From 2f3741299989ffb105bed986f7a85d567fa6cb6a Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Sat, 19 Oct 2019 12:05:45 +0200 Subject: add weights arg in generic barycenter func --- ot/bregman.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 2cd832b..ba5c7ba 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1037,11 +1037,13 @@ def barycenter(A, M, reg, weights=None, method="sinkhorn", numItermax=10000, """ if method.lower() == 'sinkhorn': - return barycenter_sinkhorn(A, M, reg, numItermax=numItermax, + return barycenter_sinkhorn(A, M, reg, weights=weights, + numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) elif method.lower() == 'sinkhorn_stabilized': - return barycenter_stabilized(A, M, reg, numItermax=numItermax, + return barycenter_stabilized(A, M, reg, weights=weights, + numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) else: -- cgit v1.2.3 From a919f960df57b4ea4beff57a3f7262b8064d8159 Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Sat, 19 Oct 2019 12:05:59 +0200 Subject: same for unbalanced --- ot/unbalanced.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/ot/unbalanced.py b/ot/unbalanced.py index d516dfc..978df08 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -1002,12 +1002,14 @@ def barycenter_unbalanced(A, M, reg, reg_m, method="sinkhorn", weights=None, if method.lower() == 'sinkhorn': return barycenter_unbalanced_sinkhorn(A, M, reg, reg_m, + weights=weights, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) elif method.lower() == 'sinkhorn_stabilized': return barycenter_unbalanced_stabilized(A, M, reg, reg_m, + weights=weights, numItermax=numItermax, stopThr=stopThr, verbose=verbose, @@ -1015,6 +1017,7 @@ def barycenter_unbalanced(A, M, reg, reg_m, method="sinkhorn", weights=None, elif method.lower() in ['sinkhorn_reg_scaling']: warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp') return barycenter_unbalanced(A, M, reg, reg_m, + weights=weights, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) -- cgit v1.2.3 From 161d68a79bc528a0d87e421f67a419cd757c7fba Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Sun, 20 Oct 2019 22:55:21 +0200 Subject: fix loop counter in barycenter + precision of dual variables --- ot/unbalanced.py | 102 +++++++++++++++++++++++++++---------------------------- 1 file changed, 50 insertions(+), 52 deletions(-) diff --git a/ot/unbalanced.py b/ot/unbalanced.py index 978df08..23f6607 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -384,10 +384,9 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, numItermax=1000, fi = reg_m / (reg_m + reg) - cpt = 0 err = 1. - while (err > stopThr and cpt < numItermax): + for i in range(numItermax): uprev = u vprev = v @@ -401,28 +400,27 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, numItermax=1000, or np.any(np.isinf(u)) or np.any(np.isinf(v))): # we have reached the machine precision # come back to previous solution and quit loop - warnings.warn('Numerical errors at iteration %s' % cpt) + warnings.warn('Numerical errors at iteration %s' % i) u = uprev v = vprev break - if cpt % 10 == 0: - # we can speed up the process by checking for the error only all - # the 10th iterations - err_u = abs(u - uprev).max() / max(abs(u).max(), abs(uprev).max(), 1.) - err_v = abs(v - vprev).max() / max(abs(v).max(), abs(vprev).max(), 1.) - err = 0.5 * (err_u + err_v) - if log: - log['err'].append(err) + + err_u = abs(u - uprev).max() / max(abs(u).max(), abs(uprev).max(), 1.) + err_v = abs(v - vprev).max() / max(abs(v).max(), abs(vprev).max(), 1.) + err = 0.5 * (err_u + err_v) + if log: + log['err'].append(err) if verbose: - if cpt % 200 == 0: + if i % 50 == 0: print( '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) - print('{:5d}|{:8e}|'.format(cpt, err)) - cpt += 1 + print('{:5d}|{:8e}|'.format(i, err)) + if err < stopThr: + break if log: - log['logu'] = np.log(u + 1e-16) - log['logv'] = np.log(v + 1e-16) + log['logu'] = np.log(u + 1e-300) + log['logv'] = np.log(v + 1e-300) if n_hists: # return only loss res = np.einsum('ik,ij,jk,ij->k', u, K, v, M) @@ -747,8 +745,8 @@ def barycenter_unbalanced_stabilized(A, M, reg, reg_m, weights=None, tau=1e3, alpha = np.zeros(dim) beta = np.zeros(dim) q = np.ones(dim) / dim - while (err > stopThr and cpt < numItermax): - qprev = q + for i in range(numItermax): + qprev = q.copy() Kv = K.dot(v) f_alpha = np.exp(- alpha / (reg + reg_m)) f_beta = np.exp(- beta / (reg + reg_m)) @@ -777,7 +775,7 @@ def barycenter_unbalanced_stabilized(A, M, reg, reg_m, weights=None, tau=1e3, warnings.warn('Numerical errors at iteration %s' % cpt) q = qprev break - if (cpt % 10 == 0 and not absorbing) or cpt == 0: + if (i % 10 == 0 and not absorbing) or i == 0: # we can speed up the process by checking for the error only all # the 10th iterations err = abs(q - qprev).max() / max(abs(q).max(), @@ -785,20 +783,21 @@ def barycenter_unbalanced_stabilized(A, M, reg, reg_m, weights=None, tau=1e3, if log: log['err'].append(err) if verbose: - if cpt % 50 == 0: + if i % 50 == 0: print( '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) - print('{:5d}|{:8e}|'.format(cpt, err)) + print('{:5d}|{:8e}|'.format(i, err)) + if err < stopThr: + break - cpt += 1 if err > stopThr: warnings.warn("Stabilized Unbalanced Sinkhorn did not converge." + "Try a larger entropy `reg` or a lower mass `reg_m`." + "Or a larger absorption threshold `tau`.") if log: - log['niter'] = cpt - log['logu'] = np.log(u + 1e-16) - log['logv'] = np.log(v + 1e-16) + log['niter'] = i + log['logu'] = np.log(u + 1e-300) + log['logv'] = np.log(v + 1e-300) return q, log else: return q @@ -882,15 +881,15 @@ def barycenter_unbalanced_sinkhorn(A, M, reg, reg_m, weights=None, fi = reg_m / (reg_m + reg) - v = np.ones((dim, n_hists)) / dim - u = np.ones((dim, 1)) / dim - - cpt = 0 + v = np.ones((dim, n_hists)) + u = np.ones((dim, 1)) + q = np.ones(dim) err = 1. - while (err > stopThr and cpt < numItermax): - uprev = u - vprev = v + for i in range(numItermax): + uprev = u.copy() + vprev = v.copy() + qprev = q.copy() Kv = K.dot(v) u = (A / Kv) ** fi @@ -905,31 +904,30 @@ def barycenter_unbalanced_sinkhorn(A, M, reg, reg_m, weights=None, or np.any(np.isinf(u)) or np.any(np.isinf(v))): # we have reached the machine precision # come back to previous solution and quit loop - warnings.warn('Numerical errors at iteration %s' % cpt) + warnings.warn('Numerical errors at iteration %s' % i) u = uprev v = vprev + q = qprev break - if cpt % 10 == 0: - # we can speed up the process by checking for the error only all - # the 10th iterations - err_u = abs(u - uprev).max() - err_u /= max(abs(u).max(), abs(uprev).max(), 1.) - err_v = abs(v - vprev).max() - err_v /= max(abs(v).max(), abs(vprev).max(), 1.) - err = 0.5 * (err_u + err_v) - if log: - log['err'].append(err) - if verbose: - if cpt % 50 == 0: - print( - '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) - print('{:5d}|{:8e}|'.format(cpt, err)) + # compute change in barycenter + err = abs(q - qprev).max() + err /= max(abs(q).max(), abs(qprev).max(), 1.) + if log: + log['err'].append(err) + # if barycenter did not change + at least 10 iterations - stop + if err < stopThr and i > 10: + break + + if verbose: + if i % 10 == 0: + print( + '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(i, err)) - cpt += 1 if log: - log['niter'] = cpt - log['logu'] = np.log(u + 1e-16) - log['logv'] = np.log(v + 1e-16) + log['niter'] = i + log['logu'] = np.log(u + 1e-300) + log['logv'] = np.log(v + 1e-300) return q, log else: return q -- cgit v1.2.3 From 2a32e2ea64d0d5096953a9b8259b0507fa58dca5 Mon Sep 17 00:00:00 2001 From: Kilian Date: Wed, 13 Nov 2019 13:55:24 +0100 Subject: fix log bug in gromov_wasserstein2 --- ot/gromov.py | 156 ++++++++++++++++++++++++++-------------------------- test/test_gromov.py | 4 ++ 2 files changed, 81 insertions(+), 79 deletions(-) diff --git a/ot/gromov.py b/ot/gromov.py index 699ae4c..9869341 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -276,7 +276,6 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwargs - p : distribution in the source space - q : distribution in the target space - L : loss function to account for the misfit between the similarity matrices - - H : entropy Parameters ---------- @@ -343,6 +342,83 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwargs return cg(p, q, 0, 1, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs) +def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwargs): + """ + Returns the gromov-wasserstein discrepancy between (C1,p) and (C2,q) + + The function solves the following optimization problem: + + .. math:: + GW = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} + + Where : + - C1 : Metric cost matrix in the source space + - C2 : Metric cost matrix in the target space + - p : distribution in the source space + - q : distribution in the target space + - L : loss function to account for the misfit between the similarity matrices + + Parameters + ---------- + C1 : ndarray, shape (ns, ns) + Metric cost matrix in the source space + C2 : ndarray, shape (nt, nt) + Metric cost matrix in the target space + p : ndarray, shape (ns,) + Distribution in the source space. + q : ndarray, shape (nt,) + Distribution in the target space. + loss_fun : str + loss function used for the solver either 'square_loss' or 'kl_loss' + max_iter : int, optional + Max number of iterations + tol : float, optional + Stop threshold on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + armijo : bool, optional + If True the steps of the line-search is found via an armijo research. Else closed form is used. + If there is convergence issues use False. + + Returns + ------- + gw_dist : float + Gromov-Wasserstein distance + log : dict + convergence information and Coupling marix + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + + .. [13] Mémoli, Facundo. Gromov–Wasserstein distances and the + metric approach to object matching. Foundations of computational + mathematics 11.4 (2011): 417-487. + + """ + + constC, hC1, hC2 = init_matrix(C1, C2, p, q, loss_fun) + + G0 = p[:, None] * q[None, :] + + def f(G): + return gwloss(constC, hC1, hC2, G) + + def df(G): + return gwggrad(constC, hC1, hC2, G) + res, log_gw = cg(p, q, 0, 1, f, df, G0, log=True, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs) + log_gw['gw_dist'] = gwloss(constC, hC1, hC2, res) + log_gw['T'] = res + if log: + return log_gw['gw_dist'], log_gw + else: + return log_gw['gw_dist'] + + def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, armijo=False, log=False, **kwargs): """ Computes the FGW transport between two graphs see [24] @@ -506,84 +582,6 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5 return log['fgw_dist'] -def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwargs): - """ - Returns the gromov-wasserstein discrepancy between (C1,p) and (C2,q) - - The function solves the following optimization problem: - - .. math:: - GW = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} - - Where : - - C1 : Metric cost matrix in the source space - - C2 : Metric cost matrix in the target space - - p : distribution in the source space - - q : distribution in the target space - - L : loss function to account for the misfit between the similarity matrices - - H : entropy - - Parameters - ---------- - C1 : ndarray, shape (ns, ns) - Metric cost matrix in the source space - C2 : ndarray, shape (nt, nt) - Metric cost matrix in the target space - p : ndarray, shape (ns,) - Distribution in the source space. - q : ndarray, shape (nt,) - Distribution in the target space. - loss_fun : str - loss function used for the solver either 'square_loss' or 'kl_loss' - max_iter : int, optional - Max number of iterations - tol : float, optional - Stop threshold on error (>0) - verbose : bool, optional - Print information along iterations - log : bool, optional - record log if True - armijo : bool, optional - If True the steps of the line-search is found via an armijo research. Else closed form is used. - If there is convergence issues use False. - - Returns - ------- - gw_dist : float - Gromov-Wasserstein distance - log : dict - convergence information and Coupling marix - - References - ---------- - .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, - "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. - - .. [13] Mémoli, Facundo. Gromov–Wasserstein distances and the - metric approach to object matching. Foundations of computational - mathematics 11.4 (2011): 417-487. - - """ - - constC, hC1, hC2 = init_matrix(C1, C2, p, q, loss_fun) - - G0 = p[:, None] * q[None, :] - - def f(G): - return gwloss(constC, hC1, hC2, G) - - def df(G): - return gwggrad(constC, hC1, hC2, G) - res, log = cg(p, q, 0, 1, f, df, G0, log=True, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs) - log['gw_dist'] = gwloss(constC, hC1, hC2, res) - log['T'] = res - if log: - return log['gw_dist'], log - else: - return log['gw_dist'] - - def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9, verbose=False, log=False): """ diff --git a/test/test_gromov.py b/test/test_gromov.py index 70fa83f..43da9fc 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -44,10 +44,14 @@ def test_gromov(): gw, log = ot.gromov.gromov_wasserstein2(C1, C2, p, q, 'kl_loss', log=True) + gw_val = ot.gromov.gromov_wasserstein2(C1, C2, p, q, 'kl_loss', log=False) + G = log['T'] np.testing.assert_allclose(gw, 0, atol=1e-1, rtol=1e-1) + np.testing.assert_allclose(gw, gw_val, atol=1e-1, rtol=1e-1) # cf log=False + # check constratints np.testing.assert_allclose( p, G.sum(1), atol=1e-04) # cf convergence gromov -- cgit v1.2.3 From 6caf3b4809993826320a662d9f3d86be61ab3ad7 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 15 Nov 2019 09:28:52 +0100 Subject: Create pythonpackage.yml --- .github/workflows/pythonpackage.yml | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 .github/workflows/pythonpackage.yml diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml new file mode 100644 index 0000000..bb94926 --- /dev/null +++ b/.github/workflows/pythonpackage.yml @@ -0,0 +1,35 @@ +name: Test Package + +on: [push] + +jobs: + build: + + runs-on: ubuntu-latest + strategy: + max-parallel: 4 + matrix: + python-version: [2.7, 3.5, 3.6, 3.7] + + steps: + - uses: actions/checkout@v1 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v1 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + - name: Lint with flake8 + run: | + pip install flake8 + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + run: | + pip install pytest "pytest-cov<2.6" + pip install . + python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot -- cgit v1.2.3 From 81a32a35a4b7ff10473ea9cc465af2d5c9337918 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 15 Nov 2019 09:34:49 +0100 Subject: correct tests --- .github/workflows/pythonpackage.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index bb94926..ec8f186 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -27,9 +27,9 @@ jobs: # stop the build if there are Python syntax errors or undefined names flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - name: Test with pytest run: | pip install pytest "pytest-cov<2.6" - pip install . + python setup.py develop python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot -- cgit v1.2.3 From 3635fc46d6fc55e6fa30b33ad07fe092dfd23241 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 15 Nov 2019 09:38:59 +0100 Subject: Remove github action tests (PR is coming) --- .github/workflows/pythonpackage.yml | 5 ----- 1 file changed, 5 deletions(-) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index ec8f186..cb3baf8 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -28,8 +28,3 @@ jobs: flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - - name: Test with pytest - run: | - pip install pytest "pytest-cov<2.6" - python setup.py develop - python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot -- cgit v1.2.3 From 0280a3441b09c781035cda3b74213ec92026ff9e Mon Sep 17 00:00:00 2001 From: Kilian Date: Fri, 15 Nov 2019 16:10:37 +0100 Subject: fix bug numItermax emd in cg --- ot/optim.py | 6 ++++-- test/test_optim.py | 33 +++++++++++++++++++++++++++++++++ 2 files changed, 37 insertions(+), 2 deletions(-) diff --git a/ot/optim.py b/ot/optim.py index 0abd9e9..4012e0d 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -134,7 +134,7 @@ def solve_linesearch(cost, G, deltaG, Mi, f_val, return alpha, fc, f_val -def cg(a, b, M, reg, f, df, G0=None, numItermax=200, +def cg(a, b, M, reg, f, df, G0=None, numItermax=200, numItermaxEmd=100000, stopThr=1e-9, stopThr2=1e-9, verbose=False, log=False, **kwargs): """ Solve the general regularized OT problem with conditional gradient @@ -172,6 +172,8 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, initial guess (default is indep joint density) numItermax : int, optional Max number of iterations + numItermaxEmd : int, optional + Max number of iterations for emd stopThr : float, optional Stop threshol on the relative variation (>0) stopThr2 : float, optional @@ -238,7 +240,7 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, Mi += Mi.min() # solve linear program - Gc = emd(a, b, Mi) + Gc = emd(a, b, Mi, numItermax=numItermaxEmd) deltaG = Gc - G diff --git a/test/test_optim.py b/test/test_optim.py index ae31e1f..aade36e 100644 --- a/test/test_optim.py +++ b/test/test_optim.py @@ -37,6 +37,39 @@ def test_conditional_gradient(): np.testing.assert_allclose(b, G.sum(0)) +def test_conditional_gradient2(): + n = 4000 # nb samples + + mu_s = np.array([0, 0]) + cov_s = np.array([[1, 0], [0, 1]]) + + mu_t = np.array([4, 4]) + cov_t = np.array([[1, -.8], [-.8, 1]]) + + xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s) + xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t) + + a, b = np.ones((n,)) / n, np.ones((n,)) / n + + # loss matrix + M = ot.dist(xs, xt) + M /= M.max() + + def f(G): + return 0.5 * np.sum(G**2) + + def df(G): + return G + + reg = 1e-1 + + G, log = ot.optim.cg(a, b, M, reg, f, df, numItermaxEmd=200000, + verbose=True, log=True) + + np.testing.assert_allclose(a, G.sum(1)) + np.testing.assert_allclose(b, G.sum(0)) + + def test_generalized_conditional_gradient(): n_bins = 100 # nb bins -- cgit v1.2.3 From 7a02c69a3791682cc3993f7a20ed6841eef75441 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 29 Nov 2019 09:12:50 +0100 Subject: complete gitignore --- .gitignore | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/.gitignore b/.gitignore index dadf84c..a2ace7c 100644 --- a/.gitignore +++ b/.gitignore @@ -106,3 +106,15 @@ ENV/ # coverage output folder cov_html/ + +docs/source/modules/generated/* +docs/source/_build/* + +# local debug folder +debug + +# vscode parameters +.vscode + +# pytest cahche +.pytest_cache \ No newline at end of file -- cgit v1.2.3 From e92ae6d155a6bed91c474a3e842581f09deceba3 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 29 Nov 2019 09:38:29 +0100 Subject: cleanup cpp code and annd emd with sparse Ot matrix --- ot/lp/EMD_wrapper.cpp | 95 ++++++++++++++++++++++++++++++++++++++++----------- 1 file changed, 75 insertions(+), 20 deletions(-) diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index fc7ca63..91110b4 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -17,18 +17,24 @@ int EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G, double* alpha, double* beta, double *cost, int maxIter) { -// beware M and C anre strored in row major C style!!! + // beware M and C anre strored in row major C style!!! int n, m, i, cur; typedef FullBipartiteDigraph Digraph; - DIGRAPH_TYPEDEFS(FullBipartiteDigraph); + DIGRAPH_TYPEDEFS(FullBipartiteDigraph); - // Get the number of non zero coordinates for r and c + std::vector indI(n), indJ(m); + std::vector weights1(n), weights2(m); + Digraph di(n, m); + NetworkSimplexSimple net(di, true, n+m, n*m, maxIter); + + // Get the number of non zero coordinates for r and c and vectors n=0; for (int i=0; i0) { - n++; + weights1[ n ] = val; + indI[n++]=i; }else if(val<0){ return INFEASIBLE; } @@ -37,42 +43,85 @@ int EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G, for (int i=0; i0) { - m++; + weights2[ m ] = -val; + indJ[m++]=i; }else if(val<0){ return INFEASIBLE; } } // Define the graph + net.supplyMap(&weights1[0], n, &weights2[0], m); + + // Set the cost of each edge + for (int i=0; i indI(n), indJ(m); std::vector weights1(n), weights2(m); Digraph di(n, m); NetworkSimplexSimple net(di, true, n+m, n*m, maxIter); - // Set supply and demand, don't account for 0 values (faster) - - cur=0; + // Get the number of non zero coordinates for r and c and vectors + n=0; for (int i=0; i0) { - weights1[ cur ] = val; - indI[cur++]=i; - } + weights1[ n ] = val; + indI[n++]=i; + }else if(val<0){ + return INFEASIBLE; + } } - - // Demand is actually negative supply... - - cur=0; + m=0; for (int i=0; i0) { - weights2[ cur ] = -val; - indJ[cur++]=i; - } + weights2[ m ] = -val; + indJ[m++]=i; + }else if(val<0){ + return INFEASIBLE; + } } - + // Define the graph net.supplyMap(&weights1[0], n, &weights2[0], m); // Set the cost of each edge @@ -90,14 +139,19 @@ int EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G, if (ret==(int)net.OPTIMAL || ret==(int)net.MAX_ITER_REACHED) { *cost = 0; Arc a; di.first(a); + cur=0 for (; a != INVALID; di.next(a)) { int i = di.source(a); int j = di.target(a); double flow = net.flow(a); *cost += flow * (*(D+indI[i]*n2+indJ[j-n])); - *(G+indI[i]*n2+indJ[j-n]) = flow; + + *(G+cur) = flow; + *(iG+cur) = i; + *(jG+cur) = j; *(alpha + indI[i]) = -net.potential(i); *(beta + indJ[j-n]) = net.potential(j); + cur++; } } @@ -105,3 +159,4 @@ int EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G, return ret; } + -- cgit v1.2.3 From df0d259ebab268517716d666ae45494b6ba998ea Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 29 Nov 2019 09:41:45 +0100 Subject: cant beleive i forgot a semicolumn --- ot/lp/EMD_wrapper.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index 91110b4..29e2303 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -139,7 +139,7 @@ int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, if (ret==(int)net.OPTIMAL || ret==(int)net.MAX_ITER_REACHED) { *cost = 0; Arc a; di.first(a); - cur=0 + cur=0; for (; a != INVALID; di.next(a)) { int i = di.source(a); int j = di.target(a); -- cgit v1.2.3 From 3a858dfa1f2795b22d1e2db3cfd94d1eb7831f8d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 29 Nov 2019 09:46:35 +0100 Subject: correct bad speedup --- ot/lp/EMD_wrapper.cpp | 48 +++++++++++++++++++++++++++++++++++------------- 1 file changed, 35 insertions(+), 13 deletions(-) diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index 29e2303..65fa80f 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -18,23 +18,17 @@ int EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G, double* alpha, double* beta, double *cost, int maxIter) { // beware M and C anre strored in row major C style!!! - int n, m, i, cur; + int n, m, i, cur; typedef FullBipartiteDigraph Digraph; DIGRAPH_TYPEDEFS(FullBipartiteDigraph); - std::vector indI(n), indJ(m); - std::vector weights1(n), weights2(m); - Digraph di(n, m); - NetworkSimplexSimple net(di, true, n+m, n*m, maxIter); - - // Get the number of non zero coordinates for r and c and vectors + // Get the number of non zero coordinates for r and c n=0; for (int i=0; i0) { - weights1[ n ] = val; - indI[n++]=i; + n++; }else if(val<0){ return INFEASIBLE; } @@ -43,14 +37,42 @@ int EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G, for (int i=0; i0) { - weights2[ m ] = -val; - indJ[m++]=i; + m++; }else if(val<0){ return INFEASIBLE; } } // Define the graph + + std::vector indI(n), indJ(m); + std::vector weights1(n), weights2(m); + Digraph di(n, m); + NetworkSimplexSimple net(di, true, n+m, n*m, maxIter); + + // Set supply and demand, don't account for 0 values (faster) + + cur=0; + for (int i=0; i0) { + weights1[ cur ] = val; + indI[cur++]=i; + } + } + + // Demand is actually negative supply... + + cur=0; + for (int i=0; i0) { + weights2[ cur ] = -val; + indJ[cur++]=i; + } + } + + net.supplyMap(&weights1[0], n, &weights2[0], m); // Set the cost of each edge @@ -147,8 +169,8 @@ int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, *cost += flow * (*(D+indI[i]*n2+indJ[j-n])); *(G+cur) = flow; - *(iG+cur) = i; - *(jG+cur) = j; + *(iG+cur) = indI[i]; + *(jG+cur) = indJ[j]; *(alpha + indI[i]) = -net.potential(i); *(beta + indJ[j-n]) = net.potential(j); cur++; -- cgit v1.2.3 From 4a6883e0ce2fd9f3edd374d54c4c219d876ceb76 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 2 Dec 2019 09:37:54 +0100 Subject: nothing explodes and it compiles --- ot/lp/EMD.h | 4 ++++ ot/lp/EMD_wrapper.cpp | 2 +- ot/lp/__init__.py | 29 ++++++++++++++++++++++------- ot/lp/emd_wrap.pyx | 38 +++++++++++++++++++++++++++++++++----- 4 files changed, 60 insertions(+), 13 deletions(-) diff --git a/ot/lp/EMD.h b/ot/lp/EMD.h index f42e222..bc513d2 100644 --- a/ot/lp/EMD.h +++ b/ot/lp/EMD.h @@ -32,4 +32,8 @@ enum ProblemType { int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int maxIter); +int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, + long *iG, long *jG, double *G, + double* alpha, double* beta, double *cost, int maxIter); + #endif diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index 65fa80f..3ca7319 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -108,7 +108,7 @@ int EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G, int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, - int *iG, int *jG, double *G, + long *iG, long *jG, double *G, double* alpha, double* beta, double *cost, int maxIter) { // beware M and C anre strored in row major C style!!! int n, m, i, cur; diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 0c92810..4fec7d9 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -27,7 +27,7 @@ __all__=['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx', 'emd_1d', 'emd2_1d', 'wasserstein_1d'] -def emd(a, b, M, numItermax=100000, log=False): +def emd(a, b, M, numItermax=100000, log=False, sparse=False): r"""Solves the Earth Movers distance problem and returns the OT matrix @@ -109,7 +109,12 @@ def emd(a, b, M, numItermax=100000, log=False): if len(b) == 0: b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] - G, cost, u, v, result_code = emd_c(a, b, M, numItermax) + if sparse: + Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse) + G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + else: + G, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse) + result_code_string = check_result(result_code) if log: log = {} @@ -123,7 +128,7 @@ def emd(a, b, M, numItermax=100000, log=False): def emd2(a, b, M, processes=multiprocessing.cpu_count(), - numItermax=100000, log=False, return_matrix=False): + numItermax=100000, log=False, sparse=False, return_matrix=False): r"""Solves the Earth Movers distance problem and returns the loss .. math:: @@ -214,19 +219,29 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), if log or return_matrix: def f(b): - G, cost, u, v, resultCode = emd_c(a, b, M, numItermax) - result_code_string = check_result(resultCode) + + if sparse: + Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse) + G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + else: + G, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse) + + result_code_string = check_result(result_code) log = {} if return_matrix: log['G'] = G log['u'] = u log['v'] = v log['warning'] = result_code_string - log['result_code'] = resultCode + log['result_code'] = result_code return [cost, log] else: def f(b): - G, cost, u, v, result_code = emd_c(a, b, M, numItermax) + if sparse: + Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse) + G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + else: + G, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse) check_result(result_code) return cost diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 2b6c495..345cb66 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -20,6 +20,9 @@ import warnings cdef extern from "EMD.h": int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int maxIter) + int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, + long *iG, long *jG, double *G, + double* alpha, double* beta, double *cost, int maxIter) cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED, MAX_ITER_REACHED @@ -39,7 +42,7 @@ def check_result(result_code): @cython.boundscheck(False) @cython.wraparound(False) -def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mode="c"] b, np.ndarray[double, ndim=2, mode="c"] M, int max_iter): +def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mode="c"] b, np.ndarray[double, ndim=2, mode="c"] M, int max_iter, bint sparse): """ Solves the Earth Movers distance problem and returns the optimal transport matrix @@ -82,12 +85,18 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod """ cdef int n1= M.shape[0] cdef int n2= M.shape[1] + cdef int nmax=n1+n2-1 + cdef int result_code = 0 cdef double cost=0 - cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) cdef np.ndarray[double, ndim=1, mode="c"] alpha=np.zeros(n1) cdef np.ndarray[double, ndim=1, mode="c"] beta=np.zeros(n2) + cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([0, 0]) + + cdef np.ndarray[double, ndim=1, mode="c"] Gv=np.zeros(0) + cdef np.ndarray[long, ndim=1, mode="c"] iG=np.zeros(0,dtype=np.int) + cdef np.ndarray[long, ndim=1, mode="c"] jG=np.zeros(0,dtype=np.int) if not len(a): a=np.ones((n1,))/n1 @@ -95,10 +104,29 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod if not len(b): b=np.ones((n2,))/n2 - # calling the function - cdef int result_code = EMD_wrap(n1, n2, a.data, b.data, M.data, G.data, alpha.data, beta.data, &cost, max_iter) + if sparse: + + Gv=np.zeros(nmax) + iG=np.zeros(nmax,dtype=np.int) + jG=np.zeros(nmax,dtype=np.int) + + + result_code = EMD_wrap_return_sparse(n1, n2, a.data, b.data, M.data, iG.data, jG.data, G.data, alpha.data, beta.data, &cost, max_iter) + + + return Gv, iG, jG, cost, alpha, beta, result_code + + + else: + + + G=np.zeros([n1, n2]) + + + # calling the function + result_code = EMD_wrap(n1, n2, a.data, b.data, M.data, G.data, alpha.data, beta.data, &cost, max_iter) - return G, cost, alpha, beta, result_code + return G, cost, alpha, beta, result_code @cython.boundscheck(False) -- cgit v1.2.3 From 57321bd0172c97b77dfc8b14972c18d063b6dda8 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 2 Dec 2019 11:13:07 +0100 Subject: add awesome sparse solver --- ot/lp/EMD_wrapper.cpp | 65 ++++++++++++++++++++++++++++++++++++--------------- ot/lp/emd_wrap.pyx | 2 +- test/test_ot.py | 20 ++++++++++++++++ 3 files changed, 67 insertions(+), 20 deletions(-) diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index 3ca7319..2aa44c1 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -111,23 +111,19 @@ int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, long *iG, long *jG, double *G, double* alpha, double* beta, double *cost, int maxIter) { // beware M and C anre strored in row major C style!!! - int n, m, i, cur; + + // Get the number of non zero coordinates for r and c and vectors + int n, m, i, cur; typedef FullBipartiteDigraph Digraph; DIGRAPH_TYPEDEFS(FullBipartiteDigraph); - std::vector indI(n), indJ(m); - std::vector weights1(n), weights2(m); - Digraph di(n, m); - NetworkSimplexSimple net(di, true, n+m, n*m, maxIter); - - // Get the number of non zero coordinates for r and c and vectors + // Get the number of non zero coordinates for r and c n=0; for (int i=0; i0) { - weights1[ n ] = val; - indI[n++]=i; + n++; }else if(val<0){ return INFEASIBLE; } @@ -136,13 +132,41 @@ int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, for (int i=0; i0) { - weights2[ m ] = -val; - indJ[m++]=i; + m++; }else if(val<0){ return INFEASIBLE; } } + // Define the graph + + std::vector indI(n), indJ(m); + std::vector weights1(n), weights2(m); + Digraph di(n, m); + NetworkSimplexSimple net(di, true, n+m, n*m, maxIter); + + // Set supply and demand, don't account for 0 values (faster) + + cur=0; + for (int i=0; i0) { + weights1[ cur ] = val; + indI[cur++]=i; + } + } + + // Demand is actually negative supply... + + cur=0; + for (int i=0; i0) { + weights2[ cur ] = -val; + indJ[cur++]=i; + } + } + // Define the graph net.supplyMap(&weights1[0], n, &weights2[0], m); @@ -166,14 +190,17 @@ int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, int i = di.source(a); int j = di.target(a); double flow = net.flow(a); - *cost += flow * (*(D+indI[i]*n2+indJ[j-n])); - - *(G+cur) = flow; - *(iG+cur) = indI[i]; - *(jG+cur) = indJ[j]; - *(alpha + indI[i]) = -net.potential(i); - *(beta + indJ[j-n]) = net.potential(j); - cur++; + if (flow>0) + { + *cost += flow * (*(D+indI[i]*n2+indJ[j-n])); + + *(G+cur) = flow; + *(iG+cur) = indI[i]; + *(jG+cur) = indJ[j-n]; + *(alpha + indI[i]) = -net.potential(i); + *(beta + indJ[j-n]) = net.potential(j); + cur++; + } } } diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 345cb66..f183995 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -111,7 +111,7 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod jG=np.zeros(nmax,dtype=np.int) - result_code = EMD_wrap_return_sparse(n1, n2, a.data, b.data, M.data, iG.data, jG.data, G.data, alpha.data, beta.data, &cost, max_iter) + result_code = EMD_wrap_return_sparse(n1, n2, a.data, b.data, M.data, iG.data, jG.data, Gv.data, alpha.data, beta.data, &cost, max_iter) return Gv, iG, jG, cost, alpha, beta, result_code diff --git a/test/test_ot.py b/test/test_ot.py index dacae0a..4d59e12 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -118,6 +118,26 @@ def test_emd_empty(): np.testing.assert_allclose(w, 0) +def test_emd_sparse(): + + n = 100 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + x2 = rng.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x2) + + G = ot.emd([], [], M) + + Gs = ot.emd([], [], M, sparse=True) + + # check G is the same + np.testing.assert_allclose(G, Gs.todense()) + # check constraints + + def test_emd2_multi(): n = 500 # nb bins -- cgit v1.2.3 From a6a654de5e78dd388a793fbd26f60045b05d519c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 2 Dec 2019 11:31:32 +0100 Subject: proper documentation and parameter --- ot/lp/EMD.h | 2 +- ot/lp/EMD_wrapper.cpp | 3 ++- ot/lp/__init__.py | 16 ++++++++++++++-- ot/lp/emd_wrap.pyx | 10 ++++++---- test/test_ot.py | 2 +- 5 files changed, 24 insertions(+), 9 deletions(-) diff --git a/ot/lp/EMD.h b/ot/lp/EMD.h index bc513d2..9896091 100644 --- a/ot/lp/EMD.h +++ b/ot/lp/EMD.h @@ -33,7 +33,7 @@ enum ProblemType { int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int maxIter); int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, - long *iG, long *jG, double *G, + long *iG, long *jG, double *G, long * nG, double* alpha, double* beta, double *cost, int maxIter); #endif diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index 2aa44c1..9be2cdc 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -108,7 +108,7 @@ int EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G, int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, - long *iG, long *jG, double *G, + long *iG, long *jG, double *G, long * nG, double* alpha, double* beta, double *cost, int maxIter) { // beware M and C anre strored in row major C style!!! @@ -202,6 +202,7 @@ int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, cur++; } } + *nG=cur; // nb of value +1 for numpy indexing } diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 4fec7d9..d476071 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -27,7 +27,7 @@ __all__=['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx', 'emd_1d', 'emd2_1d', 'wasserstein_1d'] -def emd(a, b, M, numItermax=100000, log=False, sparse=False): +def emd(a, b, M, numItermax=100000, log=False, dense=True): r"""Solves the Earth Movers distance problem and returns the OT matrix @@ -62,6 +62,10 @@ def emd(a, b, M, numItermax=100000, log=False, sparse=False): log: bool, optional (default=False) If True, returns a dictionary containing the cost and dual variables. Otherwise returns only the optimal transportation matrix. + dense: boolean, optional (default=True) + If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). + Otherwise returns a sparse representation using scipy's `coo_matrix` + format. Returns ------- @@ -103,6 +107,8 @@ def emd(a, b, M, numItermax=100000, log=False, sparse=False): b = np.asarray(b, dtype=np.float64) M = np.asarray(M, dtype=np.float64) + sparse= not dense + # if empty array given then use uniform distributions if len(a) == 0: a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0] @@ -128,7 +134,7 @@ def emd(a, b, M, numItermax=100000, log=False, sparse=False): def emd2(a, b, M, processes=multiprocessing.cpu_count(), - numItermax=100000, log=False, sparse=False, return_matrix=False): + numItermax=100000, log=False, dense=True, return_matrix=False): r"""Solves the Earth Movers distance problem and returns the loss .. math:: @@ -166,6 +172,10 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), variables. Otherwise returns only the optimal transportation cost. return_matrix: boolean, optional (default=False) If True, returns the optimal transportation matrix in the log. + dense: boolean, optional (default=True) + If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). + Otherwise returns a sparse representation using scipy's `coo_matrix` + format. Returns ------- @@ -207,6 +217,8 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), b = np.asarray(b, dtype=np.float64) M = np.asarray(M, dtype=np.float64) + sparse=not dense + # problem with pikling Forks if sys.platform.endswith('win32'): processes=1 diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index f183995..4b6cdce 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -21,7 +21,7 @@ import warnings cdef extern from "EMD.h": int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int maxIter) int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, - long *iG, long *jG, double *G, + long *iG, long *jG, double *G, long * nG, double* alpha, double* beta, double *cost, int maxIter) cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED, MAX_ITER_REACHED @@ -75,7 +75,8 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod max_iter : int The maximum number of iterations before stopping the optimization algorithm if it has not converged. - + sparse : bool + Returning a sparse transport matrix if set to True Returns ------- @@ -87,6 +88,7 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod cdef int n2= M.shape[1] cdef int nmax=n1+n2-1 cdef int result_code = 0 + cdef int nG=0 cdef double cost=0 cdef np.ndarray[double, ndim=1, mode="c"] alpha=np.zeros(n1) @@ -111,10 +113,10 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod jG=np.zeros(nmax,dtype=np.int) - result_code = EMD_wrap_return_sparse(n1, n2, a.data, b.data, M.data, iG.data, jG.data, Gv.data, alpha.data, beta.data, &cost, max_iter) + result_code = EMD_wrap_return_sparse(n1, n2, a.data, b.data, M.data, iG.data, jG.data, Gv.data, &nG, alpha.data, beta.data, &cost, max_iter) - return Gv, iG, jG, cost, alpha, beta, result_code + return Gv[:nG], iG[:nG], jG[:nG], cost, alpha, beta, result_code else: diff --git a/test/test_ot.py b/test/test_ot.py index 4d59e12..7b44fd1 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -131,7 +131,7 @@ def test_emd_sparse(): G = ot.emd([], [], M) - Gs = ot.emd([], [], M, sparse=True) + Gs = ot.emd([], [], M, dense=False) # check G is the same np.testing.assert_allclose(G, Gs.todense()) -- cgit v1.2.3 From 127adbaf4eef7a6dffbdcd4f930fc6301587f861 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 2 Dec 2019 11:41:13 +0100 Subject: remove useless variable --- test/test_ot.py | 1 - 1 file changed, 1 deletion(-) diff --git a/test/test_ot.py b/test/test_ot.py index 7b44fd1..8602022 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -125,7 +125,6 @@ def test_emd_sparse(): x = rng.randn(n, 2) x2 = rng.randn(n, 2) - u = ot.utils.unif(n) M = ot.dist(x, x2) -- cgit v1.2.3 From 84384dd9e5dc78ed5cc867a53bd1de31c05d77fc Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 2 Dec 2019 13:34:05 +0100 Subject: add test emd2 --- test/test_ot.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/test/test_ot.py b/test/test_ot.py index 8602022..507d188 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -132,9 +132,12 @@ def test_emd_sparse(): Gs = ot.emd([], [], M, dense=False) + ws = ot.emd2([], [], M, dense=False) + # check G is the same np.testing.assert_allclose(G, Gs.todense()) - # check constraints + # check value + np.testing.assert_allclose(Gs.multiply(M).sum(), ws, rtol=1e-6) def test_emd2_multi(): -- cgit v1.2.3 From 7371b2f4f931db8f67ec2967253be8d95ff9fe80 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 2 Dec 2019 13:34:55 +0100 Subject: add test emd2 --- test/test_ot.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/test/test_ot.py b/test/test_ot.py index 507d188..48ea87f 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -171,7 +171,12 @@ def test_emd2_multi(): emdn = ot.emd2(a, b, M) ot.toc('multi proc : {} s') + ot.tic() + emdn2 = ot.emd2(a, b, M, dense = False) + ot.toc('multi proc : {} s') + np.testing.assert_allclose(emd1, emdn) + np.testing.assert_allclose(emd1, emdn2) # emd loss multipro proc with log ot.tic() -- cgit v1.2.3 From dfaba55affcca606e8e041bdbd0fc5a7735c2b07 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 2 Dec 2019 13:36:08 +0100 Subject: add test emd2 multi --- test/test_ot.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_ot.py b/test/test_ot.py index 48ea87f..470fd0f 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -176,7 +176,7 @@ def test_emd2_multi(): ot.toc('multi proc : {} s') np.testing.assert_allclose(emd1, emdn) - np.testing.assert_allclose(emd1, emdn2) + np.testing.assert_allclose(emd1, emdn2, rtol=1e-6) # emd loss multipro proc with log ot.tic() -- cgit v1.2.3 From c439e3efb920086154c741b41f65d99165e875d8 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 2 Dec 2019 13:57:13 +0100 Subject: pep8 --- test/test_ot.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/test_ot.py b/test/test_ot.py index 470fd0f..fbacd8b 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -172,8 +172,8 @@ def test_emd2_multi(): ot.toc('multi proc : {} s') ot.tic() - emdn2 = ot.emd2(a, b, M, dense = False) - ot.toc('multi proc : {} s') + emdn2 = ot.emd2(a, b, M, dense=False) + ot.toc('multi proc : {} s') np.testing.assert_allclose(emd1, emdn) np.testing.assert_allclose(emd1, emdn2, rtol=1e-6) -- cgit v1.2.3 From a4afee871d8e9d5db68228d1ed5bf4853eedc294 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 3 Dec 2019 15:20:16 +0100 Subject: first implemntation sparse loss --- ot/lp/EMD.h | 5 +++ ot/lp/EMD_wrapper.cpp | 78 ++++++++++++++++++++++++++++++++++++++++++ ot/lp/emd_wrap.pyx | 4 +++ ot/lp/network_simplex_simple.h | 2 +- 4 files changed, 88 insertions(+), 1 deletion(-) diff --git a/ot/lp/EMD.h b/ot/lp/EMD.h index 9896091..fc94211 100644 --- a/ot/lp/EMD.h +++ b/ot/lp/EMD.h @@ -36,4 +36,9 @@ int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, long *iG, long *jG, double *G, long * nG, double* alpha, double* beta, double *cost, int maxIter); +int EMD_wrap_all_sparse(int n1, int n2, double *X, double *Y, + long *iD, long *jD, double *D, long nD, + long *iG, long *jG, double *G, long * nG, + double* alpha, double* beta, double *cost, int maxIter); + #endif diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index 9be2cdc..28e4af2 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -210,3 +210,81 @@ int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, return ret; } +int EMD_wrap_all_sparse(int n1, int n2, double *X, double *Y, + long *iD, long *jD, double *D, long nD, + long *iG, long *jG, double *G, long * nG, + double* alpha, double* beta, double *cost, int maxIter) { + // beware M and C anre strored in row major C style!!! + + // Get the number of non zero coordinates for r and c and vectors + int n, m, cur; + + typedef FullBipartiteDigraph Digraph; + DIGRAPH_TYPEDEFS(FullBipartiteDigraph); + + n=n1; + m=n2; + + + // Define the graph + + + std::vector weights2(m); + Digraph di(n, m); + NetworkSimplexSimple net(di, true, n+m, n*m, maxIter); + + // Set supply and demand, don't account for 0 values (faster) + + + // Demand is actually negative supply... + + cur=0; + for (int i=0; i0) { + weights2[ cur ] = -val; + } + } + + // Define the graph + net.supplyMap(X, n, &weights2[0], m); + + // Set the cost of each edge + for (int k=0; k0) + { + + *(G+cur) = flow; + *(iG+cur) = i; + *(jG+cur) = j-n; + *(alpha + i) = -net.potential(i); + *(beta + j-n) = net.potential(j); + cur++; + } + } + *nG=cur; // nb of value +1 for numpy indexing + + } + + + return ret; +} + diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 4b6cdce..4e3586d 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -23,6 +23,10 @@ cdef extern from "EMD.h": int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, long *iG, long *jG, double *G, long * nG, double* alpha, double* beta, double *cost, int maxIter) + int EMD_wrap_all_sparse(int n1, int n2, double *X, double *Y, + long *iD, long *jD, double *D, long nD, + long *iG, long *jG, double *G, long * nG, + double* alpha, double* beta, double *cost, int maxIter) cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED, MAX_ITER_REACHED diff --git a/ot/lp/network_simplex_simple.h b/ot/lp/network_simplex_simple.h index 7c6a4ce..498e921 100644 --- a/ot/lp/network_simplex_simple.h +++ b/ot/lp/network_simplex_simple.h @@ -686,7 +686,7 @@ namespace lemon { /// \see resetParams(), reset() ProblemType run() { #if DEBUG_LVL>0 - std::cout << "OPTIMAL = " << OPTIMAL << "\nINFEASIBLE = " << INFEASIBLE << "\nUNBOUNDED = " << UNBOUNDED << "\nMAX_ITER_REACHED" << MAX_ITER_REACHED\n"; + std::cout << "OPTIMAL = " << OPTIMAL << "\nINFEASIBLE = " << INFEASIBLE << "\nUNBOUNDED = " << UNBOUNDED << "\nMAX_ITER_REACHED" << MAX_ITER_REACHED << "\n" ; #endif if (!init()) return INFEASIBLE; -- cgit v1.2.3 From 92233f79e098f1930248d815e66c0a929508af59 Mon Sep 17 00:00:00 2001 From: Kilian Date: Mon, 9 Dec 2019 15:56:48 +0100 Subject: add assert for emd dimension mismatch --- ot/lp/__init__.py | 6 ++++++ test/test_ot.py | 16 ++++++++++++++++ 2 files changed, 22 insertions(+) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 0c92810..f77c3d7 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -109,6 +109,9 @@ def emd(a, b, M, numItermax=100000, log=False): if len(b) == 0: b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] + assert (a.shape[0] == M.shape[0] or b.shape[0] == M.shape[1]), \ + "Dimension mismatch, check dimensions of M with a and b" + G, cost, u, v, result_code = emd_c(a, b, M, numItermax) result_code_string = check_result(result_code) if log: @@ -212,6 +215,9 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), if len(b) == 0: b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] + assert (a.shape[0] == M.shape[0] or b.shape[0] == M.shape[1]), \ + "Dimension mismatch, check dimensions of M with a and b" + if log or return_matrix: def f(b): G, cost, u, v, resultCode = emd_c(a, b, M, numItermax) diff --git a/test/test_ot.py b/test/test_ot.py index dacae0a..1343604 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -14,6 +14,22 @@ from ot.datasets import make_1D_gauss as gauss import pytest +def test_emd_dimension_mismatch(): + # test emd and emd2 for simple identity + n_samples = 100 + n_features = 2 + rng = np.random.RandomState(0) + + x = rng.randn(n_samples, n_features) + a = ot.utils.unif(n_samples + 1) + + M = ot.dist(x, x) + + np.testing.assert_raises(AssertionError, emd, a, a, M) + + np.testing.assert_raises(AssertionError, emd2, a, a, M) + + def test_emd_emd2(): # test emd and emd2 for simple identity n = 100 -- cgit v1.2.3 From 428b44e15591071cfcd69af365d878cfd876f9d3 Mon Sep 17 00:00:00 2001 From: Kilian Date: Mon, 9 Dec 2019 16:35:49 +0100 Subject: calling ot.emd test --- test/test_ot.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/test/test_ot.py b/test/test_ot.py index 1343604..25cdfd4 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -14,8 +14,9 @@ from ot.datasets import make_1D_gauss as gauss import pytest + def test_emd_dimension_mismatch(): - # test emd and emd2 for simple identity + # test emd and emd2 for dimension mismatch n_samples = 100 n_features = 2 rng = np.random.RandomState(0) @@ -25,9 +26,9 @@ def test_emd_dimension_mismatch(): M = ot.dist(x, x) - np.testing.assert_raises(AssertionError, emd, a, a, M) + np.testing.assert_raises(AssertionError, ot.emd, a, a, M) - np.testing.assert_raises(AssertionError, emd2, a, a, M) + np.testing.assert_raises(AssertionError, ot.emd2, a, a, M) def test_emd_emd2(): -- cgit v1.2.3 From 92dbe259032d340a259209e477e9aac74897689e Mon Sep 17 00:00:00 2001 From: Kilian Date: Mon, 9 Dec 2019 16:43:54 +0100 Subject: pep8 --- test/test_ot.py | 1 - 1 file changed, 1 deletion(-) diff --git a/test/test_ot.py b/test/test_ot.py index 25cdfd4..42a3d0a 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -14,7 +14,6 @@ from ot.datasets import make_1D_gauss as gauss import pytest - def test_emd_dimension_mismatch(): # test emd and emd2 for dimension mismatch n_samples = 100 -- cgit v1.2.3 From a9bbc2cfdffd22ceee3256102e470df6c25338f3 Mon Sep 17 00:00:00 2001 From: Kilian Date: Tue, 10 Dec 2019 11:23:50 +0100 Subject: change or in assert by and --- ot/lp/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index f77c3d7..4cce41c 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -109,7 +109,7 @@ def emd(a, b, M, numItermax=100000, log=False): if len(b) == 0: b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] - assert (a.shape[0] == M.shape[0] or b.shape[0] == M.shape[1]), \ + assert (a.shape[0] == M.shape[0] and b.shape[0] == M.shape[1]), \ "Dimension mismatch, check dimensions of M with a and b" G, cost, u, v, result_code = emd_c(a, b, M, numItermax) @@ -215,7 +215,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), if len(b) == 0: b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] - assert (a.shape[0] == M.shape[0] or b.shape[0] == M.shape[1]), \ + assert (a.shape[0] == M.shape[0] and b.shape[0] == M.shape[1]), \ "Dimension mismatch, check dimensions of M with a and b" if log or return_matrix: -- cgit v1.2.3 From 3cb03158c42dde141d6f33973ea6e3394b9dc3d4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 18 Dec 2019 10:15:30 +0100 Subject: cleanup variable name dense --- ot/lp/__init__.py | 30 ++++++++++++++---------------- ot/lp/emd_wrap.pyx | 26 +++++++++++++------------- 2 files changed, 27 insertions(+), 29 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index d476071..bb9829a 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -107,7 +107,6 @@ def emd(a, b, M, numItermax=100000, log=False, dense=True): b = np.asarray(b, dtype=np.float64) M = np.asarray(M, dtype=np.float64) - sparse= not dense # if empty array given then use uniform distributions if len(a) == 0: @@ -115,11 +114,11 @@ def emd(a, b, M, numItermax=100000, log=False, dense=True): if len(b) == 0: b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] - if sparse: - Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse) - G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + if dense: + G, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense) else: - G, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse) + Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense) + G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) result_code_string = check_result(result_code) if log: @@ -217,8 +216,6 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), b = np.asarray(b, dtype=np.float64) M = np.asarray(M, dtype=np.float64) - sparse=not dense - # problem with pikling Forks if sys.platform.endswith('win32'): processes=1 @@ -231,12 +228,11 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), if log or return_matrix: def f(b): - - if sparse: - Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse) - G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + if dense: + G, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense) else: - G, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse) + Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense) + G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) result_code_string = check_result(result_code) log = {} @@ -249,11 +245,13 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), return [cost, log] else: def f(b): - if sparse: - Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse) - G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + if dense: + G, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense) else: - G, cost, u, v, result_code = emd_c(a, b, M, numItermax,sparse) + Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense) + G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + + result_code_string = check_result(result_code) check_result(result_code) return cost diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 4e3586d..636a9e3 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -46,7 +46,7 @@ def check_result(result_code): @cython.boundscheck(False) @cython.wraparound(False) -def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mode="c"] b, np.ndarray[double, ndim=2, mode="c"] M, int max_iter, bint sparse): +def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mode="c"] b, np.ndarray[double, ndim=2, mode="c"] M, int max_iter, bint dense): """ Solves the Earth Movers distance problem and returns the optimal transport matrix @@ -110,8 +110,19 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod if not len(b): b=np.ones((n2,))/n2 - if sparse: + if dense: + # init OT matrix + G=np.zeros([n1, n2]) + + # calling the function + result_code = EMD_wrap(n1, n2, a.data, b.data, M.data, G.data, alpha.data, beta.data, &cost, max_iter) + + return G, cost, alpha, beta, result_code + + else: + + # init sparse OT matrix Gv=np.zeros(nmax) iG=np.zeros(nmax,dtype=np.int) jG=np.zeros(nmax,dtype=np.int) @@ -123,17 +134,6 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod return Gv[:nG], iG[:nG], jG[:nG], cost, alpha, beta, result_code - else: - - - G=np.zeros([n1, n2]) - - - # calling the function - result_code = EMD_wrap(n1, n2, a.data, b.data, M.data, G.data, alpha.data, beta.data, &cost, max_iter) - - return G, cost, alpha, beta, result_code - @cython.boundscheck(False) @cython.wraparound(False) -- cgit v1.2.3 From d97f81dd731c4b1132939500076fd48c89f19d1f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 18 Dec 2019 10:17:31 +0100 Subject: update test --- test/test_ot.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_ot.py b/test/test_ot.py index fbacd8b..3dd544c 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -128,7 +128,7 @@ def test_emd_sparse(): M = ot.dist(x, x2) - G = ot.emd([], [], M) + G = ot.emd([], [], M, dense=True) Gs = ot.emd([], [], M, dense=False) -- cgit v1.2.3 From fc6afbd19e75e10b539062e012583c283750d9f6 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 18 Dec 2019 10:48:58 +0100 Subject: correct documentation in pyx file --- ot/lp/emd_wrap.pyx | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 636a9e3..3220d12 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -79,8 +79,8 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod max_iter : int The maximum number of iterations before stopping the optimization algorithm if it has not converged. - sparse : bool - Returning a sparse transport matrix if set to True + dense : bool + Return a sparse transport matrix if set to False Returns ------- -- cgit v1.2.3 From e9954bbd959be41c59136cf921fbf094b127eb4e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 18 Dec 2019 12:56:55 +0100 Subject: cleanup emd.h and pyx file --- ot/lp/EMD.h | 4 ---- ot/lp/emd_wrap.pyx | 4 ---- 2 files changed, 8 deletions(-) diff --git a/ot/lp/EMD.h b/ot/lp/EMD.h index fc94211..2adaace 100644 --- a/ot/lp/EMD.h +++ b/ot/lp/EMD.h @@ -36,9 +36,5 @@ int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, long *iG, long *jG, double *G, long * nG, double* alpha, double* beta, double *cost, int maxIter); -int EMD_wrap_all_sparse(int n1, int n2, double *X, double *Y, - long *iD, long *jD, double *D, long nD, - long *iG, long *jG, double *G, long * nG, - double* alpha, double* beta, double *cost, int maxIter); #endif diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 3220d12..c0d7128 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -23,10 +23,6 @@ cdef extern from "EMD.h": int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, long *iG, long *jG, double *G, long * nG, double* alpha, double* beta, double *cost, int maxIter) - int EMD_wrap_all_sparse(int n1, int n2, double *X, double *Y, - long *iD, long *jD, double *D, long nD, - long *iG, long *jG, double *G, long * nG, - double* alpha, double* beta, double *cost, int maxIter) cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED, MAX_ITER_REACHED -- cgit v1.2.3 From 3582b6ffe571cb96b69dc6c356ef8d946df57fe7 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Mon, 6 Jan 2020 11:33:01 +0100 Subject: add screenkhorn: screening Sinkhorn algorithm --- ot/bregman.py | 288 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 287 insertions(+), 1 deletion(-) diff --git a/ot/bregman.py b/ot/bregman.py index ba5c7ba..61b8605 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -8,13 +8,15 @@ Bregman projections for regularized OT # Kilian Fatras # Titouan Vayer # Hicham Janati +# Mokhtar Z. Alaya # # License: MIT License import numpy as np import warnings from .utils import unif, dist - +import bottleneck +from scipy.optimize import fmin_l_bfgs_b def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): @@ -1787,3 +1789,287 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli sinkhorn_div = sinkhorn_loss_ab - 1 / 2 * (sinkhorn_loss_a + sinkhorn_loss_b) return max(0, sinkhorn_div) + +def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=True, verbose=False): + + """" + Screening Sinkhorn Algorithm for Regularized Optimal Transport. + + Parameters + ---------- + a : `numpy.ndarray`, shape=(ns,) + samples weights in the source domain. + + b : `numpy.ndarray`, shape=(nt,) + samples weights in the target domain. + + M : `numpy.ndarray`, shape=(ns, nt) + Cost matrix. + + reg : `float` + Level of the entropy regularisation. + + ns_budget: `int` + Number budget of points to be keeped in the source domain. + + nt_budget: `int` + Number budget of points to be keeped in the target domain. + + uniform: `bool`, default=True + If `True`, a_i = 1. / ns and b_j = 1. / nt + + restricted: `bool`, default=True + If `True`, a warm-start initialization for the L-BFGS-B solver + using a restricted Sinkhorn algorithm with at most 5 iterations. + + verbose: `bool`, default=False + If `True`, dispaly informations along iterations. + + Returns + ------- + Gsc : `numpy.ndarray`, shape=(ns, nt) + Screened optimal transportation matrix for the given parameters. + + References: + ----------- + .. [1] M. Z. Alaya, Maxime Bérar, Gilles Gasso, Alain Rakotomamonjy. Screening Sinkhorn Algorithm for Regularized + Optimal Transport, NeurIPS 2019. + + """ + a = np.asarray(a, dtype=np.float64) + b = np.asarray(b, dtype=np.float64) + + # if the "autograd" package is needed for some experiments, we then have to change the instance of the cost matrix M + # from "ArrayBox"-type to "np.array"-type as follows: + if isinstance(M, np.ndarray) == False: + M = M._value + + M = np.asarray(M, dtype=np.float64) + ns, nt = M.shape + + # calculate the Gibbs kernel + K = np.empty_like(M) + np.divide(M, - reg, out=K) + np.exp(K, out=K) + + def projection(u, epsilon): + u[np.where(u <= epsilon)] = epsilon + return u + + # ----------------------------------------------------------------------------------------------------------------# + # Step 1: Screening Pre-processing # + # ----------------------------------------------------------------------------------------------------------------# + + if ns_budget == ns and nt_budget == nt: + # full number of budget points (ns, nt) = (ns_budget, nt_budget) + I = list(range(ns)) + J = list(range(nt)) + epsilon = 0.0 + kappa = 1.0 + + cst_u = 0. + cst_v = 0. + + bounds_u = [(0.0, np.inf)] * ns + bounds_v = [(0.0, np.inf)] * nt + + a_I = a + b_J = b + K_min = K.min() + K_IJ = K + K_IJc = [] + K_IcJ = [] + + vec_eps_IJc = np.zeros(nt) + vec_eps_IcJ = np.zeros(ns) + + else: + # sum of rows and columns of K + K_sum_cols = K.sum(axis=1) + K_sum_rows = K.sum(axis=0) + + if uniform: + if ns / ns_budget < 4: + aK_sort = np.sort(K_sum_cols) + epsilon_u_square = a[0] / aK_sort[ns_budget - 1] + else: + aK_sort = bottleneck.partition(K_sum_cols, ns_budget - 1)[ns_budget - 1] + epsilon_u_square = a[0] / aK_sort + + if nt / ns_budget < 4: + bK_sort = np.sort(K_sum_rows) + epsilon_v_square = b[0] / bK_sort[ns_budget - 1] + else: + bK_sort = bottleneck.partition(K_sum_rows, nt_budget - 1)[nt_budget - 1] + epsilon_v_square = b[0] / bK_sort + else: + aK = a / K_sum_cols + bK = b / K_sum_rows + + aK_sort = np.sort(aK)[::-1] + epsilon_u_square = aK_sort[ns_budget - 1] + + bK_sort = np.sort(bK)[::-1] + epsilon_v_square = bK_sort[ns_budget - 1] + + # active sets I and J (see Proposition .. in [1]) + I = np.where(a >= epsilon_u_square * K_sum_cols)[0].tolist() + J = np.where(b >= epsilon_v_square * K_sum_rows)[0].tolist() + + if len(I) != ns_budget: + if uniform: + aK = a / K_sum_cols + aK_sort = np.sort(aK)[::-1] + epsilon_u_square = aK_sort[ns_budget - 1:ns_budget + 1].mean() + I = np.where(a >= epsilon_u_square * K_sum_cols)[0].tolist() + + if len(J) != nt_budget: + if uniform: + bK = b / K_sum_rows + bK_sort = np.sort(bK)[::-1] + epsilon_v_square = bK_sort[ns_budget - 1:ns_budget + 1].mean() + J = np.where(b >= epsilon_v_square * K_sum_rows)[0].tolist() + + epsilon = (epsilon_u_square * epsilon_v_square) ** (1 / 4) + kappa = (epsilon_v_square / epsilon_u_square) ** (1 / 2) + + if verbose: + print("Epsilon = %s\n" %epsilon) + print("Scaling factor = %s\n" %kappa) + + if verbose: + print('|I_active| = %s \t |J_active| = %s ' %(len(I), len(J))) + + # Ic, Jc: complementary of the active sets I and J + Ic = list(set(list(range(ns))) - set(I)) + Jc = list(set(list(range(nt))) - set(J)) + + K_IJ = K[np.ix_(I, J)] + K_IcJ = K[np.ix_(Ic, J)] + K_IJc = K[np.ix_(I, Jc)] + + K_min = K_IJ.min() + if K_min == 0: + K_min = np.finfo(float).tiny + + # a_I, b_J, a_Ic, b_Jc + a_I = a[I] + b_J = b[J] + if not uniform: + a_I_min = a_I.min() + a_I_max = a_I.max() + b_J_max = b_J.max() + b_J_min = b_J.min() + else: + a_I_min = a_I[0] + a_I_max = a_I[0] + b_J_max = b_J[0] + b_J_min = b_J[0] + + # box constraints in L-BFGS-B (see Proposition 1 in [1]) + bounds_u = [(max(kappa * a_I_min / (epsilon * (nt - ns_budget) + ns_budget * (b_J_max / ( + epsilon * ns * K_min))), epsilon / kappa), a_I_max / (epsilon * nt * K_min))] * ns_budget + + bounds_v = [(max(b_J_min / (epsilon * (ns - ns_budget) + ns_budget * (a_I_max / (epsilon * nt * K_min))), \ + epsilon * kappa), b_J_max / (epsilon * ns * K_min))] * ns_budget + + # pre-calculated constants for the objective + vec_eps_IJc = epsilon * kappa * (K_IJc * np.ones(nt - ns_budget).reshape((1, -1))).sum(axis=1) + vec_eps_IcJ = (epsilon / kappa) * (np.ones(ns - ns_budget).reshape((-1, 1)) * K_IcJ).sum(axis=0) + + # initialisation + u0 = np.full(ns_budget, (1. / ns_budget) + epsilon / kappa) + v0 = np.full(nt_budget, (1. / nt_budget) + epsilon * kappa) + + # pre-calculed constants for Restricted Sinkhorn (see Algorithm 2 in [1]) + if restricted: + if ns_budget != ns or ns_budget != nt: + cst_u = kappa * epsilon * K_IJc.sum(axis=1) + cst_v = epsilon * K_IcJ.sum(axis=0) / kappa + + cpt = 1 + while (cpt < 5): # 5 iterations + K_IJ_v = K_IJ.T @ u0 + cst_v + v0 = b_J / (kappa * K_IJ_v) + KIJ_u = K_IJ @ v0 + cst_u + u0 = (kappa * a_I) / KIJ_u + cpt += 1 + + u0 = projection(u0, epsilon / kappa) + v0 = projection(v0, epsilon * kappa) + + else: + u0 = u0 + v0 = v0 + + def restricted_sinkhorn(usc, vsc, max_iter=5): + """ + Restricted Sinkhorn Algorithm as a warm-start initialized point for L-BFGS-B (see Algorithm 2 in [1]). + """ + cpt = 1 + while (cpt < max_iter): + K_IJ_v = K_IJ.T @ usc + cst_v + vsc = b_J / (kappa * K_IJ_v) + KIJ_u = K_IJ @ vsc + cst_u + usc = (kappa * a_I) / KIJ_u + cpt += 1 + + usc = projection(usc, epsilon / kappa) + vsc = projection(vsc, epsilon * kappa) + + return usc, vsc + + def screened_obj(usc, vsc): + part_IJ = usc @ K_IJ @ vsc - kappa * a_I @ np.log(usc) - (1. / kappa) * b_J @ np.log(vsc) + part_IJc = usc @ vec_eps_IJc + part_IcJ = vec_eps_IcJ @ vsc + psi_epsilon = part_IJ + part_IJc + part_IcJ + return psi_epsilon + + def screened_grad(usc, vsc): + # gradients of Psi_epsilon w.r.t u and v + grad_u = K_IJ @ vsc + vec_eps_IJc - kappa * a_I / usc + grad_v = K_IJ.T @ usc + vec_eps_IcJ - (1. / kappa) * b_J / vsc + return grad_u, grad_v + + def bfgspost(theta): + + u = theta[:ns_budget] + v = theta[ns_budget:] + # objective + f = screened_obj(u, v) + # gradient + g_u, g_v = screened_grad(u, v) + g = np.hstack([g_u, g_v]) + return f, g + + #----------------------------------------------------------------------------------------------------------------# + # Step 2: L-BFGS-B solver # + #----------------------------------------------------------------------------------------------------------------# + + u0, v0 = restricted_sinkhorn(u0, v0) + theta0 = np.hstack([u0, v0]) + maxiter = 10000 # max number of iterations + maxfun = 10000 # max number of function evaluations + pgtol = 1e-09 # final objective function accuracy + + bounds = bounds_u + bounds_v # constraint bounds + obj = lambda theta: bfgspost(theta) + + theta, _, _ = fmin_l_bfgs_b(func=obj, + x0=theta0, + bounds=bounds, + maxfun=maxfun, + pgtol=pgtol, + maxiter=maxiter) + + usc = theta[:ns_budget] + vsc = theta[ns_budget:] + + usc_full = np.full(ns, epsilon / kappa) + vsc_full = np.full(nt, epsilon * kappa) + usc_full[I] = usc + vsc_full[J] = vsc + + Gsc = usc_full.reshape((-1, 1)) * K * vsc_full.reshape((1, -1)) + return Gsc -- cgit v1.2.3 From d4b403ef3bda06db101d8a7c3dba1e0be36e9c80 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Tue, 7 Jan 2020 11:18:43 +0100 Subject: close "bottleneck" tag --- ot/bregman.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ot/bregman.py b/ot/bregman.py index 61b8605..58c76d0 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -15,7 +15,6 @@ Bregman projections for regularized OT import numpy as np import warnings from .utils import unif, dist -import bottleneck from scipy.optimize import fmin_l_bfgs_b def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, @@ -1893,6 +1892,7 @@ def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=Tru aK_sort = np.sort(K_sum_cols) epsilon_u_square = a[0] / aK_sort[ns_budget - 1] else: + import bottleneck aK_sort = bottleneck.partition(K_sum_cols, ns_budget - 1)[ns_budget - 1] epsilon_u_square = a[0] / aK_sort @@ -1900,6 +1900,7 @@ def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=Tru bK_sort = np.sort(K_sum_rows) epsilon_v_square = b[0] / bK_sort[ns_budget - 1] else: + import bottleneck bK_sort = bottleneck.partition(K_sum_rows, nt_budget - 1)[nt_budget - 1] epsilon_v_square = b[0] / bK_sort else: -- cgit v1.2.3 From 3979fe909c64403337ed9259de9b8673dd789a18 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Tue, 7 Jan 2020 11:47:41 +0100 Subject: update readme --- README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index d8bb051..08bacd4 100644 --- a/README.md +++ b/README.md @@ -180,6 +180,7 @@ The contributors to this library are * [Vayer Titouan](https://tvayer.github.io/) * [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT) * [Romain Tavenard](https://rtavenar.github.io/) (1d Wasserstein) +* [Mokhtar Z. Alaya](http://mzalaya.github.io/) (Screenkhorn) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): @@ -252,4 +253,6 @@ You can also post bug reports and feature requests in Github issues. Make sure t [24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http://proceedings.mlr.press/v97/titouan19a.html) Proceedings of the 36th International Conference on Machine Learning (ICML). -[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2019). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/) Advances in Neural Information Processing Systems (NIPS). +[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/) Advances in Neural Information Processing Systems (NIPS). + +[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). Screening Sinkhorn Algorithm for Regularized Optimal Transport (https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport) Advances in Neural Information Processing Systems (NIPS). -- cgit v1.2.3 From 0d33c3f42b1c22768c45299589b9b699f4f9f924 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Tue, 7 Jan 2020 11:49:27 +0100 Subject: update readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 08bacd4..ce59e59 100644 --- a/README.md +++ b/README.md @@ -255,4 +255,4 @@ You can also post bug reports and feature requests in Github issues. Make sure t [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/) Advances in Neural Information Processing Systems (NIPS). -[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). Screening Sinkhorn Algorithm for Regularized Optimal Transport (https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport) Advances in Neural Information Processing Systems (NIPS). +[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport) Advances in Neural Information Processing Systems (NIPS). -- cgit v1.2.3 From e11dbadefab138a6d1d41c1e08d9c58c9b294e99 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Tue, 7 Jan 2020 11:51:14 +0100 Subject: update README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index ce59e59..6bb8d24 100644 --- a/README.md +++ b/README.md @@ -255,4 +255,4 @@ You can also post bug reports and feature requests in Github issues. Make sure t [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/) Advances in Neural Information Processing Systems (NIPS). -[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport) Advances in Neural Information Processing Systems (NIPS). +[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 32 (NIPS). -- cgit v1.2.3 From 4488a0b577075daac13fd381f09c2a1969273bd1 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Tue, 7 Jan 2020 13:20:05 +0100 Subject: update README --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 6bb8d24..f82099e 100644 --- a/README.md +++ b/README.md @@ -28,6 +28,7 @@ It provides the following solvers: * Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) * Non regularized free support Wasserstein barycenters [20]. * Unbalanced OT with KL relaxation distance and barycenter [10, 25]. +* Screening Sinkhorn Algorithm for OT [26]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. -- cgit v1.2.3 From e821872581e5e62d984883d8b8f881e35160be56 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Tue, 7 Jan 2020 13:22:44 +0100 Subject: update README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index f82099e..987adf1 100644 --- a/README.md +++ b/README.md @@ -256,4 +256,4 @@ You can also post bug reports and feature requests in Github issues. Make sure t [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/) Advances in Neural Information Processing Systems (NIPS). -[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 32 (NIPS). +[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 33 (NIPS). -- cgit v1.2.3 From 27b6740ea95b609ecdb103fbff7c1bbc62071ddc Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Tue, 7 Jan 2020 15:29:23 +0100 Subject: improve documentation of screenkhorn add Exception at the beginning to check the installation of bottleneck module --- ot/bregman.py | 62 +++++++++++++++++++++++++++++++++++++++++++---------------- 1 file changed, 45 insertions(+), 17 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 58c76d0..456b61f 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1789,52 +1789,82 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli sinkhorn_div = sinkhorn_loss_ab - 1 / 2 * (sinkhorn_loss_a + sinkhorn_loss_b) return max(0, sinkhorn_div) -def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=True, verbose=False): +def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=True, verbose=False, log=False): """" - Screening Sinkhorn Algorithm for Regularized Optimal Transport. + Screening Sinkhorn Algorithm for Regularized Optimal Transport + + The function solves an approximate dual of Sinkhorn divergence [2] which is written as the following optimization problem: + + ..math:: + (u, v) = \argmin_{u, v} 1_{ns}.T B(u,v) 1_{nt} - <\kappa u, a> - + + where B(u,v) = \diag(e^u) K \diag(e^v), with K = e^{-M/reg} and + + s.t. e^{u_i} >= \epsilon / \kappa, for all i in {1, ..., ns} + + e^{v_j} >= \epsilon \kappa, for all j in {1, ..., nt} + + The parameters \kappa and \epsilon are determined w.r.t the couple number budget of points (ns_budget, nt_budget), see Equation (5) in [26] + Parameters ---------- a : `numpy.ndarray`, shape=(ns,) - samples weights in the source domain. + samples weights in the source domain b : `numpy.ndarray`, shape=(nt,) - samples weights in the target domain. + samples weights in the target domain M : `numpy.ndarray`, shape=(ns, nt) Cost matrix. reg : `float` - Level of the entropy regularisation. + Level of the entropy regularisation ns_budget: `int` - Number budget of points to be keeped in the source domain. + Number budget of points to be keeped in the source domain nt_budget: `int` - Number budget of points to be keeped in the target domain. + Number budget of points to be keeped in the target domain uniform: `bool`, default=True If `True`, a_i = 1. / ns and b_j = 1. / nt restricted: `bool`, default=True If `True`, a warm-start initialization for the L-BFGS-B solver - using a restricted Sinkhorn algorithm with at most 5 iterations. + using a restricted Sinkhorn algorithm with at most 5 iterations verbose: `bool`, default=False - If `True`, dispaly informations along iterations. - + If `True`, dispaly informations along iterations + + Dependency + ---------- + To gain more efficiency, screenkhorn needs to call the "Bottleneck" package (https://pypi.org/project/Bottleneck/) in the screening pre-processing step. + If Bottleneck isn't installed, the following error message appears: + "Bottleneck module doesn't exist. Install it from https://pypi.org/project/Bottleneck/" + + Returns ------- - Gsc : `numpy.ndarray`, shape=(ns, nt) - Screened optimal transportation matrix for the given parameters. + gamma : `numpy.ndarray`, shape=(ns, nt) + Screened optimal transportation matrix for the given parameters + + log : `dict`, default=False + Log dictionary return only if log==True in parameters - References: + + References ----------- - .. [1] M. Z. Alaya, Maxime Bérar, Gilles Gasso, Alain Rakotomamonjy. Screening Sinkhorn Algorithm for Regularized - Optimal Transport, NeurIPS 2019. + .. [26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). Screening Sinkhorn Algorithm for Regularized Optimal Transport (NIPS) 33, 2019 """ + # check if bottleneck module exists + try: + import bottleneck + except ImportError as e: + print("Bottleneck module doesn't exist. Install it from https://pypi.org/project/Bottleneck/") + a = np.asarray(a, dtype=np.float64) b = np.asarray(b, dtype=np.float64) @@ -1892,7 +1922,6 @@ def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=Tru aK_sort = np.sort(K_sum_cols) epsilon_u_square = a[0] / aK_sort[ns_budget - 1] else: - import bottleneck aK_sort = bottleneck.partition(K_sum_cols, ns_budget - 1)[ns_budget - 1] epsilon_u_square = a[0] / aK_sort @@ -1900,7 +1929,6 @@ def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=Tru bK_sort = np.sort(K_sum_rows) epsilon_v_square = b[0] / bK_sort[ns_budget - 1] else: - import bottleneck bK_sort = bottleneck.partition(K_sum_rows, nt_budget - 1)[nt_budget - 1] epsilon_v_square = b[0] / bK_sort else: -- cgit v1.2.3 From 7d603f0aa642dbe49da70ad3143fd4e9c74a22c5 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Tue, 7 Jan 2020 15:34:22 +0100 Subject: delete "ArrayBox"-type test of dist. matrix M --- ot/bregman.py | 8 +------- 1 file changed, 1 insertion(+), 7 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 456b61f..dedbf28 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1867,12 +1867,6 @@ def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=Tru a = np.asarray(a, dtype=np.float64) b = np.asarray(b, dtype=np.float64) - - # if the "autograd" package is needed for some experiments, we then have to change the instance of the cost matrix M - # from "ArrayBox"-type to "np.array"-type as follows: - if isinstance(M, np.ndarray) == False: - M = M._value - M = np.asarray(M, dtype=np.float64) ns, nt = M.shape @@ -1882,7 +1876,7 @@ def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=Tru np.exp(K, out=K) def projection(u, epsilon): - u[np.where(u <= epsilon)] = epsilon + u[u <= epsilon] = epsilon return u # ----------------------------------------------------------------------------------------------------------------# -- cgit v1.2.3 From be33a36eb1916968d9281c5a76e12e04b7ddb686 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Tue, 7 Jan 2020 15:51:58 +0100 Subject: replace @ operator by np.dot --- ot/bregman.py | 26 +++++++++++++------------- 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index dedbf28..4a899a6 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1885,8 +1885,8 @@ def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=Tru if ns_budget == ns and nt_budget == nt: # full number of budget points (ns, nt) = (ns_budget, nt_budget) - I = list(range(ns)) - J = list(range(nt)) + I = list(np.arange(ns)) + J = list(np.arange(nt)) epsilon = 0.0 kappa = 1.0 @@ -1989,7 +1989,7 @@ def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=Tru b_J_max = b_J[0] b_J_min = b_J[0] - # box constraints in L-BFGS-B (see Proposition 1 in [1]) + # box constraints in L-BFGS-B (see Proposition 1 in [26]) bounds_u = [(max(kappa * a_I_min / (epsilon * (nt - ns_budget) + ns_budget * (b_J_max / ( epsilon * ns * K_min))), epsilon / kappa), a_I_max / (epsilon * nt * K_min))] * ns_budget @@ -2004,7 +2004,7 @@ def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=Tru u0 = np.full(ns_budget, (1. / ns_budget) + epsilon / kappa) v0 = np.full(nt_budget, (1. / nt_budget) + epsilon * kappa) - # pre-calculed constants for Restricted Sinkhorn (see Algorithm 2 in [1]) + # pre-calculed constants for Restricted Sinkhorn (see Algorithm 2 in [26]) if restricted: if ns_budget != ns or ns_budget != nt: cst_u = kappa * epsilon * K_IJc.sum(axis=1) @@ -2012,9 +2012,9 @@ def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=Tru cpt = 1 while (cpt < 5): # 5 iterations - K_IJ_v = K_IJ.T @ u0 + cst_v + K_IJ_v = np.dot(K_IJ.T, u0) + cst_v v0 = b_J / (kappa * K_IJ_v) - KIJ_u = K_IJ @ v0 + cst_u + KIJ_u = np.dot(K_IJ, v0) + cst_u u0 = (kappa * a_I) / KIJ_u cpt += 1 @@ -2031,9 +2031,9 @@ def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=Tru """ cpt = 1 while (cpt < max_iter): - K_IJ_v = K_IJ.T @ usc + cst_v + K_IJ_v = np.dot(K_IJ.T, usc) + cst_v vsc = b_J / (kappa * K_IJ_v) - KIJ_u = K_IJ @ vsc + cst_u + KIJ_u = np.dot(K_IJ, vsc) + cst_u usc = (kappa * a_I) / KIJ_u cpt += 1 @@ -2043,16 +2043,16 @@ def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=Tru return usc, vsc def screened_obj(usc, vsc): - part_IJ = usc @ K_IJ @ vsc - kappa * a_I @ np.log(usc) - (1. / kappa) * b_J @ np.log(vsc) - part_IJc = usc @ vec_eps_IJc - part_IcJ = vec_eps_IcJ @ vsc + part_IJ = np.dot(np.dot(usc, K_IJ), vsc) - kappa * np.dot(a_I, np.log(usc)) - (1. / kappa) * np.dot(b_J, np.log(vsc)) + part_IJc = np.dot(usc, vec_eps_IJc) + part_IcJ = np.dot(vec_eps_IcJ, vsc) psi_epsilon = part_IJ + part_IJc + part_IcJ return psi_epsilon def screened_grad(usc, vsc): # gradients of Psi_epsilon w.r.t u and v - grad_u = K_IJ @ vsc + vec_eps_IJc - kappa * a_I / usc - grad_v = K_IJ.T @ usc + vec_eps_IcJ - (1. / kappa) * b_J / vsc + grad_u = np.dot(K_IJ, vsc) + vec_eps_IJc - kappa * a_I / usc + grad_v = np.dot(K_IJ.T, usc) + vec_eps_IcJ - (1. / kappa) * b_J / vsc return grad_u, grad_v def bfgspost(theta): -- cgit v1.2.3 From 69c666fc82553bed0fbbc7fc17a906eb2487ddf7 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Tue, 7 Jan 2020 16:03:18 +0100 Subject: set default param. for LBFGS in the function's prototype --- ot/bregman.py | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 4a899a6..12eaa65 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1789,7 +1789,8 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli sinkhorn_div = sinkhorn_loss_ab - 1 / 2 * (sinkhorn_loss_a + sinkhorn_loss_b) return max(0, sinkhorn_div) -def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=True, verbose=False, log=False): +def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=True, + maxiter=10000, maxfun=10000, pgtol=1e-09, verbose=False, log=False): """" Screening Sinkhorn Algorithm for Regularized Optimal Transport @@ -1834,6 +1835,15 @@ def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=Tru restricted: `bool`, default=True If `True`, a warm-start initialization for the L-BFGS-B solver using a restricted Sinkhorn algorithm with at most 5 iterations + + maxiter : `int`, default=10000 + Maximum number of iterations in LBFGS solver + + maxfun : `int`, default=10000 + Maximum number of function evaluations in LBFGS solver + + pgtol : `float`, default=1e-09 + Final objective function accuracy in LBFGS solver verbose: `bool`, default=False If `True`, dispaly informations along iterations @@ -2056,7 +2066,6 @@ def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=Tru return grad_u, grad_v def bfgspost(theta): - u = theta[:ns_budget] v = theta[ns_budget:] # objective @@ -2072,10 +2081,7 @@ def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=Tru u0, v0 = restricted_sinkhorn(u0, v0) theta0 = np.hstack([u0, v0]) - maxiter = 10000 # max number of iterations - maxfun = 10000 # max number of function evaluations - pgtol = 1e-09 # final objective function accuracy - + bounds = bounds_u + bounds_v # constraint bounds obj = lambda theta: bfgspost(theta) -- cgit v1.2.3 From 05a97b44a7137ef6cb0397cca3bb2ea1f8736ac5 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Tue, 7 Jan 2020 16:10:36 +0100 Subject: fix default values for the budget arguments --- ot/bregman.py | 16 ++++++++++++---- 1 file changed, 12 insertions(+), 4 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 12eaa65..8a20307 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1789,7 +1789,7 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli sinkhorn_div = sinkhorn_loss_ab - 1 / 2 * (sinkhorn_loss_a + sinkhorn_loss_b) return max(0, sinkhorn_div) -def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=True, +def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=True, restricted=True, maxiter=10000, maxfun=10000, pgtol=1e-09, verbose=False, log=False): """" @@ -1823,11 +1823,13 @@ def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=Tru reg : `float` Level of the entropy regularisation - ns_budget: `int` + ns_budget: `int`, deafult=None Number budget of points to be keeped in the source domain + If it is None then 50% of the source sample points will be keeped - nt_budget: `int` + nt_budget: `int`, deafult=None Number budget of points to be keeped in the target domain + If it is None then 50% of the target sample points will be keeped uniform: `bool`, default=True If `True`, a_i = 1. / ns and b_j = 1. / nt @@ -1874,11 +1876,17 @@ def screenkhorn(a, b, M, reg, ns_budget, nt_budget, uniform=True, restricted=Tru import bottleneck except ImportError as e: print("Bottleneck module doesn't exist. Install it from https://pypi.org/project/Bottleneck/") - + a = np.asarray(a, dtype=np.float64) b = np.asarray(b, dtype=np.float64) M = np.asarray(M, dtype=np.float64) ns, nt = M.shape + + # by default, we keep only 50% of the sapmle data points + if ns_budget is None: + ns_budget = int(np.floor(0.5*ns)) + if nt_budget is None: + ns_budget = int(np.floor(0.5*ns)) # calculate the Gibbs kernel K = np.empty_like(M) -- cgit v1.2.3 From 92b7075568207a468cc821cd6a21e130b9d89f96 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Tue, 7 Jan 2020 16:21:40 +0100 Subject: replace reshape by numpy slicing in return --- ot/bregman.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 8a20307..8cfea7e 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -2107,6 +2107,14 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=True, rest vsc_full = np.full(nt, epsilon * kappa) usc_full[I] = usc vsc_full[J] = vsc + + if log: + log['u'] = usc_full + log['v'] = vsc_full + + gamma = usc_full[:, None] * K * vsc_full[None, :] - Gsc = usc_full.reshape((-1, 1)) * K * vsc_full.reshape((1, -1)) - return Gsc + if log: + return gamma, log + else: + return gamma -- cgit v1.2.3 From cfe26140af85082197151d0dc50915c0bd71f602 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Tue, 7 Jan 2020 19:00:50 +0100 Subject: fix definitions complementary active sets Ic, Jc --- ot/bregman.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 8cfea7e..c0634b1 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1982,8 +1982,8 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=True, rest print('|I_active| = %s \t |J_active| = %s ' %(len(I), len(J))) # Ic, Jc: complementary of the active sets I and J - Ic = list(set(list(range(ns))) - set(I)) - Jc = list(set(list(range(nt))) - set(J)) + Ic = list(set(np.arange(ns)) - set(I)) + Jc = list(set(np.arange(nt)) - set(J)) K_IJ = K[np.ix_(I, J)] K_IcJ = K[np.ix_(Ic, J)] -- cgit v1.2.3 From 88fb534d83f42e45a42c0a9773ccfe338cd3a811 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Tue, 7 Jan 2020 19:09:30 +0100 Subject: fix typos in documentation --- ot/bregman.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index c0634b1..ceb7754 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1818,7 +1818,7 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=True, rest samples weights in the target domain M : `numpy.ndarray`, shape=(ns, nt) - Cost matrix. + Cost matrix reg : `float` Level of the entropy regularisation @@ -2045,7 +2045,7 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=True, rest def restricted_sinkhorn(usc, vsc, max_iter=5): """ - Restricted Sinkhorn Algorithm as a warm-start initialized point for L-BFGS-B (see Algorithm 2 in [1]). + Restricted Sinkhorn Algorithm as a warm-start initialized point for L-BFGS-B (see Algorithm 2 in [26]) """ cpt = 1 while (cpt < max_iter): -- cgit v1.2.3 From 45119609cbc317f59beb92382c28de6c51290c53 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Wed, 8 Jan 2020 11:03:59 +0100 Subject: using binary indexing for definition the active sets --- ot/bregman.py | 44 +++++++++++++++++++++----------------------- 1 file changed, 21 insertions(+), 23 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index ceb7754..b664ac1 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1884,13 +1884,13 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=True, rest # by default, we keep only 50% of the sapmle data points if ns_budget is None: - ns_budget = int(np.floor(0.5*ns)) + ns_budget = int(np.floor(0.5*ns)) if nt_budget is None: - ns_budget = int(np.floor(0.5*ns)) + nt_budget = int(np.floor(0.5*ns)) # calculate the Gibbs kernel K = np.empty_like(M) - np.divide(M, - reg, out=K) + np.divide(M, -reg, out=K) np.exp(K, out=K) def projection(u, epsilon): @@ -1898,13 +1898,13 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=True, rest return u # ----------------------------------------------------------------------------------------------------------------# - # Step 1: Screening Pre-processing # + # Step 1: Screening pre-processing # # ----------------------------------------------------------------------------------------------------------------# if ns_budget == ns and nt_budget == nt: # full number of budget points (ns, nt) = (ns_budget, nt_budget) - I = list(np.arange(ns)) - J = list(np.arange(nt)) + I = np.arange(ns) + J = np.arange(nt) epsilon = 0.0 kappa = 1.0 @@ -1953,37 +1953,34 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=True, rest bK_sort = np.sort(bK)[::-1] epsilon_v_square = bK_sort[ns_budget - 1] - # active sets I and J (see Proposition .. in [1]) - I = np.where(a >= epsilon_u_square * K_sum_cols)[0].tolist() - J = np.where(b >= epsilon_v_square * K_sum_rows)[0].tolist() + # active sets I and J (see Lemma 1 in [26]) + I = np.where(a >= epsilon_u_square * K_sum_cols)[0] + J = np.where(b >= epsilon_v_square * K_sum_rows)[0] if len(I) != ns_budget: if uniform: aK = a / K_sum_cols aK_sort = np.sort(aK)[::-1] epsilon_u_square = aK_sort[ns_budget - 1:ns_budget + 1].mean() - I = np.where(a >= epsilon_u_square * K_sum_cols)[0].tolist() + I = np.where(a >= epsilon_u_square * K_sum_cols)[0] if len(J) != nt_budget: if uniform: bK = b / K_sum_rows bK_sort = np.sort(bK)[::-1] - epsilon_v_square = bK_sort[ns_budget - 1:ns_budget + 1].mean() - J = np.where(b >= epsilon_v_square * K_sum_rows)[0].tolist() + epsilon_v_square = bK_sort[nt_budget - 1:nt_budget + 1].mean() + J = np.where(b >= epsilon_v_square * K_sum_rows)[0] epsilon = (epsilon_u_square * epsilon_v_square) ** (1 / 4) kappa = (epsilon_v_square / epsilon_u_square) ** (1 / 2) - + if verbose: print("Epsilon = %s\n" %epsilon) - print("Scaling factor = %s\n" %kappa) - - if verbose: print('|I_active| = %s \t |J_active| = %s ' %(len(I), len(J))) # Ic, Jc: complementary of the active sets I and J - Ic = list(set(np.arange(ns)) - set(I)) - Jc = list(set(np.arange(nt)) - set(J)) + Ic = np.arange(ns)[~np.isin(np.arange(ns), I)] + Jc = np.arange(nt)[~np.isin(np.arange(nt), J)] K_IJ = K[np.ix_(I, J)] K_IcJ = K[np.ix_(Ic, J)] @@ -2109,12 +2106,13 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=True, rest vsc_full[J] = vsc if log: - log['u'] = usc_full - log['v'] = vsc_full + log['u'] = usc_full + log['v'] = vsc_full gamma = usc_full[:, None] * K * vsc_full[None, :] - + gamma = gamma / gamma.sum() + if log: - return gamma, log + return gamma, log else: - return gamma + return gamma -- cgit v1.2.3 From e00f46aa2ea11f0e88a5b2005caa7518ca109357 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Wed, 8 Jan 2020 18:49:16 +0100 Subject: fix binary indexing --- ot/bregman.py | 109 +++++++++++++++++++++++++++++----------------------------- 1 file changed, 55 insertions(+), 54 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index b664ac1..28377b0 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -17,6 +17,7 @@ import warnings from .utils import unif, dist from scipy.optimize import fmin_l_bfgs_b + def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): r""" @@ -1788,24 +1789,24 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli sinkhorn_div = sinkhorn_loss_ab - 1 / 2 * (sinkhorn_loss_a + sinkhorn_loss_b) return max(0, sinkhorn_div) - + + def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=True, restricted=True, maxiter=10000, maxfun=10000, pgtol=1e-09, verbose=False, log=False): - """" Screening Sinkhorn Algorithm for Regularized Optimal Transport - + The function solves an approximate dual of Sinkhorn divergence [2] which is written as the following optimization problem: - + ..math:: (u, v) = \argmin_{u, v} 1_{ns}.T B(u,v) 1_{nt} - <\kappa u, a> - - + where B(u,v) = \diag(e^u) K \diag(e^v), with K = e^{-M/reg} and - + s.t. e^{u_i} >= \epsilon / \kappa, for all i in {1, ..., ns} - + e^{v_j} >= \epsilon \kappa, for all j in {1, ..., nt} - + The parameters \kappa and \epsilon are determined w.r.t the couple number budget of points (ns_budget, nt_budget), see Equation (5) in [26] @@ -1837,31 +1838,31 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=True, rest restricted: `bool`, default=True If `True`, a warm-start initialization for the L-BFGS-B solver using a restricted Sinkhorn algorithm with at most 5 iterations - + maxiter : `int`, default=10000 Maximum number of iterations in LBFGS solver - + maxfun : `int`, default=10000 Maximum number of function evaluations in LBFGS solver - + pgtol : `float`, default=1e-09 Final objective function accuracy in LBFGS solver verbose: `bool`, default=False If `True`, dispaly informations along iterations - + Dependency ---------- To gain more efficiency, screenkhorn needs to call the "Bottleneck" package (https://pypi.org/project/Bottleneck/) in the screening pre-processing step. If Bottleneck isn't installed, the following error message appears: "Bottleneck module doesn't exist. Install it from https://pypi.org/project/Bottleneck/" - - + + Returns ------- gamma : `numpy.ndarray`, shape=(ns, nt) Screened optimal transportation matrix for the given parameters - + log : `dict`, default=False Log dictionary return only if log==True in parameters @@ -1876,17 +1877,17 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=True, rest import bottleneck except ImportError as e: print("Bottleneck module doesn't exist. Install it from https://pypi.org/project/Bottleneck/") - + a = np.asarray(a, dtype=np.float64) b = np.asarray(b, dtype=np.float64) M = np.asarray(M, dtype=np.float64) ns, nt = M.shape - - # by default, we keep only 50% of the sapmle data points + + # by default, we keep only 50% of the sample data points if ns_budget is None: - ns_budget = int(np.floor(0.5*ns)) + ns_budget = int(np.floor(0.5 * ns)) if nt_budget is None: - nt_budget = int(np.floor(0.5*ns)) + nt_budget = int(np.floor(0.5 * nt)) # calculate the Gibbs kernel K = np.empty_like(M) @@ -1903,20 +1904,19 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=True, rest if ns_budget == ns and nt_budget == nt: # full number of budget points (ns, nt) = (ns_budget, nt_budget) - I = np.arange(ns) - J = np.arange(nt) - epsilon = 0.0 - kappa = 1.0 + I = np.ones(ns, dtype=bool) + J = np.ones(nt, dtype=bool) + epsilon = 0.0 + kappa = 1.0 cst_u = 0. cst_v = 0. bounds_u = [(0.0, np.inf)] * ns bounds_v = [(0.0, np.inf)] * nt - + a_I = a b_J = b - K_min = K.min() K_IJ = K K_IJc = [] K_IcJ = [] @@ -1937,9 +1937,9 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=True, rest aK_sort = bottleneck.partition(K_sum_cols, ns_budget - 1)[ns_budget - 1] epsilon_u_square = a[0] / aK_sort - if nt / ns_budget < 4: + if nt / nt_budget < 4: bK_sort = np.sort(K_sum_rows) - epsilon_v_square = b[0] / bK_sort[ns_budget - 1] + epsilon_v_square = b[0] / bK_sort[nt_budget - 1] else: bK_sort = bottleneck.partition(K_sum_rows, nt_budget - 1)[nt_budget - 1] epsilon_v_square = b[0] / bK_sort @@ -1951,36 +1951,37 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=True, rest epsilon_u_square = aK_sort[ns_budget - 1] bK_sort = np.sort(bK)[::-1] - epsilon_v_square = bK_sort[ns_budget - 1] + epsilon_v_square = bK_sort[nt_budget - 1] # active sets I and J (see Lemma 1 in [26]) - I = np.where(a >= epsilon_u_square * K_sum_cols)[0] - J = np.where(b >= epsilon_v_square * K_sum_rows)[0] + I = a >= epsilon_u_square * K_sum_cols + J = b >= epsilon_v_square * K_sum_rows - if len(I) != ns_budget: + if sum(I) != ns_budget: if uniform: aK = a / K_sum_cols aK_sort = np.sort(aK)[::-1] epsilon_u_square = aK_sort[ns_budget - 1:ns_budget + 1].mean() - I = np.where(a >= epsilon_u_square * K_sum_cols)[0] + I = a >= epsilon_u_square * K_sum_cols - if len(J) != nt_budget: + if sum(J) != nt_budget: if uniform: bK = b / K_sum_rows bK_sort = np.sort(bK)[::-1] epsilon_v_square = bK_sort[nt_budget - 1:nt_budget + 1].mean() - J = np.where(b >= epsilon_v_square * K_sum_rows)[0] + J = b >= epsilon_v_square * K_sum_rows epsilon = (epsilon_u_square * epsilon_v_square) ** (1 / 4) kappa = (epsilon_v_square / epsilon_u_square) ** (1 / 2) - + if verbose: - print("Epsilon = %s\n" %epsilon) - print('|I_active| = %s \t |J_active| = %s ' %(len(I), len(J))) + print("epsilon = %s\n" % epsilon) + print("kappa= %s\n" % kappa) + print('|I_active| = %s \t |J_active| = %s ' % (sum(I), sum(J))) # Ic, Jc: complementary of the active sets I and J - Ic = np.arange(ns)[~np.isin(np.arange(ns), I)] - Jc = np.arange(nt)[~np.isin(np.arange(nt), J)] + Ic = ~I + Jc = ~J K_IJ = K[np.ix_(I, J)] K_IcJ = K[np.ix_(Ic, J)] @@ -2005,23 +2006,23 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=True, rest b_J_min = b_J[0] # box constraints in L-BFGS-B (see Proposition 1 in [26]) - bounds_u = [(max(kappa * a_I_min / (epsilon * (nt - ns_budget) + ns_budget * (b_J_max / ( - epsilon * ns * K_min))), epsilon / kappa), a_I_max / (epsilon * nt * K_min))] * ns_budget + bounds_u = [(max(a_I_min / ((nt - nt_budget) * epsilon + nt_budget * (b_J_max / ( + ns * epsilon * kappa * K_min))), epsilon / kappa), a_I_max / (nt * epsilon * K_min))] * ns_budget - bounds_v = [(max(b_J_min / (epsilon * (ns - ns_budget) + ns_budget * (a_I_max / (epsilon * nt * K_min))), \ - epsilon * kappa), b_J_max / (epsilon * ns * K_min))] * ns_budget + bounds_v = [(max(b_J_min / ((ns - ns_budget) * epsilon + ns_budget * (kappa * a_I_max / (nt * epsilon * K_min))), + epsilon * kappa), b_J_max / (ns * epsilon * K_min))] * nt_budget # pre-calculated constants for the objective - vec_eps_IJc = epsilon * kappa * (K_IJc * np.ones(nt - ns_budget).reshape((1, -1))).sum(axis=1) + vec_eps_IJc = epsilon * kappa * (K_IJc * np.ones(nt - nt_budget).reshape((1, -1))).sum(axis=1) vec_eps_IcJ = (epsilon / kappa) * (np.ones(ns - ns_budget).reshape((-1, 1)) * K_IcJ).sum(axis=0) # initialisation u0 = np.full(ns_budget, (1. / ns_budget) + epsilon / kappa) v0 = np.full(nt_budget, (1. / nt_budget) + epsilon * kappa) - # pre-calculed constants for Restricted Sinkhorn (see Algorithm 2 in [26]) + # pre-calculed constants for Restricted Sinkhorn (see Algorithm 1 in supplementary of [26]) if restricted: - if ns_budget != ns or ns_budget != nt: + if ns_budget != ns or nt_budget != nt: cst_u = kappa * epsilon * K_IJc.sum(axis=1) cst_v = epsilon * K_IcJ.sum(axis=0) / kappa @@ -2042,7 +2043,7 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=True, rest def restricted_sinkhorn(usc, vsc, max_iter=5): """ - Restricted Sinkhorn Algorithm as a warm-start initialized point for L-BFGS-B (see Algorithm 2 in [26]) + Restricted Sinkhorn Algorithm as a warm-start initialized point for L-BFGS-B (see Algorithm 1 in supplementary of [26]) """ cpt = 1 while (cpt < max_iter): @@ -2086,9 +2087,9 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=True, rest u0, v0 = restricted_sinkhorn(u0, v0) theta0 = np.hstack([u0, v0]) - + bounds = bounds_u + bounds_v # constraint bounds - obj = lambda theta: bfgspost(theta) + def obj(theta): return bfgspost(theta) theta, _, _ = fmin_l_bfgs_b(func=obj, x0=theta0, @@ -2104,15 +2105,15 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=True, rest vsc_full = np.full(nt, epsilon * kappa) usc_full[I] = usc vsc_full[J] = vsc - + if log: log['u'] = usc_full log['v'] = vsc_full - + gamma = usc_full[:, None] * K * vsc_full[None, :] gamma = gamma / gamma.sum() - + if log: return gamma, log else: - return gamma + return gamma \ No newline at end of file -- cgit v1.2.3 From 3e77515b4f19cf1c37b2f971a54b2fe5efe9daef Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Wed, 8 Jan 2020 18:54:07 +0100 Subject: add illustration for screenkhorn --- examples/plot_screenkhorn_1D.py | 103 ++++++++++++++++++ notebooks/plot_screenkhorn_1D.ipynb | 207 ++++++++++++++++++++++++++++++++++++ 2 files changed, 310 insertions(+) create mode 100644 examples/plot_screenkhorn_1D.py create mode 100644 notebooks/plot_screenkhorn_1D.ipynb diff --git a/examples/plot_screenkhorn_1D.py b/examples/plot_screenkhorn_1D.py new file mode 100644 index 0000000..e0d7bfd --- /dev/null +++ b/examples/plot_screenkhorn_1D.py @@ -0,0 +1,103 @@ +#!/usr/bin/env python +# coding: utf-8 + +# In[ ]: + + +get_ipython().run_line_magic('matplotlib', 'inline') + + +# +# # 1D Screened optimal transport +# +# +# This example illustrates the computation of Screenkhorn: Screening Sinkhorn Algorithm for Optimal transport. +# +# + +# In[13]: + + +# Author: Mokhtar Z. Alaya +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot +import ot.plot +from ot.datasets import make_1D_gauss as gauss +from ot.bregman import screenkhorn + + +# Generate data +# ------------- +# +# + +# In[14]: + + +#%% parameters + +n = 100 # nb bins + +# bin positions +x = np.arange(n, dtype=np.float64) + +# Gaussian distributions +a = gauss(n, m=20, s=5) # m= mean, s= std +b = gauss(n, m=60, s=10) + +# loss matrix +M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) +M /= M.max() + + +# Plot distributions and loss matrix +# ---------------------------------- +# +# + +# In[15]: + + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +pl.plot(x, a, 'b', label='Source distribution') +pl.plot(x, b, 'r', label='Target distribution') +pl.legend() + +# plot distributions and loss matrix + +pl.figure(2, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') + + +# Solve Screened Sinkhorn +# -------------- +# +# + +# In[21]: + + +# Screenkhorn + +lambd = 1e-2 # entropy parameter +ns_budget = 30 # budget number of points to be keeped in the source distribution +nt_budget = 30 # budget number of points to be keeped in the target distribution + +Gsc = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True) +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Screenkhorn') + +pl.show() + + +# In[ ]: + + + + diff --git a/notebooks/plot_screenkhorn_1D.ipynb b/notebooks/plot_screenkhorn_1D.ipynb new file mode 100644 index 0000000..6126346 --- /dev/null +++ b/notebooks/plot_screenkhorn_1D.ipynb @@ -0,0 +1,207 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 1D Screened optimal transport\n", + "\n", + "\n", + "This example illustrates the computation of Screenkhorn: Screening Sinkhorn Algorithm for Optimal transport.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "# Author: Mokhtar Z. Alaya \n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n", + "import ot.plot\n", + "from ot.datasets import make_1D_gauss as gauss\n", + "from ot.bregman import screenkhorn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "#%% parameters\n", + "\n", + "n = 100 # nb bins\n", + "\n", + "# bin positions\n", + "x = np.arange(n, dtype=np.float64)\n", + "\n", + "# Gaussian distributions\n", + "a = gauss(n, m=20, s=5) # m= mean, s= std\n", + "b = gauss(n, m=60, s=10)\n", + "\n", + "# loss matrix\n", + "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\n", + "M /= M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot distributions and loss matrix\n", + "----------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "#%% plot the distributions\n", + "\n", + "pl.figure(1, figsize=(6.4, 3))\n", + "pl.plot(x, a, 'b', label='Source distribution')\n", + "pl.plot(x, b, 'r', label='Target distribution')\n", + "pl.legend()\n", + "\n", + "# plot distributions and loss matrix\n", + "\n", + "pl.figure(2, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve Screened Sinkhorn\n", + "--------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Epsilon = 0.014767606916367452\n", + "\n", + "Kappa = 3.3854408965782907\n", + "\n", + "|I_active| = 30 \t |J_active| = 30 \n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Screenkhorn\n", + "\n", + "lambd = 1e-2 # entropy parameter\n", + "ns_budget = 30 # budget number of points to be keeped in the source distribution\n", + "nt_budget = 30 # budget number of points to be keeped in the target distribution\n", + "\n", + "Gsc = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True)\n", + "pl.figure(4, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Screenkhorn')\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} -- cgit v1.2.3 From 112a2d46b80b91af399ee12c3711ba61d7aef977 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Wed, 8 Jan 2020 19:28:09 +0100 Subject: plot_screenkhorn_1D.ipynb --- notebooks/plot_screenkhorn_1D.ipynb | 207 ------------------------------------ 1 file changed, 207 deletions(-) delete mode 100644 notebooks/plot_screenkhorn_1D.ipynb diff --git a/notebooks/plot_screenkhorn_1D.ipynb b/notebooks/plot_screenkhorn_1D.ipynb deleted file mode 100644 index 6126346..0000000 --- a/notebooks/plot_screenkhorn_1D.ipynb +++ /dev/null @@ -1,207 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# 1D Screened optimal transport\n", - "\n", - "\n", - "This example illustrates the computation of Screenkhorn: Screening Sinkhorn Algorithm for Optimal transport.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Author: Mokhtar Z. Alaya \n", - "#\n", - "# License: MIT License\n", - "\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "import ot.plot\n", - "from ot.datasets import make_1D_gauss as gauss\n", - "from ot.bregman import screenkhorn" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "#%% parameters\n", - "\n", - "n = 100 # nb bins\n", - "\n", - "# bin positions\n", - "x = np.arange(n, dtype=np.float64)\n", - "\n", - "# Gaussian distributions\n", - "a = gauss(n, m=20, s=5) # m= mean, s= std\n", - "b = gauss(n, m=60, s=10)\n", - "\n", - "# loss matrix\n", - "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\n", - "M /= M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot distributions and loss matrix\n", - "----------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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E4cUX4eij4e1vh6lT/abbe9+bd1SdzUYb+aySAw+E//xPmDcP/vznvKNKBSVgIephYAB+/GPYZhsf6z3sMPjjH+F1r8s7su5g8mTf0v7ii+G++3yWyTHHeL2NNkYJWIjV8fLLvqDita+FAw7wub233uqzHSZNyju67uMjH4H77/f/i+98x4cmjj8eHn0078gaQglYiOH098Nvfwuf+hRsuKEX1Rk71ldnLVoEO+2Ud4TdzTrrwA9/6MXc3/Uur7O86ab+9Y9+BM8/n3eEdWOhC2twipH09fWFRYsW5R1GPrz4ok9z+uMfvRbBjTd6FbOJE31Z8Sc+4VOhtKy4NVmyBM49Fy66yKup9fR4CdC3vQ3e+EavtLbBBpn8/5nZ4hBCX8PHKwEL6LAEPDAAr7ziwwcvvuiP557zJcJPPeXTx5Ys8T9bH3gAli4tHTt7tk/+f+c7/aFhhvYhBP8lunAh3HAD3H57qbLammv63OLZs2HmTJgxwxd8TJsGa6/t70+Z4v/fEyd6hbY6ErYSsEiFvokTw6LNN2/eBYd/3xVfl7eHMPIxOOiPgYHSo7/fH6tW+cT90aYpmfkP4MyZXoVr6629ZGRfH6y/frr/TpEfL78Md90Fixf7kub77/dfukuW+PfJ6jCDceN86GnMGE/IPT2lx6abwvXXJ07AKsYjnHHjoJkJGEYaRvF1ebtZ5aOnp/Rc/hgzxh/jx/u/ZcIEf0yZ4o+11/ZpY+uu63UGevWt3/FMnAg77+yPckLwceInn4RnnvG/jP75T/9L6eWX/a+nV1/1JL1ypf9i7+8v/cIfHEyt0JK+C4Wz2WZwxRV5RyFE9pj5L+S11847Es2CEEKIvFACFkKInNBNOAGAmb0I3J93HKOwDtDqS5/aIUZojzjbIcatQghTGj1YY8CiyP1J7uY2AzNbpBjToR3ibJcYkxyvIQghhMgJJWAhhMgJJWBR5Jy8A6gDxZge7RBnx8eom3BCCJETMmAhhMgJJWAhhMgJJeAux8zeaWb3m9nfzOz4vOMpYmYbm9lvzew+M/uzmX02ap9qZr8xswej59zXk5pZj5n9ycyuiV7PNrPbohgvNbOxOce3lpldbmZ/jT7PnVvtczSzo6L/53vN7GdmNr4VPkcz+6GZPWVm95a1Vf3szPle9LN0t5ltP9r5lYC7GDPrAc4A9gJeA3zYzF6Tb1RD9AOfCyFsA+wEfCqK7Xjg+hDCFsD10eu8+SxwX9nrbwKnRjE+B3wsl6hKfBf4VQhha2AuHmvLfI5mtiHwGaAvhDAH6AE+RGt8jhcC7xzWVuuz2wvYInosAM4c9ewhBD269AHsDPy67PUJwAl5x1Uj1quAPfHVejOithn4ApI849oo+iHcDbgGMHz1Vm+1zziH+NYAHia64V7W3jKfI7Ah8A9gKr447BrgHa3yOQKzgHtH++yAs4EPV+tX6yED7m6K3/hFlkZtLYWZzQK2A24D1gshLAOIntOpC9g4pwHHAoPR62nA8yGEYlHivD/TTYHlwAXRMMl5ZjaJFvocQwiPAd8GlgDLgBeAxbTW51hOrc8u9s+TEnB3U63kf0vNSzSzycDPgSNDCP/MO55yzOzdwFMhhMXlzVW65vmZ9gLbA2eGELYDXqI1hm2GiMZQ9wFmAxsAk/A/54fTUt+bVYj9f68E3N0sBTYue70R8HhOsYzAzMbgyfcnIYRiseInzWxG9P4M4Km84gPeBLzXzB4BLsGHIU4D1jKzYp2VvD/TpcDSEMJt0evL8YTcSp/jHsDDIYTlIYRVwBXALrTW51hOrc8u9s+TEnB380dgi+hu81j8xsfCnGMC/I4ycD5wXwjhO2VvLQQOir4+CB8bzoUQwgkhhI1CCLPwz+6GEMK/A78F9ou65R3jE8A/zGyrqGl34C+00OeIDz3sZGYTo//3Yowt8zkOo9ZntxA4MJoNsRPwQnGooiZ5Dbzr0RoPYG/gAeDvwBfzjqcsrjfjf77dDdwZPfbGx1ivBx6MnqfmHWsU7zzgmujrTYHbgb8BlwHjco5tW2BR9FleCazdap8jcBLwV+Be4GJgXCt8jsDP8HHpVbjhfqzWZ4cPQZwR/Szdg8/qWO35tRRZCCFyItEQRKtO4hdCiHagYQOOJvE/gM/NXIqPJ344hPCX9MITQojOJcmOGDsCfwshPARgZpfgU0lqJuB11lknzJo1K8ElRVKWL4fnnoMtt6xsX/uO2fFPNnxbeQArDHs5fOv56P1h7VY8V6FQee7oden98i3rh52r0FPxeuiYnp7Kcw61F4/351AYFkNPZSyhUPZv6xnWNvQ6eo6ODT1W/f3oebB3+HHFZyqPB0J0qcFhfYa/LvYrvV95rsHeyvdHPJf9M0f07Q01zh0q34+eidqJXlv02np92nRPT/TcW5xGDWPG+NTfMT0DAIzt9edxPcVnf39C7yoAxkfPk3pWentP9Lp3BQATC94+pedVACZHz2sUXhm65sTCiqjN35sy9OznmmIheu0fyOTCeAAK6z9YbepZ3SRJwNUmHb9xeCczW4Avy2PmzJksWpRoBw+RkM9/Hs44A4b/N+xZ+ED8k5X/9VRMaiH6QYqSYxiMfuAKw94frEyexb/EbDB6v5jYotfFRGeln1MoDDsXA9FzT3RMFMpA1F5MxEUGBiteWjQiF6hsLybiodiA4homi/qWXhcp9i2ek2HvR+9GywwGR/wkFnuGEW2FqG2wxuvhFD+dwahfIeo3WLV3jfhqxFU6d/VrD//7uvTajxygGinvlJbG6aJEzGBxXUiUxJOeNsGxdU06DiGcE0LoCyH0TZ8+PcHlRBr098OYMXlHIYSAZL8bWnoSv6jOihUwNouaUkUbbpIJe5/oiyabcHl8zTPhkUfLhGOSgQnnacAtO4lf1Obll2HixLyjEEJAgt8JIYR+MzsC+DWuGj8MIfw5tchEJvzrXzB5coYXaJIJQ5Vx4WaZMIwYF87ehMt7d64JQzUbbmETTkiiUEIIvwR+mUokoik88wxMm5Z3FEIISP1Xi2h1nngCXvvaJlwoaxOG2jMkZMJVXw+nNU24dHRbmHBCVIynixgchIcfhtkNTPkVQqRPC/wOEM3ioYd8FsTwRRiZkpUJw+hzhTMyYRh9rnDaJgz1zBVO14TLo6xF2iZc2dYkE87olPUgA+4ifvc7f95ll3zjEEI4MuAuYuFCWHdd2GabHC6esglDjFVzKZsw1L9qLi0Thjir5tIxYW+rb1xYJtwYMuAuYdkyT8AHHVR5H0sIkR8y4C7hpJNcQj/+8RodzCprO2RFSibsp4pZP0ImXPX1iPOXfR13hkRSEy6PfuTrzjNhuVAX8H//B2efDUceCVtskXc0QogiSsAdzp//DB/4AGy6KZx8ct7RCCHK0RBEB3PvvbDbbtDbC7/8JUyaNMoBQ8MCbTAUAY2Xskw6FAENl7JsdCiisk/UI+OhiFIUGorIChlwBzI4CD/4Aey0kyffG2+ErbYa9TAhRJORAXcYDz8Mhx4KN9wA73gHnHcebLRRHQdaocxC28CEIXlR9zYyYe/DsD5RD5nwMNrHhGXAHcKDD/oMh622gj/+Ec49F/7f/6sz+QohckEG3ObceSd84xtw2WW+08XHPw7HHw8bbzz6sSMo7rHWDiZc3qfZJgypbW9UvwlDetsbta4Jr+76nWjCSsBtyNKlcMkl8NOfwp/+BFOm+F5vRx4J66+fd3RCiHpRAm4Tnn0WLr/ck+5NN7kwvuENcOqpcPDBsNZayc5vBRuyzHYwYW9KZ3ujuCYMKZSyjG3CkP5Gn6s34fK24WRlwpXn7nwTVgJuUfr7fefiX/8arr0WbrsNBgZ8jPfEE2H+fNh887yjFEIkQQm4hXj0UU+2114L110Hzz/vItjX5+O6738/bLddSQ7TpmiU7WDC3pTuRp/1mrAfE4XTJBP2tiLNMeFabRXXGIooLRMuxdUWJpyQ1ouoS+jvh3vugVtu8cett/oUMvCZC+9/v08j2313bSEkRKeiBNwknnsO/vCHUsK97TZ46SV/b8YMeNOb4LOfhbe/HbbeOjvLrUnZPOC2MOGyuJpvwpDVlve1TLiyrUi2JuxXyHZ7o5EmPDKuTjbh1omkg3j+eZ+dsHgx3HGHPz/wgL/X0wNz58Ihh3hh9F12gZkzc0i4QojcUQJOyLPPlpJs8fnvfy+9v/HGsMMOcOCBnmzf8IaMt4VvlIKVTC+uCUP2NjzchCviaLYJQ2YbfdYwYT8mWSnLuCbsZ2zORp8VWy/ViKsTTTj/CNqE4oaWd91V+XjkkVKfWbM82X70o/68/fYwfXpeEQshWh0l4Cq89JLfICtPtPfcAy++6O8XCr6x5RvfCIcf7ol2++1h6tR8405M0RzjmjA0b1y4/PxZbXk/mgmXn6tJJux9Gquk1rgJl9o6Y8v7WiYMeaXCrk7AIfiqsuFW++CDpZ/zNdaA17/ehxDmzoVtt4XXvhYmTsw3diFE+9M1CXjFCvjLX0Ym22efLfWZPduT7Pz5/jx3rg8rdMMNMjMrbXgZ14TL+rTEDImsTRhS395oNBMuj695JjzyaJlwunRkAn75ZS9Ss3ixrya74w7461997i3AhAnwutfBv/1bKdG+/vVuu0II0SzaPgG/8oqbbDHZLlrkpluUmfXW8xti731vKdluvnn10q5dTaFQMqy4JuyNFX062YS9T/RFs0wYMtvos7YJl/fuXBOG/GZItF0Cfvxx32Tyxht9McO993qNBPAZB3198L73+fMOO8AGG3THEIIQov1o+QS8bJkn2+KjuKBhjTV8y513v9sTbV+fL+FVsm0QsyHji2vC3tTCq+ZSNmGIXz9CJlweQauZcOnoZptwyyXgEHxHhx/9yAvS3H+/t6+xBuy6KyxYAPPm+WwEDSMIIdqZlknATz8NF18MP/yhDytMmOA7+h56aCnh9rZMtB1IoVAyvJgmDG1SPyItE4bs9pmrYcIQv35EUhOG7HbXqGXC5VHWIm0TrmxrrgmPelYz2xj4EbA+/m8+J4TwXTObClwKzAIeAT4YQngubgBLlsAxx8CVV8KqVbDjjnD22bD//rDmmnHPJoQQ7UM9ab0f+FwI4Q4zmwIsNrPfAAcD14cQvmFmxwPHA8fFufjTT8Oee/o47xFH+BLeOXPi/hNEGphZ6S5/XBOG+PUj2tmEIbXdNeo1YYhfPyKpCUN6u2vUa8Le1mgltfYz4VHPFkJYBiyLvn7RzO4DNgT2AeZF3S4CbiRGAl6xwm+gLVniY71velPMyIUQos2Jlc7NbBawHXAbsF6UnAkhLDOzdWscswBYADBz5syh9iee8Dm7fX1eIUzkTMGG7C22CUPjldTa0IShgfoRMuGK1yPOX/Z1Vjsur26357wqqRVG7+KY2WTg58CRIYR/1ntcCOGcEEJfCKFvellpsE02gXPO8bm8Bx/swxFCCNFN1JWAzWwMnnx/EkK4Imp+0sxmRO/PAJ6Ke/GPfhS+/nX42c98wcQHPwi/+lVpYYUQQnQy9cyCMOB84L4QwnfK3loIHAR8I3q+qpEATjgB3vMeOP98n4Z22WW+oOLgg2HffTXft2lYYejP5dhDEdB4Kcs2HIrwUyUt6h5zKAIy3PK++lBEZZ+oR8ZDEaUo2mUoIhn1GPCbgAOA3czszuixN5549zSzB4E9o9cNMWcOnHoqPPaYJ+A5c+BrX/Px4WnTPEH/9397UR3ZsRCiU6hnFsTvqD4uD7B7msGMGwf77eeP4hLk3/7Wn6+5xvusuaaviJs3z2dOzJ0L48enGUWXUr4lUUwThvqXLXeECUPyou5tYMLeh2F9oh4y4VRo2bVlM2bAhz/sD3A7LhbhufFGuPpqb+/tdWMu1oPo6/NSk+PG5RW5EELUR8sm4OFsuKEXSp8/318/9hjcfnupBOUvfuHjyABjxngSLibk7bf3XSxkyquh0MPQ7/WYJux94hXwaWsThvS2N6rXhCGzLe9rmzAkL+re+ia8uuvXU8oyCW2TgIez4YZedvJ97/PXIcCjj5YS8uLF8D//41PdwL93t9qqVBO4uL3Q+uvn928QQnQ3bZuAh2Pm2wfNmuVjyOBJ+aGH/OZdcQui3/3Op70VWXfdyqQ8dy5svduwYmoAABYhSURBVLVbdFdRMIr2FduEoeFSlm1pwuV9mmTCEL+AT3IThvS2N6rPhMvbhpOVCVeeu5FSlo3TMQm4Gmaw2Wb++MAHSu3PPgt33125N9z3v+/LowHGjoXXvGZkYp42LZ9/hxCiM+noBFyLqVN9FsW8eaW2Vau82Ht5Uv7Vr+Cii0p9NtywNHRRTMpbbFFZpbBd8WI8xVcxTRgaLmXZjibsTQ2WsmzQhP2Y6NpNMmFvK9IcE67VVnGNoYjSMuFSXI0U8ElCVybgaowZ4zfqXvva0o0+gCefHLmT8rXXljb4nDzZE/IOO/jNvh128LFm1S4WQoyG0sQorLcevP3t/ihS3OL+zjvhT3/yG37nnuu7MYMXk99221JC3mEH2GabFh9X7ukZMqu4JuzHxCzg08Ym7E0Ji7rHNmHIasv7WiZc2VYkWxP2KzRWyrJxEx4ZV7NMWAm4AcaNg+2288chh3jbwIBvn7R4sd/0W7zYhy/OOMPfnzDBi83vsos/dt5ZY8pCdDtKwCnR0+M37l7zGjjgAG8bHIQHH/RkfPvtcMstcMoppeGLrbYqJeRddvHZF7mNJ5sNmVRcE4YG6kckNWE/Wf3/vkaoZcJlcTXPhCGzjT5rmLAfE69+RFIT9jMmK+oe14Qr+8ZfNZcEJeAMKRQ8yW61VWlc+eWXfZ7yLbf4Y+FCuOACf2/aNNhjj9KQx0Yb5Re7ECJ7lICbzMSJXsti1139dQhuybfe6nUvrr0WLr3U33vNa0rJeNddYdKkDAMrN8u4JgyNV1Jr1ITLY262CZfH0SwTLj9Xk0zY+8SrH5HchEtt7bTlfaN0wASq9sYMttwSDjoILrzQl1jffTd8+9tuwGedBXvv7VPn9tnHk3PxZp8Qor2RAbcYZl7H4nWvg899Dl55BW6+GX75Sy/VuXChT33bd18f1thjj5RmV/QURm5/U68JQ8OV1Bo24bI+TTfhims2yYQheSW1mCZcHl/zTHjk0Z1swjLgFmfCBB+COO0038D0hhvgQx/y8px77+2LQ048EZ55Ju9IhRBxUQJuI3p64G1v8znHTzwBV17p09lOOsn32Dv6aB/CaIhCwc2np+B2V/7o6fF5wmaYmVtcwbyC2tAjarOCP6LXpWMK/iieM3o99P5QHMPOE2EFq6zF4I2VRlw8dzMIodKIw2DF+HQYDBUr54beHwz+GDpNcBseHPRH8bzR6+L73id6DD/X4ED08NdD/QcG/FE8Z/ExMOiP6Bo2GLDBshgGyh7RMTY46DY8EGCg2uvoMTAYPQIWvVfxfvQo9PujdFz5g+gRvR70vwQKA4FC2fvDXxf7ld73R/E8hX433NL5qzyK1xret9/8MezcSVECblPGjfMx4auugnvu8apw3/sezJ7tiVjjxEK0PkrAHcCcOb6f3oMP+s28U0/1lXi33FL/OUKhzFJjm7A13YQrbLgLTLjchptmwoM5mPBge5lwUpSAO4jZs3144oYbvLjQm9/s48PNWL0rhIiPZkF0IG97m09lO+IIHx9++WX45jdHkcJCYehuuA3/vTzK7AiIXz8i6ewIqGOucAvVj0g8OwIS1xSOOzsC4tePSDo7ApLXFI47O6I8ylqsvpJa4ygBdyhTpvi84kmTfPnz2LHw1a/mHZUQohwl4A7GzIsBvfoqfP3rsNdevpN0VXpGWk+9Jgx1zBVO24Qhfv2IdjZhSFBJrTEThtHnCqdtwtB4JbVGTdjbklRSaxyNAXc4Zj47YuZMOPRQWLky74iEEEVkwF3A5Mnw3e/66rkrr4QPfrBKJ7MKC4YYJgwNV1Jr2ITLz9UFJgxVxoVlwhWvGzVh79N4JbUkyIC7hPe8xxdrnH123pEIIYooAXcJhQIceKBXXHvhhbyjEUKAEnBX8da3+l/Gt9468r1QvjiiuBAjWiQRClbfQo26li2ntFAD6l+23AELNfxU9S1bTm2hRpxlyykt1Ii3bDmdhRoNLdYYLBvySYAScBex447+fMcd+cYhhHB0E66LmDLFNxl9+OEqb/ZY6YZJ8WZPvTfloPFSlg3elIPRF2t01E05GH2xRlY35SD1Le9r3ZTzPgzrE/XI+KZceRSNlLJsBBlwl7HJJl7WUgiRPzLgLmPaNFi+fGR7KBRGWkoLm3B5fF1hwpC8qHtcE4bMtryvbcJQ77LlTjBhGXCXsfba8NxzeUchhIAYBmxmPcAi4LEQwrvNbDZwCTAVuAM4IISgdVYtzsSJNWoFl48BxzRhP6a+ZcupmTDELuDT1iZc3qdJJgz1L9ZIz4QhbgGfpCZc3jacrE04jgF/Friv7PU3gVNDCFsAzwEfSykmkSETJvg+c0KI/KnLgM1sI+BdwNeAo80nZO4GzI+6XAScCJyZQYwiRcaMgf4qyyhDwcq8IZ4Je586ly2nZcLQcCnLdjRhb2qwlGWDJuzHRNdukgl7W5HmmHCttoprDEU0spRlEuo14NOAYyl9ItOA50MIxR/lpcCGqUQkMqW314u1CyHyZ1QDNrN3A0+FEBab2bxic5WuVXXBzBYACwBmzpzZYJgiLXp6KodRi4SeAgxZbNTWwibsx6x+rnAnmbA3JSzqHtuEIast72uZcGVbkWxN2K/QeCnLJNRjwG8C3mtmj+A33XbDjXgtMyv+UzcCHq92cAjhnBBCXwihb/r06SmELJJQKPiqUyFE/oxqwCGEE4ATACIDPiaE8O9mdhmwH56UDwKuyjBOkRJm1YUu9Bjlv98hhglD46UsGzRhv350rW4w4bK4mmfCkNb2RvWasB9T36q5tEzYz9h4KcskJDnPcfgNub/hY8LnpxOSyJJaCVgI0XxirYQLIdwI3Bh9/RCwY/ohiSypWQSsxyo2aHHqM2FoYNVcUhOG5EXdGzVhPxmZMtyEK+JokgmXn6tJJux96l01l5YJl9oaWTWXBK2EE0KInFAtiC6jlgFXzgMuUp8JV7Q1y4Qh/Y0+6zVhaN64cPn5s9ryvpYJQ/JKajFNuDy+5pnwyKObZcIyYCGEyAkZsADcgIvENWFvq2+ucHomDJlteT+aCZf1aYkZEhmZsPeJvmiWCUNmG33WNuHy3s01YRmwEELkhAxYADDYayO22q7XhL1PvFVzyU0YMtvyfjQT9saKPp1owhBn1Vx3mnBSZMBCCJETMmAB+Bhw0QjimnBln+aYsMc89GbxoCisbE3Ym1p41VxaJgzZ7TNXw4Sh/lVzaZkwJKuklgQZsBBC5IQMWABEtSCc+CYMSSupxTVhiLFqLmUThhir5trZhCH9feZGMWGof9VcWiYMySqpJUEGLIQQOSEDFgCEHhjuBvWbMDRcSa1RE4bMdlwe1YSh/lVzbWzCEGPVXJeacFJkwEIIkRMyYAEUx4Cr/3YfzYSBmjMkOtKEy8/VwSbsp8pmx+WaJgwZ7rhc3YQr+0Q9YlVSaxwZsBBC5IQSsBBC5ISGIARQ/NNw9Tcaag1FVDsy86EIyG7L+9GGIqC9tzeqdygCMtjos/WGIrwPw/pEPeoqZdk4MmAhhMgJGbAAYLDHyiaXy4T9/eomDPUvW25rE4YMt7yvYcKQ2Zb3tU0YkhXwaRwZsBBC5IQMWAC+EGNkqb36TBjiF/BJasJ+TH3LltM24fL4OtqEy/s0yYSh/sUa6ZkwpFPKMj4yYCGEyAkZsAAqx4DjmjA0UsAnmQl7nzoXa6RtwpD+Rp8taMLelM72RvWasB8TXbtJJuxtReKZcFJkwEIIkRMyYAFUHwOWCdcwYchuy/sWMmFvymjL+5omDFlteV/LhCvbitRnwkmRAQshRE7IgAVQeRc6vgmXt0XnyNqEIcMt71dvwn5Mnavm2tmEy+JqnglDZht91jBhP6a+VXNpm7AMWAghckIGLACiguyV1G/CEHfVXFIThgZWzaVkwn7t6FrNMmE/GZky3IQr4miSCZefq0km7H3qXTVXacJJkQELIUROyIAFAIM9tX8bj27CUO+qubRMuKKt2SYMqW/0OaoJQ/PGhcvPn9WW97VMGFLf3mg0Ey6Pr5FKakmQAQshRE7IgAUAoccYjDw0rgmXtzXLhL2tvrnCqZswZLblfU0TLuvTEjMkMjJh7xN90SwThoSV1BqnLgM2s7XM7HIz+6uZ3WdmO5vZVDP7jZk9GD2vnUpEQgjRJdRrwN8FfhVC2M/MxgITgS8A14cQvmFmxwPHA8dlFKfImMFeKES/3+OacPW2bE3Y+8RbNZeeCUNmW97XMmFvrOjTiSYMcVbNtYIJJ2NUAzazNYBdgfMBQggrQwjPA/sAF0XdLgL2TSkmIYToCuox4E2B5cAFZjYXWAx8FlgvhLAMIISwzMzWrXawmS0AFgDMnDkzlaBF+ngtCCeuCXufxupHNGrClX2abcKQ2Zb3NUzYm1p41VxaJgzZ7TNXw4Sh/lVz1eajJ6Ges/QC2wNnhhC2A17ChxvqIoRwTgihL4TQN3369AbDFEKIzqMeA14KLA0h3Ba9vhxPwE+a2YzIfmcAT2UVpMie8pVwcU3Y+ySrpBbfhEuRNtuEof5Vc2mZMMRYNdfOJgzp7zM3iglD/avmqs1HT8KoBhxCeAL4h5ltFTXtDvwFWAgcFLUdBFyVSkRCCNEl1DsL4tPAT6IZEA8Bh+DJ+3/M7GPAEuAD2YQomkH1WhBOa5pweSTNNWGIsWouLROG+lfNtbEJQ4xVcy1gwkmpKwGHEO4E+qq8tXsqUQghRBeilXACGH6Hv5JWNOHyI5tuwpDZjss1Tbj8XB1swn6qbHZcrmnCkKiSWhLS8WghhBCxUQIWQoic0BCEAKKlyKNstV1rKMLbah2TzVBEtSM7eigC2nt7o3qHIiDF7Y2yH4pIigxYCCFyQgYsgGFLkWOasLc1VsqyLU0YstvyvoYJQ/3LltvahCGDjT5HMWFIVsAnATJgIYTICRmwACD0Bhga23XqNWFovJRloyZcLb5ONuHy+DrahMv7NMmEIX4Bn7RMWAYshBA5IQMWQHEpcqV11mvCFX2bZMKQ/kaf9ZqwH1PfsuXUTBjS296ohU3Ym9LZ3qheE/Zjoms3UMoyCTJgIYTICRmwABi2Lb1MGGqbsPeJV8AnsQlD+ht9tqAJe1NGW97XNGFIUsAnCTJgIYTICRmwACD0hDL7HGqNnlvRhMvbonN0sAn7MTEL+LSjCZfF1TwThjRKWTaCDFgIIXJCBiyA4jxgpz1MeGRcQ+fI2oQhwy3vq5uwXz+6VrNM2E9Gpgw34Yo4mmTC5edqpH5EAmTAQgiREzJgAVQacJF6TRgar6TWuAmX4mi2CUMDq+aSmjAkL+oe14SheePC5efPasv7WiYM6VRSawAZsBBC5IQMWDg9gVqOM5oJex+nWSZc3tZsE65oa5YJQ3rbG9VrwmV9WmKGREYm7H2iLxqppJYAGbAQQuSEDFg4ZWPA8U0Y4s6QSGrC1duaY8LeFq9+RHIThsy2vK9lwt5Y0acTTRji149Y3Sa2cZABCyFETsiABQBWZQy4fhMu790cE/Y+6e6uUa8Je594q+aSmzBktuV9DRP2phZeNZeWCUOiSmpJkAELIUROyIAFANY7SPH3cVwThmrjwtmacPn1m23ClX2aY8IQv35EUhOGNqkfkdSEIWEltcaRAQshRE7IgAUAPT2DZb/T45kwpFc/oj1MuBRps0wY4tePSGzCEL9+RBuaMDRQP2IwHXeVAQshRE7IgAUAPb0l85IJV563WiXitGoK123CkNmOyzVNuPxcHWzCfqoEldQSIAMWQoicUAIWQoicqGsIwsyOAg7F/wq7BzgEmAFcAkwF7gAOCCGszChOkTFjxvQz/NuhlYciKs9d69rZDEWUH9nRQxGQvKh7OwxFQLJSlgkY1YDNbEPgM0BfCGEO/r/8IeCbwKkhhC2A54CPpROSEEJ0B/XehOsFJpjZKmAisAzYDZgfvX8RcCJwZtoBiuYwpqd8Ynk8E67VBtmZsLeltb1RPBOudmTmJgzZbXlfw4ShgQI+7WjCkKiUZRJGPUsI4THg28ASPPG+ACwGng8hFL81lwIbVjvezBaY2SIzW7R8+fJUghZCiE5gVAM2s7WBfYDZwPPAZcBeVbpW/RUXQjgHOAegr6+vCb8GRSOM7a22tLJ1TdjPlayou0w4ulYNEy6Pr6NNuLxPA6Usk1CPR+8BPBxCWB5CWAVcAewCrGVmxZ/QjYDHU4lICCG6hHrGgJcAO5nZROAVYHdgEfBbYD98JsRBwFVZBSmyZ1zP6oqLrN6Eof4ZEu2x5f3qTbhafFmbsB8Tr4BPYhOG5EXd28CEvSlBKcsE1DMGfBtwOT7V7J7omHOA44CjzexvwDTg/FQiEkKILqGuWRAhhK8AXxnW/BCwY+oRiVwY11OHctY0YWi0lGU7mjCkt71RvSbsfRorZdmwCUN62xu1sAl7U4JSlgnQSjghhMgJFeMRAEzoXRWj98hvm0ZXzcmEW9eE/ZiYBXza0YTL4mqklGUSZMBCCJETMmABwPhYBlykW024vC06R9YmDBlueV/dhP360bWaZcJ+MjJluAlXxBHPhJMiAxZCiJyQAQsAJvUkLWSXTiW1ek0Y0i/qXr8Jj4xr6BwZmTCkUEktrglD8qLucU0YmjcuXH7+JJXUEiADFkKInJABCwAm9DQyBlyN5piw94nO3HQTLo+jOSZc0dYsE4b0N/oczYTL+rTEDIl6KqklQAYshBA5IQMWAEzqXZHyGbM2YWi0klpSEy5va5YJe1vCSmqxTRgy2/K+lgl7Y0WfljbhhMiAhRAiJ2TAAoCJhZUZfTdkZcLlvZtrwtXbsjVh79NYJbXGTRgy2/K+hgl7UwuvmqtSSS0JMmAhhMgJGbAAYErPq6UXbWDCpSPLezfHhL1PurtrjGbClX2aY8IQv35EUhOGNqkfYem4qwxYCCFyQgYsAJhcbsBFWtiEIf195uo14cprN8uES5E2y4Sh8UpqDZswxK8fkXcltQTIgIUQIidkwAKANQqv1H5TJlzHtbM24fJImmTCkNmOyzVNuPxc7WDCCZEBCyFETigBCyFETmgIQgAwsVDHUmQNRdRx7WyGIsqP7OihCGiv7Y0SIgMWQoickAELANYoVJmGVosWMOFabZC9CVeeu9a10zXhakdmbsKQ3Zb3NUwYEpSyzMOEEyIDFkKInJABCwCmxDHgIl1qwt6W9kafMuGhf187bnnfIDJgIYTICRmwAGBKIeGWRKl/J63ehCFJKctkJuznSmt7o/pMuFp8WZuwH5O0qHtME4b0tjdqAxOWAQshRE7IgAUAUyxAUguGJpowpLe9UTwTrujbJBOG7La8r2XC3iet7Y3qNGHIbsv7FjRhGbAQQuSEDFgAMKXQC4ORWrWBCUN2W97LhPMzYT8mWVH3djJhGbAQQuSEDFgAMLkwHojmAsuEo/ejs7aECZe3RefI2oQhwy3vq5uwXz+6VrNM2E9GHsiAhRAiJyw0MfOb2XLg0aZdUMRhkxDC9LyDEKKbaGoCFkIIUUJDEEIIkRNKwEIIkRNKwEIIkRNKwEIIkRNKwEIIkRNKwEIIkRNKwEIIkRNKwEIIkRNKwEIIkRP/H6aeTnC7cT/+AAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "#%% plot the distributions\n", - "\n", - "pl.figure(1, figsize=(6.4, 3))\n", - "pl.plot(x, a, 'b', label='Source distribution')\n", - "pl.plot(x, b, 'r', label='Target distribution')\n", - "pl.legend()\n", - "\n", - "# plot distributions and loss matrix\n", - "\n", - "pl.figure(2, figsize=(5, 5))\n", - "ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve Screened Sinkhorn\n", - "--------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epsilon = 0.014767606916367452\n", - "\n", - "Kappa = 3.3854408965782907\n", - "\n", - "|I_active| = 30 \t |J_active| = 30 \n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Screenkhorn\n", - "\n", - "lambd = 1e-2 # entropy parameter\n", - "ns_budget = 30 # budget number of points to be keeped in the source distribution\n", - "nt_budget = 30 # budget number of points to be keeped in the target distribution\n", - "\n", - "Gsc = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True)\n", - "pl.figure(4, figsize=(5, 5))\n", - "ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Screenkhorn')\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.4" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} -- cgit v1.2.3 From 73de2854ef8564521e082ea706ba2ed5ab44786e Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Fri, 10 Jan 2020 06:05:54 +0100 Subject: improve documentation --- examples/plot_screenkhorn_1D.py | 45 ++++++++++++++++++----------------------- ot/bregman.py | 12 +++++------ 2 files changed, 26 insertions(+), 31 deletions(-) diff --git a/examples/plot_screenkhorn_1D.py b/examples/plot_screenkhorn_1D.py index e0d7bfd..22a9ddc 100644 --- a/examples/plot_screenkhorn_1D.py +++ b/examples/plot_screenkhorn_1D.py @@ -4,16 +4,22 @@ # In[ ]: +from ot.bregman import screenkhorn +from ot.datasets import make_1D_gauss as gauss +import ot.plot +import ot +import matplotlib.pylab as pl +import numpy as np get_ipython().run_line_magic('matplotlib', 'inline') -# +# # # 1D Screened optimal transport -# -# +# +# # This example illustrates the computation of Screenkhorn: Screening Sinkhorn Algorithm for Optimal transport. -# -# +# +# # In[13]: @@ -22,18 +28,11 @@ get_ipython().run_line_magic('matplotlib', 'inline') # # License: MIT License -import numpy as np -import matplotlib.pylab as pl -import ot -import ot.plot -from ot.datasets import make_1D_gauss as gauss -from ot.bregman import screenkhorn - # Generate data # ------------- -# -# +# +# # In[14]: @@ -56,8 +55,8 @@ M /= M.max() # Plot distributions and loss matrix # ---------------------------------- -# -# +# +# # In[15]: @@ -77,17 +76,17 @@ ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') # Solve Screened Sinkhorn # -------------- -# -# +# +# # In[21]: # Screenkhorn -lambd = 1e-2 # entropy parameter -ns_budget = 30 # budget number of points to be keeped in the source distribution -nt_budget = 30 # budget number of points to be keeped in the target distribution +lambd = 1e-2 # entropy parameter +ns_budget = 30 # budget number of points to be keeped in the source distribution +nt_budget = 30 # budget number of points to be keeped in the target distribution Gsc = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True) pl.figure(4, figsize=(5, 5)) @@ -97,7 +96,3 @@ pl.show() # In[ ]: - - - - diff --git a/ot/bregman.py b/ot/bregman.py index 28377b0..4f24cf4 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1791,7 +1791,7 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli return max(0, sinkhorn_div) -def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=True, restricted=True, +def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, restricted=True, maxiter=10000, maxfun=10000, pgtol=1e-09, verbose=False, log=False): """" Screening Sinkhorn Algorithm for Regularized Optimal Transport @@ -1824,18 +1824,18 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=True, rest reg : `float` Level of the entropy regularisation - ns_budget: `int`, deafult=None + ns_budget : `int`, deafult=None Number budget of points to be keeped in the source domain If it is None then 50% of the source sample points will be keeped - nt_budget: `int`, deafult=None + nt_budget : `int`, deafult=None Number budget of points to be keeped in the target domain If it is None then 50% of the target sample points will be keeped - uniform: `bool`, default=True - If `True`, a_i = 1. / ns and b_j = 1. / nt + uniform : `bool`, default=False + If `True`, the source and target distribution are supposed to be uniform, namely a_i = 1 / ns and b_j = 1 / nt. - restricted: `bool`, default=True + restricted : `bool`, default=True If `True`, a warm-start initialization for the L-BFGS-B solver using a restricted Sinkhorn algorithm with at most 5 iterations -- cgit v1.2.3 From 5a70afeaa1671e4af010009d47bdea1073967e1e Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Fri, 10 Jan 2020 11:52:37 +0100 Subject: update screenkhorn example --- examples/plot_screenkhorn_1D.py | 66 ++++++++++++----------------------------- ot/bregman.py | 8 ++--- 2 files changed, 23 insertions(+), 51 deletions(-) diff --git a/examples/plot_screenkhorn_1D.py b/examples/plot_screenkhorn_1D.py index 22a9ddc..0eb64b0 100644 --- a/examples/plot_screenkhorn_1D.py +++ b/examples/plot_screenkhorn_1D.py @@ -1,41 +1,26 @@ -#!/usr/bin/env python -# coding: utf-8 - -# In[ ]: - - -from ot.bregman import screenkhorn -from ot.datasets import make_1D_gauss as gauss -import ot.plot -import ot -import matplotlib.pylab as pl -import numpy as np -get_ipython().run_line_magic('matplotlib', 'inline') - - -# -# # 1D Screened optimal transport -# -# -# This example illustrates the computation of Screenkhorn: Screening Sinkhorn Algorithm for Optimal transport. -# -# - -# In[13]: +# -*- coding: utf-8 -*- +""" +=============================== +1D Screened optimal transport +=============================== +This example illustrates the computation of Screenkhorn: +Screening Sinkhorn Algorithm for Optimal transport. +""" # Author: Mokhtar Z. Alaya # # License: MIT License +import numpy as np +import matplotlib.pylab as pl +import ot.plot +from ot.datasets import make_1D_gauss as gauss +from ot.bregman import screenkhorn +############################################################################## # Generate data # ------------- -# -# - -# In[14]: - #%% parameters @@ -52,14 +37,9 @@ b = gauss(n, m=60, s=10) M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) M /= M.max() - +############################################################################## # Plot distributions and loss matrix # ---------------------------------- -# -# - -# In[15]: - #%% plot the distributions @@ -73,14 +53,9 @@ pl.legend() pl.figure(2, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') - +############################################################################## # Solve Screened Sinkhorn -# -------------- -# -# - -# In[21]: - +# ----------------------- # Screenkhorn @@ -90,9 +65,6 @@ nt_budget = 30 # budget number of points to be keeped in the target distributio Gsc = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True) pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Screenkhorn') - -pl.show() - +ot.plot.plot1D_mat(a, b, Gsc, 'OT matrix Screenkhorn') -# In[ ]: +pl.show() \ No newline at end of file diff --git a/ot/bregman.py b/ot/bregman.py index 4f24cf4..95b27e4 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1793,13 +1793,13 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, restricted=True, maxiter=10000, maxfun=10000, pgtol=1e-09, verbose=False, log=False): - """" + r"""" Screening Sinkhorn Algorithm for Regularized Optimal Transport The function solves an approximate dual of Sinkhorn divergence [2] which is written as the following optimization problem: ..math:: - (u, v) = \argmin_{u, v} 1_{ns}.T B(u,v) 1_{nt} - <\kappa u, a> - + (u, v) = \argmin_{u, v} 1_{ns}^\top B(u,v) 1_{nt} - <\kappa u, a> - where B(u,v) = \diag(e^u) K \diag(e^v), with K = e^{-M/reg} and @@ -1853,8 +1853,8 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res Dependency ---------- - To gain more efficiency, screenkhorn needs to call the "Bottleneck" package (https://pypi.org/project/Bottleneck/) in the screening pre-processing step. - If Bottleneck isn't installed, the following error message appears: + To gain more efficiency, screenkhorn needs to call the "Bottleneck" package (https://pypi.org/project/Bottleneck/) + in the screening pre-processing step. If Bottleneck isn't installed, the following error message appears: "Bottleneck module doesn't exist. Install it from https://pypi.org/project/Bottleneck/" -- cgit v1.2.3 From cadd301c4de54332378159ab55a02a48beb1d753 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Fri, 10 Jan 2020 12:00:08 +0100 Subject: improve doc --- ot/bregman.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 95b27e4..0f02226 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1799,15 +1799,15 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res The function solves an approximate dual of Sinkhorn divergence [2] which is written as the following optimization problem: ..math:: - (u, v) = \argmin_{u, v} 1_{ns}^\top B(u,v) 1_{nt} - <\kappa u, a> - + (u, v) = \argmin_{u, v} 1_{ns}^T B(u,v) 1_{nt} - <\kappa u, a> - - where B(u,v) = \diag(e^u) K \diag(e^v), with K = e^{-M/reg} and + where B(u,v) = \diag(e^u) K \diag(e^v), with K = e^{-M/reg} and - s.t. e^{u_i} >= \epsilon / \kappa, for all i in {1, ..., ns} + s.t. e^{u_i} \geq \epsilon / \kappa, for all i \in {1, ..., ns} - e^{v_j} >= \epsilon \kappa, for all j in {1, ..., nt} + e^{v_j} \geq \epsilon \kappa, for all j \in {1, ..., nt} - The parameters \kappa and \epsilon are determined w.r.t the couple number budget of points (ns_budget, nt_budget), see Equation (5) in [26] + The parameters \kappa and \epsilon are determined w.r.t the couple number budget of points (ns_budget, nt_budget), see Equation (5) in [26] Parameters @@ -1843,9 +1843,9 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res Maximum number of iterations in LBFGS solver maxfun : `int`, default=10000 - Maximum number of function evaluations in LBFGS solver + Maximum number of function evaluations in LBFGS solver - pgtol : `float`, default=1e-09 + pgtol : `float`, default=1e-09 Final objective function accuracy in LBFGS solver verbose: `bool`, default=False @@ -2116,4 +2116,4 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res if log: return gamma, log else: - return gamma \ No newline at end of file + return gamma -- cgit v1.2.3 From 0461fb539b23d90d772fe5c4dd75463f915a1da5 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Fri, 10 Jan 2020 12:17:45 +0100 Subject: improve doc --- ot/bregman.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 0f02226..16012b5 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1833,7 +1833,7 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res If it is None then 50% of the target sample points will be keeped uniform : `bool`, default=False - If `True`, the source and target distribution are supposed to be uniform, namely a_i = 1 / ns and b_j = 1 / nt. + If `True`, the source and target distribution are supposed to be uniform, i.e., a_i = 1 / ns and b_j = 1 / nt restricted : `bool`, default=True If `True`, a warm-start initialization for the L-BFGS-B solver @@ -1848,8 +1848,9 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res pgtol : `float`, default=1e-09 Final objective function accuracy in LBFGS solver - verbose: `bool`, default=False - If `True`, dispaly informations along iterations + verbose : `bool`, default=False + If `True`, dispaly informations about the cardinals of the active sets and the paramerters kappa + and epsilon Dependency ---------- @@ -2116,4 +2117,4 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res if log: return gamma, log else: - return gamma + return gamma \ No newline at end of file -- cgit v1.2.3 From 365adbccc73f7fea28811b16cbbbdbb77761e55c Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Fri, 10 Jan 2020 13:01:42 +0100 Subject: add simple test for screenkhorn --- test/test_bregman.py | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/test/test_bregman.py b/test/test_bregman.py index f70df10..eb74a9f 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -337,3 +337,14 @@ def test_implemented_methods(): ot.bregman.sinkhorn(a, b, M, epsilon, method=method) with pytest.raises(ValueError): ot.bregman.sinkhorn2(a, b, M, epsilon, method=method) + +def test_screenkhorn(): + # test screenkhorn + rng = np.random.RandomState(0) + n = 100 + a = ot.unif(n) + b = ot.unif(n) + + x = rng.randn(n, 2) + M = ot.dist(x, x) + G_screen = ot.bregman.screenkhorn(a, b, M, 1e-1) \ No newline at end of file -- cgit v1.2.3 From 18242437e73aba9cf131fafc1571e376b57f25f6 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Mon, 13 Jan 2020 09:50:49 +0100 Subject: fix simple test of screenkhorn in test/ --- test/test_bregman.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_bregman.py b/test/test_bregman.py index eb74a9f..bc8f6ae 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -347,4 +347,4 @@ def test_screenkhorn(): x = rng.randn(n, 2) M = ot.dist(x, x) - G_screen = ot.bregman.screenkhorn(a, b, M, 1e-1) \ No newline at end of file + G_screen = ot.bregman.screenkhorn(a, b, M, 1e-2, uniform=True, verbose=True) \ No newline at end of file -- cgit v1.2.3 From 4918d2c619aaa654c524c9c5dc7f4dc82b838f82 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Thu, 16 Jan 2020 16:44:40 +0100 Subject: update readme --- README.md | 2 +- test/test_bregman.py | 3 ++- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 987adf1..c115776 100644 --- a/README.md +++ b/README.md @@ -256,4 +256,4 @@ You can also post bug reports and feature requests in Github issues. Make sure t [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/) Advances in Neural Information Processing Systems (NIPS). -[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 33 (NIPS). +[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 33 (NeurIPS). diff --git a/test/test_bregman.py b/test/test_bregman.py index bc8f6ae..52e9fb2 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -338,6 +338,7 @@ def test_implemented_methods(): with pytest.raises(ValueError): ot.bregman.sinkhorn2(a, b, M, epsilon, method=method) + def test_screenkhorn(): # test screenkhorn rng = np.random.RandomState(0) @@ -347,4 +348,4 @@ def test_screenkhorn(): x = rng.randn(n, 2) M = ot.dist(x, x) - G_screen = ot.bregman.screenkhorn(a, b, M, 1e-2, uniform=True, verbose=True) \ No newline at end of file + G_screen = ot.bregman.screenkhorn(a, b, M, 1e-2, uniform=True, verbose=True) -- cgit v1.2.3 From 936b5e1eb965e1d8c71b7b26cfa5238face1aaa3 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Thu, 16 Jan 2020 17:13:01 +0100 Subject: update --- .idea/POT.iml | 11 +++++++++++ test/test_bregman.py | 2 +- 2 files changed, 12 insertions(+), 1 deletion(-) create mode 100644 .idea/POT.iml diff --git a/.idea/POT.iml b/.idea/POT.iml new file mode 100644 index 0000000..6711606 --- /dev/null +++ b/.idea/POT.iml @@ -0,0 +1,11 @@ + + + + + + + + + + \ No newline at end of file diff --git a/test/test_bregman.py b/test/test_bregman.py index 52e9fb2..bcec095 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -348,4 +348,4 @@ def test_screenkhorn(): x = rng.randn(n, 2) M = ot.dist(x, x) - G_screen = ot.bregman.screenkhorn(a, b, M, 1e-2, uniform=True, verbose=True) + G_screen = ot.bregman.screenkhorn(a, b, M, 1e-2, uniform=True, verbose=True) \ No newline at end of file -- cgit v1.2.3 From 0f753104856b7c69c6e126b2564353c1e8ccbf77 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Sat, 18 Jan 2020 06:52:42 +0100 Subject: clean --- .idea/POT.iml | 3 ++- examples/plot_screenkhorn_1D.py | 28 ++++++++++++++++----- ot/bregman.py | 56 +++++++++++++++++++++++------------------ 3 files changed, 55 insertions(+), 32 deletions(-) diff --git a/.idea/POT.iml b/.idea/POT.iml index 6711606..7c9d48f 100644 --- a/.idea/POT.iml +++ b/.idea/POT.iml @@ -6,6 +6,7 @@ - \ No newline at end of file diff --git a/examples/plot_screenkhorn_1D.py b/examples/plot_screenkhorn_1D.py index 0eb64b0..103d54c 100644 --- a/examples/plot_screenkhorn_1D.py +++ b/examples/plot_screenkhorn_1D.py @@ -14,6 +14,7 @@ Screening Sinkhorn Algorithm for Optimal transport. import numpy as np import matplotlib.pylab as pl +import time import ot.plot from ot.datasets import make_1D_gauss as gauss from ot.bregman import screenkhorn @@ -54,17 +55,32 @@ pl.figure(2, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') ############################################################################## -# Solve Screened Sinkhorn +# Solve Screenkhorn # ----------------------- # Screenkhorn - -lambd = 1e-2 # entropy parameter +lambd = 1e-3 # entropy parameter ns_budget = 30 # budget number of points to be keeped in the source distribution nt_budget = 30 # budget number of points to be keeped in the target distribution -Gsc = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True) +tic = time.time() +G_screen = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True) +tac_screen = time.time() - tic + +# Sinkhorn +tic = time.time() +G_sink = ot.sinkhorn(a, b, M, lambd, verbose=False) +tac_sink = time.time() - tic + + pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, Gsc, 'OT matrix Screenkhorn') +ot.plot.plot1D_mat(a, b, G_screen, 'OT matrix Screenkhorn') -pl.show() \ No newline at end of file +pl.show() + +############################################################################## +# Time complexity +# ----------------------- +print("Sinkhorn time complexity: %s\n" % tac_sink) +print("Screenkhorn time complexity: %s\n" % tac_screen) +print("Time_Sinkhorn / Time_Screenkhorn: %s\n" % (tac_sink / tac_screen)) diff --git a/ot/bregman.py b/ot/bregman.py index 16012b5..aff9f8c 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1876,7 +1876,7 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res # check if bottleneck module exists try: import bottleneck - except ImportError as e: + except ImportError: print("Bottleneck module doesn't exist. Install it from https://pypi.org/project/Bottleneck/") a = np.asarray(a, dtype=np.float64) @@ -1905,8 +1905,8 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res if ns_budget == ns and nt_budget == nt: # full number of budget points (ns, nt) = (ns_budget, nt_budget) - I = np.ones(ns, dtype=bool) - J = np.ones(nt, dtype=bool) + Isel = np.ones(ns, dtype=bool) + Jsel = np.ones(nt, dtype=bool) epsilon = 0.0 kappa = 1.0 @@ -1955,46 +1955,48 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res epsilon_v_square = bK_sort[nt_budget - 1] # active sets I and J (see Lemma 1 in [26]) - I = a >= epsilon_u_square * K_sum_cols - J = b >= epsilon_v_square * K_sum_rows + Isel = a >= epsilon_u_square * K_sum_cols + Jsel = b >= epsilon_v_square * K_sum_rows - if sum(I) != ns_budget: + if sum(Isel) != ns_budget: if uniform: aK = a / K_sum_cols aK_sort = np.sort(aK)[::-1] epsilon_u_square = aK_sort[ns_budget - 1:ns_budget + 1].mean() - I = a >= epsilon_u_square * K_sum_cols + Isel = a >= epsilon_u_square * K_sum_cols + ns_budget = sum(Isel) - if sum(J) != nt_budget: + if sum(Jsel) != nt_budget: if uniform: bK = b / K_sum_rows bK_sort = np.sort(bK)[::-1] epsilon_v_square = bK_sort[nt_budget - 1:nt_budget + 1].mean() - J = b >= epsilon_v_square * K_sum_rows + Jsel = b >= epsilon_v_square * K_sum_rows + nt_budget = sum(Jsel) epsilon = (epsilon_u_square * epsilon_v_square) ** (1 / 4) kappa = (epsilon_v_square / epsilon_u_square) ** (1 / 2) if verbose: print("epsilon = %s\n" % epsilon) - print("kappa= %s\n" % kappa) - print('|I_active| = %s \t |J_active| = %s ' % (sum(I), sum(J))) + print("kappa = %s\n" % kappa) + print('Cardinality of selected points: |Isel| = %s \t |Jsel| = %s \n' % (sum(Isel), sum(Jsel))) # Ic, Jc: complementary of the active sets I and J - Ic = ~I - Jc = ~J + Ic = ~Isel + Jc = ~Jsel - K_IJ = K[np.ix_(I, J)] - K_IcJ = K[np.ix_(Ic, J)] - K_IJc = K[np.ix_(I, Jc)] + K_IJ = K[np.ix_(Isel, Jsel)] + K_IcJ = K[np.ix_(Ic, Jsel)] + K_IJc = K[np.ix_(Isel, Jc)] K_min = K_IJ.min() if K_min == 0: K_min = np.finfo(float).tiny # a_I, b_J, a_Ic, b_Jc - a_I = a[I] - b_J = b[J] + a_I = a[Isel] + b_J = b[Jsel] if not uniform: a_I_min = a_I.min() a_I_max = a_I.max() @@ -2028,7 +2030,7 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res cst_v = epsilon * K_IcJ.sum(axis=0) / kappa cpt = 1 - while (cpt < 5): # 5 iterations + while cpt < 5: # 5 iterations K_IJ_v = np.dot(K_IJ.T, u0) + cst_v v0 = b_J / (kappa * K_IJ_v) KIJ_u = np.dot(K_IJ, v0) + cst_u @@ -2047,7 +2049,7 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res Restricted Sinkhorn Algorithm as a warm-start initialized point for L-BFGS-B (see Algorithm 1 in supplementary of [26]) """ cpt = 1 - while (cpt < max_iter): + while cpt < max_iter: K_IJ_v = np.dot(K_IJ.T, usc) + cst_v vsc = b_J / (kappa * K_IJ_v) KIJ_u = np.dot(K_IJ, vsc) + cst_u @@ -2067,7 +2069,7 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res return psi_epsilon def screened_grad(usc, vsc): - # gradients of Psi_epsilon w.r.t u and v + # gradients of Psi_(kappa,epsilon) w.r.t u and v grad_u = np.dot(K_IJ, vsc) + vec_eps_IJc - kappa * a_I / usc grad_v = np.dot(K_IJ.T, usc) + vec_eps_IcJ - (1. / kappa) * b_J / vsc return grad_u, grad_v @@ -2090,7 +2092,9 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res theta0 = np.hstack([u0, v0]) bounds = bounds_u + bounds_v # constraint bounds - def obj(theta): return bfgspost(theta) + + def obj(theta): + return bfgspost(theta) theta, _, _ = fmin_l_bfgs_b(func=obj, x0=theta0, @@ -2104,13 +2108,15 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res usc_full = np.full(ns, epsilon / kappa) vsc_full = np.full(nt, epsilon * kappa) - usc_full[I] = usc - vsc_full[J] = vsc + usc_full[Isel] = usc + vsc_full[Jsel] = vsc if log: + log = {} log['u'] = usc_full log['v'] = vsc_full - + log['Isel'] = Isel + log['Jsel'] = Jsel gamma = usc_full[:, None] * K * vsc_full[None, :] gamma = gamma / gamma.sum() -- cgit v1.2.3 From 55e4f76095e5cea22429846fc9d1a790d7eb691b Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Sat, 18 Jan 2020 06:56:31 +0100 Subject: remove .idea/POT.iml --- .idea/POT.iml | 12 ------------ 1 file changed, 12 deletions(-) delete mode 100644 .idea/POT.iml diff --git a/.idea/POT.iml b/.idea/POT.iml deleted file mode 100644 index 7c9d48f..0000000 --- a/.idea/POT.iml +++ /dev/null @@ -1,12 +0,0 @@ - - - - - - - - - - \ No newline at end of file -- cgit v1.2.3 From 3be0c215143e16c59ddd3be902416e91c3292937 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Sat, 18 Jan 2020 07:15:09 +0100 Subject: clean --- examples/plot_screenkhorn_1D.py | 2 +- test/test_bregman.py | 5 ++++- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/examples/plot_screenkhorn_1D.py b/examples/plot_screenkhorn_1D.py index 103d54c..7c0de82 100644 --- a/examples/plot_screenkhorn_1D.py +++ b/examples/plot_screenkhorn_1D.py @@ -59,7 +59,7 @@ ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') # ----------------------- # Screenkhorn -lambd = 1e-3 # entropy parameter +lambd = 1e-03 # entropy parameter ns_budget = 30 # budget number of points to be keeped in the source distribution nt_budget = 30 # budget number of points to be keeped in the target distribution diff --git a/test/test_bregman.py b/test/test_bregman.py index bcec095..2398d45 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -348,4 +348,7 @@ def test_screenkhorn(): x = rng.randn(n, 2) M = ot.dist(x, x) - G_screen = ot.bregman.screenkhorn(a, b, M, 1e-2, uniform=True, verbose=True) \ No newline at end of file + G_sink = ot.sinkhorn(a, b, M, 1e-03) + G_screen = ot.bregman.screenkhorn(a, b, M, 1e-03, uniform=True, verbose=True) + np.testing.assert_allclose(G_sink.sum(0), G_screen.sum(0), atol=1e-02) + np.testing.assert_allclose(G_sink.sum(1), G_screen.sum(1), atol=1e-02) \ No newline at end of file -- cgit v1.2.3 From b3fb1ef40a482f0989686b79373060d764b62d38 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Sat, 18 Jan 2020 07:45:34 +0100 Subject: clean --- ot/bregman.py | 3 ++- test/test_bregman.py | 8 ++++---- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index aff9f8c..c304b5d 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -2117,10 +2117,11 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res log['v'] = vsc_full log['Isel'] = Isel log['Jsel'] = Jsel + gamma = usc_full[:, None] * K * vsc_full[None, :] gamma = gamma / gamma.sum() if log: return gamma, log else: - return gamma \ No newline at end of file + return gamma diff --git a/test/test_bregman.py b/test/test_bregman.py index 2398d45..e376715 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -348,7 +348,7 @@ def test_screenkhorn(): x = rng.randn(n, 2) M = ot.dist(x, x) - G_sink = ot.sinkhorn(a, b, M, 1e-03) - G_screen = ot.bregman.screenkhorn(a, b, M, 1e-03, uniform=True, verbose=True) - np.testing.assert_allclose(G_sink.sum(0), G_screen.sum(0), atol=1e-02) - np.testing.assert_allclose(G_sink.sum(1), G_screen.sum(1), atol=1e-02) \ No newline at end of file + G_s = ot.sinkhorn(a, b, M, 1e-03) + G_sc = ot.bregman.screenkhorn(a, b, M, 1e-03, uniform=True, verbose=True) + np.testing.assert_allclose(G_s.sum(0), G_sc.sum(0), atol=1e-02) + np.testing.assert_allclose(G_s.sum(1), G_sc.sum(1), atol=1e-02) \ No newline at end of file -- cgit v1.2.3 From 7f7b1c547b54b394db975f4ff9d0287904a7b820 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Sat, 18 Jan 2020 09:04:48 +0100 Subject: make autopep --- test/test_bregman.py | 19 +++++++++---------- 1 file changed, 9 insertions(+), 10 deletions(-) diff --git a/test/test_bregman.py b/test/test_bregman.py index e376715..fd0679b 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -106,7 +106,6 @@ def test_sinkhorn_variants_log(): @pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) def test_barycenter(method): - n_bins = 100 # nb bins # Gaussian distributions @@ -133,7 +132,6 @@ def test_barycenter(method): def test_barycenter_stabilization(): - n_bins = 100 # nb bins # Gaussian distributions @@ -161,7 +159,6 @@ def test_barycenter_stabilization(): def test_wasserstein_bary_2d(): - size = 100 # size of a square image a1 = np.random.randn(size, size) a1 += a1.min() @@ -185,7 +182,6 @@ def test_wasserstein_bary_2d(): def test_unmix(): - n_bins = 50 # nb bins # Gaussian distributions @@ -207,7 +203,7 @@ def test_unmix(): # wasserstein reg = 1e-3 - um = ot.bregman.unmix(a, D, M, M0, h0, reg, 1, alpha=0.01,) + um = ot.bregman.unmix(a, D, M, M0, h0, reg, 1, alpha=0.01, ) np.testing.assert_allclose(1, np.sum(um), rtol=1e-03, atol=1e-03) np.testing.assert_allclose([0.5, 0.5], um, rtol=1e-03, atol=1e-03) @@ -256,7 +252,7 @@ def test_empirical_sinkhorn(): def test_empirical_sinkhorn_divergence(): - #Test sinkhorn divergence + # Test sinkhorn divergence n = 10 a = ot.unif(n) b = ot.unif(n) @@ -348,7 +344,10 @@ def test_screenkhorn(): x = rng.randn(n, 2) M = ot.dist(x, x) - G_s = ot.sinkhorn(a, b, M, 1e-03) - G_sc = ot.bregman.screenkhorn(a, b, M, 1e-03, uniform=True, verbose=True) - np.testing.assert_allclose(G_s.sum(0), G_sc.sum(0), atol=1e-02) - np.testing.assert_allclose(G_s.sum(1), G_sc.sum(1), atol=1e-02) \ No newline at end of file + # sinkhorn + G_sink = ot.sinkhorn(a, b, M, 1e-03) + # screenkhorn + G_screen = ot.bregman.screenkhorn(a, b, M, 1e-03, uniform=True, verbose=True) + # check marginals + np.testing.assert_allclose(G_sink.sum(0), G_screen.sum(0), atol=1e-02) + np.testing.assert_allclose(G_s.sum(1), G_screen.sum(1), atol=1e-02) -- cgit v1.2.3 From a1747a10e80751eacca4273af61083a853fb9dd4 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Sat, 18 Jan 2020 09:12:55 +0100 Subject: make autopep --- test/test_bregman.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_bregman.py b/test/test_bregman.py index fd0679b..f54ba9f 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -350,4 +350,4 @@ def test_screenkhorn(): G_screen = ot.bregman.screenkhorn(a, b, M, 1e-03, uniform=True, verbose=True) # check marginals np.testing.assert_allclose(G_sink.sum(0), G_screen.sum(0), atol=1e-02) - np.testing.assert_allclose(G_s.sum(1), G_screen.sum(1), atol=1e-02) + np.testing.assert_allclose(G_sink.sum(1), G_screen.sum(1), atol=1e-02) -- cgit v1.2.3 From 7c25e0725e357e4beecc3bfb6f1468a5b5140e74 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Sat, 18 Jan 2020 09:35:14 +0100 Subject: clean --- examples/plot_screenkhorn_1D.py | 22 ++-------------------- 1 file changed, 2 insertions(+), 20 deletions(-) diff --git a/examples/plot_screenkhorn_1D.py b/examples/plot_screenkhorn_1D.py index 7c0de82..bfd374e 100644 --- a/examples/plot_screenkhorn_1D.py +++ b/examples/plot_screenkhorn_1D.py @@ -14,7 +14,6 @@ Screening Sinkhorn Algorithm for Optimal transport. import numpy as np import matplotlib.pylab as pl -import time import ot.plot from ot.datasets import make_1D_gauss as gauss from ot.bregman import screenkhorn @@ -59,28 +58,11 @@ ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') # ----------------------- # Screenkhorn -lambd = 1e-03 # entropy parameter +lambd = 2e-03 # entropy parameter ns_budget = 30 # budget number of points to be keeped in the source distribution nt_budget = 30 # budget number of points to be keeped in the target distribution -tic = time.time() G_screen = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True) -tac_screen = time.time() - tic - -# Sinkhorn -tic = time.time() -G_sink = ot.sinkhorn(a, b, M, lambd, verbose=False) -tac_sink = time.time() - tic - - pl.figure(4, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, G_screen, 'OT matrix Screenkhorn') - -pl.show() - -############################################################################## -# Time complexity -# ----------------------- -print("Sinkhorn time complexity: %s\n" % tac_sink) -print("Screenkhorn time complexity: %s\n" % tac_screen) -print("Time_Sinkhorn / Time_Screenkhorn: %s\n" % (tac_sink / tac_screen)) +pl.show() \ No newline at end of file -- cgit v1.2.3 From b6fa567fcb8eaef0699cc8d8ca087ad9c1fb05de Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Sat, 18 Jan 2020 09:48:00 +0100 Subject: clean --- examples/plot_screenkhorn_1D.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/plot_screenkhorn_1D.py b/examples/plot_screenkhorn_1D.py index bfd374e..840ead8 100644 --- a/examples/plot_screenkhorn_1D.py +++ b/examples/plot_screenkhorn_1D.py @@ -65,4 +65,4 @@ nt_budget = 30 # budget number of points to be keeped in the target distributio G_screen = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True) pl.figure(4, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, G_screen, 'OT matrix Screenkhorn') -pl.show() \ No newline at end of file +pl.show() -- cgit v1.2.3 From e92b7075decc2b6b55a5b395768f733a577591ea Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Fri, 24 Jan 2020 01:39:26 +0100 Subject: add a warning for non installed Botteleneck module --- ot/bregman.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/ot/bregman.py b/ot/bregman.py index c304b5d..2707b7c 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1877,7 +1877,8 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res try: import bottleneck except ImportError: - print("Bottleneck module doesn't exist. Install it from https://pypi.org/project/Bottleneck/") + warnings.warn("Bottleneck module is not installed. Install it from https://pypi.org/project/Bottleneck/ for better performance.") + bottleneck = np a = np.asarray(a, dtype=np.float64) b = np.asarray(b, dtype=np.float64) -- cgit v1.2.3 From c5b1c7cfa3bbf0bed056f2c7c6bedc0d0148a8ce Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Fri, 24 Jan 2020 09:23:02 +0100 Subject: modify pymanopt version in requirements --- requirements.txt | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 5a3432b..d62190a 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,6 +4,7 @@ cython matplotlib sphinx-gallery autograd -pymanopt +pymanopt== 0.2.4, python_version < '3' +pymanopt, python_version >= '3' cvxopt pytest -- cgit v1.2.3 From cae39790dd09a43decc56479d0704717db2ed729 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Fri, 24 Jan 2020 09:32:47 +0100 Subject: modify pymanopt version in requirements --- requirements.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/requirements.txt b/requirements.txt index d62190a..26053e8 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,7 +4,7 @@ cython matplotlib sphinx-gallery autograd -pymanopt== 0.2.4, python_version < '3' -pymanopt, python_version >= '3' +pymanopt== 0.2.4, python_version < 3 +pymanopt, python_version >= 3 cvxopt pytest -- cgit v1.2.3 From 6d4ccaca7705646c9b46b1d01a001943b6c778a9 Mon Sep 17 00:00:00 2001 From: "Mokhtar Z. Alaya" Date: Fri, 24 Jan 2020 09:40:20 +0100 Subject: modify pymanopt version in requirements --- requirements.txt | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/requirements.txt b/requirements.txt index 26053e8..c08822e 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,7 +4,7 @@ cython matplotlib sphinx-gallery autograd -pymanopt== 0.2.4, python_version < 3 -pymanopt, python_version >= 3 +pymanopt==0.2.4; python_version <'3' +pymanopt; python_version >= '3' cvxopt -pytest +pytest \ No newline at end of file -- cgit v1.2.3 From 3844639d3dd4e0dd360ebef34dd657d26664039e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 27 Jan 2020 09:35:19 +0100 Subject: add test for constraint viuolation of duals --- test/test_ot.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/test/test_ot.py b/test/test_ot.py index 18b6294..c756e51 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -337,7 +337,10 @@ def test_dual_variables(): # Check that both cost computations are equivalent np.testing.assert_almost_equal(cost1, log['cost']) check_duality_gap(a, b, M, G, log['u'], log['v'], log['cost']) - + + viol=log['u'][:,None]+log['v'][None,:]-M + + assert viol.max()<1e-8 def check_duality_gap(a, b, M, G, u, v, cost): cost_dual = np.vdot(a, u) + np.vdot(b, v) -- cgit v1.2.3 From 30fc233f7f62d571a562971a945d68c3782f0780 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 27 Jan 2020 09:37:42 +0100 Subject: correct pep8 --- test/test_ot.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/test/test_ot.py b/test/test_ot.py index c756e51..245a107 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -337,10 +337,11 @@ def test_dual_variables(): # Check that both cost computations are equivalent np.testing.assert_almost_equal(cost1, log['cost']) check_duality_gap(a, b, M, G, log['u'], log['v'], log['cost']) - - viol=log['u'][:,None]+log['v'][None,:]-M - - assert viol.max()<1e-8 + + viol = log['u'][:, None] + log['v'][None, :] - M + + assert viol.max() < 1e-8 + def check_duality_gap(a, b, M, G, u, v, cost): cost_dual = np.vdot(a, u) + np.vdot(b, v) -- cgit v1.2.3 From e5196fa7a8c493b831fd5dac52a89bbf29e7b0e6 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 27 Jan 2020 10:40:05 +0100 Subject: correct bug in emd emd2 still todo --- ot/lp/__init__.py | 194 +++++++++++++++++++++++++++++++++++++++++++++++------ ot/lp/emd_wrap.pyx | 2 + 2 files changed, 174 insertions(+), 22 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index eabdd3a..a771ce4 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -23,11 +23,150 @@ from ..utils import parmap from .cvx import barycenter from ..utils import dist -__all__=['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx', - 'emd_1d', 'emd2_1d', 'wasserstein_1d'] +__all__ = ['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx', + 'emd_1d', 'emd2_1d', 'wasserstein_1d'] -def emd(a, b, M, numItermax=100000, log=False, dense=True): +def center_ot_dual(alpha0, beta0, a=None, b=None): + r"""Center dual OT potentials wrt theirs weights + + The main idea of this function is to find unique dual potentials + that ensure some kind of centering/fairness. It will help have + stability when multiple calling of the OT solver with small changes. + + Basically we add another constraint to the potential that will not + change the objective value but will ensure unicity. The constraint + is the following: + + .. math:: + \alpha^T a= \beta^T b + + in addition to the OT problem constraints. + + since :math:`\sum_i a_i=\sum_j b_j` this can be solved by adding/removing + a constant from both :math:`\alpha_0` and :math:`\beta_0`. + + .. math:: + c=\frac{\beta0^T b-\alpha_0^T a}{1^Tb+1^Ta} + + \alpha=\alpha_0+c + + \beta=\beta0+c + + Parameters + ---------- + alpha0 : (ns,) numpy.ndarray, float64 + Source dual potential + beta0 : (nt,) numpy.ndarray, float64 + Target dual potential + a : (ns,) numpy.ndarray, float64 + Source histogram (uniform weight if empty list) + b : (nt,) numpy.ndarray, float64 + Target histogram (uniform weight if empty list) + + Returns + ------- + alpha : (ns,) numpy.ndarray, float64 + Source centered dual potential + beta : (nt,) numpy.ndarray, float64 + Target centered dual potential + + """ + # if no weights are provided, use uniform + if a is None: + a = np.ones(alpha0.shape[0]) / alpha0.shape[0] + if b is None: + b = np.ones(beta0.shape[0]) / beta0.shape[0] + + # compute constant that balances the weighted sums of the duals + c = (b.dot(beta0) - a.dot(alpha0)) / (a.sum() + b.sum()) + + # update duals + alpha = alpha0 + c + beta = beta0 - c + + return alpha, beta + + +def estimate_dual_null_weights(alpha0, beta0, a, b, M): + r"""Estimate feasible values for 0-weighted dual potentials + + The feasible values are computed efficiently bjt rather coarsely. + First we compute the constraints violations: + + .. math:: + V=\alpha+\beta^T-M + + Next we compute the max amount of violation per row (alpha) and + columns (beta) + + .. math:: + v^a_i=\max_j V_{i,j} + + v^b_j=\max_i V_{i,j} + + Finally we update the dual potential with 0 weights if a + constraint is violated + + .. math:: + \alpha_i = \alpha_i -v^a_i \quad \text{ if } a_i=0 \text{ and } v^a_i>0 + + \beta_j = \beta_j -v^b_j \quad \text{ if } b_j=0 \text{ and } v^b_j>0 + + In the end the dual potential are centred using function + :ref:`center_ot_dual`. + + Note that all those updates do not change the objective value of the + solution but provide dual potential that do not violate the constraints. + + Parameters + ---------- + alpha0 : (ns,) numpy.ndarray, float64 + Source dual potential + beta0 : (nt,) numpy.ndarray, float64 + Target dual potential + alpha0 : (ns,) numpy.ndarray, float64 + Source dual potential + beta0 : (nt,) numpy.ndarray, float64 + Target dual potential + a : (ns,) numpy.ndarray, float64 + Source histogram (uniform weight if empty list) + b : (nt,) numpy.ndarray, float64 + Target histogram (uniform weight if empty list) + M : (ns,nt) numpy.ndarray, float64 + Loss matrix (c-order array with type float64) + + Returns + ------- + alpha : (ns,) numpy.ndarray, float64 + Source corrected dual potential + beta : (nt,) numpy.ndarray, float64 + Target corrected dual potential + + """ + + # binary indexing of non-zeros weights + asel = a != 0 + bsel = b != 0 + + # compute dual constraints violation + Viol = alpha0[:, None] + beta0[None, :] - M + + # Compute worst violation per line and columns + aviol = np.max(Viol, 1) + bviol = np.max(Viol, 0) + + # update corrects violation of + alpha_up = -1 * ~asel * np.maximum(aviol, 0) + beta_up = -1 * ~bsel * np.maximum(bviol, 0) + + alpha = alpha0 + alpha_up + beta = beta0 + beta_up + + return center_ot_dual(alpha, beta, a, b) + + +def emd(a, b, M, numItermax=100000, log=False, dense=True, center_dual=True): r"""Solves the Earth Movers distance problem and returns the OT matrix @@ -43,7 +182,7 @@ def emd(a, b, M, numItermax=100000, log=False, dense=True): - a and b are the sample weights .. warning:: - Note that the M matrix needs to be a C-order numpy.array in float64 + Note that the M matrix needs to be a C-order numpy.array in float64 format. Uses the algorithm proposed in [1]_ @@ -66,6 +205,9 @@ def emd(a, b, M, numItermax=100000, log=False, dense=True): If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). Otherwise returns a sparse representation using scipy's `coo_matrix` format. + center_dual: boolean, optional (default=True) + If True, centers the dual potential using function + :ref:`center_ot_dual`. Returns ------- @@ -107,7 +249,6 @@ def emd(a, b, M, numItermax=100000, log=False, dense=True): b = np.asarray(b, dtype=np.float64) M = np.asarray(M, dtype=np.float64) - # if empty array given then use uniform distributions if len(a) == 0: a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0] @@ -117,11 +258,21 @@ def emd(a, b, M, numItermax=100000, log=False, dense=True): assert (a.shape[0] == M.shape[0] and b.shape[0] == M.shape[1]), \ "Dimension mismatch, check dimensions of M with a and b" + asel = a != 0 + bsel = b != 0 + if dense: - G, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense) + G, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) + + if np.any(~asel) or np.any(~bsel): + u, v = estimate_dual_null_weights(u, v, a, b, M) + else: - Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense) - G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) + G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + + if np.any(~asel) or np.any(~bsel): + u, v = estimate_dual_null_weights(u, v, a, b, M) result_code_string = check_result(result_code) if log: @@ -151,7 +302,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), - a and b are the sample weights .. warning:: - Note that the M matrix needs to be a C-order numpy.array in float64 + Note that the M matrix needs to be a C-order numpy.array in float64 format. Uses the algorithm proposed in [1]_ @@ -177,7 +328,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), dense: boolean, optional (default=True) If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). Otherwise returns a sparse representation using scipy's `coo_matrix` - format. + format. Returns ------- @@ -221,7 +372,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), # problem with pikling Forks if sys.platform.endswith('win32'): - processes=1 + processes = 1 # if empty array given then use uniform distributions if len(a) == 0: @@ -235,10 +386,10 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), if log or return_matrix: def f(b): if dense: - G, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense) + G, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) else: - Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense) - G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) + G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) result_code_string = check_result(result_code) log = {} @@ -252,10 +403,10 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), else: def f(b): if dense: - G, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense) + G, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) else: - Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax,dense) - G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) + G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) result_code_string = check_result(result_code) check_result(result_code) @@ -265,7 +416,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), return f(b) nb = b.shape[1] - if processes>1: + if processes > 1: res = parmap(f, [b[:, i] for i in range(nb)], processes) else: res = list(map(f, [b[:, i].copy() for i in range(nb)])) @@ -273,7 +424,6 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), return res - def free_support_barycenter(measures_locations, measures_weights, X_init, b=None, weights=None, numItermax=100, stopThr=1e-7, verbose=False, log=None): """ Solves the free support (locations of the barycenters are optimized, not the weights) Wasserstein barycenter problem (i.e. the weighted Frechet mean for the 2-Wasserstein distance) @@ -326,7 +476,7 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None k = X_init.shape[0] d = X_init.shape[1] if b is None: - b = np.ones((k,))/k + b = np.ones((k,)) / k if weights is None: weights = np.ones((N,)) / N @@ -337,7 +487,7 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None displacement_square_norm = stopThr + 1. - while ( displacement_square_norm > stopThr and iter_count < numItermax ): + while (displacement_square_norm > stopThr and iter_count < numItermax): T_sum = np.zeros((k, d)) @@ -347,7 +497,7 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None T_i = emd(b, measure_weights_i, M_i) T_sum = T_sum + weight_i * np.reshape(1. / b, (-1, 1)) * np.matmul(T_i, measure_locations_i) - displacement_square_norm = np.sum(np.square(T_sum-X)) + displacement_square_norm = np.sum(np.square(T_sum - X)) if log: displacement_square_norms.append(displacement_square_norm) diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index c0d7128..a4987f4 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -40,6 +40,8 @@ def check_result(result_code): return message + + @cython.boundscheck(False) @cython.wraparound(False) def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mode="c"] b, np.ndarray[double, ndim=2, mode="c"] M, int max_iter, bint dense): -- cgit v1.2.3 From 9a9b3547837eac56349ce8df92bb5b0565daa2d6 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 27 Jan 2020 10:59:58 +0100 Subject: correct emd2 and add centering for dual potentials --- ot/lp/__init__.py | 28 +++++++++++++++++++++++++++- 1 file changed, 27 insertions(+), 1 deletion(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index a771ce4..aa3166f 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -264,6 +264,9 @@ def emd(a, b, M, numItermax=100000, log=False, dense=True, center_dual=True): if dense: G, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) + if center_dual: + u, v = center_ot_dual(u, v, a, b) + if np.any(~asel) or np.any(~bsel): u, v = estimate_dual_null_weights(u, v, a, b, M) @@ -271,6 +274,9 @@ def emd(a, b, M, numItermax=100000, log=False, dense=True, center_dual=True): Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + if center_dual: + u, v = center_ot_dual(u, v, a, b) + if np.any(~asel) or np.any(~bsel): u, v = estimate_dual_null_weights(u, v, a, b, M) @@ -287,7 +293,8 @@ def emd(a, b, M, numItermax=100000, log=False, dense=True, center_dual=True): def emd2(a, b, M, processes=multiprocessing.cpu_count(), - numItermax=100000, log=False, dense=True, return_matrix=False): + numItermax=100000, log=False, dense=True, return_matrix=False, + center_dual=True): r"""Solves the Earth Movers distance problem and returns the loss .. math:: @@ -329,6 +336,9 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). Otherwise returns a sparse representation using scipy's `coo_matrix` format. + center_dual: boolean, optional (default=True) + If True, centers the dual potential using function + :ref:`center_ot_dual`. Returns ------- @@ -383,14 +393,23 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), assert (a.shape[0] == M.shape[0] and b.shape[0] == M.shape[1]), \ "Dimension mismatch, check dimensions of M with a and b" + asel = a != 0 + if log or return_matrix: def f(b): + bsel = b != 0 if dense: G, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) else: Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + if center_dual: + u, v = center_ot_dual(u, v, a, b) + + if np.any(~asel) or np.any(~bsel): + u, v = estimate_dual_null_weights(u, v, a, b, M) + result_code_string = check_result(result_code) log = {} if return_matrix: @@ -402,12 +421,19 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), return [cost, log] else: def f(b): + bsel = b != 0 if dense: G, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) else: Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + if center_dual: + u, v = center_ot_dual(u, v, a, b) + + if np.any(~asel) or np.any(~bsel): + u, v = estimate_dual_null_weights(u, v, a, b, M) + result_code_string = check_result(result_code) check_result(result_code) return cost -- cgit v1.2.3 From f65073faa73b36280a19ff8b9c383e66f8bdbd2b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 30 Jan 2020 08:04:36 +0100 Subject: comlete documentation --- ot/lp/__init__.py | 30 +++++++++++++++++++----------- ot/lp/emd_wrap.pyx | 6 ++++++ test/test_ot.py | 4 ++-- 3 files changed, 27 insertions(+), 13 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index aa3166f..cdd505d 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -28,10 +28,10 @@ __all__ = ['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx', def center_ot_dual(alpha0, beta0, a=None, b=None): - r"""Center dual OT potentials wrt theirs weights + r"""Center dual OT potentials w.r.t. theirs weights The main idea of this function is to find unique dual potentials - that ensure some kind of centering/fairness. It will help have + that ensure some kind of centering/fairness. The main idea is to find dual potentials that lead to the same final objective value for both source and targets (see below for more details). It will help having stability when multiple calling of the OT solver with small changes. Basically we add another constraint to the potential that will not @@ -91,7 +91,15 @@ def center_ot_dual(alpha0, beta0, a=None, b=None): def estimate_dual_null_weights(alpha0, beta0, a, b, M): r"""Estimate feasible values for 0-weighted dual potentials - The feasible values are computed efficiently bjt rather coarsely. + The feasible values are computed efficiently but rather coarsely. + + .. warning:: + This function is necessary because the C++ solver in emd_c + discards all samples in the distributions with + zeros weights. This means that while the primal variable (transport + matrix) is exact, the solver only returns feasible dual potentials + on the samples with weights different from zero. + First we compute the constraints violations: .. math:: @@ -113,11 +121,11 @@ def estimate_dual_null_weights(alpha0, beta0, a, b, M): \beta_j = \beta_j -v^b_j \quad \text{ if } b_j=0 \text{ and } v^b_j>0 - In the end the dual potential are centred using function + In the end the dual potentials are centered using function :ref:`center_ot_dual`. Note that all those updates do not change the objective value of the - solution but provide dual potential that do not violate the constraints. + solution but provide dual potentials that do not violate the constraints. Parameters ---------- @@ -130,9 +138,9 @@ def estimate_dual_null_weights(alpha0, beta0, a, b, M): beta0 : (nt,) numpy.ndarray, float64 Target dual potential a : (ns,) numpy.ndarray, float64 - Source histogram (uniform weight if empty list) + Source distribution (uniform weights if empty list) b : (nt,) numpy.ndarray, float64 - Target histogram (uniform weight if empty list) + Target distribution (uniform weights if empty list) M : (ns,nt) numpy.ndarray, float64 Loss matrix (c-order array with type float64) @@ -150,11 +158,11 @@ def estimate_dual_null_weights(alpha0, beta0, a, b, M): bsel = b != 0 # compute dual constraints violation - Viol = alpha0[:, None] + beta0[None, :] - M + constraint_violation = alpha0[:, None] + beta0[None, :] - M - # Compute worst violation per line and columns - aviol = np.max(Viol, 1) - bviol = np.max(Viol, 0) + # Compute largest violation per line and columns + aviol = np.max(constraint_violation, 1) + bviol = np.max(constraint_violation, 0) # update corrects violation of alpha_up = -1 * ~asel * np.maximum(aviol, 0) diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index a4987f4..d345fd4 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -66,6 +66,12 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod .. warning:: Note that the M matrix needs to be a C-order :py.cls:`numpy.array` + .. warning:: + The C++ solver discards all samples in the distributions with + zeros weights. This means that while the primal variable (transport + matrix) is exact, the solver only returns feasible dual potentials + on the samples with weights different from zero. + Parameters ---------- a : (ns,) numpy.ndarray, float64 diff --git a/test/test_ot.py b/test/test_ot.py index 245a107..47df946 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -338,9 +338,9 @@ def test_dual_variables(): np.testing.assert_almost_equal(cost1, log['cost']) check_duality_gap(a, b, M, G, log['u'], log['v'], log['cost']) - viol = log['u'][:, None] + log['v'][None, :] - M + constraint_violation = log['u'][:, None] + log['v'][None, :] - M - assert viol.max() < 1e-8 + assert constraint_violation.max() < 1e-8 def check_duality_gap(a, b, M, G, u, v, cost): -- cgit v1.2.3 From e23f4d0646a3e8d28cc146c28574359585295249 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Fri, 28 Feb 2020 11:44:55 +0100 Subject: solution to osx issue --- setup.py | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/setup.py b/setup.py index bbcaf04..2cc3e50 100755 --- a/setup.py +++ b/setup.py @@ -8,9 +8,15 @@ from Cython.Build import cythonize import numpy import re import os +import sys +import subprocess here = path.abspath(path.dirname(__file__)) + +os.environ["CC"] = "g++" +os.environ["CXX"] = "g++" + # dirty but working __version__ = re.search( r'__version__\s*=\s*[\'"]([^\'"]*)[\'"]', # It excludes inline comment too @@ -24,12 +30,13 @@ ROOT = os.path.abspath(os.path.dirname(__file__)) with open(os.path.join(ROOT, 'README.md'), encoding="utf-8") as f: README = f.read() -# add platform dependant optional compilation argument opt_arg=["-O3"] -import platform -if platform.system()=='Darwin': - if platform.release()=='18.0.0': - opt_arg.append("-stdlib=libc++") # correspond to a compilation problem with Mojave and XCode 10 + +# add platform dependant optional compilation argument +if sys.platform.startswith('darwin'): + opt_arg.append("-stdlib=libc++") + sdk_path = subprocess.check_output(['xcrun', '--show-sdk-path']) + os.environ['CFLAGS'] = '-isysroot "{}"'.format(sdk_path.rstrip().decode("utf-8")) setup(name='POT', version=__version__, -- cgit v1.2.3 From d82e6eb1af99a982a4934d6bc019a9ab4ad5c880 Mon Sep 17 00:00:00 2001 From: Alex Tong Date: Thu, 5 Mar 2020 12:05:16 -0500 Subject: Fix convolutional_barycenter kernel for non-symmetric images Add authorship --- ot/bregman.py | 8 +++++++- test/test_bregman.py | 7 +++++++ 2 files changed, 14 insertions(+), 1 deletion(-) diff --git a/ot/bregman.py b/ot/bregman.py index 2707b7c..d5e3563 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -9,6 +9,7 @@ Bregman projections for regularized OT # Titouan Vayer # Hicham Janati # Mokhtar Z. Alaya +# Alexander Tong # # License: MIT License @@ -1346,12 +1347,17 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, err = 1 # build the convolution operator + # this is equivalent to blurring on horizontal then vertical directions t = np.linspace(0, 1, A.shape[1]) [Y, X] = np.meshgrid(t, t) xi1 = np.exp(-(X - Y)**2 / reg) + t = np.linspace(0, 1, A.shape[2]) + [Y, X] = np.meshgrid(t, t) + xi2 = np.exp(-(X - Y)**2 / reg) + def K(x): - return np.dot(np.dot(xi1, x), xi1) + return np.dot(np.dot(xi1, x), xi2) while (err > stopThr and cpt < numItermax): diff --git a/test/test_bregman.py b/test/test_bregman.py index f54ba9f..ec4388d 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -351,3 +351,10 @@ def test_screenkhorn(): # check marginals np.testing.assert_allclose(G_sink.sum(0), G_screen.sum(0), atol=1e-02) np.testing.assert_allclose(G_sink.sum(1), G_screen.sum(1), atol=1e-02) + + +def test_convolutional_barycenter_non_square(): + # test for image with height not equal width + A = np.ones((2, 2, 3)) / (2 * 3) + b = ot.bregman.convolutional_barycenter2d(A, 1e-03) + np.testing.assert_allclose(np.ones((2, 3)) / (2 * 3), b, atol=1e-02) -- cgit v1.2.3 From 11733534208fecbabae7b707c7b0965c9da1c752 Mon Sep 17 00:00:00 2001 From: Nemo Fournier Date: Mon, 9 Mar 2020 11:09:54 +0100 Subject: fix fgw alpha parameter implementation --- ot/gromov.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/ot/gromov.py b/ot/gromov.py index 9869341..7ad7e59 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -493,11 +493,11 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, return gwggrad(constC, hC1, hC2, G) if log: - res, log = cg(p, q, M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, log=True, **kwargs) + res, log = cg(p, q, (1-alpha) * M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, log=True, **kwargs) log['fgw_dist'] = log['loss'][::-1][0] return res, log else: - return cg(p, q, M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs) + return cg(p, q, (1-alpha) * M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs) def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, armijo=False, log=False, **kwargs): @@ -573,7 +573,7 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5 def df(G): return gwggrad(constC, hC1, hC2, G) - res, log = cg(p, q, M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, log=True, **kwargs) + res, log = cg(p, q, (1-alpha) * M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, log=True, **kwargs) if log: log['fgw_dist'] = log['loss'][::-1][0] log['T'] = res @@ -1082,7 +1082,7 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_ T_temp = [t.T for t in T] C = update_sructure_matrix(p, lambdas, T_temp, Cs) - T = [fused_gromov_wasserstein((1 - alpha) * Ms[s], C, Cs[s], p, ps[s], loss_fun, alpha, + T = [fused_gromov_wasserstein(Ms[s], C, Cs[s], p, ps[s], loss_fun, alpha, numItermax=max_iter, stopThr=1e-5, verbose=verbose) for s in range(S)] # T is N,ns -- cgit v1.2.3 From 20f9abd8633f4a905df97cc5478eae2e53c1aa96 Mon Sep 17 00:00:00 2001 From: Nemo Fournier Date: Mon, 9 Mar 2020 11:38:19 +0100 Subject: clean and complete the document of fgw related functions --- ot/gromov.py | 40 ++++++++++++++++++++-------------------- 1 file changed, 20 insertions(+), 20 deletions(-) diff --git a/ot/gromov.py b/ot/gromov.py index 7ad7e59..e329c70 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -433,8 +433,7 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, where : - M is the (ns,nt) metric cost matrix - - :math:`f` is the regularization term ( and df is its gradient) - - a and b are source and target weights (sum to 1) + - p and q are source and target weights (sum to 1) - L is a loss function to account for the misfit between the similarity matrices The algorithm used for solving the problem is conditional gradient as discussed in [24]_ @@ -453,17 +452,13 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, Distribution in the target space loss_fun : str, optional Loss function used for the solver - max_iter : int, optional - Max number of iterations - tol : float, optional - Stop threshold on error (>0) - verbose : bool, optional - Print information along iterations - log : bool, optional - record log if True + alpha : float, optional + Trade-off parameter (0 < alpha < 1) armijo : bool, optional If True the steps of the line-search is found via an armijo research. Else closed form is used. If there is convergence issues use False. + log : bool, optional + record log if True **kwargs : dict parameters can be directly passed to the ot.optim.cg solver @@ -515,8 +510,7 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5 where : - M is the (ns,nt) metric cost matrix - - :math:`f` is the regularization term ( and df is its gradient) - - a and b are source and target weights (sum to 1) + - p and q are source and target weights (sum to 1) - L is a loss function to account for the misfit between the similarity matrices The algorithm used for solving the problem is conditional gradient as discussed in [1]_ @@ -534,17 +528,13 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5 Distribution in the target space. loss_fun : str, optional Loss function used for the solver. - max_iter : int, optional - Max number of iterations - tol : float, optional - Stop threshold on error (>0) - verbose : bool, optional - Print information along iterations - log : bool, optional - Record log if True. + alpha : float, optional + Trade-off parameter (0 < alpha < 1) armijo : bool, optional If True the steps of the line-search is found via an armijo research. Else closed form is used. If there is convergence issues use False. + log : bool, optional + Record log if True. **kwargs : dict Parameters can be directly pased to the ot.optim.cg solver. @@ -994,6 +984,16 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_ Whether to fix the structure of the barycenter during the updates fixed_features : bool Whether to fix the feature of the barycenter during the updates + loss_fun : str + Loss function used for the solver either 'square_loss' or 'kl_loss' + max_iter : int, optional + Max number of iterations + tol : float, optional + Stop threshol on error (>0). + verbose : bool, optional + Print information along iterations. + log : bool, optional + Record log if True. init_C : ndarray, shape (N,N), optional Initialization for the barycenters' structure matrix. If not set a random init is used. -- cgit v1.2.3 From 18fa98fb109c935dc8d87f9c93318d8cfd118738 Mon Sep 17 00:00:00 2001 From: Nemo Fournier Date: Tue, 10 Mar 2020 15:57:41 +0100 Subject: fixing trailing and before arithmetic operation whitespace issues --- ot/gromov.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/ot/gromov.py b/ot/gromov.py index e329c70..43780a4 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -488,11 +488,11 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, return gwggrad(constC, hC1, hC2, G) if log: - res, log = cg(p, q, (1-alpha) * M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, log=True, **kwargs) + res, log = cg(p, q, (1 - alpha) * M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, log=True, **kwargs) log['fgw_dist'] = log['loss'][::-1][0] return res, log else: - return cg(p, q, (1-alpha) * M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs) + return cg(p, q, (1 - alpha) * M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs) def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, armijo=False, log=False, **kwargs): @@ -563,7 +563,7 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5 def df(G): return gwggrad(constC, hC1, hC2, G) - res, log = cg(p, q, (1-alpha) * M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, log=True, **kwargs) + res, log = cg(p, q, (1 - alpha) * M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, log=True, **kwargs) if log: log['fgw_dist'] = log['loss'][::-1][0] log['T'] = res @@ -987,13 +987,13 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_ loss_fun : str Loss function used for the solver either 'square_loss' or 'kl_loss' max_iter : int, optional - Max number of iterations + Max number of iterations tol : float, optional Stop threshol on error (>0). verbose : bool, optional Print information along iterations. log : bool, optional - Record log if True. + Record log if True. init_C : ndarray, shape (N,N), optional Initialization for the barycenters' structure matrix. If not set a random init is used. -- cgit v1.2.3 From 171b962cea369aee2513884a1fb3dca8920b77cd Mon Sep 17 00:00:00 2001 From: ievred Date: Tue, 31 Mar 2020 09:43:15 +0200 Subject: added jcpot --- ot/da.py | 288 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 287 insertions(+), 1 deletion(-) diff --git a/ot/da.py b/ot/da.py index 108a38d..fd5da4b 100644 --- a/ot/da.py +++ b/ot/da.py @@ -13,13 +13,14 @@ Domain adaptation with optimal transport import numpy as np import scipy.linalg as linalg -from .bregman import sinkhorn +from .bregman import sinkhorn, projR, projC from .lp import emd from .utils import unif, dist, kernel, cost_normalization from .utils import check_params, BaseEstimator from .unbalanced import sinkhorn_unbalanced from .optim import cg from .optim import gcg +from functools import reduce def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, @@ -745,6 +746,183 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, return A, b +def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100, + stopThr=1e-6, verbose=False, log=False, **kwargs): + """Joint OT and proportion estimation as proposed in [27] + + The function solves the following optimization problem: + + .. math:: + + \mathbf{h} = \argmin_{\mathbf{h} \in \Delta_C}\quad \sum_{k=1}^K \lambda_k + W_{reg}\left((\mathbf{D}_2^{(k)} \mathbf{h})^T \mathbf{\delta}_{\mathbf{X}^{(k)}}, \mu\right) + + + s.t. \gamma^T_k \mathbf{1}_n = \mathbf{1}_n/n + + \mathbf{D}_1^{(k)} \gamma_k \mathbf{1}_n= \mathbf{h} + + \gamma\geq 0 + where : + + - M is the (ns,nt) squared euclidean cost matrix between samples in + Xs and Xt (scaled by ns) + - :math:`L` is a ns x d linear operator on a kernel matrix that + approximates the barycentric mapping + - a and b are uniform source and target weights + + The problem consist in solving jointly an optimal transport matrix + :math:`\gamma` and the nonlinear mapping that fits the barycentric mapping + :math:`n_s\gamma X_t`. + + One can also estimate a mapping with constant bias (see supplementary + material of [8]) using the bias optional argument. + + The algorithm used for solving the problem is the block coordinate + descent that alternates between updates of G (using conditional gradient) + and the update of L using a classical kernel least square solver. + + + Parameters + ---------- + xs : np.ndarray (ns,d) + samples in the source domain + xt : np.ndarray (nt,d) + samples in the target domain + mu : float,optional + Weight for the linear OT loss (>0) + eta : float, optional + Regularization term for the linear mapping L (>0) + kerneltype : str,optional + kernel used by calling function ot.utils.kernel (gaussian by default) + sigma : float, optional + Gaussian kernel bandwidth. + bias : bool,optional + Estimate linear mapping with constant bias + verbose : bool, optional + Print information along iterations + verbose2 : bool, optional + Print information along iterations + numItermax : int, optional + Max number of BCD iterations + numInnerItermax : int, optional + Max number of iterations (inner CG solver) + stopInnerThr : float, optional + Stop threshold on error (inner CG solver) (>0) + stopThr : float, optional + Stop threshold on relative loss decrease (>0) + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + L : (ns x d) ndarray + Nonlinear mapping matrix (ns+1 x d if bias) + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, + "Mapping estimation for discrete optimal transport", + Neural Information Processing Systems (NIPS), 2016. + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + + """ + nbclasses = len(np.unique(Ys[0])) + nbdomains = len(Xs) + + # we then build, for each source domain, specific information + all_domains = [] + for d in range(nbdomains): + dom = {} + # get number of elements for this domain + nb_elem = Xs[d].shape[0] + dom['nbelem'] = nb_elem + classes = np.unique(Ys[d]) + + if np.min(classes) != 0: + Ys[d] = Ys[d] - np.min(classes) + classes = np.unique(Ys[d]) + + # build the corresponding D matrix + D1 = np.zeros((nbclasses, nb_elem)) + D2 = np.zeros((nbclasses, nb_elem)) + classes_d = np.zeros(nbclasses) + + classes_d[np.unique(Ys[d]).astype(int)] = 1 + dom['classes'] = classes_d + + for c in classes: + nbelemperclass = np.sum(Ys[d] == c) + if nbelemperclass != 0: + D1[int(c), Ys[d] == c] = 1. + D2[int(c), Ys[d] == c] = 1. / (nbelemperclass) # *nbclasses_d) + dom['D1'] = D1 + dom['D2'] = D2 + + # build the distance matrix + M = dist(Xs[d], Xt, metric=metric) + M = M / np.median(M) + + dom['K'] = np.exp(-M/reg) + + all_domains.append(dom) + + distribT = unif(np.shape(Xt)[0]) + + if log: + log = {'niter': 0, 'err': []} + + cpt = 0 + err = 1 + old_bary = np.ones((nbclasses)) + + while (err > stopThr and cpt < numItermax): + + bary = np.zeros((nbclasses)) + + for d in range(nbdomains): + all_domains[d]['K'] = projC(all_domains[d]['K'], distribT) + other = np.sum(all_domains[d]['K'], axis=1) + bary = bary + np.log(np.dot(all_domains[d]['D1'], other)) / nbdomains + + bary = np.exp(bary) + + for d in range(nbdomains): + new = np.dot(all_domains[d]['D2'].T, bary) + all_domains[d]['K'] = projR(all_domains[d]['K'], new) + + err = np.linalg.norm(bary - old_bary) + cpt = cpt + 1 + old_bary = bary + + if log: + log['err'].append(err) + + if verbose: + if cpt % 200 == 0: + print('{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) + + bary = bary / np.sum(bary) + + if log: + log['niter'] = cpt + return bary, log + else: + return bary + + def distribution_estimation_uniform(X): """estimates a uniform distribution from an array of samples X @@ -1914,3 +2092,111 @@ class UnbalancedSinkhornTransport(BaseTransport): self.log_ = dict() return self + +class JCPOTTransport(BaseTransport): + + """Domain Adapatation OT method for target shift based on sinkhorn algorithm. + + Parameters + ---------- + reg_e : float, optional (default=1) + Entropic regularization parameter + max_iter : int, float, optional (default=10) + The minimum number of iteration before stopping the optimization + algorithm if no it has not converged + max_inner_iter : int, float, optional (default=200) + The number of iteration in the inner loop + tol : float, optional (default=10e-9) + Stop threshold on error (inner sinkhorn solver) (>0) + verbose : bool, optional (default=False) + Controls the verbosity of the optimization algorithm + log : bool, optional (default=False) + Controls the logs of the optimization algorithm + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. + + Attributes + ---------- + coupling_ : array-like, shape (n_source_samples, n_target_samples) + The optimal coupling + log_ : dictionary + The dictionary of log, empty dic if parameter log is not True + + References + ---------- + + .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE + Transactions on Pattern Analysis and Machine Intelligence , + vol.PP, no.99, pp.1-1 + .. [2] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). + Generalized conditional gradient: analysis of convergence + and applications. arXiv preprint arXiv:1510.06567. + + """ + + def __init__(self, reg_e=.1, max_iter=10, + tol=10e-9, verbose=False, log=False, + metric="sqeuclidean", norm=None, + distribution_estimation=distribution_estimation_uniform, + out_of_sample_map='ferradans'): + + self.reg_e = reg_e + self.max_iter = max_iter + self.tol = tol + self.verbose = verbose + self.log = log + self.metric = metric + self.norm = norm + self.distribution_estimation = distribution_estimation + self.out_of_sample_map = out_of_sample_map + + def fit(self, Xs, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + + Returns + ------- + self : object + Returns self. + """ + + # check the necessary inputs parameters are here + if check_params(Xs=Xs, Xt=Xt, ys=ys): + + returned_ = jcpot_barycenter(Xs=Xs, Ys=ys, Xt=Xt, reg = self.reg_e, + metric=self.metric, numItermax=self.max_iter, stopThr=self.tol, + verbose=self.verbose, log=self.log) + + # deal with the value of log + if self.log: + self.coupling_, self.log_ = returned_ + else: + self.coupling_ = returned_ + self.log_ = dict() + + return self -- cgit v1.2.3 From 6aa0f1f4e275098948d4b312530119e5d95b8884 Mon Sep 17 00:00:00 2001 From: ievred Date: Tue, 31 Mar 2020 17:12:28 +0200 Subject: v1 jcpot example test --- examples/plot_otda_jcpot.py | 185 +++++++++++++++++++++++++++++++ ot/da.py | 263 ++++++++++++++++++++++++++------------------ test/test_da.py | 63 ++++++++++- 3 files changed, 404 insertions(+), 107 deletions(-) create mode 100644 examples/plot_otda_jcpot.py diff --git a/examples/plot_otda_jcpot.py b/examples/plot_otda_jcpot.py new file mode 100644 index 0000000..5e5fff8 --- /dev/null +++ b/examples/plot_otda_jcpot.py @@ -0,0 +1,185 @@ +# -*- coding: utf-8 -*- +""" +======================== +OT for multi-source target shift +======================== + +This example introduces a target shift problem with two 2D source and 1 target domain. + +""" + +# Authors: Remi Flamary +# Ievgen Redko +# +# License: MIT License + +import pylab as pl +import numpy as np +import ot + +############################################################################## +# Generate data +# ------------- +n = 50 +sigma = 0.3 +np.random.seed(1985) + + +def get_data(n, p, dec): + y = np.concatenate((np.ones(int(p * n)), np.zeros(int((1 - p) * n)))) + x = np.hstack((0 * y[:, None] - 0, 1 - 2 * y[:, None])) + sigma * np.random.randn(len(y), 2) + + x[:, 0] += dec[0] + x[:, 1] += dec[1] + + return x, y + + +p1 = .2 +dec1 = [0, 2] + +p2 = .9 +dec2 = [0, -2] + +pt = .4 +dect = [4, 0] + +xs1, ys1 = get_data(n, p1, dec1) +xs2, ys2 = get_data(n + 1, p2, dec2) +xt, yt = get_data(n, pt, dect) +all_Xr = [xs1, xs2] +all_Yr = [ys1, ys2] +# %% +da = 1.5 + + +def plot_ax(dec, name): + pl.plot([dec[0], dec[0]], [dec[1] - da, dec[1] + da], 'k', alpha=0.5) + pl.plot([dec[0] - da, dec[0] + da], [dec[1], dec[1]], 'k', alpha=0.5) + pl.text(dec[0] - .5, dec[1] + 2, name) + + +############################################################################## +# Fig 1 : plots source and target samples +# --------------------------------------- + +pl.figure(1) +pl.clf() +plot_ax(dec1, 'Source 1') +plot_ax(dec2, 'Source 2') +plot_ax(dect, 'Target') +pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9, label='Source 1 (0.8,0.2)') +pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9, label='Source 2 (0.1,0.9)') +pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9, label='Target (0.6,0.4)') +pl.title('Data') + +pl.legend() +pl.axis('equal') +pl.axis('off') + + +############################################################################## +# Instantiate Sinkhorn transport algorithm and fit them for all source domains +# ---------------------------------------------------------------------------- +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-2, metric='euclidean') + +M1 = ot.dist(xs1, xt, 'euclidean') +M2 = ot.dist(xs2, xt, 'euclidean') + + +def print_G(G, xs, ys, xt): + for i in range(G.shape[0]): + for j in range(G.shape[1]): + if G[i, j] > 5e-4: + if ys[i]: + c = 'b' + else: + c = 'r' + pl.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], xt[j, 1]], c, alpha=.2) + + +############################################################################## +# Fig 2 : plot optimal couplings and transported samples +# ------------------------------------------------------ +pl.figure(2) +pl.clf() +plot_ax(dec1, 'Source 1') +plot_ax(dec2, 'Source 2') +plot_ax(dect, 'Target') +print_G(ot_sinkhorn.fit(Xs=xs1, Xt=xt).coupling_, xs1, ys1, xt) +print_G(ot_sinkhorn.fit(Xs=xs2, Xt=xt).coupling_, xs2, ys2, xt) +pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) +pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) + +pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') +pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') + +pl.title('Independent OT') + +pl.legend() +pl.axis('equal') +pl.axis('off') + + +############################################################################## +# Instantiate JCPOT adaptation algorithm and fit it +# ---------------------------------------------------------------------------- +otda = ot.da.JCPOTTransport(reg_e=1e-2, max_iter=1000, tol=1e-9, verbose=True, log=True) +otda.fit(all_Xr, all_Yr, xt) + +ws1 = otda.proportions_.dot(otda.log_['all_domains'][0]['D2']) +ws2 = otda.proportions_.dot(otda.log_['all_domains'][1]['D2']) + +pl.figure(3) +pl.clf() +plot_ax(dec1, 'Source 1') +plot_ax(dec2, 'Source 2') +plot_ax(dect, 'Target') +print_G(ot.bregman.sinkhorn(ws1, [], M1, reg=1e-2), xs1, ys1, xt) +print_G(ot.bregman.sinkhorn(ws2, [], M2, reg=1e-2), xs2, ys2, xt) +pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) +pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) + +pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') +pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') + +pl.title('OT with prop estimation ({:1.3f},{:1.3f})'.format(otda.proportions_[0], otda.proportions_[1])) + +pl.legend() +pl.axis('equal') +pl.axis('off') + +############################################################################## +# Run oracle transport algorithm with known proportions +# ---------------------------------------------------------------------------- + +otda = ot.da.JCPOTTransport(reg_e=0.01, max_iter=1000, tol=1e-9, verbose=True, log=True) +otda.fit(all_Xr, all_Yr, xt) + +h_res = np.array([1 - pt, pt]) + +ws1 = h_res.dot(otda.log_['all_domains'][0]['D2']) +ws2 = h_res.dot(otda.log_['all_domains'][1]['D2']) + +pl.figure(4) +pl.clf() +plot_ax(dec1, 'Source 1') +plot_ax(dec2, 'Source 2') +plot_ax(dect, 'Target') +print_G(ot.bregman.sinkhorn(ws1, [], M1, reg=1e-2), xs1, ys1, xt) +print_G(ot.bregman.sinkhorn(ws2, [], M2, reg=1e-2), xs2, ys2, xt) +pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) +pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) + +pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') +pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') + +pl.title('OT with known proportion ({:1.1f},{:1.1f})'.format(h_res[0], h_res[1])) + +pl.legend() +pl.axis('equal') +pl.axis('off') +pl.show() diff --git a/ot/da.py b/ot/da.py index fd5da4b..a3da8c1 100644 --- a/ot/da.py +++ b/ot/da.py @@ -748,79 +748,58 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100, stopThr=1e-6, verbose=False, log=False, **kwargs): - """Joint OT and proportion estimation as proposed in [27] + r'''Joint OT and proportion estimation for multi-source target shift as proposed in [27] The function solves the following optimization problem: .. math:: - \mathbf{h} = \argmin_{\mathbf{h} \in \Delta_C}\quad \sum_{k=1}^K \lambda_k - W_{reg}\left((\mathbf{D}_2^{(k)} \mathbf{h})^T \mathbf{\delta}_{\mathbf{X}^{(k)}}, \mu\right) + \mathbf{h} = arg\min_{\mathbf{h}}\quad \sum_{k=1}^{K} \lambda_k + W_{reg}((\mathbf{D}_2^{(k)} \mathbf{h})^T, \mathbf{a}) + s.t. \ \forall k, \mathbf{D}_1^{(k)} \gamma_k \mathbf{1}_n= \mathbf{h} - s.t. \gamma^T_k \mathbf{1}_n = \mathbf{1}_n/n - - \mathbf{D}_1^{(k)} \gamma_k \mathbf{1}_n= \mathbf{h} - - \gamma\geq 0 where : - - M is the (ns,nt) squared euclidean cost matrix between samples in - Xs and Xt (scaled by ns) - - :math:`L` is a ns x d linear operator on a kernel matrix that - approximates the barycentric mapping - - a and b are uniform source and target weights - - The problem consist in solving jointly an optimal transport matrix - :math:`\gamma` and the nonlinear mapping that fits the barycentric mapping - :math:`n_s\gamma X_t`. + - :math:`\lambda_k` is the weight of k-th source domain + - :math:`W_{reg}(\cdot,\cdot)` is the entropic regularized Wasserstein distance (see ot.bregman.sinkhorn) + - :math:`\mathbf{D}_2^{(k)}` is a matrix of weights related to k-th source domain defined as in [p. 5, 27], its expected shape is `(n_k, C)` where `n_k` is the number of elements in the k-th source domain and `C` is the number of classes + - :math:`\mathbf{h}` is a vector of estimated proportions in the target domain of size C + - :math:`\mathbf{a}` is a uniform vector of weights in the target domain of size `n` + - :math:`\mathbf{D}_1^{(k)}` is a matrix of class assignments defined as in [p. 5, 27], its expected shape is `(n_k, C)` - One can also estimate a mapping with constant bias (see supplementary - material of [8]) using the bias optional argument. - - The algorithm used for solving the problem is the block coordinate - descent that alternates between updates of G (using conditional gradient) - and the update of L using a classical kernel least square solver. + The problem consist in solving a Wasserstein barycenter problem to estimate the proportions :math:`\mathbf{h}` in the target domain. + The algorithm used for solving the problem is the Iterative Bregman projections algorithm + with two sets of marginal constraints related to the unknown vector :math:`\mathbf{h}` and uniform tarhet distribution. Parameters ---------- - xs : np.ndarray (ns,d) - samples in the source domain - xt : np.ndarray (nt,d) + Xs : list of K np.ndarray(nsk,d) + features of all source domains' samples + Ys : list of K np.ndarray(nsk,) + labels of all source domains' samples + Xt : np.ndarray (nt,d) samples in the target domain - mu : float,optional - Weight for the linear OT loss (>0) - eta : float, optional - Regularization term for the linear mapping L (>0) - kerneltype : str,optional - kernel used by calling function ot.utils.kernel (gaussian by default) - sigma : float, optional - Gaussian kernel bandwidth. - bias : bool,optional - Estimate linear mapping with constant bias - verbose : bool, optional - Print information along iterations - verbose2 : bool, optional - Print information along iterations + reg : float + Regularization term > 0 + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem numItermax : int, optional - Max number of BCD iterations - numInnerItermax : int, optional - Max number of iterations (inner CG solver) - stopInnerThr : float, optional - Stop threshold on error (inner CG solver) (>0) + Max number of iterations stopThr : float, optional - Stop threshold on relative loss decrease (>0) + Stop threshold on relative change in the barycenter (>0) log : bool, optional record log if True - + verbose : bool, optional (default=False) + Controls the verbosity of the optimization algorithm Returns ------- - gamma : (ns x nt) ndarray - Optimal transportation matrix for the given parameters - L : (ns x d) ndarray - Nonlinear mapping matrix (ns+1 x d if bias) + gamma : List of K (nsk x nt) ndarrays + Optimal transportation matrices for the given parameters for each pair of source and target domains + h : (C,) ndarray + proportion estimation in the target domain log : dict log dictionary return only if log==True in parameters @@ -828,62 +807,59 @@ def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100, References ---------- - .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, - "Mapping estimation for discrete optimal transport", - Neural Information Processing Systems (NIPS), 2016. - - See Also - -------- - ot.lp.emd : Unregularized OT - ot.optim.cg : General regularized OT + .. [27] Ievgen Redko, Nicolas Courty, Rémi Flamary, Devis Tuia + "Optimal transport for multi-source domain adaptation under target shift", + International Conference on Artificial Intelligence and Statistics (AISTATS), 2019. - """ + ''' nbclasses = len(np.unique(Ys[0])) nbdomains = len(Xs) - # we then build, for each source domain, specific information + # For each source domain, build cost matrices M, Gibbs kernels K and corresponding matrices D_1 and D_2 all_domains = [] + + # log dictionary + if log: + log = {'niter': 0, 'err': [], 'all_domains': []} + for d in range(nbdomains): dom = {} - # get number of elements for this domain - nb_elem = Xs[d].shape[0] - dom['nbelem'] = nb_elem - classes = np.unique(Ys[d]) + nsk = Xs[d].shape[0] # get number of elements for this domain + dom['nbelem'] = nsk + classes = np.unique(Ys[d]) # get number of classes for this domain + # format classes to start from 0 for convenience if np.min(classes) != 0: Ys[d] = Ys[d] - np.min(classes) classes = np.unique(Ys[d]) - # build the corresponding D matrix - D1 = np.zeros((nbclasses, nb_elem)) - D2 = np.zeros((nbclasses, nb_elem)) - classes_d = np.zeros(nbclasses) - - classes_d[np.unique(Ys[d]).astype(int)] = 1 - dom['classes'] = classes_d + # build the corresponding D_1 and D_2 matrices + D1 = np.zeros((nbclasses, nsk)) + D2 = np.zeros((nbclasses, nsk)) for c in classes: nbelemperclass = np.sum(Ys[d] == c) if nbelemperclass != 0: D1[int(c), Ys[d] == c] = 1. - D2[int(c), Ys[d] == c] = 1. / (nbelemperclass) # *nbclasses_d) + D2[int(c), Ys[d] == c] = 1. / (nbelemperclass) dom['D1'] = D1 dom['D2'] = D2 - # build the distance matrix + # build the cost matrix and the Gibbs kernel M = dist(Xs[d], Xt, metric=metric) M = M / np.median(M) - dom['K'] = np.exp(-M/reg) + K = np.empty(M.shape, dtype=M.dtype) + np.divide(M, -reg, out=K) + np.exp(K, out=K) + dom['K'] = K all_domains.append(dom) - distribT = unif(np.shape(Xt)[0]) - - if log: - log = {'niter': 0, 'err': []} + # uniform target distribution + a = unif(np.shape(Xt)[0]) - cpt = 0 + cpt = 0 # iterations count err = 1 old_bary = np.ones((nbclasses)) @@ -891,13 +867,15 @@ def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100, bary = np.zeros((nbclasses)) + # update coupling matrices for marginal constraints w.r.t. uniform target distribution for d in range(nbdomains): - all_domains[d]['K'] = projC(all_domains[d]['K'], distribT) + all_domains[d]['K'] = projC(all_domains[d]['K'], a) other = np.sum(all_domains[d]['K'], axis=1) bary = bary + np.log(np.dot(all_domains[d]['D1'], other)) / nbdomains bary = np.exp(bary) + # update coupling matrices for marginal constraints w.r.t. unknown proportions based on [Prop 4., 27] for d in range(nbdomains): new = np.dot(all_domains[d]['D2'].T, bary) all_domains[d]['K'] = projR(all_domains[d]['K'], new) @@ -915,12 +893,14 @@ def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100, print('{:5d}|{:8e}|'.format(cpt, err)) bary = bary / np.sum(bary) + couplings = [all_domains[d]['K'] for d in range(nbdomains)] if log: log['niter'] = cpt - return bary, log + log['all_domains'] = all_domains + return couplings, bary, log else: - return bary + return couplings, bary def distribution_estimation_uniform(X): @@ -2093,9 +2073,10 @@ class UnbalancedSinkhornTransport(BaseTransport): return self + class JCPOTTransport(BaseTransport): - """Domain Adapatation OT method for target shift based on sinkhorn algorithm. + """Domain Adapatation OT method for multi-source target shift based on Wasserstein barycenter algorithm. Parameters ---------- @@ -2104,8 +2085,6 @@ class JCPOTTransport(BaseTransport): max_iter : int, float, optional (default=10) The minimum number of iteration before stopping the optimization algorithm if no it has not converged - max_inner_iter : int, float, optional (default=200) - The number of iteration in the inner loop tol : float, optional (default=10e-9) Stop threshold on error (inner sinkhorn solver) (>0) verbose : bool, optional (default=False) @@ -2126,21 +2105,20 @@ class JCPOTTransport(BaseTransport): Attributes ---------- - coupling_ : array-like, shape (n_source_samples, n_target_samples) - The optimal coupling + coupling_ : list of array-like objects, shape K x (n_source_samples, n_target_samples) + A set of optimal couplings between each source domain and the target domain + proportions_ : array-like, shape (n_classes,) + Estimated class proportions in the target domain log_ : dictionary The dictionary of log, empty dic if parameter log is not True References ---------- - .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, - "Optimal Transport for Domain Adaptation," in IEEE - Transactions on Pattern Analysis and Machine Intelligence , - vol.PP, no.99, pp.1-1 - .. [2] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). - Generalized conditional gradient: analysis of convergence - and applications. arXiv preprint arXiv:1510.06567. + .. [1] Ievgen Redko, Nicolas Courty, Rémi Flamary, Devis Tuia + "Optimal transport for multi-source domain adaptation under target shift", + International Conference on Artificial Intelligence and Statistics (AISTATS), + vol. 89, p.849-858, 2019. """ @@ -2156,20 +2134,18 @@ class JCPOTTransport(BaseTransport): self.verbose = verbose self.log = log self.metric = metric - self.norm = norm - self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map def fit(self, Xs, ys=None, Xt=None, yt=None): - """Build a coupling matrix from source and target sets of samples + """Building coupling matrices from a list of source and target sets of samples (Xs, ys) and (Xt, yt) Parameters ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - ys : array-like, shape (n_source_samples,) - The class labels + Xs : list of K array-like objects, shape K x (nk_source_samples, n_features) + A list of the training input samples. + ys : list of K array-like objects, shape K x (nk_source_samples,) + A list of the class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. yt : array-like, shape (n_target_samples,) @@ -2188,15 +2164,90 @@ class JCPOTTransport(BaseTransport): # check the necessary inputs parameters are here if check_params(Xs=Xs, Xt=Xt, ys=ys): - returned_ = jcpot_barycenter(Xs=Xs, Ys=ys, Xt=Xt, reg = self.reg_e, - metric=self.metric, numItermax=self.max_iter, stopThr=self.tol, + self.xs_ = Xs + self.xt_ = Xt + + returned_ = jcpot_barycenter(Xs=Xs, Ys=ys, Xt=Xt, reg=self.reg_e, + metric=self.metric, distrinumItermax=self.max_iter, stopThr=self.tol, verbose=self.verbose, log=self.log) # deal with the value of log if self.log: - self.coupling_, self.log_ = returned_ + self.coupling_, self.proportions_, self.log_ = returned_ else: - self.coupling_ = returned_ + self.coupling_, self.proportions_ = returned_ self.log_ = dict() return self + + def transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): + """Transports source samples Xs onto target ones Xt + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + batch_size : int, optional (default=128) + The batch size for out of sample inverse transform + """ + + transp_Xs = [] + + # check the necessary inputs parameters are here + if check_params(Xs=Xs): + + if all([np.allclose(x, y) for x, y in zip(self.xs_, Xs)]): + + # perform standard barycentric mapping for each source domain + + for coupling in self.coupling_: + transp = coupling / np.sum(coupling, 1)[:, None] + + # set nans to 0 + transp[~ np.isfinite(transp)] = 0 + + # compute transported samples + transp_Xs.append(np.dot(transp, self.xt_)) + else: + + # perform out of sample mapping + indices = np.arange(Xs.shape[0]) + batch_ind = [ + indices[i:i + batch_size] + for i in range(0, len(indices), batch_size)] + + transp_Xs = [] + + for bi in batch_ind: + transp_Xs_ = [] + + # get the nearest neighbor in the sources domains + xs = np.concatenate(self.xs_, axis=0) + idx = np.argmin(dist(Xs[bi], xs), axis=1) + + # transport the source samples + for coupling in self.coupling_: + transp = coupling / np.sum( + coupling, 1)[:, None] + transp[~ np.isfinite(transp)] = 0 + transp_Xs_.append(np.dot(transp, self.xt_)) + + transp_Xs_ = np.concatenate(transp_Xs_, axis=0) + + # define the transported points + transp_Xs_ = transp_Xs_[idx, :] + Xs[bi] - xs[idx, :] + transp_Xs.append(transp_Xs_) + + transp_Xs = np.concatenate(transp_Xs, axis=0) + + return transp_Xs diff --git a/test/test_da.py b/test/test_da.py index 2a5e50e..a8c258a 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -5,7 +5,7 @@ # License: MIT License import numpy as np -from numpy.testing.utils import assert_allclose, assert_equal +from numpy.testing import assert_allclose, assert_equal import ot from ot.datasets import make_data_classif @@ -549,3 +549,64 @@ def test_linear_mapping_class(): Cst = np.cov(Xst.T) np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2) + + +def test_jcpot_transport_class(): + """test_jcpot_transport + """ + + ns1 = 150 + ns2 = 150 + nt = 200 + + Xs1, ys1 = make_data_classif('3gauss', ns1) + Xs2, ys2 = make_data_classif('3gauss', ns2) + + Xt, yt = make_data_classif('3gauss2', nt) + + Xs = [Xs1, Xs2] + ys = [ys1, ys2] + + otda = ot.da.JCPOTTransport(reg_e=0.01, max_iter=1000, tol=1e-9, verbose=True) + + # test its computed + otda.fit(Xs=Xs, ys=ys, Xt=Xt) + print(otda.proportions_) + + assert hasattr(otda, "coupling_") + assert hasattr(otda, "proportions_") + assert hasattr(otda, "log_") + + # test dimensions of coupling + for i, xs in enumerate(Xs): + assert_equal(otda.coupling_[i].shape, ((xs.shape[0], Xt.shape[0]))) + + # test all margin constraints + mu_t = unif(nt) + + for i in range(len(Xs)): + # test margin constraints w.r.t. uniform target weights for each coupling matrix + assert_allclose( + np.sum(otda.coupling_[i], axis=0), mu_t, rtol=1e-3, atol=1e-3) + + # test margin constraints w.r.t. modified source weights for each source domain + + D1 = np.zeros((len(np.unique(ys[i])), len(ys[i]))) + for c in np.unique(ys[i]): + nbelemperclass = np.sum(ys[i] == c) + if nbelemperclass != 0: + D1[int(c), ys[i] == c] = 1. + + assert_allclose( + np.dot(D1, np.sum(otda.coupling_[i], axis=1)), otda.proportions_, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = otda.transform(Xs=Xs) + [assert_equal(x.shape, y.shape) for x, y in zip(transp_Xs, Xs)] + #assert_equal(transp_Xs.shape, Xs.shape) + + Xs_new, _ = make_data_classif('3gauss', ns1 + 1) + transp_Xs_new = otda.transform(Xs_new) + + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) -- cgit v1.2.3 From ba493aa5488507937b7f9707faa17128c9aa1872 Mon Sep 17 00:00:00 2001 From: ievred Date: Tue, 31 Mar 2020 17:36:00 +0200 Subject: readme move to bregman --- README.md | 3 + ot/bregman.py | 157 +++++++++++++++++++++++++++++++++++++++++++++- ot/da.py | 190 ++++---------------------------------------------------- test/test_da.py | 2 +- 4 files changed, 171 insertions(+), 181 deletions(-) diff --git a/README.md b/README.md index c115776..f439405 100644 --- a/README.md +++ b/README.md @@ -29,6 +29,7 @@ It provides the following solvers: * Non regularized free support Wasserstein barycenters [20]. * Unbalanced OT with KL relaxation distance and barycenter [10, 25]. * Screening Sinkhorn Algorithm for OT [26]. +* JCPOT algorithm for multi-source target shift [27]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -257,3 +258,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/) Advances in Neural Information Processing Systems (NIPS). [26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 33 (NeurIPS). + +[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019. \ No newline at end of file diff --git a/ot/bregman.py b/ot/bregman.py index d5e3563..d17aaf0 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -10,6 +10,7 @@ Bregman projections for regularized OT # Hicham Janati # Mokhtar Z. Alaya # Alexander Tong +# Ievgen Redko # # License: MIT License @@ -18,7 +19,6 @@ import warnings from .utils import unif, dist from scipy.optimize import fmin_l_bfgs_b - def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): r""" @@ -1501,6 +1501,161 @@ def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, else: return np.sum(K0, axis=1) +def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100, + stopThr=1e-6, verbose=False, log=False, **kwargs): + r'''Joint OT and proportion estimation for multi-source target shift as proposed in [27] + + The function solves the following optimization problem: + + .. math:: + + \mathbf{h} = arg\min_{\mathbf{h}}\quad \sum_{k=1}^{K} \lambda_k + W_{reg}((\mathbf{D}_2^{(k)} \mathbf{h})^T, \mathbf{a}) + + s.t. \ \forall k, \mathbf{D}_1^{(k)} \gamma_k \mathbf{1}_n= \mathbf{h} + + where : + + - :math:`\lambda_k` is the weight of k-th source domain + - :math:`W_{reg}(\cdot,\cdot)` is the entropic regularized Wasserstein distance (see ot.bregman.sinkhorn) + - :math:`\mathbf{D}_2^{(k)}` is a matrix of weights related to k-th source domain defined as in [p. 5, 27], its expected shape is `(n_k, C)` where `n_k` is the number of elements in the k-th source domain and `C` is the number of classes + - :math:`\mathbf{h}` is a vector of estimated proportions in the target domain of size C + - :math:`\mathbf{a}` is a uniform vector of weights in the target domain of size `n` + - :math:`\mathbf{D}_1^{(k)}` is a matrix of class assignments defined as in [p. 5, 27], its expected shape is `(n_k, C)` + + The problem consist in solving a Wasserstein barycenter problem to estimate the proportions :math:`\mathbf{h}` in the target domain. + + The algorithm used for solving the problem is the Iterative Bregman projections algorithm + with two sets of marginal constraints related to the unknown vector :math:`\mathbf{h}` and uniform tarhet distribution. + + Parameters + ---------- + Xs : list of K np.ndarray(nsk,d) + features of all source domains' samples + Ys : list of K np.ndarray(nsk,) + labels of all source domains' samples + Xt : np.ndarray (nt,d) + samples in the target domain + reg : float + Regularization term > 0 + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshold on relative change in the barycenter (>0) + log : bool, optional + record log if True + verbose : bool, optional (default=False) + Controls the verbosity of the optimization algorithm + + Returns + ------- + gamma : List of K (nsk x nt) ndarrays + Optimal transportation matrices for the given parameters for each pair of source and target domains + h : (C,) ndarray + proportion estimation in the target domain + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [27] Ievgen Redko, Nicolas Courty, Rémi Flamary, Devis Tuia + "Optimal transport for multi-source domain adaptation under target shift", + International Conference on Artificial Intelligence and Statistics (AISTATS), 2019. + + ''' + nbclasses = len(np.unique(Ys[0])) + nbdomains = len(Xs) + + # For each source domain, build cost matrices M, Gibbs kernels K and corresponding matrices D_1 and D_2 + all_domains = [] + + # log dictionary + if log: + log = {'niter': 0, 'err': [], 'all_domains': []} + + for d in range(nbdomains): + dom = {} + nsk = Xs[d].shape[0] # get number of elements for this domain + dom['nbelem'] = nsk + classes = np.unique(Ys[d]) # get number of classes for this domain + + # format classes to start from 0 for convenience + if np.min(classes) != 0: + Ys[d] = Ys[d] - np.min(classes) + classes = np.unique(Ys[d]) + + # build the corresponding D_1 and D_2 matrices + D1 = np.zeros((nbclasses, nsk)) + D2 = np.zeros((nbclasses, nsk)) + + for c in classes: + nbelemperclass = np.sum(Ys[d] == c) + if nbelemperclass != 0: + D1[int(c), Ys[d] == c] = 1. + D2[int(c), Ys[d] == c] = 1. / (nbelemperclass) + dom['D1'] = D1 + dom['D2'] = D2 + + # build the cost matrix and the Gibbs kernel + M = dist(Xs[d], Xt, metric=metric) + M = M / np.median(M) + + K = np.empty(M.shape, dtype=M.dtype) + np.divide(M, -reg, out=K) + np.exp(K, out=K) + dom['K'] = K + + all_domains.append(dom) + + # uniform target distribution + a = unif(np.shape(Xt)[0]) + + cpt = 0 # iterations count + err = 1 + old_bary = np.ones((nbclasses)) + + while (err > stopThr and cpt < numItermax): + + bary = np.zeros((nbclasses)) + + # update coupling matrices for marginal constraints w.r.t. uniform target distribution + for d in range(nbdomains): + all_domains[d]['K'] = projC(all_domains[d]['K'], a) + other = np.sum(all_domains[d]['K'], axis=1) + bary = bary + np.log(np.dot(all_domains[d]['D1'], other)) / nbdomains + + bary = np.exp(bary) + + # update coupling matrices for marginal constraints w.r.t. unknown proportions based on [Prop 4., 27] + for d in range(nbdomains): + new = np.dot(all_domains[d]['D2'].T, bary) + all_domains[d]['K'] = projR(all_domains[d]['K'], new) + + err = np.linalg.norm(bary - old_bary) + cpt = cpt + 1 + old_bary = bary + + if log: + log['err'].append(err) + + if verbose: + if cpt % 200 == 0: + print('{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) + + bary = bary / np.sum(bary) + couplings = [all_domains[d]['K'] for d in range(nbdomains)] + + if log: + log['niter'] = cpt + log['all_domains'] = all_domains + return couplings, bary, log + else: + return couplings, bary def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, diff --git a/ot/da.py b/ot/da.py index a3da8c1..a9c3cea 100644 --- a/ot/da.py +++ b/ot/da.py @@ -7,20 +7,20 @@ Domain adaptation with optimal transport # Nicolas Courty # Michael Perrot # Nathalie Gayraud +# Ievgen Redko # # License: MIT License import numpy as np import scipy.linalg as linalg -from .bregman import sinkhorn, projR, projC +from .bregman import sinkhorn from .lp import emd from .utils import unif, dist, kernel, cost_normalization from .utils import check_params, BaseEstimator from .unbalanced import sinkhorn_unbalanced from .optim import cg from .optim import gcg -from functools import reduce def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, @@ -128,7 +128,7 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, W = np.ones(M.shape) for (i, c) in enumerate(classes): majs = np.sum(transp[indices_labels[i]], axis=0) - majs = p * ((majs + epsilon)**(p - 1)) + majs = p * ((majs + epsilon) ** (p - 1)) W[indices_labels[i]] = majs return transp @@ -360,8 +360,8 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, def loss(L, G): """Compute full loss""" - return np.sum((xs1.dot(L) - ns * G.dot(xt))**2) + mu * \ - np.sum(G * M) + eta * np.sum(sel(L - I0)**2) + return np.sum((xs1.dot(L) - ns * G.dot(xt)) ** 2) + mu * \ + np.sum(G * M) + eta * np.sum(sel(L - I0) ** 2) def solve_L(G): """ solve L problem with fixed G (least square)""" @@ -373,10 +373,11 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, xsi = xs1.dot(L) def f(G): - return np.sum((xsi - ns * G.dot(xt))**2) + return np.sum((xsi - ns * G.dot(xt)) ** 2) def df(G): return -2 * ns * (xsi - ns * G.dot(xt)).dot(xt.T) + G = cg(a, b, M, 1.0 / mu, f, df, G0=G0, numItermax=numInnerItermax, stopThr=stopInnerThr) return G @@ -563,8 +564,8 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', def loss(L, G): """Compute full loss""" - return np.sum((K1.dot(L) - ns * G.dot(xt))**2) + mu * \ - np.sum(G * M) + eta * np.trace(L.T.dot(Kreg).dot(L)) + return np.sum((K1.dot(L) - ns * G.dot(xt)) ** 2) + mu * \ + np.sum(G * M) + eta * np.trace(L.T.dot(Kreg).dot(L)) def solve_L_nobias(G): """ solve L problem with fixed G (least square)""" @@ -581,10 +582,11 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', xsi = K1.dot(L) def f(G): - return np.sum((xsi - ns * G.dot(xt))**2) + return np.sum((xsi - ns * G.dot(xt)) ** 2) def df(G): return -2 * ns * (xsi - ns * G.dot(xt)).dot(xt.T) + G = cg(a, b, M, 1.0 / mu, f, df, G0=G0, numItermax=numInnerItermax, stopThr=stopInnerThr) return G @@ -746,163 +748,6 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, return A, b -def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100, - stopThr=1e-6, verbose=False, log=False, **kwargs): - r'''Joint OT and proportion estimation for multi-source target shift as proposed in [27] - - The function solves the following optimization problem: - - .. math:: - - \mathbf{h} = arg\min_{\mathbf{h}}\quad \sum_{k=1}^{K} \lambda_k - W_{reg}((\mathbf{D}_2^{(k)} \mathbf{h})^T, \mathbf{a}) - - s.t. \ \forall k, \mathbf{D}_1^{(k)} \gamma_k \mathbf{1}_n= \mathbf{h} - - where : - - - :math:`\lambda_k` is the weight of k-th source domain - - :math:`W_{reg}(\cdot,\cdot)` is the entropic regularized Wasserstein distance (see ot.bregman.sinkhorn) - - :math:`\mathbf{D}_2^{(k)}` is a matrix of weights related to k-th source domain defined as in [p. 5, 27], its expected shape is `(n_k, C)` where `n_k` is the number of elements in the k-th source domain and `C` is the number of classes - - :math:`\mathbf{h}` is a vector of estimated proportions in the target domain of size C - - :math:`\mathbf{a}` is a uniform vector of weights in the target domain of size `n` - - :math:`\mathbf{D}_1^{(k)}` is a matrix of class assignments defined as in [p. 5, 27], its expected shape is `(n_k, C)` - - The problem consist in solving a Wasserstein barycenter problem to estimate the proportions :math:`\mathbf{h}` in the target domain. - - The algorithm used for solving the problem is the Iterative Bregman projections algorithm - with two sets of marginal constraints related to the unknown vector :math:`\mathbf{h}` and uniform tarhet distribution. - - Parameters - ---------- - Xs : list of K np.ndarray(nsk,d) - features of all source domains' samples - Ys : list of K np.ndarray(nsk,) - labels of all source domains' samples - Xt : np.ndarray (nt,d) - samples in the target domain - reg : float - Regularization term > 0 - metric : string, optional (default="sqeuclidean") - The ground metric for the Wasserstein problem - numItermax : int, optional - Max number of iterations - stopThr : float, optional - Stop threshold on relative change in the barycenter (>0) - log : bool, optional - record log if True - verbose : bool, optional (default=False) - Controls the verbosity of the optimization algorithm - - Returns - ------- - gamma : List of K (nsk x nt) ndarrays - Optimal transportation matrices for the given parameters for each pair of source and target domains - h : (C,) ndarray - proportion estimation in the target domain - log : dict - log dictionary return only if log==True in parameters - - - References - ---------- - - .. [27] Ievgen Redko, Nicolas Courty, Rémi Flamary, Devis Tuia - "Optimal transport for multi-source domain adaptation under target shift", - International Conference on Artificial Intelligence and Statistics (AISTATS), 2019. - - ''' - nbclasses = len(np.unique(Ys[0])) - nbdomains = len(Xs) - - # For each source domain, build cost matrices M, Gibbs kernels K and corresponding matrices D_1 and D_2 - all_domains = [] - - # log dictionary - if log: - log = {'niter': 0, 'err': [], 'all_domains': []} - - for d in range(nbdomains): - dom = {} - nsk = Xs[d].shape[0] # get number of elements for this domain - dom['nbelem'] = nsk - classes = np.unique(Ys[d]) # get number of classes for this domain - - # format classes to start from 0 for convenience - if np.min(classes) != 0: - Ys[d] = Ys[d] - np.min(classes) - classes = np.unique(Ys[d]) - - # build the corresponding D_1 and D_2 matrices - D1 = np.zeros((nbclasses, nsk)) - D2 = np.zeros((nbclasses, nsk)) - - for c in classes: - nbelemperclass = np.sum(Ys[d] == c) - if nbelemperclass != 0: - D1[int(c), Ys[d] == c] = 1. - D2[int(c), Ys[d] == c] = 1. / (nbelemperclass) - dom['D1'] = D1 - dom['D2'] = D2 - - # build the cost matrix and the Gibbs kernel - M = dist(Xs[d], Xt, metric=metric) - M = M / np.median(M) - - K = np.empty(M.shape, dtype=M.dtype) - np.divide(M, -reg, out=K) - np.exp(K, out=K) - dom['K'] = K - - all_domains.append(dom) - - # uniform target distribution - a = unif(np.shape(Xt)[0]) - - cpt = 0 # iterations count - err = 1 - old_bary = np.ones((nbclasses)) - - while (err > stopThr and cpt < numItermax): - - bary = np.zeros((nbclasses)) - - # update coupling matrices for marginal constraints w.r.t. uniform target distribution - for d in range(nbdomains): - all_domains[d]['K'] = projC(all_domains[d]['K'], a) - other = np.sum(all_domains[d]['K'], axis=1) - bary = bary + np.log(np.dot(all_domains[d]['D1'], other)) / nbdomains - - bary = np.exp(bary) - - # update coupling matrices for marginal constraints w.r.t. unknown proportions based on [Prop 4., 27] - for d in range(nbdomains): - new = np.dot(all_domains[d]['D2'].T, bary) - all_domains[d]['K'] = projR(all_domains[d]['K'], new) - - err = np.linalg.norm(bary - old_bary) - cpt = cpt + 1 - old_bary = bary - - if log: - log['err'].append(err) - - if verbose: - if cpt % 200 == 0: - print('{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) - print('{:5d}|{:8e}|'.format(cpt, err)) - - bary = bary / np.sum(bary) - couplings = [all_domains[d]['K'] for d in range(nbdomains)] - - if log: - log['niter'] = cpt - log['all_domains'] = all_domains - return couplings, bary, log - else: - return couplings, bary - - def distribution_estimation_uniform(X): """estimates a uniform distribution from an array of samples X @@ -921,7 +766,6 @@ def distribution_estimation_uniform(X): class BaseTransport(BaseEstimator): - """Base class for OTDA objects Notes @@ -1079,7 +923,6 @@ class BaseTransport(BaseEstimator): transp_Xs = [] for bi in batch_ind: - # get the nearest neighbor in the source domain D0 = dist(Xs[bi], self.xs_) idx = np.argmin(D0, axis=1) @@ -1148,7 +991,6 @@ class BaseTransport(BaseEstimator): transp_Xt = [] for bi in batch_ind: - D0 = dist(Xt[bi], self.xt_) idx = np.argmin(D0, axis=1) @@ -1294,7 +1136,6 @@ class LinearTransport(BaseTransport): # check the necessary inputs parameters are here if check_params(Xs=Xs): - transp_Xs = Xs.dot(self.A_) + self.B_ return transp_Xs @@ -1328,14 +1169,12 @@ class LinearTransport(BaseTransport): # check the necessary inputs parameters are here if check_params(Xt=Xt): - transp_Xt = Xt.dot(self.A1_) + self.B1_ return transp_Xt class SinkhornTransport(BaseTransport): - """Domain Adapatation OT method based on Sinkhorn Algorithm Parameters @@ -1445,7 +1284,6 @@ class SinkhornTransport(BaseTransport): class EMDTransport(BaseTransport): - """Domain Adapatation OT method based on Earth Mover's Distance Parameters @@ -1537,7 +1375,6 @@ class EMDTransport(BaseTransport): class SinkhornLpl1Transport(BaseTransport): - """Domain Adapatation OT method based on sinkhorn algorithm + LpL1 class regularization. @@ -1639,7 +1476,6 @@ class SinkhornLpl1Transport(BaseTransport): # check the necessary inputs parameters are here if check_params(Xs=Xs, Xt=Xt, ys=ys): - super(SinkhornLpl1Transport, self).fit(Xs, ys, Xt, yt) returned_ = sinkhorn_lpl1_mm( @@ -1658,7 +1494,6 @@ class SinkhornLpl1Transport(BaseTransport): class SinkhornL1l2Transport(BaseTransport): - """Domain Adapatation OT method based on sinkhorn algorithm + l1l2 class regularization. @@ -1782,7 +1617,6 @@ class SinkhornL1l2Transport(BaseTransport): class MappingTransport(BaseEstimator): - """MappingTransport: DA methods that aims at jointly estimating a optimal transport coupling and the associated mapping @@ -1956,7 +1790,6 @@ class MappingTransport(BaseEstimator): class UnbalancedSinkhornTransport(BaseTransport): - """Domain Adapatation unbalanced OT method based on sinkhorn algorithm Parameters @@ -2075,7 +1908,6 @@ class UnbalancedSinkhornTransport(BaseTransport): class JCPOTTransport(BaseTransport): - """Domain Adapatation OT method for multi-source target shift based on Wasserstein barycenter algorithm. Parameters diff --git a/test/test_da.py b/test/test_da.py index a8c258a..958df7b 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -8,6 +8,7 @@ import numpy as np from numpy.testing import assert_allclose, assert_equal import ot +from ot.bregman import jcpot_barycenter from ot.datasets import make_data_classif from ot.utils import unif @@ -603,7 +604,6 @@ def test_jcpot_transport_class(): # test transform transp_Xs = otda.transform(Xs=Xs) [assert_equal(x.shape, y.shape) for x, y in zip(transp_Xs, Xs)] - #assert_equal(transp_Xs.shape, Xs.shape) Xs_new, _ = make_data_classif('3gauss', ns1 + 1) transp_Xs_new = otda.transform(Xs_new) -- cgit v1.2.3 From 439860609df786a877383775dd901afe28480cc9 Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 1 Apr 2020 09:00:03 +0200 Subject: fix imports remove checks --- ot/da.py | 5 ++--- test/test_da.py | 4 +++- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/ot/da.py b/ot/da.py index a9c3cea..e62e495 100644 --- a/ot/da.py +++ b/ot/da.py @@ -14,7 +14,7 @@ Domain adaptation with optimal transport import numpy as np import scipy.linalg as linalg -from .bregman import sinkhorn +from .bregman import sinkhorn, jcpot_barycenter from .lp import emd from .utils import unif, dist, kernel, cost_normalization from .utils import check_params, BaseEstimator @@ -1956,8 +1956,7 @@ class JCPOTTransport(BaseTransport): def __init__(self, reg_e=.1, max_iter=10, tol=10e-9, verbose=False, log=False, - metric="sqeuclidean", norm=None, - distribution_estimation=distribution_estimation_uniform, + metric="sqeuclidean", out_of_sample_map='ferradans'): self.reg_e = reg_e diff --git a/test/test_da.py b/test/test_da.py index 958df7b..7526f30 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -572,7 +572,6 @@ def test_jcpot_transport_class(): # test its computed otda.fit(Xs=Xs, ys=ys, Xt=Xt) - print(otda.proportions_) assert hasattr(otda, "coupling_") assert hasattr(otda, "proportions_") @@ -610,3 +609,6 @@ def test_jcpot_transport_class(): # check that the oos method is working assert_equal(transp_Xs_new.shape, Xs_new.shape) + + +test_jcpot_transport_class() \ No newline at end of file -- cgit v1.2.3 From 547a03ef87e4aa92edc1e89ee2db04114e1a8ad5 Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 1 Apr 2020 09:13:58 +0200 Subject: fix test example add M to log --- examples/plot_otda_jcpot.py | 20 ++++++-------------- ot/bregman.py | 1 + test/test_da.py | 13 ++----------- 3 files changed, 9 insertions(+), 25 deletions(-) diff --git a/examples/plot_otda_jcpot.py b/examples/plot_otda_jcpot.py index 5e5fff8..1641fb0 100644 --- a/examples/plot_otda_jcpot.py +++ b/examples/plot_otda_jcpot.py @@ -81,11 +81,7 @@ pl.axis('off') ############################################################################## # Instantiate Sinkhorn transport algorithm and fit them for all source domains # ---------------------------------------------------------------------------- -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-2, metric='euclidean') - -M1 = ot.dist(xs1, xt, 'euclidean') -M2 = ot.dist(xs2, xt, 'euclidean') - +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1, metric='sqeuclidean') def print_G(G, xs, ys, xt): for i in range(G.shape[0]): @@ -125,7 +121,7 @@ pl.axis('off') ############################################################################## # Instantiate JCPOT adaptation algorithm and fit it # ---------------------------------------------------------------------------- -otda = ot.da.JCPOTTransport(reg_e=1e-2, max_iter=1000, tol=1e-9, verbose=True, log=True) +otda = ot.da.JCPOTTransport(reg_e=1e-2, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True) otda.fit(all_Xr, all_Yr, xt) ws1 = otda.proportions_.dot(otda.log_['all_domains'][0]['D2']) @@ -136,8 +132,8 @@ pl.clf() plot_ax(dec1, 'Source 1') plot_ax(dec2, 'Source 2') plot_ax(dect, 'Target') -print_G(ot.bregman.sinkhorn(ws1, [], M1, reg=1e-2), xs1, ys1, xt) -print_G(ot.bregman.sinkhorn(ws2, [], M2, reg=1e-2), xs2, ys2, xt) +print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['all_domains'][0]['M'], reg=1e-2), xs1, ys1, xt) +print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['all_domains'][1]['M'], reg=1e-2), xs2, ys2, xt) pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) @@ -154,10 +150,6 @@ pl.axis('off') ############################################################################## # Run oracle transport algorithm with known proportions # ---------------------------------------------------------------------------- - -otda = ot.da.JCPOTTransport(reg_e=0.01, max_iter=1000, tol=1e-9, verbose=True, log=True) -otda.fit(all_Xr, all_Yr, xt) - h_res = np.array([1 - pt, pt]) ws1 = h_res.dot(otda.log_['all_domains'][0]['D2']) @@ -168,8 +160,8 @@ pl.clf() plot_ax(dec1, 'Source 1') plot_ax(dec2, 'Source 2') plot_ax(dect, 'Target') -print_G(ot.bregman.sinkhorn(ws1, [], M1, reg=1e-2), xs1, ys1, xt) -print_G(ot.bregman.sinkhorn(ws2, [], M2, reg=1e-2), xs2, ys2, xt) +print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['all_domains'][0]['M'], reg=1e-2), xs1, ys1, xt) +print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['all_domains'][1]['M'], reg=1e-2), xs2, ys2, xt) pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) diff --git a/ot/bregman.py b/ot/bregman.py index d17aaf0..fb959e9 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1603,6 +1603,7 @@ def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100, # build the cost matrix and the Gibbs kernel M = dist(Xs[d], Xt, metric=metric) M = M / np.median(M) + dom['M'] = M K = np.empty(M.shape, dtype=M.dtype) np.divide(M, -reg, out=K) diff --git a/test/test_da.py b/test/test_da.py index 7526f30..a13550c 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -568,7 +568,7 @@ def test_jcpot_transport_class(): Xs = [Xs1, Xs2] ys = [ys1, ys2] - otda = ot.da.JCPOTTransport(reg_e=0.01, max_iter=1000, tol=1e-9, verbose=True) + otda = ot.da.JCPOTTransport(reg_e=0.01, max_iter=1000, tol=1e-9, verbose=True, log = True) # test its computed otda.fit(Xs=Xs, ys=ys, Xt=Xt) @@ -591,14 +591,8 @@ def test_jcpot_transport_class(): # test margin constraints w.r.t. modified source weights for each source domain - D1 = np.zeros((len(np.unique(ys[i])), len(ys[i]))) - for c in np.unique(ys[i]): - nbelemperclass = np.sum(ys[i] == c) - if nbelemperclass != 0: - D1[int(c), ys[i] == c] = 1. - assert_allclose( - np.dot(D1, np.sum(otda.coupling_[i], axis=1)), otda.proportions_, rtol=1e-3, atol=1e-3) + np.dot(otda.log_['all_domains'][i]['D1'], np.sum(otda.coupling_[i], axis=1)), otda.proportions_, rtol=1e-3, atol=1e-3) # test transform transp_Xs = otda.transform(Xs=Xs) @@ -609,6 +603,3 @@ def test_jcpot_transport_class(): # check that the oos method is working assert_equal(transp_Xs_new.shape, Xs_new.shape) - - -test_jcpot_transport_class() \ No newline at end of file -- cgit v1.2.3 From b1f87363b160735b6e2df59380f9de56b7934b53 Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 1 Apr 2020 09:42:09 +0200 Subject: add dataset clean plot --- examples/plot_otda_jcpot.py | 28 +++++++++------------------- ot/datasets.py | 15 ++++++++++++++- 2 files changed, 23 insertions(+), 20 deletions(-) diff --git a/examples/plot_otda_jcpot.py b/examples/plot_otda_jcpot.py index 1641fb0..579ad2a 100644 --- a/examples/plot_otda_jcpot.py +++ b/examples/plot_otda_jcpot.py @@ -16,6 +16,7 @@ This example introduces a target shift problem with two 2D source and 1 target d import pylab as pl import numpy as np import ot +from ot.datasets import make_data_classif ############################################################################## # Generate data @@ -24,17 +25,6 @@ n = 50 sigma = 0.3 np.random.seed(1985) - -def get_data(n, p, dec): - y = np.concatenate((np.ones(int(p * n)), np.zeros(int((1 - p) * n)))) - x = np.hstack((0 * y[:, None] - 0, 1 - 2 * y[:, None])) + sigma * np.random.randn(len(y), 2) - - x[:, 0] += dec[0] - x[:, 1] += dec[1] - - return x, y - - p1 = .2 dec1 = [0, 2] @@ -44,15 +34,15 @@ dec2 = [0, -2] pt = .4 dect = [4, 0] -xs1, ys1 = get_data(n, p1, dec1) -xs2, ys2 = get_data(n + 1, p2, dec2) -xt, yt = get_data(n, pt, dect) +xs1, ys1 = make_data_classif('2gauss_prop', n, nz=sigma, p = p1, bias = dec1) +xs2, ys2 = make_data_classif('2gauss_prop', n+1, nz=sigma, p = p2, bias = dec2) +xt, yt = make_data_classif('2gauss_prop', n, nz=sigma, p = pt, bias = dect) + all_Xr = [xs1, xs2] all_Yr = [ys1, ys2] # %% -da = 1.5 - +da = 1.5 def plot_ax(dec, name): pl.plot([dec[0], dec[0]], [dec[1] - da, dec[1] + da], 'k', alpha=0.5) pl.plot([dec[0] - da, dec[0] + da], [dec[1], dec[1]], 'k', alpha=0.5) @@ -68,9 +58,9 @@ pl.clf() plot_ax(dec1, 'Source 1') plot_ax(dec2, 'Source 2') plot_ax(dect, 'Target') -pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9, label='Source 1 (0.8,0.2)') -pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9, label='Source 2 (0.1,0.9)') -pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9, label='Target (0.6,0.4)') +pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9, label='Source 1 ({:1.2f}, {:1.2f})'.format(1-p1, p1)) +pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9, label='Source 2 ({:1.2f}, {:1.2f})'.format(1-p2, p2)) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9, label='Target ({:1.2f}, {:1.2f})'.format(1-pt, pt)) pl.title('Data') pl.legend() diff --git a/ot/datasets.py b/ot/datasets.py index ba0cfd9..eea9f37 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -80,7 +80,7 @@ def get_2D_samples_gauss(n, m, sigma, random_state=None): return make_2D_samples_gauss(n, m, sigma, random_state=None) -def make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): +def make_data_classif(dataset, n, nz=.5, theta=0, p = .5, random_state=None, **kwargs): """Dataset generation for classification problems Parameters @@ -91,6 +91,8 @@ def make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): number of training samples nz : float noise level (>0) + p : float + proportion of one class in the binary setting random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; @@ -150,6 +152,17 @@ def make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): x = x.dot(rot) + elif dataset.lower() == '2gauss_prop': + + y = np.concatenate((np.ones(int(p * n)), np.zeros(int((1 - p) * n)))) + x = np.hstack((0 * y[:, None] - 0, 1 - 2 * y[:, None])) + nz * np.random.randn(len(y), 2) + + if ('bias' not in kwargs) and ('b' not in kwargs): + kwargs['bias'] = np.array([0, 2]) + + x[:, 0] += kwargs['bias'][0] + x[:, 1] += kwargs['bias'][1] + else: x = np.array(0) y = np.array(0) -- cgit v1.2.3 From 6b8477d1c08696a08a1b71642712d83e560f9623 Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 1 Apr 2020 09:49:24 +0200 Subject: pep8 --- examples/plot_otda_jcpot.py | 20 ++++++++++++-------- test/test_da.py | 7 +++---- 2 files changed, 15 insertions(+), 12 deletions(-) diff --git a/examples/plot_otda_jcpot.py b/examples/plot_otda_jcpot.py index 579ad2a..ce6b88f 100644 --- a/examples/plot_otda_jcpot.py +++ b/examples/plot_otda_jcpot.py @@ -34,15 +34,17 @@ dec2 = [0, -2] pt = .4 dect = [4, 0] -xs1, ys1 = make_data_classif('2gauss_prop', n, nz=sigma, p = p1, bias = dec1) -xs2, ys2 = make_data_classif('2gauss_prop', n+1, nz=sigma, p = p2, bias = dec2) -xt, yt = make_data_classif('2gauss_prop', n, nz=sigma, p = pt, bias = dect) +xs1, ys1 = make_data_classif('2gauss_prop', n, nz=sigma, p=p1, bias=dec1) +xs2, ys2 = make_data_classif('2gauss_prop', n + 1, nz=sigma, p=p2, bias=dec2) +xt, yt = make_data_classif('2gauss_prop', n, nz=sigma, p=pt, bias=dect) all_Xr = [xs1, xs2] all_Yr = [ys1, ys2] # %% da = 1.5 + + def plot_ax(dec, name): pl.plot([dec[0], dec[0]], [dec[1] - da, dec[1] + da], 'k', alpha=0.5) pl.plot([dec[0] - da, dec[0] + da], [dec[1], dec[1]], 'k', alpha=0.5) @@ -58,21 +60,24 @@ pl.clf() plot_ax(dec1, 'Source 1') plot_ax(dec2, 'Source 2') plot_ax(dect, 'Target') -pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9, label='Source 1 ({:1.2f}, {:1.2f})'.format(1-p1, p1)) -pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9, label='Source 2 ({:1.2f}, {:1.2f})'.format(1-p2, p2)) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9, label='Target ({:1.2f}, {:1.2f})'.format(1-pt, pt)) +pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9, + label='Source 1 ({:1.2f}, {:1.2f})'.format(1 - p1, p1)) +pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9, + label='Source 2 ({:1.2f}, {:1.2f})'.format(1 - p2, p2)) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9, + label='Target ({:1.2f}, {:1.2f})'.format(1 - pt, pt)) pl.title('Data') pl.legend() pl.axis('equal') pl.axis('off') - ############################################################################## # Instantiate Sinkhorn transport algorithm and fit them for all source domains # ---------------------------------------------------------------------------- ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1, metric='sqeuclidean') + def print_G(G, xs, ys, xt): for i in range(G.shape[0]): for j in range(G.shape[1]): @@ -107,7 +112,6 @@ pl.legend() pl.axis('equal') pl.axis('off') - ############################################################################## # Instantiate JCPOT adaptation algorithm and fit it # ---------------------------------------------------------------------------- diff --git a/test/test_da.py b/test/test_da.py index a13550c..f700df9 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -511,7 +511,6 @@ def test_mapping_transport_class(): def test_linear_mapping(): - ns = 150 nt = 200 @@ -529,7 +528,6 @@ def test_linear_mapping(): def test_linear_mapping_class(): - ns = 150 nt = 200 @@ -568,7 +566,7 @@ def test_jcpot_transport_class(): Xs = [Xs1, Xs2] ys = [ys1, ys2] - otda = ot.da.JCPOTTransport(reg_e=0.01, max_iter=1000, tol=1e-9, verbose=True, log = True) + otda = ot.da.JCPOTTransport(reg_e=0.01, max_iter=1000, tol=1e-9, verbose=True, log=True) # test its computed otda.fit(Xs=Xs, ys=ys, Xt=Xt) @@ -592,7 +590,8 @@ def test_jcpot_transport_class(): # test margin constraints w.r.t. modified source weights for each source domain assert_allclose( - np.dot(otda.log_['all_domains'][i]['D1'], np.sum(otda.coupling_[i], axis=1)), otda.proportions_, rtol=1e-3, atol=1e-3) + np.dot(otda.log_['all_domains'][i]['D1'], np.sum(otda.coupling_[i], axis=1)), otda.proportions_, rtol=1e-3, + atol=1e-3) # test transform transp_Xs = otda.transform(Xs=Xs) -- cgit v1.2.3 From 592f933085d5b521a440eb91eccc283c43732170 Mon Sep 17 00:00:00 2001 From: AdrienCorenflos Date: Wed, 1 Apr 2020 12:14:42 +0100 Subject: Fix ordering --- ot/lp/__init__.py | 2 +- test/test_ot.py | 38 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 39 insertions(+), 1 deletion(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index cdd505d..4c968ca 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -656,7 +656,7 @@ def emd_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', p=1., dense=True, perm_a = np.argsort(x_a_1d) perm_b = np.argsort(x_b_1d) - G_sorted, indices, cost = emd_1d_sorted(a, b, + G_sorted, indices, cost = emd_1d_sorted(a[perm_a.flatten()], b[perm_b.flatten()], x_a_1d[perm_a], x_b_1d[perm_b], metric=metric, p=p) G = coo_matrix((G_sorted, (perm_a[indices[:, 0]], perm_b[indices[:, 1]])), diff --git a/test/test_ot.py b/test/test_ot.py index 47df946..7afdae3 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -91,6 +91,44 @@ def test_emd_1d_emd2_1d(): with pytest.raises(AssertionError): ot.emd_1d(u, v, [], []) +def test_emd_1d_emd2_1d_with_weights(): + + # test emd1d gives similar results as emd + n = 20 + m = 30 + rng = np.random.RandomState(0) + u = rng.randn(n, 1) + v = rng.randn(m, 1) + + w_u = rng.uniform(0., 1., n) + w_u = w_u / w_u.sum() + + w_v = rng.uniform(0., 1., m) + w_v = w_v / w_v.sum() + + M = ot.dist(u, v, metric='sqeuclidean') + + G, log = ot.emd(w_u, w_v, M, log=True) + wass = log["cost"] + G_1d, log = ot.emd_1d(u, v, w_u, w_v, metric='sqeuclidean', log=True) + wass1d = log["cost"] + wass1d_emd2 = ot.emd2_1d(u, v, w_u, w_v, metric='sqeuclidean', log=False) + wass1d_euc = ot.emd2_1d(u, v, w_u, w_v, metric='euclidean', log=False) + + # check loss is similar + np.testing.assert_allclose(wass, wass1d) + np.testing.assert_allclose(wass, wass1d_emd2) + + # check loss is similar to scipy's implementation for Euclidean metric + wass_sp = wasserstein_distance(u.reshape((-1,)), v.reshape((-1,))) + np.testing.assert_allclose(wass_sp, wass1d_euc) + + # check constraints + np.testing.assert_allclose(w_u, G.sum(1)) + np.testing.assert_allclose(w_v, G.sum(0)) + + + def test_wass_1d(): # test emd1d gives similar results as emd -- cgit v1.2.3 From 1e2e118e3a30224932ed2f012bb8f9f0f374ef2c Mon Sep 17 00:00:00 2001 From: AdrienCorenflos Date: Thu, 2 Apr 2020 10:39:55 +0100 Subject: Fix test --- test/test_ot.py | 18 ++++++------------ 1 file changed, 6 insertions(+), 12 deletions(-) diff --git a/test/test_ot.py b/test/test_ot.py index 7afdae3..0f1357f 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -7,11 +7,11 @@ import warnings import numpy as np +import pytest from scipy.stats import wasserstein_distance import ot from ot.datasets import make_1D_gauss as gauss -import pytest def test_emd_dimension_mismatch(): @@ -75,12 +75,12 @@ def test_emd_1d_emd2_1d(): np.testing.assert_allclose(wass, wass1d_emd2) # check loss is similar to scipy's implementation for Euclidean metric - wass_sp = wasserstein_distance(u.reshape((-1, )), v.reshape((-1, ))) + wass_sp = wasserstein_distance(u.reshape((-1,)), v.reshape((-1,))) np.testing.assert_allclose(wass_sp, wass1d_euc) # check constraints - np.testing.assert_allclose(np.ones((n, )) / n, G.sum(1)) - np.testing.assert_allclose(np.ones((m, )) / m, G.sum(0)) + np.testing.assert_allclose(np.ones((n,)) / n, G.sum(1)) + np.testing.assert_allclose(np.ones((m,)) / m, G.sum(0)) # check G is similar np.testing.assert_allclose(G, G_1d) @@ -91,8 +91,8 @@ def test_emd_1d_emd2_1d(): with pytest.raises(AssertionError): ot.emd_1d(u, v, [], []) -def test_emd_1d_emd2_1d_with_weights(): +def test_emd_1d_emd2_1d_with_weights(): # test emd1d gives similar results as emd n = 20 m = 30 @@ -120,7 +120,7 @@ def test_emd_1d_emd2_1d_with_weights(): np.testing.assert_allclose(wass, wass1d_emd2) # check loss is similar to scipy's implementation for Euclidean metric - wass_sp = wasserstein_distance(u.reshape((-1,)), v.reshape((-1,))) + wass_sp = wasserstein_distance(u.reshape((-1,)), v.reshape((-1,)), w_u, w_v) np.testing.assert_allclose(wass_sp, wass1d_euc) # check constraints @@ -128,8 +128,6 @@ def test_emd_1d_emd2_1d_with_weights(): np.testing.assert_allclose(w_v, G.sum(0)) - - def test_wass_1d(): # test emd1d gives similar results as emd n = 20 @@ -173,7 +171,6 @@ def test_emd_empty(): def test_emd_sparse(): - n = 100 rng = np.random.RandomState(0) @@ -249,7 +246,6 @@ def test_emd2_multi(): def test_lp_barycenter(): - a1 = np.array([1.0, 0, 0])[:, None] a2 = np.array([0, 0, 1.0])[:, None] @@ -266,7 +262,6 @@ def test_lp_barycenter(): def test_free_support_barycenter(): - measures_locations = [np.array([-1.]).reshape((1, 1)), np.array([1.]).reshape((1, 1))] measures_weights = [np.array([1.]), np.array([1.])] @@ -282,7 +277,6 @@ def test_free_support_barycenter(): @pytest.mark.skipif(not ot.lp.cvx.cvxopt, reason="No cvxopt available") def test_lp_barycenter_cvxopt(): - a1 = np.array([1.0, 0, 0])[:, None] a2 = np.array([0, 0, 1.0])[:, None] -- cgit v1.2.3 From 60943d00bab1682d6fac22b1e1ba5e64569b4e78 Mon Sep 17 00:00:00 2001 From: AdrienCorenflos Date: Thu, 2 Apr 2020 10:41:24 +0100 Subject: Auto PEP8 --- ot/lp/__init__.py | 19 ++++++++++--------- 1 file changed, 10 insertions(+), 9 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 4c968ca..1922785 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -12,16 +12,16 @@ Solvers for the original linear program OT problem import multiprocessing import sys + import numpy as np from scipy.sparse import coo_matrix -from .import cvx - +from . import cvx +from .cvx import barycenter # import compiled emd from .emd_wrap import emd_c, check_result, emd_1d_sorted -from ..utils import parmap -from .cvx import barycenter from ..utils import dist +from ..utils import parmap __all__ = ['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx', 'emd_1d', 'emd2_1d', 'wasserstein_1d'] @@ -458,7 +458,8 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), return res -def free_support_barycenter(measures_locations, measures_weights, X_init, b=None, weights=None, numItermax=100, stopThr=1e-7, verbose=False, log=None): +def free_support_barycenter(measures_locations, measures_weights, X_init, b=None, weights=None, numItermax=100, + stopThr=1e-7, verbose=False, log=None): """ Solves the free support (locations of the barycenters are optimized, not the weights) Wasserstein barycenter problem (i.e. the weighted Frechet mean for the 2-Wasserstein distance) @@ -525,8 +526,8 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None T_sum = np.zeros((k, d)) - for (measure_locations_i, measure_weights_i, weight_i) in zip(measures_locations, measures_weights, weights.tolist()): - + for (measure_locations_i, measure_weights_i, weight_i) in zip(measures_locations, measures_weights, + weights.tolist()): M_i = dist(X, measure_locations_i) T_i = emd(b, measure_weights_i, M_i) T_sum = T_sum + weight_i * np.reshape(1. / b, (-1, 1)) * np.matmul(T_i, measure_locations_i) @@ -651,8 +652,8 @@ def emd_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', p=1., dense=True, if b.ndim == 0 or len(b) == 0: b = np.ones((x_b.shape[0],), dtype=np.float64) / x_b.shape[0] - x_a_1d = x_a.reshape((-1, )) - x_b_1d = x_b.reshape((-1, )) + x_a_1d = x_a.reshape((-1,)) + x_b_1d = x_b.reshape((-1,)) perm_a = np.argsort(x_a_1d) perm_b = np.argsort(x_b_1d) -- cgit v1.2.3 From a9e69509412338920142c0615a50bc00739144d0 Mon Sep 17 00:00:00 2001 From: AdrienCorenflos Date: Thu, 2 Apr 2020 11:11:16 +0100 Subject: Remove flatten, it's not useful. --- ot/lp/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 1922785..f4f6861 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -657,7 +657,7 @@ def emd_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', p=1., dense=True, perm_a = np.argsort(x_a_1d) perm_b = np.argsort(x_b_1d) - G_sorted, indices, cost = emd_1d_sorted(a[perm_a.flatten()], b[perm_b.flatten()], + G_sorted, indices, cost = emd_1d_sorted(a[perm_a], b[perm_b], x_a_1d[perm_a], x_b_1d[perm_b], metric=metric, p=p) G = coo_matrix((G_sorted, (perm_a[indices[:, 0]], perm_b[indices[:, 1]])), -- cgit v1.2.3 From 9200af5d795517b0772c10bb3d16022dd1a12791 Mon Sep 17 00:00:00 2001 From: ievred Date: Thu, 2 Apr 2020 15:29:12 +0200 Subject: laplace v1 --- ot/bregman.py | 72 +++++++++++++++++++++++++++++++++---------------------- ot/datasets.py | 4 ++-- ot/lp/__init__.py | 4 +--- ot/plot.py | 3 ++- 4 files changed, 49 insertions(+), 34 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index fb959e9..951d3ce 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -19,6 +19,7 @@ import warnings from .utils import unif, dist from scipy.optimize import fmin_l_bfgs_b + def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): r""" @@ -539,12 +540,12 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, old_v = v[i_2] v[i_2] = b[i_2] / (K[:, i_2].T.dot(u)) G[:, i_2] = u * K[:, i_2] * v[i_2] - #aviol = (G@one_m - a) - #aviol_2 = (G.T@one_n - b) + # aviol = (G@one_m - a) + # aviol_2 = (G.T@one_n - b) viol += (-old_v + v[i_2]) * K[:, i_2] * u viol_2[i_2] = v[i_2] * K[:, i_2].dot(u) - b[i_2] - #print('b',np.max(abs(aviol -viol)),np.max(abs(aviol_2 - viol_2))) + # print('b',np.max(abs(aviol -viol)),np.max(abs(aviol_2 - viol_2))) if stopThr_val <= stopThr: break @@ -715,7 +716,7 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, if np.abs(u).max() > tau or np.abs(v).max() > tau: if n_hists: alpha, beta = alpha + reg * \ - np.max(np.log(u), 1), beta + reg * np.max(np.log(v)) + np.max(np.log(u), 1), beta + reg * np.max(np.log(v)) else: alpha, beta = alpha + reg * np.log(u), beta + reg * np.log(v) if n_hists: @@ -940,7 +941,7 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, # the 10th iterations transp = G err = np.linalg.norm( - (np.sum(transp, axis=0) - b))**2 + np.linalg.norm((np.sum(transp, axis=1) - a))**2 + (np.sum(transp, axis=0) - b)) ** 2 + np.linalg.norm((np.sum(transp, axis=1) - a)) ** 2 if log: log['err'].append(err) @@ -966,7 +967,7 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, def geometricBar(weights, alldistribT): """return the weighted geometric mean of distributions""" - assert(len(weights) == alldistribT.shape[1]) + assert (len(weights) == alldistribT.shape[1]) return np.exp(np.dot(np.log(alldistribT), weights.T)) @@ -1108,7 +1109,7 @@ def barycenter_sinkhorn(A, M, reg, weights=None, numItermax=1000, if weights is None: weights = np.ones(A.shape[1]) / A.shape[1] else: - assert(len(weights) == A.shape[1]) + assert (len(weights) == A.shape[1]) if log: log = {'err': []} @@ -1206,7 +1207,7 @@ def barycenter_stabilized(A, M, reg, tau=1e10, weights=None, numItermax=1000, if weights is None: weights = np.ones(n_hists) / n_hists else: - assert(len(weights) == A.shape[1]) + assert (len(weights) == A.shape[1]) if log: log = {'err': []} @@ -1334,7 +1335,7 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, if weights is None: weights = np.ones(A.shape[0]) / A.shape[0] else: - assert(len(weights) == A.shape[0]) + assert (len(weights) == A.shape[0]) if log: log = {'err': []} @@ -1350,11 +1351,11 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, # this is equivalent to blurring on horizontal then vertical directions t = np.linspace(0, 1, A.shape[1]) [Y, X] = np.meshgrid(t, t) - xi1 = np.exp(-(X - Y)**2 / reg) + xi1 = np.exp(-(X - Y) ** 2 / reg) t = np.linspace(0, 1, A.shape[2]) [Y, X] = np.meshgrid(t, t) - xi2 = np.exp(-(X - Y)**2 / reg) + xi2 = np.exp(-(X - Y) ** 2 / reg) def K(x): return np.dot(np.dot(xi1, x), xi2) @@ -1501,6 +1502,7 @@ def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, else: return np.sum(K0, axis=1) + def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100, stopThr=1e-6, verbose=False, log=False, **kwargs): r'''Joint OT and proportion estimation for multi-source target shift as proposed in [27] @@ -1658,6 +1660,7 @@ def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100, else: return couplings, bary + def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs): @@ -1749,7 +1752,8 @@ def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', return pi -def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs): +def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, + verbose=False, log=False, **kwargs): r''' Solve the entropic regularization optimal transport problem from empirical data and return the OT loss @@ -1831,14 +1835,17 @@ def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', num M = dist(X_s, X_t, metric=metric) if log: - sinkhorn_loss, log = sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) + sinkhorn_loss, log = sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log, + **kwargs) return sinkhorn_loss, log else: - sinkhorn_loss = sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) + sinkhorn_loss = sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log, + **kwargs) return sinkhorn_loss -def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs): +def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, + verbose=False, log=False, **kwargs): r''' Compute the sinkhorn divergence loss from empirical data @@ -1924,11 +1931,14 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli .. [23] Aude Genevay, Gabriel Peyré, Marco Cuturi, Learning Generative Models with Sinkhorn Divergences, Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 ''' if log: - sinkhorn_loss_ab, log_ab = empirical_sinkhorn2(X_s, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) + sinkhorn_loss_ab, log_ab = empirical_sinkhorn2(X_s, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, + stopThr=1e-9, verbose=verbose, log=log, **kwargs) - sinkhorn_loss_a, log_a = empirical_sinkhorn2(X_s, X_s, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) + sinkhorn_loss_a, log_a = empirical_sinkhorn2(X_s, X_s, reg, a, b, metric=metric, numIterMax=numIterMax, + stopThr=1e-9, verbose=verbose, log=log, **kwargs) - sinkhorn_loss_b, log_b = empirical_sinkhorn2(X_t, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) + sinkhorn_loss_b, log_b = empirical_sinkhorn2(X_t, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, + stopThr=1e-9, verbose=verbose, log=log, **kwargs) sinkhorn_div = sinkhorn_loss_ab - 1 / 2 * (sinkhorn_loss_a + sinkhorn_loss_b) @@ -1943,11 +1953,14 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli return max(0, sinkhorn_div), log else: - sinkhorn_loss_ab = empirical_sinkhorn2(X_s, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) + sinkhorn_loss_ab = empirical_sinkhorn2(X_s, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, + verbose=verbose, log=log, **kwargs) - sinkhorn_loss_a = empirical_sinkhorn2(X_s, X_s, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) + sinkhorn_loss_a = empirical_sinkhorn2(X_s, X_s, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, + verbose=verbose, log=log, **kwargs) - sinkhorn_loss_b = empirical_sinkhorn2(X_t, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) + sinkhorn_loss_b = empirical_sinkhorn2(X_t, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, + verbose=verbose, log=log, **kwargs) sinkhorn_div = sinkhorn_loss_ab - 1 / 2 * (sinkhorn_loss_a + sinkhorn_loss_b) return max(0, sinkhorn_div) @@ -2039,7 +2052,8 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res try: import bottleneck except ImportError: - warnings.warn("Bottleneck module is not installed. Install it from https://pypi.org/project/Bottleneck/ for better performance.") + warnings.warn( + "Bottleneck module is not installed. Install it from https://pypi.org/project/Bottleneck/ for better performance.") bottleneck = np a = np.asarray(a, dtype=np.float64) @@ -2173,10 +2187,11 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res # box constraints in L-BFGS-B (see Proposition 1 in [26]) bounds_u = [(max(a_I_min / ((nt - nt_budget) * epsilon + nt_budget * (b_J_max / ( - ns * epsilon * kappa * K_min))), epsilon / kappa), a_I_max / (nt * epsilon * K_min))] * ns_budget + ns * epsilon * kappa * K_min))), epsilon / kappa), a_I_max / (nt * epsilon * K_min))] * ns_budget - bounds_v = [(max(b_J_min / ((ns - ns_budget) * epsilon + ns_budget * (kappa * a_I_max / (nt * epsilon * K_min))), - epsilon * kappa), b_J_max / (ns * epsilon * K_min))] * nt_budget + bounds_v = [( + max(b_J_min / ((ns - ns_budget) * epsilon + ns_budget * (kappa * a_I_max / (nt * epsilon * K_min))), + epsilon * kappa), b_J_max / (ns * epsilon * K_min))] * nt_budget # pre-calculated constants for the objective vec_eps_IJc = epsilon * kappa * (K_IJc * np.ones(nt - nt_budget).reshape((1, -1))).sum(axis=1) @@ -2225,7 +2240,8 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res return usc, vsc def screened_obj(usc, vsc): - part_IJ = np.dot(np.dot(usc, K_IJ), vsc) - kappa * np.dot(a_I, np.log(usc)) - (1. / kappa) * np.dot(b_J, np.log(vsc)) + part_IJ = np.dot(np.dot(usc, K_IJ), vsc) - kappa * np.dot(a_I, np.log(usc)) - (1. / kappa) * np.dot(b_J, + np.log(vsc)) part_IJc = np.dot(usc, vec_eps_IJc) part_IcJ = np.dot(vec_eps_IcJ, vsc) psi_epsilon = part_IJ + part_IJc + part_IcJ @@ -2247,9 +2263,9 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res g = np.hstack([g_u, g_v]) return f, g - #----------------------------------------------------------------------------------------------------------------# + # ----------------------------------------------------------------------------------------------------------------# # Step 2: L-BFGS-B solver # - #----------------------------------------------------------------------------------------------------------------# + # ----------------------------------------------------------------------------------------------------------------# u0, v0 = restricted_sinkhorn(u0, v0) theta0 = np.hstack([u0, v0]) diff --git a/ot/datasets.py b/ot/datasets.py index eea9f37..a1ca7b6 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -30,7 +30,7 @@ def make_1D_gauss(n, m, s): 1D histogram for a gaussian distribution """ x = np.arange(n, dtype=np.float64) - h = np.exp(-(x - m)**2 / (2 * s**2)) + h = np.exp(-(x - m) ** 2 / (2 * s ** 2)) return h / h.sum() @@ -80,7 +80,7 @@ def get_2D_samples_gauss(n, m, sigma, random_state=None): return make_2D_samples_gauss(n, m, sigma, random_state=None) -def make_data_classif(dataset, n, nz=.5, theta=0, p = .5, random_state=None, **kwargs): +def make_data_classif(dataset, n, nz=.5, theta=0, p=.5, random_state=None, **kwargs): """Dataset generation for classification problems Parameters diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index cdd505d..7eaa44a 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -2,8 +2,6 @@ """ Solvers for the original linear program OT problem - - """ # Author: Remi Flamary @@ -18,7 +16,7 @@ from scipy.sparse import coo_matrix from .import cvx # import compiled emd -from .emd_wrap import emd_c, check_result, emd_1d_sorted +#from .emd_wrap import emd_c, check_result, emd_1d_sorted from ..utils import parmap from .cvx import barycenter from ..utils import dist diff --git a/ot/plot.py b/ot/plot.py index f403e98..ad436b4 100644 --- a/ot/plot.py +++ b/ot/plot.py @@ -78,9 +78,10 @@ def plot2D_samples_mat(xs, xt, G, thr=1e-8, **kwargs): thr : float, optional threshold above which the line is drawn **kwargs : dict - paameters given to the plot functions (default color is black if + parameters given to the plot functions (default color is black if nothing given) """ + if ('color' not in kwargs) and ('c' not in kwargs): kwargs['color'] = 'k' mx = G.max() -- cgit v1.2.3 From 90f5d5f60af9ef25d7aba715e2398946e5ee16da Mon Sep 17 00:00:00 2001 From: ievred Date: Fri, 3 Apr 2020 16:02:39 +0200 Subject: laplace emd+sinkhorn --- examples/plot_otda_laplacian.py | 149 +++ ot1/da.py | 2551 +++++++++++++++++++++++++++++++++++++++ test/test_da.py | 107 +- 3 files changed, 2806 insertions(+), 1 deletion(-) create mode 100644 examples/plot_otda_laplacian.py create mode 100644 ot1/da.py diff --git a/examples/plot_otda_laplacian.py b/examples/plot_otda_laplacian.py new file mode 100644 index 0000000..d9ae280 --- /dev/null +++ b/examples/plot_otda_laplacian.py @@ -0,0 +1,149 @@ +# -*- coding: utf-8 -*- +""" +======================== +OT for domain adaptation +======================== + +This example introduces a domain adaptation in a 2D setting and OTDA +approaches with Laplacian regularization. + +""" + +# Authors: Ievgen Redko + +# License: MIT License + +import matplotlib.pylab as pl +import ot + +############################################################################## +# Generate data +# ------------- + +n_source_samples = 150 +n_target_samples = 150 + +Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) + + +############################################################################## +# Instantiate the different transport algorithms and fit them +# ----------------------------------------------------------- + +# EMD Transport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=.5) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# EMD Transport with Laplacian regularization +ot_emd_laplace = ot.da.EMDLaplaceTransport(reg_lap=100, reg_src=1) +ot_emd_laplace.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport with Laplacian regularization +ot_sinkhorn_laplace = ot.da.SinkhornLaplaceTransport(reg_e=.5, reg_lap=100, reg_src=1) +ot_sinkhorn_laplace.fit(Xs=Xs, Xt=Xt) + +# transport source samples onto target samples +transp_Xs_emd = ot_emd.transform(Xs=Xs) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) +transp_Xs_emd_laplace = ot_emd_laplace.transform(Xs=Xs) +transp_Xs_sinkhorn_laplace = ot_sinkhorn_laplace.transform(Xs=Xs) + +############################################################################## +# Fig 1 : plots source and target samples +# --------------------------------------- + +pl.figure(1, figsize=(10, 5)) +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Source samples') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Target samples') +pl.tight_layout() + + +############################################################################## +# Fig 2 : plot optimal couplings and transported samples +# ------------------------------------------------------ + +param_img = {'interpolation': 'nearest'} + +n_plots = 2 + +pl.figure(2, figsize=(15, 8)) +pl.subplot(2, 2*n_plots, 1) +pl.imshow(ot_emd.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nEMDTransport') + +pl.figure(2, figsize=(15, 8)) +pl.subplot(2, 2*n_plots, 2) +pl.imshow(ot_sinkhorn.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornTransport') + +pl.subplot(2, 2*n_plots, 3) +pl.imshow(ot_emd_laplace.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nEMDLaplaceTransport') + +pl.subplot(2, 2*n_plots, 4) +pl.imshow(ot_emd_laplace.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornLaplaceTransport') + +pl.subplot(2, 2*n_plots, 5) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nEmdTransport') +pl.legend(loc="lower left") + +pl.subplot(2, 2*n_plots, 6) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornTransport') + +pl.subplot(2, 2*n_plots, 7) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nEMDLaplaceTransport') + +pl.subplot(2, 2*n_plots, 8) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_sinkhorn_laplace[:, 0], transp_Xs_sinkhorn_laplace[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornLaplaceTransport') +pl.tight_layout() + +pl.show() diff --git a/ot1/da.py b/ot1/da.py new file mode 100644 index 0000000..39e8c4c --- /dev/null +++ b/ot1/da.py @@ -0,0 +1,2551 @@ +# -*- coding: utf-8 -*- +""" +Domain adaptation with optimal transport +""" + +# Author: Remi Flamary +# Nicolas Courty +# Michael Perrot +# Nathalie Gayraud +# Ievgen Redko +# +# License: MIT License + +import numpy as np +import scipy.linalg as linalg + +from .bregman import sinkhorn, jcpot_barycenter +from .lp import emd +from .utils import unif, dist, kernel, cost_normalization, laplacian +from .utils import check_params, BaseEstimator +from .unbalanced import sinkhorn_unbalanced +from .optim import cg +from .optim import gcg + + +def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, + numInnerItermax=200, stopInnerThr=1e-9, verbose=False, + log=False): + """ + Solve the entropic regularization optimal transport problem with nonconvex + group lasso regularization + + The function solves the following optimization problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma) + + \eta \Omega_g(\gamma) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the (ns,nt) metric cost matrix + - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e + (\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`\Omega_g` is the group lasso regularization term + :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^{1/2}_1` + where :math:`\mathcal{I}_c` are the index of samples from class c + in the source domain. + - a and b are source and target weights (sum to 1) + + The algorithm used for solving the problem is the generalized conditional + gradient as proposed in [5]_ [7]_ + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + labels_a : np.ndarray (ns,) + labels of samples in the source domain + b : np.ndarray (nt,) + samples weights in the target domain + M : np.ndarray (ns,nt) + loss matrix + reg : float + Regularization term for entropic regularization >0 + eta : float, optional + Regularization term for group lasso regularization >0 + numItermax : int, optional + Max number of iterations + numInnerItermax : int, optional + Max number of iterations (inner sinkhorn solver) + stopInnerThr : float, optional + Stop threshold on error (inner sinkhorn solver) (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE + Transactions on Pattern Analysis and Machine Intelligence , + vol.PP, no.99, pp.1-1 + .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). + Generalized conditional gradient: analysis of convergence + and applications. arXiv preprint arXiv:1510.06567. + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.bregman.sinkhorn : Entropic regularized OT + ot.optim.cg : General regularized OT + + """ + p = 0.5 + epsilon = 1e-3 + + indices_labels = [] + classes = np.unique(labels_a) + for c in classes: + idxc, = np.where(labels_a == c) + indices_labels.append(idxc) + + W = np.zeros(M.shape) + + for cpt in range(numItermax): + Mreg = M + eta * W + transp = sinkhorn(a, b, Mreg, reg, numItermax=numInnerItermax, + stopThr=stopInnerThr) + # the transport has been computed. Check if classes are really + # separated + W = np.ones(M.shape) + for (i, c) in enumerate(classes): + majs = np.sum(transp[indices_labels[i]], axis=0) + majs = p * ((majs + epsilon) ** (p - 1)) + W[indices_labels[i]] = majs + + return transp + + +def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10, + numInnerItermax=200, stopInnerThr=1e-9, verbose=False, + log=False): + """ + Solve the entropic regularization optimal transport problem with group + lasso regularization + + The function solves the following optimization problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+ + \eta \Omega_g(\gamma) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the (ns,nt) metric cost matrix + - :math:`\Omega_e` is the entropic regularization term + :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`\Omega_g` is the group lasso regulaization term + :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^2` + where :math:`\mathcal{I}_c` are the index of samples from class + c in the source domain. + - a and b are source and target weights (sum to 1) + + The algorithm used for solving the problem is the generalised conditional + gradient as proposed in [5]_ [7]_ + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + labels_a : np.ndarray (ns,) + labels of samples in the source domain + b : np.ndarray (nt,) + samples in the target domain + M : np.ndarray (ns,nt) + loss matrix + reg : float + Regularization term for entropic regularization >0 + eta : float, optional + Regularization term for group lasso regularization >0 + numItermax : int, optional + Max number of iterations + numInnerItermax : int, optional + Max number of iterations (inner sinkhorn solver) + stopInnerThr : float, optional + Stop threshold on error (inner sinkhorn solver) (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE Transactions + on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). + Generalized conditional gradient: analysis of convergence and + applications. arXiv preprint arXiv:1510.06567. + + See Also + -------- + ot.optim.gcg : Generalized conditional gradient for OT problems + + """ + lstlab = np.unique(labels_a) + + def f(G): + res = 0 + for i in range(G.shape[1]): + for lab in lstlab: + temp = G[labels_a == lab, i] + res += np.linalg.norm(temp) + return res + + def df(G): + W = np.zeros(G.shape) + for i in range(G.shape[1]): + for lab in lstlab: + temp = G[labels_a == lab, i] + n = np.linalg.norm(temp) + if n: + W[labels_a == lab, i] = temp / n + return W + + return gcg(a, b, M, reg, eta, f, df, G0=None, numItermax=numItermax, + numInnerItermax=numInnerItermax, stopThr=stopInnerThr, + verbose=verbose, log=log) + + +def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, + verbose2=False, numItermax=100, numInnerItermax=10, + stopInnerThr=1e-6, stopThr=1e-5, log=False, + **kwargs): + """Joint OT and linear mapping estimation as proposed in [8] + + The function solves the following optimization problem: + + .. math:: + \min_{\gamma,L}\quad \|L(X_s) -n_s\gamma X_t\|^2_F + + \mu<\gamma,M>_F + \eta \|L -I\|^2_F + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the (ns,nt) squared euclidean cost matrix between samples in + Xs and Xt (scaled by ns) + - :math:`L` is a dxd linear operator that approximates the barycentric + mapping + - :math:`I` is the identity matrix (neutral linear mapping) + - a and b are uniform source and target weights + + The problem consist in solving jointly an optimal transport matrix + :math:`\gamma` and a linear mapping that fits the barycentric mapping + :math:`n_s\gamma X_t`. + + One can also estimate a mapping with constant bias (see supplementary + material of [8]) using the bias optional argument. + + The algorithm used for solving the problem is the block coordinate + descent that alternates between updates of G (using conditionnal gradient) + and the update of L using a classical least square solver. + + + Parameters + ---------- + xs : np.ndarray (ns,d) + samples in the source domain + xt : np.ndarray (nt,d) + samples in the target domain + mu : float,optional + Weight for the linear OT loss (>0) + eta : float, optional + Regularization term for the linear mapping L (>0) + bias : bool,optional + Estimate linear mapping with constant bias + numItermax : int, optional + Max number of BCD iterations + stopThr : float, optional + Stop threshold on relative loss decrease (>0) + numInnerItermax : int, optional + Max number of iterations (inner CG solver) + stopInnerThr : float, optional + Stop threshold on error (inner CG solver) (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + L : (d x d) ndarray + Linear mapping matrix (d+1 x d if bias) + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, + "Mapping estimation for discrete optimal transport", + Neural Information Processing Systems (NIPS), 2016. + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + + """ + + ns, nt, d = xs.shape[0], xt.shape[0], xt.shape[1] + + if bias: + xs1 = np.hstack((xs, np.ones((ns, 1)))) + xstxs = xs1.T.dot(xs1) + Id = np.eye(d + 1) + Id[-1] = 0 + I0 = Id[:, :-1] + + def sel(x): + return x[:-1, :] + else: + xs1 = xs + xstxs = xs1.T.dot(xs1) + Id = np.eye(d) + I0 = Id + + def sel(x): + return x + + if log: + log = {'err': []} + + a, b = unif(ns), unif(nt) + M = dist(xs, xt) * ns + G = emd(a, b, M) + + vloss = [] + + def loss(L, G): + """Compute full loss""" + return np.sum((xs1.dot(L) - ns * G.dot(xt)) ** 2) + mu * \ + np.sum(G * M) + eta * np.sum(sel(L - I0) ** 2) + + def solve_L(G): + """ solve L problem with fixed G (least square)""" + xst = ns * G.dot(xt) + return np.linalg.solve(xstxs + eta * Id, xs1.T.dot(xst) + eta * I0) + + def solve_G(L, G0): + """Update G with CG algorithm""" + xsi = xs1.dot(L) + + def f(G): + return np.sum((xsi - ns * G.dot(xt)) ** 2) + + def df(G): + return -2 * ns * (xsi - ns * G.dot(xt)).dot(xt.T) + + G = cg(a, b, M, 1.0 / mu, f, df, G0=G0, + numItermax=numInnerItermax, stopThr=stopInnerThr) + return G + + L = solve_L(G) + + vloss.append(loss(L, G)) + + if verbose: + print('{:5s}|{:12s}|{:8s}'.format( + 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) + print('{:5d}|{:8e}|{:8e}'.format(0, vloss[-1], 0)) + + # init loop + if numItermax > 0: + loop = 1 + else: + loop = 0 + it = 0 + + while loop: + + it += 1 + + # update G + G = solve_G(L, G) + + # update L + L = solve_L(G) + + vloss.append(loss(L, G)) + + if it >= numItermax: + loop = 0 + + if abs(vloss[-1] - vloss[-2]) / abs(vloss[-2]) < stopThr: + loop = 0 + + if verbose: + if it % 20 == 0: + print('{:5s}|{:12s}|{:8s}'.format( + 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) + print('{:5d}|{:8e}|{:8e}'.format( + it, vloss[-1], (vloss[-1] - vloss[-2]) / abs(vloss[-2]))) + if log: + log['loss'] = vloss + return G, L, log + else: + return G, L + + +def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', + sigma=1, bias=False, verbose=False, verbose2=False, + numItermax=100, numInnerItermax=10, + stopInnerThr=1e-6, stopThr=1e-5, log=False, + **kwargs): + """Joint OT and nonlinear mapping estimation with kernels as proposed in [8] + + The function solves the following optimization problem: + + .. math:: + \min_{\gamma,L\in\mathcal{H}}\quad \|L(X_s) - + n_s\gamma X_t\|^2_F + \mu<\gamma,M>_F + \eta \|L\|^2_\mathcal{H} + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the (ns,nt) squared euclidean cost matrix between samples in + Xs and Xt (scaled by ns) + - :math:`L` is a ns x d linear operator on a kernel matrix that + approximates the barycentric mapping + - a and b are uniform source and target weights + + The problem consist in solving jointly an optimal transport matrix + :math:`\gamma` and the nonlinear mapping that fits the barycentric mapping + :math:`n_s\gamma X_t`. + + One can also estimate a mapping with constant bias (see supplementary + material of [8]) using the bias optional argument. + + The algorithm used for solving the problem is the block coordinate + descent that alternates between updates of G (using conditionnal gradient) + and the update of L using a classical kernel least square solver. + + + Parameters + ---------- + xs : np.ndarray (ns,d) + samples in the source domain + xt : np.ndarray (nt,d) + samples in the target domain + mu : float,optional + Weight for the linear OT loss (>0) + eta : float, optional + Regularization term for the linear mapping L (>0) + kerneltype : str,optional + kernel used by calling function ot.utils.kernel (gaussian by default) + sigma : float, optional + Gaussian kernel bandwidth. + bias : bool,optional + Estimate linear mapping with constant bias + verbose : bool, optional + Print information along iterations + verbose2 : bool, optional + Print information along iterations + numItermax : int, optional + Max number of BCD iterations + numInnerItermax : int, optional + Max number of iterations (inner CG solver) + stopInnerThr : float, optional + Stop threshold on error (inner CG solver) (>0) + stopThr : float, optional + Stop threshold on relative loss decrease (>0) + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + L : (ns x d) ndarray + Nonlinear mapping matrix (ns+1 x d if bias) + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, + "Mapping estimation for discrete optimal transport", + Neural Information Processing Systems (NIPS), 2016. + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + + """ + + ns, nt = xs.shape[0], xt.shape[0] + + K = kernel(xs, xs, method=kerneltype, sigma=sigma) + if bias: + K1 = np.hstack((K, np.ones((ns, 1)))) + Id = np.eye(ns + 1) + Id[-1] = 0 + Kp = np.eye(ns + 1) + Kp[:ns, :ns] = K + + # ls regu + # K0 = K1.T.dot(K1)+eta*I + # Kreg=I + + # RKHS regul + K0 = K1.T.dot(K1) + eta * Kp + Kreg = Kp + + else: + K1 = K + Id = np.eye(ns) + + # ls regul + # K0 = K1.T.dot(K1)+eta*I + # Kreg=I + + # proper kernel ridge + K0 = K + eta * Id + Kreg = K + + if log: + log = {'err': []} + + a, b = unif(ns), unif(nt) + M = dist(xs, xt) * ns + G = emd(a, b, M) + + vloss = [] + + def loss(L, G): + """Compute full loss""" + return np.sum((K1.dot(L) - ns * G.dot(xt)) ** 2) + mu * \ + np.sum(G * M) + eta * np.trace(L.T.dot(Kreg).dot(L)) + + def solve_L_nobias(G): + """ solve L problem with fixed G (least square)""" + xst = ns * G.dot(xt) + return np.linalg.solve(K0, xst) + + def solve_L_bias(G): + """ solve L problem with fixed G (least square)""" + xst = ns * G.dot(xt) + return np.linalg.solve(K0, K1.T.dot(xst)) + + def solve_G(L, G0): + """Update G with CG algorithm""" + xsi = K1.dot(L) + + def f(G): + return np.sum((xsi - ns * G.dot(xt)) ** 2) + + def df(G): + return -2 * ns * (xsi - ns * G.dot(xt)).dot(xt.T) + + G = cg(a, b, M, 1.0 / mu, f, df, G0=G0, + numItermax=numInnerItermax, stopThr=stopInnerThr) + return G + + if bias: + solve_L = solve_L_bias + else: + solve_L = solve_L_nobias + + L = solve_L(G) + + vloss.append(loss(L, G)) + + if verbose: + print('{:5s}|{:12s}|{:8s}'.format( + 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) + print('{:5d}|{:8e}|{:8e}'.format(0, vloss[-1], 0)) + + # init loop + if numItermax > 0: + loop = 1 + else: + loop = 0 + it = 0 + + while loop: + + it += 1 + + # update G + G = solve_G(L, G) + + # update L + L = solve_L(G) + + vloss.append(loss(L, G)) + + if it >= numItermax: + loop = 0 + + if abs(vloss[-1] - vloss[-2]) / abs(vloss[-2]) < stopThr: + loop = 0 + + if verbose: + if it % 20 == 0: + print('{:5s}|{:12s}|{:8s}'.format( + 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) + print('{:5d}|{:8e}|{:8e}'.format( + it, vloss[-1], (vloss[-1] - vloss[-2]) / abs(vloss[-2]))) + if log: + log['loss'] = vloss + return G, L, log + else: + return G, L + + +def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, + wt=None, bias=True, log=False): + """ return OT linear operator between samples + + The function estimates the optimal linear operator that aligns the two + empirical distributions. This is equivalent to estimating the closed + form mapping between two Gaussian distributions :math:`N(\mu_s,\Sigma_s)` + and :math:`N(\mu_t,\Sigma_t)` as proposed in [14] and discussed in remark + 2.29 in [15]. + + The linear operator from source to target :math:`M` + + .. math:: + M(x)=Ax+b + + where : + + .. math:: + A=\Sigma_s^{-1/2}(\Sigma_s^{1/2}\Sigma_t\Sigma_s^{1/2})^{1/2} + \Sigma_s^{-1/2} + .. math:: + b=\mu_t-A\mu_s + + Parameters + ---------- + xs : np.ndarray (ns,d) + samples in the source domain + xt : np.ndarray (nt,d) + samples in the target domain + reg : float,optional + regularization added to the diagonals of convariances (>0) + ws : np.ndarray (ns,1), optional + weights for the source samples + wt : np.ndarray (ns,1), optional + weights for the target samples + bias: boolean, optional + estimate bias b else b=0 (default:True) + log : bool, optional + record log if True + + + Returns + ------- + A : (d x d) ndarray + Linear operator + b : (1 x d) ndarray + bias + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [14] Knott, M. and Smith, C. S. "On the optimal mapping of + distributions", Journal of Optimization Theory and Applications + Vol 43, 1984 + + .. [15] Peyré, G., & Cuturi, M. (2017). "Computational Optimal + Transport", 2018. + + + """ + + d = xs.shape[1] + + if bias: + mxs = xs.mean(0, keepdims=True) + mxt = xt.mean(0, keepdims=True) + + xs = xs - mxs + xt = xt - mxt + else: + mxs = np.zeros((1, d)) + mxt = np.zeros((1, d)) + + if ws is None: + ws = np.ones((xs.shape[0], 1)) / xs.shape[0] + + if wt is None: + wt = np.ones((xt.shape[0], 1)) / xt.shape[0] + + Cs = (xs * ws).T.dot(xs) / ws.sum() + reg * np.eye(d) + Ct = (xt * wt).T.dot(xt) / wt.sum() + reg * np.eye(d) + + Cs12 = linalg.sqrtm(Cs) + Cs_12 = linalg.inv(Cs12) + + M0 = linalg.sqrtm(Cs12.dot(Ct.dot(Cs12))) + + A = Cs_12.dot(M0.dot(Cs_12)) + + b = mxt - mxs.dot(A) + + if log: + log = {} + log['Cs'] = Cs + log['Ct'] = Ct + log['Cs12'] = Cs12 + log['Cs_12'] = Cs_12 + return A, b, log + else: + return A, b + + +def emd_laplace(a, b, xs, xt, M, eta=1., alpha=0.5, + numItermax=1000, stopThr=1e-5, numInnerItermax=1000, + stopInnerThr=1e-6, log=False, verbose=False, **kwargs): + r"""Solve the optimal transport problem (OT) with Laplacian regularization + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + eta\Omega_\alpha(\gamma) + + s.t.\ \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + + where: + + - a and b are source and target weights (sum to 1) + - xs and xt are source and target samples + - M is the (ns,nt) metric cost matrix + - :math:`\Omega_\alpha` is the Laplacian regularization term + :math:`\Omega_\alpha = (1-\alpha)/n_s^2\sum_{i,j}S^s_{i,j}\|T(\mathbf{x}^s_i)-T(\mathbf{x}^s_j)\|^2+\alpha/n_t^2\sum_{i,j}S^t_{i,j}^'\|T(\mathbf{x}^t_i)-T(\mathbf{x}^t_j)\|^2` + with :math:`S^s_{i,j}, S^t_{i,j}` denoting source and target similarity matrices and :math:`T(\cdot)` being a barycentric mapping + + The algorithm used for solving the problem is the conditional gradient algorithm as proposed in [5]. + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) + samples weights in the target domain + xs : np.ndarray (ns,d) + samples in the source domain + xt : np.ndarray (nt,d) + samples in the target domain + M : np.ndarray (ns,nt) + loss matrix + eta : float + Regularization term for Laplacian regularization + alpha : float + Regularization term for source domain's importance in regularization + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshold on error (inner emd solver) (>0) + numInnerItermax : int, optional + Max number of iterations (inner CG solver) + stopInnerThr : float, optional + Stop threshold on error (inner CG solver) (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE + Transactions on Pattern Analysis and Machine Intelligence , + vol.PP, no.99, pp.1-1 + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + + """ + if 'sim' not in kwargs: + kwargs['sim'] = 'knn' + + if kwargs['sim'] == 'gauss': + if 'rbfparam' not in kwargs: + kwargs['rbfparam'] = 1 / (2 * (np.mean(dist(xs, xs, 'sqeuclidean')) ** 2)) + sS = kernel(xs, xs, method=kwargs['sim'], sigma=kwargs['rbfparam']) + sT = kernel(xt, xt, method=kwargs['sim'], sigma=kwargs['rbfparam']) + + elif kwargs['sim'] == 'knn': + if 'nn' not in kwargs: + kwargs['nn'] = 5 + + from sklearn.neighbors import kneighbors_graph + + sS = kneighbors_graph(xs, kwargs['nn']).toarray() + sS = (sS + sS.T) / 2 + sT = kneighbors_graph(xt, kwargs['nn']).toarray() + sT = (sT + sT.T) / 2 + + lS = laplacian(sS) + lT = laplacian(sT) + + def f(G): + return alpha*np.trace(np.dot(xt.T, np.dot(G.T, np.dot(lS, np.dot(G, xt))))) \ + + (1-alpha)*np.trace(np.dot(xs.T, np.dot(G, np.dot(lT, np.dot(G.T, xs))))) + + def df(G): + return alpha*np.dot(lS + lS.T, np.dot(G, np.dot(xt, xt.T)))\ + +(1-alpha)*np.dot(xs, np.dot(xs.T, np.dot(G, lT + lT.T))) + + return cg(a, b, M, reg=eta, f=f, df=df, G0=None, numItermax=numItermax, numItermaxEmd=numInnerItermax, + stopThr=stopThr, stopThr2=stopInnerThr, verbose=verbose, log=log) + +def sinkhorn_laplace(a, b, xs, xt, M, reg=.1, eta=1., alpha=0.5, + numItermax=1000, stopThr=1e-5, numInnerItermax=1000, + stopInnerThr=1e-6, log=False, verbose=False, **kwargs): + r"""Solve the entropic regularized optimal transport problem (OT) with Laplacian regularization + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\Omega_e(\gamma) + eta\Omega_\alpha(\gamma) + + s.t.\ \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + + where: + + - a and b are source and target weights (sum to 1) + - xs and xt are source and target samples + - M is the (ns,nt) metric cost matrix + - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e + (\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`\Omega_\alpha` is the Laplacian regularization term + :math:`\Omega_\alpha = (1-\alpha)/n_s^2\sum_{i,j}S^s_{i,j}\|T(\mathbf{x}^s_i)-T(\mathbf{x}^s_j)\|^2+\alpha/n_t^2\sum_{i,j}S^t_{i,j}^'\|T(\mathbf{x}^t_i)-T(\mathbf{x}^t_j)\|^2` + with :math:`S^s_{i,j}, S^t_{i,j}` denoting source and target similarity matrices and :math:`T(\cdot)` being a barycentric mapping + + The algorithm used for solving the problem is the conditional gradient algorithm as proposed in [5]. + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) + samples weights in the target domain + xs : np.ndarray (ns,d) + samples in the source domain + xt : np.ndarray (nt,d) + samples in the target domain + M : np.ndarray (ns,nt) + loss matrix + reg : float + Regularization term for entropic regularization >0 + eta : float + Regularization term for Laplacian regularization + alpha : float + Regularization term for source domain's importance in regularization + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshold on error (inner sinkhorn solver) (>0) + numInnerItermax : int, optional + Max number of iterations (inner CG solver) + stopInnerThr : float, optional + Stop threshold on error (inner CG solver) (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE + Transactions on Pattern Analysis and Machine Intelligence , + vol.PP, no.99, pp.1-1 + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + + """ + if 'sim' not in kwargs: + kwargs['sim'] = 'knn' + + if kwargs['sim'] == 'gauss': + if 'rbfparam' not in kwargs: + kwargs['rbfparam'] = 1 / (2 * (np.mean(dist(xs, xs, 'sqeuclidean')) ** 2)) + sS = kernel(xs, xs, method=kwargs['sim'], sigma=kwargs['rbfparam']) + sT = kernel(xt, xt, method=kwargs['sim'], sigma=kwargs['rbfparam']) + + elif kwargs['sim'] == 'knn': + if 'nn' not in kwargs: + kwargs['nn'] = 5 + + from sklearn.neighbors import kneighbors_graph + + sS = kneighbors_graph(xs, kwargs['nn']).toarray() + sS = (sS + sS.T) / 2 + sT = kneighbors_graph(xt, kwargs['nn']).toarray() + sT = (sT + sT.T) / 2 + + lS = laplacian(sS) + lT = laplacian(sT) + + def f(G): + return alpha*np.trace(np.dot(xt.T, np.dot(G.T, np.dot(lS, np.dot(G, xt))))) \ + + (1-alpha)*np.trace(np.dot(xs.T, np.dot(G, np.dot(lT, np.dot(G.T, xs))))) + + def df(G): + return alpha*np.dot(lS + lS.T, np.dot(G, np.dot(xt, xt.T)))\ + +(1-alpha)*np.dot(xs, np.dot(xs.T, np.dot(G, lT + lT.T))) + + return gcg(a, b, M, reg, eta, f, df, G0=None, numItermax=numItermax, stopThr=stopThr, + numInnerItermax=numInnerItermax, stopThr2=stopInnerThr, + verbose=verbose, log=log) + +def distribution_estimation_uniform(X): + """estimates a uniform distribution from an array of samples X + + Parameters + ---------- + X : array-like, shape (n_samples, n_features) + The array of samples + + Returns + ------- + mu : array-like, shape (n_samples,) + The uniform distribution estimated from X + """ + + + return unif(X.shape[0]) + + +class BaseTransport(BaseEstimator): + + """Base class for OTDA objects + + Notes + ----- + All estimators should specify all the parameters that can be set + at the class level in their ``__init__`` as explicit keyword + arguments (no ``*args`` or ``**kwargs``). + + fit method should: + - estimate a cost matrix and store it in a `cost_` attribute + - estimate a coupling matrix and store it in a `coupling_` + attribute + - estimate distributions from source and target data and store them in + mu_s and mu_t attributes + - store Xs and Xt in attributes to be used later on in transform and + inverse_transform methods + + transform method should always get as input a Xs parameter + inverse_transform method should always get as input a Xt parameter + """ + + + def fit(self, Xs=None, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + + Returns + ------- + self : object + Returns self. + """ + + # check the necessary inputs parameters are here + if check_params(Xs=Xs, Xt=Xt): + + # pairwise distance + self.cost_ = dist(Xs, Xt, metric=self.metric) + self.cost_ = cost_normalization(self.cost_, self.norm) + + if (ys is not None) and (yt is not None): + + if self.limit_max != np.infty: + self.limit_max = self.limit_max * np.max(self.cost_) + + # assumes labeled source samples occupy the first rows + # and labeled target samples occupy the first columns + classes = [c for c in np.unique(ys) if c != -1] + for c in classes: + idx_s = np.where((ys != c) & (ys != -1)) + idx_t = np.where(yt == c) + + # all the coefficients corresponding to a source sample + # and a target sample : + # with different labels get a infinite + for j in idx_t[0]: + self.cost_[idx_s[0], j] = self.limit_max + + # distribution estimation + self.mu_s = self.distribution_estimation(Xs) + self.mu_t = self.distribution_estimation(Xt) + + # store arrays of samples + self.xs_ = Xs + self.xt_ = Xt + + return self + + + def fit_transform(self, Xs=None, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) and transports source samples Xs onto target + ones Xt + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + + Returns + ------- + transp_Xs : array-like, shape (n_source_samples, n_features) + The source samples samples. + """ + + return self.fit(Xs, ys, Xt, yt).transform(Xs, ys, Xt, yt) + + + def transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): + """Transports source samples Xs onto target ones Xt + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + batch_size : int, optional (default=128) + The batch size for out of sample inverse transform + + Returns + ------- + transp_Xs : array-like, shape (n_source_samples, n_features) + The transport source samples. + """ + + # check the necessary inputs parameters are here + if check_params(Xs=Xs): + + if np.array_equal(self.xs_, Xs): + + # perform standard barycentric mapping + transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] + + # set nans to 0 + transp[~ np.isfinite(transp)] = 0 + + # compute transported samples + transp_Xs = np.dot(transp, self.xt_) + else: + # perform out of sample mapping + indices = np.arange(Xs.shape[0]) + batch_ind = [ + indices[i:i + batch_size] + for i in range(0, len(indices), batch_size)] + + transp_Xs = [] + for bi in batch_ind: + # get the nearest neighbor in the source domain + D0 = dist(Xs[bi], self.xs_) + idx = np.argmin(D0, axis=1) + + # transport the source samples + transp = self.coupling_ / np.sum( + self.coupling_, 1)[:, None] + transp[~ np.isfinite(transp)] = 0 + transp_Xs_ = np.dot(transp, self.xt_) + + # define the transported points + transp_Xs_ = transp_Xs_[idx, :] + Xs[bi] - self.xs_[idx, :] + + transp_Xs.append(transp_Xs_) + + transp_Xs = np.concatenate(transp_Xs, axis=0) + + return transp_Xs + + + def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None, + batch_size=128): + """Transports target samples Xt onto target samples Xs + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + batch_size : int, optional (default=128) + The batch size for out of sample inverse transform + + Returns + ------- + transp_Xt : array-like, shape (n_source_samples, n_features) + The transported target samples. + """ + + # check the necessary inputs parameters are here + if check_params(Xt=Xt): + + if np.array_equal(self.xt_, Xt): + + # perform standard barycentric mapping + transp_ = self.coupling_.T / np.sum(self.coupling_, 0)[:, None] + + # set nans to 0 + transp_[~ np.isfinite(transp_)] = 0 + + # compute transported samples + transp_Xt = np.dot(transp_, self.xs_) + else: + # perform out of sample mapping + indices = np.arange(Xt.shape[0]) + batch_ind = [ + indices[i:i + batch_size] + for i in range(0, len(indices), batch_size)] + + transp_Xt = [] + for bi in batch_ind: + D0 = dist(Xt[bi], self.xt_) + idx = np.argmin(D0, axis=1) + + # transport the target samples + transp_ = self.coupling_.T / np.sum( + self.coupling_, 0)[:, None] + transp_[~ np.isfinite(transp_)] = 0 + transp_Xt_ = np.dot(transp_, self.xs_) + + # define the transported points + transp_Xt_ = transp_Xt_[idx, :] + Xt[bi] - self.xt_[idx, :] + + transp_Xt.append(transp_Xt_) + + transp_Xt = np.concatenate(transp_Xt, axis=0) + + return transp_Xt + + +class LinearTransport(BaseTransport): + + """ OT linear operator between empirical distributions + + The function estimates the optimal linear operator that aligns the two + empirical distributions. This is equivalent to estimating the closed + form mapping between two Gaussian distributions :math:`N(\mu_s,\Sigma_s)` + and :math:`N(\mu_t,\Sigma_t)` as proposed in [14] and discussed in + remark 2.29 in [15]. + + The linear operator from source to target :math:`M` + + .. math:: + M(x)=Ax+b + + where : + + .. math:: + A=\Sigma_s^{-1/2}(\Sigma_s^{1/2}\Sigma_t\Sigma_s^{1/2})^{1/2} + \Sigma_s^{-1/2} + .. math:: + b=\mu_t-A\mu_s + + Parameters + ---------- + reg : float,optional + regularization added to the daigonals of convariances (>0) + bias: boolean, optional + estimate bias b else b=0 (default:True) + log : bool, optional + record log if True + + References + ---------- + + .. [14] Knott, M. and Smith, C. S. "On the optimal mapping of + distributions", Journal of Optimization Theory and Applications + Vol 43, 1984 + + .. [15] Peyré, G., & Cuturi, M. (2017). "Computational Optimal + Transport", 2018. + + """ + + + def __init__(self, reg=1e-8, bias=True, log=False, + distribution_estimation=distribution_estimation_uniform): + self.bias = bias + self.log = log + self.reg = reg + self.distribution_estimation = distribution_estimation + + + def fit(self, Xs=None, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + + Returns + ------- + self : object + Returns self. + """ + + self.mu_s = self.distribution_estimation(Xs) + self.mu_t = self.distribution_estimation(Xt) + + # coupling estimation + returned_ = OT_mapping_linear(Xs, Xt, reg=self.reg, + ws=self.mu_s.reshape((-1, 1)), + wt=self.mu_t.reshape((-1, 1)), + bias=self.bias, log=self.log) + + # deal with the value of log + if self.log: + self.A_, self.B_, self.log_ = returned_ + else: + self.A_, self.B_, = returned_ + self.log_ = dict() + + # re compute inverse mapping + self.A1_ = linalg.inv(self.A_) + self.B1_ = -self.B_.dot(self.A1_) + + return self + + + def transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): + """Transports source samples Xs onto target ones Xt + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + batch_size : int, optional (default=128) + The batch size for out of sample inverse transform + + Returns + ------- + transp_Xs : array-like, shape (n_source_samples, n_features) + The transport source samples. + """ + + # check the necessary inputs parameters are here + if check_params(Xs=Xs): + transp_Xs = Xs.dot(self.A_) + self.B_ + + return transp_Xs + + + def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None, + batch_size=128): + """Transports target samples Xt onto target samples Xs + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + batch_size : int, optional (default=128) + The batch size for out of sample inverse transform + + Returns + ------- + transp_Xt : array-like, shape (n_source_samples, n_features) + The transported target samples. + """ + + # check the necessary inputs parameters are here + if check_params(Xt=Xt): + transp_Xt = Xt.dot(self.A1_) + self.B1_ + + return transp_Xt + + +class SinkhornTransport(BaseTransport): + + """Domain Adapatation OT method based on Sinkhorn Algorithm + + Parameters + ---------- + reg_e : float, optional (default=1) + Entropic regularization parameter + max_iter : int, float, optional (default=1000) + The minimum number of iteration before stopping the optimization + algorithm if no it has not converged + tol : float, optional (default=10e-9) + The precision required to stop the optimization algorithm. + verbose : bool, optional (default=False) + Controls the verbosity of the optimization algorithm + log : int, optional (default=False) + Controls the logs of the optimization algorithm + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. + limit_max: float, optional (defaul=np.infty) + Controls the semi supervised mode. Transport between labeled source + and target samples of different classes will exhibit an cost defined + by this variable + + Attributes + ---------- + coupling_ : array-like, shape (n_source_samples, n_target_samples) + The optimal coupling + log_ : dictionary + The dictionary of log, empty dic if parameter log is not True + + References + ---------- + .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE Transactions + on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal + Transport, Advances in Neural Information Processing Systems (NIPS) + 26, 2013 + """ + + + def __init__(self, reg_e=1., max_iter=1000, + tol=10e-9, verbose=False, log=False, + metric="sqeuclidean", norm=None, + distribution_estimation=distribution_estimation_uniform, + out_of_sample_map='ferradans', limit_max=np.infty): + self.reg_e = reg_e + self.max_iter = max_iter + self.tol = tol + self.verbose = verbose + self.log = log + self.metric = metric + self.norm = norm + self.limit_max = limit_max + self.distribution_estimation = distribution_estimation + self.out_of_sample_map = out_of_sample_map + + + def fit(self, Xs=None, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + + Returns + ------- + self : object + Returns self. + """ + + super(SinkhornTransport, self).fit(Xs, ys, Xt, yt) + + # coupling estimation + returned_ = sinkhorn( + a=self.mu_s, b=self.mu_t, M=self.cost_, reg=self.reg_e, + numItermax=self.max_iter, stopThr=self.tol, + verbose=self.verbose, log=self.log) + + # deal with the value of log + if self.log: + self.coupling_, self.log_ = returned_ + else: + self.coupling_ = returned_ + self.log_ = dict() + + return self + + +class EMDTransport(BaseTransport): + + """Domain Adapatation OT method based on Earth Mover's Distance + + Parameters + ---------- + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. + log : int, optional (default=False) + Controls the logs of the optimization algorithm + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. + limit_max: float, optional (default=10) + Controls the semi supervised mode. Transport between labeled source + and target samples of different classes will exhibit an infinite cost + (10 times the maximum value of the cost matrix) + max_iter : int, optional (default=100000) + The maximum number of iterations before stopping the optimization + algorithm if it has not converged. + + Attributes + ---------- + coupling_ : array-like, shape (n_source_samples, n_target_samples) + The optimal coupling + + References + ---------- + .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE Transactions + on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + """ + + + def __init__(self, metric="sqeuclidean", norm=None, log=False, + distribution_estimation=distribution_estimation_uniform, + out_of_sample_map='ferradans', limit_max=10, + max_iter=100000): + self.metric = metric + self.norm = norm + self.log = log + self.limit_max = limit_max + self.distribution_estimation = distribution_estimation + self.out_of_sample_map = out_of_sample_map + self.max_iter = max_iter + + + def fit(self, Xs, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + + Returns + ------- + self : object + Returns self. + """ + + super(EMDTransport, self).fit(Xs, ys, Xt, yt) + + returned_ = emd( + a=self.mu_s, b=self.mu_t, M=self.cost_, numItermax=self.max_iter, + log=self.log) + + # coupling estimation + if self.log: + self.coupling_, self.log_ = returned_ + else: + self.coupling_ = returned_ + self.log_ = dict() + return self + + +class SinkhornLpl1Transport(BaseTransport): + + """Domain Adapatation OT method based on sinkhorn algorithm + + LpL1 class regularization. + + Parameters + ---------- + reg_e : float, optional (default=1) + Entropic regularization parameter + reg_cl : float, optional (default=0.1) + Class regularization parameter + max_iter : int, float, optional (default=10) + The minimum number of iteration before stopping the optimization + algorithm if no it has not converged + max_inner_iter : int, float, optional (default=200) + The number of iteration in the inner loop + log : bool, optional (default=False) + Controls the logs of the optimization algorithm + tol : float, optional (default=10e-9) + Stop threshold on error (inner sinkhorn solver) (>0) + verbose : bool, optional (default=False) + Controls the verbosity of the optimization algorithm + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. + limit_max: float, optional (defaul=np.infty) + Controls the semi supervised mode. Transport between labeled source + and target samples of different classes will exhibit a cost defined by + limit_max. + + Attributes + ---------- + coupling_ : array-like, shape (n_source_samples, n_target_samples) + The optimal coupling + + References + ---------- + + .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE + Transactions on Pattern Analysis and Machine Intelligence , + vol.PP, no.99, pp.1-1 + .. [2] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). + Generalized conditional gradient: analysis of convergence + and applications. arXiv preprint arXiv:1510.06567. + + """ + + + def __init__(self, reg_e=1., reg_cl=0.1, + max_iter=10, max_inner_iter=200, log=False, + tol=10e-9, verbose=False, + metric="sqeuclidean", norm=None, + distribution_estimation=distribution_estimation_uniform, + out_of_sample_map='ferradans', limit_max=np.infty): + self.reg_e = reg_e + self.reg_cl = reg_cl + self.max_iter = max_iter + self.max_inner_iter = max_inner_iter + self.tol = tol + self.log = log + self.verbose = verbose + self.metric = metric + self.norm = norm + self.distribution_estimation = distribution_estimation + self.out_of_sample_map = out_of_sample_map + self.limit_max = limit_max + + + def fit(self, Xs, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + + Returns + ------- + self : object + Returns self. + """ + + # check the necessary inputs parameters are here + if check_params(Xs=Xs, Xt=Xt, ys=ys): + super(SinkhornLpl1Transport, self).fit(Xs, ys, Xt, yt) + + returned_ = sinkhorn_lpl1_mm( + a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, + reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, + numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, + verbose=self.verbose, log=self.log) + + # deal with the value of log + if self.log: + self.coupling_, self.log_ = returned_ + else: + self.coupling_ = returned_ + self.log_ = dict() + return self + + +class EMDLaplaceTransport(BaseTransport): + + """Domain Adapatation OT method based on Earth Mover's Distance with Laplacian regularization + + Parameters + ---------- + reg_lap : float, optional (default=1) + Laplacian regularization parameter + reg_src : float, optional (default=0.5) + Source relative importance in regularization + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. + max_iter : int, optional (default=100) + Max number of BCD iterations + tol : float, optional (default=1e-5) + Stop threshold on relative loss decrease (>0) + max_inner_iter : int, optional (default=10) + Max number of iterations (inner CG solver) + inner_tol : float, optional (default=1e-6) + Stop threshold on error (inner CG solver) (>0) + log : int, optional (default=False) + Controls the logs of the optimization algorithm + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. + + Attributes + ---------- + coupling_ : array-like, shape (n_source_samples, n_target_samples) + The optimal coupling + + References + ---------- + .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE Transactions + on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + """ + + + def __init__(self, reg_lap = 1., reg_src=1., alpha=0.5, + metric="sqeuclidean", norm=None, max_iter=100, tol=1e-5, + max_inner_iter=100000, inner_tol=1e-6, log=False, verbose=False, + distribution_estimation=distribution_estimation_uniform, + out_of_sample_map='ferradans'): + self.reg_lap = reg_lap + self.reg_src = reg_src + self.alpha = alpha + self.metric = metric + self.norm = norm + self.max_iter = max_iter + self.tol = tol + self.max_inner_iter = max_inner_iter + self.inner_tol = inner_tol + self.log = log + self.verbose = verbose + self.distribution_estimation = distribution_estimation + self.out_of_sample_map = out_of_sample_map + + + def fit(self, Xs, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + + Returns + ------- + self : object + Returns self. + """ + + super(EMDLaplaceTransport, self).fit(Xs, ys, Xt, yt) + + returned_ = emd_laplace(a=self.mu_s, b=self.mu_t, xs=self.xs_, + xt=self.xt_, M=self.cost_, eta=self.reg_lap, alpha=self.reg_src, + numItermax=self.max_iter, stopThr=self.tol, numInnerItermax=self.max_inner_iter, + stopInnerThr=self.inner_tol, log=self.log, verbose=self.verbose) + + # coupling estimation + if self.log: + self.coupling_, self.log_ = returned_ + else: + self.coupling_ = returned_ + self.log_ = dict() + return self + +class SinkhornLaplaceTransport(BaseTransport): + + """Domain Adapatation OT method based on entropic regularized OT with Laplacian regularization + + Parameters + ---------- + reg_e : float, optional (default=1) + Entropic regularization parameter + reg_lap : float, optional (default=1) + Laplacian regularization parameter + reg_src : float, optional (default=0.5) + Source relative importance in regularization + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. + max_iter : int, optional (default=100) + Max number of BCD iterations + tol : float, optional (default=1e-5) + Stop threshold on relative loss decrease (>0) + max_inner_iter : int, optional (default=10) + Max number of iterations (inner CG solver) + inner_tol : float, optional (default=1e-6) + Stop threshold on error (inner CG solver) (>0) + log : int, optional (default=False) + Controls the logs of the optimization algorithm + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. + + Attributes + ---------- + coupling_ : array-like, shape (n_source_samples, n_target_samples) + The optimal coupling + + References + ---------- + .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE Transactions + on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + """ + + + def __init__(self, reg_e=1., reg_lap=1., reg_src=0.5, + metric="sqeuclidean", norm=None, max_iter=100, tol=1e-9, + max_inner_iter=200, inner_tol=1e-6, log=False, verbose=False, + distribution_estimation=distribution_estimation_uniform, + out_of_sample_map='ferradans'): + + self.reg_e = reg_e + self.reg_lap = reg_lap + self.reg_src = reg_src + self.metric = metric + self.norm = norm + self.max_iter = max_iter + self.tol = tol + self.max_inner_iter = max_inner_iter + self.inner_tol = inner_tol + self.log = log + self.verbose = verbose + self.distribution_estimation = distribution_estimation + self.out_of_sample_map = out_of_sample_map + + + def fit(self, Xs, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + + Returns + ------- + self : object + Returns self. + """ + + super(SinkhornLaplaceTransport, self).fit(Xs, ys, Xt, yt) + + returned_ = sinkhorn_laplace(a=self.mu_s, b=self.mu_t, xs=self.xs_, + xt=self.xt_, M=self.cost_, reg=self.reg_e, eta=self.reg_lap, alpha=self.reg_src, + numItermax=self.max_iter, stopThr=self.tol, numInnerItermax=self.max_inner_iter, + stopInnerThr=self.inner_tol, log=self.log, verbose=self.verbose) + + # coupling estimation + if self.log: + self.coupling_, self.log_ = returned_ + else: + self.coupling_ = returned_ + self.log_ = dict() + return self + + +class SinkhornL1l2Transport(BaseTransport): + + """Domain Adapatation OT method based on sinkhorn algorithm + + l1l2 class regularization. + + Parameters + ---------- + reg_e : float, optional (default=1) + Entropic regularization parameter + reg_cl : float, optional (default=0.1) + Class regularization parameter + max_iter : int, float, optional (default=10) + The minimum number of iteration before stopping the optimization + algorithm if no it has not converged + max_inner_iter : int, float, optional (default=200) + The number of iteration in the inner loop + tol : float, optional (default=10e-9) + Stop threshold on error (inner sinkhorn solver) (>0) + verbose : bool, optional (default=False) + Controls the verbosity of the optimization algorithm + log : bool, optional (default=False) + Controls the logs of the optimization algorithm + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. + limit_max: float, optional (default=10) + Controls the semi supervised mode. Transport between labeled source + and target samples of different classes will exhibit an infinite cost + (10 times the maximum value of the cost matrix) + + Attributes + ---------- + coupling_ : array-like, shape (n_source_samples, n_target_samples) + The optimal coupling + log_ : dictionary + The dictionary of log, empty dic if parameter log is not True + + References + ---------- + + .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE + Transactions on Pattern Analysis and Machine Intelligence , + vol.PP, no.99, pp.1-1 + .. [2] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). + Generalized conditional gradient: analysis of convergence + and applications. arXiv preprint arXiv:1510.06567. + + """ + + + def __init__(self, reg_e=1., reg_cl=0.1, + max_iter=10, max_inner_iter=200, + tol=10e-9, verbose=False, log=False, + metric="sqeuclidean", norm=None, + distribution_estimation=distribution_estimation_uniform, + out_of_sample_map='ferradans', limit_max=10): + self.reg_e = reg_e + self.reg_cl = reg_cl + self.max_iter = max_iter + self.max_inner_iter = max_inner_iter + self.tol = tol + self.verbose = verbose + self.log = log + self.metric = metric + self.norm = norm + self.distribution_estimation = distribution_estimation + self.out_of_sample_map = out_of_sample_map + self.limit_max = limit_max + + + def fit(self, Xs, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + + Returns + ------- + self : object + Returns self. + """ + + # check the necessary inputs parameters are here + if check_params(Xs=Xs, Xt=Xt, ys=ys): + + super(SinkhornL1l2Transport, self).fit(Xs, ys, Xt, yt) + + returned_ = sinkhorn_l1l2_gl( + a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, + reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, + numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, + verbose=self.verbose, log=self.log) + + # deal with the value of log + if self.log: + self.coupling_, self.log_ = returned_ + else: + self.coupling_ = returned_ + self.log_ = dict() + + return self + + +class MappingTransport(BaseEstimator): + + """MappingTransport: DA methods that aims at jointly estimating a optimal + transport coupling and the associated mapping + + Parameters + ---------- + mu : float, optional (default=1) + Weight for the linear OT loss (>0) + eta : float, optional (default=0.001) + Regularization term for the linear mapping L (>0) + bias : bool, optional (default=False) + Estimate linear mapping with constant bias + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. + kernel : string, optional (default="linear") + The kernel to use either linear or gaussian + sigma : float, optional (default=1) + The gaussian kernel parameter + max_iter : int, optional (default=100) + Max number of BCD iterations + tol : float, optional (default=1e-5) + Stop threshold on relative loss decrease (>0) + max_inner_iter : int, optional (default=10) + Max number of iterations (inner CG solver) + inner_tol : float, optional (default=1e-6) + Stop threshold on error (inner CG solver) (>0) + log : bool, optional (default=False) + record log if True + verbose : bool, optional (default=False) + Print information along iterations + verbose2 : bool, optional (default=False) + Print information along iterations + + Attributes + ---------- + coupling_ : array-like, shape (n_source_samples, n_target_samples) + The optimal coupling + mapping_ : array-like, shape (n_features (+ 1), n_features) + (if bias) for kernel == linear + The associated mapping + array-like, shape (n_source_samples (+ 1), n_features) + (if bias) for kernel == gaussian + log_ : dictionary + The dictionary of log, empty dic if parameter log is not True + + References + ---------- + + .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, + "Mapping estimation for discrete optimal transport", + Neural Information Processing Systems (NIPS), 2016. + + """ + + + def __init__(self, mu=1, eta=0.001, bias=False, metric="sqeuclidean", + norm=None, kernel="linear", sigma=1, max_iter=100, tol=1e-5, + max_inner_iter=10, inner_tol=1e-6, log=False, verbose=False, + verbose2=False): + self.metric = metric + self.norm = norm + self.mu = mu + self.eta = eta + self.bias = bias + self.kernel = kernel + self.sigma = sigma + self.max_iter = max_iter + self.tol = tol + self.max_inner_iter = max_inner_iter + self.inner_tol = inner_tol + self.log = log + self.verbose = verbose + self.verbose2 = verbose2 + + + def fit(self, Xs=None, ys=None, Xt=None, yt=None): + """Builds an optimal coupling and estimates the associated mapping + from source and target sets of samples (Xs, ys) and (Xt, yt) + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + + Returns + ------- + self : object + Returns self + """ + + # check the necessary inputs parameters are here + if check_params(Xs=Xs, Xt=Xt): + + self.xs_ = Xs + self.xt_ = Xt + + if self.kernel == "linear": + returned_ = joint_OT_mapping_linear( + Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, + verbose=self.verbose, verbose2=self.verbose2, + numItermax=self.max_iter, + numInnerItermax=self.max_inner_iter, stopThr=self.tol, + stopInnerThr=self.inner_tol, log=self.log) + + elif self.kernel == "gaussian": + returned_ = joint_OT_mapping_kernel( + Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, + sigma=self.sigma, verbose=self.verbose, + verbose2=self.verbose, numItermax=self.max_iter, + numInnerItermax=self.max_inner_iter, + stopInnerThr=self.inner_tol, stopThr=self.tol, + log=self.log) + + # deal with the value of log + if self.log: + self.coupling_, self.mapping_, self.log_ = returned_ + else: + self.coupling_, self.mapping_ = returned_ + self.log_ = dict() + + return self + + + def transform(self, Xs): + """Transports source samples Xs onto target ones Xt + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + + Returns + ------- + transp_Xs : array-like, shape (n_source_samples, n_features) + The transport source samples. + """ + + # check the necessary inputs parameters are here + if check_params(Xs=Xs): + + if np.array_equal(self.xs_, Xs): + # perform standard barycentric mapping + transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] + + # set nans to 0 + transp[~ np.isfinite(transp)] = 0 + + # compute transported samples + transp_Xs = np.dot(transp, self.xt_) + else: + if self.kernel == "gaussian": + K = kernel(Xs, self.xs_, method=self.kernel, + sigma=self.sigma) + elif self.kernel == "linear": + K = Xs + if self.bias: + K = np.hstack((K, np.ones((Xs.shape[0], 1)))) + transp_Xs = K.dot(self.mapping_) + + return transp_Xs + + +class UnbalancedSinkhornTransport(BaseTransport): + + """Domain Adapatation unbalanced OT method based on sinkhorn algorithm + + Parameters + ---------- + reg_e : float, optional (default=1) + Entropic regularization parameter + reg_m : float, optional (default=0.1) + Mass regularization parameter + method : str + method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or + 'sinkhorn_epsilon_scaling', see those function for specific parameters + max_iter : int, float, optional (default=10) + The minimum number of iteration before stopping the optimization + algorithm if no it has not converged + tol : float, optional (default=10e-9) + Stop threshold on error (inner sinkhorn solver) (>0) + verbose : bool, optional (default=False) + Controls the verbosity of the optimization algorithm + log : bool, optional (default=False) + Controls the logs of the optimization algorithm + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. + limit_max: float, optional (default=10) + Controls the semi supervised mode. Transport between labeled source + and target samples of different classes will exhibit an infinite cost + (10 times the maximum value of the cost matrix) + + Attributes + ---------- + coupling_ : array-like, shape (n_source_samples, n_target_samples) + The optimal coupling + log_ : dictionary + The dictionary of log, empty dic if parameter log is not True + + References + ---------- + + .. [1] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). + Scaling algorithms for unbalanced transport problems. arXiv preprint + arXiv:1607.05816. + + """ + + + def __init__(self, reg_e=1., reg_m=0.1, method='sinkhorn', + max_iter=10, tol=1e-9, verbose=False, log=False, + metric="sqeuclidean", norm=None, + distribution_estimation=distribution_estimation_uniform, + out_of_sample_map='ferradans', limit_max=10): + self.reg_e = reg_e + self.reg_m = reg_m + self.method = method + self.max_iter = max_iter + self.tol = tol + self.verbose = verbose + self.log = log + self.metric = metric + self.norm = norm + self.distribution_estimation = distribution_estimation + self.out_of_sample_map = out_of_sample_map + self.limit_max = limit_max + + + def fit(self, Xs, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + + Returns + ------- + self : object + Returns self. + """ + + # check the necessary inputs parameters are here + if check_params(Xs=Xs, Xt=Xt): + + super(UnbalancedSinkhornTransport, self).fit(Xs, ys, Xt, yt) + + returned_ = sinkhorn_unbalanced( + a=self.mu_s, b=self.mu_t, M=self.cost_, + reg=self.reg_e, reg_m=self.reg_m, method=self.method, + numItermax=self.max_iter, stopThr=self.tol, + verbose=self.verbose, log=self.log) + + # deal with the value of log + if self.log: + self.coupling_, self.log_ = returned_ + else: + self.coupling_ = returned_ + self.log_ = dict() + + return self + + +class JCPOTTransport(BaseTransport): + + """Domain Adapatation OT method for multi-source target shift based on Wasserstein barycenter algorithm. + + Parameters + ---------- + reg_e : float, optional (default=1) + Entropic regularization parameter + max_iter : int, float, optional (default=10) + The minimum number of iteration before stopping the optimization + algorithm if no it has not converged + tol : float, optional (default=10e-9) + Stop threshold on error (inner sinkhorn solver) (>0) + verbose : bool, optional (default=False) + Controls the verbosity of the optimization algorithm + log : bool, optional (default=False) + Controls the logs of the optimization algorithm + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. + + Attributes + ---------- + coupling_ : list of array-like objects, shape K x (n_source_samples, n_target_samples) + A set of optimal couplings between each source domain and the target domain + proportions_ : array-like, shape (n_classes,) + Estimated class proportions in the target domain + log_ : dictionary + The dictionary of log, empty dic if parameter log is not True + + References + ---------- + + .. [1] Ievgen Redko, Nicolas Courty, Rémi Flamary, Devis Tuia + "Optimal transport for multi-source domain adaptation under target shift", + International Conference on Artificial Intelligence and Statistics (AISTATS), + vol. 89, p.849-858, 2019. + + """ + + + def __init__(self, reg_e=.1, max_iter=10, + tol=10e-9, verbose=False, log=False, + metric="sqeuclidean", + out_of_sample_map='ferradans'): + self.reg_e = reg_e + self.max_iter = max_iter + self.tol = tol + self.verbose = verbose + self.log = log + self.metric = metric + self.out_of_sample_map = out_of_sample_map + + + def fit(self, Xs, ys=None, Xt=None, yt=None): + """Building coupling matrices from a list of source and target sets of samples + (Xs, ys) and (Xt, yt) + + Parameters + ---------- + Xs : list of K array-like objects, shape K x (nk_source_samples, n_features) + A list of the training input samples. + ys : list of K array-like objects, shape K x (nk_source_samples,) + A list of the class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + + Returns + ------- + self : object + Returns self. + """ + + # check the necessary inputs parameters are here + if check_params(Xs=Xs, Xt=Xt, ys=ys): + + self.xs_ = Xs + self.xt_ = Xt + + returned_ = jcpot_barycenter(Xs=Xs, Ys=ys, Xt=Xt, reg=self.reg_e, + metric=self.metric, distrinumItermax=self.max_iter, stopThr=self.tol, + verbose=self.verbose, log=self.log) + + # deal with the value of log + if self.log: + self.coupling_, self.proportions_, self.log_ = returned_ + else: + self.coupling_, self.proportions_ = returned_ + self.log_ = dict() + + return self + + + def transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): + """Transports source samples Xs onto target ones Xt + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + batch_size : int, optional (default=128) + The batch size for out of sample inverse transform + """ + + transp_Xs = [] + + # check the necessary inputs parameters are here + if check_params(Xs=Xs): + + if all([np.allclose(x, y) for x, y in zip(self.xs_, Xs)]): + + # perform standard barycentric mapping for each source domain + + for coupling in self.coupling_: + transp = coupling / np.sum(coupling, 1)[:, None] + + # set nans to 0 + transp[~ np.isfinite(transp)] = 0 + + # compute transported samples + transp_Xs.append(np.dot(transp, self.xt_)) + else: + + # perform out of sample mapping + indices = np.arange(Xs.shape[0]) + batch_ind = [ + indices[i:i + batch_size] + for i in range(0, len(indices), batch_size)] + + transp_Xs = [] + + for bi in batch_ind: + transp_Xs_ = [] + + # get the nearest neighbor in the sources domains + xs = np.concatenate(self.xs_, axis=0) + idx = np.argmin(dist(Xs[bi], xs), axis=1) + + # transport the source samples + for coupling in self.coupling_: + transp = coupling / np.sum( + coupling, 1)[:, None] + transp[~ np.isfinite(transp)] = 0 + transp_Xs_.append(np.dot(transp, self.xt_)) + + transp_Xs_ = np.concatenate(transp_Xs_, axis=0) + + # define the transported points + transp_Xs_ = transp_Xs_[idx, :] + Xs[bi] - xs[idx, :] + transp_Xs.append(transp_Xs_) + + transp_Xs = np.concatenate(transp_Xs, axis=0) + + return transp_Xs diff --git a/test/test_da.py b/test/test_da.py index f700df9..15f4308 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -8,7 +8,6 @@ import numpy as np from numpy.testing import assert_allclose, assert_equal import ot -from ot.bregman import jcpot_barycenter from ot.datasets import make_data_classif from ot.utils import unif @@ -602,3 +601,109 @@ def test_jcpot_transport_class(): # check that the oos method is working assert_equal(transp_Xs_new.shape, Xs_new.shape) + +def test_emd_laplace_class(): + """test_emd_laplace_transport + """ + ns = 150 + nt = 200 + + Xs, ys = make_data_classif('3gauss', ns) + Xt, yt = make_data_classif('3gauss2', nt) + + otda = ot.da.EMDLaplaceTransport(reg_lap=0.01, max_iter=1000, tol=1e-9, verbose=False, log=True) + + # test its computed + otda.fit(Xs=Xs, ys=ys, Xt=Xt) + + assert hasattr(otda, "coupling_") + assert hasattr(otda, "log_") + + # test dimensions of coupling + assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + + # test all margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + + assert_allclose( + np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose( + np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = otda.transform(Xs=Xs) + [assert_equal(x.shape, y.shape) for x, y in zip(transp_Xs, Xs)] + + Xs_new, _ = make_data_classif('3gauss', ns + 1) + transp_Xs_new = otda.transform(Xs_new) + + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) + + # test inverse transform + transp_Xt = otda.inverse_transform(Xt=Xt) + assert_equal(transp_Xt.shape, Xt.shape) + + Xt_new, _ = make_data_classif('3gauss2', nt + 1) + transp_Xt_new = otda.inverse_transform(Xt=Xt_new) + + # check that the oos method is working + assert_equal(transp_Xt_new.shape, Xt_new.shape) + + # test fit_transform + transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt) + assert_equal(transp_Xs.shape, Xs.shape) + +def test_sinkhorn_laplace_class(): + """test_sinkhorn_laplace_transport + """ + ns = 150 + nt = 200 + + Xs, ys = make_data_classif('3gauss', ns) + Xt, yt = make_data_classif('3gauss2', nt) + + otda = ot.da.SinkhornLaplaceTransport(reg_e = 1, reg_lap=0.01, max_iter=1000, tol=1e-9, verbose=False, log=True) + + # test its computed + otda.fit(Xs=Xs, ys=ys, Xt=Xt) + + assert hasattr(otda, "coupling_") + assert hasattr(otda, "log_") + + # test dimensions of coupling + assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + + # test all margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + + assert_allclose( + np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose( + np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = otda.transform(Xs=Xs) + [assert_equal(x.shape, y.shape) for x, y in zip(transp_Xs, Xs)] + + Xs_new, _ = make_data_classif('3gauss', ns + 1) + transp_Xs_new = otda.transform(Xs_new) + + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) + + # test inverse transform + transp_Xt = otda.inverse_transform(Xt=Xt) + assert_equal(transp_Xt.shape, Xt.shape) + + Xt_new, _ = make_data_classif('3gauss2', nt + 1) + transp_Xt_new = otda.inverse_transform(Xt=Xt_new) + + # check that the oos method is working + assert_equal(transp_Xt_new.shape, Xt_new.shape) + + # test fit_transform + transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt) + assert_equal(transp_Xs.shape, Xs.shape) \ No newline at end of file -- cgit v1.2.3 From fa99199c02e497354e34c6ce76e7b4ba15b44d05 Mon Sep 17 00:00:00 2001 From: ievred Date: Fri, 3 Apr 2020 16:06:39 +0200 Subject: v2 laplace emd sinkhorn --- ot/da.py | 485 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++-- ot/utils.py | 5 + 2 files changed, 481 insertions(+), 9 deletions(-) diff --git a/ot/da.py b/ot/da.py index e62e495..39e8c4c 100644 --- a/ot/da.py +++ b/ot/da.py @@ -16,7 +16,7 @@ import scipy.linalg as linalg from .bregman import sinkhorn, jcpot_barycenter from .lp import emd -from .utils import unif, dist, kernel, cost_normalization +from .utils import unif, dist, kernel, cost_normalization, laplacian from .utils import check_params, BaseEstimator from .unbalanced import sinkhorn_unbalanced from .optim import cg @@ -748,6 +748,233 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, return A, b +def emd_laplace(a, b, xs, xt, M, eta=1., alpha=0.5, + numItermax=1000, stopThr=1e-5, numInnerItermax=1000, + stopInnerThr=1e-6, log=False, verbose=False, **kwargs): + r"""Solve the optimal transport problem (OT) with Laplacian regularization + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + eta\Omega_\alpha(\gamma) + + s.t.\ \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + + where: + + - a and b are source and target weights (sum to 1) + - xs and xt are source and target samples + - M is the (ns,nt) metric cost matrix + - :math:`\Omega_\alpha` is the Laplacian regularization term + :math:`\Omega_\alpha = (1-\alpha)/n_s^2\sum_{i,j}S^s_{i,j}\|T(\mathbf{x}^s_i)-T(\mathbf{x}^s_j)\|^2+\alpha/n_t^2\sum_{i,j}S^t_{i,j}^'\|T(\mathbf{x}^t_i)-T(\mathbf{x}^t_j)\|^2` + with :math:`S^s_{i,j}, S^t_{i,j}` denoting source and target similarity matrices and :math:`T(\cdot)` being a barycentric mapping + + The algorithm used for solving the problem is the conditional gradient algorithm as proposed in [5]. + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) + samples weights in the target domain + xs : np.ndarray (ns,d) + samples in the source domain + xt : np.ndarray (nt,d) + samples in the target domain + M : np.ndarray (ns,nt) + loss matrix + eta : float + Regularization term for Laplacian regularization + alpha : float + Regularization term for source domain's importance in regularization + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshold on error (inner emd solver) (>0) + numInnerItermax : int, optional + Max number of iterations (inner CG solver) + stopInnerThr : float, optional + Stop threshold on error (inner CG solver) (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE + Transactions on Pattern Analysis and Machine Intelligence , + vol.PP, no.99, pp.1-1 + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + + """ + if 'sim' not in kwargs: + kwargs['sim'] = 'knn' + + if kwargs['sim'] == 'gauss': + if 'rbfparam' not in kwargs: + kwargs['rbfparam'] = 1 / (2 * (np.mean(dist(xs, xs, 'sqeuclidean')) ** 2)) + sS = kernel(xs, xs, method=kwargs['sim'], sigma=kwargs['rbfparam']) + sT = kernel(xt, xt, method=kwargs['sim'], sigma=kwargs['rbfparam']) + + elif kwargs['sim'] == 'knn': + if 'nn' not in kwargs: + kwargs['nn'] = 5 + + from sklearn.neighbors import kneighbors_graph + + sS = kneighbors_graph(xs, kwargs['nn']).toarray() + sS = (sS + sS.T) / 2 + sT = kneighbors_graph(xt, kwargs['nn']).toarray() + sT = (sT + sT.T) / 2 + + lS = laplacian(sS) + lT = laplacian(sT) + + def f(G): + return alpha*np.trace(np.dot(xt.T, np.dot(G.T, np.dot(lS, np.dot(G, xt))))) \ + + (1-alpha)*np.trace(np.dot(xs.T, np.dot(G, np.dot(lT, np.dot(G.T, xs))))) + + def df(G): + return alpha*np.dot(lS + lS.T, np.dot(G, np.dot(xt, xt.T)))\ + +(1-alpha)*np.dot(xs, np.dot(xs.T, np.dot(G, lT + lT.T))) + + return cg(a, b, M, reg=eta, f=f, df=df, G0=None, numItermax=numItermax, numItermaxEmd=numInnerItermax, + stopThr=stopThr, stopThr2=stopInnerThr, verbose=verbose, log=log) + +def sinkhorn_laplace(a, b, xs, xt, M, reg=.1, eta=1., alpha=0.5, + numItermax=1000, stopThr=1e-5, numInnerItermax=1000, + stopInnerThr=1e-6, log=False, verbose=False, **kwargs): + r"""Solve the entropic regularized optimal transport problem (OT) with Laplacian regularization + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\Omega_e(\gamma) + eta\Omega_\alpha(\gamma) + + s.t.\ \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + + where: + + - a and b are source and target weights (sum to 1) + - xs and xt are source and target samples + - M is the (ns,nt) metric cost matrix + - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e + (\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`\Omega_\alpha` is the Laplacian regularization term + :math:`\Omega_\alpha = (1-\alpha)/n_s^2\sum_{i,j}S^s_{i,j}\|T(\mathbf{x}^s_i)-T(\mathbf{x}^s_j)\|^2+\alpha/n_t^2\sum_{i,j}S^t_{i,j}^'\|T(\mathbf{x}^t_i)-T(\mathbf{x}^t_j)\|^2` + with :math:`S^s_{i,j}, S^t_{i,j}` denoting source and target similarity matrices and :math:`T(\cdot)` being a barycentric mapping + + The algorithm used for solving the problem is the conditional gradient algorithm as proposed in [5]. + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) + samples weights in the target domain + xs : np.ndarray (ns,d) + samples in the source domain + xt : np.ndarray (nt,d) + samples in the target domain + M : np.ndarray (ns,nt) + loss matrix + reg : float + Regularization term for entropic regularization >0 + eta : float + Regularization term for Laplacian regularization + alpha : float + Regularization term for source domain's importance in regularization + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshold on error (inner sinkhorn solver) (>0) + numInnerItermax : int, optional + Max number of iterations (inner CG solver) + stopInnerThr : float, optional + Stop threshold on error (inner CG solver) (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE + Transactions on Pattern Analysis and Machine Intelligence , + vol.PP, no.99, pp.1-1 + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + + """ + if 'sim' not in kwargs: + kwargs['sim'] = 'knn' + + if kwargs['sim'] == 'gauss': + if 'rbfparam' not in kwargs: + kwargs['rbfparam'] = 1 / (2 * (np.mean(dist(xs, xs, 'sqeuclidean')) ** 2)) + sS = kernel(xs, xs, method=kwargs['sim'], sigma=kwargs['rbfparam']) + sT = kernel(xt, xt, method=kwargs['sim'], sigma=kwargs['rbfparam']) + + elif kwargs['sim'] == 'knn': + if 'nn' not in kwargs: + kwargs['nn'] = 5 + + from sklearn.neighbors import kneighbors_graph + + sS = kneighbors_graph(xs, kwargs['nn']).toarray() + sS = (sS + sS.T) / 2 + sT = kneighbors_graph(xt, kwargs['nn']).toarray() + sT = (sT + sT.T) / 2 + + lS = laplacian(sS) + lT = laplacian(sT) + + def f(G): + return alpha*np.trace(np.dot(xt.T, np.dot(G.T, np.dot(lS, np.dot(G, xt))))) \ + + (1-alpha)*np.trace(np.dot(xs.T, np.dot(G, np.dot(lT, np.dot(G.T, xs))))) + + def df(G): + return alpha*np.dot(lS + lS.T, np.dot(G, np.dot(xt, xt.T)))\ + +(1-alpha)*np.dot(xs, np.dot(xs.T, np.dot(G, lT + lT.T))) + + return gcg(a, b, M, reg, eta, f, df, G0=None, numItermax=numItermax, stopThr=stopThr, + numInnerItermax=numInnerItermax, stopThr2=stopInnerThr, + verbose=verbose, log=log) + def distribution_estimation_uniform(X): """estimates a uniform distribution from an array of samples X @@ -762,10 +989,12 @@ def distribution_estimation_uniform(X): The uniform distribution estimated from X """ + return unif(X.shape[0]) class BaseTransport(BaseEstimator): + """Base class for OTDA objects Notes @@ -787,6 +1016,7 @@ class BaseTransport(BaseEstimator): inverse_transform method should always get as input a Xt parameter """ + def fit(self, Xs=None, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) @@ -847,6 +1077,7 @@ class BaseTransport(BaseEstimator): return self + def fit_transform(self, Xs=None, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) and transports source samples Xs onto target @@ -875,6 +1106,7 @@ class BaseTransport(BaseEstimator): return self.fit(Xs, ys, Xt, yt).transform(Xs, ys, Xt, yt) + def transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): """Transports source samples Xs onto target ones Xt @@ -942,6 +1174,7 @@ class BaseTransport(BaseEstimator): return transp_Xs + def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): """Transports target samples Xt onto target samples Xs @@ -1011,6 +1244,7 @@ class BaseTransport(BaseEstimator): class LinearTransport(BaseTransport): + """ OT linear operator between empirical distributions The function estimates the optimal linear operator that aligns the two @@ -1053,14 +1287,15 @@ class LinearTransport(BaseTransport): """ + def __init__(self, reg=1e-8, bias=True, log=False, distribution_estimation=distribution_estimation_uniform): - self.bias = bias self.log = log self.reg = reg self.distribution_estimation = distribution_estimation + def fit(self, Xs=None, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) @@ -1108,6 +1343,7 @@ class LinearTransport(BaseTransport): return self + def transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): """Transports source samples Xs onto target ones Xt @@ -1140,6 +1376,7 @@ class LinearTransport(BaseTransport): return transp_Xs + def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): """Transports target samples Xt onto target samples Xs @@ -1175,6 +1412,7 @@ class LinearTransport(BaseTransport): class SinkhornTransport(BaseTransport): + """Domain Adapatation OT method based on Sinkhorn Algorithm Parameters @@ -1223,12 +1461,12 @@ class SinkhornTransport(BaseTransport): 26, 2013 """ + def __init__(self, reg_e=1., max_iter=1000, tol=10e-9, verbose=False, log=False, metric="sqeuclidean", norm=None, distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans', limit_max=np.infty): - self.reg_e = reg_e self.max_iter = max_iter self.tol = tol @@ -1240,6 +1478,7 @@ class SinkhornTransport(BaseTransport): self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map + def fit(self, Xs=None, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) @@ -1284,6 +1523,7 @@ class SinkhornTransport(BaseTransport): class EMDTransport(BaseTransport): + """Domain Adapatation OT method based on Earth Mover's Distance Parameters @@ -1321,11 +1561,11 @@ class EMDTransport(BaseTransport): on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 """ + def __init__(self, metric="sqeuclidean", norm=None, log=False, distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans', limit_max=10, max_iter=100000): - self.metric = metric self.norm = norm self.log = log @@ -1334,6 +1574,7 @@ class EMDTransport(BaseTransport): self.out_of_sample_map = out_of_sample_map self.max_iter = max_iter + def fit(self, Xs, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) @@ -1375,6 +1616,7 @@ class EMDTransport(BaseTransport): class SinkhornLpl1Transport(BaseTransport): + """Domain Adapatation OT method based on sinkhorn algorithm + LpL1 class regularization. @@ -1429,13 +1671,13 @@ class SinkhornLpl1Transport(BaseTransport): """ + def __init__(self, reg_e=1., reg_cl=0.1, max_iter=10, max_inner_iter=200, log=False, tol=10e-9, verbose=False, metric="sqeuclidean", norm=None, distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans', limit_max=np.infty): - self.reg_e = reg_e self.reg_cl = reg_cl self.max_iter = max_iter @@ -1449,6 +1691,7 @@ class SinkhornLpl1Transport(BaseTransport): self.out_of_sample_map = out_of_sample_map self.limit_max = limit_max + def fit(self, Xs, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) @@ -1493,7 +1736,222 @@ class SinkhornLpl1Transport(BaseTransport): return self +class EMDLaplaceTransport(BaseTransport): + + """Domain Adapatation OT method based on Earth Mover's Distance with Laplacian regularization + + Parameters + ---------- + reg_lap : float, optional (default=1) + Laplacian regularization parameter + reg_src : float, optional (default=0.5) + Source relative importance in regularization + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. + max_iter : int, optional (default=100) + Max number of BCD iterations + tol : float, optional (default=1e-5) + Stop threshold on relative loss decrease (>0) + max_inner_iter : int, optional (default=10) + Max number of iterations (inner CG solver) + inner_tol : float, optional (default=1e-6) + Stop threshold on error (inner CG solver) (>0) + log : int, optional (default=False) + Controls the logs of the optimization algorithm + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. + + Attributes + ---------- + coupling_ : array-like, shape (n_source_samples, n_target_samples) + The optimal coupling + + References + ---------- + .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE Transactions + on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + """ + + + def __init__(self, reg_lap = 1., reg_src=1., alpha=0.5, + metric="sqeuclidean", norm=None, max_iter=100, tol=1e-5, + max_inner_iter=100000, inner_tol=1e-6, log=False, verbose=False, + distribution_estimation=distribution_estimation_uniform, + out_of_sample_map='ferradans'): + self.reg_lap = reg_lap + self.reg_src = reg_src + self.alpha = alpha + self.metric = metric + self.norm = norm + self.max_iter = max_iter + self.tol = tol + self.max_inner_iter = max_inner_iter + self.inner_tol = inner_tol + self.log = log + self.verbose = verbose + self.distribution_estimation = distribution_estimation + self.out_of_sample_map = out_of_sample_map + + + def fit(self, Xs, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + + Returns + ------- + self : object + Returns self. + """ + + super(EMDLaplaceTransport, self).fit(Xs, ys, Xt, yt) + + returned_ = emd_laplace(a=self.mu_s, b=self.mu_t, xs=self.xs_, + xt=self.xt_, M=self.cost_, eta=self.reg_lap, alpha=self.reg_src, + numItermax=self.max_iter, stopThr=self.tol, numInnerItermax=self.max_inner_iter, + stopInnerThr=self.inner_tol, log=self.log, verbose=self.verbose) + + # coupling estimation + if self.log: + self.coupling_, self.log_ = returned_ + else: + self.coupling_ = returned_ + self.log_ = dict() + return self + +class SinkhornLaplaceTransport(BaseTransport): + + """Domain Adapatation OT method based on entropic regularized OT with Laplacian regularization + + Parameters + ---------- + reg_e : float, optional (default=1) + Entropic regularization parameter + reg_lap : float, optional (default=1) + Laplacian regularization parameter + reg_src : float, optional (default=0.5) + Source relative importance in regularization + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. + max_iter : int, optional (default=100) + Max number of BCD iterations + tol : float, optional (default=1e-5) + Stop threshold on relative loss decrease (>0) + max_inner_iter : int, optional (default=10) + Max number of iterations (inner CG solver) + inner_tol : float, optional (default=1e-6) + Stop threshold on error (inner CG solver) (>0) + log : int, optional (default=False) + Controls the logs of the optimization algorithm + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. + + Attributes + ---------- + coupling_ : array-like, shape (n_source_samples, n_target_samples) + The optimal coupling + + References + ---------- + .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE Transactions + on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + """ + + + def __init__(self, reg_e=1., reg_lap=1., reg_src=0.5, + metric="sqeuclidean", norm=None, max_iter=100, tol=1e-9, + max_inner_iter=200, inner_tol=1e-6, log=False, verbose=False, + distribution_estimation=distribution_estimation_uniform, + out_of_sample_map='ferradans'): + + self.reg_e = reg_e + self.reg_lap = reg_lap + self.reg_src = reg_src + self.metric = metric + self.norm = norm + self.max_iter = max_iter + self.tol = tol + self.max_inner_iter = max_inner_iter + self.inner_tol = inner_tol + self.log = log + self.verbose = verbose + self.distribution_estimation = distribution_estimation + self.out_of_sample_map = out_of_sample_map + + + def fit(self, Xs, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + + Returns + ------- + self : object + Returns self. + """ + + super(SinkhornLaplaceTransport, self).fit(Xs, ys, Xt, yt) + + returned_ = sinkhorn_laplace(a=self.mu_s, b=self.mu_t, xs=self.xs_, + xt=self.xt_, M=self.cost_, reg=self.reg_e, eta=self.reg_lap, alpha=self.reg_src, + numItermax=self.max_iter, stopThr=self.tol, numInnerItermax=self.max_inner_iter, + stopInnerThr=self.inner_tol, log=self.log, verbose=self.verbose) + + # coupling estimation + if self.log: + self.coupling_, self.log_ = returned_ + else: + self.coupling_ = returned_ + self.log_ = dict() + return self + + class SinkhornL1l2Transport(BaseTransport): + """Domain Adapatation OT method based on sinkhorn algorithm + l1l2 class regularization. @@ -1550,13 +2008,13 @@ class SinkhornL1l2Transport(BaseTransport): """ + def __init__(self, reg_e=1., reg_cl=0.1, max_iter=10, max_inner_iter=200, tol=10e-9, verbose=False, log=False, metric="sqeuclidean", norm=None, distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans', limit_max=10): - self.reg_e = reg_e self.reg_cl = reg_cl self.max_iter = max_iter @@ -1570,6 +2028,7 @@ class SinkhornL1l2Transport(BaseTransport): self.out_of_sample_map = out_of_sample_map self.limit_max = limit_max + def fit(self, Xs, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) @@ -1617,6 +2076,7 @@ class SinkhornL1l2Transport(BaseTransport): class MappingTransport(BaseEstimator): + """MappingTransport: DA methods that aims at jointly estimating a optimal transport coupling and the associated mapping @@ -1673,11 +2133,11 @@ class MappingTransport(BaseEstimator): """ + def __init__(self, mu=1, eta=0.001, bias=False, metric="sqeuclidean", norm=None, kernel="linear", sigma=1, max_iter=100, tol=1e-5, max_inner_iter=10, inner_tol=1e-6, log=False, verbose=False, verbose2=False): - self.metric = metric self.norm = norm self.mu = mu @@ -1693,6 +2153,7 @@ class MappingTransport(BaseEstimator): self.verbose = verbose self.verbose2 = verbose2 + def fit(self, Xs=None, ys=None, Xt=None, yt=None): """Builds an optimal coupling and estimates the associated mapping from source and target sets of samples (Xs, ys) and (Xt, yt) @@ -1750,6 +2211,7 @@ class MappingTransport(BaseEstimator): return self + def transform(self, Xs): """Transports source samples Xs onto target ones Xt @@ -1790,6 +2252,7 @@ class MappingTransport(BaseEstimator): class UnbalancedSinkhornTransport(BaseTransport): + """Domain Adapatation unbalanced OT method based on sinkhorn algorithm Parameters @@ -1842,12 +2305,12 @@ class UnbalancedSinkhornTransport(BaseTransport): """ + def __init__(self, reg_e=1., reg_m=0.1, method='sinkhorn', max_iter=10, tol=1e-9, verbose=False, log=False, metric="sqeuclidean", norm=None, distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans', limit_max=10): - self.reg_e = reg_e self.reg_m = reg_m self.method = method @@ -1861,6 +2324,7 @@ class UnbalancedSinkhornTransport(BaseTransport): self.out_of_sample_map = out_of_sample_map self.limit_max = limit_max + def fit(self, Xs, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) @@ -1908,6 +2372,7 @@ class UnbalancedSinkhornTransport(BaseTransport): class JCPOTTransport(BaseTransport): + """Domain Adapatation OT method for multi-source target shift based on Wasserstein barycenter algorithm. Parameters @@ -1954,11 +2419,11 @@ class JCPOTTransport(BaseTransport): """ + def __init__(self, reg_e=.1, max_iter=10, tol=10e-9, verbose=False, log=False, metric="sqeuclidean", out_of_sample_map='ferradans'): - self.reg_e = reg_e self.max_iter = max_iter self.tol = tol @@ -1967,6 +2432,7 @@ class JCPOTTransport(BaseTransport): self.metric = metric self.out_of_sample_map = out_of_sample_map + def fit(self, Xs, ys=None, Xt=None, yt=None): """Building coupling matrices from a list of source and target sets of samples (Xs, ys) and (Xt, yt) @@ -2011,6 +2477,7 @@ class JCPOTTransport(BaseTransport): return self + def transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): """Transports source samples Xs onto target ones Xt diff --git a/ot/utils.py b/ot/utils.py index b71458b..b8a6f44 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -48,6 +48,11 @@ def kernel(x1, x2, method='gaussian', sigma=1, **kwargs): K = np.exp(-dist(x1, x2) / (2 * sigma**2)) return K +def laplacian(x): + """Compute Laplacian matrix""" + L = np.diag(np.sum(x, axis=0)) - x + return L + def unif(n): """ return a uniform histogram of length n (simplex) -- cgit v1.2.3 From 0baef795985f8c1afeec3667ba2c46b5d89bcc01 Mon Sep 17 00:00:00 2001 From: Ievgen Redko Date: Fri, 3 Apr 2020 16:13:11 +0200 Subject: Delete da.py --- ot1/da.py | 2551 ------------------------------------------------------------- 1 file changed, 2551 deletions(-) delete mode 100644 ot1/da.py diff --git a/ot1/da.py b/ot1/da.py deleted file mode 100644 index 39e8c4c..0000000 --- a/ot1/da.py +++ /dev/null @@ -1,2551 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Domain adaptation with optimal transport -""" - -# Author: Remi Flamary -# Nicolas Courty -# Michael Perrot -# Nathalie Gayraud -# Ievgen Redko -# -# License: MIT License - -import numpy as np -import scipy.linalg as linalg - -from .bregman import sinkhorn, jcpot_barycenter -from .lp import emd -from .utils import unif, dist, kernel, cost_normalization, laplacian -from .utils import check_params, BaseEstimator -from .unbalanced import sinkhorn_unbalanced -from .optim import cg -from .optim import gcg - - -def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, - numInnerItermax=200, stopInnerThr=1e-9, verbose=False, - log=False): - """ - Solve the entropic regularization optimal transport problem with nonconvex - group lasso regularization - - The function solves the following optimization problem: - - .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma) - + \eta \Omega_g(\gamma) - - s.t. \gamma 1 = a - - \gamma^T 1= b - - \gamma\geq 0 - where : - - - M is the (ns,nt) metric cost matrix - - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e - (\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - :math:`\Omega_g` is the group lasso regularization term - :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^{1/2}_1` - where :math:`\mathcal{I}_c` are the index of samples from class c - in the source domain. - - a and b are source and target weights (sum to 1) - - The algorithm used for solving the problem is the generalized conditional - gradient as proposed in [5]_ [7]_ - - - Parameters - ---------- - a : np.ndarray (ns,) - samples weights in the source domain - labels_a : np.ndarray (ns,) - labels of samples in the source domain - b : np.ndarray (nt,) - samples weights in the target domain - M : np.ndarray (ns,nt) - loss matrix - reg : float - Regularization term for entropic regularization >0 - eta : float, optional - Regularization term for group lasso regularization >0 - numItermax : int, optional - Max number of iterations - numInnerItermax : int, optional - Max number of iterations (inner sinkhorn solver) - stopInnerThr : float, optional - Stop threshold on error (inner sinkhorn solver) (>0) - verbose : bool, optional - Print information along iterations - log : bool, optional - record log if True - - - Returns - ------- - gamma : (ns x nt) ndarray - Optimal transportation matrix for the given parameters - log : dict - log dictionary return only if log==True in parameters - - - References - ---------- - - .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, - "Optimal Transport for Domain Adaptation," in IEEE - Transactions on Pattern Analysis and Machine Intelligence , - vol.PP, no.99, pp.1-1 - .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). - Generalized conditional gradient: analysis of convergence - and applications. arXiv preprint arXiv:1510.06567. - - See Also - -------- - ot.lp.emd : Unregularized OT - ot.bregman.sinkhorn : Entropic regularized OT - ot.optim.cg : General regularized OT - - """ - p = 0.5 - epsilon = 1e-3 - - indices_labels = [] - classes = np.unique(labels_a) - for c in classes: - idxc, = np.where(labels_a == c) - indices_labels.append(idxc) - - W = np.zeros(M.shape) - - for cpt in range(numItermax): - Mreg = M + eta * W - transp = sinkhorn(a, b, Mreg, reg, numItermax=numInnerItermax, - stopThr=stopInnerThr) - # the transport has been computed. Check if classes are really - # separated - W = np.ones(M.shape) - for (i, c) in enumerate(classes): - majs = np.sum(transp[indices_labels[i]], axis=0) - majs = p * ((majs + epsilon) ** (p - 1)) - W[indices_labels[i]] = majs - - return transp - - -def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10, - numInnerItermax=200, stopInnerThr=1e-9, verbose=False, - log=False): - """ - Solve the entropic regularization optimal transport problem with group - lasso regularization - - The function solves the following optimization problem: - - .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+ - \eta \Omega_g(\gamma) - - s.t. \gamma 1 = a - - \gamma^T 1= b - - \gamma\geq 0 - where : - - - M is the (ns,nt) metric cost matrix - - :math:`\Omega_e` is the entropic regularization term - :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - :math:`\Omega_g` is the group lasso regulaization term - :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^2` - where :math:`\mathcal{I}_c` are the index of samples from class - c in the source domain. - - a and b are source and target weights (sum to 1) - - The algorithm used for solving the problem is the generalised conditional - gradient as proposed in [5]_ [7]_ - - - Parameters - ---------- - a : np.ndarray (ns,) - samples weights in the source domain - labels_a : np.ndarray (ns,) - labels of samples in the source domain - b : np.ndarray (nt,) - samples in the target domain - M : np.ndarray (ns,nt) - loss matrix - reg : float - Regularization term for entropic regularization >0 - eta : float, optional - Regularization term for group lasso regularization >0 - numItermax : int, optional - Max number of iterations - numInnerItermax : int, optional - Max number of iterations (inner sinkhorn solver) - stopInnerThr : float, optional - Stop threshold on error (inner sinkhorn solver) (>0) - verbose : bool, optional - Print information along iterations - log : bool, optional - record log if True - - - Returns - ------- - gamma : (ns x nt) ndarray - Optimal transportation matrix for the given parameters - log : dict - log dictionary return only if log==True in parameters - - - References - ---------- - - .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, - "Optimal Transport for Domain Adaptation," in IEEE Transactions - on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 - .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). - Generalized conditional gradient: analysis of convergence and - applications. arXiv preprint arXiv:1510.06567. - - See Also - -------- - ot.optim.gcg : Generalized conditional gradient for OT problems - - """ - lstlab = np.unique(labels_a) - - def f(G): - res = 0 - for i in range(G.shape[1]): - for lab in lstlab: - temp = G[labels_a == lab, i] - res += np.linalg.norm(temp) - return res - - def df(G): - W = np.zeros(G.shape) - for i in range(G.shape[1]): - for lab in lstlab: - temp = G[labels_a == lab, i] - n = np.linalg.norm(temp) - if n: - W[labels_a == lab, i] = temp / n - return W - - return gcg(a, b, M, reg, eta, f, df, G0=None, numItermax=numItermax, - numInnerItermax=numInnerItermax, stopThr=stopInnerThr, - verbose=verbose, log=log) - - -def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, - verbose2=False, numItermax=100, numInnerItermax=10, - stopInnerThr=1e-6, stopThr=1e-5, log=False, - **kwargs): - """Joint OT and linear mapping estimation as proposed in [8] - - The function solves the following optimization problem: - - .. math:: - \min_{\gamma,L}\quad \|L(X_s) -n_s\gamma X_t\|^2_F + - \mu<\gamma,M>_F + \eta \|L -I\|^2_F - - s.t. \gamma 1 = a - - \gamma^T 1= b - - \gamma\geq 0 - where : - - - M is the (ns,nt) squared euclidean cost matrix between samples in - Xs and Xt (scaled by ns) - - :math:`L` is a dxd linear operator that approximates the barycentric - mapping - - :math:`I` is the identity matrix (neutral linear mapping) - - a and b are uniform source and target weights - - The problem consist in solving jointly an optimal transport matrix - :math:`\gamma` and a linear mapping that fits the barycentric mapping - :math:`n_s\gamma X_t`. - - One can also estimate a mapping with constant bias (see supplementary - material of [8]) using the bias optional argument. - - The algorithm used for solving the problem is the block coordinate - descent that alternates between updates of G (using conditionnal gradient) - and the update of L using a classical least square solver. - - - Parameters - ---------- - xs : np.ndarray (ns,d) - samples in the source domain - xt : np.ndarray (nt,d) - samples in the target domain - mu : float,optional - Weight for the linear OT loss (>0) - eta : float, optional - Regularization term for the linear mapping L (>0) - bias : bool,optional - Estimate linear mapping with constant bias - numItermax : int, optional - Max number of BCD iterations - stopThr : float, optional - Stop threshold on relative loss decrease (>0) - numInnerItermax : int, optional - Max number of iterations (inner CG solver) - stopInnerThr : float, optional - Stop threshold on error (inner CG solver) (>0) - verbose : bool, optional - Print information along iterations - log : bool, optional - record log if True - - - Returns - ------- - gamma : (ns x nt) ndarray - Optimal transportation matrix for the given parameters - L : (d x d) ndarray - Linear mapping matrix (d+1 x d if bias) - log : dict - log dictionary return only if log==True in parameters - - - References - ---------- - - .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, - "Mapping estimation for discrete optimal transport", - Neural Information Processing Systems (NIPS), 2016. - - See Also - -------- - ot.lp.emd : Unregularized OT - ot.optim.cg : General regularized OT - - """ - - ns, nt, d = xs.shape[0], xt.shape[0], xt.shape[1] - - if bias: - xs1 = np.hstack((xs, np.ones((ns, 1)))) - xstxs = xs1.T.dot(xs1) - Id = np.eye(d + 1) - Id[-1] = 0 - I0 = Id[:, :-1] - - def sel(x): - return x[:-1, :] - else: - xs1 = xs - xstxs = xs1.T.dot(xs1) - Id = np.eye(d) - I0 = Id - - def sel(x): - return x - - if log: - log = {'err': []} - - a, b = unif(ns), unif(nt) - M = dist(xs, xt) * ns - G = emd(a, b, M) - - vloss = [] - - def loss(L, G): - """Compute full loss""" - return np.sum((xs1.dot(L) - ns * G.dot(xt)) ** 2) + mu * \ - np.sum(G * M) + eta * np.sum(sel(L - I0) ** 2) - - def solve_L(G): - """ solve L problem with fixed G (least square)""" - xst = ns * G.dot(xt) - return np.linalg.solve(xstxs + eta * Id, xs1.T.dot(xst) + eta * I0) - - def solve_G(L, G0): - """Update G with CG algorithm""" - xsi = xs1.dot(L) - - def f(G): - return np.sum((xsi - ns * G.dot(xt)) ** 2) - - def df(G): - return -2 * ns * (xsi - ns * G.dot(xt)).dot(xt.T) - - G = cg(a, b, M, 1.0 / mu, f, df, G0=G0, - numItermax=numInnerItermax, stopThr=stopInnerThr) - return G - - L = solve_L(G) - - vloss.append(loss(L, G)) - - if verbose: - print('{:5s}|{:12s}|{:8s}'.format( - 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) - print('{:5d}|{:8e}|{:8e}'.format(0, vloss[-1], 0)) - - # init loop - if numItermax > 0: - loop = 1 - else: - loop = 0 - it = 0 - - while loop: - - it += 1 - - # update G - G = solve_G(L, G) - - # update L - L = solve_L(G) - - vloss.append(loss(L, G)) - - if it >= numItermax: - loop = 0 - - if abs(vloss[-1] - vloss[-2]) / abs(vloss[-2]) < stopThr: - loop = 0 - - if verbose: - if it % 20 == 0: - print('{:5s}|{:12s}|{:8s}'.format( - 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) - print('{:5d}|{:8e}|{:8e}'.format( - it, vloss[-1], (vloss[-1] - vloss[-2]) / abs(vloss[-2]))) - if log: - log['loss'] = vloss - return G, L, log - else: - return G, L - - -def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', - sigma=1, bias=False, verbose=False, verbose2=False, - numItermax=100, numInnerItermax=10, - stopInnerThr=1e-6, stopThr=1e-5, log=False, - **kwargs): - """Joint OT and nonlinear mapping estimation with kernels as proposed in [8] - - The function solves the following optimization problem: - - .. math:: - \min_{\gamma,L\in\mathcal{H}}\quad \|L(X_s) - - n_s\gamma X_t\|^2_F + \mu<\gamma,M>_F + \eta \|L\|^2_\mathcal{H} - - s.t. \gamma 1 = a - - \gamma^T 1= b - - \gamma\geq 0 - where : - - - M is the (ns,nt) squared euclidean cost matrix between samples in - Xs and Xt (scaled by ns) - - :math:`L` is a ns x d linear operator on a kernel matrix that - approximates the barycentric mapping - - a and b are uniform source and target weights - - The problem consist in solving jointly an optimal transport matrix - :math:`\gamma` and the nonlinear mapping that fits the barycentric mapping - :math:`n_s\gamma X_t`. - - One can also estimate a mapping with constant bias (see supplementary - material of [8]) using the bias optional argument. - - The algorithm used for solving the problem is the block coordinate - descent that alternates between updates of G (using conditionnal gradient) - and the update of L using a classical kernel least square solver. - - - Parameters - ---------- - xs : np.ndarray (ns,d) - samples in the source domain - xt : np.ndarray (nt,d) - samples in the target domain - mu : float,optional - Weight for the linear OT loss (>0) - eta : float, optional - Regularization term for the linear mapping L (>0) - kerneltype : str,optional - kernel used by calling function ot.utils.kernel (gaussian by default) - sigma : float, optional - Gaussian kernel bandwidth. - bias : bool,optional - Estimate linear mapping with constant bias - verbose : bool, optional - Print information along iterations - verbose2 : bool, optional - Print information along iterations - numItermax : int, optional - Max number of BCD iterations - numInnerItermax : int, optional - Max number of iterations (inner CG solver) - stopInnerThr : float, optional - Stop threshold on error (inner CG solver) (>0) - stopThr : float, optional - Stop threshold on relative loss decrease (>0) - log : bool, optional - record log if True - - - Returns - ------- - gamma : (ns x nt) ndarray - Optimal transportation matrix for the given parameters - L : (ns x d) ndarray - Nonlinear mapping matrix (ns+1 x d if bias) - log : dict - log dictionary return only if log==True in parameters - - - References - ---------- - - .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, - "Mapping estimation for discrete optimal transport", - Neural Information Processing Systems (NIPS), 2016. - - See Also - -------- - ot.lp.emd : Unregularized OT - ot.optim.cg : General regularized OT - - """ - - ns, nt = xs.shape[0], xt.shape[0] - - K = kernel(xs, xs, method=kerneltype, sigma=sigma) - if bias: - K1 = np.hstack((K, np.ones((ns, 1)))) - Id = np.eye(ns + 1) - Id[-1] = 0 - Kp = np.eye(ns + 1) - Kp[:ns, :ns] = K - - # ls regu - # K0 = K1.T.dot(K1)+eta*I - # Kreg=I - - # RKHS regul - K0 = K1.T.dot(K1) + eta * Kp - Kreg = Kp - - else: - K1 = K - Id = np.eye(ns) - - # ls regul - # K0 = K1.T.dot(K1)+eta*I - # Kreg=I - - # proper kernel ridge - K0 = K + eta * Id - Kreg = K - - if log: - log = {'err': []} - - a, b = unif(ns), unif(nt) - M = dist(xs, xt) * ns - G = emd(a, b, M) - - vloss = [] - - def loss(L, G): - """Compute full loss""" - return np.sum((K1.dot(L) - ns * G.dot(xt)) ** 2) + mu * \ - np.sum(G * M) + eta * np.trace(L.T.dot(Kreg).dot(L)) - - def solve_L_nobias(G): - """ solve L problem with fixed G (least square)""" - xst = ns * G.dot(xt) - return np.linalg.solve(K0, xst) - - def solve_L_bias(G): - """ solve L problem with fixed G (least square)""" - xst = ns * G.dot(xt) - return np.linalg.solve(K0, K1.T.dot(xst)) - - def solve_G(L, G0): - """Update G with CG algorithm""" - xsi = K1.dot(L) - - def f(G): - return np.sum((xsi - ns * G.dot(xt)) ** 2) - - def df(G): - return -2 * ns * (xsi - ns * G.dot(xt)).dot(xt.T) - - G = cg(a, b, M, 1.0 / mu, f, df, G0=G0, - numItermax=numInnerItermax, stopThr=stopInnerThr) - return G - - if bias: - solve_L = solve_L_bias - else: - solve_L = solve_L_nobias - - L = solve_L(G) - - vloss.append(loss(L, G)) - - if verbose: - print('{:5s}|{:12s}|{:8s}'.format( - 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) - print('{:5d}|{:8e}|{:8e}'.format(0, vloss[-1], 0)) - - # init loop - if numItermax > 0: - loop = 1 - else: - loop = 0 - it = 0 - - while loop: - - it += 1 - - # update G - G = solve_G(L, G) - - # update L - L = solve_L(G) - - vloss.append(loss(L, G)) - - if it >= numItermax: - loop = 0 - - if abs(vloss[-1] - vloss[-2]) / abs(vloss[-2]) < stopThr: - loop = 0 - - if verbose: - if it % 20 == 0: - print('{:5s}|{:12s}|{:8s}'.format( - 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) - print('{:5d}|{:8e}|{:8e}'.format( - it, vloss[-1], (vloss[-1] - vloss[-2]) / abs(vloss[-2]))) - if log: - log['loss'] = vloss - return G, L, log - else: - return G, L - - -def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, - wt=None, bias=True, log=False): - """ return OT linear operator between samples - - The function estimates the optimal linear operator that aligns the two - empirical distributions. This is equivalent to estimating the closed - form mapping between two Gaussian distributions :math:`N(\mu_s,\Sigma_s)` - and :math:`N(\mu_t,\Sigma_t)` as proposed in [14] and discussed in remark - 2.29 in [15]. - - The linear operator from source to target :math:`M` - - .. math:: - M(x)=Ax+b - - where : - - .. math:: - A=\Sigma_s^{-1/2}(\Sigma_s^{1/2}\Sigma_t\Sigma_s^{1/2})^{1/2} - \Sigma_s^{-1/2} - .. math:: - b=\mu_t-A\mu_s - - Parameters - ---------- - xs : np.ndarray (ns,d) - samples in the source domain - xt : np.ndarray (nt,d) - samples in the target domain - reg : float,optional - regularization added to the diagonals of convariances (>0) - ws : np.ndarray (ns,1), optional - weights for the source samples - wt : np.ndarray (ns,1), optional - weights for the target samples - bias: boolean, optional - estimate bias b else b=0 (default:True) - log : bool, optional - record log if True - - - Returns - ------- - A : (d x d) ndarray - Linear operator - b : (1 x d) ndarray - bias - log : dict - log dictionary return only if log==True in parameters - - - References - ---------- - - .. [14] Knott, M. and Smith, C. S. "On the optimal mapping of - distributions", Journal of Optimization Theory and Applications - Vol 43, 1984 - - .. [15] Peyré, G., & Cuturi, M. (2017). "Computational Optimal - Transport", 2018. - - - """ - - d = xs.shape[1] - - if bias: - mxs = xs.mean(0, keepdims=True) - mxt = xt.mean(0, keepdims=True) - - xs = xs - mxs - xt = xt - mxt - else: - mxs = np.zeros((1, d)) - mxt = np.zeros((1, d)) - - if ws is None: - ws = np.ones((xs.shape[0], 1)) / xs.shape[0] - - if wt is None: - wt = np.ones((xt.shape[0], 1)) / xt.shape[0] - - Cs = (xs * ws).T.dot(xs) / ws.sum() + reg * np.eye(d) - Ct = (xt * wt).T.dot(xt) / wt.sum() + reg * np.eye(d) - - Cs12 = linalg.sqrtm(Cs) - Cs_12 = linalg.inv(Cs12) - - M0 = linalg.sqrtm(Cs12.dot(Ct.dot(Cs12))) - - A = Cs_12.dot(M0.dot(Cs_12)) - - b = mxt - mxs.dot(A) - - if log: - log = {} - log['Cs'] = Cs - log['Ct'] = Ct - log['Cs12'] = Cs12 - log['Cs_12'] = Cs_12 - return A, b, log - else: - return A, b - - -def emd_laplace(a, b, xs, xt, M, eta=1., alpha=0.5, - numItermax=1000, stopThr=1e-5, numInnerItermax=1000, - stopInnerThr=1e-6, log=False, verbose=False, **kwargs): - r"""Solve the optimal transport problem (OT) with Laplacian regularization - - .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F + eta\Omega_\alpha(\gamma) - - s.t.\ \gamma 1 = a - - \gamma^T 1= b - - \gamma\geq 0 - - where: - - - a and b are source and target weights (sum to 1) - - xs and xt are source and target samples - - M is the (ns,nt) metric cost matrix - - :math:`\Omega_\alpha` is the Laplacian regularization term - :math:`\Omega_\alpha = (1-\alpha)/n_s^2\sum_{i,j}S^s_{i,j}\|T(\mathbf{x}^s_i)-T(\mathbf{x}^s_j)\|^2+\alpha/n_t^2\sum_{i,j}S^t_{i,j}^'\|T(\mathbf{x}^t_i)-T(\mathbf{x}^t_j)\|^2` - with :math:`S^s_{i,j}, S^t_{i,j}` denoting source and target similarity matrices and :math:`T(\cdot)` being a barycentric mapping - - The algorithm used for solving the problem is the conditional gradient algorithm as proposed in [5]. - - Parameters - ---------- - a : np.ndarray (ns,) - samples weights in the source domain - b : np.ndarray (nt,) - samples weights in the target domain - xs : np.ndarray (ns,d) - samples in the source domain - xt : np.ndarray (nt,d) - samples in the target domain - M : np.ndarray (ns,nt) - loss matrix - eta : float - Regularization term for Laplacian regularization - alpha : float - Regularization term for source domain's importance in regularization - numItermax : int, optional - Max number of iterations - stopThr : float, optional - Stop threshold on error (inner emd solver) (>0) - numInnerItermax : int, optional - Max number of iterations (inner CG solver) - stopInnerThr : float, optional - Stop threshold on error (inner CG solver) (>0) - verbose : bool, optional - Print information along iterations - log : bool, optional - record log if True - - - Returns - ------- - gamma : (ns x nt) ndarray - Optimal transportation matrix for the given parameters - log : dict - log dictionary return only if log==True in parameters - - - References - ---------- - - .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, - "Optimal Transport for Domain Adaptation," in IEEE - Transactions on Pattern Analysis and Machine Intelligence , - vol.PP, no.99, pp.1-1 - - See Also - -------- - ot.lp.emd : Unregularized OT - ot.optim.cg : General regularized OT - - """ - if 'sim' not in kwargs: - kwargs['sim'] = 'knn' - - if kwargs['sim'] == 'gauss': - if 'rbfparam' not in kwargs: - kwargs['rbfparam'] = 1 / (2 * (np.mean(dist(xs, xs, 'sqeuclidean')) ** 2)) - sS = kernel(xs, xs, method=kwargs['sim'], sigma=kwargs['rbfparam']) - sT = kernel(xt, xt, method=kwargs['sim'], sigma=kwargs['rbfparam']) - - elif kwargs['sim'] == 'knn': - if 'nn' not in kwargs: - kwargs['nn'] = 5 - - from sklearn.neighbors import kneighbors_graph - - sS = kneighbors_graph(xs, kwargs['nn']).toarray() - sS = (sS + sS.T) / 2 - sT = kneighbors_graph(xt, kwargs['nn']).toarray() - sT = (sT + sT.T) / 2 - - lS = laplacian(sS) - lT = laplacian(sT) - - def f(G): - return alpha*np.trace(np.dot(xt.T, np.dot(G.T, np.dot(lS, np.dot(G, xt))))) \ - + (1-alpha)*np.trace(np.dot(xs.T, np.dot(G, np.dot(lT, np.dot(G.T, xs))))) - - def df(G): - return alpha*np.dot(lS + lS.T, np.dot(G, np.dot(xt, xt.T)))\ - +(1-alpha)*np.dot(xs, np.dot(xs.T, np.dot(G, lT + lT.T))) - - return cg(a, b, M, reg=eta, f=f, df=df, G0=None, numItermax=numItermax, numItermaxEmd=numInnerItermax, - stopThr=stopThr, stopThr2=stopInnerThr, verbose=verbose, log=log) - -def sinkhorn_laplace(a, b, xs, xt, M, reg=.1, eta=1., alpha=0.5, - numItermax=1000, stopThr=1e-5, numInnerItermax=1000, - stopInnerThr=1e-6, log=False, verbose=False, **kwargs): - r"""Solve the entropic regularized optimal transport problem (OT) with Laplacian regularization - - .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F + reg\Omega_e(\gamma) + eta\Omega_\alpha(\gamma) - - s.t.\ \gamma 1 = a - - \gamma^T 1= b - - \gamma\geq 0 - - where: - - - a and b are source and target weights (sum to 1) - - xs and xt are source and target samples - - M is the (ns,nt) metric cost matrix - - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e - (\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - :math:`\Omega_\alpha` is the Laplacian regularization term - :math:`\Omega_\alpha = (1-\alpha)/n_s^2\sum_{i,j}S^s_{i,j}\|T(\mathbf{x}^s_i)-T(\mathbf{x}^s_j)\|^2+\alpha/n_t^2\sum_{i,j}S^t_{i,j}^'\|T(\mathbf{x}^t_i)-T(\mathbf{x}^t_j)\|^2` - with :math:`S^s_{i,j}, S^t_{i,j}` denoting source and target similarity matrices and :math:`T(\cdot)` being a barycentric mapping - - The algorithm used for solving the problem is the conditional gradient algorithm as proposed in [5]. - - Parameters - ---------- - a : np.ndarray (ns,) - samples weights in the source domain - b : np.ndarray (nt,) - samples weights in the target domain - xs : np.ndarray (ns,d) - samples in the source domain - xt : np.ndarray (nt,d) - samples in the target domain - M : np.ndarray (ns,nt) - loss matrix - reg : float - Regularization term for entropic regularization >0 - eta : float - Regularization term for Laplacian regularization - alpha : float - Regularization term for source domain's importance in regularization - numItermax : int, optional - Max number of iterations - stopThr : float, optional - Stop threshold on error (inner sinkhorn solver) (>0) - numInnerItermax : int, optional - Max number of iterations (inner CG solver) - stopInnerThr : float, optional - Stop threshold on error (inner CG solver) (>0) - verbose : bool, optional - Print information along iterations - log : bool, optional - record log if True - - - Returns - ------- - gamma : (ns x nt) ndarray - Optimal transportation matrix for the given parameters - log : dict - log dictionary return only if log==True in parameters - - - References - ---------- - - .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, - "Optimal Transport for Domain Adaptation," in IEEE - Transactions on Pattern Analysis and Machine Intelligence , - vol.PP, no.99, pp.1-1 - - See Also - -------- - ot.lp.emd : Unregularized OT - ot.optim.cg : General regularized OT - - """ - if 'sim' not in kwargs: - kwargs['sim'] = 'knn' - - if kwargs['sim'] == 'gauss': - if 'rbfparam' not in kwargs: - kwargs['rbfparam'] = 1 / (2 * (np.mean(dist(xs, xs, 'sqeuclidean')) ** 2)) - sS = kernel(xs, xs, method=kwargs['sim'], sigma=kwargs['rbfparam']) - sT = kernel(xt, xt, method=kwargs['sim'], sigma=kwargs['rbfparam']) - - elif kwargs['sim'] == 'knn': - if 'nn' not in kwargs: - kwargs['nn'] = 5 - - from sklearn.neighbors import kneighbors_graph - - sS = kneighbors_graph(xs, kwargs['nn']).toarray() - sS = (sS + sS.T) / 2 - sT = kneighbors_graph(xt, kwargs['nn']).toarray() - sT = (sT + sT.T) / 2 - - lS = laplacian(sS) - lT = laplacian(sT) - - def f(G): - return alpha*np.trace(np.dot(xt.T, np.dot(G.T, np.dot(lS, np.dot(G, xt))))) \ - + (1-alpha)*np.trace(np.dot(xs.T, np.dot(G, np.dot(lT, np.dot(G.T, xs))))) - - def df(G): - return alpha*np.dot(lS + lS.T, np.dot(G, np.dot(xt, xt.T)))\ - +(1-alpha)*np.dot(xs, np.dot(xs.T, np.dot(G, lT + lT.T))) - - return gcg(a, b, M, reg, eta, f, df, G0=None, numItermax=numItermax, stopThr=stopThr, - numInnerItermax=numInnerItermax, stopThr2=stopInnerThr, - verbose=verbose, log=log) - -def distribution_estimation_uniform(X): - """estimates a uniform distribution from an array of samples X - - Parameters - ---------- - X : array-like, shape (n_samples, n_features) - The array of samples - - Returns - ------- - mu : array-like, shape (n_samples,) - The uniform distribution estimated from X - """ - - - return unif(X.shape[0]) - - -class BaseTransport(BaseEstimator): - - """Base class for OTDA objects - - Notes - ----- - All estimators should specify all the parameters that can be set - at the class level in their ``__init__`` as explicit keyword - arguments (no ``*args`` or ``**kwargs``). - - fit method should: - - estimate a cost matrix and store it in a `cost_` attribute - - estimate a coupling matrix and store it in a `coupling_` - attribute - - estimate distributions from source and target data and store them in - mu_s and mu_t attributes - - store Xs and Xt in attributes to be used later on in transform and - inverse_transform methods - - transform method should always get as input a Xs parameter - inverse_transform method should always get as input a Xt parameter - """ - - - def fit(self, Xs=None, ys=None, Xt=None, yt=None): - """Build a coupling matrix from source and target sets of samples - (Xs, ys) and (Xt, yt) - - Parameters - ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - ys : array-like, shape (n_source_samples,) - The class labels - Xt : array-like, shape (n_target_samples, n_features) - The training input samples. - yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the - yt's elements with -1. - - Warning: Note that, due to this convention -1 cannot be used as a - class label - - Returns - ------- - self : object - Returns self. - """ - - # check the necessary inputs parameters are here - if check_params(Xs=Xs, Xt=Xt): - - # pairwise distance - self.cost_ = dist(Xs, Xt, metric=self.metric) - self.cost_ = cost_normalization(self.cost_, self.norm) - - if (ys is not None) and (yt is not None): - - if self.limit_max != np.infty: - self.limit_max = self.limit_max * np.max(self.cost_) - - # assumes labeled source samples occupy the first rows - # and labeled target samples occupy the first columns - classes = [c for c in np.unique(ys) if c != -1] - for c in classes: - idx_s = np.where((ys != c) & (ys != -1)) - idx_t = np.where(yt == c) - - # all the coefficients corresponding to a source sample - # and a target sample : - # with different labels get a infinite - for j in idx_t[0]: - self.cost_[idx_s[0], j] = self.limit_max - - # distribution estimation - self.mu_s = self.distribution_estimation(Xs) - self.mu_t = self.distribution_estimation(Xt) - - # store arrays of samples - self.xs_ = Xs - self.xt_ = Xt - - return self - - - def fit_transform(self, Xs=None, ys=None, Xt=None, yt=None): - """Build a coupling matrix from source and target sets of samples - (Xs, ys) and (Xt, yt) and transports source samples Xs onto target - ones Xt - - Parameters - ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - ys : array-like, shape (n_source_samples,) - The class labels - Xt : array-like, shape (n_target_samples, n_features) - The training input samples. - yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the - yt's elements with -1. - - Warning: Note that, due to this convention -1 cannot be used as a - class label - - Returns - ------- - transp_Xs : array-like, shape (n_source_samples, n_features) - The source samples samples. - """ - - return self.fit(Xs, ys, Xt, yt).transform(Xs, ys, Xt, yt) - - - def transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): - """Transports source samples Xs onto target ones Xt - - Parameters - ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - ys : array-like, shape (n_source_samples,) - The class labels - Xt : array-like, shape (n_target_samples, n_features) - The training input samples. - yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the - yt's elements with -1. - - Warning: Note that, due to this convention -1 cannot be used as a - class label - batch_size : int, optional (default=128) - The batch size for out of sample inverse transform - - Returns - ------- - transp_Xs : array-like, shape (n_source_samples, n_features) - The transport source samples. - """ - - # check the necessary inputs parameters are here - if check_params(Xs=Xs): - - if np.array_equal(self.xs_, Xs): - - # perform standard barycentric mapping - transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] - - # set nans to 0 - transp[~ np.isfinite(transp)] = 0 - - # compute transported samples - transp_Xs = np.dot(transp, self.xt_) - else: - # perform out of sample mapping - indices = np.arange(Xs.shape[0]) - batch_ind = [ - indices[i:i + batch_size] - for i in range(0, len(indices), batch_size)] - - transp_Xs = [] - for bi in batch_ind: - # get the nearest neighbor in the source domain - D0 = dist(Xs[bi], self.xs_) - idx = np.argmin(D0, axis=1) - - # transport the source samples - transp = self.coupling_ / np.sum( - self.coupling_, 1)[:, None] - transp[~ np.isfinite(transp)] = 0 - transp_Xs_ = np.dot(transp, self.xt_) - - # define the transported points - transp_Xs_ = transp_Xs_[idx, :] + Xs[bi] - self.xs_[idx, :] - - transp_Xs.append(transp_Xs_) - - transp_Xs = np.concatenate(transp_Xs, axis=0) - - return transp_Xs - - - def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None, - batch_size=128): - """Transports target samples Xt onto target samples Xs - - Parameters - ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - ys : array-like, shape (n_source_samples,) - The class labels - Xt : array-like, shape (n_target_samples, n_features) - The training input samples. - yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the - yt's elements with -1. - - Warning: Note that, due to this convention -1 cannot be used as a - class label - batch_size : int, optional (default=128) - The batch size for out of sample inverse transform - - Returns - ------- - transp_Xt : array-like, shape (n_source_samples, n_features) - The transported target samples. - """ - - # check the necessary inputs parameters are here - if check_params(Xt=Xt): - - if np.array_equal(self.xt_, Xt): - - # perform standard barycentric mapping - transp_ = self.coupling_.T / np.sum(self.coupling_, 0)[:, None] - - # set nans to 0 - transp_[~ np.isfinite(transp_)] = 0 - - # compute transported samples - transp_Xt = np.dot(transp_, self.xs_) - else: - # perform out of sample mapping - indices = np.arange(Xt.shape[0]) - batch_ind = [ - indices[i:i + batch_size] - for i in range(0, len(indices), batch_size)] - - transp_Xt = [] - for bi in batch_ind: - D0 = dist(Xt[bi], self.xt_) - idx = np.argmin(D0, axis=1) - - # transport the target samples - transp_ = self.coupling_.T / np.sum( - self.coupling_, 0)[:, None] - transp_[~ np.isfinite(transp_)] = 0 - transp_Xt_ = np.dot(transp_, self.xs_) - - # define the transported points - transp_Xt_ = transp_Xt_[idx, :] + Xt[bi] - self.xt_[idx, :] - - transp_Xt.append(transp_Xt_) - - transp_Xt = np.concatenate(transp_Xt, axis=0) - - return transp_Xt - - -class LinearTransport(BaseTransport): - - """ OT linear operator between empirical distributions - - The function estimates the optimal linear operator that aligns the two - empirical distributions. This is equivalent to estimating the closed - form mapping between two Gaussian distributions :math:`N(\mu_s,\Sigma_s)` - and :math:`N(\mu_t,\Sigma_t)` as proposed in [14] and discussed in - remark 2.29 in [15]. - - The linear operator from source to target :math:`M` - - .. math:: - M(x)=Ax+b - - where : - - .. math:: - A=\Sigma_s^{-1/2}(\Sigma_s^{1/2}\Sigma_t\Sigma_s^{1/2})^{1/2} - \Sigma_s^{-1/2} - .. math:: - b=\mu_t-A\mu_s - - Parameters - ---------- - reg : float,optional - regularization added to the daigonals of convariances (>0) - bias: boolean, optional - estimate bias b else b=0 (default:True) - log : bool, optional - record log if True - - References - ---------- - - .. [14] Knott, M. and Smith, C. S. "On the optimal mapping of - distributions", Journal of Optimization Theory and Applications - Vol 43, 1984 - - .. [15] Peyré, G., & Cuturi, M. (2017). "Computational Optimal - Transport", 2018. - - """ - - - def __init__(self, reg=1e-8, bias=True, log=False, - distribution_estimation=distribution_estimation_uniform): - self.bias = bias - self.log = log - self.reg = reg - self.distribution_estimation = distribution_estimation - - - def fit(self, Xs=None, ys=None, Xt=None, yt=None): - """Build a coupling matrix from source and target sets of samples - (Xs, ys) and (Xt, yt) - - Parameters - ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - ys : array-like, shape (n_source_samples,) - The class labels - Xt : array-like, shape (n_target_samples, n_features) - The training input samples. - yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the - yt's elements with -1. - - Warning: Note that, due to this convention -1 cannot be used as a - class label - - Returns - ------- - self : object - Returns self. - """ - - self.mu_s = self.distribution_estimation(Xs) - self.mu_t = self.distribution_estimation(Xt) - - # coupling estimation - returned_ = OT_mapping_linear(Xs, Xt, reg=self.reg, - ws=self.mu_s.reshape((-1, 1)), - wt=self.mu_t.reshape((-1, 1)), - bias=self.bias, log=self.log) - - # deal with the value of log - if self.log: - self.A_, self.B_, self.log_ = returned_ - else: - self.A_, self.B_, = returned_ - self.log_ = dict() - - # re compute inverse mapping - self.A1_ = linalg.inv(self.A_) - self.B1_ = -self.B_.dot(self.A1_) - - return self - - - def transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): - """Transports source samples Xs onto target ones Xt - - Parameters - ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - ys : array-like, shape (n_source_samples,) - The class labels - Xt : array-like, shape (n_target_samples, n_features) - The training input samples. - yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the - yt's elements with -1. - - Warning: Note that, due to this convention -1 cannot be used as a - class label - batch_size : int, optional (default=128) - The batch size for out of sample inverse transform - - Returns - ------- - transp_Xs : array-like, shape (n_source_samples, n_features) - The transport source samples. - """ - - # check the necessary inputs parameters are here - if check_params(Xs=Xs): - transp_Xs = Xs.dot(self.A_) + self.B_ - - return transp_Xs - - - def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None, - batch_size=128): - """Transports target samples Xt onto target samples Xs - - Parameters - ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - ys : array-like, shape (n_source_samples,) - The class labels - Xt : array-like, shape (n_target_samples, n_features) - The training input samples. - yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the - yt's elements with -1. - - Warning: Note that, due to this convention -1 cannot be used as a - class label - batch_size : int, optional (default=128) - The batch size for out of sample inverse transform - - Returns - ------- - transp_Xt : array-like, shape (n_source_samples, n_features) - The transported target samples. - """ - - # check the necessary inputs parameters are here - if check_params(Xt=Xt): - transp_Xt = Xt.dot(self.A1_) + self.B1_ - - return transp_Xt - - -class SinkhornTransport(BaseTransport): - - """Domain Adapatation OT method based on Sinkhorn Algorithm - - Parameters - ---------- - reg_e : float, optional (default=1) - Entropic regularization parameter - max_iter : int, float, optional (default=1000) - The minimum number of iteration before stopping the optimization - algorithm if no it has not converged - tol : float, optional (default=10e-9) - The precision required to stop the optimization algorithm. - verbose : bool, optional (default=False) - Controls the verbosity of the optimization algorithm - log : int, optional (default=False) - Controls the logs of the optimization algorithm - metric : string, optional (default="sqeuclidean") - The ground metric for the Wasserstein problem - norm : string, optional (default=None) - If given, normalize the ground metric to avoid numerical errors that - can occur with large metric values. - distribution_estimation : callable, optional (defaults to the uniform) - The kind of distribution estimation to employ - out_of_sample_map : string, optional (default="ferradans") - The kind of out of sample mapping to apply to transport samples - from a domain into another one. Currently the only possible option is - "ferradans" which uses the method proposed in [6]. - limit_max: float, optional (defaul=np.infty) - Controls the semi supervised mode. Transport between labeled source - and target samples of different classes will exhibit an cost defined - by this variable - - Attributes - ---------- - coupling_ : array-like, shape (n_source_samples, n_target_samples) - The optimal coupling - log_ : dictionary - The dictionary of log, empty dic if parameter log is not True - - References - ---------- - .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, - "Optimal Transport for Domain Adaptation," in IEEE Transactions - on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 - .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal - Transport, Advances in Neural Information Processing Systems (NIPS) - 26, 2013 - """ - - - def __init__(self, reg_e=1., max_iter=1000, - tol=10e-9, verbose=False, log=False, - metric="sqeuclidean", norm=None, - distribution_estimation=distribution_estimation_uniform, - out_of_sample_map='ferradans', limit_max=np.infty): - self.reg_e = reg_e - self.max_iter = max_iter - self.tol = tol - self.verbose = verbose - self.log = log - self.metric = metric - self.norm = norm - self.limit_max = limit_max - self.distribution_estimation = distribution_estimation - self.out_of_sample_map = out_of_sample_map - - - def fit(self, Xs=None, ys=None, Xt=None, yt=None): - """Build a coupling matrix from source and target sets of samples - (Xs, ys) and (Xt, yt) - - Parameters - ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - ys : array-like, shape (n_source_samples,) - The class labels - Xt : array-like, shape (n_target_samples, n_features) - The training input samples. - yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the - yt's elements with -1. - - Warning: Note that, due to this convention -1 cannot be used as a - class label - - Returns - ------- - self : object - Returns self. - """ - - super(SinkhornTransport, self).fit(Xs, ys, Xt, yt) - - # coupling estimation - returned_ = sinkhorn( - a=self.mu_s, b=self.mu_t, M=self.cost_, reg=self.reg_e, - numItermax=self.max_iter, stopThr=self.tol, - verbose=self.verbose, log=self.log) - - # deal with the value of log - if self.log: - self.coupling_, self.log_ = returned_ - else: - self.coupling_ = returned_ - self.log_ = dict() - - return self - - -class EMDTransport(BaseTransport): - - """Domain Adapatation OT method based on Earth Mover's Distance - - Parameters - ---------- - metric : string, optional (default="sqeuclidean") - The ground metric for the Wasserstein problem - norm : string, optional (default=None) - If given, normalize the ground metric to avoid numerical errors that - can occur with large metric values. - log : int, optional (default=False) - Controls the logs of the optimization algorithm - distribution_estimation : callable, optional (defaults to the uniform) - The kind of distribution estimation to employ - out_of_sample_map : string, optional (default="ferradans") - The kind of out of sample mapping to apply to transport samples - from a domain into another one. Currently the only possible option is - "ferradans" which uses the method proposed in [6]. - limit_max: float, optional (default=10) - Controls the semi supervised mode. Transport between labeled source - and target samples of different classes will exhibit an infinite cost - (10 times the maximum value of the cost matrix) - max_iter : int, optional (default=100000) - The maximum number of iterations before stopping the optimization - algorithm if it has not converged. - - Attributes - ---------- - coupling_ : array-like, shape (n_source_samples, n_target_samples) - The optimal coupling - - References - ---------- - .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, - "Optimal Transport for Domain Adaptation," in IEEE Transactions - on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 - """ - - - def __init__(self, metric="sqeuclidean", norm=None, log=False, - distribution_estimation=distribution_estimation_uniform, - out_of_sample_map='ferradans', limit_max=10, - max_iter=100000): - self.metric = metric - self.norm = norm - self.log = log - self.limit_max = limit_max - self.distribution_estimation = distribution_estimation - self.out_of_sample_map = out_of_sample_map - self.max_iter = max_iter - - - def fit(self, Xs, ys=None, Xt=None, yt=None): - """Build a coupling matrix from source and target sets of samples - (Xs, ys) and (Xt, yt) - - Parameters - ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - ys : array-like, shape (n_source_samples,) - The class labels - Xt : array-like, shape (n_target_samples, n_features) - The training input samples. - yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the - yt's elements with -1. - - Warning: Note that, due to this convention -1 cannot be used as a - class label - - Returns - ------- - self : object - Returns self. - """ - - super(EMDTransport, self).fit(Xs, ys, Xt, yt) - - returned_ = emd( - a=self.mu_s, b=self.mu_t, M=self.cost_, numItermax=self.max_iter, - log=self.log) - - # coupling estimation - if self.log: - self.coupling_, self.log_ = returned_ - else: - self.coupling_ = returned_ - self.log_ = dict() - return self - - -class SinkhornLpl1Transport(BaseTransport): - - """Domain Adapatation OT method based on sinkhorn algorithm + - LpL1 class regularization. - - Parameters - ---------- - reg_e : float, optional (default=1) - Entropic regularization parameter - reg_cl : float, optional (default=0.1) - Class regularization parameter - max_iter : int, float, optional (default=10) - The minimum number of iteration before stopping the optimization - algorithm if no it has not converged - max_inner_iter : int, float, optional (default=200) - The number of iteration in the inner loop - log : bool, optional (default=False) - Controls the logs of the optimization algorithm - tol : float, optional (default=10e-9) - Stop threshold on error (inner sinkhorn solver) (>0) - verbose : bool, optional (default=False) - Controls the verbosity of the optimization algorithm - metric : string, optional (default="sqeuclidean") - The ground metric for the Wasserstein problem - norm : string, optional (default=None) - If given, normalize the ground metric to avoid numerical errors that - can occur with large metric values. - distribution_estimation : callable, optional (defaults to the uniform) - The kind of distribution estimation to employ - out_of_sample_map : string, optional (default="ferradans") - The kind of out of sample mapping to apply to transport samples - from a domain into another one. Currently the only possible option is - "ferradans" which uses the method proposed in [6]. - limit_max: float, optional (defaul=np.infty) - Controls the semi supervised mode. Transport between labeled source - and target samples of different classes will exhibit a cost defined by - limit_max. - - Attributes - ---------- - coupling_ : array-like, shape (n_source_samples, n_target_samples) - The optimal coupling - - References - ---------- - - .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, - "Optimal Transport for Domain Adaptation," in IEEE - Transactions on Pattern Analysis and Machine Intelligence , - vol.PP, no.99, pp.1-1 - .. [2] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). - Generalized conditional gradient: analysis of convergence - and applications. arXiv preprint arXiv:1510.06567. - - """ - - - def __init__(self, reg_e=1., reg_cl=0.1, - max_iter=10, max_inner_iter=200, log=False, - tol=10e-9, verbose=False, - metric="sqeuclidean", norm=None, - distribution_estimation=distribution_estimation_uniform, - out_of_sample_map='ferradans', limit_max=np.infty): - self.reg_e = reg_e - self.reg_cl = reg_cl - self.max_iter = max_iter - self.max_inner_iter = max_inner_iter - self.tol = tol - self.log = log - self.verbose = verbose - self.metric = metric - self.norm = norm - self.distribution_estimation = distribution_estimation - self.out_of_sample_map = out_of_sample_map - self.limit_max = limit_max - - - def fit(self, Xs, ys=None, Xt=None, yt=None): - """Build a coupling matrix from source and target sets of samples - (Xs, ys) and (Xt, yt) - - Parameters - ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - ys : array-like, shape (n_source_samples,) - The class labels - Xt : array-like, shape (n_target_samples, n_features) - The training input samples. - yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the - yt's elements with -1. - - Warning: Note that, due to this convention -1 cannot be used as a - class label - - Returns - ------- - self : object - Returns self. - """ - - # check the necessary inputs parameters are here - if check_params(Xs=Xs, Xt=Xt, ys=ys): - super(SinkhornLpl1Transport, self).fit(Xs, ys, Xt, yt) - - returned_ = sinkhorn_lpl1_mm( - a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, - reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, - numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, - verbose=self.verbose, log=self.log) - - # deal with the value of log - if self.log: - self.coupling_, self.log_ = returned_ - else: - self.coupling_ = returned_ - self.log_ = dict() - return self - - -class EMDLaplaceTransport(BaseTransport): - - """Domain Adapatation OT method based on Earth Mover's Distance with Laplacian regularization - - Parameters - ---------- - reg_lap : float, optional (default=1) - Laplacian regularization parameter - reg_src : float, optional (default=0.5) - Source relative importance in regularization - metric : string, optional (default="sqeuclidean") - The ground metric for the Wasserstein problem - norm : string, optional (default=None) - If given, normalize the ground metric to avoid numerical errors that - can occur with large metric values. - max_iter : int, optional (default=100) - Max number of BCD iterations - tol : float, optional (default=1e-5) - Stop threshold on relative loss decrease (>0) - max_inner_iter : int, optional (default=10) - Max number of iterations (inner CG solver) - inner_tol : float, optional (default=1e-6) - Stop threshold on error (inner CG solver) (>0) - log : int, optional (default=False) - Controls the logs of the optimization algorithm - distribution_estimation : callable, optional (defaults to the uniform) - The kind of distribution estimation to employ - out_of_sample_map : string, optional (default="ferradans") - The kind of out of sample mapping to apply to transport samples - from a domain into another one. Currently the only possible option is - "ferradans" which uses the method proposed in [6]. - - Attributes - ---------- - coupling_ : array-like, shape (n_source_samples, n_target_samples) - The optimal coupling - - References - ---------- - .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, - "Optimal Transport for Domain Adaptation," in IEEE Transactions - on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 - """ - - - def __init__(self, reg_lap = 1., reg_src=1., alpha=0.5, - metric="sqeuclidean", norm=None, max_iter=100, tol=1e-5, - max_inner_iter=100000, inner_tol=1e-6, log=False, verbose=False, - distribution_estimation=distribution_estimation_uniform, - out_of_sample_map='ferradans'): - self.reg_lap = reg_lap - self.reg_src = reg_src - self.alpha = alpha - self.metric = metric - self.norm = norm - self.max_iter = max_iter - self.tol = tol - self.max_inner_iter = max_inner_iter - self.inner_tol = inner_tol - self.log = log - self.verbose = verbose - self.distribution_estimation = distribution_estimation - self.out_of_sample_map = out_of_sample_map - - - def fit(self, Xs, ys=None, Xt=None, yt=None): - """Build a coupling matrix from source and target sets of samples - (Xs, ys) and (Xt, yt) - - Parameters - ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - ys : array-like, shape (n_source_samples,) - The class labels - Xt : array-like, shape (n_target_samples, n_features) - The training input samples. - yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the - yt's elements with -1. - - Warning: Note that, due to this convention -1 cannot be used as a - class label - - Returns - ------- - self : object - Returns self. - """ - - super(EMDLaplaceTransport, self).fit(Xs, ys, Xt, yt) - - returned_ = emd_laplace(a=self.mu_s, b=self.mu_t, xs=self.xs_, - xt=self.xt_, M=self.cost_, eta=self.reg_lap, alpha=self.reg_src, - numItermax=self.max_iter, stopThr=self.tol, numInnerItermax=self.max_inner_iter, - stopInnerThr=self.inner_tol, log=self.log, verbose=self.verbose) - - # coupling estimation - if self.log: - self.coupling_, self.log_ = returned_ - else: - self.coupling_ = returned_ - self.log_ = dict() - return self - -class SinkhornLaplaceTransport(BaseTransport): - - """Domain Adapatation OT method based on entropic regularized OT with Laplacian regularization - - Parameters - ---------- - reg_e : float, optional (default=1) - Entropic regularization parameter - reg_lap : float, optional (default=1) - Laplacian regularization parameter - reg_src : float, optional (default=0.5) - Source relative importance in regularization - metric : string, optional (default="sqeuclidean") - The ground metric for the Wasserstein problem - norm : string, optional (default=None) - If given, normalize the ground metric to avoid numerical errors that - can occur with large metric values. - max_iter : int, optional (default=100) - Max number of BCD iterations - tol : float, optional (default=1e-5) - Stop threshold on relative loss decrease (>0) - max_inner_iter : int, optional (default=10) - Max number of iterations (inner CG solver) - inner_tol : float, optional (default=1e-6) - Stop threshold on error (inner CG solver) (>0) - log : int, optional (default=False) - Controls the logs of the optimization algorithm - distribution_estimation : callable, optional (defaults to the uniform) - The kind of distribution estimation to employ - out_of_sample_map : string, optional (default="ferradans") - The kind of out of sample mapping to apply to transport samples - from a domain into another one. Currently the only possible option is - "ferradans" which uses the method proposed in [6]. - - Attributes - ---------- - coupling_ : array-like, shape (n_source_samples, n_target_samples) - The optimal coupling - - References - ---------- - .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, - "Optimal Transport for Domain Adaptation," in IEEE Transactions - on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 - """ - - - def __init__(self, reg_e=1., reg_lap=1., reg_src=0.5, - metric="sqeuclidean", norm=None, max_iter=100, tol=1e-9, - max_inner_iter=200, inner_tol=1e-6, log=False, verbose=False, - distribution_estimation=distribution_estimation_uniform, - out_of_sample_map='ferradans'): - - self.reg_e = reg_e - self.reg_lap = reg_lap - self.reg_src = reg_src - self.metric = metric - self.norm = norm - self.max_iter = max_iter - self.tol = tol - self.max_inner_iter = max_inner_iter - self.inner_tol = inner_tol - self.log = log - self.verbose = verbose - self.distribution_estimation = distribution_estimation - self.out_of_sample_map = out_of_sample_map - - - def fit(self, Xs, ys=None, Xt=None, yt=None): - """Build a coupling matrix from source and target sets of samples - (Xs, ys) and (Xt, yt) - - Parameters - ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - ys : array-like, shape (n_source_samples,) - The class labels - Xt : array-like, shape (n_target_samples, n_features) - The training input samples. - yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the - yt's elements with -1. - - Warning: Note that, due to this convention -1 cannot be used as a - class label - - Returns - ------- - self : object - Returns self. - """ - - super(SinkhornLaplaceTransport, self).fit(Xs, ys, Xt, yt) - - returned_ = sinkhorn_laplace(a=self.mu_s, b=self.mu_t, xs=self.xs_, - xt=self.xt_, M=self.cost_, reg=self.reg_e, eta=self.reg_lap, alpha=self.reg_src, - numItermax=self.max_iter, stopThr=self.tol, numInnerItermax=self.max_inner_iter, - stopInnerThr=self.inner_tol, log=self.log, verbose=self.verbose) - - # coupling estimation - if self.log: - self.coupling_, self.log_ = returned_ - else: - self.coupling_ = returned_ - self.log_ = dict() - return self - - -class SinkhornL1l2Transport(BaseTransport): - - """Domain Adapatation OT method based on sinkhorn algorithm + - l1l2 class regularization. - - Parameters - ---------- - reg_e : float, optional (default=1) - Entropic regularization parameter - reg_cl : float, optional (default=0.1) - Class regularization parameter - max_iter : int, float, optional (default=10) - The minimum number of iteration before stopping the optimization - algorithm if no it has not converged - max_inner_iter : int, float, optional (default=200) - The number of iteration in the inner loop - tol : float, optional (default=10e-9) - Stop threshold on error (inner sinkhorn solver) (>0) - verbose : bool, optional (default=False) - Controls the verbosity of the optimization algorithm - log : bool, optional (default=False) - Controls the logs of the optimization algorithm - metric : string, optional (default="sqeuclidean") - The ground metric for the Wasserstein problem - norm : string, optional (default=None) - If given, normalize the ground metric to avoid numerical errors that - can occur with large metric values. - distribution_estimation : callable, optional (defaults to the uniform) - The kind of distribution estimation to employ - out_of_sample_map : string, optional (default="ferradans") - The kind of out of sample mapping to apply to transport samples - from a domain into another one. Currently the only possible option is - "ferradans" which uses the method proposed in [6]. - limit_max: float, optional (default=10) - Controls the semi supervised mode. Transport between labeled source - and target samples of different classes will exhibit an infinite cost - (10 times the maximum value of the cost matrix) - - Attributes - ---------- - coupling_ : array-like, shape (n_source_samples, n_target_samples) - The optimal coupling - log_ : dictionary - The dictionary of log, empty dic if parameter log is not True - - References - ---------- - - .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, - "Optimal Transport for Domain Adaptation," in IEEE - Transactions on Pattern Analysis and Machine Intelligence , - vol.PP, no.99, pp.1-1 - .. [2] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). - Generalized conditional gradient: analysis of convergence - and applications. arXiv preprint arXiv:1510.06567. - - """ - - - def __init__(self, reg_e=1., reg_cl=0.1, - max_iter=10, max_inner_iter=200, - tol=10e-9, verbose=False, log=False, - metric="sqeuclidean", norm=None, - distribution_estimation=distribution_estimation_uniform, - out_of_sample_map='ferradans', limit_max=10): - self.reg_e = reg_e - self.reg_cl = reg_cl - self.max_iter = max_iter - self.max_inner_iter = max_inner_iter - self.tol = tol - self.verbose = verbose - self.log = log - self.metric = metric - self.norm = norm - self.distribution_estimation = distribution_estimation - self.out_of_sample_map = out_of_sample_map - self.limit_max = limit_max - - - def fit(self, Xs, ys=None, Xt=None, yt=None): - """Build a coupling matrix from source and target sets of samples - (Xs, ys) and (Xt, yt) - - Parameters - ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - ys : array-like, shape (n_source_samples,) - The class labels - Xt : array-like, shape (n_target_samples, n_features) - The training input samples. - yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the - yt's elements with -1. - - Warning: Note that, due to this convention -1 cannot be used as a - class label - - Returns - ------- - self : object - Returns self. - """ - - # check the necessary inputs parameters are here - if check_params(Xs=Xs, Xt=Xt, ys=ys): - - super(SinkhornL1l2Transport, self).fit(Xs, ys, Xt, yt) - - returned_ = sinkhorn_l1l2_gl( - a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, - reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, - numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, - verbose=self.verbose, log=self.log) - - # deal with the value of log - if self.log: - self.coupling_, self.log_ = returned_ - else: - self.coupling_ = returned_ - self.log_ = dict() - - return self - - -class MappingTransport(BaseEstimator): - - """MappingTransport: DA methods that aims at jointly estimating a optimal - transport coupling and the associated mapping - - Parameters - ---------- - mu : float, optional (default=1) - Weight for the linear OT loss (>0) - eta : float, optional (default=0.001) - Regularization term for the linear mapping L (>0) - bias : bool, optional (default=False) - Estimate linear mapping with constant bias - metric : string, optional (default="sqeuclidean") - The ground metric for the Wasserstein problem - norm : string, optional (default=None) - If given, normalize the ground metric to avoid numerical errors that - can occur with large metric values. - kernel : string, optional (default="linear") - The kernel to use either linear or gaussian - sigma : float, optional (default=1) - The gaussian kernel parameter - max_iter : int, optional (default=100) - Max number of BCD iterations - tol : float, optional (default=1e-5) - Stop threshold on relative loss decrease (>0) - max_inner_iter : int, optional (default=10) - Max number of iterations (inner CG solver) - inner_tol : float, optional (default=1e-6) - Stop threshold on error (inner CG solver) (>0) - log : bool, optional (default=False) - record log if True - verbose : bool, optional (default=False) - Print information along iterations - verbose2 : bool, optional (default=False) - Print information along iterations - - Attributes - ---------- - coupling_ : array-like, shape (n_source_samples, n_target_samples) - The optimal coupling - mapping_ : array-like, shape (n_features (+ 1), n_features) - (if bias) for kernel == linear - The associated mapping - array-like, shape (n_source_samples (+ 1), n_features) - (if bias) for kernel == gaussian - log_ : dictionary - The dictionary of log, empty dic if parameter log is not True - - References - ---------- - - .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, - "Mapping estimation for discrete optimal transport", - Neural Information Processing Systems (NIPS), 2016. - - """ - - - def __init__(self, mu=1, eta=0.001, bias=False, metric="sqeuclidean", - norm=None, kernel="linear", sigma=1, max_iter=100, tol=1e-5, - max_inner_iter=10, inner_tol=1e-6, log=False, verbose=False, - verbose2=False): - self.metric = metric - self.norm = norm - self.mu = mu - self.eta = eta - self.bias = bias - self.kernel = kernel - self.sigma = sigma - self.max_iter = max_iter - self.tol = tol - self.max_inner_iter = max_inner_iter - self.inner_tol = inner_tol - self.log = log - self.verbose = verbose - self.verbose2 = verbose2 - - - def fit(self, Xs=None, ys=None, Xt=None, yt=None): - """Builds an optimal coupling and estimates the associated mapping - from source and target sets of samples (Xs, ys) and (Xt, yt) - - Parameters - ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - ys : array-like, shape (n_source_samples,) - The class labels - Xt : array-like, shape (n_target_samples, n_features) - The training input samples. - yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the - yt's elements with -1. - - Warning: Note that, due to this convention -1 cannot be used as a - class label - - Returns - ------- - self : object - Returns self - """ - - # check the necessary inputs parameters are here - if check_params(Xs=Xs, Xt=Xt): - - self.xs_ = Xs - self.xt_ = Xt - - if self.kernel == "linear": - returned_ = joint_OT_mapping_linear( - Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, - verbose=self.verbose, verbose2=self.verbose2, - numItermax=self.max_iter, - numInnerItermax=self.max_inner_iter, stopThr=self.tol, - stopInnerThr=self.inner_tol, log=self.log) - - elif self.kernel == "gaussian": - returned_ = joint_OT_mapping_kernel( - Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, - sigma=self.sigma, verbose=self.verbose, - verbose2=self.verbose, numItermax=self.max_iter, - numInnerItermax=self.max_inner_iter, - stopInnerThr=self.inner_tol, stopThr=self.tol, - log=self.log) - - # deal with the value of log - if self.log: - self.coupling_, self.mapping_, self.log_ = returned_ - else: - self.coupling_, self.mapping_ = returned_ - self.log_ = dict() - - return self - - - def transform(self, Xs): - """Transports source samples Xs onto target ones Xt - - Parameters - ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - - Returns - ------- - transp_Xs : array-like, shape (n_source_samples, n_features) - The transport source samples. - """ - - # check the necessary inputs parameters are here - if check_params(Xs=Xs): - - if np.array_equal(self.xs_, Xs): - # perform standard barycentric mapping - transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] - - # set nans to 0 - transp[~ np.isfinite(transp)] = 0 - - # compute transported samples - transp_Xs = np.dot(transp, self.xt_) - else: - if self.kernel == "gaussian": - K = kernel(Xs, self.xs_, method=self.kernel, - sigma=self.sigma) - elif self.kernel == "linear": - K = Xs - if self.bias: - K = np.hstack((K, np.ones((Xs.shape[0], 1)))) - transp_Xs = K.dot(self.mapping_) - - return transp_Xs - - -class UnbalancedSinkhornTransport(BaseTransport): - - """Domain Adapatation unbalanced OT method based on sinkhorn algorithm - - Parameters - ---------- - reg_e : float, optional (default=1) - Entropic regularization parameter - reg_m : float, optional (default=0.1) - Mass regularization parameter - method : str - method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or - 'sinkhorn_epsilon_scaling', see those function for specific parameters - max_iter : int, float, optional (default=10) - The minimum number of iteration before stopping the optimization - algorithm if no it has not converged - tol : float, optional (default=10e-9) - Stop threshold on error (inner sinkhorn solver) (>0) - verbose : bool, optional (default=False) - Controls the verbosity of the optimization algorithm - log : bool, optional (default=False) - Controls the logs of the optimization algorithm - metric : string, optional (default="sqeuclidean") - The ground metric for the Wasserstein problem - norm : string, optional (default=None) - If given, normalize the ground metric to avoid numerical errors that - can occur with large metric values. - distribution_estimation : callable, optional (defaults to the uniform) - The kind of distribution estimation to employ - out_of_sample_map : string, optional (default="ferradans") - The kind of out of sample mapping to apply to transport samples - from a domain into another one. Currently the only possible option is - "ferradans" which uses the method proposed in [6]. - limit_max: float, optional (default=10) - Controls the semi supervised mode. Transport between labeled source - and target samples of different classes will exhibit an infinite cost - (10 times the maximum value of the cost matrix) - - Attributes - ---------- - coupling_ : array-like, shape (n_source_samples, n_target_samples) - The optimal coupling - log_ : dictionary - The dictionary of log, empty dic if parameter log is not True - - References - ---------- - - .. [1] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). - Scaling algorithms for unbalanced transport problems. arXiv preprint - arXiv:1607.05816. - - """ - - - def __init__(self, reg_e=1., reg_m=0.1, method='sinkhorn', - max_iter=10, tol=1e-9, verbose=False, log=False, - metric="sqeuclidean", norm=None, - distribution_estimation=distribution_estimation_uniform, - out_of_sample_map='ferradans', limit_max=10): - self.reg_e = reg_e - self.reg_m = reg_m - self.method = method - self.max_iter = max_iter - self.tol = tol - self.verbose = verbose - self.log = log - self.metric = metric - self.norm = norm - self.distribution_estimation = distribution_estimation - self.out_of_sample_map = out_of_sample_map - self.limit_max = limit_max - - - def fit(self, Xs, ys=None, Xt=None, yt=None): - """Build a coupling matrix from source and target sets of samples - (Xs, ys) and (Xt, yt) - - Parameters - ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - ys : array-like, shape (n_source_samples,) - The class labels - Xt : array-like, shape (n_target_samples, n_features) - The training input samples. - yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the - yt's elements with -1. - - Warning: Note that, due to this convention -1 cannot be used as a - class label - - Returns - ------- - self : object - Returns self. - """ - - # check the necessary inputs parameters are here - if check_params(Xs=Xs, Xt=Xt): - - super(UnbalancedSinkhornTransport, self).fit(Xs, ys, Xt, yt) - - returned_ = sinkhorn_unbalanced( - a=self.mu_s, b=self.mu_t, M=self.cost_, - reg=self.reg_e, reg_m=self.reg_m, method=self.method, - numItermax=self.max_iter, stopThr=self.tol, - verbose=self.verbose, log=self.log) - - # deal with the value of log - if self.log: - self.coupling_, self.log_ = returned_ - else: - self.coupling_ = returned_ - self.log_ = dict() - - return self - - -class JCPOTTransport(BaseTransport): - - """Domain Adapatation OT method for multi-source target shift based on Wasserstein barycenter algorithm. - - Parameters - ---------- - reg_e : float, optional (default=1) - Entropic regularization parameter - max_iter : int, float, optional (default=10) - The minimum number of iteration before stopping the optimization - algorithm if no it has not converged - tol : float, optional (default=10e-9) - Stop threshold on error (inner sinkhorn solver) (>0) - verbose : bool, optional (default=False) - Controls the verbosity of the optimization algorithm - log : bool, optional (default=False) - Controls the logs of the optimization algorithm - metric : string, optional (default="sqeuclidean") - The ground metric for the Wasserstein problem - norm : string, optional (default=None) - If given, normalize the ground metric to avoid numerical errors that - can occur with large metric values. - distribution_estimation : callable, optional (defaults to the uniform) - The kind of distribution estimation to employ - out_of_sample_map : string, optional (default="ferradans") - The kind of out of sample mapping to apply to transport samples - from a domain into another one. Currently the only possible option is - "ferradans" which uses the method proposed in [6]. - - Attributes - ---------- - coupling_ : list of array-like objects, shape K x (n_source_samples, n_target_samples) - A set of optimal couplings between each source domain and the target domain - proportions_ : array-like, shape (n_classes,) - Estimated class proportions in the target domain - log_ : dictionary - The dictionary of log, empty dic if parameter log is not True - - References - ---------- - - .. [1] Ievgen Redko, Nicolas Courty, Rémi Flamary, Devis Tuia - "Optimal transport for multi-source domain adaptation under target shift", - International Conference on Artificial Intelligence and Statistics (AISTATS), - vol. 89, p.849-858, 2019. - - """ - - - def __init__(self, reg_e=.1, max_iter=10, - tol=10e-9, verbose=False, log=False, - metric="sqeuclidean", - out_of_sample_map='ferradans'): - self.reg_e = reg_e - self.max_iter = max_iter - self.tol = tol - self.verbose = verbose - self.log = log - self.metric = metric - self.out_of_sample_map = out_of_sample_map - - - def fit(self, Xs, ys=None, Xt=None, yt=None): - """Building coupling matrices from a list of source and target sets of samples - (Xs, ys) and (Xt, yt) - - Parameters - ---------- - Xs : list of K array-like objects, shape K x (nk_source_samples, n_features) - A list of the training input samples. - ys : list of K array-like objects, shape K x (nk_source_samples,) - A list of the class labels - Xt : array-like, shape (n_target_samples, n_features) - The training input samples. - yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the - yt's elements with -1. - - Warning: Note that, due to this convention -1 cannot be used as a - class label - - Returns - ------- - self : object - Returns self. - """ - - # check the necessary inputs parameters are here - if check_params(Xs=Xs, Xt=Xt, ys=ys): - - self.xs_ = Xs - self.xt_ = Xt - - returned_ = jcpot_barycenter(Xs=Xs, Ys=ys, Xt=Xt, reg=self.reg_e, - metric=self.metric, distrinumItermax=self.max_iter, stopThr=self.tol, - verbose=self.verbose, log=self.log) - - # deal with the value of log - if self.log: - self.coupling_, self.proportions_, self.log_ = returned_ - else: - self.coupling_, self.proportions_ = returned_ - self.log_ = dict() - - return self - - - def transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): - """Transports source samples Xs onto target ones Xt - - Parameters - ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - ys : array-like, shape (n_source_samples,) - The class labels - Xt : array-like, shape (n_target_samples, n_features) - The training input samples. - yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the - yt's elements with -1. - - Warning: Note that, due to this convention -1 cannot be used as a - class label - batch_size : int, optional (default=128) - The batch size for out of sample inverse transform - """ - - transp_Xs = [] - - # check the necessary inputs parameters are here - if check_params(Xs=Xs): - - if all([np.allclose(x, y) for x, y in zip(self.xs_, Xs)]): - - # perform standard barycentric mapping for each source domain - - for coupling in self.coupling_: - transp = coupling / np.sum(coupling, 1)[:, None] - - # set nans to 0 - transp[~ np.isfinite(transp)] = 0 - - # compute transported samples - transp_Xs.append(np.dot(transp, self.xt_)) - else: - - # perform out of sample mapping - indices = np.arange(Xs.shape[0]) - batch_ind = [ - indices[i:i + batch_size] - for i in range(0, len(indices), batch_size)] - - transp_Xs = [] - - for bi in batch_ind: - transp_Xs_ = [] - - # get the nearest neighbor in the sources domains - xs = np.concatenate(self.xs_, axis=0) - idx = np.argmin(dist(Xs[bi], xs), axis=1) - - # transport the source samples - for coupling in self.coupling_: - transp = coupling / np.sum( - coupling, 1)[:, None] - transp[~ np.isfinite(transp)] = 0 - transp_Xs_.append(np.dot(transp, self.xt_)) - - transp_Xs_ = np.concatenate(transp_Xs_, axis=0) - - # define the transported points - transp_Xs_ = transp_Xs_[idx, :] + Xs[bi] - xs[idx, :] - transp_Xs.append(transp_Xs_) - - transp_Xs = np.concatenate(transp_Xs, axis=0) - - return transp_Xs -- cgit v1.2.3 From 98b68f1edc916d3802eeb24a19d0e10d855e01c6 Mon Sep 17 00:00:00 2001 From: ievred Date: Fri, 3 Apr 2020 17:29:13 +0200 Subject: autopep+remove sinkhorn+add simtype --- examples/plot_otda_laplacian.py | 38 ++---- ot/da.py | 286 +++------------------------------------- ot/utils.py | 1 + test/test_da.py | 54 +------- 4 files changed, 28 insertions(+), 351 deletions(-) diff --git a/examples/plot_otda_laplacian.py b/examples/plot_otda_laplacian.py index d9ae280..965380c 100644 --- a/examples/plot_otda_laplacian.py +++ b/examples/plot_otda_laplacian.py @@ -5,7 +5,7 @@ OT for domain adaptation ======================== This example introduces a domain adaptation in a 2D setting and OTDA -approaches with Laplacian regularization. +approache with Laplacian regularization. """ @@ -36,22 +36,17 @@ ot_emd = ot.da.EMDTransport() ot_emd.fit(Xs=Xs, Xt=Xt) # Sinkhorn Transport -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=.5) +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=.01) ot_sinkhorn.fit(Xs=Xs, Xt=Xt) # EMD Transport with Laplacian regularization ot_emd_laplace = ot.da.EMDLaplaceTransport(reg_lap=100, reg_src=1) ot_emd_laplace.fit(Xs=Xs, Xt=Xt) -# Sinkhorn Transport with Laplacian regularization -ot_sinkhorn_laplace = ot.da.SinkhornLaplaceTransport(reg_e=.5, reg_lap=100, reg_src=1) -ot_sinkhorn_laplace.fit(Xs=Xs, Xt=Xt) - # transport source samples onto target samples transp_Xs_emd = ot_emd.transform(Xs=Xs) transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) transp_Xs_emd_laplace = ot_emd_laplace.transform(Xs=Xs) -transp_Xs_sinkhorn_laplace = ot_sinkhorn_laplace.transform(Xs=Xs) ############################################################################## # Fig 1 : plots source and target samples @@ -80,35 +75,27 @@ pl.tight_layout() param_img = {'interpolation': 'nearest'} -n_plots = 2 - pl.figure(2, figsize=(15, 8)) -pl.subplot(2, 2*n_plots, 1) +pl.subplot(2, 3, 1) pl.imshow(ot_emd.coupling_, **param_img) pl.xticks([]) pl.yticks([]) pl.title('Optimal coupling\nEMDTransport') pl.figure(2, figsize=(15, 8)) -pl.subplot(2, 2*n_plots, 2) +pl.subplot(2, 3, 2) pl.imshow(ot_sinkhorn.coupling_, **param_img) pl.xticks([]) pl.yticks([]) pl.title('Optimal coupling\nSinkhornTransport') -pl.subplot(2, 2*n_plots, 3) +pl.subplot(2, 3, 3) pl.imshow(ot_emd_laplace.coupling_, **param_img) pl.xticks([]) pl.yticks([]) pl.title('Optimal coupling\nEMDLaplaceTransport') -pl.subplot(2, 2*n_plots, 4) -pl.imshow(ot_emd_laplace.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornLaplaceTransport') - -pl.subplot(2, 2*n_plots, 5) +pl.subplot(2, 3, 4) pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples', alpha=0.3) pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, @@ -118,7 +105,7 @@ pl.yticks([]) pl.title('Transported samples\nEmdTransport') pl.legend(loc="lower left") -pl.subplot(2, 2*n_plots, 6) +pl.subplot(2, 3, 5) pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples', alpha=0.3) pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, @@ -127,7 +114,7 @@ pl.xticks([]) pl.yticks([]) pl.title('Transported samples\nSinkhornTransport') -pl.subplot(2, 2*n_plots, 7) +pl.subplot(2, 3, 6) pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples', alpha=0.3) pl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys, @@ -135,15 +122,6 @@ pl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys, pl.xticks([]) pl.yticks([]) pl.title('Transported samples\nEMDLaplaceTransport') - -pl.subplot(2, 2*n_plots, 8) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_sinkhorn_laplace[:, 0], transp_Xs_sinkhorn_laplace[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nSinkhornLaplaceTransport') pl.tight_layout() pl.show() diff --git a/ot/da.py b/ot/da.py index 39e8c4c..0fdd3be 100644 --- a/ot/da.py +++ b/ot/da.py @@ -361,7 +361,7 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, def loss(L, G): """Compute full loss""" return np.sum((xs1.dot(L) - ns * G.dot(xt)) ** 2) + mu * \ - np.sum(G * M) + eta * np.sum(sel(L - I0) ** 2) + np.sum(G * M) + eta * np.sum(sel(L - I0) ** 2) def solve_L(G): """ solve L problem with fixed G (least square)""" @@ -565,7 +565,7 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', def loss(L, G): """Compute full loss""" return np.sum((K1.dot(L) - ns * G.dot(xt)) ** 2) + mu * \ - np.sum(G * M) + eta * np.trace(L.T.dot(Kreg).dot(L)) + np.sum(G * M) + eta * np.trace(L.T.dot(Kreg).dot(L)) def solve_L_nobias(G): """ solve L problem with fixed G (least square)""" @@ -748,9 +748,9 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, return A, b -def emd_laplace(a, b, xs, xt, M, eta=1., alpha=0.5, - numItermax=1000, stopThr=1e-5, numInnerItermax=1000, - stopInnerThr=1e-6, log=False, verbose=False, **kwargs): +def emd_laplace(a, b, xs, xt, M, sim, eta, alpha, + numItermax, stopThr, numInnerItermax, + stopInnerThr, log=False, verbose=False, **kwargs): r"""Solve the optimal transport problem (OT) with Laplacian regularization .. math:: @@ -825,16 +825,13 @@ def emd_laplace(a, b, xs, xt, M, eta=1., alpha=0.5, ot.optim.cg : General regularized OT """ - if 'sim' not in kwargs: - kwargs['sim'] = 'knn' - - if kwargs['sim'] == 'gauss': + if sim == 'gauss': if 'rbfparam' not in kwargs: kwargs['rbfparam'] = 1 / (2 * (np.mean(dist(xs, xs, 'sqeuclidean')) ** 2)) sS = kernel(xs, xs, method=kwargs['sim'], sigma=kwargs['rbfparam']) sT = kernel(xt, xt, method=kwargs['sim'], sigma=kwargs['rbfparam']) - elif kwargs['sim'] == 'knn': + elif sim == 'knn': if 'nn' not in kwargs: kwargs['nn'] = 5 @@ -849,131 +846,16 @@ def emd_laplace(a, b, xs, xt, M, eta=1., alpha=0.5, lT = laplacian(sT) def f(G): - return alpha*np.trace(np.dot(xt.T, np.dot(G.T, np.dot(lS, np.dot(G, xt))))) \ - + (1-alpha)*np.trace(np.dot(xs.T, np.dot(G, np.dot(lT, np.dot(G.T, xs))))) + return alpha * np.trace(np.dot(xt.T, np.dot(G.T, np.dot(lS, np.dot(G, xt))))) \ + + (1 - alpha) * np.trace(np.dot(xs.T, np.dot(G, np.dot(lT, np.dot(G.T, xs))))) def df(G): - return alpha*np.dot(lS + lS.T, np.dot(G, np.dot(xt, xt.T)))\ - +(1-alpha)*np.dot(xs, np.dot(xs.T, np.dot(G, lT + lT.T))) + return alpha * np.dot(lS + lS.T, np.dot(G, np.dot(xt, xt.T)))\ + + (1 - alpha) * np.dot(xs, np.dot(xs.T, np.dot(G, lT + lT.T))) return cg(a, b, M, reg=eta, f=f, df=df, G0=None, numItermax=numItermax, numItermaxEmd=numInnerItermax, stopThr=stopThr, stopThr2=stopInnerThr, verbose=verbose, log=log) -def sinkhorn_laplace(a, b, xs, xt, M, reg=.1, eta=1., alpha=0.5, - numItermax=1000, stopThr=1e-5, numInnerItermax=1000, - stopInnerThr=1e-6, log=False, verbose=False, **kwargs): - r"""Solve the entropic regularized optimal transport problem (OT) with Laplacian regularization - - .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F + reg\Omega_e(\gamma) + eta\Omega_\alpha(\gamma) - - s.t.\ \gamma 1 = a - - \gamma^T 1= b - - \gamma\geq 0 - - where: - - - a and b are source and target weights (sum to 1) - - xs and xt are source and target samples - - M is the (ns,nt) metric cost matrix - - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e - (\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - :math:`\Omega_\alpha` is the Laplacian regularization term - :math:`\Omega_\alpha = (1-\alpha)/n_s^2\sum_{i,j}S^s_{i,j}\|T(\mathbf{x}^s_i)-T(\mathbf{x}^s_j)\|^2+\alpha/n_t^2\sum_{i,j}S^t_{i,j}^'\|T(\mathbf{x}^t_i)-T(\mathbf{x}^t_j)\|^2` - with :math:`S^s_{i,j}, S^t_{i,j}` denoting source and target similarity matrices and :math:`T(\cdot)` being a barycentric mapping - - The algorithm used for solving the problem is the conditional gradient algorithm as proposed in [5]. - - Parameters - ---------- - a : np.ndarray (ns,) - samples weights in the source domain - b : np.ndarray (nt,) - samples weights in the target domain - xs : np.ndarray (ns,d) - samples in the source domain - xt : np.ndarray (nt,d) - samples in the target domain - M : np.ndarray (ns,nt) - loss matrix - reg : float - Regularization term for entropic regularization >0 - eta : float - Regularization term for Laplacian regularization - alpha : float - Regularization term for source domain's importance in regularization - numItermax : int, optional - Max number of iterations - stopThr : float, optional - Stop threshold on error (inner sinkhorn solver) (>0) - numInnerItermax : int, optional - Max number of iterations (inner CG solver) - stopInnerThr : float, optional - Stop threshold on error (inner CG solver) (>0) - verbose : bool, optional - Print information along iterations - log : bool, optional - record log if True - - - Returns - ------- - gamma : (ns x nt) ndarray - Optimal transportation matrix for the given parameters - log : dict - log dictionary return only if log==True in parameters - - - References - ---------- - - .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, - "Optimal Transport for Domain Adaptation," in IEEE - Transactions on Pattern Analysis and Machine Intelligence , - vol.PP, no.99, pp.1-1 - - See Also - -------- - ot.lp.emd : Unregularized OT - ot.optim.cg : General regularized OT - - """ - if 'sim' not in kwargs: - kwargs['sim'] = 'knn' - - if kwargs['sim'] == 'gauss': - if 'rbfparam' not in kwargs: - kwargs['rbfparam'] = 1 / (2 * (np.mean(dist(xs, xs, 'sqeuclidean')) ** 2)) - sS = kernel(xs, xs, method=kwargs['sim'], sigma=kwargs['rbfparam']) - sT = kernel(xt, xt, method=kwargs['sim'], sigma=kwargs['rbfparam']) - - elif kwargs['sim'] == 'knn': - if 'nn' not in kwargs: - kwargs['nn'] = 5 - - from sklearn.neighbors import kneighbors_graph - - sS = kneighbors_graph(xs, kwargs['nn']).toarray() - sS = (sS + sS.T) / 2 - sT = kneighbors_graph(xt, kwargs['nn']).toarray() - sT = (sT + sT.T) / 2 - - lS = laplacian(sS) - lT = laplacian(sT) - - def f(G): - return alpha*np.trace(np.dot(xt.T, np.dot(G.T, np.dot(lS, np.dot(G, xt))))) \ - + (1-alpha)*np.trace(np.dot(xs.T, np.dot(G, np.dot(lT, np.dot(G.T, xs))))) - - def df(G): - return alpha*np.dot(lS + lS.T, np.dot(G, np.dot(xt, xt.T)))\ - +(1-alpha)*np.dot(xs, np.dot(xs.T, np.dot(G, lT + lT.T))) - - return gcg(a, b, M, reg, eta, f, df, G0=None, numItermax=numItermax, stopThr=stopThr, - numInnerItermax=numInnerItermax, stopThr2=stopInnerThr, - verbose=verbose, log=log) def distribution_estimation_uniform(X): """estimates a uniform distribution from an array of samples X @@ -989,7 +871,6 @@ def distribution_estimation_uniform(X): The uniform distribution estimated from X """ - return unif(X.shape[0]) @@ -1016,7 +897,6 @@ class BaseTransport(BaseEstimator): inverse_transform method should always get as input a Xt parameter """ - def fit(self, Xs=None, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) @@ -1077,7 +957,6 @@ class BaseTransport(BaseEstimator): return self - def fit_transform(self, Xs=None, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) and transports source samples Xs onto target @@ -1106,7 +985,6 @@ class BaseTransport(BaseEstimator): return self.fit(Xs, ys, Xt, yt).transform(Xs, ys, Xt, yt) - def transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): """Transports source samples Xs onto target ones Xt @@ -1174,7 +1052,6 @@ class BaseTransport(BaseEstimator): return transp_Xs - def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): """Transports target samples Xt onto target samples Xs @@ -1287,7 +1164,6 @@ class LinearTransport(BaseTransport): """ - def __init__(self, reg=1e-8, bias=True, log=False, distribution_estimation=distribution_estimation_uniform): self.bias = bias @@ -1295,7 +1171,6 @@ class LinearTransport(BaseTransport): self.reg = reg self.distribution_estimation = distribution_estimation - def fit(self, Xs=None, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) @@ -1343,7 +1218,6 @@ class LinearTransport(BaseTransport): return self - def transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): """Transports source samples Xs onto target ones Xt @@ -1376,7 +1250,6 @@ class LinearTransport(BaseTransport): return transp_Xs - def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): """Transports target samples Xt onto target samples Xs @@ -1461,7 +1334,6 @@ class SinkhornTransport(BaseTransport): 26, 2013 """ - def __init__(self, reg_e=1., max_iter=1000, tol=10e-9, verbose=False, log=False, metric="sqeuclidean", norm=None, @@ -1478,7 +1350,6 @@ class SinkhornTransport(BaseTransport): self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map - def fit(self, Xs=None, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) @@ -1561,7 +1432,6 @@ class EMDTransport(BaseTransport): on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 """ - def __init__(self, metric="sqeuclidean", norm=None, log=False, distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans', limit_max=10, @@ -1574,7 +1444,6 @@ class EMDTransport(BaseTransport): self.out_of_sample_map = out_of_sample_map self.max_iter = max_iter - def fit(self, Xs, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) @@ -1671,7 +1540,6 @@ class SinkhornLpl1Transport(BaseTransport): """ - def __init__(self, reg_e=1., reg_cl=0.1, max_iter=10, max_inner_iter=200, log=False, tol=10e-9, verbose=False, @@ -1691,7 +1559,6 @@ class SinkhornLpl1Transport(BaseTransport): self.out_of_sample_map = out_of_sample_map self.limit_max = limit_max - def fit(self, Xs, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) @@ -1751,6 +1618,8 @@ class EMDLaplaceTransport(BaseTransport): norm : string, optional (default=None) If given, normalize the ground metric to avoid numerical errors that can occur with large metric values. + similarity : string, optional (default="knn") + The similarity to use either knn or gaussian max_iter : int, optional (default=100) Max number of BCD iterations tol : float, optional (default=1e-5) @@ -1780,10 +1649,9 @@ class EMDLaplaceTransport(BaseTransport): on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 """ - - def __init__(self, reg_lap = 1., reg_src=1., alpha=0.5, - metric="sqeuclidean", norm=None, max_iter=100, tol=1e-5, - max_inner_iter=100000, inner_tol=1e-6, log=False, verbose=False, + def __init__(self, reg_lap=1., reg_src=1., alpha=0.5, + metric="sqeuclidean", norm=None, similarity="knn", max_iter=100, tol=1e-9, + max_inner_iter=100000, inner_tol=1e-9, log=False, verbose=False, distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans'): self.reg_lap = reg_lap @@ -1791,6 +1659,7 @@ class EMDLaplaceTransport(BaseTransport): self.alpha = alpha self.metric = metric self.norm = norm + self.similarity = similarity self.max_iter = max_iter self.tol = tol self.max_inner_iter = max_inner_iter @@ -1800,7 +1669,6 @@ class EMDLaplaceTransport(BaseTransport): self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map - def fit(self, Xs, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) @@ -1829,115 +1697,7 @@ class EMDLaplaceTransport(BaseTransport): super(EMDLaplaceTransport, self).fit(Xs, ys, Xt, yt) returned_ = emd_laplace(a=self.mu_s, b=self.mu_t, xs=self.xs_, - xt=self.xt_, M=self.cost_, eta=self.reg_lap, alpha=self.reg_src, - numItermax=self.max_iter, stopThr=self.tol, numInnerItermax=self.max_inner_iter, - stopInnerThr=self.inner_tol, log=self.log, verbose=self.verbose) - - # coupling estimation - if self.log: - self.coupling_, self.log_ = returned_ - else: - self.coupling_ = returned_ - self.log_ = dict() - return self - -class SinkhornLaplaceTransport(BaseTransport): - - """Domain Adapatation OT method based on entropic regularized OT with Laplacian regularization - - Parameters - ---------- - reg_e : float, optional (default=1) - Entropic regularization parameter - reg_lap : float, optional (default=1) - Laplacian regularization parameter - reg_src : float, optional (default=0.5) - Source relative importance in regularization - metric : string, optional (default="sqeuclidean") - The ground metric for the Wasserstein problem - norm : string, optional (default=None) - If given, normalize the ground metric to avoid numerical errors that - can occur with large metric values. - max_iter : int, optional (default=100) - Max number of BCD iterations - tol : float, optional (default=1e-5) - Stop threshold on relative loss decrease (>0) - max_inner_iter : int, optional (default=10) - Max number of iterations (inner CG solver) - inner_tol : float, optional (default=1e-6) - Stop threshold on error (inner CG solver) (>0) - log : int, optional (default=False) - Controls the logs of the optimization algorithm - distribution_estimation : callable, optional (defaults to the uniform) - The kind of distribution estimation to employ - out_of_sample_map : string, optional (default="ferradans") - The kind of out of sample mapping to apply to transport samples - from a domain into another one. Currently the only possible option is - "ferradans" which uses the method proposed in [6]. - - Attributes - ---------- - coupling_ : array-like, shape (n_source_samples, n_target_samples) - The optimal coupling - - References - ---------- - .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, - "Optimal Transport for Domain Adaptation," in IEEE Transactions - on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 - """ - - - def __init__(self, reg_e=1., reg_lap=1., reg_src=0.5, - metric="sqeuclidean", norm=None, max_iter=100, tol=1e-9, - max_inner_iter=200, inner_tol=1e-6, log=False, verbose=False, - distribution_estimation=distribution_estimation_uniform, - out_of_sample_map='ferradans'): - - self.reg_e = reg_e - self.reg_lap = reg_lap - self.reg_src = reg_src - self.metric = metric - self.norm = norm - self.max_iter = max_iter - self.tol = tol - self.max_inner_iter = max_inner_iter - self.inner_tol = inner_tol - self.log = log - self.verbose = verbose - self.distribution_estimation = distribution_estimation - self.out_of_sample_map = out_of_sample_map - - - def fit(self, Xs, ys=None, Xt=None, yt=None): - """Build a coupling matrix from source and target sets of samples - (Xs, ys) and (Xt, yt) - - Parameters - ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - ys : array-like, shape (n_source_samples,) - The class labels - Xt : array-like, shape (n_target_samples, n_features) - The training input samples. - yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the - yt's elements with -1. - - Warning: Note that, due to this convention -1 cannot be used as a - class label - - Returns - ------- - self : object - Returns self. - """ - - super(SinkhornLaplaceTransport, self).fit(Xs, ys, Xt, yt) - - returned_ = sinkhorn_laplace(a=self.mu_s, b=self.mu_t, xs=self.xs_, - xt=self.xt_, M=self.cost_, reg=self.reg_e, eta=self.reg_lap, alpha=self.reg_src, + xt=self.xt_, M=self.cost_, sim=self.similarity, eta=self.reg_lap, alpha=self.reg_src, numItermax=self.max_iter, stopThr=self.tol, numInnerItermax=self.max_inner_iter, stopInnerThr=self.inner_tol, log=self.log, verbose=self.verbose) @@ -2008,7 +1768,6 @@ class SinkhornL1l2Transport(BaseTransport): """ - def __init__(self, reg_e=1., reg_cl=0.1, max_iter=10, max_inner_iter=200, tol=10e-9, verbose=False, log=False, @@ -2028,7 +1787,6 @@ class SinkhornL1l2Transport(BaseTransport): self.out_of_sample_map = out_of_sample_map self.limit_max = limit_max - def fit(self, Xs, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) @@ -2133,7 +1891,6 @@ class MappingTransport(BaseEstimator): """ - def __init__(self, mu=1, eta=0.001, bias=False, metric="sqeuclidean", norm=None, kernel="linear", sigma=1, max_iter=100, tol=1e-5, max_inner_iter=10, inner_tol=1e-6, log=False, verbose=False, @@ -2153,7 +1910,6 @@ class MappingTransport(BaseEstimator): self.verbose = verbose self.verbose2 = verbose2 - def fit(self, Xs=None, ys=None, Xt=None, yt=None): """Builds an optimal coupling and estimates the associated mapping from source and target sets of samples (Xs, ys) and (Xt, yt) @@ -2211,7 +1967,6 @@ class MappingTransport(BaseEstimator): return self - def transform(self, Xs): """Transports source samples Xs onto target ones Xt @@ -2305,7 +2060,6 @@ class UnbalancedSinkhornTransport(BaseTransport): """ - def __init__(self, reg_e=1., reg_m=0.1, method='sinkhorn', max_iter=10, tol=1e-9, verbose=False, log=False, metric="sqeuclidean", norm=None, @@ -2324,7 +2078,6 @@ class UnbalancedSinkhornTransport(BaseTransport): self.out_of_sample_map = out_of_sample_map self.limit_max = limit_max - def fit(self, Xs, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) @@ -2419,7 +2172,6 @@ class JCPOTTransport(BaseTransport): """ - def __init__(self, reg_e=.1, max_iter=10, tol=10e-9, verbose=False, log=False, metric="sqeuclidean", @@ -2432,7 +2184,6 @@ class JCPOTTransport(BaseTransport): self.metric = metric self.out_of_sample_map = out_of_sample_map - def fit(self, Xs, ys=None, Xt=None, yt=None): """Building coupling matrices from a list of source and target sets of samples (Xs, ys) and (Xt, yt) @@ -2477,7 +2228,6 @@ class JCPOTTransport(BaseTransport): return self - def transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): """Transports source samples Xs onto target ones Xt diff --git a/ot/utils.py b/ot/utils.py index b8a6f44..a633be2 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -48,6 +48,7 @@ def kernel(x1, x2, method='gaussian', sigma=1, **kwargs): K = np.exp(-dist(x1, x2) / (2 * sigma**2)) return K + def laplacian(x): """Compute Laplacian matrix""" L = np.diag(np.sum(x, axis=0)) - x diff --git a/test/test_da.py b/test/test_da.py index 15f4308..372ebd4 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -602,6 +602,7 @@ def test_jcpot_transport_class(): # check that the oos method is working assert_equal(transp_Xs_new.shape, Xs_new.shape) + def test_emd_laplace_class(): """test_emd_laplace_transport """ @@ -654,56 +655,3 @@ def test_emd_laplace_class(): # test fit_transform transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt) assert_equal(transp_Xs.shape, Xs.shape) - -def test_sinkhorn_laplace_class(): - """test_sinkhorn_laplace_transport - """ - ns = 150 - nt = 200 - - Xs, ys = make_data_classif('3gauss', ns) - Xt, yt = make_data_classif('3gauss2', nt) - - otda = ot.da.SinkhornLaplaceTransport(reg_e = 1, reg_lap=0.01, max_iter=1000, tol=1e-9, verbose=False, log=True) - - # test its computed - otda.fit(Xs=Xs, ys=ys, Xt=Xt) - - assert hasattr(otda, "coupling_") - assert hasattr(otda, "log_") - - # test dimensions of coupling - assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) - - # test all margin constraints - mu_s = unif(ns) - mu_t = unif(nt) - - assert_allclose( - np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose( - np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) - - # test transform - transp_Xs = otda.transform(Xs=Xs) - [assert_equal(x.shape, y.shape) for x, y in zip(transp_Xs, Xs)] - - Xs_new, _ = make_data_classif('3gauss', ns + 1) - transp_Xs_new = otda.transform(Xs_new) - - # check that the oos method is working - assert_equal(transp_Xs_new.shape, Xs_new.shape) - - # test inverse transform - transp_Xt = otda.inverse_transform(Xt=Xt) - assert_equal(transp_Xt.shape, Xt.shape) - - Xt_new, _ = make_data_classif('3gauss2', nt + 1) - transp_Xt_new = otda.inverse_transform(Xt=Xt_new) - - # check that the oos method is working - assert_equal(transp_Xt_new.shape, Xt_new.shape) - - # test fit_transform - transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt) - assert_equal(transp_Xs.shape, Xs.shape) \ No newline at end of file -- cgit v1.2.3 From b32c81542c99cc48944fbeb13e4648f9947ac19d Mon Sep 17 00:00:00 2001 From: ievred Date: Fri, 3 Apr 2020 17:32:07 +0200 Subject: remove commented line --- ot/lp/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 7eaa44a..c4b5834 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -16,7 +16,7 @@ from scipy.sparse import coo_matrix from .import cvx # import compiled emd -#from .emd_wrap import emd_c, check_result, emd_1d_sorted +from .emd_wrap import emd_c, check_result, emd_1d_sorted from ..utils import parmap from .cvx import barycenter from ..utils import dist -- cgit v1.2.3 From ed34704eedb438821720509c5cddb745bc1b5056 Mon Sep 17 00:00:00 2001 From: ievred Date: Tue, 7 Apr 2020 13:29:09 +0200 Subject: add sklearn to travis yml --- .travis.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.travis.yml b/.travis.yml index 5b3a26e..072bc55 100644 --- a/.travis.yml +++ b/.travis.yml @@ -35,6 +35,7 @@ install: - pip install -r requirements.txt - pip install -U "numpy>=1.14" "scipy<1.3" # for numpy array formatting in doctests + scipy version: otherwise, pymanopt fails, cf - pip install flake8 pytest "pytest-cov<2.6" + - pip install -U "sklearn" - pip install . # command to run tests + check syntax style services: -- cgit v1.2.3 From d52a78d516a4cc3cedb8d36f14b686eec60d3c5b Mon Sep 17 00:00:00 2001 From: ievred Date: Tue, 7 Apr 2020 13:36:16 +0200 Subject: pep bregman --- ot/bregman.py | 58 ++++++++++++++++++++++++++++++---------------------------- 1 file changed, 30 insertions(+), 28 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 951d3ce..7f11e68 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1572,13 +1572,16 @@ def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100, nbclasses = len(np.unique(Ys[0])) nbdomains = len(Xs) - # For each source domain, build cost matrices M, Gibbs kernels K and corresponding matrices D_1 and D_2 - all_domains = [] - # log dictionary if log: - log = {'niter': 0, 'err': [], 'all_domains': []} + log = {'niter': 0, 'err': [], 'M': [], 'D1': [], 'D2': []} + + K = [] + M = [] + D1 = [] + D2 = [] + # For each source domain, build cost matrices M, Gibbs kernels K and corresponding matrices D_1 and D_2 for d in range(nbdomains): dom = {} nsk = Xs[d].shape[0] # get number of elements for this domain @@ -1591,28 +1594,26 @@ def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100, classes = np.unique(Ys[d]) # build the corresponding D_1 and D_2 matrices - D1 = np.zeros((nbclasses, nsk)) - D2 = np.zeros((nbclasses, nsk)) + Dtmp1 = np.zeros((nbclasses, nsk)) + Dtmp2 = np.zeros((nbclasses, nsk)) for c in classes: nbelemperclass = np.sum(Ys[d] == c) if nbelemperclass != 0: - D1[int(c), Ys[d] == c] = 1. - D2[int(c), Ys[d] == c] = 1. / (nbelemperclass) - dom['D1'] = D1 - dom['D2'] = D2 + Dtmp1[int(c), Ys[d] == c] = 1. + Dtmp2[int(c), Ys[d] == c] = 1. / (nbelemperclass) + D1.append(Dtmp1) + D2.append(Dtmp2) # build the cost matrix and the Gibbs kernel - M = dist(Xs[d], Xt, metric=metric) - M = M / np.median(M) - dom['M'] = M - - K = np.empty(M.shape, dtype=M.dtype) - np.divide(M, -reg, out=K) - np.exp(K, out=K) - dom['K'] = K + Mtmp = dist(Xs[d], Xt, metric=metric) + Mtmp = Mtmp / np.median(Mtmp) + M.append(M) - all_domains.append(dom) + Ktmp = np.empty(Mtmp.shape, dtype=Mtmp.dtype) + np.divide(Mtmp, -reg, out=Ktmp) + np.exp(Ktmp, out=Ktmp) + K.append(Ktmp) # uniform target distribution a = unif(np.shape(Xt)[0]) @@ -1627,16 +1628,16 @@ def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100, # update coupling matrices for marginal constraints w.r.t. uniform target distribution for d in range(nbdomains): - all_domains[d]['K'] = projC(all_domains[d]['K'], a) - other = np.sum(all_domains[d]['K'], axis=1) - bary = bary + np.log(np.dot(all_domains[d]['D1'], other)) / nbdomains + K[d] = projC(K[d], a) + other = np.sum(K[d], axis=1) + bary = bary + np.log(np.dot(D1[d], other)) / nbdomains bary = np.exp(bary) # update coupling matrices for marginal constraints w.r.t. unknown proportions based on [Prop 4., 27] for d in range(nbdomains): - new = np.dot(all_domains[d]['D2'].T, bary) - all_domains[d]['K'] = projR(all_domains[d]['K'], new) + new = np.dot(D2[d].T, bary) + K[d] = projR(K[d], new) err = np.linalg.norm(bary - old_bary) cpt = cpt + 1 @@ -1651,14 +1652,15 @@ def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100, print('{:5d}|{:8e}|'.format(cpt, err)) bary = bary / np.sum(bary) - couplings = [all_domains[d]['K'] for d in range(nbdomains)] if log: log['niter'] = cpt - log['all_domains'] = all_domains - return couplings, bary, log + log['M'] = M + log['D1'] = D1 + log['D2'] = D2 + return K, bary, log else: - return couplings, bary + return K, bary def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', -- cgit v1.2.3 From 34e13d467e376e9bfee2eb15771d9308518c2adb Mon Sep 17 00:00:00 2001 From: ievred Date: Tue, 7 Apr 2020 13:44:23 +0200 Subject: pep bregman --- ot/bregman.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 7f11e68..ec81924 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -716,7 +716,7 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, if np.abs(u).max() > tau or np.abs(v).max() > tau: if n_hists: alpha, beta = alpha + reg * \ - np.max(np.log(u), 1), beta + reg * np.max(np.log(v)) + np.max(np.log(u), 1), beta + reg * np.max(np.log(v)) else: alpha, beta = alpha + reg * np.log(u), beta + reg * np.log(v) if n_hists: @@ -2189,7 +2189,7 @@ def screenkhorn(a, b, M, reg, ns_budget=None, nt_budget=None, uniform=False, res # box constraints in L-BFGS-B (see Proposition 1 in [26]) bounds_u = [(max(a_I_min / ((nt - nt_budget) * epsilon + nt_budget * (b_J_max / ( - ns * epsilon * kappa * K_min))), epsilon / kappa), a_I_max / (nt * epsilon * K_min))] * ns_budget + ns * epsilon * kappa * K_min))), epsilon / kappa), a_I_max / (nt * epsilon * K_min))] * ns_budget bounds_v = [( max(b_J_min / ((ns - ns_budget) * epsilon + ns_budget * (kappa * a_I_max / (nt * epsilon * K_min))), -- cgit v1.2.3 From 2c9f992157844d6253a302905417e86580ac6b12 Mon Sep 17 00:00:00 2001 From: ievred Date: Tue, 7 Apr 2020 13:50:11 +0200 Subject: upd --- examples/plot_otda_classes.py | 1 - examples/plot_otda_jcpot.py | 16 ++++++++-------- ot/bregman.py | 2 +- test/test_da.py | 2 +- 4 files changed, 10 insertions(+), 11 deletions(-) diff --git a/examples/plot_otda_classes.py b/examples/plot_otda_classes.py index c311fbd..f028022 100644 --- a/examples/plot_otda_classes.py +++ b/examples/plot_otda_classes.py @@ -17,7 +17,6 @@ approaches currently supported in POT. import matplotlib.pylab as pl import ot - ############################################################################## # Generate data # ------------- diff --git a/examples/plot_otda_jcpot.py b/examples/plot_otda_jcpot.py index ce6b88f..316fa8b 100644 --- a/examples/plot_otda_jcpot.py +++ b/examples/plot_otda_jcpot.py @@ -118,16 +118,16 @@ pl.axis('off') otda = ot.da.JCPOTTransport(reg_e=1e-2, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True) otda.fit(all_Xr, all_Yr, xt) -ws1 = otda.proportions_.dot(otda.log_['all_domains'][0]['D2']) -ws2 = otda.proportions_.dot(otda.log_['all_domains'][1]['D2']) +ws1 = otda.proportions_.dot(otda.log_['D2'][0]) +ws2 = otda.proportions_.dot(otda.log_['D2'][1]) pl.figure(3) pl.clf() plot_ax(dec1, 'Source 1') plot_ax(dec2, 'Source 2') plot_ax(dect, 'Target') -print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['all_domains'][0]['M'], reg=1e-2), xs1, ys1, xt) -print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['all_domains'][1]['M'], reg=1e-2), xs2, ys2, xt) +print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-2), xs1, ys1, xt) +print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-2), xs2, ys2, xt) pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) @@ -146,16 +146,16 @@ pl.axis('off') # ---------------------------------------------------------------------------- h_res = np.array([1 - pt, pt]) -ws1 = h_res.dot(otda.log_['all_domains'][0]['D2']) -ws2 = h_res.dot(otda.log_['all_domains'][1]['D2']) +ws1 = h_res.dot(otda.log_['D2'][0]) +ws2 = h_res.dot(otda.log_['D2'][1]) pl.figure(4) pl.clf() plot_ax(dec1, 'Source 1') plot_ax(dec2, 'Source 2') plot_ax(dect, 'Target') -print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['all_domains'][0]['M'], reg=1e-2), xs1, ys1, xt) -print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['all_domains'][1]['M'], reg=1e-2), xs2, ys2, xt) +print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-2), xs1, ys1, xt) +print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-2), xs2, ys2, xt) pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) diff --git a/ot/bregman.py b/ot/bregman.py index ec81924..61dfa52 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1608,7 +1608,7 @@ def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100, # build the cost matrix and the Gibbs kernel Mtmp = dist(Xs[d], Xt, metric=metric) Mtmp = Mtmp / np.median(Mtmp) - M.append(M) + M.append(Mtmp) Ktmp = np.empty(Mtmp.shape, dtype=Mtmp.dtype) np.divide(Mtmp, -reg, out=Ktmp) diff --git a/test/test_da.py b/test/test_da.py index 372ebd4..4eaf193 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -589,7 +589,7 @@ def test_jcpot_transport_class(): # test margin constraints w.r.t. modified source weights for each source domain assert_allclose( - np.dot(otda.log_['all_domains'][i]['D1'], np.sum(otda.coupling_[i], axis=1)), otda.proportions_, rtol=1e-3, + np.dot(otda.log_['D1'][i], np.sum(otda.coupling_[i], axis=1)), otda.proportions_, rtol=1e-3, atol=1e-3) # test transform -- cgit v1.2.3 From c68b52d1623683e86555484bf9a4875a66957bb6 Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 8 Apr 2020 10:08:47 +0200 Subject: remove laplace from jcpot --- examples/plot_otda_jcpot.py | 10 +- examples/plot_otda_laplacian.py | 127 ----------------------- ot/bregman.py | 1 - ot/da.py | 216 ---------------------------------------- test/test_da.py | 54 ---------- 5 files changed, 5 insertions(+), 403 deletions(-) delete mode 100644 examples/plot_otda_laplacian.py diff --git a/examples/plot_otda_jcpot.py b/examples/plot_otda_jcpot.py index 316fa8b..c495690 100644 --- a/examples/plot_otda_jcpot.py +++ b/examples/plot_otda_jcpot.py @@ -115,7 +115,7 @@ pl.axis('off') ############################################################################## # Instantiate JCPOT adaptation algorithm and fit it # ---------------------------------------------------------------------------- -otda = ot.da.JCPOTTransport(reg_e=1e-2, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True) +otda = ot.da.JCPOTTransport(reg_e=1, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True) otda.fit(all_Xr, all_Yr, xt) ws1 = otda.proportions_.dot(otda.log_['D2'][0]) @@ -126,8 +126,8 @@ pl.clf() plot_ax(dec1, 'Source 1') plot_ax(dec2, 'Source 2') plot_ax(dect, 'Target') -print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-2), xs1, ys1, xt) -print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-2), xs2, ys2, xt) +print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt) +print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt) pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) @@ -154,8 +154,8 @@ pl.clf() plot_ax(dec1, 'Source 1') plot_ax(dec2, 'Source 2') plot_ax(dect, 'Target') -print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-2), xs1, ys1, xt) -print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-2), xs2, ys2, xt) +print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt) +print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt) pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) diff --git a/examples/plot_otda_laplacian.py b/examples/plot_otda_laplacian.py deleted file mode 100644 index 965380c..0000000 --- a/examples/plot_otda_laplacian.py +++ /dev/null @@ -1,127 +0,0 @@ -# -*- coding: utf-8 -*- -""" -======================== -OT for domain adaptation -======================== - -This example introduces a domain adaptation in a 2D setting and OTDA -approache with Laplacian regularization. - -""" - -# Authors: Ievgen Redko - -# License: MIT License - -import matplotlib.pylab as pl -import ot - -############################################################################## -# Generate data -# ------------- - -n_source_samples = 150 -n_target_samples = 150 - -Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) -Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) - - -############################################################################## -# Instantiate the different transport algorithms and fit them -# ----------------------------------------------------------- - -# EMD Transport -ot_emd = ot.da.EMDTransport() -ot_emd.fit(Xs=Xs, Xt=Xt) - -# Sinkhorn Transport -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=.01) -ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - -# EMD Transport with Laplacian regularization -ot_emd_laplace = ot.da.EMDLaplaceTransport(reg_lap=100, reg_src=1) -ot_emd_laplace.fit(Xs=Xs, Xt=Xt) - -# transport source samples onto target samples -transp_Xs_emd = ot_emd.transform(Xs=Xs) -transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) -transp_Xs_emd_laplace = ot_emd_laplace.transform(Xs=Xs) - -############################################################################## -# Fig 1 : plots source and target samples -# --------------------------------------- - -pl.figure(1, figsize=(10, 5)) -pl.subplot(1, 2, 1) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Source samples') - -pl.subplot(1, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Target samples') -pl.tight_layout() - - -############################################################################## -# Fig 2 : plot optimal couplings and transported samples -# ------------------------------------------------------ - -param_img = {'interpolation': 'nearest'} - -pl.figure(2, figsize=(15, 8)) -pl.subplot(2, 3, 1) -pl.imshow(ot_emd.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nEMDTransport') - -pl.figure(2, figsize=(15, 8)) -pl.subplot(2, 3, 2) -pl.imshow(ot_sinkhorn.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornTransport') - -pl.subplot(2, 3, 3) -pl.imshow(ot_emd_laplace.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nEMDLaplaceTransport') - -pl.subplot(2, 3, 4) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nEmdTransport') -pl.legend(loc="lower left") - -pl.subplot(2, 3, 5) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nSinkhornTransport') - -pl.subplot(2, 3, 6) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nEMDLaplaceTransport') -pl.tight_layout() - -pl.show() diff --git a/ot/bregman.py b/ot/bregman.py index 61dfa52..410ae85 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1607,7 +1607,6 @@ def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100, # build the cost matrix and the Gibbs kernel Mtmp = dist(Xs[d], Xt, metric=metric) - Mtmp = Mtmp / np.median(Mtmp) M.append(Mtmp) Ktmp = np.empty(Mtmp.shape, dtype=Mtmp.dtype) diff --git a/ot/da.py b/ot/da.py index 0fdd3be..90e9e92 100644 --- a/ot/da.py +++ b/ot/da.py @@ -748,115 +748,6 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, return A, b -def emd_laplace(a, b, xs, xt, M, sim, eta, alpha, - numItermax, stopThr, numInnerItermax, - stopInnerThr, log=False, verbose=False, **kwargs): - r"""Solve the optimal transport problem (OT) with Laplacian regularization - - .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F + eta\Omega_\alpha(\gamma) - - s.t.\ \gamma 1 = a - - \gamma^T 1= b - - \gamma\geq 0 - - where: - - - a and b are source and target weights (sum to 1) - - xs and xt are source and target samples - - M is the (ns,nt) metric cost matrix - - :math:`\Omega_\alpha` is the Laplacian regularization term - :math:`\Omega_\alpha = (1-\alpha)/n_s^2\sum_{i,j}S^s_{i,j}\|T(\mathbf{x}^s_i)-T(\mathbf{x}^s_j)\|^2+\alpha/n_t^2\sum_{i,j}S^t_{i,j}^'\|T(\mathbf{x}^t_i)-T(\mathbf{x}^t_j)\|^2` - with :math:`S^s_{i,j}, S^t_{i,j}` denoting source and target similarity matrices and :math:`T(\cdot)` being a barycentric mapping - - The algorithm used for solving the problem is the conditional gradient algorithm as proposed in [5]. - - Parameters - ---------- - a : np.ndarray (ns,) - samples weights in the source domain - b : np.ndarray (nt,) - samples weights in the target domain - xs : np.ndarray (ns,d) - samples in the source domain - xt : np.ndarray (nt,d) - samples in the target domain - M : np.ndarray (ns,nt) - loss matrix - eta : float - Regularization term for Laplacian regularization - alpha : float - Regularization term for source domain's importance in regularization - numItermax : int, optional - Max number of iterations - stopThr : float, optional - Stop threshold on error (inner emd solver) (>0) - numInnerItermax : int, optional - Max number of iterations (inner CG solver) - stopInnerThr : float, optional - Stop threshold on error (inner CG solver) (>0) - verbose : bool, optional - Print information along iterations - log : bool, optional - record log if True - - - Returns - ------- - gamma : (ns x nt) ndarray - Optimal transportation matrix for the given parameters - log : dict - log dictionary return only if log==True in parameters - - - References - ---------- - - .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, - "Optimal Transport for Domain Adaptation," in IEEE - Transactions on Pattern Analysis and Machine Intelligence , - vol.PP, no.99, pp.1-1 - - See Also - -------- - ot.lp.emd : Unregularized OT - ot.optim.cg : General regularized OT - - """ - if sim == 'gauss': - if 'rbfparam' not in kwargs: - kwargs['rbfparam'] = 1 / (2 * (np.mean(dist(xs, xs, 'sqeuclidean')) ** 2)) - sS = kernel(xs, xs, method=kwargs['sim'], sigma=kwargs['rbfparam']) - sT = kernel(xt, xt, method=kwargs['sim'], sigma=kwargs['rbfparam']) - - elif sim == 'knn': - if 'nn' not in kwargs: - kwargs['nn'] = 5 - - from sklearn.neighbors import kneighbors_graph - - sS = kneighbors_graph(xs, kwargs['nn']).toarray() - sS = (sS + sS.T) / 2 - sT = kneighbors_graph(xt, kwargs['nn']).toarray() - sT = (sT + sT.T) / 2 - - lS = laplacian(sS) - lT = laplacian(sT) - - def f(G): - return alpha * np.trace(np.dot(xt.T, np.dot(G.T, np.dot(lS, np.dot(G, xt))))) \ - + (1 - alpha) * np.trace(np.dot(xs.T, np.dot(G, np.dot(lT, np.dot(G.T, xs))))) - - def df(G): - return alpha * np.dot(lS + lS.T, np.dot(G, np.dot(xt, xt.T)))\ - + (1 - alpha) * np.dot(xs, np.dot(xs.T, np.dot(G, lT + lT.T))) - - return cg(a, b, M, reg=eta, f=f, df=df, G0=None, numItermax=numItermax, numItermaxEmd=numInnerItermax, - stopThr=stopThr, stopThr2=stopInnerThr, verbose=verbose, log=log) - - def distribution_estimation_uniform(X): """estimates a uniform distribution from an array of samples X @@ -1603,113 +1494,6 @@ class SinkhornLpl1Transport(BaseTransport): return self -class EMDLaplaceTransport(BaseTransport): - - """Domain Adapatation OT method based on Earth Mover's Distance with Laplacian regularization - - Parameters - ---------- - reg_lap : float, optional (default=1) - Laplacian regularization parameter - reg_src : float, optional (default=0.5) - Source relative importance in regularization - metric : string, optional (default="sqeuclidean") - The ground metric for the Wasserstein problem - norm : string, optional (default=None) - If given, normalize the ground metric to avoid numerical errors that - can occur with large metric values. - similarity : string, optional (default="knn") - The similarity to use either knn or gaussian - max_iter : int, optional (default=100) - Max number of BCD iterations - tol : float, optional (default=1e-5) - Stop threshold on relative loss decrease (>0) - max_inner_iter : int, optional (default=10) - Max number of iterations (inner CG solver) - inner_tol : float, optional (default=1e-6) - Stop threshold on error (inner CG solver) (>0) - log : int, optional (default=False) - Controls the logs of the optimization algorithm - distribution_estimation : callable, optional (defaults to the uniform) - The kind of distribution estimation to employ - out_of_sample_map : string, optional (default="ferradans") - The kind of out of sample mapping to apply to transport samples - from a domain into another one. Currently the only possible option is - "ferradans" which uses the method proposed in [6]. - - Attributes - ---------- - coupling_ : array-like, shape (n_source_samples, n_target_samples) - The optimal coupling - - References - ---------- - .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, - "Optimal Transport for Domain Adaptation," in IEEE Transactions - on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 - """ - - def __init__(self, reg_lap=1., reg_src=1., alpha=0.5, - metric="sqeuclidean", norm=None, similarity="knn", max_iter=100, tol=1e-9, - max_inner_iter=100000, inner_tol=1e-9, log=False, verbose=False, - distribution_estimation=distribution_estimation_uniform, - out_of_sample_map='ferradans'): - self.reg_lap = reg_lap - self.reg_src = reg_src - self.alpha = alpha - self.metric = metric - self.norm = norm - self.similarity = similarity - self.max_iter = max_iter - self.tol = tol - self.max_inner_iter = max_inner_iter - self.inner_tol = inner_tol - self.log = log - self.verbose = verbose - self.distribution_estimation = distribution_estimation - self.out_of_sample_map = out_of_sample_map - - def fit(self, Xs, ys=None, Xt=None, yt=None): - """Build a coupling matrix from source and target sets of samples - (Xs, ys) and (Xt, yt) - - Parameters - ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - ys : array-like, shape (n_source_samples,) - The class labels - Xt : array-like, shape (n_target_samples, n_features) - The training input samples. - yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the - yt's elements with -1. - - Warning: Note that, due to this convention -1 cannot be used as a - class label - - Returns - ------- - self : object - Returns self. - """ - - super(EMDLaplaceTransport, self).fit(Xs, ys, Xt, yt) - - returned_ = emd_laplace(a=self.mu_s, b=self.mu_t, xs=self.xs_, - xt=self.xt_, M=self.cost_, sim=self.similarity, eta=self.reg_lap, alpha=self.reg_src, - numItermax=self.max_iter, stopThr=self.tol, numInnerItermax=self.max_inner_iter, - stopInnerThr=self.inner_tol, log=self.log, verbose=self.verbose) - - # coupling estimation - if self.log: - self.coupling_, self.log_ = returned_ - else: - self.coupling_ = returned_ - self.log_ = dict() - return self - - class SinkhornL1l2Transport(BaseTransport): """Domain Adapatation OT method based on sinkhorn algorithm + diff --git a/test/test_da.py b/test/test_da.py index 4eaf193..1517cec 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -601,57 +601,3 @@ def test_jcpot_transport_class(): # check that the oos method is working assert_equal(transp_Xs_new.shape, Xs_new.shape) - - -def test_emd_laplace_class(): - """test_emd_laplace_transport - """ - ns = 150 - nt = 200 - - Xs, ys = make_data_classif('3gauss', ns) - Xt, yt = make_data_classif('3gauss2', nt) - - otda = ot.da.EMDLaplaceTransport(reg_lap=0.01, max_iter=1000, tol=1e-9, verbose=False, log=True) - - # test its computed - otda.fit(Xs=Xs, ys=ys, Xt=Xt) - - assert hasattr(otda, "coupling_") - assert hasattr(otda, "log_") - - # test dimensions of coupling - assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) - - # test all margin constraints - mu_s = unif(ns) - mu_t = unif(nt) - - assert_allclose( - np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose( - np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) - - # test transform - transp_Xs = otda.transform(Xs=Xs) - [assert_equal(x.shape, y.shape) for x, y in zip(transp_Xs, Xs)] - - Xs_new, _ = make_data_classif('3gauss', ns + 1) - transp_Xs_new = otda.transform(Xs_new) - - # check that the oos method is working - assert_equal(transp_Xs_new.shape, Xs_new.shape) - - # test inverse transform - transp_Xt = otda.inverse_transform(Xt=Xt) - assert_equal(transp_Xt.shape, Xt.shape) - - Xt_new, _ = make_data_classif('3gauss2', nt + 1) - transp_Xt_new = otda.inverse_transform(Xt=Xt_new) - - # check that the oos method is working - assert_equal(transp_Xt_new.shape, Xt_new.shape) - - # test fit_transform - transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt) - assert_equal(transp_Xs.shape, Xs.shape) -- cgit v1.2.3 From 55926517470df6ced506a934b8b9b5e23e023464 Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 8 Apr 2020 10:18:02 +0200 Subject: test+utils+readme --- README.md | 2 +- ot/da.py | 2 +- test/test_da.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index f439405..b6baf14 100644 --- a/README.md +++ b/README.md @@ -29,7 +29,7 @@ It provides the following solvers: * Non regularized free support Wasserstein barycenters [20]. * Unbalanced OT with KL relaxation distance and barycenter [10, 25]. * Screening Sinkhorn Algorithm for OT [26]. -* JCPOT algorithm for multi-source target shift [27]. +* JCPOT algorithm for multi-source domain adaptation with target shift [27]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. diff --git a/ot/da.py b/ot/da.py index 90e9e92..3a458eb 100644 --- a/ot/da.py +++ b/ot/da.py @@ -16,7 +16,7 @@ import scipy.linalg as linalg from .bregman import sinkhorn, jcpot_barycenter from .lp import emd -from .utils import unif, dist, kernel, cost_normalization, laplacian +from .utils import unif, dist, kernel, cost_normalization from .utils import check_params, BaseEstimator from .unbalanced import sinkhorn_unbalanced from .optim import cg diff --git a/test/test_da.py b/test/test_da.py index 1517cec..b58cf51 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -565,7 +565,7 @@ def test_jcpot_transport_class(): Xs = [Xs1, Xs2] ys = [ys1, ys2] - otda = ot.da.JCPOTTransport(reg_e=0.01, max_iter=1000, tol=1e-9, verbose=True, log=True) + otda = ot.da.JCPOTTransport(reg_e=1, max_iter=10000, tol=1e-9, verbose=True, log=True) # test its computed otda.fit(Xs=Xs, ys=ys, Xt=Xt) -- cgit v1.2.3 From d6ef8676cc3f94ba5d80acc9fd9745c9ed91819a Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 8 Apr 2020 10:28:57 +0200 Subject: remove jcpot from laplace --- examples/plot_otda_jcpot.py | 171 ----------------------------------------- ot/bregman.py | 160 --------------------------------------- ot/da.py | 181 +------------------------------------------- test/test_da.py | 56 +------------- 4 files changed, 3 insertions(+), 565 deletions(-) delete mode 100644 examples/plot_otda_jcpot.py diff --git a/examples/plot_otda_jcpot.py b/examples/plot_otda_jcpot.py deleted file mode 100644 index 316fa8b..0000000 --- a/examples/plot_otda_jcpot.py +++ /dev/null @@ -1,171 +0,0 @@ -# -*- coding: utf-8 -*- -""" -======================== -OT for multi-source target shift -======================== - -This example introduces a target shift problem with two 2D source and 1 target domain. - -""" - -# Authors: Remi Flamary -# Ievgen Redko -# -# License: MIT License - -import pylab as pl -import numpy as np -import ot -from ot.datasets import make_data_classif - -############################################################################## -# Generate data -# ------------- -n = 50 -sigma = 0.3 -np.random.seed(1985) - -p1 = .2 -dec1 = [0, 2] - -p2 = .9 -dec2 = [0, -2] - -pt = .4 -dect = [4, 0] - -xs1, ys1 = make_data_classif('2gauss_prop', n, nz=sigma, p=p1, bias=dec1) -xs2, ys2 = make_data_classif('2gauss_prop', n + 1, nz=sigma, p=p2, bias=dec2) -xt, yt = make_data_classif('2gauss_prop', n, nz=sigma, p=pt, bias=dect) - -all_Xr = [xs1, xs2] -all_Yr = [ys1, ys2] -# %% - -da = 1.5 - - -def plot_ax(dec, name): - pl.plot([dec[0], dec[0]], [dec[1] - da, dec[1] + da], 'k', alpha=0.5) - pl.plot([dec[0] - da, dec[0] + da], [dec[1], dec[1]], 'k', alpha=0.5) - pl.text(dec[0] - .5, dec[1] + 2, name) - - -############################################################################## -# Fig 1 : plots source and target samples -# --------------------------------------- - -pl.figure(1) -pl.clf() -plot_ax(dec1, 'Source 1') -plot_ax(dec2, 'Source 2') -plot_ax(dect, 'Target') -pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9, - label='Source 1 ({:1.2f}, {:1.2f})'.format(1 - p1, p1)) -pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9, - label='Source 2 ({:1.2f}, {:1.2f})'.format(1 - p2, p2)) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9, - label='Target ({:1.2f}, {:1.2f})'.format(1 - pt, pt)) -pl.title('Data') - -pl.legend() -pl.axis('equal') -pl.axis('off') - -############################################################################## -# Instantiate Sinkhorn transport algorithm and fit them for all source domains -# ---------------------------------------------------------------------------- -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1, metric='sqeuclidean') - - -def print_G(G, xs, ys, xt): - for i in range(G.shape[0]): - for j in range(G.shape[1]): - if G[i, j] > 5e-4: - if ys[i]: - c = 'b' - else: - c = 'r' - pl.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], xt[j, 1]], c, alpha=.2) - - -############################################################################## -# Fig 2 : plot optimal couplings and transported samples -# ------------------------------------------------------ -pl.figure(2) -pl.clf() -plot_ax(dec1, 'Source 1') -plot_ax(dec2, 'Source 2') -plot_ax(dect, 'Target') -print_G(ot_sinkhorn.fit(Xs=xs1, Xt=xt).coupling_, xs1, ys1, xt) -print_G(ot_sinkhorn.fit(Xs=xs2, Xt=xt).coupling_, xs2, ys2, xt) -pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) -pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) - -pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') -pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') - -pl.title('Independent OT') - -pl.legend() -pl.axis('equal') -pl.axis('off') - -############################################################################## -# Instantiate JCPOT adaptation algorithm and fit it -# ---------------------------------------------------------------------------- -otda = ot.da.JCPOTTransport(reg_e=1e-2, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True) -otda.fit(all_Xr, all_Yr, xt) - -ws1 = otda.proportions_.dot(otda.log_['D2'][0]) -ws2 = otda.proportions_.dot(otda.log_['D2'][1]) - -pl.figure(3) -pl.clf() -plot_ax(dec1, 'Source 1') -plot_ax(dec2, 'Source 2') -plot_ax(dect, 'Target') -print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-2), xs1, ys1, xt) -print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-2), xs2, ys2, xt) -pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) -pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) - -pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') -pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') - -pl.title('OT with prop estimation ({:1.3f},{:1.3f})'.format(otda.proportions_[0], otda.proportions_[1])) - -pl.legend() -pl.axis('equal') -pl.axis('off') - -############################################################################## -# Run oracle transport algorithm with known proportions -# ---------------------------------------------------------------------------- -h_res = np.array([1 - pt, pt]) - -ws1 = h_res.dot(otda.log_['D2'][0]) -ws2 = h_res.dot(otda.log_['D2'][1]) - -pl.figure(4) -pl.clf() -plot_ax(dec1, 'Source 1') -plot_ax(dec2, 'Source 2') -plot_ax(dect, 'Target') -print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-2), xs1, ys1, xt) -print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-2), xs2, ys2, xt) -pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) -pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) - -pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') -pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') - -pl.title('OT with known proportion ({:1.1f},{:1.1f})'.format(h_res[0], h_res[1])) - -pl.legend() -pl.axis('equal') -pl.axis('off') -pl.show() diff --git a/ot/bregman.py b/ot/bregman.py index 61dfa52..f737e81 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1503,166 +1503,6 @@ def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, return np.sum(K0, axis=1) -def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100, - stopThr=1e-6, verbose=False, log=False, **kwargs): - r'''Joint OT and proportion estimation for multi-source target shift as proposed in [27] - - The function solves the following optimization problem: - - .. math:: - - \mathbf{h} = arg\min_{\mathbf{h}}\quad \sum_{k=1}^{K} \lambda_k - W_{reg}((\mathbf{D}_2^{(k)} \mathbf{h})^T, \mathbf{a}) - - s.t. \ \forall k, \mathbf{D}_1^{(k)} \gamma_k \mathbf{1}_n= \mathbf{h} - - where : - - - :math:`\lambda_k` is the weight of k-th source domain - - :math:`W_{reg}(\cdot,\cdot)` is the entropic regularized Wasserstein distance (see ot.bregman.sinkhorn) - - :math:`\mathbf{D}_2^{(k)}` is a matrix of weights related to k-th source domain defined as in [p. 5, 27], its expected shape is `(n_k, C)` where `n_k` is the number of elements in the k-th source domain and `C` is the number of classes - - :math:`\mathbf{h}` is a vector of estimated proportions in the target domain of size C - - :math:`\mathbf{a}` is a uniform vector of weights in the target domain of size `n` - - :math:`\mathbf{D}_1^{(k)}` is a matrix of class assignments defined as in [p. 5, 27], its expected shape is `(n_k, C)` - - The problem consist in solving a Wasserstein barycenter problem to estimate the proportions :math:`\mathbf{h}` in the target domain. - - The algorithm used for solving the problem is the Iterative Bregman projections algorithm - with two sets of marginal constraints related to the unknown vector :math:`\mathbf{h}` and uniform tarhet distribution. - - Parameters - ---------- - Xs : list of K np.ndarray(nsk,d) - features of all source domains' samples - Ys : list of K np.ndarray(nsk,) - labels of all source domains' samples - Xt : np.ndarray (nt,d) - samples in the target domain - reg : float - Regularization term > 0 - metric : string, optional (default="sqeuclidean") - The ground metric for the Wasserstein problem - numItermax : int, optional - Max number of iterations - stopThr : float, optional - Stop threshold on relative change in the barycenter (>0) - log : bool, optional - record log if True - verbose : bool, optional (default=False) - Controls the verbosity of the optimization algorithm - - Returns - ------- - gamma : List of K (nsk x nt) ndarrays - Optimal transportation matrices for the given parameters for each pair of source and target domains - h : (C,) ndarray - proportion estimation in the target domain - log : dict - log dictionary return only if log==True in parameters - - - References - ---------- - - .. [27] Ievgen Redko, Nicolas Courty, Rémi Flamary, Devis Tuia - "Optimal transport for multi-source domain adaptation under target shift", - International Conference on Artificial Intelligence and Statistics (AISTATS), 2019. - - ''' - nbclasses = len(np.unique(Ys[0])) - nbdomains = len(Xs) - - # log dictionary - if log: - log = {'niter': 0, 'err': [], 'M': [], 'D1': [], 'D2': []} - - K = [] - M = [] - D1 = [] - D2 = [] - - # For each source domain, build cost matrices M, Gibbs kernels K and corresponding matrices D_1 and D_2 - for d in range(nbdomains): - dom = {} - nsk = Xs[d].shape[0] # get number of elements for this domain - dom['nbelem'] = nsk - classes = np.unique(Ys[d]) # get number of classes for this domain - - # format classes to start from 0 for convenience - if np.min(classes) != 0: - Ys[d] = Ys[d] - np.min(classes) - classes = np.unique(Ys[d]) - - # build the corresponding D_1 and D_2 matrices - Dtmp1 = np.zeros((nbclasses, nsk)) - Dtmp2 = np.zeros((nbclasses, nsk)) - - for c in classes: - nbelemperclass = np.sum(Ys[d] == c) - if nbelemperclass != 0: - Dtmp1[int(c), Ys[d] == c] = 1. - Dtmp2[int(c), Ys[d] == c] = 1. / (nbelemperclass) - D1.append(Dtmp1) - D2.append(Dtmp2) - - # build the cost matrix and the Gibbs kernel - Mtmp = dist(Xs[d], Xt, metric=metric) - Mtmp = Mtmp / np.median(Mtmp) - M.append(Mtmp) - - Ktmp = np.empty(Mtmp.shape, dtype=Mtmp.dtype) - np.divide(Mtmp, -reg, out=Ktmp) - np.exp(Ktmp, out=Ktmp) - K.append(Ktmp) - - # uniform target distribution - a = unif(np.shape(Xt)[0]) - - cpt = 0 # iterations count - err = 1 - old_bary = np.ones((nbclasses)) - - while (err > stopThr and cpt < numItermax): - - bary = np.zeros((nbclasses)) - - # update coupling matrices for marginal constraints w.r.t. uniform target distribution - for d in range(nbdomains): - K[d] = projC(K[d], a) - other = np.sum(K[d], axis=1) - bary = bary + np.log(np.dot(D1[d], other)) / nbdomains - - bary = np.exp(bary) - - # update coupling matrices for marginal constraints w.r.t. unknown proportions based on [Prop 4., 27] - for d in range(nbdomains): - new = np.dot(D2[d].T, bary) - K[d] = projR(K[d], new) - - err = np.linalg.norm(bary - old_bary) - cpt = cpt + 1 - old_bary = bary - - if log: - log['err'].append(err) - - if verbose: - if cpt % 200 == 0: - print('{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) - print('{:5d}|{:8e}|'.format(cpt, err)) - - bary = bary / np.sum(bary) - - if log: - log['niter'] = cpt - log['M'] = M - log['D1'] = D1 - log['D2'] = D2 - return K, bary, log - else: - return K, bary - - def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs): diff --git a/ot/da.py b/ot/da.py index 0fdd3be..474c944 100644 --- a/ot/da.py +++ b/ot/da.py @@ -14,7 +14,7 @@ Domain adaptation with optimal transport import numpy as np import scipy.linalg as linalg -from .bregman import sinkhorn, jcpot_barycenter +from .bregman import sinkhorn from .lp import emd from .utils import unif, dist, kernel, cost_normalization, laplacian from .utils import check_params, BaseEstimator @@ -2121,181 +2121,4 @@ class UnbalancedSinkhornTransport(BaseTransport): self.coupling_ = returned_ self.log_ = dict() - return self - - -class JCPOTTransport(BaseTransport): - - """Domain Adapatation OT method for multi-source target shift based on Wasserstein barycenter algorithm. - - Parameters - ---------- - reg_e : float, optional (default=1) - Entropic regularization parameter - max_iter : int, float, optional (default=10) - The minimum number of iteration before stopping the optimization - algorithm if no it has not converged - tol : float, optional (default=10e-9) - Stop threshold on error (inner sinkhorn solver) (>0) - verbose : bool, optional (default=False) - Controls the verbosity of the optimization algorithm - log : bool, optional (default=False) - Controls the logs of the optimization algorithm - metric : string, optional (default="sqeuclidean") - The ground metric for the Wasserstein problem - norm : string, optional (default=None) - If given, normalize the ground metric to avoid numerical errors that - can occur with large metric values. - distribution_estimation : callable, optional (defaults to the uniform) - The kind of distribution estimation to employ - out_of_sample_map : string, optional (default="ferradans") - The kind of out of sample mapping to apply to transport samples - from a domain into another one. Currently the only possible option is - "ferradans" which uses the method proposed in [6]. - - Attributes - ---------- - coupling_ : list of array-like objects, shape K x (n_source_samples, n_target_samples) - A set of optimal couplings between each source domain and the target domain - proportions_ : array-like, shape (n_classes,) - Estimated class proportions in the target domain - log_ : dictionary - The dictionary of log, empty dic if parameter log is not True - - References - ---------- - - .. [1] Ievgen Redko, Nicolas Courty, Rémi Flamary, Devis Tuia - "Optimal transport for multi-source domain adaptation under target shift", - International Conference on Artificial Intelligence and Statistics (AISTATS), - vol. 89, p.849-858, 2019. - - """ - - def __init__(self, reg_e=.1, max_iter=10, - tol=10e-9, verbose=False, log=False, - metric="sqeuclidean", - out_of_sample_map='ferradans'): - self.reg_e = reg_e - self.max_iter = max_iter - self.tol = tol - self.verbose = verbose - self.log = log - self.metric = metric - self.out_of_sample_map = out_of_sample_map - - def fit(self, Xs, ys=None, Xt=None, yt=None): - """Building coupling matrices from a list of source and target sets of samples - (Xs, ys) and (Xt, yt) - - Parameters - ---------- - Xs : list of K array-like objects, shape K x (nk_source_samples, n_features) - A list of the training input samples. - ys : list of K array-like objects, shape K x (nk_source_samples,) - A list of the class labels - Xt : array-like, shape (n_target_samples, n_features) - The training input samples. - yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the - yt's elements with -1. - - Warning: Note that, due to this convention -1 cannot be used as a - class label - - Returns - ------- - self : object - Returns self. - """ - - # check the necessary inputs parameters are here - if check_params(Xs=Xs, Xt=Xt, ys=ys): - - self.xs_ = Xs - self.xt_ = Xt - - returned_ = jcpot_barycenter(Xs=Xs, Ys=ys, Xt=Xt, reg=self.reg_e, - metric=self.metric, distrinumItermax=self.max_iter, stopThr=self.tol, - verbose=self.verbose, log=self.log) - - # deal with the value of log - if self.log: - self.coupling_, self.proportions_, self.log_ = returned_ - else: - self.coupling_, self.proportions_ = returned_ - self.log_ = dict() - - return self - - def transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): - """Transports source samples Xs onto target ones Xt - - Parameters - ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - ys : array-like, shape (n_source_samples,) - The class labels - Xt : array-like, shape (n_target_samples, n_features) - The training input samples. - yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the - yt's elements with -1. - - Warning: Note that, due to this convention -1 cannot be used as a - class label - batch_size : int, optional (default=128) - The batch size for out of sample inverse transform - """ - - transp_Xs = [] - - # check the necessary inputs parameters are here - if check_params(Xs=Xs): - - if all([np.allclose(x, y) for x, y in zip(self.xs_, Xs)]): - - # perform standard barycentric mapping for each source domain - - for coupling in self.coupling_: - transp = coupling / np.sum(coupling, 1)[:, None] - - # set nans to 0 - transp[~ np.isfinite(transp)] = 0 - - # compute transported samples - transp_Xs.append(np.dot(transp, self.xt_)) - else: - - # perform out of sample mapping - indices = np.arange(Xs.shape[0]) - batch_ind = [ - indices[i:i + batch_size] - for i in range(0, len(indices), batch_size)] - - transp_Xs = [] - - for bi in batch_ind: - transp_Xs_ = [] - - # get the nearest neighbor in the sources domains - xs = np.concatenate(self.xs_, axis=0) - idx = np.argmin(dist(Xs[bi], xs), axis=1) - - # transport the source samples - for coupling in self.coupling_: - transp = coupling / np.sum( - coupling, 1)[:, None] - transp[~ np.isfinite(transp)] = 0 - transp_Xs_.append(np.dot(transp, self.xt_)) - - transp_Xs_ = np.concatenate(transp_Xs_, axis=0) - - # define the transported points - transp_Xs_ = transp_Xs_[idx, :] + Xs[bi] - xs[idx, :] - transp_Xs.append(transp_Xs_) - - transp_Xs = np.concatenate(transp_Xs, axis=0) - - return transp_Xs + return self \ No newline at end of file diff --git a/test/test_da.py b/test/test_da.py index 4eaf193..0e31f26 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -549,60 +549,6 @@ def test_linear_mapping_class(): np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2) -def test_jcpot_transport_class(): - """test_jcpot_transport - """ - - ns1 = 150 - ns2 = 150 - nt = 200 - - Xs1, ys1 = make_data_classif('3gauss', ns1) - Xs2, ys2 = make_data_classif('3gauss', ns2) - - Xt, yt = make_data_classif('3gauss2', nt) - - Xs = [Xs1, Xs2] - ys = [ys1, ys2] - - otda = ot.da.JCPOTTransport(reg_e=0.01, max_iter=1000, tol=1e-9, verbose=True, log=True) - - # test its computed - otda.fit(Xs=Xs, ys=ys, Xt=Xt) - - assert hasattr(otda, "coupling_") - assert hasattr(otda, "proportions_") - assert hasattr(otda, "log_") - - # test dimensions of coupling - for i, xs in enumerate(Xs): - assert_equal(otda.coupling_[i].shape, ((xs.shape[0], Xt.shape[0]))) - - # test all margin constraints - mu_t = unif(nt) - - for i in range(len(Xs)): - # test margin constraints w.r.t. uniform target weights for each coupling matrix - assert_allclose( - np.sum(otda.coupling_[i], axis=0), mu_t, rtol=1e-3, atol=1e-3) - - # test margin constraints w.r.t. modified source weights for each source domain - - assert_allclose( - np.dot(otda.log_['D1'][i], np.sum(otda.coupling_[i], axis=1)), otda.proportions_, rtol=1e-3, - atol=1e-3) - - # test transform - transp_Xs = otda.transform(Xs=Xs) - [assert_equal(x.shape, y.shape) for x, y in zip(transp_Xs, Xs)] - - Xs_new, _ = make_data_classif('3gauss', ns1 + 1) - transp_Xs_new = otda.transform(Xs_new) - - # check that the oos method is working - assert_equal(transp_Xs_new.shape, Xs_new.shape) - - def test_emd_laplace_class(): """test_emd_laplace_transport """ @@ -654,4 +600,4 @@ def test_emd_laplace_class(): # test fit_transform transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt) - assert_equal(transp_Xs.shape, Xs.shape) + assert_equal(transp_Xs.shape, Xs.shape) \ No newline at end of file -- cgit v1.2.3 From a5dbac1c0088c6db816ceb12af039fef24442558 Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 8 Apr 2020 10:30:18 +0200 Subject: remove laplacian --- ot/utils.py | 6 ------ 1 file changed, 6 deletions(-) diff --git a/ot/utils.py b/ot/utils.py index a633be2..b71458b 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -49,12 +49,6 @@ def kernel(x1, x2, method='gaussian', sigma=1, **kwargs): return K -def laplacian(x): - """Compute Laplacian matrix""" - L = np.diag(np.sum(x, axis=0)) - x - return L - - def unif(n): """ return a uniform histogram of length n (simplex) -- cgit v1.2.3 From 4d77cc99ae5dd2cf3521ff2f136ff783c7d1d7ef Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 8 Apr 2020 10:41:53 +0200 Subject: pep test da --- test/test_da.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_da.py b/test/test_da.py index 0e31f26..befec43 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -600,4 +600,4 @@ def test_emd_laplace_class(): # test fit_transform transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt) - assert_equal(transp_Xs.shape, Xs.shape) \ No newline at end of file + assert_equal(transp_Xs.shape, Xs.shape) -- cgit v1.2.3 From 25cad1942166d25d2d305cf93937c1d5edc91716 Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 8 Apr 2020 10:54:56 +0200 Subject: pep test da --- test/test_da.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_da.py b/test/test_da.py index befec43..0e31f26 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -600,4 +600,4 @@ def test_emd_laplace_class(): # test fit_transform transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt) - assert_equal(transp_Xs.shape, Xs.shape) + assert_equal(transp_Xs.shape, Xs.shape) \ No newline at end of file -- cgit v1.2.3 From 37412f59a94e8607fdbd5f7f29434a70ebe18688 Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 8 Apr 2020 11:05:50 +0200 Subject: remove blank line --- ot/da.py | 2 +- test/test_da.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/da.py b/ot/da.py index 474c944..108609f 100644 --- a/ot/da.py +++ b/ot/da.py @@ -2121,4 +2121,4 @@ class UnbalancedSinkhornTransport(BaseTransport): self.coupling_ = returned_ self.log_ = dict() - return self \ No newline at end of file + return self diff --git a/test/test_da.py b/test/test_da.py index 0e31f26..befec43 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -600,4 +600,4 @@ def test_emd_laplace_class(): # test fit_transform transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt) - assert_equal(transp_Xs.shape, Xs.shape) \ No newline at end of file + assert_equal(transp_Xs.shape, Xs.shape) -- cgit v1.2.3 From 4cd4e09f89fe6f95a07d632365612b797ab760da Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 8 Apr 2020 13:56:29 +0200 Subject: Set theme jekyll-theme-slate --- _config.yml | 1 + 1 file changed, 1 insertion(+) create mode 100644 _config.yml diff --git a/_config.yml b/_config.yml new file mode 100644 index 0000000..c741881 --- /dev/null +++ b/_config.yml @@ -0,0 +1 @@ +theme: jekyll-theme-slate \ No newline at end of file -- cgit v1.2.3 From bc51793333994a1bf6263c9e9c111d754172fc82 Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 8 Apr 2020 14:35:00 +0200 Subject: added test barycenter + modif target --- ot/bregman.py | 2 +- test/test_da.py | 28 ++++++++++++++++++++++++++++ 2 files changed, 29 insertions(+), 1 deletion(-) diff --git a/ot/bregman.py b/ot/bregman.py index 410ae85..c44c141 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1528,7 +1528,7 @@ def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100, The problem consist in solving a Wasserstein barycenter problem to estimate the proportions :math:`\mathbf{h}` in the target domain. The algorithm used for solving the problem is the Iterative Bregman projections algorithm - with two sets of marginal constraints related to the unknown vector :math:`\mathbf{h}` and uniform tarhet distribution. + with two sets of marginal constraints related to the unknown vector :math:`\mathbf{h}` and uniform target distribution. Parameters ---------- diff --git a/test/test_da.py b/test/test_da.py index b58cf51..c54dab7 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -601,3 +601,31 @@ def test_jcpot_transport_class(): # check that the oos method is working assert_equal(transp_Xs_new.shape, Xs_new.shape) + + +def test_jcpot_barycenter(): + """test_jcpot_barycenter + """ + + ns1 = 150 + ns2 = 150 + nt = 200 + + sigma = 0.1 + np.random.seed(1985) + + ps1 = .2 + ps2 = .9 + pt = .4 + + Xs1, ys1 = make_data_classif('2gauss_prop', ns1, nz=sigma, p=ps1) + Xs2, ys2 = make_data_classif('2gauss_prop', ns2, nz=sigma, p=ps2) + Xt, yt = make_data_classif('2gauss_prop', nt, nz=sigma, p=pt) + + Xs = [Xs1, Xs2] + ys = [ys1, ys2] + + _, prop, = ot.bregman.jcpot_barycenter(Xs, ys, Xt, reg=.5, metric='sqeuclidean', + numItermax=10000, stopThr=1e-9, verbose=False, log=False) + + np.testing.assert_allclose(prop, [1 - pt, pt], rtol=1e-3, atol=1e-3) -- cgit v1.2.3 From 0b402fd7c7e07043afd3a9df9d75bc424730b06f Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 8 Apr 2020 16:05:01 +0200 Subject: add label prop + inverse --- ot/da.py | 171 ++++++++++++++++++++++++++++++++++++++++++++++++++++++-- test/test_da.py | 47 ++++++++++++++++ 2 files changed, 214 insertions(+), 4 deletions(-) diff --git a/ot/da.py b/ot/da.py index 3a458eb..29b0a8b 100644 --- a/ot/da.py +++ b/ot/da.py @@ -943,6 +943,46 @@ class BaseTransport(BaseEstimator): return transp_Xs + def transform_labels(self, ys=None): + """Propagate source labels ys to obtain estimated target labels + + Parameters + ---------- + ys : array-like, shape (n_source_samples,) + The class labels + + Returns + ------- + transp_ys : array-like, shape (n_target_samples,) + Estimated target labels. + """ + + # check the necessary inputs parameters are here + if check_params(ys=ys): + + classes = np.unique(ys) + n = len(classes) + D1 = np.zeros((n, len(ys))) + + # perform label propagation + transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] + + # set nans to 0 + transp[~ np.isfinite(transp)] = 0 + + if np.min(classes) != 0: + ys = ys - np.min(classes) + classes = np.unique(ys) + + for c in classes: + D1[int(c), ys == c] = 1 + + # compute transported samples + transp_ys = np.dot(D1, transp) + + return np.argmax(transp_ys,axis=0) + + def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): """Transports target samples Xt onto target samples Xs @@ -1010,6 +1050,44 @@ class BaseTransport(BaseEstimator): return transp_Xt + def inverse_transform_labels(self, yt=None): + """Propagate target labels yt to obtain estimated source labels ys + + Parameters + ---------- + yt : array-like, shape (n_target_samples,) + + Returns + ------- + transp_ys : array-like, shape (n_source_samples,) + Estimated source labels. + """ + + # check the necessary inputs parameters are here + if check_params(yt=yt): + + classes = np.unique(yt) + n = len(classes) + D1 = np.zeros((n, len(yt))) + + # perform label propagation + transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] + + # set nans to 0 + transp[~ np.isfinite(transp)] = 0 + + if np.min(classes) != 0: + yt = yt - np.min(classes) + classes = np.unique(yt) + + for c in classes: + D1[int(c), yt == c] = 1 + + # compute transported samples + transp_ys = np.dot(D1, transp.T) + + return np.argmax(transp_ys,axis=0) + class LinearTransport(BaseTransport): @@ -2017,10 +2095,10 @@ class JCPOTTransport(BaseTransport): Parameters ---------- - Xs : array-like, shape (n_source_samples, n_features) - The training input samples. - ys : array-like, shape (n_source_samples,) - The class labels + Xs : list of K array-like objects, shape K x (nk_source_samples, n_features) + A list of the training input samples. + ys : list of K array-like objects, shape K x (nk_source_samples,) + A list of the class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. yt : array-like, shape (n_target_samples,) @@ -2083,3 +2161,88 @@ class JCPOTTransport(BaseTransport): transp_Xs = np.concatenate(transp_Xs, axis=0) return transp_Xs + + def transform_labels(self, ys=None): + """Propagate source labels ys to obtain target labels + + Parameters + ---------- + ys : list of K array-like objects, shape K x (nk_source_samples,) + A list of the class labels + + Returns + ------- + yt : array-like, shape (n_target_samples,) + Estimated target labels. + """ + + # check the necessary inputs parameters are here + if check_params(ys=ys): + yt = np.zeros((len(np.unique(np.concatenate(ys))),self.xt_.shape[0])) + for i in range(len(ys)): + classes = np.unique(ys[i]) + n = len(classes) + ns = len(ys[i]) + + # perform label propagation + transp = self.coupling_[i] / np.sum(self.coupling_[i], 1)[:, None] + + # set nans to 0 + transp[~ np.isfinite(transp)] = 0 + + if self.log: + D1 = self.log_['D1'][i] + else: + D1 = np.zeros((n, ns)) + + if np.min(classes) != 0: + ys = ys - np.min(classes) + classes = np.unique(ys) + + for c in classes: + D1[int(c), ys == c] = 1 + # compute transported samples + yt = yt + np.dot(D1, transp)/len(ys) + + return np.argmax(yt,axis=0) + + def inverse_transform_labels(self, yt=None): + """Propagate source labels ys to obtain target labels + + Parameters + ---------- + yt : array-like, shape (n_source_samples,) + The target class labels + + Returns + ------- + transp_ys : list of K array-like objects, shape K x (nk_source_samples,) + A list of estimated source labels + """ + + # check the necessary inputs parameters are here + if check_params(yt=yt): + transp_ys = [] + classes = np.unique(yt) + n = len(classes) + D1 = np.zeros((n, len(yt))) + + if np.min(classes) != 0: + yt = yt - np.min(classes) + classes = np.unique(yt) + + for c in classes: + D1[int(c), yt == c] = 1 + + for i in range(len(self.xs_)): + + # perform label propagation + transp = self.coupling_[i] / np.sum(self.coupling_[i], 1)[:, None] + + # set nans to 0 + transp[~ np.isfinite(transp)] = 0 + + # compute transported labels + transp_ys.append(np.argmax(np.dot(D1, transp.T),axis=0)) + + return transp_ys diff --git a/test/test_da.py b/test/test_da.py index c54dab7..4eb6de0 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -65,6 +65,14 @@ def test_sinkhorn_lpl1_transport_class(): transp_Xs = otda.fit_transform(Xs=Xs, ys=ys, Xt=Xt) assert_equal(transp_Xs.shape, Xs.shape) + # check label propagation + transp_yt = otda.transform_labels(ys) + assert_equal(transp_yt.shape[0], yt.shape[0]) + + # check inverse label propagation + transp_ys = otda.inverse_transform_labels(yt) + assert_equal(transp_ys.shape[0], ys.shape[0]) + # test unsupervised vs semi-supervised mode otda_unsup = ot.da.SinkhornLpl1Transport() otda_unsup.fit(Xs=Xs, ys=ys, Xt=Xt) @@ -129,6 +137,14 @@ def test_sinkhorn_l1l2_transport_class(): transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) + # check label propagation + transp_yt = otda.transform_labels(ys) + assert_equal(transp_yt.shape[0], yt.shape[0]) + + # check inverse label propagation + transp_ys = otda.inverse_transform_labels(yt) + assert_equal(transp_ys.shape[0], ys.shape[0]) + Xt_new, _ = make_data_classif('3gauss2', nt + 1) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) @@ -210,6 +226,14 @@ def test_sinkhorn_transport_class(): transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) + # check label propagation + transp_yt = otda.transform_labels(ys) + assert_equal(transp_yt.shape[0], yt.shape[0]) + + # check inverse label propagation + transp_ys = otda.inverse_transform_labels(yt) + assert_equal(transp_ys.shape[0], ys.shape[0]) + Xt_new, _ = make_data_classif('3gauss2', nt + 1) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) @@ -271,6 +295,14 @@ def test_unbalanced_sinkhorn_transport_class(): transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) + # check label propagation + transp_yt = otda.transform_labels(ys) + assert_equal(transp_yt.shape[0], yt.shape[0]) + + # check inverse label propagation + transp_ys = otda.inverse_transform_labels(yt) + assert_equal(transp_ys.shape[0], ys.shape[0]) + Xs_new, _ = make_data_classif('3gauss', ns + 1) transp_Xs_new = otda.transform(Xs_new) @@ -353,6 +385,14 @@ def test_emd_transport_class(): transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) + # check label propagation + transp_yt = otda.transform_labels(ys) + assert_equal(transp_yt.shape[0], yt.shape[0]) + + # check inverse label propagation + transp_ys = otda.inverse_transform_labels(yt) + assert_equal(transp_ys.shape[0], ys.shape[0]) + Xt_new, _ = make_data_classif('3gauss2', nt + 1) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) @@ -602,6 +642,13 @@ def test_jcpot_transport_class(): # check that the oos method is working assert_equal(transp_Xs_new.shape, Xs_new.shape) + # check label propagation + transp_yt = otda.transform_labels(ys) + assert_equal(transp_yt.shape[0], yt.shape[0]) + + # check inverse label propagation + transp_ys = otda.inverse_transform_labels(yt) + [assert_equal(x.shape, y.shape) for x, y in zip(transp_ys, ys)] def test_jcpot_barycenter(): """test_jcpot_barycenter -- cgit v1.2.3 From 1a4c264cc9b2cb0bb89840ee9175177e86eef3ef Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 8 Apr 2020 16:34:39 +0200 Subject: added label normalization to utils --- ot/da.py | 75 ++++++++++++++++++++++++++++----------------------------- ot/utils.py | 22 +++++++++++++++++ test/test_da.py | 1 + 3 files changed, 60 insertions(+), 38 deletions(-) diff --git a/ot/da.py b/ot/da.py index 29b0a8b..4318c0d 100644 --- a/ot/da.py +++ b/ot/da.py @@ -16,7 +16,7 @@ import scipy.linalg as linalg from .bregman import sinkhorn, jcpot_barycenter from .lp import emd -from .utils import unif, dist, kernel, cost_normalization +from .utils import unif, dist, kernel, cost_normalization, label_normalization from .utils import check_params, BaseEstimator from .unbalanced import sinkhorn_unbalanced from .optim import cg @@ -786,6 +786,9 @@ class BaseTransport(BaseEstimator): transform method should always get as input a Xs parameter inverse_transform method should always get as input a Xt parameter + + transform_labels method should always get as input a ys parameter + inverse_transform_labels method should always get as input a yt parameter """ def fit(self, Xs=None, ys=None, Xt=None, yt=None): @@ -944,7 +947,7 @@ class BaseTransport(BaseEstimator): return transp_Xs def transform_labels(self, ys=None): - """Propagate source labels ys to obtain estimated target labels + """Propagate source labels ys to obtain estimated target labels as in [27] Parameters ---------- @@ -955,14 +958,23 @@ class BaseTransport(BaseEstimator): ------- transp_ys : array-like, shape (n_target_samples,) Estimated target labels. + + References + ---------- + + .. [27] Ievgen Redko, Nicolas Courty, Rémi Flamary, Devis Tuia + "Optimal transport for multi-source domain adaptation under target shift", + International Conference on Artificial Intelligence and Statistics (AISTATS), 2019. + """ # check the necessary inputs parameters are here if check_params(ys=ys): - classes = np.unique(ys) + ysTemp = label_normalization(np.copy(ys)) + classes = np.unique(ysTemp) n = len(classes) - D1 = np.zeros((n, len(ys))) + D1 = np.zeros((n, len(ysTemp))) # perform label propagation transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] @@ -970,18 +982,13 @@ class BaseTransport(BaseEstimator): # set nans to 0 transp[~ np.isfinite(transp)] = 0 - if np.min(classes) != 0: - ys = ys - np.min(classes) - classes = np.unique(ys) - for c in classes: - D1[int(c), ys == c] = 1 + D1[int(c), ysTemp == c] = 1 # compute transported samples transp_ys = np.dot(D1, transp) - return np.argmax(transp_ys,axis=0) - + return np.argmax(transp_ys, axis=0) def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): @@ -1066,9 +1073,10 @@ class BaseTransport(BaseEstimator): # check the necessary inputs parameters are here if check_params(yt=yt): - classes = np.unique(yt) + ytTemp = label_normalization(np.copy(yt)) + classes = np.unique(ytTemp) n = len(classes) - D1 = np.zeros((n, len(yt))) + D1 = np.zeros((n, len(ytTemp))) # perform label propagation transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] @@ -1076,17 +1084,13 @@ class BaseTransport(BaseEstimator): # set nans to 0 transp[~ np.isfinite(transp)] = 0 - if np.min(classes) != 0: - yt = yt - np.min(classes) - classes = np.unique(yt) - for c in classes: - D1[int(c), yt == c] = 1 + D1[int(c), ytTemp == c] = 1 # compute transported samples transp_ys = np.dot(D1, transp.T) - return np.argmax(transp_ys,axis=0) + return np.argmax(transp_ys, axis=0) class LinearTransport(BaseTransport): @@ -2163,7 +2167,7 @@ class JCPOTTransport(BaseTransport): return transp_Xs def transform_labels(self, ys=None): - """Propagate source labels ys to obtain target labels + """Propagate source labels ys to obtain target labels as in [27] Parameters ---------- @@ -2178,11 +2182,12 @@ class JCPOTTransport(BaseTransport): # check the necessary inputs parameters are here if check_params(ys=ys): - yt = np.zeros((len(np.unique(np.concatenate(ys))),self.xt_.shape[0])) + yt = np.zeros((len(np.unique(np.concatenate(ys))), self.xt_.shape[0])) for i in range(len(ys)): - classes = np.unique(ys[i]) + ysTemp = label_normalization(np.copy(ys[i])) + classes = np.unique(ysTemp) n = len(classes) - ns = len(ys[i]) + ns = len(ysTemp) # perform label propagation transp = self.coupling_[i] / np.sum(self.coupling_[i], 1)[:, None] @@ -2195,16 +2200,13 @@ class JCPOTTransport(BaseTransport): else: D1 = np.zeros((n, ns)) - if np.min(classes) != 0: - ys = ys - np.min(classes) - classes = np.unique(ys) - for c in classes: - D1[int(c), ys == c] = 1 + D1[int(c), ysTemp == c] = 1 + # compute transported samples - yt = yt + np.dot(D1, transp)/len(ys) + yt = yt + np.dot(D1, transp) / len(ys) - return np.argmax(yt,axis=0) + return np.argmax(yt, axis=0) def inverse_transform_labels(self, yt=None): """Propagate source labels ys to obtain target labels @@ -2223,16 +2225,13 @@ class JCPOTTransport(BaseTransport): # check the necessary inputs parameters are here if check_params(yt=yt): transp_ys = [] - classes = np.unique(yt) + ytTemp = label_normalization(np.copy(yt)) + classes = np.unique(ytTemp) n = len(classes) - D1 = np.zeros((n, len(yt))) - - if np.min(classes) != 0: - yt = yt - np.min(classes) - classes = np.unique(yt) + D1 = np.zeros((n, len(ytTemp))) for c in classes: - D1[int(c), yt == c] = 1 + D1[int(c), ytTemp == c] = 1 for i in range(len(self.xs_)): @@ -2243,6 +2242,6 @@ class JCPOTTransport(BaseTransport): transp[~ np.isfinite(transp)] = 0 # compute transported labels - transp_ys.append(np.argmax(np.dot(D1, transp.T),axis=0)) + transp_ys.append(np.argmax(np.dot(D1, transp.T), axis=0)) return transp_ys diff --git a/ot/utils.py b/ot/utils.py index b71458b..c154f99 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -200,6 +200,28 @@ def dots(*args): return reduce(np.dot, args) +def label_normalization(y, start=0): + """ Transform labels to start at a given value + + Parameters + ---------- + y : array-like, shape (n, ) + The vector of labels to be normalized. + start : int + Desired value for the smallest label in y (default=0) + + Returns + ------- + y : array-like, shape (n1, ) + The input vector of labels normalized according to given start value. + """ + + diff = np.min(np.unique(y)) - start + if diff != 0: + y -= diff + return y + + def fun(f, q_in, q_out): """ Utility function for parmap with no serializing problems """ while True: diff --git a/test/test_da.py b/test/test_da.py index 4eb6de0..d96046d 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -650,6 +650,7 @@ def test_jcpot_transport_class(): transp_ys = otda.inverse_transform_labels(yt) [assert_equal(x.shape, y.shape) for x, y in zip(transp_ys, ys)] + def test_jcpot_barycenter(): """test_jcpot_barycenter """ -- cgit v1.2.3 From 9f63ee92e281427ab3d520f75bb9c3406b547365 Mon Sep 17 00:00:00 2001 From: Laetitia Chapel Date: Thu, 9 Apr 2020 13:55:27 +0200 Subject: initial commit partial Wass and GW --- docs/source/all.rst | 6 + docs/source/readme.rst | 8 + examples/plot_partial_wass_and_gromov.py | 163 +++++ ot/partial.py | 1015 ++++++++++++++++++++++++++++++ ot/unbalanced.py | 66 +- 5 files changed, 1239 insertions(+), 19 deletions(-) create mode 100755 examples/plot_partial_wass_and_gromov.py create mode 100755 ot/partial.py diff --git a/docs/source/all.rst b/docs/source/all.rst index c968aa1..a6d9790 100644 --- a/docs/source/all.rst +++ b/docs/source/all.rst @@ -86,3 +86,9 @@ ot.unbalanced .. automodule:: ot.unbalanced :members: + +ot.partial +------------- + +.. automodule:: ot.partial + :members: diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 0871779..d5f2161 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -391,6 +391,14 @@ of the 36th International Conference on Machine Learning (ICML). `Learning with a Wasserstein Loss `__ Advances in Neural Information Processing Systems (NIPS). +[26] Caffarelli, L. A., McCann, R. J. (2020). `Free boundaries in optimal transport and +Monge-Ampere obstacle problems `__, +Annals of mathematics, 673-730. + +[27] Chapel, L., Alaya, M., Gasso, G. (2019). `Partial Gromov-Wasserstein with Applications +on Positive-Unlabeled Learning `__. arXiv preprint +arXiv:2002.08276. + .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT .. |Anaconda Cloud| image:: https://anaconda.org/conda-forge/pot/badges/version.svg diff --git a/examples/plot_partial_wass_and_gromov.py b/examples/plot_partial_wass_and_gromov.py new file mode 100755 index 0000000..2ddeb68 --- /dev/null +++ b/examples/plot_partial_wass_and_gromov.py @@ -0,0 +1,163 @@ +# -*- coding: utf-8 -*- +""" +========================== +Partial Wasserstein and Gromov-Wasserstein example +========================== + +This example is designed to show how to use the Partial (Gromov-)Wassertsein +distance computation in POT. +""" + +# Author: Laetitia Chapel +# License: MIT License + +import scipy as sp +import numpy as np +import matplotlib.pylab as pl +import ot + + +############################################################################# +# +# Sample two 2D Gaussian distributions and plot them +# -------------------------------------------------- +# +# For demonstration purpose, we sample two Gaussian distributions in 2-d +# spaces and add some random noise. + + +n_samples = 20 # nb samples (gaussian) +n_noise = 20 # nb of samples (noise) + +mu = np.array([0, 0]) +cov = np.array([[1, 0], [0, 2]]) + +xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) +xs = np.append(xs, (np.random.rand(n_noise, 2)+1)*4).reshape((-1, 2)) +xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) +xt = np.append(xt, (np.random.rand(n_noise, 2)+1)*-3).reshape((-1, 2)) + +M = sp.spatial.distance.cdist(xs, xt) + +fig = pl.figure() +ax1 = fig.add_subplot(131) +ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +ax2 = fig.add_subplot(132) +ax2.scatter(xt[:, 0], xt[:, 1], color='r') +ax3 = fig.add_subplot(133) +ax3.imshow(M) +pl.show() + +############################################################################# +# +# Compute partial Wasserstein plans and distance, +# by transporting 50% of the mass +# ---------------------------------------------- + +p = ot.unif(n_samples + n_noise) +q = ot.unif(n_samples + n_noise) + +w0, log0 = ot.partial.partial_wasserstein(p, q, M, m=0.5, log=True) +w, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=0.1, m=0.5, + log=True) + +print('Partial Wasserstein distance (m = 0.5): ' + str(log0['partial_w_dist'])) +print('Entropic partial Wasserstein distance (m = 0.5): ' + \ + str(log['partial_w_dist'])) + +pl.figure(1, (10, 5)) +pl.subplot(1, 2, 1) +pl.imshow(w0, cmap='jet') +pl.title('Partial Wasserstein') +pl.subplot(1, 2, 2) +pl.imshow(w, cmap='jet') +pl.title('Entropic partial Wasserstein') +pl.show() + + +############################################################################# +# +# Sample one 2D and 3D Gaussian distributions and plot them +# --------------------------------------------------------- +# +# The Gromov-Wasserstein distance allows to compute distances with samples that +# do not belong to the same metric space. For demonstration purpose, we sample +# two Gaussian distributions in 2- and 3-dimensional spaces. + +n_samples = 20 # nb samples +n_noise = 10 # nb of samples (noise) + +p = ot.unif(n_samples + n_noise) +q = ot.unif(n_samples + n_noise) + +mu_s = np.array([0, 0]) +cov_s = np.array([[1, 0], [0, 1]]) + +mu_t = np.array([0, 0, 0]) +cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) + + +xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) +xs = np.concatenate((xs, ((np.random.rand(n_noise, 2)+1)*4)), axis=0) +P = sp.linalg.sqrtm(cov_t) +xt = np.random.randn(n_samples, 3).dot(P) + mu_t +xt = np.concatenate((xt, ((np.random.rand(n_noise, 3)+1)*10)), axis=0) + +fig = pl.figure() +ax1 = fig.add_subplot(121) +ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +ax2 = fig.add_subplot(122, projection='3d') +ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') +pl.show() + + +############################################################################# +# +# Compute partial Gromov-Wasserstein plans and distance, +# by transporting 100% and 2/3 of the mass +# ----------------------------------------------------- + +C1 = sp.spatial.distance.cdist(xs, xs) +C2 = sp.spatial.distance.cdist(xt, xt) + +print('-----m = 1') +m = 1 +res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, + log=True) +res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, + m=m, log=True) + +print('Partial Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist'])) +print('Entropic partial Wasserstein distance (m = 1): ' + \ + str(log['partial_gw_dist'])) + +pl.figure(1, (10, 5)) +pl.title("mass to be transported m = 1") +pl.subplot(1, 2, 1) +pl.imshow(res0, cmap='jet') +pl.title('Partial Wasserstein') +pl.subplot(1, 2, 2) +pl.imshow(res, cmap='jet') +pl.title('Entropic partial Wasserstein') +pl.show() + +print('-----m = 2/3') +m = 2/3 +res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True) +res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, + m=m, log=True) + +print('Partial Wasserstein distance (m = 2/3): ' + \ + str(log0['partial_gw_dist'])) +print('Entropic partial Wasserstein distance (m = 2/3): ' + \ + str(log['partial_gw_dist'])) + +pl.figure(1, (10, 5)) +pl.title("mass to be transported m = 2/3") +pl.subplot(1, 2, 1) +pl.imshow(res0, cmap='jet') +pl.title('Partial Wasserstein') +pl.subplot(1, 2, 2) +pl.imshow(res, cmap='jet') +pl.title('Entropic partial Wasserstein') +pl.show() diff --git a/ot/partial.py b/ot/partial.py new file mode 100755 index 0000000..746f337 --- /dev/null +++ b/ot/partial.py @@ -0,0 +1,1015 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Partial OT +""" + +# Author: Laetitia Chapel +# License: MIT License + +import numpy as np + +from ot.lp import emd + + +def partial_wasserstein_lagrange(a, b, M, reg_m=None, nb_dummies=1, log=False, + **kwargs): + + r""" + Solves the partial optimal transport problem for the quadratic cost + and returns the OT plan + + The function considers the following problem: + + .. math:: + \gamma = \arg\min_\gamma <\gamma,(M-\lambda)>_F + + s.t. + \gamma\geq 0 \\ + \gamma 1 \leq a\\ + \gamma^T 1 \leq b\\ + 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} + + + or equivalently: + + .. math:: + \gamma = \arg\min_\gamma <\gamma,M>_F + \sqrt(\lambda/2) + (\|\gamma 1 - a\|_1 + \|\gamma^T 1 - b\|_1) + + s.t. + \gamma\geq 0 \\ + + + where : + + - M is the metric cost matrix + - a and b are source and target unbalanced distributions + - :math:`\lambda` is the lagragian cost. Tuning its value allows attaining + a given mass to be transported m + + The formulation of the problem has been proposed in [26]_ + + Parameters + ---------- + a : np.ndarray (dim_a,) + Unnormalized histogram of dimension dim_a + b : np.ndarray (dim_b,) + Unnormalized histograms of dimension dim_b + M : np.ndarray (dim_a, dim_b) + cost matrix for the quadratic cost + reg_m : float, optional + Lagragian cost + log : bool, optional + record log if True + + + Returns + ------- + gamma : (dim_a x dim_b) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary returned only if `log` is `True` + + + Examples + -------- + + >>> import ot + >>> a = [.1, .2] + >>> b = [.1, .1] + >>> M = [[0., 1.], [2., 3.]] + >>> np.round(partial_wasserstein_lagrange(a,b,M), 2) + array([[0.1, 0. ], + [0. , 0.1]]) + >>> np.round(partial_wasserstein_lagrange(a,b,M,reg_m=2), 2) + array([[0.1, 0. ], + [0. , 0. ]]) + + References + ---------- + + .. [26] Caffarelli, L. A., & McCann, R. J. (2010) Free boundaries in + optimal transport and Monge-Ampere obstacle problems. Annals of + mathematics, 673-730. + + See Also + -------- + ot.partial.partial_wasserstein : Partial Wasserstein with fixed mass + """ + + if np.sum(a) > 1 or np.sum(b) > 1: + raise ValueError("Problem infeasible. Check that a and b are in the " + "simplex") + + if reg_m is None: + reg_m = np.max(M) + 1 + if reg_m < -np.max(M): + return np.zeros((len(a), len(b))) + + eps = 1e-20 + M = np.asarray(M, dtype=np.float64) + b = np.asarray(b, dtype=np.float64) + a = np.asarray(a, dtype=np.float64) + + M_star = M - reg_m # modified cost matrix + + # trick to fasten the computation: select only the subset of columns/lines + # that can have marginals greater than 0 (that is to say M < 0) + idx_x = np.where(np.min(M_star, axis=1) < eps)[0] + idx_y = np.where(np.min(M_star, axis=0) < eps)[0] + + # extend a, b, M with "reservoir" or "dummy" points + M_extended = np.zeros((len(idx_x) + nb_dummies, len(idx_y) + nb_dummies)) + M_extended[:len(idx_x), :len(idx_y)] = M_star[np.ix_(idx_x, idx_y)] + + a_extended = np.append(a[idx_x], [(np.sum(a) - np.sum(a[idx_x]) + + np.sum(b)) / nb_dummies] * nb_dummies) + b_extended = np.append(b[idx_y], [(np.sum(b) - np.sum(b[idx_y]) + + np.sum(a)) / nb_dummies] * nb_dummies) + + gamma_extended, log_emd = emd(a_extended, b_extended, M_extended, log=True, + **kwargs) + gamma = np.zeros((len(a), len(b))) + gamma[np.ix_(idx_x, idx_y)] = gamma_extended[:-nb_dummies, :-nb_dummies] + + if log_emd['warning'] is not None: + raise ValueError("Error in the EMD resolution: try to increase the" + " number of dummy points") + log_emd['cost'] = np.sum(gamma*M) + if log: + return gamma, log_emd + else: + return gamma + + +def partial_wasserstein(a, b, M, m=None, nb_dummies=1, log=False, **kwargs): + r""" + Solves the partial optimal transport problem for the quadratic cost + and returns the OT plan + + The function considers the following problem: + + .. math:: + \gamma = \arg\min_\gamma <\gamma,M>_F + + s.t. + \gamma\geq 0 \\ + \gamma 1 \leq a\\ + \gamma^T 1 \leq b\\ + 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} + + + where : + + - M is the metric cost matrix + - a and b are source and target unbalanced distributions + - m is the amount of mass to be transported + + Parameters + ---------- + a : np.ndarray (dim_a,) + Unnormalized histogram of dimension dim_a + b : np.ndarray (dim_b,) + Unnormalized histograms of dimension dim_b + M : np.ndarray (dim_a, dim_b) + cost matrix for the quadratic cost + m : float, optional + amount of mass to be transported + nb_dummies : int, optional, default:1 + number of reservoir points to be added (to avoid numerical + instabilities, increase its value if an error is raised) + log : bool, optional + record log if True + + + Returns + ------- + :math:`gamma` : (dim_a x dim_b) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary returned only if `log` is `True` + + + Examples + -------- + + >>> import ot + >>> a = [.1, .2] + >>> b = [.1, .1] + >>> M = [[0., 1.], [2., 3.]] + >>> np.round(partial_wasserstein(a,b,M), 2) + array([[0.1, 0. ], + [0. , 0.1]]) + >>> np.round(partial_wasserstein(a,b,M,m=0.1), 2) + array([[0.1, 0. ], + [0. , 0. ]]) + + References + ---------- + .. [26] Caffarelli, L. A., & McCann, R. J. (2010) Free boundaries in + optimal transport and Monge-Ampere obstacle problems. Annals of + mathematics, 673-730. + .. [27] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + Wasserstein with Applications on Positive-Unlabeled Learning". + arXiv preprint arXiv:2002.08276. + + See Also + -------- + ot.partial.partial_wasserstein_lagrange: Partial Wasserstein with + regularization on the marginals + ot.partial.entropic_partial_wasserstein: Partial Wasserstein with a + entropic regularization parameter + """ + + if m is None: + return partial_wasserstein_lagrange(a, b, M, log=log, **kwargs) + elif m < 0: + raise ValueError("Problem infeasible. Parameter m should be greater" + " than 0.") + elif m > np.min((np.sum(a), np.sum(b))): + raise ValueError("Problem infeasible. Parameter m should lower or" + " equal than min(|a|_1, |b|_1).") + + b_extended = np.append(b, [(np.sum(a) - m) / nb_dummies] * nb_dummies) + a_extended = np.append(a, [(np.sum(b) - m) / nb_dummies] * nb_dummies) + M_extended = np.ones((len(a_extended), len(b_extended))) * np.max(M) * 1e2 + M_extended[-1, -1] = np.max(M) * 1e5 + M_extended[:len(a), :len(b)] = M + + gamma, log_emd = emd(a_extended, b_extended, M_extended, log=True, + **kwargs) + if log_emd['warning'] is not None: + raise ValueError("Error in the EMD resolution: try to increase the" + " number of dummy points") + log_emd['partial_w_dist'] = np.sum(M * gamma[:len(a), :len(b)]) + + if log: + return gamma[:len(a), :len(b)], log_emd + else: + return gamma[:len(a), :len(b)] + + +def partial_wasserstein2(a, b, M, m=None, nb_dummies=1, log=False, **kwargs): + r""" + Solves the partial optimal transport problem for the quadratic cost + and returns the partial GW discrepancy + + The function considers the following problem: + + .. math:: + \gamma = \arg\min_\gamma <\gamma,M>_F + + s.t. + \gamma\geq 0 \\ + \gamma 1 \leq a\\ + \gamma^T 1 \leq b\\ + 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} + + + where : + + - M is the metric cost matrix + - a and b are source and target unbalanced distributions + - m is the amount of mass to be transported + + Parameters + ---------- + a : np.ndarray (dim_a,) + Unnormalized histogram of dimension dim_a + b : np.ndarray (dim_b,) + Unnormalized histograms of dimension dim_b + M : np.ndarray (dim_a, dim_b) + cost matrix for the quadratic cost + m : float, optional + amount of mass to be transported + nb_dummies : int, optional, default:1 + number of reservoir points to be added (to avoid numerical + instabilities, increase its value if an error is raised) + log : bool, optional + record log if True + + + Returns + ------- + :math:`gamma` : (dim_a x dim_b) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary returned only if `log` is `True` + + + Examples + -------- + + >>> import ot + >>> a=[.1, .2] + >>> b=[.1, .1] + >>> M=[[0., 1.], [2., 3.]] + >>> np.round(partial_wasserstein2(a, b, M), 1) + 0.3 + >>> np.round(partial_wasserstein2(a,b,M,m=0.1), 1) + 0.0 + + References + ---------- + .. [26] Caffarelli, L. A., & McCann, R. J. (2010) Free boundaries in + optimal transport and Monge-Ampere obstacle problems. Annals of + mathematics, 673-730. + .. [27] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + Wasserstein with Applications on Positive-Unlabeled Learning". + arXiv preprint arXiv:2002.08276. + """ + + partial_gw, log_w = partial_wasserstein(a, b, M, m, nb_dummies, log=True, + **kwargs) + + log_w['T'] = partial_gw + + if log: + return np.sum(partial_gw * M), log_w + else: + return np.sum(partial_gw * M) + + +def gwgrad_partial(C1, C2, T): + """Compute the GW gradient. Note: we can not use the trick in [12]_ as + the marginals may not sum to 1. + + Parameters + ---------- + C1: array of shape (n_p,n_p) + intra-source (P) cost matrix + + C2: array of shape (n_u,n_u) + intra-target (U) cost matrix + + T : array of shape(n_p+nb_dummies, n_u) (default: None) + Transport matrix + + Returns + ------- + numpy.array of shape (n_p+nb_dummies, n_u) + gradient + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + """ + cC1 = np.dot(C1 ** 2 / 2, np.dot(T, np.ones(C2.shape[0]).reshape(-1, 1))) + cC2 = np.dot(np.dot(np.ones(C1.shape[0]).reshape(1, -1), T), C2 ** 2 / 2) + constC = cC1 + cC2 + A = -np.dot(C1, T).dot(C2.T) + tens = constC + A + return tens * 2 + + +def gwloss_partial(C1, C2, T): + """Compute the GW loss. + + Parameters + ---------- + C1: array of shape (n_p,n_p) + intra-source (P) cost matrix + + C2: array of shape (n_u,n_u) + intra-target (U) cost matrix + + T : array of shape(n_p+nb_dummies, n_u) (default: None) + Transport matrix + + Returns + ------- + GW loss + """ + g = gwgrad_partial(C1, C2, T) * 0.5 + return np.sum(g * T) + + +def partial_gromov_wasserstein(C1, C2, p, q, m=None, nb_dummies=1, G0=None, + thres=1, numItermax=1000, tol=1e-7, + log=False, verbose=False, **kwargs): + r""" + Solves the partial optimal transport problem + and returns the OT plan + + The function considers the following problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + + s.t. \gamma 1 \leq a \\ + \gamma^T 1 \leq b \\ + \gamma\geq 0 \\ + 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} \\ + + where : + + - M is the metric cost matrix + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma) + =\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are the sample weights + - m is the amount of mass to be transported + + The formulation of the problem has been proposed in [27]_ + + + Parameters + ---------- + C1 : ndarray, shape (ns, ns) + Metric cost matrix in the source space + C2 : ndarray, shape (nt, nt) + Metric costfr matrix in the target space + p : ndarray, shape (ns,) + Distribution in the source space + q : ndarray, shape (nt,) + Distribution in the target space + m : float, optional + Amount of mass to be transported (default: min (|p|_1, |q|_1)) + nb_dummies : int, optional + Number of dummy points to add (avoid instabilities in the EMD solver) + G0 : ndarray, shape (ns, nt), optional + Initialisation of the transportation matrix + thres : float, optional + quantile of the gradient matrix to populate the cost matrix when 0 + (default: 1) + numItermax : int, optional + Max number of iterations + log : bool, optional + return log if True + verbose : bool, optional + Print information along iterations + armijo : bool, optional + If True the steps of the line-search is found via an armijo research. Else closed form is used. + If there is convergence issues use False. + **kwargs : dict + parameters can be directly passed to the emd solver + + + Returns + ------- + gamma : (dim_a x dim_b) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary returned only if `log` is `True` + + + Examples + -------- + >>> import ot + >>> import scipy as sp + >>> a = np.array([0.25] * 4) + >>> b = np.array([0.25] * 4) + >>> x = np.array([1,2,100,200]).reshape((-1,1)) + >>> y = np.array([3,2,98,199]).reshape((-1,1)) + >>> C1 = sp.spatial.distance.cdist(x, x) + >>> C2 = sp.spatial.distance.cdist(y, y) + >>> np.round(partial_gromov_wasserstein(C1, C2, a, b),2) + array([[0. , 0.25, 0. , 0. ], + [0.25, 0. , 0. , 0. ], + [0. , 0. , 0.25, 0. ], + [0. , 0. , 0. , 0.25]]) + >>> np.round(partial_gromov_wasserstein(C1, C2, a, b, m=0.25),2) + array([[0. , 0. , 0. , 0. ], + [0. , 0. , 0. , 0. ], + [0. , 0. , 0. , 0. ], + [0. , 0. , 0. , 0.25]]) + + References + ---------- + .. [27] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + Wasserstein with Applications on Positive-Unlabeled Learning". + arXiv preprint arXiv:2002.08276. + + """ + + if m is None: + m = np.min((np.sum(p), np.sum(q))) + elif m < 0: + raise ValueError("Problem infeasible. Parameter m should be greater" + " than 0.") + elif m > np.min((np.sum(p), np.sum(q))): + raise ValueError("Problem infeasible. Parameter m should lower or" + " equal than min(|a|_1, |b|_1).") + + if G0 is None: + G0 = np.outer(p, q) + + dim_G_extended = (len(p) + nb_dummies, len(q) + nb_dummies) + q_extended = np.append(q, [(np.sum(p) - m) / nb_dummies] * nb_dummies) + p_extended = np.append(p, [(np.sum(q) - m) / nb_dummies] * nb_dummies) + + cpt = 0 + err = 1 + eps = 1e-20 + if log: + log = {'err': []} + + while (err > tol and cpt < numItermax): + + Gprev = G0 + + M = gwgrad_partial(C1, C2, G0) + M[M < eps] = np.quantile(M[M > eps], thres) + + M_emd = np.ones(dim_G_extended) * np.max(M) * 1e2 + M_emd[:len(p), :len(q)] = M + M_emd[-nb_dummies:, -nb_dummies:] = np.max(M) * 1e5 + M_emd = np.asarray(M_emd, dtype=np.float64) + + Gc, logemd = emd(p_extended, q_extended, M_emd, log=True, **kwargs) + + if logemd['warning'] is not None: + raise ValueError("Error in the EMD resolution: try to increase the" + " number of dummy points") + + G0 = Gc[:len(p), :len(q)] + + if cpt % 10 == 0: # to speed up the computations + err = np.linalg.norm(G0 - Gprev) + if log: + log['err'].append(err) + if verbose: + if cpt % 200 == 0: + print('{:5s}|{:12s}|{:12s}'.format( + 'It.', 'Err', 'Loss') + '\n' + '-' * 31) + print('{:5d}|{:8e}|{:8e}'.format(cpt, err, + gwloss_partial(C1, C2, G0))) + + cpt += 1 + + if log: + log['partial_gw_dist'] = gwloss_partial(C1, C2, G0) + return G0[:len(p), :len(q)], log + else: + return G0[:len(p), :len(q)] + + +def partial_gromov_wasserstein2(C1, C2, p, q, m=None, nb_dummies=1, G0=None, + thres=0.75, numItermax=1000, tol=1e-7, + log=False, verbose=False, **kwargs): + r""" + Solves the partial optimal transport problem + and returns the partial Gromov-Wasserstein discrepancy + + The function considers the following problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + + s.t. \gamma 1 \leq a \\ + \gamma^T 1 \leq b \\ + \gamma\geq 0 \\ + 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} \\ + + where : + + - M is the metric cost matrix + - :math:`\Omega` is the entropic regularization term + :math:`\Omega=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are the sample weights + - m is the amount of mass to be transported + + The formulation of the problem has been proposed in [27]_ + + + Parameters + ---------- + C1 : ndarray, shape (ns, ns) + Metric cost matrix in the source space + C2 : ndarray, shape (nt, nt) + Metric costfr matrix in the target space + p : ndarray, shape (ns,) + Distribution in the source space + q : ndarray, shape (nt,) + Distribution in the target space + m : float, optional + Amount of mass to be transported (default: min (|p|_1, |q|_1)) + nb_dummies : int, optional + Number of dummy points to add (avoid instabilities in the EMD solver) + G0 : ndarray, shape (ns, nt), optional + Initialisation of the transportation matrix + thres : float, optional + quantile of the gradient matrix to populate the cost matrix when 0 + (default: 1) + numItermax : int, optional + Max number of iterations + log : bool, optional + return log if True + verbose : bool, optional + Print information along iterations + **kwargs : dict + parameters can be directly passed to the emd solver + + + Returns + ------- + partial_gw_dist : (dim_a x dim_b) ndarray + partial GW discrepancy + log : dict + log dictionary returned only if `log` is `True` + + + Examples + -------- + >>> import ot + >>> import scipy as sp + >>> a = np.array([0.25] * 4) + >>> b = np.array([0.25] * 4) + >>> x = np.array([1,2,100,200]).reshape((-1,1)) + >>> y = np.array([3,2,98,199]).reshape((-1,1)) + >>> C1 = sp.spatial.distance.cdist(x, x) + >>> C2 = sp.spatial.distance.cdist(y, y) + >>> np.round(partial_gromov_wasserstein2(C1, C2, a, b),2) + 1.69 + >>> np.round(partial_gromov_wasserstein2(C1, C2, a, b, m=0.25),2) + 0.0 + + References + ---------- + .. [27] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + Wasserstein with Applications on Positive-Unlabeled Learning". + arXiv preprint arXiv:2002.08276. + + """ + + partial_gw, log_gw = partial_gromov_wasserstein(C1, C2, p, q, m, + nb_dummies, G0, thres, + numItermax, tol, True, + verbose, **kwargs) + + log_gw['T'] = partial_gw + + if log: + return log_gw['partial_gw_dist'], log_gw + else: + return log_gw['partial_gw_dist'] + + +def entropic_partial_wasserstein(a, b, M, reg, m=None, numItermax=1000, + stopThr=1e-100, verbose=False, log=False): + r""" + Solves the partial optimal transport problem + and returns the OT plan + + The function considers the following problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + + s.t. \gamma 1 \leq a \\ + \gamma^T 1 \leq b \\ + \gamma\geq 0 \\ + 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} \\ + + where : + + - M is the metric cost matrix + - :math:`\Omega` is the entropic regularization term + :math:`\Omega=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are the sample weights + - m is the amount of mass to be transported + + The formulation of the problem has been proposed in [3]_ + + + Parameters + ---------- + a : np.ndarray (dim_a,) + Unnormalized histogram of dimension dim_a + b : np.ndarray (dim_b,) + Unnormalized histograms of dimension dim_b + M : np.ndarray (dim_a, dim_b) + cost matrix + reg : float + Regularization term > 0 + m : float, optional + Amount of mass to be transported + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshold on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (dim_a x dim_b) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary returned only if `log` is `True` + + + Examples + -------- + >>> import ot + >>> a = [.1, .2] + >>> b = [.1, .1] + >>> M = [[0., 1.], [2., 3.]] + >>> np.round(entropic_partial_wasserstein(a, b, M, 1, 0.1), 2) + array([[0.06, 0.02], + [0.01, 0. ]]) + + + References + ---------- + .. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. + (2015). Iterative Bregman projections for regularized transportation + problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + + See Also + -------- + ot.partial.partial_wasserstein: exact Partial Wasserstein + """ + + a = np.asarray(a, dtype=np.float64) + b = np.asarray(b, dtype=np.float64) + M = np.asarray(M, dtype=np.float64) + + dim_a, dim_b = M.shape + dx = np.ones(dim_a) + dy = np.ones(dim_b) + + if len(a) == 0: + a = np.ones(dim_a, dtype=np.float64) / dim_a + if len(b) == 0: + b = np.ones(dim_b, dtype=np.float64) / dim_b + + if m is None: + m = np.min((np.sum(a), np.sum(b))) + if m < 0: + raise ValueError("Problem infeasible. Parameter m should be greater" + " than 0.") + if m > np.min((np.sum(a), np.sum(b))): + raise ValueError("Problem infeasible. Parameter m should lower or" + " equal than min(|a|_1, |b|_1).") + + log_e = {'err': []} + + # Next 3 lines equivalent to K=np.exp(-M/reg), but faster to compute + K = np.empty(M.shape, dtype=M.dtype) + np.divide(M, -reg, out=K) + np.exp(K, out=K) + np.multiply(K, m/np.sum(K), out=K) + + err, cpt = 1, 0 + + while (err > stopThr and cpt < numItermax): + Kprev = K + K1 = np.dot(np.diag(np.minimum(a / np.sum(K, axis=1), dx)), K) + K2 = np.dot(K1, np.diag(np.minimum(b / np.sum(K1, axis=0), dy))) + K = K2 * (m / np.sum(K2)) + + if np.any(np.isnan(K)) or np.any(np.isinf(K)): + print('Warning: numerical errors at iteration', cpt) + break + if cpt % 10 == 0: + err = np.linalg.norm(Kprev - K) + if log: + log_e['err'].append(err) + if verbose: + if cpt % 200 == 0: + print( + '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 11) + print('{:5d}|{:8e}|'.format(cpt, err)) + + cpt = cpt + 1 + log_e['partial_w_dist'] = np.sum(M * K) + if log: + return K, log_e + else: + return K + + +def entropic_partial_gromov_wasserstein(C1, C2, p, q, reg, m=None, G0=None, + numItermax=1000, tol=1e-7, log=False, + verbose=False): + r""" + Returns the partial Gromov-Wasserstein transport between (C1,p) and (C2,q) + + The function solves the following optimization problem: + + .. math:: + GW = \arg\min_{\gamma} \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})\cdot + \gamma_{i,j}\cdot\gamma_{k,l} + reg\cdot\Omega(\gamma) + + s.t. + \gamma\geq 0 \\ + \gamma 1 \leq a\\ + \gamma^T 1 \leq b\\ + 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} + + where : + + - C1 is the metric cost matrix in the source space + - C2 is the metric cost matrix in the target space + - p and q are the sample weights + - L : quadratic loss function + - :math:`\Omega` is the entropic regularization term + :math:`\Omega=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - m is the amount of mass to be transported + + The formulation of the problem has been proposed in [12]. + + Parameters + ---------- + C1 : ndarray, shape (ns, ns) + Metric cost matrix in the source space + C2 : ndarray, shape (nt, nt) + Metric costfr matrix in the target space + p : ndarray, shape (ns,) + Distribution in the source space + q : ndarray, shape (nt,) + Distribution in the target space + reg: float + entropic regularization parameter + m : float, optional + Amount of mass to be transported (default: min (|p|_1, |q|_1)) + G0 : ndarray, shape (ns, nt), optional + Initialisation of the transportation matrix + numItermax : int, optional + Max number of iterations + log : bool, optional + return log if True + verbose : bool, optional + Print information along iterations + + Examples + -------- + >>> import ot + >>> import scipy as sp + >>> a = np.array([0.25] * 4) + >>> b = np.array([0.25] * 4) + >>> x = np.array([1,2,100,200]).reshape((-1,1)) + >>> y = np.array([3,2,98,199]).reshape((-1,1)) + >>> C1 = sp.spatial.distance.cdist(x, x) + >>> C2 = sp.spatial.distance.cdist(y, y) + >>> np.round(entropic_partial_gromov_wasserstein(C1, C2, a, b,50), 2) + array([[0.12, 0.13, 0. , 0. ], + [0.13, 0.12, 0. , 0. ], + [0. , 0. , 0.25, 0. ], + [0. , 0. , 0. , 0.25]]) + >>> np.round(entropic_partial_gromov_wasserstein(C1, C2, a, b, 50, m=0.25) + , 2) + array([[0.02, 0.03, 0. , 0.03], + [0.03, 0.03, 0. , 0.03], + [0. , 0. , 0.03, 0. ], + [0.02, 0.02, 0. , 0.03]]) + + Returns + ------- + :math: `gamma` : (dim_a x dim_b) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary returned only if `log` is `True` + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + + See Also + -------- + ot.partial.partial_gromov_wasserstein: exact Partial Gromov-Wasserstein + """ + + if G0 is None: + G0 = np.outer(p, q) + + if m is None: + m = np.min((np.sum(p), np.sum(q))) + elif m < 0: + raise ValueError("Problem infeasible. Parameter m should be greater" + " than 0.") + elif m > np.min((np.sum(p), np.sum(q))): + raise ValueError("Problem infeasible. Parameter m should lower or" + " equal than min(|a|_1, |b|_1).") + + cpt = 0 + err = 1 + + loge = {'err': []} + + while (err > tol and cpt < numItermax): + Gprev = G0 + M_entr = gwgrad_partial(C1, C2, G0) + G0 = entropic_partial_wasserstein(p, q, M_entr, reg, m) + if cpt % 10 == 0: # to speed up the computations + err = np.linalg.norm(G0 - Gprev) + if log: + loge['err'].append(err) + if verbose: + if cpt % 200 == 0: + print('{:5s}|{:12s}|{:12s}'.format( + 'It.', 'Err', 'Loss') + '\n' + '-' * 31) + print('{:5d}|{:8e}|{:8e}'.format(cpt, err, + gwloss_partial(C1, C2, G0))) + + cpt += 1 + + if log: + loge['partial_gw_dist'] = gwloss_partial(C1, C2, G0) + return G0, loge + else: + return G0 + + +def entropic_partial_gromov_wasserstein2(C1, C2, p, q, reg, m=None, G0=None, + numItermax=1000, tol=1e-7, log=False, + verbose=False): + r""" + Returns the partial Gromov-Wasserstein discrepancy between (C1,p) and + (C2,q) + + The function solves the following optimization problem: + + .. math:: + GW = \arg\min_{\gamma} \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})\cdot + \gamma_{i,j}\cdot\gamma_{k,l} + reg\cdot\Omega(\gamma) + + s.t. + \gamma\geq 0 \\ + \gamma 1 \leq a\\ + \gamma^T 1 \leq b\\ + 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} + + where : + + - C1 is the metric cost matrix in the source space + - C2 is the metric cost matrix in the target space + - p and q are the sample weights + - L : quadratic loss function + - :math:`\Omega` is the entropic regularization term + :math:`\Omega=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - m is the amount of mass to be transported + + The formulation of the problem has been proposed in [12]. + + + Parameters + ---------- + C1 : ndarray, shape (ns, ns) + Metric cost matrix in the source space + C2 : ndarray, shape (nt, nt) + Metric costfr matrix in the target space + p : ndarray, shape (ns,) + Distribution in the source space + q : ndarray, shape (nt,) + Distribution in the target space + reg: float + entropic regularization parameter + m : float, optional + Amount of mass to be transported (default: min (|p|_1, |q|_1)) + G0 : ndarray, shape (ns, nt), optional + Initialisation of the transportation matrix + numItermax : int, optional + Max number of iterations + log : bool, optional + return log if True + verbose : bool, optional + Print information along iterations + + + Returns + ------- + partial_gw_dist: float + Gromov-Wasserstein distance + log : dict + log dictionary returned only if `log` is `True` + + Examples + -------- + >>> import ot + >>> import scipy as sp + >>> a = np.array([0.25] * 4) + >>> b = np.array([0.25] * 4) + >>> x = np.array([1,2,100,200]).reshape((-1,1)) + >>> y = np.array([3,2,98,199]).reshape((-1,1)) + >>> C1 = sp.spatial.distance.cdist(x, x) + >>> C2 = sp.spatial.distance.cdist(y, y) + >>> np.round(entropic_partial_gromov_wasserstein2(C1, C2, a, b,50), 2) + 1.87 + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + """ + + partial_gw, log_gw = entropic_partial_gromov_wasserstein(C1, C2, p, q, reg, + m, G0, numItermax, + tol, True, + verbose) + + log_gw['T'] = partial_gw + + if log: + return log_gw['partial_gw_dist'], log_gw + else: + return log_gw['partial_gw_dist'] diff --git a/ot/unbalanced.py b/ot/unbalanced.py index 23f6607..66a8830 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -14,7 +14,7 @@ from scipy.special import logsumexp # from .utils import unif, dist -def sinkhorn_unbalanced(a, b, M, reg, reg_m, method='sinkhorn', numItermax=1000, +def sinkhorn_unbalanced(a, b, M, reg, reg_m, method='sinkhorn', div = "TV", numItermax=1000, stopThr=1e-6, verbose=False, log=False, **kwargs): r""" Solve the unbalanced entropic regularization optimal transport problem @@ -120,20 +120,20 @@ def sinkhorn_unbalanced(a, b, M, reg, reg_m, method='sinkhorn', numItermax=1000, """ if method.lower() == 'sinkhorn': - return sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, + return sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, div, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) elif method.lower() == 'sinkhorn_stabilized': - return sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, + return sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, div, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) elif method.lower() in ['sinkhorn_reg_scaling']: warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp') - return sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, + return sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, reg, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) @@ -261,8 +261,8 @@ def sinkhorn_unbalanced2(a, b, M, reg, reg_m, method='sinkhorn', else: raise ValueError('Unknown method %s.' % method) - -def sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, numItermax=1000, +# TODO: update the doc +def sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, div="KL", numItermax=1000, stopThr=1e-6, verbose=False, log=False, **kwargs): r""" Solve the entropic regularization unbalanced optimal transport problem and return the loss @@ -349,6 +349,7 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, numItermax=1000, """ a = np.asarray(a, dtype=np.float64) + print(a) b = np.asarray(b, dtype=np.float64) M = np.asarray(M, dtype=np.float64) @@ -376,24 +377,39 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, numItermax=1000, else: u = np.ones(dim_a) / dim_a v = np.ones(dim_b) / dim_b + u = np.ones(dim_a) + v = np.ones(dim_b) # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute K = np.empty(M.shape, dtype=M.dtype) - np.divide(M, -reg, out=K) + np.true_divide(M, -reg, out=K) np.exp(K, out=K) - - fi = reg_m / (reg_m + reg) + + if div == "KL": + fi = reg_m / (reg_m + reg) + elif div == "TV": + fi = reg_m / reg err = 1. + + dx = np.ones(dim_a) / dim_a + dy = np.ones(dim_b) / dim_b + z = 1 for i in range(numItermax): uprev = u vprev = v - Kv = K.dot(v) - u = (a / Kv) ** fi - Ktu = K.T.dot(u) - v = (b / Ktu) ** fi + Kv = z*K.dot(v*dy) + u = scaling_iter_prox(Kv, a, fi, div) + #u = (a / Kv) ** fi + Ktu = z*K.T.dot(u*dx) + v = scaling_iter_prox(Ktu, b, fi, div) + #v = (b / Ktu) ** fi + #print(v*dy) + z = np.dot((u*dx).T, np.dot(K,v*dy))/0.35 + print(z) + if (np.any(Ktu == 0.) or np.any(np.isnan(u)) or np.any(np.isnan(v)) @@ -434,12 +450,12 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, numItermax=1000, if log: return u[:, None] * K * v[None, :], log else: - return u[:, None] * K * v[None, :] - + return z*u[:, None] * K * v[None, :] -def sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, tau=1e5, numItermax=1000, - stopThr=1e-6, verbose=False, log=False, - **kwargs): +# TODO: update the doc +def sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, div = "KL", tau=1e5, + numItermax=1000, stopThr=1e-6, + verbose=False, log=False, **kwargs): r""" Solve the entropic regularization unbalanced optimal transport problem and return the loss @@ -564,7 +580,10 @@ def sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, tau=1e5, numItermax=1000 np.divide(M, -reg, out=K) np.exp(K, out=K) - fi = reg_m / (reg_m + reg) + if div == "KL": + fi = reg_m / (reg_m + reg) + elif div == "TV": + fi = reg_m / reg cpt = 0 err = 1. @@ -650,6 +669,15 @@ def sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, tau=1e5, numItermax=1000 else: return ot_matrix +def scaling_iter_prox(s, p, fi, div): + if div == "KL": + return (p / s) ** fi + elif div == "TV": + return np.minimum(s*np.exp(fi), np.maximum(s*np.exp(-fi), p)) / s + else: + raise ValueError("Unknown divergence '%s'." % div) + + def barycenter_unbalanced_stabilized(A, M, reg, reg_m, weights=None, tau=1e3, numItermax=1000, stopThr=1e-6, -- cgit v1.2.3 From 749378a50abd763c87f5cf24a4b2e0dff2a6ec6a Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 15 Apr 2020 11:12:23 +0200 Subject: fix soft labels, remove gammas from jcpot --- ot/bregman.py | 9 ++++----- ot/da.py | 40 +++++++++++++++++++++------------------- test/test_da.py | 14 +++++++++++++- 3 files changed, 38 insertions(+), 25 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index c44c141..543dbaa 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1553,8 +1553,6 @@ def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100, Returns ------- - gamma : List of K (nsk x nt) ndarrays - Optimal transportation matrices for the given parameters for each pair of source and target domains h : (C,) ndarray proportion estimation in the target domain log : dict @@ -1574,7 +1572,7 @@ def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100, # log dictionary if log: - log = {'niter': 0, 'err': [], 'M': [], 'D1': [], 'D2': []} + log = {'niter': 0, 'err': [], 'M': [], 'D1': [], 'D2': [], 'gamma': []} K = [] M = [] @@ -1657,9 +1655,10 @@ def jcpot_barycenter(Xs, Ys, Xt, reg, metric='sqeuclidean', numItermax=100, log['M'] = M log['D1'] = D1 log['D2'] = D2 - return K, bary, log + log['gamma'] = K + return bary, log else: - return K, bary + return bary def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', diff --git a/ot/da.py b/ot/da.py index 4318c0d..30e5a61 100644 --- a/ot/da.py +++ b/ot/da.py @@ -956,8 +956,8 @@ class BaseTransport(BaseEstimator): Returns ------- - transp_ys : array-like, shape (n_target_samples,) - Estimated target labels. + transp_ys : array-like, shape (n_target_samples, nb_classes) + Estimated soft target labels. References ---------- @@ -985,10 +985,10 @@ class BaseTransport(BaseEstimator): for c in classes: D1[int(c), ysTemp == c] = 1 - # compute transported samples + # compute propagated labels transp_ys = np.dot(D1, transp) - return np.argmax(transp_ys, axis=0) + return transp_ys.T def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): @@ -1066,8 +1066,8 @@ class BaseTransport(BaseEstimator): Returns ------- - transp_ys : array-like, shape (n_source_samples,) - Estimated source labels. + transp_ys : array-like, shape (n_source_samples, nb_classes) + Estimated soft source labels. """ # check the necessary inputs parameters are here @@ -1087,10 +1087,10 @@ class BaseTransport(BaseEstimator): for c in classes: D1[int(c), ytTemp == c] = 1 - # compute transported samples + # compute propagated samples transp_ys = np.dot(D1, transp.T) - return np.argmax(transp_ys, axis=0) + return transp_ys.T class LinearTransport(BaseTransport): @@ -2083,13 +2083,15 @@ class JCPOTTransport(BaseTransport): returned_ = jcpot_barycenter(Xs=Xs, Ys=ys, Xt=Xt, reg=self.reg_e, metric=self.metric, distrinumItermax=self.max_iter, stopThr=self.tol, - verbose=self.verbose, log=self.log) + verbose=self.verbose, log=True) + + self.coupling_ = returned_[1]['gamma'] # deal with the value of log if self.log: - self.coupling_, self.proportions_, self.log_ = returned_ + self.proportions_, self.log_ = returned_ else: - self.coupling_, self.proportions_ = returned_ + self.proportions_ = returned_ self.log_ = dict() return self @@ -2176,8 +2178,8 @@ class JCPOTTransport(BaseTransport): Returns ------- - yt : array-like, shape (n_target_samples,) - Estimated target labels. + yt : array-like, shape (n_target_samples, nb_classes) + Estimated soft target labels. """ # check the necessary inputs parameters are here @@ -2203,10 +2205,10 @@ class JCPOTTransport(BaseTransport): for c in classes: D1[int(c), ysTemp == c] = 1 - # compute transported samples + # compute propagated labels yt = yt + np.dot(D1, transp) / len(ys) - return np.argmax(yt, axis=0) + return yt.T def inverse_transform_labels(self, yt=None): """Propagate source labels ys to obtain target labels @@ -2218,8 +2220,8 @@ class JCPOTTransport(BaseTransport): Returns ------- - transp_ys : list of K array-like objects, shape K x (nk_source_samples,) - A list of estimated source labels + transp_ys : list of K array-like objects, shape K x (nk_source_samples, nb_classes) + A list of estimated soft source labels """ # check the necessary inputs parameters are here @@ -2241,7 +2243,7 @@ class JCPOTTransport(BaseTransport): # set nans to 0 transp[~ np.isfinite(transp)] = 0 - # compute transported labels - transp_ys.append(np.argmax(np.dot(D1, transp.T), axis=0)) + # compute propagated labels + transp_ys.append(np.dot(D1, transp.T).T) return transp_ys diff --git a/test/test_da.py b/test/test_da.py index d96046d..70296bf 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -68,10 +68,12 @@ def test_sinkhorn_lpl1_transport_class(): # check label propagation transp_yt = otda.transform_labels(ys) assert_equal(transp_yt.shape[0], yt.shape[0]) + assert_equal(transp_yt.shape[1], len(np.unique(ys))) # check inverse label propagation transp_ys = otda.inverse_transform_labels(yt) assert_equal(transp_ys.shape[0], ys.shape[0]) + assert_equal(transp_ys.shape[1], len(np.unique(yt))) # test unsupervised vs semi-supervised mode otda_unsup = ot.da.SinkhornLpl1Transport() @@ -140,10 +142,12 @@ def test_sinkhorn_l1l2_transport_class(): # check label propagation transp_yt = otda.transform_labels(ys) assert_equal(transp_yt.shape[0], yt.shape[0]) + assert_equal(transp_yt.shape[1], len(np.unique(ys))) # check inverse label propagation transp_ys = otda.inverse_transform_labels(yt) assert_equal(transp_ys.shape[0], ys.shape[0]) + assert_equal(transp_ys.shape[1], len(np.unique(yt))) Xt_new, _ = make_data_classif('3gauss2', nt + 1) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) @@ -229,10 +233,12 @@ def test_sinkhorn_transport_class(): # check label propagation transp_yt = otda.transform_labels(ys) assert_equal(transp_yt.shape[0], yt.shape[0]) + assert_equal(transp_yt.shape[1], len(np.unique(ys))) # check inverse label propagation transp_ys = otda.inverse_transform_labels(yt) assert_equal(transp_ys.shape[0], ys.shape[0]) + assert_equal(transp_ys.shape[1], len(np.unique(yt))) Xt_new, _ = make_data_classif('3gauss2', nt + 1) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) @@ -298,10 +304,12 @@ def test_unbalanced_sinkhorn_transport_class(): # check label propagation transp_yt = otda.transform_labels(ys) assert_equal(transp_yt.shape[0], yt.shape[0]) + assert_equal(transp_yt.shape[1], len(np.unique(ys))) # check inverse label propagation transp_ys = otda.inverse_transform_labels(yt) assert_equal(transp_ys.shape[0], ys.shape[0]) + assert_equal(transp_ys.shape[1], len(np.unique(yt))) Xs_new, _ = make_data_classif('3gauss', ns + 1) transp_Xs_new = otda.transform(Xs_new) @@ -388,10 +396,12 @@ def test_emd_transport_class(): # check label propagation transp_yt = otda.transform_labels(ys) assert_equal(transp_yt.shape[0], yt.shape[0]) + assert_equal(transp_yt.shape[1], len(np.unique(ys))) # check inverse label propagation transp_ys = otda.inverse_transform_labels(yt) assert_equal(transp_ys.shape[0], ys.shape[0]) + assert_equal(transp_ys.shape[1], len(np.unique(yt))) Xt_new, _ = make_data_classif('3gauss2', nt + 1) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) @@ -645,10 +655,12 @@ def test_jcpot_transport_class(): # check label propagation transp_yt = otda.transform_labels(ys) assert_equal(transp_yt.shape[0], yt.shape[0]) + assert_equal(transp_yt.shape[1], len(np.unique(ys))) # check inverse label propagation transp_ys = otda.inverse_transform_labels(yt) - [assert_equal(x.shape, y.shape) for x, y in zip(transp_ys, ys)] + [assert_equal(x.shape[0], y.shape[0]) for x, y in zip(transp_ys, ys)] + [assert_equal(x.shape[1], len(np.unique(y))) for x, y in zip(transp_ys, ys)] def test_jcpot_barycenter(): -- cgit v1.2.3 From 54a129f8f17cbdbfa03c3caa296f99423536cc32 Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 15 Apr 2020 11:20:14 +0200 Subject: fix jcpot_bary test --- test/test_da.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_da.py b/test/test_da.py index 70296bf..472dc19 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -685,7 +685,7 @@ def test_jcpot_barycenter(): Xs = [Xs1, Xs2] ys = [ys1, ys2] - _, prop, = ot.bregman.jcpot_barycenter(Xs, ys, Xt, reg=.5, metric='sqeuclidean', + prop = ot.bregman.jcpot_barycenter(Xs, ys, Xt, reg=.5, metric='sqeuclidean', numItermax=10000, stopThr=1e-9, verbose=False, log=False) np.testing.assert_allclose(prop, [1 - pt, pt], rtol=1e-3, atol=1e-3) -- cgit v1.2.3 From 7889484b79a425ebf3632444547a6092e814bf20 Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 15 Apr 2020 11:27:25 +0200 Subject: fix indent test_da --- test/test_da.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_da.py b/test/test_da.py index 472dc19..7d0fdda 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -686,6 +686,6 @@ def test_jcpot_barycenter(): ys = [ys1, ys2] prop = ot.bregman.jcpot_barycenter(Xs, ys, Xt, reg=.5, metric='sqeuclidean', - numItermax=10000, stopThr=1e-9, verbose=False, log=False) + numItermax=10000, stopThr=1e-9, verbose=False, log=False) np.testing.assert_allclose(prop, [1 - pt, pt], rtol=1e-3, atol=1e-3) -- cgit v1.2.3 From 93ef47c2d408cec7aba46815639b9bede52be92d Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 15 Apr 2020 11:46:36 +0200 Subject: description example laplacian --- examples/plot_otda_laplacian.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/plot_otda_laplacian.py b/examples/plot_otda_laplacian.py index 965380c..67c8f67 100644 --- a/examples/plot_otda_laplacian.py +++ b/examples/plot_otda_laplacian.py @@ -1,11 +1,11 @@ # -*- coding: utf-8 -*- """ -======================== -OT for domain adaptation -======================== +====================================================== +OT with Laplacian regularization for domain adaptation +====================================================== This example introduces a domain adaptation in a 2D setting and OTDA -approache with Laplacian regularization. +approach with Laplacian regularization. """ -- cgit v1.2.3 From 8c724ad3579959e9d369c0b7fbaa22ea19ced614 Mon Sep 17 00:00:00 2001 From: Laetitia Chapel Date: Wed, 15 Apr 2020 15:32:56 +0200 Subject: partial with tests --- examples/plot_partial_wass_and_gromov.py | 18 +++---- ot/__init__.py | 81 -------------------------------- ot/partial.py | 23 +++++---- ot/unbalanced.py | 66 ++++++++------------------ 4 files changed, 39 insertions(+), 149 deletions(-) delete mode 100644 ot/__init__.py diff --git a/examples/plot_partial_wass_and_gromov.py b/examples/plot_partial_wass_and_gromov.py index 2ddeb68..30b3fc0 100755 --- a/examples/plot_partial_wass_and_gromov.py +++ b/examples/plot_partial_wass_and_gromov.py @@ -33,9 +33,9 @@ mu = np.array([0, 0]) cov = np.array([[1, 0], [0, 2]]) xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) -xs = np.append(xs, (np.random.rand(n_noise, 2)+1)*4).reshape((-1, 2)) +xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2)) xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) -xt = np.append(xt, (np.random.rand(n_noise, 2)+1)*-3).reshape((-1, 2)) +xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2)) M = sp.spatial.distance.cdist(xs, xt) @@ -62,7 +62,7 @@ w, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=0.1, m=0.5, log=True) print('Partial Wasserstein distance (m = 0.5): ' + str(log0['partial_w_dist'])) -print('Entropic partial Wasserstein distance (m = 0.5): ' + \ +print('Entropic partial Wasserstein distance (m = 0.5): ' + str(log['partial_w_dist'])) pl.figure(1, (10, 5)) @@ -98,10 +98,10 @@ cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) -xs = np.concatenate((xs, ((np.random.rand(n_noise, 2)+1)*4)), axis=0) +xs = np.concatenate((xs, ((np.random.rand(n_noise, 2) + 1) * 4)), axis=0) P = sp.linalg.sqrtm(cov_t) xt = np.random.randn(n_samples, 3).dot(P) + mu_t -xt = np.concatenate((xt, ((np.random.rand(n_noise, 3)+1)*10)), axis=0) +xt = np.concatenate((xt, ((np.random.rand(n_noise, 3) + 1) * 10)), axis=0) fig = pl.figure() ax1 = fig.add_subplot(121) @@ -128,7 +128,7 @@ res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, m=m, log=True) print('Partial Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist'])) -print('Entropic partial Wasserstein distance (m = 1): ' + \ +print('Entropic partial Wasserstein distance (m = 1): ' + str(log['partial_gw_dist'])) pl.figure(1, (10, 5)) @@ -142,14 +142,14 @@ pl.title('Entropic partial Wasserstein') pl.show() print('-----m = 2/3') -m = 2/3 +m = 2 / 3 res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True) res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, m=m, log=True) -print('Partial Wasserstein distance (m = 2/3): ' + \ +print('Partial Wasserstein distance (m = 2/3): ' + str(log0['partial_gw_dist'])) -print('Entropic partial Wasserstein distance (m = 2/3): ' + \ +print('Entropic partial Wasserstein distance (m = 2/3): ' + str(log['partial_gw_dist'])) pl.figure(1, (10, 5)) diff --git a/ot/__init__.py b/ot/__init__.py deleted file mode 100644 index 89c7936..0000000 --- a/ot/__init__.py +++ /dev/null @@ -1,81 +0,0 @@ -""" - -This is the main module of the POT toolbox. It provides easy access to -a number of sub-modules and functions described below. - -.. note:: - - - Here is a list of the submodules and short description of what they contain. - - - :any:`ot.lp` contains OT solvers for the exact (Linear Program) OT problems. - - :any:`ot.bregman` contains OT solvers for the entropic OT problems using - Bregman projections. - - :any:`ot.lp` contains OT solvers for the exact (Linear Program) OT problems. - - :any:`ot.smooth` contains OT solvers for the regularized (l2 and kl) smooth OT - problems. - - :any:`ot.gromov` contains solvers for Gromov-Wasserstein and Fused Gromov - Wasserstein problems. - - :any:`ot.optim` contains generic solvers OT based optimization problems - - :any:`ot.da` contains classes and function related to Monge mapping - estimation and Domain Adaptation (DA). - - :any:`ot.gpu` contains GPU (cupy) implementation of some OT solvers - - :any:`ot.dr` contains Dimension Reduction (DR) methods such as Wasserstein - Discriminant Analysis. - - :any:`ot.utils` contains utility functions such as distance computation and - timing. - - :any:`ot.datasets` contains toy dataset generation functions. - - :any:`ot.plot` contains visualization functions - - :any:`ot.stochastic` contains stochastic solvers for regularized OT. - - :any:`ot.unbalanced` contains solvers for regularized unbalanced OT. - -.. warning:: - The list of automatically imported sub-modules is as follows: - :py:mod:`ot.lp`, :py:mod:`ot.bregman`, :py:mod:`ot.optim` - :py:mod:`ot.utils`, :py:mod:`ot.datasets`, - :py:mod:`ot.gromov`, :py:mod:`ot.smooth` - :py:mod:`ot.stochastic` - - The following sub-modules are not imported due to additional dependencies: - - - :any:`ot.dr` : depends on :code:`pymanopt` and :code:`autograd`. - - :any:`ot.gpu` : depends on :code:`cupy` and a CUDA GPU. - - :any:`ot.plot` : depends on :code:`matplotlib` - -""" - -# Author: Remi Flamary -# Nicolas Courty -# -# License: MIT License - - -# All submodules and packages -from . import lp -from . import bregman -from . import optim -from . import utils -from . import datasets -from . import da -from . import gromov -from . import smooth -from . import stochastic -from . import unbalanced - -# OT functions -from .lp import emd, emd2, emd_1d, emd2_1d, wasserstein_1d -from .bregman import sinkhorn, sinkhorn2, barycenter -from .unbalanced import sinkhorn_unbalanced, barycenter_unbalanced, sinkhorn_unbalanced2 -from .da import sinkhorn_lpl1_mm - -# utils functions -from .utils import dist, unif, tic, toc, toq - -__version__ = "0.6.0" - -__all__ = ['emd', 'emd2', 'emd_1d', 'sinkhorn', 'sinkhorn2', 'utils', 'datasets', - 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', - 'emd_1d', 'emd2_1d', 'wasserstein_1d', - 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim', - 'sinkhorn_unbalanced', 'barycenter_unbalanced', - 'sinkhorn_unbalanced2'] diff --git a/ot/partial.py b/ot/partial.py index 746f337..3425acb 100755 --- a/ot/partial.py +++ b/ot/partial.py @@ -9,12 +9,11 @@ Partial OT import numpy as np -from ot.lp import emd +from .lp import emd def partial_wasserstein_lagrange(a, b, M, reg_m=None, nb_dummies=1, log=False, **kwargs): - r""" Solves the partial optimal transport problem for the quadratic cost and returns the OT plan @@ -136,7 +135,7 @@ def partial_wasserstein_lagrange(a, b, M, reg_m=None, nb_dummies=1, log=False, if log_emd['warning'] is not None: raise ValueError("Error in the EMD resolution: try to increase the" " number of dummy points") - log_emd['cost'] = np.sum(gamma*M) + log_emd['cost'] = np.sum(gamma * M) if log: return gamma, log_emd else: @@ -233,7 +232,7 @@ def partial_wasserstein(a, b, M, m=None, nb_dummies=1, log=False, **kwargs): b_extended = np.append(b, [(np.sum(a) - m) / nb_dummies] * nb_dummies) a_extended = np.append(a, [(np.sum(b) - m) / nb_dummies] * nb_dummies) - M_extended = np.ones((len(a_extended), len(b_extended))) * np.max(M) * 1e2 + M_extended = np.ones((len(a_extended), len(b_extended))) * 0 M_extended[-1, -1] = np.max(M) * 1e5 M_extended[:len(a), :len(b)] = M @@ -381,7 +380,7 @@ def gwloss_partial(C1, C2, T): Returns ------- - GW loss + GW loss """ g = gwgrad_partial(C1, C2, T) * 0.5 return np.sum(g * T) @@ -432,7 +431,7 @@ def partial_gromov_wasserstein(C1, C2, p, q, m=None, nb_dummies=1, G0=None, G0 : ndarray, shape (ns, nt), optional Initialisation of the transportation matrix thres : float, optional - quantile of the gradient matrix to populate the cost matrix when 0 + quantile of the gradient matrix to populate the cost matrix when 0 (default: 1) numItermax : int, optional Max number of iterations @@ -566,7 +565,7 @@ def partial_gromov_wasserstein2(C1, C2, p, q, m=None, nb_dummies=1, G0=None, where : - M is the metric cost matrix - - :math:`\Omega` is the entropic regularization term + - :math:`\Omega` is the entropic regularization term :math:`\Omega=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - a and b are the sample weights - m is the amount of mass to be transported @@ -591,7 +590,7 @@ def partial_gromov_wasserstein2(C1, C2, p, q, m=None, nb_dummies=1, G0=None, G0 : ndarray, shape (ns, nt), optional Initialisation of the transportation matrix thres : float, optional - quantile of the gradient matrix to populate the cost matrix when 0 + quantile of the gradient matrix to populate the cost matrix when 0 (default: 1) numItermax : int, optional Max number of iterations @@ -666,7 +665,7 @@ def entropic_partial_wasserstein(a, b, M, reg, m=None, numItermax=1000, where : - M is the metric cost matrix - - :math:`\Omega` is the entropic regularization term + - :math:`\Omega` is the entropic regularization term :math:`\Omega=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - a and b are the sample weights - m is the amount of mass to be transported @@ -754,7 +753,7 @@ def entropic_partial_wasserstein(a, b, M, reg, m=None, numItermax=1000, K = np.empty(M.shape, dtype=M.dtype) np.divide(M, -reg, out=K) np.exp(K, out=K) - np.multiply(K, m/np.sum(K), out=K) + np.multiply(K, m / np.sum(K), out=K) err, cpt = 1, 0 @@ -809,7 +808,7 @@ def entropic_partial_gromov_wasserstein(C1, C2, p, q, reg, m=None, G0=None, - C2 is the metric cost matrix in the target space - p and q are the sample weights - L : quadratic loss function - - :math:`\Omega` is the entropic regularization term + - :math:`\Omega` is the entropic regularization term :math:`\Omega=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - m is the amount of mass to be transported @@ -944,7 +943,7 @@ def entropic_partial_gromov_wasserstein2(C1, C2, p, q, reg, m=None, G0=None, - C2 is the metric cost matrix in the target space - p and q are the sample weights - L : quadratic loss function - - :math:`\Omega` is the entropic regularization term + - :math:`\Omega` is the entropic regularization term :math:`\Omega=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - m is the amount of mass to be transported diff --git a/ot/unbalanced.py b/ot/unbalanced.py index 66a8830..23f6607 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -14,7 +14,7 @@ from scipy.special import logsumexp # from .utils import unif, dist -def sinkhorn_unbalanced(a, b, M, reg, reg_m, method='sinkhorn', div = "TV", numItermax=1000, +def sinkhorn_unbalanced(a, b, M, reg, reg_m, method='sinkhorn', numItermax=1000, stopThr=1e-6, verbose=False, log=False, **kwargs): r""" Solve the unbalanced entropic regularization optimal transport problem @@ -120,20 +120,20 @@ def sinkhorn_unbalanced(a, b, M, reg, reg_m, method='sinkhorn', div = "TV", numI """ if method.lower() == 'sinkhorn': - return sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, div, + return sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) elif method.lower() == 'sinkhorn_stabilized': - return sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, div, + return sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) elif method.lower() in ['sinkhorn_reg_scaling']: warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp') - return sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, reg, + return sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) @@ -261,8 +261,8 @@ def sinkhorn_unbalanced2(a, b, M, reg, reg_m, method='sinkhorn', else: raise ValueError('Unknown method %s.' % method) -# TODO: update the doc -def sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, div="KL", numItermax=1000, + +def sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, numItermax=1000, stopThr=1e-6, verbose=False, log=False, **kwargs): r""" Solve the entropic regularization unbalanced optimal transport problem and return the loss @@ -349,7 +349,6 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, div="KL", numItermax=1000, """ a = np.asarray(a, dtype=np.float64) - print(a) b = np.asarray(b, dtype=np.float64) M = np.asarray(M, dtype=np.float64) @@ -377,39 +376,24 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, div="KL", numItermax=1000, else: u = np.ones(dim_a) / dim_a v = np.ones(dim_b) / dim_b - u = np.ones(dim_a) - v = np.ones(dim_b) # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute K = np.empty(M.shape, dtype=M.dtype) - np.true_divide(M, -reg, out=K) + np.divide(M, -reg, out=K) np.exp(K, out=K) - - if div == "KL": - fi = reg_m / (reg_m + reg) - elif div == "TV": - fi = reg_m / reg + + fi = reg_m / (reg_m + reg) err = 1. - - dx = np.ones(dim_a) / dim_a - dy = np.ones(dim_b) / dim_b - z = 1 for i in range(numItermax): uprev = u vprev = v - Kv = z*K.dot(v*dy) - u = scaling_iter_prox(Kv, a, fi, div) - #u = (a / Kv) ** fi - Ktu = z*K.T.dot(u*dx) - v = scaling_iter_prox(Ktu, b, fi, div) - #v = (b / Ktu) ** fi - #print(v*dy) - z = np.dot((u*dx).T, np.dot(K,v*dy))/0.35 - print(z) - + Kv = K.dot(v) + u = (a / Kv) ** fi + Ktu = K.T.dot(u) + v = (b / Ktu) ** fi if (np.any(Ktu == 0.) or np.any(np.isnan(u)) or np.any(np.isnan(v)) @@ -450,12 +434,12 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, div="KL", numItermax=1000, if log: return u[:, None] * K * v[None, :], log else: - return z*u[:, None] * K * v[None, :] + return u[:, None] * K * v[None, :] + -# TODO: update the doc -def sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, div = "KL", tau=1e5, - numItermax=1000, stopThr=1e-6, - verbose=False, log=False, **kwargs): +def sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, tau=1e5, numItermax=1000, + stopThr=1e-6, verbose=False, log=False, + **kwargs): r""" Solve the entropic regularization unbalanced optimal transport problem and return the loss @@ -580,10 +564,7 @@ def sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, div = "KL", tau=1e5, np.divide(M, -reg, out=K) np.exp(K, out=K) - if div == "KL": - fi = reg_m / (reg_m + reg) - elif div == "TV": - fi = reg_m / reg + fi = reg_m / (reg_m + reg) cpt = 0 err = 1. @@ -669,15 +650,6 @@ def sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, div = "KL", tau=1e5, else: return ot_matrix -def scaling_iter_prox(s, p, fi, div): - if div == "KL": - return (p / s) ** fi - elif div == "TV": - return np.minimum(s*np.exp(fi), np.maximum(s*np.exp(-fi), p)) / s - else: - raise ValueError("Unknown divergence '%s'." % div) - - def barycenter_unbalanced_stabilized(A, M, reg, reg_m, weights=None, tau=1e3, numItermax=1000, stopThr=1e-6, -- cgit v1.2.3 From 13444cabb8318a7759e2d0941baf4aba67308a51 Mon Sep 17 00:00:00 2001 From: Laetitia Chapel Date: Wed, 15 Apr 2020 15:35:16 +0200 Subject: partial with tests --- test/test_partial.py | 141 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 141 insertions(+) create mode 100755 test/test_partial.py diff --git a/test/test_partial.py b/test/test_partial.py new file mode 100755 index 0000000..fbcd3c2 --- /dev/null +++ b/test/test_partial.py @@ -0,0 +1,141 @@ +"""Tests for module partial """ + +# Author: +# Laetitia Chapel +# +# License: MIT License + +import numpy as np +import scipy as sp +import ot + + +def test_partial_wasserstein(): + + n_samples = 20 # nb samples (gaussian) + n_noise = 20 # nb of samples (noise) + + mu = np.array([0, 0]) + cov = np.array([[1, 0], [0, 2]]) + + xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) + xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2)) + xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) + xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2)) + + M = ot.dist(xs, xt) + + p = ot.unif(n_samples + n_noise) + q = ot.unif(n_samples + n_noise) + + m = 0.5 + + w0, log0 = ot.partial.partial_wasserstein(p, q, M, m=m, log=True) + w, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=1, m=m, + log=True) + + # check constratints + np.testing.assert_equal( + w0.sum(1) - p <= 1e-5, [True] * len(p)) # cf convergence wasserstein + np.testing.assert_equal( + w0.sum(0) - q <= 1e-5, [True] * len(q)) # cf convergence wasserstein + np.testing.assert_equal( + w.sum(1) - p <= 1e-5, [True] * len(p)) # cf convergence wasserstein + np.testing.assert_equal( + w.sum(0) - q <= 1e-5, [True] * len(q)) # cf convergence wasserstein + + # check transported mass + np.testing.assert_allclose( + np.sum(w0), m, atol=1e-04) + np.testing.assert_allclose( + np.sum(w), m, atol=1e-04) + + w0, log0 = ot.partial.partial_wasserstein2(p, q, M, m=m, log=True) + w0_val = ot.partial.partial_wasserstein2(p, q, M, m=m, log=False) + + G = log0['T'] + + np.testing.assert_allclose(w0, w0_val, atol=1e-1, rtol=1e-1) + + # check constratints + np.testing.assert_equal( + G.sum(1) <= p, [True] * len(p)) # cf convergence wasserstein + np.testing.assert_equal( + G.sum(0) <= q, [True] * len(q)) # cf convergence wasserstein + np.testing.assert_allclose( + np.sum(G), m, atol=1e-04) + + +def test_partial_gromov_wasserstein(): + n_samples = 20 # nb samples + n_noise = 10 # nb of samples (noise) + + p = ot.unif(n_samples + n_noise) + q = ot.unif(n_samples + n_noise) + + mu_s = np.array([0, 0]) + cov_s = np.array([[1, 0], [0, 1]]) + + mu_t = np.array([0, 0, 0]) + cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) + + xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) + xs = np.concatenate((xs, ((np.random.rand(n_noise, 2) + 1) * 4)), axis=0) + P = sp.linalg.sqrtm(cov_t) + xt = np.random.randn(n_samples, 3).dot(P) + mu_t + xt = np.concatenate((xt, ((np.random.rand(n_noise, 3) + 1) * 10)), axis=0) + xt2 = xs[::-1].copy() + + C1 = ot.dist(xs, xs) + C2 = ot.dist(xt, xt) + C3 = ot.dist(xt2, xt2) + + m = 2 / 3 + res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C3, p, q, m=m, + log=True) + res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C3, p, q, 10, + m=m, log=True) + np.testing.assert_allclose(res0, 0, atol=1e-1, rtol=1e-1) + np.testing.assert_allclose(res, 0, atol=1e-1, rtol=1e-1) + + C1 = sp.spatial.distance.cdist(xs, xs) + C2 = sp.spatial.distance.cdist(xt, xt) + + m = 1 + res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, + log=True) + G = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss') + np.testing.assert_allclose(G, res0, atol=1e-04) + + res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, + m=m, log=True) + G = ot.gromov.entropic_gromov_wasserstein( + C1, C2, p, q, 'square_loss', epsilon=10) + np.testing.assert_allclose(G, res, atol=1e-02) + + w0, log0 = ot.partial.partial_gromov_wasserstein2(C1, C2, p, q, m=m, + log=True) + w0_val = ot.partial.partial_gromov_wasserstein2(C1, C2, p, q, m=m, + log=False) + G = log0['T'] + np.testing.assert_allclose(w0, w0_val, atol=1e-1, rtol=1e-1) + + m = 2 / 3 + res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, + log=True) + res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, + m=m, log=True) + # check constratints + np.testing.assert_equal( + res0.sum(1) <= p, [True] * len(p)) # cf convergence wasserstein + np.testing.assert_equal( + res0.sum(0) <= q, [True] * len(q)) # cf convergence wasserstein + np.testing.assert_allclose( + np.sum(res0), m, atol=1e-04) + + np.testing.assert_equal( + res.sum(1) <= p, [True] * len(p)) # cf convergence wasserstein + np.testing.assert_equal( + res.sum(0) <= q, [True] * len(q)) # cf convergence wasserstein + np.testing.assert_allclose( + np.sum(res), m, atol=1e-04) -- cgit v1.2.3 From 590b934b746ab2dc6d67c34990428f94c419c084 Mon Sep 17 00:00:00 2001 From: Laetitia Chapel Date: Wed, 15 Apr 2020 16:28:27 +0200 Subject: partial with init --- ot/__init__.py | 83 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 83 insertions(+) create mode 100644 ot/__init__.py diff --git a/ot/__init__.py b/ot/__init__.py new file mode 100644 index 0000000..4fcb800 --- /dev/null +++ b/ot/__init__.py @@ -0,0 +1,83 @@ +""" + +This is the main module of the POT toolbox. It provides easy access to +a number of sub-modules and functions described below. + +.. note:: + + + Here is a list of the submodules and short description of what they contain. + + - :any:`ot.lp` contains OT solvers for the exact (Linear Program) OT problems. + - :any:`ot.bregman` contains OT solvers for the entropic OT problems using + Bregman projections. + - :any:`ot.lp` contains OT solvers for the exact (Linear Program) OT problems. + - :any:`ot.smooth` contains OT solvers for the regularized (l2 and kl) smooth OT + problems. + - :any:`ot.gromov` contains solvers for Gromov-Wasserstein and Fused Gromov + Wasserstein problems. + - :any:`ot.optim` contains generic solvers OT based optimization problems + - :any:`ot.da` contains classes and function related to Monge mapping + estimation and Domain Adaptation (DA). + - :any:`ot.gpu` contains GPU (cupy) implementation of some OT solvers + - :any:`ot.dr` contains Dimension Reduction (DR) methods such as Wasserstein + Discriminant Analysis. + - :any:`ot.utils` contains utility functions such as distance computation and + timing. + - :any:`ot.datasets` contains toy dataset generation functions. + - :any:`ot.plot` contains visualization functions + - :any:`ot.stochastic` contains stochastic solvers for regularized OT. + - :any:`ot.unbalanced` contains solvers for regularized unbalanced OT. + - :any:`ot.partial` contains solvers for partial OT. + +.. warning:: + The list of automatically imported sub-modules is as follows: + :py:mod:`ot.lp`, :py:mod:`ot.bregman`, :py:mod:`ot.optim` + :py:mod:`ot.utils`, :py:mod:`ot.datasets`, + :py:mod:`ot.gromov`, :py:mod:`ot.smooth` + :py:mod:`ot.stochastic` + + The following sub-modules are not imported due to additional dependencies: + + - :any:`ot.dr` : depends on :code:`pymanopt` and :code:`autograd`. + - :any:`ot.gpu` : depends on :code:`cupy` and a CUDA GPU. + - :any:`ot.plot` : depends on :code:`matplotlib` + +""" + +# Author: Remi Flamary +# Nicolas Courty +# +# License: MIT License + + +# All submodules and packages +from . import lp +from . import bregman +from . import optim +from . import utils +from . import datasets +from . import da +from . import gromov +from . import smooth +from . import stochastic +from . import unbalanced +from . import partial + +# OT functions +from .lp import emd, emd2, emd_1d, emd2_1d, wasserstein_1d +from .bregman import sinkhorn, sinkhorn2, barycenter +from .unbalanced import sinkhorn_unbalanced, barycenter_unbalanced, sinkhorn_unbalanced2 +from .da import sinkhorn_lpl1_mm + +# utils functions +from .utils import dist, unif, tic, toc, toq + +__version__ = "0.6.0" + +__all__ = ['emd', 'emd2', 'emd_1d', 'sinkhorn', 'sinkhorn2', 'utils', 'datasets', + 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', + 'emd_1d', 'emd2_1d', 'wasserstein_1d', + 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim', + 'sinkhorn_unbalanced', 'barycenter_unbalanced', + 'sinkhorn_unbalanced2'] -- cgit v1.2.3 From 77cae32e956d87cfc1f69a0ea7a28c906347070d Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 15 Apr 2020 16:43:40 +0200 Subject: check conflict da --- ot/da.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/da.py b/ot/da.py index 108609f..272af91 100644 --- a/ot/da.py +++ b/ot/da.py @@ -16,7 +16,7 @@ import scipy.linalg as linalg from .bregman import sinkhorn from .lp import emd -from .utils import unif, dist, kernel, cost_normalization, laplacian +from .utils import unif, dist, kernel, cost_normalization, label_normalization from .utils import check_params, BaseEstimator from .unbalanced import sinkhorn_unbalanced from .optim import cg -- cgit v1.2.3 From dda4941eb9bd184aee7db8eff229edc3ea1979df Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 15 Apr 2020 16:44:54 +0200 Subject: conflict readme --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index f439405..c96731c 100644 --- a/README.md +++ b/README.md @@ -29,7 +29,7 @@ It provides the following solvers: * Non regularized free support Wasserstein barycenters [20]. * Unbalanced OT with KL relaxation distance and barycenter [10, 25]. * Screening Sinkhorn Algorithm for OT [26]. -* JCPOT algorithm for multi-source target shift [27]. +* JCPOT algorithm for multi-source domain adaptation with target shift [27]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -259,4 +259,4 @@ You can also post bug reports and feature requests in Github issues. Make sure t [26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 33 (NeurIPS). -[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019. \ No newline at end of file +[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019. -- cgit v1.2.3 From ef50bae5a22c6e69bcc77b8f925551208079b19e Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 15 Apr 2020 16:47:01 +0200 Subject: readme --- README.md | 242 +++++++++++++++++++++++++++++++------------------------------- 1 file changed, 121 insertions(+), 121 deletions(-) diff --git a/README.md b/README.md index c96731c..8bd833c 100644 --- a/README.md +++ b/README.md @@ -1,58 +1,60 @@ # POT: Python Optimal Transport -[![PyPI version](https://badge.fury.io/py/POT.svg)](https://badge.fury.io/py/POT) -[![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot) -[![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/rflamary/POT) -[![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) -[![Downloads](https://pepy.tech/badge/pot)](https://pepy.tech/project/pot) -[![Anaconda downloads](https://anaconda.org/conda-forge/pot/badges/downloads.svg)](https://anaconda.org/conda-forge/pot) -[![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/rflamary/POT/blob/master/LICENSE) - +import ot +[![PyPI version](https: // badge.fury.io / py / POT.svg)](https: // badge.fury.io / py / POT) +[![Anaconda Cloud](https: // anaconda.org / conda - forge / pot / badges / version.svg)](https: // anaconda.org / conda - forge / pot) +[![Build Status](https: // travis - ci.org / rflamary / POT.svg?branch=master)](https: // travis - ci.org / rflamary / POT) +[![Documentation Status](https: // readthedocs.org / projects / pot / badge /?version=latest)](http: // pot.readthedocs.io / en / latest /?badge=latest) +[![Downloads](https: // pepy.tech / badge / pot)](https: // pepy.tech / project / pot) +[![Anaconda downloads](https: // anaconda.org / conda - forge / pot / badges / downloads.svg)](https: // anaconda.org / conda - forge / pot) +[![License](https: // anaconda.org / conda - forge / pot / badges / license.svg)](https: // github.com / rflamary / POT / blob / master / LICENSE) This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. It provides the following solvers: -* OT Network Flow solver for the linear program/ Earth Movers Distance [1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU implementation (requires cupy). -* Sinkhorn divergence [23] and entropic regularization OT from empirical data. -* Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. -* Non regularized Wasserstein barycenters [16] with LP solver (only small scale). -* Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4]. -* Optimal transport for domain adaptation with group lasso regularization [5] -* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. -* Linear OT [14] and Joint OT matrix and mapping estimation [8]. -* Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). -* Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) -* Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) -* Non regularized free support Wasserstein barycenters [20]. -* Unbalanced OT with KL relaxation distance and barycenter [10, 25]. -* Screening Sinkhorn Algorithm for OT [26]. -* JCPOT algorithm for multi-source domain adaptation with target shift [27]. - -Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. +* OT Network Flow solver for the linear program / Earth Movers Distance[1]. +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm[2], stabilized version[9][10] and greedy Sinkhorn[22] with optional GPU implementation(requires cupy). +* Sinkhorn divergence[23] and entropic regularization OT from empirical data. +* Smooth optimal transport solvers(dual and semi - dual) for KL and squared L2 regularizations[17]. +* Non regularized Wasserstein barycenters[16] with LP solver(only small scale). +* Bregman projections for Wasserstein barycenter[3], convolutional barycenter[21] and unmixing[4]. +* Optimal transport for domain adaptation with group lasso regularization[5] +* Conditional gradient[6] and Generalized conditional gradient for regularized OT[7]. +* Linear OT[14] and Joint OT matrix and mapping estimation[8]. +* Wasserstein Discriminant Analysis[11](requires autograd + pymanopt). +* Gromov - Wasserstein distances and barycenters([13] and regularized[12]) +* Stochastic Optimization for Large - scale Optimal Transport(semi - dual problem[18] and dual problem[19]) +* Non regularized free support Wasserstein barycenters[20]. +* Unbalanced OT with KL relaxation distance and barycenter[10, 25]. +* Screening Sinkhorn Algorithm for OT[26]. +* JCPOT algorithm for multi - source domain adaptation with target shift[27]. + +Some demonstrations(both in Python and Jupyter Notebook format) are available in the examples folder. #### Using and citing the toolbox If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference: ``` + + @misc{flamary2017pot, -title={POT Python Optimal Transport library}, -author={Flamary, R{'e}mi and Courty, Nicolas}, -url={https://github.com/rflamary/POT}, -year={2017} -} + title = {POT Python Optimal Transport library}, + author = {Flamary, R{'e}mi and Courty, Nicolas}, + url = {https: // github.com / rflamary / POT}, + year = {2017} + } ``` ## Installation -The library has been tested on Linux, MacOSX and Windows. It requires a C++ compiler for building/installing the EMD solver and relies on the following Python modules: +The library has been tested on Linux, MacOSX and Windows. It requires a C + + compiler for building / installing the EMD solver and relies on the following Python modules: -- Numpy (>=1.11) -- Scipy (>=1.0) -- Cython (>=0.23) -- Matplotlib (>=1.5) +- Numpy ( >= 1.11) +- Scipy ( >= 1.0) +- Cython ( >= 0.23) +- Matplotlib ( >= 1.5) #### Pip installation @@ -68,35 +70,33 @@ pip install POT ``` or get the very latest version by downloading it and then running: ``` -python setup.py install --user # for user install (no root) +python setup.py install - -user # for user install (no root) ``` - #### Anaconda installation with conda-forge -If you use the Anaconda python distribution, POT is available in [conda-forge](https://conda-forge.org). To install it and the required dependencies: +If you use the Anaconda python distribution, POT is available in [conda - forge](https: // conda - forge.org). To install it and the required dependencies: ``` -conda install -c conda-forge pot +conda install - c conda - forge pot ``` #### Post installation check After a correct installation, you should be able to import the module without errors: ```python -import ot ``` Note that for easier access the module is name ot instead of pot. ### Dependencies -Some sub-modules require additional dependences which are discussed below +Some sub - modules require additional dependences which are discussed below -* **ot.dr** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: +* **ot.dr ** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: ``` pip install pymanopt autograd ``` -* **ot.gpu** (GPU accelerated OT) depends on cupy that have to be installed following instructions on [this page](https://docs-cupy.chainer.org/en/stable/install.html). +* **ot.gpu ** (GPU accelerated OT) depends on cupy that have to be installed following instructions on[this page](https: // docs - cupy.chainer.org / en / stable / install.html). obviously you need CUDA installed and a compatible GPU. @@ -107,156 +107,156 @@ obviously you need CUDA installed and a compatible GPU. * Import the toolbox ```python -import ot ``` * Compute Wasserstein distances ```python # a,b are 1D histograms (sum to 1 and positive) # M is the ground cost matrix -Wd=ot.emd2(a,b,M) # exact linear program -Wd_reg=ot.sinkhorn2(a,b,M,reg) # entropic regularized OT +Wd = ot.emd2(a, b, M) # exact linear program +Wd_reg = ot.sinkhorn2(a, b, M, reg) # entropic regularized OT # if b is a matrix compute all distances to a and return a vector ``` * Compute OT matrix ```python # a,b are 1D histograms (sum to 1 and positive) # M is the ground cost matrix -T=ot.emd(a,b,M) # exact linear program -T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT +T = ot.emd(a, b, M) # exact linear program +T_reg = ot.sinkhorn(a, b, M, reg) # entropic regularized OT ``` * Compute Wasserstein barycenter ```python # A is a n*d matrix containing d 1D histograms # M is the ground cost matrix -ba=ot.barycenter(A,M,reg) # reg is regularization parameter +ba = ot.barycenter(A, M, reg) # reg is regularization parameter ``` - - ### Examples and Notebooks -The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http://pot.readthedocs.io/). +The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http: // pot.readthedocs.io / ). -Here is a list of the Python notebooks available [here](https://github.com/rflamary/POT/blob/master/notebooks/) if you want a quick look: +Here is a list of the Python notebooks available [here](https: // github.com / rflamary / POT / blob / master / notebooks / ) if you want a quick look: -* [1D optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_1D.ipynb) -* [OT Ground Loss](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_L1_vs_L2.ipynb) -* [Multiple EMD computation](https://github.com/rflamary/POT/blob/master/notebooks/plot_compute_emd.ipynb) -* [2D optimal transport on empirical distributions](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_2D_samples.ipynb) -* [1D Wasserstein barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_1D.ipynb) -* [OT with user provided regularization](https://github.com/rflamary/POT/blob/master/notebooks/plot_optim_OTreg.ipynb) -* [Domain adaptation with optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_d2.ipynb) -* [Color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_color_images.ipynb) -* [OT mapping estimation for domain adaptation](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping.ipynb) -* [OT mapping estimation for color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping_colors_images.ipynb) -* [Wasserstein Discriminant Analysis](https://github.com/rflamary/POT/blob/master/notebooks/plot_WDA.ipynb) -* [Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov.ipynb) -* [Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov_barycenter.ipynb) -* [Fused Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_fgw.ipynb) -* [Fused Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_fgw.ipynb) +* [1D optimal transport](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_1D.ipynb) +* [OT Ground Loss](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_L1_vs_L2.ipynb) +* [Multiple EMD computation](https: // github.com / rflamary / POT / blob / master / notebooks / plot_compute_emd.ipynb) +* [2D optimal transport on empirical distributions](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_2D_samples.ipynb) +* [1D Wasserstein barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_barycenter_1D.ipynb) +* [OT with user provided regularization](https: // github.com / rflamary / POT / blob / master / notebooks / plot_optim_OTreg.ipynb) +* [Domain adaptation with optimal transport](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_d2.ipynb) +* [Color transfer in images](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_color_images.ipynb) +* [OT mapping estimation for domain adaptation](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_mapping.ipynb) +* [OT mapping estimation for color transfer in images](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_mapping_colors_images.ipynb) +* [Wasserstein Discriminant Analysis](https: // github.com / rflamary / POT / blob / master / notebooks / plot_WDA.ipynb) +* [Gromov Wasserstein](https: // github.com / rflamary / POT / blob / master / notebooks / plot_gromov.ipynb) +* [Gromov Wasserstein Barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_gromov_barycenter.ipynb) +* [Fused Gromov Wasserstein](https: // github.com / rflamary / POT / blob / master / notebooks / plot_fgw.ipynb) +* [Fused Gromov Wasserstein Barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_barycenter_fgw.ipynb) -You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/notebooks/). +You can also see the notebooks with [Jupyter nbviewer](https: // nbviewer.jupyter.org / github / rflamary / POT / tree / master / notebooks / ). ## Acknowledgements This toolbox has been created and is maintained by -* [Rémi Flamary](http://remi.flamary.com/) -* [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) +* [Rémi Flamary](http: // remi.flamary.com / ) +* [Nicolas Courty](http: // people.irisa.fr / Nicolas.Courty / ) -The contributors to this library are +The contributors to this library are -* [Alexandre Gramfort](http://alexandre.gramfort.net/) -* [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) -* [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation) -* [Léo Gautheron](https://github.com/aje) (GPU implementation) -* [Nathalie Gayraud](https://www.linkedin.com/in/nathalie-t-h-gayraud/?ppe=1) -* [Stanislas Chambon](https://slasnista.github.io/) -* [Antoine Rolet](https://arolet.github.io/) -* Erwan Vautier (Gromov-Wasserstein) -* [Kilian Fatras](https://kilianfatras.github.io/) -* [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home) -* [Vayer Titouan](https://tvayer.github.io/) -* [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT) -* [Romain Tavenard](https://rtavenar.github.io/) (1d Wasserstein) -* [Mokhtar Z. Alaya](http://mzalaya.github.io/) (Screenkhorn) +* [Alexandre Gramfort](http: // alexandre.gramfort.net / ) +* [Laetitia Chapel](http: // people.irisa.fr / Laetitia.Chapel / ) +* [Michael Perrot](http: // perso.univ - st - etienne.fr / pem82055 / ) (Mapping estimation) +* [Léo Gautheron](https: // github.com / aje)(GPU implementation) +* [Nathalie Gayraud](https: // www.linkedin.com / in / nathalie - t - h - gayraud /?ppe=1) +* [Stanislas Chambon](https: // slasnista.github.io / ) +* [Antoine Rolet](https: // arolet.github.io / ) +* Erwan Vautier(Gromov - Wasserstein) +* [Kilian Fatras](https: // kilianfatras.github.io / ) +* [Alain Rakotomamonjy](https: // sites.google.com / site / alainrakotomamonjy / home) +* [Vayer Titouan](https: // tvayer.github.io / ) +* [Hicham Janati](https: // hichamjanati.github.io / ) (Unbalanced OT) +* [Romain Tavenard](https: // rtavenar.github.io / ) (1d Wasserstein) +* [Mokhtar Z. Alaya](http: // mzalaya.github.io / ) (Screenkhorn) -This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): +This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code(in various languages): -* [Gabriel Peyré](http://gpeyre.github.io/) (Wasserstein Barycenters in Matlab) -* [Nicolas Bonneel](http://liris.cnrs.fr/~nbonneel/) ( C++ code for EMD) -* [Marco Cuturi](http://marcocuturi.net/) (Sinkhorn Knopp in Matlab/Cuda) +* [Gabriel Peyré](http: // gpeyre.github.io / ) (Wasserstein Barycenters in Matlab) +* [Nicolas Bonneel](http: // liris.cnrs.fr / ~nbonneel /) (C++ code for EMD) +* [Marco Cuturi](http: // marcocuturi.net / ) (Sinkhorn Knopp in Matlab/Cuda) ## Contributions and code of conduct -Every contribution is welcome and should respect the [contribution guidelines](CONTRIBUTING.md). Each member of the project is expected to follow the [code of conduct](CODE_OF_CONDUCT.md). +Every contribution is welcome and should respect the[contribution guidelines](CONTRIBUTING.md). Each member of the project is expected to follow the[code of conduct](CODE_OF_CONDUCT.md). ## Support You can ask questions and join the development discussion: -* On the [POT Slack channel](https://pot-toolbox.slack.com) -* On the POT [mailing list](https://mail.python.org/mm3/mailman3/lists/pot.python.org/) +* On the[POT Slack channel](https: // pot - toolbox.slack.com) +* On the POT [mailing list](https: // mail.python.org / mm3 / mailman3 / lists / pot.python.org / ) -You can also post bug reports and feature requests in Github issues. Make sure to read our [guidelines](CONTRIBUTING.md) first. +You can also post bug reports and feature requests in Github issues. Make sure to read our[guidelines](CONTRIBUTING.md) first. ## References -[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). [Displacement interpolation using Lagrangian mass transport](https://people.csail.mit.edu/sparis/publi/2011/sigasia/Bonneel_11_Displacement_Interpolation.pdf). In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. +[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). [Displacement interpolation using Lagrangian mass transport](https: // people.csail.mit.edu / sparis / publi / 2011 / sigasia / Bonneel_11_Displacement_Interpolation.pdf). In ACM Transactions on Graphics(TOG)(Vol. 30, No. 6, p. 158). ACM. -[2] Cuturi, M. (2013). [Sinkhorn distances: Lightspeed computation of optimal transport](https://arxiv.org/pdf/1306.0895.pdf). In Advances in Neural Information Processing Systems (pp. 2292-2300). +[2] Cuturi, M. (2013). [Sinkhorn distances: Lightspeed computation of optimal transport](https: // arxiv.org / pdf / 1306.0895.pdf). In Advances in Neural Information Processing Systems(pp. 2292 - 2300). -[3] Benamou, J. 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Corpetti, [Supervised planetary unmixing with optimal transport](https: // hal.archives - ouvertes.fr / hal - 01377236 / document), Whorkshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing(WHISPERS), 2016. -[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, [Optimal Transport for Domain Adaptation](https://arxiv.org/pdf/1507.00504.pdf), in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 +[5] N. Courty +R. Flamary +D. Tuia +A. Rakotomamonjy, [Optimal Transport for Domain Adaptation](https: // arxiv.org / pdf / 1507.00504.pdf), in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.PP, no.99, pp.1 - 1 -[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). [Regularized discrete optimal transport](https://arxiv.org/pdf/1307.5551.pdf). SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). 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Habrard(2016), [Mapping estimation for discrete optimal transport](http: // remi.flamary.com / biblio / perrot2016mapping.pdf), Neural Information Processing Systems(NIPS). -[9] Schmitzer, B. (2016). [Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems](https://arxiv.org/pdf/1610.06519.pdf). arXiv preprint arXiv:1610.06519. +[9] Schmitzer, B. (2016). [Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems](https: // arxiv.org / pdf / 1610.06519.pdf). arXiv preprint arXiv: 1610.06519. -[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). [Scaling algorithms for unbalanced transport problems](https://arxiv.org/pdf/1607.05816.pdf). arXiv preprint arXiv:1607.05816. +[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). [Scaling algorithms for unbalanced transport problems](https: // arxiv.org / pdf / 1607.05816.pdf). arXiv preprint arXiv: 1607.05816. -[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https://arxiv.org/pdf/1608.08063.pdf). arXiv preprint arXiv:1608.08063. +[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https: // arxiv.org / pdf / 1608.08063.pdf). arXiv preprint arXiv: 1608.08063. -[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon (2016), [Gromov-Wasserstein averaging of kernel and distance matrices](http://proceedings.mlr.press/v48/peyre16.html) International Conference on Machine Learning (ICML). +[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon(2016), [Gromov - Wasserstein averaging of kernel and distance matrices](http: // proceedings.mlr.press / v48 / peyre16.html) International Conference on Machine Learning(ICML). -[13] Mémoli, Facundo (2011). 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[Optimal Transport for structured data with application on graphs](http://proceedings.mlr.press/v97/titouan19a.html) Proceedings of the 36th International Conference on Machine Learning (ICML). +[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http: // proceedings.mlr.press / v97 / titouan19a.html) Proceedings of the 36th International Conference on Machine Learning(ICML). -[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/) Advances in Neural Information Processing Systems (NIPS). +[25] Frogner C., Zhang C., Mobahi H., Araya - Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http: // cbcl.mit.edu / wasserstein / ) Advances in Neural Information Processing Systems (NIPS). -[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 33 (NeurIPS). +[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https: // papers.nips.cc / paper / 9386 - screening - sinkhorn - algorithm - for-regularized - optimal - transport), Advances in Neural Information Processing Systems 33 (NeurIPS). [27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019. -- cgit v1.2.3 From 33ba700a97a5f76ef0845bb2187fc3d8c594191a Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 15 Apr 2020 16:47:48 +0200 Subject: readme conflict --- README.md | 242 +++++++++++++++++++++++++++++++------------------------------- 1 file changed, 121 insertions(+), 121 deletions(-) diff --git a/README.md b/README.md index 8bd833c..c96731c 100644 --- a/README.md +++ b/README.md @@ -1,60 +1,58 @@ # POT: Python Optimal Transport -import ot -[![PyPI version](https: // badge.fury.io / py / POT.svg)](https: // badge.fury.io / py / POT) -[![Anaconda Cloud](https: // anaconda.org / conda - forge / pot / badges / version.svg)](https: // anaconda.org / conda - forge / pot) -[![Build Status](https: // travis - ci.org / rflamary / POT.svg?branch=master)](https: // travis - ci.org / rflamary / POT) -[![Documentation Status](https: // readthedocs.org / projects / pot / badge /?version=latest)](http: // pot.readthedocs.io / en / latest /?badge=latest) -[![Downloads](https: // pepy.tech / badge / pot)](https: // pepy.tech / project / pot) -[![Anaconda downloads](https: // anaconda.org / conda - forge / pot / badges / downloads.svg)](https: // anaconda.org / conda - forge / pot) -[![License](https: // anaconda.org / conda - forge / pot / badges / license.svg)](https: // github.com / rflamary / POT / blob / master / LICENSE) +[![PyPI version](https://badge.fury.io/py/POT.svg)](https://badge.fury.io/py/POT) +[![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot) +[![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/rflamary/POT) +[![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) +[![Downloads](https://pepy.tech/badge/pot)](https://pepy.tech/project/pot) +[![Anaconda downloads](https://anaconda.org/conda-forge/pot/badges/downloads.svg)](https://anaconda.org/conda-forge/pot) +[![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/rflamary/POT/blob/master/LICENSE) + This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. It provides the following solvers: -* OT Network Flow solver for the linear program / Earth Movers Distance[1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm[2], stabilized version[9][10] and greedy Sinkhorn[22] with optional GPU implementation(requires cupy). -* Sinkhorn divergence[23] and entropic regularization OT from empirical data. -* Smooth optimal transport solvers(dual and semi - dual) for KL and squared L2 regularizations[17]. -* Non regularized Wasserstein barycenters[16] with LP solver(only small scale). -* Bregman projections for Wasserstein barycenter[3], convolutional barycenter[21] and unmixing[4]. -* Optimal transport for domain adaptation with group lasso regularization[5] -* Conditional gradient[6] and Generalized conditional gradient for regularized OT[7]. -* Linear OT[14] and Joint OT matrix and mapping estimation[8]. -* Wasserstein Discriminant Analysis[11](requires autograd + pymanopt). -* Gromov - Wasserstein distances and barycenters([13] and regularized[12]) -* Stochastic Optimization for Large - scale Optimal Transport(semi - dual problem[18] and dual problem[19]) -* Non regularized free support Wasserstein barycenters[20]. -* Unbalanced OT with KL relaxation distance and barycenter[10, 25]. -* Screening Sinkhorn Algorithm for OT[26]. -* JCPOT algorithm for multi - source domain adaptation with target shift[27]. - -Some demonstrations(both in Python and Jupyter Notebook format) are available in the examples folder. +* OT Network Flow solver for the linear program/ Earth Movers Distance [1]. +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU implementation (requires cupy). +* Sinkhorn divergence [23] and entropic regularization OT from empirical data. +* Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. +* Non regularized Wasserstein barycenters [16] with LP solver (only small scale). +* Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4]. +* Optimal transport for domain adaptation with group lasso regularization [5] +* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. +* Linear OT [14] and Joint OT matrix and mapping estimation [8]. +* Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). +* Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) +* Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) +* Non regularized free support Wasserstein barycenters [20]. +* Unbalanced OT with KL relaxation distance and barycenter [10, 25]. +* Screening Sinkhorn Algorithm for OT [26]. +* JCPOT algorithm for multi-source domain adaptation with target shift [27]. + +Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. #### Using and citing the toolbox If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference: ``` - - @misc{flamary2017pot, - title = {POT Python Optimal Transport library}, - author = {Flamary, R{'e}mi and Courty, Nicolas}, - url = {https: // github.com / rflamary / POT}, - year = {2017} - } +title={POT Python Optimal Transport library}, +author={Flamary, R{'e}mi and Courty, Nicolas}, +url={https://github.com/rflamary/POT}, +year={2017} +} ``` ## Installation -The library has been tested on Linux, MacOSX and Windows. It requires a C + + compiler for building / installing the EMD solver and relies on the following Python modules: +The library has been tested on Linux, MacOSX and Windows. It requires a C++ compiler for building/installing the EMD solver and relies on the following Python modules: -- Numpy ( >= 1.11) -- Scipy ( >= 1.0) -- Cython ( >= 0.23) -- Matplotlib ( >= 1.5) +- Numpy (>=1.11) +- Scipy (>=1.0) +- Cython (>=0.23) +- Matplotlib (>=1.5) #### Pip installation @@ -70,33 +68,35 @@ pip install POT ``` or get the very latest version by downloading it and then running: ``` -python setup.py install - -user # for user install (no root) +python setup.py install --user # for user install (no root) ``` + #### Anaconda installation with conda-forge -If you use the Anaconda python distribution, POT is available in [conda - forge](https: // conda - forge.org). To install it and the required dependencies: +If you use the Anaconda python distribution, POT is available in [conda-forge](https://conda-forge.org). To install it and the required dependencies: ``` -conda install - c conda - forge pot +conda install -c conda-forge pot ``` #### Post installation check After a correct installation, you should be able to import the module without errors: ```python +import ot ``` Note that for easier access the module is name ot instead of pot. ### Dependencies -Some sub - modules require additional dependences which are discussed below +Some sub-modules require additional dependences which are discussed below -* **ot.dr ** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: +* **ot.dr** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: ``` pip install pymanopt autograd ``` -* **ot.gpu ** (GPU accelerated OT) depends on cupy that have to be installed following instructions on[this page](https: // docs - cupy.chainer.org / en / stable / install.html). +* **ot.gpu** (GPU accelerated OT) depends on cupy that have to be installed following instructions on [this page](https://docs-cupy.chainer.org/en/stable/install.html). obviously you need CUDA installed and a compatible GPU. @@ -107,156 +107,156 @@ obviously you need CUDA installed and a compatible GPU. * Import the toolbox ```python +import ot ``` * Compute Wasserstein distances ```python # a,b are 1D histograms (sum to 1 and positive) # M is the ground cost matrix -Wd = ot.emd2(a, b, M) # exact linear program -Wd_reg = ot.sinkhorn2(a, b, M, reg) # entropic regularized OT +Wd=ot.emd2(a,b,M) # exact linear program +Wd_reg=ot.sinkhorn2(a,b,M,reg) # entropic regularized OT # if b is a matrix compute all distances to a and return a vector ``` * Compute OT matrix ```python # a,b are 1D histograms (sum to 1 and positive) # M is the ground cost matrix -T = ot.emd(a, b, M) # exact linear program -T_reg = ot.sinkhorn(a, b, M, reg) # entropic regularized OT +T=ot.emd(a,b,M) # exact linear program +T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT ``` * Compute Wasserstein barycenter ```python # A is a n*d matrix containing d 1D histograms # M is the ground cost matrix -ba = ot.barycenter(A, M, reg) # reg is regularization parameter +ba=ot.barycenter(A,M,reg) # reg is regularization parameter ``` + + ### Examples and Notebooks -The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http: // pot.readthedocs.io / ). +The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http://pot.readthedocs.io/). -Here is a list of the Python notebooks available [here](https: // github.com / rflamary / POT / blob / master / notebooks / ) if you want a quick look: +Here is a list of the Python notebooks available [here](https://github.com/rflamary/POT/blob/master/notebooks/) if you want a quick look: -* [1D optimal transport](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_1D.ipynb) -* [OT Ground Loss](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_L1_vs_L2.ipynb) -* [Multiple EMD computation](https: // github.com / rflamary / POT / blob / master / notebooks / plot_compute_emd.ipynb) -* [2D optimal transport on empirical distributions](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_2D_samples.ipynb) -* [1D Wasserstein barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_barycenter_1D.ipynb) -* [OT with user provided regularization](https: // github.com / rflamary / POT / blob / master / notebooks / plot_optim_OTreg.ipynb) -* [Domain adaptation with optimal transport](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_d2.ipynb) -* [Color transfer in images](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_color_images.ipynb) -* [OT mapping estimation for domain adaptation](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_mapping.ipynb) -* [OT mapping estimation for color transfer in images](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_mapping_colors_images.ipynb) -* [Wasserstein Discriminant Analysis](https: // github.com / rflamary / POT / blob / master / notebooks / plot_WDA.ipynb) -* [Gromov Wasserstein](https: // github.com / rflamary / POT / blob / master / notebooks / plot_gromov.ipynb) -* [Gromov Wasserstein Barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_gromov_barycenter.ipynb) -* [Fused Gromov Wasserstein](https: // github.com / rflamary / POT / blob / master / notebooks / plot_fgw.ipynb) -* [Fused Gromov Wasserstein Barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_barycenter_fgw.ipynb) +* [1D optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_1D.ipynb) +* [OT Ground Loss](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_L1_vs_L2.ipynb) +* [Multiple EMD computation](https://github.com/rflamary/POT/blob/master/notebooks/plot_compute_emd.ipynb) +* [2D optimal transport on empirical distributions](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_2D_samples.ipynb) +* [1D Wasserstein barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_1D.ipynb) +* [OT with user provided regularization](https://github.com/rflamary/POT/blob/master/notebooks/plot_optim_OTreg.ipynb) +* [Domain adaptation with optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_d2.ipynb) +* [Color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_color_images.ipynb) +* [OT mapping estimation for domain adaptation](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping.ipynb) +* [OT mapping estimation for color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping_colors_images.ipynb) +* [Wasserstein Discriminant Analysis](https://github.com/rflamary/POT/blob/master/notebooks/plot_WDA.ipynb) +* [Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov.ipynb) +* [Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov_barycenter.ipynb) +* [Fused Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_fgw.ipynb) +* [Fused Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_fgw.ipynb) -You can also see the notebooks with [Jupyter nbviewer](https: // nbviewer.jupyter.org / github / rflamary / POT / tree / master / notebooks / ). +You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/notebooks/). ## Acknowledgements This toolbox has been created and is maintained by -* [Rémi Flamary](http: // remi.flamary.com / ) -* [Nicolas Courty](http: // people.irisa.fr / Nicolas.Courty / ) +* [Rémi Flamary](http://remi.flamary.com/) +* [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) -The contributors to this library are +The contributors to this library are -* [Alexandre Gramfort](http: // alexandre.gramfort.net / ) -* [Laetitia Chapel](http: // people.irisa.fr / Laetitia.Chapel / ) -* [Michael Perrot](http: // perso.univ - st - etienne.fr / pem82055 / ) (Mapping estimation) -* [Léo Gautheron](https: // github.com / aje)(GPU implementation) -* [Nathalie Gayraud](https: // www.linkedin.com / in / nathalie - t - h - gayraud /?ppe=1) -* [Stanislas Chambon](https: // slasnista.github.io / ) -* [Antoine Rolet](https: // arolet.github.io / ) -* Erwan Vautier(Gromov - Wasserstein) -* [Kilian Fatras](https: // kilianfatras.github.io / ) -* [Alain Rakotomamonjy](https: // sites.google.com / site / alainrakotomamonjy / home) -* [Vayer Titouan](https: // tvayer.github.io / ) -* [Hicham Janati](https: // hichamjanati.github.io / ) (Unbalanced OT) -* [Romain Tavenard](https: // rtavenar.github.io / ) (1d Wasserstein) -* [Mokhtar Z. Alaya](http: // mzalaya.github.io / ) (Screenkhorn) +* [Alexandre Gramfort](http://alexandre.gramfort.net/) +* [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) +* [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation) +* [Léo Gautheron](https://github.com/aje) (GPU implementation) +* [Nathalie Gayraud](https://www.linkedin.com/in/nathalie-t-h-gayraud/?ppe=1) +* [Stanislas Chambon](https://slasnista.github.io/) +* [Antoine Rolet](https://arolet.github.io/) +* Erwan Vautier (Gromov-Wasserstein) +* [Kilian Fatras](https://kilianfatras.github.io/) +* [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home) +* [Vayer Titouan](https://tvayer.github.io/) +* [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT) +* [Romain Tavenard](https://rtavenar.github.io/) (1d Wasserstein) +* [Mokhtar Z. Alaya](http://mzalaya.github.io/) (Screenkhorn) -This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code(in various languages): +This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): -* [Gabriel Peyré](http: // gpeyre.github.io / ) (Wasserstein Barycenters in Matlab) -* [Nicolas Bonneel](http: // liris.cnrs.fr / ~nbonneel /) (C++ code for EMD) -* [Marco Cuturi](http: // marcocuturi.net / ) (Sinkhorn Knopp in Matlab/Cuda) +* [Gabriel Peyré](http://gpeyre.github.io/) (Wasserstein Barycenters in Matlab) +* [Nicolas Bonneel](http://liris.cnrs.fr/~nbonneel/) ( C++ code for EMD) +* [Marco Cuturi](http://marcocuturi.net/) (Sinkhorn Knopp in Matlab/Cuda) ## Contributions and code of conduct -Every contribution is welcome and should respect the[contribution guidelines](CONTRIBUTING.md). Each member of the project is expected to follow the[code of conduct](CODE_OF_CONDUCT.md). +Every contribution is welcome and should respect the [contribution guidelines](CONTRIBUTING.md). Each member of the project is expected to follow the [code of conduct](CODE_OF_CONDUCT.md). ## Support You can ask questions and join the development discussion: -* On the[POT Slack channel](https: // pot - toolbox.slack.com) -* On the POT [mailing list](https: // mail.python.org / mm3 / mailman3 / lists / pot.python.org / ) +* On the [POT Slack channel](https://pot-toolbox.slack.com) +* On the POT [mailing list](https://mail.python.org/mm3/mailman3/lists/pot.python.org/) -You can also post bug reports and feature requests in Github issues. Make sure to read our[guidelines](CONTRIBUTING.md) first. +You can also post bug reports and feature requests in Github issues. Make sure to read our [guidelines](CONTRIBUTING.md) first. ## References -[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). [Displacement interpolation using Lagrangian mass transport](https: // people.csail.mit.edu / sparis / publi / 2011 / sigasia / Bonneel_11_Displacement_Interpolation.pdf). In ACM Transactions on Graphics(TOG)(Vol. 30, No. 6, p. 158). ACM. +[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). [Displacement interpolation using Lagrangian mass transport](https://people.csail.mit.edu/sparis/publi/2011/sigasia/Bonneel_11_Displacement_Interpolation.pdf). In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. -[2] Cuturi, M. (2013). [Sinkhorn distances: Lightspeed computation of optimal transport](https: // arxiv.org / pdf / 1306.0895.pdf). In Advances in Neural Information Processing Systems(pp. 2292 - 2300). +[2] Cuturi, M. (2013). [Sinkhorn distances: Lightspeed computation of optimal transport](https://arxiv.org/pdf/1306.0895.pdf). In Advances in Neural Information Processing Systems (pp. 2292-2300). -[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). [Iterative Bregman projections for regularized transportation problems](https: // arxiv.org / pdf / 1412.5154.pdf). SIAM Journal on Scientific Computing, 37(2), A1111 - A1138. +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). [Iterative Bregman projections for regularized transportation problems](https://arxiv.org/pdf/1412.5154.pdf). SIAM Journal on Scientific Computing, 37(2), A1111-A1138. -[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, [Supervised planetary unmixing with optimal transport](https: // hal.archives - ouvertes.fr / hal - 01377236 / document), Whorkshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing(WHISPERS), 2016. +[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, [Supervised planetary unmixing with optimal transport](https://hal.archives-ouvertes.fr/hal-01377236/document), Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016. -[5] N. Courty -R. Flamary -D. Tuia -A. Rakotomamonjy, [Optimal Transport for Domain Adaptation](https: // arxiv.org / pdf / 1507.00504.pdf), in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.PP, no.99, pp.1 - 1 +[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, [Optimal Transport for Domain Adaptation](https://arxiv.org/pdf/1507.00504.pdf), in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 -[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). [Regularized discrete optimal transport](https: // arxiv.org / pdf / 1307.5551.pdf). SIAM Journal on Imaging Sciences, 7(3), 1853 - 1882. +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). [Regularized discrete optimal transport](https://arxiv.org/pdf/1307.5551.pdf). SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). [Generalized conditional gradient: analysis of convergence and applications](https: // arxiv.org / pdf / 1510.06567.pdf). arXiv preprint arXiv: 1510.06567. +[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). [Generalized conditional gradient: analysis of convergence and applications](https://arxiv.org/pdf/1510.06567.pdf). arXiv preprint arXiv:1510.06567. -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard(2016), [Mapping estimation for discrete optimal transport](http: // remi.flamary.com / biblio / perrot2016mapping.pdf), Neural Information Processing Systems(NIPS). +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard (2016), [Mapping estimation for discrete optimal transport](http://remi.flamary.com/biblio/perrot2016mapping.pdf), Neural Information Processing Systems (NIPS). -[9] Schmitzer, B. (2016). [Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems](https: // arxiv.org / pdf / 1610.06519.pdf). arXiv preprint arXiv: 1610.06519. +[9] Schmitzer, B. (2016). [Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems](https://arxiv.org/pdf/1610.06519.pdf). arXiv preprint arXiv:1610.06519. -[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). [Scaling algorithms for unbalanced transport problems](https: // arxiv.org / pdf / 1607.05816.pdf). arXiv preprint arXiv: 1607.05816. +[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). [Scaling algorithms for unbalanced transport problems](https://arxiv.org/pdf/1607.05816.pdf). arXiv preprint arXiv:1607.05816. -[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https: // arxiv.org / pdf / 1608.08063.pdf). arXiv preprint arXiv: 1608.08063. +[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https://arxiv.org/pdf/1608.08063.pdf). arXiv preprint arXiv:1608.08063. -[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon(2016), [Gromov - Wasserstein averaging of kernel and distance matrices](http: // proceedings.mlr.press / v48 / peyre16.html) International Conference on Machine Learning(ICML). +[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon (2016), [Gromov-Wasserstein averaging of kernel and distance matrices](http://proceedings.mlr.press/v48/peyre16.html) International Conference on Machine Learning (ICML). -[13] Mémoli, Facundo(2011). [Gromov–Wasserstein distances and the metric approach to object matching](https: // media.adelaide.edu.au / acvt / Publications / 2011 / 2011 - Gromov % E2 % 80 % 93Wasserstein % 20Distances % 20and % 20the % 20Metric % 20Approach % 20to % 20Object % 20Matching.pdf). Foundations of computational mathematics 11.4: 417 - 487. +[13] Mémoli, Facundo (2011). 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[Barycenters in the Wasserstein space](https: // hal.archives - ouvertes.fr / hal - 00637399 / document). SIAM Journal on Mathematical Analysis, 43(2), 904 - 924. +[16] Agueh, M., & Carlier, G. (2011). [Barycenters in the Wasserstein space](https://hal.archives-ouvertes.fr/hal-00637399/document). SIAM Journal on Mathematical Analysis, 43(2), 904-924. -[17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https: // arxiv.org / abs / 1710.06276). Proceedings of the Twenty - First International Conference on Artificial Intelligence and Statistics(AISTATS). +[17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https://arxiv.org/abs/1710.06276). Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). -[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016)[Stochastic Optimization for Large - scale Optimal Transport](https: // arxiv.org / abs / 1605.08527). 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(2014) [Fast Computation of Wasserstein Barycenters](http://proceedings.mlr.press/v32/cuturi14.html). International Conference in Machine Learning -[21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). [Convolutional wasserstein distances: Efficient optimal transportation on geometric domains](https: // dl.acm.org / citation.cfm?id=2766963). ACM Transactions on Graphics(TOG), 34(4), 66. +[21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). [Convolutional wasserstein distances: Efficient optimal transportation on geometric domains](https://dl.acm.org/citation.cfm?id=2766963). ACM Transactions on Graphics (TOG), 34(4), 66. -[22] J. Altschuler, J.Weed, P. Rigollet, (2017)[Near - linear time approximation algorithms for optimal transport via Sinkhorn iteration](https: // papers.nips.cc / paper / 6792 - near - linear - time - approximation - algorithms - for-optimal - transport - via - sinkhorn - iteration.pdf), Advances in Neural Information Processing Systems(NIPS) 31 +[22] J. Altschuler, J.Weed, P. Rigollet, (2017) [Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration](https://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration.pdf), Advances in Neural Information Processing Systems (NIPS) 31 -[23] Aude, G., Peyré, G., Cuturi, M., [Learning Generative Models with Sinkhorn Divergences](https: // arxiv.org / abs / 1706.00292), Proceedings of the Twenty - First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 +[23] Aude, G., Peyré, G., Cuturi, M., [Learning Generative Models with Sinkhorn Divergences](https://arxiv.org/abs/1706.00292), Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 -[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http: // proceedings.mlr.press / v97 / titouan19a.html) Proceedings of the 36th International Conference on Machine Learning(ICML). +[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http://proceedings.mlr.press/v97/titouan19a.html) Proceedings of the 36th International Conference on Machine Learning (ICML). -[25] Frogner C., Zhang C., Mobahi H., Araya - Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http: // cbcl.mit.edu / wasserstein / ) Advances in Neural Information Processing Systems (NIPS). +[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/) Advances in Neural Information Processing Systems (NIPS). -[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https: // papers.nips.cc / paper / 9386 - screening - sinkhorn - algorithm - for-regularized - optimal - transport), Advances in Neural Information Processing Systems 33 (NeurIPS). +[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 33 (NeurIPS). [27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019. -- cgit v1.2.3 From 7b98abfe9769475afe3b34e2ac4c7f0275fa0e6f Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 15 Apr 2020 16:50:17 +0200 Subject: conflict readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c96731c..b6baf14 100644 --- a/README.md +++ b/README.md @@ -259,4 +259,4 @@ You can also post bug reports and feature requests in Github issues. Make sure t [26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 33 (NeurIPS). -[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019. +[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019. \ No newline at end of file -- cgit v1.2.3 From 2571a3ead11a7fc010ed20b1af6faeef464565a1 Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 15 Apr 2020 17:00:32 +0200 Subject: conflict test_da --- test/test_da.py | 132 ++++++++++++++++++++++++++++++++++++++++++++++---------- 1 file changed, 110 insertions(+), 22 deletions(-) diff --git a/test/test_da.py b/test/test_da.py index befec43..7d0fdda 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -65,6 +65,16 @@ def test_sinkhorn_lpl1_transport_class(): transp_Xs = otda.fit_transform(Xs=Xs, ys=ys, Xt=Xt) assert_equal(transp_Xs.shape, Xs.shape) + # check label propagation + transp_yt = otda.transform_labels(ys) + assert_equal(transp_yt.shape[0], yt.shape[0]) + assert_equal(transp_yt.shape[1], len(np.unique(ys))) + + # check inverse label propagation + transp_ys = otda.inverse_transform_labels(yt) + assert_equal(transp_ys.shape[0], ys.shape[0]) + assert_equal(transp_ys.shape[1], len(np.unique(yt))) + # test unsupervised vs semi-supervised mode otda_unsup = ot.da.SinkhornLpl1Transport() otda_unsup.fit(Xs=Xs, ys=ys, Xt=Xt) @@ -129,6 +139,16 @@ def test_sinkhorn_l1l2_transport_class(): transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) + # check label propagation + transp_yt = otda.transform_labels(ys) + assert_equal(transp_yt.shape[0], yt.shape[0]) + assert_equal(transp_yt.shape[1], len(np.unique(ys))) + + # check inverse label propagation + transp_ys = otda.inverse_transform_labels(yt) + assert_equal(transp_ys.shape[0], ys.shape[0]) + assert_equal(transp_ys.shape[1], len(np.unique(yt))) + Xt_new, _ = make_data_classif('3gauss2', nt + 1) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) @@ -210,6 +230,16 @@ def test_sinkhorn_transport_class(): transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) + # check label propagation + transp_yt = otda.transform_labels(ys) + assert_equal(transp_yt.shape[0], yt.shape[0]) + assert_equal(transp_yt.shape[1], len(np.unique(ys))) + + # check inverse label propagation + transp_ys = otda.inverse_transform_labels(yt) + assert_equal(transp_ys.shape[0], ys.shape[0]) + assert_equal(transp_ys.shape[1], len(np.unique(yt))) + Xt_new, _ = make_data_classif('3gauss2', nt + 1) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) @@ -271,6 +301,16 @@ def test_unbalanced_sinkhorn_transport_class(): transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) + # check label propagation + transp_yt = otda.transform_labels(ys) + assert_equal(transp_yt.shape[0], yt.shape[0]) + assert_equal(transp_yt.shape[1], len(np.unique(ys))) + + # check inverse label propagation + transp_ys = otda.inverse_transform_labels(yt) + assert_equal(transp_ys.shape[0], ys.shape[0]) + assert_equal(transp_ys.shape[1], len(np.unique(yt))) + Xs_new, _ = make_data_classif('3gauss', ns + 1) transp_Xs_new = otda.transform(Xs_new) @@ -353,6 +393,16 @@ def test_emd_transport_class(): transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) + # check label propagation + transp_yt = otda.transform_labels(ys) + assert_equal(transp_yt.shape[0], yt.shape[0]) + assert_equal(transp_yt.shape[1], len(np.unique(ys))) + + # check inverse label propagation + transp_ys = otda.inverse_transform_labels(yt) + assert_equal(transp_ys.shape[0], ys.shape[0]) + assert_equal(transp_ys.shape[1], len(np.unique(yt))) + Xt_new, _ = make_data_classif('3gauss2', nt + 1) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) @@ -549,55 +599,93 @@ def test_linear_mapping_class(): np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2) -def test_emd_laplace_class(): - """test_emd_laplace_transport +def test_jcpot_transport_class(): + """test_jcpot_transport """ - ns = 150 + + ns1 = 150 + ns2 = 150 nt = 200 - Xs, ys = make_data_classif('3gauss', ns) + Xs1, ys1 = make_data_classif('3gauss', ns1) + Xs2, ys2 = make_data_classif('3gauss', ns2) + Xt, yt = make_data_classif('3gauss2', nt) - otda = ot.da.EMDLaplaceTransport(reg_lap=0.01, max_iter=1000, tol=1e-9, verbose=False, log=True) + Xs = [Xs1, Xs2] + ys = [ys1, ys2] + + otda = ot.da.JCPOTTransport(reg_e=1, max_iter=10000, tol=1e-9, verbose=True, log=True) # test its computed otda.fit(Xs=Xs, ys=ys, Xt=Xt) assert hasattr(otda, "coupling_") + assert hasattr(otda, "proportions_") assert hasattr(otda, "log_") # test dimensions of coupling - assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + for i, xs in enumerate(Xs): + assert_equal(otda.coupling_[i].shape, ((xs.shape[0], Xt.shape[0]))) # test all margin constraints - mu_s = unif(ns) mu_t = unif(nt) - assert_allclose( - np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose( - np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + for i in range(len(Xs)): + # test margin constraints w.r.t. uniform target weights for each coupling matrix + assert_allclose( + np.sum(otda.coupling_[i], axis=0), mu_t, rtol=1e-3, atol=1e-3) + + # test margin constraints w.r.t. modified source weights for each source domain + + assert_allclose( + np.dot(otda.log_['D1'][i], np.sum(otda.coupling_[i], axis=1)), otda.proportions_, rtol=1e-3, + atol=1e-3) # test transform transp_Xs = otda.transform(Xs=Xs) [assert_equal(x.shape, y.shape) for x, y in zip(transp_Xs, Xs)] - Xs_new, _ = make_data_classif('3gauss', ns + 1) + Xs_new, _ = make_data_classif('3gauss', ns1 + 1) transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working assert_equal(transp_Xs_new.shape, Xs_new.shape) - # test inverse transform - transp_Xt = otda.inverse_transform(Xt=Xt) - assert_equal(transp_Xt.shape, Xt.shape) + # check label propagation + transp_yt = otda.transform_labels(ys) + assert_equal(transp_yt.shape[0], yt.shape[0]) + assert_equal(transp_yt.shape[1], len(np.unique(ys))) - Xt_new, _ = make_data_classif('3gauss2', nt + 1) - transp_Xt_new = otda.inverse_transform(Xt=Xt_new) + # check inverse label propagation + transp_ys = otda.inverse_transform_labels(yt) + [assert_equal(x.shape[0], y.shape[0]) for x, y in zip(transp_ys, ys)] + [assert_equal(x.shape[1], len(np.unique(y))) for x, y in zip(transp_ys, ys)] - # check that the oos method is working - assert_equal(transp_Xt_new.shape, Xt_new.shape) - # test fit_transform - transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt) - assert_equal(transp_Xs.shape, Xs.shape) +def test_jcpot_barycenter(): + """test_jcpot_barycenter + """ + + ns1 = 150 + ns2 = 150 + nt = 200 + + sigma = 0.1 + np.random.seed(1985) + + ps1 = .2 + ps2 = .9 + pt = .4 + + Xs1, ys1 = make_data_classif('2gauss_prop', ns1, nz=sigma, p=ps1) + Xs2, ys2 = make_data_classif('2gauss_prop', ns2, nz=sigma, p=ps2) + Xt, yt = make_data_classif('2gauss_prop', nt, nz=sigma, p=pt) + + Xs = [Xs1, Xs2] + ys = [ys1, ys2] + + prop = ot.bregman.jcpot_barycenter(Xs, ys, Xt, reg=.5, metric='sqeuclidean', + numItermax=10000, stopThr=1e-9, verbose=False, log=False) + + np.testing.assert_allclose(prop, [1 - pt, pt], rtol=1e-3, atol=1e-3) -- cgit v1.2.3 From 1c60175fee4eb7f29b49f693e91f59720369edb1 Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 15 Apr 2020 17:06:02 +0200 Subject: conflict test_da --- test/test_da.py | 64 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 64 insertions(+) diff --git a/test/test_da.py b/test/test_da.py index 7d0fdda..3b28119 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -689,3 +689,67 @@ def test_jcpot_barycenter(): numItermax=10000, stopThr=1e-9, verbose=False, log=False) np.testing.assert_allclose(prop, [1 - pt, pt], rtol=1e-3, atol=1e-3) + + +def test_emd_laplace_class(): + """test_emd_laplace_transport + """ + ns = 150 + nt = 200 + + Xs, ys = make_data_classif('3gauss', ns) + Xt, yt = make_data_classif('3gauss2', nt) + + otda = ot.da.EMDLaplaceTransport(reg_lap=0.01, max_iter=1000, tol=1e-9, verbose=False, log=True) + + # test its computed + otda.fit(Xs=Xs, ys=ys, Xt=Xt) + + assert hasattr(otda, "coupling_") + assert hasattr(otda, "log_") + + # test dimensions of coupling + assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + + # test all margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + + assert_allclose( + np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose( + np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = otda.transform(Xs=Xs) + [assert_equal(x.shape, y.shape) for x, y in zip(transp_Xs, Xs)] + + Xs_new, _ = make_data_classif('3gauss', ns + 1) + transp_Xs_new = otda.transform(Xs_new) + + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) + + # test inverse transform + transp_Xt = otda.inverse_transform(Xt=Xt) + assert_equal(transp_Xt.shape, Xt.shape) + + Xt_new, _ = make_data_classif('3gauss2', nt + 1) + transp_Xt_new = otda.inverse_transform(Xt=Xt_new) + + # check that the oos method is working + assert_equal(transp_Xt_new.shape, Xt_new.shape) + + # test fit_transform + transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt) + assert_equal(transp_Xs.shape, Xs.shape) + + # check label propagation + transp_yt = otda.transform_labels(ys) + assert_equal(transp_yt.shape[0], yt.shape[0]) + assert_equal(transp_yt.shape[1], len(np.unique(ys))) + + # check inverse label propagation + transp_ys = otda.inverse_transform_labels(yt) + assert_equal(transp_ys.shape[0], ys.shape[0]) + assert_equal(transp_ys.shape[1], len(np.unique(yt))) -- cgit v1.2.3 From 6c64f16acd7421ff6278119eb68877d845820fac Mon Sep 17 00:00:00 2001 From: ievred Date: Wed, 15 Apr 2020 17:13:31 +0200 Subject: import laplacian --- ot/da.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/da.py b/ot/da.py index ef05181..a6d7d9b 100644 --- a/ot/da.py +++ b/ot/da.py @@ -16,7 +16,7 @@ import scipy.linalg as linalg from .bregman import sinkhorn, jcpot_barycenter from .lp import emd -from .utils import unif, dist, kernel, cost_normalization, label_normalization +from .utils import unif, dist, kernel, cost_normalization, label_normalization, laplacian from .utils import check_params, BaseEstimator from .unbalanced import sinkhorn_unbalanced from .optim import cg -- cgit v1.2.3 From 18b64556aaa477b5499dc05110c96d32b04147ff Mon Sep 17 00:00:00 2001 From: Laetitia Chapel Date: Thu, 16 Apr 2020 13:56:53 +0200 Subject: partial with doctests --- ot/partial.py | 38 ++++++++++++++++++-------------------- 1 file changed, 18 insertions(+), 20 deletions(-) diff --git a/ot/partial.py b/ot/partial.py index 3425acb..8698d9d 100755 --- a/ot/partial.py +++ b/ot/partial.py @@ -80,10 +80,10 @@ def partial_wasserstein_lagrange(a, b, M, reg_m=None, nb_dummies=1, log=False, >>> M = [[0., 1.], [2., 3.]] >>> np.round(partial_wasserstein_lagrange(a,b,M), 2) array([[0.1, 0. ], - [0. , 0.1]]) + [0. , 0.1]]) >>> np.round(partial_wasserstein_lagrange(a,b,M,reg_m=2), 2) array([[0.1, 0. ], - [0. , 0. ]]) + [0. , 0. ]]) References ---------- @@ -199,10 +199,10 @@ def partial_wasserstein(a, b, M, m=None, nb_dummies=1, log=False, **kwargs): >>> M = [[0., 1.], [2., 3.]] >>> np.round(partial_wasserstein(a,b,M), 2) array([[0.1, 0. ], - [0. , 0.1]]) + [0. , 0.1]]) >>> np.round(partial_wasserstein(a,b,M,m=0.1), 2) array([[0.1, 0. ], - [0. , 0. ]]) + [0. , 0. ]]) References ---------- @@ -466,14 +466,14 @@ def partial_gromov_wasserstein(C1, C2, p, q, m=None, nb_dummies=1, G0=None, >>> C2 = sp.spatial.distance.cdist(y, y) >>> np.round(partial_gromov_wasserstein(C1, C2, a, b),2) array([[0. , 0.25, 0. , 0. ], - [0.25, 0. , 0. , 0. ], - [0. , 0. , 0.25, 0. ], - [0. , 0. , 0. , 0.25]]) + [0.25, 0. , 0. , 0. ], + [0. , 0. , 0.25, 0. ], + [0. , 0. , 0. , 0.25]]) >>> np.round(partial_gromov_wasserstein(C1, C2, a, b, m=0.25),2) array([[0. , 0. , 0. , 0. ], - [0. , 0. , 0. , 0. ], - [0. , 0. , 0. , 0. ], - [0. , 0. , 0. , 0.25]]) + [0. , 0. , 0. , 0. ], + [0. , 0. , 0. , 0. ], + [0. , 0. , 0. , 0.25]]) References ---------- @@ -711,8 +711,7 @@ def entropic_partial_wasserstein(a, b, M, reg, m=None, numItermax=1000, >>> M = [[0., 1.], [2., 3.]] >>> np.round(entropic_partial_wasserstein(a, b, M, 1, 0.1), 2) array([[0.06, 0.02], - [0.01, 0. ]]) - + [0.01, 0. ]]) References ---------- @@ -849,15 +848,14 @@ def entropic_partial_gromov_wasserstein(C1, C2, p, q, reg, m=None, G0=None, >>> C2 = sp.spatial.distance.cdist(y, y) >>> np.round(entropic_partial_gromov_wasserstein(C1, C2, a, b,50), 2) array([[0.12, 0.13, 0. , 0. ], - [0.13, 0.12, 0. , 0. ], - [0. , 0. , 0.25, 0. ], - [0. , 0. , 0. , 0.25]]) - >>> np.round(entropic_partial_gromov_wasserstein(C1, C2, a, b, 50, m=0.25) - , 2) + [0.13, 0.12, 0. , 0. ], + [0. , 0. , 0.25, 0. ], + [0. , 0. , 0. , 0.25]]) + >>> np.round(entropic_partial_gromov_wasserstein(C1, C2, a, b, 50, m=0.25), 2) array([[0.02, 0.03, 0. , 0.03], - [0.03, 0.03, 0. , 0.03], - [0. , 0. , 0.03, 0. ], - [0.02, 0.02, 0. , 0.03]]) + [0.03, 0.03, 0. , 0.03], + [0. , 0. , 0.03, 0. ], + [0.02, 0.02, 0. , 0.03]]) Returns ------- -- cgit v1.2.3 From ef7c11a5df3cf6c82864472f0cfa65d6b2036f2f Mon Sep 17 00:00:00 2001 From: Laetitia Chapel Date: Thu, 16 Apr 2020 15:52:00 +0200 Subject: partial with python 3.8 --- .travis.yml | 2 +- ot/partial.py | 12 ++++++------ test/test_partial.py | 9 ++++----- 3 files changed, 11 insertions(+), 12 deletions(-) diff --git a/.travis.yml b/.travis.yml index 072bc55..7ff1b3c 100644 --- a/.travis.yml +++ b/.travis.yml @@ -16,7 +16,7 @@ matrix: python: 3.7 - os: linux sudo: required - python: 2.7 + python: 3.8 # - os: osx # sudo: required # language: generic diff --git a/ot/partial.py b/ot/partial.py index 8698d9d..726a590 100755 --- a/ot/partial.py +++ b/ot/partial.py @@ -232,7 +232,7 @@ def partial_wasserstein(a, b, M, m=None, nb_dummies=1, log=False, **kwargs): b_extended = np.append(b, [(np.sum(a) - m) / nb_dummies] * nb_dummies) a_extended = np.append(a, [(np.sum(b) - m) / nb_dummies] * nb_dummies) - M_extended = np.ones((len(a_extended), len(b_extended))) * 0 + M_extended = np.zeros((len(a_extended), len(b_extended))) M_extended[-1, -1] = np.max(M) * 1e5 M_extended[:len(a), :len(b)] = M @@ -510,9 +510,9 @@ def partial_gromov_wasserstein(C1, C2, p, q, m=None, nb_dummies=1, G0=None, Gprev = G0 M = gwgrad_partial(C1, C2, G0) - M[M < eps] = np.quantile(M[M > eps], thres) + M[M < eps] = np.quantile(M, thres) - M_emd = np.ones(dim_G_extended) * np.max(M) * 1e2 + M_emd = np.zeros(dim_G_extended) M_emd[:len(p), :len(q)] = M M_emd[-nb_dummies:, -nb_dummies:] = np.max(M) * 1e5 M_emd = np.asarray(M_emd, dtype=np.float64) @@ -729,8 +729,8 @@ def entropic_partial_wasserstein(a, b, M, reg, m=None, numItermax=1000, M = np.asarray(M, dtype=np.float64) dim_a, dim_b = M.shape - dx = np.ones(dim_a) - dy = np.ones(dim_b) + dx = np.ones(dim_a, dtype=np.float64) + dy = np.ones(dim_b, dtype=np.float64) if len(a) == 0: a = np.ones(dim_a, dtype=np.float64) / dim_a @@ -738,7 +738,7 @@ def entropic_partial_wasserstein(a, b, M, reg, m=None, numItermax=1000, b = np.ones(dim_b, dtype=np.float64) / dim_b if m is None: - m = np.min((np.sum(a), np.sum(b))) + m = np.min((np.sum(a), np.sum(b))) * 1.0 if m < 0: raise ValueError("Problem infeasible. Parameter m should be greater" " than 0.") diff --git a/test/test_partial.py b/test/test_partial.py index fbcd3c2..1799fd4 100755 --- a/test/test_partial.py +++ b/test/test_partial.py @@ -93,10 +93,7 @@ def test_partial_gromov_wasserstein(): m = 2 / 3 res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C3, p, q, m=m, log=True) - res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C3, p, q, 10, - m=m, log=True) np.testing.assert_allclose(res0, 0, atol=1e-1, rtol=1e-1) - np.testing.assert_allclose(res, 0, atol=1e-1, rtol=1e-1) C1 = sp.spatial.distance.cdist(xs, xs) C2 = sp.spatial.distance.cdist(xt, xt) @@ -123,8 +120,10 @@ def test_partial_gromov_wasserstein(): m = 2 / 3 res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True) - res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, - m=m, log=True) + res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, + 100, m=m, + log=True) + # check constratints np.testing.assert_equal( res0.sum(1) <= p, [True] * len(p)) # cf convergence wasserstein -- cgit v1.2.3 From 14fbb88333971f575510747fd6e9217ec50d9780 Mon Sep 17 00:00:00 2001 From: ievred Date: Thu, 16 Apr 2020 16:09:22 +0200 Subject: references added --- README.md | 7 +++++-- ot/da.py | 6 ++++++ 2 files changed, 11 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index b6baf14..304f249 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ It provides the following solvers: * Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. * Non regularized Wasserstein barycenters [16] with LP solver (only small scale). * Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4]. -* Optimal transport for domain adaptation with group lasso regularization [5] +* Optimal transport for domain adaptation with group lasso and Laplacian regularization [5] * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. * Linear OT [14] and Joint OT matrix and mapping estimation [8]. * Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). @@ -183,6 +183,7 @@ The contributors to this library are * [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT) * [Romain Tavenard](https://rtavenar.github.io/) (1d Wasserstein) * [Mokhtar Z. Alaya](http://mzalaya.github.io/) (Screenkhorn) +* [Ievgen Redko](https://ievred.github.io/) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): @@ -259,4 +260,6 @@ You can also post bug reports and feature requests in Github issues. Make sure t [26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 33 (NeurIPS). -[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019. \ No newline at end of file +[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019. + +[28] Flamary R., Courty N., Tuia D., Rakotomamonjy A. (2014). [Optimal transport with Laplacian regularization: Applications to domain adaptation and shape matching](https://remi.flamary.com/biblio/flamary2014optlaplace.pdf), NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014. diff --git a/ot/da.py b/ot/da.py index a6d7d9b..be959d6 100644 --- a/ot/da.py +++ b/ot/da.py @@ -818,6 +818,9 @@ def emd_laplace(a, b, xs, xt, M, sim, eta, alpha, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + .. [28] R. Flamary, N. Courty, D. Tuia, A. Rakotomamonjy, + "Optimal transport with Laplacian regularization: Applications to domain adaptation and shape matching," + in NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014. See Also -------- @@ -1729,6 +1732,9 @@ class EMDLaplaceTransport(BaseTransport): .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + .. [2] R. Flamary, N. Courty, D. Tuia, A. Rakotomamonjy, + "Optimal transport with Laplacian regularization: Applications to domain adaptation and shape matching," + in NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014. """ def __init__(self, reg_lap=1., reg_src=1., alpha=0.5, -- cgit v1.2.3 From 47306ad23d0c9943c14149ffd85d1c3d0544a3df Mon Sep 17 00:00:00 2001 From: Laetitia Chapel Date: Thu, 16 Apr 2020 16:25:16 +0200 Subject: partial with python 3.8 --- test/test_partial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_partial.py b/test/test_partial.py index 1799fd4..ce363bd 100755 --- a/test/test_partial.py +++ b/test/test_partial.py @@ -123,7 +123,7 @@ def test_partial_gromov_wasserstein(): res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 100, m=m, log=True) - + # check constratints np.testing.assert_equal( res0.sum(1) <= p, [True] * len(p)) # cf convergence wasserstein -- cgit v1.2.3 From d2ecce4a79228cd10f4beba8b6b2b28239be796d Mon Sep 17 00:00:00 2001 From: Laetitia Chapel Date: Thu, 16 Apr 2020 16:42:59 +0200 Subject: partial with python 3.8 --- test/test_partial.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_partial.py b/test/test_partial.py index ce363bd..8b1ca89 100755 --- a/test/test_partial.py +++ b/test/test_partial.py @@ -123,7 +123,7 @@ def test_partial_gromov_wasserstein(): res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 100, m=m, log=True) - + # check constratints np.testing.assert_equal( res0.sum(1) <= p, [True] * len(p)) # cf convergence wasserstein -- cgit v1.2.3 From dc942ac386423870277ea69fae723f216ea61030 Mon Sep 17 00:00:00 2001 From: Laetitia Chapel Date: Fri, 17 Apr 2020 09:08:02 +0200 Subject: partial with readme updated --- README.md | 4 +++- ot/partial.py | 12 ++++++------ 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index b6baf14..fecaa35 100644 --- a/README.md +++ b/README.md @@ -259,4 +259,6 @@ You can also post bug reports and feature requests in Github issues. Make sure t [26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 33 (NeurIPS). -[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019. \ No newline at end of file +[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019. + +[28] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning"] (https://arxiv.org/abs/2002.08276), arXiv preprint arXiv:2002.08276. \ No newline at end of file diff --git a/ot/partial.py b/ot/partial.py index 726a590..d32e054 100755 --- a/ot/partial.py +++ b/ot/partial.py @@ -209,7 +209,7 @@ def partial_wasserstein(a, b, M, m=None, nb_dummies=1, log=False, **kwargs): .. [26] Caffarelli, L. A., & McCann, R. J. (2010) Free boundaries in optimal transport and Monge-Ampere obstacle problems. Annals of mathematics, 673-730. - .. [27] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + .. [28] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- Wasserstein with Applications on Positive-Unlabeled Learning". arXiv preprint arXiv:2002.08276. @@ -314,7 +314,7 @@ def partial_wasserstein2(a, b, M, m=None, nb_dummies=1, log=False, **kwargs): .. [26] Caffarelli, L. A., & McCann, R. J. (2010) Free boundaries in optimal transport and Monge-Ampere obstacle problems. Annals of mathematics, 673-730. - .. [27] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + .. [28] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- Wasserstein with Applications on Positive-Unlabeled Learning". arXiv preprint arXiv:2002.08276. """ @@ -411,7 +411,7 @@ def partial_gromov_wasserstein(C1, C2, p, q, m=None, nb_dummies=1, G0=None, - a and b are the sample weights - m is the amount of mass to be transported - The formulation of the problem has been proposed in [27]_ + The formulation of the problem has been proposed in [28]_ Parameters @@ -477,7 +477,7 @@ def partial_gromov_wasserstein(C1, C2, p, q, m=None, nb_dummies=1, G0=None, References ---------- - .. [27] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + .. [28] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- Wasserstein with Applications on Positive-Unlabeled Learning". arXiv preprint arXiv:2002.08276. @@ -570,7 +570,7 @@ def partial_gromov_wasserstein2(C1, C2, p, q, m=None, nb_dummies=1, G0=None, - a and b are the sample weights - m is the amount of mass to be transported - The formulation of the problem has been proposed in [27]_ + The formulation of the problem has been proposed in [28]_ Parameters @@ -627,7 +627,7 @@ def partial_gromov_wasserstein2(C1, C2, p, q, m=None, nb_dummies=1, G0=None, References ---------- - .. [27] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + .. [28] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- Wasserstein with Applications on Positive-Unlabeled Learning". arXiv preprint arXiv:2002.08276. -- cgit v1.2.3 From 429abe06d53e1ebdd2492b275f70ba1bfe751f0f Mon Sep 17 00:00:00 2001 From: Laetitia Chapel Date: Fri, 17 Apr 2020 11:49:28 +0200 Subject: partial added on quick start guide --- README.md | 4 +- docs/source/quickstart.rst | 64 ++++++++++++++++++++++++++++ docs/source/readme.rst | 104 +++++++++++++++++++++++++++------------------ ot/partial.py | 68 ++++++++++++++++++++++------- 4 files changed, 181 insertions(+), 59 deletions(-) diff --git a/README.md b/README.md index fecaa35..1f62c2c 100644 --- a/README.md +++ b/README.md @@ -261,4 +261,6 @@ You can also post bug reports and feature requests in Github issues. Make sure t [27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019. -[28] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning"] (https://arxiv.org/abs/2002.08276), arXiv preprint arXiv:2002.08276. \ No newline at end of file +[28] Caffarelli, L. A., McCann, R. J. (2020). [Free boundaries in optimal transport and Monge-Ampere obstacle problems](http://www.math.toronto.edu/~mccann/papers/annals2010.pdf), Annals of mathematics, 673-730. + +[29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning](https://arxiv.org/abs/2002.08276), arXiv preprint arXiv:2002.08276. \ No newline at end of file diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index 978eaff..d56f812 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -645,6 +645,53 @@ implemented the main function :any:`ot.barycenter_unbalanced`. - :any:`auto_examples/plot_UOT_barycenter_1D` +Partial optimal transport +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Partial OT is a variant of the optimal transport problem when only a fixed amount of mass m +is to be transported. The partial OT metric between two histograms a and b is defined as [28]_: + +.. math:: + \gamma = \arg\min_\gamma <\gamma,M>_F + + s.t. + \gamma\geq 0 \\ + \gamma 1 \leq a\\ + \gamma^T 1 \leq b\\ + 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} + + +Interestingly the problem can be casted into a regular OT problem by adding reservoir points +in which the surplus mass is sent [29]_. We provide a solver for partial OT +in :any:`ot.partial`. The exact resolution of the problem is computed in :any:`ot.partial.partial_wasserstein` +and :any:`ot.partial.partial_wasserstein2` that return respectively the OT matrix and the value of the +linear term. The entropic solution of the problem is computed in :any:`ot.partial.entropic_partial_wasserstein` +(see [3]_). + +The partial Gromov-Wasserstein formulation of the problem + +.. math:: + GW = \min_\gamma \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*\gamma_{i,j}*\gamma_{k,l} + + s.t. + \gamma\geq 0 \\ + \gamma 1 \leq a\\ + \gamma^T 1 \leq b\\ + 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} + +is computed in :any:`ot.partial.partial_gromov_wasserstein` and in +:any:`ot.partial.entropic_partial_gromov_wasserstein` when considering the entropic +regularization of the problem. + + +.. hint:: + + Examples of the use of :any:`ot.partial` are available in : + + - :any:`auto_examples/plot_partial` + + + Gromov-Wasserstein ^^^^^^^^^^^^^^^^^^ @@ -921,3 +968,20 @@ References .. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : Learning with a Wasserstein Loss, Advances in Neural Information Processing Systems (NIPS) 2015 + +.. [26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). Screening Sinkhorn + Algorithm for Regularized Optimal Transport , + Advances in Neural Information Processing Systems 33 (NeurIPS). + +.. [27] Redko I., Courty N., Flamary R., Tuia D. (2019). Optimal Transport for Multi-source + Domain Adaptation under Target Shift , + Proceedings of the Twenty-Second International Conference on Artificial Intelligence + and Statistics (AISTATS) 22, 2019. + +.. [28] Caffarelli, L. A., McCann, R. J. (2020). Free boundaries in optimal transport and + Monge-Ampere obstacle problems , + Annals of mathematics, 673-730. + +.. [29] Chapel, L., Alaya, M., Gasso, G. (2019). Partial Gromov-Wasserstein with + Applications on Positive-Unlabeled Learning , + arXiv preprint arXiv:2002.08276. diff --git a/docs/source/readme.rst b/docs/source/readme.rst index d5f2161..6d98dc5 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -36,6 +36,9 @@ It provides the following solvers: problem [18] and dual problem [19]) - Non regularized free support Wasserstein barycenters [20]. - Unbalanced OT with KL relaxation distance and barycenter [10, 25]. +- Screening Sinkhorn Algorithm for OT [26]. +- JCPOT algorithm for multi-source domain adaptation with target shift + [27]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -48,19 +51,19 @@ POT using the following bibtex reference: :: - @misc{flamary2017pot, - title={POT Python Optimal Transport library}, - author={Flamary, R{'e}mi and Courty, Nicolas}, - url={https://github.com/rflamary/POT}, - year={2017} - } + @misc{flamary2017pot, + title={POT Python Optimal Transport library}, + author={Flamary, R{'e}mi and Courty, Nicolas}, + url={https://github.com/rflamary/POT}, + year={2017} + } Installation ------------ The library has been tested on Linux, MacOSX and Windows. It requires a -C++ compiler for using the EMD solver and relies on the following Python -modules: +C++ compiler for building/installing the EMD solver and relies on the +following Python modules: - Numpy (>=1.11) - Scipy (>=1.0) @@ -75,19 +78,19 @@ be installed prior to installing POT. This can be done easily with :: - pip install numpy cython + pip install numpy cython You can install the toolbox through PyPI with: :: - pip install POT + pip install POT or get the very latest version by downloading it and then running: :: - python setup.py install --user # for user install (no root) + python setup.py install --user # for user install (no root) Anaconda installation with conda-forge ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -98,7 +101,7 @@ required dependencies: :: - conda install -c conda-forge pot + conda install -c conda-forge pot Post installation check ^^^^^^^^^^^^^^^^^^^^^^^ @@ -108,7 +111,7 @@ without errors: .. code:: python - import ot + import ot Note that for easier access the module is name ot instead of pot. @@ -121,9 +124,9 @@ below - **ot.dr** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: - :: +:: - pip install pymanopt autograd + pip install pymanopt autograd - **ot.gpu** (GPU accelerated OT) depends on cupy that have to be installed following instructions on `this @@ -139,36 +142,36 @@ Short examples - Import the toolbox - .. code:: python +.. code:: python - import ot + import ot - Compute Wasserstein distances - .. code:: python +.. code:: python - # a,b are 1D histograms (sum to 1 and positive) - # M is the ground cost matrix - Wd=ot.emd2(a,b,M) # exact linear program - Wd_reg=ot.sinkhorn2(a,b,M,reg) # entropic regularized OT - # if b is a matrix compute all distances to a and return a vector + # a,b are 1D histograms (sum to 1 and positive) + # M is the ground cost matrix + Wd=ot.emd2(a,b,M) # exact linear program + Wd_reg=ot.sinkhorn2(a,b,M,reg) # entropic regularized OT + # if b is a matrix compute all distances to a and return a vector - Compute OT matrix - .. code:: python +.. code:: python - # a,b are 1D histograms (sum to 1 and positive) - # M is the ground cost matrix - T=ot.emd(a,b,M) # exact linear program - T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT + # a,b are 1D histograms (sum to 1 and positive) + # M is the ground cost matrix + T=ot.emd(a,b,M) # exact linear program + T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT - Compute Wasserstein barycenter - .. code:: python +.. code:: python - # A is a n*d matrix containing d 1D histograms - # M is the ground cost matrix - ba=ot.barycenter(A,M,reg) # reg is regularization parameter + # A is a n*d matrix containing d 1D histograms + # M is the ground cost matrix + ba=ot.barycenter(A,M,reg) # reg is regularization parameter Examples and Notebooks ~~~~~~~~~~~~~~~~~~~~~~ @@ -207,6 +210,10 @@ want a quick look: Wasserstein `__ - `Gromov Wasserstein Barycenter `__ +- `Fused Gromov + Wasserstein `__ +- `Fused Gromov Wasserstein + Barycenter `__ You can also see the notebooks with `Jupyter nbviewer `__. @@ -237,6 +244,7 @@ The contributors to this library are - `Vayer Titouan `__ - `Hicham Janati `__ (Unbalanced OT) - `Romain Tavenard `__ (1d Wasserstein) +- `Mokhtar Z. Alaya `__ (Screenkhorn) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various @@ -274,11 +282,11 @@ References [1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). `Displacement interpolation using Lagrangian mass transport `__. -In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. +In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. [2] Cuturi, M. (2013). `Sinkhorn distances: Lightspeed computation of optimal transport `__. In Advances -in Neural Information Processing Systems (pp. 2292-2300). +in Neural Information Processing Systems (pp. 2292-2300). [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). `Iterative Bregman projections for regularized transportation @@ -387,17 +395,29 @@ and Statistics, (AISTATS) 21, 2018 graphs `__ Proceedings of the 36th International Conference on Machine Learning (ICML). -[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2019). +[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2015). `Learning with a Wasserstein Loss `__ Advances in Neural Information Processing Systems (NIPS). -[26] Caffarelli, L. A., McCann, R. J. (2020). `Free boundaries in optimal transport and -Monge-Ampere obstacle problems `__, -Annals of mathematics, 673-730. - -[27] Chapel, L., Alaya, M., Gasso, G. (2019). `Partial Gromov-Wasserstein with Applications -on Positive-Unlabeled Learning `__. arXiv preprint -arXiv:2002.08276. +[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). +`Screening Sinkhorn Algorithm for Regularized Optimal +Transport `__, +Advances in Neural Information Processing Systems 33 (NeurIPS). + +[27] Redko I., Courty N., Flamary R., Tuia D. (2019). `Optimal Transport +for Multi-source Domain Adaptation under Target +Shift `__, Proceedings +of the Twenty-Second International Conference on Artificial Intelligence +and Statistics (AISTATS) 22, 2019. + +[28] Caffarelli, L. A., McCann, R. J. (2020). [Free boundaries in +optimal transport and Monge-Ampere obstacle problems] +(http://www.math.toronto.edu/~mccann/papers/annals2010.pdf), Annals of +mathematics, 673-730. + +[29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial +Gromov-Wasserstein with Applications on Positive-Unlabeled Learning"] +(https://arxiv.org/abs/2002.08276), arXiv preprint arXiv:2002.08276. .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT diff --git a/ot/partial.py b/ot/partial.py index d32e054..f325d98 100755 --- a/ot/partial.py +++ b/ot/partial.py @@ -1,4 +1,3 @@ -#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ Partial OT @@ -30,7 +29,9 @@ def partial_wasserstein_lagrange(a, b, M, reg_m=None, nb_dummies=1, log=False, 1^T \gamma^T 1 = m \leq \min\{\|a\|_1, \|b\|_1\} - or equivalently: + or equivalently (see Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. + (2018). An interpolating distance between optimal transport and Fisher–Rao + metrics. Foundations of Computational Mathematics, 18(1), 1-44.) .. math:: \gamma = \arg\min_\gamma <\gamma,M>_F + \sqrt(\lambda/2) @@ -47,7 +48,8 @@ def partial_wasserstein_lagrange(a, b, M, reg_m=None, nb_dummies=1, log=False, - :math:`\lambda` is the lagragian cost. Tuning its value allows attaining a given mass to be transported m - The formulation of the problem has been proposed in [26]_ + The formulation of the problem has been proposed in [28]_ + Parameters ---------- @@ -59,9 +61,17 @@ def partial_wasserstein_lagrange(a, b, M, reg_m=None, nb_dummies=1, log=False, cost matrix for the quadratic cost reg_m : float, optional Lagragian cost + nb_dummies : int, optional, default:1 + number of reservoir points to be added (to avoid numerical + instabilities, increase its value if an error is raised) log : bool, optional record log if True + .. warning:: + When dealing with a large number of points, the EMD solver may face + some instabilities, especially when the mass associated to the dummy + point is large. To avoid them, increase the number of dummy points + (allows a smoother repartition of the mass over the points). Returns ------- @@ -88,7 +98,7 @@ def partial_wasserstein_lagrange(a, b, M, reg_m=None, nb_dummies=1, log=False, References ---------- - .. [26] Caffarelli, L. A., & McCann, R. J. (2010) Free boundaries in + .. [28] Caffarelli, L. A., & McCann, R. J. (2010) Free boundaries in optimal transport and Monge-Ampere obstacle problems. Annals of mathematics, 673-730. @@ -182,6 +192,13 @@ def partial_wasserstein(a, b, M, m=None, nb_dummies=1, log=False, **kwargs): record log if True + .. warning:: + When dealing with a large number of points, the EMD solver may face + some instabilities, especially when the mass associated to the dummy + point is large. To avoid them, increase the number of dummy points + (allows a smoother repartition of the mass over the points). + + Returns ------- :math:`gamma` : (dim_a x dim_b) ndarray @@ -206,10 +223,10 @@ def partial_wasserstein(a, b, M, m=None, nb_dummies=1, log=False, **kwargs): References ---------- - .. [26] Caffarelli, L. A., & McCann, R. J. (2010) Free boundaries in + .. [28] Caffarelli, L. A., & McCann, R. J. (2010) Free boundaries in optimal transport and Monge-Ampere obstacle problems. Annals of mathematics, 673-730. - .. [28] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + .. [29] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- Wasserstein with Applications on Positive-Unlabeled Learning". arXiv preprint arXiv:2002.08276. @@ -289,6 +306,13 @@ def partial_wasserstein2(a, b, M, m=None, nb_dummies=1, log=False, **kwargs): record log if True + .. warning:: + When dealing with a large number of points, the EMD solver may face + some instabilities, especially when the mass associated to the dummy + point is large. To avoid them, increase the number of dummy points + (allows a smoother repartition of the mass over the points). + + Returns ------- :math:`gamma` : (dim_a x dim_b) ndarray @@ -311,10 +335,10 @@ def partial_wasserstein2(a, b, M, m=None, nb_dummies=1, log=False, **kwargs): References ---------- - .. [26] Caffarelli, L. A., & McCann, R. J. (2010) Free boundaries in + .. [28] Caffarelli, L. A., & McCann, R. J. (2010) Free boundaries in optimal transport and Monge-Ampere obstacle problems. Annals of mathematics, 673-730. - .. [28] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + .. [29] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- Wasserstein with Applications on Positive-Unlabeled Learning". arXiv preprint arXiv:2002.08276. """ @@ -411,7 +435,7 @@ def partial_gromov_wasserstein(C1, C2, p, q, m=None, nb_dummies=1, G0=None, - a and b are the sample weights - m is the amount of mass to be transported - The formulation of the problem has been proposed in [28]_ + The formulation of the problem has been proposed in [29]_ Parameters @@ -435,13 +459,12 @@ def partial_gromov_wasserstein(C1, C2, p, q, m=None, nb_dummies=1, G0=None, (default: 1) numItermax : int, optional Max number of iterations + tol : float, optional + tolerance for stopping iterations log : bool, optional return log if True verbose : bool, optional Print information along iterations - armijo : bool, optional - If True the steps of the line-search is found via an armijo research. Else closed form is used. - If there is convergence issues use False. **kwargs : dict parameters can be directly passed to the emd solver @@ -477,7 +500,7 @@ def partial_gromov_wasserstein(C1, C2, p, q, m=None, nb_dummies=1, G0=None, References ---------- - .. [28] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + .. [29] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- Wasserstein with Applications on Positive-Unlabeled Learning". arXiv preprint arXiv:2002.08276. @@ -546,7 +569,7 @@ def partial_gromov_wasserstein(C1, C2, p, q, m=None, nb_dummies=1, G0=None, def partial_gromov_wasserstein2(C1, C2, p, q, m=None, nb_dummies=1, G0=None, - thres=0.75, numItermax=1000, tol=1e-7, + thres=1, numItermax=1000, tol=1e-7, log=False, verbose=False, **kwargs): r""" Solves the partial optimal transport problem @@ -570,7 +593,7 @@ def partial_gromov_wasserstein2(C1, C2, p, q, m=None, nb_dummies=1, G0=None, - a and b are the sample weights - m is the amount of mass to be transported - The formulation of the problem has been proposed in [28]_ + The formulation of the problem has been proposed in [29]_ Parameters @@ -594,6 +617,8 @@ def partial_gromov_wasserstein2(C1, C2, p, q, m=None, nb_dummies=1, G0=None, (default: 1) numItermax : int, optional Max number of iterations + tol : float, optional + tolerance for stopping iterations log : bool, optional return log if True verbose : bool, optional @@ -602,6 +627,13 @@ def partial_gromov_wasserstein2(C1, C2, p, q, m=None, nb_dummies=1, G0=None, parameters can be directly passed to the emd solver + .. warning:: + When dealing with a large number of points, the EMD solver may face + some instabilities, especially when the mass associated to the dummy + point is large. To avoid them, increase the number of dummy points + (allows a smoother repartition of the mass over the points). + + Returns ------- partial_gw_dist : (dim_a x dim_b) ndarray @@ -627,7 +659,7 @@ def partial_gromov_wasserstein2(C1, C2, p, q, m=None, nb_dummies=1, G0=None, References ---------- - .. [28] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + .. [29] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- Wasserstein with Applications on Positive-Unlabeled Learning". arXiv preprint arXiv:2002.08276. @@ -831,6 +863,8 @@ def entropic_partial_gromov_wasserstein(C1, C2, p, q, reg, m=None, G0=None, Initialisation of the transportation matrix numItermax : int, optional Max number of iterations + tol : float, optional + Stop threshold on error (>0) log : bool, optional return log if True verbose : bool, optional @@ -966,6 +1000,8 @@ def entropic_partial_gromov_wasserstein2(C1, C2, p, q, reg, m=None, G0=None, Initialisation of the transportation matrix numItermax : int, optional Max number of iterations + tol : float, optional + Stop threshold on error (>0) log : bool, optional return log if True verbose : bool, optional -- cgit v1.2.3 From 126903381374a1d2f934190b208d134a0495dc65 Mon Sep 17 00:00:00 2001 From: ievred Date: Fri, 17 Apr 2020 16:41:14 +0200 Subject: added regulrization from [6]+fix other issues --- ot/da.py | 29 +++++++++++++++++++++++------ 1 file changed, 23 insertions(+), 6 deletions(-) diff --git a/ot/da.py b/ot/da.py index be959d6..9e00dce 100644 --- a/ot/da.py +++ b/ot/da.py @@ -16,7 +16,7 @@ import scipy.linalg as linalg from .bregman import sinkhorn, jcpot_barycenter from .lp import emd -from .utils import unif, dist, kernel, cost_normalization, label_normalization, laplacian +from .utils import unif, dist, kernel, cost_normalization, label_normalization, laplacian, dots from .utils import check_params, BaseEstimator from .unbalanced import sinkhorn_unbalanced from .optim import cg @@ -748,7 +748,7 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, return A, b -def emd_laplace(a, b, xs, xt, M, sim, eta, alpha, +def emd_laplace(a, b, xs, xt, M, sim, reg, eta, alpha, numItermax, stopThr, numInnerItermax, stopInnerThr, log=False, verbose=False, **kwargs): r"""Solve the optimal transport problem (OT) with Laplacian regularization @@ -785,6 +785,8 @@ def emd_laplace(a, b, xs, xt, M, sim, eta, alpha, samples in the target domain M : np.ndarray (ns,nt) loss matrix + reg : string + Type of Laplacian regularization eta : float Regularization term for Laplacian regularization alpha : float @@ -844,6 +846,8 @@ def emd_laplace(a, b, xs, xt, M, sim, eta, alpha, sS = (sS + sS.T) / 2 sT = kneighbors_graph(xt, kwargs['nn']).toarray() sT = (sT + sT.T) / 2 + else: + raise ValueError('Unknown similarity type {sim}. Currently supported similarity types are "knn" and "gauss".'.format(sim=sim)) lS = laplacian(sS) lT = laplacian(sT) @@ -852,9 +856,18 @@ def emd_laplace(a, b, xs, xt, M, sim, eta, alpha, return alpha * np.trace(np.dot(xt.T, np.dot(G.T, np.dot(lS, np.dot(G, xt))))) \ + (1 - alpha) * np.trace(np.dot(xs.T, np.dot(G, np.dot(lT, np.dot(G.T, xs))))) + ls2 = lS + lS.T + lt2 = lT + lT.T + xt2 = np.dot(xt, xt.T) + + if reg == 'disp': + Cs = -eta * alpha / xs.shape[0] * dots(ls2, xs, xt.T) + Ct = -eta * (1 - alpha) / xt.shape[0] * dots(xs, xt.T, lt2) + M = M + Cs + Ct + def df(G): - return alpha * np.dot(lS + lS.T, np.dot(G, np.dot(xt, xt.T)))\ - + (1 - alpha) * np.dot(xs, np.dot(xs.T, np.dot(G, lT + lT.T))) + return alpha * np.dot(ls2, np.dot(G, xt2))\ + + (1 - alpha) * np.dot(xs, np.dot(xs.T, np.dot(G, lt2))) return cg(a, b, M, reg=eta, f=f, df=df, G0=None, numItermax=numItermax, numItermaxEmd=numInnerItermax, stopThr=stopThr, stopThr2=stopInnerThr, verbose=verbose, log=log) @@ -1694,6 +1707,9 @@ class EMDLaplaceTransport(BaseTransport): Parameters ---------- + reg_type : string optional (default='pos') + Type of the regularization term: 'pos' and 'disp' for + regularization term defined in [2] and [6], respectively. reg_lap : float, optional (default=1) Laplacian regularization parameter reg_src : float, optional (default=0.5) @@ -1737,11 +1753,12 @@ class EMDLaplaceTransport(BaseTransport): in NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014. """ - def __init__(self, reg_lap=1., reg_src=1., alpha=0.5, + def __init__(self, reg_type='pos', reg_lap=1., reg_src=1., alpha=0.5, metric="sqeuclidean", norm=None, similarity="knn", max_iter=100, tol=1e-9, max_inner_iter=100000, inner_tol=1e-9, log=False, verbose=False, distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans'): + self.reg = reg_type self.reg_lap = reg_lap self.reg_src = reg_src self.alpha = alpha @@ -1785,7 +1802,7 @@ class EMDLaplaceTransport(BaseTransport): super(EMDLaplaceTransport, self).fit(Xs, ys, Xt, yt) returned_ = emd_laplace(a=self.mu_s, b=self.mu_t, xs=self.xs_, - xt=self.xt_, M=self.cost_, sim=self.similarity, eta=self.reg_lap, alpha=self.reg_src, + xt=self.xt_, M=self.cost_, reg=self.reg, sim=self.similarity, eta=self.reg_lap, alpha=self.reg_src, numItermax=self.max_iter, stopThr=self.tol, numInnerItermax=self.max_inner_iter, stopInnerThr=self.inner_tol, log=self.log, verbose=self.verbose) -- cgit v1.2.3 From 5ed4a27c8054397aae51ca49ddfcc8fa01e64db7 Mon Sep 17 00:00:00 2001 From: Laetitia Chapel Date: Mon, 20 Apr 2020 09:09:57 +0200 Subject: partial update refs --- ot/partial.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/ot/partial.py b/ot/partial.py index f325d98..5f4b836 100755 --- a/ot/partial.py +++ b/ot/partial.py @@ -702,7 +702,7 @@ def entropic_partial_wasserstein(a, b, M, reg, m=None, numItermax=1000, - a and b are the sample weights - m is the amount of mass to be transported - The formulation of the problem has been proposed in [3]_ + The formulation of the problem has been proposed in [3]_ (prop. 5) Parameters @@ -843,7 +843,8 @@ def entropic_partial_gromov_wasserstein(C1, C2, p, q, reg, m=None, G0=None, :math:`\Omega=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - m is the amount of mass to be transported - The formulation of the problem has been proposed in [12]. + The formulation of the GW problem has been proposed in [12]_ and the + partial GW in [29]_. Parameters ---------- @@ -903,6 +904,9 @@ def entropic_partial_gromov_wasserstein(C1, C2, p, q, reg, m=None, G0=None, .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." International Conference on Machine Learning (ICML). 2016. + .. [29] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + Wasserstein with Applications on Positive-Unlabeled Learning". + arXiv preprint arXiv:2002.08276. See Also -------- @@ -979,7 +983,8 @@ def entropic_partial_gromov_wasserstein2(C1, C2, p, q, reg, m=None, G0=None, :math:`\Omega=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - m is the amount of mass to be transported - The formulation of the problem has been proposed in [12]. + The formulation of the GW problem has been proposed in [12]_ and the + partial GW in [29]_. Parameters @@ -1033,6 +1038,9 @@ def entropic_partial_gromov_wasserstein2(C1, C2, p, q, reg, m=None, G0=None, .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." International Conference on Machine Learning (ICML). 2016. + .. [29] Chapel, L., Alaya, M., Gasso, G. (2019). "Partial Gromov- + Wasserstein with Applications on Positive-Unlabeled Learning". + arXiv preprint arXiv:2002.08276. """ partial_gw, log_gw = entropic_partial_gromov_wasserstein(C1, C2, p, q, reg, -- cgit v1.2.3 From 07463285317ed5c989040edcefb5c0e8cd3ac034 Mon Sep 17 00:00:00 2001 From: ievred Date: Mon, 20 Apr 2020 10:39:13 +0200 Subject: added kwargs to sim + doc --- ot/da.py | 48 ++++++++++++++++++++++++++++++++---------------- 1 file changed, 32 insertions(+), 16 deletions(-) diff --git a/ot/da.py b/ot/da.py index 9e00dce..8e26e31 100644 --- a/ot/da.py +++ b/ot/da.py @@ -748,7 +748,7 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, return A, b -def emd_laplace(a, b, xs, xt, M, sim, reg, eta, alpha, +def emd_laplace(a, b, xs, xt, M, reg, eta, alpha, numItermax, stopThr, numInnerItermax, stopInnerThr, log=False, verbose=False, **kwargs): r"""Solve the optimal transport problem (OT) with Laplacian regularization @@ -803,7 +803,11 @@ def emd_laplace(a, b, xs, xt, M, sim, reg, eta, alpha, Print information along iterations log : bool, optional record log if True - + kwargs : dict + Dictionary with attributes 'sim' ('knn' or 'gauss') and + 'param' (int, float or None) for similarity type and its parameter to be used. + If 'param' is None, it is computed as mean pairwise Euclidean distance over the data set + or set to 3 when sim is 'gauss' or 'knn', respectively. Returns ------- @@ -830,24 +834,28 @@ def emd_laplace(a, b, xs, xt, M, sim, reg, eta, alpha, ot.optim.cg : General regularized OT """ - if sim == 'gauss': - if 'rbfparam' not in kwargs: - kwargs['rbfparam'] = 1 / (2 * (np.mean(dist(xs, xs, 'sqeuclidean')) ** 2)) - sS = kernel(xs, xs, method=kwargs['sim'], sigma=kwargs['rbfparam']) - sT = kernel(xt, xt, method=kwargs['sim'], sigma=kwargs['rbfparam']) + if not isinstance(kwargs['param'], (int, float, type(None))): + raise ValueError( + 'Similarity parameter should be an int or a float. Got {type} instead.'.format(type=type(kwargs['param']))) + + if kwargs['sim'] == 'gauss': + if kwargs['param'] is None: + kwargs['param'] = 1 / (2 * (np.mean(dist(xs, xs, 'sqeuclidean')) ** 2)) + sS = kernel(xs, xs, method=kwargs['sim'], sigma=kwargs['param']) + sT = kernel(xt, xt, method=kwargs['sim'], sigma=kwargs['param']) - elif sim == 'knn': - if 'nn' not in kwargs: - kwargs['nn'] = 5 + elif kwargs['sim'] == 'knn': + if kwargs['param'] is None: + kwargs['param'] = 3 from sklearn.neighbors import kneighbors_graph - sS = kneighbors_graph(xs, kwargs['nn']).toarray() + sS = kneighbors_graph(X=xs, n_neighbors=int(kwargs['param'])).toarray() sS = (sS + sS.T) / 2 - sT = kneighbors_graph(xt, kwargs['nn']).toarray() + sT = kneighbors_graph(xt, n_neighbors=int(kwargs['param'])).toarray() sT = (sT + sT.T) / 2 else: - raise ValueError('Unknown similarity type {sim}. Currently supported similarity types are "knn" and "gauss".'.format(sim=sim)) + raise ValueError('Unknown similarity type {sim}. Currently supported similarity types are "knn" and "gauss".'.format(sim=kwargs['sim'])) lS = laplacian(sS) lT = laplacian(sT) @@ -1721,6 +1729,9 @@ class EMDLaplaceTransport(BaseTransport): can occur with large metric values. similarity : string, optional (default="knn") The similarity to use either knn or gaussian + similarity_param : int or float, optional (default=3) + Parameter for the similarity: number of nearest neighbors or bandwidth + if similarity="knn" or "gaussian", respectively. max_iter : int, optional (default=100) Max number of BCD iterations tol : float, optional (default=1e-5) @@ -1754,7 +1765,7 @@ class EMDLaplaceTransport(BaseTransport): """ def __init__(self, reg_type='pos', reg_lap=1., reg_src=1., alpha=0.5, - metric="sqeuclidean", norm=None, similarity="knn", max_iter=100, tol=1e-9, + metric="sqeuclidean", norm=None, similarity="knn", similarity_param=None, max_iter=100, tol=1e-9, max_inner_iter=100000, inner_tol=1e-9, log=False, verbose=False, distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans'): @@ -1765,6 +1776,7 @@ class EMDLaplaceTransport(BaseTransport): self.metric = metric self.norm = norm self.similarity = similarity + self.sim_param = similarity_param self.max_iter = max_iter self.tol = tol self.max_inner_iter = max_inner_iter @@ -1801,10 +1813,14 @@ class EMDLaplaceTransport(BaseTransport): super(EMDLaplaceTransport, self).fit(Xs, ys, Xt, yt) + kwargs = dict() + kwargs["sim"] = self.similarity + kwargs["param"] = self.sim_param + returned_ = emd_laplace(a=self.mu_s, b=self.mu_t, xs=self.xs_, - xt=self.xt_, M=self.cost_, reg=self.reg, sim=self.similarity, eta=self.reg_lap, alpha=self.reg_src, + xt=self.xt_, M=self.cost_, reg=self.reg, eta=self.reg_lap, alpha=self.reg_src, numItermax=self.max_iter, stopThr=self.tol, numInnerItermax=self.max_inner_iter, - stopInnerThr=self.inner_tol, log=self.log, verbose=self.verbose) + stopInnerThr=self.inner_tol, log=self.log, verbose=self.verbose, **kwargs) # coupling estimation if self.log: -- cgit v1.2.3 From de5679ad6e284c7d8751bcef05d4be5fd79d0aec Mon Sep 17 00:00:00 2001 From: Laetitia Chapel Date: Mon, 20 Apr 2020 11:57:48 +0200 Subject: partial update readme --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 1f62c2c..9d113bd 100644 --- a/README.md +++ b/README.md @@ -30,6 +30,7 @@ It provides the following solvers: * Unbalanced OT with KL relaxation distance and barycenter [10, 25]. * Screening Sinkhorn Algorithm for OT [26]. * JCPOT algorithm for multi-source domain adaptation with target shift [27]. +* Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic [3] formulations). Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. -- cgit v1.2.3 From 4eeaf49fa2289e6c48c83db1bbf17071f6fcdf96 Mon Sep 17 00:00:00 2001 From: ievred Date: Mon, 20 Apr 2020 12:53:16 +0200 Subject: conflit readme --- README.md | 248 +++++++++++++++++++++++++++++++------------------------------- 1 file changed, 123 insertions(+), 125 deletions(-) diff --git a/README.md b/README.md index 304f249..5b7f505 100644 --- a/README.md +++ b/README.md @@ -1,58 +1,60 @@ # POT: Python Optimal Transport -[![PyPI version](https://badge.fury.io/py/POT.svg)](https://badge.fury.io/py/POT) -[![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot) -[![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/rflamary/POT) -[![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) -[![Downloads](https://pepy.tech/badge/pot)](https://pepy.tech/project/pot) -[![Anaconda downloads](https://anaconda.org/conda-forge/pot/badges/downloads.svg)](https://anaconda.org/conda-forge/pot) -[![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/rflamary/POT/blob/master/LICENSE) - +import ot +[![PyPI version](https: // badge.fury.io / py / POT.svg)](https: // badge.fury.io / py / POT) +[![Anaconda Cloud](https: // anaconda.org / conda - forge / pot / badges / version.svg)](https: // anaconda.org / conda - forge / pot) +[![Build Status](https: // travis - ci.org / rflamary / POT.svg?branch=master)](https: // travis - ci.org / rflamary / POT) +[![Documentation Status](https: // readthedocs.org / projects / pot / badge /?version=latest)](http: // pot.readthedocs.io / en / latest /?badge=latest) +[![Downloads](https: // pepy.tech / badge / pot)](https: // pepy.tech / project / pot) +[![Anaconda downloads](https: // anaconda.org / conda - forge / pot / badges / downloads.svg)](https: // anaconda.org / conda - forge / pot) +[![License](https: // anaconda.org / conda - forge / pot / badges / license.svg)](https: // github.com / rflamary / POT / blob / master / LICENSE) This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. It provides the following solvers: -* OT Network Flow solver for the linear program/ Earth Movers Distance [1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU implementation (requires cupy). -* Sinkhorn divergence [23] and entropic regularization OT from empirical data. -* Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. -* Non regularized Wasserstein barycenters [16] with LP solver (only small scale). -* Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4]. -* Optimal transport for domain adaptation with group lasso and Laplacian regularization [5] -* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. -* Linear OT [14] and Joint OT matrix and mapping estimation [8]. -* Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). -* Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) -* Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) -* Non regularized free support Wasserstein barycenters [20]. -* Unbalanced OT with KL relaxation distance and barycenter [10, 25]. -* Screening Sinkhorn Algorithm for OT [26]. -* JCPOT algorithm for multi-source domain adaptation with target shift [27]. - -Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. +* OT Network Flow solver for the linear program / Earth Movers Distance[1]. +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm[2], stabilized version[9][10] and greedy Sinkhorn[22] with optional GPU implementation(requires cupy). +* Sinkhorn divergence[23] and entropic regularization OT from empirical data. +* Smooth optimal transport solvers(dual and semi - dual) for KL and squared L2 regularizations[17]. +* Non regularized Wasserstein barycenters[16] with LP solver(only small scale). +* Bregman projections for Wasserstein barycenter[3], convolutional barycenter[21] and unmixing[4]. +* Optimal transport for domain adaptation with group lasso and Laplacian regularization[5] +* Conditional gradient[6] and Generalized conditional gradient for regularized OT[7]. +* Linear OT[14] and Joint OT matrix and mapping estimation[8]. +* Wasserstein Discriminant Analysis[11](requires autograd + pymanopt). +* Gromov - Wasserstein distances and barycenters([13] and regularized[12]) +* Stochastic Optimization for Large - scale Optimal Transport(semi - dual problem[18] and dual problem[19]) +* Non regularized free support Wasserstein barycenters[20]. +* Unbalanced OT with KL relaxation distance and barycenter[10, 25]. +* Screening Sinkhorn Algorithm for OT[26]. +* JCPOT algorithm for multi - source domain adaptation with target shift[27]. + +Some demonstrations(both in Python and Jupyter Notebook format) are available in the examples folder. #### Using and citing the toolbox If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference: ``` + + @misc{flamary2017pot, -title={POT Python Optimal Transport library}, -author={Flamary, R{'e}mi and Courty, Nicolas}, -url={https://github.com/rflamary/POT}, -year={2017} -} + title = {POT Python Optimal Transport library}, + author = {Flamary, R{'e}mi and Courty, Nicolas}, + url = {https: // github.com / rflamary / POT}, + year = {2017} + } ``` ## Installation -The library has been tested on Linux, MacOSX and Windows. It requires a C++ compiler for building/installing the EMD solver and relies on the following Python modules: +The library has been tested on Linux, MacOSX and Windows. It requires a C + + compiler for building / installing the EMD solver and relies on the following Python modules: -- Numpy (>=1.11) -- Scipy (>=1.0) -- Cython (>=0.23) -- Matplotlib (>=1.5) +- Numpy ( >= 1.11) +- Scipy ( >= 1.0) +- Cython ( >= 0.23) +- Matplotlib ( >= 1.5) #### Pip installation @@ -68,35 +70,33 @@ pip install POT ``` or get the very latest version by downloading it and then running: ``` -python setup.py install --user # for user install (no root) +python setup.py install - -user # for user install (no root) ``` - #### Anaconda installation with conda-forge -If you use the Anaconda python distribution, POT is available in [conda-forge](https://conda-forge.org). To install it and the required dependencies: +If you use the Anaconda python distribution, POT is available in [conda - forge](https: // conda - forge.org). To install it and the required dependencies: ``` -conda install -c conda-forge pot +conda install - c conda - forge pot ``` #### Post installation check After a correct installation, you should be able to import the module without errors: ```python -import ot ``` Note that for easier access the module is name ot instead of pot. ### Dependencies -Some sub-modules require additional dependences which are discussed below +Some sub - modules require additional dependences which are discussed below -* **ot.dr** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: +* **ot.dr ** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: ``` pip install pymanopt autograd ``` -* **ot.gpu** (GPU accelerated OT) depends on cupy that have to be installed following instructions on [this page](https://docs-cupy.chainer.org/en/stable/install.html). +* **ot.gpu ** (GPU accelerated OT) depends on cupy that have to be installed following instructions on[this page](https: // docs - cupy.chainer.org / en / stable / install.html). obviously you need CUDA installed and a compatible GPU. @@ -107,159 +107,157 @@ obviously you need CUDA installed and a compatible GPU. * Import the toolbox ```python -import ot ``` * Compute Wasserstein distances ```python # a,b are 1D histograms (sum to 1 and positive) # M is the ground cost matrix -Wd=ot.emd2(a,b,M) # exact linear program -Wd_reg=ot.sinkhorn2(a,b,M,reg) # entropic regularized OT +Wd = ot.emd2(a, b, M) # exact linear program +Wd_reg = ot.sinkhorn2(a, b, M, reg) # entropic regularized OT # if b is a matrix compute all distances to a and return a vector ``` * Compute OT matrix ```python # a,b are 1D histograms (sum to 1 and positive) # M is the ground cost matrix -T=ot.emd(a,b,M) # exact linear program -T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT +T = ot.emd(a, b, M) # exact linear program +T_reg = ot.sinkhorn(a, b, M, reg) # entropic regularized OT ``` * Compute Wasserstein barycenter ```python # A is a n*d matrix containing d 1D histograms # M is the ground cost matrix -ba=ot.barycenter(A,M,reg) # reg is regularization parameter +ba = ot.barycenter(A, M, reg) # reg is regularization parameter ``` - - ### Examples and Notebooks -The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http://pot.readthedocs.io/). +The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http: // pot.readthedocs.io / ). -Here is a list of the Python notebooks available [here](https://github.com/rflamary/POT/blob/master/notebooks/) if you want a quick look: +Here is a list of the Python notebooks available [here](https: // github.com / rflamary / POT / blob / master / notebooks / ) if you want a quick look: -* [1D optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_1D.ipynb) -* [OT Ground Loss](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_L1_vs_L2.ipynb) -* [Multiple EMD computation](https://github.com/rflamary/POT/blob/master/notebooks/plot_compute_emd.ipynb) -* [2D optimal transport on empirical distributions](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_2D_samples.ipynb) -* [1D Wasserstein barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_1D.ipynb) -* [OT with user provided regularization](https://github.com/rflamary/POT/blob/master/notebooks/plot_optim_OTreg.ipynb) -* [Domain adaptation with optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_d2.ipynb) -* [Color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_color_images.ipynb) -* [OT mapping estimation for domain adaptation](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping.ipynb) -* [OT mapping estimation for color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping_colors_images.ipynb) -* [Wasserstein Discriminant Analysis](https://github.com/rflamary/POT/blob/master/notebooks/plot_WDA.ipynb) -* [Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov.ipynb) -* [Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov_barycenter.ipynb) -* [Fused Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_fgw.ipynb) -* [Fused Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_fgw.ipynb) +* [1D optimal transport](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_1D.ipynb) +* [OT Ground Loss](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_L1_vs_L2.ipynb) +* [Multiple EMD computation](https: // github.com / rflamary / POT / blob / master / notebooks / plot_compute_emd.ipynb) +* [2D optimal transport on empirical distributions](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_2D_samples.ipynb) +* [1D Wasserstein barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_barycenter_1D.ipynb) +* [OT with user provided regularization](https: // github.com / rflamary / POT / blob / master / notebooks / plot_optim_OTreg.ipynb) +* [Domain adaptation with optimal transport](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_d2.ipynb) +* [Color transfer in images](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_color_images.ipynb) +* [OT mapping estimation for domain adaptation](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_mapping.ipynb) +* [OT mapping estimation for color transfer in images](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_mapping_colors_images.ipynb) +* [Wasserstein Discriminant Analysis](https: // github.com / rflamary / POT / blob / master / notebooks / plot_WDA.ipynb) +* [Gromov Wasserstein](https: // github.com / rflamary / POT / blob / master / notebooks / plot_gromov.ipynb) +* [Gromov Wasserstein Barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_gromov_barycenter.ipynb) +* [Fused Gromov Wasserstein](https: // github.com / rflamary / POT / blob / master / notebooks / plot_fgw.ipynb) +* [Fused Gromov Wasserstein Barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_barycenter_fgw.ipynb) -You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/notebooks/). +You can also see the notebooks with [Jupyter nbviewer](https: // nbviewer.jupyter.org / github / rflamary / POT / tree / master / notebooks / ). ## Acknowledgements This toolbox has been created and is maintained by -* [Rémi Flamary](http://remi.flamary.com/) -* [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) +* [Rémi Flamary](http: // remi.flamary.com / ) +* [Nicolas Courty](http: // people.irisa.fr / Nicolas.Courty / ) -The contributors to this library are +The contributors to this library are -* [Alexandre Gramfort](http://alexandre.gramfort.net/) -* [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) -* [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation) -* [Léo Gautheron](https://github.com/aje) (GPU implementation) -* [Nathalie Gayraud](https://www.linkedin.com/in/nathalie-t-h-gayraud/?ppe=1) -* [Stanislas Chambon](https://slasnista.github.io/) -* [Antoine Rolet](https://arolet.github.io/) -* Erwan Vautier (Gromov-Wasserstein) -* [Kilian Fatras](https://kilianfatras.github.io/) -* [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home) -* [Vayer Titouan](https://tvayer.github.io/) -* [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT) -* [Romain Tavenard](https://rtavenar.github.io/) (1d Wasserstein) -* [Mokhtar Z. Alaya](http://mzalaya.github.io/) (Screenkhorn) -* [Ievgen Redko](https://ievred.github.io/) +* [Alexandre Gramfort](http: // alexandre.gramfort.net / ) +* [Laetitia Chapel](http: // people.irisa.fr / Laetitia.Chapel / ) +* [Michael Perrot](http: // perso.univ - st - etienne.fr / pem82055 / ) (Mapping estimation) +* [Léo Gautheron](https: // github.com / aje)(GPU implementation) +* [Nathalie Gayraud](https: // www.linkedin.com / in / nathalie - t - h - gayraud /?ppe=1) +* [Stanislas Chambon](https: // slasnista.github.io / ) +* [Antoine Rolet](https: // arolet.github.io / ) +* Erwan Vautier(Gromov - Wasserstein) +* [Kilian Fatras](https: // kilianfatras.github.io / ) +* [Alain Rakotomamonjy](https: // sites.google.com / site / alainrakotomamonjy / home) +* [Vayer Titouan](https: // tvayer.github.io / ) +* [Hicham Janati](https: // hichamjanati.github.io / ) (Unbalanced OT) +* [Romain Tavenard](https: // rtavenar.github.io / ) (1d Wasserstein) +* [Mokhtar Z. Alaya](http: // mzalaya.github.io / ) (Screenkhorn) +* [Ievgen Redko](https: // ievred.github.io /) -This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): +This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code(in various languages): -* [Gabriel Peyré](http://gpeyre.github.io/) (Wasserstein Barycenters in Matlab) -* [Nicolas Bonneel](http://liris.cnrs.fr/~nbonneel/) ( C++ code for EMD) -* [Marco Cuturi](http://marcocuturi.net/) (Sinkhorn Knopp in Matlab/Cuda) +* [Gabriel Peyré](http: // gpeyre.github.io / ) (Wasserstein Barycenters in Matlab) +* [Nicolas Bonneel](http: // liris.cnrs.fr / ~nbonneel /) (C++ code for EMD) +* [Marco Cuturi](http: // marcocuturi.net / ) (Sinkhorn Knopp in Matlab/Cuda) ## Contributions and code of conduct -Every contribution is welcome and should respect the [contribution guidelines](CONTRIBUTING.md). Each member of the project is expected to follow the [code of conduct](CODE_OF_CONDUCT.md). +Every contribution is welcome and should respect the[contribution guidelines](CONTRIBUTING.md). Each member of the project is expected to follow the[code of conduct](CODE_OF_CONDUCT.md). ## Support You can ask questions and join the development discussion: -* On the [POT Slack channel](https://pot-toolbox.slack.com) -* On the POT [mailing list](https://mail.python.org/mm3/mailman3/lists/pot.python.org/) +* On the[POT Slack channel](https: // pot - toolbox.slack.com) +* On the POT [mailing list](https: // mail.python.org / mm3 / mailman3 / lists / pot.python.org / ) -You can also post bug reports and feature requests in Github issues. Make sure to read our [guidelines](CONTRIBUTING.md) first. +You can also post bug reports and feature requests in Github issues. Make sure to read our[guidelines](CONTRIBUTING.md) first. ## References -[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). [Displacement interpolation using Lagrangian mass transport](https://people.csail.mit.edu/sparis/publi/2011/sigasia/Bonneel_11_Displacement_Interpolation.pdf). In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. - -[2] Cuturi, M. (2013). [Sinkhorn distances: Lightspeed computation of optimal transport](https://arxiv.org/pdf/1306.0895.pdf). In Advances in Neural Information Processing Systems (pp. 2292-2300). +[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). [Displacement interpolation using Lagrangian mass transport](https: // people.csail.mit.edu / sparis / publi / 2011 / sigasia / Bonneel_11_Displacement_Interpolation.pdf). In ACM Transactions on Graphics(TOG)(Vol. 30, No. 6, p. 158). ACM. -[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). [Iterative Bregman projections for regularized transportation problems](https://arxiv.org/pdf/1412.5154.pdf). SIAM Journal on Scientific Computing, 37(2), A1111-A1138. +[2] Cuturi, M. (2013). [Sinkhorn distances: Lightspeed computation of optimal transport](https: // arxiv.org / pdf / 1306.0895.pdf). In Advances in Neural Information Processing Systems(pp. 2292 - 2300). -[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, [Supervised planetary unmixing with optimal transport](https://hal.archives-ouvertes.fr/hal-01377236/document), Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016. +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). [Iterative Bregman projections for regularized transportation problems](https: // arxiv.org / pdf / 1412.5154.pdf). SIAM Journal on Scientific Computing, 37(2), A1111 - A1138. -[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, [Optimal Transport for Domain Adaptation](https://arxiv.org/pdf/1507.00504.pdf), in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 +[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, [Supervised planetary unmixing with optimal transport](https: // hal.archives - ouvertes.fr / hal - 01377236 / document), Whorkshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing(WHISPERS), 2016. -[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). [Regularized discrete optimal transport](https://arxiv.org/pdf/1307.5551.pdf). SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +[5] N. Courty +R. Flamary +D. Tuia +A. Rakotomamonjy, [Optimal Transport for Domain Adaptation](https: // arxiv.org / pdf / 1507.00504.pdf), in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.PP, no.99, pp.1 - 1 -[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). [Generalized conditional gradient: analysis of convergence and applications](https://arxiv.org/pdf/1510.06567.pdf). arXiv preprint arXiv:1510.06567. +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). [Regularized discrete optimal transport](https: // arxiv.org / pdf / 1307.5551.pdf). SIAM Journal on Imaging Sciences, 7(3), 1853 - 1882. -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard (2016), [Mapping estimation for discrete optimal transport](http://remi.flamary.com/biblio/perrot2016mapping.pdf), Neural Information Processing Systems (NIPS). +[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). [Generalized conditional gradient: analysis of convergence and applications](https: // arxiv.org / pdf / 1510.06567.pdf). arXiv preprint arXiv: 1510.06567. -[9] Schmitzer, B. (2016). [Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems](https://arxiv.org/pdf/1610.06519.pdf). arXiv preprint arXiv:1610.06519. +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard(2016), [Mapping estimation for discrete optimal transport](http: // remi.flamary.com / biblio / perrot2016mapping.pdf), Neural Information Processing Systems(NIPS). -[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). [Scaling algorithms for unbalanced transport problems](https://arxiv.org/pdf/1607.05816.pdf). arXiv preprint arXiv:1607.05816. +[9] Schmitzer, B. (2016). [Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems](https: // arxiv.org / pdf / 1610.06519.pdf). arXiv preprint arXiv: 1610.06519. -[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https://arxiv.org/pdf/1608.08063.pdf). arXiv preprint arXiv:1608.08063. +[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). [Scaling algorithms for unbalanced transport problems](https: // arxiv.org / pdf / 1607.05816.pdf). arXiv preprint arXiv: 1607.05816. -[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon (2016), [Gromov-Wasserstein averaging of kernel and distance matrices](http://proceedings.mlr.press/v48/peyre16.html) International Conference on Machine Learning (ICML). +[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https: // arxiv.org / pdf / 1608.08063.pdf). arXiv preprint arXiv: 1608.08063. -[13] Mémoli, Facundo (2011). [Gromov–Wasserstein distances and the metric approach to object matching](https://media.adelaide.edu.au/acvt/Publications/2011/2011-Gromov%E2%80%93Wasserstein%20Distances%20and%20the%20Metric%20Approach%20to%20Object%20Matching.pdf). Foundations of computational mathematics 11.4 : 417-487. +[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon(2016), [Gromov - Wasserstein averaging of kernel and distance matrices](http: // proceedings.mlr.press / v48 / peyre16.html) International Conference on Machine Learning(ICML). -[14] Knott, M. and Smith, C. S. (1984).[On the optimal mapping of distributions](https://link.springer.com/article/10.1007/BF00934745), Journal of Optimization Theory and Applications Vol 43. +[13] Mémoli, Facundo(2011). [Gromov–Wasserstein distances and the metric approach to object matching](https: // media.adelaide.edu.au / acvt / Publications / 2011 / 2011 - Gromov % E2 % 80 % 93Wasserstein % 20Distances % 20and % 20the % 20Metric % 20Approach % 20to % 20Object % 20Matching.pdf). Foundations of computational mathematics 11.4: 417 - 487. -[15] Peyré, G., & Cuturi, M. (2018). [Computational Optimal Transport](https://arxiv.org/pdf/1803.00567.pdf) . +[14] Knott, M. and Smith, C. S. (1984).[On the optimal mapping of distributions](https: // link.springer.com / article / 10.1007 / BF00934745), Journal of Optimization Theory and Applications Vol 43. -[16] Agueh, M., & Carlier, G. (2011). [Barycenters in the Wasserstein space](https://hal.archives-ouvertes.fr/hal-00637399/document). SIAM Journal on Mathematical Analysis, 43(2), 904-924. +[15] Peyré, G., & Cuturi, M. (2018). [Computational Optimal Transport](https: // arxiv.org / pdf / 1803.00567.pdf) . -[17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https://arxiv.org/abs/1710.06276). Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). +[16] Agueh, M., & Carlier, G. (2011). [Barycenters in the Wasserstein space](https: // hal.archives - ouvertes.fr / hal - 00637399 / document). SIAM Journal on Mathematical Analysis, 43(2), 904 - 924. -[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) [Stochastic Optimization for Large-scale Optimal Transport](https://arxiv.org/abs/1605.08527). Advances in Neural Information Processing Systems (2016). +[17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https: // arxiv.org / abs / 1710.06276). Proceedings of the Twenty - First International Conference on Artificial Intelligence and Statistics(AISTATS). -[19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A.& Blondel, M. [Large-scale Optimal Transport and Mapping Estimation](https://arxiv.org/pdf/1711.02283.pdf). International Conference on Learning Representation (2018) +[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016)[Stochastic Optimization for Large - scale Optimal Transport](https: // arxiv.org / abs / 1605.08527). Advances in Neural Information Processing Systems(2016). -[20] Cuturi, M. and Doucet, A. (2014) [Fast Computation of Wasserstein Barycenters](http://proceedings.mlr.press/v32/cuturi14.html). International Conference in Machine Learning +[19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A. & Blondel, M. [Large - scale Optimal Transport and Mapping Estimation](https: // arxiv.org / pdf / 1711.02283.pdf). International Conference on Learning Representation(2018) -[21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). [Convolutional wasserstein distances: Efficient optimal transportation on geometric domains](https://dl.acm.org/citation.cfm?id=2766963). ACM Transactions on Graphics (TOG), 34(4), 66. +[20] Cuturi, M. and Doucet, A. (2014)[Fast Computation of Wasserstein Barycenters](http: // proceedings.mlr.press / v32 / cuturi14.html). International Conference in Machine Learning -[22] J. Altschuler, J.Weed, P. Rigollet, (2017) [Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration](https://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration.pdf), Advances in Neural Information Processing Systems (NIPS) 31 +[21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). [Convolutional wasserstein distances: Efficient optimal transportation on geometric domains](https: // dl.acm.org / citation.cfm?id=2766963). ACM Transactions on Graphics(TOG), 34(4), 66. -[23] Aude, G., Peyré, G., Cuturi, M., [Learning Generative Models with Sinkhorn Divergences](https://arxiv.org/abs/1706.00292), Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 +[22] J. Altschuler, J.Weed, P. Rigollet, (2017)[Near - linear time approximation algorithms for optimal transport via Sinkhorn iteration](https: // papers.nips.cc / paper / 6792 - near - linear - time - approximation - algorithms - for-optimal - transport - via - sinkhorn - iteration.pdf), Advances in Neural Information Processing Systems(NIPS) 31 -[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http://proceedings.mlr.press/v97/titouan19a.html) Proceedings of the 36th International Conference on Machine Learning (ICML). +[23] Aude, G., Peyré, G., Cuturi, M., [Learning Generative Models with Sinkhorn Divergences](https: // arxiv.org / abs / 1706.00292), Proceedings of the Twenty - First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 -[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/) Advances in Neural Information Processing Systems (NIPS). +[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http: // proceedings.mlr.press / v97 / titouan19a.html) Proceedings of the 36th International Conference on Machine Learning(ICML). -[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 33 (NeurIPS). +[25] Frogner C., Zhang C., Mobahi H., Araya - Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http: // cbcl.mit.edu / wasserstein / ) Advances in Neural Information Processing Systems (NIPS). -[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019. +[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https: // papers.nips.cc / paper / 9386 - screening - sinkhorn - algorithm - for-regularized - optimal - transport), Advances in Neural Information Processing Systems 33 (NeurIPS). -[28] Flamary R., Courty N., Tuia D., Rakotomamonjy A. (2014). [Optimal transport with Laplacian regularization: Applications to domain adaptation and shape matching](https://remi.flamary.com/biblio/flamary2014optlaplace.pdf), NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014. +[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi - source Domain Adaptation under Target Shift](http: // proceedings.mlr.press / v89 / redko19a.html), Proceedings of the Twenty - Second International Conference on Artificial Intelligence and Statistics(AISTATS) 22, 2019. -- cgit v1.2.3 From 5eedfcf89171163c2bc60cf51c438b34fc514caf Mon Sep 17 00:00:00 2001 From: ievred Date: Mon, 20 Apr 2020 12:54:25 +0200 Subject: conflit readme --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index 5b7f505..39acdd3 100644 --- a/README.md +++ b/README.md @@ -261,3 +261,7 @@ A. Rakotomamonjy, [Optimal Transport for Domain Adaptation](https: // arxiv.org [26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https: // papers.nips.cc / paper / 9386 - screening - sinkhorn - algorithm - for-regularized - optimal - transport), Advances in Neural Information Processing Systems 33 (NeurIPS). [27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi - source Domain Adaptation under Target Shift](http: // proceedings.mlr.press / v89 / redko19a.html), Proceedings of the Twenty - Second International Conference on Artificial Intelligence and Statistics(AISTATS) 22, 2019. + +[28] Caffarelli, L. A., McCann, R. J. (2020). [Free boundaries in optimal transport and Monge - Ampere obstacle problems](http: // www.math.toronto.edu / ~mccann / papers / annals2010.pdf), Annals of mathematics, 673 - 730. + +[29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial Gromov - Wasserstein with Applications on Positive - Unlabeled Learning](https: // arxiv.org / abs / 2002.08276), arXiv preprint arXiv: 2002.08276. -- cgit v1.2.3 From 6da7586f0f08ca07a380d23ea96281e1511d333c Mon Sep 17 00:00:00 2001 From: ievred Date: Mon, 20 Apr 2020 12:57:33 +0200 Subject: conflit readme --- README.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/README.md b/README.md index 39acdd3..5b7f505 100644 --- a/README.md +++ b/README.md @@ -261,7 +261,3 @@ A. Rakotomamonjy, [Optimal Transport for Domain Adaptation](https: // arxiv.org [26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https: // papers.nips.cc / paper / 9386 - screening - sinkhorn - algorithm - for-regularized - optimal - transport), Advances in Neural Information Processing Systems 33 (NeurIPS). [27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi - source Domain Adaptation under Target Shift](http: // proceedings.mlr.press / v89 / redko19a.html), Proceedings of the Twenty - Second International Conference on Artificial Intelligence and Statistics(AISTATS) 22, 2019. - -[28] Caffarelli, L. A., McCann, R. J. (2020). [Free boundaries in optimal transport and Monge - Ampere obstacle problems](http: // www.math.toronto.edu / ~mccann / papers / annals2010.pdf), Annals of mathematics, 673 - 730. - -[29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial Gromov - Wasserstein with Applications on Positive - Unlabeled Learning](https: // arxiv.org / abs / 2002.08276), arXiv preprint arXiv: 2002.08276. -- cgit v1.2.3 From 72b1a2822be81a877210d0bf11520ae4559b6d51 Mon Sep 17 00:00:00 2001 From: ievred Date: Mon, 20 Apr 2020 13:00:51 +0200 Subject: conflit readme --- README.md | 250 +++++++++++++++++++++++++++++++------------------------------- 1 file changed, 127 insertions(+), 123 deletions(-) diff --git a/README.md b/README.md index 5b7f505..28d2b2a 100644 --- a/README.md +++ b/README.md @@ -1,60 +1,59 @@ # POT: Python Optimal Transport -import ot -[![PyPI version](https: // badge.fury.io / py / POT.svg)](https: // badge.fury.io / py / POT) -[![Anaconda Cloud](https: // anaconda.org / conda - forge / pot / badges / version.svg)](https: // anaconda.org / conda - forge / pot) -[![Build Status](https: // travis - ci.org / rflamary / POT.svg?branch=master)](https: // travis - ci.org / rflamary / POT) -[![Documentation Status](https: // readthedocs.org / projects / pot / badge /?version=latest)](http: // pot.readthedocs.io / en / latest /?badge=latest) -[![Downloads](https: // pepy.tech / badge / pot)](https: // pepy.tech / project / pot) -[![Anaconda downloads](https: // anaconda.org / conda - forge / pot / badges / downloads.svg)](https: // anaconda.org / conda - forge / pot) -[![License](https: // anaconda.org / conda - forge / pot / badges / license.svg)](https: // github.com / rflamary / POT / blob / master / LICENSE) +[![PyPI version](https://badge.fury.io/py/POT.svg)](https://badge.fury.io/py/POT) +[![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot) +[![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/rflamary/POT) +[![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) +[![Downloads](https://pepy.tech/badge/pot)](https://pepy.tech/project/pot) +[![Anaconda downloads](https://anaconda.org/conda-forge/pot/badges/downloads.svg)](https://anaconda.org/conda-forge/pot) +[![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/rflamary/POT/blob/master/LICENSE) + This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. It provides the following solvers: -* OT Network Flow solver for the linear program / Earth Movers Distance[1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm[2], stabilized version[9][10] and greedy Sinkhorn[22] with optional GPU implementation(requires cupy). -* Sinkhorn divergence[23] and entropic regularization OT from empirical data. -* Smooth optimal transport solvers(dual and semi - dual) for KL and squared L2 regularizations[17]. -* Non regularized Wasserstein barycenters[16] with LP solver(only small scale). -* Bregman projections for Wasserstein barycenter[3], convolutional barycenter[21] and unmixing[4]. -* Optimal transport for domain adaptation with group lasso and Laplacian regularization[5] -* Conditional gradient[6] and Generalized conditional gradient for regularized OT[7]. -* Linear OT[14] and Joint OT matrix and mapping estimation[8]. -* Wasserstein Discriminant Analysis[11](requires autograd + pymanopt). -* Gromov - Wasserstein distances and barycenters([13] and regularized[12]) -* Stochastic Optimization for Large - scale Optimal Transport(semi - dual problem[18] and dual problem[19]) -* Non regularized free support Wasserstein barycenters[20]. -* Unbalanced OT with KL relaxation distance and barycenter[10, 25]. -* Screening Sinkhorn Algorithm for OT[26]. -* JCPOT algorithm for multi - source domain adaptation with target shift[27]. - -Some demonstrations(both in Python and Jupyter Notebook format) are available in the examples folder. +* OT Network Flow solver for the linear program/ Earth Movers Distance [1]. +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU implementation (requires cupy). +* Sinkhorn divergence [23] and entropic regularization OT from empirical data. +* Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. +* Non regularized Wasserstein barycenters [16] with LP solver (only small scale). +* Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4]. +* Optimal transport for domain adaptation with group lasso regularization [5] +* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. +* Linear OT [14] and Joint OT matrix and mapping estimation [8]. +* Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). +* Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) +* Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) +* Non regularized free support Wasserstein barycenters [20]. +* Unbalanced OT with KL relaxation distance and barycenter [10, 25]. +* Screening Sinkhorn Algorithm for OT [26]. +* JCPOT algorithm for multi-source domain adaptation with target shift [27]. +* Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic [3] formulations). + +Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. #### Using and citing the toolbox If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference: ``` - - @misc{flamary2017pot, - title = {POT Python Optimal Transport library}, - author = {Flamary, R{'e}mi and Courty, Nicolas}, - url = {https: // github.com / rflamary / POT}, - year = {2017} - } +title={POT Python Optimal Transport library}, +author={Flamary, R{'e}mi and Courty, Nicolas}, +url={https://github.com/rflamary/POT}, +year={2017} +} ``` ## Installation -The library has been tested on Linux, MacOSX and Windows. It requires a C + + compiler for building / installing the EMD solver and relies on the following Python modules: +The library has been tested on Linux, MacOSX and Windows. It requires a C++ compiler for building/installing the EMD solver and relies on the following Python modules: -- Numpy ( >= 1.11) -- Scipy ( >= 1.0) -- Cython ( >= 0.23) -- Matplotlib ( >= 1.5) +- Numpy (>=1.11) +- Scipy (>=1.0) +- Cython (>=0.23) +- Matplotlib (>=1.5) #### Pip installation @@ -70,33 +69,35 @@ pip install POT ``` or get the very latest version by downloading it and then running: ``` -python setup.py install - -user # for user install (no root) +python setup.py install --user # for user install (no root) ``` + #### Anaconda installation with conda-forge -If you use the Anaconda python distribution, POT is available in [conda - forge](https: // conda - forge.org). To install it and the required dependencies: +If you use the Anaconda python distribution, POT is available in [conda-forge](https://conda-forge.org). To install it and the required dependencies: ``` -conda install - c conda - forge pot +conda install -c conda-forge pot ``` #### Post installation check After a correct installation, you should be able to import the module without errors: ```python +import ot ``` Note that for easier access the module is name ot instead of pot. ### Dependencies -Some sub - modules require additional dependences which are discussed below +Some sub-modules require additional dependences which are discussed below -* **ot.dr ** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: +* **ot.dr** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: ``` pip install pymanopt autograd ``` -* **ot.gpu ** (GPU accelerated OT) depends on cupy that have to be installed following instructions on[this page](https: // docs - cupy.chainer.org / en / stable / install.html). +* **ot.gpu** (GPU accelerated OT) depends on cupy that have to be installed following instructions on [this page](https://docs-cupy.chainer.org/en/stable/install.html). obviously you need CUDA installed and a compatible GPU. @@ -107,157 +108,160 @@ obviously you need CUDA installed and a compatible GPU. * Import the toolbox ```python +import ot ``` * Compute Wasserstein distances ```python # a,b are 1D histograms (sum to 1 and positive) # M is the ground cost matrix -Wd = ot.emd2(a, b, M) # exact linear program -Wd_reg = ot.sinkhorn2(a, b, M, reg) # entropic regularized OT +Wd=ot.emd2(a,b,M) # exact linear program +Wd_reg=ot.sinkhorn2(a,b,M,reg) # entropic regularized OT # if b is a matrix compute all distances to a and return a vector ``` * Compute OT matrix ```python # a,b are 1D histograms (sum to 1 and positive) # M is the ground cost matrix -T = ot.emd(a, b, M) # exact linear program -T_reg = ot.sinkhorn(a, b, M, reg) # entropic regularized OT +T=ot.emd(a,b,M) # exact linear program +T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT ``` * Compute Wasserstein barycenter ```python # A is a n*d matrix containing d 1D histograms # M is the ground cost matrix -ba = ot.barycenter(A, M, reg) # reg is regularization parameter +ba=ot.barycenter(A,M,reg) # reg is regularization parameter ``` + + ### Examples and Notebooks -The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http: // pot.readthedocs.io / ). +The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http://pot.readthedocs.io/). -Here is a list of the Python notebooks available [here](https: // github.com / rflamary / POT / blob / master / notebooks / ) if you want a quick look: +Here is a list of the Python notebooks available [here](https://github.com/rflamary/POT/blob/master/notebooks/) if you want a quick look: -* [1D optimal transport](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_1D.ipynb) -* [OT Ground Loss](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_L1_vs_L2.ipynb) -* [Multiple EMD computation](https: // github.com / rflamary / POT / blob / master / notebooks / plot_compute_emd.ipynb) -* [2D optimal transport on empirical distributions](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_2D_samples.ipynb) -* [1D Wasserstein barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_barycenter_1D.ipynb) -* [OT with user provided regularization](https: // github.com / rflamary / POT / blob / master / notebooks / plot_optim_OTreg.ipynb) -* [Domain adaptation with optimal transport](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_d2.ipynb) -* [Color transfer in images](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_color_images.ipynb) -* [OT mapping estimation for domain adaptation](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_mapping.ipynb) -* [OT mapping estimation for color transfer in images](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_mapping_colors_images.ipynb) -* [Wasserstein Discriminant Analysis](https: // github.com / rflamary / POT / blob / master / notebooks / plot_WDA.ipynb) -* [Gromov Wasserstein](https: // github.com / rflamary / POT / blob / master / notebooks / plot_gromov.ipynb) -* [Gromov Wasserstein Barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_gromov_barycenter.ipynb) -* [Fused Gromov Wasserstein](https: // github.com / rflamary / POT / blob / master / notebooks / plot_fgw.ipynb) -* [Fused Gromov Wasserstein Barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_barycenter_fgw.ipynb) +* [1D optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_1D.ipynb) +* [OT Ground Loss](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_L1_vs_L2.ipynb) +* [Multiple EMD computation](https://github.com/rflamary/POT/blob/master/notebooks/plot_compute_emd.ipynb) +* [2D optimal transport on empirical distributions](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_2D_samples.ipynb) +* [1D Wasserstein barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_1D.ipynb) +* [OT with user provided regularization](https://github.com/rflamary/POT/blob/master/notebooks/plot_optim_OTreg.ipynb) +* [Domain adaptation with optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_d2.ipynb) +* [Color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_color_images.ipynb) +* [OT mapping estimation for domain adaptation](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping.ipynb) +* [OT mapping estimation for color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping_colors_images.ipynb) +* [Wasserstein Discriminant Analysis](https://github.com/rflamary/POT/blob/master/notebooks/plot_WDA.ipynb) +* [Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov.ipynb) +* [Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov_barycenter.ipynb) +* [Fused Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_fgw.ipynb) +* [Fused Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_fgw.ipynb) -You can also see the notebooks with [Jupyter nbviewer](https: // nbviewer.jupyter.org / github / rflamary / POT / tree / master / notebooks / ). +You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/notebooks/). ## Acknowledgements This toolbox has been created and is maintained by -* [Rémi Flamary](http: // remi.flamary.com / ) -* [Nicolas Courty](http: // people.irisa.fr / Nicolas.Courty / ) +* [Rémi Flamary](http://remi.flamary.com/) +* [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) -The contributors to this library are +The contributors to this library are -* [Alexandre Gramfort](http: // alexandre.gramfort.net / ) -* [Laetitia Chapel](http: // people.irisa.fr / Laetitia.Chapel / ) -* [Michael Perrot](http: // perso.univ - st - etienne.fr / pem82055 / ) (Mapping estimation) -* [Léo Gautheron](https: // github.com / aje)(GPU implementation) -* [Nathalie Gayraud](https: // www.linkedin.com / in / nathalie - t - h - gayraud /?ppe=1) -* [Stanislas Chambon](https: // slasnista.github.io / ) -* [Antoine Rolet](https: // arolet.github.io / ) -* Erwan Vautier(Gromov - Wasserstein) -* [Kilian Fatras](https: // kilianfatras.github.io / ) -* [Alain Rakotomamonjy](https: // sites.google.com / site / alainrakotomamonjy / home) -* [Vayer Titouan](https: // tvayer.github.io / ) -* [Hicham Janati](https: // hichamjanati.github.io / ) (Unbalanced OT) -* [Romain Tavenard](https: // rtavenar.github.io / ) (1d Wasserstein) -* [Mokhtar Z. Alaya](http: // mzalaya.github.io / ) (Screenkhorn) -* [Ievgen Redko](https: // ievred.github.io /) +* [Alexandre Gramfort](http://alexandre.gramfort.net/) +* [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) +* [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation) +* [Léo Gautheron](https://github.com/aje) (GPU implementation) +* [Nathalie Gayraud](https://www.linkedin.com/in/nathalie-t-h-gayraud/?ppe=1) +* [Stanislas Chambon](https://slasnista.github.io/) +* [Antoine Rolet](https://arolet.github.io/) +* Erwan Vautier (Gromov-Wasserstein) +* [Kilian Fatras](https://kilianfatras.github.io/) +* [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home) +* [Vayer Titouan](https://tvayer.github.io/) +* [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT) +* [Romain Tavenard](https://rtavenar.github.io/) (1d Wasserstein) +* [Mokhtar Z. Alaya](http://mzalaya.github.io/) (Screenkhorn) -This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code(in various languages): +This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): -* [Gabriel Peyré](http: // gpeyre.github.io / ) (Wasserstein Barycenters in Matlab) -* [Nicolas Bonneel](http: // liris.cnrs.fr / ~nbonneel /) (C++ code for EMD) -* [Marco Cuturi](http: // marcocuturi.net / ) (Sinkhorn Knopp in Matlab/Cuda) +* [Gabriel Peyré](http://gpeyre.github.io/) (Wasserstein Barycenters in Matlab) +* [Nicolas Bonneel](http://liris.cnrs.fr/~nbonneel/) ( C++ code for EMD) +* [Marco Cuturi](http://marcocuturi.net/) (Sinkhorn Knopp in Matlab/Cuda) ## Contributions and code of conduct -Every contribution is welcome and should respect the[contribution guidelines](CONTRIBUTING.md). Each member of the project is expected to follow the[code of conduct](CODE_OF_CONDUCT.md). +Every contribution is welcome and should respect the [contribution guidelines](CONTRIBUTING.md). Each member of the project is expected to follow the [code of conduct](CODE_OF_CONDUCT.md). ## Support You can ask questions and join the development discussion: -* On the[POT Slack channel](https: // pot - toolbox.slack.com) -* On the POT [mailing list](https: // mail.python.org / mm3 / mailman3 / lists / pot.python.org / ) +* On the [POT Slack channel](https://pot-toolbox.slack.com) +* On the POT [mailing list](https://mail.python.org/mm3/mailman3/lists/pot.python.org/) -You can also post bug reports and feature requests in Github issues. Make sure to read our[guidelines](CONTRIBUTING.md) first. +You can also post bug reports and feature requests in Github issues. Make sure to read our [guidelines](CONTRIBUTING.md) first. ## References -[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). [Displacement interpolation using Lagrangian mass transport](https: // people.csail.mit.edu / sparis / publi / 2011 / sigasia / Bonneel_11_Displacement_Interpolation.pdf). In ACM Transactions on Graphics(TOG)(Vol. 30, No. 6, p. 158). ACM. +[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). [Displacement interpolation using Lagrangian mass transport](https://people.csail.mit.edu/sparis/publi/2011/sigasia/Bonneel_11_Displacement_Interpolation.pdf). In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. + +[2] Cuturi, M. (2013). [Sinkhorn distances: Lightspeed computation of optimal transport](https://arxiv.org/pdf/1306.0895.pdf). In Advances in Neural Information Processing Systems (pp. 2292-2300). + +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). [Iterative Bregman projections for regularized transportation problems](https://arxiv.org/pdf/1412.5154.pdf). SIAM Journal on Scientific Computing, 37(2), A1111-A1138. -[2] Cuturi, M. (2013). [Sinkhorn distances: Lightspeed computation of optimal transport](https: // arxiv.org / pdf / 1306.0895.pdf). In Advances in Neural Information Processing Systems(pp. 2292 - 2300). +[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, [Supervised planetary unmixing with optimal transport](https://hal.archives-ouvertes.fr/hal-01377236/document), Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016. -[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). [Iterative Bregman projections for regularized transportation problems](https: // arxiv.org / pdf / 1412.5154.pdf). SIAM Journal on Scientific Computing, 37(2), A1111 - A1138. +[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, [Optimal Transport for Domain Adaptation](https://arxiv.org/pdf/1507.00504.pdf), in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 -[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, [Supervised planetary unmixing with optimal transport](https: // hal.archives - ouvertes.fr / hal - 01377236 / document), Whorkshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing(WHISPERS), 2016. +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). [Regularized discrete optimal transport](https://arxiv.org/pdf/1307.5551.pdf). SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -[5] N. Courty -R. Flamary -D. Tuia -A. Rakotomamonjy, [Optimal Transport for Domain Adaptation](https: // arxiv.org / pdf / 1507.00504.pdf), in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.PP, no.99, pp.1 - 1 +[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). [Generalized conditional gradient: analysis of convergence and applications](https://arxiv.org/pdf/1510.06567.pdf). arXiv preprint arXiv:1510.06567. -[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). [Regularized discrete optimal transport](https: // arxiv.org / pdf / 1307.5551.pdf). SIAM Journal on Imaging Sciences, 7(3), 1853 - 1882. +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard (2016), [Mapping estimation for discrete optimal transport](http://remi.flamary.com/biblio/perrot2016mapping.pdf), Neural Information Processing Systems (NIPS). -[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). [Generalized conditional gradient: analysis of convergence and applications](https: // arxiv.org / pdf / 1510.06567.pdf). arXiv preprint arXiv: 1510.06567. +[9] Schmitzer, B. (2016). [Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems](https://arxiv.org/pdf/1610.06519.pdf). arXiv preprint arXiv:1610.06519. -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard(2016), [Mapping estimation for discrete optimal transport](http: // remi.flamary.com / biblio / perrot2016mapping.pdf), Neural Information Processing Systems(NIPS). +[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). [Scaling algorithms for unbalanced transport problems](https://arxiv.org/pdf/1607.05816.pdf). arXiv preprint arXiv:1607.05816. -[9] Schmitzer, B. (2016). [Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems](https: // arxiv.org / pdf / 1610.06519.pdf). arXiv preprint arXiv: 1610.06519. +[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https://arxiv.org/pdf/1608.08063.pdf). arXiv preprint arXiv:1608.08063. -[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). 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(2016) [Stochastic Optimization for Large-scale Optimal Transport](https://arxiv.org/abs/1605.08527). Advances in Neural Information Processing Systems (2016). -[17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https: // arxiv.org / abs / 1710.06276). Proceedings of the Twenty - First International Conference on Artificial Intelligence and Statistics(AISTATS). +[19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A.& Blondel, M. [Large-scale Optimal Transport and Mapping Estimation](https://arxiv.org/pdf/1711.02283.pdf). International Conference on Learning Representation (2018) -[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016)[Stochastic Optimization for Large - scale Optimal Transport](https: // arxiv.org / abs / 1605.08527). Advances in Neural Information Processing Systems(2016). +[20] Cuturi, M. and Doucet, A. (2014) [Fast Computation of Wasserstein Barycenters](http://proceedings.mlr.press/v32/cuturi14.html). 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[Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/) Advances in Neural Information Processing Systems (NIPS). -[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http: // proceedings.mlr.press / v97 / titouan19a.html) Proceedings of the 36th International Conference on Machine Learning(ICML). +[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 33 (NeurIPS). -[25] Frogner C., Zhang C., Mobahi H., Araya - Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http: // cbcl.mit.edu / wasserstein / ) Advances in Neural Information Processing Systems (NIPS). +[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019. -[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https: // papers.nips.cc / paper / 9386 - screening - sinkhorn - algorithm - for-regularized - optimal - transport), Advances in Neural Information Processing Systems 33 (NeurIPS). +[28] Caffarelli, L. A., McCann, R. J. (2020). [Free boundaries in optimal transport and Monge-Ampere obstacle problems](http://www.math.toronto.edu/~mccann/papers/annals2010.pdf), Annals of mathematics, 673-730. -[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi - source Domain Adaptation under Target Shift](http: // proceedings.mlr.press / v89 / redko19a.html), Proceedings of the Twenty - Second International Conference on Artificial Intelligence and Statistics(AISTATS) 22, 2019. +[29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning](https://arxiv.org/abs/2002.08276), arXiv preprint arXiv:2002.08276. -- cgit v1.2.3 From d25d9e8401b0bbdbecadedce977fd4a3a4fe6c87 Mon Sep 17 00:00:00 2001 From: ievred Date: Mon, 20 Apr 2020 13:01:47 +0200 Subject: conflit readme --- README.md | 250 +++++++++++++++++++++++++++++++------------------------------- 1 file changed, 125 insertions(+), 125 deletions(-) diff --git a/README.md b/README.md index 28d2b2a..57a3dcf 100644 --- a/README.md +++ b/README.md @@ -1,59 +1,61 @@ # POT: Python Optimal Transport -[![PyPI version](https://badge.fury.io/py/POT.svg)](https://badge.fury.io/py/POT) -[![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot) -[![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/rflamary/POT) -[![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) -[![Downloads](https://pepy.tech/badge/pot)](https://pepy.tech/project/pot) -[![Anaconda downloads](https://anaconda.org/conda-forge/pot/badges/downloads.svg)](https://anaconda.org/conda-forge/pot) -[![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/rflamary/POT/blob/master/LICENSE) - +import ot +[![PyPI version](https: // badge.fury.io / py / POT.svg)](https: // badge.fury.io / py / POT) +[![Anaconda Cloud](https: // anaconda.org / conda - forge / pot / badges / version.svg)](https: // anaconda.org / conda - forge / pot) +[![Build Status](https: // travis - ci.org / rflamary / POT.svg?branch=master)](https: // travis - ci.org / rflamary / POT) +[![Documentation Status](https: // readthedocs.org / projects / pot / badge /?version=latest)](http: // pot.readthedocs.io / en / latest /?badge=latest) +[![Downloads](https: // pepy.tech / badge / pot)](https: // pepy.tech / project / pot) +[![Anaconda downloads](https: // anaconda.org / conda - forge / pot / badges / downloads.svg)](https: // anaconda.org / conda - forge / pot) +[![License](https: // anaconda.org / conda - forge / pot / badges / license.svg)](https: // github.com / rflamary / POT / blob / master / LICENSE) This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. It provides the following solvers: -* OT Network Flow solver for the linear program/ Earth Movers Distance [1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU implementation (requires cupy). -* Sinkhorn divergence [23] and entropic regularization OT from empirical data. -* Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. -* Non regularized Wasserstein barycenters [16] with LP solver (only small scale). -* Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4]. -* Optimal transport for domain adaptation with group lasso regularization [5] -* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. -* Linear OT [14] and Joint OT matrix and mapping estimation [8]. -* Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). -* Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) -* Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) -* Non regularized free support Wasserstein barycenters [20]. -* Unbalanced OT with KL relaxation distance and barycenter [10, 25]. -* Screening Sinkhorn Algorithm for OT [26]. -* JCPOT algorithm for multi-source domain adaptation with target shift [27]. -* Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic [3] formulations). - -Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. +* OT Network Flow solver for the linear program / Earth Movers Distance[1]. +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm[2], stabilized version[9][10] and greedy Sinkhorn[22] with optional GPU implementation(requires cupy). +* Sinkhorn divergence[23] and entropic regularization OT from empirical data. +* Smooth optimal transport solvers(dual and semi - dual) for KL and squared L2 regularizations[17]. +* Non regularized Wasserstein barycenters[16] with LP solver(only small scale). +* Bregman projections for Wasserstein barycenter[3], convolutional barycenter[21] and unmixing[4]. +* Optimal transport for domain adaptation with group lasso regularization[5] +* Conditional gradient[6] and Generalized conditional gradient for regularized OT[7]. +* Linear OT[14] and Joint OT matrix and mapping estimation[8]. +* Wasserstein Discriminant Analysis[11](requires autograd + pymanopt). +* Gromov - Wasserstein distances and barycenters([13] and regularized[12]) +* Stochastic Optimization for Large - scale Optimal Transport(semi - dual problem[18] and dual problem[19]) +* Non regularized free support Wasserstein barycenters[20]. +* Unbalanced OT with KL relaxation distance and barycenter[10, 25]. +* Screening Sinkhorn Algorithm for OT[26]. +* JCPOT algorithm for multi - source domain adaptation with target shift[27]. +* Partial Wasserstein and Gromov - Wasserstein(exact[29] and entropic[3] formulations). + +Some demonstrations(both in Python and Jupyter Notebook format) are available in the examples folder. #### Using and citing the toolbox If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference: ``` + + @misc{flamary2017pot, -title={POT Python Optimal Transport library}, -author={Flamary, R{'e}mi and Courty, Nicolas}, -url={https://github.com/rflamary/POT}, -year={2017} -} + title = {POT Python Optimal Transport library}, + author = {Flamary, R{'e}mi and Courty, Nicolas}, + url = {https: // github.com / rflamary / POT}, + year = {2017} + } ``` ## Installation -The library has been tested on Linux, MacOSX and Windows. It requires a C++ compiler for building/installing the EMD solver and relies on the following Python modules: +The library has been tested on Linux, MacOSX and Windows. It requires a C + + compiler for building / installing the EMD solver and relies on the following Python modules: -- Numpy (>=1.11) -- Scipy (>=1.0) -- Cython (>=0.23) -- Matplotlib (>=1.5) +- Numpy ( >= 1.11) +- Scipy ( >= 1.0) +- Cython ( >= 0.23) +- Matplotlib ( >= 1.5) #### Pip installation @@ -69,35 +71,33 @@ pip install POT ``` or get the very latest version by downloading it and then running: ``` -python setup.py install --user # for user install (no root) +python setup.py install - -user # for user install (no root) ``` - #### Anaconda installation with conda-forge -If you use the Anaconda python distribution, POT is available in [conda-forge](https://conda-forge.org). To install it and the required dependencies: +If you use the Anaconda python distribution, POT is available in [conda - forge](https: // conda - forge.org). To install it and the required dependencies: ``` -conda install -c conda-forge pot +conda install - c conda - forge pot ``` #### Post installation check After a correct installation, you should be able to import the module without errors: ```python -import ot ``` Note that for easier access the module is name ot instead of pot. ### Dependencies -Some sub-modules require additional dependences which are discussed below +Some sub - modules require additional dependences which are discussed below -* **ot.dr** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: +* **ot.dr ** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: ``` pip install pymanopt autograd ``` -* **ot.gpu** (GPU accelerated OT) depends on cupy that have to be installed following instructions on [this page](https://docs-cupy.chainer.org/en/stable/install.html). +* **ot.gpu ** (GPU accelerated OT) depends on cupy that have to be installed following instructions on[this page](https: // docs - cupy.chainer.org / en / stable / install.html). obviously you need CUDA installed and a compatible GPU. @@ -108,160 +108,160 @@ obviously you need CUDA installed and a compatible GPU. * Import the toolbox ```python -import ot ``` * Compute Wasserstein distances ```python # a,b are 1D histograms (sum to 1 and positive) # M is the ground cost matrix -Wd=ot.emd2(a,b,M) # exact linear program -Wd_reg=ot.sinkhorn2(a,b,M,reg) # entropic regularized OT +Wd = ot.emd2(a, b, M) # exact linear program +Wd_reg = ot.sinkhorn2(a, b, M, reg) # entropic regularized OT # if b is a matrix compute all distances to a and return a vector ``` * Compute OT matrix ```python # a,b are 1D histograms (sum to 1 and positive) # M is the ground cost matrix -T=ot.emd(a,b,M) # exact linear program -T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT +T = ot.emd(a, b, M) # exact linear program +T_reg = ot.sinkhorn(a, b, M, reg) # entropic regularized OT ``` * Compute Wasserstein barycenter ```python # A is a n*d matrix containing d 1D histograms # M is the ground cost matrix -ba=ot.barycenter(A,M,reg) # reg is regularization parameter +ba = ot.barycenter(A, M, reg) # reg is regularization parameter ``` - - ### Examples and Notebooks -The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http://pot.readthedocs.io/). +The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http: // pot.readthedocs.io / ). -Here is a list of the Python notebooks available [here](https://github.com/rflamary/POT/blob/master/notebooks/) if you want a quick look: +Here is a list of the Python notebooks available [here](https: // github.com / rflamary / POT / blob / master / notebooks / ) if you want a quick look: -* [1D optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_1D.ipynb) -* [OT Ground Loss](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_L1_vs_L2.ipynb) -* [Multiple EMD computation](https://github.com/rflamary/POT/blob/master/notebooks/plot_compute_emd.ipynb) -* [2D optimal transport on empirical distributions](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_2D_samples.ipynb) -* [1D Wasserstein barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_1D.ipynb) -* [OT with user provided regularization](https://github.com/rflamary/POT/blob/master/notebooks/plot_optim_OTreg.ipynb) -* [Domain adaptation with optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_d2.ipynb) -* [Color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_color_images.ipynb) -* [OT mapping estimation for domain adaptation](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping.ipynb) -* [OT mapping estimation for color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping_colors_images.ipynb) -* [Wasserstein Discriminant Analysis](https://github.com/rflamary/POT/blob/master/notebooks/plot_WDA.ipynb) -* [Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov.ipynb) -* [Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov_barycenter.ipynb) -* [Fused Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_fgw.ipynb) -* [Fused Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_fgw.ipynb) +* [1D optimal transport](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_1D.ipynb) +* [OT Ground Loss](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_L1_vs_L2.ipynb) +* [Multiple EMD computation](https: // github.com / rflamary / POT / blob / master / notebooks / plot_compute_emd.ipynb) +* [2D optimal transport on empirical distributions](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_2D_samples.ipynb) +* [1D Wasserstein barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_barycenter_1D.ipynb) +* [OT with user provided regularization](https: // github.com / rflamary / POT / blob / master / notebooks / plot_optim_OTreg.ipynb) +* [Domain adaptation with optimal transport](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_d2.ipynb) +* [Color transfer in images](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_color_images.ipynb) +* [OT mapping estimation for domain adaptation](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_mapping.ipynb) +* [OT mapping estimation for color transfer in images](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_mapping_colors_images.ipynb) +* [Wasserstein Discriminant Analysis](https: // github.com / rflamary / POT / blob / master / notebooks / plot_WDA.ipynb) +* [Gromov Wasserstein](https: // github.com / rflamary / POT / blob / master / notebooks / plot_gromov.ipynb) +* [Gromov Wasserstein Barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_gromov_barycenter.ipynb) +* [Fused Gromov Wasserstein](https: // github.com / rflamary / POT / blob / master / notebooks / plot_fgw.ipynb) +* [Fused Gromov Wasserstein Barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_barycenter_fgw.ipynb) -You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/notebooks/). +You can also see the notebooks with [Jupyter nbviewer](https: // nbviewer.jupyter.org / github / rflamary / POT / tree / master / notebooks / ). ## Acknowledgements This toolbox has been created and is maintained by -* [Rémi Flamary](http://remi.flamary.com/) -* [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) +* [Rémi Flamary](http: // remi.flamary.com / ) +* [Nicolas Courty](http: // people.irisa.fr / Nicolas.Courty / ) -The contributors to this library are +The contributors to this library are -* [Alexandre Gramfort](http://alexandre.gramfort.net/) -* [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) -* [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation) -* [Léo Gautheron](https://github.com/aje) (GPU implementation) -* [Nathalie Gayraud](https://www.linkedin.com/in/nathalie-t-h-gayraud/?ppe=1) -* [Stanislas Chambon](https://slasnista.github.io/) -* [Antoine Rolet](https://arolet.github.io/) -* Erwan Vautier (Gromov-Wasserstein) -* [Kilian Fatras](https://kilianfatras.github.io/) -* [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home) -* [Vayer Titouan](https://tvayer.github.io/) -* [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT) -* [Romain Tavenard](https://rtavenar.github.io/) (1d Wasserstein) -* [Mokhtar Z. Alaya](http://mzalaya.github.io/) (Screenkhorn) +* [Alexandre Gramfort](http: // alexandre.gramfort.net / ) +* [Laetitia Chapel](http: // people.irisa.fr / Laetitia.Chapel / ) +* [Michael Perrot](http: // perso.univ - st - etienne.fr / pem82055 / ) (Mapping estimation) +* [Léo Gautheron](https: // github.com / aje)(GPU implementation) +* [Nathalie Gayraud](https: // www.linkedin.com / in / nathalie - t - h - gayraud /?ppe=1) +* [Stanislas Chambon](https: // slasnista.github.io / ) +* [Antoine Rolet](https: // arolet.github.io / ) +* Erwan Vautier(Gromov - Wasserstein) +* [Kilian Fatras](https: // kilianfatras.github.io / ) +* [Alain Rakotomamonjy](https: // sites.google.com / site / alainrakotomamonjy / home) +* [Vayer Titouan](https: // tvayer.github.io / ) +* [Hicham Janati](https: // hichamjanati.github.io / ) (Unbalanced OT) +* [Romain Tavenard](https: // rtavenar.github.io / ) (1d Wasserstein) +* [Mokhtar Z. Alaya](http: // mzalaya.github.io / ) (Screenkhorn) -This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): +This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code(in various languages): -* [Gabriel Peyré](http://gpeyre.github.io/) (Wasserstein Barycenters in Matlab) -* [Nicolas Bonneel](http://liris.cnrs.fr/~nbonneel/) ( C++ code for EMD) -* [Marco Cuturi](http://marcocuturi.net/) (Sinkhorn Knopp in Matlab/Cuda) +* [Gabriel Peyré](http: // gpeyre.github.io / ) (Wasserstein Barycenters in Matlab) +* [Nicolas Bonneel](http: // liris.cnrs.fr / ~nbonneel /) (C++ code for EMD) +* [Marco Cuturi](http: // marcocuturi.net / ) (Sinkhorn Knopp in Matlab/Cuda) ## Contributions and code of conduct -Every contribution is welcome and should respect the [contribution guidelines](CONTRIBUTING.md). Each member of the project is expected to follow the [code of conduct](CODE_OF_CONDUCT.md). +Every contribution is welcome and should respect the[contribution guidelines](CONTRIBUTING.md). Each member of the project is expected to follow the[code of conduct](CODE_OF_CONDUCT.md). ## Support You can ask questions and join the development discussion: -* On the [POT Slack channel](https://pot-toolbox.slack.com) -* On the POT [mailing list](https://mail.python.org/mm3/mailman3/lists/pot.python.org/) +* On the[POT Slack channel](https: // pot - toolbox.slack.com) +* On the POT [mailing list](https: // mail.python.org / mm3 / mailman3 / lists / pot.python.org / ) -You can also post bug reports and feature requests in Github issues. Make sure to read our [guidelines](CONTRIBUTING.md) first. +You can also post bug reports and feature requests in Github issues. Make sure to read our[guidelines](CONTRIBUTING.md) first. ## References -[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). [Displacement interpolation using Lagrangian mass transport](https://people.csail.mit.edu/sparis/publi/2011/sigasia/Bonneel_11_Displacement_Interpolation.pdf). In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. +[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). [Displacement interpolation using Lagrangian mass transport](https: // people.csail.mit.edu / sparis / publi / 2011 / sigasia / Bonneel_11_Displacement_Interpolation.pdf). In ACM Transactions on Graphics(TOG)(Vol. 30, No. 6, p. 158). ACM. -[2] Cuturi, M. (2013). [Sinkhorn distances: Lightspeed computation of optimal transport](https://arxiv.org/pdf/1306.0895.pdf). In Advances in Neural Information Processing Systems (pp. 2292-2300). +[2] Cuturi, M. (2013). [Sinkhorn distances: Lightspeed computation of optimal transport](https: // arxiv.org / pdf / 1306.0895.pdf). In Advances in Neural Information Processing Systems(pp. 2292 - 2300). -[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). [Iterative Bregman projections for regularized transportation problems](https://arxiv.org/pdf/1412.5154.pdf). SIAM Journal on Scientific Computing, 37(2), A1111-A1138. +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). [Iterative Bregman projections for regularized transportation problems](https: // arxiv.org / pdf / 1412.5154.pdf). SIAM Journal on Scientific Computing, 37(2), A1111 - A1138. -[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, [Supervised planetary unmixing with optimal transport](https://hal.archives-ouvertes.fr/hal-01377236/document), Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016. +[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, [Supervised planetary unmixing with optimal transport](https: // hal.archives - ouvertes.fr / hal - 01377236 / document), Whorkshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing(WHISPERS), 2016. -[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, [Optimal Transport for Domain Adaptation](https://arxiv.org/pdf/1507.00504.pdf), in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 +[5] N. Courty +R. Flamary +D. Tuia +A. Rakotomamonjy, [Optimal Transport for Domain Adaptation](https: // arxiv.org / pdf / 1507.00504.pdf), in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.PP, no.99, pp.1 - 1 -[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). [Regularized discrete optimal transport](https://arxiv.org/pdf/1307.5551.pdf). SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). 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Habrard(2016), [Mapping estimation for discrete optimal transport](http: // remi.flamary.com / biblio / perrot2016mapping.pdf), Neural Information Processing Systems(NIPS). -[9] Schmitzer, B. (2016). [Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems](https://arxiv.org/pdf/1610.06519.pdf). arXiv preprint arXiv:1610.06519. +[9] Schmitzer, B. (2016). [Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems](https: // arxiv.org / pdf / 1610.06519.pdf). arXiv preprint arXiv: 1610.06519. -[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). [Scaling algorithms for unbalanced transport problems](https://arxiv.org/pdf/1607.05816.pdf). arXiv preprint arXiv:1607.05816. +[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). [Scaling algorithms for unbalanced transport problems](https: // arxiv.org / pdf / 1607.05816.pdf). arXiv preprint arXiv: 1607.05816. -[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https://arxiv.org/pdf/1608.08063.pdf). arXiv preprint arXiv:1608.08063. +[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https: // arxiv.org / pdf / 1608.08063.pdf). arXiv preprint arXiv: 1608.08063. -[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon (2016), [Gromov-Wasserstein averaging of kernel and distance matrices](http://proceedings.mlr.press/v48/peyre16.html) International Conference on Machine Learning (ICML). +[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon(2016), [Gromov - Wasserstein averaging of kernel and distance matrices](http: // proceedings.mlr.press / v48 / peyre16.html) International Conference on Machine Learning(ICML). -[13] Mémoli, Facundo (2011). [Gromov–Wasserstein distances and the metric approach to object matching](https://media.adelaide.edu.au/acvt/Publications/2011/2011-Gromov%E2%80%93Wasserstein%20Distances%20and%20the%20Metric%20Approach%20to%20Object%20Matching.pdf). Foundations of computational mathematics 11.4 : 417-487. +[13] Mémoli, Facundo(2011). [Gromov–Wasserstein distances and the metric approach to object matching](https: // media.adelaide.edu.au / acvt / Publications / 2011 / 2011 - Gromov % E2 % 80 % 93Wasserstein % 20Distances % 20and % 20the % 20Metric % 20Approach % 20to % 20Object % 20Matching.pdf). Foundations of computational mathematics 11.4: 417 - 487. -[14] Knott, M. and Smith, C. S. (1984).[On the optimal mapping of distributions](https://link.springer.com/article/10.1007/BF00934745), Journal of Optimization Theory and Applications Vol 43. +[14] Knott, M. and Smith, C. S. (1984).[On the optimal mapping of distributions](https: // link.springer.com / article / 10.1007 / BF00934745), Journal of Optimization Theory and Applications Vol 43. -[15] Peyré, G., & Cuturi, M. (2018). [Computational Optimal Transport](https://arxiv.org/pdf/1803.00567.pdf) . +[15] Peyré, G., & Cuturi, M. (2018). [Computational Optimal Transport](https: // arxiv.org / pdf / 1803.00567.pdf) . -[16] Agueh, M., & Carlier, G. (2011). [Barycenters in the Wasserstein space](https://hal.archives-ouvertes.fr/hal-00637399/document). SIAM Journal on Mathematical Analysis, 43(2), 904-924. +[16] Agueh, M., & Carlier, G. (2011). [Barycenters in the Wasserstein space](https: // hal.archives - ouvertes.fr / hal - 00637399 / document). SIAM Journal on Mathematical Analysis, 43(2), 904 - 924. -[17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https://arxiv.org/abs/1710.06276). Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). +[17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https: // arxiv.org / abs / 1710.06276). Proceedings of the Twenty - First International Conference on Artificial Intelligence and Statistics(AISTATS). -[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) [Stochastic Optimization for Large-scale Optimal Transport](https://arxiv.org/abs/1605.08527). Advances in Neural Information Processing Systems (2016). +[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016)[Stochastic Optimization for Large - scale Optimal Transport](https: // arxiv.org / abs / 1605.08527). Advances in Neural Information Processing Systems(2016). -[19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A.& Blondel, M. [Large-scale Optimal Transport and Mapping Estimation](https://arxiv.org/pdf/1711.02283.pdf). International Conference on Learning Representation (2018) +[19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A. & Blondel, M. [Large - scale Optimal Transport and Mapping Estimation](https: // arxiv.org / pdf / 1711.02283.pdf). International Conference on Learning Representation(2018) -[20] Cuturi, M. and Doucet, A. (2014) [Fast Computation of Wasserstein Barycenters](http://proceedings.mlr.press/v32/cuturi14.html). International Conference in Machine Learning +[20] Cuturi, M. and Doucet, A. (2014)[Fast Computation of Wasserstein Barycenters](http: // proceedings.mlr.press / v32 / cuturi14.html). International Conference in Machine Learning -[21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). [Convolutional wasserstein distances: Efficient optimal transportation on geometric domains](https://dl.acm.org/citation.cfm?id=2766963). ACM Transactions on Graphics (TOG), 34(4), 66. +[21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). [Convolutional wasserstein distances: Efficient optimal transportation on geometric domains](https: // dl.acm.org / citation.cfm?id=2766963). ACM Transactions on Graphics(TOG), 34(4), 66. -[22] J. Altschuler, J.Weed, P. Rigollet, (2017) [Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration](https://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration.pdf), Advances in Neural Information Processing Systems (NIPS) 31 +[22] J. Altschuler, J.Weed, P. Rigollet, (2017)[Near - linear time approximation algorithms for optimal transport via Sinkhorn iteration](https: // papers.nips.cc / paper / 6792 - near - linear - time - approximation - algorithms - for-optimal - transport - via - sinkhorn - iteration.pdf), Advances in Neural Information Processing Systems(NIPS) 31 -[23] Aude, G., Peyré, G., Cuturi, M., [Learning Generative Models with Sinkhorn Divergences](https://arxiv.org/abs/1706.00292), Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 +[23] Aude, G., Peyré, G., Cuturi, M., [Learning Generative Models with Sinkhorn Divergences](https: // arxiv.org / abs / 1706.00292), Proceedings of the Twenty - First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 -[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http://proceedings.mlr.press/v97/titouan19a.html) Proceedings of the 36th International Conference on Machine Learning (ICML). +[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http: // proceedings.mlr.press / v97 / titouan19a.html) Proceedings of the 36th International Conference on Machine Learning(ICML). -[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/) Advances in Neural Information Processing Systems (NIPS). +[25] Frogner C., Zhang C., Mobahi H., Araya - Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http: // cbcl.mit.edu / wasserstein / ) Advances in Neural Information Processing Systems (NIPS). -[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 33 (NeurIPS). +[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https: // papers.nips.cc / paper / 9386 - screening - sinkhorn - algorithm - for-regularized - optimal - transport), Advances in Neural Information Processing Systems 33 (NeurIPS). -[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019. +[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi - source Domain Adaptation under Target Shift](http: // proceedings.mlr.press / v89 / redko19a.html), Proceedings of the Twenty - Second International Conference on Artificial Intelligence and Statistics(AISTATS) 22, 2019. -[28] Caffarelli, L. A., McCann, R. J. (2020). [Free boundaries in optimal transport and Monge-Ampere obstacle problems](http://www.math.toronto.edu/~mccann/papers/annals2010.pdf), Annals of mathematics, 673-730. +[28] Caffarelli, L. A., McCann, R. J. (2020). [Free boundaries in optimal transport and Monge - Ampere obstacle problems](http: // www.math.toronto.edu / ~mccann / papers / annals2010.pdf), Annals of mathematics, 673 - 730. -[29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning](https://arxiv.org/abs/2002.08276), arXiv preprint arXiv:2002.08276. +[29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial Gromov - Wasserstein with Applications on Positive - Unlabeled Learning](https: // arxiv.org / abs / 2002.08276), arXiv preprint arXiv: 2002.08276. -- cgit v1.2.3 From 6ea4169133523b74b38c5fcca1c45de7acdf4226 Mon Sep 17 00:00:00 2001 From: ievred Date: Mon, 20 Apr 2020 13:08:04 +0200 Subject: clean readme --- README.md | 267 -------------------------------------------------------------- 1 file changed, 267 deletions(-) diff --git a/README.md b/README.md index 57a3dcf..e69de29 100644 --- a/README.md +++ b/README.md @@ -1,267 +0,0 @@ -# POT: Python Optimal Transport - -import ot -[![PyPI version](https: // badge.fury.io / py / POT.svg)](https: // badge.fury.io / py / POT) -[![Anaconda Cloud](https: // anaconda.org / conda - forge / pot / badges / version.svg)](https: // anaconda.org / conda - forge / pot) -[![Build Status](https: // travis - ci.org / rflamary / POT.svg?branch=master)](https: // travis - ci.org / rflamary / POT) -[![Documentation Status](https: // readthedocs.org / projects / pot / badge /?version=latest)](http: // pot.readthedocs.io / en / latest /?badge=latest) -[![Downloads](https: // pepy.tech / badge / pot)](https: // pepy.tech / project / pot) -[![Anaconda downloads](https: // anaconda.org / conda - forge / pot / badges / downloads.svg)](https: // anaconda.org / conda - forge / pot) -[![License](https: // anaconda.org / conda - forge / pot / badges / license.svg)](https: // github.com / rflamary / POT / blob / master / LICENSE) - - -This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. - -It provides the following solvers: - -* OT Network Flow solver for the linear program / Earth Movers Distance[1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm[2], stabilized version[9][10] and greedy Sinkhorn[22] with optional GPU implementation(requires cupy). -* Sinkhorn divergence[23] and entropic regularization OT from empirical data. -* Smooth optimal transport solvers(dual and semi - dual) for KL and squared L2 regularizations[17]. -* Non regularized Wasserstein barycenters[16] with LP solver(only small scale). -* Bregman projections for Wasserstein barycenter[3], convolutional barycenter[21] and unmixing[4]. -* Optimal transport for domain adaptation with group lasso regularization[5] -* Conditional gradient[6] and Generalized conditional gradient for regularized OT[7]. -* Linear OT[14] and Joint OT matrix and mapping estimation[8]. -* Wasserstein Discriminant Analysis[11](requires autograd + pymanopt). -* Gromov - Wasserstein distances and barycenters([13] and regularized[12]) -* Stochastic Optimization for Large - scale Optimal Transport(semi - dual problem[18] and dual problem[19]) -* Non regularized free support Wasserstein barycenters[20]. -* Unbalanced OT with KL relaxation distance and barycenter[10, 25]. -* Screening Sinkhorn Algorithm for OT[26]. -* JCPOT algorithm for multi - source domain adaptation with target shift[27]. -* Partial Wasserstein and Gromov - Wasserstein(exact[29] and entropic[3] formulations). - -Some demonstrations(both in Python and Jupyter Notebook format) are available in the examples folder. - -#### Using and citing the toolbox - -If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference: -``` - - -@misc{flamary2017pot, - title = {POT Python Optimal Transport library}, - author = {Flamary, R{'e}mi and Courty, Nicolas}, - url = {https: // github.com / rflamary / POT}, - year = {2017} - } -``` - -## Installation - -The library has been tested on Linux, MacOSX and Windows. It requires a C + + compiler for building / installing the EMD solver and relies on the following Python modules: - -- Numpy ( >= 1.11) -- Scipy ( >= 1.0) -- Cython ( >= 0.23) -- Matplotlib ( >= 1.5) - -#### Pip installation - -Note that due to a limitation of pip, `cython` and `numpy` need to be installed -prior to installing POT. This can be done easily with -``` -pip install numpy cython -``` - -You can install the toolbox through PyPI with: -``` -pip install POT -``` -or get the very latest version by downloading it and then running: -``` -python setup.py install - -user # for user install (no root) -``` - - -#### Anaconda installation with conda-forge - -If you use the Anaconda python distribution, POT is available in [conda - forge](https: // conda - forge.org). To install it and the required dependencies: -``` -conda install - c conda - forge pot -``` - -#### Post installation check -After a correct installation, you should be able to import the module without errors: -```python -``` -Note that for easier access the module is name ot instead of pot. - - -### Dependencies - -Some sub - modules require additional dependences which are discussed below - -* **ot.dr ** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: -``` -pip install pymanopt autograd -``` -* **ot.gpu ** (GPU accelerated OT) depends on cupy that have to be installed following instructions on[this page](https: // docs - cupy.chainer.org / en / stable / install.html). - - -obviously you need CUDA installed and a compatible GPU. - -## Examples - -### Short examples - -* Import the toolbox -```python -``` -* Compute Wasserstein distances -```python -# a,b are 1D histograms (sum to 1 and positive) -# M is the ground cost matrix -Wd = ot.emd2(a, b, M) # exact linear program -Wd_reg = ot.sinkhorn2(a, b, M, reg) # entropic regularized OT -# if b is a matrix compute all distances to a and return a vector -``` -* Compute OT matrix -```python -# a,b are 1D histograms (sum to 1 and positive) -# M is the ground cost matrix -T = ot.emd(a, b, M) # exact linear program -T_reg = ot.sinkhorn(a, b, M, reg) # entropic regularized OT -``` -* Compute Wasserstein barycenter -```python -# A is a n*d matrix containing d 1D histograms -# M is the ground cost matrix -ba = ot.barycenter(A, M, reg) # reg is regularization parameter -``` - - -### Examples and Notebooks - -The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http: // pot.readthedocs.io / ). - - -Here is a list of the Python notebooks available [here](https: // github.com / rflamary / POT / blob / master / notebooks / ) if you want a quick look: - -* [1D optimal transport](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_1D.ipynb) -* [OT Ground Loss](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_L1_vs_L2.ipynb) -* [Multiple EMD computation](https: // github.com / rflamary / POT / blob / master / notebooks / plot_compute_emd.ipynb) -* [2D optimal transport on empirical distributions](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_2D_samples.ipynb) -* [1D Wasserstein barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_barycenter_1D.ipynb) -* [OT with user provided regularization](https: // github.com / rflamary / POT / blob / master / notebooks / plot_optim_OTreg.ipynb) -* [Domain adaptation with optimal transport](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_d2.ipynb) -* [Color transfer in images](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_color_images.ipynb) -* [OT mapping estimation for domain adaptation](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_mapping.ipynb) -* [OT mapping estimation for color transfer in images](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_mapping_colors_images.ipynb) -* [Wasserstein Discriminant Analysis](https: // github.com / rflamary / POT / blob / master / notebooks / plot_WDA.ipynb) -* [Gromov Wasserstein](https: // github.com / rflamary / POT / blob / master / notebooks / plot_gromov.ipynb) -* [Gromov Wasserstein Barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_gromov_barycenter.ipynb) -* [Fused Gromov Wasserstein](https: // github.com / rflamary / POT / blob / master / notebooks / plot_fgw.ipynb) -* [Fused Gromov Wasserstein Barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_barycenter_fgw.ipynb) - - -You can also see the notebooks with [Jupyter nbviewer](https: // nbviewer.jupyter.org / github / rflamary / POT / tree / master / notebooks / ). - -## Acknowledgements - -This toolbox has been created and is maintained by - -* [Rémi Flamary](http: // remi.flamary.com / ) -* [Nicolas Courty](http: // people.irisa.fr / Nicolas.Courty / ) - -The contributors to this library are - -* [Alexandre Gramfort](http: // alexandre.gramfort.net / ) -* [Laetitia Chapel](http: // people.irisa.fr / Laetitia.Chapel / ) -* [Michael Perrot](http: // perso.univ - st - etienne.fr / pem82055 / ) (Mapping estimation) -* [Léo Gautheron](https: // github.com / aje)(GPU implementation) -* [Nathalie Gayraud](https: // www.linkedin.com / in / nathalie - t - h - gayraud /?ppe=1) -* [Stanislas Chambon](https: // slasnista.github.io / ) -* [Antoine Rolet](https: // arolet.github.io / ) -* Erwan Vautier(Gromov - Wasserstein) -* [Kilian Fatras](https: // kilianfatras.github.io / ) -* [Alain Rakotomamonjy](https: // sites.google.com / site / alainrakotomamonjy / home) -* [Vayer Titouan](https: // tvayer.github.io / ) -* [Hicham Janati](https: // hichamjanati.github.io / ) (Unbalanced OT) -* [Romain Tavenard](https: // rtavenar.github.io / ) (1d Wasserstein) -* [Mokhtar Z. Alaya](http: // mzalaya.github.io / ) (Screenkhorn) - -This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code(in various languages): - -* [Gabriel Peyré](http: // gpeyre.github.io / ) (Wasserstein Barycenters in Matlab) -* [Nicolas Bonneel](http: // liris.cnrs.fr / ~nbonneel /) (C++ code for EMD) -* [Marco Cuturi](http: // marcocuturi.net / ) (Sinkhorn Knopp in Matlab/Cuda) - - -## Contributions and code of conduct - -Every contribution is welcome and should respect the[contribution guidelines](CONTRIBUTING.md). Each member of the project is expected to follow the[code of conduct](CODE_OF_CONDUCT.md). - -## Support - -You can ask questions and join the development discussion: - -* On the[POT Slack channel](https: // pot - toolbox.slack.com) -* On the POT [mailing list](https: // mail.python.org / mm3 / mailman3 / lists / pot.python.org / ) - - -You can also post bug reports and feature requests in Github issues. Make sure to read our[guidelines](CONTRIBUTING.md) first. - -## References - -[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). [Displacement interpolation using Lagrangian mass transport](https: // people.csail.mit.edu / sparis / publi / 2011 / sigasia / Bonneel_11_Displacement_Interpolation.pdf). In ACM Transactions on Graphics(TOG)(Vol. 30, No. 6, p. 158). ACM. - -[2] Cuturi, M. (2013). [Sinkhorn distances: Lightspeed computation of optimal transport](https: // arxiv.org / pdf / 1306.0895.pdf). In Advances in Neural Information Processing Systems(pp. 2292 - 2300). - -[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). [Iterative Bregman projections for regularized transportation problems](https: // arxiv.org / pdf / 1412.5154.pdf). SIAM Journal on Scientific Computing, 37(2), A1111 - A1138. - -[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, [Supervised planetary unmixing with optimal transport](https: // hal.archives - ouvertes.fr / hal - 01377236 / document), Whorkshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing(WHISPERS), 2016. - -[5] N. Courty -R. Flamary -D. Tuia -A. Rakotomamonjy, [Optimal Transport for Domain Adaptation](https: // arxiv.org / pdf / 1507.00504.pdf), in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.PP, no.99, pp.1 - 1 - -[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). [Regularized discrete optimal transport](https: // arxiv.org / pdf / 1307.5551.pdf). SIAM Journal on Imaging Sciences, 7(3), 1853 - 1882. - -[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). [Generalized conditional gradient: analysis of convergence and applications](https: // arxiv.org / pdf / 1510.06567.pdf). arXiv preprint arXiv: 1510.06567. - -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard(2016), [Mapping estimation for discrete optimal transport](http: // remi.flamary.com / biblio / perrot2016mapping.pdf), Neural Information Processing Systems(NIPS). - -[9] Schmitzer, B. (2016). [Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems](https: // arxiv.org / pdf / 1610.06519.pdf). arXiv preprint arXiv: 1610.06519. - -[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). [Scaling algorithms for unbalanced transport problems](https: // arxiv.org / pdf / 1607.05816.pdf). arXiv preprint arXiv: 1607.05816. - -[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https: // arxiv.org / pdf / 1608.08063.pdf). arXiv preprint arXiv: 1608.08063. - -[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon(2016), [Gromov - Wasserstein averaging of kernel and distance matrices](http: // proceedings.mlr.press / v48 / peyre16.html) International Conference on Machine Learning(ICML). - -[13] Mémoli, Facundo(2011). [Gromov–Wasserstein distances and the metric approach to object matching](https: // media.adelaide.edu.au / acvt / Publications / 2011 / 2011 - Gromov % E2 % 80 % 93Wasserstein % 20Distances % 20and % 20the % 20Metric % 20Approach % 20to % 20Object % 20Matching.pdf). Foundations of computational mathematics 11.4: 417 - 487. - -[14] Knott, M. and Smith, C. S. (1984).[On the optimal mapping of distributions](https: // link.springer.com / article / 10.1007 / BF00934745), Journal of Optimization Theory and Applications Vol 43. - -[15] Peyré, G., & Cuturi, M. (2018). [Computational Optimal Transport](https: // arxiv.org / pdf / 1803.00567.pdf) . - -[16] Agueh, M., & Carlier, G. (2011). [Barycenters in the Wasserstein space](https: // hal.archives - ouvertes.fr / hal - 00637399 / document). SIAM Journal on Mathematical Analysis, 43(2), 904 - 924. - -[17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https: // arxiv.org / abs / 1710.06276). Proceedings of the Twenty - First International Conference on Artificial Intelligence and Statistics(AISTATS). - -[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016)[Stochastic Optimization for Large - scale Optimal Transport](https: // arxiv.org / abs / 1605.08527). Advances in Neural Information Processing Systems(2016). - -[19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A. & Blondel, M. [Large - scale Optimal Transport and Mapping Estimation](https: // arxiv.org / pdf / 1711.02283.pdf). International Conference on Learning Representation(2018) - -[20] Cuturi, M. and Doucet, A. (2014)[Fast Computation of Wasserstein Barycenters](http: // proceedings.mlr.press / v32 / cuturi14.html). International Conference in Machine Learning - -[21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). [Convolutional wasserstein distances: Efficient optimal transportation on geometric domains](https: // dl.acm.org / citation.cfm?id=2766963). ACM Transactions on Graphics(TOG), 34(4), 66. - -[22] J. Altschuler, J.Weed, P. Rigollet, (2017)[Near - linear time approximation algorithms for optimal transport via Sinkhorn iteration](https: // papers.nips.cc / paper / 6792 - near - linear - time - approximation - algorithms - for-optimal - transport - via - sinkhorn - iteration.pdf), Advances in Neural Information Processing Systems(NIPS) 31 - -[23] Aude, G., Peyré, G., Cuturi, M., [Learning Generative Models with Sinkhorn Divergences](https: // arxiv.org / abs / 1706.00292), Proceedings of the Twenty - First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 - -[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http: // proceedings.mlr.press / v97 / titouan19a.html) Proceedings of the 36th International Conference on Machine Learning(ICML). - -[25] Frogner C., Zhang C., Mobahi H., Araya - Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http: // cbcl.mit.edu / wasserstein / ) Advances in Neural Information Processing Systems (NIPS). - -[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https: // papers.nips.cc / paper / 9386 - screening - sinkhorn - algorithm - for-regularized - optimal - transport), Advances in Neural Information Processing Systems 33 (NeurIPS). - -[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi - source Domain Adaptation under Target Shift](http: // proceedings.mlr.press / v89 / redko19a.html), Proceedings of the Twenty - Second International Conference on Artificial Intelligence and Statistics(AISTATS) 22, 2019. - -[28] Caffarelli, L. A., McCann, R. J. (2020). [Free boundaries in optimal transport and Monge - Ampere obstacle problems](http: // www.math.toronto.edu / ~mccann / papers / annals2010.pdf), Annals of mathematics, 673 - 730. - -[29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial Gromov - Wasserstein with Applications on Positive - Unlabeled Learning](https: // arxiv.org / abs / 2002.08276), arXiv preprint arXiv: 2002.08276. -- cgit v1.2.3 From d12ccc2880fcb413efae2e6e4663fe9704c8aa85 Mon Sep 17 00:00:00 2001 From: ievred Date: Mon, 20 Apr 2020 13:09:46 +0200 Subject: clean readme --- README.md | 267 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 267 insertions(+) diff --git a/README.md b/README.md index e69de29..57a3dcf 100644 --- a/README.md +++ b/README.md @@ -0,0 +1,267 @@ +# POT: Python Optimal Transport + +import ot +[![PyPI version](https: // badge.fury.io / py / POT.svg)](https: // badge.fury.io / py / POT) +[![Anaconda Cloud](https: // anaconda.org / conda - forge / pot / badges / version.svg)](https: // anaconda.org / conda - forge / pot) +[![Build Status](https: // travis - ci.org / rflamary / POT.svg?branch=master)](https: // travis - ci.org / rflamary / POT) +[![Documentation Status](https: // readthedocs.org / projects / pot / badge /?version=latest)](http: // pot.readthedocs.io / en / latest /?badge=latest) +[![Downloads](https: // pepy.tech / badge / pot)](https: // pepy.tech / project / pot) +[![Anaconda downloads](https: // anaconda.org / conda - forge / pot / badges / downloads.svg)](https: // anaconda.org / conda - forge / pot) +[![License](https: // anaconda.org / conda - forge / pot / badges / license.svg)](https: // github.com / rflamary / POT / blob / master / LICENSE) + + +This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. + +It provides the following solvers: + +* OT Network Flow solver for the linear program / Earth Movers Distance[1]. +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm[2], stabilized version[9][10] and greedy Sinkhorn[22] with optional GPU implementation(requires cupy). +* Sinkhorn divergence[23] and entropic regularization OT from empirical data. +* Smooth optimal transport solvers(dual and semi - dual) for KL and squared L2 regularizations[17]. +* Non regularized Wasserstein barycenters[16] with LP solver(only small scale). +* Bregman projections for Wasserstein barycenter[3], convolutional barycenter[21] and unmixing[4]. +* Optimal transport for domain adaptation with group lasso regularization[5] +* Conditional gradient[6] and Generalized conditional gradient for regularized OT[7]. +* Linear OT[14] and Joint OT matrix and mapping estimation[8]. +* Wasserstein Discriminant Analysis[11](requires autograd + pymanopt). +* Gromov - Wasserstein distances and barycenters([13] and regularized[12]) +* Stochastic Optimization for Large - scale Optimal Transport(semi - dual problem[18] and dual problem[19]) +* Non regularized free support Wasserstein barycenters[20]. +* Unbalanced OT with KL relaxation distance and barycenter[10, 25]. +* Screening Sinkhorn Algorithm for OT[26]. +* JCPOT algorithm for multi - source domain adaptation with target shift[27]. +* Partial Wasserstein and Gromov - Wasserstein(exact[29] and entropic[3] formulations). + +Some demonstrations(both in Python and Jupyter Notebook format) are available in the examples folder. + +#### Using and citing the toolbox + +If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference: +``` + + +@misc{flamary2017pot, + title = {POT Python Optimal Transport library}, + author = {Flamary, R{'e}mi and Courty, Nicolas}, + url = {https: // github.com / rflamary / POT}, + year = {2017} + } +``` + +## Installation + +The library has been tested on Linux, MacOSX and Windows. It requires a C + + compiler for building / installing the EMD solver and relies on the following Python modules: + +- Numpy ( >= 1.11) +- Scipy ( >= 1.0) +- Cython ( >= 0.23) +- Matplotlib ( >= 1.5) + +#### Pip installation + +Note that due to a limitation of pip, `cython` and `numpy` need to be installed +prior to installing POT. This can be done easily with +``` +pip install numpy cython +``` + +You can install the toolbox through PyPI with: +``` +pip install POT +``` +or get the very latest version by downloading it and then running: +``` +python setup.py install - -user # for user install (no root) +``` + + +#### Anaconda installation with conda-forge + +If you use the Anaconda python distribution, POT is available in [conda - forge](https: // conda - forge.org). To install it and the required dependencies: +``` +conda install - c conda - forge pot +``` + +#### Post installation check +After a correct installation, you should be able to import the module without errors: +```python +``` +Note that for easier access the module is name ot instead of pot. + + +### Dependencies + +Some sub - modules require additional dependences which are discussed below + +* **ot.dr ** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: +``` +pip install pymanopt autograd +``` +* **ot.gpu ** (GPU accelerated OT) depends on cupy that have to be installed following instructions on[this page](https: // docs - cupy.chainer.org / en / stable / install.html). + + +obviously you need CUDA installed and a compatible GPU. + +## Examples + +### Short examples + +* Import the toolbox +```python +``` +* Compute Wasserstein distances +```python +# a,b are 1D histograms (sum to 1 and positive) +# M is the ground cost matrix +Wd = ot.emd2(a, b, M) # exact linear program +Wd_reg = ot.sinkhorn2(a, b, M, reg) # entropic regularized OT +# if b is a matrix compute all distances to a and return a vector +``` +* Compute OT matrix +```python +# a,b are 1D histograms (sum to 1 and positive) +# M is the ground cost matrix +T = ot.emd(a, b, M) # exact linear program +T_reg = ot.sinkhorn(a, b, M, reg) # entropic regularized OT +``` +* Compute Wasserstein barycenter +```python +# A is a n*d matrix containing d 1D histograms +# M is the ground cost matrix +ba = ot.barycenter(A, M, reg) # reg is regularization parameter +``` + + +### Examples and Notebooks + +The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http: // pot.readthedocs.io / ). + + +Here is a list of the Python notebooks available [here](https: // github.com / rflamary / POT / blob / master / notebooks / ) if you want a quick look: + +* [1D optimal transport](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_1D.ipynb) +* [OT Ground Loss](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_L1_vs_L2.ipynb) +* [Multiple EMD computation](https: // github.com / rflamary / POT / blob / master / notebooks / plot_compute_emd.ipynb) +* [2D optimal transport on empirical distributions](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_2D_samples.ipynb) +* [1D Wasserstein barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_barycenter_1D.ipynb) +* [OT with user provided regularization](https: // github.com / rflamary / POT / blob / master / notebooks / plot_optim_OTreg.ipynb) +* [Domain adaptation with optimal transport](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_d2.ipynb) +* [Color transfer in images](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_color_images.ipynb) +* [OT mapping estimation for domain adaptation](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_mapping.ipynb) +* [OT mapping estimation for color transfer in images](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_mapping_colors_images.ipynb) +* [Wasserstein Discriminant Analysis](https: // github.com / rflamary / POT / blob / master / notebooks / plot_WDA.ipynb) +* [Gromov Wasserstein](https: // github.com / rflamary / POT / blob / master / notebooks / plot_gromov.ipynb) +* [Gromov Wasserstein Barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_gromov_barycenter.ipynb) +* [Fused Gromov Wasserstein](https: // github.com / rflamary / POT / blob / master / notebooks / plot_fgw.ipynb) +* [Fused Gromov Wasserstein Barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_barycenter_fgw.ipynb) + + +You can also see the notebooks with [Jupyter nbviewer](https: // nbviewer.jupyter.org / github / rflamary / POT / tree / master / notebooks / ). + +## Acknowledgements + +This toolbox has been created and is maintained by + +* [Rémi Flamary](http: // remi.flamary.com / ) +* [Nicolas Courty](http: // people.irisa.fr / Nicolas.Courty / ) + +The contributors to this library are + +* [Alexandre Gramfort](http: // alexandre.gramfort.net / ) +* [Laetitia Chapel](http: // people.irisa.fr / Laetitia.Chapel / ) +* [Michael Perrot](http: // perso.univ - st - etienne.fr / pem82055 / ) (Mapping estimation) +* [Léo Gautheron](https: // github.com / aje)(GPU implementation) +* [Nathalie Gayraud](https: // www.linkedin.com / in / nathalie - t - h - gayraud /?ppe=1) +* [Stanislas Chambon](https: // slasnista.github.io / ) +* [Antoine Rolet](https: // arolet.github.io / ) +* Erwan Vautier(Gromov - Wasserstein) +* [Kilian Fatras](https: // kilianfatras.github.io / ) +* [Alain Rakotomamonjy](https: // sites.google.com / site / alainrakotomamonjy / home) +* [Vayer Titouan](https: // tvayer.github.io / ) +* [Hicham Janati](https: // hichamjanati.github.io / ) (Unbalanced OT) +* [Romain Tavenard](https: // rtavenar.github.io / ) (1d Wasserstein) +* [Mokhtar Z. Alaya](http: // mzalaya.github.io / ) (Screenkhorn) + +This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code(in various languages): + +* [Gabriel Peyré](http: // gpeyre.github.io / ) (Wasserstein Barycenters in Matlab) +* [Nicolas Bonneel](http: // liris.cnrs.fr / ~nbonneel /) (C++ code for EMD) +* [Marco Cuturi](http: // marcocuturi.net / ) (Sinkhorn Knopp in Matlab/Cuda) + + +## Contributions and code of conduct + +Every contribution is welcome and should respect the[contribution guidelines](CONTRIBUTING.md). Each member of the project is expected to follow the[code of conduct](CODE_OF_CONDUCT.md). + +## Support + +You can ask questions and join the development discussion: + +* On the[POT Slack channel](https: // pot - toolbox.slack.com) +* On the POT [mailing list](https: // mail.python.org / mm3 / mailman3 / lists / pot.python.org / ) + + +You can also post bug reports and feature requests in Github issues. Make sure to read our[guidelines](CONTRIBUTING.md) first. + +## References + +[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). [Displacement interpolation using Lagrangian mass transport](https: // people.csail.mit.edu / sparis / publi / 2011 / sigasia / Bonneel_11_Displacement_Interpolation.pdf). In ACM Transactions on Graphics(TOG)(Vol. 30, No. 6, p. 158). ACM. + +[2] Cuturi, M. (2013). [Sinkhorn distances: Lightspeed computation of optimal transport](https: // arxiv.org / pdf / 1306.0895.pdf). In Advances in Neural Information Processing Systems(pp. 2292 - 2300). + +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). [Iterative Bregman projections for regularized transportation problems](https: // arxiv.org / pdf / 1412.5154.pdf). SIAM Journal on Scientific Computing, 37(2), A1111 - A1138. + +[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, [Supervised planetary unmixing with optimal transport](https: // hal.archives - ouvertes.fr / hal - 01377236 / document), Whorkshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing(WHISPERS), 2016. + +[5] N. Courty +R. Flamary +D. Tuia +A. Rakotomamonjy, [Optimal Transport for Domain Adaptation](https: // arxiv.org / pdf / 1507.00504.pdf), in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.PP, no.99, pp.1 - 1 + +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). [Regularized discrete optimal transport](https: // arxiv.org / pdf / 1307.5551.pdf). SIAM Journal on Imaging Sciences, 7(3), 1853 - 1882. + +[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). [Generalized conditional gradient: analysis of convergence and applications](https: // arxiv.org / pdf / 1510.06567.pdf). arXiv preprint arXiv: 1510.06567. + +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard(2016), [Mapping estimation for discrete optimal transport](http: // remi.flamary.com / biblio / perrot2016mapping.pdf), Neural Information Processing Systems(NIPS). + +[9] Schmitzer, B. (2016). [Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems](https: // arxiv.org / pdf / 1610.06519.pdf). arXiv preprint arXiv: 1610.06519. + +[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). [Scaling algorithms for unbalanced transport problems](https: // arxiv.org / pdf / 1607.05816.pdf). arXiv preprint arXiv: 1607.05816. + +[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https: // arxiv.org / pdf / 1608.08063.pdf). arXiv preprint arXiv: 1608.08063. + +[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon(2016), [Gromov - Wasserstein averaging of kernel and distance matrices](http: // proceedings.mlr.press / v48 / peyre16.html) International Conference on Machine Learning(ICML). + +[13] Mémoli, Facundo(2011). [Gromov–Wasserstein distances and the metric approach to object matching](https: // media.adelaide.edu.au / acvt / Publications / 2011 / 2011 - Gromov % E2 % 80 % 93Wasserstein % 20Distances % 20and % 20the % 20Metric % 20Approach % 20to % 20Object % 20Matching.pdf). Foundations of computational mathematics 11.4: 417 - 487. + +[14] Knott, M. and Smith, C. S. (1984).[On the optimal mapping of distributions](https: // link.springer.com / article / 10.1007 / BF00934745), Journal of Optimization Theory and Applications Vol 43. + +[15] Peyré, G., & Cuturi, M. (2018). [Computational Optimal Transport](https: // arxiv.org / pdf / 1803.00567.pdf) . + +[16] Agueh, M., & Carlier, G. (2011). [Barycenters in the Wasserstein space](https: // hal.archives - ouvertes.fr / hal - 00637399 / document). SIAM Journal on Mathematical Analysis, 43(2), 904 - 924. + +[17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https: // arxiv.org / abs / 1710.06276). Proceedings of the Twenty - First International Conference on Artificial Intelligence and Statistics(AISTATS). + +[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016)[Stochastic Optimization for Large - scale Optimal Transport](https: // arxiv.org / abs / 1605.08527). Advances in Neural Information Processing Systems(2016). + +[19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A. & Blondel, M. [Large - scale Optimal Transport and Mapping Estimation](https: // arxiv.org / pdf / 1711.02283.pdf). International Conference on Learning Representation(2018) + +[20] Cuturi, M. and Doucet, A. (2014)[Fast Computation of Wasserstein Barycenters](http: // proceedings.mlr.press / v32 / cuturi14.html). International Conference in Machine Learning + +[21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). [Convolutional wasserstein distances: Efficient optimal transportation on geometric domains](https: // dl.acm.org / citation.cfm?id=2766963). ACM Transactions on Graphics(TOG), 34(4), 66. + +[22] J. Altschuler, J.Weed, P. Rigollet, (2017)[Near - linear time approximation algorithms for optimal transport via Sinkhorn iteration](https: // papers.nips.cc / paper / 6792 - near - linear - time - approximation - algorithms - for-optimal - transport - via - sinkhorn - iteration.pdf), Advances in Neural Information Processing Systems(NIPS) 31 + +[23] Aude, G., Peyré, G., Cuturi, M., [Learning Generative Models with Sinkhorn Divergences](https: // arxiv.org / abs / 1706.00292), Proceedings of the Twenty - First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 + +[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http: // proceedings.mlr.press / v97 / titouan19a.html) Proceedings of the 36th International Conference on Machine Learning(ICML). + +[25] Frogner C., Zhang C., Mobahi H., Araya - Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http: // cbcl.mit.edu / wasserstein / ) Advances in Neural Information Processing Systems (NIPS). + +[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https: // papers.nips.cc / paper / 9386 - screening - sinkhorn - algorithm - for-regularized - optimal - transport), Advances in Neural Information Processing Systems 33 (NeurIPS). + +[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi - source Domain Adaptation under Target Shift](http: // proceedings.mlr.press / v89 / redko19a.html), Proceedings of the Twenty - Second International Conference on Artificial Intelligence and Statistics(AISTATS) 22, 2019. + +[28] Caffarelli, L. A., McCann, R. J. (2020). [Free boundaries in optimal transport and Monge - Ampere obstacle problems](http: // www.math.toronto.edu / ~mccann / papers / annals2010.pdf), Annals of mathematics, 673 - 730. + +[29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial Gromov - Wasserstein with Applications on Positive - Unlabeled Learning](https: // arxiv.org / abs / 2002.08276), arXiv preprint arXiv: 2002.08276. -- cgit v1.2.3 From 55b5da6e04653b30ea69f56f1fc28db33ddb67a9 Mon Sep 17 00:00:00 2001 From: ievred Date: Mon, 20 Apr 2020 13:24:18 +0200 Subject: hell --- README.md | 250 +++++++++++++++++++++++++++++++------------------------------- 1 file changed, 125 insertions(+), 125 deletions(-) diff --git a/README.md b/README.md index 57a3dcf..28d2b2a 100644 --- a/README.md +++ b/README.md @@ -1,61 +1,59 @@ # POT: Python Optimal Transport -import ot -[![PyPI version](https: // badge.fury.io / py / POT.svg)](https: // badge.fury.io / py / POT) -[![Anaconda Cloud](https: // anaconda.org / conda - forge / pot / badges / version.svg)](https: // anaconda.org / conda - forge / pot) -[![Build Status](https: // travis - ci.org / rflamary / POT.svg?branch=master)](https: // travis - ci.org / rflamary / POT) -[![Documentation Status](https: // readthedocs.org / projects / pot / badge /?version=latest)](http: // pot.readthedocs.io / en / latest /?badge=latest) -[![Downloads](https: // pepy.tech / badge / pot)](https: // pepy.tech / project / pot) -[![Anaconda downloads](https: // anaconda.org / conda - forge / pot / badges / downloads.svg)](https: // anaconda.org / conda - forge / pot) -[![License](https: // anaconda.org / conda - forge / pot / badges / license.svg)](https: // github.com / rflamary / POT / blob / master / LICENSE) +[![PyPI version](https://badge.fury.io/py/POT.svg)](https://badge.fury.io/py/POT) +[![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot) +[![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/rflamary/POT) +[![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) +[![Downloads](https://pepy.tech/badge/pot)](https://pepy.tech/project/pot) +[![Anaconda downloads](https://anaconda.org/conda-forge/pot/badges/downloads.svg)](https://anaconda.org/conda-forge/pot) +[![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/rflamary/POT/blob/master/LICENSE) + This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. It provides the following solvers: -* OT Network Flow solver for the linear program / Earth Movers Distance[1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm[2], stabilized version[9][10] and greedy Sinkhorn[22] with optional GPU implementation(requires cupy). -* Sinkhorn divergence[23] and entropic regularization OT from empirical data. -* Smooth optimal transport solvers(dual and semi - dual) for KL and squared L2 regularizations[17]. -* Non regularized Wasserstein barycenters[16] with LP solver(only small scale). -* Bregman projections for Wasserstein barycenter[3], convolutional barycenter[21] and unmixing[4]. -* Optimal transport for domain adaptation with group lasso regularization[5] -* Conditional gradient[6] and Generalized conditional gradient for regularized OT[7]. -* Linear OT[14] and Joint OT matrix and mapping estimation[8]. -* Wasserstein Discriminant Analysis[11](requires autograd + pymanopt). -* Gromov - Wasserstein distances and barycenters([13] and regularized[12]) -* Stochastic Optimization for Large - scale Optimal Transport(semi - dual problem[18] and dual problem[19]) -* Non regularized free support Wasserstein barycenters[20]. -* Unbalanced OT with KL relaxation distance and barycenter[10, 25]. -* Screening Sinkhorn Algorithm for OT[26]. -* JCPOT algorithm for multi - source domain adaptation with target shift[27]. -* Partial Wasserstein and Gromov - Wasserstein(exact[29] and entropic[3] formulations). - -Some demonstrations(both in Python and Jupyter Notebook format) are available in the examples folder. +* OT Network Flow solver for the linear program/ Earth Movers Distance [1]. +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU implementation (requires cupy). +* Sinkhorn divergence [23] and entropic regularization OT from empirical data. +* Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. +* Non regularized Wasserstein barycenters [16] with LP solver (only small scale). +* Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4]. +* Optimal transport for domain adaptation with group lasso regularization [5] +* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. +* Linear OT [14] and Joint OT matrix and mapping estimation [8]. +* Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). +* Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) +* Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) +* Non regularized free support Wasserstein barycenters [20]. +* Unbalanced OT with KL relaxation distance and barycenter [10, 25]. +* Screening Sinkhorn Algorithm for OT [26]. +* JCPOT algorithm for multi-source domain adaptation with target shift [27]. +* Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic [3] formulations). + +Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. #### Using and citing the toolbox If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference: ``` - - @misc{flamary2017pot, - title = {POT Python Optimal Transport library}, - author = {Flamary, R{'e}mi and Courty, Nicolas}, - url = {https: // github.com / rflamary / POT}, - year = {2017} - } +title={POT Python Optimal Transport library}, +author={Flamary, R{'e}mi and Courty, Nicolas}, +url={https://github.com/rflamary/POT}, +year={2017} +} ``` ## Installation -The library has been tested on Linux, MacOSX and Windows. It requires a C + + compiler for building / installing the EMD solver and relies on the following Python modules: +The library has been tested on Linux, MacOSX and Windows. It requires a C++ compiler for building/installing the EMD solver and relies on the following Python modules: -- Numpy ( >= 1.11) -- Scipy ( >= 1.0) -- Cython ( >= 0.23) -- Matplotlib ( >= 1.5) +- Numpy (>=1.11) +- Scipy (>=1.0) +- Cython (>=0.23) +- Matplotlib (>=1.5) #### Pip installation @@ -71,33 +69,35 @@ pip install POT ``` or get the very latest version by downloading it and then running: ``` -python setup.py install - -user # for user install (no root) +python setup.py install --user # for user install (no root) ``` + #### Anaconda installation with conda-forge -If you use the Anaconda python distribution, POT is available in [conda - forge](https: // conda - forge.org). To install it and the required dependencies: +If you use the Anaconda python distribution, POT is available in [conda-forge](https://conda-forge.org). To install it and the required dependencies: ``` -conda install - c conda - forge pot +conda install -c conda-forge pot ``` #### Post installation check After a correct installation, you should be able to import the module without errors: ```python +import ot ``` Note that for easier access the module is name ot instead of pot. ### Dependencies -Some sub - modules require additional dependences which are discussed below +Some sub-modules require additional dependences which are discussed below -* **ot.dr ** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: +* **ot.dr** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: ``` pip install pymanopt autograd ``` -* **ot.gpu ** (GPU accelerated OT) depends on cupy that have to be installed following instructions on[this page](https: // docs - cupy.chainer.org / en / stable / install.html). +* **ot.gpu** (GPU accelerated OT) depends on cupy that have to be installed following instructions on [this page](https://docs-cupy.chainer.org/en/stable/install.html). obviously you need CUDA installed and a compatible GPU. @@ -108,160 +108,160 @@ obviously you need CUDA installed and a compatible GPU. * Import the toolbox ```python +import ot ``` * Compute Wasserstein distances ```python # a,b are 1D histograms (sum to 1 and positive) # M is the ground cost matrix -Wd = ot.emd2(a, b, M) # exact linear program -Wd_reg = ot.sinkhorn2(a, b, M, reg) # entropic regularized OT +Wd=ot.emd2(a,b,M) # exact linear program +Wd_reg=ot.sinkhorn2(a,b,M,reg) # entropic regularized OT # if b is a matrix compute all distances to a and return a vector ``` * Compute OT matrix ```python # a,b are 1D histograms (sum to 1 and positive) # M is the ground cost matrix -T = ot.emd(a, b, M) # exact linear program -T_reg = ot.sinkhorn(a, b, M, reg) # entropic regularized OT +T=ot.emd(a,b,M) # exact linear program +T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT ``` * Compute Wasserstein barycenter ```python # A is a n*d matrix containing d 1D histograms # M is the ground cost matrix -ba = ot.barycenter(A, M, reg) # reg is regularization parameter +ba=ot.barycenter(A,M,reg) # reg is regularization parameter ``` + + ### Examples and Notebooks -The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http: // pot.readthedocs.io / ). +The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http://pot.readthedocs.io/). -Here is a list of the Python notebooks available [here](https: // github.com / rflamary / POT / blob / master / notebooks / ) if you want a quick look: +Here is a list of the Python notebooks available [here](https://github.com/rflamary/POT/blob/master/notebooks/) if you want a quick look: -* [1D optimal transport](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_1D.ipynb) -* [OT Ground Loss](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_L1_vs_L2.ipynb) -* [Multiple EMD computation](https: // github.com / rflamary / POT / blob / master / notebooks / plot_compute_emd.ipynb) -* [2D optimal transport on empirical distributions](https: // github.com / rflamary / POT / blob / master / notebooks / plot_OT_2D_samples.ipynb) -* [1D Wasserstein barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_barycenter_1D.ipynb) -* [OT with user provided regularization](https: // github.com / rflamary / POT / blob / master / notebooks / plot_optim_OTreg.ipynb) -* [Domain adaptation with optimal transport](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_d2.ipynb) -* [Color transfer in images](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_color_images.ipynb) -* [OT mapping estimation for domain adaptation](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_mapping.ipynb) -* [OT mapping estimation for color transfer in images](https: // github.com / rflamary / POT / blob / master / notebooks / plot_otda_mapping_colors_images.ipynb) -* [Wasserstein Discriminant Analysis](https: // github.com / rflamary / POT / blob / master / notebooks / plot_WDA.ipynb) -* [Gromov Wasserstein](https: // github.com / rflamary / POT / blob / master / notebooks / plot_gromov.ipynb) -* [Gromov Wasserstein Barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_gromov_barycenter.ipynb) -* [Fused Gromov Wasserstein](https: // github.com / rflamary / POT / blob / master / notebooks / plot_fgw.ipynb) -* [Fused Gromov Wasserstein Barycenter](https: // github.com / rflamary / POT / blob / master / notebooks / plot_barycenter_fgw.ipynb) +* [1D optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_1D.ipynb) +* [OT Ground Loss](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_L1_vs_L2.ipynb) +* [Multiple EMD computation](https://github.com/rflamary/POT/blob/master/notebooks/plot_compute_emd.ipynb) +* [2D optimal transport on empirical distributions](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_2D_samples.ipynb) +* [1D Wasserstein barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_1D.ipynb) +* [OT with user provided regularization](https://github.com/rflamary/POT/blob/master/notebooks/plot_optim_OTreg.ipynb) +* [Domain adaptation with optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_d2.ipynb) +* [Color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_color_images.ipynb) +* [OT mapping estimation for domain adaptation](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping.ipynb) +* [OT mapping estimation for color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping_colors_images.ipynb) +* [Wasserstein Discriminant Analysis](https://github.com/rflamary/POT/blob/master/notebooks/plot_WDA.ipynb) +* [Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov.ipynb) +* [Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov_barycenter.ipynb) +* [Fused Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_fgw.ipynb) +* [Fused Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_fgw.ipynb) -You can also see the notebooks with [Jupyter nbviewer](https: // nbviewer.jupyter.org / github / rflamary / POT / tree / master / notebooks / ). +You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/notebooks/). ## Acknowledgements This toolbox has been created and is maintained by -* [Rémi Flamary](http: // remi.flamary.com / ) -* [Nicolas Courty](http: // people.irisa.fr / Nicolas.Courty / ) +* [Rémi Flamary](http://remi.flamary.com/) +* [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) -The contributors to this library are +The contributors to this library are -* [Alexandre Gramfort](http: // alexandre.gramfort.net / ) -* [Laetitia Chapel](http: // people.irisa.fr / Laetitia.Chapel / ) -* [Michael Perrot](http: // perso.univ - st - etienne.fr / pem82055 / ) (Mapping estimation) -* [Léo Gautheron](https: // github.com / aje)(GPU implementation) -* [Nathalie Gayraud](https: // www.linkedin.com / in / nathalie - t - h - gayraud /?ppe=1) -* [Stanislas Chambon](https: // slasnista.github.io / ) -* [Antoine Rolet](https: // arolet.github.io / ) -* Erwan Vautier(Gromov - Wasserstein) -* [Kilian Fatras](https: // kilianfatras.github.io / ) -* [Alain Rakotomamonjy](https: // sites.google.com / site / alainrakotomamonjy / home) -* [Vayer Titouan](https: // tvayer.github.io / ) -* [Hicham Janati](https: // hichamjanati.github.io / ) (Unbalanced OT) -* [Romain Tavenard](https: // rtavenar.github.io / ) (1d Wasserstein) -* [Mokhtar Z. Alaya](http: // mzalaya.github.io / ) (Screenkhorn) +* [Alexandre Gramfort](http://alexandre.gramfort.net/) +* [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) +* [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation) +* [Léo Gautheron](https://github.com/aje) (GPU implementation) +* [Nathalie Gayraud](https://www.linkedin.com/in/nathalie-t-h-gayraud/?ppe=1) +* [Stanislas Chambon](https://slasnista.github.io/) +* [Antoine Rolet](https://arolet.github.io/) +* Erwan Vautier (Gromov-Wasserstein) +* [Kilian Fatras](https://kilianfatras.github.io/) +* [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home) +* [Vayer Titouan](https://tvayer.github.io/) +* [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT) +* [Romain Tavenard](https://rtavenar.github.io/) (1d Wasserstein) +* [Mokhtar Z. Alaya](http://mzalaya.github.io/) (Screenkhorn) -This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code(in various languages): +This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): -* [Gabriel Peyré](http: // gpeyre.github.io / ) (Wasserstein Barycenters in Matlab) -* [Nicolas Bonneel](http: // liris.cnrs.fr / ~nbonneel /) (C++ code for EMD) -* [Marco Cuturi](http: // marcocuturi.net / ) (Sinkhorn Knopp in Matlab/Cuda) +* [Gabriel Peyré](http://gpeyre.github.io/) (Wasserstein Barycenters in Matlab) +* [Nicolas Bonneel](http://liris.cnrs.fr/~nbonneel/) ( C++ code for EMD) +* [Marco Cuturi](http://marcocuturi.net/) (Sinkhorn Knopp in Matlab/Cuda) ## Contributions and code of conduct -Every contribution is welcome and should respect the[contribution guidelines](CONTRIBUTING.md). Each member of the project is expected to follow the[code of conduct](CODE_OF_CONDUCT.md). +Every contribution is welcome and should respect the [contribution guidelines](CONTRIBUTING.md). Each member of the project is expected to follow the [code of conduct](CODE_OF_CONDUCT.md). ## Support You can ask questions and join the development discussion: -* On the[POT Slack channel](https: // pot - toolbox.slack.com) -* On the POT [mailing list](https: // mail.python.org / mm3 / mailman3 / lists / pot.python.org / ) +* On the [POT Slack channel](https://pot-toolbox.slack.com) +* On the POT [mailing list](https://mail.python.org/mm3/mailman3/lists/pot.python.org/) -You can also post bug reports and feature requests in Github issues. Make sure to read our[guidelines](CONTRIBUTING.md) first. +You can also post bug reports and feature requests in Github issues. Make sure to read our [guidelines](CONTRIBUTING.md) first. ## References -[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). [Displacement interpolation using Lagrangian mass transport](https: // people.csail.mit.edu / sparis / publi / 2011 / sigasia / Bonneel_11_Displacement_Interpolation.pdf). In ACM Transactions on Graphics(TOG)(Vol. 30, No. 6, p. 158). ACM. +[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). [Displacement interpolation using Lagrangian mass transport](https://people.csail.mit.edu/sparis/publi/2011/sigasia/Bonneel_11_Displacement_Interpolation.pdf). In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. -[2] Cuturi, M. (2013). [Sinkhorn distances: Lightspeed computation of optimal transport](https: // arxiv.org / pdf / 1306.0895.pdf). In Advances in Neural Information Processing Systems(pp. 2292 - 2300). +[2] Cuturi, M. (2013). [Sinkhorn distances: Lightspeed computation of optimal transport](https://arxiv.org/pdf/1306.0895.pdf). In Advances in Neural Information Processing Systems (pp. 2292-2300). -[3] Benamou, J. 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Corpetti, [Supervised planetary unmixing with optimal transport](https://hal.archives-ouvertes.fr/hal-01377236/document), Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016. -[5] N. Courty -R. Flamary -D. Tuia -A. Rakotomamonjy, [Optimal Transport for Domain Adaptation](https: // arxiv.org / pdf / 1507.00504.pdf), in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.PP, no.99, pp.1 - 1 +[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, [Optimal Transport for Domain Adaptation](https://arxiv.org/pdf/1507.00504.pdf), in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 -[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). [Regularized discrete optimal transport](https: // arxiv.org / pdf / 1307.5551.pdf). SIAM Journal on Imaging Sciences, 7(3), 1853 - 1882. +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). [Regularized discrete optimal transport](https://arxiv.org/pdf/1307.5551.pdf). SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). [Generalized conditional gradient: analysis of convergence and applications](https: // arxiv.org / pdf / 1510.06567.pdf). arXiv preprint arXiv: 1510.06567. +[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). [Generalized conditional gradient: analysis of convergence and applications](https://arxiv.org/pdf/1510.06567.pdf). arXiv preprint arXiv:1510.06567. -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard(2016), [Mapping estimation for discrete optimal transport](http: // remi.flamary.com / biblio / perrot2016mapping.pdf), Neural Information Processing Systems(NIPS). +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard (2016), [Mapping estimation for discrete optimal transport](http://remi.flamary.com/biblio/perrot2016mapping.pdf), Neural Information Processing Systems (NIPS). -[9] Schmitzer, B. (2016). [Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems](https: // arxiv.org / pdf / 1610.06519.pdf). arXiv preprint arXiv: 1610.06519. +[9] Schmitzer, B. (2016). [Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems](https://arxiv.org/pdf/1610.06519.pdf). arXiv preprint arXiv:1610.06519. -[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). [Scaling algorithms for unbalanced transport problems](https: // arxiv.org / pdf / 1607.05816.pdf). arXiv preprint arXiv: 1607.05816. +[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). [Scaling algorithms for unbalanced transport problems](https://arxiv.org/pdf/1607.05816.pdf). arXiv preprint arXiv:1607.05816. -[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https: // arxiv.org / pdf / 1608.08063.pdf). arXiv preprint arXiv: 1608.08063. +[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https://arxiv.org/pdf/1608.08063.pdf). arXiv preprint arXiv:1608.08063. -[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon(2016), [Gromov - Wasserstein averaging of kernel and distance matrices](http: // proceedings.mlr.press / v48 / peyre16.html) International Conference on Machine Learning(ICML). +[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon (2016), [Gromov-Wasserstein averaging of kernel and distance matrices](http://proceedings.mlr.press/v48/peyre16.html) International Conference on Machine Learning (ICML). -[13] Mémoli, Facundo(2011). [Gromov–Wasserstein distances and the metric approach to object matching](https: // media.adelaide.edu.au / acvt / Publications / 2011 / 2011 - Gromov % E2 % 80 % 93Wasserstein % 20Distances % 20and % 20the % 20Metric % 20Approach % 20to % 20Object % 20Matching.pdf). Foundations of computational mathematics 11.4: 417 - 487. +[13] Mémoli, Facundo (2011). [Gromov–Wasserstein distances and the metric approach to object matching](https://media.adelaide.edu.au/acvt/Publications/2011/2011-Gromov%E2%80%93Wasserstein%20Distances%20and%20the%20Metric%20Approach%20to%20Object%20Matching.pdf). Foundations of computational mathematics 11.4 : 417-487. -[14] Knott, M. and Smith, C. S. (1984).[On the optimal mapping of distributions](https: // link.springer.com / article / 10.1007 / BF00934745), Journal of Optimization Theory and Applications Vol 43. +[14] Knott, M. and Smith, C. S. (1984).[On the optimal mapping of distributions](https://link.springer.com/article/10.1007/BF00934745), Journal of Optimization Theory and Applications Vol 43. -[15] Peyré, G., & Cuturi, M. (2018). [Computational Optimal Transport](https: // arxiv.org / pdf / 1803.00567.pdf) . +[15] Peyré, G., & Cuturi, M. (2018). [Computational Optimal Transport](https://arxiv.org/pdf/1803.00567.pdf) . -[16] Agueh, M., & Carlier, G. (2011). [Barycenters in the Wasserstein space](https: // hal.archives - ouvertes.fr / hal - 00637399 / document). SIAM Journal on Mathematical Analysis, 43(2), 904 - 924. +[16] Agueh, M., & Carlier, G. (2011). [Barycenters in the Wasserstein space](https://hal.archives-ouvertes.fr/hal-00637399/document). SIAM Journal on Mathematical Analysis, 43(2), 904-924. -[17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https: // arxiv.org / abs / 1710.06276). Proceedings of the Twenty - First International Conference on Artificial Intelligence and Statistics(AISTATS). +[17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https://arxiv.org/abs/1710.06276). Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). -[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016)[Stochastic Optimization for Large - scale Optimal Transport](https: // arxiv.org / abs / 1605.08527). Advances in Neural Information Processing Systems(2016). +[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) [Stochastic Optimization for Large-scale Optimal Transport](https://arxiv.org/abs/1605.08527). Advances in Neural Information Processing Systems (2016). -[19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A. & Blondel, M. [Large - scale Optimal Transport and Mapping Estimation](https: // arxiv.org / pdf / 1711.02283.pdf). International Conference on Learning Representation(2018) +[19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A.& Blondel, M. [Large-scale Optimal Transport and Mapping Estimation](https://arxiv.org/pdf/1711.02283.pdf). International Conference on Learning Representation (2018) -[20] Cuturi, M. and Doucet, A. (2014)[Fast Computation of Wasserstein Barycenters](http: // proceedings.mlr.press / v32 / cuturi14.html). International Conference in Machine Learning +[20] Cuturi, M. and Doucet, A. (2014) [Fast Computation of Wasserstein Barycenters](http://proceedings.mlr.press/v32/cuturi14.html). International Conference in Machine Learning -[21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). [Convolutional wasserstein distances: Efficient optimal transportation on geometric domains](https: // dl.acm.org / citation.cfm?id=2766963). ACM Transactions on Graphics(TOG), 34(4), 66. +[21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). [Convolutional wasserstein distances: Efficient optimal transportation on geometric domains](https://dl.acm.org/citation.cfm?id=2766963). ACM Transactions on Graphics (TOG), 34(4), 66. -[22] J. Altschuler, J.Weed, P. Rigollet, (2017)[Near - linear time approximation algorithms for optimal transport via Sinkhorn iteration](https: // papers.nips.cc / paper / 6792 - near - linear - time - approximation - algorithms - for-optimal - transport - via - sinkhorn - iteration.pdf), Advances in Neural Information Processing Systems(NIPS) 31 +[22] J. Altschuler, J.Weed, P. Rigollet, (2017) [Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration](https://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration.pdf), Advances in Neural Information Processing Systems (NIPS) 31 -[23] Aude, G., Peyré, G., Cuturi, M., [Learning Generative Models with Sinkhorn Divergences](https: // arxiv.org / abs / 1706.00292), Proceedings of the Twenty - First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 +[23] Aude, G., Peyré, G., Cuturi, M., [Learning Generative Models with Sinkhorn Divergences](https://arxiv.org/abs/1706.00292), Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 -[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http: // proceedings.mlr.press / v97 / titouan19a.html) Proceedings of the 36th International Conference on Machine Learning(ICML). +[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http://proceedings.mlr.press/v97/titouan19a.html) Proceedings of the 36th International Conference on Machine Learning (ICML). -[25] Frogner C., Zhang C., Mobahi H., Araya - Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http: // cbcl.mit.edu / wasserstein / ) Advances in Neural Information Processing Systems (NIPS). +[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2015). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/) Advances in Neural Information Processing Systems (NIPS). -[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https: // papers.nips.cc / paper / 9386 - screening - sinkhorn - algorithm - for-regularized - optimal - transport), Advances in Neural Information Processing Systems 33 (NeurIPS). +[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). [Screening Sinkhorn Algorithm for Regularized Optimal Transport](https://papers.nips.cc/paper/9386-screening-sinkhorn-algorithm-for-regularized-optimal-transport), Advances in Neural Information Processing Systems 33 (NeurIPS). -[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi - source Domain Adaptation under Target Shift](http: // proceedings.mlr.press / v89 / redko19a.html), Proceedings of the Twenty - Second International Conference on Artificial Intelligence and Statistics(AISTATS) 22, 2019. +[27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019. -[28] Caffarelli, L. A., McCann, R. J. (2020). [Free boundaries in optimal transport and Monge - Ampere obstacle problems](http: // www.math.toronto.edu / ~mccann / papers / annals2010.pdf), Annals of mathematics, 673 - 730. +[28] Caffarelli, L. A., McCann, R. J. (2020). [Free boundaries in optimal transport and Monge-Ampere obstacle problems](http://www.math.toronto.edu/~mccann/papers/annals2010.pdf), Annals of mathematics, 673-730. -[29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial Gromov - Wasserstein with Applications on Positive - Unlabeled Learning](https: // arxiv.org / abs / 2002.08276), arXiv preprint arXiv: 2002.08276. +[29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning](https://arxiv.org/abs/2002.08276), arXiv preprint arXiv:2002.08276. -- cgit v1.2.3 From 3409778d6f8ed2f9b7ed9977566348cec38a3dd7 Mon Sep 17 00:00:00 2001 From: ievred Date: Mon, 20 Apr 2020 13:27:32 +0200 Subject: hell --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 28d2b2a..9d113bd 100644 --- a/README.md +++ b/README.md @@ -264,4 +264,4 @@ You can also post bug reports and feature requests in Github issues. Make sure t [28] Caffarelli, L. A., McCann, R. J. (2020). [Free boundaries in optimal transport and Monge-Ampere obstacle problems](http://www.math.toronto.edu/~mccann/papers/annals2010.pdf), Annals of mathematics, 673-730. -[29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning](https://arxiv.org/abs/2002.08276), arXiv preprint arXiv:2002.08276. +[29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning](https://arxiv.org/abs/2002.08276), arXiv preprint arXiv:2002.08276. \ No newline at end of file -- cgit v1.2.3 From 1a36193c6616b8ad89fe0e0f2a5a7ab137e9d820 Mon Sep 17 00:00:00 2001 From: ievred Date: Mon, 20 Apr 2020 13:31:37 +0200 Subject: readme with laplace --- README.md | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 9d113bd..6b43eae 100644 --- a/README.md +++ b/README.md @@ -20,7 +20,7 @@ It provides the following solvers: * Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. * Non regularized Wasserstein barycenters [16] with LP solver (only small scale). * Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4]. -* Optimal transport for domain adaptation with group lasso regularization [5] +* Optimal transport for domain adaptation with group lasso regularization and Laplacian regularization [5][30] * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. * Linear OT [14] and Joint OT matrix and mapping estimation [8]. * Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). @@ -184,6 +184,7 @@ The contributors to this library are * [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT) * [Romain Tavenard](https://rtavenar.github.io/) (1d Wasserstein) * [Mokhtar Z. Alaya](http://mzalaya.github.io/) (Screenkhorn) +* [Ievgen Redko](https://ievred.github.io/) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): @@ -264,4 +265,6 @@ You can also post bug reports and feature requests in Github issues. Make sure t [28] Caffarelli, L. A., McCann, R. J. (2020). [Free boundaries in optimal transport and Monge-Ampere obstacle problems](http://www.math.toronto.edu/~mccann/papers/annals2010.pdf), Annals of mathematics, 673-730. -[29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning](https://arxiv.org/abs/2002.08276), arXiv preprint arXiv:2002.08276. \ No newline at end of file +[29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning](https://arxiv.org/abs/2002.08276), arXiv preprint arXiv:2002.08276. + +[30] Flamary R., Courty N., Tuia D., Rakotomamonjy A. (2014). [Optimal transport with Laplacian regularization: Applications to domain adaptation and shape matching](https://remi.flamary.com/biblio/flamary2014optlaplace.pdf), NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014. -- cgit v1.2.3 From fcf23770c5ed5252dee233b578239118d9d8ba62 Mon Sep 17 00:00:00 2001 From: Laetitia Chapel Date: Mon, 20 Apr 2020 13:50:14 +0200 Subject: doc kwargs + fix rst issues --- examples/plot_partial_wass_and_gromov.py | 24 +++++++++++------------- ot/partial.py | 6 ++++++ 2 files changed, 17 insertions(+), 13 deletions(-) diff --git a/examples/plot_partial_wass_and_gromov.py b/examples/plot_partial_wass_and_gromov.py index 30b3fc0..a5af441 100755 --- a/examples/plot_partial_wass_and_gromov.py +++ b/examples/plot_partial_wass_and_gromov.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -========================== +================================================== Partial Wasserstein and Gromov-Wasserstein example -========================== +================================================== This example is designed to show how to use the Partial (Gromov-)Wassertsein distance computation in POT. @@ -50,8 +50,7 @@ pl.show() ############################################################################# # -# Compute partial Wasserstein plans and distance, -# by transporting 50% of the mass +# Compute partial Wasserstein plans and distance # ---------------------------------------------- p = ot.unif(n_samples + n_noise) @@ -113,34 +112,33 @@ pl.show() ############################################################################# # -# Compute partial Gromov-Wasserstein plans and distance, -# by transporting 100% and 2/3 of the mass +# Compute partial Gromov-Wasserstein plans and distance # ----------------------------------------------------- C1 = sp.spatial.distance.cdist(xs, xs) C2 = sp.spatial.distance.cdist(xt, xt) +# transport 100% of the mass print('-----m = 1') m = 1 -res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, - log=True) +res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True) res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, m=m, log=True) -print('Partial Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist'])) -print('Entropic partial Wasserstein distance (m = 1): ' + - str(log['partial_gw_dist'])) +print('Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist'])) +print('Entropic Wasserstein distance (m = 1): ' + str(log['partial_gw_dist'])) pl.figure(1, (10, 5)) pl.title("mass to be transported m = 1") pl.subplot(1, 2, 1) pl.imshow(res0, cmap='jet') -pl.title('Partial Wasserstein') +pl.title('Wasserstein') pl.subplot(1, 2, 2) pl.imshow(res, cmap='jet') -pl.title('Entropic partial Wasserstein') +pl.title('Entropic Wasserstein') pl.show() +# transport 2/3 of the mass print('-----m = 2/3') m = 2 / 3 res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True) diff --git a/ot/partial.py b/ot/partial.py index 5f4b836..c03ec25 100755 --- a/ot/partial.py +++ b/ot/partial.py @@ -66,6 +66,8 @@ def partial_wasserstein_lagrange(a, b, M, reg_m=None, nb_dummies=1, log=False, instabilities, increase its value if an error is raised) log : bool, optional record log if True + **kwargs : dict + parameters can be directly passed to the emd solver .. warning:: When dealing with a large number of points, the EMD solver may face @@ -190,6 +192,8 @@ def partial_wasserstein(a, b, M, m=None, nb_dummies=1, log=False, **kwargs): instabilities, increase its value if an error is raised) log : bool, optional record log if True + **kwargs : dict + parameters can be directly passed to the emd solver .. warning:: @@ -304,6 +308,8 @@ def partial_wasserstein2(a, b, M, m=None, nb_dummies=1, log=False, **kwargs): instabilities, increase its value if an error is raised) log : bool, optional record log if True + **kwargs : dict + parameters can be directly passed to the emd solver .. warning:: -- cgit v1.2.3 From fd115a538deb8fa9dcf3169fcfa6b85aebd36b07 Mon Sep 17 00:00:00 2001 From: ievred Date: Mon, 20 Apr 2020 13:55:45 +0200 Subject: sim+sim param fixed --- ot/da.py | 53 +++++++++++++++++++++++++---------------------------- 1 file changed, 25 insertions(+), 28 deletions(-) diff --git a/ot/da.py b/ot/da.py index 8e26e31..300af30 100644 --- a/ot/da.py +++ b/ot/da.py @@ -748,7 +748,7 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, return A, b -def emd_laplace(a, b, xs, xt, M, reg, eta, alpha, +def emd_laplace(a, b, xs, xt, M, sim, sim_param, reg, eta, alpha, numItermax, stopThr, numInnerItermax, stopInnerThr, log=False, verbose=False, **kwargs): r"""Solve the optimal transport problem (OT) with Laplacian regularization @@ -785,6 +785,11 @@ def emd_laplace(a, b, xs, xt, M, reg, eta, alpha, samples in the target domain M : np.ndarray (ns,nt) loss matrix + sim : string, optional + Type of similarity ('knn' or 'gauss') used to construct the Laplacian. + sim_param : int or float, optional + Parameter (number of the nearest neighbors for sim='knn' + or bandwidth for sim='gauss' used to compute the Laplacian. reg : string Type of Laplacian regularization eta : float @@ -803,11 +808,6 @@ def emd_laplace(a, b, xs, xt, M, reg, eta, alpha, Print information along iterations log : bool, optional record log if True - kwargs : dict - Dictionary with attributes 'sim' ('knn' or 'gauss') and - 'param' (int, float or None) for similarity type and its parameter to be used. - If 'param' is None, it is computed as mean pairwise Euclidean distance over the data set - or set to 3 when sim is 'gauss' or 'knn', respectively. Returns ------- @@ -824,7 +824,7 @@ def emd_laplace(a, b, xs, xt, M, reg, eta, alpha, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 - .. [28] R. Flamary, N. Courty, D. Tuia, A. Rakotomamonjy, + .. [30] R. Flamary, N. Courty, D. Tuia, A. Rakotomamonjy, "Optimal transport with Laplacian regularization: Applications to domain adaptation and shape matching," in NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014. @@ -834,28 +834,28 @@ def emd_laplace(a, b, xs, xt, M, reg, eta, alpha, ot.optim.cg : General regularized OT """ - if not isinstance(kwargs['param'], (int, float, type(None))): + if not isinstance(sim_param, (int, float, type(None))): raise ValueError( - 'Similarity parameter should be an int or a float. Got {type} instead.'.format(type=type(kwargs['param']))) + 'Similarity parameter should be an int or a float. Got {type} instead.'.format(type=type(sim_param).__name__)) - if kwargs['sim'] == 'gauss': - if kwargs['param'] is None: - kwargs['param'] = 1 / (2 * (np.mean(dist(xs, xs, 'sqeuclidean')) ** 2)) - sS = kernel(xs, xs, method=kwargs['sim'], sigma=kwargs['param']) - sT = kernel(xt, xt, method=kwargs['sim'], sigma=kwargs['param']) + if sim == 'gauss': + if sim_param is None: + sim_param = 1 / (2 * (np.mean(dist(xs, xs, 'sqeuclidean')) ** 2)) + sS = kernel(xs, xs, method=sim, sigma=sim_param) + sT = kernel(xt, xt, method=sim, sigma=sim_param) - elif kwargs['sim'] == 'knn': - if kwargs['param'] is None: - kwargs['param'] = 3 + elif sim == 'knn': + if sim_param is None: + sim_param = 3 from sklearn.neighbors import kneighbors_graph - sS = kneighbors_graph(X=xs, n_neighbors=int(kwargs['param'])).toarray() + sS = kneighbors_graph(X=xs, n_neighbors=int(sim_param)).toarray() sS = (sS + sS.T) / 2 - sT = kneighbors_graph(xt, n_neighbors=int(kwargs['param'])).toarray() + sT = kneighbors_graph(xt, n_neighbors=int(sim_param)).toarray() sT = (sT + sT.T) / 2 else: - raise ValueError('Unknown similarity type {sim}. Currently supported similarity types are "knn" and "gauss".'.format(sim=kwargs['sim'])) + raise ValueError('Unknown similarity type {sim}. Currently supported similarity types are "knn" and "gauss".'.format(sim=sim)) lS = laplacian(sS) lT = laplacian(sT) @@ -1729,9 +1729,10 @@ class EMDLaplaceTransport(BaseTransport): can occur with large metric values. similarity : string, optional (default="knn") The similarity to use either knn or gaussian - similarity_param : int or float, optional (default=3) + similarity_param : int or float, optional (default=None) Parameter for the similarity: number of nearest neighbors or bandwidth - if similarity="knn" or "gaussian", respectively. + if similarity="knn" or "gaussian", respectively. If None is provided, + it is set to 3 or the average pairwise squared Euclidean distance, respectively. max_iter : int, optional (default=100) Max number of BCD iterations tol : float, optional (default=1e-5) @@ -1813,14 +1814,10 @@ class EMDLaplaceTransport(BaseTransport): super(EMDLaplaceTransport, self).fit(Xs, ys, Xt, yt) - kwargs = dict() - kwargs["sim"] = self.similarity - kwargs["param"] = self.sim_param - returned_ = emd_laplace(a=self.mu_s, b=self.mu_t, xs=self.xs_, - xt=self.xt_, M=self.cost_, reg=self.reg, eta=self.reg_lap, alpha=self.reg_src, + xt=self.xt_, M=self.cost_, sim=self.similarity, sim_param=self.sim_param, reg=self.reg, eta=self.reg_lap, alpha=self.reg_src, numItermax=self.max_iter, stopThr=self.tol, numInnerItermax=self.max_inner_iter, - stopInnerThr=self.inner_tol, log=self.log, verbose=self.verbose, **kwargs) + stopInnerThr=self.inner_tol, log=self.log, verbose=self.verbose) # coupling estimation if self.log: -- cgit v1.2.3 From 36b2e92d9ad5148208cc1bec9bc9133999bcdb1c Mon Sep 17 00:00:00 2001 From: ievred Date: Mon, 20 Apr 2020 14:04:32 +0200 Subject: added defaults for emd_laplace --- ot/da.py | 15 +++++++-------- 1 file changed, 7 insertions(+), 8 deletions(-) diff --git a/ot/da.py b/ot/da.py index 300af30..e615993 100644 --- a/ot/da.py +++ b/ot/da.py @@ -748,9 +748,9 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, return A, b -def emd_laplace(a, b, xs, xt, M, sim, sim_param, reg, eta, alpha, - numItermax, stopThr, numInnerItermax, - stopInnerThr, log=False, verbose=False, **kwargs): +def emd_laplace(a, b, xs, xt, M, sim='knn', sim_param=None, reg='pos', eta=1, alpha=.5, + numItermax=100, stopThr=1e-9, numInnerItermax=100000, + stopInnerThr=1e-9, log=False, verbose=False): r"""Solve the optimal transport problem (OT) with Laplacian regularization .. math:: @@ -1765,15 +1765,14 @@ class EMDLaplaceTransport(BaseTransport): in NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014. """ - def __init__(self, reg_type='pos', reg_lap=1., reg_src=1., alpha=0.5, - metric="sqeuclidean", norm=None, similarity="knn", similarity_param=None, max_iter=100, tol=1e-9, + def __init__(self, reg_type='pos', reg_lap=1., reg_src=1., metric="sqeuclidean", + norm=None, similarity="knn", similarity_param=None, max_iter=100, tol=1e-9, max_inner_iter=100000, inner_tol=1e-9, log=False, verbose=False, distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans'): self.reg = reg_type self.reg_lap = reg_lap self.reg_src = reg_src - self.alpha = alpha self.metric = metric self.norm = norm self.similarity = similarity @@ -1815,8 +1814,8 @@ class EMDLaplaceTransport(BaseTransport): super(EMDLaplaceTransport, self).fit(Xs, ys, Xt, yt) returned_ = emd_laplace(a=self.mu_s, b=self.mu_t, xs=self.xs_, - xt=self.xt_, M=self.cost_, sim=self.similarity, sim_param=self.sim_param, reg=self.reg, eta=self.reg_lap, alpha=self.reg_src, - numItermax=self.max_iter, stopThr=self.tol, numInnerItermax=self.max_inner_iter, + xt=self.xt_, M=self.cost_, sim=self.similarity, sim_param=self.sim_param, reg=self.reg, eta=self.reg_lap, + alpha=self.reg_src, numItermax=self.max_iter, stopThr=self.tol, numInnerItermax=self.max_inner_iter, stopInnerThr=self.inner_tol, log=self.log, verbose=self.verbose) # coupling estimation -- cgit v1.2.3 From b706bad0653407b14a8b79a0315aed0ced29e833 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 20 Apr 2020 14:11:22 +0200 Subject: Change rflamary to PythonOT in Readme --- README.md | 38 +++++++++++++++++++------------------- 1 file changed, 19 insertions(+), 19 deletions(-) diff --git a/README.md b/README.md index 6b43eae..44d35a7 100644 --- a/README.md +++ b/README.md @@ -2,11 +2,11 @@ [![PyPI version](https://badge.fury.io/py/POT.svg)](https://badge.fury.io/py/POT) [![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot) -[![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/rflamary/POT) +[![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/PythonOT/POT) [![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) [![Downloads](https://pepy.tech/badge/pot)](https://pepy.tech/project/pot) [![Anaconda downloads](https://anaconda.org/conda-forge/pot/badges/downloads.svg)](https://anaconda.org/conda-forge/pot) -[![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/rflamary/POT/blob/master/LICENSE) +[![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/PythonOT/POT/blob/master/LICENSE) @@ -140,26 +140,26 @@ ba=ot.barycenter(A,M,reg) # reg is regularization parameter The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http://pot.readthedocs.io/). -Here is a list of the Python notebooks available [here](https://github.com/rflamary/POT/blob/master/notebooks/) if you want a quick look: +Here is a list of the Python notebooks available [here](https://github.com/PythonOT/POT/blob/master/notebooks/) if you want a quick look: -* [1D optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_1D.ipynb) -* [OT Ground Loss](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_L1_vs_L2.ipynb) -* [Multiple EMD computation](https://github.com/rflamary/POT/blob/master/notebooks/plot_compute_emd.ipynb) -* [2D optimal transport on empirical distributions](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_2D_samples.ipynb) -* [1D Wasserstein barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_1D.ipynb) -* [OT with user provided regularization](https://github.com/rflamary/POT/blob/master/notebooks/plot_optim_OTreg.ipynb) -* [Domain adaptation with optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_d2.ipynb) -* [Color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_color_images.ipynb) -* [OT mapping estimation for domain adaptation](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping.ipynb) -* [OT mapping estimation for color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping_colors_images.ipynb) -* [Wasserstein Discriminant Analysis](https://github.com/rflamary/POT/blob/master/notebooks/plot_WDA.ipynb) -* [Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov.ipynb) -* [Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov_barycenter.ipynb) -* [Fused Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_fgw.ipynb) -* [Fused Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_fgw.ipynb) +* [1D optimal transport](https://github.com/PythonOT/POT/blob/master/notebooks/plot_OT_1D.ipynb) +* [OT Ground Loss](https://github.com/PythonOT/POT/blob/master/notebooks/plot_OT_L1_vs_L2.ipynb) +* [Multiple EMD computation](https://github.com/PythonOT/POT/blob/master/notebooks/plot_compute_emd.ipynb) +* [2D optimal transport on empirical distributions](https://github.com/PythonOT/POT/blob/master/notebooks/plot_OT_2D_samples.ipynb) +* [1D Wasserstein barycenter](https://github.com/PythonOT/POT/blob/master/notebooks/plot_barycenter_1D.ipynb) +* [OT with user provided regularization](https://github.com/PythonOT/POT/blob/master/notebooks/plot_optim_OTreg.ipynb) +* [Domain adaptation with optimal transport](https://github.com/PythonOT/POT/blob/master/notebooks/plot_otda_d2.ipynb) +* [Color transfer in images](https://github.com/PythonOT/POT/blob/master/notebooks/plot_otda_color_images.ipynb) +* [OT mapping estimation for domain adaptation](https://github.com/PythonOT/POT/blob/master/notebooks/plot_otda_mapping.ipynb) +* [OT mapping estimation for color transfer in images](https://github.com/PythonOT/POT/blob/master/notebooks/plot_otda_mapping_colors_images.ipynb) +* [Wasserstein Discriminant Analysis](https://github.com/PythonOT/POT/blob/master/notebooks/plot_WDA.ipynb) +* [Gromov Wasserstein](https://github.com/PythonOT/POT/blob/master/notebooks/plot_gromov.ipynb) +* [Gromov Wasserstein Barycenter](https://github.com/PythonOT/POT/blob/master/notebooks/plot_gromov_barycenter.ipynb) +* [Fused Gromov Wasserstein](https://github.com/PythonOT/POT/blob/master/notebooks/plot_fgw.ipynb) +* [Fused Gromov Wasserstein Barycenter](https://github.com/PythonOT/POT/blob/master/notebooks/plot_barycenter_fgw.ipynb) -You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/notebooks/). +You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/PythonOT/POT/tree/master/notebooks/). ## Acknowledgements -- cgit v1.2.3 From 470fce2b6ae01134e3e2fb7a27d9966fd776dae8 Mon Sep 17 00:00:00 2001 From: ievred Date: Mon, 20 Apr 2020 14:12:49 +0200 Subject: added defaults for emd_laplace --- ot/da.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/da.py b/ot/da.py index e615993..6249f08 100644 --- a/ot/da.py +++ b/ot/da.py @@ -789,7 +789,7 @@ def emd_laplace(a, b, xs, xt, M, sim='knn', sim_param=None, reg='pos', eta=1, al Type of similarity ('knn' or 'gauss') used to construct the Laplacian. sim_param : int or float, optional Parameter (number of the nearest neighbors for sim='knn' - or bandwidth for sim='gauss' used to compute the Laplacian. + or bandwidth for sim='gauss') used to compute the Laplacian. reg : string Type of Laplacian regularization eta : float -- cgit v1.2.3 From 2dc6dd874036b05eef02063c04a83a16a0568784 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 20 Apr 2020 14:31:30 +0200 Subject: test build new version of scipy --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 7ff1b3c..b9cc88f 100644 --- a/.travis.yml +++ b/.travis.yml @@ -33,7 +33,7 @@ before_install: # command to install dependencies install: - pip install -r requirements.txt - - pip install -U "numpy>=1.14" "scipy<1.3" # for numpy array formatting in doctests + scipy version: otherwise, pymanopt fails, cf + - pip install -U "numpy>=1.14" scipy # for numpy array formatting in doctests - pip install flake8 pytest "pytest-cov<2.6" - pip install -U "sklearn" - pip install . -- cgit v1.2.3 From 1ea47b72fa3ee8e9ee4c9a1e17871506b4d50066 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 20 Apr 2020 14:47:55 +0200 Subject: read image using pylab --- examples/plot_otda_color_images.py | 5 ++--- examples/plot_otda_mapping_colors_images.py | 5 ++--- 2 files changed, 4 insertions(+), 6 deletions(-) diff --git a/examples/plot_otda_color_images.py b/examples/plot_otda_color_images.py index 62383a2..d9cbd2b 100644 --- a/examples/plot_otda_color_images.py +++ b/examples/plot_otda_color_images.py @@ -18,7 +18,6 @@ SIAM Journal on Imaging Sciences, 7(3), 1853-1882. # License: MIT License import numpy as np -from scipy import ndimage import matplotlib.pylab as pl import ot @@ -45,8 +44,8 @@ def minmax(I): # ------------- # Loading images -I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 +I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 X1 = im2mat(I1) X2 = im2mat(I2) diff --git a/examples/plot_otda_mapping_colors_images.py b/examples/plot_otda_mapping_colors_images.py index a20eca8..bc9afba 100644 --- a/examples/plot_otda_mapping_colors_images.py +++ b/examples/plot_otda_mapping_colors_images.py @@ -22,7 +22,6 @@ estimation [8]. # License: MIT License import numpy as np -from scipy import ndimage import matplotlib.pylab as pl import ot @@ -48,8 +47,8 @@ def minmax(I): # ------------- # Loading images -I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 +I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 X1 = im2mat(I1) -- cgit v1.2.3 From d5efa20c70a6988a251acc15759dcd9d182c6dc1 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 20 Apr 2020 14:50:02 +0200 Subject: read image using pylab --- examples/plot_gromov_barycenter.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py index 58fc51a..101c6c5 100755 --- a/examples/plot_gromov_barycenter.py +++ b/examples/plot_gromov_barycenter.py @@ -17,7 +17,6 @@ computation in POT. import numpy as np import scipy as sp -import scipy.ndimage as spi import matplotlib.pylab as pl from sklearn import manifold from sklearn.decomposition import PCA @@ -90,10 +89,10 @@ def im2mat(I): return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) -square = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256 -cross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256 -triangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256 -star = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256 +square = pl.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256 +cross = pl.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256 +triangle = pl.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256 +star = pl.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256 shapes = [square, cross, triangle, star] -- cgit v1.2.3 From 7da2d99d628f71b08cefb01fc31d57d76196bcb7 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 20 Apr 2020 14:54:30 +0200 Subject: change example for working (default parameter change) --- examples/plot_UOT_barycenter_1D.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/plot_UOT_barycenter_1D.py b/examples/plot_UOT_barycenter_1D.py index c8d9d3b..acb5892 100644 --- a/examples/plot_UOT_barycenter_1D.py +++ b/examples/plot_UOT_barycenter_1D.py @@ -77,7 +77,7 @@ bary_l2 = A.dot(weights) reg = 1e-3 alpha = 1. -bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights) +bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights) pl.figure(2) pl.clf() @@ -111,7 +111,7 @@ for i in range(0, n_weight): weight = weight_list[i] weights = np.array([1 - weight, weight]) B_l2[:, i] = A.dot(weights) - B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights) + B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights) # plot interpolation -- cgit v1.2.3 From 13054c65dc0f4731a1d9a9e5294160035627f910 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 20 Apr 2020 15:03:30 +0200 Subject: working partial gromov with 3d plot --- examples/plot_partial_wass_and_gromov.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/examples/plot_partial_wass_and_gromov.py b/examples/plot_partial_wass_and_gromov.py index 30b3fc0..01141f2 100755 --- a/examples/plot_partial_wass_and_gromov.py +++ b/examples/plot_partial_wass_and_gromov.py @@ -11,6 +11,8 @@ distance computation in POT. # Author: Laetitia Chapel # License: MIT License +# necessary for 3d plot even if not used +from mpl_toolkits.mplot3d import Axes3D # noqa import scipy as sp import numpy as np import matplotlib.pylab as pl -- cgit v1.2.3 From 8acaf262baa04a4d2bdd9c774c45c5bb2fb2d12a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 20 Apr 2020 15:12:02 +0200 Subject: add and update notebooks --- notebooks/plot_OT_1D.ipynb | 104 +-- notebooks/plot_OT_1D_smooth.ipynb | 133 ++-- notebooks/plot_OT_2D_samples.ipynb | 68 +- notebooks/plot_OT_L1_vs_L2.ipynb | 40 +- notebooks/plot_UOT_1D.ipynb | 39 +- notebooks/plot_UOT_barycenter_1D.ipynb | 64 +- notebooks/plot_barycenter_1D.ipynb | 73 ++- notebooks/plot_barycenter_fgw.ipynb | 88 ++- notebooks/plot_barycenter_lp_vs_entropic.ipynb | 411 +++++++----- notebooks/plot_compute_emd.ipynb | 29 +- notebooks/plot_convolutional_barycenter.ipynb | 8 +- notebooks/plot_fgw.ipynb | 46 +- notebooks/plot_free_support_barycenter.ipynb | 10 +- notebooks/plot_gromov.ipynb | 52 +- notebooks/plot_gromov_barycenter.ipynb | 44 +- notebooks/plot_optim_OTreg.ipynb | 799 +++++++++++------------- notebooks/plot_otda_classes.ipynb | 56 +- notebooks/plot_otda_color_images.ipynb | 44 +- notebooks/plot_otda_d2.ipynb | 8 +- notebooks/plot_otda_jcpot.ipynb | 397 ++++++++++++ notebooks/plot_otda_linear_mapping.ipynb | 28 +- notebooks/plot_otda_mapping.ipynb | 50 +- notebooks/plot_otda_mapping_colors_images.ipynb | 42 +- notebooks/plot_otda_semi_supervised.ipynb | 8 +- notebooks/plot_partial_wass_and_gromov.ipynb | 352 +++++++++++ notebooks/plot_screenkhorn_1D.ipynb | 213 +++++++ notebooks/plot_stochastic.ipynb | 96 +-- 27 files changed, 2180 insertions(+), 1122 deletions(-) create mode 100644 notebooks/plot_otda_jcpot.ipynb create mode 100644 notebooks/plot_partial_wass_and_gromov.ipynb create mode 100644 notebooks/plot_screenkhorn_1D.ipynb diff --git a/notebooks/plot_OT_1D.ipynb b/notebooks/plot_OT_1D.ipynb index bf9f9cd..9b2b446 100644 --- a/notebooks/plot_OT_1D.ipynb +++ b/notebooks/plot_OT_1D.ipynb @@ -61,8 +61,6 @@ }, "outputs": [], "source": [ - "#%% parameters\n", - "\n", "n = 100 # nb bins\n", "\n", "# bin positions\n", @@ -95,35 +93,55 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" + "" ] }, + "execution_count": 4, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% plot the distributions\n", - "\n", "pl.figure(1, figsize=(6.4, 3))\n", "pl.plot(x, a, 'b', label='Source distribution')\n", "pl.plot(x, b, 'r', label='Target distribution')\n", - "pl.legend()\n", - "\n", - "#%% plot distributions and loss matrix\n", - "\n", + "pl.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ "pl.figure(2, figsize=(5, 5))\n", "ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')" ] @@ -139,25 +157,25 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% EMD\n", - "\n", "G0 = ot.emd(a, b, M)\n", "\n", "pl.figure(3, figsize=(5, 5))\n", @@ -175,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -186,34 +204,46 @@ "text": [ "It. |Err \n", "-------------------\n", - " 0|8.187970e-02|\n", - " 10|3.460174e-02|\n", - " 20|6.633335e-03|\n", - " 30|9.797798e-04|\n", - " 40|1.389606e-04|\n", - " 50|1.959016e-05|\n", - " 60|2.759079e-06|\n", - " 70|3.885166e-07|\n", - " 80|5.470605e-08|\n", - " 90|7.702918e-09|\n", - " 100|1.084609e-09|\n", - " 110|1.527180e-10|\n" + " 0|2.861463e-01|\n", + " 10|1.860154e-01|\n", + " 20|8.144529e-02|\n", + " 30|3.130143e-02|\n", + " 40|1.178815e-02|\n", + " 50|4.426078e-03|\n", + " 60|1.661047e-03|\n", + " 70|6.233110e-04|\n", + " 80|2.338932e-04|\n", + " 90|8.776627e-05|\n", + " 100|3.293340e-05|\n", + " 110|1.235791e-05|\n", + " 120|4.637176e-06|\n", + " 130|1.740051e-06|\n", + " 140|6.529356e-07|\n", + " 150|2.450071e-07|\n", + " 160|9.193632e-08|\n", + " 170|3.449812e-08|\n", + " 180|1.294505e-08|\n", + " 190|4.857493e-09|\n", + "It. |Err \n", + "-------------------\n", + " 200|1.822723e-09|\n", + " 210|6.839572e-10|\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% Sinkhorn\n", - "\n", "lambd = 1e-3\n", "Gs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n", "\n", @@ -240,7 +270,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_OT_1D_smooth.ipynb b/notebooks/plot_OT_1D_smooth.ipynb index 69e71da..603a18c 100644 --- a/notebooks/plot_OT_1D_smooth.ipynb +++ b/notebooks/plot_OT_1D_smooth.ipynb @@ -61,8 +61,6 @@ }, "outputs": [], "source": [ - "#%% parameters\n", - "\n", "n = 100 # nb bins\n", "\n", "# bin positions\n", @@ -95,35 +93,55 @@ "outputs": [ { "data": { - "image/png": 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\n", 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" + "" ] }, + "execution_count": 4, "metadata": {}, - "output_type": "display_data" + "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", 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" + "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% plot the distributions\n", - "\n", "pl.figure(1, figsize=(6.4, 3))\n", "pl.plot(x, a, 'b', label='Source distribution')\n", "pl.plot(x, b, 'r', label='Target distribution')\n", - "pl.legend()\n", - "\n", - "#%% plot distributions and loss matrix\n", - "\n", + "pl.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ "pl.figure(2, figsize=(5, 5))\n", "ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')" ] @@ -139,25 +157,25 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% EMD\n", - "\n", "G0 = ot.emd(a, b, M)\n", "\n", "pl.figure(3, figsize=(5, 5))\n", @@ -175,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -186,28 +204,34 @@ "text": [ "It. |Err \n", "-------------------\n", - " 0|7.958844e-02|\n", - " 10|5.921715e-03|\n", - " 20|1.238266e-04|\n", - " 30|2.469780e-06|\n", - " 40|4.919966e-08|\n", - " 50|9.800197e-10|\n" + " 0|2.821142e-01|\n", + " 10|7.695268e-02|\n", + " 20|1.112774e-02|\n", + " 30|1.571553e-03|\n", + " 40|2.218100e-04|\n", + " 50|3.130527e-05|\n", + " 60|4.418267e-06|\n", + " 70|6.235716e-07|\n", + " 80|8.800770e-08|\n", + " 90|1.242095e-08|\n", + " 100|1.753030e-09|\n", + " 110|2.474136e-10|\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% Sinkhorn\n", - "\n", "lambd = 2e-3\n", "Gs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n", "\n", @@ -228,46 +252,55 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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ilyb/6EepI0lOCVikO9TU+Bi5224L3/1u6mjS2m03n3Lpxht99ucCpgQs0h2uv95HPbv6aujZM3U06f34xz5k5RlnFPTccUrAIl3t3Xe9zfeww+Dzn08dTXbo3x+uuAKefdansS9QSsAiXSkE7/FQVeUzBefDbBedZdIkPyl33nmwaFHqaJJQAhbpSrffDlOmwOWXw447po4muxQVwe9+519OJ5/sX1YFRglYpKssXAhnnw377ee3srkddvBeEQ8+6Mm4wCgBi3SF6mo49ljv/XDLLVBcnDqi7HXWWT4T9Fln+cSkBUQJWKQrfP/78MwzftWbmh4+WlER3HEH9O0LX/pSQU1fpAQs0tnuucfH+j31VK+CZcuGDYM774TXX4dvfKNguqYpAYt0pmefha9/Hfbd1/v8SusdcoiPFXHPPf4LogCUpA5AJG+88YbPAjxyJDzwAFRUpI4o95x/Prz1lo8VMWoUnHZa6oi6lBKwSGd4/XWv4Mz8jP6gQakjyk1mcN113oPk9NP95OXJJ6eOqsuoCUKko2bN8rP4dXXw5JM66dZRJSUwebKPH3zKKX4BS55SAhbpiIce8vZeM3jiCdh119QR5Yfych85buJE75523nlQW5s6qk6nBCzSHrW1fnXb0Uf7xQT/+hd8/OOpo8ovmSR87rlwzTVw5JE+nVMeUQIWaau5c73J4fvf936rTz0FI0akjio/FRd7b5KbbvL3efRouP/+1FF1GiVgkdZat85HNdt1Vx/H9rbb4K67oHfv1JHlv5NOghkz/Ivu//0/bx+ePTt1VB2mBCyyJatWebeo7bbzcWy/8AV49VXv76vRzbrPrrvCtGlw5ZXw9NO+fsIJ/rfIUUrAIs2pqYG//x1OPNGv0rroIthrL7+8+M471eSQSlmZ9xWePRvOOcfbiHfbDQ480Ke7X7EidYRtYqEAh4CTzY0dOzZMnz49dRjp1Nb6hRTPPOO9GR55BD74wJsXvvpV7w41ZkzqKKWpZct8FLWbb4Y33/Q24wMPhAkT/HbMGOjVq8ue3sxmhBDGtvvxSsACBZCAq6q8Olq2DN57zzv6z5sHc+Z44p01C9av932HDoVDD/W2xsMP79IPsHSSEOD55+G+++Cvf/Xpn8CbiHbayXuo7LgjVFb6lYrDhvnEqAMH+t+3nU1JSsDSKcYOHhymT5zY9U/U0v+3zPbG94ew+VJX57e1tQ1LTY0v1dWwcaMvGzb4snYtrFnj/27OiBGw886w++5eLY0b5x9Yte3mtvff93E5Zs70E6ZvvOFftlVVm+9bUgJ9+vjSsyf06OFd4MrLvcmjtNT3KS5uWLbZBq66SglYOsfYsrIwvbumSm8puWW2N77fbNOlqMhvG38Yiov9Q1Ja2vDB6dHDlz59vBmhf39fBg+GIUM88Y4Y4ftKYair818/8+f77dKl/qtoxQr/kl6zxn8FbdjQ8EVeVeVf7DU1DV/4dXU+u/Ujj3Q4AWssCHF77AH53AQhUlTkTQ/DhqWOpJ56QYiIJKIELCKSiNqABQAzWwO8kTqOLRgELE8dxBbkQoyQG3HmQow7hxD6tPfBagOWjDc6cjKhO5jZdMXYOXIhzlyJsSOPVxOEiEgiSsAiIokoAUvGTakDaAXF2HlyIc68j1En4UREElEFLCKSiBKwiEgiSsAFzsyOMLM3zGyOmX0vdTwZZjbSzJ4ws1fN7BUzOyduH2hmj5rZm/F2QBbEWmxmM81sSlzfzsymxff0bjMrSxxffzObbGavm9lrZrZPtr2PZnZe/DvPMrO7zKwiG95HM/udmS01s1mNtjX73pn73xjvS2a2xfFLlYALmJkVA9cDnwV2BY4zs2yZ1rcGOD+EsCswHjgjxvY94PEQwo7A43E9tXOA1xqtXwFcHULYAVgBfDNJVA2uBR4OIewCfAKPNWveRzMbDpwNjA0h7A4UA8eSHe/j74Ejmmxr6b37LLBjXE4Gbtzi0UMIWgp0AfYBHmm0fhFwUeq4Woj1AeAz+NV6w+K2YfgFJCnjGhE/hIcAUwDDr94qae49ThBfP+Bt4gn3Rtuz5n0EhgMLgIH4xWFTgMOz5X0EKoFZW3rvgF8DxzW3X0uLKuDClvmPn7EwbssqZlYJ7AlMA4aEEBbHu5YAQxKFlXENcAFQF9e3AlaGEGrieur3dDtgGXBLbCb5rZn1IovexxDCIuBKYD6wGFgFzCC73sfGWnrv2vx5UgKWrGZmvYF7gXNDCKsb3xe8zEjWj9LMjgaWhhBmpIqhFUqAMcCNIYQ9gXU0aW7IgvdxADAR/7LYBujF5j/7s1JH3zsl4MK2CBjZaH1E3JYVzKwUT753hBD+FDe/Z2bD4v3DgKWp4gP2A/7DzN4B/g9vhrgW6G9mmXFWUr+nC4GFIYRpcX0ynpCz6X08FHg7hLAshFAN/Al/b7PpfWyspfeuzZ8nJeDC9jywYzzbXIaf+Phz4pgAP6MM3Ay8FkK4qtFdfwYmxX9PwtuGkwghXBRCGBFCqMTfu7+HEI4HngC+GHdLHeMSYIGZ7Rw3TQBeJYveR7zpYbyZ9Yx/90yMWfM+NtHSe/dn4ITYG2I8sKpRU0XzUjW8a8mOBTgSmA28BXw/dTyN4tof/2n3EvBiXI7E21gfB94EHgMGpo41xnsQMCX+e3vgX8Ac4B6gPHFsnwSmx/fyfmBAtr2PwKXA68As4A9AeTa8j8BdeLt0Nf5r4pstvXf4Cdjr42fpZbxXx0cev0OXIpvZEfhPrmLgtyGEn7b7YCIiBabdCTj2IZ2Ndw1aiP+cPS6E8GrnhScikr86MiD7p4A5IYS5AGb2f/iZzBYT8KBBg0JlZWUHnlI6auVKn/x15MhNt8+YMWN5CGFwZv0zRV/SKE0ijTxad08L03m3X0cScHN93sY13cnMTsavCmHUqFFM18y7SV1wAVx33eYTIJvZvDQRiRSuLu8FEUK4KYQwNoQwdvDgwVt+gHSp6mooLU0dhYhAxxJwVvchleZVVUFZ0qFhRCSjIwk4a/uQSsvWr4devVJHISLQgTbgEEKNmZ0JPIJ3Q/tdCOGVTotMusTq1dC7d+ooRAQ6OC19COFB4MFOikW6wfvvw1ZbpY4ii1g8sd2B/vAi7aVLkQvMokUwdGjqKEQElIALSm0tvPMOfOxjqSNJyKzJUhSXptubLCJdQAm4gMyaBTU1sPvuqSMREehgG7Dkln/8w28PPDBtHNnEiry6DbWb3eG3IY6z3lIVrLZj6QBVwAXk9tth1103vwxZRNJQBVwgpk6F55/3y5ALmm1ac1hJCx+B4mK/rYsVbqyU69czlTGZ1ea3q0KWj6IKuABUVcFpp8GQIXDCCamjEZEMVcB5LgT4/vfhxRfh/vuhb9/UEWWXzYZjjRWyZSrg4sz2Jm3ATR5ndXWbHq9JRbxZhazKWFAFnNdC8NHPrrzSK+CJE1NHJCKNqQLOU6tWwdlnw223wRlnwP/+b+qIskTTNtq6FirRJhWvlcaPSpM25Pq24cx+tbWbHDfE9cz2UJupgFUZiyrgvHT//d7b4fbb4Yc/9BNvRfpLi2QdVcB55Lnn4Cc/gb/8BfbYwxPx3nunjirLZCpMy/T/3bQDsBXVNbtffeVbnLn1xmHLDK7cdEyJuG41Nb45thHXr1dV+3p9ZRzjyKyrMi4IqotyXG0t3Hcf7Lcf7Lsv/POfcPnlPuOFkq9IdlMFnKNeew3uvhvuuAPmzIHKSm/n/c//1HCTrVJfUTbtzxtrkkzbbX1lm9mvaNPtJV4Jh7JNK+FQXrLJ4TO9JKjx4xbFCjizHjZW+frGjb6eqZSr422mQlZFnFeUgHPI7NmedP/4Rx/XwcwvK77sMvjCF6ClawpEJDvpI5vF1q6Fp5+Gxx6DRx+Fl1/2pLv//n5i7ZhjYNiw1FHmuM0qydg7IVae9VfEZdpsM70b4t4W4vxOTSrf2l7lvl7iFXMojm3C8fmsum6T4xVtiMffUBXXYyX8od/WV8ZVfn+mN4Uq49ymBJxFqqth2jRPuI8/7pcP19T4HG777QfXXANf/CIMH546UhHpDErACS1d6gk3szz3HKxb58XUXnvB+efDoYd68u3RI3W0BaJJ23CoadpvuEmlmekNkWkTbtIvuK7MK+CaHr5fbXmT+0t9vXijH6/4Qz9+6XqvwIvXxop4zYd+u95vw4YNfvth823GqohzgxJwN9m40S8HnjrVk+3UqfD2235fcTGMHg2TJsGECXDQQTBwYNJwRaQbKAF3gbVr4aWXPOHOnOm3L73kg+KANyGMH++XB48b59WuZirOMk0qx1ATey2E4mbvz1z5ZrFttrg07hcr4tpyr4TrSmNFXOHbq/rGtuNMd+M6f1xxlX80S9ZXAFC+xv+DlK30CrdklVe+RWvW++1avw3rY2WcaSuuadqLQhVxNlEC7qD33ts00c6cCW++2fD/fOBA2HNPOOccT7bjxsGIEWljFpHsoATcShs2wKuvek+ExsuSJQ37VFZ6sj3+ePjkJ/3fI0ZoSrG8EDYd26F+DInMbWZ7rDgzVzhZfRtyT398rIjrSjIVsa9Xx19AtT3i88QC2uLDizf4R7Vstd+Wr/BeFhUr/IEVy73iLVkRK+LV6/w4mbbiDbHtWP2Ks4oScBO1tTB37uaJds6chvMvFRU+1sLhhzck2k98Avr3Txu7iOSWgk7AS5dunmhfeQXWexGBmc8gPHo0HHus344eDTvs0NA9VApMplKMk8iF0GR0s1hZFmWupKv2tuOSWHlaLHWLarxttyb2isj0E66JlXBN/9gLolfs1RDHqFhb45Xz6rV+5V3ZB/4fsWKZV9g9l/pxeyzt4/e/75Vv8cq1/jxr4m2mIo79m1URp1EQCXj9ek+sTZPt0qUN+wwe7Mn1pJMaEu1uu+nkmIh0nbxLwB984CfCXnih4Xb27IYv9IoKn5b9qKMaEu3o0T5dj0ibtdA2XJfpFVGTqXxjJRwrzqIP/Zvd6nzgjvUb40cxeIW7IY4xQayAt+rvlevgnt62W2L+fKuqvIP4klVe8S5ZGtuEF3sbca/FZfE2bl/qU6IUf+DHszWxrbhp7wn1J+4WOZuAQ4B339082c6f37DPyJEwZsymzQcf+5iaD0QkO+RMAq6r82aEp57y8RGeegoWL264f6edYJ99fPaHPff0ZdCgdPFKgWmhbbjpjBiWGVMiXsFWsTH2613rFWpp7PdbssGrhLXVvr4sPk3fMn/czgPeA2BU+Qd+RxwTZP52fgXPyyu3AWDOksEArJrvlXLvhV4p937X24x7LPG24JLlXhEXrfbbunWxX7Eq4i6VtQm4uhpmzGhIts88AytW+H3Dh/vVYuPHe4X7iU9Anz5JwxURabOsSsAh+IDit93mQy6uXu3bd9rJh1s84AAffrGyUn1rJcs1rYirYuWYaSuObcKZirh0nbfBFq+OvRdWxSvfVnsb7trVXsHOXuel7rpRvr10iB9vfO85ABzQczYAH/b3j/br2/j+T2+/EwDT3t0WgHffiZXwfH+ePgu90u652LeXLFsDqCLualtMwGY2ErgNGIKPwndTCOFaMxsI3A1UAu8AXw4hrGhPEHPnwh/+4Il37lzveXDMMfC5z/nQi0OHtueoIiLZrTUVcA1wfgjhBTPrA8wws0eBE4HHQwg/NbPvAd8DLmxrAL/4BXznO17RTpgAl1zi1a66f0leyfSWaDI2Q2bsCIvj/Vq8cq1inVeipbF3Q8UH3ma75gP/yC5937vtTN7Oe1HMH+Vtv0dv9W8AxlUsAGCP3gsBOLznXAD+PWgrAB7edjQA/1i4AwCL3/ariHq/48/TZ4H3oui52I+fqYgtVsT1vSYyo7GpH3G7bDEBhxAWA4vjv9eY2WvAcGAicFDc7VbgSdqYgG+6yZPvMcfA1Vd7rwURkULRpjZgM6sE9gSmAUNicgZYgjdRtNrbb8Opp8LYsXDnnT7ouEjBqK+IN70SzTJXzMXKMjPKWa+VXomWv+/9eHvFK95WL/Htzy3eBYBXtvf2ukNGeFvw5/q/CMCe5X78I3r6cceVPwXAtH4vAzBlm08C8OQor4iXzPXKu887mTZir4h7vOvrJcu9Ig71/YgzbcTNXFmnarhFrZ4V2cx6A/dlzgyZAAAPx0lEQVQC54YQVje+L4QQaJilpenjTjaz6WY2fdmyZfXbR4yAgw/2/rsPPti+4EVEcpmFVnw7mVkpMAV4JIRwVdz2BnBQCGGxmQ0Dngwh7PxRxxk7dmyYPn16/fqaNfCZz8Dzz/vANpMmwcSJfrWadC8zmxFCGJtZ/0zRl1S2pJDp3mNx9uVS/5Fq8Sei9YxTo/TzCrVmsN+uH+YV6pqRsf/wtt4W27tyFQCfHuG9JA7t9woAu5X5dfg949O9W+vHf3b9jgA8uvzjAMya5/2Jy97x4/ee5/v3WeSVesUSr4CLVsSKeG1mFLbMWBNVeTMW8aN193R636stVsDmc63cDLyWSb7Rn4FJ8d+TgAfa+uR9+sDDD8OFF/rYDMce6z0eTj7Z+/3W1W35GCIiuWqLFbCZ7Q88DbxMZqIsuBhvB/4jMAqYh3dD++CjjtW0Am6sthaefBJuvRXuvdcH0BkwwLuhHXig9wEeMwZKS9vy8qS1VAFnqRYq4qJyr0gzFXGIFXH14Di2xDb+M3LNCH/culH+0e0VK+K9h3oviX37eWW8Y7kPbF0cP+ILqr23xLNrvE34uSXbAbB8vveW6DXf4+gzPx53sfcPLlvqFbCtjBXxunWbz1uXoxVxV1TArekF8U+gpSee0FmBFBd7N7QJE+CGG+CBB+CJJ/xKuL/8xffp2dMvN85ckPGpT6m7mojkrqy6Ei6jd2+fVeL44319yRK/Qi4zDsSll/qXpxnsvLOP+zBmTMMYEJrQUvJGC1fU1WZ6S8T+w0WxX27ZKq88S5d5RdxrUayI53vFvHbuAACeGtE/3nqF+7GhfoJ8t37esWl4+UoARlb4j9q6IV5JzyrzSnd+L/+QVffx427sF9uI+3pK6RFHYSv+oLx+xDVijKjvcL2sTMBNDR0KX/yiLwArV8Kzz/rJu5kzPTnfdVfD/ttu25CQx4zxWSu22UaXL4tIdsmJBNxU//5w5JG+ZCxf7sk4s7zwAtx/f8OX6oABDUNS7rGH3+6+uwbxkRzTyorY4tgNJSu9x2jfpV4J91rotxvmeRvxuiHehjxvmI8R8caw4QD0HOxV69Z9/cq3fmXeq6FnqVfAW23l29+PRc2aUq+Aa3qUxFtvG+zZq5TS9/2+ohXxKrq1Teary/G24Y7IyQTcnEGDvEvbZz7TsG3NGvj3v315+WWfGv6223x7RmXlpgOzjx7tg//oZJ/khBYScmawHIuD51j8+V8cT471iQO391zotxsHeSLeMNhTwvqt+wGwaJBf+PHOwDjVUi+/0KK4JF5CXeTPX9PX7/+wLjObqDdZ1JaV06NHnDapLHMC0T9ctiqmnw89uVOAA/3kTQJuTp8+3oti//0btoUA8+ZtPj3RQw/VT2hLWRnsssvmiVkzHItIZ8rrBNwcM696Kyt9tLWMjRvhjTe8Ss4k5X/8A+64o2Gf/v292aJpU0bfvt39KkRasKWKOJ4As3jpcNEqb6LoudQH4ano500UfQZ4RbxxK69WNwz0KnbjAE8Z1bHpLlTEqZcys8zEp49zjrKxvxEsk2b8mGWlvnNJnJqmKE4wGtbFpglr0m0tTu9EXW1r34WcUXAJuCXl5Z5Q99hj0+0rVsCsWZtWy3fc0TBWMfgsyZnp6TO3Q4eqWhaRj6YEvAUDBni/4wMOaNgWAixY4NXyiy/68sILMHlywz5bb92QkD/5SdhrL0/USsrSrbY0VVKTk3YWT5CUve/VaumSON19X1+v7uelbVUc8H1jH2/rrfHdqC3b9D+41UJdHGiruncsk0O8rDrGVlzkj7GieGFubD8mxkas3utbgvOobVgJuB3MYNQoX44+umH7qlWelGfO9KQ8cyZcdVX95AcMHAjjxvkyfrxfSDJgQJrXICLpKQF3on79Nq+Wq6rg1Vdh+nSYNg2mTvXxLzJf3jvt5Ml43DjYd19vAilq9Rh1Im3UtCLOFJOZijhzJjoOpmNrM4PteNVa0cMr4fLefturt1fENb29Hbemp1e5NRX+n7iuxAiZwrY2DsFZ7BVvbUUcaKgmdlOLIVpmEJjiTCXsvSMyvSQy0zrlQ7c1JeAuVlbW0AzxrW/5ttWrPSFPnepJ+eGHvXsceHe6gw+GQw/1y7K3317NFiL5Sgk4gb594ZBDfIGGrnFPPQWPP+7LPff4fZWVDWNkHHGEmiykizSZMqm+ytys94T3J7Y1XvFmBgUqr/DbsngbenjFXNujlLryTBeJTRVVx0o3Vhgh9o6wsthPuOkD6jbdvz70HK6ElYCzQOOucSec4P+P3nijIRnfey/cfLNfHHLYYfDlL/u4yf36pY5cRDpCCTgLmfmFILvsAmec4cXI8897Iv7jH+Gvf/WmjSOO8GR8zDEaxF46WUu9J+IUSpkr7OrbikuaDBwfq9jSsjJCRewGUeIVboj9f+sbfTOFbdMKN+5vdfGy1DiBaX11HmPM1MP1lXDT15DFdLonBxQX+4m6n/8c3nkHnnvOE/OMGfC1r/ngQz/+Mbz/fupIRaQtWjUlUWf5qAHZpe3q6nzM5F/8wi+l7tEDvvEN+O53PSm3hQZkl3ZpOmB87NNLrHKtpKR+EHkyVXLJpuv1x8g8trZJP99YGYdM1R2vjAsbNx3Wsn4MiYxO7i+cZEoiyV5FRX5y7sEHG6Z0uukm2HVXuOaa+l9qIpKllIDzxO67w+9+B2++CQcdBOed5/2KX3kldWSS10Lwpa4W6moJtXGpqiJUVVG34UPq1m3wZc1aX1av8WXVaupWrSasWePL2nW+bNjgy8aNvlRXE6qrvaKorY1T3TdMGGlmvhRtujTaIWv7cioB55ltt4UpU+DOO2HuXE/CzzyTOioRaY56QeQhMzjuOB+G89BDvevan//szRUiXappe2uore9BUb+pSXtx/Xpxk3qwfmwI2/TYmfVM23BLbbzW5Hgh+9rkVAHnsZEj/eKO7bf36ZwWLUodkYg0pgSc54YMgfvu84GlTjopJ7pGSr7JtBO31F5cU02oqaauatMl044cqqp9qa7xng6xLTjU1hFq67xXRF1u/sdWAi4AO+wAl1/uXdWeeCJ1NCKSoQRcIE491YfDvOGG1JGIRC1Uxi1VyE0r5fr7M0td2GRp2lsiGykBF4iKCpg0CR54AOLMLyKSmBJwATnsMJ94dOrU1JGItEKmMm6j+n7AVgRWtHm/4CyiBFxA9tnHb//1r7RxiIhTP+AC0q+f94p4663UkYi0QTN9iz9y97qPXs8mqoALTGUlzJ+fOgoRASXggjNokIatFMkWSsAFZsAAWLEidRQiAm1IwGZWbGYzzWxKXN/OzKaZ2Rwzu9vMyrouTOksPXvChg2poxARaFsFfA7wWqP1K4CrQwg7ACuAb3ZmYNI1KiqUgEWyRasSsJmNAI4CfhvXDTgEmBx3uRX4fFcEKJ2rtBSqq1NHISLQ+gr4GuAC6qfPYytgZQghMwfIQmB4cw80s5PNbLqZTV+2bFmHgpWOKynxizFEJL0tJmAzOxpYGkKY0Z4nCCHcFEIYG0IYO3jw4PYcQjpRUVH9MKoiklhrLsTYD/gPMzsSqAD6AtcC/c2sJFbBIwCNNpsDlIBFsscWK+AQwkUhhBEhhErgWODvIYTjgSeAL8bdJgEPdFmU0mnMNCawSLboSD/gC4Fvm9kcvE345s4JSbqSErBI9mjTWBAhhCeBJ+O/5wKf6vyQpCtl6eSwIgVJV8KJiCSiBFxgVAGLZA8lYBGRRJSARUQSUQIWEUlECVhEJBElYBGRRJSARUQSUQIWEUlECVhEJBElYBGRRJSARUQSUQIWEUlECVhEJBElYBGRRJSARUQSUQIWEUlECVhEJBElYBGRRJSARUQSUQIWEUlECVhEJBElYBGRRJSARUQSUQIWEUlECVhEJBElYBGRRJSARUQSUQIWEUlECVhEJBElYBGRRFqVgM2sv5lNNrPXzew1M9vHzAaa2aNm9ma8HdDVwYqI5JPWVsDXAg+HEHYBPgG8BnwPeDyEsCPweFwXEZFW2mICNrN+wIHAzQAhhKoQwkpgInBr3O1W4PNdFaSISD5qTQW8HbAMuMXMZprZb82sFzAkhLA47rMEGNLcg83sZDObbmbTly1b1jlRi4jkgdYk4BJgDHBjCGFPYB1NmhtCCAEIzT04hHBTCGFsCGHs4MGDOxqviEjeaE0CXggsDCFMi+uT8YT8npkNA4i3S7smRBGR/LTFBBxCWAIsMLOd46YJwKvAn4FJcdsk4IEuiVBEJE+VtHK/s4A7zKwMmAv8J568/2hm3wTmAV/umhBFRPJTqxJwCOFFYGwzd03o3HBERAqHroQTEUlECVhEJBElYBGRRJSARUQSUQIWEUlECVhEJBElYBGRRJSARUQSUQIWEUlECVhEJBElYBGRRJSARUQSUQIWEUlECVhEJBElYBGRRJSARUQSUQIWEUlECVhEJBElYBGRRJSARUQSUQIWEUlECVhEJBElYBGRRJSARUQSUQIWEUlECVhEJBElYBGRRJSARUQSUQIWEUlECVhEJJFWJWAzO8/MXjGzWWZ2l5lVmNl2ZjbNzOaY2d1mVtbVwYqI5JMtJmAzGw6cDYwNIewOFAPHAlcAV4cQdgBWAN/sykBFRPJNa5sgSoAeZlYC9AQWA4cAk+P9twKf7/zwRETy1xYTcAhhEXAlMB9PvKuAGcDKEEJN3G0hMLy5x5vZyWY23cymL1u2rHOiFhHJA61pghgATAS2A7YBegFHtPYJQgg3hRDGhhDGDh48uN2Biojkm9Y0QRwKvB1CWBZCqAb+BOwH9I9NEgAjgEVdFKOISF5qTQKeD4w3s55mZsAE4FXgCeCLcZ9JwANdE6KISH5qTRvwNPxk2wvAy/ExNwEXAt82sznAVsDNXRiniEjeKdnyLhBC+BHwoyab5wKf6vSIREQKhK6EExFJRAlYRCQRJWARkUSUgEVEElECFhFJRAlYRCQRJWARkUSUgEVEElECFhFJRAlYRCQRJWARkUSUgEVEElECFhFJRAlYRCQRJWARkUSUgEWkMJj5kkWUgEVEEmnVjBgiIjkvhNQRbEYVsIhIIkrAIiKJKAGLiCSiBCwikogSsIhIIkrAIiKJKAGLiCSiBCwikogSsIhIIkrAIlIYiop9ySJKwCIiiWgsCBEpDHW1qSPYjCpgEZFElIBFRBJRAi4w48fDOeekjkJEACx04xiZZrYMmNdtTyhtsW0IYXDqIEQKSbcmYBERaaAmCBGRRJSARUQSUQIWEUlECVhEJBElYBGRRJSARUQSUQIWEUlECVhEJBElYBGRRJSARUQSUQIWEUlECVhEJBElYBGRRJSARUQSUQIWEUlECVhEJBElYBGRRJSARUQSUQIWEUlECVhEJBElYBGRRJSARUQS+f/Js7GEh8ojHQAAAABJRU5ErkJggg==\n", 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\n", 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\n", - "text/plain": [ - "
" - ] + "metadata": { + "needs_background": "light" }, - "metadata": {}, "output_type": "display_data" } ], "source": [ - "#%% Smooth OT with KL regularization\n", - "\n", "lambd = 2e-3\n", "Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl')\n", "\n", "pl.figure(5, figsize=(5, 5))\n", "ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.')\n", "\n", - "pl.show()\n", - "\n", - "\n", - "#%% Smooth OT with KL regularization\n", - "\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ "lambd = 1e-1\n", "Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2')\n", "\n", @@ -294,7 +327,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_OT_2D_samples.ipynb b/notebooks/plot_OT_2D_samples.ipynb index cd1b541..d5967bd 100644 --- a/notebooks/plot_OT_2D_samples.ipynb +++ b/notebooks/plot_OT_2D_samples.ipynb @@ -61,8 +61,6 @@ }, "outputs": [], "source": [ - "#%% parameters and data generation\n", - "\n", "n = 50 # nb samples\n", "\n", "mu_s = np.array([0, 0])\n", @@ -100,7 +98,7 @@ { "data": { "text/plain": [ - "Text(0.5,1,'Cost matrix M')" + "Text(0.5, 1.0, 'Cost matrix M')" ] }, "execution_count": 4, @@ -109,28 +107,30 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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NbxghxDa+YYSQlhufiD5DRDNE9MSG13qJ6KtE9Hzjf10exjCMbctmjHt/BuAPAHx2w2sfBfA159zHieijjfZHWk1EVYfUXNPo4chnqRACFkWZY48zSEQEb2w8xotkJnigS2qBO/kkVn0GKC5feo47YEQ92YCiIptLZIl72mSndQYemUhGlvPyBSZFRdmnzKwnu6osOS4deqp6LetJabTia5tY08eJlUTJ7rXW5b2CojCU5lr6nKhMOLIkNgCk57iDTjXDb/HMrPZaiggHnpoIIIrP64Cb9KwI/hHT5kf11spM8xMIsjrgKZ4XJeOKwvDoMQ6nFpvyy1LiZ6LlN75z7h8BLIiXbwJwV+PvuwDcvLnDGYaxHThXHX/IOTfZ+HsKgE7q3oCIbiOiw0R0OAg81RMNw2g7L9u459ad/c/o8O+cu8M5d8g5dyge1/HHhmG0n3N14JkmohHn3CQRjQCYaTkCQKUXOPqe5mfNjt16WFxU0pnPc8+OYlHrRcO986z9wvF+1ecjP3Ava//uN25k7VSP9gZ598UPsPbnnjrE2nsGpQYE/GDvCdb+SuRNrE0/xGUFgHSC2ySGRAbd13Tzyi4AsCg8Xh6a3a36rIqqN3WRMbdS0d4eMtnFJaPjrD1X0B/ekwu8ek1vD/9lJ5N5AMBQ1ypr98V1YNX+HE86MVPm9pGJNZ3leGyOe8Vct/cIaz88vkuNCUp8Gxw68BxrP/4PIkMzgJHX8+ArmY34V/b8kxrzscGbWPsHL/2u6nN4ist37TBf/9VAOxM9Pz9w+u/6A54U0x7O9Rv/ywBuafx9C4B7z9LXMIxtxmYe530OwDcBXEJE40R0K4CPA3gbET0P4K2NtmEYFwgtf+o75957hrfecp5lMQyjTbQ9Ecc1b/oPp9tBzvODQwSsJFZa6yylXvEcf0GPqSVlsAlvZ05pHT/o5M/xqxlxnFmdCaLSw/XZzDGRxCGmz7m4g+vNxX7+eZyd0n4JK7tFcscjWpbCEJcluczXJVbUD32XDogKu0LcjjEtS6WTr0t6lvcJOvT3S0Vc+9Xdel26X+DyykCYYo8e03mC2wrmXs114r4ntS0hVuLHCbJc3vQpkfUEwMJV3JYgk5HM/Zh+9r/vt/lx5q/WNgoZLNZ1nK/lqR/w+Qc0xxy5+xMoTlkiDsMwPNjGN4wQYhvfMEKIbXzDCCFtzcATCepIjzUdN+LdukKMNO5Fi9y4QYEnS2qJp6xJzGrX4NWLuTEmd4wbbHzzlvu4YSg7zg029bj+3JSBL3Rqmo85sFONSS5ww1ykwg1F1ax2tMlOC0NdXhvdcuMiwKkk1rKsz1kmhamm+S0iZQWAhKjcLLP5+mSLZ0X1o0AHbMVXuXyRgM8b18lvkZzh177rOJc/Na2NblThRk5H3CEpMq0dtXLj3IEqUhGyPq6DsSg/xdqZae0MpTIhi3PueUYNYcFjMhPxmbBvfMMIIbbxDSOE2MY3jBDSVh0/yEUx/f3NZD2FYe1n4KJCPyxw/d2XWbXcw8fkxnQgw8r1PF1v7hvceSLghVHW5buU67PZJ7jeVurX+lS1n+uzB0r7WHv8ep9dg89TE11qw1qvdnmhe8/mVB9ZBVbGUPoq6ZBQ+4uDMjGE1l0Ty7xdEYYC3zULOsQ56zge1HJC3xXJO2IFT1KKeX7t1/ZwO0fmgE5tKyvyrO7lY0b696oxs9eI70zhXBS9TBsgJlaGWXvlIm1jSc1ye05pUPSp63suOd+8F4LDrROaAPaNbxihxDa+YYQQ2/iGEULaquNTDUisNnWUWsqj44vn+DL5YETHWKgKu76EkJFj3FYgq77G9eNd1BJcX0ysCH1X6tAASgWurEbzPOFE7qS2P1RFYEYg1PV6XtsF6nGRbHNSdVGVVWWCTpm8cv3YYv1FEtKkeGYPAFHxrFnq9OR5tExVWb3GU4V3lZ+AT15JcpHPU+oX8nuqCsfEutSS/PswsaRtLPEVfh2lbJ4iRchMi0q4Pdo/Q9obMqd4n+Kg597ecDDfWvuwb3zDCCG28Q0jhNjGN4wQYhvfMEJIW4170cAhd6ppBYkVN1FJR2Q2iQTauJHI83ky057MODluIMtNiIAVT1UWQGTTmeEeLzKLDwDEV7kxJjbDvVs6xrVxr5aSVWV4W2YPAnRmnNyk9saR8kWkca+s17LSzW+JxDI/UGpRO53IwJLNEOSE0WrNs5YFaYzkbbkGAJCZ5te1luTXsNOTQSgqgpWiFX6NUhPcQAsAnf38npPBNJN7tENVbow7kVVTuja4rA4ksw5FAn3SqTm34X31thf7xjeMEGIb3zBCiG18wwghbdXxXQQIcs1DVtOtHXhk0Eg9ocdUssLpJKdPSwaklLu4jikz0AJab5N6qazkC2gHinpOJGwItD5c6eTyS4eeWEGPKfXyz2zpxARoW0F8E94dpW4+piqcrBJ5fZxaQhxHOFD5HLVkNtlyT+uKwBDXvsp9sta7rPBrVOmSY/R3naxWLAoEo57REUSyum+Mq++IDOrIJHmNqlrFh4tImwpfS19gGNWa80rZz4R94xtGCLGNbxghxDa+YYSQNuv4xHRGqeet9+FtEgpYpKrHSFtBNaU/z6QOGS2LIJEOrRyVRaUWGaQjdVsAKHfzeV1c6JzdesmDbES0+Ry+5/gykKfcqWWR+rlMxFH3VPWpC9eKmtCjpd0A0Dqy1FPrsdY6vjwuoPVz+YzaN6aakT4Q8rge+aPyXhDrn9LXTN5z8r6NxrTNKOjgviTyOgNARAwrC/uP8+jwbB02l4fDvvENI4zYxjeMEGIb3zBCiG18wwgh7Xfg2WDI8jltSCOJNGwppw4AFVFtOLGqP89WD3IPnuQCt5JIQx4ALB/kzhNdz/I+5V4tf7mXjxl8iC/x0gFtnVHnLBLulAc8zkUi42ykpuWXxjDpGORzJpIOVNKIKN8HeFYlACgOcFlkVhkACESyXplRCAAKO3hbZvZRWYQBlHqkk4zIyNOr10neU/lR/n7HuLYiSvll5qJqVV/n/Ai/F8p9noy5i3ye4oDo4PHBYvePGfcMwzgTtvENI4S03PhEtIuI7ieip4joSSL6QOP1XiL6KhE93/i/p9VchmFsDzaj41cB/Lxz7hEi6gDwMBF9FcBPAfiac+7jRPRRAB8F8JGzTRSpOpbMIlrx6LtCR5EZc2XQDgDEC3yezKxOStH/oKiausznTZzUY1ILoqKrSNjQdUIH6ZRE5tTYMo/eGDqsz7nUy48jA2W6n2+ty3ae0PJXOkTwTIHLGy1p+fPDIsGEWG9ZpRcAaiLhR4eo0iuz1gLaEchXVSl9RGRYFkkqfEEuuVN8HYIcPx/fOkVL/JwSq3xMalKnYM4M8dJLskrtzBX6fHqe5fdCPaajjFyEz9Nxkrfnr9TzxjaIJxN3nImW3/jOuUnn3CONv1cBPA1gJ4CbANzV6HYXgJs3d0jDMLaal6TjE9FeANcAeBDAkHPuxWzuUwCGzqtkhmG8Ymx64xNRDsAXAXzQOccqAjrnHLwPGgAiuo2IDhPR4aCSf1nCGoZxftjUxieiONY3/d3OuS81Xp4mopHG+yMAZnxjnXN3OOcOOecOxRPZ8yGzYRgvk5bGPSIiAHcCeNo594kNb30ZwC0APt74/95Wc1U6CSdvaH7WJEf0L4BIhFsnCivSm0Ubx7KDvCTx3Aldynn3lROsffwI10xI1psC0L2L14tanOPzUkT/yBkc5GOmM9wDY/FVnhJIfdzDpbeLr0sh0LLt6eHHeeLYTtUnmuB1naolMU/Z4/ST414yg/18bcdnhbcUACczvwr7E3nqcWe7uKFLng8ALJW48atQ4UY38mQUOjHOy2BfdfkLrP3Ys7vVGBLrkhnlWXVXHtTnHH/DAmuvCdnuvPZuNeb9p/4ta+9+zSnVZ3aNfzmuifffMDyuxjw2s8HT6W88tc89bMaq/0YAPwHgu0T0aOO1X8T6hv8CEd0K4ASAd2/qiIZhbDktN75z7hs4syPgW86vOIZhtAPz3DOMENLeSjoloOvZpo5eWNbVRurioyiTl0E6et7yHHem6NJqEE729/I+T/FT9zmDLCb4vLnnuR5X9pR2ni5zB8b9R7ieXRzSlXTqi1yXXUjzdq1Xl0f57hS3NySnW2cWTlTP/j4AgPg8swPcxpL0VbwRimggbLi+6i7lDp659ulMp+pTT4gqSiUZ/KNl6Zjlrz1e3cvauTFtI4qIeyovInCGjmqnpdlufp2lY9ntyXepMdlxLtuxzIjqk1iQGZv4Gnx9QtuvEvPNMfXC5ra0feMbRgixjW8YIcQ2vmGEkLbq+JHAoWNDEEWs0DophQzSkYEaAFBa4YOyU55AksNcb85Oi4AVT+XYxDJfnqQI7Ok4qYag1COqzc7w59P9j+tzlsE0MmMuHfUkghAZWjvGtPwyy64MJIl5zrnYy+WLFvkc6TlPFIhQtaXtwFfdRSauKPbr76CEuK4ycYgve29OXPt6gh+8y6Ovy0QcCVHxODemg3SKfdyQIW1PC3u10WjPs9ze4yK6Qo9cu9wYP8fVfXoxU/PNPrS5x/j2jW8YYcQ2vmGEENv4hhFCbOMbRghpq3GvHiPkB5vGieJg6yy75cImHHhk0i+PNWnlMm71iIpgn3pCL8Xqfm7Iyh0Xpa60/xHKfXxM31PcqLh4UB+nLmw81awoz93hMaiJVEX1uP4MlzEskUBk2a20LqElyzJXs3pMTNi+ZJZgX9YkuXZBThtty+LYEeGw43MMAvHrWhwURtyivjdaZdlNz4sTAlAYkaXd+Pu5nC6TvbKbBxDlR/U5J1ZEtl7hDFXNeoK8NmSy8pXY8mHf+IYRQmzjG0YIsY1vGCGkrTo+OSC2QfXx6etSx4/J6imeSjq10tkdVQCAAtmHv1/1ZA6TzhBRMW9NlqEGEKkI3a/IJ/EFxkhd3EWEnpdufZxoRXVR8stj+yrpyD6xTdhY5LpI+X26uLQleMs/x+SxpY3CI4u4P2JFOYc+Z3lOam19Y8T9I7MR5ws6GEvGWnnXkucnUesU9XxXxzaM8eQm8WLf+IYRQmzjG0YIsY1vGCGk7UE62ammouNLcCn1Q6mv+4JpYkUZpKOVyvgKf1iem+B9ZHANABRF1d3cFFeA18hXCUjIP8uTVcbX9MN/qZ/XhF4nKwYDQGqOv5Zc1spdvCCNB+K5uOf5utT7pb9DtKKPk1rkExV7+VrKCj4AEIi1rel4FQQdQtcW94JMAAIAqSURpBPj8mdntCzynqpm+AVIzuogndwpnThkI5UXdJWc9AKXLZjQ91x6gcuSH5KBSvpYqcXmGJ/dw4d94xtGCLGNbxghxDa+YYQQ2/iGEULaatyrdAPHbm5+1nTsWFZ9osIDYX5NZJwtaYNazwCfZ+ZEt+pz6JrnWPuhvgPiwNrStX8fr75z7EA/a8eTOhBj38A8a08u8coti9d6ynF383kGOrnVqiOiDVIjGW40PHxSV4iJRPm4oMIvd92zltE0X4fRAV4ZbWJBG7WqJVFaO8G9UFxdGye7RbWgnZ0rqk9VpFxeKafO+j4AnDzFI7a+7zJ+3Q8f2avGOBG4M7B7lrXHung1JADIvpH3KYlqR3/0qi+oMbd1vJ+1L7v6mOpzaplX7cklubXu4m5+XAB4fLaZrdd9e3MpeOwb3zBCiG18wwghtvENI4SQc5v06j8PZPt2uSvf/sHT7WKf53NHqIOxvHAo8TgwlHr5oOyU1omXDgpnnJNyXr0Oa6N8THJeZrZoLcvoP/DKq0sX61LhFeGoEnTyttMqMmrCPyQ77gleSrRyhvIkv+jmJ1URKn16znO/iOXWQUd6iHTOKfV6HJDyZw/28c2bneTCLF7C5+g6osfEZCIO4TQz8Li25cxcy+0N0nEmuF7br3rv5s5bC5d6qvqIc5RBU2u79L2dmm3Ke/wzn0Bxcsxzx4h5W3UwDON7D9v4hhFCbOMbRghp63N8EFCPNtUPX1UcFeTiSarhnXgDvsotHcdFAkuhamdm9XN8GbAi9d/spKcqi3g2XsvwJe44qbMvrOwVz6eFbF3H9XEWL+bHkcEdALA2IgJshC4bL3rsGrvUS3xMXo8pdctgJi5vqcuTCFSckkwwCgA5UfWYxCmLnroPAAAWnklEQVQW+z0JSmQVH5nIwpd8RLyWWhCJRUgfR9pd5NpeNDitxiwuCbtAoG9Ude2P8JNevVpH4QSVZtIPS7ZpGMYZsY1vGCHENr5hhJCWG5+IUkT0bSJ6jIieJKJfa7y+j4geJKIjRPR5IvKkUjAMYzuyGeNeGcCbnXNrRBQH8A0i+gqA2wH8vnPuHiL6YwC3Avj02SaqJYHlgxsmHvSkgImKLDGrMgOMNrQE/dzgURnTn0H0Kh4EUn2ug7Xzu/RSVHdzxw2a4ZlTF67Un5uRAR6gEv0yN+jMX+mRv5sbcKiLn8/aVXpMtoM7Bk0M6uAZl+DrGxHZiCNlLX81J8b0cVmmh3X22IjIyrN6QDogaYNavYt7qmR7iqrP6gi/JtVKa8tV0MHlS1+6xNpzpAO4ZPbe8k4uW/CQvp/WrhBGWlGV6Ee6hGUSwOdedzFrV6/SKYQqeX6s0h6+dsOD2jFoGs3AHhffnENey298t86LEsYb/xyANwP4q8brdwG4eVNHNAxjy9mUjk9EUSJ6FMAMgK8CeAHAknPuxYcn4wB2nmHsbUR0mIgO1/J5XxfDMNrMpja+c67mnLsawCiA6wBcutkDOOfucM4dcs4dima1n7phGO3nJTnwOOeWiOh+AK8H0E1Esca3/iiAU63GR0tAz9NNHaQ82TpLbWKV6yxU9ziQ9HG9KDOtnVkKIoFEdk04bSzpMcXjXD+XTicqiy2AUg+Pnuk4zn/lxNc8lVcH+DqUe/kcCU8G3cIQn2fwmJa/LBxnZFbaWMnj9LNDOCBNcFmyE55gIKH2y+NUPRWHghy/ZoWRuOqTmeLjZGUg6ewCAB1j/Jzm61yn73tayy+DdEpjXJbu57XTVTXD1z8m7oX7dl2pxgw+wm0Hc1WdcVkkBVZBUdNX82QwAJCaag6icsv4HACbs+oPEK1bRIgoDeBtAJ4GcD+AH210uwXAvZs6omEYW85mvvFHANxFRFGsf1B8wTl3HxE9BeAeIvpNAN8BcOcrKKdhGOeRlhvfOfc4gGs8rx/Fur5vGMYFhnnuGUYIaWt0nosAQa5pfPCVf5YZeGoyGMmTjkZG2lVyuo+M8qt0iRJUOskrYiJ6rdQjMvKsaONYpCqz6XAjVrSknZacsOhUZXYdn0FN2wgVQVZmsJEltPQ6VXiSWtSFQ5Wn6hkqImOQjODzXedqRsja61mXGXEw0Qw69LpIQ2LQya9RLfnSv+uqGU8UnZCfxFoeGhhTY56I8MWVRlEACESUYlok1U0M6HJe5Q0bwG1yR9s3vmGEENv4hhFCbOMbRghpa5bdrviAe33Pv2q+MNCrO0XEZ5GQj/I6mMMlRRDF/KLqM/ke7mw4cveTrF0/qCvRTF/HA3l23HuCtVcPaS/lIM3l7/zct1h77V2vVWM6jnKPFypzfffUv9TrNPLPfEw9rvXQxBiv6qPWKer53J+e4+1+fux6xhOkM86r7VQP7mDt+CQPlAEAlxKyBLoCTPEi7qwSy/M+iVP6OqPOdXqXFvJ6jkM1PqZ4kFfOSX2LV+MBAAzzPi7BnX7qOR3YEylyBx6X0Ap5dJwr9WuH+H2ZfUZcHwC01tT7H5j9PJYrM5Zl1zAMjW18wwghtvENI4S0vZLOFe9oVtIpDOrPHVkdJSYeW3qrv/RwlSZ3Sj9fn34dbw88zMdUPc/FVy7i7U5RhaXSrVWpsqgIs/+LXBefej23GwA6M6p8jl/u9fgLiIQkaY9aJ4OKZFZjWbXFJ0upT2Q99kRWx0XAU7WF/wDA/TkA/VwcAOoiqURUJhLxyJ+eFUlB9vL3szo/BqIiBmdtFz/O8Ld1ZtvZq7kOL9c6f7W2RfX/Hb/Jli/S1ywh8mwEIr9KLanXMjnfnOfoZz+B4pRV0jEMw4NtfMMIIbbxDSOE2MY3jBDS9hJatTixdisiNWHM8IyRBilZHhoA0tMieCPD502saaNJepp/LsoAleSCHiMzCNWSXLjMjCfTjygX7mR5rwn9+VzqF+WwPKWtZPntqLBR+YxjRWEorUufn0VPwJAIwpHZaHzXQxpxZTANACTneSdZHssX5CKRhlJZhsv3WlwYMF1Eyy/vhaiQbffwghoTFIb5cT31rgJh+42L4LHSpT4HpKYwVkLLMIwzYhvfMEKIbXzDCCFt1fFjhRr6H24GVlT6dZpUqftFy1wBi5a0jiOTXSSndIWSoKOPtYce4AEeVZEdFwCCLHe46P8On7ewU3udRAKR2fbxo6wd69OZyXue5cp2Pc7nWD6gM9AOPsLXJb6qE1nIoBZV0MaTsbjnaT5vpVeUdq5oJTm+wj1gKt1c+Y4V9DWrivLhtZRH3xW3R0zcC7E1fc6JZW7ISM/z65qe5tWRAIACPk9+D89+m3lyUo0Zqg6p1zayWNEBXINjvPpRpKbvucQSX6viIL+3O0/o7+rkYvOcp1Y355Bn3/iGEUJs4xtGCLGNbxghpK06fjUTxcJVzYSDhSHPQ3kZpCOeqW4mSCc7oXXi5Yu5HhdfFYkPfUE6B+QrXPcr+4J0erh8PU/v4XPu0Utej4mqsEL1q/RovTo/yhcqPa11ZKqJQBKhakc9wTPy+XSpVybSVEMQFxVeg4wMptEP3OXzal+QjouJoKIyFy5a0WuZmuWyrO7j7+fGtF2pVZBOLM+fvwPA7DVnD9IpeoJ0Eiv8/lk6qLogsSzuBVFsp5rS1yy10Fzf6pPnqZKOYRjfe9jGN4wQYhvfMEKIbXzDCCFtNe7VEsDK/qbxodyvHTBkEE5s5eyBGgBQHuQv1lKe04ryYy0JP5rEsieQRHi8rOwXsnkMXbUMN8St7ufGJOmgBADFIZHBpp879MTmtLESe3hqonJZG60q3cLJZ00E7RT1OZdGxDXp4LJUT2lDXV4stzQA+gxS1W5+nM7hVdVnZZpbtqgqgpky+maoJbl8iSt4ht8116XGxMQ6lIUxdXVUr3/+AF8XKnDj6sde87/VmE88+G7WDvZoZ6KgLkqDL/BjX3SVTiH0wnQzG3H9C54oJA/2jW8YIcQ2vmGEENv4hhFC2lstN+FQ2tUMKBgYWVZ9ohGuo8wvc901CLSjys4BPs8p6lN9rrr0JGs/foIHUQSejL87d/JkCjNLXOeseSr3Hhzk1WumjvJKKCueRAq5IR78s6ub66WLO3Qwx6v7Jlj7ftLeIF1pHrCSL3D9t1jSlz/XzR1PDvTy83k2PajGQFSKrdb5WkZj2pYz0sXP+bKeKdVnsofr46sVob9H9LzPR7h8bxl9gbX/tnC5GlMp8HUYHuXXfWlFB+Ts3zfNZStz2W7I8qpLAPCxi7i81x04rvpMF7hnUzDM7/c39POgLwCgDbaoubjHCObBvvENI4TYxjeMELLpjU9EUSL6DhHd12jvI6IHiegIEX2eiHSVQMMwtiWbrqRDRLcDOASg0zn3TiL6AoAvOefuIaI/BvCYc+7TZ5sj17vLvfotHzjdLvW2rqQjk0j6EkTKQJLslH6WuXgx15U6T/A+ssoMAKzu5GPS86ISqycJo5Rlx/084cfKxaI0CoAgy0+60snn8D37l0FF2VOeJJjiozhWEpVpKp6Apy5+sEAk7EzNe+4XmQ9VJPjwrVMggk/kugFAXFRRiohkob7EktlJrkfL6979gr435Drkhb2n/7s64Gb2Gh5VJIPHln5Qjxn9C/5MfuEy7R8g728ZWJXf5QnSmW2u3Qt/cR4r6RDRKIB3APjTRpsAvBnAXzW63AXg5s3MZRjG1rPZn/qfBPBhAC9+XPYBWHLOvfh5NA5A5xoCQES3EdFhIjoclHVKLMMw2k/LjU9E7wQw45x7+FwO4Jy7wzl3yDl3KJ7MtR5gGMYrzmae478RwA8T0Y0AUgA6AXwKQDcRxRrf+qMATr1yYhqGcT5pufGdc78A4BcAgIiuB/Ah59z7iOgvAfwogHsA3ALg3pZHczzTq8z2AmhDkDRQScMRoKvXkMdgWRziRp3uI7xPuVP/+CmM8D5p7suC4oAnA083H0NVftz8sC9TDm9Lo1V+hz4fGVQkDYIAECuevaKNz+gmxwSi5LXMDgQAiRWxll0ig422c6ljx3S8ijIAykw5viCpelwEIgljWS3usXuJ5Q2UcbV1Vhs5b31OBzPJa1T3xF7JtZT3mC+YbGOZcnkvnYmX8xz/IwBuJ6IjWNf573wZcxmG0UZeksuuc+7rAL7e+PsogOvOv0iGYbzSmOeeYYSQtgbpRCp1ZMeaXhnxNa0HKd1vTSQ8kNVzASS7uSEgNVNQfQYf4sEPmSmuMGbHtHKUXOVOGulp7kGS9VSxrXSLJZ3jDjyDj2gluZ6QDjx8jtwpj6OTSBKSFecDADVRkSda4ucYqehzlpVz4qLybWpBeNF4yI1Jjx6fAw8/x1Kftn1I562oqOJTj3mqIk9yg4IjHuTVeUwbHCJl7iWTWOHXPTEmjDsAOrt45l0pW2GnVuC7nuL3QiTQSUFkdejchEgGs9uTWXixeWxP3JIX+8Y3jBBiG98wQohtfMMIIe2tpJOLYPq1TV27OOh5Jq8q6XBdST7LBYByn9B3x3UgzOJVXPkpPCNKt3ge1a7t4XpbepLr5zVtokC5j4/Zu7KLtWev1oNkFZmgg59PPeFZp4SQbdzzgF2spQwA8a2lfE5f7uXHSSzq40SF2u/zz5DIc6x2eZJEVsUzeVFJB54hmWluy1k5yPX3/EjrSjqFnVy2weyIGjNzSCSBrXAbxehrtT/bzEnu1b54ub6usbxIKCpOudqno9RWZ5t7JPhn9bYX+8Y3jBBiG98wQohtfMMIIbbxDSOEtNeBJwDSsxstMq0z8MigEZ9BKiKMQJlZ7cWwusKNL5lpbhmqpjwBK338tfQMl6XSpce4KD+B5Bx3GImv6QxlspKLquzSq4YgusgvXXJR95HOIDKbi68qUUL40UQCfj4bA0JOvyaCZQJhrPRlTZLnWF3TDjzSSCivvTQqAkBqTgYZ8UnSc1p+mT2nlubnnJrXJ5Ce5o5OMjjm6EmdjXhkWThDzej7P7HC2ypQqaQdg5LzzbX0rbUP+8Y3jBBiG98wQohtfMMIIW3V8YPuOqbf2VTUdg1pxVRW0ple4Q4ZlYrWBfeL6jXPnRxWfQ5ddJy1H93NnSmqFb0Ue0b4vHNXcOePalXLcvHgLGsfW+Mldtcu1kpY7zCvBHRF7xxrj692qzFvHOIVVe47eoXq05nh2S1WClwvLZe0vaG3m+dFvKaXV4x5bFqnVqyIyjm1mrALeKq77Ojkyuy1PWOqz9FCP2uvBdz5KSWzbAB4dGyUtd9zOc8Y95fPXqPGBKKi0O4RXklnrEs78Fz2Jl6hZ6nMHZsevfKv1Zh9pdtY+4cOPab6TBe589mqOOd37dAZ8L6+eMnpv0/9rSejiQf7xjeMEGIb3zBCiG18wwghtvENI4RsuoTW+SA9tMtd9OO3n25XdaAUZOXppK6krSgO8HZqTveRDhYVkfyk87gO9ZJOPcVB3u4Y02NkWbCOMW7YigR6vZcOcKeM4jDv0/2MGoL5q3ifnie1M1F+lL8mnXxiBY8sl/K2i/A+HUf1d4V0MJLlvMqe8lgBt9mivF8bpTJPCScZsdzFAS1/p6givfgabgDs/o42aEZL0oGHy5ub0A5h0yI6L7HCx/ybf/23asz/+pW38Tmu02tZ7eAn2fU0NyAn3zGjZTnVc/rvqd/4rygfHz8/JbQMw/jewja+YYQQ2/iGEULa6sDjoly3Kw15UoKKj6KgQ2SK9QRmlPv5PNGSPq3Kq3jm3dhzPJJk6RL9GVjazQ8Wn+a6+KywLQBAvV9k753kssxe45GtU2SP7eJ2gRntP4JElsu2VNcGk3pMBCKJqjjRkifISGTvrfVyWVZd69LOS5fIHloXr3bxaxaP63uhcDnX+11ZOEx5zFPFIS5fooOvU2FEyy/XoTjKzzla1I5aQR/vUxX36UlPZNXSQT5PbZfO+OvWuHxLr+aL21nxlN85BzOdfeMbRgixjW8YIcQ2vmGEkLbq+NEK0HGyqZDEV7XuJD+K4qtcgfEljyiu8NPITunn62t1rtOnFvi8vgQThUX+zDcmnvdGKp5qub08qCIzybNUuAh/Ng0AxT5+0uUecVxdGAjlPq7rdR/XfYIcnzcmKtPEynqdCoN8THWZy5KZ1uskE2bI6riy4jGgqwWVBvS9kFng6yttCb4sxznhQ7CQ4raPbh5bA0CvQ2KZy9Z5Qmd/KfXzg8tzfujgbjWm5zl+89YSnszIYnkTq7y97IQDBIDUVFNeClpX9gXsG98wQoltfMMIIbbxDSOE2MY3jBDSVuNePQYU+5vGB5lBFIAuZVWns78PoNwrDYD680xmgi0M84k6j3myxwqDX3FIBOmc1MaxiDCuFIe5MU9mDQZ0UEilS5QEm1BDsLaP95HlxQGg1MfbSdnH87GfH+Xn5ITNLbHsCdIRx8mOiyCdHl+ZbLEOPmeWZW6QFYl9UBrU6x9fE9mARrgTUDChjat14RMjMz2Xe/Q2kfdcXQTp/NyeB9SY/xG7icvS6XFs6uSOTImn+LH37NdBOidTzQvgPOXWfNg3vmGEENv4hhFCbOMbRghpayIOIpoFcAJAPwBPuoxtyYUkK3BhyXshyQpcGPLucc55wsc4bd34pw9KdNg5d6jtBz4HLiRZgQtL3gtJVuDCk/ds2E99wwghtvENI4Rs1ca/Y4uOey5cSLICF5a8F5KswIUn7xnZEh3fMIytxX7qG0YIsY1vGCGkrRufiG4gomeJ6AgRfbSdx94MRPQZIpohoic2vNZLRF8loucb//ecbY52QUS7iOh+InqKiJ4kog80Xt+u8qaI6NtE9FhD3l9rvL6PiB5s3BOfJyJP2o6tgYiiRPQdIrqv0d62sr5U2rbxiSgK4A8BvB3A5QDeS0SXt+v4m+TPANwgXvsogK855w4C+FqjvR2oAvh559zlAF4H4N831nO7ylsG8Gbn3FUArgZwAxG9DsDvAvh959xFABYB3LqFMko+AODpDe3tLOtLop3f+NcBOOKcO+qcqwC4B8BNLca0FefcPwJYEC/fBOCuxt93Abi5rUKdAefcpHPukcbfq1i/QXdi+8rrnHNrjWa88c8BeDOAv2q8vm3kJaJRAO8A8KeNNmGbynoutHPj7wQwtqE93nhtuzPknJts/D0FYGgrhfFBRHsBXAPgQWxjeRs/nR8FMAPgqwBeALDknHsxGd12uic+CeDDAF6M/e3D9pX1JWPGvZeAW3/2ua2efxJRDsAXAXzQObey8b3tJq9zruacuxrAKNZ/AV7aYsiWQETvBDDjnHt4q2V5pWhnIo5TAHZtaI82XtvuTBPRiHNukohGsP5ttS0gojjWN/3dzrkvNV7etvK+iHNuiYjuB/B6AN1EFGt8k26Xe+KNAH6YiG4EkALQCeBT2J6ynhPt/MZ/CMDBhmU0AeA9AL7cxuOfK18GcEvj71sA3LuFspymoXPeCeBp59wnNry1XeUdIKLuxt9pAG/Dul3ifgA/2ui2LeR1zv2Cc27UObcX6/fpPzjn3odtKOs545xr2z8ANwJ4Duu63S+189iblO9zACYBBFjX4W7Fum73NQDPA/h7AL1bLWdD1jdh/Wf84wAebfy7cRvL+2oA32nI+wSAX228vh/AtwEcAfCXAJJbLauQ+3oA910Isr6Uf+ayaxghxIx7hhFCbOMbRgixjW8YIcQ2vmGEENv4hhFCbOMbRgixjW8YIeT/A9EctK99zlCjAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% plot samples\n", - "\n", "pl.figure(1)\n", "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", @@ -161,7 +161,7 @@ { "data": { "text/plain": [ - "Text(0.5,1,'OT matrix with samples')" + "Text(0.5, 1.0, 'OT matrix with samples')" ] }, "execution_count": 5, @@ -170,28 +170,30 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% EMD\n", - "\n", "G0 = ot.emd(a, b, M)\n", "\n", "pl.figure(3)\n", @@ -224,28 +226,30 @@ "outputs": [ { "data": { - "image/png": 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uGuGfS9XGF0LPF2ZbI5ekWahTt8VLNVzk0qeiPruZbTez54OvDwJYD2AKgGsALAouW4TCHwARaVJl9dlJngrgPADLAEwws755njsATKhqy0SkqhIHO8kRAH4E4DNm5mzYZoW+QNH+AMkbSa4gueIEavNxkYjESxTsJFtRCPT7zOyBIHtn0J/v69cX7Vyb2Z1m1mVmXa2I9nFFpD5id6ohSQB3A1hvZv/U76GHACwEcHvw/4Nl115kcJBFBu1ii1m7yUm3pjjyJ83kkTQDT9UYjCvG1jTPzrf5LncQkv+1ujoFh3cInnhK5JLebd1lF1u33YSqMCC3/SfRAd5J1yZ7TiXZlupdAP4QwIsk+35rt6AQ5EtI3gBgK4DrEtUoIg0RG+xm9hSAgV5uLxsgX0SajGbQiXiioQthGonz3EkotjI6AeXwhy9y0sN/uCy+3K45brkrXkrRuvrIjI5Oeszt29eAlqD4BKvOTiedpm3hHXwOvndm5JphP47/vcYJL4qq2Sk5MbQQRkQU7CK+ULCLeOKk3LwiO31atNyt20rek3/3uZG8lqdKfz4c3pUUiN+ZlBecE8mz59wddMO77lqCeQNN1f+ukcwE93P1JIukkgjv+Nv72utVKTcsTb++3O9ZfXYRUbCL+ELBLuIJBbuIJ7yZVJOZdbqTzm18OfaeNDuuZMaFjhneu9+9oAa7k6SVagfU8G4rQFW+p2KDnS3H3XLDOwInEfkdHi8yKFaFGIjsNGzRg6CTDLRWumuOBuhERMEu4gsFu4gnvOmzC/DY6+4kocsnRycSyeCmPruIKNhFfKFgF/FEkj3omkvKz3lbhrmnxOSPHCm76lQLMcKbMqQYIwkvjAGSfWYbFu6jFy03F/pZhjd5nBDd5BHHTzjJyAKcYpuIhn8OSa6pk70Pz3LSY67eGH9TqP2ZMUUWJu3ZG19ODU+N0Su7iCcU7CKeULCLeELBLuKJwTdAl3LAomW8u0Alv7X8Abo0O6O0DB3q1ptiYDDNYFytyk21O0yRgbatf7PASU95MrpAJbs0tOtPigG7NAOziQbkwkJty+17s/wyAOQvdncyitstqRx6ZRfxhIJdxBMKdhFPnJQLYcIbVQDJNquoh1rtqHv88q5I3pDHVlRcbhotc92TRqt1cm120kQnfWLGhMg1VdngIsXpvIlcGNpZePmLxa+rgBbCiIiCXcQXCnYRT5yUfXapnsnPjnTSr88/2KCWNE6zn7bTe+m8t75esfwbOHigW312EZ8p2EU8oWAX8URssJNsJ7mc5BqSa0neFuTPILmM5GaS95McEleWiDROkoUwPQAuNbNDJFsBPEXyUQCfBfDPZraY5B0AbgDwrUobtG+hu0Bi9KJnYu/JTnQnV/QWW6zRoF1PkqjGpI5q7WYTlmZAjvPOdtuxcm3F7Sgm/HsHgN4dO6teTzMNxu381MWRvAlff/qtr2kDL/SJfWW3gkNBsjX4ZwAuBfDDIH8RgGuTNlhE6i9Rn51khuRqALsAPA7gZQD7zazvpaMbwJQB7r2R5AqSK06gRtMQRSRWomA3s5yZnQtgKoALAbwjaQVmdqeZdZlZVyva4m8QkZooa/MKM9tPcimABQA6SWaDV/epAF6LLYB0Tru0E9ENC5L00cNq0U+rp2osvKjVBhdp1KqPHtbI3/uPu5c76d+bemFd6u3fPy9XktH48SQ7g6+HAng/gPUAlgL4cHDZQgAPpm6FiNRcklf2SQAWkcyg8MdhiZk9THIdgMUk/xbAKgB317CdIlKh2GA3sxcAnFckfwsK/XcRGQQ0g07EE/XdXdas6KBcI5y7yk2vjrx3ATLjxzvp3O7d8QVX4bgnqZ2WOdEPkvIv/brscuo1IFdNemUX8YSCXcQTCnYRTzT0RJjwbqFAkYkSCfq84VM/bPaMyDW2yp3oEe6js2tO5J7cipdK1xvaRRWI30mVF5wTybPnKt9ltNhRyqlOb2lmVTjOuFj/vPeyeU46+8TKsstNpMHjOXplF/GEgl3EEwp2EU80/e6yO37i9osnXlud00Wa2YnfcU93af15Y052AYDsVHflcm93/HonqZ7wWEzcOIxOhBERBbuILxTsIp5QsIt4oqEDdG9+dH7kmlH3PVt+waHJCi1Dh0YuyR896maEvu+eKy+I3NP26HMlqz12dXQxRPvD7g4m4Qk/4Z1kgQS7l4YnYwDRCRlJrkmiChNXBpvstKlOundbd+WFhn+OQLqfZZkTcTRAJyIKdhFfKNhFPNH0k2pETlbHL++K5A15rPwJVGeufHs92/0ffQw716nPLuI1BbuIJxTsIp5o6OYVSez+E/dU1/F3lH9izGCz5xPu9zz2rsZ9z+HNNqqx0YYUJOmf28VznTSfXhO5Zv28t08DOlpiCE6v7CKeULCLeELBLuIJBbuIJ7yZVMOsOxaZ5IjjzJkznXRu/abYe1re6Z44kn+h/NNG6uXI710UyRv242Wlb6rWAo+Q7JTJkbyeme7uw5knny+73JaRI50024ZErsm9safscsMyY8e4Ze7Zm66czlFuOfvfLOt+LYQREQW7iC8U7CKeaGyfvUb9v2Jazj3LrWb1urLLCG88YT09sff0XOVuitH2SOkNMYrJzDo9kpfb+HLZ5SSq6+zZbj1rN9SknrBim3ok+fk2q4MfiW7MMnJx/MYsmXFjnXS54wnqs4uIgl3EF4mDnWSG5CqSDwfpGSSXkdxM8n6S0c80RKRpJO6zk/wsgC4AHWZ2NcklAB4ws8Uk7wCwxsy+VaoMbV5RPblLznfSaT6DlsaaszL6WvvSvHzZ5fQf73i251EcyO9J32cnORXABwDcFaQJ4FIAPwwuWQTg2rJbKSJ1k/Rt/FcBfB5A35+dsQD2m1nfNLRuAFOK3UjyRpIrSK44gcE7uioy2MUGO8mrAewys1Qn1JvZnWbWZWZdrYh+vCIi9ZFk84p3AfggyasAtAPoAPA1AJ0ks8Gr+1QAOt5TpImVNamG5CUAPhcM0P0AwI/6DdC9YGb/Uur+agzQ7f/Ygkhe57012snlJD8dpaW9PZKXP3as5D3hSUIAcHii+5ox5p4m3k2oWifnhGTOmOGkc5tfqbjMNGo1qeYmAJ8luRmFPvzdFZQlIjVW1h50ZvYkgCeDr7cAiB52JiJNSTPoRDzhzeYV1bDrkxc76VO++XRd6s3MPC2Sl9u0pSZ1ZSe5G0b0bt9RcZnVOvmkXsKn84ZP5k2kjou8+tNCGBFRsIv4QsEu4glv+uw9HwhtIvHTBJtIhD+TTfCzYqu7+I9DWp10/vDh+HrrJDs1OsO5tztmblSN+qL595wXyRuyzd24offV35RfcL3mSoSeK9mJEyKXJBn/CG8CGrsBaIj67CKiYBfxhYJdxBMKdhFPeDNAd7LJvS+0U83S6E41r3zFXTQ04wv1WaCSZhfeJFqGDXPrGT48ck1u9+6q1NVfdvq0SF7v1m1ll1OPnXs1QCciCnYRXyjYRTzR0D77geujp2Z0fD/+1Iyw8EQWy0UnTkRO2Qz17cKnrwLxJ7AmuYddc9wLVkfLjD1RtkYbLhTVoEkozLZGLrETx2tTd0hmwilOOrdzV+Vljh4dycvt2xd/X0eHe8+BA2XVqz67iCjYRXyhYBfxhIJdxBP+TKpJsYItPIkjf+RIfDUXnONW89yL8W1rkEznqEhebv+bdam7Ze6ZTprHTkSuyXcMddJpfpb7/9CdWNR5X5FdZ5po1+CWkSOddP7gwbLu1wCdiCjYRXyhYBfxRFn7xjeD7GmnRvJ6t7waf2OKsYne82c56ZanVsfewxc3udWWXWv92PTJ0cyYPnt4AgqQbhJKfs362GvCC2rS6Px3d/FPeEwFqM64SvikorSnFJXbRy+HXtlFPKFgF/GEgl3EE/X9nH34FJs/54/fSjfzZ9D1dOj33R1FR/ygvB1F5W0b73JPn5n1idqcPMOsO9wVu5ipTvQ5u4go2EV8oWAX8YSCXcQTWghThjdudCdOjLszwcSJKuz8cuRDF0Xyhj1Qm0G83svmOensEysrLjMz6/RIXm7jyxWXWyuV7hYDRBe0ALWdMNNHA3QiomAX8YWCXcQTde2zk9wNYCuAcQDeqFvFlRlMbQUGV3sHU1uBwdHe6WY2vtgDdQ32tyolV5hZV/yVjTeY2goMrvYOprYCg6+9YXobL+IJBbuIJxoV7Hc2qN40BlNbgcHV3sHUVmDwtdfRkD67iNSf3saLeELBLuKJugY7yStIbiC5meTN9aw7CZL3kNxF8qV+eWNIPk5yU/B/9HjOBiA5jeRSkutIriX56SC/WdvbTnI5yTVBe28L8meQXBY8J+4nOSSurHohmSG5iuTDQbpp25pE3YKdZAbANwFcCeAsANeTPKte9Sf0HQBXhPJuBvCEmc0E8ESQbga9AP7CzM4CMB/AJ4OfZ7O2twfApWY2F8C5AK4gOR/A3wP4ZzM7A8A+ADc0sI1hnwbQfxvcZm5rrHq+sl8IYLOZbTGz4wAWA7imjvXHMrNfAdgbyr4GwKLg60UArq1rowZgZtvN7Png64MoPCmnoHnba2Z2KEi2Bv8MwKUAfhjkN017SU4F8AEAdwVpoknbmlQ9g30KgG390t1BXrObYGbbg693AJjQyMYUQ/JUAOcBWIYmbm/wtng1gF0AHgfwMoD9Zta3gVszPSe+CuDzAPJBeiyat62JaICuDFb4nLKpPqskOQLAjwB8xsychdfN1l4zy5nZuQCmovBO7x0NblJRJK8GsMvMKl/M30TqeSLMawCm9UtPDfKa3U6Sk8xsO8lJKLwqNQWSrSgE+n1m9kCQ3bTt7WNm+0kuBbAAQCfJbPCK2SzPiXcB+CDJqwC0A+gA8DU0Z1sTq+cr+3MAZgYjmkMAfATAQ3WsP62HACwMvl4I4MEGtuUtQR/ybgDrzeyf+j3UrO0dT7Iz+HoogPejMM6wFMCHg8uaor1m9gUzm2pmp6LwPP2lmX0UTdjWsphZ3f4BuArARhT6an9Zz7oTtu/7ALYDOIFCn+wGFPpqTwDYBOAXAMY0up1BW9+Nwlv0FwCsDv5d1cTtfSeAVUF7XwLwxSD/NADLAWwG8AMAbY1ua6jdlwB4eDC0Ne6fpsuKeEIDdCKeULCLeELBLuIJBbuIJxTsIp5QsIt4QsEu4on/Bmutl6aTJbY5AAAAAElFTkSuQmCC\n", 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% sinkhorn\n", - "\n", "# reg term\n", "lambd = 1e-3\n", "\n", @@ -292,36 +296,38 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/rflamary/PYTHON/POT/ot/bregman.py:374: RuntimeWarning: divide by zero encountered in true_divide\n", + "/home/rflamary/PYTHON/POT/ot/bregman.py:363: RuntimeWarning: divide by zero encountered in true_divide\n", " v = np.divide(b, KtransposeU)\n", - "/home/rflamary/PYTHON/POT/ot/plot.py:83: RuntimeWarning: invalid value encountered in double_scalars\n", + "/home/rflamary/PYTHON/POT/ot/plot.py:90: RuntimeWarning: invalid value encountered in double_scalars\n", " if G[i, j] / mx > thr:\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% sinkhorn\n", - "\n", "# reg term\n", "lambd = 1e-3\n", "\n", @@ -358,7 +364,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_OT_L1_vs_L2.ipynb b/notebooks/plot_OT_L1_vs_L2.ipynb index 8cc4e5d..d6a8761 100644 --- a/notebooks/plot_OT_L1_vs_L2.ipynb +++ b/notebooks/plot_OT_L1_vs_L2.ipynb @@ -64,22 +64,26 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% EMD\n", "G1 = ot.emd(a, b, M1)\n", "G2 = ot.emd(a, b, M2)\n", "Gp = ot.emd(a, b, Mp)\n", @@ -214,22 +219,26 @@ "outputs": [ { "data": { - "image/png": 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\n", 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jmT1Nu2s5OqCZ2XDyj6GR5IsTW/U03yoGPDnWZsdTPqyJA5qVw42R9cpTPqyJA5qVw42R9chTPqyZA5qVwo2R9cpTPqyZA5qVwo2R9cxTPqyJA5qVw42R9crz0ayJ0/atHE65NrM58Dw0MxsJW38cWW15HppVi+egWZ+sXFl2DaxMDmg2eJ6DZmZ94IBmg+c5aFagyUmQsgWefOyhx9HjgGYD5zloVqTJSYjIFnjysb9Po8cBzQbOc9DMrB8c0GzwPAfN+mTFirJrYGVyQLPB84RY6xP38kdbIfPQJC0G3gdsA5wfEe9pKt8B+ARwALAZeH1E3NZum56HZmZmzfo6D03SNsAHgcOARcCxkhY1rXYC8L8RsQ/wz8BZve7XhpjnoZlZHxQx5HggsCEibo2Ix4A1wFFN6xwFXJAeXwwcIs0k2drI8Tw0M+uDIgLansAdDc/vTK+1XCcitgAPALs1b0jSSZKmJU1v2rSpgKpZJXkempn1QaWSQiLivIgYj4jxefPmlV0d6xPPQzOzfigioG0E5jc83yu91nIdSdsCv0qWHGIjyPPQzKwfigho64F9JT1P0vbAMcClTetcChyXHr8WmIqqXubf+s/z0MysD3oOaOmc2KnAZcBNwNqIuFHSKklHptU+AuwmaQPwN8Dpve7XhpjnoZlZH/h+aGZmNjR8PzSrFs9DM7M+cECzwfM8NDPrg23LroCNoKfMQzsFlqz2PDQz65l7aDZwnodmZv3ggGYD53loZtYPDmg2eJ6HZmZ94IBmg+d5aGbWB56HZmZmQ8Pz0KyaPB/NzArkgGbl8Xw0MyuQ56FZeTwfzcwK5B6alcbz0cysSA5oVhrPRzOzIjmgWXk8H83MCuSAZuXxfDQzK5DnoZmZ2dDwPDSrNs9HM7MCOKBZ+TwfzcwK4HloVj7PRzOzAriHZqXzfDQzK4IDmpXO89HMrAgOaFY+z0czswI4oFn5PB/NzArggGblW7p0awLI5I4phX9igslHlmblTuE3sy44oFm1OIXfzObIaftWLU7hN7M5cg/NKsUp/GY2Vw5oVilO4TezueopoEn6NUlXSPpx+nfXnPV+Kem6tFzayz6t5pzCb2Zz1GsP7XTgaxGxL/C19LyVRyPit9NyZI/7tDpzCr+ZzVGvAe0o4IL0+ALgNT1uz0ZdYwr/JE+m6y9d6ivxm1lbvQa03SPirvT4bmD3nPWeIWla0tWScoOepJPSetObNm3qsWpWC07jN7MudUzbl/RV4Dktis5ofBIRISnvbqF7R8RGSc8HpiR9PyJuaV4pIs4DzoPsBp8da2/15zR+M+tSxx5aRBwaES9qsXweuEfScwHSv/fmbGNj+vdW4ErgJYW9A6s1p/GbWbd6HXK8FDguPT4O+HzzCpJ2lbRDejwG/A7wgx73ayPCafxm1q1eA9p7gN+X9GPg0PQcSeOSzk/rvBCYlnQ9sA54T0Q4oFl3nMZvZl3q6dJXEbEZOKTF69PAienxfwEv7mU/NsLapfH7PJqZNfCVQqzamtP4IQtmKcvRqfxmNsMBzYaPU/nNrAVfbd+Gj1P5zawF99Bs6DiV38xacUCzoeNU/ho4++ynZ6r6PKj1yAHNho9T+Ydfw3lQwOdBrRAOaDZ8WqXyH300rFkDOPNxKDScB2X58q0/UHwe1HqhiGpeMnF8fDymp6fLroYNi4Zemw6eIKbWuZEcBsuXw5lnwrJlsGpV2bWxISDp2ogYb1XmHprVw1MyH/2LfyisWwerV2fBbPVqDxlbzxzQrBac+ThkGnrUrPJ5UCuGA5rVgjMfK6ohm/Ep5zbPOeepPWjfmdwK4HNoVg8+h1ZN/lysYD6HZvWXdxHjc85p3UNw9uNg+NymDZADmtVDq4sYT0zA29/u6z6WyOc2bZA85Gj1l4LYqvtOYfmYr/s4cD7+ViAPOdrIcg9hQPKSP04+2Vd1sYFxQLNay81+3DGnAfa5tbnJu6UP5N+g1axoEVHJ5YADDgiznk1NRYyNRUxNBTQ8/6d/av361FTZNR5e6RiuZJmPpfUNMB05ccM9NKu3vOzHLVucfVcgD+1aJeRFurIX99Csn1asiIDIehMQK1kWkL1uOc46a2uva+txmprKXp957B6a9RltemilB668xQHN+i6vAe7UcI+qvOHbqan2ZWYFahfQPORoo6ndPdXyEhxGfe5au0nSeUO7Tv6wQcqLdGUv7qFZX3n4bNY8TGtVgIcczbo30g23A71VnAOa2Wy1a7jrfI7N58ms4toFNJ9DM2vW7vwaDPc5trwresxMKPd5MhtmeZGu7MU9NCtNNz2wYc2Q7NDLGunhVhsKeMjRrDhtG/2yh+V6CcbdlpuVqG8BDXgdcCPwBDDeZr3FwM3ABuD0brbtgGaV1q7R7+X8W6/lvfbAyg7IZh30M6C9ENgPuDIvoAHbALcAzwe2B64HFnXatgOaVVabRr/ngNFrecM6c+qBVX3I1EZe34ccOwS0VwCXNTx/B/COTtt0QLPK6jW1vY/l7oFZ3ZUd0F4LnN/w/I3AB3LWPQmYBqYXLFjQ58Ni1gc9Dvn1Wt5YB/fArI56CmjAV4EbWixHNaxTSEBrXNxDs6E0iKSMLs7fuQdmdVV2D81DjmYz+n0OzT0wq7l2AW0QE6vXA/tKep6k7YFjgEsHsF+z6uk0ObnX8qVLt97TbevE6YmJ7HWzmlMW8Ob4x9IfAf8CzAPuB66LiD+UtAfZMOPhab3DgfeSZTx+NCLe1Wnb4+PjMT09Pee6mZlZ/Ui6NiLGW5Vt28uGI+JzwOdavP5T4PCG518GvtzLvszMzNrxtRzNzKwWHNDMzKwWejqH1k+SNgG3l12PHowB95VdiSHg49QdH6fu+Dh1Z5iP094RMa9VQWUD2rCTNJ134tKe5OPUHR+n7vg4daeux8lDjmZmVgsOaGZmVgsOaP1zXtkVGBI+Tt3xceqOj1N3anmcfA7NzMxqwT00MzOrBQe0PpF0jqQfSvqepM9J2qXsOlWVpNdJulHSE5Jql3nVC0mLJd0saYOk08uuT1VJ+qikeyXdUHZdqkzSfEnrJP0g/Z97W9l1KpIDWv9cAbwoIvYHfkR2lwFr7QbgaOAbZVekSiRtA3wQOAxYBBwraVG5taqsjwOLy67EENgCnBYRi4CDgL+o03fKAa1PIuLyiNiSnl4N7FVmfaosIm6KiJvLrkcFHQhsiIhbI+IxYA1wVMl1qqSI+Abws7LrUXURcVdE/Hd6/BBwE7BnubUqjgPaYLwZ+ErZlbChsydwR8PzO6lR42PlkrQQeAlwTbk1KU5PV9sfdZK+CjynRdEZEfH5tM4ZZN38Tw6yblXTzbEys8GQtDPwWeCvIuLBsutTFAe0HkTEoe3KJR0PHAEcEiM+P6LTsbKWNgLzG57vlV4zmzNJ25EFs09GxCVl16dIHnLsE0mLgaXAkRHxSNn1saHku71boSQJ+AhwU0ScW3Z9iuaA1j8fAJ4FXCHpOkkfKrtCVSXpjyTdCbwC+JKky8quUxWkpKJTgcvITt6vjYgby61VNUn6FHAVsJ+kOyWdUHadKup3gDcCB6d26TpJh3f6o2HhK4WYmVktuIdmZma14IBmZma14IBmZma14IBmZma14IBmZma14IBmZma14IBmZma14IBmZma18P9MORVkuis1FwAAAABJRU5ErkJggg==\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% EMD\n", "G1 = ot.emd(a, b, M1)\n", "G2 = ot.emd(a, b, M2)\n", "Gp = ot.emd(a, b, Mp)\n", @@ -366,7 +376,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_UOT_1D.ipynb b/notebooks/plot_UOT_1D.ipynb index 2354d4f..e0289d1 100644 --- a/notebooks/plot_UOT_1D.ipynb +++ b/notebooks/plot_UOT_1D.ipynb @@ -60,8 +60,6 @@ }, "outputs": [], "source": [ - "#%% parameters\n", - "\n", "n = 100 # nb bins\n", "\n", "# bin positions\n", @@ -97,28 +95,30 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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GG8O0aXD++fDww758wAH+d5FRasAi5SxZAv/7v/C97/lVWqeeClts4ZcXX3+9TjnEMnCgjxV+8UU45hg/R7zJJrDDDj7d/YIFsSvsFQtNeAs4Wdr48ePD9OnTY5cRT1ubX0jxyCM+muHee+Hvf/fTC/vt58Ohxo2LXaV0NX++30XtiivgpZf8nPEOO8CECf48bhwsv3xqhzezGSGE8X3+eTVggSZowIsWeTqaPx/eeccH+s+eDS+/7I336afh009929VWg1128XONu+6a6i+wVEkI8Le/wa23wh//6NM/gZ8i2mADH6Gy/vrQ2upXKo4a5ROjrryy//328VSSGrBUxfhVVw3TJ05M/0Dd/f9WeL10fQhLP9rb/bmtrfhYssQfixfDZ5/5Y+FCf3z8MXz0kf+5nNGjYcMNYcwYT0tbbum/sDq3m23vvef35Zg5078wfeEF/7BdtGjpbVtaYNgwfwwZAsst50PgBg3yUx4DBvg2+XzxsfrqcMEFasBSHeMHDgzTazVVenfNrfB66Xqzzo9czp9Lfxnyef8lGTCg+Iuz3HL+GDbMTyMMH+6PVVeFkSO98Y4e7dtKc2hv93/9vP66P8+b5/8qWrDAP6Q/+sj/FbRwYfGDfNEi/2BfsqT4gd/e7rNb33tvvxuw7gUhbtNNoZFPQYjkcn7qYdSo2JV00CgIEZFI1IBFRCLROWABwMw+Al6IXUcPRgDvxi6iB1moEbJRZxZq3DCEMKyvP6xzwFLwQn++TKgFM5uuGqsjC3Vmpcb+/LxOQYiIRKIGLCISiRqwFEyJXUAFVGP1ZKHOhq9RX8KJiESiBCwiEokasIhIJGrATc7MdjOzF8zsZTM7JXY9BWa2ppk9YGbPmtkzZnZM8vrKZvYnM3speV6pDmrNm9lMM7szWV7HzKYl7+mNZjYwcn3DzexmM3vezJ4zs63r7X00s+OSv+enzewGMxtcD++jmf3GzOaZ2dMlr5V978z9Z1Lvk2bW4/1L1YCbmJnlgUuArwIbA/uaWb1M67sEOCGEsDGwFXBEUtspwP0hhPWB+5Pl2I4BnitZ/hlwYQhhPWABcGCUqoouAu4JIWwEbIbXWjfvo5mtARwNjA8hjAHywD7Ux/v438BuXV7r7r37KrB+8jgYuKzHvYcQ9GjSB7A1cG/J8qnAqbHr6qbW24Ev41frjUpeG4VfQBKzrtHJL+HOwJ2A4VdvtZR7jyPUtyLwKskX7iWv1837CKwBvAGsjF8cdiewa728j0Ar8HRP7x3wK2Dfctt191ACbm6F//EL5iSv1RUzawW+AEwDRoYQ3kpWvQ2MjFRWwWTgJKA9WV4FeD+EsCRZjv2ergPMB65MTpNcbmbLU0fvYwhhLnA+8DrwFvABMIP6eh9Ldffe9fr3SQ1Y6pqZDQVuAY4NIXxYui54zIg2jtLM9gTmhRBmxKqhAi3AOOCyEMIXgE/ocrqhDt7HlYCJ+IfF6sDyLP3P/rrU3/dODbi5zQXWLFkenbxWF8xsAN58rwsh/D55+R0zG5WsHwXMi1UfsC3wL2b2GvBb/DTERcBwMyvcZyX2ezoHmBNCmJYs34w35Hp6H3cBXg0hzA8hLAZ+j7+39fQ+luruvev175MacHP7G7B+8m3zQPyLjz9Ergnwb5SBK4DnQggXlKz6AzAp+fMk/NxwFCGEU0MIo0MIrfh7978hhP2BB4BvJpvFrvFt4A0z2zB5aQLwLHX0PuKnHrYysyHJ33uhxrp5H7vo7r37A3BAMhpiK+CDklMV5cU68a5HfTyA3YEXgVeA02PXU1LXdvg/7Z4Enkgeu+PnWO8HXgLuA1aOXWtS747Ancmf1wX+CrwM3AQMilzb5sD05L28DVip3t5H4CzgeeBp4BpgUD28j8AN+Hnpxfi/Jg7s7r3Dv4C9JPldegof1bHM/ffrUmQz2w3/J1ceuDyEcF6fdyYi0mT63ICTMaQv4kOD5uD/nN03hPBs9coTEWlc/bkh+5eAl0MIswDM7Lf4N5ndNuARI0aE1tbWfhxS+uv9933y1zXX7Pz6jBkz3g0hrFpY/nLuW73/ZO4627Hluix2s77L61bYTy7Xeb/JcnF9YRblLvvJ5TuWO7bN5zvvq+P1ws/6c8h1OXa+cw2h4/XulpPn5OdC3sqvT57bW7r+XOGZzsvJYdq7rO+6XNiuuL7zftpbOq9f6rlwnK7btYRu9hs6r0+eSV6nJWDJn63FR+rl88lzsjxggI80G5BvA2Bgiz8Pyheeff1yLYsBGJw8L5/3KeaXyyfLLZ8BMCTnrw/L/wOAocnzCrmFyfrPkmV/fVjHs+9nmIVk2d+EobnBAORWe6mb6bz7rj8NuNyYty27bmRmB+NXhbDWWmsxXTPvRnXSSXDxxUtPgGxms+NUJNK8Up+SKIQwheSemePHj9e9LyNbvBgGDEhp54XTWYV0GZJrE5KEGtqTJJTrsr69c4ItnBaz9mR9IWUmy4XUaYVLH3Jd9kNb8pzvSHTWlrxWSMIFbe2dFi0ZGBTo/HohCRdqKlzDZHRdLugoLllPl/XJ2uQyg/alfhMLW3b+yVyy3N7NcleFd6Q92S6XbNdedusydXVTT3G/5Y8byv7Zf6qNrqrchvq7uyQJ0164BiRJ0P3cbdlD9eNn63oMqZS3aBEMjHprGBEp6M9nRccYUrzx7gPsV5WqJDWffgrLL5/yQeoqCeeTbZMSlISTZSXhHnVJwmkk4D6XGEJYYmZHAvfi/5f/JoTwTNUqk1R8+CEMHRq7ChGBfn5GhBDuAu6qUi1SA++9B6usUqOD1UUSLp4P9m2TEpSEk+X0k3DXL34ym4RToEuRm8zcubDaarGrEBGowSgIqR9tbfDaa/DNb/a4aXXFTMJlRkb4tkkJSsLJspJwDErATeTpp2HJEhgzJnYlIgJ1+Zkgafnzn/15hx0iFRAjCS9jjLBvm5SgJJwsKwnXkhJwE7n2Wth446UvQxaROOros0DSNHUq/O1vfhlydDVMwpVcLefbJiUoCSfL1UvClYwR7rzcPElYCbgJLFoEhx0GI0fCAQfErkZECurgM0DSFAKcfjo88QTcdhusUOnlPGbFpJqWGiTh3tw3ApSE00jCvblarvNy4ydhJeAGFoLf/ez88z0BT5wYuyIRKaUE3KA++ACOPhquvhqOOAL+8z/7sJOOZJrhJNyHO6j59kkJSsLJcn+ScO/vG9F5uXGTsBJwA7rtNh/tcO21cMYZ/sVbTn/TInVHCbiBPPYYnHsu3HEHbLqpN+IvfrGPO7NcSRLNcBLux72EffukBCXhZLkvSbjvd1DrvNx4SVi5KOPa2uDWW2HbbWGbbeAvf4FzzvEZL/rcfEWkJpSAM+q55+DGG+G66+Dll6G11c/zfv/7VbzdZGGutQwn4WrMquHbJyUoCSfLlSfhatxLuPNy4yRhNeAMefFFb7q/+53f18HMLys++2z4xjegRX+bIpmiX9k69vHH8PDDcN998Kc/wVNPedPdbjv/Ym2vvWDUqHSObTnrSJpZTsLVnF/Ot09KUBJOlntOwtWcVaPzcvaTsBpwHVm8GKZN84Z7//1++fCSJT6H27bbwuTJfivJNdaIXamIVIMacETz5nnDLTweeww++cQD3xZbwAknwC67ePNdbrna11dIlplOwinMtOzbJyUoCSfLTZCEU1D/FTaIzz7zy4GnTvVmO3UqvPqqr8vnYexYmDQJJkyAHXeElVeOWq6I1IAacAo+/hiefNIb7syZ/vzkk35THPBTCFtt5ZcHb7mlp93UZyrurZJxwJlOwqnMtAxKwqVHVxLuq/qtLCPeeadzo505E156qdgrVl4ZvvAFOOYYb7ZbbgmjR8etWUTqgxpwhRYuhGef9ZEIpY+33y5u09rqzXb//WHzzf3Po0cXA1um5KyY+rKchFOZaRmUhLvsv6MaJeHeqL+KImtrg1mzlm60L7/c8bvJ4MF+r4Vddy022s02g+HD49YuItnS1A143rylG+0zz8Cnn/p6M/j85/0Lsn328eexY2G99ZYOOA2pcC41w0k4lZmWS/ejJNx5/x3VFJNwf+4l3HmfjZeE66eSFH36qTfWrs123rziNquu6s31oIOKjXaTTerwyzERaRgN14D//nf/Iuzxx4vPL75YDEuDB/u07HvsUWy0Y8f6dD1SZFa8iizLSTiVmZZBSbgXSbgWMy2XVrz0cv0m4fgV9FEI8OabSzfb118vbrPmmjBuXOfTB5//fJOcPhCRupeZBtze7qcRHnrI74/w0EPw1lvF9RtsAFtv7bM/fOEL/hgxIl69mZfLFVNXlpNwCjMt+/rkmErCnZa7ypHOTMvLOmaWknDdNuDFi2HGjGKzfeQRWLDA162xhl8tttVWnnA32wyGDYtarohIr9VVAw7Bbyh+9dV+y8UPP/TXN9jAb7e4/fZ++8XW1oyOrc0Ss470l+kknMJMy6Ak3Jsk3Jcxwp1q6qaWRkjCPR7RzNYErgZG4v8NU0IIF5nZysCNQCvwGrB3CGFBX4qYNQuuucYb76xZPvJgr73ga1/zWy+utlpf9ioiUt8qaflLgBNCCI+b2TBghpn9CfgecH8I4TwzOwU4BTi5twX8x3/AiSd6WJkwAc4809Ouhn9FVjKjcKaTcBozLYOSsJJwVfR4pBDCW8BbyZ8/MrPngDWAicCOyWZXAQ/SywY8ZYo33732ggsv9FELIiLNolet3sxagS8A04CRSXMGeBs/RVGxV1+FQw+F8ePh+uv9puNSP8ysI91lOgmnMdMyKAkrCVdFrudNnJkNBW4Bjg0hfFi6Lvj/nWV/i8zsYDObbmbT58+f3/H66NGw004+fveuu/pWvIhIllXU4s1sAN58rwsh/D55+R0zGxVCeMvMRgHzyv1sCGEKMAVg/PjxHU16wAC47Tb48pf9FMSuu/oNySdO9KvVJLKcdaQ5JWGUhPuRhNOYabnzdtlNwj0mYDMz4ArguRDCBSWr/gBMSv48Cbi9twcfNgzuuQdOPtnvzbDPPj7i4eCDfdxve09/EyIiGVZJa98W+C7wlJk9kbx2GnAe8DszOxCYDezdlwKGD4dzzoGf/hQefBCuugquuw5+/WtYaSUfhrbDDj4GeNw4T85SA5brSG9KwigJ9yMJp3Ev4U41dVNL9ZNw9VUyCuIvLP33UTChWoXk8z4MbcIEuPRSuP12eOABvxLujjt8myFD/HLjwgUZX/qShquJSHbV1ZVwBUOH+qwS++/vy2+/7VfIFe4DcdZZxSGeG27o930YN654DwhNaFkFpTNiKAkrCRfW9iEJpzmrRqeauqmlnpNwXTbgrlZbDb75TX8AvP8+PPoo/O1vPoriL3+BG24obr/22sWGPG6cz1qx+uq6fFlE6ksmGnBXw4fD7rv7o+Ddd70ZFx6PP+6jLApBZ6WVirek3HRTfx4zRjfx6VYuT8dnv5KwknDnvfYqCddifrlONXVTS3+TcBoy2YDLGTHCh7R9+cvF1z76CP7v//zx1FM+NfzVV/vrBa2tnW/MPnas3/yn6b/syxmFZqBGrEbcv0bc+4s1ikeup0ZcfQ3TgMsZNsxHUWy3XfG1EGD27KWnJ7r7bliS/MUNHAgbbbR0Y87sDMciUpcaugGXY+apt7XV77ZW8Nln8MILnpILTfnPf/YhcQXDh/tpi66nMlZYodb/FenzS5ELS0rCSsL9ScJ9v2y5eOT4STgNTdeAuzNokDfUTTft/PqCBfD0053T8nXXFe9VDD5LcmF6+sLzaqspLYvIsqkB92CllXzc8fbbF18LAd54w9PyE0/44/HH4eabi9t87nPFhrz55rDFFt6oM9OU8/mOtKUkjJJwv5Jw/2/gUzxyYyVhNeA+MIO11vLHnnsWX//gA2/KM2d6U545Ey64wKdXAh+fvOWW/thqK7+QZKWV4vw3iEh8asBVtOKKS6flRYvg2Wdh+nSYNg2mTvX7XxQC1gYbeDPeckvYZhs/BZLLld9/TZl1pCslYZSEO36geZNwGtSAUzZwYPE0xA9/6K99+KE35KlTvSnfc48PjwMfTrfTTrDLLn5Z9rrrZui0hYj0ihpwBCusADvv7A8oDo176CG4/35/3HSTr2ttLd4jY7fdanjKorTrKwkrCffPc1UpAAAO+klEQVQjCac55b2vz24SVgOuA6VD4w44wH/XX3ih2IxvuQWuuMIvDvnKV2Dvvf2+ySuuGLtyEekPNeA6ZOYXgmy0ERxxBLS1+X0vbrkFfvc7+OMf/dTGbrt5M95rrxRuYp/PLZWelIRREu5DEk5zeqPikbOZhOvh6x7pQT7vX9T94hfw2mvw2GPemGfMgO98x28+9NOfwnvvxa5URHpDDThjzLwZX3ABvP463HefjzE+4wyfVfrII/18cr/lcp6G8jk/aOkjn/dxwmaYmae6nPkNfHL54rLl/JEsF7fP+aOwv2S5Y31HDV32U3gPclZMmP5Cp/Ud+01TCJ2Tdmgvpl08CXck9dL17cEfHbsJnobb2/1R2G+yXFyfPLrup70tefhyx/Ztbf4o7K/waGv3R7J/aw9Ye8nxC+uT7a293dNwW4C2csvJo609eQQsWddpfXsgt8QfxZ8pPEgeyXK7J/9cWyBXsr7rcmG74np/FPaTW+Ipt7j/Lo/Ccbput8T80WW/aVADzrBczr+cu+uu4pROU6bAxhvD5Mn++yci9UsNuEGMGQO/+Q289BLsuCMcd5yPK37mmb7tL+RKkmqWk3DalIR7TMKlKTjLSTgNasANZu214c474frrYdYsb8KPPBK7KhEpR6MgGpAZ7Luv34Zzl1186Nof/uCnKyqWy3V8Q25dP6ezNDqiFiMjSvev0RGJ4uiIvt1Bzfdauhx/dET1KQE3sDXX9Is71l3Xp3OaOzd2RSJSSgm4wY0cCbfe6veYOOggH0Nc0anRfPGzOdNJuJZjhEv3ryScaKdv940o3bJeknD1KQE3gfXWg3PO8Vk/HnggdjUiUqAG3CQOPdRvh3nppRX+gFlx9EMyOiHkLHujI2KMEQaNjigdAVHBGOEsjI5Igxpwkxg8GCZNgttvh08+iV2NiIAacFP5yld84tGpU3veNpQm0Awn4ZIFJeFISbg3V8vVcxJOgxpwE9l6a3/+61/j1iEiTqMgmsiKK/qoiFdeqWDjvBW/0S58E5/v/HmdhdERdXEHtdL9N+HoiHRmWl76J2s1OqKalICbTGur38RHROJTAm4yI0bAW2/1vF3I5ZYe25nFJFxP9xIu3X8TJeF05pcr3TK7SVgJuMmstBIsWBC7ChGBXiRgM8sD04G5IYQ9zWwd4LfAKsAM4LshhEXplCnVMmQILFxYwYal54CznITrcVaN0v03QRJOd6bl0i2zl4R7k4CPAZ4rWf4ZcGEIYT1gAXBgNQuTdAweXGEDFpHUVZSAzWw0sAdwNnC8+SDMnYH9kk2uAs4ELkuhRqmiAQNg8eKetws5K8kVGU7C9Ty/XOn+GzgJpzHTMtQ+Caeh0gQ8GTiJ4ju0CvB+CKFwgd4cYI1yP2hmB5vZdDObPn/+/H4VK/3X0uIXY4hIfD0mYDPbE5gXQphhZjv29gAhhCnAFIDx48enHCekJ7lcMTAuS8jnoCPJJq9lMAlnYqbl0v03YhJOYaZlqH0STkMlpyC2Bf7FzHYHBgMrABcBw82sJUnBowHdbTYDKm3AIpK+HhtwCOFU4FSAJAGfGELY38xuAr6Jj4SYBNyeYp1SJWaVhbmQN0ozAGQzCVdlVg1QElYSTkV/9n0y/oXcy/g54SuqU5KkqdIGLCLp69WVcCGEB4EHkz/PAr5U/ZIkTRXfACxvJWe+MpyEqzm/HCgJ9yEJV3N+OV9Pl/XJ2pSTcBp0JZyISCS6F0STqTQBdx4HXJDBJJzGTMugJNyLJJzGTMu+ni7rk7UZSsJKwCIikSgBS1mhZDaJLCfhVGZaLlmvJJyUoSTcJ0rAIiKRKAFLWe0tttRMsFlMwtWYVQOUhPuVhKswq0bn5YLaJuE0KAGLiESiBCxlhZx1JIZMJ+Eqzi8HSsLNnITToAQsIhKJErCU5feCcJlOwinMtAxKwr1KwinMtNx5uSDdJJwGJWARkUiUgKWskIeu2SGbSTiFmZZBSbgXSTiNmZZ9+9om4TQoAYuIRKIELGX5OeDyCSBbSTiFmZZL96MknKzvPgmnMdOyr691Eq4+JWARkUiUgKUsTy3LPheWhSScykzLoCTcqyRc/ZmWS2uqXRKuPiVgEZFIlIClrPa8lYx/zG4STmOmZVAS7l0STmF+OYiQhKtPCVhEJBIlYCkr5MvdDSqDSTiFmZZLa1ISriAJpznTMmQ6CSsBi4hEogQsZZWeA1YSVhLuVxJOYaZlqH0SToMSsIhIJErAUla5c8CZTMJpzLQMSsK9SMJpzC8HtU/CaVACFhGJRAlYygolH81KwigJ9yMJpznTMmQ7CSsBi4hEogQsZYX80q9lMQmnMdOyHzM5hpKwPy8rCacxvxzUPAmnQQlYRCQSJWApqz3f/adzlpJwKjMtg5Jwb5JwmjMtQ82ScBqUgEVEIqkoAZvZcOByYAweUH4AvADcCLQCrwF7hxAWpFKl1FzIG+1JFlUSBiXhvifhVGZahghJuPoqTcAXAfeEEDYCNgOeA04B7g8hrA/cnyyLiEiFekzAZrYisAPwPYAQwiJgkZlNBHZMNrsKeBA4OY0ipfbaWyCXZIBMJ+FUZloGJeHKk3AaMy1DjCRcfZUk4HWA+cCVZjbTzC43s+WBkSGEt5Jt3gZGlvthMzvYzKab2fT58+dXp2oRkQZQyTngFmAccFQIYZqZXUSX0w0hhGBmZT+KQwhTgCkA48ePT/njWqrF7wXhspyE05lpGZSEe5GEU5hp2bdPSqhREk5DJXueA8wJIUxLlm/GG/I7ZjYKIHmel06JIiKNqccEHEJ428zeMLMNQwgvABOAZ5PHJOC85Pn2VCuVmiq9Ei7LSTiNmZZBSbhXSTiVmZah1kk4DZVeiHEUcJ2ZDQRmAd/H/+//nZkdCMwG9k6nRBGRxlRRAw4hPAGML7NqQnXLkXpR/l4QLktJOI2ZlkFJuFdJOJWZlqHWSTgNuhJORCQS3QtCygrL+GjOUhJOZX45UBIuqCAJpzLTcul+apSE06AELCISiRKwlNXe0vNssFlIwlWdVQOUhLuzjCScykzLUPMknAYlYBGRSJSApaxOV8JlOgn34r4RoCTcX+WScAozLfv65Ji1SsIpUAIWEYlECVjKCi0BOhKty2YSrvy+EaAkXDWlSTiFmZahMZKwErCISCRKwFKWXwnXOXlmMQmnMdOyb68kXJEQenffCKjbJJwGJWARkUiUgKWszrMiZzcJV2VWDVAS7o80ZlqGmifhNCgBi4hEogQsZYV8KEmgHa8mz0rCdGyvJFwxJeGlKAGLiESiBCxl+Thgl+UknMpMy6Ak3B9ZTcIpUAIWEYlECVjKKk3ABVlMwmnMtAxKwlWhJKwELCISixKwlJcPdJd9lISVhKsqK0k4BUrAIiKRKAFLeSXngLOdhPtzL+FidUrCSsJpUAOWsqzMKYhsNuLqTW/Ueb0acWrqtBGnQacgREQiUQKWsqylncLnc7aTcN9vZVn6U0rCSsJpUAIWEYlECVjKyufbSz73s5uE05zyvvN6JeHU1E0Srj4lYBGRSJSApax8SzGJZTkJpzvlfbE6JeEmSMIpUAIWEYmkogRsZscBP8QDwFPA94FRwG+BVYAZwHdDCItSqlNqbMCAJXT93yOLSTjdKe9Lj64k7MsNnIRT0GMCNrM1gKOB8SGEMfjf9D7Az4ALQwjrAQuAA9MrU0Sk8VR6DrgFWM7MFgNDgLeAnYH9kvVXAWcCl1W7QIljQL507GN2k3CaU96X/pSScOMn4TT0uOcQwlzgfOB1vPF+gJ9yeD+EUPjfcg6wRrmfN7ODzWy6mU2fP39+daoWEWkAPSZgM1sJmAisA7wP3ATsVukBQghTgCkA48ePT/HjUKppYEu5q3+UhJWE+5CE00zBXmTxWJ2OXd0knIZKsvUuwKshhPkhhMXA74FtgeFmVviNHA3MTalGEZGGVMk54NeBrcxsCLAQmABMBx4AvomPhJgE3J5WkVJ7g/LLuv5dSVhJuBdJuBbng0v3n1YSTkEl54CnATcDj+ND0HL4KYWTgePN7GV8KNoVqVUpItKAKhoFEUL4MfDjLi/PAr5U9YqkLgzK9xA7gSwk4d7cN6J0WUm4ikm4liMjSvdf7SScAl0JJyISie4FIWUt17K4F1vXbxJOc6JPUBKuKAnHGCNcuv8qJeE0KAGLiESiBCxlDe5VAi6ovyRciynvQUl4WUk46tVypfvvZxJOgxKwiEgkSsBS1vL5/tzYrn6ScF/uoObrlYT9uMlx+pGE6+K+EaX772sSToESsIhIJErAUtZy+b6cA+5KSVhJuDgOOPNJOAVKwCIikSgBS1nLt3xWxb0pCTd1Eq7HewmX7r/SJJwCJWARkUiUgKWsIblFKfzfoSTclEm4nmfVKN1/D0k4DUrAIiKRKAFLWcPy/yguZDgJpzHTsq9XEvbjJsdZRhLOxPxypfvvJgmnQQlYRCQSJWApa2hpAi7IYBLuz72EO++zu2MqCftxk+OUScKZmmm5dP9dk3AKlIBFRCJRApayVsgt7H5lppJw/2fV6LzP7o6pJOzHTY5TmoTTmGnZd0SquibhFCgBi4hEogQsZQ3JVXAlXCaScPXmlyvuc1nHVBL24ybHyeXSmWkZap+EU6AELCISiRKwlLVCrswoiO7UcRLuz9VynbZTEvbt+pCEU5lpuWR9zZJwCpSARUQiUQKWsob1JgEX1GESTmOmZV9WEobKknAqMy37C53WZzEJKwGLiESiBCxlDcv1Y0YMJeEyx23iJJzCTMu+mP0krAQsIhKJErCUNcwC9CcFg5Jw2eM2YRJOYaZlaIwkrAQsIhKJErCUNSzXAu1J3MpwEk5zVo1O2ykJ+3Zlk3AKMy1DQyRhJWARkUiUgKWsobnBQDIWWElYSbhfSTiFmZZL95PhJKwE3GS22gqOOSZ2FSICYKGGnwZmNh+YXbMDSm+sHUJYNXYRIs2kpg1YRESKdApCRCQSNWARkUjUgEVEIlEDFhGJRA1YRCQSNWARkUjUgEVEIlEDFhGJRA1YRCQSNWARkUjUgEVEIlEDFhGJRA1YRCQSNWARkUjUgEVEIlEDFhGJRA1YRCQSNWARkUjUgEVEIlEDFhGJRA1YRCQSNWARkUj+P4VunBEI1yzqAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% plot the distributions\n", - "\n", "pl.figure(1, figsize=(6.4, 3))\n", "pl.plot(x, a, 'b', label='Source distribution')\n", "pl.plot(x, b, 'r', label='Target distribution')\n", @@ -146,29 +146,16 @@ "collapsed": false }, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It. |Err \n", - "-------------------\n", - " 0|1.838786e+00|\n", - " 10|1.242379e-01|\n", - " 20|2.581314e-03|\n", - " 30|5.674552e-05|\n", - " 40|1.252959e-06|\n", - " 50|2.768136e-08|\n", - " 60|6.116090e-10|\n" - ] - }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -202,7 +189,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_UOT_barycenter_1D.ipynb b/notebooks/plot_UOT_barycenter_1D.ipynb index 43c8105..0ef7f62 100644 --- a/notebooks/plot_UOT_barycenter_1D.ipynb +++ b/notebooks/plot_UOT_barycenter_1D.ipynb @@ -106,12 +106,14 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -141,24 +143,16 @@ "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/rflamary/PYTHON/POT/ot/unbalanced.py:501: RuntimeWarning: overflow encountered in square\n", - " np.sum((v - vprev) ** 2) / np.sum((v) ** 2)\n", - "/home/rflamary/PYTHON/POT/ot/unbalanced.py:501: RuntimeWarning: invalid value encountered in double_scalars\n", - " np.sum((v - vprev) ** 2) / np.sum((v) ** 2)\n" - ] - }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -175,7 +169,7 @@ "reg = 1e-3\n", "alpha = 1.\n", "\n", - "bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)\n", + "bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights)\n", "\n", "pl.figure(2)\n", "pl.clf()\n", @@ -212,34 +206,46 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/rflamary/PYTHON/POT/ot/unbalanced.py:500: RuntimeWarning: overflow encountered in square\n", - " err = np.sum((u - uprev) ** 2) / np.sum((u) ** 2) + \\\n", - "/home/rflamary/PYTHON/POT/ot/unbalanced.py:500: RuntimeWarning: invalid value encountered in double_scalars\n", - " err = np.sum((u - uprev) ** 2) / np.sum((u) ** 2) + \\\n", - "/home/rflamary/PYTHON/POT/ot/unbalanced.py:501: RuntimeWarning: overflow encountered in square\n", - " np.sum((v - vprev) ** 2) / np.sum((v) ** 2)\n", - "/home/rflamary/PYTHON/POT/ot/unbalanced.py:501: RuntimeWarning: invalid value encountered in double_scalars\n", - " np.sum((v - vprev) ** 2) / np.sum((v) ** 2)\n" + "/home/rflamary/PYTHON/POT/ot/unbalanced.py:895: RuntimeWarning: overflow encountered in true_divide\n", + " u = (A / Kv) ** fi\n", + "/home/rflamary/PYTHON/POT/ot/unbalanced.py:900: RuntimeWarning: invalid value encountered in true_divide\n", + " v = (Q / Ktu) ** fi\n", + "/home/rflamary/PYTHON/POT/ot/unbalanced.py:907: UserWarning: Numerical errors at iteration 595\n", + " warnings.warn('Numerical errors at iteration %s' % i)\n", + "/home/rflamary/PYTHON/POT/ot/unbalanced.py:900: RuntimeWarning: overflow encountered in true_divide\n", + " v = (Q / Ktu) ** fi\n", + "/home/rflamary/PYTHON/POT/ot/unbalanced.py:907: UserWarning: Numerical errors at iteration 974\n", + " warnings.warn('Numerical errors at iteration %s' % i)\n", + "/home/rflamary/PYTHON/POT/ot/unbalanced.py:907: UserWarning: Numerical errors at iteration 615\n", + " warnings.warn('Numerical errors at iteration %s' % i)\n", + "/home/rflamary/PYTHON/POT/ot/unbalanced.py:907: UserWarning: Numerical errors at iteration 455\n", + " warnings.warn('Numerical errors at iteration %s' % i)\n", + "/home/rflamary/PYTHON/POT/ot/unbalanced.py:907: UserWarning: Numerical errors at iteration 361\n", + " warnings.warn('Numerical errors at iteration %s' % i)\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -258,7 +264,7 @@ " weight = weight_list[i]\n", " weights = np.array([1 - weight, weight])\n", " B_l2[:, i] = A.dot(weights)\n", - " B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)\n", + " B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights)\n", "\n", "\n", "# plot interpolation\n", @@ -328,7 +334,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_barycenter_1D.ipynb b/notebooks/plot_barycenter_1D.ipynb index bd73a99..564028c 100644 --- a/notebooks/plot_barycenter_1D.ipynb +++ b/notebooks/plot_barycenter_1D.ipynb @@ -67,8 +67,6 @@ }, "outputs": [], "source": [ - "#%% parameters\n", - "\n", "n = 100 # nb bins\n", "\n", "# bin positions\n", @@ -105,18 +103,18 @@ "outputs": [ { "data": { - "image/png": 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1RpNiiFpTUkG028XoAUlOhwLAMYOTEbHGTWpSjCCNtbD4j7D4cfDWQVQc9BsHx1wMdRVWgtz0DmAgYySc9WsYfiY4NK5Wqc5oUgxRq4srGDMwiWhPcNSAJ8ZGkZeZoDPbRAqfD9a+Ah89ANV7YeyFcNJt0Hc0uNxfPbahBrZ9BB/eD3+/1CoxnvUb6DfGkdCVOpLg+ERVXeJt9rGupNLxQfttjbc72xijK2aENW8DvD4b/vUDSBwA310Alz4H/cd9PSECxCTAmFlw01I4+0HYvQKePAU2/LPXQ1eqM5oUQ9DWAzXUNTUHXVKckJXCwdpGSsrrnA5F9ZSGavj7t2DDGzDjPrj+I8ie5t9rPdFw/E3wo1UwcBL84zpY/lTPxqtUF2lSDEH/XRkj+JIi6IoZYau2DOacBzs+hVl/hpNuBddRfITEp8PV/4QRZ8M7P4OFD4LWLqggoUkxBK0priC5TxS56XFOh/IVI/snEuNxabtiOKophWfOhgMb4fKXYOKV3TtfdBxc9hJMuBL+/RAsuCswcSrVTdrRJgStLq5gfFaKYytjdCTK7WLcoGQtKYYbbyO89h2oLLFKeDknBOa8bg/MegJiEuE/T0D6MJgyOzDnVuooaUkxxNQ2eNmyv5oJg51ZVLgz4wensH5PJU3NPqdDUYFgDMy/DYqWWAksUAmxhQic/VsYfha8e7tVNauUgzQphpj1uyvxGYJmJpu2JmSnUN/kY8t+XTEjLCz7P1g5B076GRxzSc9cw+WGi5+CtKFWibR8Z89cRyk/aFIMMWtKgmsmm7Za5mLVKtQwsH0RvHcHjDwXTru7Z68VmwxXvALGBy9fYfVyVcoBmhRDzOriCrLS+pCeEON0KO3KSutDWny0drYJdTUHYO53IWMEXPTk0fUy7ar0YdZ4x9LN8M5tPX89pdqhSTHErCmuDNpSIlgrZowfrJ1tQpox8NZPrJloLn3O6gjTW4adBiffZs2Ws/Ht3ruuUjZNiiHkQHU9uyvqgm7Qflvjs1LYeqCGmgav06Goo7H2Vdj8Dsy4B/qO6v3rn3SbNYn42z+xxkYq1Ys0KYaQNcXWIr7BnhQnZKVgDKzTRYdDT+VumH87ZB8P025yJgZPNFz4V6ivhLd/qgP7Va/SpBhC1hRX4HYJYwcG53CMFuO1s01oMgbm3Qy+Jrjgz+3PY9pb+o2FU++EjfNg/evOxaEijl9JUURmishmESkUkTva2R8jIq/a+5eKSG6b/dkiUiMi2nreDWtKKhjZL5E+0Q5+WPkhNT6anPQ47WwTalY8Z62JeNavrOERTjvhxzB4ijUVXPV+p6NREaLTpCgibuAJ4BxgDHCFiLRd82U2UG6MyQMeAx5us/9R4N3uhxu5fD7D6uKKoB2f2NYEe8UMFSJqy+DD+yD3JMgPklll3B644C/QdBg+uMfpaFSE8KekOBUoNMZsN8Y0Aq8As9ocMwuYYz+eC8wQew4yEbkA2AFsCEzIkWnHwVqq671fjgMMduMHp7Cvqp59lfVOh6L88eF91oLB3/hDcC0AnDEcpt9idf7ZudjpaFQE8CcpDgKKWz0vsbe1e4wxxgtUAukikgD8HPjlkS4gIjeISIGIFJSWlvobe0RZXRScK2N0pCXOlskGVBArXgarXoTjfwiZI52O5utOvBWSs63p5pqbnI5Ghbme7mhzP/CYMabmSAcZY540xuQbY/IzMzN7OKTQtKakgvhoN3l9E5wOxS9jBybhcYlWoQY7X7PVZpc4EE6+3elo2hcdB+c8BAe+gGVPOh2NCnP+rJKxG8hq9Xywva29Y0pExAMkAweB44BLROR3QArgE5F6Y8yfuh15hFlZVM6xg1Nwu4KoausIYqPcjBmYxMpd5U6Hoo6k4BnYt9YepB/EX7hGnmtNGr7wQRh3MST2dzoiFab8KSkuB4aLyBARiQYuB+a1OWYecI39+BLgY2M5yRiTa4zJBf4X+K0mxK6rbfCycW81+bmpTofSJZNzUllTUqErZgSrmlL4+Fcw9FQYc4HT0RyZCMx8CJob4P0enodVRbROk6LdRngzsADYCLxmjNkgIg+IyPn2YU9jtSEWArcCXxu2oY7emuIKmn2GSTmhlxTrm3x8safK6VBUexb91upcc84jwdW5piPpw6xON+v+AcXLnY5GhSm/Fhk2xswH5rfZdm+rx/XApZ2c4/6jiE8BK+wqyEnZoZcUwYo/VDoIRYwDm6xxiVO+B5kjnI7Gf9N/AiufhwW/gNnvh0YyVyFFZ7QJAQW7yhnRL4HkPlFOh9IlA5L7MCilz5dJXQWRD+6B6EQ45edOR9I1MQlw+t1Qsgy++JfT0agwpEkxyPl8hpVF5UzOSXM6lKMyKSeVgl2HMDp/ZfDYthC2vm+tRhGf7nQ0XTfhSug3Dj64D7wNTkejwowmxSC39UAN1fXeL6siQ01+Tir7qxrYXVHndCgKrCEY798NKTlw3I1OR3N0XG5rKrqKXTpEQwWcJsUg11L1mB+iSbF1u6IKAqtfgv3r4Yz7wROcC1X7ZdjpkHcm/PsRqD3odDQqjGhSDHIrdpWTbk+wHYpG9U8kLtqt4xWDQWMtfPwbGDwVxl7odDTdd9avoLEaPvmd05GoMKJJMcit2HWIyTmpSIj2svO4XUzISqFAk6LzPv8z1OyDs34dHr02+46GiVfD8qfh0A6no1FhQpNiECuraWDnwcMh257YIj8nlY17q6ht8DodSuSqLYPFj8Oob0L2cU5HEzin3gkuD3z8a6cjUWFCk2IQ+7I9McRmsmlrUk4qPqOLDjvqk99DUy3MuM/pSAIraQAcfxOsnwt7VjsdjQoDmhSD2Mpd5US7XYwdmOx0KN0yMTsVEe1s45jynbD8KauqMZQG6vtr+i3QJw0+vN/pSFQY0KQYxAp2lXPM4GRio9xOh9ItyX2iGNE3UdsVnfLxr60qxlPvdDqSnhGbDCf/D2xfCNs+djoaFeI0KQapBm8z60oqQ749scWknFRW7SrH59NB/L1q7xprrtDjb7KqGsPVlNmQkm2VFn06Ab06epoUg9T63ZU0NvtCbr7TjuTnpFLd4GXLgWqnQ4ksH94PfVKtKsZw5omB0++xvgRseMPpaFQI06QYpJbtsKoaw6Wk2NJZaPmOQw5HEkG22dWJJ91mVTGGu3GXQL9jrOWwvI1OR6NClCbFILVkWxkj+iWQmRjCs460kp0Wx8DkWBYX6uwjvcLns0qJydkw9XtOR9M7XC44836rY9GKZ52ORoUoTYpBqMHbzPKdhzhhWIbToQSMiHBCXgafbz9Is7Yr9rwNb8De1XD6XaE9nVtXDZsBQ06Gfz8M9bqOp+o6TYpBaOWuCuqbfEzPC5+kCDA9L53KuiZddLineRutKsR+4+CYIy5zGn5ErHldDx+Ez//kdDQqBGlSDEJLtpXhEjhuaGguF9WRlpLvkm1lDkcS5lY8Z1UhnnG/taJEpBk02ZrbdcmfoHq/09GoEKNJMQgtLizj2MEpJMWG1qLCnemXFEte3wQWb9N2xR7TUG1VHeaeBHlnOB2Nc06/B5obrPdCqS7QpBhkquubWFNSyfS8EFz81Q/Th6WzfMchGr06lqxHLPl/cLgMzvxleEz6fbTSh8Hk66xSc9lWp6NRIUSTYpBZtuMQzT7D9DDqZNPaCXkZ1DU1s6pIZ7cJuKo9sPiPVtXhoMlOR+O8U26HqD46/ZvqEk2KQWZx4UFiPC4mhcn4xLamDU3HJWgVak9Y+BswzVZbooKEvnDiT2DT27BzsdPRqBChSTHILNlWRn5uasjPd9qR5D5RHDMomSWF2tkmoPatg1UvwdQbIDXX6WiCx7QfQtIgeP8unf5N+UWTYhApq2lg077qsBqf2J4T8jJYXVyh6ysGijHw/t3QJwVOvs3paIJLdJzV6WbPKlj/utPRqBCgSTGILLGrFMNtfGJb04dl4PUZlumUb4FR+BFsXwSn/Nya51R91bGXQf9j4KNfQlO909GoIKdJMYgsKSwjMdbDMYPCe57K/NxUoj0uFmsVavc1e61SYuoQyJ/tdDTByeWCs34DlcWw9K9OR6OCnCbFILJ4WxnThqbjdoV3V/rYKDeTs1O1s00grJwDpRutIRieaKejCV5DT4HhZ8Mnv9cB/eqINCkGiV0Hayk+VMf0YeE5PrGt6XnpbNxbRWl1g9OhhK7Dh6zp3HJPgtHnOx1N8Dv7N+Cth48ecDoSFcT8SooiMlNENotIoYjc0c7+GBF51d6/VERy7e1nisgKEVln/z49sOGHjw++sL69zhjdz+FIesfpo6y/86ON+q39qC38jTXp9TkPR/ZAfX9lDIdpP4DVL0JJgdPRqCDVaVIUETfwBHAOMAa4QkTGtDlsNlBujMkDHgNa5lYqA84zxhwDXAO8EKjAw8176/cxekASWWlxTofSK0YPSCQrrQ/vbdjndCihad86KHgGplwP/cY6HU3oOOV2SOgH8/9Hh2iodvlTUpwKFBpjthtjGoFXgFltjpkFzLEfzwVmiIgYY1YZY/bY2zcAfUQkgtax8U9pdQMrisqZOba/06H0GhFh5tj+LCk8SHV9k9PhhBZjYP7tEJsCp93pdDShJSYRznwA9qyE1S85HY0KQv4kxUFAcavnJfa2do8xxniBSqBt49jFwEpjjDYitfHBF/sxBs4eFxlVpy3OHtufxmYfCzeXOh1KaFn/OhQtgRn36hCMo3HsZTB4qjX9W12F09GoINMrHW1EZCxWleqNHey/QUQKRKSgtDTyPiAXbNhHTnocI/slOh1Kr5qUnUpGQgwLtArVfw3V8P49MGA8TPqO09GEJhE49xFrzcVFDzodjQoy/iTF3UBWq+eD7W3tHiMiHiAZOGg/Hwz8E/iOMWZbexcwxjxpjMk3xuRnZmZ27S8IcVX1TSzZVsbZY/sjEdZZwuUSzhzTj0WbDlDf1Ox0OKHhowegei+c+4fIXCsxUAZOgCmzYenfoGSF09GoIOJPUlwODBeRISISDVwOzGtzzDysjjQAlwAfG2OMiKQA7wB3GGN0Rt52LNx0gKZmw9kR1J7Y2sxx/altbNaB/P4oXgbL/g+mfg+ypjgdTeibcS8k9od5P4JmbddWlk6Tot1GeDOwANgIvGaM2SAiD4hIy+Cop4F0ESkEbgVahm3cDOQB94rIavunb8D/ihC2YMM++ibGMDErxelQHHH80HQSYz1ahdoZbyPM+zEkDbQ+zFX3xSbDub+HAxtgyR+djkYFCY8/Bxlj5gPz22y7t9XjeuDSdl73a+DX3YwxbNU3NbNwUykXTRqEK8xnselItMfF6aP68sEX+/E2+/C4dT6Jdi1+3Jq55opXrR6UKjBGf9Oa+GDRwzDmAmtxYhXR9BPIQZ9uLaOuqZmZ4yKz6rTFzLH9KT/cxPKduvBwu8q2wie/sxYPHjnT6WjCz7mPgCcW3rrFGu6iIpomRQct2LCPpFgP04ZGxtRuHTllZCYxHpdWobbH1wxv3mytID/z4c6PV12X2N+aO3bnp7DiWaejUQ7TpOiQusZm3t+wjzNG9yMqwqsM46I9nDwik3fW7aWpWWcZ+YrPHoXi/8A5j0BiZI1j7VWTroGhp8GCu6Cs0OlolIMi+9PYQW+v3UNVvZdvTcnq/OAIcFl+FqXVDToXamu7V8Cih2DcxXDst5yOJry5XHDBX8ATA29cr71RI5gmRYe8tLSIvL4JHDckzelQgsJpo/oyMDmWl5YWOR1KcGiogde/Bwn94RuP6oTfvSFpAJz3R9izSgf1RzBNig5Yv7uS1cUVXHlcdsQN2O+I2yVcMTWbT7eWsbOs1ulwnLfgF3BoO1z0N+gTmcN1HDHmfJh4FXz6KOxa4nQ0ygGaFB3w92VFxEa5uGjiYKdDCSqXTcnC7RJeXhbhpcUv5lmLB0+/BXJPdDqayDPzYUjNhTdutNasVBFFk2Ivq2nw8uaq3Zx37ECS46KcDieo9E2K5awx/XitoJgGb4RO+3ZgE/zrBzBwEpx2l9PRRKaYBLj4aWs6vddnWz2AVcTQpNjL/rlqN7WNzVw5LcfpUILSlcflUH64iXfXReDwjLoKeOXb1vCLy14ET7TTEUWuwZPhG3+AbR/DR790OhrVizQp9iJjDC/9ZxfnNdk3AAAPLUlEQVRjByYxfnCy0+EEpROGpZObHsdLS3c5HUrv8vngjRugYhd863lIbrs6m+p1k6+B/O9aswmtf93paFQv0aTYi1YWVbBpXzVXHpejHWw64HIJ3z4um+U7y9m8r9rpcHrPot/C1gUw8yHIOcHpaFSLmQ9D1jRrAoV965yORvUCTYq96JnPdhAf7eb8CQOdDiWoXTI5i2iPi6c/2+50KL1j9cvwySMw8WqYcr3T0ajWPNFWyT02GV6+AipLnI5I9TBNir1kVVE576zby+yThpIQ49c87BErLT6aq47LYe6KErbsD/PS4qb58OYPYcgpVhuW1iAEn8R+8O1XrTbfFy6E2oNOR6R6kCbFXmCM4cH5m8hIiOaGk4c6HU5I+NHpecTHeHjo3U1Oh9JzdnwK/7jWWvD28pes2VRUcBowHr79ClQUwUsXQ0OYf1mLYJoUe8GHGw+wbOchbjljhJYS/ZQaH80PT8vj400HWLItDBcg3r3Sqo5LGwJXztXloEJB7olw6RzYu9b6t2uqdzoi1QM0KfYwb7OPh97dyNDMeC7XeU675NoTchmYHMtD727C5wujJX1KCuDFiyAuFa7+J8TpVH8hY+RMuPCv1ooar1xhTcenwoomxR72akEx20pr+fnMURG/GkZXxUa5ue3skawtqeSttXucDicwtn4Ac86D2BT4zjxI0k5XIefYb8GsJ2D7Inj+fG1jDDP6Kd2Dahu8PPbBVvJzUjlrjC77czQumDCI0QOSeGTB5tCf5Wbta/Dy5ZCeB7Pft6pOVWiaeBVc9hLs3wDPnA0VxU5HpAJEk2IPeuCtLyiraeDOc0fruMSj5HIJd507mpLyOh5+d7PT4RwdY+Czx+CN70H28XDtO5DQ1+moVHeNOteq/q45AE+fZa2uoUKeJsUe8uryIl4tKObm0/KYnJPqdDgh7cThGVx7Qi7PLN7B26FWjXr4kNUp48P7YeyFVqea2CSno1KBknMCXDcfxGUlxmX/Z30JUiFLk2IPWFdSyT1vbuCk4Rn89MwRTocTFn5x7mgmZadw+9y1FB4Ike7wJSvgb6dA4YfWzCiXPAtRsU5HpQKt/zi48RNrrOn822Dud6G+yumo1FHSpBhgFYcb+cFLK8iIj+bxyyfidmm1aSBEe1z8+crJ9Ilyc+MLK6hp8DodUse8DbDoYautCeC7C2Da93VgfjiLT4dvvwYz7oMv3oS/nWx1xFEhR5NiADU1+/jJq6vZX1XPE1dOIi1eVzkIpP7Jsfy/Kyayo6yW2+euoTkYh2lsXwR/OcGay3T0eXDjv60VF1T4c7ngpFvh2ret58/Pgtevh+r9zsalukSTYoBU1Tdx3bPLWbS5lPvPH8vEbG1H7Akn5GVwxzmjmL9uHze+UMDhxiApMR7cBnNnWx+Evma46g249FkdgxiJck6Am/4Dp9xhlRr/NAU+fwKa6pyOTPlBTJA1Cufn55uCggKnw+iS3RV1XPfsMraX1vLQxcdyyeTBTocU9l74fCf3zdvA2IHJPH1tPn0THWqrK9sKn/we1r0G7miYfguceKu2HSpLWSG8+z/WuozxfWH6j63lqKLjnY4s4ojICmNMfqfHaVLsnnUllcyes5y6pmb+etVkpudlOB1SxPho435u/vsq0uKjefrafEb176Venb5m2L4QVsyBjW9ZiwJPmQ3H/8iaPFqptnYuhk9+Z1Wvx6XD5GutsY5pOhdyb9Gk2MNKqxv43w+38MryYvonxfLsdVMY0U/nr+xt60oq+e6c5ZTXNnL18TncMmM4KXE90JZrDJRtgXVzYfXfoaoE+qRZC9EefzPE65ch5YfiZfDpH2Dr+2B8kHMiTLwSRp4DfbTJpSdpUuwhtQ1enluyk78s2kZ9UzNXTbM+iFO1U41jSqsbePSDLby6vIiEGA8/njGcq6blEBvl7t6JG2qgeKn1AbblPSjfCQjkzbC+5Y88V1e2UEenao/15WrVi1C+A8QN2dNgxNmQdwZkjrY67qiACWhSFJGZwOOAG3jKGPNQm/0xwPPAZOAgcJkxZqe9705gNtAM/NgYs+BI1wrGpFjf1My/t5Ty1po9fLTxAHVNzZw5ph93njOKoZkJToenbJv3VfPb+Rv595ZS4qPdnDmmH+eNH8hJwzOJ9nTyAdN42CoJHtgIuwusb/T7N4BpBk8sDD0Vhp9lfaPX+UpVoBhjTRC/5T3YsgD2r7O2xyRbvZYHT7WWFsscBSk5mii7IWBJUUTcwBbgTKAEWA5cYYz5otUxNwHHGmO+LyKXAxcaYy4TkTHAy8BUYCDwITDCGNPhJJZOJ8X6pmZ2V9SxcW8V63ZXsq6kkrUlldQ0eEmLj+bcY/pz0aTBTNLepUFr6faD/Gv1buav20dlXRNJsS6mD/QwOdPLMckNDImtIbVpP1HVJdb6eAcLoXwXYP9fiE6AQZMhaypkTbN6E0bHOfo3qQhRWQI7PrG+lJUshwNfWNWsAJ4+kDkCUnMhOQtSsiF5MCT0s6rv4zO1A88RBDIpHg/cb4w5235+J4Ax5sFWxyywj/lcRDzAPiATuKP1sa2P6+h6gUqKyz/8B95mLz4fNBtDs8/gbfbR1Gxo9PpobPZR1+jlcKOPw41eKuuaOHS4kZr6/3bxd7uErLQ4hmTEMSk7jTEDEnFHwje1LlWptzn2K681bbaZdh4b6z99y2Ofz37us0ppvmbwea3nzU3ga7J+NzdBcwN4G63fTfXQVGuV+JoOQ0MVpq4S7+EK3I1VuPB9LfIKEilz9+VgzGAO9hlKRcJQDqcMpy5xCNFRUcR4XER73HjcgscluO0flwguARGhZTh+y+OW8flf/uYIA/Z1LL/qhLupmvjKQuIrtxJfWUhcZSF9akuIObwHd3PD145vdsfgjUrCG52INyqJZk+c/dMHnycOnysan9v+cUVjXFEY8eBzeTAuD0bcIG6MuOzHgsEF4sKIAGJv++/jlhvZIK3u6bY391efmy5MZDFs8pkkJHW/EOJvUvRnxdtBQOsp4EuA4zo6xhjjFZFKIN3e/p82rx3UTrA3ADcAZGdn+xFS50Z/+iMS5CjGBbVtGqyyf7YHICgVWK4oq03PHW31AI2Ks0p0UfGQOADJGElUbDLEJkN8Jo2xaRQ3xLP1cDw7mlIpqhH2VNRTfriRqromqiq8VBc20dS8zem/TKk2htg/Z9rPDRlUMUAOki6VZEgV6VSR5q0iseEwSVJHErXESylxNNCHBuKkgWiaiKGJaJpwS3D1J+nIzv4fkZDUaS4LmKBYBt4Y8yTwJFglxUCcs/zSuVQY35ff7t0iRLldRHsEj9uFJxJKfN3ShWLM1w5tteHLb4Sti1CtHouLr3zjdLmtbSJW5wOXx97mBrfHSoTuKOt3F/8No4Fh9s+ReJutmoSGJh8NXh9en8+qafBZNQ7GgM+ufWhhDBi7ZNxSGD7SjRxsHdxU+Kq3f8rb7vB5EZ8X8TVZv41VI2M9tmprxPgAH+LzYd3Rxt5u/vucll+taoFaka/d612797OzR3bp+O7yJynuBlovGT/Y3tbeMSV29WkyVocbf17bI7LGndgbl1FhyON24XG76ImRHUqp4ObPV+3lwHARGSIi0cDlwLw2x8wDrrEfXwJ8bKyvwvOAy0UkRkSGAMOBZYEJXSmllAqsTkuKdhvhzcACrCEZzxhjNojIA0CBMWYe8DTwgogUAoewEif2ca8BXwBe4IdH6nmqlFJKOUkH7yullAp7/vY+1d4mSimllE2TolJKKWULuupTESkFdgXodBlAWYDOFc70ffKfvlf+0/fKP/o++a8771WOMSazs4OCLikGkogU+FOHHOn0ffKfvlf+0/fKP/o++a833iutPlVKKaVsmhSVUkopW7gnxSedDiBE6PvkP32v/KfvlX/0ffJfj79XYd2mqJRSSnVFuJcUlVJKKb9pUlRKKaVsYZkURWSmiGwWkUIRucPpeIKJiGSJyEIR+UJENojILfb2NBH5QES22r+7v6pnGBARt4isEpG37edDRGSpfW+9ak+SH/FEJEVE5orIJhHZKCLH6z3VPhH5qf1/b72IvCwisXpfWUTkGRE5ICLrW21r9z4Syx/t92ytiEwKRAxhlxRFxA08AZwDjAGuEJExzkYVVLzAz4wxY4BpwA/t9+cO4CNjzHDgI/u5gluAja2ePww8ZozJw1qibrYjUQWfx4H3jDGjgPFY75neU22IyCDgx0C+MWYc1iILl6P3VYvngJlttnV0H52DtfLScKxF6v8SiADCLikCU4FCY8x2Y0wj8Aowy+GYgoYxZq8xZqX9uBrrw2sQ1ns0xz5sDnCBMxEGDxEZDHwDeMp+LsDpwFz7EH2fABFJBk7GWi0HY0yjMaYCvac64gH62GvPxgF70fsKAGPMJ1grLbXW0X00C3jeWP4DpIjIgO7GEI5JcRBQ3Op5ib1NtSEiucBEYCnQzxiz1961D+jnUFjB5H+B2wGf/TwdqDDGeO3nem9ZhgClwLN2VfNTIhKP3lNfY4zZDfweKMJKhpXACvS+OpKO7qMe+awPx6So/CAiCcDrwE+MMVWt99kLREf0WB0R+SZwwBizwulYQoAHmAT8xRgzEailTVWp3lMWuz1sFtYXiYFAPF+vLlQd6I37KByT4m4gq9XzwfY2ZRORKKyE+JIx5g178/6Wqgf79wGn4gsS04HzRWQnVhX86VjtZil2tRfovdWiBCgxxiy1n8/FSpJ6T33dGcAOY0ypMaYJeAPrXtP7qmMd3Uc98lkfjklxOTDc7s0VjdWIPc/hmIKG3S72NLDRGPNoq13zgGvsx9cAb/Z2bMHEGHOnMWawMSYX6x762BhzJbAQuMQ+LOLfJwBjzD6gWERG2ptmAF+g91R7ioBpIhJn/19sea/0vupYR/fRPOA7di/UaUBlq2rWoxaWM9qIyLlY7UFu4BljzG8cDiloiMiJwKfAOv7bVvYLrHbF14BsrKW7vmWMadvgHZFE5FTgNmPMN0VkKFbJMQ1YBVxljGlwMr5gICITsDokRQPbgeuwvnTrPdWGiPwSuAyrJ/gq4HqstrCIv69E5GXgVKwlovYD9wH/op37yP5S8Ses6ufDwHXGmIJuxxCOSVEppZQ6GuFYfaqUUkodFU2KSimllE2TolJKKWXTpKiUUkrZNCkqpZRSNk2KSimllE2TolJKKWX7/0J1xNhklKqsAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% plot the distributions\n", - "\n", "pl.figure(1, figsize=(6.4, 3))\n", "for i in range(n_distributions):\n", " pl.plot(x, A[:, i])\n", @@ -142,18 +140,18 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% barycenter computation\n", - "\n", "alpha = 0.2 # 0<=alpha<=1\n", "weights = np.array([1 - alpha, alpha])\n", "\n", @@ -194,47 +192,56 @@ "metadata": { "collapsed": false }, + "outputs": [], + "source": [ + "n_alpha = 11\n", + "alpha_list = np.linspace(0, 1, n_alpha)\n", + "\n", + "\n", + "B_l2 = np.zeros((n, n_alpha))\n", + "\n", + "B_wass = np.copy(B_l2)\n", + "\n", + "for i in range(0, n_alpha):\n", + " alpha = alpha_list[i]\n", + " weights = np.array([1 - alpha, alpha])\n", + " B_l2[:, i] = A.dot(weights)\n", + " B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% barycenter interpolation\n", - "\n", - "n_alpha = 11\n", - "alpha_list = np.linspace(0, 1, n_alpha)\n", - "\n", - "\n", - "B_l2 = np.zeros((n, n_alpha))\n", - "\n", - "B_wass = np.copy(B_l2)\n", - "\n", - "for i in range(0, n_alpha):\n", - " alpha = alpha_list[i]\n", - " weights = np.array([1 - alpha, alpha])\n", - " B_l2[:, i] = A.dot(weights)\n", - " B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n", - "\n", - "#%% plot interpolation\n", - "\n", "pl.figure(3)\n", "\n", "cmap = pl.cm.get_cmap('viridis')\n", @@ -300,7 +307,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_barycenter_fgw.ipynb b/notebooks/plot_barycenter_fgw.ipynb index 8da80a6..6fb19af 100644 --- a/notebooks/plot_barycenter_fgw.ipynb +++ b/notebooks/plot_barycenter_fgw.ipynb @@ -42,9 +42,17 @@ "source": [ "# Author: Titouan Vayer \n", "#\n", - "# License: MIT License\n", - "\n", - "#%% load libraries\n", + "# License: MIT License" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import networkx as nx\n", @@ -52,10 +60,17 @@ "from scipy.sparse.csgraph import shortest_path\n", "import matplotlib.colors as mcol\n", "from matplotlib import cm\n", - "from ot.gromov import fgw_barycenters\n", - "#%% Graph functions\n", - "\n", - "\n", + "from ot.gromov import fgw_barycenters" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ "def find_thresh(C, inf=0.5, sup=3, step=10):\n", " \"\"\" Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected\n", " Tthe threshold is found by a linesearch between values \"inf\" and \"sup\" with \"step\" thresholds tested.\n", @@ -160,19 +175,23 @@ "\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We build a dataset of noisy circular graphs.\n", + "Noise is added on the structures by random connections and on the features by gaussian noise.\n", + "\n" + ] + }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "#%% circular dataset\n", - "# We build a dataset of noisy circular graphs.\n", - "# Noise is added on the structures by random connections and on the features by gaussian noise.\n", - "\n", - "\n", "np.random.seed(30)\n", "X0 = []\n", "for k in range(9):\n", @@ -190,14 +209,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -207,8 +226,6 @@ } ], "source": [ - "#%% Plot graphs\n", - "\n", "plt.figure(figsize=(8, 10))\n", "for i in range(len(X0)):\n", " plt.subplot(3, 3, i + 1)\n", @@ -228,16 +245,22 @@ "\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Features distances are the euclidean distances\n", + "\n" + ] + }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "#%% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph\n", - "# Features distances are the euclidean distances\n", "Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0]\n", "ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0]\n", "Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0]\n", @@ -258,14 +281,27 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0]))\n", + "for i, v in enumerate(A.ravel()):\n", + " bary.add_node(i, attr_name=v)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -275,12 +311,6 @@ } ], "source": [ - "#%% Create the barycenter\n", - "bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0]))\n", - "for i, v in enumerate(A.ravel()):\n", - " bary.add_node(i, attr_name=v)\n", - "\n", - "#%%\n", "pos = nx.kamada_kawai_layout(bary)\n", "nx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False)\n", "plt.suptitle('Barycenter', fontsize=20)\n", @@ -304,7 +334,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_barycenter_lp_vs_entropic.ipynb b/notebooks/plot_barycenter_lp_vs_entropic.ipynb index 9c8e83e..b5d7895 100644 --- a/notebooks/plot_barycenter_lp_vs_entropic.ipynb +++ b/notebooks/plot_barycenter_lp_vs_entropic.ipynb @@ -70,64 +70,8 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed time : 0.010422945022583008 s\n", - "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n", - "1.0 1.0 1.0 - 1.0 1700.336700337 \n", - "0.006776453137632 0.006776453137633 0.006776453137633 0.9932238647293 0.006776453137633 125.6700527543 \n", - "0.004018712867874 0.004018712867874 0.004018712867874 0.4301142633 0.004018712867874 12.26594150093 \n", - "0.001172775061627 0.001172775061627 0.001172775061627 0.7599932455029 0.001172775061627 0.3378536968897 \n", - "0.0004375137005385 0.0004375137005385 0.0004375137005385 0.6422331807989 0.0004375137005385 0.1468420566358 \n", - "0.000232669046734 0.0002326690467341 0.000232669046734 0.5016999460893 0.000232669046734 0.09381703231432 \n", - "7.430121674303e-05 7.430121674303e-05 7.430121674303e-05 0.7035962305812 7.430121674303e-05 0.0577787025717 \n", - "5.321227838876e-05 5.321227838875e-05 5.321227838876e-05 0.308784186441 5.321227838876e-05 0.05266249477203 \n", - "1.990900379199e-05 1.990900379196e-05 1.990900379199e-05 0.6520472013244 1.990900379199e-05 0.04526054405519 \n", - "6.305442046799e-06 6.30544204682e-06 6.3054420468e-06 0.7073953304075 6.305442046798e-06 0.04237597591383 \n", - "2.290148391577e-06 2.290148391582e-06 2.290148391578e-06 0.6941812711492 2.29014839159e-06 0.041522849321 \n", - "1.182864875387e-06 1.182864875406e-06 1.182864875427e-06 0.508455204675 1.182864875445e-06 0.04129461872827 \n", - "3.626786381529e-07 3.626786382468e-07 3.626786382923e-07 0.7101651572101 3.62678638267e-07 0.04113032448923 \n", - "1.539754244902e-07 1.539754249276e-07 1.539754249356e-07 0.6279322066282 1.539754253892e-07 0.04108867636379 \n", - "5.193221323143e-08 5.193221463044e-08 5.193221462729e-08 0.6843453436759 5.193221708199e-08 0.04106859618414 \n", - "1.888205054507e-08 1.888204779723e-08 1.88820477688e-08 0.6673444085651 1.888205650952e-08 0.041062141752 \n", - "5.676855206925e-09 5.676854518888e-09 5.676854517651e-09 0.7281705804232 5.676885442702e-09 0.04105958648713 \n", - "3.501157668218e-09 3.501150243546e-09 3.501150216347e-09 0.414020345194 3.501164437194e-09 0.04105916265261 \n", - "1.110594251499e-09 1.110590786827e-09 1.11059083379e-09 0.6998954759911 1.110636623476e-09 0.04105870073485 \n", - "5.770971626386e-10 5.772456113791e-10 5.772456200156e-10 0.4999769658132 5.77013379477e-10 0.04105859769135 \n", - "1.535218204536e-10 1.536993317032e-10 1.536992771966e-10 0.7516471627141 1.536205005991e-10 0.04105851679958 \n", - "6.724209350756e-11 6.739211232927e-11 6.739210470901e-11 0.5944802416166 6.735465384341e-11 0.04105850033766 \n", - "1.743382199199e-11 1.736445896691e-11 1.736448490761e-11 0.7573407808104 1.734254328931e-11 0.04105849088824 \n", - "Optimization terminated successfully.\n", - "Elapsed time : 2.927520990371704 s\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "#%% parameters\n", - "\n", "problems = []\n", "\n", "n = 100 # nb bins\n", @@ -146,19 +90,93 @@ "\n", "# loss matrix + normalization\n", "M = ot.utils.dist0(n)\n", - "M /= M.max()\n", - "\n", - "\n", - "#%% plot the distributions\n", - "\n", + "M /= M.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ "pl.figure(1, figsize=(6.4, 3))\n", "for i in range(n_distributions):\n", " pl.plot(x, A[:, i])\n", "pl.title('Distributions')\n", - "pl.tight_layout()\n", - "\n", - "#%% barycenter computation\n", - "\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time : 0.008066177368164062 s\n", + "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n", + "1.0 1.0 1.0 - 1.0 1700.336700337 \n", + "0.006776453137632 0.006776453137632 0.006776453137632 0.9932238647293 0.006776453137632 125.6700527543 \n", + "0.004018712867873 0.004018712867873 0.004018712867873 0.4301142633001 0.004018712867873 12.26594150092 \n", + "0.001172775061627 0.001172775061627 0.001172775061627 0.7599932455027 0.001172775061627 0.3378536968898 \n", + "0.0004375137005386 0.0004375137005386 0.0004375137005386 0.6422331807989 0.0004375137005386 0.1468420566359 \n", + "0.0002326690467339 0.0002326690467339 0.0002326690467339 0.5016999460898 0.0002326690467339 0.09381703231428 \n", + "7.430121674299e-05 7.4301216743e-05 7.430121674299e-05 0.7035962305811 7.430121674299e-05 0.05777870257169 \n", + "5.321227838943e-05 5.321227838945e-05 5.321227838944e-05 0.3087841864307 5.321227838944e-05 0.05266249477219 \n", + "1.990900379216e-05 1.99090037922e-05 1.990900379216e-05 0.6520472013271 1.990900379216e-05 0.04526054405523 \n", + "6.305442046834e-06 6.305442046856e-06 6.305442046837e-06 0.7073953304085 6.305442046837e-06 0.04237597591384 \n", + "2.290148391591e-06 2.290148391631e-06 2.290148391602e-06 0.6941812711476 2.29014839161e-06 0.04152284932101 \n", + "1.182864875578e-06 1.182864875548e-06 1.182864875555e-06 0.5084552046229 1.182864875567e-06 0.04129461872829 \n", + "3.626786386894e-07 3.626786386985e-07 3.626786386845e-07 0.7101651569095 3.626786385995e-07 0.0411303244893 \n", + "1.539754244475e-07 1.539754247164e-07 1.539754247197e-07 0.6279322077522 1.539754251915e-07 0.04108867636377 \n", + "5.193221608537e-08 5.19322169648e-08 5.193221696942e-08 0.6843453280956 5.193221892276e-08 0.04106859618454 \n", + "1.888205219929e-08 1.88820500654e-08 1.888205006369e-08 0.6673443828803 1.888205852187e-08 0.04106214175236 \n", + "5.676837529301e-09 5.676842740457e-09 5.676842761502e-09 0.7281712198286 5.676877044229e-09 0.04105958648535 \n", + "3.501170987746e-09 3.501167688027e-09 3.501167721804e-09 0.4140142115019 3.501183058995e-09 0.04105916265728 \n", + "1.110582426269e-09 1.110580273241e-09 1.110580239523e-09 0.6999003212726 1.110624310022e-09 0.04105870073273 \n", + "5.768753963318e-10 5.769422203363e-10 5.769421938248e-10 0.5002521235315 5.767522037401e-10 0.04105859764872 \n", + "1.534102102874e-10 1.535920569433e-10 1.535921107494e-10 0.7516900610544 1.535251083958e-10 0.04105851678411 \n", + "6.717475002202e-11 6.735435784522e-11 6.735430717133e-11 0.5944268235824 6.732253215483e-11 0.04105850033323 \n", + "1.751321118837e-11 1.74043080851e-11 1.740429001123e-11 0.7566075167358 1.736956306927e-11 0.0410584908946 \n", + "Optimization terminated successfully.\n", + " Current function value: 0.041058 \n", + " Iterations: 22\n", + "Elapsed time : 2.3570468425750732 s\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ "alpha = 0.5 # 0<=alpha<=1\n", "weights = np.array([1 - alpha, alpha])\n", "\n", @@ -198,14 +216,65 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Dirac Data\n", + "Stair Data\n", "----------\n", "\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a1 = 1.0 * (x > 10) * (x < 50)\n", + "a2 = 1.0 * (x > 60) * (x < 80)\n", + "\n", + "a1 /= a1.sum()\n", + "a2 /= a2.sum()\n", + "\n", + "# creating matrix A containing all distributions\n", + "A = np.vstack((a1, a2)).T\n", + "n_distributions = A.shape[1]\n", + "\n", + "# loss matrix + normalization\n", + "M = ot.utils.dist0(n)\n", + "M /= M.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1, figsize=(6.4, 3))\n", + "for i in range(n_distributions):\n", + " pl.plot(x, A[:, i])\n", + "pl.title('Distributions')\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, "metadata": { "collapsed": false }, @@ -214,92 +283,42 @@ "name": "stdout", "output_type": "stream", "text": [ - "Elapsed time : 0.014856815338134766 s\n", + "Elapsed time : 0.007988214492797852 s\n", "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n", "1.0 1.0 1.0 - 1.0 1700.336700337 \n", - "0.006776466288966 0.006776466288966 0.006776466288966 0.9932238515788 0.006776466288966 125.6649255808 \n", - "0.004036918865495 0.004036918865495 0.004036918865495 0.4272973099316 0.004036918865495 12.3471617011 \n", - "0.00121923268707 0.00121923268707 0.00121923268707 0.749698685599 0.00121923268707 0.3243835647408 \n", - "0.0003837422984432 0.0003837422984432 0.0003837422984432 0.6926882608284 0.0003837422984432 0.1361719397493 \n", - "0.0001070128410183 0.0001070128410183 0.0001070128410183 0.7643889137854 0.0001070128410183 0.07581952832518 \n", - "0.0001001275033711 0.0001001275033711 0.0001001275033711 0.07058704837812 0.0001001275033712 0.0734739493635 \n", - "4.550897507844e-05 4.550897507841e-05 4.550897507844e-05 0.5761172484828 4.550897507845e-05 0.05555077655047 \n", - "8.557124125522e-06 8.5571241255e-06 8.557124125522e-06 0.8535925441152 8.557124125522e-06 0.04439814660221 \n", - "3.611995628407e-06 3.61199562841e-06 3.611995628414e-06 0.6002277331554 3.611995628415e-06 0.04283007762152 \n", - "7.590393750365e-07 7.590393750491e-07 7.590393750378e-07 0.8221486533416 7.590393750381e-07 0.04192322976248 \n", - "8.299929287441e-08 8.299929286079e-08 8.299929287532e-08 0.9017467938799 8.29992928758e-08 0.04170825633295 \n", - "3.117560203449e-10 3.117560130137e-10 3.11756019954e-10 0.997039969226 3.11756019952e-10 0.04168179329766 \n", - "1.559749653711e-14 1.558073160926e-14 1.559756940692e-14 0.9999499686183 1.559750643989e-14 0.04168169240444 \n", + "0.006776466288938 0.006776466288938 0.006776466288938 0.9932238515788 0.006776466288938 125.66492558 \n", + "0.004036918865472 0.004036918865472 0.004036918865472 0.4272973099325 0.004036918865472 12.347161701 \n", + "0.001219232687076 0.001219232687076 0.001219232687076 0.7496986855957 0.001219232687076 0.3243835647418 \n", + "0.0003837422984467 0.0003837422984467 0.0003837422984467 0.6926882608271 0.0003837422984467 0.1361719397498 \n", + "0.0001070128410194 0.0001070128410194 0.0001070128410194 0.7643889137854 0.0001070128410194 0.07581952832542 \n", + "0.0001001275033713 0.0001001275033714 0.0001001275033713 0.07058704838615 0.0001001275033713 0.07347394936346 \n", + "4.550897507807e-05 4.550897507807e-05 4.550897507807e-05 0.576117248486 4.550897507807e-05 0.05555077655034 \n", + "8.557124125834e-06 8.557124125853e-06 8.557124125835e-06 0.853592544106 8.557124125835e-06 0.0443981466023 \n", + "3.611995628666e-06 3.611995628643e-06 3.611995628672e-06 0.6002277331398 3.611995628673e-06 0.0428300776216 \n", + "7.590393750111e-07 7.590393750273e-07 7.590393750129e-07 0.8221486533655 7.590393750133e-07 0.04192322976247 \n", + "8.299929287077e-08 8.299929283415e-08 8.299929287126e-08 0.901746793884 8.299929287181e-08 0.04170825633295 \n", + "3.117560207452e-10 3.117560192413e-10 3.117560199213e-10 0.9970399692253 3.117560200234e-10 0.04168179329766 \n", + "1.559774508975e-14 1.559825507727e-14 1.559755309294e-14 0.9999499686993 1.559748033629e-14 0.04168169240444 \n", "Optimization terminated successfully.\n", - "Elapsed time : 2.703077793121338 s\n", - "Elapsed time : 0.0029761791229248047 s\n", - "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n", - "1.0 1.0 1.0 - 1.0 1700.336700337 \n", - "0.006774675520727 0.006774675520727 0.006774675520727 0.9932256422636 0.006774675520727 125.6956034743 \n", - "0.002048208707562 0.002048208707562 0.002048208707562 0.7343095368143 0.002048208707562 5.213991622123 \n", - "0.000269736547478 0.0002697365474781 0.0002697365474781 0.8839403501193 0.000269736547478 0.505938390389 \n", - "6.832109993943e-05 6.832109993944e-05 6.832109993944e-05 0.7601171075965 6.832109993943e-05 0.2339657807272 \n", - "2.437682932219e-05 2.43768293222e-05 2.437682932219e-05 0.6663448297475 2.437682932219e-05 0.1471256246325 \n", - "1.13498321631e-05 1.134983216308e-05 1.13498321631e-05 0.5553643816404 1.13498321631e-05 0.1181584941171 \n", - "3.342312725885e-06 3.342312725884e-06 3.342312725885e-06 0.7238133571615 3.342312725885e-06 0.1006387519747 \n", - "7.078561231603e-07 7.078561231509e-07 7.078561231604e-07 0.8033142552512 7.078561231603e-07 0.09474734646269 \n", - "1.966870956916e-07 1.966870954537e-07 1.966870954468e-07 0.752547917788 1.966870954633e-07 0.09354342735766 \n", - "4.19989524849e-10 4.199895164852e-10 4.199895238758e-10 0.9984019849375 4.19989523951e-10 0.09310367785861 \n", - "2.101015938666e-14 2.100625691113e-14 2.101023853438e-14 0.999949974425 2.101023691864e-14 0.09310274466458 \n", - "Optimization terminated successfully.\n", - "Elapsed time : 2.6085386276245117 s\n" + " Current function value: 0.041682 \n", + " Iterations: 13\n", + "Elapsed time : 1.8278961181640625 s\n" ] }, { "data": { - "image/png": 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MrlMMikJxgUhCsDfbS2+2V6EokoLK0SpFtU+DoFBcIJJzij3ZHnqyPZp9KpKCxhNtVCmGRKG4QCTnFPOdUftUF++LtJ8m2oRPobhAjJRGyFiG7s5uejp71D4VSUFy0+9J9z7VRJugKBQXiNHyKD2dPZgZvbletU9FUjDRPp1aKep5imFQKC4QI6UR8tk8ELVQdfG+SPtNtE8nKkU6OsBM7dNAKBQXiJHSCD3ZHiCabDNSGsHdUx6VyMLScKKNGZbLqX0aCIXiAlEfiuVKmWJF/whF2qnRRBuIQlKVYhgUigtEfSgmy0SkfRrd0QaikFSlGAaF4gKhUBRJX6OJNtH7XHVmqqRLobhAjJZGp4SiZqCKtJfap+FTKC4Qw6Vh8p3x7NN4Fqou4Bdpr+TxUBm1T4OlUJwF7s7bvvk2vv3Ut9MeChCNp1GlGEr79MDhA7z5n9/MkwefTHsoIrOq0Q3BQZViSBSKs+DgkYM8OPggW/ZuSXsoABQrRcpepjfXC0Q3BYdw2qdPHHiCp154ioeHHk57KCKzqloN1oeiKsVgKBRnwdDoUPTn2FDKI4lUHxsVt09DqxST/ZTsN5H5ykslLJfDzCYtt5xCMRQKxVmwb3QfAIOjgymPJDJSjMIvCcPQzikm+ynZbyLzlReLU1qnEJ1jVPs0DArFWZBUPMGEYrkuFOOKMZT2abKfQqmsRWZLpVicco0iAGqfBqOpUDSzNWb2hJltN7NrG6x/v5ltM7NHzOxOMzu99UOdO5If8vvH9jNeGU95NBNt0iQUOzOddHd2B9M+TfZXKL9EiMyWpH1aT5ViOGYMRTPrANYDFwKrgcvMbHXdZg8CA+7+auA24C9bPdC5ZHAs+uE+7uMcPHIw5dFMDUUI66bgoVXWIrNluvapJtqEo5lK8Vxgu7vvcPcicCuwtnYDd7/b3ZNe3H3AitYOc26p/eEewg/6pE1aG4rJTcFDkJxLPHD4AKWKfluW+cuLjStFy+qG4KFoJhSXA7tq3u+Ol03nSuCbjVaY2Toz22pmW4eG5u/5o6HRIZZ0LQHCCMVkQk2IoVjxCvvH9lf31/7R/SmPSGT2eKnUuFJU+zQYLZ1oY2aXAwPApxqtd/cN7j7g7gOFQqGVXx2UfaP7eOXSVwJhhGKj9mkooXjg8AHGfXxif42lv79EZotPM9FG7dNwNBOKe4BTa96viJdNYmZvAD4CXOzuR1ozvLmnVClx4PABXr7k5WQsE0QoJu3TZNYpRKEYwuzTZP+8qu9Vk96LzEfTTbSxXI6KKsUgNBOKW4BVZnaGmeWAS4FNtRuY2TnA3xIF4oL+qfbc2HMAnNJ7Cku7lgZxmcFIaYTuzm46Mh3VZflsPohKMZlko1CUheCoE20UikGYMRTdvQxcDdwBPAZsdPdHzex6M7s43uxTQC/wVTN7yMw2TfNx814yaaQ/308hXwjigvTam4EnerI9QVy8n+yfs04+i85Mp0JR5rWofdr4nCKlEl6ppDAqqdXZzEbuvhnYXLfsuprXb2jxuOaspPLpz/fTn+/nmeFnUh7R5MdGJXo6w2ifDo0NYRh93X30d/frVm8yr0UTbRq3TwG8XG58cb+0je5o02JJ5VPoLgTzQ36kPDI1FHM9HB4/TLlSTmlUkcHRQZZ2L6Uz00khX9BEG5nXpq0U45aqJtukT6HYYkOjQ3RmOjm562T68/0cPHKQ4ni6B/pIqUEodsYPGi6nWy0Ojg7Sn+8Houpa7VOZz7xUmvIsRaipFBWKqVMottjg6CCF7gIZy1R/2Kc92aZh+zR+n3YLdXB0kP7uiVAMobIWmS1eLE55bBRQrR412SZ9CsUWGxybqHwK+ehazLSrn+HScPXJGIkkFIeL6U62GRodmthf3QWGS8OpB7XIbPFisXGlmFWlGAqFYovVtwOTZWkaKY1UHyycqD5TMcX7nxbHixw8crD6y0Mo+0tktkw/0UaVYigUii1WW/kkbcG0f8gfrX2a5rWKSVt5WX4ZoFCU+U8TbcKnUGyh0dIow6VhCt1R5bN40WJymVyq58nKlTKHxw9P2z5Ns1WZhN+USlEzUGUecveZL8lQKKZOodhCyQ/55Ie7maV+AX/1vqedkyvFJCTTvIC/fn+pUpR5LW6NNqoUk/OMap+mT6HYQvU/5CFqDaY5+zSpBHtzk88pJucY02yfVvdX3GbuyfbQk+3RDFSZlyrFOBQbVYpqnwZDodhCSduvNhQL+UKqP+ST0AuxfTo0OkQuk2PxosXVZYXugipFmZe8FAXedDcEj7ZRpZg2hWIL1d7iLdGf72ff6D7cPZUxJbNL69unuY4cnZnOdCvFsUEK+QJmVl22LL9MoSjzklcrRU20CZlCsYUGRwerLcBEf3c/Y+Wx1MJnpDj1WYqJtG8KPjg6WJ15mijkC6nf7EBkNjRVKSoUU6dQbKHkbja10r6Av1opNgjF3mxv6u3TZP8kCvmofZpWZS0yW45aKap9GgyFYgvVXrifSPsyg+rs0wahmOYzFd2dfaP7pvwSsSy/jFKlxKEjh1IZl8hsOWqlGAdlRZVi6pp6dJQ0Z2hsiHP6z5m0LO3LDI4Wij2dPand0WakNMJYeWxq+7R7orI+uevkNIYmraTnA1b5kSNA40sy1D4Nh0KxRdw9ap/WtwO7022fJu3R6c4pvlB8od1DAqZeuJ+o/SXi7CVnt31c0kK3vx+23pj2KILhQzmgDxsfm7KuOtFG7dPUKRRb5NCRQ5QqpSmVTz6b54TsCaldljFcGqYz00muY2rLJp/N88xIOg9BbnT5Su17TbaZ4577CTzwRTjzjbDiF9MeTRB820648y7sydvhDRdNWjdRKSoU06ZQbJFq5VN3jgzSfU5go5uBJ3qzvamdU2x0owOY2H9p3gVIWuDeG6AjB2vXwwnLZt5+AfCOe4G7yPz4dhj9M8gvqa6bqBTVPk2bQrFFpvshD8S3ehtk5Ej7n3L/wpFhujvzDb87l+lmpDSayrj2vLAXgJ6OJXXfb5y86GSefXFfKuMKVUfG6Mp2pD2M5hx8Ch6+Bc5dp0CsUT1fOD4K930OXveR6jrr6ICODlWKAVAotsjRQvHI4V4eGXycV37sjnYPi64VPyWTrTT87lxhL7mlI7zyY98CbOpfnkWLlm0lu7iLgT+9Z8q6/BldfHX/o9x8e/v3V6g6MsYfrTmbdb/6srSHMrN7b4BMJ5z/nrRHEpQkFDOrLoD7/xZ+6SroPqm63nI5TbQJQFOhaGZrgE8DHcAX3P0TdesXATcDrwWeAy5x96daO9SwJefI6tunX39oD/c/WSa39AU+dOFZmLX3Kph/fraDcT+Jt1z08inrHji0je8edD6wZiW5THdbx7V539c4WCrw7gbj2rT3FEbHD3Lpq6euW6ju33GAv9j8OOMV+P0LAg7GQ7vgoX+A114BJ74k7dEEJZlEY+e9C77xr/D9DfBrH6yut2xWE20CMGMomlkHsB54I7Ab2GJmm9x9W81mVwIH3f1MM7sU+CRwyWwMOFSDo4Ms6VpCtmNiuvXXHtzD+zc+xMte9h/YaxV+69yT6evua+u47r7dWdy1tGGFsfGJ0/nufXDJucumzAKdbf/3G6MUsssbjuuZ767kO7uemhtVUZu84/wzeN/Gh/nktx6n4s5Vv35m2kNq7N//Kvrz/PemO44AJVWgLf85OPsi+N56OO/3YdEJ0XJVikFoplI8F9ju7jsAzOxWYC1QG4prgY/Hr28DPmtm5m24Lcntf30N48XDs/01Mxod+REDPs4jf/3nABwYKfLIk0N89KQ8Ly0XuXFnhftv+ghLc40nvcyW/md3sLK7n+GhT09Z1/f8k/zCMxV+8OXrWNyZb/C3Z89Jz/yY1b0rGH5u6rhePvQTduzfz30brml3Vzdov+3OWR2H2PnVUW68p5e+3kVpD2mySpllO7/Fi0sG2L/x1rRHE5y+hx9nBfDFH36Hzr5fZtXOu9j/1XW8uOTnAHj1+BgHHrqH7//Pq9IdaEA6uvK86apPtfU7babcMrO3AGvc/Z3x+98F/qO7X12zzY/ibXbH738Sb7O/7rPWAesATjvttNc+/fTTx/0/4P7XvIIT07tTmYhI00od8I73dnAkN/W3vU98scxL96YwqIAdPAF+ectjLfksM3vA3Qdm2q6tE23cfQOwAWBgYKAlVeTIB67k+QAqRYBluaUsykTXG2UyximLu+nIRAf/nrH9jI0fSWVcp+aXsSgz9f9qd9h1eJDiePvPY5gZp3cvozMzdUZlxZ2dY/soV8bbPq65wN0ZGikyPh7e3WIquV7Gu5amPYxglRefyLX90SkUGx8j98LOiZV/OMquId3esFa2q70dLGguFPcAp9a8XxEva7TNbjPrBBYTTbiZda/77Q+042uOW6BngAj1njGvSHsAIrIgNTMVcguwyszOMLMccCmwqW6bTcAV8eu3AHe143yiiIhIK81YKbp72cyuBu4guiTjJnd/1MyuB7a6+ybgRuArZrYdOEAUnCIiInNKU+cU3X0zsLlu2XU1rw8D/6W1QxMREWkvPU9RREQkplAUERGJzXid4qx9sdkQcPwXKkb6gP0zbiUJ7a/maV8dG+2vY6P9dWyOZ3+d7u4z3rortVBsJTPb2sxFmRLR/mqe9tWx0f46Ntpfx6Yd+0vtUxERkZhCUUREJDZfQnFD2gOYY7S/mqd9dWy0v46N9texmfX9NS/OKYqIiLTCfKkURUREjptCUUREJDanQ9HM1pjZE2a23cyuTXs8oTGzU83sbjPbZmaPmtl74uVLzOxfzezJ+M+T0x5rSMysw8weNLPb4/dnmNn98XH2j/GN8QUws5PM7DYze9zMHjOzX9Lx1ZiZvS/+d/gjM7vFzLp0bE1mZjeZ2WD8jN5kWcPjySKfiffdI2b2mlaMYc6Gopl1AOuBC4HVwGVmtjrdUQWnDFzj7quB84Cr4n10LXCnu68C7ozfy4T3ALVPNv0k8L/c/UzgIHBlKqMK06eBb7n7y4GfJ9pvOr7qmNly4N3AgLu/iujhCpeiY6vel4A1dcumO54uBFbF/60DPteKAczZUATOBba7+w53LwK3AmtTHlNQ3P1Zd/9B/PpFoh9Yy4n205fjzb4M/Od0RhgeM1sB/Cbwhfi9Aa8Dbos30f6Kmdli4FeJnpKDuxfd/RA6vqbTCXTHz5zNA8+iY2sSd7+H6ElLtaY7ntYCN3vkPuAkM3vJ8Y5hLoficmBXzfvd8TJpwMxWAucA9wPL3P3ZeNVeYFlKwwrRXwEfBJLH2i8FDrl7OX6v42zCGcAQ8MW43fwFM+tBx9cU7r4H+B/ATqIwfB54AB1bzZjueJqVDJjLoShNMrNe4P8A73X3F2rXxQ+D1nU5gJm9CRh09wfSHssc0Qm8Bvicu58DjFDXKtXxFYnPg60l+kXiFKCHqW1CmUE7jqe5HIp7gFNr3q+Il0kNM8sSBeL/dvd/ihfvS9oM8Z+DaY0vMOcDF5vZU0Tt+NcRnTM7KW55gY6zWruB3e5+f/z+NqKQ1PE11RuAn7r7kLuXgH8iOt50bM1suuNpVjJgLofiFmBVPHsrR3TSelPKYwpKfD7sRuAxd7+hZtUm4Ir49RXA19s9thC5+4fcfYW7ryQ6nu5y998B7gbeEm+m/RVz973ALjM7O170emAbOr4a2QmcZ2b5+N9lsq90bM1suuNpE/C2eBbqecDzNW3Wn9mcvqONmV1EdA6oA7jJ3f885SEFxcx+BbgX+CET58g+THRecSNwGtHju97q7vUntxc0M7sA+IC7v8nMXkpUOS4BHgQud/cjaY4vFGb2C0STknLADuDtRL9s6/iqY2Z/AlxCNCv8QeCdROfAdGzFzOwW4AKiR0TtAz4GfI0Gx1P8y8VnidrQo8Db3X3rcY9hLoeiiIhIK83l9qmIiEhLKRRFRERiCkUREZGYQlFERCSmUBQREYkpFEVERGIKRRERkdj/B6LkDTMg/T/xAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% parameters\n", - "\n", - "a1 = 1.0 * (x > 10) * (x < 50)\n", - "a2 = 1.0 * (x > 60) * (x < 80)\n", - "\n", - "a1 /= a1.sum()\n", - "a2 /= a2.sum()\n", - "\n", - "# creating matrix A containing all distributions\n", - "A = np.vstack((a1, a2)).T\n", - "n_distributions = A.shape[1]\n", - "\n", - "# loss matrix + normalization\n", - "M = ot.utils.dist0(n)\n", - "M /= M.max()\n", - "\n", - "\n", - "#%% plot the distributions\n", - "\n", - "pl.figure(1, figsize=(6.4, 3))\n", - "for i in range(n_distributions):\n", - " pl.plot(x, A[:, i])\n", - "pl.title('Distributions')\n", - "pl.tight_layout()\n", - "\n", - "\n", - "#%% barycenter computation\n", - "\n", "alpha = 0.5 # 0<=alpha<=1\n", "weights = np.array([1 - alpha, alpha])\n", "\n", @@ -333,10 +352,26 @@ "pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n", "pl.legend()\n", "pl.title('Barycenters')\n", - "pl.tight_layout()\n", - "\n", - "#%% parameters\n", - "\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dirac Data\n", + "----------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ "a1 = np.zeros(n)\n", "a2 = np.zeros(n)\n", "\n", @@ -355,20 +390,82 @@ "\n", "# loss matrix + normalization\n", "M = ot.utils.dist0(n)\n", - "M /= M.max()\n", - "\n", - "\n", - "#%% plot the distributions\n", - "\n", + "M /= M.max()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ "pl.figure(1, figsize=(6.4, 3))\n", "for i in range(n_distributions):\n", " pl.plot(x, A[:, i])\n", "pl.title('Distributions')\n", - "pl.tight_layout()\n", - "\n", - "\n", - "#%% barycenter computation\n", - "\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time : 0.0016779899597167969 s\n", + "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n", + "1.0 1.0 1.0 - 1.0 1700.336700337 \n", + "0.00677467552072 0.006774675520719 0.006774675520719 0.9932256422636 0.006774675520719 125.6956034741 \n", + "0.002048208707556 0.002048208707555 0.002048208707555 0.734309536815 0.002048208707555 5.213991622102 \n", + "0.0002697365474791 0.0002697365474791 0.0002697365474791 0.8839403501183 0.0002697365474791 0.5059383903908 \n", + "6.832109993919e-05 6.832109993918e-05 6.832109993918e-05 0.7601171075982 6.832109993918e-05 0.2339657807271 \n", + "2.437682932221e-05 2.437682932221e-05 2.437682932221e-05 0.6663448297463 2.437682932221e-05 0.1471256246325 \n", + "1.134983216308e-05 1.134983216308e-05 1.134983216308e-05 0.5553643816417 1.134983216308e-05 0.1181584941171 \n", + "3.342312725863e-06 3.34231272585e-06 3.342312725863e-06 0.7238133571629 3.342312725863e-06 0.1006387519746 \n", + "7.078561231536e-07 7.078561231537e-07 7.078561231535e-07 0.803314255252 7.078561231535e-07 0.09474734646268 \n", + "1.966870949422e-07 1.966870952674e-07 1.966870952717e-07 0.7525479180433 1.966870953014e-07 0.09354342735758 \n", + "4.199895266495e-10 4.199895367352e-10 4.19989526535e-10 0.9984019849265 4.199895265747e-10 0.09310367785861 \n", + "2.101053559204e-14 2.100331212975e-14 2.101054034304e-14 0.9999499736903 2.101053604307e-14 0.09310274466458 \n", + "Optimization terminated successfully.\n", + " Current function value: 0.093103 \n", + " Iterations: 11\n", + "Elapsed time : 1.8065369129180908 s\n" + ] + }, + { + "data": { + "image/png": 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3vTu3qw7vPm4nEvsulGkxykvr3n+CgBqUdZJoThZQTVBRWcXabcXVtSaAA7ukk5KY4MvAqIEDxQbKykz1pQa1emtxdfMeUNOJxGpQpqWoN6Csia85WUA1wbrC0lqdAACSEhPon50R844SFZVV7CgtDxlQznh8sQ2oisoq1haU1Do24NyH+sa+C2VairLSusMcgTXxxYgFVBN4oyJ4X9D1DOiWGfMa1PZSp2dc8D0ocIc7ivF4fOuLSimrrKq+7+TxsxOJMQ1Wvid0DSqpXc1202wsoJrAqyUd1K32l/gGdm9P/vY9lJbF7iIcapgjjx81qJoefEE1KDew1hRYLcq0AOUloQMqIcEJKetm3qwiCigRmSoiX4vIahG5PsT2VBF51t3+kYj0jXZB49GqrcX07tyO9JSkWutzumWiCmtj2J3aGykiZEBlOgEVy/H4vNrlQXUCyu1EYs18piUo3xO6iQ+c9VaDalb1BpSIJAIPAscCg4EzRGRw0MvOB7ar6gDgXuDOaBc0Hn2zZXedGgLUXIRj2Z3aqyEF9+Jz1qVQVllFcQyb1b7ZUkyvTu3ITK0d3l4nkm9s0FjTEpSF6SQBznob6qhZSX2fqkVkAnCLqk5xl28AUNU/BLzmdfc1H4pIEvA9kK372fno0aN1yZIljS548a7t7Lh3fKN/PxoqqpSOacl0Tk+utV6BDUWliEBC8Bf8mokqVFYpB3RpR2LQexbvq2BbcRlJibEpCzhlSUtOpHv7uoH53c69VFRW1Rrxwph41EO38u+kqdyfelGdbY+XXkI33UaRdPKhZPFhc4dhjLny+SbvR0SWquro4PVJoV4cpBewMWA5HxgX7jWqWiEiO4EsYFtQIS4CLgLo06dPxIUPJTExie/bD23SPppKROjSLRPSageUAOWpxewoje19n/SUJBJ6dKizPqmikqLvdlEV4yk3DuySASECSjP3sHWnNY2Y+LcZ4dvO0xnRrm4ILdz5Mw4u+cSHUsWPii45zbr/SAIqalR1NjAbnBpUU/bVLqM9o696ISrlag4D/C5AgDRgpN+FCNDL/TGmJRgTdsuIGJaibYqkk8Qm4ICA5d7uupCvcZv4OgKF0SigMcaYtimSgPoEyBGRfiKSApwOzAt6zTzgbPf5KcDb+7v/ZIwxxtSn3k4SACJyHHAfkAg8pqp3iMhtwBJVnSciacCTOHXeIuB0VV1bzz4LgPVN/QcAXQm612Wq2bEJz45NeHZswrNjs3+NPT4Hqmp28MqIAiqeiciSUL0/jB2b/bFjE54dm/Ds2OxftI+PjSRhjDEmLllAGWOMiUutIaBm+12AOGbHJjw7NuHZsQnPjs3+RfX4tPh7UMYYY1qn1lCDMsYY0wpZQBljjIlLLTag6psCpC0RkQNE5B0RyROR5SJyubu+i4j8T0RWuY+d/S6rX0QkUUSWicgr7nI/d2qY1e5UMXXnKWkjRKSTiMwVkZUiskJEJti54xCRK93/p74SkadFJK2tnjsi8piIbBWRrwLWhTxPxPGAe4y+EJFGjbbWIgMqwilA2pIK4GpVHQyMB37lHo/rgbdUNQd4y11uqy4HVgQs3wnc604Rsx1nypi26n7gv6o6CBiOc5za/LkjIr2Ay4DRqjoUZ6CC02m7584cYGrQunDnybFAjvtzEfC3xrxhiwwoYCywWlXXqmoZ8Axwos9l8o2qblbVT93nu3EuML1wjsnj7sseB2b4U0J/iUhv4HjgEXdZgCOAue5L2vKx6QgcBjwKoKplqroDO3c8SUA7d4zRdGAzbfTcUdX3cUYKChTuPDkReEIdi4FOItKjoe/ZUgMq1BQgNkA24M5mPAL4COiuqpvdTd8D3X0qlt/uA34NVLnLWcAOVfVmcGzL508/oAD4h9sE+oiIZGDnDqq6CbgL2IATTDuBpdi5EyjceRKVa3RLDSgTgohkAi8AV6jqrsBt7uC9be47BSIyDdiqqkv9LkucSsKZjeVvqjoCKCGoOa8NnzudcWoC/YCeQAZ1m7iMqznOk5YaUJFMAdKmiEgyTjg9paovuqu3eNVq93GrX+Xz0SRguoisw2kKPgLnnksnt9kG2vb5kw/kq+pH7vJcnMCycweOAr5V1QJVLQdexDmf7NypEe48ico1uqUGVCRTgLQZ7j2VR4EVqnpPwKbAaVDOBv4d67L5TVVvUNXeqtoX5zx5W1VnAu/gTA0DbfTYAKjq98BGETnYXXUkkIedO+A07Y0XkXT3/zHv2Ni5UyPceTIPOMvtzTce2BnQFBixFjuSRKgpQHwukm9EZDLwAfAlNfdZfoNzH+o5oA/O1CY/VdXgm5xthogcDlyjqtNEpD9OjaoLsAyYpar7/CyfX0QkF6cDSQqwFjgX58Nrmz93RORW4DScnrLLgAtw7qW0uXNHRJ4GDseZUmML8DvgZUKcJ26g/xWnSbQUOFdVlzT4PVtqQBljjGndWmoTnzHGmFbOAsoYY0xcsoAyxhgTlyygjDHGxCULKGOMMXHJAsoYY0xcsoAyxhgTlyygjDHGxCULKGOMMXHJAsoYY0xcsoAyxhgTlyygjDHGxCULKGOMMXHJAsq0OSKyTkT2iEixiGwXkVdF5ID6fzM+iMgtIvJPv8thTHOzgDJt1Qmqmgn0wJnb5i8N3UHArKotSkstt2l7LKBMm6aqe3GmOR8MICLHi8gyEdklIhtF5BbvtSLSV0RURM4XkQ3A227t69LAfYrIFyLyE/f5EBH5n4gUicgWEfmNuz5BRK4XkTUiUigiz4lIl6D3OVtENojINhG50d02FWcyytPcGuDn7vqOIvKoiGwWkU0i8nsRSXS3nSMiC0XkXhEpBG4RkQEi8p6I7HT3/2yzHmhjGsECyrRpIpKOM2PqYndVCXAW0Ak4HviliMwI+rUfAYcAU4DHgVkB+xuOM+PqqyLSHngT+C/QExgAvOW+9FJghruvnsB24MGg95kMHIwz1fjNInKIqv4X+H/As6qaqarD3dfOwZn1dQAwAjgGZ/ZXzzic2XK7A3cAtwNvAJ2B3jSiBmlMc7OAMm3VyyKyA9gJHA38GUBV31XVL1W1SlW/AJ7GCZFAt6hqiaruAeYBA0Ukx932M5zwKAOmAd+r6t2quldVd6vqR+7rfgHcqKr57nThtwCnBDW/3aqqe1T1c+BzYDghiEh34DjgCrdcW4F7gdMDXvadqv5FVSvccpcDBwI93bItaNjhM6b5WUCZtmqGqnYC0oBLgPdE5AciMk5E3hGRAhHZiRMkXYN+d6P3xG0ifBaYJSIJwBnAk+7mA4A1Yd7/QOAlEdnhBuUKoBKnhuP5PuB5KZC5n30lA5sD9vcw0C1UmV2/BgT4WESWi8h5YfZtjG8soEybpqqVqvoiTjhMBv6FUys6QFU7Ag/hXMhr/VrQ8uPATJymuFJV/dBdvxHoH+atNwLHqmqngJ80Vd0USbFD7Gsf0DVgXx1UdUi431HV71X1QlXtCfwc+D8RGRDBexsTMxZQpk0Tx4k492JWAO2BIlXdKyJjgTPr24cbSFXA3dTUngBeAXqIyBUikioi7UVknLvtIeAOETnQLUe2W45IbAH6ujU2VHUzzv2ku0Wkg9sB4yARCW6aDPx3nyoivd3F7TgBVhXh+xsTExZQpq2aLyLFwC6cTgNnq+py4GLgNhHZDdwMPBfh/p4ADgWqv5+kqrtx7m+dgNNctwr4sbv5fpya2hvuey3G6cgQiefdx0IR+dR9fhaQAuThBM5cnC704YwBPnKPwTzgclVdG+H7GxMTohrcWmCMaSgROQu4SFUn+10WY1oLq0EZ00RuV/WLgdl+l8WY1sQCypgmEJEpQAHOfaF/+VwcY1oVa+IzxhgTl6wGZYwxJi75Nmhk165dtW/fvn69vTHGmDixdOnSbaqaHbzet4Dq27cvS5Ys8evtjTHGxAkRWR9qvTXxGWOMiUsWUMb47JVXYM8ev0th6rVnD7z6qt+laFMsoIzx0bp1cMIJ8OKLfpfE1GvuXJg2DTYGj7trmovNrGmMj3bsqP1ooLy8nPz8fPbu3et3UWobOBD+8x8oKoLiYr9L0yKlpaXRu3dvkpOTI3q9BZQxPiotrf1oID8/n/bt29O3b19EggeS99HmzZCYCAMGQEaG36VpcVSVwsJC8vPz6devX0S/Y018xviopKT2o4G9e/eSlZUVX+EEUOUO9l5Z6W85WigRISsrq0E1YwsoY3xkNajQ4i6coCagqmxWksZq6N/VAsoYH1kNqgWxgIo5CyhjfGQBFZ8yMzMB+Oyzz5gwYQJDhgxh2JQpPPvGGxZQMWSdJIzxkTXxxbf09HSeeOIJcnJy+G7hQkadeCJTTj2VTl27+l20NsECyhgfWQ0qvg0cOLD6ec/sbLp16UJBQQGdcnJ8LFXbYQFljI+sBlWPK66Azz6L7j5zc+G++xr8ax9/9hll5eUc1KdPdMtjwrKAMsZHVoNqGTZv3szPrruOx2++mQSbQy9mLKCM8ZHVoOrRiJpOtO3atYvjjz+eOy69lPGHHmqdJGLIevEZ4yOrQcW3srIyfvKTn3DWWWdxylFHOSstoGLGalDG+MhqUPHtueee4/3336ewsJA5f/sbqDLn7rvJjXCoHtM0EQWUiEwF7gcSgUdU9Y9hXncyMBcYo6o2G6Ex9bAaVHwqdgeDnTVrFrNmzXJWLl0KqtCxo48la1vqbeITkUTgQeBYYDBwhogMDvG69sDlwEfRLqQxrZUXTKWlzrXPxCnVmj+QNfHFTCT3oMYCq1V1raqWAc8AJ4Z43e3AnUCcjZFvTPzymvZUId5mlzABAkPJAipmIgmoXkDgDF357rpqIjISOEBVbbpJYxogsGnPmvniWGAo2WjmMdPkXnwikgDcA1wdwWsvEpElIrKkoKCgqW9tTItXWgreAM/WUSKOWQ3KF5EE1CbggIDl3u46T3tgKPCuiKwDxgPzRGR08I5UdbaqjlbV0dnZ2Y0vtTGtREkJZGXVPDdxygulpCQLqBiKJKA+AXJEpJ+IpACnA/O8jaq6U1W7qmpfVe0LLAamWy8+Y+pXWgreZzWrQcUxr1nPAiqm6g0oVa0ALgFeB1YAz6nqchG5TUSmN3cBjWmtKith3z7o1s1ZthpU/EhMTCQ3N5ehQ4dywgknsKOoyNmQnOwEVCO6XO7YsYOsrCzU/d0PP/wQESE/Px+AnTt30qVLF6riIABffvll8vLy6n3dQw89xBNPPNFs5YjoHpSqvqaqA1X1IFW9w113s6rOC/Haw632ZEz9vEDyalAWUPGjXbt2fPbZZ3z11Vd06dKFB2fPdjYkuV8dbUSIdOrUiR49erBixQoAFi1axIgRI1i0aBEAixcvZuzYsSQkxG6An8owHT4iDahf/OIXnHXWWdEuVjUb6sgYn3hNetbEF98mTJjApu++cxaSkvjzk08yZtw4hg0bxu9+97vq191+++0cfPDBTJ48mTPOOIO77rqrzr4mTpxYHUiLFi3iyiuvrLU8adIkAP7+978zZswYhg8fzsknn0ype3I8//zzDB06lOHDh3PYYYcBsHz5csaOHUtubi7Dhg1j1apVAPzzn/+sXv/zn/+8OowyMzO5+uqrGT58OB9++CHXX389gwcPZtiwYVxzzTUsWrSIefPmce2115Kbm8uaNWtYs2YNU6dOZdSoUfzwhz9k5cqVANxyyy3V/87DDz+c6667jrFjxzJw4EA++OCDJh97G+rIGJ9YDap+V/z3Cj77PrrTbeT+IJf7pkY2CG1lZSVvvfUW559yCgBvLFzIqg0b+HjBAjQlhenTp/P+++/Trl07XnjhBT7//HPKy8sZOXIko0aNqrO/SZMm8d5773HBBRewdu1aTj31VB5++GHACajrr78egJNOOokLL7wQgJtuuolHH32USy+9lNtuu43XX3+dXr16sWPHDsBpZrv88suZOXMmZWVlVFZWsmLFCp599lkWLlxIcnIyF198MU899RRnnXUWJSUljBs3jrvvvpvCwkLOP/98Vq5ciYiwY8cOOnXqxPTp05k2bRqnuP/uI488koceeoicnBw++ugjLr74Yt5+++06/76Kigo+/vhjXnvtNW699VbefPPNBv51arOAMsYnVoOKX3v27CE3N+Fd6SwAAB7ZSURBVJdNmzZxyCGHcPRhh8GmTbyxYAFvfPQRI8aOhYQEiouLWbVqFbt37+bEE08kLS2NtLQ0TjjhhJD7nThxIn/4wx/49ttv6du3L2lpaagqxcXFLF26lHHjxgHw1VdfcdNNN7Fjxw6Ki4uZMmUK4ATcOeecw09/+lNOOukkwKnh3XHHHeTn53PSSSeRk5PDW2+9xdKlSxkzZkz1v6ebe7MzMTGRk08+GYCOHTuSlpbG+eefz7Rp05g2bVqdMhcXF7No0SJOPfXU6nX79u0L+e/zyjRq1CjWrVvX0MNehwWUMT6xGlT9Iq3pRJt3D6q0tJQpU6bw4KOPctnUqagIN5xzDj+/8UbIyKgpZ4TTguTk5LBjxw7mz5/PhAkTAOdi/o9//IO+ffuSmZkJwDnnnMPLL7/M8OHDmTNnDu+++y7g1JY++ugjXn31VUaNGsXSpUs588wzGTduHK+++irHHXccDz/8MKrK2WefzR/+8Ic6ZUhLSyMxMRGApKQkPv74Y9566y3mzp3LX//61zo1o6qqKjp16sRnEUwcmZqaCjghWFFREdEx2R+7B2WMT7waU9eutZdN/EhPT+eBBx7g7oceoqKigilHHcVj8+ZRvGsXAJs2bWLr1q1MmjSJ+fPns3fvXoqLi3nllVfC7nP8+PHcf//91QE1YcIE7rvvvur7TwC7d++mR48elJeX89RTT1WvX7NmDePGjeO2224jOzubjRs3snbtWvr3789ll13GiSeeyBdffMGRRx7J3Llz2bp1KwBFRUWsX7++TlmKi4vZuXMnxx13HPfeey+ff/45AO3bt2f37t0AdOjQgX79+vH8888DoKrVr2tuFlDG+MSrMXXsCCkpVoOKVyNGjGDYoEE8/b//cczRR3PmlClMOOooDj30UE455RR2797NmDFjmD59OsOGDePYY4/l0EMPpWOYUc8nTZrExo0bGT3aGctgwoQJrF27lokTJ1a/5vbbb2fcuHFMmjSJQYMGVa+/9tprOfTQQxk6dCgTJ05k+PDhPPfccwwdOpTc3Fy++uorzjrrLAYPHszvf/97jjnmGIYNG8bRRx/N5s2b65Rl9+7dTJs2jWHDhjF58mTuueceAE4//XT+/Oc/M2LECNasWcNTTz3Fo48+yvDhwxkyZAj//ve/o3mIwxL1aQjl0aNH65Il1hvdtF3PPANnnAF5eTBpEsycCX/5i9+l8t+KFSs45JBD/C5GbevXw/btMHCg8wfr3x+6dKn1kuLiYjIzMyktLeWwww5j9uzZjBw50qcCx69Qf18RWaqqdUYfsntQxvjEa9JLT3d+rIkvjlVVQUKC8+MtB7nooovIy8tj7969nH322RZOUWABZYxPvCa9jAznx5r44pgXUG7nglAB9a9//SvGhWr97B6UMT6xGlQLEkENykSfBZQxPvFqTO3aWQ0q7lVVObUnC6iYsoAyxielpU7NScRqUHGvstIJJxHnxwIqJiygjPFJSUnNdz2tBhXnvCY+cB4toGLCAsoYn1hAxS9vRIdqVVXc8sAD9OrVi9wzzmDo0Uczb17tyRxUla5du7J9+3YANm/ejIiwYMGC6tdkZ2dTWFjY7OWvz7vvvls9SO3+zJs3jz/+8Y8xKFFoFlDG+MRr4gNr4ot7VVUgwpVXXslnc+fy/AMPcN5559Wau0lEGD9+PB9++CFQdzqNr7/+mqysLLK8KZRjINx0GpEG1PTp06sHsPWDBZQxPrEaVAviBhQACQkc0r8/SUlJbNu2rdbLQk2nERhY3nBG8+fPZ9y4cYwYMYKjjjqKLVu2APDee++Rm5tLbm4uI0aMYPfu3WzevJnDDjusegJFbxqLN954gwkTJjBy5EhOPfVUiouLAejbty/XXXcdI0eO5Pnnn+eBBx6onk7j9NNPZ926dTz00EPce++95Obm8sEHH1BQUMDJJ5/MmDFjGDNmDAsXLgRgzpw5XHLJJYAzPuBll13GxIkT6d+/P3Pnzm3GA+6w70EZ45NQNSjVmuuggSuugAjGKG2Q3FyIcGxXh2qdgPpo2TISEhLI9kb6dU2aNIlbb70VgI8//phbb72V+++/H3ACyhvOaPLkySxevBgR4ZFHHuFPf/oTd999N3fddRcPPvggkyZNori4mLS0NGbPns2UKVO48cYbqayspLS0lG3btvH73/+eN998k4yMDO68807uuecebr75ZgCysrL49NNPAejZsyfffvstqamp1dNp/OIXvyAzM5NrrrkGgDPPPJMrr7ySyZMns2HDBqZMmVI9sWKgzZs3s2DBAlauXMn06dOrp+NoLhZQxvikpAS6d3eeZ2Q4HcXKysAdENrEC68ZLyGBe++9l38++ijt09N59tlnkaBPE2PGjGHZsmWUlJRQXl5OZmYm/fv3Z/Xq1SxatIirr74agPz8fE477TQ2b95MWVkZ/fr1A5yAu+qqq5g5cyYnnXQSvXv3ZsyYMZx33nmUl5czY8YMcnNzee+998jLy6uukZWVlVUPPgtw2mmnVT8fNmwYM2fOZMaMGcyYMSPkP/HNN9+sNYPurl27qmtkgWbMmEFCQgKDBw+urvU1JwsoY3wSXIPy1llA1WhQTae5eAHl3oO6ZsYM2LcPhgyp89L09HRycnJ47LHHqoc6Gj9+PK+99hpbt27l4IMPBuDSSy/lqquuYvr06bz77rvccsstAFx//fUcf/zxvPbaa0yaNInXX3+dww47jPfff59XX32Vc845h6uuuorOnTtz9NFH8/TTT4csckbAVCCvvvoq77//PvPnz+eOO+7gyy+/DPFPrGLx4sWkpaXt91CkBpycsRjH1e5BtVZffw1B7eMmulasgKKixv9+8D0ob11jVFbC4sWNL4vZj4CAAurtZj5x4kTuu+++WtNp3H///YwfP766xrVz50569eoFwOOPP179u2vWrOHQQw/luuuuY8yYMaxcuZL169fTvXt3LrzwQi644AI+/fRTxo8fz8KFC1m9ejUAJSUlfPPNNyGKXsXGjRv58Y9/zJ133snOnTspLi6uNZ0GwDHHHMNfAkYqjmTup1iwgGqtpk6F3/7W71K0aj/+MdxxR+N/v7S0bkA1tiffyy/DhAngXq9ME5WWltK7d2/np39/7nnqqZqASkzcb0BNmjSJtWvXVgfUyJEjyc/PrzWdxi233MKpp57KqFGj6OpNCIYz8eHQoUMZNmwYycnJHHvssbz77rsMHz6cESNG8Oyzz3L55ZeTnZ3NnDlzOOOMMxg2bBgTJkxg5cqVdcpSWVnJrFmzOPTQQxkxYgSXXXYZnTp14oQTTuCll16q7iTxwAMPsGTJEoYNG8bgwYN56KGHonQkm0hVffkZNWqUmmZSWamalKR6/PF+l6TV2rdPFVRPPbXx+2jXTvWaa5znL73k7O/TTxu3r3vvdX7/nXcaX554kZeX53cRaisuVv3kE9Xt253lDRtUly71t0wtWKi/L7BEQ+SE1aBaox07oKIC3Nk0TfQVFDiPjT3EVVWwZ0/0mvi8ctifvBkEdJKofqyqcnr3mWZlAdUaeVdP79FEXVMP8Z49zmOoThJ+lMfsR6iAAguoGLCAao3satXsmnqIA+eCCnxsbA2qtf3JNZ4u/t5oDN5cUDaieaM19O9qAdUaeVepkpKaj+omqrxDXFjYuOtU4FxQgY9Wg4K0tDQKCwvjJ6TC1aAsoBpEVSksLKy3K3ugiL4HJSJTgfuBROARVf1j0PargAuACqAAOE9V10dcChNdgVepggLo08e/srRS3iGuqnK6mgd0xIqI1aDC6927N/n5+RTEyz9m927nj7xqlVOLKilxvsKxciUkJ/tduhYlLS2N3r17R/z6egNKRBKBB4GjgXzgExGZp6p5AS9bBoxW1VIR+SXwJ+C0unszMWEB1eyCD3FDA8qrKUWrm3lrCqjk5OTqkRXiwl13wbXXOkGVmQn//jfMmAFLl8KwYX6XrlWLpIlvLLBaVdeqahnwDHBi4AtU9R1V9f7XWgxEHpEm+oKvnibqAnvLNabnnFdTCm7ia0wNqrzc6bjZ2LKYegROfQxNr+6aiEUSUL2AjQHL+e66cM4H/hNqg4hcJCJLRGRJ3FTfW6OCAkhKqnluoq6phzi4iS8lxdlfY6553oAhSUn2524WpaWQllbTSaKpNwxNxKLaSUJEZgGjgT+H2q6qs1V1tKqODh4F2ETR1q2Qk1Pz3ERdQUHNIW5MKAR3kvCeN+aa5/2Jc3Ia32nD7EdJSe0/lNWgYiaSgNoEHBCw3NtdV4uIHAXcCExX1X3RKZ5plIICOOgg5waufaRuFgUFMGhQzfOGCq5Bec8bc83z3n/w4JpOGyaKAsekAqtBxVAkAfUJkCMi/UQkBTgdqDXXsYiMAB7GCSf7yO63ggLo1s25c28B1SwKCqBnT+jUyf8aVGBABS6bKLEalG/qDShVrQAuAV4HVgDPqepyEblNRKa7L/szkAk8LyKfici8MLszzU3VuUJlZzs/drWKuvJy2L69aYe4uWpQgcsmSqwG5ZuIvgelqq8BrwWtuzng+VFRLpdprF27nCtodrZTi7KrVdQVFjqP3bo1/hB71zavYxg0rQaVkADuVEP2J482q0H5xkaSaG28q1O3blaDaiZepwSvBtXYbuaBHcOgaTWorCz4wQ9ql89ESeDEXeDc201OtoCKAQuo1sYLJGviazbROMTB1zxoWkBlZ9d8Wdj+5FEW3MQHja/umgaxgGptgj/e79rlTE9toiY4oLZta3jX7sDp3j1N6Waene18qG9spw2zH8FNfND4TxOmQSygWpvgq2fgOhMVwYe4srJmJIdINUcNyiuT/bmjzGpQvrGAam0soJpdQYEz+3eXLo0/xNGsQVlANTOrQfnGAqq1KShw/udp187pKOGtM1HjDQ6bmNj4QxytGlRFhfPFXK8cFlBRphrd6q5pEAuoaFm4EM45x/9xZoI/Tnvr/LRxozP6c0PbweKUd88Hah4b2nMuXA2qvNz5iZTX5T2wPNaLL4r27XNCKlrVXdMgFlDR8vTT8PjjsGGDv+WIx4B69VVnioIFC/wtR5RE4xCH+1AODbvuBbboeo+N6bRhwgj1jWpv2WpQzc4CKlry8mo/+sUb5gicLl2Jif4H1PLlzqPfxyZKAgOqsV27Q913j0ZAdevWuE4bJozgibs8VoOKCQuoaPEuwt6jXwKvngkJ8TEenxdMfh+bKAk8xKmp0KFD42pQXqtRlTrVncbMCRWqBhW43jRR8MRdHqtBxYQFVDRs21bT8O9nLUG19g0SiI+bEvFSu4yCykqnU0LwIW5MQJWylZkvziTl9hQOn3M4X+/8tHpbpAK/9hb4aAEVJVaD8pUFVDSsWOE8pqb6exEuLnZu6jb16hlNRUXw/ffOsVmxosXfHCksdD4HNOUQf/rdMkpKq3hyxcPM+3oePxv+M9ZsX8Mdi38DwH/y3ot4X977ZmXVlCVwvWkiq0H5ygIqGrxQOvZY57mqP+UIbu8B/weMDTw2JSVOj74WLHCoQ09DDvGS75bwo0emgCZw9KBJbLhiA/848R+suWwNNxxxOQA3/Od2Hlv2WMTlycqqmd3XAirK9tdJoqICyspiX6Y2xAIqGpYvh8xMOOYYpxbj10U4VED5XYPyAuqUU5zHFn4fKrhJzXseSStqXkEeU/85lc5JvQCYNuQIOrfrDEBKYgonDz8WgNyuk7hw/oU8v/z5evcZeD8ssFx+t+q2GqEm7gpctma+ZmUBFQ15ec5kPEOG1Cz7IVxA7djRsC/XRFNenvNpc+rUmuUWLNwh3rZt/xXntdvXctQTR5GcmMw/p70EhO/Fd9mIG5jQewIzX5zJf1f/t97yBJYlNRXat7caVNTsrwYVuN00CwuoaIj3gALnCuqH5cvhkENq5oNopQFVXg47d4b+nXU71nHUE0exr3If//vZ/+iW0hcIf82rKkvjlTNfYUi3IZz07Em8tfat/ZYnsCxeeSygomR/nSQCt5tmYQHVVNu3w+bNTjhlZTk3JPxqxgp1g8TvmxJ5eTXBPXhwi2/iC+6UAPs/xIs2LmLs38eyfe92/jPzPwztNjTsfffAbuad0jrx+qzX6d+5P1Ofmsojnz4StjzBAeX3bcdWZX+dJAK3m2ZhAdVUXo3Am2978GD/aglbtzpj8AV+2vPzpsSOHfDdd3WPjV+dSKKgoMAZJDYpYC7qcAH1ry//xRGPH0HHtI4sPn8xY3uNBSJvNeqW0Y2F5y3kyH5HcuH8C7n2jWuprKqsfn1lpVMxthpUMwp3D6ox36o2DRbRlO9mP4IDasgQePJJ5yIsEtuyhGvv8bbFWqhjU1wM+flwwAGxL08UFBRA12zlvXXv89a3b1FWWca+zUOBWcxftpgvUz5nc/Fmvi78mme+eoYfHfgjXvjpC2Sl11S5wl3zUlOdUybwmtcxrSOvnPkKV/z3Cu768C4++e4TJh4wkR6ZPUgvPxDV6awoeY/fvv0meyv2MqnPJDp2OY6CpSnNfzDagpKSmhl0AzXmW9WmwSygmiovzzlZ+/RxlgcPdiYJ/O476NUrtmUJ197jbYu1ULVLb30LDKi3v32bt7/KZvu+XRz++OEkSiIJkkD59u7ALP74+qOw7REEoVtGNy4efTH3Tr2XlMTaYRGuBiUS+us1SQlJ/PW4vzI4ezB3LryThRsXUlFVAVsPAabzwoa/kbDgeZISkrjrw7uQNXfClit5ZOkTnDfyXBLEGkoaLdSgiWBNfDFiZ25TLV/uXHgT3EPpXYT9uNcSKqC6dHHK5ldAtWsHffs6y34emybYUryFWS/O4sgnjmRnUQoH9kjnpdNeoui6IvbdtI/vb/4KgF8NuYWNV2501l3zPQ8e/2CdcILwNShvXbhWo4vHXMz6K9az76Z9bLlmC48cOQ+AF859mLKbyth9w27ePftdfjxkKFqZzIXPX82kxybxxZYvonIc2qRQw86DdZKIEQuopvJ68HkCawmxFiqgEhKcO/p+BJTXg88L765dnfK1kJ58qsrfl/6dQQ8O4rnlz3HTD2+ic9VAjhk2ghmDZtAhtQMiQvdOHcnMhOS9vejdoTfJicn73W+4GpS3rr4P5QmSQLeMbnSsGgDAgAM6kpiQSEpiCj/q+yPOnngcAHdO/Aeri1Yz8uGRXPvGtZSW28W0wawG5SsLqKbYuRM2baodUN26ORfieAko8O+ueXB4g3MfqgUE1KZdm5j61FQueuUihncfzhe//IJbD7+dwkJp8iEO13PZWxfph/JQXd4Dlyd3/Qkrf7WSc3PP5a4P72LEwyP4eNPHke3cOEINOw9Wg4oRC6im8MbgC74I+9GTr7TU+YmXgNq1y+kMEe7YxHFPvqe/fJqhfxvKgg0L+L/j/o93zn6HQV0HUVTkDCXY1EMcrueyty7SD+Xe+3lTfgSWxduelZ7F36f/nbfOeos95XuY+OhEfvfO7yiv9OmL2y1NqOnewWpQMWIB1RTevRTvez4e7/s+sbwIh/s47a2LdUB54R3q2Ozc6XQiiTNfbPmCaf+axpkvnsmgroP47Oef8csxv0Tc3pjROsQlJZCSUruruqchY5AWFDhTfgV3MAvVcfOIfkfw5S+/ZOawmdz2/m2Mmj2KV795FY3jDwpxIVwNKiXFmWvNalDNKqKAEpGpIvK1iKwWketDbE8VkWfd7R+JSN9oFzQueZ0ADjyw9vohQ5zvAH3/fezK4n3PKfBLup5u3WL/PSgvvEM18UFcNfOtKVrDzBdnkvtQLgs3LuRPR/2JD879gJysnFqvC/U9aE9Dvhwb7r47NGwWh61bQ5cl3DcLOqZ15PEZj/PSaS9RWl7KtKencdicw/hg/QeRvWFbFK4GJdKw6q5plHq7mYtIIvAgcDSQD3wiIvNUNfAKcz6wXVUHiMjpwJ3Aac1R4LiSlweDBjmfpAIFdpTo0SM2Zanv431RkTP6cqiP7c0hL8/5Yk+/frXXBx6bo4+OTVmCqCort61k/jfzmff1PBZtXERaUhrXTbqOX0/6dfUArsFCDRTr8QaMjeTrb+Huu0PDa1ChypKe7uwn3GeSGYNmcHzO8Ty67FFue+82DptzGId0PYTpB0/nhIEnML73eBITEkP/clsTrT+WaZRIrlZjgdWquhZARJ4BTgQCA+pE4Bb3+VzgryIi2oztB3tL9/Lgn+c11+4jszYNBhwBTy2rvX53Ivzgx/Dw+7CgMDZl+eYb5z0/3g1rgsqzNcvZ9tt/QVpabMqzsAgGnwLPBHVxVoX+02D+atjxXIN3G3hKKaAELiuVWkUVSpVWsa+qnNKqveypKqO4cg+by4r4rqyQ7/YVsquyGICcdv35WYdpTOsynqzPO7Ls83XAupDvvejDLKAP2V8vgG21p1nI3nkAZWU5vPaHz2mXuv85r9YtO5B00uDtxXW2pe88hO1bsnj77vq74q9fOYhh/Yvh7S/rbMtuP4HlH+zh7bvXh/39gYzjkcqXeH3vJyzI+4o/f7KUO/Vj0hJT6ZmSRc/krvRM7ULHxAzaJaSSnphKakIKiZJAIgnOfwFpLAiB2Syx/qJ6c9BDoKRP3f/HAbImw8oEuLXh53Fr0bNnB864cGrzvYGq7vcHOAV4JGD5Z8Bfg17zFdA7YHkN0DXEvi4ClgBL+vTpo02xftUGda529mM/sftJp1jLSKqz4XlObtB+JvN+yA3X8YcG7edy7g254XDe9v1Y2U/r/+l8wDtNuo57gCWqdfMnpiNJqOpsYDbA6NGjtSn76tYzmwf+3wtRKVejiUCfA0M3m20vcprVYql9h9A3JbQKNmyEihj33OrdG1JD1NhKiqN2fy7wU7sg7ugOQgIJpCYkk56QSrvEVNISUkiUpjdb9czaR3LvuqOL/6QSFn+9jH3lkfU7GtQnCTrXnTn3d/sSOH7lZyj11z4EZeTA0dCu7n6e25HMivWfR1SW/VFV9laVsadqH6WVe9mrZVSpU1OtVGdcQHXrsYG12ValTx9IDjF01K5dUNC2J97q0aNDs+4/koDaBASOS9PbXRfqNfkikgR0BJq1bSstPY1Lbzi5Od+ilRnhdwFatURg3I+bvp92wA+jcGsu2/0xpiWL5OPeJ0COiPQTkRTgdCD45s884Gz3+SnA2261zRhjjGmUemtQqlohIpcAr+N8UHxMVZeLyG047YbzgEeBJ0VkNVCEE2LGGGNMo4lfFR0RKQDCdzGKXFfAp+li454dm/Ds2IRnxyY8Ozb719jjc6Cq1mmV9i2gokVElqjqaL/LEY/s2IRnxyY8Ozbh2bHZv2gfHxvqyBhjTFyygDLGGBOXWkNAzfa7AHHMjk14dmzCs2MTnh2b/Yvq8Wnx96CMMca0Tq2hBmWMMaYVsoAyxhgTl1psQNU3R1VbIiIHiMg7IpInIstF5HJ3fRcR+Z+IrHIfQ88j0QaISKKILBORV9zlfu7cZavducxCDLbWNohIJxGZKyIrRWSFiEywc8chIle6/099JSJPi0haWz13ROQxEdkqIl8FrAt5nojjAfcYfSEiIxvzni0yoALmqDoWGAycISKD9/9brVoFcLWqDgbGA79yj8f1wFuqmgO85S63VZcDKwKW7wTuVdUBwHacOc3aqvuB/6rqIGA4znFq8+eOiPQCLgNGq+pQnJF0vPnu2uK5MwcInlsj3HlyLJDj/lwE/K0xb9giA4qAOapUtQzw5qhqk1R1s6p+6j7fjXOB6YVzTB53X/Y4MMOfEvpLRHoDxwOPuMsCHIEzdxm07WPTETgMZ7gyVLVMVXdg544nCWjnDoKdDmymjZ47qvo+zlB2gcKdJycCT7izaSwGOolIg2dvbakB1QvYGLCc765r80SkL87Q5R8B3VV1s7vpe6C7T8Xy233ArwFvJsEsYIeqVrjLbfn86QcUAP9wm0AfEZEM7NxBVTcBdwEbcIJpJ7AUO3cChTtPonKNbqkBZUIQkUzgBeAKVd0VuM0dXb7NfadARKYBW1V1qd9liVNJwEjgb6o6AighqDmvDZ87nXFqAv2AnkAGdZu4jKs5zpOWGlCRzFHVpohIMk44PaWqL7qrt3jVavexLc6uNgmYLiLrcJqCj8C559LJbbaBtn3+5AP5qvqRuzwXJ7Ds3IGjgG9VtUBVy4EXcc4nO3dqhDtPonKNbqkBFckcVW2Ge0/lUWCFqt4TsClwnq6zgX/Humx+U9UbVLW3qvbFOU/eVtWZwDs4c5dBGz02AKr6PbBRRA52Vx0J5GHnDjhNe+NFJN39f8w7Nnbu1Ah3nswDznJ7840HdgY0BUasxY4kISLH4dxb8OaousPnIvlGRCYDHwBfUnOf5Tc496GeA/rgTG3yU1WN8Tz08UNEDgeuUdVpItIfp0bVBVgGzFLVfX6Wzy8ikovTgSQFWAuci/Phtc2fOyJyK3AaTk/ZZcAFOPdS2ty5IyJPA4fjTKmxBfgd8DIhzhM30P+K0yRaCpyrqksa/J4tNaCMMca0bi21ic8YY0wrZwFljDEmLllAGWOMiUsWUMYYY+KSBZQxxpi4ZAFljDEmLllAGWOMiUv/H6Th9UMKPXJAAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ "alpha = 0.5 # 0<=alpha<=1\n", "weights = np.array([1 - alpha, alpha])\n", "\n", @@ -417,14 +514,14 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -434,8 +531,6 @@ } ], "source": [ - "#%% plot\n", - "\n", "nbm = len(problems)\n", "nbm2 = (nbm // 2)\n", "\n", @@ -489,7 +584,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_compute_emd.ipynb b/notebooks/plot_compute_emd.ipynb index 26c125e..5ee6641 100644 --- a/notebooks/plot_compute_emd.ipynb +++ b/notebooks/plot_compute_emd.ipynb @@ -61,8 +61,6 @@ }, "outputs": [], "source": [ - "#%% parameters\n", - "\n", "n = 100 # nb bins\n", "n_target = 50 # nb target distributions\n", "\n", @@ -105,18 +103,18 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% plot the distributions\n", - "\n", "pl.figure(1)\n", "pl.subplot(2, 1, 1)\n", "pl.plot(x, a, 'b', label='Source distribution')\n", @@ -146,7 +144,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -155,18 +153,18 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% Compute and plot distributions and loss matrix\n", - "\n", "d_emd = ot.emd2(a, B, M) # direct computation of EMD\n", "d_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2\n", "\n", @@ -196,17 +194,18 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%%\n", "reg = 1e-2\n", "d_sinkhorn = ot.sinkhorn2(a, B, M, reg)\n", "d_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg)\n", @@ -240,7 +239,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_convolutional_barycenter.ipynb b/notebooks/plot_convolutional_barycenter.ipynb index d0df486..4dba332 100644 --- a/notebooks/plot_convolutional_barycenter.ipynb +++ b/notebooks/plot_convolutional_barycenter.ipynb @@ -106,12 +106,14 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -168,7 +170,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_fgw.ipynb b/notebooks/plot_fgw.ipynb index b41f280..a4c7329 100644 --- a/notebooks/plot_fgw.ipynb +++ b/notebooks/plot_fgw.ipynb @@ -56,6 +56,14 @@ "\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We create two 1D random measures\n", + "\n" + ] + }, { "cell_type": "code", "execution_count": 3, @@ -64,8 +72,6 @@ }, "outputs": [], "source": [ - "#%% parameters\n", - "# We create two 1D random measures\n", "n = 20 # number of points in the first distribution\n", "n2 = 30 # number of points in the second distribution\n", "sig = 1 # std of first distribution\n", @@ -104,18 +110,18 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% plot the distributions\n", - "\n", "pl.close(10)\n", "pl.figure(10, (7, 7))\n", "\n", @@ -153,7 +159,6 @@ }, "outputs": [], "source": [ - "#%% Structure matrices and across-features distance matrix\n", "C1 = ot.dist(xs)\n", "C2 = ot.dist(xt)\n", "M = ot.dist(ys, yt)\n", @@ -180,17 +185,18 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%%\n", "cmap = 'Reds'\n", "pl.close(10)\n", "pl.figure(10, (5, 5))\n", @@ -252,11 +258,11 @@ "text": [ "It. |Loss |Relative loss|Absolute loss\n", "------------------------------------------------\n", - " 0|4.734462e+01|0.000000e+00|0.000000e+00\n", - " 1|2.508258e+01|8.875498e-01|2.226204e+01\n", - " 2|2.189329e+01|1.456747e-01|3.189297e+00\n", - " 3|2.189329e+01|0.000000e+00|0.000000e+00\n", - "Elapsed time : 0.005539894104003906 s\n", + " 0|4.734412e+01|0.000000e+00|0.000000e+00\n", + " 1|2.508254e+01|8.875326e-01|2.226158e+01\n", + " 2|2.189327e+01|1.456740e-01|3.189279e+00\n", + " 3|2.189327e+01|0.000000e+00|0.000000e+00\n", + "Elapsed time : 0.0014753341674804688 s\n", "It. |Loss |Relative loss|Absolute loss\n", "------------------------------------------------\n", " 0|4.683978e+04|0.000000e+00|0.000000e+00\n", @@ -267,7 +273,6 @@ } ], "source": [ - "#%% Computing FGW and GW\n", "alpha = 1e-3\n", "\n", "ot.tic()\n", @@ -296,17 +301,18 @@ "outputs": [ { "data": { - "image/png": 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yNI8Cp2bmNRGxA3B1RFyWmTd19bsiM48aQ3ySNAxzmqRJYT6T1LpiCtXMvBu4u779YETcDOwOdCdBqVWjWCO1FIOssXXCGSf13e76poOZzzmtrbVyR+HUdf13p2u1SrM3n/OZVIKm96rz3/qexjGa3ndLeV8epWJO/e0UEXsBBwBXTrP5ZyPiuoj4dETs22eM1RGxPiLWb9m6ZY4ilaRms81p5jNJpTCfSWpLcYVqRDwd+CRwSmY+0LX5GmDPzNwf+Evg4l7jZOa5mbkiM1csXrR47gKWpD5GkdPMZ5JKYD6T1KaiCtWI2I4qAV6QmX/XvT0zH8jMb9e3LwW2i4hFLYcpSQMxp0maFOYzSW0rplCNiADOA27OzD/v0edZdT8i4kCq+L/ZXpSSNBhzmqRJYT6TNA7FXEwJOAh4FfCViLi2bnszsBQgM98HvAL47Yh4FHgYOCYzcxzBSlIDc5qkSWE+k9S6YgrVzPwcEA193g1M3iWtJE0cc5qkSWE+kzQOxZz6K0mSJEkSWKhKkiRJkgpTzKm/0lzY+QUnN/aZxAWSeynlb21a+Bpgzap9WohE0ynleTLI6/eEM05qIRJJkqY3iveqUt53S+MRVUmSJElSUSxUJUmSJElFsVCVJEmSJBXFQlWSJEmSVBQLVUmSJElSUSxUJUmSJElFsVCVJEmSJBXFdVQ10UpZl2qhrefatE6qa6ROvtMuubmxz3lnndN3+yS9JiRJk6mt96qmueQgcTS9N5+9ctlQMc01j6hKkiRJkopSXKEaEXdExFci4tqIWD/N9oiId0XEbRFxfUQ8bxxxSlIT85mkSWE+k9S2Uk/9fXFmbu2x7QjgOfXPC4H31v9KUonMZ5ImhflMUmuKO6I6gKOBD2bli8BOEbHruIOSpBkwn0maFOYzSSNVYqGawGci4uqIWD3N9t2BDR33N9ZtTxARqyNifUSs37J1yxyFKkl9mc8kTQrzmaRWlVioHpyZz6M6heSkiDhkJoNk5rmZuSIzVyxetHi0EUrSYMxnkiaF+UxSq4orVDNzU/3vZuAi4MCuLpuAJR3396jbJKko5jNJk8J8JqltRRWqEbF9ROwwdRs4DLihq9s64NX11eV+Brg/M+9uOVRJ6st8JmlSmM8kjUNpV/3dBbgoIqCK7cOZ+Y8R8TqAzHwfcClwJHAb8BDwm2OKVRrYKBZhPu+sc0byOG1Ys2qfcYdQggWdzwZZNPzslWU8XyfptSfNkQWdzzR/7fyCk/tuP+GMkxrHGOT9rA2jeJ8p5W8ZVFGFambeDuw/Tfv7Om4n0PyskqQxMp9JmhTmM0njUNSpv5IkSZIkWahKkiRJkopioSpJkiRJKoqFqiRJkiSpKBaqkiRJkqSiWKhKkiRJkopS1PI00kLWtLbVIGtONq0XNsgaXKeuu6nvdtdI1XzS9JoAOP70E/tud41USZqfmvJ305wHRjO30sx4RFWSJEmSVBQLVUmSJElSUSxUJUmSJElFsVCVJEmSJBXFQlWSJEmSVJRiCtWI2Dsiru34eSAiTunqc2hE3N/R54xxxStJ/ZjTJE0K85mkcShmeZrMvAVYDhAR2wCbgIum6XpFZh7VZmySNCxzmqRJYT6TNA7FHFHt8lLga5l557gDkaQRMKdJmhTmM0mtKOaIapdjgAt7bPvZiLgOuAs4LTNvnK5TRKwGVgMsWbp0ToKUppSyGPQoHmfNqn1GEEl/p11yc2Ofs1cum/M4WjSrnGY+m7m2XntNOeD4009sHKON1540AuYzteLUdTf13X7+W9/TOEbTe8AgeXfNqnbeR/TDijuiGhFPBlYBH59m8zXAnpm5P/CXwMW9xsnMczNzRWauWLxo8dwEK0kNRpHTzGeSSmA+k9Sm4gpV4Ajgmsy8p3tDZj6Qmd+ub18KbBcRi9oOUJKGYE6TNCnMZ5JaU2Kheiw9TimJiGdFRNS3D6SK/5stxiZJwzKnSZoU5jNJrSnqO6oRsT3w88BrO9peB5CZ7wNeAfx2RDwKPAwck5k5jlglqYk5TdKkMJ9JaltRhWpmfgf4sa6293XcfjfgN5olzQvmNEmTwnwmqW0lnvorSZIkSVrALFQlSZIkSUUp6tRfqURN63hBe2s1TopRrJHatG7ld2/5xqwfQxpUUw4YZO3gUtZjlqS51pTvAE4446S+282Jk88jqpIkSZKkolioSpIkSZKKYqEqSZIkSSqKhaokSZIkqSgWqpIkSZKkolioSpIkSZKKYqEqSZIkSSpK30I1IixkJUmSCuMcTdKk27Zh+zcj4qDMvCkiTgCuB27IzIdbiG1kNtz/CKeuu6nn9jWr9mklDhdzn5/aen5oOE2vl4Ne+MWWIpGanb1y2QB9+j+nm95DAE4446RZx6F5YyLmaJp/+s2pp5z/1vf03e6cV4No+jTu94EH6tvvAL4IPBARt0TExyPijIh4eUT8+DAPGhHnR8TmiLiho+0ZEXFZRNxa/7tzj999Td3n1oh4zTCPK0mjZj6TNCYjn6OZzySVpG+hmpl/k5kb67s/CvwU8CvABXXbrwGfAG6LiAemGaKXtcDhXW1vBP4lM58D/Et9/wki4hnAW4AXAgcCb+mVMCWpJWsxn0lq2RzN0dZiPpNUiKZTf38gMxP4Wv1z8VR7RDwV2K/+GXSsyyNir67mo4FD69sfAD4LvKGrzy8Al2XmvfVjX0aVUC8c9LElaZTMZ5LGbVRzNPOZpJIMXKj2kpmPAOvrn9nYJTPvrm//F7DLNH12BzZ03N9Yt/2QiFgNrAZ4+qJdZxmaJA1lzvLZkqVLRximpEk2ojma+UzSWBR5xbj6k8Gc5RjnZuaKzFzxtB2fMaLIJGk4o85nixctHlFkkjQc85mkNpVUqN4TEbsC1P9unqbPJmBJx/096jZJKon5TNKkMJ9JGouSCtV1wNRV4l4D/P00ff4JOCwidq6/pH9Y3SZJJTGfSZoU5jNJYzHr76jORERcSPXF/EURsZHqSnFvBz5WrwV2J/DKuu8K4HWZ+VuZeW9EvBW4qh7qrKkv7vezZMenFrEW5iStGdW0htYo9vcgawZO0j7V/NR2PtPCNUi+a8rN5lX1Yz4TNOeJpvWawTyi0RhLoZqZx/bY9NJp+q4Hfqvj/vnA+XMUmiQNxXwmaVKYzySVpKRTfyVJkiRJslCVJEmSJJXFQlWSJEmSVBQLVUmSJElSUSxUJUmSJElFsVCVJEmSJBXFQlWSJEmSVJSxrKOqsjUtGA+wZtU+s36cpgWlXSxakobTlJvXrGrOq025+YQzTmoc4+yVyxr7SBq90y65ue/28846p3EM518qhUdUJUmSJElFsVCVJEmSJBXFQlWSJEmSVBQLVUmSJElSUVovVCPi/IjYHBE3dLT9WUR8NSKuj4iLImKnHr97R0R8JSKujYj17UUtSdMzp0maFOYzSSUZxxHVtcDhXW2XAftl5nOB/wTe1Of3X5yZyzNzxRzFJ0nDWIs5TdJkWIv5TFIhWi9UM/Ny4N6uts9k5qP13S8Ce7QdlyTNhDlN0qQwn0kqSYnrqB4PfLTHtgQ+ExEJvD8zz+01SESsBlYDLFm6dORBTrKImPUYTevwget0acGYdU4zn6lNTbl5kLW2XSd7YpnPxmiQudXxp5/Yd7uvPc0nRV1MKSL+EHgUuKBHl4Mz83nAEcBJEXFIr7Ey89zMXJGZKxYvWjwH0UpSf6PKaeYzSeNmPpPUtmIK1Yg4DjgK+PXMzOn6ZOam+t/NwEXAga0FKElDMKdJmhTmM0njUEShGhGHA68HVmXmQz36bB8RO0zdBg4DbpiurySNkzlN0qQwn0kal3EsT3Mh8AVg74jYGBEnAO8GdgAuqy9r/r66724RcWn9q7sAn4uI64AvAf+Qmf/YdvyS1MmcJmlSmM8klaT1iyll5rHTNJ/Xo+9dwJH17duB/ecwNEkamjlN0qQwn0kqSRGn/kqSJEmSNMVCVZIkSZJUFAtVSZIkSVJRWv+OaokGWbx8zap9Wohk9gZZDLppseezVy6bdRwuKC1Jk2mQ98M1q/q/BwzyXnXCGSf13T6K9yqpLaN4zju30kLjEVVJkiRJUlEsVCVJkiRJRbFQlSRJkiQVxUJVkiRJklQUC1VJkiRJUlEsVCVJkiRJRbFQlSRJkiQVxXVUmT9rpELzOlxtrbF12iU3993u+naSpF4Gea9qWuN8FOuGa/KV8jwZxXNeWmg8oipJkiRJKkrrhWpEnB8RmyPiho62MyNiU0RcW/8c2eN3D4+IWyLitoh4Y3tRS9L0zGmSJoX5TFJJxnFEdS1w+DTtf5GZy+ufS7s3RsQ2wDnAEcA+wLERMX/O2ZU0qdZiTpM0GdZiPpNUiNYL1cy8HLh3Br96IHBbZt6emd8DPgIcPdLgJGlI5jRJk8J8JqkkJX1H9eSIuL4+7WTnabbvDmzouL+xbptWRKyOiPURsX7L1i2jjlWSmowsp5nPJI2Z+UxS60opVN8L/ASwHLgbWDPbATPz3MxckZkrFi9aPNvhJGkYI81p5jNJY2Q+kzQWRRSqmXlPZn4/Mx8D/orqFJJum4AlHff3qNskqSjmNEmTwnwmaVyKKFQjYteOu78I3DBNt6uA50TEsyPiycAxwLo24pOkYZjTJE0K85mkcdm27QeMiAuBQ4FFEbEReAtwaEQsBxK4A3ht3Xc34K8z88jMfDQiTgb+CdgGOD8zb2wr7qZFmNesaufidqUsXn72ymXjDgFoXsi7lP2lyTVfc5pUuqb31TWrZp/fm95DAI4//cSGOCbn4raTmM8GmQc0PQ+angMwmufBJD2XpFFovVDNzGOnaT6vR9+7gCM77l8K/NBl0SVpXMxpkiaF+UxSSYo49VeSJEmSpCkWqpIkSZKkolioSpIkSZKKYqEqSZIkSSqKhaokSZIkqSgWqpIkSZKkorS+PM18FRHjDmFkJmnt0fkUqySpXaN4vzvtkptHFY4K1fQ8GOQ5MElzK6kUHlGVJEmSJBXFQlWSJEmSVBQLVUmSJElSUSxUJUmSJElFsVCVJEmSJBWl9av+RsT5wFHA5szcr277KLB33WUn4L7MXD7N794BPAh8H3g0M1e0ErQk9WBOkzQpzGeSSjKO5WnWAu8GPjjVkJm/OnU7ItYA9/f5/Rdn5tY5i06ShrMWc5qkybAW85mkQrReqGbm5RGx13Tbolqs9JXAS9qMSZJmypwmaVKYzySVZBxHVPt5EXBPZt7aY3sCn4mIBN6fmef2GigiVgOrAZYsXdr3QQdZyPnslcv6bm9a6BnKWey5lDikBWAkOW2YfCbpcaN4v2t6/z913U2NY6xZtU/f7U1ziO/e8o3Gx2jByPMZ2z29799+/OknNgbVtG9Hoek5UPVxbiWNWmmF6rHAhX22H5yZmyLimcBlEfHVzLx8uo51gjwX4PnPX5GjD1WSGo0kp5nPJBVg5PnsST/yTPOZpJ6KuepvRGwL/BLw0V59MnNT/e9m4CLgwHaik6ThmNMkTQrzmaRxKKZQBV4GfDUzN063MSK2j4gdpm4DhwE3tBifJA3DnCZpUpjPJLWu9UI1Ii4EvgDsHREbI+KEetMxdJ1SEhG7RcSl9d1dgM9FxHXAl4B/yMx/bCtuSZqOOU3SpDCfSSrJOK76e2yP9uOmabsLOLK+fTuw/5wGJ0lDMqdJmhTmM0klKenUX0mSJEmSLFQlSZIkSWUpbXmaObHh/kf6rpU6yPpYTUaxVtt8WotVkiRBRDT2aXp/b3pvP+iFXxwqpvnigGVL+fyVvf/2Qda5n+2+lVQuj6hKkiRJkopioSpJkiRJKoqFqiRJkiSpKBaqkiRJkqSiWKhKkiRJkopioSpJkiRJKoqFqiRJkiSpKBaqkiRJkqSiRGaOO4Y5FxFbgDvHHYekVu2ZmYvHHcSomc+kBWkh5bNFwNYxhDMs4xy9+RLrfIkTyo11oJy2IApVSZIklS8i1mfminHH0cQ4R2++xDpf4oT5Fet0PPVXkiRJklQUC1VJkiRJUlEsVCVJklSKc8cdwICMc/TmS6zzJU6YX7H+EL+jKkmSJEkqikdUJUmSJElFsVCVJEmSJBXFQlWSJEljFRGHR8QtEXFbRLxx3PH0ExF3RMRXIuLaiFg/7niYuI1vAAAEC0lEQVSmRMT5EbE5Im7oaHtGRFwWEbfW/+48zhin9Ij1zIjYVO/XayPiyHHGWMe0JCL+LSJuiogbI+L36vai9mufOIvbp8PwO6qSJEkam4jYBvhP4OeBjcBVwLGZedNYA+shIu4AVmTm1nHH0ikiDgG+DXwwM/er2/4UuDcz315/ALBzZr5hnHHWcU0X65nAtzPz7HHG1ikidgV2zcxrImIH4Grg5cBxFLRf+8T5Sgrbp8PwiKokSZLG6UDgtsy8PTO/B3wEOHrMMc07mXk5cG9X89HAB+rbH6AqXsauR6zFycy7M/Oa+vaDwM3A7hS2X/vEOa9ZqEqSJGmcdgc2dNzfSNmT7AQ+ExFXR8TqcQfTYJfMvLu+/V/ALuMMZgAnR8T19anBRZymPCUi9gIOAK6k4P3aFScUvE+bWKhKkiRJgzs4M58HHAGcVJ/GWrysvu9X8nf+3gv8BLAcuBtYM95wHhcRTwc+CZySmQ90bitpv04TZ7H7dBAWqpIkSRqnTcCSjvt71G1FysxN9b+bgYuoTl0u1T319xenvse4eczx9JSZ92Tm9zPzMeCvKGS/RsR2VMXfBZn5d3Vzcft1ujhL3aeDslCVJEnSOF0FPCcinh0RTwaOAdaNOaZpRcT29cVqiIjtgcOAG/r/1litA15T334N8PdjjKWvqcKv9osUsF8jIoDzgJsz8887NhW1X3vFWeI+HYZX/ZUkSdJY1ctmvAPYBjg/M//fmEOaVkT8ONVRVIBtgQ+XEmtEXAgcCiwC7gHeAlwMfAxYCtwJvDIzx34Rox6xHkp1imoCdwCv7fge6FhExMHAFcBXgMfq5jdTff+zmP3aJ85jKWyfDsNCVZIkSZJUFE/9lSRJkiQVxUJVkiRJklQUC1VJkiRJUlEsVCVJkiRJRbFQlSRJkiQVxUJVkiRJklQUC1VJkiRJUlEsVCVJkiS1KiJ+KSK+GxHbjzsWlSkyc9wxSJIkSVpAImJ3YHFmXjvuWFQmC1VJkiRJUlE89VeSJElSqyJiS0ScMu44VC4LVUmSJEmtiYjdgEWAp/2qJwtVSZIkSW3av/73urFGoaJZqEqSJElq0/7Ahsz81rgDUbksVCVJkiS16bl4NFUNLFQlSZIktWl/LFTVwEJVkiRJUisi4inA3lioqoGFqiRJkqS27Atsg4WqGlioSpIkSWrL/sBDwG3jDkRls1CVJEmS1JaDgC9l5mPjDkRls1CVJEmSNKciYveIOAY4BvjUuONR+SIzxx2DJEmSpAkWEe8Hfhn4GHBKZn5vzCGpcBaqkiRJkqSieOqvJEmSJKkoFqqSJEmSpKJYqEqSJEmSimKhKkmSJEkqioWqJEmSJKkoFqqSJEmSpKJYqEqSJEmSivL/ASQ5WP1N4VHtAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% visu OT matrix\n", "cmap = 'Blues'\n", "fs = 15\n", "pl.figure(2, (13, 5))\n", @@ -351,7 +357,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_free_support_barycenter.ipynb b/notebooks/plot_free_support_barycenter.ipynb index b8df589..018353c 100644 --- a/notebooks/plot_free_support_barycenter.ipynb +++ b/notebooks/plot_free_support_barycenter.ipynb @@ -124,12 +124,14 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -137,7 +139,7 @@ "pl.figure(1)\n", "for (x_i, b_i) in zip(measures_locations, measures_weights):\n", " color = np.random.randint(low=1, high=10 * N)\n", - " pl.scatter(x_i[:, 0], x_i[:, 1], s=b * 1000, label='input measure')\n", + " pl.scatter(x_i[:, 0], x_i[:, 1], s=b_i * 1000, label='input measure')\n", "pl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter')\n", "pl.title('Data measures and their barycenter')\n", "pl.legend(loc=0)\n", @@ -161,7 +163,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_gromov.ipynb b/notebooks/plot_gromov.ipynb index f565bfb..11cfeec 100644 --- a/notebooks/plot_gromov.ipynb +++ b/notebooks/plot_gromov.ipynb @@ -97,12 +97,14 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -177,38 +181,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 0|3.135088e-02|0.000000e+00\n", - " 1|1.414971e-02|-1.215656e+00\n", - " 2|9.934682e-03|-4.242736e-01\n", - " 3|7.486162e-03|-3.270729e-01\n", - " 4|7.415422e-03|-9.539599e-03\n", - " 5|6.433930e-03|-1.525492e-01\n", - " 6|6.020392e-03|-6.868966e-02\n", - " 7|6.012738e-03|-1.272962e-03\n", - " 8|6.012661e-03|-1.278831e-05\n", - " 9|6.012660e-03|-1.278889e-07\n", - " 10|6.012660e-03|-1.278889e-09\n", - " 11|6.012660e-03|-1.278958e-11\n", + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 0|7.602249e-02|0.000000e+00|0.000000e+00\n", + " 1|4.138485e-02|8.369641e-01|3.463764e-02\n", + " 2|2.382207e-02|7.372484e-01|1.756278e-02\n", + " 3|2.149536e-02|1.082425e-01|2.326712e-03\n", + " 4|2.149536e-02|0.000000e+00|0.000000e+00\n", "It. |Err \n", "-------------------\n", - " 0|7.283759e-02|\n", - " 10|4.751585e-03|\n", - " 20|4.981526e-09|\n", - " 30|3.401818e-14|\n", - "Gromov-Wasserstein distances: 0.0060126599835825445\n", - "Entropic Gromov-Wasserstein distances: 0.00591822665714488\n" + " 0|8.206392e-02|\n", + " 10|2.775943e-07|\n", + " 20|5.372013e-14|\n", + "Gromov-Wasserstein distances: 0.02149535867154042\n", + "Entropic Gromov-Wasserstein distances: 0.019910889144636214\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -257,7 +255,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_gromov_barycenter.ipynb b/notebooks/plot_gromov_barycenter.ipynb index 2271fdb..eb51305 100644 --- a/notebooks/plot_gromov_barycenter.ipynb +++ b/notebooks/plot_gromov_barycenter.ipynb @@ -41,7 +41,6 @@ "import numpy as np\n", "import scipy as sp\n", "\n", - "import scipy.ndimage as spi\n", "import matplotlib.pylab as pl\n", "from sklearn import manifold\n", "from sklearn.decomposition import PCA\n", @@ -132,40 +131,17 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:6: DeprecationWarning: `imread` is deprecated!\n", - "`imread` is deprecated in SciPy 1.0.0.\n", - "Use ``matplotlib.pyplot.imread`` instead.\n", - " \n", - "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:7: DeprecationWarning: `imread` is deprecated!\n", - "`imread` is deprecated in SciPy 1.0.0.\n", - "Use ``matplotlib.pyplot.imread`` instead.\n", - " import sys\n", - "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:8: DeprecationWarning: `imread` is deprecated!\n", - "`imread` is deprecated in SciPy 1.0.0.\n", - "Use ``matplotlib.pyplot.imread`` instead.\n", - " \n", - "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:9: DeprecationWarning: `imread` is deprecated!\n", - "`imread` is deprecated in SciPy 1.0.0.\n", - "Use ``matplotlib.pyplot.imread`` instead.\n", - " if __name__ == '__main__':\n" - ] - } - ], + "outputs": [], "source": [ "def im2mat(I):\n", " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n", " return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n", "\n", "\n", - "square = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256\n", - "cross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256\n", - "triangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256\n", - "star = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256\n", + "square = pl.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256\n", + "cross = pl.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256\n", + "triangle = pl.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256\n", + "star = pl.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256\n", "\n", "shapes = [square, cross, triangle, star]\n", "\n", @@ -263,7 +239,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -272,12 +248,14 @@ }, { "data": { - "image/png": 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\n", 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uQib8edvlVV9FSdoYYsh9BnVRyKRp8X7o+caWp6rY2Eja0eXnAxy0vyx6MtZFiR64y0JeIow9OexAgnahTfBWKUOJo5CXkKpoekViNTzG0KvRdQz5QOKoyndR927Kos6ErO2arO4wht5V94nWRau3zg55ktYqU1UMvEIiGeta6LnCYk0OO8AacJIVYIa+e8aqzKJfJTzG0Ksxl2RsCN9F0474oY87dJ9gXZSX8Ax1ktYqHQhD+E9QgGSsSyEvEWYlcX1ODjvAGnByFWCOLsaMtR0TVrUXZeg5/VySsa6+izqJUt3LlwNpIyuZXF3U5gwwxiStVe4aydrvgCokkrGulE1xXvcSYezJYQdochVggdCXkJrc79EkDIbeqzGXZMy92XdRdpNHiJtGxpC0l5lcXdQ0uYk1SWvT/Q4oyEjGulInOBavh+jl6iLhG+gp6uQqwJ6E6vmagjklY3WFuAOzzrHGHG+Tq4uaJjdFbVNR71RZstXkbs6Rdbu2iaFBTfo6OHkz+Z45s3uSO0l6y1uSn0OcHHaksxJPQReTq1aZfb/KLPpM/DptZZO3hlzFYaSrNkxX3uzR0rAmaW2734kgGStSdybfxdTSWUsgHTqU/Z8jT+iEb6yzEo9c1SVq6iZEISafnvPM6GNUFidZ75clW1ObxBxLDh063yaYlSc3RTPtly2VdOhQftDs39/NfqcWpE271Lou0S8LhL5EGHty2IFeM9DULg2sCDE3VJYQX/PAb0yqrOsY8gHEUdMxgmVXeELeNNJkuyGZel30htCD9heaTNJadkk0b0qOgbZpbWKo0wqsTYkauFlBJbUbExZ7ctiBmnoFWDbFRJVkrWjwdZP3qn62sZhDMtZ03riiyVgXQt00Une7oZl6XfSGrC8o1iStTecxG2gwkYyFVlTzNZnqIvbksAM29QqwrBEtu+Gpy14zesbGE0dliXPItSTzVI2XscbV1OuiHVaDIq9N6WqS1kVAZCVeVeYxGyiSsdCaLH/kPszJYQcewFOvANvczda0Uav6e2PtwVg1h2Ssac9Y3f/+RYlbiEXGh2zqdVGhKj1fISdpbXtJdKBIxkJrkvAMdXLYgQfwHCrApvM8Ne3tqNMYLu9nta4dS1I2h2Ssj3nlQk2DQc/YMGOoUJOer77nMRt6AHm7GOq0AmtTBjdmrM/1I0NODjvwAJ51BZjKS6yajgNqEgpj7iWbQzLmXn65se3lyFA3m4w1lmZfF60GUFeTtBa1o0XzjQ09gLxdDHVagbUp0QM3q2YLMSZs8XrIyWEXn3eENeDsK8ACfd0h5z7aXN7du48hH3kcrcpL2kItRF5nuyGhLlrR1SStTS5djiGAvF0MdVqBtSmDDNzQY8JCXNYc6V2UC1SAxbIatZCN5sJYx/m4dx9DPoE4WihK1MeckIdAXbSiadI0s0H7y0jG+lSnx6yo1B0A0nRfA0cFWF+dAfpVE7IxN8QkY9WV3Sg+4qqkNeqiFU2mwHCf3aD9ZW1iiBn468pa8yNv2Yn19ex97N2bv0bJ8ePZs+s32RcmKS/cjhw5/7zu7PpV9onxK5qRP2/Cdokls2YpKyDcs7ddzN6/Z4/00kvShRfufL9spn1WjqFnLJisbojQNwJM6C7KBXE22khZr1fTQfxjG+fj3n0M+YTiqG5czKm3jLqogqq9XquTtBa1eSMftL+sTQzRMxZKVo9Z3qlm3llAWS/Xm950/vXFguGcUcxS2aLMTRaAZqHn6avbA1q20LjEYvOzkhVAWb1er76arKO8qEyaLj4+o0qIZKxrdS5rnjuXvY/FNaYzZ86/9vLL0r/8S9IlvIrrS7NXtmYvDec85Z0f5rV5ZUk9i83PTJ1Ll08/fT5Tb7pI+IyQjMUQqsfszjt3JmjS+R6zmQUydsrK9/ftk/71X2k4565OD2heUr94vUrPGSZmNYDy2q39+89n6tLunq+iJG6GSMZiCdFjlhXMb3kLiRgy8/3f+z3pd7/buR0NJ4qUXdZscjkcE5MXJNLuTH2554shNju0SsbMbL+ZfcfMnkh/XpKz3Tkz+1la7m1zzEmr22OWZYS1IHHUjdV8/8UXs7cbYcjsQgx1o+yyZlnP2dgQRw3kBUlehbO48zLr0uWMh9i07Rm7Q9J33f0qSd9Nn2d52d3/JC03tTzmtFXtMVsN4oVx1oLEUQ+m1nCuIIY6UnRZc4JTohBHTWQFSV7FwqD9TG2TsZslHUsfH5P04Zb7Q5asM49PfGJKtSBx1IMJNpzLiKEI6t4QMALEUShV77yc8aD9ZW2Tscvd/bn08fOSLs/Z7mIz2zazH5kZwd3E6pnH3//9lGpB4qgHE2w4lxFDkUxsShTiKJS6d17O3AVlG5jZA5LemvHWjmG/7u5mlvMvrQ13f9bMrpT0PTN72N2fzDjWlqQtSTowkWsnnVrMZTYC119/vZ5//vmst/5g+Qlx1K0RhcwufcaQRBxNFXVRj1YrnMVYsVVz/LdZUZqMufv1ee+Z2a/N7Ap3f87MrpD0m5x9PJv+fMrMfiDp3ZJ2Ba67H5V0VJI2Nzfz/hNghB544IHM183s/0g6RxyhTJ8xlG5DHE0QdVFER44k010s32U5obESbbS9THmvpFvTx7dK+tbqBmZ2iZldlD6+VNJ1kh5teVxMC3GEtoghhEAcdWniYyXaaJuMfUHSB83sCUnXp89lZptmdle6zbskbZvZQ5K+L+kL7k7gYhlxhLaIIYRAHHVtYoMMQym9TFnE3c9I+kDG69uSPp4+/l+S/l2b42DaiCO0RQwhBOIIsTADPwAAQEQkYwAAABGRjAEAAEREMgYAABARyRgAAEBEJGMAAAARkYwBAABERDIGAAAQEckYAABARCRjAAAAEZGMAQAAREQyBgAAEBHJGAAAQEQkYwAAABGRjAEAAEREMgYAABARyRgAAEBEJGMAAAARkYwBAABERDIGAAAQUatkzMz+s5k9Ymavm9lmwXY3mNnjZnbCzO5oc0xMD3GEtoghhEAcIZa2PWO/kPSfJP0wbwMz2yvpS5I+JOkaSR81s2taHhfTQhyhLWIIIRBHiOKCNr/s7o9JkpkVbfZeSSfc/al0269JulnSo22OjekgjtAWMYQQiCPE0seYsbdJ+tXS82fS14A6iCO0RQwhBOIIwZX2jJnZA5LemvHWYXf/VsgPY2ZbkrbSp6+Y2S9C7r+GSyW9wHGD+r8kXZjx+h+FPtBA4ijWdxnz2F0ft7cYkmYfR1OOX+qi6R871nH/bdNfLE3G3P36pjtPPSvpHUvP356+lnWso5KOSpKZbbt77gDKLsU69tyOuzh2xU1HFUex/03n9Dd3EUPSvONorvFbcVPqooEfewQxtEsflyl/KukqM3unme2TdIuke3s4LqaFOEJbxBBCII4QXNupLf7MzJ6R9H5J/8PM7k9f/0Mzu0+S3P01SZ+WdL+kxyR93d0fafexMSXEEdoihhACcYRY2t5N+c+S/jnj9f8t6cal5/dJuq/m7o+2+WwtxTr23I4rSUcnGkfEb4/H7TiGpBn+m0Y6bsxjUxdN59ijO665e8gPAgAAgBpYDgkAACCiwSRjMZehMLP9ZvYdM3si/XlJznbnzOxnaWk8YLPsbzCzi8zsnvT9H5vZwabHqnnc28zs9NLf+PFAx/1HM/tN3m3dlvjb9HP93Mze0+JYUeJoLjFU8djEUfPjziKOiKEd2406htJ9EUc7368fR+4+iCLpXUrm6PiBpM2cbfZKelLSlZL2SXpI0jUBjv3fJd2RPr5D0n/L2e6lAMcq/RskfVLSnenjWyTd09Nxb5P0dx18t/9B0nsk/SLn/Rsl/U9JJul9kn48tjiaQwwRR8QRdRExRBx1E0eD6Rlz98fc/fGSzd5YhsLdX5W0WIairZslHUsfH5P04QD7zFPlb1j+PN+U9AGz4vU5Ah23E+7+Q0kvFmxys6R/8sSPJP2BmV3R8Fix4mgOMVT12J0gjoKjLtqNGKqPONqtdhwNJhmrqKtlKC539+fSx89Lujxnu4vNbNvMfmRmTQO8yt/wxjae3Eb9W0nrDY9X57iS9JG0W/WbZvaOjPe70PfyIl0cbw4xVPXYEnHU1BziiBjq9nh9xpBEHGWp/b22mtqiLutxaaU6x15+4u5uZnm3mG64+7NmdqWk75nZw+7+ZOjPGtG3JX3V3V8xs/+i5EzmP0b+TLvEiiNiqDLiqOFxl5/MPI6IoYbHXX4y8xiSRhJHUs/JmPe4tFKdY5vZr83sCnd/Lu1K/E3OPp5Nfz5lZj+Q9G4l16zrqPI3LLZ5xswukPRvJJ2peZzax3X35WPcpWTsQR/qLlMTJY6IoWrHJo6KEUfEUNPjVTluzzEkEUdZan+vY7tM2dUyFPdKujV9fKukXWc0ZnaJmV2UPr5U0nWSHm1wrCp/w/Ln+XNJ3/N0VGALpcdduaZ9k5LZpftwr6S/TO9AeZ+k3y51s3ehiziaQwxVOjZx1Moc4ogYOm/sMSQRR1nqx5EHvsugaZH0Z0quq74i6deS7k9f/0NJ9y1td6OkXyrJ4A8HOva6pO9KekLSA5L2p69vSrorffzvJT2s5I6NhyX9dYvj7fobJH1e0k3p44slfUPSCUk/kXRloL+z7Lj/VdIj6d/4fUlXBzruVyU9J+l36Xf815I+IekT6fsm6Uvp53pYOXceDTmO5hJDxBFxRAwRQ8RR+DhiBn4AAICIxnaZEgAAYFJIxgAAACIiGQMAAIiIZAwAACCiIMmY9bj4KgAAXaE9Qwyhesa+LOmGgvc/JOmqtGxJ+odAxwUAIKQvi/YMPQuSjHmPi68CANAV2jPE0NeYsb4XXwUAoAu0Zwiu17Upy5jZlpJuX735zW++9uqrr478idC1Bx988AV3vyz25wCA0GjT5qVNe9ZXMlZp0Ux3PyrpqCRtbm769vZ2P58O0ZjZqdifAQBqqLwING3avLRpz/q6TNn34qsAAHSB9gzBBekZM7OvSvpTSZea2TOS/kbShZLk7ndKuk/Jgp4nJJ2V9FchjgsAQEi0Z4ghSDLm7h8ted8lfSrEsQAA6ArtGWJgBn4AAICISMYAAAAiIhkDAACIiGQMAAAgIpIxAACAiEjGAAAAIiIZAwAAiIhkDAAAICKSMQAAgIhIxgAAACIiGQMAAIiIZAwAACAikjEAAICISMYAAAAiIhkDAACIiGQMAAAgIpIxAACAiEjGAAAAIiIZAwAAiIhkDAAAICKSMQAAgIiCJGNmdoOZPW5mJ8zsjoz3bzOz02b2s7R8PMRxAQAIjTYNfbug7Q7MbK+kL0n6oKRnJP3UzO5190dXNr3H3T/d9ngAAHSFNg0xhOgZe6+kE+7+lLu/Kulrkm4OsF8AAPpGm4behUjG3ibpV0vPn0lfW/URM/u5mX3TzN4R4LgAAIRGm4be9TWA/9uSDrr7H0v6jqRjWRuZ2ZaZbZvZ9unTp3v6aAAA1EKbhqBCJGPPSlo+K3h7+tob3P2Mu7+SPr1L0rVZO3L3o+6+6e6bl112WYCPFsfx49LBg9KePcnP48djfyIAQEW0aehdiGTsp5KuMrN3mtk+SbdIund5AzO7YunpTZIeC3DcQTp+XNrakk6dktyTn1tbJGQAMBK0aehd62TM3V+T9GlJ9ysJyK+7+yNm9nkzuynd7DNm9oiZPSTpM5Jua3vcoTp8WDp7dudrZ88mry+j9wwAhoc2LUEb1S9z99ifIdPm5qZvb2/H/hi17dmT9IitMpNefz15vOg9W07a1tako0elQ4f6+ZxDYWYPuvtm7M8BAF0aU5tGG9VMm/aMGfgDO3Cg/PWqvWcAAPStSRtFT1o7JGOBHTmSnEEsW1tLXl94+uns3817HQCAvtRtoxgr3R7JWGCHDiVduRsbyaXJjY3dXbtVes+WccYBAOhL3TaKqz3tkYx14NAh6eTJZIzYyZO7r7FX6T1b4IwDANCnOm2UFP5qzxw7IEjGIqjSe7bAGQcAoG9vetP5x+vrxYP36/akSfkJ11w7IEjGepAVdMu9Z0eOJMlV1lkA48sAAH1ZJENnzpx/7eWXi3+nbk9aUcI11w4IkrGOlWX5Ze83OeMAAKCJJslQ3tUeKbv3q+gYc+2AIBnrWFlgl71f9z6pR6wAACAASURBVIxDmuf1dgBAe02TodWx0lJ+R0PRMap0QEyxjSMZa6ksKMoCu+z9OuPLFp9njtfbAQDthboaU9TRUHSMsg6IybZx7j7Icu211/rQ3X23+9qaexISSVlbS15f2NjY+f6ibGxUe7+u0PvrmqRtH0C8USgUSpdlDG2ae3m7dvfdSXtilvxcbu+WmXlmW2TW7hhDbuPatGfRAzSvjCFwqwRFlaArS+hWFQVq0X+AISIZo1AocyhjaNMW8tqYOu1VWftYNalbNeQ2jmQskqpBURZ0dYKy7D/DkM8aspCMUSiUOZQht2lV26A67UuTjoYqhtzGkYxFEiMoqpxtdPEfoCskYxQKZQ5lqG1anTajbq9U096vUJ+3b23aMwbwt1BloGHoOz7aDvif4l0oAIBm6kxlUXdwf9ZqNHXaoLw5Ouvc1DYaTbO4rstQzyJWtb22XvfMoU1v3BDPKETPGIVCmUEZaptWp7erbRtS5/eH2F6VadOeRQ/QvDLUwK0qxOD+LG0CdIjX2knGKBTKHMpQ27S67UJRB0JZ50KdYw2xvSrTpj2z5PeHZ3Nz07e3t2N/jMb27ElCZ5VZ0mUrJd2up07t3mZj4/ykeVkWS0YsJsg7cqRaF22Vz9Q3M3vQ3TfjHB0A+jHUNm0xb9fypcq1tfqX/qrsp04bNMT2qkyb9owxYx2pcm29bPxX3rX1rOvwoT4TAGA+Qo3BqjL2rE4bNLf2imSsI1WWMSoKthCzDK8mczfeWH9pJQDAtGUtZVT3Rq8qyyjVWd6vyVKAo9b0+mbXZajX15e1nT+saPxX2+vlefu+/fbwtxq3IcaMUSiUGZQxtGnuzcclV22z6s6rOaT2qkyb9ixIkEm6QdLjkk5IuiPj/Ysk3ZO+/2NJB8v2OfTADXWnR16wtZ1QdiyDH0nGKBTK0Moc27SFpjPnj/Hux9CiJmOS9kp6UtKVkvZJekjSNSvbfFLSnenjWyTdU7bfoQdu18lO27sxh7xkxDKSMQqFMqQy1zZtoas1JeegTXsWYszYeyWdcPen3P1VSV+TdPPKNjdLOpY+/qakD5iZBTh2L7IG0le5Pt5GlevlRQMm5zb4EQACmXybVqSo7SgbpF/15rKmk49PedLyEMnY2yT9aun5M+lrmdu4+2uSfitpPcCxO5c3kH7//uztF4Pv2wZMlTtcihLC2Q1+BIAwJt2mLctqq4rajjqdEHntYNOb00Lc1DZoTbvUFkXSn0u6a+n5xyT93co2v5D09qXnT0q6NGNfW5K2JW0fOHCgg07E+vIuF66v5w+Q7+u6edNr+0MiLlNSKJQBlam3aQtFlxzbjkVuc3PamMdBt2nPQgTu+yXdv/T8c5I+t7LN/ZLenz6+QNILUjLhbF4ZyvX1suvnq0HTVYKU9XtTGDBJMkahUIZUpt6mLTRJbqq2OUX7bjombQzjoGMnYxdIekrSO3V+sOMfrWzzKe0c7Pj1sv0OJXDrBmybwY95mpzBjAXJGIVCGVKZepu20DS5qdLmFO27qE1t+t5QRE3GkuPrRkm/TLtqD6evfV7STenjiyV9Q8ltwD+RdGXZPocSuHUTqC6CaQxB2BTJGIVCGVqZcpu20GW7UrTvpr1fY7gSFD0Z66IMKXDrTlLXNNDyjjGG7tmmSMYoFMocypDaNPduk5umU2CMfRw0yVgkRZPf1Qm0vJsBmg5cHHrALiMZo1AocyhDbNNCtRV5Y5rr7nsMvV9FSMYiaBI0eb+zvu6lZwNVjzW2YCYZo1AocyhDb9OWhboa1PWxh4ZkLIKm19uzAq3KZciqATq28WUkYxQKZQ5l6G3aQshx0nPTpj2z5PeHZ3Nz07e3t2N/jFx79iQht8osmX24joMHkwnsVm1sJLMYx/pcfTCzB919M/bnAIAuDb1NW6jbHo2tzelSm/YsxAz8k5Y3i3DI5YZCzpbPMkgAgKbqLvVX1uZMeQmjkEjGChQtvxAygaqy9FHe56uzlAUAAEXqntAXtTmTX8IopKbXN7suQ7i+Hvo225DbT2UiWDFmjEKhzKAMoU2rosm0FCGWMCpr78bQprVpz6IHaF4ZQuCGnN+r7qDIsu2nMmiSZIxCocyhDKFNWyhLboqmbarTjlVtQ8s6F8YyQ0Cb9owB/AVCDqyvu6+y7acyaJIB/ADmYAhtmnT+0uHZs+dfW1urNjQmdDtWZTspXDvcNQbwd6Tu+KuigYp1B0WWvc5AfQBAXYcP70zEpOT54cPlv1u3varahhbtt+4xx4pkrECdgfVlAxXrJk9lrzNQHwBQV5XkJtQsAlXb0KL9zqbjoen1za7LkK6vV1FlsH/IMWOLbcYwqLGIGDNGoVBmUIbSprVpq9qO32oyFm0uY8aiB2heGUrgVhVyFv2m248RyRiFQplDGUqb1vbmsKbtUtPFw9scs28kYwPQ5O7GsQRYl0jGKBTKHMqQ2rSitqfpLAJl7dlUZgAo0qY9Y8xYIE0G+zMZHgCgb4cOJXcivv568nN5DFeTMVpV2rO5DMRvimQskLqz6Fe5o4VlJAAAfWpyc1iV9qxqkjfXdo9kLKCis41VZWcJ9JwBAPrWZHm+Kr1eVZK8Obd7JGORlJ0lFJ1pzPXMAQDQvTodC1K1Xq8qSV6bOdDGjmQskrKzhLwzjcWZwhzPHAAAw1P10mZZkjfncWUkY5GUnSXknWns3TvfMwcAwPA0ubSZZTYTvGYgGYuo6Cwh70zj3Lnsfc3hzAEAMEx1L21mmfPKMq2SMTPbb2bfMbMn0p+X5Gx3zsx+lpZ72xxzLvLONBYLp66aw5kDAHSJNi2uUD1sY3RBy9+/Q9J33f0LZnZH+vz/ztjuZXf/k5bHmp1Dh7KDcGtr56XKuZw5AEDHaNMiy2v3pq7tZcqbJR1LHx+T9OGW+0OJOZ85AEDHaNMQRduescvd/bn08fOSLs/Z7mIz25b0mqQvuPv/0/K4szbXMwcA6BhtGqIoTcbM7AFJb814a8f9e+7uZuY5u9lw92fN7EpJ3zOzh939yYxjbUnakqQDDIICAARGm4YhKk3G3P36vPfM7NdmdoW7P2dmV0j6Tc4+nk1/PmVmP5D0bkm7Atfdj0o6Kkmbm5t5/wkAAGiENg1D1HbM2L2Sbk0f3yrpW6sbmNklZnZR+vhSSddJerTlcQEACI02DVG0Tca+IOmDZvaEpOvT5zKzTTO7K93mXZK2zewhSd9Xcn2dwAUADA1tGqJoNYDf3c9I+kDG69uSPp4+/l+S/l2b4wAA0DXaNMTCDPwAAAARkYwBAABERDIGAAAQEckYAABARCRjAAAAEZGMAQAAREQyBgAAEBHJGAAAQEQkYwAAABGRjAEAAEREMgYAABARyRgAAEBEJGMAAAARkYwBAABERDIGAAAQEckYAABARCRjAAAAEZGMAQAAREQyBgAAEBHJGAAAQEStkjEz+89m9oiZvW5mmwXb3WBmj5vZCTO7o80xAQDoAm0aYmnbM/YLSf9J0g/zNjCzvZK+JOlDkq6R9FEzu6blcQEACI02DVFc0OaX3f0xSTKzos3eK+mEuz+Vbvs1STdLerTNsQEACIk2DbH0MWbsbZJ+tfT8mfQ1AADGhjYNwZX2jJnZA5LemvHWYXf/VsgPY2ZbkrbSp6+Y2S9C7r+GSyW9wHF78W8jHRfADM2wTYtZv8+tTWvcnpUmY+5+fdOdp56V9I6l529PX8s61lFJRyXJzLbdPXcAZZdiHXtux10cO8ZxAczT3Nq02PX7nP7mNu1ZH5cpfyrpKjN7p5ntk3SLpHt7OC4AAKHRpiG4tlNb/JmZPSPp/ZL+h5ndn77+h2Z2nyS5+2uSPi3pfkmPSfq6uz/S7mMDABAWbRpiaXs35T9L+ueM1/+3pBuXnt8n6b6auz/a5rO1FOvYcztu7GMDwBsm2qbNsX4f3XHN3UN+EAAAANTAckgAAAARDSYZi7kMhZntN7PvmNkT6c9LcrY7Z2Y/S0vjAZtlf4OZXWRm96Tv/9jMDjY9Vs3j3mZmp5f+xo8HOu4/mtlv8m7rtsTfpp/r52b2nhDHBYBYYrVpfbdn6b5o03a+X79Nc/dBFEnvUjJHxw8kbeZss1fSk5KulLRP0kOSrglw7P8u6Y708R2S/lvOdi8FOFbp3yDpk5LuTB/fIumeno57m6S/6+C7/Q+S3iPpFznv3yjpf0oySe+T9OPY8UihUChtSqw2rc/2rOrfQJtW3qYNpmfM3R9z98dLNntjGQp3f1XSYhmKtm6WdCx9fEzShwPsM0+Vv2H583xT0gesZH2OQMfthLv/UNKLBZvcLOmfPPEjSX9gZlf08dkAoAsR27Q+2zOJNi1L7TZtMMlYRV0tQ3G5uz+XPn5e0uU5211sZttm9iMzaxrgVf6GN7bx5Dbq30pab3i8OseVpI+k3arfNLN3ZLzfBZYXATBHXdR9fbZnEm1altrfa6upLeqyHpehqHPs5Sfu7maWd4vphrs/a2ZXSvqemT3s7k+G/qwRfVvSV939FTP7L0rOZP5j5M8EAIMUq02jPatsNG1ar8mY97gMRZ1jm9mvzewKd38u7Ur8Tc4+nk1/PmVmP5D0biXXrOuo8jcstnnGzC6Q9G8knal5nNrHdfflY9ylZOxBHxp/rwAQS6w2bUDtmUSblqX29zq2y5RdLUNxr6Rb08e3Stp1RmNml5jZRenjSyVdJ+nRBseq8jcsf54/l/Q9T0cFtlB63JVr2jcpmV26D/dK+sv0DpT3SfrtUjc7AExVF21an+2ZRJuWpX6bFvougxZ3J/yZkuuqr0j6taT709f/UNJ9K3cp/FJJBn840LHXJX1X0hOSHpC0P319U9Jd6eN/L+lhJXdsPCzpr1scb9ffIOnzkm5KH18s6RuSTkj6iaQrA/2dZcf9r5IeSf/G70u6OtBxvyrpOUm/S7/jv5b0CUmfSN83SV9KP9fDyrnziEKhUMZSYrVpfbdneX8DbVq9No0Z+AEAACIa22VKAACASSEZAwAAiIhkDAAAICKSMQAAgIiCJGOdLJoJAEDPaM8QQ6iesS9LuqHg/Q9JuiotW5L+IdBxAQAI6cuiPUPPgiRjzkLQAIAJoD1DDH2NGWMhaADAFNCeIbhe16YsY2ZbSrp99eY3v/naq6++OvInQtcefPDBF9z9stifAwBCo02blzbtWV/JWKVFM939qKSjkrS5uenb29v9fDpEY2anYn8GAKih8iLQtGnz0qY96+syJQtBAwCmgPYMwQXpGTOzr0r6U0mXmtkzkv5G0oWS5O53SrpPyYKeJySdlfRXIY4LAEBItGeIIUgy5u4fLXnfJX0qxLEAAOgK7RliYAZ+AACAiEjGAAAAIiIZAwAAiIhkDAAAICKSMQAAgIhIxgAAACIiGQMAAIiIZAwAACAikjEAAICISMYAAAAiIhkDAACIiGQMAAAgIpIxAACAiEjGAAAAIiIZAwAAiIhkDAAAICKSMQAAgIhIxgAAACIiGQMAAIiIZAwAACCiIMmYmd1gZo+b2QkzuyPj/dvM7LSZ/SwtHw9xXAAAQqNNQ98uaLsDM9sr6UuSPijpGUk/NbN73f3RlU3vcfdPtz0eAABdoU1DDCF6xt4r6YS7P+Xur0r6mqSbA+wXAIC+0aahdyGSsbdJ+tXS82fS11Z9xMx+bmbfNLN3BDguAACh0aahd30N4P+2pIPu/seSviPpWNZGZrZlZttmtn369OmePhoAALXQpiGoEMnYs5KWzwrenr72Bnc/4+6vpE/vknRt1o7c/ai7b7r75mWXXRbgo8V3/Lh08KC0Z0/y8/jx2J8IAFCANg29C5GM/VTSVWb2TjPbJ+kWSfcub2BmVyw9vUnSYwGOO3jHj0tbW9KpU5J78nNri4QMAAaMNg29a52Muftrkj4t6X4lAfl1d3/EzD5vZjelm33GzB4xs4ckfUbSbW2POwaHD0tnz+587ezZ5HUAwPDQpiEGc/fYnyHT5uamb29vx/4YrezZk/SIrTKTXn+9/88zRGb2oLtvxv4cANClKbRpKNamPWMG/g4dOFDvdYkxZgAAzA3JWIeOHJHW1na+traWvJ6FMWYAgKGq21lA50J1JGMde9Obzj9eX5eOHpUOHcreljFmAIC+VUma6nYW0LlQD8lYRxaBeObM+ddefrn4d55+ut7rAAC0UTVpqttZ0KRzYc49aSRjHWkSiE3GmAEA0FTVtqpuZ0Hd1+fek0Yy1pEmvVx1x5gBANBG1baqbmdB3dfnPkyHZKwjdQJx0TX7sY8lY8zW15PpLzY2iseYAQDQRtW2qm5nQd3tqyaFU72USTLWkaqBuNo1e+ZMMrbsK1+RTp7cnYhNNRABAP2r2lYdOpR0DmxsVOssqLt9laRw0pcy3X2Q5dprr/UxuPtu940Nd7Pk5913V3tvYWPDPQmrnWVjI/tYa2s7t1tb273fKscdCknbPoB4o1AolC7LkNu0vDajrC1p0tYUHausfavTXsbQpj2LHqB5ZciBu1A1OSpilh1cZru3rRKIIT5Tn0jGKBTKHMoY2rRlZW1Jk7amyj6Lkrs67WUMbdozlkNq4eDBpJt01cZGcokx9D6qLK8U4jP1ieWQAMzBGNq0ZWVtSZO2pm37NPT2jeWQIgkxL1idQY5VrqkzVxkAoK2ytqRJW9O2fZryjAMkYy00uWNydeB9nUGOVQKRucoAAG2VtSVN2pqqg/TzblKre1PAqDS9vtl1GcP19ToD6kON46oyoJIxYxQKhTKsMoY2bVmsMWNjar9WtWnPogdoXhlL4Ia+Y7LrzzS0Oy1JxigUyhzKUNu0Nu1FyLsp3Yd/t2QZkrGBK7oDJGRyNMZeM5IxCoUyhzLENq3PqzZVDP1uyTJt2jPGjAWUd6077zr5/v3hJrCrMhne3JebAACcF6pNKGt/qk5WPusxz02zuK7LEM8iihSdYeS9t76efRbQpEu2SvfuEM86RM8YhUKZQRlim1a1TSjr9Spqf+r0vg3x6k0dbdozesYCKTrDyLsD5MUXs/e1uM23ztJHVW4ZnvVZBwBgh1BLEBW1P3V638rulpz0coBNs7iuyxDPIorODpr0OoU6myjb1/LnH9pZh+gZo1AoMyhDbdNCLEFUtE2VtrHKeLMhtl+r2rRn0QM0rwwtcMsCocldIEX7LEvUVgN3rOtWkoxRKJQ5lKG1aQshliBq2paV/e6yMdxpGT0Zk3SDpMclnZB0R8b7F0m6J33/x5IOlu1zaIEbKqBW5f1HyPsPsNhv1nGGlmhVQTJGoVCGVubQplVVNQlqugB41f0PcczzqqjJmKS9kp6UdKWkfZIeknTNyjaflHRn+vgWSfeU7XdogRuqq7WqvADdu7da4I4FyRiFQhlSmUubVlWIy4MhhvhMvWcsxAD+90o64e5Pufurkr4m6eaVbW6WdCx9/E1JHzAzC3Ds3lQZ6HjoULJY6euvJz+Xl2ioO/Awb+mjc+eyt2ftSQAIYhZtWlUhliAqahur3lg25XUppTBrU75N0q+Wnj+Tvpa5jbu/Jum3ktYDHLs3bQKhyt0oq/L+A2xsZG/PHZEAEMQs2rQ6ipKptqq2rZNel1IDWyjczLbMbNvMtk+fPh374+zQJhCaTqyX9R+gTlI46duAAWDghtymreqjvcg6Rp22tcukMLqm1zcXRdL7Jd2/9Pxzkj63ss39kt6fPr5A0guSrGi/Y72+niX0wMOp3Abs3u4aO4VCoYQuc2zTqrQXbcdEj6VNaqNNexYicC+Q9JSkd+r8YMc/WtnmU9o52PHrZfsdcuAu62Kh8BA3AoxhsKN7u+ClUCiU0GWObVpXswXUOcYURE3GkuPrRkm/VHIHyuH0tc9Luil9fLGkbyi5Dfgnkq4s2+eQA3ehztxeXS0HUXdqjCHdBuzeLngpFAqlizK3Nq2svaiSrJV1IIylTWojejLWRRly4C7UyfSr9nbV3WfTifaGgmSMQqHMoQy5TStrL4oSqZCTto5xrsxlJGMd6DLTD7WsUsjllGIhGaNQKHMosdu0Im0mZq0zKWzRMYreH0uSRjIWWJfLM4RcVqkscRtDAJOMUSiUOZShJ2Pr6/5GG7K+vnvwfl67VacDoahNymv71tfH0bHg7iRjobXN9G+/vX7ANRkoOZZLkUVIxigUyhzKUJOxtusah2qHipYAHEs7RzIWWJtM//bbiwM75LJKY7kUWYRkjEKhzKEMNRlrm0yFmhYj73PklSEO/CcZC6xNcJb9bujerDFciixCMkahUOZQhpqMhbjLsagdajvrwPLl0yn3jA1qBv6haLP0Ud4akYvXQ6+vNekZiQEAnaq6NmSRonao6go0eTPxf/GL016TcoFkLEPZ8gxFy0aUBXaTZZVY1ggA0IUmHQR12qSyDoplWUnd1NekfEPTLrWuy1C7dNvcntvF8Za3G+PlSnGZkkKhzKAMtU1zr9d+1G3jpnCjWVVt2rPoAZpXhhq4fU9cV/V4Yx3ITzJGoVDmUIbWpjVtp5os7zfW9qmuNu0ZlylrqtLlWmccV1l3b5XjVb0mDwDA8ePS1pZ06lSSHp06lTyvMgSmzmVHaUaXGVsiGaspxGDHhSr/Iaocr+5/DgDAfLU5gW/SBi46KL7yleT5xz7G+OdVJGM1hbwbssp/iCrHC5kgAgCmrcoJfN5Vm6ZtYFnnw+xvVGt6fbPrMrTr68vqXmvP277q/C5lxxvzNXkxZoxCocygDKlNa7sSTJPxZk3XUh7TzWlt2rPoAZpXhhS4bRQFWci7TMYUsMtIxigUyhzKkNq0kGskr+43rx0q6nyYwrqU7s4A/iEruhQZ8pInk78CAKooG1TfZBxy2WXIouE0efs9c2Y+N6eRjHWsKKjz/kNIM792DgDoVNEJfJNxyGVjoIs6H+qOb57izWkkYx2rMiP/8n8IqfktxwAAtNXkqk1Zb1pRb1ze8dbXs/c5xZvTSMY6Vjeoy84uZn/HCQCgU03mBqvSm5bXGzf3dSkl6YLYH2DqFsF2+HByhnDgQBJIeUFddHaxuCa/SNYWvWbLxwEAoK3FupBVHTmys32S6iVORcer2n6OmSU3AAzP5uamb29vx/4YvTt4MEmyVm1sJD/z3ltc4hwbM3vQ3Tdjfw4A6NIc2rTjx+eROOVp0561ukxpZvvN7Dtm9kT685Kc7c6Z2c/Scm+bY05d0WVNZtoHgO7QprXDXf3NtR0zdoek77r7VZK+mz7P8rK7/0labmp5zEkrulbPTPsA0CnaNETRNhm7WdKx9PExSR9uuT8o/+wi5LxkAIBdaNMQRdtk7HJ3fy59/Lyky3O2u9jMts3sR2ZGcDfU5A4XAEBltGmIovRuSjN7QNJbM97aMQeuu7uZ5d0NsOHuz5rZlZK+Z2YPu/uTGcfakrQlSQe49pap7h0uAIDzaNMwRKXJmLtfn/eemf3azK5w9+fM7ApJv8nZx7Ppz6fM7AeS3i1pV+C6+1FJR6XkzpNKfwEAABXRpmGI2l6mvFfSrenjWyV9a3UDM7vEzC5KH18q6TpJj7Y8LgAAodGmIYq2ydgXJH3QzJ6QdH36XGa2aWZ3pdu8S9K2mT0k6fuSvuDuBC4AYGho0xBFqxn43f2MpA9kvL4t6ePp4/8l6d+1OQ4AAF2jTUMsrE0JAAAQEckYAABARCRjAAAAEZGMAQAAREQyBgAAEBHJGAAAQEQkYwAAABGRjAEAAEREMgYAABARyRgAAEBEJGMAAAARkYwBAABERDIGAAAQEckYAABARCRjAAAAEZGMAQAAREQyBgAAEBHJGAAAQEQkYwAAABGRjAEAAEREMgYAABBRq2TMzP6zmT1iZq+b2WbBdjeY2eNmdsLM7mhzTAAAukCbhlja9oz9QtJ/kvTDvA3MbK+kL0n6kKRrJH3UzK5peVwAAEKjTUMUF7T5ZXd/TJLMrGiz90o64e5Ppdt+TdLNkh5tc2wAAEKiTUMsfYwZe5ukXy09fyZ9DQCAsaFNQ3ClPWNm9oCkt2a8ddjdvxXyw5jZlqSt9OkrZvaLkPuv4VJJL3DcXvzbSMcFMEMzbNNi1u9za9Mat2elyZi7X99056lnJb1j6fnb09eyjnVU0lFJMrNtd88dQNmlWMee23EXx45xXADzNLc2LXb9Pqe/uU171sdlyp9KusrM3mlm+yTdIuneHo4LAEBotGkIru3UFn9mZs9Ier+k/2Fm96ev/6GZ3SdJ7v6apE9Lul/SY5K+7u6PtPvYAACERZuGWNreTfnPkv454/X/LenGpef3Sbqv5u6PtvlsLcU69tyOG/vYAPCGibZpc6zfR3dcc/eQHwQAAAA1sBwSAABARINJxmIuQ2Fm+83sO2b2RPrzkpztzpnZz9LSeMBm2d9gZheZ2T3p+z82s4NNj1XzuLeZ2emlv/HjgY77j2b2m7zbui3xt+nn+rmZvSfEcQEgllhtWt/tWbov2rSd79dv09x9EEXSu5TM0fEDSZs52+yV9KSkKyXtk/SQpGsCHPu/S7ojfXyHpP+Ws91LAY5V+jdI+qSkO9PHt0i6p6fj3ibp7zr4bv+DpPdI+kXO+zdK+p+STNL7JP04djxSKBRKmxKrTeuzPav6N9Cmlbdpg+kZc/fH3P3xks3eWIbC3V+VtFiGoq2bJR1LHx+T9OEA+8xT5W9Y/jzflPQBK1mfI9BxO+HuP5T0YsEmN0v6J0/8SNIfmNkVfXw2AOhCxDatz/ZMok3LUrtNG0wyVlFXy1Bc7u7PpY+fl3R5znYXm9m2mf3IzJoGeJW/4Y1tPLmN+reS1hser85xJekjabfqN83sHRnvd4HlRQDMURd1X5/tmUSblqX299pqaou6rMdlKOoce/mJu7uZ5d1iuuHuz5rZlZK+Z2YPu/uToT9rRN+W9FV3f8XM/ouSM5n/GPkzAcAgxWrTaM8qG02b1msy5j0uQ1Hn2Gb2azO7wt2fS7sSf5Ozj2fTn0+Z2Q8kvVvJ7bXBWgAAHMNJREFUNes6qvwNi22eMbMLJP0bSWdqHqf2cd19+Rh3KRl70IfG3ysAxBKrTRtQeybRpmWp/b2O7TJlV8tQ3Cvp1vTxrZJ2ndGY2SVmdlH6+FJJ10l6tMGxqvwNy5/nzyV9z9NRgS2UHnflmvZNSmaX7sO9kv4yvQPlfZJ+u9TNDgBT1UWb1md7JtGmZanfpoW+y6DF3Ql/puS66iuSfi3p/vT1P5R038pdCr9UksEfDnTsdUnflfSEpAck7U9f35R0V/r430t6WMkdGw9L+usWx9v1N0j6vKSb0scXS/qGpBOSfiLpykB/Z9lx/6ukR9K/8fuSrg503K9Kek7S79Lv+K8lfULSJ9L3TdKX0s/1sHLuPKJQKJSxlFhtWt/tWd7fQJtWr01jBn4AAICIxnaZEgAAYFJIxgAAACIiGQMAAIiIZAwAACCiIMlYJ4tmYlaIIYRAHKEtYggxhOoZ+7KkGwre/5Ckq9KyJekfAh0X0/FlEUNo78sijtDOl0UMoWdBkjFnIWi0RAwhBOIIbRFDiKGvMWMsBI22iCGEQByhLWIIwfW6NmUZM9tS0u2rN7/5zddeffXVkT8Ruvbggw++4O6XhdwncTQvXcSQRBzNDXUR2moTQ30lY5UWzXT3o5KOStLm5qZvb2/38+kQjZmdqrhp5YVXiaN5qRFDEnGEHNRFaKtmXbRDX5cpWQgabRFDCIE4QlvEEIIL0jNmZl+V9KeSLjWzZyT9jaQLJcnd75R0n5IFPU9IOivpr0IcF9NBDCEE4ghtEUOIIUgy5u4fLXnfJX0qxLEwTcQQQiCO0BYxhBiYgR8AACAikjEAAICISMYAAAAiIhkDAACIiGQMAAAgIpIxAACAiEjGAAAAIiIZAwAAiIhkDAAAICKSMQAAgIhIxgAAACIiGQMAAIiIZAwAACAikjEAAICISMYAAAAiIhkDAACIiGQMAAAgIpIxAACAiEjGAAAAIiIZAwAAiChIMmZmN5jZ42Z2wszuyHj/NjM7bWY/S8vHQxwX00IcoS1iCCEQR+hb62TMzPZK+pKkD0m6RtJHzeyajE3vcfc/SctdbY87KcePSwcPSnv2JD+PH4/9iXpHHKEtYgghEEeIIUTP2HslnXD3p9z9VUlfk3RzgP3Ow/Hj0taWdOqU5J783NqaY0JGHKEtYgghEEfoXYhk7G2SfrX0/Jn0tVUfMbOfm9k3zewdAY47DYcPS2fP7nzt7Nnk9XkhjtAWMYQQiCP0rq8B/N+WdNDd/1jSdyQdy9rIzLbMbNvMtk+fPt3TR4vs6afrvT5vxFELXA2XVDGGJOIIhaiLEFSIZOxZSctnBW9PX3uDu59x91fSp3dJujZrR+5+1N033X3zsssuC/DRBmq5VdyT8xUs3ptPq0kcdWgmV8ODxVC6LXE0T9RF6F2IZOynkq4ys3ea2T5Jt0i6d3kDM7ti6elNkh4LcNxxWm0Vz53L3u7cuUm3mhmIow7N5Go4MYQQiCP07oK2O3D318zs05Lul7RX0j+6+yNm9nlJ2+5+r6TPmNlNkl6T9KKk29oed7SyWkVJ2rtXev31pDdsNUFbtJqHDvXzGSMgjro1h6vhxBBCII4Qg7l77M+QaXNz07e3t2N/jPD27El6vFaZnU/Git6fGDN70N03u9r/ZOOopoMHk07WVRsb0smTfX+asLqOIYk4moNZ10XHjycn/E8/LR04IB05MumT/660iSFm4O/bgQPFr5e9D6TqDMg/ckRaW9v52tpa8jqAGSsaUJpXyXA3UHAkY11bDdobb9zdKu7bJ730UrLNSy9JF1648/21teT3CH6k6g7IP3RIOno06QkzS34ePcrJ71yFaktpkycgb0DpZz+bXcl88pP1kzeJYCnj7oMs1157rY/e3Xe7r625JyGblLU199tvd9/YcDdzX193v/DCndvs25e8bpZsd/vt2fu5++7Yf2FrSsZgEEc1bWzsDIdF2dhI3r/77vMhtrExiVDJ1XUM+cTiKK9aqhsjofYzFLOti8yyK5O8sndv9uvr6/kBURQseZXVCCuxNjHUaQXWpgw2cOsoazFDbjNSs60AW8qrP82m10iWIRmrJ1R1UnU/Y2lTZ1sX5X2RocrGRv4x8hK4og6IAQdUmxjiMmWXqtzCFmobzErR0MKZTGMxe02H89SpTor2VWU/M5nfbtzyBpSur2dvv3dvvf0//XR+sJw5k11ZHT1a79LpBAKKZCy0KhO67t9fb9LX/fvLt5lAMKK6ogH5TXJ3hnOMS16SUzScZ6HqPUJliVSV/XBiMAKHDkm33no+ydq7N3n+xS9mVzJbW/WStwMH6t+Aljf/Zl7yNoWAatql1nUZbJdukazrQ6tl377dY8TKSpXfGel1KM310kCGur3vedvXvQw19suaXceQDzCO8r7jvOE8y9991e+7yrjEsv0UXU4fmtnWRUVf5O23nw+qvXuT5+7Zr5eNC8t6b329XiDnlYEEVJsY6rQCa1MGG7hFimrIRYtZFHxmxYMjFy1vlRp3JGZbAa4ImRDV3dfYhyTOMRmrO+Z6ta2qkvhXSaTK9jOm2JptXRRyPFde8nb33TvbvvX14iQt7xh57edAAqpNDHGZMqS860Cvv56UkyelF18s3iZvYtcXX0x+v2gbxpCNVsjLOXWnsWBI4vjkXfXJG87TZJrCKpchDx06Xy2dPLk7xpjfbgRCjuc6duz8JcZz55Lni2vnZ86c3/7ll88/ftObzj9eX0/2f9112a//xV8kldqyqQRU0yyu6zLYs4hVy6eGeT1WffWMLR9nJNeYNNez0RV9X86pErYDOdks1XUM+QDjqG6HwnJ1ULXntG4P69hnKJhtXdT13ZR1p8LIC+Ks183O974NQJsY6rQCa1MGG7jLqowRWy1djRlbLSMZ9DPbCnBF08s5TRq6KmE7kvBx9+5jyAcaR02TnzqxVjW+xj7u0H3GdVHX47lCJW8jOGskGYulbIxYqF6upr1pAwrSPLOtAFc0acyaNoBVhjbSiI4zjqroohd2TGPD8sy2Lup6PFfXydtABu+7t4shxoy1UTZGrM34rxDjzBj0E13VKSNWx3mtrydDJj72sfzfazrOrMrQRpZJmq6my9+2nXMMA7SYvyTEeK5QU2HkDXwMOSBygEjG6litjcrm/+prnrEqx2HyqN41WT/y5EnpK19J6sMzZ4p/r+qkm6shwFr089ZkUH2IOccwQGXrUq4maf/yL/WTt7//+/x5zFbXYb7wwvzkLe/1KQzel7hMWVlWl21f479C/c4AB3FowpcGml66qfp7TeeBmtpSp13HkA+xPmqp7ljDEHOODd2U66JcMdelvP32pJ1abbfqzm82IG1iqNMKrE0ZXOAWzcUyxLspBz4fy8KUK8CmY3Oq/l5ZA1jUgI7lDrcqSMZ2C/39tplzbCyxNuW6KFfMdSlD3WU5oIAiGetDldooxDZ9HWcgplwBdt0z5l7c0IUIgTE0pHNNxoqSnxBTUixrc7fvwNvPN0y5LsrVx8z4dXvf8soIblQjGetDldqozTZ934E5kJZ1yhVg04YoVAPW9g63sTSkc0zGir6busl8F3OOLYzpLssp10WF6ixt1GRm/Lo9Y3XLgDoYSMa6snzKuL6ePQbr9tuLt9m3L3l9kQBlBXNZmfB6llOvAJv2LJX9XpX9tk2mxtKQzjEZK/pu6vSIVv2O82Y/KDOSDnp3n35dlCnmupShpsgYUIVEMtaFvAH7ZYnV8jZVErjY48wiB/IsK8CW6iRZbS4zjqUhnWMyVvTd1Emiq44Fa5rUjyWhd59pXRR7XcoQ85sNqKueZKwLIS5LVtnHkMaZRTDLCnBJk2SpqwZu9bOM5B6QWSZjZTdnVG2zQo2+yDOWS93uM62Lur6bss7SRss9aXWStwGJnoxJukHS45JOSLoj4/2LJN2Tvv9jSQfL9hk9cPtKkvpK+gZ6irocvJOMo1RW0tW0oeoir646c8sQG9KuY8gHFEcLZbETchmjtvE2hptA3OdTF+3Q9d2UdZc2GvGdlO47Y6huafRLO3Yg7ZX0pKQrJe2T9JCka1a2+aSkO9PHt0i6p2y/UQJ3udbo6/Jh03FmdS+HVjlOhMBeBO+k4mhF3RuWluduymrE2vZU1NnncqgOtSHtOoZ8IHG0qm6S03TqiVB39w7dHOqiXbq+m7LrpG5g3fSxk7H3S7p/6fnnJH1uZZv7Jb0/fXyBpBckWdF+ew/crKAsK6EG1neRWDVN4HquPZcqwGnEUYa6J59mzcfDlg36z9vnQK9iV9J1DPlA4qiNNpcLu77jcijmUBftEmrcVt3kbYJ3Urqfj6EmJcRySG+T9Kul58+kr2Vu4+6vSfqtpJyFqSLJWhZCSpZuMMteF+vVV6Xf//3zCwoWralllmz3e78n/e53u/fzlrckCwO+5S273z97VrrvvvPrWWZts7yPkyeT7Vf/nirHKVvYsDvTiKMMddfnO3CgeN3J1XUsNzaS1UWOHSteeqlonxNZzmayMdRW03VMpex4O3p09/qlbY4xMPOIo9DrUoZY2qjuepUjq6CKDGptSjPbMrNtM9s+ffp0vwePvej34vhVFhwMsc2EV/aNGkcZ8uqL9fX8pdbKvp7FOpZFufdqQ1i0zybrFU7d0OKojbb/3Q8dSmLhwIHkdw4fbrZW6twMOoZCr0tptnNfZknylrUuZdF6lXNcl1LiMuUbhjIZa+zj9HwNXjO4NFB2yTHEuLAQ94qMdbxP1zHkA4mjNvqYAHig9whVNoe6aIdQd1IWTewacoqMga9L6X4+hpqURr+0YwdJID4l6Z06P9jxj1a2+ZR2Dnb8etl+ZzVmbCi/E3fM2DTiKEeTwdZ1xt9UaQjHPqYnT9cx5AOKo6b6mAB47PE1l7roDaHupGyy5BF3U+4qjX5p106kGyX9UskdKIfT1z4v6ab08cWSvqHkNuCfSLqybJ+zuZtySMeJeDelTymOAqmTwNUZZB2i92tIvWhdx5APNI6KvoO8qVSa3E3pXv0GjzbHiG12dVGoOymLesZClYFcySkTPRnrokQP3CFNxsqkr+ONox6FaPCq7GNoPSBdx5APMI6a3G3b5rucw8Svs6yL8pY2Wr2qcuGF5RO1xpgiY0J3U3ZagbUp0QO3bAzZVHrGBjTpaxclehwF0FevQh8NcxfmmIwVfQd1vp+q27ZJqIYWL3lmVxcVTV+xb9/O1/ftKx7PFWuKjIEFEclYF6qMIRvy+C8WCh9GHJUoS7RC9iqEmtxzaJ2sc0zGir6DOt9PnW2bnhQMLV7yzK4uKupwyHq9yXiuoiRtYutSureLoU4rsDZlEIFbZQzZkHq56hxnIAM3ZlcBLgl5h1qIpK5qozm0no45JmN994zlqZKgDS1e8syuLqo76D6vxEzeBoZkrA9jG/81ktPR2VWAS6o0UlW+xlBJXR+XrLowx2Ss7zFjdT9DqGP0aXZ1Ud2esVBlondSureLoU4rsDZlNIFbpceqTq9Wm32MYIzYqtlVgEtCzA1WdZtQSd3ytkO5O26OyZh7/bsp6+4n1GXtup8nltnVRV2P5+o6eRtYW+beLoY6rcDalFEEbpVxWU3GezXZx0jGiK2aXQW4JNTcTaGSusXxFo3m+nr0deQrmWsy1lSoO2ZH0vle2ezqoq7Hc83sTkr3djE0qOWQBi1rgbasdSal8rUol7dpup5lk22yFpRDNFWWIKqyLmCVdSWrLne0WGbpK19JVjo5cyap+bLWusT4LJYjLFrDVKq2zuRE1jOdp7x1KcuWPMpalzLU0kYzXpdSEj1jrTBGrDXN7Wx0Rai5wUJP+DqWQdfu3ceQjyCOqgp5x+xYxoJVNau6KNSdlGWDFOssbTTyOynd28VQpxVYmzKowM0TYkBPqEFBY2o9l8yqAqyp6bifEJcXx5Tbk4xVF/qO2TGMBatqVnVRqDspi27fndm6lO7tYojLlG1kXfvZt0966SVpzx7p4EHpxhuLt3npJenCC3e+v7aW/N7Bg/nbVDnO1Fa1n5Gql5MWQl9e5BLUNFX9Xute1n799eS9w4fPV0lc0h6wvEDIuySY5+mnk5LlzJnsa91Hj2a//tnPSseOSefOJa+dO5c8/+Qns1+fWoA1zeK6LoM6iyiy2iWxOoh+cSZQtM2+fTu7MrLOHJa3qXKckZymak5nozU07egM1UE6pktQXceQDzSOmtxN2dUds2X7HUMP2qzqolCD8Yt6xkIV7qYkGautr0uOI70kmWVWFWANTS8TFv1e3QZxDA2oe/cx5AOMozbzjHXxvRZVSWNJ7GdXF4UYz1UUcNxNWat0WoG1KYML3CqGNDHsSMyuAqwodM9Y0fjbsZtjMhZiBv6QyXlRlTSWc8dZ1UVNBsuzLmWpNjHEmLGQ8q7D799/fvzXnpx/8sV7Bw8m2xdtk7cPBvRMRtUxO1V/TyqfqgDjkTdMp2gIz/Lrdccklm1fNBatyudBz/LmLmkynqvuVBjXXZf9et2pMCY2HppkLKS8Af3/+q/na7FFMK86d+58Lff//X+7B+wvb5O1jwkG55xVmV+szu+9+GL29jSI41SU/FQZpF9lHrFlZdsXnTxwM8gA5f3Hz2ufmgzGDzWPWV7yNrU5M5t2qXVdBtWlW8dqX35fC4OP9HqT5nRpILCpzhtWV9cx5AOMo7ZrU9Yd6VB13rG2Nw3ENKu6iHUpO9EmhjqtwNqUQQVuG31N+jpSs6oAA6rbwI2lQWxijsmYe7u1Kesm522T+THcDDKrumhoSxvVTd4GehZJMjZkRWcgE1v0u4lZVYANZTVkTRrHMTSITcw1GWuDZH632dVFdVaIH1ryNtCOCJKxIcsK4rIy0kW/m5hdBVhTXh04sjqqUyRjzUx1qpOmqItKDCl5G2hHRJsYuqD/UWozsxhkePhwMmhyz57sQZJ79ybTWB84kMysvzzAMWubI0emN4ARu+QNnN67NzuMGBSNqg4dqleF1N0eE5MXAEWBsWj3ltus667b/bqUDOxfruzW1pIFx48d2/36BG9Wa5WMmdl+SfdIOijppKS/cPf/N2O7c5IeTp8+7e43tTnu6CwHa960FK+/npSq20wIcZSv6KantbVZ1FGVEEMIgTgKqMvkbYJnBW2ntrhD0nfd/SpJ302fZ3nZ3f8kLfMO2ryuiyrzjE2324M4ypH3lS+mrKg79cWEEUMIgTiKZXmh05Mnz1dmea9PTNtk7GZJx9LHxyR9uOX+pi9rQh6pfJ6xaXd7EEc5iuZvmkkdVRUxhBCII0TRNhm73N2fSx8/L+nynO0uNrNtM/uRmc07uFdn5dy7d/c2r74q/f7vz6nbgzjK0XTy1xkihhACcYQoSseMmdkDkt6a8daOuZrd3c3Mc3az4e7PmtmVkr5nZg+7+5MZx9qStCVJB6Z7Sa7aGLIXX5ReeKG/z9Sx66+/Xs8//3zWW3+w/IQ42o2B04k+Y0iaXhwhQV2EISpNxtz9+rz3zOzXZnaFuz9nZldI+k3OPp5Nfz5lZj+Q9G5JuwLX3Y9KOipJm5ubef8JpuXAgeTSZNbrE/LAAw9kvm5m/0fSOeIIZfqMoXQb4miCqIswRG0vU94r6db08a2SvrW6gZldYmYXpY8vlXSdpEdbHnc6mq4IPS3EEdoihhACcYQo2iZjX5D0QTN7QtL16XOZ2aaZ3ZVu8y5J22b2kKTvS/qCuxO4CwwKkogjtEcMIQTiCFFYMmns8Gxubvr29nbsj4GOmdmD7r7Z1f6Jo+nrOoYk4mgOqIvQVpsYatszBgAAgBZIxgAAACIiGQMAAIiIZAwAACAikjEAAICISMYAAAAiIhkDAACIiGQMAAAgIpIxAACAiEjGAAD/f3v38ipJeYBh/HnRqLvEC3hJJHFAjeNKGYIXyEKzSGbhxKigGxUUFfEPGHCXTUiWoiCDCGZjvIBkQiYMUSOuNLpQZ8bBOLpxxvGSCIKb0SRfFl1jWs/pc7q6qvs73fX8oDlV3d/pt+r0S/Od7q4uSRU5GZMkSarIyZgkSVJFTsYkSZIqcjImSZJUkZMxSZKkipyMSZIkVeRkTJIkqSInY5IkSRU5GZMkSarIyZgkSVJFTsYkSZIq6jQZS3JLkkNJ/ptkxwbjfp7knSRHkuzukqnVY4/UlR1SH+yRaun6ythB4FfAy5MGJDkFeAT4BbAduC3J9o65Wi32SF3ZIfXBHqmKU7v8cinlMECSjYb9BDhSSnm/GfsHYBfwdpdsrQ57pK7skPpgj1TLIj4z9n3gg7H1o811Uhv2SF3ZIfXBHql3m74yluR54Lx1bnqwlPLHPjcmyT3APc3qiSQH+7z/Fs4B/mlury4BvrPO9Zf3HbRFelTrsayZPe/chXUIBt+jVe6vz0Wrn10r99JZf3HTyVgp5Wez3nnjGHDh2PoPmuvWy9oD7AFI8nopZeIHKOepVvbQck9mTzl0qXpU+286pH2eR4dg2D0aan+nHOpz0RbPXoIOrbGItylfAy5OclGS04Bbgb0LyNVqsUfqyg6pD/ZIvev61RY3JjkKXA38Ocn+5voLkuwDKKX8G3gA2A8cBp4upRzqttlaJfZIXdkh9cEeqZauR1M+Bzy3zvUfAjvH1vcB+1re/Z4u29ZRreyh5QLsWdEe2d8F5s65QzDAv2ml3JrZPhetTvbS5aaU0ueGSJIkqQVPhyRJklTRlpmM1TwNRZKzkvw1ybvNzzMnjPtPkjeay8wf2NxsH5KcnuSp5vZXk/xo1qyWuXcm+XRsH+/uKffxJJ9MOqw7Iw812/VWkis7ZFXp0VA6NGW2PZo9dxA9skPfGLfUHWruyx598/b2PSqlbIkLcBmj7+h4CdgxYcwpwHvANuA04E1gew/ZvwN2N8u7gd9OGPdFD1mb7gNwP/Bos3wr8NSCcu8EHp7DY/tT4Erg4ITbdwJ/AQJcBby6bD0aQofskT3yucgO2aP59GjLvDJWSjlcSnlnk2Ffn4ailPIlcPI0FF3tAp5olp8AftnDfU4yzT6Mb8+zwPXJxufn6Cl3LkopLwOfbTBkF/D7MvIK8L0k58+YVatHQ+jQtNlzYY9653PRWnaoPXu0VusebZnJ2JTmdRqKc0spx5vlj4BzJ4w7I8nrSV5JMmvBp9mHr8eU0WHUnwNnz5jXJhfgpuZl1WeTXLjO7fOw6NOLzCNvCB2aNhvs0ayG0CM7NN+8RXYI7NF6Wj+unb7aoq0s8NRKbbLHV0opJcmkQ0x/WEo5lmQb8GKSA6WU9/re1or+BDxZSjmR5F5G/8lcV3mb1qjVIzs0NXs0Y+74ysB7ZIdmzB1fGXiHYEl6BAuejJUFnlqpTXaSj5OcX0o53ryU+MmE+zjW/Hw/yUvAFYzes25jmn04OeZoklOB7wL/apnTOreUMp7xGKPPHixC29PUVOmRHZou2x5tzB7ZoVnzpsldcIfAHq2n9eO6bG9Tzus0FHuBO5rlO4A1/9EkOTPJ6c3yOcC1wNszZE2zD+PbczPwYmk+FdjBprnfek/7BkbfLr0Ie4HbmyNQrgI+H3uZfR7m0aMhdGiqbHvUyRB6ZIf+b9k7BPZoPe17VHo+ymDWC3Ajo/dVTwAfA/ub6y8A9o2N2wn8g9EM/sGess8GXgDeBZ4Hzmqu3wE81ixfAxxgdMTGAeCuDnlr9gH4NXBDs3wG8AxwBPg7sK2n/dws9zfAoWYf/wb8uKfcJ4HjwFfNY3wXcB9wX3N7gEea7TrAhCOPtnKPhtIhe2SP7JAdskf998hv4JckSapo2d6mlCRJWilOxiRJkipyMiZJklSRkzFJkqSKnIxJkiRV5GRMkiSpIidjkiRJFTkZkyRJquh/ybAjHz+m1RMAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -383,7 +361,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_optim_OTreg.ipynb b/notebooks/plot_optim_OTreg.ipynb index e3784df..34f42af 100644 --- a/notebooks/plot_optim_OTreg.ipynb +++ b/notebooks/plot_optim_OTreg.ipynb @@ -72,8 +72,6 @@ }, "outputs": [], "source": [ - "#%% parameters\n", - "\n", "n = 100 # nb bins\n", "\n", "# bin positions\n", @@ -106,18 +104,18 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% EMD\n", - "\n", "G0 = ot.emd(a, b, M)\n", "\n", "pl.figure(3, figsize=(5, 5))\n", @@ -144,246 +142,245 @@ "name": "stdout", "output_type": "stream", "text": [ - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 0|1.760578e-01|0.000000e+00\n", - " 1|1.669467e-01|-5.457501e-02\n", - " 2|1.665639e-01|-2.298130e-03\n", - " 3|1.664378e-01|-7.572776e-04\n", - " 4|1.664077e-01|-1.811855e-04\n", - " 5|1.663912e-01|-9.936787e-05\n", - " 6|1.663852e-01|-3.555826e-05\n", - " 7|1.663814e-01|-2.305693e-05\n", - " 8|1.663785e-01|-1.760450e-05\n", - " 9|1.663767e-01|-1.078011e-05\n", - " 10|1.663751e-01|-9.525192e-06\n", - " 11|1.663737e-01|-8.396466e-06\n", - " 12|1.663727e-01|-6.086938e-06\n", - " 13|1.663720e-01|-4.042609e-06\n", - " 14|1.663713e-01|-4.160914e-06\n", - " 15|1.663707e-01|-3.823502e-06\n", - " 16|1.663702e-01|-3.022440e-06\n", - " 17|1.663697e-01|-3.181249e-06\n", - " 18|1.663692e-01|-2.698532e-06\n", - " 19|1.663687e-01|-3.258253e-06\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 20|1.663682e-01|-2.741118e-06\n", - " 21|1.663678e-01|-2.624135e-06\n", - " 22|1.663673e-01|-2.645179e-06\n", - " 23|1.663670e-01|-1.957237e-06\n", - " 24|1.663666e-01|-2.261541e-06\n", - " 25|1.663663e-01|-1.851305e-06\n", - " 26|1.663660e-01|-1.942296e-06\n", - " 27|1.663657e-01|-2.092896e-06\n", - " 28|1.663653e-01|-1.924361e-06\n", - " 29|1.663651e-01|-1.625455e-06\n", - " 30|1.663648e-01|-1.641123e-06\n", - " 31|1.663645e-01|-1.566666e-06\n", - " 32|1.663643e-01|-1.338514e-06\n", - " 33|1.663641e-01|-1.222711e-06\n", - " 34|1.663639e-01|-1.221805e-06\n", - " 35|1.663637e-01|-1.440781e-06\n", - " 36|1.663634e-01|-1.520091e-06\n", - " 37|1.663632e-01|-1.288193e-06\n", - " 38|1.663630e-01|-1.123055e-06\n", - " 39|1.663628e-01|-1.024487e-06\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 40|1.663627e-01|-1.079606e-06\n", - " 41|1.663625e-01|-1.172093e-06\n", - " 42|1.663623e-01|-1.047880e-06\n", - " 43|1.663621e-01|-1.010577e-06\n", - " 44|1.663619e-01|-1.064438e-06\n", - " 45|1.663618e-01|-9.882375e-07\n", - " 46|1.663616e-01|-8.532647e-07\n", - " 47|1.663615e-01|-9.930189e-07\n", - " 48|1.663613e-01|-8.728955e-07\n", - " 49|1.663612e-01|-9.524214e-07\n", - " 50|1.663610e-01|-9.088418e-07\n", - " 51|1.663609e-01|-7.639430e-07\n", - " 52|1.663608e-01|-6.662611e-07\n", - " 53|1.663607e-01|-7.133700e-07\n", - " 54|1.663605e-01|-7.648141e-07\n", - " 55|1.663604e-01|-6.557516e-07\n", - " 56|1.663603e-01|-7.304213e-07\n", - " 57|1.663602e-01|-6.353809e-07\n", - " 58|1.663601e-01|-7.968279e-07\n", - " 59|1.663600e-01|-6.367159e-07\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 60|1.663599e-01|-5.610790e-07\n", - " 61|1.663598e-01|-5.787466e-07\n", - " 62|1.663596e-01|-6.937777e-07\n", - " 63|1.663596e-01|-5.599432e-07\n", - " 64|1.663595e-01|-5.813048e-07\n", - " 65|1.663594e-01|-5.724600e-07\n", - " 66|1.663593e-01|-6.081892e-07\n", - " 67|1.663592e-01|-5.948732e-07\n", - " 68|1.663591e-01|-4.941833e-07\n", - " 69|1.663590e-01|-5.213739e-07\n", - " 70|1.663589e-01|-5.127355e-07\n", - " 71|1.663588e-01|-4.349251e-07\n", - " 72|1.663588e-01|-5.007084e-07\n", - " 73|1.663587e-01|-4.880265e-07\n", - " 74|1.663586e-01|-4.931950e-07\n", - " 75|1.663585e-01|-4.981309e-07\n", - " 76|1.663584e-01|-3.952959e-07\n", - " 77|1.663584e-01|-4.544857e-07\n", - " 78|1.663583e-01|-4.237579e-07\n", - " 79|1.663582e-01|-4.382386e-07\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 80|1.663582e-01|-3.646051e-07\n", - " 81|1.663581e-01|-4.197994e-07\n", - " 82|1.663580e-01|-4.072764e-07\n", - " 83|1.663580e-01|-3.994645e-07\n", - " 84|1.663579e-01|-4.842721e-07\n", - " 85|1.663578e-01|-3.276486e-07\n", - " 86|1.663578e-01|-3.737346e-07\n", - " 87|1.663577e-01|-4.282043e-07\n", - " 88|1.663576e-01|-4.020937e-07\n", - " 89|1.663576e-01|-3.431951e-07\n", - " 90|1.663575e-01|-3.052335e-07\n", - " 91|1.663575e-01|-3.500538e-07\n", - " 92|1.663574e-01|-3.063176e-07\n", - " 93|1.663573e-01|-3.576367e-07\n", - " 94|1.663573e-01|-3.224681e-07\n", - " 95|1.663572e-01|-3.673221e-07\n", - " 96|1.663572e-01|-3.635561e-07\n", - " 97|1.663571e-01|-3.527236e-07\n", - " 98|1.663571e-01|-2.788548e-07\n", - " 99|1.663570e-01|-2.727141e-07\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 100|1.663570e-01|-3.127278e-07\n", - " 101|1.663569e-01|-2.637504e-07\n", - " 102|1.663569e-01|-2.922750e-07\n", - " 103|1.663568e-01|-3.076454e-07\n", - " 104|1.663568e-01|-2.911509e-07\n", - " 105|1.663567e-01|-2.403398e-07\n", - " 106|1.663567e-01|-2.439790e-07\n", - " 107|1.663567e-01|-2.634542e-07\n", - " 108|1.663566e-01|-2.452203e-07\n", - " 109|1.663566e-01|-2.852991e-07\n", - " 110|1.663565e-01|-2.165490e-07\n", - " 111|1.663565e-01|-2.450250e-07\n", - " 112|1.663564e-01|-2.685294e-07\n", - " 113|1.663564e-01|-2.821800e-07\n", - " 114|1.663564e-01|-2.237390e-07\n", - " 115|1.663563e-01|-1.992842e-07\n", - " 116|1.663563e-01|-2.166739e-07\n", - " 117|1.663563e-01|-2.086064e-07\n", - " 118|1.663562e-01|-2.435945e-07\n", - " 119|1.663562e-01|-2.292497e-07\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 120|1.663561e-01|-2.366209e-07\n", - " 121|1.663561e-01|-2.138746e-07\n", - " 122|1.663561e-01|-2.009637e-07\n", - " 123|1.663560e-01|-2.386258e-07\n", - " 124|1.663560e-01|-1.927442e-07\n", - " 125|1.663560e-01|-2.081681e-07\n", - " 126|1.663559e-01|-1.759123e-07\n", - " 127|1.663559e-01|-1.890771e-07\n", - " 128|1.663559e-01|-1.971315e-07\n", - " 129|1.663558e-01|-2.101983e-07\n", - " 130|1.663558e-01|-2.035645e-07\n", - " 131|1.663558e-01|-1.984492e-07\n", - " 132|1.663557e-01|-1.849064e-07\n", - " 133|1.663557e-01|-1.795703e-07\n", - " 134|1.663557e-01|-1.624087e-07\n", - " 135|1.663557e-01|-1.689557e-07\n", - " 136|1.663556e-01|-1.644308e-07\n", - " 137|1.663556e-01|-1.618007e-07\n", - " 138|1.663556e-01|-1.483013e-07\n", - " 139|1.663555e-01|-1.708771e-07\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 140|1.663555e-01|-2.013847e-07\n", - " 141|1.663555e-01|-1.721217e-07\n", - " 142|1.663554e-01|-2.027911e-07\n", - " 143|1.663554e-01|-1.764565e-07\n", - " 144|1.663554e-01|-1.677151e-07\n", - " 145|1.663554e-01|-1.351982e-07\n", - " 146|1.663553e-01|-1.423360e-07\n", - " 147|1.663553e-01|-1.541112e-07\n", - " 148|1.663553e-01|-1.491601e-07\n", - " 149|1.663553e-01|-1.466407e-07\n", - " 150|1.663552e-01|-1.801524e-07\n", - " 151|1.663552e-01|-1.714107e-07\n", - " 152|1.663552e-01|-1.491257e-07\n", - " 153|1.663552e-01|-1.513799e-07\n", - " 154|1.663551e-01|-1.354539e-07\n", - " 155|1.663551e-01|-1.233818e-07\n", - " 156|1.663551e-01|-1.576219e-07\n", - " 157|1.663551e-01|-1.452791e-07\n", - " 158|1.663550e-01|-1.262867e-07\n", - " 159|1.663550e-01|-1.316379e-07\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 160|1.663550e-01|-1.295447e-07\n", - " 161|1.663550e-01|-1.283286e-07\n", - " 162|1.663550e-01|-1.569222e-07\n", - " 163|1.663549e-01|-1.172942e-07\n", - " 164|1.663549e-01|-1.399809e-07\n", - " 165|1.663549e-01|-1.229432e-07\n", - " 166|1.663549e-01|-1.326191e-07\n", - " 167|1.663548e-01|-1.209694e-07\n", - " 168|1.663548e-01|-1.372136e-07\n", - " 169|1.663548e-01|-1.338395e-07\n", - " 170|1.663548e-01|-1.416497e-07\n", - " 171|1.663548e-01|-1.298576e-07\n", - " 172|1.663547e-01|-1.190590e-07\n", - " 173|1.663547e-01|-1.167083e-07\n", - " 174|1.663547e-01|-1.069425e-07\n", - " 175|1.663547e-01|-1.217780e-07\n", - " 176|1.663547e-01|-1.140754e-07\n", - " 177|1.663546e-01|-1.160707e-07\n", - " 178|1.663546e-01|-1.101798e-07\n", - " 179|1.663546e-01|-1.114904e-07\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 180|1.663546e-01|-1.064022e-07\n", - " 181|1.663546e-01|-9.258231e-08\n", - " 182|1.663546e-01|-1.213120e-07\n", - " 183|1.663545e-01|-1.164296e-07\n", - " 184|1.663545e-01|-1.188762e-07\n", - " 185|1.663545e-01|-9.394153e-08\n", - " 186|1.663545e-01|-1.028656e-07\n", - " 187|1.663545e-01|-1.115348e-07\n", - " 188|1.663544e-01|-9.768310e-08\n", - " 189|1.663544e-01|-1.021806e-07\n", - " 190|1.663544e-01|-1.086303e-07\n", - " 191|1.663544e-01|-9.879008e-08\n", - " 192|1.663544e-01|-1.050210e-07\n", - " 193|1.663544e-01|-1.002463e-07\n", - " 194|1.663543e-01|-1.062747e-07\n", - " 195|1.663543e-01|-9.348538e-08\n", - " 196|1.663543e-01|-7.992512e-08\n", - " 197|1.663543e-01|-9.558020e-08\n", - " 198|1.663543e-01|-9.993772e-08\n", - " 199|1.663543e-01|-8.588499e-08\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 200|1.663543e-01|-8.737134e-08\n" + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 0|1.760578e-01|0.000000e+00|0.000000e+00\n", + " 1|1.669467e-01|5.457501e-02|9.111116e-03\n", + " 2|1.665639e-01|2.298130e-03|3.827855e-04\n", + " 3|1.664378e-01|7.572776e-04|1.260396e-04\n", + " 4|1.664077e-01|1.811855e-04|3.015066e-05\n", + " 5|1.663912e-01|9.936787e-05|1.653393e-05\n", + " 6|1.663852e-01|3.555826e-05|5.916369e-06\n", + " 7|1.663814e-01|2.305693e-05|3.836245e-06\n", + " 8|1.663785e-01|1.760450e-05|2.929009e-06\n", + " 9|1.663767e-01|1.078011e-05|1.793559e-06\n", + " 10|1.663751e-01|9.525192e-06|1.584755e-06\n", + " 11|1.663737e-01|8.396466e-06|1.396951e-06\n", + " 12|1.663727e-01|6.086938e-06|1.012700e-06\n", + " 13|1.663720e-01|4.042609e-06|6.725769e-07\n", + " 14|1.663713e-01|4.160914e-06|6.922568e-07\n", + " 15|1.663707e-01|3.823502e-06|6.361187e-07\n", + " 16|1.663702e-01|3.022440e-06|5.028438e-07\n", + " 17|1.663697e-01|3.181249e-06|5.292634e-07\n", + " 18|1.663692e-01|2.698532e-06|4.489527e-07\n", + " 19|1.663687e-01|3.258253e-06|5.420712e-07\n", + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 20|1.663682e-01|2.741118e-06|4.560349e-07\n", + " 21|1.663678e-01|2.624135e-06|4.365715e-07\n", + " 22|1.663673e-01|2.645179e-06|4.400714e-07\n", + " 23|1.663670e-01|1.957237e-06|3.256196e-07\n", + " 24|1.663666e-01|2.261541e-06|3.762450e-07\n", + " 25|1.663663e-01|1.851305e-06|3.079948e-07\n", + " 26|1.663660e-01|1.942296e-06|3.231320e-07\n", + " 27|1.663657e-01|2.092896e-06|3.481860e-07\n", + " 28|1.663653e-01|1.924361e-06|3.201470e-07\n", + " 29|1.663651e-01|1.625455e-06|2.704189e-07\n", + " 30|1.663648e-01|1.641123e-06|2.730250e-07\n", + " 31|1.663645e-01|1.566666e-06|2.606377e-07\n", + " 32|1.663643e-01|1.338514e-06|2.226810e-07\n", + " 33|1.663641e-01|1.222711e-06|2.034152e-07\n", + " 34|1.663639e-01|1.221805e-06|2.032642e-07\n", + " 35|1.663637e-01|1.440781e-06|2.396935e-07\n", + " 36|1.663634e-01|1.520091e-06|2.528875e-07\n", + " 37|1.663632e-01|1.288193e-06|2.143080e-07\n", + " 38|1.663630e-01|1.123055e-06|1.868347e-07\n", + " 39|1.663628e-01|1.024487e-06|1.704365e-07\n", + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 40|1.663627e-01|1.079606e-06|1.796061e-07\n", + " 41|1.663625e-01|1.172093e-06|1.949922e-07\n", + " 42|1.663623e-01|1.047880e-06|1.743277e-07\n", + " 43|1.663621e-01|1.010577e-06|1.681217e-07\n", + " 44|1.663619e-01|1.064438e-06|1.770820e-07\n", + " 45|1.663618e-01|9.882375e-07|1.644049e-07\n", + " 46|1.663616e-01|8.532647e-07|1.419505e-07\n", + " 47|1.663615e-01|9.930189e-07|1.652001e-07\n", + " 48|1.663613e-01|8.728955e-07|1.452161e-07\n", + " 49|1.663612e-01|9.524214e-07|1.584459e-07\n", + " 50|1.663610e-01|9.088418e-07|1.511958e-07\n", + " 51|1.663609e-01|7.639430e-07|1.270902e-07\n", + " 52|1.663608e-01|6.662611e-07|1.108397e-07\n", + " 53|1.663607e-01|7.133700e-07|1.186767e-07\n", + " 54|1.663605e-01|7.648141e-07|1.272349e-07\n", + " 55|1.663604e-01|6.557516e-07|1.090911e-07\n", + " 56|1.663603e-01|7.304213e-07|1.215131e-07\n", + " 57|1.663602e-01|6.353809e-07|1.057021e-07\n", + " 58|1.663601e-01|7.968279e-07|1.325603e-07\n", + " 59|1.663600e-01|6.367159e-07|1.059240e-07\n", + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 60|1.663599e-01|5.610790e-07|9.334102e-08\n", + " 61|1.663598e-01|5.787466e-07|9.628015e-08\n", + " 62|1.663596e-01|6.937777e-07|1.154166e-07\n", + " 63|1.663596e-01|5.599432e-07|9.315190e-08\n", + " 64|1.663595e-01|5.813048e-07|9.670555e-08\n", + " 65|1.663594e-01|5.724600e-07|9.523409e-08\n", + " 66|1.663593e-01|6.081892e-07|1.011779e-07\n", + " 67|1.663592e-01|5.948732e-07|9.896260e-08\n", + " 68|1.663591e-01|4.941833e-07|8.221188e-08\n", + " 69|1.663590e-01|5.213739e-07|8.673523e-08\n", + " 70|1.663589e-01|5.127355e-07|8.529811e-08\n", + " 71|1.663588e-01|4.349251e-07|7.235363e-08\n", + " 72|1.663588e-01|5.007084e-07|8.329722e-08\n", + " 73|1.663587e-01|4.880265e-07|8.118744e-08\n", + " 74|1.663586e-01|4.931950e-07|8.204723e-08\n", + " 75|1.663585e-01|4.981309e-07|8.286832e-08\n", + " 76|1.663584e-01|3.952959e-07|6.576082e-08\n", + " 77|1.663584e-01|4.544857e-07|7.560750e-08\n", + " 78|1.663583e-01|4.237579e-07|7.049564e-08\n", + " 79|1.663582e-01|4.382386e-07|7.290460e-08\n", + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 80|1.663582e-01|3.646051e-07|6.065503e-08\n", + " 81|1.663581e-01|4.197994e-07|6.983702e-08\n", + " 82|1.663580e-01|4.072764e-07|6.775370e-08\n", + " 83|1.663580e-01|3.994645e-07|6.645410e-08\n", + " 84|1.663579e-01|4.842721e-07|8.056248e-08\n", + " 85|1.663578e-01|3.276486e-07|5.450691e-08\n", + " 86|1.663578e-01|3.737346e-07|6.217366e-08\n", + " 87|1.663577e-01|4.282043e-07|7.123508e-08\n", + " 88|1.663576e-01|4.020937e-07|6.689135e-08\n", + " 89|1.663576e-01|3.431951e-07|5.709310e-08\n", + " 90|1.663575e-01|3.052335e-07|5.077789e-08\n", + " 91|1.663575e-01|3.500538e-07|5.823407e-08\n", + " 92|1.663574e-01|3.063176e-07|5.095821e-08\n", + " 93|1.663573e-01|3.576367e-07|5.949549e-08\n", + " 94|1.663573e-01|3.224681e-07|5.364492e-08\n", + " 95|1.663572e-01|3.673221e-07|6.110670e-08\n", + " 96|1.663572e-01|3.635561e-07|6.048017e-08\n", + " 97|1.663571e-01|3.527236e-07|5.867807e-08\n", + " 98|1.663571e-01|2.788548e-07|4.638946e-08\n", + " 99|1.663570e-01|2.727141e-07|4.536791e-08\n", + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 100|1.663570e-01|3.127278e-07|5.202445e-08\n", + " 101|1.663569e-01|2.637504e-07|4.387670e-08\n", + " 102|1.663569e-01|2.922750e-07|4.862195e-08\n", + " 103|1.663568e-01|3.076454e-07|5.117891e-08\n", + " 104|1.663568e-01|2.911509e-07|4.843492e-08\n", + " 105|1.663567e-01|2.403398e-07|3.998215e-08\n", + " 106|1.663567e-01|2.439790e-07|4.058755e-08\n", + " 107|1.663567e-01|2.634542e-07|4.382735e-08\n", + " 108|1.663566e-01|2.452203e-07|4.079401e-08\n", + " 109|1.663566e-01|2.852991e-07|4.746137e-08\n", + " 110|1.663565e-01|2.165490e-07|3.602434e-08\n", + " 111|1.663565e-01|2.450250e-07|4.076149e-08\n", + " 112|1.663564e-01|2.685294e-07|4.467159e-08\n", + " 113|1.663564e-01|2.821800e-07|4.694245e-08\n", + " 114|1.663564e-01|2.237390e-07|3.722040e-08\n", + " 115|1.663563e-01|1.992842e-07|3.315219e-08\n", + " 116|1.663563e-01|2.166739e-07|3.604506e-08\n", + " 117|1.663563e-01|2.086064e-07|3.470297e-08\n", + " 118|1.663562e-01|2.435945e-07|4.052346e-08\n", + " 119|1.663562e-01|2.292497e-07|3.813711e-08\n", + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 120|1.663561e-01|2.366209e-07|3.936334e-08\n", + " 121|1.663561e-01|2.138746e-07|3.557935e-08\n", + " 122|1.663561e-01|2.009637e-07|3.343153e-08\n", + " 123|1.663560e-01|2.386258e-07|3.969683e-08\n", + " 124|1.663560e-01|1.927442e-07|3.206415e-08\n", + " 125|1.663560e-01|2.081681e-07|3.463000e-08\n", + " 126|1.663559e-01|1.759123e-07|2.926406e-08\n", + " 127|1.663559e-01|1.890771e-07|3.145409e-08\n", + " 128|1.663559e-01|1.971315e-07|3.279398e-08\n", + " 129|1.663558e-01|2.101983e-07|3.496771e-08\n", + " 130|1.663558e-01|2.035645e-07|3.386414e-08\n", + " 131|1.663558e-01|1.984492e-07|3.301317e-08\n", + " 132|1.663557e-01|1.849064e-07|3.076024e-08\n", + " 133|1.663557e-01|1.795703e-07|2.987255e-08\n", + " 134|1.663557e-01|1.624087e-07|2.701762e-08\n", + " 135|1.663557e-01|1.689557e-07|2.810673e-08\n", + " 136|1.663556e-01|1.644308e-07|2.735399e-08\n", + " 137|1.663556e-01|1.618007e-07|2.691644e-08\n", + " 138|1.663556e-01|1.483013e-07|2.467075e-08\n", + " 139|1.663555e-01|1.708771e-07|2.842636e-08\n", + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 140|1.663555e-01|2.013847e-07|3.350146e-08\n", + " 141|1.663555e-01|1.721217e-07|2.863339e-08\n", + " 142|1.663554e-01|2.027911e-07|3.373540e-08\n", + " 143|1.663554e-01|1.764565e-07|2.935449e-08\n", + " 144|1.663554e-01|1.677151e-07|2.790030e-08\n", + " 145|1.663554e-01|1.351982e-07|2.249094e-08\n", + " 146|1.663553e-01|1.423360e-07|2.367836e-08\n", + " 147|1.663553e-01|1.541112e-07|2.563722e-08\n", + " 148|1.663553e-01|1.491601e-07|2.481358e-08\n", + " 149|1.663553e-01|1.466407e-07|2.439446e-08\n", + " 150|1.663552e-01|1.801524e-07|2.996929e-08\n", + " 151|1.663552e-01|1.714107e-07|2.851507e-08\n", + " 152|1.663552e-01|1.491257e-07|2.480784e-08\n", + " 153|1.663552e-01|1.513799e-07|2.518282e-08\n", + " 154|1.663551e-01|1.354539e-07|2.253345e-08\n", + " 155|1.663551e-01|1.233818e-07|2.052519e-08\n", + " 156|1.663551e-01|1.576219e-07|2.622121e-08\n", + " 157|1.663551e-01|1.452791e-07|2.416792e-08\n", + " 158|1.663550e-01|1.262867e-07|2.100843e-08\n", + " 159|1.663550e-01|1.316379e-07|2.189863e-08\n", + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 160|1.663550e-01|1.295447e-07|2.155041e-08\n", + " 161|1.663550e-01|1.283286e-07|2.134810e-08\n", + " 162|1.663550e-01|1.569222e-07|2.610479e-08\n", + " 163|1.663549e-01|1.172942e-07|1.951247e-08\n", + " 164|1.663549e-01|1.399809e-07|2.328651e-08\n", + " 165|1.663549e-01|1.229432e-07|2.045221e-08\n", + " 166|1.663549e-01|1.326191e-07|2.206184e-08\n", + " 167|1.663548e-01|1.209694e-07|2.012384e-08\n", + " 168|1.663548e-01|1.372136e-07|2.282614e-08\n", + " 169|1.663548e-01|1.338395e-07|2.226484e-08\n", + " 170|1.663548e-01|1.416497e-07|2.356410e-08\n", + " 171|1.663548e-01|1.298576e-07|2.160242e-08\n", + " 172|1.663547e-01|1.190590e-07|1.980603e-08\n", + " 173|1.663547e-01|1.167083e-07|1.941497e-08\n", + " 174|1.663547e-01|1.069425e-07|1.779038e-08\n", + " 175|1.663547e-01|1.217780e-07|2.025834e-08\n", + " 176|1.663547e-01|1.140754e-07|1.897697e-08\n", + " 177|1.663546e-01|1.160707e-07|1.930890e-08\n", + " 178|1.663546e-01|1.101798e-07|1.832892e-08\n", + " 179|1.663546e-01|1.114904e-07|1.854694e-08\n", + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 180|1.663546e-01|1.064022e-07|1.770049e-08\n", + " 181|1.663546e-01|9.258231e-08|1.540149e-08\n", + " 182|1.663546e-01|1.213120e-07|2.018080e-08\n", + " 183|1.663545e-01|1.164296e-07|1.936859e-08\n", + " 184|1.663545e-01|1.188762e-07|1.977559e-08\n", + " 185|1.663545e-01|9.394153e-08|1.562760e-08\n", + " 186|1.663545e-01|1.028656e-07|1.711216e-08\n", + " 187|1.663545e-01|1.115348e-07|1.855431e-08\n", + " 188|1.663544e-01|9.768310e-08|1.625002e-08\n", + " 189|1.663544e-01|1.021806e-07|1.699820e-08\n", + " 190|1.663544e-01|1.086303e-07|1.807113e-08\n", + " 191|1.663544e-01|9.879008e-08|1.643416e-08\n", + " 192|1.663544e-01|1.050210e-07|1.747071e-08\n", + " 193|1.663544e-01|1.002463e-07|1.667641e-08\n", + " 194|1.663543e-01|1.062747e-07|1.767925e-08\n", + " 195|1.663543e-01|9.348538e-08|1.555170e-08\n", + " 196|1.663543e-01|7.992512e-08|1.329589e-08\n", + " 197|1.663543e-01|9.558020e-08|1.590018e-08\n", + " 198|1.663543e-01|9.993772e-08|1.662507e-08\n", + " 199|1.663543e-01|8.588499e-08|1.428734e-08\n", + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 200|1.663543e-01|8.737134e-08|1.453459e-08\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% Example with Frobenius norm regularization\n", - "\n", - "\n", "def f(G):\n", " return 0.5 * np.sum(G**2)\n", "\n", @@ -420,215 +417,124 @@ "name": "stdout", "output_type": "stream", "text": [ - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 0|1.692289e-01|0.000000e+00\n", - " 1|1.617643e-01|-4.614437e-02\n", - " 2|1.612639e-01|-3.102965e-03\n", - " 3|1.611291e-01|-8.371098e-04\n", - " 4|1.610468e-01|-5.110558e-04\n", - " 5|1.610198e-01|-1.672927e-04\n", - " 6|1.610130e-01|-4.232417e-05\n", - " 7|1.610090e-01|-2.513455e-05\n", - " 8|1.610002e-01|-5.443507e-05\n", - " 9|1.609996e-01|-3.657071e-06\n", - " 10|1.609948e-01|-2.998735e-05\n", - " 11|1.609695e-01|-1.569217e-04\n", - " 12|1.609533e-01|-1.010779e-04\n", - " 13|1.609520e-01|-8.043897e-06\n", - " 14|1.609465e-01|-3.415246e-05\n", - " 15|1.609386e-01|-4.898605e-05\n", - " 16|1.609324e-01|-3.837052e-05\n", - " 17|1.609298e-01|-1.617826e-05\n", - " 18|1.609184e-01|-7.080015e-05\n", - " 19|1.609083e-01|-6.273206e-05\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 20|1.608988e-01|-5.940805e-05\n", - " 21|1.608853e-01|-8.380030e-05\n", - " 22|1.608844e-01|-5.185045e-06\n", - " 23|1.608824e-01|-1.279113e-05\n", - " 24|1.608819e-01|-3.156821e-06\n", - " 25|1.608783e-01|-2.205746e-05\n", - " 26|1.608764e-01|-1.189894e-05\n", - " 27|1.608755e-01|-5.474607e-06\n", - " 28|1.608737e-01|-1.144227e-05\n", - " 29|1.608676e-01|-3.775335e-05\n", - " 30|1.608638e-01|-2.348020e-05\n", - " 31|1.608627e-01|-6.863136e-06\n", - " 32|1.608529e-01|-6.110230e-05\n", - " 33|1.608487e-01|-2.641106e-05\n", - " 34|1.608409e-01|-4.823638e-05\n", - " 35|1.608373e-01|-2.256641e-05\n", - " 36|1.608338e-01|-2.132444e-05\n", - " 37|1.608310e-01|-1.786649e-05\n", - " 38|1.608260e-01|-3.103848e-05\n", - " 39|1.608206e-01|-3.321265e-05\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 40|1.608201e-01|-3.054747e-06\n", - " 41|1.608195e-01|-4.198335e-06\n", - " 42|1.608193e-01|-8.458736e-07\n", - " 43|1.608159e-01|-2.153759e-05\n", - " 44|1.608115e-01|-2.738314e-05\n", - " 45|1.608108e-01|-3.960032e-06\n", - " 46|1.608081e-01|-1.675447e-05\n", - " 47|1.608072e-01|-5.976340e-06\n", - " 48|1.608046e-01|-1.604130e-05\n", - " 49|1.608020e-01|-1.617036e-05\n", - " 50|1.608014e-01|-3.957795e-06\n", - " 51|1.608011e-01|-1.292411e-06\n", - " 52|1.607998e-01|-8.431795e-06\n", - " 53|1.607964e-01|-2.127054e-05\n", - " 54|1.607947e-01|-1.021878e-05\n", - " 55|1.607947e-01|-3.560621e-07\n", - " 56|1.607900e-01|-2.929781e-05\n", - " 57|1.607890e-01|-5.740229e-06\n", - " 58|1.607858e-01|-2.039550e-05\n", - " 59|1.607836e-01|-1.319545e-05\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 60|1.607826e-01|-6.378947e-06\n", - " 61|1.607808e-01|-1.145102e-05\n", - " 62|1.607776e-01|-1.941743e-05\n", - " 63|1.607743e-01|-2.087422e-05\n", - " 64|1.607741e-01|-1.310249e-06\n", - " 65|1.607738e-01|-1.682752e-06\n", - " 66|1.607691e-01|-2.913936e-05\n", - " 67|1.607671e-01|-1.288855e-05\n", - " 68|1.607654e-01|-1.002448e-05\n", - " 69|1.607641e-01|-8.209492e-06\n", - " 70|1.607632e-01|-5.588467e-06\n", - " 71|1.607619e-01|-8.050388e-06\n", - " 72|1.607618e-01|-9.417493e-07\n", - " 73|1.607598e-01|-1.210509e-05\n", - " 74|1.607591e-01|-4.392914e-06\n", - " 75|1.607579e-01|-7.759587e-06\n", - " 76|1.607574e-01|-2.760280e-06\n", - " 77|1.607556e-01|-1.146469e-05\n", - " 78|1.607550e-01|-3.689456e-06\n", - " 79|1.607550e-01|-4.065631e-08\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 80|1.607539e-01|-6.555681e-06\n", - " 81|1.607528e-01|-7.177470e-06\n", - " 82|1.607527e-01|-5.306068e-07\n", - " 83|1.607514e-01|-7.816045e-06\n", - " 84|1.607511e-01|-2.301970e-06\n", - " 85|1.607504e-01|-4.281072e-06\n", - " 86|1.607503e-01|-7.821886e-07\n", - " 87|1.607480e-01|-1.403013e-05\n", - " 88|1.607480e-01|-1.169298e-08\n", - " 89|1.607473e-01|-4.235982e-06\n", - " 90|1.607470e-01|-1.717105e-06\n", - " 91|1.607470e-01|-6.148402e-09\n", - " 92|1.607462e-01|-5.396481e-06\n", - " 93|1.607461e-01|-5.194954e-07\n", - " 94|1.607450e-01|-6.525707e-06\n", - " 95|1.607442e-01|-5.332060e-06\n", - " 96|1.607439e-01|-1.682093e-06\n", - " 97|1.607437e-01|-1.594796e-06\n", - " 98|1.607435e-01|-7.923812e-07\n", - " 99|1.607420e-01|-9.738552e-06\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 100|1.607419e-01|-1.022448e-07\n", - " 101|1.607419e-01|-4.865999e-07\n", - " 102|1.607418e-01|-7.092012e-07\n", - " 103|1.607408e-01|-5.861815e-06\n", - " 104|1.607402e-01|-3.953266e-06\n", - " 105|1.607395e-01|-3.969572e-06\n", - " 106|1.607390e-01|-3.612075e-06\n", - " 107|1.607377e-01|-7.683735e-06\n", - " 108|1.607365e-01|-7.777599e-06\n", - " 109|1.607364e-01|-2.335096e-07\n", - " 110|1.607364e-01|-4.562036e-07\n", - " 111|1.607360e-01|-2.089538e-06\n", - " 112|1.607356e-01|-2.755355e-06\n", - " 113|1.607349e-01|-4.501960e-06\n", - " 114|1.607347e-01|-1.160544e-06\n", - " 115|1.607346e-01|-6.289450e-07\n", - " 116|1.607345e-01|-2.092146e-07\n", - " 117|1.607336e-01|-5.990866e-06\n", - " 118|1.607330e-01|-3.348498e-06\n", - " 119|1.607328e-01|-1.256222e-06\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 120|1.607320e-01|-5.418353e-06\n", - " 121|1.607318e-01|-8.296189e-07\n", - " 122|1.607311e-01|-4.381608e-06\n", - " 123|1.607310e-01|-8.913901e-07\n", - " 124|1.607309e-01|-3.808821e-07\n", - " 125|1.607302e-01|-4.608994e-06\n", - " 126|1.607294e-01|-5.063777e-06\n", - " 127|1.607290e-01|-2.532835e-06\n", - " 128|1.607285e-01|-2.870049e-06\n", - " 129|1.607284e-01|-4.892812e-07\n", - " 130|1.607281e-01|-1.760452e-06\n", - " 131|1.607279e-01|-1.727139e-06\n", - " 132|1.607275e-01|-2.220706e-06\n", - " 133|1.607271e-01|-2.516930e-06\n", - " 134|1.607269e-01|-1.201434e-06\n", - " 135|1.607269e-01|-2.183459e-09\n", - " 136|1.607262e-01|-4.223011e-06\n", - " 137|1.607258e-01|-2.530202e-06\n", - " 138|1.607258e-01|-1.857260e-07\n", - " 139|1.607256e-01|-1.401957e-06\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 140|1.607250e-01|-3.242751e-06\n", - " 141|1.607247e-01|-2.308071e-06\n", - " 142|1.607247e-01|-4.730700e-08\n", - " 143|1.607246e-01|-4.240229e-07\n", - " 144|1.607242e-01|-2.484810e-06\n", - " 145|1.607238e-01|-2.539206e-06\n", - " 146|1.607234e-01|-2.535574e-06\n", - " 147|1.607231e-01|-1.954802e-06\n", - " 148|1.607228e-01|-1.765447e-06\n", - " 149|1.607228e-01|-1.620007e-08\n", - " 150|1.607222e-01|-3.615783e-06\n", - " 151|1.607222e-01|-8.668516e-08\n", - " 152|1.607215e-01|-4.000673e-06\n", - " 153|1.607213e-01|-1.774103e-06\n", - " 154|1.607213e-01|-6.328834e-09\n", - " 155|1.607209e-01|-2.418783e-06\n", - " 156|1.607208e-01|-2.848492e-07\n", - " 157|1.607207e-01|-8.836043e-07\n", - " 158|1.607205e-01|-1.192836e-06\n", - " 159|1.607202e-01|-1.638022e-06\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 160|1.607202e-01|-3.670914e-08\n", - " 161|1.607197e-01|-3.153709e-06\n", - " 162|1.607197e-01|-2.419565e-09\n", - " 163|1.607194e-01|-2.136882e-06\n", - " 164|1.607194e-01|-1.173754e-09\n", - " 165|1.607192e-01|-8.169238e-07\n", - " 166|1.607191e-01|-9.218755e-07\n", - " 167|1.607189e-01|-9.459255e-07\n", - " 168|1.607187e-01|-1.294835e-06\n", - " 169|1.607186e-01|-5.797668e-07\n", - " 170|1.607186e-01|-4.706272e-08\n", - " 171|1.607183e-01|-1.753383e-06\n", - " 172|1.607183e-01|-1.681573e-07\n", - " 173|1.607183e-01|-2.563971e-10\n" + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 0|1.692289e-01|0.000000e+00|0.000000e+00\n", + " 1|1.617643e-01|4.614437e-02|7.464513e-03\n", + " 2|1.612639e-01|3.102965e-03|5.003963e-04\n", + " 3|1.611291e-01|8.371098e-04|1.348827e-04\n", + " 4|1.610468e-01|5.110558e-04|8.230389e-05\n", + " 5|1.610198e-01|1.672927e-04|2.693743e-05\n", + " 6|1.610130e-01|4.232417e-05|6.814742e-06\n", + " 7|1.610090e-01|2.513455e-05|4.046887e-06\n", + " 8|1.610002e-01|5.443507e-05|8.764057e-06\n", + " 9|1.609996e-01|3.657071e-06|5.887869e-07\n", + " 10|1.609948e-01|2.998735e-05|4.827807e-06\n", + " 11|1.609695e-01|1.569217e-04|2.525962e-05\n", + " 12|1.609533e-01|1.010779e-04|1.626881e-05\n", + " 13|1.609520e-01|8.043897e-06|1.294681e-06\n", + " 14|1.609465e-01|3.415246e-05|5.496718e-06\n", + " 15|1.609386e-01|4.898605e-05|7.883745e-06\n", + " 16|1.609324e-01|3.837052e-05|6.175060e-06\n", + " 17|1.609298e-01|1.617826e-05|2.603564e-06\n", + " 18|1.609184e-01|7.080015e-05|1.139305e-05\n", + " 19|1.609083e-01|6.273206e-05|1.009411e-05\n", + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 20|1.608988e-01|5.940805e-05|9.558681e-06\n", + " 21|1.608853e-01|8.380030e-05|1.348223e-05\n", + " 22|1.608844e-01|5.185045e-06|8.341930e-07\n", + " 23|1.608824e-01|1.279113e-05|2.057868e-06\n", + " 24|1.608819e-01|3.156821e-06|5.078753e-07\n", + " 25|1.608783e-01|2.205746e-05|3.548567e-06\n", + " 26|1.608764e-01|1.189894e-05|1.914259e-06\n", + " 27|1.608755e-01|5.474607e-06|8.807303e-07\n", + " 28|1.608737e-01|1.144227e-05|1.840760e-06\n", + " 29|1.608676e-01|3.775335e-05|6.073291e-06\n", + " 30|1.608638e-01|2.348020e-05|3.777116e-06\n", + " 31|1.608627e-01|6.863136e-06|1.104023e-06\n", + " 32|1.608529e-01|6.110230e-05|9.828482e-06\n", + " 33|1.608487e-01|2.641106e-05|4.248184e-06\n", + " 34|1.608409e-01|4.823638e-05|7.758383e-06\n", + " 35|1.608373e-01|2.256641e-05|3.629519e-06\n", + " 36|1.608338e-01|2.132444e-05|3.429691e-06\n", + " 37|1.608310e-01|1.786649e-05|2.873484e-06\n", + " 38|1.608260e-01|3.103848e-05|4.991794e-06\n", + " 39|1.608206e-01|3.321265e-05|5.341279e-06\n", + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 40|1.608201e-01|3.054747e-06|4.912648e-07\n", + " 41|1.608195e-01|4.198335e-06|6.751739e-07\n", + " 42|1.608193e-01|8.458736e-07|1.360328e-07\n", + " 43|1.608159e-01|2.153759e-05|3.463587e-06\n", + " 44|1.608115e-01|2.738314e-05|4.403523e-06\n", + " 45|1.608108e-01|3.960032e-06|6.368161e-07\n", + " 46|1.608081e-01|1.675447e-05|2.694254e-06\n", + " 47|1.608072e-01|5.976340e-06|9.610383e-07\n", + " 48|1.608046e-01|1.604130e-05|2.579515e-06\n", + " 49|1.608020e-01|1.617036e-05|2.600226e-06\n", + " 50|1.608014e-01|3.957795e-06|6.364188e-07\n", + " 51|1.608011e-01|1.292411e-06|2.078211e-07\n", + " 52|1.607998e-01|8.431795e-06|1.355831e-06\n", + " 53|1.607964e-01|2.127054e-05|3.420225e-06\n", + " 54|1.607947e-01|1.021878e-05|1.643126e-06\n", + " 55|1.607947e-01|3.560621e-07|5.725288e-08\n", + " 56|1.607900e-01|2.929781e-05|4.710793e-06\n", + " 57|1.607890e-01|5.740229e-06|9.229659e-07\n", + " 58|1.607858e-01|2.039550e-05|3.279306e-06\n", + " 59|1.607836e-01|1.319545e-05|2.121612e-06\n", + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 60|1.607826e-01|6.378947e-06|1.025624e-06\n", + " 61|1.607808e-01|1.145102e-05|1.841105e-06\n", + " 62|1.607776e-01|1.941743e-05|3.121889e-06\n", + " 63|1.607743e-01|2.087422e-05|3.356037e-06\n", + " 64|1.607741e-01|1.310249e-06|2.106541e-07\n", + " 65|1.607738e-01|1.682752e-06|2.705425e-07\n", + " 66|1.607691e-01|2.913936e-05|4.684709e-06\n", + " 67|1.607671e-01|1.288855e-05|2.072055e-06\n", + " 68|1.607654e-01|1.002448e-05|1.611590e-06\n", + " 69|1.607641e-01|8.209492e-06|1.319792e-06\n", + " 70|1.607632e-01|5.588467e-06|8.984199e-07\n", + " 71|1.607619e-01|8.050388e-06|1.294196e-06\n", + " 72|1.607618e-01|9.417493e-07|1.513973e-07\n", + " 73|1.607598e-01|1.210509e-05|1.946012e-06\n", + " 74|1.607591e-01|4.392914e-06|7.062009e-07\n", + " 75|1.607579e-01|7.759587e-06|1.247415e-06\n", + " 76|1.607574e-01|2.760280e-06|4.437356e-07\n", + " 77|1.607556e-01|1.146469e-05|1.843012e-06\n", + " 78|1.607550e-01|3.689456e-06|5.930984e-07\n", + " 79|1.607550e-01|4.065631e-08|6.535705e-09\n", + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 80|1.607539e-01|6.555681e-06|1.053852e-06\n", + " 81|1.607528e-01|7.177470e-06|1.153798e-06\n", + " 82|1.607527e-01|5.306068e-07|8.529648e-08\n", + " 83|1.607514e-01|7.816045e-06|1.256440e-06\n", + " 84|1.607511e-01|2.301970e-06|3.700442e-07\n", + " 85|1.607504e-01|4.281072e-06|6.881840e-07\n", + " 86|1.607503e-01|7.821886e-07|1.257370e-07\n", + " 87|1.607480e-01|1.403013e-05|2.255315e-06\n", + " 88|1.607480e-01|1.169298e-08|1.879624e-09\n", + " 89|1.607473e-01|4.235982e-06|6.809227e-07\n", + " 90|1.607470e-01|1.717105e-06|2.760195e-07\n", + " 91|1.607470e-01|6.148402e-09|9.883374e-10\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% Example with entropic regularization\n", - "\n", - "\n", "def f(G):\n", " return np.sum(G * np.log(G))\n", "\n", @@ -665,30 +571,35 @@ "name": "stdout", "output_type": "stream", "text": [ - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 0|1.693084e-01|0.000000e+00\n", - " 1|1.610121e-01|-5.152589e-02\n", - " 2|1.609378e-01|-4.622297e-04\n", - " 3|1.609284e-01|-5.830043e-05\n", - " 4|1.609284e-01|-1.111407e-12\n" + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 0|1.693084e-01|0.000000e+00|0.000000e+00\n", + " 1|1.610202e-01|5.147342e-02|8.288260e-03\n", + " 2|1.609508e-01|4.309685e-04|6.936474e-05\n", + " 3|1.609484e-01|1.524885e-05|2.454278e-06\n", + " 4|1.609477e-01|3.863641e-06|6.218444e-07\n", + " 5|1.609475e-01|1.433633e-06|2.307397e-07\n", + " 6|1.609474e-01|6.332412e-07|1.019185e-07\n", + " 7|1.609474e-01|2.950826e-07|4.749276e-08\n", + " 8|1.609473e-01|1.508393e-07|2.427718e-08\n", + " 9|1.609473e-01|7.859971e-08|1.265041e-08\n", + " 10|1.609473e-01|4.337432e-08|6.980981e-09\n" ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "#%% Example with Frobenius norm + entropic regularization with gcg\n", - "\n", - "\n", "def f(G):\n", " return 0.5 * np.sum(G**2)\n", "\n", @@ -724,7 +635,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_otda_classes.ipynb b/notebooks/plot_otda_classes.ipynb index 2af6fb6..450365f 100644 --- a/notebooks/plot_otda_classes.ipynb +++ b/notebooks/plot_otda_classes.ipynb @@ -86,31 +86,31 @@ "name": "stdout", "output_type": "stream", "text": [ - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 0|9.537526e+00|0.000000e+00\n", - " 1|2.505426e+00|-2.806748e+00\n", - " 2|2.264025e+00|-1.066249e-01\n", - " 3|2.210620e+00|-2.415841e-02\n", - " 4|2.191601e+00|-8.677880e-03\n", - " 5|2.182712e+00|-4.072416e-03\n", - " 6|2.178054e+00|-2.138572e-03\n", - " 7|2.176320e+00|-7.971427e-04\n", - " 8|2.174237e+00|-9.578098e-04\n", - " 9|2.172978e+00|-5.792305e-04\n", - " 10|2.172514e+00|-2.138295e-04\n", - " 11|2.171279e+00|-5.689220e-04\n", - " 12|2.169799e+00|-6.819885e-04\n", - " 13|2.169215e+00|-2.692594e-04\n", - " 14|2.168810e+00|-1.868050e-04\n", - " 15|2.168289e+00|-2.401519e-04\n", - " 16|2.168018e+00|-1.249509e-04\n", - " 17|2.167885e+00|-6.124717e-05\n", - " 18|2.167623e+00|-1.211692e-04\n", - " 19|2.167335e+00|-1.327875e-04\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 20|2.166808e+00|-2.432572e-04\n" + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 0|1.068487e+01|0.000000e+00|0.000000e+00\n", + " 1|2.265157e+00|3.717055e+00|8.419713e+00\n", + " 2|2.027223e+00|1.173695e-01|2.379341e-01\n", + " 3|1.969810e+00|2.914611e-02|5.741231e-02\n", + " 4|1.946438e+00|1.200787e-02|2.337257e-02\n", + " 5|1.935059e+00|5.880253e-03|1.137864e-02\n", + " 6|1.927743e+00|3.795152e-03|7.316078e-03\n", + " 7|1.923064e+00|2.433205e-03|4.679208e-03\n", + " 8|1.917781e+00|2.754726e-03|5.282962e-03\n", + " 9|1.914769e+00|1.572823e-03|3.011594e-03\n", + " 10|1.913550e+00|6.373943e-04|1.219686e-03\n", + " 11|1.911332e+00|1.160273e-03|2.217668e-03\n", + " 12|1.910399e+00|4.882451e-04|9.327431e-04\n", + " 13|1.908762e+00|8.578270e-04|1.637388e-03\n", + " 14|1.908107e+00|3.430078e-04|6.544958e-04\n", + " 15|1.907106e+00|5.253391e-04|1.001877e-03\n", + " 16|1.906394e+00|3.734588e-04|7.119595e-04\n", + " 17|1.906076e+00|1.664949e-04|3.173520e-04\n", + " 18|1.905829e+00|1.295989e-04|2.469934e-04\n", + " 19|1.905120e+00|3.720389e-04|7.087790e-04\n", + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 20|1.904801e+00|1.676571e-04|3.193534e-04\n" ] } ], @@ -157,7 +157,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -297,7 +297,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_otda_color_images.ipynb b/notebooks/plot_otda_color_images.ipynb index e2bd92b..cc060ee 100644 --- a/notebooks/plot_otda_color_images.ipynb +++ b/notebooks/plot_otda_color_images.ipynb @@ -42,7 +42,6 @@ "# License: MIT License\n", "\n", "import numpy as np\n", - "from scipy import ndimage\n", "import matplotlib.pylab as pl\n", "import ot\n", "\n", @@ -79,26 +78,11 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:2: DeprecationWarning: `imread` is deprecated!\n", - "`imread` is deprecated in SciPy 1.0.0.\n", - "Use ``matplotlib.pyplot.imread`` instead.\n", - " \n", - "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: `imread` is deprecated!\n", - "`imread` is deprecated in SciPy 1.0.0.\n", - "Use ``matplotlib.pyplot.imread`` instead.\n", - " This is separate from the ipykernel package so we can avoid doing imports until\n" - ] - } - ], + "outputs": [], "source": [ "# Loading images\n", - "I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\n", - "I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n", + "I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256\n", + "I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n", "\n", "X1 = im2mat(I1)\n", "X2 = im2mat(I2)\n", @@ -131,7 +115,7 @@ { "data": { "text/plain": [ - "Text(0.5,1,'Image 2')" + "Text(0.5, 1.0, 'Image 2')" ] }, "execution_count": 4, @@ -140,12 +124,14 @@ }, { "data": { - "image/png": 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GJSvjOsC46TOTmk5XNvntOE8HF87bd2mCaF7WLSIXWapJQuTlPGYKIolKXsoCJbb1b9JdS3j1N/+tN9ov9LILZys8teQWZ4pGz05Ep5d5ZDOShBQOupIpdIEzQs8zZxdEnNjOi/fKNzyYY0ign2djcWfVgq8HVoIziQGtFL7PGmdXIpXsI0+YXSEXDNvuL4VIqs5kbdw0WBO6BORC8RzXJoLIinE3pNpZuBO46YGoEotDUZRklmQSKGZ0dzQ62m1IkgUMcMlN7VaZ9EQtla4VCMxGSfFQCuaOZ6OV4XxKSXoGN5LciOOidE2aGM0CjSMLQa2FKTrFhAUnrXJkyKIlGy0KB5s4Z6PiZLGR5aQSOhOhnCzpCBMHDlSKNyoTwTK4Jl/JjXBffNydZcteSjbChKINicIMVOn0VJopZCElcTFOaRBOEcVlcDDHqeGlcJSJ+ywgjWdrcF+VEBnkfw/CjBjMEfNyzyJKFENTmFrSLXCPj75Bv018KhwNPBg7/9D/EwgVNOWyDUanaWYOCeBmzMtmOTNHjXtEAIIIuCYorxkzfW37w3fK5rRcEkHGvhiG8cNWSDZ1EIDbUIGoyCaxfP17drjkpncZP+ujLNWHnUbzYb+ZieuIimCToXoQJkg8rLcTQ22jiuQwsrtDEnk4HzCMtjz63sfHE5vjks0gZyhqhkh77b0ubMf6+vrZ1hSZFFV8c3IfhuoIEIhRnumml2s7HHfd1pyIwCTKq+pMsTvTocjZz1FmDkcHBEZLp9qDo8ktANk97O6gieGkWrz5nlSRQc7eu2JVUJ0InbG453vkntag1YmzgnrD12AmiFAkDQZzQEmjpHAqcEzlld/jKbyfnS+40lg5yZAeP8+klnFDfD0mbsK5tWTxhlBxGcqlanDCWWWUm8T7kFDXYPbgLENgK5GEBFOtLNkoWRCMkOQmhGEjB2Gt4bhCFUX3gCxgsiFTFhshkahQMsjuzKVgXpBUZlNcA6OPkpoVaklqCitjfy1lKPGicJwETeXcnWCo56YinHtnKhX10cNSSmGNIBAsCl2VRUfwcm+Vydv4BQpFNJh9pWsgaUxmTFJo3iGTKg3LAOkQI2vs3kEMR6k6gtkbc0IrMEFREOHgzr0khwjMnLtMcKfGUAc6Tm7c2VGMlwjuzpwrqxZCndtwTj4c1VkT6QuqhnmCBTVAlwWy0s1RN0Q+hxkNqhdlnjCM4O5PJblkLI8+QCTkbhBFhvxzM6KxpYOqgkcMg/nIiDVNJB8M6p7JJGChrAaFpDMau2oMXbompDhx+eIYNep9hFiOCMoBxC6rTeES8ecoRo8II3NE5LmvjJFhMZrD5NHnFdAcPREBUMb2MFBPQoekE4LdlgpC2uYkIzen9shpbg40Ih6cbD4cx+4xxIb5UtXXnReQCKJsMdqWoZFEjnPQt3WMhkAhQ8bndMhyx/VKFMUyN+cjSCZexnd0BEtokkwoKbmtBXL7hbDt39jOswuUVDL3Y0uCcfJSR/Y0nJzC9lnVj/C832FINsQD1+TlatzYMLAHVV5tQcy6OnVuRFVmB4vgpgjnWIc81YMGNJ2Y0rhrjTkLvZ+xhHdFKAk3VJ7aGZUDJjrIej/TonBGMBXMkiKBeyEEjjIxxZmVQplncKenoHXG1kZIH1mCC807qUZTvQQtYiOD1arDyRTFU5l05ShKS5imykHBo9PTRpNiHfdAz070pFZD3TEZTZLVCunwfiRfrM5NhWcxs2iSHmSZaRHMNnNrneZGi+RVDmHD08ORd9qKpnGXIzN2qxQJWjqWxrMwztJJKagGIQo+ssFAOJoMol+DCkzSSUl2a1YZwWhkUIoSGEXP9CwcEkLr5f6vJqQKs1RWnKWNEkANOJB0+pbBG2uM38u7iPF7ZzNPcsWs8CoU7SvqQjKUbU8UXjGyqPtTsIoQoaiOrLINAuxjva8/FY4mN41/8bzUWh8ymq0Mw4fm5WzNZLBlASjJXhbaHA9DOz8+/0C86bZf3QpKr63FhMKWLW372rOO0dQ2SNQB21Icu6wJNmexlbRGExbk5nj2774c46j5wZYBmUCmIprkFmHrdvwptmUirxtEMdmcy6PXL13YmwOQoRLSfHBospUE08rmmPaS3KWodiktjuu0OQ3dy05byepxppQ5nNPjc5o5Op8BVYZjTmOf8VlShjNG8AQyKZsMdP98PiqLjfOxncM9i92c7H4tlBGI7O5aRNDNkXkmoo6Ijasho3T2+qrfDP4eztyFckOlyR0lhGc60sTWlW6FpRzB+4hELRBXnvczb2nnLmw44CZ0WTjnTM0g64L0imsi5IjOOeEJnROT3xBU1AwkuQ/HsnKIkVUUDbwJXk4UGdGzexuRsQjaA1G4UWNdnXM4LgWTTvdCk2E4D9o5lo77TJUkEGYVjjr+b1upqpBMZSYzCJISfTT8mOFSxqWeK6U3qs3MnHCFo06s8ZRb7nl2fI+TPWVaV1oYL+vMXULPA1Pt4AXX4NQF18KTKTg24675KHdJYNMRW06oBA3nRg2ncZBKzyFkaJL41mjZRVGbWWNMXShWOZkNtR8xFHibETcplF6QqYBO22SCYLLCOyJEF97VSojgaayS3Fgj0nhagjUc7c4rH9WBWZVS4P3WOdrEuS28Fzc8ozBPycoEZrzMwFvgOegDElwLp+wce+NZBGfpH+t9/alwNCKCmeESJEJBhjGQjWPZ37MZgticT9+sjAK2mcp9f5kjgjXNoaHXMgzWw5dulWle+8xumFyhbmMrLp4lk9z6C7h8227kRhAg8hDxl21JqXIp6wVjH3vwHAKS+mhno0wk6B5oj+a8zWFpcjHkFyULwxg/5k/YnO2ebUVsKdlWfxTVzSnYaAiEUZfXh7LfcGqj1wEeOsd3pzuOe9SKQ+U1h3RZRT6c24fzrJd/x/vjkp6KDL5nyPgfrud+zR/OkeAi2LbfnonIA9cSW9ihjLEcFqAyDLaIINSxOBMkRrSY+eYdzW80wSOZvXNrypTCexl8KRU1KNm5xXBz1DuiyVSUSkEM3nZFc0WtcPbOiZUzxmlNVl0QSUoaU4JvyqImSmiwcoLoFJ6gBqKDryCEszcmO7BH511HR71tuewaIxfvfSG2cSglVlaZqLuAQIQVI73ytiY3arSELuNevw+hSqAMLk0kmCyIFA61snhDZSKLo62wrivrrOCB1Yqb8ESUL9aFv91vaesTvjA1vr9O3MSKanDIwvsoJ245HIPnLTjUzp0nZw5kcb5sK09j5RtSeCc6RaCqoThuQkXwHFwsLowiXMElqJviy2T8LrUMJDoHq1gIczTWGGN8UlaYbZS8TYkMznHkpQhdOulKjQCBJZ3DkoStkM7LENomKXdzigpLFwxY3fiN7NQ+c1MaLsKSzr2vnNtoxlSCmsP5n88rTQJJ5SZXohrn9XOY0agqPUa9V0fh/2I0RUYJDIaCBMDywQDBN/Mm+zbZOl/3jOgxT/LY+D1+jYuTkFHDj9ejdflQtN5ky3w2rmf/ir0cJwx7tvMBFmMkhsRueEHEXzO0O/b11ZTXhAzjXDxkGo/Xv5ejJnQQ8JtD6TaMcuiHzsGHzuP4+aGO+SHX9TrXtBP7Gw+z//zYKeRHkIofns+5f2deSn6/9/v90X933srdt+MEfDgYYWQ/IygZ86V2Jz0ksf6h/b95R/MWwSKMLvoxNYVbVdwEtFDnEbS8rYH0UWN+SucwdaYUPpCVZ9ZZo9Gl86IfOUtSw4dxFGW2hofxSsd5C1V6nEgzIhJspYsRWQZ/pQlUXASRCcQpDPXVEsndukIoTyVGadp9zDhDKTkaLh2YRMhItCr3OuagVTNaNF6iPLdhmCPhPQ9Sje9PZSpQolGsEiRHMXJO0maW7IgFxRS00qvy6814X5WnKnytG18V5wtW+S5LVjVuYzRfvsB4qwTBRJGgR3CfYzbce1S6OF+QIFWHKKEUuleOVNSE1hb6NEbPqMMaRpjRNjl4YatQkOjaKCbbBAYgHStGpCGqLOtKyMQ9SmkrRz9tXPNEk+DQ1yHkWRYsygjMDbJOoyTXHAlhacFaOrdd0HTWuxU0MavgC5PMFOuYVSIbh/O4VtNWWv96BseunD/mecufCkfjmZgObfeulrkYKpMLbyM7Wavjr72EtBvTlME32Ba5KormmO8jsSuhdie11fk3OzjmOD0Y9AxQfOMAtlR369jeeYHMZEK4cMgyomqRhzLfzn/IFkmnysZ37MZdSYzU2HfBfpAXtRt7BjQI8JF5QWxGdhSVhlpvSr1kboMTGe+o+6K27TAiyfIoilfZq80Pmd44+XlRh4UMQ18DVILOGKWR4nSDupUTPUYNmaJIJIZeVHt7ue3iPLdrw1baAr1kY5dj2a5NRGxKxBEo9Me1ZBXK1tGc2/7YshVBSR3lx5Kya9E2fi+2jJA3jmeanHuyJlisaE7MugAH3vHGV18dKAJTmVmasKiPOVwR3MTKXRyGQ8UR7vmKCt8jzhN1cqrbXCsdPJUKE5VQiFpoqUgZM7GI5GCNLsnCmIvWEUQG6X/sMGlHQjEr3PuCuGwillGXUHHEBgdWANPExJiyc1Clp/Jub3y5FCY6z0plaSvVKi8SziRnVU7hPFOYWekh2HQYM9ik8bSMjPvUA7FAKTRTDmGYBSLKGsp7MEp56awVCkpN4+tlZl5WunYOYihG85eIddoqnNc25qFROWhSaIQKp5i4nZSnuXB25V6SmCeWDCaCldx+n2DpQWRw13zMqNMAgipCT2FxaC5YLkzxasway/G7HuIcGRLnc/RR7C+MmWSeZB2NpgqEJFNWju40FGvOREO8ENI4aGGJoERDY8jCX8UQd5zCEak80UoJuMvPYUazG3B4UEMNJxBbHX2zO/ZRkfDDzwKo2oWbuJRctlKPMJzSvt+UBxI89/B9d3QISIEIzKaLLPhDC9j4lfEZ2wn+YUWH8dKxt7zwGIJRvmntERfTd3k9ZHckeznKRhlR9aK6284gXOa9DqWbxeC9dvJ/FDaMJHETSuxOIR6VjDZnp7tyKy+lusIDJ2MbbzXE2INP173fYPtbdLu1MkkN/MH1XsqPe9aaW61Y4iHT9IwtJxkd3oGA5yY82D63lfaAB6f1SCEIF0X4Fllujm4PJvq4fmqDq4lPwcSee28cVTmms4SBdHo/cKbwfiZvHYKlG10MsYqx0ANEjfcQTEZ1dDQG3vIrC/yadooGbzXhe63zvCZPi9Oz8hIb2aiMcrWKUL2TpngUUgumTprhGUw5kbnQdfBpjU6ujoTxQowbOjUqXeFshWSCWFCZ0CgcSrLauK/eB9424yTCfRZeuHGnT8loPDejpvOK4KjGu5pEO/AFbeCNg3Zu8gjhvGXBXZ15pzsrnecleWqDYRQRXshEz4mX9YCI8L4EUyQSwbo4xY0pDdfObQQLMxbCOgkxHZjaCUVpDPFP5ELdTNYrNwrCs9pYo/F2MeYxc4dzdlqUobKzypSFc7QxGy4K4ckdHU3FpNHjxDM7jrllS3DK4CDGcnYsGk/nSm/DyRiDpwwa56wb99uYstHTiFzpKVhN2goLgnanEhCDH30VnXdzYs0xdHP1zmmjZj7u6VWfCkcDD05j9EbImBZrHyLqv8VnBpFrl5MjlhdSepeW6c4DZGKqW4nGLinETr57JFWHFBOGUfK99+TDJZ99TTr4oxgkzYOzEOHytOQtik5ykwTn5T1sZPn++Y+Cbob9NYd3UYo9lMCc5EHWLeN7txLaltxspb5NEPBIHScxMpZdVKGxO8FH+2PIUUsIyNbzIxf/vPnq0f+wn7PEsH37NsxvdBDsmeImgdbtdQHTh+tJDh5gKOhGhpQSSChahpzzcv9sgcTQiMuDGi1iBAIkLYekM3U4mj3rs4/Se3+H8Y5PuCiWcBMLb09JT+Ve4VmOiD9lZlpecJYDpsbE6GMpARmN2CTSMIKVLkOxVa3wUhN15T4qv8kNtZz53mqcezDpfr3H/ZzaxzlK6Gsfzi0aYcHiW0NowJRCYcFzdPiXAp576XpcE2EEU++3BdbKeSq0cN7XidpHwHerQtPOMZV3ZeGpzBylM6Ec5cDvDMkg0yn523LkrYPzfWJ8Yz1QsvEjxZinTtwHdxNEHfstudJM8DaYQb2DAAAgAElEQVSUk/d2ZNnmsN22CnHmGfC7PvMunSdRmKTRU3gqneMBnrhzF4mEYKVwG2cE435KtHUWblA9czRhMrZpyIqooUV44q944UoP5dwT753ARouCGCaKlsrdutCBVZW6QgtH1KilsAqUojyJhRDBrEAPMl/SU6mijJxshJVZndP5AEUxbyjKbIX314VSBBPBsmAplBwDQVv4Novt403vPx2ORu0RSc+ljn5RHW2bQmBSG2oseVCMyRZp79nKrjWKHLLfylYiU3mIgIVLM6Hr6FtBR/9BwqVRdMTUoyy1u6d9RboZzF2kkJthG0KE3fDH1hC6cQkar/XIPOaKdC/N5QPHMbIvfVBZ6Zg11R/lPsN56eV4YJjwRAkZ9eLcs5Ntn6mjbPa4dcREEJLYnY9tRyoPXI9mGZGUQqZTN8ch6ZgZ3ROX2Ij3cSwlHzirwa8lRZRFR2Y1Cn/xIGaIbZbWfl4ZfUTGw7UTLSSBmCJ7CWwrwZnZ6PzmUQOn2UUwUTZxQNJQqbjGpfP+TeMggVrhmPeIHjbRRef7C9yW4B2UNVZ+q01oOL2veIx5VtEY6i7WIZSIQuSCbRf5/eycrFLVeWrKVJI5C9+4T6JMZJw5qNDDqWIUV7o7TsVZCfFRXovgrh9IdYqAhA8nJIVGssQg8WvGuP+00tnKRaZ4TngmB3VqBpbBwWCqRmuFuzSIyldzZTLh1gyPRLWwZCdVsQ7LnfIbgFtHa+V/PzvHe+O9SI63J+7fd57XA14M58wq41kutTR0miAKX82G85x32nus4nyPNm5EOKvwtgirHLkLWMrKW1mZdBDrr+LIjTWexWhVKOa86k+GalUmntSGlMB9YdHKYZop9w2tyZ0LUWF1YQlnDadnsPRRai6hlDrUnZZCLcq89ZotLIO4C/h6gwPOjHK0Qjdh9s4pFHIhvPHEKqUuqBd+KyfWPtSWxYODKN/Fitg4pkJuZT2IEt/6Jv028KlwNDuh+1HYCevxn2EkVco3kfv7e1Ufyk2jI/1RNvKYyJctSt8Sn0upZVuKDsJnRGOb/PbDjmYv/+wlFxW9ZBN71lKGJphdLLiLFD7qOD/qtXE8r29zeG0fl++L+KY975lIjYcy0oVAF17Lrro+OILHa7pkCR9aq4huIz6ErMZ64cdeX1vXh4ynjbroKINtAop9X7ugwJBNcjm2XcqO+bDP3Nbl7hfe6vE5HNftI84FjCm9AsJ0aQ69ZFNvGD9gyVrueCLJWjplM1JqRreJ03nhPYSDFk4amCsuQfhwHBJJ3wZhIuN5LrsgIqKDJB7KewK2dKoa1ZwWjScy8Qx4ViA5Y2mQRueewLjHESakC5N0TtLHtACTwYdqct+F1ZRMGUooKRxwwIgc89B6Xwe5jnIoI/McwgKjqrCGj4eSSSUyedkFsm2PPqiYgM37A9hGrfA2z9x642s68QN1Yl0CivKqn/lRFs5t5Ys3T/nlOHD3qrOUpBQn1hMtX8BUgIXfZUT0s8HZJoo1bsoN9z35+jl4bznz1uHADY23pFPnAt65qTcwVV4y8dslee5Orcmcd3QJphCeTzMF59DHCKH3IvEUGo7KRK3D0FuFXBM159yVr/nETQYTSVVoMdb3PTamNR+AxZP3IvjABfFG1YoW5dyCdk5Ck5mFmcF1FZ2ZcyFNcGw0miaINYSJ+fM4GeCxM3kwcw+GYafWbf9RhIemQjZOZHMam3ppp1xMbSh8dwny1ttyCY5V0IhRqst4FDUL2OZktqkCEh9yiLInYPvrw2vthjtUsK35az/RISP95rERZfAUD6o22cjs7dzsJNMl4n59nAzsI170kt2NVx/xPY9VcI8Mcnhe9qtwGe0Dw6kX0YvUnG37XgYLse16JeoyGBvZv2XPNnMrlTk1YHR/xsXJI3t/kqBb70/YON4HDmcbL7OXMHWXveelNCZbWUxtOMVExvyt3DI4RlNnijBtx150GKm9ZzY/5ia1bwfnqXJfDKHw3EagUcrM7RScTbA+My/BfWmc71cslKJBLEHYypHCKsayiV2mCMhCzTPNIHohxcnWkaKsqWQWSOEe+HWbeDuNg028VRPpgYTgYUDlnI4bKJ2WSpNC3e4RzRUM3A5UcRClqiFVEXfyLmFOJoMSyVQFVWPWCtLobWS2dfsdWMPRKJg2dJsQ4J5M83jY2I0UXrU76nzDS3vCB2VMP/7VNNblhE4zCPySFuwAf1fGg98O4jSc1pKqSnFn6Y2IMbn67GfuYubrekc9PCdipSqUc3DvEy96sppy48rhbgXg+OpMmVf68pIXN7doPXBqQeYzauvMc+FI521Tntsr5shRNjN4roGfk5OMKkXvDfPgZQqO8sQWWhTIzkxnEsjuW8VGeZlGC2MleCLJPN/QoyORnHNMPCibsrWjzFX57miYJojjmcQm3jnnEGfdlc9h6Wzvl7kQ9vAwplo+Kv7fPsODIUIejOnjQWK2lcT2fezOKmFT5ySxOxT0wnvseZFbYiFY5HAGojwez5DExTPu+rQ929p94n5M2wdAB/EKg6TuPKi1do7iYq8ZDqNbbusdtH+Sl4kKJbhMRRC25s2My2uXc5aPdsoY7vdYYDEc1C5nBcHGxIzdSSfjmy/Z4KWgt3VAb+fhURAA28OesDHXKpOebN0GG3+29e1Ijif+7X0tlz3I5gxsyM6d4RxH1rllNPhDeUz1cmOLyiW7iY0fi+3aSwwnuTtRK8mbRrm54Ul1nlCY58I8TXg672WlT0ZZPsDvXrCex0PO1DrhcJyc52l8A8UkmRicX2ZyyM7zuXMblW/UxtnHqHjPzu2aTMeJV22lS9DWlffEURN+y0aZdsJIcWoOrmc0TrbhTDAKidnoixqzGIMi45HEVpTT6YS7c8PgQYsoWo1pa6bGF6yAq1DLgfPS6L1DgVKOqIzy2jzPOEHvZ2KtvG/BND+nMaTbiVJ0lHEPt082QcSWVeN4a6gnNaEWo9hostSpcFqTJsdRcq2JMeHbs1ru7u6w45FJhvy7e9DDeDVN3EllXVfmc7AuwZf6iW+ckq/oO3xZhFaSr54r+aRyV5VX5yPvpXKW4BAwaXIK4zAJmQ1WoUjhVXHKmtQSaFOWPBPtnpc6pnZX+jaEU3imQFFe+sqNwF3EmEnnN3xpWtmoWlpUOspZjG/UMz9UKiHJ0oPbjUsuIXxVQPz48d7XH+vevk0ko7YijP6QkjbIe0bE7wyCfi+T7Ibmwnts2UDJ0cRpW8QbNqbDtnwYQimbbDBF6DnmBVS2pkUbkVkjxk1FUrcMKbdnTBAPBn81oIGWrbS0dZqrjPfBiJLd5KH3By7NjZKjDFb217ZjVQZRJ54sZWRgJR+cySDzH2TOGJehleOcJaK2keb7WRZEH2UFKZcb8NGFGBzWo+bKPavwoUu+OOLhszZxhT3K4mLwN/s52hs2ndyO7ZHK0PtWPivA9iCvZAgz8kE0kTCeFNhlm4s1HIrZOH+qkFEwxqN0t8W/NhWA2J6JLjIeTmX2kJFuM9BU3/yvwx/4ynNKVVooocpkIL6SS+OrL17x3vuvmMN4Zo1vUOgBWg0N5at95VZO1BROXmkEWiZUhKa3vODEQZVJjA+ygFWyBO9NnafFuM2VvmU3dGViBBlnHYMsG0ExY8IwF6JAMI8Ie8rRBqgFMcW1sNyd6aeVL8+F1OR375PmRp3OyLlyOw8HktHRplAq6/mMafJ0SiIrWlasDZYoV6dKRTjQ65nlpDyZxoTjVY3Jkrvu3B4PnFvDqlDvG8doHFz4nUxKKbylhQ9ODv6St2plnoIvWfLb3PLVDO6WV5xXpQrclwmzCe8Lr9bGrEe6L2QU1rODGMnKB6FoS36zTJh3flsmygpPZeVpSc5noa4rk554UoLvc+Ec43EKo/k4mCM4AgdGg+6LaZQaFzVUoZSnYwisJkcvTBJUgymcJTu3UlglOVThlmCdgztTXi6OSqFV4ZTGJBNLPuH/7I2vDw0skitfofGBHLmLRv8WoqRvF2/+N4vx7OuIwWKojjEpe9e/9LE9Pbb3jbKLml2aAStjGqvEUDBd+jLYjaU+9G8glzEkIxIec8/c9sxKvmnEy2MOYDRZ7dvHjZvyoKzZ6+GXRlEbJbnc9zFUApjpZS7YngldSn0JPYOYhLlvDYp7WW0zlIXXS2D7T/t79jU8zgxeExnsJbAP3U/DsYzj841/ss0pie7Z0uNCJ68LGnTMF9sbVEcmAfahWW575vGwn4fAYR9+2YcfG4MyJaCO1PRBsu1M0zT2VYUlg+qboMKGc3k8kWCaJlpr45pFEjJGxretP0k/BaWzV7fPOHXQqpTsrO3E6XTmdN8o941nNXi5NFyPHHHWHlhf+WKBH5mMd/pEFuXrklgUtBilVtyDe5moLVmjURnd/iFCidGv4lmZ3fme6tzryr0qX6jjgVurO2U2eqxYBmITUyjv2ytOmRyWykuCxRfmDl/O4IePC98I5d0lObVGlcJBnaKV2eBHS+Mk8IqZ++34xfbp6DoegLZ2vlAWfl2esZZC6Y3Des8PmXNzmPm/m/Ml7xwOjcNS+G078t7dmbk13i+VL633HJ8/5dfOd8wVnobyYll4cjQmV96djVKP/MY5iBb84fKCJ4fG7xwP/IonN/eNv+OnMfuMidCVWitrEyZ1PDquSt2CMIntGS9r4FS4T757ekWZGk/F+FI9c+eCSOdthebBi1BkekLTzhclmbVzZxU44IfCJMYxFNNG9spkQaEhKrQwhOCLueAGa++8Gwc6ypKNuiZfNvAp+Epf+XVtfC1mntL5UkmiDX71WJLvVuW72ivu7C3eax9vdv+pcDR7hgKj/LTKGLLoOeqx6kkvOvq4d3I+k9xKJUEyh12edNd01Oh9KwXBMDRFxwA/z1HbjwgoQo9H05QVDlv0ZjEi8UkFV92m4j68r6L0jBEpP+o9KQlRhnRAc0TgRGK6jdnJZJXcuIZBhLrkhfQOHWWr6qPnwBDYZsDF7iAf3QexqbjicrQPmdPuBHYOo8Qg5EsAtsmiHxnjh09zmfWWjFljuw96Tbigmxhiv37JZdwPDKdh+jAg1PKhtOhbr5Mwym17n45aHb+wMqbwhgqHHBmmJaQO51x0dK7HVopUZHTQs6ndbAxjjRxTFDwDMxvnSnY+Ljhu5R7dmobfJLr/P9y9z48lWZbn9Tnn3mtm7z13j98ZmZWZnZXV1T+G6W4ETc9IgAaB0IDYILFGzCzYISGBhJBYwYZ/AQmxA8GG2SAkFiAhjQaJHw10N11d3TXV3VWVVfkzIjzC3d97ZnbvPYfFve95VLMYTZNSpdpiEeERHs/tmdm7557z/aUcb17w8sUd61KQpQVdiUcO5iziRE8IC6MEhqgc8siPi/FZnwxcEIkUVjfcmn3+1cXE7nYh6kpRYVL43FqR3ooRSkYijDHwGmeXAjsLHE242EQeLsbgM5HA+8PMEAsXIiTNHK3yNAkvl8qlCzFNfL5WRBdubQco22kipMgmBHYjhBj5spSG+VXn0iqrOZsU2JUjQZR3k/FHt3vGKogcmVd4Ugr/1GjcDYE1v+S3ry74f24mPmTkRzFQliMfjAOvxwlbVl6nLS9yxcPAnS3c1MzlsOVVrVjYsB6U4Jm9LSRP/Hc5ECUxExnDFmMhWiMWDEFY9we+PbyG+IC1wBdl5cqVr7xZ0FAqQUek3PJ0GCm6MonAsnIMW37qIw9C5gMd2IU7gkI1uK7KV1K5tR0v1sAYmyGtUtkVJQ8tb+i4SZg6bhtUMrtcKSK8zJHZAtjCIoFDhaQNj2xOE8odxoea+PXBGB1epkRaRq5R3h+EC8vUTWBT7vh4+CvodQZt1FL7QpOkqeJF2u7+nn58v4go2r/uAr+3qGNJ2kIXzc/ivFx7mysnFXoDIpWGUxS3ZiWhLY5ATpbJIqy1L1C0VbKN76xrP5qbcNu1yxkDOFUC7+aQ0gulup5HSND38tqyb5B7axkTQNuO+zRrV9X77qG/blUYav+ZffyWm3oLOtAvnTiQRCnSmGE1dCC97+JbBtDP35Mgjf5sfVx4IlmciQjN170VR/r8Xn6+aEk4PWL97725PZt1gsWZ8ABupeMszRm3uR80PGfRhmedLq2L9LTHeyyvOQ7YOUKgOh37O40/W1BU7VR47WaVqDYH7G8A6+z4pz9kXo6UZWIndyy0Hf4r3SO6I9WmHSqrcUcLSVutNINEd7ZDotaV90KmxEKpxpyVR/nAR9vCS9+wdWfJxuy3LD7w0J1HY7PVL9VQjbx/qTyszlW54TrueBBAg7AvlTc18FUVsJHVEhHhuQnmkb1VbnJljMrz6SF3OO9E5QEZ8hFi5KLCu/GWK9lwFOMuCLtNIpaFrVdu3bk1iHnL33q243dvjuyBZ6uz3SX+19sj66r89bjjf3gZCHnh5aj4Wni8HfneWpGlu40PA+aZmCam9JQiR26zkHOmrCu/Nr/GHzzhVY3c2YHlMHIVrpliwvSW30wzDAObdODDlPgplc9JfFxvubt8iK3wcg9XNvN8G/hiNlLZs0mFtTiLJr53KwwK39IV21SOB+MHaeSZJJ6kyG6cmO2Gpwpv/IhI4s3+jiVeUcbM09qcBkaNrPs9e3OuXNlthTtL7IOyizBL5FIOBAtMaeBgic+j89oCpQpJA1EjN7V1l4/nwOehfVZeWeCqDgySMTGe/QUN4//f4xtRaFShSmBwZdZG0fVTXrYIgyuLNMFdbZvQthCd2E8qhNLYTe4njceJicQ596T9e+s06KOVdtx3JGpO6d3GGcg+UaY7Dbb0jut0uHSXZu0gfc+cSN0doAbBiaiXFpaGtfEgLQ3Paf+31ooSG/uK5j0UHQjaDPB6YXERQqUJT90bC8jbeax6T6m2qGerlmZn0UdUekpxpI/2Ok5EGzWd8BU/j9Ha/2tNejtfja1Ly71YxNrSC8U70+vkMiD3ljeq2tMLjaACoudRqHn3Y+IE0N9Tott5N1p7KyJ+1glFs94B99f31v2dsJ0gMHrL6pFeOIPLOWYAg9xB4/Q1z6X/MsdLF4awRcYFLyNaVwYq75O48YUlDm2cGCLP5MBskRqaDckk8PG48mBstOLP8oRaZRSBNXItTgorex0pKlz5FVEzHw2VQ42ssvIwXnEpR7ZhQaKQw5bRnIMMEFY2mjiWArNT1dEUqQKf07JlRJ2NK1KF4/HIo7ghSm7WOsF5R/ZoTNSQuK2VTaw8WZRSCoc48g9vA4daWcPE3QrFFanOE4fBj1ysGyxOfOXwf5cW0FYQjmthDIlPD5kpJmI9EgjcHY/ENIDfEYdbSk4MCTYGIa38+fSM3XHhV3cLP7lx3rus/HC5QGzmwzHyw3nh0hNDHbi9acFiv3Ox8n/ur1iu73iswrusZArH/ZaPUqHKysOUeLRxXh0PvK4J9cKTITDWkf99XtjfLnyKsG7ht9ItcRwR2fNAC4/lDR8PrQP56X7is2CMNXEbjcECOgjXplRzrBrvjIVLNwYNLGXLGx24tsBXKDUrqwghShulqRLCBkN56Y1V2hidgVehED2iQdmn8I9+WP8xjm9IoVHQQCRAXZvJJo4JZ+qk+Al74FwE6ttb8BTODCK1RmnN3Xb8hNMka1iMmZFcyfLzM3kT8KRsMy0gqP/S0wgK79qTezEiNFxDOSntBQ16JihYOLHm5Od+aadTn9c27Sy1Doi3TsjOLLIqbdFG2qJuoavnzVp2i3gTL9rJuuWMfJzfn7cVimLGoHoeJZ2wrmCtM7QT4K8tke9EuS7uLVKWrmHwSuqLtwbpvmecx1XtXvRFvSeRnrqyRvrojLB+rqfrJ3RNUP8QtPt6H3Z2inwA7p+J/vVJ0VyE+27TmsdW+0BVgt4zss7stb9wrX5Rx4hy1JXkO2RsOfB5vWMxYxMCwRdGr8SgbBEeBiNrYPHEjgGPK5/KwFJgSIlBC6M6H28iBx9JxTnmwjE4OzPmmFi44NefB5grc9lTpoHrJTLakYGJD7YLx+M1uyL86exMFxs+XAzRletsHELgYhVIMFfhUUqMk3GYYVsP1E7u+I2tICjXNfF8WNjrxGdz5XMM9Qve3GbmvBLjQK7O6MY0r1QCUWYup0SxlU0KjIuwFyfa3H0S2/RDkyBSCeMVc124DC3ULOoEBtskeMlsouE28NTfMG2EZ1r46MFCRfgorFzGhWPO/PZHlTfrHbcWGt61HNiz8LenwsYnfjYfWBmoZaVK5bbAFxIQC/zksLbNlAprjvysKHelMsbEGBu78sti/MyVx3bkpSprCKw+cFyFRYWaKpeilJrJMrIZmz7pUQJKpgbnkzohBYpGJhmQUviuFNRHPo21RT3U2CSzZkALMBRXwijgERFjDInBneCV4Th/rc/1N6LQFFVUjL2VNs7StlNttKDelfQEPuUkPLwHrYO1ribSBHxVmw4lipK6R5EgWOguANo9w7wVn9y3zqkvNDm+lYHTbTWkY0PB7wvYWdfibeE/LVinpMcTwaC9joMERA312H2legQ1TZkurpSOBQGEU4YL9wVRvWX2qPhb2FazwTRrEbnSR29B9GwmejpO+BTSSBcn76+MNc3DW2C/mBP76Ku5AjTWVjGDWs9RCnbCcDpeIp3fn7qIr+E7b7HkRM5YVxu7tXFelJNAtRG+T6mZ4URFjs39wGiv7Qg1VAJtzJekkyqqMaZE9tY/BqHjO4KHhuucCB5weszk/zM6/EUcmykxXLzDfr/H1gVfFh4TSYPypi6MBHaD80FocdVf5JWDBb67SUgy7kx4rsJ+NO7WmRhHgiifVzmPCqeHOzbryqDC6i2e+QeHBcy5soXjfmGpE3WNzFb4vw6RxR6Q68pHu4H1UNkvym88zvxOMqZaWK4a+8vdKRrZiPP4sfDZXrFUuVuU1yXxxarMIfCDVwNbdR6ps5FIsIXtJnKIA9UCG62UpByLElSQumGJGfGR6wISnQcK1ZUxVVxSWyDHNhkp2oD/oQZ0FK6ycxzbBuNqEL67C7w8ZK5qYbdVPtsvvLbAd3evefZgyyZmPskTf++rp6gqWwo/2NN0TFEYinHnmVu/IqlxQeRCA4M60NaId2I8syPX1OIOrsXJFsjq3Erg0pvg1UUoUik6E3zinW1l1YEbAjfjjlGgWGIanL0bGiZynNjGgTpMXLoRg2O5YKvz41yJxze8WwJZjaojr01wK3hoRXmjws0KEgPTZiCIs2ZhtQX/eiGab0ah2RCa66m0GVAxP4+rzvqQtgI23Qgn9lVTvC9dQWgCQ4jdJLB9XQHlfkTTNB098kxbPKtLs0I5QQzNSubkESaNFBC6bsSatbmdVPj+lkgyNAqw9PGUdqGgoN1nCxBwa69fg6DWXKM9NlPH5nLfxmv3h5ztbgTp+TVvWfB0uxaR0ASSfZQk1uxnjnoqZG9FWQsd11BcjNh3hCKtK1jFCV38GOQeGzJ3NLbuIFrLYm8WOXSyhYM2aqhaKziKQwjnoLoTc044OS/8vLLfPfw8Q+4tsshJc+V9fJbe0jRVEbDmvVa7BU2tFUJo964XHAsn0W4rLt5JEaeR4S/yuNxGruyGNFaSZtYJlqOSrfKsxB4KFjl0QeZmM/BIlUEVC4UpbmEtBJxnY2SgsgmFjcNLdyS2MfM0OWbGejiyGpTqiCwsS2KXDPGMbZQrlJoHasgURlgjq0SuNhnc+f155JULT1jZDU11PtvAiMA+oT6zsYExLBQPvJcqu02heiTnhWG34UlZoCwEU7ZbI8SB2yP8aRl5kAIbN2YtbLPw2jKbpHgQhliRBFOKBDsyKdyYs02BVGbGQSGNPBsL5qWxCrPhCodD5YkfmYaJpcLfeBc+vVv58jjy08PK3i54lY2fuXCsK5chMATji5pYc4VhRG3AxRmyc6eV96NgqnyZnSs1ihiTCFs1UkpsOoV8PwckZQ6zsWhlrsouFrwEsJESwBlYxFokhBfmGHlumZeeSDowkXla4NYW3s0L1+7c5g1P02s+NuPzDD+RyCsZcSmwZB5q5sUa2ITEFGFcjaCFuyXCXWCOwuAJ4Y64/BUcnXnUNu6ijYdU+875zDJqO/8o4SyadODkKda8Fk+aGj/TZMUbG6v0ERHA2IVJp7CvoNr8tt6K8Q20UdVpll+pCBA1kEMbD2XpVinSzjWEhh/kUs6jv/gWtnPWr4ggsZ3L4M0pwKSeaddOy6zxtzCKk6P1ufielK2n87WWFKgEVs9Nq4IgvQMYe7CatRdpXRxN8W3Rz/ELLgpeKV1Rn1CW0AgDFoRYaQmH3danjQVbHsdJ5X+6ro53z7FWqBrU1Bb01WsnGpxcGxyze2seP1fkFi9gctLV+Pl6oIpiXVDazrEZKGjvUBpxXDv7z7GzL10Tcfb3KIohTWBo98/AL+p4b7OjqMEyk1y4LgvbVHlUBB8Lc1UooHEkhMAolSGCqvPQAjZU0mAkh5uwcuWJoM5aVzbrgZQSN2uLB44poZYRAu8NmQtxvggrtWw52or4gJvxNB45SGRZjO145FfHzDYO/GRpo8jfGJWjD4zR2cWRGI4EG7AYOZSHHGolTBNXZsQpcjcHLrhjuhhwMkJhGBvlX8VJZeXJtPLrCvtyxFNgWzI/1oHLJbMbIs+2TuDIRZgZ0oQW48Wi/OAQkGxchcKX8g6P1q8Y047VMmuFV9U5rAOfLhdUuSSZ8SsXzn/1sy2zC3I88O6mcDRnH+GfHRYexDterInMyNMYWcwZwp5REm/WxDzA0UeyCHckhjFyF1f2NbPDeJaEpWS+3Acupy0myr7OvDs6Q3B+NideevNym02xGKnMXMSpdfWTsmXARmUkclgzr+oW2VVGVz4uhaCFF2nPH94O/FD2PDVlX4RhfYNrYBpGJAfeDZmLUJmscB2FR659sztzWZ1va+RKCnr15mt9rr8RhUZVCd2U0cyw1CjNozRh4ywNkC8n5hQn88RTcmIf3/R1IshbIcibXYYAACAASURBVM0qDfuhjde81GZfotI7owamB2/6m3IaB8lb45XYRlgFUKMv1ve6FNd7YsFJ63PSqARRsggmxtijmGsfZ526iuARxPoIMEASqOWUhXbGemLfoYfubn3S81hoC3KxwngadVXrVO9WsE5K/qSBXHOrzgJDhdIZJieml/Qf3EZPCrSxWE6h0477dTZ61k/7WkRY1dv7xFnNGtvOWrfQiHANgMwYI40FaA410goZnBliKtKZeoadCB3exnnBDUOJ4dRNnrJqmrYpdgfks+7KhtYFCgRrjgpJwMkEAtGU8g0w1SxS0WHbmX17HsWElUsOhxmsbYLiqKxFOdSZn5TmtCvaNkYXWvlgNK4G2JhAKEitjLUyhA3ZjCdjxi0QrOBbKBgvD8LeCsVHNrLwKFY2pbJJAYkBXZfGZiTgukFk5rvbNoG4CjMX6qwmzB0fldhSM5+kPcMmwDpTtRDiBe9faos6sJWQC65GrtpYhMPAwY396mzikSEUEis3VDaqlFFQFva3K+MG5sOOW4xqkauk/NbFAdWIEfhlvuTzu8jr7KwWmEJFeoTBtzczW2kbrlsC/8RFIdTK3SbyMDaiUZTKGgPX64Z5CASDD6PzTJ26UeZjQTaZ7MreEtfZmUOheMMoX1ThZki8GypxNYKuSMlsXNiGQJGIVeXBUBlqc0kQbZYwtUbuspCjkg+ODUYtlRkn+sr7sfD6JrN44A/WhUNNjEHZhcCzErhLlef2mqvdhl19QzDl4ThwUONI4rBWvu3CV/nA+xdP+PRoiC28WZ0vdcTsr6AzQBUgNtzEtSnirdN3c2hYBTQA+pTDAH2n3xfr9lsbqRWaffnJkuR0RIMlNExj7NiCnOjHCB6k4UAuDH0EZGYMJ+xG/EwM0Le6ldpZUkH1DIKflqwgSrbWUQUJzFRC14+cDpP2XtQaw0q9McZOnmJuRkJZscbyOTHD/F5BL97e79kIs18rk+ba3LQzPYFziKi3nJ4S7jsn6b5sb+tpogk1Nhpf8jaeOrkFKA0bKcnx2ogNJ5ZbcEgau2PCOUG6dYKNRNYYdN6ud3N47l1mZ+udRnYqfdhpJyp7w+wahnMaibYCE7r7gWijrKd+TVQL0Ar9gPWUTgVPiPhZI/SLPm6KYPtr1AqjGuITN6y80MqTGBFrJvBLhTgoH0xOKs5rbZqYWQu/l5W8wuDGswQxH4kJntrEtHV2yxGPpZlYhsBqztVOidYZjyVy4U4KEfeCp8B7o/Cd6KRSqEH4iV2whkjIynY3kPPCHJUv64Hke2I2tmlhqgvToIS08HKFJ3LAh4zmOyCxdPJHdCPqES0HCK1QTZtA6WSU7VVAzdiykEXIYWBdhbwNPJDIsiz4MHB7UFZJmMGNVJ5dTohldA2IZqZg3C0DUSsHhBi3PEoGBdZpi/vI379reTHCnqsl8E4MpHHiZlk5rAt1EymLshEjesVrYS6Vb0djXxMHAhLg2SD8YFb+YDU+joqr8ufH0AgCi/LRmHmmc7P87zhwLiDJSW5tg+Yjtzo0F21pYXJjUWypkECPlfcGQ8fIVbzhWxI5rgt7SfxYR36wz7ySJ1xFyCscrPChwmfZ2KpzWS74jt6wK8q3txnzDVkPVHZf63P9jSg09Fk9tN20dRsWc2tBV909WbpGxbs4D/dmCwNnDMNpQHxLrPTzGAsaZjBK6ItMU/Ge2F/Nx6RhJITGhHJ33p5SnTsc6Roch9krkYbTFPw+JqDvvgvGEFp++FxLWzxjOKdnNsyh4TSigls9s62qNoGo0h7CoWNNri1d9MR+C70gniKlV6tEERaF1PGP2OZabWTVc2/MjSgte6eNCtv1P6Ee1VpcrGgTThZxkoWza4NJuw9iTjh1cn0AVgMtK0Pa9Sy9IEhtLLGkqRd6pXa6c7TejZg1irlbc1U2B1EkGOIRqAwOWQUxOQP5xr0xaXE7MwHdrGV31JZHP6u0eGxaBLA7WIDhG1Bp/vnplrtQOejAyzXy+WysxXkQAjkHxkEYMHw0QLnGIARGWdFqLRSrCBex3d+jbQkpUqPxOQGrA8qW6M5DV56Gwi4pXjMp1WZ4mYStbkj9wZ/Glp0iNWECGeGX0shscFcqr3NhN1VeFXhUhOcPhZfHCRc4yAU3YYerU6Lxfd2yL0c28pQ7iazhQFkadjaVwqO08kwrDy/hiOIxsKAcjgNz8P5ZjlCdkp1pgiE7pAuuV2EbhRJCE0yHyvezsNPIdTJemxNlC7vCJyYsEqAurDkQckVXGL02r7hkLCq8WQMvygbbVz6cApYi1/OOj8KBH+QtbwR+La5sw54733LHgeU48jLAGIy9wLGOfE8S0ZxVAI8oC39QE8kSz83RUQhFmMiMxbiKyl2BXZ2py8wwRKIndqNTPBM88WGdeX5ZmH3kk3rgR3nHJ164KwOXorgVKAtbDJ0u2R4DixZu48QkOy79js/HxCfrFsX43tKipmPcITnzd77G5/obUWjOEcneDB1PQV5oW9Br7QWh71xPO91mQCln3crJT8z9HoRWEQa6YE/ohat1Ai1mtuMrPb1RDY5SmfqlCdqA/AZgt91/cWt6m+4OoKrknBlCPC90jV3FWwWn7RDb0To2oGfV9EUcYYztw0xtYHZACCmRrRKR84Ke1bvJfduIh9BHRapMJ7ubrimK3eEgBCW7E/X+PDu/rDHKegELBos6UaW5Mrh2o02hdto3vCXKlKZ1GEKk9AKq7ngUJrkPQYPWnZoZORhBQi/SLUWzNa4Vjw5VkBAptcVeuzWxpahRaaaroT8j7RwahVnNm0VI91E7PQ8nKrOJ0pJsYOwx3dLf+6lz/kUe/0u5osSJB3rLoSRcZ355KCiVNTlaMsUyasYwDI34UA9Y2JHXmQ/HTA1txz6OY2PcxciNJzYhk804rIaGSpHKC674SQGJFzz3N0zSrE4SA7vURsEvQ2rUWoVjOTKuyrg2gP3ChBep8jOe8GC3cvUgctTnxEvh5fXCtFHSspBSYh9GBkuYVOJGuagjFnbUuZBrYRoDELiWyhqNx6nZ1XiFeTmwkwgBltWQgRa1XIWXUokFdlNlXxyRitTMC5u4jA3HeGesPD86Kb3iC4+EooQxgs34kNgNgZoLFpX3a+CggVQF38IhZ44qHOuGOVaeT2/48/wO5gd+SYzrJbOdRm6XQJLAsFM2WdDUPhNfuPBYB8RXri2zt7bpTW4spnwVA8ULOwu4R0ZLXJpxGSvvDZkPB0WpzOXI4fAaixfcWeRLJn62Btwz25B45K/xeslLV16XwsUs3Aw7XCL5rrAdb/hlIn58w7c2AkvCpHA7DhRLDDqx6IFaMwfPX+tz/Y0oNG3cJQzSGVB91x1CpNZKSuE+uIw2WitBCNW6aLHrUmjpiRIaCydXO7PLxBwJ93Ysp4yV2StXJGo82dkYE9rxEcU7a2muhRFtC61JCw6T+3THlBKlVqI2lfyizoUHVmu8+VCa6v20sz9pQkydSAP4S61nbclphCYifWR332l4ELYksvy8wNKkzdsRzpRf9c5CU+kjOz//bKWxvyzIWRB6OieljdZISnBvRRZA9VxMtlW4Ccbo2oSlHZgPtOJfpOl7WmZ8Z/5pI2iMBjG0TYR0zZA37huOEaP3Z0Do+4HOJutOBvTi2t+7SktH9UFZ6cVXjWrtP5+KWUCI2gZ5wRrVveDkWvma7Z3+UsfNYgx+x7U6WSpPp5GjwxNW3o1GXoTb4iQC5pkZ4b3xgjRlpkcTIW35wiN05t4PjsbtrKS652rYsHe4ITCGkamGlqGlgctNouoz1pSoS8EwXjEQciasd0x2y2uruO5Yk7AEZdSBmJT31ElBsDBxGzbNvulYeLITpqjcjVccPTJKE1deaGSsK3uF1Zy0CWyLwP6G3S5xPW4YLq74ktCnG8pw8YhSW8c6XBixVjxUpDpjMBYzXtUKKmRfmaLw8VQJuUIsDPPMiyRcHzdMo3B1CYM5Q3K8b2ZvcJ6OKyUrKWZuCygLN6K8LjObTeH7h4GvPPPXxmseD/CZBnRxPpUd7+5mtjKywTn2OIR3ZM93V3gyGRs1XhydG0+U3KQSbwi8LplnU0RroShIMl4sCVnueD0OeB55mQvZE6/mB9RJ0SoEFY4B1uExQwysPMKjM7hT9rfUqfI0QMwzxSuHg/EZKxYTn985l9vEgYLWzJO4Mi23rCGSSuW11H/0w/qPcXwjCs1p0ZMz6MJ5lBZjKzYhxLNFiMXepWjHQ7SHa9Eoz0MX5IUQuiiyaWgUzoQD+p8vLXAMTiyGR+2OJNrB5b7TdRj7zxfo5pJyfo3Tn1uxaekrO2+ZIDGcnIbbbvmEI53OoxXXhhHEGM9FMdBdEFxINCuWkyfZiJLFGQmY230qgghjXyxPUdRmzYy0CUgBP11TQ2IgEXAMsfsORaVnu/yFhbexu1pHBG08dlkVi/cUuUlaIam066DeOkt3b9iSxMYeC4JQ+u32RmmWhu3UbuJ5dnn2ZifyNtNOVXET1vMIEtTsHHTXqmI8M9VEWsEaMFJ3GMjWxrLBCoPqOQ7hF3m8Vw7MKIHAZcxsU+C2bpg18YMh8VILoRjfKpkyGK+z83mZ2d4K714EruKGIs4UIhu747dSZJqcF3bBoQbQgBXH04ah7plw4pLJ84FDHUiTMA3CxyFhcktU5YtF2deExIljGqisjC5cpcJmCBSDK10YqiGsDCiHNPA6TLzUgQPw2JVrUVThWwa6iTzJlbulcKwVp7A8viQNiXcDvM7GJgoLRnRBZObCMwJYmbjTkbpb2SxGHIXn2Qk1gGcGMV7eGrc3R2qdGIaVjcIHusfiHTpe8GJZuVuNP8tbHurMO5vApVTezLCbNhzrnn2tXIWRbSrIdiD7xMfpyDBc8Ej3fLkqSxk4ToFa4B965dfHiYtYeVdWKsbRhIPAJ/mS0Su1GF86FDvynjgfb46I7KgDvDxkQpnI5Y7nYcN1ClwvyhIG9qEln67bEQGGmhmicemF/XqDlIHtWOGwJ5Q3XMRCris3eSRoZAmBVypc6sCCcKPKAy88ksiX80IMgbsKdc28OWV4fY3HN6LQhHjCKRpFt2lEYmuBPaCxCzX7bL12Oq2HhBrU0MSEkYaFhHDSlEizFxEhnBybaQtY9h4VrI19ROxOyil2gL3N0YIE1hPLrTEPwJxFnanjH6VjQ4MoYxBqZ1ilDozTnQJEpGEiHcso8d5XbehiUnFa4aF5tdExqHi2gmm79dPYTPW+0LXRYt+JnCjLQbvm5f57SinoW/Y0odJFsdK1Je3aZJrvXHZDT4UoyDkDR0WooX2vy/33IkIB4lsJOkGcSVsXBkLCqdYEomd86GRbc3pv3ZfO/SSu9N6t9ffSiSInfQ8azqFmcNJDwSCBILk5IWholOkgDKFx6s0aDfutLNdf2PHwIjTxnTpXtfKpZUJw/uhYOSyVd6KyLcJNENwCOyCkKx7vCjtV9mnke8tAKCB6wToJF77yyDOhFsa85xIlrXdskrK1lbAN7InkrfLCBg5r5c8wNE6oGtu05VJWDgF+VV8xmbEEoRJZ5JI1JOYYeSHK0/4+nhJYgzCXtoCtwclVKRVeSiDMyoOoTFOleHtStixkDVwF4ZcnRUvmd+vI4EKsIzUkSimsuXA7H9G6ELyACd7FkS4w10TQyrVsqVI5ZKHMDuO7UBbsaIgkhMQYjQX4Yq48GRPTaFgtDGMLNtuXIzFGHgzwxPeUaWZMA18dAk92K98SIc/OQRwPiTeW+JO98Sw8pMrMXGqjGYeB6ka1uW9+N+wppHLB9bKyErgpI5MqG7+gVMWGxOB7HuueD+qRVwkeHJVbEQZfGZdMtcJvbvbsjsIQLrmJG/63O+HVxcDil6xhYNWAiTFl4dkVrNd73pkqwRce64YLKpdDYqKyupFNuSlfb6X5RhSan7eM71SAbhXvUlFrHUnqBIG281RErMX9dp2I+32YWQih2ch33UzTc5xEkz0OIDSWmDrnjgicQUJT0CNkaVn2Z2sTc1JQYhczcsIKoBlUhpZtc5DKSLgPB/PGQGtZKAr1Pj/nxHhqJIIeDIXjoancJQQWK4waCR0o8reENi4NRzouKzG1YqzJscpZeIoq7rWxvFTP2Ia6MJw0SNYquNdWjE+amMG1EbQaNw9xPwemBbTHMjRHg6ABFWm26dDGG92Us4gRvJEvVm2YC7S0RTqTTDtzr+EqfVOQcyv0yL2ItGcO0e9nce8RE41hN7gzRCWokYAijWlnZixemxaqQtSm40onY9Jf9LEuHHxitMz/URJvhsTLXJAw8tAXljnwhTiSE0NY+bVJiPmIl4mf2srNsuFFXSgJtscNPi4EjCnCXIStKiYjq0xcG7zZXTAdK4+8UmxHSE4YBpIqngvHceTWYEojA8KPxwsGH9hIo/CLOUepvKyX5DRwLTPJEn+cjI1FogjzBLEWiE62TgKxQpZClsSxCiPKQwaWtZDdqUcFSW2fJZlHzEgdcd2wbCqb8YLZlJhvm/uyTw33kCO7LOCZ99OGw7oQrLKOE3MwLkpgwkAjOiVCdW5FKDHyyZpZfeTCWzaMxyObcSDujeO48llVXswPeYOR5JK1+/bdeOBBde6KUQa4qxOOMA4BSxOXGrn2lZScQ95S1oxVY1ghuDEwEHJhrrAPwjNZ+KXhiLHjx/tCPWT+pAobAlebmec18oYN6wTHLPxP+3cwV5brhZScoyamZcP7VHSolLIy0YL9fvpV4bUkjgeheCBn5VEq2N4QS4xTYJ63aPwrODoLoaP0tD27ufQMsdjCu8LJwuV+nNKKQmBFzk4w2nfH5t7iYD00ILnvdN42x7S+W1/E2HhoMapdAJpp4kKqt0W4kw3MWiaKuzR/MGnCP4XzqM5FKNXY0dMDOwkhau+iOkOqxtZphHo/+krd6l+B2MdjUQO5VrYhISLMwdh4RUhnAUu1wjvHl/y3/87f4nf+898jXUywXxlr4NGDSz7Sa/7suPKqDjgnWnAnROg9EaO7xRGGoWEnQbuyXnuK5+kOnY72fqyPF8tpRCZN46JU8HZ/imsrptI0QYO3MYpZCyE7mWBax5ScVjiQZvdfvY1Q2oZAkdCMOE8BBNOJ7afKVBe22wDWirAbWGnsOaqwSiMpxFSotZ6jF74JH4bv54F9mchpYPAFzQsXKmyqc4xjTzAvXPmRb0kgrDODwVz2rF74zibzvkb2Rbiut9TDwC4aD4tRgzHLxFdUHonzpga2cyKOA9drYcoLU1CmNFCCk4bIxWy8kxqY/6Yq1SNvvLBYogAXMTO78dhnjqVgmrjTFkNh1VHNXGjgqSg3ZrymspHQdD9FUDGmmNBiLDgajEtPvIqgNbBg1DySg3Bryo5KZeEizzwPK3GphEEw9lgtrGnEk3NYKhdDw2+GEtmyMi8FH5xZL6E6wRI3Wpiq4KvxngRG7niQBKszK8ZSarNoCRCD8Hi7IEXxOCNEXtnY9DPLCjmTfUBC5li3PCqJlzj7+ZqIsDkYi2gzoHVnoDAU7QxJ48kaeLa940Ue+N7RudQ72O14P608W0fmBD+5bWvlGCslP8Bk5fkkbMbE7bGlkGZxCsqxwqa0ZNNFEreeGS42XBVnlxJSlLKrrDUhGrHiLCXzKN3y4Gv+MHwTPltdEX/CBxShGSeaOTJEgp1GKO33uWQ2nTk1hUTuedfi3fX3hPmIkGshxEA0OefHn8gAWWFjgYNUVEIrRCdtigorLWBMTxTqtwwhRQQ6x/+U/BlD6E4FegbyZ69nKrKbM6TEihOrN/A8xhYfeyIodHLBCVjXzgg7CUHhhAs5qomcM//z3/0O7737N8h15T/+Jz/gP/3hV/ybvxr4D/+FX+ULhXB4lz/85FP+3X+wb5iM+5nEcHJRMDN8iC1LpxdNozG4inXzz7ewILgXrIbQsuVjVKy2c8PkbOsf5X4s1jCojuGcmG/SMnlORSPoSYDrXYTZOqXggitENfAWdndOujmJUtVI4eSErSzVexFsOzZkZarKEo2xmwkOtMjibwJGc1zv+CgtbFz4ygaOpujgPI+Fz/tIcfDK+1PkV+KeL4+J52HlxeLsRBnykes68HkZ+dZU+LO8MueBNSghRC5Cs9sXET6MTl5uubLCB+PAJzLyaQ6IZR4NUBZYN4VrBg41canOt+KRy8UYpbCXhOeRx3HlUFZirLypiRtfeSRK1cKNg6yFa4FAwEV5VzIbK+Q0MlbhY39D0sImFSKVT8JTjqLoXJB4y3f1QPAZ7/4OdwtsRjBL/MgHPr9VdrKyTYbdLKwaGD1Qcncrjo6QeDw6OWeu0g0vHH746sgvXVwxhpWryUl7KKFyXTZAYpUV0wsyBfeB69sj1zZQCVSFS8lUd6bg+PHIk2qs+ch7F0q2lbuD8YErm+HAQ3M+swIWKLXRn6cAtx64whhc+GlcmW8SDx9H6rrjR2TqInyVJ3aamO/AgzNX4Z/eChd24IucGTywyQtPLwome8Jux+HQtqyLCssCZSxsXNkEJcpMIlBSxKpwOx9YGDgqpEPmaaycRXxf0yFvCxp/UcfH//5/79X97Fvl0gDh09FsZBqWUvBzpkuU7kYa7pM3zZqdfLEKGsAyEcE0tKyWEzlAGjXZ+2u4Nt+0k+uA9XInfZQEXaMi97HRJweCpi85LZhdx2HNtju7EbWFq0GzondpLgNNV9IW8VNHADTAPcYevgZKIUo8kx+c2owII/wn//Jv8q/9NSFNQ8OO1j1vXs8UNy63O9a1sB0Dy1r5t//L3+P374aOaSSQAt749kUi8cw0MWqRpu0J9z5x6gUkEPykc9EzclQ7kw2vbbRXu4mmtohmkftMm6bl8fP9aor/xl4z4ecEr6fr+XMWPt4GrG+bYJ5cA4KXM9Gi4V29a+qMM9UmelutIhYI7g0T7IzDP/4v/q1faLX59/71v+veLZPUMlFhIwMilVSN9x8NyPwGDYnFCp8cIne1EHzl2ZDAKs9Suyd3VTgyktypsXIw5aNh4tYrxQtXtLyjy+DcKrxufSTbIBzNqN7sUHZBWYnchEQNAcmZqwTvWIGxWf6/XgrbJOwMjlGJrtSQWKoxUzGNBIlNPxYikcrBQ99AwlgzKSoDRo7OwZUQm67rIiUoHYsTpZaZJI5o4dEy8x07knxFSAxSmdaXhHDJwVeOJXG0yH5/xxJG7iwjHnhnLI1koMaLeeDOjFUTpWTWOvAoGLMXcoVslWwVXNmKsSwLaRiJCGsV5rrwRa48HC9wy2zciGXlPV1Z68APsnKdMyYDGzfejwceTSPRM9drw1tlcKZgbFIk58gyjNwcF6axWSQds7LH8CKgwiZkHlZ4ORuvOPCBJ8rVYz5584I67NiEe1Paq2Q83LQO5meeKWXkke75ro38/ZK5mReOcYuHlYu8Iw9tXfzP/se/97V9Fr4RHU3UgFrDSWLXvjThUMS7BiQC5WSgqA1XqSh0fyQ/RThro/AOobnSqnU9DN2+pL+GumDeHp4QwjloDDeSV7K313a9N7h0dwZvNyHEcDaUHE4FzrsCXtoHmF7MTO4vdOivFhSkNCo2gLRhWOs2TjbeVhk0kglYULxkZhL/xi9t+Tt/8ykvX93wG09n3KcmOFUY4xXvpC3rvABwubuklsJ+PvDXv/M+v/+Hr4heybUgetIuCaNYu0rmmAdCaONGr9Y7E4cOvK99lEZoRdjF0BhQL03lLAFPfRNgbTTaZCwNG3I3kuo5pTTTSBdF7k0/s5/cpFvROTEIobkTSJuknjtd724ATqJYczaOsW1eQkhIL0QtHqCRBlyao0EtggZn/AYkbE5iVE3NK08Kz2PgIUe+tW3BYi/syH7YsrOMjsozBh7qwsN5IcnEwMLrORBTwxmi1qZEX5Vswme68FWGh+uRd64WXsnEV+vAB0Pg11TYDgPPppdMDn+2XPEP3sy8TAN/c7hmqldcq3EYBh7UyI9y4KEdqXJgw4YvF2EbFt6XgvuGbXjNV/6QL4sx4yQcC8aDeWXZVNRGRnGSOiU4d+oEG6m5bVrK6kQ3bk0IuZKC0eehHNXRDlj/MDzksh4Iw4a6OjZsuC5KmYWrfMfenbTd8DRm3ttXZofXZQIvzAWyJ8xe88yFN/nAg5QRc+ZaeV03jDWwiZULX3nNFk2J53FmQPlRdnYifHuTqH7kkswUjdulcIwDx1z54OHKry3CnjseWeDH2Vlyoe62fForv6IFs8KSLqhTYFLlW7zho21l0Q17SySZuSkRv1zJdxOPkvNHBep2xOuWH+mIFSc/eoZqxCXiFnF9xU/nC366RIrPaJ1YZeBlDPwJEw9k4dn4HnfckrSNSo/hyEG2X+tz/Y0oNC6gURil0ZMFw1IgmTTnYQ8Urc02pc9wTmFmAFgbz5zjlLszr7o1W3ggeKH0EUlt0EnXInQGExUhtLhlArF3Phk4xSiL+L1rAT0PpneE0aVjD42ErSncCwVP7sZyYlXRtCkpdlpuPY+lojf3WpFK8MY12wTlNjv/ygdb/qN/6V2eXE7c7hd++ekTHj7a4rmvuqcdfgwMQ+Nfec9d2W4S/8G/+jH/zfdfoRYI2hZdUyFY6vgJ/e9rOy8COQXGXmjrOT+nRSGD911noEpFTJqosr+XEJrFe4ukbfeubRq0JWdWx6Iy1OYXZ2ZYH0u2MUvr+E4O3kWaVYf6fdqnnbJ0YrsGsRNB1Jo+ZnVHqjEEcLu/TEEU1+b9VsZ2vhu+tg3cX/q40kLW9owGS7yuUEPC58wYCtcl4HrAwsDV2kw0b+dLynjFO7V1vs+2xhfHgWyZgylDML41Od9NhQei5LTyZ9MlfyAXPMzOo1j48TLyxyyEuyNLfIqngFumMHCZlU/jlru68M89HHjX9/xuvSTWjPdNy9NBKChzqWg2sFtWU2J+Q/QtXoxjrKCVrM7Do7GdmuvB3lv33sDWxg5sOrWRVY1hWRk0MFghmbM1uKp3bIaIMBgmhwAAIABJREFU5gPUNyw+sOwXxrRgS+U9Wxgn5c3xiPjEcan8uWUelsDgB462sEmJYxau5ZajD+RhIS6RFDasy573o3ERVl77zJQj7wXhWaz8bL3lpl5xt84sFMbtyHa+pciGOl3y5XLNnJTffjjyp68+RW3Ldy6d//rzh/yLj478M9uRnQf+8C6zQ/lzNUp4zIUdGV4ZYZz4vjzjShaSC9tsfKEjd4zYzfD/cvcmMbed2Xnes75mN6f723t52RVZVSyVSiwpkmwpUJAIVgLJCpQYgUcCMlGQDJJJJhkJgQDHBgIEBgzYkxhIAmTkUTrYmcSS4zgQpAiS1VSpKbGqWCySt79/c9rdfM3K4Nvnv1QGASwQIFF7Qhbuz/+eOmefvb611vs+L0O2aEy82rZ8vT5g3YrnaeRBzAxmpA/CB53hj7PFmgoTbmmc4+t14T72RIYRvp3XzGPkaXrGZmhRNVA5fDBEc/hU7+vPRaERb+/ktxjIWajEEZ1QH7EipuwMGin0YJchIpPj3gK5mAqNwZhE4fUanJaHaUpmUrBMLngyGVfkkZRRXXCKz0ft0XGMA3VOxNIT4bXQnAFELFAAl7hSMBLTOGAaKmnOVFKgl1sDdZaSEGkcJmdEFJmIAnCsFaV7MNMI5a+9kvibXz7h3XcuuVfV4A2rZQEvdvsiv7Rj4hjLnEtzM2F7DKREPyQkRlqjZK/kYFDrsDqRGFwROQwpURnDkCNOimIrTT6grPbu/SrL84krdxQcmyn3RYQ8qeiSKRyz4+FAcr4LrLOVg5Sm1EsYDXgp8MIoRbJce38XUlZbh6YOMc2dMu0oVjRTZ2PEowrBREQz3hcfVlIhakKz4CTjvCvmWAU/iQHS52CMfL+GNBo2XtnEikMWGt3gbVFC3gwja9uSpsNPOGQiShUy342Qqhl6GLkUy9uV5T49+5TIPTyVmj/oI++P51gvwMAhC99XVyTQ1fF9jcQ0UpNoG89hNAzBMXeG39seEOe53XccvGE+KpINN3GgtjWgfCc4ughZMosKumGgcRmbPVFLCuzNdNBxAkMeMThCLrvXkwyjWl7QUYkQclFYqlOGmKjCU7x1tLtIci2prqiqYmR+lE7oTREMmFEZzAyiIn3AD4adZM7HnrdWgZvR8VMVPDZznueeeU54J+zCSEtm7xuud1s2o2Jqw0cHeHs+MEsG3IE8yzR9pA6Rd9oa1QMP+x1fmishGt5f79mGU25v9piLFadN4Hux4Rs58XYV+eKqxXQ9r7YLvrc7oNazrAO4Ht/17MaRR1VLmyNZe96VxJ8kZYkSdhFM4v+2wpOwQ62CBnKsycYjWbAmE3OPJo9xyjf2wt5W5HEotg7b8LEtz6rWK3kaIVe1fbn7/JSuz0WhqSaFFbxcuCMTMHFCvFi4g0kmINkp0ErLG2Sz3C3UAwVCyTGtUqBxpTAdpdRqijNYJ+yIaMGr3C2kNRONw2fINpOR0gEhVFMtSscx3PHEbwyVKbsWl8ufxel1JoET9eS67Cx02unIFJSUdAKHHo2gGdJEBPj1J47/5KdXmGTZGuHMOcTaEnJ0TIo8qsi8RWzJTwfI0whyNRe+++iKg/OsUsHDkAvORUwCKUgbteByxlaWNHlloBSNmowaW6TVMFENhKBQT69fpTj7j93V8Qb7JA1BOcZNC85NQWQ6kQwoIFDRTxhIjbn73EQKpl1U8VaZ6HQvxQbTP51Q4sCnvxstfhrx5YBgjLnzBpn8kpbwWV9/uHdYC2kQHJlKIjv1PErCX1v0/Fid+Xbs+aM4Yz2WMaVRJU0gykqVaDxr9fxRFxnbhjObeafOrLzyhSryM6nn/4w1h6HG2chOA+e2QqXjrbnnDb/HIlwYuJGa32lnXIeB4B0pVuxQ7i2EXjL7NGOX4b4pgXwHm2GIrGblvb8eE6axGMnMBWpJrCMMYyCq0HjDawlC6jlkxRlPFM+qNny9VbYh8f3BYmyZFOTK89i+zuNpdLbwBqixOXCRHWPaMa8qDntDLVvezQMhBbYuk1zNWC14srNshoFFrfxRimiSgt9Xy1f9QE+iS3M+vBmxYji3woNaWFaZgT3oHHJgHsdyUFLPt7tE5VuCCfz2uqILI6euAGer83sgO96dW16pArf7GXuTeDr0XMwsfVCujWWvNVYTOWbMoIirOMsjKSoLA1rtOR8TY24xNtPVGbcLLE1LGwz3Pbi05ZCkJATnOeoMZ1JiBAJKkh2+EiATqHng4J7bMrMGm3uSdaQ0Ys0P6OjMm5fKriOn6uipOFL4xZQTtLt7qEEtpdPBlj2MF0GMh1ROS1nK6V6YvClSogeyTDiZyZ+R0HJ6d8oiGXozkZwFEEM1LUoVucP3Q8KoLfDGCW6mmjFZwJQESkvhdOFAtaB27DTrE0CzxUsRHjiKCKGYPKEShxPljdOK3/3giuadCxaaOPEOHUZizgSBmbFoiGWnFBI6xr/4YJ9UdF996zX+1rtr/t6frjFal7CsnBHnX3ZUWnY1FvBOGCb/Uc5l+f/Jc45IkT97ORKsy2BKVe46jWOPcAxMswgOXhZIYJDMXBzVceamiawGKyVL3rpSmIMGRCzWFel56RmPQoVPHCC0IIKOAoLje3As4gXIOsmdKYw7a4420c/2CuLIUzJop9BhqdQxSOSfbT0Gx00O9DnhsqP3qUh89SW5PInFMGCMw4WKTgz/W8zcaw68rqd8t/MEn0i5dL+klkeUDvppgD+0K7yFy6oAVAMRI54hQm89F2K5t9zyfGhRjcjY0JrEqe9JcYR5xaBwO2QaJ1gGsJYwsbyaGJk7Txg7Ho2OQR11Tqw8fKUe2aeeoXN8v9vztq/4OgGz3WDMnO9E2O4G7nsh1RU3I+w1U8XE1mZOxOHHjtPa8mwX+HZteHJI3HeO19sddFveEsNhBG+FU2OozA3XY+ZeveCP9xWjJiRvmDWGE19jNNLIQF17mgi2DtQ6ch06XoSKk2pAfMWMSCBz1gb8eU3GISFwO2ZC8IzqeNL19H7HpR6Yr+bMQkSWhougaNiAC8SYkLOeFB3GVVjJbNWxC/DmsqHWLRs7p9vDgyUc7Jb7AjNfM2bL81FZR+WPNzccdpGtrUim4WDbYleoMrWvSCT+bBBcf0qniskLwjSJuG+Un/4U7+vPRaE5EpaPijrn3F3Wip0eEt6+TGE8ei40Z2rrGD+xrM/CHYFZtXg5S2EppkJhogMcfRdWMJk730aVlNFP+TTHCjftYgxlP3BcDjkpXC2vhRpgBI6bf5vKaGkwSj3993Yy+quWbiuZo/vdYDWTcsldcWqwAmNO3POWt5eB7WhYHyLXQ/F9NJXBSfHe9LnEGVdV6Q5yzmXOnfLdA32IkV2/5ed/4jX+9rcCrU44HV4+iItyzMJxlJQz9eRxqSZvjcGQJiRMShk3hbhJKmIOkFI4jybY/4+kXFMun42Yu8RNLyWPpznGGGjZIdmYqesjPicjtvwOo2Ctw5pPxFTnIq82Cn2OZaxnhBhKObLWYEWBKb5ajzudl7ETx3vus7wkj2X3l/MdRaIoJQM32nPiGxobWCbPxiayOGJVQRwx1uJUS5CVMWQFa6GE8w38Yj2n9c/44ukJu3XkX6TFncTdmFxUiBRSQgjKIxk5kQqpLKQRp47TnBA/sNu2zElolTlvE33MXPmG2nvUeA59QGY191LCZ0WNMO96HriaFxWkPPDIGB6kHkdmNtkZNtcVD+oNp1YJoeE3rjrENlykgbfqzCJXPPA929CQhi0//sYl19cdVdUg+zUn88g3ssOFzMpE1uOM+045nyUOsaWuBkRHXm+FWeN4fDMgVeDVmaO2I5jAqak5WQnbMBK6jsooyZ8xSs+sqRn6xL6Zcd0tCPMNKZ9zajNZO8RWBBUe7zbcryocji81kegi3o28OHjS3vJ76ZzT3Y6TrDyXG37h/ILf3AQ+HIoSMnGGyoFBHTiPkDmPCw555FoekGRHio7zXllLTa2ZUQPZeOpscSrU9YKmNgx2xGfDRVJwRcVmxoiRxFdM5lZ6rq1wT5Z8tz8UjFP6ASQDOOcYTDFOli0Hd+FbWcoJODGlaWaKsswW4OVoKAmRtoAdU9HagjV4CjLfmqJswxo0JpI9PlqKd8IZA1kIEhBXKADZTVJla+4c6E4n7LxYJBaJb32EehoDk9LfKmhVXn9NeT0iEJ2UkZG+xLVkAw5P1lBc8LkooFDL3BlGV0ZaVe246no+2Ad2u57XzhpWyzmNsaQxYa0w7g80xuKqiqE7YK0tlIFFi4mR5zfP+Ae/+5ClNxjNk/zBQJjUesex5RRCl4bJkMnU0dhiujRTQqcxoBNEc8wJZ920VzLENHUQJuKsR0kTCcFOu5rIS+9xURUmk3HGICHSGotpbDkIqCXHdCcKYDLbqiaOWAaXTYnANsrcV+RYimFrdeKaKSKOnCNimegFpYO7Q/joZ09vXrjSiY6ayjiUjLOlCHZpSUekDcLXCPxUExgsvDd4bpyjIxPEF9q49RhjCbkkrmqs+F9uBHiFWFmcFVyTUfWorZAcsNKUg1weMQYkuSLAGA6cVIaU9nRZSbFmoOO89gyDJekWXM24ieQwomEgq+FcA2+1lq8tewyJjQkMJL5oW+ys4Vk/8PHB8dFQcZsz0QJh5L2+YkyZd9s9P1k7htRxSU8zb7lZK7Zp+PFlYGYCt7snnC4u+fDmGafLGRoP/OyZ0ncJ43rioJzWEaHi8jzw/PoGPzvlmx9c065mvHY+59wOLNsIZuTtqGSTeXZwmGyomoarAKY/8KhTBjtw0SgX+8S5G7kdKm5ijzYRbx03h0i2iVoXnM9rnl3f8tv7NcgZURsuw8BclH/rYsOz3pX777Di1x9GxCtvupFLn9ip8nwYSaMwt3skGc79FjVCkBtOq0I3mVc1bd/h6kilEd96no1ztrsduVLGoDR2hlpXWGuHxHMxhKj0xpMls1SlDXOSXHNphBbDO9XuU72vPxeFJhvhRITORiqdEPbTIMNLIqst0tVPyF/v/BZSPBpZJvaWThDJCZgilUPy0XdxhFtOZUam2VjMuKo4hZMWwOSR7mxMQetDiZq2k/dGtCis4sTuOo6NjLGQ8l0KZYE5lgdYmrqrY9RB1nLSTjlgpAA16xgQU4ZoGc+QE0OKxDHz6Nqx7oXfvD3wM1jeqQZ8Jeyn00frDVSKS6V4VyeL0uEYMNlwsWoxhw0ilxiJU9CZopUtOP5P8NOOMM4oijuWhGzv3l+VEriV4S4qu6jYLElfmjVRf6eoKx3pSzqDnThsVmOhS08wTeN8MWceVXvIFBx3/NzAOjBqiPnoXVLcpNQTFbJziGbUFhNvJUI2EaLBTobOMB1mdPI4mU+bJPiXuKy1PCDwhdkOi0PFsNfMKgsr1jgHlz4iJmBpuA6OXVNzs47sa6UeKfePKbL/2nvQAWeLeVl18oflRLyT/u9R61FNxJjwVYOmSCaWOAvj6VLkpKrZGssy7tBcse+Ee3nDZb9nOVfOTGTue04WEfEONY7dmPhOWPKYltrOuTokrsVS70CZE5JDfI/J0GiJpj6ZJapgOJWG07rjo23i7JVXeHK75a15z8oWhaqpLHZI1PKMt1ul9gNRIudiGM88+7Uyv6joQ8X7Nz1/GJYkPeHBYJCTc76fT7jd7sBdsLvuqcWzDRWv1gN7zeRdRLxnMxyYmx4CuPmMWnu+Hc7xYlnZgVdauO9GcqWEzYiy4WzRYMISm5WfXyx4GK4x5pTGKS9izwc7y6VELqpIO5/zeJ/YGfhgbDlk5WIW+FIzI8dMCEseOst3c+nsNSuPRkOPYgbYhxbtBTUBv45o8GR/gY4HlqYl70eSwJAT/Vgzjx1jFTnPB24qyyw5WrlF84wPukgnyj/Pc37xU7yvPxeFprLlATSblF5HfL0XQxKP0YSZOpoyvyrFwuYiGMDZsqbXI/jS4MmMIlRHs6U1dwY+A3fRy8ZadMLMqFE8jiwTANOVTmfuiznOUlDzOVG+SAJ1Krk0TosJNFOQ+s3kE5FJumulrK3FyvQ68qTGAi/+pdveuWJINSDTPuoqCDfDSMhlz3NmErtDR8pz+rEn5aKwu90Glo3jbDXjdDEjjaVLMgk0lH3S74wrvJRMEMEUtI8a1JcAsywgufhiJAvOKDAZII2FiShgUwJXWG4eUx7ivDRUpk9sPDRHrDiSLUTno4H2CMos+5HM3FXkY+gZQqYo6ZoJmGimmIeYhZxKtHeadM6q5eGZZCKEihCVCatTVGyoxZiXS3/VEpBmp2I1vly+fWZXn+DjbPi4m9OIgAzcswtux56DP2XbDYSsBBzqLSkWCraq4g5KNAabMiZkBhMR2smgmvFS9pw5FjKEUQskdHRkGdlXcJ6hTnuic5xl+GoInCwGLl2FNJF3ZgNKYMyWYCpMGnDO8bCHj66FvTxg2wuLeUUeEzcaEDyHFJAUqcXymhdejEqtHW+dBprkaWPknu2oVzM+Wo9sx8htFpa+4gtnjr6/5qSyON8S08Cojts+gbek0OPEEeKOeTVjlxJ+zOh8yTo4nm93vHVSsRl2dMPI+TywCvBFfczZ+Sn9DrZ2QMyC3N1yPQTuDyMzZ4kzGOoi0Dc64vMO55Svnz7GixL8GU9eXPNoOOfq2YbVTHjcX/DNZx2XuqWpDdJkHswv2PUDvnKcqPBEPDtj2Q6ZMXUsneGiqvj6amQIVYGcJuVFNFy4hkuN7HMxjF5WysHNWd9ckwycVgFjDM+0R6Pn4zSwPowE53mhh/LdcGVFcDAjt1hMtFwn0DGQSBg1BN0BhionHvwg0puLDknvlroZQ2WOjoeSYZInMjOUEZaZjIEvdzuGKIVNpprLcl8mJlbOWClIhiP597ioT0zeEPsSgXJEziiTso1EPe2Icion8WOEMr48qMQW6W4loGLvluPiixektSNZDF1uWJIIJpDUYcdA7z311GWVrieTJWGoSTLwIhuScWhKmJzZhcwY4Xaz53TRkBGud1sqV3HvwnN6skArh7WmkCMBWZSZ+n/0lVf4n7/bsc7mzg1vjv4Ya8o4yRShQnSFWaVZQEuKaOG5MSVTJuyxQ9HqEy7/wpqD8u9qih9IVTFaPCKqiWr6fYjQWl+UYirYXGjcWDdBRUGzIaUwdU7KsY5lq/hcFvsiL93QUUGscsSs5hSRLGSmRE8t5AMn5XCDgP8kauAzuoZuwE9ChbUZsVR0OhQqeJ/ogUopSY1jyZjXZO/2K0ZLJ+Jo0GAQ6RERKslEQKNi1CI5H49uCAErwryHAQgYTK88N4YXJmCvDckaVKoS9+wsjnIws7SICN5AnDr0WizX+4F+HJHsUFeSTV0UglfeDFt+qOnQXLHuLFXOmJz4yFScxQMryTSrBIPFERgySA+p9izHTC8lx9W2S9qqZ3+zYBMjqpZb23K7DwTT4A9rZOa42sL3brZ81R3YhDf5zvMr1HtCXjE88rzZZt6sBe+2bHulcY5xMec2CFXOjIcR5goHj1lYfJ7xGx92hFAjJlKFEzozYm1LvBFs6siquLbBIjwZEx8cAkkMly5hqpbL8YBSQ+twaUUeO7COR0OGbsdiVr63F1IT4g1aZ87iCX1W/vw605sDq9qz1MyNJow1bLuG17TjDSvcW2RWeWBhduxiZqShsQnRGbLsubRKjpGgFU0awCayKKfGMJcWtT+IwWdTdLJM8laVXLAmVvDp5biFY3qjNXcKokqPklowKSMyLdSnJa9VLbHCWFQTefpd1TTjvxsXIUQRfM70FnyedgDHjfkkFpieixNCBRSPkUCUSGVc8dgozCamWDDwb9/z/OxrS377e0/5Gz/xBZ5s1vzdP6r4J//+BW1b81//xge83ycedgt+eB550g936o8twtwrM8nMnFA7Q4zKth95tBGuu4D1jsu2wXphsw+8ejLJnWO6W3RLjJiq5pffXfCPv/O8oHBsITJkMRRP/BSGPQkAHGAFdErXjBzlzJNfJxuMlMiAkakL0gTGkOO0qBe5SzT1FIHFUW7sbUkVjVJ2OmpMici2kZCL2MCqmVAhijd1+T2++KDUgNcS4WyNkHLiGGmUsoBa4p2arnScOhlGj6ZdRe6K1ueB32yPQgVVSL6YeU0gAzNr6LKSk8UQYMoishPpHCheKgOB7i7DR9USxWBzySQSIlGOJRiYBBt31Iw8CQNyhdhc4jVCV6gOZHI2ROOoKIIPEaGyJSAvqoEciVrCM05kIGTLKjznXms5czV1XXPRDmxjjdzuEQznvue9K0s8KTHNJ6Zl3kRGVWy2NMsVV13PWixdnzn4xM11Qp2wl8zZNBbvr3oa7fHpwJtnA2lwXMwrvFngq1OePn/O2YkjDlu+2NZQ3ZICSC6RE/Om4KzMEFgtZtzeRBZtZO4r+pnyeBOw+owf8Y5qnnBS4LXzU8OTZwe2lfD26ZysyjiOBFdTY4gqXCWICq3LZF+xzuU+PIjDNivuuZ7YjxgnSKzx3tPbkdwL3T4Sqxu8Jk6y443lSKMzun7Hyifq5HirgnUI1LasE3aj4dlYc9HWjF3HYMtO0mV4FA1iGup04Fmq6XD4NFJXhhzgVmf82Kd4X38uCk3tDOkIqsxFRsxRdTY99I4wS6A8HKcbPKWMM7acIKbgsIHyhU25LLNVwBhHSlow9dMup/zuqcNBMZIZrWGRhGBSUYfB3Y4B/qK348j/0mkebshIEqJQHnAC1mW+sFD+tfvwSz/6M5Cv+Qfvd/z9f/OCi0ph1vBf/eKX+IV/9E3ULPiVd+esx4Z/9CdPeRrgXCyr0LE+JBYLT4wlBO4wBrpQoJpvnM/58r1zoiiHOLDf76ljha0rxB8X3EX50y5q/u4vPOC/+Odb9oWXC3cP2EnhZyagZToiY/T4p5jMXcQAOpBtTWcVn6adFo6ElB3Q9Jm5PIkgBLIpI0j1gk5ybigeKWOEkBPJCJILNVczRQXjSnFSLfsx9CUTrYSqWcInx1+a7jrP4+dmtOwDieW0WPJ34suf+RyMzpyNOPUkE/Eoxo18OfeMyYGzfNGAt5nnaWAZDY2MnNUTJNU4HI6hDmRp+WivPFFPF2I5BKDUKlxWmdOsvGocs1XPR1vDh4Mw5mKmVDETR67HxfIwdObl94RUki1HV6jHcxzRHJhXwio6+hx5pypOjS5ExF8QZME6J662Arstl8aSdOSizpy6UpS+/uaKb320RtqRx8ExH1qiKam77B2vVRW5u2HIls3Ocit7TlPHW/OKha0xIXOoAk9fDNy/mPFsqHjVR/puz+1oGZ3lrXrkNiV8JRwUbPTMmgO1nWFsIm9esFiek/0kF69u6OMJ3T6zVMdlO2enNbWuuWgtMiY+3gkHUU5Pz5nvrrFJ2Q2FEnJvGQhDpGqUeVfUkF0I2GrGW8YymEJ7/95+oN+37EaIyTJrR3brSI4J6zJV1fAw7XlVF1gZud0JMe/AZLwawjCyDRHrGk5MZOYNK+N4fXmgj4L3hrVVyIEXo+UQDUqAXDx5YVQaV8EQmDlPleT/9z79V76vP9Xf9pe8GqA/jmCOYys9WvGKyixlpZ6SHcUoeXpo+GkUYibycqDALDOZWl5yxlRi4Y+Z4qOJClDm/sd8E80FFV+C1PKdmROZCpdAmgLPYAI9mgL4rCfviJpSzI4jOhXDbz0a+YUvNdz2L2it4z//+a9we7OjN5ZGilT3V376Df6PP+3JRnh6s2VZ11yJL4Fn2RCd48WYELGcVwnna/ZD4N/52musWk9tBy6WSw5dphtzUbGNI7WvsIsWpEQbZIkFWup7TBYaAyG/HDkxkZfLZ5EL9SBlgmiJexa9GyMmaTEUxpuYsmtRkZIv48rv8LkoINKdc99OsQEJa0wZYUn5jOOU1eMVpCrdrTORpKaMuCaFmJLKKFXLyDVNBt9sy2frxUyRBRlIqBUqyk7HZcX4I++u+KsUh5XIp5xe+5e6nAEIxQg40S2+x4pllXhbBr5yVvHhzZ5YG14A8yQ87h291IRxJFgh9y1kwVvBpcCruSW7A5k5D9yB11yiclqW8MnylXmgMo5vBcHiiOMxGqKMhBvKqDMiIBkbM9465hqpXMWJG0EjMSqVApJ5v1e88xipSfsrTmYVsY9YHJIHtnlJb5RhqHm8SzgJLHdbXpkn3mhmpHHNn20zF87xV16r+Gjf0VrLjlNWaY2XPQ/OlDF7Zk2F9le4C4tVy9ffzDi2bF9kqlq412RS9hziAetH3p7N2XeWAeHpTc/TcMEsZ6qD4fVXC4nC5MzpvOe1S4G5gXgDZg55JB4iVzct39k6NlSkdMsrrkWGF5jZkodhybbf0BrDo6eRJJ58VbPXjlXrWe8jKffgMwtbcQi3bGjQvOH1xtEPPUmXWLvhxBsqZqzZ82qfWPo1J3NPVItzI2ei3I4ZKsGZlj5H1ipcNpaxO5BNS6hyOVuZin48cOIbnkZl4UasnxGHTF13VHlOZTKSElfpBzCP5uvznpvoeE+aaXmfOS54k6EkydtCAHD5aMI0pXBY7kK4yhL+uLcxxdh3540okc8A2OLdOHpynJtMn/nljF60vDWZaUw2GTg9E3tTFZnGaMc9hc0FR2+MkHOR13ob+ZIL7HY77i1PqOctJKh8KXo3V1esh5Hff2rQxvHe1ZbKwWltuB+F585woOVXfmTFr//5FT986vjmi5GqqqlNESjXtaf2puBunGPWONa7HVEN1paTfZknjfiq4kHr+Mm553f35aHt0TIO0/I+YUGYXPoJcKUAl1wfmXwxRVlsTBERRIovJk87ryPJ2ppc4gaqAtAccpxC1YR6Ug5WHBVm6S4uWoqSGpddKf6aPpF7U4qOHs2wYkFLhk8kFol7eRGgE4DTlAcv1t4p/8oOp0i9wZDMZz86y2Mm2lQ8Vykxc4bLFu7ZnnUe+c4uAIkTDbwxs4xqeHYInKhh5iMvVLhH4KQdeWORuGwi664cmBbNNTMTWa3CosI5AAAgAElEQVRO+a2rzG9ezyb/1BSnIB5HpJaSTGusYRxHFIdvLTUBnyveXW4YEzR2ZJ0CVztLrirmLnI1BtK84qtWWQy33LoaaLisepKNPBst5/GCR+EprTH8aOsI84qb7UiKgcv5Jc9vH7HH8aWVsPKBg7PcdwbUcP/klk0XaFawH2BZefbpES/SA6qrLfdWHu17VidzFpcbUpjx4eOae6cbls0MmJEOB87uC8w6zmew38FobnhlacC9oL/x3A4rPnp+YL18g7f312yGJbVL1Dqwi8Jud80PLeeMg7BcefbdM9Se8zQqNl2xVMsowrnzfHd3gNQQfebpDvZY7vtX2OQtRkfumcjKRXZdIHSFfv0KHXUNTgNBRvJY0y6UfhQ6EaIb0KFmPxMenDXsNnuCMTwcDYdgeXibGeZL3s6lm21dYFY5Op2TGPnZE3hvV/PxIYE6+jwj58T5FD1y+yl395+LQnOrjpnL/Kg58B4NTiBNCBcLJWNEpugAAxmZPBmZNhvCsX5MEmMvL3lfR6OFYxoHMaFM1BBlwJgleThQNxVRMgYYRXF3iialnsLWjsKDMt8vgMejH8ZaW1zyWk7poS5KtDo53j4rNN4n645VO6NPA2PIOK84Z6hTy/c2N4zB8fu3jorMj54IXzuJ2Ox4muDZ9Z4vrmpO28R/+GNn3A6Z924zVkZerCNPNHN/2dD4Yn4s6qxERYUOofhRSoXEGuWX/+pr/MlvvmBUUElY9QQt8tfjKClnxTohTQ77Y6inkRJAZtM0UjNQ67FAHa/jolmmTrT4WRrnCjtNyqhMphVJeWlTpzHpQCzCaIsAA3GkHAk5I2ILYUFN8URpKRgpl+6mhPRCtoJO3pgsUpRsynQQKIZVA3dpqZX77L8Oq8axS5OM30InwoeHju/TUJtMlAWWDmscV+s9ry0ib80tQ+7YxDmHfcWhCmyi53eu5xxspk0Bnw6MZk6VepI3+FzR2FL0uzDgTcUsRxoPbRV5EgwpG6raY+NICIqvDZKveT8sMZqZjZF7jeHBaaYKL1j4JToP7NIpp75jtjil3Ry4WGWebTveuvRcrjfcP1lzsbOcN0oeBuL6GchbzOwLmsMjjJ/xV+4JsxjItWHMV8xOThEsEufMT3YMY+Zk2TOGp9yb3eeerEn1DpUOZzL5OvB4M+fj7wn/+ld3UFfsD4mUEoulZdxnqp1y6Bxn85E/+ACePRUul2/x/s2WNwVeVEvef7Yn1CNvL+ZsDvAoVfTqmFnhrI2MWXi8DVz51+h3N/Spph8sJwvDs3XHk8pNWU3XLH3Ddn3LOycNJznzQezYZsOTtKBNgWxanBzYR3htaWmrzL4TOlEeHwKdbRjGyHbXczAzHpDZ3h449crDLnFqEsjAK77Gmp409jyLNQtmfKCBZzETsyer4VUfWRjhdSoexo4qQzYlKts5x4JPVwzwucij+aV/+FtqwpSeeKQBa7rjWyWxhRd2fKk5gSmRxQeTizN/KgAlK8aXYiBFjlxObeXB0xy5ZGL4774cqR9cotny979zy798Fj8xOSq7i5Tkbk+UBEx+6UY/qtSSQEqpjGfIZEphc5Sl3KWO/NxF4q++teLiZMbmEFi2JY+9bRo2MXBa1fz6ey/4fx6N/Nz9irZSJCmvvdry5Enm959veX3l8TaBFgf/a/dOeH3VYiiBTrfbPecnSxqTOETB21JUzuoZzOrJM5QY9h3PB8OTh8/4O99JWFU0G8Zcuoo7f4mmEq4W4+Rtoozgci6FJn+CYTZVGMMxgO64hC/8uKKMLlQDVMkqNG4CkKaSF3O8EpMEPBUAp1UhyctUUEsBbopYci6vbUrVJk3G0OOe4fhZJc2gBQNKLveV1XKwsFOSqBXDP/21v/7pDqf/Fa9f/cW/oXY6vOhEr7CT+EKtwU/jYUsp+kfPWZp+xhrBC9TWcK82vN16VvGaDS3bLuK9Z6cD/SDsNbPOLSEMiHEspEBKd5T4BDGTkpCAcZbEyNLMOSFgHFRGWFhwhzWz2tAHOKshJsU7Q8qWe43QDZHTxtIPQhc3fOUNz82LhK2UMMImBr72mmW9HzldLWHYQl3G01wWCTyHAIMnh5HdruP7H3sW8xVvX2ZytcaenHP94Q1Vs8JjMfUWb3cMB8XYM54+h02XGOOMr/3INetHNXlQHrx9A3qf7ZAwu1O+9fApP/RFz24TOT9fcL3N7Abh8cawVuGHTs/4+OFzHtyf0Q0DD4dMq5nGtnRS0409tso01ZwqB4Zhz+WyZT/0DNFyrTX0e944n3NW9cQO1jbQrRNDqng4ODobuY22pAtLyfKZpUxI8JWF4dGYSVboc0ZCTR937F3LMh2zyT0HDahAGwWpptFzyLQzSz8MJKkIMZF1oBZhMe2cR7XTIlT4tW/8waf2Xfjsj3BQTio+T2Mph1L2AY3N7FXLol2Uavr51gtdhoTlq0ZBIh+qwekxT6T8bL4zZpYYAplk0F4MP9ce4OIUXxtmHn713TP+oe64VuG3rwZa0cKKsjpN1AQvOnHOPgFhVEOWNGWxF84WajjGLAuwkYYn6viTFwOzjaI5smx6zuqa86XhZOHYHw788Eq437aMhz2nbYtHqcTy5gPDxdkJD3cDwzbwyvmc7z4/sL9ZM9SGZWPZjKF0YDkyaPGljKnQqG/oWNkCsOy6jm0/EIaMOVkx44poyo3pJkJzNQWbJS1S8crdSe+AQtc+jhATeuTqlD+b/pmmbi+lgLpptOVcCYJDsDL5aXJGzcucIIDKKGPMZKe0Kowy0R/gbrRmzJRcao+BWBnJpfggJReoWGwmyKbKtLNhSg3PdweFSJGqf7LYfVbXqQVHIhvhwgWcsWgIaOhxPnDIDd1oycbSAbVRbnPESjORJTpCdmhWngwHrjvo1ZHiFP8NJOOopHShNh1QY6lt4vU2kyJcxIHTtkLshqaeU0nmukvss+F5OCB1TTsOzLxyqrBzwsJYXj9VrMu4kMhVxYvnG1xlGHYHol9welLRP+159lGHbSrGdeILb3guxhFxntNTy/OrNfcenPD04xfcrue80wshDDRGebHfEQZHP1bcn1vuv5oRIrmfYf0O7wQNPfVFgpDYhBmzs0y69lhj+PK9GR8//5gXHzacLufYVc+3vnVJBqp6SdYDr7xuCJ1jY+/x7e9DUw0wbrhoZ6yi8Pr9nlPTEHRPlSyYEz7cb1CbGMYR42vuzRJ5iFiXmM8WPNltGQ6epIm67cgOHl9Hvk3F9aHjcrZg223ppMRfW8mc2BrNkc56YrI0JuON8mGnxCSkafQfY8bZGfdEobVcimXm4aaL1NIQqxHRTFDoLcT9QFVVLK2wNhlva/IQqIwgajBeCFoo9Z/m9bnoaP7mf/+7mqai4IqciIjgJNHkzNvGMK9HHo+GbUrUleVH6Jm5hn8xznmbNe80ysoq/9OmRYxnIxVVGulsSdb0U5qeZGEwRQ74t75mqWaei6Vncxjp+gjqOas9V/0tf+9PlZ+8t+KfXm3JanDT6f3Oi0NxgJZEzuP7aBBSYaJRvsxOAl4zp07oY2JO5rSG/+ynv8JiFjgE5eHVDdtDJknEiqG2jrNFAzCNdSwPX6ypnCeJwUqRajsSja8ISfFWqCt/55Q3ppzg67rGS6YfAkMor3M/Zv7Xj+FPbwbyJF0WVVLMGFv+d55SFo+vwRmmILqyKNSJjh1ESpfxiZTTjFJPuxqyfiL1srQeQglWq7Rk2xgtPqgSlvayG4KXXdMxLbNgUqa3e+p6Vf/ifiXmNEnlJxL29PeJCO44ClS9i9zOuXiv/tl/+e9+ph3Nf/uLv6SLuWKzw7LHZfCmYXCBj/aFaNB4h80Gb5VAwpM45IozO9InW8bMAjYHlq5ilEjG4lV4NBrUGUiRmkzrLReVwZrMIQ+EwaE248NItiW19QuLgIxCjom2atmGnreWLetuz7zOxC6Ar1m6DoOFyrHtMydVoqlq3nt0zeVyxmGfCpiyGqnsnNMLAfG40PP8xQvaxYLNLnO2GHHO41QYnefj55naVNze3lKbzOrU83zjyLYlJ+H1i4aF3dDMSuyGqRSSIegNw3qF5IS1Fc83I6+8Oufho5HDsOXVkxVDyqj3dH1ktVgxdB3v7zJm9JB3fOFiSRciIQycNI7tKHRjZqclPXTVOnbjyLkvtI+Ap++F62HP1c6yd4YmQPYKxtCMHWjFxk8+tpwxSTiZjaRxzjqlcsBjZIFj1pTD4zoJ1sFcwKWEw1ATOYjFeMcwjnRRpjTiDKpElE4sr/tyv1cYDghDKraFIIaYR6wxjKl0xZ4yRt5k+LVv/vGn9l34XBSaX/4ffvvYKhRHvAh2yk9uNXNmysNkpWNp78TTkZjlyKlV3hsr7mvPlxcG74tzXxWimeGG4lyuTOKPteX9Xvg3msTH256fOFV+9sfeIuWR7RA49MW8OObIvdWCTRdZ95nDeODPN5n/a9eQ1d9FCMdPnIBFylL8eHkxDJSiJApWUpH1aiJZz9/+kSXLuoDwLpdz+ph4fH0oqpQ+8GLX3UFGH5y0eG85mXmeXQ883vdcHRKvLiyzyrJoavq+p2nqEuSlmaiKN6Z0KTljrZJCGceEEBAcagL/4x894qFcMqSC1okaSzKpAlkZp4fyXfZMLsa/46V3HcwR019Mrs7K9P9XGGO8M3JqKuILkxVnPrkPKgv5oyjgKNR4+fcIVov6zVBGZsdo6OP7/xJZc7wymaPs+uVrPf7c0dtzLHAG4dd/9bMdnf3jf++vq3OBxSzwe7uaqBUPnGNperIOqLXsOqWmIZixRFdI5Ma3hH2mrgyv1gEJAxd1TZRYMPtBuGct0QxUWvFByGhINEbpvYUoJKlIusGGht4MOFrISqUDJ6JUruONasXTfktCaYBaR2pbU7WGJmzZhopkhbH3nJ2PuKDsRTlPwugcl2cjrzaWf/ndhDYGiZnLVcV6FNohMD9JnFTCdrvnfFljTY3EAfPKlE6XDsTdyKBQLSBd1aRhRJb1xNBT6ksLY8d2HVjOLtntNizOFjBE0Mju1nM9eBrbcX+hpPMOuzPcXi3ZaMtrqwO3h0zVLGlcz+9/tGZm5rRVSXJ1RD44VOz7ihHLISYqHWic5apXbJV5UFVoytzGnl0ynFcOifA8WF6fW/qkuCrhQuRgLVddpmkr3rEDtzFzXxp6qzwePLdxZMmIaqB2NTlHTKw4mVV0KSB1zfPbDWNoOVRS6M8kcLCSGXPb4bUgo26tsBsNKSnXmhDnp3F3IaW3puQ3Baf8nW9+4wdrdNZ7mKVEEkttAmjNJIQkCNzmssR9aBsa7YlYbPa0LqNh4E2JmKy8fzAcTENrEzOE130mLwKHsWdUyzsm81MX0A2Rt988pfHCx8+uWLSeUQ3rfUfO5ZR3tdmz2/ekpCwXc95yG/6D1Yz/fZMxvpwsj+MkN3HPss1o9sXtHhLUikRFreACxDojASoJ/DffeMR/+mXPqxcXGCmGxN2hgxz5wsWcL5w35FjzJ49e0FSeygjr3cjZ6RznSyoiFIaZ5sRq2WCkvA6NZby0qARjLcMQ2ffl4b1oPbV3aOxpmhn/8Y9/gX/y3Q3fWHtelZ4/kxmVZGycFH/GEI+U5Slf1H2CFWdyORDkbKitJ1Iyco6td8kCsqRUzhJqpdAGbL6TpZcaU4rH5Nyd2Gsvx2UqgpoSqaApYzgCQKdCl8qYKU0/X0LMPFEDmOK90iMdNcVy40tGxE8RA2W39FlfY5u4Gh3fWdd8cZ54NSn16RoGwyEkojieeSWnyKBKr3BK5OuzQHOPkikShdE23GrguhNc7bhNHV1KuGx4s7a8K4ptO4IYqrZCt0oOB6IoO9kTTGJMW2o11I2HWEgbzISFeOZayOAhGcQp561nn2sWNtF6h2t2NE3i/P6S1I88Xg9c9Zn9I+F7JO4tQFIgaiB2wqUcmF9kTuZnROvwi8y4i7y4HRBW3JtvmS0bUoCnm5rlbIVfKc509BvH7hCoamU+y9BYsBVLdw7J0iw99DM2w47KtPgTeMPuMNKw3yrz/oLf+/6aBzMhbXd8sA9cNIFx+H+5e5NY27YsPesbs1jFLk9xq/fuixcRjiIzAckppawEGSUgIQvJlsASyA2LDrSR6GC3LUSPPoaGaUMDRMegbCAkkOVEZBHOzCAjo371LU6xy7XWrAaNuc65L0wvFdILxeqc++7b9xT7rDXnHGP8//cPvPYFna75fw8TTVtFS43AORSQSJcH+tbTYFi3hq0cyWI5JcXmxDeWwnLleHsH4jMXbeFgDDZHXngYpshqYXjJwF0s3Ivh+arFxztKWfK8C5hj4Bg8fdPyfCFIsEy+cJNOuFgwZJaNQ3XE0VVRTlZaWo5h4kaElA29MbhJKTaRraEvSpoPkiEpThRlwjeWxS/ZUvYrUdH8w//+/9TReWKBpauQyMnUUr8OmpXrHDkIjNKRbY18tlpwEkDbOQY60RM5FctoLBsNrDAcLDyPe9biue4K2TjQRJwiL69WPN0u2I2BEGtImJcyt13emRmzOJZkflxW/K9vKluoZH1Q0M5+EMMLDfzd9wz/y13DJ1NFxogp/N7ScDaOf3FUUoa/cz2SsvCbz1ZsOlAMH789suoN33x2xUIE0xt+/urE7jQRVbjsHSklQlHcg6pLhEXv6X1DLJnWC1OC0znWICxTB+7jNM3Mo/o5nm96GgNiLLenTNsZTHb8/g9v+T9uCmdbkKwUcWipm1qqMBfKQ0ib8u79IeNnUOiX4xwekPyFPHua5NGU+9AGS7OCT/Wddv+hVfbwM2p+97VUgPJAcK7qGKl5BJRHQ65FybN0uQbZPSR5Zs3zxjNn1jxUOjj+6T/4W19pRfNP/sO/rWoNrWRcospMNQAOY+sG6WKq0NASK3C2TWi0RAsbhY2rB4nLtkWmI5P1jFl40rfsxoQMI8lGUrRYV9V9kwqlVDOoFc8wBqzNiIkU29DGOkdbNiMbGq4uIo10+HZijI6ihuM+MhZHmo5k2XDR7vFWWPUGysRnd4mvX7e4ZkkJI75fgBkwtoEg5C4QzlXYcjicuL6+pusasr1DtCPgsKbB5lxnkCnT9J6bV2f6JrHYXhH7VxXoqpYSA+5yw+0XytXVGuyKT//4Fcbcs+kWjJNlnPysVHRM5ciH1z0//uiILCaKXrLwynLTcNxFxnEizmpY3zaEWDiWukYtTeTVydI4jyOTi+OcLV2fWJqIU0s0gUYc+yQcToVdENYbU7FMSUgpkcVyKFUFupZCsfDEed6ca/7NyTY8sZYmBYK1FCNojjQogxpS8OAgWDiHyMJXNWzEYcUwpYKbzcvGWIZUCRIB0OwomjDSUKTwX/75n/56VTS/sRzw6Yjx0Pc9N5PwR1OLiNDawDcbw1ItP84GSCQsBYsaT1cMsSQmEYrUYKUiI0uB314t+MvDDc9pOPhrtjZwX5ThNJBzREzB7CZenyrd1ig0vhJrpSjbRcf5fCLajpwDZr3ka3bg7z3z/LO3J76QJdnAU2O4McpvE9m4yBeD8rvuxLL0/CQ1/But8u9+oyPZnuFPX/O7TxpWq4aurRXHeQycc6K1yme3Ix9eZ9xywTAGhMSq8/zg0x3Lp0tyzgQ1FMPMg6sLxJQiIsLFYkGMkVXbcJ4yMZdH9V6MCZsL27bBG6HrWg7DxNopT7YrQpz4299t+YM/OsJkSbaQSqxwUR7I00KUVNsUqTDNcrMHUx9SuduJuln4OT3uMSG71IdHNVHmaIWmJNTM8nXm2Yk8zOwqSkhcoTzuPAWsIlpmFFCFx9RQuspJM7P78jFrZvY2IQJSSRRZa2z1gzfI6Jebgl/N9WJV2W45B1QtLyRjGNh0HafzSO8bdqGmr06nibvJYrThdDqh0lCc8GkYWTrhi93AIVYPrtczP2hGVgbAsHG1LdbliO8MOiS2OvG1DcCZ1YstUQu985ymifNJeXo9p8zKmn0Y+PkXiRbD2gW6buIbz56gdmA3nOmzwMYznQe6S8EH4TevPTjHcFBKF2g95NAzpTPnQ6Azlv7ZQGktL9fP4JVlGgRvrhBdYeMO38HhTuj7QLGGdIoYN7L4Wg8p1htvbVCfcG8umH44ctUbXv/hSCqvWPUr+u1T7g8nxDlebhvwR7CO/+cvl3z+aaon+mhp3cSqs9y+OrGThil1eBcIOcHJsWoMrkwYqyRxbJyyaBwx75nUcrXwOBokGLzJHIsnFGXrEpvW4ZpInhynLLSucN15jC1sp4mJTMmWXiyjTriu8GHbUTSSjeVsMi2OHiE6R9LCOhe0mcBZSoHcVRn/gHLVZFJRslVCmp2KkvHOEopgraJkAkJONVrll3n9Smw0n00dH9jA9dIQS6RD+b3OsguCSmAl1b/9OyvlNnl+HDqsRpblBMAJRypKUs/k4N+/UJZLQykTT3zDarHg+58d0GJBA086yK4nDZGxFI77hJljiY0xOCJFHA6haxp+9sWO3no0KRerWo7+jaXlnx8C/841OBJ/vmv53aeKZoNrGoYSsV8ULiQRTnf84HVk2Qe+s8pMeubKLPnasytuj0dM13P/dsfawreebTgMI8vWM+aAtZaUMl+/7ik5ENUSUqyL9wMhR2C16fGuzmSM1PZUTIkhFKZpIudM0sLCGvres2gbUOicYVTD3f7AerWk73v+4/cy/+TzQJuVtRdui62RAblWVl4tkRpEVs2edZbzICc3Ut39Reom8zDKsgrx0cckj3ObSuWuXLv6A821S6nSVpn/8gF6qfpO6v4Q2iYiiGFWotXo71pR6Sx0AG/dY5U1g87Q8gDWlF9ADX1V17OloZQ8G4QDrTWVLZbOuL6tjDJr2VLwbcdLV5CSmc4OoxPT1HDyUDzEo/BimZiyJXcWSfMsUS2NKTxdLriZCt/uC6bbs1044iwAkDzw5NJijpYbt+FTM/Ljzw2nIGz1zPNe+J3vXiH+FeNYiPceNgdEEm33hEY7hiIY7fnJx0t2wwlVZdUkjBv4Vz78kBTPiDtwHAuyCXhpCdMly7aFwwgraF8Wym4gNK9oVx58Ye17zm8nvMmMxbApCa6O0AscwJRA7hXODW1JpNFy9fJIKOtH9RfNgkYDP/3owMm3hJhordACFod3Db1RzuFI2ze8LPd0i4a7MXFxVc2txig3u9qxuFw5hqmwO+9wUk2PYg1FD6gPLJaOp7EKdYYc6bcrnpXAGCyH6VRnumVk4RzrrXI4Wk6SOZ93ZNNgcjUdm2zIeWLTN4SScKWSM4ZSMOKJJSNWCVKRUUksz13LmCJWM8m32Jn2XQSsBJa2IYqSjX1A7D12LX5Z16/ERnPNQCDz8c4wFMOYEpftwFWvvE4tn50TV0vhEFqKGl7aiMYJUiaK5d62aK6S3Gclsx8Du3PAuUKzdNyd96ycMk7CzTjiO8dwnjilzJQsUSasVJhe61ocSibR+4Er7fj60w2vDyOH88QQMilb3pjE12XABcuyb/gblyNqHGjNl1mK8o028G0mXGdJYeLTQ2CaRqZ54LZet6z6np988Zab/ciz95+S4sg0GqYY8G7FRzefQqkL6LL3nMcziGc3jIjCcrnk7hRpmoYXF9VwFZJymgLHcyDrTC8wCuKYUiSUerMbFfbngJiMbXr+6C8/4Vsvr/nR6Z4PpGPfGK5N4F+3J4YEP0yWl03kz0aDKz1FJ+6lwcg7snaeld0qUuGaMyEBrT6QSijI8yYzeyWsqdQz89DCMo9ufgBTG2+Phk6hmkYfPSYPG4R915KzaslSalSAGKwkEspKa0WYbB2AWuaYBwzpV6CiebauhwJjMw7BS8FJNalmGTmMDT/62Z43OBhWDDIizuOyEtVTitI5gwnKals4B0VNRnPCpiox33YRYzx3wysufcdHY+Zpe8X9LnJMsLQLDqFw/CxxSoJJJ9a+4PG83zeMpZoK//zndxTfEtQSxozeT/Rmg1PhyIHOCFcXDd948QXZCo1rmSbFUxg4MWkA9WBbmrZjMg1LteRDwnZhPpk4TFnhpxashykyHgvp9glvvpgok4fmgC3wcmvxywXT8jVcRczwGkkOp44sStkXtquB82S5uztwtVxz+cywON/z7AMhJ0cMC0oYWV5uCLHQNJ7jLtKvK2lkEzMUR0hvaZqefq5AOSc6m9n66p1zbUuYpqrAFMtwglM507Yt+QyH80BUqabqpuX+FOiWFxzzxO5eSbblMA70fo2URNs21ctHopHaXkxaiDSIVlVqjtWMbJ2DFHDFENA6+zSKs9DGyGhgyvVZzVqVe07KjNqqIGFxv4ZkgCEW3u4zJStPNpb3lw1jiXx0NCxXGbGQbGahe3rTcDvUYfB+DKhYXppbyMotjv3mgu+dWy7zHpuV61RbRzEk9kNlY6kqoYCSuLxe8fqtIRAIU0aagHHK5uKSSORuVLScGKLFWENJ1efx1j/hvfiK/VjIpio2TMyUUAfc66bh/RbGpExh4qOz51mjOG8o84J6Op0Yp0hOwsdvD7RiWXeGrvd8dnvmbviCJra8Ho40TccUA6bxkOvivWocJU2A483dDiuRl08v2e133JwmcjQ0rhpOF23DeQr4dsGwO3CfK1dOZ8ps09TW248+fc0zk1l3B37Giq/ZwovFmrf7MxfnCWMa/u1tZH/a85PBcq2RV+IJc1jcBCAtQQKgWFtPcQ/yZldmAbiAfVjY1dUxydxeE1uDI9KXJMs1WbXOY9TUNajOePzja3LJWMkYUezcTLOu9r6dwt+8MvzWkw2fv7L8b7sDSJ4ziGqqq/sVeBxu3wY0HNHi2WwXfH4u5CGy7Co4szWBr7UDzbKh6yz7I9wOd7x/vWWbjpx9B8bx5ggmRkrf0KuybJU8JowdmaLhYp1ZZCFvLT9/VdDjHWjH2hTyNNKo4xLhvT5xjJmiLdZPFDLb1pJLZeVtXWaaMu6iI0blOGpFtYrjxbMeZOL1TvDe895vOPwpwyioOWFguwAAACAASURBVNFvVjVrOh4hFuLU8bP7kfOh53S0dI1l2UyUKdOuOnZvd2TXY/DYMtHahHcRV6BplN0wcGkdzXWL2ED0BT9Xr+nkaa9PmH7DKsN3N1sECMdEd9mQbeJwjMR4xvuGcIKQJprBEGPLcCfVLpYXND4zpcsajCaewp6rzaK2g33hvHOM91AZMg4b4TBOfDKtSftEEcOUYW0DfSsMw4S4JccxsouZhg3p/IZlvyDmjIrFGsM4jowZwFJifZ+9sYQ80qinqMc3hjHU5FRhtgKYeuDSIogDCiyayv4bXCCJpWQF4wkPys/4axjlHDFVdngunKeR105YNS3dUignZVsi5txUlL+bIZxhoGlaDlOgbz1vJJEOE88vM39tq3y+Ez4dE5/+6I6nVyucMXzj6Yauhcu+xYvyo0OCqNxw5Bgg4ll3DaVkes2IUcaUueg7lMwxVR+I88K3zR37tuOT88Q61pyU8X5EnGUYT3hnaNuWQzEkWXI6nbk5KFet8P7lgotVy/25EPOAWPj28y2xFE4ZxlPkemmQ7LgJZzZ9S4yBZedpW8swZsQZjDEs+pZxHLA4Pnp95nhMjJrwCGhBi7DsHbeHmkuSSv2ookzDSCiV8TaEgWNIRFXOwfM6wH48sG/gOExseg858vY2svPCaYjsZYG6Ooi/7jI7NfRToZHAMClnydC1j2QFZJ6ZPCi8HlthYeYFzTMaJqS8M3HWGUyuLZ0HaKdU6KnMAoKC4kxVxSFCzAUrwipn/q0nLX/4+cD9KXNYT7xOlkbrA19jDEBFUfPVVzTf+WvCwjwlDomb85ExCO2qVsB7cUSrEA3d1LE7RoybWHvP6zeRz7PDmII3mYwQo2LNic4KQ7E0tOxD3bBevwnYUeDVHdk4QmpZNGYWw+QqLGkNjYH3+kLrFE0toZtYd46l8Swag7WRiYgJhoTQNRHxHo0T6gI5FZL3aBEOP0x0reftzrFsWkIYSMMSt8rEvaNbTiw10hBZNS3oiWnMODNSzpnLdc/lEvrLkTMnlu0LYITJoSOEfIDliKYDMQTcxR0lrymHhF87zNDDtCf6LeQbPC2NbfjkPrHxStte4MIJK7CfBGuFtdkQzImzNzQOwrijGEVd4LL5gGIi7cV7AExjocTC4qVyfhV4/eaEWV/wk7eBSYXW97g8IjFyMpl719GMGdOu0BE01oiNk5nIZsMpVPWkF4MfI8lZRB1WoPWKJI/YiJiuGpqdZT+M1axuLGnObzI4mspqpi8VuHtKhq4ojRfEdkSbEKmIHmM8Y/k1hGoam5mKYSqQUoYMm3YiTg3B59r+0JHPzpFpmhCxRBylnNj0HqPwQdvQrns+Gu7ZB08Okb/59Uu+uF4Tx0jXWl4fRl5Kw006cblcsqBwGyN+5Um3kX7TI96i8cgPbs58uF2wi4m74cCi65imREI4JaF3E2qFGAtjqkFax5KJp4TvWi5cw/40sh8yy96xHyINBklC02XenO5xAk6knrpj5aVhamIiukQ10zthCgkQDmPmHCdaC2NMDPsTIsLrY8Aa4TwEsjhaC995vmHRV1nqm/sTYyoMUybqg9lSGcZCKLWimdK7VFEjAlPhWpScHacRfnAfKH7JsVOW08DFZYeWnhfDGcOJlVq+4w2HCb53mhibhrZta+zzjLRIKdF4/+6/tYoKktb3ocwu/qQzCXqmNdftqRK2oUZTlzIHlcmD8VJRfZA1Q2OhD4mvLYU/uzkijbAwhUNMfH84k53Da612bTI0XjnrL/cU91e5PvrYs7nMNH1PNgeePeno+5ZDJ1yzosiIUO+VLr3Fmve4vc+c78+82Rv6mKq6yXlOsWCi5ws1hJNSXGSLsO4N62Tx24wtW85lYOsdZx1oejDasRXh/etSkTWHyKoDVU/TnXFPjwynM4UL7KojfCZ8fJPAF4bjlvcvWjZ9ZLdvmMZCLCeMdCwWlpwyy8bgXMCu95jVLX1nIKzZvf6EjVwTTMvqyR3SLIlDxKcGUmJYjTTrI9I09L9xB8ufodNzCifiBy3Nsie/eoL8sdJ0LXzcUXiF+/o34P8+VT5O7tD7t+TvvECbjD/e8sHThttPEovxx7jvvEfe37C8j6w/dBzefEaz3rJNB5w3dAtfla5tQ9q/od0IqCPnjkJEyhmbJl5sPKtlQ4Plwy2Mx8IUDshyZOMLy75weDsR21r9pbTh/nhmzEJIYzVm54z31S4xlkyMEWuhZ8RkCOowJjDFBdFmcjjwdNHQOkshkZNwAuIcNhhTyyElmLsMwTlIIzBxdi2LEusBMBei/HIPXb8SG83zvuXZssMYw+3xjCbDOUVWXUeMkWNMjAMcU81q8MbT2cyTq4alOPYhE6dMzIYX0tI0BgnCp7uBP//8xPs9HFPDZ4c490zh5lR44msJe7sXlsuexdMNF8OJ4heEFLkfA2W1YL9T7sfMelHfrriupNPU9pWxFU5cThMv+oZdyEybJcPtgfe20ArsTmM94ceJtyi7aeJb11V51hjhR6+PxKwctPCk67GSOYXCNEWcc+xPkVKUvps9IrFQVOdy2HB7GvDWMWWlyInLZgFlx/OLjlgy+7EwJssUIyFnjmNk0VgaaxlLovctpQhjrgv7xhbUetDCbSh8MgoXXuiJ/NbFkj/++Zk3x5GUz7zOcJTEaYys12tsb5G2Y9FZjJZKDDCFEhPfWq75dDiRqCmd3ltijLhHZ3/dJCxzYJ3M8xbzjisHwFytUPTRKCpao6ZlNl/aCFMM7KeGYRQwhdRWksLvXRn+5LZw5ZUnFwt+/PrAtSj78NVL/V/dTdycHaHsWHZLVJUxnOjUMJkdbm6JpBKJxeObe0zJTMmwFCjesHQNeMtl4/Am0kRbgalRsAtLq0rqGpomsFpkvnm5IhRDlsTxkIn7O3rX0IlBY6hD8VOm9Wd2UyR9qizWS6Tfsv/UEV1L33zKm9OaePqEP91vuOyEdulo8omrxS3P3rdwZeB5C+c1mARHQ5oy3GbyzRdsG0OxZ1pzS3g7YJdLvDTkmxF7Uejf28LFEj4+k44vaW4uke0Wu7jE5pH8eqI86bB/y8H3P0NfvMIcvwmhJ/3eiLvP4AdUtvTdE7AT7Nbw0z1X7wH2GYeP75imnieX3+B807Hf7TiPNyTzlIXxhDQQgkNtQ2OWlM+PdO2ChKHxQhHH6Zzom0hPxi33XL2/JJ3gzZsdkxHenhrOWSimAi6tayhqGCk0zrNa1Cj0lApWMiELkxhM16NFOWhDwtVW19gwoaTssGYJtqkoHGCg0KUWbCBkR5EJ6yrFw9mqeC2hZnhpmlhby0jGmoanX2pJ/zKuXwkfzT/4b35fjatqpKX3NM5ynkYaNZzOOxbOEHMhUnf3EMcaCORXnDQxJcvl9YJFtyRN9+Tcsj+eubjcUPIB59fVpW4zr96eCDTEm1tMV1ibhuVyTe8DQYXzWHfysXRslg7pqb3PIuTOQAZvM/Gwp7MNPkaSMVAWJDPSd57UWJpT5C6PnA7Ks8tLPr7bkYaJMI5VL+8cv/H191kYEFeH9744Pru7J8VI2whSKt7m7hhI1ByY7XZL0sJq0fJmdyKneqJfbxs2Fyusd5xvdxg1TAOcxhN//dsvud0fOE+BEgu3hxO+7RmmEdc6bKqYmquVq2W6ZKZcF/IpZTR3jH0knA0LG0l+xaUr3B4HDiXwrafXnO5GhrVnmpQxR07DRGMd/cWypnRmAyXwzV64z0pRz26es3zZsf/wUaVuHtbaSjSw8v97nWglElQlVYE5ZiClNKvR6nt4Oo5cNoaFV7a+4f0NXK8sP/s8YUrmr3/tgv/xL24R4L/9h3/3K9Wefe/v/44GC0kNJXUgGWcifZOxoky6rNBKr6SUiNFjkzDmHdvtltaCtZ7GT1gKfSe8Oii+TDhzQnTB0gl9O9E3gvVCaTxIoUwZ1zTkNGLWlmI8dn0FaYJoScXj3ns2D+kV2FegrFgkCD4HCBmirUQGahiZ8a6KQFohaKJRV2XuoRDTCaMOkwqqJ1wraB8pJWELlFhnc9Un1aAHgxwzRMdp90Ps7kT321+Df8+DjvDdD6B5Df/sHn7mYTqitxmVlvSZpVltKbnBRIs+DYSpBiZaYyg3jle7Iz4vyGnP9XXPEOr/b9oN0/mOcD5z++YzLq5fcOKeFx/0+MZDXoKs+eznP2IMltO0QPWSxbZnEMPbQ2QIdY1LpfIZUU+qSNTHe13U4EwF0D7EWTzQ2DOekBMpVxWuN4oUuCqJybcM4cxxiATTsnQjY+iIFFyOnLOirgEc51JAHIERr+0MTq0bm7janm5K4T//w18eguZXoqJp1hUE98JU/MjzRSKr54fHwKXrebZo2Kx7/uLuyL922bG2HdqtaUpkF4QfHSY+XFhaG0CW/MFd4cliWU+3LIBcFx2BJ88WFXHz/MXs03uQ1HY0RXHbB7d6ZRORa06JaHVjO2tBLHa1JQFpsXiU15qyYAiJdUjEEtkqeBOR8Y6vrzzdZU+JLQOKGE/IhSmcAMHZymL7zvMVq7Xyk4+PNE2Dc47ri54wZa7XjtFviGnAG0u33GKKwjgyFsWME50U3KJnK3DwA89WKz652dOYWNV0Vrm8WLOxBTZLYuvZ3Q+8v3Q0Rnl5teAndyOSlYmEEeVoJ2w0XC88ooZzCozGExtLaxbsh8T2iZKj0kvi/Sct7617/uxGuR8ngrFkMWANP9uN/JsfLPnep3uS7x43DSeFWOxjBSOp4ma0FCQnTM3jrhtIzpXVlAvOGDopXLSGznm8Jo6q7Epi4YXpnNnFM+qXFLXchES4V376ZqynRi3ImxMlZzbNL1fS+Ve5fjqt8PPj3WvEd5ZJPOfJMNiBHA3bbYfkO9qLBXbImLLjavGEm7c7fhoN26WDSbEysrBrvvbNNf2ipUMREzEtlRNmtXLrxOI14yygTa0WjcGmSkEPmijUOUsJkKxQYsbwBEmVfI2piCXTWEynuHzC60Sxe0ycCQ6lCmmKi5gQKGWgNb7O2loLuoBeGL7/GYvtlr1ONCM03RpJR9hsSesR3wQ4v2X55BlcL4nliPnTFnuxZfzn97SDQ9JzdFohF5aSTujkKYsADnJnGfYH5NayP5xZXfSsPnTI63vyEsoUmNyW8ezw9szz1QX78IrLpz3m7RW+WRJ7zzPzhNe3C352CAylIKkh6XdQDNmCKZl4XwP4UrSoFHyAYCAnR5IMriHnjLML4hQR6iajSdn4BsqJ9cLTz4blPEVaB/lUE2xvUyY4gx2VsyiFpoY26hJtCiVkAg5jlTFmei906GwC9qRypmoulaYRYrQoSpRfQ9XZpRUkRqStyPYf7SJXC4sVR9NZzqlgThP/atfyxXFkbFrCYcdq6bg5Tiyy59UpszCez8fCpW8IkvGauUuF1ih751gkZSWWCSXPyq9CBTBasbXnr5ac6kORJOJFMLm66/3s77BSZwYVdVId8aAkAW2EfXEUk8F5jGvJIaLWEqapyo2tI8fEPsPSdjQ205sM1vGbVy3/4tM3vHexIMfAYuG46ArX62v+8vWB0/mebdchaeR60fP5/ZnrvuOkhY03dGL4s2HEusLT3rKLlj5kRrX1ZGTra0zbcDpOjKlC/D4+Fja95+bzPZd9w0m1GrnEEYClMeQyIdbQGMdpHEgi+ARTmzGlRYczGEOMhY/uqpqmcx5CZpBMNjBF+GQXCVpnQbkULoH3Np63u4nPnKeJ0NnM+73hzQg5G570ma2DPxk8cRwZrcNhiFEJOXJzLjztexYSGYvhNAXuzp7L3vGN7YInF577w8huEHYjdKawyxkt8JM4kLKyD199xOaTxYQmh/M1fnzTe3IpqDMUtSylobVwHFpIwjRmuuUzDre3rLqGp1YonOlT5vnTDe9dZay7RWwHzleVlylVFp6pwU0MUGpSbE4TOXsideM/p4IU+9iiVJvQ3GFn8rZzGS9KE45IMKyadp573SNNh4ZKcBxPAWKg22w4fP4Wu7lktV2SYyCEM/2LBcgdOnkW371CXWSxXmBuCsYD6T3YHPE6oj6z7zOL3zpgvjHhzUuyHoCpynTDW/gsIv/dDeGb7yH3PSk72rIlDKWaIVmTI7hmxeEsHL4Pky4AKFJIzhKTcspL7t4EUrnk5/eQU70f9Vw32N6MtAY+WC/Y2In1xRpn7yhjAMmIP/LJz5VV03F/WpDHQN/AcTry0XmLSxMmj5hJ+Dg1YBNr24CM2GxqtMBOKVmRWeyiQUkqtIwYOiYFZxKmFNB6oHQlshKLbR2dT0i2tF7IU53jqjUEMikYRpMfq6uouebS/JLv61+J1tl/8Y//qZ6k0mFVCgsrtGLn7BHL4KBPhcHWE3BTRlrr0IfXSI281bmnnzXhrUOMI5c4l6S1DPXOMBWZcSfmMesjCyzbjiFHWus4jhGy0LSOrHVTEVEyipQqGY45Y7GPMlwrSp6rG4cQ4yzHTbV0dTM2p7aGKirlNJ9UhEKLMqUqDrho69drreBnHf3dCGvX8tk0sJBaaS19yyEkWmfZD4FtV6uCUzLVoGUMY44sjaX1DacY8Vr9GYOa+nM8IMBy1RibuaVVZC6nJWNT3aTE+RntLzO9ufDtq4af3AesZjrnGVLBSOFqYdiPhcOU2c8y89Zalt4yokyxvq9PNy02ZO7GSEYJMdN3DVYThyFhffXVLJzj7RRYOkunsB8DZoawDnPFM4VUyQi956Lv2I9nnArPli37qVJ4HyKdSqmqvIeKFFX+5//q73+lrbPX//Xf0bNpmMKJjpYrY2jCDrWGY1HGFBFrYTS8eDZLUbuI809BlWxyJTLICKlD/UzMllJPSaqguc5ITKjScvKjDFbEw2TRPEvf1RIHGI0jxogWj29qbHMMQuMsAWGhEcXV7CFjUDPhsKgYPI6kt8hpSZYRZ874tKHzhRgT5y7Qp/XsZwM7FqQNuNl/xXKClQE/whOPyohcDaRXA/Z1g4YzRnrIW8gZhiWDnOkHgeSAJWgmxzKLYQyahIChZCGjFLXVtJjqGpKwFaeUC6glaaQYIcY8qzdnRWTJJFEk92iOlDxizoaLjWO9NTxZWtrVHkKixIY4TcS04hDe8snNgruxZx8y39kKX5yn2dMldL6wEo+VQFZHKQISiVloxDG5ltsxsDKGobQcSyZINXg/MS2vDiPNquJnmNOCrcLCt0yh0k6SBByFJilnU52apkTUNkxa+E//9z/59aI3/2f/+PfVzNwpsQWHxZi6MWS1eEkU43ko5mq8cvVWGGNIbiYnzwa/UhwQqKLxeumscBKT0eKIBnyuD1/NoTfVeMU4v74FmWYDYv08xoJJhUc+g+YasPaAQbGCpELJ7ePwWuscukYUiNBrwkohamW5Dam66tt5wUwGxLjqirczxytV2ekD3FJUiTaxEEMoGUtLLBNRDa3xnFNAKXRqGI1i87z5WX3MgLHGQ8rsjbL2PacpgSSUekNaaxlTxkjlh41plkqqoZh6A1tTZcU5ZyQVmqbhnEAIFB7iBfTdQg6UmRL9YKyEefg/J5QCFDG1Ry+Zh/siFfMlOvYD5XneyFUppcq9K7WphtdJtpiH11hTQ+v0XU5RxXAIMQV61xNz4H/6R3/vK91ovvgf/hO1qqBKIaJGSFOooXEl0bUtxjETrAsPt32KE23bIkXxMaHURSeMAyIWYwxNU03JU57J2rOZ1pSMlVCJ29YiWpFAgoFU47xTNEy5UGKdgeX0Loqh0sEtMhtxH6IerNY5m5k7kl6U3ineKdqeEWdRSTWKvRjyQbC4+rOXCaY7zLKrYV5nJUxvaTYXqCuIV1g00ATI9Tm12QGREgUZO2SEMjpMtJAslAlKT4kJUUsytSrR4kklzpSK+T4UW/1e8wE1IeQEY5wD/KiZMFDQMmciPaT4ZiEbiKWGv4X5GfAlEROEkmlsy0RhKJa17tmz5qWFW8kMpedDd+J+gvMwoH3PFWdaf8FpuketJeuCEieMLWRjsbZGbYsaGjIxKUhmheNWLQvyY0TApusYxkxnLcyopmOogXWlCL1LnILyH/1ff/HrNaP5gz/8KTBnhViAKm11zmGlGveM4V0OjAGntmLrjSGWjJFf3IgqSqamaj78rXNuTn50iNSTTevt7NNweKsPyc+0LiKiqJF3D4r3iFSVBgBaT29lXkitd/gHXtf8vRZVrKlZD4JQxFcOlxgKhmSUNKTHrBREEDEUB2b+9Yi3RFXK/H3Url/DgFKckIrUQALVeRBeF5RRalUkKNkKU+ERs1M3CkdjHOcQiWKwODKV6CpagZrM5TSuJeSMGIOZN4SoQn0chdRAVAXjMKZF502hzBvMQ8ZMcfXrS65pjvU1hmIMD40rNy+MqSgFwRhw5t2GVYrWdpI6yKWacE1GjQFZzJ+ltjfTrJ6xM6ImaXncsHKp1Gb1liE75FeAQXO1NSAWMakKKACoUlcRIZdY/UTWIFLvXWPA0KJqiXHCFEsOjpwn7GJNjomQM+FYMDYj4shkMgmjHlEBbXFUzJIz80FJh1mskWoFbg3GCG3nSENGYyGFukCns61oeuqi62Vmz1EgzQcIC6epenPc5GkaA/0JFoESGzAGjT3ZKK4rpK5H1WAGQYylWT4DNWgoYCY4JMRXlJLNgCSKBdOCbo7kC4sRJU8Rc3bIZGCaMMcOzQmPxRsH9lxbiFogj6AWshIlzp0MSy4TRXusGWukuzggIUVQPQMQYsspQBwK682CY4DthWdpBj7/aEL7nqUZ+el9oG0cGw+HMTGMlqczauiKA9ZCzsJ16+i9IasgLJlKwCEcR4Omit/amw6r54rXMoJIrcZyqW94wLAthhsxHEzFULX7ynmcCJQ5R6pTy1AKRSpdosivIYLmRx/fgMwNjfmkUWOQ5fHEJLOZrrY4LFbK44L/sEAV1UorpgZ/iVIJvsaggDN+zqqvA2RjLI4aS2zK3Pay7uGTkXKpM5YkTDmRUaK8Uz01YhGJOJmTH6XgVCjG4maToojWOY9UTXxra4BaO2exOFfbUw8VgBUlitJSiQhWQcUCBetqSuaDPNHOsMy+b6sL2De0NuMBn0Gp8mVVJeeCJGUqyhgLw1gYYv3zYYoVXZMdKSvnnMlz1ZDmPJ3aLsiVoebq9x7Tu6CzR96Yzh0aqSc7mSOYBR7hnilnjAhFqykz6zvw5wOf7KHaqL+LMn/4xdcZhYcEzX/5euCrPXA4zSNH7V3oWUXYVGacK1QPzj/6D/7K9/Ev40qHY2WyyRkrplbCJFwpFONxpRK0xRQorpLDrQVNgMeIIj6TnaBqAcHSVG+U9pRyS1GHU2EaPSlCDkemEY5xjy31RO9UQCzOA0UpJeK84huLsZZ+KaytYNICKcoUB0qpMnUjDRQhpAmPRbNFTMYbWw8MBYoWUkzo0CM3S6BWP6q5+lROYEuLWFBb46TF1vLNtAl8qtgjnylMVJhQxIiDpEhpsfnhd6zoQuHJofoNDi1y4+F2XRV1oyMXRWykBEVVyBJIqUAJqK9z1Lari7szE0g3Zxg5SpLqbXKB1ifGzgI7Vp3heGPYGYi+MMUDR3V0vir7XicPkkguoHlErdB7C4cTstqwkEBbDELLoBZjC8MgLEriXh3LpmNKE5IbXNtQguD8yDQIjSmkYJByx9n2dM6xzfW+n+yINw0NjnOGVBITjgzEkCgC5dfRRzOme9KXMk6IwjgvEA+Z878QhDXvtg+8q1oFfOk0qu9c5Q+f1yGoGbHU9oCXmqgINVI4iavD8jQvYoaZEzbykP6ZcpkTWR6qFanVSil4M28GCFnKY6BW7xweBaN0DqzWKsPNcl2Z81hEagtK81yNWQGjswhBsMZh5vjjpjVYU2Oe+wbujwcab/HGcju3MR7pq1IXDlXhOASScRzHwBiF0zQREwxjZshVKjzlQtTaYov6DskPPCZnWjGPC7UKaLHvWoWP79bD7yK/q9YAijy250opZBHKw2bwMHCed4dHAnN5IEDPSA3y3DL6xbC5X7xq+0nne0ioCxnGQplnEjlTZvJzhMeK+Ku87r73EbkIYhI5JlrboFrxQNlmSmpIZLK1hFCQbFCb6MTXf5Mzaa5o63s+tzBV6qJY6mKeSkZyj9ixzlZcnUeUUqse7z0+K10TaQlE0xKBQdr5OUxYBG8LxhY6Z4lxpG0aTBkQCpuupeQIUpiGglgDXY9MIxatVYk3dRUqDo0jYrXOivAI49wZKHUu5QO5PWMvEun9CWc7CAdMI6RJYAduMBAbiEqMDsmViFwmh7u/IKeEFIM5OkqeMCahvWBzgGiJdlHd8WVutbuE93VArkUIyRImzzhXm7F4wpQZszCm2nqzs5giUCA3BA3U+Kq2Rm0kQym2CjxSARbzAbag2VKKcpkzt+JQLQz5gFHB+UiJhZNC0pbPz0d6KUiCcRrxKtzlQmfrjC20gk2XmCTcYZGUSVQTdrUPCK1aTDFEc6KYHmM9okpjfg3JADGdH/+spQ6wzfyAVMqoBfOlDYcKMCr53eZirCXP/gmoLRtVnTcAKHZuFzlPypU4bKyi4phUaUwkZyESMdbgghDMA2R+jl1VASnkubVTq6CKbNd5Y/Pz4pZr04BJU52pGAPzINH5qpaqUl197Gs/tOFQRTJIsniTUetI+UhnHHR1gGmzkI0gGJwzTFOgMZUtJlrFDmIN8NCHthzCgIonq5CKcgxjFQ6opVAq2jQrkzW0qWIsSn5XTVR0TamtuvKukjDGkv9lnYqaL72mIGrf+V9MjSC2rkaoGRFyrsPW+TPClyCayYa5rVgrU5nBf2CJUtH/Ygu+vKtUsqsxBiIzY42ab5MNPDRZxc5+nfKwaX71W811P9bjiikYIlkKoVQ0yM0dhJQIRaiN2BZnIiVnimrt1xdDkfg4N1SpJ32LxUjAW0vjczXS2htydKRS0FI3YX0oBcP4/3H3Zr22rtl912883dvMOVe3uzrVV9kOwVYACexA2CX8OgAAIABJREFUIhv7mouEOwhO7vgAfAQ+At8AJIQgFzQSEkhBKChIUWQCEQi5K9vlOlWn2c3q5pxv83SDi+ddax/DJSWdUk1pa6+192rnfN9njPEf/4YqQlpGinMYElYcVtoO02hpjVBp+8cY2z1R5hkrFmSFsrRmwVkGb1GdSKcjVirFCZIUNZFSDS5E3H4EUjNlzQkkIsFS3YrsLIwFMUvLTT2vbXczWOjBXTj41gpdaIXmzuB/ujRdz9FjbIFUsLWiZwfOYDSiy4BWIasll7arqbVuUKUSY4/OkJIl1oIi1OqZU0SrJWEpxbCS2/slY2jx5jlrozhrayaLUShQTWnXaNLWZGp53gWVCrVa3tf83FTlalCp1DyQpWKcpeaK2gEpsWnmUtqu7ebEsdSK2g5dUzPJnBMFSwacCawlN7H5NrkU9eQqpCeykvn53gu/EIWGLZUR2pITTc39FzZ/rIIplfLc+TZ8VJ+KSK3Y0jrjFjnSPtBay1JWADQ1O41lXVqRMAaqQ3RBa2U2FmoGDKlWErWxNb4SXSxqUGMxZPI2OVSaV5hucE7cDrQKLReigDWOTMvNGYIlxkzYJo2c84a3byw1K9gNgghesRaQzGD85hlWMAreQ2MyVGp1BNcmoa5vwWZBbEvN80KqylrbjurhOIENxBTpnHCam6cbWghVmFEGraxeMaUi4qjayAUiZps2MtaZr3TN8NUxpt0gGTFPJIAnaO0JdrTbp6Rt2hDgK8VLcsPit0V+C2mSj8t/eWpC0rPmRLM+58pYayHX9r6uKNrMD7Vi0kdq+9MmXbfMHuu+fq+zs/eg7Tm3rkMrmKAYDK9Gg1JwtqJlbJMrkSqKl4yY1gShBuMyqMMbWtPhUoM+vW/hVtqihsXMQEWNQyXyrDkzBXzF1BUQ8ECO0K2w7TSN3aZGW9EU29cZPUjEeoVeoe4gAP0dIgveHNDjA/y4Il8ENM/0RdtEs66IWxozziSkXlHXhIkejgrRYlzf9psSUBEkNAeLhmz4RtzJuS3/174VHdoZIlmoOROXC5aSiGkkVaEg1GIpmht7tTqQxiYt2ZFqI8Z4BPGW85JYqm+ze6oUUVKxbfpSKLVSiyepUF2hZEMpsG47x8q2Z5RWaKoaquZGSpDUdltFASXVJ4V+u2eKgVrKdi5NrfmuLehPTNtnF6SRdiShmqnFE01zaM7a6N2KByqJshF0LDFHUm1R7/+fxvH/5+MXotCIqeiGnbfkeFrXshWEWhsTS7Yqb4xQq8UYty0ezUaFZku+bAiJiLRwBckIARGLSmlaGCeb269gXHMb8G5AS4HgeKJ4ibWt+6sVY4X6lAIpzQamvd3+iNDihFXpfLPs9xiqVBztYs9YQvDbeK644J73FTmXtlx3hmWdsaZjUUOQFvfW2aZSdr4RADprmFNhDEKMBUSpWui6jjUXpBZKqcwxEmO7iaxTSm7MrDVGcm104IRtO9taiSqUXFEEkbxRnJ+gq+aAXWt9LjQi0oSV0mCxJyJA+/dtd7MxcoxIM++rCSrb7qAVD7cVqJbH/HGv8/T9ZGMi6maVwxZJkKXtdVQb26zW/DylWNtemCcdSKVgvpIC+jSxiXlK8fx6Hz9+O+Ocw+iCV7+ZZCohBJY1A22KxtzhaYW/JbxakEhVj7Vtoq01AqZt4amNpKHNzsiZFkcgeMSB6orRdh85r+jqEN/or2vJGG1PtjFtR/pE2Gn3o2/3ZO4QIggE57AVrLjt3j6AjO1gtAeEnpgCcCKZdn9SFqwd8bbZ4BvpUVcpUinJ43zFcqSEivO+3Xe2NL2KXkJJkB36UJHa0iJTspyOkWXpOS/CnByn1TJloaJkdVQzYbOSxVOLI6mgJpMWJRbIXmGBRdt+VHJHcnO7/4shs6NKagaYduSYVyxCqh5jOu7qSiKwLhMV5TwVjnri3WklyIBxhVU7ALqQydFj+0Sq4G0grqWdK6UQnKVaRbDMKbXpRlrmWyqROce2p4LWIFaPshKB3l21kLa6YtVgup6ywtWLV5wfjnRjwPqOahPl50yM+YUoNK1Lf+p+y8Yyc1jT1Px164hDCNtYu0ULp7l1+TRNR6mVjbTWYDLrqbq0jlkqtcZn2/pUctPtuMY2cqFZn6uRLZSrHaBV68d0SBRjt31QffrZG6SnWhGFtB1op6UVxepMOwRzIaggRcg1E5eZm4vL9nNUWEsmBEcqhZQWnHPEnBh8YImxCUN3ocXXuo4QOqS2ouIdxKJIrUCllETKCWcsyzS3UTpGxDqcc5zmM5i++R1lpRsO1HlurDVnMQqdWFLOGBGCt5vmBIwqqUTEGNxmxl9LeXZodkgLGtsmHWNMU7lZgwiQMuoMFnnOm2loTWONWTUU23zPnhwaikDY4DqBZkcjT/HZilTFeNfgopq/UgD5SFLYXAbaXq9QS8FYj0ExziO5/r+3S1/L4z/+Hxy9P7DEjEyJi93ImyuoNXKaDdei/OA7Bot/1nQZ06jfOfWoXdq00gVKFqxZKDEx+CtE1mYXUy1JF2p+yVmVtGau+oR4S64VFzukRHCNNCDWMNP0GI1YUjDa7kU2CJg0UxG0DsyTQ0KbrFt2XQEcKWmzQlFPHgxhOaGLEv2AD0KdBe87yqocsWhW+lEQenJaiTHixxvmubmjT1K4CB4nPSErp1o5x8roYS0zIQRCqLjS8y6tOGfZlcJUJsQ4ptSeu1MtUM90xjB6z2KUG9uzFuWc7/h2d8EqkNZMDZ7jY8QEy84XhhrZ94WjKcQqDKlNHWuZeFBIecVYz/sc0QJKJtKRCRQvrGIpKE4NBMepWvAOp0Nr+FLm5avvkGohLStJK9k7BhOw3oIovii+Gq58JJ4ysndk7XHlxGNaGXTgYbqnHw4M/Z5aCiWvhBp5m+5ZHu4ppnB3npCN3ZnzL+NEQwW3VeFaMWIwBnKOWARnGhWYqpuA6inHpDFYZIOhnBPQTNW2t3iCYlSVnBLOO2p5wvtbwfBGqbWQVsW4JvAEsBv7zDmLqmwFLraWWQveDZsg06AmY6yhZEVrO9CstQhCSivOGELfE0TbhWEF6S3n6QGcJfQdZY48zmesGPq+R6yga0WcEIxF1dAPYStySsq5hXbNj5TQoVT2+z0lrohWilbOc2z0TYTdYd9ozOuKauG8nJsFSy3kMlFso6aiSskbRFUr+St7D1Qbw08VTZmsDcMTMTRdeaMdIwJPUxDNykdzapBIAVvLMwHgiT2mRpCaUWs3ajXkraFAIJaK9+33L6UdYGrNx91Wis8kgraXap/rrEWfpi/d9FfWYI1Bq2JUyPP6kbL+NT+891hJXO0+oYRHDMpDciiO2ZzQGPjiJ4FgLGvJSF3pe9eiycUjXFApZM0425FKz+3txEV35OXVK1YaXNsQ1MQxJar2JFMR7VniSpy+5HI38uHhDqtsU/QB68/c3yaurq4IfcGUlWIbE3LXNRp1E37uOBdhMI4iCe8961Lwe09dF0QzPitmfMk8rlxIR62Jh+4KYxyyN/SlOXEsupJrwu/2iAhrWUld4kjmwve8Fwi1sA89XYZYCotrOp48DNwdj0Qv7HdQy8DbHDFyRfSBrkqDwGtjxOEtn6aKT5V3VjBGOew+4Q+p1GqYyW1Cvu5AlxaPUAun45mLfuT2dGSuK2WNVNOKmDeexIrHMzgD2w5lLQXvd8S4UNUx9NLEtkm59EpcH+lsj1h4/9mfoiKMoWOpylKUORhubE9SiGlhP1631z8V5vtbanGcq/KdK8OaHYeLSwqJsh7JNVNy5nE5Y70SvOXKdG2XZxuE/7D8EtKbm8CrHQzWmL9ygOhXltFLPv+VTrmzzY4MZ3C57WgaxbZBLcE0Y8Cn4K24zLjNpr5znuIK1QhLbMvTWgrueezU50JVNprkE722UZIzVeMGCzVtTZEVa81zsqR1Di891ISSKTUTjMP4sS2ntZEWnnYqzgnGN2bQPnQspi2BsYJV4fbxAYAX454YI5TMi6vm8EvKPLz7EuvDs0faxdi1XBZrGfuRdHfkYZqIqaCuw290WckN7jqn1BbCG4X4yeL/SXdTtsA1LS03o24xzVojqXw8qK0YnmKYldoKTHvRAKVq0ydJU+U+656k1PZ2bVBW2TROTp+gzrXFSKjDkKk1PF8PT/qap/f9Bl0+/V9b9gvGFTBha2gaVh360PRH5eufaN6eDEkqXZew0tE7x/k0A5azHlr87rLSba/FpAdMFPZA7XfYkjBVyKFFf4dlIQk4P/BHn3/Gq/5A1UgeBmIVjDgONQGFx/UdX57vGi14tvT7Cx4L7HHIOBCWym98e0d2lYe58lgL3y2Rgx05ns7EvFCCpcwrNVdWZxh84PvDDrxFtPLndUKdIRllKJWSM3VwZBbGczvoQi04PEOo1Ji4dhVvF9QGPrufGZ3jXiwhFA7nmX0/8KeniV1ZODtLWQxRLDdxpfoXrFRWXeisIs4wr4qmlWx6Yl4wJTIBdSksy5lk9qwlEiXyFsfFriNkx5pm+jBAXrm5uuaUMyZXLl58ExXhcPGSpVZSXtu1VzILicHuSaLEdUYFXvSXJCeUeGbsDpyWyOuuMkfleFEYzECyBmMtXcz0xWB7wxJXOvVcWIOI2eBFhyMTi3IuIAeDNTc4hVoS79WQPJgCVRMxCAFPCIGhVq6MbS4PNbHkyuADacjc7H++heYXwhnA/86/ryJN3VpQjHjKJth8Wu7qdiCVUnCbQ6+KPLv1BhuomjfsPjzj9Dm3z3fOEKsyBEPJbZpZ0sJhNzTVfSzElMC6RhpYGiOrc/65o8/ANkIBIFsRK7kVHO8sVVs2vdn2CmHom1ZkXbaRNDIODY/1NLFpCOHZcfg8Txx2e4yzaErbIZ/QmrnZj6zrytAFBu+gRMZxZDlPjENg7Fvc6ziOLEv7fphWOOclIcGxzA26+vAQKQLn88JxXlg3p2iVlo1Ta0XFNFhwe22oDeartRErsuZGPZaK+4qQtXdNgQxQUtsHiWyq86qkqk3zUM2z1Q3wlb2KpVAQbewcZ+zGxvlIbzbYTcD4UU9ljCGndft+Fs2l7VB1s13BI6Y8u2I3UWj7mlYbFb7+wX/6tao2/86/9rv65ADhU9NmqNrWfXeWP3r7BWldceOB3nnS+chhGHgxOuZSuNi9oHftd5/PE4szHOpKdgPvTUVSoRPLGjNSEkUs6i3f6X2b1L1nztqU7bkS1zNdN+CSY98r1ndIXblPhb7z+AjVOfp4xnnDQxmp0yPS9/SSOFgDzqApc1tWbu8eKK5dxzU0vVlHi4soXU9M8PrQ82IYuT/f8rNZSCKEuqOaW/r1hK3gBdY4YatlDY59XRlurknTwkvnKaJ8GoVlPtK7Hd84DAR3yTudiOcVb2GtkZd2x5sx8IAwTgu3Hs7Sms8pGZIVck5IeqCLlYu+56bvASh0dF1HSpkv3Z7OOpI3lJgZ7cijWeiL55gtIoWqmc57apqxYdysfwq1OErMVGfIUlmsslsbQ/WcM1YzqWsGPxRBqqekRmpKcSJawxhGjDQIu7ONAOHWIwd7wHSeXRByKUiOODxxO8PWalBniDWxxMhgm8ln7h3/5F/8zz+3e+EXotD0v/cP9GmpnFLLYBFnQRtrrPfheZp4sipRbaOtiCPnp8NsO3xKQqyH2phqwdj2MaYVEVNbMFJKm9Br29n03Uiq7XAjp+Z1ZIRcNluajQEiX+n2W0Txk2K92VEYKc+wnLFNpGlsJaVECOFZTDj6jpSXZqmeI0Y8c41cDrv2tVIEMp1Ysq0cuoFhGJinI33f41R49/4zPnnzCcMYGMxH9lrf9xhviOeGbeciaPB8+PItqTrmqtxPC3bs6UxHwfEY1+13aEthbHtunyGp0ux3Sla8byQL4KP3W2osMnWBMp1xzpG0YLYC8yToTKWRCoCPz2Euz3HPT4JMLWlrJCo2WDRtWTOmWdgg9XlaqbVixTQbkFLwPjSh4UZCUC0N8lN9bmBk0+E87WacGOI/+3oLzd/64b+qaV5AMlEta4ocxp7LcY8xnvvzLWM3okbpadb+tutZJeDLI6V0WBe5Py347hrKCijn5RZcRxBLZw2kiDjDmhMxJozfbGow9D6Q0kq1ypwVyULnDTuvfBIO3D8+cLi6xNUVR+BeJjQqcUl0XbN4mZxlcO2eXLVQrGNeV3yFx5zoxXKWxKiek272RtJxc/kNclGkJKoPfHn3js513C3vuA4vUefoRBglIruRPDke0+2m2xnx9mK77oBcmDwcMNyuR4K0tFcrrXl7GQKfL0LwKyrNSNLE1AgYAv1aUU4Mu1es2hqZUFecHcDMUC2z66nrTJ0i+77yqVq+Oe7Jx4lZhDF4dq7tPM6yY0emlMqkK6MfiXFlFosRT+e0PV/zQnGOzihzyvgwICRqsfSuAMpaHRMNahtdc/RI20qh2d0YVrsQqUiBgYCuC6sFo4G5JrwGfNc+fskZKwarSt7cQf6PH//zX65C4/7231OxH3+npz2EqiLBNSaWbWaOOAs5kRTc0yHSDPAbm8zaZ6+0VOKzdsMgZGkH5JNHWilNuHZzccl5nhl2e+Y4k2rBF8MUV0LniFNuy/l43jqRZkbp7KanMZBzJudM3/dYF0gbXu1NS8ZbtWCnM7v9BVLyM6W2iGnsEDGYotsCs6fvA3NJvAw7sq7UnHBGMLkincUqXB1GOusIm1JfNWF84OLigg8fPuCNcH983NhegVgbAy6uUE3gFBdirozdji+mqRkGbsp+a20zDjT6fCjXtU1WT9Nasb4RLiiIbgzBlDdfsU1smSKu65FaWOZ5mwbbn2YJJA2SQ6gbfPokYH16WNvMGZ1pzUiMzTrjqTl5Zifm0gS1pkF3jczRfg5rHLpRz2XzfmsmkZvIdoPZ9F/8l19rofnXv/2rekkr4kvNnGtiVw2j76gB5pgJ1hLrwt47vAS6UsBa3p5XshaCEUx2dJfNQPaNdRxz5PLimhoTX55uWXB8N+ypVrgywo9Pd0zV8OVyZHQdLzrP64sL3p9O/LXDBX0QOgv5YaUbeu5SItvCKI7iCq/dNfPpPVd+hxFhrTMudFAM3zkU5lJZ/cD5tLIv8KiRP596ToPyK24k58xjnDl5YczCg458dnrkathhOmEfXrIvE39+fM8PDwM3CNPQsng+yV/yq7s95MLnUfivPhQe5gUTPFfDjk6F3gu71O7X3h0ZgsfLwlVw7M2B21woarGd5WItnErC+cK5Fs4TFJsxCCPg3cgfPbxjNCOrgzifeW/gMY38G/sDfzx/4DZOjGFPwTBs+rm3VenFg1cGVRZpjDL2cIVh8IYL4PNUiMXgTduROqN40yD6nZ/xKlzqQF+Fz1LE2EY0chucNhrP4kDMmWsCaDtDTVo4dXAVA3cD7ObKeaP622qokrHGUQw8qPDf/cn/+ctVaOzv/n212opAm1YUrCEuC9IFfBWqaBPgBUdcEz44yrZwFhModcF0npIzznTY6ig2PsNe+25gmiaGiz1pWp472zVFwiY4rGuhiDDsRkS3QpQjinuepIxvNi1m2y+IFJwLf6WzrhS87dshSMZqwUtguOopSwvb2nU9MUbOeSGEwOPSHKmnaWqShRJxFYbrS8gF5wyddwTryDXx4uKKYJQ4L5zmCeeFne/agnI7eK3A5dVL7u7u6Pu+7aXUcHd7ZimZ8/lMLJb7vOCrw1rPKo15BI0Ndp4Wdrtdo9cuLUY4xm035UaqZmqKGN/gv2eh4AZ7ifNIydS40g8DKxVTpTlBSzvgfQhtsticttOaMe4jLNo4FJViG1nBaPO9e7p266YRqrl8hFxz2V6P3F4n43DekmtBU9ng0sjm3ordil76p1/vRPM3v/1ruuTCRCGVismZv77fcTkGJirOBsRU7CJkSRQC9zVyrW0vpSUSTEeyBikrQRrVN+w6WBPBtQjfY7mipHvi/Mjse+JyItaOBwlM6YFUV/rxG4ziN+Guab6Dm6NFlcRQHedNHDpqh46O5e6Rfgz0BJJk8jzhttfpYCwaXNtVjgM9lZ1arncHdsYz2yM5CnsjUBOjVyiBl93AahdUC4dS+e6w8JNz4OJww3Q+8lIMXK9omvn8/hKThRduQoPw6YdHMkOz9M8Lq8v8+WNi9MLn88w5HPhucMx14keyZ3Z9u4aSo1hLmCboPX+9i3jruKgrP+yuyOUdPvR4Onpf+ckMf3memKPwM1am05k3YeBFsNxVy69fj1zWleAHJE+ob+uBok1Lk7b7YUgQjZJTu35X2+y4Btd2osRMMpZUE14MtjgWjTi3oT4l00jtlsUqQuC0fawQWGomS236HypRDH5jFqZqQCJemr/af/1nf/jLVWjCv/37mupm2FibLYwx7hlz997zFBXivaci5Lg29bQx2Go2Qznd2GfhI5Rl5NlAEte8zpxzdGKZcqTf7M9FmmZGVZlKgjk2wSWCDZaQmgdZrgXzhOt33TYBNGGj3UwsY27MnoAitrF2wkZCWNbE0LvnZfW+Gzgej2RRejWMY0/UQlki9+cHvPe8vLpmCO13Oq8P7KpndxhZ48LFxQXrPIFUhi7w5vqKh4cHcoWxDxx2F3z62Vtev7wiabuA3t89NBZXv+M8L0xReJzPVG2xCPfHTWNRcmP0eYusjShQtE0fOWd812FoAU/xucDQQsm2iIRm+GgxKOu6YvqemjMYxbJFDtRmMmo2KMxgMd43BmGtGBvQmhBrcGthNQ0eNbXNsdY0Sw0tFS0LPLlM86T9MNTSpjNR0Jxx1lEEvLWtcG5st/IH/8XXWmh+79f+tloiVgxfAEOqlGDRaujMihro5sq4GxAR5mXh0Hfk9AQJlsY7C617v48JYz3J2EYpTwXrNxsmFdaaWdRiammef2VlyOD7jkQgaSXUJoz1NBKJIXIxjsR55TFGgnT4IJw1I7XDVKUzkVQqO9dxkoxgcWniph/4IrV4jMFljrGyDz0xTQT2lHxGbUt9XGvc7l8llkgyDl8zISmPxWK8UojYomQ6siiMO3xu7NRFwVlDpVDXM3ciXMuJtGZ2tlJKpR/33C933Oyu0HnmLIkuO6YqvAiVn8ZCzAYvI3t68uGSYXrgvtwSjDYYD3jpR97PR87WcF8SsnvTcnrSTNTEtBR6zZgKsavNIb0a0ILpDD63uIE5KzFLg9VFKTkTjLJkZe87lpzwzrTmzbbiEqSjGuGsK2Zrxqy6RpCtFpdbhEfxoRWUZaE6g60JIx3RV7pqt3s2IMERQs+PP/v5uTf/QhSa/vd+X5+mAe89KUZshVULu27PktetiGzU4SeSgG/L75rbIYiuONdCxZwfECew+fs8wTGd2Lansb4tvmtlnmc677DWkXMixkjYApx8N1C2Li5FkLqCGoZhYEmRmjKeTEwT1Q5YawnO0jvP/elI34+MwbOmiGom2FZM1nXFbDuEbAy+NJv9UgrH45EQHOc1MjrDqxcvAYjrypsXF9zfP/Ly+ophGLi/fccwDBxC4NMvfoqzgaF3BOcYdgfuP9wyjiM/ubtjcD3THDkuE303EivM68I3vvUDaq3MKbPECGoaqw0DzjJNS/O+kpbg2yzrN9GkbXBZyW1yoPOYTUArIhjbt6V8Xp4t/2ttjgYaC9Z7StzsRBSoTw4E9bnwG+OopSDmI6wKrbDYrmswaIofdzvbtfQEj1LXZ+IGarDbDk3VUtKM+GHL2ynkP/iHX2uh+d63/4babSfpMHSuI6eVsxR2WTmlBK5ZtEiu9IcDnfSkEjftWMB1HqMbE08EJ0pJldVn+tpxrCtjMYhRzNixzgu1ZnzXU+IM6hBN9GFgjRNOLEsI1NORTgPJLBjj6KVjlQYZD96Sc6YrlShtOq01IdWx2khnHFIHap7odcHbHg2V4XDgh3bP4/zAzW7km53nvCRedoqVgLUrBwzOBQbXMpHqdGIc97y/m7iogdsKR6/85PjIj1bltRshFVbXo2r5ov6MA4YzI3GtHLvAxUaSnHNszc7SsrD2nXCf5rYU14GB1nR9YOGagMaZ3nmCJII6LI5jOjP7SjWWQ7VMVbEUKoYoC6YGZs3sQmAtypsamoVN5xCxnEvCIaBbftbgmWixBKVGTAHn+o0yHXHZU82E1wHtPRaL3zRsE4KagisOK4VE843znUPWTKyFVFvzLKpEhKtkMMHgbH0WSfdY/qef/uiXq9Dsfvvf0yhCTgnrAmIKuVYCBsS3hW7NW05EE3U551jXtTG20rrZdWzLaOPRkumCAXVN6JmOFDMwDB3LsmzUV4P3G+OlsDkO1KasL40hpjlxeXXT8tlzRHNBvG/FyPnG7U+Z3hky0DtPzYV+HIgxMk1ndn1H2O+REhmGHdPpyH5ozJt1PYI4rq+vOd8/NhcBF9gNHd1uwEyRsBt498XnvH7zktPpxOU4MqWVw36HxzDPzcRQSuZ733zNZx9uubi4oO97Hk8n3r59y25/xTQtLMvKy5cvwXU8nCdKFe7mmVoM03wiqzRnA+tYYrvJVARNTZW+mZNhNndfFcNTiBy1ghF8MM8QqGxkCy1PjgoC4kET3g1AO6i6boex7W1rPMs6NfaeMeTSHHWt9RvTbGqEhZpx22tWVPG+J6YWQ91o4u3HfYI1jTQ6r5aVzcnx2UiSUrAhkP+3r3dH82/+yq/r677HGMMeSzBC5YgPgS63ZX3IinYdscJDWlg2XUoOHb5UjkU5hJ6iD1S/w2fLRMXmQsngxkASwccFEeFkhDE1+ObaCcY4Vk0c1syxRO5NI8VUN9IXYayGWSZ8KQwusE5HvnQDD2vm+zvDZ48L177wIXseE1TtWPOX5NBxwDNt5Asfp+bH5ytuHihOyWUG5wm+53X6gp+VPS96w6/liYfxijI9Ypzj330TePNGuH+45Nu7hZ+87/gw3fJnC0S/431c+VaN7ELPty8L3zmA7a/R5RbmivPQje3Al2yxdhNvrjt+Nj1wqZkvMNxIx364ZJqQAAAgAElEQVSI2DIQXSRUjzET89nQ9RYthX53YKkTXfU85oSqxWNYn3JrauUhL3S2w0gjKT3ayq4EYl4RL00Yayrn7MgoQ1q5N1dc1hNvxTYm50PFdnBaZ+xuT18EOscpLo38IB4pitVKroUqPTMFJ4HVrByKcDYWUyIPAjsVTn5kXBaKaUbAS23TVkX4h5/+khWa8Nv/gSYyXgx+6+Bc13Ycy3xGbWN4iXfUlJC4YsNAVksYBuJ0pg8NNkgpPcNSUhPYJ61F66JzzhudVcilEvq+TSfzjFbBdx0ZZd8P3N5+4M2L17w73iMiBOfJWOI0tU66JqgV10J0SCmTXcYumfFw0QRbteI7x8N54np/gZOyKYwd5/O5TWZasd3YgsPiiYvdHpsrx/OZ6+trTucHjPX42phrL68u2Q890/nMt7/xmvd373nz5g3n4wOxKu/fv+fm6pKbfsf7L99z7g0vLl7x6e2XhETrpmzg4nDN+8dH7k+NIVayIF2Pt8K0KvvBcn9/z+Fw4Hw+s9vtOM+Jkias6YHaqLJ5JW+WQYKl5AVnmjLcOEvVtv/QUhBX0ao4Azk1ESjO4Kyjq8JZExI6TIwY2yN5bceSdZTSwrx8KZSww5hISh890rxrk1CH4RwbU7CmBek6pAoY/wyvGmNQUTRX9mFENbOsE/mf/zdfL+vs135Lg22sxmYRVChxoaJ09MTQII9SCuJ7Us5IMOw18JASnROyGpxvprNLXBAglqb56MKIk8yxZLS0vaiqklKk6y8pdqZEAS+UNJFPM2EYEQIxH7lMkfH6skE2RG52B6wm5my4co6dGGpIhOx4Nex4mB4x3YGqE7VYPqC4lFnXFa+QLPSdZzWel1hSrPxUI9Y61qXw6JUrdmSpSF75rX3g73z/xK7e8kkQ1iEy4LmXHXZdGIcLSpoJ/SUfvnzAYVjywqfHhe5wYO8gxzb1DmFgTpGSmsN4KtJcrqvy33965Ae7Cy52FpsbBHndC9VlvOyY18LgBWokVkOxQl8sM4XqoDeOh2llsB23ZWVvA7mCtS1uw4vhtEyI8W3SNx4lEXOTTcy5QaGn8yOEPViHlomOZnidTEeOCesdMResgzmD15ZPVFVAlbNmclGi8+iaORnHTmgTkzpe2MRb9YTaLKwecqazjSr/j37yZ79chab7nd/XUhMXw445NfZPis3RWTfHXlSxxuOcexb+YRq+3ntDVUOMKzUlfN8in41vwVgpJYxGfH+gPoVeUTG1UGqDrHTzhbrYX7KkyLI0eCCntXUhQMqJbjfgN1HjvLYgItP3jZ5ZW+KmswajdaNqB2pJHI8PvLq6AVWG4Hl3+4EQArvgCLsdp9MJYwwX+/b1Y8nM02mjYzpuTyecRrquo2Rl6Bo0d3UYca5NEN/9xjeosU15GMdnn31GrfDy+obzGnGh4/Offc6rT77Bh9tHbPCk2vyejDE8nI7kqtgKbNDhE608p4QPzciw5tQonl6Ij2fMMGDJm0u0ABGdV8S13Unf71ji0mJ2RbHdiGCfdU/OuUZ7FkXWZpb6xHobvGmaIO/R1EgITR/TlqedD8QYN2GrtmV5VfJmiyNAihFnG6Eg54yWjHi/5dk7grVUjc1h93//b7/WQvPy8rX24qBz7MVxGB2B5gc4Sk/MkUhljg0mXvHEJDgD89xsV0zMuN5hi9L3lZ0Y3vQ71AY8GScgeeWiU5Y5E0Ig1cKHeaEUy83gEK+E3CyLVmsIVpmi8GG+5Qf9vrE3t92X9XAdHMdYeDGMfKgrp2PGEkl4WB65DwdeacRpITnPHz/MXPY73kfPVBZWEawbqOmRJS1U4xG7R0zhbC3h4W1rZPyIc46/pTPVwJ8uie+Pr/jdm8S/9eL/Zu2vub9/oE4eUqHbX/HgDG9eG9xbZbKGz+JCnF/xoy9/xrC/ZHe44rVPdAjvc+Rmv4fjiWibs/K7esN//u6BH0qgSOCbOlHGjj/8/At+85vfYZ5Xfloe+afvT3w/dPxg94bbsvCqN/z5h3v2g+e8VN7WQqkLv34zUOKImsSNVV6PBx7LzE4Kg/YsXaCPM1MIjDHBU2CfFNQGUnHEkulspSLE2rwJJ2ubZi9HJvFU2vk3WUEqLFJYKxTN9Hg6A6u2DK2zFHZi+TJNFPUE0/G//OSPf7kKjfmdv6cX3UiMERd8iyF1LdNhNMrpdKLbHVBt0IrktFmeNBX8mqe233EDwLOA0hnboKASudy/4OH0gWACUiPqKjW1ScJmxfaBJUWMd8zzjC3NAtP7EbZ4Aj+0Redpmp9FpL4LjENPTopYJU0JvEXXBks43/Jqzu++YH+1Awl855Nv8PZ43zrxeSW4VsjmlNjtR3rfMU1nRJSry0tEm4Px9W6HWMPt8Y7gHZdjc/BNceGy6wihseiurq7QpfD+/MjlrpENTiWxH/bUYjiviS9ubxn3F3zx5XvGi0vmeSbnzFqgc55zWrHGEKNB15nw8hJdVrQa+qocTUFSJmuFNTcha9dGd2rE7m4wNZHKR88k3expnoPLtr9r3j7fugZ7OdOYgGXdRJXNJbueF4ZhIBlDjjPGeXofWGtue5sccRv1OkvCi6PmluUi1lBqo8nbrQkQo21ark1/FcQx/8HXC539h//Kb7RY8yKb152FqoydkGOic13Taa1LcycXeH3Yc4wRrYZchLvjSraeS1Pwg+c+rpR55VsHgzHN7t77gThPfPPqBbPOvHuI3GrPbAwPywm3RK52B2qaeGDgw8M7xqEHo3yDC26uPJ+MhfujYeiExzwzesWwR0PllRh2Vz3/+E8eyRiqTTyskVkbxXfSzGmNJLXUmtm5SjdeslPDq3Viv9/z9nRkGD13a+Tbs/Di1SV59Hx5f8+3jeMfv/ucw3jgrx1e8JPzHXk98b3DJbH3uJhYVmHWzFQswWb2oefT6YQxlQsNnNf4LEQ2tiXKLptx700G3znu5oXLYUfoB9KasR10BQKVd9VhMCRRHnMiqWHJmWpgXVdUKycpXLgDa273X86RYgx7UxkU7NAxGs/DcqaURqoptfJJ5/n16wOXKWNZqUV4J45eEjlXTuJxktiLZ9YWI6+57U6LK5ANGgZMjCy1ySiSJpIEPi0LTgJDKezUsg+GKEKnhfNWDg4i/Gd/8fODzn4hLGg0rpw2Wurp+ICcJuqhpw/XTHWGrMTTkd43h+ZlmaHvyccj4eUBh8GahpdmaZNPzJn9bsfd2y9g2BHLskUxh+YwnAtDZ1lswQ2B9f6eaj2DE4ax5zRNGNNDWXBGscZSTwurrWgq1Gq5uLgixshySi3edbO6z6fEOI7Pu4GSM5evX28whfCzv/wUxoGr/YGpGsLQ8fj4gOkcd/f3XB+a6OxiN7LMjdZ5+3jPO2kXcBCL6Tzx5U1brqfM435g6DzDsOOf/ZP/lV/53vcYxHJ0FjuMTJ9/TtddcHt3z25occe1ZmKaqUsgF1hjasabYw+3Mzkp1iq5s8jpyJwrnQ8c00IxAVNWLAEZewy0ol8KXR0pUhqpwzm8NOHZMwW8FEiZ4gU2CMgZT9ryckSEeZ02htgKXd/igDvLIs2Vuh/HRvTQgpbS3LJNT1GBvOBsT6qKDQMpZ5C2j9O8YrsDJjRYT0ompwa95RK/tnvg6fEbrw21NrJIGPa8f5jpjEdM4iEbDp1wihWzD0xZWeeVn64nutwsjPqu57Vp1kof1hVdE7EKL8eeHx2VUSLqPV08kyVgHqa2QzQ9FMOdibwJ18SdEupMlUBfHvnB4YqrvaVFNaxYlFJH1BYI7bV4sd/xF7Pw2Y8/4x9NC5cXe/Y+gx8wZ2VNkaHfs9aCN2CzQ3YBlwO/87rjL94eedkN1NHxxfnMTVCCLvza9RX39Q5jFr5ZDG92nmKF304dF13gFff85pvCp3c93++FpesZexh8YJomHopQykg/RE59T5bCty5GHuZLSBAlUdKJzjims4Wuo5hGevFXjRr9aBOXtsGHxnpmHKe68sOwtryZ4NiFPWue8YwUcSTvsEvliLIYy6Fk/jAKn6nnZQ8/VI9XRczMcug51Yqsmdvq+VRm0oeVF/uEni2uE/wKhMwdht50qBk5l5UYDMO6cO+hOEfUjlETYT6TsERRrO3RKoRU+JYfoApGPNllHnOmGs87lU3MWvhcf7791i/EROP/5t9VMBhTET9S40oqiTCOxPNjU6Qbwe+usOmESmCNK65r8I6lI0si2IGYHxj8npgW1Dp2+yvmed68uwxUYVkWLi8vOdfMJ7uRKa4YbayuKh7xlpoiBWFOscEECqsAOaNPfmibDYSkzZRRPHiLUyFaIBesZtQ6JCVubm4o85Fht+f+8R6rEGNqHsi+qZan+ch6nNldXeG8ZZ1mrq8uWKaZ88MteMuvfveb1FyIQDzPIJW7hyPfevWK7rBreyorgGF6PPHqkzfc3d0hPnB59ZqffPZTvLf0JnCaEyKGruv46Wc/I/ieZeso76e5qexdR9K20wh+RF2DrUwRpCbWtNL1bS+mVcjryrAbmM8rQ++p2pHyvAko07Oqn5poFCptccX9jqxNL+RqZZofwbX8+jyf2HKLsdLYOYaM83tqmhFpWhnzRFd3tpmcLmcQoRu36IQiOCnPE1UslWBcY9lZg/5f/+PXOtH8R7/5N5TNAaGq0AXDeYrN8LAL1ApBetJSUdeew4c4M7o23R9ro80mwMbC4JQwWKa5Mg6B46p0ZObjRAiOqxB4nyMJSwL6YeC8FtQLuzWTO8taWtbL7TKRfNudEiMqldPSCDi97ai6MNmCw1KmxCmdMBK4toZ/+dUlg0SuusCYKmIKVgu7vuMgFnqPSRGychfPXHWGlzd7lmklVyXHlVc3l9iYqLEidqXrHMF4NC287AeimcgJarFMJ+WwN20XNPYYqax15DEqUkpzBdmkEVNxrEZJVXiMni8fHwh15mer8C+9avuysev4Zu1Y8gkjPcnmNkF0e5ZUWVU5z2d+dDxTjMF0nh9c7/lEDV7O+FiJ2lFEOLOyGy9YfcE/Os7GgRrepkS1QjweOZjaipUoxgaKLzjx2JRAfUtGzYUgBY0tZqSWRBFHI7Jb1jK3Jk6FJEo1BhcL1QkGQwTsJsjWalhozF6/OVj/J3/yl79c0Jn9rX9HTWn+V64fyElQt8UCzDOuC6zZsveF7C9IS+b/4e5dfjXLzjyt5123vfd3Ode4ZKbtLJddLrfVNCAVDVIzANFSq2m1xIwBA/4ARvCHwASJZsSAAQ0SA5CYgIQESKiBQVWLRt2qKmyX7UxnRkbEuXzft2/r9jJYO6KKEROjtByTTMUgMuOc8+211/v+fs+zPx6Yp0uDW4pFaD2Lzt8wvv05/eFArhm7ebLt/kgnhacZ9raCFMYUCdmh3kKZkNVghsBSLkj3gi4I+fJMOB6YHp4wxwPkirUdIkKenrHh8HGMZruenGboDqR8YRc6+m6HsZWnhzM3NzdcTo+YWjhcHShrJGtmzIXdZgDdhY5qLF7gcXmmc57b3YHeDnzx9DV+jISjZXf9gvtd4DjsiTFjqPzq5z/j888/J8bIu2++4Wm68Pr2Bdf3d3SxMqH8n//3z9ldHcFabm4+4TSfeLG/RVU5xdwUANh2MDlP13nK9ISajjkpkPDa4sFRAsQJDBgbGpTUNY5bJmLNQCkZqRNW+1aQtAVcTysYNUkXaW1SLV2hHyA7nEBxYHxPnVbwFm9a/yOWijUeLSuuP5CnkaqZApuSITePkPENH7SuIIpxXStwCh8wzojt0JxbqMNa0p/8d9/qQfPv/81/VR+mGR8Mz0vGijCtE845ZjGYWMjVc9l2Ys4qThzn2IqriZa0S1Q6LcxrQrqOmCe8OrILmM5iJZMvgKeVOkuirAuDFcQ6ghGCN+RScKbSVeEwGO5ChzOVPigHFW78nk9cO/QXL8QYsE4JCFaUZZlYTIf3Hb1YTCk8L5GbYcBUw+1tpNbAGBeureFpztwOL1jXlburkcsMugoqM70LXPdAmXlaAu++ufB4eY/VK1yp2OM1rz+Bq53n/v6JWA9AoD4nHpaVl9cz1juOxZO1IybP42VFO8eb58zXl4hUJXx6z8s+Mi8Z2098cnegXyJ1Fi7nmViFnTjmVFiyxzooJREtWNuRYrOguo1esdBebKx6+tDGSKsKKoZVC5KhD/1H788HVP9Htp8qlVbCTiWi0lFyE5g53eL+tVIsZBy6qUfEGmIR1pxQgUVb8MNUpZiML02kV00b3RfhL1FQKP/RT3/5u3XQyL/091U2TpdBmvVQlTJdsP2+nbL7Adn3DA6mWOnUoTmxPJ8x+77tMaq2klauhMFSs35MrGku7Huh1ICGjScUE8Nh3yK1msFZljF+fGPvug5iJKaCBNfQ4zkz7A4N9CgbyiblJiNDqMuFu/2RS0pM08Tt/QvK5UzZHSjnJ6xWjlc7Dt2BqivL44juAuM4cjwe8Z3DiuP5fAIq99c3PJ4emzXQWRzSCNQ148Two+98xp/+8hfcHPbsbaBI4Yuvv+I73/kMlyHGzLt37yhWuH/9GSlXXt6/4O3ziZubK37xiy95czlzdXXFPC+kIogWai7konRdR66GGM+E4YC3jmleWyihFIyx5JzY7fbtQ7Us7HYDa0rkdUTEEqeVMOyoBmrObQ8XF4xs1tBSaLSbuu3aOkrMqM1NWBYzZkP+pJSocWkfLgvWtANLqoDzZFU641lZGbDktJIKTbyWEoSOPnRbpDkTc/q4G7LWkv74v/1WD5r/+N/8gdo10PWNgDGLo0plniI3g8Wq4rodvmtYJOccMY2tnPmw0vc7gpsIVri5sTjjeTytvLq2FCu8f/+MmB3GrQy7A9+8LRz6jk9uPNNiMXnh7Xrh6c1E13uK6bAUhCuu9pnn9yOlzuxuXpFyRHLlbtfT7wpCB+7CYD3RQiiV62FPpNLZTM2ZLmbywVCnEVkzPhn2h4Dmhbn0eGdwXumd0PsJpxZD3YjolS40oCV+AReBBD7QovdK1YgZryhmwVYL2bYRcK4sqyEnQ6Qn1eZ1zakS8KiMlGKZs6PgEbGcU3t4S6lY10EtnOZC7zzrurDmxLg0fmGwlbqJ49bcSjqxQioZK81G+uFZm3Pc5HGtUBkBh5AwrKWlUD8UzquxLXGm5SOA1iIt5CSK/NURV25hpIxAaUgZVUv8IM+q0ugCpW4uo9aL+6BoT0UxappIUIT/8C9+/bu1o7HqMaGdtnk6EYaelAXn9hgXsH1tI6KUiKVwc3vH+fkZZy019Fjj+Oz+nsfzBZsza4pM5zP2eIfRyHHYsVzOPD2vmPqwQRwjYpXl+Yn+cE0qQhoXTB/wzqKXBeMNfd9ipsduTzWeOD/TU1vDNq2YBPPTe/a3tygBh/I8TqzTc2vyv/sScQN3TihXV8THL1lneHj3yLqOvL6/4+lhwgXL+/MzZjQcd3tKSVxd3TCukWVN3N5e0+8GHh4e+OrXX/Li1T177/mL92+ZcsSujmezUOaZtWSWKXK6PHH38iWlN/z63TsOr17zNJ/54s++xqnjcTwzp5U4PjFbTzCGru+Ypoo6xfqV5fKM+tDUsfOKdsJ+v2c+X0AiKRZ87zmPK5RC5zrm88q6+WdMbl1aUFhnrDEQla4Ksa7EtbbOy7JivMWKEOeFWrTpgGvF+QMpLq0UmjP4vnWe4tJMgEVb5DeeW+ktBKQUFlPRVMH1aImAQFqIddNIl4JzHq21CQ1K/f/4Sf3//9fvf3qLWoe3E9727K52pLm0EVCoVAbQlWo6TG5UimG4g1qo3w30vqPaHucNzjWn0ct7xzAMXM4zr1+8oEqHIcPF8GmXmJZIpzD0K87D1dKxDrDGRi5GHcYvGKe8/tGA91cYUWptFk3sCS0d1lhSsnSuclAIpkK8cBBLXtbW0i8Je3bsbY/dO4IrDbE0DPiSyWtBqsVoQpNt45wls9uUEUVOiGudN+zGzcsK5Qhxh+kmGE7YfARZiUmakrwoPYVFhZpWxnFhTB4RT6IVIsUaxjVTaHs/I4nQabPnLolYMj5Z6jITHOx95abzLHNkSVC0yQoLjpQqUwLnoW44JrP1ZWB71pUNrWU8c15JPuBqo5i4vP13DWQak9CbjmWeyMZ/RDwlTIP0iidXRTcPVyoFHwUoGIE1FYptVOu07ZOTzSgGUyrZG5z3H/eVSX8HNQHyL/wdNblS9zusMTjbE1iY6ciPX7QceRjwpaXC4rJivcc5wdJQ2mIDLnSUJaJGubu5YVVFN6Ad1PYWM65I53FDB0Wo5cKSlUEr+/2ey7Ltc/ojeV0YL084BHsYSLE0Z7vfY+Yzub9CavpLYjKwphY62A+tsBkOe3JMqDN0xmEFDkOP7zvevXvH09MTN9fX2I0GHWPTBSxp4abvOT++5/rla+bzCS2Z19/5jNPzM69fXuO9ZzpdMFSqc9zuj4zjiaurWx5Pj5zPZ37vB79PJ5avvnrD2zdfY0L7//r+j37CH//jP2mhg7uXnM9nUkp89p3PeZ5boun09NDy/evaRoVFoSassxQ30EslxwWxfnuLqm15j2eT1UNeMa7HayFpodbSbhSaSbqhgpYJ7NBGWkaayjlPbdxlbdMTBws1QkmUKjgt5Kq4/ti4Zdre/qzx5HX8mGizUhDXU9bWJ5GtyCnSAKWxzPS27XVwgfzH3+7o7H/5D/6Wzqqsz4mr/YDWmcvG5ht2Ab+raFFMV9n7PSlXnPPEWkASpggmN9156Mx2+03ESyXXM+tscK7jcBeQ2CgYRQ15zTgXgE21oZm8KSyqCWTN5FKopYU1hA96hYRWh1GgFFTBi2JyBUmEIptOPeLoMWbBeMFrJtiGBGq4qUqJBfFgKwxB6S2gmRQzfnvDN51tcpVOwWvzvkuGUNE8oXqDWcZ28FApq8Nmh66eU1mJxbEW0Nwe9KrtwVyyoFiiFpak24O2YDbNhlFpdstkOZuCxsrOuTaGysJS1iZkNIJzkJKSGVA1CM3lVEyL8dect3GYo6o2thqeWUwjVVTPgAHJPDuLakZq60Xl3OLp/RY8GmmsskilJMBZ1ppZ44dRnmXNBi8VamV0SqhCwRLUUkxBSiVSsWqZfUvYhmT5T7744nfrRoMa1ETCOBO9AInoFZ8fkcM9h92R8/N7Ui0Y18RbudtRKHS0D45qbj2crJSu8v7NV1AqbhgaVdk5jDhCf2S0lTSe2IcDY0mYbuDyzTuYKnHvoSrdMm/Mnx1LnLlZIZVCd9hRHr9C/TVpemTYX298NYfb7XEcGbqeOK6I80yXkfz+Heb2jjkmsJlnHL5vdAMJjueycut7xmlkHR/o+p6rYSCdJ0wsnKdnlnXhO5988hGZ8e7hwsE6vBiWlPjOJ3c8vn9gOj3x9enE7W7H67sX/Pn/8Y+5+r1POb974vu//yPKGnn//j2//OpLbg475nFlmp+Z5jOo5xd/8ecoDtf3aFoxPjVsu/dQVlw/4G3ApQsXBY0T2AC6YsTQ7V8iJTPHiOv7jw+rNUN/2LFMM8kYvEzoqigwBMucMywr5jAg3mLEUco2GshNQWx8R991rKeVHMEdHOJdM32WSr08I/sDmkdKVoJvSB8ThFIXBtuxPj+irjmKyAHKxBIOzbPybX8OADUrJln21w5r20vM3XBETGJZE3k19H1gvWSepS2/nWTWZWI6AzrjneUwtKV9LYWbqyOVtjTupNGAy2NE60xcMy4YXt3tUTJaW3+p0hxAJUOcWzCB6lEn7cEHWLXNiGmEdJ7ZQMDE1Ob8YJmnE7c3N+3lJM1YK1jZRp6GNpYzBciYzpFTgmpZF6iuNp6hODRUjDWoKYjTRpjQAhRwFbQi5QriSi0ekxMksMVDXRCJ7LVRz4MJFB8Zi2Jss8ZWpy0yj9JtDqiaIKkiJpEyBFVsrVgcg1hyzMwoTh2dVmITZrXlYgJL5DQncG3BXmP9f+uwfcUX30ZbktAq2OqwtpXLF6vcxsrFGkpOOGMwTilVGHMlG4foSjKtpGtdpZiC3zBYrW4YUN8oAQU4CExZMMBaK18bw6KFJfaMZFxDFLak5m/w12/FjWb4V/4tTXGkmF1bPscRNo87gFVYpgeQDkIAEVzfoR9Q/N5Taf0A1oQ6cGG3eWraEk031hgucOgH1vnSbIUY4vgA3VUjNMeIHzyUSuj3OCBuNIEcC1GA+YLvr/BDTxVYz09bGCCwd5bzNDN0rbmPXDjsXxGpLRm0PyKaCbYlnd4+P0JOXF3fIxu9uBp4df2CcZwxNrOeR95PZ26OR87nM3/wwx+S0srzwzOvP7knBAdL4dWrO87nM+u68urVK8Zx5Pl84u3zI7fHW4oone/JCR5rpKvCL37xC16/fo13e948fM1hf0O1He+/+ZK7u3vGIhwcPD08oMMRa5X1dAK7owttZxI6x7IsH4uXVhvhuapByMhmTP3w5DI4dLfHSVto19IeFL7ryeuM73eN3G2b/A3bI6L43lNjAt+3/0bd9i5pRqxHtWJ9ty1RlZoy0JhpXdiR1gslJvAd3nuCZWPYNQiqWov+k//hWz1v/pt/95/XEjfxXp7o7cAyNyL34CraW/pOCDcNpNlLYNVmdJznmbREPA63TQGvjwe0TuyvA+TE9c2B0A3N1VOmtpfSghht7hIR1tm2Yq5UtASSrjBvyCe3SfCysppC6ASTA3W6YLzDh377nkasFIxVbGx4lSY3b94gHyo7pxgBLxmtnjklSmk3goM1oAljFF8iXQhNvWoqaisaCia0LAlMHx/gEh1E01RJs4NqUGIrOUZDLIZlLYhYpmKptUF3MRWtDvGQxWNMRQvkqpQMVGFelcuUyDmz68z2TGiadZFmh60rFFaSGLRa1MgWsAkgdpMFblimrBRvSKnd0lYMikUxVJNx2kgFkgqnjWpiJDDlRLDNYeNyovHlK4ncFvpqKdm0A1wDxUYs7SZvbEWKb/SAKkSzAA5TmzdI2heUROU//dVvbrlescUAACAASURBVEfzW3HQ7P71f0d9raxrokrGlEK0DicVRejEMK1n+uEWJZNTocZEuLthWDNjSQQbcPs9T+/fs+ssbnfFOl7w+yPz+QlTG3W46zbcvfdYHzAxkdWQy4xzvsEu/cBx2PH0/hvc/kiWwvF4hGkii0docL91npGS0KL0e8euvyfGBbwl+wPm9GtUhbIuLHmlDz1Rm676g4tlsJb++pppPLMPjnFMHK4PVPRjQGDwjq9++ueE2yNd1+G9pWRHqpGaF8Zx5A9/9CMeH98Tp4ndcGR8fuL169d01vHixQu+fD7RVWGuK+9OI/NlRrrAcX9gOp2pojxPC1f9FVjbRia1cBojZgjMX72DzjAM15SaUGvoNJNrpRbImqkxIW6HdQVvWvpsTheMgrMdYjIpxob5MIZBPORIGnpMXFm7HUEKg+u4LAkxrkEBl7ntWOz2kIuZoo2LV1Ibo9aaEeuopRk2WZuhE2joG/EYJ4gP5LVge2mcs7RFrUsF79B/+j99qwfNP/zbP9GpCtbRmt+lHaLDHqIKu/1AjgnfK9Sem5c7vv7Zr3DB4WvgeHcDNjK4AKXSHWBxlbxGdnvPlEaM9NtYsdUDDj7RaWaZ28J6ncF4A7kQi0OqMMaC83yMhUsq7A+BhMOVSNGKlbaziRVMzWRd6RGCc0iNdCYgNjZVhq0cRLHO42xlnjJiAlSLs+0lQMxCzZm8QqozOXqudsK6jjizEoLBmh5rBszQdoJUBUlglHypSD22xXhMLLlwmSvzanmKlTG3kVbNHUUMaixSEzm1A8E420C+3hJXyGJZs/1odBVZ0ZyI1bH3LSSzSFOUV+OJJbZaRG3swLFAMRVbAlkUYyqGihFFawsXfUibBWe2fQ5UUwnSDidRUFrwAKA1vypF5aM5tiC4D0GlUoii5NyIH8Vs73vSYuCFtB1OhoIQTcGqUCv8Z1+++d0anS2P3zAL7I43lHklYyBdKFhIkdkPWD/gJDNpE4W5LpDOE8t0whzvqKp0qeCsae7rmPDDARtTe4t3gqsWNbEhaaxhvVwQHdF0xtgbVjX4PlCXmYuzhOM1WANjZGHGhr4h66dn1stKtz9QbaDrPK47Mo7P2+2pUtb3VDUs80zXB4JzxJzxruPu9pqvv/51w3vUSlcKr1+/xlF4nt6zxEQtK3FZec6FixNuv/M99sOOh8uJ+emZeTyzv77j/ZuvePW9z/niq7d01pC0EREe0sq7b76kvDvx6ruf4YznQuXKdXz2+p43+S1TqXxy94o/efcNh5t7urUyz2ecc5xOH3wuK0y2xZKLY16eCc6SpgV/OBDjjAD97kg0hnx+1670LqJbpwjVlsibG0In5sxgPQtCqQX3/A3RdG3sEQJTnds45vkbQm/JfkDziqGn5KYcFoS6aaIbDdpTiyLSo7UiO4fUirXtjbV+hIAWZPCIQug9NeSPO5tS12/3gwA8REhRqTUipqK+a46fMYLtOf9yBirkRDdcCD8zEDzjAkN35ruLsHt94PLuQlki72b47P6a/iYwnZSkjrQU5iXi4pmcM1H2hP2IhCb029/dcsknUGnVJe9wXbNiJ21E6GI7TvPEwVXaUAYQMCFTloRXi0m26c1zxtMo676Hbrcjl4W5ZExe8bVSjcH3QMmsWai5gMmQVw6HHQe9pjLiReleWErf491ArY+Y2oP11FLQi0E0Y4rHucwyJqY5MV4ca43MUbC6Y1yFagw5W9BEbwtrzPT9jiUVQgCC0snQXna8tvTfWun7vu02qkLpWFLbp+wPjpwgZWHNSg2N3ye2NqwUlZwFrUrSyrwuWAYKleILmUpazbb7UrxCNuCrYGjjNrUG1XY7AaiSEdGmtd8kgslWYvVNpSItXWetbbc/ASOtk1MEFEsytBcFbeNQa9r3/Tf567fiRuP/1t9TXRvosE7PdPsjU23cnlom6rpsGuEI+9cENiujtaRlwclKMT3D7opVhc4GtKxNA4ClrBPWB9CI8TskrXgjXJYWDLiuhcUKmEB8eubFixcslyf8sOd5XNjtdmA8l5SopYE0rWtxZ6krOVcGtdjrgCVQ00iWjnme8WLxh57BO87nMxI6euOx3nJ3feDNm7e8fP0KUcPP337FnRGeU8WcH/CHQ/szbIcdJ37413/SiNCXJ1588h3eTI+8f/8eLZneGk6PD7zqj/z4xz8mlsqfffUlfW1vbZenZ158//u8NAMPZeEvfvUFxxcvGWPC1eb5OZ8e+e7nn/Prtw/UIpBmhmHPGjOV2noubodTg7IZKY2nzhcQxYU9Zh3JNVNp5dVDv+OyrH+lazQg2kCRUuIW2RS6/ZFdNTznpakXTItilhjbqNQGjG2HSl4XiBGGA3RbOiZWfAgfoamKacEE3YbOtSJhQEyHpILpA1ISGI8pzdrpO1j+5L//Vm80/9Xf/76W1NwqFrBFMc6i1lCmqUnfbLPMzmLobKDUTNaMpEI1juA6NM24kLgKARsMPgyMqeC9YMVx3CW67oxzfdMD1IzkyjoLc14JtuOSBI0Tl7O0m40TOgL7XjBe6XpLzTODGxDb8fR0Yh0bJmrn2veilEKwERMGvK4EsyfWkaOzSG8RB0GFYB2+izgb0LoweEPnQaSAjdgawDW8EbZF/TE9+BVNpQFSVbEloKnh7okGLW03so6VMQfO80SsES09U4wcfMd+v6dQeZoNc1amacI5T1Ka9hqIuVJqxNo9nQWlEGtB9S9vGlVsS3XNK6hjlfaMWJ1itI32zQabzTlTrSfXxnOkbIoLaVF8t/VbDHm7xbQDSGtlzdqoF7RYclVLFUPOsSlR1BLVozVSWigTaLHnnIREJIvDbBH0ptEGEFQ+yBvt797o7PZf+7e1bETa4n0TEnU7akx0TpnmlXWZQARbE+o61Do624CLWIPdOhjiBo67juen9wzHezSvlOVCKgXmGY73iK5oXdgP16zJkDesRI0NvNkWdR2+D6R5Qktu6oG8onFFNlqzSsfx5pp5XCmdY7CWaVkJmhHjubu74/F8QjeEfylt6bnbHVAcx11HSoVxmTHecR0GUp75wSevqAKnObVkybxQUqVq66/IMvPZp58Sdh1PT0/M44liHbddzzovxBj58R/+EMXw8//rT7m/v2c2lQlhnTJfvnvDv/jjv8bbx2cuMXIex0a8VtPGdYcdtQih75pCIVUyyjqeMc4htqPkBW9sS+wYD8sF3+9JMW5xZqEKsAUlckrtwNCImEA2FmsNOUacb/PmaBSZE9ZkKoZgXfuZsBYRQ1mb1Ixc6HY7UlywmrfEWLdpClrazVltBVG1hP2eskaME3I1WJTqDMR2m2Fz1pd1Qf/sH32rB81//bf/UBVHSjNZWmnSetPGG3EzkMaCVyU2CTUlGfK2z5QKxm5jGYFgHcZYbJmRHBiGgVkTeU2otF2QMwWPJYTmgxILxiV654k1Y61jScpaKve9o9SVFJX94ClqWXPFSCUuGWeapsPkSsaCUXbG4HxqN0cqYhySmjVWRCg2YavBZsOaIr73qDUEXwjiebrM3HQNmfT6usNaoRsScV1ZJuFxmgn2Cq0eq5HDTult5ZIDl6ljSplYCsuy6StMIJVKlU0drg5SUznHjXUmSjtMBTRlFiylGkLIuAJiHYUmKVtLG2tmzYQkZNd+D5pyg7KVLV3jka3abjzOWKoktNoWipDWIezVYOymv3CGUhXJUMRRtAUQamqjsaiNYPJctZFCaC3/nfF42UZ8taG5irR9dxWFaMjGgZnxmPb52pw20Ay2/+B3bXQG7apcS2FnHFOaWDckfhJI4wXxBu8OoI5oFGcM63zGWUupbewhBWoemeoVPrSCVY1rQ5Bcv6Z/2XE5PVLqEXDg9+T1hBvu6EPTSM/zqT0Ic4Ikm4/Fcn99w/nyHukHclFCZxjkwMM3v4D9PVfdQBGDW2Zev37NV1//iml0vLy94/H9E92wkXDHiZd3t7x7fODrX7/l7u4FTgxxmphzIZH52a/fsK4rn9zf8vz0hD8cuH9x4OlZefPmS9Zl4hdvfgW+h5r49JNPuD/s+Wc//TldcFxdX/NPfvpTbm9vefU3fszDwxNv3nxFSokOz1//8R9wWiaEROcdueuIRTg/P/Dy5SdEXZnGmZwW0jq2u0spkAqaHf1eWctKigWqAVdxXUdax7ZkzgnxA0PfsypUwO1uyGklrwXTW7xa4uk9/X4PNbOk2OKxtBm0ht3H3yMLsBXfcusIrOMZpC1L/XDVZsxL2+PQOYoahBVdV0oF2V1j04QaB/GCEYuKxdlKXmdKVky4+jY/BMD2oADUOZxVaha8RFQc1Qm15JYAL4IJQo1tbPMR8uoq1raFsENwVpiXRI6K1ZVTiiiGvrPcHWd2rwd2OyEuC+fVkRHOo2O5VLzvyNkiJWKwiBF+cYlNClZnLud50wtZTLHta66teCsBVDPOGtQkpmwxtpUdrVHoLIMIKa3shXZC9oZ+2LPkhNbKMlZW+cs9jwI/e7ty6Dz63uDcEWs7NI9MSSkl8ukejnbBH4FzIewc3SosSXBiWAtYkRZhLpt9V5TQ9aw5ohWCKRTsRnrP7HrD0Vi8FbK0PaFLlVlh8tDnpluO1ZJDwTrPUhKleoqW9h3VNtK3wdFLBYRc2gvUkhNVPsSo26pJTDNpOoFqDOKFnCviHH0pDHvDXBK19NRc+MQY4uZmymo3+2y75SVtPw/tn7X93XqgVrT2zUwqgpFGic7a6PO/yV+/FTea8Ed/V7MumC05UksmdD1xnhHT3tC0CsZ15DQiWFDP7v6G8eERyJBPUDuwlt31NYhjjRkhk9cVFyw5ZVDB+uaA8N2eZR7bm0RppAA77P8K+LHxr4ab143obC2mpjY/Bpgmiol4e0TTCX/zur2RWYdIK2V1nSXlhbu7O85j5PT4CDVyvLlpCgBreXrf+ir7zjKvGWNBXCMW7fd7nh8eSOuJ7//wx9RauTvesnfwmGZySiyPT3z99htevX7N5598xj/63/83Aob+03t+8Onv8fDVG77z+feoKN88PWDFMI8R69rNoq4rT2tkHS/c39/zODfar9sf0NJsjmVZwAusEzhH31+RbUvk6Pm5GTJTJtzeAkIap0ZY8AeCLVjTeGdV/JZKm9jh28vEMmH3e0pO9Fc3aM0ISiwZW1dSEoz3hL5nOT8i1uL8gArkcWR/ODCn/DFdaK2lpIqxkLabcPCOFFes8+TgQIShmkbLjRHjfes3/LP/+Vu90fyXf++vaa0RVsFs/RijcCqVmhRbDUoiq6cWwbHSm80UGwHnoBh6aV8PY5oBtXYOTZnOgjcFqmBd6w/tdwFrtKWOTMEkh+8coWO7mTQGVswemzKXtbLbGZzPlLGSSuvCOC/ohieypdA5j3GJIKWNuiRvpOyNM1cVFyu9yXixqN2eRU4/8tOsgErFmxZG8P4Fp/E9Ngtd3wI1ayzUFOh3mV6eue8zYV8oi+Pd6cA5VaYJsDCvbaRnrSEXw6ILCwZnKhSPyhZTrkJUR5CIV0GNZZ3jRzW70UowhkSDAWtOTLEFCIgDT3mhONnI4RuJQlvfLpkAwFpyW7yLfpxvFWnJbYfBVlhUKBoxtBh0rEKslbIR5WVr8Uttr2jQDiqsYdr+zKrN5PuRNoChFks0Qqxt3Oo2fYqjUG3TZ//Dr7/53brRpHlGfEfRBOIxZYKlEATM1Wcs6QSpUtMM4lAUTGR8/4AfBnwJTDHBcOBwvGGdTnSDoawj1vsWk82m1WzThcoHy6KCOIZqWMVRvEFMT83QpweKU1K6MD8FjFRqTQ3B3fRD7W07HEiqeG+YLw9gHGtUfHCYHNH+FX2352lMjG/fYDyoOzRdc9chYrm6vcFjOC0TXe+oCstlYndzII8Txhtkcrw7z9z7wPz8yBfLide3twzWYm9vuRfl6/GJd3/8NT/64R/wOJ5JMbE3wv0Pv8+79w/IzvGpG/hff/5P+Rv/3B/xp3/6p8SYOd4fud0FnhbP+/MjVjt8tyOujw1UmhLhMFBSph6uQIWlRkiZ/c0L5v6AqdvCMU9oLnTXd5AKtU6sp6lVpAVITxQM7K+YRbHugA2Gopmw35HmJ8qy0l/dU4nUeYVujy+JvAomHDEukS5n8DvCrmMaz00fkNImkrIUOVPtHnPcUZMQVcFU8jDgqqGsF+ZaEXWEvm+Fthi+3Q8CUFPEJmkOlpxbH1FhHSeqD2gtWK30vZKTsKwtUJEVsnHUrCCVgqGkwlJaE9wsBWMguIqp4EQIvmCt53SpmE1FIaaNWtxU0QiPdeWq35FyK78mLbzaw7KAzZ5A8+JYB8ucsVYAbRUXkzlUiMmQqThpCUDrWs9mCJ5YlYTDWNeEYACljYVqUYzxLR5sBRGgPONLRFxHqSs5Nc5YqBckwrAb8H4lx8w0HRjXSM49RRJxckRd8M5gxOEQfNlCQiVh1Dchn1HEVFZN+I9dtcr+OlC2/bCjEQdQg6aCN5V912oW42C4XgLRGtgYhmLbThhROmkg0g+mWjVQ/uohUGECOuMIkikSGppLQKJuidV2cOTcbJpqXNujQiNlFGX4cPCgbesjUEVYValG0SJYarvlqJKNEGuLdFN+B+nN/R/9HcUPqGZSqgTb8ufWWubljNfWhi5bZFZLe0sFh3GOne8Yl7kdHsW1paE1oO0H2pgWXRyGwHi6bPFExXT7dgXeNKYlzSAWa3q0JopWDnvH6Xlh6BxJQdOK1kgIO5I20RZ+z9Xgmed5W0IvqLTbz/ohcmmEIMpUMr3rqdqEU8fdDmMs76czYc4M+45vHp4wtVLmkd3LW+KSOAw7DtdXPJzfg3T0CjI4DsOOzhr+7Kd/xndff8qr733GL3/5S14MR372y1/wve/9Xmv5Lwt3hwOny8j1/kicI++miccvvuD1D/6Ah2Xkk8Md7y8nfvL59/jq3SNzyuSayNMJVWUZZ7CC4FBDIxLMZ9z+umX8jcEaR1pH7P4WYlvaNpBfwu8H5nklhL5Z/mpPsBPnMUPNsDvCNIILkEYsDn99wKTCdHlsN0/b46yCKMEMjKL4bZyRU6NwN/vkQInndqsMB1QrsUSkZChwGDwrlhgLXgppHpHuSP2n/+O3eqP5B//GDzUssGJIufImFaS2JGGQ2thv2jTW7ee4sa9ibqmkD+yqYoWcGrs0aFscW3HsukpHJhjFawX1IJWYM4s6BmtYYqRzsDOyxWG3trgkigktpr4oe+sYvCGnBcTRmQpbkz4T6Yyy7y1TBsm1FUUR+tpuPVk28RxgbRsBhtBEdn3fU9MJ1FNQnG/PKSkWK5YghbVmcgKK0FnFB8EawbOwE8dFYInCGs12oLU/G3VUGhI/50yS1vNSGkV50U0lL56lfkCytPF+rB9IyIIr7aC2JtBJwpSV6JpwLYsybXutdvMQnFiqrY16viXEjDEUBXFtCW/rljozofV/qidJxOT2M25dbemyWllKYioBo+0GWKzB5MpSKzMW1W3PI9BboYnV5aP+PEsjQbfvl7TYM9q03bXyn//6N7ej+e04aP7m39WCtAb50hAuJWWSFjrfik54bTFD1zPkyjSOuGFPXFfkdEK9pTsesUXJUogxg1Q6vwdAgmPfd4zjDNAeVhjWywJ5xR6vGk+rtGu+zyvqesSYZsmsFa2R3WFgHhuiZTpvD0XTcPk6PUMI3L38Lufnp2Z4NAqp4HvLuq4NlSPCuiVPjLV0ITSESspIaaMknCcc95TTxP7+BWOcud8PTPOJOCa6Xcf5+RsOLz4l58zed8TLhb3tubm5gc7TD4F8ujCtCxo8WWEcz5zPM6ZWDjc3vP/lr7j6zmeE/TVeDE/ryPz2EVSbpiFXhv2BwUHKimpr/ddacTZQSnvTrYSmg66VkiNWFHU9rBXrmqVTvSXHSN/vsWlmXibqPIMdsf5us6kmRANGQLfFpNZCxTC4gfn0HrfbYVVZ6wzdvh1owYIxGGuwufWcZF5ImrD2YwC3rc+lYgtUjajxH8VzJvTMf/ztFjb/vZ/8WIcP8rbQMURFaC9HTyLsMHRqKMTG1CIi0mbyvWlffxXa/kkzVVpGwpuGEPKmEhWMKCW13Y6vBjWVu215nGwlS6YzHdU2A2PY/p+Om9HUUGmp3i2EYNrXVrTphDu/UtVyLYZiFsAQXGCMyppaX6STwjB05AqewpoLxkOgqaiv9u0gAMNh397kD96wxJXp0iR4S05oNE2HECzOVO52I4ejwtrxMBlyDCCJpDvWpTKzcBk71LW0YVQhqW3BkywkNbDxDPe+kObM6gxLMhsyxrIKzKngccTajL3O1GaYrbLtPRwlt7FVtYJTpZoWdU5akaTolmpTbb+nGMZiUFacCnOC6ravqYVLNTgqWSM72/xWditFf1jkqwhZBbT10CoGUyBKbT0nKrNs2CbWFhqo0tJqso0+8fwXv/4dg2rur/fM40QtK7UsRKmItGJeNhbjHOl0Bhy+Uyax+DCwPr5B/A7ZHzA1UlNtX+SSIY2AYVUDaYbUkZaA1kpNlzb20gVkBlXSk8GZI2I7fO2JmiGOGGuhzu3BagzLKbOe3oG1LU01nfDHW1LNuOMd1ijPz19hakcW0FSbYMvuScbSW0ulcjjuIRemHJnmif3xwPX1ka/evoOcGPqO+fGb1pF4/gYJHTl71qVgB0s/DBj5nHS+cPvihoN1/PlXX/HZD15TguHl9Q2pJsaY+OL5gW6/YyeB6j33vfD1+Zn1zVtefv45OM/p4R1ht0eLYzgcWNeVtDw3r/rze2bXQZyRvseHQLkkUnoE5xqUspwoxoD1kFaq1e2traVdjAtIEiRDqpB1oc4nsAHpXuFCj5TEYm8xLEhqEjbvfUPgkFnXC+wF1YVoAs4dKKrYvsOqUoqQ2YCDc8Jaj6ffPuS5/Tw5T7CN3WVNaIW72JxDeU7f9keBuwCqlbkIUiOLK3gUL8pB2lIZk8nZgUS8GqRWVDyqiVgNGMHKSlTfDlZtO5ZKpRQ4q0c04SUgKljAFeXXNlOyxRewYkm0g0mKgSAcrVLLSCc7kJVQG2VdRDBWyBVsaS8VXgeKRDCWzg/bqKgxzG6GQC4z47rZTa2yFiWZQqgd2SmlFp4uSo6CamVaWljHWujEUaXFr031dK5yTgZflGAtNu3RWJmy8jRVplIZoyPllVos1QZibjeNWMBUAQdrMlSUNW/7jqR4kUa7TpneKGsB0cyaKgcbGEuliuLFENSQLQiZnUpLT9qWQqMUkmmpLpNg8gbnYK6CEW0UBNGmILAgahhE2dt28IDhqSaKFpYqROd4yhZDJtWArUIwwr5tY8hS8RtNoDiDGqWvyiLt7xi04GolYhAJiClIhbAlSaX+ZiWAvxU3GvnJv6xI2GisuRXy3LBdoxesCDhPyQuamsQKoNqO2vjyreVdFF8mqu/RnKjzjD8cNk98wYYdV7sOYwzP05ldd2i01RoxKLkq/XBASvv34CzLeKLYAVNH0tpsgQDoiu+uG6J+o/4aSVhx25u5IZSJMS4UFTQ3B464Dl2esfsrdrsdMVWMKvP7d9irHVY911cHqrGcTieccyynR7y3xHlkt39FrRNIIQP5eWxfs67n5vaWKspnn32P5XLieLjimzdvqLVye3vLl2/f8r1PPuH57TOnmvn05QtO04y1yjffPFDmM8fjLfP5RA47vOvIcQVJ7ELHeJlbPjLVRlawHrUWV5Voe1yZSZdnZHcF4pusSwwueGL8YNgsqFZcGKC2oAbSEj0ltVGXmPD/tHcurbZk2V7/jTHnjFiPvc8jM+thXcuLFhRSIuoFe4J+FL+DPTt+Ljs2FEHs6AVBEMQC8VZWVr722XuvFRFzjjFsjMi8INi55KGKJH6d0zknz+bkWjFizvF/ABm4KerYukePLHf0zVuiL6ieCFvQNu110EaZ8u592zYYnVIrjsH8HrGBeKAK1m+7QTNy0Fmn1kofg/gf//mPeqL5N3/xmzBfuY1gi5JxRRQayosOZg+mEngvIFsGiSJ0NxqDwRkLR6UT3rDwvP/HWbxSIh/2EtmebWbfL4lrK/TNaa0gBorRwjBRxAaXqXLuK+c2aKIUqVQ8kwKq7HuOafeDBKUGJ1XKHkFk4RQzisI0KciZcGAvootdWtzE0RI8lJpyaAoy5ekD0gE/9rd3G1vudcWY1ZmqM4dwnYzFJp5HoS8bXqD3jJzp6nRzYlRCJevIVVLUgGFk0oSWYDLhWwFGoasxYvcH7XH8LfLPS1SGdCIKQzRLDWPFafn/RgvDLNMGRKljP7UWo0aFadAlOPtEFOXaGrLemazSxdjUqCO4uWIKr95RaaBB641a8jqtl4GasIbwvHvITOEsFbfC6oKX9OmsPqjlxLoM1gqgBOlVC4F/+/s//LhONIyF2p8ZlwZzpiqXyyObGZfLhddlQ30Qy4B+YS0G3qn1jjIj04VGxzAWn+H+hIyKzBf664bOM+XcKOcHnpaNUkAt2LaNoZpO59srUR8o84lYOxrBsvSUTCPY/YXTw2f7UjuVLo3O6htsntd+HmDG49u3LKvx+vwFcT4TfYOxAIPYFhDl2mbOdeJ0Uqw77c075NNH5DaYHx95fX5hMvjJJ+/53XbnfH3g4fwJb983Pv8/N/7OL/4uXy7f8Oe//BV/+PA13wD3p2/xAtvzjevlwn//L3/Jp7/4OWMMfvfhmfnxyhf9Tu9Gv33gfz79nsvlgR7OPM1s/g4tM2N6A/2ZbinDlDbzug6knZkePiFKZXt9JVgwUQy43n/HoldoF6KvufinU66PmM7Mc0+/S3eiNcbyQj2fkbkRUlJosAS0RpBJxKAQTi2VUMcf3uMfvgCtWMkhoTpjY6DzzLh/oJSgacGKg5+JSdHoVBG2+zdYe8OJoG9bmoAnJbYlT2M/sKTzb8LrurF0IWK/3hVADA+jjTw5bMPAHdVCHxmOWLWA5MAJESIaPRxzxcgW2okB0vE+cy6R3+8mwwAAEmVJREFUZsDmFJQiyomAU74MFBmsOKceqfysylVvRDsT0ZhR1uJcpmv6SG7PhDaqZPLxVFMhdXelRRCRb+VVG8Xh/rqBLpgFpznfotMADZs67sJz3WjRCNmYmuCWcUGKM+l3V02CW6dpgakwVtKJb5U1smPldQtGFdwFLc7oKbj5PgWUVO1lDYHiHvRQqsMtMk28yy4Tti1/jymqwW2kXLiWwRhGCLh3NhU0CoOBS6Yui6bfyUTZVClVKTQcZRupoHvah9g3u3CgeKfuVdAAW2SJTNWJ7s4UhZvCbMow4YzyjXTGEJY9Eqh7EKKUMPouNlit0MXptlKKEqE02YcmfCdg+8H40zjR/KN/HnVKU5/WRuGE6G7gHBvcX+HyjvNp4v7hS6DmF3D9inJ9T21X2nzh5TXrgqO/MJ8uSD2zPt+ZxZk+ec9UG+rCh/vKaMpZnJcRxHZDTfC4w7ah04R3R9oCMROnCl6pIftie89Iig30TGn5sDudz5mDpsJ6XwgzzueHlOHuR/Wijn145Xq94u4s0hn7snXWSn1/5uWLP+BD+eznP+X24Rts3Xj79pHt6can7x75/dO3rDb2BaBwup6YphP312fePGTw5lhyoH1HEWXrqRTTaUrDWe+wDebpTda30hl9o52gLwvt8ll+eUanzieGs7dhvkC75O5sfQWdkPmUV4lL1lvr5UzfFtJIswdhnt7Qxp3VB2H5xuj39DmJZERMPT8SnjW5dnuiXi6M1xemx3cEKQ+tBUTyTt2ngpZMqpV1j+mIQKYJX7ZMzvUZitHXXeMaAAO0IapM00QPpyKs/+2PK2/+V//wVyEirMNpltEgVXMHs6C4OgXP01hUulpK0Hc5c3XQIjS+C0esDDaqzwQrNgSmwE0pYjRviG2EZsoQZIJ08YlTDer+gLqcKjFfKdtTnoJGxpfwnZy5ZL6JOURVdJcwhxfmkrvPZunPqJr7Bo0cgU1gsU4rhQh4c9KMitFAzPdcvZTdguXQ8lyAa4BLutsnhVYKJxnU0nGZuG3GKhPznHvX7sFmYDFhzn46+U7AAOg+FCh0GwzPgbZ6ZYhTzWhYvviYcJcLtt0wNeZTnsyvOuNFWLrnwh9SgKArJ05ssu9ShiFUvto2fr8Gl1ZYRWhS9hNToFYouiHeMRqoYgauaf6MCF60cgvDbHApmT6vEpwkh5M7dAXIa80qmnslAYlI+XqUbNuU706fwr/78oeTN/9JDJryT/5F+Nop5ywfim3BNgNVihRMnNN0YVn3LKroXK8P9NXYbrddCZXVz601yrXy/GHBn7+E+borWvKD9ebtW4zCZht1bHiZWNeOeAcfxLhzOp1YNqedT7R5wvfwvb5BFaW9fcP99ZVJjbFu+LgzzTNdZ2Is0DsyP+QDt79COeE9F7pWH2jNvledzNMD96//CsORy5mqj0TcYTXM1r3HZsrInJcXPv1bPyOG8XR74f3DGy7ziX5b+MO3X9PePtBK4fnLb6hTw4fxk7fv+fzzz/nsFz9HzGll4qvnJ97/9M/AVp6WG/ff5SmB88ybd+94/uqr/DJbdrQwAj3NeU1yecwrQBHuL/tQIRhBGmPrnOnb7Edwiz3ZNvK/FwPKDLF9n0sGpGEvfN/JSP47TgrbRhmBtVPWLkfs8fB7300EZZqwbkzXwnbLndulFW59hXWhVMXqO1he8u8h862QhpwfKb6mtNQh/td//aMOmn/9D34dq3aknii+Me8DlXVwnwrRN6aqdMuInqYlT3Ru9MjMN3ZPxFSC+4BTSRe6xWBkTC37ujtfVsLzKs0GrkIXZQLCnBZCn/IquZG+mvR/GT9R5VWC8xasM5xjZlje7Z+qsEnwUJy2Z3vhyqSFU03JL9vCkODse3vmqTGVwWlv0lzrK8NPfL013kXQTWllwv0DrQRaN4pcKZZ+kNvWucyF+byib0+0DV6fgpvCWJVKQMws8UqUTLiuUTODLGBY8HCGc73ytCysPagN6kgh0tqD276XslIZDkZhjAWrNVVr4ow9ANOH4Bg1813QIthwukjWoiuZUhHKzfeOGlXEApHAJBgxY9aJYhA1+24ykCbL0/ZrVccYSFZ4ixCudM1BFwgbqdjT4twdJjK6BtmT6T3TnheT/IxI4T/92AaN/P1/HFJPxJpDo7YTY80sK3GI5SW/PHNhur7FDCCwl1fa27eZ5IyyaEqZdWpc5xOvr6+0ekJ08Pr0gXI6I6Mzto3zmytWJ3p/IizvTMf9BeYTOk1c24X72ndhwcb05oFzUZ6evmA+XdhcUmUmBbZv80Mwv6HOV9r5gfvTN9AKpU7M1wvNgqEwFeXl6QPmG4/vPoWeHeRjWSl1AwvOj58Qms2CTBWGEfcb7eHMsizcP7xwupwpDOp8RTxrXu9fP/HrX/+a29j47W9/y+l6Znm9wTDOn77HzFhvdz797DNKVb74/HNAaNcHysiHiWtmzI0etDLYltdc+JcJXp6gNdDz3k0z49Hw9SWjM85vWJZnQKGneEOmt1ndvC5M08Rww/tCbVcsfK+3dSqVaBO23DIzbWrIWFnvT8CUysA24bEhNdtOJTZi2XLg1EaZGk0zyJRtyYNvq+Sm906ZGkM1I29FwA38nt0oZMTHH3vQ/Mvf/Ca+Bm6bcRZHLfOqmigPIZwcntQoW77VtuKIbAgZ24KBiuDFOLeSjdcdWgTDekpXS6rDBn8dSPru8YT0Jy7v3iB/gOadr8uEb50xZW33KW78LLKWQ9Q4X5TT9kS9nIjlQkTntkcOtehILUgZXEc+AKkN9eBhymvUN6NgslA5ZT1GLFSvbG7M85mHa6P4B9ZFEW98uK1EnLkPuPcFt5lv1LmJcCslpcAjuHWw8yvTaDy2BaziBBd7S5k+ZBxUKEbhVhugiA/qUFQ3iufVrABlBFsTKulfGpQ99NJRApdK0MHPDO5oVO6xZ/IR6cP0PCqqTngZFC/ct2ArzgZ0KbyJHDRBLutVnQfPCB8R4VUCkcbdR5pi9+F80wKm3MR4JdWrJ2ffw+w3BfsJ50ze/IeXbJkVEE3BwDry7ynf7YDC+Y9fffXjGjQHBwfJP/vlrwICq8Zjd+YKNQpaX2mcmM2RMnOX4BtJhZRrtipOIUgpOUxH3yXljRidqUrKhmN/+ISxRaFTeJKVd11R0n9TwplIfwtkpMysuTM6hVDYqKasGpwAN0W0M0qkngd49MIi8K6kik3ryqtn8ZgbVFXunsoojbwpQJ0IowlUhIsHXjKQElmZtKCuVDVqy9QDG8GrAb6yqnIKxaKgu4fINN/Y5yr8rFRgsGydm08QnSjTLgvOh/7wyJ8xL5KwAd+gPGj68kyUtYOo0SKbTAHuobx4RvHfgEkig2ml5CAClFzSI4OuMzWyUG5jZCFbqTxH+qZOqvTI7h71VCJGBJtKXndF2V8YjOGwhjFpgahoyd1UhDCFEAVOElQfvBZFhtJ3q2GPwYjCqwV3SdXZ5gYe/IevvvyRiQEODg6APGipKJM0SlkRwKQwc2H1fFgVvzNr5VdaeNeCVxY2TfWjhqEh0Ar3vvHgeQf/OIRVNkYoVgo9gpfuPMvgQRRRSWVmFM6iTJl7TkNzlyIGplw03+4vxakSTFGQaWEirzGHkIGsGHMRYKW2VLf9pBSqGCLKVNmLB2O/fjW27rydTngsuOcgIRST9JSIDVaNvY8pcO9QhLfaOZ32Sgox5mq02Lj1CUO55xkB8ztLD+YyMZWVs06YddYQ7ivcxCii4HsMTClYNWrP8NKSKmguzTBXumbVAMAbz1uVWpUrYKZMNeiuLDKx+OAWgoWgMeeAiBQhVC0IcHdnooA7rnAXoYkwFTJTkDRU4spSK+7OOZQGnDUl2U03NATRwZ3GOoLVCl9LiiHEoe9XcxPChYrsqdFFlG6W+pMf+HN9DJqDgz8hVjOqKhWjEkwiXHRQHd4CpTpNJ2496Gb8b3EkCkOdqytFwaLy2oUvR2UrwvtS+DN3HmVORV/sgYutZhxNwOySMfcMPgwQUeoeTV8s6wFS/Co0yXf0OYSrOO6K7yo5NcOoKZ81Z9L0wYgIDMdU0Ngz5szY3JlLqshEhNdhNFdoJd/id8nwMjqlBmE1H9bFkGliZmAjm11dB2qZFGFUYgp0CBVYe6evKSJ47gtlatzDOSlMTIzauVrN666SKv5twKs7ocFi6VMZLhSCpmQPjHfCK6s0UMfdcFpmqYXR1eijoEAdRikZJ2MSUArqKY5YI9DIBFptJZOUTdPcKUbzTBFoEYQ45z2eyNlj0mQ31ZpTtLCR0n7VykN03hcFyZPRt1UyXUIEj2CL3O/Yrjgre7DnD8kxaA4O/qTIeKQeyhAYmr6RRxtsopTuPDX/vqr5OSoblXtPP8tmzt3Tla4+KEz81eh8q8FPYnCqQjNnLsqbHmxivFrh23CGgnlhjnwAFXXUHKemzDaEZ1cqE7UFNpRFUoV1EaVSeJHB+wG1ZL3x+62k9wdllM68/+B1KNeA2hrdld4XLqVxc0Au3NcN04mmncdSeY6VT6xSAypnIu58GHdeLXiYJ0YoPZSLjiw5NFBT7mbc+kqtU4a8ojTdQ8FHcC8FtZ4mxtbAjRPCixlbWFZvrCunChHKrQRbVHqsTD2HC7Fyi8oqAhS6bbhkqWEhxUwRmcQsrKhPfMDQPgBhkUxKqG5s2vDuaBU6ebqaxDInzvPXjlBEWG3woJW7ChvB8MIaMDyTp7UIbrChKJUpMnB2rI4Xg4ALwibGiVT83UUzMUB/hFlnBwcHyV/88u/FifQCTebkzYzS2XixSlNLwYZnl0qlUNJNknlkBOaC18EHP1HI/DMi3+znChVnJVjR3CMAwl+nB5c9YJP9Oo797b1QUkqMcCJ9L1Yz8XeSRt1TCIJOoVAUTgZv1aghdIUyPJcDw9K/4Zm7Vvd64tjjboaChmRvEMHslaF5GtvcqN3ZcH5a4DIplzp4rzNT8WzMVMHVWYewjKCLoNQ9Nt9w342agHHGZeAIvQjVdum0Zr7htm3MkdLy2z7QKkHT3bcUwbankn+XNbeOwGpQozD2bLclgpWsuq6RfU0R2cCZEULCy76U15pROCUGs+gusAz6XvvcNMUGiykuMGcpNFtk1YaJsGLcvdDDaFKYdym6xcCZuMdIo+tur8hstPxcWQj//ttvjx3NwcGPkUZWJNRunObK5s5qaeJrBUYI3dLvcFE4xyCkZLKvQHfhVYOlT/siPHc+Jy3556xzqRNXM6wW1mHpodmHy1QqLqBuXDVwCWJPH9Aw3k9B8fRorGHcdWLZMl5liOC9AROqC8WFTZwI4aSVy3CiwqkYaLBZ7P6pLN4qQ5BqWSI2BnNthAtFwCXrEgrKoyoyD4peAOcLDF8qvRcuUzCHU61yV4e9eqT4nSKdqZ15X/NaTkqq4DQWjOyOoe+hpEHGMIXTRYkShCplGxCajZaRIaGdrBW57bLjocGCEJvhEsxVuIRyKXCuKRtfXTJBxPdmh6Gs8V3EjlCjYASLKkukkjAAoqBaeNnVYpCGc98l7QNlcueFfCFRN1yEmztDhRLZPjt5vsyU2CumI18c+l4J7aH//w/p34DjRHNw8CfEP/3bfx5N9Hsl1rrHiKQpcsoUakll1IbjnpJY2b/GgkOZvvfShAwsGkX698kHIrq79fbopEiXe9H0t8xaUREqHVVlFqWpc3Z48bzTX1QIVa6hRFV6L0TZaCOYNH0keSqAq5Ahkx4gxicRXPda4lIyikUicDI9QAOQzoNMZBZO49bT4HuCfZH9nSF1UKPAHpEfUbgR3Dy4huEmmGatcdFdeFAm3AwRsqEXaG1iijydlZp7npNqnghdWH0hvHK9zNxWBw2qp6F2izxVBIpJp3vWMndXngPOtaDf2Rt6HhK8CF5h29MQlrDc23hge0+SqjKVwMgAzIgcPMMNodDUCFfu5M+iAZvWjHkSZds9apdaeNODVffTF/lzQ55c+n4avo/s2BkSLN35y6cf7kRzDJqDg4ODg4/KD3s+Ojg4ODg4+H84Bs3BwcHBwUflGDQHBwcHBx+VY9AcHBwcHHxUjkFzcHBwcPBROQbNwcHBwcFH5Rg0BwcHBwcflWPQHBwcHBx8VI5Bc3BwcHDwUTkGzcHBwcHBR+UYNAcHBwcHH5Vj0BwcHBwcfFSOQXNwcHBw8FE5Bs3BwcHBwUflGDQHBwcHBx+VY9AcHBwcHHxUjkFzcHBwcPBROQbNwcHBwcFH5Rg0BwcHBwcflWPQHBwcHBx8VI5Bc3BwcHDwUTkGzcHBwcHBR+UYNAcHBwcHH5X/C3DhnNRx+moYAAAAAElFTkSuQmCC\n", + "image/png": 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\n", 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DKaVyFFmltaOXvR3drFi7k4a6at565RkHnVw/FK2t7WD7QE2SaK8OnEWLifeJoJuhOlRU2CAiVFdXM+/UeVRVn/DFukf1HPR4RoLhFMVlwCkiModkIr4NeMeAfTYBrwK+JyILgWpg9zCO6aD4zepW/vZnq+mL+n1eFgHnCAdYSHHsCMOgvBxY0aJMtqlzqZdxMEK6cjoEcZQn4yKSRAWXLNuWvO4smEARI8QOjLXkc1ES2INgwpAgGiwgZoh/+UC1wlgdiuCAKLa0dXRz8x3LyQbC9EmNLDljNg11hx70s2bdFpwthBT1Y7X/i+lUsar9SRbpwEJjy5ZVgyBgxqwZNDY3Ul1bfchjGaOM2jno8YwUwyaKqhqLyIeAX5EEUX5HVVeIyKeB5ap6B/BXwI0i8lESJXmvVmqDPsz05i1PbmxnelM1DTUh/3bvWvpyUdo3SBHtt+psVUBgBHGJklk1BGESb5p6EgEQGxFHDpMxmDhGA4GwcqBLxaDU1AdpcDgLYpKgm0q7OgdBACqOTFgqMIqLI8SGDJLFgjFYulmVUCuLN2oJcclrCl19MUaFzq5e1m1u5Y+uOpvq6gzV2cwgq845x5atu9m4eRdhYJg7ZxqTJzUnOYZDUBo8Y50jCAq1SBMj2Az4DFVlwpQJBMEJ5zccktE0Bz2e4wUZbd//JUuW6PLly4/4OCu3dfLrlbv5nye20Nod99d8BqqyBkkF0SgkTWj7E73DUDCmXyiNEcKaLGJMGkjjMDZGUKolubkbAyYMQcwA0VGCQMhk06TygrMRCG2+bN+aIBiyuEoQQFWcfGbRD5lWiAmtkHWDE9qzDZT7MlWpsm6wKKpSlQa0lNrCYsCokgmTzwfIhAHnL57HwpNnAIml9+DDz9Da2oFNA3+CwLBg/kz2tHXQ2dlT8XyqSvyANTVVmDiHS3+IBOlrBdE0xnDSyScxedrkyhfnGCMiT6rqkpEex3BxtOagxzNcHMkcHOlAmxHhs3e/xPcf20I+cohJOsmKCMXciPR3QrkgQkEGrYXCPVtIfHMuHxNkAsQ5RBNhKdgsxaARaxPXWamyqUWJ0CiDCYOkoos6jNpB4qTqEKkQNCJgXJLuUbIzSXKIIQ4g4wZ0fECJuh3ZuqD8PQOtRyBMjzvQH4qDTEaLS7UAcRSz9MlVLHtyFVVVGWZMncDOXXtJ4jZSy89aVq3ewOJF8+ju7ksCbUrGEIhDXTIQEWXBqbN46fkXi7tY64o/NBqbG5k1Zxb1jfWDr4vH4/EcIiecKN793E7++8GNuNj1+6ik3wrEgFOHqPbnF5YhZemEBTSKMaTW1H4iMzW1dkgLUxtJolKdjQlQxEWgDieSfH7JsSxJseuBYzIooR1iKVIdSIAzEcYVFiAFwRHYCLoCyIbJMR1YgcCUf4YZwh9a9J2m+4oqQbr8qkBfLmLNhiTtLWOKxmviHxR4YdUa6saNo3ZcPe3tXahLlouNKoXgx8IPhVMXL2DVc6vLKrNNnz2D2XN9eoDH4zl6nFCi+Nzmdv7spqdRm9zITTC484FaJU6XEIMqU0EUSVMrXCH8FDEQGkXTJgsqgmSzSfhIWVZFidgUq64kx0iWBuOyz3A2RjTABEGyv42QIASS52ItJooSoawOh6yYo85htDzaxmi6IGwtkgfCMD0XSTrRc3BpDFJ2zErimaiY037r2gIZEl9jZ1cXjY3jOH3hbFa/sHZQhRt1jqamBrJVWc6/9Dz27Eqq4LRMaKa6xgfUeDyeo8sJIYrPbengbd94nLaeuHjTDs3QARmFFcR8PqaqakDgiDqyxBCXCIADF0AQ9Aue5nJgFa3OQGhwTtJlxv6bflASQRkO1bHBJZVdJH1fbC2BCEHcHwB0IKrcYCvSohj6hVHTENjYCLV1AerixF+K4IRBn6VppE7ppdnfWJz2Lycr5T8Utm7byTlnL2TTxq309vQvpwaBYc7cmWTTOqSZTIYp06ccxBl7PB7P4THms5qXrmnjis8/nAhioQ1Q6XLpflBN/FeF0mKqSoBDKiwnWttfc7PwZnWOfE8Ol7OoVWykoDHGxAQmLmtdVLmvg5Id8HnqFBfbQTmPLnYMCppKToCh5N9WuAbOKX9w4UJIA4lIz9ulxesK/yWRuRZntfi5+7ui+0/CV4wxXHTxOZw8/yQaG+uZMLGZs85ZxMnz5+znnR6Px3N0GdOW4qMvtfLmLy9FgsHLoKWWSyEopSBLpXvGscU5S2AMgThCM5R/rYKfMT1WlMuTsVlULTilpi6kTL8UVAaLYlBSQaeIQOAGL/vGkSMTGErlR1QJ4ggNK/hGJVmaVE1yIJEw/b0gbN6yi4YA9jmbBv6UnlfhB4Wm6SqCaJKMYil4LMuuSnIuUvpcyq5TS0sjxhiMMcydN4u587yf0OPxjAxjUhRVlc/8/AW+du9L4CCo0HXWqeKsw2CTmqQpIibJbC8Ej0ihJJsjMOktfogKK4PG4VzaHgOcSxyO1ZksH71iEf/z+Dp27etFRBCnOAcDV3QrWaRQkKTSqNiEqC8mCAUxQhBHYG0S6Vqp5qcqBpcezYI6lAxCzO9XbSzp2iFImClJ3SipfO4cWSNceflZbN7WRm9vjt7eXna1tvfvl/7fGCn6VDPpP0dBCM85+7QDXkuPx+M5FoxJUfzrHz3L9x7Y0O8HS1MrBgaiOBclEZ9lzjKHczFBkCluT7I1HFYTH9tQa86lxb+pkO8nwBf/5HzmTW7gsgVT6cnH/PSh1dy9bD25fEx1Uw35UAhMEuE6u6WRHXv2YQcsi1pRQh0qqEYRGxeLC6gq1jqCUms5tYyNlif6gyXEDvIdqk3rsBYiYZ3rD9YNDNMmtzBtckvxPe0dXTzyxPPsaessnksQCC0tTZy5aB4dezvZs7eDxoY65syZQfUY6V3o8XhGP2NOFJ/d3M7Nj2xAY4uIJD44YwiyA6wlVcKBglh8zSUBLqFBpL99k3NK3llqjPSHUqYEasEFaCFPsbThRfr/d7x8HvMm97couuW3K/nN0xvJRRYRpWdfTzKeTFI27rLz53Fra0f5uqxqGtHqGOgSNihh+vmlRFGMakAgkhQaEEXc4DzIpHSOTQoMlCT1W43BBWneJpD6FDNGWHjKjEGXr6mxjtdecSFRFNPZ1UNtTTXV1f3CN2lCC6dUuOwej8cz0owpUcxFljd98UH6epKlyjBdYHT5PAHZ/mQ5BWKLDNVeUJVMPkIikKoQlWTdVKOY0EVoJpMm6WvycIlVqH1JlKcGSa9EScuyGQP/94oFXHPxvOJH9OYi7ntqA7k4aRmVLFemye1pzdXv3/cc48IMucBBugQsaSm2RBgLXjySqFgdQuRJAnFM5NAATLVBNalnWmY9qsO6JG8ymy3/ali1WLVkgyBJ2g8ME1oaOPv0oQNhMpmQluYx2afQ4/GMUcaMKHb3Rcz+i58T2YLPKy0knT7Nuz6yLlvmCyy4/IZ0DypoX1y2QQKwcUwYhhhbwdqCpKJ1Jgkuqa0yfOX9L2PelHJx2NPZh9X+3ESr4KwQmPKgoKb6Kna3d4HtT9+Q0KTGoyRVbhACy35jiU3aBgp1uDhfekZIkE3EO7UwrdM0IrQkgd8IUyc2c+aCWXR19zFlYhMzp084KN+qx+PxjBbGhCju3tfHuX9/T9L/r8QRqGhhtRGcDuq5F8eQzWpRYPprjrpBQSwFCrVJ4yjab09BQXndkllce8VCGio0At7a2kFs47Jtqo7YadI1HkDhzJMn88BTvcSUdOyIXRJMY4RXnTWHKY11vLSpld1tnezu6OyPbE3/H8YFS9QRhuWfCYraPIEElbt4SBIQM2ViE2989XnUVHv/n8fjGbuMalHsy1u++IsXuOEXK8AMTlNABCta9IWpdcQ2TjopCBBZolgIs4IEJKIYpxl5ZmAahxIMSLB3Wr7sWcq4wHD96xYTVmhwa53j1gdWDj6hVJidtUkVG4FXnT2HV5x5Ev9995Ns2N2OCGTSwuB/fvX5LJo9CYDLzjwJVeWJlZv53/ufI58uy4ZxYkBmMwFKPIRVrGk5uOTFwBiufvUSxjfVEcWW2poqmuprK73R4/F4xhSjVhRVlas/9zseeWk3EgoDe08UKcnTV0DjGLF5sC4tSyZIPi3NFgYQGJwIkXNkjEl1QgmNDoytIVZHVkxZeoQA1VnDP7/zvIqCuHLTLj5760P09pX4AwfgVKkODW95+UImN48D4B/fdRmdvTlWbNhNGBgWz5lE1YAmvyLCBYtmcf5pM9m8s50dezoZ31hLb0+efGRZtuJFtu5sq3w909EExnDSjImcOnda5evp8Xg8Y5hRK4qPrNrNU+vbUGfRvGIqdVlXTZx1JeZR4OLE9Zb6G6VQnSVNPSAMkGwGwSEl6QmqgmohdzHJuUMgr47LFk6lrjpDX2Q546QWrloym/ENg+tydvfl+ecf3k9vPsYQJOOoYLpVZwyfe/8rmDGh3A9ZX1PFhQsHR3sORESYNaWZWVOay7a3d3Wws7Wd2LpB7wmDpOjbgnlTed0rzjngZ3g8Hs9YZNSK4rI1e+jJ5YAkd5DIlbRioOgfTNq4F5omafGEpeRRRmxRicnUlNc8daqo1TTvTjGS9Jq4fNFUbnjfRRWtwoE8snIzLh2XU4uRcHC6hSoNNdlBgng0uOCMU1j+/Dp6+vLFwtuZMOC8xfO45OwFZLMh2cyo/Up4PB7PETMq74DOKbc/tj6pkAJpuoRNKs1k0nxEl1iJxfaIqkkh72Kbo6EWLxVxSpyLCavSzhEFzSKJWD1tZhPXXHoyFy+cwqSmg/e1dfXm+q00KQijKS7vkqZ4RH1Dd6Q/EsbVVHHd267g/ide4MWN26mpynLx2fM5Y8EsH0Xq8Xg8jFJR/MGDa3nypZ1pg18oLgZGYOIMQRBSWmNT4xhyEVoVoCFF/9+gfrqFGqhxjHOQj2PCbKZYJi4bGq6/+gze/apTD2vcZ86dwi0PPIdNq824NMDFkCi3pPmTzROOrCVSLh8RpwEyA8WuflwNr/fLox6Px1ORUSeKCnzs20tx1hEwOOLURVFJ01uHy7liRZo4n5RvUxQnlFRoAYljTFySrhACgWPq+HFcecFcLl00lQtPnVKWu3eozJvawoWnzmTpqi3koji1FpNuGkFqHFZlAl5/ycLDOn5vX45bf/4QK17aDEBLYx1vvfpS5s327ZY8Ho/nYBh1ori1tZv2rr6kiHZB1EQwJbU91cYYYyCKE6uwxGUX9/ZhwhAJDBYIEEwcD07ET/VxV2sn77vi1OIy6f2/38A3frGMba2dzJzYyIfeeB4Xn37wXR0++qaLeHTlJn799FrykaWtrZt9Hb2EVQGRtbzmgvlcetZJh3Vt/uuH97Jleys2XaLd3baPb/3wV/z1n72JCS2+sozH4/EciGEVRRG5CvgySZem/1bVGyrscw3wKRIj8BlVfcf+jrl7Xy+BLTHx0uAU5xwmEyaRodbBgAjL0s5FGsVoBFVhstXYIdpBxRDUCI+v2M7rL57Hr59cyz9//wFyaRm2Ndva+Nsb7+Nf3/8qLjl9Fr9a+iI/e2AF+cjyyvPm8ZZXLqamqryWnBHhkkWzuWTR7OK2jTv20tbRy9zpLTTWVeOcsnn7HowRpk9uPih/37adbWzb2VYUxOIpWMtDT6zgTVdddMBjeMYewzEHPZ6xzLCJoogEwNeBK4AtwDIRuUNVV5bscwrwd8DFqrpXRCYd9AekNUfLNkUxZEJMiYgkrZ8S0QtcjImTgBwNDM4EBAdYDs1Hjgef3cBFp0/lKz99oiiIBXKR5Ss/fZyHn1zHA0+toy+fmJhbdnVw//J1fPPv3kQmDFBVenMRVZmQYECk6uwpzcxO0ydWrd/OV27+Nb19SSm2hroaPvqe13DS9An7HWdbe9KRYmCIjnPKztaO/b7XMzYZ9jno8YxBhtNSPB9Yo6rrAETkVuANQGkpl2uBr6vqXgBV3XWwBy8IYnnXJ1csaKokxbNNIBiFwFlMKlhJ2TdLFFtMhRJspVin/Gb5Wh58Zj2u1AlZwqYd7XTs3ke+RDDzkWXr7g4eeGo9zXVZvnbLg+ze20kQGK68aCHX/tHFZDPlnTv2dffybzfeRS7f79vc3dbJv3zzDr72D++4vrT8AAAgAElEQVSiumqoCuYwbXJL5fzDMGDuLO9TPEEZ1jno8YxFhlMUpwObS55vAS4YsM98ABF5hGR551Oqes/AA4nIB4APAFDTQlBBEIvPU2tRNUasxcUQZrOYfFy2f1rClDgfE9ZkIK4geAGEJiIfK1FsGVefwbrB+wWaVNgZSG8u5nfLX2LFi5uLQmed5d7HXqCrN8ffvu+Ksv0ffWpNMY+xFGeV5SvWc8k589nX2cP/3vsoz6xcTxgGXHzuabz2FUtoaarnrNPm8MwL64lScRYRqrIZXrbk8KJlPaOeYZmDs2YdvA/d4xltjHSgTQicAlwOzAAeFJHFqtpeupOqfgv4FoA0zqpsrvXvDS5t4ZSKVdSXI5DB3eeFpKXSTX9/Fc+8tJtfPbaeTTs76erLIYElCFxZjmI+FxNkBvdldDicG5y8nwkNW3e2k4/Ki3DnI8sjT6+j/S09ZTVF2/d1FwWtlMhaOjp7yeUjPvuN29jX2ZO0eAJ+/fDTrNu8g798/xt5+xsuZdqUFh56fCW5fMTCU2bw2lcuoa72yFI8PGOaQ56DS5YsOcAc9HhGL8MpiluBmSXPZ6TbStkCPK6qEbBeRF4kmaDL9nvkOEbTDhWlQSg6sJ+gatI30Eh55ZgSpk6o46JF07ho0TT+7I1n4pxy0XXfprtv8FKkKGQDyJdZlQ4EwtDgIkupoRcYg1FHBeOPTBiwe29XmSgunDeNex9ZQV++3DMYGsOpc6by+O9X093bVxREgCi2rNu0g41bdzF7+iRecdFiXnHR4iEunOcEY/jmoMczRjlwbbLDZxlwiojMEZEs8DbgjgH7/IzkFyoiMoFkKWfdfo+qCi5G4z5c3IeNenE2Li5fSqGTRRqVCmkhcNVBS5zV2YB//NMkKrMvF/OpG3/DWe/6Gu1tndi+HK7ER5cNDVedP5+aqgCwxYegVGUCPvGnr2T2lGaqsiE1VSHN9TX86wdfw+mnTC0L/CkQW8u0iY1l2xbPn8lJMyaUlVqryoYsXjCDebMmsW7zTvL5ga2fEjZvb93vZfOckAzPHPR4xjDDZimqaiwiHwJ+ReKr+I6qrhCRTwPLVfWO9LUrRWQlicr8taruOfDRB0SdughxFrFph/pMBqwr+hCNs1iNCIOqMoNRTMwvl67kjFMmcMP37ueRZzaSTxP4nQNylpqGOsQYTp09gU+86xI27FzE9V+9C+scCsSx47o3nM/lZ8/l8rPnsmVXB/nIctLUZowRpoyv46Gn1tKXi9JxK9lMhtdftphxNeVFzI0R/u4Dr+M3j63kweWrCYzhlRcu5LLzFgAwdWIzmTAgisuXWEWECb7DvWcAwzsHPZ6xiVQKEDmekYaZmrnwLwe/4JTQJVZhmMkgqQ1scARqCVMLMggMJhDEOESS7vI1VRmMtcQ2DVApOWxLQy3f/uRbWDh7YnFbFFuWr95KTy7i3PnTaKqr2e+Yn1+zjU9983/pyfUhCMYIr75gER9+25WHVCGns7uXv//CzfTl8sVtxggTWxr554+884iq7XiOHiLypKouGelxDBdLlizR5cuXj/QwPJ4hOZI5OJzLp8eWkux8awupF6kQljQHDjIOE9jiMqtzSl8uImcrR7Tu7ewlY8ovUyYMuGjRLF51zrwDCiLA7fctJR/n09Ep1jnuf/IF7n7494d0ivXjavibD7yZWdMmYowhMIbTTp7FX1/7Zi+IHo/HcxQ4qOVTEakF/gqYparXpgm/C1T1zmEd3aGgJf93lkLb4UxJT0QZ4ieAdYoDwsEBqgRGeH7dDk6eOf6whtXdm+OpVRsH5RDm8jE/vu8JXvfysw/peDOmTuAfPvRWevvyBIHxrZ5OEEbFHPR4xgAHayl+F8gBhVphW4F/GZYRHQwDl3xVCUo1xzlcPiKweQz9++rggFIg8TE2DJHEnwkDpg8IiDkQW3ft5cmV69nT3jkokrSU3Xs7+eTXbx3kIzwYaqqzXhBPLI6vOejxjFEO9q46T1XfKiJvB1DVHhmpBnwlUaXJczBpQn3S+alQ1UawkSMw5Z00RBM/XGmSfHU25EvX/yEf+eIdRHG/cgZGmDqhniULpx/U0Hr78nzyG7fz7IubyYQB+SjmkrPnY20F0VMFHCvWbObnv32Ct1zpa5N69svxMwc9njHMwVqKeRGpIV2kFJF5JL9aRwTN2yTaVB2iSUQn6hCNEbWgDnUO55Qob1FVAiNUZ0Pe+srFvPbi+WTDgKpMwOSWcXztL1/Hq86bxw8+/TbmTGsmGwZkQsMFp8/kpn+65qAb8H7pB/fw7IubyEcx3b05otjy4JOriOOoLCVEVdMydJZ8FHPvY88M38XyjBWOqzno8YxVDtZS/CfgHmCmiPwQuBh473AN6mBwkSOQQsHvAagFq4gJUKtUS8D//vtbWTRvYrFrRU9fRGdPjknN44qid+YpU/nll/+UPR09ZMOA+nFVA488JPko5nfLVg5aCrXO4fJKNuuS8WDSddz+lJH4MJZPPSccx90c9HjGIgcliqr6axF5CriQRIOuV9URyRaf2FjDXjSp5qaOcGDUpbOJ+asOIeCKC+by+euvYM705rLdaqsz1FZXLrA9vrG24vb9kY/iAXVLFSOu6NN0sQPiYnurApkw4NJzT6O7t5fN23fT3FjP5PHlY/V4jqc56PGMZQ42+vTl6Z+d6f9PExFU9cHhGdbQ1NVm6crE2LwmS6VWkEJdU+23vsLA8IsvvZ1Lz5495LEq0ZeL6Mnlaa6vpaO7l2wYUludBOHE1rKvq5eGuhrCoDxUdVxNFVMnNLFlZxuQpIMYdFBlORclwmhEqKnOMrGpgUAirv3kF8iEAbG1zD9pJh9739sYV+NrlnoSjqc56Bk59u5q5dlHl9KxZy8zTp7LovPPpcrfJ44qB7t8+tclf1eTtKR5EnjlUR/RAdiyuwNmREigIA6JMqlfsR9FueSsWSw+ZRLfvftxdu7t5PyFs7n8rHkYU9mN2tWb4x+/+XN+/fhKFKWgeSLwsjNOZvFJU7nll0uJYksmDHj/my7jPa+/pGj1iQh/9Z4/5ONf+hFRHCf+ziFckQ21VcyeNoErLjyLqoxw4+2/IIpjorSazqr1m/jqD37Cx69959G5aJ6xwHEzBz0jw8bVL3H392/FWos6x9Z1G3jm4cd42/XXUVM3bsj3OWvZ+sIq9m7fTl1LC7MWn06Y3X/LvBOZw6poIyIzgS+p6h8d/SHtn6Bxugbn/h+ERHQkNhgbIKmNWCgKXt0UU1tlQITeXMS46iyL507l9n95Hx1dvXz2+7/kvmWrCAPDmy47izUbdvLMi5vJx5YgLK8dbiQpKB5qv/+yuirD9e+4kre+5sKy8W3a3sptv3qc+5c9Q36IdIwwGyVVdALDtJYmtu0avAoWhgHf+tTHqB936Eu5npHlWFS0Gck56CvaHHvUOb7z2S/Q09lVtt0EAYsvOo+XX/2HFd+X7+3l3v/8L3r37SPO5wmzWYJMhiuv+7/UtbQci6GPCEcyBw830W0LsPAw33tkKIDtzz4MHU4UsQGiAoHDjIuIUfblk/DaAEN3X55n1mzlmz97hFt+vZTd7V3FhPqb7l6KqiOAtHpNuYnnNPFhaskrfbmIb//0wUGiOHPKeGqqlCjKpXsPNBcT0c5FEUSwfXflMpOBMfT09nlR9AzFyM1BzzFn39528n2Dg42dtaxbsWpIUXzm3l/TtXcvmqaFxfk8cRTx4E03M662mnxPD1MXLuSUSy+latzQ1uaJxMH6FL9Kf80YA5wFPDVcg9rvWIzipL9TREAAgUGDGFCy1Y7SFVIHiW8PoTcf8927HqMv31dWYcYVu2koKjpkCsZAm7qto2vQPg8/tYK7H1pGbCOMJIE8yfGSd4cZW2aFOlUCkUHNhbOZDBNamvZ/MTwnDMfTHPQcezJVWdRVrj5SVV05St5Zy6ZnnysKYhFVOnbvpleTal/de/aw+fe/59Uf+QiZau+fPFhLsXStJAZuUdVHhmE8B8Q6pTTExWJJFk8Lvr3B73FQfM+u9n1kgqFFr5BPWEkYB26ZNXVw6bef/+7xtCMGOI0QTJKGgZLJOsyAz1YRqquryOcjYmsRETJhyLVveR3BEP5PzwnJcTMHPcee2ro6psyeybYNm8rEMcxkOOPi8tUqG0Usu+sXrH1qORL33xvLUMVaS2AMzlpy3d2se/xxFlx22XCfynHPwaZk3DTcAzkiTEQQOhDFqhAQIIVCp6qoOJQg+Wqo4lTTguDJ8mbpl8Y6JQxA1SHGIsYCimiA2JBCvYOqbIa/fNcfABDFMU+vWksuH9HV3VM2NMWBOrKZkGwYYrX8V1tzYz3//pFrufOBx3j+pQ1MGt/M1a94GafMnjEsl8ozOjnu56Bn2LnqndfwsxtvYl/bXhDBWcup557JaeedAyQ/6Fc/+hjL77kLZ5Mf5oYAgykXxrQqmADOOYwxuDhm54svelHkAKIoIs8xeNUQEjVRVT1jWEa1X8otuSCwhGFp6oNiNSYg7BdGLCo2ETYTgZSeVFI2vFQac1FMdZVL2kulexqNcCYiG1SzYOZMPvz2K7lg8TxWrN3Ix754I1EcoyixjcAI4gyGsBD+QxgIZ8yfwwvrNxHFMZlMSGAMn3j/O2hubOBdV79m2K+cZ/RxfM5Bz0hQW1/H2z/6QXZt2UZXxz4mz5hGXVN/XeYn77yLlY88gkpcvD86bJkgFhoGBS51QanirMWEITWNh1bjeaxyIEvxdcdkFIeCKuryKGACCENTcanTqk0lKS0DB2SCCDFavsaqihITFgUMjEkFUUCcIiVRp7HL0ZXr4IxTZtDd28eHbvg6UWwRkwhzcmhFjcWqJSvJ9tjFRHEvH3//W9mwbRfN9XVcfNYiqqt8aLRnvxx/c9BzVNFUmILwwAt3IsLkmdOZPLO8HnO+t5eVDz2MtTEmW34/tMRJH1cMgXNIaiWmB0ysRuc4+eKLj9IZjW72+6+gqhsHbhORCcAeHfHuxIrbb3U0Tcq94dJVUocJGCygAuCoq8oSxRanlsCkp6blgphsUra37uWOh5bz9KrVaVm3UkEsP3bBn2mt5dnVa3FO+fxffvAIzttzInF8z0HPkaCqLP3N/Tx0z7309vTS0NjIFW++mtPPO/eQj9WxezcmDLFxXHkHAVGHqdBhqFA7umnq1LKXurZvY8+Lq8nU1DJx8Rlkag7cO3YscKDl0wuBG4A24DPAzcAEwIjIu1X1nuEf4pCjAxTnlKBS4ExScTvZUyAsS6goJzCGb378XZw6eyr/9v1f8KvHnyHWfMV9IUmneOjpFaxY8xKKsr/+vqVBPrG1rFy7gZ172pg8fuzmCHmOHsf3HDxxyfXlWLNqLWEmw8mnziVIq32sXbWGu390Fzu2bmfS1En8wR+/lvmnL6h4jEd//VseuOseonxyr9nX3s4dP7iFMJvl1DMXH9J46pqbcakgqgWC8mDBIJNl6pTptK5d2//rPRVISRsU7HjuWeqnTmPcxIk8/4Pvs+P3T5HcwUBuCznnzz5Iy8mnHNK4RiMHste/BnwCaAR+C/yBqi4VkVOBW0gKFI8oNlaMGRAtqlps/5EUDHcYHGiAMjiytLm+lpctPhmAD7zxcu574mmSDlKlnYtTq5OkrNyEpoZi8e+h5VYHbQ+DgLaOTi+KnoPluJ+DY404itm9u43Gxnpqxw22jh5/aBk3f/OWYnWsTCbkw5+4jnxfHzf++38RpUU71neu58bPf5M/es9bOP3cM6hrqgcg19vHvr0dPHTPvUVBLBDl8tz3k58xa94cauvqDnrMNfX1zFx0GptXrsRGcXKrSouQhGGGbHtE67aXcBmHVAdJ3ANaXEo1+Zgn/+vb4JTq8Q1oX2fZjU1dzFPf/BoX/c3fkR1XR2Zc5bHZvl7y7e0gSqa+ibB29OVZ77eijYj8XlXPSv9+QVUXlrz2tKoeWtv4o4DUT1I59xoKei5YMtmQIDTpcwWNCCRxMAdiMKKERhAxmDAojL8YsPOF667h9RefxZ2PLOOGm35CT28OESEMq9Icx4jCEikACm98xctYuXYDW3a2AlJWFq6U7ID6p1XZDLd94TPUDJFb5Bn9HM2KNsfjHBzLFW3uufN+fvSDO3Cpn+/Ci8/h2j9/J9lsknO8c9tOPv2xG4rCV6CmtoYpk5vZuWV7cZs4JYhcKkwhM085iWkzJvP7h55AjMC4ct+MxJbAOsQqxgmNLeO58n1vY/aiBax9+hk2v7Ca+pZmFl1yEXXNSQ5zadBhHEU8/tOfsXbZcoh6kDTKNOwE3IDEDIGgIYOpCgh68wR5W3w9UyNJ6ljp2FBCHCb1ezadPJ+F73wfmdok4d/FEZtuu4W25U8k/WxRJGtoOuMsZv3xuwiqjm3+43BWtCnNFu0d8NoI+jMsYBExhBmLEhHlhIwBk5H+1QEcTpOuGS79/rlICIIqkCR38KLT5vGKc07lqus/xd7O7qTSDIBzSNSHGJBAysVO4BcPLeVzf/4+PvfdW8nnIqztF8bqbIYwCAlwRFFUbDJclc3y7tdf5QXRcygcp3NwbPHcs6v4xldvom1Xe1m05uOPPo0xhuuufzcAj/x2aXnTcE2srTjXx5b1W5J2doFBlEQQATQRrE0rX2TzCy9iJGl8Htb2d8wR5xJBzCuSTwqJdGxv5fYbvk5tQy1iYqJcjiAMWXbnLzntgvNYv/xpct09tMyYxqV/8lamL1zAxdf8MTNPPYVHbr6JOJ9Pvj2uUns9cN0RRuNUEEsiVAcU9SoIogioTZZo29es5vnv/Cdnf+hjAGz+yW3sfXIZqEXC9GvpLB3PPsUGGzPvPdcd+T/SMeJAonimiOwjuUQ16d+kzw8o/SJyFfBlErfaf6vqDUPs90fA7cB5qnrQP0E1zfkTAQLFOkpiSJNhBjJweVWxtg8jWUJj+NL17+DzN/+E3e37sIWkWFVCpwg2+YJXqgigiS/ya3/z53z3jnvZuGMn82fN4MoLz2FSSzMnz5xGR2cXt9x9H8tXrqKloYE/fs0ruOjM0w/29DweOM7n4FjgxRfXccPnvkHcEw1KdM/nIx59aDnvvfYaamqr6ersxqXVsAqNzgFswdJSkMgSFstFalKnOV2KVAWrijGC7bYEdQGiioli1ComT9kY1Cnd7d1kqpNoexvH4JRVD/TXbWjbso2f3/AfzD5tIZe85+1sX7WKqK8vHWOlUpOFYztM3qEqRf9ikv8omJLAQcPgSjpqLV1bN9OzayfVTc20LXsctTESlq+MqVM6Vz5H1LmPTH3Dwf6TDIlGeezW9Ugmi5l20kE3gD8UDhR9Guzv9f0hST+nrwNXkNRpXCYid6jqygH71QPXA48fzudommEhApiS5QRVQrUEGRl84TSpNjN14iSmjm/iN8uf6xdEIHBarFqqTnHiECk/jgg01ddx2tzZ/PtHrq04tvFNjXzoHce8XrNnDDEa5uBo57Zb7ySfz2OGEA9jDJ2d3dTUVnPGktNZ9vBycn19SSecAfsmZSUhdi75ga4xaXwnkNw3jBicUzSXRH2GJkIDMNYAA6tYKWIc6pJgGEQwWiHUXZWNz69gxz9+iky14mx/FKoxIYEb+DVSjHFgLWrLoyLiXIZs2G8uCpUrhUkQkOvYS5itApGkc9HAfZIKAfTt3nHEoph/dim5u34AJmnULjXjqHn7XxBMmn7gNx8Cw1lH7HxgjaquU9U8cCvwhgr7fQb4N6DvyD9Scc6hqSCKDLG6lH6nvvgX70zKqg3ojZh8HRTEojZC83lcLofN5Sj4YBvr6jhr/twjH7LHM3yMwBwcfWzZsg0AlWTZciBhJmT8hMSHd8Y5pzN3/hyCA1koqqBJPWYoyIuiSuLSsTFhPkK6c7hOh+1UnB382UFoCTMOYwqpE5YYy6BYEBHEgAnzZYIIEFXHuDJrLxHa0EQgDhcqNnDFc3f5iDgvhGkKhtN+Q7LsFOOYumkzyDQ0YLLZ4n21EsYcbu+JBLtrK7k7vw9RDnK9kM+hHW303vxFdP+5eYfMcIridGBzyfMt6bYiInIOMFNV79rfgUTkAyKyXESWExXcKv3pFqWoi3BxPvklsR+Pi6jlo1/4L3549/284bLzyWZK/9EUTJwsexR/5yXl2lw+R3N9Hd/+5EeH7M3o8RwnDMsc3L1799Ef6Qgyc1ZySdT0NwYokK3K8CfvfXMx5UJR5s6ZgUYRxDGazyePKEprkiaPUOIkurNkMVQoJMpD0J8eiHOgVrF9rkzsRBQTDGxULiXlSACnmN6IoCtH0BUR77OoGyiYEI9z1M+cTFiTwYSWTCafHLfk4UItnvvsyy6nqiZEtA91OQqVxAqYbJapF13K5rtuZ/knPkgutxenrqJ4IpIG3xw+0ZMPgh2cg6lRHrt+9REdeyAjdleXpAbbF4G/OtC+qvotVV2iqkvIVFP4SoSZElHUdO0+eUKc/iOo08G/qlQxNmZ7axv/fOMtNNbWsmjuLGqrslRnM6ix6UckjYyTR9rUWByfve7dzJ4y6ahcB49npDjcOThx4sThH9wx5JprXpdElwq4QNFUJJrHN/GXH/8AL7v0XH72w5/x8Wv/lo9c82F++aO7ElEsvdGrQhyRISYkSkSvwnKsiFSwRfuJxSbdelJrbsj9sKBK0JtHbP8yrvZB1DbAklRFXUR722YWXPlyAlPJ0uz/f1hdTaY6S35fO+KSY1ubRzV5X6aunlPe/HZ6Xnqe1iceweVyqLVpXefB91pRx4ZvfJ5N3/7q4I4dB0BVyT+zlPzvH61srsY5XFfl9nuHy5HZtPtnKzCz5PmMdFuBeuB04P7UVzcFuENErj6Qo9+QJ7HcQtQlgTCitswhXLj0atNGiIWUCVUCFxdrmvbm8nzjtrt55tav8PzaTazauIV//fZNEFPcZ+DX+Maf/IKLzlx0KNfC4xkJhm0OjiVOmT+Hv/vEh/je937M5k1bqWuq4+qrr+DqN1yBtZZ/uO7v2bl9B846jAqBmooFQ8IguVdIMW5l6AzmgTgHQZBYorbWInGa0lHxGGkgRWSTtc1if9kksEKcoHlFqvrfZyJL0BOx6qd3JsurqoS14eCuPSgujqhuGNxb0bkk6r/+pDnUNjWSa91ZjEYFUHVYI9Q0thC1tyWCaGOMS8bX+cJz7HnoN0y4/MqDuiYAvff8mNyj9yEuwmSDCvEhjnjPM2S59KCPeSCGUxSXAaeIyBySifg24B2FF1W1g6QyBwAicj/wsQNPRsUUqs1E/U5sY0zyrSqp1lD8NeQcYvOIpJXh0wT8wgXOxzHtnd2cvWAuZy+Yy+e/dzNRLocEIFbTL16KEZ558aUh20t5PMcRwzQHxx6nL17AF/7fPwzaftt3fsT2rYnPMSnbmKwYBQxuRh6YktuPFIqFlO+lWt76rpzC3UzQAOLeiExt5Vu0BAq9UdKFh2RpVlN3j7ogcWdWkahz3pHpdmlKWn9+ZdwTk6lL0kIER2CiJAVN8mz43b0VS8aZbJamk+fTs2MragdbsjbKUTNnLjy5Cx3wfo3ytD36u4MWRdfdSe6ReyGOkivjDBj677tGoTnG7Xj6qN6Ph00UVTUWkQ8BvyKx076jqitE5NPAclW94zCPXHFroQVKISnfOJushRsIg2Q9pCTQuaSTRhJo01Sf/DLa0bqHqKcvsTBFygURwClxVJ646/EcjwzfHDwxiKOY++68r3yjMHjpsQIqEKsjxBQ9MZA0PK900y2EJ2gUo30gkUOcEnfnCWuzxc8WwIQkdUxjV55fmEgigk12jhJBDHtiNJ/6+wQIBZMJEp9mrEgGQonKOg11bt8CCDXjalEX9/tCMUxYuJh86y4kMIn4ll6eMIPrHTpeSw/h3mm3bYQghDjtT5uLkdAggYFAMSfFSL2DXMHLepyLIoCq3g3cPWDbJ4fY9/Ij/Tznkl9KgYsx2CQ8WiG2EATBoBwkRamtquJ9V7+KF9ZvZNrECXzx+7eiNv2yDRTElAxD5C56PMcZx3oOjgb6+nJs2biVxqYGJk6eUHGfPTtbeeHZF8qDXJLQUVQEJ4rR8qIezlGWE61GiVyMAQJJGxSoIam/VniXpCllDkhyHSWXNBsPnE2WTzt7EzMUEuusPsTkK/sbpVATuieHyQcYq5hcqe8TiBSHxWQC1DkCrZDdL8n5mkxt0javrQ0JAsQqT3zir5j7lrdT1TSe3t07Ke3MoFFE57PLCStZbqoEYcjGz/wtIkLDJa+k8ZJXIkN0BzENTQzs+qCxQ2OLTFSkIb0/V0tJm8AjZ1hF8ZijjkzaJ6zwz+FcYUnDoiLFKDKA6mzIOfOn872f/Ywf3nkn+SguT88Y4sdHFMd++dTjGYX8/Md3c/ONt2KMIYoiZs2ewaxZUwnDkMuuvJR5C+bylU9+idXPrk4soUwaxJK3SIkQRUFMproKY/qFMbYxWROU5UojSS9WIFl+1RiXy2PCEDGCCcBU6iigSlhaO7m4VKnYzjxVQaWcxsIehYBPRzY/uP7y/2/vzOMlqarD/z23qrrfmxVmhn3YQVBAERCNJuKCBCXqj6ioEY0RIeKuGI0xMa5RMRoXNApi4hJF3HGLUSQQDCgoBgWRVWQYYBiY9b3urrr3nt8f91Zvr984w8zjzRvvl08z/bqrq05Vd9Wps4cdUDT3qKvAZ8H6GrGmiVX3MM+Edai1XZfqbV+7gCNe+yZWX3kpa375M9Q5xHmyqkJVsSLkRWiN1z0Wqrjbf9v19a3+yn8wce3P2fNVbxp5LTVLdyHbdU/cXXcMKkcD2R79BosLQ+G3kWLcoZRi7i1kI8LSsc7Go5i+H6wvJ7nm1zdgnaMTexmWVcXY76mp2WeP3ZJCTCTmGFdfeQ2fPe8COu0OAEaV22+6jd/ddBsAV1z6ExYtmE/r/g3YePHPsxyxHimHCvWd4ifbZLlBvA0dYEy4dkujCH2WCd1guomdXgqffjsAACAASURBVCk68eIe42QOkPFG6IXqfbQYFabt2VDn19f9cnQoMKTBPUZw88qmKiHizFivSlZ3QRkiI148h97zVcXqn13FwX95JtXERn75xjMHSiZUlaosMVlGLoI4R+78gBrXskPrxl/TvvVGxg/sTRKxa+5g/WUfx66+FRpCsWhn2FCGoyUes7eFwqM+Q4wg83b9A7cUYwLNFKVkPVmxmV7lWkuqw7qpnyi9o2kyvEoIp/ctUuQ5Z73kBVuzB4lEYhb42he/1VWI9SSd/rO/0+5wb7tDw9PtbmMnLE0z+rriVXFlScP1FJUCNErMogwjg2GWvIxtKYfW4zoVeVPIsH2dbi0WQyGjLtGK8z4kxWCmNByQepoPoKKIjpJeEeNABFv5MKydPuWnCi7q15EfV+66+Pu0brqRPU88ado6RO+CS7juEjZlNdbSuumGrlL07Q2s+dbfo+VkWCCDatc1ZPvuRnPBEvz9vwwitoCWRReO0zzsL0as+YEzJ5WiLytMEeaiiDpwFtGMzVKJ6oNv2zlkbPTyntDazajBa+xwI8JOixbwnte/nMc+8uHbdJcSicTMc/99a7rPN2VX+P73g4tp2lYt+aiyuxJcx5OP9W1leGB5PdwXQX2w1LrF9BGnId5nBqwgxaA4auuul30KilQlRhUdK8jEoZmithjKp1BoOsQoYhVTOaqOpbGwGTSCAlZD7NFWMD5CTagiVcWGG2/gNzf9hrF5wR08vIyp46vCyBxJKQryRTt1/27deMlAmQcA3uHaq7F2FVMqYSYt+bJt20967ilFQJ3DSHB3dmuCxFHZnEY+nATT3/nGg7PxlQz1AsOzGONnKmfJTcafPeFx7LfXHjz50cdw6P77zeyOJRKJGeORj3o4d628B2c3v4DcDFzJNz9koi2P8x2ysUZ3dqHiUXHgPXVdvgBZLoTk4BHXIa1oUPSmaeC77StLbym8gBGM92TedZ2rxrYxDcVnxGYljd5qc4sUoRm4sRqtYkXWtkKxpTHB8nOh9ZsrhKxv5F7IRA3ZsWFzStmyNOvykV6hJlkcYOyVkaUoYgwLjnp092+3dgW4EQPevQ0lHvMVxny4rE9moBnVndfQ2O+PNuNb2TzmpFLM4i+q1mX1v95bnM/ITf/h90CF+pzcuNoRjxOPqYS82ZurWKdaGw2uCfC8+9UvoxEDxolEYu7ynBf8Py794Y+Z2DiJs3ZaazE30nVjitSZmRn9kTwYjBdOwSqCxZdVyCgtBApBXNe46yUDumlCQoRsU6dt8iKP7tj6DQVr8WoQlV5zclGKeb7buk0VXMOC65VvGByFC5arz0AtZA7IM8Q6uj1bg3+WqlOhYsNkIRTjHDLkDvVe8e0K06xNzSCC76vLdOrJqJUrYbnOWlZ9+gMse8HLyRfvTL7LwXDr/4LtDB8Jsl0cjEXXnQLzLbJmPf6S99JZdjDFY1+OWbr1/ajnZPNO6SuSHUQxVIS+xh2gjVDG+GHVnwUdGikpVB0fevYFpynYEnUWYwyPfvjhSSEmEjsIS5btzDn/9n5OfMaTERF6DdV6/+213x781ete0r1oa7f7cd3CLDzE+KGrp4Jx4YEnz4J7sh7BpE6xsfZwyEsaPLQjmoEDGKehZjqWm3XzIZxHnA81hF67omRFUIiiCtZ3B8m6LLxuxFPEbNKu4swVL4TQkquCUlQXnrvQNUd9mNSRVR1wbVQ7eK16NZvq8VUHOzmJdirwSmPJLuTqMLZEOi20amHtRoy0EdoY2ohaJn95NSvPfiPqHNmC3fHrW/h1LfyGNlo5yAqKXXdGaoVYy25Adw6lL7r6RsrvvRmdWL3Vv5M5qRSnbR6o9P1wfNeE77Pmu9R3VRQelzkcFlXXnVa986JFvPs1c2cwZiKR+P0sWbYzZ77uNJ72jCcxr5nTncKLp2hknPHal3LCn/8pr3r7qzC575p1CiAeMS72JI2q0njIKhhvQ7OEZomMdcgy37OG4jVpU5mgvgzNwPsfWB+uR4AvHVJWSGWRssRY21WsmdazHBWTe6TjYNJB20PLwWQovleUXPxUg0IEX2iYT1so5PFRKIirxwYhlEEJ96RGCT1RC1Nh8jCOSn0HrVrsd+ZZSOWgVUHpoQPGhe45RsLthlQes7GDX3E3937y3dz/lX+EshMOm1N0oqRY9jCa+x04Wls5RZvxGLsK++tN9rXfLOag+zSMhxqeb6hxArZ4FxRi/boIZPkU14RpEnwG8XiKgom1QJkx/Ne5H2bh/Kn9/xKJxNzn9NeeRqNR8F8X/QDnPQsXLeSlr3kJDz/6CO696x4+8dazoRPKJqxA1hjDGwmzASQU4xtVMqNQ2AFFE67dPlhqfSgZjKpJDG+ibUc3k8T50FS8W89B94Z9+IO1QsyoQv2hHYpOekLXl8JMly8Eokiu3W11yQjWosY4oxBdty547DJAXCiNGFr5rR9+J5kdlNfUbdpUMS0XqiwI22j9+ErMzlDsN6j9qjtuYOzAx9DvusYp2f0WKbUnp1f8yl+gV6xna5iDShFEXeyD13fwvAet6nrZHqrgLKYomDfWpKwsJlfmz28y0W6HOzinGBd+XEWecdyjjk4KMZHYgcnznNNe/RJedOYLabfaLFi4oHtRf9tLzwq9j4nXEgXXmUTE0MjzkBKTx2uPGZG0owoj3KHeudggdepHTH29t+EufbgUTAqmKruoJFXiegkZoyNrqF24DuqYICOamZta3GnCUkDofONDxx0zTqgJl2CQWFVyTDfbFPWUq1fRdDpg4HkXDRinPYXYtxm/BvxuihnvvePbG8mXPg676jLwHVAlX13FoQ19chtH9vNfw5qBGdpbzBxUirEw1fs4vywoSdTTKPqnl/V/RHFVyYLGYv73wk9jveeWFStYtngn/uFDH+fn190QAuECy3fblfec9aoHc4cSicRmctdd9/C7365g3/33ZvdtML6tKAqKvryBW2+4iTX3rO4ZaP2JLc7hRMiMCVZgJqhMNf421Rq1dI5GXqeehKxUoVe6UCtEI720HprgY1yylwEUs+ydxUlIGg2jNqazRBUsWFWK+UMZ+qpIZaEYkR8qIC5M2FANkdW8MJDJlERHi6fQ/nUrKhrrMQLeKqqCsdP7kv0GgtKtRchy8mVH0NjvuZS//SJSAq6aeqVXxS+AbM3wG1vGHFSKPUQ9GWXPorZgJBsx/DfcUd2/di3rNkyw1267cPShhwLw2fe9g+tuvpUbbv0t++yxG8cc/rDUrSaR2M6oqoq3vOk9XHbZlTSKgrKqOO64x/Cu9755QKlBsER+cvlP+c5Xv0On0+H4px3Pk5/6pG5Zwab42WVXMFDGpTpwAdeyxBcNssyEeBaKDiX+berq4Z3Hx0L5ulAjMwT3JQpVyBT1Eiw6ycFMKlnQioP7aUKDAFXiJCAPYqI8o6XwVnGTlmwsdIMJUzQsah0UUyd/GO8o6phlOBxIPm3ZJh4lq5dWxVJivEE0NBTA5JQT0MxHl2ggYf19K0TXlWz4yluQhiPf72SMrEdX/+fU0g0jMb64dcw9paiEydKi5JS9LKpIVTkajan+7To9uCim7vJhBx3AYQdtfSpvIpGYGc75yKf5n8uupOyUlJ1wMbzs0iv5+Ef/nde8/vSBZT/+/o/zna99l3ac1vCra37FD779A973r+/t3jDfftNtXP6fl+C953EnPIEDHnoQECzH/l6dxtZdaPryF3xs9SIG7zTUHPbFyrxMc2GuE//q8gliWYT1GFd1ayJrT6o68GWMYRqZqoi8Qw0Y0VDDJzlqPdLo9V/tlplZ7W4z8xZpDRbIe8CVnrwRDYq4L7l3g9uV6fMcQfHiMJqH4+UtDav0TbcNO2RyrHVkFIyqzTQL6HX2We+QNZb2vVeTHwh25fWYRbvQNCOkcIpZ/weqFF3bk2UdKGTkLYtznrx7Vxh89JkxHHHwAey6ZOcHVdxEIrH1fPXCb9PpDFoGnU7Jly/81oBSvPOOlXzrK9+mbHe6Sqg92eL6/7uOn17+Ux7z+MfwlU99gS9+7DNhmjzKVz/9eRYv2Zk/fvJxXPn9S6ha7TClIgeDmRKScc6ycMliyskJvAPfMWH2alaHtrSnOfrbpgG58eB9dI0apHJk1oa66L5klf4txvkYjFIgkpWYmJETep1KyPTMJAzkiOUUBsHUpSLTmHmucuROg8UoilgXE2mGlrOKyQatUa078uBRHMZD3r2hGMJWCB4rnjxr9jr5CORLKuRORcYVme/DTMhdgAlBJwyysINfvwr2PRTW3wau17YPB9l9I3dti5h7JRnikWICY6bzSYfs1DrmqOpp5Dl7LFvKeW9/84MqaiKReOCoKhde8E2e8NhnMjEx2XvDe6SqMGVJa+06fnP9jd23rvnpNahzsb4uKCCspbVhIxd/54fcfcdKLvj4Z6iqTqhCjFfsjfeu4b8u+AYb1qzrbttWih8KEIooRdMyufF+VCyeNk47+MpD6UPxu7W4stNXw1dbgJ6BITwaFSJMyaYfOA6/70AZsHUPOYkuXefRyoUxeGoxfhLT7jCdERsFRL0iHYtpO8SOXthbxVV95SM+WLxmooVpleArBCVzGqxZX8WH6x6TUP3pqOwkfmGJ2bvEHNTBqMc0FFnkkSLcU0gOLFR8PSnElVRtMAccF8oG8KCW4gY/MsFpS5l7liLEzu697ghTUIfGqReHH3ow/3DmaRz/mGP7rMdEIrG980/v/BAX/sc3QleUIpZVeY+xfePhVHnpX7ySV7/hr/n1/13HbbfcRnuyFSw2YxAfJzOo8j/f+SG/vuoXYQJGVzsEf2CIptW1WfE9L1ReQ/wQACVvRLejam8yvbrgTfU5pqowMQbnOy2yIrRXy3FkRR4NvliSMGJy/Sg2lbgjtawCFZbC5CFxRoPaMeoptDfYVzsK40MxxzpjXwSL0qhfRHBWyabEEBVXKtmERxoayiwyG9yeANjQXUzpxRfDAY31kgby2GQAcGtL8t1zpKPIWoX9hOGhF+Fv7SnV8UWw6ieYrDfQ2B0qZLflsGHTx/P3MSeVIoSEJq8+uDfqprPRhDd9dYo33HgLnVY7KcREYg6x+t77+PIF38RriIV565BCupZVjQDtVpsPvPsj5HXsqnZVOqWQnvPTO8e9d68C9RSZRmUgZBKvDZkPbsPa4ymK+pAMA70s06nF7+AIitH0JaUAuKrsLpNBKCOrV9RnialXVIZckvSajAQF0/++hobeUVkIiorHGwc+D05f9eS2J4/LPaYTSkJ0rE98B5qD31hiFsUd9VnI3xDFI5iYQSoSFFRmLaaymCIOSW4OHhc1SjUGOuHJzGBGqqAD1XQYpVphaQKUAvn0GbSqIUGp8bA/gysvHXx/nuIOqzbDtN40c1YpAlh1GPXk0WJUDe6J/uNtnePl//AudlmyM489+sjZETSRSGwR1193IwMFVqq4spq24sB5T106WFtiI1tsR4VZjwhUFKeOQrIBhVivBqM448g06yrHUYgxNPMmftKOfN/7eEH3PpYoCL5vfaphma6yiFZUbeU6tYDBmAyTQ5YrkikioWi/p3I8GIunoFEN1ghiwBWOrJNBR7vWmC5WZKNDKgVr8A3Ic9+3Tg19S72DWul7iZUWcWbHZJQ5F6SISjCLSUfek/f5jc1wbacIOhmWzcigVJhmghFrSnLxdL7yVhr7jCO+Ne138kCZezHFIbz3+MrhK0suGoZiRjRmkLXabf75vH+fPSETicQWscuuS4H6pj809YcSqxVeN8PtKCMrlrvowPNgZY1eD5jMQCE05jcYaYaostvey3nmq06nOTY28n18yDINMTjfLa+oyw+yIg+tR23IhTBqybAYLIKN2fMe6ypEPCa2kTO4GJMcstSous/68blSjVn8PAcLPSx2SNVGvA2Leg2Jn1n9WQ2WtSsxucUs9bCbxy91VPNjr1ftbUorxZd9x3LMo4Xr+84UvMVZ24u5RqwL34W/y+OHuwF5DQlEVdzPybXYu1u95mWqoa1d6ZGKrWJuK0VVsBrv4EI/vd6dYHC71K7U2+5YMXtyJhKJLeLQhx7M8n33RLoKMTTEVvVYhhXj4M1weCm28J4mIDesMPOxBmaaFmx/fNJTeN+XPoVpT8Yrt/YCffF5oyh4/HNOZunyPTHZ1FBN5hVfeWzb4iuPUGFoYV0LS4uymkS8JfeOzDkywtxEqdu4RaEzI1AqvowlI4zoZRoXzsamcwQqdCYxbhJjW4g4mGfR3EJD2PnQwzjkJWdi5gOFI5MSmorsojAWuuswD1gKLhtxfG1f7A8gA83qfrHBRkY93laohufgKbIKLTr4Tgd3Sxu7ugw3ETb0SMUqZB4dd2jucWtb2FUWvb9CbppErm9hrmkh109Ms9+bx4wqRRE5UUR+IyI3i8jfjnj/9SJyvYhcKyIXi8i+m7Xi+kdp69RnG0z4vma6dR/UOuB+9BGHbdudSyTmADN2Dm5Dbr7pNs79xGf5zL9dwD1331vLxb999iOMNbNeuzV6TyxV1xMk6hCNF9g+bHcgQL/WULIhNykoC5YsGp39qcpVP/wRP/7Gt0ObNq9QxX+dj027hYOPOhIR4WUf+wD7HnEYeaPRVY6Z6yk2EUGwGK0QZ0MPUevAlvjYwiwaZwMJNpIZpBAae4xjdmtw0JOPZ5d9Dpk2fmbynP2feDxeyqh44jXTeaTVCYZqPA7d3Z7v2P/P/5w/eteHWPG1r+LXTMCGFlJ6ZGcdmE5Ul1HYBdMI4BRxPStWYxP1XKpu6UY2bsnG25ixNg1TAtGSrruW3WfRlRUSM2EFJZsfm5aPe1jkEG1j7i6Ru0E2gLSBdaNF2lxmLKYoIhnwMeApwArgKhG5SFX7G9NdAxyjqpMiciZwNvDcTa5YoxmtAIrpy76yhBlpC/MmZVkPIRbGm03e+Ncv2ab7l0hs78zYObgNef97z+HT538B7xwmy3jvuz/C2R94K09/5oks22Up1o4YOAvBEqzaGFWKPFpLGhp4IwahES7GDcMjjjmKifUbaTQKfvN/12Kkv+YvuAiddcwbH2PjxiErwzvKTsmae+/Du9D4WiF0j4nkec5TTn0eAIuWLeXV55/DuntXs/H+NXznAx/j9l/8kizPcZWlGC9wk6tRqxhHdwpGLUmVlWQmA5+x24EH4F1oNn7kySdzyJOfRDmxgUW77UHeaAJw1Wc+ybVf/wK+6vMZqqJVxS3f+1qwJicdEgOI3e1pKPHLo7e37mF663cvRCbXM3HnLb0jJDq6fZyEBJ1RZN4PDmg2Co0KsWHj+bwYs6wgi68pPcvexOQcb123BZ6ZZzG4MBkwA6lALMhaQHvu8k25zTeHmUy0ORa4WVVvBRCRC4BnAt0TUlUv6Vv+SuDU379aRfADdy2qMUCtIdD8xMcey623r+De++7nUQ8/nH98zZk8ZP8H/QY4kZhtZugc3Db83zW/4t8//UU67ViAXYUklTe+4R38yeP/iJ12XszCRQtZu2b0rb9ER1FdMdEz9DzQBtOkOT7GWz74dhbvtBhrLac+9gQ23L823FxnGWIgx0OrRXPeGBvXrRsouBfAO8tBjzycay7+bzqtVl8GTLjpfu3HPsiS3Qb7sC7eZRmLd1nGGed+iHtu/S03X/kTbrnqSm69+qc4b8mJw4GHd8o5HB5pltx3x3U0xsdRlEs/8c+gFUc95/kDFu0jn/diVlx9Bat/fR2UNmSONgqYqPANyGJstV/5do9SBfSFQE2hFAsnufOSr8cUJ4kieTLNRmubKasNMyTNwCvByswqjyqYIipEDQpx5GrrjjzOI3lFPt9j1vWmGtEtT1S0ymLDvGl6X28hM6kU9wLu6Pt7BfDoTSx/GvC9UW+IyBnAGQDkBYpFyHruBWeRLIu+/ZxjjzyCL37o7K3fg0RibjMj5+A+++yzTYT75jf+k05neMI6ZFnGJT+6nJOfdRKnvvj5nPev/9ZTnBCUlY2uUxOukKNcn7vvuStnf/JDLN5pMQDvf+2baW/YGAr6Aeo6wwzW3nsvrbExjDHdQQPdzTnL59/7PoxkoRjdWshyinnzOOmlL+IhRx2JquKtJYt9WCfXr+dH55/PdT+6FPBsWHUX3rnuuiv1NEZ0ywk2rFKY4A0rN27EdIL2v/Ts93HFx87hxLe/i/3/+E/I8oLJ+1az7saboR0tRa9QdVAPti1I5smmucz3DpkiYsmaDt+CrMqnyrXRIQuHmrx6hbWK9726QhnzZFZidm9sMyeQd1pQhfyiEOuUPv/w1O8uWIyKFB4qj6yj1/otfkTFI1bDkOHuBw1Gt678brsoyRCRU4FjgONGva+q5wLnAsj4PPXq8OoQhazuwK6hfZExhuec+JQHSfJEYsdgS87BY445ZisrweI60ZGF6baq2LBxIwAnPPVJnPvRcwcDbN4hNvQ9zsNMiZHrP+KoI9jvoP0BuOmX1/OTiy+lGmoVF1x24VrfabdpjjUpGg2yPKc9ORnila6iqnTAqhEjLFm6E0987rO44O3v4sqvf4OqLNlp1134o5Ofwf98/nPYiQ6gFI3YfLsfgUocDSdBCsm6ij0rYoaq80ipsd4/1Bfazga++w+vQURYvNfeLNllT6qJjVPWjQmlEEEHx3UMKB/FZA4tlXw8zknsgLYV50KcVPot5paDRg5NU2tuaLuQeLSkAzHhRjODncwpOlk3v6NZdTCTcTf7xWwqOJk2LopA1qyQDtAYkbxUEeonBw6tn3Z1m8tMKsU7gb37/l4eXxtARI4H3gIcp6pTbxunozahI+NjDYwxnPuOf2T57rs/QJETiR2KmT0Ht5JnPPNELvziN2i12gOvl2XJu//+HXzyQ+ew06JFlK06zhcViMbJFJ4pbdj6ueKHl/DsxxzHcSeewM//+3I6ZTs62AYVlNeeWi07JU//qxeituTiL16ALStQneLm89Zy/8q7ePeznsv61XcGyyaDNavu4eLzzmMgbDnCo2ecJ+/vaKMOlQxMRqYVtEMZR32ZE/WYjG6xPCjr7vwd7TvunNZhKBKUXSklDd+so3ZkpiQjlDeYZow11msRwRlH7rJuBn/mquCW3WhhgljcX/uuFZP3vgP1oU5So7GSiceUDChE3/KYccHs7KGVTVGKKmGdxeKq19mmvnPpI69GHFoJIbStYSazT68CDhaR/UWkATwPuKh/ARF5JPBJ4Bmqumqz11zn1mgwszMDZ//NWfz2R9/nWX+arMREIjJz5+A24JFHHcELXvhsGo1GzI4Mc1GNlogq9917H3fc/ru+T9QnfigcVwidYHSw9KK2UFrr13L/vffy7S/8BytX3o7PLC6vcMYOFOL3X2uLZpOlu++G4LFlJ7pqHaPMmbLdZtWKlWFdMc5oTOy5XL82OmBG7vzUt9QBZejt6nsf7yoTF/Z34CObaiggvWUq08GZkrxoIQ2Lb3g0V0wxnJ0LGKHMytBrVEMpTN8GQ3/RaTyfAmjh8QsqWGyRMd83DFhBPL5Voq1oXS5zoXNQ3bNVFJrQWEqvuYAfsY+b7H23dbbijClFVbXAK4HvA78GLlTV60TkHSLyjLjY+4EFwJdF5BcictE0q+tbMbEhsIPKotayx7JlnH7Ks1g4f/4M7U0iMfeYsXNwG/Lmv38tBx6wO6KTIb1eW2Rq493+5t3xlx0blWOsj1MQa4OF19chpn6oie3QIv2ezbLdppqc5NCjj6bRbEI1tdRjAKlLMxxUDmk7tPT4jsN1QoG68zqgtM2oizwAGornN9HU2lVA20LLQunwUo1QjEP1jYAK5HmFFj5c9U1QitOhAq5j8Z0YqywH96HeTjY+Yh0CND3a8LisNzZKxMVHUMZ0FG0o7OlgJw+LPCzzsNSjpucuFQdMDNWHRhlHMm/rlOKMxhRV9bvAd4dee2vf8+O3eKVekXYIkHvA5IZnPOkJWyNmIrHDMiPn4Dbkqxd+nZtu7E25CP09e+87P9w7k9AU3HvERx+lGLRyiDHdi6ZQ4VCKfETWZF0354PSrDMhAaQs+eLZH+Ksj/8LRZbR3xylzm/sE4SGVAPjAl1OrKkLpqwvHRaQPAuNs+nb2BZTl6P1lLTtgFkwXBvhMeIHtIaIR/Jw3dS6jt57rIG8mQ81CNfQOYbgJUUVbcWyjYJuTNFkStaMw4OH5FSR8P1UDi0MlCB1fWhtAa7zyFiGiiKxmbh6RSYtlS1jb9fw/dIhJEaNG8gF48EbDQqT2v0b/ODZPkNt5LaQ7SLR5oGjjBc5L3zW02dbkEQi8QD4+Ec+gYtz90a5rZzv1alBKC3Iy3LQpQeIadCtXcaG97UbkRu9cSlxqnivFC4YfcZ4rN3AB1/1CsbzRk8OLBl5sMpieCuXajDWFa/LLoO8T1HiHOJLJBMQid14GqPlMqETTzFKboVs2Gr1YEtLMeYQp33uToNSdJVFo1kGr6INsdjufrUtokI+Fi2z2BTFtGJqjjiUGPebhO4UEYVs4Yg98AothxqHNArylsMVFVkGjjDv0fjYldaC3uOQRQZtEDrhbHDkeYUxinexFlEFpAFOkPU2uHyjvM6A8SGT1ezsyQ4qkWVbF1Ock0qxdhdkhfDyFz2fYx5++CxLlEgkHgir7umFMYUpuRQAVM6SiSEzhqwqp7gGQbG2TZ5BJqZbqI4II/IzQpyw1jlRkXoczTgIXkSwVYe2dZi+LTls11LMcLEzztSgWlDF2lVI4h2m0F5yioA3burou9q9W1lcloUifrT7lsGPvHEIw+z7xmmFV/GUGAqK3HbHDtaxyn5spyKzijGC8THPKlq1KoL1SiYZRkwM1znyccAb/FpCO7hGVLbrKmTCQiZk2gpx1ly6PmoVxWWWzBaYHMQKen9fX9TcQq7djjkqoE6p1FJIjlGPdBRKB7kJccnxCp0P2aMIEza2slRx7ilFATJP0cx4+2tezZvPPGO2JUokEg+AC79wIe12L/PUoVVz2QAAFypJREFUEwyRUYrRqIK14aI4wh8aQokepw5RjQoFKutpFFG1Sa82rm6EFZ57irCaASXn1CMMum5DyzQXb8w3LyXDGD8YHlWwrkTyItQ+qkA9Bso6jPNY59H5ilFD5pXceDI72uqdPslVEVMSDoUwXc9zgKqwjFfBrzroSvV4BBFPnvfGUPlOKL/Iixy5xyL9iUOq5GVQ52bRkOs73ohUlaXQAlP0tdzLHGKGynQEyMDZ0A82i3ZwrZyzXUMWT7YXYGTkb2dLmYNKUWmOFxy47z6cddqLZ1uaRCLxAGhNtnj7W96Bqyoki5ehgbmovRo51KPekdem1HTe0Piv4lE1CIpxFWoVsjh8V8CIAwSVUGgvOjTfL+LxOCBTM2CB4StUBJcZMh1WIlGx11aiODJxQ3XnoWi/shWNguAe9gqVD8o/rsh5i2kW7HP4I7n7F1fHV4fjmqF8YeQhqftAW0PQ+tOhSAZUo5sgqHqKEdtQ5/GTJbljQCEWdXx2unsGATWK73hMw3dlqzc9TV92ALyBOnfHLHUxpKww4fHXEe5TlmydZpxzSrFRNDjrtL/iTWecHlK5E4nEnOO6X10XRjKhqLMhSQaDVSWX2grTUIfgfTSm6liZIsbTzbJQgaEuJqohCUa8hn6bsTeoEi6spqEIJWE2fHSzjpDTqcNoFd2oIaszuEgV6zwmM1NaqIWLdujLnJupc4xCZm3sZmNttFpHWIAKWavDyp/+GMlzNHMYVwzk6YiEOYeqZqRCo+3xzmKWNru1naO2Q6tCGb0OcY56BmQ4KB5jfTeeqlmvOUF/su+mkoclU/IFLjYkjxa881236ajj4QuLyeajG2OpTDPMk5TCh36oEJKeVm/H2aczwRGHPIR3n/W62RYjkUhsBYsWLcK5XjaKekeGHY6ydfEAXlG1mAYgvZFJiiLiUe1lURrjEB/G/cHQJdaDWsIIJHxIJHFATrdgHfVhigVQj5KoL96isdm1d1jNaYaCacSHjXljaOZ5UKDOhxILAcmkqzxU4nZcKD8YjpECFGWJMXH/XCfIYB119xsjwbLUCmiaERY25GWQyd7fJlvUwMgIN6qzUHl8Q3oWsypZabuNz20JpmHImxIUYn1MVam8pSjykaO3tFKYUgup5IUjVAzliFNMWXbX6UuQBQ0kj91zPJjMIuJwbMQYMB0PapB8hObdOp0495RiIpGY+xx8yMHsvffe3HzTzfjYD9QTYn0j40JCdH8G/+RAmCrGqTyeDANImK9Y1yyOWJ2GFE/qBawJcTDJQXyFaC9GFio/hNxkZNpLaFEUXO9i3sU5ysrS0FhiYCQ0ye6rFNDcEKZ5WFQzMI2giH2oxzN5cKsKQQljpKfg1YdSEt8rz7AbHfm8YA2GZZRism42Gsoh/Hob4nF5Hpqhi8RYYIhVWuvJsmAtmmooTgj40oeykhFfkLWORiOfoo9cS0NZZJ+LNMsdpm4L5ytM6afctLgNJdlOzZBtnNneNCRvqZqKKQVdJ7DLNL+XrWBuDxlOJBJzEhHh/P84nz332jO8oBpbtpWotlBaKJ3gahTI6+K66S6A0aWJQp7pgLdvJKpQVjQbDXI8xpdoZxImJ7tzWPvxquBDQwCxHkqHlA6p3NSidlUKq93OL6L1wGB6D+vB2mhtOSgnMWUbsXHOYsdSTpZ460ZepbVWiNFyFetw6ydxayfQyYp8ooP4Kqab+jDf0CvGKdqu0Ik2OtnuNUcnxAitr9DoHt0SXVM38K6LYPobCviWx663aGsSykkE233PuN7w4X6kMY7xnkYszxh8E8qFDnefC03Gt9IyHCYpxUQiMSss33svzv7Qe1gwXmDUktNB6c/a8ECH+QsKCiOoYaoCqlGCq7JTIhMTMDG5iatlHD/nPeNFA3EWKYOCklFzA/s2IS4U/Xd1rkJV+QG5cjsUYxxhXdWfDbupGB1UmvUGK2sp21Xo2NN/tfYWrxZPGDVFX5mKlmVQ4NFa9lZxLihp0b5teMV34rBm75HK4jeU2M40Myw3hcRMV6lAKoJbOvg+RSx53gkuZ6vY9RZXOZDQZGDUuCf1HtOY3/8C4iqMKxFXgSiagb/bdxOKesdm67RkUoqJRGLWOOLIR6DOBSWFTnGFFUXB0cc+itNe/2oW7rSIvNmsr/VDxHFSxAu/gm93cBLLJ7rDdWP8L1orrY0bcZ2KroYadT2t44VV2VWIw9i+5t5mEwbtlFVXVejUMs113NtgdZUbKrz3IZ9Ig9LpJ+xlrVinkTHuyxQZOhW57ZCpw6iHsprm5kOxdkgBqUO0Q6ad8D1qjPUaD6YizztkWd/xjetxkxaTWSh0wKrsIsL4k18IJgP1GF/1fiNosKYbHfxYB1Z5pKT73TKRlGIikZijLFiwgNf93RtpNkdnkldVxYYNG3jFG17Pt6/6Ma9565t54tNOYt68edFPF5VcFS6WJsYCu1ZTVSKuHTJr1AaFop3QtFuVstPBSa/MYUrfUVUyV5E7Gyyd6QzVB3IdVj/gvpwWFxpl27bFZx7BjVysHpq0CWN3dEJobaVKHZ9VbFX2esmGheKiNihnVURLMkoMHlHFdSyVrfBaW4mKYrsPrzauT1AXrHUyhWxQMSowdvTxzD/5jeSHPA7j7cB3KigNo5g4a9FVJaz2sNLDneG1rSEpxUQiMaucduYZvOuD7yfLpl6OsjznIQ89FICFixfzF2ecxtmf+gTfu+bnPPwRRzFmGixozCczWWxsNnhB7JUIWEQrhF68sDvIdkijuegO1Wghmmj9TFM5EdZf+0NV8XV8M2LdCLevKrlWKD64QadpOp5JPaVe8dbDZDW9Yh798u9ZJsQ7h/fFqeKrTsxI8qH0I6tCwo63ONvBqBt09wLqFCceFSWTztAWo5Ksj2dsm6rjHm161Hg087AwY8nL/wURYeHpn5widR5jjP11qexTYh5aYh47QfaICbaGlH2aSCRmnZNPeTbf/cbX+MnlP6bT6Y10bDQa/NXLXjZl+XkL5vOp736TW2/4Db+75VZW3HIz573znVu0zTBRQxB8LImP5RIebMdhMqHoSwqBnrIbLqDPMgkxMg2NqrN6eDDBirQOMhNKJoSgEE1fbYSjAg9ZXxcBoVd20lPkv3evqPukj2KUFZQNJ7J0t6WIL8mKbEpMdFMF9ioZxaL5sH660Zyh4YBrg8yPtZGNMDFDGuMsPP4MTHMcABlbgFm4DN2wuvvp2qrtx096zFKLrGTzfdfTkCzFRCKxXfCRT32Kpz/7WTSaTYwxHHzIIZz/pQvY74ADpv3MAYcewhNOeirPO/NljPeNjtOhf0dhjFBoaB7uu1G52pXnUdfq1S1GHD6kkHRdix4oUevAalCv6oGKEMVzgEW1wrkKazsUdAYUYnfdGuJmRjyZWDIzuExdwzldHkkoRglKrp5P2DsKisGHBJXuf55C/LQKTvqsyGFLV6frdoCw+ymvYv7+D50+UTjTECq0YCd9GEhsMmRsAQtPfDmLT37TwPKNE14BjfFp1hb3fbGF++Oubp33NFmKiURi+2B83jze/cEP8o73v5+qLBkb3/SFsJ+8KDjv4h/yhuecwr133hlmHALF2DgZYdJGFS3Q5tg4uy7fiw133UlnMrjaFCUMeVJyPFkcVOvUY2SwuN5HxWhwXUXl1CI+D5Zid3xVXaDQR21ATqMxnHcU+bAlGj8qQRnlOy1jr8MOY+XVV+CrkMQiopgsulp9VpdoYiQoNmM8RrQbhq1FCdO2srh+6Za1ZP2apbRQ5INlFj7UdE6RsWiwywnPYX0DJq+9fOQ+hhrFuH9mnJ1f8mEWPOoEpBiPnY0GaR5/Jrr2LsrLPgtZga82YqS++ajXGZNetwFJKSYSie2KLMvItkAh1ux94IF86ec/Y8Utt9JutyiKAmctex98MBdf+CW+97nPYauK45/7XE58wam86akncNdtt2LLXgmCwECMzdfJNTEBpYvqkIWlwfoTQbKM3Q44iPtv/y22r+F5MTbOs9/3Ab7/j39DZ+OGkfug9f+Gp8erxtpERZzjGed/hYlVd3PVOf/ELd/7Mr7sdD/vxIVRWPGVrBhyN0bFXO+SiOvb62BqmWGlXNmBzzaX78++L3oFt5/zd8F0NQYxGfuf9QHGlh9I45TXs/rCD6NVX3lHbV16UK8Uex3Ebq/8MOOHPnrkseiKawzjp7yLsT/7G/zq22HBEqqPnoLefWNvnXaTq9giklJMJBI7FMsPnOpuPfEFp3LiC04deO093/oun/+nd3H5N76GaphE1Fqzekq8yuXCY058Otde/APKVgvw5ENddfKsQL2nMTbGYU88nr/86Ce55GMf5orP/Rvl5CSHHPck/uwf3s6Svfdh4u47ueycf6Zqtfq2IhRjBQt32ZVj/+qvuecXP+M33/tmeEuVzPeK6ceXLAVg/q6787i/fQ9rbvoVa397E3Zygnx8Plmjwe4HP5x7rri0/njYwtB+iaWvUXi0ukxGc8luzF+0lPbK34Wm3p2NvQ8pmLF57Hf6m9j9pOey60l/wbqrL0VtxaKjjyNfsCisptHgoE9dxW2vOwF7393dDxv1aAXeNNnppJf9XoU4IO+8xWT7PDxIO39f3PpfhVRbp/j7wOxkptxLPBBk2mLY7ZRjjjlGr7766tkWI5GYFhH5maoeM9tyzBQ76jl4+Vcv5Lw3vIZOa7L7Wt5ocNRTTuT1538OgCu/9mU+98ZXY8sS7xyN8XksXb43j3/+iygnJzj0T57A/kc/anRz7oj3nss++s/8+JMfxVUljXnzedIb3sKxLzxtYLlvvfwvueXi7+H6Eo/y8Xk85d3/wsP+/Hnd19R7Vvzvxaz61c9ZsPtyDjjhmRTzFnDtx8/muvM/jKvaZFloNSfGML7bXowtWEDr9ltwrcnQTi76Und//Ikc+bZzaCzeOaxbldvP/2d+97mP4MuSbHwe+7/s71h+yumbdUzdhjXc8qLD8a2NA6+b8QUc+PnryeYv3qz1DFOd81L85RdSu6fNXpZst0aw1BXMm9Y94HMwKcVEYhuTlOLcRFX58tn/xLf+9SMURYOqKnnoYx7H6z71GcYXLOwud8d1v+S/P3s+6+65h0ec8FQeffJzaDwAd6+zls6G9YwtWozJprZCLzdu4FuveDF3XPk/ZI0mriw55vRX8riz/h5fVfzuR//JxD0r2e2oR7PLEY8cvU/eU25YRzFvAeodrtOmWLgYdY6VP/gmK77/NYqFi1n+1Gex7KjHko2N3g9vLW7jevKFi5ERsm6Kyet/wp3vfCG+HRSjGV/A8r//POMPO3aL1jMgz/WXU73vWdCZhELJj4gt83wOGLI3b0hKMZHYXkhKcW4zuX4dK278DUt234Nly/eebXFYv3IFG+9ayZKDHsLY4p1Ye+tNfP3kJ2Bbk3hrERGWP/54TjzvQky+fUbE1Hs6t1wLIjQPOGJkQs2WYr/yT7hvfhDZqSJb3gkzISPZ33Qe8DmYSjISiUSij3mLFvOQY47dLhQiwKI9l7Pn0ccytngnAL5/+im0Vq+i2rgB125hW5OsuOyHXPfZT86ypNMjxjB28JGMHfSIbaIQAfJn/x2ND19LdsIrodh2s3VnVCmKyIki8hsRuVlE/nbE+00R+VJ8/ycist9MypNI/KGRzsEdiw0rfsfaW2+aUsVvW5Nc9/nzZkmq2UOW7Ik56e3I+BK2umo/MmNKUUQy4GPAU4GHAc8XkYcNLXYasEZVDwL+BXjfTMmTSPyhkc7BHQ9vq2ktLVc+gOkWOwBicuSU/4RF+0CxABqLtmp9M2kpHgvcrKq3qmoJXAA8c2iZZwKfic+/AjxZNpW2lUgktoR0Du5gLNr3AMaWLJvyetYc4yF//vxZkGj7QJYeipx+A/K8HyAnf3Wr1jWTUdm9gDv6/l4BDBeldJdRVSsi64ClwOr+hUTkDOCM+GdHRH41IxJvGcsYknOWSHIMsj3Iccgsb78mnYMPDtuBHBNw1luXcdZb0/EIPOBzcPtMVRpCVc8FzgUQkau3h8y+JEeSY1MyzOb2Z4J0DiY55pIcW3MOzqT79E6gP31reXxt5DIikgOLgftmUKZE4g+JdA4mElvITCrFq4CDRWR/EWkAzwMuGlrmIuAv4/NnAz/SuVY4mUhsv6RzMJHYQmbMfRrjE68Evk+YevJpVb1ORN4BXK2qFwHnA58TkZsJgz+eN/0au5w7UzJvIUmOQZIcPbYHGdI5+OCR5Bhke5DjAcsw5zraJBKJRCIxU6SONolEIpFIRJJSTCQSiUQist0qxe2lPdVmyPF6EbleRK4VkYtFZN/ZkKNvuWeJiIrINk+J3hwZROSUeDyuE5EvbGsZNkcOEdlHRC4RkWvi9/K0GZLj0yKyarqaPQl8JMp5rYgcNRNyzBTpHNwyOfqWS+fgXD4HVXW7exCSAm4BDgAawP8BDxta5uXAJ+Lz5wFfmiU5ngjMi8/PnC054nILgcuAK4FjZuFYHAxcA+wc/951lr6Tc4Ez4/OHAb+dod/p44GjgF9N8/7TgO8RmjI+BvjJTMgxQ/uWzsEtlCMul85Bndvn4PZqKW4v7al+rxyqeomq1lNJryTUgm1rNud4ALyT0LuyPUsynA58TFXXAKjqqlmSQ4G6AeJiYOUMyIGqXkbI2JyOZwKf1cCVwE4issdMyDIDpHNwC+WIpHMwMGfPwe1VKY5qT7XXdMuoqgXq9lQPthz9nEa4K9nW/F45oltgb1X9zgxsf7NkAB4CPEREfiwiV4rIibMkx9uAU0VkBfBd4FUzIMfmsKW/n+2JdA5uoRzpHBzgbczRc3BOtHmbC4jIqcAxwHGzsG0DfBB48YO97SFygvvmCYS79ctE5AhVXfsgy/F84N9V9QMi8keEOrzDVdU/yHIkHkTSOQikc3Cr2V4txe2lPdXmyIGIHA+8BXiGqna2sQybI8dC4HDgv0XktwTf+UXbONC/OcdiBXCRqlaqehtwI+EE3ZZsjhynARcCqOoVwBihSfGDzWb9frZT0jm4ZXKkc3CQuXsOzkTwcxsET3PgVmB/eoHcw4aWeQWDQf4LZ0mORxKCzgfP5vEYWv6/2fZB/s05FicCn4nPlxHcFktnQY7vAS+Ozx9KiGfIDH03+zF9kP8kBoP8P52p38hs/ObSOZjOwR3xHJyRH9A22tGnEe5ybgHeEl97B+FOEMKdx5eBm4GfAgfMkhw/BO4BfhEfF82GHEPLbvMTcjOPhRBcSNcDvwSeN0vfycOAH8eT9RfACTMkxxeBu4CKcId+GvAy4GV9x+NjUc5fzsR3MpOPdA5umRxDy6ZzcI6eg6nNWyKRSCQSke01pphIJBKJxINOUoqJRCKRSESSUkwkEolEIpKUYiKRSCQSkaQUE4lEIpGIJKW4gyMiTkR+ISK/EpFvichOW/j5t4nIG2ZKvkRiRyedg3OLpBR3fFqqeqSqHk5onPuK2RYokfgDI52Dc4ikFP+wuIK+Zrgi8jciclWcM/b2vtffIiI3isjlwCGzIWgisYOSzsHtnNQQ/A8EEcmAJwPnx79PIPREPJbQ9eEiEXk8MEFo2XUk4ffxc+BnsyFzIrEjkc7BuUFSijs+4yLyC8Ld6a+BH8TXT4iPa+LfCwgn6ELg6xrn04nIRQ+uuInEDkc6B+cQyX2649NS1SOBfQl3o3U8Q4D3xFjHkap6kKqeP2tSJhI7LukcnEMkpfgHQrzrfDVwVhzz833gJSKyAEBE9hKRXYHLgP8nIuMishB4+qwJnUjsQKRzcG6Q3Kd/QKjqNSJyLfB8Vf2ciDwUuEJEADYCp6rqz0XkS4Tu9quAq2ZP4kRixyKdg9s/aUpGIpFIJBKR5D5NJBKJRCKSlGIikUgkEpGkFBOJRCKRiCSlmEgkEolEJCnFRCKRSCQiSSkmEolEIhFJSjGRSCQSicj/B5QIFWbkAoorAAAAAElFTkSuQmCC\n", + "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -329,7 +319,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_otda_d2.ipynb b/notebooks/plot_otda_d2.ipynb index 68d3b66..c39b7fb 100644 --- a/notebooks/plot_otda_d2.ipynb +++ b/notebooks/plot_otda_d2.ipynb @@ -127,7 +127,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] @@ -313,7 +313,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_otda_jcpot.ipynb b/notebooks/plot_otda_jcpot.ipynb new file mode 100644 index 0000000..cc70d59 --- /dev/null +++ b/notebooks/plot_otda_jcpot.ipynb @@ -0,0 +1,397 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# OT for multi-source target shift\n", + "\n", + "\n", + "This example introduces a target shift problem with two 2D source and 1 target domain.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Authors: Remi Flamary \n", + "# Ievgen Redko \n", + "#\n", + "# License: MIT License\n", + "\n", + "import pylab as pl\n", + "import numpy as np\n", + "import ot\n", + "from ot.datasets import make_data_classif" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n = 50\n", + "sigma = 0.3\n", + "np.random.seed(1985)\n", + "\n", + "p1 = .2\n", + "dec1 = [0, 2]\n", + "\n", + "p2 = .9\n", + "dec2 = [0, -2]\n", + "\n", + "pt = .4\n", + "dect = [4, 0]\n", + "\n", + "xs1, ys1 = make_data_classif('2gauss_prop', n, nz=sigma, p=p1, bias=dec1)\n", + "xs2, ys2 = make_data_classif('2gauss_prop', n + 1, nz=sigma, p=p2, bias=dec2)\n", + "xt, yt = make_data_classif('2gauss_prop', n, nz=sigma, p=pt, bias=dect)\n", + "\n", + "all_Xr = [xs1, xs2]\n", + "all_Yr = [ys1, ys2]" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "da = 1.5\n", + "\n", + "\n", + "def plot_ax(dec, name):\n", + " pl.plot([dec[0], dec[0]], [dec[1] - da, dec[1] + da], 'k', alpha=0.5)\n", + " pl.plot([dec[0] - da, dec[0] + da], [dec[1], dec[1]], 'k', alpha=0.5)\n", + " pl.text(dec[0] - .5, dec[1] + 2, name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig 1 : plots source and target samples\n", + "---------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1.85, 5.85, -4.167353448800062, 4.244952120369078)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1)\n", + "pl.clf()\n", + "plot_ax(dec1, 'Source 1')\n", + "plot_ax(dec2, 'Source 2')\n", + "plot_ax(dect, 'Target')\n", + "pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9,\n", + " label='Source 1 ({:1.2f}, {:1.2f})'.format(1 - p1, p1))\n", + "pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9,\n", + " label='Source 2 ({:1.2f}, {:1.2f})'.format(1 - p2, p2))\n", + "pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9,\n", + " label='Target ({:1.2f}, {:1.2f})'.format(1 - pt, pt))\n", + "pl.title('Data')\n", + "\n", + "pl.legend()\n", + "pl.axis('equal')\n", + "pl.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instantiate Sinkhorn transport algorithm and fit them for all source domains\n", + "----------------------------------------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1, metric='sqeuclidean')\n", + "\n", + "\n", + "def print_G(G, xs, ys, xt):\n", + " for i in range(G.shape[0]):\n", + " for j in range(G.shape[1]):\n", + " if G[i, j] > 5e-4:\n", + " if ys[i]:\n", + " c = 'b'\n", + " else:\n", + " c = 'r'\n", + " pl.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], xt[j, 1]], c, alpha=.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig 2 : plot optimal couplings and transported samples\n", + "------------------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1.85, 5.85, -4.170525419290473, 4.251885380465107)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(2)\n", + "pl.clf()\n", + "plot_ax(dec1, 'Source 1')\n", + "plot_ax(dec2, 'Source 2')\n", + "plot_ax(dect, 'Target')\n", + "print_G(ot_sinkhorn.fit(Xs=xs1, Xt=xt).coupling_, xs1, ys1, xt)\n", + "print_G(ot_sinkhorn.fit(Xs=xs2, Xt=xt).coupling_, xs2, ys2, xt)\n", + "pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9)\n", + "pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9)\n", + "pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9)\n", + "\n", + "pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1')\n", + "pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2')\n", + "\n", + "pl.title('Independent OT')\n", + "\n", + "pl.legend()\n", + "pl.axis('equal')\n", + "pl.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instantiate JCPOT adaptation algorithm and fit it\n", + "----------------------------------------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(-1.85, 5.85, -4.170525419290473, 4.251885380465107)" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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PWblyZfTuu++uHT9+/OTDDjtsyrXXXrullFFpaem0q6++euS4ceOmnHDCCeP//ve/lx577LETRo4cecQDDzywTXmln/3sZ7VHH310l0+4AOeff37nMcccky4+bk9eH8DRRx+dnjp16u5Wk9kuAks3wFZIJBuyyUT9opCbv9jJu5T1dNeWZDPjsfW6AvQDK1ZE6OnZswZNaanHuHE7tVqvvPLKltmzZw+/7LLL2pYuXVr6+c9/vvkf//hHOcDUqVPTr7766juRSIQ//OEPFddff/3IuXPnrrzzzjvrrrvuuo3XXnttSzqdFtd1eeSRR6qGDRuWe+6551YANDc3b2W2XnbZZe2vvPLK5vLy8vxtt922cdmyZdG1a9fGfvGLX7w3c+bM1atXr47ceuutiQULFiytq6tzTzrppPH3339/9ZVXXtmWSqVCM2fO7Lj77rsbzzzzzLE33XRT4oUXXli+cOHC+Gc/+9lDP/WpT21VJn7JkiUlRx99dM/Orn1PXt/eREC6AbaC1XDn9FRWPhzr6RlKLnN22nilcZH7gd9hTMe+HmOA7eO4445LNTY2xu65557aM844YyvyamlpCV922WWHrl69Oi4iJpfLCcDxxx/fffvttw9vbGyMXn755a1HHHFE5uijj07deOON9ddee23ioosuaj/77LO7+u6xgOHDh2dnzpzZDfDiiy+WzZgxo3PEiBEuwGWXXdYyb9688iuvvLItEomYSy+9tANgypQpqVgs5sViMXPsscemksnkbidt2tvXt6cQkG6ALUgm6mdS0HA/nSktDWfc6ENxJ/2RcGfHpojnTUDkKWAe2686HKAfFunexNlnn902e/bs+qeffnrZpk2btvzGb7jhhsQpp5zS+cwzz6xctmxZ9PTTT58AcM0117ScdNJJ3Y899ljV+eeff9idd9655sILL+xcuHDh248++mjVzTffnHj22Wc7br/99vXb7xVKS0v7leTIcRwTCumDQCgUIhaLGYBwOEw+n5fex0+ZMiX9/PPPl++s3b19fXsKAekGKMbfgE8BD1kNN59M1H887ZR/tLa9bQHwceASYDoij6ERbwH2M1x77bVN1dXV+WOPPTb15JNPVvjbOzo6wiNHjswC3H333YP97W+//XZ00qRJmSlTpmxau3ZtdNGiRSVHHnlkesiQIe51113XUlNTk//FL34xuK++toeTTjqp+/rrr69fv369U1dX5z788MO11113Xb9DZYvxxS9+sfnHP/7xsAcffLDq8ssvbwd46qmnygcPHrxVCtIP8vreD4KFtABbkEg2mESy4TfFi2aJZEMukWx4UDTc+IdoCPEQ4FpELkekel+NN0DfGDt2bO6mm27ahuBuuOGGDbfeeuvISZMmTXbdAl/9+te/rh0/fvyUiRMnTl66dGnJ1Vdf3bxgwYKSo446atLEiRMnf/vb3x5xyy237JIVOGrUqNzs2bOTp5xyyvhJkyZNmTp1avcVV1zRtjvXU15ebh5//PEVP/3pT4eMGjXq8LFjx0756U9/OmTYsGFbke6evr5f/epX1UOHDj1y0aJFZRdffPFhJ5544mG7M/7eCCLSDnIkE/Uh4KxEsuEvRdviwAmJZMPfdqtRkRHAR9DMYy3AX4FXMGafPlbvSwQRaQMXQUTaAEYyUX9EMlHfeyHie8BTt1VV/+ackpJrk4n6UuAfwLPJRP2EZKJ+mj33REvGO4cmS/858CCa2+ES4IuIjEdkG00uQIAABQSke5AgmagfhpLpQz7xJhP1lwNfBbyKUOgTZ8ZL7gLeA6ahZPlpYGEyUf9DVM99MJmov6pXu/9mF9i27m/EyBzGvAz8DHgFGIVmNLsEW50iQIAA2yIg3QMQyUS9JBP1kV6bm4Gvo4/9DyUT9Z8GHgBeBC4w4H2qrTVUuXH9EFIpFzDAN4B1KDEvAmLAvclE/WdtP18G7kDJeEhR/1cATyUT9SUYsxF4DPV42AQcB3wakZPQKhYBAgQoQuC9cGDiJ8DoZKL+Y4lkQyaZqI8Bj6BW7H+WtLf9MJrLfqSjvOJ1Ey85Fygxba0bq2B4OJ8n3trs5FshBZQhIzrLStPpqprzgC7gD8Av1g2uu5FYfCzwPHAM8Fdr8Z6FVqOYhyY2B037uBiRJPAh4Gh73ARE/gG8gzFBzbQAAQgs3QMVS4ELgIeTifoKlHDPB2qAr4c8j0g+z+DW1nG1mze9IqnU6lB1zfC/RaJbqkWG0cQKgqGquzs6dF3jS4l1jafgefNinR1S2942trK5maHrGq+1bY910umNuO79KOGen0g2bB0lZEwTKlM8AbwDJOw4P2IX3wIEGPAILN0DEIlkw38nE/WQTv9XWaq7o7ukDOLxa4EK4Irumlq60+l8VVdnhZN3p9R2tPPhkJgFkUi+qabWKd20Ad/TXLSETxgY48LjNZs2RjtLSsiEI8TyObxQ6OnEusb711fV/Kk0m7kUgVgm+0g07/ZtuarVuwSRjUASTa4+BRiOJjR/FWN2VhooQICDFoGle+Di3nguS0k2y+D2Voasa5wmmcwc4F2Ayq7OcFdJCe3llXjhkBntunJhOu2UdnZsTNcNpcmJYgCjE2/eQNhANOLlGdTd1V2SSf21KxbzsuFIIhsKXVXd3X2Jm8/jIeScyJw8/AiR8xDpO2zTmM3Ay8DfgcVABNV7P4bIkYh8YLHuAwVBasc9i6uvvnrkoYceOmX8+PGTzzzzzLFNTU175DsbkO4BCF/DTVdU0l5R2ZOXMCYU/tzglqbNZa0tY0JtbcSzGQa1t1HT3ko2m5X5YWdD1vOaSrKZoTUtzc2D87mvdlTVfC6F5Pvw1I4JnFLZ3Z0Br7k7FhuK8TZXZTPzy3p6XsyFQhU98dJL8nAxcCsip/VJvsZkMGYpSr6vAY2oBHIacBEih+zF2zTgUJzaEeCDSu24dOnSt6dMmbIlG5fneeTzey5K3E/tePXVV29es2bNkrfffnvpddddt3lvp3acNWtWx/Lly99avnz52+PGjUvffPPNw/ZEuwHpHph4ANVZr82VllW3Dh78XsrLO2LMISWpVLg6myYdia7z0JWuMuAcNzc4DY3ZsPNqNO++HTLmqtr21gvd8rKXmssrHQ+aQ5AVMNb6dcIQiedyZaWpVGss7/45BK86mNbyVM9SJ5tpFfV8iKK1025A5AS0YOXWUKv3VZR43wY6UBezcxCZicg26fwC7B6C1I57LrXjJZdc0hGJ6Nf5+OOP734/yXiKEWi6BybuBZ5JJBvuBkgm6v9d4I8AIQxh13UFhuSdCBk3Z+IgAs5hcLiTzeQz8HQ6Gju6JJcbUtaTKi0Jh1ZF4CuZUOhMx/OuQTVeDOCA40AlWmjyHWCZg1niePla1FNhDdAKDEJz8Z6IyHPA61tFqBmTAZYh0gq0A8NQq/dwYCQiS4A3DoaothUriPT07FmDprQUb9w4gtSOO8DeTO143333Db700ktbdnRMfxGQ7gGIRLLhz/77ZKL+BuDbnVU1SGcHcS+PUSsVx81lHWjPOZG6zW6O4fb/HYZzSrMZJwMbSw3zxcsPycOtuWhscnck0lDV2fEucKqBiAuu/ZLE0TLvhwErgPXoItxIoBb10c3Yz5cDx1l3sTd6ke8mRNqB0fbYapR8ZwCjEFkEvEcQn75bCFI77vnru+GGG4aFw2FzzTXXBKQ70JFM1E9Aw3yhrOy+jrKyf+ns7l5Z2d5WH8NgICpQF3Vz2VIItYAz2P7PBdwyOETU4vynC4loNpMrS6dWhuHFPPw6D3cKlHpspUOVAVOBQ4EGNPeCgy6UtQHd9vDxqMvYMYjMB5ZgjGYh2drqHQWkUZliKKr3jkfkVYxp3ou3b6+hPxbp3kSQ2nHPXd8dd9wxaO7cudUvvPDCcn/M7xcB6R7YWA68BHwYtRq/ZsrK6ttLSzc769d7tXhDAQQiNZAv/kUYMB5IWMnuxBik8563StTyPDcMDQK3eHBICK5ArdHiH0Ql6g7WAWxGJYZSlIQ7gSxK0FOAEcCHLPm+U0S+xVbvMNRSLrWfByOyAliEMVtpdwF2jCC14565vkceeaTyJz/5ybAXXnhhWUVFxR4L7gkW0g5gJJINBjgZ+EZ5S/MZtZs23Bbp6nwJkf/rjhgxtKW0HPuMngec4n+2gBjA1X3kVT6Y5MGUHNQBk124MR8KHYuGFz+LWqRs3QxVwBg049gE1MKtRwnX18nqUO32I8AViEzZ4jKmVu9yVC/egJL4SlRSPgK4AJGJiATf1X4iSO24Z67vK1/5yiHd3d3h008/ffzEiRMnf/KTn9wj3jZBascDHMlE/U3ALaWtLZHSTJqQMZlsJBLripekvXjJcA/Oj7e13V+RTW8xUwVcA471VBD/s7/PHua6EA9BKgJvilYKXgX8CzCWAqEWI49GF3eiuSAagY2oBYs9pxu1jBvRCsPLtlSh0FwNo4DBqMVrUAveAE3AAozZsEdu3B5GkNpx4GJXUzsG8sKBjzAQ6ampzad6eu6t7On6YsTNU9vZmQt1tN9l4H83DhvR8eyGdZXnouYsbEWujin6HhS9dxxwQ9Btdd+L0MWyP6P67SfYVnLwo4tLUJliCEqwq+3fnN03CvV2SABHIrIYWI4xaUSWo9pwApUwVqGRdkOBmYisQT0jgqi2AAckAkv3AEYyUT8SfTQP4XlOdXNTOB2N4YZDVPT0EHXdzflQqCwdjZY+Ew7RYGj+dDbbVOnmxouVFcwOJt6wki7osR2oTptHrdeXUE33w2h2smL4XyoPJdoue84a1GLNoGRtUKt4M7AWjVxbgTF5a/WORuWLUnvOcJTU08BbFOvD+xiBpTtwEVi6AwiJZENjMlF/PBAim/1nKJelPJclH4nQFY3mnFistiSTDsdzWT6UMfmEyCAxZk1TdW2urL2NUuOFfIu3L/L1gytELdga9NhO9JH/o6jl+wiahWwchTUCsaeDLtTVoNbqMDQfwzpUfkjZ7SWo5TsSWIPIG6iFuwy1lhMo8W5AxzICzQk8BpGFQHI/cDHzPM+TUCi0r8cR4AOE53nF3/V+ISDdAx/LgD8Qj4dzsRjxTIZwLkdVLhdxURMzBoREzCGel6mAo8vaWuiBTW1lFZVl3Z0SgXAfuq4YcPKWlEP6coCaPOQMZB39PBQlX9/yHWTH5ROwn7vXQa3WcpREN9jXZlTndVDyHYIuxK0B3kTTVfoeDhUoia9CpYchwCnAOkQW7OPy8Es2b948ua6urj0g3oEBz/Nk8+bNVcCSXTkvkBf2UyQT9QJcD9ybSDZstttiwA3ADxPJhlQyUT8I+HfgZmAxmcxRVZ0dONnMNqtcXdDVBpFhEHNQYvXApJCII7zoGDMjjFq8RXqvYMOCxUoNNkkOKKmmHZUdPFR2aEE5fiQF+Rh7ir+Y5ocJuxQW1TagxN2JyhFZ+3cdSrpvobpwHWrlRoraHYzqxx4qtby5L6LaFixYMMRxnJ+jXhqBp8XAgAcscV33C9OnT++3O1xAuvspkon6Sejq/rvATJTcHgXOAy5ENdJnUdK9Go3o+i3GXBZtbg6VZ9NbPcaI+uXmgZyni2ODwwVpARc6DNREIBMCwmrlEkJP6kXGIcAraj+NGtX2UDpRi7earQkoi0oKUfsy9pxuVG7YjEa6ddrjMugXuwHVfJfY6z4ElRv8eSBq+6sEetAqGKv3A8khQIBtEJDufgxbqeFJstm1QCvR6HHANYlkw93JRH0Z6klwImoRvoB6FChyOVPa3CxlnruVe4ElTtdAPq/ZxLby3w0puaXDqsOWYhfJrEawRX7oQ5fyrdke+9f/YtWgskHxcd34hSu0fcv79KCeC00o+bZRsH491M1sDWr5plGN2F+QE9tPnR33JtTFbI+EbgYIsKcQkO5+jmSi/tyK1pY/hb08eQn/vjLd8wmbKByfeEvaWk82IqRLyzYQidyNnyfBmEQok5lQ3tIUCQMRJa7ei2euV0S8VttFoMtRYnM8iHlQUky6VuPtCz75plASFZQEK9jatzePRrG5qIUaKdqeRi37JtTKbbcv1+5vROWGFeiY4na72M9lKPk6aKDFG0FUW4D9BcFC2n4Mq+Fel4lGKU2nieZyZ+bh92GRJ4DfJIzpSibqH2IIrJUAACAASURBVIznsieHPY+qnu5Wx5j3gF8nR4wMIfJbLx4/3HEipN0cecjHwSsmT6OE6wmkPH1Mjwi4YV3wKsvrMSGjZN2JJV+vaIGtFwQlwaj961u/KVRuiNtjwqgem0Y13RKUmCMULGx/sawFJd9m1PodherGo1Dy3YRNxm778dCxVqOBHPWIvAms3BKIESDAPkJg6e6nsITra7jXYMyKaKrnydJUqieeSb8RhpaeWKyrraLqSuM488va20fEc5l6x3XXOvr4/eCGuqFL85HIS7S3x+juJAZUKQnGYKsACXoRcS6ix5W4BevUOLqty1VSLPWZcyfIo1puBiVY3/2s94TfhpJvLdp+RIeFZ8/vQS3jBtQCbqXgB9yASiydqEXcY/uJ6iUxxPbZAiy0FYwDBNgnCEh3P0UyUT8WdcOaXZQ3dybGPFLZ2vKteDZzihsOny+wMZLL/cR1nCdaKqt/XpJKHVee6n5TQDKR6PhUPJ51crnK7kyanDFenYRCUeM1ATEDFb39dK10kA/p9hyq/cbDBQIDSOehI6yGbi39W633ZQMXJeA4atkWS84uSqA9qCtanCLSp7AQ126P8xPt5CiQbztK4EkKujHo9dXbNtei5LvTHK0BAuxpBKS7HyOZqK9KJBva+9qWjsVL09HIjyq6uiSsj9o9eVjSWllVVdvR/ogHl7th55OCKXXz+cgaJF8pEhsMmYjxXguB48I4gWpj/XSL+7FmaJ6CpekTZWnRYVn00T6Oeg9sk5avD/ikm7Ptl7L1QhsoWS4vajfGljgNxJ7rk+961KOhxbadsp9TKPGusm2W2fFWoN4PebSKxbL9JaotwMBAQLoHOkQqgeOBWWiO2yzwtgvvtJdX/DDi5oaUptOvr4PqDAwfEQpXRb38ygg0ZmFwHpniYLocCHkQMZY4e0kHnn35EkFvosyh5FeOWr47g+8qlqLgZlbBtpJDCxr8MRQNrAjbl29Z5yi4q21E5Ylmu83f3oFmMPMDKsJ23wjbbjvqYrY/RLUFGAAISPdggIigi1JHo0nADzXgpaOxQzHeiyW53MKn4OKxMG6USF3YmA0OdHkgLoxwYH1Izx+ch3IDkTB9mq0+WWZR4iqhsDAGammuRxewKrY9vc/2chRczAQl7uKu87bNRpQky1HSdVAClaLx9KB670ZUekhRCLTYACyw+2qK+hxrx9qIJtLZl1FtAQYAAtI9mKA5aocDk9BENGPQBanuP0JFA2y+TgnsaFTfBPWZzaHkVYdag5UU8uH2JRn4lq+LkloEG21s97so6fnJanaGYvL1bFvxXsdkUR9dP/DCt7SjKAELBU+JNKrrNqMk22mPzaDeDq/Y9mKoJVyBliECtYqX+m55AQLsaQSkezBCM3SNRPMVHAOMXQ5T05A9Ep5EQ2vHo8Q8BLUmm1DyK0ODDsrsK46S6Y7I1/ciCLE1+foZxrYEWewEvS3fEgr+uz660AUzj4Jc4HsqRGzfaXQy8f+2o/puhz2mA41uW2KvK4+S9CH2vnUBbwBrMWaPVQwIEAAC0j24oXrvSGD4o/D5MZCYpgTUgZLKJtTqnYpKAp12Xwi1mCtR4itDyWp7HmIehbQMvkZbfLyLEmCc/vmG+xFqGTsWn/iL+2tDrXaHgjXtW9yOPT5FgXxz9voa7HY/MfrrKCFnKJQZmmzvx0ZUcjgga7UF2D8RkO7BDqv3XgXXJKD020ok01BCbUUjtnJoOfVhKMn2oARUg2q9JRQWz3w91f/i+Baw71NL0WfXHusTbd5uC7Nr5Ouf07tSrGuvoZlCPgcXJd4YBTe3bnQyyRV9XkdBWmlEM5o12eM22fszyfa7Ci2smerHmBGRQcBf7cdh9ro328/Hmr0gXYjI0cAQY8xf9nTbAfYsAtIdIIiLfPY4qJ4H81Ct9ThUYohTWICqQi3catRq7LF/q1AS8iPGIhQWsnwL18+BAEq+vpWbL/rsH++Ts9D/jFx+CLAvIRQjjXo6dFOwsD373o+OA51I2uy+qD3eL6TZjoYVN1JIO7kJ1cVH2z6WAKt2JapNRG4Fuowxt+/COWGzi5FzIvIF4HBjzJd35bwAHzwC0h0gEJGrAAw8iOazrUalh8kUUjH6Fqivl/qZvCIoSVWgLmE+sfnWZXEiZ/+9/8XyF+Py9tV7cc6wfc24L/gk3lvrzaOTRDsqH/gTA7Z9PyzZz4LWRCEvRIZCrocNaPBEE4X8Dhm0SOZgu30RsKk/Lma9SVdE/oi6q8WBHxtjfi4ijm33PuB0NGtcHfBDVF/+B1BvjPmIiJQDd6H/twhwC5ptbhn6JJIEvmWMeWRnYwuwbxDkXhho0MQvK63em0Itv5EooQxB3bLqUHJssn+rKDyapylIDr7nQpxC2K5v9WLfpymQtG+BmqL9oaL3/SHe4nYoOt+vzxZDiarbjk/sNj9Pb5SC1e4HV8TsNQ9GCXE4qv2ORL0a3ka13yqUfE/Dr3Cx67XaPmOMaRGRUuA1EXkUnQSqgOeNMV+2+5ajC51rgd8VnX8L8BdjzFUiUoN6YhwJ3EZg6R4QCEh3oMKYDkSWUqjc4CeeEbT0zhEoAfWgFt8glLhK0Uf9Kgpk3IMSnm8t++TrW8n+QlrxIhds7f3gu31B/wi4L7IWlFSr7N8elIB77PFltq8uO65qCtr2Ojv+GtSa96tXDEK9Gqagfr5/R4n4UGAEIm+jdd36mzj9/4jIhfb9SNRPeBE6ITxmt08Glhlj1gCIyG+BT9t9ZwHniMjX7Oe4HV+AAwQB6Q5k6OPxZkRa0AW2I1Aieg19pD0NOAGthrABtb58q3gTStaDUTLtRsmsyn4OY4tfUvCl9f1oSynIDP530M+fUKzZ9od8+yJq3+r15Y9u1JrMoBZ6uT0uZc+ptdfdgj6eR9AFsKNQTXetvc5xqNzwHPpIPw31/jjUVjRetyPJQUTOAE4GZhhjUiLyIgV/5JTpn9YnwEeMMSt7tX1yP84NsB8gIN09CBE5G/2x7o84EXxnhr5RA86l6lY2LAPua7ChHl65DE4cAkfkYUoTbMxBdjAMiUHcg0wZxCJQYoXbdAjcmH52/IoVoqkhywx4LmQ9cCMQFZtPQSBiv4xZCi5nu0O+xfDJ3ncla0et2naUZKvscX4poVq9DTSjVm4ElRqOQDXfJCrBTEAX1Z5BvT+OQMm00UoOW+XLKEIV0GIJdwrqQ90X3gYmiEg9urB3WdG+ucC/AV8GEJFpxpjX0UmlP1GAAfYxgoW0PYgDgXSBF3d24DQovxzGVkHJcmh+DZqugsknwLRyqOqG9nboiEGkUkv8OBGIW6INAZK3xOtA3BKu54IX1vchA25eE6hjt0l0a4nBrxZRnN2st5varsAP1EhRyMvrk6pfVsjv03eJ8/P4xlGt27FttKDknUQ11XmoDDHOnvcuWh4+U7yQJhq08jgqXSyz53wDmA80GWOq/cGKyEeA79v+XgPixpjPiEgZ8H/R8kwhYIUx5iIRqQOe0tvJt4OFtP0XAekOEGzxXjDmvn6eEEYfrw9DiepN1Aq8CH2kjqALbRn0cd3XQv0oNihkLvPDdou9GHzvAz/bmEshBNg/1hS1UUzIHrtf/NEPmPB9dZtRuWMshTSVxb7BHoWIthC64BZBNeoMqomvRCWH11H5JYFanm+ym1FtIlJujOkSfTS5G3jTGHPnbl1xgP0KAekOEOwy6RZO9LXNSgrVG2agxDuMglbru30lUAL2F818uaC0aLsf7uvLCMWf/YTl1RTIt5i0/PSOsPvk61HQoDtRnbYVnRzGoxKCT7gpCgUw29GJJo9ONH7+hxyqcb+FBkWsoeCWtx5YvKu12kTkP4FPoffrNeCLJig5dFAgIN0Bgt0mXT25DNUxR1IIKBiFPk4nwC9KQRQlpDjq+VBh3/veC75nQUXRsb5bV4RCKXffwyCFWpZ+JjM/yKJYZng/koOLkm43upC4nMLi4BR0UvHHlabgldGNknQHhYU53wd4M/Aq8DdUwvAJfCWaSCdInD7AEZDuAIFPusfd8pe/Al8Cbpw/Z1YeYMbsudOAM+fPmfWDHTTgoG5Sw1CCaUWJtxYlFt8LwCcqByWjQSjRhij4zlZRqBLsezlkKIQM+768WduP2HaKw4CL80C8H+LF9t2FkuR7KPlGUUt1Mjqx+KTqJ2CHQjDGZrtvKIXFrCbgZbT6R9reixRqDa8OEqcPXASkO0BQRLrlwJ3Ar4DPoY71z6KkM3X+nFltO2loOGrF+gEHw9EwWVBSqbT7h6CSQjlKkH7msjxqKTuoRVlNIQNZFrU+o2ydJjKNyhp+UEOIwoJXsQdOnl2LbuuNLGr5rkcf6TehlrZv6R9ir8mfENyi9/6iW8YeN9iOpQldbJtPIRPbZvyEQ8EPcMAhIN0BgmJ5YcbsuTcDt3WsLV9WeUjXMNRaO23+nFmrdtRGUWOVqNXroEQ7FNV4o+hjdBS1Dn0reKjdFqPgBdCNWpZ+Uh3fr9avUJGjkGDHT1TejhJwDQV5wtdXe5d370fNzD7h68vtqBfCn21fY+x4DkVJtaxoXL7skEHJd7m9L2PRySaMThqvowEWfnXkNcBbGOPn+w0wABCQ7gBBb013ynXPPbn6j0eeFy1PE4q65zQvPmTXslOJ+ERUhpJnFJiJWoaN6OLUWFQWKLfH+hJBrd3mk5uHEq9fCdgvp+5HkhVbvimU2KIUyDaFWp2D2Nbyfb/k24pqtL9CpYaJdoz1FNJfClvXmEujGvEy1HIej+rhjr3epcBC2/Zm+3lVkDh9YCAg3QGCXpbuNGN4NvlSfW3rO8PIpyKbMq2lP4Xwt4yh/+5N6s6UQK3XbpRAzkKJKQe8gJJyPUpUQ1ArsQYl5yqUGDtQK7GMggZc7GLmh+36CdI9e04HSnq+3tqMkuxQtk6I837J16+E8RjwG3RyOdyOcwSFicVf2PNDnTtRiWJZXkvXnxyCIQLZEDSFNGXk6+gk9R4acLE+SJx+cCMg3QGCIk13MQUN97SWVVXXNi2q/4+exqrNmbbSVyH0A2OYt4uN16CLagYlj0koMVWhFt06ClZhNWrRDkW14BEoQfrldXyfXz+dpK/3+lUg/LpsoMTehpJqBUqsGZToylCS94MrivNB7C6yaDj0ncDTwLmoO10dasn6XhmgxBsHjAuZPFTlHMfLG6JhY7IR4xnHmE1hva71qE/vEvv3LYzZsbYe4IBFQLoDBEWkmwK+C5zha7gzZs+9OfnyyM82PjOxAcJRNO/CHGPof5FGjbbydc/1KMFdiEoMDeijdohC0Uo/Cuxw1DIejBLmGlQuGEkhIU0pSmbG7uui4I7mb2uzfft+wB2oDFFDgXzfr5eDjyyaBOib6ILbecB01Oo/pGhcBih1tcy942/IhiOYkOQiudyKEkzWjtFfYFyCejzMB94l8M096BCQ7gBBL3khPn/OrK1+zDNmz42/ctusEuBb6KJYJ/Bz4GFj6N+XRCSEks4glPQ2oklzpqJW6SpUx4yjFm0FKhUMQUlrEgV3tHdQwh1tj/NrthVHieVQYvbdzLpQ4vYT6vhVgHOoNeonZ4ddS6C+PWTQPAm3o94Ip6GBI4eik0bMg1QaRkWKtGYPIBQij3SXevmfopb/Eeiim9h7txx4Ao10a9yVxOkB9m8EpDtAsCvBESKch/ry1qCW13eNoX+eDdrAYJR8XVRumIpm5KpCrV4/320VSoK+p8Jk4FhUA86j1mSj/TyKbYtluqiFCwXL2UUX4Dzbpm/1rkOJvo6CfzAU8vPuLnyvhVezodCf26pqZkbc3PHRTCYfzWaawlDqwTAPHN+frYsQ8YhDxHVTUeM1rIPONfDO8fqEMB2daKKoBb8Mzaf7Z4xpeh/jDLCfICDdAYAZs+dGl/7qhhs7Vi9+zyfdGbPnVgOJ+XNmvdXXOSJUAF9Hs2dlgEeBe4zZEhiwY2gi7jEoeTSiZHccatVuoFBcEtSS7USJdgxK0FNRUm5DPSGyFCLgoij5xii4bbWgZFxGIXlNmoIvbx7Nn7ABJXHf08H/ARQT8S4jr9nUJEMolC0vC+UiEZxMNlWe7nlPPC8hOi7HQ8jGoqSjsVQk5/5vdbpnSAucEIZwlQZOPGPHfxKa96IMJd93gV8Cj2NMdzJRPxgNE65Hkxg9mUg2BAEXBwAC0h0AmDF77vc2vT73q23vvnJHy9KXvmoJdy76gx03f86sPkNTRRD0cfkrKNmtBu4AXumX5KBJc0ajVmgrSoQnootqG1HSy1GobeaT5xB0gcrPZ+uhluoGlKDHoCQeoyA5gE4O7WxNyH5Jdz+UOI1KAlmU1KoohPb69eB2OeWpz3a+dusCJholHw5nSlOpH2ec6JXiZof6bg0GOh34ncDPvgGXfgpOn6J9x9H8vY/YcZ2Wh+lGJwkXeDcTdv7UVjfkK4RCEXSy6EIJ+7REsqFfxTMD7DsEpDsAMGP23NoN/3x8YSgSTwyZNusKlESnAZfOnzPriZ2dL0I5cA26YCRoUpe7jKG1XwMQGYqSdhYl2+NR4tyIkmIctURTKME2oQQ4CrX4JqFk24YutIHqn4dQkCiK9d4uCqV5ijOe+VnM/LSNC+35EyiUH2qi4N7Wu/rwdmFD07YiawO4EspFhAcinvdCRkJDBE4MG++QcCEnQ9t8WDkbnp6rVu4NeTjB6PU0dkZjq7xY/IJYqsdx3JzZUtM+HHZS8VJSZWXgONh7dztwW2Dx7t8ISHeAIFo5+EvlR35idvuC79eVDG9nzLlLLn7jrjP/0N/zrdV7FPDvqLP/RrSQ4p+MYeeLPFpQcQxKNElUv52CWp5+WZw4apX6hSNjdts0VOYYipJLI0rAfikdPx9ElIJM4Nl2iuu4hVBy9xPs5FCrcpG9ptF2e6cdY5Vt33db2y4sy4nZVqJwQ/B6WLXsJg9WZMLhweG8NymMqQ/DiDSUZKCnEn65fsiwOuO6n6jq6pBozg2HjAm5TjjUGi8hIkI8lSLsujgUHjU2V9dCaSkU/Je/lkg23L2zMQfYNwhId4AgWl57XfmYm25pf/eaoV4uAnk6IHIPcP2uBERYq/cTwEdRIlsA/I8xvNuPkyPoyn4FSqo1aPWEGPp4HEVdx/zIrUYKpXdqUN/fI21rDfZVjnoKHEohH67vYuZru10USgH5KSf9tJB+RNtbtr0jKSTwaUZljRp0wijd3qUVywv2LwKurUeUA9pceDcXjU73kEg+HMrnjYTimdQr7cbUV8KgKIQ8iHWXlkZ6qmognaayu4t4Vgtb5EMhmuuGEkv1UNrZhWMdGrrCEVI1NRDdYpj3AJ9MJBse3+n/JMAHjoB0BwBmzJ5bveGVPywIx0pG1R016wtv3X/k97saa2vJhV1wNgPfRhfJ+kW+1uqdhJYKn4ZanU8ADxjDjjVFjWIbgVqnPSghHY+SrR826wdQpFHvBxcl13J0Me0Me/4GYDFqvQ63+w6lkBfBJ19QC9pPYO6HEPvk6//tRCWHPOo7XGn73ohGpFXZ6+6TfIue6bfUhwtBm6eTUxwI5SGUBcLRGAbwhNwbmcw/HUhNhRlhW7/NAK2xEkxlJdGuLkqyGTwMPdU1hPN5zxhjPGPCJdks4XwegyETidFTWuqT76uJZMOxO/xfBNgnCEh3AGDG7Lnf37xo7lfbVrx2V/Nbz395xuy5tV0bo8+u+MORkzIbq7MQDqHkdhvw+10g3wpU5/04ag0uQ1fYX9lpGyJVKEGCLrJ9CNVwG1CSy6PkHKXgZuaghFyDLsgdhfLTAtSvdzAajDGFQp5fUMmhOJlOJ/oYXm7b8yUBP8R4I2r5Vtgx+YU3/WKcVag84uvFW8GjkGndT0PmguNX4oRCImEnEvM2uNmWrDGrE2HnsHDerfJLZKRDYXKVleQlhCeC42nGy7zjpHNOJEkoNARjKqLd3ZSlU4S8PK2V1XjxOMD6RLJhxA7/BwH2CQLSHQCYMXtu7J1f33hz+6oFK4pcxmqBw165bVYt8COU4DzIL4LwD4C/9MdDwVq9E1H3peNR6/QF4BfGsHknJ8coPLa3UCDMDlTn7UZJsRy1pttRPipBiXc8utBWi8oAc1HLuR61SKdRSLHol2D3k6H7iXM6UKu5gkJJID9dYyPqO1uL6sd+WaEwhYoZwyhUkNgGxQtsveOQm6JxQuVlrGxtaTzEmNRQHWuFsT69bixOJho1hMMmZEh74ZDJRqKYcPhy4M9OLncG8FsxptaEBDfvQSyGvb5HEsmGy7YZUIB9jvcbkRNgP4B1AdvutvlzZmXaVy1Y0euQYcDvj7tlbgQNxf0R4mYJeyeC+yvgQRFOt6S6XRiDMYalaBHFO1Ad9Czg/xfhQpEdeAAYk0Gt480osTWg4a9xlDSrUaL1S91UUwhG2IBGgT2KWqVDUYv7UFQieBr4LZpIvJNCuZ0MBZ3YlznWo5ZyBwWVwM+idiSFKDE/n0MMXYDzE9a8jU42O4QfAuenL4tHHEKeoUdCm1pgYwgW5iFrJOSGgEgm7ZZ1d7tVHe2PiOfemonFrzLh8LDEusa/JNY11g/dvLE5lknf7DrhHteJGEu4LvpUcPPOxhNg3yCwdA9wzJg993Tg98DH58+Z9bTd9iVgNnDC/DmzVsC2EWlFvrrTgEuBdHdz7IlVT07q7lkz2LU5GJqB54H/BV7cmeVrCXoCmnPhFJRnFqNeDst3eL5ILfoon0d56RiUZFfYccQoRJq56KKYH9I7FLW2T0KtzmXAkyjBDkXJ82Q7trhto4yCi1cGtZRX2eNHUQgl9qtYZFBi9Wum+V4X61ALtQa1huuxOrJl75DZjnHjASYUYoUIm0Mhd7oTebw81fNUOuycE/a8Y8PGiwj0hHXSeRv9Py+0/YRQuWN9csTID6FVhQ9DJ5nvJJINK7d7rwPsUwSke4Bjxuy5g9GsYRPQSr2HAXehC1sfmz9nVhb6DgP2iXfFE+OODTkeY85dtTifCc987ftnnAhcjy5MCeo+9Q/gAeDlfpBvBUqA59k2WuwYf2cM20/YLVKCEmQcJdipKIk1oNaoT3btFMr++I/2fgrJE+09aEaJ93U0kMKve3Y66gbmE6Gfncy3hN9GZYWjsfkTKEgSadTrwid9x/bzKmqtH46SdsLAkLwS7k4DLXqATRJiUGlZLiTyy+qujmdROUWAU1HZZpgd80rgl1kn8sTmIUP/FbjO3oMngBsTyYaNO+svwL5FQLoHAXziffPeaVMjlSnGnP/OE9E4HwNy8+fMMrBVlrFf+tvsuR9b8KMP/y6fiWCMNOFGv4mSdhwl3o+ij9RZ1LJ7BXgIeHVH5Fuk9c5Erd4SdLHuN/bcvh34NYptFGo5plG5YAKFwAi/PHozSsTDUanA12vr0OQxH0YJb77t07HHDkKj7E62xxqUWH2PBN9bYQ1qaY+gkFjdj3DrQuUGB7VqXdRKnodOMsfnYZCBsv6Qro88kIuXZDMlJT/NxuJLTCj0u8S6xgw6AVyGTih1QC4VdmJtZeWDvfJyf9w5O+6JiWRDd3/7DPDBIyDdgwRH/ufcW5bcccockw9Dns5QaWbJ2I8t7qod1XXh/Dmz0iJy1ZDp551+6Hn/lgKunT9nljdj9tyzgCc6O1m/9rGpNT3rqsu8rJMGZy3wPeBB1Gq8CfUuKEf10Qbgn6ieunAn5FsBnIBamFPQx/NXgV8bw7rtXpDIENTSdFECnopam6tQ41BQ6/JdO64EhXSPEdRiPhUl8HXAvaiXgx9MMRydEPzy8sae70sWebSiwx9tG6ejhO2XjO+hUDSztGh7B7AqD54Lp4R6ZRcz7DzBQwborBnUQzRKPJP+Xkmq5+WusvLhLvLWsJamyS5ca5BjEZxsKERHWQVeeTnowuP/SSQb7tlJFwH2IQLSPYAwY/bcUUB2/pxZ64u2HYH6rf6os5OX1v7hyIndyZoqk3U8nHwkWplqyXaUfKl28qcvqB498lN1R531OPAxlMReRBeQZgL59mTJs2v/MunInmRNt11AXwX8GCWeC4EvoKQlFKK2FgB/AF7fHvkWab0noFZvNWqVPQk8awx9lyXX0u9jKAQ1TEUt2tXoY34U1TVXoGQ+DH2894tdVqJJdnx/1eeAn1AgZd+39zx0comjnFjsCtZmz1kKXGLvdRUFvbfb/vUj3nz9tzMjoYq8SAVe3gmzpdiba/T4CDvJ65sBumpqycdLQLYc2owxNzmdnd+v6OmujHgaILGpshqUeH+RSDZ8YUftBti3CEj3AMGM2XND6KJUBC0iuX7G7LmXAb9AiegJlEyPzaXkrlVPj5vYtmRkGqSSvCewifiIxY359pnn5rpLllivhW8CP5g/Z1bzjNlzBZUVBr/y3VPuIxefDd5Ywm4ZYhbjxu5CH9U/h3onVKO84OfNXYSS7xs7IN9K9NF+BioBgC56/QZY1mc4caH0eyXKW5MoeBwkKRDvSoxpt7XbEuhjeC1KvqPRiWUY6nVwJzrhjLDXMh31UvBDfl0KlYx9vI3qp1EDX/a0vZi1Wn0XNMlDzBQIeX06EpFcJDIsksl4kXzeCYMRDRfGBcdn3x2hE0gNGwGhEBhD2HVTkVwuJJhYPpfD8Tx6ampBre+bEsmGH++kyQD7EAHpHkCYMXvuScBT6ELPL4HvoJbYPagE8A379zzgiFe+PfMeGbzuLbNxeEJbyGcg/h6qPT4FzDNG0yvOmD23CiWiscAFa589bL6HO7/lnaGH51rLOjBOO1pK5iFUT/0kSpx+yG4aJb830AlgSV/kK0II9a89BrVCh6HW4vPA48bQd87YQul3UBIeY/t9z45hM/DelpyzmuthJEq+NShpn4Dqozl0lX8hqlcPoeDLOw7VoktQ4hxOIcjCzcFrPfGSyY7rVobd3BahNwy4NvrXhgD7cFHeTIf01YVKD6OBKgOO7/i7I7PX2EaoqUWMwYisykaiIzzH8a1yP7H7uESyoWV7V7J80AAAIABJREFU7QTY9whI9wDDjNlzT2p6p/b59S+NoWbChvzQ4xrDkQhfRx9vZ6PuXV+wmu2XgLuWPzaG1jdHAhHXUoQH+fVOdVe721b+Kwg/Byw+7pa5NcDfujbGJ6daYtm6Se3xjrUV/7H0vhNGAOeg1m0XanH/FX3MPgMlJt+rwEXJ9y1UPnhrO+RbgVq8R9pXCWq5Pgq8Zkwffq9bl34fjpJ3Hl3R91CNdTXGrC86ZxBq+Q6z45+ESiU1drxPoeVx3rBtTLLjOpPCQlsUOMR6I2DAyQHGcQi7bj4EXtiSrbHWK2yREzzbLgaSEfUdFuBoVy3syuLgiS4nghGhLJftU/ttrx1ELhLBC4W/hsgY4NN2fPOAaxLJhuV9nBZgP0JAugcYZsye+6H1/xz26sbXRuPlHBfxOr2cqYlUZhh+/HuPDTm89VJLuNcC/wU8Dnx7w4Kn529cdE4onTzZhv0awDhAE0QWoiT5z+HHr3i3c33Fa6mNlYTLshuzTVUfQT0WjkUzjE1HCbLNnvMmah0fjj6SN8EWjbbJHvMnYGlv8rVW7wTU+jycQnHLhWg+2YZtwolVPjiUQgjvZDue1bbfdlRCWIv/5dYyQiPRcjpHoLrvWPu3A7gfuBNjctZ7YoQ97lLUY6AUwLVasU+SHuCGHfLRaFrc3IuRXO7k8A7SQYpOSFlHnxSygOtCSRuM2JKg0cKlEEyRxyaOcKJ4dXUg4gEnJpINLycT9QJIItkQVBA+QBCQ7gGEGbPnfgj1d23NdMotGxfW/0/7O8Ocno2lUWtX5cBZAfxw+vXPNjrx/OeAz8yfMys76PBT76geO/1L4Yqzm5Lzx6d6Vg8dRd5xLX/kgXbIr6wYszkRrmo7JNU4lFxn3PWykTY8ZwGal+F14GJUWhiLPhW3oPW8WlHL07cOG9mSbItWlJyfQrXb3uRbiZL6eJTsqlAy/Avwd2NoZ+sTiku/l1EobLmOAumvQaUHg5L0Uaj3wVAKgQ+CShwV6OTwTYxZjkg1GjRyLGr1Hg5Uu+oCtk3IbwoyTth5N2S8CSHPi/SWCfwyPTYfg2vJNY0uYs77PQw/BD56eEEjBsANhclGo/SUleNFo8WLaQuAYxLJhuDHewAiIN0DBHYh7U2UME6dP2fWmhmz597reXx20+vDk2ueqw/RXTocwq71PGgGHga+bwzrh37o/F/Fa0ccs+G1H0500xHC8czruVA2RHP1OIhFwAuBB07WQWQ1OWdRbNTGE72WyqpcV2kXxulEF8t+gj7OX4EuJtWhRl8LSrQxClFZHXabv1bUaa9hLr0i1Iq03qkUctvGUZewx1BLObvVTSmUfi9BvQ9GUsjbkEWtcT9vQsyOs4eCVZxACfk01OLuRINAltpr8F3IDgMmuSppOL11WGvBYoA84mj2MCFsvG0kAku6flpJF9iQhFwpjC4vWlNLIaRqB+HGYiDSjc6OPah+f0si2ZAhwAGJgHQPIMyYPXc8kLGEezqqq/4R+My6l0d1hUrST65fOOSsbOPgDoj6ZcC7gVfKDv1R5eBJ6z/c2fgl2lcMIZ913JDjORL9f+y9eZxcZ3nn+33PUnt1Ve+bWqsl2ZJlGe9bkNksBwImQMIASW642WCyMPfm5iZ3so2TTIYsTMgNGRKYJCTDTAIEEgghGIMRxsayLRlZsi0vkiyp1ep9rb3qnPPOH895+5Rk2ZaMAbf6/D6f+nR31alT7znd/TvP+T3P83saJ/2ljoMQXAtBt9CI4wMl8CbSaxaGapPpo7SyfUhUWUYirU8gBPcOJCrMIdHbQrhND3I77iER6CRCjgohj0eRSQnPnEW+psxrHUK+fUiw+E2ErCfOiJTFNGcTQpDrw/cZ05pe5GIwgZDwNLAPrefC93YhCbnLEQ33OiKJZB9y4TCj4JsaFppwo4K0inRYr73HNxD7RmWHeTGzEB9QUn1QS2n9RLjOAqCME5kGp+kmqGaztNIZUKoK/Orw2OhHXujvIsbKQky6KxRhiddbgc/vvXN3ED7neA3rLfv/4A2ngJ8FdgM94FvQslRqxum6skRzPkPTruOfGG55lXQF7LCbKaiCNYIkmcJ72aACljEM70EiwCQSFe5HKhUspHlgI1FEVgq36w+fW0IizCWEzBykzOogcvFYJt82rfcKomi0QFTbu19ryssn48zR79cibbPpcPtG+PVrwEG09trel0Nkh52INryZaG6aH651GrlojAPNAHqDaM6bpZH7A0O8YeJMaVC+UmgN2DaBpQiU5TnNxlfTcvHaDBR8cA3hAlSVRXlw2ZFxz/DY6Gue508gxgpFTLoXKZTCRsjkPdB6O/jDYS/Ucr7G6Sw1vLr1eWrFbUh2H4RomkjkaOaPmQ6sU0gU2E00Q2wJiVq/hZCs0U6t8DU//LkbIaQJRANuhWvJhPs+hBDjkTbyLSAkOoxEsWYS8LeBLwHPLrcTS5XC65GSr2S4bQGRGg4g9cCH0boUku1OJNrsDo+9J3xPd/h8Plz7aaSDLoNcjEyJVrYFmxVkdHiBOqtUDMKD9F0XrSxs3/dSvnfUjjx8PQ8qM7A2D67JwE0l09DdDfDg8NjoDcS4qBCT7kUOpXCAbfDVD+JcdgN+V6cULWjA8mRCjDWJVAwMIhFjWHZKi2i4o6lVrSGR4xxCQsVwmwpCbE8gZLqOiKBM1DuMRHntiS4//LwORJY4BHwdOCrlqGdovcaJrD/cx1dv50sP/RtvGkEkhla47vbpEVa41nFE57YRgi0gVQr94XFYCNF2EA21XBvuxycyV8+H22ea0KMh3S4vtCfNDEzNmCPjeyqOSDDPAPf5oI7Z9q93+L5TDLdrZHNUsrkWjvPR4bHRD5z7NxtjpSIm3VWCwZve82Xsnbvrc++6f+GJNRpaV4DukMTbcml+mSgJ1UvUFmtsA8yAR4gm5y4iRJQNX68j0eEYcmtfJBqiUEcIb5CoqeJouH0dIboiQtKPIW27x0LyLSCa6yDQpwhGruPBkR/k35o38sChKzh4cIApENI0VQxZxO9hPVGkXSOKvnvC7x2ESE2HnY8QfJGosw2ice4KaXDIB5DW4dh1Q7ZGwzXdFhDNTANOuhKlT4f7119wnNfOOYnLfshxkulWA9v3g8CyPNv3P5D0/b80pW9jwyM2cvGqxZULKxcx6a4S9F5528cS+e61s4/91ZsbC5lLgTc5hfJPe4tuByQdRAM10kMLKfOaJxpvnmp7zXjZmsGPZvyNiYpdhJRLSJRZJYqcTd5ohIgIZxC9dxqJAjPha0sI+d6L+EBYwJbr2Hvzm/mXbS6tkeOsz1ZIezfw0KnrePjABo59rZv5Y2itUWod4iZmJIpieIxGY15AIt4kQqbNtvXXkAtOBpEsuojuAMpAzQffgyErnCQRhHwbErBjCFgBnlL4juMlW97HXXQJ4eWngD0WvOZdmeyuPyx23mJ5rd50uXQsV62WHdnvMz58aGJozbuBXwzXdxz4heGx0S+f9x9AjFcMYtJdJVBK3Q6gtf6y/IyLdF/tRohpIxLVmYGNCiGGGkJQDYSATJ1qCyEFQ74eIjFUibq4TJTsESXX5sJ9eQiBrCeqfJgk0o1NJNhN2IhxJY889DDXFafp3X4PrxnZz9VBDzM9Hu7aA+x09nDr4Xm6v/Sj/MPhT/GuNQhZmrE+WxG5wEEuJiahVguPhfDYjEm5TaT1uuG5MCPhCY+1FHotZAM53qYFldB8twC4AcrybFv7juPZOngg22x+HZFV7kfalvW5vI5Rag2SDL21admbaslkTzmTTYTTIQjP563DY6MPP/e3HeOVjJh0VznCcTrbkJrbGxE9tB8hDZPbCf1ZqCFRnnH9MmRrcSZRm8m75jXV9noTIfEJRF7QCLEaY/ESQr6z4dcJRdBzM/dtvJU9yTWcGu9n4p7buWtyH9de/21e1ffP3DG2h9dYGSqbd3Hv4Gu5Z+HVfOPYZTzmZWluQMgzE66tNzw2Y2dg5ATzSBPJCiZirxHJtFlEwjAXlKXwOJQGN4CkBYGCqgfzTdtR2nGqruc9kvC9/Ugp2jNovWzuc07SDdFUamcjmXrI9b2E9gPqqRTVYicopYF/Gh4bffsL/4ZjvNJw3gbLMS5OhA0HB5TiCeAexIP2OiTy7SOazuAgZJMnGltjvF4U0ewxCyG4FNGIG0PYfviaMZlZIkpwPYpIDkZLzgOFjRxd+xY+r9ZxonGETcEDXN95DY+8c5KBiW5mv7aHW//2KbZe/TN87KorORC0cJSitXWG4lUz9NUCZpey1MYtiZaHw/U1EULNIRePMtGY9y4iY/L58DSZpgVzkTHz0hJI2VdCi6FFWsq/rJYieDQJexzfa+J700hp3JNobaLq88LMwNCi8v1WqlxKZII6uXqNai0FmYxCIvgYKwwx6cYAlsn32yH53ouM27kKqQroRaK/NJHfbIpokriH/C1p5FY9CLdLhw9Dvi2iOWc5hMQLCCFPIbLCDLAuTWXgB7iv91IONxskG5/jrac28ezSD/EldYo19of5D/lTrLnux/nkpZ/l7TWXpjtJb66fyVSOSqpMNv0MG5MFet11PJspsKSTBJVw/x1EXWqG4I2k4CEEbaJeeK4JmPF4yCAVDDnkoJxWIonnOI5ynNcG9foD6Wbja8DjaP2igyvPgIyov2IAXlXK5m3tOJSSRfwggEyG8Fzef0H7jPGKQCwvxDgnlCKF+CD8ANKxtR65TS8QTUowka0hW59IwzV/WGY7iG7VzcgbkIjRSBV1C3/uavZ5V/FIzscuPsFlXoXM5I08SJKGeoqt4w9zzaHbuDuxhac3ZykXe5j0dvCYv46TqQYJd56Cn8DDIuirk8o2cfxO5hf6mPHTNJQdrblIlFwzFow1ouYNMxsNIu8Zc4wuUm+HH1Vj4CmLakcHvutqz3b+ZWji9B0vfq5DeUEcyNYgHX7rw3VMzhWKm2rpzAewLDOax0c05SuHx0affbH9x3hlISbdGC+IkHx3Il1YlyJ1ssYm0dTvmmQanEm8hqQgGuQY8hQ1JPotI6ScHGY0fR0PpbuYC6bpLR3k8tltPFkcZDy/QLH+bXY+NcIYV/BoYZDxYIQTpTWMpjso93vYtoe11M1crchStoGrGiTrNh42wTDodIKWn6A+m6XWTBBkrTNreU00GyAXh0a4zgQRSZvjMYRc9WGsnEzdrPwglfbEGmI614Hu6AA4ODw2uvNFTrB9Lfz8q6HvQ5Jg6w0/+1lE/z2N1sHY8MgPI37JQ0hL9G8Oj40+c96/yBivGMSku4KhlPp1xPHLEN3Paa0f/O58FmmEfG9GqgLWEPkSJFkuF/vjJPyVLbzUo+Gvq7DBSBBBuJ3pcvOARoZy9WbuVxs4niiTtR7mGs/Bd6/iETdBs3GYS8s+dvoyDieHOFVby8n5HmZSfczkHVpNh+ZCmqqTolVo4FIhM9vJvNfJQk+SZkahvQqpZoVsOkc1naEaOHgVF6/kCnkWiAjYJzQaJ4rSG4ikYMrNWkgd8pMBVCup9L8PbCfVTLh4LY9ACLcFfGx4bPQXznEyFSJxDAGXfAp+NAXuHeJFcRh4Aq0XXsZfX4xXEGLSXaFQSt0I/FfgVq11QynVAyS01s8/7PH89uvodn+C57xOBmn1vRmJetcQ1cCm4CtZuNmGrAN/7sIeGz5jSsRMpNgCXAs/sZ3H1Q4O4uJ5T7K1fJQNlRt4KDXIuDtNTzBP0VrLqDvAab+LObfAojvIhEpRq6apLCVppHNUExqruURmNEMt3cP8QIZqxoe6h1MPsFs5ykkXz62TtMtktUXgF1nSSerKBs+W9Zk64wSRWZCZwmAkkDnEknEMIeXpxVy+p5LNvU/bdvvt/xKwc3hsdLTt5Jn64/XheesDgg/Dlgdh/O/hT9C69h38+mKsAMSku0KhlHob8F6t9ZvP8drrgD9GorKHgfeHxHwcuEZrPaOUugb4Y631rUqp/4QkzDYiBuA/BvwBcDsS8X1ca/1nSqmrEaLPgTMPn/4z+OGtCOkOI5FbEYkaHdiXhV/MwwNNomYtD9BDnPKu50HVwZJzmiHrYa7xhzkdXMkBXBr1Mnk/R5khTtPNlJuj4nQx5zh4OgCrk0UnS0VbBJUpOieStLIbOVHsoGRrgmaNVM3DtVI0EgmaDqCb2BWXVtVBFzQq7WEpDdUUzVKKBgmp6U2H66yFD8K1l5BmhmeQi8YcUnHxCFrPjw2PvAn4/xAy/Tpw5/DY6HGUcpGKCDOBeBghcNMKfSQPt5XBP1fJWIyLD3H1wsrFV4DfUko9jRibf0pr/Q2lVAqxXXyd1vpppdTfAe8HPvwi+9sG3KK1riml3o9EY1dqrT2lVJcS8vgz4A6t9bRS6p3wtjtA/xKS+LmJaCDkENAFH83B60qIdpsFnCwl9xr2qbWcdKtk9B5ubZbIN2/mW04fUwlFoAJwNnAsGGQ8SFNVncwHSRq+Q8svsuAmaVgeCf8Im3BpZHZweHOReU+jvRkKdQttZakmsizZGuwamUaNZCtNPeXi58D3HLz5BhkngBQ0hzUWTYK6kjZdEJLsRoj3SaQluYaQ7xGEbCfNyRseG/1XZEKGcT0rotTm6FwsO7fNIVHys8hk36Cs1HMHcsa4aBGT7gqF1rocRp4/gJhwf0op9WuIA9ezWmszK+tvgZ/nxUn3Czq6tX098BdGZtBazymlLkeqGO4WSRIbGA8tFr+pFAeQErObgSn44DUy5efh40DeplXfwaHU5Tye1Sj3EDvUYS7Vl/Fk4gf4ptXPROBj+y51NcSE7dJ0MlRJUfdT1HQP04kUDQJsf4L+hkXLuZIDiQwVy8NmnF4ngW/lKJOgqSy0KpFvNrEXU7RUgUpa4dsNEkEJlzT1jg5KtoPvNnGDSTprPm65h7k8NFMW1GwpHVtANN1seG73AaOc6xZR3Mu6kQtPN6LbGvP0MaTb7hRaLz7nvTFWDWLSXcHQ0tW0B9ijlDoE/B8IMTwfTIcYRO2sBpUX+TgFPK61vvHca6EEfEMpvg0//7PwmXfB/Z+GdPISnl5zPQ+uSVNLnmJN+QFubCVpZG/nrvQWnrKzVGih7H5mnST1QBPoDLUgz6LVw7ybok6DRDBFT5Ck6WzguJOhSgs7KJHFIaCDqgrQto+TKZHwGyQqHq6fo9phU1cQNJs4dRcvmaOccdGOhx0skS0p9FyeWsGi2tck6c3RPVEndV8/0wtFypcQdahVgKkzCFdM1LuJolnTyecSefmKQ5nWVWKsesSku0KhlNoKBFprUzZ0JaIRPgWsV0pdorU+Avw4MikWxCjlamRW2Qu1j94N/JxS6utGXgj326uUulFr/UAoN2zRWj9+1so2Ae+HV992Ka2hW/lvb+tlujVH1+hXeb13jE0d1/PA0Kv5ph7gdLOOa+eouA7aamKToK46mbd6mFFp6oGHrZfIWC6e6mPGdWgFLRxdIUOaus5TxcPSGoXG1gtkmKOTJF62j+nAoenXcIMkQTZD2U7i2QEEi6Rai+S8JEE2T63QAm+e1OJ+Lls6zkYF3nU15scWOX3yag4vXIV3SQC/+U1484eVOpQDdxPk+iHdC+kEOC5YFqg6eKehfAjmD0H3omjl7TPOzsYt4e/0vH//32NMGM+OGN85YtJducgBf6ZkiKKH6Iw/q7WuK6XeC3xGKWUSaX8RvudO4K+UUr+LRMjPh/+OtJgeVEq1kETaR5RS7wD+fyXdUg4iWZxFuvwRkMtw7z9MsT31Zez5t/NfPvoxfnZgM09vuJPfzG7kWEOjbQ87q7H9GkkvRc3pZ8HtYdbOUMPHohkOZChQ1k1c6iR1lpbVwRIaRZOEbpAIXHzqJINZOv0aGT3AlJOmYoPvKHy7gypJGkqhrCWy/hLJRoqm7qGU8FFM0F0/xLbmCfpqNvPlMvXag9yo9nKlW2UxP8jdYz/NF5rvYGzb7XDLdlh7PzzthY0TTki2i1A/CeXDsHgQyq2oQSRGjGXE1QsxXl4oNYJouzlEx9wHNEcZfuc8xbfVSG5skEpVyeglsih0Pk8p18mcm6diSWubo3xsWiSsMlmAoJs5q5N5AizKZPBwSdOigcM0vd4cXX4/U3QzY6eoqQQ1UjStJC1loamS0ktkGho7yFN3LHzVIFmdoO/0ES6dmaPLHmNN5jF2NPZyw+Oz9D4FdBRY6F3PcXUDD4y9h79T17LvmhReNzL94m5E811C/COmgIVz6r0veMqe3/AmxsWHmHRjvDyQJNK1SLa+jOmmkhHmb0VusZMVEqVjbMrNURxO0OgssJjKU077OE6dhNMkQZWMqpDxPVzVz6STp2wpYIkMDVI6R035WEzSq8cZ0BlqrGXMyrFIhqrOUsGlpcSFJ6EXKPh1kipHXaWo6xpJ/xTrK4+xrbZAV71Jcuo0w6N384a5E2zIJqn39TNpXcKRsW5mW6C7HDy7g6WZV7Nn+nbu2l5gcdAWuebjwINo/WKa+Aucuph0VxNi0o3xnUHKo65AvHk1Ijc8jgxe/BFEQy4iVo4LiF9BZwtGGqTXNEmkGiTtGgmvRro5T7fTwEl0UkoVWbAcWpTJ6hop0jScAIdpepiiJwiwGOa0VWSBDubJUSVBEwW0cFkg75fJ6ix1laahWjjBKGuCx9lem2GgOk9nc4yR+gPcUBpjTamfycX1HK8UWci3cLps/KRLq56hdsKlmUeSj6VBxk+8m08Wt/D0hjSNcQv+Eth7oRFudApj0l1NiEk3xkuHUmsRKSGLZOj3I6bfbwRuQzL+C8htt4dk9o19ox1Ao4VVm6E7mKM31yCRSlJzCiy6GjJV0laNjOUQoAhYoMOfpSuo0OF2Mm93Mat6mKbAIi4tAiyaJCiTp0KaJK0gTU1pFNP0+Y9xuX+cDd4cPd4U/Y1HuGp+mt5aH9Oql+mES8uz8SoOXjlBs94kkU/Q7LAIfB97vkly/iibnJOsraSpnfoTfsm5gy9ck6dUTeL9NfBFtA6ee6Je7DTGpLuaEJNujAuHUnnEc3cA0TP3ISVStwB3IIbkDSS6LSNkuw4hZIuoyWAK0AGoBnaiTH5dA7fDx3GbuL4Cz8NJLVJwZum2K2TIUQ3yLNLLhNvNomPTwseiRZIaaaqkNGiVoYlNwDwFDnOpd5QtrQkG9Qx9+nG2BQt0+j3M+hmqFUVQCbA8i0DZBI6D10pTLTl49SpZtUAxe5K1eoq+OY21gJTPpWy8uZ/iY0v/kQ9e3sVsMkHzH5N4H+MF2qjPfTpj0l1NiEk3xvlDqiGuQEbfBMj8shIyceJ2xIVMIZ61k0j96ghSv+oiBLyAJJ2MW1eARMr5AJwWijkKbpWOjgZJt0K66eHWWrjZAJXrYSrZz4zr0iSAoEGaBkmrTkIHQIpAObSokOFptugn2cYoI3qOHn2aodYsPc0MNQugKoN8/SSNZorGUoLGEiKR2D6OPUdXdYzh8Xm66ojnble47ulw3UkgeBX7pj/Gz4xs5ESHJnigm8XfvJCa3Jh0Vxdi0o1xfpAhj1cj3gQnkCTZtcBrkYm7GYRUTyEa7gBCtjbSVDCPkFX7xHI33F+G0CoRwAergeNN0J9epLPQwk4XmE/2Mpd28BMNLNUgpXxcZBik8hTKcaTjTB9nQ3CYy4KjbLRKFJilR08yoFokaJDUTdyWReDlpB/E9rGUja8tgrqFnp+le+YUa5pVssYvuEY0vt10mpWJjMzdTmZKH+Hf51/LnpyGZ4+y6Vdu0Q+cOL9TG5PuakJMujFeGFKTey0yN20JMcS5FBlmuQ0h1hYS3SbCnzsQQi0hka0hW+NZa+rD00i0WCeKfEHIt+FDooZd9HHXK1TOB+Xh+hoVBFhWnaSjUY6LD+hgir76IS5XR9lsLVHQZTrUES5RSxSdBQoKCHJUrCQNfCzt4WiXVsvB95sknEn6mabX93CryBih9gkSAUK0S0jUWwyPsYJIKd02XvAr/BfrJ/m7fJ7S+D8OvPljn/ypH7nRsvWtSKXDH+69c/e3nnuKY9JdTYhJN8a5IVLCTkRK8BESGkZmqG1CIj4QwmkhEoGZJGwi21mEZM1kYAeJDs28NWNo3j5huEI0EqgHmUFmtaBVJ5X0SKQ9LKVQuHgEWNYEfTzBNnWKEa9GtrpEsX6AnYkTbEhWSftJGnaRxRRg10jZARZJGtrBt2qk/QkG9BxdvsZqH6RpSHYKIVozet4Lj6GFWDOasUX18Pvim/i8+2t8sOB10fOZV72FB6+6znXSnnEu+/G9d+7+3JmnOibd1YSYdGM8F0qtR6oS0gjB9CJJsg1IUsz4NlQRgkoTRX0mQWbmqJnJDLMIiXWFzwdEU3XN7DQv3FcH0dDIZvhZji8buC2SqRZuYom8P07/0hw9CxMMekfZ3Pcg17uH2da0CJZ6mVEp6h0eTqJM1tMoJ0s1rdDOIgV7kn6WKAREtpMgBOqFzxlT82rb+s0MOHOsLnIXYBPNf+vbbB9Y97vqznyvO+18ac1r+fKu15Lpa2I5egIY3nvn7uUqh5h0Vxdi0o0RQVqKr0NIViMEeS3S2JBm2SeXBkI6ZgRPCyHnhbbtQAhrEqliyBLpoTmEiE00WUNu2Tva9umH2xiCdwhnrflQrpKcPcl67xA7EifY4NzPTVMHuHK0k8VclsoVGtVVI+3VSNWSNPwclWyA5c7Q0xpn0KqSzRAZ0xhoIgI243nMP4iZoVYJHx4S3c6E6y+G50sDS519Y7sK8+O5d7T+DY8k1YLL/W+8Hre/GSRyjTFlcQj4nb137n4wJt3VhZh0VwmUUrcDnNO4RKSEKxG/hTQSuV1ORLZmmoLRbs2UBZMkq4bbpIkSYqcQ/beJyBFDRPKDuR2vEJmet89WyxARfPv8tXbXriMNnNHP8cOn/pBfHc5TuSFNrV+jglm6Zmuk7TT1y5I0Oj0cPUl/fYKBZpOk2acO928GYyaIotv2WlvNmSTcQEi2Gh6bDyy8y+RZAAAgAElEQVTZeOPdzNLFXF+RhQ6VrV/qaTdfaWXY2nqS+XSK1nCWybU92MM+2cEl7ab8GvCh8W/94w9rYOimd/we8Jn2KDjGxYeYdFcJnjeaUmojIiX0IMblW5Ga2ny4hYuQ7WkkIdaJEFUrfJjhlBqJdo8h1Q1TCIlvDd9jfGWNlGBGupuR59Vwu662fRqy9RCyP4WUqT0DHAjXU1ygkPx73tXzSd5zySKFV3UzN5ykoZokFp9lg3+KNYMBdjdnDs+EaOBkhmhcvMOZFRYG7eTrAY0EjVoXc60u5nSBRe3SqgK1aXq7ZlSxWM6nusm1HMu2cKtNNngnSCdqHFeD2Nsgu3aJ3JqlYO6xuyyA3itvqwCf23vn7p84x+fHuEgQk+4qwXNIV+war0V02h1I2+4ahFADoqm4JxES7SXSLn2EGJ1wm1ngKDIN4TRSLrYTSbzliW7RjYyQDb+fQIi0HyF802prItEAmbRwCjgYPk4i0a4Zq2MMZ9IzdA98njvW/gtv3vIg118ywWAh/FwzVLIPkThSCLF6nOkxbMg3Q0S+JvpFEQRZKqrAIkUWggzVwCKoN0k05ukM5ulMlshbAXYNOG53lBLp7srOpKrbOV12rckandUWNnAq14O3NYmTabI4/xUymUU23X61WetNe+/c/ej5/F5jrDzEpLtKsEy68D+R8TpXAtcjngmDCNmY6oESUuJ0DJEFNiJEBJGsUEUI9hnEVnJGds/OcJ/D4XYWQswZhOzqCHE+hBDg1eG2RsYwt/HziIxwKHxMIxUUZrDldLiGbiRC1ghBTyl0C7mI3Bzuvy9ceyvcLoUQvTEcNxUYAUK2Rt5I2nhugUVVYNHpYMlyaWmN0iXyapGCv0jBq5M+I+mWolYusNgqslDvYqaZoJ6ouKntDS+dKOscLWVR7vTo6algTSpSiwEVkvT81jjhPn557527//R8f7cxVhZiP91VhDuEfN6D+CLsQCQFFyHC0+HjKBI9DiOjgMxsL9M9VkJI80kksh1HIk2z/TaiqgSNyAQFJLJ9GvgSEl2+DiH+cIowikhqOIWMIj+MkPlY+BlLCNkqhDSHEJKaQCY6tGA5rH5CKY4Ae4nItxe5CPjhMQRIVN4TrrkB1LOU5zuZrxdZyGao5gC3hRssUrDm6bSW6LB8HKMJOwkaOk+pWWDR72ApmaCWAG23sNQU3apGzvVbKNdutIo9p9yubIXyZBqeztIgwSIdLFnz9Mi6beSCE+MiRUy6qwFKdX8Ernm9RLZ9CMGYCbdPIkY1ZqT4VkR26EX+PkwEN4tEvofDr+MISVvAboRER9reYxJWVeAJ4J/D994O/BAiZZjEm4cQ6gkiQp9CSH0CId45RH4YIYpOR4GZ5zOZ0ZomcEgpnga+BexCiL4PuRgATNp4RzuZX1NgcV2Bxd4kDc8iaNZJLU3RV5mhJ1Uhm9VYNkKyiSILbp6SylMKnc10wsNxSqSaTSub0YGNTeDkKFNkUVs0KrYTLM0vZnqPTY2osi5YJbKWb/4FnTxaz5vhEnvO8zcbYwUilhcuZiiVQMjwP9TgBhsSiUiDfRD4HEJeIwghb0GkBpP0aiDkdwgZ13OYiGyXkIGYP4pICka7NdUG5fA9/4iMC7oB+Ckkws4T6cEVhPAnEaniFNGo8+lwP31INGqHP08CixduFk4SkT52pale083sYBdzqSILdprago2/MMFAYZzBkVm6M02SGqgkqVcLzOWz1uLarK6mk7RQWgcerl8hj4+tNMp2aJGlrCx8B8DDpkyGMjmqZP0Kea2XbwCWq9McrBky6w4wcnMX+TWlKTvhXw58ABkQehz40N47dz98Icca45WLmHQvIowNj+wBGB4bvRWlbgY+hNzuuw0ISjDbA3+FTAjOIDW5r0XKwwaRCNJHpIBR4BEkCjYVCbNoXUGpDcB7kQjXJNdMhFtCItu/B76JEOb7gDcQRc9e+BnjyAXgBCI9HEYGa04RSQid4eEtIMMdX5pZuAwgywDFI2zq/QJv2b6fq68ZY3jzBAO5GXqseTpbAfZYmup4F3NDGao7cpS7E9STvh10+LZt13TS8bCwFJ6jAz/XapY0VkqjElWSVoUsJbJOmRxNkohUbKRtFbqPhaXAqt5KrJ137caM4zfTOpHv8tBq7/Cuo5cV1i3mlbV88asDP7b3zt3/9JKOPcYrCjHpXkQYGx7ZY83NZQbr1TxS9mUh/7D7/wAO/gE8PgefRnwT7kDcwfoRsjXF/+ZW/GEkQTYOlNBah5Nv34ZMHb6UqIJBh+99FPgkUs7lINrxjyGSRXv0PEvkRPYoQrQPIJFtR7imHHIBmEH0WtOZdv5Qyg73Vwy/mrVWgMXf4Heb/5nfuCRD5bVFFq7rYKk/TymTpeKlqI9b+KVZutdXSG7xLVW0Lc9JBFVIeliuw1K94FWaxWqZolUml/RBhYry88h2KgxxgyAsmGiiWk3yj9vZzslWsmNnvjGXVhrlpDqrOJ0TdL5qlmIRkAvRYFzDu/IRk+5FgLHhkT3MzdFdr+4KHWU8Db4rBPgbaD3hKvWT74E1n5Db9B9EiM3U4M4ixHcXQrYnkVlf0T+4UtuB/wvxXigSJcqWkIj4fyFarItUFPwEcntsEnENIltH83n3I9HwQviefqLW3ylErzU1tecHpVJI4s40XbRrxovhw0IkjjyQe5rNxc/y9s2PsvPqSfq21kgP+tgdFoGdoLWUKJT8alFdOjmxxik3uiiTo5ZW4Lk+LceLmtoCO+yrsEGfY7RvgNW1RLrDozmf9VvlpMZ3PfBsVE1lNi04fdunOf1EN81nBgEbO1vlml/eC3JnsHXvnbtHL+h8xHjFIU6kXUSwCftqbedIva9/cvj0qZ8GQKmuL8AP3STVBblwU1OD+3Xgn5Cmg+nnGHArlQF+DpETTKLM1Mc+gsgIx8KPdpEqgV9CutCMlFBGiHYufM9XgXuRiLMX0XmNUc4YFzLcUcYF5YiINhG+UsNov0L4OYRkB4laiy1Ab+GZ2vv5b4//M29d+jI/qKboy87RlVqk4JbJuUuLyXSz7qC0gqRG40EzCb5t1hj6S5jOZnyZt67tMxdrEcxlqCw26LxswdY+fmW8I2gtuA467VSPZDh+ZIDlsmE7oHPHyejNcs5jrHDEpHsRYHhs9FaAsXR2D11d0QtCSO8Gfu8NUl6lELnhSSQy/WfgFFo3zrljpW4Cfg/J+KcJW14RnfcfkPKyVvgcyIj3NxHV21YRol1AItsvIpFtFYlqN4ZrMnpt+bwOWCkXIdkCIhsYtish1Q4VJGI2MoshWdN0sWxuUyZr7eHWrs/xtjWPsnPtBANTM/R8LmwX3ga8DoJuGj4az7NygWNj4XmBF7pVnkWsprIu0KLh+hoCFU5ptyABvotfa2G5LTu/YT4zd3BG4+8g0n4FfVcdZ8Ntp82PGrk4PccaMsbKQiwvXEQwiTQAa3o6M9hqDCHkRgO8cTi6XiSCB16Q4MRD9w8Q/bYjfLaEkK2REdqj138HfBCRLoyUMI+Q8WMIud8bvqefyNbR6LXnJv1oPSYJZojWNGo0kUjWHIuJZlMIgxkd2fyRm89plMhVP8Qvp/6Onxg6wbotAbbpkjsVfn11ePy9QCv0vukCrKjyQGmwzyEjLF9wngWy0FofRr7Osq2DHYDfJKqaOxsenbueZNMtU+20XgJG9t65e/EFz1eMVzTiSPdiw9SU6vaar7YBT7TdwIVP3wHfvhvmfK3vft73Crn9GPD7RI0EFSRK/ThweKKv/6OBZbeGxsdejVKDSORlwjQT9S4gJV//DNyDkEU/cmvfQiSEmRecJRYlwQzRmr/VMhLNmu6xfLjWBFGEbXx6q0QEWAYqT3BZeTtPFBD5Y3u4b1PzWw3X+O7w9RbSepwHe5jI6Gd5leFX85nmq3Eh2yLbO0AzAN8jgUNTIVVl5/j3cz3Slz1Lslmk/NQa9j10CW6qxVW/+DDh7+MdSAVKjBWKmHQvPlhG262iTlQHBk8Nj4/9+F1hG3A7ziox2wB8ASEKCyGi/cCfItUIc8Ci77gSLSr1MYScjNmNmRJxHPgy8K9ItNuLmNjUwtfmnlevjZJgBSRqNUkwQ2IgUa7RZQ3J6nC9S23bl8NH1SQElaILIVrT+lxDou1quN8fRbrXEkgir4Lo2Mac54X+X0qIVn0QqbHtDZ/XoEbBOQDNq2im1px7P1WswSpB3aF2cC01LLBcCKBVN30cJIjM42OsUMSkexFhWdt1k9+gr08Pj43e2vki78HzQKmvILfTxszmYeA3kKaIebT2lqWLhYVdvdUynpATjhBWCSkt+zrwWaT0q4AQ2yKi15ae89lnJsEKRF1iTYQQA6K5ZOm21+WaIqReJ4xigTJa15/7Mcuj3zcjk4qND24r3O/ViBbdE37u6XBdpmtu2cu3Dcbv17iO1ZGa57dIAs2r07FQtYJUKiin+sF+iwTlHpHaAVFO0iMYbzdCA4KAwmUTrLvtSfNMC/jac85jjBWFmHQvRvT1vaBQbwjUGT+9q1MHeGEU6UhTw08Ch89JXp7nZKplc0/thx9yGvE3+DRyi268FuYQsj1zP+dOgkHkpasRdjrbAKeCELiRCoRoX0CiUIo84gmxEXEx8xGGC8L9DyFEuTVcxyISXa8jIltTrUHbWowD2yRSAXI54uEA6IDUUmnN646kyyc7+xYOFZCEWtB2qEpLSZkh3AJnkC2a9PppNr/1CdIdy1V7FeCze+/cvf/5jjfGykBMuhchTMT7YijqwNy/N+aS6QND9eot59xQKWsYfgf4f1pQU2A7cvt9EGnzPRRuaWN8d9vJUKksZybBLCIzc0UkFZhI1kymMG3ARi6onk8pmVJkEUIdAS4h0qbNaKE8Mkr+WiID9SYS6bZHtmdPkTCPOSRBmA/3kWY5eeglqKe7Tv3rFW3/W9oR4jWnRAfg27Ks9nsRTWJolo27j1AYqYJEz9PAPuDvgM+/2LHHeOUjJt1ViGUZwnb3A+Vhv7Vr6Pk2Ft/dX0USON0KLB9VddAfRZobKgg5TCJ6bYBSNkp1EhFtAiFbjZBZAvnbM3pwNdzPKGdGsS9c1fCcpZJCItsBJBGWIapYyIU/DyGdeINICVkQPp8MfzZkC1FyzFhCmjriFuIlkQ33fwCpdb4Z1O4zR661Q2twx6E59By1Ij3Khtvn6b5s1rcdbSGVD78P/E3chXZxISbd1YyBgefqrAZSyfBG4LeIkmsTDnzFQX8R0USXEAlhKUyC9YblZh2cOQbH1MeabrMqcl89g+jBhmQvrPtseakkERI1ZuhFJKxsn1KcQ3yENxPJGhYR0Zq2snbC9Yhal+cR0jXG7B6SGNwTfu6vE001PgcaASQUMBRJwQbTpIqzpAotbEebNYwAH0ai3NjQ/CJCTLqrGM8rQ0h0+yHEgjGDkOsDSI3uMYxeK0RVCKcHmzE+WYRoTXRmpIIFIrlAhlF+h0XiSuEiZNuHRLD9RDPO0kTRrZmOYXwmzESIdtKFM2UEkyAzGnKaZd2WGaSyI4WU2LWRrYlyVdgUoQFLQ9I6qx7XSw3NOiO3nmT6kTHqs5uDU3vWW+meip1ZM0P/FbNmQOefIq3XMS4SxKQbI4JEt/8Oua3tR0jnEPDXSEXDHFFL7dUI0XYSRYkthKCmkcaJacwE3ZdiWPO8y8QJ19cXfu1HyNUQqYlse5ASsXVE7c8gpGzI14Sd7ZEtRBG5Cj/HDOF8MjzON3DOyNYmLI/2pCHCOus1AGuy+9pn96679fhb3LTn+OX7VLP2iKpMvJepR4dg/zCLx06y5a1HFCJjxLiIEJNuDIFSfcCngGsQdphEyPYrCPmkkdvzLiJ9tr3zbIozSfZl1yGVwiIi2S4kyjWasUeUJOtCiPZSpNzMXBR85G/ejBwyTRSmIoG24zJTiY1h+rMI6V5F1BF3NkLydkJ2NYmz5X+zcHCn1aid6tw5/8yi17tj2i1ND7L01JuUVxvEsjVWqk7vFePmPfEUiYsMMemudkh0+9vALxINjPwXxOA8idyWmwGPNSTaPY6Q7CRSG1v77i4RC4laB5GIdYDI/rEBy68bs/Pt4Tam5KwVbpMkckczk4l9ohDUjFhXRFUNE8hFZXP43PPB1IS1ZciW/72qyMXro8C+ZFfpzYlC4xOn7lvrPPvFzeDdgnJbpHurDL/mSbo2ldvf9yfnf6ZirATEpLsKsdyJdvrULyBdaEMIER1FsvB1JHlWQSKt/Qj5TCLeus/fvvsyQikUEqkOIoTXhySyighh1sLXexGZYxOSSMty5rRfk8wzeq9JshlN15SMeQhRB8hxl5CE1sbzWO65xrY3kdrn/47MhhvTmqDnioWO0kzCb810OEbnTYx8kR0/ViT87HK45o8Df3wenx1jBSEm3dUIz6M4NXE5QqYK+Sf/J8QvYQqZ5HAck/j6LkgFLwal6EQuBlmEUNcjpGsIsRshQ7PdhvB701VXIypVM8+1woeZ4abanjMGOaYuuB/pSHspaCEXqC8insaHwvX8oFL8HAzdAtpGaex8lVTnffRf/hBwWwP4CDLZ42RsbHNxIibdVYTf6ij82tjwyE/mpyZ2JYAWtGow2yFeuUcRm8fvqlTwYghbdg3ZdiDEasa5jyNSx3aEgLuRtl5jeGOqDZzw/YZszYj19moFE+2aVuNG+OgidGZ7CTDTN76N6OH3IuT7VsTU/UZZvx2QrJWGXn0kOXLj6cz0gXsg0pL/6947d58+595jXBSISXcVouQm0a0G5b6Bb+E4dIyNfvn7vSalyCHkmkMSYpuQ6DWFtNqmEH15AIloexFyNg0WpuMsQ9S62yTSfJ2znveIDHV8JAHXc57LNd6O5nsTWZ9EnNU+h1zE7gDehVR6FMPtJ4C/6Bguf2jkxtO/DHzAzRY7vXr5EPDjMeFe/Ij9dFcJVOgyprX+xBnuYt9nKEUGIdsOhEDXI0mrPNJS7BNNKe5CIt1hIiPaZriNIVtDpCYhZoZmQqTdmqoFiJo4zgftkyICIg+GGeAhZIrGAcTP4Z1IRN5NZLLwWeC3tY4nQKxmxKS7StBOut/flQjClt0hJGp1kITVdiQiNN1um8PnjSfCEGdaPprEVyJ8zkgJ7aPgTR2ukRhMlGrKwc4Hph3Y7MuQdxXRwf8JuA+RD96CGOgUifTye4D/pDXPXMApinGRIpYXYnxPoRQJhDy7kb+/AUQ26EXqWE8gNbbXhtvkEGLuINJoPSQ6NdOIPaKI15CtqUpoIdUYhojNe84HpobXRNDNcH8NJOF4P2K1uBn4Q0QOKRIZ7OwH/gj4ptZ8z5ORMV6ZiEk3xvcEYcvuAEKuDpII2xk+V0WkhG5gV7hNBxLdGrI1lQYuUXdZgGipXvi8SZKZ2twKUUtwinOXdZ0LRjYwxjxNIsKtIA5jD4Tr/UUkGjftz2YG3ceBz2nNcywyY6xuxKQb47sKpbARYu0jGs++E9Flm0i7cBaJbAc405XMJMAgKukyxGkiTpuIUC2iyNZ0lCU59xCyc8GQrUvUiVYjinhHkfKvBCIj9BFVSXjAU4jV5d9qzfR5fmaMVYaYdGN8VxB2kfUhRJpEJIIdSIkXSMToIq26a4i0XdPVZawfnbbnISJG48NryNZH9NMmUbnZ+cJovsZ2soVIHUYSmEfagC2kDbiTqJXYdK3dDfwN8FQsJcR4IcSkG+NlRdhF1ouQbRqpOLgU0TsdJAq1EfJdi0S+pk7W+CeYJgczk9xYd5lEWOKs52vh+433wvnCELixdvSRulojV1QR7dZF6oWN54KRMGYRI6D/ATyg9bJ3b4wYz4uYdGO8LAjJtouosaELqbW9hMjsO0Ci33VIRYJx7jLRaT+RP4KRBExVgkloteuyRmdNcOYIhheDiWwVkZH5FNLYYIh1hijRZxMl6syaH0ekhK9qzdwFfHaMVY6YdGN8x2hr2e1AyHQNUlvbSZSE6iAiWwepW51DyHktkhwz0at5GFMac9tvIltTleByYZFte7Rs/vZnkGkQxj3NRL494eeXws9LEo1q/yIy8fhZrc85IiJGjOdFTLoxXjKUogNJiHUiEewgEtkawmogpDiCyAgWcktuSO4yIinBEK1FZEpjIlHzmikXM22+FwLT8mu60paAp5GLQR9RZF0IP2cekS2MbnwamXb8b8CjWtO6wM+PEQOISTfGS0DYsjuEkOsQQlobkFtxo5PmiKJdhSSbjiGk9qrwPe1ygSE9423bLjGY6NYMlnw+tLfnGpg2X1NrW0IqEBwkIjcJMRNlTyEXhhxC+AtIedhXgQe1JjahifEdISbdlxFKqduJRrq80nALGPvcl4o+F67vhEs6YGMX9ORguBsKhTAQDSCZgGIPJFMy9XZxHqYmIZWENW+AXCdYYfSqNdhtxOoYKcFEo+bR3sp7LhiybT84ExWb99aRWWPzSGKv2LZPkxQ7hUTQ+XD7A8A3kBHzJ2MpIcbLgZh0Y5wHOh24vgjbirC5B3ry0FuEzmJItj6k0tDbDYkUeA2YnoTx05BOwLpNUOgFJ6w60IClwArbagPjg2AmPBhya492Xwhnb9Mk6kprIJ62h5Gx65e2fU6AlIYdD7cvhts/jjiE7QMOac33xD84xupA7L2wSvBSvBfClt1BRJPdgJSCmUGQJqnVidymp5Fb96cQgisgDQ/biBoIIDKhMaYzJqllNFvjdftSYMjR7OsZJFK9HPF1yBDpxUtIZFsLj8FDtOb7gIPAw1pTJkaMlxlxpBvjOQgHPw4gtakbiTrKCkS38WYCr4vcmj+EkNUgcDPSdZbnufW2RvM1ZGsaG0xk+lLQTrYBYrH4r0hVxI+E6zBlYmVEt50nkhhOE0a1wH6tOfUS1xEjxosiJt0YywhbdvsRAxdjp9hJZKOYDJ8bJMro70Vu39chE3Kv5MzmBiMRtHeSGRI27l0XQrZmFpn5vj15No142WaBtxFVTNSQhNgSUiKWQaoWphEfhYMI4R7WetnyMUaM7wpi0o1hWnZ7Eb1zGyIXmLpZHX5v5AUTSd6NkO0W4M1IZNtPlLgyZGsaGyCKbE2C7EL+/kxSzBBuOzmWkFlvS8Dt4fptJBkmI4fk4YTHMo/IIAeRior9WlO5gLXEiPGSEZPuKkbYRWam5+5ADMQTRJ1eAwipdoc/P4NEkk8hBP1OxItgkMgjweizAVHLryHL9mqE84GZ8mD2a54z0W0N8ap9GiHb9Ug0Xkf02UWiLrIkQrbHkCqG0wjZLs86jxHje4GYdFchQrLtRLL51yBkpRCSqiANDpcRZfMPIv4CT4Tv+QngeoRsU0TeBe1G4hZRF5m5/T9fsjWE7bTtA6JEWxOZQ/YtxApyFyKBeEhybDz8fDNNYgbx6X0MqRc+RGxME+P7hJh0VxmUoog0J9yEJJp8JLFUQwj1CiRhVgEeBP4KIakdwPuAWxEZwRCaiWTbx5qbiQxnNz68GLxwHWayw9narQ8cQTrDrgZ+EkmSBYiMcCT83rQazyDuYIfC148Bj8QetzG+n4hJd9VgQwrevRHRZkeQaPQ4oofehJBYFtFF70FMuA8gMsL7gB9CWn7NSHPzMO2+JiHW3s57vmjCcnlWB8+VEnxELngwXMOPEGnOC4i8sIhouXnE0+EIQrbjhG5gscdtjFcC4jrdixxKkQVuhm/8EnR1wY4DSB1tBdiNyAsphLzuA/4CkRNGkNHh70TKxtq9aw0pnt1ie77NDAZ1hOR9RMpob/HV4f5nEfLPIq5lZpJEDYlijyDyiJlA8RQiPYwjF5SDwDNxN1mMVwriSPciRThl91bgNqAPMil45DDsuBd4B5IASyNR4ReAv9Cap0LHsHcCP4tUMphpDSZ6NcbdRg9t97Y9X9QQMm0RzT9rj4xN88JT4edchkgeRsY4hRBxD3BDuM0RJBI+jkTOzwAHtKZ5AeuKEeO7jjjSvcgQTtl9EzIGvKdr2+mbcyPzCyfvuuce2H0zbOlFZIAZpIHgo1pzInzfjcCvIlJDkjNLu1yi6BPOrME9X1SRRFYDiWx7OLNG10ci8FEkSu0nimxBLhCHESniVUj0OwF8E0mSeeFxPRx73MZ4pSKOdFcwbvjtu/YA7L1z961KkQTeVtw6/sd+w3FKx3s/BXwys3bm0pmDQ0Pw3neD5SBZ/M8CH7v+t+76Bx1wg1K7fwH4PaSTLEXkY+uEP8OZ7l8X+ndTJmq57UDkgHYpwVQrnEZkDkPIJvpdRDTdZ5CWXiMlfAWRRILw5wPEHrcxXuGISXeFo1mxlVK8F3g1kLUSftC1bfx057bTu2YeXfPOifsu6Qs8Bcm5WqrvwFJ99E23as08wOXvS9jHv3zZRqQaII0Qaw2JPjNE9bYaIcDkBS6vglQMLCHywCaiBBhErbkzSA2tGc9uUCdqZOgAXhOu6THg8wiZKySRFnvcxlgRiOWFFQgT4U48PLRr6WQXXiUxleyo1zu3TczYyeZVk/vW0ywlW5pA+xU3Udw+gVJ/+VQiWePEXX956XW/fvc3xh8a6Bp/YP3lfi0FWrfAa6IzNSJzmvYW25cS2T6JkGkXkohrt1IEkQLmkFIum6jV2PxB1hDpYBrpdssh88v+AemIS4av74s9bmOsJMSR7gqGV02QKtYoXDV6orBhsQpQnkwQ+BZdW6aP9F59ckorSCTgwd/500+AxSU/8tP7xx+4cnt1Oh8QaHAa0LR9cM3k3XbD8As1oCkhUegppGV4G6LLnt3gUEJqg80YHJtojI5GIuSjiJ/DZoSAP4+UsmXD992nNScucH0xYnzfEUe6KxhX/8e79riuaLrmuXadN/z+SuDAg7/z0X+FD75DJddfjla2bibHsWoDBI4NrolqA2glzgxuTd7sea/PGiHRw0hd7DAS2a4hkijgTN3WyAAOUVNFishe0QO2ImT8GPA/iZJ2hxGP29iYJsaKRBzprmC45xGHVucSjx76yHiHUtcAAAsxSURBVGuO4lz3nwlcZdm+4xNA0xoiSGvwFLRscD0gcQHFCIZsn0SqB4aR1uARIktHgwYiDSwhhJpESHgekQ3SRMmydYjXwwTwaaSSIYWQdexxG2PFI450L0KYaPfEPSO7pg+sw6+7TWUvWNparFIf6UBbgOWB44F31oVXhz8rL5RX234GcMyE3KeQIY29wHUI6XYTtQBDVMI1iRCvi5DxAiITFJCa2rHwtXWIlHAvIiVkEH14n9aMfccnJkaMVwDiSPcigiHb+qJrTT28dmDpRKeY4HYtLjTmahkaaxTYXhTNek5EsgDqrFv29ryWRmae8QjwGSRafSPRJIn2EjAfiVxPIdGtGSi5gJDrQPjcbPhYFz5/CCHyKkLeB4HHY2OaGBcTYtK9iNCqwtQj6/pLJ7o6A1+pVFcZH4/GRKEDv+C0VWqFrKvOcZuzHNGGP3temO96BPh7hEzfhUS2pnmhXZMoIZLAFNFUYOOtMIyQbwORCzKI/juBOIY9He5jFIluay/9bMSI8cpELC9cBFiWE75yya7GQoZkZ9VPrZ3k+L/ssKkmESkBogqwMySEsy68RlZwfaCW6lnIdl8x1hy7Z/uPAj+N1Nr2Iu277e+tIZHt8fD7RPh6I9y+J1zAFELAvURDIw8gEfEiottOvCwnJkaMVyDiSPciQvGySQJf0Viwaye+uN2lmtJhJYIfVmSFya32CTcgsoJuT3wFTmGp3rVtcqpz8+SW6UfWu6mepc/Xl9wWzXTAmU0SLaRu9llEKgDRazVCvCPhVzObLIkk2o4ike1xpETsIPBk3E0W42JHHOleJFAKtf1n7msd+dI6uzE2ED6rQ/3W9aCloO6SsjT1REiw6uwIWIP7GPA3r/qVu98RVBPO6X1DN8090Y9fsluQVG3X6YCoNXcciVKLSAScRCLZHKJNzCCyg4tEtFOIQc0EQrr7Y4/bGKsFMemuUBhJAaC+6F554p4Rd+Gp4QwtV7gTEFlBA24JFlvQ7BMePPsGxy07+VLQtWN8YupbW3Yi5Pj63NZTf1o+0tGPnw4rEpalhxmkfnYMafMtIH4KHYhm241UKVSQyoUaUslQRgj4MELUD2vNzMt6YmLEeIUjlhdWMJpVW5361kh/+WhPurGYStA0RmBtzV920LSS9Y6gWteojrAYwQzUVb5bqNJadH/LK+X/ct2tD3ypc+PC1OxjA/MzB9bOlp/qHzyzgqHVUoVGTS8Wv4LIAT5iETmEkGwRYfUAIdtTSBLNNF88icgQB4AjsZQQYzUijnRXGEyEu3C0uGts3xoa40Usx8fJVanMJ6CUlQ1tDysZoGzlJTvqTrW1t87cjSm8LMpVOJkmrcX8/4uYlpeB7X3XPvv5mZOZNcFkp4JEm+jb8MjW61QKXwD2Ixrua5A23wQSGfcQ1eAeQyoQ0kTTd/chpBt73MZY1Ygj3RWKREeDbLFBpjBB15ZpTnztElQ9i1tsULhilIUnhnATmuzaSac2WYDJH0gRuIGT9/3uHSet4ZtOs/+PXvdHSjE8eMszj5QnsiNTD/clo94GzwPfh+Sh/IaFNbnhxaXx+wofQYZA/gxiZFNDpIQsEtEeRiJgTTS08ttI/e2Dxt0sRozVjDjSXaFo03SvBBjb11dozOYYvuXYokVCLTxTtDIDtfT8k1321IEhvHqT7NC+YMvbE+VHPvTaIpAbef0TDy4e7youncrlqSdTLF+ENaRqXm6gPls+3n8n8A1kFPu7iLrGMkiyzEJqbu9HkmJbkXrc00hn2X6tOfY9OCUxYqwIxKS7QtGeSAtxZeApHv79264FOrb9n3s/atm+rs8n7VrF3aor/2vi9L1/8vvQ/B8b3nJg//zhgd7KTGqwNZ8hquMFK1sn3VWbrYz2GrIF+HlCckdqa82AykXERPxexODmSkRq+Fb43KNaLzvmxIgRg5h0Lxpc9X9//b7GfCb1xCeufz/RKHS98xfu/fNksbZt4qHPTZz8SvpT8OGeZO/Cuxol26KeKsi7lYfrtdJ95Ur3tslMYWTxscf/+qbdyJy0NyK67AwiGXQiCbRHkNlqS8gctjWIlvtFREpY+l4ef4wYKwUx6a5wKIWD3PJ3IRpqg8hoZlxrWkpxCdz3Z7B2HaytI6VdXeDlsX3f7aqUu7dOz0zct/UORCJ4I/BTCMnOIXW36xD54Rjwj0gUfBsyV81HRufcFXvcxojxwogTaSsYSlFE6mN7kLKsxR3vu//PU53VxkO//4ZdStGlFB8AdsGmkdAM0pjTNN3OSrnrsonJnu0zC5meamPivq0J4H+3dyexbVRxGMC/GY/XjOMEJ87mtGobSoMCSqEUN1IWERUD4kSPXLgg4MyNSlhQDoDYDkiAWIQAcWmDkBCiBgFGqJQEWlDAlUKInK1xEidOnHgbLzMcXqJENNBS2lGcfr+j9eTxyNanv+e9939vAzgAMTGWgOiN4IZYgTAAEbj1AJ5cu+4FAB8BGGaPW6LLY+hWIEmCBWJ77V5s/PVPALjo8qXzWg6dzT0jGWdDg5abqxkFYAXUWsDhgnj0EAPw/v5jvx1z+XKFUl7pmfxmD+ze1PeGIUuFpPs7ALsg+iwUAHwF4F2IXWSPQ5wWvLr22mn2uCW6cgzdCiNJcAO4DWIyawViG+703U+HPwOA+E/NvcloE7SsUjaKsEJ0AqsHHDKQXQE8bwL4EMCE2px50DCAmbN+pGJeSEqpqJcVCSJUZYglYK8D+BpiXe5rEFXvEIC32OOW6L9j6FaAQCgcMXRg6LngYxBHkMsQ1erY+trXQAgA0Jmbd6OYk1HUYEHWZYFUaoGlXEZp+Azw/JfAwCvrp+ZKUvABALf6+3+PyPaCnJ1qWAXgslZnqmvb4/H5wbaH1z7CMxAbIRYBvAjgW+4mI7o6DN0KoK3YldWJWjeAQxDNYn4FsLBV8DXdM1LWPr3doo15AYuOql2LxZbu2PAfHwQ/BtLltYk1GUDr7vujny/HPO65c61V5bwNsiPXqLYmF1t6xy+4m9K5+cG2+wA8BPE7OQ3gDfa4Jfp/uHphGwuEwpFSXpaXR33dkmzAUZcZdNWn84MnNg6i3Dw2NVXtXIo21mVXLS1lvaD7OpLR8YHDXSJopUfESOMUxPE6Rzy3XHyikHI6Cimn1+lbhe/wxDnvzcm27KLDE/9hL/ILatKqFvKp0YZ7DQNRM++daKdipbvNKQ5dV1uXYVPzkJVL2x8GQuFIfkWRk9Fd3VqyCnpRmmrqnC149izpsmJkYqfWT961zTX3vfSqURw7Hj+z7wsAvtRIy48AZlqPDvf5Ds4t//zC0Z5AKBwpZ+29xYwdN7XPJpq6JmeHTgQZuETXCEN3G1s/Wn3zsepbjVNsZb2YsUHdvYiatkTM6jCMzWMlCbWA1qAtTFdpaasFYhIuCdGE5mTzkfgnm68ZCIUjzvpfcP7lYB/Qfp3ujujGxNCtcOvheuh4OKIoG6EZCIUjg88G+wH013VOvVNIy1JJU/x2tYCqu8YPejtmEtH3uh4V73JpmNtUE2+C6AbC0K0A/1Thbqb87Ztcma52AXgKQKPFXio2dsQT6Yl6v+pfhmdfclKWt3qXK78eEV0dTqTtIIFQOFIqAYnz/t58wg29JE3W7J9P/nnyzjsMA8blHlMQ0fXHSneHURTA0CWo/iXUHpiPKXYdo1xTS7RtsNLdgVjREm1f//Jkj4iIrjVWukREJmKlS0RkIoYuEZGJGLpERCZi6BIRmYihS0RkIoYuEZGJGLpERCZi6BIRmYihS0RkIoYuEZGJGLpERCZi6BIRmYihS0RkIoYuEZGJGLpERCZi6BIRmegvcVdDGZl0Ir0AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "otda = ot.da.JCPOTTransport(reg_e=1, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True)\n", + "otda.fit(all_Xr, all_Yr, xt)\n", + "\n", + "ws1 = otda.proportions_.dot(otda.log_['D2'][0])\n", + "ws2 = otda.proportions_.dot(otda.log_['D2'][1])\n", + "\n", + "pl.figure(3)\n", + "pl.clf()\n", + "plot_ax(dec1, 'Source 1')\n", + "plot_ax(dec2, 'Source 2')\n", + "plot_ax(dect, 'Target')\n", + "print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt)\n", + "print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt)\n", + "pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9)\n", + "pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9)\n", + "pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9)\n", + "\n", + "pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1')\n", + "pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2')\n", + "\n", + "pl.title('OT with prop estimation ({:1.3f},{:1.3f})'.format(otda.proportions_[0], otda.proportions_[1]))\n", + "\n", + "pl.legend()\n", + "pl.axis('equal')\n", + "pl.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run oracle transport algorithm with known proportions\n", + "----------------------------------------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "h_res = np.array([1 - pt, pt])\n", + "\n", + "ws1 = h_res.dot(otda.log_['D2'][0])\n", + "ws2 = h_res.dot(otda.log_['D2'][1])\n", + "\n", + "pl.figure(4)\n", + "pl.clf()\n", + "plot_ax(dec1, 'Source 1')\n", + "plot_ax(dec2, 'Source 2')\n", + "plot_ax(dect, 'Target')\n", + "print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt)\n", + "print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt)\n", + "pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9)\n", + "pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9)\n", + "pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9)\n", + "\n", + "pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1')\n", + "pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2')\n", + "\n", + "pl.title('OT with known proportion ({:1.1f},{:1.1f})'.format(h_res[0], h_res[1]))\n", + "\n", + "pl.legend()\n", + "pl.axis('equal')\n", + "pl.axis('off')\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_otda_linear_mapping.ipynb b/notebooks/plot_otda_linear_mapping.ipynb index 4b6713d..e1e9c17 100644 --- a/notebooks/plot_otda_linear_mapping.ipynb +++ b/notebooks/plot_otda_linear_mapping.ipynb @@ -99,7 +99,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 4, @@ -108,12 +108,14 @@ }, { "data": { - "image/png": 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\n", 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2zag5E+ocr7RgRIE0OIorraUEdzEVvdV54/Wu++rL1zPVldkGGh7FFdET3KUIF+3yReDCxe76pxaadz67tR6CBzS+A4u4t4K7lOGiXZ7zgiW5amItQhqLiugJDjO5HegbqHoXFrLEa6RQZj2xQ4joCe4yPgXc89hCAm5T6R9c+Lk1DGzc6zVSmG+oStJYVEPO9AS3UX/EapYELQE4tMNZPbl80RM1f5OE8SkRuQBE9AS38UdVmyZ4igunvOYLgAhdDCJ6gttUXeZlE1cueVFmoJw2WY4iZ3qC29SxsmBoBNj4eLbvqlZZ0jE5FBE9wW1crixY0gcs6V/8mYkIq3RMjkTcW8E95odgn/bKxvoGzFdmlMHVK0BrCBgY7HVFd68xe686WsQZEdET3MIfuHA9aXju58DWE72fmxYply1iw4h7K7hF3QIXYWIU1fggLZKUvAgRvRzsPvRK1VtoHq66aa3h8PO7oufT3vJeid5qiOj5iBOy3Ydemb9mz/SxMrYk6LjmpvW3vEjs1hPAuz/VWyEBBEdbTbrtx54zt1YNkDM9jd2HXsGe6WPYsv6m+fcA5t8DC0KnfyaUyOT2+BZP/YNelULV+Kskgiokdq8JjrZS30KL97y4ah0XhFh6Gn7Lbc/0sfnP7tu7uFnl6m3PzL+u3vaMuLplMT6l1ZQC3qhDjf4WcM21pW+rZw8bH/fasce5lWGCZErwAPes44IxMex7KYCvA7gWnuX4NDN/POo7ts3IUBZeGJsnx0J/v3lyLNbqC7IYBUPo6Ssq5WP/JhTe3bh/EHjT8ELaDOBFYtNWQETN2zCFGmjegHO9JDMyTIgeARhk5l8SUT+AbwDYzMyHw75ji+ilPZtLIn67D73SI27KKjy56+75+4oAFohxIfENzu5vmetYUkhH5oBB3yb3bDGlDAZij1923/Z3/zkxRECd322eHEt8vUIJmPp5y/qbcN/e5xddE+bySgCkYCa39/aSy8rQCLBxX3EtmpS7bor+weB0F6nKmMdIIIOI+gAcBfDrAB5j5iMm1rWRzZNjOHz8bODvjpzwIm66BakLnLL4hIIZnwK+/TfAia+l+FKIdaRc1SItpPGphdZYebl8MTyIIwENAIbn3hLRMgAHAHyEmV/0/W4TgE0AcMMNN9z2wx/+0Nh90xB3flc2ysoUd9cgBx/2xjAmZccF7zXofLAsdzCRmxsgzGmo6axbnVLO9AJuuh3Ar5j5L8KuseVML4/lpcQqq4Dq7vHqbc/g5K675azPFP99OF30U4le1fhriq+8DlzqWm2tYeA3700n5jpypjdP7jM9IlrRtfBARC0A6wF8P++6RaFST/KmmPhd16Tngjr+PeyZPiapLyZII3i0xJ62S+NTniW2cR/wxtyC4AHe+xvWJV+rNSyt4kMwEb0dB/AZAH3wRLTNzJEnplVaesqqUuKnzuHKZO3ocOS9dStQyMCOZUjlBtpmBYVFn1vDXlpM3LMt6feqP2x5nhIpK3rbYeZbmXmcmdfECZ4N7D70Co6cOGdU8DZPjmHt6HCPYK1ctrTn2rh7S7JzDjptgCj+Oh3bIpthAYe5c0gk5te+pZGCl5RGlKHdsWsaZ86/Nv8+7Tnc2tFhfOGB2wEszrnTzwT9pWsK/b5p2DN9DIePn52/r6DhP/sCPAuotTyZJRSETZHNoVX5IrlzPze3lxpS+zK0+/Y+n1l4FEdOnFskZvr53ebJsfn3YZFh5c6mYfPk2CJrUCy/LirKqQr0584tWEBJLaEgbCrVytsGyqZnsZDai54pF1YFGfSyM/Wzer9l/U04uevunqBG0j285dq+Rffz319AMf306tRvrk7PUhC1dG+ztn5SZWYqfUR9PyywEJZesmX9TdgzfWzRempfUXt69fXeqKMkNPsowg21KYgBLEw0Sw1J77wE1MrSC6uESIr+nTTlaUEkaUSQlsZ2dNGbbJLh/2SHRuwSiU47Ry89lt55CaiVpaf3wotqDqATZI0p8oiWvg//Z4ePn83kdjcylaVnmLfBlks2uoJ5o8g2BWQsxXhFRhKKyNPLW15WhGUWhnJZkwqznzL3WjkmOqaohpytbkApSwuoskibY+inAaVmUSTJ03Pe0gsTOyUoa0eH560qXWRUyol6LVtEsrrOjRI8ILvlQkuAe/faJ2px5E1Xsc1ytZDaWHr37X3eSKS2KFEJE+eVy5YmTqnR63MbU6eb1dJrDQePVrSdtM0SdFx9ZoM0wtJLgx6V1QVEWXxFoqe2BN0vLkqrNzhQ0eFGiN7kdmD/h9J/z6UEXT3ZOm01iaK/5XVHFmJxOnqrgg6qrCwOv7DYJBpx7u6e6WPz+29c9DYLriTo+pOt+Wr6NaShQCqctvTSWjtrR4cDr8+TmpIF//2iXPOgYId6rwdEbBJwo2SJZtoYlQ0ja7J13wBwz2MidBlw+kwvTeKuK8Kwetszi4IvOv5630aksOwYSnf9kgHg3Q6JQdZorZzfBVLLM70sqSm2C57/mcKsPr/Ir972jPXPlotOG6m7Bff1xV9jC522F2XOknvo0pmlZTh3pqf+wNNYObY351Q1u+qZkrjbmyfH6m/pPbsVqa0g29pEKfSqkt1rvCjtFz+cPdnalTNLC3HG0tNTNYB6ubZ+Sy+JJauuqW0UN085lm1VCf6qkgunsqelAADInTNLC3FG9LLW09oueEBvOkscK5ctxTe3TQKocfeVPNaabVaQ0c4wBEx80J0zSwtxRvQA9HQ/SYLtgpeFM+dfWySOtYziZrXWbIzcmrI8h0bsLJ1zDKtFzy9w/silOsiPEkH1Hb37sc0o4Upi8fmbJdSKVOVY3WCHraKQt7Rs4+P2PZPDWC16YVUMaqiPuiZK9FwSBCXySbvDxD2708RVYgyNVDOfNguT2xPMtI3A5mdzEKtFLwhVfaEsoTiLyKUaVb/Ih9Xlrly2NLB1lbMEDdkGEJqu0hp2q5OIEq1nt6YPzgwMmt9Pw7Fa9KLc26xtmVwiTPD0z10R9EA67V4huHAKOPBH3WahIekqv3lvKdszzhsZLL03Lnn/O4m1ZwwTw75HiOgfieh7RPQSEW02sTGgN39NRwle1NAdFw/2VUpO2HOtWv6mMrdTDJ028Mio574GWT58Bbh6Ofz7Lx0obm9FkTWCe/WynXmHDmMiOfkNAH/CzDcDWAfgQSK62cC6PegCeHLX3aHlWkB4na3txFmv6nmdbR2vctYyt0RHvu9WRZ5Ahm15h45jYtj3T5j5he7PrwJ4GcDKvOv6CTq7ihI8/9hGl0jaNQbwWs87RRHTzGxGWbV5sC3v0HGMlqER0WoAtwI4EvC7TUQ0Q0Qzs7OzqdfWrba1o8ORAQyVmuLKmd/uQ6/MW27Awr5XLlsa+11TIy5Lw4TV0ko/R7gSTFi1IGDsTmNbEgyKHhG9GcDfAXiImX/h/z0z72PmCWaeWLFiReb7JLGC/Im7tlt8/rNL9RrVUTnsrNN68lotfQPuNMs0YtUy8N3PdZsvCCYwInpE1A9P8D7LzPtNrBlGWHAjqgDf9oYDYUSlo+iWoVPne5PbvaqJVHS7CQ+NuNVDztRZnK1NFBwldz89IiIAnwFwjpkfSvKdvP300pSiuWQNpXkuvXLDpWcEkG4OhK1VFnF02tna3IdCwI7zBterJ0n66Zmw9O4A8D4Av0NE3+n++08G1g1Fne9FpasonLB+ukSl6PjR28c7RacNHH0q4cXkJSG7KHhf/HC274adV0owwxi5k5OZ+RuY9z/KJepsTzUncI20DRWSCL81qIP9pD3kXPxD77S95OosffKUVesvWbOxiYLDWF2R4SdLh5UqZtrmQe017jmdcGn95WWXLiY/2HftD73TBr78EHD5Yrbvq+dVVq2/LM81a9dinBK9pIIALLRSd5Gnj+ZIZLWBsPKyNLg03Uu5s1FVJKFQr7CNT7nz7A7iZLv4pFaOPjXMmegmvFSVk7vujhRtffylVRjJTYNbf/TTO7ML3sZ9bp5bOoxzopcGJRoqOOCCm6uLWNB+leCnsXpLpSkVF/rMi8wlZiypKBXglHub5UzPOlEIwT/7Niwya33EtgkVF/6ZF3Es6Q+3BKWutnScnXub5Y/f9q4rWcZb6ljxfLvX5Cuud2GIdZZnpL7giO7QiFu9AS2nlnNvdfzDr8OwQgwiCBO7qJ6Bys21Ljl5cjvwpQeBK5dSfMnydu9+slhnfMWL0EoqSuU4eaaXdgykzYIHBAdn4oTMygDG7jXA/k3AlaSH+uQJ3cZ9wI4L7hzoZ80fXPU273nVc7sUoa4RTlp6aUYmrh0dxn17n8e6G6+zRvz0FvZhVp76/C3X9uHV13vdIqvOKtOecc1T/tFKLubzDjO67ye/AXzcsa44NcRJS89PVHrHuhuvw5ET56wSCX0vysrz719dEyR46lprUnLyRGwvnPIE0/YuIgcf9mpp85xXZqnSEIzjvOilSUD257bZ5CImsULVswa1oqo0JSdvBNL2LiKddvIGCVFQX/41hNw46d7qqD/0LetvwuHjZ3vqcXWrSh+vqNJZyhKKsCFHYRPPgnj66Ck7q0zyznUF7E7dMCXIt73fzDpCLpy39ICFzsNpugjrs3PLwO/GqldVfZGkccCZ86/19Aa0QgRNRCBtbi6QV5BpCTBxP7DhUTP7EXLhvKUHZKtO0AfsAOWltag9+veqzh6DUKkrQRFdW4IzubA9dSOPJStiZx21sPTysHZ0uEdMTJz1pVlj9bZnIgVb/c6mM8hFPLs1w5e0bsi2pG7opWW713jvO22vO0xqSATPUpytyAjjjl3Tic/IFGp6WtaEXz0FRaGvEVdpETe4XPUGtNaq2zGU7npbqhD01let5cDrry4uF1vSDxClTLSGV0Z31yN2CHnDKKtzslV8c9tk6u/obmUWayrOrY7riBxXdaHWqA02TPdSuYUXTgFgryuMvz726uV0gtcaBjY+Dmw9IYJnMbU40/MTNQQ8Cv8UNWDhrM+fUAxg/nNFWIR28+RYYGQ5CSpQYa2llyW/7qUD1bt9RXSDGRgUsXOA2olemkHZceiWlkpv0buhhAmc+p2eVxdVORLm3urfKTO9JjGdNnDggfTfmzvnfbdKgSgiRcbmtBthntq5t1vW32QsjWP3oVdwx67pRZ/pgqqLol8g1WuSiLJ+jVVJx1F02l5jAb6a7ftVJyMXkSJjc9qNME/tAhk61veeC0G552EWoBVdYx4ZzdkducKRhkHt7PPS37InCt1gSmstRURPAtgA4KfMvMbEmnkIi5bGiUkYWc8I/cSd7enWojrD0xsrWNNCqtPOLxhVWUUHHwZmnkRPs4PWMPDa+XSW68AgcOlXMrzHMUy5t08BeIehtXITFC09uetufOGB2wEAh4+fTbWeqTPCw8fPYt2N1/V87lzScaa8PB9VRHA77WDBAzwBSyx43Ry8P/uxZ6260hJLAGDI0mPmrxPRahNrmSIskgqYE7G0HDlxLvDeuw+9EluGZkW5GWDGygOAY8/lXyMt0zsR2s7qwunw7sYAAqeWCU5Su0CGwl/rGjddrCrWjg5jy/qb5q3QMKyw/NQgaxNUEemMumdreXTrJxG82lCa6BHRJiKaIaKZ2dnZsm67SCysEA4fR06cC+yFZ0XJmV6W9choN1prqCdcFWd6cfccGgn/nSt9/4RYShM9Zt7HzBPMPLFixYqybovdh16x0sJT6Ll8irwDgowQVLGQthwrjKoaDETdc+6c9/v+Vvg1tvf9ExJRW/dWsWf6GA4fPzvfXTgJSdo8mSKomUDlggeYr1hoDcPobIig5gBBHHzYu2bHkNf5OI53fjLG4pMEZNcxkqdHRJ8H8HYA1wP4FwAfZ+bQVrNF5emFFf7rpWRWCEpKKsnL27EMZmZYEDDxQbNlZ0EzOfx5cp02cPChdB1SWsNe3SwQPubRlmYJQiBJ8vRqlZysctn8g7PjCBu+k6arcVGsHR2ODXIUQt75tYDXPPO2D5ivsw3bm4q+0pLslSI7LnivSYRVsI5GdlkBMN8mKmky782/Ftwa6T/f1uvmqDX1qLAir1u8eXKsJ7ewqvSa+DM3il+DrwIv/A/zh/9hLqYKsmQVPJ3xKc3VlZGNdcJ50VOt4pu01ZcAAAzwSURBVNV5nXq9b+/zsWd4SrjCLKk908ewctnSnhbvQG8kePPkGL7wwO3YPDmWOXDibwVfKeNT3XO4AIZGkNj1vXo5/PA/6blcz/1LivyOT3murCQg1wrnRS9shGIaCylKHM+cfw2Hj59ddK7mF0H9d3nP3oISqpWolz3XA3c9EhzNTOv2XjjVK2r+6HCalJC4KGtWwkReqBXOi55CiY3eoQSIrmSIC2ooF/nIiXOB+X5hQqc6vaS1+PzX+13o0l1d5eKZEAO/qAVFh4NSQoKswfEpYNXb8u9JZ0m/J/JC7amN6AGeSCj3UFlJurD5RcUvkErkTOT16c0CgvYZhF+ErZiNMT7l1aWaQBe1sHM5/fMwa/Az7wJOfM3MngDPXX/3p8R9bQhONxFVKSr+jiSqgWeYiMShZuhGdVJOigkBDWtWWloai8ncNLVWa3lwDa86r1Mlb/4KkMtzZgVv4+Midg3D6ZQVlaKSZghPEEFVEUH3MYmy3rLstRSxO/gwcPQpc2VniqER70xu/wMAfFHWvgHgnse8n/3pIkmZuB+YCU0RDd6P5N3VhtL66dlAFrGruj9dlkRp1Ta+0JkZBx9OJxxpuHQR+PJD6BE8wCtzS1I1EUXafUuFReNw7kwvLEVFCUhQekkYap24MzPTtbt5rFFFoZUlR58qbu25c8DlLHNkC0JavDeOxru3VbdeV6K9dnQY6268LvQ8MojC9p52jq2rSIVF7WhsRUYU/ooHG9pNbZ4cW5SOklS090wfS2Sppob6zK5nE1Jh0XicPdPzp6akHQJkS7spfR/WnEne9v7izvSqRIIWAhy29IKSkf15dwqVKBxUQVElyhW3rvPLhke9KGidLL6qevgJ1uGc6IUFMnQXT/3sT0UxVSpmCv8AI/WatHFBIa6tYsOjwMfPRfeWsx6CuLKCH2cDGcpKStNKyhYLLwg9KJOWQp/LWF+9kpEgRSOpdSBDdwnX3XhdT/lYkAVlq+ABwU0M4lDPWOhz2ZzS0T/oJTT7aQ2L4AmhOBfI0JNylTAoAQwb+eh3hW20+Pxlb0nO+QpNUFZMbgf2b4KV1t5/+7FXrja900sylollQgKcEj3/wX+SIIASuCJKyUyy7sbrqmsYGouFgqfOGsenROSEVDjl3vojtjrWRUBT4g9q+LsoB1F401HV5cQ2JBIr5MCJQEZYlYXfvY2islkTGUgSzCjFajUxJ8MYBIAXGhaIdScEUPuGA4ePn411CW12acNQZWi6aw54wq2et5TzvCzF+H0Dvvm4XbHKghr0I0InGMTUCMh3ANgDoA/AE8y8K+r6PCkr+tncHbumY6eVuSR61tUNx00d89Ma9roP64GFsTuBmScRLnzdEZHHnpNghJCbUiw9IuoD8BiA9QBOA/gWEf09M38v79pR7D70SqDgKWvIljKzNOiNUPVIc5gQqs8LE77J7cFjEG95L/Ddz/V+ftcj4YGFqLI20yMiBSECE+7t2wD8gJmPAwAR/S2AewAUInqqJXyYECj3T/+9bekpUfifLc7yK1T4lHgFpYTcsC55qsiGR4GXDkR3ShaEkjAheisB6D7QaQBrDawbShIX0CW3VkdZe2HBDP1cDyjhOcMst7SpInc9Emw1ShRWKJnSUlaIaBMRzRDRzOzsbOZ1VGpHGLpba80M2YT464r9BNXkJm2EWjkyPFuwhNyBDCK6HcAOZv697vuPAQAz/3nYd7IGMrJ2HHbJvVWs3vZM6HmenqrjqkUrCEVQVsrKtwCMEdEogDMA3gPgvQbWFUKofDqaIDhMbveWmd8A8McAvgrgZQBtZn4p77pBqD/qpK2XgAK7CxfM2tFh56tMBMFGnK7ISILfAiolqdcgYW2zlOsr7q0gLFCb1lJBzTbjrD29nZRu5bliPamgRljFiSvPIQi24YToBRFXR7t62zNYuWwpADcFYsv6m5xMsBYE23G29jbJGd2Z86/1DBDSf7b18D+NO6/X5brSUEEQqsQJ0fOLQJqW6kHiUadzsDo9iyCUgRPubdCZXtI/duUi+tvJ20pSKy9NBFsQhAWcED0/6pA/Dr/7qto12cp9e59P7NaqAIczFRmCYAnOiZ4SsiQRXJWjp2PjGZ5CncmlcVlVh2Wbn0sQbMI50dP/uIMO7sMsOX04UJ0so8JbxgtCzXAiOTmILA03bU3mzZN8bePzCEJV1CY5OQh/cMPlg/2gQE0Yb7m2b9H7ulmuglA0jbH0AE8Y19143XyVhi3nYEmf5eSuu+fb5avviKUnCAs0ytJTRFl86268bv4MTBeZMq2koHv5R1vGnUvaJNiC4Bq1tfRWLluaeGhQmYPAg+6VZMBREFKFIQiLqfUISGXphAlfEhFJU9lRBFmDMYAEMAQhK85aejpR7dWVS5uUIupxw8TNP+8iC7bWDwtCFSSx9JwWvTypHn6qcG/1s7koq1Pfm7pOLD1B6KXWgQwgPtUjjTCs3vYM7tg13RNoKDLIoYIqcW62pKMIgjmcFr00xOXAndx1N86cf63HcoyzJMNEUv/8vr3Pz79Xc3sVqm9e0prgNNcKgtBLbURPCcHmybH5tJWgHnphVlWUtRVmaQW51+q9/vmRE+d6Bnjr+9kzfQyHj58NfS79zE7NxRUEIRu1ET0lBFvW35SpcF9HVTkoYVIipVts6vMgdGvPL5hqn7pIA14dsd+Ck0YCgmAepwMZUaiAgX7w7/8sDN268n9f/b6MFvQSmRWEdNQ+euvHZDQ3LXmFUB/gHfQ7ET9BiKfw6C0R/T4RvUREV4ko8kZlENVhWX9NEghQfeqSklds9e/79y+CJwjmyFuR8SKAjQD2GtiLcYICGWFzZP3smT5WidUokVlBKJZcosfMLwMAEZnZjUH853KqMwmA3FUQSUhydqhf60fETxCKwdna2ziCXMLDx8+WIniA10QgKWpG7ze3Tc5/Ji6tIBRD7JkeEf0vInox4N89aW5ERJuIaIaIZmZnZ7PvOAMqIbgswQPCGx74U1XU2aEueIIgFEespcfMv2viRsy8D8A+wIvemlgzKXoOH7B42DcQH4RQjQHSuKxh+O8lFp0glEttkpOzoFc3hDUf1TuhpHFZ/fjP6JT4SV2tIJRL3pSVe4noNIDbATxDRF81s61i8devRrV40j+P6tEXlQqjB1UkHUUQqiVv9PYAgAOG9lIafqFRZWu665q0191AH+G/vP3XE91XIrKCUD21jd7mRW/DHlSKprh0hecbBgSJpG7l+etuBUEon8aLXljpmn/4TlAzgLDZGnGNPsWlFYTqaLzo6cGMMBHTrbU0fe8EQbCPxoteEvz97BRJAheCINhFo1NW/KS1zsKETQRPEOxFRE9DxEoQ6o+IniAIjUJETxCERiGiJwhCoxDREwShUYjoCYLQKET0BEFoFCJ6giA0ChE9QRAaRSVzb4loFsAPS7+xea4H8LOqN1Ew8oz1oCnPOMjMK6IuqkT06gIRzcQNFnYdecZ6IM+4gLi3giA0ChE9QRAahYhePvZVvYESkGesB/KMXeRMTxCERiGWniAIjUJELwNE9A4i+ici+gERbat6P6YhohEi+kci+h4RvUREm6veU1EQUR8RfZuIDla9lyIgomVE9DQRfZ+IXiai2+O/5RZEtKX73+mLRPR5Iloadb2IXkqIqA/AYwDuAnAzgD8gopur3ZVx3gDwJ8x8M4B1AB6s4TMqNgN4uepNFMgeAF9h5t8AcAtq9qxEtBLARwFMMPMaAH0A3hP1HRG99LwNwA+Y+TgzXwLwtwDuqXhPRmHmnzDzC92fX4X3h7Ky2l2Zh4hWAbgbwBNV76UIiGgIwG8D+DQAMPMlZj5f7a4K4RoALSK6BsCbAPw46mIRvfSsBHBKe38aNRQEBRGtBnArgCPV7qQQPgHgTwFcrXojBTEKYBbAX3dd+CeIaLDqTZmEmc8A+AsAPwLwEwAXmPm5qO+I6AmhENGbAfwdgIeY+RdV78ckRLQBwE+Z+WjVeymQawD8FoC/ZOZbAVwEUKszaCJaDs/TGgXwawAGiegPo74jopeeMwBGtPerup/VCiLqhyd4n2Xm/VXvpwDuAPAuIjoJ74jid4job6rdknFOAzjNzMpKfxqeCNaJ3wVwgplnmfkygP0A/l3UF0T00vMtAGNENEpEA/AOTf++4j0ZhYgI3jnQy8z8aNX7KQJm/hgzr2Lm1fD+b/i/mTnSQnANZv5nAKeI6K3djyYBfK/CLRXBjwCsI6I3df+7nURMsEaGfaeEmd8goj8G8FV4kaInmfmlirdlmjsAvA/A/yWi73Q/+zNm/ocK9yRk4yMAPtv9f9DHAXyg4v0YhZmPENHTAF6Al3XwbcRUZkhFhiAIjULcW0EQGoWIniAIjUJETxCERiGiJwhCoxDREwShUYjoCYLQKET0BEFoFCJ6giA0iv8PbNJ3zZTDAswAAAAASUVORK5CYII=\n", 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -248,9 +252,7 @@ "xts = mapping.inverse_transform(Xt=X2)\n", "\n", "I1t = minmax(mat2im(xst, I1.shape))\n", - "I2t = minmax(mat2im(xts, I2.shape))\n", - "\n", - "# %%" + "I2t = minmax(mat2im(xts, I2.shape))" ] }, { @@ -272,7 +274,7 @@ { "data": { "text/plain": [ - "Text(0.5,1,'Inverse mapping Im. 2')" + "Text(0.5, 1.0, 'Inverse mapping Im. 2')" ] }, "execution_count": 9, @@ -281,12 +283,14 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -331,7 +335,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_otda_mapping.ipynb b/notebooks/plot_otda_mapping.ipynb index 9a7ae04..c604278 100644 --- a/notebooks/plot_otda_mapping.ipynb +++ b/notebooks/plot_otda_mapping.ipynb @@ -100,7 +100,7 @@ { "data": { "text/plain": [ - "Text(0.5,1,'Source and target distributions')" + "Text(0.5, 1.0, 'Source and target distributions')" ] }, "execution_count": 4, @@ -109,12 +109,14 @@ }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -149,26 +151,24 @@ "text": [ "It. |Loss |Delta loss\n", "--------------------------------\n", - " 0|4.132385e+03|0.000000e+00\n", - " 1|4.124370e+03|-1.939427e-03\n", - " 2|4.124043e+03|-7.928706e-05\n", - " 3|4.123904e+03|-3.369312e-05\n", - " 4|4.123827e+03|-1.881208e-05\n", - " 5|4.123778e+03|-1.184435e-05\n", - " 6|4.123764e+03|-3.358329e-06\n", + " 0|4.130455e+03|0.000000e+00\n", + " 1|4.124174e+03|-1.520585e-03\n", + " 2|4.123972e+03|-4.893906e-05\n", + " 3|4.123892e+03|-1.957570e-05\n", + " 4|4.123852e+03|-9.690449e-06\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 0|4.143700e+02|0.000000e+00\n", - " 1|4.111590e+02|-7.748977e-03\n", - " 2|4.109509e+02|-5.062453e-04\n", - " 3|4.108581e+02|-2.257162e-04\n", - " 4|4.107918e+02|-1.614130e-04\n", - " 5|4.107473e+02|-1.083067e-04\n", - " 6|4.107110e+02|-8.833802e-05\n", - " 7|4.106839e+02|-6.600463e-05\n", - " 8|4.106615e+02|-5.455553e-05\n", - " 9|4.106428e+02|-4.548650e-05\n", - " 10|4.106278e+02|-3.649926e-05\n" + " 0|4.155681e+02|0.000000e+00\n", + " 1|4.121954e+02|-8.115904e-03\n", + " 2|4.120356e+02|-3.877130e-04\n", + " 3|4.119541e+02|-1.978089e-04\n", + " 4|4.118961e+02|-1.406833e-04\n", + " 5|4.118524e+02|-1.061404e-04\n", + " 6|4.118195e+02|-7.984227e-05\n", + " 7|4.117940e+02|-6.188410e-05\n", + " 8|4.117747e+02|-4.692100e-05\n", + " 9|4.117580e+02|-4.045536e-05\n", + " 10|4.117441e+02|-3.393923e-05\n" ] } ], @@ -218,12 +218,14 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -280,7 +282,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_otda_mapping_colors_images.ipynb b/notebooks/plot_otda_mapping_colors_images.ipynb index b66640b..5313e3b 100644 --- a/notebooks/plot_otda_mapping_colors_images.ipynb +++ b/notebooks/plot_otda_mapping_colors_images.ipynb @@ -46,7 +46,6 @@ "# License: MIT License\n", "\n", "import numpy as np\n", - "from scipy import ndimage\n", "import matplotlib.pylab as pl\n", "import ot\n", "\n", @@ -82,26 +81,11 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:2: DeprecationWarning: `imread` is deprecated!\n", - "`imread` is deprecated in SciPy 1.0.0.\n", - "Use ``matplotlib.pyplot.imread`` instead.\n", - " \n", - "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: `imread` is deprecated!\n", - "`imread` is deprecated in SciPy 1.0.0.\n", - "Use ``matplotlib.pyplot.imread`` instead.\n", - " This is separate from the ipykernel package so we can avoid doing imports until\n" - ] - } - ], + "outputs": [], "source": [ "# Loading images\n", - "I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\n", - "I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n", + "I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256\n", + "I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n", "\n", "\n", "X1 = im2mat(I1)\n", @@ -220,12 +204,14 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", "text/plain": [ "
" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], @@ -370,7 +360,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_otda_semi_supervised.ipynb b/notebooks/plot_otda_semi_supervised.ipynb index 484c2ee..6386840 100644 --- a/notebooks/plot_otda_semi_supervised.ipynb +++ b/notebooks/plot_otda_semi_supervised.ipynb @@ -128,7 +128,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] @@ -234,7 +234,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -286,7 +286,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/notebooks/plot_partial_wass_and_gromov.ipynb b/notebooks/plot_partial_wass_and_gromov.ipynb new file mode 100644 index 0000000..017fd8c --- /dev/null +++ b/notebooks/plot_partial_wass_and_gromov.ipynb @@ -0,0 +1,352 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Partial Wasserstein and Gromov-Wasserstein example\n", + "\n", + "\n", + "This example is designed to show how to use the Partial (Gromov-)Wassertsein\n", + "distance computation in POT.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Laetitia Chapel \n", + "# License: MIT License\n", + "\n", + "# necessary for 3d plot even if not used\n", + "from mpl_toolkits.mplot3d import Axes3D # noqa\n", + "import scipy as sp\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sample two 2D Gaussian distributions and plot them\n", + "--------------------------------------------------\n", + "\n", + "For demonstration purpose, we sample two Gaussian distributions in 2-d\n", + "spaces and add some random noise.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "n_samples = 20 # nb samples (gaussian)\n", + "n_noise = 20 # nb of samples (noise)\n", + "\n", + "mu = np.array([0, 0])\n", + "cov = np.array([[1, 0], [0, 2]])\n", + "\n", + "xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)\n", + "xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2))\n", + "xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)\n", + "xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2))\n", + "\n", + "M = sp.spatial.distance.cdist(xs, xt)\n", + "\n", + "fig = pl.figure()\n", + "ax1 = fig.add_subplot(131)\n", + "ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", + "ax2 = fig.add_subplot(132)\n", + "ax2.scatter(xt[:, 0], xt[:, 1], color='r')\n", + "ax3 = fig.add_subplot(133)\n", + "ax3.imshow(M)\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute partial Wasserstein plans and distance,\n", + "by transporting 50% of the mass\n", + "----------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Partial Wasserstein distance (m = 0.5): 0.485157824314826\n", + "Entropic partial Wasserstein distance (m = 0.5): 0.5048991597745197\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "p = ot.unif(n_samples + n_noise)\n", + "q = ot.unif(n_samples + n_noise)\n", + "\n", + "w0, log0 = ot.partial.partial_wasserstein(p, q, M, m=0.5, log=True)\n", + "w, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=0.1, m=0.5,\n", + " log=True)\n", + "\n", + "print('Partial Wasserstein distance (m = 0.5): ' + str(log0['partial_w_dist']))\n", + "print('Entropic partial Wasserstein distance (m = 0.5): ' +\n", + " str(log['partial_w_dist']))\n", + "\n", + "pl.figure(1, (10, 5))\n", + "pl.subplot(1, 2, 1)\n", + "pl.imshow(w0, cmap='jet')\n", + "pl.title('Partial Wasserstein')\n", + "pl.subplot(1, 2, 2)\n", + "pl.imshow(w, cmap='jet')\n", + "pl.title('Entropic partial Wasserstein')\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sample one 2D and 3D Gaussian distributions and plot them\n", + "---------------------------------------------------------\n", + "\n", + "The Gromov-Wasserstein distance allows to compute distances with samples that\n", + "do not belong to the same metric space. For demonstration purpose, we sample\n", + "two Gaussian distributions in 2- and 3-dimensional spaces.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ivrfjvd1uN+qExIOeUn7QdXp7e0lLS8Nutydl7fGiN37WrRe3283w8DBjY2PHEvWzFsYYbd6Pf/zjXL16lf/wH/6D8fN3v/vdAL/y4NtfAV7Uf6eEXKE4Z0QT8b6+PqxWK1VVVXFHosDhO/LBwUFMJhNOp/PUaq3o1ktBQQFVVVVYrVZ6e3uj1hCPh9P0yOP5DH/yk5/wX//rf+WHP/wh9fX11NfX89JLL/HMM88AvF0IMQT8DPC8/hrlkT9mqGiR8000ER8cHASgurraSAuPV3QPEv3R0VF8Ph/Xrl3bVZ/7tJBSYjabyc/Px+Vysb29zdzcHG1tbWRmZuJ0OsnMzDxUpE9rRx7v5/emN70p5lgp5dui/VwJ+WOGslPON5EiLoRgZGQEv99PXV2dISqJCnm0sdPT06ysrNDQ0GBc67SFfC9paWlGT87V1VWmpqbY3t7eV252L6e5I4eT6bakhFyhOCfsFfGxsTHcbve+ZsnHtVYWFhaYmpqiubnZEKqz3OpNCEFubi65ubkEAwE2vvc95l58EX9pKZlvfCP5+fm7silP0yM/qc9RCblCcQ4IBoNG/WshBJOTk6ysrFBfX79PHBLZPe8du7KywsjICM3NzftSyU97R36ocEpJ6h/9EfaXXwZ2dszTCwu01teTnZ2N0+nEbrfvmsf8v/4Xts99Dra3Cbz//fh///fBYolrPZFx+/Gu/6RQQq5QnHGCwSCdnZ3U1tYihGBmZob5+fmYzZL1OtjxEGnDbGxs0NfXR1NTE5Y9YnYWrZW9aENDWL73PcKFhaBpCL+f0u98h7wPf5hVj4fJyUm2t7eNm2JqWxspv/qriAd1X6x//dcgBP7nnz/4Qg+QUh5aNyWSQCCw73NNFipqRXEgylM/XXTRWV9fRwjB/Pw8U1NT1NfXxxSRo1gr29vb3Lt3L2bDifMg5LjdSJMJ9JubxYIIhxEeD7m5udTW1lJfXw/sRPmsfu1rhogDCI8Hy9/9XdyXS9Ra8Xq9J1LCFpSQKw7hrKT07+VxuMGEQqFddsrCwgKjo6OHNktO9LDT6/UatcrT09NjjjtsnpPmMOEMV1WBzYZYW4NQCLGwsPOzHKNIIBaLBZvNRn19PZklJYT3fI4ygYYbkYedWn8/pu9/H621FWLcRH0+nxJyhSKSs3qDSRZ7O96Hw2GGh4dpbGw89PE8EWtFn/fKlSv70sAjORc78pwcfC+8gCwoQKyuEr5+Hd9/+S+vpzQ/QL8hiF//dcjORlosSCBks9Hz0Y8yMTGx045tawvtzh1M//zPsLy873L6PKbvfQ/r5z+P5a//GusLL2D5y7+M2v3kJIVceeSKfeit1XT0/w+effbx2AmfNuFwGJ/PZ4j42toaHo+Hp556Kqkd70OhEAsLCxQXF8cscxvJmRdyIHz5Mt6/+ZsDx+gCLIuK2P7nf8by9a/DxgbBf/kvKWlsZH5+nt5XX6Xqb/6GrK0tLDYbZrsd/zPPIIuLX79WOIwWCGD55jeRTidYrRAOY/rJTwi+853Iqqpd1z2p7kCgduSKKJzVlP7HoWbMXhHf2Nigp6eHzMzMuA/K4rFWwuEwHR0dRkr/YZyFHflJFM2SRUX4/+N/xP/7v0/4ySexWCyUlJTQtL5Ort/PZmEh01YrG8vLhL797X3zaMHgjpWi/9toGphMCJ9v33WVtaJQcHZvMMlEL4AlhGBzc9M4gLRYLAkdYB40VkpJd3c3ubm5ZGVlxV3G9qBxbrc7rnrixyVhL35jA9unP01aczOp73wnWktLXPMItxtTWho5OTk4nU6smZmsj4/T2trK5OSkUftd2O2EL19GTE+D349YWEDa7YRLS/fNqQ47FaeGSul/uGiahhCC7e1tOjs7uXHjBunp6QmHFMYaK6Wkv78fm81GZWXlkVu9ReJ2u+nu7jZauY2Pj+OLsiM9DWyf/Szm738fhEDMzJD6a79Gyvz8oa8LX7u2E9GyvY3w+0n1eMj/+Z83kq+6urpYWlpifWMD76c+RejmTdjeJlxejv8//2eI0nTjJK0V5ZErDuSs7nYf5RuMHkUS2YUnESE/SJzv379PMBikrq7OmDeRDkHR1trV1cW1a9ew2WyEw2Hm5+e5d+8eVqsVl8sVl/9+1OsfSDiM+ZVXkJmZO5aH2Qybm9j7++Ff/IuDX3rtGoFf+zXMf/d34PUSeN/7CL3tbVg0jZKSEkpKSujq6mJzc5OWuTlyf+7ncH7sYwd2TVKHnQrFHs7qDea46F3nr169usu7TkRwY4n+5OQk6+vru7JBE9mR7x0XCARob2+npqaGjIwMAoEAZrOZ4uJiiouL2dzcZGZmhpGREQKBAB6Ph9TU1Ljew0HrSGAwMjUVgsGdg8gHXlwozjWE3vQmQm96U8zfm81mysrKSEtLY2VlxSgyVlRURFFR0b6DaSXkCsVjwubmJpcvXyYnIvYZjl8/ZW5ujtnZWZqamnZlgx6l+TK8flhaWVlJTk5O1FKyGRkZVFdXEwqFuHPnDv39/QC4XK599U9OBCHw/97vYfu934OtLTCZCF2/zmpDQ1Km1+PINU0jPz+f/Px8/H4/8/PzRoMPp9NJbm4umqYpIVcoHhfy8/MJBAL7fn4cj3x5eZnR0dGo9VOO4pFLKbl37x4FBQVxdQwymUxYLBbq6+vxeDzMzs4yNjZGTk4OLpcroSbOiR52Bt/7XsLl5Zju3kXm5RF817uQXV0JzRGLaAlKVquV0tJSSktLcbvdzM3NMTIyQm5uLrOzs4cK+cc+9jH+/u//nsLCQrq7uwG4desWX/nKVygoKKCzs7MD+M9SypciX6eEXKE4BxzVI19fX2dgYCBq/ZS9Y+Odc3BwEJvNRkVFRfxv4AFpaWlcuHCByspKVlZWuH//PoFAAIfDQVFR0YEZq0eNiAk3NhJ+0JU+mUgpDyxja7fbsdvthMNhFhYW+Pa3v838/DzV1dV84AMfiPqaj3zkI3zqU5/iwx/+8K6f/9Zv/Ra//du/DVAf7XVKyBWKc8BRduSbm5t0d3fT0NAQcyeYiGUjpWR8fByv18v169fjXnvk6yNrput2hM/nY25ujrt372K323G5XHE1iHhYiOVlrF/4AlpfH+HKyp3EIJcr7uqHmqbhcDj44Ac/yObmJlV7EoUiefrppxkbG0t4jSr8UKE4ByS6I9crJl6/fp20A+qHJLIj9/l8LCwscO3ataTW4bbZbJSXlxu9Oaenp2ltbX09VT7OeU6EYBDbpz+N6X//b/D7MbW2kvLJT4LXm3AZW5/PR15eHjdv3kx4GX/2Z3/G9evXEUJ8TQiRs/f3SsgVMXlUI0POMrGEIREhD4VCrKysUFNTc2jD5HiFfH19HY/HQ319/ZG64sSD3puzpqbGuE5XVxf37t1jeXk57vef1DXNzaGNjSELCsBmQ+bnIxYX0UZHD7VW9uL3+6NWljyM3/iN32BkZISOjg6AWeCFvWOUkCti8qgXpjpPxCvkwWCQ3t5e0tPT90W+xJr3MCHf3Nykv7+fjIyME6unvRc9Vb65uZmKigqWlpZYXFxkZmYGr9f7UNYAQErKTgq+/tk/+FqmpDy0MrZFRUWYTCb9pvEV4Im9Y5SQKxTngHiEXA8JdLlccQvuYTtyn89HZ2fnkeyUZGG327l8+TL5+fnYbDZ6e3vp6OhgYWHhxHfpMj+fwPvfj1haQszPIxYXCf7MzyArKhIW8qPuyGdnZyO/fS/QvXeMOuxU7EJVPjybaJoWNSxRRw8JzM/Px+l0srCwENe8Bx12BoNB2tvbuXLlitEi7agko+iWEIL8/HwqKyvZ3t5mZmaG0dFRcnNzcblcO7XU/X7ExgYyKyvulm2HEfjsZwk3NqKNjBAuKyP09rcb/2Mk6pEftiP/pV/6JW7fvs3S0hIlJSU899xz3L59m46ODv1abwH+7d7XKSFX7OLWrdcFW4ioZZUVp8BBQiilpK+vj7S0NCoqKggGg8dO59d39+Xl5eTl5SGlPFCIH3Z1xLS0NC5evEhVVRXLy8sMDw+T0tfHlW98A1soBBkZ+J99lnBt7fEvJgSht76V0Fvfeqxp4qm18q1vfWvfzz7+8Y9HfvvuaK9T1opCcQ44yFoZGRlBSsnFixeB4zVfhp0bQ09PD3l5eTidTmNcPHOdJNHek6ZpFBQUcKOykmt/+7eEgWWrlS23G+13fxe5vZ30dYj797H8wR9w4a/+CtN//++wJ7ImFucis1MIYQJagWkp5cEVaRTngke5MNVZJdGolYmJCTY3N42qfAeNjXW9vQI5PDyM2Ww+UsLPSRPr8xHz85hCIVILC0llZ/frn5qi54c/JPvGDRwOBxaL5fj2zuIi1t//fbSJCezb21g2NxGhEMF//a8Pfe25EHLg3wN9QOx+UYpzhfLEzw7RxHl2dpb5+Xmampp2CVwiO+O9USuTk5Nsbm7uKqyVLE7SepF5eTtf+Hxgs2ENh7HZ7Vx54xuZ29qio6OD1NRU4wnjqJh+/GNMt28jLBbSAgFMS0tgtcYt5Ec57IyHpFgrQogS4OeB/ycZ8ykUjzPRBHSvkC8tLTE+Pk5DQ8Ox4rojd+QLCwvMzs7qiSdHnjPWdU6U3Fz8/+7fIdbWEAsLiPV1/J/9LJbcXEpLS2lubqasrIyFhQW2trYYGxs7Us107Sc/QYTDyPR0QikpSCHQRkfjeu1Ro1biIVk78i8C/xGImX0ghPgE8AmAsrKyJF1WoXg8iBTytbU1BgcHaW5uPrA2STzoUStra2sMDw9HLax1Vjgs3C/0jnfgvXEDMT+PdDiQhYXG74QQZGZmkpaWxvb2Nlarle7ubsxmMy6Xi7y8vPhuiLm5O7Hl29togQACCMVpQZ1pa0UI8S+ABSllmxDizbHGSSm/DHwZoLm5WcVCKBQJEFk/paenh8bGxrgaMR+GEIJAIJDUOU8TWVSELCqK/fsH2ZgulwuXy8XW1hYzMzPcv3+fvLw8XC7XgSUNgu95D+Z/+Afweglub2NNTyfwb/dFA0blTAs58FPAu4UQ7wJSgEwhxP8npfxQEuZWKBTsCLnf7zfavx23QYNOIBBgZWWFmzdvJm3Os8xenz49PZ1Lly4RDodZWlpicHCQcDiM0+mksLBw39NJuK4O75/8CeZvfpO1mRlMv/7rhH7qp+K69kl65McWcinl7wC/A/BgR/7bSsQViuQSDAZZXV2lubk5ofrdh805MDCA3W4nM/NkYxSSEWeeaCZlLKJZKJqmUVhYSGFhIV6vl9nZWdra2sjKysLlcu2qWRO+cQNPXR3D7e1kNzfHfd3z4JErFIoksVf0IuunZGdnJ+Ua4XCYzs5OHA4HGxsbx55venqa+fl5iouLyc3NPTMlaPcSz80kJSWFyspKKioqWFlZMUr36jXT9TDGRA+ZQ6HQsc80YpHUWaWUt4HbyZxToXicCYfDtLe3U1paytzcXFLmlFLS29tLdnY2DoeD9fX1Y823tLTEzMwMlZWVzM/PMzIyQmFhIU6n88Q84aOSyK5eCEFeXh55eXn4/X7m5ubo6OggLS2NwoiD1JO4dqKoHblCcUaRUtLZ2UlRURFOp5OZmZmkzHv//n2EEFRVVeHz+Y5lebjdbqMDEUB2djahUIj5+Xm6u7uxWq0UFxcnJYY8GUJ41DmsVitlZWWUlpaysbHB5OQkbreb8fFxHA7Hqd+wlJArFGcQPU3ebrdTVlZGOBxOWAyjidbU1BTr6+s0NDQghDiWd+3z+ejq6uLGjRtYrVajCYTJZDKiQtxuN9PT07jdbiYnJykpKTnVyJjj3gyEEGRlZWGz2QgGg5jNZrq7u7FYLLhcLqPRcrTrniRKyBWKM8jQ0BCapnHhwgUgsZZs+vi9orW4uMj09DTNzc3GzxOdVycUChmVETMyMmLOYbfbuXLlCh6PB5PJRFdXFykpKRQXF5Odnf3QvfRk2RvhcBiz2UxxcTHFxcVsbm4yMzPDyMiIUYEyWhjjSb1fVTRLoThjjI6O4vF4uHr16i7BTYS9maDr6+sMDQ3R0NCwK6TuKDtyKSVdXV2UlJSQp6fGH4IQAqfTSVNTE2VlZczNzcVs53bQdZNmrWxuYv3CF0h9//tJ+bVfQ+veV+I7obVkZGRQXV1tRNeUhTEAACAASURBVBUNDAzQ3t7O3NwcoVAIiO/f8GMf+xiFhYXU1dUZP1tZWeHtb387ly5dQgjxfdXqTaE4B9jt9mM3coisobK9vU13dzf19fX7bI2jCPng4CDp6emUlJTsm+sw9AzLq1evGuUFOjs76enpYW1t7cQtCF2ArV/8IqYf/QiZng6rq9h+93cRuxs4HEg4HI5qoZhMJoqKimhoaODKlStsb2/T2trKN77xjbiafXzkIx/h5Zdf3vWz559/nre97W0MDQ0B/AB4Zu/rlJArFGeMgoKCY/fF1C0Tv99PR0cHdXV1MR/1ExHPyclJPB4Ply5dSng9ezGbzUY7t5KSEmZmZmhtbWVycvLAJhrHRkpM/+f/IJ3OneYTWVng96MNDCQwxeFPB6mpqVRVVfHEE0+QnZ3NxMQEb3rTmw6s8fL000+Tm5u762cvvvgiv/Irv6J/+3XgX+19nfLIFYpHECEEwWCQ7u5uLl26RFZWVsxx8Qp5MBhkZmZml8eeCLGuox8gZmVlEQgEjDC/9PR0iouLyczMPLLFFG0NQghkRgZ4vZCWttM9RUrkAan5e4m1I4+GEIK3vOUtVFZW8vd///cJR7jMz89HVm2cA/bVIFBCrlA8gggh6O3tpbi4mIKCggPHxSPkbrcbr9fLG97whhMtqmWxWCgtLaWkpIT19XWmpqbY3t7G6XQaXvNxkFIiNI3Apz6F9fnnYXUVgFBDA+HGxsTmOUKbt+MmdEkppRBi3z+YEnKF4hFDSsnm5iZ5eXn7fOy9xCNGephhampq3E2dj4sQguzsbLKzswkEAszOzrKxscHQ0BBlZWXY7fYjPxUIIQg9/TRepxPT4CDSbif01FOQQNZlIjtyOF7BrKKiImZnZ3E6nQghnMC+hqzKI1cozhjHtQ9GH9THLi0tPfZaIsMMT6u8rcVioaysjKysLAoLC5mYmKCtrY3p6WmCwWBCc0XupOWlSwR//ucJPf10wo2aj7ojPwrvfve7+frXv65/+yvAi3vHKCFXKM4J8dggMzMzrK6ukpOzL0ItYfaGGR52/YfRnDk7O5u6ujquX79OMBjk7t279Pf3x10vJllx5Ccl5L/0S7/EU089xcDAACUlJXz1q1/lmWee4fvf/75+wPwzwPN7X6esFYXinKBHosTaGS8vLzMxMUFzczMDAwNHSvSJZG+YYbLEOBlYrVbKy8spKytjdXWV8fFxfD4fLpeLwsLCmMWpkpkQlKi1Ek9G67e+9a2oP//BD36gf/kz0X6vhFyhOCfoST7RhDyy5onZbN7XizNR9DDDGzduGD87TMhPOkszmggLIcjNzSU3Nxefz8fs7Cx3796NWn421hxHIRwOJzTPSZawBSXkCsW5IVoDZgCPx0NXVxf19fXG4/tRU+/h9WqGiYQZnoWytTabjYqKCsrLy1lZWWF0dJRAIGDs0k0mU9KeKBItY+v1ek+0sJYScoXijBFLFKMJeSAQoKOjg9raWtLT03fNcRQh13f2N2/e3LfzP4618jBtmcjysz6fj5mZGdra2sjOziY1NfVUPHK/36+EXKFQ7BdyPaLkwoUL++KTj2Kt7K1muJfT9siPYovYbDajScTy8rKxS09JSYnayi1e9KJZ8aJ25AqFAtgt5FJK7t27h8PhiNrkIFEh31vNMBqnLeTHQQhBfn4+oVCIzc1NPB4Pra2t5Obm4nK5dj3NxIPakSsUiiMRKeQDAwOkpaVRVlYWdWwi1kq81QzPs5DrSCmNuHR9lz48PEwoFDK89Hi876NErajDToVCYQj52NgYfr+fa9euHTg2XtH1+XwUFBQcmgV6XM7CTSByJ61pGgUFBRQUFODxeJiZmaGlpYW8vDxcLlfUImPR5okHtSNXKB4zDjrsXFpaYn19ncbGxgOFJN4d+eTkJOFwOK5qhgftyDc3N5mcnMThcES1Zk7jgDGROVJTU7lw4QKVlZUsLS0xODiIlJLi4mLy8/P37b4fZop+PCghVyjOCV6vF7fbzVNPPXWoiMQKVYxEDzNMTU2N6/qxhNzv99PV1UVxcTEjIyOEw+GYAnjaHHYz0DSNwsJCCgsL2d7eZmZmhtHRUfLz83G5XMZnlehNxev1kpmZeez1x0IJuUJxDnC73aysrFBdXR1XtMRhO/LIMMO2tra4hCmakIfDYTo7O41SuS6XC4/Hw/T0NKOjoxQUFFBcXBzfm3wIJBL/nZaWxsWLF6mqqmJxcZH+/n6EELhcLkKhkLJWFApF/Hi9Xrq6unA6nXGHvGmaFrM5w94ww0T89MhxUkr6+vrIz8+noKDAaNmWmprKxYsXqaysZGFhge7ubrxeL+vr68eK406WtZIomqZRVFREUVER29vbTE9PMz8/TygUIiUlJa4nGiXkCsVjTCAQoL29nZqaGtbW1uKORIllg0QLM4w3GmWviE5MTBAOh6moqIg63mQy4XQ6cTqdtLe3s7S0xNTUFE6nE4fDkVAcdjI5zs0gLS2NS5cuEQgESEtLo6+vD5PJhMvlIi8vL+ZuX0WtKBSPKeFwmI6ODiorK8nJyWFjYyNuIY+2y44VZhjvwWik4C8tLTE/P2+k8R92I7BarVRUVGC1WnfVQykuLo4Zt34SJKvWCkB+fj4VFRVsbW0xPT3N/fv3KSwsxOl07hNtddipUDxm6MJ47949CgoKcDgcQHwHmJFz7B17UNPkeHfkUkq2trYYGBigubk54cNMPYa7tLSUlZUV7t+/TzAYNDoZHTTfSUatHGee9PR0qqurCYVCLCws0NPTg8ViMXbpQggl5ArF48jg4KBRBErnIN97L3tFP1o1Q51EhDwQCNDd3c3169ePJUyR9VD0GO6xsTEKCgpwuVwnZkOcZBnbSCtpc3PT2KXb7XY8Hk/Cn1dFRQV2ux2TyYTZbKa1tTXmWCXkCsUZIxgMIoSgurp6188T3ZFH2iAHVTOMV8illAwODnLx4sV95WHjIdY19BjuiooKFhcX6enpwWq1UlxcTE5OTlIrKyZzR37Q00NGRgaXL18mFArx2muv8corrzAzM8OXvvQl6urq4r7Oj370I/Lz8w8dd2whF0KUAn/DTmdnCXxZSvml486rUDyumM1mLl++vE/4EhFyfazb7WZwcJCmpqaYBaLiFfL19XVycnKi1nY5jHjE02Qy4XA4cDgcuN1upqamGBkZMX4W7zwH8bDrkZtMJp566ikaGhr40Ic+lHBNl3hJRrR+EPislLIGeAPwSSFETRLmVSgUESS6Iw8Gg3R1dR1qg8Qj5HqEitPpjDnmuB2JIrHb7Vy9epX6+noA2tvbjYSo43Card4aGhqorKyM+zVCCN7xjnfQ1NTEl7/85QPHHntHLqWcBWYffO0WQvQBxUDvcedWKBSvk4iQSylZWlrixo0bh0aFHBZHvry8zOzs7IGP+OFw2PijaVrSGjVbLBZKS0spKSnh1VdfZXR0lGAwmFCBq0hOs9Vboh75K6+8QnFxMQsLC7z97W/nypUrPP3001HHJjV/VghRATQAd6L87hNCiFYhROvi4mIyL3vq3Lp12itQPA7EK+RSSoaHh0lLSzuwmqHOQTvyra0t+vv7qa+vjzlOSkk4HMZisWAymQiHwwQCAUKhUNJ26UIIzGYz169fp6amhq2tLVpaWhgZGcHr9cY9TzI7BJ10ZqeeEVtYWMh73/teXnvttZhjkybkQogM4L8Dn5FS7mtpLaX8spSyWUrZXFBQkKzLngmee+60V6B41IgmEvFmYA4ODpKWlhZ35EesOPJAIEBnZyfXrl3DZrNFXZOU0khXN5lMWCwWrFar0VYtGAwakTbJEtGUlBQuXLjAzZs3SU9Pp7e3l66uLlZWVo6U2HQUjmKtJBKJs7W1ZdhIW1tb/MM//MOBh6RJiVoRQljYEfFvSCn/LhlzKhSK3cSTuKOHGV66dInBwcG4542VPFRVVWUUe9o7ThfxvREcmqYZ34dCIQKBAF6vl1AoZIh+MoppaZq263B0enqa4eFhI3PUYrHse00yE4JOckc+Pz/Pe9/7XmAniumXf/mXeec73xlz/LE/TbHzbr4K9Ekp/+i480XjLFoXt26BEDt/4PWvz+JaFY8Gh1krepjhtWvXDIsjHqIJ+cDAAFlZWUa0yN5xup1ymDCaTCbm5+fJyMggMzOTcDhMKBQiGAwm/XD0ypUrNDQ0ANDR0UFfX9++w9FkCnkiSCkTOjeoqqqis7OTzs5Oenp6+NznPnfg+GRYKz8F/BvgrUKIjgd/3pWEeQ3OonVx6xZIufMHXv9aCbnipDhIyPVqhvX19ZhMpoS6+ewdOzU1hdfr5cKFCzHH6QebQogDhXF5eZnFxUUuX75s2C56jRV9t55MQdcPR5ubmykqKmJsbIy2tjbm5ubiuvGcV5IRtfIK8Oh9MgrFGSOWkEdWM9Qf3xONOdcFemVlhampKW7evBlT8PTd+GEi7vF4GBwcpLGx0bBSdNvFbDYTDocJBoOG5aJpWtJsFyEEubm55Obm4vV6je4/QFwJNueNs1X1ndd3tOfJunj22dNegeJxIJo4h0IhOjo69jVNPsqOfHt7m76+PmNXH22cLr6HiXgoFOLevXvU1NTE9IY1TcNqtRqHo7rtkuxdekpKClVVVdy8eROz2czo6ChdXV0sLy8/lPZzD+MaZ07IdRvlPFkXZ3FNivNNrKiVSIHTDySLi4v3hRkeJXmos7OTurq6A6MrQqFQzPVFrqunp4eSkhKysrIOvb6maYbtYrFY0DTtRARd0zRSUlKoqamhqqqKpaUlWlpamJiYiLuGzXE4SUtH1VpRKM4Je8U5VjVDSLzj/ejoKBUVFTGFV0pJSkoKExMTRtPiWMI0Pj6O1WrF5XLFfX3AsFT03bluuwSDQSOc8bjoc+i1UILBIHNzc3R0dJCRkUFxcfGJtmQ7Kc7EjvwwG0VZFwrFbnGODDOMNTZeVlZWSElJOTT9vqioiNraWlZXV3n11VcZGxvbt5NdXl5meXl5X8GvRIm0XcxmsxHlctxd+t7DTrPZTElJCc3NzTgcDsbHx2lra2N2dtZ4+jguD8NaORM78lu3XhdtIV63UyJ/r1A87ugCdFg1w0SYnp4mEAhQWloac4yeoSmEID093djJzszM0NbWRmZmJmVlZWiaxtDQ0K7DzeOiH44ODQ1RUlKCEGLXLv0opQBiVYDMyckhJycHn8/H9PQ0ra2t5OXlUVxcvKudW6LCHAgEosa0J5MzIeQKhSI+QqGQ0dThuPVMVldXmZiYOHAnHitCxWw2Gw0ilpeXGRgYYH19nQsXLiRdtGZmZgiFQpSVlRkHrnqki762yCSkg4gn/NBms1FVVUVFRQVLS0v09/ejaZpxFnGUrM6TbCoBZ8RaiUTZKApF9F2jz+czmkMcVxg8Hg+9vb1GhEqsGip6JmYs4dIbRJjNZqqqqvB4PLz66quMj48n5QDR7XYzOTlJTU2NsYbIw9HImHS/33+o7ZKICGuaRmFhIQ0NDVy4cIHl5WVaWloYHx9P6D08DCE/cztyZaMoFPvRwwxTUlKO3eMyGAzS0dFBbW0tqampMft7Rku/j8bY2BgpKSlGN6NI2yUrK4vS0tIjrTkQCNDT02Nkqu4lMiZd36EfFpN+1ISgyMPR6elptra26O3tpaSk5NDDUa/X+/jtyM8i6uaiOE0iwwyP23le7wVaXl5OdnY2EL2GSrxZkEtLS6ysrOw6dNVtlyeffJL8/HwGBwe5e/cui4uLcfvLUkp6e3uprKyMqxmDyWTaF5MeDAb3lQI4bman2WzG4XCQlZWF0+lkYmKC1tZWw/6JxlEqHyaKEvI4OIslAhSPD3o1w2hhhomil7eNDA3cK+Txpt9vbW0xPDzMtWvXou7ahRAUFBTQ2NjI5cuXWV5e5s6dO3HFbY+Pj5OSkkJRUVFC7y/SdrFarcDuUgDJauCsaRo5OTnU1dVx7do1fD4fra2tDA0Nsb29vWv8Y+mRR+PNbz7tFSgUp4MeZhgZznfUcLaZmRncbve+0MDIqorxpt8Hg0G6u7upra01BPMg0tPTuXLlCk1NTUgpaWtro7+/n62trX1jV1dXWVxcjBlaGQ96cwubzYbVajWSjPQD0uOEMO5tKmGz2aisrOTmzZtkZWUxMDBAZ2cnS0tLSCmPJOQvv/wyly9f5uLFizz//POHjj8XQv5P//Twr3meSgQoHj2EELuqGeqimmiij87a2hrj4+Ncv359n0Drc+r1ww8TcT1zs6ysLOEmzBaLhfLycp588kny8vLo7+/n7t27u0Svv78/5i7/KOgx6evr61F36YkSa1cfeTh68eJFVlZWaGlp4fvf/35C7yUUCvHJT36S7373u/T29vKtb32L3t6DG66dCyE/Dc5TiQDFo8feaoY6iaTew47oeL1eenp6uHHjRlSPPTKkT//+IEZHR0lNTT0wbPEwdNulqamJ6upqFhcXuXPnDq2trVy4cCGhJgzx4PF4uH//PnV1ddhstn2lABJJ/omn8XJ6ejrV1dU0NjaytrbGd7/7XZ555pm45n/ttde4ePEiVVVVWK1WfvEXf5EXX3zxwNecWSF/85uj74iPYrMo8VWcNzRNixpmmGhVw0AgQEdHBzU1NaSlpUUdpwt5PBEqi4uLrK2tcfHixfjeSBxkZGRw9epVsrOzSU1NZWRkhIGBgX1e81EJh8P09PRw5coVw2aJPBzVP1O/3x9Xe7p4Picds9nMm9/8Zj74wQ/ymc98Jq7XTE9P70rQKikpYXp6+uDrxDXzKXD79utfR8v2TITnnjuemKvYdsXDJj09Par3nOiOXC9elZOTE3OMxWJhfn6etLS0Axsab21tMTIyktTMTZ2FhQU8Ho/RGGJxcZG+vj40TaOsrIzc3NwjH1Lev3+f3NzcqJ+BbrvoTyT633rrumjvMxEhh9fbvEU26Ug2Z1bIzxJqR684KyQi5D6fD7vdfmC0SygUIjMzkxs3bjA1NcXo6ChOp5Pi4uJdGZrBYJB79+7FfbiZCNvb29y/f5+mpiZDrAsLCyksLGRzc5OJiQmGhoYoLi7G6XQmFIK5vLzM+vo6jY2NB47b255ub8GuSHsrHmslEr/fn5BVVFxczOTkpPH91NSU0Yg55vrjnv0U+emfTvw16rBScZ45KJMynsPOubk5QqHQvi4/kURGqKSlpVFdXc3NmzfRNM2IKtne3kZKSXd3NxUVFQkfbh5GKBSiu7ubmpqaqKn9GRkZ1NTU0NTURCgUoqWlJW7bxefzMTg4SF1dXULCGxntojfACAQCRkz6Safo37x5k6GhIUZHR/H7/fzt3/4t7373uw98zbnYkUfaLPFyWCEuheI8Es+OfH19ndHRUTIzM2OKfqz0+8gaKrq94fV6sdvtCcd0x0N/fz8ul+vQ7EiLxUJFRQVlZWUsLS0darvokTWXLl06cgy3vkuPbHqhlwI4SSE3m8382Z/9GT/7sz9LKBTiYx/7GLW1tQe/Ju7ZFQrFqXOYkHu9Xrq7u2loaGBwcPDAGioHeb1CCAoLC4GdFHxN02hpaaGkpASHw5EUj3x6ehop5aG2QSR6iF9hYaFRh0W3XVwul2GBjI2NYbfbk9LWbW97uvX1ddLT0wkEAnG1p9MtrkR417vexbveFX/r43NhrRyXeA8rle2iOOscJOShUIjOjg6uulykeb2IKIkviaTfb21tcf/+fRoaGqirq+PGjRtsb29z584d7t+/j9/vP/L7cLvdTE1NcfXq1SMfYtrtdmpqamhsbCQYDPLaa68xODjI/Pw8S0tLB9pKR2V1dRWfz0dJScmuUgAHxaQn6pEfhcdCyOMVaJWKrzjrxBJyKSXdXV1UTU6S//LLaN/+Nrm3byP3eMnxpt8HAgHu3btHXV2d4V3bbDYuXrzIE088gc1mo729nZ6eHjY3NxN6D3oxrLq6umOX4gWwWq1UVlby5JNPkp6eTnd3N5qmsba2ltSmDn6/n8HBQWpqajCZTLtKARzUnu6xrH6oUChiE0vI79+/T8bCAkVzc1BWBpqGpa0Nra0Nfu7ngPjT7/XDzcrKyqhVC00mk2FlrKysMDQ0hJSS0tJS8vPz48oKjbcYViIIIVhcXKSuro7U1NRdtovT6TzWTUNKSV9fHxcuXNglyoe1pxNCqKJZ0Ui2/aGiWxRnkVhiGE3I5+fnWV1dpSovD1JT4YG4hO12tMVFgLjT7wFGRkbiOtzUa5E3NDRw+fJllpaWuHPnDpOTkzEzJcfGxkhLSzuRg9PJyUmj0FZmZia1tbU0NDQQCAR47bXXGBoawuPxHGnumZkZLBaLcW4Qjb3t6fQD0uXl5WNXrTyMcyfkybY/VCq+4jyxV8g3NjYYGRnhxo0biNxccLvhgYia3W6CBQXIcJjw4CDaP/4j2quv7oyJwfz8PG63O2F/OT09natXr9LU1GT41UNDQ3i9XmPMysoKy8vLSc0K1dnY2GBubm5foa1I28Vut9Pd3U1nZycrKytx2y7b29tMTk5y+fLluMZHVmAE+OEPf0gwGEzsDSWIslYUijNKtJjxSCH3+Xzcu3eP+vp6LO3tmL71LeTsLKK1FVlVBTYbvitXSO/rQ/vBDxDZ2TA9DSMjhN73PtiTsr+5ucno6OiuxJxEsVgsVFZWUl5ezvz8PF1dXUZdlqGhIRoaGpKeFRoMBunt7Y3ZgAJ2PjeHw4HD4WBjY8NIMtKjcGK9Tk/vv3r1asLWjKZpvPDCC3z4wx/mfe97X8LvKxHOhZDfurV7J67/N/bss2rnrHi80Lv5hMNhOjo6uHz5Mum9vZifew6ZkYEwmxHt7Wg/+Qn5mZmE2tvhrW+F1FS01lbExgbSaiXc2IisqTHmDQQCdHd37zrcPO46nU4nDoeD1dVVurq6sNlsRgXCZIm57l2XlZXF7blnZmZSV1dnNFl+7bXXyM/Pp7S0dF90yejoKHl5eWRlZSW8tra2Nm7fvs3toyTCJMi5sFZO0v5QHrniPKFHR/T09OBwOCgYH8f0h38IAwOI3l5EWxtibg4sFgKFhZiGhzG/9BLmf/xHxOwsSIkYGUH77neNOfXDzaqqqmO3kduLXo63vLyc+vp61tfXuXPnDmNjY0np6TkzM4MQYlejjHjRmyw/+eSTZGRkcO/ePTo7O1ldXUVKydraGqurq1RWViY8t8fj4TOf+Qxf+cpXkt6MOhrnQshPkqPeJJTQK04DXRg1TaPMYkG89hrC7QaTCSwWxNbWjkfu82GTEn9qKlvT0wTm5pBWK/h84HKhTU7Cgzhw/XDzoIO8ozI/P8/29jYVFRWkpqbGLANwFDY3N5mcnOTKlSvHWqP+9HDz5k0qKiqMXfq9e/eOFOcupeS5557jQx/6EDURTz0nSVKEXAjxTiHEgBBiWAgRX9HdI3JWKhGqmHPFabC5ucnW1taOwHg8YDYjs7ORmZk7Iu31gs+HWF/H0ttLxtwctsZGtgoKmPP72bDbCV66BCkpYDId+XAzHra2thgdHaW2tjZqGYAnn3yS3Nxc+vr66OjoSOgAUn8qqa2tTWpESFZWlhG+aLfb6erqYnh4eNeh7WH8+Mc/pqenh09/+tNJW9dhHPsTEEKYgD8H3g5MAS1CiP8ppTy4pcUROcmd8Fm5SSgUsP+w0+12Mzc393qKfEYGBINQWQmhEHJubsc+0TRkKARbWwhA/MIvYJ+awt7VhdvnY7G7G9973oNtbe3Yh5ux0Ith1dbWxrQW9DIAhYWFxgHk8PBwXGUABgYGcLlcSS/iBTtPEQA3btxASmkc2qakpFBWVkZWVlbMz2tjY4P/9J/+Ey+++GJSkp3iJRk78ieAYSnlfSmlH/hb4D1JmPehE4+dovx0xWng9/u5d+8elZWVr4tIURHyTW9COp1gt8ODdm3S4UAWFYHVupOK39lJ6AMfIPyRj5DxgQ+Qe+sWoZ/6KTo6OkhNTcXn8yV1rfoBZElJSdxCqx9AxlMGYH5+nkAgkJRm1Hvxer2Mjo4alopuuzzxxBOUl5czOTlJS0sLMzMzUcsf/M7v/A6f/vSnqaioSPraDiIZzyTFwGTE91PAk3sHCSE+AXwCoKysLAmXffioioqK00CPULl06RKapu3ylOW1a8hLlxAdHYjnn0dYrYjNTQiHwe9HBAKYX3oJmZVF+KMf3fHJpWSxvZ26ujrMZjODg4MAlJeXH6uBg8709DRCiISKYenoZQAqKyuZm5ujvb2djIwMysvLycjIMGqXNzc3J/0pQkpJb28v1dXVUZ8isrKyuHbtGl6vl6mpKe7cuUNBQQElJSWkpKTw8ssvs7y8zEc/+tGkriseHlr4oZTyy8CXAZqbm5UEKhRxoItLYWEhBQUFrK6u7k/RX16G731vxyMPhcDj2fnbYkFWVSGdTkytrcif+zlkWRnDw8NkZWUZ2ZV5eXlsbm4yPj7O8PAwpaWlR65wuLGxwczMDE1NTcd639HKAITDYXw+X8za5cdlYmICu91Obm7ugeNSUlKMnppzc3N0dXXx9a9/nddee40f/OAHSY+Tj4dkXHEaKI34vuTBz5LGWbQulJ+ueBiMj48DO7tliJ6iL370I0RLC9r0NNjtyLQ0sFggMxN5/fpOyv6DcKy5uTm2traoqqraNUdGRga1tbXU19cb1kaiIYKBQIDe3t6kFcOC3WUAUlJSMJvN9Pf3H1gG4Ci43W7m5+cTOvTVNA2Xy0VzczOrq6tkZ2fz+c9/PmlrSoRkCHkLcEkIUSmEsAK/CPzPJMxrcBYjRM7izUXxaLG4uMjCwgI1NTWGjbBXyKXPh+zu3tmBS4ksLIScHGReHtJmg7U1WFlB1taykZbG+Pj4gR1zIiscappGa2srAwMDh9YoiYxFj9Xk+TgsLi7i9/u5efPmgWUAjkIoFKK3t5eampoj7aa/853vkJ6eziuvvMKf/MmfHGstR+XY1oqUMiiE+BTwPcAEfE1K2XPslSkUL9G85QAAGixJREFUjzlut5v6+vpd4qJ3vIcH1QwDAcweD8LnA7cbbWsLabEQeuMbEaurO9bKzZt43vEOegYHuXbtWlzheiaTyegUtLCwQHd3NzabjfLy8qhZjqOjo2RkZJxILLrX62V4eNiIrolVBkCPKEmU4eFhXC7XkZKhZmZm+OM//mNu376NEOLEi2PFIilXlVK+BLyUjLl0EknLjzyEVCgeFS5cuLDPPtA0jZTBQbRXXyUcCEB9/U7suMtFOD8f0d+PCIWgooLACy9AYSHhcJjujg4uXLiQcOlYIQRFRUUUFRWxtrZm2C1lZWUUFBQghGB5eZnV1VUaGhqS+faB18veXr58eV/T58gyAOvr64yPj+Pz+Yy1xbO7Xl5eZnt7m+rq6oTXFg6H+eQnP8kf/MEfHOqrnzQimYXX46W5uVm2trbGPf6wCBEVQaKIRAjRJqVsPqXLJ+2/RL22dSS+/n5W/uqvcDY2IjUNMTmJWFqCzEzEygoyPR2Znk74F34B0dmJdv8+c4EAW297G+VPPJGUdW1vbzMxMcHa2hpFRUXMzs7S1NR0IjW3R0ZGAOL2rj0eD5OTkywvL+N0OikuLo55MOr3+2lra6OxsfFIa//qV79Kb28vf/EXf5H0CJoDiHqhxzJFX+3eFecV0+QkIZttJ4zQbIb8/J0Dzqwswo2NUFwMZWVor7yC1t/PmqYRXl2l6sc/hq2tpKwhLS2NK1eu0NDQwNTUFOFwmMnJyaTHo6+srCRc6yTeMgB6rPvFixePJOL379/na1/7Gn/4h3/4MEU8JudCyKNFiBwnOecsHp4qFPGgZWYS2t5+vUmEx0P4iScIv+ENyNRUwtXVhN7xDsTkJJ68PJY3Nii6ehXh9e7s3JPI2NgYpaWlvPGNbyQtLY2Ojg56e3sTbv0WDb/fz8DAAHV1dUc6gDysDIDeKKKgoCDhuYPBIL/5m7/Jn/7pnya9y9FRORfWymEkaq0oK+bR5lGxVvSWYcbEUhLe3mbja19jc3gYW0oK2aWlmH75lyHykC8UQnzhC4y73RRXVWE1mxFTUwR/9VfB6UzK2ubm5pifn+f69evGjlRKyfLyMhMTEwghKC8vJycn50hFpzo6OigpKTmS0MbC7XYzPj6O2+0mGAzyhje84Ujx6F/84hdZX1/nC1/4QtLWlgCPt7Wi0usV5xm9BnnYasX+8Y/j+tSn0N73PjqvX6dzbIz19XVjbFgIei9cwAHY5ufRJicJNzWBw5GUtWxtbTE2NrYrLBJ2Dkbz8/NpbGzk4sWLzMzM0NLSwuzsbMwO89EYHx8nLS0tqSIOYLfbjRDD3NxcWltbY5YBiEVPTw//43/8j1OLF4/FI7EjTzRqJZ4duYqEOb88ijvyUCgUs3Hy2toa4+PjBINBysvLWVpawmazUZmSglhehrQ0ZGXl67uYY66ptbWVmpqauOqoeL1eJicnWVpaOvTwEWB9fZ2BgQGam5tPJENyZGQETdOorKwkFAoxNzfH1NTUrjIAsfD7/fzsz/4sf/mXf3kiETpxEvUf8ZEQ8kSJR8iV/XJ+edSEPN7GyVtbW/T19eF2u7l8+fKR0+xjoSf95OXlJdzIIRgMMjMzw/T0NLm5uZSVlZGamrprTCAQoK2tjevXr59IUtHa2tqueHQdKSUrKytMTEwgpaS0tJT8/Px9n/XnP/957HY7n/vc55K+tgSI+h/AuWj1lmxUer3ivBCviMOO8IdCIZ544glmZma4c+cOLpeL4uLipCSqTE1NYTKZjtSNRz98LCkpYXFxke7ublJSUigvLyczM9OIIqmoqDgREQ8Gg/T39+80qd7zOeplAPLy8tja2mJiYoKRkRGj1ovJZKKlpYWf/OQn/OhHP0r62pLBY7kjj8XeJCSdn/5peAht9xRJ4lHakXu9XqSUh+6s9ZjoGzduGEIYDAaZnp5mZmaGgoICysrK9iXVxItueTQ1NSWljoreSm18fJxQKERaWhrhcJja2tpjzx0N/UnCGedhbyAQYGpqirm5OW7fvs1LL73EN7/5TS5fvnwi60uAx/uwMx5itX37p3861WUpHlP+6I/+iO985zuHHhSGw2Hu3btHdXX1rt2s2WymvLycJ598krS0NNrb2+nr60u4tZrf76evr+/ALvWJIoQgJyeH+vp6SktLjU5FU1NTSS2GBTv1y8PhMI4EDnv1MgBPPPEEAwMDrK2t8dWvfjWp60omSsgVijPKBz7wATo6Onj66af5/9u796iqy3SB49+XjcpEIY1cdFBAvJEpEYrOmZkgA7Q1Kmo2pS5N5HhrhSnHyanjzMpyzUQdV8c1aa4UpqgMp1XHy0zqUUdbaiWIl7yggaLc5BIKqSQh8J4/YHOwuO3Nvvt81nIt9mb/fu+zdfns335/z/u8mzZtardxVW5ubsvUQFuMXfrGjBmDj48POTk5nDp16o5Kl/YY58UHDRr0kzltS2hoaGjpLx4REUFdXR1ZWVlcvHjRpGqS9tTW1pKfn2/W3pvQtG1bdXU1ubm5vPDCC92Ox1okkbcjOlrKFYV9BQYGsnbtWvbt20dVVRXR0dGsWbOG6urqltdcuXKFurq6lja3HVFK4evry+jRowkMDOTy5cscO3aMysrKdvfKzM/Px8vLy+KlgEbnz59nwIAB3HvvvfTs2ZOQkBDGjBmDh4cHx48fJycnhxozV6S27tNiTr34d999x0svvURqaioGg8FqfweWIHPkXSAVLM7FVebIf6ympobU1FTS0tKIi4tj7NixfP/990yfPt3sKY+ampqWRTKBgYH4+/u3zMdXVlZSUFBARESEVZahl5aWUllZ2W5bXa01lZWVFBYWYjAYCAoKwtvbu8uxGBt8DRkyxOTYtNYsXryY2NhY5s6da/LxViRz5EI4M09PT5YuXcqxY8cIDg5m0aJFHDhwgPz8/G6dc/jw4YSHh3Pz5k0yMzMpLCzk5s2b5OXlMXLkSKskceMHSEdTHsZvEKNGjSIkJITi4mKOHj1KWVlZp/cNbty4QUVFhUkbRbT22WefUVNTw5w5c8w6PjExET8/P0aMGNHy3KpVqwgICCA8PJzw8HB27rRcw9i7svzQVFKuKBxJjx49UErx97//ndraWpYsWYKvry/Jyck8/PDDZiXeXr16MWTIEAYOHNiyH6VxKzhLa2xs5OzZswwfPrzLZZFeXl4t+2UWFhZy6dKldksrjRtFPPjgg2bV0VdUVLB69Wr27dtndh1+QkICSUlJPPPMM3c8n5yczO9//3uzztkRSeRdIPPiwtEkJSW1/Dx58mQOHTpESkoKtbW1JCcnEx0dbXazqdraWgYNGkTPnj05ceIEXl5eBAUFWay+Ozc3l759++Ll5WXysR4eHgwdOrSltPLo0aP06dOHwMBAPDw8AMjLyzN7o4jGxkaWLVvGq6++2q0PsqioKC5fvmz28aaSqRUhnJxSiqioKP7xj3/w5ptvsmXLFmJjY9m6davJpXylpaUtN0/NrXTpSEVFBT/88AMDBgzo/MUdaF1a6eXlxalTpzh9+jSFhYXcunWL/v37m3XeLVu24OXlxdSpU7sVX3vWrVtHWFgYiYmJVFVVWey8ksiFcBFKKcLCwvjggw/IyMjgq6++IioqinfffbdL+1revHmTgoKCO5phmVPp0p5bt26Rn5//k2Zb3eHm5kbfvn2JjIzE39+fCxcuUF9fz9WrV02Or7i4mLfeeou//vWvVrkv8Oyzz3Lx4kVOnjxJv379WL58ucXOLYlcCBc0cOBA1q1bx549eygrKyM6Opq1a9dy/fr1Nl9fX1/P2bNnGTFiRLvz1t7e3jz00EOEhoZSUVFBVlZWlzsbNjY2cubMGUJDQ80qBeyKK1euMHLkSIYPH05FRQWZmZmUlJR06VuJcdu2NWvW4O3tbZX4/P39MRgMuLm5sWDBArKysix2bknkQrgwX19fVq9ezZdffkmvXr2Ii4vj5Zdfpry8vOU1WmtycnIIDAzs0rxye5UurXun/9jFixfx8fGxWpIsKSmhV69e+Pr6tsQXERFBbW0tWVlZnbarTU1NZdiwYcTGxlolPmiatjLaunXrHRUt3SV15MLluGoduSXU1dXx4Ycfsn79ekaNGsXzzz9PUVERfn5+PPDAA2ads7OeLlevXqWgoMDsiprO1NTUcPr0aSIjI9usp29oaKC0tJTi4mJ69+5NYGDgHTv75OXlMW/ePA4fPmyxG7ozZ87k888/p7KyEn9/f1555RU+//xzTp48iVKK4OBg3nnnnS73fmlF2tiKu4Mk8s41NDSwY8cOVq1axfXr13n//fe7vfCnsbGRsrIyioqKWipdDAYDx48fN3uD466MeezYMYYNG9ZpFcyPFxgFBwfj6enJxIkTWbNmDb/85S8tHp8VyIIgIUQTg8FAfHw8np6evPbaa/z5z3/miSee4ODBgybt5tNaWz1djhw5QkBAgFWSODS1EPDx8elSKWPrBUYDBw7kiy++YMyYMfj5+REZGWmV+GxFErkQdymDwcD+/fuZMWMGu3btIiUlhfT0dCZMmMCOHTvM7kJoTJjGRl5VVVVmV7p0pLq6murqaoKDg00+tnfv3gwYMAAvLy8CAwP55JNPLBaXPcjUinA5MrXSPRcvXmTNmjVkZmaycOFCnn76aZOvqI278URERODm5tZhTxdz1NfXk52dzUMPPWRWV8YffviB8ePHk5aWRlhYmNlx2IFMrQghOjdo0CA2bNjAzp07KSgoICoqirfeeosbN2506fjbt29z7ty5O5bIm1Pp0pHz588TFBRkdmvdv/zlLzz55JPOlsTbJYlcCNGmvn378tprr3H48GGUUsTGxrJ69Wq+/fbbdo8xto5tr3+5sadLZGQkjY2NHD16lAsXLpjUe7ysrAyttTkVHwAcOXKErKysbi3Iaasp1rVr14iLi2PIkCHExcVZdOVmZySRCyE61Lt3b1asWMHRo0cZOHAgU6dOZfny5RQWFv7ktUVFRXh4eODn59fhOd3d3QkODjZ596La2louXbpEaGioWe/l5s2bLF++nNTU1G7tY5qQkMDu3bvveC4lJYWYmBjy8vKIiYkhJSXF7PObyuESuTSoEsIxeXh4sHDhQrKzs3nsscdITExk/vz5nD17Fq01169fp6ysjKFDh3b5nKbsXtTdjSK01vzpT39i4cKFZvUoby0qKoqf//zndzy3ffv2lt7lc+fOZdu2bd0awxTdutmplPovYDJQB1wE5mmtqzs+quObnc64icOqVfIB5EjkZqdtNDY2sn//ft544w3c3Nz47rvv2Lp1q1ldDVszbspcX19PUFAQffr0QSnVrY0iAP71r3/x9ttv89lnn3XrRqvR5cuXmTRpEmfOnAGaWhgYd2/SWnP//fffsZuThVjlZudeYITWOgzIBV7q5vmc0iuv2DsCIWzPzc2N2NjYlikGd3d3fve737Fz506za9Gh7Z4uly5dory83OyNIqqqqli5ciWbNm2ySBLvjFLKKqtY29Otd6S13qO1Nt52PgKY1Tty1SrZH1MIZ6W1JiEhgUOHDpGWlsaePXt49NFH+eijj7h9+7bZ5zVWuowcObKlwqW4uNjk+natNS+88AJ/+MMfzG5v2xX+/v4t/VRKS0s7vU9gSZb8aEoEdrX3S6XUQqVUtlIq+8d3vVetappOMU6pGH925EQuHz5CNDEYDMyYMQOAoUOHsnHjRnbs2EFubi6PPPIIb7/9ttkbKAMUFhYSEhLC2LFjaWxsJCsry6RKl+3bt3P79m1mzZpldgxdER8fT3p6OgDp6elMmTLFquO11ukcuVJqH9C3jV+t1Fpvb37NSmA08ITuwqS7q82RO2PMrszV5sh3797N0qVLaWhoYP78+bz44ouWHsJqqqqq2LBhAxkZGUyZMoVFixbRp0+fLh9fWVlJUVER4eHhLVMVbfV0aa/ZVXl5OfHx8ezfvx9fX1+LvCdouynW1KlTeeqppygsLCQoKIiPP/74JzdELcA6TbOUUgnAIiBGa91x7VCzjhK5M944lETuWFwpkTc0NDB06FD27t1L//79iYyMJCMjg+HDh1tyGKu7desW7733Hu+88w6PPPIIS5Ys6XSao66ujmPHjjFq1Kg7uikaGZtgFRQU0LNnT4KCgujdu3fL7xsbG5k1axbz588nPj7e4u/JTix/s1Mp9TiwAojvahLvjLMlcZDNmYX1ZGVlMXjwYEJCQujZsyczZsxg+/bt9g7LZD/72c949tlnyc7O5le/+hVz5sxh0aJFnDt3rs3+K8Ye6YMHD24ziUPnuxdt3rwZHx8fJk+ebO23Z3fd3Xx5HdAL2Nv8teeI1npxt6NyMs744SOcQ0lJyR37W/bv35/MzEw7RtQ97u7uzJw5k6effpq9e/eyYsUKPD09SU5OZsyYMS3TJ603iugKb29vvL29qamp4fLlyyQkJHDlypWWVamurluJXGs92FKBCCHuHm5ubkyYMIHx48eTlZXF66+/zrVr11i2bBlBQUGUlJQQHR1t8nk9PT0JDQ3Fzc2NiIgIkpOT+eCDD6zwDhyLw63sFEL8v4CAAIqKiloeFxcXExAQYMeILEspxdixY/n000/ZsGEDW7du5be//S0nTpwwuxZ948aNjBgxgs2bN/P+++9bOGLH1N2pFSGEFUVGRpKXl8elS5cICAhgy5YtfPTRR/YOy+KUUjzwwAOEhYUREBBAVVUVUVFRzJs3j9mzZ3d5C7ZvvvmGjIwMDh06dFdMqRhJIhfCgbm7u7Nu3TomTJhAQ0MDiYmJPPjgg/YOy2oWL16Mh4cHBoOBq1evsn79esaNG8f06dNZsGAB999/f7vH3r59m6SkJDZs2GB2e9sfCw4O5r777sNgMODu7o6j7qMgG0sIl+NK5YcCvv/+e9LS0khNTWXcuHEkJSXxi1/84ieve/3119Fa8+qrr1ps7ODgYLKzs/Hx8bHYObtJNpYQ4m4WHBzMyJEjCQ8PZ/Roe33Ome6ee+5hyZIlZGdnM3r0aGbOnMlzzz1Hbm5uS+niyZMn2bt3L3/84x/tHK19SCK3EClBFM7gwIEDnDx50mGnCDrSo0cPZs+eTWZmJtOnT2fZsmXMnj2bL7/8kueff55Nmza1W3NuLqUU48ePZ9SoUWzcuNGi57YkmSO3kFdekWQuhC24ubkxadIkJk6cyBdffMGyZcsICwuzyr2Dw4cPExAQQEVFBXFxcYSGhhIVFWXxcbrLKa/IJWEKYTpnubrsKqUUv/nNb8jOziYtLc0qYxhLPf38/Jg2bRpZWVlWGae7nDKRO0r/b+mAKJzJ4cOHOX78OLt27WL9+vUcPHjQ3iFZjDVKDWtqalo2nK6pqWHPnj137NHpSGRqpRtaN/iSxlnC0bV1demI0wSOory8nGnTpgFQX1/PrFmzePzxx+0cVduc5opcrn6FMJ8zXV06ipCQEL7++mu+/vprzp49y8qVK+0dUruc5orc0a9+pQOicGTOdHUpTOeUC4IcMZELxyELgoQLc50FQXL1K4TtJCYm4ufnd8dUzLVr14iLi2PIkCHExcVRVVVlxwiFUyZymRcXwnYSEhLYvXv3Hc+lpKQQExNDXl4eMTExpKSk2Ck6AU6ayIUQthMVFfWTvSe3b9/O3LlzAZg7dy7btm2zR2iimSRyIYTJysvL6devHwB9+/alvLzczhGZbvfu3QwbNozBgwc7/TcKSeRCiG5RSjld7++Ghgaee+45du3aRU5ODhkZGeTk5Ng7LLNJIhdCmMzf35/S0lIASktL8fPzs3NEpnGVTa2NJJELIUwWHx9Peno6AOnp6UyZMsXOEZmmrU2tS0pK7BhR99iljlwp9S1QYIOhfIBKG4wj4zvW+EFa665tvy46pZTKAB6l6d+zHHgZ2AZ8DATS9H/5Ka31NXvFaCql1JPA41rr+c2P5wBjtdZJ9o3MPHZZ2Wmr/2RKqWw7LgyR8e08vrAMrfXMdn4VY8p5lFJ/AyYBFVrrEc3PrQIWAN82v+w/tdY7zQzVFCXAgFaP+zc/55RkakUIYSvvAW31BfhvrXV48x9bJHGAo8AQpdRApVRPYAaww0ZjW5zT9FoRQjg3rfVBpVSwveMA0FrXK6WSgP8FDMDftNZn7RyW2Vw9kdu7e76ML0TnkpRSzwDZwHKttU3W+zdf/dvqG4BV2eVmpxDi7tR8Rf7PVnPk/jTdENfAaqCf1jrRbgE6KZkjF0LYjda6XGvdoLVuBDYBY+wdkzOSRC6EsBulVL9WD6cBZ+wVizNzyUSulHpcKfWNUuqCUupFO4w/QCl1QCmVo5Q6q5RaausYmuMwKKVOKKX+aYexvZVSnyilziulziml/s3WMQjH0lyP/hUwTClVrJT6d+ANpdRppdQpYByQbNcgnZTLzZErpQxALhAHFNNUZjRTa22zRgrNVxn9tNbHlVL3AceAqbaMoTmO/wBGA15a60k2HjsdOKS1Tm0u77pHa11tyxiEuFu44hX5GOCC1jpfa10HbAFsun5Ya12qtT7e/PMN4BwQYMsYlFL9gYlAqi3HbR67NxAFpAForeskiQthPa6YyAOAolaPi7FxEm2t+S79w0CmjYdeC6wAGm08LsBAmlbqvds8tZOqlPK0QxxC3BVcMZE7DKXUvcCnwDKt9XUbjmtcBn3MVmP+iDsQAWzQWj8M1AA2v1chxN3CFRO5Q/RQUEr1oCmJb9Za/4+Nh/81EK+UukzT1NJjSqkPbTh+MVCstTZ+C/mEpsQuhLACV0zkdu+hoJq67KcB57TWb9pybACt9Uta6/5a62Ca3v9+rfVsG45fBhQppYY1PxUDOG/XfiEcnMst0XeQHgq/BuYAp5VSJ5ufs1VXN0exBNjc/GGaD8yzczxCuCyXKz8UQoi7jStOrQghxF1FErkQQjg5SeRCCOHkJJELIYSTk0QuhBBOThK5EEI4OUnkQgjh5P4PNZItA/fc2zIAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "n_samples = 20 # nb samples\n", + "n_noise = 10 # nb of samples (noise)\n", + "\n", + "p = ot.unif(n_samples + n_noise)\n", + "q = ot.unif(n_samples + n_noise)\n", + "\n", + "mu_s = np.array([0, 0])\n", + "cov_s = np.array([[1, 0], [0, 1]])\n", + "\n", + "mu_t = np.array([0, 0, 0])\n", + "cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n", + "\n", + "\n", + "xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)\n", + "xs = np.concatenate((xs, ((np.random.rand(n_noise, 2) + 1) * 4)), axis=0)\n", + "P = sp.linalg.sqrtm(cov_t)\n", + "xt = np.random.randn(n_samples, 3).dot(P) + mu_t\n", + "xt = np.concatenate((xt, ((np.random.rand(n_noise, 3) + 1) * 10)), axis=0)\n", + "\n", + "fig = pl.figure()\n", + "ax1 = fig.add_subplot(121)\n", + "ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", + "ax2 = fig.add_subplot(122, projection='3d')\n", + "ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute partial Gromov-Wasserstein plans and distance,\n", + "by transporting 100% and 2/3 of the mass\n", + "-----------------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----m = 1\n", + "Partial Wasserstein distance (m = 1): 63.419317539744505\n", + "Entropic partial Wasserstein distance (m = 1): 64.89236221074341\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-----m = 2/3\n", + "Partial Wasserstein distance (m = 2/3): 0.17456327357887044\n", + "Entropic partial Wasserstein distance (m = 2/3): 1.070213997054379\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "C1 = sp.spatial.distance.cdist(xs, xs)\n", + "C2 = sp.spatial.distance.cdist(xt, xt)\n", + "\n", + "print('-----m = 1')\n", + "m = 1\n", + "res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m,\n", + " log=True)\n", + "res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10,\n", + " m=m, log=True)\n", + "\n", + "print('Partial Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist']))\n", + "print('Entropic partial Wasserstein distance (m = 1): ' +\n", + " str(log['partial_gw_dist']))\n", + "\n", + "pl.figure(1, (10, 5))\n", + "pl.title(\"mass to be transported m = 1\")\n", + "pl.subplot(1, 2, 1)\n", + "pl.imshow(res0, cmap='jet')\n", + "pl.title('Partial Wasserstein')\n", + "pl.subplot(1, 2, 2)\n", + "pl.imshow(res, cmap='jet')\n", + "pl.title('Entropic partial Wasserstein')\n", + "pl.show()\n", + "\n", + "print('-----m = 2/3')\n", + "m = 2 / 3\n", + "res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True)\n", + "res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10,\n", + " m=m, log=True)\n", + "\n", + "print('Partial Wasserstein distance (m = 2/3): ' +\n", + " str(log0['partial_gw_dist']))\n", + "print('Entropic partial Wasserstein distance (m = 2/3): ' +\n", + " str(log['partial_gw_dist']))\n", + "\n", + "pl.figure(1, (10, 5))\n", + "pl.title(\"mass to be transported m = 2/3\")\n", + "pl.subplot(1, 2, 1)\n", + "pl.imshow(res0, cmap='jet')\n", + "pl.title('Partial Wasserstein')\n", + "pl.subplot(1, 2, 2)\n", + "pl.imshow(res, cmap='jet')\n", + "pl.title('Entropic partial Wasserstein')\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_screenkhorn_1D.ipynb b/notebooks/plot_screenkhorn_1D.ipynb new file mode 100644 index 0000000..0bd4aad --- /dev/null +++ b/notebooks/plot_screenkhorn_1D.ipynb @@ -0,0 +1,213 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 1D Screened optimal transport\n", + "\n", + "\n", + "This example illustrates the computation of Screenkhorn:\n", + "Screening Sinkhorn Algorithm for Optimal transport.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Mokhtar Z. Alaya \n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot.plot\n", + "from ot.datasets import make_1D_gauss as gauss\n", + "from ot.bregman import screenkhorn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n = 100 # nb bins\n", + "\n", + "# bin positions\n", + "x = np.arange(n, dtype=np.float64)\n", + "\n", + "# Gaussian distributions\n", + "a = gauss(n, m=20, s=5) # m= mean, s= std\n", + "b = gauss(n, m=60, s=10)\n", + "\n", + "# loss matrix\n", + "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\n", + "M /= M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot distributions and loss matrix\n", + "----------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1, figsize=(6.4, 3))\n", + "pl.plot(x, a, 'b', label='Source distribution')\n", + "pl.plot(x, b, 'r', label='Target distribution')\n", + "pl.legend()\n", + "\n", + "# plot distributions and loss matrix\n", + "\n", + "pl.figure(2, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve Screenkhorn\n", + "-----------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "epsilon = 0.020986042861303855\n", + "\n", + "kappa = 3.7476531411890917\n", + "\n", + "Cardinality of selected points: |Isel| = 30 \t |Jsel| = 30 \n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rflamary/PYTHON/POT/ot/bregman.py:2056: UserWarning: Bottleneck module is not installed. Install it from https://pypi.org/project/Bottleneck/ for better performance.\n", + " \"Bottleneck module is not installed. Install it from https://pypi.org/project/Bottleneck/ for better performance.\")\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Screenkhorn\n", + "lambd = 2e-03 # entropy parameter\n", + "ns_budget = 30 # budget number of points to be keeped in the source distribution\n", + "nt_budget = 30 # budget number of points to be keeped in the target distribution\n", + "\n", + "G_screen = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True)\n", + "pl.figure(4, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, G_screen, 'OT matrix Screenkhorn')\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_stochastic.ipynb b/notebooks/plot_stochastic.ipynb index 0911c28..aa0f1b3 100644 --- a/notebooks/plot_stochastic.ipynb +++ b/notebooks/plot_stochastic.ipynb @@ -184,15 +184,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "[3.88833283 7.64041833 3.93000933 2.68489048 1.42837354 3.25840738\n", - " 2.80033951] [-2.50038759 -2.4083026 -0.96389053 5.87258072]\n", - "[[2.49326139e-02 1.01118047e-01 1.68018025e-02 4.67918477e-06]\n", - " [1.20543018e-01 1.29218840e-02 1.25860644e-03 8.13363473e-03]\n", - " [3.52425849e-03 7.83826265e-02 6.09501106e-02 1.47316769e-07]\n", - " [2.62727985e-02 3.49290291e-02 8.11998888e-02 4.55426386e-04]\n", - " [9.00986942e-03 7.15412954e-04 9.65318348e-03 1.23478677e-01]\n", - " [1.98446848e-02 8.60145164e-04 1.67017745e-03 1.20482135e-01]\n", - " [4.16774129e-02 2.77550575e-02 7.07529364e-02 2.67173611e-03]]\n" + "[3.7937628 7.65961287 3.80848103 2.58141742 1.61215093 3.44897695\n", + " 2.71747327] [-2.52391608 -2.29387992 -0.82558991 5.64338591]\n", + "[[2.21553327e-02 1.03145567e-01 1.75528576e-02 3.38501746e-06]\n", + " [1.20021720e-01 1.47691349e-02 1.47329335e-03 6.59299438e-03]\n", + " [3.04838905e-03 7.78276435e-02 6.19810066e-02 1.03737333e-07]\n", + " [2.31393025e-02 3.53135903e-02 8.40777056e-02 3.26544498e-04]\n", + " [1.05758118e-02 9.63969840e-04 1.33213201e-02 1.17996041e-01]\n", + " [2.34525044e-02 1.16688539e-03 2.32054035e-03 1.15917213e-01]\n", + " [3.74708983e-02 2.86448739e-02 7.47858286e-02 1.95554208e-03]]\n" ] } ], @@ -226,13 +226,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[2.55535622e-02 9.96413843e-02 1.76578860e-02 4.31043335e-06]\n", - " [1.21640742e-01 1.25369034e-02 1.30234529e-03 7.37715259e-03]\n", - " [3.56096458e-03 7.61460101e-02 6.31500344e-02 1.33788624e-07]\n", - " [2.61499607e-02 3.34255577e-02 8.28741973e-02 4.07427179e-04]\n", - " [9.85698720e-03 7.52505948e-04 1.08291770e-02 1.21418473e-01]\n", - " [2.16947591e-02 9.04086158e-04 1.87228707e-03 1.18386011e-01]\n", - " [4.15442692e-02 2.65998963e-02 7.23192701e-02 2.39370724e-03]]\n" + "[[2.55553508e-02 9.96395661e-02 1.76579142e-02 4.31178193e-06]\n", + " [1.21640234e-01 1.25357448e-02 1.30225079e-03 7.37891333e-03]\n", + " [3.56123974e-03 7.61451746e-02 6.31505947e-02 1.33831455e-07]\n", + " [2.61515201e-02 3.34246014e-02 8.28734709e-02 4.07550425e-04]\n", + " [9.85500876e-03 7.52288523e-04 1.08262629e-02 1.21423583e-01]\n", + " [2.16904255e-02 9.03825804e-04 1.87178504e-03 1.18391107e-01]\n", + " [4.15462212e-02 2.65987989e-02 7.23177217e-02 2.39440105e-03]]\n" ] } ], @@ -268,12 +268,14 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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a/docs/source/auto_examples/images/thumb/sphx_glr_plot_stochastic_thumb.png and b/docs/source/auto_examples/images/thumb/sphx_glr_plot_stochastic_thumb.png differ diff --git a/docs/source/auto_examples/index.rst b/docs/source/auto_examples/index.rst index fe6702d..60536b6 100644 --- a/docs/source/auto_examples/index.rst +++ b/docs/source/auto_examples/index.rst @@ -1,5 +1,9 @@ :orphan: + + +.. _sphx_glr_auto_examples: + POT Examples ============ @@ -13,9 +17,9 @@ This is a gallery of all the POT example files. .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_1D_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_1D_thumb.png - :ref:`sphx_glr_auto_examples_plot_OT_1D.py` + :ref:`sphx_glr_auto_examples_plot_OT_1D.py` .. raw:: html @@ -33,9 +37,9 @@ This is a gallery of all the POT example files. .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_UOT_1D_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_UOT_1D_thumb.png - :ref:`sphx_glr_auto_examples_plot_UOT_1D.py` + :ref:`sphx_glr_auto_examples_plot_UOT_1D.py` .. raw:: html @@ -49,13 +53,13 @@ This is a gallery of all the POT example files. .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_optim_OTreg_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_screenkhorn_1D_thumb.png - :ref:`sphx_glr_auto_examples_plot_optim_OTreg.py` + :ref:`sphx_glr_auto_examples_plot_screenkhorn_1D.py` .. raw:: html @@ -65,17 +69,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_optim_OTreg + /auto_examples/plot_screenkhorn_1D .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_free_support_barycenter_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_optim_OTreg_thumb.png - :ref:`sphx_glr_auto_examples_plot_free_support_barycenter.py` + :ref:`sphx_glr_auto_examples_plot_optim_OTreg.py` .. raw:: html @@ -85,7 +89,7 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_free_support_barycenter + /auto_examples/plot_optim_OTreg .. raw:: html @@ -93,9 +97,9 @@ This is a gallery of all the POT example files. .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_1D_smooth_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_1D_smooth_thumb.png - :ref:`sphx_glr_auto_examples_plot_OT_1D_smooth.py` + :ref:`sphx_glr_auto_examples_plot_OT_1D_smooth.py` .. raw:: html @@ -109,13 +113,13 @@ This is a gallery of all the POT example files. .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_free_support_barycenter_thumb.png - :ref:`sphx_glr_auto_examples_plot_gromov.py` + :ref:`sphx_glr_auto_examples_plot_free_support_barycenter.py` .. raw:: html @@ -125,7 +129,7 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_gromov + /auto_examples/plot_free_support_barycenter .. raw:: html @@ -133,9 +137,9 @@ This is a gallery of all the POT example files. .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png - :ref:`sphx_glr_auto_examples_plot_compute_emd.py` + :ref:`sphx_glr_auto_examples_plot_compute_emd.py` .. raw:: html @@ -147,15 +151,35 @@ This is a gallery of all the POT example files. /auto_examples/plot_compute_emd +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png + + :ref:`sphx_glr_auto_examples_plot_gromov.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_gromov + .. raw:: html
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_convolutional_barycenter_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_convolutional_barycenter_thumb.png - :ref:`sphx_glr_auto_examples_plot_convolutional_barycenter.py` + :ref:`sphx_glr_auto_examples_plot_convolutional_barycenter.py` .. raw:: html @@ -173,9 +197,9 @@ This is a gallery of all the POT example files. .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_linear_mapping_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_linear_mapping_thumb.png - :ref:`sphx_glr_auto_examples_plot_otda_linear_mapping.py` + :ref:`sphx_glr_auto_examples_plot_otda_linear_mapping.py` .. raw:: html @@ -189,13 +213,13 @@ This is a gallery of all the POT example files. .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png - :ref:`sphx_glr_auto_examples_plot_WDA.py` + :ref:`sphx_glr_auto_examples_plot_OT_2D_samples.py` .. raw:: html @@ -205,17 +229,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_WDA + /auto_examples/plot_OT_2D_samples .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png - :ref:`sphx_glr_auto_examples_plot_OT_2D_samples.py` + :ref:`sphx_glr_auto_examples_plot_WDA.py` .. raw:: html @@ -225,7 +249,7 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_OT_2D_samples + /auto_examples/plot_WDA .. raw:: html @@ -233,9 +257,9 @@ This is a gallery of all the POT example files. .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_stochastic_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_stochastic_thumb.png - :ref:`sphx_glr_auto_examples_plot_stochastic.py` + :ref:`sphx_glr_auto_examples_plot_stochastic.py` .. raw:: html @@ -253,9 +277,9 @@ This is a gallery of all the POT example files. .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_color_images_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_color_images_thumb.png - :ref:`sphx_glr_auto_examples_plot_otda_color_images.py` + :ref:`sphx_glr_auto_examples_plot_otda_color_images.py` .. raw:: html @@ -273,9 +297,9 @@ This is a gallery of all the POT example files. .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_1D_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_1D_thumb.png - :ref:`sphx_glr_auto_examples_plot_barycenter_1D.py` + :ref:`sphx_glr_auto_examples_plot_barycenter_1D.py` .. raw:: html @@ -293,9 +317,9 @@ This is a gallery of all the POT example files. .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_colors_images_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_colors_images_thumb.png - :ref:`sphx_glr_auto_examples_plot_otda_mapping_colors_images.py` + :ref:`sphx_glr_auto_examples_plot_otda_mapping_colors_images.py` .. raw:: html @@ -313,9 +337,9 @@ This is a gallery of all the POT example files. .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_UOT_barycenter_1D_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_UOT_barycenter_1D_thumb.png - :ref:`sphx_glr_auto_examples_plot_UOT_barycenter_1D.py` + :ref:`sphx_glr_auto_examples_plot_UOT_barycenter_1D.py` .. raw:: html @@ -333,9 +357,9 @@ This is a gallery of all the POT example files. .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_thumb.png - :ref:`sphx_glr_auto_examples_plot_otda_mapping.py` + :ref:`sphx_glr_auto_examples_plot_otda_mapping.py` .. raw:: html @@ -349,13 +373,13 @@ This is a gallery of all the POT example files. .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_semi_supervised_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_fgw_thumb.png - :ref:`sphx_glr_auto_examples_plot_otda_semi_supervised.py` + :ref:`sphx_glr_auto_examples_plot_fgw.py` .. raw:: html @@ -365,17 +389,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_otda_semi_supervised + /auto_examples/plot_fgw .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_fgw_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_semi_supervised_thumb.png - :ref:`sphx_glr_auto_examples_plot_fgw.py` + :ref:`sphx_glr_auto_examples_plot_otda_semi_supervised.py` .. raw:: html @@ -385,7 +409,7 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_fgw + /auto_examples/plot_otda_semi_supervised .. raw:: html @@ -393,9 +417,9 @@ This is a gallery of all the POT example files. .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_classes_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_classes_thumb.png - :ref:`sphx_glr_auto_examples_plot_otda_classes.py` + :ref:`sphx_glr_auto_examples_plot_otda_classes.py` .. raw:: html @@ -407,15 +431,35 @@ This is a gallery of all the POT example files. /auto_examples/plot_otda_classes +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_partial_wass_and_gromov_thumb.png + + :ref:`sphx_glr_auto_examples_plot_partial_wass_and_gromov.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_partial_wass_and_gromov + .. raw:: html
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_d2_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_d2_thumb.png - :ref:`sphx_glr_auto_examples_plot_otda_d2.py` + :ref:`sphx_glr_auto_examples_plot_otda_d2.py` .. raw:: html @@ -433,9 +477,9 @@ This is a gallery of all the POT example files. .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_L1_vs_L2_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_L1_vs_L2_thumb.png - :ref:`sphx_glr_auto_examples_plot_OT_L1_vs_L2.py` + :ref:`sphx_glr_auto_examples_plot_OT_L1_vs_L2.py` .. raw:: html @@ -447,15 +491,35 @@ This is a gallery of all the POT example files. /auto_examples/plot_OT_L1_vs_L2 +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_jcpot_thumb.png + + :ref:`sphx_glr_auto_examples_plot_otda_jcpot.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_otda_jcpot + .. raw:: html
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_lp_vs_entropic_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_lp_vs_entropic_thumb.png - :ref:`sphx_glr_auto_examples_plot_barycenter_lp_vs_entropic.py` + :ref:`sphx_glr_auto_examples_plot_barycenter_lp_vs_entropic.py` .. raw:: html @@ -473,9 +537,9 @@ This is a gallery of all the POT example files. .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_fgw_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_fgw_thumb.png - :ref:`sphx_glr_auto_examples_plot_barycenter_fgw.py` + :ref:`sphx_glr_auto_examples_plot_barycenter_fgw.py` .. raw:: html @@ -493,9 +557,9 @@ This is a gallery of all the POT example files. .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_gromov_barycenter_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_gromov_barycenter_thumb.png - :ref:`sphx_glr_auto_examples_plot_gromov_barycenter.py` + :ref:`sphx_glr_auto_examples_plot_gromov_barycenter.py` .. raw:: html @@ -508,22 +572,23 @@ This is a gallery of all the POT example files. /auto_examples/plot_gromov_barycenter .. raw:: html -
+
.. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-gallery - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download all examples in Python source code: auto_examples_python.zip ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip ` @@ -532,4 +597,4 @@ This is a gallery of all the POT example files. .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_1D.ipynb b/docs/source/auto_examples/plot_OT_1D.ipynb index bd0439e..f679a30 100644 --- a/docs/source/auto_examples/plot_OT_1D.ipynb +++ b/docs/source/auto_examples/plot_OT_1D.ipynb @@ -44,7 +44,7 @@ }, "outputs": [], "source": [ - "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" + "n = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" ] }, { @@ -62,7 +62,18 @@ }, "outputs": [], "source": [ - "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n#%% plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')" + "pl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')" ] }, { @@ -80,7 +91,7 @@ }, "outputs": [], "source": [ - "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" + "G0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" ] }, { @@ -98,7 +109,7 @@ }, "outputs": [], "source": [ - "#%% Sinkhorn\n\nlambd = 1e-3\nGs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n\npl.show()" + "lambd = 1e-3\nGs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n\npl.show()" ] } ], @@ -118,7 +129,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_OT_1D.rst b/docs/source/auto_examples/plot_OT_1D.rst index b97d67c..ec21845 100644 --- a/docs/source/auto_examples/plot_OT_1D.rst +++ b/docs/source/auto_examples/plot_OT_1D.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_OT_1D.py: + .. _sphx_glr_auto_examples_plot_OT_1D.py: ==================== @@ -12,8 +18,7 @@ and their visualization. - -.. code-block:: python +.. code-block:: default # Author: Remi Flamary @@ -32,17 +37,14 @@ and their visualization. + Generate data ------------- - -.. code-block:: python +.. code-block:: default - - #%% parameters - n = 100 # nb bins # bin positions @@ -63,55 +65,60 @@ Generate data + Plot distributions and loss matrix ---------------------------------- +.. code-block:: default -.. code-block:: python - - - #%% plot the distributions pl.figure(1, figsize=(6.4, 3)) pl.plot(x, a, 'b', label='Source distribution') pl.plot(x, b, 'r', label='Target distribution') pl.legend() - #%% plot distributions and loss matrix - pl.figure(2, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') +.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_001.png + :class: sphx-glr-single-img -.. rst-class:: sphx-glr-horizontal +.. rst-class:: sphx-glr-script-out + Out: - * + .. code-block:: none - .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_001.png - :scale: 47 - * + - .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_002.png - :scale: 47 +.. code-block:: default + + + pl.figure(2, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') + -Solve EMD ---------- +.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_002.png + :class: sphx-glr-single-img -.. code-block:: python - #%% EMD + +Solve EMD +--------- + + +.. code-block:: default + G0 = ot.emd(a, b, M) @@ -121,8 +128,9 @@ Solve EMD -.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_005.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_003.png + :class: sphx-glr-single-img + @@ -131,12 +139,8 @@ Solve Sinkhorn -------------- +.. code-block:: default -.. code-block:: python - - - - #%% Sinkhorn lambd = 1e-3 Gs = ot.sinkhorn(a, b, M, lambd, verbose=True) @@ -148,46 +152,71 @@ Solve Sinkhorn -.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_007.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_004.png + :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out - Out:: + Out: + .. code-block:: none + + It. |Err + ------------------- + 0|2.861463e-01| + 10|1.860154e-01| + 20|8.144529e-02| + 30|3.130143e-02| + 40|1.178815e-02| + 50|4.426078e-03| + 60|1.661047e-03| + 70|6.233110e-04| + 80|2.338932e-04| + 90|8.776627e-05| + 100|3.293340e-05| + 110|1.235791e-05| + 120|4.637176e-06| + 130|1.740051e-06| + 140|6.529356e-07| + 150|2.450071e-07| + 160|9.193632e-08| + 170|3.449812e-08| + 180|1.294505e-08| + 190|4.857493e-09| It. |Err ------------------- - 0|8.187970e-02| - 10|3.460174e-02| - 20|6.633335e-03| - 30|9.797798e-04| - 40|1.389606e-04| - 50|1.959016e-05| - 60|2.759079e-06| - 70|3.885166e-07| - 80|5.470605e-08| - 90|7.702918e-09| - 100|1.084609e-09| - 110|1.527180e-10| + 200|1.822723e-09| + 210|6.839572e-10| + /home/rflamary/PYTHON/POT/examples/plot_OT_1D.py:84: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() + + -**Total running time of the script:** ( 0 minutes 0.561 seconds) +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 0.665 seconds) + + +.. _sphx_glr_download_auto_examples_plot_OT_1D.py: .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_OT_1D.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_OT_1D.ipynb ` @@ -196,4 +225,4 @@ Solve Sinkhorn .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_1D_smooth.ipynb b/docs/source/auto_examples/plot_OT_1D_smooth.ipynb index d523f6a..493e6bb 100644 --- a/docs/source/auto_examples/plot_OT_1D_smooth.ipynb +++ b/docs/source/auto_examples/plot_OT_1D_smooth.ipynb @@ -44,7 +44,7 @@ }, "outputs": [], "source": [ - "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" + "n = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" ] }, { @@ -62,7 +62,18 @@ }, "outputs": [], "source": [ - "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n#%% plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')" + "pl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')" ] }, { @@ -80,7 +91,7 @@ }, "outputs": [], "source": [ - "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" + "G0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" ] }, { @@ -98,7 +109,7 @@ }, "outputs": [], "source": [ - "#%% Sinkhorn\n\nlambd = 2e-3\nGs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n\npl.show()" + "lambd = 2e-3\nGs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n\npl.show()" ] }, { @@ -116,7 +127,18 @@ }, "outputs": [], "source": [ - "#%% Smooth OT with KL regularization\n\nlambd = 2e-3\nGsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl')\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.')\n\npl.show()\n\n\n#%% Smooth OT with KL regularization\n\nlambd = 1e-1\nGsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2')\n\npl.figure(6, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.')\n\npl.show()" + "lambd = 2e-3\nGsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl')\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.')\n\npl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "lambd = 1e-1\nGsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2')\n\npl.figure(6, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.')\n\npl.show()" ] } ], @@ -136,7 +158,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_OT_1D_smooth.rst b/docs/source/auto_examples/plot_OT_1D_smooth.rst index 5a0ebd3..de42689 100644 --- a/docs/source/auto_examples/plot_OT_1D_smooth.rst +++ b/docs/source/auto_examples/plot_OT_1D_smooth.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_OT_1D_smooth.py: + .. _sphx_glr_auto_examples_plot_OT_1D_smooth.py: =========================== @@ -12,8 +18,7 @@ and their visualization. - -.. code-block:: python +.. code-block:: default # Author: Remi Flamary @@ -32,16 +37,13 @@ and their visualization. + Generate data ------------- +.. code-block:: default -.. code-block:: python - - - - #%% parameters n = 100 # nb bins @@ -63,55 +65,60 @@ Generate data + Plot distributions and loss matrix ---------------------------------- +.. code-block:: default -.. code-block:: python - - - #%% plot the distributions pl.figure(1, figsize=(6.4, 3)) pl.plot(x, a, 'b', label='Source distribution') pl.plot(x, b, 'r', label='Target distribution') pl.legend() - #%% plot distributions and loss matrix - pl.figure(2, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') +.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_001.png + :class: sphx-glr-single-img -.. rst-class:: sphx-glr-horizontal +.. rst-class:: sphx-glr-script-out + Out: - * + .. code-block:: none - .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_001.png - :scale: 47 - * + - .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_002.png - :scale: 47 +.. code-block:: default -Solve EMD ---------- + + pl.figure(2, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') + + + + +.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_002.png + :class: sphx-glr-single-img -.. code-block:: python +Solve EMD +--------- + + +.. code-block:: default - #%% EMD G0 = ot.emd(a, b, M) @@ -121,8 +128,9 @@ Solve EMD -.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_005.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_003.png + :class: sphx-glr-single-img + @@ -131,13 +139,9 @@ Solve Sinkhorn -------------- - -.. code-block:: python +.. code-block:: default - - #%% Sinkhorn - lambd = 2e-3 Gs = ot.sinkhorn(a, b, M, lambd, verbose=True) @@ -149,34 +153,42 @@ Solve Sinkhorn -.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_007.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_004.png + :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out - Out:: + Out: + + .. code-block:: none It. |Err ------------------- - 0|7.958844e-02| - 10|5.921715e-03| - 20|1.238266e-04| - 30|2.469780e-06| - 40|4.919966e-08| - 50|9.800197e-10| - + 0|2.821142e-01| + 10|7.695268e-02| + 20|1.112774e-02| + 30|1.571553e-03| + 40|2.218100e-04| + 50|3.130527e-05| + 60|4.418267e-06| + 70|6.235716e-07| + 80|8.800770e-08| + 90|1.242095e-08| + 100|1.753030e-09| + 110|2.474136e-10| + /home/rflamary/PYTHON/POT/examples/plot_OT_1D_smooth.py:84: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() -Solve Smooth OT --------------- -.. code-block:: python +Solve Smooth OT +-------------- +.. code-block:: default - #%% Smooth OT with KL regularization lambd = 2e-3 Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl') @@ -187,7 +199,28 @@ Solve Smooth OT pl.show() - #%% Smooth OT with KL regularization + + + +.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_005.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_OT_1D_smooth.py:99: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() + + + + + +.. code-block:: default + lambd = 1e-1 Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2') @@ -199,38 +232,45 @@ Solve Smooth OT -.. rst-class:: sphx-glr-horizontal +.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_006.png + :class: sphx-glr-single-img - * +.. rst-class:: sphx-glr-script-out + + Out: - .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_009.png - :scale: 47 + .. code-block:: none - * + /home/rflamary/PYTHON/POT/examples/plot_OT_1D_smooth.py:110: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() - .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_010.png - :scale: 47 -**Total running time of the script:** ( 0 minutes 1.053 seconds) +.. rst-class:: sphx-glr-timing + **Total running time of the script:** ( 0 minutes 0.732 seconds) + + +.. _sphx_glr_download_auto_examples_plot_OT_1D_smooth.py: .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_OT_1D_smooth.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_OT_1D_smooth.ipynb ` @@ -239,4 +279,4 @@ Solve Smooth OT .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_2D_samples.ipynb b/docs/source/auto_examples/plot_OT_2D_samples.ipynb index dad138b..ff7abde 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.ipynb +++ b/docs/source/auto_examples/plot_OT_2D_samples.ipynb @@ -44,7 +44,7 @@ }, "outputs": [], "source": [ - "#%% parameters and data generation\n\nn = 50 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4])\ncov_t = np.array([[1, -.8], [-.8, 1]])\n\nxs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s)\nxt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t)\n\na, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples\n\n# loss matrix\nM = ot.dist(xs, xt)\nM /= M.max()" + "n = 50 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4])\ncov_t = np.array([[1, -.8], [-.8, 1]])\n\nxs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s)\nxt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t)\n\na, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples\n\n# loss matrix\nM = ot.dist(xs, xt)\nM /= M.max()" ] }, { @@ -62,7 +62,7 @@ }, "outputs": [], "source": [ - "#%% plot samples\n\npl.figure(1)\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')\n\npl.figure(2)\npl.imshow(M, interpolation='nearest')\npl.title('Cost matrix M')" + "pl.figure(1)\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')\n\npl.figure(2)\npl.imshow(M, interpolation='nearest')\npl.title('Cost matrix M')" ] }, { @@ -80,7 +80,7 @@ }, "outputs": [], "source": [ - "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3)\npl.imshow(G0, interpolation='nearest')\npl.title('OT matrix G0')\n\npl.figure(4)\not.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix with samples')" + "G0 = ot.emd(a, b, M)\n\npl.figure(3)\npl.imshow(G0, interpolation='nearest')\npl.title('OT matrix G0')\n\npl.figure(4)\not.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix with samples')" ] }, { @@ -98,7 +98,7 @@ }, "outputs": [], "source": [ - "#%% sinkhorn\n\n# reg term\nlambd = 1e-3\n\nGs = ot.sinkhorn(a, b, M, lambd)\n\npl.figure(5)\npl.imshow(Gs, interpolation='nearest')\npl.title('OT matrix sinkhorn')\n\npl.figure(6)\not.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn with samples')\n\npl.show()" + "# reg term\nlambd = 1e-3\n\nGs = ot.sinkhorn(a, b, M, lambd)\n\npl.figure(5)\npl.imshow(Gs, interpolation='nearest')\npl.title('OT matrix sinkhorn')\n\npl.figure(6)\not.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn with samples')\n\npl.show()" ] }, { @@ -116,7 +116,7 @@ }, "outputs": [], "source": [ - "#%% sinkhorn\n\n# reg term\nlambd = 1e-3\n\nGes = ot.bregman.empirical_sinkhorn(xs, xt, lambd)\n\npl.figure(7)\npl.imshow(Ges, interpolation='nearest')\npl.title('OT matrix empirical sinkhorn')\n\npl.figure(8)\not.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn from samples')\n\npl.show()" + "# reg term\nlambd = 1e-3\n\nGes = ot.bregman.empirical_sinkhorn(xs, xt, lambd)\n\npl.figure(7)\npl.imshow(Ges, interpolation='nearest')\npl.title('OT matrix empirical sinkhorn')\n\npl.figure(8)\not.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn from samples')\n\npl.show()" ] } ], @@ -136,7 +136,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_OT_2D_samples.rst b/docs/source/auto_examples/plot_OT_2D_samples.rst index 1f1d713..460bb95 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.rst +++ b/docs/source/auto_examples/plot_OT_2D_samples.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_OT_2D_samples.py: + .. _sphx_glr_auto_examples_plot_OT_2D_samples.py: ==================================================== @@ -12,8 +18,7 @@ sum of diracs. The OT matrix is plotted with the samples. - -.. code-block:: python +.. code-block:: default # Author: Remi Flamary @@ -32,15 +37,13 @@ sum of diracs. The OT matrix is plotted with the samples. + Generate data ------------- +.. code-block:: default -.. code-block:: python - - - #%% parameters and data generation n = 50 # nb samples @@ -65,15 +68,13 @@ Generate data + Plot data --------- +.. code-block:: default -.. code-block:: python - - - #%% plot samples pl.figure(1) pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') @@ -94,25 +95,31 @@ Plot data * .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_001.png - :scale: 47 + :class: sphx-glr-multi-img * .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_002.png - :scale: 47 + :class: sphx-glr-multi-img +.. rst-class:: sphx-glr-script-out + Out: -Compute EMD ------------ + .. code-block:: none + + + Text(0.5, 1.0, 'Cost matrix M') -.. code-block:: python +Compute EMD +----------- + +.. code-block:: default - #%% EMD G0 = ot.emd(a, b, M) @@ -136,26 +143,32 @@ Compute EMD * - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_005.png - :scale: 47 + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_003.png + :class: sphx-glr-multi-img * - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_006.png - :scale: 47 + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_004.png + :class: sphx-glr-multi-img +.. rst-class:: sphx-glr-script-out + Out: -Compute Sinkhorn ----------------- + .. code-block:: none + Text(0.5, 1.0, 'OT matrix with samples') -.. code-block:: python - #%% sinkhorn +Compute Sinkhorn +---------------- + + +.. code-block:: default + # reg term lambd = 1e-3 @@ -184,26 +197,33 @@ Compute Sinkhorn * - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_009.png - :scale: 47 + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_005.png + :class: sphx-glr-multi-img * - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_010.png - :scale: 47 + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_006.png + :class: sphx-glr-multi-img +.. rst-class:: sphx-glr-script-out + Out: -Emprirical Sinkhorn ----------------- + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_OT_2D_samples.py:103: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() -.. code-block:: python + +Emprirical Sinkhorn +---------------- - #%% sinkhorn +.. code-block:: default + # reg term lambd = 1e-3 @@ -230,38 +250,55 @@ Emprirical Sinkhorn * - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_013.png - :scale: 47 + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_007.png + :class: sphx-glr-multi-img * - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_014.png - :scale: 47 + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_008.png + :class: sphx-glr-multi-img .. rst-class:: sphx-glr-script-out - Out:: + Out: + + .. code-block:: none + /home/rflamary/PYTHON/POT/ot/bregman.py:363: RuntimeWarning: divide by zero encountered in true_divide + v = np.divide(b, KtransposeU) Warning: numerical errors at iteration 0 + /home/rflamary/PYTHON/POT/ot/plot.py:90: RuntimeWarning: invalid value encountered in double_scalars + if G[i, j] / mx > thr: + /home/rflamary/PYTHON/POT/examples/plot_OT_2D_samples.py:128: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() + -**Total running time of the script:** ( 0 minutes 2.616 seconds) +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 2.154 seconds) + + +.. _sphx_glr_download_auto_examples_plot_OT_2D_samples.py: + .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_OT_2D_samples.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_OT_2D_samples.ipynb ` @@ -270,4 +307,4 @@ Emprirical Sinkhorn .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb index 125d720..12a09f0 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb @@ -62,7 +62,7 @@ }, "outputs": [], "source": [ - "#%% EMD\nG1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(3, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()" + "G1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(3, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()" ] }, { @@ -98,7 +98,7 @@ }, "outputs": [], "source": [ - "#%% EMD\nG1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(6, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()" + "G1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(6, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()" ] } ], @@ -118,7 +118,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst index 5db4b55..16b20f9 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_OT_L1_vs_L2.py: + .. _sphx_glr_auto_examples_plot_OT_L1_vs_L2.py: ========================================== @@ -15,8 +21,7 @@ https://arxiv.org/pdf/1706.07650.pdf - -.. code-block:: python +.. code-block:: default # Author: Remi Flamary @@ -34,12 +39,12 @@ https://arxiv.org/pdf/1706.07650.pdf + Dataset 1 : uniform sampling ---------------------------- - -.. code-block:: python +.. code-block:: default n = 20 # nb samples @@ -98,12 +103,13 @@ Dataset 1 : uniform sampling * .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_001.png - :scale: 47 + :class: sphx-glr-multi-img * .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_002.png - :scale: 47 + :class: sphx-glr-multi-img + @@ -112,12 +118,8 @@ Dataset 1 : Plot OT Matrices ---------------------------- +.. code-block:: default -.. code-block:: python - - - - #%% EMD G1 = ot.emd(a, b, M1) G2 = ot.emd(a, b, M2) Gp = ot.emd(a, b, Mp) @@ -156,8 +158,18 @@ Dataset 1 : Plot OT Matrices -.. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_005.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_003.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_OT_L1_vs_L2.py:113: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() @@ -166,8 +178,7 @@ Dataset 2 : Partial circle -------------------------- - -.. code-block:: python +.. code-block:: default n = 50 # nb samples @@ -228,13 +239,14 @@ Dataset 2 : Partial circle * - .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_007.png - :scale: 47 + .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_004.png + :class: sphx-glr-multi-img * - .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_008.png - :scale: 47 + .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_005.png + :class: sphx-glr-multi-img + @@ -243,12 +255,8 @@ Dataset 2 : Plot OT Matrices ----------------------------- +.. code-block:: default -.. code-block:: python - - - - #%% EMD G1 = ot.emd(a, b, M1) G2 = ot.emd(a, b, M2) Gp = ot.emd(a, b, Mp) @@ -285,28 +293,45 @@ Dataset 2 : Plot OT Matrices -.. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_011.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_006.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + Out: + .. code-block:: none + /home/rflamary/PYTHON/POT/examples/plot_OT_L1_vs_L2.py:208: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() -**Total running time of the script:** ( 0 minutes 0.958 seconds) + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 1.002 seconds) + + +.. _sphx_glr_download_auto_examples_plot_OT_L1_vs_L2.py: + + .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_OT_L1_vs_L2.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_OT_L1_vs_L2.ipynb ` @@ -315,4 +340,4 @@ Dataset 2 : Plot OT Matrices .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_UOT_1D.ipynb b/docs/source/auto_examples/plot_UOT_1D.ipynb index c695306..640e398 100644 --- a/docs/source/auto_examples/plot_UOT_1D.ipynb +++ b/docs/source/auto_examples/plot_UOT_1D.ipynb @@ -44,7 +44,7 @@ }, "outputs": [], "source": [ - "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# make distributions unbalanced\nb *= 5.\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" + "n = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# make distributions unbalanced\nb *= 5.\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" ] }, { @@ -62,7 +62,7 @@ }, "outputs": [], "source": [ - "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n# plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')" + "pl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n# plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')" ] }, { @@ -100,7 +100,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_UOT_1D.rst b/docs/source/auto_examples/plot_UOT_1D.rst index 8e618b4..f43b0c1 100644 --- a/docs/source/auto_examples/plot_UOT_1D.rst +++ b/docs/source/auto_examples/plot_UOT_1D.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_UOT_1D.py: + .. _sphx_glr_auto_examples_plot_UOT_1D.py: =============================== @@ -11,8 +17,7 @@ This example illustrates the computation of Unbalanced Optimal transport using a Kullback-Leibler relaxation. - -.. code-block:: python +.. code-block:: default # Author: Hicham Janati @@ -31,17 +36,14 @@ using a Kullback-Leibler relaxation. + Generate data ------------- - -.. code-block:: python +.. code-block:: default - - #%% parameters - n = 100 # nb bins # bin positions @@ -65,15 +67,13 @@ Generate data + Plot distributions and loss matrix ---------------------------------- +.. code-block:: default -.. code-block:: python - - - #%% plot the distributions pl.figure(1, figsize=(6.4, 3)) pl.plot(x, a, 'b', label='Source distribution') @@ -95,12 +95,13 @@ Plot distributions and loss matrix * .. image:: /auto_examples/images/sphx_glr_plot_UOT_1D_001.png - :scale: 47 + :class: sphx-glr-multi-img * .. image:: /auto_examples/images/sphx_glr_plot_UOT_1D_002.png - :scale: 47 + :class: sphx-glr-multi-img + @@ -109,8 +110,7 @@ Solve Unbalanced Sinkhorn -------------- - -.. code-block:: python +.. code-block:: default @@ -127,41 +127,45 @@ Solve Unbalanced Sinkhorn -.. image:: /auto_examples/images/sphx_glr_plot_UOT_1D_006.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_UOT_1D_003.png + :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out - Out:: + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_UOT_1D.py:76: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() + - It. |Err - ------------------- - 0|1.838786e+00| - 10|1.242379e-01| - 20|2.581314e-03| - 30|5.674552e-05| - 40|1.252959e-06| - 50|2.768136e-08| - 60|6.116090e-10| -**Total running time of the script:** ( 0 minutes 0.259 seconds) +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 0.274 seconds) + + +.. _sphx_glr_download_auto_examples_plot_UOT_1D.py: .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_UOT_1D.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_UOT_1D.ipynb ` @@ -170,4 +174,4 @@ Solve Unbalanced Sinkhorn .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_UOT_barycenter_1D.ipynb b/docs/source/auto_examples/plot_UOT_barycenter_1D.ipynb index e59cdc2..549a78b 100644 --- a/docs/source/auto_examples/plot_UOT_barycenter_1D.ipynb +++ b/docs/source/auto_examples/plot_UOT_barycenter_1D.ipynb @@ -80,7 +80,7 @@ }, "outputs": [], "source": [ - "# non weighted barycenter computation\n\nweight = 0.5 # 0<=weight<=1\nweights = np.array([1 - weight, weight])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nalpha = 1.\n\nbary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()" + "# non weighted barycenter computation\n\nweight = 0.5 # 0<=weight<=1\nweights = np.array([1 - weight, weight])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nalpha = 1.\n\nbary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()" ] }, { @@ -98,7 +98,7 @@ }, "outputs": [], "source": [ - "# barycenter interpolation\n\nn_weight = 11\nweight_list = np.linspace(0, 1, n_weight)\n\n\nB_l2 = np.zeros((n, n_weight))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_weight):\n weight = weight_list[i]\n weights = np.array([1 - weight, weight])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)\n\n\n# plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = weight_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel(r'$\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = weight_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel(r'$\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" + "# barycenter interpolation\n\nn_weight = 11\nweight_list = np.linspace(0, 1, n_weight)\n\n\nB_l2 = np.zeros((n, n_weight))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_weight):\n weight = weight_list[i]\n weights = np.array([1 - weight, weight])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights)\n\n\n# plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = weight_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel(r'$\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = weight_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel(r'$\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" ] } ], @@ -118,7 +118,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_UOT_barycenter_1D.py b/docs/source/auto_examples/plot_UOT_barycenter_1D.py index c8d9d3b..acb5892 100644 --- a/docs/source/auto_examples/plot_UOT_barycenter_1D.py +++ b/docs/source/auto_examples/plot_UOT_barycenter_1D.py @@ -77,7 +77,7 @@ bary_l2 = A.dot(weights) reg = 1e-3 alpha = 1. -bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights) +bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights) pl.figure(2) pl.clf() @@ -111,7 +111,7 @@ for i in range(0, n_weight): weight = weight_list[i] weights = np.array([1 - weight, weight]) B_l2[:, i] = A.dot(weights) - B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights) + B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights) # plot interpolation diff --git a/docs/source/auto_examples/plot_UOT_barycenter_1D.rst b/docs/source/auto_examples/plot_UOT_barycenter_1D.rst index ac17587..2688d2e 100644 --- a/docs/source/auto_examples/plot_UOT_barycenter_1D.rst +++ b/docs/source/auto_examples/plot_UOT_barycenter_1D.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_UOT_barycenter_1D.py: + .. _sphx_glr_auto_examples_plot_UOT_barycenter_1D.py: =========================================================== @@ -15,8 +21,7 @@ as proposed in [10] for Unbalanced inputs. - -.. code-block:: python +.. code-block:: default # Author: Hicham Janati @@ -36,12 +41,12 @@ as proposed in [10] for Unbalanced inputs. + Generate data ------------- - -.. code-block:: python +.. code-block:: default # parameters @@ -72,12 +77,12 @@ Generate data + Plot data --------- - -.. code-block:: python +.. code-block:: default # plot the distributions @@ -92,7 +97,8 @@ Plot data .. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_001.png - :align: center + :class: sphx-glr-single-img + @@ -101,8 +107,7 @@ Barycenter computation ---------------------- - -.. code-block:: python +.. code-block:: default # non weighted barycenter computation @@ -117,7 +122,7 @@ Barycenter computation reg = 1e-3 alpha = 1. - bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights) + bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights) pl.figure(2) pl.clf() @@ -136,8 +141,9 @@ Barycenter computation -.. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_003.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_002.png + :class: sphx-glr-single-img + @@ -146,8 +152,7 @@ Barycentric interpolation ------------------------- - -.. code-block:: python +.. code-block:: default # barycenter interpolation @@ -164,7 +169,7 @@ Barycentric interpolation weight = weight_list[i] weights = np.array([1 - weight, weight]) B_l2[:, i] = A.dot(weights) - B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights) + B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights) # plot interpolation @@ -223,33 +228,66 @@ Barycentric interpolation * - .. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_005.png - :scale: 47 + .. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_003.png + :class: sphx-glr-multi-img * - .. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_006.png - :scale: 47 + .. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_004.png + :class: sphx-glr-multi-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + /home/rflamary/PYTHON/POT/ot/unbalanced.py:895: RuntimeWarning: overflow encountered in true_divide + u = (A / Kv) ** fi + /home/rflamary/PYTHON/POT/ot/unbalanced.py:900: RuntimeWarning: invalid value encountered in true_divide + v = (Q / Ktu) ** fi + /home/rflamary/PYTHON/POT/ot/unbalanced.py:907: UserWarning: Numerical errors at iteration 595 + warnings.warn('Numerical errors at iteration %s' % i) + /home/rflamary/PYTHON/POT/ot/unbalanced.py:900: RuntimeWarning: overflow encountered in true_divide + v = (Q / Ktu) ** fi + /home/rflamary/PYTHON/POT/ot/unbalanced.py:907: UserWarning: Numerical errors at iteration 974 + warnings.warn('Numerical errors at iteration %s' % i) + /home/rflamary/PYTHON/POT/ot/unbalanced.py:907: UserWarning: Numerical errors at iteration 615 + warnings.warn('Numerical errors at iteration %s' % i) + /home/rflamary/PYTHON/POT/ot/unbalanced.py:907: UserWarning: Numerical errors at iteration 455 + warnings.warn('Numerical errors at iteration %s' % i) + /home/rflamary/PYTHON/POT/ot/unbalanced.py:907: UserWarning: Numerical errors at iteration 361 + warnings.warn('Numerical errors at iteration %s' % i) + /home/rflamary/PYTHON/POT/examples/plot_UOT_barycenter_1D.py:164: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() -**Total running time of the script:** ( 0 minutes 0.344 seconds) +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 1.567 seconds) + + +.. _sphx_glr_download_auto_examples_plot_UOT_barycenter_1D.py: + .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_UOT_barycenter_1D.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_UOT_barycenter_1D.ipynb ` @@ -258,4 +296,4 @@ Barycentric interpolation .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_barycenter_1D.ipynb b/docs/source/auto_examples/plot_barycenter_1D.ipynb index fc60e1f..387c41a 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.ipynb +++ b/docs/source/auto_examples/plot_barycenter_1D.ipynb @@ -44,7 +44,7 @@ }, "outputs": [], "source": [ - "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()" + "n = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()" ] }, { @@ -62,7 +62,7 @@ }, "outputs": [], "source": [ - "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()" + "pl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()" ] }, { @@ -80,7 +80,7 @@ }, "outputs": [], "source": [ - "#%% barycenter computation\n\nalpha = 0.2 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()" + "alpha = 0.2 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()" ] }, { @@ -98,7 +98,18 @@ }, "outputs": [], "source": [ - "#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n alpha = alpha_list[i]\n weights = np.array([1 - alpha, alpha])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" + "n_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n alpha = alpha_list[i]\n weights = np.array([1 - alpha, alpha])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" ] } ], @@ -118,7 +129,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_barycenter_1D.rst b/docs/source/auto_examples/plot_barycenter_1D.rst index 66ac042..a65ac3d 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.rst +++ b/docs/source/auto_examples/plot_barycenter_1D.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_barycenter_1D.py: + .. _sphx_glr_auto_examples_plot_barycenter_1D.py: ============================== @@ -17,8 +23,7 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. - -.. code-block:: python +.. code-block:: default # Author: Remi Flamary @@ -38,16 +43,14 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + Generate data ------------- - -.. code-block:: python +.. code-block:: default - #%% parameters - n = 100 # nb bins # bin positions @@ -71,15 +74,13 @@ Generate data + Plot data --------- +.. code-block:: default -.. code-block:: python - - - #%% plot the distributions pl.figure(1, figsize=(6.4, 3)) for i in range(n_distributions): @@ -91,7 +92,8 @@ Plot data .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_001.png - :align: center + :class: sphx-glr-single-img + @@ -100,12 +102,9 @@ Barycenter computation ---------------------- - -.. code-block:: python +.. code-block:: default - #%% barycenter computation - alpha = 0.2 # 0<=alpha<=1 weights = np.array([1 - alpha, alpha]) @@ -133,8 +132,9 @@ Barycenter computation -.. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_003.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_002.png + :class: sphx-glr-single-img + @@ -143,11 +143,8 @@ Barycentric interpolation ------------------------- +.. code-block:: default -.. code-block:: python - - - #%% barycenter interpolation n_alpha = 11 alpha_list = np.linspace(0, 1, n_alpha) @@ -163,7 +160,16 @@ Barycentric interpolation B_l2[:, i] = A.dot(weights) B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights) - #%% plot interpolation + + + + + + + + +.. code-block:: default + pl.figure(3) @@ -219,33 +225,50 @@ Barycentric interpolation * - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_005.png - :scale: 47 + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_003.png + :class: sphx-glr-multi-img * - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_006.png - :scale: 47 + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_004.png + :class: sphx-glr-multi-img + +.. rst-class:: sphx-glr-script-out + Out: + .. code-block:: none -**Total running time of the script:** ( 0 minutes 0.413 seconds) + /home/rflamary/PYTHON/POT/examples/plot_barycenter_1D.py:160: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 0.769 seconds) + + +.. _sphx_glr_download_auto_examples_plot_barycenter_1D.py: + + .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_barycenter_1D.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_barycenter_1D.ipynb ` @@ -254,4 +277,4 @@ Barycentric interpolation .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_barycenter_fgw.ipynb b/docs/source/auto_examples/plot_barycenter_fgw.ipynb index 28229b2..4e4704c 100644 --- a/docs/source/auto_examples/plot_barycenter_fgw.ipynb +++ b/docs/source/auto_examples/plot_barycenter_fgw.ipynb @@ -26,7 +26,29 @@ }, "outputs": [], "source": [ - "# Author: Titouan Vayer \n#\n# License: MIT License\n\n#%% load libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport networkx as nx\nimport math\nfrom scipy.sparse.csgraph import shortest_path\nimport matplotlib.colors as mcol\nfrom matplotlib import cm\nfrom ot.gromov import fgw_barycenters\n#%% Graph functions\n\n\ndef find_thresh(C, inf=0.5, sup=3, step=10):\n \"\"\" Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected\n Tthe threshold is found by a linesearch between values \"inf\" and \"sup\" with \"step\" thresholds tested.\n The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix\n and the original matrix.\n Parameters\n ----------\n C : ndarray, shape (n_nodes,n_nodes)\n The structure matrix to threshold\n inf : float\n The beginning of the linesearch\n sup : float\n The end of the linesearch\n step : integer\n Number of thresholds tested\n \"\"\"\n dist = []\n search = np.linspace(inf, sup, step)\n for thresh in search:\n Cprime = sp_to_adjency(C, 0, thresh)\n SC = shortest_path(Cprime, method='D')\n SC[SC == float('inf')] = 100\n dist.append(np.linalg.norm(SC - C))\n return search[np.argmin(dist)], dist\n\n\ndef sp_to_adjency(C, threshinf=0.2, threshsup=1.8):\n \"\"\" Thresholds the structure matrix in order to compute an adjency matrix.\n All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0\n Parameters\n ----------\n C : ndarray, shape (n_nodes,n_nodes)\n The structure matrix to threshold\n threshinf : float\n The minimum value of distance from which the new value is set to 1\n threshsup : float\n The maximum value of distance from which the new value is set to 1\n Returns\n -------\n C : ndarray, shape (n_nodes,n_nodes)\n The threshold matrix. Each element is in {0,1}\n \"\"\"\n H = np.zeros_like(C)\n np.fill_diagonal(H, np.diagonal(C))\n C = C - H\n C = np.minimum(np.maximum(C, threshinf), threshsup)\n C[C == threshsup] = 0\n C[C != 0] = 1\n\n return C\n\n\ndef build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None):\n \"\"\" Create a noisy circular graph\n \"\"\"\n g = nx.Graph()\n g.add_nodes_from(list(range(N)))\n for i in range(N):\n noise = float(np.random.normal(mu, sigma, 1))\n if with_noise:\n g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise)\n else:\n g.add_node(i, attr_name=math.sin(2 * i * math.pi / N))\n g.add_edge(i, i + 1)\n if structure_noise:\n randomint = np.random.randint(0, p)\n if randomint == 0:\n if i <= N - 3:\n g.add_edge(i, i + 2)\n if i == N - 2:\n g.add_edge(i, 0)\n if i == N - 1:\n g.add_edge(i, 1)\n g.add_edge(N, 0)\n noise = float(np.random.normal(mu, sigma, 1))\n if with_noise:\n g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise)\n else:\n g.add_node(N, attr_name=math.sin(2 * N * math.pi / N))\n return g\n\n\ndef graph_colors(nx_graph, vmin=0, vmax=7):\n cnorm = mcol.Normalize(vmin=vmin, vmax=vmax)\n cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis')\n cpick.set_array([])\n val_map = {}\n for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items():\n val_map[k] = cpick.to_rgba(v)\n colors = []\n for node in nx_graph.nodes():\n colors.append(val_map[node])\n return colors" + "# Author: Titouan Vayer \n#\n# License: MIT License" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\nimport matplotlib.pyplot as plt\nimport networkx as nx\nimport math\nfrom scipy.sparse.csgraph import shortest_path\nimport matplotlib.colors as mcol\nfrom matplotlib import cm\nfrom ot.gromov import fgw_barycenters" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def find_thresh(C, inf=0.5, sup=3, step=10):\n \"\"\" Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected\n Tthe threshold is found by a linesearch between values \"inf\" and \"sup\" with \"step\" thresholds tested.\n The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix\n and the original matrix.\n Parameters\n ----------\n C : ndarray, shape (n_nodes,n_nodes)\n The structure matrix to threshold\n inf : float\n The beginning of the linesearch\n sup : float\n The end of the linesearch\n step : integer\n Number of thresholds tested\n \"\"\"\n dist = []\n search = np.linspace(inf, sup, step)\n for thresh in search:\n Cprime = sp_to_adjency(C, 0, thresh)\n SC = shortest_path(Cprime, method='D')\n SC[SC == float('inf')] = 100\n dist.append(np.linalg.norm(SC - C))\n return search[np.argmin(dist)], dist\n\n\ndef sp_to_adjency(C, threshinf=0.2, threshsup=1.8):\n \"\"\" Thresholds the structure matrix in order to compute an adjency matrix.\n All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0\n Parameters\n ----------\n C : ndarray, shape (n_nodes,n_nodes)\n The structure matrix to threshold\n threshinf : float\n The minimum value of distance from which the new value is set to 1\n threshsup : float\n The maximum value of distance from which the new value is set to 1\n Returns\n -------\n C : ndarray, shape (n_nodes,n_nodes)\n The threshold matrix. Each element is in {0,1}\n \"\"\"\n H = np.zeros_like(C)\n np.fill_diagonal(H, np.diagonal(C))\n C = C - H\n C = np.minimum(np.maximum(C, threshinf), threshsup)\n C[C == threshsup] = 0\n C[C != 0] = 1\n\n return C\n\n\ndef build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None):\n \"\"\" Create a noisy circular graph\n \"\"\"\n g = nx.Graph()\n g.add_nodes_from(list(range(N)))\n for i in range(N):\n noise = float(np.random.normal(mu, sigma, 1))\n if with_noise:\n g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise)\n else:\n g.add_node(i, attr_name=math.sin(2 * i * math.pi / N))\n g.add_edge(i, i + 1)\n if structure_noise:\n randomint = np.random.randint(0, p)\n if randomint == 0:\n if i <= N - 3:\n g.add_edge(i, i + 2)\n if i == N - 2:\n g.add_edge(i, 0)\n if i == N - 1:\n g.add_edge(i, 1)\n g.add_edge(N, 0)\n noise = float(np.random.normal(mu, sigma, 1))\n if with_noise:\n g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise)\n else:\n g.add_node(N, attr_name=math.sin(2 * N * math.pi / N))\n return g\n\n\ndef graph_colors(nx_graph, vmin=0, vmax=7):\n cnorm = mcol.Normalize(vmin=vmin, vmax=vmax)\n cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis')\n cpick.set_array([])\n val_map = {}\n for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items():\n val_map[k] = cpick.to_rgba(v)\n colors = []\n for node in nx_graph.nodes():\n colors.append(val_map[node])\n return colors" ] }, { @@ -36,6 +58,13 @@ "Generate data\n-------------\n\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We build a dataset of noisy circular graphs.\nNoise is added on the structures by random connections and on the features by gaussian noise.\n\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -44,7 +73,7 @@ }, "outputs": [], "source": [ - "#%% circular dataset\n# We build a dataset of noisy circular graphs.\n# Noise is added on the structures by random connections and on the features by gaussian noise.\n\n\nnp.random.seed(30)\nX0 = []\nfor k in range(9):\n X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3))" + "np.random.seed(30)\nX0 = []\nfor k in range(9):\n X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3))" ] }, { @@ -62,7 +91,7 @@ }, "outputs": [], "source": [ - "#%% Plot graphs\n\nplt.figure(figsize=(8, 10))\nfor i in range(len(X0)):\n plt.subplot(3, 3, i + 1)\n g = X0[i]\n pos = nx.kamada_kawai_layout(g)\n nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100)\nplt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20)\nplt.show()" + "plt.figure(figsize=(8, 10))\nfor i in range(len(X0)):\n plt.subplot(3, 3, i + 1)\n g = X0[i]\n pos = nx.kamada_kawai_layout(g)\n nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100)\nplt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20)\nplt.show()" ] }, { @@ -72,6 +101,13 @@ "Barycenter computation\n----------------------\n\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Features distances are the euclidean distances\n\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -80,7 +116,7 @@ }, "outputs": [], "source": [ - "#%% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph\n# Features distances are the euclidean distances\nCs = [shortest_path(nx.adjacency_matrix(x)) for x in X0]\nps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0]\nYs = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0]\nlambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel()\nsizebary = 15 # we choose a barycenter with 15 nodes\n\nA, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True)" + "Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0]\nps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0]\nYs = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0]\nlambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel()\nsizebary = 15 # we choose a barycenter with 15 nodes\n\nA, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True)" ] }, { @@ -98,7 +134,18 @@ }, "outputs": [], "source": [ - "#%% Create the barycenter\nbary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0]))\nfor i, v in enumerate(A.ravel()):\n bary.add_node(i, attr_name=v)\n\n#%%\npos = nx.kamada_kawai_layout(bary)\nnx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False)\nplt.suptitle('Barycenter', fontsize=20)\nplt.show()" + "bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0]))\nfor i, v in enumerate(A.ravel()):\n bary.add_node(i, attr_name=v)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pos = nx.kamada_kawai_layout(bary)\nnx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False)\nplt.suptitle('Barycenter', fontsize=20)\nplt.show()" ] } ], @@ -118,7 +165,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_barycenter_fgw.rst b/docs/source/auto_examples/plot_barycenter_fgw.rst index 2c44a65..ad4c275 100644 --- a/docs/source/auto_examples/plot_barycenter_fgw.rst +++ b/docs/source/auto_examples/plot_barycenter_fgw.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_barycenter_fgw.py: + .. _sphx_glr_auto_examples_plot_barycenter_fgw.py: ================================= @@ -18,15 +24,23 @@ Requires networkx >=2 - -.. code-block:: python +.. code-block:: default # Author: Titouan Vayer # # License: MIT License - #%% load libraries + + + + + + + + +.. code-block:: default + import numpy as np import matplotlib.pyplot as plt import networkx as nx @@ -35,7 +49,16 @@ Requires networkx >=2 import matplotlib.colors as mcol from matplotlib import cm from ot.gromov import fgw_barycenters - #%% Graph functions + + + + + + + + +.. code-block:: default + def find_thresh(C, inf=0.5, sup=3, step=10): @@ -138,17 +161,16 @@ Requires networkx >=2 + Generate data ------------- +We build a dataset of noisy circular graphs. +Noise is added on the structures by random connections and on the features by gaussian noise. -.. code-block:: python - +.. code-block:: default - #%% circular dataset - # We build a dataset of noisy circular graphs. - # Noise is added on the structures by random connections and on the features by gaussian noise. np.random.seed(30) @@ -162,15 +184,13 @@ Generate data + Plot data --------- +.. code-block:: default -.. code-block:: python - - - #%% Plot graphs plt.figure(figsize=(8, 10)) for i in range(len(X0)): @@ -185,7 +205,17 @@ Plot data .. image:: /auto_examples/images/sphx_glr_plot_barycenter_fgw_001.png - :align: center + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_barycenter_fgw.py:155: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + plt.show() @@ -193,13 +223,11 @@ Plot data Barycenter computation ---------------------- +Features distances are the euclidean distances -.. code-block:: python +.. code-block:: default - - #%% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph - # Features distances are the euclidean distances Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0] ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0] Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0] @@ -214,20 +242,27 @@ Barycenter computation + Plot Barycenter ------------------------- +.. code-block:: default -.. code-block:: python - - - #%% Create the barycenter bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0])) for i, v in enumerate(A.ravel()): bary.add_node(i, attr_name=v) - #%% + + + + + + + + +.. code-block:: default + pos = nx.kamada_kawai_layout(bary) nx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False) plt.suptitle('Barycenter', fontsize=20) @@ -236,27 +271,44 @@ Plot Barycenter .. image:: /auto_examples/images/sphx_glr_plot_barycenter_fgw_002.png - :align: center + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + .. code-block:: none + /home/rflamary/PYTHON/POT/examples/plot_barycenter_fgw.py:184: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + plt.show() -**Total running time of the script:** ( 0 minutes 2.065 seconds) +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 1.949 seconds) + + +.. _sphx_glr_download_auto_examples_plot_barycenter_fgw.py: + + .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_barycenter_fgw.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_barycenter_fgw.ipynb ` @@ -265,4 +317,4 @@ Plot Barycenter .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.ipynb b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.ipynb index 2199162..b976aae 100644 --- a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.ipynb +++ b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.ipynb @@ -44,7 +44,69 @@ }, "outputs": [], "source": [ - "#%% parameters\n\nproblems = []\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n#%% barycenter computation\n\nalpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])" + "problems = []\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "alpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Stair Data\n----------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "a1 = 1.0 * (x > 10) * (x < 50)\na2 = 1.0 * (x > 60) * (x < 80)\n\na1 /= a1.sum()\na2 /= a2.sum()\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "alpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()" ] }, { @@ -62,7 +124,29 @@ }, "outputs": [], "source": [ - "#%% parameters\n\na1 = 1.0 * (x > 10) * (x < 50)\na2 = 1.0 * (x > 60) * (x < 80)\n\na1 /= a1.sum()\na2 /= a2.sum()\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n\n#%% barycenter computation\n\nalpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\n#%% parameters\n\na1 = np.zeros(n)\na2 = np.zeros(n)\n\na1[10] = .25\na1[20] = .5\na1[30] = .25\na2[80] = 1\n\n\na1 /= a1.sum()\na2 /= a2.sum()\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n\n#%% barycenter computation\n\nalpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()" + "a1 = np.zeros(n)\na2 = np.zeros(n)\n\na1[10] = .25\na1[20] = .5\na1[30] = .25\na2[80] = 1\n\n\na1 /= a1.sum()\na2 /= a2.sum()\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "alpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()" ] }, { @@ -80,7 +164,7 @@ }, "outputs": [], "source": [ - "#%% plot\n\nnbm = len(problems)\nnbm2 = (nbm // 2)\n\n\npl.figure(2, (20, 6))\npl.clf()\n\nfor i in range(nbm):\n\n A = problems[i][0]\n bary_l2 = problems[i][1][0]\n bary_wass = problems[i][1][1]\n bary_wass2 = problems[i][1][2]\n\n pl.subplot(2, nbm, 1 + i)\n for j in range(n_distributions):\n pl.plot(x, A[:, j])\n if i == nbm2:\n pl.title('Distributions')\n pl.xticks(())\n pl.yticks(())\n\n pl.subplot(2, nbm, 1 + i + nbm)\n\n pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)')\n pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n if i == nbm - 1:\n pl.legend()\n if i == nbm2:\n pl.title('Barycenters')\n\n pl.xticks(())\n pl.yticks(())" + "nbm = len(problems)\nnbm2 = (nbm // 2)\n\n\npl.figure(2, (20, 6))\npl.clf()\n\nfor i in range(nbm):\n\n A = problems[i][0]\n bary_l2 = problems[i][1][0]\n bary_wass = problems[i][1][1]\n bary_wass2 = problems[i][1][2]\n\n pl.subplot(2, nbm, 1 + i)\n for j in range(n_distributions):\n pl.plot(x, A[:, j])\n if i == nbm2:\n pl.title('Distributions')\n pl.xticks(())\n pl.yticks(())\n\n pl.subplot(2, nbm, 1 + i + nbm)\n\n pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)')\n pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n if i == nbm - 1:\n pl.legend()\n if i == nbm2:\n pl.title('Barycenters')\n\n pl.xticks(())\n pl.yticks(())" ] } ], @@ -100,7 +184,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.py b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.py index b82765e..d7c72d0 100644 --- a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.py +++ b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.py @@ -102,7 +102,7 @@ pl.tight_layout() problems.append([A, [bary_l2, bary_wass, bary_wass2]]) ############################################################################## -# Dirac Data +# Stair Data # ---------- #%% parameters @@ -168,6 +168,11 @@ pl.legend() pl.title('Barycenters') pl.tight_layout() + +############################################################################## +# Dirac Data +# ---------- + #%% parameters a1 = np.zeros(n) diff --git a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.rst b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.rst index bd1c710..5e83fbf 100644 --- a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.rst +++ b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_barycenter_lp_vs_entropic.py: + .. _sphx_glr_auto_examples_plot_barycenter_lp_vs_entropic.py: ================================================================================= @@ -20,8 +26,7 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. - -.. code-block:: python +.. code-block:: default # Author: Remi Flamary @@ -43,15 +48,13 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + Gaussian Data ------------- +.. code-block:: default -.. code-block:: python - - - #%% parameters problems = [] @@ -74,7 +77,16 @@ Gaussian Data M /= M.max() - #%% plot the distributions + + + + + + + + +.. code-block:: default + pl.figure(1, figsize=(6.4, 3)) for i in range(n_distributions): @@ -82,7 +94,19 @@ Gaussian Data pl.title('Distributions') pl.tight_layout() - #%% barycenter computation + + + +.. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_001.png + :class: sphx-glr-single-img + + + + + + +.. code-block:: default + alpha = 0.5 # 0<=alpha<=1 weights = np.array([1 - alpha, alpha]) @@ -121,62 +145,55 @@ Gaussian Data -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_001.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_002.png - :scale: 47 +.. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_002.png + :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out - Out:: + Out: + + .. code-block:: none - Elapsed time : 0.010712385177612305 s + Elapsed time : 0.0049059391021728516 s Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective 1.0 1.0 1.0 - 1.0 1700.336700337 - 0.006776453137632 0.006776453137633 0.006776453137633 0.9932238647293 0.006776453137633 125.6700527543 - 0.004018712867874 0.004018712867874 0.004018712867874 0.4301142633 0.004018712867874 12.26594150093 - 0.001172775061627 0.001172775061627 0.001172775061627 0.7599932455029 0.001172775061627 0.3378536968897 - 0.0004375137005385 0.0004375137005385 0.0004375137005385 0.6422331807989 0.0004375137005385 0.1468420566358 - 0.000232669046734 0.0002326690467341 0.000232669046734 0.5016999460893 0.000232669046734 0.09381703231432 - 7.430121674303e-05 7.430121674303e-05 7.430121674303e-05 0.7035962305812 7.430121674303e-05 0.0577787025717 - 5.321227838876e-05 5.321227838875e-05 5.321227838876e-05 0.308784186441 5.321227838876e-05 0.05266249477203 - 1.990900379199e-05 1.990900379196e-05 1.990900379199e-05 0.6520472013244 1.990900379199e-05 0.04526054405519 - 6.305442046799e-06 6.30544204682e-06 6.3054420468e-06 0.7073953304075 6.305442046798e-06 0.04237597591383 - 2.290148391577e-06 2.290148391582e-06 2.290148391578e-06 0.6941812711492 2.29014839159e-06 0.041522849321 - 1.182864875387e-06 1.182864875406e-06 1.182864875427e-06 0.508455204675 1.182864875445e-06 0.04129461872827 - 3.626786381529e-07 3.626786382468e-07 3.626786382923e-07 0.7101651572101 3.62678638267e-07 0.04113032448923 - 1.539754244902e-07 1.539754249276e-07 1.539754249356e-07 0.6279322066282 1.539754253892e-07 0.04108867636379 - 5.193221323143e-08 5.193221463044e-08 5.193221462729e-08 0.6843453436759 5.193221708199e-08 0.04106859618414 - 1.888205054507e-08 1.888204779723e-08 1.88820477688e-08 0.6673444085651 1.888205650952e-08 0.041062141752 - 5.676855206925e-09 5.676854518888e-09 5.676854517651e-09 0.7281705804232 5.676885442702e-09 0.04105958648713 - 3.501157668218e-09 3.501150243546e-09 3.501150216347e-09 0.414020345194 3.501164437194e-09 0.04105916265261 - 1.110594251499e-09 1.110590786827e-09 1.11059083379e-09 0.6998954759911 1.110636623476e-09 0.04105870073485 - 5.770971626386e-10 5.772456113791e-10 5.772456200156e-10 0.4999769658132 5.77013379477e-10 0.04105859769135 - 1.535218204536e-10 1.536993317032e-10 1.536992771966e-10 0.7516471627141 1.536205005991e-10 0.04105851679958 - 6.724209350756e-11 6.739211232927e-11 6.739210470901e-11 0.5944802416166 6.735465384341e-11 0.04105850033766 - 1.743382199199e-11 1.736445896691e-11 1.736448490761e-11 0.7573407808104 1.734254328931e-11 0.04105849088824 + 0.006776453137632 0.006776453137632 0.006776453137632 0.9932238647293 0.006776453137632 125.6700527543 + 0.004018712867873 0.004018712867873 0.004018712867873 0.4301142633001 0.004018712867873 12.26594150092 + 0.001172775061627 0.001172775061627 0.001172775061627 0.7599932455027 0.001172775061627 0.3378536968898 + 0.0004375137005386 0.0004375137005386 0.0004375137005386 0.6422331807989 0.0004375137005386 0.1468420566359 + 0.0002326690467339 0.0002326690467339 0.0002326690467339 0.5016999460898 0.0002326690467339 0.09381703231428 + 7.430121674299e-05 7.4301216743e-05 7.430121674299e-05 0.7035962305811 7.430121674299e-05 0.05777870257169 + 5.321227838943e-05 5.321227838945e-05 5.321227838944e-05 0.3087841864307 5.321227838944e-05 0.05266249477219 + 1.990900379216e-05 1.99090037922e-05 1.990900379216e-05 0.6520472013271 1.990900379216e-05 0.04526054405523 + 6.305442046834e-06 6.305442046856e-06 6.305442046837e-06 0.7073953304085 6.305442046837e-06 0.04237597591384 + 2.290148391591e-06 2.290148391631e-06 2.290148391602e-06 0.6941812711476 2.29014839161e-06 0.04152284932101 + 1.182864875578e-06 1.182864875548e-06 1.182864875555e-06 0.5084552046229 1.182864875567e-06 0.04129461872829 + 3.626786386894e-07 3.626786386985e-07 3.626786386845e-07 0.7101651569095 3.626786385995e-07 0.0411303244893 + 1.539754244475e-07 1.539754247164e-07 1.539754247197e-07 0.6279322077522 1.539754251915e-07 0.04108867636377 + 5.193221608537e-08 5.19322169648e-08 5.193221696942e-08 0.6843453280956 5.193221892276e-08 0.04106859618454 + 1.888205219929e-08 1.88820500654e-08 1.888205006369e-08 0.6673443828803 1.888205852187e-08 0.04106214175236 + 5.676837529301e-09 5.676842740457e-09 5.676842761502e-09 0.7281712198286 5.676877044229e-09 0.04105958648535 + 3.501170987746e-09 3.501167688027e-09 3.501167721804e-09 0.4140142115019 3.501183058995e-09 0.04105916265728 + 1.110582426269e-09 1.110580273241e-09 1.110580239523e-09 0.6999003212726 1.110624310022e-09 0.04105870073273 + 5.768753963318e-10 5.769422203363e-10 5.769421938248e-10 0.5002521235315 5.767522037401e-10 0.04105859764872 + 1.534102102874e-10 1.535920569433e-10 1.535921107494e-10 0.7516900610544 1.535251083958e-10 0.04105851678411 + 6.717475002202e-11 6.735435784522e-11 6.735430717133e-11 0.5944268235824 6.732253215483e-11 0.04105850033323 + 1.751321118837e-11 1.74043080851e-11 1.740429001123e-11 0.7566075167358 1.736956306927e-11 0.0410584908946 Optimization terminated successfully. - Elapsed time : 2.883899211883545 s + Current function value: 0.041058 + Iterations: 22 + Elapsed time : 2.149055242538452 s -Dirac Data ----------- +Stair Data +---------- -.. code-block:: python +.. code-block:: default - #%% parameters a1 = 1.0 * (x > 10) * (x < 50) a2 = 1.0 * (x > 60) * (x < 80) @@ -193,7 +210,16 @@ Dirac Data M /= M.max() - #%% plot the distributions + + + + + + + + +.. code-block:: default + pl.figure(1, figsize=(6.4, 3)) for i in range(n_distributions): @@ -202,7 +228,19 @@ Dirac Data pl.tight_layout() - #%% barycenter computation + + + +.. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_003.png + :class: sphx-glr-single-img + + + + + + +.. code-block:: default + alpha = 0.5 # 0<=alpha<=1 weights = np.array([1 - alpha, alpha]) @@ -239,7 +277,50 @@ Dirac Data pl.title('Barycenters') pl.tight_layout() - #%% parameters + + + + +.. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_004.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + Elapsed time : 0.008316993713378906 s + Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective + 1.0 1.0 1.0 - 1.0 1700.336700337 + 0.006776466288938 0.006776466288938 0.006776466288938 0.9932238515788 0.006776466288938 125.66492558 + 0.004036918865472 0.004036918865472 0.004036918865472 0.4272973099325 0.004036918865472 12.347161701 + 0.001219232687076 0.001219232687076 0.001219232687076 0.7496986855957 0.001219232687076 0.3243835647418 + 0.0003837422984467 0.0003837422984467 0.0003837422984467 0.6926882608271 0.0003837422984467 0.1361719397498 + 0.0001070128410194 0.0001070128410194 0.0001070128410194 0.7643889137854 0.0001070128410194 0.07581952832542 + 0.0001001275033713 0.0001001275033714 0.0001001275033713 0.07058704838615 0.0001001275033713 0.07347394936346 + 4.550897507807e-05 4.550897507807e-05 4.550897507807e-05 0.576117248486 4.550897507807e-05 0.05555077655034 + 8.557124125834e-06 8.557124125853e-06 8.557124125835e-06 0.853592544106 8.557124125835e-06 0.0443981466023 + 3.611995628666e-06 3.611995628643e-06 3.611995628672e-06 0.6002277331398 3.611995628673e-06 0.0428300776216 + 7.590393750111e-07 7.590393750273e-07 7.590393750129e-07 0.8221486533655 7.590393750133e-07 0.04192322976247 + 8.299929287077e-08 8.299929283415e-08 8.299929287126e-08 0.901746793884 8.299929287181e-08 0.04170825633295 + 3.117560207452e-10 3.117560192413e-10 3.117560199213e-10 0.9970399692253 3.117560200234e-10 0.04168179329766 + 1.559774508975e-14 1.559825507727e-14 1.559755309294e-14 0.9999499686993 1.559748033629e-14 0.04168169240444 + Optimization terminated successfully. + Current function value: 0.041682 + Iterations: 13 + Elapsed time : 2.0333712100982666 s + + + + +Dirac Data +---------- + + +.. code-block:: default + a1 = np.zeros(n) a2 = np.zeros(n) @@ -262,7 +343,16 @@ Dirac Data M /= M.max() - #%% plot the distributions + + + + + + + + +.. code-block:: default + pl.figure(1, figsize=(6.4, 3)) for i in range(n_distributions): @@ -271,7 +361,19 @@ Dirac Data pl.tight_layout() - #%% barycenter computation + + + +.. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_005.png + :class: sphx-glr-single-img + + + + + + +.. code-block:: default + alpha = 0.5 # 0<=alpha<=1 weights = np.array([1 - alpha, alpha]) @@ -312,70 +414,45 @@ Dirac Data -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_003.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_004.png - :scale: 47 +.. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_006.png + :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out - Out:: + Out: - Elapsed time : 0.014938592910766602 s - Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective - 1.0 1.0 1.0 - 1.0 1700.336700337 - 0.006776466288966 0.006776466288966 0.006776466288966 0.9932238515788 0.006776466288966 125.6649255808 - 0.004036918865495 0.004036918865495 0.004036918865495 0.4272973099316 0.004036918865495 12.3471617011 - 0.00121923268707 0.00121923268707 0.00121923268707 0.749698685599 0.00121923268707 0.3243835647408 - 0.0003837422984432 0.0003837422984432 0.0003837422984432 0.6926882608284 0.0003837422984432 0.1361719397493 - 0.0001070128410183 0.0001070128410183 0.0001070128410183 0.7643889137854 0.0001070128410183 0.07581952832518 - 0.0001001275033711 0.0001001275033711 0.0001001275033711 0.07058704837812 0.0001001275033712 0.0734739493635 - 4.550897507844e-05 4.550897507841e-05 4.550897507844e-05 0.5761172484828 4.550897507845e-05 0.05555077655047 - 8.557124125522e-06 8.5571241255e-06 8.557124125522e-06 0.8535925441152 8.557124125522e-06 0.04439814660221 - 3.611995628407e-06 3.61199562841e-06 3.611995628414e-06 0.6002277331554 3.611995628415e-06 0.04283007762152 - 7.590393750365e-07 7.590393750491e-07 7.590393750378e-07 0.8221486533416 7.590393750381e-07 0.04192322976248 - 8.299929287441e-08 8.299929286079e-08 8.299929287532e-08 0.9017467938799 8.29992928758e-08 0.04170825633295 - 3.117560203449e-10 3.117560130137e-10 3.11756019954e-10 0.997039969226 3.11756019952e-10 0.04168179329766 - 1.559749653711e-14 1.558073160926e-14 1.559756940692e-14 0.9999499686183 1.559750643989e-14 0.04168169240444 - Optimization terminated successfully. - Elapsed time : 2.642659902572632 s - Elapsed time : 0.002908945083618164 s + .. code-block:: none + + Elapsed time : 0.001787424087524414 s Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective 1.0 1.0 1.0 - 1.0 1700.336700337 - 0.006774675520727 0.006774675520727 0.006774675520727 0.9932256422636 0.006774675520727 125.6956034743 - 0.002048208707562 0.002048208707562 0.002048208707562 0.7343095368143 0.002048208707562 5.213991622123 - 0.000269736547478 0.0002697365474781 0.0002697365474781 0.8839403501193 0.000269736547478 0.505938390389 - 6.832109993943e-05 6.832109993944e-05 6.832109993944e-05 0.7601171075965 6.832109993943e-05 0.2339657807272 - 2.437682932219e-05 2.43768293222e-05 2.437682932219e-05 0.6663448297475 2.437682932219e-05 0.1471256246325 - 1.13498321631e-05 1.134983216308e-05 1.13498321631e-05 0.5553643816404 1.13498321631e-05 0.1181584941171 - 3.342312725885e-06 3.342312725884e-06 3.342312725885e-06 0.7238133571615 3.342312725885e-06 0.1006387519747 - 7.078561231603e-07 7.078561231509e-07 7.078561231604e-07 0.8033142552512 7.078561231603e-07 0.09474734646269 - 1.966870956916e-07 1.966870954537e-07 1.966870954468e-07 0.752547917788 1.966870954633e-07 0.09354342735766 - 4.19989524849e-10 4.199895164852e-10 4.199895238758e-10 0.9984019849375 4.19989523951e-10 0.09310367785861 - 2.101015938666e-14 2.100625691113e-14 2.101023853438e-14 0.999949974425 2.101023691864e-14 0.09310274466458 + 0.00677467552072 0.006774675520719 0.006774675520719 0.9932256422636 0.006774675520719 125.6956034741 + 0.002048208707556 0.002048208707555 0.002048208707555 0.734309536815 0.002048208707555 5.213991622102 + 0.0002697365474791 0.0002697365474791 0.0002697365474791 0.8839403501183 0.0002697365474791 0.5059383903908 + 6.832109993919e-05 6.832109993918e-05 6.832109993918e-05 0.7601171075982 6.832109993918e-05 0.2339657807271 + 2.437682932221e-05 2.437682932221e-05 2.437682932221e-05 0.6663448297463 2.437682932221e-05 0.1471256246325 + 1.134983216308e-05 1.134983216308e-05 1.134983216308e-05 0.5553643816417 1.134983216308e-05 0.1181584941171 + 3.342312725863e-06 3.34231272585e-06 3.342312725863e-06 0.7238133571629 3.342312725863e-06 0.1006387519746 + 7.078561231536e-07 7.078561231537e-07 7.078561231535e-07 0.803314255252 7.078561231535e-07 0.09474734646268 + 1.966870949422e-07 1.966870952674e-07 1.966870952717e-07 0.7525479180433 1.966870953014e-07 0.09354342735758 + 4.199895266495e-10 4.199895367352e-10 4.19989526535e-10 0.9984019849265 4.199895265747e-10 0.09310367785861 + 2.101053559204e-14 2.100331212975e-14 2.101054034304e-14 0.9999499736903 2.101053604307e-14 0.09310274466458 Optimization terminated successfully. - Elapsed time : 2.690450668334961 s + Current function value: 0.093103 + Iterations: 11 + Elapsed time : 2.1853578090667725 s -Final figure ------------- +Final figure +------------ -.. code-block:: python +.. code-block:: default - #%% plot nbm = len(problems) nbm2 = (nbm // 2) @@ -414,28 +491,36 @@ Final figure -.. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_006.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_007.png + :class: sphx-glr-single-img + + -**Total running time of the script:** ( 0 minutes 8.892 seconds) +.. rst-class:: sphx-glr-timing + **Total running time of the script:** ( 0 minutes 7.697 seconds) + + +.. _sphx_glr_download_auto_examples_plot_barycenter_lp_vs_entropic.py: .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_barycenter_lp_vs_entropic.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_barycenter_lp_vs_entropic.ipynb ` @@ -444,4 +529,4 @@ Final figure .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_compute_emd.ipynb b/docs/source/auto_examples/plot_compute_emd.ipynb index 562eff8..24a2fff 100644 --- a/docs/source/auto_examples/plot_compute_emd.ipynb +++ b/docs/source/auto_examples/plot_compute_emd.ipynb @@ -44,7 +44,7 @@ }, "outputs": [], "source": [ - "#%% parameters\n\nn = 100 # nb bins\nn_target = 50 # nb target distributions\n\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\nlst_m = np.linspace(20, 90, n_target)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\n\nB = np.zeros((n, n_target))\n\nfor i, m in enumerate(lst_m):\n B[:, i] = gauss(n, m=m, s=5)\n\n# loss matrix and normalization\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean')\nM /= M.max()\nM2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean')\nM2 /= M2.max()" + "n = 100 # nb bins\nn_target = 50 # nb target distributions\n\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\nlst_m = np.linspace(20, 90, n_target)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\n\nB = np.zeros((n, n_target))\n\nfor i, m in enumerate(lst_m):\n B[:, i] = gauss(n, m=m, s=5)\n\n# loss matrix and normalization\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean')\nM /= M.max()\nM2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean')\nM2 /= M2.max()" ] }, { @@ -62,7 +62,7 @@ }, "outputs": [], "source": [ - "#%% plot the distributions\n\npl.figure(1)\npl.subplot(2, 1, 1)\npl.plot(x, a, 'b', label='Source distribution')\npl.title('Source distribution')\npl.subplot(2, 1, 2)\npl.plot(x, B, label='Target distributions')\npl.title('Target distributions')\npl.tight_layout()" + "pl.figure(1)\npl.subplot(2, 1, 1)\npl.plot(x, a, 'b', label='Source distribution')\npl.title('Source distribution')\npl.subplot(2, 1, 2)\npl.plot(x, B, label='Target distributions')\npl.title('Target distributions')\npl.tight_layout()" ] }, { @@ -80,7 +80,7 @@ }, "outputs": [], "source": [ - "#%% Compute and plot distributions and loss matrix\n\nd_emd = ot.emd2(a, B, M) # direct computation of EMD\nd_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2\n\n\npl.figure(2)\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.title('EMD distances')\npl.legend()" + "d_emd = ot.emd2(a, B, M) # direct computation of EMD\nd_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2\n\n\npl.figure(2)\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.title('EMD distances')\npl.legend()" ] }, { @@ -98,7 +98,7 @@ }, "outputs": [], "source": [ - "#%%\nreg = 1e-2\nd_sinkhorn = ot.sinkhorn2(a, B, M, reg)\nd_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg)\n\npl.figure(2)\npl.clf()\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn')\npl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn')\npl.title('EMD distances')\npl.legend()\n\npl.show()" + "reg = 1e-2\nd_sinkhorn = ot.sinkhorn2(a, B, M, reg)\nd_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg)\n\npl.figure(2)\npl.clf()\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn')\npl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn')\npl.title('EMD distances')\npl.legend()\n\npl.show()" ] } ], @@ -118,7 +118,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_compute_emd.rst b/docs/source/auto_examples/plot_compute_emd.rst index 27bca2c..e4cc143 100644 --- a/docs/source/auto_examples/plot_compute_emd.rst +++ b/docs/source/auto_examples/plot_compute_emd.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_compute_emd.py: + .. _sphx_glr_auto_examples_plot_compute_emd.py: ================= @@ -13,8 +19,7 @@ ground metrics and plot their values for diffeent distributions. - -.. code-block:: python +.. code-block:: default # Author: Remi Flamary @@ -33,15 +38,13 @@ ground metrics and plot their values for diffeent distributions. + Generate data ------------- +.. code-block:: default -.. code-block:: python - - - #%% parameters n = 100 # nb bins n_target = 50 # nb target distributions @@ -72,16 +75,14 @@ Generate data + Plot data --------- - -.. code-block:: python +.. code-block:: default - #%% plot the distributions - pl.figure(1) pl.subplot(2, 1, 1) pl.plot(x, a, 'b', label='Source distribution') @@ -96,7 +97,8 @@ Plot data .. image:: /auto_examples/images/sphx_glr_plot_compute_emd_001.png - :align: center + :class: sphx-glr-single-img + @@ -105,11 +107,8 @@ Compute EMD for the different losses ------------------------------------ +.. code-block:: default -.. code-block:: python - - - #%% Compute and plot distributions and loss matrix d_emd = ot.emd2(a, B, M) # direct computation of EMD d_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2 @@ -124,21 +123,27 @@ Compute EMD for the different losses -.. image:: /auto_examples/images/sphx_glr_plot_compute_emd_003.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_compute_emd_002.png + :class: sphx-glr-single-img +.. rst-class:: sphx-glr-script-out + Out: -Compute Sinkhorn for the different losses ------------------------------------------ + .. code-block:: none + -.. code-block:: python - #%% +Compute Sinkhorn for the different losses +----------------------------------------- + + +.. code-block:: default + reg = 1e-2 d_sinkhorn = ot.sinkhorn2(a, B, M, reg) d_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg) @@ -156,28 +161,45 @@ Compute Sinkhorn for the different losses -.. image:: /auto_examples/images/sphx_glr_plot_compute_emd_004.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_compute_emd_003.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + Out: + .. code-block:: none + /home/rflamary/PYTHON/POT/examples/plot_compute_emd.py:102: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() -**Total running time of the script:** ( 0 minutes 0.446 seconds) + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 0.436 seconds) + + +.. _sphx_glr_download_auto_examples_plot_compute_emd.py: + + .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_compute_emd.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_compute_emd.ipynb ` @@ -186,4 +208,4 @@ Compute Sinkhorn for the different losses .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_convolutional_barycenter.ipynb b/docs/source/auto_examples/plot_convolutional_barycenter.ipynb index 4981ba3..f94a32e 100644 --- a/docs/source/auto_examples/plot_convolutional_barycenter.ipynb +++ b/docs/source/auto_examples/plot_convolutional_barycenter.ipynb @@ -82,7 +82,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_convolutional_barycenter.rst b/docs/source/auto_examples/plot_convolutional_barycenter.rst index a28db2f..9c9a596 100644 --- a/docs/source/auto_examples/plot_convolutional_barycenter.rst +++ b/docs/source/auto_examples/plot_convolutional_barycenter.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_convolutional_barycenter.py: + .. _sphx_glr_auto_examples_plot_convolutional_barycenter.py: ============================================ @@ -11,8 +17,7 @@ This example is designed to illustrate how the Convolutional Wasserstein Barycen function of POT works. - -.. code-block:: python +.. code-block:: default # Author: Nicolas Courty @@ -30,14 +35,14 @@ function of POT works. + Data preparation ---------------- The four distributions are constructed from 4 simple images - -.. code-block:: python +.. code-block:: default @@ -73,13 +78,13 @@ The four distributions are constructed from 4 simple images + Barycenter computation and visualization ---------------------------------------- - -.. code-block:: python +.. code-block:: default pl.figure(figsize=(10, 10)) @@ -119,27 +124,44 @@ Barycenter computation and visualization .. image:: /auto_examples/images/sphx_glr_plot_convolutional_barycenter_001.png - :align: center + :class: sphx-glr-single-img + +.. rst-class:: sphx-glr-script-out + Out: + .. code-block:: none -**Total running time of the script:** ( 1 minutes 11.608 seconds) + /home/rflamary/PYTHON/POT/examples/plot_convolutional_barycenter.py:92: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 34.615 seconds) + + +.. _sphx_glr_download_auto_examples_plot_convolutional_barycenter.py: + + .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_convolutional_barycenter.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_convolutional_barycenter.ipynb ` @@ -148,4 +170,4 @@ Barycenter computation and visualization .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_fgw.ipynb b/docs/source/auto_examples/plot_fgw.ipynb index 1b150bd..20c0a3f 100644 --- a/docs/source/auto_examples/plot_fgw.ipynb +++ b/docs/source/auto_examples/plot_fgw.ipynb @@ -36,6 +36,13 @@ "Generate data\n---------\n\n" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We create two 1D random measures\n\n" + ] + }, { "cell_type": "code", "execution_count": null, @@ -44,7 +51,7 @@ }, "outputs": [], "source": [ - "#%% parameters\n# We create two 1D random measures\nn = 20 # number of points in the first distribution\nn2 = 30 # number of points in the second distribution\nsig = 1 # std of first distribution\nsig2 = 0.1 # std of second distribution\n\nnp.random.seed(0)\n\nphi = np.arange(n)[:, None]\nxs = phi + sig * np.random.randn(n, 1)\nys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1)\n\nphi2 = np.arange(n2)[:, None]\nxt = phi2 + sig * np.random.randn(n2, 1)\nyt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1)\nyt = yt[::-1, :]\n\np = ot.unif(n)\nq = ot.unif(n2)" + "n = 20 # number of points in the first distribution\nn2 = 30 # number of points in the second distribution\nsig = 1 # std of first distribution\nsig2 = 0.1 # std of second distribution\n\nnp.random.seed(0)\n\nphi = np.arange(n)[:, None]\nxs = phi + sig * np.random.randn(n, 1)\nys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1)\n\nphi2 = np.arange(n2)[:, None]\nxt = phi2 + sig * np.random.randn(n2, 1)\nyt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1)\nyt = yt[::-1, :]\n\np = ot.unif(n)\nq = ot.unif(n2)" ] }, { @@ -62,7 +69,7 @@ }, "outputs": [], "source": [ - "#%% plot the distributions\n\npl.close(10)\npl.figure(10, (7, 7))\n\npl.subplot(2, 1, 1)\n\npl.scatter(ys, xs, c=phi, s=70)\npl.ylabel('Feature value a', fontsize=20)\npl.title('$\\mu=\\sum_i \\delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1)\npl.xticks(())\npl.yticks(())\npl.subplot(2, 1, 2)\npl.scatter(yt, xt, c=phi2, s=70)\npl.xlabel('coordinates x/y', fontsize=25)\npl.ylabel('Feature value b', fontsize=20)\npl.title('$\\\\nu=\\sum_j \\delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1)\npl.yticks(())\npl.tight_layout()\npl.show()" + "pl.close(10)\npl.figure(10, (7, 7))\n\npl.subplot(2, 1, 1)\n\npl.scatter(ys, xs, c=phi, s=70)\npl.ylabel('Feature value a', fontsize=20)\npl.title('$\\mu=\\sum_i \\delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1)\npl.xticks(())\npl.yticks(())\npl.subplot(2, 1, 2)\npl.scatter(yt, xt, c=phi2, s=70)\npl.xlabel('coordinates x/y', fontsize=25)\npl.ylabel('Feature value b', fontsize=20)\npl.title('$\\\\nu=\\sum_j \\delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1)\npl.yticks(())\npl.tight_layout()\npl.show()" ] }, { @@ -80,7 +87,7 @@ }, "outputs": [], "source": [ - "#%% Structure matrices and across-features distance matrix\nC1 = ot.dist(xs)\nC2 = ot.dist(xt)\nM = ot.dist(ys, yt)\nw1 = ot.unif(C1.shape[0])\nw2 = ot.unif(C2.shape[0])\nGot = ot.emd([], [], M)" + "C1 = ot.dist(xs)\nC2 = ot.dist(xt)\nM = ot.dist(ys, yt)\nw1 = ot.unif(C1.shape[0])\nw2 = ot.unif(C2.shape[0])\nGot = ot.emd([], [], M)" ] }, { @@ -98,7 +105,7 @@ }, "outputs": [], "source": [ - "#%%\ncmap = 'Reds'\npl.close(10)\npl.figure(10, (5, 5))\nfs = 15\nl_x = [0, 5, 10, 15]\nl_y = [0, 5, 10, 15, 20, 25]\ngs = pl.GridSpec(5, 5)\n\nax1 = pl.subplot(gs[3:, :2])\n\npl.imshow(C1, cmap=cmap, interpolation='nearest')\npl.title(\"$C_1$\", fontsize=fs)\npl.xlabel(\"$k$\", fontsize=fs)\npl.ylabel(\"$i$\", fontsize=fs)\npl.xticks(l_x)\npl.yticks(l_x)\n\nax2 = pl.subplot(gs[:3, 2:])\n\npl.imshow(C2, cmap=cmap, interpolation='nearest')\npl.title(\"$C_2$\", fontsize=fs)\npl.ylabel(\"$l$\", fontsize=fs)\n#pl.ylabel(\"$l$\",fontsize=fs)\npl.xticks(())\npl.yticks(l_y)\nax2.set_aspect('auto')\n\nax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1)\npl.imshow(M, cmap=cmap, interpolation='nearest')\npl.yticks(l_x)\npl.xticks(l_y)\npl.ylabel(\"$i$\", fontsize=fs)\npl.title(\"$M_{AB}$\", fontsize=fs)\npl.xlabel(\"$j$\", fontsize=fs)\npl.tight_layout()\nax3.set_aspect('auto')\npl.show()" + "cmap = 'Reds'\npl.close(10)\npl.figure(10, (5, 5))\nfs = 15\nl_x = [0, 5, 10, 15]\nl_y = [0, 5, 10, 15, 20, 25]\ngs = pl.GridSpec(5, 5)\n\nax1 = pl.subplot(gs[3:, :2])\n\npl.imshow(C1, cmap=cmap, interpolation='nearest')\npl.title(\"$C_1$\", fontsize=fs)\npl.xlabel(\"$k$\", fontsize=fs)\npl.ylabel(\"$i$\", fontsize=fs)\npl.xticks(l_x)\npl.yticks(l_x)\n\nax2 = pl.subplot(gs[:3, 2:])\n\npl.imshow(C2, cmap=cmap, interpolation='nearest')\npl.title(\"$C_2$\", fontsize=fs)\npl.ylabel(\"$l$\", fontsize=fs)\n#pl.ylabel(\"$l$\",fontsize=fs)\npl.xticks(())\npl.yticks(l_y)\nax2.set_aspect('auto')\n\nax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1)\npl.imshow(M, cmap=cmap, interpolation='nearest')\npl.yticks(l_x)\npl.xticks(l_y)\npl.ylabel(\"$i$\", fontsize=fs)\npl.title(\"$M_{AB}$\", fontsize=fs)\npl.xlabel(\"$j$\", fontsize=fs)\npl.tight_layout()\nax3.set_aspect('auto')\npl.show()" ] }, { @@ -116,7 +123,7 @@ }, "outputs": [], "source": [ - "#%% Computing FGW and GW\nalpha = 1e-3\n\not.tic()\nGwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True)\not.toc()\n\n#%reload_ext WGW\nGg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True)" + "alpha = 1e-3\n\not.tic()\nGwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True)\not.toc()\n\n#%reload_ext WGW\nGg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True)" ] }, { @@ -134,7 +141,7 @@ }, "outputs": [], "source": [ - "#%% visu OT matrix\ncmap = 'Blues'\nfs = 15\npl.figure(2, (13, 5))\npl.clf()\npl.subplot(1, 3, 1)\npl.imshow(Got, cmap=cmap, interpolation='nearest')\n#pl.xlabel(\"$y$\",fontsize=fs)\npl.ylabel(\"$i$\", fontsize=fs)\npl.xticks(())\n\npl.title('Wasserstein ($M$ only)')\n\npl.subplot(1, 3, 2)\npl.imshow(Gg, cmap=cmap, interpolation='nearest')\npl.title('Gromov ($C_1,C_2$ only)')\npl.xticks(())\npl.subplot(1, 3, 3)\npl.imshow(Gwg, cmap=cmap, interpolation='nearest')\npl.title('FGW ($M+C_1,C_2$)')\n\npl.xlabel(\"$j$\", fontsize=fs)\npl.ylabel(\"$i$\", fontsize=fs)\n\npl.tight_layout()\npl.show()" + "cmap = 'Blues'\nfs = 15\npl.figure(2, (13, 5))\npl.clf()\npl.subplot(1, 3, 1)\npl.imshow(Got, cmap=cmap, interpolation='nearest')\n#pl.xlabel(\"$y$\",fontsize=fs)\npl.ylabel(\"$i$\", fontsize=fs)\npl.xticks(())\n\npl.title('Wasserstein ($M$ only)')\n\npl.subplot(1, 3, 2)\npl.imshow(Gg, cmap=cmap, interpolation='nearest')\npl.title('Gromov ($C_1,C_2$ only)')\npl.xticks(())\npl.subplot(1, 3, 3)\npl.imshow(Gwg, cmap=cmap, interpolation='nearest')\npl.title('FGW ($M+C_1,C_2$)')\n\npl.xlabel(\"$j$\", fontsize=fs)\npl.ylabel(\"$i$\", fontsize=fs)\n\npl.tight_layout()\npl.show()" ] } ], @@ -154,7 +161,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.8" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_fgw.rst b/docs/source/auto_examples/plot_fgw.rst index aec725d..1c81d10 100644 --- a/docs/source/auto_examples/plot_fgw.rst +++ b/docs/source/auto_examples/plot_fgw.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_fgw.py: + .. _sphx_glr_auto_examples_plot_fgw.py: ============================== @@ -16,8 +22,7 @@ This example illustrates the computation of FGW for 1D measures[18]. - -.. code-block:: python +.. code-block:: default # Author: Titouan Vayer @@ -35,16 +40,15 @@ This example illustrates the computation of FGW for 1D measures[18]. + Generate data --------- +We create two 1D random measures -.. code-block:: python - +.. code-block:: default - #%% parameters - # We create two 1D random measures n = 20 # number of points in the first distribution n2 = 30 # number of points in the second distribution sig = 1 # std of first distribution @@ -70,15 +74,13 @@ Generate data + Plot data --------- +.. code-block:: default -.. code-block:: python - - - #%% plot the distributions pl.close(10) pl.figure(10, (7, 7)) @@ -102,21 +104,28 @@ Plot data -.. image:: /auto_examples/images/sphx_glr_plot_fgw_010.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_fgw_001.png + :class: sphx-glr-single-img +.. rst-class:: sphx-glr-script-out + Out: -Create structure matrices and across-feature distance matrix ---------- + .. code-block:: none + /home/rflamary/PYTHON/POT/examples/plot_fgw.py:73: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() -.. code-block:: python - #%% Structure matrices and across-features distance matrix +Create structure matrices and across-feature distance matrix +--------- + + +.. code-block:: default + C1 = ot.dist(xs) C2 = ot.dist(xt) M = ot.dist(ys, yt) @@ -130,15 +139,13 @@ Create structure matrices and across-feature distance matrix + Plot matrices --------- +.. code-block:: default -.. code-block:: python - - - #%% cmap = 'Reds' pl.close(10) pl.figure(10, (5, 5)) @@ -180,21 +187,28 @@ Plot matrices -.. image:: /auto_examples/images/sphx_glr_plot_fgw_011.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_fgw_002.png + :class: sphx-glr-single-img +.. rst-class:: sphx-glr-script-out + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_fgw.py:128: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() -Compute FGW/GW ---------- -.. code-block:: python +Compute FGW/GW +--------- + +.. code-block:: default - #%% Computing FGW and GW alpha = 1e-3 ot.tic() @@ -210,15 +224,17 @@ Compute FGW/GW .. rst-class:: sphx-glr-script-out - Out:: + Out: + + .. code-block:: none It. |Loss |Relative loss|Absolute loss ------------------------------------------------ - 0|4.734462e+01|0.000000e+00|0.000000e+00 - 1|2.508258e+01|8.875498e-01|2.226204e+01 - 2|2.189329e+01|1.456747e-01|3.189297e+00 - 3|2.189329e+01|0.000000e+00|0.000000e+00 - Elapsed time : 0.0016989707946777344 s + 0|4.734412e+01|0.000000e+00|0.000000e+00 + 1|2.508254e+01|8.875326e-01|2.226158e+01 + 2|2.189327e+01|1.456740e-01|3.189279e+00 + 3|2.189327e+01|0.000000e+00|0.000000e+00 + Elapsed time : 0.0023026466369628906 s It. |Loss |Relative loss|Absolute loss ------------------------------------------------ 0|4.683978e+04|0.000000e+00|0.000000e+00 @@ -227,15 +243,14 @@ Compute FGW/GW 3|2.182948e+04|0.000000e+00|0.000000e+00 -Visualize transport matrices ---------- +Visualize transport matrices +--------- -.. code-block:: python +.. code-block:: default - #%% visu OT matrix cmap = 'Blues' fs = 15 pl.figure(2, (13, 5)) @@ -264,28 +279,45 @@ Visualize transport matrices -.. image:: /auto_examples/images/sphx_glr_plot_fgw_004.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_fgw_003.png + :class: sphx-glr-single-img +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_fgw.py:173: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() + -**Total running time of the script:** ( 0 minutes 1.468 seconds) +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 1.184 seconds) + + +.. _sphx_glr_download_auto_examples_plot_fgw.py: + .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_fgw.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_fgw.ipynb ` @@ -294,4 +326,4 @@ Visualize transport matrices .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_free_support_barycenter.ipynb b/docs/source/auto_examples/plot_free_support_barycenter.ipynb index 05a81c8..25ce60f 100644 --- a/docs/source/auto_examples/plot_free_support_barycenter.ipynb +++ b/docs/source/auto_examples/plot_free_support_barycenter.ipynb @@ -80,7 +80,7 @@ }, "outputs": [], "source": [ - "pl.figure(1)\nfor (x_i, b_i) in zip(measures_locations, measures_weights):\n color = np.random.randint(low=1, high=10 * N)\n pl.scatter(x_i[:, 0], x_i[:, 1], s=b * 1000, label='input measure')\npl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter')\npl.title('Data measures and their barycenter')\npl.legend(loc=0)\npl.show()" + "pl.figure(1)\nfor (x_i, b_i) in zip(measures_locations, measures_weights):\n color = np.random.randint(low=1, high=10 * N)\n pl.scatter(x_i[:, 0], x_i[:, 1], s=b_i * 1000, label='input measure')\npl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter')\npl.title('Data measures and their barycenter')\npl.legend(loc=0)\npl.show()" ] } ], @@ -100,7 +100,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_free_support_barycenter.py b/docs/source/auto_examples/plot_free_support_barycenter.py index b6efc59..64b89e4 100644 --- a/docs/source/auto_examples/plot_free_support_barycenter.py +++ b/docs/source/auto_examples/plot_free_support_barycenter.py @@ -62,7 +62,7 @@ X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, pl.figure(1) for (x_i, b_i) in zip(measures_locations, measures_weights): color = np.random.randint(low=1, high=10 * N) - pl.scatter(x_i[:, 0], x_i[:, 1], s=b * 1000, label='input measure') + pl.scatter(x_i[:, 0], x_i[:, 1], s=b_i * 1000, label='input measure') pl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter') pl.title('Data measures and their barycenter') pl.legend(loc=0) diff --git a/docs/source/auto_examples/plot_free_support_barycenter.rst b/docs/source/auto_examples/plot_free_support_barycenter.rst index d1b3b80..f349604 100644 --- a/docs/source/auto_examples/plot_free_support_barycenter.rst +++ b/docs/source/auto_examples/plot_free_support_barycenter.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_free_support_barycenter.py: + .. _sphx_glr_auto_examples_plot_free_support_barycenter.py: ==================================================== @@ -12,8 +18,7 @@ sum of diracs. - -.. code-block:: python +.. code-block:: default # Author: Vivien Seguy @@ -31,13 +36,13 @@ sum of diracs. + Generate data ------------- %% parameters and data generation - -.. code-block:: python +.. code-block:: default N = 3 d = 2 @@ -67,12 +72,12 @@ Generate data + Compute free support barycenter ------------- - -.. code-block:: python +.. code-block:: default k = 10 # number of Diracs of the barycenter @@ -88,18 +93,18 @@ Compute free support barycenter + Plot data --------- - -.. code-block:: python +.. code-block:: default pl.figure(1) for (x_i, b_i) in zip(measures_locations, measures_weights): color = np.random.randint(low=1, high=10 * N) - pl.scatter(x_i[:, 0], x_i[:, 1], s=b * 1000, label='input measure') + pl.scatter(x_i[:, 0], x_i[:, 1], s=b_i * 1000, label='input measure') pl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter') pl.title('Data measures and their barycenter') pl.legend(loc=0) @@ -108,27 +113,44 @@ Plot data .. image:: /auto_examples/images/sphx_glr_plot_free_support_barycenter_001.png - :align: center + :class: sphx-glr-single-img +.. rst-class:: sphx-glr-script-out + Out: -**Total running time of the script:** ( 0 minutes 0.129 seconds) + .. code-block:: none + /home/rflamary/PYTHON/POT/examples/plot_free_support_barycenter.py:69: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 0.080 seconds) + + +.. _sphx_glr_download_auto_examples_plot_free_support_barycenter.py: .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_free_support_barycenter.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_free_support_barycenter.ipynb ` @@ -137,4 +159,4 @@ Plot data .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_gromov.ipynb b/docs/source/auto_examples/plot_gromov.ipynb index dc1f179..e5a88e7 100644 --- a/docs/source/auto_examples/plot_gromov.ipynb +++ b/docs/source/auto_examples/plot_gromov.ipynb @@ -118,7 +118,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_gromov.rst b/docs/source/auto_examples/plot_gromov.rst index 3ed4e11..13d0d09 100644 --- a/docs/source/auto_examples/plot_gromov.rst +++ b/docs/source/auto_examples/plot_gromov.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_gromov.py: + .. _sphx_glr_auto_examples_plot_gromov.py: ========================== @@ -11,8 +17,7 @@ This example is designed to show how to use the Gromov-Wassertsein distance computation in POT. - -.. code-block:: python +.. code-block:: default # Author: Erwan Vautier @@ -32,6 +37,7 @@ computation in POT. + Sample two Gaussian distributions (2D and 3D) --------------------------------------------- @@ -40,8 +46,7 @@ do not belong to the same metric space. For demonstration purpose, we sample two Gaussian distributions in 2- and 3-dimensional spaces. - -.. code-block:: python +.. code-block:: default @@ -64,12 +69,12 @@ two Gaussian distributions in 2- and 3-dimensional spaces. + Plotting the distributions -------------------------- - -.. code-block:: python +.. code-block:: default @@ -84,7 +89,17 @@ Plotting the distributions .. image:: /auto_examples/images/sphx_glr_plot_gromov_001.png - :align: center + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_gromov.py:56: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() @@ -93,8 +108,7 @@ Compute distance kernels, normalize them and then display --------------------------------------------------------- - -.. code-block:: python +.. code-block:: default @@ -115,7 +129,17 @@ Compute distance kernels, normalize them and then display .. image:: /auto_examples/images/sphx_glr_plot_gromov_002.png - :align: center + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_gromov.py:75: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() @@ -124,8 +148,7 @@ Compute Gromov-Wasserstein plans and distance --------------------------------------------- - -.. code-block:: python +.. code-block:: default p = ot.unif(n_samples) @@ -157,52 +180,60 @@ Compute Gromov-Wasserstein plans and distance .. image:: /auto_examples/images/sphx_glr_plot_gromov_003.png - :align: center + :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out - Out:: - - It. |Loss |Delta loss - -------------------------------- - 0|4.328711e-02|0.000000e+00 - 1|2.281369e-02|-8.974178e-01 - 2|1.843659e-02|-2.374139e-01 - 3|1.602820e-02|-1.502598e-01 - 4|1.353712e-02|-1.840179e-01 - 5|1.285687e-02|-5.290977e-02 - 6|1.284537e-02|-8.952931e-04 - 7|1.284525e-02|-8.989584e-06 - 8|1.284525e-02|-8.989950e-08 - 9|1.284525e-02|-8.989949e-10 + Out: + + .. code-block:: none + + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 0|8.019265e-02|0.000000e+00|0.000000e+00 + 1|3.734805e-02|1.147171e+00|4.284460e-02 + 2|2.923853e-02|2.773572e-01|8.109516e-03 + 3|2.478957e-02|1.794691e-01|4.448961e-03 + 4|2.444720e-02|1.400444e-02|3.423693e-04 + 5|2.444720e-02|0.000000e+00|0.000000e+00 It. |Err ------------------- - 0|7.263293e-02| - 10|1.737784e-02| - 20|7.783978e-03| - 30|3.399419e-07| - 40|3.751207e-11| - Gromov-Wasserstein distances: 0.012845252089244688 - Entropic Gromov-Wasserstein distances: 0.013543882352191079 + 0|8.259147e-02| + 10|6.113732e-04| + 20|1.650651e-08| + 30|5.671192e-12| + Gromov-Wasserstein distances: 0.024447198979060496 + Entropic Gromov-Wasserstein distances: 0.02488439679981518 + /home/rflamary/PYTHON/POT/examples/plot_gromov.py:106: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() + + + -**Total running time of the script:** ( 0 minutes 1.916 seconds) +.. rst-class:: sphx-glr-timing + **Total running time of the script:** ( 0 minutes 0.999 seconds) + + +.. _sphx_glr_download_auto_examples_plot_gromov.py: .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_gromov.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_gromov.ipynb ` @@ -211,4 +242,4 @@ Compute Gromov-Wasserstein plans and distance .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_gromov_barycenter.ipynb b/docs/source/auto_examples/plot_gromov_barycenter.ipynb index 4c2f28f..17ba374 100644 --- a/docs/source/auto_examples/plot_gromov_barycenter.ipynb +++ b/docs/source/auto_examples/plot_gromov_barycenter.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -8,35 +9,35 @@ "outputs": [], "source": [ "%matplotlib inline" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "\n# Gromov-Wasserstein Barycenter example\n\n\nThis example is designed to show how to use the Gromov-Wasserstein distance\ncomputation in POT.\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "# Author: Erwan Vautier \n# Nicolas Courty \n#\n# License: MIT License\n\n\nimport numpy as np\nimport scipy as sp\n\nimport scipy.ndimage as spi\nimport matplotlib.pylab as pl\nfrom sklearn import manifold\nfrom sklearn.decomposition import PCA\n\nimport ot" - ], - "cell_type": "code" + "# Author: Erwan Vautier \n# Nicolas Courty \n#\n# License: MIT License\n\n\nimport numpy as np\nimport scipy as sp\n\nimport matplotlib.pylab as pl\nfrom sklearn import manifold\nfrom sklearn.decomposition import PCA\n\nimport ot" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Smacof MDS\n----------\n\nThis function allows to find an embedding of points given a dissimilarity matrix\nthat will be given by the output of the algorithm\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -44,35 +45,35 @@ "outputs": [], "source": [ "def smacof_mds(C, dim, max_iter=3000, eps=1e-9):\n \"\"\"\n Returns an interpolated point cloud following the dissimilarity matrix C\n using SMACOF multidimensional scaling (MDS) in specific dimensionned\n target space\n\n Parameters\n ----------\n C : ndarray, shape (ns, ns)\n dissimilarity matrix\n dim : int\n dimension of the targeted space\n max_iter : int\n Maximum number of iterations of the SMACOF algorithm for a single run\n eps : float\n relative tolerance w.r.t stress to declare converge\n\n Returns\n -------\n npos : ndarray, shape (R, dim)\n Embedded coordinates of the interpolated point cloud (defined with\n one isometry)\n \"\"\"\n\n rng = np.random.RandomState(seed=3)\n\n mds = manifold.MDS(\n dim,\n max_iter=max_iter,\n eps=1e-9,\n dissimilarity='precomputed',\n n_init=1)\n pos = mds.fit(C).embedding_\n\n nmds = manifold.MDS(\n 2,\n max_iter=max_iter,\n eps=1e-9,\n dissimilarity=\"precomputed\",\n random_state=rng,\n n_init=1)\n npos = nmds.fit_transform(C, init=pos)\n\n return npos" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Data preparation\n----------------\n\nThe four distributions are constructed from 4 simple images\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "def im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\nsquare = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256\ncross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256\ntriangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256\nstar = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256\n\nshapes = [square, cross, triangle, star]\n\nS = 4\nxs = [[] for i in range(S)]\n\n\nfor nb in range(4):\n for i in range(8):\n for j in range(8):\n if shapes[nb][i, j] < 0.95:\n xs[nb].append([j, 8 - i])\n\nxs = np.array([np.array(xs[0]), np.array(xs[1]),\n np.array(xs[2]), np.array(xs[3])])" - ], - "cell_type": "code" + "def im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\nsquare = pl.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256\ncross = pl.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256\ntriangle = pl.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256\nstar = pl.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256\n\nshapes = [square, cross, triangle, star]\n\nS = 4\nxs = [[] for i in range(S)]\n\n\nfor nb in range(4):\n for i in range(8):\n for j in range(8):\n if shapes[nb][i, j] < 0.95:\n xs[nb].append([j, 8 - i])\n\nxs = np.array([np.array(xs[0]), np.array(xs[1]),\n np.array(xs[2]), np.array(xs[3])])" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Barycenter computation\n----------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -80,17 +81,17 @@ "outputs": [], "source": [ "ns = [len(xs[s]) for s in range(S)]\nn_samples = 30\n\n\"\"\"Compute all distances matrices for the four shapes\"\"\"\nCs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)]\nCs = [cs / cs.max() for cs in Cs]\n\nps = [ot.unif(ns[s]) for s in range(S)]\np = ot.unif(n_samples)\n\n\nlambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]]\n\nCt01 = [0 for i in range(2)]\nfor i in range(2):\n Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]],\n [ps[0], ps[1]\n ], p, lambdast[i], 'square_loss', # 5e-4,\n max_iter=100, tol=1e-3)\n\nCt02 = [0 for i in range(2)]\nfor i in range(2):\n Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]],\n [ps[0], ps[2]\n ], p, lambdast[i], 'square_loss', # 5e-4,\n max_iter=100, tol=1e-3)\n\nCt13 = [0 for i in range(2)]\nfor i in range(2):\n Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]],\n [ps[1], ps[3]\n ], p, lambdast[i], 'square_loss', # 5e-4,\n max_iter=100, tol=1e-3)\n\nCt23 = [0 for i in range(2)]\nfor i in range(2):\n Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]],\n [ps[2], ps[3]\n ], p, lambdast[i], 'square_loss', # 5e-4,\n max_iter=100, tol=1e-3)" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Visualization\n-------------\n\nThe PCA helps in getting consistency between the rotations\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -98,29 +99,28 @@ "outputs": [], "source": [ "clf = PCA(n_components=2)\nnpos = [0, 0, 0, 0]\nnpos = [smacof_mds(Cs[s], 2) for s in range(S)]\n\nnpost01 = [0, 0]\nnpost01 = [smacof_mds(Ct01[s], 2) for s in range(2)]\nnpost01 = [clf.fit_transform(npost01[s]) for s in range(2)]\n\nnpost02 = [0, 0]\nnpost02 = [smacof_mds(Ct02[s], 2) for s in range(2)]\nnpost02 = [clf.fit_transform(npost02[s]) for s in range(2)]\n\nnpost13 = [0, 0]\nnpost13 = [smacof_mds(Ct13[s], 2) for s in range(2)]\nnpost13 = [clf.fit_transform(npost13[s]) for s in range(2)]\n\nnpost23 = [0, 0]\nnpost23 = [smacof_mds(Ct23[s], 2) for s in range(2)]\nnpost23 = [clf.fit_transform(npost23[s]) for s in range(2)]\n\n\nfig = pl.figure(figsize=(10, 10))\n\nax1 = pl.subplot2grid((4, 4), (0, 0))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r')\n\nax2 = pl.subplot2grid((4, 4), (0, 1))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b')\n\nax3 = pl.subplot2grid((4, 4), (0, 2))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b')\n\nax4 = pl.subplot2grid((4, 4), (0, 3))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r')\n\nax5 = pl.subplot2grid((4, 4), (1, 0))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b')\n\nax6 = pl.subplot2grid((4, 4), (1, 3))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b')\n\nax7 = pl.subplot2grid((4, 4), (2, 0))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b')\n\nax8 = pl.subplot2grid((4, 4), (2, 3))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b')\n\nax9 = pl.subplot2grid((4, 4), (3, 0))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r')\n\nax10 = pl.subplot2grid((4, 4), (3, 1))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b')\n\nax11 = pl.subplot2grid((4, 4), (3, 2))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b')\n\nax12 = pl.subplot2grid((4, 4), (3, 3))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r')" - ], - "cell_type": "code" + ] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python", "codemirror_mode": { "name": "ipython", "version": 3 }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", "nbconvert_exporter": "python", - "version": "3.5.2", "pygments_lexer": "ipython3", - "file_extension": ".py", - "mimetype": "text/x-python" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3", - "language": "python" + "version": "3.6.9" } }, - "nbformat_minor": 0, - "nbformat": 4 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_gromov_barycenter.py b/docs/source/auto_examples/plot_gromov_barycenter.py index 58fc51a..101c6c5 100644 --- a/docs/source/auto_examples/plot_gromov_barycenter.py +++ b/docs/source/auto_examples/plot_gromov_barycenter.py @@ -17,7 +17,6 @@ computation in POT. import numpy as np import scipy as sp -import scipy.ndimage as spi import matplotlib.pylab as pl from sklearn import manifold from sklearn.decomposition import PCA @@ -90,10 +89,10 @@ def im2mat(I): return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) -square = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256 -cross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256 -triangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256 -star = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256 +square = pl.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256 +cross = pl.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256 +triangle = pl.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256 +star = pl.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256 shapes = [square, cross, triangle, star] diff --git a/docs/source/auto_examples/plot_gromov_barycenter.rst b/docs/source/auto_examples/plot_gromov_barycenter.rst index 531ee22..995cca7 100644 --- a/docs/source/auto_examples/plot_gromov_barycenter.rst +++ b/docs/source/auto_examples/plot_gromov_barycenter.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_gromov_barycenter.py: + .. _sphx_glr_auto_examples_plot_gromov_barycenter.py: ===================================== @@ -11,8 +17,7 @@ This example is designed to show how to use the Gromov-Wasserstein distance computation in POT. - -.. code-block:: python +.. code-block:: default # Author: Erwan Vautier @@ -24,7 +29,6 @@ computation in POT. import numpy as np import scipy as sp - import scipy.ndimage as spi import matplotlib.pylab as pl from sklearn import manifold from sklearn.decomposition import PCA @@ -37,6 +41,7 @@ computation in POT. + Smacof MDS ---------- @@ -44,8 +49,7 @@ This function allows to find an embedding of points given a dissimilarity matrix that will be given by the output of the algorithm - -.. code-block:: python +.. code-block:: default @@ -101,14 +105,14 @@ that will be given by the output of the algorithm + Data preparation ---------------- The four distributions are constructed from 4 simple images - -.. code-block:: python +.. code-block:: default @@ -117,10 +121,10 @@ The four distributions are constructed from 4 simple images return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) - square = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256 - cross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256 - triangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256 - star = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256 + square = pl.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256 + cross = pl.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256 + triangle = pl.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256 + star = pl.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256 shapes = [square, cross, triangle, star] @@ -143,12 +147,12 @@ The four distributions are constructed from 4 simple images + Barycenter computation ---------------------- - -.. code-block:: python +.. code-block:: default @@ -200,14 +204,14 @@ Barycenter computation + Visualization ------------- The PCA helps in getting consistency between the rotations - -.. code-block:: python +.. code-block:: default @@ -297,27 +301,43 @@ The PCA helps in getting consistency between the rotations .. image:: /auto_examples/images/sphx_glr_plot_gromov_barycenter_001.png - :align: center + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + -**Total running time of the script:** ( 0 minutes 5.906 seconds) +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 1.747 seconds) + + +.. _sphx_glr_download_auto_examples_plot_gromov_barycenter.py: + .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_gromov_barycenter.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_gromov_barycenter.ipynb ` @@ -326,4 +346,4 @@ The PCA helps in getting consistency between the rotations .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_optim_OTreg.ipynb b/docs/source/auto_examples/plot_optim_OTreg.ipynb index 107c299..01e0689 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.ipynb +++ b/docs/source/auto_examples/plot_optim_OTreg.ipynb @@ -44,7 +44,7 @@ }, "outputs": [], "source": [ - "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\nb = ot.datasets.make_1D_gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" + "n = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\nb = ot.datasets.make_1D_gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" ] }, { @@ -62,7 +62,7 @@ }, "outputs": [], "source": [ - "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" + "G0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" ] }, { @@ -80,7 +80,7 @@ }, "outputs": [], "source": [ - "#%% Example with Frobenius norm regularization\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg = 1e-1\n\nGl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(3)\not.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg')" + "def f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg = 1e-1\n\nGl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(3)\not.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg')" ] }, { @@ -98,7 +98,7 @@ }, "outputs": [], "source": [ - "#%% Example with entropic regularization\n\n\ndef f(G):\n return np.sum(G * np.log(G))\n\n\ndef df(G):\n return np.log(G) + 1.\n\n\nreg = 1e-3\n\nGe = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg')" + "def f(G):\n return np.sum(G * np.log(G))\n\n\ndef df(G):\n return np.log(G) + 1.\n\n\nreg = 1e-3\n\nGe = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg')" ] }, { @@ -116,7 +116,7 @@ }, "outputs": [], "source": [ - "#%% Example with Frobenius norm + entropic regularization with gcg\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg1 = 1e-3\nreg2 = 1e-1\n\nGel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')\npl.show()" + "def f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg1 = 1e-3\nreg2 = 1e-1\n\nGel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')\npl.show()" ] } ], @@ -136,7 +136,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_optim_OTreg.rst b/docs/source/auto_examples/plot_optim_OTreg.rst index 844cba0..cd5bcf5 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.rst +++ b/docs/source/auto_examples/plot_optim_OTreg.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_optim_OTreg.py: + .. _sphx_glr_auto_examples_plot_optim_OTreg.py: ================================== @@ -28,8 +34,7 @@ arXiv preprint arXiv:1510.06567. - -.. code-block:: python +.. code-block:: default import numpy as np @@ -43,15 +48,13 @@ arXiv preprint arXiv:1510.06567. + Generate data ------------- +.. code-block:: default -.. code-block:: python - - - #%% parameters n = 100 # nb bins @@ -72,16 +75,14 @@ Generate data + Solve EMD --------- - -.. code-block:: python +.. code-block:: default - #%% EMD - G0 = ot.emd(a, b, M) pl.figure(3, figsize=(5, 5)) @@ -90,8 +91,9 @@ Solve EMD -.. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_003.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_001.png + :class: sphx-glr-single-img + @@ -100,12 +102,9 @@ Solve EMD with Frobenius norm regularization -------------------------------------------- - -.. code-block:: python +.. code-block:: default - #%% Example with Frobenius norm regularization - def f(G): return 0.5 * np.sum(G**2) @@ -125,248 +124,249 @@ Solve EMD with Frobenius norm regularization -.. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_004.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_002.png + :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out - Out:: - - It. |Loss |Delta loss - -------------------------------- - 0|1.760578e-01|0.000000e+00 - 1|1.669467e-01|-5.457501e-02 - 2|1.665639e-01|-2.298130e-03 - 3|1.664378e-01|-7.572776e-04 - 4|1.664077e-01|-1.811855e-04 - 5|1.663912e-01|-9.936787e-05 - 6|1.663852e-01|-3.555826e-05 - 7|1.663814e-01|-2.305693e-05 - 8|1.663785e-01|-1.760450e-05 - 9|1.663767e-01|-1.078011e-05 - 10|1.663751e-01|-9.525192e-06 - 11|1.663737e-01|-8.396466e-06 - 12|1.663727e-01|-6.086938e-06 - 13|1.663720e-01|-4.042609e-06 - 14|1.663713e-01|-4.160914e-06 - 15|1.663707e-01|-3.823502e-06 - 16|1.663702e-01|-3.022440e-06 - 17|1.663697e-01|-3.181249e-06 - 18|1.663692e-01|-2.698532e-06 - 19|1.663687e-01|-3.258253e-06 - It. |Loss |Delta loss - -------------------------------- - 20|1.663682e-01|-2.741118e-06 - 21|1.663678e-01|-2.624135e-06 - 22|1.663673e-01|-2.645179e-06 - 23|1.663670e-01|-1.957237e-06 - 24|1.663666e-01|-2.261541e-06 - 25|1.663663e-01|-1.851305e-06 - 26|1.663660e-01|-1.942296e-06 - 27|1.663657e-01|-2.092896e-06 - 28|1.663653e-01|-1.924361e-06 - 29|1.663651e-01|-1.625455e-06 - 30|1.663648e-01|-1.641123e-06 - 31|1.663645e-01|-1.566666e-06 - 32|1.663643e-01|-1.338514e-06 - 33|1.663641e-01|-1.222711e-06 - 34|1.663639e-01|-1.221805e-06 - 35|1.663637e-01|-1.440781e-06 - 36|1.663634e-01|-1.520091e-06 - 37|1.663632e-01|-1.288193e-06 - 38|1.663630e-01|-1.123055e-06 - 39|1.663628e-01|-1.024487e-06 - It. |Loss |Delta loss - -------------------------------- - 40|1.663627e-01|-1.079606e-06 - 41|1.663625e-01|-1.172093e-06 - 42|1.663623e-01|-1.047880e-06 - 43|1.663621e-01|-1.010577e-06 - 44|1.663619e-01|-1.064438e-06 - 45|1.663618e-01|-9.882375e-07 - 46|1.663616e-01|-8.532647e-07 - 47|1.663615e-01|-9.930189e-07 - 48|1.663613e-01|-8.728955e-07 - 49|1.663612e-01|-9.524214e-07 - 50|1.663610e-01|-9.088418e-07 - 51|1.663609e-01|-7.639430e-07 - 52|1.663608e-01|-6.662611e-07 - 53|1.663607e-01|-7.133700e-07 - 54|1.663605e-01|-7.648141e-07 - 55|1.663604e-01|-6.557516e-07 - 56|1.663603e-01|-7.304213e-07 - 57|1.663602e-01|-6.353809e-07 - 58|1.663601e-01|-7.968279e-07 - 59|1.663600e-01|-6.367159e-07 - It. |Loss |Delta loss - -------------------------------- - 60|1.663599e-01|-5.610790e-07 - 61|1.663598e-01|-5.787466e-07 - 62|1.663596e-01|-6.937777e-07 - 63|1.663596e-01|-5.599432e-07 - 64|1.663595e-01|-5.813048e-07 - 65|1.663594e-01|-5.724600e-07 - 66|1.663593e-01|-6.081892e-07 - 67|1.663592e-01|-5.948732e-07 - 68|1.663591e-01|-4.941833e-07 - 69|1.663590e-01|-5.213739e-07 - 70|1.663589e-01|-5.127355e-07 - 71|1.663588e-01|-4.349251e-07 - 72|1.663588e-01|-5.007084e-07 - 73|1.663587e-01|-4.880265e-07 - 74|1.663586e-01|-4.931950e-07 - 75|1.663585e-01|-4.981309e-07 - 76|1.663584e-01|-3.952959e-07 - 77|1.663584e-01|-4.544857e-07 - 78|1.663583e-01|-4.237579e-07 - 79|1.663582e-01|-4.382386e-07 - It. |Loss |Delta loss - -------------------------------- - 80|1.663582e-01|-3.646051e-07 - 81|1.663581e-01|-4.197994e-07 - 82|1.663580e-01|-4.072764e-07 - 83|1.663580e-01|-3.994645e-07 - 84|1.663579e-01|-4.842721e-07 - 85|1.663578e-01|-3.276486e-07 - 86|1.663578e-01|-3.737346e-07 - 87|1.663577e-01|-4.282043e-07 - 88|1.663576e-01|-4.020937e-07 - 89|1.663576e-01|-3.431951e-07 - 90|1.663575e-01|-3.052335e-07 - 91|1.663575e-01|-3.500538e-07 - 92|1.663574e-01|-3.063176e-07 - 93|1.663573e-01|-3.576367e-07 - 94|1.663573e-01|-3.224681e-07 - 95|1.663572e-01|-3.673221e-07 - 96|1.663572e-01|-3.635561e-07 - 97|1.663571e-01|-3.527236e-07 - 98|1.663571e-01|-2.788548e-07 - 99|1.663570e-01|-2.727141e-07 - It. |Loss |Delta loss - -------------------------------- - 100|1.663570e-01|-3.127278e-07 - 101|1.663569e-01|-2.637504e-07 - 102|1.663569e-01|-2.922750e-07 - 103|1.663568e-01|-3.076454e-07 - 104|1.663568e-01|-2.911509e-07 - 105|1.663567e-01|-2.403398e-07 - 106|1.663567e-01|-2.439790e-07 - 107|1.663567e-01|-2.634542e-07 - 108|1.663566e-01|-2.452203e-07 - 109|1.663566e-01|-2.852991e-07 - 110|1.663565e-01|-2.165490e-07 - 111|1.663565e-01|-2.450250e-07 - 112|1.663564e-01|-2.685294e-07 - 113|1.663564e-01|-2.821800e-07 - 114|1.663564e-01|-2.237390e-07 - 115|1.663563e-01|-1.992842e-07 - 116|1.663563e-01|-2.166739e-07 - 117|1.663563e-01|-2.086064e-07 - 118|1.663562e-01|-2.435945e-07 - 119|1.663562e-01|-2.292497e-07 - It. |Loss |Delta loss - -------------------------------- - 120|1.663561e-01|-2.366209e-07 - 121|1.663561e-01|-2.138746e-07 - 122|1.663561e-01|-2.009637e-07 - 123|1.663560e-01|-2.386258e-07 - 124|1.663560e-01|-1.927442e-07 - 125|1.663560e-01|-2.081681e-07 - 126|1.663559e-01|-1.759123e-07 - 127|1.663559e-01|-1.890771e-07 - 128|1.663559e-01|-1.971315e-07 - 129|1.663558e-01|-2.101983e-07 - 130|1.663558e-01|-2.035645e-07 - 131|1.663558e-01|-1.984492e-07 - 132|1.663557e-01|-1.849064e-07 - 133|1.663557e-01|-1.795703e-07 - 134|1.663557e-01|-1.624087e-07 - 135|1.663557e-01|-1.689557e-07 - 136|1.663556e-01|-1.644308e-07 - 137|1.663556e-01|-1.618007e-07 - 138|1.663556e-01|-1.483013e-07 - 139|1.663555e-01|-1.708771e-07 - It. |Loss |Delta loss - -------------------------------- - 140|1.663555e-01|-2.013847e-07 - 141|1.663555e-01|-1.721217e-07 - 142|1.663554e-01|-2.027911e-07 - 143|1.663554e-01|-1.764565e-07 - 144|1.663554e-01|-1.677151e-07 - 145|1.663554e-01|-1.351982e-07 - 146|1.663553e-01|-1.423360e-07 - 147|1.663553e-01|-1.541112e-07 - 148|1.663553e-01|-1.491601e-07 - 149|1.663553e-01|-1.466407e-07 - 150|1.663552e-01|-1.801524e-07 - 151|1.663552e-01|-1.714107e-07 - 152|1.663552e-01|-1.491257e-07 - 153|1.663552e-01|-1.513799e-07 - 154|1.663551e-01|-1.354539e-07 - 155|1.663551e-01|-1.233818e-07 - 156|1.663551e-01|-1.576219e-07 - 157|1.663551e-01|-1.452791e-07 - 158|1.663550e-01|-1.262867e-07 - 159|1.663550e-01|-1.316379e-07 - It. |Loss |Delta loss - -------------------------------- - 160|1.663550e-01|-1.295447e-07 - 161|1.663550e-01|-1.283286e-07 - 162|1.663550e-01|-1.569222e-07 - 163|1.663549e-01|-1.172942e-07 - 164|1.663549e-01|-1.399809e-07 - 165|1.663549e-01|-1.229432e-07 - 166|1.663549e-01|-1.326191e-07 - 167|1.663548e-01|-1.209694e-07 - 168|1.663548e-01|-1.372136e-07 - 169|1.663548e-01|-1.338395e-07 - 170|1.663548e-01|-1.416497e-07 - 171|1.663548e-01|-1.298576e-07 - 172|1.663547e-01|-1.190590e-07 - 173|1.663547e-01|-1.167083e-07 - 174|1.663547e-01|-1.069425e-07 - 175|1.663547e-01|-1.217780e-07 - 176|1.663547e-01|-1.140754e-07 - 177|1.663546e-01|-1.160707e-07 - 178|1.663546e-01|-1.101798e-07 - 179|1.663546e-01|-1.114904e-07 - It. |Loss |Delta loss - -------------------------------- - 180|1.663546e-01|-1.064022e-07 - 181|1.663546e-01|-9.258231e-08 - 182|1.663546e-01|-1.213120e-07 - 183|1.663545e-01|-1.164296e-07 - 184|1.663545e-01|-1.188762e-07 - 185|1.663545e-01|-9.394153e-08 - 186|1.663545e-01|-1.028656e-07 - 187|1.663545e-01|-1.115348e-07 - 188|1.663544e-01|-9.768310e-08 - 189|1.663544e-01|-1.021806e-07 - 190|1.663544e-01|-1.086303e-07 - 191|1.663544e-01|-9.879008e-08 - 192|1.663544e-01|-1.050210e-07 - 193|1.663544e-01|-1.002463e-07 - 194|1.663543e-01|-1.062747e-07 - 195|1.663543e-01|-9.348538e-08 - 196|1.663543e-01|-7.992512e-08 - 197|1.663543e-01|-9.558020e-08 - 198|1.663543e-01|-9.993772e-08 - 199|1.663543e-01|-8.588499e-08 - It. |Loss |Delta loss - -------------------------------- - 200|1.663543e-01|-8.737134e-08 + Out: + + .. code-block:: none + + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 0|1.760578e-01|0.000000e+00|0.000000e+00 + 1|1.669467e-01|5.457501e-02|9.111116e-03 + 2|1.665639e-01|2.298130e-03|3.827855e-04 + 3|1.664378e-01|7.572776e-04|1.260396e-04 + 4|1.664077e-01|1.811855e-04|3.015066e-05 + 5|1.663912e-01|9.936787e-05|1.653393e-05 + 6|1.663852e-01|3.555826e-05|5.916369e-06 + 7|1.663814e-01|2.305693e-05|3.836245e-06 + 8|1.663785e-01|1.760450e-05|2.929009e-06 + 9|1.663767e-01|1.078011e-05|1.793559e-06 + 10|1.663751e-01|9.525192e-06|1.584755e-06 + 11|1.663737e-01|8.396466e-06|1.396951e-06 + 12|1.663727e-01|6.086938e-06|1.012700e-06 + 13|1.663720e-01|4.042609e-06|6.725769e-07 + 14|1.663713e-01|4.160914e-06|6.922568e-07 + 15|1.663707e-01|3.823502e-06|6.361187e-07 + 16|1.663702e-01|3.022440e-06|5.028438e-07 + 17|1.663697e-01|3.181249e-06|5.292634e-07 + 18|1.663692e-01|2.698532e-06|4.489527e-07 + 19|1.663687e-01|3.258253e-06|5.420712e-07 + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 20|1.663682e-01|2.741118e-06|4.560349e-07 + 21|1.663678e-01|2.624135e-06|4.365715e-07 + 22|1.663673e-01|2.645179e-06|4.400714e-07 + 23|1.663670e-01|1.957237e-06|3.256196e-07 + 24|1.663666e-01|2.261541e-06|3.762450e-07 + 25|1.663663e-01|1.851305e-06|3.079948e-07 + 26|1.663660e-01|1.942296e-06|3.231320e-07 + 27|1.663657e-01|2.092896e-06|3.481860e-07 + 28|1.663653e-01|1.924361e-06|3.201470e-07 + 29|1.663651e-01|1.625455e-06|2.704189e-07 + 30|1.663648e-01|1.641123e-06|2.730250e-07 + 31|1.663645e-01|1.566666e-06|2.606377e-07 + 32|1.663643e-01|1.338514e-06|2.226810e-07 + 33|1.663641e-01|1.222711e-06|2.034152e-07 + 34|1.663639e-01|1.221805e-06|2.032642e-07 + 35|1.663637e-01|1.440781e-06|2.396935e-07 + 36|1.663634e-01|1.520091e-06|2.528875e-07 + 37|1.663632e-01|1.288193e-06|2.143080e-07 + 38|1.663630e-01|1.123055e-06|1.868347e-07 + 39|1.663628e-01|1.024487e-06|1.704365e-07 + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 40|1.663627e-01|1.079606e-06|1.796061e-07 + 41|1.663625e-01|1.172093e-06|1.949922e-07 + 42|1.663623e-01|1.047880e-06|1.743277e-07 + 43|1.663621e-01|1.010577e-06|1.681217e-07 + 44|1.663619e-01|1.064438e-06|1.770820e-07 + 45|1.663618e-01|9.882375e-07|1.644049e-07 + 46|1.663616e-01|8.532647e-07|1.419505e-07 + 47|1.663615e-01|9.930189e-07|1.652001e-07 + 48|1.663613e-01|8.728955e-07|1.452161e-07 + 49|1.663612e-01|9.524214e-07|1.584459e-07 + 50|1.663610e-01|9.088418e-07|1.511958e-07 + 51|1.663609e-01|7.639430e-07|1.270902e-07 + 52|1.663608e-01|6.662611e-07|1.108397e-07 + 53|1.663607e-01|7.133700e-07|1.186767e-07 + 54|1.663605e-01|7.648141e-07|1.272349e-07 + 55|1.663604e-01|6.557516e-07|1.090911e-07 + 56|1.663603e-01|7.304213e-07|1.215131e-07 + 57|1.663602e-01|6.353809e-07|1.057021e-07 + 58|1.663601e-01|7.968279e-07|1.325603e-07 + 59|1.663600e-01|6.367159e-07|1.059240e-07 + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 60|1.663599e-01|5.610790e-07|9.334102e-08 + 61|1.663598e-01|5.787466e-07|9.628015e-08 + 62|1.663596e-01|6.937777e-07|1.154166e-07 + 63|1.663596e-01|5.599432e-07|9.315190e-08 + 64|1.663595e-01|5.813048e-07|9.670555e-08 + 65|1.663594e-01|5.724600e-07|9.523409e-08 + 66|1.663593e-01|6.081892e-07|1.011779e-07 + 67|1.663592e-01|5.948732e-07|9.896260e-08 + 68|1.663591e-01|4.941833e-07|8.221188e-08 + 69|1.663590e-01|5.213739e-07|8.673523e-08 + 70|1.663589e-01|5.127355e-07|8.529811e-08 + 71|1.663588e-01|4.349251e-07|7.235363e-08 + 72|1.663588e-01|5.007084e-07|8.329722e-08 + 73|1.663587e-01|4.880265e-07|8.118744e-08 + 74|1.663586e-01|4.931950e-07|8.204723e-08 + 75|1.663585e-01|4.981309e-07|8.286832e-08 + 76|1.663584e-01|3.952959e-07|6.576082e-08 + 77|1.663584e-01|4.544857e-07|7.560750e-08 + 78|1.663583e-01|4.237579e-07|7.049564e-08 + 79|1.663582e-01|4.382386e-07|7.290460e-08 + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 80|1.663582e-01|3.646051e-07|6.065503e-08 + 81|1.663581e-01|4.197994e-07|6.983702e-08 + 82|1.663580e-01|4.072764e-07|6.775370e-08 + 83|1.663580e-01|3.994645e-07|6.645410e-08 + 84|1.663579e-01|4.842721e-07|8.056248e-08 + 85|1.663578e-01|3.276486e-07|5.450691e-08 + 86|1.663578e-01|3.737346e-07|6.217366e-08 + 87|1.663577e-01|4.282043e-07|7.123508e-08 + 88|1.663576e-01|4.020937e-07|6.689135e-08 + 89|1.663576e-01|3.431951e-07|5.709310e-08 + 90|1.663575e-01|3.052335e-07|5.077789e-08 + 91|1.663575e-01|3.500538e-07|5.823407e-08 + 92|1.663574e-01|3.063176e-07|5.095821e-08 + 93|1.663573e-01|3.576367e-07|5.949549e-08 + 94|1.663573e-01|3.224681e-07|5.364492e-08 + 95|1.663572e-01|3.673221e-07|6.110670e-08 + 96|1.663572e-01|3.635561e-07|6.048017e-08 + 97|1.663571e-01|3.527236e-07|5.867807e-08 + 98|1.663571e-01|2.788548e-07|4.638946e-08 + 99|1.663570e-01|2.727141e-07|4.536791e-08 + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 100|1.663570e-01|3.127278e-07|5.202445e-08 + 101|1.663569e-01|2.637504e-07|4.387670e-08 + 102|1.663569e-01|2.922750e-07|4.862195e-08 + 103|1.663568e-01|3.076454e-07|5.117891e-08 + 104|1.663568e-01|2.911509e-07|4.843492e-08 + 105|1.663567e-01|2.403398e-07|3.998215e-08 + 106|1.663567e-01|2.439790e-07|4.058755e-08 + 107|1.663567e-01|2.634542e-07|4.382735e-08 + 108|1.663566e-01|2.452203e-07|4.079401e-08 + 109|1.663566e-01|2.852991e-07|4.746137e-08 + 110|1.663565e-01|2.165490e-07|3.602434e-08 + 111|1.663565e-01|2.450250e-07|4.076149e-08 + 112|1.663564e-01|2.685294e-07|4.467159e-08 + 113|1.663564e-01|2.821800e-07|4.694245e-08 + 114|1.663564e-01|2.237390e-07|3.722040e-08 + 115|1.663563e-01|1.992842e-07|3.315219e-08 + 116|1.663563e-01|2.166739e-07|3.604506e-08 + 117|1.663563e-01|2.086064e-07|3.470297e-08 + 118|1.663562e-01|2.435945e-07|4.052346e-08 + 119|1.663562e-01|2.292497e-07|3.813711e-08 + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 120|1.663561e-01|2.366209e-07|3.936334e-08 + 121|1.663561e-01|2.138746e-07|3.557935e-08 + 122|1.663561e-01|2.009637e-07|3.343153e-08 + 123|1.663560e-01|2.386258e-07|3.969683e-08 + 124|1.663560e-01|1.927442e-07|3.206415e-08 + 125|1.663560e-01|2.081681e-07|3.463000e-08 + 126|1.663559e-01|1.759123e-07|2.926406e-08 + 127|1.663559e-01|1.890771e-07|3.145409e-08 + 128|1.663559e-01|1.971315e-07|3.279398e-08 + 129|1.663558e-01|2.101983e-07|3.496771e-08 + 130|1.663558e-01|2.035645e-07|3.386414e-08 + 131|1.663558e-01|1.984492e-07|3.301317e-08 + 132|1.663557e-01|1.849064e-07|3.076024e-08 + 133|1.663557e-01|1.795703e-07|2.987255e-08 + 134|1.663557e-01|1.624087e-07|2.701762e-08 + 135|1.663557e-01|1.689557e-07|2.810673e-08 + 136|1.663556e-01|1.644308e-07|2.735399e-08 + 137|1.663556e-01|1.618007e-07|2.691644e-08 + 138|1.663556e-01|1.483013e-07|2.467075e-08 + 139|1.663555e-01|1.708771e-07|2.842636e-08 + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 140|1.663555e-01|2.013847e-07|3.350146e-08 + 141|1.663555e-01|1.721217e-07|2.863339e-08 + 142|1.663554e-01|2.027911e-07|3.373540e-08 + 143|1.663554e-01|1.764565e-07|2.935449e-08 + 144|1.663554e-01|1.677151e-07|2.790030e-08 + 145|1.663554e-01|1.351982e-07|2.249094e-08 + 146|1.663553e-01|1.423360e-07|2.367836e-08 + 147|1.663553e-01|1.541112e-07|2.563722e-08 + 148|1.663553e-01|1.491601e-07|2.481358e-08 + 149|1.663553e-01|1.466407e-07|2.439446e-08 + 150|1.663552e-01|1.801524e-07|2.996929e-08 + 151|1.663552e-01|1.714107e-07|2.851507e-08 + 152|1.663552e-01|1.491257e-07|2.480784e-08 + 153|1.663552e-01|1.513799e-07|2.518282e-08 + 154|1.663551e-01|1.354539e-07|2.253345e-08 + 155|1.663551e-01|1.233818e-07|2.052519e-08 + 156|1.663551e-01|1.576219e-07|2.622121e-08 + 157|1.663551e-01|1.452791e-07|2.416792e-08 + 158|1.663550e-01|1.262867e-07|2.100843e-08 + 159|1.663550e-01|1.316379e-07|2.189863e-08 + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 160|1.663550e-01|1.295447e-07|2.155041e-08 + 161|1.663550e-01|1.283286e-07|2.134810e-08 + 162|1.663550e-01|1.569222e-07|2.610479e-08 + 163|1.663549e-01|1.172942e-07|1.951247e-08 + 164|1.663549e-01|1.399809e-07|2.328651e-08 + 165|1.663549e-01|1.229432e-07|2.045221e-08 + 166|1.663549e-01|1.326191e-07|2.206184e-08 + 167|1.663548e-01|1.209694e-07|2.012384e-08 + 168|1.663548e-01|1.372136e-07|2.282614e-08 + 169|1.663548e-01|1.338395e-07|2.226484e-08 + 170|1.663548e-01|1.416497e-07|2.356410e-08 + 171|1.663548e-01|1.298576e-07|2.160242e-08 + 172|1.663547e-01|1.190590e-07|1.980603e-08 + 173|1.663547e-01|1.167083e-07|1.941497e-08 + 174|1.663547e-01|1.069425e-07|1.779038e-08 + 175|1.663547e-01|1.217780e-07|2.025834e-08 + 176|1.663547e-01|1.140754e-07|1.897697e-08 + 177|1.663546e-01|1.160707e-07|1.930890e-08 + 178|1.663546e-01|1.101798e-07|1.832892e-08 + 179|1.663546e-01|1.114904e-07|1.854694e-08 + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 180|1.663546e-01|1.064022e-07|1.770049e-08 + 181|1.663546e-01|9.258231e-08|1.540149e-08 + 182|1.663546e-01|1.213120e-07|2.018080e-08 + 183|1.663545e-01|1.164296e-07|1.936859e-08 + 184|1.663545e-01|1.188762e-07|1.977559e-08 + 185|1.663545e-01|9.394153e-08|1.562760e-08 + 186|1.663545e-01|1.028656e-07|1.711216e-08 + 187|1.663545e-01|1.115348e-07|1.855431e-08 + 188|1.663544e-01|9.768310e-08|1.625002e-08 + 189|1.663544e-01|1.021806e-07|1.699820e-08 + 190|1.663544e-01|1.086303e-07|1.807113e-08 + 191|1.663544e-01|9.879008e-08|1.643416e-08 + 192|1.663544e-01|1.050210e-07|1.747071e-08 + 193|1.663544e-01|1.002463e-07|1.667641e-08 + 194|1.663543e-01|1.062747e-07|1.767925e-08 + 195|1.663543e-01|9.348538e-08|1.555170e-08 + 196|1.663543e-01|7.992512e-08|1.329589e-08 + 197|1.663543e-01|9.558020e-08|1.590018e-08 + 198|1.663543e-01|9.993772e-08|1.662507e-08 + 199|1.663543e-01|8.588499e-08|1.428734e-08 + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 200|1.663543e-01|8.737134e-08|1.453459e-08 -Solve EMD with entropic regularization --------------------------------------- +Solve EMD with entropic regularization +-------------------------------------- -.. code-block:: python +.. code-block:: default - #%% Example with entropic regularization def f(G): @@ -387,217 +387,128 @@ Solve EMD with entropic regularization -.. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_006.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_003.png + :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out - Out:: - - It. |Loss |Delta loss - -------------------------------- - 0|1.692289e-01|0.000000e+00 - 1|1.617643e-01|-4.614437e-02 - 2|1.612639e-01|-3.102965e-03 - 3|1.611291e-01|-8.371098e-04 - 4|1.610468e-01|-5.110558e-04 - 5|1.610198e-01|-1.672927e-04 - 6|1.610130e-01|-4.232417e-05 - 7|1.610090e-01|-2.513455e-05 - 8|1.610002e-01|-5.443507e-05 - 9|1.609996e-01|-3.657071e-06 - 10|1.609948e-01|-2.998735e-05 - 11|1.609695e-01|-1.569217e-04 - 12|1.609533e-01|-1.010779e-04 - 13|1.609520e-01|-8.043897e-06 - 14|1.609465e-01|-3.415246e-05 - 15|1.609386e-01|-4.898605e-05 - 16|1.609324e-01|-3.837052e-05 - 17|1.609298e-01|-1.617826e-05 - 18|1.609184e-01|-7.080015e-05 - 19|1.609083e-01|-6.273206e-05 - It. |Loss |Delta loss - -------------------------------- - 20|1.608988e-01|-5.940805e-05 - 21|1.608853e-01|-8.380030e-05 - 22|1.608844e-01|-5.185045e-06 - 23|1.608824e-01|-1.279113e-05 - 24|1.608819e-01|-3.156821e-06 - 25|1.608783e-01|-2.205746e-05 - 26|1.608764e-01|-1.189894e-05 - 27|1.608755e-01|-5.474607e-06 - 28|1.608737e-01|-1.144227e-05 - 29|1.608676e-01|-3.775335e-05 - 30|1.608638e-01|-2.348020e-05 - 31|1.608627e-01|-6.863136e-06 - 32|1.608529e-01|-6.110230e-05 - 33|1.608487e-01|-2.641106e-05 - 34|1.608409e-01|-4.823638e-05 - 35|1.608373e-01|-2.256641e-05 - 36|1.608338e-01|-2.132444e-05 - 37|1.608310e-01|-1.786649e-05 - 38|1.608260e-01|-3.103848e-05 - 39|1.608206e-01|-3.321265e-05 - It. |Loss |Delta loss - -------------------------------- - 40|1.608201e-01|-3.054747e-06 - 41|1.608195e-01|-4.198335e-06 - 42|1.608193e-01|-8.458736e-07 - 43|1.608159e-01|-2.153759e-05 - 44|1.608115e-01|-2.738314e-05 - 45|1.608108e-01|-3.960032e-06 - 46|1.608081e-01|-1.675447e-05 - 47|1.608072e-01|-5.976340e-06 - 48|1.608046e-01|-1.604130e-05 - 49|1.608020e-01|-1.617036e-05 - 50|1.608014e-01|-3.957795e-06 - 51|1.608011e-01|-1.292411e-06 - 52|1.607998e-01|-8.431795e-06 - 53|1.607964e-01|-2.127054e-05 - 54|1.607947e-01|-1.021878e-05 - 55|1.607947e-01|-3.560621e-07 - 56|1.607900e-01|-2.929781e-05 - 57|1.607890e-01|-5.740229e-06 - 58|1.607858e-01|-2.039550e-05 - 59|1.607836e-01|-1.319545e-05 - It. |Loss |Delta loss - -------------------------------- - 60|1.607826e-01|-6.378947e-06 - 61|1.607808e-01|-1.145102e-05 - 62|1.607776e-01|-1.941743e-05 - 63|1.607743e-01|-2.087422e-05 - 64|1.607741e-01|-1.310249e-06 - 65|1.607738e-01|-1.682752e-06 - 66|1.607691e-01|-2.913936e-05 - 67|1.607671e-01|-1.288855e-05 - 68|1.607654e-01|-1.002448e-05 - 69|1.607641e-01|-8.209492e-06 - 70|1.607632e-01|-5.588467e-06 - 71|1.607619e-01|-8.050388e-06 - 72|1.607618e-01|-9.417493e-07 - 73|1.607598e-01|-1.210509e-05 - 74|1.607591e-01|-4.392914e-06 - 75|1.607579e-01|-7.759587e-06 - 76|1.607574e-01|-2.760280e-06 - 77|1.607556e-01|-1.146469e-05 - 78|1.607550e-01|-3.689456e-06 - 79|1.607550e-01|-4.065631e-08 - It. |Loss |Delta loss - -------------------------------- - 80|1.607539e-01|-6.555681e-06 - 81|1.607528e-01|-7.177470e-06 - 82|1.607527e-01|-5.306068e-07 - 83|1.607514e-01|-7.816045e-06 - 84|1.607511e-01|-2.301970e-06 - 85|1.607504e-01|-4.281072e-06 - 86|1.607503e-01|-7.821886e-07 - 87|1.607480e-01|-1.403013e-05 - 88|1.607480e-01|-1.169298e-08 - 89|1.607473e-01|-4.235982e-06 - 90|1.607470e-01|-1.717105e-06 - 91|1.607470e-01|-6.148402e-09 - 92|1.607462e-01|-5.396481e-06 - 93|1.607461e-01|-5.194954e-07 - 94|1.607450e-01|-6.525707e-06 - 95|1.607442e-01|-5.332060e-06 - 96|1.607439e-01|-1.682093e-06 - 97|1.607437e-01|-1.594796e-06 - 98|1.607435e-01|-7.923812e-07 - 99|1.607420e-01|-9.738552e-06 - It. |Loss |Delta loss - -------------------------------- - 100|1.607419e-01|-1.022448e-07 - 101|1.607419e-01|-4.865999e-07 - 102|1.607418e-01|-7.092012e-07 - 103|1.607408e-01|-5.861815e-06 - 104|1.607402e-01|-3.953266e-06 - 105|1.607395e-01|-3.969572e-06 - 106|1.607390e-01|-3.612075e-06 - 107|1.607377e-01|-7.683735e-06 - 108|1.607365e-01|-7.777599e-06 - 109|1.607364e-01|-2.335096e-07 - 110|1.607364e-01|-4.562036e-07 - 111|1.607360e-01|-2.089538e-06 - 112|1.607356e-01|-2.755355e-06 - 113|1.607349e-01|-4.501960e-06 - 114|1.607347e-01|-1.160544e-06 - 115|1.607346e-01|-6.289450e-07 - 116|1.607345e-01|-2.092146e-07 - 117|1.607336e-01|-5.990866e-06 - 118|1.607330e-01|-3.348498e-06 - 119|1.607328e-01|-1.256222e-06 - It. |Loss |Delta loss - -------------------------------- - 120|1.607320e-01|-5.418353e-06 - 121|1.607318e-01|-8.296189e-07 - 122|1.607311e-01|-4.381608e-06 - 123|1.607310e-01|-8.913901e-07 - 124|1.607309e-01|-3.808821e-07 - 125|1.607302e-01|-4.608994e-06 - 126|1.607294e-01|-5.063777e-06 - 127|1.607290e-01|-2.532835e-06 - 128|1.607285e-01|-2.870049e-06 - 129|1.607284e-01|-4.892812e-07 - 130|1.607281e-01|-1.760452e-06 - 131|1.607279e-01|-1.727139e-06 - 132|1.607275e-01|-2.220706e-06 - 133|1.607271e-01|-2.516930e-06 - 134|1.607269e-01|-1.201434e-06 - 135|1.607269e-01|-2.183459e-09 - 136|1.607262e-01|-4.223011e-06 - 137|1.607258e-01|-2.530202e-06 - 138|1.607258e-01|-1.857260e-07 - 139|1.607256e-01|-1.401957e-06 - It. |Loss |Delta loss - -------------------------------- - 140|1.607250e-01|-3.242751e-06 - 141|1.607247e-01|-2.308071e-06 - 142|1.607247e-01|-4.730700e-08 - 143|1.607246e-01|-4.240229e-07 - 144|1.607242e-01|-2.484810e-06 - 145|1.607238e-01|-2.539206e-06 - 146|1.607234e-01|-2.535574e-06 - 147|1.607231e-01|-1.954802e-06 - 148|1.607228e-01|-1.765447e-06 - 149|1.607228e-01|-1.620007e-08 - 150|1.607222e-01|-3.615783e-06 - 151|1.607222e-01|-8.668516e-08 - 152|1.607215e-01|-4.000673e-06 - 153|1.607213e-01|-1.774103e-06 - 154|1.607213e-01|-6.328834e-09 - 155|1.607209e-01|-2.418783e-06 - 156|1.607208e-01|-2.848492e-07 - 157|1.607207e-01|-8.836043e-07 - 158|1.607205e-01|-1.192836e-06 - 159|1.607202e-01|-1.638022e-06 - It. |Loss |Delta loss - -------------------------------- - 160|1.607202e-01|-3.670914e-08 - 161|1.607197e-01|-3.153709e-06 - 162|1.607197e-01|-2.419565e-09 - 163|1.607194e-01|-2.136882e-06 - 164|1.607194e-01|-1.173754e-09 - 165|1.607192e-01|-8.169238e-07 - 166|1.607191e-01|-9.218755e-07 - 167|1.607189e-01|-9.459255e-07 - 168|1.607187e-01|-1.294835e-06 - 169|1.607186e-01|-5.797668e-07 - 170|1.607186e-01|-4.706272e-08 - 171|1.607183e-01|-1.753383e-06 - 172|1.607183e-01|-1.681573e-07 - 173|1.607183e-01|-2.563971e-10 + Out: + + .. code-block:: none + + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 0|1.692289e-01|0.000000e+00|0.000000e+00 + 1|1.617643e-01|4.614437e-02|7.464513e-03 + 2|1.612639e-01|3.102965e-03|5.003963e-04 + 3|1.611291e-01|8.371098e-04|1.348827e-04 + 4|1.610468e-01|5.110558e-04|8.230389e-05 + 5|1.610198e-01|1.672927e-04|2.693743e-05 + 6|1.610130e-01|4.232417e-05|6.814742e-06 + 7|1.610090e-01|2.513455e-05|4.046887e-06 + 8|1.610002e-01|5.443507e-05|8.764057e-06 + 9|1.609996e-01|3.657071e-06|5.887869e-07 + 10|1.609948e-01|2.998735e-05|4.827807e-06 + 11|1.609695e-01|1.569217e-04|2.525962e-05 + 12|1.609533e-01|1.010779e-04|1.626881e-05 + 13|1.609520e-01|8.043897e-06|1.294681e-06 + 14|1.609465e-01|3.415246e-05|5.496718e-06 + 15|1.609386e-01|4.898605e-05|7.883745e-06 + 16|1.609324e-01|3.837052e-05|6.175060e-06 + 17|1.609298e-01|1.617826e-05|2.603564e-06 + 18|1.609184e-01|7.080015e-05|1.139305e-05 + 19|1.609083e-01|6.273206e-05|1.009411e-05 + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 20|1.608988e-01|5.940805e-05|9.558681e-06 + 21|1.608853e-01|8.380030e-05|1.348223e-05 + 22|1.608844e-01|5.185045e-06|8.341930e-07 + 23|1.608824e-01|1.279113e-05|2.057868e-06 + 24|1.608819e-01|3.156821e-06|5.078753e-07 + 25|1.608783e-01|2.205746e-05|3.548567e-06 + 26|1.608764e-01|1.189894e-05|1.914259e-06 + 27|1.608755e-01|5.474607e-06|8.807303e-07 + 28|1.608737e-01|1.144227e-05|1.840760e-06 + 29|1.608676e-01|3.775335e-05|6.073291e-06 + 30|1.608638e-01|2.348020e-05|3.777116e-06 + 31|1.608627e-01|6.863136e-06|1.104023e-06 + 32|1.608529e-01|6.110230e-05|9.828482e-06 + 33|1.608487e-01|2.641106e-05|4.248184e-06 + 34|1.608409e-01|4.823638e-05|7.758383e-06 + 35|1.608373e-01|2.256641e-05|3.629519e-06 + 36|1.608338e-01|2.132444e-05|3.429691e-06 + 37|1.608310e-01|1.786649e-05|2.873484e-06 + 38|1.608260e-01|3.103848e-05|4.991794e-06 + 39|1.608206e-01|3.321265e-05|5.341279e-06 + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 40|1.608201e-01|3.054747e-06|4.912648e-07 + 41|1.608195e-01|4.198335e-06|6.751739e-07 + 42|1.608193e-01|8.458736e-07|1.360328e-07 + 43|1.608159e-01|2.153759e-05|3.463587e-06 + 44|1.608115e-01|2.738314e-05|4.403523e-06 + 45|1.608108e-01|3.960032e-06|6.368161e-07 + 46|1.608081e-01|1.675447e-05|2.694254e-06 + 47|1.608072e-01|5.976340e-06|9.610383e-07 + 48|1.608046e-01|1.604130e-05|2.579515e-06 + 49|1.608020e-01|1.617036e-05|2.600226e-06 + 50|1.608014e-01|3.957795e-06|6.364188e-07 + 51|1.608011e-01|1.292411e-06|2.078211e-07 + 52|1.607998e-01|8.431795e-06|1.355831e-06 + 53|1.607964e-01|2.127054e-05|3.420225e-06 + 54|1.607947e-01|1.021878e-05|1.643126e-06 + 55|1.607947e-01|3.560621e-07|5.725288e-08 + 56|1.607900e-01|2.929781e-05|4.710793e-06 + 57|1.607890e-01|5.740229e-06|9.229659e-07 + 58|1.607858e-01|2.039550e-05|3.279306e-06 + 59|1.607836e-01|1.319545e-05|2.121612e-06 + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 60|1.607826e-01|6.378947e-06|1.025624e-06 + 61|1.607808e-01|1.145102e-05|1.841105e-06 + 62|1.607776e-01|1.941743e-05|3.121889e-06 + 63|1.607743e-01|2.087422e-05|3.356037e-06 + 64|1.607741e-01|1.310249e-06|2.106541e-07 + 65|1.607738e-01|1.682752e-06|2.705425e-07 + 66|1.607691e-01|2.913936e-05|4.684709e-06 + 67|1.607671e-01|1.288855e-05|2.072055e-06 + 68|1.607654e-01|1.002448e-05|1.611590e-06 + 69|1.607641e-01|8.209492e-06|1.319792e-06 + 70|1.607632e-01|5.588467e-06|8.984199e-07 + 71|1.607619e-01|8.050388e-06|1.294196e-06 + 72|1.607618e-01|9.417493e-07|1.513973e-07 + 73|1.607598e-01|1.210509e-05|1.946012e-06 + 74|1.607591e-01|4.392914e-06|7.062009e-07 + 75|1.607579e-01|7.759587e-06|1.247415e-06 + 76|1.607574e-01|2.760280e-06|4.437356e-07 + 77|1.607556e-01|1.146469e-05|1.843012e-06 + 78|1.607550e-01|3.689456e-06|5.930984e-07 + 79|1.607550e-01|4.065631e-08|6.535705e-09 + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 80|1.607539e-01|6.555681e-06|1.053852e-06 + 81|1.607528e-01|7.177470e-06|1.153798e-06 + 82|1.607527e-01|5.306068e-07|8.529648e-08 + 83|1.607514e-01|7.816045e-06|1.256440e-06 + 84|1.607511e-01|2.301970e-06|3.700442e-07 + 85|1.607504e-01|4.281072e-06|6.881840e-07 + 86|1.607503e-01|7.821886e-07|1.257370e-07 + 87|1.607480e-01|1.403013e-05|2.255315e-06 + 88|1.607480e-01|1.169298e-08|1.879624e-09 + 89|1.607473e-01|4.235982e-06|6.809227e-07 + 90|1.607470e-01|1.717105e-06|2.760195e-07 + 91|1.607470e-01|6.148402e-09|9.883374e-10 -Solve EMD with Frobenius norm + entropic regularization -------------------------------------------------------- +Solve EMD with Frobenius norm + entropic regularization +------------------------------------------------------- -.. code-block:: python +.. code-block:: default - #%% Example with Frobenius norm + entropic regularization with gcg def f(G): @@ -619,39 +530,58 @@ Solve EMD with Frobenius norm + entropic regularization -.. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_008.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_004.png + :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out - Out:: + Out: - It. |Loss |Delta loss - -------------------------------- - 0|1.693084e-01|0.000000e+00 - 1|1.610121e-01|-5.152589e-02 - 2|1.609378e-01|-4.622297e-04 - 3|1.609284e-01|-5.830043e-05 - 4|1.609284e-01|-1.111407e-12 + .. code-block:: none + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 0|1.693084e-01|0.000000e+00|0.000000e+00 + 1|1.610202e-01|5.147342e-02|8.288260e-03 + 2|1.609508e-01|4.309685e-04|6.936474e-05 + 3|1.609484e-01|1.524885e-05|2.454278e-06 + 4|1.609477e-01|3.863641e-06|6.218444e-07 + 5|1.609475e-01|1.433633e-06|2.307397e-07 + 6|1.609474e-01|6.332412e-07|1.019185e-07 + 7|1.609474e-01|2.950826e-07|4.749276e-08 + 8|1.609473e-01|1.508393e-07|2.427718e-08 + 9|1.609473e-01|7.859971e-08|1.265041e-08 + 10|1.609473e-01|4.337432e-08|6.980981e-09 + /home/rflamary/PYTHON/POT/examples/plot_optim_OTreg.py:129: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() -**Total running time of the script:** ( 0 minutes 1.990 seconds) + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 0.985 seconds) + + +.. _sphx_glr_download_auto_examples_plot_optim_OTreg.py: + + .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_optim_OTreg.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_optim_OTreg.ipynb ` @@ -660,4 +590,4 @@ Solve EMD with Frobenius norm + entropic regularization .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_classes.ipynb b/docs/source/auto_examples/plot_otda_classes.ipynb index 643e760..283d227 100644 --- a/docs/source/auto_examples/plot_otda_classes.ipynb +++ b/docs/source/auto_examples/plot_otda_classes.ipynb @@ -118,7 +118,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_otda_classes.py b/docs/source/auto_examples/plot_otda_classes.py index c311fbd..f028022 100644 --- a/docs/source/auto_examples/plot_otda_classes.py +++ b/docs/source/auto_examples/plot_otda_classes.py @@ -17,7 +17,6 @@ approaches currently supported in POT. import matplotlib.pylab as pl import ot - ############################################################################## # Generate data # ------------- diff --git a/docs/source/auto_examples/plot_otda_classes.rst b/docs/source/auto_examples/plot_otda_classes.rst index 19756ff..9cf31ee 100644 --- a/docs/source/auto_examples/plot_otda_classes.rst +++ b/docs/source/auto_examples/plot_otda_classes.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_otda_classes.py: + .. _sphx_glr_auto_examples_plot_otda_classes.py: ======================== @@ -12,8 +18,7 @@ approaches currently supported in POT. - -.. code-block:: python +.. code-block:: default # Authors: Remi Flamary @@ -35,8 +40,7 @@ Generate data ------------- - -.. code-block:: python +.. code-block:: default n_source_samples = 150 @@ -52,12 +56,12 @@ Generate data + Instantiate the different transport algorithms and fit them ----------------------------------------------------------- - -.. code-block:: python +.. code-block:: default # EMD Transport @@ -90,41 +94,44 @@ Instantiate the different transport algorithms and fit them .. rst-class:: sphx-glr-script-out - Out:: - - It. |Loss |Delta loss - -------------------------------- - 0|9.566309e+00|0.000000e+00 - 1|2.169680e+00|-3.409088e+00 - 2|1.914989e+00|-1.329986e-01 - 3|1.860251e+00|-2.942498e-02 - 4|1.838073e+00|-1.206621e-02 - 5|1.827064e+00|-6.025122e-03 - 6|1.820899e+00|-3.386082e-03 - 7|1.817290e+00|-1.985705e-03 - 8|1.814644e+00|-1.458223e-03 - 9|1.812661e+00|-1.093816e-03 - 10|1.810239e+00|-1.338121e-03 - 11|1.809100e+00|-6.296940e-04 - 12|1.807939e+00|-6.420646e-04 - 13|1.806965e+00|-5.389118e-04 - 14|1.806822e+00|-7.889599e-05 - 15|1.806193e+00|-3.482356e-04 - 16|1.805735e+00|-2.536930e-04 - 17|1.805321e+00|-2.292667e-04 - 18|1.804389e+00|-5.170222e-04 - 19|1.803908e+00|-2.661907e-04 - It. |Loss |Delta loss - -------------------------------- - 20|1.803696e+00|-1.178279e-04 + Out: + + .. code-block:: none + + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 0|9.484039e+00|0.000000e+00|0.000000e+00 + 1|1.976107e+00|3.799355e+00|7.507932e+00 + 2|1.749871e+00|1.292876e-01|2.262365e-01 + 3|1.692667e+00|3.379504e-02|5.720374e-02 + 4|1.676256e+00|9.790077e-03|1.641068e-02 + 5|1.667458e+00|5.276422e-03|8.798212e-03 + 6|1.661775e+00|3.419693e-03|5.682762e-03 + 7|1.658009e+00|2.271789e-03|3.766646e-03 + 8|1.655167e+00|1.716870e-03|2.841707e-03 + 9|1.651825e+00|2.023380e-03|3.342270e-03 + 10|1.649431e+00|1.451076e-03|2.393450e-03 + 11|1.648649e+00|4.742894e-04|7.819369e-04 + 12|1.647901e+00|4.538219e-04|7.478538e-04 + 13|1.647356e+00|3.313134e-04|5.457909e-04 + 14|1.646923e+00|2.627246e-04|4.326871e-04 + 15|1.646038e+00|5.375014e-04|8.847478e-04 + 16|1.645629e+00|2.483240e-04|4.086492e-04 + 17|1.645616e+00|8.248172e-06|1.357332e-05 + 18|1.645377e+00|1.452648e-04|2.390153e-04 + 19|1.644745e+00|3.838976e-04|6.314139e-04 + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 20|1.644164e+00|3.538439e-04|5.817773e-04 + + Fig 1 : plots source and target samples --------------------------------------- - -.. code-block:: python +.. code-block:: default pl.figure(1, figsize=(10, 5)) @@ -148,7 +155,8 @@ Fig 1 : plots source and target samples .. image:: /auto_examples/images/sphx_glr_plot_otda_classes_001.png - :align: center + :class: sphx-glr-single-img + @@ -157,8 +165,7 @@ Fig 2 : plot optimal couplings and transported samples ------------------------------------------------------ - -.. code-block:: python +.. code-block:: default param_img = {'interpolation': 'nearest'} @@ -230,28 +237,45 @@ Fig 2 : plot optimal couplings and transported samples -.. image:: /auto_examples/images/sphx_glr_plot_otda_classes_003.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_otda_classes_002.png + :class: sphx-glr-single-img +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_otda_classes.py:149: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() + -**Total running time of the script:** ( 0 minutes 1.423 seconds) +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 2.083 seconds) + + +.. _sphx_glr_download_auto_examples_plot_otda_classes.py: + .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_otda_classes.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_otda_classes.ipynb ` @@ -260,4 +284,4 @@ Fig 2 : plot optimal couplings and transported samples .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_color_images.ipynb b/docs/source/auto_examples/plot_otda_color_images.ipynb index 103bdec..c2afd41 100644 --- a/docs/source/auto_examples/plot_otda_color_images.ipynb +++ b/docs/source/auto_examples/plot_otda_color_images.ipynb @@ -26,7 +26,7 @@ }, "outputs": [], "source": [ - "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport numpy as np\nfrom scipy import ndimage\nimport matplotlib.pylab as pl\nimport ot\n\n\nr = np.random.RandomState(42)\n\n\ndef im2mat(I):\n \"\"\"Converts an image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)" + "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n\nr = np.random.RandomState(42)\n\n\ndef im2mat(I):\n \"\"\"Converts an image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)" ] }, { @@ -44,7 +44,7 @@ }, "outputs": [], "source": [ - "# Loading images\nI1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)\n\n# training samples\nnb = 1000\nidx1 = r.randint(X1.shape[0], size=(nb,))\nidx2 = r.randint(X2.shape[0], size=(nb,))\n\nXs = X1[idx1, :]\nXt = X2[idx2, :]" + "# Loading images\nI1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)\n\n# training samples\nnb = 1000\nidx1 = r.randint(X1.shape[0], size=(nb,))\nidx2 = r.randint(X2.shape[0], size=(nb,))\n\nXs = X1[idx1, :]\nXt = X2[idx2, :]" ] }, { @@ -136,7 +136,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_otda_color_images.py b/docs/source/auto_examples/plot_otda_color_images.py index 62383a2..d9cbd2b 100644 --- a/docs/source/auto_examples/plot_otda_color_images.py +++ b/docs/source/auto_examples/plot_otda_color_images.py @@ -18,7 +18,6 @@ SIAM Journal on Imaging Sciences, 7(3), 1853-1882. # License: MIT License import numpy as np -from scipy import ndimage import matplotlib.pylab as pl import ot @@ -45,8 +44,8 @@ def minmax(I): # ------------- # Loading images -I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 +I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 X1 = im2mat(I1) X2 = im2mat(I2) diff --git a/docs/source/auto_examples/plot_otda_color_images.rst b/docs/source/auto_examples/plot_otda_color_images.rst index ab0406e..a5b0d53 100644 --- a/docs/source/auto_examples/plot_otda_color_images.rst +++ b/docs/source/auto_examples/plot_otda_color_images.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_otda_color_images.py: + .. _sphx_glr_auto_examples_plot_otda_color_images.py: ============================= @@ -15,8 +21,7 @@ Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. - -.. code-block:: python +.. code-block:: default # Authors: Remi Flamary @@ -25,7 +30,6 @@ SIAM Journal on Imaging Sciences, 7(3), 1853-1882. # License: MIT License import numpy as np - from scipy import ndimage import matplotlib.pylab as pl import ot @@ -53,17 +57,17 @@ SIAM Journal on Imaging Sciences, 7(3), 1853-1882. + Generate data ------------- - -.. code-block:: python +.. code-block:: default # Loading images - I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 - I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 + I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 + I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 X1 = im2mat(I1) X2 = im2mat(I2) @@ -83,12 +87,12 @@ Generate data + Plot original image ------------------- - -.. code-block:: python +.. code-block:: default pl.figure(1, figsize=(6.4, 3)) @@ -108,17 +112,25 @@ Plot original image .. image:: /auto_examples/images/sphx_glr_plot_otda_color_images_001.png - :align: center + :class: sphx-glr-single-img +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + + Text(0.5, 1.0, 'Image 2') + Scatter plot of colors ---------------------- - -.. code-block:: python +.. code-block:: default pl.figure(2, figsize=(6.4, 3)) @@ -142,8 +154,9 @@ Scatter plot of colors -.. image:: /auto_examples/images/sphx_glr_plot_otda_color_images_003.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_otda_color_images_002.png + :class: sphx-glr-single-img + @@ -152,8 +165,7 @@ Instantiate the different transport algorithms and fit them ----------------------------------------------------------- - -.. code-block:: python +.. code-block:: default # EMDTransport @@ -184,12 +196,12 @@ Instantiate the different transport algorithms and fit them + Plot new images --------------- - -.. code-block:: python +.. code-block:: default pl.figure(3, figsize=(8, 4)) @@ -229,28 +241,45 @@ Plot new images -.. image:: /auto_examples/images/sphx_glr_plot_otda_color_images_005.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_otda_color_images_003.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_otda_color_images.py:164: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() + -**Total running time of the script:** ( 3 minutes 55.541 seconds) +.. rst-class:: sphx-glr-timing + **Total running time of the script:** ( 2 minutes 28.821 seconds) + + +.. _sphx_glr_download_auto_examples_plot_otda_color_images.py: .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_otda_color_images.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_otda_color_images.ipynb ` @@ -259,4 +288,4 @@ Plot new images .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_d2.ipynb b/docs/source/auto_examples/plot_otda_d2.ipynb index b9002ee..a2a7839 100644 --- a/docs/source/auto_examples/plot_otda_d2.ipynb +++ b/docs/source/auto_examples/plot_otda_d2.ipynb @@ -136,7 +136,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_otda_d2.rst b/docs/source/auto_examples/plot_otda_d2.rst index 80cc34c..6d8e429 100644 --- a/docs/source/auto_examples/plot_otda_d2.rst +++ b/docs/source/auto_examples/plot_otda_d2.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_otda_d2.py: + .. _sphx_glr_auto_examples_plot_otda_d2.py: =================================================== @@ -16,8 +22,7 @@ transported samples are represented in order to give a visual understanding of what the transport methods are doing. - -.. code-block:: python +.. code-block:: default # Authors: Remi Flamary @@ -35,12 +40,12 @@ of what the transport methods are doing. + generate data ------------- - -.. code-block:: python +.. code-block:: default n_samples_source = 150 @@ -59,12 +64,12 @@ generate data + Instantiate the different transport algorithms and fit them ----------------------------------------------------------- - -.. code-block:: python +.. code-block:: default # EMD Transport @@ -91,12 +96,12 @@ Instantiate the different transport algorithms and fit them + Fig 1 : plots source and target samples + matrix of pairwise distance --------------------------------------------------------------------- - -.. code-block:: python +.. code-block:: default pl.figure(1, figsize=(10, 10)) @@ -126,7 +131,8 @@ Fig 1 : plots source and target samples + matrix of pairwise distance .. image:: /auto_examples/images/sphx_glr_plot_otda_d2_001.png - :align: center + :class: sphx-glr-single-img + @@ -135,8 +141,7 @@ Fig 2 : plots optimal couplings for the different methods --------------------------------------------------------- - -.. code-block:: python +.. code-block:: default pl.figure(2, figsize=(10, 6)) @@ -187,8 +192,9 @@ Fig 2 : plots optimal couplings for the different methods -.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_003.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_002.png + :class: sphx-glr-single-img + @@ -197,8 +203,7 @@ Fig 3 : plot transported samples -------------------------------- - -.. code-block:: python +.. code-block:: default # display transported samples @@ -236,28 +241,45 @@ Fig 3 : plot transported samples -.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_006.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_003.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + .. code-block:: none + /home/rflamary/PYTHON/POT/examples/plot_otda_d2.py:172: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() -**Total running time of the script:** ( 0 minutes 35.515 seconds) +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 21.323 seconds) + + +.. _sphx_glr_download_auto_examples_plot_otda_d2.py: + + .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_otda_d2.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_otda_d2.ipynb ` @@ -266,4 +288,4 @@ Fig 3 : plot transported samples .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_linear_mapping.ipynb b/docs/source/auto_examples/plot_otda_linear_mapping.ipynb index 027b6cb..96eccbe 100644 --- a/docs/source/auto_examples/plot_otda_linear_mapping.ipynb +++ b/docs/source/auto_examples/plot_otda_linear_mapping.ipynb @@ -134,7 +134,7 @@ }, "outputs": [], "source": [ - "mapping = ot.da.LinearTransport()\n\nmapping.fit(Xs=X1, Xt=X2)\n\n\nxst = mapping.transform(Xs=X1)\nxts = mapping.inverse_transform(Xt=X2)\n\nI1t = minmax(mat2im(xst, I1.shape))\nI2t = minmax(mat2im(xts, I2.shape))\n\n# %%" + "mapping = ot.da.LinearTransport()\n\nmapping.fit(Xs=X1, Xt=X2)\n\n\nxst = mapping.transform(Xs=X1)\nxts = mapping.inverse_transform(Xt=X2)\n\nI1t = minmax(mat2im(xst, I1.shape))\nI2t = minmax(mat2im(xts, I2.shape))" ] }, { @@ -172,7 +172,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_otda_linear_mapping.rst b/docs/source/auto_examples/plot_otda_linear_mapping.rst index 8e2e0cf..63848d2 100644 --- a/docs/source/auto_examples/plot_otda_linear_mapping.rst +++ b/docs/source/auto_examples/plot_otda_linear_mapping.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_otda_linear_mapping.py: + .. _sphx_glr_auto_examples_plot_otda_linear_mapping.py: ============================ @@ -10,8 +16,7 @@ Linear OT mapping estimation - -.. code-block:: python +.. code-block:: default # Author: Remi Flamary @@ -28,12 +33,12 @@ Linear OT mapping estimation + Generate data ------------- - -.. code-block:: python +.. code-block:: default n = 1000 @@ -64,12 +69,12 @@ Generate data + Plot data --------- - -.. code-block:: python +.. code-block:: default pl.figure(1, (5, 5)) @@ -81,8 +86,17 @@ Plot data .. image:: /auto_examples/images/sphx_glr_plot_otda_linear_mapping_001.png - :align: center + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + Out: + + .. code-block:: none + + + [] @@ -90,8 +104,7 @@ Estimate linear mapping and transport ------------------------------------- - -.. code-block:: python +.. code-block:: default Ae, be = ot.da.OT_mapping_linear(xs, xt) @@ -105,12 +118,12 @@ Estimate linear mapping and transport + Plot transported samples ------------------------ - -.. code-block:: python +.. code-block:: default pl.figure(1, (5, 5)) @@ -125,7 +138,17 @@ Plot transported samples .. image:: /auto_examples/images/sphx_glr_plot_otda_linear_mapping_002.png - :align: center + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_otda_linear_mapping.py:73: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() @@ -134,8 +157,7 @@ Load image data --------------- - -.. code-block:: python +.. code-block:: default @@ -167,12 +189,12 @@ Load image data + Estimate mapping and adapt ---------------------------- - -.. code-block:: python +.. code-block:: default mapping = ot.da.LinearTransport() @@ -186,8 +208,6 @@ Estimate mapping and adapt I1t = minmax(mat2im(xst, I1.shape)) I2t = minmax(mat2im(xts, I2.shape)) - # %% - @@ -199,8 +219,7 @@ Plot transformed images ----------------------- - -.. code-block:: python +.. code-block:: default pl.figure(2, figsize=(10, 7)) @@ -227,28 +246,44 @@ Plot transformed images -.. image:: /auto_examples/images/sphx_glr_plot_otda_linear_mapping_004.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_otda_linear_mapping_003.png + :class: sphx-glr-single-img + +.. rst-class:: sphx-glr-script-out + Out: + .. code-block:: none -**Total running time of the script:** ( 0 minutes 0.635 seconds) + Text(0.5, 1.0, 'Inverse mapping Im. 2') + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 0.787 seconds) + + +.. _sphx_glr_download_auto_examples_plot_otda_linear_mapping.py: .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_otda_linear_mapping.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_otda_linear_mapping.ipynb ` @@ -257,4 +292,4 @@ Plot transformed images .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_mapping.ipynb b/docs/source/auto_examples/plot_otda_mapping.ipynb index 898466d..ac02255 100644 --- a/docs/source/auto_examples/plot_otda_mapping.ipynb +++ b/docs/source/auto_examples/plot_otda_mapping.ipynb @@ -118,7 +118,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_otda_mapping.rst b/docs/source/auto_examples/plot_otda_mapping.rst index 1d95fc6..99787f7 100644 --- a/docs/source/auto_examples/plot_otda_mapping.rst +++ b/docs/source/auto_examples/plot_otda_mapping.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_otda_mapping.py: + .. _sphx_glr_auto_examples_plot_otda_mapping.py: =========================================== @@ -16,8 +22,7 @@ a linear or a kernelized mapping as introduced in [8]. Neural Information Processing Systems (NIPS), 2016. - -.. code-block:: python +.. code-block:: default # Authors: Remi Flamary @@ -36,12 +41,12 @@ a linear or a kernelized mapping as introduced in [8]. + Generate data ------------- - -.. code-block:: python +.. code-block:: default n_source_samples = 100 @@ -66,12 +71,12 @@ Generate data + Plot data --------- - -.. code-block:: python +.. code-block:: default pl.figure(1, (10, 5)) @@ -86,17 +91,25 @@ Plot data .. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_001.png - :align: center + :class: sphx-glr-single-img +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + + Text(0.5, 1.0, 'Source and target distributions') + Instantiate the different transport algorithms and fit them ----------------------------------------------------------- - -.. code-block:: python +.. code-block:: default # MappingTransport with linear kernel @@ -132,38 +145,41 @@ Instantiate the different transport algorithms and fit them .. rst-class:: sphx-glr-script-out - Out:: + Out: + + .. code-block:: none It. |Loss |Delta loss -------------------------------- - 0|4.299275e+03|0.000000e+00 - 1|4.290443e+03|-2.054271e-03 - 2|4.290040e+03|-9.389994e-05 - 3|4.289876e+03|-3.830707e-05 - 4|4.289783e+03|-2.157428e-05 - 5|4.289724e+03|-1.390941e-05 - 6|4.289706e+03|-4.051054e-06 + 0|4.212661e+03|0.000000e+00 + 1|4.198567e+03|-3.345626e-03 + 2|4.198198e+03|-8.797101e-05 + 3|4.198027e+03|-4.059527e-05 + 4|4.197928e+03|-2.355659e-05 + 5|4.197886e+03|-1.002352e-05 + 6|4.197853e+03|-7.873125e-06 It. |Loss |Delta loss -------------------------------- - 0|4.326465e+02|0.000000e+00 - 1|4.282533e+02|-1.015416e-02 - 2|4.279473e+02|-7.145955e-04 - 3|4.277941e+02|-3.580104e-04 - 4|4.277069e+02|-2.039229e-04 - 5|4.276462e+02|-1.418698e-04 - 6|4.276011e+02|-1.054172e-04 - 7|4.275663e+02|-8.145802e-05 - 8|4.275405e+02|-6.028774e-05 - 9|4.275191e+02|-5.005886e-05 - 10|4.275019e+02|-4.021935e-05 + 0|4.231694e+02|0.000000e+00 + 1|4.185911e+02|-1.081889e-02 + 2|4.182717e+02|-7.631953e-04 + 3|4.181271e+02|-3.455908e-04 + 4|4.180328e+02|-2.255461e-04 + 5|4.179645e+02|-1.634435e-04 + 6|4.179136e+02|-1.216359e-04 + 7|4.178752e+02|-9.198108e-05 + 8|4.178465e+02|-6.870868e-05 + 9|4.178243e+02|-5.321390e-05 + 10|4.178054e+02|-4.521725e-05 + + Plot transported samples ------------------------ - -.. code-block:: python +.. code-block:: default pl.figure(2) @@ -202,28 +218,45 @@ Plot transported samples -.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_003.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_002.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_otda_mapping.py:125: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() + -**Total running time of the script:** ( 0 minutes 0.795 seconds) +.. rst-class:: sphx-glr-timing + **Total running time of the script:** ( 0 minutes 0.843 seconds) + + +.. _sphx_glr_download_auto_examples_plot_otda_mapping.py: .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_otda_mapping.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_otda_mapping.ipynb ` @@ -232,4 +265,4 @@ Plot transported samples .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb b/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb index baffef4..de46629 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb @@ -26,7 +26,7 @@ }, "outputs": [], "source": [ - "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport numpy as np\nfrom scipy import ndimage\nimport matplotlib.pylab as pl\nimport ot\n\nr = np.random.RandomState(42)\n\n\ndef im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)" + "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\nr = np.random.RandomState(42)\n\n\ndef im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)" ] }, { @@ -44,7 +44,7 @@ }, "outputs": [], "source": [ - "# Loading images\nI1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)\n\n# training samples\nnb = 1000\nidx1 = r.randint(X1.shape[0], size=(nb,))\nidx2 = r.randint(X2.shape[0], size=(nb,))\n\nXs = X1[idx1, :]\nXt = X2[idx2, :]" + "# Loading images\nI1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)\n\n# training samples\nnb = 1000\nidx1 = r.randint(X1.shape[0], size=(nb,))\nidx2 = r.randint(X2.shape[0], size=(nb,))\n\nXs = X1[idx1, :]\nXt = X2[idx2, :]" ] }, { @@ -136,7 +136,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.py b/docs/source/auto_examples/plot_otda_mapping_colors_images.py index a20eca8..bc9afba 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.py +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.py @@ -22,7 +22,6 @@ estimation [8]. # License: MIT License import numpy as np -from scipy import ndimage import matplotlib.pylab as pl import ot @@ -48,8 +47,8 @@ def minmax(I): # ------------- # Loading images -I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 +I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 X1 = im2mat(I1) diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst b/docs/source/auto_examples/plot_otda_mapping_colors_images.rst index 2afdc8a..26664e3 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_otda_mapping_colors_images.py: + .. _sphx_glr_auto_examples_plot_otda_mapping_colors_images.py: ===================================================== @@ -19,8 +25,7 @@ estimation [8]. - -.. code-block:: python +.. code-block:: default # Authors: Remi Flamary @@ -29,7 +34,6 @@ estimation [8]. # License: MIT License import numpy as np - from scipy import ndimage import matplotlib.pylab as pl import ot @@ -56,17 +60,17 @@ estimation [8]. + Generate data ------------- - -.. code-block:: python +.. code-block:: default # Loading images - I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 - I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 + I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 + I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 X1 = im2mat(I1) @@ -87,12 +91,12 @@ Generate data + Domain adaptation for pixel distribution transfer ------------------------------------------------- - -.. code-block:: python +.. code-block:: default # EMDTransport @@ -128,7 +132,9 @@ Domain adaptation for pixel distribution transfer .. rst-class:: sphx-glr-script-out - Out:: + Out: + + .. code-block:: none It. |Loss |Delta loss -------------------------------- @@ -167,12 +173,13 @@ Domain adaptation for pixel distribution transfer 10|3.639419e+02|-3.209753e-05 + + Plot original images -------------------- - -.. code-block:: python +.. code-block:: default pl.figure(1, figsize=(6.4, 3)) @@ -192,7 +199,8 @@ Plot original images .. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_colors_images_001.png - :align: center + :class: sphx-glr-single-img + @@ -201,8 +209,7 @@ Plot pixel values distribution ------------------------------ - -.. code-block:: python +.. code-block:: default pl.figure(2, figsize=(6.4, 5)) @@ -226,8 +233,9 @@ Plot pixel values distribution -.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_colors_images_003.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_colors_images_002.png + :class: sphx-glr-single-img + @@ -236,8 +244,7 @@ Plot transformed images ----------------------- - -.. code-block:: python +.. code-block:: default pl.figure(2, figsize=(10, 5)) @@ -277,28 +284,45 @@ Plot transformed images -.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_colors_images_004.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_colors_images_003.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + .. code-block:: none + /home/rflamary/PYTHON/POT/examples/plot_otda_mapping_colors_images.py:173: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() -**Total running time of the script:** ( 3 minutes 14.206 seconds) +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 2 minutes 24.007 seconds) + + +.. _sphx_glr_download_auto_examples_plot_otda_mapping_colors_images.py: + + .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_otda_mapping_colors_images.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_otda_mapping_colors_images.ipynb ` @@ -307,4 +331,4 @@ Plot transformed images .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_semi_supervised.ipynb b/docs/source/auto_examples/plot_otda_semi_supervised.ipynb index e3192da..d2157fb 100644 --- a/docs/source/auto_examples/plot_otda_semi_supervised.ipynb +++ b/docs/source/auto_examples/plot_otda_semi_supervised.ipynb @@ -136,7 +136,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_otda_semi_supervised.rst b/docs/source/auto_examples/plot_otda_semi_supervised.rst index 2ed7819..4a355e7 100644 --- a/docs/source/auto_examples/plot_otda_semi_supervised.rst +++ b/docs/source/auto_examples/plot_otda_semi_supervised.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_otda_semi_supervised.py: + .. _sphx_glr_auto_examples_plot_otda_semi_supervised.py: ============================================ @@ -16,8 +22,7 @@ transported samples are represented in order to give a visual understanding of what the transport methods are doing. - -.. code-block:: python +.. code-block:: default # Authors: Remi Flamary @@ -35,12 +40,12 @@ of what the transport methods are doing. + Generate data ------------- - -.. code-block:: python +.. code-block:: default n_samples_source = 150 @@ -56,12 +61,12 @@ Generate data + Transport source samples onto target samples -------------------------------------------- - -.. code-block:: python +.. code-block:: default @@ -94,12 +99,12 @@ Transport source samples onto target samples + Fig 1 : plots source and target samples + matrix of pairwise distance --------------------------------------------------------------------- - -.. code-block:: python +.. code-block:: default pl.figure(1, figsize=(10, 10)) @@ -139,7 +144,8 @@ Fig 1 : plots source and target samples + matrix of pairwise distance .. image:: /auto_examples/images/sphx_glr_plot_otda_semi_supervised_001.png - :align: center + :class: sphx-glr-single-img + @@ -148,8 +154,7 @@ Fig 2 : plots optimal couplings for the different methods --------------------------------------------------------- - -.. code-block:: python +.. code-block:: default pl.figure(2, figsize=(8, 4)) @@ -172,8 +177,9 @@ Fig 2 : plots optimal couplings for the different methods -.. image:: /auto_examples/images/sphx_glr_plot_otda_semi_supervised_003.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_otda_semi_supervised_002.png + :class: sphx-glr-single-img + @@ -182,8 +188,7 @@ Fig 3 : plot transported samples -------------------------------- - -.. code-block:: python +.. code-block:: default # display transported samples @@ -212,28 +217,45 @@ Fig 3 : plot transported samples -.. image:: /auto_examples/images/sphx_glr_plot_otda_semi_supervised_006.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_otda_semi_supervised_003.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + .. code-block:: none + /home/rflamary/PYTHON/POT/examples/plot_otda_semi_supervised.py:148: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() -**Total running time of the script:** ( 0 minutes 0.256 seconds) +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 0.660 seconds) + + +.. _sphx_glr_download_auto_examples_plot_otda_semi_supervised.py: + + .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_otda_semi_supervised.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_otda_semi_supervised.ipynb ` @@ -242,4 +264,4 @@ Fig 3 : plot transported samples .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_stochastic.ipynb b/docs/source/auto_examples/plot_stochastic.ipynb index 7f6ff3d..c29f75a 100644 --- a/docs/source/auto_examples/plot_stochastic.ipynb +++ b/docs/source/auto_examples/plot_stochastic.ipynb @@ -287,7 +287,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.7" + "version": "3.6.9" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_stochastic.rst b/docs/source/auto_examples/plot_stochastic.rst index d531045..63fc74f 100644 --- a/docs/source/auto_examples/plot_stochastic.rst +++ b/docs/source/auto_examples/plot_stochastic.rst @@ -1,6 +1,12 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title -.. _sphx_glr_auto_examples_plot_stochastic.py: + .. _sphx_glr_auto_examples_plot_stochastic.py: ========================== @@ -12,8 +18,7 @@ algorithms for descrete and semicontinous measures from the POT library. - -.. code-block:: python +.. code-block:: default # Author: Kilian Fatras @@ -32,6 +37,7 @@ algorithms for descrete and semicontinous measures from the POT library. + COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM ############################################################################ ############################################################################ @@ -44,8 +50,7 @@ COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM and the target measures and finally the cost matrix c. - -.. code-block:: python +.. code-block:: default n_source = 7 @@ -67,6 +72,7 @@ COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM + Call the "SAG" method to find the transportation matrix in the discrete case --------------------------------------------- @@ -74,8 +80,7 @@ Define the method "SAG", call ot.solve_semi_dual_entropic and plot the results. - -.. code-block:: python +.. code-block:: default method = "SAG" @@ -89,7 +94,9 @@ results. .. rst-class:: sphx-glr-script-out - Out:: + Out: + + .. code-block:: none [[2.55553509e-02 9.96395660e-02 1.76579142e-02 4.31178196e-06] [1.21640234e-01 1.25357448e-02 1.30225078e-03 7.37891338e-03] @@ -100,6 +107,8 @@ results. [4.15462212e-02 2.65987989e-02 7.23177216e-02 2.39440107e-03]] + + SEMICONTINOUS CASE: Sample one general measure a, one discrete measures b for the semicontinous @@ -110,8 +119,7 @@ Define one general measure a, one discrete measures b, the points where are defined the source and the target measures and finally the cost matrix c. - -.. code-block:: python +.. code-block:: default n_source = 7 @@ -134,6 +142,7 @@ are defined the source and the target measures and finally the cost matrix c. + Call the "ASGD" method to find the transportation matrix in the semicontinous case --------------------------------------------- @@ -142,8 +151,7 @@ Define the method "ASGD", call ot.solve_semi_dual_entropic and plot the results. - -.. code-block:: python +.. code-block:: default method = "ASGD" @@ -158,17 +166,21 @@ results. .. rst-class:: sphx-glr-script-out - Out:: + Out: + + .. code-block:: none + + [3.89943264 7.64823414 3.9284189 2.67501041 1.42825446 3.26039819 + 2.79237712] [-2.50786905 -2.42684838 -0.93647774 5.87119517] + [[2.50229922e-02 1.00367920e-01 1.74615056e-02 4.72486104e-06] + [1.20583329e-01 1.27839737e-02 1.30373565e-03 8.18610462e-03] + [3.49243139e-03 7.68200813e-02 6.25444833e-02 1.46879008e-07] + [2.58205995e-02 3.39501207e-02 8.26360982e-02 4.50324517e-04] + [8.94164918e-03 7.02183713e-04 9.92028326e-03 1.23293027e-01] + [1.97360234e-02 8.46022708e-04 1.72001583e-03 1.20555081e-01] + [4.10386980e-02 2.70289873e-02 7.21425804e-02 2.64687723e-03]] + - [3.98220325 7.76235856 3.97645524 2.72051681 1.23219313 3.07696856 - 2.84476972] [-2.65544161 -2.50838395 -0.9397765 6.10360206] - [[2.34528761e-02 1.00491956e-01 1.89058354e-02 6.47543413e-06] - [1.16616747e-01 1.32074516e-02 1.45653361e-03 1.15764107e-02] - [3.16154850e-03 7.42892944e-02 6.54061055e-02 1.94426150e-07] - [2.33152216e-02 3.27486992e-02 8.61986263e-02 5.94595747e-04] - [6.34131496e-03 5.31975896e-04 8.12724003e-03 1.27856612e-01] - [1.41744829e-02 6.49096245e-04 1.42704389e-03 1.26606520e-01] - [3.73127657e-02 2.62526499e-02 7.57727161e-02 3.51901117e-03]] Compare the results with the Sinkhorn algorithm @@ -177,8 +189,7 @@ Compare the results with the Sinkhorn algorithm Call the Sinkhorn algorithm from POT - -.. code-block:: python +.. code-block:: default sinkhorn_pi = ot.sinkhorn(a, b, M, reg) @@ -191,27 +202,29 @@ Call the Sinkhorn algorithm from POT .. rst-class:: sphx-glr-script-out - Out:: + Out: + + .. code-block:: none + + [[2.55553508e-02 9.96395661e-02 1.76579142e-02 4.31178193e-06] + [1.21640234e-01 1.25357448e-02 1.30225079e-03 7.37891333e-03] + [3.56123974e-03 7.61451746e-02 6.31505947e-02 1.33831455e-07] + [2.61515201e-02 3.34246014e-02 8.28734709e-02 4.07550425e-04] + [9.85500876e-03 7.52288523e-04 1.08262629e-02 1.21423583e-01] + [2.16904255e-02 9.03825804e-04 1.87178504e-03 1.18391107e-01] + [4.15462212e-02 2.65987989e-02 7.23177217e-02 2.39440105e-03]] + - [[2.55535622e-02 9.96413843e-02 1.76578860e-02 4.31043335e-06] - [1.21640742e-01 1.25369034e-02 1.30234529e-03 7.37715259e-03] - [3.56096458e-03 7.61460101e-02 6.31500344e-02 1.33788624e-07] - [2.61499607e-02 3.34255577e-02 8.28741973e-02 4.07427179e-04] - [9.85698720e-03 7.52505948e-04 1.08291770e-02 1.21418473e-01] - [2.16947591e-02 9.04086158e-04 1.87228707e-03 1.18386011e-01] - [4.15442692e-02 2.65998963e-02 7.23192701e-02 2.39370724e-03]] PLOT TRANSPORTATION MATRIX ############################################################################# - Plot SAG results ---------------- - -.. code-block:: python +.. code-block:: default pl.figure(4, figsize=(5, 5)) @@ -222,8 +235,18 @@ Plot SAG results -.. image:: /auto_examples/images/sphx_glr_plot_stochastic_004.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_stochastic_001.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_stochastic.py:119: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() @@ -232,8 +255,7 @@ Plot ASGD results ----------------- - -.. code-block:: python +.. code-block:: default pl.figure(4, figsize=(5, 5)) @@ -244,8 +266,18 @@ Plot ASGD results -.. image:: /auto_examples/images/sphx_glr_plot_stochastic_005.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_stochastic_002.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_stochastic.py:128: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() @@ -254,8 +286,7 @@ Plot Sinkhorn results --------------------- - -.. code-block:: python +.. code-block:: default pl.figure(4, figsize=(5, 5)) @@ -266,8 +297,18 @@ Plot Sinkhorn results -.. image:: /auto_examples/images/sphx_glr_plot_stochastic_006.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_stochastic_003.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_stochastic.py:137: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() @@ -285,8 +326,7 @@ COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM are defined the source and the target measures and finally the cost matrix c. - -.. code-block:: python +.. code-block:: default n_source = 7 @@ -311,6 +351,7 @@ COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM + Call the "SGD" dual method to find the transportation matrix in the semicontinous case --------------------------------------------- @@ -318,8 +359,7 @@ semicontinous case Call ot.solve_dual_entropic and plot the results. - -.. code-block:: python +.. code-block:: default sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg, @@ -334,17 +374,21 @@ Call ot.solve_dual_entropic and plot the results. .. rst-class:: sphx-glr-script-out - Out:: + Out: + + .. code-block:: none + + [0.91421006 2.78075506 1.06828701 0.01979397 0.60914807 1.81887037 + 0.1152939 ] [0.33964624 0.47604281 1.57223631 4.93843308] + [[2.18038772e-02 9.24355133e-02 1.08426805e-02 9.39355366e-08] + [1.59966167e-02 1.79248770e-03 1.23251128e-04 2.47779034e-05] + [3.44864558e-03 8.01760930e-02 4.40119061e-02 3.30922887e-09] + [3.12954103e-02 4.34915712e-02 7.13747533e-02 1.24533534e-05] + [6.79742497e-02 5.64192090e-03 5.37416946e-02 2.13851205e-02] + [8.05141568e-02 3.64790957e-03 5.00040902e-03 1.12213345e-02] + [4.86643900e-02 3.38763749e-02 6.09634969e-02 7.16139950e-05]] + - [0.92449986 2.75486107 1.07923806 0.02741145 0.61355413 1.81961594 - 0.12072562] [0.33831611 0.46806842 1.5640451 4.96947652] - [[2.20001105e-02 9.26497883e-02 1.08654588e-02 9.78995555e-08] - [1.55669974e-02 1.73279561e-03 1.19120878e-04 2.49058251e-05] - [3.48198483e-03 8.04151063e-02 4.41335396e-02 3.45115752e-09] - [3.14927954e-02 4.34760520e-02 7.13338154e-02 1.29442395e-05] - [6.81836550e-02 5.62182457e-03 5.35386584e-02 2.21568095e-02] - [8.04671052e-02 3.62163462e-03 4.96331605e-03 1.15837801e-02] - [4.88644009e-02 3.37903481e-02 6.07955004e-02 7.42743505e-05]] Compare the results with the Sinkhorn algorithm @@ -353,8 +397,7 @@ Compare the results with the Sinkhorn algorithm Call the Sinkhorn algorithm from POT - -.. code-block:: python +.. code-block:: default sinkhorn_pi = ot.sinkhorn(a, b, M, reg) @@ -366,23 +409,26 @@ Call the Sinkhorn algorithm from POT .. rst-class:: sphx-glr-script-out - Out:: + Out: + + .. code-block:: none + + [[2.55553508e-02 9.96395661e-02 1.76579142e-02 4.31178193e-06] + [1.21640234e-01 1.25357448e-02 1.30225079e-03 7.37891333e-03] + [3.56123974e-03 7.61451746e-02 6.31505947e-02 1.33831455e-07] + [2.61515201e-02 3.34246014e-02 8.28734709e-02 4.07550425e-04] + [9.85500876e-03 7.52288523e-04 1.08262629e-02 1.21423583e-01] + [2.16904255e-02 9.03825804e-04 1.87178504e-03 1.18391107e-01] + [4.15462212e-02 2.65987989e-02 7.23177217e-02 2.39440105e-03]] + - [[2.55535622e-02 9.96413843e-02 1.76578860e-02 4.31043335e-06] - [1.21640742e-01 1.25369034e-02 1.30234529e-03 7.37715259e-03] - [3.56096458e-03 7.61460101e-02 6.31500344e-02 1.33788624e-07] - [2.61499607e-02 3.34255577e-02 8.28741973e-02 4.07427179e-04] - [9.85698720e-03 7.52505948e-04 1.08291770e-02 1.21418473e-01] - [2.16947591e-02 9.04086158e-04 1.87228707e-03 1.18386011e-01] - [4.15442692e-02 2.65998963e-02 7.23192701e-02 2.39370724e-03]] Plot SGD results ----------------- - -.. code-block:: python +.. code-block:: default pl.figure(4, figsize=(5, 5)) @@ -393,8 +439,18 @@ Plot SGD results -.. image:: /auto_examples/images/sphx_glr_plot_stochastic_007.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_stochastic_004.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_stochastic.py:199: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() @@ -403,8 +459,7 @@ Plot Sinkhorn results --------------------- - -.. code-block:: python +.. code-block:: default pl.figure(4, figsize=(5, 5)) @@ -413,28 +468,45 @@ Plot Sinkhorn results -.. image:: /auto_examples/images/sphx_glr_plot_stochastic_008.png - :align: center +.. image:: /auto_examples/images/sphx_glr_plot_stochastic_005.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_stochastic.py:208: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() -**Total running time of the script:** ( 0 minutes 20.889 seconds) +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 8.885 seconds) + + +.. _sphx_glr_download_auto_examples_plot_stochastic.py: .. only :: html .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_stochastic.py ` - .. container:: sphx-glr-download + .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_stochastic.ipynb ` @@ -443,4 +515,4 @@ Plot Sinkhorn results .. rst-class:: sphx-glr-signature - `Gallery generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ -- cgit v1.2.3 From 8df1b72d664a57bfbee085f994539a843f25c942 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 20 Apr 2020 15:32:05 +0200 Subject: bump beta version and update setup.py --- ot/__init__.py | 2 +- setup.py | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/ot/__init__.py b/ot/__init__.py index 4fcb800..1e57b78 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -73,7 +73,7 @@ from .da import sinkhorn_lpl1_mm # utils functions from .utils import dist, unif, tic, toc, toq -__version__ = "0.6.0" +__version__ = "0.7.0b" __all__ = ['emd', 'emd2', 'emd_1d', 'sinkhorn', 'sinkhorn2', 'utils', 'datasets', 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', diff --git a/setup.py b/setup.py index bb00854..e780187 100755 --- a/setup.py +++ b/setup.py @@ -45,7 +45,7 @@ setup(name='POT', long_description_content_type='text/markdown', author=u'Remi Flamary, Nicolas Courty', author_email='remi.flamary@gmail.com, ncourty@gmail.com', - url='https://github.com/rflamary/POT', + url='https://github.com/PythonOT/POT', packages=find_packages(), ext_modules = cythonize(Extension( "ot.lp.emd_wrap", # the extension name @@ -56,7 +56,7 @@ setup(name='POT', extra_compile_args=opt_arg )), platforms=['linux','macosx','windows'], - download_url='https://github.com/rflamary/POT/archive/{}.tar.gz'.format(__version__), + download_url='https://github.com/PythonOT/POT/archive/{}.tar.gz'.format(__version__), license = 'MIT', scripts=[], data_files=[], -- cgit v1.2.3 From 8933a84a14bfda3da66983ea35784ad90091f439 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 20 Apr 2020 15:53:40 +0200 Subject: pep8 setup.py and remove cython cpp files --- setup.py | 94 ++++++++++++++++++++++++++++++++++------------------------------ 1 file changed, 50 insertions(+), 44 deletions(-) diff --git a/setup.py b/setup.py index e780187..4640d00 100755 --- a/setup.py +++ b/setup.py @@ -9,12 +9,12 @@ import numpy import re import os import sys -import subprocess +import subprocess here = path.abspath(path.dirname(__file__)) -os.environ["CC"] = "g++" +os.environ["CC"] = "g++" os.environ["CXX"] = "g++" # dirty but working @@ -30,61 +30,67 @@ ROOT = os.path.abspath(os.path.dirname(__file__)) with open(os.path.join(ROOT, 'README.md'), encoding="utf-8") as f: README = f.read() -opt_arg=["-O3"] +opt_arg = ["-O3"] + +# clean cython output is clean is called +if 'clean' in sys.argv[1:]: + if os.path.isfile('ot/lp/emd_wrap.cpp'): + os.remove('ot/lp/emd_wrap.cpp') + # add platform dependant optional compilation argument if sys.platform.startswith('darwin'): - opt_arg.append("-stdlib=libc++") - sdk_path = subprocess.check_output(['xcrun', '--show-sdk-path']) - os.environ['CFLAGS'] = '-isysroot "{}"'.format(sdk_path.rstrip().decode("utf-8")) + opt_arg.append("-stdlib=libc++") + sdk_path = subprocess.check_output(['xcrun', '--show-sdk-path']) + os.environ['CFLAGS'] = '-isysroot "{}"'.format(sdk_path.rstrip().decode("utf-8")) setup(name='POT', version=__version__, description='Python Optimal Transport Library', long_description=README, - long_description_content_type='text/markdown', + long_description_content_type='text/markdown', author=u'Remi Flamary, Nicolas Courty', author_email='remi.flamary@gmail.com, ncourty@gmail.com', url='https://github.com/PythonOT/POT', packages=find_packages(), - ext_modules = cythonize(Extension( - "ot.lp.emd_wrap", # the extension name - sources=["ot/lp/emd_wrap.pyx", "ot/lp/EMD_wrapper.cpp"], # the Cython source and - # additional C++ source files - language="c++", # generate and compile C++ code, - include_dirs=[numpy.get_include(),os.path.join(ROOT,'ot/lp')], - extra_compile_args=opt_arg - )), - platforms=['linux','macosx','windows'], + ext_modules=cythonize(Extension( + "ot.lp.emd_wrap", # the extension name + sources=["ot/lp/emd_wrap.pyx", "ot/lp/EMD_wrapper.cpp"], # the Cython source and + # additional C++ source files + language="c++", # generate and compile C++ code, + include_dirs=[numpy.get_include(), os.path.join(ROOT, 'ot/lp')], + extra_compile_args=opt_arg + )), + platforms=['linux', 'macosx', 'windows'], download_url='https://github.com/PythonOT/POT/archive/{}.tar.gz'.format(__version__), - license = 'MIT', + license='MIT', scripts=[], data_files=[], - requires=["numpy","scipy","cython"], - install_requires=["numpy","scipy","cython"], + requires=["numpy", "scipy", "cython"], + install_requires=["numpy", "scipy", "cython"], classifiers=[ - 'Development Status :: 5 - Production/Stable', - 'Intended Audience :: Developers', - 'Intended Audience :: Education', - 'Intended Audience :: Science/Research', - 'License :: OSI Approved :: MIT License', - 'Environment :: Console', - 'Operating System :: OS Independent', - 'Operating System :: MacOS', - 'Operating System :: POSIX', - 'Programming Language :: Python', - 'Programming Language :: C++', - 'Programming Language :: C', - 'Programming Language :: Cython', - 'Topic :: Utilities', - 'Topic :: Scientific/Engineering :: Artificial Intelligence', - 'Topic :: Scientific/Engineering :: Mathematics', - 'Topic :: Scientific/Engineering :: Information Analysis', - 'Programming Language :: Python :: 2', - 'Programming Language :: Python :: 2.7', - 'Programming Language :: Python :: 3', - 'Programming Language :: Python :: 3.4', - 'Programming Language :: Python :: 3.5', - 'Programming Language :: Python :: 3.6', - ] - ) + 'Development Status :: 5 - Production/Stable', + 'Intended Audience :: Developers', + 'Intended Audience :: Education', + 'Intended Audience :: Science/Research', + 'License :: OSI Approved :: MIT License', + 'Environment :: Console', + 'Operating System :: OS Independent', + 'Operating System :: MacOS', + 'Operating System :: POSIX', + 'Programming Language :: Python', + 'Programming Language :: C++', + 'Programming Language :: C', + 'Programming Language :: Cython', + 'Topic :: Utilities', + 'Topic :: Scientific/Engineering :: Artificial Intelligence', + 'Topic :: Scientific/Engineering :: Mathematics', + 'Topic :: Scientific/Engineering :: Information Analysis', + 'Programming Language :: Python :: 2', + 'Programming Language :: Python :: 2.7', + 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3.4', + 'Programming Language :: Python :: 3.5', + 'Programming Language :: Python :: 3.6', + ] + ) -- cgit v1.2.3 From 88054b5d514a5380e217ebd889c31366aba9c726 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 20 Apr 2020 15:59:33 +0200 Subject: update doc --- docs/source/readme.rst | 81 ++++++++++++++++++++++++++------------------------ 1 file changed, 42 insertions(+), 39 deletions(-) diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 6d98dc5..4f6af01 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -39,6 +39,8 @@ It provides the following solvers: - Screening Sinkhorn Algorithm for OT [26]. - JCPOT algorithm for multi-source domain adaptation with target shift [27]. +- Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic + [3] formulations). Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -51,12 +53,12 @@ POT using the following bibtex reference: :: - @misc{flamary2017pot, - title={POT Python Optimal Transport library}, - author={Flamary, R{'e}mi and Courty, Nicolas}, - url={https://github.com/rflamary/POT}, - year={2017} - } + @misc{flamary2017pot, + title={POT Python Optimal Transport library}, + author={Flamary, R{'e}mi and Courty, Nicolas}, + url={https://github.com/rflamary/POT}, + year={2017} + } Installation ------------ @@ -78,19 +80,19 @@ be installed prior to installing POT. This can be done easily with :: - pip install numpy cython + pip install numpy cython You can install the toolbox through PyPI with: :: - pip install POT + pip install POT or get the very latest version by downloading it and then running: :: - python setup.py install --user # for user install (no root) + python setup.py install --user # for user install (no root) Anaconda installation with conda-forge ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -101,7 +103,7 @@ required dependencies: :: - conda install -c conda-forge pot + conda install -c conda-forge pot Post installation check ^^^^^^^^^^^^^^^^^^^^^^^ @@ -111,7 +113,7 @@ without errors: .. code:: python - import ot + import ot Note that for easier access the module is name ot instead of pot. @@ -124,9 +126,9 @@ below - **ot.dr** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: -:: + :: - pip install pymanopt autograd + pip install pymanopt autograd - **ot.gpu** (GPU accelerated OT) depends on cupy that have to be installed following instructions on `this @@ -142,36 +144,36 @@ Short examples - Import the toolbox -.. code:: python + .. code:: python - import ot + import ot - Compute Wasserstein distances -.. code:: python + .. code:: python - # a,b are 1D histograms (sum to 1 and positive) - # M is the ground cost matrix - Wd=ot.emd2(a,b,M) # exact linear program - Wd_reg=ot.sinkhorn2(a,b,M,reg) # entropic regularized OT - # if b is a matrix compute all distances to a and return a vector + # a,b are 1D histograms (sum to 1 and positive) + # M is the ground cost matrix + Wd=ot.emd2(a,b,M) # exact linear program + Wd_reg=ot.sinkhorn2(a,b,M,reg) # entropic regularized OT + # if b is a matrix compute all distances to a and return a vector - Compute OT matrix -.. code:: python + .. code:: python - # a,b are 1D histograms (sum to 1 and positive) - # M is the ground cost matrix - T=ot.emd(a,b,M) # exact linear program - T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT + # a,b are 1D histograms (sum to 1 and positive) + # M is the ground cost matrix + T=ot.emd(a,b,M) # exact linear program + T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT - Compute Wasserstein barycenter -.. code:: python + .. code:: python - # A is a n*d matrix containing d 1D histograms - # M is the ground cost matrix - ba=ot.barycenter(A,M,reg) # reg is regularization parameter + # A is a n*d matrix containing d 1D histograms + # M is the ground cost matrix + ba=ot.barycenter(A,M,reg) # reg is regularization parameter Examples and Notebooks ~~~~~~~~~~~~~~~~~~~~~~ @@ -282,11 +284,11 @@ References [1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). `Displacement interpolation using Lagrangian mass transport `__. -In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. +In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. [2] Cuturi, M. (2013). `Sinkhorn distances: Lightspeed computation of optimal transport `__. In Advances -in Neural Information Processing Systems (pp. 2292-2300). +in Neural Information Processing Systems (pp. 2292-2300). [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). `Iterative Bregman projections for regularized transportation @@ -410,14 +412,15 @@ Shift `__, Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019. -[28] Caffarelli, L. A., McCann, R. J. (2020). [Free boundaries in -optimal transport and Monge-Ampere obstacle problems] -(http://www.math.toronto.edu/~mccann/papers/annals2010.pdf), Annals of -mathematics, 673-730. +[28] Caffarelli, L. A., McCann, R. J. (2020). `Free boundaries in +optimal transport and Monge-Ampere obstacle +problems `__, +Annals of mathematics, 673-730. -[29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial -Gromov-Wasserstein with Applications on Positive-Unlabeled Learning"] -(https://arxiv.org/abs/2002.08276), arXiv preprint arXiv:2002.08276. +[29] Chapel, L., Alaya, M., Gasso, G. (2019). `Partial +Gromov-Wasserstein with Applications on Positive-Unlabeled +Learning `__, arXiv preprint +arXiv:2002.08276. .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT -- cgit v1.2.3 From 6ac8d405f16832e671c432d7b03ce3da38f8fedc Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 20 Apr 2020 16:01:15 +0200 Subject: add all pages in documentation --- docs/source/auto_examples/plot_otda_jcpot.ipynb | 173 +++++++++++ docs/source/auto_examples/plot_otda_jcpot.py | 171 +++++++++++ docs/source/auto_examples/plot_otda_jcpot.rst | 336 +++++++++++++++++++++ .../plot_partial_wass_and_gromov.ipynb | 126 ++++++++ .../auto_examples/plot_partial_wass_and_gromov.py | 165 ++++++++++ .../source/auto_examples/plot_screenkhorn_1D.ipynb | 108 +++++++ docs/source/auto_examples/plot_screenkhorn_1D.py | 68 +++++ 7 files changed, 1147 insertions(+) create mode 100644 docs/source/auto_examples/plot_otda_jcpot.ipynb create mode 100644 docs/source/auto_examples/plot_otda_jcpot.py create mode 100644 docs/source/auto_examples/plot_otda_jcpot.rst create mode 100644 docs/source/auto_examples/plot_partial_wass_and_gromov.ipynb create mode 100644 docs/source/auto_examples/plot_partial_wass_and_gromov.py create mode 100644 docs/source/auto_examples/plot_screenkhorn_1D.ipynb create mode 100644 docs/source/auto_examples/plot_screenkhorn_1D.py diff --git a/docs/source/auto_examples/plot_otda_jcpot.ipynb b/docs/source/auto_examples/plot_otda_jcpot.ipynb new file mode 100644 index 0000000..a81d47a --- /dev/null +++ b/docs/source/auto_examples/plot_otda_jcpot.ipynb @@ -0,0 +1,173 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# OT for multi-source target shift\n\n\nThis example introduces a target shift problem with two 2D source and 1 target domain.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Authors: Remi Flamary \n# Ievgen Redko \n#\n# License: MIT License\n\nimport pylab as pl\nimport numpy as np\nimport ot\nfrom ot.datasets import make_data_classif" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n-------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n = 50\nsigma = 0.3\nnp.random.seed(1985)\n\np1 = .2\ndec1 = [0, 2]\n\np2 = .9\ndec2 = [0, -2]\n\npt = .4\ndect = [4, 0]\n\nxs1, ys1 = make_data_classif('2gauss_prop', n, nz=sigma, p=p1, bias=dec1)\nxs2, ys2 = make_data_classif('2gauss_prop', n + 1, nz=sigma, p=p2, bias=dec2)\nxt, yt = make_data_classif('2gauss_prop', n, nz=sigma, p=pt, bias=dect)\n\nall_Xr = [xs1, xs2]\nall_Yr = [ys1, ys2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "da = 1.5\n\n\ndef plot_ax(dec, name):\n pl.plot([dec[0], dec[0]], [dec[1] - da, dec[1] + da], 'k', alpha=0.5)\n pl.plot([dec[0] - da, dec[0] + da], [dec[1], dec[1]], 'k', alpha=0.5)\n pl.text(dec[0] - .5, dec[1] + 2, name)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig 1 : plots source and target samples\n---------------------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(1)\npl.clf()\nplot_ax(dec1, 'Source 1')\nplot_ax(dec2, 'Source 2')\nplot_ax(dect, 'Target')\npl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9,\n label='Source 1 ({:1.2f}, {:1.2f})'.format(1 - p1, p1))\npl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9,\n label='Source 2 ({:1.2f}, {:1.2f})'.format(1 - p2, p2))\npl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9,\n label='Target ({:1.2f}, {:1.2f})'.format(1 - pt, pt))\npl.title('Data')\n\npl.legend()\npl.axis('equal')\npl.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instantiate Sinkhorn transport algorithm and fit them for all source domains\n----------------------------------------------------------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1, metric='sqeuclidean')\n\n\ndef print_G(G, xs, ys, xt):\n for i in range(G.shape[0]):\n for j in range(G.shape[1]):\n if G[i, j] > 5e-4:\n if ys[i]:\n c = 'b'\n else:\n c = 'r'\n pl.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], xt[j, 1]], c, alpha=.2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig 2 : plot optimal couplings and transported samples\n------------------------------------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(2)\npl.clf()\nplot_ax(dec1, 'Source 1')\nplot_ax(dec2, 'Source 2')\nplot_ax(dect, 'Target')\nprint_G(ot_sinkhorn.fit(Xs=xs1, Xt=xt).coupling_, xs1, ys1, xt)\nprint_G(ot_sinkhorn.fit(Xs=xs2, Xt=xt).coupling_, xs2, ys2, xt)\npl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9)\npl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9)\npl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9)\n\npl.plot([], [], 'r', alpha=.2, label='Mass from Class 1')\npl.plot([], [], 'b', alpha=.2, label='Mass from Class 2')\n\npl.title('Independent OT')\n\npl.legend()\npl.axis('equal')\npl.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instantiate JCPOT adaptation algorithm and fit it\n----------------------------------------------------------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "otda = ot.da.JCPOTTransport(reg_e=1, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True)\notda.fit(all_Xr, all_Yr, xt)\n\nws1 = otda.proportions_.dot(otda.log_['D2'][0])\nws2 = otda.proportions_.dot(otda.log_['D2'][1])\n\npl.figure(3)\npl.clf()\nplot_ax(dec1, 'Source 1')\nplot_ax(dec2, 'Source 2')\nplot_ax(dect, 'Target')\nprint_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt)\nprint_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt)\npl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9)\npl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9)\npl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9)\n\npl.plot([], [], 'r', alpha=.2, label='Mass from Class 1')\npl.plot([], [], 'b', alpha=.2, label='Mass from Class 2')\n\npl.title('OT with prop estimation ({:1.3f},{:1.3f})'.format(otda.proportions_[0], otda.proportions_[1]))\n\npl.legend()\npl.axis('equal')\npl.axis('off')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run oracle transport algorithm with known proportions\n----------------------------------------------------------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "h_res = np.array([1 - pt, pt])\n\nws1 = h_res.dot(otda.log_['D2'][0])\nws2 = h_res.dot(otda.log_['D2'][1])\n\npl.figure(4)\npl.clf()\nplot_ax(dec1, 'Source 1')\nplot_ax(dec2, 'Source 2')\nplot_ax(dect, 'Target')\nprint_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt)\nprint_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt)\npl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9)\npl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9)\npl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9)\n\npl.plot([], [], 'r', alpha=.2, label='Mass from Class 1')\npl.plot([], [], 'b', alpha=.2, label='Mass from Class 2')\n\npl.title('OT with known proportion ({:1.1f},{:1.1f})'.format(h_res[0], h_res[1]))\n\npl.legend()\npl.axis('equal')\npl.axis('off')\npl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_jcpot.py b/docs/source/auto_examples/plot_otda_jcpot.py new file mode 100644 index 0000000..c495690 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_jcpot.py @@ -0,0 +1,171 @@ +# -*- coding: utf-8 -*- +""" +======================== +OT for multi-source target shift +======================== + +This example introduces a target shift problem with two 2D source and 1 target domain. + +""" + +# Authors: Remi Flamary +# Ievgen Redko +# +# License: MIT License + +import pylab as pl +import numpy as np +import ot +from ot.datasets import make_data_classif + +############################################################################## +# Generate data +# ------------- +n = 50 +sigma = 0.3 +np.random.seed(1985) + +p1 = .2 +dec1 = [0, 2] + +p2 = .9 +dec2 = [0, -2] + +pt = .4 +dect = [4, 0] + +xs1, ys1 = make_data_classif('2gauss_prop', n, nz=sigma, p=p1, bias=dec1) +xs2, ys2 = make_data_classif('2gauss_prop', n + 1, nz=sigma, p=p2, bias=dec2) +xt, yt = make_data_classif('2gauss_prop', n, nz=sigma, p=pt, bias=dect) + +all_Xr = [xs1, xs2] +all_Yr = [ys1, ys2] +# %% + +da = 1.5 + + +def plot_ax(dec, name): + pl.plot([dec[0], dec[0]], [dec[1] - da, dec[1] + da], 'k', alpha=0.5) + pl.plot([dec[0] - da, dec[0] + da], [dec[1], dec[1]], 'k', alpha=0.5) + pl.text(dec[0] - .5, dec[1] + 2, name) + + +############################################################################## +# Fig 1 : plots source and target samples +# --------------------------------------- + +pl.figure(1) +pl.clf() +plot_ax(dec1, 'Source 1') +plot_ax(dec2, 'Source 2') +plot_ax(dect, 'Target') +pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9, + label='Source 1 ({:1.2f}, {:1.2f})'.format(1 - p1, p1)) +pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9, + label='Source 2 ({:1.2f}, {:1.2f})'.format(1 - p2, p2)) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9, + label='Target ({:1.2f}, {:1.2f})'.format(1 - pt, pt)) +pl.title('Data') + +pl.legend() +pl.axis('equal') +pl.axis('off') + +############################################################################## +# Instantiate Sinkhorn transport algorithm and fit them for all source domains +# ---------------------------------------------------------------------------- +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1, metric='sqeuclidean') + + +def print_G(G, xs, ys, xt): + for i in range(G.shape[0]): + for j in range(G.shape[1]): + if G[i, j] > 5e-4: + if ys[i]: + c = 'b' + else: + c = 'r' + pl.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], xt[j, 1]], c, alpha=.2) + + +############################################################################## +# Fig 2 : plot optimal couplings and transported samples +# ------------------------------------------------------ +pl.figure(2) +pl.clf() +plot_ax(dec1, 'Source 1') +plot_ax(dec2, 'Source 2') +plot_ax(dect, 'Target') +print_G(ot_sinkhorn.fit(Xs=xs1, Xt=xt).coupling_, xs1, ys1, xt) +print_G(ot_sinkhorn.fit(Xs=xs2, Xt=xt).coupling_, xs2, ys2, xt) +pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) +pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) + +pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') +pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') + +pl.title('Independent OT') + +pl.legend() +pl.axis('equal') +pl.axis('off') + +############################################################################## +# Instantiate JCPOT adaptation algorithm and fit it +# ---------------------------------------------------------------------------- +otda = ot.da.JCPOTTransport(reg_e=1, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True) +otda.fit(all_Xr, all_Yr, xt) + +ws1 = otda.proportions_.dot(otda.log_['D2'][0]) +ws2 = otda.proportions_.dot(otda.log_['D2'][1]) + +pl.figure(3) +pl.clf() +plot_ax(dec1, 'Source 1') +plot_ax(dec2, 'Source 2') +plot_ax(dect, 'Target') +print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt) +print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt) +pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) +pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) + +pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') +pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') + +pl.title('OT with prop estimation ({:1.3f},{:1.3f})'.format(otda.proportions_[0], otda.proportions_[1])) + +pl.legend() +pl.axis('equal') +pl.axis('off') + +############################################################################## +# Run oracle transport algorithm with known proportions +# ---------------------------------------------------------------------------- +h_res = np.array([1 - pt, pt]) + +ws1 = h_res.dot(otda.log_['D2'][0]) +ws2 = h_res.dot(otda.log_['D2'][1]) + +pl.figure(4) +pl.clf() +plot_ax(dec1, 'Source 1') +plot_ax(dec2, 'Source 2') +plot_ax(dect, 'Target') +print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt) +print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt) +pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) +pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) + +pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') +pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') + +pl.title('OT with known proportion ({:1.1f},{:1.1f})'.format(h_res[0], h_res[1])) + +pl.legend() +pl.axis('equal') +pl.axis('off') +pl.show() diff --git a/docs/source/auto_examples/plot_otda_jcpot.rst b/docs/source/auto_examples/plot_otda_jcpot.rst new file mode 100644 index 0000000..3433190 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_jcpot.rst @@ -0,0 +1,336 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title + + .. _sphx_glr_auto_examples_plot_otda_jcpot.py: + + +======================== +OT for multi-source target shift +======================== + +This example introduces a target shift problem with two 2D source and 1 target domain. + + + +.. code-block:: default + + + # Authors: Remi Flamary + # Ievgen Redko + # + # License: MIT License + + import pylab as pl + import numpy as np + import ot + from ot.datasets import make_data_classif + + + + + + + + +Generate data +------------- + + +.. code-block:: default + + n = 50 + sigma = 0.3 + np.random.seed(1985) + + p1 = .2 + dec1 = [0, 2] + + p2 = .9 + dec2 = [0, -2] + + pt = .4 + dect = [4, 0] + + xs1, ys1 = make_data_classif('2gauss_prop', n, nz=sigma, p=p1, bias=dec1) + xs2, ys2 = make_data_classif('2gauss_prop', n + 1, nz=sigma, p=p2, bias=dec2) + xt, yt = make_data_classif('2gauss_prop', n, nz=sigma, p=pt, bias=dect) + + all_Xr = [xs1, xs2] + all_Yr = [ys1, ys2] + + + + + + + + +.. code-block:: default + + + da = 1.5 + + + def plot_ax(dec, name): + pl.plot([dec[0], dec[0]], [dec[1] - da, dec[1] + da], 'k', alpha=0.5) + pl.plot([dec[0] - da, dec[0] + da], [dec[1], dec[1]], 'k', alpha=0.5) + pl.text(dec[0] - .5, dec[1] + 2, name) + + + + + + + + + +Fig 1 : plots source and target samples +--------------------------------------- + + +.. code-block:: default + + + pl.figure(1) + pl.clf() + plot_ax(dec1, 'Source 1') + plot_ax(dec2, 'Source 2') + plot_ax(dect, 'Target') + pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9, + label='Source 1 ({:1.2f}, {:1.2f})'.format(1 - p1, p1)) + pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9, + label='Source 2 ({:1.2f}, {:1.2f})'.format(1 - p2, p2)) + pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9, + label='Target ({:1.2f}, {:1.2f})'.format(1 - pt, pt)) + pl.title('Data') + + pl.legend() + pl.axis('equal') + pl.axis('off') + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_jcpot_001.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + + (-1.85, 5.85, -4.1171725099266725, 4.197384527473105) + + + +Instantiate Sinkhorn transport algorithm and fit them for all source domains +---------------------------------------------------------------------------- + + +.. code-block:: default + + ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1, metric='sqeuclidean') + + + def print_G(G, xs, ys, xt): + for i in range(G.shape[0]): + for j in range(G.shape[1]): + if G[i, j] > 5e-4: + if ys[i]: + c = 'b' + else: + c = 'r' + pl.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], xt[j, 1]], c, alpha=.2) + + + + + + + + + +Fig 2 : plot optimal couplings and transported samples +------------------------------------------------------ + + +.. code-block:: default + + pl.figure(2) + pl.clf() + plot_ax(dec1, 'Source 1') + plot_ax(dec2, 'Source 2') + plot_ax(dect, 'Target') + print_G(ot_sinkhorn.fit(Xs=xs1, Xt=xt).coupling_, xs1, ys1, xt) + print_G(ot_sinkhorn.fit(Xs=xs2, Xt=xt).coupling_, xs2, ys2, xt) + pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) + pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) + pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) + + pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') + pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') + + pl.title('Independent OT') + + pl.legend() + pl.axis('equal') + pl.axis('off') + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_jcpot_002.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + + (-1.85, 5.85, -4.11901398007908, 4.201462272227509) + + + +Instantiate JCPOT adaptation algorithm and fit it +---------------------------------------------------------------------------- + + +.. code-block:: default + + otda = ot.da.JCPOTTransport(reg_e=1, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True) + otda.fit(all_Xr, all_Yr, xt) + + ws1 = otda.proportions_.dot(otda.log_['D2'][0]) + ws2 = otda.proportions_.dot(otda.log_['D2'][1]) + + pl.figure(3) + pl.clf() + plot_ax(dec1, 'Source 1') + plot_ax(dec2, 'Source 2') + plot_ax(dect, 'Target') + print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt) + print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt) + pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) + pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) + pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) + + pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') + pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') + + pl.title('OT with prop estimation ({:1.3f},{:1.3f})'.format(otda.proportions_[0], otda.proportions_[1])) + + pl.legend() + pl.axis('equal') + pl.axis('off') + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_jcpot_003.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + + (-1.85, 5.85, -4.11901398007908, 4.201462272227509) + + + +Run oracle transport algorithm with known proportions +---------------------------------------------------------------------------- + + +.. code-block:: default + + h_res = np.array([1 - pt, pt]) + + ws1 = h_res.dot(otda.log_['D2'][0]) + ws2 = h_res.dot(otda.log_['D2'][1]) + + pl.figure(4) + pl.clf() + plot_ax(dec1, 'Source 1') + plot_ax(dec2, 'Source 2') + plot_ax(dect, 'Target') + print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt) + print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt) + pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) + pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) + pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) + + pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') + pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') + + pl.title('OT with known proportion ({:1.1f},{:1.1f})'.format(h_res[0], h_res[1])) + + pl.legend() + pl.axis('equal') + pl.axis('off') + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_jcpot_004.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_otda_jcpot.py:171: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 4.725 seconds) + + +.. _sphx_glr_download_auto_examples_plot_otda_jcpot.py: + + +.. only :: html + + .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + + + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_otda_jcpot.py ` + + + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_otda_jcpot.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_partial_wass_and_gromov.ipynb b/docs/source/auto_examples/plot_partial_wass_and_gromov.ipynb new file mode 100644 index 0000000..0f69ec1 --- /dev/null +++ b/docs/source/auto_examples/plot_partial_wass_and_gromov.ipynb @@ -0,0 +1,126 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Partial Wasserstein and Gromov-Wasserstein example\n\n\nThis example is designed to show how to use the Partial (Gromov-)Wassertsein\ndistance computation in POT.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Laetitia Chapel \n# License: MIT License\n\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nimport scipy as sp\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sample two 2D Gaussian distributions and plot them\n--------------------------------------------------\n\nFor demonstration purpose, we sample two Gaussian distributions in 2-d\nspaces and add some random noise.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_samples = 20 # nb samples (gaussian)\nn_noise = 20 # nb of samples (noise)\n\nmu = np.array([0, 0])\ncov = np.array([[1, 0], [0, 2]])\n\nxs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)\nxs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2))\nxt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)\nxt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2))\n\nM = sp.spatial.distance.cdist(xs, xt)\n\nfig = pl.figure()\nax1 = fig.add_subplot(131)\nax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\nax2 = fig.add_subplot(132)\nax2.scatter(xt[:, 0], xt[:, 1], color='r')\nax3 = fig.add_subplot(133)\nax3.imshow(M)\npl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute partial Wasserstein plans and distance,\nby transporting 50% of the mass\n----------------------------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "p = ot.unif(n_samples + n_noise)\nq = ot.unif(n_samples + n_noise)\n\nw0, log0 = ot.partial.partial_wasserstein(p, q, M, m=0.5, log=True)\nw, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=0.1, m=0.5,\n log=True)\n\nprint('Partial Wasserstein distance (m = 0.5): ' + str(log0['partial_w_dist']))\nprint('Entropic partial Wasserstein distance (m = 0.5): ' +\n str(log['partial_w_dist']))\n\npl.figure(1, (10, 5))\npl.subplot(1, 2, 1)\npl.imshow(w0, cmap='jet')\npl.title('Partial Wasserstein')\npl.subplot(1, 2, 2)\npl.imshow(w, cmap='jet')\npl.title('Entropic partial Wasserstein')\npl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sample one 2D and 3D Gaussian distributions and plot them\n---------------------------------------------------------\n\nThe Gromov-Wasserstein distance allows to compute distances with samples that\ndo not belong to the same metric space. For demonstration purpose, we sample\ntwo Gaussian distributions in 2- and 3-dimensional spaces.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_samples = 20 # nb samples\nn_noise = 10 # nb of samples (noise)\n\np = ot.unif(n_samples + n_noise)\nq = ot.unif(n_samples + n_noise)\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([0, 0, 0])\ncov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n\n\nxs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)\nxs = np.concatenate((xs, ((np.random.rand(n_noise, 2) + 1) * 4)), axis=0)\nP = sp.linalg.sqrtm(cov_t)\nxt = np.random.randn(n_samples, 3).dot(P) + mu_t\nxt = np.concatenate((xt, ((np.random.rand(n_noise, 3) + 1) * 10)), axis=0)\n\nfig = pl.figure()\nax1 = fig.add_subplot(121)\nax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\nax2 = fig.add_subplot(122, projection='3d')\nax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\npl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute partial Gromov-Wasserstein plans and distance,\nby transporting 100% and 2/3 of the mass\n-----------------------------------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "C1 = sp.spatial.distance.cdist(xs, xs)\nC2 = sp.spatial.distance.cdist(xt, xt)\n\nprint('-----m = 1')\nm = 1\nres0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m,\n log=True)\nres, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10,\n m=m, log=True)\n\nprint('Partial Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist']))\nprint('Entropic partial Wasserstein distance (m = 1): ' +\n str(log['partial_gw_dist']))\n\npl.figure(1, (10, 5))\npl.title(\"mass to be transported m = 1\")\npl.subplot(1, 2, 1)\npl.imshow(res0, cmap='jet')\npl.title('Partial Wasserstein')\npl.subplot(1, 2, 2)\npl.imshow(res, cmap='jet')\npl.title('Entropic partial Wasserstein')\npl.show()\n\nprint('-----m = 2/3')\nm = 2 / 3\nres0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True)\nres, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10,\n m=m, log=True)\n\nprint('Partial Wasserstein distance (m = 2/3): ' +\n str(log0['partial_gw_dist']))\nprint('Entropic partial Wasserstein distance (m = 2/3): ' +\n str(log['partial_gw_dist']))\n\npl.figure(1, (10, 5))\npl.title(\"mass to be transported m = 2/3\")\npl.subplot(1, 2, 1)\npl.imshow(res0, cmap='jet')\npl.title('Partial Wasserstein')\npl.subplot(1, 2, 2)\npl.imshow(res, cmap='jet')\npl.title('Entropic partial Wasserstein')\npl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_partial_wass_and_gromov.py b/docs/source/auto_examples/plot_partial_wass_and_gromov.py new file mode 100644 index 0000000..01141f2 --- /dev/null +++ b/docs/source/auto_examples/plot_partial_wass_and_gromov.py @@ -0,0 +1,165 @@ +# -*- coding: utf-8 -*- +""" +========================== +Partial Wasserstein and Gromov-Wasserstein example +========================== + +This example is designed to show how to use the Partial (Gromov-)Wassertsein +distance computation in POT. +""" + +# Author: Laetitia Chapel +# License: MIT License + +# necessary for 3d plot even if not used +from mpl_toolkits.mplot3d import Axes3D # noqa +import scipy as sp +import numpy as np +import matplotlib.pylab as pl +import ot + + +############################################################################# +# +# Sample two 2D Gaussian distributions and plot them +# -------------------------------------------------- +# +# For demonstration purpose, we sample two Gaussian distributions in 2-d +# spaces and add some random noise. + + +n_samples = 20 # nb samples (gaussian) +n_noise = 20 # nb of samples (noise) + +mu = np.array([0, 0]) +cov = np.array([[1, 0], [0, 2]]) + +xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) +xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2)) +xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) +xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2)) + +M = sp.spatial.distance.cdist(xs, xt) + +fig = pl.figure() +ax1 = fig.add_subplot(131) +ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +ax2 = fig.add_subplot(132) +ax2.scatter(xt[:, 0], xt[:, 1], color='r') +ax3 = fig.add_subplot(133) +ax3.imshow(M) +pl.show() + +############################################################################# +# +# Compute partial Wasserstein plans and distance, +# by transporting 50% of the mass +# ---------------------------------------------- + +p = ot.unif(n_samples + n_noise) +q = ot.unif(n_samples + n_noise) + +w0, log0 = ot.partial.partial_wasserstein(p, q, M, m=0.5, log=True) +w, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=0.1, m=0.5, + log=True) + +print('Partial Wasserstein distance (m = 0.5): ' + str(log0['partial_w_dist'])) +print('Entropic partial Wasserstein distance (m = 0.5): ' + + str(log['partial_w_dist'])) + +pl.figure(1, (10, 5)) +pl.subplot(1, 2, 1) +pl.imshow(w0, cmap='jet') +pl.title('Partial Wasserstein') +pl.subplot(1, 2, 2) +pl.imshow(w, cmap='jet') +pl.title('Entropic partial Wasserstein') +pl.show() + + +############################################################################# +# +# Sample one 2D and 3D Gaussian distributions and plot them +# --------------------------------------------------------- +# +# The Gromov-Wasserstein distance allows to compute distances with samples that +# do not belong to the same metric space. For demonstration purpose, we sample +# two Gaussian distributions in 2- and 3-dimensional spaces. + +n_samples = 20 # nb samples +n_noise = 10 # nb of samples (noise) + +p = ot.unif(n_samples + n_noise) +q = ot.unif(n_samples + n_noise) + +mu_s = np.array([0, 0]) +cov_s = np.array([[1, 0], [0, 1]]) + +mu_t = np.array([0, 0, 0]) +cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) + + +xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) +xs = np.concatenate((xs, ((np.random.rand(n_noise, 2) + 1) * 4)), axis=0) +P = sp.linalg.sqrtm(cov_t) +xt = np.random.randn(n_samples, 3).dot(P) + mu_t +xt = np.concatenate((xt, ((np.random.rand(n_noise, 3) + 1) * 10)), axis=0) + +fig = pl.figure() +ax1 = fig.add_subplot(121) +ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +ax2 = fig.add_subplot(122, projection='3d') +ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') +pl.show() + + +############################################################################# +# +# Compute partial Gromov-Wasserstein plans and distance, +# by transporting 100% and 2/3 of the mass +# ----------------------------------------------------- + +C1 = sp.spatial.distance.cdist(xs, xs) +C2 = sp.spatial.distance.cdist(xt, xt) + +print('-----m = 1') +m = 1 +res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, + log=True) +res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, + m=m, log=True) + +print('Partial Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist'])) +print('Entropic partial Wasserstein distance (m = 1): ' + + str(log['partial_gw_dist'])) + +pl.figure(1, (10, 5)) +pl.title("mass to be transported m = 1") +pl.subplot(1, 2, 1) +pl.imshow(res0, cmap='jet') +pl.title('Partial Wasserstein') +pl.subplot(1, 2, 2) +pl.imshow(res, cmap='jet') +pl.title('Entropic partial Wasserstein') +pl.show() + +print('-----m = 2/3') +m = 2 / 3 +res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True) +res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, + m=m, log=True) + +print('Partial Wasserstein distance (m = 2/3): ' + + str(log0['partial_gw_dist'])) +print('Entropic partial Wasserstein distance (m = 2/3): ' + + str(log['partial_gw_dist'])) + +pl.figure(1, (10, 5)) +pl.title("mass to be transported m = 2/3") +pl.subplot(1, 2, 1) +pl.imshow(res0, cmap='jet') +pl.title('Partial Wasserstein') +pl.subplot(1, 2, 2) +pl.imshow(res, cmap='jet') +pl.title('Entropic partial Wasserstein') +pl.show() diff --git a/docs/source/auto_examples/plot_screenkhorn_1D.ipynb b/docs/source/auto_examples/plot_screenkhorn_1D.ipynb new file mode 100644 index 0000000..1c27d3b --- /dev/null +++ b/docs/source/auto_examples/plot_screenkhorn_1D.ipynb @@ -0,0 +1,108 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# 1D Screened optimal transport\n\n\nThis example illustrates the computation of Screenkhorn:\nScreening Sinkhorn Algorithm for Optimal transport.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Mokhtar Z. Alaya \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot.plot\nfrom ot.datasets import make_1D_gauss as gauss\nfrom ot.bregman import screenkhorn" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n-------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot distributions and loss matrix\n----------------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n# plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve Screenkhorn\n-----------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Screenkhorn\nlambd = 2e-03 # entropy parameter\nns_budget = 30 # budget number of points to be keeped in the source distribution\nnt_budget = 30 # budget number of points to be keeped in the target distribution\n\nG_screen = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True)\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G_screen, 'OT matrix Screenkhorn')\npl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_screenkhorn_1D.py b/docs/source/auto_examples/plot_screenkhorn_1D.py new file mode 100644 index 0000000..840ead8 --- /dev/null +++ b/docs/source/auto_examples/plot_screenkhorn_1D.py @@ -0,0 +1,68 @@ +# -*- coding: utf-8 -*- +""" +=============================== +1D Screened optimal transport +=============================== + +This example illustrates the computation of Screenkhorn: +Screening Sinkhorn Algorithm for Optimal transport. +""" + +# Author: Mokhtar Z. Alaya +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot.plot +from ot.datasets import make_1D_gauss as gauss +from ot.bregman import screenkhorn + +############################################################################## +# Generate data +# ------------- + +#%% parameters + +n = 100 # nb bins + +# bin positions +x = np.arange(n, dtype=np.float64) + +# Gaussian distributions +a = gauss(n, m=20, s=5) # m= mean, s= std +b = gauss(n, m=60, s=10) + +# loss matrix +M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) +M /= M.max() + +############################################################################## +# Plot distributions and loss matrix +# ---------------------------------- + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +pl.plot(x, a, 'b', label='Source distribution') +pl.plot(x, b, 'r', label='Target distribution') +pl.legend() + +# plot distributions and loss matrix + +pl.figure(2, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') + +############################################################################## +# Solve Screenkhorn +# ----------------------- + +# Screenkhorn +lambd = 2e-03 # entropy parameter +ns_budget = 30 # budget number of points to be keeped in the source distribution +nt_budget = 30 # budget number of points to be keeped in the target distribution + +G_screen = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True) +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, G_screen, 'OT matrix Screenkhorn') +pl.show() -- cgit v1.2.3 From d54184c233cd211a693e4cdf4b25dd68b07ed00b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 20 Apr 2020 16:10:18 +0200 Subject: add rst file for documentation --- .../auto_examples/plot_partial_wass_and_gromov.rst | 314 +++++++++++++++++++++ docs/source/auto_examples/plot_screenkhorn_1D.rst | 178 ++++++++++++ 2 files changed, 492 insertions(+) create mode 100644 docs/source/auto_examples/plot_partial_wass_and_gromov.rst create mode 100644 docs/source/auto_examples/plot_screenkhorn_1D.rst diff --git a/docs/source/auto_examples/plot_partial_wass_and_gromov.rst b/docs/source/auto_examples/plot_partial_wass_and_gromov.rst new file mode 100644 index 0000000..7f47f83 --- /dev/null +++ b/docs/source/auto_examples/plot_partial_wass_and_gromov.rst @@ -0,0 +1,314 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title + + .. _sphx_glr_auto_examples_plot_partial_wass_and_gromov.py: + + +========================== +Partial Wasserstein and Gromov-Wasserstein example +========================== + +This example is designed to show how to use the Partial (Gromov-)Wassertsein +distance computation in POT. + + +.. code-block:: default + + + # Author: Laetitia Chapel + # License: MIT License + + # necessary for 3d plot even if not used + from mpl_toolkits.mplot3d import Axes3D # noqa + import scipy as sp + import numpy as np + import matplotlib.pylab as pl + import ot + + + + + + + + + +Sample two 2D Gaussian distributions and plot them +-------------------------------------------------- + +For demonstration purpose, we sample two Gaussian distributions in 2-d +spaces and add some random noise. + + +.. code-block:: default + + + + n_samples = 20 # nb samples (gaussian) + n_noise = 20 # nb of samples (noise) + + mu = np.array([0, 0]) + cov = np.array([[1, 0], [0, 2]]) + + xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) + xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2)) + xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) + xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2)) + + M = sp.spatial.distance.cdist(xs, xt) + + fig = pl.figure() + ax1 = fig.add_subplot(131) + ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + ax2 = fig.add_subplot(132) + ax2.scatter(xt[:, 0], xt[:, 1], color='r') + ax3 = fig.add_subplot(133) + ax3.imshow(M) + pl.show() + + + + +.. image:: /auto_examples/images/sphx_glr_plot_partial_wass_and_gromov_001.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:51: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() + + + + +Compute partial Wasserstein plans and distance, +by transporting 50% of the mass +---------------------------------------------- + + +.. code-block:: default + + + p = ot.unif(n_samples + n_noise) + q = ot.unif(n_samples + n_noise) + + w0, log0 = ot.partial.partial_wasserstein(p, q, M, m=0.5, log=True) + w, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=0.1, m=0.5, + log=True) + + print('Partial Wasserstein distance (m = 0.5): ' + str(log0['partial_w_dist'])) + print('Entropic partial Wasserstein distance (m = 0.5): ' + + str(log['partial_w_dist'])) + + pl.figure(1, (10, 5)) + pl.subplot(1, 2, 1) + pl.imshow(w0, cmap='jet') + pl.title('Partial Wasserstein') + pl.subplot(1, 2, 2) + pl.imshow(w, cmap='jet') + pl.title('Entropic partial Wasserstein') + pl.show() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_partial_wass_and_gromov_002.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + Partial Wasserstein distance (m = 0.5): 0.29721185147886475 + Entropic partial Wasserstein distance (m = 0.5): 0.31204119793315976 + /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:77: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() + + + + +Sample one 2D and 3D Gaussian distributions and plot them +--------------------------------------------------------- + +The Gromov-Wasserstein distance allows to compute distances with samples that +do not belong to the same metric space. For demonstration purpose, we sample +two Gaussian distributions in 2- and 3-dimensional spaces. + + +.. code-block:: default + + + n_samples = 20 # nb samples + n_noise = 10 # nb of samples (noise) + + p = ot.unif(n_samples + n_noise) + q = ot.unif(n_samples + n_noise) + + mu_s = np.array([0, 0]) + cov_s = np.array([[1, 0], [0, 1]]) + + mu_t = np.array([0, 0, 0]) + cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) + + + xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) + xs = np.concatenate((xs, ((np.random.rand(n_noise, 2) + 1) * 4)), axis=0) + P = sp.linalg.sqrtm(cov_t) + xt = np.random.randn(n_samples, 3).dot(P) + mu_t + xt = np.concatenate((xt, ((np.random.rand(n_noise, 3) + 1) * 10)), axis=0) + + fig = pl.figure() + ax1 = fig.add_subplot(121) + ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + ax2 = fig.add_subplot(122, projection='3d') + ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') + pl.show() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_partial_wass_and_gromov_003.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:113: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() + + + + +Compute partial Gromov-Wasserstein plans and distance, +by transporting 100% and 2/3 of the mass +----------------------------------------------------- + + +.. code-block:: default + + + C1 = sp.spatial.distance.cdist(xs, xs) + C2 = sp.spatial.distance.cdist(xt, xt) + + print('-----m = 1') + m = 1 + res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, + log=True) + res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, + m=m, log=True) + + print('Partial Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist'])) + print('Entropic partial Wasserstein distance (m = 1): ' + + str(log['partial_gw_dist'])) + + pl.figure(1, (10, 5)) + pl.title("mass to be transported m = 1") + pl.subplot(1, 2, 1) + pl.imshow(res0, cmap='jet') + pl.title('Partial Wasserstein') + pl.subplot(1, 2, 2) + pl.imshow(res, cmap='jet') + pl.title('Entropic partial Wasserstein') + pl.show() + + print('-----m = 2/3') + m = 2 / 3 + res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True) + res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, + m=m, log=True) + + print('Partial Wasserstein distance (m = 2/3): ' + + str(log0['partial_gw_dist'])) + print('Entropic partial Wasserstein distance (m = 2/3): ' + + str(log['partial_gw_dist'])) + + pl.figure(1, (10, 5)) + pl.title("mass to be transported m = 2/3") + pl.subplot(1, 2, 1) + pl.imshow(res0, cmap='jet') + pl.title('Partial Wasserstein') + pl.subplot(1, 2, 2) + pl.imshow(res, cmap='jet') + pl.title('Entropic partial Wasserstein') + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_partial_wass_and_gromov_004.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + -----m = 1 + Partial Wasserstein distance (m = 1): 56.18870587756925 + Entropic partial Wasserstein distance (m = 1): 57.63642536818668 + /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:144: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() + -----m = 2/3 + Partial Wasserstein distance (m = 2/3): 0.18550643334550976 + Entropic partial Wasserstein distance (m = 2/3): 1.0781947761552997 + /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:159: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance. + pl.subplot(1, 2, 1) + /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:162: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance. + pl.subplot(1, 2, 2) + /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:165: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 1.656 seconds) + + +.. _sphx_glr_download_auto_examples_plot_partial_wass_and_gromov.py: + + +.. only :: html + + .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + + + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_partial_wass_and_gromov.py ` + + + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_partial_wass_and_gromov.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_screenkhorn_1D.rst b/docs/source/auto_examples/plot_screenkhorn_1D.rst new file mode 100644 index 0000000..039479e --- /dev/null +++ b/docs/source/auto_examples/plot_screenkhorn_1D.rst @@ -0,0 +1,178 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title + + .. _sphx_glr_auto_examples_plot_screenkhorn_1D.py: + + +=============================== +1D Screened optimal transport +=============================== + +This example illustrates the computation of Screenkhorn: +Screening Sinkhorn Algorithm for Optimal transport. + + +.. code-block:: default + + + # Author: Mokhtar Z. Alaya + # + # License: MIT License + + import numpy as np + import matplotlib.pylab as pl + import ot.plot + from ot.datasets import make_1D_gauss as gauss + from ot.bregman import screenkhorn + + + + + + + + +Generate data +------------- + + +.. code-block:: default + + + n = 100 # nb bins + + # bin positions + x = np.arange(n, dtype=np.float64) + + # Gaussian distributions + a = gauss(n, m=20, s=5) # m= mean, s= std + b = gauss(n, m=60, s=10) + + # loss matrix + M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) + M /= M.max() + + + + + + + + +Plot distributions and loss matrix +---------------------------------- + + +.. code-block:: default + + + pl.figure(1, figsize=(6.4, 3)) + pl.plot(x, a, 'b', label='Source distribution') + pl.plot(x, b, 'r', label='Target distribution') + pl.legend() + + # plot distributions and loss matrix + + pl.figure(2, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_screenkhorn_1D_001.png + :class: sphx-glr-multi-img + + * + + .. image:: /auto_examples/images/sphx_glr_plot_screenkhorn_1D_002.png + :class: sphx-glr-multi-img + + + + + +Solve Screenkhorn +----------------------- + + +.. code-block:: default + + + # Screenkhorn + lambd = 2e-03 # entropy parameter + ns_budget = 30 # budget number of points to be keeped in the source distribution + nt_budget = 30 # budget number of points to be keeped in the target distribution + + G_screen = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True) + pl.figure(4, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, G_screen, 'OT matrix Screenkhorn') + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_screenkhorn_1D_003.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/ot/bregman.py:2056: UserWarning: Bottleneck module is not installed. Install it from https://pypi.org/project/Bottleneck/ for better performance. + "Bottleneck module is not installed. Install it from https://pypi.org/project/Bottleneck/ for better performance.") + epsilon = 0.020986042861303855 + + kappa = 3.7476531411890917 + + Cardinality of selected points: |Isel| = 30 |Jsel| = 30 + + /home/rflamary/PYTHON/POT/examples/plot_screenkhorn_1D.py:68: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 0.228 seconds) + + +.. _sphx_glr_download_auto_examples_plot_screenkhorn_1D.py: + + +.. only :: html + + .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + + + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_screenkhorn_1D.py ` + + + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_screenkhorn_1D.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery 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29112 bytes docs/source/auto_examples/index.rst | 20 ++ .../source/auto_examples/plot_otda_laplacian.ipynb | 126 +++++++++++ docs/source/auto_examples/plot_otda_laplacian.py | 127 +++++++++++ docs/source/auto_examples/plot_otda_laplacian.rst | 233 +++++++++++++++++++++ .../plot_partial_wass_and_gromov.ipynb | 6 +- .../auto_examples/plot_partial_wass_and_gromov.py | 24 +-- .../auto_examples/plot_partial_wass_and_gromov.rst | 50 +++-- 18 files changed, 545 insertions(+), 43 deletions(-) create mode 100644 docs/source/auto_examples/images/sphx_glr_plot_otda_laplacian_001.png create mode 100644 docs/source/auto_examples/images/sphx_glr_plot_otda_laplacian_002.png create mode 100644 docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_laplacian_thumb.png create mode 100644 docs/source/auto_examples/plot_otda_laplacian.ipynb create mode 100644 docs/source/auto_examples/plot_otda_laplacian.py create mode 100644 docs/source/auto_examples/plot_otda_laplacian.rst diff --git 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+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_laplacian_thumb.png + + :ref:`sphx_glr_auto_examples_plot_otda_laplacian.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_otda_laplacian + .. raw:: html
diff --git a/docs/source/auto_examples/plot_otda_laplacian.ipynb b/docs/source/auto_examples/plot_otda_laplacian.ipynb new file mode 100644 index 0000000..c1e9efe --- /dev/null +++ b/docs/source/auto_examples/plot_otda_laplacian.ipynb @@ -0,0 +1,126 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# OT with Laplacian regularization for domain adaptation\n\n\nThis example introduces a domain adaptation in a 2D setting and OTDA\napproach with Laplacian regularization.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Authors: Ievgen Redko \n\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n-------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_source_samples = 150\nn_target_samples = 150\n\nXs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples)\nXt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# EMD Transport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=.01)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# EMD Transport with Laplacian regularization\not_emd_laplace = ot.da.EMDLaplaceTransport(reg_lap=100, reg_src=1)\not_emd_laplace.fit(Xs=Xs, Xt=Xt)\n\n# transport source samples onto target samples\ntransp_Xs_emd = ot_emd.transform(Xs=Xs)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\ntransp_Xs_emd_laplace = ot_emd_laplace.transform(Xs=Xs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig 1 : plots source and target samples\n---------------------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(1, figsize=(10, 5))\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\npl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig 2 : plot optimal couplings and transported samples\n------------------------------------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "param_img = {'interpolation': 'nearest'}\n\npl.figure(2, figsize=(15, 8))\npl.subplot(2, 3, 1)\npl.imshow(ot_emd.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nEMDTransport')\n\npl.figure(2, figsize=(15, 8))\npl.subplot(2, 3, 2)\npl.imshow(ot_sinkhorn.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornTransport')\n\npl.subplot(2, 3, 3)\npl.imshow(ot_emd_laplace.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nEMDLaplaceTransport')\n\npl.subplot(2, 3, 4)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=\"lower left\")\n\npl.subplot(2, 3, 5)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornTransport')\n\npl.subplot(2, 3, 6)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nEMDLaplaceTransport')\npl.tight_layout()\n\npl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_laplacian.py b/docs/source/auto_examples/plot_otda_laplacian.py new file mode 100644 index 0000000..67c8f67 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_laplacian.py @@ -0,0 +1,127 @@ +# -*- coding: utf-8 -*- +""" +====================================================== +OT with Laplacian regularization for domain adaptation +====================================================== + +This example introduces a domain adaptation in a 2D setting and OTDA +approach with Laplacian regularization. + +""" + +# Authors: Ievgen Redko + +# License: MIT License + +import matplotlib.pylab as pl +import ot + +############################################################################## +# Generate data +# ------------- + +n_source_samples = 150 +n_target_samples = 150 + +Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) + + +############################################################################## +# Instantiate the different transport algorithms and fit them +# ----------------------------------------------------------- + +# EMD Transport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=.01) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# EMD Transport with Laplacian regularization +ot_emd_laplace = ot.da.EMDLaplaceTransport(reg_lap=100, reg_src=1) +ot_emd_laplace.fit(Xs=Xs, Xt=Xt) + +# transport source samples onto target samples +transp_Xs_emd = ot_emd.transform(Xs=Xs) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) +transp_Xs_emd_laplace = ot_emd_laplace.transform(Xs=Xs) + +############################################################################## +# Fig 1 : plots source and target samples +# --------------------------------------- + +pl.figure(1, figsize=(10, 5)) +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Source samples') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Target samples') +pl.tight_layout() + + +############################################################################## +# Fig 2 : plot optimal couplings and transported samples +# ------------------------------------------------------ + +param_img = {'interpolation': 'nearest'} + +pl.figure(2, figsize=(15, 8)) +pl.subplot(2, 3, 1) +pl.imshow(ot_emd.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nEMDTransport') + +pl.figure(2, figsize=(15, 8)) +pl.subplot(2, 3, 2) +pl.imshow(ot_sinkhorn.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornTransport') + +pl.subplot(2, 3, 3) +pl.imshow(ot_emd_laplace.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nEMDLaplaceTransport') + +pl.subplot(2, 3, 4) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nEmdTransport') +pl.legend(loc="lower left") + +pl.subplot(2, 3, 5) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornTransport') + +pl.subplot(2, 3, 6) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nEMDLaplaceTransport') +pl.tight_layout() + +pl.show() diff --git a/docs/source/auto_examples/plot_otda_laplacian.rst b/docs/source/auto_examples/plot_otda_laplacian.rst new file mode 100644 index 0000000..12cd7b9 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_laplacian.rst @@ -0,0 +1,233 @@ +.. only:: html + + .. note:: + :class: sphx-glr-download-link-note + + Click :ref:`here ` to download the full example code + .. rst-class:: sphx-glr-example-title + + .. _sphx_glr_auto_examples_plot_otda_laplacian.py: + + +====================================================== +OT with Laplacian regularization for domain adaptation +====================================================== + +This example introduces a domain adaptation in a 2D setting and OTDA +approach with Laplacian regularization. + + + +.. code-block:: default + + + # Authors: Ievgen Redko + + # License: MIT License + + import matplotlib.pylab as pl + import ot + + + + + + + + +Generate data +------------- + + +.. code-block:: default + + + n_source_samples = 150 + n_target_samples = 150 + + Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) + Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) + + + + + + + + + +Instantiate the different transport algorithms and fit them +----------------------------------------------------------- + + +.. code-block:: default + + + # EMD Transport + ot_emd = ot.da.EMDTransport() + ot_emd.fit(Xs=Xs, Xt=Xt) + + # Sinkhorn Transport + ot_sinkhorn = ot.da.SinkhornTransport(reg_e=.01) + ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + + # EMD Transport with Laplacian regularization + ot_emd_laplace = ot.da.EMDLaplaceTransport(reg_lap=100, reg_src=1) + ot_emd_laplace.fit(Xs=Xs, Xt=Xt) + + # transport source samples onto target samples + transp_Xs_emd = ot_emd.transform(Xs=Xs) + transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) + transp_Xs_emd_laplace = ot_emd_laplace.transform(Xs=Xs) + + + + + + + + +Fig 1 : plots source and target samples +--------------------------------------- + + +.. code-block:: default + + + pl.figure(1, figsize=(10, 5)) + pl.subplot(1, 2, 1) + pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') + pl.xticks([]) + pl.yticks([]) + pl.legend(loc=0) + pl.title('Source samples') + + pl.subplot(1, 2, 2) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') + pl.xticks([]) + pl.yticks([]) + pl.legend(loc=0) + pl.title('Target samples') + pl.tight_layout() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_laplacian_001.png + :class: sphx-glr-single-img + + + + + +Fig 2 : plot optimal couplings and transported samples +------------------------------------------------------ + + +.. code-block:: default + + + param_img = {'interpolation': 'nearest'} + + pl.figure(2, figsize=(15, 8)) + pl.subplot(2, 3, 1) + pl.imshow(ot_emd.coupling_, **param_img) + pl.xticks([]) + pl.yticks([]) + pl.title('Optimal coupling\nEMDTransport') + + pl.figure(2, figsize=(15, 8)) + pl.subplot(2, 3, 2) + pl.imshow(ot_sinkhorn.coupling_, **param_img) + pl.xticks([]) + pl.yticks([]) + pl.title('Optimal coupling\nSinkhornTransport') + + pl.subplot(2, 3, 3) + pl.imshow(ot_emd_laplace.coupling_, **param_img) + pl.xticks([]) + pl.yticks([]) + pl.title('Optimal coupling\nEMDLaplaceTransport') + + pl.subplot(2, 3, 4) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) + pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, + marker='+', label='Transp samples', s=30) + pl.xticks([]) + pl.yticks([]) + pl.title('Transported samples\nEmdTransport') + pl.legend(loc="lower left") + + pl.subplot(2, 3, 5) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) + pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, + marker='+', label='Transp samples', s=30) + pl.xticks([]) + pl.yticks([]) + pl.title('Transported samples\nSinkhornTransport') + + pl.subplot(2, 3, 6) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) + pl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys, + marker='+', label='Transp samples', s=30) + pl.xticks([]) + pl.yticks([]) + pl.title('Transported samples\nEMDLaplaceTransport') + pl.tight_layout() + + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_laplacian_002.png + :class: sphx-glr-single-img + + +.. rst-class:: sphx-glr-script-out + + Out: + + .. code-block:: none + + /home/rflamary/PYTHON/POT/examples/plot_otda_laplacian.py:127: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + pl.show() + + + + + +.. rst-class:: sphx-glr-timing + + **Total running time of the script:** ( 0 minutes 1.195 seconds) + + +.. _sphx_glr_download_auto_examples_plot_otda_laplacian.py: + + +.. only :: html + + .. container:: sphx-glr-footer + :class: sphx-glr-footer-example + + + + .. container:: sphx-glr-download sphx-glr-download-python + + :download:`Download Python source code: plot_otda_laplacian.py ` + + + + .. container:: sphx-glr-download sphx-glr-download-jupyter + + :download:`Download Jupyter notebook: plot_otda_laplacian.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_partial_wass_and_gromov.ipynb b/docs/source/auto_examples/plot_partial_wass_and_gromov.ipynb index 0f69ec1..539d575 100644 --- a/docs/source/auto_examples/plot_partial_wass_and_gromov.ipynb +++ b/docs/source/auto_examples/plot_partial_wass_and_gromov.ipynb @@ -51,7 +51,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Compute partial Wasserstein plans and distance,\nby transporting 50% of the mass\n----------------------------------------------\n\n" + "Compute partial Wasserstein plans and distance\n----------------------------------------------\n\n" ] }, { @@ -87,7 +87,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Compute partial Gromov-Wasserstein plans and distance,\nby transporting 100% and 2/3 of the mass\n-----------------------------------------------------\n\n" + "Compute partial Gromov-Wasserstein plans and distance\n-----------------------------------------------------\n\n" ] }, { @@ -98,7 +98,7 @@ }, "outputs": [], "source": [ - "C1 = sp.spatial.distance.cdist(xs, xs)\nC2 = sp.spatial.distance.cdist(xt, xt)\n\nprint('-----m = 1')\nm = 1\nres0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m,\n log=True)\nres, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10,\n m=m, log=True)\n\nprint('Partial Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist']))\nprint('Entropic partial Wasserstein distance (m = 1): ' +\n str(log['partial_gw_dist']))\n\npl.figure(1, (10, 5))\npl.title(\"mass to be transported m = 1\")\npl.subplot(1, 2, 1)\npl.imshow(res0, cmap='jet')\npl.title('Partial Wasserstein')\npl.subplot(1, 2, 2)\npl.imshow(res, cmap='jet')\npl.title('Entropic partial Wasserstein')\npl.show()\n\nprint('-----m = 2/3')\nm = 2 / 3\nres0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True)\nres, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10,\n m=m, log=True)\n\nprint('Partial Wasserstein distance (m = 2/3): ' +\n str(log0['partial_gw_dist']))\nprint('Entropic partial Wasserstein distance (m = 2/3): ' +\n str(log['partial_gw_dist']))\n\npl.figure(1, (10, 5))\npl.title(\"mass to be transported m = 2/3\")\npl.subplot(1, 2, 1)\npl.imshow(res0, cmap='jet')\npl.title('Partial Wasserstein')\npl.subplot(1, 2, 2)\npl.imshow(res, cmap='jet')\npl.title('Entropic partial Wasserstein')\npl.show()" + "C1 = sp.spatial.distance.cdist(xs, xs)\nC2 = sp.spatial.distance.cdist(xt, xt)\n\n# transport 100% of the mass\nprint('-----m = 1')\nm = 1\nres0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True)\nres, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10,\n m=m, log=True)\n\nprint('Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist']))\nprint('Entropic Wasserstein distance (m = 1): ' + str(log['partial_gw_dist']))\n\npl.figure(1, (10, 5))\npl.title(\"mass to be transported m = 1\")\npl.subplot(1, 2, 1)\npl.imshow(res0, cmap='jet')\npl.title('Wasserstein')\npl.subplot(1, 2, 2)\npl.imshow(res, cmap='jet')\npl.title('Entropic Wasserstein')\npl.show()\n\n# transport 2/3 of the mass\nprint('-----m = 2/3')\nm = 2 / 3\nres0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True)\nres, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10,\n m=m, log=True)\n\nprint('Partial Wasserstein distance (m = 2/3): ' +\n str(log0['partial_gw_dist']))\nprint('Entropic partial Wasserstein distance (m = 2/3): ' +\n str(log['partial_gw_dist']))\n\npl.figure(1, (10, 5))\npl.title(\"mass to be transported m = 2/3\")\npl.subplot(1, 2, 1)\npl.imshow(res0, cmap='jet')\npl.title('Partial Wasserstein')\npl.subplot(1, 2, 2)\npl.imshow(res, cmap='jet')\npl.title('Entropic partial Wasserstein')\npl.show()" ] } ], diff --git a/docs/source/auto_examples/plot_partial_wass_and_gromov.py b/docs/source/auto_examples/plot_partial_wass_and_gromov.py index 01141f2..9f95a70 100644 --- a/docs/source/auto_examples/plot_partial_wass_and_gromov.py +++ b/docs/source/auto_examples/plot_partial_wass_and_gromov.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -========================== +================================================== Partial Wasserstein and Gromov-Wasserstein example -========================== +================================================== This example is designed to show how to use the Partial (Gromov-)Wassertsein distance computation in POT. @@ -52,8 +52,7 @@ pl.show() ############################################################################# # -# Compute partial Wasserstein plans and distance, -# by transporting 50% of the mass +# Compute partial Wasserstein plans and distance # ---------------------------------------------- p = ot.unif(n_samples + n_noise) @@ -115,34 +114,33 @@ pl.show() ############################################################################# # -# Compute partial Gromov-Wasserstein plans and distance, -# by transporting 100% and 2/3 of the mass +# Compute partial Gromov-Wasserstein plans and distance # ----------------------------------------------------- C1 = sp.spatial.distance.cdist(xs, xs) C2 = sp.spatial.distance.cdist(xt, xt) +# transport 100% of the mass print('-----m = 1') m = 1 -res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, - log=True) +res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True) res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, m=m, log=True) -print('Partial Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist'])) -print('Entropic partial Wasserstein distance (m = 1): ' + - str(log['partial_gw_dist'])) +print('Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist'])) +print('Entropic Wasserstein distance (m = 1): ' + str(log['partial_gw_dist'])) pl.figure(1, (10, 5)) pl.title("mass to be transported m = 1") pl.subplot(1, 2, 1) pl.imshow(res0, cmap='jet') -pl.title('Partial Wasserstein') +pl.title('Wasserstein') pl.subplot(1, 2, 2) pl.imshow(res, cmap='jet') -pl.title('Entropic partial Wasserstein') +pl.title('Entropic Wasserstein') pl.show() +# transport 2/3 of the mass print('-----m = 2/3') m = 2 / 3 res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True) diff --git a/docs/source/auto_examples/plot_partial_wass_and_gromov.rst b/docs/source/auto_examples/plot_partial_wass_and_gromov.rst index 7f47f83..2d51210 100644 --- a/docs/source/auto_examples/plot_partial_wass_and_gromov.rst +++ b/docs/source/auto_examples/plot_partial_wass_and_gromov.rst @@ -9,9 +9,9 @@ .. _sphx_glr_auto_examples_plot_partial_wass_and_gromov.py: -========================== +================================================== Partial Wasserstein and Gromov-Wasserstein example -========================== +================================================== This example is designed to show how to use the Partial (Gromov-)Wassertsein distance computation in POT. @@ -90,8 +90,7 @@ spaces and add some random noise. -Compute partial Wasserstein plans and distance, -by transporting 50% of the mass +Compute partial Wasserstein plans and distance ---------------------------------------------- @@ -132,9 +131,9 @@ by transporting 50% of the mass .. code-block:: none - Partial Wasserstein distance (m = 0.5): 0.29721185147886475 - Entropic partial Wasserstein distance (m = 0.5): 0.31204119793315976 - /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:77: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + Partial Wasserstein distance (m = 0.5): 0.507323938973194 + Entropic partial Wasserstein distance (m = 0.5): 0.5205305886057896 + /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:76: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. pl.show() @@ -191,14 +190,13 @@ two Gaussian distributions in 2- and 3-dimensional spaces. .. code-block:: none - /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:113: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:112: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. pl.show() -Compute partial Gromov-Wasserstein plans and distance, -by transporting 100% and 2/3 of the mass +Compute partial Gromov-Wasserstein plans and distance ----------------------------------------------------- @@ -208,27 +206,27 @@ by transporting 100% and 2/3 of the mass C1 = sp.spatial.distance.cdist(xs, xs) C2 = sp.spatial.distance.cdist(xt, xt) + # transport 100% of the mass print('-----m = 1') m = 1 - res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, - log=True) + res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True) res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, m=m, log=True) - print('Partial Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist'])) - print('Entropic partial Wasserstein distance (m = 1): ' + - str(log['partial_gw_dist'])) + print('Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist'])) + print('Entropic Wasserstein distance (m = 1): ' + str(log['partial_gw_dist'])) pl.figure(1, (10, 5)) pl.title("mass to be transported m = 1") pl.subplot(1, 2, 1) pl.imshow(res0, cmap='jet') - pl.title('Partial Wasserstein') + pl.title('Wasserstein') pl.subplot(1, 2, 2) pl.imshow(res, cmap='jet') - pl.title('Entropic partial Wasserstein') + pl.title('Entropic Wasserstein') pl.show() + # transport 2/3 of the mass print('-----m = 2/3') m = 2 / 3 res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True) @@ -263,18 +261,18 @@ by transporting 100% and 2/3 of the mass .. code-block:: none -----m = 1 - Partial Wasserstein distance (m = 1): 56.18870587756925 - Entropic partial Wasserstein distance (m = 1): 57.63642536818668 - /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:144: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + Wasserstein distance (m = 1): 63.65368600872179 + Entropic Wasserstein distance (m = 1): 65.23659085946916 + /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:141: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. pl.show() -----m = 2/3 - Partial Wasserstein distance (m = 2/3): 0.18550643334550976 - Entropic partial Wasserstein distance (m = 2/3): 1.0781947761552997 - /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:159: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance. + Partial Wasserstein distance (m = 2/3): 0.23235485397666825 + Entropic partial Wasserstein distance (m = 2/3): 1.4645434781619244 + /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:157: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance. pl.subplot(1, 2, 1) - /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:162: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance. + /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:160: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance. pl.subplot(1, 2, 2) - /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:165: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. + /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:163: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. pl.show() @@ -283,7 +281,7 @@ by transporting 100% and 2/3 of the mass .. rst-class:: sphx-glr-timing - **Total running time of the script:** ( 0 minutes 1.656 seconds) + **Total running time of the script:** ( 0 minutes 1.543 seconds) .. _sphx_glr_download_auto_examples_plot_partial_wass_and_gromov.py: -- cgit v1.2.3 From e106537ee2fd5c3b2dac87789ed9f2dc40766a55 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 20 Apr 2020 22:19:26 +0200 Subject: update readme + notebooks --- README.md | 2 +- notebooks/plot_otda_laplacian.ipynb | 252 +++++++++++++++++++++++++++ notebooks/plot_partial_wass_and_gromov.ipynb | 42 +++-- 3 files changed, 273 insertions(+), 23 deletions(-) create mode 100644 notebooks/plot_otda_laplacian.ipynb diff --git a/README.md b/README.md index 44d35a7..931a252 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ [![PyPI version](https://badge.fury.io/py/POT.svg)](https://badge.fury.io/py/POT) [![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot) -[![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/PythonOT/POT) +[![Build Status](https://travis-ci.org/PythonOT/POT.svg?branch=master)](https://travis-ci.org/PythonOT/POT) [![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) [![Downloads](https://pepy.tech/badge/pot)](https://pepy.tech/project/pot) [![Anaconda downloads](https://anaconda.org/conda-forge/pot/badges/downloads.svg)](https://anaconda.org/conda-forge/pot) diff --git a/notebooks/plot_otda_laplacian.ipynb b/notebooks/plot_otda_laplacian.ipynb new file mode 100644 index 0000000..07e4653 --- /dev/null +++ b/notebooks/plot_otda_laplacian.ipynb @@ -0,0 +1,252 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# OT with Laplacian regularization for domain adaptation\n", + "\n", + "\n", + "This example introduces a domain adaptation in a 2D setting and OTDA\n", + "approach with Laplacian regularization.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Authors: Ievgen Redko \n", + "\n", + "# License: MIT License\n", + "\n", + "import matplotlib.pylab as pl\n", + "import ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_source_samples = 150\n", + "n_target_samples = 150\n", + "\n", + "Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples)\n", + "Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instantiate the different transport algorithms and fit them\n", + "-----------------------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# EMD Transport\n", + "ot_emd = ot.da.EMDTransport()\n", + "ot_emd.fit(Xs=Xs, Xt=Xt)\n", + "\n", + "# Sinkhorn Transport\n", + "ot_sinkhorn = ot.da.SinkhornTransport(reg_e=.01)\n", + "ot_sinkhorn.fit(Xs=Xs, Xt=Xt)\n", + "\n", + "# EMD Transport with Laplacian regularization\n", + "ot_emd_laplace = ot.da.EMDLaplaceTransport(reg_lap=100, reg_src=1)\n", + "ot_emd_laplace.fit(Xs=Xs, Xt=Xt)\n", + "\n", + "# transport source samples onto target samples\n", + "transp_Xs_emd = ot_emd.transform(Xs=Xs)\n", + "transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\n", + "transp_Xs_emd_laplace = ot_emd_laplace.transform(Xs=Xs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig 1 : plots source and target samples\n", + "---------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1, figsize=(10, 5))\n", + "pl.subplot(1, 2, 1)\n", + "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.legend(loc=0)\n", + "pl.title('Source samples')\n", + "\n", + "pl.subplot(1, 2, 2)\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.legend(loc=0)\n", + "pl.title('Target samples')\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig 2 : plot optimal couplings and transported samples\n", + "------------------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "param_img = {'interpolation': 'nearest'}\n", + "\n", + "pl.figure(2, figsize=(15, 8))\n", + "pl.subplot(2, 3, 1)\n", + "pl.imshow(ot_emd.coupling_, **param_img)\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Optimal coupling\\nEMDTransport')\n", + "\n", + "pl.figure(2, figsize=(15, 8))\n", + "pl.subplot(2, 3, 2)\n", + "pl.imshow(ot_sinkhorn.coupling_, **param_img)\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Optimal coupling\\nSinkhornTransport')\n", + "\n", + "pl.subplot(2, 3, 3)\n", + "pl.imshow(ot_emd_laplace.coupling_, **param_img)\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Optimal coupling\\nEMDLaplaceTransport')\n", + "\n", + "pl.subplot(2, 3, 4)\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", + " label='Target samples', alpha=0.3)\n", + "pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n", + " marker='+', label='Transp samples', s=30)\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Transported samples\\nEmdTransport')\n", + "pl.legend(loc=\"lower left\")\n", + "\n", + "pl.subplot(2, 3, 5)\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", + " label='Target samples', alpha=0.3)\n", + "pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n", + " marker='+', label='Transp samples', s=30)\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Transported samples\\nSinkhornTransport')\n", + "\n", + "pl.subplot(2, 3, 6)\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", + " label='Target samples', alpha=0.3)\n", + "pl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys,\n", + " marker='+', label='Transp samples', s=30)\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Transported samples\\nEMDLaplaceTransport')\n", + "pl.tight_layout()\n", + "\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.9" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_partial_wass_and_gromov.ipynb b/notebooks/plot_partial_wass_and_gromov.ipynb index 017fd8c..6bf0bc6 100644 --- a/notebooks/plot_partial_wass_and_gromov.ipynb +++ b/notebooks/plot_partial_wass_and_gromov.ipynb @@ -64,7 +64,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -103,8 +103,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Compute partial Wasserstein plans and distance,\n", - "by transporting 50% of the mass\n", + "Compute partial Wasserstein plans and distance\n", "----------------------------------------------\n", "\n" ] @@ -120,13 +119,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Partial Wasserstein distance (m = 0.5): 0.485157824314826\n", - "Entropic partial Wasserstein distance (m = 0.5): 0.5048991597745197\n" + "Partial Wasserstein distance (m = 0.5): 0.45151590745848863\n", + "Entropic partial Wasserstein distance (m = 0.5): 0.46654948274375097\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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" ] @@ -224,8 +223,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Compute partial Gromov-Wasserstein plans and distance,\n", - "by transporting 100% and 2/3 of the mass\n", + "Compute partial Gromov-Wasserstein plans and distance\n", "-----------------------------------------------------\n", "\n" ] @@ -242,13 +240,13 @@ "output_type": "stream", "text": [ "-----m = 1\n", - "Partial Wasserstein distance (m = 1): 63.419317539744505\n", - "Entropic partial Wasserstein distance (m = 1): 64.89236221074341\n" + "Wasserstein distance (m = 1): 62.612867557378095\n", + "Entropic Wasserstein distance (m = 1): 64.09799387131392\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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Jf9jDWDBGrnGktH01j9ye7iv6rzqH7aD25pAlTTBzzSVLMvJPMuPzCubIxXptwRy54+xdMEdun1yjTSl/zp5fMEeuicW+mfGSBpe5Y9Akc9qhjQEAAEAlCigAAIBKFFAAAACVKKAAAAAqUUABAABUooACAACoRAEFAABQqdvPwkMfTae+R7meVdPpvgDj2irp8Zbxkv5Ld2fGS3on5ZTEkes5tKZgjlysJXPkYi35DZXbp+0xG7WgyzlK4szNUfK48Rt7qLACBQAAUIkCCgAAoBIFFAAAQCUKKAAAgEoUUAAAAJUooAAAACpRQAEAAFSigAIAAKhEWy70RK5RJo02Me1Z7Rkz15xSkuZmxncpmGNjZrwkjlxTx9wxSuYoaQyZ26ekGWfut1hJHLn7m3tcSn6T5h6XkoafGCqsQAEAAFSigAIAAKhEAQUAAFCJAgoAAKASBRQAAEAlCigAAIBKFFAAAACV6AM1QNtTb6SZdF+wndoqaVPLeK7HkzI/L5X1cDo6M76yYI5cb6S/KJgjd5ySOEYy4/sWzJE7Z8sL5licGR8pmCMnd1960UsKU4oVKAAAgEoUUAAAAJUooAAAACpRQAEAAFSigAIAAKhEAQUAAFCJAgoAAKASBRQAAEClbOsu2xdKOlbSuog4OG2bL+nLatqPjUg6PiIe7F+Y5XLNKaXhaeo4LHEAM1nPctgOam+WWdLkcHFm/PGCOe4u2Ccnd5wNBXPkfnv0oglmLxpHLi7YJ3c+FmTGS+LMHaPkscdQKVmBukjSMWO2fVDS9yLiQEnfS98DwDC6SOQwAD2WLaAi4jpJD4zZfJyki9PtiyW9rsdxAUBPkMMA9MNkr4FaGBGr0+01khb2KB4AmArkMABd6foi8ogISTHRuO2ltpfbXi491u3hAKCn2nLYNvnr0fVTHBmAYTbZAmqt7UWSlP5dN9GOEXFeRCyJiCXS7EkeDgB6qiiHbZO/5uw5pQECGG6TLaCulHRSun2SpG/0JhwAmBLkMABdyRZQti+T9GNJz7O90vY7JJ0t6ZW275J0dPoeAIYOOQxAP2T7QEXECRMMvaLHsfTEsPRWmk79qICZrGc5bIukjS3jJX18cv2ESno8/dfMgT62S36Oee3Dzz7ztuwU93z4Be07rMmHoTsz40sK5tiUGS85p7meVW2Pu1TWB+pNmfGSOHvRFws9QydyAACAShRQAAAAlSigAAAAKlFAAQAAVKKAAgAAqEQBBQAAUIkCCgAAoBIFFAAAQKVsI01MzlQ1ycw17KRZJ9AjO6u94eKGgjlyPS4PKZjj7ZlJSppPZppc3nNkpkmmJB3e5bgkHZUZX14wx9zM+LsK5vhJZjx3X0oaXP5TwT45/MYeKqxAAQAAVKKAAgAAqEQBBQAAUIkCCgAAoBIFFAAAQCUKKAAAgEoUUAAAAJXoKjHN0efpKbmeWBLnC114Qu39k+YVzJHra1QyxzGZ8ZKeRHtnxhcXzJFzfcE+ub5YJecj91uspP9SW38vSbqzyxhKjpE7Fxg6rEABAABUooACAACoRAEFAABQiQIKAACgEgUUAABAJQooAACAShRQAAAAlSigAAAAKg1VI00aIaIb/N9AX1ntzQ5Lsmlun7kFc4xkxhcXzPF4ZjzXOFKSDsiMl9yXXKPMkqagObmmoSVycZY89m1NWEuOgaHDChQAAEAlCigAAIBKFFAAAACVKKAAAAAqUUABAABUooACAACoRAEFAABQKdu9wvaFko6VtC4iDk7blkk6WdL6tNsZEXFVt8HQx2f7RQ8w9EvPctgOau8DleutJOV7/bTNPyrXT6ik71Eu1twxJGnfzPiCgjly+4wUzJGTi1PK399cT6uSPlC5c76pYI6h6tyIkhWoiyQdM872T0TEIemr6+IJAPrkIpHDAPRYtoCKiOskPTAFsQBAz5HDAPRDN9dAnWr7F7YvtL1HzyICgKlBDgMwaZMtoD4r6TmSDpG0WtI5E+1oe6nt5baXS49N8nAA0FNFOWyb/PXo+vF2AbCdmlQBFRFrI2JLRGyVdL6kw1r2PS8ilkTEEmn2ZOMEgJ4pzWHb5K85e05tkACG2qQKKNuLOr59vaRbexMOAPQfOQxAt0raGFwm6ShJC2yvlHSmpKNsHyIp1Pyh6Sl9jBEAJo0cBqAfsgVURJwwzuYL+hALhtRU9GiixxP6pWc5bJba+zjdXTDHUZnxkv5LG7ocl6SNmfGSnkS5OY4omCP3G+jOgjk2Z8aPKpjj5sz44Znxkh5guThXFsxR0icMU4ZO5AAAAJUooAAAACpRQAEAAFSigAIAAKhEAQUAAFCJAgoAAKASBRQAAEAlCigAAIBK2Uaa26OpaBw5nWxP9xWY0BNqb3S5d8EcV2fGS+Y4ODO+b8EcJcfpdo5rC+Zoa0wqSXML5sj9FiuJY3FmfKTLGKT8fcnFgKHDChQAAEAlCigAAIBKFFAAAACVKKAAAAAqUUABAABUooACAACoRAEFAABQiT5Q46Dv0cyV6/HFY48JhaTHW8ZLsmlun40FcxyeGR8pmCPXk+iYgjlyx9lUMMeGzPjigjk2Z8ZHCubIHaet/5dU9tjn+nOVzJG7r5hSrEABAABUooACAACoRAEFAABQiQIKAACgEgUUAABAJQooAACAShRQAAAAlSigAAAAKtFIE9tVc8mZdF8wxWZJmtcyXtLksO3npXyDS0m6OjP+/II5cg0sry2Y4+DM+OKCOXL3N9fAUsr/FltSMEfufJSc05y7M+O7FMzBb+yhwgoUAABAJQooAACAShRQAAAAlSigAAAAKlFAAQAAVKKAAgAAqEQBBQAAUImuEqA3UodcTyyJ87XdWnWf9MFlg45ieFwx6AAw1jmxLrvPCu3XOr5Zs7JznHvBB1rHL3/Ha7NzHKKbW8dn67HsHLO0pXV80b3rs3N89JnvbR0/3ROPZVegbO9n+xrbt9u+zfZ70vb5tr9r+6707x7ZSAFgCpG/APRLyVt4myW9LyIOknS4pHfbPkjSByV9LyIOlPS99D0ADBPyF4C+yBZQEbE6In6Wbj8i6Q5J+0g6TtLFabeLJb2uX0ECwGSQvwD0S9U1ULYXSzpU0g2SFkbE6jS0RtLCCX5mqaSlzXe7Ty5KAOgS+QtALxX/FZ7tuZK+Kum0iHi4cywiQlKM93MRcV5ELImIJdLsroIFgMkgfwHotaICyvZOapLPFyLia2nzWtuL0vgiSfnL/wFgipG/APRDyV/hWdIFku6IiI93DF0p6aR0+yRJ3+h9eAAweeQvAP1Scg3Un0g6UdIttkcbN5wh6WxJl9t+h6R7JR3fnxABYNLIXwD6IltARcT1kiZqJfWK3oYDDNZUNcnMNeykWWdvkL+wPXms4Dq91+iq1vG9tDY7x1HvPL11/LJ3XJKd4/hdv5k5SHYKaUn7cFzc0gUzOeue9qagbfgoFwAAgEoUUAAAAJUooAAAACpRQAEAAFSigAIAAKhEAQUAAFCJAgoAAKBS1YcJA+iNXJ+nXJ+okjkAbF+2aFZ2ny/oza3jvynoJXX5aSe1jl+kN2bn+Mo1J7bvUPDZ3fH09nEfPu5HXG7j7/T+/IEmwAoUAABAJQooAACAShRQAAAAlSigAAAAKlFAAQAAVKKAAgAAqEQBBQAAUIkCCgAAoBKNNIEh1IsmmTTjHEY7ZcafmJIoMDP9Wouz+9yhg1rHN2hB/kBXtw+v+MR++TmOz4y/IT+FX5HZIROnJK3404JYJ8AKFAAAQCUKKAAAgEoUUAAAAJUooAAAACpRQAEAAFSigAIAAKhEAQUAAFCJPlBAjw1L/yV6PA0j+jyhf47W97L7vE2XtI7P1mPZOV548y2t45/RX2Xn8CnRvsPR2Sk0/5BVreNxsLNzXKo/bx0/r2WMFSgAAIBKFFAAAACVKKAAAAAqUUABAABUooACAACoRAEFAABQiQIKAACgEgUUAABApWwjTdv7Sfq8pIWSQtJ5EfEp28sknSxpfdr1jIi4ql+BAsMi1yiTBpbDg/yF7clGzcvus59WtI7vqkeyc8x5w9bW8RVX7pedQ1/JjG/MT/HAyn3ad7gmP8e6T+6V32kCJZ3IN0t6X0T8zPaukm6y/d009omI+Nikjw4A/UX+AtAX2QIqIlZLWp1uP2L7DkmZsg8ABo/8BaBfqq6Bsr1Y0qGSbkibTrX9C9sX2t6jx7EBQM+QvwD0UnEBZXuupK9KOi0iHpb0WUnPkXSImld450zwc0ttL7e9XAUfUggAvUb+AtBrRQWU7Z3UJJ8vRMTXJCki1kbElojYKul8SYeN97MRcV5ELImIJdLsXsUNAEXIXwD6IVtA2bakCyTdEREf79i+qGO310u6tffhAcDkkb8A9EvJX+H9iaQTJd1i++a07QxJJ9g+RM2fBo9IOqUvEQLA5JG/APRFyV/hXS/J4wzRMwXbJfo8bSvfF2twyF/YnizU2uw+j2hu6/jvtHN2jn+68ujW8UN1c+u4JOmi9uEDX/Dz7BTP069ax68+7mXZOfbSuuw+E6ETOQAAQCUKKAAAgEoUUAAAAJUooAAAACpRQAEAAFSigAIAAKhEAQUAAFCJAgoAAKBSSSdyADNUvglmvmlofp9BttIEth/X6cjsPr/S81rHN2pedo6fvry9QeUnr8k39o+PjdfftsPh2SmajwJv4WsjO8Vpp38ks8dXJxxhBQoAAKASBRQAAEAlCigAAIBKFFAAAACVKKAAAAAqUUABAABUooACAACoRB8oYDuW6+GU6xNVMgeAqfEsjWT3eb2uaB1fqLXZOV5w7z2t46fok9k5fH2mR9Mu2Smy+8RNmV5Tkj6i0woOND5WoAAAACpRQAEAAFSigAIAAKhEAQUAAFCJAgoAAKASBRQAAEAlCigAAIBKFFAAAACVaKQJTFO5Jpe9aHBJk8xOO0j6g5bxzQVzzM+MP1wwR8lxhsGuBfvk7stvehBH22NWepxe/Krs/+N2pf5ddp+//e3ftI4/NLJ3/kC/bh9+q87PTnHm3e1NLpc9Nx+GXts+7PWZZp2SXqVvZPaYuCkoK1AAAACVKKAAAAAqUUABAABUooACAACoRAEFAABQiQIKAACgEgUUAABAJUfk+yT07GD2ekn3dmxaIGnDlAUwedMlTmn6xEqcvTessT4zIvYcdBDdGid/ScN7zscizt6aLnFK0yfWYY1zwvw1pQXU7x3cXh4RSwYWQKHpEqc0fWIlzt6bTrHOFNPlnBNnb02XOKXpE+t0ibMTb+EBAABUooACAACoNOgC6rwBH7/UdIlTmj6xEmfvTadYZ4rpcs6Js7emS5zS9Il1usT5pIFeAwUAADAdDXoFCgAAYNoZWAFl+xjbv7R9t+0PDiqOHNsjtm+xfbPt5YOOp5PtC22vs31rx7b5tr9r+6707x6DjDHFNF6cy2yvSuf1ZtuvGWSMKab9bF9j+3bbt9l+T9o+VOe0Jc6hO6czFfmre+Sv3iJ/Tb2BvIVne5akX0l6paSVkm6UdEJE3D7lwWTYHpG0JCKGrj+F7ZdK2iTp8xFxcNr295IeiIizU2LfIyJOH8I4l0naFBEfG2RsnWwvkrQoIn5me1dJN0l6naS3a4jOaUucx2vIzulMRP7qDfJXb5G/pt6gVqAOk3R3RNwTEb+T9CVJxw0olmkrIq6T9MCYzcdJujjdvljNf8yBmiDOoRMRqyPiZ+n2I5LukLSPhuyctsSJqUH+6gHyV2+Rv6beoAqofSSt6Ph+pYb3BIak79i+yfbSQQdTYGFErE6310haOMhgMk61/Yu0RD7wpfpOthdLOlTSDRriczomTmmIz+kMQv7qn6F9ro1jaJ9r5K+pwUXkeUdExB9JerWkd6fl3Gkhmvdnh/XPLD8r6TmSDpG0WtI5gw3nKbbnSvqqpNMi4uHOsWE6p+PEObTnFAND/uqPoX2ukb+mzqAKqFWS9uv4ft+0behExKr07zpJX1ezfD/M1qb3mEffa1434HjGFRFrI2JLRGyVdL6G5Lza3knNk/oLEfG1tHnozul4cQ7rOZ2ByF/9M3TPtfEM63ON/DW1BlVA3SjpQNvPsr2zpDdJunJAsUzI9px0kZtsz5H0Kkm3tv/UwF0p6aR0+yRJ3xhgLBMafZNsvHIAAADeSURBVEInr9cQnFfblnSBpDsi4uMdQ0N1TieKcxjP6QxF/uqfoXquTWQYn2vkr6k3sEaa6U8UPylplqQLI+LDAwmkhe1nq3nVJkk7SvriMMVp+zJJR6n5FOu1ks6UdIWkyyXtr+aT44+PiIFeADlBnEepWaoNSSOSTul4n34gbB8h6QeSbpG0NW0+Q83780NzTlviPEFDdk5nKvJX98hfvUX+mnp0IgcAAKjEReQAAACVKKAAAAAqUUABAABUooACAACoRAEFAABQiQIKAACgEgUUAABAJQooAACASv8fCi5jD+vsRMUAAAAASUVORK5CYII=\n", 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" ] @@ -263,13 +261,13 @@ "output_type": "stream", "text": [ "-----m = 2/3\n", - "Partial Wasserstein distance (m = 2/3): 0.17456327357887044\n", - "Entropic partial Wasserstein distance (m = 2/3): 1.070213997054379\n" + "Partial Wasserstein distance (m = 2/3): 0.252736149616858\n", + "Entropic partial Wasserstein distance (m = 2/3): 1.407282181449262\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -284,27 +282,27 @@ "C1 = sp.spatial.distance.cdist(xs, xs)\n", "C2 = sp.spatial.distance.cdist(xt, xt)\n", "\n", + "# transport 100% of the mass\n", "print('-----m = 1')\n", "m = 1\n", - "res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m,\n", - " log=True)\n", + "res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True)\n", "res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10,\n", " m=m, log=True)\n", "\n", - "print('Partial Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist']))\n", - "print('Entropic partial Wasserstein distance (m = 1): ' +\n", - " str(log['partial_gw_dist']))\n", + "print('Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist']))\n", + "print('Entropic Wasserstein distance (m = 1): ' + str(log['partial_gw_dist']))\n", "\n", "pl.figure(1, (10, 5))\n", "pl.title(\"mass to be transported m = 1\")\n", "pl.subplot(1, 2, 1)\n", "pl.imshow(res0, cmap='jet')\n", - "pl.title('Partial Wasserstein')\n", + "pl.title('Wasserstein')\n", "pl.subplot(1, 2, 2)\n", "pl.imshow(res, cmap='jet')\n", - "pl.title('Entropic partial Wasserstein')\n", + "pl.title('Entropic Wasserstein')\n", "pl.show()\n", "\n", + "# transport 2/3 of the mass\n", "print('-----m = 2/3')\n", "m = 2 / 3\n", "res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True)\n", -- cgit v1.2.3 From 1efd48d61f871d6833158ad18a2a2920bf515d77 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 21 Apr 2020 10:32:42 +0200 Subject: circle CI --- .circleci/artifact_path | 1 + .circleci/config.yml | 137 ++++++++++++++++++++++++++++++++++++++++++++++++ docs/requirements.txt | 5 ++ 3 files changed, 143 insertions(+) create mode 100644 .circleci/artifact_path create mode 100644 .circleci/config.yml create mode 100644 docs/requirements.txt diff --git a/.circleci/artifact_path b/.circleci/artifact_path new file mode 100644 index 0000000..aa9acb8 --- /dev/null +++ b/.circleci/artifact_path @@ -0,0 +1 @@ +0/docs/build/html/index.html diff --git a/.circleci/config.yml b/.circleci/config.yml new file mode 100644 index 0000000..9701ad1 --- /dev/null +++ b/.circleci/config.yml @@ -0,0 +1,137 @@ +# Tagging a commit with [circle front] will build the front page and perform test-doc. +# Tagging a commit with [circle full] will build everything. +version: 2 +jobs: + build_docs: + docker: + - image: circleci/python:3.7-stretch + steps: + - checkout + - run: + name: Set BASH_ENV + command: | + echo "set -e" >> $BASH_ENV + echo "export DISPLAY=:99" >> $BASH_ENV + echo "export OPENBLAS_NUM_THREADS=4" >> $BASH_ENV + echo "BASH_ENV:" + cat $BASH_ENV + + - run: + name: Merge with upstream + command: | + echo $(git log -1 --pretty=%B) | tee gitlog.txt + echo ${CI_PULL_REQUEST//*pull\//} | tee merge.txt + if [[ $(cat merge.txt) != "" ]]; then + echo "Merging $(cat merge.txt)"; + git remote add upstream git://github.com/PythonOT/POT.git; + git pull --ff-only upstream "refs/pull/$(cat merge.txt)/merge"; + git fetch upstream master; + fi + + # Load our data + - restore_cache: + keys: + - data-cache-0 + - pip-cache + + - run: + name: Spin up Xvfb + command: | + /sbin/start-stop-daemon --start --quiet --pidfile /tmp/custom_xvfb_99.pid --make-pidfile --background --exec /usr/bin/Xvfb -- :99 -screen 0 1400x900x24 -ac +extension GLX +render -noreset; + + # https://github.com/ContinuumIO/anaconda-issues/issues/9190#issuecomment-386508136 + # https://github.com/golemfactory/golem/issues/1019 + - run: + name: Fix libgcc_s.so.1 pthread_cancel bug + command: | + sudo apt-get install qt5-default + + - run: + name: Get Python running + command: | + python -m pip install --user --upgrade --progress-bar off pip + python -m pip install --user --upgrade --progress-bar off -r requirements.txt + python -m pip install --user --upgrade --progress-bar off -r docs/requirements.txt + python -m pip install --user --upgrade --progress-bar off ipython "https://api.github.com/repos/sphinx-gallery/sphinx-gallery/zipball/master" memory_profiler + python -m pip install --user -e . + + - save_cache: + key: pip-cache + paths: + - ~/.cache/pip + + # Look at what we have and fail early if there is some library conflict + - run: + name: Check installation + command: | + which python + python -c "import ot" + + # Build docs + - run: + name: make html + command: | + cd docs; + make html; + + # Save the outputs + - store_artifacts: + path: docs/build/html/ + destination: dev + - persist_to_workspace: + root: docs/build + paths: + - html + + deploy: + docker: + - image: circleci/python:3.6-jessie + steps: + - attach_workspace: + at: /tmp/build + - run: + name: Fetch docs + command: | + set -e + mkdir -p ~/.ssh + echo -e "Host *\nStrictHostKeyChecking no" > ~/.ssh/config + chmod og= ~/.ssh/config + if [ ! -d ~/PythonOT.github.io ]; then + git clone git@github.com:/PythonOT/PythonOT.github.io.git ~/PythonOT.github.io --depth=1 + fi + - run: + name: Deploy docs + command: | + set -e; + if [ "${CIRCLE_BRANCH}" == "master" ]; then + git config --global user.email "circle@PythonOT.com"; + git config --global user.name "Circle CI"; + cd ~/PythonOT.github.io; + git checkout master + git remote -v + git fetch origin + git reset --hard origin/master + git clean -xdf + echo "Deploying dev docs for ${CIRCLE_BRANCH}."; + cp -a /tmp/build/html/* .; + touch .nojekyll; + git add -A; + git commit -m "CircleCI update of dev docs (${CIRCLE_BUILD_NUM})."; + git push origin master; + else + echo "No deployment (build: ${CIRCLE_BRANCH})."; + fi + +workflows: + version: 2 + + default: + jobs: + - build_docs + - deploy: + requires: + - build_docs + filters: + branches: + only: + - master diff --git a/docs/requirements.txt b/docs/requirements.txt new file mode 100644 index 0000000..1fe37c2 --- /dev/null +++ b/docs/requirements.txt @@ -0,0 +1,5 @@ +sphinx_gallery +sphinx_rtd_theme +numpydoc +memory_profiler +pillow -- cgit v1.2.3 From f141b7ecec8f69db29fb3dcf1efd1d00d96db87d Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 21 Apr 2020 10:53:59 +0200 Subject: add artifact redirect --- .github/workflows/main.yml | 12 ++++++++++++ 1 file changed, 12 insertions(+) create mode 100644 .github/workflows/main.yml diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml new file mode 100644 index 0000000..ca10c8c --- /dev/null +++ b/.github/workflows/main.yml @@ -0,0 +1,12 @@ +on: [status] +jobs: + circleci_artifacts_redirector_job: + runs-on: ubuntu-latest + name: Run CircleCI artifacts redirector + steps: + - name: GitHub Action step + uses: larsoner/circleci-artifacts-redirector-action@master + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + artifact-path: 0/docs/build/html/index.html + circleci-jobs: build_doc -- cgit v1.2.3 From 8c6a976c9d1225e469e22f7ef1c700eaf1e08dd7 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 21 Apr 2020 11:00:24 +0200 Subject: try codecov setup --- .github/workflows/main.yml | 2 +- .travis.yml | 5 ++++- codecov.yml | 14 ++++++++++++++ 3 files changed, 19 insertions(+), 2 deletions(-) create mode 100644 codecov.yml diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index ca10c8c..1a8a759 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -9,4 +9,4 @@ jobs: with: repo-token: ${{ secrets.GITHUB_TOKEN }} artifact-path: 0/docs/build/html/index.html - circleci-jobs: build_doc + circleci-jobs: build_docs diff --git a/.travis.yml b/.travis.yml index b9cc88f..3f63867 100644 --- a/.travis.yml +++ b/.travis.yml @@ -34,7 +34,7 @@ before_install: install: - pip install -r requirements.txt - pip install -U "numpy>=1.14" scipy # for numpy array formatting in doctests - - pip install flake8 pytest "pytest-cov<2.6" + - pip install flake8 pytest "pytest-cov<2.6" codecov - pip install -U "sklearn" - pip install . # command to run tests + check syntax style @@ -45,3 +45,6 @@ script: - flake8 examples/ ot/ test/ - python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot # - py.test ot test +after_script: + # Need to run from source dir to execute "git" commands + - codecov; diff --git a/codecov.yml b/codecov.yml new file mode 100644 index 0000000..1e7b888 --- /dev/null +++ b/codecov.yml @@ -0,0 +1,14 @@ +coverage: + precision: 2 + round: down + range: "70...100" + status: + project: + default: + target: auto + threshold: 0.01 + patch: false + changes: false +comment: + layout: "header, diff, sunburst, uncovered" + behavior: default -- cgit v1.2.3 From 555f8426a8cb66ae5255885ce555037dc8c72c53 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 21 Apr 2020 11:11:19 +0200 Subject: add codecov button --- .github/workflows/main.yml | 1 - README.md | 1 + 2 files changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 1a8a759..6b52555 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -7,6 +7,5 @@ jobs: - name: GitHub Action step uses: larsoner/circleci-artifacts-redirector-action@master with: - repo-token: ${{ secrets.GITHUB_TOKEN }} artifact-path: 0/docs/build/html/index.html circleci-jobs: build_docs diff --git a/README.md b/README.md index 931a252..64630e6 100644 --- a/README.md +++ b/README.md @@ -3,6 +3,7 @@ [![PyPI version](https://badge.fury.io/py/POT.svg)](https://badge.fury.io/py/POT) [![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot) [![Build Status](https://travis-ci.org/PythonOT/POT.svg?branch=master)](https://travis-ci.org/PythonOT/POT) +[![Codecov Status](https://codecov.io/gh/PythonOT/POT/branch/master/graph/badge.svg)](https://codecov.io/gh/PythonOT/POT) [![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) [![Downloads](https://pepy.tech/badge/pot)](https://pepy.tech/project/pot) [![Anaconda downloads](https://anaconda.org/conda-forge/pot/badges/downloads.svg)](https://anaconda.org/conda-forge/pot) -- cgit v1.2.3 From f48d51579d843f12a73f29d44374bb63e627238d Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 21 Apr 2020 11:19:41 +0200 Subject: try to push doc to github.io --- .circleci/config.yml | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 9701ad1..455b700 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -103,24 +103,24 @@ jobs: name: Deploy docs command: | set -e; - if [ "${CIRCLE_BRANCH}" == "master" ]; then - git config --global user.email "circle@PythonOT.com"; - git config --global user.name "Circle CI"; - cd ~/PythonOT.github.io; - git checkout master - git remote -v - git fetch origin - git reset --hard origin/master - git clean -xdf - echo "Deploying dev docs for ${CIRCLE_BRANCH}."; - cp -a /tmp/build/html/* .; - touch .nojekyll; - git add -A; - git commit -m "CircleCI update of dev docs (${CIRCLE_BUILD_NUM})."; - git push origin master; - else - echo "No deployment (build: ${CIRCLE_BRANCH})."; - fi + # if [ "${CIRCLE_BRANCH}" == "master" ]; then + git config --global user.email "circle@PythonOT.com"; + git config --global user.name "Circle CI"; + cd ~/PythonOT.github.io; + git checkout master + git remote -v + git fetch origin + git reset --hard origin/master + git clean -xdf + echo "Deploying dev docs for ${CIRCLE_BRANCH}."; + cp -a /tmp/build/html/* .; + touch .nojekyll; + git add -A; + git commit -m "CircleCI update of dev docs (${CIRCLE_BUILD_NUM})."; + git push origin master; + # else + # echo "No deployment (build: ${CIRCLE_BRANCH})."; + # fi workflows: version: 2 -- cgit v1.2.3 From d3ebb36fc4a3614c5640d731ea793c0afd400e4e Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 21 Apr 2020 11:23:34 +0200 Subject: run gh actions all the time --- .github/workflows/main.yml | 11 ++++++++++- .github/workflows/pythonpackage.yml | 10 +++++++++- 2 files changed, 19 insertions(+), 2 deletions(-) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 6b52555..46f278a 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -1,4 +1,13 @@ -on: [status] +on: + push: + branches: + - '**' + create: + branches: + - 'master' + tags: + - '**' + jobs: circleci_artifacts_redirector_job: runs-on: ubuntu-latest diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index cb3baf8..d728edd 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -1,6 +1,14 @@ name: Test Package -on: [push] +on: + push: + branches: + - '**' + create: + branches: + - 'master' + tags: + - '**' jobs: build: -- cgit v1.2.3 From 624fd44c729a3b761d09a01b71338c0332b6b9fe Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 21 Apr 2020 11:24:42 +0200 Subject: fix --- .github/workflows/main.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 46f278a..07686d9 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -16,5 +16,6 @@ jobs: - name: GitHub Action step uses: larsoner/circleci-artifacts-redirector-action@master with: + repo-token: ${{ secrets.GITHUB_TOKEN }} artifact-path: 0/docs/build/html/index.html circleci-jobs: build_docs -- cgit v1.2.3 From 5c88642a27e6c330a5c795898c0de7f3b4c0cd8d Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 21 Apr 2020 12:00:03 +0200 Subject: fix? --- .circleci/config.yml | 3 ++- .github/workflows/main.yml | 13 ++----------- 2 files changed, 4 insertions(+), 12 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 455b700..ea5981e 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -134,4 +134,5 @@ workflows: filters: branches: only: - - master + # - master + - doc_ci_build diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 07686d9..7153fe6 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -1,13 +1,4 @@ -on: - push: - branches: - - '**' - create: - branches: - - 'master' - tags: - - '**' - +on: [status] jobs: circleci_artifacts_redirector_job: runs-on: ubuntu-latest @@ -17,5 +8,5 @@ jobs: uses: larsoner/circleci-artifacts-redirector-action@master with: repo-token: ${{ secrets.GITHUB_TOKEN }} - artifact-path: 0/docs/build/html/index.html + artifact-path: 0/dev/index.html circleci-jobs: build_docs -- cgit v1.2.3 From 8497cd523085ea69d949a0de67db252b36a81d8e Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 21 Apr 2020 12:03:44 +0200 Subject: run tests in GH actions --- .github/workflows/pythonpackage.yml | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index d728edd..d60acd2 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -29,10 +29,18 @@ jobs: run: | python -m pip install --upgrade pip pip install -r requirements.txt + pip install flake8 pytest "pytest-cov<2.6" codecov + pip install -U "sklearn" - name: Lint with flake8 run: | - pip install flake8 # stop the build if there are Python syntax errors or undefined names flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Run tests + run: | + pip install . + python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot + - name: Upload codecov + run: | + codecov -- cgit v1.2.3 From bdf608bfb417a15545d581aa2d57160401aacb6e Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 21 Apr 2020 12:09:53 +0200 Subject: fix? --- .github/workflows/pythonpackage.yml | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index d60acd2..c4b0165 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -17,7 +17,7 @@ jobs: strategy: max-parallel: 4 matrix: - python-version: [2.7, 3.5, 3.6, 3.7] + python-version: [3.5, 3.6, 3.7, 3.8] steps: - uses: actions/checkout@v1 @@ -37,9 +37,11 @@ jobs: flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Install POT + run: | + pip install -e . - name: Run tests run: | - pip install . python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot - name: Upload codecov run: | -- cgit v1.2.3 From 3e10d08ab7bb1a58053505e6e5bb6c8715e90854 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 21 Apr 2020 12:12:40 +0200 Subject: fix? --- .circleci/config.yml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.circleci/config.yml b/.circleci/config.yml index ea5981e..6817880 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -129,6 +129,10 @@ workflows: jobs: - build_docs - deploy: + steps: + - add_ssh_keys: + fingerprints: + - "ff:30:7b:ba:bd:5c:ec:27:49:12:11:cb:78:aa:c2:6e" requires: - build_docs filters: -- cgit v1.2.3 From 8da537d72414a7ce347f39ef64c6c819610c660c Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 21 Apr 2020 12:14:21 +0200 Subject: fix? --- .circleci/config.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 6817880..80fe935 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -130,9 +130,9 @@ workflows: - build_docs - deploy: steps: - - add_ssh_keys: - fingerprints: - - "ff:30:7b:ba:bd:5c:ec:27:49:12:11:cb:78:aa:c2:6e" + - add_ssh_keys: + fingerprints: + - "ff:30:7b:ba:bd:5c:ec:27:49:12:11:cb:78:aa:c2:6e" requires: - build_docs filters: -- cgit v1.2.3 From 9b66b194cdfc6b7e4b35488fa093d936fa08e011 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 21 Apr 2020 12:15:01 +0200 Subject: fix? --- .circleci/config.yml | 4 ---- 1 file changed, 4 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index 80fe935..ea5981e 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -129,10 +129,6 @@ workflows: jobs: - build_docs - deploy: - steps: - - add_ssh_keys: - fingerprints: - - "ff:30:7b:ba:bd:5c:ec:27:49:12:11:cb:78:aa:c2:6e" requires: - build_docs filters: -- cgit v1.2.3 From 3eed3cad36ec5f5691dc029853bc36bfad5ee389 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 21 Apr 2020 12:25:36 +0200 Subject: all good --- .circleci/config.yml | 39 +++++++++++++++++++-------------------- 1 file changed, 19 insertions(+), 20 deletions(-) diff --git a/.circleci/config.yml b/.circleci/config.yml index ea5981e..9701ad1 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -103,24 +103,24 @@ jobs: name: Deploy docs command: | set -e; - # if [ "${CIRCLE_BRANCH}" == "master" ]; then - git config --global user.email "circle@PythonOT.com"; - git config --global user.name "Circle CI"; - cd ~/PythonOT.github.io; - git checkout master - git remote -v - git fetch origin - git reset --hard origin/master - git clean -xdf - echo "Deploying dev docs for ${CIRCLE_BRANCH}."; - cp -a /tmp/build/html/* .; - touch .nojekyll; - git add -A; - git commit -m "CircleCI update of dev docs (${CIRCLE_BUILD_NUM})."; - git push origin master; - # else - # echo "No deployment (build: ${CIRCLE_BRANCH})."; - # fi + if [ "${CIRCLE_BRANCH}" == "master" ]; then + git config --global user.email "circle@PythonOT.com"; + git config --global user.name "Circle CI"; + cd ~/PythonOT.github.io; + git checkout master + git remote -v + git fetch origin + git reset --hard origin/master + git clean -xdf + echo "Deploying dev docs for ${CIRCLE_BRANCH}."; + cp -a /tmp/build/html/* .; + touch .nojekyll; + git add -A; + git commit -m "CircleCI update of dev docs (${CIRCLE_BUILD_NUM})."; + git push origin master; + else + echo "No deployment (build: ${CIRCLE_BRANCH})."; + fi workflows: version: 2 @@ -134,5 +134,4 @@ workflows: filters: branches: only: - # - master - - doc_ci_build + - master -- cgit v1.2.3 From 0b2d808aaebb1cab60a272ea7901d5f77df43a9f Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 21 Apr 2020 17:20:46 +0200 Subject: Windows CI (#148) * add Windows build on GH actions --- .github/workflows/pythonpackage.yml | 55 ++++++++++++++++++++++++++++++++++++- README.md | 1 + 2 files changed, 55 insertions(+), 1 deletion(-) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index c4b0165..19527c3 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -11,7 +11,7 @@ on: - '**' jobs: - build: + linux: runs-on: ubuntu-latest strategy: @@ -46,3 +46,56 @@ jobs: - name: Upload codecov run: | codecov + + # macos: + # runs-on: macOS-latest + # strategy: + # max-parallel: 4 + # matrix: + # python-version: [3.7] + + # steps: + # - uses: actions/checkout@v1 + # - name: Set up Python ${{ matrix.python-version }} + # uses: actions/setup-python@v1 + # with: + # python-version: ${{ matrix.python-version }} + # - name: Install dependencies + # run: | + # python -m pip install --upgrade pip + # pip install -r requirements.txt + # pip install pytest "pytest-cov<2.6" + # pip install -U "sklearn" + # - name: Install POT + # run: | + # pip install -e . + # - name: Run tests + # run: | + # python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot + + + windows: + runs-on: windows-2019 + strategy: + max-parallel: 4 + matrix: + python-version: [3.7] + + steps: + - uses: actions/checkout@v1 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v1 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + pip install pytest "pytest-cov<2.6" + pip install -U "sklearn" + - name: Install POT + run: | + pip install -e . + - name: Run tests + run: | + python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot diff --git a/README.md b/README.md index 64630e6..65193ff 100644 --- a/README.md +++ b/README.md @@ -3,6 +3,7 @@ [![PyPI version](https://badge.fury.io/py/POT.svg)](https://badge.fury.io/py/POT) [![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot) [![Build Status](https://travis-ci.org/PythonOT/POT.svg?branch=master)](https://travis-ci.org/PythonOT/POT) +[![Build Status](https://github.com/PythonOT/POT/workflows/Linux%7CWin%7CMacOS/badge.svg)](https://github.com/PythonOT/POT/actions) [![Codecov Status](https://codecov.io/gh/PythonOT/POT/branch/master/graph/badge.svg)](https://codecov.io/gh/PythonOT/POT) [![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) [![Downloads](https://pepy.tech/badge/pot)](https://pepy.tech/project/pot) -- cgit v1.2.3 From f9638166521a1160838fae75e2a2e318d645460e Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 21 Apr 2020 17:40:32 +0200 Subject: rm travis --- .github/workflows/pythonpackage.yml | 8 +++--- .travis.yml | 50 ------------------------------------- .travis/before_install.sh | 15 ----------- README.md | 1 - 4 files changed, 3 insertions(+), 71 deletions(-) delete mode 100644 .travis.yml delete mode 100755 .travis/before_install.sh diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index 19527c3..755937a 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -3,12 +3,10 @@ name: Test Package on: push: branches: - - '**' - create: + - master + pull_request: branches: - - 'master' - tags: - - '**' + - master jobs: linux: diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 3f63867..0000000 --- a/.travis.yml +++ /dev/null @@ -1,50 +0,0 @@ -dist: xenial # required for Python >= 3.7 -language: python -matrix: - # allow_failures: - # - os: osx - # - os: windows - include: - - os: linux - sudo: required - python: 3.5 - - os: linux - sudo: required - python: 3.6 - - os: linux - sudo: required - python: 3.7 - - os: linux - sudo: required - python: 3.8 - # - os: osx - # sudo: required - # language: generic - # - name: "Python 3.7.3 on Windows" - # os: windows # Windows 10.0.17134 N/A Build 17134 - # language: shell # 'language: python' is an error on Travis CI Windows - # before_install: choco install python - # env: PATH=/c/Python37:/c/Python37/Scripts:$PATH -# before_script: # configure a headless display to test plot generation -# - "export DISPLAY=:99.0" -# - sleep 3 # give xvfb some time to start -before_install: - - ./.travis/before_install.sh -# command to install dependencies -install: - - pip install -r requirements.txt - - pip install -U "numpy>=1.14" scipy # for numpy array formatting in doctests - - pip install flake8 pytest "pytest-cov<2.6" codecov - - pip install -U "sklearn" - - pip install . -# command to run tests + check syntax style -services: - - xvfb -script: - - python setup.py develop - - flake8 examples/ ot/ test/ - - python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot - # - py.test ot test -after_script: - # Need to run from source dir to execute "git" commands - - codecov; diff --git a/.travis/before_install.sh b/.travis/before_install.sh deleted file mode 100755 index 0ae6249..0000000 --- a/.travis/before_install.sh +++ /dev/null @@ -1,15 +0,0 @@ -#!/bin/bash - -if [[ $TRAVIS_OS_NAME == 'osx' ]]; then - - # Install some custom requirements on OS X - # e.g. brew install pyenv-virtualenv - #brew update - #brew install python - sudo easy_install -U pip - -else - # Install some custom requirements on Linux - sudo apt-get update -q - sudo apt-get install libblas-dev liblapack-dev libatlas-base-dev -fi diff --git a/README.md b/README.md index 65193ff..ca8481c 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,6 @@ [![PyPI version](https://badge.fury.io/py/POT.svg)](https://badge.fury.io/py/POT) [![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot) -[![Build Status](https://travis-ci.org/PythonOT/POT.svg?branch=master)](https://travis-ci.org/PythonOT/POT) [![Build Status](https://github.com/PythonOT/POT/workflows/Linux%7CWin%7CMacOS/badge.svg)](https://github.com/PythonOT/POT/actions) [![Codecov Status](https://codecov.io/gh/PythonOT/POT/branch/master/graph/badge.svg)](https://codecov.io/gh/PythonOT/POT) [![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) -- cgit v1.2.3 From a303cc6b483d3cd958c399621e22e40574bcbbc8 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 21 Apr 2020 17:48:37 +0200 Subject: [MRG] Actually run sphinx-gallery (#146) * generate gallery * remove mock * add sklearn to requirermnt?txt for example * remove latex from fgw example * add networks for graph example * remove all * add requirement.txt rtd * rtd debug * update readme * eradthedoc with redirection * add conf rtd --- README.md | 10 +- docs/requirements.txt | 1 + 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Status](https://travis-ci.org/PythonOT/POT.svg?branch=master)](https://travis-ci.org/PythonOT/POT) [![Build Status](https://github.com/PythonOT/POT/workflows/Linux%7CWin%7CMacOS/badge.svg)](https://github.com/PythonOT/POT/actions) [![Codecov Status](https://codecov.io/gh/PythonOT/POT/branch/master/graph/badge.svg)](https://codecov.io/gh/PythonOT/POT) -[![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) [![Downloads](https://pepy.tech/badge/pot)](https://pepy.tech/project/pot) [![Anaconda downloads](https://anaconda.org/conda-forge/pot/badges/downloads.svg)](https://anaconda.org/conda-forge/pot) [![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/PythonOT/POT/blob/master/LICENSE) +This open source Python library provide several solvers for optimization +problems related to Optimal Transport for signal, image processing and machine +learning. -This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. +Website and documentation: [https://PythonOT.github.io/](https://PythonOT.github.io/) -It provides the following solvers: +POT provides the following solvers: * OT Network Flow solver for the linear program/ Earth Movers Distance [1]. * Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU implementation (requires cupy). @@ -139,7 +141,7 @@ ba=ot.barycenter(A,M,reg) # reg is regularization parameter ### Examples and Notebooks -The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http://pot.readthedocs.io/). +The examples folder contain several examples and use case for the library. The full documentation is available on [https://PythonOT.github.io/](https://PythonOT.github.io/). Here is a list of the Python notebooks available [here](https://github.com/PythonOT/POT/blob/master/notebooks/) if you want a quick look: diff --git a/docs/requirements.txt b/docs/requirements.txt index 1fe37c2..256706b 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -3,3 +3,4 @@ sphinx_rtd_theme numpydoc memory_profiler pillow +networkx diff --git a/docs/requirements_rtd.txt b/docs/requirements_rtd.txt new file mode 100644 index 0000000..e3999d6 --- /dev/null +++ b/docs/requirements_rtd.txt @@ -0,0 +1,14 @@ +sphinx_gallery +numpydoc +memory_profiler +pillow +networkx +numpy +scipy>=1.0 +cython +matplotlib +autograd +pymanopt==0.2.4; python_version <'3' +pymanopt; python_version >= '3' +cvxopt +scikit-learn \ No newline at end of file diff --git a/docs/rtd/conf.py b/docs/rtd/conf.py new file mode 100644 index 0000000..814db75 --- /dev/null +++ b/docs/rtd/conf.py @@ -0,0 +1,6 @@ +from recommonmark.parser import CommonMarkParser + +source_parsers = {'.md': 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- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_1D_thumb.png - - :ref:`sphx_glr_auto_examples_plot_OT_1D.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_OT_1D - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_UOT_1D_thumb.png - - :ref:`sphx_glr_auto_examples_plot_UOT_1D.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_UOT_1D - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_screenkhorn_1D_thumb.png - - :ref:`sphx_glr_auto_examples_plot_screenkhorn_1D.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_screenkhorn_1D - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_optim_OTreg_thumb.png - - :ref:`sphx_glr_auto_examples_plot_optim_OTreg.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_optim_OTreg - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_1D_smooth_thumb.png - - :ref:`sphx_glr_auto_examples_plot_OT_1D_smooth.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_OT_1D_smooth - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_free_support_barycenter_thumb.png - - :ref:`sphx_glr_auto_examples_plot_free_support_barycenter.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_free_support_barycenter - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png - - :ref:`sphx_glr_auto_examples_plot_compute_emd.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_compute_emd - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png - - :ref:`sphx_glr_auto_examples_plot_gromov.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_gromov - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_convolutional_barycenter_thumb.png - - :ref:`sphx_glr_auto_examples_plot_convolutional_barycenter.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_convolutional_barycenter - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_linear_mapping_thumb.png - - :ref:`sphx_glr_auto_examples_plot_otda_linear_mapping.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_otda_linear_mapping - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png - - :ref:`sphx_glr_auto_examples_plot_OT_2D_samples.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_OT_2D_samples - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png - - :ref:`sphx_glr_auto_examples_plot_WDA.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_WDA - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_stochastic_thumb.png - - :ref:`sphx_glr_auto_examples_plot_stochastic.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_stochastic - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_color_images_thumb.png - - :ref:`sphx_glr_auto_examples_plot_otda_color_images.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_otda_color_images - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_1D_thumb.png - - :ref:`sphx_glr_auto_examples_plot_barycenter_1D.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_barycenter_1D - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_laplacian_thumb.png - - :ref:`sphx_glr_auto_examples_plot_otda_laplacian.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_otda_laplacian - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_colors_images_thumb.png - - :ref:`sphx_glr_auto_examples_plot_otda_mapping_colors_images.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_otda_mapping_colors_images - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_UOT_barycenter_1D_thumb.png - - :ref:`sphx_glr_auto_examples_plot_UOT_barycenter_1D.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_UOT_barycenter_1D - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_thumb.png - - :ref:`sphx_glr_auto_examples_plot_otda_mapping.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_otda_mapping - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_fgw_thumb.png - - :ref:`sphx_glr_auto_examples_plot_fgw.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_fgw - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_semi_supervised_thumb.png - - :ref:`sphx_glr_auto_examples_plot_otda_semi_supervised.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_otda_semi_supervised - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_classes_thumb.png - - :ref:`sphx_glr_auto_examples_plot_otda_classes.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_otda_classes - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_partial_wass_and_gromov_thumb.png - - :ref:`sphx_glr_auto_examples_plot_partial_wass_and_gromov.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_partial_wass_and_gromov - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_d2_thumb.png - - :ref:`sphx_glr_auto_examples_plot_otda_d2.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_otda_d2 - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_L1_vs_L2_thumb.png - - :ref:`sphx_glr_auto_examples_plot_OT_L1_vs_L2.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_OT_L1_vs_L2 - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_jcpot_thumb.png - - :ref:`sphx_glr_auto_examples_plot_otda_jcpot.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_otda_jcpot - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_lp_vs_entropic_thumb.png - - :ref:`sphx_glr_auto_examples_plot_barycenter_lp_vs_entropic.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_barycenter_lp_vs_entropic - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_fgw_thumb.png - - :ref:`sphx_glr_auto_examples_plot_barycenter_fgw.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_barycenter_fgw - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_gromov_barycenter_thumb.png - - :ref:`sphx_glr_auto_examples_plot_gromov_barycenter.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_gromov_barycenter -.. raw:: html - -
- - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-gallery - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download all examples in Python source code: auto_examples_python.zip ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_1D.ipynb b/docs/source/auto_examples/plot_OT_1D.ipynb deleted file mode 100644 index f679a30..0000000 --- a/docs/source/auto_examples/plot_OT_1D.ipynb +++ /dev/null @@ -1,137 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# 1D optimal transport\n\n\nThis example illustrates the computation of EMD and Sinkhorn transport plans\nand their visualization.\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot\nfrom ot.datasets import make_1D_gauss as gauss" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n-------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot distributions and loss matrix\n----------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve EMD\n---------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "G0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve Sinkhorn\n--------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "lambd = 1e-3\nGs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OT_1D.py b/docs/source/auto_examples/plot_OT_1D.py deleted file mode 100644 index f33e2a4..0000000 --- a/docs/source/auto_examples/plot_OT_1D.py +++ /dev/null @@ -1,84 +0,0 @@ -# -*- coding: utf-8 -*- -""" -==================== -1D optimal transport -==================== - -This example illustrates the computation of EMD and Sinkhorn transport plans -and their visualization. - -""" - -# Author: Remi Flamary -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot -import ot.plot -from ot.datasets import make_1D_gauss as gauss - -############################################################################## -# Generate data -# ------------- - - -#%% parameters - -n = 100 # nb bins - -# bin positions -x = np.arange(n, dtype=np.float64) - -# Gaussian distributions -a = gauss(n, m=20, s=5) # m= mean, s= std -b = gauss(n, m=60, s=10) - -# loss matrix -M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) -M /= M.max() - - -############################################################################## -# Plot distributions and loss matrix -# ---------------------------------- - -#%% plot the distributions - -pl.figure(1, figsize=(6.4, 3)) -pl.plot(x, a, 'b', label='Source distribution') -pl.plot(x, b, 'r', label='Target distribution') -pl.legend() - -#%% plot distributions and loss matrix - -pl.figure(2, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') - -############################################################################## -# Solve EMD -# --------- - - -#%% EMD - -G0 = ot.emd(a, b, M) - -pl.figure(3, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') - -############################################################################## -# Solve Sinkhorn -# -------------- - - -#%% Sinkhorn - -lambd = 1e-3 -Gs = ot.sinkhorn(a, b, M, lambd, verbose=True) - -pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn') - -pl.show() diff --git a/docs/source/auto_examples/plot_OT_1D.rst b/docs/source/auto_examples/plot_OT_1D.rst deleted file mode 100644 index ec21845..0000000 --- a/docs/source/auto_examples/plot_OT_1D.rst +++ /dev/null @@ -1,228 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_OT_1D.py: - - -==================== -1D optimal transport -==================== - -This example illustrates the computation of EMD and Sinkhorn transport plans -and their visualization. - - - -.. code-block:: default - - - # Author: Remi Flamary - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot - import ot.plot - from ot.datasets import make_1D_gauss as gauss - - - - - - - - -Generate data -------------- - - -.. code-block:: default - - - n = 100 # nb bins - - # bin positions - x = np.arange(n, dtype=np.float64) - - # Gaussian distributions - a = gauss(n, m=20, s=5) # m= mean, s= std - b = gauss(n, m=60, s=10) - - # loss matrix - M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) - M /= M.max() - - - - - - - - - -Plot distributions and loss matrix ----------------------------------- - - -.. code-block:: default - - - pl.figure(1, figsize=(6.4, 3)) - pl.plot(x, a, 'b', label='Source distribution') - pl.plot(x, b, 'r', label='Target distribution') - pl.legend() - - - - -.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_001.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - - - - - - -.. code-block:: default - - - pl.figure(2, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') - - - - -.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_002.png - :class: sphx-glr-single-img - - - - - -Solve EMD ---------- - - -.. code-block:: default - - - G0 = ot.emd(a, b, M) - - pl.figure(3, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') - - - - -.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_003.png - :class: sphx-glr-single-img - - - - - -Solve Sinkhorn --------------- - - -.. code-block:: default - - - lambd = 1e-3 - Gs = ot.sinkhorn(a, b, M, lambd, verbose=True) - - pl.figure(4, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn') - - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_004.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - It. |Err - ------------------- - 0|2.861463e-01| - 10|1.860154e-01| - 20|8.144529e-02| - 30|3.130143e-02| - 40|1.178815e-02| - 50|4.426078e-03| - 60|1.661047e-03| - 70|6.233110e-04| - 80|2.338932e-04| - 90|8.776627e-05| - 100|3.293340e-05| - 110|1.235791e-05| - 120|4.637176e-06| - 130|1.740051e-06| - 140|6.529356e-07| - 150|2.450071e-07| - 160|9.193632e-08| - 170|3.449812e-08| - 180|1.294505e-08| - 190|4.857493e-09| - It. |Err - ------------------- - 200|1.822723e-09| - 210|6.839572e-10| - /home/rflamary/PYTHON/POT/examples/plot_OT_1D.py:84: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 0.665 seconds) - - -.. _sphx_glr_download_auto_examples_plot_OT_1D.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_OT_1D.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_OT_1D.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_1D_smooth.ipynb b/docs/source/auto_examples/plot_OT_1D_smooth.ipynb deleted file mode 100644 index 493e6bb..0000000 --- a/docs/source/auto_examples/plot_OT_1D_smooth.ipynb +++ /dev/null @@ -1,166 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# 1D smooth optimal transport\n\n\nThis example illustrates the computation of EMD, Sinkhorn and smooth OT plans\nand their visualization.\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot\nfrom ot.datasets import make_1D_gauss as gauss" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n-------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot distributions and loss matrix\n----------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve EMD\n---------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "G0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve Sinkhorn\n--------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "lambd = 2e-3\nGs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n\npl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve Smooth OT\n--------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "lambd = 2e-3\nGsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl')\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.')\n\npl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "lambd = 1e-1\nGsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2')\n\npl.figure(6, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.')\n\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OT_1D_smooth.py b/docs/source/auto_examples/plot_OT_1D_smooth.py deleted file mode 100644 index b690751..0000000 --- a/docs/source/auto_examples/plot_OT_1D_smooth.py +++ /dev/null @@ -1,110 +0,0 @@ -# -*- coding: utf-8 -*- -""" -=========================== -1D smooth optimal transport -=========================== - -This example illustrates the computation of EMD, Sinkhorn and smooth OT plans -and their visualization. - -""" - -# Author: Remi Flamary -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot -import ot.plot -from ot.datasets import make_1D_gauss as gauss - -############################################################################## -# Generate data -# ------------- - - -#%% parameters - -n = 100 # nb bins - -# bin positions -x = np.arange(n, dtype=np.float64) - -# Gaussian distributions -a = gauss(n, m=20, s=5) # m= mean, s= std -b = gauss(n, m=60, s=10) - -# loss matrix -M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) -M /= M.max() - - -############################################################################## -# Plot distributions and loss matrix -# ---------------------------------- - -#%% plot the distributions - -pl.figure(1, figsize=(6.4, 3)) -pl.plot(x, a, 'b', label='Source distribution') -pl.plot(x, b, 'r', label='Target distribution') -pl.legend() - -#%% plot distributions and loss matrix - -pl.figure(2, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') - -############################################################################## -# Solve EMD -# --------- - - -#%% EMD - -G0 = ot.emd(a, b, M) - -pl.figure(3, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') - -############################################################################## -# Solve Sinkhorn -# -------------- - - -#%% Sinkhorn - -lambd = 2e-3 -Gs = ot.sinkhorn(a, b, M, lambd, verbose=True) - -pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn') - -pl.show() - -############################################################################## -# Solve Smooth OT -# -------------- - - -#%% Smooth OT with KL regularization - -lambd = 2e-3 -Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl') - -pl.figure(5, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.') - -pl.show() - - -#%% Smooth OT with KL regularization - -lambd = 1e-1 -Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2') - -pl.figure(6, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.') - -pl.show() diff --git a/docs/source/auto_examples/plot_OT_1D_smooth.rst b/docs/source/auto_examples/plot_OT_1D_smooth.rst deleted file mode 100644 index de42689..0000000 --- a/docs/source/auto_examples/plot_OT_1D_smooth.rst +++ /dev/null @@ -1,282 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_OT_1D_smooth.py: - - -=========================== -1D smooth optimal transport -=========================== - -This example illustrates the computation of EMD, Sinkhorn and smooth OT plans -and their visualization. - - - -.. code-block:: default - - - # Author: Remi Flamary - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot - import ot.plot - from ot.datasets import make_1D_gauss as gauss - - - - - - - - -Generate data -------------- - - -.. code-block:: default - - - n = 100 # nb bins - - # bin positions - x = np.arange(n, dtype=np.float64) - - # Gaussian distributions - a = gauss(n, m=20, s=5) # m= mean, s= std - b = gauss(n, m=60, s=10) - - # loss matrix - M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) - M /= M.max() - - - - - - - - - -Plot distributions and loss matrix ----------------------------------- - - -.. code-block:: default - - - pl.figure(1, figsize=(6.4, 3)) - pl.plot(x, a, 'b', label='Source distribution') - pl.plot(x, b, 'r', label='Target distribution') - pl.legend() - - - - -.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_001.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - - - - - - -.. code-block:: default - - - pl.figure(2, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') - - - - -.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_002.png - :class: sphx-glr-single-img - - - - - -Solve EMD ---------- - - -.. code-block:: default - - - G0 = ot.emd(a, b, M) - - pl.figure(3, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') - - - - -.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_003.png - :class: sphx-glr-single-img - - - - - -Solve Sinkhorn --------------- - - -.. code-block:: default - - - lambd = 2e-3 - Gs = ot.sinkhorn(a, b, M, lambd, verbose=True) - - pl.figure(4, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn') - - pl.show() - - - - -.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_004.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - It. |Err - ------------------- - 0|2.821142e-01| - 10|7.695268e-02| - 20|1.112774e-02| - 30|1.571553e-03| - 40|2.218100e-04| - 50|3.130527e-05| - 60|4.418267e-06| - 70|6.235716e-07| - 80|8.800770e-08| - 90|1.242095e-08| - 100|1.753030e-09| - 110|2.474136e-10| - /home/rflamary/PYTHON/POT/examples/plot_OT_1D_smooth.py:84: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -Solve Smooth OT --------------- - - -.. code-block:: default - - - lambd = 2e-3 - Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl') - - pl.figure(5, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.') - - pl.show() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_005.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_OT_1D_smooth.py:99: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. code-block:: default - - - lambd = 1e-1 - Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2') - - pl.figure(6, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.') - - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_006.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_OT_1D_smooth.py:110: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 0.732 seconds) - - -.. _sphx_glr_download_auto_examples_plot_OT_1D_smooth.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_OT_1D_smooth.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_OT_1D_smooth.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_2D_samples.ipynb b/docs/source/auto_examples/plot_OT_2D_samples.ipynb deleted file mode 100644 index ff7abde..0000000 --- a/docs/source/auto_examples/plot_OT_2D_samples.ipynb +++ /dev/null @@ -1,144 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# 2D Optimal transport between empirical distributions\n\n\nIllustration of 2D optimal transport between discributions that are weighted\nsum of diracs. The OT matrix is plotted with the samples.\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Remi Flamary \n# Kilian Fatras \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n-------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 50 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4])\ncov_t = np.array([[1, -.8], [-.8, 1]])\n\nxs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s)\nxt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t)\n\na, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples\n\n# loss matrix\nM = ot.dist(xs, xt)\nM /= M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n---------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(1)\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')\n\npl.figure(2)\npl.imshow(M, interpolation='nearest')\npl.title('Cost matrix M')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute EMD\n-----------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "G0 = ot.emd(a, b, M)\n\npl.figure(3)\npl.imshow(G0, interpolation='nearest')\npl.title('OT matrix G0')\n\npl.figure(4)\not.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix with samples')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute Sinkhorn\n----------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# reg term\nlambd = 1e-3\n\nGs = ot.sinkhorn(a, b, M, lambd)\n\npl.figure(5)\npl.imshow(Gs, interpolation='nearest')\npl.title('OT matrix sinkhorn')\n\npl.figure(6)\not.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn with samples')\n\npl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Emprirical Sinkhorn\n----------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# reg term\nlambd = 1e-3\n\nGes = ot.bregman.empirical_sinkhorn(xs, xt, lambd)\n\npl.figure(7)\npl.imshow(Ges, interpolation='nearest')\npl.title('OT matrix empirical sinkhorn')\n\npl.figure(8)\not.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn from samples')\n\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OT_2D_samples.py b/docs/source/auto_examples/plot_OT_2D_samples.py deleted file mode 100644 index 63126ba..0000000 --- a/docs/source/auto_examples/plot_OT_2D_samples.py +++ /dev/null @@ -1,128 +0,0 @@ -# -*- coding: utf-8 -*- -""" -==================================================== -2D Optimal transport between empirical distributions -==================================================== - -Illustration of 2D optimal transport between discributions that are weighted -sum of diracs. The OT matrix is plotted with the samples. - -""" - -# Author: Remi Flamary -# Kilian Fatras -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot -import ot.plot - -############################################################################## -# Generate data -# ------------- - -#%% parameters and data generation - -n = 50 # nb samples - -mu_s = np.array([0, 0]) -cov_s = np.array([[1, 0], [0, 1]]) - -mu_t = np.array([4, 4]) -cov_t = np.array([[1, -.8], [-.8, 1]]) - -xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s) -xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t) - -a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples - -# loss matrix -M = ot.dist(xs, xt) -M /= M.max() - -############################################################################## -# Plot data -# --------- - -#%% plot samples - -pl.figure(1) -pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') -pl.legend(loc=0) -pl.title('Source and target distributions') - -pl.figure(2) -pl.imshow(M, interpolation='nearest') -pl.title('Cost matrix M') - -############################################################################## -# Compute EMD -# ----------- - -#%% EMD - -G0 = ot.emd(a, b, M) - -pl.figure(3) -pl.imshow(G0, interpolation='nearest') -pl.title('OT matrix G0') - -pl.figure(4) -ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1]) -pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') -pl.legend(loc=0) -pl.title('OT matrix with samples') - - -############################################################################## -# Compute Sinkhorn -# ---------------- - -#%% sinkhorn - -# reg term -lambd = 1e-3 - -Gs = ot.sinkhorn(a, b, M, lambd) - -pl.figure(5) -pl.imshow(Gs, interpolation='nearest') -pl.title('OT matrix sinkhorn') - -pl.figure(6) -ot.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1]) -pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') -pl.legend(loc=0) -pl.title('OT matrix Sinkhorn with samples') - -pl.show() - - -############################################################################## -# Emprirical Sinkhorn -# ---------------- - -#%% sinkhorn - -# reg term -lambd = 1e-3 - -Ges = ot.bregman.empirical_sinkhorn(xs, xt, lambd) - -pl.figure(7) -pl.imshow(Ges, interpolation='nearest') -pl.title('OT matrix empirical sinkhorn') - -pl.figure(8) -ot.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1]) -pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') -pl.legend(loc=0) -pl.title('OT matrix Sinkhorn from samples') - -pl.show() diff --git a/docs/source/auto_examples/plot_OT_2D_samples.rst b/docs/source/auto_examples/plot_OT_2D_samples.rst deleted file mode 100644 index 460bb95..0000000 --- a/docs/source/auto_examples/plot_OT_2D_samples.rst +++ /dev/null @@ -1,310 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_OT_2D_samples.py: - - -==================================================== -2D Optimal transport between empirical distributions -==================================================== - -Illustration of 2D optimal transport between discributions that are weighted -sum of diracs. The OT matrix is plotted with the samples. - - - -.. code-block:: default - - - # Author: Remi Flamary - # Kilian Fatras - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot - import ot.plot - - - - - - - - -Generate data -------------- - - -.. code-block:: default - - - n = 50 # nb samples - - mu_s = np.array([0, 0]) - cov_s = np.array([[1, 0], [0, 1]]) - - mu_t = np.array([4, 4]) - cov_t = np.array([[1, -.8], [-.8, 1]]) - - xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s) - xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t) - - a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples - - # loss matrix - M = ot.dist(xs, xt) - M /= M.max() - - - - - - - - -Plot data ---------- - - -.. code-block:: default - - - pl.figure(1) - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.legend(loc=0) - pl.title('Source and target distributions') - - pl.figure(2) - pl.imshow(M, interpolation='nearest') - pl.title('Cost matrix M') - - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_001.png - :class: sphx-glr-multi-img - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_002.png - :class: sphx-glr-multi-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - - Text(0.5, 1.0, 'Cost matrix M') - - - -Compute EMD ------------ - - -.. code-block:: default - - - G0 = ot.emd(a, b, M) - - pl.figure(3) - pl.imshow(G0, interpolation='nearest') - pl.title('OT matrix G0') - - pl.figure(4) - ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1]) - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.legend(loc=0) - pl.title('OT matrix with samples') - - - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_003.png - :class: sphx-glr-multi-img - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_004.png - :class: sphx-glr-multi-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - - Text(0.5, 1.0, 'OT matrix with samples') - - - -Compute Sinkhorn ----------------- - - -.. code-block:: default - - - # reg term - lambd = 1e-3 - - Gs = ot.sinkhorn(a, b, M, lambd) - - pl.figure(5) - pl.imshow(Gs, interpolation='nearest') - pl.title('OT matrix sinkhorn') - - pl.figure(6) - ot.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1]) - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.legend(loc=0) - pl.title('OT matrix Sinkhorn with samples') - - pl.show() - - - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_005.png - :class: sphx-glr-multi-img - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_006.png - :class: sphx-glr-multi-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_OT_2D_samples.py:103: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -Emprirical Sinkhorn ----------------- - - -.. code-block:: default - - - # reg term - lambd = 1e-3 - - Ges = ot.bregman.empirical_sinkhorn(xs, xt, lambd) - - pl.figure(7) - pl.imshow(Ges, interpolation='nearest') - pl.title('OT matrix empirical sinkhorn') - - pl.figure(8) - ot.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1]) - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.legend(loc=0) - pl.title('OT matrix Sinkhorn from samples') - - pl.show() - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_007.png - :class: sphx-glr-multi-img - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_008.png - :class: sphx-glr-multi-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/ot/bregman.py:363: RuntimeWarning: divide by zero encountered in true_divide - v = np.divide(b, KtransposeU) - Warning: numerical errors at iteration 0 - /home/rflamary/PYTHON/POT/ot/plot.py:90: RuntimeWarning: invalid value encountered in double_scalars - if G[i, j] / mx > thr: - /home/rflamary/PYTHON/POT/examples/plot_OT_2D_samples.py:128: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 2.154 seconds) - - -.. _sphx_glr_download_auto_examples_plot_OT_2D_samples.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_OT_2D_samples.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_OT_2D_samples.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb deleted file mode 100644 index 12a09f0..0000000 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb +++ /dev/null @@ -1,126 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# 2D Optimal transport for different metrics\n\n\n2D OT on empirical distributio with different gound metric.\n\nStole the figure idea from Fig. 1 and 2 in\nhttps://arxiv.org/pdf/1706.07650.pdf\n\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Dataset 1 : uniform sampling\n----------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 20 # nb samples\nxs = np.zeros((n, 2))\nxs[:, 0] = np.arange(n) + 1\nxs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex...\n\nxt = np.zeros((n, 2))\nxt[:, 1] = np.arange(n) + 1\n\na, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM1 = ot.dist(xs, xt, metric='euclidean')\nM1 /= M1.max()\n\n# loss matrix\nM2 = ot.dist(xs, xt, metric='sqeuclidean')\nM2 /= M2.max()\n\n# loss matrix\nMp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\nMp /= Mp.max()\n\n# Data\npl.figure(1, figsize=(7, 3))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\npl.title('Source and target distributions')\n\n\n# Cost matrices\npl.figure(2, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\npl.imshow(M1, interpolation='nearest')\npl.title('Euclidean cost')\n\npl.subplot(1, 3, 2)\npl.imshow(M2, interpolation='nearest')\npl.title('Squared Euclidean cost')\n\npl.subplot(1, 3, 3)\npl.imshow(Mp, interpolation='nearest')\npl.title('Sqrt Euclidean cost')\npl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Dataset 1 : Plot OT Matrices\n----------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "G1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(3, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Dataset 2 : Partial circle\n--------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 50 # nb samples\nxtot = np.zeros((n + 1, 2))\nxtot[:, 0] = np.cos(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\nxtot[:, 1] = np.sin(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\n\nxs = xtot[:n, :]\nxt = xtot[1:, :]\n\na, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM1 = ot.dist(xs, xt, metric='euclidean')\nM1 /= M1.max()\n\n# loss matrix\nM2 = ot.dist(xs, xt, metric='sqeuclidean')\nM2 /= M2.max()\n\n# loss matrix\nMp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\nMp /= Mp.max()\n\n\n# Data\npl.figure(4, figsize=(7, 3))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\npl.title('Source and traget distributions')\n\n\n# Cost matrices\npl.figure(5, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\npl.imshow(M1, interpolation='nearest')\npl.title('Euclidean cost')\n\npl.subplot(1, 3, 2)\npl.imshow(M2, interpolation='nearest')\npl.title('Squared Euclidean cost')\n\npl.subplot(1, 3, 3)\npl.imshow(Mp, interpolation='nearest')\npl.title('Sqrt Euclidean cost')\npl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Dataset 2 : Plot OT Matrices\n-----------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "G1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(6, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.py b/docs/source/auto_examples/plot_OT_L1_vs_L2.py deleted file mode 100644 index 37b429f..0000000 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.py +++ /dev/null @@ -1,208 +0,0 @@ -# -*- coding: utf-8 -*- -""" -========================================== -2D Optimal transport for different metrics -========================================== - -2D OT on empirical distributio with different gound metric. - -Stole the figure idea from Fig. 1 and 2 in -https://arxiv.org/pdf/1706.07650.pdf - - -""" - -# Author: Remi Flamary -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot -import ot.plot - -############################################################################## -# Dataset 1 : uniform sampling -# ---------------------------- - -n = 20 # nb samples -xs = np.zeros((n, 2)) -xs[:, 0] = np.arange(n) + 1 -xs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex... - -xt = np.zeros((n, 2)) -xt[:, 1] = np.arange(n) + 1 - -a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples - -# loss matrix -M1 = ot.dist(xs, xt, metric='euclidean') -M1 /= M1.max() - -# loss matrix -M2 = ot.dist(xs, xt, metric='sqeuclidean') -M2 /= M2.max() - -# loss matrix -Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean')) -Mp /= Mp.max() - -# Data -pl.figure(1, figsize=(7, 3)) -pl.clf() -pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') -pl.axis('equal') -pl.title('Source and target distributions') - - -# Cost matrices -pl.figure(2, figsize=(7, 3)) - -pl.subplot(1, 3, 1) -pl.imshow(M1, interpolation='nearest') -pl.title('Euclidean cost') - -pl.subplot(1, 3, 2) -pl.imshow(M2, interpolation='nearest') -pl.title('Squared Euclidean cost') - -pl.subplot(1, 3, 3) -pl.imshow(Mp, interpolation='nearest') -pl.title('Sqrt Euclidean cost') -pl.tight_layout() - -############################################################################## -# Dataset 1 : Plot OT Matrices -# ---------------------------- - - -#%% EMD -G1 = ot.emd(a, b, M1) -G2 = ot.emd(a, b, M2) -Gp = ot.emd(a, b, Mp) - -# OT matrices -pl.figure(3, figsize=(7, 3)) - -pl.subplot(1, 3, 1) -ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1]) -pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') -pl.axis('equal') -# pl.legend(loc=0) -pl.title('OT Euclidean') - -pl.subplot(1, 3, 2) -ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1]) -pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') -pl.axis('equal') -# pl.legend(loc=0) -pl.title('OT squared Euclidean') - -pl.subplot(1, 3, 3) -ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1]) -pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') -pl.axis('equal') -# pl.legend(loc=0) -pl.title('OT sqrt Euclidean') -pl.tight_layout() - -pl.show() - - -############################################################################## -# Dataset 2 : Partial circle -# -------------------------- - -n = 50 # nb samples -xtot = np.zeros((n + 1, 2)) -xtot[:, 0] = np.cos( - (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) -xtot[:, 1] = np.sin( - (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) - -xs = xtot[:n, :] -xt = xtot[1:, :] - -a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples - -# loss matrix -M1 = ot.dist(xs, xt, metric='euclidean') -M1 /= M1.max() - -# loss matrix -M2 = ot.dist(xs, xt, metric='sqeuclidean') -M2 /= M2.max() - -# loss matrix -Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean')) -Mp /= Mp.max() - - -# Data -pl.figure(4, figsize=(7, 3)) -pl.clf() -pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') -pl.axis('equal') -pl.title('Source and traget distributions') - - -# Cost matrices -pl.figure(5, figsize=(7, 3)) - -pl.subplot(1, 3, 1) -pl.imshow(M1, interpolation='nearest') -pl.title('Euclidean cost') - -pl.subplot(1, 3, 2) -pl.imshow(M2, interpolation='nearest') -pl.title('Squared Euclidean cost') - -pl.subplot(1, 3, 3) -pl.imshow(Mp, interpolation='nearest') -pl.title('Sqrt Euclidean cost') -pl.tight_layout() - -############################################################################## -# Dataset 2 : Plot OT Matrices -# ----------------------------- - - -#%% EMD -G1 = ot.emd(a, b, M1) -G2 = ot.emd(a, b, M2) -Gp = ot.emd(a, b, Mp) - -# OT matrices -pl.figure(6, figsize=(7, 3)) - -pl.subplot(1, 3, 1) -ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1]) -pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') -pl.axis('equal') -# pl.legend(loc=0) -pl.title('OT Euclidean') - -pl.subplot(1, 3, 2) -ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1]) -pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') -pl.axis('equal') -# pl.legend(loc=0) -pl.title('OT squared Euclidean') - -pl.subplot(1, 3, 3) -ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1]) -pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') -pl.axis('equal') -# pl.legend(loc=0) -pl.title('OT sqrt Euclidean') -pl.tight_layout() - -pl.show() diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst deleted file mode 100644 index 16b20f9..0000000 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst +++ /dev/null @@ -1,343 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_OT_L1_vs_L2.py: - - -========================================== -2D Optimal transport for different metrics -========================================== - -2D OT on empirical distributio with different gound metric. - -Stole the figure idea from Fig. 1 and 2 in -https://arxiv.org/pdf/1706.07650.pdf - - - - -.. code-block:: default - - - # Author: Remi Flamary - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot - import ot.plot - - - - - - - - -Dataset 1 : uniform sampling ----------------------------- - - -.. code-block:: default - - - n = 20 # nb samples - xs = np.zeros((n, 2)) - xs[:, 0] = np.arange(n) + 1 - xs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex... - - xt = np.zeros((n, 2)) - xt[:, 1] = np.arange(n) + 1 - - a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples - - # loss matrix - M1 = ot.dist(xs, xt, metric='euclidean') - M1 /= M1.max() - - # loss matrix - M2 = ot.dist(xs, xt, metric='sqeuclidean') - M2 /= M2.max() - - # loss matrix - Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean')) - Mp /= Mp.max() - - # Data - pl.figure(1, figsize=(7, 3)) - pl.clf() - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - pl.title('Source and target distributions') - - - # Cost matrices - pl.figure(2, figsize=(7, 3)) - - pl.subplot(1, 3, 1) - pl.imshow(M1, interpolation='nearest') - pl.title('Euclidean cost') - - pl.subplot(1, 3, 2) - pl.imshow(M2, interpolation='nearest') - pl.title('Squared Euclidean cost') - - pl.subplot(1, 3, 3) - pl.imshow(Mp, interpolation='nearest') - pl.title('Sqrt Euclidean cost') - pl.tight_layout() - - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_001.png - :class: sphx-glr-multi-img - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_002.png - :class: sphx-glr-multi-img - - - - - -Dataset 1 : Plot OT Matrices ----------------------------- - - -.. code-block:: default - - G1 = ot.emd(a, b, M1) - G2 = ot.emd(a, b, M2) - Gp = ot.emd(a, b, Mp) - - # OT matrices - pl.figure(3, figsize=(7, 3)) - - pl.subplot(1, 3, 1) - ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1]) - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - # pl.legend(loc=0) - pl.title('OT Euclidean') - - pl.subplot(1, 3, 2) - ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1]) - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - # pl.legend(loc=0) - pl.title('OT squared Euclidean') - - pl.subplot(1, 3, 3) - ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1]) - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - # pl.legend(loc=0) - pl.title('OT sqrt Euclidean') - pl.tight_layout() - - pl.show() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_003.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_OT_L1_vs_L2.py:113: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -Dataset 2 : Partial circle --------------------------- - - -.. code-block:: default - - - n = 50 # nb samples - xtot = np.zeros((n + 1, 2)) - xtot[:, 0] = np.cos( - (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) - xtot[:, 1] = np.sin( - (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) - - xs = xtot[:n, :] - xt = xtot[1:, :] - - a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples - - # loss matrix - M1 = ot.dist(xs, xt, metric='euclidean') - M1 /= M1.max() - - # loss matrix - M2 = ot.dist(xs, xt, metric='sqeuclidean') - M2 /= M2.max() - - # loss matrix - Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean')) - Mp /= Mp.max() - - - # Data - pl.figure(4, figsize=(7, 3)) - pl.clf() - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - pl.title('Source and traget distributions') - - - # Cost matrices - pl.figure(5, figsize=(7, 3)) - - pl.subplot(1, 3, 1) - pl.imshow(M1, interpolation='nearest') - pl.title('Euclidean cost') - - pl.subplot(1, 3, 2) - pl.imshow(M2, interpolation='nearest') - pl.title('Squared Euclidean cost') - - pl.subplot(1, 3, 3) - pl.imshow(Mp, interpolation='nearest') - pl.title('Sqrt Euclidean cost') - pl.tight_layout() - - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_004.png - :class: sphx-glr-multi-img - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_005.png - :class: sphx-glr-multi-img - - - - - -Dataset 2 : Plot OT Matrices ------------------------------ - - -.. code-block:: default - - G1 = ot.emd(a, b, M1) - G2 = ot.emd(a, b, M2) - Gp = ot.emd(a, b, Mp) - - # OT matrices - pl.figure(6, figsize=(7, 3)) - - pl.subplot(1, 3, 1) - ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1]) - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - # pl.legend(loc=0) - pl.title('OT Euclidean') - - pl.subplot(1, 3, 2) - ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1]) - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - # pl.legend(loc=0) - pl.title('OT squared Euclidean') - - pl.subplot(1, 3, 3) - ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1]) - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - # pl.legend(loc=0) - pl.title('OT sqrt Euclidean') - pl.tight_layout() - - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_006.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_OT_L1_vs_L2.py:208: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 1.002 seconds) - - -.. _sphx_glr_download_auto_examples_plot_OT_L1_vs_L2.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_OT_L1_vs_L2.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_OT_L1_vs_L2.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_UOT_1D.ipynb b/docs/source/auto_examples/plot_UOT_1D.ipynb deleted file mode 100644 index 640e398..0000000 --- a/docs/source/auto_examples/plot_UOT_1D.ipynb +++ /dev/null @@ -1,108 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# 1D Unbalanced optimal transport\n\n\nThis example illustrates the computation of Unbalanced Optimal transport\nusing a Kullback-Leibler relaxation.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Hicham Janati \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot\nfrom ot.datasets import make_1D_gauss as gauss" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n-------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# make distributions unbalanced\nb *= 5.\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot distributions and loss matrix\n----------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n# plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve Unbalanced Sinkhorn\n--------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Sinkhorn\n\nepsilon = 0.1 # entropy parameter\nalpha = 1. # Unbalanced KL relaxation parameter\nGs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn')\n\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_UOT_1D.py b/docs/source/auto_examples/plot_UOT_1D.py deleted file mode 100644 index 2ea8b05..0000000 --- a/docs/source/auto_examples/plot_UOT_1D.py +++ /dev/null @@ -1,76 +0,0 @@ -# -*- coding: utf-8 -*- -""" -=============================== -1D Unbalanced optimal transport -=============================== - -This example illustrates the computation of Unbalanced Optimal transport -using a Kullback-Leibler relaxation. -""" - -# Author: Hicham Janati -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot -import ot.plot -from ot.datasets import make_1D_gauss as gauss - -############################################################################## -# Generate data -# ------------- - - -#%% parameters - -n = 100 # nb bins - -# bin positions -x = np.arange(n, dtype=np.float64) - -# Gaussian distributions -a = gauss(n, m=20, s=5) # m= mean, s= std -b = gauss(n, m=60, s=10) - -# make distributions unbalanced -b *= 5. - -# loss matrix -M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) -M /= M.max() - - -############################################################################## -# Plot distributions and loss matrix -# ---------------------------------- - -#%% plot the distributions - -pl.figure(1, figsize=(6.4, 3)) -pl.plot(x, a, 'b', label='Source distribution') -pl.plot(x, b, 'r', label='Target distribution') -pl.legend() - -# plot distributions and loss matrix - -pl.figure(2, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') - - -############################################################################## -# Solve Unbalanced Sinkhorn -# -------------- - - -# Sinkhorn - -epsilon = 0.1 # entropy parameter -alpha = 1. # Unbalanced KL relaxation parameter -Gs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True) - -pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn') - -pl.show() diff --git a/docs/source/auto_examples/plot_UOT_1D.rst b/docs/source/auto_examples/plot_UOT_1D.rst deleted file mode 100644 index f43b0c1..0000000 --- a/docs/source/auto_examples/plot_UOT_1D.rst +++ /dev/null @@ -1,177 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_UOT_1D.py: - - -=============================== -1D Unbalanced optimal transport -=============================== - -This example illustrates the computation of Unbalanced Optimal transport -using a Kullback-Leibler relaxation. - - -.. code-block:: default - - - # Author: Hicham Janati - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot - import ot.plot - from ot.datasets import make_1D_gauss as gauss - - - - - - - - -Generate data -------------- - - -.. code-block:: default - - - n = 100 # nb bins - - # bin positions - x = np.arange(n, dtype=np.float64) - - # Gaussian distributions - a = gauss(n, m=20, s=5) # m= mean, s= std - b = gauss(n, m=60, s=10) - - # make distributions unbalanced - b *= 5. - - # loss matrix - M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) - M /= M.max() - - - - - - - - - -Plot distributions and loss matrix ----------------------------------- - - -.. code-block:: default - - - pl.figure(1, figsize=(6.4, 3)) - pl.plot(x, a, 'b', label='Source distribution') - pl.plot(x, b, 'r', label='Target distribution') - pl.legend() - - # plot distributions and loss matrix - - pl.figure(2, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') - - - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_UOT_1D_001.png - :class: sphx-glr-multi-img - - * - - .. image:: /auto_examples/images/sphx_glr_plot_UOT_1D_002.png - :class: sphx-glr-multi-img - - - - - -Solve Unbalanced Sinkhorn --------------- - - -.. code-block:: default - - - - # Sinkhorn - - epsilon = 0.1 # entropy parameter - alpha = 1. # Unbalanced KL relaxation parameter - Gs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True) - - pl.figure(4, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn') - - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_UOT_1D_003.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_UOT_1D.py:76: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 0.274 seconds) - - -.. _sphx_glr_download_auto_examples_plot_UOT_1D.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_UOT_1D.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_UOT_1D.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_UOT_barycenter_1D.ipynb b/docs/source/auto_examples/plot_UOT_barycenter_1D.ipynb deleted file mode 100644 index 549a78b..0000000 --- a/docs/source/auto_examples/plot_UOT_barycenter_1D.ipynb +++ /dev/null @@ -1,126 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# 1D Wasserstein barycenter demo for Unbalanced distributions\n\n\nThis example illustrates the computation of regularized Wassersyein Barycenter\nas proposed in [10] for Unbalanced inputs.\n\n\n[10] Chizat, L., Peyr\u00e9, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816.\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Hicham Janati \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n-------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# make unbalanced dists\na2 *= 3.\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n---------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycenter computation\n----------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# non weighted barycenter computation\n\nweight = 0.5 # 0<=weight<=1\nweights = np.array([1 - weight, weight])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nalpha = 1.\n\nbary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycentric interpolation\n-------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# barycenter interpolation\n\nn_weight = 11\nweight_list = np.linspace(0, 1, n_weight)\n\n\nB_l2 = np.zeros((n, n_weight))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_weight):\n weight = weight_list[i]\n weights = np.array([1 - weight, weight])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights)\n\n\n# plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = weight_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel(r'$\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = weight_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel(r'$\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_UOT_barycenter_1D.py b/docs/source/auto_examples/plot_UOT_barycenter_1D.py deleted file mode 100644 index acb5892..0000000 --- a/docs/source/auto_examples/plot_UOT_barycenter_1D.py +++ /dev/null @@ -1,164 +0,0 @@ -# -*- coding: utf-8 -*- -""" -=========================================================== -1D Wasserstein barycenter demo for Unbalanced distributions -=========================================================== - -This example illustrates the computation of regularized Wassersyein Barycenter -as proposed in [10] for Unbalanced inputs. - - -[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. - -""" - -# Author: Hicham Janati -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot -# necessary for 3d plot even if not used -from mpl_toolkits.mplot3d import Axes3D # noqa -from matplotlib.collections import PolyCollection - -############################################################################## -# Generate data -# ------------- - -# parameters - -n = 100 # nb bins - -# bin positions -x = np.arange(n, dtype=np.float64) - -# Gaussian distributions -a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std -a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) - -# make unbalanced dists -a2 *= 3. - -# creating matrix A containing all distributions -A = np.vstack((a1, a2)).T -n_distributions = A.shape[1] - -# loss matrix + normalization -M = ot.utils.dist0(n) -M /= M.max() - -############################################################################## -# Plot data -# --------- - -# plot the distributions - -pl.figure(1, figsize=(6.4, 3)) -for i in range(n_distributions): - pl.plot(x, A[:, i]) -pl.title('Distributions') -pl.tight_layout() - -############################################################################## -# Barycenter computation -# ---------------------- - -# non weighted barycenter computation - -weight = 0.5 # 0<=weight<=1 -weights = np.array([1 - weight, weight]) - -# l2bary -bary_l2 = A.dot(weights) - -# wasserstein -reg = 1e-3 -alpha = 1. - -bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights) - -pl.figure(2) -pl.clf() -pl.subplot(2, 1, 1) -for i in range(n_distributions): - pl.plot(x, A[:, i]) -pl.title('Distributions') - -pl.subplot(2, 1, 2) -pl.plot(x, bary_l2, 'r', label='l2') -pl.plot(x, bary_wass, 'g', label='Wasserstein') -pl.legend() -pl.title('Barycenters') -pl.tight_layout() - -############################################################################## -# Barycentric interpolation -# ------------------------- - -# barycenter interpolation - -n_weight = 11 -weight_list = np.linspace(0, 1, n_weight) - - -B_l2 = np.zeros((n, n_weight)) - -B_wass = np.copy(B_l2) - -for i in range(0, n_weight): - weight = weight_list[i] - weights = np.array([1 - weight, weight]) - B_l2[:, i] = A.dot(weights) - B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights) - - -# plot interpolation - -pl.figure(3) - -cmap = pl.cm.get_cmap('viridis') -verts = [] -zs = weight_list -for i, z in enumerate(zs): - ys = B_l2[:, i] - verts.append(list(zip(x, ys))) - -ax = pl.gcf().gca(projection='3d') - -poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) -poly.set_alpha(0.7) -ax.add_collection3d(poly, zs=zs, zdir='y') -ax.set_xlabel('x') -ax.set_xlim3d(0, n) -ax.set_ylabel(r'$\alpha$') -ax.set_ylim3d(0, 1) -ax.set_zlabel('') -ax.set_zlim3d(0, B_l2.max() * 1.01) -pl.title('Barycenter interpolation with l2') -pl.tight_layout() - -pl.figure(4) -cmap = pl.cm.get_cmap('viridis') -verts = [] -zs = weight_list -for i, z in enumerate(zs): - ys = B_wass[:, i] - verts.append(list(zip(x, ys))) - -ax = pl.gcf().gca(projection='3d') - -poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) -poly.set_alpha(0.7) -ax.add_collection3d(poly, zs=zs, zdir='y') -ax.set_xlabel('x') -ax.set_xlim3d(0, n) -ax.set_ylabel(r'$\alpha$') -ax.set_ylim3d(0, 1) -ax.set_zlabel('') -ax.set_zlim3d(0, B_l2.max() * 1.01) -pl.title('Barycenter interpolation with Wasserstein') -pl.tight_layout() - -pl.show() diff --git a/docs/source/auto_examples/plot_UOT_barycenter_1D.rst b/docs/source/auto_examples/plot_UOT_barycenter_1D.rst deleted file mode 100644 index 2688d2e..0000000 --- a/docs/source/auto_examples/plot_UOT_barycenter_1D.rst +++ /dev/null @@ -1,299 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_UOT_barycenter_1D.py: - - -=========================================================== -1D Wasserstein barycenter demo for Unbalanced distributions -=========================================================== - -This example illustrates the computation of regularized Wassersyein Barycenter -as proposed in [10] for Unbalanced inputs. - - -[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. - - - -.. code-block:: default - - - # Author: Hicham Janati - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot - # necessary for 3d plot even if not used - from mpl_toolkits.mplot3d import Axes3D # noqa - from matplotlib.collections import PolyCollection - - - - - - - - -Generate data -------------- - - -.. code-block:: default - - - # parameters - - n = 100 # nb bins - - # bin positions - x = np.arange(n, dtype=np.float64) - - # Gaussian distributions - a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std - a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) - - # make unbalanced dists - a2 *= 3. - - # creating matrix A containing all distributions - A = np.vstack((a1, a2)).T - n_distributions = A.shape[1] - - # loss matrix + normalization - M = ot.utils.dist0(n) - M /= M.max() - - - - - - - - -Plot data ---------- - - -.. code-block:: default - - - # plot the distributions - - pl.figure(1, figsize=(6.4, 3)) - for i in range(n_distributions): - pl.plot(x, A[:, i]) - pl.title('Distributions') - pl.tight_layout() - - - - -.. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_001.png - :class: sphx-glr-single-img - - - - - -Barycenter computation ----------------------- - - -.. code-block:: default - - - # non weighted barycenter computation - - weight = 0.5 # 0<=weight<=1 - weights = np.array([1 - weight, weight]) - - # l2bary - bary_l2 = A.dot(weights) - - # wasserstein - reg = 1e-3 - alpha = 1. - - bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights) - - pl.figure(2) - pl.clf() - pl.subplot(2, 1, 1) - for i in range(n_distributions): - pl.plot(x, A[:, i]) - pl.title('Distributions') - - pl.subplot(2, 1, 2) - pl.plot(x, bary_l2, 'r', label='l2') - pl.plot(x, bary_wass, 'g', label='Wasserstein') - pl.legend() - pl.title('Barycenters') - pl.tight_layout() - - - - -.. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_002.png - :class: sphx-glr-single-img - - - - - -Barycentric interpolation -------------------------- - - -.. code-block:: default - - - # barycenter interpolation - - n_weight = 11 - weight_list = np.linspace(0, 1, n_weight) - - - B_l2 = np.zeros((n, n_weight)) - - B_wass = np.copy(B_l2) - - for i in range(0, n_weight): - weight = weight_list[i] - weights = np.array([1 - weight, weight]) - B_l2[:, i] = A.dot(weights) - B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights) - - - # plot interpolation - - pl.figure(3) - - cmap = pl.cm.get_cmap('viridis') - verts = [] - zs = weight_list - for i, z in enumerate(zs): - ys = B_l2[:, i] - verts.append(list(zip(x, ys))) - - ax = pl.gcf().gca(projection='3d') - - poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) - poly.set_alpha(0.7) - ax.add_collection3d(poly, zs=zs, zdir='y') - ax.set_xlabel('x') - ax.set_xlim3d(0, n) - ax.set_ylabel(r'$\alpha$') - ax.set_ylim3d(0, 1) - ax.set_zlabel('') - ax.set_zlim3d(0, B_l2.max() * 1.01) - pl.title('Barycenter interpolation with l2') - pl.tight_layout() - - pl.figure(4) - cmap = pl.cm.get_cmap('viridis') - verts = [] - zs = weight_list - for i, z in enumerate(zs): - ys = B_wass[:, i] - verts.append(list(zip(x, ys))) - - ax = pl.gcf().gca(projection='3d') - - poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) - poly.set_alpha(0.7) - ax.add_collection3d(poly, zs=zs, zdir='y') - ax.set_xlabel('x') - ax.set_xlim3d(0, n) - ax.set_ylabel(r'$\alpha$') - ax.set_ylim3d(0, 1) - ax.set_zlabel('') - ax.set_zlim3d(0, B_l2.max() * 1.01) - pl.title('Barycenter interpolation with Wasserstein') - pl.tight_layout() - - pl.show() - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_003.png - :class: sphx-glr-multi-img - - * - - .. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_004.png - :class: sphx-glr-multi-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/ot/unbalanced.py:895: RuntimeWarning: overflow encountered in true_divide - u = (A / Kv) ** fi - /home/rflamary/PYTHON/POT/ot/unbalanced.py:900: RuntimeWarning: invalid value encountered in true_divide - v = (Q / Ktu) ** fi - /home/rflamary/PYTHON/POT/ot/unbalanced.py:907: UserWarning: Numerical errors at iteration 595 - warnings.warn('Numerical errors at iteration %s' % i) - /home/rflamary/PYTHON/POT/ot/unbalanced.py:900: RuntimeWarning: overflow encountered in true_divide - v = (Q / Ktu) ** fi - /home/rflamary/PYTHON/POT/ot/unbalanced.py:907: UserWarning: Numerical errors at iteration 974 - warnings.warn('Numerical errors at iteration %s' % i) - /home/rflamary/PYTHON/POT/ot/unbalanced.py:907: UserWarning: Numerical errors at iteration 615 - warnings.warn('Numerical errors at iteration %s' % i) - /home/rflamary/PYTHON/POT/ot/unbalanced.py:907: UserWarning: Numerical errors at iteration 455 - warnings.warn('Numerical errors at iteration %s' % i) - /home/rflamary/PYTHON/POT/ot/unbalanced.py:907: UserWarning: Numerical errors at iteration 361 - warnings.warn('Numerical errors at iteration %s' % i) - /home/rflamary/PYTHON/POT/examples/plot_UOT_barycenter_1D.py:164: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 1.567 seconds) - - -.. _sphx_glr_download_auto_examples_plot_UOT_barycenter_1D.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_UOT_barycenter_1D.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_UOT_barycenter_1D.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_WDA.ipynb b/docs/source/auto_examples/plot_WDA.ipynb deleted file mode 100644 index 1661c53..0000000 --- a/docs/source/auto_examples/plot_WDA.ipynb +++ /dev/null @@ -1,144 +0,0 @@ -{ - "nbformat_minor": 0, - "nbformat": 4, - "cells": [ - { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "\n# Wasserstein Discriminant Analysis\n\n\nThis example illustrate the use of WDA as proposed in [11].\n\n\n[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016).\nWasserstein Discriminant Analysis.\n\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\n\nfrom ot.dr import wda, fda" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "Generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% parameters\n\nn = 1000 # nb samples in source and target datasets\nnz = 0.2\n\n# generate circle dataset\nt = np.random.rand(n) * 2 * np.pi\nys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1\nxs = np.concatenate(\n (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)\nxs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2)\n\nt = np.random.rand(n) * 2 * np.pi\nyt = np.floor((np.arange(n) * 1.0 / n * 3)) + 1\nxt = np.concatenate(\n (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)\nxt = xt * yt.reshape(-1, 1) + nz * np.random.randn(n, 2)\n\nnbnoise = 8\n\nxs = np.hstack((xs, np.random.randn(n, nbnoise)))\nxt = np.hstack((xt, np.random.randn(n, nbnoise)))" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "Plot data\n---------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% plot samples\npl.figure(1, figsize=(6.4, 3.5))\n\npl.subplot(1, 2, 1)\npl.scatter(xt[:, 0], xt[:, 1], c=ys, marker='+', label='Source samples')\npl.legend(loc=0)\npl.title('Discriminant dimensions')\n\npl.subplot(1, 2, 2)\npl.scatter(xt[:, 2], xt[:, 3], c=ys, marker='+', label='Source samples')\npl.legend(loc=0)\npl.title('Other dimensions')\npl.tight_layout()" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "Compute Fisher Discriminant Analysis\n------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% Compute FDA\np = 2\n\nPfda, projfda = fda(xs, ys, p)" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "Compute Wasserstein Discriminant Analysis\n-----------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% Compute WDA\np = 2\nreg = 1e0\nk = 10\nmaxiter = 100\n\nPwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter)" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "Plot 2D projections\n-------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% plot samples\n\nxsp = projfda(xs)\nxtp = projfda(xt)\n\nxspw = projwda(xs)\nxtpw = projwda(xt)\n\npl.figure(2)\n\npl.subplot(2, 2, 1)\npl.scatter(xsp[:, 0], xsp[:, 1], c=ys, marker='+', label='Projected samples')\npl.legend(loc=0)\npl.title('Projected training samples FDA')\n\npl.subplot(2, 2, 2)\npl.scatter(xtp[:, 0], xtp[:, 1], c=ys, marker='+', label='Projected samples')\npl.legend(loc=0)\npl.title('Projected test samples FDA')\n\npl.subplot(2, 2, 3)\npl.scatter(xspw[:, 0], xspw[:, 1], c=ys, marker='+', label='Projected samples')\npl.legend(loc=0)\npl.title('Projected training samples WDA')\n\npl.subplot(2, 2, 4)\npl.scatter(xtpw[:, 0], xtpw[:, 1], c=ys, marker='+', label='Projected samples')\npl.legend(loc=0)\npl.title('Projected test samples WDA')\npl.tight_layout()\n\npl.show()" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, - "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", - "codemirror_mode": { - "version": 2, - "name": "ipython" - } - } - } -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_WDA.py b/docs/source/auto_examples/plot_WDA.py deleted file mode 100644 index 93cc237..0000000 --- a/docs/source/auto_examples/plot_WDA.py +++ /dev/null @@ -1,127 +0,0 @@ -# -*- coding: utf-8 -*- -""" -================================= -Wasserstein Discriminant Analysis -================================= - -This example illustrate the use of WDA as proposed in [11]. - - -[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). -Wasserstein Discriminant Analysis. - -""" - -# Author: Remi Flamary -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl - -from ot.dr import wda, fda - - -############################################################################## -# Generate data -# ------------- - -#%% parameters - -n = 1000 # nb samples in source and target datasets -nz = 0.2 - -# generate circle dataset -t = np.random.rand(n) * 2 * np.pi -ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 -xs = np.concatenate( - (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1) -xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2) - -t = np.random.rand(n) * 2 * np.pi -yt = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 -xt = np.concatenate( - (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1) -xt = xt * yt.reshape(-1, 1) + nz * np.random.randn(n, 2) - -nbnoise = 8 - -xs = np.hstack((xs, np.random.randn(n, nbnoise))) -xt = np.hstack((xt, np.random.randn(n, nbnoise))) - -############################################################################## -# Plot data -# --------- - -#%% plot samples -pl.figure(1, figsize=(6.4, 3.5)) - -pl.subplot(1, 2, 1) -pl.scatter(xt[:, 0], xt[:, 1], c=ys, marker='+', label='Source samples') -pl.legend(loc=0) -pl.title('Discriminant dimensions') - -pl.subplot(1, 2, 2) -pl.scatter(xt[:, 2], xt[:, 3], c=ys, marker='+', label='Source samples') -pl.legend(loc=0) -pl.title('Other dimensions') -pl.tight_layout() - -############################################################################## -# Compute Fisher Discriminant Analysis -# ------------------------------------ - -#%% Compute FDA -p = 2 - -Pfda, projfda = fda(xs, ys, p) - -############################################################################## -# Compute Wasserstein Discriminant Analysis -# ----------------------------------------- - -#%% Compute WDA -p = 2 -reg = 1e0 -k = 10 -maxiter = 100 - -Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter) - - -############################################################################## -# Plot 2D projections -# ------------------- - -#%% plot samples - -xsp = projfda(xs) -xtp = projfda(xt) - -xspw = projwda(xs) -xtpw = projwda(xt) - -pl.figure(2) - -pl.subplot(2, 2, 1) -pl.scatter(xsp[:, 0], xsp[:, 1], c=ys, marker='+', label='Projected samples') -pl.legend(loc=0) -pl.title('Projected training samples FDA') - -pl.subplot(2, 2, 2) -pl.scatter(xtp[:, 0], xtp[:, 1], c=ys, marker='+', label='Projected samples') -pl.legend(loc=0) -pl.title('Projected test samples FDA') - -pl.subplot(2, 2, 3) -pl.scatter(xspw[:, 0], xspw[:, 1], c=ys, marker='+', label='Projected samples') -pl.legend(loc=0) -pl.title('Projected training samples WDA') - -pl.subplot(2, 2, 4) -pl.scatter(xtpw[:, 0], xtpw[:, 1], c=ys, marker='+', label='Projected samples') -pl.legend(loc=0) -pl.title('Projected test samples WDA') -pl.tight_layout() - -pl.show() diff --git a/docs/source/auto_examples/plot_WDA.rst b/docs/source/auto_examples/plot_WDA.rst deleted file mode 100644 index 2d83123..0000000 --- a/docs/source/auto_examples/plot_WDA.rst +++ /dev/null @@ -1,244 +0,0 @@ - - -.. _sphx_glr_auto_examples_plot_WDA.py: - - -================================= -Wasserstein Discriminant Analysis -================================= - -This example illustrate the use of WDA as proposed in [11]. - - -[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). -Wasserstein Discriminant Analysis. - - - - -.. code-block:: python - - - # Author: Remi Flamary - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - - from ot.dr import wda, fda - - - - - - - - -Generate data -------------- - - - -.. code-block:: python - - - #%% parameters - - n = 1000 # nb samples in source and target datasets - nz = 0.2 - - # generate circle dataset - t = np.random.rand(n) * 2 * np.pi - ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 - xs = np.concatenate( - (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1) - xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2) - - t = np.random.rand(n) * 2 * np.pi - yt = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 - xt = np.concatenate( - (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1) - xt = xt * yt.reshape(-1, 1) + nz * np.random.randn(n, 2) - - nbnoise = 8 - - xs = np.hstack((xs, np.random.randn(n, nbnoise))) - xt = np.hstack((xt, np.random.randn(n, nbnoise))) - - - - - - - -Plot data ---------- - - - -.. code-block:: python - - - #%% plot samples - pl.figure(1, figsize=(6.4, 3.5)) - - pl.subplot(1, 2, 1) - pl.scatter(xt[:, 0], xt[:, 1], c=ys, marker='+', label='Source samples') - pl.legend(loc=0) - pl.title('Discriminant dimensions') - - pl.subplot(1, 2, 2) - pl.scatter(xt[:, 2], xt[:, 3], c=ys, marker='+', label='Source samples') - pl.legend(loc=0) - pl.title('Other dimensions') - pl.tight_layout() - - - - -.. image:: /auto_examples/images/sphx_glr_plot_WDA_001.png - :align: center - - - - -Compute Fisher Discriminant Analysis ------------------------------------- - - - -.. code-block:: python - - - #%% Compute FDA - p = 2 - - Pfda, projfda = fda(xs, ys, p) - - - - - - - -Compute Wasserstein Discriminant Analysis ------------------------------------------ - - - -.. code-block:: python - - - #%% Compute WDA - p = 2 - reg = 1e0 - k = 10 - maxiter = 100 - - Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter) - - - - - - -.. rst-class:: sphx-glr-script-out - - Out:: - - Compiling cost function... - Computing gradient of cost function... - iter cost val grad. norm - 1 +9.0167295050534191e-01 2.28422652e-01 - 2 +4.8324990550878105e-01 4.89362707e-01 - 3 +3.4613154515357075e-01 2.84117562e-01 - 4 +2.5277108387195002e-01 1.24888750e-01 - 5 +2.4113858393736629e-01 8.07491482e-02 - 6 +2.3642108593032782e-01 1.67612140e-02 - 7 +2.3625721372202199e-01 7.68640008e-03 - 8 +2.3625461994913738e-01 7.42200784e-03 - 9 +2.3624493441436939e-01 6.43534105e-03 - 10 +2.3621901383686217e-01 2.17960585e-03 - 11 +2.3621854258326572e-01 2.03306749e-03 - 12 +2.3621696458678049e-01 1.37118721e-03 - 13 +2.3621569489873540e-01 2.76368907e-04 - 14 +2.3621565599232983e-01 1.41898134e-04 - 15 +2.3621564465487518e-01 5.96602069e-05 - 16 +2.3621564232556647e-01 1.08709521e-05 - 17 +2.3621564230277003e-01 9.17855656e-06 - 18 +2.3621564224857586e-01 1.73728345e-06 - 19 +2.3621564224748123e-01 1.17770019e-06 - 20 +2.3621564224658587e-01 2.16179383e-07 - Terminated - min grad norm reached after 20 iterations, 9.20 seconds. - - -Plot 2D projections -------------------- - - - -.. code-block:: python - - - #%% plot samples - - xsp = projfda(xs) - xtp = projfda(xt) - - xspw = projwda(xs) - xtpw = projwda(xt) - - pl.figure(2) - - pl.subplot(2, 2, 1) - pl.scatter(xsp[:, 0], xsp[:, 1], c=ys, marker='+', label='Projected samples') - pl.legend(loc=0) - pl.title('Projected training samples FDA') - - pl.subplot(2, 2, 2) - pl.scatter(xtp[:, 0], xtp[:, 1], c=ys, marker='+', label='Projected samples') - pl.legend(loc=0) - pl.title('Projected test samples FDA') - - pl.subplot(2, 2, 3) - pl.scatter(xspw[:, 0], xspw[:, 1], c=ys, marker='+', label='Projected samples') - pl.legend(loc=0) - pl.title('Projected training samples WDA') - - pl.subplot(2, 2, 4) - pl.scatter(xtpw[:, 0], xtpw[:, 1], c=ys, marker='+', label='Projected samples') - pl.legend(loc=0) - pl.title('Projected test samples WDA') - pl.tight_layout() - - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_WDA_003.png - :align: center - - - - -**Total running time of the script:** ( 0 minutes 16.182 seconds) - - - -.. container:: sphx-glr-footer - - - .. container:: sphx-glr-download - - :download:`Download Python source code: plot_WDA.py ` - - - - .. container:: sphx-glr-download - - :download:`Download Jupyter notebook: plot_WDA.ipynb ` - -.. rst-class:: sphx-glr-signature - - `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_barycenter_1D.ipynb b/docs/source/auto_examples/plot_barycenter_1D.ipynb deleted file mode 100644 index 387c41a..0000000 --- a/docs/source/auto_examples/plot_barycenter_1D.ipynb +++ /dev/null @@ -1,137 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# 1D Wasserstein barycenter demo\n\n\nThis example illustrates the computation of regularized Wassersyein Barycenter\nas proposed in [3].\n\n\n[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyr\u00e9, G. (2015).\nIterative Bregman projections for regularized transportation problems\nSIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n-------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n---------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycenter computation\n----------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "alpha = 0.2 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycentric interpolation\n-------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n alpha = alpha_list[i]\n weights = np.array([1 - alpha, alpha])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_barycenter_1D.py b/docs/source/auto_examples/plot_barycenter_1D.py deleted file mode 100644 index 6864301..0000000 --- a/docs/source/auto_examples/plot_barycenter_1D.py +++ /dev/null @@ -1,160 +0,0 @@ -# -*- coding: utf-8 -*- -""" -============================== -1D Wasserstein barycenter demo -============================== - -This example illustrates the computation of regularized Wassersyein Barycenter -as proposed in [3]. - - -[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). -Iterative Bregman projections for regularized transportation problems -SIAM Journal on Scientific Computing, 37(2), A1111-A1138. - -""" - -# Author: Remi Flamary -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot -# necessary for 3d plot even if not used -from mpl_toolkits.mplot3d import Axes3D # noqa -from matplotlib.collections import PolyCollection - -############################################################################## -# Generate data -# ------------- - -#%% parameters - -n = 100 # nb bins - -# bin positions -x = np.arange(n, dtype=np.float64) - -# Gaussian distributions -a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std -a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) - -# creating matrix A containing all distributions -A = np.vstack((a1, a2)).T -n_distributions = A.shape[1] - -# loss matrix + normalization -M = ot.utils.dist0(n) -M /= M.max() - -############################################################################## -# Plot data -# --------- - -#%% plot the distributions - -pl.figure(1, figsize=(6.4, 3)) -for i in range(n_distributions): - pl.plot(x, A[:, i]) -pl.title('Distributions') -pl.tight_layout() - -############################################################################## -# Barycenter computation -# ---------------------- - -#%% barycenter computation - -alpha = 0.2 # 0<=alpha<=1 -weights = np.array([1 - alpha, alpha]) - -# l2bary -bary_l2 = A.dot(weights) - -# wasserstein -reg = 1e-3 -bary_wass = ot.bregman.barycenter(A, M, reg, weights) - -pl.figure(2) -pl.clf() -pl.subplot(2, 1, 1) -for i in range(n_distributions): - pl.plot(x, A[:, i]) -pl.title('Distributions') - -pl.subplot(2, 1, 2) -pl.plot(x, bary_l2, 'r', label='l2') -pl.plot(x, bary_wass, 'g', label='Wasserstein') -pl.legend() -pl.title('Barycenters') -pl.tight_layout() - -############################################################################## -# Barycentric interpolation -# ------------------------- - -#%% barycenter interpolation - -n_alpha = 11 -alpha_list = np.linspace(0, 1, n_alpha) - - -B_l2 = np.zeros((n, n_alpha)) - -B_wass = np.copy(B_l2) - -for i in range(0, n_alpha): - alpha = alpha_list[i] - weights = np.array([1 - alpha, alpha]) - B_l2[:, i] = A.dot(weights) - B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights) - -#%% plot interpolation - -pl.figure(3) - -cmap = pl.cm.get_cmap('viridis') -verts = [] -zs = alpha_list -for i, z in enumerate(zs): - ys = B_l2[:, i] - verts.append(list(zip(x, ys))) - -ax = pl.gcf().gca(projection='3d') - -poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list]) -poly.set_alpha(0.7) -ax.add_collection3d(poly, zs=zs, zdir='y') -ax.set_xlabel('x') -ax.set_xlim3d(0, n) -ax.set_ylabel('$\\alpha$') -ax.set_ylim3d(0, 1) -ax.set_zlabel('') -ax.set_zlim3d(0, B_l2.max() * 1.01) -pl.title('Barycenter interpolation with l2') -pl.tight_layout() - -pl.figure(4) -cmap = pl.cm.get_cmap('viridis') -verts = [] -zs = alpha_list -for i, z in enumerate(zs): - ys = B_wass[:, i] - verts.append(list(zip(x, ys))) - -ax = pl.gcf().gca(projection='3d') - -poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list]) -poly.set_alpha(0.7) -ax.add_collection3d(poly, zs=zs, zdir='y') -ax.set_xlabel('x') -ax.set_xlim3d(0, n) -ax.set_ylabel('$\\alpha$') -ax.set_ylim3d(0, 1) -ax.set_zlabel('') -ax.set_zlim3d(0, B_l2.max() * 1.01) -pl.title('Barycenter interpolation with Wasserstein') -pl.tight_layout() - -pl.show() diff --git a/docs/source/auto_examples/plot_barycenter_1D.rst b/docs/source/auto_examples/plot_barycenter_1D.rst deleted file mode 100644 index a65ac3d..0000000 --- a/docs/source/auto_examples/plot_barycenter_1D.rst +++ /dev/null @@ -1,280 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_barycenter_1D.py: - - -============================== -1D Wasserstein barycenter demo -============================== - -This example illustrates the computation of regularized Wassersyein Barycenter -as proposed in [3]. - - -[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). -Iterative Bregman projections for regularized transportation problems -SIAM Journal on Scientific Computing, 37(2), A1111-A1138. - - - -.. code-block:: default - - - # Author: Remi Flamary - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot - # necessary for 3d plot even if not used - from mpl_toolkits.mplot3d import Axes3D # noqa - from matplotlib.collections import PolyCollection - - - - - - - - -Generate data -------------- - - -.. code-block:: default - - - n = 100 # nb bins - - # bin positions - x = np.arange(n, dtype=np.float64) - - # Gaussian distributions - a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std - a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) - - # creating matrix A containing all distributions - A = np.vstack((a1, a2)).T - n_distributions = A.shape[1] - - # loss matrix + normalization - M = ot.utils.dist0(n) - M /= M.max() - - - - - - - - -Plot data ---------- - - -.. code-block:: default - - - pl.figure(1, figsize=(6.4, 3)) - for i in range(n_distributions): - pl.plot(x, A[:, i]) - pl.title('Distributions') - pl.tight_layout() - - - - -.. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_001.png - :class: sphx-glr-single-img - - - - - -Barycenter computation ----------------------- - - -.. code-block:: default - - - alpha = 0.2 # 0<=alpha<=1 - weights = np.array([1 - alpha, alpha]) - - # l2bary - bary_l2 = A.dot(weights) - - # wasserstein - reg = 1e-3 - bary_wass = ot.bregman.barycenter(A, M, reg, weights) - - pl.figure(2) - pl.clf() - pl.subplot(2, 1, 1) - for i in range(n_distributions): - pl.plot(x, A[:, i]) - pl.title('Distributions') - - pl.subplot(2, 1, 2) - pl.plot(x, bary_l2, 'r', label='l2') - pl.plot(x, bary_wass, 'g', label='Wasserstein') - pl.legend() - pl.title('Barycenters') - pl.tight_layout() - - - - -.. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_002.png - :class: sphx-glr-single-img - - - - - -Barycentric interpolation -------------------------- - - -.. code-block:: default - - - n_alpha = 11 - alpha_list = np.linspace(0, 1, n_alpha) - - - B_l2 = np.zeros((n, n_alpha)) - - B_wass = np.copy(B_l2) - - for i in range(0, n_alpha): - alpha = alpha_list[i] - weights = np.array([1 - alpha, alpha]) - B_l2[:, i] = A.dot(weights) - B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights) - - - - - - - - - -.. code-block:: default - - - pl.figure(3) - - cmap = pl.cm.get_cmap('viridis') - verts = [] - zs = alpha_list - for i, z in enumerate(zs): - ys = B_l2[:, i] - verts.append(list(zip(x, ys))) - - ax = pl.gcf().gca(projection='3d') - - poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list]) - poly.set_alpha(0.7) - ax.add_collection3d(poly, zs=zs, zdir='y') - ax.set_xlabel('x') - ax.set_xlim3d(0, n) - ax.set_ylabel('$\\alpha$') - ax.set_ylim3d(0, 1) - ax.set_zlabel('') - ax.set_zlim3d(0, B_l2.max() * 1.01) - pl.title('Barycenter interpolation with l2') - pl.tight_layout() - - pl.figure(4) - cmap = pl.cm.get_cmap('viridis') - verts = [] - zs = alpha_list - for i, z in enumerate(zs): - ys = B_wass[:, i] - verts.append(list(zip(x, ys))) - - ax = pl.gcf().gca(projection='3d') - - poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list]) - poly.set_alpha(0.7) - ax.add_collection3d(poly, zs=zs, zdir='y') - ax.set_xlabel('x') - ax.set_xlim3d(0, n) - ax.set_ylabel('$\\alpha$') - ax.set_ylim3d(0, 1) - ax.set_zlabel('') - ax.set_zlim3d(0, B_l2.max() * 1.01) - pl.title('Barycenter interpolation with Wasserstein') - pl.tight_layout() - - pl.show() - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_003.png - :class: sphx-glr-multi-img - - * - - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_004.png - :class: sphx-glr-multi-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_barycenter_1D.py:160: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 0.769 seconds) - - -.. _sphx_glr_download_auto_examples_plot_barycenter_1D.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_barycenter_1D.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_barycenter_1D.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_barycenter_fgw.ipynb b/docs/source/auto_examples/plot_barycenter_fgw.ipynb deleted file mode 100644 index 4e4704c..0000000 --- a/docs/source/auto_examples/plot_barycenter_fgw.ipynb +++ /dev/null @@ -1,173 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n=================================\nPlot graphs' barycenter using FGW\n=================================\n\nThis example illustrates the computation barycenter of labeled graphs using FGW\n\nRequires networkx >=2\n\n.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain\n and Courty Nicolas\n \"Optimal Transport for structured data with application on graphs\"\n International Conference on Machine Learning (ICML). 2019.\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Titouan Vayer \n#\n# License: MIT License" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\nimport matplotlib.pyplot as plt\nimport networkx as nx\nimport math\nfrom scipy.sparse.csgraph import shortest_path\nimport matplotlib.colors as mcol\nfrom matplotlib import cm\nfrom ot.gromov import fgw_barycenters" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def find_thresh(C, inf=0.5, sup=3, step=10):\n \"\"\" Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected\n Tthe threshold is found by a linesearch between values \"inf\" and \"sup\" with \"step\" thresholds tested.\n The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix\n and the original matrix.\n Parameters\n ----------\n C : ndarray, shape (n_nodes,n_nodes)\n The structure matrix to threshold\n inf : float\n The beginning of the linesearch\n sup : float\n The end of the linesearch\n step : integer\n Number of thresholds tested\n \"\"\"\n dist = []\n search = np.linspace(inf, sup, step)\n for thresh in search:\n Cprime = sp_to_adjency(C, 0, thresh)\n SC = shortest_path(Cprime, method='D')\n SC[SC == float('inf')] = 100\n dist.append(np.linalg.norm(SC - C))\n return search[np.argmin(dist)], dist\n\n\ndef sp_to_adjency(C, threshinf=0.2, threshsup=1.8):\n \"\"\" Thresholds the structure matrix in order to compute an adjency matrix.\n All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0\n Parameters\n ----------\n C : ndarray, shape (n_nodes,n_nodes)\n The structure matrix to threshold\n threshinf : float\n The minimum value of distance from which the new value is set to 1\n threshsup : float\n The maximum value of distance from which the new value is set to 1\n Returns\n -------\n C : ndarray, shape (n_nodes,n_nodes)\n The threshold matrix. Each element is in {0,1}\n \"\"\"\n H = np.zeros_like(C)\n np.fill_diagonal(H, np.diagonal(C))\n C = C - H\n C = np.minimum(np.maximum(C, threshinf), threshsup)\n C[C == threshsup] = 0\n C[C != 0] = 1\n\n return C\n\n\ndef build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None):\n \"\"\" Create a noisy circular graph\n \"\"\"\n g = nx.Graph()\n g.add_nodes_from(list(range(N)))\n for i in range(N):\n noise = float(np.random.normal(mu, sigma, 1))\n if with_noise:\n g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise)\n else:\n g.add_node(i, attr_name=math.sin(2 * i * math.pi / N))\n g.add_edge(i, i + 1)\n if structure_noise:\n randomint = np.random.randint(0, p)\n if randomint == 0:\n if i <= N - 3:\n g.add_edge(i, i + 2)\n if i == N - 2:\n g.add_edge(i, 0)\n if i == N - 1:\n g.add_edge(i, 1)\n g.add_edge(N, 0)\n noise = float(np.random.normal(mu, sigma, 1))\n if with_noise:\n g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise)\n else:\n g.add_node(N, attr_name=math.sin(2 * N * math.pi / N))\n return g\n\n\ndef graph_colors(nx_graph, vmin=0, vmax=7):\n cnorm = mcol.Normalize(vmin=vmin, vmax=vmax)\n cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis')\n cpick.set_array([])\n val_map = {}\n for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items():\n val_map[k] = cpick.to_rgba(v)\n colors = []\n for node in nx_graph.nodes():\n colors.append(val_map[node])\n return colors" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n-------------\n\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We build a dataset of noisy circular graphs.\nNoise is added on the structures by random connections and on the features by gaussian noise.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "np.random.seed(30)\nX0 = []\nfor k in range(9):\n X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n---------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "plt.figure(figsize=(8, 10))\nfor i in range(len(X0)):\n plt.subplot(3, 3, i + 1)\n g = X0[i]\n pos = nx.kamada_kawai_layout(g)\n nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100)\nplt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20)\nplt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycenter computation\n----------------------\n\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Features distances are the euclidean distances\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0]\nps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0]\nYs = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0]\nlambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel()\nsizebary = 15 # we choose a barycenter with 15 nodes\n\nA, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot Barycenter\n-------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0]))\nfor i, v in enumerate(A.ravel()):\n bary.add_node(i, attr_name=v)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pos = nx.kamada_kawai_layout(bary)\nnx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False)\nplt.suptitle('Barycenter', fontsize=20)\nplt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_barycenter_fgw.py b/docs/source/auto_examples/plot_barycenter_fgw.py deleted file mode 100644 index 77b0370..0000000 --- a/docs/source/auto_examples/plot_barycenter_fgw.py +++ /dev/null @@ -1,184 +0,0 @@ -# -*- coding: utf-8 -*- -""" -================================= -Plot graphs' barycenter using FGW -================================= - -This example illustrates the computation barycenter of labeled graphs using FGW - -Requires networkx >=2 - -.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain - and Courty Nicolas - "Optimal Transport for structured data with application on graphs" - International Conference on Machine Learning (ICML). 2019. - -""" - -# Author: Titouan Vayer -# -# License: MIT License - -#%% load libraries -import numpy as np -import matplotlib.pyplot as plt -import networkx as nx -import math -from scipy.sparse.csgraph import shortest_path -import matplotlib.colors as mcol -from matplotlib import cm -from ot.gromov import fgw_barycenters -#%% Graph functions - - -def find_thresh(C, inf=0.5, sup=3, step=10): - """ Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected - Tthe threshold is found by a linesearch between values "inf" and "sup" with "step" thresholds tested. - The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix - and the original matrix. - Parameters - ---------- - C : ndarray, shape (n_nodes,n_nodes) - The structure matrix to threshold - inf : float - The beginning of the linesearch - sup : float - The end of the linesearch - step : integer - Number of thresholds tested - """ - dist = [] - search = np.linspace(inf, sup, step) - for thresh in search: - Cprime = sp_to_adjency(C, 0, thresh) - SC = shortest_path(Cprime, method='D') - SC[SC == float('inf')] = 100 - dist.append(np.linalg.norm(SC - C)) - return search[np.argmin(dist)], dist - - -def sp_to_adjency(C, threshinf=0.2, threshsup=1.8): - """ Thresholds the structure matrix in order to compute an adjency matrix. - All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0 - Parameters - ---------- - C : ndarray, shape (n_nodes,n_nodes) - The structure matrix to threshold - threshinf : float - The minimum value of distance from which the new value is set to 1 - threshsup : float - The maximum value of distance from which the new value is set to 1 - Returns - ------- - C : ndarray, shape (n_nodes,n_nodes) - The threshold matrix. Each element is in {0,1} - """ - H = np.zeros_like(C) - np.fill_diagonal(H, np.diagonal(C)) - C = C - H - C = np.minimum(np.maximum(C, threshinf), threshsup) - C[C == threshsup] = 0 - C[C != 0] = 1 - - return C - - -def build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None): - """ Create a noisy circular graph - """ - g = nx.Graph() - g.add_nodes_from(list(range(N))) - for i in range(N): - noise = float(np.random.normal(mu, sigma, 1)) - if with_noise: - g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise) - else: - g.add_node(i, attr_name=math.sin(2 * i * math.pi / N)) - g.add_edge(i, i + 1) - if structure_noise: - randomint = np.random.randint(0, p) - if randomint == 0: - if i <= N - 3: - g.add_edge(i, i + 2) - if i == N - 2: - g.add_edge(i, 0) - if i == N - 1: - g.add_edge(i, 1) - g.add_edge(N, 0) - noise = float(np.random.normal(mu, sigma, 1)) - if with_noise: - g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise) - else: - g.add_node(N, attr_name=math.sin(2 * N * math.pi / N)) - return g - - -def graph_colors(nx_graph, vmin=0, vmax=7): - cnorm = mcol.Normalize(vmin=vmin, vmax=vmax) - cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis') - cpick.set_array([]) - val_map = {} - for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items(): - val_map[k] = cpick.to_rgba(v) - colors = [] - for node in nx_graph.nodes(): - colors.append(val_map[node]) - return colors - -############################################################################## -# Generate data -# ------------- - -#%% circular dataset -# We build a dataset of noisy circular graphs. -# Noise is added on the structures by random connections and on the features by gaussian noise. - - -np.random.seed(30) -X0 = [] -for k in range(9): - X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3)) - -############################################################################## -# Plot data -# --------- - -#%% Plot graphs - -plt.figure(figsize=(8, 10)) -for i in range(len(X0)): - plt.subplot(3, 3, i + 1) - g = X0[i] - pos = nx.kamada_kawai_layout(g) - nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100) -plt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20) -plt.show() - -############################################################################## -# Barycenter computation -# ---------------------- - -#%% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph -# Features distances are the euclidean distances -Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0] -ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0] -Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0] -lambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel() -sizebary = 15 # we choose a barycenter with 15 nodes - -A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True) - -############################################################################## -# Plot Barycenter -# ------------------------- - -#%% Create the barycenter -bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0])) -for i, v in enumerate(A.ravel()): - bary.add_node(i, attr_name=v) - -#%% -pos = nx.kamada_kawai_layout(bary) -nx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False) -plt.suptitle('Barycenter', fontsize=20) -plt.show() diff --git a/docs/source/auto_examples/plot_barycenter_fgw.rst b/docs/source/auto_examples/plot_barycenter_fgw.rst deleted file mode 100644 index ad4c275..0000000 --- a/docs/source/auto_examples/plot_barycenter_fgw.rst +++ /dev/null @@ -1,320 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_barycenter_fgw.py: - - -================================= -Plot graphs' barycenter using FGW -================================= - -This example illustrates the computation barycenter of labeled graphs using FGW - -Requires networkx >=2 - -.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain - and Courty Nicolas - "Optimal Transport for structured data with application on graphs" - International Conference on Machine Learning (ICML). 2019. - - - -.. code-block:: default - - - # Author: Titouan Vayer - # - # License: MIT License - - - - - - - - - -.. code-block:: default - - import numpy as np - import matplotlib.pyplot as plt - import networkx as nx - import math - from scipy.sparse.csgraph import shortest_path - import matplotlib.colors as mcol - from matplotlib import cm - from ot.gromov import fgw_barycenters - - - - - - - - -.. code-block:: default - - - - def find_thresh(C, inf=0.5, sup=3, step=10): - """ Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected - Tthe threshold is found by a linesearch between values "inf" and "sup" with "step" thresholds tested. - The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix - and the original matrix. - Parameters - ---------- - C : ndarray, shape (n_nodes,n_nodes) - The structure matrix to threshold - inf : float - The beginning of the linesearch - sup : float - The end of the linesearch - step : integer - Number of thresholds tested - """ - dist = [] - search = np.linspace(inf, sup, step) - for thresh in search: - Cprime = sp_to_adjency(C, 0, thresh) - SC = shortest_path(Cprime, method='D') - SC[SC == float('inf')] = 100 - dist.append(np.linalg.norm(SC - C)) - return search[np.argmin(dist)], dist - - - def sp_to_adjency(C, threshinf=0.2, threshsup=1.8): - """ Thresholds the structure matrix in order to compute an adjency matrix. - All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0 - Parameters - ---------- - C : ndarray, shape (n_nodes,n_nodes) - The structure matrix to threshold - threshinf : float - The minimum value of distance from which the new value is set to 1 - threshsup : float - The maximum value of distance from which the new value is set to 1 - Returns - ------- - C : ndarray, shape (n_nodes,n_nodes) - The threshold matrix. Each element is in {0,1} - """ - H = np.zeros_like(C) - np.fill_diagonal(H, np.diagonal(C)) - C = C - H - C = np.minimum(np.maximum(C, threshinf), threshsup) - C[C == threshsup] = 0 - C[C != 0] = 1 - - return C - - - def build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None): - """ Create a noisy circular graph - """ - g = nx.Graph() - g.add_nodes_from(list(range(N))) - for i in range(N): - noise = float(np.random.normal(mu, sigma, 1)) - if with_noise: - g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise) - else: - g.add_node(i, attr_name=math.sin(2 * i * math.pi / N)) - g.add_edge(i, i + 1) - if structure_noise: - randomint = np.random.randint(0, p) - if randomint == 0: - if i <= N - 3: - g.add_edge(i, i + 2) - if i == N - 2: - g.add_edge(i, 0) - if i == N - 1: - g.add_edge(i, 1) - g.add_edge(N, 0) - noise = float(np.random.normal(mu, sigma, 1)) - if with_noise: - g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise) - else: - g.add_node(N, attr_name=math.sin(2 * N * math.pi / N)) - return g - - - def graph_colors(nx_graph, vmin=0, vmax=7): - cnorm = mcol.Normalize(vmin=vmin, vmax=vmax) - cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis') - cpick.set_array([]) - val_map = {} - for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items(): - val_map[k] = cpick.to_rgba(v) - colors = [] - for node in nx_graph.nodes(): - colors.append(val_map[node]) - return colors - - - - - - - - -Generate data -------------- - -We build a dataset of noisy circular graphs. -Noise is added on the structures by random connections and on the features by gaussian noise. - - -.. code-block:: default - - - - np.random.seed(30) - X0 = [] - for k in range(9): - X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3)) - - - - - - - - -Plot data ---------- - - -.. code-block:: default - - - plt.figure(figsize=(8, 10)) - for i in range(len(X0)): - plt.subplot(3, 3, i + 1) - g = X0[i] - pos = nx.kamada_kawai_layout(g) - nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100) - plt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20) - plt.show() - - - - -.. image:: /auto_examples/images/sphx_glr_plot_barycenter_fgw_001.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_barycenter_fgw.py:155: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - plt.show() - - - - -Barycenter computation ----------------------- - -Features distances are the euclidean distances - - -.. code-block:: default - - Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0] - ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0] - Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0] - lambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel() - sizebary = 15 # we choose a barycenter with 15 nodes - - A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True) - - - - - - - - -Plot Barycenter -------------------------- - - -.. code-block:: default - - bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0])) - for i, v in enumerate(A.ravel()): - bary.add_node(i, attr_name=v) - - - - - - - - - -.. code-block:: default - - pos = nx.kamada_kawai_layout(bary) - nx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False) - plt.suptitle('Barycenter', fontsize=20) - plt.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_barycenter_fgw_002.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_barycenter_fgw.py:184: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - plt.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 1.949 seconds) - - -.. _sphx_glr_download_auto_examples_plot_barycenter_fgw.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_barycenter_fgw.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_barycenter_fgw.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.ipynb b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.ipynb deleted file mode 100644 index b976aae..0000000 --- a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.ipynb +++ /dev/null @@ -1,192 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# 1D Wasserstein barycenter comparison between exact LP and entropic regularization\n\n\nThis example illustrates the computation of regularized Wasserstein Barycenter\nas proposed in [3] and exact LP barycenters using standard LP solver.\n\nIt reproduces approximately Figure 3.1 and 3.2 from the following paper:\nCuturi, M., & Peyr\u00e9, G. (2016). A smoothed dual approach for variational\nWasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343.\n\n[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyr\u00e9, G. (2015).\nIterative Bregman projections for regularized transportation problems\nSIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection # noqa\n\n#import ot.lp.cvx as cvx" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Gaussian Data\n-------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "problems = []\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "alpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Stair Data\n----------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "a1 = 1.0 * (x > 10) * (x < 50)\na2 = 1.0 * (x > 60) * (x < 80)\n\na1 /= a1.sum()\na2 /= a2.sum()\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "alpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Dirac Data\n----------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "a1 = np.zeros(n)\na2 = np.zeros(n)\n\na1[10] = .25\na1[20] = .5\na1[30] = .25\na2[80] = 1\n\n\na1 /= a1.sum()\na2 /= a2.sum()\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "alpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Final figure\n------------\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "nbm = len(problems)\nnbm2 = (nbm // 2)\n\n\npl.figure(2, (20, 6))\npl.clf()\n\nfor i in range(nbm):\n\n A = problems[i][0]\n bary_l2 = problems[i][1][0]\n bary_wass = problems[i][1][1]\n bary_wass2 = problems[i][1][2]\n\n pl.subplot(2, nbm, 1 + i)\n for j in range(n_distributions):\n pl.plot(x, A[:, j])\n if i == nbm2:\n pl.title('Distributions')\n pl.xticks(())\n pl.yticks(())\n\n pl.subplot(2, nbm, 1 + i + nbm)\n\n pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)')\n pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n if i == nbm - 1:\n pl.legend()\n if i == nbm2:\n pl.title('Barycenters')\n\n pl.xticks(())\n pl.yticks(())" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.py b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.py deleted file mode 100644 index d7c72d0..0000000 --- a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.py +++ /dev/null @@ -1,286 +0,0 @@ -# -*- coding: utf-8 -*- -""" -================================================================================= -1D Wasserstein barycenter comparison between exact LP and entropic regularization -================================================================================= - -This example illustrates the computation of regularized Wasserstein Barycenter -as proposed in [3] and exact LP barycenters using standard LP solver. - -It reproduces approximately Figure 3.1 and 3.2 from the following paper: -Cuturi, M., & Peyré, G. (2016). A smoothed dual approach for variational -Wasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343. - -[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). -Iterative Bregman projections for regularized transportation problems -SIAM Journal on Scientific Computing, 37(2), A1111-A1138. - -""" - -# Author: Remi Flamary -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot -# necessary for 3d plot even if not used -from mpl_toolkits.mplot3d import Axes3D # noqa -from matplotlib.collections import PolyCollection # noqa - -#import ot.lp.cvx as cvx - -############################################################################## -# Gaussian Data -# ------------- - -#%% parameters - -problems = [] - -n = 100 # nb bins - -# bin positions -x = np.arange(n, dtype=np.float64) - -# Gaussian distributions -# Gaussian distributions -a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std -a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) - -# creating matrix A containing all distributions -A = np.vstack((a1, a2)).T -n_distributions = A.shape[1] - -# loss matrix + normalization -M = ot.utils.dist0(n) -M /= M.max() - - -#%% plot the distributions - -pl.figure(1, figsize=(6.4, 3)) -for i in range(n_distributions): - pl.plot(x, A[:, i]) -pl.title('Distributions') -pl.tight_layout() - -#%% barycenter computation - -alpha = 0.5 # 0<=alpha<=1 -weights = np.array([1 - alpha, alpha]) - -# l2bary -bary_l2 = A.dot(weights) - -# wasserstein -reg = 1e-3 -ot.tic() -bary_wass = ot.bregman.barycenter(A, M, reg, weights) -ot.toc() - - -ot.tic() -bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) -ot.toc() - -pl.figure(2) -pl.clf() -pl.subplot(2, 1, 1) -for i in range(n_distributions): - pl.plot(x, A[:, i]) -pl.title('Distributions') - -pl.subplot(2, 1, 2) -pl.plot(x, bary_l2, 'r', label='l2') -pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') -pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') -pl.legend() -pl.title('Barycenters') -pl.tight_layout() - -problems.append([A, [bary_l2, bary_wass, bary_wass2]]) - -############################################################################## -# Stair Data -# ---------- - -#%% parameters - -a1 = 1.0 * (x > 10) * (x < 50) -a2 = 1.0 * (x > 60) * (x < 80) - -a1 /= a1.sum() -a2 /= a2.sum() - -# creating matrix A containing all distributions -A = np.vstack((a1, a2)).T -n_distributions = A.shape[1] - -# loss matrix + normalization -M = ot.utils.dist0(n) -M /= M.max() - - -#%% plot the distributions - -pl.figure(1, figsize=(6.4, 3)) -for i in range(n_distributions): - pl.plot(x, A[:, i]) -pl.title('Distributions') -pl.tight_layout() - - -#%% barycenter computation - -alpha = 0.5 # 0<=alpha<=1 -weights = np.array([1 - alpha, alpha]) - -# l2bary -bary_l2 = A.dot(weights) - -# wasserstein -reg = 1e-3 -ot.tic() -bary_wass = ot.bregman.barycenter(A, M, reg, weights) -ot.toc() - - -ot.tic() -bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) -ot.toc() - - -problems.append([A, [bary_l2, bary_wass, bary_wass2]]) - -pl.figure(2) -pl.clf() -pl.subplot(2, 1, 1) -for i in range(n_distributions): - pl.plot(x, A[:, i]) -pl.title('Distributions') - -pl.subplot(2, 1, 2) -pl.plot(x, bary_l2, 'r', label='l2') -pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') -pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') -pl.legend() -pl.title('Barycenters') -pl.tight_layout() - - -############################################################################## -# Dirac Data -# ---------- - -#%% parameters - -a1 = np.zeros(n) -a2 = np.zeros(n) - -a1[10] = .25 -a1[20] = .5 -a1[30] = .25 -a2[80] = 1 - - -a1 /= a1.sum() -a2 /= a2.sum() - -# creating matrix A containing all distributions -A = np.vstack((a1, a2)).T -n_distributions = A.shape[1] - -# loss matrix + normalization -M = ot.utils.dist0(n) -M /= M.max() - - -#%% plot the distributions - -pl.figure(1, figsize=(6.4, 3)) -for i in range(n_distributions): - pl.plot(x, A[:, i]) -pl.title('Distributions') -pl.tight_layout() - - -#%% barycenter computation - -alpha = 0.5 # 0<=alpha<=1 -weights = np.array([1 - alpha, alpha]) - -# l2bary -bary_l2 = A.dot(weights) - -# wasserstein -reg = 1e-3 -ot.tic() -bary_wass = ot.bregman.barycenter(A, M, reg, weights) -ot.toc() - - -ot.tic() -bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) -ot.toc() - - -problems.append([A, [bary_l2, bary_wass, bary_wass2]]) - -pl.figure(2) -pl.clf() -pl.subplot(2, 1, 1) -for i in range(n_distributions): - pl.plot(x, A[:, i]) -pl.title('Distributions') - -pl.subplot(2, 1, 2) -pl.plot(x, bary_l2, 'r', label='l2') -pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') -pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') -pl.legend() -pl.title('Barycenters') -pl.tight_layout() - - -############################################################################## -# Final figure -# ------------ -# - -#%% plot - -nbm = len(problems) -nbm2 = (nbm // 2) - - -pl.figure(2, (20, 6)) -pl.clf() - -for i in range(nbm): - - A = problems[i][0] - bary_l2 = problems[i][1][0] - bary_wass = problems[i][1][1] - bary_wass2 = problems[i][1][2] - - pl.subplot(2, nbm, 1 + i) - for j in range(n_distributions): - pl.plot(x, A[:, j]) - if i == nbm2: - pl.title('Distributions') - pl.xticks(()) - pl.yticks(()) - - pl.subplot(2, nbm, 1 + i + nbm) - - pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)') - pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') - pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') - if i == nbm - 1: - pl.legend() - if i == nbm2: - pl.title('Barycenters') - - pl.xticks(()) - pl.yticks(()) diff --git a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.rst b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.rst deleted file mode 100644 index 5e83fbf..0000000 --- a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.rst +++ /dev/null @@ -1,532 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_barycenter_lp_vs_entropic.py: - - -================================================================================= -1D Wasserstein barycenter comparison between exact LP and entropic regularization -================================================================================= - -This example illustrates the computation of regularized Wasserstein Barycenter -as proposed in [3] and exact LP barycenters using standard LP solver. - -It reproduces approximately Figure 3.1 and 3.2 from the following paper: -Cuturi, M., & Peyré, G. (2016). A smoothed dual approach for variational -Wasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343. - -[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). -Iterative Bregman projections for regularized transportation problems -SIAM Journal on Scientific Computing, 37(2), A1111-A1138. - - - -.. code-block:: default - - - # Author: Remi Flamary - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot - # necessary for 3d plot even if not used - from mpl_toolkits.mplot3d import Axes3D # noqa - from matplotlib.collections import PolyCollection # noqa - - #import ot.lp.cvx as cvx - - - - - - - - -Gaussian Data -------------- - - -.. code-block:: default - - - problems = [] - - n = 100 # nb bins - - # bin positions - x = np.arange(n, dtype=np.float64) - - # Gaussian distributions - # Gaussian distributions - a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std - a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) - - # creating matrix A containing all distributions - A = np.vstack((a1, a2)).T - n_distributions = A.shape[1] - - # loss matrix + normalization - M = ot.utils.dist0(n) - M /= M.max() - - - - - - - - - - -.. code-block:: default - - - pl.figure(1, figsize=(6.4, 3)) - for i in range(n_distributions): - pl.plot(x, A[:, i]) - pl.title('Distributions') - pl.tight_layout() - - - - -.. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_001.png - :class: sphx-glr-single-img - - - - - - -.. code-block:: default - - - alpha = 0.5 # 0<=alpha<=1 - weights = np.array([1 - alpha, alpha]) - - # l2bary - bary_l2 = A.dot(weights) - - # wasserstein - reg = 1e-3 - ot.tic() - bary_wass = ot.bregman.barycenter(A, M, reg, weights) - ot.toc() - - - ot.tic() - bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) - ot.toc() - - pl.figure(2) - pl.clf() - pl.subplot(2, 1, 1) - for i in range(n_distributions): - pl.plot(x, A[:, i]) - pl.title('Distributions') - - pl.subplot(2, 1, 2) - pl.plot(x, bary_l2, 'r', label='l2') - pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') - pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') - pl.legend() - pl.title('Barycenters') - pl.tight_layout() - - problems.append([A, [bary_l2, bary_wass, bary_wass2]]) - - - - -.. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_002.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - Elapsed time : 0.0049059391021728516 s - Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective - 1.0 1.0 1.0 - 1.0 1700.336700337 - 0.006776453137632 0.006776453137632 0.006776453137632 0.9932238647293 0.006776453137632 125.6700527543 - 0.004018712867873 0.004018712867873 0.004018712867873 0.4301142633001 0.004018712867873 12.26594150092 - 0.001172775061627 0.001172775061627 0.001172775061627 0.7599932455027 0.001172775061627 0.3378536968898 - 0.0004375137005386 0.0004375137005386 0.0004375137005386 0.6422331807989 0.0004375137005386 0.1468420566359 - 0.0002326690467339 0.0002326690467339 0.0002326690467339 0.5016999460898 0.0002326690467339 0.09381703231428 - 7.430121674299e-05 7.4301216743e-05 7.430121674299e-05 0.7035962305811 7.430121674299e-05 0.05777870257169 - 5.321227838943e-05 5.321227838945e-05 5.321227838944e-05 0.3087841864307 5.321227838944e-05 0.05266249477219 - 1.990900379216e-05 1.99090037922e-05 1.990900379216e-05 0.6520472013271 1.990900379216e-05 0.04526054405523 - 6.305442046834e-06 6.305442046856e-06 6.305442046837e-06 0.7073953304085 6.305442046837e-06 0.04237597591384 - 2.290148391591e-06 2.290148391631e-06 2.290148391602e-06 0.6941812711476 2.29014839161e-06 0.04152284932101 - 1.182864875578e-06 1.182864875548e-06 1.182864875555e-06 0.5084552046229 1.182864875567e-06 0.04129461872829 - 3.626786386894e-07 3.626786386985e-07 3.626786386845e-07 0.7101651569095 3.626786385995e-07 0.0411303244893 - 1.539754244475e-07 1.539754247164e-07 1.539754247197e-07 0.6279322077522 1.539754251915e-07 0.04108867636377 - 5.193221608537e-08 5.19322169648e-08 5.193221696942e-08 0.6843453280956 5.193221892276e-08 0.04106859618454 - 1.888205219929e-08 1.88820500654e-08 1.888205006369e-08 0.6673443828803 1.888205852187e-08 0.04106214175236 - 5.676837529301e-09 5.676842740457e-09 5.676842761502e-09 0.7281712198286 5.676877044229e-09 0.04105958648535 - 3.501170987746e-09 3.501167688027e-09 3.501167721804e-09 0.4140142115019 3.501183058995e-09 0.04105916265728 - 1.110582426269e-09 1.110580273241e-09 1.110580239523e-09 0.6999003212726 1.110624310022e-09 0.04105870073273 - 5.768753963318e-10 5.769422203363e-10 5.769421938248e-10 0.5002521235315 5.767522037401e-10 0.04105859764872 - 1.534102102874e-10 1.535920569433e-10 1.535921107494e-10 0.7516900610544 1.535251083958e-10 0.04105851678411 - 6.717475002202e-11 6.735435784522e-11 6.735430717133e-11 0.5944268235824 6.732253215483e-11 0.04105850033323 - 1.751321118837e-11 1.74043080851e-11 1.740429001123e-11 0.7566075167358 1.736956306927e-11 0.0410584908946 - Optimization terminated successfully. - Current function value: 0.041058 - Iterations: 22 - Elapsed time : 2.149055242538452 s - - - - -Stair Data ----------- - - -.. code-block:: default - - - a1 = 1.0 * (x > 10) * (x < 50) - a2 = 1.0 * (x > 60) * (x < 80) - - a1 /= a1.sum() - a2 /= a2.sum() - - # creating matrix A containing all distributions - A = np.vstack((a1, a2)).T - n_distributions = A.shape[1] - - # loss matrix + normalization - M = ot.utils.dist0(n) - M /= M.max() - - - - - - - - - - -.. code-block:: default - - - pl.figure(1, figsize=(6.4, 3)) - for i in range(n_distributions): - pl.plot(x, A[:, i]) - pl.title('Distributions') - pl.tight_layout() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_003.png - :class: sphx-glr-single-img - - - - - - -.. code-block:: default - - - alpha = 0.5 # 0<=alpha<=1 - weights = np.array([1 - alpha, alpha]) - - # l2bary - bary_l2 = A.dot(weights) - - # wasserstein - reg = 1e-3 - ot.tic() - bary_wass = ot.bregman.barycenter(A, M, reg, weights) - ot.toc() - - - ot.tic() - bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) - ot.toc() - - - problems.append([A, [bary_l2, bary_wass, bary_wass2]]) - - pl.figure(2) - pl.clf() - pl.subplot(2, 1, 1) - for i in range(n_distributions): - pl.plot(x, A[:, i]) - pl.title('Distributions') - - pl.subplot(2, 1, 2) - pl.plot(x, bary_l2, 'r', label='l2') - pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') - pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') - pl.legend() - pl.title('Barycenters') - pl.tight_layout() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_004.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - Elapsed time : 0.008316993713378906 s - Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective - 1.0 1.0 1.0 - 1.0 1700.336700337 - 0.006776466288938 0.006776466288938 0.006776466288938 0.9932238515788 0.006776466288938 125.66492558 - 0.004036918865472 0.004036918865472 0.004036918865472 0.4272973099325 0.004036918865472 12.347161701 - 0.001219232687076 0.001219232687076 0.001219232687076 0.7496986855957 0.001219232687076 0.3243835647418 - 0.0003837422984467 0.0003837422984467 0.0003837422984467 0.6926882608271 0.0003837422984467 0.1361719397498 - 0.0001070128410194 0.0001070128410194 0.0001070128410194 0.7643889137854 0.0001070128410194 0.07581952832542 - 0.0001001275033713 0.0001001275033714 0.0001001275033713 0.07058704838615 0.0001001275033713 0.07347394936346 - 4.550897507807e-05 4.550897507807e-05 4.550897507807e-05 0.576117248486 4.550897507807e-05 0.05555077655034 - 8.557124125834e-06 8.557124125853e-06 8.557124125835e-06 0.853592544106 8.557124125835e-06 0.0443981466023 - 3.611995628666e-06 3.611995628643e-06 3.611995628672e-06 0.6002277331398 3.611995628673e-06 0.0428300776216 - 7.590393750111e-07 7.590393750273e-07 7.590393750129e-07 0.8221486533655 7.590393750133e-07 0.04192322976247 - 8.299929287077e-08 8.299929283415e-08 8.299929287126e-08 0.901746793884 8.299929287181e-08 0.04170825633295 - 3.117560207452e-10 3.117560192413e-10 3.117560199213e-10 0.9970399692253 3.117560200234e-10 0.04168179329766 - 1.559774508975e-14 1.559825507727e-14 1.559755309294e-14 0.9999499686993 1.559748033629e-14 0.04168169240444 - Optimization terminated successfully. - Current function value: 0.041682 - Iterations: 13 - Elapsed time : 2.0333712100982666 s - - - - -Dirac Data ----------- - - -.. code-block:: default - - - a1 = np.zeros(n) - a2 = np.zeros(n) - - a1[10] = .25 - a1[20] = .5 - a1[30] = .25 - a2[80] = 1 - - - a1 /= a1.sum() - a2 /= a2.sum() - - # creating matrix A containing all distributions - A = np.vstack((a1, a2)).T - n_distributions = A.shape[1] - - # loss matrix + normalization - M = ot.utils.dist0(n) - M /= M.max() - - - - - - - - - - -.. code-block:: default - - - pl.figure(1, figsize=(6.4, 3)) - for i in range(n_distributions): - pl.plot(x, A[:, i]) - pl.title('Distributions') - pl.tight_layout() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_005.png - :class: sphx-glr-single-img - - - - - - -.. code-block:: default - - - alpha = 0.5 # 0<=alpha<=1 - weights = np.array([1 - alpha, alpha]) - - # l2bary - bary_l2 = A.dot(weights) - - # wasserstein - reg = 1e-3 - ot.tic() - bary_wass = ot.bregman.barycenter(A, M, reg, weights) - ot.toc() - - - ot.tic() - bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) - ot.toc() - - - problems.append([A, [bary_l2, bary_wass, bary_wass2]]) - - pl.figure(2) - pl.clf() - pl.subplot(2, 1, 1) - for i in range(n_distributions): - pl.plot(x, A[:, i]) - pl.title('Distributions') - - pl.subplot(2, 1, 2) - pl.plot(x, bary_l2, 'r', label='l2') - pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') - pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') - pl.legend() - pl.title('Barycenters') - pl.tight_layout() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_006.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - Elapsed time : 0.001787424087524414 s - Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective - 1.0 1.0 1.0 - 1.0 1700.336700337 - 0.00677467552072 0.006774675520719 0.006774675520719 0.9932256422636 0.006774675520719 125.6956034741 - 0.002048208707556 0.002048208707555 0.002048208707555 0.734309536815 0.002048208707555 5.213991622102 - 0.0002697365474791 0.0002697365474791 0.0002697365474791 0.8839403501183 0.0002697365474791 0.5059383903908 - 6.832109993919e-05 6.832109993918e-05 6.832109993918e-05 0.7601171075982 6.832109993918e-05 0.2339657807271 - 2.437682932221e-05 2.437682932221e-05 2.437682932221e-05 0.6663448297463 2.437682932221e-05 0.1471256246325 - 1.134983216308e-05 1.134983216308e-05 1.134983216308e-05 0.5553643816417 1.134983216308e-05 0.1181584941171 - 3.342312725863e-06 3.34231272585e-06 3.342312725863e-06 0.7238133571629 3.342312725863e-06 0.1006387519746 - 7.078561231536e-07 7.078561231537e-07 7.078561231535e-07 0.803314255252 7.078561231535e-07 0.09474734646268 - 1.966870949422e-07 1.966870952674e-07 1.966870952717e-07 0.7525479180433 1.966870953014e-07 0.09354342735758 - 4.199895266495e-10 4.199895367352e-10 4.19989526535e-10 0.9984019849265 4.199895265747e-10 0.09310367785861 - 2.101053559204e-14 2.100331212975e-14 2.101054034304e-14 0.9999499736903 2.101053604307e-14 0.09310274466458 - Optimization terminated successfully. - Current function value: 0.093103 - Iterations: 11 - Elapsed time : 2.1853578090667725 s - - - - -Final figure ------------- - - - -.. code-block:: default - - - nbm = len(problems) - nbm2 = (nbm // 2) - - - pl.figure(2, (20, 6)) - pl.clf() - - for i in range(nbm): - - A = problems[i][0] - bary_l2 = problems[i][1][0] - bary_wass = problems[i][1][1] - bary_wass2 = problems[i][1][2] - - pl.subplot(2, nbm, 1 + i) - for j in range(n_distributions): - pl.plot(x, A[:, j]) - if i == nbm2: - pl.title('Distributions') - pl.xticks(()) - pl.yticks(()) - - pl.subplot(2, nbm, 1 + i + nbm) - - pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)') - pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') - pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') - if i == nbm - 1: - pl.legend() - if i == nbm2: - pl.title('Barycenters') - - pl.xticks(()) - pl.yticks(()) - - - -.. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_007.png - :class: sphx-glr-single-img - - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 7.697 seconds) - - -.. _sphx_glr_download_auto_examples_plot_barycenter_lp_vs_entropic.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_barycenter_lp_vs_entropic.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_barycenter_lp_vs_entropic.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_compute_emd.ipynb b/docs/source/auto_examples/plot_compute_emd.ipynb deleted file mode 100644 index 24a2fff..0000000 --- a/docs/source/auto_examples/plot_compute_emd.ipynb +++ /dev/null @@ -1,126 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Plot multiple EMD\n\n\nShows how to compute multiple EMD and Sinkhorn with two differnt\nground metrics and plot their values for diffeent distributions.\n\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import make_1D_gauss as gauss" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n-------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 100 # nb bins\nn_target = 50 # nb target distributions\n\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\nlst_m = np.linspace(20, 90, n_target)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\n\nB = np.zeros((n, n_target))\n\nfor i, m in enumerate(lst_m):\n B[:, i] = gauss(n, m=m, s=5)\n\n# loss matrix and normalization\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean')\nM /= M.max()\nM2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean')\nM2 /= M2.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n---------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(1)\npl.subplot(2, 1, 1)\npl.plot(x, a, 'b', label='Source distribution')\npl.title('Source distribution')\npl.subplot(2, 1, 2)\npl.plot(x, B, label='Target distributions')\npl.title('Target distributions')\npl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute EMD for the different losses\n------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "d_emd = ot.emd2(a, B, M) # direct computation of EMD\nd_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2\n\n\npl.figure(2)\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.title('EMD distances')\npl.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute Sinkhorn for the different losses\n-----------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "reg = 1e-2\nd_sinkhorn = ot.sinkhorn2(a, B, M, reg)\nd_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg)\n\npl.figure(2)\npl.clf()\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn')\npl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn')\npl.title('EMD distances')\npl.legend()\n\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_compute_emd.py b/docs/source/auto_examples/plot_compute_emd.py deleted file mode 100644 index 7ed2b01..0000000 --- a/docs/source/auto_examples/plot_compute_emd.py +++ /dev/null @@ -1,102 +0,0 @@ -# -*- coding: utf-8 -*- -""" -================= -Plot multiple EMD -================= - -Shows how to compute multiple EMD and Sinkhorn with two differnt -ground metrics and plot their values for diffeent distributions. - - -""" - -# Author: Remi Flamary -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot -from ot.datasets import make_1D_gauss as gauss - - -############################################################################## -# Generate data -# ------------- - -#%% parameters - -n = 100 # nb bins -n_target = 50 # nb target distributions - - -# bin positions -x = np.arange(n, dtype=np.float64) - -lst_m = np.linspace(20, 90, n_target) - -# Gaussian distributions -a = gauss(n, m=20, s=5) # m= mean, s= std - -B = np.zeros((n, n_target)) - -for i, m in enumerate(lst_m): - B[:, i] = gauss(n, m=m, s=5) - -# loss matrix and normalization -M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean') -M /= M.max() -M2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean') -M2 /= M2.max() - -############################################################################## -# Plot data -# --------- - -#%% plot the distributions - -pl.figure(1) -pl.subplot(2, 1, 1) -pl.plot(x, a, 'b', label='Source distribution') -pl.title('Source distribution') -pl.subplot(2, 1, 2) -pl.plot(x, B, label='Target distributions') -pl.title('Target distributions') -pl.tight_layout() - - -############################################################################## -# Compute EMD for the different losses -# ------------------------------------ - -#%% Compute and plot distributions and loss matrix - -d_emd = ot.emd2(a, B, M) # direct computation of EMD -d_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2 - - -pl.figure(2) -pl.plot(d_emd, label='Euclidean EMD') -pl.plot(d_emd2, label='Squared Euclidean EMD') -pl.title('EMD distances') -pl.legend() - -############################################################################## -# Compute Sinkhorn for the different losses -# ----------------------------------------- - -#%% -reg = 1e-2 -d_sinkhorn = ot.sinkhorn2(a, B, M, reg) -d_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg) - -pl.figure(2) -pl.clf() -pl.plot(d_emd, label='Euclidean EMD') -pl.plot(d_emd2, label='Squared Euclidean EMD') -pl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn') -pl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn') -pl.title('EMD distances') -pl.legend() - -pl.show() diff --git a/docs/source/auto_examples/plot_compute_emd.rst b/docs/source/auto_examples/plot_compute_emd.rst deleted file mode 100644 index e4cc143..0000000 --- a/docs/source/auto_examples/plot_compute_emd.rst +++ /dev/null @@ -1,211 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_compute_emd.py: - - -================= -Plot multiple EMD -================= - -Shows how to compute multiple EMD and Sinkhorn with two differnt -ground metrics and plot their values for diffeent distributions. - - - - -.. code-block:: default - - - # Author: Remi Flamary - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot - from ot.datasets import make_1D_gauss as gauss - - - - - - - - - -Generate data -------------- - - -.. code-block:: default - - - n = 100 # nb bins - n_target = 50 # nb target distributions - - - # bin positions - x = np.arange(n, dtype=np.float64) - - lst_m = np.linspace(20, 90, n_target) - - # Gaussian distributions - a = gauss(n, m=20, s=5) # m= mean, s= std - - B = np.zeros((n, n_target)) - - for i, m in enumerate(lst_m): - B[:, i] = gauss(n, m=m, s=5) - - # loss matrix and normalization - M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean') - M /= M.max() - M2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean') - M2 /= M2.max() - - - - - - - - -Plot data ---------- - - -.. code-block:: default - - - pl.figure(1) - pl.subplot(2, 1, 1) - pl.plot(x, a, 'b', label='Source distribution') - pl.title('Source distribution') - pl.subplot(2, 1, 2) - pl.plot(x, B, label='Target distributions') - pl.title('Target distributions') - pl.tight_layout() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_compute_emd_001.png - :class: sphx-glr-single-img - - - - - -Compute EMD for the different losses ------------------------------------- - - -.. code-block:: default - - - d_emd = ot.emd2(a, B, M) # direct computation of EMD - d_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2 - - - pl.figure(2) - pl.plot(d_emd, label='Euclidean EMD') - pl.plot(d_emd2, label='Squared Euclidean EMD') - pl.title('EMD distances') - pl.legend() - - - - -.. image:: /auto_examples/images/sphx_glr_plot_compute_emd_002.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - - - - - -Compute Sinkhorn for the different losses ------------------------------------------ - - -.. code-block:: default - - reg = 1e-2 - d_sinkhorn = ot.sinkhorn2(a, B, M, reg) - d_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg) - - pl.figure(2) - pl.clf() - pl.plot(d_emd, label='Euclidean EMD') - pl.plot(d_emd2, label='Squared Euclidean EMD') - pl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn') - pl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn') - pl.title('EMD distances') - pl.legend() - - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_compute_emd_003.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_compute_emd.py:102: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 0.436 seconds) - - -.. _sphx_glr_download_auto_examples_plot_compute_emd.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_compute_emd.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_compute_emd.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_convolutional_barycenter.ipynb b/docs/source/auto_examples/plot_convolutional_barycenter.ipynb deleted file mode 100644 index f94a32e..0000000 --- a/docs/source/auto_examples/plot_convolutional_barycenter.ipynb +++ /dev/null @@ -1,90 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Convolutional Wasserstein Barycenter example\n\n\nThis example is designed to illustrate how the Convolutional Wasserstein Barycenter\nfunction of POT works.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Nicolas Courty \n#\n# License: MIT License\n\n\nimport numpy as np\nimport pylab as pl\nimport ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Data preparation\n----------------\n\nThe four distributions are constructed from 4 simple images\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2]\nf2 = 1 - pl.imread('../data/duck.png')[:, :, 2]\nf3 = 1 - pl.imread('../data/heart.png')[:, :, 2]\nf4 = 1 - pl.imread('../data/tooth.png')[:, :, 2]\n\nA = []\nf1 = f1 / np.sum(f1)\nf2 = f2 / np.sum(f2)\nf3 = f3 / np.sum(f3)\nf4 = f4 / np.sum(f4)\nA.append(f1)\nA.append(f2)\nA.append(f3)\nA.append(f4)\nA = np.array(A)\n\nnb_images = 5\n\n# those are the four corners coordinates that will be interpolated by bilinear\n# interpolation\nv1 = np.array((1, 0, 0, 0))\nv2 = np.array((0, 1, 0, 0))\nv3 = np.array((0, 0, 1, 0))\nv4 = np.array((0, 0, 0, 1))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycenter computation and visualization\n----------------------------------------\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(figsize=(10, 10))\npl.title('Convolutional Wasserstein Barycenters in POT')\ncm = 'Blues'\n# regularization parameter\nreg = 0.004\nfor i in range(nb_images):\n for j in range(nb_images):\n pl.subplot(nb_images, nb_images, i * nb_images + j + 1)\n tx = float(i) / (nb_images - 1)\n ty = float(j) / (nb_images - 1)\n\n # weights are constructed by bilinear interpolation\n tmp1 = (1 - tx) * v1 + tx * v2\n tmp2 = (1 - tx) * v3 + tx * v4\n weights = (1 - ty) * tmp1 + ty * tmp2\n\n if i == 0 and j == 0:\n pl.imshow(f1, cmap=cm)\n pl.axis('off')\n elif i == 0 and j == (nb_images - 1):\n pl.imshow(f3, cmap=cm)\n pl.axis('off')\n elif i == (nb_images - 1) and j == 0:\n pl.imshow(f2, cmap=cm)\n pl.axis('off')\n elif i == (nb_images - 1) and j == (nb_images - 1):\n pl.imshow(f4, cmap=cm)\n pl.axis('off')\n else:\n # call to barycenter computation\n pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm)\n pl.axis('off')\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_convolutional_barycenter.py b/docs/source/auto_examples/plot_convolutional_barycenter.py deleted file mode 100644 index e74db04..0000000 --- a/docs/source/auto_examples/plot_convolutional_barycenter.py +++ /dev/null @@ -1,92 +0,0 @@ - -#%% -# -*- coding: utf-8 -*- -""" -============================================ -Convolutional Wasserstein Barycenter example -============================================ - -This example is designed to illustrate how the Convolutional Wasserstein Barycenter -function of POT works. -""" - -# Author: Nicolas Courty -# -# License: MIT License - - -import numpy as np -import pylab as pl -import ot - -############################################################################## -# Data preparation -# ---------------- -# -# The four distributions are constructed from 4 simple images - - -f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2] -f2 = 1 - pl.imread('../data/duck.png')[:, :, 2] -f3 = 1 - pl.imread('../data/heart.png')[:, :, 2] -f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2] - -A = [] -f1 = f1 / np.sum(f1) -f2 = f2 / np.sum(f2) -f3 = f3 / np.sum(f3) -f4 = f4 / np.sum(f4) -A.append(f1) -A.append(f2) -A.append(f3) -A.append(f4) -A = np.array(A) - -nb_images = 5 - -# those are the four corners coordinates that will be interpolated by bilinear -# interpolation -v1 = np.array((1, 0, 0, 0)) -v2 = np.array((0, 1, 0, 0)) -v3 = np.array((0, 0, 1, 0)) -v4 = np.array((0, 0, 0, 1)) - - -############################################################################## -# Barycenter computation and visualization -# ---------------------------------------- -# - -pl.figure(figsize=(10, 10)) -pl.title('Convolutional Wasserstein Barycenters in POT') -cm = 'Blues' -# regularization parameter -reg = 0.004 -for i in range(nb_images): - for j in range(nb_images): - pl.subplot(nb_images, nb_images, i * nb_images + j + 1) - tx = float(i) / (nb_images - 1) - ty = float(j) / (nb_images - 1) - - # weights are constructed by bilinear interpolation - tmp1 = (1 - tx) * v1 + tx * v2 - tmp2 = (1 - tx) * v3 + tx * v4 - weights = (1 - ty) * tmp1 + ty * tmp2 - - if i == 0 and j == 0: - pl.imshow(f1, cmap=cm) - pl.axis('off') - elif i == 0 and j == (nb_images - 1): - pl.imshow(f3, cmap=cm) - pl.axis('off') - elif i == (nb_images - 1) and j == 0: - pl.imshow(f2, cmap=cm) - pl.axis('off') - elif i == (nb_images - 1) and j == (nb_images - 1): - pl.imshow(f4, cmap=cm) - pl.axis('off') - else: - # call to barycenter computation - pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm) - pl.axis('off') -pl.show() diff --git a/docs/source/auto_examples/plot_convolutional_barycenter.rst b/docs/source/auto_examples/plot_convolutional_barycenter.rst deleted file mode 100644 index 9c9a596..0000000 --- a/docs/source/auto_examples/plot_convolutional_barycenter.rst +++ /dev/null @@ -1,173 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_convolutional_barycenter.py: - - -============================================ -Convolutional Wasserstein Barycenter example -============================================ - -This example is designed to illustrate how the Convolutional Wasserstein Barycenter -function of POT works. - - -.. code-block:: default - - - # Author: Nicolas Courty - # - # License: MIT License - - - import numpy as np - import pylab as pl - import ot - - - - - - - - -Data preparation ----------------- - -The four distributions are constructed from 4 simple images - - -.. code-block:: default - - - - f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2] - f2 = 1 - pl.imread('../data/duck.png')[:, :, 2] - f3 = 1 - pl.imread('../data/heart.png')[:, :, 2] - f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2] - - A = [] - f1 = f1 / np.sum(f1) - f2 = f2 / np.sum(f2) - f3 = f3 / np.sum(f3) - f4 = f4 / np.sum(f4) - A.append(f1) - A.append(f2) - A.append(f3) - A.append(f4) - A = np.array(A) - - nb_images = 5 - - # those are the four corners coordinates that will be interpolated by bilinear - # interpolation - v1 = np.array((1, 0, 0, 0)) - v2 = np.array((0, 1, 0, 0)) - v3 = np.array((0, 0, 1, 0)) - v4 = np.array((0, 0, 0, 1)) - - - - - - - - - -Barycenter computation and visualization ----------------------------------------- - - - -.. code-block:: default - - - pl.figure(figsize=(10, 10)) - pl.title('Convolutional Wasserstein Barycenters in POT') - cm = 'Blues' - # regularization parameter - reg = 0.004 - for i in range(nb_images): - for j in range(nb_images): - pl.subplot(nb_images, nb_images, i * nb_images + j + 1) - tx = float(i) / (nb_images - 1) - ty = float(j) / (nb_images - 1) - - # weights are constructed by bilinear interpolation - tmp1 = (1 - tx) * v1 + tx * v2 - tmp2 = (1 - tx) * v3 + tx * v4 - weights = (1 - ty) * tmp1 + ty * tmp2 - - if i == 0 and j == 0: - pl.imshow(f1, cmap=cm) - pl.axis('off') - elif i == 0 and j == (nb_images - 1): - pl.imshow(f3, cmap=cm) - pl.axis('off') - elif i == (nb_images - 1) and j == 0: - pl.imshow(f2, cmap=cm) - pl.axis('off') - elif i == (nb_images - 1) and j == (nb_images - 1): - pl.imshow(f4, cmap=cm) - pl.axis('off') - else: - # call to barycenter computation - pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm) - pl.axis('off') - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_convolutional_barycenter_001.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_convolutional_barycenter.py:92: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 34.615 seconds) - - -.. _sphx_glr_download_auto_examples_plot_convolutional_barycenter.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_convolutional_barycenter.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_convolutional_barycenter.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_fgw.ipynb b/docs/source/auto_examples/plot_fgw.ipynb deleted file mode 100644 index 20c0a3f..0000000 --- a/docs/source/auto_examples/plot_fgw.ipynb +++ /dev/null @@ -1,169 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Plot Fused-gromov-Wasserstein\n\n\nThis example illustrates the computation of FGW for 1D measures[18].\n\n.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain\n and Courty Nicolas\n \"Optimal Transport for structured data with application on graphs\"\n International Conference on Machine Learning (ICML). 2019.\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Titouan Vayer \n#\n# License: MIT License\n\nimport matplotlib.pyplot as pl\nimport numpy as np\nimport ot\nfrom ot.gromov import gromov_wasserstein, fused_gromov_wasserstein" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n---------\n\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We create two 1D random measures\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 20 # number of points in the first distribution\nn2 = 30 # number of points in the second distribution\nsig = 1 # std of first distribution\nsig2 = 0.1 # std of second distribution\n\nnp.random.seed(0)\n\nphi = np.arange(n)[:, None]\nxs = phi + sig * np.random.randn(n, 1)\nys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1)\n\nphi2 = np.arange(n2)[:, None]\nxt = phi2 + sig * np.random.randn(n2, 1)\nyt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1)\nyt = yt[::-1, :]\n\np = ot.unif(n)\nq = ot.unif(n2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n---------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.close(10)\npl.figure(10, (7, 7))\n\npl.subplot(2, 1, 1)\n\npl.scatter(ys, xs, c=phi, s=70)\npl.ylabel('Feature value a', fontsize=20)\npl.title('$\\mu=\\sum_i \\delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1)\npl.xticks(())\npl.yticks(())\npl.subplot(2, 1, 2)\npl.scatter(yt, xt, c=phi2, s=70)\npl.xlabel('coordinates x/y', fontsize=25)\npl.ylabel('Feature value b', fontsize=20)\npl.title('$\\\\nu=\\sum_j \\delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1)\npl.yticks(())\npl.tight_layout()\npl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create structure matrices and across-feature distance matrix\n---------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "C1 = ot.dist(xs)\nC2 = ot.dist(xt)\nM = ot.dist(ys, yt)\nw1 = ot.unif(C1.shape[0])\nw2 = ot.unif(C2.shape[0])\nGot = ot.emd([], [], M)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot matrices\n---------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "cmap = 'Reds'\npl.close(10)\npl.figure(10, (5, 5))\nfs = 15\nl_x = [0, 5, 10, 15]\nl_y = [0, 5, 10, 15, 20, 25]\ngs = pl.GridSpec(5, 5)\n\nax1 = pl.subplot(gs[3:, :2])\n\npl.imshow(C1, cmap=cmap, interpolation='nearest')\npl.title(\"$C_1$\", fontsize=fs)\npl.xlabel(\"$k$\", fontsize=fs)\npl.ylabel(\"$i$\", fontsize=fs)\npl.xticks(l_x)\npl.yticks(l_x)\n\nax2 = pl.subplot(gs[:3, 2:])\n\npl.imshow(C2, cmap=cmap, interpolation='nearest')\npl.title(\"$C_2$\", fontsize=fs)\npl.ylabel(\"$l$\", fontsize=fs)\n#pl.ylabel(\"$l$\",fontsize=fs)\npl.xticks(())\npl.yticks(l_y)\nax2.set_aspect('auto')\n\nax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1)\npl.imshow(M, cmap=cmap, interpolation='nearest')\npl.yticks(l_x)\npl.xticks(l_y)\npl.ylabel(\"$i$\", fontsize=fs)\npl.title(\"$M_{AB}$\", fontsize=fs)\npl.xlabel(\"$j$\", fontsize=fs)\npl.tight_layout()\nax3.set_aspect('auto')\npl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute FGW/GW\n---------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "alpha = 1e-3\n\not.tic()\nGwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True)\not.toc()\n\n#%reload_ext WGW\nGg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Visualize transport matrices\n---------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "cmap = 'Blues'\nfs = 15\npl.figure(2, (13, 5))\npl.clf()\npl.subplot(1, 3, 1)\npl.imshow(Got, cmap=cmap, interpolation='nearest')\n#pl.xlabel(\"$y$\",fontsize=fs)\npl.ylabel(\"$i$\", fontsize=fs)\npl.xticks(())\n\npl.title('Wasserstein ($M$ only)')\n\npl.subplot(1, 3, 2)\npl.imshow(Gg, cmap=cmap, interpolation='nearest')\npl.title('Gromov ($C_1,C_2$ only)')\npl.xticks(())\npl.subplot(1, 3, 3)\npl.imshow(Gwg, cmap=cmap, interpolation='nearest')\npl.title('FGW ($M+C_1,C_2$)')\n\npl.xlabel(\"$j$\", fontsize=fs)\npl.ylabel(\"$i$\", fontsize=fs)\n\npl.tight_layout()\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_fgw.py b/docs/source/auto_examples/plot_fgw.py deleted file mode 100644 index 43efc94..0000000 --- a/docs/source/auto_examples/plot_fgw.py +++ /dev/null @@ -1,173 +0,0 @@ -# -*- coding: utf-8 -*- -""" -============================== -Plot Fused-gromov-Wasserstein -============================== - -This example illustrates the computation of FGW for 1D measures[18]. - -.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain - and Courty Nicolas - "Optimal Transport for structured data with application on graphs" - International Conference on Machine Learning (ICML). 2019. - -""" - -# Author: Titouan Vayer -# -# License: MIT License - -import matplotlib.pyplot as pl -import numpy as np -import ot -from ot.gromov import gromov_wasserstein, fused_gromov_wasserstein - -############################################################################## -# Generate data -# --------- - -#%% parameters -# We create two 1D random measures -n = 20 # number of points in the first distribution -n2 = 30 # number of points in the second distribution -sig = 1 # std of first distribution -sig2 = 0.1 # std of second distribution - -np.random.seed(0) - -phi = np.arange(n)[:, None] -xs = phi + sig * np.random.randn(n, 1) -ys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1) - -phi2 = np.arange(n2)[:, None] -xt = phi2 + sig * np.random.randn(n2, 1) -yt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1) -yt = yt[::-1, :] - -p = ot.unif(n) -q = ot.unif(n2) - -############################################################################## -# Plot data -# --------- - -#%% plot the distributions - -pl.close(10) -pl.figure(10, (7, 7)) - -pl.subplot(2, 1, 1) - -pl.scatter(ys, xs, c=phi, s=70) -pl.ylabel('Feature value a', fontsize=20) -pl.title('$\mu=\sum_i \delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1) -pl.xticks(()) -pl.yticks(()) -pl.subplot(2, 1, 2) -pl.scatter(yt, xt, c=phi2, s=70) -pl.xlabel('coordinates x/y', fontsize=25) -pl.ylabel('Feature value b', fontsize=20) -pl.title('$\\nu=\sum_j \delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1) -pl.yticks(()) -pl.tight_layout() -pl.show() - -############################################################################## -# Create structure matrices and across-feature distance matrix -# --------- - -#%% Structure matrices and across-features distance matrix -C1 = ot.dist(xs) -C2 = ot.dist(xt) -M = ot.dist(ys, yt) -w1 = ot.unif(C1.shape[0]) -w2 = ot.unif(C2.shape[0]) -Got = ot.emd([], [], M) - -############################################################################## -# Plot matrices -# --------- - -#%% -cmap = 'Reds' -pl.close(10) -pl.figure(10, (5, 5)) -fs = 15 -l_x = [0, 5, 10, 15] -l_y = [0, 5, 10, 15, 20, 25] -gs = pl.GridSpec(5, 5) - -ax1 = pl.subplot(gs[3:, :2]) - -pl.imshow(C1, cmap=cmap, interpolation='nearest') -pl.title("$C_1$", fontsize=fs) -pl.xlabel("$k$", fontsize=fs) -pl.ylabel("$i$", fontsize=fs) -pl.xticks(l_x) -pl.yticks(l_x) - -ax2 = pl.subplot(gs[:3, 2:]) - -pl.imshow(C2, cmap=cmap, interpolation='nearest') -pl.title("$C_2$", fontsize=fs) -pl.ylabel("$l$", fontsize=fs) -#pl.ylabel("$l$",fontsize=fs) -pl.xticks(()) -pl.yticks(l_y) -ax2.set_aspect('auto') - -ax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1) -pl.imshow(M, cmap=cmap, interpolation='nearest') -pl.yticks(l_x) -pl.xticks(l_y) -pl.ylabel("$i$", fontsize=fs) -pl.title("$M_{AB}$", fontsize=fs) -pl.xlabel("$j$", fontsize=fs) -pl.tight_layout() -ax3.set_aspect('auto') -pl.show() - -############################################################################## -# Compute FGW/GW -# --------- - -#%% Computing FGW and GW -alpha = 1e-3 - -ot.tic() -Gwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True) -ot.toc() - -#%reload_ext WGW -Gg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True) - -############################################################################## -# Visualize transport matrices -# --------- - -#%% visu OT matrix -cmap = 'Blues' -fs = 15 -pl.figure(2, (13, 5)) -pl.clf() -pl.subplot(1, 3, 1) -pl.imshow(Got, cmap=cmap, interpolation='nearest') -#pl.xlabel("$y$",fontsize=fs) -pl.ylabel("$i$", fontsize=fs) -pl.xticks(()) - -pl.title('Wasserstein ($M$ only)') - -pl.subplot(1, 3, 2) -pl.imshow(Gg, cmap=cmap, interpolation='nearest') -pl.title('Gromov ($C_1,C_2$ only)') -pl.xticks(()) -pl.subplot(1, 3, 3) -pl.imshow(Gwg, cmap=cmap, interpolation='nearest') -pl.title('FGW ($M+C_1,C_2$)') - -pl.xlabel("$j$", fontsize=fs) -pl.ylabel("$i$", fontsize=fs) - -pl.tight_layout() -pl.show() diff --git a/docs/source/auto_examples/plot_fgw.rst b/docs/source/auto_examples/plot_fgw.rst deleted file mode 100644 index 1c81d10..0000000 --- a/docs/source/auto_examples/plot_fgw.rst +++ /dev/null @@ -1,329 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_fgw.py: - - -============================== -Plot Fused-gromov-Wasserstein -============================== - -This example illustrates the computation of FGW for 1D measures[18]. - -.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain - and Courty Nicolas - "Optimal Transport for structured data with application on graphs" - International Conference on Machine Learning (ICML). 2019. - - - -.. code-block:: default - - - # Author: Titouan Vayer - # - # License: MIT License - - import matplotlib.pyplot as pl - import numpy as np - import ot - from ot.gromov import gromov_wasserstein, fused_gromov_wasserstein - - - - - - - - -Generate data ---------- - -We create two 1D random measures - - -.. code-block:: default - - n = 20 # number of points in the first distribution - n2 = 30 # number of points in the second distribution - sig = 1 # std of first distribution - sig2 = 0.1 # std of second distribution - - np.random.seed(0) - - phi = np.arange(n)[:, None] - xs = phi + sig * np.random.randn(n, 1) - ys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1) - - phi2 = np.arange(n2)[:, None] - xt = phi2 + sig * np.random.randn(n2, 1) - yt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1) - yt = yt[::-1, :] - - p = ot.unif(n) - q = ot.unif(n2) - - - - - - - - -Plot data ---------- - - -.. code-block:: default - - - pl.close(10) - pl.figure(10, (7, 7)) - - pl.subplot(2, 1, 1) - - pl.scatter(ys, xs, c=phi, s=70) - pl.ylabel('Feature value a', fontsize=20) - pl.title('$\mu=\sum_i \delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1) - pl.xticks(()) - pl.yticks(()) - pl.subplot(2, 1, 2) - pl.scatter(yt, xt, c=phi2, s=70) - pl.xlabel('coordinates x/y', fontsize=25) - pl.ylabel('Feature value b', fontsize=20) - pl.title('$\\nu=\sum_j \delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1) - pl.yticks(()) - pl.tight_layout() - pl.show() - - - - -.. image:: /auto_examples/images/sphx_glr_plot_fgw_001.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_fgw.py:73: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -Create structure matrices and across-feature distance matrix ---------- - - -.. code-block:: default - - C1 = ot.dist(xs) - C2 = ot.dist(xt) - M = ot.dist(ys, yt) - w1 = ot.unif(C1.shape[0]) - w2 = ot.unif(C2.shape[0]) - Got = ot.emd([], [], M) - - - - - - - - -Plot matrices ---------- - - -.. code-block:: default - - cmap = 'Reds' - pl.close(10) - pl.figure(10, (5, 5)) - fs = 15 - l_x = [0, 5, 10, 15] - l_y = [0, 5, 10, 15, 20, 25] - gs = pl.GridSpec(5, 5) - - ax1 = pl.subplot(gs[3:, :2]) - - pl.imshow(C1, cmap=cmap, interpolation='nearest') - pl.title("$C_1$", fontsize=fs) - pl.xlabel("$k$", fontsize=fs) - pl.ylabel("$i$", fontsize=fs) - pl.xticks(l_x) - pl.yticks(l_x) - - ax2 = pl.subplot(gs[:3, 2:]) - - pl.imshow(C2, cmap=cmap, interpolation='nearest') - pl.title("$C_2$", fontsize=fs) - pl.ylabel("$l$", fontsize=fs) - #pl.ylabel("$l$",fontsize=fs) - pl.xticks(()) - pl.yticks(l_y) - ax2.set_aspect('auto') - - ax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1) - pl.imshow(M, cmap=cmap, interpolation='nearest') - pl.yticks(l_x) - pl.xticks(l_y) - pl.ylabel("$i$", fontsize=fs) - pl.title("$M_{AB}$", fontsize=fs) - pl.xlabel("$j$", fontsize=fs) - pl.tight_layout() - ax3.set_aspect('auto') - pl.show() - - - - -.. image:: /auto_examples/images/sphx_glr_plot_fgw_002.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_fgw.py:128: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -Compute FGW/GW ---------- - - -.. code-block:: default - - alpha = 1e-3 - - ot.tic() - Gwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True) - ot.toc() - - #%reload_ext WGW - Gg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True) - - - - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 0|4.734412e+01|0.000000e+00|0.000000e+00 - 1|2.508254e+01|8.875326e-01|2.226158e+01 - 2|2.189327e+01|1.456740e-01|3.189279e+00 - 3|2.189327e+01|0.000000e+00|0.000000e+00 - Elapsed time : 0.0023026466369628906 s - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 0|4.683978e+04|0.000000e+00|0.000000e+00 - 1|3.860061e+04|2.134468e-01|8.239175e+03 - 2|2.182948e+04|7.682787e-01|1.677113e+04 - 3|2.182948e+04|0.000000e+00|0.000000e+00 - - - - -Visualize transport matrices ---------- - - -.. code-block:: default - - cmap = 'Blues' - fs = 15 - pl.figure(2, (13, 5)) - pl.clf() - pl.subplot(1, 3, 1) - pl.imshow(Got, cmap=cmap, interpolation='nearest') - #pl.xlabel("$y$",fontsize=fs) - pl.ylabel("$i$", fontsize=fs) - pl.xticks(()) - - pl.title('Wasserstein ($M$ only)') - - pl.subplot(1, 3, 2) - pl.imshow(Gg, cmap=cmap, interpolation='nearest') - pl.title('Gromov ($C_1,C_2$ only)') - pl.xticks(()) - pl.subplot(1, 3, 3) - pl.imshow(Gwg, cmap=cmap, interpolation='nearest') - pl.title('FGW ($M+C_1,C_2$)') - - pl.xlabel("$j$", fontsize=fs) - pl.ylabel("$i$", fontsize=fs) - - pl.tight_layout() - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_fgw_003.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_fgw.py:173: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 1.184 seconds) - - -.. _sphx_glr_download_auto_examples_plot_fgw.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_fgw.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_fgw.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_free_support_barycenter.ipynb b/docs/source/auto_examples/plot_free_support_barycenter.ipynb deleted file mode 100644 index 25ce60f..0000000 --- a/docs/source/auto_examples/plot_free_support_barycenter.ipynb +++ /dev/null @@ -1,108 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# 2D free support Wasserstein barycenters of distributions\n\n\nIllustration of 2D Wasserstein barycenters if discributions that are weighted\nsum of diracs.\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Vivien Seguy \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n -------------\n%% parameters and data generation\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "N = 3\nd = 2\nmeasures_locations = []\nmeasures_weights = []\n\nfor i in range(N):\n\n n_i = np.random.randint(low=1, high=20) # nb samples\n\n mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean\n\n A_i = np.random.rand(d, d)\n cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix\n\n x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations\n b_i = np.random.uniform(0., 1., (n_i,))\n b_i = b_i / np.sum(b_i) # Dirac weights\n\n measures_locations.append(x_i)\n measures_weights.append(b_i)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute free support barycenter\n-------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "k = 10 # number of Diracs of the barycenter\nX_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations\nb = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized)\n\nX = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n---------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(1)\nfor (x_i, b_i) in zip(measures_locations, measures_weights):\n color = np.random.randint(low=1, high=10 * N)\n pl.scatter(x_i[:, 0], x_i[:, 1], s=b_i * 1000, label='input measure')\npl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter')\npl.title('Data measures and their barycenter')\npl.legend(loc=0)\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_free_support_barycenter.py b/docs/source/auto_examples/plot_free_support_barycenter.py deleted file mode 100644 index 64b89e4..0000000 --- a/docs/source/auto_examples/plot_free_support_barycenter.py +++ /dev/null @@ -1,69 +0,0 @@ -# -*- coding: utf-8 -*- -""" -==================================================== -2D free support Wasserstein barycenters of distributions -==================================================== - -Illustration of 2D Wasserstein barycenters if discributions that are weighted -sum of diracs. - -""" - -# Author: Vivien Seguy -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot - - -############################################################################## -# Generate data -# ------------- -#%% parameters and data generation -N = 3 -d = 2 -measures_locations = [] -measures_weights = [] - -for i in range(N): - - n_i = np.random.randint(low=1, high=20) # nb samples - - mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean - - A_i = np.random.rand(d, d) - cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix - - x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations - b_i = np.random.uniform(0., 1., (n_i,)) - b_i = b_i / np.sum(b_i) # Dirac weights - - measures_locations.append(x_i) - measures_weights.append(b_i) - - -############################################################################## -# Compute free support barycenter -# ------------- - -k = 10 # number of Diracs of the barycenter -X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations -b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized) - -X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b) - - -############################################################################## -# Plot data -# --------- - -pl.figure(1) -for (x_i, b_i) in zip(measures_locations, measures_weights): - color = np.random.randint(low=1, high=10 * N) - pl.scatter(x_i[:, 0], x_i[:, 1], s=b_i * 1000, label='input measure') -pl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter') -pl.title('Data measures and their barycenter') -pl.legend(loc=0) -pl.show() diff --git a/docs/source/auto_examples/plot_free_support_barycenter.rst b/docs/source/auto_examples/plot_free_support_barycenter.rst deleted file mode 100644 index f349604..0000000 --- a/docs/source/auto_examples/plot_free_support_barycenter.rst +++ /dev/null @@ -1,162 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_free_support_barycenter.py: - - -==================================================== -2D free support Wasserstein barycenters of distributions -==================================================== - -Illustration of 2D Wasserstein barycenters if discributions that are weighted -sum of diracs. - - - -.. code-block:: default - - - # Author: Vivien Seguy - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot - - - - - - - - - -Generate data - ------------- -%% parameters and data generation - - -.. code-block:: default - - N = 3 - d = 2 - measures_locations = [] - measures_weights = [] - - for i in range(N): - - n_i = np.random.randint(low=1, high=20) # nb samples - - mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean - - A_i = np.random.rand(d, d) - cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix - - x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations - b_i = np.random.uniform(0., 1., (n_i,)) - b_i = b_i / np.sum(b_i) # Dirac weights - - measures_locations.append(x_i) - measures_weights.append(b_i) - - - - - - - - - -Compute free support barycenter -------------- - - -.. code-block:: default - - - k = 10 # number of Diracs of the barycenter - X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations - b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized) - - X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b) - - - - - - - - - -Plot data ---------- - - -.. code-block:: default - - - pl.figure(1) - for (x_i, b_i) in zip(measures_locations, measures_weights): - color = np.random.randint(low=1, high=10 * N) - pl.scatter(x_i[:, 0], x_i[:, 1], s=b_i * 1000, label='input measure') - pl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter') - pl.title('Data measures and their barycenter') - pl.legend(loc=0) - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_free_support_barycenter_001.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_free_support_barycenter.py:69: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 0.080 seconds) - - -.. _sphx_glr_download_auto_examples_plot_free_support_barycenter.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_free_support_barycenter.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_free_support_barycenter.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_gromov.ipynb b/docs/source/auto_examples/plot_gromov.ipynb deleted file mode 100644 index e5a88e7..0000000 --- a/docs/source/auto_examples/plot_gromov.ipynb +++ /dev/null @@ -1,126 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Gromov-Wasserstein example\n\n\nThis example is designed to show how to use the Gromov-Wassertsein distance\ncomputation in POT.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Erwan Vautier \n# Nicolas Courty \n#\n# License: MIT License\n\nimport scipy as sp\nimport numpy as np\nimport matplotlib.pylab as pl\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nimport ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sample two Gaussian distributions (2D and 3D)\n---------------------------------------------\n\nThe Gromov-Wasserstein distance allows to compute distances with samples that\ndo not belong to the same metric space. For demonstration purpose, we sample\ntwo Gaussian distributions in 2- and 3-dimensional spaces.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_samples = 30 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4, 4])\ncov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n\n\nxs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)\nP = sp.linalg.sqrtm(cov_t)\nxt = np.random.randn(n_samples, 3).dot(P) + mu_t" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotting the distributions\n--------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "fig = pl.figure()\nax1 = fig.add_subplot(121)\nax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\nax2 = fig.add_subplot(122, projection='3d')\nax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\npl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute distance kernels, normalize them and then display\n---------------------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "C1 = sp.spatial.distance.cdist(xs, xs)\nC2 = sp.spatial.distance.cdist(xt, xt)\n\nC1 /= C1.max()\nC2 /= C2.max()\n\npl.figure()\npl.subplot(121)\npl.imshow(C1)\npl.subplot(122)\npl.imshow(C2)\npl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute Gromov-Wasserstein plans and distance\n---------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "p = ot.unif(n_samples)\nq = ot.unif(n_samples)\n\ngw0, log0 = ot.gromov.gromov_wasserstein(\n C1, C2, p, q, 'square_loss', verbose=True, log=True)\n\ngw, log = ot.gromov.entropic_gromov_wasserstein(\n C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)\n\n\nprint('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))\nprint('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))\n\n\npl.figure(1, (10, 5))\n\npl.subplot(1, 2, 1)\npl.imshow(gw0, cmap='jet')\npl.title('Gromov Wasserstein')\n\npl.subplot(1, 2, 2)\npl.imshow(gw, cmap='jet')\npl.title('Entropic Gromov Wasserstein')\n\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_gromov.py b/docs/source/auto_examples/plot_gromov.py deleted file mode 100644 index deb2f86..0000000 --- a/docs/source/auto_examples/plot_gromov.py +++ /dev/null @@ -1,106 +0,0 @@ -# -*- coding: utf-8 -*- -""" -========================== -Gromov-Wasserstein example -========================== - -This example is designed to show how to use the Gromov-Wassertsein distance -computation in POT. -""" - -# Author: Erwan Vautier -# Nicolas Courty -# -# License: MIT License - -import scipy as sp -import numpy as np -import matplotlib.pylab as pl -from mpl_toolkits.mplot3d import Axes3D # noqa -import ot - -############################################################################# -# -# Sample two Gaussian distributions (2D and 3D) -# --------------------------------------------- -# -# The Gromov-Wasserstein distance allows to compute distances with samples that -# do not belong to the same metric space. For demonstration purpose, we sample -# two Gaussian distributions in 2- and 3-dimensional spaces. - - -n_samples = 30 # nb samples - -mu_s = np.array([0, 0]) -cov_s = np.array([[1, 0], [0, 1]]) - -mu_t = np.array([4, 4, 4]) -cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) - - -xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) -P = sp.linalg.sqrtm(cov_t) -xt = np.random.randn(n_samples, 3).dot(P) + mu_t - -############################################################################# -# -# Plotting the distributions -# -------------------------- - - -fig = pl.figure() -ax1 = fig.add_subplot(121) -ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -ax2 = fig.add_subplot(122, projection='3d') -ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') -pl.show() - -############################################################################# -# -# Compute distance kernels, normalize them and then display -# --------------------------------------------------------- - - -C1 = sp.spatial.distance.cdist(xs, xs) -C2 = sp.spatial.distance.cdist(xt, xt) - -C1 /= C1.max() -C2 /= C2.max() - -pl.figure() -pl.subplot(121) -pl.imshow(C1) -pl.subplot(122) -pl.imshow(C2) -pl.show() - -############################################################################# -# -# Compute Gromov-Wasserstein plans and distance -# --------------------------------------------- - -p = ot.unif(n_samples) -q = ot.unif(n_samples) - -gw0, log0 = ot.gromov.gromov_wasserstein( - C1, C2, p, q, 'square_loss', verbose=True, log=True) - -gw, log = ot.gromov.entropic_gromov_wasserstein( - C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True) - - -print('Gromov-Wasserstein distances: ' + str(log0['gw_dist'])) -print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist'])) - - -pl.figure(1, (10, 5)) - -pl.subplot(1, 2, 1) -pl.imshow(gw0, cmap='jet') -pl.title('Gromov Wasserstein') - -pl.subplot(1, 2, 2) -pl.imshow(gw, cmap='jet') -pl.title('Entropic Gromov Wasserstein') - -pl.show() diff --git a/docs/source/auto_examples/plot_gromov.rst b/docs/source/auto_examples/plot_gromov.rst deleted file mode 100644 index 13d0d09..0000000 --- a/docs/source/auto_examples/plot_gromov.rst +++ /dev/null @@ -1,245 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_gromov.py: - - -========================== -Gromov-Wasserstein example -========================== - -This example is designed to show how to use the Gromov-Wassertsein distance -computation in POT. - - -.. code-block:: default - - - # Author: Erwan Vautier - # Nicolas Courty - # - # License: MIT License - - import scipy as sp - import numpy as np - import matplotlib.pylab as pl - from mpl_toolkits.mplot3d import Axes3D # noqa - import ot - - - - - - - - -Sample two Gaussian distributions (2D and 3D) ---------------------------------------------- - -The Gromov-Wasserstein distance allows to compute distances with samples that -do not belong to the same metric space. For demonstration purpose, we sample -two Gaussian distributions in 2- and 3-dimensional spaces. - - -.. code-block:: default - - - - n_samples = 30 # nb samples - - mu_s = np.array([0, 0]) - cov_s = np.array([[1, 0], [0, 1]]) - - mu_t = np.array([4, 4, 4]) - cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) - - - xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) - P = sp.linalg.sqrtm(cov_t) - xt = np.random.randn(n_samples, 3).dot(P) + mu_t - - - - - - - - -Plotting the distributions --------------------------- - - -.. code-block:: default - - - - fig = pl.figure() - ax1 = fig.add_subplot(121) - ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - ax2 = fig.add_subplot(122, projection='3d') - ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') - pl.show() - - - - -.. image:: /auto_examples/images/sphx_glr_plot_gromov_001.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_gromov.py:56: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -Compute distance kernels, normalize them and then display ---------------------------------------------------------- - - -.. code-block:: default - - - - C1 = sp.spatial.distance.cdist(xs, xs) - C2 = sp.spatial.distance.cdist(xt, xt) - - C1 /= C1.max() - C2 /= C2.max() - - pl.figure() - pl.subplot(121) - pl.imshow(C1) - pl.subplot(122) - pl.imshow(C2) - pl.show() - - - - -.. image:: /auto_examples/images/sphx_glr_plot_gromov_002.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_gromov.py:75: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -Compute Gromov-Wasserstein plans and distance ---------------------------------------------- - - -.. code-block:: default - - - p = ot.unif(n_samples) - q = ot.unif(n_samples) - - gw0, log0 = ot.gromov.gromov_wasserstein( - C1, C2, p, q, 'square_loss', verbose=True, log=True) - - gw, log = ot.gromov.entropic_gromov_wasserstein( - C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True) - - - print('Gromov-Wasserstein distances: ' + str(log0['gw_dist'])) - print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist'])) - - - pl.figure(1, (10, 5)) - - pl.subplot(1, 2, 1) - pl.imshow(gw0, cmap='jet') - pl.title('Gromov Wasserstein') - - pl.subplot(1, 2, 2) - pl.imshow(gw, cmap='jet') - pl.title('Entropic Gromov Wasserstein') - - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_gromov_003.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 0|8.019265e-02|0.000000e+00|0.000000e+00 - 1|3.734805e-02|1.147171e+00|4.284460e-02 - 2|2.923853e-02|2.773572e-01|8.109516e-03 - 3|2.478957e-02|1.794691e-01|4.448961e-03 - 4|2.444720e-02|1.400444e-02|3.423693e-04 - 5|2.444720e-02|0.000000e+00|0.000000e+00 - It. |Err - ------------------- - 0|8.259147e-02| - 10|6.113732e-04| - 20|1.650651e-08| - 30|5.671192e-12| - Gromov-Wasserstein distances: 0.024447198979060496 - Entropic Gromov-Wasserstein distances: 0.02488439679981518 - /home/rflamary/PYTHON/POT/examples/plot_gromov.py:106: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 0.999 seconds) - - -.. _sphx_glr_download_auto_examples_plot_gromov.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_gromov.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_gromov.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_gromov_barycenter.ipynb b/docs/source/auto_examples/plot_gromov_barycenter.ipynb deleted file mode 100644 index 17ba374..0000000 --- a/docs/source/auto_examples/plot_gromov_barycenter.ipynb +++ /dev/null @@ -1,126 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Gromov-Wasserstein Barycenter example\n\n\nThis example is designed to show how to use the Gromov-Wasserstein distance\ncomputation in POT.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Erwan Vautier \n# Nicolas Courty \n#\n# License: MIT License\n\n\nimport numpy as np\nimport scipy as sp\n\nimport matplotlib.pylab as pl\nfrom sklearn import manifold\nfrom sklearn.decomposition import PCA\n\nimport ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Smacof MDS\n----------\n\nThis function allows to find an embedding of points given a dissimilarity matrix\nthat will be given by the output of the algorithm\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def smacof_mds(C, dim, max_iter=3000, eps=1e-9):\n \"\"\"\n Returns an interpolated point cloud following the dissimilarity matrix C\n using SMACOF multidimensional scaling (MDS) in specific dimensionned\n target space\n\n Parameters\n ----------\n C : ndarray, shape (ns, ns)\n dissimilarity matrix\n dim : int\n dimension of the targeted space\n max_iter : int\n Maximum number of iterations of the SMACOF algorithm for a single run\n eps : float\n relative tolerance w.r.t stress to declare converge\n\n Returns\n -------\n npos : ndarray, shape (R, dim)\n Embedded coordinates of the interpolated point cloud (defined with\n one isometry)\n \"\"\"\n\n rng = np.random.RandomState(seed=3)\n\n mds = manifold.MDS(\n dim,\n max_iter=max_iter,\n eps=1e-9,\n dissimilarity='precomputed',\n n_init=1)\n pos = mds.fit(C).embedding_\n\n nmds = manifold.MDS(\n 2,\n max_iter=max_iter,\n eps=1e-9,\n dissimilarity=\"precomputed\",\n random_state=rng,\n n_init=1)\n npos = nmds.fit_transform(C, init=pos)\n\n return npos" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Data preparation\n----------------\n\nThe four distributions are constructed from 4 simple images\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\nsquare = pl.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256\ncross = pl.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256\ntriangle = pl.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256\nstar = pl.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256\n\nshapes = [square, cross, triangle, star]\n\nS = 4\nxs = [[] for i in range(S)]\n\n\nfor nb in range(4):\n for i in range(8):\n for j in range(8):\n if shapes[nb][i, j] < 0.95:\n xs[nb].append([j, 8 - i])\n\nxs = np.array([np.array(xs[0]), np.array(xs[1]),\n np.array(xs[2]), np.array(xs[3])])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycenter computation\n----------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ns = [len(xs[s]) for s in range(S)]\nn_samples = 30\n\n\"\"\"Compute all distances matrices for the four shapes\"\"\"\nCs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)]\nCs = [cs / cs.max() for cs in Cs]\n\nps = [ot.unif(ns[s]) for s in range(S)]\np = ot.unif(n_samples)\n\n\nlambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]]\n\nCt01 = [0 for i in range(2)]\nfor i in range(2):\n Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]],\n [ps[0], ps[1]\n ], p, lambdast[i], 'square_loss', # 5e-4,\n max_iter=100, tol=1e-3)\n\nCt02 = [0 for i in range(2)]\nfor i in range(2):\n Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]],\n [ps[0], ps[2]\n ], p, lambdast[i], 'square_loss', # 5e-4,\n max_iter=100, tol=1e-3)\n\nCt13 = [0 for i in range(2)]\nfor i in range(2):\n Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]],\n [ps[1], ps[3]\n ], p, lambdast[i], 'square_loss', # 5e-4,\n max_iter=100, tol=1e-3)\n\nCt23 = [0 for i in range(2)]\nfor i in range(2):\n Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]],\n [ps[2], ps[3]\n ], p, lambdast[i], 'square_loss', # 5e-4,\n max_iter=100, tol=1e-3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Visualization\n-------------\n\nThe PCA helps in getting consistency between the rotations\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "clf = PCA(n_components=2)\nnpos = [0, 0, 0, 0]\nnpos = [smacof_mds(Cs[s], 2) for s in range(S)]\n\nnpost01 = [0, 0]\nnpost01 = [smacof_mds(Ct01[s], 2) for s in range(2)]\nnpost01 = [clf.fit_transform(npost01[s]) for s in range(2)]\n\nnpost02 = [0, 0]\nnpost02 = [smacof_mds(Ct02[s], 2) for s in range(2)]\nnpost02 = [clf.fit_transform(npost02[s]) for s in range(2)]\n\nnpost13 = [0, 0]\nnpost13 = [smacof_mds(Ct13[s], 2) for s in range(2)]\nnpost13 = [clf.fit_transform(npost13[s]) for s in range(2)]\n\nnpost23 = [0, 0]\nnpost23 = [smacof_mds(Ct23[s], 2) for s in range(2)]\nnpost23 = [clf.fit_transform(npost23[s]) for s in range(2)]\n\n\nfig = pl.figure(figsize=(10, 10))\n\nax1 = pl.subplot2grid((4, 4), (0, 0))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r')\n\nax2 = pl.subplot2grid((4, 4), (0, 1))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b')\n\nax3 = pl.subplot2grid((4, 4), (0, 2))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b')\n\nax4 = pl.subplot2grid((4, 4), (0, 3))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r')\n\nax5 = pl.subplot2grid((4, 4), (1, 0))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b')\n\nax6 = pl.subplot2grid((4, 4), (1, 3))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b')\n\nax7 = pl.subplot2grid((4, 4), (2, 0))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b')\n\nax8 = pl.subplot2grid((4, 4), (2, 3))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b')\n\nax9 = pl.subplot2grid((4, 4), (3, 0))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r')\n\nax10 = pl.subplot2grid((4, 4), (3, 1))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b')\n\nax11 = pl.subplot2grid((4, 4), (3, 2))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b')\n\nax12 = pl.subplot2grid((4, 4), (3, 3))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_gromov_barycenter.py b/docs/source/auto_examples/plot_gromov_barycenter.py deleted file mode 100644 index 101c6c5..0000000 --- a/docs/source/auto_examples/plot_gromov_barycenter.py +++ /dev/null @@ -1,247 +0,0 @@ -# -*- coding: utf-8 -*- -""" -===================================== -Gromov-Wasserstein Barycenter example -===================================== - -This example is designed to show how to use the Gromov-Wasserstein distance -computation in POT. -""" - -# Author: Erwan Vautier -# Nicolas Courty -# -# License: MIT License - - -import numpy as np -import scipy as sp - -import matplotlib.pylab as pl -from sklearn import manifold -from sklearn.decomposition import PCA - -import ot - -############################################################################## -# Smacof MDS -# ---------- -# -# This function allows to find an embedding of points given a dissimilarity matrix -# that will be given by the output of the algorithm - - -def smacof_mds(C, dim, max_iter=3000, eps=1e-9): - """ - Returns an interpolated point cloud following the dissimilarity matrix C - using SMACOF multidimensional scaling (MDS) in specific dimensionned - target space - - Parameters - ---------- - C : ndarray, shape (ns, ns) - dissimilarity matrix - dim : int - dimension of the targeted space - max_iter : int - Maximum number of iterations of the SMACOF algorithm for a single run - eps : float - relative tolerance w.r.t stress to declare converge - - Returns - ------- - npos : ndarray, shape (R, dim) - Embedded coordinates of the interpolated point cloud (defined with - one isometry) - """ - - rng = np.random.RandomState(seed=3) - - mds = manifold.MDS( - dim, - max_iter=max_iter, - eps=1e-9, - dissimilarity='precomputed', - n_init=1) - pos = mds.fit(C).embedding_ - - nmds = manifold.MDS( - 2, - max_iter=max_iter, - eps=1e-9, - dissimilarity="precomputed", - random_state=rng, - n_init=1) - npos = nmds.fit_transform(C, init=pos) - - return npos - - -############################################################################## -# Data preparation -# ---------------- -# -# The four distributions are constructed from 4 simple images - - -def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) - - -square = pl.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256 -cross = pl.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256 -triangle = pl.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256 -star = pl.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256 - -shapes = [square, cross, triangle, star] - -S = 4 -xs = [[] for i in range(S)] - - -for nb in range(4): - for i in range(8): - for j in range(8): - if shapes[nb][i, j] < 0.95: - xs[nb].append([j, 8 - i]) - -xs = np.array([np.array(xs[0]), np.array(xs[1]), - np.array(xs[2]), np.array(xs[3])]) - -############################################################################## -# Barycenter computation -# ---------------------- - - -ns = [len(xs[s]) for s in range(S)] -n_samples = 30 - -"""Compute all distances matrices for the four shapes""" -Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)] -Cs = [cs / cs.max() for cs in Cs] - -ps = [ot.unif(ns[s]) for s in range(S)] -p = ot.unif(n_samples) - - -lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]] - -Ct01 = [0 for i in range(2)] -for i in range(2): - Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], - [ps[0], ps[1] - ], p, lambdast[i], 'square_loss', # 5e-4, - max_iter=100, tol=1e-3) - -Ct02 = [0 for i in range(2)] -for i in range(2): - Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], - [ps[0], ps[2] - ], p, lambdast[i], 'square_loss', # 5e-4, - max_iter=100, tol=1e-3) - -Ct13 = [0 for i in range(2)] -for i in range(2): - Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], - [ps[1], ps[3] - ], p, lambdast[i], 'square_loss', # 5e-4, - max_iter=100, tol=1e-3) - -Ct23 = [0 for i in range(2)] -for i in range(2): - Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], - [ps[2], ps[3] - ], p, lambdast[i], 'square_loss', # 5e-4, - max_iter=100, tol=1e-3) - - -############################################################################## -# Visualization -# ------------- -# -# The PCA helps in getting consistency between the rotations - - -clf = PCA(n_components=2) -npos = [0, 0, 0, 0] -npos = [smacof_mds(Cs[s], 2) for s in range(S)] - -npost01 = [0, 0] -npost01 = [smacof_mds(Ct01[s], 2) for s in range(2)] -npost01 = [clf.fit_transform(npost01[s]) for s in range(2)] - -npost02 = [0, 0] -npost02 = [smacof_mds(Ct02[s], 2) for s in range(2)] -npost02 = [clf.fit_transform(npost02[s]) for s in range(2)] - -npost13 = [0, 0] -npost13 = [smacof_mds(Ct13[s], 2) for s in range(2)] -npost13 = [clf.fit_transform(npost13[s]) for s in range(2)] - -npost23 = [0, 0] -npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)] -npost23 = [clf.fit_transform(npost23[s]) for s in range(2)] - - -fig = pl.figure(figsize=(10, 10)) - -ax1 = pl.subplot2grid((4, 4), (0, 0)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r') - -ax2 = pl.subplot2grid((4, 4), (0, 1)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b') - -ax3 = pl.subplot2grid((4, 4), (0, 2)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b') - -ax4 = pl.subplot2grid((4, 4), (0, 3)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r') - -ax5 = pl.subplot2grid((4, 4), (1, 0)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b') - -ax6 = pl.subplot2grid((4, 4), (1, 3)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b') - -ax7 = pl.subplot2grid((4, 4), (2, 0)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b') - -ax8 = pl.subplot2grid((4, 4), (2, 3)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b') - -ax9 = pl.subplot2grid((4, 4), (3, 0)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r') - -ax10 = pl.subplot2grid((4, 4), (3, 1)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b') - -ax11 = pl.subplot2grid((4, 4), (3, 2)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b') - -ax12 = pl.subplot2grid((4, 4), (3, 3)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r') diff --git a/docs/source/auto_examples/plot_gromov_barycenter.rst b/docs/source/auto_examples/plot_gromov_barycenter.rst deleted file mode 100644 index 995cca7..0000000 --- a/docs/source/auto_examples/plot_gromov_barycenter.rst +++ /dev/null @@ -1,349 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_gromov_barycenter.py: - - -===================================== -Gromov-Wasserstein Barycenter example -===================================== - -This example is designed to show how to use the Gromov-Wasserstein distance -computation in POT. - - -.. code-block:: default - - - # Author: Erwan Vautier - # Nicolas Courty - # - # License: MIT License - - - import numpy as np - import scipy as sp - - import matplotlib.pylab as pl - from sklearn import manifold - from sklearn.decomposition import PCA - - import ot - - - - - - - - -Smacof MDS ----------- - -This function allows to find an embedding of points given a dissimilarity matrix -that will be given by the output of the algorithm - - -.. code-block:: default - - - - def smacof_mds(C, dim, max_iter=3000, eps=1e-9): - """ - Returns an interpolated point cloud following the dissimilarity matrix C - using SMACOF multidimensional scaling (MDS) in specific dimensionned - target space - - Parameters - ---------- - C : ndarray, shape (ns, ns) - dissimilarity matrix - dim : int - dimension of the targeted space - max_iter : int - Maximum number of iterations of the SMACOF algorithm for a single run - eps : float - relative tolerance w.r.t stress to declare converge - - Returns - ------- - npos : ndarray, shape (R, dim) - Embedded coordinates of the interpolated point cloud (defined with - one isometry) - """ - - rng = np.random.RandomState(seed=3) - - mds = manifold.MDS( - dim, - max_iter=max_iter, - eps=1e-9, - dissimilarity='precomputed', - n_init=1) - pos = mds.fit(C).embedding_ - - nmds = manifold.MDS( - 2, - max_iter=max_iter, - eps=1e-9, - dissimilarity="precomputed", - random_state=rng, - n_init=1) - npos = nmds.fit_transform(C, init=pos) - - return npos - - - - - - - - - -Data preparation ----------------- - -The four distributions are constructed from 4 simple images - - -.. code-block:: default - - - - def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) - - - square = pl.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256 - cross = pl.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256 - triangle = pl.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256 - star = pl.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256 - - shapes = [square, cross, triangle, star] - - S = 4 - xs = [[] for i in range(S)] - - - for nb in range(4): - for i in range(8): - for j in range(8): - if shapes[nb][i, j] < 0.95: - xs[nb].append([j, 8 - i]) - - xs = np.array([np.array(xs[0]), np.array(xs[1]), - np.array(xs[2]), np.array(xs[3])]) - - - - - - - - -Barycenter computation ----------------------- - - -.. code-block:: default - - - - ns = [len(xs[s]) for s in range(S)] - n_samples = 30 - - """Compute all distances matrices for the four shapes""" - Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)] - Cs = [cs / cs.max() for cs in Cs] - - ps = [ot.unif(ns[s]) for s in range(S)] - p = ot.unif(n_samples) - - - lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]] - - Ct01 = [0 for i in range(2)] - for i in range(2): - Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], - [ps[0], ps[1] - ], p, lambdast[i], 'square_loss', # 5e-4, - max_iter=100, tol=1e-3) - - Ct02 = [0 for i in range(2)] - for i in range(2): - Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], - [ps[0], ps[2] - ], p, lambdast[i], 'square_loss', # 5e-4, - max_iter=100, tol=1e-3) - - Ct13 = [0 for i in range(2)] - for i in range(2): - Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], - [ps[1], ps[3] - ], p, lambdast[i], 'square_loss', # 5e-4, - max_iter=100, tol=1e-3) - - Ct23 = [0 for i in range(2)] - for i in range(2): - Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], - [ps[2], ps[3] - ], p, lambdast[i], 'square_loss', # 5e-4, - max_iter=100, tol=1e-3) - - - - - - - - - -Visualization -------------- - -The PCA helps in getting consistency between the rotations - - -.. code-block:: default - - - - clf = PCA(n_components=2) - npos = [0, 0, 0, 0] - npos = [smacof_mds(Cs[s], 2) for s in range(S)] - - npost01 = [0, 0] - npost01 = [smacof_mds(Ct01[s], 2) for s in range(2)] - npost01 = [clf.fit_transform(npost01[s]) for s in range(2)] - - npost02 = [0, 0] - npost02 = [smacof_mds(Ct02[s], 2) for s in range(2)] - npost02 = [clf.fit_transform(npost02[s]) for s in range(2)] - - npost13 = [0, 0] - npost13 = [smacof_mds(Ct13[s], 2) for s in range(2)] - npost13 = [clf.fit_transform(npost13[s]) for s in range(2)] - - npost23 = [0, 0] - npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)] - npost23 = [clf.fit_transform(npost23[s]) for s in range(2)] - - - fig = pl.figure(figsize=(10, 10)) - - ax1 = pl.subplot2grid((4, 4), (0, 0)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r') - - ax2 = pl.subplot2grid((4, 4), (0, 1)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b') - - ax3 = pl.subplot2grid((4, 4), (0, 2)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b') - - ax4 = pl.subplot2grid((4, 4), (0, 3)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r') - - ax5 = pl.subplot2grid((4, 4), (1, 0)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b') - - ax6 = pl.subplot2grid((4, 4), (1, 3)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b') - - ax7 = pl.subplot2grid((4, 4), (2, 0)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b') - - ax8 = pl.subplot2grid((4, 4), (2, 3)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b') - - ax9 = pl.subplot2grid((4, 4), (3, 0)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r') - - ax10 = pl.subplot2grid((4, 4), (3, 1)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b') - - ax11 = pl.subplot2grid((4, 4), (3, 2)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b') - - ax12 = pl.subplot2grid((4, 4), (3, 3)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r') - - - -.. image:: /auto_examples/images/sphx_glr_plot_gromov_barycenter_001.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 1.747 seconds) - - -.. _sphx_glr_download_auto_examples_plot_gromov_barycenter.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_gromov_barycenter.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_gromov_barycenter.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_optim_OTreg.ipynb b/docs/source/auto_examples/plot_optim_OTreg.ipynb deleted file mode 100644 index 01e0689..0000000 --- a/docs/source/auto_examples/plot_optim_OTreg.ipynb +++ /dev/null @@ -1,144 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Regularized OT with generic solver\n\n\nIllustrates the use of the generic solver for regularized OT with\nuser-designed regularization term. It uses Conditional gradient as in [6] and\ngeneralized Conditional Gradient as proposed in [5][7].\n\n\n[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for\nDomain Adaptation, in IEEE Transactions on Pattern Analysis and Machine\nIntelligence , vol.PP, no.99, pp.1-1.\n\n[6] Ferradans, S., Papadakis, N., Peyr\u00e9, G., & Aujol, J. F. (2014).\nRegularized discrete optimal transport. SIAM Journal on Imaging Sciences,\n7(3), 1853-1882.\n\n[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized\nconditional gradient: analysis of convergence and applications.\narXiv preprint arXiv:1510.06567.\n\n\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n-------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\nb = ot.datasets.make_1D_gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve EMD\n---------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "G0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve EMD with Frobenius norm regularization\n--------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg = 1e-1\n\nGl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(3)\not.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve EMD with entropic regularization\n--------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def f(G):\n return np.sum(G * np.log(G))\n\n\ndef df(G):\n return np.log(G) + 1.\n\n\nreg = 1e-3\n\nGe = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve EMD with Frobenius norm + entropic regularization\n-------------------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg1 = 1e-3\nreg2 = 1e-1\n\nGel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_optim_OTreg.py b/docs/source/auto_examples/plot_optim_OTreg.py deleted file mode 100644 index 2c58def..0000000 --- a/docs/source/auto_examples/plot_optim_OTreg.py +++ /dev/null @@ -1,129 +0,0 @@ -# -*- coding: utf-8 -*- -""" -================================== -Regularized OT with generic solver -================================== - -Illustrates the use of the generic solver for regularized OT with -user-designed regularization term. It uses Conditional gradient as in [6] and -generalized Conditional Gradient as proposed in [5][7]. - - -[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for -Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine -Intelligence , vol.PP, no.99, pp.1-1. - -[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). -Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, -7(3), 1853-1882. - -[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized -conditional gradient: analysis of convergence and applications. -arXiv preprint arXiv:1510.06567. - - - -""" - -import numpy as np -import matplotlib.pylab as pl -import ot -import ot.plot - -############################################################################## -# Generate data -# ------------- - -#%% parameters - -n = 100 # nb bins - -# bin positions -x = np.arange(n, dtype=np.float64) - -# Gaussian distributions -a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std -b = ot.datasets.make_1D_gauss(n, m=60, s=10) - -# loss matrix -M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) -M /= M.max() - -############################################################################## -# Solve EMD -# --------- - -#%% EMD - -G0 = ot.emd(a, b, M) - -pl.figure(3, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') - -############################################################################## -# Solve EMD with Frobenius norm regularization -# -------------------------------------------- - -#%% Example with Frobenius norm regularization - - -def f(G): - return 0.5 * np.sum(G**2) - - -def df(G): - return G - - -reg = 1e-1 - -Gl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True) - -pl.figure(3) -ot.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg') - -############################################################################## -# Solve EMD with entropic regularization -# -------------------------------------- - -#%% Example with entropic regularization - - -def f(G): - return np.sum(G * np.log(G)) - - -def df(G): - return np.log(G) + 1. - - -reg = 1e-3 - -Ge = ot.optim.cg(a, b, M, reg, f, df, verbose=True) - -pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg') - -############################################################################## -# Solve EMD with Frobenius norm + entropic regularization -# ------------------------------------------------------- - -#%% Example with Frobenius norm + entropic regularization with gcg - - -def f(G): - return 0.5 * np.sum(G**2) - - -def df(G): - return G - - -reg1 = 1e-3 -reg2 = 1e-1 - -Gel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True) - -pl.figure(5, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg') -pl.show() diff --git a/docs/source/auto_examples/plot_optim_OTreg.rst b/docs/source/auto_examples/plot_optim_OTreg.rst deleted file mode 100644 index cd5bcf5..0000000 --- a/docs/source/auto_examples/plot_optim_OTreg.rst +++ /dev/null @@ -1,593 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_optim_OTreg.py: - - -================================== -Regularized OT with generic solver -================================== - -Illustrates the use of the generic solver for regularized OT with -user-designed regularization term. It uses Conditional gradient as in [6] and -generalized Conditional Gradient as proposed in [5][7]. - - -[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for -Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine -Intelligence , vol.PP, no.99, pp.1-1. - -[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). -Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, -7(3), 1853-1882. - -[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized -conditional gradient: analysis of convergence and applications. -arXiv preprint arXiv:1510.06567. - - - - - -.. code-block:: default - - - import numpy as np - import matplotlib.pylab as pl - import ot - import ot.plot - - - - - - - - -Generate data -------------- - - -.. code-block:: default - - - n = 100 # nb bins - - # bin positions - x = np.arange(n, dtype=np.float64) - - # Gaussian distributions - a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std - b = ot.datasets.make_1D_gauss(n, m=60, s=10) - - # loss matrix - M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) - M /= M.max() - - - - - - - - -Solve EMD ---------- - - -.. code-block:: default - - - G0 = ot.emd(a, b, M) - - pl.figure(3, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') - - - - -.. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_001.png - :class: sphx-glr-single-img - - - - - -Solve EMD with Frobenius norm regularization --------------------------------------------- - - -.. code-block:: default - - - - def f(G): - return 0.5 * np.sum(G**2) - - - def df(G): - return G - - - reg = 1e-1 - - Gl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True) - - pl.figure(3) - ot.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg') - - - - -.. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_002.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 0|1.760578e-01|0.000000e+00|0.000000e+00 - 1|1.669467e-01|5.457501e-02|9.111116e-03 - 2|1.665639e-01|2.298130e-03|3.827855e-04 - 3|1.664378e-01|7.572776e-04|1.260396e-04 - 4|1.664077e-01|1.811855e-04|3.015066e-05 - 5|1.663912e-01|9.936787e-05|1.653393e-05 - 6|1.663852e-01|3.555826e-05|5.916369e-06 - 7|1.663814e-01|2.305693e-05|3.836245e-06 - 8|1.663785e-01|1.760450e-05|2.929009e-06 - 9|1.663767e-01|1.078011e-05|1.793559e-06 - 10|1.663751e-01|9.525192e-06|1.584755e-06 - 11|1.663737e-01|8.396466e-06|1.396951e-06 - 12|1.663727e-01|6.086938e-06|1.012700e-06 - 13|1.663720e-01|4.042609e-06|6.725769e-07 - 14|1.663713e-01|4.160914e-06|6.922568e-07 - 15|1.663707e-01|3.823502e-06|6.361187e-07 - 16|1.663702e-01|3.022440e-06|5.028438e-07 - 17|1.663697e-01|3.181249e-06|5.292634e-07 - 18|1.663692e-01|2.698532e-06|4.489527e-07 - 19|1.663687e-01|3.258253e-06|5.420712e-07 - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 20|1.663682e-01|2.741118e-06|4.560349e-07 - 21|1.663678e-01|2.624135e-06|4.365715e-07 - 22|1.663673e-01|2.645179e-06|4.400714e-07 - 23|1.663670e-01|1.957237e-06|3.256196e-07 - 24|1.663666e-01|2.261541e-06|3.762450e-07 - 25|1.663663e-01|1.851305e-06|3.079948e-07 - 26|1.663660e-01|1.942296e-06|3.231320e-07 - 27|1.663657e-01|2.092896e-06|3.481860e-07 - 28|1.663653e-01|1.924361e-06|3.201470e-07 - 29|1.663651e-01|1.625455e-06|2.704189e-07 - 30|1.663648e-01|1.641123e-06|2.730250e-07 - 31|1.663645e-01|1.566666e-06|2.606377e-07 - 32|1.663643e-01|1.338514e-06|2.226810e-07 - 33|1.663641e-01|1.222711e-06|2.034152e-07 - 34|1.663639e-01|1.221805e-06|2.032642e-07 - 35|1.663637e-01|1.440781e-06|2.396935e-07 - 36|1.663634e-01|1.520091e-06|2.528875e-07 - 37|1.663632e-01|1.288193e-06|2.143080e-07 - 38|1.663630e-01|1.123055e-06|1.868347e-07 - 39|1.663628e-01|1.024487e-06|1.704365e-07 - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 40|1.663627e-01|1.079606e-06|1.796061e-07 - 41|1.663625e-01|1.172093e-06|1.949922e-07 - 42|1.663623e-01|1.047880e-06|1.743277e-07 - 43|1.663621e-01|1.010577e-06|1.681217e-07 - 44|1.663619e-01|1.064438e-06|1.770820e-07 - 45|1.663618e-01|9.882375e-07|1.644049e-07 - 46|1.663616e-01|8.532647e-07|1.419505e-07 - 47|1.663615e-01|9.930189e-07|1.652001e-07 - 48|1.663613e-01|8.728955e-07|1.452161e-07 - 49|1.663612e-01|9.524214e-07|1.584459e-07 - 50|1.663610e-01|9.088418e-07|1.511958e-07 - 51|1.663609e-01|7.639430e-07|1.270902e-07 - 52|1.663608e-01|6.662611e-07|1.108397e-07 - 53|1.663607e-01|7.133700e-07|1.186767e-07 - 54|1.663605e-01|7.648141e-07|1.272349e-07 - 55|1.663604e-01|6.557516e-07|1.090911e-07 - 56|1.663603e-01|7.304213e-07|1.215131e-07 - 57|1.663602e-01|6.353809e-07|1.057021e-07 - 58|1.663601e-01|7.968279e-07|1.325603e-07 - 59|1.663600e-01|6.367159e-07|1.059240e-07 - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 60|1.663599e-01|5.610790e-07|9.334102e-08 - 61|1.663598e-01|5.787466e-07|9.628015e-08 - 62|1.663596e-01|6.937777e-07|1.154166e-07 - 63|1.663596e-01|5.599432e-07|9.315190e-08 - 64|1.663595e-01|5.813048e-07|9.670555e-08 - 65|1.663594e-01|5.724600e-07|9.523409e-08 - 66|1.663593e-01|6.081892e-07|1.011779e-07 - 67|1.663592e-01|5.948732e-07|9.896260e-08 - 68|1.663591e-01|4.941833e-07|8.221188e-08 - 69|1.663590e-01|5.213739e-07|8.673523e-08 - 70|1.663589e-01|5.127355e-07|8.529811e-08 - 71|1.663588e-01|4.349251e-07|7.235363e-08 - 72|1.663588e-01|5.007084e-07|8.329722e-08 - 73|1.663587e-01|4.880265e-07|8.118744e-08 - 74|1.663586e-01|4.931950e-07|8.204723e-08 - 75|1.663585e-01|4.981309e-07|8.286832e-08 - 76|1.663584e-01|3.952959e-07|6.576082e-08 - 77|1.663584e-01|4.544857e-07|7.560750e-08 - 78|1.663583e-01|4.237579e-07|7.049564e-08 - 79|1.663582e-01|4.382386e-07|7.290460e-08 - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 80|1.663582e-01|3.646051e-07|6.065503e-08 - 81|1.663581e-01|4.197994e-07|6.983702e-08 - 82|1.663580e-01|4.072764e-07|6.775370e-08 - 83|1.663580e-01|3.994645e-07|6.645410e-08 - 84|1.663579e-01|4.842721e-07|8.056248e-08 - 85|1.663578e-01|3.276486e-07|5.450691e-08 - 86|1.663578e-01|3.737346e-07|6.217366e-08 - 87|1.663577e-01|4.282043e-07|7.123508e-08 - 88|1.663576e-01|4.020937e-07|6.689135e-08 - 89|1.663576e-01|3.431951e-07|5.709310e-08 - 90|1.663575e-01|3.052335e-07|5.077789e-08 - 91|1.663575e-01|3.500538e-07|5.823407e-08 - 92|1.663574e-01|3.063176e-07|5.095821e-08 - 93|1.663573e-01|3.576367e-07|5.949549e-08 - 94|1.663573e-01|3.224681e-07|5.364492e-08 - 95|1.663572e-01|3.673221e-07|6.110670e-08 - 96|1.663572e-01|3.635561e-07|6.048017e-08 - 97|1.663571e-01|3.527236e-07|5.867807e-08 - 98|1.663571e-01|2.788548e-07|4.638946e-08 - 99|1.663570e-01|2.727141e-07|4.536791e-08 - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 100|1.663570e-01|3.127278e-07|5.202445e-08 - 101|1.663569e-01|2.637504e-07|4.387670e-08 - 102|1.663569e-01|2.922750e-07|4.862195e-08 - 103|1.663568e-01|3.076454e-07|5.117891e-08 - 104|1.663568e-01|2.911509e-07|4.843492e-08 - 105|1.663567e-01|2.403398e-07|3.998215e-08 - 106|1.663567e-01|2.439790e-07|4.058755e-08 - 107|1.663567e-01|2.634542e-07|4.382735e-08 - 108|1.663566e-01|2.452203e-07|4.079401e-08 - 109|1.663566e-01|2.852991e-07|4.746137e-08 - 110|1.663565e-01|2.165490e-07|3.602434e-08 - 111|1.663565e-01|2.450250e-07|4.076149e-08 - 112|1.663564e-01|2.685294e-07|4.467159e-08 - 113|1.663564e-01|2.821800e-07|4.694245e-08 - 114|1.663564e-01|2.237390e-07|3.722040e-08 - 115|1.663563e-01|1.992842e-07|3.315219e-08 - 116|1.663563e-01|2.166739e-07|3.604506e-08 - 117|1.663563e-01|2.086064e-07|3.470297e-08 - 118|1.663562e-01|2.435945e-07|4.052346e-08 - 119|1.663562e-01|2.292497e-07|3.813711e-08 - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 120|1.663561e-01|2.366209e-07|3.936334e-08 - 121|1.663561e-01|2.138746e-07|3.557935e-08 - 122|1.663561e-01|2.009637e-07|3.343153e-08 - 123|1.663560e-01|2.386258e-07|3.969683e-08 - 124|1.663560e-01|1.927442e-07|3.206415e-08 - 125|1.663560e-01|2.081681e-07|3.463000e-08 - 126|1.663559e-01|1.759123e-07|2.926406e-08 - 127|1.663559e-01|1.890771e-07|3.145409e-08 - 128|1.663559e-01|1.971315e-07|3.279398e-08 - 129|1.663558e-01|2.101983e-07|3.496771e-08 - 130|1.663558e-01|2.035645e-07|3.386414e-08 - 131|1.663558e-01|1.984492e-07|3.301317e-08 - 132|1.663557e-01|1.849064e-07|3.076024e-08 - 133|1.663557e-01|1.795703e-07|2.987255e-08 - 134|1.663557e-01|1.624087e-07|2.701762e-08 - 135|1.663557e-01|1.689557e-07|2.810673e-08 - 136|1.663556e-01|1.644308e-07|2.735399e-08 - 137|1.663556e-01|1.618007e-07|2.691644e-08 - 138|1.663556e-01|1.483013e-07|2.467075e-08 - 139|1.663555e-01|1.708771e-07|2.842636e-08 - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 140|1.663555e-01|2.013847e-07|3.350146e-08 - 141|1.663555e-01|1.721217e-07|2.863339e-08 - 142|1.663554e-01|2.027911e-07|3.373540e-08 - 143|1.663554e-01|1.764565e-07|2.935449e-08 - 144|1.663554e-01|1.677151e-07|2.790030e-08 - 145|1.663554e-01|1.351982e-07|2.249094e-08 - 146|1.663553e-01|1.423360e-07|2.367836e-08 - 147|1.663553e-01|1.541112e-07|2.563722e-08 - 148|1.663553e-01|1.491601e-07|2.481358e-08 - 149|1.663553e-01|1.466407e-07|2.439446e-08 - 150|1.663552e-01|1.801524e-07|2.996929e-08 - 151|1.663552e-01|1.714107e-07|2.851507e-08 - 152|1.663552e-01|1.491257e-07|2.480784e-08 - 153|1.663552e-01|1.513799e-07|2.518282e-08 - 154|1.663551e-01|1.354539e-07|2.253345e-08 - 155|1.663551e-01|1.233818e-07|2.052519e-08 - 156|1.663551e-01|1.576219e-07|2.622121e-08 - 157|1.663551e-01|1.452791e-07|2.416792e-08 - 158|1.663550e-01|1.262867e-07|2.100843e-08 - 159|1.663550e-01|1.316379e-07|2.189863e-08 - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 160|1.663550e-01|1.295447e-07|2.155041e-08 - 161|1.663550e-01|1.283286e-07|2.134810e-08 - 162|1.663550e-01|1.569222e-07|2.610479e-08 - 163|1.663549e-01|1.172942e-07|1.951247e-08 - 164|1.663549e-01|1.399809e-07|2.328651e-08 - 165|1.663549e-01|1.229432e-07|2.045221e-08 - 166|1.663549e-01|1.326191e-07|2.206184e-08 - 167|1.663548e-01|1.209694e-07|2.012384e-08 - 168|1.663548e-01|1.372136e-07|2.282614e-08 - 169|1.663548e-01|1.338395e-07|2.226484e-08 - 170|1.663548e-01|1.416497e-07|2.356410e-08 - 171|1.663548e-01|1.298576e-07|2.160242e-08 - 172|1.663547e-01|1.190590e-07|1.980603e-08 - 173|1.663547e-01|1.167083e-07|1.941497e-08 - 174|1.663547e-01|1.069425e-07|1.779038e-08 - 175|1.663547e-01|1.217780e-07|2.025834e-08 - 176|1.663547e-01|1.140754e-07|1.897697e-08 - 177|1.663546e-01|1.160707e-07|1.930890e-08 - 178|1.663546e-01|1.101798e-07|1.832892e-08 - 179|1.663546e-01|1.114904e-07|1.854694e-08 - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 180|1.663546e-01|1.064022e-07|1.770049e-08 - 181|1.663546e-01|9.258231e-08|1.540149e-08 - 182|1.663546e-01|1.213120e-07|2.018080e-08 - 183|1.663545e-01|1.164296e-07|1.936859e-08 - 184|1.663545e-01|1.188762e-07|1.977559e-08 - 185|1.663545e-01|9.394153e-08|1.562760e-08 - 186|1.663545e-01|1.028656e-07|1.711216e-08 - 187|1.663545e-01|1.115348e-07|1.855431e-08 - 188|1.663544e-01|9.768310e-08|1.625002e-08 - 189|1.663544e-01|1.021806e-07|1.699820e-08 - 190|1.663544e-01|1.086303e-07|1.807113e-08 - 191|1.663544e-01|9.879008e-08|1.643416e-08 - 192|1.663544e-01|1.050210e-07|1.747071e-08 - 193|1.663544e-01|1.002463e-07|1.667641e-08 - 194|1.663543e-01|1.062747e-07|1.767925e-08 - 195|1.663543e-01|9.348538e-08|1.555170e-08 - 196|1.663543e-01|7.992512e-08|1.329589e-08 - 197|1.663543e-01|9.558020e-08|1.590018e-08 - 198|1.663543e-01|9.993772e-08|1.662507e-08 - 199|1.663543e-01|8.588499e-08|1.428734e-08 - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 200|1.663543e-01|8.737134e-08|1.453459e-08 - - - - -Solve EMD with entropic regularization --------------------------------------- - - -.. code-block:: default - - - - def f(G): - return np.sum(G * np.log(G)) - - - def df(G): - return np.log(G) + 1. - - - reg = 1e-3 - - Ge = ot.optim.cg(a, b, M, reg, f, df, verbose=True) - - pl.figure(4, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg') - - - - -.. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_003.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 0|1.692289e-01|0.000000e+00|0.000000e+00 - 1|1.617643e-01|4.614437e-02|7.464513e-03 - 2|1.612639e-01|3.102965e-03|5.003963e-04 - 3|1.611291e-01|8.371098e-04|1.348827e-04 - 4|1.610468e-01|5.110558e-04|8.230389e-05 - 5|1.610198e-01|1.672927e-04|2.693743e-05 - 6|1.610130e-01|4.232417e-05|6.814742e-06 - 7|1.610090e-01|2.513455e-05|4.046887e-06 - 8|1.610002e-01|5.443507e-05|8.764057e-06 - 9|1.609996e-01|3.657071e-06|5.887869e-07 - 10|1.609948e-01|2.998735e-05|4.827807e-06 - 11|1.609695e-01|1.569217e-04|2.525962e-05 - 12|1.609533e-01|1.010779e-04|1.626881e-05 - 13|1.609520e-01|8.043897e-06|1.294681e-06 - 14|1.609465e-01|3.415246e-05|5.496718e-06 - 15|1.609386e-01|4.898605e-05|7.883745e-06 - 16|1.609324e-01|3.837052e-05|6.175060e-06 - 17|1.609298e-01|1.617826e-05|2.603564e-06 - 18|1.609184e-01|7.080015e-05|1.139305e-05 - 19|1.609083e-01|6.273206e-05|1.009411e-05 - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 20|1.608988e-01|5.940805e-05|9.558681e-06 - 21|1.608853e-01|8.380030e-05|1.348223e-05 - 22|1.608844e-01|5.185045e-06|8.341930e-07 - 23|1.608824e-01|1.279113e-05|2.057868e-06 - 24|1.608819e-01|3.156821e-06|5.078753e-07 - 25|1.608783e-01|2.205746e-05|3.548567e-06 - 26|1.608764e-01|1.189894e-05|1.914259e-06 - 27|1.608755e-01|5.474607e-06|8.807303e-07 - 28|1.608737e-01|1.144227e-05|1.840760e-06 - 29|1.608676e-01|3.775335e-05|6.073291e-06 - 30|1.608638e-01|2.348020e-05|3.777116e-06 - 31|1.608627e-01|6.863136e-06|1.104023e-06 - 32|1.608529e-01|6.110230e-05|9.828482e-06 - 33|1.608487e-01|2.641106e-05|4.248184e-06 - 34|1.608409e-01|4.823638e-05|7.758383e-06 - 35|1.608373e-01|2.256641e-05|3.629519e-06 - 36|1.608338e-01|2.132444e-05|3.429691e-06 - 37|1.608310e-01|1.786649e-05|2.873484e-06 - 38|1.608260e-01|3.103848e-05|4.991794e-06 - 39|1.608206e-01|3.321265e-05|5.341279e-06 - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 40|1.608201e-01|3.054747e-06|4.912648e-07 - 41|1.608195e-01|4.198335e-06|6.751739e-07 - 42|1.608193e-01|8.458736e-07|1.360328e-07 - 43|1.608159e-01|2.153759e-05|3.463587e-06 - 44|1.608115e-01|2.738314e-05|4.403523e-06 - 45|1.608108e-01|3.960032e-06|6.368161e-07 - 46|1.608081e-01|1.675447e-05|2.694254e-06 - 47|1.608072e-01|5.976340e-06|9.610383e-07 - 48|1.608046e-01|1.604130e-05|2.579515e-06 - 49|1.608020e-01|1.617036e-05|2.600226e-06 - 50|1.608014e-01|3.957795e-06|6.364188e-07 - 51|1.608011e-01|1.292411e-06|2.078211e-07 - 52|1.607998e-01|8.431795e-06|1.355831e-06 - 53|1.607964e-01|2.127054e-05|3.420225e-06 - 54|1.607947e-01|1.021878e-05|1.643126e-06 - 55|1.607947e-01|3.560621e-07|5.725288e-08 - 56|1.607900e-01|2.929781e-05|4.710793e-06 - 57|1.607890e-01|5.740229e-06|9.229659e-07 - 58|1.607858e-01|2.039550e-05|3.279306e-06 - 59|1.607836e-01|1.319545e-05|2.121612e-06 - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 60|1.607826e-01|6.378947e-06|1.025624e-06 - 61|1.607808e-01|1.145102e-05|1.841105e-06 - 62|1.607776e-01|1.941743e-05|3.121889e-06 - 63|1.607743e-01|2.087422e-05|3.356037e-06 - 64|1.607741e-01|1.310249e-06|2.106541e-07 - 65|1.607738e-01|1.682752e-06|2.705425e-07 - 66|1.607691e-01|2.913936e-05|4.684709e-06 - 67|1.607671e-01|1.288855e-05|2.072055e-06 - 68|1.607654e-01|1.002448e-05|1.611590e-06 - 69|1.607641e-01|8.209492e-06|1.319792e-06 - 70|1.607632e-01|5.588467e-06|8.984199e-07 - 71|1.607619e-01|8.050388e-06|1.294196e-06 - 72|1.607618e-01|9.417493e-07|1.513973e-07 - 73|1.607598e-01|1.210509e-05|1.946012e-06 - 74|1.607591e-01|4.392914e-06|7.062009e-07 - 75|1.607579e-01|7.759587e-06|1.247415e-06 - 76|1.607574e-01|2.760280e-06|4.437356e-07 - 77|1.607556e-01|1.146469e-05|1.843012e-06 - 78|1.607550e-01|3.689456e-06|5.930984e-07 - 79|1.607550e-01|4.065631e-08|6.535705e-09 - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 80|1.607539e-01|6.555681e-06|1.053852e-06 - 81|1.607528e-01|7.177470e-06|1.153798e-06 - 82|1.607527e-01|5.306068e-07|8.529648e-08 - 83|1.607514e-01|7.816045e-06|1.256440e-06 - 84|1.607511e-01|2.301970e-06|3.700442e-07 - 85|1.607504e-01|4.281072e-06|6.881840e-07 - 86|1.607503e-01|7.821886e-07|1.257370e-07 - 87|1.607480e-01|1.403013e-05|2.255315e-06 - 88|1.607480e-01|1.169298e-08|1.879624e-09 - 89|1.607473e-01|4.235982e-06|6.809227e-07 - 90|1.607470e-01|1.717105e-06|2.760195e-07 - 91|1.607470e-01|6.148402e-09|9.883374e-10 - - - - -Solve EMD with Frobenius norm + entropic regularization -------------------------------------------------------- - - -.. code-block:: default - - - - def f(G): - return 0.5 * np.sum(G**2) - - - def df(G): - return G - - - reg1 = 1e-3 - reg2 = 1e-1 - - Gel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True) - - pl.figure(5, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg') - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_004.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 0|1.693084e-01|0.000000e+00|0.000000e+00 - 1|1.610202e-01|5.147342e-02|8.288260e-03 - 2|1.609508e-01|4.309685e-04|6.936474e-05 - 3|1.609484e-01|1.524885e-05|2.454278e-06 - 4|1.609477e-01|3.863641e-06|6.218444e-07 - 5|1.609475e-01|1.433633e-06|2.307397e-07 - 6|1.609474e-01|6.332412e-07|1.019185e-07 - 7|1.609474e-01|2.950826e-07|4.749276e-08 - 8|1.609473e-01|1.508393e-07|2.427718e-08 - 9|1.609473e-01|7.859971e-08|1.265041e-08 - 10|1.609473e-01|4.337432e-08|6.980981e-09 - /home/rflamary/PYTHON/POT/examples/plot_optim_OTreg.py:129: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 0.985 seconds) - - -.. _sphx_glr_download_auto_examples_plot_optim_OTreg.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_optim_OTreg.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_optim_OTreg.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_classes.ipynb b/docs/source/auto_examples/plot_otda_classes.ipynb deleted file mode 100644 index 283d227..0000000 --- a/docs/source/auto_examples/plot_otda_classes.ipynb +++ /dev/null @@ -1,126 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# OT for domain adaptation\n\n\nThis example introduces a domain adaptation in a 2D setting and the 4 OTDA\napproaches currently supported in POT.\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n-------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_source_samples = 150\nn_target_samples = 150\n\nXs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples)\nXt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# EMD Transport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization\not_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\not_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization l1l2\not_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20,\n verbose=True)\not_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# transport source samples onto target samples\ntransp_Xs_emd = ot_emd.transform(Xs=Xs)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\ntransp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)\ntransp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 1 : plots source and target samples\n---------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(1, figsize=(10, 5))\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\npl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 2 : plot optimal couplings and transported samples\n------------------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "param_img = {'interpolation': 'nearest'}\n\npl.figure(2, figsize=(15, 8))\npl.subplot(2, 4, 1)\npl.imshow(ot_emd.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nEMDTransport')\n\npl.subplot(2, 4, 2)\npl.imshow(ot_sinkhorn.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornTransport')\n\npl.subplot(2, 4, 3)\npl.imshow(ot_lpl1.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornLpl1Transport')\n\npl.subplot(2, 4, 4)\npl.imshow(ot_l1l2.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornL1l2Transport')\n\npl.subplot(2, 4, 5)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=\"lower left\")\n\npl.subplot(2, 4, 6)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornTransport')\n\npl.subplot(2, 4, 7)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornLpl1Transport')\n\npl.subplot(2, 4, 8)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornL1l2Transport')\npl.tight_layout()\n\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_classes.py b/docs/source/auto_examples/plot_otda_classes.py deleted file mode 100644 index f028022..0000000 --- a/docs/source/auto_examples/plot_otda_classes.py +++ /dev/null @@ -1,149 +0,0 @@ -# -*- coding: utf-8 -*- -""" -======================== -OT for domain adaptation -======================== - -This example introduces a domain adaptation in a 2D setting and the 4 OTDA -approaches currently supported in POT. - -""" - -# Authors: Remi Flamary -# Stanislas Chambon -# -# License: MIT License - -import matplotlib.pylab as pl -import ot - -############################################################################## -# Generate data -# ------------- - -n_source_samples = 150 -n_target_samples = 150 - -Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) -Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) - - -############################################################################## -# Instantiate the different transport algorithms and fit them -# ----------------------------------------------------------- - -# EMD Transport -ot_emd = ot.da.EMDTransport() -ot_emd.fit(Xs=Xs, Xt=Xt) - -# Sinkhorn Transport -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - -# Sinkhorn Transport with Group lasso regularization -ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) -ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) - -# Sinkhorn Transport with Group lasso regularization l1l2 -ot_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20, - verbose=True) -ot_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt) - -# transport source samples onto target samples -transp_Xs_emd = ot_emd.transform(Xs=Xs) -transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) -transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) -transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs) - - -############################################################################## -# Fig 1 : plots source and target samples -# --------------------------------------- - -pl.figure(1, figsize=(10, 5)) -pl.subplot(1, 2, 1) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Source samples') - -pl.subplot(1, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Target samples') -pl.tight_layout() - - -############################################################################## -# Fig 2 : plot optimal couplings and transported samples -# ------------------------------------------------------ - -param_img = {'interpolation': 'nearest'} - -pl.figure(2, figsize=(15, 8)) -pl.subplot(2, 4, 1) -pl.imshow(ot_emd.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nEMDTransport') - -pl.subplot(2, 4, 2) -pl.imshow(ot_sinkhorn.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornTransport') - -pl.subplot(2, 4, 3) -pl.imshow(ot_lpl1.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornLpl1Transport') - -pl.subplot(2, 4, 4) -pl.imshow(ot_l1l2.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornL1l2Transport') - -pl.subplot(2, 4, 5) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nEmdTransport') -pl.legend(loc="lower left") - -pl.subplot(2, 4, 6) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nSinkhornTransport') - -pl.subplot(2, 4, 7) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nSinkhornLpl1Transport') - -pl.subplot(2, 4, 8) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nSinkhornL1l2Transport') -pl.tight_layout() - -pl.show() diff --git a/docs/source/auto_examples/plot_otda_classes.rst b/docs/source/auto_examples/plot_otda_classes.rst deleted file mode 100644 index 9cf31ee..0000000 --- a/docs/source/auto_examples/plot_otda_classes.rst +++ /dev/null @@ -1,287 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_otda_classes.py: - - -======================== -OT for domain adaptation -======================== - -This example introduces a domain adaptation in a 2D setting and the 4 OTDA -approaches currently supported in POT. - - - -.. code-block:: default - - - # Authors: Remi Flamary - # Stanislas Chambon - # - # License: MIT License - - import matplotlib.pylab as pl - import ot - - - - - - - - -Generate data -------------- - - -.. code-block:: default - - - n_source_samples = 150 - n_target_samples = 150 - - Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) - Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) - - - - - - - - - -Instantiate the different transport algorithms and fit them ------------------------------------------------------------ - - -.. code-block:: default - - - # EMD Transport - ot_emd = ot.da.EMDTransport() - ot_emd.fit(Xs=Xs, Xt=Xt) - - # Sinkhorn Transport - ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) - ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - - # Sinkhorn Transport with Group lasso regularization - ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) - ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) - - # Sinkhorn Transport with Group lasso regularization l1l2 - ot_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20, - verbose=True) - ot_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt) - - # transport source samples onto target samples - transp_Xs_emd = ot_emd.transform(Xs=Xs) - transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) - transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) - transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs) - - - - - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 0|9.484039e+00|0.000000e+00|0.000000e+00 - 1|1.976107e+00|3.799355e+00|7.507932e+00 - 2|1.749871e+00|1.292876e-01|2.262365e-01 - 3|1.692667e+00|3.379504e-02|5.720374e-02 - 4|1.676256e+00|9.790077e-03|1.641068e-02 - 5|1.667458e+00|5.276422e-03|8.798212e-03 - 6|1.661775e+00|3.419693e-03|5.682762e-03 - 7|1.658009e+00|2.271789e-03|3.766646e-03 - 8|1.655167e+00|1.716870e-03|2.841707e-03 - 9|1.651825e+00|2.023380e-03|3.342270e-03 - 10|1.649431e+00|1.451076e-03|2.393450e-03 - 11|1.648649e+00|4.742894e-04|7.819369e-04 - 12|1.647901e+00|4.538219e-04|7.478538e-04 - 13|1.647356e+00|3.313134e-04|5.457909e-04 - 14|1.646923e+00|2.627246e-04|4.326871e-04 - 15|1.646038e+00|5.375014e-04|8.847478e-04 - 16|1.645629e+00|2.483240e-04|4.086492e-04 - 17|1.645616e+00|8.248172e-06|1.357332e-05 - 18|1.645377e+00|1.452648e-04|2.390153e-04 - 19|1.644745e+00|3.838976e-04|6.314139e-04 - It. |Loss |Relative loss|Absolute loss - ------------------------------------------------ - 20|1.644164e+00|3.538439e-04|5.817773e-04 - - - - -Fig 1 : plots source and target samples ---------------------------------------- - - -.. code-block:: default - - - pl.figure(1, figsize=(10, 5)) - pl.subplot(1, 2, 1) - pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') - pl.xticks([]) - pl.yticks([]) - pl.legend(loc=0) - pl.title('Source samples') - - pl.subplot(1, 2, 2) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') - pl.xticks([]) - pl.yticks([]) - pl.legend(loc=0) - pl.title('Target samples') - pl.tight_layout() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_classes_001.png - :class: sphx-glr-single-img - - - - - -Fig 2 : plot optimal couplings and transported samples ------------------------------------------------------- - - -.. code-block:: default - - - param_img = {'interpolation': 'nearest'} - - pl.figure(2, figsize=(15, 8)) - pl.subplot(2, 4, 1) - pl.imshow(ot_emd.coupling_, **param_img) - pl.xticks([]) - pl.yticks([]) - pl.title('Optimal coupling\nEMDTransport') - - pl.subplot(2, 4, 2) - pl.imshow(ot_sinkhorn.coupling_, **param_img) - pl.xticks([]) - pl.yticks([]) - pl.title('Optimal coupling\nSinkhornTransport') - - pl.subplot(2, 4, 3) - pl.imshow(ot_lpl1.coupling_, **param_img) - pl.xticks([]) - pl.yticks([]) - pl.title('Optimal coupling\nSinkhornLpl1Transport') - - pl.subplot(2, 4, 4) - pl.imshow(ot_l1l2.coupling_, **param_img) - pl.xticks([]) - pl.yticks([]) - pl.title('Optimal coupling\nSinkhornL1l2Transport') - - pl.subplot(2, 4, 5) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) - pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, - marker='+', label='Transp samples', s=30) - pl.xticks([]) - pl.yticks([]) - pl.title('Transported samples\nEmdTransport') - pl.legend(loc="lower left") - - pl.subplot(2, 4, 6) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) - pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, - marker='+', label='Transp samples', s=30) - pl.xticks([]) - pl.yticks([]) - pl.title('Transported samples\nSinkhornTransport') - - pl.subplot(2, 4, 7) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) - pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, - marker='+', label='Transp samples', s=30) - pl.xticks([]) - pl.yticks([]) - pl.title('Transported samples\nSinkhornLpl1Transport') - - pl.subplot(2, 4, 8) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) - pl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys, - marker='+', label='Transp samples', s=30) - pl.xticks([]) - pl.yticks([]) - pl.title('Transported samples\nSinkhornL1l2Transport') - pl.tight_layout() - - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_classes_002.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_otda_classes.py:149: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 2.083 seconds) - - -.. _sphx_glr_download_auto_examples_plot_otda_classes.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_otda_classes.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_otda_classes.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_color_images.ipynb b/docs/source/auto_examples/plot_otda_color_images.ipynb deleted file mode 100644 index c2afd41..0000000 --- a/docs/source/auto_examples/plot_otda_color_images.ipynb +++ /dev/null @@ -1,144 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# OT for image color adaptation\n\n\nThis example presents a way of transferring colors between two images\nwith Optimal Transport as introduced in [6]\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).\nRegularized discrete optimal transport.\nSIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n\nr = np.random.RandomState(42)\n\n\ndef im2mat(I):\n \"\"\"Converts an image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n-------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Loading images\nI1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)\n\n# training samples\nnb = 1000\nidx1 = r.randint(X1.shape[0], size=(nb,))\nidx2 = r.randint(X2.shape[0], size=(nb,))\n\nXs = X1[idx1, :]\nXt = X2[idx2, :]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot original image\n-------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(1, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Scatter plot of colors\n----------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(2, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\npl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# EMDTransport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# SinkhornTransport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# prediction between images (using out of sample prediction as in [6])\ntransp_Xs_emd = ot_emd.transform(Xs=X1)\ntransp_Xt_emd = ot_emd.inverse_transform(Xt=X2)\n\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)\ntransp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2)\n\nI1t = minmax(mat2im(transp_Xs_emd, I1.shape))\nI2t = minmax(mat2im(transp_Xt_emd, I2.shape))\n\nI1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\nI2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot new images\n---------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(3, figsize=(8, 4))\n\npl.subplot(2, 3, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(2, 3, 2)\npl.imshow(I1t)\npl.axis('off')\npl.title('Image 1 Adapt')\n\npl.subplot(2, 3, 3)\npl.imshow(I1te)\npl.axis('off')\npl.title('Image 1 Adapt (reg)')\n\npl.subplot(2, 3, 4)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')\n\npl.subplot(2, 3, 5)\npl.imshow(I2t)\npl.axis('off')\npl.title('Image 2 Adapt')\n\npl.subplot(2, 3, 6)\npl.imshow(I2te)\npl.axis('off')\npl.title('Image 2 Adapt (reg)')\npl.tight_layout()\n\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_color_images.py b/docs/source/auto_examples/plot_otda_color_images.py deleted file mode 100644 index d9cbd2b..0000000 --- a/docs/source/auto_examples/plot_otda_color_images.py +++ /dev/null @@ -1,164 +0,0 @@ -# -*- coding: utf-8 -*- -""" -============================= -OT for image color adaptation -============================= - -This example presents a way of transferring colors between two images -with Optimal Transport as introduced in [6] - -[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). -Regularized discrete optimal transport. -SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -""" - -# Authors: Remi Flamary -# Stanislas Chambon -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot - - -r = np.random.RandomState(42) - - -def im2mat(I): - """Converts an image to matrix (one pixel per line)""" - return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) - - -def mat2im(X, shape): - """Converts back a matrix to an image""" - return X.reshape(shape) - - -def minmax(I): - return np.clip(I, 0, 1) - - -############################################################################## -# Generate data -# ------------- - -# Loading images -I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 - -X1 = im2mat(I1) -X2 = im2mat(I2) - -# training samples -nb = 1000 -idx1 = r.randint(X1.shape[0], size=(nb,)) -idx2 = r.randint(X2.shape[0], size=(nb,)) - -Xs = X1[idx1, :] -Xt = X2[idx2, :] - - -############################################################################## -# Plot original image -# ------------------- - -pl.figure(1, figsize=(6.4, 3)) - -pl.subplot(1, 2, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Image 1') - -pl.subplot(1, 2, 2) -pl.imshow(I2) -pl.axis('off') -pl.title('Image 2') - - -############################################################################## -# Scatter plot of colors -# ---------------------- - -pl.figure(2, figsize=(6.4, 3)) - -pl.subplot(1, 2, 1) -pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) -pl.axis([0, 1, 0, 1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 1') - -pl.subplot(1, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) -pl.axis([0, 1, 0, 1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 2') -pl.tight_layout() - - -############################################################################## -# Instantiate the different transport algorithms and fit them -# ----------------------------------------------------------- - -# EMDTransport -ot_emd = ot.da.EMDTransport() -ot_emd.fit(Xs=Xs, Xt=Xt) - -# SinkhornTransport -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - -# prediction between images (using out of sample prediction as in [6]) -transp_Xs_emd = ot_emd.transform(Xs=X1) -transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) - -transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) -transp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2) - -I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) -I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) - -I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) -I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) - - -############################################################################## -# Plot new images -# --------------- - -pl.figure(3, figsize=(8, 4)) - -pl.subplot(2, 3, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Image 1') - -pl.subplot(2, 3, 2) -pl.imshow(I1t) -pl.axis('off') -pl.title('Image 1 Adapt') - -pl.subplot(2, 3, 3) -pl.imshow(I1te) -pl.axis('off') -pl.title('Image 1 Adapt (reg)') - -pl.subplot(2, 3, 4) -pl.imshow(I2) -pl.axis('off') -pl.title('Image 2') - -pl.subplot(2, 3, 5) -pl.imshow(I2t) -pl.axis('off') -pl.title('Image 2 Adapt') - -pl.subplot(2, 3, 6) -pl.imshow(I2te) -pl.axis('off') -pl.title('Image 2 Adapt (reg)') -pl.tight_layout() - -pl.show() diff --git a/docs/source/auto_examples/plot_otda_color_images.rst b/docs/source/auto_examples/plot_otda_color_images.rst deleted file mode 100644 index a5b0d53..0000000 --- a/docs/source/auto_examples/plot_otda_color_images.rst +++ /dev/null @@ -1,291 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_otda_color_images.py: - - -============================= -OT for image color adaptation -============================= - -This example presents a way of transferring colors between two images -with Optimal Transport as introduced in [6] - -[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). -Regularized discrete optimal transport. -SIAM Journal on Imaging Sciences, 7(3), 1853-1882. - - -.. code-block:: default - - - # Authors: Remi Flamary - # Stanislas Chambon - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot - - - r = np.random.RandomState(42) - - - def im2mat(I): - """Converts an image to matrix (one pixel per line)""" - return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) - - - def mat2im(X, shape): - """Converts back a matrix to an image""" - return X.reshape(shape) - - - def minmax(I): - return np.clip(I, 0, 1) - - - - - - - - - -Generate data -------------- - - -.. code-block:: default - - - # Loading images - I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 - I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 - - X1 = im2mat(I1) - X2 = im2mat(I2) - - # training samples - nb = 1000 - idx1 = r.randint(X1.shape[0], size=(nb,)) - idx2 = r.randint(X2.shape[0], size=(nb,)) - - Xs = X1[idx1, :] - Xt = X2[idx2, :] - - - - - - - - - -Plot original image -------------------- - - -.. code-block:: default - - - pl.figure(1, figsize=(6.4, 3)) - - pl.subplot(1, 2, 1) - pl.imshow(I1) - pl.axis('off') - pl.title('Image 1') - - pl.subplot(1, 2, 2) - pl.imshow(I2) - pl.axis('off') - pl.title('Image 2') - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_color_images_001.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - - Text(0.5, 1.0, 'Image 2') - - - -Scatter plot of colors ----------------------- - - -.. code-block:: default - - - pl.figure(2, figsize=(6.4, 3)) - - pl.subplot(1, 2, 1) - pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) - pl.axis([0, 1, 0, 1]) - pl.xlabel('Red') - pl.ylabel('Blue') - pl.title('Image 1') - - pl.subplot(1, 2, 2) - pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) - pl.axis([0, 1, 0, 1]) - pl.xlabel('Red') - pl.ylabel('Blue') - pl.title('Image 2') - pl.tight_layout() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_color_images_002.png - :class: sphx-glr-single-img - - - - - -Instantiate the different transport algorithms and fit them ------------------------------------------------------------ - - -.. code-block:: default - - - # EMDTransport - ot_emd = ot.da.EMDTransport() - ot_emd.fit(Xs=Xs, Xt=Xt) - - # SinkhornTransport - ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) - ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - - # prediction between images (using out of sample prediction as in [6]) - transp_Xs_emd = ot_emd.transform(Xs=X1) - transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) - - transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) - transp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2) - - I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) - I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) - - I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) - I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) - - - - - - - - - -Plot new images ---------------- - - -.. code-block:: default - - - pl.figure(3, figsize=(8, 4)) - - pl.subplot(2, 3, 1) - pl.imshow(I1) - pl.axis('off') - pl.title('Image 1') - - pl.subplot(2, 3, 2) - pl.imshow(I1t) - pl.axis('off') - pl.title('Image 1 Adapt') - - pl.subplot(2, 3, 3) - pl.imshow(I1te) - pl.axis('off') - pl.title('Image 1 Adapt (reg)') - - pl.subplot(2, 3, 4) - pl.imshow(I2) - pl.axis('off') - pl.title('Image 2') - - pl.subplot(2, 3, 5) - pl.imshow(I2t) - pl.axis('off') - pl.title('Image 2 Adapt') - - pl.subplot(2, 3, 6) - pl.imshow(I2te) - pl.axis('off') - pl.title('Image 2 Adapt (reg)') - pl.tight_layout() - - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_color_images_003.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_otda_color_images.py:164: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 2 minutes 28.821 seconds) - - -.. _sphx_glr_download_auto_examples_plot_otda_color_images.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_otda_color_images.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_otda_color_images.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_d2.ipynb b/docs/source/auto_examples/plot_otda_d2.ipynb deleted file mode 100644 index a2a7839..0000000 --- a/docs/source/auto_examples/plot_otda_d2.ipynb +++ /dev/null @@ -1,144 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# OT for domain adaptation on empirical distributions\n\n\nThis example introduces a domain adaptation in a 2D setting. It explicits\nthe problem of domain adaptation and introduces some optimal transport\napproaches to solve it.\n\nQuantities such as optimal couplings, greater coupling coefficients and\ntransported samples are represented in order to give a visual understanding\nof what the transport methods are doing.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "generate data\n-------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_samples_source = 150\nn_samples_target = 150\n\nXs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)\nXt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)\n\n# Cost matrix\nM = ot.dist(Xs, Xt, metric='sqeuclidean')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# EMD Transport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization\not_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\not_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# transport source samples onto target samples\ntransp_Xs_emd = ot_emd.transform(Xs=Xs)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\ntransp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 1 : plots source and target samples + matrix of pairwise distance\n---------------------------------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(1, figsize=(10, 10))\npl.subplot(2, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\n\npl.subplot(2, 2, 3)\npl.imshow(M, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Matrix of pairwise distances')\npl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 2 : plots optimal couplings for the different methods\n---------------------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(2, figsize=(10, 6))\n\npl.subplot(2, 3, 1)\npl.imshow(ot_emd.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nEMDTransport')\n\npl.subplot(2, 3, 2)\npl.imshow(ot_sinkhorn.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornTransport')\n\npl.subplot(2, 3, 3)\npl.imshow(ot_lpl1.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornLpl1Transport')\n\npl.subplot(2, 3, 4)\not.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nEMDTransport')\n\npl.subplot(2, 3, 5)\not.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nSinkhornTransport')\n\npl.subplot(2, 3, 6)\not.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nSinkhornLpl1Transport')\npl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 3 : plot transported samples\n--------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# display transported samples\npl.figure(4, figsize=(10, 4))\npl.subplot(1, 3, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=0)\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 3, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornTransport')\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 3, 3)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornLpl1Transport')\npl.xticks([])\npl.yticks([])\n\npl.tight_layout()\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_d2.py b/docs/source/auto_examples/plot_otda_d2.py deleted file mode 100644 index cf22c2f..0000000 --- a/docs/source/auto_examples/plot_otda_d2.py +++ /dev/null @@ -1,172 +0,0 @@ -# -*- coding: utf-8 -*- -""" -=================================================== -OT for domain adaptation on empirical distributions -=================================================== - -This example introduces a domain adaptation in a 2D setting. It explicits -the problem of domain adaptation and introduces some optimal transport -approaches to solve it. - -Quantities such as optimal couplings, greater coupling coefficients and -transported samples are represented in order to give a visual understanding -of what the transport methods are doing. -""" - -# Authors: Remi Flamary -# Stanislas Chambon -# -# License: MIT License - -import matplotlib.pylab as pl -import ot -import ot.plot - -############################################################################## -# generate data -# ------------- - -n_samples_source = 150 -n_samples_target = 150 - -Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) -Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) - -# Cost matrix -M = ot.dist(Xs, Xt, metric='sqeuclidean') - - -############################################################################## -# Instantiate the different transport algorithms and fit them -# ----------------------------------------------------------- - -# EMD Transport -ot_emd = ot.da.EMDTransport() -ot_emd.fit(Xs=Xs, Xt=Xt) - -# Sinkhorn Transport -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - -# Sinkhorn Transport with Group lasso regularization -ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) -ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) - -# transport source samples onto target samples -transp_Xs_emd = ot_emd.transform(Xs=Xs) -transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) -transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) - - -############################################################################## -# Fig 1 : plots source and target samples + matrix of pairwise distance -# --------------------------------------------------------------------- - -pl.figure(1, figsize=(10, 10)) -pl.subplot(2, 2, 1) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Source samples') - -pl.subplot(2, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Target samples') - -pl.subplot(2, 2, 3) -pl.imshow(M, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Matrix of pairwise distances') -pl.tight_layout() - - -############################################################################## -# Fig 2 : plots optimal couplings for the different methods -# --------------------------------------------------------- -pl.figure(2, figsize=(10, 6)) - -pl.subplot(2, 3, 1) -pl.imshow(ot_emd.coupling_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nEMDTransport') - -pl.subplot(2, 3, 2) -pl.imshow(ot_sinkhorn.coupling_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornTransport') - -pl.subplot(2, 3, 3) -pl.imshow(ot_lpl1.coupling_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornLpl1Transport') - -pl.subplot(2, 3, 4) -ot.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1]) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.title('Main coupling coefficients\nEMDTransport') - -pl.subplot(2, 3, 5) -ot.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1]) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.title('Main coupling coefficients\nSinkhornTransport') - -pl.subplot(2, 3, 6) -ot.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1]) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.title('Main coupling coefficients\nSinkhornLpl1Transport') -pl.tight_layout() - - -############################################################################## -# Fig 3 : plot transported samples -# -------------------------------- - -# display transported samples -pl.figure(4, figsize=(10, 4)) -pl.subplot(1, 3, 1) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Transported samples\nEmdTransport') -pl.legend(loc=0) -pl.xticks([]) -pl.yticks([]) - -pl.subplot(1, 3, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Transported samples\nSinkhornTransport') -pl.xticks([]) -pl.yticks([]) - -pl.subplot(1, 3, 3) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Transported samples\nSinkhornLpl1Transport') -pl.xticks([]) -pl.yticks([]) - -pl.tight_layout() -pl.show() diff --git a/docs/source/auto_examples/plot_otda_d2.rst b/docs/source/auto_examples/plot_otda_d2.rst deleted file mode 100644 index 6d8e429..0000000 --- a/docs/source/auto_examples/plot_otda_d2.rst +++ /dev/null @@ -1,291 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_otda_d2.py: - - -=================================================== -OT for domain adaptation on empirical distributions -=================================================== - -This example introduces a domain adaptation in a 2D setting. It explicits -the problem of domain adaptation and introduces some optimal transport -approaches to solve it. - -Quantities such as optimal couplings, greater coupling coefficients and -transported samples are represented in order to give a visual understanding -of what the transport methods are doing. - - -.. code-block:: default - - - # Authors: Remi Flamary - # Stanislas Chambon - # - # License: MIT License - - import matplotlib.pylab as pl - import ot - import ot.plot - - - - - - - - -generate data -------------- - - -.. code-block:: default - - - n_samples_source = 150 - n_samples_target = 150 - - Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) - Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) - - # Cost matrix - M = ot.dist(Xs, Xt, metric='sqeuclidean') - - - - - - - - - -Instantiate the different transport algorithms and fit them ------------------------------------------------------------ - - -.. code-block:: default - - - # EMD Transport - ot_emd = ot.da.EMDTransport() - ot_emd.fit(Xs=Xs, Xt=Xt) - - # Sinkhorn Transport - ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) - ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - - # Sinkhorn Transport with Group lasso regularization - ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) - ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) - - # transport source samples onto target samples - transp_Xs_emd = ot_emd.transform(Xs=Xs) - transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) - transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) - - - - - - - - - -Fig 1 : plots source and target samples + matrix of pairwise distance ---------------------------------------------------------------------- - - -.. code-block:: default - - - pl.figure(1, figsize=(10, 10)) - pl.subplot(2, 2, 1) - pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') - pl.xticks([]) - pl.yticks([]) - pl.legend(loc=0) - pl.title('Source samples') - - pl.subplot(2, 2, 2) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') - pl.xticks([]) - pl.yticks([]) - pl.legend(loc=0) - pl.title('Target samples') - - pl.subplot(2, 2, 3) - pl.imshow(M, interpolation='nearest') - pl.xticks([]) - pl.yticks([]) - pl.title('Matrix of pairwise distances') - pl.tight_layout() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_001.png - :class: sphx-glr-single-img - - - - - -Fig 2 : plots optimal couplings for the different methods ---------------------------------------------------------- - - -.. code-block:: default - - pl.figure(2, figsize=(10, 6)) - - pl.subplot(2, 3, 1) - pl.imshow(ot_emd.coupling_, interpolation='nearest') - pl.xticks([]) - pl.yticks([]) - pl.title('Optimal coupling\nEMDTransport') - - pl.subplot(2, 3, 2) - pl.imshow(ot_sinkhorn.coupling_, interpolation='nearest') - pl.xticks([]) - pl.yticks([]) - pl.title('Optimal coupling\nSinkhornTransport') - - pl.subplot(2, 3, 3) - pl.imshow(ot_lpl1.coupling_, interpolation='nearest') - pl.xticks([]) - pl.yticks([]) - pl.title('Optimal coupling\nSinkhornLpl1Transport') - - pl.subplot(2, 3, 4) - ot.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1]) - pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') - pl.xticks([]) - pl.yticks([]) - pl.title('Main coupling coefficients\nEMDTransport') - - pl.subplot(2, 3, 5) - ot.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1]) - pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') - pl.xticks([]) - pl.yticks([]) - pl.title('Main coupling coefficients\nSinkhornTransport') - - pl.subplot(2, 3, 6) - ot.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1]) - pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') - pl.xticks([]) - pl.yticks([]) - pl.title('Main coupling coefficients\nSinkhornLpl1Transport') - pl.tight_layout() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_002.png - :class: sphx-glr-single-img - - - - - -Fig 3 : plot transported samples --------------------------------- - - -.. code-block:: default - - - # display transported samples - pl.figure(4, figsize=(10, 4)) - pl.subplot(1, 3, 1) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) - pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, - marker='+', label='Transp samples', s=30) - pl.title('Transported samples\nEmdTransport') - pl.legend(loc=0) - pl.xticks([]) - pl.yticks([]) - - pl.subplot(1, 3, 2) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) - pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, - marker='+', label='Transp samples', s=30) - pl.title('Transported samples\nSinkhornTransport') - pl.xticks([]) - pl.yticks([]) - - pl.subplot(1, 3, 3) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) - pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, - marker='+', label='Transp samples', s=30) - pl.title('Transported samples\nSinkhornLpl1Transport') - pl.xticks([]) - pl.yticks([]) - - pl.tight_layout() - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_003.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_otda_d2.py:172: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 21.323 seconds) - - -.. _sphx_glr_download_auto_examples_plot_otda_d2.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_otda_d2.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_otda_d2.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_jcpot.ipynb b/docs/source/auto_examples/plot_otda_jcpot.ipynb deleted file mode 100644 index a81d47a..0000000 --- a/docs/source/auto_examples/plot_otda_jcpot.ipynb +++ /dev/null @@ -1,173 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# OT for multi-source target shift\n\n\nThis example introduces a target shift problem with two 2D source and 1 target domain.\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Authors: Remi Flamary \n# Ievgen Redko \n#\n# License: MIT License\n\nimport pylab as pl\nimport numpy as np\nimport ot\nfrom ot.datasets import make_data_classif" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n-------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 50\nsigma = 0.3\nnp.random.seed(1985)\n\np1 = .2\ndec1 = [0, 2]\n\np2 = .9\ndec2 = [0, -2]\n\npt = .4\ndect = [4, 0]\n\nxs1, ys1 = make_data_classif('2gauss_prop', n, nz=sigma, p=p1, bias=dec1)\nxs2, ys2 = make_data_classif('2gauss_prop', n + 1, nz=sigma, p=p2, bias=dec2)\nxt, yt = make_data_classif('2gauss_prop', n, nz=sigma, p=pt, bias=dect)\n\nall_Xr = [xs1, xs2]\nall_Yr = [ys1, ys2]" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "da = 1.5\n\n\ndef plot_ax(dec, name):\n pl.plot([dec[0], dec[0]], [dec[1] - da, dec[1] + da], 'k', alpha=0.5)\n pl.plot([dec[0] - da, dec[0] + da], [dec[1], dec[1]], 'k', alpha=0.5)\n pl.text(dec[0] - .5, dec[1] + 2, name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 1 : plots source and target samples\n---------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(1)\npl.clf()\nplot_ax(dec1, 'Source 1')\nplot_ax(dec2, 'Source 2')\nplot_ax(dect, 'Target')\npl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9,\n label='Source 1 ({:1.2f}, {:1.2f})'.format(1 - p1, p1))\npl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9,\n label='Source 2 ({:1.2f}, {:1.2f})'.format(1 - p2, p2))\npl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9,\n label='Target ({:1.2f}, {:1.2f})'.format(1 - pt, pt))\npl.title('Data')\n\npl.legend()\npl.axis('equal')\npl.axis('off')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instantiate Sinkhorn transport algorithm and fit them for all source domains\n----------------------------------------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1, metric='sqeuclidean')\n\n\ndef print_G(G, xs, ys, xt):\n for i in range(G.shape[0]):\n for j in range(G.shape[1]):\n if G[i, j] > 5e-4:\n if ys[i]:\n c = 'b'\n else:\n c = 'r'\n pl.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], xt[j, 1]], c, alpha=.2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 2 : plot optimal couplings and transported samples\n------------------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(2)\npl.clf()\nplot_ax(dec1, 'Source 1')\nplot_ax(dec2, 'Source 2')\nplot_ax(dect, 'Target')\nprint_G(ot_sinkhorn.fit(Xs=xs1, Xt=xt).coupling_, xs1, ys1, xt)\nprint_G(ot_sinkhorn.fit(Xs=xs2, Xt=xt).coupling_, xs2, ys2, xt)\npl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9)\npl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9)\npl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9)\n\npl.plot([], [], 'r', alpha=.2, label='Mass from Class 1')\npl.plot([], [], 'b', alpha=.2, label='Mass from Class 2')\n\npl.title('Independent OT')\n\npl.legend()\npl.axis('equal')\npl.axis('off')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instantiate JCPOT adaptation algorithm and fit it\n----------------------------------------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "otda = ot.da.JCPOTTransport(reg_e=1, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True)\notda.fit(all_Xr, all_Yr, xt)\n\nws1 = otda.proportions_.dot(otda.log_['D2'][0])\nws2 = otda.proportions_.dot(otda.log_['D2'][1])\n\npl.figure(3)\npl.clf()\nplot_ax(dec1, 'Source 1')\nplot_ax(dec2, 'Source 2')\nplot_ax(dect, 'Target')\nprint_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt)\nprint_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt)\npl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9)\npl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9)\npl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9)\n\npl.plot([], [], 'r', alpha=.2, label='Mass from Class 1')\npl.plot([], [], 'b', alpha=.2, label='Mass from Class 2')\n\npl.title('OT with prop estimation ({:1.3f},{:1.3f})'.format(otda.proportions_[0], otda.proportions_[1]))\n\npl.legend()\npl.axis('equal')\npl.axis('off')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run oracle transport algorithm with known proportions\n----------------------------------------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "h_res = np.array([1 - pt, pt])\n\nws1 = h_res.dot(otda.log_['D2'][0])\nws2 = h_res.dot(otda.log_['D2'][1])\n\npl.figure(4)\npl.clf()\nplot_ax(dec1, 'Source 1')\nplot_ax(dec2, 'Source 2')\nplot_ax(dect, 'Target')\nprint_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt)\nprint_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt)\npl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9)\npl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9)\npl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9)\n\npl.plot([], [], 'r', alpha=.2, label='Mass from Class 1')\npl.plot([], [], 'b', alpha=.2, label='Mass from Class 2')\n\npl.title('OT with known proportion ({:1.1f},{:1.1f})'.format(h_res[0], h_res[1]))\n\npl.legend()\npl.axis('equal')\npl.axis('off')\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_jcpot.py b/docs/source/auto_examples/plot_otda_jcpot.py deleted file mode 100644 index c495690..0000000 --- a/docs/source/auto_examples/plot_otda_jcpot.py +++ /dev/null @@ -1,171 +0,0 @@ -# -*- coding: utf-8 -*- -""" -======================== -OT for multi-source target shift -======================== - -This example introduces a target shift problem with two 2D source and 1 target domain. - -""" - -# Authors: Remi Flamary -# Ievgen Redko -# -# License: MIT License - -import pylab as pl -import numpy as np -import ot -from ot.datasets import make_data_classif - -############################################################################## -# Generate data -# ------------- -n = 50 -sigma = 0.3 -np.random.seed(1985) - -p1 = .2 -dec1 = [0, 2] - -p2 = .9 -dec2 = [0, -2] - -pt = .4 -dect = [4, 0] - -xs1, ys1 = make_data_classif('2gauss_prop', n, nz=sigma, p=p1, bias=dec1) -xs2, ys2 = make_data_classif('2gauss_prop', n + 1, nz=sigma, p=p2, bias=dec2) -xt, yt = make_data_classif('2gauss_prop', n, nz=sigma, p=pt, bias=dect) - -all_Xr = [xs1, xs2] -all_Yr = [ys1, ys2] -# %% - -da = 1.5 - - -def plot_ax(dec, name): - pl.plot([dec[0], dec[0]], [dec[1] - da, dec[1] + da], 'k', alpha=0.5) - pl.plot([dec[0] - da, dec[0] + da], [dec[1], dec[1]], 'k', alpha=0.5) - pl.text(dec[0] - .5, dec[1] + 2, name) - - -############################################################################## -# Fig 1 : plots source and target samples -# --------------------------------------- - -pl.figure(1) -pl.clf() -plot_ax(dec1, 'Source 1') -plot_ax(dec2, 'Source 2') -plot_ax(dect, 'Target') -pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9, - label='Source 1 ({:1.2f}, {:1.2f})'.format(1 - p1, p1)) -pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9, - label='Source 2 ({:1.2f}, {:1.2f})'.format(1 - p2, p2)) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9, - label='Target ({:1.2f}, {:1.2f})'.format(1 - pt, pt)) -pl.title('Data') - -pl.legend() -pl.axis('equal') -pl.axis('off') - -############################################################################## -# Instantiate Sinkhorn transport algorithm and fit them for all source domains -# ---------------------------------------------------------------------------- -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1, metric='sqeuclidean') - - -def print_G(G, xs, ys, xt): - for i in range(G.shape[0]): - for j in range(G.shape[1]): - if G[i, j] > 5e-4: - if ys[i]: - c = 'b' - else: - c = 'r' - pl.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], xt[j, 1]], c, alpha=.2) - - -############################################################################## -# Fig 2 : plot optimal couplings and transported samples -# ------------------------------------------------------ -pl.figure(2) -pl.clf() -plot_ax(dec1, 'Source 1') -plot_ax(dec2, 'Source 2') -plot_ax(dect, 'Target') -print_G(ot_sinkhorn.fit(Xs=xs1, Xt=xt).coupling_, xs1, ys1, xt) -print_G(ot_sinkhorn.fit(Xs=xs2, Xt=xt).coupling_, xs2, ys2, xt) -pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) -pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) - -pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') -pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') - -pl.title('Independent OT') - -pl.legend() -pl.axis('equal') -pl.axis('off') - -############################################################################## -# Instantiate JCPOT adaptation algorithm and fit it -# ---------------------------------------------------------------------------- -otda = ot.da.JCPOTTransport(reg_e=1, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True) -otda.fit(all_Xr, all_Yr, xt) - -ws1 = otda.proportions_.dot(otda.log_['D2'][0]) -ws2 = otda.proportions_.dot(otda.log_['D2'][1]) - -pl.figure(3) -pl.clf() -plot_ax(dec1, 'Source 1') -plot_ax(dec2, 'Source 2') -plot_ax(dect, 'Target') -print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt) -print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt) -pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) -pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) - -pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') -pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') - -pl.title('OT with prop estimation ({:1.3f},{:1.3f})'.format(otda.proportions_[0], otda.proportions_[1])) - -pl.legend() -pl.axis('equal') -pl.axis('off') - -############################################################################## -# Run oracle transport algorithm with known proportions -# ---------------------------------------------------------------------------- -h_res = np.array([1 - pt, pt]) - -ws1 = h_res.dot(otda.log_['D2'][0]) -ws2 = h_res.dot(otda.log_['D2'][1]) - -pl.figure(4) -pl.clf() -plot_ax(dec1, 'Source 1') -plot_ax(dec2, 'Source 2') -plot_ax(dect, 'Target') -print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt) -print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt) -pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) -pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) - -pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') -pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') - -pl.title('OT with known proportion ({:1.1f},{:1.1f})'.format(h_res[0], h_res[1])) - -pl.legend() -pl.axis('equal') -pl.axis('off') -pl.show() diff --git a/docs/source/auto_examples/plot_otda_jcpot.rst b/docs/source/auto_examples/plot_otda_jcpot.rst deleted file mode 100644 index 3433190..0000000 --- a/docs/source/auto_examples/plot_otda_jcpot.rst +++ /dev/null @@ -1,336 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_otda_jcpot.py: - - -======================== -OT for multi-source target shift -======================== - -This example introduces a target shift problem with two 2D source and 1 target domain. - - - -.. code-block:: default - - - # Authors: Remi Flamary - # Ievgen Redko - # - # License: MIT License - - import pylab as pl - import numpy as np - import ot - from ot.datasets import make_data_classif - - - - - - - - -Generate data -------------- - - -.. code-block:: default - - n = 50 - sigma = 0.3 - np.random.seed(1985) - - p1 = .2 - dec1 = [0, 2] - - p2 = .9 - dec2 = [0, -2] - - pt = .4 - dect = [4, 0] - - xs1, ys1 = make_data_classif('2gauss_prop', n, nz=sigma, p=p1, bias=dec1) - xs2, ys2 = make_data_classif('2gauss_prop', n + 1, nz=sigma, p=p2, bias=dec2) - xt, yt = make_data_classif('2gauss_prop', n, nz=sigma, p=pt, bias=dect) - - all_Xr = [xs1, xs2] - all_Yr = [ys1, ys2] - - - - - - - - -.. code-block:: default - - - da = 1.5 - - - def plot_ax(dec, name): - pl.plot([dec[0], dec[0]], [dec[1] - da, dec[1] + da], 'k', alpha=0.5) - pl.plot([dec[0] - da, dec[0] + da], [dec[1], dec[1]], 'k', alpha=0.5) - pl.text(dec[0] - .5, dec[1] + 2, name) - - - - - - - - - -Fig 1 : plots source and target samples ---------------------------------------- - - -.. code-block:: default - - - pl.figure(1) - pl.clf() - plot_ax(dec1, 'Source 1') - plot_ax(dec2, 'Source 2') - plot_ax(dect, 'Target') - pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9, - label='Source 1 ({:1.2f}, {:1.2f})'.format(1 - p1, p1)) - pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9, - label='Source 2 ({:1.2f}, {:1.2f})'.format(1 - p2, p2)) - pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9, - label='Target ({:1.2f}, {:1.2f})'.format(1 - pt, pt)) - pl.title('Data') - - pl.legend() - pl.axis('equal') - pl.axis('off') - - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_jcpot_001.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - - (-1.85, 5.85, -4.1171725099266725, 4.197384527473105) - - - -Instantiate Sinkhorn transport algorithm and fit them for all source domains ----------------------------------------------------------------------------- - - -.. code-block:: default - - ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1, metric='sqeuclidean') - - - def print_G(G, xs, ys, xt): - for i in range(G.shape[0]): - for j in range(G.shape[1]): - if G[i, j] > 5e-4: - if ys[i]: - c = 'b' - else: - c = 'r' - pl.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], xt[j, 1]], c, alpha=.2) - - - - - - - - - -Fig 2 : plot optimal couplings and transported samples ------------------------------------------------------- - - -.. code-block:: default - - pl.figure(2) - pl.clf() - plot_ax(dec1, 'Source 1') - plot_ax(dec2, 'Source 2') - plot_ax(dect, 'Target') - print_G(ot_sinkhorn.fit(Xs=xs1, Xt=xt).coupling_, xs1, ys1, xt) - print_G(ot_sinkhorn.fit(Xs=xs2, Xt=xt).coupling_, xs2, ys2, xt) - pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) - pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) - pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) - - pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') - pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') - - pl.title('Independent OT') - - pl.legend() - pl.axis('equal') - pl.axis('off') - - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_jcpot_002.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - - (-1.85, 5.85, -4.11901398007908, 4.201462272227509) - - - -Instantiate JCPOT adaptation algorithm and fit it ----------------------------------------------------------------------------- - - -.. code-block:: default - - otda = ot.da.JCPOTTransport(reg_e=1, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True) - otda.fit(all_Xr, all_Yr, xt) - - ws1 = otda.proportions_.dot(otda.log_['D2'][0]) - ws2 = otda.proportions_.dot(otda.log_['D2'][1]) - - pl.figure(3) - pl.clf() - plot_ax(dec1, 'Source 1') - plot_ax(dec2, 'Source 2') - plot_ax(dect, 'Target') - print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt) - print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt) - pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) - pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) - pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) - - pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') - pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') - - pl.title('OT with prop estimation ({:1.3f},{:1.3f})'.format(otda.proportions_[0], otda.proportions_[1])) - - pl.legend() - pl.axis('equal') - pl.axis('off') - - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_jcpot_003.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - - (-1.85, 5.85, -4.11901398007908, 4.201462272227509) - - - -Run oracle transport algorithm with known proportions ----------------------------------------------------------------------------- - - -.. code-block:: default - - h_res = np.array([1 - pt, pt]) - - ws1 = h_res.dot(otda.log_['D2'][0]) - ws2 = h_res.dot(otda.log_['D2'][1]) - - pl.figure(4) - pl.clf() - plot_ax(dec1, 'Source 1') - plot_ax(dec2, 'Source 2') - plot_ax(dect, 'Target') - print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt) - print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt) - pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) - pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) - pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) - - pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') - pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') - - pl.title('OT with known proportion ({:1.1f},{:1.1f})'.format(h_res[0], h_res[1])) - - pl.legend() - pl.axis('equal') - pl.axis('off') - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_jcpot_004.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_otda_jcpot.py:171: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 4.725 seconds) - - -.. _sphx_glr_download_auto_examples_plot_otda_jcpot.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_otda_jcpot.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_otda_jcpot.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_laplacian.ipynb b/docs/source/auto_examples/plot_otda_laplacian.ipynb deleted file mode 100644 index c1e9efe..0000000 --- a/docs/source/auto_examples/plot_otda_laplacian.ipynb +++ /dev/null @@ -1,126 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# OT with Laplacian regularization for domain adaptation\n\n\nThis example introduces a domain adaptation in a 2D setting and OTDA\napproach with Laplacian regularization.\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Authors: Ievgen Redko \n\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n-------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_source_samples = 150\nn_target_samples = 150\n\nXs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples)\nXt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# EMD Transport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=.01)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# EMD Transport with Laplacian regularization\not_emd_laplace = ot.da.EMDLaplaceTransport(reg_lap=100, reg_src=1)\not_emd_laplace.fit(Xs=Xs, Xt=Xt)\n\n# transport source samples onto target samples\ntransp_Xs_emd = ot_emd.transform(Xs=Xs)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\ntransp_Xs_emd_laplace = ot_emd_laplace.transform(Xs=Xs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 1 : plots source and target samples\n---------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(1, figsize=(10, 5))\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\npl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 2 : plot optimal couplings and transported samples\n------------------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "param_img = {'interpolation': 'nearest'}\n\npl.figure(2, figsize=(15, 8))\npl.subplot(2, 3, 1)\npl.imshow(ot_emd.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nEMDTransport')\n\npl.figure(2, figsize=(15, 8))\npl.subplot(2, 3, 2)\npl.imshow(ot_sinkhorn.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornTransport')\n\npl.subplot(2, 3, 3)\npl.imshow(ot_emd_laplace.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nEMDLaplaceTransport')\n\npl.subplot(2, 3, 4)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=\"lower left\")\n\npl.subplot(2, 3, 5)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornTransport')\n\npl.subplot(2, 3, 6)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nEMDLaplaceTransport')\npl.tight_layout()\n\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_laplacian.py b/docs/source/auto_examples/plot_otda_laplacian.py deleted file mode 100644 index 67c8f67..0000000 --- a/docs/source/auto_examples/plot_otda_laplacian.py +++ /dev/null @@ -1,127 +0,0 @@ -# -*- coding: utf-8 -*- -""" -====================================================== -OT with Laplacian regularization for domain adaptation -====================================================== - -This example introduces a domain adaptation in a 2D setting and OTDA -approach with Laplacian regularization. - -""" - -# Authors: Ievgen Redko - -# License: MIT License - -import matplotlib.pylab as pl -import ot - -############################################################################## -# Generate data -# ------------- - -n_source_samples = 150 -n_target_samples = 150 - -Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) -Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) - - -############################################################################## -# Instantiate the different transport algorithms and fit them -# ----------------------------------------------------------- - -# EMD Transport -ot_emd = ot.da.EMDTransport() -ot_emd.fit(Xs=Xs, Xt=Xt) - -# Sinkhorn Transport -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=.01) -ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - -# EMD Transport with Laplacian regularization -ot_emd_laplace = ot.da.EMDLaplaceTransport(reg_lap=100, reg_src=1) -ot_emd_laplace.fit(Xs=Xs, Xt=Xt) - -# transport source samples onto target samples -transp_Xs_emd = ot_emd.transform(Xs=Xs) -transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) -transp_Xs_emd_laplace = ot_emd_laplace.transform(Xs=Xs) - -############################################################################## -# Fig 1 : plots source and target samples -# --------------------------------------- - -pl.figure(1, figsize=(10, 5)) -pl.subplot(1, 2, 1) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Source samples') - -pl.subplot(1, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Target samples') -pl.tight_layout() - - -############################################################################## -# Fig 2 : plot optimal couplings and transported samples -# ------------------------------------------------------ - -param_img = {'interpolation': 'nearest'} - -pl.figure(2, figsize=(15, 8)) -pl.subplot(2, 3, 1) -pl.imshow(ot_emd.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nEMDTransport') - -pl.figure(2, figsize=(15, 8)) -pl.subplot(2, 3, 2) -pl.imshow(ot_sinkhorn.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornTransport') - -pl.subplot(2, 3, 3) -pl.imshow(ot_emd_laplace.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nEMDLaplaceTransport') - -pl.subplot(2, 3, 4) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nEmdTransport') -pl.legend(loc="lower left") - -pl.subplot(2, 3, 5) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nSinkhornTransport') - -pl.subplot(2, 3, 6) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nEMDLaplaceTransport') -pl.tight_layout() - -pl.show() diff --git a/docs/source/auto_examples/plot_otda_laplacian.rst b/docs/source/auto_examples/plot_otda_laplacian.rst deleted file mode 100644 index 12cd7b9..0000000 --- a/docs/source/auto_examples/plot_otda_laplacian.rst +++ /dev/null @@ -1,233 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_otda_laplacian.py: - - -====================================================== -OT with Laplacian regularization for domain adaptation -====================================================== - -This example introduces a domain adaptation in a 2D setting and OTDA -approach with Laplacian regularization. - - - -.. code-block:: default - - - # Authors: Ievgen Redko - - # License: MIT License - - import matplotlib.pylab as pl - import ot - - - - - - - - -Generate data -------------- - - -.. code-block:: default - - - n_source_samples = 150 - n_target_samples = 150 - - Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) - Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) - - - - - - - - - -Instantiate the different transport algorithms and fit them ------------------------------------------------------------ - - -.. code-block:: default - - - # EMD Transport - ot_emd = ot.da.EMDTransport() - ot_emd.fit(Xs=Xs, Xt=Xt) - - # Sinkhorn Transport - ot_sinkhorn = ot.da.SinkhornTransport(reg_e=.01) - ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - - # EMD Transport with Laplacian regularization - ot_emd_laplace = ot.da.EMDLaplaceTransport(reg_lap=100, reg_src=1) - ot_emd_laplace.fit(Xs=Xs, Xt=Xt) - - # transport source samples onto target samples - transp_Xs_emd = ot_emd.transform(Xs=Xs) - transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) - transp_Xs_emd_laplace = ot_emd_laplace.transform(Xs=Xs) - - - - - - - - -Fig 1 : plots source and target samples ---------------------------------------- - - -.. code-block:: default - - - pl.figure(1, figsize=(10, 5)) - pl.subplot(1, 2, 1) - pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') - pl.xticks([]) - pl.yticks([]) - pl.legend(loc=0) - pl.title('Source samples') - - pl.subplot(1, 2, 2) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') - pl.xticks([]) - pl.yticks([]) - pl.legend(loc=0) - pl.title('Target samples') - pl.tight_layout() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_laplacian_001.png - :class: sphx-glr-single-img - - - - - -Fig 2 : plot optimal couplings and transported samples ------------------------------------------------------- - - -.. code-block:: default - - - param_img = {'interpolation': 'nearest'} - - pl.figure(2, figsize=(15, 8)) - pl.subplot(2, 3, 1) - pl.imshow(ot_emd.coupling_, **param_img) - pl.xticks([]) - pl.yticks([]) - pl.title('Optimal coupling\nEMDTransport') - - pl.figure(2, figsize=(15, 8)) - pl.subplot(2, 3, 2) - pl.imshow(ot_sinkhorn.coupling_, **param_img) - pl.xticks([]) - pl.yticks([]) - pl.title('Optimal coupling\nSinkhornTransport') - - pl.subplot(2, 3, 3) - pl.imshow(ot_emd_laplace.coupling_, **param_img) - pl.xticks([]) - pl.yticks([]) - pl.title('Optimal coupling\nEMDLaplaceTransport') - - pl.subplot(2, 3, 4) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) - pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, - marker='+', label='Transp samples', s=30) - pl.xticks([]) - pl.yticks([]) - pl.title('Transported samples\nEmdTransport') - pl.legend(loc="lower left") - - pl.subplot(2, 3, 5) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) - pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, - marker='+', label='Transp samples', s=30) - pl.xticks([]) - pl.yticks([]) - pl.title('Transported samples\nSinkhornTransport') - - pl.subplot(2, 3, 6) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) - pl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys, - marker='+', label='Transp samples', s=30) - pl.xticks([]) - pl.yticks([]) - pl.title('Transported samples\nEMDLaplaceTransport') - pl.tight_layout() - - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_laplacian_002.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_otda_laplacian.py:127: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 1.195 seconds) - - -.. _sphx_glr_download_auto_examples_plot_otda_laplacian.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_otda_laplacian.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_otda_laplacian.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_linear_mapping.ipynb b/docs/source/auto_examples/plot_otda_linear_mapping.ipynb deleted file mode 100644 index 96eccbe..0000000 --- a/docs/source/auto_examples/plot_otda_linear_mapping.ipynb +++ /dev/null @@ -1,180 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Linear OT mapping estimation\n\n\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport pylab as pl\nimport ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n-------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 1000\nd = 2\nsigma = .1\n\n# source samples\nangles = np.random.rand(n, 1) * 2 * np.pi\nxs = np.concatenate((np.sin(angles), np.cos(angles)),\n axis=1) + sigma * np.random.randn(n, 2)\nxs[:n // 2, 1] += 2\n\n\n# target samples\nanglet = np.random.rand(n, 1) * 2 * np.pi\nxt = np.concatenate((np.sin(anglet), np.cos(anglet)),\n axis=1) + sigma * np.random.randn(n, 2)\nxt[:n // 2, 1] += 2\n\n\nA = np.array([[1.5, .7], [.7, 1.5]])\nb = np.array([[4, 2]])\nxt = xt.dot(A) + b" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n---------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(1, (5, 5))\npl.plot(xs[:, 0], xs[:, 1], '+')\npl.plot(xt[:, 0], xt[:, 1], 'o')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Estimate linear mapping and transport\n-------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "Ae, be = ot.da.OT_mapping_linear(xs, xt)\n\nxst = xs.dot(Ae) + be" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot transported samples\n------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(1, (5, 5))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+')\npl.plot(xt[:, 0], xt[:, 1], 'o')\npl.plot(xst[:, 0], xst[:, 1], '+')\n\npl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load image data\n---------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)\n\n\n# Loading images\nI1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Estimate mapping and adapt\n----------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "mapping = ot.da.LinearTransport()\n\nmapping.fit(Xs=X1, Xt=X2)\n\n\nxst = mapping.transform(Xs=X1)\nxts = mapping.inverse_transform(Xt=X2)\n\nI1t = minmax(mat2im(xst, I1.shape))\nI2t = minmax(mat2im(xts, I2.shape))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot transformed images\n-----------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(2, figsize=(10, 7))\n\npl.subplot(2, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Im. 1')\n\npl.subplot(2, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Im. 2')\n\npl.subplot(2, 2, 3)\npl.imshow(I1t)\npl.axis('off')\npl.title('Mapping Im. 1')\n\npl.subplot(2, 2, 4)\npl.imshow(I2t)\npl.axis('off')\npl.title('Inverse mapping Im. 2')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_linear_mapping.py b/docs/source/auto_examples/plot_otda_linear_mapping.py deleted file mode 100644 index c65bd4f..0000000 --- a/docs/source/auto_examples/plot_otda_linear_mapping.py +++ /dev/null @@ -1,144 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -============================ -Linear OT mapping estimation -============================ - - -""" - -# Author: Remi Flamary -# -# License: MIT License - -import numpy as np -import pylab as pl -import ot - -############################################################################## -# Generate data -# ------------- - -n = 1000 -d = 2 -sigma = .1 - -# source samples -angles = np.random.rand(n, 1) * 2 * np.pi -xs = np.concatenate((np.sin(angles), np.cos(angles)), - axis=1) + sigma * np.random.randn(n, 2) -xs[:n // 2, 1] += 2 - - -# target samples -anglet = np.random.rand(n, 1) * 2 * np.pi -xt = np.concatenate((np.sin(anglet), np.cos(anglet)), - axis=1) + sigma * np.random.randn(n, 2) -xt[:n // 2, 1] += 2 - - -A = np.array([[1.5, .7], [.7, 1.5]]) -b = np.array([[4, 2]]) -xt = xt.dot(A) + b - -############################################################################## -# Plot data -# --------- - -pl.figure(1, (5, 5)) -pl.plot(xs[:, 0], xs[:, 1], '+') -pl.plot(xt[:, 0], xt[:, 1], 'o') - - -############################################################################## -# Estimate linear mapping and transport -# ------------------------------------- - -Ae, be = ot.da.OT_mapping_linear(xs, xt) - -xst = xs.dot(Ae) + be - - -############################################################################## -# Plot transported samples -# ------------------------ - -pl.figure(1, (5, 5)) -pl.clf() -pl.plot(xs[:, 0], xs[:, 1], '+') -pl.plot(xt[:, 0], xt[:, 1], 'o') -pl.plot(xst[:, 0], xst[:, 1], '+') - -pl.show() - -############################################################################## -# Load image data -# --------------- - - -def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) - - -def mat2im(X, shape): - """Converts back a matrix to an image""" - return X.reshape(shape) - - -def minmax(I): - return np.clip(I, 0, 1) - - -# Loading images -I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 - - -X1 = im2mat(I1) -X2 = im2mat(I2) - -############################################################################## -# Estimate mapping and adapt -# ---------------------------- - -mapping = ot.da.LinearTransport() - -mapping.fit(Xs=X1, Xt=X2) - - -xst = mapping.transform(Xs=X1) -xts = mapping.inverse_transform(Xt=X2) - -I1t = minmax(mat2im(xst, I1.shape)) -I2t = minmax(mat2im(xts, I2.shape)) - -# %% - - -############################################################################## -# Plot transformed images -# ----------------------- - -pl.figure(2, figsize=(10, 7)) - -pl.subplot(2, 2, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Im. 1') - -pl.subplot(2, 2, 2) -pl.imshow(I2) -pl.axis('off') -pl.title('Im. 2') - -pl.subplot(2, 2, 3) -pl.imshow(I1t) -pl.axis('off') -pl.title('Mapping Im. 1') - -pl.subplot(2, 2, 4) -pl.imshow(I2t) -pl.axis('off') -pl.title('Inverse mapping Im. 2') diff --git a/docs/source/auto_examples/plot_otda_linear_mapping.rst b/docs/source/auto_examples/plot_otda_linear_mapping.rst deleted file mode 100644 index 63848d2..0000000 --- a/docs/source/auto_examples/plot_otda_linear_mapping.rst +++ /dev/null @@ -1,295 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_otda_linear_mapping.py: - - -============================ -Linear OT mapping estimation -============================ - - - - -.. code-block:: default - - - # Author: Remi Flamary - # - # License: MIT License - - import numpy as np - import pylab as pl - import ot - - - - - - - - -Generate data -------------- - - -.. code-block:: default - - - n = 1000 - d = 2 - sigma = .1 - - # source samples - angles = np.random.rand(n, 1) * 2 * np.pi - xs = np.concatenate((np.sin(angles), np.cos(angles)), - axis=1) + sigma * np.random.randn(n, 2) - xs[:n // 2, 1] += 2 - - - # target samples - anglet = np.random.rand(n, 1) * 2 * np.pi - xt = np.concatenate((np.sin(anglet), np.cos(anglet)), - axis=1) + sigma * np.random.randn(n, 2) - xt[:n // 2, 1] += 2 - - - A = np.array([[1.5, .7], [.7, 1.5]]) - b = np.array([[4, 2]]) - xt = xt.dot(A) + b - - - - - - - - -Plot data ---------- - - -.. code-block:: default - - - pl.figure(1, (5, 5)) - pl.plot(xs[:, 0], xs[:, 1], '+') - pl.plot(xt[:, 0], xt[:, 1], 'o') - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_linear_mapping_001.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - - [] - - - -Estimate linear mapping and transport -------------------------------------- - - -.. code-block:: default - - - Ae, be = ot.da.OT_mapping_linear(xs, xt) - - xst = xs.dot(Ae) + be - - - - - - - - - -Plot transported samples ------------------------- - - -.. code-block:: default - - - pl.figure(1, (5, 5)) - pl.clf() - pl.plot(xs[:, 0], xs[:, 1], '+') - pl.plot(xt[:, 0], xt[:, 1], 'o') - pl.plot(xst[:, 0], xst[:, 1], '+') - - pl.show() - - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_linear_mapping_002.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_otda_linear_mapping.py:73: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -Load image data ---------------- - - -.. code-block:: default - - - - def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) - - - def mat2im(X, shape): - """Converts back a matrix to an image""" - return X.reshape(shape) - - - def minmax(I): - return np.clip(I, 0, 1) - - - # Loading images - I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 - I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 - - - X1 = im2mat(I1) - X2 = im2mat(I2) - - - - - - - - -Estimate mapping and adapt ----------------------------- - - -.. code-block:: default - - - mapping = ot.da.LinearTransport() - - mapping.fit(Xs=X1, Xt=X2) - - - xst = mapping.transform(Xs=X1) - xts = mapping.inverse_transform(Xt=X2) - - I1t = minmax(mat2im(xst, I1.shape)) - I2t = minmax(mat2im(xts, I2.shape)) - - - - - - - - -Plot transformed images ------------------------ - - -.. code-block:: default - - - pl.figure(2, figsize=(10, 7)) - - pl.subplot(2, 2, 1) - pl.imshow(I1) - pl.axis('off') - pl.title('Im. 1') - - pl.subplot(2, 2, 2) - pl.imshow(I2) - pl.axis('off') - pl.title('Im. 2') - - pl.subplot(2, 2, 3) - pl.imshow(I1t) - pl.axis('off') - pl.title('Mapping Im. 1') - - pl.subplot(2, 2, 4) - pl.imshow(I2t) - pl.axis('off') - pl.title('Inverse mapping Im. 2') - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_linear_mapping_003.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - - Text(0.5, 1.0, 'Inverse mapping Im. 2') - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 0.787 seconds) - - -.. _sphx_glr_download_auto_examples_plot_otda_linear_mapping.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_otda_linear_mapping.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_otda_linear_mapping.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_mapping.ipynb b/docs/source/auto_examples/plot_otda_mapping.ipynb deleted file mode 100644 index ac02255..0000000 --- a/docs/source/auto_examples/plot_otda_mapping.ipynb +++ /dev/null @@ -1,126 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# OT mapping estimation for domain adaptation\n\n\nThis example presents how to use MappingTransport to estimate at the same\ntime both the coupling transport and approximate the transport map with either\na linear or a kernelized mapping as introduced in [8].\n\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard,\n \"Mapping estimation for discrete optimal transport\",\n Neural Information Processing Systems (NIPS), 2016.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n-------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_source_samples = 100\nn_target_samples = 100\ntheta = 2 * np.pi / 20\nnoise_level = 0.1\n\nXs, ys = ot.datasets.make_data_classif(\n 'gaussrot', n_source_samples, nz=noise_level)\nXs_new, _ = ot.datasets.make_data_classif(\n 'gaussrot', n_source_samples, nz=noise_level)\nXt, yt = ot.datasets.make_data_classif(\n 'gaussrot', n_target_samples, theta=theta, nz=noise_level)\n\n# one of the target mode changes its variance (no linear mapping)\nXt[yt == 2] *= 3\nXt = Xt + 4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n---------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(1, (10, 5))\npl.clf()\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# MappingTransport with linear kernel\not_mapping_linear = ot.da.MappingTransport(\n kernel=\"linear\", mu=1e0, eta=1e-8, bias=True,\n max_iter=20, verbose=True)\n\not_mapping_linear.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_linear = ot_mapping_linear.transform(Xs=Xs)\n\n# for out of source samples, transform applies the linear mapping\ntransp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new)\n\n\n# MappingTransport with gaussian kernel\not_mapping_gaussian = ot.da.MappingTransport(\n kernel=\"gaussian\", eta=1e-5, mu=1e-1, bias=True, sigma=1,\n max_iter=10, verbose=True)\not_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs)\n\n# for out of source samples, transform applies the gaussian mapping\ntransp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot transported samples\n------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(2)\npl.clf()\npl.subplot(2, 2, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+',\n label='Mapped source samples')\npl.title(\"Bary. mapping (linear)\")\npl.legend(loc=0)\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1],\n c=ys, marker='+', label='Learned mapping')\npl.title(\"Estim. mapping (linear)\")\n\npl.subplot(2, 2, 3)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys,\n marker='+', label='barycentric mapping')\npl.title(\"Bary. mapping (kernel)\")\n\npl.subplot(2, 2, 4)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys,\n marker='+', label='Learned mapping')\npl.title(\"Estim. mapping (kernel)\")\npl.tight_layout()\n\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_mapping.py b/docs/source/auto_examples/plot_otda_mapping.py deleted file mode 100644 index 5880adf..0000000 --- a/docs/source/auto_examples/plot_otda_mapping.py +++ /dev/null @@ -1,125 +0,0 @@ -# -*- coding: utf-8 -*- -""" -=========================================== -OT mapping estimation for domain adaptation -=========================================== - -This example presents how to use MappingTransport to estimate at the same -time both the coupling transport and approximate the transport map with either -a linear or a kernelized mapping as introduced in [8]. - -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, - "Mapping estimation for discrete optimal transport", - Neural Information Processing Systems (NIPS), 2016. -""" - -# Authors: Remi Flamary -# Stanislas Chambon -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot - - -############################################################################## -# Generate data -# ------------- - -n_source_samples = 100 -n_target_samples = 100 -theta = 2 * np.pi / 20 -noise_level = 0.1 - -Xs, ys = ot.datasets.make_data_classif( - 'gaussrot', n_source_samples, nz=noise_level) -Xs_new, _ = ot.datasets.make_data_classif( - 'gaussrot', n_source_samples, nz=noise_level) -Xt, yt = ot.datasets.make_data_classif( - 'gaussrot', n_target_samples, theta=theta, nz=noise_level) - -# one of the target mode changes its variance (no linear mapping) -Xt[yt == 2] *= 3 -Xt = Xt + 4 - -############################################################################## -# Plot data -# --------- - -pl.figure(1, (10, 5)) -pl.clf() -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.legend(loc=0) -pl.title('Source and target distributions') - - -############################################################################## -# Instantiate the different transport algorithms and fit them -# ----------------------------------------------------------- - -# MappingTransport with linear kernel -ot_mapping_linear = ot.da.MappingTransport( - kernel="linear", mu=1e0, eta=1e-8, bias=True, - max_iter=20, verbose=True) - -ot_mapping_linear.fit(Xs=Xs, Xt=Xt) - -# for original source samples, transform applies barycentric mapping -transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs) - -# for out of source samples, transform applies the linear mapping -transp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new) - - -# MappingTransport with gaussian kernel -ot_mapping_gaussian = ot.da.MappingTransport( - kernel="gaussian", eta=1e-5, mu=1e-1, bias=True, sigma=1, - max_iter=10, verbose=True) -ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) - -# for original source samples, transform applies barycentric mapping -transp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs) - -# for out of source samples, transform applies the gaussian mapping -transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new) - - -############################################################################## -# Plot transported samples -# ------------------------ - -pl.figure(2) -pl.clf() -pl.subplot(2, 2, 1) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) -pl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+', - label='Mapped source samples') -pl.title("Bary. mapping (linear)") -pl.legend(loc=0) - -pl.subplot(2, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) -pl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1], - c=ys, marker='+', label='Learned mapping') -pl.title("Estim. mapping (linear)") - -pl.subplot(2, 2, 3) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) -pl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys, - marker='+', label='barycentric mapping') -pl.title("Bary. mapping (kernel)") - -pl.subplot(2, 2, 4) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) -pl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys, - marker='+', label='Learned mapping') -pl.title("Estim. mapping (kernel)") -pl.tight_layout() - -pl.show() diff --git a/docs/source/auto_examples/plot_otda_mapping.rst b/docs/source/auto_examples/plot_otda_mapping.rst deleted file mode 100644 index 99787f7..0000000 --- a/docs/source/auto_examples/plot_otda_mapping.rst +++ /dev/null @@ -1,268 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_otda_mapping.py: - - -=========================================== -OT mapping estimation for domain adaptation -=========================================== - -This example presents how to use MappingTransport to estimate at the same -time both the coupling transport and approximate the transport map with either -a linear or a kernelized mapping as introduced in [8]. - -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, - "Mapping estimation for discrete optimal transport", - Neural Information Processing Systems (NIPS), 2016. - - -.. code-block:: default - - - # Authors: Remi Flamary - # Stanislas Chambon - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot - - - - - - - - - -Generate data -------------- - - -.. code-block:: default - - - n_source_samples = 100 - n_target_samples = 100 - theta = 2 * np.pi / 20 - noise_level = 0.1 - - Xs, ys = ot.datasets.make_data_classif( - 'gaussrot', n_source_samples, nz=noise_level) - Xs_new, _ = ot.datasets.make_data_classif( - 'gaussrot', n_source_samples, nz=noise_level) - Xt, yt = ot.datasets.make_data_classif( - 'gaussrot', n_target_samples, theta=theta, nz=noise_level) - - # one of the target mode changes its variance (no linear mapping) - Xt[yt == 2] *= 3 - Xt = Xt + 4 - - - - - - - - -Plot data ---------- - - -.. code-block:: default - - - pl.figure(1, (10, 5)) - pl.clf() - pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') - pl.legend(loc=0) - pl.title('Source and target distributions') - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_001.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - - Text(0.5, 1.0, 'Source and target distributions') - - - -Instantiate the different transport algorithms and fit them ------------------------------------------------------------ - - -.. code-block:: default - - - # MappingTransport with linear kernel - ot_mapping_linear = ot.da.MappingTransport( - kernel="linear", mu=1e0, eta=1e-8, bias=True, - max_iter=20, verbose=True) - - ot_mapping_linear.fit(Xs=Xs, Xt=Xt) - - # for original source samples, transform applies barycentric mapping - transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs) - - # for out of source samples, transform applies the linear mapping - transp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new) - - - # MappingTransport with gaussian kernel - ot_mapping_gaussian = ot.da.MappingTransport( - kernel="gaussian", eta=1e-5, mu=1e-1, bias=True, sigma=1, - max_iter=10, verbose=True) - ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) - - # for original source samples, transform applies barycentric mapping - transp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs) - - # for out of source samples, transform applies the gaussian mapping - transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new) - - - - - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - It. |Loss |Delta loss - -------------------------------- - 0|4.212661e+03|0.000000e+00 - 1|4.198567e+03|-3.345626e-03 - 2|4.198198e+03|-8.797101e-05 - 3|4.198027e+03|-4.059527e-05 - 4|4.197928e+03|-2.355659e-05 - 5|4.197886e+03|-1.002352e-05 - 6|4.197853e+03|-7.873125e-06 - It. |Loss |Delta loss - -------------------------------- - 0|4.231694e+02|0.000000e+00 - 1|4.185911e+02|-1.081889e-02 - 2|4.182717e+02|-7.631953e-04 - 3|4.181271e+02|-3.455908e-04 - 4|4.180328e+02|-2.255461e-04 - 5|4.179645e+02|-1.634435e-04 - 6|4.179136e+02|-1.216359e-04 - 7|4.178752e+02|-9.198108e-05 - 8|4.178465e+02|-6.870868e-05 - 9|4.178243e+02|-5.321390e-05 - 10|4.178054e+02|-4.521725e-05 - - - - -Plot transported samples ------------------------- - - -.. code-block:: default - - - pl.figure(2) - pl.clf() - pl.subplot(2, 2, 1) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) - pl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+', - label='Mapped source samples') - pl.title("Bary. mapping (linear)") - pl.legend(loc=0) - - pl.subplot(2, 2, 2) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) - pl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1], - c=ys, marker='+', label='Learned mapping') - pl.title("Estim. mapping (linear)") - - pl.subplot(2, 2, 3) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) - pl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys, - marker='+', label='barycentric mapping') - pl.title("Bary. mapping (kernel)") - - pl.subplot(2, 2, 4) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) - pl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys, - marker='+', label='Learned mapping') - pl.title("Estim. mapping (kernel)") - pl.tight_layout() - - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_002.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_otda_mapping.py:125: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 0.843 seconds) - - -.. _sphx_glr_download_auto_examples_plot_otda_mapping.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_otda_mapping.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_otda_mapping.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb b/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb deleted file mode 100644 index de46629..0000000 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb +++ /dev/null @@ -1,144 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# OT for image color adaptation with mapping estimation\n\n\nOT for domain adaptation with image color adaptation [6] with mapping\nestimation [8].\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized\n discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3),\n 1853-1882.\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n discrete optimal transport\", Neural Information Processing Systems (NIPS),\n 2016.\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\nr = np.random.RandomState(42)\n\n\ndef im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n-------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Loading images\nI1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)\n\n# training samples\nnb = 1000\nidx1 = r.randint(X1.shape[0], size=(nb,))\nidx2 = r.randint(X2.shape[0], size=(nb,))\n\nXs = X1[idx1, :]\nXt = X2[idx2, :]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Domain adaptation for pixel distribution transfer\n-------------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# EMDTransport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_emd = ot_emd.transform(Xs=X1)\nImage_emd = minmax(mat2im(transp_Xs_emd, I1.shape))\n\n# SinkhornTransport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)\nImage_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\n\not_mapping_linear = ot.da.MappingTransport(\n mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True)\not_mapping_linear.fit(Xs=Xs, Xt=Xt)\n\nX1tl = ot_mapping_linear.transform(Xs=X1)\nImage_mapping_linear = minmax(mat2im(X1tl, I1.shape))\n\not_mapping_gaussian = ot.da.MappingTransport(\n mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True)\not_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n\nX1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping\nImage_mapping_gaussian = minmax(mat2im(X1tn, I1.shape))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot original images\n--------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(1, figsize=(6.4, 3))\npl.subplot(1, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')\npl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot pixel values distribution\n------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(2, figsize=(6.4, 5))\n\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\npl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot transformed images\n-----------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(2, figsize=(10, 5))\n\npl.subplot(2, 3, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Im. 1')\n\npl.subplot(2, 3, 4)\npl.imshow(I2)\npl.axis('off')\npl.title('Im. 2')\n\npl.subplot(2, 3, 2)\npl.imshow(Image_emd)\npl.axis('off')\npl.title('EmdTransport')\n\npl.subplot(2, 3, 5)\npl.imshow(Image_sinkhorn)\npl.axis('off')\npl.title('SinkhornTransport')\n\npl.subplot(2, 3, 3)\npl.imshow(Image_mapping_linear)\npl.axis('off')\npl.title('MappingTransport (linear)')\n\npl.subplot(2, 3, 6)\npl.imshow(Image_mapping_gaussian)\npl.axis('off')\npl.title('MappingTransport (gaussian)')\npl.tight_layout()\n\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.py b/docs/source/auto_examples/plot_otda_mapping_colors_images.py deleted file mode 100644 index bc9afba..0000000 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.py +++ /dev/null @@ -1,173 +0,0 @@ -# -*- coding: utf-8 -*- -""" -===================================================== -OT for image color adaptation with mapping estimation -===================================================== - -OT for domain adaptation with image color adaptation [6] with mapping -estimation [8]. - -[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized - discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), - 1853-1882. -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for - discrete optimal transport", Neural Information Processing Systems (NIPS), - 2016. - -""" - -# Authors: Remi Flamary -# Stanislas Chambon -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot - -r = np.random.RandomState(42) - - -def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) - - -def mat2im(X, shape): - """Converts back a matrix to an image""" - return X.reshape(shape) - - -def minmax(I): - return np.clip(I, 0, 1) - - -############################################################################## -# Generate data -# ------------- - -# Loading images -I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 - - -X1 = im2mat(I1) -X2 = im2mat(I2) - -# training samples -nb = 1000 -idx1 = r.randint(X1.shape[0], size=(nb,)) -idx2 = r.randint(X2.shape[0], size=(nb,)) - -Xs = X1[idx1, :] -Xt = X2[idx2, :] - - -############################################################################## -# Domain adaptation for pixel distribution transfer -# ------------------------------------------------- - -# EMDTransport -ot_emd = ot.da.EMDTransport() -ot_emd.fit(Xs=Xs, Xt=Xt) -transp_Xs_emd = ot_emd.transform(Xs=X1) -Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape)) - -# SinkhornTransport -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn.fit(Xs=Xs, Xt=Xt) -transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) -Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) - -ot_mapping_linear = ot.da.MappingTransport( - mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True) -ot_mapping_linear.fit(Xs=Xs, Xt=Xt) - -X1tl = ot_mapping_linear.transform(Xs=X1) -Image_mapping_linear = minmax(mat2im(X1tl, I1.shape)) - -ot_mapping_gaussian = ot.da.MappingTransport( - mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True) -ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) - -X1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping -Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape)) - - -############################################################################## -# Plot original images -# -------------------- - -pl.figure(1, figsize=(6.4, 3)) -pl.subplot(1, 2, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Image 1') - -pl.subplot(1, 2, 2) -pl.imshow(I2) -pl.axis('off') -pl.title('Image 2') -pl.tight_layout() - - -############################################################################## -# Plot pixel values distribution -# ------------------------------ - -pl.figure(2, figsize=(6.4, 5)) - -pl.subplot(1, 2, 1) -pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) -pl.axis([0, 1, 0, 1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 1') - -pl.subplot(1, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) -pl.axis([0, 1, 0, 1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 2') -pl.tight_layout() - - -############################################################################## -# Plot transformed images -# ----------------------- - -pl.figure(2, figsize=(10, 5)) - -pl.subplot(2, 3, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Im. 1') - -pl.subplot(2, 3, 4) -pl.imshow(I2) -pl.axis('off') -pl.title('Im. 2') - -pl.subplot(2, 3, 2) -pl.imshow(Image_emd) -pl.axis('off') -pl.title('EmdTransport') - -pl.subplot(2, 3, 5) -pl.imshow(Image_sinkhorn) -pl.axis('off') -pl.title('SinkhornTransport') - -pl.subplot(2, 3, 3) -pl.imshow(Image_mapping_linear) -pl.axis('off') -pl.title('MappingTransport (linear)') - -pl.subplot(2, 3, 6) -pl.imshow(Image_mapping_gaussian) -pl.axis('off') -pl.title('MappingTransport (gaussian)') -pl.tight_layout() - -pl.show() diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst b/docs/source/auto_examples/plot_otda_mapping_colors_images.rst deleted file mode 100644 index 26664e3..0000000 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst +++ /dev/null @@ -1,334 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_otda_mapping_colors_images.py: - - -===================================================== -OT for image color adaptation with mapping estimation -===================================================== - -OT for domain adaptation with image color adaptation [6] with mapping -estimation [8]. - -[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized - discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), - 1853-1882. -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for - discrete optimal transport", Neural Information Processing Systems (NIPS), - 2016. - - - -.. code-block:: default - - - # Authors: Remi Flamary - # Stanislas Chambon - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot - - r = np.random.RandomState(42) - - - def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) - - - def mat2im(X, shape): - """Converts back a matrix to an image""" - return X.reshape(shape) - - - def minmax(I): - return np.clip(I, 0, 1) - - - - - - - - - -Generate data -------------- - - -.. code-block:: default - - - # Loading images - I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 - I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 - - - X1 = im2mat(I1) - X2 = im2mat(I2) - - # training samples - nb = 1000 - idx1 = r.randint(X1.shape[0], size=(nb,)) - idx2 = r.randint(X2.shape[0], size=(nb,)) - - Xs = X1[idx1, :] - Xt = X2[idx2, :] - - - - - - - - - -Domain adaptation for pixel distribution transfer -------------------------------------------------- - - -.. code-block:: default - - - # EMDTransport - ot_emd = ot.da.EMDTransport() - ot_emd.fit(Xs=Xs, Xt=Xt) - transp_Xs_emd = ot_emd.transform(Xs=X1) - Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape)) - - # SinkhornTransport - ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) - ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) - Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) - - ot_mapping_linear = ot.da.MappingTransport( - mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True) - ot_mapping_linear.fit(Xs=Xs, Xt=Xt) - - X1tl = ot_mapping_linear.transform(Xs=X1) - Image_mapping_linear = minmax(mat2im(X1tl, I1.shape)) - - ot_mapping_gaussian = ot.da.MappingTransport( - mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True) - ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) - - X1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping - Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape)) - - - - - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - It. |Loss |Delta loss - -------------------------------- - 0|3.680534e+02|0.000000e+00 - 1|3.592501e+02|-2.391854e-02 - 2|3.590682e+02|-5.061555e-04 - 3|3.589745e+02|-2.610227e-04 - 4|3.589167e+02|-1.611644e-04 - 5|3.588768e+02|-1.109242e-04 - 6|3.588482e+02|-7.972733e-05 - 7|3.588261e+02|-6.166174e-05 - 8|3.588086e+02|-4.871697e-05 - 9|3.587946e+02|-3.919056e-05 - 10|3.587830e+02|-3.228124e-05 - 11|3.587731e+02|-2.744744e-05 - 12|3.587648e+02|-2.334451e-05 - 13|3.587576e+02|-1.995629e-05 - 14|3.587513e+02|-1.761058e-05 - 15|3.587457e+02|-1.542568e-05 - 16|3.587408e+02|-1.366315e-05 - 17|3.587365e+02|-1.221732e-05 - 18|3.587325e+02|-1.102488e-05 - 19|3.587303e+02|-6.062107e-06 - It. |Loss |Delta loss - -------------------------------- - 0|3.784871e+02|0.000000e+00 - 1|3.646491e+02|-3.656142e-02 - 2|3.642975e+02|-9.642655e-04 - 3|3.641626e+02|-3.702413e-04 - 4|3.640888e+02|-2.026301e-04 - 5|3.640419e+02|-1.289607e-04 - 6|3.640097e+02|-8.831646e-05 - 7|3.639861e+02|-6.487612e-05 - 8|3.639679e+02|-4.994063e-05 - 9|3.639536e+02|-3.941436e-05 - 10|3.639419e+02|-3.209753e-05 - - - - -Plot original images --------------------- - - -.. code-block:: default - - - pl.figure(1, figsize=(6.4, 3)) - pl.subplot(1, 2, 1) - pl.imshow(I1) - pl.axis('off') - pl.title('Image 1') - - pl.subplot(1, 2, 2) - pl.imshow(I2) - pl.axis('off') - pl.title('Image 2') - pl.tight_layout() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_colors_images_001.png - :class: sphx-glr-single-img - - - - - -Plot pixel values distribution ------------------------------- - - -.. code-block:: default - - - pl.figure(2, figsize=(6.4, 5)) - - pl.subplot(1, 2, 1) - pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) - pl.axis([0, 1, 0, 1]) - pl.xlabel('Red') - pl.ylabel('Blue') - pl.title('Image 1') - - pl.subplot(1, 2, 2) - pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) - pl.axis([0, 1, 0, 1]) - pl.xlabel('Red') - pl.ylabel('Blue') - pl.title('Image 2') - pl.tight_layout() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_colors_images_002.png - :class: sphx-glr-single-img - - - - - -Plot transformed images ------------------------ - - -.. code-block:: default - - - pl.figure(2, figsize=(10, 5)) - - pl.subplot(2, 3, 1) - pl.imshow(I1) - pl.axis('off') - pl.title('Im. 1') - - pl.subplot(2, 3, 4) - pl.imshow(I2) - pl.axis('off') - pl.title('Im. 2') - - pl.subplot(2, 3, 2) - pl.imshow(Image_emd) - pl.axis('off') - pl.title('EmdTransport') - - pl.subplot(2, 3, 5) - pl.imshow(Image_sinkhorn) - pl.axis('off') - pl.title('SinkhornTransport') - - pl.subplot(2, 3, 3) - pl.imshow(Image_mapping_linear) - pl.axis('off') - pl.title('MappingTransport (linear)') - - pl.subplot(2, 3, 6) - pl.imshow(Image_mapping_gaussian) - pl.axis('off') - pl.title('MappingTransport (gaussian)') - pl.tight_layout() - - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_colors_images_003.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_otda_mapping_colors_images.py:173: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 2 minutes 24.007 seconds) - - -.. _sphx_glr_download_auto_examples_plot_otda_mapping_colors_images.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_otda_mapping_colors_images.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_otda_mapping_colors_images.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_semi_supervised.ipynb b/docs/source/auto_examples/plot_otda_semi_supervised.ipynb deleted file mode 100644 index d2157fb..0000000 --- a/docs/source/auto_examples/plot_otda_semi_supervised.ipynb +++ /dev/null @@ -1,144 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# OTDA unsupervised vs semi-supervised setting\n\n\nThis example introduces a semi supervised domain adaptation in a 2D setting.\nIt explicits the problem of semi supervised domain adaptation and introduces\nsome optimal transport approaches to solve it.\n\nQuantities such as optimal couplings, greater coupling coefficients and\ntransported samples are represented in order to give a visual understanding\nof what the transport methods are doing.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n-------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_samples_source = 150\nn_samples_target = 150\n\nXs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)\nXt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Transport source samples onto target samples\n--------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# unsupervised domain adaptation\not_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn_un.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs)\n\n# semi-supervised domain adaptation\not_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt)\ntransp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs)\n\n# semi supervised DA uses available labaled target samples to modify the cost\n# matrix involved in the OT problem. The cost of transporting a source sample\n# of class A onto a target sample of class B != A is set to infinite, or a\n# very large value\n\n# note that in the present case we consider that all the target samples are\n# labeled. For daily applications, some target sample might not have labels,\n# in this case the element of yt corresponding to these samples should be\n# filled with -1.\n\n# Warning: we recall that -1 cannot be used as a class label" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 1 : plots source and target samples + matrix of pairwise distance\n---------------------------------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(1, figsize=(10, 10))\npl.subplot(2, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\n\npl.subplot(2, 2, 3)\npl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Cost matrix - unsupervised DA')\n\npl.subplot(2, 2, 4)\npl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Cost matrix - semisupervised DA')\n\npl.tight_layout()\n\n# the optimal coupling in the semi-supervised DA case will exhibit \" shape\n# similar\" to the cost matrix, (block diagonal matrix)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 2 : plots optimal couplings for the different methods\n---------------------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(2, figsize=(8, 4))\n\npl.subplot(1, 2, 1)\npl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nUnsupervised DA')\n\npl.subplot(1, 2, 2)\npl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSemi-supervised DA')\n\npl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 3 : plot transported samples\n--------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# display transported samples\npl.figure(4, figsize=(8, 4))\npl.subplot(1, 2, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=0)\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornTransport')\npl.xticks([])\npl.yticks([])\n\npl.tight_layout()\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_semi_supervised.py b/docs/source/auto_examples/plot_otda_semi_supervised.py deleted file mode 100644 index 8a67720..0000000 --- a/docs/source/auto_examples/plot_otda_semi_supervised.py +++ /dev/null @@ -1,148 +0,0 @@ -# -*- coding: utf-8 -*- -""" -============================================ -OTDA unsupervised vs semi-supervised setting -============================================ - -This example introduces a semi supervised domain adaptation in a 2D setting. -It explicits the problem of semi supervised domain adaptation and introduces -some optimal transport approaches to solve it. - -Quantities such as optimal couplings, greater coupling coefficients and -transported samples are represented in order to give a visual understanding -of what the transport methods are doing. -""" - -# Authors: Remi Flamary -# Stanislas Chambon -# -# License: MIT License - -import matplotlib.pylab as pl -import ot - - -############################################################################## -# Generate data -# ------------- - -n_samples_source = 150 -n_samples_target = 150 - -Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) -Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) - - -############################################################################## -# Transport source samples onto target samples -# -------------------------------------------- - - -# unsupervised domain adaptation -ot_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn_un.fit(Xs=Xs, Xt=Xt) -transp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs) - -# semi-supervised domain adaptation -ot_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt) -transp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs) - -# semi supervised DA uses available labaled target samples to modify the cost -# matrix involved in the OT problem. The cost of transporting a source sample -# of class A onto a target sample of class B != A is set to infinite, or a -# very large value - -# note that in the present case we consider that all the target samples are -# labeled. For daily applications, some target sample might not have labels, -# in this case the element of yt corresponding to these samples should be -# filled with -1. - -# Warning: we recall that -1 cannot be used as a class label - - -############################################################################## -# Fig 1 : plots source and target samples + matrix of pairwise distance -# --------------------------------------------------------------------- - -pl.figure(1, figsize=(10, 10)) -pl.subplot(2, 2, 1) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Source samples') - -pl.subplot(2, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Target samples') - -pl.subplot(2, 2, 3) -pl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Cost matrix - unsupervised DA') - -pl.subplot(2, 2, 4) -pl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Cost matrix - semisupervised DA') - -pl.tight_layout() - -# the optimal coupling in the semi-supervised DA case will exhibit " shape -# similar" to the cost matrix, (block diagonal matrix) - - -############################################################################## -# Fig 2 : plots optimal couplings for the different methods -# --------------------------------------------------------- - -pl.figure(2, figsize=(8, 4)) - -pl.subplot(1, 2, 1) -pl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nUnsupervised DA') - -pl.subplot(1, 2, 2) -pl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSemi-supervised DA') - -pl.tight_layout() - - -############################################################################## -# Fig 3 : plot transported samples -# -------------------------------- - -# display transported samples -pl.figure(4, figsize=(8, 4)) -pl.subplot(1, 2, 1) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Transported samples\nEmdTransport') -pl.legend(loc=0) -pl.xticks([]) -pl.yticks([]) - -pl.subplot(1, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Transported samples\nSinkhornTransport') -pl.xticks([]) -pl.yticks([]) - -pl.tight_layout() -pl.show() diff --git a/docs/source/auto_examples/plot_otda_semi_supervised.rst b/docs/source/auto_examples/plot_otda_semi_supervised.rst deleted file mode 100644 index 4a355e7..0000000 --- a/docs/source/auto_examples/plot_otda_semi_supervised.rst +++ /dev/null @@ -1,267 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_otda_semi_supervised.py: - - -============================================ -OTDA unsupervised vs semi-supervised setting -============================================ - -This example introduces a semi supervised domain adaptation in a 2D setting. -It explicits the problem of semi supervised domain adaptation and introduces -some optimal transport approaches to solve it. - -Quantities such as optimal couplings, greater coupling coefficients and -transported samples are represented in order to give a visual understanding -of what the transport methods are doing. - - -.. code-block:: default - - - # Authors: Remi Flamary - # Stanislas Chambon - # - # License: MIT License - - import matplotlib.pylab as pl - import ot - - - - - - - - - -Generate data -------------- - - -.. code-block:: default - - - n_samples_source = 150 - n_samples_target = 150 - - Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) - Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) - - - - - - - - - -Transport source samples onto target samples --------------------------------------------- - - -.. code-block:: default - - - - # unsupervised domain adaptation - ot_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1) - ot_sinkhorn_un.fit(Xs=Xs, Xt=Xt) - transp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs) - - # semi-supervised domain adaptation - ot_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1) - ot_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt) - transp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs) - - # semi supervised DA uses available labaled target samples to modify the cost - # matrix involved in the OT problem. The cost of transporting a source sample - # of class A onto a target sample of class B != A is set to infinite, or a - # very large value - - # note that in the present case we consider that all the target samples are - # labeled. For daily applications, some target sample might not have labels, - # in this case the element of yt corresponding to these samples should be - # filled with -1. - - # Warning: we recall that -1 cannot be used as a class label - - - - - - - - - -Fig 1 : plots source and target samples + matrix of pairwise distance ---------------------------------------------------------------------- - - -.. code-block:: default - - - pl.figure(1, figsize=(10, 10)) - pl.subplot(2, 2, 1) - pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') - pl.xticks([]) - pl.yticks([]) - pl.legend(loc=0) - pl.title('Source samples') - - pl.subplot(2, 2, 2) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') - pl.xticks([]) - pl.yticks([]) - pl.legend(loc=0) - pl.title('Target samples') - - pl.subplot(2, 2, 3) - pl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest') - pl.xticks([]) - pl.yticks([]) - pl.title('Cost matrix - unsupervised DA') - - pl.subplot(2, 2, 4) - pl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest') - pl.xticks([]) - pl.yticks([]) - pl.title('Cost matrix - semisupervised DA') - - pl.tight_layout() - - # the optimal coupling in the semi-supervised DA case will exhibit " shape - # similar" to the cost matrix, (block diagonal matrix) - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_semi_supervised_001.png - :class: sphx-glr-single-img - - - - - -Fig 2 : plots optimal couplings for the different methods ---------------------------------------------------------- - - -.. code-block:: default - - - pl.figure(2, figsize=(8, 4)) - - pl.subplot(1, 2, 1) - pl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest') - pl.xticks([]) - pl.yticks([]) - pl.title('Optimal coupling\nUnsupervised DA') - - pl.subplot(1, 2, 2) - pl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest') - pl.xticks([]) - pl.yticks([]) - pl.title('Optimal coupling\nSemi-supervised DA') - - pl.tight_layout() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_semi_supervised_002.png - :class: sphx-glr-single-img - - - - - -Fig 3 : plot transported samples --------------------------------- - - -.. code-block:: default - - - # display transported samples - pl.figure(4, figsize=(8, 4)) - pl.subplot(1, 2, 1) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) - pl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys, - marker='+', label='Transp samples', s=30) - pl.title('Transported samples\nEmdTransport') - pl.legend(loc=0) - pl.xticks([]) - pl.yticks([]) - - pl.subplot(1, 2, 2) - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) - pl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys, - marker='+', label='Transp samples', s=30) - pl.title('Transported samples\nSinkhornTransport') - pl.xticks([]) - pl.yticks([]) - - pl.tight_layout() - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_semi_supervised_003.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_otda_semi_supervised.py:148: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 0.660 seconds) - - -.. _sphx_glr_download_auto_examples_plot_otda_semi_supervised.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_otda_semi_supervised.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_otda_semi_supervised.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_partial_wass_and_gromov.ipynb b/docs/source/auto_examples/plot_partial_wass_and_gromov.ipynb deleted file mode 100644 index 539d575..0000000 --- a/docs/source/auto_examples/plot_partial_wass_and_gromov.ipynb +++ /dev/null @@ -1,126 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Partial Wasserstein and Gromov-Wasserstein example\n\n\nThis example is designed to show how to use the Partial (Gromov-)Wassertsein\ndistance computation in POT.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Laetitia Chapel \n# License: MIT License\n\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nimport scipy as sp\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sample two 2D Gaussian distributions and plot them\n--------------------------------------------------\n\nFor demonstration purpose, we sample two Gaussian distributions in 2-d\nspaces and add some random noise.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_samples = 20 # nb samples (gaussian)\nn_noise = 20 # nb of samples (noise)\n\nmu = np.array([0, 0])\ncov = np.array([[1, 0], [0, 2]])\n\nxs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)\nxs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2))\nxt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)\nxt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2))\n\nM = sp.spatial.distance.cdist(xs, xt)\n\nfig = pl.figure()\nax1 = fig.add_subplot(131)\nax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\nax2 = fig.add_subplot(132)\nax2.scatter(xt[:, 0], xt[:, 1], color='r')\nax3 = fig.add_subplot(133)\nax3.imshow(M)\npl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute partial Wasserstein plans and distance\n----------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "p = ot.unif(n_samples + n_noise)\nq = ot.unif(n_samples + n_noise)\n\nw0, log0 = ot.partial.partial_wasserstein(p, q, M, m=0.5, log=True)\nw, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=0.1, m=0.5,\n log=True)\n\nprint('Partial Wasserstein distance (m = 0.5): ' + str(log0['partial_w_dist']))\nprint('Entropic partial Wasserstein distance (m = 0.5): ' +\n str(log['partial_w_dist']))\n\npl.figure(1, (10, 5))\npl.subplot(1, 2, 1)\npl.imshow(w0, cmap='jet')\npl.title('Partial Wasserstein')\npl.subplot(1, 2, 2)\npl.imshow(w, cmap='jet')\npl.title('Entropic partial Wasserstein')\npl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sample one 2D and 3D Gaussian distributions and plot them\n---------------------------------------------------------\n\nThe Gromov-Wasserstein distance allows to compute distances with samples that\ndo not belong to the same metric space. For demonstration purpose, we sample\ntwo Gaussian distributions in 2- and 3-dimensional spaces.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_samples = 20 # nb samples\nn_noise = 10 # nb of samples (noise)\n\np = ot.unif(n_samples + n_noise)\nq = ot.unif(n_samples + n_noise)\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([0, 0, 0])\ncov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n\n\nxs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)\nxs = np.concatenate((xs, ((np.random.rand(n_noise, 2) + 1) * 4)), axis=0)\nP = sp.linalg.sqrtm(cov_t)\nxt = np.random.randn(n_samples, 3).dot(P) + mu_t\nxt = np.concatenate((xt, ((np.random.rand(n_noise, 3) + 1) * 10)), axis=0)\n\nfig = pl.figure()\nax1 = fig.add_subplot(121)\nax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\nax2 = fig.add_subplot(122, projection='3d')\nax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\npl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute partial Gromov-Wasserstein plans and distance\n-----------------------------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "C1 = sp.spatial.distance.cdist(xs, xs)\nC2 = sp.spatial.distance.cdist(xt, xt)\n\n# transport 100% of the mass\nprint('-----m = 1')\nm = 1\nres0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True)\nres, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10,\n m=m, log=True)\n\nprint('Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist']))\nprint('Entropic Wasserstein distance (m = 1): ' + str(log['partial_gw_dist']))\n\npl.figure(1, (10, 5))\npl.title(\"mass to be transported m = 1\")\npl.subplot(1, 2, 1)\npl.imshow(res0, cmap='jet')\npl.title('Wasserstein')\npl.subplot(1, 2, 2)\npl.imshow(res, cmap='jet')\npl.title('Entropic Wasserstein')\npl.show()\n\n# transport 2/3 of the mass\nprint('-----m = 2/3')\nm = 2 / 3\nres0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True)\nres, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10,\n m=m, log=True)\n\nprint('Partial Wasserstein distance (m = 2/3): ' +\n str(log0['partial_gw_dist']))\nprint('Entropic partial Wasserstein distance (m = 2/3): ' +\n str(log['partial_gw_dist']))\n\npl.figure(1, (10, 5))\npl.title(\"mass to be transported m = 2/3\")\npl.subplot(1, 2, 1)\npl.imshow(res0, cmap='jet')\npl.title('Partial Wasserstein')\npl.subplot(1, 2, 2)\npl.imshow(res, cmap='jet')\npl.title('Entropic partial Wasserstein')\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_partial_wass_and_gromov.py b/docs/source/auto_examples/plot_partial_wass_and_gromov.py deleted file mode 100644 index 9f95a70..0000000 --- a/docs/source/auto_examples/plot_partial_wass_and_gromov.py +++ /dev/null @@ -1,163 +0,0 @@ -# -*- coding: utf-8 -*- -""" -================================================== -Partial Wasserstein and Gromov-Wasserstein example -================================================== - -This example is designed to show how to use the Partial (Gromov-)Wassertsein -distance computation in POT. -""" - -# Author: Laetitia Chapel -# License: MIT License - -# necessary for 3d plot even if not used -from mpl_toolkits.mplot3d import Axes3D # noqa -import scipy as sp -import numpy as np -import matplotlib.pylab as pl -import ot - - -############################################################################# -# -# Sample two 2D Gaussian distributions and plot them -# -------------------------------------------------- -# -# For demonstration purpose, we sample two Gaussian distributions in 2-d -# spaces and add some random noise. - - -n_samples = 20 # nb samples (gaussian) -n_noise = 20 # nb of samples (noise) - -mu = np.array([0, 0]) -cov = np.array([[1, 0], [0, 2]]) - -xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) -xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2)) -xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) -xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2)) - -M = sp.spatial.distance.cdist(xs, xt) - -fig = pl.figure() -ax1 = fig.add_subplot(131) -ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -ax2 = fig.add_subplot(132) -ax2.scatter(xt[:, 0], xt[:, 1], color='r') -ax3 = fig.add_subplot(133) -ax3.imshow(M) -pl.show() - -############################################################################# -# -# Compute partial Wasserstein plans and distance -# ---------------------------------------------- - -p = ot.unif(n_samples + n_noise) -q = ot.unif(n_samples + n_noise) - -w0, log0 = ot.partial.partial_wasserstein(p, q, M, m=0.5, log=True) -w, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=0.1, m=0.5, - log=True) - -print('Partial Wasserstein distance (m = 0.5): ' + str(log0['partial_w_dist'])) -print('Entropic partial Wasserstein distance (m = 0.5): ' + - str(log['partial_w_dist'])) - -pl.figure(1, (10, 5)) -pl.subplot(1, 2, 1) -pl.imshow(w0, cmap='jet') -pl.title('Partial Wasserstein') -pl.subplot(1, 2, 2) -pl.imshow(w, cmap='jet') -pl.title('Entropic partial Wasserstein') -pl.show() - - -############################################################################# -# -# Sample one 2D and 3D Gaussian distributions and plot them -# --------------------------------------------------------- -# -# The Gromov-Wasserstein distance allows to compute distances with samples that -# do not belong to the same metric space. For demonstration purpose, we sample -# two Gaussian distributions in 2- and 3-dimensional spaces. - -n_samples = 20 # nb samples -n_noise = 10 # nb of samples (noise) - -p = ot.unif(n_samples + n_noise) -q = ot.unif(n_samples + n_noise) - -mu_s = np.array([0, 0]) -cov_s = np.array([[1, 0], [0, 1]]) - -mu_t = np.array([0, 0, 0]) -cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) - - -xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) -xs = np.concatenate((xs, ((np.random.rand(n_noise, 2) + 1) * 4)), axis=0) -P = sp.linalg.sqrtm(cov_t) -xt = np.random.randn(n_samples, 3).dot(P) + mu_t -xt = np.concatenate((xt, ((np.random.rand(n_noise, 3) + 1) * 10)), axis=0) - -fig = pl.figure() -ax1 = fig.add_subplot(121) -ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -ax2 = fig.add_subplot(122, projection='3d') -ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') -pl.show() - - -############################################################################# -# -# Compute partial Gromov-Wasserstein plans and distance -# ----------------------------------------------------- - -C1 = sp.spatial.distance.cdist(xs, xs) -C2 = sp.spatial.distance.cdist(xt, xt) - -# transport 100% of the mass -print('-----m = 1') -m = 1 -res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True) -res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, - m=m, log=True) - -print('Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist'])) -print('Entropic Wasserstein distance (m = 1): ' + str(log['partial_gw_dist'])) - -pl.figure(1, (10, 5)) -pl.title("mass to be transported m = 1") -pl.subplot(1, 2, 1) -pl.imshow(res0, cmap='jet') -pl.title('Wasserstein') -pl.subplot(1, 2, 2) -pl.imshow(res, cmap='jet') -pl.title('Entropic Wasserstein') -pl.show() - -# transport 2/3 of the mass -print('-----m = 2/3') -m = 2 / 3 -res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True) -res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, - m=m, log=True) - -print('Partial Wasserstein distance (m = 2/3): ' + - str(log0['partial_gw_dist'])) -print('Entropic partial Wasserstein distance (m = 2/3): ' + - str(log['partial_gw_dist'])) - -pl.figure(1, (10, 5)) -pl.title("mass to be transported m = 2/3") -pl.subplot(1, 2, 1) -pl.imshow(res0, cmap='jet') -pl.title('Partial Wasserstein') -pl.subplot(1, 2, 2) -pl.imshow(res, cmap='jet') -pl.title('Entropic partial Wasserstein') -pl.show() diff --git a/docs/source/auto_examples/plot_partial_wass_and_gromov.rst b/docs/source/auto_examples/plot_partial_wass_and_gromov.rst deleted file mode 100644 index 2d51210..0000000 --- a/docs/source/auto_examples/plot_partial_wass_and_gromov.rst +++ /dev/null @@ -1,312 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_partial_wass_and_gromov.py: - - -================================================== -Partial Wasserstein and Gromov-Wasserstein example -================================================== - -This example is designed to show how to use the Partial (Gromov-)Wassertsein -distance computation in POT. - - -.. code-block:: default - - - # Author: Laetitia Chapel - # License: MIT License - - # necessary for 3d plot even if not used - from mpl_toolkits.mplot3d import Axes3D # noqa - import scipy as sp - import numpy as np - import matplotlib.pylab as pl - import ot - - - - - - - - - -Sample two 2D Gaussian distributions and plot them --------------------------------------------------- - -For demonstration purpose, we sample two Gaussian distributions in 2-d -spaces and add some random noise. - - -.. code-block:: default - - - - n_samples = 20 # nb samples (gaussian) - n_noise = 20 # nb of samples (noise) - - mu = np.array([0, 0]) - cov = np.array([[1, 0], [0, 2]]) - - xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) - xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2)) - xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) - xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2)) - - M = sp.spatial.distance.cdist(xs, xt) - - fig = pl.figure() - ax1 = fig.add_subplot(131) - ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - ax2 = fig.add_subplot(132) - ax2.scatter(xt[:, 0], xt[:, 1], color='r') - ax3 = fig.add_subplot(133) - ax3.imshow(M) - pl.show() - - - - -.. image:: /auto_examples/images/sphx_glr_plot_partial_wass_and_gromov_001.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:51: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -Compute partial Wasserstein plans and distance ----------------------------------------------- - - -.. code-block:: default - - - p = ot.unif(n_samples + n_noise) - q = ot.unif(n_samples + n_noise) - - w0, log0 = ot.partial.partial_wasserstein(p, q, M, m=0.5, log=True) - w, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=0.1, m=0.5, - log=True) - - print('Partial Wasserstein distance (m = 0.5): ' + str(log0['partial_w_dist'])) - print('Entropic partial Wasserstein distance (m = 0.5): ' + - str(log['partial_w_dist'])) - - pl.figure(1, (10, 5)) - pl.subplot(1, 2, 1) - pl.imshow(w0, cmap='jet') - pl.title('Partial Wasserstein') - pl.subplot(1, 2, 2) - pl.imshow(w, cmap='jet') - pl.title('Entropic partial Wasserstein') - pl.show() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_partial_wass_and_gromov_002.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - Partial Wasserstein distance (m = 0.5): 0.507323938973194 - Entropic partial Wasserstein distance (m = 0.5): 0.5205305886057896 - /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:76: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -Sample one 2D and 3D Gaussian distributions and plot them ---------------------------------------------------------- - -The Gromov-Wasserstein distance allows to compute distances with samples that -do not belong to the same metric space. For demonstration purpose, we sample -two Gaussian distributions in 2- and 3-dimensional spaces. - - -.. code-block:: default - - - n_samples = 20 # nb samples - n_noise = 10 # nb of samples (noise) - - p = ot.unif(n_samples + n_noise) - q = ot.unif(n_samples + n_noise) - - mu_s = np.array([0, 0]) - cov_s = np.array([[1, 0], [0, 1]]) - - mu_t = np.array([0, 0, 0]) - cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) - - - xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) - xs = np.concatenate((xs, ((np.random.rand(n_noise, 2) + 1) * 4)), axis=0) - P = sp.linalg.sqrtm(cov_t) - xt = np.random.randn(n_samples, 3).dot(P) + mu_t - xt = np.concatenate((xt, ((np.random.rand(n_noise, 3) + 1) * 10)), axis=0) - - fig = pl.figure() - ax1 = fig.add_subplot(121) - ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - ax2 = fig.add_subplot(122, projection='3d') - ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') - pl.show() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_partial_wass_and_gromov_003.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:112: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -Compute partial Gromov-Wasserstein plans and distance ------------------------------------------------------ - - -.. code-block:: default - - - C1 = sp.spatial.distance.cdist(xs, xs) - C2 = sp.spatial.distance.cdist(xt, xt) - - # transport 100% of the mass - print('-----m = 1') - m = 1 - res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True) - res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, - m=m, log=True) - - print('Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist'])) - print('Entropic Wasserstein distance (m = 1): ' + str(log['partial_gw_dist'])) - - pl.figure(1, (10, 5)) - pl.title("mass to be transported m = 1") - pl.subplot(1, 2, 1) - pl.imshow(res0, cmap='jet') - pl.title('Wasserstein') - pl.subplot(1, 2, 2) - pl.imshow(res, cmap='jet') - pl.title('Entropic Wasserstein') - pl.show() - - # transport 2/3 of the mass - print('-----m = 2/3') - m = 2 / 3 - res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True) - res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, - m=m, log=True) - - print('Partial Wasserstein distance (m = 2/3): ' + - str(log0['partial_gw_dist'])) - print('Entropic partial Wasserstein distance (m = 2/3): ' + - str(log['partial_gw_dist'])) - - pl.figure(1, (10, 5)) - pl.title("mass to be transported m = 2/3") - pl.subplot(1, 2, 1) - pl.imshow(res0, cmap='jet') - pl.title('Partial Wasserstein') - pl.subplot(1, 2, 2) - pl.imshow(res, cmap='jet') - pl.title('Entropic partial Wasserstein') - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_partial_wass_and_gromov_004.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - -----m = 1 - Wasserstein distance (m = 1): 63.65368600872179 - Entropic Wasserstein distance (m = 1): 65.23659085946916 - /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:141: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - -----m = 2/3 - Partial Wasserstein distance (m = 2/3): 0.23235485397666825 - Entropic partial Wasserstein distance (m = 2/3): 1.4645434781619244 - /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:157: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance. - pl.subplot(1, 2, 1) - /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:160: MatplotlibDeprecationWarning: Adding an axes using the same arguments as a previous axes currently reuses the earlier instance. In a future version, a new instance will always be created and returned. Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance. - pl.subplot(1, 2, 2) - /home/rflamary/PYTHON/POT/examples/plot_partial_wass_and_gromov.py:163: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 1.543 seconds) - - -.. _sphx_glr_download_auto_examples_plot_partial_wass_and_gromov.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_partial_wass_and_gromov.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_partial_wass_and_gromov.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_screenkhorn_1D.ipynb b/docs/source/auto_examples/plot_screenkhorn_1D.ipynb deleted file mode 100644 index 1c27d3b..0000000 --- a/docs/source/auto_examples/plot_screenkhorn_1D.ipynb +++ /dev/null @@ -1,108 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# 1D Screened optimal transport\n\n\nThis example illustrates the computation of Screenkhorn:\nScreening Sinkhorn Algorithm for Optimal transport.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Mokhtar Z. Alaya \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot.plot\nfrom ot.datasets import make_1D_gauss as gauss\nfrom ot.bregman import screenkhorn" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n-------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot distributions and loss matrix\n----------------------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n# plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve Screenkhorn\n-----------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Screenkhorn\nlambd = 2e-03 # entropy parameter\nns_budget = 30 # budget number of points to be keeped in the source distribution\nnt_budget = 30 # budget number of points to be keeped in the target distribution\n\nG_screen = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True)\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G_screen, 'OT matrix Screenkhorn')\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_screenkhorn_1D.py b/docs/source/auto_examples/plot_screenkhorn_1D.py deleted file mode 100644 index 840ead8..0000000 --- a/docs/source/auto_examples/plot_screenkhorn_1D.py +++ /dev/null @@ -1,68 +0,0 @@ -# -*- coding: utf-8 -*- -""" -=============================== -1D Screened optimal transport -=============================== - -This example illustrates the computation of Screenkhorn: -Screening Sinkhorn Algorithm for Optimal transport. -""" - -# Author: Mokhtar Z. Alaya -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot.plot -from ot.datasets import make_1D_gauss as gauss -from ot.bregman import screenkhorn - -############################################################################## -# Generate data -# ------------- - -#%% parameters - -n = 100 # nb bins - -# bin positions -x = np.arange(n, dtype=np.float64) - -# Gaussian distributions -a = gauss(n, m=20, s=5) # m= mean, s= std -b = gauss(n, m=60, s=10) - -# loss matrix -M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) -M /= M.max() - -############################################################################## -# Plot distributions and loss matrix -# ---------------------------------- - -#%% plot the distributions - -pl.figure(1, figsize=(6.4, 3)) -pl.plot(x, a, 'b', label='Source distribution') -pl.plot(x, b, 'r', label='Target distribution') -pl.legend() - -# plot distributions and loss matrix - -pl.figure(2, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') - -############################################################################## -# Solve Screenkhorn -# ----------------------- - -# Screenkhorn -lambd = 2e-03 # entropy parameter -ns_budget = 30 # budget number of points to be keeped in the source distribution -nt_budget = 30 # budget number of points to be keeped in the target distribution - -G_screen = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True) -pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, G_screen, 'OT matrix Screenkhorn') -pl.show() diff --git a/docs/source/auto_examples/plot_screenkhorn_1D.rst b/docs/source/auto_examples/plot_screenkhorn_1D.rst deleted file mode 100644 index 039479e..0000000 --- a/docs/source/auto_examples/plot_screenkhorn_1D.rst +++ /dev/null @@ -1,178 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_screenkhorn_1D.py: - - -=============================== -1D Screened optimal transport -=============================== - -This example illustrates the computation of Screenkhorn: -Screening Sinkhorn Algorithm for Optimal transport. - - -.. code-block:: default - - - # Author: Mokhtar Z. Alaya - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot.plot - from ot.datasets import make_1D_gauss as gauss - from ot.bregman import screenkhorn - - - - - - - - -Generate data -------------- - - -.. code-block:: default - - - n = 100 # nb bins - - # bin positions - x = np.arange(n, dtype=np.float64) - - # Gaussian distributions - a = gauss(n, m=20, s=5) # m= mean, s= std - b = gauss(n, m=60, s=10) - - # loss matrix - M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) - M /= M.max() - - - - - - - - -Plot distributions and loss matrix ----------------------------------- - - -.. code-block:: default - - - pl.figure(1, figsize=(6.4, 3)) - pl.plot(x, a, 'b', label='Source distribution') - pl.plot(x, b, 'r', label='Target distribution') - pl.legend() - - # plot distributions and loss matrix - - pl.figure(2, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') - - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_screenkhorn_1D_001.png - :class: sphx-glr-multi-img - - * - - .. image:: /auto_examples/images/sphx_glr_plot_screenkhorn_1D_002.png - :class: sphx-glr-multi-img - - - - - -Solve Screenkhorn ------------------------ - - -.. code-block:: default - - - # Screenkhorn - lambd = 2e-03 # entropy parameter - ns_budget = 30 # budget number of points to be keeped in the source distribution - nt_budget = 30 # budget number of points to be keeped in the target distribution - - G_screen = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True) - pl.figure(4, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, G_screen, 'OT matrix Screenkhorn') - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_screenkhorn_1D_003.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/ot/bregman.py:2056: UserWarning: Bottleneck module is not installed. Install it from https://pypi.org/project/Bottleneck/ for better performance. - "Bottleneck module is not installed. Install it from https://pypi.org/project/Bottleneck/ for better performance.") - epsilon = 0.020986042861303855 - - kappa = 3.7476531411890917 - - Cardinality of selected points: |Isel| = 30 |Jsel| = 30 - - /home/rflamary/PYTHON/POT/examples/plot_screenkhorn_1D.py:68: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 0.228 seconds) - - -.. _sphx_glr_download_auto_examples_plot_screenkhorn_1D.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_screenkhorn_1D.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_screenkhorn_1D.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_stochastic.ipynb b/docs/source/auto_examples/plot_stochastic.ipynb deleted file mode 100644 index c29f75a..0000000 --- a/docs/source/auto_examples/plot_stochastic.ipynb +++ /dev/null @@ -1,295 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n# Stochastic examples\n\n\nThis example is designed to show how to use the stochatic optimization\nalgorithms for descrete and semicontinous measures from the POT library.\n\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Kilian Fatras \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport numpy as np\nimport ot\nimport ot.plot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM\n############################################################################\n############################################################################\n DISCRETE CASE:\n\n Sample two discrete measures for the discrete case\n ---------------------------------------------\n\n Define 2 discrete measures a and b, the points where are defined the source\n and the target measures and finally the cost matrix c.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_source = 7\nn_target = 4\nreg = 1\nnumItermax = 1000\n\na = ot.utils.unif(n_source)\nb = ot.utils.unif(n_target)\n\nrng = np.random.RandomState(0)\nX_source = rng.randn(n_source, 2)\nY_target = rng.randn(n_target, 2)\nM = ot.dist(X_source, Y_target)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call the \"SAG\" method to find the transportation matrix in the discrete case\n---------------------------------------------\n\nDefine the method \"SAG\", call ot.solve_semi_dual_entropic and plot the\nresults.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "method = \"SAG\"\nsag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,\n numItermax)\nprint(sag_pi)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "SEMICONTINOUS CASE:\n\nSample one general measure a, one discrete measures b for the semicontinous\ncase\n---------------------------------------------\n\nDefine one general measure a, one discrete measures b, the points where\nare defined the source and the target measures and finally the cost matrix c.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_source = 7\nn_target = 4\nreg = 1\nnumItermax = 1000\nlog = True\n\na = ot.utils.unif(n_source)\nb = ot.utils.unif(n_target)\n\nrng = np.random.RandomState(0)\nX_source = rng.randn(n_source, 2)\nY_target = rng.randn(n_target, 2)\nM = ot.dist(X_source, Y_target)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call the \"ASGD\" method to find the transportation matrix in the semicontinous\ncase\n---------------------------------------------\n\nDefine the method \"ASGD\", call ot.solve_semi_dual_entropic and plot the\nresults.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "method = \"ASGD\"\nasgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,\n numItermax, log=log)\nprint(log_asgd['alpha'], log_asgd['beta'])\nprint(asgd_pi)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compare the results with the Sinkhorn algorithm\n---------------------------------------------\n\nCall the Sinkhorn algorithm from POT\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "sinkhorn_pi = ot.sinkhorn(a, b, M, reg)\nprint(sinkhorn_pi)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "PLOT TRANSPORTATION MATRIX\n#############################################################################\n\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot SAG results\n----------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG')\npl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot ASGD results\n-----------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD')\npl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot Sinkhorn results\n---------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')\npl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM\n############################################################################\n############################################################################\n SEMICONTINOUS CASE:\n\n Sample one general measure a, one discrete measures b for the semicontinous\n case\n ---------------------------------------------\n\n Define one general measure a, one discrete measures b, the points where\n are defined the source and the target measures and finally the cost matrix c.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_source = 7\nn_target = 4\nreg = 1\nnumItermax = 100000\nlr = 0.1\nbatch_size = 3\nlog = True\n\na = ot.utils.unif(n_source)\nb = ot.utils.unif(n_target)\n\nrng = np.random.RandomState(0)\nX_source = rng.randn(n_source, 2)\nY_target = rng.randn(n_target, 2)\nM = ot.dist(X_source, Y_target)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call the \"SGD\" dual method to find the transportation matrix in the\nsemicontinous case\n---------------------------------------------\n\nCall ot.solve_dual_entropic and plot the results.\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg,\n batch_size, numItermax,\n lr, log=log)\nprint(log_sgd['alpha'], log_sgd['beta'])\nprint(sgd_dual_pi)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compare the results with the Sinkhorn algorithm\n---------------------------------------------\n\nCall the Sinkhorn algorithm from POT\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "sinkhorn_pi = ot.sinkhorn(a, b, M, reg)\nprint(sinkhorn_pi)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot SGD results\n-----------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD')\npl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot Sinkhorn results\n---------------------\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')\npl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_stochastic.py b/docs/source/auto_examples/plot_stochastic.py deleted file mode 100644 index 742f8d9..0000000 --- a/docs/source/auto_examples/plot_stochastic.py +++ /dev/null @@ -1,208 +0,0 @@ -""" -========================== -Stochastic examples -========================== - -This example is designed to show how to use the stochatic optimization -algorithms for descrete and semicontinous measures from the POT library. - -""" - -# Author: Kilian Fatras -# -# License: MIT License - -import matplotlib.pylab as pl -import numpy as np -import ot -import ot.plot - - -############################################################################# -# COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM -############################################################################# -############################################################################# -# DISCRETE CASE: -# -# Sample two discrete measures for the discrete case -# --------------------------------------------- -# -# Define 2 discrete measures a and b, the points where are defined the source -# and the target measures and finally the cost matrix c. - -n_source = 7 -n_target = 4 -reg = 1 -numItermax = 1000 - -a = ot.utils.unif(n_source) -b = ot.utils.unif(n_target) - -rng = np.random.RandomState(0) -X_source = rng.randn(n_source, 2) -Y_target = rng.randn(n_target, 2) -M = ot.dist(X_source, Y_target) - -############################################################################# -# -# Call the "SAG" method to find the transportation matrix in the discrete case -# --------------------------------------------- -# -# Define the method "SAG", call ot.solve_semi_dual_entropic and plot the -# results. - -method = "SAG" -sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, - numItermax) -print(sag_pi) - -############################################################################# -# SEMICONTINOUS CASE: -# -# Sample one general measure a, one discrete measures b for the semicontinous -# case -# --------------------------------------------- -# -# Define one general measure a, one discrete measures b, the points where -# are defined the source and the target measures and finally the cost matrix c. - -n_source = 7 -n_target = 4 -reg = 1 -numItermax = 1000 -log = True - -a = ot.utils.unif(n_source) -b = ot.utils.unif(n_target) - -rng = np.random.RandomState(0) -X_source = rng.randn(n_source, 2) -Y_target = rng.randn(n_target, 2) -M = ot.dist(X_source, Y_target) - -############################################################################# -# -# Call the "ASGD" method to find the transportation matrix in the semicontinous -# case -# --------------------------------------------- -# -# Define the method "ASGD", call ot.solve_semi_dual_entropic and plot the -# results. - -method = "ASGD" -asgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, - numItermax, log=log) -print(log_asgd['alpha'], log_asgd['beta']) -print(asgd_pi) - -############################################################################# -# -# Compare the results with the Sinkhorn algorithm -# --------------------------------------------- -# -# Call the Sinkhorn algorithm from POT - -sinkhorn_pi = ot.sinkhorn(a, b, M, reg) -print(sinkhorn_pi) - - -############################################################################## -# PLOT TRANSPORTATION MATRIX -############################################################################## - -############################################################################## -# Plot SAG results -# ---------------- - -pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG') -pl.show() - - -############################################################################## -# Plot ASGD results -# ----------------- - -pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD') -pl.show() - - -############################################################################## -# Plot Sinkhorn results -# --------------------- - -pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') -pl.show() - - -############################################################################# -# COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM -############################################################################# -############################################################################# -# SEMICONTINOUS CASE: -# -# Sample one general measure a, one discrete measures b for the semicontinous -# case -# --------------------------------------------- -# -# Define one general measure a, one discrete measures b, the points where -# are defined the source and the target measures and finally the cost matrix c. - -n_source = 7 -n_target = 4 -reg = 1 -numItermax = 100000 -lr = 0.1 -batch_size = 3 -log = True - -a = ot.utils.unif(n_source) -b = ot.utils.unif(n_target) - -rng = np.random.RandomState(0) -X_source = rng.randn(n_source, 2) -Y_target = rng.randn(n_target, 2) -M = ot.dist(X_source, Y_target) - -############################################################################# -# -# Call the "SGD" dual method to find the transportation matrix in the -# semicontinous case -# --------------------------------------------- -# -# Call ot.solve_dual_entropic and plot the results. - -sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg, - batch_size, numItermax, - lr, log=log) -print(log_sgd['alpha'], log_sgd['beta']) -print(sgd_dual_pi) - -############################################################################# -# -# Compare the results with the Sinkhorn algorithm -# --------------------------------------------- -# -# Call the Sinkhorn algorithm from POT - -sinkhorn_pi = ot.sinkhorn(a, b, M, reg) -print(sinkhorn_pi) - -############################################################################## -# Plot SGD results -# ----------------- - -pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD') -pl.show() - - -############################################################################## -# Plot Sinkhorn results -# --------------------- - -pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') -pl.show() diff --git a/docs/source/auto_examples/plot_stochastic.rst b/docs/source/auto_examples/plot_stochastic.rst deleted file mode 100644 index 63fc74f..0000000 --- a/docs/source/auto_examples/plot_stochastic.rst +++ /dev/null @@ -1,518 +0,0 @@ -.. only:: html - - .. note:: - :class: sphx-glr-download-link-note - - Click :ref:`here ` to download the full example code - .. rst-class:: sphx-glr-example-title - - .. _sphx_glr_auto_examples_plot_stochastic.py: - - -========================== -Stochastic examples -========================== - -This example is designed to show how to use the stochatic optimization -algorithms for descrete and semicontinous measures from the POT library. - - - -.. code-block:: default - - - # Author: Kilian Fatras - # - # License: MIT License - - import matplotlib.pylab as pl - import numpy as np - import ot - import ot.plot - - - - - - - - - -COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM -############################################################################ -############################################################################ - DISCRETE CASE: - - Sample two discrete measures for the discrete case - --------------------------------------------- - - Define 2 discrete measures a and b, the points where are defined the source - and the target measures and finally the cost matrix c. - - -.. code-block:: default - - - n_source = 7 - n_target = 4 - reg = 1 - numItermax = 1000 - - a = ot.utils.unif(n_source) - b = ot.utils.unif(n_target) - - rng = np.random.RandomState(0) - X_source = rng.randn(n_source, 2) - Y_target = rng.randn(n_target, 2) - M = ot.dist(X_source, Y_target) - - - - - - - - -Call the "SAG" method to find the transportation matrix in the discrete case ---------------------------------------------- - -Define the method "SAG", call ot.solve_semi_dual_entropic and plot the -results. - - -.. code-block:: default - - - method = "SAG" - sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, - numItermax) - print(sag_pi) - - - - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - [[2.55553509e-02 9.96395660e-02 1.76579142e-02 4.31178196e-06] - [1.21640234e-01 1.25357448e-02 1.30225078e-03 7.37891338e-03] - [3.56123975e-03 7.61451746e-02 6.31505947e-02 1.33831456e-07] - [2.61515202e-02 3.34246014e-02 8.28734709e-02 4.07550428e-04] - [9.85500870e-03 7.52288517e-04 1.08262628e-02 1.21423583e-01] - [2.16904253e-02 9.03825797e-04 1.87178503e-03 1.18391107e-01] - [4.15462212e-02 2.65987989e-02 7.23177216e-02 2.39440107e-03]] - - - - -SEMICONTINOUS CASE: - -Sample one general measure a, one discrete measures b for the semicontinous -case ---------------------------------------------- - -Define one general measure a, one discrete measures b, the points where -are defined the source and the target measures and finally the cost matrix c. - - -.. code-block:: default - - - n_source = 7 - n_target = 4 - reg = 1 - numItermax = 1000 - log = True - - a = ot.utils.unif(n_source) - b = ot.utils.unif(n_target) - - rng = np.random.RandomState(0) - X_source = rng.randn(n_source, 2) - Y_target = rng.randn(n_target, 2) - M = ot.dist(X_source, Y_target) - - - - - - - - -Call the "ASGD" method to find the transportation matrix in the semicontinous -case ---------------------------------------------- - -Define the method "ASGD", call ot.solve_semi_dual_entropic and plot the -results. - - -.. code-block:: default - - - method = "ASGD" - asgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, - numItermax, log=log) - print(log_asgd['alpha'], log_asgd['beta']) - print(asgd_pi) - - - - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - [3.89943264 7.64823414 3.9284189 2.67501041 1.42825446 3.26039819 - 2.79237712] [-2.50786905 -2.42684838 -0.93647774 5.87119517] - [[2.50229922e-02 1.00367920e-01 1.74615056e-02 4.72486104e-06] - [1.20583329e-01 1.27839737e-02 1.30373565e-03 8.18610462e-03] - [3.49243139e-03 7.68200813e-02 6.25444833e-02 1.46879008e-07] - [2.58205995e-02 3.39501207e-02 8.26360982e-02 4.50324517e-04] - [8.94164918e-03 7.02183713e-04 9.92028326e-03 1.23293027e-01] - [1.97360234e-02 8.46022708e-04 1.72001583e-03 1.20555081e-01] - [4.10386980e-02 2.70289873e-02 7.21425804e-02 2.64687723e-03]] - - - - -Compare the results with the Sinkhorn algorithm ---------------------------------------------- - -Call the Sinkhorn algorithm from POT - - -.. code-block:: default - - - sinkhorn_pi = ot.sinkhorn(a, b, M, reg) - print(sinkhorn_pi) - - - - - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - [[2.55553508e-02 9.96395661e-02 1.76579142e-02 4.31178193e-06] - [1.21640234e-01 1.25357448e-02 1.30225079e-03 7.37891333e-03] - [3.56123974e-03 7.61451746e-02 6.31505947e-02 1.33831455e-07] - [2.61515201e-02 3.34246014e-02 8.28734709e-02 4.07550425e-04] - [9.85500876e-03 7.52288523e-04 1.08262629e-02 1.21423583e-01] - [2.16904255e-02 9.03825804e-04 1.87178504e-03 1.18391107e-01] - [4.15462212e-02 2.65987989e-02 7.23177217e-02 2.39440105e-03]] - - - - -PLOT TRANSPORTATION MATRIX -############################################################################# - -Plot SAG results ----------------- - - -.. code-block:: default - - - pl.figure(4, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG') - pl.show() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_stochastic_001.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_stochastic.py:119: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -Plot ASGD results ------------------ - - -.. code-block:: default - - - pl.figure(4, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD') - pl.show() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_stochastic_002.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_stochastic.py:128: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -Plot Sinkhorn results ---------------------- - - -.. code-block:: default - - - pl.figure(4, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') - pl.show() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_stochastic_003.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_stochastic.py:137: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM -############################################################################ -############################################################################ - SEMICONTINOUS CASE: - - Sample one general measure a, one discrete measures b for the semicontinous - case - --------------------------------------------- - - Define one general measure a, one discrete measures b, the points where - are defined the source and the target measures and finally the cost matrix c. - - -.. code-block:: default - - - n_source = 7 - n_target = 4 - reg = 1 - numItermax = 100000 - lr = 0.1 - batch_size = 3 - log = True - - a = ot.utils.unif(n_source) - b = ot.utils.unif(n_target) - - rng = np.random.RandomState(0) - X_source = rng.randn(n_source, 2) - Y_target = rng.randn(n_target, 2) - M = ot.dist(X_source, Y_target) - - - - - - - - -Call the "SGD" dual method to find the transportation matrix in the -semicontinous case ---------------------------------------------- - -Call ot.solve_dual_entropic and plot the results. - - -.. code-block:: default - - - sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg, - batch_size, numItermax, - lr, log=log) - print(log_sgd['alpha'], log_sgd['beta']) - print(sgd_dual_pi) - - - - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - [0.91421006 2.78075506 1.06828701 0.01979397 0.60914807 1.81887037 - 0.1152939 ] [0.33964624 0.47604281 1.57223631 4.93843308] - [[2.18038772e-02 9.24355133e-02 1.08426805e-02 9.39355366e-08] - [1.59966167e-02 1.79248770e-03 1.23251128e-04 2.47779034e-05] - [3.44864558e-03 8.01760930e-02 4.40119061e-02 3.30922887e-09] - [3.12954103e-02 4.34915712e-02 7.13747533e-02 1.24533534e-05] - [6.79742497e-02 5.64192090e-03 5.37416946e-02 2.13851205e-02] - [8.05141568e-02 3.64790957e-03 5.00040902e-03 1.12213345e-02] - [4.86643900e-02 3.38763749e-02 6.09634969e-02 7.16139950e-05]] - - - - -Compare the results with the Sinkhorn algorithm ---------------------------------------------- - -Call the Sinkhorn algorithm from POT - - -.. code-block:: default - - - sinkhorn_pi = ot.sinkhorn(a, b, M, reg) - print(sinkhorn_pi) - - - - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - [[2.55553508e-02 9.96395661e-02 1.76579142e-02 4.31178193e-06] - [1.21640234e-01 1.25357448e-02 1.30225079e-03 7.37891333e-03] - [3.56123974e-03 7.61451746e-02 6.31505947e-02 1.33831455e-07] - [2.61515201e-02 3.34246014e-02 8.28734709e-02 4.07550425e-04] - [9.85500876e-03 7.52288523e-04 1.08262629e-02 1.21423583e-01] - [2.16904255e-02 9.03825804e-04 1.87178504e-03 1.18391107e-01] - [4.15462212e-02 2.65987989e-02 7.23177217e-02 2.39440105e-03]] - - - - -Plot SGD results ------------------ - - -.. code-block:: default - - - pl.figure(4, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD') - pl.show() - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_stochastic_004.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_stochastic.py:199: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - -Plot Sinkhorn results ---------------------- - - -.. code-block:: default - - - pl.figure(4, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') - pl.show() - - - -.. image:: /auto_examples/images/sphx_glr_plot_stochastic_005.png - :class: sphx-glr-single-img - - -.. rst-class:: sphx-glr-script-out - - Out: - - .. code-block:: none - - /home/rflamary/PYTHON/POT/examples/plot_stochastic.py:208: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure. - pl.show() - - - - - -.. rst-class:: sphx-glr-timing - - **Total running time of the script:** ( 0 minutes 8.885 seconds) - - -.. _sphx_glr_download_auto_examples_plot_stochastic.py: - - -.. only :: html - - .. container:: sphx-glr-footer - :class: sphx-glr-footer-example - - - - .. container:: sphx-glr-download sphx-glr-download-python - - :download:`Download Python source code: plot_stochastic.py ` - - - - .. container:: sphx-glr-download sphx-glr-download-jupyter - - :download:`Download Jupyter notebook: plot_stochastic.ipynb ` - - -.. only:: html - - .. rst-class:: sphx-glr-signature - - `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/searchindex b/docs/source/auto_examples/searchindex deleted file mode 100644 index 2cad500..0000000 Binary files a/docs/source/auto_examples/searchindex and /dev/null differ diff --git a/docs/source/conf.py b/docs/source/conf.py index d29b829..880c71d 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -34,9 +34,11 @@ class Mock(MagicMock): @classmethod def __getattr__(cls, name): return MagicMock() -MOCK_MODULES = ['ot.lp.emd_wrap','autograd','pymanopt','cupy','autograd.numpy','pymanopt.manifolds','pymanopt.solvers'] + + +MOCK_MODULES = ['ot.lp.emd_wrap', 'autograd', 'pymanopt', 'cupy', 'autograd.numpy', 'pymanopt.manifolds', 'pymanopt.solvers'] # 'autograd.numpy','pymanopt.manifolds','pymanopt.solvers', -sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) +#sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # !!!! # If extensions (or modules to document with autodoc) are in another directory, @@ -65,7 +67,7 @@ extensions = [ 'sphinx.ext.ifconfig', 'sphinx.ext.viewcode', 'sphinx.ext.napoleon', - #'sphinx_gallery.gen_gallery', + 'sphinx_gallery.gen_gallery', ] napoleon_numpy_docstring = True @@ -248,17 +250,17 @@ htmlhelp_basename = 'POTdoc' # -- Options for LaTeX output --------------------------------------------- latex_elements = { -# The paper size ('letterpaper' or 'a4paper'). -#'papersize': 'letterpaper', + # The paper size ('letterpaper' or 'a4paper'). + #'papersize': 'letterpaper', -# The font size ('10pt', '11pt' or '12pt'). -#'pointsize': '10pt', + # The font size ('10pt', '11pt' or '12pt'). + #'pointsize': '10pt', -# Additional stuff for the LaTeX preamble. -#'preamble': '', + # Additional stuff for the LaTeX preamble. + #'preamble': '', -# Latex figure (float) alignment -#'figure_align': 'htbp', + # Latex figure (float) alignment + #'figure_align': 'htbp', } # Grouping the document tree into LaTeX files. List of tuples @@ -334,7 +336,7 @@ intersphinx_mapping = {'python': ('https://docs.python.org/3', None), 'matplotlib': ('http://matplotlib.sourceforge.net/', None)} sphinx_gallery_conf = { - 'examples_dirs': ['../../examples','../../examples/da'], + 'examples_dirs': ['../../examples', '../../examples/da'], 'gallery_dirs': 'auto_examples', 'backreferences_dir': '../modules/generated/', 'reference_url': { diff --git a/docs/source/index.md b/docs/source/index.md new file mode 100644 index 0000000..9acdcf3 --- /dev/null +++ b/docs/source/index.md @@ -0,0 +1 @@ +` \ No newline at end of file diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 4f6af01..279e5da 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -1,14 +1,16 @@ POT: Python Optimal Transport ============================= -|PyPI version| |Anaconda Cloud| |Build Status| |Documentation Status| +|PyPI version| |Anaconda Cloud| |Build Status| |Codecov Status| |Downloads| |Anaconda downloads| |License| This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. -It provides the following solvers: +Website and documentation: https://PythonOT.github.io/ + +POT provides the following solvers: - OT Network Flow solver for the linear program/ Earth Movers Distance [1]. @@ -24,7 +26,7 @@ It provides the following solvers: - Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4]. - Optimal transport for domain adaptation with group lasso - regularization [5] + regularization and Laplacian regularization [5][30] - Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. - Linear OT [14] and Joint OT matrix and mapping estimation [8]. @@ -180,45 +182,45 @@ Examples and Notebooks The examples folder contain several examples and use case for the library. The full documentation is available on -`Readthedocs `__. +https://PythonOT.github.io/. Here is a list of the Python notebooks available -`here `__ if you +`here `__ if you want a quick look: - `1D optimal - transport `__ + transport `__ - `OT Ground - Loss `__ + Loss `__ - `Multiple EMD - computation `__ + computation `__ - `2D optimal transport on empirical - distributions `__ + distributions `__ - `1D Wasserstein - barycenter `__ + barycenter `__ - `OT with user provided - regularization `__ + regularization `__ - `Domain adaptation with optimal - transport `__ + transport `__ - `Color transfer in - images `__ + images `__ - `OT mapping estimation for domain - adaptation `__ + adaptation `__ - `OT mapping estimation for color transfer in - images `__ + images `__ - `Wasserstein Discriminant - Analysis `__ + Analysis `__ - `Gromov - Wasserstein `__ + Wasserstein `__ - `Gromov Wasserstein - Barycenter `__ + Barycenter `__ - `Fused Gromov - Wasserstein `__ + Wasserstein `__ - `Fused Gromov Wasserstein - Barycenter `__ + Barycenter `__ You can also see the notebooks with `Jupyter -nbviewer `__. +nbviewer `__. Acknowledgements ---------------- @@ -247,6 +249,7 @@ The contributors to this library are - `Hicham Janati `__ (Unbalanced OT) - `Romain Tavenard `__ (1d Wasserstein) - `Mokhtar Z. Alaya `__ (Screenkhorn) +- `Ievgen Redko `__ This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various @@ -422,17 +425,23 @@ Gromov-Wasserstein with Applications on Positive-Unlabeled Learning `__, arXiv preprint arXiv:2002.08276. +[30] Flamary R., Courty N., Tuia D., Rakotomamonjy A. (2014). `Optimal +transport with Laplacian regularization: Applications to domain +adaptation and shape +matching `__, +NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014. + .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT .. |Anaconda Cloud| image:: https://anaconda.org/conda-forge/pot/badges/version.svg :target: https://anaconda.org/conda-forge/pot -.. |Build Status| image:: https://travis-ci.org/rflamary/POT.svg?branch=master - :target: https://travis-ci.org/rflamary/POT -.. |Documentation Status| image:: https://readthedocs.org/projects/pot/badge/?version=latest - :target: http://pot.readthedocs.io/en/latest/?badge=latest +.. |Build Status| image:: https://travis-ci.org/PythonOT/POT.svg?branch=master + :target: https://travis-ci.org/PythonOT/POT +.. |Codecov Status| image:: https://codecov.io/gh/PythonOT/POT/branch/master/graph/badge.svg + :target: https://codecov.io/gh/PythonOT/POT .. |Downloads| image:: https://pepy.tech/badge/pot :target: https://pepy.tech/project/pot .. |Anaconda downloads| image:: https://anaconda.org/conda-forge/pot/badges/downloads.svg :target: https://anaconda.org/conda-forge/pot .. |License| image:: https://anaconda.org/conda-forge/pot/badges/license.svg - :target: https://github.com/rflamary/POT/blob/master/LICENSE + :target: https://github.com/PythonOT/POT/blob/master/LICENSE diff --git a/examples/plot_fgw.py b/examples/plot_fgw.py index 43efc94..cfdc33b 100644 --- a/examples/plot_fgw.py +++ b/examples/plot_fgw.py @@ -60,14 +60,14 @@ pl.subplot(2, 1, 1) pl.scatter(ys, xs, c=phi, s=70) pl.ylabel('Feature value a', fontsize=20) -pl.title('$\mu=\sum_i \delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1) +pl.title('$\mu=\sum_i \delta_{x_i,a_i}$', fontsize=25, y=1) pl.xticks(()) pl.yticks(()) pl.subplot(2, 1, 2) pl.scatter(yt, xt, c=phi2, s=70) pl.xlabel('coordinates x/y', fontsize=25) pl.ylabel('Feature value b', fontsize=20) -pl.title('$\\nu=\sum_j \delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1) +pl.title('$\\nu=\sum_j \delta_{y_j,b_j}$', fontsize=25, y=1) pl.yticks(()) pl.tight_layout() pl.show() diff --git a/requirements.txt b/requirements.txt index c08822e..bee22f7 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,9 +2,9 @@ numpy scipy>=1.0 cython matplotlib -sphinx-gallery autograd pymanopt==0.2.4; python_version <'3' pymanopt; python_version >= '3' cvxopt +scikit-learn pytest \ No newline at end of file -- cgit v1.2.3 From bcda6054e44481a571e89c3d64c9056c75ff244b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 21 Apr 2020 17:57:55 +0200 Subject: rtd redirect index.md --- docs/rtd/index.md | 1 + docs/source/index.md | 1 - 2 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 docs/rtd/index.md delete mode 100644 docs/source/index.md diff --git a/docs/rtd/index.md b/docs/rtd/index.md new file mode 100644 index 0000000..9acdcf3 --- /dev/null +++ b/docs/rtd/index.md @@ -0,0 +1 @@ +` \ No newline at end of file diff --git a/docs/source/index.md b/docs/source/index.md deleted file mode 100644 index 9acdcf3..0000000 --- a/docs/source/index.md +++ /dev/null @@ -1 +0,0 @@ -` \ No newline at end of file -- cgit v1.2.3 From 72dc1d57bb0b94603b7d08bd5830f8391dd59502 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 21 Apr 2020 23:07:16 +0200 Subject: fix GH Action badge in readme (#153) --- .github/workflows/pythonpackage.yml | 2 +- README.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index 19527c3..af6efb7 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -1,4 +1,4 @@ -name: Test Package +name: build on: push: diff --git a/README.md b/README.md index 40f43e0..1cc39e6 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ [![PyPI version](https://badge.fury.io/py/POT.svg)](https://badge.fury.io/py/POT) [![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot) [![Build Status](https://travis-ci.org/PythonOT/POT.svg?branch=master)](https://travis-ci.org/PythonOT/POT) -[![Build Status](https://github.com/PythonOT/POT/workflows/Linux%7CWin%7CMacOS/badge.svg)](https://github.com/PythonOT/POT/actions) +[![Build Status](https://github.com/PythonOT/POT/workflows/build/badge.svg)](https://github.com/PythonOT/POT/actions) [![Codecov Status](https://codecov.io/gh/PythonOT/POT/branch/master/graph/badge.svg)](https://codecov.io/gh/PythonOT/POT) [![Downloads](https://pepy.tech/badge/pot)](https://pepy.tech/project/pot) [![Anaconda downloads](https://anaconda.org/conda-forge/pot/badges/downloads.svg)](https://anaconda.org/conda-forge/pot) -- cgit v1.2.3 From b61d414da0179eda951d6f8de2cfa5899dd500f3 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 22 Apr 2020 07:32:29 +0200 Subject: cleanup example + moe information rtd --- docs/rtd/index.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/docs/rtd/index.md b/docs/rtd/index.md index 9acdcf3..734574e 100644 --- a/docs/rtd/index.md +++ b/docs/rtd/index.md @@ -1 +1,5 @@ -` \ No newline at end of file + + +# POT: Python Optimal Transport + +The documentation has been moved to : [https://PythonOT.github.io](https://PythonOT.github.io) \ No newline at end of file -- cgit v1.2.3 From 3f9d14d2f91eefb748a353df7907a8e20a3168c4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 22 Apr 2020 07:32:53 +0200 Subject: cleanup example --- examples/plot_otda_mapping_colors_images.py | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/examples/plot_otda_mapping_colors_images.py b/examples/plot_otda_mapping_colors_images.py index bc9afba..a0938a0 100644 --- a/examples/plot_otda_mapping_colors_images.py +++ b/examples/plot_otda_mapping_colors_images.py @@ -8,11 +8,9 @@ OT for domain adaptation with image color adaptation [6] with mapping estimation [8]. [6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized - discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), - 1853-1882. +discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for - discrete optimal transport", Neural Information Processing Systems (NIPS), - 2016. +discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. """ -- cgit v1.2.3 -- cgit v1.2.3 From ab561b240be6c6597736e79a5082a05e2707c593 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 22 Apr 2020 10:45:45 +0200 Subject: working gromov barycenter example --- examples/plot_gromov_barycenter.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py index 101c6c5..753bdc8 100755 --- a/examples/plot_gromov_barycenter.py +++ b/examples/plot_gromov_barycenter.py @@ -89,10 +89,10 @@ def im2mat(I): return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) -square = pl.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256 -cross = pl.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256 -triangle = pl.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256 -star = pl.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256 +square = pl.imread('../data/square.png').astype(np.float64)[:, :, 2] +cross = pl.imread('../data/cross.png').astype(np.float64)[:, :, 2] +triangle = pl.imread('../data/triangle.png').astype(np.float64)[:, :, 2] +star = pl.imread('../data/star.png').astype(np.float64)[:, :, 2] shapes = [square, cross, triangle, star] -- cgit v1.2.3 From 6654741efcd4a211278a9253bf22920186ecf777 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 22 Apr 2020 10:55:22 +0200 Subject: add codecov token --- codecov.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/codecov.yml b/codecov.yml index 1e7b888..fbd1b07 100644 --- a/codecov.yml +++ b/codecov.yml @@ -12,3 +12,5 @@ coverage: comment: layout: "header, diff, sunburst, uncovered" behavior: default +codecov: + token: 057953e4-d263-41c0-913c-5d45c0371df9 \ No newline at end of file -- cgit v1.2.3 From 2a8c86cdad1948ad3c907ad8de2ec14d0d689a2e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 22 Apr 2020 11:05:45 +0200 Subject: add flake8 fail to github actions --- .github/workflows/pythonpackage.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index af6efb7..394f453 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -36,7 +36,7 @@ jobs: # stop the build if there are Python syntax errors or undefined names flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + flake8 examples/ ot/ test/ --count --max-line-length=127 --statistics - name: Install POT run: | pip install -e . -- cgit v1.2.3 From c728696d9aaf7e18a018e07af3e3a0d9725db991 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 22 Apr 2020 11:07:54 +0200 Subject: pep8 error detected and corrected --- examples/plot_gromov_barycenter.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py index 753bdc8..6b29687 100755 --- a/examples/plot_gromov_barycenter.py +++ b/examples/plot_gromov_barycenter.py @@ -90,8 +90,8 @@ def im2mat(I): square = pl.imread('../data/square.png').astype(np.float64)[:, :, 2] -cross = pl.imread('../data/cross.png').astype(np.float64)[:, :, 2] -triangle = pl.imread('../data/triangle.png').astype(np.float64)[:, :, 2] +cross = pl.imread('../data/cross.png').astype(np.float64)[:, :, 2] +triangle = pl.imread('../data/triangle.png').astype(np.float64)[:, :, 2] star = pl.imread('../data/star.png').astype(np.float64)[:, :, 2] shapes = [square, cross, triangle, star] -- cgit v1.2.3 From 249d46c2aa29e7f975e3acbe8c9ae29a1dfc5490 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 22 Apr 2020 11:17:08 +0200 Subject: rename circleci redicrector action --- .github/workflows/main.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml index 7153fe6..ae7bfca 100644 --- a/.github/workflows/main.yml +++ b/.github/workflows/main.yml @@ -1,3 +1,4 @@ +name: circleci-redirector on: [status] jobs: circleci_artifacts_redirector_job: -- cgit v1.2.3 From 135c011092cb442b0b874b565b6a2ca3f09234c4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 22 Apr 2020 11:39:23 +0200 Subject: remove notebooks and cleanup readme and doc --- README.md | 40 +- docs/source/readme.rst | 60 +-- notebooks/plot_OT_1D.ipynb | 278 ---------- notebooks/plot_OT_1D_smooth.ipynb | 335 ------------ notebooks/plot_OT_2D_samples.ipynb | 372 -------------- notebooks/plot_OT_L1_vs_L2.ipynb | 384 -------------- notebooks/plot_UOT_1D.ipynb | 197 -------- notebooks/plot_UOT_barycenter_1D.ipynb | 342 ------------- notebooks/plot_WDA.ipynb | 316 ------------ notebooks/plot_barycenter_1D.ipynb | 315 ------------ notebooks/plot_barycenter_fgw.ipynb | 342 ------------- notebooks/plot_barycenter_lp_vs_entropic.ipynb | 592 ---------------------- notebooks/plot_compute_emd.ipynb | 247 --------- notebooks/plot_convolutional_barycenter.ipynb | 178 ------- notebooks/plot_fgw.ipynb | 365 -------------- notebooks/plot_free_support_barycenter.ipynb | 171 ------- notebooks/plot_gromov.ipynb | 263 ---------- notebooks/plot_gromov_barycenter.ipynb | 369 -------------- notebooks/plot_optim_OTreg.ipynb | 643 ------------------------ notebooks/plot_otda_classes.ipynb | 305 ----------- notebooks/plot_otda_color_images.ipynb | 327 ------------ notebooks/plot_otda_d2.ipynb | 321 ------------ notebooks/plot_otda_jcpot.ipynb | 397 --------------- notebooks/plot_otda_laplacian.ipynb | 252 ---------- notebooks/plot_otda_linear_mapping.ipynb | 343 ------------- notebooks/plot_otda_mapping.ipynb | 290 ----------- notebooks/plot_otda_mapping_colors_images.ipynb | 368 -------------- notebooks/plot_otda_semi_supervised.ipynb | 294 ----------- notebooks/plot_partial_wass_and_gromov.ipynb | 350 ------------- notebooks/plot_screenkhorn_1D.ipynb | 213 -------- notebooks/plot_stochastic.ipynb | 573 --------------------- 31 files changed, 22 insertions(+), 9820 deletions(-) delete mode 100644 notebooks/plot_OT_1D.ipynb delete mode 100644 notebooks/plot_OT_1D_smooth.ipynb delete mode 100644 notebooks/plot_OT_2D_samples.ipynb delete mode 100644 notebooks/plot_OT_L1_vs_L2.ipynb delete mode 100644 notebooks/plot_UOT_1D.ipynb delete mode 100644 notebooks/plot_UOT_barycenter_1D.ipynb delete mode 100644 notebooks/plot_WDA.ipynb delete mode 100644 notebooks/plot_barycenter_1D.ipynb delete mode 100644 notebooks/plot_barycenter_fgw.ipynb delete mode 100644 notebooks/plot_barycenter_lp_vs_entropic.ipynb delete mode 100644 notebooks/plot_compute_emd.ipynb delete mode 100644 notebooks/plot_convolutional_barycenter.ipynb delete mode 100644 notebooks/plot_fgw.ipynb delete mode 100644 notebooks/plot_free_support_barycenter.ipynb delete mode 100644 notebooks/plot_gromov.ipynb delete mode 100644 notebooks/plot_gromov_barycenter.ipynb delete mode 100644 notebooks/plot_optim_OTreg.ipynb delete mode 100644 notebooks/plot_otda_classes.ipynb delete mode 100644 notebooks/plot_otda_color_images.ipynb delete mode 100644 notebooks/plot_otda_d2.ipynb delete mode 100644 notebooks/plot_otda_jcpot.ipynb delete mode 100644 notebooks/plot_otda_laplacian.ipynb delete mode 100644 notebooks/plot_otda_linear_mapping.ipynb delete mode 100644 notebooks/plot_otda_mapping.ipynb delete mode 100644 notebooks/plot_otda_mapping_colors_images.ipynb delete mode 100644 notebooks/plot_otda_semi_supervised.ipynb delete mode 100644 notebooks/plot_partial_wass_and_gromov.ipynb delete mode 100644 notebooks/plot_screenkhorn_1D.ipynb delete mode 100644 notebooks/plot_stochastic.ipynb diff --git a/README.md b/README.md index 1cc39e6..c42af39 100644 --- a/README.md +++ b/README.md @@ -136,35 +136,11 @@ T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT ba=ot.barycenter(A,M,reg) # reg is regularization parameter ``` - - - ### Examples and Notebooks The examples folder contain several examples and use case for the library. The full documentation is available on [https://PythonOT.github.io/](https://PythonOT.github.io/). -Here is a list of the Python notebooks available [here](https://github.com/PythonOT/POT/blob/master/notebooks/) if you want a quick look: - -* [1D optimal transport](https://github.com/PythonOT/POT/blob/master/notebooks/plot_OT_1D.ipynb) -* [OT Ground Loss](https://github.com/PythonOT/POT/blob/master/notebooks/plot_OT_L1_vs_L2.ipynb) -* [Multiple EMD computation](https://github.com/PythonOT/POT/blob/master/notebooks/plot_compute_emd.ipynb) -* [2D optimal transport on empirical distributions](https://github.com/PythonOT/POT/blob/master/notebooks/plot_OT_2D_samples.ipynb) -* [1D Wasserstein barycenter](https://github.com/PythonOT/POT/blob/master/notebooks/plot_barycenter_1D.ipynb) -* [OT with user provided regularization](https://github.com/PythonOT/POT/blob/master/notebooks/plot_optim_OTreg.ipynb) -* [Domain adaptation with optimal transport](https://github.com/PythonOT/POT/blob/master/notebooks/plot_otda_d2.ipynb) -* [Color transfer in images](https://github.com/PythonOT/POT/blob/master/notebooks/plot_otda_color_images.ipynb) -* [OT mapping estimation for domain adaptation](https://github.com/PythonOT/POT/blob/master/notebooks/plot_otda_mapping.ipynb) -* [OT mapping estimation for color transfer in images](https://github.com/PythonOT/POT/blob/master/notebooks/plot_otda_mapping_colors_images.ipynb) -* [Wasserstein Discriminant Analysis](https://github.com/PythonOT/POT/blob/master/notebooks/plot_WDA.ipynb) -* [Gromov Wasserstein](https://github.com/PythonOT/POT/blob/master/notebooks/plot_gromov.ipynb) -* [Gromov Wasserstein Barycenter](https://github.com/PythonOT/POT/blob/master/notebooks/plot_gromov_barycenter.ipynb) -* [Fused Gromov Wasserstein](https://github.com/PythonOT/POT/blob/master/notebooks/plot_fgw.ipynb) -* [Fused Gromov Wasserstein Barycenter](https://github.com/PythonOT/POT/blob/master/notebooks/plot_barycenter_fgw.ipynb) - - -You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/PythonOT/POT/tree/master/notebooks/). - ## Acknowledgements This toolbox has been created and is maintained by @@ -174,21 +150,21 @@ This toolbox has been created and is maintained by The contributors to this library are -* [Alexandre Gramfort](http://alexandre.gramfort.net/) -* [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) +* [Alexandre Gramfort](http://alexandre.gramfort.net/) (CI) +* [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) (Partial OT) * [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation) * [Léo Gautheron](https://github.com/aje) (GPU implementation) -* [Nathalie Gayraud](https://www.linkedin.com/in/nathalie-t-h-gayraud/?ppe=1) -* [Stanislas Chambon](https://slasnista.github.io/) -* [Antoine Rolet](https://arolet.github.io/) +* [Nathalie Gayraud](https://www.linkedin.com/in/nathalie-t-h-gayraud/?ppe=1) (DA classes) +* [Stanislas Chambon](https://slasnista.github.io/) (DA classes) +* [Antoine Rolet](https://arolet.github.io/) (EMD solver debug) * Erwan Vautier (Gromov-Wasserstein) -* [Kilian Fatras](https://kilianfatras.github.io/) +* [Kilian Fatras](https://kilianfatras.github.io/) (Stochastic solvers) * [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home) -* [Vayer Titouan](https://tvayer.github.io/) +* [Vayer Titouan](https://tvayer.github.io/) (Gromov-Wasserstein -, Fused-Gromov-Wasserstein) * [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT) * [Romain Tavenard](https://rtavenar.github.io/) (1d Wasserstein) * [Mokhtar Z. Alaya](http://mzalaya.github.io/) (Screenkhorn) -* [Ievgen Redko](https://ievred.github.io/) +* [Ievgen Redko](https://ievred.github.io/) (Laplacian DA, JCPOT) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 279e5da..4da1ceb 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -1,8 +1,8 @@ POT: Python Optimal Transport ============================= -|PyPI version| |Anaconda Cloud| |Build Status| |Codecov Status| -|Downloads| |Anaconda downloads| |License| +|PyPI version| |Anaconda Cloud| |Build Status| |Build Status| |Codecov +Status| |Downloads| |Anaconda downloads| |License| This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and @@ -184,44 +184,6 @@ The examples folder contain several examples and use case for the library. The full documentation is available on https://PythonOT.github.io/. -Here is a list of the Python notebooks available -`here `__ if you -want a quick look: - -- `1D optimal - transport `__ -- `OT Ground - Loss `__ -- `Multiple EMD - computation `__ -- `2D optimal transport on empirical - distributions `__ -- `1D Wasserstein - barycenter `__ -- `OT with user provided - regularization `__ -- `Domain adaptation with optimal - transport `__ -- `Color transfer in - images `__ -- `OT mapping estimation for domain - adaptation `__ -- `OT mapping estimation for color transfer in - images `__ -- `Wasserstein Discriminant - Analysis `__ -- `Gromov - Wasserstein `__ -- `Gromov Wasserstein - Barycenter `__ -- `Fused Gromov - Wasserstein `__ -- `Fused Gromov Wasserstein - Barycenter `__ - -You can also see the notebooks with `Jupyter -nbviewer `__. - Acknowledgements ---------------- @@ -232,24 +194,28 @@ This toolbox has been created and is maintained by The contributors to this library are -- `Alexandre Gramfort `__ +- `Alexandre Gramfort `__ (CI) - `Laetitia Chapel `__ + (Partial OT) - `Michael Perrot `__ (Mapping estimation) - `Léo Gautheron `__ (GPU implementation) - `Nathalie Gayraud `__ -- `Stanislas Chambon `__ -- `Antoine Rolet `__ + (DA classes) +- `Stanislas Chambon `__ (DA classes) +- `Antoine Rolet `__ (EMD solver debug) - Erwan Vautier (Gromov-Wasserstein) -- `Kilian Fatras `__ +- `Kilian Fatras `__ (Stochastic + solvers) - `Alain Rakotomamonjy `__ -- `Vayer Titouan `__ +- `Vayer Titouan `__ (Gromov-Wasserstein -, + Fused-Gromov-Wasserstein) - `Hicham Janati `__ (Unbalanced OT) - `Romain Tavenard `__ (1d Wasserstein) - `Mokhtar Z. Alaya `__ (Screenkhorn) -- `Ievgen Redko `__ +- `Ievgen Redko `__ (Laplacian DA, JCPOT) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various @@ -437,6 +403,8 @@ NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014. :target: https://anaconda.org/conda-forge/pot .. |Build Status| image:: https://travis-ci.org/PythonOT/POT.svg?branch=master :target: https://travis-ci.org/PythonOT/POT +.. |Build Status| image:: https://github.com/PythonOT/POT/workflows/build/badge.svg + :target: https://github.com/PythonOT/POT/actions .. |Codecov Status| image:: https://codecov.io/gh/PythonOT/POT/branch/master/graph/badge.svg :target: https://codecov.io/gh/PythonOT/POT .. |Downloads| image:: https://pepy.tech/badge/pot diff --git a/notebooks/plot_OT_1D.ipynb b/notebooks/plot_OT_1D.ipynb deleted file mode 100644 index 9b2b446..0000000 --- a/notebooks/plot_OT_1D.ipynb +++ /dev/null @@ -1,278 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# 1D optimal transport\n", - "\n", - "\n", - "This example illustrates the computation of EMD and Sinkhorn transport plans\n", - "and their visualization.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Remi Flamary \n", - "#\n", - "# License: MIT License\n", - "\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "import ot.plot\n", - "from ot.datasets import make_1D_gauss as gauss" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 100 # nb bins\n", - "\n", - "# bin positions\n", - "x = np.arange(n, dtype=np.float64)\n", - "\n", - "# Gaussian distributions\n", - "a = gauss(n, m=20, s=5) # m= mean, s= std\n", - "b = gauss(n, m=60, s=10)\n", - "\n", - "# loss matrix\n", - "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\n", - "M /= M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot distributions and loss matrix\n", - "----------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "G0 = ot.emd(a, b, M)\n", - "\n", - "pl.figure(3, figsize=(5, 5))\n", - "ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve Sinkhorn\n", - "--------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It. |Err \n", - "-------------------\n", - " 0|2.861463e-01|\n", - " 10|1.860154e-01|\n", - " 20|8.144529e-02|\n", - " 30|3.130143e-02|\n", - " 40|1.178815e-02|\n", - " 50|4.426078e-03|\n", - " 60|1.661047e-03|\n", - " 70|6.233110e-04|\n", - " 80|2.338932e-04|\n", - " 90|8.776627e-05|\n", - " 100|3.293340e-05|\n", - " 110|1.235791e-05|\n", - " 120|4.637176e-06|\n", - " 130|1.740051e-06|\n", - " 140|6.529356e-07|\n", - " 150|2.450071e-07|\n", - " 160|9.193632e-08|\n", - " 170|3.449812e-08|\n", - " 180|1.294505e-08|\n", - " 190|4.857493e-09|\n", - "It. |Err \n", - "-------------------\n", - " 200|1.822723e-09|\n", - " 210|6.839572e-10|\n" - ] - }, - { - "data": { - "image/png": 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fH648enTOL9ijABZpqvJyOO00+Mc/4JZb/AADabxevXzZTjM48kh4//3YFbUYBbBIU2zY4IH797/D9df7VCppuiFDfKGeLVvgM5+BN96IXVGLUACLNNaSJXDEEd753nwzXHhh7Ipyy7BhMGmSz6E+/HB46qnYFTU7BbBIYzz7LAwfDtOn+5jld78bu6LcNGwYTJ7sK6eNHg3/8z85tXqaAlhkR6xbBxdfDEcfDd26+U63L385dlW5rV8/n1Vyxhnwq1/BqFHw9tuxq2oWCmCRhqiogL/8Bfbay8d6zzsPXn0V9t03dmX5oWNHP6X9fffBzJk+y+RHP/L1NrKYAlhkezZu9AMq9tkHTj/d5/ZOnuyzHTp0iF1d/vnmN2HWLP+/+MMffGji8svhww9jV9YoCmCRmsrL4YUX4Hvfg759fVGdNm386KypU+Hgg2NXmN969IC77vLF3I87ztdZHjzY/33vvbB6dewKG8xCHq7BKZ9UWloapk6dGruMONat82lOr77qaxFMmuSrmJWU+GHF3/mOT4XSYcWZaf58GDsWxo3z1dQKC30J0M99Dj71KV9prU+fFvn/M7NpIYTSRj9fASyQYwFcUQGbNvnwwbp1flm1yg8RXrrUp4/Nn+8fW2fPhgULqp87aJBP/j/mGL9omCF7hOB/RCdOhOefh1deqV5ZrXNnn1s8aBAMGAC9e/sBH927Q9eufn+nTv7/XVLiK7Q1ILAVwNIsSktKwtTdd2+9F6z5c5e6nn57CJ+8VFb6paKi+lJe7peyMp+4X980JTP/BRwwwFfhGjrUl4wsLYVddmne71Pi2bgR3nwTpk3zQ5pnzfI/uvPn+8/J9phB27Y+9FRc7IFcWFh9GTwYnnuuyQGsxXjEtW0LrRnA8MkOI3U9/XazbS+FhdVf0y/FxX5p186/l/bt/dKpk1+6dvVpYzvv7OsMFOlHP+eVlMAhh/glXQg+TrxkCaxY4Z+M1q71T0obN/qnp82bPaS3bvU/7OXl1X/wKyubbaEl/RSK2203ePjh2FWItDwz/4PctWvsSjQLQkQkFgWwiEgk2gknAJjZOmBW7Drq0QPI9EOfsqFGyI46s6HGISGETo19ssaAJWVWU/bmtgYzm6oam0c21JktNTbl+RqCEBGJRAEsIhKJAlhSbo9dQAOoxuaTDXXmfI3aCSciEok6YBGRSBTAIiKRKIDznJkdY2azzOw9M7s8dj0pZtbfzF4ws3fM7G0z+2Fyezcze8bM5iRfox9PamaFZva6mT2eXB9kZlOS93SCmbWJXF8XM3vIzN41s5lmdkimvY9mdlHy/zzDzB4ws3aZ8D6a2V1mttTMZqTdVut7Z+6PSb1vmdnI+ravAM5jZlYI3AyMBvYGTjWzveNWVaUcuCSEsDdwMPC9pLbLgedCCHsAzyXXY/shMDPt+jXA9SGE3YFVwLlRqqp2I/BkCGEoMByvNWPeRzPrC/wAKA0hDAMKgVPIjPfxHuCYGrfV9d6NBvZILmOAW+rdeghBlzy9AIcAT6Vd/wnwk9h11VHro8Dn8aP1eie39cYPIIlZV7/kl/AI4HHA8KO3imp7jyPU1xmYR7LDPe32jHkfgb7AR0A3/OCwx4EvZMr7CAwEZtT33gG3AafW9ri6LuqA81vqBz9lQXJbRjGzgcAIYArQK4SwKLlrMdArUlkpNwCXApXJ9e7A6hBCalHi2O/pIGAZcHcyTHKHmXUgg97HEMJC4DpgPrAIWANMI7Pex3R1vXc7/PukAJaMZmYdgb8DF4YQ1qbfF7zNiDaP0sy+CCwNIUyLVUMDFAEjgVtCCCOADdQYbsiA97ErcDz+x6IP0IFPfuzPSE197xTA+W0h0D/ter/ktoxgZsV4+N4fQkgtVrzEzHon9/cGlsaqDzgM+LKZfQCMx4chbgS6mFlqnZXY7+kCYEEIYUpy/SE8kDPpfTwKmBdCWBZCKAMext/bTHof09X13u3w75MCOL+9CuyR7G1ug+/4mBi5JsD3KAN3AjNDCH9Iu2sicGby7zPxseEoQgg/CSH0CyEMxN+750MI3wBeAE5KHha7xsXAR2Y2JLnpSOAdMuh9xIceDjazkuT/PVVjxryPNdT13k0EzkhmQxwMrEkbqqhdrIF3XTLjAhwLzAbeB34Wu560uj6Nf7R7C3gjuRyLj7E+B8wBngW6xa41qXcU8Hjy78HAK8B7wINA28i17Q9MTd7LR4CumfY+AlcC7wIzgPuAtpnwPgIP4OPSZfiniXPreu/wHbA3J79L0/FZHdvdvg5FFhGJpElDEJk6iV9EJBs0ugNOJvHPxudmLsDHE08NIbzTfOWJiOSuppwR4yDgvRDCXAAzG49PJakzgHv06BEGDhzYhJeUplq2DFatgj333Pb2rq8NilOQSBZ7pvJBa8rzmxLAtU06/lTNB5nZGPywPAYMGMDUqU06g4c00Y9/DDffDDX/Gz5f8LU4BYnksRafhhZCuD2EUBpCKO3Zs2dLv5zUo7wciotjVyEi0LQAzuhJ/FK7LVugTdS1uUQkpSkBnLGT+KVuGzdCSUnsKkQEmjAGHEIoN7PvA0/hy8fdFUJ4u9kqkxaxfj107Bi7ChGBpu2EI4TwBPBEM9UirWDFCujePXYVIgJaCyLvLF4MO+8cu4pWZrZjF5FWogDOI5WVMG8eDNKUX5GM0KQhCMkuc+f6LIiaB2HkHaun70g1waFy29u1boo0M3XAeeQ///Gvhx4atw4RceqA88jEiT7+u9desStpZanONTW+m+psk07YCmyb66SuV9boeNM64pC6L3WbumNpBHXAeWLRIg/gM8+EAv2vi2QEdcB54sorvUn79rdjVxJRzU64Dpa6v03dvx6WbCtUVPgNlTWuqzOWBlAvlAf+9S+47Ta48ELYY4/Y1YhIigI4x739NnztazB4MFx1VexqRCSdhiBy2IwZcMQRUFQETzwBHTrErihDpIYFQkVy1YccrJBtrycPt+Lk16SwsHobyTBFaiiCZOghVCRDD2Vl21zX0ITURh1wDqqshD//GQ4+2MN30iQYMqTep4lIK1MHnGPmzYNvfQuefx6+8AW44w7o1y92VRmuxg61quMwUvcn00YsrQO21KLKRcmvUDJ1zVLT08rLfRupTnjLVr89dT11f1VnrI44H6kDzhFz5vgMhyFD4NVXYexY+Oc/Fb4imUwdcJZ74w347W/hwQf9TBff/jZcfjn071//c6WGOqaW1arAu2FLOuDQNlnlPhkvDoVJR1zh27Ct3vnaZu+Ew6ZNydfN/nVrasxYY8X5RAGchRYsgPHj4a9/hddfh06d/FxvF14Iu+wSuzoRaSgFcJZYuRIeeshD98UXvTE68EC4/no46yzo0iV2hTmk5iyJrUk3mt4RV6Y6VP+aOpw5JB1wZYl3xJVtvVMOqTHict9G4SbveAvWeQdcsCHpiDds9K+pDjkZK/ZyNF6caxTAGaq83M9c/NRT8PTTMGWKz3QaMgSuuAJOOw123z12lSLSFArgDPLhhx62Tz8Nzz4Lq1f7dNPSUh/X/cpXYMQIrRne6lJjw+Vl1Tcl3WhBqitN5vumZkEUJP9JlcXtASjr5L9q5e2TTrmgLQCFZT45u3i9b6fNqi3+/NUbfHtr11e/5nq/rWq8OFWPOuKspQCOpLwcpk+Hl1/2y+TJPoUMfObCV77i08iOPFKnEBLJVQrgVrJqFfz3v9WBO2UKbPCGht694bDD4Ic/hKOPhqFD1eVmpPROMxkfrtySdL6pDnirz3IoSOb7FpclY7jBz4Ra2cY73007+X9weQefCRqSWRWFm31+cbvV3hm3W9616iXbLvPx4cIVa/056707rkzNpEi9VmVFE75JaU0K4BawerXPTpg2DV57zb/Onu33FRbC8OFw9tm+MPqhh8KAAQpckXykAG6ilSurQzb19f33q+/v3x8OOADOOMPD9sADdVr4nJIaHy5L5vem1oRIZi8UJEfAtUm+Fm7pDIBV+Njw+mLvfDf39L/AG3v7Ztclf5GLNrSteql2y/3fHRbvBEDJx975Fi9Z46+5JumMk5kUlVtTY8SaU5ypFMANlDqh5Ztvbnv54IPqxwwc6GF7zjn+deRI6NkzVsUikukUwLXYsMF3kKUH7fTpsG6d319Q4Ce2/NSn4PzzPWhHjoRu3eLWLRmgctu5w5Wp9SW2JLMbkvHajht84nbRhk4ArNvq84bX9/fOd3PvZDy339aqTa9PFlRavdYf2+5jHyfuNL8k+ep7a9su8k64cPlqr2VDavbEtl26OuL48jqAQ/Cjymp2tXPmVP9s7rQT7LefDyEMHw777w/77AMlJXFrF5HslzcBvGULvPPOJ8N25crqxwwa5CF72mn+dfhwH1bQDjLZYVVzh7dd9cySmQqpTrj9Wu9O26z22Q5t13hXu2aj/2qu3616k936ekfbv+8iAMqH+gyKD1b6R68VH3o33Wmuj3t1metddvuP/KNb4dJVAFSu89kTIenK1RHHk5MBvHGjL1IzbZofTfbaa/Duu1UrBNK+Pey7L3z1q9VBu99+3u2KiLSWrA/gTZu8k02F7dSp3ummDtXv1ct3iH35y9Vhu/vu257cQKTF1ZgtUVFzbDiZudB5pc+SaL/UO+I1y9pVbWL1EB/jfW+IzxU+rN9cAI7qOROAjbv7LImXRwwG4O25fQHoMMs75K5zfNsd5nkHXNURr05mUdTsiNPqlpaRdQH88cd+kslJk/xghhkzqs4GQ8+eftjuiSf61wMOgD59NIQgIpkp4wN40SIP29QldUDDTjv5KXe++EUP2tJSP4RXYStZITVbYot/ragaG/ZV0IrX+rhtj+XVU2s6LPIOdtXH/vXpvfcFYOFePtb79V1eBeDKXacDsKKfjyc/vt/+ADw5Z28Ait7253ed5RPSO831brtwcdIRJ68NaauyaZy4RWRcAIfgZ3S4915fkGbWLL99p53g8MNhzBgYNcpnI2gYQUSymYVW/ItWWloapk6dWut9y5fDfffBXXf5sEL79n5G31GjqgO3KOP+XOSOzxd8LXYJAtUf4cxnOBS0qz4Szjp5xxp6eVe8YZDvNV451H8xNu3r3epxQ2cAcGb3lwAYUuw7RD5OutgHVh8IwIQ5I317b/l2ur3rj+v0fnUHXNUV1zVOnOcd8TOVDzbpM3e9kWZm/YF7gV74eQpvDyHcaGbdgAnAQOAD4OshhFU7WsD8+fCjH8Ejj/j5Cg86CG67DU4+GTp33tGtiYhkj3o7YDPrDfQOIbxmZp2AacAJwFnAyhDCb83scqBrCOGy7W2rZge8fLmvArZokZ/J95xzYNiwJn5H0ijqgDNU2k6N1FmZra13xQU7+bzfil18dsT63bxDXjk0OQvHcO9kT9lzGgBndHkFgEHF/rh5ZT4b4t7VBwEwfvYBvv03O1W9Zrd3vdPt+H4yc2LxCqB6nDjfO+IW74BDCIuARcm/15nZTKAvcDwwKnnYOGASsN0ATrdli+9Amz/fx3oPO2wHKxcRyXI7NAZsZgOBF4FhwPwQQpfkdgNWpa7XeM4YYAzAgAEDDvjwww8BP/vDbrv57IUXX4Q2bZr4nUiTqAPOIklX/ImOuIuP2VXs4rMa1u3mnezK5Ig528/XiDh5j9cAOLWLz5rok2xnVpk/btyK6m7oH+/6R9L20331tm7v+myNDvN8W7bEDyUNydF1lZu9I86XFdia2gEXNPSBZtYR+DtwYQhhbfp9wVO81nc6hHB7CKE0hFDaM21psF13hdtv97m8Z53lwxEiIvmkQQFsZsV4+N4fQng4uXlJMj6cGideuqMvfs45cPXV8MADfsDE178OTz5ZfWCFiEgua8hOOMPHeFeGEC5Mu/1aYEXaTrhuIYRLt7etuqahzZgBd97p09BWrPADKs46C044QfN9W4uGILJYzSGJ9j5cUL2TLhmSGOw731YN8ceV7+MLAR2zxzsAfLHLGwB0L9xQtenpW/oB8NBi30H39sz+AHR+x3cfdZ3th1a3n+/T1FiWDEkkJxCtWhQ+R0+T1BpDEIcBpwNHmNkbyeVY4LfA581sDnBUcr1Rhg2D66+HhQvhwQf9+m9+4+PD3bvDl74Ev/+9L6qj7lhEckXGHIhRU8asHQIAAA0fSURBVOoQ5Bde8K9z5vjtnTv7EXGjRvnMieHDoV277WxIGkQdcA6pbyfdzt4RbxiUdMR7+OM2DPFudp/BC6s2dWg3X/CnpNB3rs3c4OdMemmBL/izdZYfxNElOWK18/u+zGabhX5IQFjlnXHlRl9sKNdOHNri09Bi6d0bTj3VL+DdcWoRnkmT4LHH/PaiIu+YU+tBlJb6UpNt29a1ZRGRzJCxHXB9Fi6EV16pXoJy6tTqxdWLiz2EU4E8cqSfxUKdct3UAeewujri5NDmyqQj3tTfx4xXDy6ueuq6wT6drP2ufuDFwG7+S1ZkfvtHa72rXrnQZ6B2fN97us5zkwM4PvSx4NQhzSE5gKMyWZA+lKdOHJqd09VytgOuT9++vuzkiSf69RB8bnEqjKdNg7/9zae6ge/IGzKkek3g1OmFdtkl3vcgIvktawO4JjM/fdDAgXDSSX5bCDB3ru+8S52C6D//8WlvKTvvvG0oDx8OQ4d6Fy2SE+o4PVLqMGJLutKSRb58Zcnc6uOptvT2Md51A/zr+wO8493cx7dVtNPWbb6mTqFUXuLRsqWzd9U7dfauu23yGgUrkhOGpk6PtLX65KP5dFhzzgRwbcz8aLvddoOvpX3CXrkS3npr23PD/elPfng0+FF5e+/9yWDu3j3O9yEiuSmnA7gu3bpVL3OZUlbmi72nh/KTT8K4cdWP6du3eugiFcp77OGnqRfJGnV0xCSHEResq16Osu1SP/13uw+S2Q69vAPe2Md3qGzYxe/f3CN5QgffdnmJf93Q14dIK5K1Bjp29O66pIN3xIXJKZfCmurXzNUZE7XJywCuTXGx76jbZx8/K3LKkiWfPJPy009Xn+CzY0cP5AMO8J19BxzgY81au1hE6qOYqEevXnD00X5JSZ3i/o034PXXfYff2LF+NmbwxeT33786kA84APbaS+PKkqFSY63BO83KzWkdZ3IkmyVHthWt9NkMnT9KTmfU3TvjLT1TnbD/kG/uknS+ycyjyjZ+fWOPZKnMwqSzbuvXi9tWr8ZVuMb/XVk1Puw1ZPuMidoogBuhbVsYMcIvZ5/tt1VU+OmTpk3znX7Tpvnwxc03+/3t2/ti84ce6pdDDtGYski+UwA3k8JC33G3995w+ul+W2WlH8E3bZrPWX75Zbj22urhiyFDqgP50EN99oXGkyWj1Dx5aDJbwTb46Y9slc9maLfYZze02ymZW9zFr2/t6mO9ZR29060o9k64MlnfZWuXpPO16kXgi4o9lgqSj4whGZMOm/2Xo2p9iRxY8lIB3IIKCjxkhwypHlfeuNHnKb/8sl8mToS77/b7uneHo46qHvLo1y9e7SLS8hTAraykxNeyOPxwvx6Cd8mTJ/u6F08/DRMm+H17710dxocfDh06xKtbBKieQVHmnXBqXNZSc4rX+FLhBct9Rbb2HZKx3g5+vbKTDwpXtE+626LkQLK0T36VHb0rtuA/8FaQPKbId7IUpI6iy4GxYX3gjcwM9twTzjwT7rnHD7F+6y247jrvgG+9FY491qfOHX+8h3NqZ5+IZDd1wBnGzNex2HdfuOQS2LQJ/v1veOIJX6pz4kSf+nbCCT6scdRRml0hEdU1p7jG7Alr4z+kBe29Ay5M1iwO7ZIx4DbVP8ShcNvlFUIyQ8Iqa3S4qR0mW/3xVfOGs2hsWB1whmvf3ocgbrjBT2D6/PNwyinw+OPeGfftC1dc4QvZi0h2ydrV0PLdli1+pN5dd3lX3KEDjBnjXXPfvju+Pa2GJi2q5opsqSOVko9vljYP2FIf6VKPSY0BpzrgVLedzMioGgtOrSeRWuuiFdaUaLWTckpmadvWx4QffRSmT/dV4f74Rxg0CC6+WOPEItlAAZwDhg3z8+nNmeM7866/3o/Ee/nl2JWJJEKAEAjl5YTyciq3bPHLho1+WbOu+rJqtV9Wr6Fy9RrC2vV+2bjRL2VlhLKyqk1bYYFfioq8sy4shMJCLLlgVtWBZxoFcA4ZNMgPiX7+eV9c6NOf9vHhLNgXIZKXNAsiB33ucz6V7fvfhyuv9OGIa67J2CZA8lGN9SdC2opnoTz5QU3NpEidFr1g23Hkun6gLbk9WEHy+NRL1VhVLQM6EwVwjurUyecVd+jghz+3aQO//nXsqkQknQI4h5n5YkCbN8PVV8Po0X4maZGMVld3XLOzTc2OsBojqQV1fNRLPS41Tzi9g47UDWsMOMeZ+eyIAQPgW9+CtDO/iEhkCuA80LEj3HgjvPsuPPJI7GpEGimZSUFlBVRWVM2oCOVlfqmo8EtZuV9S1ysDIf0oOiv4ZNccSWZUIS3uS1+CXXeF226LXYmIpCiA80RBAZxxhq+4tmZN7GpEBBTAeeWzn/VPcJMnx65EpBnVGJqouoTK7V/SRTpYQwGcRw46yL++9lrcOkTEaRpaHunUyU8yOm9e7EpEWkEGHGhRH3XAeWbXXX1ZSxGJTwGcZ7p3h5UrY1chIqAAzjtdu8KqVbGrEBHYgQA2s0Ize93MHk+uDzKzKWb2nplNMLM29W1D4isp0VrBIpliRzrgHwIz065fA1wfQtgdWAWc25yFScto397PMyci8TUogM2sH3AccEdy3YAjgIeSh4wDTmiJAqV5FRdXndFFRCJraAd8A3ApkJq93B1YHUJI/SovABpxJjJpbUVFvli7iMRXbwCb2ReBpSGEaY15ATMbY2ZTzWzqsmXLGrMJaUaFhVBZWf/jRKTlNaQDPgz4spl9AIzHhx5uBLqYWepAjn7AwtqeHEK4PYRQGkIo7dmzZzOULE1RUFB10lgRiazeAA4h/CSE0C+EMBA4BXg+hPAN4AXgpORhZwKPtliV0mzMsuIAIZG80JR5wJcBF5vZe/iY8J3NU5K0JAWwSObYobUgQgiTgEnJv+cCBzV/SdKSdGJOkcyhI+FERCJRAOcZdcAimUMBLCISiQJYRCQSBbCISCQKYBGRSBTAIiKRKIBFRCJRAIuIRKIAFhGJRAEsIhKJAlhEJBIFsIhIJApgEZFIFMAiIpEogEVEIlEAi4hEogAWEYlEASwiEokCWEQkEgWwiEgkCmARkUgUwCIikSiARUQiUQCLiESiABYRiUQBLCISiQJYRCQSBbCISCQKYBGRSBTAIiKRKIBFRCJRAIuIRNKgADazLmb2kJm9a2YzzewQM+tmZs+Y2Zzka9eWLlZEJJc0tAO+EXgyhDAUGA7MBC4Hngsh7AE8l1wXEZEGqjeAzawzcDhwJ0AIYWsIYTVwPDAuedg44ISWKlJEJBc1pAMeBCwD7jaz183sDjPrAPQKISxKHrMY6FXbk81sjJlNNbOpy5Yta56qRURyQEMCuAgYCdwSQhgBbKDGcEMIIQChtieHEG4PIZSGEEp79uzZ1HpFRHJGQwJ4AbAghDAluf4QHshLzKw3QPJ1acuUKCKSm+oN4BDCYuAjMxuS3HQk8A4wETgzue1M4NEWqVBEJEcVNfBxFwD3m1kbYC5wNh7efzOzc4EPga+3TIkiIrmpQQEcQngDKK3lriObtxwRkfyhI+FERCJRAIuIRKIAFhGJRAEsIhKJAlhEJBIFsIhIJApgEZFIFMAiIpEogEVEIlEAi4hEogAWEYlEASwiEokCWEQkEgWwiEgkCmARkUgUwCIikSiARUQiUQCLiESiABYRiUQBLCISiQJYRCQSBbCISCQKYBGRSBTAIiKRKIBFRCJRAIuIRKIAFhGJRAEsIhKJAlhEJBIFsIhIJApgEZFIFMAiIpE0KIDN7CIze9vMZpjZA2bWzswGmdkUM3vPzCaYWZuWLlZEJJfUG8Bm1hf4AVAaQhgGFAKnANcA14cQdgdWAee2ZKEiIrmmoUMQRUB7MysCSoBFwBHAQ8n944ATmr88EZHcVW8AhxAWAtcB8/HgXQNMA1aHEMqThy0A+tb2fDMbY2ZTzWzqsmXLmqdqEZEc0JAhiK7A8cAgoA/QATimoS8QQrg9hFAaQijt2bNnowsVEck1DRmCOAqYF0JYFkIoAx4GDgO6JEMSAP2AhS1Uo4hITmpIAM8HDjazEjMz4EjgHeAF4KTkMWcCj7ZMiSIiuakhY8BT8J1trwHTk+fcDlwGXGxm7wHdgTtbsE4RkZxTVP9DIITwS+CXNW6eCxzU7BWJiOQJHQknIhKJAlhEJBIFsIhIJApgEZFIFMAiIpEogEVEIlEAi4hEogAWEYlEASwiEokCWEQkEgWwiEgkCmARkUgUwCIikSiARUQiUQCLiESiABYRiUQBLCISiQJYRCQSBbCISCQKYBGRSBTAIiKRKIBFRCJRAIuIRKIAFhGJRAEsIhKJAlhEJBIFsIhIJApgEZFIFMAiIpEogEVEIlEAi4hEogAWEYlEASwiEokCWEQkEgshtN6LmS0DPmy1F5QdsWsIoWfsIkTySasGsIiIVNMQhIhIJApgEZFIFMAiIpEogEVEIlEAi4hEogAWEYlEASwiEokCWEQkEgWwiEgk/w+/asBPRYt0dwAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "lambd = 1e-3\n", - "Gs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n", - "\n", - "pl.figure(4, figsize=(5, 5))\n", - "ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n", - "\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_OT_1D_smooth.ipynb b/notebooks/plot_OT_1D_smooth.ipynb deleted file mode 100644 index 603a18c..0000000 --- a/notebooks/plot_OT_1D_smooth.ipynb +++ /dev/null @@ -1,335 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# 1D smooth optimal transport\n", - "\n", - "\n", - "This example illustrates the computation of EMD, Sinkhorn and smooth OT plans\n", - "and their visualization.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Remi Flamary \n", - "#\n", - "# License: MIT License\n", - "\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "import ot.plot\n", - "from ot.datasets import make_1D_gauss as gauss" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 100 # nb bins\n", - "\n", - "# bin positions\n", - "x = np.arange(n, dtype=np.float64)\n", - "\n", - "# Gaussian distributions\n", - "a = gauss(n, m=20, s=5) # m= mean, s= std\n", - "b = gauss(n, m=60, s=10)\n", - "\n", - "# loss matrix\n", - "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\n", - "M /= M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot distributions and loss matrix\n", - "----------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "G0 = ot.emd(a, b, M)\n", - "\n", - "pl.figure(3, figsize=(5, 5))\n", - "ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve Sinkhorn\n", - "--------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It. |Err \n", - "-------------------\n", - " 0|2.821142e-01|\n", - " 10|7.695268e-02|\n", - " 20|1.112774e-02|\n", - " 30|1.571553e-03|\n", - " 40|2.218100e-04|\n", - " 50|3.130527e-05|\n", - " 60|4.418267e-06|\n", - " 70|6.235716e-07|\n", - " 80|8.800770e-08|\n", - " 90|1.242095e-08|\n", - " 100|1.753030e-09|\n", - " 110|2.474136e-10|\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "lambd = 2e-3\n", - "Gs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n", - "\n", - "pl.figure(4, figsize=(5, 5))\n", - "ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve Smooth OT\n", - "--------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "lambd = 2e-3\n", - "Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl')\n", - "\n", - "pl.figure(5, figsize=(5, 5))\n", - "ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.')\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "lambd = 1e-1\n", - "Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2')\n", - "\n", - "pl.figure(6, figsize=(5, 5))\n", - "ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.')\n", - "\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_OT_2D_samples.ipynb b/notebooks/plot_OT_2D_samples.ipynb deleted file mode 100644 index d5967bd..0000000 --- a/notebooks/plot_OT_2D_samples.ipynb +++ /dev/null @@ -1,372 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# 2D Optimal transport between empirical distributions\n", - "\n", - "\n", - "Illustration of 2D optimal transport between discributions that are weighted\n", - "sum of diracs. The OT matrix is plotted with the samples.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Remi Flamary \n", - "# Kilian Fatras \n", - "#\n", - "# License: MIT License\n", - "\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "import ot.plot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 50 # nb samples\n", - "\n", - "mu_s = np.array([0, 0])\n", - "cov_s = np.array([[1, 0], [0, 1]])\n", - "\n", - "mu_t = np.array([4, 4])\n", - "cov_t = np.array([[1, -.8], [-.8, 1]])\n", - "\n", - "xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s)\n", - "xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t)\n", - "\n", - "a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples\n", - "\n", - "# loss matrix\n", - "M = ot.dist(xs, xt)\n", - "M /= M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n", - "---------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Cost matrix M')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1)\n", - "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", - "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Source and target distributions')\n", - "\n", - "pl.figure(2)\n", - "pl.imshow(M, interpolation='nearest')\n", - "pl.title('Cost matrix M')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute EMD\n", - "-----------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'OT matrix with samples')" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "G0 = ot.emd(a, b, M)\n", - "\n", - "pl.figure(3)\n", - "pl.imshow(G0, interpolation='nearest')\n", - "pl.title('OT matrix G0')\n", - "\n", - "pl.figure(4)\n", - "ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])\n", - "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", - "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", - "pl.legend(loc=0)\n", - "pl.title('OT matrix with samples')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute Sinkhorn\n", - "----------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# reg term\n", - "lambd = 1e-3\n", - "\n", - "Gs = ot.sinkhorn(a, b, M, lambd)\n", - "\n", - "pl.figure(5)\n", - "pl.imshow(Gs, interpolation='nearest')\n", - "pl.title('OT matrix sinkhorn')\n", - "\n", - "pl.figure(6)\n", - "ot.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])\n", - "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", - "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", - "pl.legend(loc=0)\n", - "pl.title('OT matrix Sinkhorn with samples')\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Emprirical Sinkhorn\n", - "----------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Warning: numerical errors at iteration 0\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/rflamary/PYTHON/POT/ot/bregman.py:363: RuntimeWarning: divide by zero encountered in true_divide\n", - " v = np.divide(b, KtransposeU)\n", - "/home/rflamary/PYTHON/POT/ot/plot.py:90: RuntimeWarning: invalid value encountered in double_scalars\n", - " if G[i, j] / mx > thr:\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# reg term\n", - "lambd = 1e-3\n", - "\n", - "Ges = ot.bregman.empirical_sinkhorn(xs, xt, lambd)\n", - "\n", - "pl.figure(7)\n", - "pl.imshow(Ges, interpolation='nearest')\n", - "pl.title('OT matrix empirical sinkhorn')\n", - "\n", - "pl.figure(8)\n", - "ot.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1])\n", - "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", - "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", - "pl.legend(loc=0)\n", - "pl.title('OT matrix Sinkhorn from samples')\n", - "\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_OT_L1_vs_L2.ipynb b/notebooks/plot_OT_L1_vs_L2.ipynb deleted file mode 100644 index d6a8761..0000000 --- a/notebooks/plot_OT_L1_vs_L2.ipynb +++ /dev/null @@ -1,384 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# 2D Optimal transport for different metrics\n", - "\n", - "\n", - "2D OT on empirical distributio with different gound metric.\n", - "\n", - "Stole the figure idea from Fig. 1 and 2 in\n", - "https://arxiv.org/pdf/1706.07650.pdf\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Remi Flamary \n", - "#\n", - "# License: MIT License\n", - "\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "import ot.plot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Dataset 1 : uniform sampling\n", - "----------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "n = 20 # nb samples\n", - "xs = np.zeros((n, 2))\n", - "xs[:, 0] = np.arange(n) + 1\n", - "xs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex...\n", - "\n", - "xt = np.zeros((n, 2))\n", - "xt[:, 1] = np.arange(n) + 1\n", - "\n", - "a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n", - "\n", - "# loss matrix\n", - "M1 = ot.dist(xs, xt, metric='euclidean')\n", - "M1 /= M1.max()\n", - "\n", - "# loss matrix\n", - "M2 = ot.dist(xs, xt, metric='sqeuclidean')\n", - "M2 /= M2.max()\n", - "\n", - "# loss matrix\n", - "Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\n", - "Mp /= Mp.max()\n", - "\n", - "# Data\n", - "pl.figure(1, figsize=(7, 3))\n", - "pl.clf()\n", - "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", - "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", - "pl.axis('equal')\n", - "pl.title('Source and target distributions')\n", - "\n", - "\n", - "# Cost matrices\n", - "pl.figure(2, figsize=(7, 3))\n", - "\n", - "pl.subplot(1, 3, 1)\n", - "pl.imshow(M1, interpolation='nearest')\n", - "pl.title('Euclidean cost')\n", - "\n", - "pl.subplot(1, 3, 2)\n", - "pl.imshow(M2, interpolation='nearest')\n", - "pl.title('Squared Euclidean cost')\n", - "\n", - "pl.subplot(1, 3, 3)\n", - "pl.imshow(Mp, interpolation='nearest')\n", - "pl.title('Sqrt Euclidean cost')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Dataset 1 : Plot OT Matrices\n", - "----------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "G1 = ot.emd(a, b, M1)\n", - "G2 = ot.emd(a, b, M2)\n", - "Gp = ot.emd(a, b, Mp)\n", - "\n", - "# OT matrices\n", - "pl.figure(3, figsize=(7, 3))\n", - "\n", - "pl.subplot(1, 3, 1)\n", - "ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\n", - "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", - "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", - "pl.axis('equal')\n", - "# pl.legend(loc=0)\n", - "pl.title('OT Euclidean')\n", - "\n", - "pl.subplot(1, 3, 2)\n", - "ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\n", - "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", - "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", - "pl.axis('equal')\n", - "# pl.legend(loc=0)\n", - "pl.title('OT squared Euclidean')\n", - "\n", - "pl.subplot(1, 3, 3)\n", - "ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\n", - "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", - "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", - "pl.axis('equal')\n", - "# pl.legend(loc=0)\n", - "pl.title('OT sqrt Euclidean')\n", - "pl.tight_layout()\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Dataset 2 : Partial circle\n", - "--------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "n = 50 # nb samples\n", - "xtot = np.zeros((n + 1, 2))\n", - "xtot[:, 0] = np.cos(\n", - " (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\n", - "xtot[:, 1] = np.sin(\n", - " (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\n", - "\n", - "xs = xtot[:n, :]\n", - "xt = xtot[1:, :]\n", - "\n", - "a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n", - "\n", - "# loss matrix\n", - "M1 = ot.dist(xs, xt, metric='euclidean')\n", - "M1 /= M1.max()\n", - "\n", - "# loss matrix\n", - "M2 = ot.dist(xs, xt, metric='sqeuclidean')\n", - "M2 /= M2.max()\n", - "\n", - "# loss matrix\n", - "Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\n", - "Mp /= Mp.max()\n", - "\n", - "\n", - "# Data\n", - "pl.figure(4, figsize=(7, 3))\n", - "pl.clf()\n", - "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", - "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", - "pl.axis('equal')\n", - "pl.title('Source and traget distributions')\n", - "\n", - "\n", - "# Cost matrices\n", - "pl.figure(5, figsize=(7, 3))\n", - "\n", - "pl.subplot(1, 3, 1)\n", - "pl.imshow(M1, interpolation='nearest')\n", - "pl.title('Euclidean cost')\n", - "\n", - "pl.subplot(1, 3, 2)\n", - "pl.imshow(M2, interpolation='nearest')\n", - "pl.title('Squared Euclidean cost')\n", - "\n", - "pl.subplot(1, 3, 3)\n", - "pl.imshow(Mp, interpolation='nearest')\n", - "pl.title('Sqrt Euclidean cost')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Dataset 2 : Plot OT Matrices\n", - "-----------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "G1 = ot.emd(a, b, M1)\n", - "G2 = ot.emd(a, b, M2)\n", - "Gp = ot.emd(a, b, Mp)\n", - "\n", - "# OT matrices\n", - "pl.figure(6, figsize=(7, 3))\n", - "\n", - "pl.subplot(1, 3, 1)\n", - "ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\n", - "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", - "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", - "pl.axis('equal')\n", - "# pl.legend(loc=0)\n", - "pl.title('OT Euclidean')\n", - "\n", - "pl.subplot(1, 3, 2)\n", - "ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\n", - "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", - "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", - "pl.axis('equal')\n", - "# pl.legend(loc=0)\n", - "pl.title('OT squared Euclidean')\n", - "\n", - "pl.subplot(1, 3, 3)\n", - "ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\n", - "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", - "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", - "pl.axis('equal')\n", - "# pl.legend(loc=0)\n", - "pl.title('OT sqrt Euclidean')\n", - "pl.tight_layout()\n", - "\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_UOT_1D.ipynb b/notebooks/plot_UOT_1D.ipynb deleted file mode 100644 index e0289d1..0000000 --- a/notebooks/plot_UOT_1D.ipynb +++ /dev/null @@ -1,197 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# 1D Unbalanced optimal transport\n", - "\n", - "\n", - "This example illustrates the computation of Unbalanced Optimal transport\n", - "using a Kullback-Leibler relaxation.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Hicham Janati \n", - "#\n", - "# License: MIT License\n", - "\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "import ot.plot\n", - "from ot.datasets import make_1D_gauss as gauss" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 100 # nb bins\n", - "\n", - "# bin positions\n", - "x = np.arange(n, dtype=np.float64)\n", - "\n", - "# Gaussian distributions\n", - "a = gauss(n, m=20, s=5) # m= mean, s= std\n", - "b = gauss(n, m=60, s=10)\n", - "\n", - "# make distributions unbalanced\n", - "b *= 5.\n", - "\n", - "# loss matrix\n", - "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\n", - "M /= M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot distributions and loss matrix\n", - "----------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1, figsize=(6.4, 3))\n", - "pl.plot(x, a, 'b', label='Source distribution')\n", - "pl.plot(x, b, 'r', label='Target distribution')\n", - "pl.legend()\n", - "\n", - "# plot distributions and loss matrix\n", - "\n", - "pl.figure(2, figsize=(5, 5))\n", - "ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve Unbalanced Sinkhorn\n", - "--------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Sinkhorn\n", - "\n", - "epsilon = 0.1 # entropy parameter\n", - "alpha = 1. # Unbalanced KL relaxation parameter\n", - "Gs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True)\n", - "\n", - "pl.figure(4, figsize=(5, 5))\n", - "ot.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn')\n", - "\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_UOT_barycenter_1D.ipynb b/notebooks/plot_UOT_barycenter_1D.ipynb deleted file mode 100644 index 0ef7f62..0000000 --- a/notebooks/plot_UOT_barycenter_1D.ipynb +++ /dev/null @@ -1,342 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# 1D Wasserstein barycenter demo for Unbalanced distributions\n", - "\n", - "\n", - "This example illustrates the computation of regularized Wassersyein Barycenter\n", - "as proposed in [10] for Unbalanced inputs.\n", - "\n", - "\n", - "[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Hicham Janati \n", - "#\n", - "# License: MIT License\n", - "\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "# necessary for 3d plot even if not used\n", - "from mpl_toolkits.mplot3d import Axes3D # noqa\n", - "from matplotlib.collections import PolyCollection" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# parameters\n", - "\n", - "n = 100 # nb bins\n", - "\n", - "# bin positions\n", - "x = np.arange(n, dtype=np.float64)\n", - "\n", - "# Gaussian distributions\n", - "a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\n", - "a2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n", - "\n", - "# make unbalanced dists\n", - "a2 *= 3.\n", - "\n", - "# creating matrix A containing all distributions\n", - "A = np.vstack((a1, a2)).T\n", - "n_distributions = A.shape[1]\n", - "\n", - "# loss matrix + normalization\n", - "M = ot.utils.dist0(n)\n", - "M /= M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n", - "---------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# plot the distributions\n", - "\n", - "pl.figure(1, figsize=(6.4, 3))\n", - "for i in range(n_distributions):\n", - " pl.plot(x, A[:, i])\n", - "pl.title('Distributions')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycenter computation\n", - "----------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# non weighted barycenter computation\n", - "\n", - "weight = 0.5 # 0<=weight<=1\n", - "weights = np.array([1 - weight, weight])\n", - "\n", - "# l2bary\n", - "bary_l2 = A.dot(weights)\n", - "\n", - "# wasserstein\n", - "reg = 1e-3\n", - "alpha = 1.\n", - "\n", - "bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights)\n", - "\n", - "pl.figure(2)\n", - "pl.clf()\n", - "pl.subplot(2, 1, 1)\n", - "for i in range(n_distributions):\n", - " pl.plot(x, A[:, i])\n", - "pl.title('Distributions')\n", - "\n", - "pl.subplot(2, 1, 2)\n", - "pl.plot(x, bary_l2, 'r', label='l2')\n", - "pl.plot(x, bary_wass, 'g', label='Wasserstein')\n", - "pl.legend()\n", - "pl.title('Barycenters')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycentric interpolation\n", - "-------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/rflamary/PYTHON/POT/ot/unbalanced.py:895: RuntimeWarning: overflow encountered in true_divide\n", - " u = (A / Kv) ** fi\n", - "/home/rflamary/PYTHON/POT/ot/unbalanced.py:900: RuntimeWarning: invalid value encountered in true_divide\n", - " v = (Q / Ktu) ** fi\n", - "/home/rflamary/PYTHON/POT/ot/unbalanced.py:907: UserWarning: Numerical errors at iteration 595\n", - " warnings.warn('Numerical errors at iteration %s' % i)\n", - "/home/rflamary/PYTHON/POT/ot/unbalanced.py:900: RuntimeWarning: overflow encountered in true_divide\n", - " v = (Q / Ktu) ** fi\n", - "/home/rflamary/PYTHON/POT/ot/unbalanced.py:907: UserWarning: Numerical errors at iteration 974\n", - " warnings.warn('Numerical errors at iteration %s' % i)\n", - "/home/rflamary/PYTHON/POT/ot/unbalanced.py:907: UserWarning: Numerical errors at iteration 615\n", - " warnings.warn('Numerical errors at iteration %s' % i)\n", - "/home/rflamary/PYTHON/POT/ot/unbalanced.py:907: UserWarning: Numerical errors at iteration 455\n", - " warnings.warn('Numerical errors at iteration %s' % i)\n", - "/home/rflamary/PYTHON/POT/ot/unbalanced.py:907: UserWarning: Numerical errors at iteration 361\n", - " warnings.warn('Numerical errors at iteration %s' % i)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# barycenter interpolation\n", - "\n", - "n_weight = 11\n", - "weight_list = np.linspace(0, 1, n_weight)\n", - "\n", - "\n", - "B_l2 = np.zeros((n, n_weight))\n", - "\n", - "B_wass = np.copy(B_l2)\n", - "\n", - "for i in range(0, n_weight):\n", - " weight = weight_list[i]\n", - " weights = np.array([1 - weight, weight])\n", - " B_l2[:, i] = A.dot(weights)\n", - " B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights)\n", - "\n", - "\n", - "# plot interpolation\n", - "\n", - "pl.figure(3)\n", - "\n", - "cmap = pl.cm.get_cmap('viridis')\n", - "verts = []\n", - "zs = weight_list\n", - "for i, z in enumerate(zs):\n", - " ys = B_l2[:, i]\n", - " verts.append(list(zip(x, ys)))\n", - "\n", - "ax = pl.gcf().gca(projection='3d')\n", - "\n", - "poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])\n", - "poly.set_alpha(0.7)\n", - "ax.add_collection3d(poly, zs=zs, zdir='y')\n", - "ax.set_xlabel('x')\n", - "ax.set_xlim3d(0, n)\n", - "ax.set_ylabel(r'$\\alpha$')\n", - "ax.set_ylim3d(0, 1)\n", - "ax.set_zlabel('')\n", - "ax.set_zlim3d(0, B_l2.max() * 1.01)\n", - "pl.title('Barycenter interpolation with l2')\n", - "pl.tight_layout()\n", - "\n", - "pl.figure(4)\n", - "cmap = pl.cm.get_cmap('viridis')\n", - "verts = []\n", - "zs = weight_list\n", - "for i, z in enumerate(zs):\n", - " ys = B_wass[:, i]\n", - " verts.append(list(zip(x, ys)))\n", - "\n", - "ax = pl.gcf().gca(projection='3d')\n", - "\n", - "poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])\n", - "poly.set_alpha(0.7)\n", - "ax.add_collection3d(poly, zs=zs, zdir='y')\n", - "ax.set_xlabel('x')\n", - "ax.set_xlim3d(0, n)\n", - "ax.set_ylabel(r'$\\alpha$')\n", - "ax.set_ylim3d(0, 1)\n", - "ax.set_zlabel('')\n", - "ax.set_zlim3d(0, B_l2.max() * 1.01)\n", - "pl.title('Barycenter interpolation with Wasserstein')\n", - "pl.tight_layout()\n", - "\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_WDA.ipynb b/notebooks/plot_WDA.ipynb deleted file mode 100644 index df46812..0000000 --- a/notebooks/plot_WDA.ipynb +++ /dev/null @@ -1,316 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Wasserstein Discriminant Analysis\n", - "\n", - "\n", - "This example illustrate the use of WDA as proposed in [11].\n", - "\n", - "\n", - "[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016).\n", - "Wasserstein Discriminant Analysis.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/rflamary/.local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", - " from ._conv import register_converters as _register_converters\n" - ] - } - ], - "source": [ - "# Author: Remi Flamary \n", - "#\n", - "# License: MIT License\n", - "\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "\n", - "from ot.dr import wda, fda" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#%% parameters\n", - "\n", - "n = 1000 # nb samples in source and target datasets\n", - "nz = 0.2\n", - "\n", - "# generate circle dataset\n", - "t = np.random.rand(n) * 2 * np.pi\n", - "ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1\n", - "xs = np.concatenate(\n", - " (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)\n", - "xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2)\n", - "\n", - "t = np.random.rand(n) * 2 * np.pi\n", - "yt = np.floor((np.arange(n) * 1.0 / n * 3)) + 1\n", - "xt = np.concatenate(\n", - " (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)\n", - "xt = xt * yt.reshape(-1, 1) + nz * np.random.randn(n, 2)\n", - "\n", - "nbnoise = 8\n", - "\n", - "xs = np.hstack((xs, np.random.randn(n, nbnoise)))\n", - "xt = np.hstack((xt, np.random.randn(n, nbnoise)))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n", - "---------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#%% plot samples\n", - "pl.figure(1, figsize=(6.4, 3.5))\n", - "\n", - "pl.subplot(1, 2, 1)\n", - "pl.scatter(xt[:, 0], xt[:, 1], c=ys, marker='+', label='Source samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Discriminant dimensions')\n", - "\n", - "pl.subplot(1, 2, 2)\n", - "pl.scatter(xt[:, 2], xt[:, 3], c=ys, marker='+', label='Source samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Other dimensions')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute Fisher Discriminant Analysis\n", - "------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#%% Compute FDA\n", - "p = 2\n", - "\n", - "Pfda, projfda = fda(xs, ys, p)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute Wasserstein Discriminant Analysis\n", - "-----------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compiling cost function...\n", - "Computing gradient of cost function...\n", - " iter\t\t cost val\t grad. norm\n", - " 1\t+8.1744675052927340e-01\t5.18943290e-01\n", - " 2\t+4.6574082332790778e-01\t1.98300827e-01\n", - " 3\t+4.4912307637701449e-01\t1.38157890e-01\n", - " 4\t+4.4030490938831912e-01\t7.00834944e-02\n", - " 5\t+4.3780885707533013e-01\t4.35903168e-02\n", - " 6\t+4.3753767094086027e-01\t5.81158060e-02\n", - " 7\t+4.3658534767678403e-01\t4.44189462e-02\n", - " 8\t+4.3516262357849916e-01\t4.15971448e-02\n", - " 9\t+4.3332622549435446e-01\t7.82365182e-02\n", - " 10\t+4.2847338855201011e-01\t5.28789263e-02\n", - " 11\t+4.1510883118208680e-01\t6.83664317e-02\n", - " 12\t+4.1332168544999542e-01\t1.01013343e-01\n", - " 13\t+4.0818672134323475e-01\t5.07935089e-02\n", - " 14\t+4.0502824759368472e-01\t9.56110665e-02\n", - " 15\t+3.9786250825732905e-01\t6.68177888e-02\n", - " 16\t+3.5518514892526853e-01\t2.01958719e-01\n", - " 17\t+2.5048183658126072e-01\t1.82260477e-01\n", - " 18\t+2.4055179583813954e-01\t1.48301002e-01\n", - " 19\t+2.2127351995549377e-01\t1.77690944e-02\n", - " 20\t+2.2106865089529223e-01\t6.44501056e-03\n", - " 21\t+2.2105965534255226e-01\t5.35361879e-03\n", - " 22\t+2.2104056962319110e-01\t3.63028924e-04\n", - " 23\t+2.2104051220659457e-01\t2.27022248e-04\n", - " 24\t+2.2104049071158929e-01\t1.43369832e-04\n", - " 25\t+2.2104048094656506e-01\t7.87652127e-05\n", - " 26\t+2.2104047690862835e-01\t1.39217014e-05\n", - " 27\t+2.2104047689800282e-01\t1.33400288e-05\n", - " 28\t+2.2104047685931880e-01\t1.09433285e-05\n", - " 29\t+2.2104047677977950e-01\t3.27906935e-07\n", - "Terminated - min grad norm reached after 29 iterations, 8.50 seconds.\n", - "\n" - ] - } - ], - "source": [ - "#%% Compute WDA\n", - "p = 2\n", - "reg = 1e0\n", - "k = 10\n", - "maxiter = 100\n", - "\n", - "Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot 2D projections\n", - "-------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#%% plot samples\n", - "\n", - "xsp = projfda(xs)\n", - "xtp = projfda(xt)\n", - "\n", - "xspw = projwda(xs)\n", - "xtpw = projwda(xt)\n", - "\n", - "pl.figure(2)\n", - "\n", - "pl.subplot(2, 2, 1)\n", - "pl.scatter(xsp[:, 0], xsp[:, 1], c=ys, marker='+', label='Projected samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Projected training samples FDA')\n", - "\n", - "pl.subplot(2, 2, 2)\n", - "pl.scatter(xtp[:, 0], xtp[:, 1], c=ys, marker='+', label='Projected samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Projected test samples FDA')\n", - "\n", - "pl.subplot(2, 2, 3)\n", - "pl.scatter(xspw[:, 0], xspw[:, 1], c=ys, marker='+', label='Projected samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Projected training samples WDA')\n", - "\n", - "pl.subplot(2, 2, 4)\n", - "pl.scatter(xtpw[:, 0], xtpw[:, 1], c=ys, marker='+', label='Projected samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Projected test samples WDA')\n", - "pl.tight_layout()\n", - "\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_barycenter_1D.ipynb b/notebooks/plot_barycenter_1D.ipynb deleted file mode 100644 index 564028c..0000000 --- a/notebooks/plot_barycenter_1D.ipynb +++ /dev/null @@ -1,315 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# 1D Wasserstein barycenter demo\n", - "\n", - "\n", - "This example illustrates the computation of regularized Wassersyein Barycenter\n", - "as proposed in [3].\n", - "\n", - "\n", - "[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015).\n", - "Iterative Bregman projections for regularized transportation problems\n", - "SIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Remi Flamary \n", - "#\n", - "# License: MIT License\n", - "\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "# necessary for 3d plot even if not used\n", - "from mpl_toolkits.mplot3d import Axes3D # noqa\n", - "from matplotlib.collections import PolyCollection" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 100 # nb bins\n", - "\n", - "# bin positions\n", - "x = np.arange(n, dtype=np.float64)\n", - "\n", - "# Gaussian distributions\n", - "a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\n", - "a2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n", - "\n", - "# creating matrix A containing all distributions\n", - "A = np.vstack((a1, a2)).T\n", - "n_distributions = A.shape[1]\n", - "\n", - "# loss matrix + normalization\n", - "M = ot.utils.dist0(n)\n", - "M /= M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n", - "---------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1, figsize=(6.4, 3))\n", - "for i in range(n_distributions):\n", - " pl.plot(x, A[:, i])\n", - "pl.title('Distributions')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycenter computation\n", - "----------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "alpha = 0.2 # 0<=alpha<=1\n", - "weights = np.array([1 - alpha, alpha])\n", - "\n", - "# l2bary\n", - "bary_l2 = A.dot(weights)\n", - "\n", - "# wasserstein\n", - "reg = 1e-3\n", - "bary_wass = ot.bregman.barycenter(A, M, reg, weights)\n", - "\n", - "pl.figure(2)\n", - "pl.clf()\n", - "pl.subplot(2, 1, 1)\n", - "for i in range(n_distributions):\n", - " pl.plot(x, A[:, i])\n", - "pl.title('Distributions')\n", - "\n", - "pl.subplot(2, 1, 2)\n", - "pl.plot(x, bary_l2, 'r', label='l2')\n", - "pl.plot(x, bary_wass, 'g', label='Wasserstein')\n", - "pl.legend()\n", - "pl.title('Barycenters')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycentric interpolation\n", - "-------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_alpha = 11\n", - "alpha_list = np.linspace(0, 1, n_alpha)\n", - "\n", - "\n", - "B_l2 = np.zeros((n, n_alpha))\n", - "\n", - "B_wass = np.copy(B_l2)\n", - "\n", - "for i in range(0, n_alpha):\n", - " alpha = alpha_list[i]\n", - " weights = np.array([1 - alpha, alpha])\n", - " B_l2[:, i] = A.dot(weights)\n", - " B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(3)\n", - "\n", - "cmap = pl.cm.get_cmap('viridis')\n", - "verts = []\n", - "zs = alpha_list\n", - "for i, z in enumerate(zs):\n", - " ys = B_l2[:, i]\n", - " verts.append(list(zip(x, ys)))\n", - "\n", - "ax = pl.gcf().gca(projection='3d')\n", - "\n", - "poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\n", - "poly.set_alpha(0.7)\n", - "ax.add_collection3d(poly, zs=zs, zdir='y')\n", - "ax.set_xlabel('x')\n", - "ax.set_xlim3d(0, n)\n", - "ax.set_ylabel('$\\\\alpha$')\n", - "ax.set_ylim3d(0, 1)\n", - "ax.set_zlabel('')\n", - "ax.set_zlim3d(0, B_l2.max() * 1.01)\n", - "pl.title('Barycenter interpolation with l2')\n", - "pl.tight_layout()\n", - "\n", - "pl.figure(4)\n", - "cmap = pl.cm.get_cmap('viridis')\n", - "verts = []\n", - "zs = alpha_list\n", - "for i, z in enumerate(zs):\n", - " ys = B_wass[:, i]\n", - " verts.append(list(zip(x, ys)))\n", - "\n", - "ax = pl.gcf().gca(projection='3d')\n", - "\n", - "poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\n", - "poly.set_alpha(0.7)\n", - "ax.add_collection3d(poly, zs=zs, zdir='y')\n", - "ax.set_xlabel('x')\n", - "ax.set_xlim3d(0, n)\n", - "ax.set_ylabel('$\\\\alpha$')\n", - "ax.set_ylim3d(0, 1)\n", - "ax.set_zlabel('')\n", - "ax.set_zlim3d(0, B_l2.max() * 1.01)\n", - "pl.title('Barycenter interpolation with Wasserstein')\n", - "pl.tight_layout()\n", - "\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_barycenter_fgw.ipynb b/notebooks/plot_barycenter_fgw.ipynb deleted file mode 100644 index 6fb19af..0000000 --- a/notebooks/plot_barycenter_fgw.ipynb +++ /dev/null @@ -1,342 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "=================================\n", - "Plot graphs' barycenter using FGW\n", - "=================================\n", - "\n", - "This example illustrates the computation barycenter of labeled graphs using FGW\n", - "\n", - "Requires networkx >=2\n", - "\n", - ".. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain\n", - " and Courty Nicolas\n", - " \"Optimal Transport for structured data with application on graphs\"\n", - " International Conference on Machine Learning (ICML). 2019.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Titouan Vayer \n", - "#\n", - "# License: MIT License" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import networkx as nx\n", - "import math\n", - "from scipy.sparse.csgraph import shortest_path\n", - "import matplotlib.colors as mcol\n", - "from matplotlib import cm\n", - "from ot.gromov import fgw_barycenters" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def find_thresh(C, inf=0.5, sup=3, step=10):\n", - " \"\"\" Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected\n", - " Tthe threshold is found by a linesearch between values \"inf\" and \"sup\" with \"step\" thresholds tested.\n", - " The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix\n", - " and the original matrix.\n", - " Parameters\n", - " ----------\n", - " C : ndarray, shape (n_nodes,n_nodes)\n", - " The structure matrix to threshold\n", - " inf : float\n", - " The beginning of the linesearch\n", - " sup : float\n", - " The end of the linesearch\n", - " step : integer\n", - " Number of thresholds tested\n", - " \"\"\"\n", - " dist = []\n", - " search = np.linspace(inf, sup, step)\n", - " for thresh in search:\n", - " Cprime = sp_to_adjency(C, 0, thresh)\n", - " SC = shortest_path(Cprime, method='D')\n", - " SC[SC == float('inf')] = 100\n", - " dist.append(np.linalg.norm(SC - C))\n", - " return search[np.argmin(dist)], dist\n", - "\n", - "\n", - "def sp_to_adjency(C, threshinf=0.2, threshsup=1.8):\n", - " \"\"\" Thresholds the structure matrix in order to compute an adjency matrix.\n", - " All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0\n", - " Parameters\n", - " ----------\n", - " C : ndarray, shape (n_nodes,n_nodes)\n", - " The structure matrix to threshold\n", - " threshinf : float\n", - " The minimum value of distance from which the new value is set to 1\n", - " threshsup : float\n", - " The maximum value of distance from which the new value is set to 1\n", - " Returns\n", - " -------\n", - " C : ndarray, shape (n_nodes,n_nodes)\n", - " The threshold matrix. Each element is in {0,1}\n", - " \"\"\"\n", - " H = np.zeros_like(C)\n", - " np.fill_diagonal(H, np.diagonal(C))\n", - " C = C - H\n", - " C = np.minimum(np.maximum(C, threshinf), threshsup)\n", - " C[C == threshsup] = 0\n", - " C[C != 0] = 1\n", - "\n", - " return C\n", - "\n", - "\n", - "def build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None):\n", - " \"\"\" Create a noisy circular graph\n", - " \"\"\"\n", - " g = nx.Graph()\n", - " g.add_nodes_from(list(range(N)))\n", - " for i in range(N):\n", - " noise = float(np.random.normal(mu, sigma, 1))\n", - " if with_noise:\n", - " g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise)\n", - " else:\n", - " g.add_node(i, attr_name=math.sin(2 * i * math.pi / N))\n", - " g.add_edge(i, i + 1)\n", - " if structure_noise:\n", - " randomint = np.random.randint(0, p)\n", - " if randomint == 0:\n", - " if i <= N - 3:\n", - " g.add_edge(i, i + 2)\n", - " if i == N - 2:\n", - " g.add_edge(i, 0)\n", - " if i == N - 1:\n", - " g.add_edge(i, 1)\n", - " g.add_edge(N, 0)\n", - " noise = float(np.random.normal(mu, sigma, 1))\n", - " if with_noise:\n", - " g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise)\n", - " else:\n", - " g.add_node(N, attr_name=math.sin(2 * N * math.pi / N))\n", - " return g\n", - "\n", - "\n", - "def graph_colors(nx_graph, vmin=0, vmax=7):\n", - " cnorm = mcol.Normalize(vmin=vmin, vmax=vmax)\n", - " cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis')\n", - " cpick.set_array([])\n", - " val_map = {}\n", - " for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items():\n", - " val_map[k] = cpick.to_rgba(v)\n", - " colors = []\n", - " for node in nx_graph.nodes():\n", - " colors.append(val_map[node])\n", - " return colors" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We build a dataset of noisy circular graphs.\n", - "Noise is added on the structures by random connections and on the features by gaussian noise.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "np.random.seed(30)\n", - "X0 = []\n", - "for k in range(9):\n", - " X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n", - "---------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(figsize=(8, 10))\n", - "for i in range(len(X0)):\n", - " plt.subplot(3, 3, i + 1)\n", - " g = X0[i]\n", - " pos = nx.kamada_kawai_layout(g)\n", - " nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100)\n", - "plt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20)\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycenter computation\n", - "----------------------\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Features distances are the euclidean distances\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0]\n", - "ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0]\n", - "Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0]\n", - "lambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel()\n", - "sizebary = 15 # we choose a barycenter with 15 nodes\n", - "\n", - "A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot Barycenter\n", - "-------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0]))\n", - "for i, v in enumerate(A.ravel()):\n", - " bary.add_node(i, attr_name=v)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pos = nx.kamada_kawai_layout(bary)\n", - "nx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False)\n", - "plt.suptitle('Barycenter', fontsize=20)\n", - "plt.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_barycenter_lp_vs_entropic.ipynb b/notebooks/plot_barycenter_lp_vs_entropic.ipynb deleted file mode 100644 index b5d7895..0000000 --- a/notebooks/plot_barycenter_lp_vs_entropic.ipynb +++ /dev/null @@ -1,592 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# 1D Wasserstein barycenter comparison between exact LP and entropic regularization\n", - "\n", - "\n", - "This example illustrates the computation of regularized Wasserstein Barycenter\n", - "as proposed in [3] and exact LP barycenters using standard LP solver.\n", - "\n", - "It reproduces approximately Figure 3.1 and 3.2 from the following paper:\n", - "Cuturi, M., & Peyré, G. (2016). A smoothed dual approach for variational\n", - "Wasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343.\n", - "\n", - "[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015).\n", - "Iterative Bregman projections for regularized transportation problems\n", - "SIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Remi Flamary \n", - "#\n", - "# License: MIT License\n", - "\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "# necessary for 3d plot even if not used\n", - "from mpl_toolkits.mplot3d import Axes3D # noqa\n", - "from matplotlib.collections import PolyCollection # noqa\n", - "\n", - "#import ot.lp.cvx as cvx" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Gaussian Data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "problems = []\n", - "\n", - "n = 100 # nb bins\n", - "\n", - "# bin positions\n", - "x = np.arange(n, dtype=np.float64)\n", - "\n", - "# Gaussian distributions\n", - "# Gaussian distributions\n", - "a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\n", - "a2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n", - "\n", - "# creating matrix A containing all distributions\n", - "A = np.vstack((a1, a2)).T\n", - "n_distributions = A.shape[1]\n", - "\n", - "# loss matrix + normalization\n", - "M = ot.utils.dist0(n)\n", - "M /= M.max()" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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0xpJijFJVVhZX8KVBHb8yRmsOz2yzo8KSYqJpOASLfwfvPQT11U4i7DESRpwPgVrYvQaK5oMGoWsfmH4PjLkcfFZZZaKLJcUYtauyltJqb1bGaE3jihnW2SaBqMKaV2DBfVBZDMPOhS/fDieMBn+zj5eGQ7D9A1hwP/ztZlj8OMx4APJP8yZ2Y1pgX9Ni1OH2RI9WxmjNSf2zLCkmikA9vHoTvHojpGfDta/BlS9A75O+mBABkjvB4Onwzbfg4ifgQBk8fQ68/5CTXI2JApYUY5TXK2O0pnHFjNLqWq9DMe2prgZevALWvAxn/Bi++TYMmBLea30+GHsF3FIIoy+F+ffC63dCKNSeERsTFqs+jVErd1Qw0sOVMVpzUpN2xbNG9fQ4GtMuasrghctg12q44DEYd82xnSclHb76f9Clh9MeeaAULnocklIjG68xbWAlxRgUCIZYs7MyqtoTG43uk0mST6wKNV7VlMKTM6B0Pcx6/tgTYiOfz2lXPPM+WPtXeHGWUy1rjEcsKcagjXuqOdQQPDy1WjRJS/YzvFcGq0osKcadQB28dBVUfQpf/wcMOzsy5xWByd+H8x+FLQth7g+sjdF4xpJiDIq2QfvNje1rK2bEHVX45/eh5CO4+HHoPyny1xh/LUy+FZY/Ax89EfnzGxMGS4oxaFVxBd3Sk+mfHZ0DoG3FjDj04WOw6gWYeieMurj9rnPG3TDsHKfjzZaF7XcdY1phSTEGRcvKGK2xFTPizKY34I27YeRFcPrt7Xstnw+++gTkDoO/XAd7i9r3esY0Y0kxxkTTyhitsRUz4khlCfz1Rmeatose75gZaFIz4MqXwJcEL1/jtGUa00EsKcaYNSXRszJGa2zFjDgRCsHfvwOhAFz+p46dr7RbHlz0eyj9GN56oOOuaxKeJcUYsyLKO9k0shUz4sDSP8In78CMn0H2gI6//tCzYNzXYdEjsGNxx1/fJCRLijFmZXF0rYzRmrFNVswwMWhvEbx5j7OixfjrvYtjxgOQ1Q/+9i1nFh1j2pklxRjSuDJGtJcS4fMz25gYEwzA37/lzCxzwWPOOEKvpGY41ajl25wkbUw7CyspishMEdkoIkUickcL+1NF5M/u/iUikt9sf38RqRGRH0Qm7MS0q7KWsihbGaM1tmJGDPvgUShZCuf+Grr28joaZxWNU/8DCmc7azUa046OmhRFxA/8FjgbGAlcKSIjmx12A1CuqoOBh4BfNNv/IPDv4w83sUX7oP3mbMWMGFS+Dd75BYy4AMZc6nU0nznjbug+BP51GzTYZPOm/YRTUpwIFKnqVlWtB14CLmx2zIXAM+7jV4Dp4g6iE5GLgE+AdZEJOXE1rowxPMpWxmhN44oZZdXWpT5m/PuHIH6Y+XOvI/m85DQ491dQ/gksetjraEwcCycp9gGKmzwvcbe1eIyqBoBKoLuIdAF+CNx3pAuIyE0iUigihWVlZeHGnnCidWWM1oxtbFe00mJs2DAXNr0OU++AzOZ/4lFg4FQY9VV470HYv9XraEycau+ONvcCD6nqEbuNqeoTqlqgqgW5ubntHFJsiuaVMVozuncmfp+wsrjc61DM0dQfdEqJuSNg0re9jqZ1Mx4AfwrMvd0mDTftIpykuBPo1+R5X3dbi8eISBKQCewDTgF+KSLbgO8DPxKRW44z5oS0YXf0rozRmk4pfkb0ymD5dispRr33fg2VO5wqSn+y19G0rmsvmHYnFL0JG17zOhoTh8JJikuBISIyQERSgFnAnGbHzAGudR9fCixUxxRVzVfVfOBh4AFVfSxCsSeUwm37ASjIz/Y4krYpyMtmZXEFDUFbVT1q7S1yBsifeAXkT/Y6mqObeDP0GAX/vgPqD3gdjYkzR02KbhvhLcA8YD3wsqquE5H7ReQC97DZOG2IRcCtwBeGbZjjU7i9nF6ZafTJ6uR1KG0yPq8bhxqCrN9V5XUopjVv3AVJafCVn3odSXj8Sc5wkaoSWPSo19GYOJMUzkGqOheY22zbPU0e1wKXHeUc9x5DfMa1bHs54/O6eR1GmxXkOzEXbivnxL6xU/WbMLa85XSuOfM+yDjB62jCl3eqs4TVokecqeCisWOQiUk2o00M2FlxiF2VtRTEYFLsldmJPlmdWLbdOttEnVAQ5t0FWf3hlG95HU3bnXkvaBAWxkgJ18QES4oxIFbbExuNz+tG4fb9qPUWjC4rnoPSdfCV+51xgLGmWz5M+g6sehF2Lvc6GhMnLCnGgGXby0lP8TO8Z2wM2m+uIL8be6rqKCk/5HUoplFdNSz8GfQ7xVk8OFZNuRXSc+CNH9sQDRMRlhRjQOG2ck7un0WSPzb/dzW2hVoVahR5/2E4UOqM+/Nywu/jlZYJ034E2xfZEA0TEbH5KZtAauoCbNhdxfi82Kw6BRjesytdUpMo3L7f61AMQGUJfPgYjLkM+hZ4Hc3xG3ct5A6HN+6GQL3X0ZgYZ0kxyq3cUUFIiclONo38PuHk/lkUbrOSYlRY+N9OVeP0OFmKyZ8EZ/3MmRd12VNeR2NinCXFKFe4fT8+IaZmsmnJ+LxubNxTTVVtg9ehJLbda5yOKafc7PQ6jReDz4T8Kc4KH7U2JtYcO0uKUW7Z9nKG9exKRloUT70VhoK8bFRhhS067K359zrtcFNu9TqSyBJxetEe3OeMXTTmGFlSjGLBkLJiR0VMV502Oql/Fj6BZdusXdEzW9+Govlw+g+gU+zfU1/QZxyMvgQ+/C1U7fI6GhOjLClGsQ27q6ipCxyeFSaWdUlNYkSvrhRaD1RvhELw5j2Q2Q8mfNPraNrPGXdDKABvP+B1JCZGWVKMYo1DGGJxereWFOR1Y2VxBQGbHLzjrf0r7FrlJI1YHKgfruwBMOFGd2KCDV5HY2KQJcUoVritnJ5dY28S8NaMz8/mYH2Q9buqvQ4lsQTqYOH90HOMMwwj3p3+/yClCyw44trmxrTIkmKUUlWWbtvP+PxuSCwPrm5iglsNvOSTfR5HkmCWzoaKHc6k374E+JPv3B0mfx82zoXtH3gdjYkxCfAXEpu27j3ArspaThuU43UoEdMrsxMDcjrzwRZLih3mUAW8+0sYOA0GT/c6mo5zyrcho7fTjmrTv5k2sKQYpT4o2gvAaYO7exxJZH1pUHeWbN1niw53lEUPw6Fy+EqCVSWmpMO0O6FkKaxvvia6Ma2zpBil3i/aS99uneifne51KBE1eXAOB+qDrCq28YrtrnInLH4cxlwOvcZ6HU3HG/s1yB0B8++DoE0aYcJjSTEKBUPKh1v2cdqgnLhpT2x06qDuiMCiIqtCbXdvPwAagjN+7HUk3vAnOWsu7t8Cy572OBgTKywpRqG1Oyupqg1w2pD4aU9slJWewujemSxyq4dNO9nzMax8ASbeBN3yvI7GO0NnQN5kePvnznJZxhyFJcUotGiLkzC+NCi+2hMbnTY4hxXF5RyoC3gdSvya/xNIyYApt3kdibcOT/+2FxY96nU0JgZYUoxCi4r2MrxnBjldUr0OpV2cNrg7DUHlI5vyrX1seQs2vwGn3wbpsbvkWMT0HQ+jvgof/MZpZzXmCCwpRpnahiBLt5Vz2uD4qzptNCE/m5Qk3+EetiaCQkFnFfqsPJh4s9fRRI8zfwIahIU/8zoSE+UsKUaZZdvLqQ+EmBzHSTEt2U9BXjfet842kbfyediz1ulgEs/TubVVt3yY9G1Y9QJ8utLraEwUs6QYZRYV7SXJJ0wcEN/VXqcNzmH9rir21dR5HUr8qKtxSkJ9J8Koi72OJvpMuQ3SuzslaRvQb1phSTHKLCray8n9s+icmuR1KO2qsXrYZreJoEWPQM0emPGA08HEfF5aJky9E7a950wBZ0wLLClGkcqDDazeWRnX7YmNxvTJJCMtyYZmRErlTqcjyehLoN8Er6OJXuOvg5yhzvRvNqDftMCSYhT5cOs+VEmIpOj3CacO7H54+Ik5TvN/4gzUn/4TryOJbv5k+MpPYV8RfPSE19GYKGRJMYq8t7mM9BQ/Y/tmeR1Kh5g8JIfi/YfYtveA16HEtm2LYM1f4LTvJfZA/XANnQGDz3QG9Ffv8ToaE2XCSooiMlNENopIkYjc0cL+VBH5s7t/iYjku9u/IiLLRGSN+/uMyIYfP0Ih5c2P9/DlobmkJCXGd5Vpw3oA8MbHuz2OJIYFA/Dv2yGzH0y+1etoYoMIzPwFNByC+fd6HY2JMkf99BURP/Bb4GxgJHCliIxsdtgNQLmqDgYeAn7hbt8LnK+qY4BrgWcjFXi8WVFcQWl1HTNH9/Q6lA7TLzud0X268vpaS4rHrPBJZwjGjP92VoYw4ckZDF+6xRmisWOJ19GYKBJOkWQiUKSqW1W1HngJuLDZMRcCz7iPXwGmi4io6gpV/dTdvg7oJCLxOU3LcZq3bjfJfmHa8B5eh9KhZo7qyfIdFeypqvU6lNhzYC+89TMYOBVGXOB1NLFnyg+cNRfn/sCZ9MAYwkuKfYDiJs9L3G0tHqOqAaASaD5x5yXAclW1gWnNqCqvr93NaYNz6JqW7HU4HaqxZPzGOistttmC+6D+AJz9SxuCcSxSu8BZP4Xdq2H5M0c/3iSEDmm8EpFROFWqLc47JSI3iUihiBSWlZV1REhRZf2uanbsP8jMUYlTddpocI8MBuV25nVLim1TUgjLn4VTvgW5w7yOJnaNvsRZRWPB/U7J2yS8cJLiTqBfk+d93W0tHiMiSUAmsM993hf4G/B1Vd3S0gVU9QlVLVDVgtzc3Lb9C+LA6+t24xM4c+QJXofiiZmje7J4637KD9R7HUpsCNTDnO9C197w5R96HU1sE4Fzf+XMBvT6F/oQmgQUTlJcCgwRkQEikgLMAuY0O2YOTkcagEuBhaqqIpIF/Au4Q1UXRSroeDNv7W4m5GfH7aoYRzNzVC+CIWX+euseH5ZFD0Ppx3Dug5DW1etoYl+PEc4UcGv+Apve8Doa47GjJkW3jfAWYB6wHnhZVdeJyP0i0ti6PxvoLiJFwK1A41euW4DBwD0istL9SayeJEextayGjXuqE6rXaXOj+3SlT1Yn5lkV6tGVbYR3/9dZCmnYTK+jiR9TboWcYfDaf9lixAkurAk2VXUuMLfZtnuaPK4FLmvhdT8DbK2WI2hsS5uRgO2JjUSEGaN68tyS7dTUBegS5/O+HrNQCOZ8D1I6O51rTOQkpcKFj8Hss5z2xXP+1+uIjEcSY5R4FJu3djdj+2bSO6uT16F4aubontQHQry9sdTrUKJX4WwoXuxM+N0l8dre212/iTDxJvjo/2zsYgKzpOihnRWHWFVSyYwErjptND6vGzldUmwgf2v2f+LMvjJwGoy90uto4tf0u6FrH5hzC9Qf9Doa4wFLih765ypnXoNEHIrRnN8nnDWqJwvWl1JVa6sXfE4wAK/eBOKDCx61MYntKTXDqUbdu8lZd9EkHEuKHgmFlBeW7GDigGwG5nbxOpyocEVBPw41BPn7iuYjfhLcu/8LJR/BeQ9BVn+vo4l/g6bBqbc41dUbbN3FRGNJ0SPvbi5jx/6DXD3JVjVoNLZfFmP6ZPLc4u2orYzu2LEE3v0lnDgLxlzqdTSJY/o90HOMU41abVX6icSSokeeX7KD7p1TrOq0masn9WfTnhqWbiv3OhTv1VbCqzc6K2BYb8iOlZQKl8x22hX//m2n569JCJYUPfBpxSEWrN/D5RP6JcwyUeE6f2xvMtKSeG7xdq9D8ZYqvHYrVO6ES/5og/S9kDvMWX1ky0L48DGvozEdxD6RPfDSRztQ4GsTrX2oufSUJC4Z15d/r93F3poEnjv+w9/C2ldg2p3OUAHjjYJvwIjznZ6/W9/2OhrTASwpdrCGYIiXlhYzdWgu/bJt/buWXD2pPw1B5eXC4qMfHI+2LIQ373Y+jCff5nU0iU0ELnoccobCX65zhsaYuGZJsYO9+fEeSqvrrIPNEQzukcGkgdm8sGQHwVCCdbjZvxX+cj3kDoeLfg8++xP1XGoGzHoeNAQvXeVMHm7ilv3FdbDnFm+nT1Ynpg6zKWCP5OpJeZSUH+LdTQm0lFhdjfOhC86HcKoN1Yka3QfBpU9B2Xr4x3ecNl8TlywpdqB1n1bywZZ9fO2U/vh9Ni6e2GsAAA67SURBVAD7SM4a2ZPcjFT+772tiTE8I1APr1wPZRvgsqche6DXEZnmBk+HM++Dj//hzI9q4pIlxQ6iqjwwdz3d0pOt6jQMKUk+vv3lQXywZR/vxHtpMRSEv90Mm9+Ac3/tDB430elL34Xx18P7D8L7D3sdjWkHlhQ7yNubylhUtI/vTR9CZqdkr8OJCVdPyiOvezr/M3dD/LYtqsK/boV1rzqlkIJveB2RORIR54vL6Etg/k+g8CmvIzIRZkmxAwSCIf5n7nryu6dz1SlWSgxXSpKPH84czsY91byyLE57os6/F5Y9DZNvhcnf9zoaEw6fHy7+AwyZ4ay/uOYVryMyEWRJsQO8sqyETXtq+OHM4TZYv43OHt2Tcf2z+PUbmzhYH/A6nMhRhTfvgUUPQ8ENzrRiJnb4k+HyZyDvS07V96o/ex2RiRD7hG5nB+oC/PrNTYzP68ZMWyKqzUSEu84dQWl1Hf/3bpyMEQs2OFOHLXrESYjn/MpWvohFyZ3gypeg/6nwt5vgg994HZGJAEuK7eyJd7dSVl3Hj84ZgdgH3zEZn5fNOWN68od3t1BaVet1OMen/gC8eCWsehGm3eW0T9lYxNiV1hWu/iuMvMhZamreXTZPaoyzv8Z2tLqkgsff2cK5Y3oxPq+b1+HEtNtnDCcQVG7/62pCsdrppnInPHM+bFkA5z8CX77dSojxICkVLn0SJt7kzJH66o3Olx8TkywptpP9B+r59nPLye2Syk8vGu11ODEvP6cz95w/krc3lvHows1eh9N2m9+E30+G0g1w+bMw/jqvIzKR5PPD2b+E6T+Bta/CE9OgdL3XUZljYEmxHQRDyn++tIKy6jp+d9U4sjuneB1SXLjqlP5cMq4vjyzYzFsbSr0OJzzBgNPD9PlLIaMX3PwOjDjP66hMexCBKbfC1/8Oh8qdxLjiea+jMm1kSbEdPPTmJt7bvJf7LxzF2H5ZXocTN0SE/754NCN6duU/X1rBjn0HvQ7pyHavhadmwvsPOSXDby6AnCFeR2Xa28Cp8K33od8EZ0q4v1wHVbs8DsqEy5JihL2+dhePvVXEFQX9mGVLQ0VcWrKfP1wzHhHh5ueWUV3b4HVIX1RXDa//CP5wujPB9yWznTbE5E5eR2Y6SsYJcM3f4Ywfw4a58NgEWPy4U3NgopolxQh6fsl2/uOFFYztl8V9F47yOpy41S87nUdmncSmPdVc9vsP+bTikNchOYINsPIF9wPwdzDu63BLIYy51OvIjBd8fjj9/8F3PnTWxHz9DnhiKhQtsAnFo5hE22TLBQUFWlhY6HUYbRIKKb94fQN/eHcr04bl8puvjaNLapLXYcW99zaX8Z3nltMpxc+T101gdJ9MbwIJ1DnJ8P2HoGI79BoL5z4IfQu8icdEH1VYP8epQagqgT7jnYQ5dKb1QPaAiCxT1Rb/QC0pHqeD9QFue3kV/167m2sm5fGT80eS5LcCeEfZtKea659ayv4D9Twy6yTOGtWBEyRUljjjDQufgqqd0HucM8zCPuhMaw5/gXoQKnbACWOg4HpnLtVO1v+go1hSbAfBkPLXZSX86o2NlNXUcdc5I7hh8gAboO+B0upabnymkNUllXxl5AnccfZwBuW201qEtVXOahYrX4AtCwGFAafDad+HQWdYMjThCTY4c6Z+8BsoXQdJaTDifBh7JeRPgSTrsd6eLClGkKryzqYyfv7vDWzYXc3J/bO465wRFORnex1aQqttCDL7/U94/O0tHGoI8rWJ/fnu9MH0yEg7vhOHQrB3ExTNh83zYPuHEGqArn3h5KucD7HsAZH5R5jEowqfroCVz8Oav0BtJaR0cXqwDp0BA6dBZl/7shVhx50URWQm8AjgB/6oqj9vtj8V+BMwHtgHXKGq29x9dwI3AEHge6o670jXisakqKqs2VnJa6t38a/Vu9hZcYj+2en8cOZwzhnT00qHUWRvTR2PLtjM80t2oKp8aVAO553Yi5mje5KVfpRv36GQ0yZYthF2rYTij2BnofNBBZA7Aoae5ayO0H+S05HCmEhpOARb34ZN85zaiKqdzvaMXtB3gvPTcwzkDoeMnpYoj8NxJUUR8QObgK8AJcBS4EpV/bjJMd8BTlTVb4nILOBiVb1CREYCLwITgd7AfGCoqgZbu57XSTEQDFFaXcfm0hrW7qxkTUklq0sq+LSylmS/MGVILued2ItzT+xFapJ9KEarT/Ye4K/LSnht9ads23eQJB8U9EphYo8gJ3arZ0jnWnqEykg7sBOpLIbybbB3MwQae7IK9BjpjDXrO8Gp0upmy36ZDqIKe9bB9g+g5CPnC1rF9s/2p2VCzjDI6g9Z/SCzn1Oi7Jzr/uTYEKAjON6keCpwr6rOcJ/fCaCq/9PkmHnuMR+KSBKwG8gF7mh6bNPjWrtepJLiqg/mcbB6P6EQBFUJhZRAUGkIhWgIhKgPhjhUH+RgQ5CDdUFq6hrYf6CBikP1n+st3SMjlbyczozu3ZWCvGw6J0qv0jZVqzc79nOv1WbbtIXHChpq8lid1eg1BBp0H7u/QwGnPSbU4Iz5CtY5nRcCdRCohYaDzjfu+gNQX4PWVhI8WAG1lSTpF8c0HtIUyvy5lCf3oqzTACrSB1LddRAHM4cgnbqSmuQnNclHsl/w+3wk+QS/T/CJ4BNnQgERENzH7nkbv8Q7+47yjd6+8JswJNfuo3PlZjpXFpFeVUTnyi2kHSgh9eBufPrF8Y9BfycCKRkEkjMIpHQlmJTu/PjTCCWlE/KnEPKlHP6tvmRUkgj5klBfEip+ED8qPvexoPhAfKgIIO62zx433szaeFPL4f808fnn2oYSb3bfYfQbMjbs41tzpKQYzid8H6DpCq8lwCmtHaOqARGpBLq72xc3e22fFgK8CbgJoH//yAx477Twx4wNbGr7C5ObPa8Ddro/S48/LhNh4nc6KSSlOL+T052flHRIy0Ky8khKy3RWM0jvTig9h9JQVzbXpLGtoRtbD6axq7KO0upaqmoDVO9toKo4wKGGXYDNQmKijR8Y5v44fITIpYLeso9sqaK7VJFDJVmBA2TUHaSrHKArB+kse+hEHenUkS51pNBACgFSaMAv0dW3pDUf9v0G/YY81K7XiIpij6o+ATwBTkkxEufMuPwPlNTV4G/yzT7ZLyT7faQk+fCLWFvgUbXh/fnCoU02HH6fmxSfmj4WH5/7punzO9tEnKTn83/225fkLPDqS3Z/t60K2wf0dH+mHOG4UEipD4aoC4SoawgSCCnBkBIIKYFgCAVCqoRCzm/4rACsfPb8aDdztHV0M/Gt1v3Z33xHKICEAkiowfmtAdCQ+zjkPNYQEEJCIZw7W93t+tlzaHLTK83/AuQL93vb7v8BPdp/lrBwkuJOoF+T533dbS0dU+JWn2bidLgJ57XtotfQcR1xGROnfD4hzecnLdkPnZpXHxhj4lU4o8yXAkNEZICIpACzgDnNjpkDXOs+vhRYqM5X4DnALBFJFZEBwBDgo8iEbowxxkTWUUuKbhvhLcA8nArtJ1V1nYjcDxSq6hxgNvCsiBThlMxnua9dJyIvAx8DAeA/jtTz1BhjjPGSDd43xhiTUI7U+9Qm6TTGGGNclhSNMcYYV9RVn4pIGbD9qAeGJwfYG6FzxTt7r9rG3q/w2XsVPnuv2uZY3688Vc1taUfUJcVIEpHC1uqNzefZe9U29n6Fz96r8Nl71Tbt8X5Z9akxxhjjsqRojDHGuOI9KT7hdQAxxN6rtrH3K3z2XoXP3qu2ifj7FddtisYYY0xbxHtJ0RhjjAmbJUVjjDHGFZdJUURmishGESkSkTu8jifaiEg/EXlLRD4WkXUi8p/u9mwReVNENru/u3kda7QQEb+IrBCR19znA0RkiXuP/dmdLD/hiUiWiLwiIhtEZL2InGr3VetE5L/cv8G1IvKiiKTZveUQkSdFpFRE1jbZ1uK9JI5H3fdstYgc8zJJcZcURcQP/BY4GxgJXCkiI72NKuoEgNtUdSQwCfgP9z26A1igqkOABe5z4/hPYH2T578AHlLVwUA5cIMnUUWfR4DXVXU4MBbnPbP7qgUi0gf4HlCgqqNxFlyYhd1bjZ4GZjbb1tq9dDbOKkxDcBasf/xYLxp3SRGYCBSp6lZVrQdeAi70OKaooqq7VHW5+7ga54OrD8779Ix72DPARd5EGF1EpC9wLvBH97kAZwCvuIfYewWISCZwOs6qOahqvapWYPfVkSQBndx1aNOBXdi9BYCqvssX10Nu7V66EPiTOhYDWSLS61iuG49JsQ9Q3OR5ibvNtEBE8oGTgSXACaq6y921GzjBo7CizcPA7UDIfd4dqFDVgPvc7jHHAKAMeMqtav6jiHTG7qsWqepO4FfADpxkWAksw+6tI2ntXorY5348JkUTJhHpAvwV+L6qVjXd5y4SnfDjdUTkPKBUVZd5HUsMSALGAY+r6snAAZpVldp99Rm3PexCnC8TvYHOfLG60LSive6leEyKO4F+TZ73dbeZJkQkGSchPq+qr7qb9zRWObi/S72KL4qcBlwgIttwquLPwGk3y3KrvMDusUYlQImqLnGfv4KTJO2+atmZwCeqWqaqDcCrOPeb3Vuta+1eitjnfjwmxaXAELcHVwpOw/Ucj2OKKm6b2Gxgvao+2GTXHOBa9/G1wD86OrZoo6p3qmpfVc3HuZcWqupVwFvApe5h9l4BqrobKBaRYe6m6cDH2H3Vmh3AJBFJd/8mG98vu7da19q9NAf4utsLdRJQ2aSatU3ickYbETkHpx3IDzypqv/tcUhRRUQmA+8Ba/isnexHOO2KLwP9cZbvulxVmzd0JywRmQr8QFXPE5GBOCXHbGAFcLWq1nkZXzQQkZNwOiSlAFuB63G+fNt91QIRuQ+4AqdH+ArgRpy2sIS/t0TkRWAqzvJQe4CfAH+nhXvJ/VLxGE7180HgelUtPKbrxmNSNMYYY45FPFafGmOMMcfEkqIxxhjjsqRojDHGuCwpGmOMMS5LisYYY4zLkqIxxhjjsqRojDHGuP4/Ol70eqbM5JAAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1, figsize=(6.4, 3))\n", - "for i in range(n_distributions):\n", - " pl.plot(x, A[:, i])\n", - "pl.title('Distributions')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed time : 0.008066177368164062 s\n", - "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n", - "1.0 1.0 1.0 - 1.0 1700.336700337 \n", - "0.006776453137632 0.006776453137632 0.006776453137632 0.9932238647293 0.006776453137632 125.6700527543 \n", - "0.004018712867873 0.004018712867873 0.004018712867873 0.4301142633001 0.004018712867873 12.26594150092 \n", - "0.001172775061627 0.001172775061627 0.001172775061627 0.7599932455027 0.001172775061627 0.3378536968898 \n", - "0.0004375137005386 0.0004375137005386 0.0004375137005386 0.6422331807989 0.0004375137005386 0.1468420566359 \n", - "0.0002326690467339 0.0002326690467339 0.0002326690467339 0.5016999460898 0.0002326690467339 0.09381703231428 \n", - "7.430121674299e-05 7.4301216743e-05 7.430121674299e-05 0.7035962305811 7.430121674299e-05 0.05777870257169 \n", - "5.321227838943e-05 5.321227838945e-05 5.321227838944e-05 0.3087841864307 5.321227838944e-05 0.05266249477219 \n", - "1.990900379216e-05 1.99090037922e-05 1.990900379216e-05 0.6520472013271 1.990900379216e-05 0.04526054405523 \n", - "6.305442046834e-06 6.305442046856e-06 6.305442046837e-06 0.7073953304085 6.305442046837e-06 0.04237597591384 \n", - "2.290148391591e-06 2.290148391631e-06 2.290148391602e-06 0.6941812711476 2.29014839161e-06 0.04152284932101 \n", - "1.182864875578e-06 1.182864875548e-06 1.182864875555e-06 0.5084552046229 1.182864875567e-06 0.04129461872829 \n", - "3.626786386894e-07 3.626786386985e-07 3.626786386845e-07 0.7101651569095 3.626786385995e-07 0.0411303244893 \n", - "1.539754244475e-07 1.539754247164e-07 1.539754247197e-07 0.6279322077522 1.539754251915e-07 0.04108867636377 \n", - "5.193221608537e-08 5.19322169648e-08 5.193221696942e-08 0.6843453280956 5.193221892276e-08 0.04106859618454 \n", - "1.888205219929e-08 1.88820500654e-08 1.888205006369e-08 0.6673443828803 1.888205852187e-08 0.04106214175236 \n", - "5.676837529301e-09 5.676842740457e-09 5.676842761502e-09 0.7281712198286 5.676877044229e-09 0.04105958648535 \n", - "3.501170987746e-09 3.501167688027e-09 3.501167721804e-09 0.4140142115019 3.501183058995e-09 0.04105916265728 \n", - "1.110582426269e-09 1.110580273241e-09 1.110580239523e-09 0.6999003212726 1.110624310022e-09 0.04105870073273 \n", - "5.768753963318e-10 5.769422203363e-10 5.769421938248e-10 0.5002521235315 5.767522037401e-10 0.04105859764872 \n", - "1.534102102874e-10 1.535920569433e-10 1.535921107494e-10 0.7516900610544 1.535251083958e-10 0.04105851678411 \n", - "6.717475002202e-11 6.735435784522e-11 6.735430717133e-11 0.5944268235824 6.732253215483e-11 0.04105850033323 \n", - "1.751321118837e-11 1.74043080851e-11 1.740429001123e-11 0.7566075167358 1.736956306927e-11 0.0410584908946 \n", - "Optimization terminated successfully.\n", - " Current function value: 0.041058 \n", - " Iterations: 22\n", - "Elapsed time : 2.3570468425750732 s\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "alpha = 0.5 # 0<=alpha<=1\n", - "weights = np.array([1 - alpha, alpha])\n", - "\n", - "# l2bary\n", - "bary_l2 = A.dot(weights)\n", - "\n", - "# wasserstein\n", - "reg = 1e-3\n", - "ot.tic()\n", - "bary_wass = ot.bregman.barycenter(A, M, reg, weights)\n", - "ot.toc()\n", - "\n", - "\n", - "ot.tic()\n", - "bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\n", - "ot.toc()\n", - "\n", - "pl.figure(2)\n", - "pl.clf()\n", - "pl.subplot(2, 1, 1)\n", - "for i in range(n_distributions):\n", - " pl.plot(x, A[:, i])\n", - "pl.title('Distributions')\n", - "\n", - "pl.subplot(2, 1, 2)\n", - "pl.plot(x, bary_l2, 'r', label='l2')\n", - "pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n", - "pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n", - "pl.legend()\n", - "pl.title('Barycenters')\n", - "pl.tight_layout()\n", - "\n", - "problems.append([A, [bary_l2, bary_wass, bary_wass2]])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Stair Data\n", - "----------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "a1 = 1.0 * (x > 10) * (x < 50)\n", - "a2 = 1.0 * (x > 60) * (x < 80)\n", - "\n", - "a1 /= a1.sum()\n", - "a2 /= a2.sum()\n", - "\n", - "# creating matrix A containing all distributions\n", - "A = np.vstack((a1, a2)).T\n", - "n_distributions = A.shape[1]\n", - "\n", - "# loss matrix + normalization\n", - "M = ot.utils.dist0(n)\n", - "M /= M.max()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1, figsize=(6.4, 3))\n", - "for i in range(n_distributions):\n", - " pl.plot(x, A[:, i])\n", - "pl.title('Distributions')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed time : 0.007988214492797852 s\n", - "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n", - "1.0 1.0 1.0 - 1.0 1700.336700337 \n", - "0.006776466288938 0.006776466288938 0.006776466288938 0.9932238515788 0.006776466288938 125.66492558 \n", - "0.004036918865472 0.004036918865472 0.004036918865472 0.4272973099325 0.004036918865472 12.347161701 \n", - "0.001219232687076 0.001219232687076 0.001219232687076 0.7496986855957 0.001219232687076 0.3243835647418 \n", - "0.0003837422984467 0.0003837422984467 0.0003837422984467 0.6926882608271 0.0003837422984467 0.1361719397498 \n", - "0.0001070128410194 0.0001070128410194 0.0001070128410194 0.7643889137854 0.0001070128410194 0.07581952832542 \n", - "0.0001001275033713 0.0001001275033714 0.0001001275033713 0.07058704838615 0.0001001275033713 0.07347394936346 \n", - "4.550897507807e-05 4.550897507807e-05 4.550897507807e-05 0.576117248486 4.550897507807e-05 0.05555077655034 \n", - "8.557124125834e-06 8.557124125853e-06 8.557124125835e-06 0.853592544106 8.557124125835e-06 0.0443981466023 \n", - "3.611995628666e-06 3.611995628643e-06 3.611995628672e-06 0.6002277331398 3.611995628673e-06 0.0428300776216 \n", - "7.590393750111e-07 7.590393750273e-07 7.590393750129e-07 0.8221486533655 7.590393750133e-07 0.04192322976247 \n", - "8.299929287077e-08 8.299929283415e-08 8.299929287126e-08 0.901746793884 8.299929287181e-08 0.04170825633295 \n", - "3.117560207452e-10 3.117560192413e-10 3.117560199213e-10 0.9970399692253 3.117560200234e-10 0.04168179329766 \n", - "1.559774508975e-14 1.559825507727e-14 1.559755309294e-14 0.9999499686993 1.559748033629e-14 0.04168169240444 \n", - "Optimization terminated successfully.\n", - " Current function value: 0.041682 \n", - " Iterations: 13\n", - "Elapsed time : 1.8278961181640625 s\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "alpha = 0.5 # 0<=alpha<=1\n", - "weights = np.array([1 - alpha, alpha])\n", - "\n", - "# l2bary\n", - "bary_l2 = A.dot(weights)\n", - "\n", - "# wasserstein\n", - "reg = 1e-3\n", - "ot.tic()\n", - "bary_wass = ot.bregman.barycenter(A, M, reg, weights)\n", - "ot.toc()\n", - "\n", - "\n", - "ot.tic()\n", - "bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\n", - "ot.toc()\n", - "\n", - "\n", - "problems.append([A, [bary_l2, bary_wass, bary_wass2]])\n", - "\n", - "pl.figure(2)\n", - "pl.clf()\n", - "pl.subplot(2, 1, 1)\n", - "for i in range(n_distributions):\n", - " pl.plot(x, A[:, i])\n", - "pl.title('Distributions')\n", - "\n", - "pl.subplot(2, 1, 2)\n", - "pl.plot(x, bary_l2, 'r', label='l2')\n", - "pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n", - "pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n", - "pl.legend()\n", - "pl.title('Barycenters')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Dirac Data\n", - "----------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "a1 = np.zeros(n)\n", - "a2 = np.zeros(n)\n", - "\n", - "a1[10] = .25\n", - "a1[20] = .5\n", - "a1[30] = .25\n", - "a2[80] = 1\n", - "\n", - "\n", - "a1 /= a1.sum()\n", - "a2 /= a2.sum()\n", - "\n", - "# creating matrix A containing all distributions\n", - "A = np.vstack((a1, a2)).T\n", - "n_distributions = A.shape[1]\n", - "\n", - "# loss matrix + normalization\n", - "M = ot.utils.dist0(n)\n", - "M /= M.max()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1, figsize=(6.4, 3))\n", - "for i in range(n_distributions):\n", - " pl.plot(x, A[:, i])\n", - "pl.title('Distributions')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed time : 0.0016779899597167969 s\n", - "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n", - "1.0 1.0 1.0 - 1.0 1700.336700337 \n", - "0.00677467552072 0.006774675520719 0.006774675520719 0.9932256422636 0.006774675520719 125.6956034741 \n", - "0.002048208707556 0.002048208707555 0.002048208707555 0.734309536815 0.002048208707555 5.213991622102 \n", - "0.0002697365474791 0.0002697365474791 0.0002697365474791 0.8839403501183 0.0002697365474791 0.5059383903908 \n", - "6.832109993919e-05 6.832109993918e-05 6.832109993918e-05 0.7601171075982 6.832109993918e-05 0.2339657807271 \n", - "2.437682932221e-05 2.437682932221e-05 2.437682932221e-05 0.6663448297463 2.437682932221e-05 0.1471256246325 \n", - "1.134983216308e-05 1.134983216308e-05 1.134983216308e-05 0.5553643816417 1.134983216308e-05 0.1181584941171 \n", - "3.342312725863e-06 3.34231272585e-06 3.342312725863e-06 0.7238133571629 3.342312725863e-06 0.1006387519746 \n", - "7.078561231536e-07 7.078561231537e-07 7.078561231535e-07 0.803314255252 7.078561231535e-07 0.09474734646268 \n", - "1.966870949422e-07 1.966870952674e-07 1.966870952717e-07 0.7525479180433 1.966870953014e-07 0.09354342735758 \n", - "4.199895266495e-10 4.199895367352e-10 4.19989526535e-10 0.9984019849265 4.199895265747e-10 0.09310367785861 \n", - "2.101053559204e-14 2.100331212975e-14 2.101054034304e-14 0.9999499736903 2.101053604307e-14 0.09310274466458 \n", - "Optimization terminated successfully.\n", - " Current function value: 0.093103 \n", - " Iterations: 11\n", - "Elapsed time : 1.8065369129180908 s\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "alpha = 0.5 # 0<=alpha<=1\n", - "weights = np.array([1 - alpha, alpha])\n", - "\n", - "# l2bary\n", - "bary_l2 = A.dot(weights)\n", - "\n", - "# wasserstein\n", - "reg = 1e-3\n", - "ot.tic()\n", - "bary_wass = ot.bregman.barycenter(A, M, reg, weights)\n", - "ot.toc()\n", - "\n", - "\n", - "ot.tic()\n", - "bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\n", - "ot.toc()\n", - "\n", - "\n", - "problems.append([A, [bary_l2, bary_wass, bary_wass2]])\n", - "\n", - "pl.figure(2)\n", - "pl.clf()\n", - "pl.subplot(2, 1, 1)\n", - "for i in range(n_distributions):\n", - " pl.plot(x, A[:, i])\n", - "pl.title('Distributions')\n", - "\n", - "pl.subplot(2, 1, 2)\n", - "pl.plot(x, bary_l2, 'r', label='l2')\n", - "pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n", - "pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n", - "pl.legend()\n", - "pl.title('Barycenters')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Final figure\n", - "------------\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "nbm = len(problems)\n", - "nbm2 = (nbm // 2)\n", - "\n", - "\n", - "pl.figure(2, (20, 6))\n", - "pl.clf()\n", - "\n", - "for i in range(nbm):\n", - "\n", - " A = problems[i][0]\n", - " bary_l2 = problems[i][1][0]\n", - " bary_wass = problems[i][1][1]\n", - " bary_wass2 = problems[i][1][2]\n", - "\n", - " pl.subplot(2, nbm, 1 + i)\n", - " for j in range(n_distributions):\n", - " pl.plot(x, A[:, j])\n", - " if i == nbm2:\n", - " pl.title('Distributions')\n", - " pl.xticks(())\n", - " pl.yticks(())\n", - "\n", - " pl.subplot(2, nbm, 1 + i + nbm)\n", - "\n", - " pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)')\n", - " pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n", - " pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n", - " if i == nbm - 1:\n", - " pl.legend()\n", - " if i == nbm2:\n", - " pl.title('Barycenters')\n", - "\n", - " pl.xticks(())\n", - " pl.yticks(())" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_compute_emd.ipynb b/notebooks/plot_compute_emd.ipynb deleted file mode 100644 index 5ee6641..0000000 --- a/notebooks/plot_compute_emd.ipynb +++ /dev/null @@ -1,247 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Plot multiple EMD\n", - "\n", - "\n", - "Shows how to compute multiple EMD and Sinkhorn with two differnt\n", - "ground metrics and plot their values for diffeent distributions.\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Remi Flamary \n", - "#\n", - "# License: MIT License\n", - "\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "from ot.datasets import make_1D_gauss as gauss" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 100 # nb bins\n", - "n_target = 50 # nb target distributions\n", - "\n", - "\n", - "# bin positions\n", - "x = np.arange(n, dtype=np.float64)\n", - "\n", - "lst_m = np.linspace(20, 90, n_target)\n", - "\n", - "# Gaussian distributions\n", - "a = gauss(n, m=20, s=5) # m= mean, s= std\n", - "\n", - "B = np.zeros((n, n_target))\n", - "\n", - "for i, m in enumerate(lst_m):\n", - " B[:, i] = gauss(n, m=m, s=5)\n", - "\n", - "# loss matrix and normalization\n", - "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean')\n", - "M /= M.max()\n", - "M2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean')\n", - "M2 /= M2.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n", - "---------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1)\n", - "pl.subplot(2, 1, 1)\n", - "pl.plot(x, a, 'b', label='Source distribution')\n", - "pl.title('Source distribution')\n", - "pl.subplot(2, 1, 2)\n", - "pl.plot(x, B, label='Target distributions')\n", - "pl.title('Target distributions')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute EMD for the different losses\n", - "------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "d_emd = ot.emd2(a, B, M) # direct computation of EMD\n", - "d_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2\n", - "\n", - "\n", - "pl.figure(2)\n", - "pl.plot(d_emd, label='Euclidean EMD')\n", - "pl.plot(d_emd2, label='Squared Euclidean EMD')\n", - "pl.title('EMD distances')\n", - "pl.legend()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute Sinkhorn for the different losses\n", - "-----------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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O/MiQIxwZcqRABHFb+XeE/tNE+DeHJyAe8oDHM96BqHPnzrzzzjs0aNCAjh07MnDgQNq2bcvNmzcZMWIEW7Zs4ZFHHsm0nK6tL774ggcffJDY2Fh8fHzo27cvlStX5vr16/j6+vLxxx8TFxdH27Zt0y2dO2zYMObOnUubNm147bXXMrzP6dOnU5TInTNnTprUhC1/f3/atm3LmjVrSEhIsAbi9JhMJhwdHTGbzYSEhNC4cWMAoqKimD59Ops3b6ZMmTJ88MEHzJo1i7fffptx48bx9ttvAzB48GA2bNjAE088AUB8fDz79u1j48aNTJ06lc2bN2fr++nbty/Dhg3j1VdfZf369Sxfvpwvv/wyw/43btyYjz76KMPzomj6/VQUnx+dzzWzM0/51mbi4y6UL1XC3t26b/k3oNtB2bJlOXjwIDt37mTr1q0MHDiQGTNm4O3tjbOzM/Xr1wfgmWeeSTHizsjs2bNZs2YNAGfPnuXkyZNUrlwZBwcH+vbtC2RcOjc6Opro6GjatGkDWALjTz/9lO59spty2bJlizUv7eDgQIUKFTJsu2PHDvz9/QHw9PTE09MTgL179xIaGkqrVq0AuH37trU2zdatW/nwww+5ceMGly9fxs3NzRrQkzfRaNKkifWTTHa+n8qVK1OpUiVWrFiB0WjE0THzCSsp+StsXY2N4/2NZlbsP0s5I6wY2ZzmdSunaFMQcuUZyb8BPZORdG5ycHCgXbt2tGvXDg8PD5YuXZpitJiabVlduFNad9u2bWzevJk9e/bg6OhIu3btrOdKlSplzZtnVDo3Ojr6vr8X213oc6Pkb6dOnfjmm5Tbx968eZMxY8Zw4MABatWqxZQpU1LcOzmd5eDgQHx8/D3de+DAgYwdOzZLe5xKyV+R7NfQC7z+20fEl/+Zckk/EiN2toOdBS9XnhHJods4fvw4J0+etL4OCgri4YcfxsXFhfDwcE6fPg2QIojVqVOHQ4cOAXDo0CH+/PNPAK5evUqlSpVwdHQkLCyMvXv3pnvPjErnVqxYkYoVK7Jrl2XW/V5K51atWhWz2UxiYqL1kwJYdioymUyA5RPB1atXM7xGmzZt+PrrrwHLZhshISEANG/enN27d3PqlGUH8+vXr3PixAlr8HZyciImJiZXJld79+7N66+/nmKzj/SEhIQwbdo0xo7NkU20RAEUGBTIpZhbjP/mMCOWHaBqQk+Wd9hZ4HPlGcm/I3Q7iImJYfz48URHR1O8eHEeeeQRFixYQKlSpViwYAHdu3fH0dGR1q1bc+3aNcCS0122bBlubm74+vrSoIFlmrxr167MmzcPo9FIw4YNad68ebr3TC6d6+/vz9WrV4mPj+ell17Czc2NxYsXM3z4cJRSdO7cOcN+p845Dx8+HH9/f2bMmEGPHj0wGAw0bdrUmiv/7LPPGDlyJIsWLcLBwQGTyWRNl6Q2evRohg0bhtFoxGg00qRJEwAMBgNLlixh0KBB3Lp1C4Dp06fToEEDRowYgbu7Ow899BA+Pj7Z/K+Q8feTrFy5ckyYMCHd9+7cuZNGjRpx48YNqlSpwuzZs2WFSxGltaVW+efr6xNzM55XOjXghbb1eKB44R3HSvnce7Bt2zZmzpwpa5xFGgXlZ7iwO381lklrjrJPDeeR6/P4sK8n9auWS9EmMCiwQI7MMyufW3h/VQkhihytNaM3vE/ntc3Yp4YDcKrMKPpsapmi/goU7Fx5RiTlcg+SJ02FEPlDYFAg3WsNYeLqI+w540nLeo8xo48n3Tf45vv6KzkpSwFdKdUV+AzLJtGfa63TLEFRSg0ApgAaCNZaP5WD/RRCiHQlJO3rOXu1MyWKFeP9Ph74+dRKscqrqLhrQFdKOQABQCcgAtivlFqntQ61aVMfeANopbW+opSqklsdFkKIZCcuXOP1VSFQFlrVc2J6b3eqVShtPV+Q15Tfi6zk0JsBp7TWZ7TWt4EVwJOp2owAArTWVwC01hdztptCCHFHXEIiQ76fRt9NLTlddhQA+9RwOq9tlm9rleeFrKRcagBnbV5HAL6p2jQAUErtxpKWmaK13pT6QkqpkcBIgNq1a99Lf4UQRVhgUCCtnZ7mtVXBhP3bmB6e3ZnS0y1f7etpTzm1yqU4UB9oBwwCFiqlKqZupLVeoLVuqrVuajDkv/34wPIEo23p1hkz7u2J1Tp16hAVFQVAy5Yt020zdOjQXK9qmFE54Oeff57Q0NBM35tR/2y/NyHyys24BEzBJnoF7uby9dssGNyEuU81xqlsSXt3Ld/Iygj9H6CWzeuaScdsRQB/aK3jgD+VUiewBPj9OdLLu8jJ9aSlS5fO0VK0QJpqhnkls3LAn3/+uV36FB8fT/HisrhKZM/+8MtMWBUCBujbuAZvdnelQuk7xbSKWq48I1kZoe8H6iulnJVSDwB+wLpUbdZiGZ2jlHLCkoI5k4P9zJQp2JTr97AdlR44cMC6bDEmJoZhw4bh4eGBp6cnq1evTvPe5DK2WmvGjRtHw4YN6dixIxcv3plqyKiE7sKFC/Hx8cHLy4u+ffty48YNwDJ69vf3p2XLltStWzfdkXRm5YBtN6ooW7Ysb775Jl5eXjRv3pwLFy6kudZbb73F0KFDSUhIACwVEBs3boyHhwdhYWEAXL58mV69euHp6Unz5s2tZQKmTJnC4MGDadWqFYMHD2bJkiX06dOHrl27Ur9+fV5//fXs/KcQRcj1W/H0XzGF4dvbEmkYD8BP15/h0W8bF+lceUbuGtC11vHAOOBnwAx8q7U+ppR6RynVM6nZz8AlpVQosBV4TWt9Kbc6nZtiY2NTpFySa29nZNq0aVSoUIEjR44QEhJC+/btM2y7Zs0ajh8/TmhoKMuWLbOO3OPi4hg/fjyrVq3i4MGDDB8+nDfffBOwVCfcv38/wcHBGI1GFi1aZL3e+fPn2bVrFxs2bGDixIlp7te5c2fOnj1LgwYNGDNmDNu3b0+3X9evX6d58+YEBwfTpk0bFi5cmOL8a6+9RmRkJIsXL7YWFXNycuLQoUOMHj3aWg9+8uTJNGrUiJCQEN577z2effZZ6zVCQ0PZvHmztQ5OUFAQK1eu5MiRI6xcuZKzZ88iRLLAoEB2noyk8yc7OBDsQ99KK9jrdxgofPVXclKWPvtqrTcCG1Mde9vmaw28nPQnTwQGBaYYmXss9QBSVk27F9lNuWzevJkVK1ZYX1eqVCnDtjt27GDQoEE4ODhQvXp1a/DPqIQuWApiTZo0iejoaGJiYlIUpOrVqxfFihXD1dU13VF1RuWAkze6SPbAAw9YN+xo0qQJv/76q/XctGnT8PX1TVMu2LYM7vfffw/Arl27rJ9Q2rdvz6VLl/jvv/8A6NmzJ6VL31lO1qFDB2vZXldXV/766y9q1bLN7Imi6uqNOEzBJq6ZH6auoQzfvtACnzoP2rtbBUKBTWaO8R5jDdweSz1yfYbbtkxubpSiTa+ELlhSK2vXrsXLy4slS5awbds267nkVEryNdKTXjng1AG9RIkS1ocwUpe19fHx4eDBg1y+fJkHH7zzP1V2y+CWKVMmxWvbvt9PKV1RuPxy7F8mrT0KNWF0u3q82KE+pUrc2aJRcuWZk1ouWVSnTh0OHjwIkCJP3qlTpxTbw125ciXDa7Rp04aVK1eSkJDA+fPn2bp1K5BxCV2w7KFZrVo14uLisl1CN6NywNnRtWtXJk6cSPfu3a0VJjPSunVrax+3bduGk5MT5cuXz9b9RNF0KeYWPb58i1cOdCK25v8A+OpCf3y+9pZceTYU2BG6rZz8rZ2cQ0/WtWtXZsyYweTJk3nuued46623UtRxmTRpEmPHjsXd3R0HBwcmT55sTUek1rt3b7Zs2YKrqyu1a9e2lqzNrIRucsrDYDDg6+t716BqK6NywNnVv39/rl27Rs+ePdm4cWOG7aZMmcLw4cPx9PTE0dGRpUuXZvteomgJOBxATdWLKeuOcf1WS8a3f5YX2tajyXIvWVd+D6R8rhA5SH6Gs+7fqzfptNaHa+YZeNeqyEf97pS4zYs0akGVWfncQjFCF0IUHFprVu4/y7sbzeAMk7obGdbKGYdid4ppSa783khAF0Lkmb8v3WDYD9O56LAenC3HPjvzJJ+dKTz7etqTBHQhRK4KDArkBc/RLPk9nJk/H8ehWFv+r9so/Hxq4fWlp6RWcpAEdCFErjIFm9j8uxeH/o7msYYG3u3tQfWKpe/+RpFtEtCFELkiLiGR+dtPA3Am6jqfDvTmSe/qKTaekFx5zpKALoTIcVN2zGL1n4utrxMefoW3QuCcSvkkt+TKc5Y8WJRKRuVm7S08PBx3d/d0j5cuXTpF/Zlly5bd0z2Si4idO3eOfv36pdvGtqhXbkhMTMTf3x93d3c8PDzw8fHhzz//BKBbt25ER0dn+v6M+pf8vYncExgUyM24BD7cFMaXm1woFfEJM5taykhI/ZW8UShG6JFz5mIYP+6+r5NZudnckpCQYC14da/q1auXoyV/q1evnut12jOycuVKzp07R0hICMWKFSMiIsJaNiCzh5pyk5T8zRpTsInVv7lyOvI6/ZrU5K3urlRwLMGruff7X6RSKEboUTaP3t+PzMrNbtq0CRcXFxo3boy/v7+1mNWUKVOs1QYB3N3dCQ8PByzFs5o0aYKbm1uKJzTLli3LK6+8gpeXF3v27MmwdO7Bgwfx8vLCy8srRXmBrLIdla5atcpaw+XChQv07t3beu3U9dptPw3Exsbi5+eH0Wikd+/exMbGWtv98ssvtGjRgsaNG9O/f39iYmIAeOedd/Dx8cHd3Z2RI0da68y0a9eOCRMm0KxZMxo0aMDOnTvT9Pn8+fNUq1aNYsUsP5o1a9a0FjxLLmEcHh6O0WhkxIgRuLm50blz5xT9AstIf+jQoUyaNMl6LL0SweHh4bRv3x5PT086dOjA33//DVhq6IwaNQpfX19ef/1161Ow7dq1o27dusyePTub/zUKrxu345m63lKq4mZcIkuHN2Nmfy8qOFrqlUuePO8UioCeUzIqN3vz5k1GjBjB+vXrOXjwIP/++2+WrvfFF19w8OBBDhw4wOzZs7l0yVJR+Pr16/j6+hIcHIyvr2+GpXOHDRvGnDlzCA4OzvQ+p0+fTpFySS9Q2vL396dt27YEBwdz6NAh3NzcMmxrMplwdHTEbDYzdepUaz2bqKgopk+fzubNmzl06BBNmzZl1qxZAIwbN479+/dz9OhRYmNj2bBhg/V68fHx7Nu3j08//ZSpU6emud+AAQNYv3493t7evPLKKxw+fDjdfp08eZKxY8dy7NgxKlasmKK+Tnx8PE8//TT169dn+vTpQMYlgsePH8+QIUMICQnh6aefxt/f33qdiIgIfv/9d+v3FRYWxs8//8y+ffuYOnUqcXFxmf47FwUTt8zE95tGrLrsB8B/1V5k3J72Un/FTgpsQI+cMxezixGzi+Ux6+SvI+fMvedrJpebXbBgAQaDgYEDB7JkyRLCwsJwdnamfv36KKV45plnsnS92bNnW0eEZ8+etRbKcnBwoG/fvkDK0rne3t5Mnz6diIgIoqOjiY6Opk2bNgAMHjw4w/skp1yS/7Ru3TrTfm3ZsoXRo0db+5JcxjY9O3bssH6/np6eeHp6ArB3715CQ0Np1aoV3t7eLF26lL/++guArVu34uvri4eHB1u2bLEWGoOUZXeTP8nYqlmzJsePH+f999+nWLFidOjQgd9++y1NO2dnZ2vNndTXeuGFF3B3d7f+YoS0JYKT2+/Zs4ennnoKsPwb79q1y/qe/v37p0iHde/enZIlS+Lk5ESVKlXSLVlcFAQGBfLfzTgmrg5hxS9GnCLnsKiNZfAjuXL7KrCJQcP4cda8udnFiDHMnCPXTa/crG2xrtRsy+rCndK627ZtY/PmzezZswdHR0fatWtnPVeqVClroMiodO7dJv+ywnZ5WG6U/O3UqZN1wwrb+4wZM4YDBw5Qq1YtpkyZkuLeWSm7W7JkSR5//HEef/xxqlatytq1a+nQoUOaNskcHBxSpFxatmzJ1q1beeWVVyhVqhSQeYngjEjJ3/SZgk0s3diAyGu3eKFtXf7XsYGlxO0Oe/dMFNgRem7IqNysi4sL4eHhnJZCkP0AACAASURBVD5tWVNrG8Tq1KnDoUOHADh06JB1RcbVq1epVKkSjo6OhIWFsXfv3nTvmVHp3IoVK1KxYkXriDG7pXMBqlatitlsJjExkTVr1liPd+jQAZPJsjlIQkICV69ezfAabdq04euvvwYsm20kbyvXvHlzdu/ezalTpwBLSuPEiRPW4O3k5ERMTEy2J1cPHTrEuXPnAEsePCQkJNslf5977jm6devGgAED7hp0W7Zsad2gZPny5Xf9dFOUXYq5hf83lhRYJccHWDu2FW88brTWK5dcuf0VioDuNHZsjlwnJiaGIUOG4OrqiqenJ6GhoUyZMoVSpUqxYMECunfvTuPGjalSpYr1PX379uXy5cu4ubkxd+5cGjRoAFjK7sbHx2M0Gpk4cSLNmzdP957JpXMnTJiAl5cX3t7e1knKxYsXM3bsWLy9vTPcwALS5tCTJ+xmzJhBjx49aNmypXUHJIDPPvuMrVu34uHhQZMmTQgNDc3w2qNHjyYmJgaj0cjbb79NkyZNADAYDCxZsoRBgwbh6elJixYtCAsLo2LFiowYMQJ3d3e6dOmCj49PFv/1LS5evMgTTzyBu7s7np6eFC9enHHjsr+C6eWXX6ZRo0YMHjw4xSeo1ObMmcPixYvx9PTkyy+/5LPPPsv2vQo7rTUvbvqAdqubsvW2ZVvBfyqN5enfWkuuPJ+R8rn3YNu2bcycOTPFZJ8QUHB+hrMiMCiQvnWH8+aao2w2X8CrZgU+7OdFv59bSv0VO8qsfG6WRuhKqa5KqeNKqVNKqTS7ESulhiqlIpVSQUl/nr/fTgsh7EdrjSnYRMdZ29l5MpL/6+bC6tEtafhQOXt3TWTirpOiSikHIADoBEQA+5VS67TWqT+nr9Ra3//TPQVA8qSpEIXR2cs3+L81R6A4GKuV54O+njg73Zkgllz5/cuphyFTy8oIvRlwSmt9Rmt9G1gBPJnjPUlirxSQEPeroP/sJiZqRq57l27rfQkqbvmQbS41kp4/NpdceQ7LqYchU8tKQK8BnLV5HZF0LLW+SqkQpdQqpVSt9C6klBqplDqglDoQGRmZ5nypUqW4dOlSgf8fQxQ9WmsuXbpkXSZZUCQH6tORMQyYv4dffvemccIiNj25D5B15bki9v6XJGckp9ahrwe+0VrfUkq9ACwF2qdupLVeACwAy6Ro6vM1a9YkIiKC9IK9EPldqVKlqFmzpr27kS2mYBNEd+bTzScpXcKBj/t70adxjRTPMIicETlnboqRefJDkU5jx+ZY+iUrAf0fwHbEXTPpmJXW+pLNy8+BD++lMyVKlMDZ2fle3iqEyKbQc/8B8OGm4zzu/hBTn3SjSrk7nzAkV56zDM1LYbhyESo5Yw64nmMPQ9rKSsplP1BfKeWslHoA8APW2TZQSlWzedkTyPmeCiFyxOxDc/FY6sHAX1sBUM44kV0JQ1l1+osU7STNcn+sZUjib8P6l+DHV6BeexiRtpRFTrnrCF1rHa+UGgf8DDgAX2itjyml3gEOaK3XAf5KqZ5APHAZGJprPRZCZFtgUCBjvMdw6O8rrN/mwbWLM+jTqAa/3hwsa8pzSVRAAIZhA+HbwfD3Hnj0ZWg/CYo55NjDkKllKYeutd4IbEx17G2br98A3sjZrgkhcoop2MSliHZ8sftPHipfisVDfXjMpQoeS+3ds0JuQTu4cQn6LgKPO5vG5MaSRSjAxbmEEFmz57RlimvRrj952rc2Ex93oVwpqVWeG9JMfC5IBCrhVOFfDB65f/989ei/ECLnfHJgDl8cW5Dm+Giv0ZIfzy1aw/YPYdt7mFdUx3hgO5Stcvf3ZcN9P/ovhCgYkteVbw27yLe/unI9bAZ+TisBWVOeW6yTn3GxsPo52PYeeA2yHMvhYH43knIRohAxBZs4ebwlaw7/Q4OqZQl8uiWNaldiheTKc01UQACGof1gxVPwzyHoOAVavYRTRO48DZoZCehCFAJaazYesWyNuD74HP4d6jP2sXqULC61yvPEwvYQewUGfgVGy85YuTXxmRkJ6EIUcB/9MZtlYQutr0s3nMDic1Dq6J1cuaRZclbayU8NVMSpfDgGO1ZPloAuRAEUGBTIaK/RrDoYwZc/NeRW/Ie83KkBAeG9ZF15HjCMG4uhUTxsnoJ5RTWM+7dBuar27pZMigpREJmCTQxZvJ/XVoXQ8KFybHqxNaPa1rN3twq1FE9+/jAONk8Gt96WY/kgmIOM0IUoUBITNV/98RcAB8Iv886Tbjzj+zDFilmKaUmuPPdEBQRgeO4pWDkY/toFbSdA24k4/Rt49zfnEVmHLkQB8e7uT1lxalGa47KuPG+YXYwYR5eE/87Bk3PBc4Bd+pHZOnQZoQuRjwUGBTLSYxSf7/qTZb82pGTxmbzVw5V3jnWTXHkeSDP5aboFVMap4kUMnvbrV0YkoAuRj5mCTWza5UlIxFW6uFVl2pPuVClfineO2btnRYNh/DgMLcrAxlcxf10F455NUOlhe3crQxLQhciHbscnMnfrKQD+uRJLwFON6ebxkHXjCcmV54HEBPhlEuwNhEc6AcfydTAHCehC5Dtvb5/FmvDF1te3a7/MxMPwV6KsK88LkXPmYhjxLKx+Hk7+DL6joPO7OF2ZZ++u3ZUEdCHsLLlWeeztBD7ZfIKvdjakSrlPea+POy/+0VFy5XksKiAAg8NyiDwO3T8GH8uG2fZ48jO7JKALYWemYBNNyg9kwuoQwi/dYFCz2rzRzYXypUrAH/buXRFz1rI5Nlf/gWdWWXYYKkDkwSIh7CjmVjwAAxfsJUFrvn7el/f7eFiCOZIrzyuRc+ZidjFi7jQEAPPSMpi7j73zMFEBISN0IewgMCgQU7DJ+rqccSLRQFDMaFpyJz8uufI8kJiIwSUSg985ePhRzB+cyZUNnPOCjNCFyCPJtcqjb9zm9IlWXDPPoOolywhQapXnvcg5c+HWNVj5NOz+FJoMhcFr7N2t+5KlgK6U6qqUOq6UOqWUmphJu75KKa2USvcpJiGKMlOwiZ+OnKfjrB2sDfqHcY89wo/+j9q7W0VWVEAALOoCJzZB1w+gx6dQ/IFc28A5L9w15aKUcgACgE5ABLBfKbVOax2aql054EVkGkeINCKv3QJg9PJDuFUvz9LhPrhVr2A5JnnyvPf3XsvfVyPg6VXwSAfrqYKwmiUjWRmhNwNOaa3PaK1vAyuAJ9NpNw34ALiZg/0TokALOByAx1IP2n9v+dBazjiRvyuMYfvF5dY2kmbJO9bJz87DgKTJzx7jCtzkZ0ayMilaAzhr8zoC8LVtoJRqDNTSWv+olHotB/snRIGTvK78n+hY9gf5cO14HZo8XIkTji/ImnJ7SojH8EiEZfKzbjvM750osJOfGbnvSVGlVDFgFvBKFtqOVEodUEodiIyMvN9bC5EvmYJNfLn3LzrP2s4fZy4z+QlXvn2hhb27VSRZR943LsPyvvCHCZqPgadX27djuSQrAf0foJbN65pJx5KVA9yBbUqpcKA5sC69iVGt9QKtdVOtdVODwXDvvRYinwqPug7AW2uP4l27Ir/8rw3DWjnjUExJrtwOogIC4KLZsufnX7/DkwHQ9X1wKF6gJz8zctd66Eqp4sAJoAOWQL4feEprnW69N6XUNuBVrXWmxc6lHrooTOYeDmB+SNpaH1Kr3L7MLkaMz/4HD5SxbOBcq5m9u3Tf7qseutY6Xik1DvgZcAC+0FofU0q9AxzQWq/L2e4KUbAc//cav/7uzbWzM+horMIfDJdcuR2lqWG+rDwATiX3YRhf8AN6ZrL0pKjWeiOwMdWxtzNo2+7+uyVE/jfnUACJlzszd+tJypUqwWd+3vT0qo7nMnv3rGgzjByCwbAHwjZgXlEd45FDUKK0vbuVJ+RJUSHuwZGIqyw4Mo9PNp+gq3s1fv1fG570roFSkiu3B+vkZ9QpWNgBjv8EXWdYjhWRYA5Sy0WIbLkZZylxu3DHGcq4wMJnm9LJNeWO75Izz3tRAQEYOj8C348AhxLw7FpwboPT2AR7dy1PySbRQmTR/vDLjNs4gxtlfkpzTiY/7SgxEbOrG0a/8/CQB/gth4q17d2rXCObRAtxHz49MJcr/7Rj2d6/qFGxK6ZOr/NofSc8lnrI5KcdpZn8XFENiMIpbl2Bfnz/fkhAFyITO09GsujYfGLCnBnSog6vdWlImZLyv01+YBjwGAa9CKL/tmzgbA6FpD1XiyqZFBUiHVdvxPHad8EMXmTZwea7F1owpadbimAuk595zzr5GfIdfN4Rbl+HIRssx4p4MAcZoQuRxs/H/mXClpkklP+ZckbLsWHb28L2lLlyyZnnvaiAAAz1/oZ986F2S+i/GMo9VCif+rwXEtCFwFJQa8AjzzF53TF+DDmPa7Un+bDjW7jXqCC58vziv/OWv/fNh+ZjodNUy4oWCnbJ25wkAV0UeVprTMEmFq57hOu3Eni1cwNeaFuPEg6SkcwP0k5+VocVa3AaW10CeSoS0EWRdv5qLJPWHAUFdZzK8GFfT+pXLZeijeTK817knLmWYJ2YiMEzFsOgf6FyfcxzrxW6krc5SYYgokjSWjP6x/fpvLYZ+9RwAE6VGUWfTS2te38mk1x53osKCLCUvP16AGydDu79YMQWe3cr35MRuihSAoMC6V5rCBNXH2HPGU9a1nuMGX086b7BV/Lk+c281nD9InSfBU2Hg1Iy+XkXEtBFkZGQaMmVz17tTIlixXi/jwd+PrVQstwtX0iTK1+QCDjhVDEWg4/lv5HkzDMnAV0UCScvXOP11SFQBlrWc+Ld3u5Uq3CnaJPkye3P8PzTKaskHv4dSleyd7cKFMmhi0ItLiGRId9Po8+mlpwqMwqA/Wo4ndc2S5Erlzy5fVgfFIo4APPawImfoct7lmMSzLNNRuiiUAoMCqSN4WleXxVC6PnGdPfsztSebjy2uqnkyvORqIAADE00bJ4C5avD8J+hZhOcxtqnaGBBJwFdFDo34xIwBZv4+HgdHizzAPMHN6GL20P27pZI7foly9+/TALjE9BzLpSuCEiu/F5JQBeFysG/LvP6qhBwgj6NajCpuysVHEtYz0uu3P7SfVCIgzhd/EoC+X2SeuiiULh+K55ha6djvrk6zTmpVZ6PJMTD9g9g50yo5Iw5MFYeFMqmzOqhZ2lSVCnVVSl1XCl1Sik1MZ3zo5RSR5RSQUqpXUop1/vttBBZERgUyK6TUXT5dAf7DvvQu+I37PE7DMCRIUc4MuSIBHM7s058Rv8NS7rBjg/B6yl4YYd9O1YI3TXlopRyAAKATkAEsF8ptU5rHWrT7Gut9byk9j2BWUDXXOivEFZXY+MwBZu4Zn4YZ6cyfPtCC5o5P2jvbolUogICMLSvAeteBDT0XQQe/QDkQaEclpUcejPglNb6DIBSagXwJGAN6Frr/2zalwFkilrkqs2hF3hz7RGoAaPa1uOljvUpVcLBel5y5fnE7euWv78bCjV9oO/nUKmO9bTkzHNWVgJ6DeCszesIwDd1I6XUWOBl4AGgfY70TohULsXcYuia6YQnrrX8ZALLL/Zn+ddSqzw/SX/i8x+cYjdIEM9FObbKRWsdAAQopZ4CJgFDUrdRSo0ERgLUrl14N3EVOS/gcAC1ivVmyrpjXLvZgvHtn2VU23o0We4l68rziTsVEhMweN3E8NRFKFsV8wItE595JCuTov8AtWxe10w6lpEVQK/0TmitF2itm2qtmxoMhqz3UhRpF/67ybyQefh/c5haDzqyYXxr/DvU54Hi8qBzfhIVEABX/oIl3WHLNDD2hNG77d2tIiUrI/T9QH2llDOWQO4HPGXbQClVX2t9Mulld+AkQtwnrTXfHjjL9B/N4AxvdjMy/FFnHIrdKaYlufJ8Zt6joDX0ng+eA6VCYh7L0jp0pVQ34FPAAfhCa/2uUuod4IDWep1S6jOgIxAHXAHGaa2PZXZNWYcuMnP28g2GrZ3OBYf1ac7JuvL8I3WuPJnT2LGSK88lma1Dz1IOXWu9EdiY6tjbNl+/eF89FALLmvJRnqNZuiecDzcdp5hqwxvdXuCpZrXx+tJTcuX5hDVXDhi6NsRwKxFuRGH+ugrGY0fAQR5AtxdJQop8wxRsov/8PUxdH0oz5wf55eW2PNP8YYoVk3rl+UlUQADcioH1L8HyfpaqiM//Zjkpwdyu5F9f2F1cQiILdpwB4NTFGGYN8KJ3oxopNp6QXHk+M6+VZQK05Xh4bBKUKCW58nxAarkIu5qycxarzyxOc1zy5PmL5Mrzj/vOoQuRkwKDAnnO/QXm/HaKr7a7UNHxE6Y96cbrhzpLnjwfSZEr79MCg1oGUcctuwkF74OS5ezcQ5Ga5NBFnjMFm+gxexdzt56ip3d1Nr/chsc9qtm7WyKVqIAAiL8Fm6fC5x3h1jV4OqmapQTzfElG6CLPxN5OYOYvxwGIuRXP4mE+PNawivW85MnzofltIdIM3s9Al3ehdEXJledjkkMXeeKNLTPZcHZpmuOSK89fJFee/0kOXdhFYFAgz7iM4P2NYXyzz0idyrOZ0deTETvbSa48n7DNkwMYejVLmSs//Lts1lyASA5d5BpTsInOs3awcv/fjGxTl59ebEPzupXt3S1hwzoavxUDP02ERZ0tJW+fXmU5LsG8QJERushxV67f5p0NlnL5FUqXYN7gJnjXqmg9L7nyfOb0Flj/omVHIZ8R0HEylCwnufICSHLoIsdorfnfLx/y279fpTknufL8I8M8+TM9MUz6wA49Etlx33uKCpGZwKBALv53kxe+PMjare44XzPxXRdL2VTZ1zN/sO7rCRjGjcX43TSMz8UBYJzrh/HIYQnmhYCkXMR90VpjCjYxb209bsUn8sbjLjz3qDPFHWSskJ9EBQRYJj+vhMOPr8CpzVC9MfCvJcUiCgUJ6OKenb18g/9bcwSKg8tD5ZnR14O6hrLW85Irz2d2z4Zt74MqBl0/gGYjcLplsnevRA6SHLrItsREzagf32fP5W/SnJNcef6RYa78+cEYXv0/O/RI5ATJoYv7FhgUCMCZyBgGLtjDL7u98I7/nJ96/gFIrjy/SJErf/4ZjDMfx+h3HgDj9zMwmkMlmBdiknIRWWIKNqGiu/DJ5hOUKl6Mj/p50q9JzRQlboX9RQUEYBg3FkK+hV/ehBuXwHcUrPgBXJ+0d/dELpOALu7KfP4/AD7YFEYXt6pMe9KdKuVLWc9LrjyfWfoEhO+EGk3hmdVQzQunUzXt3SuRBySHLjI0+9BcFh6Zn+a45Mnzlwxz5WPGYPAfb4ceidwktVxElgUGBTLGewxBZ6PZsN2Daxdm0LtRDTbfHCz1V/IRaw0WrTE8Vg3DbeDaOUv9lQM7oKzB3l0UdpClSVGlVFel1HGl1Cml1MR0zr+slApVSoUopX5TSj2c810VecEUbOLdH0PpE7ib/2Lj+WJoUz4Z6G3vbolUogIC4EKoJb2yarglgD/3q+WkBPMi664jdKWUAxAAdAIigP1KqXVa61CbZoeBplrrG0qp0cCHwMDc6LDIPXvPXAJg4c4/ecq3NhMfd6F8qRKA5Mnzldhoy9/zHoVS5aHHJ9B4CBRzkPorRVxWRujNgFNa6zNa69vACiDFdLnWeqvW+kbSy72AzMAUIJ8cmIPHUg9G7GwHQDnjRNb/9xRfhS20tpGcuX3YLkOMnD0bs4sRc6MWAJi/qYp5cWki99yAYg4AUrO8iMtKDr0GcNbmdQTgm0n754Cf0juhlBoJjASoXbt2FrsockNyrnzr8Yt896srMf/NYHgrZ769NFBy5fmI9ZH98F0YHvgWg985qNUc80d/Ywwz27t7Ip/J0UlRpdQzQFOgbXrntdYLgAVgWeWSk/cW2WMKNnHqeEu+P/wP9auUZfXoljSuXYlv024qJOzt22ch9AeoUAv6fQFufeAjV3v3SuRDWQno/wC1bF7XTDqWglKqI/Am0FZrfStnuidyw09HLE8Orgs+x/j2jzCu/SOULG75yC65cvtLvQzR/PZ+oDpOo5/D4N4XQHLlIl13XYeulCoOnAA6YAnk+4GntNbHbNo0AlYBXbXWJ7NyY1mHnvc++mM2y2zy4slkXbl9pdgGLiEeDi21FNG6HmlZhvjHZqhQw76dFPnGfdVy0VrHA+OAnwEz8K3W+phS6h2lVM+kZh8BZYHvlFJBSql1OdR3cZ8CgwLRWrP6YARfbXLh1skPGf3wGkDqr+QXUQEBoDUc3wSmlvDjy1C5PozYYmkgwVxkUZZy6FrrjcDGVMfetvm6Yw73S+QQU7CJPw41ZfuJSJo+XIkP+nlSz1AWk+TK85dlPeHPHfBgPRi4HFy6g1KSWhHZItUWC6nERM1Xe/8CYH/4ZaY84cq3L7SgXlK9csmV570USxDnzLUsQXQxAmB+/xTmFdWJZAgYe0BS0TNZhiiyQ2q5FELv/f4p35xclOa45Mrty+xitCw1vB4FO2bC/s+hWHHMX1XCGLQXSlWwdxdFASD10IuAwKBAEhI1C3acZunGhvDnTN5ytWTJJFeej2z/ED7zhn3zwfsp8D9kOS7BXOQACeiFhCnYRJ/A3by3MYw2DQxsfrktA3xq3f2NIsdlmlp5YTHmZeWILPUi9JwN5atLnlzkGKm2WMDdjk/EtO00AGevxDJnUCN6eFazbjwhufK8Z326MyEeQ6vyGOId4OpZyxLEX5dCrWYp2kueXOQUGaEXYJN3zKLJci8+/6cPAHG1X+b/grpgCr6z8a+kWewk5DsI8IF146GMAZ753nI8VTAXIifJCL0ASa6/cjMugU82n+DLHQ0xlPuEd3t58L/9HaUGix2lebpzgGVVr9PAoRhGfCpLEEWekIBegJiCTTSt4MeE1SH8GXUdP59avNHNSIXSJSzP74o8ZX3CMzERQ4faGIo/CBeOWlIrq6aDa28odudDsKRWRG6TlEsBcf1WPAAD5u8hPjGR5c/7MqOvpyWYI7lye4gKCIDQdTC/NXw7GOJvQp+k0grufVMEcyHygozQ87nAoMAUOfFyxolEA8Exo2nFnfy45MrzUGIihK23fP3tYKj8iCWQu/dN2mTion37J4osCej5UHKu/OqNOM6cbMU188PUM5ThotN4yZPbgTW1khBP5ORxRK3abj1nXlEduIETFzF4yiYTwr4koOdDpmATdYv34a0fjnL5+m3GPWYpcevztb17VjRFBQRgaOEIuz7FUPwvDP5u0PplzP3fkk0mRL4iSb58JirGUkp+1FcHMZQtyQ9jW/Fql4aUKuEgefJcZPswkNWtGNiTtHJlw/+gjBP4fQOjdoFHv7ztoBBZICP0fCLgcADzQuZZX5czTuQssCNyNO41LPlxyZPnHuvDQAAxF4l8eyxRG49az1tSK+dxunkGg4tlHCTLEEV+IwHdjpJz5eeiYzkY3IxrYXVoVLsip8qMkly5PVw6Db/PgaCvMZS/jWFqD2j5IuZOz6abWpFcuchvJKDbkSnYRIVb3Xl/YxgJiZq3ergytGUdvL+0d88KL9vdgdI8DNSqBwBOnXwwTP4MnB6xSx+FuFcS0O3kr0vXAXhzzVFa1qvMjD6e1K7sCMia8tyUos7KY9UxlK4BEfstDwPN9QPfUVCuaor3SGpFFBRSDz2PzT0cwHybXHkyqVWeN8wuRoxfjIE/5sPVs1DJGZqPwTzkE1mxIgqEzOqhywg9D524cI3Nv3tz7ewMOrhUYZ8aLrnyXJJpamV4IABOA4dgGP9J0sNAt+3STyFyUpaWLSqluiqljiulTimlJqZzvo1S6pBSKl4pJeu5UplzKIA5v52kx+xd/HXpOp/5efP5kHR/wYocEhUQAIkJcPwnDBV/w+h3DuNTkQAYt3yNMcyMYepsKCYPA4nC464BXSnlAAQAjwOuwCCllGuqZn8DQwF59CWVo/9cZcGReXz86wk6u1Xl15fb8qR3DZRSkivPAemuH79x2fL37EbwjR9cNMNjk+DlUMvx6o3yroNC5KGspFyaAae01mcAlFIrgCeB0OQGWuvwpHOJudDHAulmXAKf/XaSBTvO4NgQ5g9uQhe3h1K0kZz5/bNOcmpN5Hv/R9SXa63nzPPigOo4jRmFoe2LgExwisItKwG9BnDW5nUE4HsvN1NKjQRGAtSuXfteLlEgHAi/zNifZnDD8SccG1qOvXqgE68ekMnPXLH/cziwGEPcUQzPlgXPAZhf3SRrx0WRk6eTolrrBcACsKxyyct754VPD8wl+txjLN0TTvUKXQno8BptGhjwWOohk5/3KeUk5xyiAgKt58yDPwbAqXc/DG98CiXLwaub7NJPIewpKwH9H8B2t+GaSceEjV0no1h0bD4xYc4MaVGH17o0pExJWUSUU6ICAjAM94PgFRiKLcfgdw6Kl8b8VSWMm7+EGk0gaR9VkNSKKJqyssplP1BfKeWslHoA8APW5W63Co6rsXFMWBXCM4v+AODbF1owpadbimAuk59Zk+4EZ0I8nPjF8vXHLvDLm/CAI/T4BF49bjles2mKYA6SWhFFU5YeLFJKdQM+BRyAL7TW7yql3gEOaK3XKaV8gDVAJeAm8K/W2i2zaxaGB4t+Db3A6799RHz5n9Ock1x59pldjHfy3v8eJXLGW0RtCkvTzmns2BRrzCV4i6IksweL5EnRbAoMCmTgI88xZX0o64PP4fJQOT7q54VHzQqSK8+ijIKw2cWIcfE4CPoGLhyBYsWhfhfw8sPcZ4I8ySkE8qRojtFaYwo28fn6+ly7GcfLnRowqm09HiguZeWzw7ZUbeQnHxM1/3PrOfMwS9rFqVcfDG/NhDKVk85MyOtuClHgSEDPon+v3mTSWsvou9aDjnzUz5MGVculaCO58pQyTYeE/gBHvsMQ8wsGv1tQsTbmefEYd6yBKi5pmsskpxB3JymXu9BaM3bjDHZGpX0IVvLkmbPNiUd+9ilRpvlp2jh1boDhdi5oOQAAC5tJREFUtTehpg9mo6ukVYS4C0m53IPAoECeqD2Uid+HsPuUJ77O7figrydP/Nhc8uSpZDoSN6+H0B8wXN+Ewe8alK6EeXFpjD8GQp3W4HDnR1BG4ULcHxmhpyMhUeP9pSfxpz7CoZhi4uMuPNWsNsWKKZn4TEeKkfgnM4mavyhNG6eOdTG89DI4t8Hs5ikjcSHukYzQs+HUxWtMWH0EHMG37oO819uD6hVLW88X1Tz5XZcH7lsIYT9iuLYTg188lKmCeVFxjBtN8PCjMhIXIg/ICD1JXEIiI9a9x8H/VqY5J7nyVGvEExOJfH8SUV+uSdPO6XFXDC9PhBpNMLu6yUhciBwmI/RMBAYF0rbK07y+KoRj5xrRzeNxpvZ0p/33TYtkaiXTkfjR7+HkL3DyVwxxURgGFYMaTTHPjMC48wcwNEjRXEbiQuStIr2A+lZ8AqZgE0/O3c2F/24x75nGBD7dBEO5kvbuWq5L9zF7uLOzT0I8ke++gdnFiNnFCIC535uY39hOZHg96PM5vHYanv/V0j5VMAd5/F6IvFZkR+iH/r7C66tCoDL0alSDSd2NVHR8wHq+sOfKbR/uAUBruBJu+XrlYPhzO4a4qxj8FFRvhPn/27v3GKnqK4Dj38PC8lSQnQGRN4GWCwJLoTyqGIGqtDUuUR6rUokxoVYgtGlDFpoAhVJfLUJAsAYFteiiFnTTYJAiCZioPATK41ahSkXkMSsgy2NZF07/uHeXYWd22WUfs3PnfJLJzP3NnTu/E+4efjm/O/e34BjO+yu9+6b4q/yUsJG4MfVDytXQzxcV8+ja+ewvfDvmvSDWyiv8mf3H6+HLLUSWv0b+xv/F7BMaO4LwjHnQrPXVNXRjTMJYDR2vVp7ZYhw5a/bw1cmBPDL0AaaP6snQ3P6BqJWXl7ivWtHnmXnkr3ij9D13yD0AhDKLceYOhS7DcB9ZgOPuj7l7oY3Cjan/UiKhnyn8nmW7l1HgdqZrqDmrJw1hcLeMa3+wnqlowjKmhFJcBMf8/6hWT4CvPiZ8IUI4G2jaGndFE5yV06DrMAg70KBkOmVBTDIHq4cbkwwCPym60T3O3Qs2A/CrO7rx3rRhVyXz+lgrv+aEZVklZbM9b8N7OUQeHYR7az/cn04AwJ29A/elRkSKxsLkrTD9C2//IY9D295RydxG4sYks8DV0JfuWsoTmU9w8lwRE9fO49Cld2L2qS+18grr23Hq1W5PxyuHfHcYvtnl1b7X7Y3ZL/STloTHj4QOA3EfmBn3WHYfcWOSU0rV0JftXkZHGc2cvH2cKRzK5OG/5Ik7uzNgVb+E1cqvWd8uT3ERkWf/RP4rb5U2uU4vAEK9Cwj3PU94mgPt+uHmbMLZlAttekFao6iDzIx7aEvmxgRPoBL68TOFAEx9Yyd9O7Rk1ZjB9Lz5xjr7/utO3Je+J/KXP5O/Ire0qeTa71DvApzsAkhLx10Vwnl6JNySCe36Q9te0Mi/LUGOl9jLshKKMakjKRN6SVmlxPM7n+eFf79Qun2Dk8OXwAfHfk3Pm739qlorLy85V2liMpoqnD1OZNFz5P/9ypKsMYkbcHNvwZnV16tvt+0NbXpDRndY1Qey4tfXy0vcNhI3JnUkZULPX7IElnuJ+vDJ83z86UAKDjzFoC6tuXX7Y8xavj/mM2O3XIbM2GNVdVRdbtIumYs4uhtOHyay8i3y391a+nZ0qcTJLoAGDXFfb4Mz50cQ+gGEf+g9h3pA7iB4KPaeMhWNti1xG2MqldBFZBSwCG+R6OWq+lSZ9xsDrwIDgG+B8ap6qGa7esXYD5XLl5VXPzrEM+s/Q4B5Wb15eHBnPlsYf5K3ygm6hCoUnoZz38K5iNf2yd/gzDdQcJTIur3kf3iqdHd3eDbgJ+4JxdCqI+6SszgLR0PrblceLTvC630ge1XMV9po2xhzPa6Z0EUkDXgeuAv4GtgmInmqGj0Mfgw4pardRSQbeBoYX5MdXbprKct2LwPgTaDfa30BaN/1PlaM/gMdbmoW+6HLl6C4EIovetuRz6GoAIrOXXkAbPkrXDhNJG8H+f/6svTjJeWQpuGLXIhcub+LO3EhAKEBQnh4e8J3d4cb2uH+Ng9nzVNesm7VCZpleNd0L3Fg1JMx3bPEbYypSde8bFFEhgJzVPUef3sGgKo+GbXPen+fj0SkIXAMCGsFB6/qZYuRxUviXoedMUAQIH9H7FeVTcTXag/1V8K33QhNW+E+exjn6RHQPAzNQt5z8wzce6fibN/sJesGV1/GX97lhnaJoDGmplT3ssX2wOGo7a+BweXto6rFIvIdkAHkl+nIJGASQKdOnSrV+RLhqVMIT53Cgc2rKZ40h66zf0yT9HSQNGiQRnicgKThTt+As+h+aNgY0hpDw3RIa4z76GKct+ZBegtIb+4/WuAOy8LZsxMaNbn6C591IKucH/K0CMdtthG3MSaR6nRSVFVfBF4Eb4R+Pcfoccd4XObQ5MFX4+8w3YF75sd5YzH0GRP/M2WTOeUnZ5uYNMbUV5X56f8RoGPUdge/Le4+fsmlJd7kaK2oKKlWNRFXdVRtSdsYU19VpobeEPgcGImXuLcBD6nqvqh9JgN9VPVxf1L0flUdV9Fx69sSdMYYkwyqVUP3a+JTgPV4ly2+rKr7RGQusF1V84CXgNdE5CBwEsiuue4bY4ypjErV0FV1HbCuTNusqNeFwNia7ZoxxpiqCPztc40xJlVYQjfGmICwhG6MMQFhCd0YYwIiYSsWiUgEiF1qvnJClPkVaopI1bghdWO3uFNLZeLurKpxf66esIReHSKyvbzrMIMsVeOG1I3d4k4t1Y3bSi7GGBMQltCNMSYgkjWhv5joDiRIqsYNqRu7xZ1aqhV3UtbQjTHGxErWEboxxpgyLKEbY0xAJF1CF5FRIvKZiBwUkZxE96e2iMjLInJCRPZGtbUWkQ0icsB/vimRfawNItJRRDaJyH4R2Sci0/z2QMcuIk1EZKuI7Pbj/qPf3lVEPvHP99Uikp7ovtYGEUkTkZ0i8k9/O/Bxi8ghEdkjIrtEZLvfVq3zPKkSetSC1T8DegEPikivxPaq1qwERpVpywE2qmoPYKO/HTTFwO9UtRcwBJjs/xsHPfaLwAhV7QdkAqNEZAjeguvPqWp34BTeguxBNA2IXpA3VeIerqqZUdeeV+s8T6qEDgwCDqrqF6paBOQCWQnuU61Q1c1495aPlgW84r9+BRhdp52qA6p6VFU/9V8X4P2RtyfgsavnrL/ZyH8oMAJ4228PXNwAItIB+AWw3N8WUiDuclTrPE+2hB5vwer2CepLIrRV1aP+62NA20R2praJSBegP/AJKRC7X3bYBZwANgD/BU6rarG/S1DP94XAdOCyv51BasStwPsiskNEJvlt1TrP63SRaFNzVFVFJLDXnIpIC+AfwG9U9Yw3aPMENXZVvQRkikgrYC3QM8FdqnUici9wQlV3iMidie5PHbtdVY+ISBtgg4j8J/rN6znPk22EXpkFq4PsuIi0A/CfTyS4P7VCRBrhJfNVqrrGb06J2AFU9TSwCRgKtPLX9YVgnu+3AfeJyCG8EuoIYBHBjxtVPeI/n8D7D3wQ1TzPky2hbwN6+DPg6Xhrl+YluE91KQ+Y6L+eCLybwL7UCr9++hLgquqCqLcCHbuIhP2ROSLSFLgLb/5gEzDG3y1wcavqDFXtoKpd8P6eP1DVhwl43CLSXERuKHkN3A3spZrnedL9UlREfo5XcytZsHp+grtUK0TkDeBOvNtpHgdmA+8AbwKd8G49PE5Vy06cJjURuR3YAuzhSk11Jl4dPbCxi0hfvEmwNLyB1puqOldEuuGNXFsDO4EJqnoxcT2tPX7J5feqem/Q4/bjW+tvNgReV9X5IpJBNc7zpEvoxhhj4ku2kosxxphyWEI3xpiAsIRujDEBYQndGGMCwhK6McYEhCV0Y4wJCEvoxhgTEP8Hw1P6NtKayG0AAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "reg = 1e-2\n", - "d_sinkhorn = ot.sinkhorn2(a, B, M, reg)\n", - "d_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg)\n", - "\n", - "pl.figure(2)\n", - "pl.clf()\n", - "pl.plot(d_emd, label='Euclidean EMD')\n", - "pl.plot(d_emd2, label='Squared Euclidean EMD')\n", - "pl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn')\n", - "pl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn')\n", - "pl.title('EMD distances')\n", - "pl.legend()\n", - "\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_convolutional_barycenter.ipynb b/notebooks/plot_convolutional_barycenter.ipynb deleted file mode 100644 index 4dba332..0000000 --- a/notebooks/plot_convolutional_barycenter.ipynb +++ /dev/null @@ -1,178 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Convolutional Wasserstein Barycenter example\n", - "\n", - "\n", - "This example is designed to illustrate how the Convolutional Wasserstein Barycenter\n", - "function of POT works.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Nicolas Courty \n", - "#\n", - "# License: MIT License\n", - "\n", - "\n", - "import numpy as np\n", - "import pylab as pl\n", - "import ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Data preparation\n", - "----------------\n", - "\n", - "The four distributions are constructed from 4 simple images\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2]\n", - "f2 = 1 - pl.imread('../data/duck.png')[:, :, 2]\n", - "f3 = 1 - pl.imread('../data/heart.png')[:, :, 2]\n", - "f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2]\n", - "\n", - "A = []\n", - "f1 = f1 / np.sum(f1)\n", - "f2 = f2 / np.sum(f2)\n", - "f3 = f3 / np.sum(f3)\n", - "f4 = f4 / np.sum(f4)\n", - "A.append(f1)\n", - "A.append(f2)\n", - "A.append(f3)\n", - "A.append(f4)\n", - "A = np.array(A)\n", - "\n", - "nb_images = 5\n", - "\n", - "# those are the four corners coordinates that will be interpolated by bilinear\n", - "# interpolation\n", - "v1 = np.array((1, 0, 0, 0))\n", - "v2 = np.array((0, 1, 0, 0))\n", - "v3 = np.array((0, 0, 1, 0))\n", - "v4 = np.array((0, 0, 0, 1))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycenter computation and visualization\n", - "----------------------------------------\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(figsize=(10, 10))\n", - "pl.title('Convolutional Wasserstein Barycenters in POT')\n", - "cm = 'Blues'\n", - "# regularization parameter\n", - "reg = 0.004\n", - "for i in range(nb_images):\n", - " for j in range(nb_images):\n", - " pl.subplot(nb_images, nb_images, i * nb_images + j + 1)\n", - " tx = float(i) / (nb_images - 1)\n", - " ty = float(j) / (nb_images - 1)\n", - "\n", - " # weights are constructed by bilinear interpolation\n", - " tmp1 = (1 - tx) * v1 + tx * v2\n", - " tmp2 = (1 - tx) * v3 + tx * v4\n", - " weights = (1 - ty) * tmp1 + ty * tmp2\n", - "\n", - " if i == 0 and j == 0:\n", - " pl.imshow(f1, cmap=cm)\n", - " pl.axis('off')\n", - " elif i == 0 and j == (nb_images - 1):\n", - " pl.imshow(f3, cmap=cm)\n", - " pl.axis('off')\n", - " elif i == (nb_images - 1) and j == 0:\n", - " pl.imshow(f2, cmap=cm)\n", - " pl.axis('off')\n", - " elif i == (nb_images - 1) and j == (nb_images - 1):\n", - " pl.imshow(f4, cmap=cm)\n", - " pl.axis('off')\n", - " else:\n", - " # call to barycenter computation\n", - " pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm)\n", - " pl.axis('off')\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_fgw.ipynb b/notebooks/plot_fgw.ipynb deleted file mode 100644 index a4c7329..0000000 --- a/notebooks/plot_fgw.ipynb +++ /dev/null @@ -1,365 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Plot Fused-gromov-Wasserstein\n", - "\n", - "\n", - "This example illustrates the computation of FGW for 1D measures[18].\n", - "\n", - ".. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain\n", - " and Courty Nicolas\n", - " \"Optimal Transport for structured data with application on graphs\"\n", - " International Conference on Machine Learning (ICML). 2019.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Titouan Vayer \n", - "#\n", - "# License: MIT License\n", - "\n", - "import matplotlib.pyplot as pl\n", - "import numpy as np\n", - "import ot\n", - "from ot.gromov import gromov_wasserstein, fused_gromov_wasserstein" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "---------\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We create two 1D random measures\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 20 # number of points in the first distribution\n", - "n2 = 30 # number of points in the second distribution\n", - "sig = 1 # std of first distribution\n", - "sig2 = 0.1 # std of second distribution\n", - "\n", - "np.random.seed(0)\n", - "\n", - "phi = np.arange(n)[:, None]\n", - "xs = phi + sig * np.random.randn(n, 1)\n", - "ys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1)\n", - "\n", - "phi2 = np.arange(n2)[:, None]\n", - "xt = phi2 + sig * np.random.randn(n2, 1)\n", - "yt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1)\n", - "yt = yt[::-1, :]\n", - "\n", - "p = ot.unif(n)\n", - "q = ot.unif(n2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n", - "---------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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6eQenhmWz3MzuAV4A7iV1gIn0gpnxr3OuZvGrP2Bdy3o6kjEcpypUycjoCP51zj9QE6kudZgiIjKI5LXPu8ebmf0MONzdj+yprfZ5721N8zqe3baCRDLBQaP259DRBxEyrSkUERmOCrbPuxc2AQcU+J7DRv2ImdSPmFnqMEREZJArWLcuOJjkNKBXFdZERESkb/LZKnZCjnvsA3wCOAq4sQBxiYiISBb5DJs/SmobWDZG6uCSa/oVUT+809rE0jcf5uHNK0h4kiPq9uWvZ57GgaOmlyokERGRgssnef8HmZN3EtgKPOXujxckqj54butqrn3+x8SSCeLBlquH3n2Rx997hc/MPo8Lpmc9ilxERKSs5LNVbNDu325PxPji8z+hNdHR5XnHaU/G+MHrv+WoMfsxs3ZiiSIUEREpnCGxD+mBd58nmWNEP55MsPTNRwY8Dnfnha3rufftl2h8b13OA0dERET6qtBbxUripW3r9up1p0uQZMX2tQMawzNb3uTa5Xeytb2FkBlJnJpwBV896jxOnXzggL63iIgML1mTt5mt7OM93d2Lmq2qwxW7i6pnUxXu+QCQvnpp2zt88vGf05aIdXm+Jd7BPz69lO8d+2FOnLj/gL2/iIgML7mGzWuA6j781AxgvBmdOulwKnOczlUVquDsKfMG7P2/veJPeyXuTm2JOF9//g8UspJdPra0tXDfW6+zbP3rbG1vKUkMIiJSWFl73u5eNvurDh41g9kjp/LqjreIdTvcI4RRE6nkzMkDk7x3xtp4bsubOdtsatvJ2uYt7Dty/IDEkElrPMa/PHEPf3jzVSrCYQBiiQTn73sIXzvmLCrDQ2LGRERkWBoSC9bMjG8f+TccUVdPZShK1MKECVEdrmBazXh+2PB31EQqB+S9d8baCIfCOdtELMSOWOuAvH8m7s7HH7iVe958lY5kguZYB82xDtqTCX679mU++eDSko0EiIhI/w2Z7ldtpIrvzr2Stbs28eSWV4knkxxWN4vDRs/CzAbsfcdXjuixTUcywbSaMQMWQ3ePb1zHiqaNtCcTe11rS8R55r23eea9t5k3oWwGV0REJE3eydvMosA8YBqQsTvr7r/qZ1x9Nqt2ErNqJxXt/SrCEc7f53DuWvfcXkP2AGEzjh0/i/FVPSf5Qrll1XO0xDPPwUNqSP3WVc8reYuIlKm8kreZXQp8B8g2edu56LtkybsUPnfwfJ7YvIaNrTvoSOvtRizE6IpqvnLUeUWN5722XTmvey/aiIjI4NXrOW8zOxP4CbAF+GdSifq3wL8DDwaPbweuLHyYg9voimqWnnoVl+13PKOiVQDUhCu4eFYDd572KSZXjy5qPHPGTCSS4xzwilCYOXWqNiciUq6stwuXzOxPwFyg3t13mFkSuM7dvxpcvwr4HnBqb2qcNzQ0eGNjY98jH8QSniScI3kOtDd2NHHO72+kLRHPeL0yHOH+869kWm1xv1SIiEjvmdlyd2/IdC2fDDMP+I27p5/Xvfv17r4EeAIYtDXQi6WUiRtg31Fj+fvDTqQ6HN3rWnU4wheOPEWJW0SkjOUz510LbEh73A6M7NbmKeDy/gYl/fe3h57AAXUT+N8XHuWlpo0AHDFuKlcffhKnTtuvxNGJiEh/5JO8NwIT0h6/A3Qvgzoqz3vKAJo/fTbzp88mkUwdkBIODYlt/SIiw14+v81fpmuyfgw4w8yOBzCzOcCioJ0MIuFQSIlbRGQIyec3+j3AiWY2JXj8bVK7jh41sw3Ai6R63t8obIgiIiKSLp/kvQSYCTQBuPsKYAFwH9BMarvYue7+u0IHKSIiInv0en7a3TuAt7s99xhwdqGDEhERkezyKdIyaiADERERkd7JZ9h8g5n90szOtIE86UNERERyyid5vw18hNTCtbfM7JvBCnMREREpol4nb3c/ADgR+P+AGuBaYIWZPWlmf2tmxTvzUkREZBjLa/Ovu//F3a8CppDqhd9Lqt7594F3zOx2Mzu38GGKiIhIpz5V7nD3dne/1d3PAaYD1wArgQuBXxcwPhEREemm32W33H0TsBx4FoiROhpUREREBkif65Cb2f7AZcAlwD6kkvYbwM8KE5qIiIhkklfyNrPRwMWkkvZxpBJ2M/BT4CZ3f7jgEUqPVja9xy9WPMea7VuZOmIkHzn4CI6cOBnt6BMRGZp6nbzN7FbgPKAyeOoh4CbgDndvKXhk0iN35+uPP8QvX3qeWDJBwp2QGb9d9SqnzqjnewvOJaIDSUREhpx8frNfRGqv978D+7r7Ge7+cyXu0rn11Rf51cvP05aIk3AHIOlOazzOQ2+u4b+eerTEEYqIyEDIJ3mf7O6z3f3r7v7mgEUkveLu/F/jX2iNxzNeb43H+dmKZ2nLcl1ERMpXPkVaHhvIQCQ/77W28F5r7kEPw3h1y+YiRSQiIsWiCVEREZEyo+RdpsZX1zC+uiZnG8c5aNyEIkUkIiLFouRdpsyMqxtOoDqSecNAdSTCZYceRVWW6yIiQ9narVu58enl/PCJJ3nkjbUkg0W9Q4V+s5exRQcdyutbt/CLFc912SpWGQ5z2sx6/umYk0odoohIUbXFYnzud3/g4bVrcXfiSac6EmFkVSU3fuiDHDhhaIxGmpfo20hDQ4M3NjaW5L2HmlVbt/CLl55nzbYmpo4YyV8ffASHT5xc6rBERIruU3fdzSNr19GeYafNqMpK/vQ3H2d8bW3xA+sDM1vu7g2ZrqnnPQTsP2Yc1510eqnDEBEpqTeatmZN3ADt8Ti/ePZ5PnvSCUWOrPDynvM2s4iZzTezvzezL6Y9X2FmY001OUVEpASWrVpFMpnMer09keA3r7xSxIgGTl7J28zmA2tIneP9v8DX0y7PAzaTqn0uIiJSVO3xBPEcybuzzVDQ6+RtZnOB35Eaav8CcEv6dXf/C7AW+GAB4xMREemVQydPpKYimvW6AYdPHhrrgfLpeX8ZaAUa3P27wGsZ2jwNHFmIwERERPJx8qxZ1EQrsl6vikS44piM67/KTj7J+yTgLnd/J0ebN4Ep/QtJREQkf+FQiBs/9EFGVlZQEQ7vft5I1b749HHHMnfa1NIFWED5rDYfQWpOO5dqVPhFRERK5OBJE7n3Ex/nZ888y29feY2ORJwjpkzhymOOHjKJG/JL3m8Dh/TQ5kjgjb6HIyIi0j8TR4zg8+87mc+/7+RShzJg8ukl3wucbWbHZ7poZmcCJ5Ja1CYiIiIDJJ/k/R/AdmCZmX0DOAjAzM4KHt8BbAK+W/AoRUREZLdeD5u7+3ozOwu4Dfgi4KTWAfwh+HMtcKG76wBpERGRAZRXeVR3bzSzA4APAMcB40j1xp8gtRK9o/AhioiISLpeJ28zmwrEgp71HcGPiIjIoNQei3PPSyt56LU1AJx6YD1/dcgBVEbL/1iPfD7BW8DPgMsHKBYREZGCeP3d97jsx7fTHo/T0hED4JHX1/LtPz7MTz+xkNkTx5c4wv7JZ8HaNuDdgQpERESkEFo7Ylz246VsbWndnbgBWjpibG1p5bIfL6UtlvnksXKRT/J+EjhqoAIREREphHtWrMx5AEl7PME9KzJV+C4f+STvrwCnmNnHBygWERGRfrvv5VVdetzdtXTE+NPLq4oYUeHlM+d9BvAAcKOZfYrUISQbSW0ZS+fu/s0CxSciIpKXpOc+FhTIee53Ocgneaef3X1M8JOJA0reIiJSEqcdWM/Ta9+mNZa5910djXLagfVFjqqw8kneCwYsChERkQI574g5/Peyx2jNMnIeDYc474g5xQ2qwPKpsHb/QAYiIiJSCLWVFdx42YV84qd3Ek8kaA1WlldHI0TCYW687EJqK7Of+10Oyn+nuoiIFFVzaztPv/ImbR1xDpwxkfqp40od0l4OnTaZB/7xb7jr2Zd54LXVAJxx0P5ccOQcRlRVlji6/jP37uvNiqOhocEbGxtL8t4iIpJbIpnk6VXreXd7MxNHj+Do/adjGD+861FuXvYskXAIdyeRdOqnjuPbf3seU8aNKnXYQ4qZLXf3hkzX8imPGmPvleWZuLuX/9caEZFh6uGX1/Bvt/yJjlgCd8cMKqIR5k6dwtMvrKM9Fqc9bT75tbfe5bJv/IqlX72M0SOqSxf4MJLPsPmTZE7edcD+QCXwIrCjAHGJiEgJPPX6W3z+p7/fqwLZrvYY97+2hkjC9yoQkkw6u1o7uOOh5/nEuccVL9hhLJ8Faydlu2Zmo4D/AxqA8woQl4iIlMC3f/1Q9tKhBvEqqNi196X2WJxfP7pCybtI8qmwlpW77wD+hlTP/BuFuKeIiBTXxm07Wbd5a+5GIXDLfKmlLXtVMymsgiRvAHdPAA8CHyzUPUVEpHh2tXUQCYd7bJctec+cPKbAEUk2BUvegQpA//VERMrQ5LqRxBPZD/ToZBkqi1ZXRrnkrIwLo2UAFCx5m9ls4CJgdaHuKSIixVNbVcGCI2YTDWdODeGQEUkYYeva9a6uiHLaUftzypH7FSNMIb+tYtfnuMc+wPuCv19bgLhERKQErr3gNJ594x0279hFR9qxmhWRMBNG1fKtj/wVt97/LH9+djWxRIKZk8Zw+TnH8FfHzcEsy3i6FFyvi7SYZRoo6WIV8B13v6E391ORFhGRwWlnazs/f/gZbnvseba3tDG6popFJxzOJafMY2S1yngUS64iLfkk72zjIUlgq7tvyycoJW8RESk3bW0xHvjzK/zx3hdpbevg0IOnc+EF89hn+tiCv1dBKqy5u+ayRURk2Hr33R383ed+QXNzG63Btrg31r7HH+59gc986nTOf/9RRYul1wvWzOx6Mzu3hzbn5JgbFxERKUvuzj//21K2NDXvTtwAiUSSjo44P1zyAK++tqFo8eSz2vyTwNwe2hxFqliLiIjIkPHKaxvYsHE7yWTmqeaOjgQ33/ZE0eIZiH3ePW8SFBERKSMrVqwnHs+e3tydF15cX7R48k3eWVe3mVkUOBnY1K+IREREBplQONTjVrhQqHhb5XIuWDOzld2eutrMLsnQNAxMBGoAzXmLiJTYpg3b+ONvn+Od9U1Mmjyas88/iqkDsCJ6uDhm3r7c8OM/Z70eDoc48fjZRYunp9XmNezpbTsQBTId1poAVgL3A18pWHQiIpK3m65/kNt/8ReS7sRjCSKREHfe+iTnfGAun/7cWSqm0gczZozjsEOn88KLbxGL7T18Ho2EWLTw6KLFk3PY3N2nu/s+7r4PYMB/dT7u9jPL3ee5+zXunuGwOBERKYZ7f/ccd/zqCTo64sSDJBOPJ+loj3PPb57lzlueLHGE5esrX7qAA2ZPoqoqSuf3n6qqKNXVUb5+3YeYPq14Ixu93ucNLADWDFQgIiLSP+7Oz254iPYsR3O2t8W4+aZHuOCiYwhHCr1eeeirra3ke9/9GC+/8g4P/PkVWnZ1cPDBU5l/2sFUV1cUNZZ8irTcP5CBiIhI/2zetIPt21pytol1JHhz3Xvsu9/EIkU1tJgZhxw8jUMOnlbSOPLpeQO7V5XPA6YBGYvcuvuv+hmXiIjkKZFM9jifbQbJZE9HVchgl1fyNrNLge8A47M1IbWwTclbRKTIJk4aTWVlJOuwOaR+Qe8zI9uvcCkX+ZRHPRP4CbAF+GdSifq3wL8DDwaPbweuLHyYIiLSk3A4xKKPnUBlVTTj9crKCOd9qIGKyrwHXWWQyWfFwueBrcBx7v6d4Lln3P3r7j4f+DRwAfBKgWMUEZFeWvjREzj+5AO6rIgGqKqOcsS8WXz8qtNKF5wUTD5fv+YBd7v7jrTndid/d19iZh8FvgScU6D4REQkD6GQ8cWvXsiK59/k7qVPs+HtrUycNJrzLzqaI+fN0h7vISKf5F0LpB+Z0g6M7NbmKeDy/gYlIiJ9Z2YcduRMDjtyZqlDkQGSz7D5RmBC2uN3gAO7tRlFH1awi4iISO/lk2hfpmuyfgy4yMyOd/e/mNkcYFHQTkREeqm9I8b9j6/kz0+9TtKdE+fWc+bJc6ipKm7hDykf5p71oLCuDc3+HvhvYB9332Bmh5IaJq8E3iXVKw8B57v773q6X0NDgzc2NvY5cBGRoeCN9Vv4zHW30t4RpzXY4lVdGSUcCfF//3YRB9VPKnGEUipmtqkhRcoAACAASURBVNzdGzJdy2fYfAkwE2gCcPcVpEqm3gc0k9oudm5vEreIiEBHLM7ffeU2tu9o3Z24AVrbYzTvaucfvrqU5pb2EkYog1Wvk7e7d7j72+7envbcY+5+trvPdvcF7v6HgQlTRGToefCJ12lvj5Ft/DOeSPKHh14qakxSHlSZXkSkRB5tXEVLjmpobe0xHmlcVcSIpFz0pbb5IcBHgDlArbufHTw/A2gAHnD3bQWNUkRERHbLq+dtZl8Gngf+BfggqTnvTlFgKfCxgkUnIjKEnThvP6qzlDIFqKqMcNK8/YoYkZSLfGqbLwKuAx4g1cNenH7d3VcDy4HzCxifiMiQddpxB1BZkX0ANBwK8f7TDi1iRFIu8ul5Xw2sBs5z92eAtgxtXgZmFyIwEZGhrrIiwvf/fRGjR1ZTXbmnB15VGWVETSX/+28XMaIm48nLMszlM+d9OHBT+mrzDDYA2pQoItJL9fuM564fXsGyx17lwSdfx4MiLWefcgi11SrSIpnlk7wN6OkE9wmkap6LiEgvVVVGOff0wzj39MNKHUrJuTsrGt9gzSsbqKqp4NjT51A3dkSpwxp08kneq4Djs100sxBwEiqPKiIifbB25Uau+9RN7GjaRTyRJBwK8YOv/JqzFh7Np750PuGwdjd3yudf4jZgnpldneX6NaTmu2/ud1QiIjKsvLdxO5//yI/YtH4rrS0dxNrjtLWm/rzvzkb+39d/U+oQB5V8kvf/AC8C3zWzx4CzAMzsW8HjbwBPkyqjKiIi0mt33fQI7VkK1rS3xrj39qfZtqW5yFENXvmUR20BTiXVsz4WOI7UPPg1wd9vAc509+zlgkRERDJ48DfPEo8lsl4Ph0I8+YBmZTvlVWEtqJz2MTP7HHAMMA7YDjzp7hsHID4RERkGOtrjOa8nEknaWtU37JR3eVQAd98M/L7AsYiIyDA1Y/9JvPLsuqzXw5EQ+x44uYgRDW45h83N7FIzO7xYwYiIyPC06MpTqcqxr31UXQ2HHVNfxIgGt57mvG8CLkh/wswuM7MHBiwiEREZdo49fQ6nnnfkXgk8Eg1TM6KKf//RZZhZiaIbfPoybD4LOKXAcYiIyDBmZvzD1y7kmFMP4rbrH+LNVZuoqIxy6nlHcuEnTmbC5LpShzio9GnOW0REpNDMjOPnH8Lx8w8pdSiDnsrViIiIlBn1vEVEgKaNW3nolsfZ+u42ptRP5pRFx1M7qqbUYYlk1Jvk7QMehYhIibg7P/7Szdzx3d9hBh1tMapqK/nh1T/mH350BWdeemqpQxTZS2+S93Vmdl33J80sWykcd3f16EWkLNzxP7/jrv/9A7H2PQVA2nalDkf8v0/fwNjJY2g484hShSeSUW/mvC3PH82ji0hZiMfi/PJrd9Dekvkk4/bWDn7yr78qclQiPcvZQ3Z3JWIRGbJWLl9DMpnM2Wb18+vYtaNF898FlEgkWXbLX7j9+39i05tbqKyp4LSFx3DR35/FhKljSh1eWdDwtogMW7H2WI+FP0JhI96Ru+629F4ikeQrH/shLzy+kvaWDgBiHXHuuekRHrjtSf7rD19g5oFTSxzl4KeetYgMW/seNqPLXHcmI+pqGTl2RJEiGvr+9KvHeDEtcXeKxxO07GzlG5+4vkSRlRclbxEZtkaNHcmJHzyWaGU04/XKmgou+vz5hEL6VVkot3//Ptq6Je5O7vDu+iZWPf9mkaMqP/o/UkSGtat/dAXTD5hCVW1ll+erRlQxb8ERXPjZ95cosqFp07r3cl43M9a99k6RoilfmvMWkWGtdlQN33/qW/z51se5+wf3sO3dHUypn8SFn30/x75/rnrdBVZRHaW1OfPqfkgl75qRVUWMqDwpeYvIsFdRGWXBpaew4FKduTTQTrmggftu+QuJeOZV/p5MctQpBxc5qvKjr5QiIlI0F3/2r6jMcm53ZU0FH/7cOVTVZD/XW1KUvEVEpGgmzxzPd37zeabMGk9VbSVVNRVUj6iisrqCD3/2r1h09VmlDrEsaNhcRESKqv7Q6dz41Nd4tfEN3ly5gdpR1cw77WCqR2iuu7eUvEVEpOjMjDlH1zPn6PpSh1KWNGwuIiJSZpS8RUREyoy5l+a4bjPbDKwryZuLiIgMfjPdfUKmCyVL3iIiItI3GjYXEREpM0reIiIiZUbJW0REpMwoeYuIiJQZJW8REZEyo+QtIiJSZpS8RUREyoySt4iISJlR8hYRESkzSt4iIiJlRslbRESkzCh5i5Q5M1tsZl7gn62l/lwikp0OJhEpc2ZWD6wOHl7r7t/O47V1QAMwF1gAzE+7vMDdlxUs0D3vOTd4n23AGqDR3bcV+n1EhjIlb5EhwMwWAkuDh/Pc/Zk+3qcOWAxcCdzu7hcVKMTO+18Z/LUJuBhYCGxz9zGFfB+RoU7JW2SIMLOlpJLhGnffr5/3WggsdXcrSHB77rvE3a9Ke1wH1Pf1y4bIcKXkLTKEmNlqoJ4C9JrN7BpSXwRuL0hwqXsud/d5hbqfyHClBWsiQ0tnwl4Y9J77LJg7P7r/IXWxxswWF/ieIsOOet4iQ0zQY+5MkPu5+5pSxpPOzOYD9zFAi+FEhgslb5EhyMzuI7Wi+5nBNkwdDO2PBfbVKnORvtGwuUgaM6szs+VmtjXY73xllnaLgwVig9VFpLZizR1Mw9RBz3sxUAfcUOJwRMqWkrdIGnffFvRUvxk8tdfQbrBC+hpSe5QHpaBH2zn/fU2QNEvGzOaa2VJ3X+bu15P6d10Y7PkWkTwpeYtkdjSp/ceZEnRnb/zWIsaTt2BO+frg4dLgS0fRBQn6fuCKtKeXBH9ePJDvG4yiaG5QhhzNeYtkEJQHXZZpu1UwZ7vG3Rf04j5L6Fq1LB9L8qmWliOGzu1jy3oTc6EF79/lswRfJLYyAIVgur33lcBFpfjcIgMpUuoARAaboNxoHalV0d2vLSSVCHuVDNILkpTQAlLlU+eb2dxiFkQJkmc9e0YAgNSwvplBqtLaQFpAhv+OIuVOyVtkb5095S7z3UFv8QbgqsG0/aon7r7GzJaRGi0odiWzi0j1+LOtKl+d5flCmc+e9QsiQ4bmvEX2toDM8933k0pE12d4zaAV7PseW6JRgAZgry8MwegG6dfMbL6ZXdl9dbyZLe3Lwra096g3s4XBDoH6nC8SKRPqeYvsbT7QmP5EsC1sTb7zs6We8w5WmX8R2Lc/98lx/2tIFVzJNY2wJcNznTXYlwX3qQPq3P36YIHZtd3aXpHhHj3ZPYISDNOvIXV4y6Da9y7SF0reImm6z3cHSeV+4Na+JNJSznkHn2UpcMYAFkO5nm7z2d00ApkOSbmKPVXgABrc/fbgy0aX3jipUZBtac/VATf04ovUAlJHpHa+dhupo093y+NeIoOKVpuLpAkWWC0h9Yt/LqktY9eW0xx3p2CV9+JSDvMHXyCWk1ZNrXNY3N2vzdB+CbC684tS0LM/ui/JNdgxMK/zv12w2PAGHT8qQ4F63iJdzSNVfGUBfextDwbBMH+/5+fNrD7TF5cgEY4llRyzji4Ei+XOAG4ws6dJ9cJX5/h3XUTXYe0F7DmnPJ+4O4fh02NfQIaiOyLlSD1vkSEm6NnO729N86DXfG335ByMTiwLEvNWClSjPHi/1elniAfz3/M6V8mnbdW7PddoSHCv+9LPNQ9iPSPfe4kMRlptLjKEBHPEVwJnFOB2S9lTCS1dU5C460itDyiUJlLz0sDuLwmkJdv5pHrOmeau69NrzQfJOP1ei4Hre3MvkXKgnrfIEJE2v7y7d9nH+9SRStwNueaHgwS4JL1321/BHDekkuoCUkPfC7q1WRK8b/eFbUtJVVPrXMG+kNSahS2kFr3tNYWQ6V4i5UDJW2QICBLuclLD3Lf34x5XktpaVgd8O9OisrT2i0kl1wFZUR8ca3pf9/lxM1uebUrAzBbm8/lz3UtkMNOCNZGhoXPIeGy2Y0y7qQPGBX+OJTV03L2ASU8HrxS0eln6nHTwRaKBPSejpctYUjUYech37nqgy7OKDAglb5EyFyTrzoIkmeao+6I3pVTnUqDV20Gyvj7tPW8Arui+EK77PvBuFuazO6CHe4kMakreImUumMst6l7uoFzptu7Jta+CCmirgy8i+5Gah870xSDjQSNB8s83EevQEilbmvMWkbz1p3hKH99vYVCBrd9z1IW8l0ipqOctIn1xMcU9retiMxtL32qcD+S9REpCPW8R6VEwLP0GqepndcDSQm4RE5H8qOctIr21jNTCuP3QyVwiJaWet4iISJlReVQREZEyU7Jh8/Hjx/usWbNK9fYiIiKD2vLly99z9wmZrpUsec+aNYvGxsZSvb2IiMigZmbrsl3TsLmIiEiZ0WpzEREZMjy5HToagQREj8TCE0sd0oBQ8hYRkbLn3oHv+Cq03g0WDZ7swCtPxUZ/CwuNKG2ABaZhcxERKXu+7bPQ+hugHbw59UMHtD+EN12Ce7zUIRaUkreIiJQ1j70E7Y8CbRmudkDiDWh/qMhRDSwlbxERKWveehfQkaNBC97S0/H05UXJW0REyltyC5Dsoc3WooRSLEreIiJS3iIHAZU5GoQhemCxoikKJW8RESlrVv2hHlpEsZpLihJLsSh5i4hIWbPweBj1b0BVhqvVUHs5Fj2o2GENKO3zFhGRsheqWYSHZ+DN34PYcsAhchA24jNY1Zk5X9sRX8/WXXcQT2ygIlLPmNoPEQmPK07gfaTkLSIiQ4JVHodVHkfqqGvHLPfgsruzYdtXaGr+OU4SiGFUsWn7YibX/QvjR/5NUeLuCyVvEREpmWSyifbW35CIv004MpXK6g8QCo3t1z3NDLAe223e+SOadv0Sp333cx7sFd+4/VtEw9MYXXN2v2IZKHknbzM7DrgcOAoYDWwHngFucvcnChueiIgMVS3NS9i1YzGpRNsGVNG8/WvUjrqGmhGfGtD3do+xecf3cW/Ncr2VTdu/PTSSt5n9N/AP7P2VpgG4wsz+193/sVDBiYjI0NTWche7dnwH0nq9nRXSdu38Tyw0geqanlaR911rx0v0tDe8I/4G8cRWIuExAxZHX/V6tbmZ/S1wNbAOuAKYDYwM/rwSeBO42sw+PQBxiojIEOHu7Nr5TSBzrxdvpWXn4mDueoBiIE7PQ+uhoN3gk0/P+2+BjUCDuzelPb8aWG1mvwZeAD4D/KhwIYqIyFCSTLxJMtHUQ5smkol1hCOzBiSGquhBOLGcbcKhUURCg3PVeT77vPcDlnZL3Lu5+3vAHUE7ERGRjNw7elwJbhbCPUe98n4Kh0ZQV3MhlqUym1k140d+usc4SyWfqJroOjmRSRuwpe/hiIjIUBeOzOixjeO9atcfU+u+QnXFoYSstsvzIathVNUCxo/85IC+f3/kk7zvBs43s4xD7WYWBc4P2omIiGRkVklVzSVkrogGUEV1zccwy3a9MEKhauon3sH0sf9DbeVJVEYOYGTVAmaO/zH7jPvBoO11A1hvFwSY2WjgAVJbw/7Z3Z9Ku3Ys8C1SC9hOd/cdPd2voaHBGxsb+xS0iIiUN/d2tm/5a2KxF8Bb9lywGiLRw6gbdzNmuQ4bGfrMbLm7N2S6lnXBmpmtzPB0JXAk8Bczawc2AxPYc5zLeuBpYGgd3yIiIgVlVsnocbfS0fZHWnbdSDLxDqHwFGpqP0lF1dlkGeSVQK5/nRogU7f8nbS/h+g6xx0KXiciIpKTWYTK6nOprD631KGUnazJ292nFzMQERER6Z3BOxsvIiIiGSl5i4iIlBmtCBARkUEr6XHW73qE17bdSWtiC6OiM5hTt4iJ1UcFp4cNT0reIiJDyPb2FWxpeQQnyZiqoxlTdXTZJrl4so373v4M29rXEA9O/9re8QYbWp5gxojTOWHSv5XtZ+svJW8RkSGgI7GVZzdeRXNsFUlvB5ywVVMZnsjcyf8f1dFped1vc9sa3mp5ATBm1B7J+MqZAxJ3Lk+9+x2a2l8n2aVMqhP3NtY1P8D4qkM4sG7gTh4bzJS8RUTKnHuS5Rs+zq7Ymi6nYCW8hZb4mzy94a85cfofCYeqe7xXS3wbd62/js1ta/Bgt7ABk6sO5AP7fJnq8KgB+hRddSR2srb5vm6Je4+Et7Fi6085YPSFw7L3rQVrIiJlrqntL7TG12c5vjJJPNnMxl339HifpCe4Zd0/san1deLeTsI7SHgHce9gQ+sr3LbuWtxzn4FdKFvbXydk0Zxt2uJb6Uj2WNBzSFLyFhEpcxub/0givcRoNwlvYUPzb3q8z6qdf2FnfAvJDF8CEsTZ1rGBtbue6VesvWUW7rGNkyQ0TCux5Z28zewQM/u6md1hZn9Me36GmV1oZnWFDVFERHJJBou5crdp67HNS9uXEUtmv1fMW3l5+/15xdZX4yrn0NPZG3UV9URDtTnbDFV5JW8z+zLwPPAvwAeBBWmXo8BS4GMFi05ERHpUV9VA2LLPZxsVjKk6usf7dCSz997zaVMI4VAFh4z5KOEsJ4uFrZIjx3+qKLEMRr1O3ma2CLiO1MliDcDi9OvuvhpYTupYUBERKZIpI84jtawsMzNj+qiP9HifqdUHE84xzxyxCqZWH9yXEPvksLGXs9/IcwhbBaFgfXXYKglbBXPH/z3Ta08sWiyDTT6TBVcDq4Hz3L3dzM7L0OZl4JSCRCYiIr0SCdVy5KQf8dymT5P0GE4suBImZFEOGf8NqiNTe7zPkWPez/KmO3K2Oazu7AJE3DtmIY6ddA0Hj/0oa3bcQ0t8M6MrZlI/6hyqwsN7hjaf5H04cJO7t+doswGY1L+QREQkX2Orj+H4ab/hrR2/4N2WZThJxlYdz8zRlzOiYr9e3WNkdAJnTv4sf9r4vyQ8hpNaWW6ECFuUc6Z+gZrI6IH8GFnimsYR4z5Z9PcdzPJJ3gb0tEdgApAruYuIyACpjk7jgHHXcsC4a/t8j4PrzmBc5Qye3HIbb7Y8i2HMrJ3HseMWMaGqvoDRSn/kk7xXAcdnu2hmIeAkUkPnIiJSpiZVz+b86f9a6jAkh3xWm98GzDOzq7NcvwaYDdzc76hEREQkq3x63v8DLAK+G6w8NwAz+xZwMnAc8DSwpNBBioiIyB69Tt7u3mJmpwLfBz7Mnl77NYADtwCfdvdY5juIiIhIIeRVV87dtwEfM7PPAccA44DtwJPuvnEA4hMREZFu+lQU1t03A78vcCwiIiLSC8OzoruIiADwTusbrNv1KmYhDhh5JGMrVKqjHPQ6eZvZ9b1s6u5+VR/jERGRItgRa+Kna7/Fu23rcRzD+B03MXvEEXxk5mepCKVqijfHm1m26U88vPkhWhItjIqOZv7EBZw68XQqQhUl/hTDl/V0asvuhmY9FWhxUivQ3d17PMutoaHBGxsbe/XeIiJSOLFkO//12mfZEWsiSaLLtYhFmVFzAFfUX8eO+Ha+/vJX2BnfSdz3HBMatSiTqybzzwd9icpwZbHDHzbMbLm7N2S6ls8+79lZfo4G/pZUadRbgQP6Fa2IiAyo57c9Tkt8516JGyDuMd5qWcX61lX8dO1P2B7b3iVxA8Q8xoa2Ddz9zl3FClm6yWer2Oocl5eb2T3AC8C9pA4wERGRQaix6QE6cpzvHfMOntryAC/veIlklqrYcY/z8OaHuHDaQja0vccd6+/l6aYXSHiCfWv34UPTz6Jh7KED9RGGvbzO887F3dcBdwOfLdQ9RUSk8DqSrT20cLbFmojmOB4UIOEJnmp6ji88v5hHNjfSkmijPRnj1Z1r+M/XbuRna39duKCli4Il78AmNGwuIjKoTa+ZTYjsS5MqrJIpVftm7XV3SpLkB6t+SXuyY6+27ckO/rDhz7yyQwOxA6FgyTs4mOQ0YEeh7ikiIoV34vhzCFn25O3A6RPPpyZck/M+4yomkXTLer0jGePut+/va5iSQ6+Tt5mdkOXnfWb2UeA+4CjgNwMWrYiI9Nukqn04e/JHiVolwTEVAIQIEbVKPjbzn6iK1HDxPn9N1DJvB6sIVTC1ajZtyeynQDvOG7vWFzp8Ib8iLY+S+kKWjQGPk6p1LiIig9hJE97P9Jr9ePDdO3cXaZkzqoFTJnyASVX7ANAw9mg6kh3c/NYvcHeSJFMJPlTBFfVX8cqO9YQJk8iwar1TtbaSDYh8kvd/kDl5J4GtwFPu/nhBohIRkQE3q/YgLt/3X3K2OWH8iRwz9lhe2rGCHbHtjK+cwIEjDyJkIeqiE7j1zXtIeObkXRmqYP6kEwYi9GEvn61iXxrIQEREZHCKhCIcUXfkXs9PrZ7IMeMO5+mmF+hIdj1QMkSI2kg1p088bq/XtSdiPPTu89y/8Vk6knHmjp3NedOOZUzFyAH7DENNryusFZoqrImIlL9YMs7/W30zj2xuJGxhHAecadWT+eKcqxhfOaZL+w2tW/i7xh/QkminNZGaL68IRQkB1x12KceNn1P8DzFI5aqwpuQtIiL9tq1jB89ue4VYMs7skTPZt3b6Xm2SnuSjj3+LjW1bgyTfVVUoyk+O+wJTqscWI+RBL1fyzjpsbmYr+/h+7u4H9vG1IiJShuoqRnHaxGNztlne9DrbYrsyJm6AuCe5861H+cwB5w9EiENKrjnvGnKvLhcRkUFoW0cLS9c9wa/fWk5zvJ0ZteO5tP4kzph8MCErdG2u3lve9PruofJM4p7giS2v8BmUvHuSNXm7+95jHiIiMqhtaN3GJY/9P3bF22hPpg4UeWn7eq574U7+tOFFFh91cckSeMiyF3TZ3Yae20jhy6OKiEgJXfvMLWyL7dqduDu1Jjp4bPNK7n7rmRJFBkePPZDqcPYzwKMW4cQJOsykN5S8RUSGiDeaN/P6zo0ksyxEbkvEuGnNw0WOao8jx+zHhMo6wll6/pFQiAuma194b+RTpAUAM4sC84BpQMbSOe7+q37GJSIieVq5YyMRC9NOPGub9S1NuDvWiyHsQjMz/uuoq7j6mR+ytaN59/x3VagCM+M/jriciVV1RY+rHOWVvM3sUuA7wPhsTUgtclPyFhEpsupwlJ6mjCOhcEkSd6cJVaP52XHX8MSWV3hw0/N0JGPMHbM/Z05poDZSVbK4yk2vk7eZnQn8BHgN+E9gMalDSBqBU4HTgduBewsepYiI9Ojo8fUkPfsxniGMUyeWvghKJBTmpAmHcpLmt/ssn57350nVMD/O3XeY2WLgGXf/OvB1M7sK+B7wPwMQp4iI9KA6XMFl9Sdz05pHaEvE9rpeEY5w5ezTShBZYbzXtovfrXuZDS07mFY7mvNnHkJdZXXO1yTdue+dV7lx5eOs29XEyGgli2bN48P7zmNURfn29HtdYc3MtgB3u/sngsdJ4Kvufl1am4eBZnc/p6f7qcKaiEjhuTvff+0+frn2ccJmxJNJoqEwFaEI35n3EeaN3bfUIfbJD196jO+9+ChgtCfjVIUjOHDtkafx8QOPzviapDtXP7mURzetpiXty0xlKMLoiipuP+2TTKoeVZwP0Ad9qrCWQS2wIe1xO9C9ivxTwOX5hSciIoViZvz9QWdySf1JPLTpFZpjbcwcMZ4TJszOusp7sLt11XP8YMXjtCf3nF7WlkgtyvvOcw8ysWoE58zcezrgtjee4ZFNq2ntNgrRnoyzpb2Ff3zqTn55yscHNPaBkk/y3ghMSHv8DtC9DOqoPO8pIiIDoK6ihgv2mVfqMPot6c53X/jzXgm4U2sizneef5C/mnHQXgvxblj5WNbXJTzJi1vf4c3mJmaMKL9a6vl8DXuZrsn6MeAMMzsewMzmAIuCdiIiIv22ZscWdsU7crbZ2NrMxtadXZ5LeJK3W7blfF00FObV7Zv6HWMp5JO87wFONLMpweNvk9oW9qiZbQBeJNXz/kZhQxQRkeGqI5nosWRqGKMjkejyXAjrVRnYynB5Dhbnk7yXADOBJgB3XwEsAO4DmoEHgXPd/XeFDlJERIanfUeOJdHDwupwKMTU2q4Lz8yMUybtnzPtJz3JsRNm9T/IEuj1Vw537wDe7vbcY8DZhQ5KRET26EgkuHfdSm5/fQXNsQ6OmjCFSw+ey4yRQ78aWXUkykX1h3PrmudpT+xdOa4qHOHSA+YRDYX3uvYPB5/K45vX7F7c1uW+4ShXHngSVeHogMQ90PLZKjbK3XcU6o21VUxEpGfvte7iQ7//FZtbmtkVTy2+ioZChCzEV487gw8feESJIxx4bfEYlzxwM69s3dRly1dNJMrc8dO58dRFGZM3wOPvruGzT95O3JO0J+JEQ2GS7ly2/7H84yGnl7TaXE9ybRXLJ3nvAn4N/BS4z3v7wiyUvEVEenbBb3/Bi+9tJJ6hclpVOMKt53yEIydMyfDKoSWeTPKn9a9x02tPs7FlJ9NqR3P5gUdzxrTZhEO5Z4BjyQR/3vg6bzRvYXS0mgVTD2JMZU2RIu+7QiXvlcD+pBapbQB+DvzM3V/pS1BK3iIiub3atJkP/PbnGYd9IVXGfMGM2dww/4PFDWyY29XRwbod26iKRNh39JgB670XpEiLux8QbAv7OHARcC1wjZk1kuqN3+zuWwsQr4iIAE9sfItc3SsHntz4VrHCGfZ2dXTwtcce4tevvUIkFCLhScZUVfPFE97HebMPKmoseZXbcfe/uPtVwBTgI6QOIZkLfB94x8xuN7NzCx+miMjwEzLr6ZAwQoN3ynZIaYvHWXjnLdz12su0JeI0xzpojcd5p3knX3jgXn7+4nNFjadPtfLcvd3dbw1qmE8HrgFWAheSmhcXEZF+OmnqzJw977AZp0yvL1o8w9mdr73E2u1bae+2nxxSif0bj/+Z5o7cxWQKqd+Fbt19E7AceBaI0eNpsiIi0hv1o8dyzKRpVGRZSR0Nhfn0YccWOarh6aYXnqU1nnntAaSKwvxxzetFi6fPydvM9jezr5nZWmAZcCmwHvhqgWITERn2fnj6BcwZO4GaSHR3IRYTFwAAIABJREFUz6gqHKEqHOH/Tj2Pg8ZOyPl6KYz3WlpyXm9PxNm0q7lI0eR5iIiZjQYuBi4DjiPVy24mtWDtJnd/uOARiogMY6MqKrn7vEt4YuNb3LX6JXZ2dDB3wlQWHXAYoyvL9zzqcjOpdgRNba1Zr1dGIkwd0f2gzYHT6+RtZrcC5wGVwVMPATcBd7h77q8kIiLSZ2bG8VNmcPyUGaUOZdj6xBFz+fLD92cdOnd3zq6fXbR48hk2v4hUedR/B/Z19zPc/edK3NKdu/P2jh2saWqiPccckYhIufjAAXM4aNwEKsN7rz+oikT42vvmUx0tXqnVfIbNTw5qmYtkde/rr7P44UfY2NxMxAwHLj7sMD5/0olUFfF/bBGRQqoIh7n5gov4zyce4+aXX8CBRDLJPqNG88UT3scZs/Yrajy9rrBWaKqwNvTc8sILfO3Bh2jr1tuuDIeZM3ECN198MRUZvrWKiJST9kScDTt3UhWJMHkA57lzVVjr91YxEYDmjo6MiRugPZHgtffe4/evvVaCyETk/2/vvuOlKM8Fjv+e3T0NDr2KIGhUxAqCAlYQEVHARI3R2DWxJ95oTGJu9BKvphE1PfaWXLtGQVGTUERFQFpsKCC9CSjttD1bnvvHzMK6Z+s5Ww/P9/OZz+7OvDPz7Hv27LMz8877muyq8Pro17FTThN3Kpa8TVa8vnQZniT9+9YHgjy6YGEeIzLGmNbLkrfJik01u6gPBJKW+bwmf/dAGmNMa2bJ22RFz+rqlC0tu7Vtm6dojDGmdbPkbbJizEEHEUrS+LGqzMdlRx+dx4iMMab1suRtsqJdRQW3nnQSlb6mdx9WeL0c2LkL4w/pX4DIjDGm9cmoe1QAEfEBI4ABQLWq/tKdXw5UA9u0UPefmYK6eNBAOlVV8pu33uaLujpnvNtwmLMPO4xbTz6JijiJ3RhjTOYy7dv8VOARYF+cfs0V+KW7eDDwNnAh8HQWYzQlZNwhh3Bm//6s3r4dfyjEfh065LXXIWOM2RukfdpcRI4GXsFJ+LcQk6BV9V1gFfCNLMZnSpCI0K9TJ/p37WqJ2xhjciCTI+/bgXpgiKpuEJH/iVPmPWBQViIzOfX5rho+3byVCq+XQX16Wc9nxhhTQjJJ3icA/1DVDUnKrAHOaFlIJpe+rKvnJ5PfYPbKNVT4vCiAwrUnHMt3hg9BknS0YowxpjhkkryrgS0pylSxF7Zgr28MMG3JZ2zcvpNu7doy+rCDaFtRXuiwmqhrDPDNR55i485dBMNhGkOh3cv+9NYcdvkbuWnk8QWM0BhjTDoySd7rgcNSlBkIrGx+OC3TEAjy4twPefLtRXxRU0fn6jZ8+/iBnDPsCCrLctPSecriJUx8eRoi4A8GqfD5uGPKdG4ecyIXDhuYk3021wv/+YgtNbUEw+Emy+oDQR6du4DLhh5N5zZVBYjOGGNMujI5Sn4DOF1EhsdbKCKnAcfjNGrLuzp/Ixf+4SnuffUtVm/dTk1DI2u2bud3U9/mgt8/SW1DY9b3OWvpSv7n5X9THwhQ1xggFFbqGgM0BILc/cZbvLJ4Sdb32RJPLvhP3IFDIgTh9SVL8xiRMcaY5sgkef8C2AH8W0TuAg4BEJEx7usXgM+Be7IeZRp+O2UWq7ZsoyHw1eTUEAiyZut2fjtlVtb3effrbzXZX/R+7/7n2xTTLe876huSLvcHg2yrq89TNMYYY5or7eStquuAMTgJ+lbgPJx7vae6r7cAY1U11XXxrKtvDDBlwRIag6G4yxuDIV5ZuIQ6f/KBMzKxtaaW1V9sT1pmZ72fz7Z8mbV9ttR+nTomXV5VXkbfFGWMMcYUXkYXglV1vogcDJwFDAO64ByNz8FpiZ79c9Np2LhtJx5P8lbSXo+HDdt2cGDPrlnZZ0NjEK/HA6H4PxicfQr+BEfmhXD5sKP55OUt1CUY/UuA0YccmN+gjDHGZCzt5C0ivYCAe2T9gjsVharyMkKhpo2wogXDYarKs9dhSPf21XhSnLcIhEL07VI8R7Kj+x/IS/svYfbK1dTH/Kio9PmYdNZY68LUGGNKQCbXvNcCv8lVIC2xT6f27NOpfdIyPTu2o1eKMpko93k575gjqfDF79yk3OvljCP6U11ZkbV9tpRHhD+dO46bRhxP9+q2eD2CR4ShfXvz2IXncGr/rxU6RGOMMWnI5DBrO7A5V4G01C3jT+Kmv70a9zR1ZZmPH447MesdkHx/1HEsWr2BTzdt+cqRbFWZj96dO/DTcSOzur9s8Ho8XDr0aC45dhD1gSA+r8d6VzPGmBIj6baGFpGpgE9VT8vGjocMGaLz58/PxqZ2e2XBEv73xWkIEAiGKfN5UIX/PvsUJgw5NKv7imgMhpj6/ic8PnsRn+/cRZe2bbho+CDOGnRozu4tN8YY0/qJyAJVHRJ3WQbJeygwC7haVR9raVC5SN4A/kCQmR+vYPOOGrp3qGbEoQdQYUnUGGNMiUmWvDPJaqOA6cDDInINziAkm3CGBY2mkTG+C6GizMeYow4u1O6NMcaYnMsked8Z9fxYd4oneoxvY4wxxmRZJsl7dM6iMDm3dst2lm3YSpuKMo7+2r6U26UEY4wpWWl/g6vqtFwGYnJj07Zd3PrYa3y8ZhO+SKtyVa4eO4yLRw22IUCNMaYE2eFXmpau3cI/537Crno/A/r1YMyxh1BVkb1OX3JhR20DF016km019YTCij+wpze4v0x9l4ZAiKvGDi1ghMYYY5rDkncKDY0BfvyXV5j/6VoCgRBhVaoqyrj7qZn86tpxHH/E/oUOMaGn31zMzjo/oXDTOwoaGoM8/M+5XDBiIO2qiqcjGWOMMaml3cOaiAREpDGNyZ/LgPPtjkfeYP4na/A3Bgm7t9XV+wPU+wP8+C9TWL5ua4EjTOzF2R8kHKwFnA5b3vzgszxGZIwxJhsy6R51boLpUyCEcxS/BJiX5RgLZvO2Xcxc9NlXTjdHawyGeGxq8b7dmhRjmAdCIXbWtarfWsYYs1fIpMHaCYmWiUh74A/AEGB8FuIqCnM+Wo3X64EER6/hsPL2+yvyHFX69u3SgaXrE4/QWub10qdb8QycYowxJj2ZHHknpKo7gStx7vG+KxvbLAbBUJhUPdDFu56ciXR7uGuOS0cNTjqSWrnPx3ED+uZs/8YYY3Ijaw3WVDUkIjOAc4Hrs7XdQjrya/ukLHPY/j0z3u7ylZt59OnZzF7wGaFQmD69OnPROUM5feRhWb116/Qh/Zk6/xMWLF9HQ+OegVNEnJ7oJl15pjMmuTHGmJKS7W/ucqBTlrdZMAf27sYBvbrg9cRPqJXlPi4/M1FHc/HN/88qrv3J//HWvGUEg2FUYc36L7nn/n9z5++mZvVI3Ovx8Purz+L7E06gR8dqRASvRzj5iK/xxM3nc8zBfbK2L2OMMfmT9sAkKTckchAwB9ioqoenKp+rgUmybcu2Gi7/xVPsqG2g3h8AwOMRyn1eLj/zWK4cNyztbQUCISZc9mdqauM3EqusLGPizeM5/pjcjKsdCIbwejx4EvwYMcYYUzyyMjCJiDyQYJEP6AOc5D7/ccYRFrFunap5/s7LeH3eJ7w06wNq6xsZ0K8HF542mP77dc9oW2+/t5xwkmvkDQ0Bnn7pvZwl7zKfjdttjDGtQSbXvL+TYvlyYJKqPtSCeIpSZUUZXz/xCL5+4hEt2s7a9V9Sn+L2rTXrv2jRPowxxrR+mSTvgxLMDwPbVHV7FuJp1arbVlBe5sMf1XgsVts21tuZMcaY5NJusKaqnyWYVlriTs/Jww9O2iCtotzHhNOOymNExhhjSlEm3aM+ICLjUpQ5I8m18b1el07VTBgzkMqKpic8vF4P7aorGTf6yLzF428MMvf9Vcx8bxlrN23L236NMca0TKbXvNcBryQpMwins5arWhJUa/a9K0ZSVeHj2SkLnN7bcDqD6X9AD35+y3iq2+b+tLmq8uSr83nkxXd331ceCIbov38P7rjhTHp2bZ/zGIwxxjRftkcVK8fp59wk4PEIV118EhedO4xFHzgDnhy4f3f227dz3mJ47KU5PDF5Hg3+r157/2j5Rq687f948jeX0aFdVd7iMcYYk5lMk3fCC7YiUgacCHzeooj2Em2qyjn+2APzvt+aOj+PvzQPf6Bpo7lwWKmp8/P8vxZz5dnD8x6bMcaY9CRN3iKyNGbWjSJycZyiXqA70Aawa95F7O2Fn+HxCgTiL28MhJg8/X1L3sYYU8RSHXm3Yc/RtgJlQLzzqSFgKTAN+HnWojNZt7OmgWAonLRMbX3ye9GNMcYUVtLkraq9I89FJAzcrap35DwqkzN99ulEmddDIMEY5QC9unXIY0TGGGMylck179FA8Q5ebdJy7BF9qSgvo64h/nnzyooyLhx3TJ6jMsYYk4lMOmmZpqorcxlMa6OqfLxwFVOfnsP0yYuo2Vlf6JDwejzcdeN4Kit8xI4+WlnuY9CA3px6XP/CBGeMMSYtGd8q5rYqHwzsC8S9KVlVn2xhXCVv1dJN3HHdE2zbugsNKx6PEAqFmXDJ8Vx+8+l48jCOdjAYYsknG6lvaGT/vl3p1s25f3vQgN48+PNv8/ALs3lr4QrCoTDdu7TjwnHHcPboo2yMb2OMKXIZDQkqIpcAk4CuiYoAqqoph68qlSFBm2PLpu1ce+a91O5qaLKsoqqM8Rcdx5W3nNFkWX19I8s+3QTAQQf3pKpNebNjeGnyQh5+bBbhsCICjY0hBh7Zh5/cciadO1fvLqeqhMKKz2sJ2xhjikm2hgQ9DXgU+BT4LfBrYDIwHxgBnAI8D7zRwnhL3gsPzcJfH/+asr8+wOQn3uFbV4+kur3TcD8YDPHQX2fwyssLdyfRYDDM2PFHcdX1p1JWltlQnk89M4fH//4O/phOWBYuXsM133uCR+6/gurqSgBEBJ/Xxvc2xphSksnh1g+BbcAwVZ3kzluoqneq6qnAtcDXgSVZjrHkzJiyiGAwcWtur9fDvBlONakqd97+D155aQH+hgC1tX5qa/34/QFem7KYiT99PulgJrFqa/089remiRsgFAqzY0c9k19dnPmbMsYYUzQySd6DgcmqujPe+qp6PzAH+FmWYitZ/gQtuSNCYaWu1g/Ap0s2MH/eirjJ1u8P8v7i1Xz0/rq09/3Ou8vwJjmSbmwMMuUVS97GGFPKMknebYGNUa/9QLuYMvOAoS0NqtT13r9b0uUej9Dv4J4ATJ2ymMY4iTvC3xDglckL0973jh31BIPJO2GpqWl6Ld4YY0zpyCR5bwKis9IGIPaeovZkf7CTkvPN746gsipxY7OOXao5bHA/AL7YsivpaXFV+HJrTdr77tWrI2W+5NfIu3e3UcOMMaaUZZK8P+aryfodYJSIDAcQkQHAeW65vdpJZxzJsFGHNkngPp+HNtUV3P7nS3YPxdmnX1d8vsR/Bq9X6NO3S5P5oWCIhXM+Y/pr7/PR4jW7fwAMPeaA3UONxlNZWcZ55x7bnLdljDGmSGRylPwacK+I7KOqG4HfAOcCb4vIZpyjcg9wV/bDLC0iwo/uPp83X/0Pzz04k/WrtlJeUcaI8QM598qT6N6r0+6y4846mikvLgDin+r2+bxM+Mbgr8yb9a8P+cNdrxAKhogctLdrX8WPf3EOhw/qy89+Op7bf/6PJtfRKyp8HDagF6eecmhW368xxpj8Svs+bxEpx0nQW1XV7847HrgN+BqwCrhXVaems73WfJ93ph5/6E2ee3puk4ZulZVlTDh7CN+97pTd82bPWMKvbn0+bgO3isoy7n7kCg4a0ItPPt3Iw4/OYuHi1XhEqG5XybfOPZZzzx6CL8VpdWOMMYWX7D7vjDppySZL3l81c9rHPP7Qm2zcuB2AHj06cPEVJzLqtMN3n2JXVS4eew9bPt+ZcDuDhh7Ar+67dPfrQCBEIBCkqqp893aMMcYUv6x00mJya8SoQxkx6lBq3ZbgbdpWNEm2K5d9zq4U/aN/sGAV9XV+qto4PdeWlXkz7uTFGGNMcWtO3+aHARcAA4C2qnq6O38/YAgwXVW3ZzXKvUhbt+ezeGp3NSRtjAbg8Xqor2vcnbyNMca0PhklbxG5HbidPa3Uo8+5lwHPATcCf8pKdOYrevXpTGNj4p7bwOm9rX2HNnmKyBhjTCGkfauYiJwHTASm4xxh/zp6uap+BiwAJmQxPhOlS/f2HD5oPzwJjr7Lyr2MPXswPjtNbowxrVom93nfCHwGjFfVhUC8bro+Bg7KRmAmvpsmfp32HarwlX31T1de7mOf3p25+JqRBYrMGGNMvmSSvI8EXo/cJpbARqBHy0IyyXTv2YH7nr2OCd8aSttqp1Fbx85tueA7J/HHv19Fm7Z2rdsYY1q7TK55C4l6EtmjG06f5yaHOnWp5uqbT+fqm08vdCjGGGMKIJMj7+XA8EQLRcQDnIB1j2qMMcbkVCbJ+1lgsIjcmGD5j3Cudz/V4qiMMcYYk1Amp81/hzPwyD1uy3MBEJFfAScCw4D3gPuzHaQxxhhj9kg7eatqnYiMwLmH+3z2HLX/COd+76eBa1U1EH8LxhhjjMmGjDppcXtOu0hEfgAcC3QBdgBzVXVTDuIzxhhjTIxm9W2uqluAV7McizHGGGPSkLTBmohcIiJH5isYY4wxxqSW6sj7MZwuUd+PzBCRS4FLVfWUBOuYvczyD9Yy+dE3WbN0E517dODMi09g0En98XgyuZnBGGNMuppz2rwfcHKW4zAlSFW5//YXeP2p2QQaQ4RDTh8+i2Z9wkFH7cedf7+O8sqyAkdpjDGtjx0amWb793Nzef2pd/HXB3YnboCGukY+Xbiav972fAGjM8aY1suSt2kWVeXJ372Ov74x7vJGf4DpL75H7c76PEdmjDGtX7Nam5vMhcNhFk77iJf++i82rd5Cl16dOOvqUxk6diDeBEN8FrO6mga2rN+etIyvzMuy99cw8IT+eYrKGGP2Dukkb815FK1cKBjijgv/xH9mLaGh1hm3Zd2yTXw6fwUHHtWXX7z0w5K7Nuw0Rkv90Ug09rgxxpjmS+ebdaKIhCITcDtA9LyYKZjbkEvPM/e8yuKZH+9O3BENtX6WLlzJw7c/W6DImq+qbQV9Dkw++ms4FObggX3zFJExxuw90knekuFkh1pRQsEQL/7pn4mvDTcEeOOJt2iocxK7qvLJvGW8/Y+5fDJvGarFe+Ljkh+No6KqPO6yiqpyxl16IpUJlhtjjGm+pKfNVdUScQtt3bCNgD95d+8er7B26UZqt9Uw6fI/s+vLGsQjhENK+y7V3PLo9QwceXieIk7f8DFHcuFNY/nbb18FhUBjEI9XKCvzMXzMEVx264RCh2iMMa2SNVjLMV+ZN+XRs6qy6sO1/P6a+/DXffUIvaG2gZ+N/yW/euM2Dj/+kFyG2izfvO5URnx9MG889S5rlm2iS48OnHb+cPYf0KvQoRljTKslhTotO2TIEJ0/f35B9p1PqsoVA3/CxpWbE5bp2K0dnTpVsnzhyoRlDh5yAH+e9+tchGiMMaYIicgCVR0Sb5mdFs8xEeGy289OfG24TTlnXTOa1R+tS7qdVR+uZeuGL3MRojHGmBJjyTsPTj5nKJf87BuUV5TtviWsrMJHWUUZ37juNIaNPQpfefIrGL4yHzXbavMRrjHGmCJn17zz5Jzvnc6oC45j+jPvsnHFZrr16cIp5w2na69O1O6oJRhIfoddMBCkW+/OeYrWGGNMMbPknUcdu7bn7OvHNJnftkNbho8fwjv/mEcoGGqy3OvzMnzCMbTt0DYfYRpjjClydtq8SNzwhyvo2KNDk9PnvnIfHXt04PrfX16gyIwxxhQbS95FolOPjty/aBJnXT+GqnaViAhV1ZVMuG4M9y+aRKceHQsdojHGmCJht4oVIVUlGAjiK/MhIoUOxxhjTAEku1XMrnkXIRGhrLy0BioxxhiTP3ba3BhjjCkxlryNMcaYElOwa94isgVYXZCd519XYGuhgygCVg9WB2B1AFYHYHUAqeugr6p2i7egYMl7byIi8xM1OtibWD1YHYDVAVgdgNUBtKwO7LS5McYYU2IseRtjjDElxpJ3fjxQ6ACKhNWD1QFYHYDVAVgdQAvqwK55G2OMMSXGjryNMcaYEmPJ2xhjjCkxlryzSEROF5FPRWS5iPwkzvIKEXnGXT5XRPrlP8rcSqMObhKRj0XkfRGZJiJ9CxFnrqWqh6hy54iIikiru2UmnToQkfPcz8NHIvJkvmPMtTT+H/YTkRkissj9nzijEHHmiog8IiKbReTDBMtFRP7g1s/7InJ0vmPMhzTq4UL3/X8gIrNF5KiUG1VVm7IwAV7gM+AAoBz4D3BoTJnrgPvc5+cDzxQ67gLUwUigjfv82tZWB+nWg1uuHTALmAMMKXTcBfgsHAQsAjq5r7sXOu4C1MEDwLXu80OBVYWOO8t1cBJwNPBhguVnAK8BAgwD5hY65gLVw3FR/wdj06kHO/LOnmOB5aq6QlUbgaeBs2LKnAU87j5/HhglrWvYsJR1oKozVLXOfTkH6J3nGPMhnc8CwP8CvwYa8hlcnqRTB98F/qyq2wBUdXOeY8y1dOpAgfbu8w7AhjzGl3OqOgv4MkmRs4An1DEH6Cgi++QnuvxJVQ+qOjvyf0Ca34uWvLNnX2Bt1Ot17ry4ZVQ1COwAuuQluvxIpw6iXYnzq7u1SVkP7unBPqr6aj4Dy6N0PgsHAweLyDsiMkdETs9bdPmRTh1MBC4SkXXAVOB7+QmtaGT6nbE3SOt70YYENQUhIhcBQ4CTCx1LvomIB7gHuKzAoRSaD+fU+QicI41ZInKEqm4vaFT5dQHwmKreLSLDgb+JyOGqGi50YCb/RGQkTvI+IVVZO/LOnvVAn6jXvd15ccuIiA/nNNkXeYkuP9KpA0TkVOC/gQmq6s9TbPmUqh7aAYcDM0VkFc61vsmtrNFaOp+FdcBkVQ2o6kpgKU4yby3SqYMrgWcBVPVdoBJnsIq9RVrfGXsDETkSeAg4S1VT5gVL3tnzHnCQiOwvIuU4DdImx5SZDFzqPj8XmK5uC4VWImUdiMgg4H6cxN3arnFGJK0HVd2hql1VtZ+q9sO5xjVBVecXJtycSOf/4SWco25EpCvOafQV+Qwyx9KpgzXAKAARGYCTvLfkNcrCmgxc4rY6HwbsUNWNhQ4q30RkP+BF4GJVXZrOOnbaPEtUNSgiNwBv4LQyfURVPxKRO4D5qjoZeBjntNhynMYL5xcu4uxLsw4mAdXAc25bvTWqOqFgQedAmvXQqqVZB28Ap4nIx0AIuCWdI45SkWYd3Aw8KCI/wGm8dllr+kEvIk/h/EDr6l7X/x+gDEBV78O5zn8GsByoAy4vTKS5lUY93I7T/ukv7vdiUFOMNmbdoxpjjDElxk6bG2OMMSXGkrcxxhhTYix5G2OMMSXGkrcxxhhTYix5G2OMMSXGkrcxRcYdZUxFZETM/H5Ry/oVJDhjTFGw5G2MaRER6SgiE92pY6HjKQQRGej+qFqburQxLWedtBhTOgLAp1HPi0VHnE4nAB4D9qa+ySMio4W9XNAozF7DkrcxJUJV1wOHFDoOE5clb5NXdtrcGGNawO2XehDOEL8zCxuN2VtY8jZFS0T6iMhvRGSxiOwQkXoR+UxEXhaRS0SkMs46XhG5QkSmi8hWEfGLyHoReS62AViCfY5wy653190qItNE5HIR8SZYZ6J7vXOm+/ocEfmniGwWkbCITIwp30lEJrnvpUFENrr7HJwitoQN1ty4VUTUfX2giDwiImvd97FORB4UkbhjJYuIR0RGicgfxBlbe52INIrIFyLypohcIyJlcdabCayMmrUyKsbddRKzTrmIXCciM9z6bRSRTe7fdWyS918lIj8UkXdFZJuIBERki4h8LCKPi8g5yeovzvb+6sa4PVEDQBG51i0TFJGTEmwqctT9mqoG3L9vnbveeSli+F+33ApxO7U2Ji2qapNNRTcBFwP1OIM1KOAHtuJc643MGxizTgdgRtTyILANCEfNm5Rkn/dElQu76waj5k0D2sVZb6K7fCZwd9T6X7rrT4wq2w9YFfO+dkQ9nxC1bETMfvpFLesXs2xE1LKRwC73+c6YOlsP7BvnPURvW931t8fMmwVUxaz3Is4oWJEyW4BNUdOLMeX7Ah/G1HPsfv4aJ752wOI4f5/o97Yqw89YVVQsswFfzPLD2fMZ/HmS7fzbLfOtqHmPufP+nWQ9L86wqAr8tND/czaV1lTwAGyyKXYCzmRPwn0bZ2B6j7us3H39AHBozHrPRyXB7wFt3Pk9cUZ0i3zJXxNnnzdELb8f6OnObwv8V1SSeDrOuhOjEp4CvwK6ucsqgL7ucy/OMJGKk9i/GUkYwKFuctwWFceImP30i1rWL2bZiKhlX+Jcez0kqs7Ow0nkCjwR5z30Bv4OjAc6R82vBi7DSfoK3BNn3YRxxZRrCyxxy80ATgYq3GUdgB9E1eGNMev+zJ3/BXB21HoeoBfOj70HmvFZOxxnNCsF7oqaH53Y3wa8Cdbv6H42/ED7qPlD2fMj44AE6453ywQinzebbEp3KngANtkUPeE0olzhfqm9BZSnuV7ky1KBqxKUiST3LUBl1PwqNyko8GSCdb8Xtf3BMcsmRi27O0mM50WVGxVneRucoRFbmryn4/7YSfAe6og5ykyjfoe469ZE112quGLK3caeMxRlCcp8I+pv5IuaP9Wdf2sOPnPXuNsOASPdefe587YB+yVZ99tuuTfiLFvkLvtlgnWnuMtfyMX/kk2te7Jr3qbYjAT2d5//QFUb01zvW+7jOuChBGVucx+7AqOj5o8GOrvPJyZY9y/ARvf5txOUCQO/ThJjZPz2d1R1WuxCVa0DfpNk/XT9QlXDceZHWkJXAQdlskFVnQ9sxjl6HtjMuK50H+9R1US3ur2Ec4agKxDdBiBy+9k+zdwrcPsFAAAGq0lEQVR3QuqMp/wizlH830XkKuBqd/F3VXVNktWTtTL/q/t4WWx7AbftQeT6/v3NCtzs1Sx5m2JznPu4yU0Y6YoMXD8jQeJCVZfgnP6NLh/9fK2qLk2wbgjniDZ23WjLVXVzGjFOT1Im2bJ0zU0wf0PU886xC92GZNe4je02uA3ddjc+A7q7RXtnGpCbrPq6Lx92G6g1mXB+IFW75fpGbeIV9/EGEXlKRL4uIl0zjSOJ7wBrcE7BR5LpQ6r6fKIVRKQcJwErMDlOkSdxLgP0xDlFHu0KnMsoK4F/tShys1ey5G2KTU/3cXWG60USy/qkpZwj8+jyLV03WrLEne5+1iVZlhZV3ZVgfjDqZeyRYHdgPs7R4micI9wwTiPBz90p8qOobTPC6hX1vCvQI8kU+V5qExX7k8DvcRLl+cA/gC0iskxE/pyqpX4qqroNuD5q1grgxhSrnYLTkG6Bqjb5u6lqDU47AoCrIvNFxMOesxAPqqo2N26z97LkbYpNKX+RhQodQAvcCxyBc+3/CmAfVa1S1W6q2lNVe7LnyL05tzRF32Y3QFUljemx6A2o6n8B/YGfAq/hnEo/ELgOmC8iv2tGXNG+G/V8X3fbyaTTMUvk1PnoqNvRTsM5qxAEHs0sRGMclrxNsdnkPvZNWqqpyFFvqlO6keXRR8ktWTcTkfXi3mudxrKccK/Hnu2+vEFVH1XVTTFlvDhHzM0Vvb1M/7a7qepyVf2lqp4BdAGG41wnB7hRRCY0Z7sicgPObXoh4GOcuwSeFpE2CcqLWx6SJG9V/QDnNrToo+3Ij4SXY+vZmHRZ8jbFZrb72FNEEl1bjidyfXyke1qyCRE5hD3J8b046/YWkYMTrOvFaUwXu24mdseYpMwpzdx2S3QDIh3eLEpQ5oSoMrGi2xjEPSpX1VXsuVwQe/23WVQ1rKpzgHNxrlfDVxsipkVEjgAmuS/vAM7AOaofgHNGIp5jcC4FrHATdDKRo+8r3Gv/kff/QKaxGhNhydsUmxk41xsB7nUbBaXjafdxX5zGR/Hc4T5uxelYI+JfOKeLIXFr86vZc932qTRjivWM+3iCxOntTUSqgFuaue2WiNz/DXBU7EIR8QF3pVg/ItmoYg+6j1eKyKBkAYlI55jXFYnKuo0JI3clxG2smGQ/VTifnUqc+7nvUtXV7LlGfVWCntsy6cv8OZzPVy+cRmxlWEM100KWvE1Rcb+IIx2mnABME5ETIkfTbovoESLydxE5NGq9ecAL7ss/isgNkVOeItJTRB7E6RQF4DZVbYhat549SfsCEblPRHq467YRke8Dkeupz6jqgma+vReAhZHn4nSj6nX3MwDnOm63Zm672dyGVe+4L+8RkVOi6vtwnHushwC1Cdbfzp6j6svdZB/P3cAHOIlyhvs36hJZKM7QomNF5Amce/yjzRWn69YRItI2ap1eIvJH9lyfnprm2464F6eDnO3Ahe7nD1V9DqdjH4AHRaRPzHppJ29V9eP0uAYQ6WLVGqqZlin0jeY22RRvAi4BGtjT+UcD6XWPOjNqeQCnt7Hmdo/6Zcz+ppOie9Q03tcBOKd4o99XpHvQrHSPmmL/ibY9GKcDlui4IkfkAZwezFa5ry+Ls92fxay7xi3/dEy5XsC7MfW8jT1dxEamZTHrrYqzTk3MOk16f0tRF2dHrXtunOVt2NMj3CzcXtaAr7nztpKg57U42zow6nNoParZ1OLJjrxNUVLVJ3CGv/wdTgOiIE7nIqtxGihdjPPFGr3ODmAUTsOgmTj32FbjNJZ6Aaf3rISnpVX1Jpxrzi/g3BpV7W5jBk4L7NGa4DasDN7XCpxOTu7BOXUqOMnueeA4VY13v3DOqXM24VjgWZyk5MF578+6cf0txSZ+gXNr1Xyc5NQbp2Faz+hCqroB54zKBTj3Rm/ESZLlOAl6Ck53tLGDgJyPM2b4NJx6K8c5/bwa53LEKPfvlxb3SDrSmc/DGud+bnU6zbkA50fViTg/UGDPUfer6h6pp6Kqy3H6ZgdrqGayQFTtzI0xxqRLRGbhJPNzVPXFNNfpCazF6f53jKr+M4chmr2AHXkbY0ya3F7djsM5W/JGBqteg5O4l2MN1UwWJGpYYowxpqnOwJ04XenGbcAXy73l8Wb35T1qpztNFthpc2OMyQERWYXT2Uvkuv8iYKgmHpTFmLRZ8jbGmBxwB3MBp8Hk68BPVPXzAoZkWhFL3sYYY0yJsQZrxhhjTImx5G2MMcaUGEvexhhjTImx5G2MMcaUGEvexhhjTIn5f5EY+tTB7D3cAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.close(10)\n", - "pl.figure(10, (7, 7))\n", - "\n", - "pl.subplot(2, 1, 1)\n", - "\n", - "pl.scatter(ys, xs, c=phi, s=70)\n", - "pl.ylabel('Feature value a', fontsize=20)\n", - "pl.title('$\\mu=\\sum_i \\delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1)\n", - "pl.xticks(())\n", - "pl.yticks(())\n", - "pl.subplot(2, 1, 2)\n", - "pl.scatter(yt, xt, c=phi2, s=70)\n", - "pl.xlabel('coordinates x/y', fontsize=25)\n", - "pl.ylabel('Feature value b', fontsize=20)\n", - "pl.title('$\\\\nu=\\sum_j \\delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1)\n", - "pl.yticks(())\n", - "pl.tight_layout()\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create structure matrices and across-feature distance matrix\n", - "---------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "C1 = ot.dist(xs)\n", - "C2 = ot.dist(xt)\n", - "M = ot.dist(ys, yt)\n", - "w1 = ot.unif(C1.shape[0])\n", - "w2 = ot.unif(C2.shape[0])\n", - "Got = ot.emd([], [], M)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot matrices\n", - "---------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "cmap = 'Reds'\n", - "pl.close(10)\n", - "pl.figure(10, (5, 5))\n", - "fs = 15\n", - "l_x = [0, 5, 10, 15]\n", - "l_y = [0, 5, 10, 15, 20, 25]\n", - "gs = pl.GridSpec(5, 5)\n", - "\n", - "ax1 = pl.subplot(gs[3:, :2])\n", - "\n", - "pl.imshow(C1, cmap=cmap, interpolation='nearest')\n", - "pl.title(\"$C_1$\", fontsize=fs)\n", - "pl.xlabel(\"$k$\", fontsize=fs)\n", - "pl.ylabel(\"$i$\", fontsize=fs)\n", - "pl.xticks(l_x)\n", - "pl.yticks(l_x)\n", - "\n", - "ax2 = pl.subplot(gs[:3, 2:])\n", - "\n", - "pl.imshow(C2, cmap=cmap, interpolation='nearest')\n", - "pl.title(\"$C_2$\", fontsize=fs)\n", - "pl.ylabel(\"$l$\", fontsize=fs)\n", - "#pl.ylabel(\"$l$\",fontsize=fs)\n", - "pl.xticks(())\n", - "pl.yticks(l_y)\n", - "ax2.set_aspect('auto')\n", - "\n", - "ax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1)\n", - "pl.imshow(M, cmap=cmap, interpolation='nearest')\n", - "pl.yticks(l_x)\n", - "pl.xticks(l_y)\n", - "pl.ylabel(\"$i$\", fontsize=fs)\n", - "pl.title(\"$M_{AB}$\", fontsize=fs)\n", - "pl.xlabel(\"$j$\", fontsize=fs)\n", - "pl.tight_layout()\n", - "ax3.set_aspect('auto')\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute FGW/GW\n", - "---------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 0|4.734412e+01|0.000000e+00|0.000000e+00\n", - " 1|2.508254e+01|8.875326e-01|2.226158e+01\n", - " 2|2.189327e+01|1.456740e-01|3.189279e+00\n", - " 3|2.189327e+01|0.000000e+00|0.000000e+00\n", - "Elapsed time : 0.0014753341674804688 s\n", - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 0|4.683978e+04|0.000000e+00|0.000000e+00\n", - " 1|3.860061e+04|2.134468e-01|8.239175e+03\n", - " 2|2.182948e+04|7.682787e-01|1.677113e+04\n", - " 3|2.182948e+04|0.000000e+00|0.000000e+00\n" - ] - } - ], - "source": [ - "alpha = 1e-3\n", - "\n", - "ot.tic()\n", - "Gwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True)\n", - "ot.toc()\n", - "\n", - "#%reload_ext WGW\n", - "Gg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Visualize transport matrices\n", - "---------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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SG0q66+9jwBmZeV1E7A5cGxGXZ+b6rnZXZuZxLcQnSVNhTpM0LsxnkkaumCOqmXlXZl5XP34Q2ADs225UkjQ95jRJ48J8JqkNxRSqnSLiAOBw4OpJVv9URNwQEZ+OiENGGpgkTYM5TdK4MJ9JGpWSTv0FICKeAXwCOD0zH+hafR2wf2Y+FBHHAp8EDuzRzwpgBcCChQtnMGLNFSuXLmo7hKFqmgz65LNObexjkImc57ph5LQ28lnT30dJ//dnrO0++3B7w5i0XNLszWdSKZo+rwBWv/M9fdcP8vlb0mf0jijqiGpE7EKVAC/KzL/rXp+ZD2TmQ/Xjy4BdImLeZH1l5gWZuSQzl8yfN39G45akyQwrp5nPJLXNfCZp1IopVCMigAuBDZn5Zz3aPKduR0QcQRX/N0cXpSQNxpwmaVyYzyS1oaRTf18EvA74ckRcXy97G7AQIDPfB7wG+I2IeAx4BFiemdlGsJLUwJwmaVyYzySNXDGFamZ+DoiGNu8GxuOka0ljzZwmaVyYzyS1oZhTfyVJkiRJAgtVSZIkSVJhLFQlSZIkSUUp5hpVaSY1zQkJ4zPn1KBK+X0HmVPMeTBHr5S/j0Heu4PM+StJ0kxzjvrh8oiqJEmSJKkoFqqSJEmSpKJYqEqSJEmSimKhKkmSJEkqioWqJEmSJKkoFqqSJEmSpKJYqEqSJEmSimKhKkmSJEkqys5tB9AtIjYCDwLfAx7LzCVd6wM4DzgWeBg4MTOvG3Wcml1KmTy5aSJoKCfWYTlj7fq+61ctO3hEkYzeXM9nZ166obHNheec33f9uL0fpNlqruczaRCj+Mwa1liy6TN65dJFA8c0U4orVGsvzcxtPda9Cjiw/nkh8N76X0kqkflM0rgwn0kamdl46u/xwIey8gVgz4h4bttBSdI0mM8kjQvzmaShKrFQTeAzEXFtRKyYZP2+wKaO55vrZZJUGvOZpHFhPpM0UiWe+ntkZm6JiGcDl0fEzZl5xVQ7qZPoCoAFCxcOO0ZJGoT5TNK4MJ9JGqnijqhm5pb6363AJcARXU22AAs6nu9XL+vu54LMXJKZS+bPmz9T4UpST+YzSePCfCZp1IoqVCNit4jYfeIxcDRwU1eztcDro/KTwP2ZedeIQ5WkvsxnksaF+UxSG0o79Xdv4JLqDufsDHwkM/8xIn4dIDPfB1xGdevzW6luf/6rLcUqSf2YzySNC/OZpJErqlDNzNuBwyZZ/r6OxwmcOsq4JGmqzGeSxoX5TFIbiipUpXE3jAmYAS485/wd3s6orFp2cNshqCWDTBa+cmkZf6vj9r6TJD1hrxec1nf9yWc1f8cyyGfaKAzrs6aU36efoq5RlSRJkiTJQlWSJEmSVBQLVUmSJElSUSxUJUmSJElFsVCVJEmSJBXFQlWSJEmSVBQLVUmSJElSUZxHVSrMMOaebJovDJrn4Tpj7frGPpwjVbPBIO+Hk95+SmMb50mVpNlpGGOeYYytNDUeUZUkSZIkFcVCVZIkSZJUFAtVSZIkSVJRLFQlSZIkSUUpplCNiIMi4vqOnwci4vSuNkdFxP0dbc5qK15J6secJmlcmM8ktaGYu/5m5leBxQARsROwBbhkkqZXZuZxo4xNkqbKnCZpXJjPJLWhmCOqXV4O3JaZd7QdiCQNgTlN0rgwn0kaiVIL1eXAxT3W/VRE3BARn46IQ0YZlCRNkzlN0rgwn0kaicjMtmPYTkQ8FbgTOCQz7+5a90zg8cx8KCKOBc7LzAN79LMCWAGwYOHCn/jabX7xp5nXNBm0E0E/2ZmXbmhss3Lpoh3axoteuIRrr10XO9TJNA0jp5nPZodBJoM/6e2n9F2/atnBwwpHY2zXXeLazFwy6u2az9SGM9au77t+9Tvf09iH469y9ctnJR5RfRVwXXcCBMjMBzLzofrxZcAuETFvsk4y84LMXJKZS+bPmz+zEUtSbzuc08xnkgphPpM0MiUWqifQ45SSiHhORET9+Aiq+L85wtgkaarMaZLGhflM0sgUc9dfgIjYDXgl8MaOZb8OkJnvA14D/EZEPAY8AizP0s5dlqSaOU3SuDCfSRq1ogrVzPw28INdy97X8fjdgCeZS5oVzGmSxoX5TNKolXjqryRJkiRpDrNQlSRJkiQVxUJVkiRJklSUoq5RlUrWNI8XOE/XdOzoHKnQPH/ld776jR3ehtRkkPd/07zBg8zFap6RNC4GyXknn3Vq3/XmxPHlEVVJkiRJUlEsVCVJkiRJRbFQlSRJkiQVxUJVkiRJklQUC1VJkiRJUlEsVCVJkiRJRelbqEaEhawkSVJhHKNJGndNSe6bEXEwQEScHBEviIhdRxCXJEmSenOMJmms7dyw/neAB+rH7wJ+AHg8Im4HbgS+XP97Y2bePuhGI2I1cBywNTMPrZc9C/gYcACwEXhtZn5rkte+Afj9+un/zcwPNm1v0/2Pcsba9X3brFp28KDhT5sTuc9uo/gb0fQ0vW9e9MIvzNi2R53PNLutXLqoYX3zZ8AgnyUnn3XqDsWhWWHoYzTzmaaiaWy9+p3vaezDca/66XtENTP/KjM310+fCfwo8AvARfWyXwI+DtwaEQ9M0kUva4Bjupa9BfiXzDwQ+Jf6+XbqZPkO4IXAEcA7ImKvKWxXkoZtDeYzSSM2Q2O0NZjPJBWi6Yjq92VmArfVP5+cWB4RTwcOrX8G7euKiDiga/HxwFH14w8CnwXe3NXmZ4DLM/PeetuXUyXUiwfdtiQNk/lMUtuGNUYzn0kqycCFai+Z+Siwrv7ZEXtn5l314/8E9p6kzb7Apo7nm+tlklQS85mk1g1pjGY+k9SKIu8YV38zmDvSR0SsiIh1EbHukfvvHVJkkjQ1w85n92y7Z0iRSdLUmM8kjVJJherdEfFcgPrfrZO02QIs6Hi+X73sSTLzgsxckplLdt3jWUMPVpL6mLF8Nn/e/KEHK0l9mM8ktaKkQnUt8Ib68RuAv5+kzT8BR0fEXvVF+kfXyySpJOYzSePCfCapFa0UqhFxMXAVcFBEbI6Ik4FzgVdGxC3AK+rnRMSSiPgAQH2R/juBa+qfcyYu3JekNpjPJI0L85mkkuzwzZSmIzNP6LHq5ZO0XQf8747nq4HVMxSaJE2J+UzSuDCfSSpJK4XqqC3Y4+msWnZw22GM3aTGTRM9A0PZ702T24/bfpWkfgbJeU35uSmvDrodSeNpkBxx8lmn9l1vDtGOKukaVUmSJEmSLFQlSZIkSWWxUJUkSZIkFcVCVZIkSZJUFAtVSZIkSVJRLFQlSZIkSUWxUJUkSZIkFWVOzKOqqStljlRwHi5Jmqqm/LxqWXNebcrPTXMoAqxcuqixjaThOvPSDY1tLjzn/L7rHXupBB5RlSRJkiQVxUJVkiRJklQUC1VJkiRJUlEsVCVJkiRJRRl5oRoRqyNia0Tc1LHsTyPi5oi4MSIuiYg9e7x2Y0R8OSKuj4h1o4takiZnTpM0LsxnkkrSxhHVNcAxXcsuBw7NzOcDXwPe2uf1L83MxZm5ZIbik6SpWIM5TdJ4WIP5TFIhRl6oZuYVwL1dyz6TmY/VT78A7DfquCRpOsxpksaF+UxSSUq8RvUk4NM91iXwmYi4NiJWjDAmSZouc5qkcWE+kzQyO7cdQKeI+D3gMeCiHk2OzMwtEfFs4PKIuLn+9m+yvlYAKwAWLFw4I/GOs4gYSj9NE8Y7obTG2bBymvlMbWjKz2esXd/YR9NnwCDbURnMZ2UY5D110ttPaWzj+06zQTFHVCPiROA44JczMydrk5lb6n+3ApcAR/TqLzMvyMwlmblk/rz5MxCxJPU2zJxmPpPUJvOZpDYUUahGxDHAm4Blmflwjza7RcTuE4+Bo4GbJmsrSW0yp0kaF+YzSW1pY3qai4GrgIMiYnNEnAy8G9id6lSR6yPifXXbfSLisvqlewOfi4gbgC8C/5CZ/zjq+CWpkzlN0rgwn0kqycivUc3MEyZZfGGPtncCx9aPbwcOm8HQJGnKzGmSxoX5TFJJijj1V5IkSZKkCRaqkiRJkqSiWKhKkiRJkopS1DyqbWqaD27VsoNHFMmOG8a8dSuXLhpKLM7TJUnjaZDPxVXLmj8Dmj6zTj7r1MY+hvWZJc2kQcZnTX/vjqs0l3hEVZIkSZJUFAtVSZIkSVJRLFQlSZIkSUWxUJUkSZIkFcVCVZIkSZJUFAtVSZIkSVJRLFQlSZIkSUWxUJUkSZIkFWXnUW8wIlYDxwFbM/PQetnZwK8B99TN3paZl03y2mOA84CdgA9k5rnDimuQictL0TRh9Kgmgz7z0g2NbZyEXeOu1JwmzRZNn1lnrF3f2Ecpn4uz3bjms1L+PgbZziB/79Jc0cYR1TXAMZMs//PMXFz/TJYAdwLOB14FHAycEBGzp7qUNK7WYE6TNB7WYD6TVIiRF6qZeQVw7zReegRwa2benpnfBT4KHD/U4CRpisxpksaF+UxSSUq6RvW0iLgxIlZHxF6TrN8X2NTxfHO9TJJKZE6TNC7MZ5JGrpRC9b3ADwOLgbuAVTvaYUSsiIh1EbHunm33NL9AkoZnqDnNfCapReYzSa0oolDNzLsz83uZ+Tjwl1SnkHTbAizoeL5fvaxXnxdk5pLMXDJ/3vzhBixJfQw7p5nPJLXFfCapLUUUqhHx3I6nPwvcNEmza4ADI+J5EfFUYDmwdhTxSdJUmNMkjQvzmaS2tDE9zcXAUcC8iNgMvAM4KiIWAwlsBN5Yt92H6hbnx2bmYxFxGvBPVLc+X52ZXxl1/JLUyZwmaVyYzySVZOSFamaeMMniC3u0vRM4tuP5ZcCTbosuSW0xp0kaF+YzSSUZeaE6Ww0yAfOqZaOZMqyUictXLl3UdghA80TeUM4+kyRNzSCfrauW7XiOH+Sz5KS3n9IQh1OHlqhpDDCM/3sYzv+/f0PSE4q4RlWSJEmSpAkWqpIkSZKkolioSpIkSZKKYqEqSZIkSSqKhaokSZIkqSgWqpIkSZKkolioSpIkSZKK4jyqA4qItkMYqqY5w2bTvKOzKVZJ0ugNa77tMy/dMIxwVJhh/d+P09hKKoFHVCVJkiRJRbFQlSRJkiQVxUJVkiRJklQUC1VJkiRJUlFGfjOliFgNHAdszcxD62UfAw6qm+wJ3JeZiyd57UbgQeB7wGOZuWQkQUtSD+Y0SePCfCapJG3c9XcN8G7gQxMLMvMXJx5HxCrg/j6vf2lmbpux6CRpatZgTpM0HtZgPpNUiJEXqpl5RUQcMNm6qOaAeS3wslHGJEnTZU6TNC7MZ5JKUto1qi8G7s7MW3qsT+AzEXFtRKwYYVySNB3mNEnjwnwmaaTaOPW3nxOAi/usPzIzt0TEs4HLI+LmzLxisoZ1klwBsGDhwsYNN03kvHLposY+ZtNEzyXFIo2xoeS0qeYzSdsb1mde01jgjLXrG/tYtezgvuubxhItGno+Y5dnNP6+J739lL7rm/bnsAwyDly51LGVNEzFHFGNiJ2BnwM+1qtNZm6p/90KXAIc0aftBZm5JDOXzJ83f9jhSlJfw8xp5jNJbZqpfBY77zoT4UoaE8UUqsArgJszc/NkKyNit4jYfeIxcDRw0wjjk6SpMKdJGhfmM0kjN/JCNSIuBq4CDoqIzRFxcr1qOV2nlETEPhFxWf10b+BzEXED8EXgHzLzH0cVtyRNxpwmaVyYzySVpI27/p7QY/mJkyy7Ezi2fnw7cNiMBidJU2ROkzQuzGeSSlLSqb+SJEmSJFmoSpIkSZLKYqEqSZIkSSpKafOozohN9z86lHlSmwxjnrZB5k9zDlRJksoTEY1thjHn+q67nD9wTCU7fNFCPn91/9+3afzmuEkaXx5RlSRJkiQVxUJVkiRJklQUC1VJkiRJUlEsVCVJkiRJRbFQlSRJkiQVxUJVkiRJklQUC1VJkiRJUlEsVCVJkiRJRYnMbDuGGRcR9wB3tB2HpNbsn5nz2w5iGMxnkhiTnNYjn80DtrUQznQY68ww1plRaqw989mcKFQlSZJUvohYl5lL2o5jEMY6M4x1ZsymWCd46q8kSZIkqSgWqpIkSZKkolioSpIkqRQXtB3AFBjrzDDWmTGbYgW8RlWSJEmSVBiPqEqSJEmSimKhKkmSpFZFxDER8QyshRwAAAQmSURBVNWIuDUi3tJ2PP1ExMaI+HJEXB8R69qOp1NErI6IrRFxU8eyZ0XE5RFxS/3vXm3GOKFHrGdHxJZ6314fEce2GeOEiFgQEf8WEesj4isR8dv18uL2bZ9Yi9y3/XjqryRJkloTETsBXwNeCWwGrgFOyMz1rQbWQ0RsBJZkZnFzUkbES4CHgA9l5qH1sj8B7s3Mc+svAfbKzDe3GWcd12Sxng08lJkr24ytW0Q8F3huZl4XEbsD1wKvBk6ksH3bJ9bXUuC+7ccjqpIkSWrTEcCtmXl7Zn4X+ChwfMsxzUqZeQVwb9fi44EP1o8/SFW0tK5HrEXKzLsy87r68YPABmBfCty3fWKddSxUJUmS1KZ9gU0dzzdT9sA6gc9ExLURsaLtYAawd2beVT/+T2DvNoMZwGkRcWN9anDrp9J2i4gDgMOBqyl833bFCoXv224WqpIkSdLgjszMHwdeBZxan8I6K2R1zV/J1/29F/hhYDFwF7Cq3XC2FxHPAD4BnJ6ZD3SuK23fThJr0ft2MhaqkiRJatMWYEHH8/3qZUXKzC31v1uBS6hOXS7Z3fV1ixPXL25tOZ6eMvPuzPxeZj4O/CUF7duI2IWq8LsoM/+uXlzkvp0s1pL3bS8WqpIkSWrTNcCBEfG8iHgqsBxY23JMk4qI3eob1BARuwFHAzf1f1Xr1gJvqB+/Afj7FmPpa6Loq/0shezbiAjgQmBDZv5Zx6ri9m2vWEvdt/14119JkiS1qp4q413ATsDqzPx/LYc0qYj4IaqjqAA7Ax8pKdaIuBg4CpgH3A28A/gk8DfAQuAO4LWZ2fpNjHrEehTVqakJbATe2HENaGsi4kjgSuDLwOP14rdRXftZ1L7tE+sJFLhv+7FQlSRJkiQVxVN/JUmSJElFsVCVJEmSJBXFQlWSJEmSVBQLVUmSJElSUSxUJUmSJElFsVCVJEmSJBXFQlWSJEmSVBQLVUmSJEkjFxE/FxHfiYjd2o5F5YnMbDsGSZIkSXNMROwLzM/M69uOReWxUJUkSZIkFcVTfyVJkiSNXETcExGntx2HymShKkmSJGmkImIfYB7gab+alIWqJEmSpFE7rP73hlajULEsVCVJkiSN2mHApsz8VtuBqEwWqpIkSZJG7fl4NFV9WKhKkiRJGrXDsFBVHxaqkiRJkkYmIp4GHISFqvqwUJUkSZI0SocAO2Ghqj4sVCVJkiSN0mHAw8CtbQeiclmoSpIkSRqlFwFfzMzH2w5E5bJQlSRJkjTjImLfiFgOLAc+1XY8KltkZtsxSJIkSRpzEfF+4OeBvwFOz8zvthySCmahKkmSJEkqiqf+SpIkSZKKYqEqSZIkSSqKhaokSZIkqSgWqpIkSZKkolioSpIkSZKKYqEqSZIkSSqKhaokSZIkqSgWqpIkSZKkovw3xkbJsCjktrMAAAAASUVORK5CYII=\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "cmap = 'Blues'\n", - "fs = 15\n", - "pl.figure(2, (13, 5))\n", - "pl.clf()\n", - "pl.subplot(1, 3, 1)\n", - "pl.imshow(Got, cmap=cmap, interpolation='nearest')\n", - "#pl.xlabel(\"$y$\",fontsize=fs)\n", - "pl.ylabel(\"$i$\", fontsize=fs)\n", - "pl.xticks(())\n", - "\n", - "pl.title('Wasserstein ($M$ only)')\n", - "\n", - "pl.subplot(1, 3, 2)\n", - "pl.imshow(Gg, cmap=cmap, interpolation='nearest')\n", - "pl.title('Gromov ($C_1,C_2$ only)')\n", - "pl.xticks(())\n", - "pl.subplot(1, 3, 3)\n", - "pl.imshow(Gwg, cmap=cmap, interpolation='nearest')\n", - "pl.title('FGW ($M+C_1,C_2$)')\n", - "\n", - "pl.xlabel(\"$j$\", fontsize=fs)\n", - "pl.ylabel(\"$i$\", fontsize=fs)\n", - "\n", - "pl.tight_layout()\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_free_support_barycenter.ipynb b/notebooks/plot_free_support_barycenter.ipynb deleted file mode 100644 index 018353c..0000000 --- a/notebooks/plot_free_support_barycenter.ipynb +++ /dev/null @@ -1,171 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# 2D free support Wasserstein barycenters of distributions\n", - "\n", - "\n", - "Illustration of 2D Wasserstein barycenters if discributions that are weighted\n", - "sum of diracs.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Vivien Seguy \n", - "#\n", - "# License: MIT License\n", - "\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - " -------------\n", - "%% parameters and data generation\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "N = 3\n", - "d = 2\n", - "measures_locations = []\n", - "measures_weights = []\n", - "\n", - "for i in range(N):\n", - "\n", - " n_i = np.random.randint(low=1, high=20) # nb samples\n", - "\n", - " mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean\n", - "\n", - " A_i = np.random.rand(d, d)\n", - " cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix\n", - "\n", - " x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations\n", - " b_i = np.random.uniform(0., 1., (n_i,))\n", - " b_i = b_i / np.sum(b_i) # Dirac weights\n", - "\n", - " measures_locations.append(x_i)\n", - " measures_weights.append(b_i)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute free support barycenter\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "k = 10 # number of Diracs of the barycenter\n", - "X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations\n", - "b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized)\n", - "\n", - "X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n", - "---------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1)\n", - "for (x_i, b_i) in zip(measures_locations, measures_weights):\n", - " color = np.random.randint(low=1, high=10 * N)\n", - " pl.scatter(x_i[:, 0], x_i[:, 1], s=b_i * 1000, label='input measure')\n", - "pl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter')\n", - "pl.title('Data measures and their barycenter')\n", - "pl.legend(loc=0)\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_gromov.ipynb b/notebooks/plot_gromov.ipynb deleted file mode 100644 index 11cfeec..0000000 --- a/notebooks/plot_gromov.ipynb +++ /dev/null @@ -1,263 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Gromov-Wasserstein example\n", - "\n", - "\n", - "This example is designed to show how to use the Gromov-Wassertsein distance\n", - "computation in POT.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Erwan Vautier \n", - "# Nicolas Courty \n", - "#\n", - "# License: MIT License\n", - "\n", - "import scipy as sp\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "from mpl_toolkits.mplot3d import Axes3D # noqa\n", - "import ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sample two Gaussian distributions (2D and 3D)\n", - "---------------------------------------------\n", - "\n", - "The Gromov-Wasserstein distance allows to compute distances with samples that\n", - "do not belong to the same metric space. For demonstration purpose, we sample\n", - "two Gaussian distributions in 2- and 3-dimensional spaces.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_samples = 30 # nb samples\n", - "\n", - "mu_s = np.array([0, 0])\n", - "cov_s = np.array([[1, 0], [0, 1]])\n", - "\n", - "mu_t = np.array([4, 4, 4])\n", - "cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n", - "\n", - "\n", - "xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)\n", - "P = sp.linalg.sqrtm(cov_t)\n", - "xt = np.random.randn(n_samples, 3).dot(P) + mu_t" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotting the distributions\n", - "--------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = pl.figure()\n", - "ax1 = fig.add_subplot(121)\n", - "ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", - "ax2 = fig.add_subplot(122, projection='3d')\n", - "ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute distance kernels, normalize them and then display\n", - "---------------------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "C1 = sp.spatial.distance.cdist(xs, xs)\n", - "C2 = sp.spatial.distance.cdist(xt, xt)\n", - "\n", - "C1 /= C1.max()\n", - "C2 /= C2.max()\n", - "\n", - "pl.figure()\n", - "pl.subplot(121)\n", - "pl.imshow(C1)\n", - "pl.subplot(122)\n", - "pl.imshow(C2)\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute Gromov-Wasserstein plans and distance\n", - "---------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 0|7.602249e-02|0.000000e+00|0.000000e+00\n", - " 1|4.138485e-02|8.369641e-01|3.463764e-02\n", - " 2|2.382207e-02|7.372484e-01|1.756278e-02\n", - " 3|2.149536e-02|1.082425e-01|2.326712e-03\n", - " 4|2.149536e-02|0.000000e+00|0.000000e+00\n", - "It. |Err \n", - "-------------------\n", - " 0|8.206392e-02|\n", - " 10|2.775943e-07|\n", - " 20|5.372013e-14|\n", - "Gromov-Wasserstein distances: 0.02149535867154042\n", - "Entropic Gromov-Wasserstein distances: 0.019910889144636214\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "p = ot.unif(n_samples)\n", - "q = ot.unif(n_samples)\n", - "\n", - "gw0, log0 = ot.gromov.gromov_wasserstein(\n", - " C1, C2, p, q, 'square_loss', verbose=True, log=True)\n", - "\n", - "gw, log = ot.gromov.entropic_gromov_wasserstein(\n", - " C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)\n", - "\n", - "\n", - "print('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))\n", - "print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))\n", - "\n", - "\n", - "pl.figure(1, (10, 5))\n", - "\n", - "pl.subplot(1, 2, 1)\n", - "pl.imshow(gw0, cmap='jet')\n", - "pl.title('Gromov Wasserstein')\n", - "\n", - "pl.subplot(1, 2, 2)\n", - "pl.imshow(gw, cmap='jet')\n", - "pl.title('Entropic Gromov Wasserstein')\n", - "\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_gromov_barycenter.ipynb b/notebooks/plot_gromov_barycenter.ipynb deleted file mode 100644 index eb51305..0000000 --- a/notebooks/plot_gromov_barycenter.ipynb +++ /dev/null @@ -1,369 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Gromov-Wasserstein Barycenter example\n", - "\n", - "\n", - "This example is designed to show how to use the Gromov-Wasserstein distance\n", - "computation in POT.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Erwan Vautier \n", - "# Nicolas Courty \n", - "#\n", - "# License: MIT License\n", - "\n", - "\n", - "import numpy as np\n", - "import scipy as sp\n", - "\n", - "import matplotlib.pylab as pl\n", - "from sklearn import manifold\n", - "from sklearn.decomposition import PCA\n", - "\n", - "import ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Smacof MDS\n", - "----------\n", - "\n", - "This function allows to find an embedding of points given a dissimilarity matrix\n", - "that will be given by the output of the algorithm\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def smacof_mds(C, dim, max_iter=3000, eps=1e-9):\n", - " \"\"\"\n", - " Returns an interpolated point cloud following the dissimilarity matrix C\n", - " using SMACOF multidimensional scaling (MDS) in specific dimensionned\n", - " target space\n", - "\n", - " Parameters\n", - " ----------\n", - " C : ndarray, shape (ns, ns)\n", - " dissimilarity matrix\n", - " dim : int\n", - " dimension of the targeted space\n", - " max_iter : int\n", - " Maximum number of iterations of the SMACOF algorithm for a single run\n", - " eps : float\n", - " relative tolerance w.r.t stress to declare converge\n", - "\n", - " Returns\n", - " -------\n", - " npos : ndarray, shape (R, dim)\n", - " Embedded coordinates of the interpolated point cloud (defined with\n", - " one isometry)\n", - " \"\"\"\n", - "\n", - " rng = np.random.RandomState(seed=3)\n", - "\n", - " mds = manifold.MDS(\n", - " dim,\n", - " max_iter=max_iter,\n", - " eps=1e-9,\n", - " dissimilarity='precomputed',\n", - " n_init=1)\n", - " pos = mds.fit(C).embedding_\n", - "\n", - " nmds = manifold.MDS(\n", - " 2,\n", - " max_iter=max_iter,\n", - " eps=1e-9,\n", - " dissimilarity=\"precomputed\",\n", - " random_state=rng,\n", - " n_init=1)\n", - " npos = nmds.fit_transform(C, init=pos)\n", - "\n", - " return npos" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Data preparation\n", - "----------------\n", - "\n", - "The four distributions are constructed from 4 simple images\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def im2mat(I):\n", - " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n", - " return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n", - "\n", - "\n", - "square = pl.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256\n", - "cross = pl.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256\n", - "triangle = pl.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256\n", - "star = pl.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256\n", - "\n", - "shapes = [square, cross, triangle, star]\n", - "\n", - "S = 4\n", - "xs = [[] for i in range(S)]\n", - "\n", - "\n", - "for nb in range(4):\n", - " for i in range(8):\n", - " for j in range(8):\n", - " if shapes[nb][i, j] < 0.95:\n", - " xs[nb].append([j, 8 - i])\n", - "\n", - "xs = np.array([np.array(xs[0]), np.array(xs[1]),\n", - " np.array(xs[2]), np.array(xs[3])])" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycenter computation\n", - "----------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ns = [len(xs[s]) for s in range(S)]\n", - "n_samples = 30\n", - "\n", - "\"\"\"Compute all distances matrices for the four shapes\"\"\"\n", - "Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)]\n", - "Cs = [cs / cs.max() for cs in Cs]\n", - "\n", - "ps = [ot.unif(ns[s]) for s in range(S)]\n", - "p = ot.unif(n_samples)\n", - "\n", - "\n", - "lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]]\n", - "\n", - "Ct01 = [0 for i in range(2)]\n", - "for i in range(2):\n", - " Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]],\n", - " [ps[0], ps[1]\n", - " ], p, lambdast[i], 'square_loss', # 5e-4,\n", - " max_iter=100, tol=1e-3)\n", - "\n", - "Ct02 = [0 for i in range(2)]\n", - "for i in range(2):\n", - " Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]],\n", - " [ps[0], ps[2]\n", - " ], p, lambdast[i], 'square_loss', # 5e-4,\n", - " max_iter=100, tol=1e-3)\n", - "\n", - "Ct13 = [0 for i in range(2)]\n", - "for i in range(2):\n", - " Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]],\n", - " [ps[1], ps[3]\n", - " ], p, lambdast[i], 'square_loss', # 5e-4,\n", - " max_iter=100, tol=1e-3)\n", - "\n", - "Ct23 = [0 for i in range(2)]\n", - "for i in range(2):\n", - " Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]],\n", - " [ps[2], ps[3]\n", - " ], p, lambdast[i], 'square_loss', # 5e-4,\n", - " max_iter=100, tol=1e-3)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Visualization\n", - "-------------\n", - "\n", - "The PCA helps in getting consistency between the rotations\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "clf = PCA(n_components=2)\n", - "npos = [0, 0, 0, 0]\n", - "npos = [smacof_mds(Cs[s], 2) for s in range(S)]\n", - "\n", - "npost01 = [0, 0]\n", - "npost01 = [smacof_mds(Ct01[s], 2) for s in range(2)]\n", - "npost01 = [clf.fit_transform(npost01[s]) for s in range(2)]\n", - "\n", - "npost02 = [0, 0]\n", - "npost02 = [smacof_mds(Ct02[s], 2) for s in range(2)]\n", - "npost02 = [clf.fit_transform(npost02[s]) for s in range(2)]\n", - "\n", - "npost13 = [0, 0]\n", - "npost13 = [smacof_mds(Ct13[s], 2) for s in range(2)]\n", - "npost13 = [clf.fit_transform(npost13[s]) for s in range(2)]\n", - "\n", - "npost23 = [0, 0]\n", - "npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)]\n", - "npost23 = [clf.fit_transform(npost23[s]) for s in range(2)]\n", - "\n", - "\n", - "fig = pl.figure(figsize=(10, 10))\n", - "\n", - "ax1 = pl.subplot2grid((4, 4), (0, 0))\n", - "pl.xlim((-1, 1))\n", - "pl.ylim((-1, 1))\n", - "ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r')\n", - "\n", - "ax2 = pl.subplot2grid((4, 4), (0, 1))\n", - "pl.xlim((-1, 1))\n", - "pl.ylim((-1, 1))\n", - "ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b')\n", - "\n", - "ax3 = pl.subplot2grid((4, 4), (0, 2))\n", - "pl.xlim((-1, 1))\n", - "pl.ylim((-1, 1))\n", - "ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b')\n", - "\n", - "ax4 = pl.subplot2grid((4, 4), (0, 3))\n", - "pl.xlim((-1, 1))\n", - "pl.ylim((-1, 1))\n", - "ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r')\n", - "\n", - "ax5 = pl.subplot2grid((4, 4), (1, 0))\n", - "pl.xlim((-1, 1))\n", - "pl.ylim((-1, 1))\n", - "ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b')\n", - "\n", - "ax6 = pl.subplot2grid((4, 4), (1, 3))\n", - "pl.xlim((-1, 1))\n", - "pl.ylim((-1, 1))\n", - "ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b')\n", - "\n", - "ax7 = pl.subplot2grid((4, 4), (2, 0))\n", - "pl.xlim((-1, 1))\n", - "pl.ylim((-1, 1))\n", - "ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b')\n", - "\n", - "ax8 = pl.subplot2grid((4, 4), (2, 3))\n", - "pl.xlim((-1, 1))\n", - "pl.ylim((-1, 1))\n", - "ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b')\n", - "\n", - "ax9 = pl.subplot2grid((4, 4), (3, 0))\n", - "pl.xlim((-1, 1))\n", - "pl.ylim((-1, 1))\n", - "ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r')\n", - "\n", - "ax10 = pl.subplot2grid((4, 4), (3, 1))\n", - "pl.xlim((-1, 1))\n", - "pl.ylim((-1, 1))\n", - "ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b')\n", - "\n", - "ax11 = pl.subplot2grid((4, 4), (3, 2))\n", - "pl.xlim((-1, 1))\n", - "pl.ylim((-1, 1))\n", - "ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b')\n", - "\n", - "ax12 = pl.subplot2grid((4, 4), (3, 3))\n", - "pl.xlim((-1, 1))\n", - "pl.ylim((-1, 1))\n", - "ax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_optim_OTreg.ipynb b/notebooks/plot_optim_OTreg.ipynb deleted file mode 100644 index 34f42af..0000000 --- a/notebooks/plot_optim_OTreg.ipynb +++ /dev/null @@ -1,643 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Regularized OT with generic solver\n", - "\n", - "\n", - "Illustrates the use of the generic solver for regularized OT with\n", - "user-designed regularization term. It uses Conditional gradient as in [6] and\n", - "generalized Conditional Gradient as proposed in [5][7].\n", - "\n", - "\n", - "[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for\n", - "Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine\n", - "Intelligence , vol.PP, no.99, pp.1-1.\n", - "\n", - "[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014).\n", - "Regularized discrete optimal transport. SIAM Journal on Imaging Sciences,\n", - "7(3), 1853-1882.\n", - "\n", - "[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized\n", - "conditional gradient: analysis of convergence and applications.\n", - "arXiv preprint arXiv:1510.06567.\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "import ot.plot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 100 # nb bins\n", - "\n", - "# bin positions\n", - "x = np.arange(n, dtype=np.float64)\n", - "\n", - "# Gaussian distributions\n", - "a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\n", - "b = ot.datasets.make_1D_gauss(n, m=60, s=10)\n", - "\n", - "# loss matrix\n", - "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\n", - "M /= M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve EMD\n", - "---------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "G0 = ot.emd(a, b, M)\n", - "\n", - "pl.figure(3, figsize=(5, 5))\n", - "ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve EMD with Frobenius norm regularization\n", - "--------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 0|1.760578e-01|0.000000e+00|0.000000e+00\n", - " 1|1.669467e-01|5.457501e-02|9.111116e-03\n", - " 2|1.665639e-01|2.298130e-03|3.827855e-04\n", - " 3|1.664378e-01|7.572776e-04|1.260396e-04\n", - " 4|1.664077e-01|1.811855e-04|3.015066e-05\n", - " 5|1.663912e-01|9.936787e-05|1.653393e-05\n", - " 6|1.663852e-01|3.555826e-05|5.916369e-06\n", - " 7|1.663814e-01|2.305693e-05|3.836245e-06\n", - " 8|1.663785e-01|1.760450e-05|2.929009e-06\n", - " 9|1.663767e-01|1.078011e-05|1.793559e-06\n", - " 10|1.663751e-01|9.525192e-06|1.584755e-06\n", - " 11|1.663737e-01|8.396466e-06|1.396951e-06\n", - " 12|1.663727e-01|6.086938e-06|1.012700e-06\n", - " 13|1.663720e-01|4.042609e-06|6.725769e-07\n", - " 14|1.663713e-01|4.160914e-06|6.922568e-07\n", - " 15|1.663707e-01|3.823502e-06|6.361187e-07\n", - " 16|1.663702e-01|3.022440e-06|5.028438e-07\n", - " 17|1.663697e-01|3.181249e-06|5.292634e-07\n", - " 18|1.663692e-01|2.698532e-06|4.489527e-07\n", - " 19|1.663687e-01|3.258253e-06|5.420712e-07\n", - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 20|1.663682e-01|2.741118e-06|4.560349e-07\n", - " 21|1.663678e-01|2.624135e-06|4.365715e-07\n", - " 22|1.663673e-01|2.645179e-06|4.400714e-07\n", - " 23|1.663670e-01|1.957237e-06|3.256196e-07\n", - " 24|1.663666e-01|2.261541e-06|3.762450e-07\n", - " 25|1.663663e-01|1.851305e-06|3.079948e-07\n", - " 26|1.663660e-01|1.942296e-06|3.231320e-07\n", - " 27|1.663657e-01|2.092896e-06|3.481860e-07\n", - " 28|1.663653e-01|1.924361e-06|3.201470e-07\n", - " 29|1.663651e-01|1.625455e-06|2.704189e-07\n", - " 30|1.663648e-01|1.641123e-06|2.730250e-07\n", - " 31|1.663645e-01|1.566666e-06|2.606377e-07\n", - " 32|1.663643e-01|1.338514e-06|2.226810e-07\n", - " 33|1.663641e-01|1.222711e-06|2.034152e-07\n", - " 34|1.663639e-01|1.221805e-06|2.032642e-07\n", - " 35|1.663637e-01|1.440781e-06|2.396935e-07\n", - " 36|1.663634e-01|1.520091e-06|2.528875e-07\n", - " 37|1.663632e-01|1.288193e-06|2.143080e-07\n", - " 38|1.663630e-01|1.123055e-06|1.868347e-07\n", - " 39|1.663628e-01|1.024487e-06|1.704365e-07\n", - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 40|1.663627e-01|1.079606e-06|1.796061e-07\n", - " 41|1.663625e-01|1.172093e-06|1.949922e-07\n", - " 42|1.663623e-01|1.047880e-06|1.743277e-07\n", - " 43|1.663621e-01|1.010577e-06|1.681217e-07\n", - " 44|1.663619e-01|1.064438e-06|1.770820e-07\n", - " 45|1.663618e-01|9.882375e-07|1.644049e-07\n", - " 46|1.663616e-01|8.532647e-07|1.419505e-07\n", - " 47|1.663615e-01|9.930189e-07|1.652001e-07\n", - " 48|1.663613e-01|8.728955e-07|1.452161e-07\n", - " 49|1.663612e-01|9.524214e-07|1.584459e-07\n", - " 50|1.663610e-01|9.088418e-07|1.511958e-07\n", - " 51|1.663609e-01|7.639430e-07|1.270902e-07\n", - " 52|1.663608e-01|6.662611e-07|1.108397e-07\n", - " 53|1.663607e-01|7.133700e-07|1.186767e-07\n", - " 54|1.663605e-01|7.648141e-07|1.272349e-07\n", - " 55|1.663604e-01|6.557516e-07|1.090911e-07\n", - " 56|1.663603e-01|7.304213e-07|1.215131e-07\n", - " 57|1.663602e-01|6.353809e-07|1.057021e-07\n", - " 58|1.663601e-01|7.968279e-07|1.325603e-07\n", - " 59|1.663600e-01|6.367159e-07|1.059240e-07\n", - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 60|1.663599e-01|5.610790e-07|9.334102e-08\n", - " 61|1.663598e-01|5.787466e-07|9.628015e-08\n", - " 62|1.663596e-01|6.937777e-07|1.154166e-07\n", - " 63|1.663596e-01|5.599432e-07|9.315190e-08\n", - " 64|1.663595e-01|5.813048e-07|9.670555e-08\n", - " 65|1.663594e-01|5.724600e-07|9.523409e-08\n", - " 66|1.663593e-01|6.081892e-07|1.011779e-07\n", - " 67|1.663592e-01|5.948732e-07|9.896260e-08\n", - " 68|1.663591e-01|4.941833e-07|8.221188e-08\n", - " 69|1.663590e-01|5.213739e-07|8.673523e-08\n", - " 70|1.663589e-01|5.127355e-07|8.529811e-08\n", - " 71|1.663588e-01|4.349251e-07|7.235363e-08\n", - " 72|1.663588e-01|5.007084e-07|8.329722e-08\n", - " 73|1.663587e-01|4.880265e-07|8.118744e-08\n", - " 74|1.663586e-01|4.931950e-07|8.204723e-08\n", - " 75|1.663585e-01|4.981309e-07|8.286832e-08\n", - " 76|1.663584e-01|3.952959e-07|6.576082e-08\n", - " 77|1.663584e-01|4.544857e-07|7.560750e-08\n", - " 78|1.663583e-01|4.237579e-07|7.049564e-08\n", - " 79|1.663582e-01|4.382386e-07|7.290460e-08\n", - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 80|1.663582e-01|3.646051e-07|6.065503e-08\n", - " 81|1.663581e-01|4.197994e-07|6.983702e-08\n", - " 82|1.663580e-01|4.072764e-07|6.775370e-08\n", - " 83|1.663580e-01|3.994645e-07|6.645410e-08\n", - " 84|1.663579e-01|4.842721e-07|8.056248e-08\n", - " 85|1.663578e-01|3.276486e-07|5.450691e-08\n", - " 86|1.663578e-01|3.737346e-07|6.217366e-08\n", - " 87|1.663577e-01|4.282043e-07|7.123508e-08\n", - " 88|1.663576e-01|4.020937e-07|6.689135e-08\n", - " 89|1.663576e-01|3.431951e-07|5.709310e-08\n", - " 90|1.663575e-01|3.052335e-07|5.077789e-08\n", - " 91|1.663575e-01|3.500538e-07|5.823407e-08\n", - " 92|1.663574e-01|3.063176e-07|5.095821e-08\n", - " 93|1.663573e-01|3.576367e-07|5.949549e-08\n", - " 94|1.663573e-01|3.224681e-07|5.364492e-08\n", - " 95|1.663572e-01|3.673221e-07|6.110670e-08\n", - " 96|1.663572e-01|3.635561e-07|6.048017e-08\n", - " 97|1.663571e-01|3.527236e-07|5.867807e-08\n", - " 98|1.663571e-01|2.788548e-07|4.638946e-08\n", - " 99|1.663570e-01|2.727141e-07|4.536791e-08\n", - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 100|1.663570e-01|3.127278e-07|5.202445e-08\n", - " 101|1.663569e-01|2.637504e-07|4.387670e-08\n", - " 102|1.663569e-01|2.922750e-07|4.862195e-08\n", - " 103|1.663568e-01|3.076454e-07|5.117891e-08\n", - " 104|1.663568e-01|2.911509e-07|4.843492e-08\n", - " 105|1.663567e-01|2.403398e-07|3.998215e-08\n", - " 106|1.663567e-01|2.439790e-07|4.058755e-08\n", - " 107|1.663567e-01|2.634542e-07|4.382735e-08\n", - " 108|1.663566e-01|2.452203e-07|4.079401e-08\n", - " 109|1.663566e-01|2.852991e-07|4.746137e-08\n", - " 110|1.663565e-01|2.165490e-07|3.602434e-08\n", - " 111|1.663565e-01|2.450250e-07|4.076149e-08\n", - " 112|1.663564e-01|2.685294e-07|4.467159e-08\n", - " 113|1.663564e-01|2.821800e-07|4.694245e-08\n", - " 114|1.663564e-01|2.237390e-07|3.722040e-08\n", - " 115|1.663563e-01|1.992842e-07|3.315219e-08\n", - " 116|1.663563e-01|2.166739e-07|3.604506e-08\n", - " 117|1.663563e-01|2.086064e-07|3.470297e-08\n", - " 118|1.663562e-01|2.435945e-07|4.052346e-08\n", - " 119|1.663562e-01|2.292497e-07|3.813711e-08\n", - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 120|1.663561e-01|2.366209e-07|3.936334e-08\n", - " 121|1.663561e-01|2.138746e-07|3.557935e-08\n", - " 122|1.663561e-01|2.009637e-07|3.343153e-08\n", - " 123|1.663560e-01|2.386258e-07|3.969683e-08\n", - " 124|1.663560e-01|1.927442e-07|3.206415e-08\n", - " 125|1.663560e-01|2.081681e-07|3.463000e-08\n", - " 126|1.663559e-01|1.759123e-07|2.926406e-08\n", - " 127|1.663559e-01|1.890771e-07|3.145409e-08\n", - " 128|1.663559e-01|1.971315e-07|3.279398e-08\n", - " 129|1.663558e-01|2.101983e-07|3.496771e-08\n", - " 130|1.663558e-01|2.035645e-07|3.386414e-08\n", - " 131|1.663558e-01|1.984492e-07|3.301317e-08\n", - " 132|1.663557e-01|1.849064e-07|3.076024e-08\n", - " 133|1.663557e-01|1.795703e-07|2.987255e-08\n", - " 134|1.663557e-01|1.624087e-07|2.701762e-08\n", - " 135|1.663557e-01|1.689557e-07|2.810673e-08\n", - " 136|1.663556e-01|1.644308e-07|2.735399e-08\n", - " 137|1.663556e-01|1.618007e-07|2.691644e-08\n", - " 138|1.663556e-01|1.483013e-07|2.467075e-08\n", - " 139|1.663555e-01|1.708771e-07|2.842636e-08\n", - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 140|1.663555e-01|2.013847e-07|3.350146e-08\n", - " 141|1.663555e-01|1.721217e-07|2.863339e-08\n", - " 142|1.663554e-01|2.027911e-07|3.373540e-08\n", - " 143|1.663554e-01|1.764565e-07|2.935449e-08\n", - " 144|1.663554e-01|1.677151e-07|2.790030e-08\n", - " 145|1.663554e-01|1.351982e-07|2.249094e-08\n", - " 146|1.663553e-01|1.423360e-07|2.367836e-08\n", - " 147|1.663553e-01|1.541112e-07|2.563722e-08\n", - " 148|1.663553e-01|1.491601e-07|2.481358e-08\n", - " 149|1.663553e-01|1.466407e-07|2.439446e-08\n", - " 150|1.663552e-01|1.801524e-07|2.996929e-08\n", - " 151|1.663552e-01|1.714107e-07|2.851507e-08\n", - " 152|1.663552e-01|1.491257e-07|2.480784e-08\n", - " 153|1.663552e-01|1.513799e-07|2.518282e-08\n", - " 154|1.663551e-01|1.354539e-07|2.253345e-08\n", - " 155|1.663551e-01|1.233818e-07|2.052519e-08\n", - " 156|1.663551e-01|1.576219e-07|2.622121e-08\n", - " 157|1.663551e-01|1.452791e-07|2.416792e-08\n", - " 158|1.663550e-01|1.262867e-07|2.100843e-08\n", - " 159|1.663550e-01|1.316379e-07|2.189863e-08\n", - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 160|1.663550e-01|1.295447e-07|2.155041e-08\n", - " 161|1.663550e-01|1.283286e-07|2.134810e-08\n", - " 162|1.663550e-01|1.569222e-07|2.610479e-08\n", - " 163|1.663549e-01|1.172942e-07|1.951247e-08\n", - " 164|1.663549e-01|1.399809e-07|2.328651e-08\n", - " 165|1.663549e-01|1.229432e-07|2.045221e-08\n", - " 166|1.663549e-01|1.326191e-07|2.206184e-08\n", - " 167|1.663548e-01|1.209694e-07|2.012384e-08\n", - " 168|1.663548e-01|1.372136e-07|2.282614e-08\n", - " 169|1.663548e-01|1.338395e-07|2.226484e-08\n", - " 170|1.663548e-01|1.416497e-07|2.356410e-08\n", - " 171|1.663548e-01|1.298576e-07|2.160242e-08\n", - " 172|1.663547e-01|1.190590e-07|1.980603e-08\n", - " 173|1.663547e-01|1.167083e-07|1.941497e-08\n", - " 174|1.663547e-01|1.069425e-07|1.779038e-08\n", - " 175|1.663547e-01|1.217780e-07|2.025834e-08\n", - " 176|1.663547e-01|1.140754e-07|1.897697e-08\n", - " 177|1.663546e-01|1.160707e-07|1.930890e-08\n", - " 178|1.663546e-01|1.101798e-07|1.832892e-08\n", - " 179|1.663546e-01|1.114904e-07|1.854694e-08\n", - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 180|1.663546e-01|1.064022e-07|1.770049e-08\n", - " 181|1.663546e-01|9.258231e-08|1.540149e-08\n", - " 182|1.663546e-01|1.213120e-07|2.018080e-08\n", - " 183|1.663545e-01|1.164296e-07|1.936859e-08\n", - " 184|1.663545e-01|1.188762e-07|1.977559e-08\n", - " 185|1.663545e-01|9.394153e-08|1.562760e-08\n", - " 186|1.663545e-01|1.028656e-07|1.711216e-08\n", - " 187|1.663545e-01|1.115348e-07|1.855431e-08\n", - " 188|1.663544e-01|9.768310e-08|1.625002e-08\n", - " 189|1.663544e-01|1.021806e-07|1.699820e-08\n", - " 190|1.663544e-01|1.086303e-07|1.807113e-08\n", - " 191|1.663544e-01|9.879008e-08|1.643416e-08\n", - " 192|1.663544e-01|1.050210e-07|1.747071e-08\n", - " 193|1.663544e-01|1.002463e-07|1.667641e-08\n", - " 194|1.663543e-01|1.062747e-07|1.767925e-08\n", - " 195|1.663543e-01|9.348538e-08|1.555170e-08\n", - " 196|1.663543e-01|7.992512e-08|1.329589e-08\n", - " 197|1.663543e-01|9.558020e-08|1.590018e-08\n", - " 198|1.663543e-01|9.993772e-08|1.662507e-08\n", - " 199|1.663543e-01|8.588499e-08|1.428734e-08\n", - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 200|1.663543e-01|8.737134e-08|1.453459e-08\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "def f(G):\n", - " return 0.5 * np.sum(G**2)\n", - "\n", - "\n", - "def df(G):\n", - " return G\n", - "\n", - "\n", - "reg = 1e-1\n", - "\n", - "Gl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n", - "\n", - "pl.figure(3)\n", - "ot.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve EMD with entropic regularization\n", - "--------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 0|1.692289e-01|0.000000e+00|0.000000e+00\n", - " 1|1.617643e-01|4.614437e-02|7.464513e-03\n", - " 2|1.612639e-01|3.102965e-03|5.003963e-04\n", - " 3|1.611291e-01|8.371098e-04|1.348827e-04\n", - " 4|1.610468e-01|5.110558e-04|8.230389e-05\n", - " 5|1.610198e-01|1.672927e-04|2.693743e-05\n", - " 6|1.610130e-01|4.232417e-05|6.814742e-06\n", - " 7|1.610090e-01|2.513455e-05|4.046887e-06\n", - " 8|1.610002e-01|5.443507e-05|8.764057e-06\n", - " 9|1.609996e-01|3.657071e-06|5.887869e-07\n", - " 10|1.609948e-01|2.998735e-05|4.827807e-06\n", - " 11|1.609695e-01|1.569217e-04|2.525962e-05\n", - " 12|1.609533e-01|1.010779e-04|1.626881e-05\n", - " 13|1.609520e-01|8.043897e-06|1.294681e-06\n", - " 14|1.609465e-01|3.415246e-05|5.496718e-06\n", - " 15|1.609386e-01|4.898605e-05|7.883745e-06\n", - " 16|1.609324e-01|3.837052e-05|6.175060e-06\n", - " 17|1.609298e-01|1.617826e-05|2.603564e-06\n", - " 18|1.609184e-01|7.080015e-05|1.139305e-05\n", - " 19|1.609083e-01|6.273206e-05|1.009411e-05\n", - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 20|1.608988e-01|5.940805e-05|9.558681e-06\n", - " 21|1.608853e-01|8.380030e-05|1.348223e-05\n", - " 22|1.608844e-01|5.185045e-06|8.341930e-07\n", - " 23|1.608824e-01|1.279113e-05|2.057868e-06\n", - " 24|1.608819e-01|3.156821e-06|5.078753e-07\n", - " 25|1.608783e-01|2.205746e-05|3.548567e-06\n", - " 26|1.608764e-01|1.189894e-05|1.914259e-06\n", - " 27|1.608755e-01|5.474607e-06|8.807303e-07\n", - " 28|1.608737e-01|1.144227e-05|1.840760e-06\n", - " 29|1.608676e-01|3.775335e-05|6.073291e-06\n", - " 30|1.608638e-01|2.348020e-05|3.777116e-06\n", - " 31|1.608627e-01|6.863136e-06|1.104023e-06\n", - " 32|1.608529e-01|6.110230e-05|9.828482e-06\n", - " 33|1.608487e-01|2.641106e-05|4.248184e-06\n", - " 34|1.608409e-01|4.823638e-05|7.758383e-06\n", - " 35|1.608373e-01|2.256641e-05|3.629519e-06\n", - " 36|1.608338e-01|2.132444e-05|3.429691e-06\n", - " 37|1.608310e-01|1.786649e-05|2.873484e-06\n", - " 38|1.608260e-01|3.103848e-05|4.991794e-06\n", - " 39|1.608206e-01|3.321265e-05|5.341279e-06\n", - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 40|1.608201e-01|3.054747e-06|4.912648e-07\n", - " 41|1.608195e-01|4.198335e-06|6.751739e-07\n", - " 42|1.608193e-01|8.458736e-07|1.360328e-07\n", - " 43|1.608159e-01|2.153759e-05|3.463587e-06\n", - " 44|1.608115e-01|2.738314e-05|4.403523e-06\n", - " 45|1.608108e-01|3.960032e-06|6.368161e-07\n", - " 46|1.608081e-01|1.675447e-05|2.694254e-06\n", - " 47|1.608072e-01|5.976340e-06|9.610383e-07\n", - " 48|1.608046e-01|1.604130e-05|2.579515e-06\n", - " 49|1.608020e-01|1.617036e-05|2.600226e-06\n", - " 50|1.608014e-01|3.957795e-06|6.364188e-07\n", - " 51|1.608011e-01|1.292411e-06|2.078211e-07\n", - " 52|1.607998e-01|8.431795e-06|1.355831e-06\n", - " 53|1.607964e-01|2.127054e-05|3.420225e-06\n", - " 54|1.607947e-01|1.021878e-05|1.643126e-06\n", - " 55|1.607947e-01|3.560621e-07|5.725288e-08\n", - " 56|1.607900e-01|2.929781e-05|4.710793e-06\n", - " 57|1.607890e-01|5.740229e-06|9.229659e-07\n", - " 58|1.607858e-01|2.039550e-05|3.279306e-06\n", - " 59|1.607836e-01|1.319545e-05|2.121612e-06\n", - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 60|1.607826e-01|6.378947e-06|1.025624e-06\n", - " 61|1.607808e-01|1.145102e-05|1.841105e-06\n", - " 62|1.607776e-01|1.941743e-05|3.121889e-06\n", - " 63|1.607743e-01|2.087422e-05|3.356037e-06\n", - " 64|1.607741e-01|1.310249e-06|2.106541e-07\n", - " 65|1.607738e-01|1.682752e-06|2.705425e-07\n", - " 66|1.607691e-01|2.913936e-05|4.684709e-06\n", - " 67|1.607671e-01|1.288855e-05|2.072055e-06\n", - " 68|1.607654e-01|1.002448e-05|1.611590e-06\n", - " 69|1.607641e-01|8.209492e-06|1.319792e-06\n", - " 70|1.607632e-01|5.588467e-06|8.984199e-07\n", - " 71|1.607619e-01|8.050388e-06|1.294196e-06\n", - " 72|1.607618e-01|9.417493e-07|1.513973e-07\n", - " 73|1.607598e-01|1.210509e-05|1.946012e-06\n", - " 74|1.607591e-01|4.392914e-06|7.062009e-07\n", - " 75|1.607579e-01|7.759587e-06|1.247415e-06\n", - " 76|1.607574e-01|2.760280e-06|4.437356e-07\n", - " 77|1.607556e-01|1.146469e-05|1.843012e-06\n", - " 78|1.607550e-01|3.689456e-06|5.930984e-07\n", - " 79|1.607550e-01|4.065631e-08|6.535705e-09\n", - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 80|1.607539e-01|6.555681e-06|1.053852e-06\n", - " 81|1.607528e-01|7.177470e-06|1.153798e-06\n", - " 82|1.607527e-01|5.306068e-07|8.529648e-08\n", - " 83|1.607514e-01|7.816045e-06|1.256440e-06\n", - " 84|1.607511e-01|2.301970e-06|3.700442e-07\n", - " 85|1.607504e-01|4.281072e-06|6.881840e-07\n", - " 86|1.607503e-01|7.821886e-07|1.257370e-07\n", - " 87|1.607480e-01|1.403013e-05|2.255315e-06\n", - " 88|1.607480e-01|1.169298e-08|1.879624e-09\n", - " 89|1.607473e-01|4.235982e-06|6.809227e-07\n", - " 90|1.607470e-01|1.717105e-06|2.760195e-07\n", - " 91|1.607470e-01|6.148402e-09|9.883374e-10\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "def f(G):\n", - " return np.sum(G * np.log(G))\n", - "\n", - "\n", - "def df(G):\n", - " return np.log(G) + 1.\n", - "\n", - "\n", - "reg = 1e-3\n", - "\n", - "Ge = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n", - "\n", - "pl.figure(4, figsize=(5, 5))\n", - "ot.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve EMD with Frobenius norm + entropic regularization\n", - "-------------------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 0|1.693084e-01|0.000000e+00|0.000000e+00\n", - " 1|1.610202e-01|5.147342e-02|8.288260e-03\n", - " 2|1.609508e-01|4.309685e-04|6.936474e-05\n", - " 3|1.609484e-01|1.524885e-05|2.454278e-06\n", - " 4|1.609477e-01|3.863641e-06|6.218444e-07\n", - " 5|1.609475e-01|1.433633e-06|2.307397e-07\n", - " 6|1.609474e-01|6.332412e-07|1.019185e-07\n", - " 7|1.609474e-01|2.950826e-07|4.749276e-08\n", - " 8|1.609473e-01|1.508393e-07|2.427718e-08\n", - " 9|1.609473e-01|7.859971e-08|1.265041e-08\n", - " 10|1.609473e-01|4.337432e-08|6.980981e-09\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "def f(G):\n", - " return 0.5 * np.sum(G**2)\n", - "\n", - "\n", - "def df(G):\n", - " return G\n", - "\n", - "\n", - "reg1 = 1e-3\n", - "reg2 = 1e-1\n", - "\n", - "Gel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)\n", - "\n", - "pl.figure(5, figsize=(5, 5))\n", - "ot.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_otda_classes.ipynb b/notebooks/plot_otda_classes.ipynb deleted file mode 100644 index 450365f..0000000 --- a/notebooks/plot_otda_classes.ipynb +++ /dev/null @@ -1,305 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# OT for domain adaptation\n", - "\n", - "\n", - "This example introduces a domain adaptation in a 2D setting and the 4 OTDA\n", - "approaches currently supported in POT.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Authors: Remi Flamary \n", - "# Stanislas Chambon \n", - "#\n", - "# License: MIT License\n", - "\n", - "import matplotlib.pylab as pl\n", - "import ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_source_samples = 150\n", - "n_target_samples = 150\n", - "\n", - "Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples)\n", - "Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instantiate the different transport algorithms and fit them\n", - "-----------------------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 0|1.068487e+01|0.000000e+00|0.000000e+00\n", - " 1|2.265157e+00|3.717055e+00|8.419713e+00\n", - " 2|2.027223e+00|1.173695e-01|2.379341e-01\n", - " 3|1.969810e+00|2.914611e-02|5.741231e-02\n", - " 4|1.946438e+00|1.200787e-02|2.337257e-02\n", - " 5|1.935059e+00|5.880253e-03|1.137864e-02\n", - " 6|1.927743e+00|3.795152e-03|7.316078e-03\n", - " 7|1.923064e+00|2.433205e-03|4.679208e-03\n", - " 8|1.917781e+00|2.754726e-03|5.282962e-03\n", - " 9|1.914769e+00|1.572823e-03|3.011594e-03\n", - " 10|1.913550e+00|6.373943e-04|1.219686e-03\n", - " 11|1.911332e+00|1.160273e-03|2.217668e-03\n", - " 12|1.910399e+00|4.882451e-04|9.327431e-04\n", - " 13|1.908762e+00|8.578270e-04|1.637388e-03\n", - " 14|1.908107e+00|3.430078e-04|6.544958e-04\n", - " 15|1.907106e+00|5.253391e-04|1.001877e-03\n", - " 16|1.906394e+00|3.734588e-04|7.119595e-04\n", - " 17|1.906076e+00|1.664949e-04|3.173520e-04\n", - " 18|1.905829e+00|1.295989e-04|2.469934e-04\n", - " 19|1.905120e+00|3.720389e-04|7.087790e-04\n", - "It. |Loss |Relative loss|Absolute loss\n", - "------------------------------------------------\n", - " 20|1.904801e+00|1.676571e-04|3.193534e-04\n" - ] - } - ], - "source": [ - "# EMD Transport\n", - "ot_emd = ot.da.EMDTransport()\n", - "ot_emd.fit(Xs=Xs, Xt=Xt)\n", - "\n", - "# Sinkhorn Transport\n", - "ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\n", - "ot_sinkhorn.fit(Xs=Xs, Xt=Xt)\n", - "\n", - "# Sinkhorn Transport with Group lasso regularization\n", - "ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\n", - "ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n", - "\n", - "# Sinkhorn Transport with Group lasso regularization l1l2\n", - "ot_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20,\n", - " verbose=True)\n", - "ot_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt)\n", - "\n", - "# transport source samples onto target samples\n", - "transp_Xs_emd = ot_emd.transform(Xs=Xs)\n", - "transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\n", - "transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)\n", - "transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 1 : plots source and target samples\n", - "---------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1, figsize=(10, 5))\n", - "pl.subplot(1, 2, 1)\n", - "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.legend(loc=0)\n", - "pl.title('Source samples')\n", - "\n", - "pl.subplot(1, 2, 2)\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.legend(loc=0)\n", - "pl.title('Target samples')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 2 : plot optimal couplings and transported samples\n", - "------------------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "param_img = {'interpolation': 'nearest'}\n", - "\n", - "pl.figure(2, figsize=(15, 8))\n", - "pl.subplot(2, 4, 1)\n", - "pl.imshow(ot_emd.coupling_, **param_img)\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Optimal coupling\\nEMDTransport')\n", - "\n", - "pl.subplot(2, 4, 2)\n", - "pl.imshow(ot_sinkhorn.coupling_, **param_img)\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Optimal coupling\\nSinkhornTransport')\n", - "\n", - "pl.subplot(2, 4, 3)\n", - "pl.imshow(ot_lpl1.coupling_, **param_img)\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Optimal coupling\\nSinkhornLpl1Transport')\n", - "\n", - "pl.subplot(2, 4, 4)\n", - "pl.imshow(ot_l1l2.coupling_, **param_img)\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Optimal coupling\\nSinkhornL1l2Transport')\n", - "\n", - "pl.subplot(2, 4, 5)\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", - " label='Target samples', alpha=0.3)\n", - "pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n", - " marker='+', label='Transp samples', s=30)\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Transported samples\\nEmdTransport')\n", - "pl.legend(loc=\"lower left\")\n", - "\n", - "pl.subplot(2, 4, 6)\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", - " label='Target samples', alpha=0.3)\n", - "pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n", - " marker='+', label='Transp samples', s=30)\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Transported samples\\nSinkhornTransport')\n", - "\n", - "pl.subplot(2, 4, 7)\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", - " label='Target samples', alpha=0.3)\n", - "pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n", - " marker='+', label='Transp samples', s=30)\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Transported samples\\nSinkhornLpl1Transport')\n", - "\n", - "pl.subplot(2, 4, 8)\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", - " label='Target samples', alpha=0.3)\n", - "pl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys,\n", - " marker='+', label='Transp samples', s=30)\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Transported samples\\nSinkhornL1l2Transport')\n", - "pl.tight_layout()\n", - "\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_otda_color_images.ipynb b/notebooks/plot_otda_color_images.ipynb deleted file mode 100644 index cc060ee..0000000 --- a/notebooks/plot_otda_color_images.ipynb +++ /dev/null @@ -1,327 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# OT for image color adaptation\n", - "\n", - "\n", - "This example presents a way of transferring colors between two images\n", - "with Optimal Transport as introduced in [6]\n", - "\n", - "[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).\n", - "Regularized discrete optimal transport.\n", - "SIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Authors: Remi Flamary \n", - "# Stanislas Chambon \n", - "#\n", - "# License: MIT License\n", - "\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "\n", - "\n", - "r = np.random.RandomState(42)\n", - "\n", - "\n", - "def im2mat(I):\n", - " \"\"\"Converts an image to matrix (one pixel per line)\"\"\"\n", - " return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n", - "\n", - "\n", - "def mat2im(X, shape):\n", - " \"\"\"Converts back a matrix to an image\"\"\"\n", - " return X.reshape(shape)\n", - "\n", - "\n", - "def minmax(I):\n", - " return np.clip(I, 0, 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Loading images\n", - "I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256\n", - "I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n", - "\n", - "X1 = im2mat(I1)\n", - "X2 = im2mat(I2)\n", - "\n", - "# training samples\n", - "nb = 1000\n", - "idx1 = r.randint(X1.shape[0], size=(nb,))\n", - "idx2 = r.randint(X2.shape[0], size=(nb,))\n", - "\n", - "Xs = X1[idx1, :]\n", - "Xt = X2[idx2, :]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot original image\n", - "-------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Image 2')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1, figsize=(6.4, 3))\n", - "\n", - "pl.subplot(1, 2, 1)\n", - "pl.imshow(I1)\n", - "pl.axis('off')\n", - "pl.title('Image 1')\n", - "\n", - "pl.subplot(1, 2, 2)\n", - "pl.imshow(I2)\n", - "pl.axis('off')\n", - "pl.title('Image 2')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Scatter plot of colors\n", - "----------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(2, figsize=(6.4, 3))\n", - "\n", - "pl.subplot(1, 2, 1)\n", - "pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\n", - "pl.axis([0, 1, 0, 1])\n", - "pl.xlabel('Red')\n", - "pl.ylabel('Blue')\n", - "pl.title('Image 1')\n", - "\n", - "pl.subplot(1, 2, 2)\n", - "pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\n", - "pl.axis([0, 1, 0, 1])\n", - "pl.xlabel('Red')\n", - "pl.ylabel('Blue')\n", - "pl.title('Image 2')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instantiate the different transport algorithms and fit them\n", - "-----------------------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# EMDTransport\n", - "ot_emd = ot.da.EMDTransport()\n", - "ot_emd.fit(Xs=Xs, Xt=Xt)\n", - "\n", - "# SinkhornTransport\n", - "ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\n", - "ot_sinkhorn.fit(Xs=Xs, Xt=Xt)\n", - "\n", - "# prediction between images (using out of sample prediction as in [6])\n", - "transp_Xs_emd = ot_emd.transform(Xs=X1)\n", - "transp_Xt_emd = ot_emd.inverse_transform(Xt=X2)\n", - "\n", - "transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)\n", - "transp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2)\n", - "\n", - "I1t = minmax(mat2im(transp_Xs_emd, I1.shape))\n", - "I2t = minmax(mat2im(transp_Xt_emd, I2.shape))\n", - "\n", - "I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\n", - "I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot new images\n", - "---------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(3, figsize=(8, 4))\n", - "\n", - "pl.subplot(2, 3, 1)\n", - "pl.imshow(I1)\n", - "pl.axis('off')\n", - "pl.title('Image 1')\n", - "\n", - "pl.subplot(2, 3, 2)\n", - "pl.imshow(I1t)\n", - "pl.axis('off')\n", - "pl.title('Image 1 Adapt')\n", - "\n", - "pl.subplot(2, 3, 3)\n", - "pl.imshow(I1te)\n", - "pl.axis('off')\n", - "pl.title('Image 1 Adapt (reg)')\n", - "\n", - "pl.subplot(2, 3, 4)\n", - "pl.imshow(I2)\n", - "pl.axis('off')\n", - "pl.title('Image 2')\n", - "\n", - "pl.subplot(2, 3, 5)\n", - "pl.imshow(I2t)\n", - "pl.axis('off')\n", - "pl.title('Image 2 Adapt')\n", - "\n", - "pl.subplot(2, 3, 6)\n", - "pl.imshow(I2te)\n", - "pl.axis('off')\n", - "pl.title('Image 2 Adapt (reg)')\n", - "pl.tight_layout()\n", - "\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_otda_d2.ipynb b/notebooks/plot_otda_d2.ipynb deleted file mode 100644 index c39b7fb..0000000 --- a/notebooks/plot_otda_d2.ipynb +++ /dev/null @@ -1,321 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# OT for domain adaptation on empirical distributions\n", - "\n", - "\n", - "This example introduces a domain adaptation in a 2D setting. It explicits\n", - "the problem of domain adaptation and introduces some optimal transport\n", - "approaches to solve it.\n", - "\n", - "Quantities such as optimal couplings, greater coupling coefficients and\n", - "transported samples are represented in order to give a visual understanding\n", - "of what the transport methods are doing.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Authors: Remi Flamary \n", - "# Stanislas Chambon \n", - "#\n", - "# License: MIT License\n", - "\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "import ot.plot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_samples_source = 150\n", - "n_samples_target = 150\n", - "\n", - "Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)\n", - "Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)\n", - "\n", - "# Cost matrix\n", - "M = ot.dist(Xs, Xt, metric='sqeuclidean')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instantiate the different transport algorithms and fit them\n", - "-----------------------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# EMD Transport\n", - "ot_emd = ot.da.EMDTransport()\n", - "ot_emd.fit(Xs=Xs, Xt=Xt)\n", - "\n", - "# Sinkhorn Transport\n", - "ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\n", - "ot_sinkhorn.fit(Xs=Xs, Xt=Xt)\n", - "\n", - "# Sinkhorn Transport with Group lasso regularization\n", - "ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\n", - "ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n", - "\n", - "# transport source samples onto target samples\n", - "transp_Xs_emd = ot_emd.transform(Xs=Xs)\n", - "transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\n", - "transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 1 : plots source and target samples + matrix of pairwise distance\n", - "---------------------------------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1, figsize=(10, 10))\n", - "pl.subplot(2, 2, 1)\n", - "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.legend(loc=0)\n", - "pl.title('Source samples')\n", - "\n", - "pl.subplot(2, 2, 2)\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.legend(loc=0)\n", - "pl.title('Target samples')\n", - "\n", - "pl.subplot(2, 2, 3)\n", - "pl.imshow(M, interpolation='nearest')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Matrix of pairwise distances')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 2 : plots optimal couplings for the different methods\n", - "---------------------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(2, figsize=(10, 6))\n", - "\n", - "pl.subplot(2, 3, 1)\n", - "pl.imshow(ot_emd.coupling_, interpolation='nearest')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Optimal coupling\\nEMDTransport')\n", - "\n", - "pl.subplot(2, 3, 2)\n", - "pl.imshow(ot_sinkhorn.coupling_, interpolation='nearest')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Optimal coupling\\nSinkhornTransport')\n", - "\n", - "pl.subplot(2, 3, 3)\n", - "pl.imshow(ot_lpl1.coupling_, interpolation='nearest')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Optimal coupling\\nSinkhornLpl1Transport')\n", - "\n", - "pl.subplot(2, 3, 4)\n", - "ot.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1])\n", - "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Main coupling coefficients\\nEMDTransport')\n", - "\n", - "pl.subplot(2, 3, 5)\n", - "ot.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1])\n", - "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Main coupling coefficients\\nSinkhornTransport')\n", - "\n", - "pl.subplot(2, 3, 6)\n", - "ot.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1])\n", - "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Main coupling coefficients\\nSinkhornLpl1Transport')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 3 : plot transported samples\n", - "--------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# display transported samples\n", - "pl.figure(4, figsize=(10, 4))\n", - "pl.subplot(1, 3, 1)\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", - " label='Target samples', alpha=0.5)\n", - "pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n", - " marker='+', label='Transp samples', s=30)\n", - "pl.title('Transported samples\\nEmdTransport')\n", - "pl.legend(loc=0)\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "\n", - "pl.subplot(1, 3, 2)\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", - " label='Target samples', alpha=0.5)\n", - "pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n", - " marker='+', label='Transp samples', s=30)\n", - "pl.title('Transported samples\\nSinkhornTransport')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "\n", - "pl.subplot(1, 3, 3)\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", - " label='Target samples', alpha=0.5)\n", - "pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n", - " marker='+', label='Transp samples', s=30)\n", - "pl.title('Transported samples\\nSinkhornLpl1Transport')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "\n", - "pl.tight_layout()\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_otda_jcpot.ipynb b/notebooks/plot_otda_jcpot.ipynb deleted file mode 100644 index cc70d59..0000000 --- a/notebooks/plot_otda_jcpot.ipynb +++ /dev/null @@ -1,397 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# OT for multi-source target shift\n", - "\n", - "\n", - "This example introduces a target shift problem with two 2D source and 1 target domain.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Authors: Remi Flamary \n", - "# Ievgen Redko \n", - "#\n", - "# License: MIT License\n", - "\n", - "import pylab as pl\n", - "import numpy as np\n", - "import ot\n", - "from ot.datasets import make_data_classif" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 50\n", - "sigma = 0.3\n", - "np.random.seed(1985)\n", - "\n", - "p1 = .2\n", - "dec1 = [0, 2]\n", - "\n", - "p2 = .9\n", - "dec2 = [0, -2]\n", - "\n", - "pt = .4\n", - "dect = [4, 0]\n", - "\n", - "xs1, ys1 = make_data_classif('2gauss_prop', n, nz=sigma, p=p1, bias=dec1)\n", - "xs2, ys2 = make_data_classif('2gauss_prop', n + 1, nz=sigma, p=p2, bias=dec2)\n", - "xt, yt = make_data_classif('2gauss_prop', n, nz=sigma, p=pt, bias=dect)\n", - "\n", - "all_Xr = [xs1, xs2]\n", - "all_Yr = [ys1, ys2]" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "da = 1.5\n", - "\n", - "\n", - "def plot_ax(dec, name):\n", - " pl.plot([dec[0], dec[0]], [dec[1] - da, dec[1] + da], 'k', alpha=0.5)\n", - " pl.plot([dec[0] - da, dec[0] + da], [dec[1], dec[1]], 'k', alpha=0.5)\n", - " pl.text(dec[0] - .5, dec[1] + 2, name)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 1 : plots source and target samples\n", - "---------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(-1.85, 5.85, -4.167353448800062, 4.244952120369078)" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1)\n", - "pl.clf()\n", - "plot_ax(dec1, 'Source 1')\n", - "plot_ax(dec2, 'Source 2')\n", - "plot_ax(dect, 'Target')\n", - "pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9,\n", - " label='Source 1 ({:1.2f}, {:1.2f})'.format(1 - p1, p1))\n", - "pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9,\n", - " label='Source 2 ({:1.2f}, {:1.2f})'.format(1 - p2, p2))\n", - "pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9,\n", - " label='Target ({:1.2f}, {:1.2f})'.format(1 - pt, pt))\n", - "pl.title('Data')\n", - "\n", - "pl.legend()\n", - "pl.axis('equal')\n", - "pl.axis('off')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instantiate Sinkhorn transport algorithm and fit them for all source domains\n", - "----------------------------------------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1, metric='sqeuclidean')\n", - "\n", - "\n", - "def print_G(G, xs, ys, xt):\n", - " for i in range(G.shape[0]):\n", - " for j in range(G.shape[1]):\n", - " if G[i, j] > 5e-4:\n", - " if ys[i]:\n", - " c = 'b'\n", - " else:\n", - " c = 'r'\n", - " pl.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], xt[j, 1]], c, alpha=.2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 2 : plot optimal couplings and transported samples\n", - "------------------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(-1.85, 5.85, -4.170525419290473, 4.251885380465107)" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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tsMIe8xrgbOzuFC5cuPgoXNI9ABEO1Ug4VOPfaXEj8GPgTJR4Lwf+CbwGnGbA+npri6dic11P2tvTgAF+AmxGiXkxkAfcEw7VXGkf57vAH1Ay7plz/EuB58KhmnzULP2PwKOoX+/JwFcRmYJ2sXDhwkUO3OyFAxO/BwaEQzXnhcK1iXCoJg94DI1if1jQ0vybQCp5ZmtRySKTn38ykG9amrf5oI8X6Nne6su0t9IBFELf9kBePF5ZdQoQAf4N3L25vOJmCgoHAPOAw4CX7Yj3S2g3ildRY3PQjhPPIrLKXj8KOAsYhsgbwAcY4/ZMc+ECN9I9ULESOA14NByqKUYJ91SgHPixx7LwZzJUNTcNqdi+7W3p6NjgKSvv85rXhzNt6kXFWAFKk4lAz811K0Ob607Fsl4taGmRqtaWAeX12+i1ue6b9tiDApHINpLJ+1HCPTUUru1cJWTMOrQ/2zNo5D0WJd8zEen72b4lLlwcGHAj3QMQoXDtX8KhGohG/1zSEW1ryy+EwsIZQDFwaaSiB9GOjkxppL3In0mP7NHawpEeMQvzApkRhT18hfXbKLTHEpUEvAJlafhX+dbNvvZgPgmPlzxNu5kX2lz30Jbi0ueK4h1nE4/hTaVfCRhr15GrRr3PI7IWmAoMAg4H+qCG5u9gzCe1BnLh4gsLN9I9cHFPQSpJMJmkZ2szPTfXHS2JxCxgDUBZe6u3PRCgtaAIy+Mxg9JpOaMj7iuItG/rqOpFI4IBjN54Mwa8Bnx+oEe8IxnIpN9uC+RZKfGUpeGSimjkzLhlYSFYHu91GXgIkTMQ2XXZphZOPIhO0G0FQsCRwHmIHILI51br3l3gWjvuXVx99dXVAwcOHDV06NCRJ5xwwqCGhoa98p11SfcAhKPhxsrKaSkqiRmPB4Hzqhrr2wsbGw7yNDXiT6fpEY1QGmkjmU7J+x5vA8ZqKUgmepU3NTT2EL7fVlL2lYQS7s7weGB8cSKetoxpjnk8RWJloqWZ9PpAMvFe0ucNJH3+qRn4NvA7RI7dJfkaE8OYl1C7yFXo5F0/4FjgDET6fYZvU7dDrrUjwOdl7bhy5coVo0aN2uHGZVkWmczeqxJ3rB2vvvrq+o0bNy5bsWLFymuvvbb+s7Z2nD59etvq1auXr169esXgwYPjN910U++9Ma5Lugcm/onqrDOSJSVl9T2q1ic1Yu1ZmIh7y1JJ4l7/ZoNqtgXAUVamLAp1KVgRyGTWe4y5sqKt5fRkUdHrjXn5PguiHkgLGAM+Az4veIKYoqBltXphtgc2+iG/IBHfTDpVLxrB9kfJ9wZEJqMNKztDtd5HUb/eRqAQGAychMg0RD5i5+diz+BaO+49a8ezzz67ze/Xr/MRRxwR/b+Y8eTC1XQPTNwDvBgK194JEA7VfDsNTzts589k0l4yPdNeH5lM2gSVe30DYbgvkzEpeC3u9Y0KZjK9CmMdwXyv50M/fC8BF/q0oaQXNCz1gXh1zu004ANgiw/KfJAP1KApZ0E0w6E/cBQic4FFnSrUjIkBryCyCdV4e6H3g4OBakSWAe9/Eara1q7FH4vt3YCmoABr8GBca8fd4LO0drz33nsrzz333KbdbdNVuKR7ACIUrn3W+T0cqrke+Hl7SRmetlbyMBjwCRDIpFMWtCS9vqpEJk0x+NIazU4JZtK+NNTnWZnFYmWKMnBLKpg/Murz15ZG2loFRhl7e/tL4kVTwRLAaqADqEAJuQ6dkKsCJgHVwOF2utj7O5HvWkQ229sNBQIo+U4C+iOyGFiPW5++R3CtHff+9V1//fW9vV6vueaaa1zS7e4Ih2qGoWW+UFR0b2tR0de9ra0byqPtfT2AAb9AVSCTTrSCNwO+MvszF0gHoa9AT+C9NPQIJBOpwnjHOi/My8AdBv4H8Ft00qHygNFAO1AP+IEhqNTQjBLoYKAMnTw7DJEFwDKMUReSzlHvYShZR9FzORYYisg7GNP4mb15nyG6EpF+lnCtHffe9f3hD3/oMXv27LL58+evds75/wqXdA9srAZeR7MCqoEfZUpL+zYUFtYXbt/qK9S8XQQClfCRmQ0L8Op3YGIexDOWtV6ULE/2Qq3ATwyM9cD5KLnmohiNctuAOBqxVgAf2r/3srcpBfoCh9rk+0EO+eZGvYNQRaMDGABU2mlnizGmk3bnYvdwrR33zvU99thjJb///e97z58/f1VxcfFeK+5xJ9IOYITCtQY4GvhJYXPT8eX12271dXTMxef7XbRvdXlL9uPNiC055MKgmgBARnXZ4RkYlYIewIg0/CTt8QwC/gtYtotTEJRUq1CC74UWRJSj1W2FKIEORrXbM4FLERm1I2VMo9459qsFJf1m+/RGA6chMhwR97vaRbjWjnvn+r73ve/1i0aj3uOOO27o8OHDR1588cV7JdvGtXY8wBEO1dwI3Jzf0uwvTMQRY+LxQF4wUlAYN8FgH4lGzyppbb4nN4lSIG3rvsaAOH876wAMpDMQ9ECHH5aKZh40oJkKH+eha6GabxyVH9bYy/xADJUjWu2fdWiH4VU7ulCIONruAD0VtqKEbuxjL8SYrXvhbdvrcK0duy8+rbWjKy8c+PAC/o6y8kyiI3ZvcTTy1bxUkryWRIfHsq5NBoN/b+lb3bZ6c13JUTk7STbI9Zmc70HO7z4fpD3QLhr5noqme92HShmn89HvjwfNashDtd0SYBtQi0oR+Sh5FqBjhoBDEFkCrMaYGCJzUKlhAipLNKJE3guYhshGNDPCrWpzcUDCjXQPYIRDNdWorushk/GVNTV64/4AlsdDUTyGP5PZmvL5CyL5+SWPAtFksvEb8Q4J6ON/BjqR7EfgVdLF3rYZJT8LJc53gWPQSbSPg2XvG0WJt00PSTtKximyE3KbgCXAWozJ2FHvESjBe4CN6HkXoZH0cnL14X0MN9LtvnBNzLsRQuHaOpSYjiCVMp5UkqJYhMJEB23BYCoSzK8SY5UUx6IcEYtlaqBHa7BgWWOwIOXcakVTyHZJXBY4BjleoBKNNgNAb+BEVDJ4CtVvdwUPSuqlwAhgOBrlVqFkXYFOtvVD/XhPQc1xhqAE/wo6URhDJYcMsEFPm3HAyYhU7yfG6ZZlWfvDebj4HGF/5p9qks2VFw58rAL+TTDoNV4vnkwGUilKUyl/Bg0j84GeIqY8nYpVwtEW0BIIrslPxvsH7RvvLnRdMeDL2ITs0ZcP6JWBpIEOn5Kxk7HgpI7t/J2yLR7w2duWAE1kK9Mq0CjX55wqWnSxEViK2lU6GQ4hNNLdhJJ/T9RUZzMiC/dxe/hl9fX1I6uqqlo9Ho/7+NgNYFmW1NfXl7LrSeaPhSsv7KcIh2oEuA64JxSurbeX5aHNIX8TCtd2hEM1PdCJrZuAJUSjYyvbWvDs4jNthMYoFFdDwAMIJFOIFfN4g6VWuhUlQKewwol8BbssWGypIeeW7gGiPs04MPaqdpQIe+zikiz75bO3T6Hk245GsmF04iwFJO2fm1HSXY5GuAOB8WS14Qb7WGX22KuBpfuiqm3hwoU9fT7f39AsDfcJsnvAApal0+mvTZgwocvpcC7p7qcIh2pGoLP7a4BpqB76OPoIfjqqib6Eku7VaCT4IKnUBcX12zz5Hx3SxCHjhYgXxAOlosQmCTAeaPNr1JkB23oMZY8MHyFjD2DZIa1BNdu27KYIWSmi0zmgsoGPLPkmUHkigka/H9rjdZDVkGvR6HYZqv9ORCWONErWFkq+JSiBLwY2uFVtLvZHuKS7H8Pu1PAMqdQmoBm//3DgmlC49s5wqKYQeBY4Co0I5wMXAZBOU9jUaPLTqY8kt9rEuS4DPTwaIe6QA9JAHtQbKPUqsToEit3fZ4f8sAtdypkga0fJ3IvqtbtKL8ughBogy+8plDDb0Ih3I0q8EXudhaaZbUQjXx+aE+xHI+Z6NNe4CtWNt6MpZnuldNOFi70Fl3T3c4RDNScXtbb8x2NliAeCv6tqafqvnHWFwLPB1tajjUdIFJdsBe4EgljW2ILWllH5HbHe3ixZWgY8NvGm0pBvO4vt4FCBSAYCXjA+m3jt8NWTS7q2xrsrZNBc3ChKrI5O+1H3MSXZDEqSjnqRRrMTmtEIN2yP02avy6DkuwFYh2Y39LL32YISdCFKvj57m/fdqjYX+wtc0t2PYWu4j/s7YqcUdMTwWKbZn05/329lHrI7NBAO1cwoa6j/s9fKADwbTKfPwZh4OFTjx7IeDHbEzilubbGbmZEWOkeslsoGlkC7UbLyCSS9sEWgIgMB2+DcIWtPrsa7G/EyjRJlFCXEclS+2HkXRwv2oZGqMxucsferRyP5rfZY7fa6DErKG+zlvdHoeivZ1LQylPCT6KTcuh2FGC5c7CO4pLufwiFcVMO9JhCNNualEv/ypzMRfyr5e58xD2zu3XeK8XjuIZFYUBZpP8iXTleJsV4PWNaVwPpw3+pRWNY7ga2b80rYwXZpdqo+QyfL/DnLLJ8SV2saKg0Uiu6UAdJpLX5AsD0gd48UGvlG0P17orLDzkiixywgqwU7E3QO+W5Fo1lHA7bs8WvJWkz6UGLeal+r2McsR2WI9zBm2yeftgsXnw1c0t1PEQ7VDEJzVGc6vrlbevY+ORiLPVLUEX3NEk99a0npJZbfP9+InOSxLE8w0r44mEoO9KUzryX9/vuae1T+wng8UrZ9W5WkU3jB8mjEmsJOCcsp+3WIOOGBmCdburslA+VefVz3otydyoDH2yXO3QGHVKPosSqxyXsntKFyQhHZyTYn+o2ik21O9OtkPqTIkm/CHrfZ3iZCNmWtBiXmTSj5fqJHqwsXexsu6e7HCIdqSkPh2k6eqPWVVT2qGhumGjg27g+MDqRTd3qNWQbUbqvqlQ5G2u8o6Yj1sMDbXlTUNxHML0onU31S7a1UGRPwQ8qr2Qomo6QpRhtT5hZIWD4lL0GjzI0oafVBI0aHBB1CdF5dQQpNM4ujeu/HSQ4N6PkV2GM72xiUSJtR8t2Ckm8UjaQ70Em0PPt3J89XUOJ3ijEywArU+2G/qGpz0T3gku6BCHXomgychBLTSjRHtQGN9iZb8I2E3398Ii8v4DF858RopODPyA2jMD0EYj7wpu1mlAJpLxhLJ9p2RK8+JUiDEuQG9O8ilLRy9VchS9q7mjDbGU6erpPpUGS/dkYSJdZCspKDc34WSr4taPS7jWwknbTPOWWfTx1aYhxDyTuO+jr0QqWPxUDYTTFz8XnAJd0DFVr6ejCq+Zags/TrUCJrMNAn7vf/2Z9KxXzw6kzwr4W2OwJ5pxYkEwO9ELQgzwKvBzIeO5LMQMqA36vuY5Cd1EqS9U/IQyeuKtH9nCg3gxJbPl2rdjT2uO0omeaSay6iKDkW2OPaEjMm55gtqGa73R4vYo+dRG8QcXQybbF9/gl7/0Fo9FuHGunsy6o2F90ALukeyFDiHQBMR0tkN6F9zBIo2ZSghRQDXoX8d2DND+B+tLjgevTRPkC2BsIh0BhKgsGcozm5tNvQiDpl71+NkpgTgToFD44u2xXd14l842SzGHaWHAxZw/Q8sr4OTj6xIy00odJDI9m8X2fcAlSOmI+mogXs9cVkjXs+AFZiTLIL5+3CxaeGS7pfBIj0Qc3Mh5J9XG5DCa8KmLwMTmiFtiPhLpScB6Plw33IklyGbFJCBJUMSums16bJklor+vgeQivCnOg2N+vAQsm3K5qvQ75pe9xdSRVOHrAjHXjJTvA5X+aYfX5NZEnXcUlzSoZXAq+hRJ1BI+V+6E0kArwPbMKYvdYxwIULcEn3iwORSrTf2MEoiSxHydUDHPQifKUXVB6ipbSvoRpwNXADSsBO9JhjQEYMJaAefJQAnbLdCPr4X2aPV2CvdybanDJhH7a/QxeQ29TCw66j5STZG4PHPr/c6Nhjr2+yXwn7eprsdcWoDPE2Kjs4xutJYKR9PdtQyeGA7NXmYv+ES7pfJIiUoS1uDkFJKIySb/vP4LsTYMBJSixp4E10cqkMlRoOIysTOJNjkI0Sy8nKDQ45p9EIsY1s3mwIzYuFbBGDz962AyXlrky2QTZihl0TrzPJFyUbHTtRr2OuI+g1b0cjZGefZlR79qKSwxuorttmb1uC2lF6UT+IZU5BykNyPLEAACAASURBVCdBRHoAL9t/9kbfg3r774nmM5AuRGQ80NMY8/zeHtvF3oVLul80iBShvrVjUBKNAssOhyMnQuXtSpIHo4S00H61AzeinXgL6ExwzmRXMxqpOiW72Ns5ZbtR+xVByWwASoLOJJwjA8RRInR8F7oKJ2reVRGcM5nWYW+TR+e0NmdyzimacCrgYmQ9IJKoTeZylIS3ouR7kH0tcfQp4cNPU9UmIrcAEWPMbZ9iH6/5lJVzIvI14GBjzHc/zX4uPn+4FnRfNBgTQYnjHbIt0sceBT03KiH+B20CGUMn1E5AZYEfAo/S2S3MycN1Ov3GyeqpjvbqRUm2BM1mqLL3WYMez5mQc/qn5aGE12GP0VVyyc3V3RmOuU6pfb3t9vgO0TuOZcWolDLIPoeAfc5Be904tC3Rkahr2yEo+b6a834dj0ivPTVOF5GnRWShiCy3iRIR8YlIi4j8TkTeByaKyOkissre9nYR+be9bZGI3Csib4vIIhE5TUTygZuBS0RksYicuyfn5uLzgWti/kWEMR2IrEKJrhrofQj0KVKSyUNb7WxHCXcASqgVwG0oyVyB5rB6yUaXfpTUoqguWoYSmbPOkSac7AOnSCGDEloe2agX+2/H9zZXtvi/wCH0PPTm0EC2N5tT0eZ0siixt9loX6OTd1yESil1aAXbELSIYpG932j0iWAjIu/vQa+2LxtjmkTbEb0rIo+jN4lSYJ4x5rv2utUo+W8CHsnZ/2bgeWPMFSJSDryF3hxuxY10Dwi4pPtFhTEJRD7A7lEWh0n9lSj7oY/Pjeg/87HoI/TRKPHOttd/E40IHY3XSdEqsn9vJNtrLYGSlkO6TgZEPtmCBIeUgyjxOr66oBGxs78n53h7Ek06+b55ZHuxeexrK0Sjcyclrgwl32aUfC30BlFpr+uNkl4/YBQqxcxBiXgg0BeRFWhft64ap/+XiJxu/16NvseL0ffkCXv5SGCVMWYjgIg8CFxur/sScJKI/Mj+O2ifn4sDBC7pfpFhTBqR1UB6GWwfpMTjeOiGUVKch5LswSixlKLSwG+Ar6OP3E4hghclQkfXbSCb6xu1lzn6qRPJesj660JWY/WhxAtZnwcPnW0ec4/5aeGzr7UAvUGsRQm2h73MQqNxIfu+NKEFIC2oTNIPJeDN9n6D7XHmogby49BOFgPtjsabd1fVJiLHoze3ScaYDhF5jWyU32G6NsEiwJnGmHU7jX10F/Z1sR/AJd29CBE5EY2O9it4gDEw9EPIa4dxVVCYgPQaaKqE/AxYJRAYDT1KoF8UxkcgFoXVk6CgGoZ5tVxY7DJhj90/LRiHiB/EDyVpSBjIGDXDyXhtAs7YMoLtz+vJQMqjlXAFFmQsLUMuCCgJN6PRdG7RhkO+n1Z+EJT8e6KEuwXVuytQ+aSQbG6wZS8vR0m6zj52X2CYvX0YJd9h6KTai2gV4GiUTOtsyaGTX0YOSoEmm3BHoRkju8IKYJiI1NjncUHOutnAt4DvAojIOGPMIjSq35V7m4v9DO5EWjeABSyCyCaI10LbWmj0gWcYVDZB3G9/D96AtfXQKmDyIa8HlK6EtRtgRUoJ1RJ7ck2UWH35UJqBdFz9HPI86ueQiUOsA9rSWmIcEPCk9BHa+NSj18qoo5nXr8TopKkVktWNHWtGJ0vCKd39tHByhPuj0WkCfaRfika1uZN9aZRYD7G334pGuiXoY/9Ae/kJwPfRzh2L0Uq23sAJiIxBZFcOav8BCkQliZ+heuxHYNT97JtoNP2ufY4Okc8CCkVkqYgsB26xl78CjLEn19yJtP0YbspYN4GIXAFg4Hk0l1bQiaJ81MzG6cy7HY3cisiWyBaghHM4Ggk6WQGOny0oMcTQx3Hn0d3RVPvZ+zlSgyMh5Obu+shWsTnrYva6nTtLQHbi69PCKVNuRqvS2uxr7Y9Gs0507WRmWCjhhe1zLLeXNaM3kRgqScxFJ9uq0fe3HSX1PapqE5EiY0xENEviTmCpMeb2PbheF/sZXNLtJthBusbciybv9ydrKt4TfYwVlDC2oVkNzvISe/koNP+3nGwhAmSJN4LqvE5xhNNm5wNUMx1C1k3MRzY7Io0SmSMpOB2BndSytL2fo386PhBO1sSewPFq2Izm5zr5xUPsa/WTLaRwikFa0Qgc+1ycmwb2da5FI86N9hhlqKSx5NP2ahORHwKXkM02ucq4LYe+EHBJt5ugE+nqglI0ayGNRpKDyfopHIRGchUoOdeiJDgUzVUdiT6CB8gSp+PbEEMfySvIVqK1oV4GzejEUx80snYq05yJNotsFA3ZiS6HID3oDcA5ppPnG6CzOc+nQRqNStei3rtObvEY+9r9ZH2FoXP3Y0cLdqJij718KRr5tqA3IC+q/a50jdNduKTbTeCQ7uE3P/8y8A3ghgW3npgPDL5v8nlDF9eMPOx/Hp71LtkODINQAg2i0Z/Tp2wwmrY0HJ3h95NNE3MKHRL2tk73B6cwYin6CD4MzZbIbd3jkJETuTopYyl7Xz9Z97IgWXNzJ+p1GlzuaeTrePeuQrM3UvZY41HydeSMZM51xtGbgVMK7bevx4PeYBajum3cfi860Im8Da5xeveFS7rdBDmkWwTcDtwHfKV//abD8tLJ571WJnbF/Ie+NHX1m4PJ6ry9yWYODEOJZBlKvCeh2m9vsjm4Dgk6ZLkNJV0/SrwWGvG9jEasB9vj9rH3d0p5i/lotkKcLNk5BRZBNMp1tGUnRa2Irvs77IwEKqksRskX9OYyBtWmC3K2c5BGo98tqCZeimrbPlRuWWy/nPOrRyP/7a5xeveDS7rdBLnywqSZs28Cbm1aU7KmYkhbT3861fr95/789TMXPVePThiNRieVttN5Zn80Soyvo8R7IhoJ9iZrHpNLvBYqWTjNMJ0JpS1oae1WVLI4FI2m81FCdSwjnUIMR1PN9dx1iNYpvHAm3xL2cqe0d0/TIiOoNvsKanhTgercI+nsppYkW6KcIavtbkVvJk5lXyMa5a4ke3PZCCzHmPY9PEcXByBc0u0m2FnTHfG1ec9++L9jTwoUJcjrEflSw+KD5qBEWohWYQ1ACdFx5CpAyeww++ccNAI8E5iAThw5WQhOpoGDVrKarENSrcB7aARYao87iazc0IGSk1NCnFtc4fRDc0jesXd0CjMSZG0fe9jX9GmaaDpwWgKtAh5Cb0JD0RuEk7vrkG+uy1kSnXBbae9Tjeb7etH3co39akGj3pWokY5rnN4N4JJuN8FOke444KUPnx9c0bq6F1bK25yO+n/xCBf9z3k8dhBKgptRQjkMJZPNZCWCKfaw81FyvAQtDnBIyCFJhwyNvV0b2UkphzzXohaTDWgO7TSybYCc/UCj4GDO8jTZiNiJZnMntCyUtJvtbfrYY+xJbrqFTra9CdxhH38SSr7DydpeOu+PY1/ZgZLqsgy0p0UmC9LTY6yYB2o9KuGss9/b9ah0s8U1Tv9iwyXdboIcTXcJmnQfAY7d8k6f6+oX1VydbM1vy3QE5uYTvzZGoR+NEOtR4pqCPl43oNKAAMegUeg7KFl8DZ1gczTeQnu7XFP0BEpexh43br+2oOS7Gs0dPhWNoiFL4I6U4CdLvg4ZOu5lAbI5tg65WijZOxkV1ex5jq8jHzwF/B6NXp2OHcNRndqROXz2++PLaMv6oqTPZ2WQgBeSPmNl/JnMOq9+DttQCWM1Otm4HGNa9uD8XBwAcCvSuh+clj7HLpg1/cONzx5yzfBL37u1qF9TAsyIDgqeEcy0Fkq3o8RXghZUbLL/HoqS3DMokU1BOxP/DjXQ2YISqWMR6WQhOD63ZSg5FpGVI8rRqPEwlIQeRyPADvsYTtWYQ2htZFvEl6FkGkMJGLL6MvZxS9A0uBSqJa+l80RYV+G1z/VS4FngCHRS8l77/XC8iZ2bxBag0UCRgC+QTgeC6RTGEEh6vL6kx4t9PQOBqcA5wGXAeYiMRmRP0+Bc7MdwI91ugp3kheCCWdM7JdpPmjk7+Nat089AfXWLgLev479/+9/8yIMSyXr08f8QlCjfRCPf44Gx9voH0e7EX0eJBLIdfnMjXqeowPnZhEbVjlfvNpS4D0Uj5zaUcAfQOVXM0Dnft90+p2KykXbSPo7jDRyzz7XePu9qdt2BuCtIo5kO/4OS7qEoEY9EbyIFFiTi0N+n3hWALXZ7PGSQaIGV+RN6UxhM1ou4DY18X0Hzfes+jXG6i/0bLul2E3ykOOJjt6MajVoPBppG8/7dczh2aQ+anAKC/ugjdRmwAJUWpgLHofrp/Sjh3GD/FPRx3unm4LycjhNO9VkDGk07xusZe/0Qsk5hTlToNNPM7UThPNpn0MmrOBoBO166To4v9nZN6KN8BvWt7cmep5mlgA+SIo+2lFdM8WQyR+QlEqm8eMdmHxRloL8Bn/OfFkEI+v340+mOgLFqN0P7RvjgCNV2R6ASSwCN+tehng3PY0zDHp6fi/0ILul2A0yaOTuw8r7rb2jbsGS9Q7qTZs4uA0ILZk1fvvP2InhRF6tLAH9vtiz4DT988lL+uQGddS9FI9waNPtgLho1noMS4MNoBHubvdyRBvLtQ+SmgTldHZwuv2vQ2fxClNi9KMmCZhFY9nEPQiNDR5+N2uPnk9WPa1GyryBrNRlFSd7RfzegZbZ9UPMap5LOiYy7hAw7ctg88aJiT8rvx5dMdRTFIms8xgwUyDfgsxCSeQHigbwOfyr997J4rGcTTPaCt1RTyl6035sJaIFKgX3Oq9HOHs9hTDQcqqm0P58atNHoM6FwrVtwcQDAJd1ugEkzZ/9q+6LZ329Z89Yfmla+/n2bcGej/7CDF8yavsvSVBEmoxHrwBJat1zIQ8/dxE/nVhNeYW9yAurHsAadXOoNfAUlyxfQlLDfoZGxk2Hg5NQ6puhOFOq0a3cI5jV7G+exuwyNdN9AI1mnWKEfSpSOOXoMvSk4RRPtqATQi2w0nEAJ3rL3i6KVcotRsptMlvC7BIftnIg2DRifDxFJ5aVSz8W9vomSsSp9WM7dptUHjwn89Sdw7iVw3Cg97yAa8T+mm3FkBsYa6GkgKbA04Q+81NKj8gY8Hj96k3FaNB0bCtd2qXmmi30Hl3S7ASbNnF2x9e0n3/P4g6Ge46ZfCnwP1WfPXTBr+lO721eESuA6YFo+Maby6uKzeOKpc3nslQqaO9CJtCPQCa9HUFL7CkqWi4EnUfvB08k+6gfJmpk7k7kOYVr27x+iE3hh1N1sOJpKFkON1+faxx2FEmo/NOMi1yTH8WlIobJFBL0xOLm1bSj5OpkPTj+0jWgmxmH2GJ8Y8dqi8UcKMTJI2ot52A9zkyIHAWM8xlT59EbiA9rehE23wIuzNcq9PgOTjUb6de2BvA+tvOBpeYm4z5dKGa+xsMBK+/2BjmAB8bwgEvBD9sniVjfi3b/hkm43QaCk8hsVh140s33VLVUlAxupnrL+jHd+uXvCdWDLDecDVwZI9JrAwm3jWPTKobx7/5XcuxmNOqejEeNjqP56HqqVbkTlhq+gLWdKyRY2QLavGWS7+qZR4t2CRuRz0LzYaWiaVgzNEX4YJeKT0AmxXqjmW4KSejtK8Du6M6BRstOQ0jHkaSAbIWfQSPsplGzPQScPnQm8XcJmOcfkvdMqD7zl1ZtHiwUb0+Lph7EGeKHUC30iUJKAaDncv7Vn794mnb6oNNImgVTa6zHGk/Z5PQ3+fPI8kJ9K4k2n8VoZ2igiSjFWmSFQ4INsetyPQuHaOz/uXF3sW7ik200QKKq4tmL0d29u3/TNXulYgHSHf7mVyPuRMTzT1TFEOBj4tpf0uNEsZRDrFgdI3vsgFy8wSD/gLDRyfRqVHI5DsxkiaP+vycC3UcJzSoW96ASWM4nl9FxLoMTbiGZK3I/qrleiUW8HOpH3L1SXPRWNfMvQ6riBKKE7vrxFZKPeiP0qyVkeRSeyAvbydrTc+Wl7rIvQKrRdGurktE/OjXbTAvj0mI0pWJoK5E0xQiAjnowxljeYSCxqhPIiqCgAb0Y8RbGCAn+spBTicUqiEfzJBG9zOBYehvTaQF4sikQsoqYYwSLf20KivBQCO5IwYsDFoXDtk7v9QF3sE7ik2w0waebssq1v/XuhNy+/f+Uh07+2/vkhP2v9sLJXsjV/Gxn/m8D1xvBhV8YSoQT4uofM9OF8UFVDbThO8OlXOeZBg/jRCLcElQDeRtOnLkTJ7EX08f46lEAdYxzHP8EhNEdiyCXeJWgp7gbgx6jkAKobP4wScE/7WMPR6HaYfTxHx3V0ZMcnN0o288FJa2tEU9aq7GV1aNT7GnrTuAgl4Y9kOuQ803uxzXk8kLay5cqeDHg68ODPC4ABg0mtTSaWeiE+BEZ7oLiFIHfwXY7wvcyo4o1saK8hki6hhCYGl9WSSXusxnS56fDkeYdkPsRvpWlJ5THfewxHlL6FJxgAeCcUrp34yZ+oi88bbnFE98CPvXn5/VvWvvvHt346/d6DTl5zyJDzFi4rHVhfDpmxwFMi/FRkh8H4x8IY2oDfWXh/u5IRy9czsLeXzCUTeWtWgETf+Rz1MBoxOmlka9DOBw2oQU4S+DmaK+vouo7HQZSsq5njkRtEyfRQ4Mso2d6IRs5J1N/366jE4Iz9D/Rxfjk68RYhW9nmpI05hjigj+ROpVwVStYRVOOtBq4GfmBfy9XA3Wh6XCf42OHMnnHaIls6sSb2BJvHAyzgSO5NXEwynUQ84i0QqQ5Cfsbjo4F8/s4MfKQIpg21sRo2Sz98BUmGF6/HSnuolZrUloJQuKq0rb29vIzt3jJe51hSmXzWJA5yTqf6kz5LF/sGLul2D9zcuGzer5pWzF8MsGDW9KaiXslpwy9eehx4v43mrF4IPOfxJy8X2b0zlzGkjeFZg2fWaoa9tJH+Jp+OI8ew5CfTePmkC3lwDpr2NQ597I8Ad6EpX4eiZPf/UFJ0TMidjsDtZFuwB3JeFfZ4Z6AmO/cDf0PJfCiqGZ+J6suvoFkXc1ByfA1t9pgmq+86FXIl9t9R++W0hh+E6sfr7eWHAr9Ao+wC1J7yVbK9y3bASUTOgQ/gMU7jbcaxgaFY+NloVdFaUupZ4fO3xyDSbhW1PsKV6QAJjuFlgpJiZWqIqfZstCbI+/EY+R1LfQd3zPFM++6c6LRBJdGWa6w2q/3t9GRqvf0J5rczrLQO+/2bv7vP0MW+gysvfAEwaebssgWzprfsbtnOxRGTZs4eiT7uz3jr1ukvANeLP3mZryDZL5PwvWrFgzcDC4zZfSNIEQqBS/uz4ZQBbOjTQX7DIsa9NZD1L87nyOKeNA5HifF1dBLrVJQY6+3X5Wgur1MQ0U62XDi3HVAiZ/0aNHvhDVS+OAst2ojax3kZlR3qUKK+DM2mAC36cHqhOc0wLfTG04pOpjmtgRwNOIzKEU7RQjOq9b5mH/cIVHooZie+dbIa/s7lgJcIBbzNoVzFbzjcs4poaRl/i0WfKkkUNqW46Kg8EgPGsJgAKd9aBqTHsIJhsuaNzXl93vqX/5KGB+KXv/Js6qTN/ag7ZjO9x/ybM05e7h0+/OiS17xH5b/lFJ3EgMNC4drVu/vsXOwbuKR7gGPSzNnHAf8LnL9g1vQX7GXfAGYCkxfMmr4Wdkm6Tq7uOOBcIN62teCpupeGRNs/rNoO3hgaMd5hDOt2dw52ZDylhk2XDmLd8DjB+GLGri2m/c2HOb9+KvP6eTAx1Lj7A9Qs53CyPcouQom3gKx7mI9sSyCnHVCcrGH4h+gE23y0iutINDo1aOXcXJSUP0CJ8HT7VYGS6nA6tyx3SoRX28cMkS0xdvq2NaAEXWz/3AD8BXgO9RqegUoqBbCDcD3/4WRPIxWs4yCe5ETCDORo5vE4F2CA5RQxl9PTHsra+7B1fgFtFXHyRw5ntQxjVSRKceQ9Dt3eSvHLJzJ7aT6JoWs5qOKn3DTiEc6Pnh187JVfV9xwGlq99ybwi1C4drefmYt9B5d0D3BMmjm7EnUNG4Y+eg8B/ohO/py3YNb0JOy6DNgh3rrXqyd6vBZ9J21e0rKux4mr/nnocejjel9U13wceNgYdluGKkL/EHVXDeeDw+IEg0sYs8lL5sOf8Iu1X+OuQAUtFvq4/jZK9o5F5GaU+MejhJghq5nmVp1lUN02jhJknT3Wm2gkOwoly3zUJGcReuNYiDHNiPRFsx8m2mP2Qv0ccl3HnCq19+3tetM5s6IBjYor7f2a7ff/V2gUfiTwXwYOS0NgDkf7VjGMORzL20wgQgmjWMFzTCMfaKaYlziBRiqoYUPK65XX2jKV8XJaVk/jxWcbqDzkQwadVcW2foP50O/BanqPsfPP5vFAha9l9I+Kf9l3Sv4Cn/153xAK127b3WfkYt/DJd0vABzi/fDZIWPyymKEJoefQrMIUgtmTTfQydrxH84ye9/zlv7t0EfSsTyslGdlOlrwXWN4wfZguAatOstH9dh/Ac8bw8dWPYlQ2Jfw+SNZcXoKf8kSxjSk8Decz8N13+BPjaNZmgiQbkJlgBBahOBDyf10lOiK0Ii1BY2GnUaWjsuY49vQjhL2O2ghxkBU3w2SNbzZghLvPDRn2KBywOX29gX2+L3Jdih2ulJssI/Rj6w7GvaxV6Ek3N/erw5tgfQgMLID79WvceRZr3K07zHOYRXDKKGFo3iThziHINBAKf/gClooZRSLGMU6ttA3nfLlzRtftuTR/0RPfWZzR+/xp/CfsRNYOMqDNfwdxvX4M98sShDwXpR/n/ew8pVOabVjFDQ8FK6Nftzn42LfwyXdLwjGXDf75tX3HT7LSvhId/g+KKppeX7oBUsGeb2cv2DW9LiIXNFzwinHDTzlWx3AjAWzpluTZs7+EvBURwdb6l4YXhLZVFGSag9uN2n/m2gV2SrUj+BKNIpMoZHlI8CbxuzoWdYJIviqqZ08gpVfsfD0XsKYtg7ym6fxcuJM/nfdl3gxEmJLK+p5YIDTUPLbhpL80WRbtTtNH/uQrTBzOvAmUVLciJrXLEQj3oPYkTa7Q2ddiMoAizEmbtsmXmAfuxeqI4dQSSPXFnITSqaHobqtM/FmoYS+CiXlKntZXQLfssc5e+hspk14mtN9zZRRST0nMJs/MYMi0tRTwZ1cRQM9iVHECJYzjLUU0cYRvE5zoLRjq7+fFCbjc2oym16P5Bclvpe4rfG99KFnj2D5uO/wu16TZYFvo+nJguAkjq1YCCq7/FcoXHtXV74zLvYNXNI9gDBp5uz+QHLBrOlbcpaNRs1n/qejg9e3zBkyuHV9jx7paF57sCJaVjZs+/seI5OT7T+4oyg07LKqsV96Eo2Cx6ATQR+glV6Z9i3BlzbPG3RIy9reW8j4Iujs/K/QR/nzgZPRiLAejVSfBJZ93GTbSFnRbwhrrk2QN2IpoxPNlLcdxWuBsbz34Vk8uWUMi6MFxLVbghZRVKIR79FoBOwQr2PZWEW2ksyxOEiRtWtcjEa9w8i2GmpAPXB72mM/Dby6w7FLpBr4DqozO+3ScyfSQKPtV9E84YtQWSS3QaVjxDOwA2/B05xm/ZlreJMjAj4s6cd633G8xM+4mWLi6TBV3M3XvFsIiY8UeUTwIBzDXE5gDmk8NNkcnheIECurZEHqcJ6Ln0jG+DpG+FfefpXnL1fXtvYq/Q530EE+DwTPoqzCA3B3KFz7tY/5CrnYD+CS7gGCSTNne9ACAT9qQL5l0szZF6A5o4XYGi4wMRHx/HHz/IOGNy7v2+z1Z3qlMhkJ9Hqaiup5i/pNPWfSglnTk5Nmzg4APwV+vWDW9MZJM2cLqgVXvvWrY+4imfcDsA7yl3SUI+ahVGvRf6OkdBGaPhVEH7/nAs8aQ+0uz1sWFB/Eh19up3jqckZlttI7Mpk38nuzpeUY5q45jlc6DmLDB2j62DT0kT+MRpZnopGqQaPdRjTS7EP2Ud8hXqfR49so8Q5BMxvS6MSaF51w86EE+hRK1AejUfxRqM5cQrat+wCy5Gqhk3ffMtoJ4rcCI71ZYo5Eyat7gAur/8S1RasYRi/q6ccGjmUuVwbvSZabVt/2ROX2e7iydzM98ZDxpbBYxyBWMIYaNnIpf2cbAyilgW/zZ+IEeYmjeS7vbDxei1OD/+Hw4MLYG7EJ/Lz9poKE5ec7nv/Hab1eAb353BgK1/72E79QLvYZXNI9gDBp5uwp6CNyHVoA8Av08fsutGDgJ/bPU4DRb916/H3BvlsWxzf3sq0RY41Q+hPg/p112UkzZ5eike8g4LRNLw1ZYLzJN1rX9TwkUV+0xUoFVqO+Cs+hpHsK+lhtoTm5rwIvGMNH2sycL4/4a6g9aSP9T1vByML1DGyfzBuFxbRlBrB+7fk82j6cVRsqaF6AEt8YNPodgtoXlpNNF2tECT+37Y6jwXbY78279vkMQonXj0ajjnlOCI2AV9pj+uzjbUA15dPt8aP2vo5uSxoSCY8vEy0tCfiiUU9RMmF5wRcjn7/xZX7Pd4hRSF+2MJQ1HMIivsw/NlfS/MESxm29n0vPTBAoSBGgkQK20tcXp4iDWE8/1hOjkLUMpxcf8CbTSZFhEnN5mgs5jFd4vM+3+E/0BG6LXBcLSCJwffGvfNMKXsN+D1qBwaFwbdMnfZdc7Du4pHuAYdLM2VOa1pbN2/LGQCqGb81Ujd/i9fn4MUpAM4G/A1+zNdtvAH/c8HI/ti0IQaYgDZIG72ZI3XHIN18P5Fckfr1g1vSUPXYV8Eq8JW9kvDWQLOvfHmzbVPyDlfdOrkAljELUVPsJspHpRHSSKYLqqnOBecbs1A5HRO7k6+MWMOm8JYzp9wHD2ifzZn6QuNdCtl7Jvc0jWbFtCKvnBEj3RrMAWuyxv4ZG2U6PskaUBGvItml3eqMlUAJ9D83X7YlGswF7+TK0Mm4YSqhL0NY7r6Ck3d8e90L0BuBH08iG2mbkO+wbI3lBwyW83QAAIABJREFUPH5/pj3iifyVa/Me4HJfPh2+KrYzjNVUs4mvcI9VSYu1kmHmL3zdpCnwNFPJGgaHg0QKSmgrH8gGn5BmM31YzATG8ypvcALNO9rEqbWvhyS3Fs3inx2X0WyVbfhp8Y0LTyp65RT72l4FrnFzc/d/uKR7gGHSzNmHblvU851t7w7ASvrSYFrEn6rMr4zQa2LdI6XVkYtswp0B/BnVXX++5d0XFzQu+5InunlqgrQPsLwUxn15RYm7E9sqf2iMpmhNmjm7es1TI2pjdWX4ixIftm+o+hL6WD0RJb/x9qk4htsRVA89CJ2M2mavewVYuPNk21Ny+qDXOfLk+UwZt5gxqSN5w1tALFBLqOV8HuuYzOv1lTS8NpIPEva4jnzwDZQMnayGRnvIvmgk7LXXWWS78C5BbwJ5aF6u0wZ9O5qR4USwb6PdGepRHdiRDIai/dBGom13Ml4dx/cIZ1PDJgawgV/IjzteNl9KV9GQX80mXxltFNLCd7idcppYwmju4wrilKY3UUMb/vZSEvG+bK8qpoUWinif0b4NDOZU/sXjXMLOvjpCkrO4nxXeKZRLkzU1OPeiX7bf9Eg4VCOAhMK1bgfhAwQu6R5AmDRz9qFoTmhzvFVublhac0fLqp6+6LaiAMYDECHjfwn41eE3zy4Gvgp8ecGs6ckeBx/zh7JBE74R6HFSQ9384Ynopsoak/SlwOc0UHwwWNl215hr3/xLrD54cnj+QKJby9LJ1uCHJhV4Hi3brUcfvc9Btdc4GjkuRKOtiWjxgaCz/ivs812dO9m2Wob2fplpRz/BWUe8yaSCibzjLaFNVjAiNpVXfafw7PZC2t+fxNsfFBGdStZo/Co0O0HQXNntaJRbiU6y5ZEtpHCMcpbZ59GHbJaC0wZnGaodj7G3fwrN+XVaE2VQieIa4IQ09E1BydOczAKOpoEKKqjnPQ5NFxKjL1sA4+vDZnqxjSv5B/OZxANcSpQy6qimiIZ0LxpNISlJEGAjg1LvMVoS+H3Tedz3HOezszuklxin8CyrvYfQx1PHTcU/XTw0uHF8KFzr/vMegHBJ9wCBPZG2FJ3YOWbBrOkbJ82cfY9lceW2Rb3Dta/XYKIFIVK+BPhiKNH8Hvj3/2fvzcPkuKtz/8+3unqdfV81Go32XbYl25JtZONFBozNFnYChMTADTf3JvzIJckNYLJdEkLYAiQQMGYNgQAmxovwbkuyJUuWtWu0zqYZzT49Sy/V9f39ceo71TMeybLse5FG9T5PPz3TXWtPz1un3nPOe7QmW7P2tnti5fXrhts+syQ7GkPZ2c2pZKghN1TchBuxIeeoWJryZacKKxYP/HnZwoFvDRwtfur0s83zRk5UtOlspBe5Xf8WQkYmi1+FRJ6HkcRUGUJUBcit/gnvuB/WmsmqC5Qq28xNV9zNB9bdx+ubLuP5RBW9o9tZm1nB3vht3JepoftQGQPbNvLUaqSUaxR4NyIX2AipnkKItwwhVZP4MvW8494y7d7xhBE99xhyESlGEmbLkRbgXYgZeBKRJsy4+KVZ+Kv7uXnJfdxut1HHfPbRxnKKGHYipCljhDL67QRj/E7BL3h6bDX38TZOU0U/VVTSTj09To44A1Swg1X0iNmaM4cHU+28vnT63z1Cko08lmmzFlst9lHnj4u+9K1l0cP/X0Nn+/lMMw5wASAg3YsIV3/6wUVA2iPc1yIk+Cvg/V1b546qaPq+3j1VN0+crBqAsJkJ1g58r3L1R19bPDdxa2b89xg8WEt2PJIJRbMRFXJeGO+o6gB3LbjloLJgnwIeKmw+/f2l79n1xaO/XPpfA3ub1iLJpSFkFM9PkQjzrQgJxpDos8N7rvAeYYQs2xCd9RGtvVHpShXtZfmKe/jdJd/mg8uXs7+2lu6hZ7lyuJLe4jv4ZdEiDh8ZpWDHO/lxWYK08Ve4DUnmRbx9tSHEW4pEtIX4Y99dfOI9gES4xuwmjUS0YW+91QhxjyGyzIPIBaYCmPvP3HnbVq5+52kq1Cp22qdoIkTOiZGiQaJcbNLWO/muepxb1GZupYM5pInSyFHKGCFJubOXFRymhRwWMRyW8kR2F5vCTJs8kWDAqWXghWMs1UhFxqe15vT5fn8CXBgISPcihVfi9Sbgl9vu2uR6r9lO2rr9uc/d3IHcEt8IlEIOwoORRPXB2JybINlRiuPmGDvcmBrrLD0GoRHgNLglYC1GSMsBBsF9EqyvIPW870HMZSqQmtfHvMcCxHOgCSG7YYQMU0jJlxnPM4R0kG0FtmhNBqUSg5Qu/glvn/PX/MWyZk4ur6I3/QKrjjmEyt/Kz2oXc6g/Q+TZG3jEXc7BJoQsX4t0lsXwB1paSHRaC1NsKnMI8bciUbfnLc6QnDft3mu1yJSIRd46u4D7cqjir/ORG+/n9W8YoMRZzXPjaeLzNZpK+ihj2B7xKs0+xlfdh7hJ3ctbVQeNJBingeMkyNDKUmcnlzNOQQa0ZZNRc9k7epTLS5hGuKX0MD/U1vtcbsNxpJvucy/Vhh3g4kBAurMU3oid1cD7IHs7KtuEtnKE3CgqRDiRoailb2Ksu+DL6e7qFUgUa6blFuC33mrEX2A74ovbhmT2r0OIrQtplDiE1MFejt8FNu49jH2jha+1diIZ970aZQML7+fW2k/yd42lDF9fypB9kCUv9FKVeBM/n7ucfaEoqe3NnBh5LY/WJUg5yAgfcxxJmIwCC71jL8FPsJlRQD1IdNuJT3RdSBSeRqLllUiDRmUOhu7hPe0/4R3X5oi4zRxvG6VgUSnDhXV0hHKEQt3UqAnCvJe72ccKfsp7mCBODT2U0cMYRWzjWnqoc0BlvOKHNKROgr1SDkFPkm4Bp1gTOkK/rk4edFf8I/BVrScThwEucgSkO8vhOYAtg4f/D/aSq1WovEynvUSNsjJo6zRYTyFR3VWIvmnGo4cRzdOMNU8iRjD3IMT5eoSgoki0+DwSdbZ4D+PrbfRV076bRaLgEUTzfWQfy9qXcWDBfpaW/wn/WJwh+voikuUHWNraQePpW3hw1VqeK4sxfqyCvmM38mh9Ax0TISF5U86W8o4jhxB/GInKp0+kaMe3axzyHocQx7I4Erkvy8H6R7hu0Te5s9zCzSQYb7VQzRX0FZQzFOqhQnVTbRczSBmjHKeB/aymhCT1dOESYh+LOMwycthA1oGwA+Ex5E5g3qQXmXcBiDLAWvaQsRLO/MixrT9OvffNAeHOLgSke4mgbsN7HiC0elOq/11PDx1oBCuzAleXQCgLoQxYY0jy7Tkk6p2LRIw2Qrhm3HcYId/9iO45htzmN3u76kKSVCFEHy33Xjfm4cYmcQQp7cp62zvYzPHHjtNSMkRJ6Wf4jNXKwltsnMX7WN5znHk717Nl5Wt5dG6ciWQxg7tvZvOSJtoqYzj1SClXAUK4x71HFZJgm5O3f9dbZgiRG7oQAraQC8YugBSR5l/whg33csetIXLxetrTaWJ2OcOWQyR0ilpcoJLTjFJsP8cSJqigmTYKGKOdRnayknFMbixLCBwNEy6JIeROwoMM+ingtL2Gg4Rx3Ndzn/s6HvzsCg79Nd4/aWfDnBByAZkIKhcuXgSke4mgas0t/xopqmjq3/tvb0wPJZYAb7BLkr/vJCPFuFGFH82mEAlhD0K49YhOGkUISyMEFkFu11uRCDGJEF+Ft8tTiI5qBj3aCLEVe88WQroD3nI5YDiEs/sxrm+/lqejX+cjeivrrxilcMMeVo4eYeHeleyOvJ0fX7ea3VUVDPRX050uY9AqZaQ2JBUICe8cDnrHMA+5eFTij1PX+DLHcXxf3xRgTRA+9RPeWbSZG9+ZJBGfz5GJOOOVBYzbndSTIk4pI9rGye1niX2EudQyYDfQrYcoye1iDV3U2n7pV4YQmihpZ5yiYYiUTP3rOED/yBrrRKQkNBp5jX607U7nm12NnI4CJ3Pw+e76xrcA/x2//fpjDZ3tD5zHVyHAbxkB6V4iUErdCqC1fkB+J4xosJsQXbQFIcwEQpCGlFqRaLYCIWBjKp5CiDqGkJapTkgiEWYRfkRpRoZF8SfzmmGQlrfMKe89gMH38P1jX+BPTh1l/uhP+J3Fp6h/7UEWhS5jN7dyvxPCWeCi7EJGJ+Zz1Clg3CnhdLaI3AaEYM3EhyNItFvnHVd+ZYNJsJ3w9j0xQTj7CDdY3+V9q0KoWCPtw+UMVA5TEuunJFTCCFEytFOvt7FhtJSRUAvHEy6K/SzjMIvIEpEWMuF2K4TWUdJ6nHgaEjNMEx48DUMDMG8f0qTxXxqVQkzR78hY1oJ0NFo5kiiMEJ1cfRy4vqGzffs5fgUCXCAISPcSh1JEkI6rGxGZYC5CTsWIlKCR2+4u/LpWYzhjyrES+KNtTiM1u8YjIYIQtImmwwihjyGJrmJvfeN9ewIhQ+sKdkx8iH87fju/3LuPZVemib7+KAtKdnJZ53yOnLyCnSss3JqjzOtJMHbiCnbG6+ioqmR4fUguEhmkhK3X25dG5I46/CRa1ltueJzI6JNcM/J93j0/TiZezanRBNloJ7XxBGkrxpiVpJgnWOeOU6yWsp84OdVJE7u4jCQF3iZNIJ/NQk6FyLk5GQM3OSM9D0fgeAaGRuCyrwH35/tXTNjheY5tHwznnEirU8vfchefr/803rn8vKGz/a3n/McOcEHgrAMIA8x+aE0GeF4p9iOtu9cjXVrNSMRoiNE0EfQhiag4fkTrIBptDCG0aiR6PYGUloEQdL+3vQLvMYKQYhFChvVI2dZJ4OhzrI2U03dtFadvX8f2kw7WU6MUVK3CXjlGYcW93H4IeLCJ9msHqWjIEpnYyBMorEOlDC62ocISecG0+hrHMgt/xHoYCI8TsZ9hXeQ+3tBUwqjdSHtmiOLCIWJ2FX12lig7WefuZnV6CQdCyzhiD1NhPclqOmmc5m2ZA3ImaZbJyblO/1/TSM3wMeiNwbMH4bL7pxsGDdTUukD2/tNXRf6SL5AizvX9m7mt4imFlLYFuMgQRLoBpkApokizw3WI4UsTQr5l+BFtBrkt70PIxGi2piwr4i1nKh468TVTIyHU4Ce+BvENx8uAgkJGJm7nV4NXsCPnEEqMEx9fSGv/NWwZGaAi/vf8qf533pUDRl/L5tZ386MbC0i2REnnmjmRqaejuITBhWEoCclFoR0mZ72VI8nCOsAeIc7zrNS/5jY0ShUx5EyQcOOkwxkiqpNGHuHGXBnDzmIOKY1iP4sjh1hCZlJtUfhlX1lC5Jwc0QmImM8iH653LEeAw/ClKHxtv9aHvj7979HZMCf8t4N/3P/DifcWKTQfjnyVj1XeLTuBbzd0tn/knP+4AS4IBKQbYEYoRQwpB7sOIeFGhKwqEF3UEEkKkR8spg5xzHo/h7zXx5Cotw0hbOONW49EzSlgJITjXs3W+lW80KjQsUMs6nWxdt3Br0oq6KvrpP50Fw1PLqJ1+Ke8rWQ761rWs6Xoddw/fhm7SgYorc9hRWrpzjVzgnJ6FsZw6xS4IX/CRK13PvYghRV7WR7/DTeQI0yYFIVMMEGEQap4gmvpp0qvZG+uiHE6aHSfZ01kmDLv9B3v2RCui80EoLRDPOfb/k7CVFccYnLaxeI6ODyRP7/O+xso4EdhJl5fTn/8c8V/Yt9UuNVsYwxY09DZfvxl/WED/NYRkG6As8Ij39WIyfcSJDosQwi4BEmOGaJN4SeogEl7R42wj40k3foQAuxH9NY6oLqZYyU38nCsij66qRnZwtWZZRyaW09nYpxE70EWP9pER9TFKnAIJa9m20Aj7YlWFi3toiEzQFnXe/jBeIz0ilNURYsZsS9jZ0ENXc0xcpWAZUvkfQpo76Gqaj+L67eyrjZLxCpglChpuqllLyvYyRXM5zgNdOokRXo3q3MdzAu7UwLX6aRrTjsCWM40VcFBJJe9SE3zXuBRUG+BqUNDlaIOsdCcD3R8veD9X35jySP/DblIPQn8ZUNne+u5/h0DXDgISPcihlLqLxADmBxyy/phrfUz/3f2RRwh32uQ5oE6pHurFCHhOBCCz0Xh2zEIaahw4DvdsGACf6hkFIl80wjpdpUy0PVmfl5Yx6nmAcpjW1g/GiHrrGdrUYxU5hmuSj3NtYU5bOKMn3wj9x5byv45TbTVFDGa7aBx1918YLyO7oWlDDqXsfPkWna0jFLQME4ivJi9kSUcWhrBLQe0gr4OGvbtZXn8AAuXu4SLEoypFFEOsYgtbCBCjgUIp7Uyn4MszaUp8Ng2n0jNR8+ktCCnp5xpy2aQKP95pBb6EPC41oxMn9SsFDcitpzlSCfgW7Qm9Qr/hAEuEASke5FCKbUe+AJwvdY6rZSqBCJa665XuF1ba+2c+X0SwBqEfOciSbMyJOoth4fK4Botcu2X4/C4DT87jdwOmy+bC8QscvZKXmAd2zNFjA4dpeXAblb2b+SpJVWcLu+i3nmYG4dOU2s6uCpKGajbxIMFN/DoaDEjJ55iQzJDzC5n0B2ipKudObUFjIdaONp7HU+kihleW0Xv4mKG4iUMRMPkivuotluZN36cBTpJYdzFUqeo0Tu4zO6giYUcp5hhuqjjBVYwSBkvlgnyiTdrnm2PcHP+udogdwBmXPwOpHnkSa0Z8T7zD4CQrlJ8AqnHjSADQP+n1gReubMIQfXCxYs6oE9rnQbQZtAioJS6EbEntJFI6aMeMZ8A1mqt+5RSa4HPa62vV0p9BrmNbQHalFLvBT6HTFhwgW9qrb+ilLoCIfpCsAfhJ1+BNy9Gxt9UASVwSwmi+xbAegU/NNUPpk13BAg10uZuYIsqZiTSSUP0Ga4MNXPy8jfy64liRnqe5NojW7gmncMuBCI22ca1bLc38vj4GAWxb/GhmMaau5bnTqzh+VQWuzyHVVpMsvsI850O6gqWsXfpGnaVKvRgO019bTQWFDCiUxRU9FNRmCJKH5XuUZpTe1hhl5PkCl5ghAK2cDUdzCGHYgZdFgnazb9PWENWQ2TaxcoG3+HsCSTKPQk8ZQjXR0NIKe5BSvcc4K+Bfz7T0M8AFy8C0r148RDwKaXUYcQo/N+11o8rGS1+N3Cj1vqwUuoepMj+iy+xvWXAtVrrCaXUR5HysDVaa0cpVa6UCgNfAe7QWvcqpd4Bb7kD9B8hVQ4bEPItZbKe959Xw40jiJQQA4oKSCau4hmnhWOZcQr0w9w4mqQodx1PFVTSX3SUluwWNkSHKTsBDCtc5nEsdg1P11TSV3qM+bnN3NQ2TGnfQg4vSBG57DRVThmDh6o5PdJBQ3wNu4o38WBDEcnYU1w3ECX1QjPH4gOUrUgTtwcodU/QYh1mIR3UqV5qogs5GbJwOcAifYglaoI4vk/PmeDgmde4EJ5eoQB+8nAzMsWiE3j6xYT7lir4wgeQi1Uv8AmtCbrNZikC0r1IobUe9SLP64AbgH9XSn0S8Q44rrU2s7K+i4y6eSnSvVdrbYZV3gR8w8gMWusBpdQKpIphs1IKpCrhlNaMAk8qxfOI+cw1QA387RrYBuzcDlSGyBat4oXCZewvBqI7uTy2j2Wjq9gT2cjjYQebLawfPshSrbHKgOJKeuevYE/vSva4OUJqKxuOb2ddWqHLVvHCkqXsz4bQp46yoAz0khpOJd/ND8ZrOF3UQ3V4Kxv6d7FmaD6tZVHSLUUMl3ZQz3OsVSdopp8iiphQ8zke6qaOPaxw+2UumRJCPRvhGjga7JkIN4k4mv0X4uPbCWydTrhKcTt8/Q+hoNT72/2p1gRdZrMYAelexNBa5/A8bZVSe4D34xm2nAEOktAC3wDGYOwldqeAfVrr9TMfC0ngcaXYBX94J/zHu+Dpn0NCL+ZA8hqeboqStk7SnHmaDTpKuvQWNpeUMkw7TRPPcGVylGIL0IUkx5o5Eamhu6KSvqoDLB3dy8pj3dS5ccZD1/DUxAKOFPZTWfQCK9MOoc7rebz6KrbPKWA02k9Z/wGWbj9Oc+YanlrcSGdDH1X2fhaH97CyoIN6HSKbred0eJgynuFK2mnCIWx59bZZCIdf4tOXs0ZZU6UGNFIS14NUHxxC5IVt+YTrlYP9b+DdkCiGEwdg+Z1aE1QkzHIEpHuRQim1GHC11uafdA2iFx4CmpVSC7TWR4D3Ib61IOVKVyBj1M/WProZ+LBS6lEjL3jbrVJKrddab/XkhkVa633Tjmw+8FF4zS1LyNZfz9feUkVv0QjF/b/hptbDLC7YwJaWhRwOjZNIPcm1sZM0hzVWeZTURBNtuXL6VQX9rkJPHGKxOshSO0V8cSHJ0Rv5zUQT7W4HjUNtzOmqpbtyCQdaVvJCgU0m08qiiePMcyvoW/s2/sMB7XZTywGWxY8xt3SAUlVNt2uBfYDlHGYxY8Tx3SyzvJhwTfOD+dkMpAgp38bB8d500jA+DjsOwokK6FwO34/DsXplNsGqBHz/vdDUIus+0g5/fhQOXaMU17zU3/63gG7j2RHglSOoXrhI4UkLX0E0VAfpbrrTS5KdKZF2HfBvSDLrMSSpZhJpo1rrz3vbtoG/RxJpWSSR9lWl1Brgy0ilgg18UWv9zWnH9RtgZQIGYxArJjT4Vv7u61/mj2rnc7T5Fh6qLiIZeYFVoc3cHEuRKAjhFNbTVV5DTySEo2OknTSRXA+17knmjruERhtpS1zD00VhsrSycERDcgX7sivZbTfRbvdQYz3OxlEL172FzRX1tBUMU2K10+QeYKlzknnlEVKRYkbUKWrYx0rVRwV6kmxBNNzpAe6ZCNee9r6rxWshNQDPPgNtfdA5Aj84Bsfyyr0+3AR/9g4orYb0GOzcAb87Cr0O8NTL/R78P0JAuq8iAtIN8OpCqTmItluI6Jg7gMxuVr7mEItvep41c3/Bm9IHWD6scJ25nGxupHOuhsJx4okw2cIcdvgEzbl+Kh2Fqy9jp7WG53PjxMdbWUA1fQVNnLQWcljHmMj2UDuwl5WdzZyILKS1uoBk1MKNtjMnfJQWu4O6RBljoVGKcgdZwkma7eykygJeP4cGW81wRnnLgBfh5r2mgBwRMk6MVF8NPT9vZdlWsHqA7ZPz4ACl+F3gY0ilxwlkztw3Qb0bpjZHBJi9CEg3wKsDpQoRo5x6RNPcgdadHgmvQzTk1hvZfOIRblo7lxOvmc/RlkJGnWGKR5IUVRWTnDtMSeFBlsQnSMQLGbGv48lQDadDvVS6oxToZk5m6+lIVdJbMEFCHWKx00+Fs4K97jxOpCOk0wOUW0eZFxumKJ4lWuxihdqY4xxiOUlKjScEQqAunjZ7FsI1cF2wrKmvORQyTh1d7k385mgYva2H2i0PsenfB3R5Uj4aIsBngdch/hUvAD8DfqI1zvTmiACzGwHpBnhlUMpChjkuRUK/fd4jgpBtEyJnPIPWp1GqIEO48TmuqP8OH1zyMK9truF0UxW9udNU921nXUKjmudzpHEtOyqipMKnqaKACVVJb6SEYQoZJUUsfYhFyQhOtJljsVKGFOB2Up/to9pJE1M26dJT1NkHWZjroVZpbC9RNr3Y4Iy9IOTJCy4wjXAzFJJiDid5B//hzqMtW8LIkSvY+e+NdN4P7FboWuBLiMF6DpF7fgo8aJoeAtK9tBAk0gKcP5RqQqSEAsTF6zmkGWCe93oYIWCZQqFUC1AWIZtdz7Y9a3h+x9Ncc9l9vOGye7l9zjHmF1fSm7qJzYfmcdwdJ57up6KimZNxm6xVSDKdYMLqocY+yvxwPacqmjmeKSaZ66fU6mBuaJhiO4Rrj5NQh1kTOkmjkyGspMoAfGuIfOI1Y9ymQ+c9XhTh6kJSqplj/A4/ZQEnrFp67LU811JM8h2AeiffuwJy74FQNWIKtAP4MVI6FkQ7lyiCSDfAy4dSRcCViFvXCCIlnEKpBDKhtw5p230GIRvjsauRUqpuxNqxHkidovbYBrYUL2P/bfV0Xl9NT2MxI+kE48NZwuVd1FUXM1Ibwo0cYYEzREm6hRPxaroTFq7qpD43RHl6gnjIxQ4fpzl0hIXWsMwncyFnMYXjjKXu2SJePW3ZKcha5JxGTqbfzY/0SvaX1dPN5eyimKSbJZT5HH88/i98zOmlYixN4ihYe4HvaM3+F3+cQaR7KSGIdAOcO6SqYRWwGLndfh4p/NdICdsa5F58NzK4shKxhwzhj10HaTku9l5rq6O78Dgt1TlUVwcNu/awMtXG3DmnqKkuYDR9HU939VHZ+wg3lOSwGi7j+UQVvWqEgsHjzIsMUlGgsRK9VHGIJXRTa+U5gVmy+0n91nv5TBGvWc6Ugk1PuLkuWBkX+1iE3H4F9SUMNl/OrrpikvYQhdYn+dvoU1wXK2LQqaNtdISKjqMs+FJWRzpehb9CgIscAekGODcoNRep8Y0j9cDPofUEShUj0W0V0sK6FczYd6KY6RCybBEiPYS8bQzhm6TPD6Hr59IRb6Rz5wEWbznOvGtHKFm4m1XqAItPXs/jPQ10TvRS1dTKokQvlbE0MWecRPIwixLHaQmnib0oLBWYETrTo9kpjQ3TllPTlk1RRNoqYDTnEj52hKVPPcwt8U1sXhomdcUuFi/6Sz4XO06zKmGYZo6HFnG07oN8u3IunfUoOgluLS95BPJCgLNDqRIkIVaDEOh2tO72EmhLkUhWI1FvB2IOXoj45nag9Yi3nTo8OQFx2Yoho9FLve3UITXBhxDWm59DsZ11Jx5l45I5dF5fwGjFBJHBCQo62phbdIz5y1pZWHGYxdYg5cZE/RyqEGbSb2eKPya9coEUxUwwn6PcyoOpYUqObebWZ1tZ+u3/zpdSWfizp7juFodQQRNtNNDNSnbzfr6rw6EMnSXVA/vnrrjrLbse+MZ04g3khUsLAekGmBkiJaxGpIQckgw7gNYapcqQ6LYcGcOzy/u5DCHOLqDfW9ZGottiROftQoxxKhFNeLm33gnEb7bae/0UIlNc5sLyPsoLn+OK+G539aSzAAAgAElEQVTWVJ6iobyDhsweVmWP0VKWw25BSrHwjtXiJcnXYWrjA5yZeF0dJc1iDug38ktrKa3U0DMxQeTZu/ir557j6jpFdnUZ/cUr2RWZS0fp9TwR+R1+SowUGbBzVlh3lVbndsy/4m/es/3ez6L1pF1jQLqXFgJ5IcCLoVQzUn1gpIQdaJ1CKQulViPSQRZxtEkjlpAaIcruSUKR2t0W5Ht20ltmKeKmNRcZrOgipD3svRZBsvw54DagxoLhagaOXcfTyTaa6x7gdct2sG7pGIX1SFQdxtcDZjKfmQHTKxZmkhrQ4iLmOjkyTiEjTg19iTo67dXsiQ2TuKqA5IIQqUiO2OAA5TtOM6fodu6rvJl7F0RJFWiwt3AV691nVP1At319+uk/d+CArdR/5BNvgEsHZ9C/AlySUKoUpW5BnMLSwGa0fsoj3CrgDUhk2oZot0WI7NAP7EXrrjzCrcUn1SNINLsYWIjIFcuRyHcr0kzR4j0/h5Dv7UjU2wUcHaGw5/u8N/M1/nDocW44PUbhKeT7W+w95xmHnyumt/LCNOnB6/m1Ug6Fnfu57NSTXNdbRX9qN0v5CN+K9VPVsIbtqoDBIYjUHGTx4yvZf9fulrUcotr+fb7BB/gx/4OvEiZH6dhAaChW/P171t2xe+Of33v1yzveALMBQaR7iUApdSvAjD30IgGsQUjSwYyTEXkghES9ixCddre3ltF4JUk2dVvNiD/DIFK3uxCRFOoQHTcEtCI1vNXesp3ISJubEe13GNF++05R03cXnwl9j/fVjlOwxNu30YNz3iPKi/tzzwEz1ehOiXiziANbZIiKgfu57dQJGhdlSdRrFGvZQpRcZTMd7GHlDw6zwr2FR5rWFj7Y1ROZX5LKxJnLMT7EV72D01Y8NWLd9sJvlseymUevCdn/0HTT779ZA1d/+sEJ4D+23bUpiIBnMQJN9xLBGXVDaVi4HCGt48BOtE5579Uj9bgFCCn2IgmwqUkyf1uFiH4bBk57683xXitHSHIMKTNrQyJaU+FQj5SjRZBGiy6gYydr+t7PPYm9rFyCJO0qkYi5GX/WWhTfqvJlEG4+DPGamZohwHa9481455wCisFJFDFsvYmfhOM44XIGeCc/ZJx450f4xvdf4OrrwVkVjqbj15Vstr+c+wSlQz2U55LYwFf5IHdyN5lwjJ1zV7gfXnKNpS2LqjW3jAH/ue2uTb/78o8/wMWCQF64VKFUOUptAtYj0ehDaL3FkxLCKLUBMUe3kag0iV/qdWAGwq1BomGNRLiNwFVIBF2DJLo6EJvJYwgRR5BI8ipk8oSDRL8HgN3f5PcPX8Wz4b2sXIFMp6j09tGMEG6GqYRrGPM8YOf9lCVBEsi63n7MPuqBcrCtJKXDv+ZNIxZu5gN8x13KYd1FVcMYJZ9QZFaDPegWTTzcs7R27Nfzbk73VdSzmbUsYw9/wRf5Q75CNDvBuhN7rH+974fkukpALlJvvfrTD64+//MIcKEjiHQvEUxGuvADhMAWIoT3PNA6WcYkrb1rEZIcQCLWHKaTbHryZ6qcYKb+LvC2b0YvTCCR67MIAbcgWm8VEr1GEDI/ARwF2tazZWwb65d4x7oM0W5bvG0bso4ghAjnXLVwRnj9vjkijOkWTugQWh9gqevKaZhytBRycQgBE/NoHfoiH6n+Ke8oeJSbLIXmap5K72HVjoNc1gq5ZEGkP1QX6fxQ1+i8AheLBeznW7yNFeo0/6A/xj/zcWzSzP1UqzmPj2+7a9OXzvM8AlzgCDTdSwh3iH56B0JURxEpIQOAzFa7EpEDQG7xR4E+oAutsy/aoFIFCBEaEqxFSNRorUPeNo4j0euVSEItipBnk7fMASSaPg6cVOgYIjVcjkgJcW8/C/ErFaL4pG7MaM6XcPOScCEnQ+HYCCXWCvaEM4TCrSyOQFghkbXtHUMKyB1nYdkd/DpWyojVwlH+Dx/nNWyJtjLvireHfzT/QHb1yFimas6RTHkkrFLOh+NfsP+ML7FlfDXv1/dxgiYyRLmWX1DRn+ZURX0IuVMIMEsRkO6lAKUqPgsrq+X29QXgMfKmB6PUfITgSpDotg1JZE1Nkk3dZg2SHAt5j9VIxBtCSsf6EEniKEJqNyD7r0LKxqJIVcMh4LC3zx7PlWs1fjQeQQh6eoRrCHdGc4SXgQx+328OGAdrvIu5uREKym3ceDkDuQGK0xCP4LerWcgFJAxRhigev5Ov5dayuwjAIhuLZ9ON3uIay5nIxnLH9lWsWPCOru/Zu7nKGiNqudjEmCBClD949Jt879rf4Xjt/MdewfkEuMARyAuzGUpFEDKd/yVY/xi0/Vzrv897vwBpcpiLsEMrkix7cZLMXyeEkGsZEoEaiaAQidD2IhrxGJJ8a/KWr/KeG5ESM+M+dhJoR+uMUsxByHY1EtmGEQ13PkLSWe/ZjHc4z6TZJAzh4h1zByKFlCMySDROMr2G7dk+aqLHmBfPYXkVEiZesTWSzHMssrnP8vHiLmrCP+GDJCmknH7i8S66yhfmUgOxDBMFgGuDViGyFDNq38oPWNj0CNczgG1nU+VjQ2/70Ie/uh4ZEHoC+Mdtd20KhlXOEgSkO4vQ2TDnMYCGzvbrUWoRQl4RoLUEVozITLW7kXG+SxBCLkci08Pkd5LNBF9OKPUeyxBJYQIh24NIpJtDotxliKQxFyFOG4lsd2GiW62HvSGNzd7xrEaI2vKOsQVJYs0U4b4S/XbQ20cUIc1+75hzCOHGvNedBMMlc63jxQW619qhr7RF7laON7YnCZaSc3Ms0BELx9YoLBzu4qPsYiMPcRtJKr0SCQW4LOQAP+TN1Nk99pHCCn3f8nel373nV+0TRdE5P7/yVvXEyo1RZCcp4L3b7tr08/M83wAXEALSnUXobJjzmOU4dt3p7r9ByLQfeBYZof4BAA3/CVyLkKCDyA3HmSlJlg+lqhEyrEVIdKH3Titi4diNVBdohHiXeMstQKLc00in2W4kuu1Ga1cp8VlAknerkFpe4+swDyG/HELYrwbhut6xZr3jHEM+Jxu5QJR5y7QikW8TKlsfUwP1Mde1xykggw3RHCjLIRUdBTsLFIDjHZ+2wUWR9WawhbHI4BLWYGlUzsXOjBZlJ/o+qz5dc5u+N17EIF21Tdyz/G289tizdkVqgHtqbmfrzVcRj4P3+dUFNbwXPwJNdxags2HOY7gu4Wx2o51zGCwp/YdMODJa09sz2fFkA++WW/t3IZUArUjEOXOSzMCXExoRIl2EaLMdyCDFPQhJzkHI0cgIq711QIh2C6LhtqF1WjZNCEmUrQNW4HvuLvO2EcWfFpk/MfKVJMw6kYtNGNGdtyJJw00IqU8glRol3vEME3Z0ihJSGUWECSoZYCiawAFFKqTALgJs+Rhc5BwspScLKzQKRSmDqiLUpbob6ywrZBXlMlb4Tzv+8UAfRUvfyU+jDT3d9gdTP+af1r6XfVtvoPXUUuZ2HoaPjoF0/zV4xxrgIkZAurMIluuStW0cO9w3ZdCBUuX/BFfOFc+DR5AR7EfPmCTz10sgJLsMIdEqJNH2EEKipxEiqEa+S/MQfXcFovGeQupynwdOovWgv2nCSDR7lbf9MoRwV+ATrqmTzSfc6eN5z5WAs8iFwpD4AOIdMYRop0XAE0g33kbv2OJAJZlwlJCLimUJF6WJxkdxB4shGdNgFfi7sJCpwConx6U9/VcRI8lXuZM9ueX86uTr6Jg3PxSOWTGraWzx5wY/YveNldrXur/hj4e+TfY3IZKUUhvrxVo7gtdoZ3nHGuAiR0C6swANne3XA3TWNTw2jWwtJFG2vgkqtsPJN8KPzpgky4fICauB1yAyQRqJCjfjR1vGjLzMW+ZaRB7IAE8jJHYUOIXWOX/TRBByXo+QehFCoKvwTW+09zz9O6rO8PPZkEUSUi5CpINIzXAUeK23nc3AA0giL4PoqN6o+RDkXG0VOo5VPWEPjJbipsPOzFVqtvIq0LTovgAuY5RZH+T7loUizhiNw+20FTSGiGaKcqF+/S/uh/kXPoFZPkKa5UueZmRdtdmw9j6zLed4zgEuUASa7iyCSaQBWNlsuK63524kCm2/Fsq2wnDupewDRU5YgEyuXYuwyn7gV0gnWhalTJ1tEUKylyMG52GEkB9AtOKT06NppYghZH4dkiSLe/tYhUgUpizLZirhnq+OO4EQrkZkkWFEEilGCH4Imcy7C7jRe5QhkbrRkwchN0Ios5pcKJFXoeZ61QszOZvlvH2NQrYelA3aDjPBIrYRtYrodKP0sMD7CKauupLdXNb0OAOvr6G3uta8kQTmbLtr0/B5fA4BLhAEke5sQy5HNJPeGM5mGSkorEnFYoPV/X1XPQ0v3c8vcsKNwFuQ6oQ24F7gaePH0FXX8GS4sipW1df7Zwhp3oRk+5PAw8CjwBG07n/x5ilAIsnrEMILIwxm9F+bmQl3hkm854QxJNIGiVpHvN+b8D18v4tIDb+HSB0RhAVzSMLvB7Kd0IfIxadHKBZT5/+YC0MWuTMIAfVgh0RGdpxsKGLvy10HroPfvZyHcJaihR10puqZNziPvnsq6KmsouYDXWZ/bwP+7Tw+iwAXCALSnX2wwk6WbDhMOhLtJBTSaO2iXhwkTisxawE+iuirg0j090u0Tk6uoFRluLwyHsplY8AtSIeZQpJyv0Ci2y60ftFoBqUo9pa/BvEwsBDWWen9HkIIb7on7vkS7jA+4ZYjBNyDXCgspJLiHkST/hT+GKEQ0kW3DbgPuB65SDR7xze93dg8p73tP4no0suZ1KJVEqynw+XpudmB0FKI2S/611MpQsUOrhsieaQOFDxU9FqiGZdoj+miJoLo8gEuYgSkO4swqe3W1j9OKKTN72eF64JSdyKG4SFEi70brbsml1FKdVfXbgmVlUdCqYnLo9ksactqdJWVjeecv0ai26NoPTbTLpSiDNFvr0HkDhe5fV+JRMkKX1bIJ7Tz7TYbRColFEJSKYR0G5EmiAeAnwNvQtqiy7z9pJDqhp8hkfGHvWMswp9UORO0t+4EcCW4NZDLEc7kUGgy0RBYG7MDpTF/YkU+sqBz5IYT+BV34JalKdnYTdmik3j8nUXuJgJcxAhIdzYiFDqrUG8iXCud2lg8NkoqFF7phqxUIpO+HfFj8NcXjXdeJJ1K2NlsgcpmCLmanB0eHY8n+uLDgz8Ces/UUKEUlUikuAF/nE8FQmYVCJnFEKaZTrjno+H24ke4NXjzdpBotxsh1EPAXyAOaHFvmQGMj7BcHFYienUo7zjMRcFlsmV4sqKgGlgEGkIZp+bqkzRc25boerKB7i0tjq+cACgNWrZZ3A/JEOhS/zOLZSle1E3zDceIlZhiC8aAn227a9Nz5/GZBLiAEJDuLMQ5RbhAwfg4ynUZj8e6JwqLehOnOqf+Q0sb8ULgivLhoYeAmtFoPDoRVmNl4+O3JzLp42er8VWKWuT2/Goksk0jRLYM0YxthHBninBfLuG6SAnbUW9b1d42wvim6T/x9vtZJOoNI9HpSaTSohmJfBd6x6W8Rz7ZugjZ9iEWlaXevjxyz2lCTkHPC1V2z9ZGcCMw6ZQ+qbq4ckwOjOSrBS6FLf00XHuC0uYkSPTciy+F/PJlfiYBLkAEpHsJYlKGqKp5GnAaens2vmghMSRfiZSMLUW+K4dT8fiKTCw2VjY2dvhs+1CKJsTkZh1SmpVCElhL8EfsvFqEm0Nqgo9569bgT5IYR0jrSSTavgG/66wf2IlouNciem8FfkhqjssQbtZbp897vRm/RXkb8LBdMXyHM1B4NRnTGDEd9iAkSyEWyv/3U0VtNG0cpXJlT84OawvpEvxb4DtBF9rsQkC6lzIikZmjVKXKERK6BUk09SNNFQ9XDg381Rm9GQDPR2Get+4ahLAy3muLkIjX8p7Nrfv51N4amKaHk966jQipa4Qcn0ESXG9CLh5mPPxJREqoRyoqGvATeeBfDMzEyhFESkh72zdoR3Tw3cBHnf6SK+Tl6R+RBnKnwS6EuJr6fhI7PE60dAI7rM1nMAf4In7rdIBZgqBO9xLBOY/5VmoOcou9Ebn93oPc1r4w6b17xlWxkLbe1yESguM9FiOkG/e2WYQfRb4Sl7AUQnonEQJvwSfEDqT2NuwdSx0S/Q4gZOsgkWotfuRtEnfm2PKlBI3v5ZtBqiNOIjrw65E642LIGUINTeq2HuPKZkMYmSFWO2DXXdNO3+4e0n2LXDteYUUrkxTPP03tqn6z3hPb7tp0/Sv4jAJcYAgi3QAC6V5bBXwQIaleJMN/f3777plXJ4SUSd2GaKIp763lSPRpHMIKeXG51fnAND20I9/jhUjFgek+a0Oi17lIEg3vtU7EmKcOkRnyTXRs/CqFNBLZpvA9IIa9/Q4gUsZS4BPefr0yt0lSzU0OE578NzOVcKHh0hXtz8y7tfXmSMKxVfZhlRnfpoa6PsDgwXoG99YxfLSNxW8+ohA9PMAsQkC6AUzC7E3AexHfwl3A3UgH2kvqiUphpgnfjui2owjDrED0VdNwYKQFOL9SMAPT9NDubXcpEq2akq8kQvTV3j4zSOSbRaLuMu88jQ1lCPlfMFLCqLcPGyFcY/s45r1XDrwVSaLl+0Ig27C9ujDHBsvK+zcbA06BSk30lKwa2F/l1FxxKjw2WMnwwdcpJ1VDKOxCLEP16smKvWCKxCxDQLqXOpQqAf4MKZMaRLqdfvGSZjiTqxNBmh7eiNyqjyCEthQhpyivLuGOIDW47Qh55uu0vUhoWYk/FmgMkQeKEZIsxv/eG8nAYAyREzSSIOvHt4F0vfVXetuIMjVSN2GtkSjyE2VpRFv+MvBouGT0rZGC9L92bJljn3ygBdxrsKIZ4jVjNLymlbKWUbPeOPBP5/1JBbggEZDuJYjJTrSujo8j3VhVSLLm79H6+Llux/NRuBYh3DKEpMqQCoUShGwTiOfBq0G4g0jpVzsSyS5CyDGDSAEhb59FCJmOIJFrDT5RmnE7+YWzWYTg0t56o4jJ+oS3XBVyQTF+DNPPwRDudAecHH6r8Q+Bdq1xazd0Z0a6Yk5uuNQ2sm+44deseG8J3nZGkbuDbwKfP7+PKsCFioB0L1EUDg7UA19CCOcbwA/yncBeCkqRQBy63ogQXT9CbvMRkk3gE+5MVQoG51oi1ocQYTui35oRPhmEMM2MM6OvGi221DsO813PMVXHHcfXn0Gi6H5v3TlI1FzAiy0mzfrm+PMJ10UuEI8CX0cqECaAdUrx36D5DYCN0oSKx4mUbKNuxTPALWngqwhJtwXGNrMTAeleQvhUccknOxvmfCCaHNkYzWYYC4eLh4tKjtX3997zcrajFEVIxv4WhIhOI1ruHITgivBJ99Ug3G6EDLuQZF8Tkw0HZBBCyyJyRsj72UTZpgzMRKImWZbyHhnv9T6kNtZGkm9mZI8h2+lSArw4Ieh6x3IIiVLv8459A/Ah4GagEmytohPJutccic5Z35Xoff4hs8008IVtd23yW7ADzDoEpHsJIh2Rwv10UfHBl7uu56PwZqTJwEV01Bak3jWORJYFCNmFOLN/wrkQrsaf3zaAWE3W4RNuynvdQiJSkxgrwJcBTDRqyN+07xrNOo0Qega5aNR76+Y7nr3UcZvGiW6EaL+LP3L+b5A7AmOM2wN8o6hh9B/nrO/6OPA/wgWlZU5qdA/wvoBwZz+COt1LBPl1ulPcxV7WNqgC3o7ouGnkFroZiQoTiPZpanFNdHi+Gq6LVBwcQcjVTK4wUesoQviFSGRtCNcQpsIflW4IN42Qbdrbxin86ReN+JpvKG8bZ4Ihc9M48TxS8fEkktx7H+I5YQx9jHPbp7UOJkBcyghI9xLBOTdHnHF96pGSsrX4Ri8tSOKsCJ9wzQDJMxHuuUS4OUS7NT4Kq/EbKgzJDSNRtXEAz3cqM9q0IVAT3Y5763ulW5Qg0W0hfkSen2Cb6ZhN0iyHkHcbxgZTLkDvQCLcKm9bSaSb7zNa0/oS5x3gEkAgLwR4SSjFPMQEfQ1yOz+G6J6lCOmW48sJZyNceGnCdZCMf6e3/UUIsZqochi5la9Fvr8mMWYSWfnRbX6TQ9Z7r99bbjl+o4ZpjDjT/4M55hx++dgAMjrnZ0h0/efIxaHaO54xJIH2D8CTWhP4JwQAAtINcBZ4PgpLkKkKCxHCGkeiwxL8elgjJ+ST3/kggySzBhFt1XSymcjSNCwU5a1j3MBy+ORpIuJRJNLMedtOI1JCKT7Zmgj5bMec87aXw0+UPeAdz7sQAjc1yWnEzP2bwH9qPaUyIkCAgHQDzAyPcNcAv4+QbC9CYo0I4dbj2zMaws33nn25GEdu1XNIdYJJjBnCNfNtbPwpwSaa9qbvEvbeS3vHm8Efn1OEX/VgolXjRGYwXfow2zJRdjfiJnYKMWWfj5Bt2NvXIeCnwHe1pvc8P4cAsxwB6QZ4ETzjmg2ID0MNQjajSJRYhpCX6TAL4SegzpdwxxA5IY40MZhmCuNfa+HXyKq8/RlSNd/jLCIlDOcdWwS/4sFouzGm1uoaPTf/+Cfwo+dhpIKiG7kArcevkDBVC5uB7wCHAikhwNkQkG6AKfCMa25Gsu+lSG3sEEK0VfiJJ5DvT5xXRrgmKVaOn4gzcoGZWWbaa40MoBHCNe87CEmexp9EEUYIPIzfBGEx1ZbRzDvL159TCJGaWt4ORL8txPcGNheAHmA78D1gq9akz/MzCHAJISDdAJNQijBi6/guhADbESJbgES5DUiE5+Cb2LwU4Z6tWmHQ25aRKfJtFY2WaxJjxhXcwY9uzWyyISRajnoPUycMfgNEnhPYJInnSwtZhLhN9cOAt90oUpWQv88ksA+REn6jNQNnOf8AAaYgIN0AAChFFHg34jYWRcq1uhGbxxZ8P9osPrG91PfnbISb75WQZxIzOQ3Y6LhGu80nSoUQ6RgSJdsIqeZ3oBk/hgr8yDzH1DZg8EvIzIw24yYWQXRlo/9a3uvtwH8hibTjWp9xWGWAADMiIN0AxkfhTuBWhFz2IMmiy5GSLVMGlUZu3fO9DGZCvk46E0bxh1GaSNZEuvmVCFH8uljy3ptA5IIMkiAz3gjm/X7v9bq8fabxk33mGJP4TmNmmm/IOz9jgGOi217ES+F+YLfWnHE2XIAAZ0NAupc4lKIY+J/IpIgcolGewp+NVoZ8T1IIURbyYuOXfJjGhNAM7xk5wJSWGe00X6s1OqvpPIOpZJvFJ8PKvG2ZGl6N1PCaCNssmz+0bBQh7Cj+dAhD8sP40XMIkUC2Ar8BntGawIQmwCtC0JH2KkIpdSvyD38h4lrv+Sn/pTVF8OVbYd58SGfg8CHoHYarVkNVA0SioBQ4DthhiCRAnaWtV3uEqWaQFDTgOhCyZIyN68i2rJCvQtjmNh78W3oHIU6TpHKQyNZUMOQQEh3Fr1IAv7Ih/wKRQqJbU3pmJAyNJPS68TvqUkhr7+NImVhbICUEeDUQRLqXLG4sg797IzTOgdEk7N4PA6Nw41VQUQd2xCtPzUA4+tKEm8uCZc9MuLIAhDw91nVAhWSqggZUvj1ivg5sjGnyk2zF+BUNowhZliMROfjmM/mRrYPouxH8oZVGqx1HdFoHf0z8PmQk+w5gj9b+7PQAAV4pgkj3EkG+94JSLAb+N9JtdhLxDcgBH0AqFcxt9xj+xIWzSQqjiA56Jq8FF58os/iGMmdCFolIjdabY6pN5ARCtlEkgWb2O4HfoYa3v2Hv2ST+8u0gO/E9HBzv96eQjrLtWjM5wiFAgFcLQaR7iUEp1iLjeeYhEd2PkEjxD5AqBdNdNYEQmtF0Z4K5tTdmNNORb6sI/sSGmfReEHJMesuZcTgmsWVcwgwRVuYdl9GGjfmNqcsdxy8f0945mXreXuSCUojUIu9AEojPaU3HGY4vQIBXjIB0Lyl8ahniglWLzOz6DpIsuxNp7w3jk5W5ZT/Td8TooxWc3U0sv5nhTB4HhhDHvWMwJGvKxUwnWRp/KgX4dbz5JWBp/JrdMm/9NBIZDyK6rZnZ1gvsRSLbPcABrScTgQEC/F9BQLqXAMRH4Utr4a03IgT0AGLIcitiZlODRJRjCDmZ8eRnikhN4qqal3YTy6+NnYlwcwh5gy8B5Ou7EwjBh5DoNn+7ps0XJNo15jJG9zWuYn34rcEFCPkeQsj2GBLdjp3hPAIEeFURkO4sh+ej8Afw9td7Sa67gW8hZPu7+NHgML4peTlnJtMBhAjrzrKMgWloOJMhuIluTSeZKRUzia4UQq75E3xdfNkBfI3WxW//zSGJs1P47b9h7/yOIUM4uxCyPfUS5xAgwKuKgHRnMTwfhU8Cb5dyrl8/Cr/3FeB/ITKDsUgcQKLBJfgkPBPavef6syxjkN8gMZMpuIk8i/GjWvCdvdKIDBDNez+/4cKUe4EQsEmgma6xEW/9iHduJxEpoRuREgJjmgC/FQTVC7MUno/C3wObgNNw13b48kHoX45M8C1ASKpH3mc1ksU/kz67DyHIxjMsMx3GbnH6smZ6r4lKwW+EcLz3QojmOpPtYhZfWoCpbcGdCKmayNgMm9yD6LfHgJ2Bx22A3yYC0p2FUIpCZPT3OsRD4ZNQfRU8+D64bA5CeBq5xe4GrsaPOKfDQZJujYjD2LkQrjGlma4Jp/F9cQ1MSVm+MU142vt475lKiPxjcBHd9hhyISlCIvcj+O3M/UgJWOBxG+C3joB0Zxm84ZHfRQy2dwB/AgzDgcehsQWKTKR4Eon+rkOIbiakgIcQD4YGzm16r8NUYsyPUA0RG6nAJNnG8Uem58PF12vNOB3zhTVlYWbuWKX3+yFgF0K2SSRZ1hp0kwW4UBBourMISrEASZRVIRUK/wv5G/8c5syXLjAmkPHgSeBGfAeu6RhC3LRu4txam82Eh/C010wrr5EBDDG7+GPQy5lK6KbEzBjsmNIxQ2xxkvwAAA1VSURBVNZpJEof8I7NRSLbZ5D5ahmEjJ/Xmsw5HHuAAP/PEES6swRKcR3wRSRa/A7weaAkWjl0NDMai+uUkwLtQNFj3io3I7fjM6ELmYRwO3577dkwXb81XypDstP12BwSRU8vSzOEajrLjCuYcR0zdo2d3rpxRB55EkmSOYiOuz3wuA1woSIg3YsYV3/6wccAnvnspq8BnwJouGmv27ihc+CZz256N3CviqSX65yryTlj0LoPLu9HoteEn/yfcsNzFNFCb+LMUXA+8k1qpr823bTGEGchUxsa8N435V2mXlfnrZtCCDaM6LZjSMvuU/geCs8TeNwGuMARyAsXOY49sKAR+CukWeFjjRs6/67vSFHcLho9lEvbtiYXs+MKYs8mSdetcZKT5VUzYT9SyvU6phrGnA0zlYPlSwHjSKTqIkRbxlQpwUGkDhchU2NuYyb4TnjHNOGt6yIXhV9656yQ+WWBx22AiwJBpHsRwkS4R361eGPyeCWW7Q7Nf9OuvYX1qVzf4eKNbfevQLshJ9F4enyis7K4fOUJ+nfbSWd0Xly41ES42lx0HaIT46SLu5BZaNOjUIOZJkHkl2+Z941MkMY3rTH1tgYOUktrrBpN669Jno15z8P4kW838GNkanDU+31H4HEb4GJCEOlexMiMxIiWjTPvTbt2x02bQy4EIahff3h/3ZU9g4PtR6y2X12xzBktLJxaRJCP8THSsShieHOmkrDpMoLpGssnaEO4xpQmwovJ1njXjiFEW45f0ZDFnwiRRL6fZd7P/wU8ghBwDnhKa06e/RMKEODCQxDpXsRY/acPPhaPw7a7Nl1vXrv60w8+Nj4OL/zDpusv+5OHnzj4g8uuSp0utqbmtkD4LAtkM5DIMGnNOF3nNR7fU5wds94bxtXLRLtmqoMZJDk9STaBJLoK8I3IVd56E/gRchFCvruBH+BXPxxAPG4DY5oAFyWCSPciRjw+8+uJBChFUbhs3arsYMLyCVbhSwrZLIQHgTJwImf/KkxGxmbcjoVPuGYUej4Rz9QU0eu9bkatm0SbsYdMIdGvIeSTwE+Qlt4YUlEReNwGuOgRRLqzECv/6OEnDn73ytXOSKzYe8nxidOQrpuGaBaysWklst77ypn6u6mtDZtGBZMkMwSar+0aZJFuMFMeZsbk4C1vzMonkIMo9JZ/FJESEggh79CazvP5LAIEuNAQkO4swtWffvCx0dNhdegHV13uJKMxyNpec1jeuBnTdTvpnpgX4qocaI8480lXm21ob44ZCOEWMTNyiKPXAOLnYKQEcwBG9zUDIouRaPh54Nf4FQ/7gX2BMU2A2YRAXphFGO2OqYPfX7c2Nx6JiHwQsqdFsRpybt7rM1xxDdnaALnJIFnbXYhhuY0/+nwmDOFLCbVMHXsOQq7D+KY3hUht8KNI6ReIpLBD68mOtQABZg0C0p0FMCVkh3969WtyqTCQyclgyXzCdQFXgR2SaFbjE6yBDuW9lgVyVjwdchNJh/7aCvx6s5nU5HFEdzVeu0ZKMF66RrsdxK/JHQSeBXYiFQ0jiG7b/Qo+jgABLmgEpDtLMNJRGHVC4+CGgYieSqq2C441tWchn5DzZQWBFRtz7crRZJZ0IR11NmS9kNee/p3J4PsglCFOZGaigykFm0AIdgQhW41ICbsQr4QxxJjmYNBNFmC2I9B0ZwGUYgXwN4RSbyAXyiNP5fjlXo6GTAhcDdFpum0+7GPAb0qWddw23lNQmO2Pl3vVWvmyA0i0egppULCQkT8JJBrOn202jJSJKe89M5fskLfuCWSCQ+BxG+CSQBDpXqQwksLg0ZKEXblgtdNXYk0lXAMFkIJsDjIlUz1uTBItnCY25hQv7Dk1sqflW8Cq4f2NaXDqxSQHW5ZVjjfQtx8p6RpFSsCMdmsqGUDkhn5E4/3/27u7mLbuM47j3+NjH2xs4xhWSCANhSakLNCQLWtAWUNblabNtFWT9qJVXbXtYpvWSYtUTZOmdUhJh9pJ6y5aNZu0i92sF2taaaNL6y1LaLQ0IS8iTZZ3kgIFEgMGEww2x+f47OJvB6ft2qxrLJw8n6tAHFvm4peHx///85Sg2hXnUdPATqMC+7DjMPFp/UyEKAYSukXs8rHK4OBfGxrAyF215drWQQZwMmD7IOV8cH6NZuOZh7TnaVL+QR33c+6KyWesWEBTLQpYuGQGYNt60Jy3Z4J92W+uRLUU3l/d5rbu5kYzxlBXd/+N2uRwDOiXVoK4FUl7ocjkKtz39t3ePtpTB2QPIpQmYDa3gQeuTlt0a5a7LOG2JodMuMu4epBAT4Nd+jPgLWA98BDuZAeW25Pt3ea9qpkB04TAW6j1PvXAaq7dYZbbTzaGOn2QC+I4qsI9ApxBZtyKW9z1rF4Ri1BJ+RwlFSZGOEHVvech6UOFrE14wznwOLh8FtqyEbc1aQBrDPWBmpMOtVy01/7okI36Nf9xvIlnwdyC5fZxtZVgAaYDDHqrElfCa+K5lsIm4G7UB2K5EwoZVK/2GKqazV3ZHUVdctgFdDsOhyRwxa1OKt0ilat4gRaAc6/Xh+ZGQqx+vG9adwxt9MAyXS/RvNEjy3Q74QMcWNKXWfe92UTf8w+0AVs84fjP03GvF8edHayQf1EibRm3zV8xx8O/R33Y9RjQhKpec0fBQPVuB4DDqIsQNagQfgdVRR91HC7eoB+DEEVHQrdI5YVuTkvG0jjc9dB9QFtV68WnPKVm2rST81PDgTvLl/7hcnT/q7+B/jFf9fiOZDQYxNaz523zTjHoFkYolTAnl+wAelCzdb+BujWWm4mbG+YQRVWyUVT4V6F6ubuBfagZtx9yQkKIW5eE7k1i3da9/4qfvy08sKvpT6jeQD8wsPbH+140QsnPDu0+HIv2PvwurK8FcwVoeR++aRZkHFdZaraiYdwbqo+f7v/z538BPAvcwcJg8hIWZi68DbwBNKJ6wjpwEDVcvNdxuFK4dy9E8ZDQLXKahhtYB3wRdZJgBLVZYRh1LMsAfgDjPwF/OZS6UC0C7erYRu98KnRnbGL6ZO0TwEnU+vaO7L+1UGHrzv75LGrb8ATwLdRliEvATiAiM26F+GhyZKyIaRqfQf3634ia5NXb/MP9T3vDc/OHujraNY37gS6gDgIB0A3yxo2hm2aoYSJWeffYRFltPHH0ZO1dwMuo8E6jqtrcwd4J1FzbncCjwLdR1e1e1CLM4zLjVoiPJ6FbhDQNHWhDrVAPoqZxHQAGSysTP52JuVoCtWMplxEiY5bMAD5wl+Rd/Y0Dr6362qn1ZXfEE6kpz6YTf1wHpNsADTxXUId6Xahrvv8Afpv9ehsL1e1LwJsy41aI6yehW2Q0jSrg68Aq1KWD14ATG34Z6Qa48Pqq9qkzS7HnXDZkcoNl9OzMGQvc3cALwJHy1bE3zFmPfv7VZszJIGDbagIZZagq9wJqy/A/gaeAL6OeaBfwvMy4FeJ/J6FbBFo7Iz22BUe6Nv8aVd26UKvHdzsOU+oxALTMXg5hzzmASwdNB8tSIx0vnYPf/QW6tjsOKU3D1btt81bgCU/V+AZ129eXvTlhZkLNw8PTJ+rvB6pRLYXlqD7xM8AeuU0mxCcjoVsEZkYC3onjNZXAl1BnYl8BBj8s+Ko6jtsDL7fqpLOtBF/SXv7A+VPDf3t4B4ynHacrpWkEgMf8y2O/mh3zGOloKHsLzbLc5fHJlY+eHfAvSySPnqjfCnwl+9SvANtlxq0Q/x85vbCItXZGelLTbvel/Ss3anqGYO3ksfKGiene7QuLKPMfe2HXypqpU0ur7TndgKSzpG34bPxA8xdUZat9Jzsa4TjwJNBISaKJtOElo3kwTGo6TvdVr43Vx9/zh0b3NJKK++Y8gflUKhp+0HHoK/DbF+KmJJXuIucNWVZF0yillVdwe5l+/9+3dkZ6pi76jaG/39OWnvGiuZxk1cZ3U8s3DttubyZ28O3m7MjE1VNlq158yZyO+1JjS4aApcwHTCAabj6bqX9kKHrkuY5NrZ2RHmu6tD09ZxBeHb1Ut+Xc8KHtmyVwhfiUSOguYrnV6rnbZ/mr1vN5/Om0lTQI3B5jxSOnD5eGMk7+YzWNNjjz3ZnRRNCxXDrq9IGJ+oDsyYavDuzMf83WzkhPcMUk77zw4H1Qd4PenRC3JgndIpcL1+ZApMfvXwjN1s5IT++2zVuAbUY48X1zznFIu4MuF+ih2cyye88ODnZ/7pvqWT4Y5r5yOXIrxI0goVsE/luFm8/vv/br0d7qCuBNYIkRTCYrN1wYiB5cc0+ofoy6Lf1HXR8xX+56Xk8I8cnIB2k3kdbOSE8qBYPdTe2p8SAOTFc0j4yM7m1schycj2tTCCFuPKl0bzJeLzgZjcCKGDXt/ce8wQwjexrlf1YhFgmpdG9CUtEKsXjJ5gghhCggqXSFEKKApNIVQogCktAVQogCktAVQogCktAVQogCktAVQogCktAVQogCktAVQogCktAVQogCktAVQogCktAVQogCktAVQogCktAVQogCktAVQogCktAVQogCktAVQogCktAVQogC+g/9tmbfhaW0kgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(2)\n", - "pl.clf()\n", - "plot_ax(dec1, 'Source 1')\n", - "plot_ax(dec2, 'Source 2')\n", - "plot_ax(dect, 'Target')\n", - "print_G(ot_sinkhorn.fit(Xs=xs1, Xt=xt).coupling_, xs1, ys1, xt)\n", - "print_G(ot_sinkhorn.fit(Xs=xs2, Xt=xt).coupling_, xs2, ys2, xt)\n", - "pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9)\n", - "pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9)\n", - "pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9)\n", - "\n", - "pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1')\n", - "pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2')\n", - "\n", - "pl.title('Independent OT')\n", - "\n", - "pl.legend()\n", - "pl.axis('equal')\n", - "pl.axis('off')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instantiate JCPOT adaptation algorithm and fit it\n", - "----------------------------------------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(-1.85, 5.85, -4.170525419290473, 4.251885380465107)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "otda = ot.da.JCPOTTransport(reg_e=1, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True)\n", - "otda.fit(all_Xr, all_Yr, xt)\n", - "\n", - "ws1 = otda.proportions_.dot(otda.log_['D2'][0])\n", - "ws2 = otda.proportions_.dot(otda.log_['D2'][1])\n", - "\n", - "pl.figure(3)\n", - "pl.clf()\n", - "plot_ax(dec1, 'Source 1')\n", - "plot_ax(dec2, 'Source 2')\n", - "plot_ax(dect, 'Target')\n", - "print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt)\n", - "print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt)\n", - "pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9)\n", - "pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9)\n", - "pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9)\n", - "\n", - "pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1')\n", - "pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2')\n", - "\n", - "pl.title('OT with prop estimation ({:1.3f},{:1.3f})'.format(otda.proportions_[0], otda.proportions_[1]))\n", - "\n", - "pl.legend()\n", - "pl.axis('equal')\n", - "pl.axis('off')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Run oracle transport algorithm with known proportions\n", - "----------------------------------------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "h_res = np.array([1 - pt, pt])\n", - "\n", - "ws1 = h_res.dot(otda.log_['D2'][0])\n", - "ws2 = h_res.dot(otda.log_['D2'][1])\n", - "\n", - "pl.figure(4)\n", - "pl.clf()\n", - "plot_ax(dec1, 'Source 1')\n", - "plot_ax(dec2, 'Source 2')\n", - "plot_ax(dect, 'Target')\n", - "print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt)\n", - "print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt)\n", - "pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9)\n", - "pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9)\n", - "pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9)\n", - "\n", - "pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1')\n", - "pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2')\n", - "\n", - "pl.title('OT with known proportion ({:1.1f},{:1.1f})'.format(h_res[0], h_res[1]))\n", - "\n", - "pl.legend()\n", - "pl.axis('equal')\n", - "pl.axis('off')\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_otda_laplacian.ipynb b/notebooks/plot_otda_laplacian.ipynb deleted file mode 100644 index 07e4653..0000000 --- a/notebooks/plot_otda_laplacian.ipynb +++ /dev/null @@ -1,252 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# OT with Laplacian regularization for domain adaptation\n", - "\n", - "\n", - "This example introduces a domain adaptation in a 2D setting and OTDA\n", - "approach with Laplacian regularization.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Authors: Ievgen Redko \n", - "\n", - "# License: MIT License\n", - "\n", - "import matplotlib.pylab as pl\n", - "import ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_source_samples = 150\n", - "n_target_samples = 150\n", - "\n", - "Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples)\n", - "Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instantiate the different transport algorithms and fit them\n", - "-----------------------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# EMD Transport\n", - "ot_emd = ot.da.EMDTransport()\n", - "ot_emd.fit(Xs=Xs, Xt=Xt)\n", - "\n", - "# Sinkhorn Transport\n", - "ot_sinkhorn = ot.da.SinkhornTransport(reg_e=.01)\n", - "ot_sinkhorn.fit(Xs=Xs, Xt=Xt)\n", - "\n", - "# EMD Transport with Laplacian regularization\n", - "ot_emd_laplace = ot.da.EMDLaplaceTransport(reg_lap=100, reg_src=1)\n", - "ot_emd_laplace.fit(Xs=Xs, Xt=Xt)\n", - "\n", - "# transport source samples onto target samples\n", - "transp_Xs_emd = ot_emd.transform(Xs=Xs)\n", - "transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\n", - "transp_Xs_emd_laplace = ot_emd_laplace.transform(Xs=Xs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 1 : plots source and target samples\n", - "---------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1, figsize=(10, 5))\n", - "pl.subplot(1, 2, 1)\n", - "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.legend(loc=0)\n", - "pl.title('Source samples')\n", - "\n", - "pl.subplot(1, 2, 2)\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.legend(loc=0)\n", - "pl.title('Target samples')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 2 : plot optimal couplings and transported samples\n", - "------------------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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J/0febPolmdloM/sbSTdL+qhzLl3lNYdIOiLpuJldqMJx06X6cK2ZZbO2h+UFuVTOIR82s2FmNlHS38obRyd5JdofMG9JrSHyygDvLfEenpPkzGxq4HcFoCVkqg9utMxkbpkhHW+WtLKa9pxz98obDnG/mfndxOYfv0PSckm3mdkAM5slrwS5khv43iKOA4iCsO5j+2XiafZPQl6cOynpsHmTc97s0+zbzOwiMxsorzphaaYCIVd/SW3ykq1J8yZkzl1u+w5J7zKzKzNzIo0zswucc7vlDTX5gpm1Z/adZ2b5w1iCGiJvKMh+eZUdtyhTYVGOmV1oZi/OJGNOZv7k/v4WmDcRaT9JH5R0TNJqeZ9VSUk3mlm/zFxNL1d3XPezVxJxu0mQwEA1fmE918/uMZtvZizeA865UmV0h82bGfhJeUHnDc65b/eiTzfKW+rqmLyneKWCWL6FklZn+vMTSe/PjCvP+oWktZIekzc28ruZ7d/MXOcPkjZnrv13xS6SKc2+TdKqTNleoNn6AbSEY/Ji0apMLFoprxrhxmobdM59T97N73+b2eQAp7xZ3jwSHfJi3c2ZscX1QhwHUA/1uo/9tbq/mJ+U9+X+XyWdJe8L/0pJv/Vp+y55MWqPvDki/jb/gEws+ltJ98kbtvEW5ayW4px7RJmJPeUlhh9Ud7XZdfISIE9lzl0qbz6kavxa0v2SNsibJPSopN0Bz22TN2fGfnnvdbikj+Xs/6m8SZYPyltF5LWZOZI6JV0jb6jkfnnzGr3FObehxLW+JWm2eSu/LA3YP0SU9ayMBJCVyZSfkTQlM2EcAKAPIY4D6EvM7PeS7nbOfavRfWkkM/uUpPHOuXc2ui+IHiowAAAAAABA5JHAAAAAAAAAkccQEgAAAAAAEHlUYAAAAAAAgMhLNLoDjdTf2tyAYCv99NqMWSckSeufGFiX6wHN6JSO67TrDLS2OPqWYvHYEt7HlEsmQ7tWerh3ndih4zVvy/r3kyS502cqajc1wms3frD3fQRq5ZgO7XfOjWp0PxC+ojG5rb8kyXWeDu1aNqDNa/NUZ9ljXbt3H21HT/juPzPa63O/vfWLndbWP9TfR8/GM7c8VMyjjFa6R27pBMYADdJCu7Iu11q2bK0k6aqxc+pyPaAZrXIPNLoLqJFi8Th+9jmSpNTe5wpPisWldKriax27apEkacgPV1Z8bqVtJcZOkCQlt+2oqN2jV3vttn+/930EauV+t3Rbo/uA2vCNyWaKT5oiSUpt2BzateLTzvfaXPds2WNPXzZfktT/t6sL+iZJHdctliSNvX25fwOxuPffSj47zCTLFK27dEEyIT5xilIbtwRvrwLWlknudJZP7qC1tdI9ckvPgdFuI1y9EhhRt6xjLckVRN4q94COuoMtkV1uNZXEY7tkpiTJPbbOd79bMluSlHh6u1KHDoXTwRCkr7hEkhR78LGSx22/ZYkm3lLk5huIkPvd0jXOuXmN7gfCF+Y9cmL8OElScueuUNrrjdyEwKF3eMmO4d9bUfKcTfdcovPe5j2IpBICUdVK98gkMPpIAoMEA9BawbnV1Coex6dPlRTu08Lrnt2huy/xnhimT/iXMQOtgARG86omJicmT1Ry6/aSx+y+cYkk6dwvhJek3fqpxZr62T9JktLHjvW6vdhAb5iKb3w3i2YSI6r9Qt200j0yk3gCAAAAAIDII4Ehr7oh6qi+ANAKPrrpiVDbS23Y3KvqC+vXX9avf49td54/QekTJ6quvsg+3UM0xIcPb3QXgMiKDRgQ+Nhy1ReSV3kRZvWFJE3++Aqljx2ruvpi3/sW9/i5ZHxvVJWDWfeEnn5q3K/4jPOUmDJJiSmTfPcnJk8M7Vrpy8P7zpMdMlSp+MzzFZ95frCDY/HuuVVQFy09iWcWyQGEJZsM4+8UUJ3bzpvV6C704M6EP7N8Kw87yd78JreUnvsxN2lU7f+D6571Jk698/wJeY33nNU/SvOkAFGTPnWq0V2ouVFfLz0HRq3E29uVOno02ME1SlDs+og3pGfcZ0snlVIbNpfsQ5DkVfyiGV5bT63vsX37LV4fsnM/xR4K78FytZOfBplQtksVk4mjd6jAAAAAAAAAkUcFhg+eoqNa/J0BwrX1U15p7+SPFz4h23brEk26OdqrdcTb2yUp+FO27Hlnj/TO238g9D41UrnKi6wwKl8KKi+6GmeiO6BahzPLlA67M7yqhYriXV4FVcHuWiw7aqbt910sSZr4hidDa7bSz4VaKFd50SWEuJlfeZHFqluoFBUYAAAAAAAg8qjA8NFXnqKztCqAZhQbNEiSlD5+vKvy4tQ1CyRJA37xSNdxudUXiTGjldyzt1fXtYT3keiSyYrP3f2zC3Xuq5/Oacx7SljtE7Zmq7wA0ByCVl4cffMitd+7svgBOct+VhTvylQCZCsvznpwtE5e0bvPhNxrhll5AaB3SGCEpBHDTkheAGhG6ePHC7blJi6ys53nThqWm7zY8QlvQrAJn1rhe7ObGDO64BypusRFVo/khdR13ewKF6lDh4peN9/zb1ioIT9dU7pPZcqoAaCRcpMX2dWlekzSnBu7ahDPiiUvEuPHSZKSO3cFbiub3JZ69zkBIBwMIQEAAAAAAJFHBUZIGl0NwXASAK2i3HJtE/65e2hJbOBAST2XLvWrgHCXzZE9XMHSbdk1332WT7twTUJPz/We0uUu0Rl0iMvgH61S2eeQVF4A6CNyKy82fHWhJGn6Dau6D+hlPIsNGqT0ycxyrzkxOVut1/HK8Trna97nQiWVF13do+qiPqgsREBUYDSJSpMXyzrWdg17AYBmlT5xokfyophKkhfxmed7N8lF1n7PJi+C2vWTmdr1k5m++zpfPr9wYzZ5AgARsvOmJWWPOf+mp3T+TU+Fe933z5b1S8j69Xwum9y6Xcmt27uSF0ElJk/sSn7kiw8bWnU/AYSDBAYAAAAAAIg8hpC0qHIVG42YlBQAomDHx5Zowr8Uf2KXWvdsaNeKDxuqca9dV3R/269Xa/3XvRVYZrwvM5FpkcoPAAhVzkohQYz/9HJ1Xu1VjbX9ZrXvMeljxyruRmLSBCW37Si6f+znlpcfdhfQqWsW9Jg0Ol/q8BFt/dRiSepaJQshYegIAiKB0UIqSUqQuADQatxlXtwrlbwIy773eTfAo/6jxDKDGf2OMGQEQANU8YWyWOKiN5Lbd4beZr6t/+zF5Gnf7lDZQYBW8+4AKIEhJAAAAAAAIPKowIiIeqwiQlUFgGaz42PepHFhVE1kJ/LccOelmn7dH3vdXimjvh689HjKP1KmDKCPqMFKErduelQ3T50bWnt+Jn/Ci7NJK19eMe0bXkUIa5MAjUEFBgAAAAAAiLyWTmDMmHUiMkuJUh0BoJWdGTNIuz5Sfgm++MgRPX6e8C/LA1dfxEefo/joc8oeV4vqi02fXxx6mwBQK9a/f9GlRHsemFex4Fzg6gvr11/Wr39he3ltFq2+8Dk2qHc9u81/R4C+J7ftKDmpKEJU5f/fmonFWco8Alo6gbH+iYGRTxws61gbmSQLANRKvz3HNe6z5RMRqQMHq75Gau9zSu19LtjBPjfGyfsnKnl/gBt6H+d9qDbDQLIz/qN1JM4do8S5YxrdDTQ5d/q0klu3K37RjDIHVj9UxJ05LXfmdGF7Pm0mxo9TYvy4ntsmTVBi0oSqrv2d8ydVdV4xsSFDFBsyRJ2vICaHKsShSNbWVrAt3t5ecn+u9BWXeKuA5a0E5hbPlls8O5xOIpCWTmAAAAAAAIC+wVwLr7nbbiPcQruyonPqMdkmAH+r3AM66g5GrJ4QYSgWj3d/wBtWcu4XC6sztty2WFM+Gu0JLhNjRkuSknv2VnZe5kljcueu0PsEhOV+t3SNc25eo/uB8BWLydmhfn4Vc9bWJtfZWfG1SsX5wouUniQ0NmCAJCl96lTwDpiVfdJv81/gXXb1k4X7Egm5JFN6orFa6R6ZBEaFCQwgDCTCqtNKwbnVEI/RKNV+6epLfrxzpV43flHo7ZLAaF7EZDRKvL1dqaNHw2nr/GlKPbvR+yEWLxj+0UPu/uw8F6WO74WNd1+iaW97rMe2zlfMV9uvVhceHCDBJrXWPTJDSAAAAAAAQOSRwAgZE24iCKovgOrFhw1VfNjQHttiAwYoPmqU4qNGdW3bdt8L6t21ktKXz1H68vL/9rPDTlAfzV59Iakm1RdoHSdftUAnX7VAp65Z0Kt2Nn6phn8Pq1iRxNrayk7ciPoLq/pCUnf1hVS+miJ3v89knWHKr76Q5F99IYU6kWmzIIEBAAAAAAAiL9HoDjSbZnuynq0oabb3BaDvSh0+UrAtfeqUlDdp26RrCydbq6kyY2ZjDwWr0Cs24ecvd62RJL1y3NzK+wYAVTrr54+E0s60f1gZSju+fJ5SH32zV/HR/oNVvvvLVl+Vien9fn+uJOnMi3ZX0FEAvUUFRi+0wnCRq8bOIXkBIPJOLptScv/Bv1xc+07UuOT0lePmkrwA0CcM+t9RJfdnkwu11H7vSrXfu7L6EvwyMf3Mi3aTvAAagAQGAAAAAACIPBIYvUBlAoJa1rG2JSp2gEY566otJfeP+PaKOvUkui5cU37UaHz4cMWHDy97nPXrH0aXGsLmv0A2P1oTvAJ9RsDJMo//2b6S+9vvreFwknqrcPLQShx//UIdf/3Csse1PTimZn2otdiAAYoNGNDobqAPYQ4MtKR6z+1BsgtAoz09N1ly/x3bH9L1Ey8P1JY7czqMLklS14oyfnOb1IJbXee5UYBmwooIhWr0O0m+ZK4GLV0V6NjOK/aUPSa7Cla5+Zh2/nimJGn869YFunZvpfPmrwLKoQIDAAAAAABEHhUYLWhZx9qWrwho9fcPNIvUiy6VJMV//8eqzo+3t3vthLjufCUmP3KWti442ZBr5wtafRG2elVeAKiQWcXVBUfe6k3OOfSe6oaINDomP//bqRr8ss0NuXa+xH+vCbW9oCth1avyAqgWFRhNrNicC3x5B9As4r//Y9XJC8m7SQ56o3z47Yt1+O3VrWay4av+Y5ijkrxAfWRLuIE+oYqhEUPvWVl18kIqHpNjcy5SbM5FPbalL59T9b8pa2vz3d7b5IXNu7hX56O+0i+8pPuHMnOZ9Ho1sxrOldJqSGAAAAAAAIDII4HRxGpVacFqGgCanbtsjtxlPWPo8HtXa/i9q6tqb/oNwSZiCyo+bYri06ZID4wPtV3UVtASbiAqTr9sfvUnB1yxJFBTZ1KyM6ke2+KdKcU7U0XOKM11dobRre6+nD1S8bNHhtomaq//5r1dry0eL3nsqKX+Q2ts3sXBKm9yK5pipa+F0khgAAAAAACAyGMSTwRW76VHAaBR+m32lqTLXXjUJUsvQ1pP1/7yYUnSj158qaLTKwDN5tj4hKquKwhxeVG3dWfBtmxFRiQWds281+cnD9KgRxvcF/jzmZQ2tf9A12uXKl3Nk3r+uP+Oav6ep6urHIKHBEaNNdOX/mZ4DwCaVJnZ8mODBmnPO2dLks75t+Xd2zOTwqXXPqXE1MmSpOTmrUru3lO7vkra+7dLJEmjv7K8zJH+7r1gbOZVbfsJoLWN/NaKmrRrbW3aceNcSdL4T/vHQUt4X1NcMqn08cIvj+m1T4XWn/V3zJMkzbi+uuxD6sBBSdKgpeEOF0SIfO4RegwlKpeIKJJ0cGtYtaXeGEICAAAAAAAijwqMGitXtdBMFRoA0DBlnpykjx/vUXnRtT3nCZ6747T34sWh9szXgAPp2l8EACLKnUlq/G2lqzt+u92rhqjHPXK1lRcA6o8KjAa7auycooGZ1T4AwBMfNUrxUaNqeo3UizuUenGH7773bdhY8tyC2efLzL4/9J6VGnrPyor7CABRcPpl83u3Qkk65SWeSySfS90jd3xwScnmY7MuqL5vGc+/YaGef8PCXrcDIFwkMAAAAAAAQOQxhCTC8rPODDcB0EpOvHahBv7EmxAttW9fwf54e7tSR4+WbOPIWxdJUq+rHb4+fVqPn3d+1Hv6N/42b1hK7kzmkkKdfR8AoiDe3t61EkP/364u2J+YPFHJrdvr0pext/ccErjjE15MnvDP3vb0E8/4nrfvrxZLkkb9Rzjff2EAACAASURBVPnJSQf/qHtCzsSUSZKk5JZtlXcWQKiowAAAAAAAAJFnroWfErXbCLfQrmx0NwpQaQH4W+Ue0FF3sPjEAuizsvE4NnCg0idO1PRa1q+/JMmdOV322K9ue1iSdMOky0K9fpBrA1F3v1u6xjk3r9H9QPiyMdna2nouNdlgsUGDJMl3WdVqHXrnYg3/bm2Wi20ai2Yp8ZxX8ZjcvLVgd3zYUKUOHwnlUu6yObKHQ5oHMBYvuvxpydNmXyhJSj/+dGUnZue+qub7dZnl4MtppXtkhpBEUKMSFyROADRafvIiMX6cJCm5c1fBsZZIyCWTFV/jxMu9GHfWzx8pe2y5xMXx13kTvA368Srf/YnJEyWpR1l1kOTFoXd4Zc7Dv8dNNYDG6ZG8MJPmX+y9fuTJ0K6RnSgzd8hGMbFR3oTJxRIY+96XGSLy9eCxM0jyoiuWb9tR8CUzNmSI0seOBb5el15+Ya2rlU+o1KdtWMkLSeElL6SqkhdSFYmLrN78/+wrfxcigCEkAAAAAAAg8hhCEsEhJPVE1QX6klYqj2s1kY/HdXhS9utdf5QkvXzcpTW9DhAWhpA0r4bH5HKl/xUMDdj9gSU694vLyx+YZ+sPZ0mSJr/xiYrPBeqtle6RGULSAFFKGkShDwDQKFc8cVKS9OCss0ofWKPkRfqFlyj2h8ckkbgAEDE1TtzGhw2V1HP4wV07vHmH3j6hzLxDFQwNqCZ5IZG4AKKKISQAAAAAACDyqMBogFpVPUSpsgMAwpSYMF6SlNyxM9R2s5UX8fZ2pY4eDadRM13/7GZJ0h0zppQ8NFt9AQCRU6L64syfz5Uk9bt/TdXN+038WLbywk+ZSpH05XO0/Ya0JKoqgGZABQYAAAAAAIg8KjBK6GsVDX2lnwBQqac+ca4kacZ7w63AyCpXfRGfcZ5S6zcV3X/47Ys17K7MUnzO6bvzZmf2hFTVAQAR4uKNnStw6w9ndVdT+FRf2NyZcmvWSZJiD63V5Icqv0Zs0CBJxZdsBdAYJDBKCJIQ6GtJDgDoi2a8d3XXa0t4H10u2b0qfXz4cJ1ZOliSFLtyh/ffWRco/cQzkqTEuWOU3L2n6uuXSl5I6k5eZI/3SYjEBg6UJKVPnKi6HwAQBf2XPVr2mH6/9xLPZ160W5J05K2LNPSelaFcv9xQkGzyoiTLJGGKDD8hcQFEE0NIAAAAAABA5JHAqMKyjrU9Ki+ovgCA3jk99SxtuXd2+QPlVV5kqy86PrhEHR9cotShQ4pduaOr+kJSV/WFJP/qC6tvCXT6xAmqLwD0CWdf3Kl3Pbut4vMS545R4twxkrzKi2z1haSy1Rfxs0dWfL2qZSf+rOEysYiwWNz709eY1f3eJYpIYFShLyYtsgkXAIii/ptPasqbH6/4vLG3L9fY25dXd9EAN67xYUMVHza0uvaLuGN7FYOxAaCO9v+pTd85f5Ksra2i85K791Q9XC+1/0D5g8L64pkT/0++ekHv26tGkPcRkS/aqRddqvi0KYpPy6yslfdFOj5yRGjXSr/wktDaKvq7S6e8P8VOm3ORYnMuCq8fBReo8v8pSTdJJDAAAAAAAEAfwCSeLaKvVYz0Rcs61vJ7BppM6vCR0Nu8fuLlobcJT7ZaZsNNF2nqh1f4HnPsTYskSYN2d0qSYg8+Vp/OAX2Q6+xsdBd6KvHUvFpn/eyR3jWQHY5SqXTKd1Lq/GOCiJ8/TalnN1beh4Div/+jevQk7/2mDhws2i9J2vTJgcUnXs1UI8Rmne/9/IcQY3KVf1/Sa58Krw++Fwj/73ErIYGRhy+hqBZ/b4BwxGZfKEnq95VD6ryi+pVDFIsrdtYASeHPJp8YN1aSlNzV4X9AmdntS7F+/eXOnK62ay0tm3AqlryQpCE/CGcVBKBVHH2zl/TTdfvUfnXpFZlKiQ8frtShQyH1KgS9iNM99OL8oomLCtUyedEb2X5NfmOJgzJf5mueNEDTYAgJAAAAAACIPCow8jTbU/Tc1VIAoC9IP/60JKnzCv/9B96zWJI08ps9n7Inxo/zzh8xRJJkKafUumdr0seilRdZPk/kDl/n9XvYncWrAyTJ+iWowAAQGe33ZqqW7vXfXy62Wb/+khSt6gtJB38xXZI04pXre91WbOBASWKlKaAOqMAAAAAAAACRRwVGE8qtuqDyAkBfZW1tvhPIZSsvLJHoMX44uXOXt32v97QvHYEqhp0/nilJGv+6dRp2V7C5F1Kzpyt+wut7thoFABotPmyo78TG5arKXPJM6YbDmosigPV3zJMkzbj+0cCVFx0fXqKxnyu9XDeVF0D9kMCIkLCGe5C0ANAM/JIX6Ssu6Vo1In/ys/jocyRJqb3P9frao5YPkyTtW3K4V+2Mf9267h8C3pzbiseV7tVVASAkOSts1GJVJkmBYuPeG5ZIkkZ/tXQioZwZ1z9a8TnlkheStP6b8yVJF93a0ZVMR4uoYwIOHoaQAAAAAACAyKMCIyMKk11SOQEA0kue9JY8/e8XDOraFh81ynvxoP/68InJE5Xcuj20PvS28iI2ILN866lTYXQHABrDOW3+rDdJ59SPdA8VqagiIvtketEsaeUTVXWjt5UXvkJ8cj7jPaslSeEsioo+hcqLuiOBkRFm8iAKyRAA6KtyExdZqX37Sp5TLHmx6fZFkqTzPlh6/on46HMqGnqy4xPezfuEf/a/qc4f3gIAfVVu4iKrqoTCyicUG+KtEpU+dqzkoQfevVgjv1V6bo384yX5nmNtbTp07aWSpGF35ezniyfQJzGEBAAAAAAARJ65Fs4+ttsIt9CubHQ3eoVqD7SSVe4BHXUHrdH9QPiaIR7/eKdX5fH6867wnYAUaDb3u6VrnHPzGt0PhK8ZYvLun10oSTr31dWt5pQYP06Sik7KeeStXoXf0HuCrTAF1FIr3SNTgQEAAAAAACKPOTBqaFnH2ppXRlB5AaCVJMaNVXJXR+H2c8cotW+/JP/5JzZ871KN/UU/SdKgpatq0rfXjV+UeUX1BYAWkbPMar4zL/WKc/r9rnDp0pPLpmjwm7zJklOHDpW9THYi53LzIeWqtvIiq9xyqFReAI1BAqOGyiUX6pHgAIBm4pe8kKTk7j2+2+Pt7ZKk6e/4Y8l24/8zVqkX+7ftJzbIm2g0ffx4wb70Cy9RYs2z3usTJwK3CQB9Tomh6H6Ji+zKH2ddtUWpEs0e/c15ar96U9fP5RIXbvFsr/kVjxfsi48aJRvixezk5q0l28ntI5N8AtHEEBIAAAAAABB5VGA0ENUX6CuYLBb1kjh3TNFqimqkjh4NdlwF1ReSf+VFVuwPjyldUWtAcJ/f6pWtf2jyojJHAr2XvHKuEg+sCa/BbFWDWeG2HLnVF5L/EJL05d49Seyhtb6VF1mpffukCoaelK288KnQiJ890nsxcrhSz24Mfi3UTWzAAKVPneqxLTF+XNmhQl3nDxzoW1V58lULJEln/fyR3ncSgZDAAIrgS3s3fgeolzCTF4iuxJjRSu7ZG/j4L25dIUn6wOTFtepSn0HiAvUUavIiV4XDM/yGkMQeWhtWbyrj0/fU/gPei+x/+5jYnIuUXvtU4ONf9/RzkqQfX3hOrboUuvzkhVR+npMe5xcZEppNXFi//nJnTpduhOFJoWAICQAAAAAAiDwqMNAn1aM6gqoDAOgpt2Q7a9dHlkiSxn12eeB2Kqm+kPwrLxJTJ0uS9v3ZuRr+3RWlG+CpF4AG8oudYYiPHCFJSh042LVt+81eTJ54a/CYXEn1heRfeWEJ72vl/nfN18hvlonJTahs9YXEZ1BIqMAAAAAAAACRRwUGIq3YUrNURwBAfb1vw0Z9fXrh9koqL/zEL5ohSUo9tb6i87LLIQ4PsiwiT70ANFBVlRdmJWPXhjsv1fTrCpcIr6Tyws+Bd3sVbyO/VVkVhUsmvfO+uaKrQi7QsrVAhUhgINL6cqKCSUABRFV8+lRJUmrD5sDnfH36tJL7N31/js57S+U36ZUmLqpx5G3exJdD715Z82sBQKU2ftGLUdM+kBOjyiRe/ZIXkrT5M14C4rxPrAk2rCFPpYkLP+USF9bWJklynZ29vhZaD0NIAAAAAABA5FGBgapQXVAevxsA9dD24BhJUucV3hK08dHnKLX3uZLnBK28sPkvkFv9ZKBjc6svAi0n53vB2ky26Vd5ERs4UFLxpfEAoBrv3+BVlf3bdG94XLmhIFJe5UUJsUGDlD5+PGdD3PtvOtW1aeo/ehUUYUTRxPhxkipbbjQIKi/QGyQwUJV6fjknWQIAxZ2+cn+Pn8slLyoR27pHqZyfN3zZK3Oe/nfdN9s7b/JmvB//6e5x11UlL6S6zlVB4gJALXQlLrJCjGvugsnSmnVdP2/5/kxJ0pQ3PVFw7InXLtTAn6zq1fXCTlwAYWAICQAAAAAAiDwqMBCaWlVKUHkBAMVlZ37P9cInTkmS/jBrQK/aTu3b1+Pn3MqLrNzKi0ZIX3GJYg8+1tA+AEApIx8eLkk6cNkh3/2xWRdIktJPPFOyHZdTfSH5V15k9bb6olqJSROU3Laj5DHxYUMlSanDR+rRJTQZEhgITT0SDQwnAYDyKkpc+Mw78dxfe8NCzvn34smJzd/34vDUClYeibe3S5JSR48G718ZJC8ARF2xxEVWucRFYvJESVJy6/aixxz/rbe61KCXBVxdKsDcHNUol7yQuhMXna+Yr7ZfrQ69D2huDCEBAAAAAACRRwIDochWRtTaVWPnUH0BACV0Xj2/6L4zL52nMy+d1/Xzc3+zxHsCl/cU7px/X16y+kLyKi+KVV8kpk723Z46ejTU6gu/6xa7dgGz7uoTAKiRjXdfUnRfYsJ4JSaM795QJCYlt25Xcut2HfzLxUXbGvSyzcGrL6T6TJoci3evlOKj7Veru2MxMRkBkcAAAAAAAACRxxwYCEUYVRHMbwEAvdf2m+Ljifvd33O+iHO+trz76Vi6e8HUQ+/wnvIN/96Kom099zdLutvIk9y81fec7NPDEd8u3m5vFLuurzou2QqgdU17W/F5epI7dvbcUCQurf+PBZKkGX9VPHYmxoz22tyzN3DfOj7kxfGxn6/RZMw5nyt+nzWSiMWomLkW/kvTbiPcQruy0d1AL5H4aB2r3AM66g5SX9iEiMfN4ZXrvInqfjlzeNc2mztTUuHs+fkskfBdUQXRdb9busY5N6/8kehriMnNYf03vSGFM97TndhOjBsrSUru6ih5bnzY0KpXCdn/Xi9ZffY3apOsRqFWukdmCAkAAAAAAIg8hpCgzytXeUGFBgDUR27lRVZu5cXGLy2SJE37h5WFx4VcfVFs2cFd/+iVTI/7TI1KpgG0ljCXI622rVi8cGiGelZeZOVWXrgls73LLn+84Lhqqy+k7sqLA9dnKjG+t7pojN/8Oe+YqR+mWgPBkMBA02t04mJZx9qG9wEAGi4W70pcxAYMUPrUqeCnDhggSRWdk5+4kKSvbntYN0wK3AQAlBfmcPwibSXOHSNJSu7e439eOqX0C73VTmJ/KD7nRg9mvomLMI28w0tKFPsNjV7RLi0mcYHKMIQEAAAAAABEHgkM9BnZoSB9DdUXAJrNv297qGBbfHjh8JEecsqbK6mkyB5f6Tl+bph0WfcPZt4fAMHF4t2rSdRLmX+nlihTUF7hv/PkS+Yq+ZK5vjHi1DULCo7f8NWFFbVfjeTuPcWrLyRZW5tif3gsePWFFInVP/YuPtroLqAPIoEBAAAAAAAijwQG+oxqKhn6atUGAETZX0+6vGBb6tChkuf8h0/VRrVigwZVdZ7169/9g3OReAIJ9CnplO9kkTVV5t9p2QmAK/x3nvjvNUr89xrfGDHgF48UHD/9hlUVtV8LrrNTiXFju5ZIDeJdz25TbOBAxQYOrPyC+dUpQapcfCparK2t8muj5TGJJ5oSK48AQANkS8tzvuCMXtEuSforn6RHrlPXLPD9cuAnffx4Vd1zZ05XdR4ARF3u6iKlfH6rN5nyhyYvknSi+IGlVkTJ3573czaR0qNPPm25zs6y/QXyUYEBAAAAAAAijwQGIieMYR9XjZ1D9QWAlmDzXyCb/4Kun8OcUK6ScmRJXeXlsVkXdG3au/hooInaglZfhCU+4zzFZ5xX12sCaD27/nFJ8IPLDMU48+dzq+pDx4e6+/ChyYsy1Rdl9GKIXXJXR+CKEKBS5lp4/Ge7jXAL7cpGd6PlMdwDQa1yD+ioO8iyAU2IeBxc4twxJWej742epcVAafe7pWucc/Ma3Q+Er6KYnP3SXYfvFJ1Xz5cktf1mtf8BPsPYai0xZrSSe/ZWfJ716192WFvn7yZLktpeurWKnqGVtNI9MhUYAAAAAAAg8khgoO7yh4gw3AMAgutN9cX+9y7W/vcu7vp5+y1LesxCH7i0uIz4yBG9bqOrreHDu14nJk9UYvLE0NoGEII6rujT9pvVxasvpIasklJN9YXkTSp89C2LdPQt3TF3w1cW9lito+2lWyNXfZG7aolbMltuyewG9gatiFVIUDcMFQGAxjr7GyskSdc+7SVB7rtwudI1uE7qwMHw2spZnjW5dXtVbdjcmZIkt2ZdKH0CgIoUGdrS/n1v2N5zf+PNUTH9b5fXtVvVSJ/oXrnElj9esD/5krneMrQlbLvVe7+Tbo7++0X0UIEBAAAAAAAijwoM1A2VF1ShAIiG+y4cE+xAs7qVhochfvZISdKmf5ihyR9b0bU9t/Iifv40SVLHVedIkkZ/hSeAAGqszLCWc77WuzgUm32h0o8/3as2SspdHaXMZ0Ju9UXqxZdKkvbNHqAx/9r9HnMrL7JDUo693FtNa9DSVb3uLpobFRgAAAAAACDyWEaVZfta2rKOtVRD9CGttERUqyEeR1f8ohmSpNRT66s7P1MVkdp/oORxG+68VNOv+2PBdrd4tmyFN846ca5XOVKrZWRRGZZRbV7E5MZJjBsrSUru6ujaFjSOVs2n2m7P3y3RmC8XVoZs/OIiTfvAyu7zpD5VqdesWukemQoM9Gn5K5pUiuQFABS352cXKvXU+kDJi0PvXOy7PbX/QKCbbr/khaSu5IXkJS7CSl7Ehw1VfNjQUNoCgLAkd3X0SF5I3XH0+OsX9jw4Fu+eIDTP0IdGBr+oTwLCL3khqTt5kT0vrORFzuorQCkkMAAAAAAAQOSRwECfFoUKimUda3tdCQIAdVHiaZ2fMa95JvCxw7+7ovxBNTZ+5WCNXzlYlig/R/mpedN0at60OvQKAIqoMCYPWfZUzw3plPfHp3LhyOU1Gm5Sgb03LNHeG5YEiskb75yjjXc2/r4e0ccqJGiIZlqNoxneA4DWED9vkiQptWFz17aNd18iSZr2tse6th170yJJ0pAf5JQKN0rO2Gzr11+S5M6c9j1056LnAzfb7/415Q8CgBqKT50oSUpt3OJtMNPQP4yQlJeAyCQo0seO+TdUzzkocmPyfG/lELf6Sd9DR3/VG4YSpHfT3v5Y+YMAUYEBAAAAAAD6ACow0BB9qWqhmapFALS4Q0d6/BifNkXTrnui4LDeVl7EBgxQ+tSpis6JT5/aozIka+8NizX6K95TvP3vnCtJGvnNxg9XAYDess4zPX7ectsiTbncJ771tsLCZ5WRcpJXzlXigcJKtfiwYUodOuQdM7ift613vQMqQgUGAAAAAACIPHMtvG4va1wDlVnWsbahlSittMZ1qyEeA5VpdDyWpPvd0jXOuXkN7QRqgphcQ9kJN8t9B4vFvQk6S7UT0ve4bbcu0aSblwfuX2z2hZKk9ONPh3L9ZhCbdYHc014VYbF5mipx4rXekrkDf7Iq0PGtdI/MEJI+iCENaBT+zqHesjOXu2QytDY7PrhEkjT2dv817iux9V8WS5Imf8x/SENswABJqnw4xYzzJEmp9Zt60Ts0M+Ix6s5Mskzxdqkv1pU2O3emJMmtWVf22F3/6MXvcZ/xj9+dV8+XJLX9ZnVIvfOUmkA4fcUlij1YwQSUQZMO6VTpz8AQH0J3JS8CtkviolD6ieCrdgURNHHRihhCAgAAAAAAIo8hJJTHoUpUwtRfK5XHtRrice3ER42SJKX27av5tRITxkuSkjt21vxaua59eo8k6b4Lx9T1uq2OISTNi5hcO/GR3jKpqQMHa36tbZ/0qgQn/VOVEx8HHe6SZ+MXvaW4p33AZ0LockNjUJVWukdmCAlQhSiMPQaAIEJNXPjczMYGDZIkpY8fr3viQvKG8dx3Yemb841fytxM/0PvVlcBgKrkxE6/xIW1tXm7Ozt7fanElEmSpOSWbdUnLrKqeNDd8cElmvaBEkM006nSCQ6gDIaQAAAAAACAyGMICeVxTYVhHc2tlcrjWg3xuPkQj5sfQ0iaFzG5+Vy97rAk6TczhzW4J00qxFVhJMld5n122sNrAx3fSvfIVGAAAAAAAIDIYw4M1Fw9n8LxpA8AistfYjA+fLgOv/R8SdKQH3aPRS61ZGBQYcVjm/8CudVPSpKev9YbNz34PsZNA+j7dnzCWxZ2wj9n5oww657kc/8B/5OqnFiz0soLSyR8l2+NDRjQtTT45s96k4RO/Ugv59poBiGPaghaedGKGEJCeRwqQEl0Y7VSeVyrIR73XmzWBZIya9HH4t7GCM/0Hh82VJKUOnyke1t7u7ft6NGy509c5U0eun3h8dIHhlzWi24MIWlexORwhZEU7l0HysfBxJjRkqTknr1d2/KT3qWkH5ggSYpduaPaXqIXWukemSEkAAAAAAAg8hhCAuQoV2FB5QWAqEo/8UzOD9GtvMjKrbzo2papvLC2NsVHnS1JSu7cVXDchzc9qc+d94JgF6L6Aoi2SoZEVDl8omybNY4TDau86OpAkfeX8/vMrbzIylZeJKZO1uF5YyRlhvDl/X84fN1iDbuyimEkPr/72MCBSp84UXlbPm0mxo/z/QwpJ325d78fe6jCYRy1+PuJAiQwEEmNGqpBggIAejr2Rm/eieEP76jqRtDP9puXaOKt3pjvM38+V5LU7/41XftdZ2fJawVOXgCIvkq+7NXii2Ef+7IZ/5+xkiR3Q7vSf3qmzNFllHjvbvFs2YrHJUnJzVs1ePPWoucNu7PKOTB8rt+r5EVem9V+ZlWcuPC5NmqHISQAAAAAACDyqMBA6JZ1rO11JQOVEABQoZzS1fi0KZKkv//tL/WFaTN71Wx2dZLCueirl62+kHpWXgBA08iJybGBAyVJt657ULcseLkkKbVvX1XNpl7ckXnVUfK43spWXwBRQwUGAskO6QiC5AMABJcYN1aJcWN995165YKCbZ2/m+zfkHNd5aupjVuU2ril18kLAGgIC76YQnaFj7Bsv3mJtt+8xHffpi8sKtiWXfa0QE5MTp84ofSJE/rElPlK7dtXmLwwq+g9R1qzvA9EFgkMAAAAAAAQeQwhQSDNUlXRqMlBAaCY5K7iZcADfvlIwba2l26tYW9KO/lqryLkrJ919ys7XCW1cUtFbR1492JJ0shvdU/+dvAvvW0jvl3lhHAAmkMFkyGGvcJH7hC3fOfduLJgW+rAwd5ftMrJH/e/14uZZ38jJ2ZWuRLG1n/22pr8ieri7+mXzZck9f/t6qrOB4KiAgMAAAAAAESeuRZe7qXdRriFdmWju4EmFMZEpii0yj2go+4ggyubEPG4OcQGDJAkpU+dKnusJbwiUJdMltzWW/+x7SH91aTLQ2sP3e53S9c45+Y1uh8IHzG59+LDh0uSUocONa4Tsbj333SqcX3Ic8f2h3T9RGJy2FrpHpkhJOgVhmT44/cBoN6ueOKkJOnBWWcpNmiQJMkGDqx6pvtqBElcZPklKSpJXCQmT5QkJbduL3kcyQsAjXDH47+QJL2zkV/W65m4CDh05b2zr5HUwKQO+jyGkAAAAAAAgMgjgYEClS6ZSrUBAFTv+WsX6flrC5fmk6QNX11YsO3MS/0r9h+cdZYenHWWJCl9/LjSx4/Xtfqi3pJbt5etvgBQPx0f9F96NFds4EDFBg4M9bqJKZNCXYb05KsXdE1YnC87UWWuYsu4vnPi5fWrvvB77/VezjRn2dhSGjqkBk2BBAYKkJCoLIkDAL0x+L6VGnxf4cz2kjT9hlUF2/r97tFQrx8bMkSxIUOK7l9/xzytvyPkaQ4quNlOXjm367VbMltuyexw+wIgFGNvL756R1b6xAmlT5wI9brJLdsCf3kO4qyfPdJjpaVcfitshL0KSmzORYrNuajHtlPXLNCpa7ykivXrX5g08Xvv1f4+Msmg3Niba9c/lk9UAbVEAgMAAAAAAEQek3iipvrqahx9sc8AUEr68jmKPVRYXZY+dqzkeTOuD7fiQ1LZp4Wbvj9HE+7sJ6nnE09b/nj4fQHQdwScKLIv+Nety/X3kwurGdJrnyrYNuAX3RUhoVZ8+P0+M68TD6zpeVxm+7jPlK+08RMfNlSSlDp8pGcX2tq8y3Z2VtUuWg8VGAAAAAAAIPKowEBNsLwqAETDjRvXSZK+MK3BHanAeW+pbB6i+PSpkqTUhs216A6AqOijlRfZKoP42DH6u//6tST5Vl/UXdDfZwi/9/zKi66mqbxAhUhgoCYakbjoq8NVAKA3EmNGS5KSe/ZKkmzuTLk167r2f2HazLr3acfSizXh9X8qeUxi3FhJUnJXR/e2CeO9bTt2VnQ9EhcAoiI+83xJUmrds5IkSyS6vqQnt2xrSEze8oNZmvKmJyo+7/jrvJWwBv24cEJpoFEYQgIAAAAAACKPCgw0Daov6oPhQUC0ZCsvsnKrLxqlXPWF1LPyomtbhZUXUZMYP07Jnbvqdr1Pb/Em9rtpyoK6XRNAadnKiyyXTDaoJ938qi/iF81Q6qn1Jc/rUXkRi3v/TacKjjvz53PV7/41BdsbPemqtbVVN0Slyn53/NRb/nbsawonYkV4SGCgZTDEJBz8DgH01o6PLdGEf/Fmst9wXiqGkgAAIABJREFU56WSpOnX/bGRXQpFPZMXEokLoBUde9MiSVI6IQ29e2XlDWS+nOcmL3Z8fIkmfCqzukixRIVP4iLLN3khNXzOkqrn16iy3yQu6oMhJAAAAAAAIPJIYKBlUDnQ07KOtV3DQQCgnrLVF5JXedHI6ot/3bq8/EE1Fh99juKjz2l0N4BC2VL6Jlb1vz2z7t9P7utKzi93SL/+sn79e2wb8oOVGvKDldVVX0hedUFehUFX9YXkVVrkVVt0vmK+4jPOU3zGed19z+l/dgLmUvzei5+Trw6vsiw+fHh15+W+1xqIjxxRs7ZbAUNIgIxWG2LSSu8VQN9y4N2LdeaVhyVJY179dMH+2MCBSp84Ecq1orCUYWrvc43uAuAvasuW1mBOhar//eX2oZr+BDjHnTldebtB5SZQyvRl4B+elTJLwQY53s/la45Kkv4wa0DJ48762SMVt11M6tCh6s5bvym0Pvi2f+BgTdtvdlRgAAAAAACAyKMCAy2PVTUAIKAQn35u+oI3Ed15N6702bZC+lbxc8OqvgDQx0StIqQvy/tdbro9E38/2B2Tt9/iVahNvKX0ULvkjp1KjB/nvS4ymXG5ygsgKCowAAAAAABA5FGBgZZH5UW0UBEDRFiITz9zKy9KbWtV8fOnKfXsxspPDLFK5n0bvOt/ffq0XreFJpP5e5aYPFHJLdtqfrnsZIy5cxokpk6WJCU3by08wazkvwHr17+280v0QbmVF1nlKi9y1XsZ6TCkr7hEsQcfC3awmTpuXCxJGnt7YyZ/trkzJUluzbqGXD8qSGAAaLjcCVRJXABoZbEBXpl1ueRFYsxoJffsLdyR86XNXebFU3s4+IpTQ/5wto69cL8kEhcoIfP3rB7JC8l/MkbfxEVWmQQeyQtICpa8yEkKl0pcXPv0Hv3oBd5qLC6Z7HXfsiuV2JDBSm7d7rXb4omLLIaQAAAAAACAyCOBATSBZR1ru4Ze9EVUXQBoFr2Nx+lTp5Q+darscb7VF3ns4bUVVV9I6qq+AIAoSr5krhLnjlHi3DGBjt/50SXa+dHMctmxuPcnK3cpWR+xORd51TxFKnrc4tldr++7cIxcMhlK9YXkLbWaOnCwq/oC3UhgAE3gqrFzQkkC9OUkCABEQTYexwYOLHlcYsL4kvv/YePTYXYLqEp8+HDvS16ZL3oNEaV+RakvTS7x32uU3L1Hyd17Ah0//rblGn/bcsVnni+lU96fjPiI4d3tZlZRyZVe+1TX6503LSnYbyse1/NvWKjn37CwkreAXiKBAQAAAAAAIo8EBhAhjR4KwlAOAPB8dssqfXbLqqrPT584UXJ/csfOkvu/NO3Cqq6bGDdWiXFjqzoXyJc6dKhkCX1DRalfUepLE7F5F3f/sGiW96fcOZmVOvKl1j1buO3Awa5hJT1WUfGpqBn/af8JPAf/aJUG/6jnZ0VuBd7pl83X6ZfNL9vvINxlc7omZ85KTJkUStt9CQkMAAAAAAAQeSyjCkRIK1VA5C6dCgBR85Ep3pjm596/RAOf88ZM5z9lC1t89DlK7X2u6v2SlNzVUfF1T7xmoQb+tLbvDQAq5R79U/cPj3hLiHZ8eIkm/NKbbDj11PrCc/KWGo23t3vHHj3qzemivGV5c+bE6G7Eq6Y58rZFGnr3yqL9i589Uqn9Bwq251bg9f/t6qLnS+qxTGtWbMgQpY8dKzzUZ1Lmei1lHCXmWrjcqd1GuIV2ZaO7ATRcdthK1BMKq9wDOuoOMktWEyIeIyqyk2uWG+JRKyMf9m6wD1x2qMyR5WVLjStdiSSo+93SNc65eTVpHA1FTEbDmEVqOM6pVy6QJA345SOlD8zvt09i4tgbF0mShvyweFIk3+bPLdbUD68oe1wr3SMzhAQAAAAAAEQeQ0iAPqDWFRJRr7wAgHopV3lx5K3eE7Sh9wR/glaJMCovsqquvPB5crjj494SghM+tVxH3+z9DvT9pb3pHgAUcs5/qEcR2SVMazLEz0yD1ntDRHIHmnS+wpuUs+1XOcND8qtG8n6Onz2yosqLrKkfXtG1xGvuRKPW1uZdprNT6RdeIv3Rf5LRZsQQEsrjGop5EFCJViqPazXEY6CxqkmUM4SkeRGTG2/uY2mtuaT+xfKxIUMkyXcOhkjySbg2A7dktiTJlj8e6PhWukdmCAkAAAAAAIg8EhhoKKovGi/71A0A0FiNjMdXjZ3DZzIQIY2ovpC8yosoVl9YIiFLJHTmpfMUHzVK8VGjvB3Olay+OP76hTr+em+YSWLSBCUmTQh8zeykm8VkJ32uill39Ui+WFy2/PHA1RethgQGAAAAAACIPCbxBAJq1vk6mvE9AUAjxM8eqdT+AxWf9+/bHpIkXTX28rC7BERftXMYhDT3QWzQIKWPH+9VG83O5l2s5ycNkiQN+nHhZJmxWRco/cQzoVwrdvEFSv+psC2XTEqS+v3u0R4TapZkpkFLV3W9Tm7bEei07MSg5SbdrHq57VhcSpd4F6X2gQQGEBRf9Iur9SopANAXFEtelJsU768nhZe4uGvHw5Kkt0+4LLQ2gZqqNgER0qSNJC/Kc4/+SYMeLb4/rOSFJN/khaTqEla5x+a83nmTt6rS+E/7r9wR5oomm+65RJJ03lsf695IgqJXGEICAAAAAAAijwoMAFXJHVJD5QWAVnXr5jW6eercksfUc0K8SiovLOHdBmZLs4NIjBmt5J69FfcLACoVv2iGUk+t915PnypJSq3fVPIct3i20gPi3jn/80ffY4pVXoQlN05Oe8eTXr/CaHfcWElScldH17YTr12ogT8Jr2KkLyCBAfRBUZiPo9HXB4Ao+OTVb5S0MfDx6++YJ0macX2Jeuw6OfjW+ZKk4d9bEficaub4AIBqZJMXkqQjwRLBsT8+I+vsDHyNDV/2VhqZ/nel57uoRG6St5IEsc272Dvn0T/5t5uTuOhqv8hCJs2MISQAAAAAACDyzIU0AU5f1G4j3EK7stHdAJgEM6BV7gEddQdbMNfc/IjHiIqdH81M7nZbYYnxhi8vCvUpXV+RGDfW98nf/W7pGufcvAZ0CTVGTEZUxM+fJklKPVtY6WaJREUVDr4TgeZsO/nqBZKks372SNfu9BXeJJyxB3Mm4Wyw7Tcv0cRbe35GtdI9MgkMgjP6uCgMJ6mXVgrOrYZ4jGbw3vWb9Y0ZU+t2vcTkiZKk5NbtFZ8bGzKk13NzkMBoXsRk9GWx2RdKkja/YZgmfzz4ELnuBrw5NAKtFpKTAMkuv1rNKia9XYq2le6RGUICAAAAAAAijwQG0Mc1a/VFdlgNAPQVQaovEuPHKTF+XCjXS27d3lV9sf7fF2j9vy8IfG4l1RcdH1pScd8AIAy7PrJEuz5SWQxKP/600o8/Haj6wjcmp1Pd1RexeHdFhh/nuoakDF76iAYv9YafnLpmgU5dU0FMrqD64tibFgU+thmRwAAAAAAAAJHHMqpAH9XsE3826/sC0Hz2/Mwbbz3m1U+XPTa5c1fo13/u/Us0468LJx0Ny9jP165tACjm9FXzNO6zlcUfu2Rm12v32Lqyx5eNydlKDLOek3/6yezf8unFmnJTFXNvlBEbOFCSNOQHOZNJB+lXk6ECA+ijrho7p+BLPsMuAKD+xrz66YLkxdXrDpc8xy2eHdr1z/k3EgyoEWuJOQERJTl/5/ove7Ti091j67r+5LriiZOlzysXk4slCcwK/p2UTV6UGpJSQvrECaVPnAjWryZGAgMAAAAAAEQeCQygQZZ1rA29YoJhFwBQufjM8xWfeX6gY6evbtP01W1lj/vNzGEl99uKxwNdLyri06YoPm1Ko7uBemvBp7toMOe07ZOLte2TiwMd/vy1i/T8teUntXxw1lkl99uKx32rKfwm8YxdfIFiF1/Q1d9A/05y2w2yPGu5yUNzD511geysAYGObQbMgQE0SF9JNizrWNtn+goA1UitezbwsRvmd9awJ6U1Mh6nNm5pyHUBtJ5J/1Q4BMP69ZckxaZN0sFLR0qSht6zUoPvW1lwbK74RTMkSamn1is++hzv9d7n/A/OS0TER41Sat++gsPSf/JWDDlw/WKNvCPgXBf5SY5scqJYMqNUkiNv3ov0E8/IuVPB+tEEqMAAAAAAAACRRwUG0KTCWqWE6gsA6B3iMfoai8cUH9yu1NGjFZ134D1e2f/Ib1a+AsPJVy3QWT9/pOLzaiZb8p//5LzYdvx/9u48zs6yvP/45zrL7PtkMkv2hbAnAQKBACqgUrFqXSqCgku1+qu21VJta1tc6taqP1uXX8UVhRIQkLYuLQrKnoQkkBC2JGRfZiaz73PW+/fH85zJmZkzayaZM5Pv+/U6L+Y86/0c4DrPuZ7rvu+TysWiACRe2k3pS7vHvV/ixV3H//YrLxr+ch0ANf82+gDIQ6svUlUgqbaMu/oik/F0IxnJaf7fniowRERERERERCTrqQJDZJaajid1Gi9DRGQ4xWOZaVwiOeHqC5hc5UVKVlVfwMhPuU/zp99ZJRCcVCXDWJUXKXbBuYOmY01VXgQrKwBItLSO+5xH/mYd8/55fOcNVlYMOnbrB7zKpoofHf//K70axEIhiI+7KTOeKjDGYapnihCZrXSzLCdb5DeLJ71v6/svo/X94xvVXGSmUzyWU2H3t9dOet+9X7mMvV85gZicacaI8Rrn7A6S5U6kG8Y4pCcv0iVaWieUvADGnbxIHT9dxY82DEpegJe4SCVUXDwOp1FeTQkMEREREREREcl66kIyDlP5FEMlnSIik5f7+v2T3rfix4OfXigei4icmDM+tmnQ+9CiBQDEDxwac9+lfzs4Jtvv5uGuPjL+k59IV46T/OReslv02jUA5Dy4ZZpbIpOhCoxTTDfLcqqpC5RIZorHcqpd+/zExhRw61bh1q06Sa0ROXF9b7lk0Pv4gUPjSl5k4q45OuK6YGXFwLgDKaEF8wktmD+pcwUKCia1n8wOOQ9uIefBLSRec+GE9otfcxHxay46Sa0aW+KqC0lcNbE2z0ZKYIiIiIiIiIhI1lMXEpFZLhueMqeqQLKhLSIi0+XB80omtL09tX3q26B4LFNoSmcOGaVLSKYBE+OHDk/6VMne3knvK7NH8JFnJrR96OGtU3dyfyDZBw97xxxPTA7+fmLtna1UgSEiIiIiIiIiWU8JDJHTyHSNh3Ft3Wo97RORWavzxkvpvPHSCe0TLJlYNcao5/+fZePeVvFYRGaKrndNLK4CBMtKiV67ZmCgznHvd+6ZEz7XSFo+OI7pgZMJSCYmFJMtFMJCQzpQTHYq4RlMCQyR08i1dat58Og2DewpIjKFSu7aSMldGye0T6Kzk/1fvIz9XxzHje5Y53/DnhPaf9d3Lxl7IxGRU8UMzCi+e/S4euBz64YtS7R3DAzSORGJF3YSWHkWgZVnTWi/TCp/sGHsjUZx6B+GXxeAi8dx8bj3xv+MTmg2nhlKCQwRERERERERyXpKYIicZqazfFiVHyIixy3++w0s/vsTe1I3HpmmjLz70FMDf6/4yBQOxCgyUaknySJwvKpgHJUFiz7zVMblodoaQrU1BFafM+7Txq+5iORzL5N87uVx7zNUsKyUYFnpmNuF5s8btiy9Gm/BFzJf1yDj/IxmI81CIiInzdDR7tXvWkTk1Ev29g6Lx+9akLlEWeSUO01/hMkIxvPfgz+DB8lExtXx+gbvj/oGbM153mG3PD/qITPOMDJGF43gimUkdh3vwpdo7xj1HAPtO3yE4sfnANB1ZTPAKUlmzxaqwBARERERERGRrKcEhsg0m83dKjTavYjMJJHfLJ7wPhbOmfqGnARTFY873jPxWQFERCYj8ZoLB/4OVlURrKry3vgzeGQSv+YiQosXElq8EPAqL0atvggEj1d0DDVGNUh69UW6UE318b/97ixDdV3ZPFB9MV6B884icN7gQUaTV5x+99lKYIhMs9n6A382J2ZEZHbKff3+Ce/jYlFCSxcTWrp48IpLV466X+kTlRM+VzYovXNis62ISJaaAeOOBB95ZuDvRFMTiaamMfcJPbwVYnGIxYfH5Uz8ZEjjX6R1q5vIuCxp21o4BwvnEG9oHFgdr2843qXF1/vWtcObMY5ERPL5l0k+P3iMjsATp9/9thIYIiIiIiIiIpL1NIiniJwU46ksGTqonIjIdPjX/d6I7x9fPLmBLeN79w9fuPG5UffpuKJlUuc6WYLVcwFINB6b5paIyCmRxYOn7vuyNyPHkr8bx8CWqUqJtOuJHzk64XNWfzNt5o8xPht32Srv1Bu2D9rWxaLjOlfBA5uGLRtaSdH8Ye8zmHPbhjEHLbVwDsSyv6JmqqgCQ0RERERERESyniowxklPikWmnv5/kslo+EvvKXnNvw2eJz01r3rPqjoACjbuIdHSemobN4rotWsAyHlwy+gbBoIjPmUZ2KS4GIBkV9eUtO10N9nKi9lElRcyaQ/P9/55zeFBi1MxOX74yKlu0fhkeHI/WQPx/Tdbs7qyYaYYV+VFyjR83rZh+0k/x5zb0j6DMe4JXCx6Wv13pwTGOOmHlpxqSpqJZDY0cZGSuknO9f85+tf9qTdm4iJljBsVUOLiVLLcXFZtjACw7YKJ7Zt8tbdD4NFnp7pZItljSOIiJWsTFylT+INv3PFdJi81UGYozIG/9xJGCz+b+X4g0344R9f13ixKxfdMzWDEPe9YS+F9w7uDyMmlLiQiIiIiIiIikvVUgSGSpWZC5YWqRERkttn1A+/J3ooPek9UXSQy4cqLlFNZeXH0k143mLqvHn8iGSwvByDR1nbK2iEiMpWCZ58BQOKl3YDXXWJclRcpaZU2k6m8SLzmQq8daVO6poxWffHKN7xqj+WfOH7O0Y41YVPYBWqmUQJDRCbtVCYuHjy6jUuu7T1l5xOR01MqcTHTpCcuUiabuNj13UsAWPGRpweWWW4u4CV0Dn7WHzPkM/dN6vgiIqMJFBQAkOztHUhcjCRYUgJAorNzytsRqqkmtNOb0SQ+ZDlAvKFxxH3TExcAbt2qcScugpUVg8bwirzhYgBy/2fz8eNduhLwxuMIrliG7X9iXMeeDdSFRERERERERESynrnTsOwkpcQq3Fq7ZrqbITLMg0e3qVtGBpvcw3S61tNnouvTiOKxZKvobxeR8/qD3pspvmcKFBYCkOzpmdLjTlTX9ZdOqrT6IXffVufcmpPQJJlmismSbUK1NQC4vj4S7R3D1rd86DIAKr8/8gwmwTmVACSaWwaWWTjHO24sil18vvf35h0n3uBJdPFIVZO0vfEcStaPEpPNhh33dLpHVgWGiIiIiIiIiGQ9jYEhkoWmuvpCg22KiExOzusOTOnx2m/2nhKW/XTDlFZeBFafA0By24sT3nes6otd37+YFR/aPOo2IiInQ8NfemPujDSFespolRcp6ZUXKcHqKsCb9jdT5YWFvJ/LLh4ftm40PW/zxhIqvH/806ymxvEoWb9x1AqOruvXUnz31EwFOxOpC4nK4yTLqTvJcadTedzpRvFYZoL6W9ZR+/UJjH4/ht23XwTAGe/bOmXHPBmCZaUZS7bVhWT2UkyWmWDXbRez4sMnmFxNSxS03+QnmO8YOxlyMtkF5+KefWGUDdJuhf3f8qfTPbK6kIiIiIiIiIhI1lMCQyTLqfrieBcYEZGTqeET60Zdn7H6IhCc9PnOeN/WrK++AAZVX+y/Z+U0tkRETiepQS0Bkq++gOSrLxi0PlP1Rfc7L/WmP/WnOh2TcwNVDGV3bBi9+sJscPXDSO32BwsFCJ65nOCZy8fXllSTRqi+GLiuVJudI371RRM69mygMTBEJGtp7A4ROZVqvvHU8YREMjFsvbtsFbbxOf+NG3G7E5EtJcxD7f7WWgDOuH78/blFRE5EorNzIGEQePTZjNtEr/V6seU8uAWAop9tZGKjVYwuWFbqtaW9Y9wziqSPtZHY+coJtyE1A0u8vmFg2a7bLga8JE7gvLOwV5484fPMFKrAEBEREREREZGsp0E8NUCRyCDZXPVwOg1QdLpRPBYZzMI5/NH2IwA8cE7VtLRhrJlNNIjn7DXemBxYeRbJ514+6e0Z9BQ8tay83FvW1jbx41VVkWhqmprGpTMb91N6mX7uslXYhu3j3r7+Fq+b4VQO5jwhl5zv/fPp4bOlnE73yOpCIjKDnIrkQjYmLkREss267VEAnlqVM7EdR5kaL52LRU964sIuOtc719bM/a0zJS4CxcUku7pOartk5jgVyQsg4yw4k0lcDOx7MpIXoOTFSWS5uQC4aDTj53z0r73kQt3Xxp9csA3bCS1ZBEB83wGCK5YBkNi1J+P2JztxMWaCJEPi4nSkLiQiIiIiIiIikvVUgSEyg4ynOiKbu4CIiMwUgfPOAiD5fOYnzOOpvPiTXfsA+OGKJccXZtET2pEqL0aj6gsRmQ7db/Luawvv24SFvJ+wLn58uM7xVF7s+5I3SPKSTx8fJDm+78DA3yNVXozoBLoM9b7NGxi54IGnvQXOTV/XlBlGFRgiIiIiIiIikvVUgSEyy0x35YUqQERkNhip8mIiBlVeTIO2X50BQPkbd497n+YPX8ac27JrClcRkcL7jk/hnF55kdEIYw2lV16MV7CkBPCndB1qgtUXu757CQArPvI0BT8f35TUh/5xHQv+SZUZ6ZTAEJGMJpuIUOJCRGRq/et+7+b144vXjXufYPXcCSUuUpS8EJExjXMw4kkLBL1/JhNjb5pptqT0dk3gWMOYkezrH7Y4cdWFAAR//8y4D+XWrWLFR54e93lT17Dgn57KeI2hmmoA4g2NRN54Me7x0yd2qwuJiIiIiIiIiGQ9VWCISEYnq5JCXUxEZDboebs3AFvh/eMrA04Za5q+TCZSeZGSaDw25jZ7/uMCAJa9+9lxH1fTqIrISR+MOFO1hF9JYQEb1IUkVZXQeeOlAJTctXHsY6VJr6YIzqn0ljW3eCudw8Wiw/aZSOVFij21fcxtDt7qxfqFnx/cZSTTlNbxhkbvj0CQ3F9txlzvhNs0U5nLotGwT7USq3Br7ZrpboaIML7Exib3MJ2u1U5Vm+TUUTwWGdlYM6JMtd3f9pIzZ3wsc3Km6SPeSP7b//2Wrc65NaekUXJKKSbLtJpAF5VgVRUAiaamk9KUUG0NAPH6hoFldvH5ALjNO8Z9nMRVF04q8QHAJd75ePr4+dxlq7y2bNhO8soL2PzMd+jsOnJa3COrC4mIiIiIiIiIZL3TugLDzJqAA2NuKCLZYpFzrmq6GyFTT/FYZEZSTJ6lFJNFZpzTJh6f1gkMEREREREREZkZ1IVERERERERERLKeEhgiIiIiIiIikvWUwBARERERERGRrKcEhoiIiIiIiIhkPSUwRERERERERCTrKYEhIiIiIiIiIllPCQwRERERERERyXpKYIiIiIiIiIhI1lMCQ0RERERERESynhIYIiIiIiIiIpL1lMAQERERERERkaynBIaIiIiIiIiIZD0lMEREREREREQk6ymBIacdM3vCzN433e0YiZm91sz2T3c7RERONsVjEZHsoZgsM4ESGLOUmXWnvZJm1pf2/t3T3b7JMrPlZuamux0iIuOleCwikj0Uk0VmttB0N0BODudcUepvP1P5QefcQyNtb2Yh51z8VLRtssxM/72KyIyjeCwikj0Uk0VmNlVgnKbM7Atmdo+ZrTezLuA9ZnaZmW00s3Yzqzezb5pZ2N8+ZGbOzD5sZq+YWZuZfTPteCvM7DEz6zCzZjO7a8h+f25m+/x1XzGzgL8+YGa3mtkBMztmZrebWYm/brm/7/vN7CDwG+Axf10qU36x//6DZvay367/MbMFaW37AzPb6bft3wAb5XO51MyeMbNOM2s0s6+mtfM+M2vwP59HzOzstP3uNLNvmdmDfrseM7Nqf1m7mb1kZqvStj9sZn/jL28zsx+aWe4IbZpvZg+YWZP/GX50rPaKyMyheDzi56J4LCKnnGLyiJ+LYrJkB+ecXrP8BewHXjtk2ReAKPAmvERWPnAxsBavMmcpsAv4mL99CHDAfwGlwGKgNXVc4F7gb/xj5QGXD9nvIaAcWAS8ArzPX/+n/nmWAMX+8X/sr1vu7/tjoMBv43LvP9tB1/J2YCdwpn++zwKP++vmAt3AW4Ew8Ekgnjp/hs9qM3CD/3cxsNb/OwC8z1+WB3wb2JK2353AMeACf/2jwD7gRiAIfAX4bdr2h4HngPnAHGAj8Fl/3WuB/Wnn3QZ8Gsjxr38/cM1o7dVLL72y86V4rHisl156Zc9LMVkxWa+Z95r2Buh1Cv4ljxycfzfGfn8N3Ov/nQqyl6at/znw1/7fdwH/DswbcozUfq9NW/YXwIP+348Cf5q27lwg4gelVHBemLY+U3D+LfDeIeeMAPOADwBPpK0LAPWjBOengFuByjE+mzl+2wr993cC/562/hPAjrT3FwDNae8P45Uspt6/Gdjp/50enC8H9g459z8C359Ie/XSS6/seCkeKx7rpZde2fNSTFZM1mvmvdSF5PR2KP2NmZ1lZr/yS8A6gc/jBaF0DWl/9wKpfoS34GVvt5jZDjN77yjnOgDU+X/X+e/T1+UAVSO1M4NFwHf8MrR2oBlI4mVu69L3d84l8QLjSN4PnAPsNLOnzew6ADMLmtm/mNle/7N5xd8+/fNpTPu7L8P7IgYb6TMZem0LU9fmX9+ngJrR2isiM47i8XCKxyIyXRSTh1NMlqygBMbpzQ15fxvwPLDcOVeCl7UcsS/coAM5V++c+6Bzrhb4KPA9M1uStsmCtL8XAkf9v4/iBaD0dVGgKe3Y6e0c2mbwgtyfOOfK0l75zrlNeJnk9L5+AbygPdJ17HTOvQuvrO7rwP1mlgfcDFwHXI1XHrg8dciRjjUOI30m6Q4Bu4dcW7Fz7k1jtFdEZhbF4+HXoXgsItNFMXn4dSgmS1ZQAkPSFQMdQI8/+M6Hx7vKm73wAAAgAElEQVSjmb3TzOb5b9vxgmgibZNPmVmZmS3EK4+7x1++HvgrM1tsZsXAF4H1fhY4k2OAM7Olacu+C/x9asAg/zzv8Nf9ElhtZm8xb7ClTzA4cz30Om4yszn++Tv860jifTYRoAWvr+EXx/pMxuFjZjbPzCqBv+P4Z5JuAxA1s1vMLM/Pcp9vZheN0V4RmdkUjxWPRSR7KCYrJkuWUAJD0t0CvBfowss0ZwoWI1kLbDazHrx+fx91zh1MW/8LvIF2ngUeAG73l3/fP8/jwF7/3H850kmcc13Al4FNfrnYGufcvcD/Be71S9eeA671t28Erge+ilc2txDYNMp1XAe8ZN6o018DrnfORfEGSTrqv17A61d3otbjDdy0B2+ApS8N3cB503ZdB1yC10+zGe/fTckY7RWRmU3xWPFYRLKHYrJismQJG1x5JDK1zJuXOgYscc7tn+bmZA0zOwy8xzn3yHS3RUROD4rHmSkei8h0UEzOTDFZxqIKDBERERERERHJekpgiIiIiIiIiEjWUxcSEREREREREcl6qsAQERERERERkaynBIZkBTN7n5k9Md3tEBHJNmb2bjP7zTi3HTGWKs6KiMxc/nSqzh/8U+S0pQSGTIiZ7TezPjPrTnt9ewqPv3DIsZ2Z9aS9v3KqznWqmdlrzWz/dLdDRLKTmV1hZk+ZWYeZtZrZk2Z2sXPuP5xzr5/u9k3EkDieHPK98e7pbt9kmdlyM1PfW5EZaqT7WD/B68zsG0O2f4u//Hb/fSqJkNq30cx+aWavy3Ce157CSzthZvbptOvqN7NE2vsXprt9J8LM7jSzz053O2RqKIEhk/Em51xR2utjU3Vg59zB9GP7i1elLXt86D5mFpyq858sypaLyGjMrAT4JfAtoAKYB3wOiExnu0YyVkwbEscPMvh74z8merxsMBPaKCLjMtJ97B7gnUP+X38vsCvDMcr8+LYK+C3wgJm976S2+iRzzn0pLW5/BNiQ9hmdO3T7mRITZ8LvBJkYJTBkSviZ6yfN7Btm1m5me81snb/8kJkdM7P3pm1faWb/bWadZvY0sGwC57rTzL5jZv9rZj3AlWb2ZjPb5h/voJn9Y9r2y/1s+c1mdtjMmszsb9PWX2pmz/j7NprZV4fs9yEzO+q/PpG2X56ZfdPM6s3siJn9XzPL8de91s++f9rMGoDvA78A0itM5p7ARy4is8sKAOfceudcwjnX55z7jXPuORvS9cOPSx8xs91+vP2OmVmmg5rZV83sCTMrTVv2NTNrM7N9ZvaGtOV1flxuNbNXzOxDaes+a2b3+fG3E3ifv+xnZvZTM+sysxfMbM14LtbMvmBm95jZejPrAt5jZpeZ2Ub/mur9+Br2tw/51/1hv21tZvbNtOOtMLPHzKteaTazu4bs9+f+9Tab2VfMLOCvD5jZrWZ2wP+eut1PJqV/B7zfzA4CvwEe89el4vjF47leEZkRGoAdwLUAZlYBrAP+e6QdnHMNzrl/Az4L/HMqtozEzMrNq9ho8uPYL81sftr6R8zsy2b2tH9f+l9+OzId6/1m9pIff/ea2YeHrH+LHb833mNmf+AvLzWzH6bdv37BxvEjPy2e/pmZvQK87C//tnn3151mttnM1qXt8wU/zt/pt/N5M7swbf2nzbu/7jSzl83sNWn73WNm9/r7bTGz89P2O9fMHvW/L3aY2RvT1g39nfBh4HogVWHywFjXKtlNCQyZSmuB54BK4C7gbuBiYDnwHuDbZpaqqvgO0A/UAh/wXxNxI97TyWJgA9ANvBsoA94E/KWZ/eGQfdb5bbkW+JyZneEv/xbwVedcib/+viH7vcpf/gbgH1LBFbgVWAOsBC4ALgf+Lm2/+UARsBD4M79d6RUmxyZ4zSIye+0CEmb2EzN7g5mVj7H9H+LF15XAO/FvuFP8H+bf99e/3jnX4a9aC+wE5gD/AvzQbCD5cTdwGKgD3gF8ycyuTjvsW/DiYxmQqqJ4s79fGd5N/kS6FL4V77uiFLgHiAN/6bftcuAP8G48010HXIQXc99jx0u0vwj8CijHi73fGbLfW4AL/X3fAdzsL/8g3vfTa/AS6eXAvw3Z91XAWcAb/b/TK0w2T+B6RST7/ZTj8eFdwH8xvkq4nwNzgTPH2C4A/BhYhHd/2MfwuHkz3n1xLV5c/CaZHcP7LigB3g98I5UcMLNL/Gv5JF58fhWw39/vdv+4y/Fi6evxYuF4vRnv+yeVUNiE911Tgfcdca+Z5aZt/0fAHX47/id1PWZ2Ll6Mv9C/B38DXsVeytvwviNSx33AT6Lk4FUs/gqoAj4B3GNmy9P2Tf+d8EO875hUhclbJ3CtkoWUwJDJ+E8/45l6pZ7S7XPO/dg5l8ALFAuAzzvnIs653wBRYLmf5X07cKtzrsc59zzwkwm24QHn3AbnXNI//u+ccy/477fj3VC/esg+n3XO9TvnngFewCv7A4gBZ5hZpXOuyzm3ach+n3PO9frH/Qlwg7/83f4xm/xkxOeBm9L2i/vro865vglen4icRpxzncAVgMOr2GoyrxqieoRdvuKca3fOHQR+D6xOWxcG1uPd9L3JOdebtu6Ac+77fpz+Cd4NcrWZLcBLGvyNHye3AT/g+I08eOXE/+nH2VRMe8I592v/eHdwPK6OxxPOuV+kjuec2+yc2+Scizvn9gLfY3gc/7JzrsM5tx94JO26Y8BioNZv/5MZPq8259wBvJvn9Dj+NefcPudcF/Bp4MYhT1E/438HKI6LzA4j3ccCPAC8xryqtZvxkgDjcdT/Z8ZqiRTnXItz7n4/pnThJV+Hxrk7nHPPO+d6gH/E69YyrELCOfcr59we53kUr0osNVbcnwA/cs791o+xR5xzL/vfKdcBH/fvwY8B38BL1ozXl/x42ue34w7nXKtzLo6XGE89EEx51Dn3YNr3RCpux4E84FwzC/lxeG/afpuccw8452LAV/3jXoz3XZWD9/Ax5px7CC8xkn4Ng34nTODaZAZQAkMm44+cc2Vpr+/7yxvTtkkFtaHLivCypSHgUNq6AxNsQ/q+mFd6/IhfkteBl0mek76Nc64h7W2v3xbwstbnADv9kr3rRjnXAbynk/j/PDBk3by0943OuegErklETmPOuZecc+9zzs0HzsOLMf86wuYjxTPwbhzfgpd8HRqDBvZLS2wU+edq9W+oU4bGtEFxd4R25Nn4+0UPjeNnmdmvzKzBvG4qn2dIHM9wvtR134KXuNnilxO/d8h+E4njOXjfUxnbKSIz3kj3sfg/yn8F/ANQmSEZOpJUrGwdbSMzKzCz28zrttaJ1y2tbEiCYmi8CjM8FuJX6200r9tfO15iIrXdArwxPYZa5B+vPpXAAW7Dqx4Zr6Gx+1N+948OoA0oHNLeoXG7EMA5txMvdn8eOOZ3NanJdB4/+XEEL2bX4VU0pw+oPJ7vK5kllMCQ6dCEl3VdkLZs4QSPMXQU+LuB+4EFzrlSvCeHGfuEDzuQczudc+/CC95fB+43s7y0TYa2M5VlP4r3RZC+7sgobdTI9SIyLs65l/HKfM+bxO4v4SVm/8fMxipnTjkKVJhZcdqysWLaiRp6vNuA54HlfjnxrYw/jtc75z7onKsFPgp8z8yWpG0ykTgexfueSh07vZ2K4yKz30/xfljfOYF93orXpWPnGNvdgtfNZK0f517lL0+PdUPjVQxoTj+I30XjfuBrQLVzrgz4ddpxDpF5fLlDeF1i5qQlcEpchkE6RzEQB83sKuCv8Cqry/C64XUz/th9p3PucmAJEAS+nLZ64HPwq+Lm4cXso8CCtO6PoHvw04oSGHLK+VnUnwOf9TPR5+CN8nwiivGeHvab2aVMoBTOzG4ysznOuSTQgRfkkmmb/KOZ5fuDB70Xr3sMeCXat5rZHDOrwivzG+3LrhGYM+QHgohIqvrgFvMHc/O7dNwAbJzM8Zxz6/G6QzxkZmMOkuycOwQ8BXzZvAGKV+KVIE/kBv5EFePF4B4zO5vh41+MyMzeaWapp2/teHE8kbbJp8yszMwWAn/B4Dj+V+ZNjViMV8693v8+yOQY4Mxs6bivSkRmmkeB1+GNkTYqM6s2s48BnwH+bkjsCPvxNPUK4cW5PqDdvME5P5PhsO8xs3PMrACvOuE+/945XQ6Qi/9Q0LwBmdOn2/4h8H4zu8YfE2memZ3lnKvH62rydTMr8dctM7Oh3VjGqxjvoWQzXmXHZ/ErLMZiZmeb2VV+MqbPf6V/fpeYNxBpGPhroAvYjPddFQduMbOwP1bTdRyP65k0Aorbs4QSGDIZv7DB82dPZjTfj+GV/jbgPWX88Qm26f/g3Xin+jD/bAL7Xge85O/7NeD6IWXXTwB78QL+l51zv/OXfw7YjvfE8Dm8QYzSM8eDOG+sj/uB/X7ZnmYhEZGULrwBNjeZN2r6RrzYcstkD+ic+wneze/vzGzxOHa5AW8ciaN4/cA/4/ctPlVuwUsSd+FVY4x2MzrUWmCz/9n9HPioPz5Iyi+AbcCzeNd2u7/8+/55HseL8114A4lm5Hex+TLev6d2G+esKyKSVUa9j/XHlHjYOTdad5B2P97swLuP/GPn3I+GbPNrjv8w78P7cf+vQD7eD/6NwP9mOPYdeDGqAW+MiL8YuoEfi/4C7363DW/Qyv9OW/80/sCeeInhRzlebXYzXgLkRX/f+/DGQ5qMXwMPAbvxBgntBOrHuW8u3pgZzXjXWg78fdr6B/AGWW7Fm0Xkbf4YSRG8gfHf4u/7TeBG59zuUc71A2CVeTO/DB2sX2YYG1wZKSIp5o1mvNs5N64yOBERyS7+E88YsMQf+FNEJGuZ2SPAnc65H0x3W6aTmX0BmO+ce990t0WyjyowRERERERERCTrKYEhIiIiIiIiIllPXUhEREREREREJOupAkNEREREREREsl5oIhvPmTPHLV68+CQ1RUTk9LN169Zm51zVRPdTPBYRmVqTjcegmCwiMtVGiskTSmAsXryYLVu2TF2rREROc2Z2YDL7KR6LiEytycZjUEwWEZlqI8VkdSERERERERERkaynBIaIiIiIiIiIZD0lMEREREREREQk6ymBISIiIiIiIiJZTwkMEREREREREcl6SmCIiIiIiMikOefojcWIxOPT3RQRmeUmNI2qiIhINnHO0dzbS08sSlFOLpX5+ZjZdDdLROS00dzby6YjB2nv68fMWFJWzoW1deSG9DNDRKaeIouIiMxIkXicxw/up767i4AZzjnml5Ry+YJFhIPB6W6eiMis1xON8tDeV8gLhagpKibpHHvbW4kmk7x60eLpbp6IzEJKYExCNJGgsbubuEtSkZdPaV7edDdJROS080JTI4093dQVlQwsO9zRycsFzZw/t3oaWyYicno40N5O0jmKc3IBCJhRXVDEwY52OiMRSnJzp7mFIjLbKIExQS29vfx+/17+3+ZNALz/gos4f241q6prVLYsInKKOOfY1dLCT7dvw4BPrrsSgMqCAna3KIEhInIqdMei5AQGV7yZGQEzIok4oASGiEwtDeI5AUnnePzgAcKBIDlB71VTWMRzjQ009fZMd/NERE57hpfcEBGRk6+6sJDeRGzQsngyiQElOUpeiMjUUwXGBLT39/HNp58iJxBkV2sLAF/f8ASxZJKzq+Yyt7BomlsoInJ6uPHnP6MrEmG3H4u/+tTjALxn5WpVX4iInCJ1xSXMLSikvquLktxcEi5JZzTCJXXzNYiniJwUiixTRKUsIiKnVmFOzsDf0UQCgOrCIs6aUzVdTRIRmXW6IhF6YzGKcnIGxV2AcDDI1UuWsbetlQMd7eQFQ6ydv4C64pIRjiYicmKUwJiAsrx8Pr52HUnn+O6WpwG45bIrqO/pYkFp6TS3TkRkdumKRNjf0c7fPvQgOcEgP3vHuwZmF1n/9usBeNd99xBLJvjna66lKDeHuYVFBDQekYjICYsnkzx95DB721oHZnpaUTmHi+rmDYqzOcEgZ82pUvJYRE4JFQ5MQMCMKxYuIpF0RBMJbzaSnm5Wza2hqqBwupsnIjJrHOvp5pe7X+b5xkYi8Tgd/f38fv/egUqLFDPv5nl5ZSU1RcVKXoiITJEXjzWyp62VmsIiqguLmFtYxEvNTbzid93LZvFkkqTGQxKZlVSBkSbpHEc6O9jb1krCOZaWV7CwtGzQDXFFfgFvOvMsLp2/gGgyQWV+PiW5mkZVRGSqJJ1jw6FDFIZy+H9bNrG3vQ2ArzzxGN/ZvIn/etd7BrZNVWJkg85IPx39EfJCISoLCpRMEZEZyznHyy3N3Pnc8ZmeAmZU5ufzcnMTn3nkYSC7YjBAc28vW48eoam3h9xQiPOq5nLmnCrFY5FZRAmMNM/UH+HFY03csWMbADetXM3yikoum79g0BSpOcEg80rUt09E5GToiUbpjkWpGTIwcjAQIBKPT1OrRpZ0ji1Hj7CzpZkfb9sKDj55+ZW8ZtES8sPh6W6eiMiE3fDzn3Gsp5v97e3A8YGSP752HX3x6HQ2bUSdkX5+u3c3ecEwtUXFxBIJnj56hGgiwaqa2ulunohMESUwfB39/bzc3ExtcTFBP1lRV1TM3rZWVlTOYU5BwTS3cLhoIkF9Vye9sRjl+fnq+y0is0I4GADnSDrHJ9ddOXDj/KEL11BTVDzNrRvuYEc7Lzc3UVtUTE7AG6Ojo7+frUePcMWixdPbOBGRSTAgLzj8Z0Jrfx93Pb+dF5uaALjh/nuA7KjEeKW1BTOjJNebvjUcDFJTWMRLzcc4u2ouOf4YSiIysymB4Wvv7+dHz24lJ3h8itSvbXiCaCLBpfMWZF0CozPSz8P79vKtTRswM963+kIWlJRw+YJFA4PciYjMRHmhMIvLKzjQ3sZcf3whB/TEYiyvqJzexmWwu6WFO57bRtBs4Pvj9m3PcNOqC7g4HtdUgiIy46x/+/V0RSK87Wd3kUgm+ZML1hBJxCnOyaUwnDP2AaZBa18fBcHBVW+hQICEg0g8rgSGyCyhuypfbmjkoJYXzr6PafORIyQSyYFgXFdUzKGOTvYVt7Gics40t05E5MSsqa0jnkxyqKOdm1atJmgBLqyto7qoaOydT7EkmQeKc87hRlgnIpLtinNzqcjPJxJPsLS8nPL8fOaXlHLX89spzsnhnKq5WVF5kVJdWMSOnsZBU73GEgnCgYC684nMItn3y3yaVBUU8olLL6cvHuNHz24F4EMXXkwgYFQXZtcNcyQep767izuf2zbwtO+rTz1O0jnmFhYqgSEiM15uKMSrFy2mKxIhkkhQkpubtU/PlpVXcPPK1dQVlwx0d/ngBWsoz88nL6SbZhGZue55x7sGvb/h/nvYdOQwAC82HeOG++/JmiTGsopKdrW00NTbQ1luHpFEgvZIH5fNX0gooIkXRWYLJTB8wUCAq5YsZdORQ9y0cjUAxTk5rJ2/ICu7ZGQa68KNsFxEZKYqzs0l+0a9GGxxWTlHOjs52NFOLJnAOQgGA1w8b/50N01E5KQ5p2rudDdhkIJwmNcvW85LzU0c6eykMCeHi2rrmF9aOuljZtMYHyLiUQIjTVFODtcsWUZvLIZzblAJWjbJDYVYWFLKe1dfyE+2PQPALZddQX13F2dUZl//cBGR2SwUCHDlosU09fRw8bz55IdC1BaXZG3FiIicvhLJJM29vUQTCcry8ij2B7wcr/Vvv/6EftQn/QGaT1ZFRHFuLpfMmw/zTvxY6dUmSmSIZA8lMDIomAH95C6qm0f3gSjRRAKAhp5uzp5TxcLSsmlumYhIZk29PexsbqYz0k9tUTErKudMKlHsnCOaSBAKBAiO8ya4o7+ftv4+wsEgcwsKp7yyLmBGdVFRVo7RISIC0BWJ8Mj+fXRE+ge6S3/j2utYWV2DjVHB2x2N4pyjKCcHhzcT3hvv+ilmcPtb3jHmYPdJ59jZ3MTzTceIxOPMLSjkwrp5WTdIvohkv9MigZF0DufcuG90Z4L8cJhrl53BRXXziMTjlOTmUpKbN93NEhHJ6EhnB7/bv4+fbHuGgD9z0r72Nq5ddsaISQznHE29PdR3dREMBJhfUkJPNMaWo0foiUYIBYOcV1XNWVVVI3afc87xbEM9LzQdI7VFQTjM1UuWUpaXf5KuVkQku8STSX67dw/RRMyb8jkYJInjsYP7cXjdQTJVjXVFImw8cojG7m4AyvPyaO/rI5pIsL+jHYB33rue4txc/utd7xnx/DsaG9jW2EB1QSEVefl0RiJcf996KvILuPePbzgp13yiTrTaREROjlmdwIjE4zx/rJFdrc24pGNhaRmra+somsQTv47+fvr9RMF4RjLui8U40NFOS18flfn5LC4rm/LB3MyMKn+KQRGRbJR0jqNdndz3wg7yQmECZpgZzjmeqa/nWE83r1t6BovLygYlmb31R3mh6Ri5wSDfe2Yz8WSSt551Lr/evZOAGR9fu47N9V557zlzM/fFbuju5vljjdQWFRMw46tPPU7CJckLhXjD8hVjPnUUEZnp9rS28OShg2ytP8qmI4fIDYY41NkBwE+3b+OuHc/x8UvX8aqFi6gtLhnYL+kcjxzYR3/MS3oAHOnspKWvj4r84w/NwsEgXdEI/fFYxnvdaCLBS83HqCksIhQIDAx2vK+9nX3t7UoSiMiEzNoEhnOOJw8dpL67k59u34YB77/gQo7t6eG65SvIGyEJ0RWJsLOlmYbuLkpz81haXsHu1mY+/+jvweBPVl/EyuoazptbPeKNb2ckwm/3vMJ3tmwkgPH+1RfxUlMTr1u2fFLJExGRmSiRTPLkoYPsamnmP57fQTBgHOvpAeDzj/0egHeft4onDx3gSFcnVy5cNBBXm/t6ebG5aSDxEA4EicQT7O9oI+kcZkY4GKS6oIjnmxo5c86cjFV2+9rbKAyHB1VoBC1AW38/XdGIKtdEZFZr6O7iyUMHKArnUpKTS8gC9MdjA+s7I/2YGUXhMI8e2M9bzzqH3JD386Cpp4fO/n5qio4Ppdwbi3L2nDksKCmlLx4nNxjiU+uupKGni9a+PuqKh99f98fjJBxTMu7FLVd9BoCv//5zJ3ys8VBSRST7zMoERltfH5sOH+LWRx4mLxTicFcnAN/atJH+RJz67k6uWLCYc+dWDwqmXZEI//vKbhIuyY+e3UrSOV61eAmV+fkDZXVVBYU821BPWV4eC0YYb2J7Qz0JlyQn4O1TU1REU28PLxxrZO38BSf56kVEskN9dxf729tYUFxCKGAE7Xi8NQyHozw/n7qiYg50tHF2X9VAVVljdzdhC/D1DU8ADEwZ/ctdO0k6B8AXHn+E3GCQm1euJpZMjNpNMPXEL3WcHz6zhf98+cUTKl1OOkdTTw+RRJyyvDwlQ0Qk67zc3ERxTi7FObkUhMO8YfkZ9MXi/GL3y0TiCaJJbyy1bz29kWgiweULFrHAn7XDG2ftePK3LxZjV0vLwPTWsUSCaCJBe38/zkE4kHlsoYJwmHAgQCyRIBwM8sl1VwLwxSceoSicM2KSIOaP8zbamEXOOfa2tfL8sWP0xKPMKyphVU2NugiKzGKzLoHR0tvLg3t20x2J0hHppyNyfF3SOboiEe594QVKc/KJJhKDprnb2dJMwiWpKigcKHPu7O/nlztfprmvF4D/u/FJEs4xr7g4YwLDOcehzg5+uv3ZgRvlrz71OA74wOqLlMAQkdPGwY4OCsM5fGPTU+QGQ1y+YCEP7dtDS1/fwE3zf+96iV/t3slNK1fTFYkMJDDCgQAJP1GRLr3urS8Ww4DCnFxyg5m/zhaXlbHHj8XpQoFAxqeB/fEY9V1dxJJJKvMLqMjPz1ht1xON8sj+fax/9/cAuPaHN3HOnCouqK3TdNYikjV6ojFygkHMjBUVlWw4fJCGnh5qCopoi/QP3N+mOLy4G4nH6Y5GaenroSTXS34c7uogPxQiJxiiIBzmj885j/54nO2N9aypnUflCANyhgIBVtfUsuHwQcpy88kNBmmP9BPAMlZEd0ejPFN/lEMd7ZgZi8rKue+mHxAw47lHXwSOV2J88GcfZfPRI1Tm51OVX0hTTw8P7nmFNyxfQckEZ1gRkZlh1iUwdhxrIDcYorg4h/K8fEKBAJFEnPb+fgIBI5ZMcrCzgzt3bOOmlas5v7p6oL9eQ3cXP3p2KwGzgeRDQ3cXvbHjpXaRRIJwIEA0mcx4fjMjLxgiyeAbb+cceaET/7gbu7s52NmBSyZZWFZGdWGR+nCLSFb60hOP0BeLs6+9DYDevXto7e/DpSUmUlUZDm+K6Ggiwc6WZl441sj2xnredtY5zC8p5fOP/x4cnDlnDjtbmglagFcvXEwwGGBNXd2IcbCmsIhzquby7pWrMfBifMC45x3vojx/8BO6pt4efrdvL7FEggBGwiU5u6qKi2rnDTv+00cO0xOLDlTn1RQW8cKxY1QVFLKwTLNBiUh2mF9SwgtNxwhagFfaWgkHQxSFw7QFA1y1eAnPNNQT9McUaunrZU5BIfVdnTx6YD/f27qZSCLOhbXzOGdOFUe6Ook7xwW1dcQScdr6+8BBzCW5fOGijMnbd957N9FEnH953R9wce08Dnd10RONsrS8ggeuf/ewaVzjySS/37eX3niUuYXerE4HO9pp7++nYkjMds6xvbGBan9sDYDy/HyaenvY09rCBbV1J+lTFZHpNOsSGA3d3dy+7RkAjvV6fa2DZjggnhicdDAz+uNx8kJhEskkIf+J39C+0oXhHPrjceYUFHD5goUsKi1jSVn5iG04u6qKd5+/ivU7ngPglsuu4Gh3J+dUVQ3bNp5McqC9nT1trYQCAZaXV7CgtDTjzfiOxgaebajn2orPA/Dgnls5b241FypAi0gWyguG6Y0eTwCHAgHm5BcQTSTojkWpLSrmry+7gtb+PvJCIaoKCnn8wD7qu7v56XPPEk8kiSYSHOroIJ5IkhMKct0ZK9jf3k48meCxgwCyJv0AACAASURBVPvJDYX4p6teO+zczb29bGs4SmN3N0U5uaysrqE4nMNrFi+hurBoWEly0jkeP7if/GCIOfkFA8tebGpifknpoD7gfbEYP3znd8gJBjn09D4A1r/7eyScY8Hd/0cJDBHJGl964lFa+/p43ZLlNPf2UJCTw9yiIs6vqWFPaytd0QgFoTCtfb1cOn8BoUCAxw4eoDgnh5xgkJxgkMvmL2B3SwulubmU5xWwqLwM5xy9sRjxZJKkc8MSwuDNPtXc592Lbz56hKRzrK6p5fy51SO2t7G7m/ZIH3VFxwcTnVtQyNXfu5Frly3nn//wq4A3BkZXJMJ/73p5WDVdQSg8rLJERGaPWZfAKM3NG5aEcMCC4hJetWgxGw4fImDGJy69nLb+PgrCORxob+fpo4dp7evl3Kq5LC4tI5ZMcrizg1AgMBAEm3p7+N89u/nomrUsLa/IeP6uSISKggLOrKzy+g6at9+q6lqWVVQO2jbpHE8ePMCBjnZ+96d34YCrb7uB86trhiUluiIRtjc2DIzgDFBbVMyLTcdYUlae8YtDRGQ63ffOG9jT2sLN/3kf0USCpD+ddbufXK7v7uJwVyeLy8pYUzeftr4+jnZ3U1dUTAAjJxjkDctX8EpbCx9ZcwmVBQWU5OSSHwoBoYFKuRvuv2dQH+q2vj4e3LObglCY6sIir8S5oZ518xcyv6Q0Y1vb+/voi8Wp8Z/4AQTMyA+FOdjePiiBMbxjy3HJDN1eRESmS8CMivx8SvJyMCukNC+PmqJi/t/mTSSc4/pzzucPlq9gfkkJxbm5HO3q5LYtT5MTDA7E2J9uf5ZoIsE/XfVa9rS1EosnyA2FyA+FaezpZk3tvGHVF9ffdzfNvb3sb/emWr1j+7P81aWXs63+KPOKi6nwE8VDZyDpjcUIMrx7XwDvoWO6vFCIUNrYGik98djAOB6TkWzxpoMNVN456WOIyMkz6xIYK6uruWnlKjr7I9y5Yzvt/f2U5uVycd082vq9eavDwSANPd2srZtPZyTCYwf3U5mXzw9fepFYMkFeKMQltfPoikQozAnT3Ocdu6aoGOccl8ybP2yu7GgiwcbDh/jsIw+DwQcvWMPXXvcGFpWVUZgTzjit1LGebg52tDOvuGQg8NcVe6V+Z1RUDiqra+3r45qyz5ITDFIe3AHAhXl/QzScoLXvh0pgiEhW6YpE6IvHqC0uoSgnh1giSTAQIGhGvb/NvOIS3rTiTCr9cS8aurr48bNbB904f2Pjk0QTCb5+7Rt4ubmJ/niMhHMkRujGB/BScxM5gcBA/+f8cJg5AWN7YwNLKyoG3Winbp7//Y1vHujakhrw85PrrsThhg0OWhAOc/P6j9AVjfKrD/wEgBv/40852t3FkvKRq/NERE6Vt//sLnpjMXa2NAPefeSfX3wphWmz4QXMqC0uZkVl5aiDIANgUFVYSF1JCc/WH6Ut0k/QjPtefJ5fv7KLu4cMxBlPJgd1FwQIBgKEggHqu7r46K9/AcCmI95U2KlY/G9/8EYSbnB8d87hcBTn5A6afSQcDLJybjVPHz1CZV4+uaGQ12XcjOVDHhqORypxQezpQe+VyBDJLrMugTGvpJQrFi7mti2bAYgmEzT19rLtWAP9sThXLVnKFQsWcfacKuaXlrLh0EF+su0ZQoHAwA1zKBDk+nPP45y5cynPy+fbmzcC8PG162ju682Y1X22oZ5Dne0DiY3K/AJ2NDVSXVxEZSjzoEZtfX385kP/QTgQGFSGHE0muPqhzwxKYISDI3+xjDY6s4jIqRRPJtl85DB72loJ+DON/Mnqi4gmk9T6VQypgY0/eOEaStNGii/IyTy9NcD84lKWlVeyr62VW191FTVFRfz9736LA25/y9sHtrvh/nto6e3l42vXDdo/Nxiiva+PaCKeMaFcmptHWV4enZH+QdcSicdZmGHA5rXzF/C7fXuJJhIYXjXJ0vIKFo0wO5WIyKmyq6WZ1r4+QoHjydpYIkFzXy/54TBf3/DEwD3vnTu28V87XxqogKjML+BPL7qY4pxcvvX0BgA+cenlHOvtoaaoiJLcPBaWlNIfjxMw4+4XdtDR38+mw4dYWl5BVaGXkP7OdW/i4b17uOO5bQADM484x6jJkjkFhSwoLeVQZyfluXk4HG39/SyvqMz4sO6sOVXkhkK8cKyRlr5e6opLWFldPWxsDRGZPWZNAqMzEuFQRzuReJy5RUWcN3cuzzYeHRgHI4CRHw6zuqaW1y1bPrBfbyw2bLwJA3JDYS6tm8+WhqP+NFLQ0tfLZfMXDJsqL5pIsKe1hTu2bxv01DDhHAtLSplXXEIm+eHwsOw0AA5yQoOTElUFhfziyBcxZ7yq5B8BeKLrC0QTCd5cVzT8GCIi0+DFY4280tZKrT/AcNI5DnS0E0t405yW5uaScI54MsmFNbWD+i5XFxZxy7or6IxE+Mm2Z3DATSsvoKqwkMqCAgJmrKqpxTnH803HaOnrxQH3v/QC51bN5fzqGsBL6nbHouSmDZwcicfJC4fJSZut5Ib77xl4+nfjz3/GC03HqC4sYk9bKwD//ORjFOXkctOqC4ZdZ2leHm9ccSZrHrqVvniM8rx85hQUaFBlEZlW0USCZ+qP8jfrriQcDA5UlN14/ipKc3M51tM9MAsUQGHYq8iIxOMc6uwkNxTgigWLeOLQ/oH73+beHtbOmz9w/xsMBMgLhXjj+jvY5Vd4fOqhBzGDH/zhW1leWcmcgkJyQyH+bM3agaqPWCKBc4664pKBhMnQLiQAly9YxJ62Vva0thK0AOsWLBxx7DkzY2l5xYhduyciVWmhyguR7DYrEhhHOjt45MB+fvTsVgDet+oCemIxPrDqIm7f7g3o+cl1V9LQ3c3SIQFwXkkJ7111AbVFxQNB/s8vuZRoMsmSigrml5Zy+YKFJJ2jKCeX9v4+Xmo6xpyCwoGb1UQy6aWUhzAgkogPW55SW1TM2376AZxjoAz59T94D6V5eVTmD67aCAeDXL14KU8cPDDwhQJw9ZKlg27SRUSmi3OOnS3NVOUf/yEfMKOmsIi+eJxlFeUc6uzkby+/krOr5lJXXEJvLMamw4d4rrGegnAOF9bWUZmXz81+0uCMikpWVtcM6vaxt62VjYcOEksmiSeSHOpop7G7i1sfeZgdxxoBuG3r0xjGp9ZdSX88TnuknysWZB4lP8WAyvx89niTpjCnoJBgYOTtc4LBE+pnLSIy1XqiUZLODVTnpiofvvLkY4QDAe794xt47dLlfOx/fsGulmYCZnxy3RW86vbvk3SOdfMXUldczA3nreKy+YtI4qgqKKQoresJwNGuTnpj0YH3Dd1dADy4ZxcLSi8mNxTi1YuX8Mj+fXT66wJmXLZg4ZjTmzogHAhSkZ9PaW4edcUlY3dxEZHTxoz/5RtPJnnq0CHKc/MIpw1uua+9jd5EzCvvNeNodxeV+fmcNcebCSSeSNAVjTK/uIQ9ea3Ud3eRcI6k80rVrlq8hIAZuaEQ80tKae7t5aG9r/DvWzbhHNy8ajVnz5nL2vkLyAuFKM8v4CMXXcJ3t3r95j657krqu7tGLSfODYW4ZskyNh0+xDXfu9Fre3EJF9cNHwwJoCwvnzeecSYdkfU4B2/Kyxv1ZlxE5FRyMFBpkS4UCBAwuLB2HhfWzhtYHonH+e6WTextbWNL/REcjosb5vPms87iXeetBMgY47Y11HOoo4NoIkHQvNmj6ru7aevrG9imPL+AnmiU1v4+SnPzuHrxUuYPSTasf/v1g57+Nff2suHQwYHKvY9dcimXzJuHiMhMkRsK4fz72YDZwMO5VGXZB3/xAAD3/vEN3HD/PUQTCX6y/VkCFiAcNBaVlVHf1cUdO7bxsTVryQuHMz4o+/Cv/pv+WJzaoqKBhLVzsKu1lfquLhaXl1NVUMgfnXk2Tb09OAeVBfnDuvCtHzJ2Rn88xkN799De309+KMy+9jaeb2rkdUuXn7Lx3lR5IZLdZnwCo72/n3/fuomcwPFB37624QniySSfe801/ODNb6UrEqE8L5+6khJCgQBPHNjPr1/ZRU/UKzG+cuEi1tTUUVdcTHFuHsvKywdGRwZ/er0D+2no6uJol5dF3tfWRmN3D/NKSlhYWsYl8+bz272vEE0mCGAc7e5kTkHhmCVtxbm5rKypobGnh+JwDgvLyoZNB5XOzCjL04CdIpJ9AmbcsWM70USCT1/x6oHl/7LhcQrCYd5+znmDtn/bz+6isbubG89fybZj3tCeNUVF3Pfi8wQtQG1RsT8Q8uAnf/+66SliieRAouHXr+ziTWecxUW1ddQWFxMwG3ZTPJbeWIyH9+0hLxgiJ+A9uTzS2cmTiThXL1mmriEiMiMUhMPc9fxz9MVi/O3lrwIgkaFKOL0L3Y5jjcT9gZF//vKLJJJJaguLuT38DFWFRZxRUcnqmtpBY66lBtZs8RPHqergJw8dYG9bK7+44SbAqyCuG6ErdSY7m5vp6I8MjJkE0NHfz5b6I7xu6fJR9hSR08WMT2CEApZxTjsHFOXkDEsgPFt/hPUvPEdNYTE/f/lFnHP0xKIUnBniVYuXZhwQsysSYW9bC/e++AIxP8D/fv8+IokEyysqWFhaRmVBAW9acRYr59bQFY1QXVTEvOKSUQfYjCYSPHZgH/VdXdy+/Rmcg09cdjnXLFlGQXjkwexERLJJ0jk6+vv501/+58CI9198/BEC/hgY+zu8afTeee/dmMHdb78eM6M3FqMrGuHnL784kBx+YOdLRBJxVs6tobGnmx1Njbxu6bJBSeWABXAkBrWhPx6nLC+Pz7zm/7P33uFxXee572/tvWdPH2DQe2PvRaIa1WXLUmS524qa5RLbKefk5FhxkntyTnST3NybE1k3iY+dxEqUI6valmXHiSXbktVMiUWkRFKFRSRAECB6x/TZZZ0/9mCIIQASFCkCIPfvefSIszmzsQAO1qz1rvd7v+tPS+SdEDoODA5g2hYhfyBvuQYnnHMsk3aFYxcXl3lLxjQ5MjpCbzxGxOu0mhbCyW67e+16on4///zmLizb5p51Gwh6dP5ux9Zp72XbksFkkkTW4GPLllMeCHJwcBAbuLS2DnDEj4m5XleU/NoYQBPKGbmDj46NEvUVZs1FvF76EgmyljWlC6CLi8uFx4IXMIq8Pr6x+SrimSwP7d4FON1CBlLJaZPjn29r41dtrQgh8mrx822tDKdSdMXjlAeDXFRdQ0XweDCmU4ISZzR93J48mEoipaRjbCx/LajrrKyomPXY3xsapDcepyYcwZM78Utks+zu7WFzfcPp/SBcXFxc5oChZJItHUdJZDMMJZP56xGvF9O2Cxxl7aMjCAGP7d3NT947wNGcsDE46XW2dDp//OTgfv7bldcwnkmzs6uLjyxegmFZDCQT/OEVV/JSWxtbuzoYSaUwbRtb2jQUFeN7n5lASSObn4cnIxBkTGuaV7i4uLjMPWnT4PnWVsYzGR7e+ya2lBwZdYJ8fnpwPxL42xtvZjiVImkY/OLQISSwvqqKlGGgKgoNkSL29vUymEpi2jaWlMSNLE+88xaKENx7+ZW0Dg+xrrKS4VSqoFxPQn6eLwsEuW3Vam5ZujxfwnK6+DQPKcNgckqGJSWqEKiuE87FxYUFKmCYtk33+Dh9yQRBzcOGymrenNQtZDSTZlN1LYPJBPsH+4n6/NQXFRPSdYZTSU6c/qSUvNvfz3Aqxe9cfAm/aj3MTUuW5k/8wrqOT9WI+v30JRzLcpk/gGHbpwwiOhmtw0M8snc3ihD58peH97zJ59eu57LaOjewyMXFZV6TMc182UVVKMyfXnUtf/3ar5FIfvS5O9AUhb29Pfzus/+BaVtoiopE8u5Af4HYEfX6GMmksWw7v3g+MjrC32zdwh/l8oSOjY2x7VgnGcvkX97cRXcshhBg2Db9iQS/bD2MR1VZXlbOyvKK066VrgyGeae/v+CaadsIAUU+tx2fi4vL/OTQ8DBjmTTVofCUTb4Qjkv51c4Orm1sxquq+DxORsZwKkU8m6U0EGBNhdPBaVdPFynzePh8wsji0zwoQjghzYMD7O3r47cvuoR/fON1Dg8PYUuZL1Hpicf4h52vU+IPsK+/n2uamqZ07jsVy0vLePnoEXyahqYo2FLSn4izuqLSXRe7uLgAC1DAMHJlF93xGN/bsxsbyVc2bOKG5hZ+9LnbMW2bZDbLL1oP8dDuNxjLZIh4vdy1eh03L11GS7SEW5euoDoc4sE3dmFJm6BHZzyb4dDwEP+063VM26Y5WsLG6hqOjI7QMTZKeSDAZbX1PHv4PQCua2om4NGpjxRhWNZJS0VmYjplWgJCEW69tYuLy7ynLxEna1kFXZNUIchaNv2JOGWBIO8M9GNJG01REcJxNPg9Hm5ZvJSXjx5BUxXWV1Qzkk7xQntbQUOneDZLynRS7u/56dOoQvDHm692FrHCCaszss7fq4pgKJXkvpdf4O6167lx0RKqw2FmS1Wu7K87Nk7E68O0beJGhk3VdVNC51xcXFzmC3/43M+RUvLHm6/Ol7/dv3ULWcvioY99iqRh8G8H3sWWNj6PI8YKIdBVlY8vW8FldXVcUlvPru5jLC8rZ3dPN1s6j6IKhY8uWUYsm6VjbJSArnNgcIjyQJC/37GVjrFRJFPzNbK2xaNv7SFtGnSMjfLx5SsccWWW4kN9UREbqmt4u68XiQQJi0tKWV1ReVZ/bi5zj5RO6ZEQrjDlcnosOAGjY2yM7phTdjFhWQt6PGzv6uTWpcvJWhbPtR6mc3wMXVVRhMCnagwkE/z6aDub6+s5ODhAV2wciURKWdAPe8LyNpBM8FJ7G4PJJN/buxvTtmgqdlwcHkUlkc3yy9bD/LLtEOPZDJfW1tNYPHPHkelYUlrG3WvXUx0K881trwLw+XUbWBwtdbuLuLi4zHuypsWJU5XTsjqGadtkLQvDskCCUMjnXDzXdhgJBD06Pk3j1qXL2D8wQGUwRMo0eOVoOwi4rrGZPb19bKqpQUqJoij8z9d+nbdHBzQPGdMk6vNz0+Kl/PpoOynD4K2+PtpGRrihZREX19TOKlNIVRSubmyiY2yM9tERvJrKFdGG0xJBXOYnUkqwB5D2IOBFaDUI4WaauJwfqEKQnZRBAfD1yzYznE7iyXWAEghOnKxlLkBuNJ3ml62H+P+3vUbGMvnk8pVsPdbhrJEBVSi8M9DPF9dvZG9fL7qqIoFiny/vShb5ezqMZzJIKTk8MsTP3jvI4pISrmlsntVhnxCCNRWVLC0pJZbN4te0KUHOLgsbaSeRxttgHQEEUluC8KxGCPff2WV2LDgB4+jYCI+8tRt1UtnFP+zawd1r1xPPZhnPZPjnN3fSG4/nQ4W64zF+tP9dKoJBrqxv4L9etpnn2w5jWTatI8P0JuLYtiTq93FVY1O+9elgIkl1OIwQkLEsyv0hoI+sZTGaThPy6uiKimnb/PDdt/nI4sUsKy2ftt3UdCwuKWUomeTI6Ei+/KU8EGRtZdXZ/8G5zAlSStdN43LeUhoIICUFtc6WbSOBqM/vLDw9OkLkNpE5nHpmhU8vX0XQ66F9bJS3B3vZcrQDXVUxpQ25+z576CBv9HTlg0C9JyyApXQW4D977yADua4kO7o6ub65he7YOC+3Z7lp8ZJZicIeVWVRSQmLSk7ePcpl4SCljczuALMVko8AEhn8KnivQ6hlcz08F5cz4vanf8CBXJjm32zdggDuvfxK+hJxVlVU4FFVoj4/laEQXbExntr3Dqqi8PGly8mYFhGvoHNsjJpwBF1V6YqN88N97+QFkWcPvYciBHeuXUdjUTHv9Pdi2ja/fdEl7O7t5sX2Nizb5sMti1GE4LnWwxi2xc2LlxDSdUZSKcqDAXpiMVpHhlleVj7r782raXg1jbF0mr54nLDX6wbcnwdIaSIzL4MdB6UMkGAeRNqjzrzsrpldZsGCEzC8qlO7N+XYD07afnSCzrEx4kYWVQiCus6HFy3hibf3IpEoQmE0lSLk0akNR/jWjm1oisLhYad39tauDuLZLBI4MDRIbyIOwLd2bGM4neLfDu7n3lwXkSLfqWv+NEVhc0MjK8sruLqxCb/HQ5k/4P7yngfYZgcYb4E9jlQrEJ51CHX2H9wuLguBqN/PyrJy3hnox695AEnKMtlQWU04lw90cW0tv7l6HR2jI7zW2YEErm5oojoUQlUENaEIv/+Ln2Hadl50XlJSgi3hktp6tncdK0idrwqF6I7FKA8Eua6pBUUIfnXkcF4EBkf4CHl0wrqXrtg4g4kEFaEQs2U8k2Zffz+9iTjFPh8ryysKgp1dFhBWN5iHQakBJjY/PmR2G/huca3LLucNnWNjSCQDyQRLy8pZW1kNOI6GqxubiGezPHPoPQzT5FhsnMaiKNWhMA/t3oWmKPlDwbF0On/PkK5jS0l5IEhQ11lRVsHevl7CuhdbSmxbIiUsiZZyYHgQS0psKQl6PBiWM5/HMwYjqRRbOztoLo6e8pDv9qd/AMD3PvEZtnV20DE+xsRv6aqKStZVVs3bdfLE2E+3jfcFhd0P9ghCrT5+TVQirR6wh8AVll1mwYITMBaXlPL5deupDIb52+2vAXDPuo3URSKOACAEX924if0DA7x0tI2hVIpSf4BrG5sI6zpHRkd4ev+7SGBjVQ1PvL2X/typnZSSHV3HuK65mYpQaEp31sFEMr/AHp7UkURTFQSOjU9K2HGskxsXL5n19xT1+4n6/YymU/QnEhT5vG7N9QLGNtohuwWSTwAKBL+KTD8Pvo8g1NK5Hp6Ly1llQ3UN1eEInWOjCEWhsaioYLPfXBzltlWr2X6sk+1dx1CF4GPLljOUTDq25hyTHRKObVkQNzLc/+GPsLG6lk/+4HHGMml+c9Va3unvJ+zVaYk6i+GMaTCWTbO7pxdVUdhc18BPDu7jxwf2cWltHV5F5aPLlhW0Yp2J8Uyanx8+hJDw0J43sKXN59du4PrmFmojRWf1Z+fywSOtDkg+DqhgHXIuJv4JAneCjIFw/01dFi5Pfvq2/KZZSrCkzadWrJyyhvzaz36KBAZy4clHx8boicf58vqLTupOs6XEsG3WV1ajCMHqikpMy+bg0CC6qnJtUzMbq2oo9vkYTCW5pLYWaTuuOFNKXjzSxvNtrVzf1MJgMsGzh97jiXf2oinKKTf5e3t7ODY+Tk0onB/LW729RH0+GoujZ/BTc5lLpJ0AphGOhQIyOfW6i8s0LDgBozIU4tK6Bt7s6XKyK6Rz7ZJcb2qvpnFtUwvxbJasZWFLSdYyKQsE0VWVpw+8y+ERx1ERy2YYmSRERLxeDNumLlJMU1ExX9l4MQHNw/3bXqU/dxI3IXaU+PykTIOkYQBOEv6x2Djfen0btyxZyrqqaipneeKXMU1e7Wjnr7a8AgJ+a8PFrK2sYlV5xbxVmV2mR0oJ5l5HvLBanYuJBwELqdUj1KvmdHwuLmcbIQTV4TBff+5ZYPqTp6pQmE8sX8knlq/MX3ulvY3/Z8vLqELJp97rqootJXesXoeqCCoCIVaVV5IxTWwp8Sgq1aEQYV1nLJPBr3mwkVSGw5RZQXZ2daMIwSsdR/IL9R1dx3h7oA+/R+Njy1ac8vRv/8AAQjrlMY4wrVDs8/NmTzc14Yg7Jy84VJhyHEGuaN91X7icH+wb6CeWCzT+4k9/DEydiyfPXP7cPFgZCvHF9RdREQxy/9YtdI6PURUK53OGEE7WRUtJCQPJBNs7OxnPpJHA5vpGdE2jc2yUvkScxSUl3Lx4Kbu6j9GfSKApCkKAJhSEEGzr6mBbV2f+3ie6FSYe7+g6BsCfvPBLNEXlj3LBpIoQFPt8HBganCJgjKXTfPGnP0ZVBE999vaz8BM9PU4cu+vEmBmhhJGicE6WUjoKnOI6HV1mx4ITMACWlZbRVFTMh5oXo6vqlHKN2kiEe9Zv4IbmRQwmkxT5vFSGwvz88Hsok6Zwn6pR7POhCEGR18dXN24ia1ssipYQ9nq5sr6R1zo7uH3VGg4MDbAoWsqzh9/DsCxWVVSyrbMDANM6Hp7UG4/x1L53qAyGuL55EfVFpz7d2dl1jL5EIm+TLvUHeKOnmxK/n5pw5Gz8yFzOGQbYSaYujBWwh+diQC4u55SsZeFRlFNu9BeXlmHaEmVSpIXAKa27tqmZoEenLBAgbZo833aYz61cQ9DjIW4YSODapmYypommKNRGIoR0L+urqvmLLS8xGD9+ijOWSVPs85GxLLpi47RET55v0ZuI8y97dqFwPGfpOzu3k7Usbly0JC+ADCQT3P3jpzBsm29ccSVrKqqojbjz9XxDaM3IwN2gVEL875yLwS+BEkEobkCry8JnwoUxsXnOmCY2MJJK5dtJnygSTDyOZzLs6e3m0MgQlpTUhMPcvXYDPzmwD5+mHX9eNssLba34NQ9VoTC2lPQl4tSEw3xm5WpM28avaQghaI5G+fj3H2M4lWIw5czFr3YezYsms0VSKLqAE7ZsTCoXtGybHV3HaB0ZZjSTAgmvtB/h8vqGgtJDl3mEUgFKBdLuBVGCE3g1BGoTQnHzp1xmx4IUMMBxWpSfZDL0aR4Wl5ayuNSx7EspCes6v7fpMr6zczvgBB29O9BH2rSoi0SI+LxsqKrB7/Gws+sYh4aHkFIS8fr42NIVjKTTfH7dBgRQHQpx86LF7OzppiIY5OE9u/EoSr5dYHkgyPZjnVSFQidNXc6YJkfHRnn0rT35xfLfbn8N07ZpKiouEDAs22YgmSBrWRT7fKfdW9vlXOBxFOTQ70L8HwAQ4XuR9ggobgaGy8JlphOlE0+ebnrsYVRF4bsf/fiMYoGUkoxpctuq1RwaHiaWyaCrKn92zfVsrm8ocEl87kffJ2Fk+dMrrwUgjJdYJsORkRFuXrK04L7XNrfwwPbXKPJ6GUo57rrGomK+ccVVDCQSpHKOuZNRTo0zkAAAIABJREFU7PVh2xJFKSxpUYTIz+VDySTPHz6Eadt4FIWMafHCkVaua2qZlWjtcu4QagVSvwiye4Bs7qIPoV8+p+NycTkdxjMZ4lnHdTYhSkzmyU/fxmefepKRdIpPrliJQPDMoYMsLyvjgW2v5Z8zef6OZ7P8uqMdy5aMpFJsrKpmTWUlH25Zws8Pv1dw/47RUSdbKNcNRBGCikCQY+PjpE2jYD3q1TTCXi/jmUz+mqYofOOKqzAsi/u3vUpFMDjls+REkeW3NlzM6CSXNMBIOsWGquPZCYeGh/jvLz6Prqq0jzpBz3/+yosEdZ3/uP3uWf50z5yZBCKXqQihgPdqpHEQrDZAgH4RQpt96b2Ly4IVMADSpkF3LEbKMCgPBikPBGc89RNCsLq8gmcOHSRtmuiqymg6RVUozE2LlhDyevMhoLt7ujkwNMjjb+0B4L9cegXd8Rg3NC8i7NXxqk4yspSSqnCEZw8dZCSdQkA+I+Nbr28ja1tc19xCWWDmumszl9g/3Xgzk1TmWCbDS+1t/N32rQB8ccNFrCqvYENVtWtpnkcIIZCedZB5BbABBWmPgcwiPMvnenguLqeFadscHBzg4PAgA4kEPo/TtvTEMoyMeXyumigD2dLRjiIETSdYfW9/+gcksgafWbmK6lAYv+YhonuJeHU2VlcX3Pv2p3/AuwP9ANy/dQvgtGkNe730xGNTxjKeSXPXmnW83tXJL1oPA/DxZSuwbBvDtigLBE/5Pa8sr+Ce9Rsp9nr51uvbsaXkN1et4YqGxnyt+F0/eQrDsmjLWaH/cdcObCmJ+nzURdwyk/mG4lmBVBvBdw2OyFzqhne6LAhsKdnV3cV7uU4jAHWRCJfXFQq9UkrG0mkETjDyxGv3DQyQsawp3ZsAtnYeJZHNUuIPMJpOoykqe/v6WFlWOeW5cSOLrhTeQwjhrFVNC7zHr8cyGT6zcjX98TiPvb0HVShcVltPd2wcYNadRC6uqeW51kMcGh5kLJ3ByDmkJ3eJOjg4MCXA36OqJA1jTrrAucLF7BBCR+hrgDVzPRSXBcq8FDBimQzDqRSaolARDE7rYBhKJnnxSCv/uOt1hBB8Yf0GWopLuLy+YdpAoq7xcV7v7uJH+98lY1pcXF2Drqhc09xMse+4mm1YFgcGB3jsrT0cyjki/n7HVkzbprk4ylWNTfnnCiFYWV7O3t4ewrqOqigMJicF0EjQlJNPngGPhxK/n9++6BL+6Y3XAWeB3h2P0VxcnH/etmMdZEwrb4mrCoZ4p7+PylCIWrfMZF6haA3YXO9YluUoKFGEvta1xrksOLZ1dtA+Nsqjb+3Ob9Zv/f5jlPj8fP8zzkLtyU/fxi8Ov8cD215DFYJv5OqVU4bB2329UwQMW0oSRpZSf4ADgwOMpFJoqqB9dJRH9+7hnvUbTukuM20bTVFQJy1cpZS81tFB0KOzprKK7V1dKEJweHjICaGrqqY8eGoBozwY5Iq6en747tusKCvHq6qMZzJ4Jy3eDdsq+NrgnEiOZTJYUqK5Asa8QygB4NQhri4u84kjIyPsHxygJhTOr227YuO83d/HxTW1+efFshnuWb+RqkkByg9sexXTtvNz9+1P/yC/wR7PZOhPJAjpOm92d6NrKiV+H0OpFP/p5/+R77I34Sj46xtu5MDgAFGOr5dN20YRTn7cZA4NDyKlZHl5OT7Nk88w2t3bw6dWrOInt9150q6Bk0WAh/fuZiyd5uPLVlDqd0oK9w8MsKG6Jj+Gr1+2GY+q5kXuey+/koFkYtoSFBcXl/ODeSdgvNvfx+7eHv519xsg4HcuvpTrm1ooneRikFKytbMDj6rmN/TVwTCHR4aojxTRMGnjD45TY0vHEUIeL4pQEMLGo6ps7+5kXVVVgYAx4Yg4cdJThCBuZAuuWbbNi0eOYOOUjWiKY6nTFIUvrd9IxOej6BQLcSEEl9bW83zrYRKGgWlbvNJ+hNWVlTQWOd9HPJtlIJEoKDOZ+GBqiUZdAWMeomi1oNWe+okuLvOUkVSKo2OjVAdDBdlBpmU5AcqTGEtnpgjHfo+HvkQ8fwo2sRDe2d0FwDe3vcrm+gZKAs7861FUxjJpdnV3cX3zIsBZyH7qh08wkkrxB5degSfn7uhPJFhTWVmwCB7PZBjPpKkKhQl5de69fDN98RjjmQz1kQibZxC3p6N9dISmaJSXj7YjgOuaW3i18yhFPh9Rv5//ftW1DCdTPPjmTsARnVOGgSUlqiteuLi4nCUODg1Q4vMXzF3l/iCHhgfZUFU9SUgVThDiLLGljQCOjY2hqSLvivBrnmnnydpIEVWhMN2xcYq8PgzbJmFkuaSmboojbyCRJOjRURD82dXXkTZN0pbBWCrNhqrqk4oXE9z+9A8wbZv9gwMAvHDECUW/9/IreWegn+ZolGKfn0Ulpbzb30/VpND8oVSSluLorOd7FxeXhce8EjAGk0ne7OmmMhjKCxO6orKl4ygfW7Y8PxnFslnGsxke3bs7v6H/5rZXsaRNc3HJFAGjP5HgwTd2IZEcHRsD4CcH9xPRvVSHwtyxZn2+zMOnabmsjEv5zs4dgLM47Y3HqTtBKOhPJPjm1i3oqspArjuJR1GQgC3gyoamWdnXSvx+SgMBbl22jP84eIBfd7TTXFLCuwP9bKiucT6UprmPEGDP/vPKxcXFZdYkjCwCxyL8jSuuyp9ufWHdRtZUFlqMK0NBvrpxE8WTApVjmQxlJynry1omIf24lThj29SHw/TE4wWlIV5VJeL1MppJY0vpnOyVlbG6onAMQhzvNSEQlPoDlPoDjKScUsETHRMzMZ7J8FdbXkFX1bwL71s7tmHYNqsrKon6/ayqqOQXhw/x2xddQkjXSRpZRtJprmlsdstHXFxczhqGbeM5odxJCIFtF/bWCes6pYEAY+l0Ptj+3suvpCce498O7serqgXOhojXR0DXOTw8hC8310oJadPkP196Gd/dtbPAsaYpCtc1NXNkdISjY6NEVY0lpQ1UhaYG4Ub9ftpHRvKiiE/TUIXA9MpTdoGajGnbU64pwmm+PZxKUezzs6KsnJ54jO54jC+s34hp2wQ9Omsn5WS4uLicf8wrAePY+BgP73XCMCeEiX/ctYOsbXFVQ2PehaEI4cy0JyAl055+2VIicSbmCbKWxVgmzdP73+Wi6lrKGhoB54NhU00dLx5pxbBtFCHojccJe3UWl5QW3DeRLXRkANQXFWFaNpfV1ufDjk7FQDJBbyLGxsoaXmxrA6AuHOGdgX4Wl5QS0nVK/H6+dtEmvvuGc+L3h5dfSXc8RlNR8clu7eLi4vK+CHg8yGkSegxpTen8tKaiil+2HmI4lcpv6FOmyebcvAqFIWfxbJZV5ZVkTRuPAknDQAEqgmESRhZFCD771JNkLYv/+aGPUB0KE/B4SBhZfJqGT5taQx3WvZQGnFruCSHFlpKEadAcjU55/kwYljWt71hAPgS0PBDkI4sWs6e3l75EnCKfj2ubmmlw52MXF5ezSEtxlL19vVRPEgpG0inqiiIFTgYhBFfUNfBiexs98RjgCByrKyqnBHKCs47eXN/A/v5++hJxIl4fpmVTFQo57glFIBC8t/sItpQ8tfxtmqMlrCyvYGlp2UnHvLS0lMMjQ4xnMoR1naxlMZhMsqm2dlbuC3A+L7rGx7n7J0+hq2q+NHECT66kz6tpfLhlMX3xOKPpFGHdS3U4fNLwfBcXl4XPvBIwbDlVbQVAFirNIV2nMhTiyxsu4qHdbwDw9cs205OIFYT7TFAeCPL5tes5NDzEjw/sI5sLx4z6/XnbMjilKVnLoiwY5DeWLmNZWTljmTTVoTAt0ZIpynHIq/PF9RupCUcKAua64+OE9MKawJMxlEzy8J438ShqQYlI1rK4rqmZsNfL5XX1vHikLT/2nkScpaVl1LmJ9y4uLh8AUZ+f+kgRneNjlPkDfP2yzQylk0R035RTt9JAgJsWL2H/wAADyQQVwRAryiumDTB+8tO3YUvJS21tPHP4AFnLpCQQYFG0lISRpaU4SsfYKEO5PKE3erqxpWRTTS3Ly2bu5CNyC/IXjzgLeIHzubGusorK4Ox7yxf5fPzOxZcS1Dx86/VtgDOvd8XGqZvUJrUiGOLGRYvnJCjOxcXlwmBpaRldsXG64zF0RcG0bQK6zsaqminPLfL5uHXpcvoScQzLIurzU+TzzRgsWREM8YX1G/nhvrcBSU24CF1Vc0K0l3g2w0iFIxb/y+43+ML6DXTHxrlp8dK8S3q6rhvFPj83tixhd283/fE4Pt3D5fX1Uw4BT0VFMIiiCKxJB5axbAafplE5qWRkopW228baxeXCYV4JGPWRYu5Zv4HqYJgHtr0KwO9efCkmNtETTvwuq2vglfYj+Q39QDLBxuraaReqQV3nyoZG9g/0U+T1MppOE/X5+U+bLkNTFKpCIQaSCXZ1dTGcSqIqCktLy1hbWXVSFbciGKIqFKYnHssLLD2xGPVFRZRO0+ZqJvwez3SGEgC8qvNPVOzz89Gly9lUU0faNIn6/ZT6/e7C2cXF5QNBCMEV9Q3sG+jn4NAglpS0REtZW1E57SlaiT9Q4Lg4GYoQXNfSQpHfy/7BQRQEsWyG6lCIB7a/xlAqSfuY0xLvm99/lvUvjCAevIOacGRKYNwEEwvpRz/5WQYSCQzbotjnn/H5M6EpTmL+r48ewbCdOvHuWIyGE9paT+DOwS4uLh8UXk3jQzmHwVAyQdjrpSYcmbEUQ1OU08pFq4lEuHvtBt7p76MnHsOralzfVMv/+tFzjhutypk/+/f38Py393HDg3dwbHxsxhbZE5QFAny4ZTG2lAje3zzpUVWe+sztvNpxNO8qiXi9XN/ckhdQXFymwx66CwCl9LE5HonLB8W8EjDKg0HWVVTxVn9fXphIWSbXN7dMqV8O6To3L1nKJbV1ZC2LYp+P4ElKNpaUlvHFDRdRGQ7z7wf341FUNFVBRVAVivBCWys+TeOFrz6BBIzv3o5hWVxaVz/jPRUhuLqxiQODA3x148UIIVhaWsay0rLTmqyrQ2F+b9NlCMiHwn1xw8a8JXoCXVWpdx0XLi4u5wiPqrKuqpp1VdVn3WmgCMHFNXUsKy1nPJvBp2qU+P18e+eOAstdJpbm2OtH+MWXH2Wb18d3Xv2rKfe6/ekfsKPrGAB3/+Qp4Mza2dUXFfHRpctZXVFJ2jSpjUSoPo0cDRcXF5ezxQftMCgNBLimqbng2uWvxBhJpXjjcudQcMPrKQB0VWM4leJPX3QE44l5d7IT497r7gPggZf+/IyDNKN+Px9duoyxTBopHZeJG87p4uIyrwQMgHVV1TQVR9lc34CmKFQGQzMqzYoQs2qLN8GK8gpK/AEuqq5hLJOhJhRmeVk5bSPDPPOl7+FRVDpfPwLAS197kl/aFmtf+2v8J+lZ7dW0/AL//eLVNG5oaWH7sWPcvXY9AJXBEJtq6tyJ2sXFZV7wQTkNVEUhZRiMp9PYSL510y28crSdB37wcwDKvr1v0iA+kCFMS5HPd0bzuouLi8tCwJaS9tER9g30kzFNmoqj/Pef/wm/bD3MO7l5+M4nPg9ATzxG1De9w7h1Tzv3fvs+3nrFmbPvve4+Hnjpz/N/b+Vy5U73s0QIUdAt0MXlZNhDd4Hx+vE/4zoxzkfmnYABzsLxxJC4s0VlKERlKOSExgnwaR7GMxkUMfVkTeAEf55MwDhbFPv83LR4CYmsE2B3Lr6mi4uLy1zSF49zx49/iJSSr2y8GFNKWoqjaIrCX37+k/g9Hh5/YQQpJTf+y918YvmKae/z5Kdvm7YWeySV4k8+9BdIKbnvl/+N6lDYFYVd3hf20B0gDUTxN0EpQShTuy+4uCxE3u7rZW9fL1Gfn6BH573hQY6Nj9NYVMyq5wad9tVfkoykUgR1nbpIpCCUGXLOi2/fN+39BxIJ3uzpZjCZIKDrrK2opCVa4pbfuZwRUqaQxiGwukEJIbSlCLViroflco6YlwLGB8loOsX2Y8cYSiZACOoiEaJePx/+5zupDoV5/I7vAvC5R3+L0Uz6pGUp02HZNqPpNKoiKPL6TnuCPt2v53J+I2UWUBHCrfd0Ob+wbJs7f/xDjoyOAPC9vbsBuGPNOtZXVXFgcJDRTJqsZSGE4OrGRvweD5Zt05uI05+I49c81EeKpp0324aH2Xqsg6RhIIAX2lpZFC3h8voGV8RwOS2kNQT2EGAjs6+BlEh9PYpn1VwPzcXljEibBu8O9BeIuxWBED3xGMtKS/njZ/6IA7lw5saiYtZWVuHVNDKmyf7BAT613Pkd2NPbw//7/P/Aq2kFJSTDqSTPtx4i4NGpCoXJmCavdXZgSsmyU3QzcXGZCSnTyPTzYCdAiYA1gDTbkfpVKKWPuc6LC4ALSsDImCYvtLUhgEff2gPAFzZsZDSVJqx76Y3H8y1X+5JxLqtryAcFpU2DlGES8HhmLGnpjo2ztbODf7vnfwNw55Nf48r6RsKnGSLn4iLtEWT2TbD6QGhIbSnCswohXGeOy/nBaDqNPU16sV/TyFg2n1y+ksFkkutfvI9SfwCvpmHaNq92tNMxNoYv93hPbw83NC8qcF5kTJP7bvorNEWld9dRAF7+2pM8Z1kseun/ntJFxcVlJuyhOx3xwnLKS0k8nvsbiVSqEOrpdVZwcZlPxLMGEjlF1PVrHkbSaS6vb2BNRWVBBpItJb8+2k5/Mk6pz8lpe6e/j6FkguuaFxXcZ//AAB5Vy6+Dv/X6NiROeP3iaImbK+TyvpBmG8gEQq10Lgg/UvrAeAOp1c3t4FzOCReUgNEdG+c7O7ejq8fblT68+02ylsX3PvEZRtMpih79MgGPzvKyMmrCEWwp2dPbzf7BwVySMqypqGJVeUWBuyKezfLK0SNEdG9e9Ehksvz6aDs3L1nqnvi5zBppJ5HpFwAVkrnFcuAOpEwivFfM6dhcXM4WihB8acNFPJYTk79xxVUADCYTaELgUVWqw4VCQ8fYKB1jYwUp+4lslq2dHdy6bHl+nh1Np0FOjcxQhKAnHpuVgGFYFpqiuDbnCx4TmK7Fu460jrkChsuCxq9pIB1RYvI6NW0ZRCd105s8Dw4kEvQm4tRMmkerQ2G64+MMJZMFuRcj6RSBE0qiBWCYJoZtnVTAkFIynEqRtkwiutc9DHQ5jtULFHadFMKLlCMgU67z4gLgghIw0qY542JUEYI1lVWsqawquH5gYIC3+/t45WvfB+C2x77CG91dBHWd5uJo/nnHxsd49kuPFASBPvvlR8haFpe+8peUTeom4uJyMqTVAYkHAQ9Yh5yLyScAA1n6Y4QytVWwi8tCo9jno9jnw7TtfFtW07bJWhYNRcXTvqZzbJSIXriIDeo6vYk48Ww23zJVUxQ+8tDdBWWBdz7xNfoScbzKyT/2umPj7O7tZjSVxq97WFdR5dZrX8CI4u8g08/lxWQRvhcAaQ3O5bBcXM4KQV1ncWkZ7w0OUB4IoioKo+k0XlWjcYZ5OGFkp81TFgjiRpZyjofrVwRDtI+M4NM07t+6JX94+L/37ubfDx3k+zN0i0qbhuPySMQRKEgkK8sr2FBV7c7FLiDCwDBMeq9JaQEKCLcU/0LgghIwSvwBvrBuAzXhCPdv3QLAvZdfSW8inl/4TkZKyb7Bfl7+2vc5lhMlfnDXP2NLSfljv1UgYGRMk5ki8k3bOum4xjMZjowMEzcMqkMh6iNFeNwe1xcu9jgztluQaU5UnV1cFiJCCK5qaMKSkngmQ088hiIEl9TVF7SPnoxHdcpGJiOlBCnzIghAid9Pid/PcCqVv5Y2TYCTtqIeSCR4oa2VR97agyoEv3/J5bza4ZSgLCpxT9ovSJQoKH4cF4bzHnMWygZCda3KLgufi6trCHg8HBjox7AsaiNFrK+qnjFMPuiZfoMokQRPeM3ysjLaRoYZTiUnd8cmpOsnbSr1Rk8PQ8kU1SHHbWdLyTt9fZT6/TROWnu7XJgIz2KkeQhpJxFKwJmT7X7wrEC4AsYFwQUlYJQHgzQWR2kfHcHK1V73xGOsqaic1pomgaxpTplkhRAkDaPgWlUozI25INAn7nwQcIJARzKpGVtOgZPC/0J7K9cX3Uc1guc67qMsGOD65kX5UhSXCwylDAL3INQqZOwB51roD8AeBNd94XIeUeTz8dElyxhKJTFsmxKf/6QdmBZFSzg8MkTY9uYFi8FUktpIpMCmPCGOvNp5lBsevAMQJE2DaxqbT2pDfnegj6BHR82d8Hk1jfJAkL19vTRHS9xSwAsQIVTQr0IGLZBZpNUDCPCsc8tHXM4LVEVhTUUlq8srkHDKea48GKQiGKI3EaPM75yAD6YSVIfClAWCBc+NeH3ctHgJ7/b385WNF/PPb+4i6NH58W13ANO3Vs2YJkdHRyifdC9FCCI+L4eGh1wBwwWhRJHe68HYibT6QCjgWY3wrJ7V692Qz4XPBSVgKEJwRX0DdZEINeEwmqKwuKS0oJ76xOdXRyLc8q+f55kvPQI4NuSBZIL6E6x15cEgi0pKOTw8hGnbSGAgleSKuvoZQz9tKdl+rJOwR8ejOGJFdThMTyxG++gIS92E5gsSodUhzWJnUkY6/9m94FmPEB9Me2EXl7lCVRQqgrMT5ipDIS6pruPN3m7AmUMrgkEura2f8tyw18tNi5Ywlklj2ZIinw9NUegeH6dtdASQ1EWKqIsU5cWQv9ryCooQHMrZnO/fuoVvXHEVI5k0pm27ovICx3HrjIDMglI86/lUqGXg/5gTqowFSqnbRtXlvEMIcVJXxASKEFzT2MS+gf78XLmyrIKV5RXTih/FPj+bGxoBeHr/uwAcHR1hR9cxhpJJon4/qysqWVFWjqo45SKSqT5UVQgse2rws8vCRco00uwEe8RpT63VI8Tssk4UrQqpfhRIA9oFFXIvZQZkBkQAIS6orXyeC+671hSFlmgJLdGSWT1/Q1U1z7UexrAtFKHQm4gR0HRWlRf2GlaE4LK6ehqLi1n+4/+Cpio0FkUpDQRImwZDqRQKgrJAIF8ekjQM4kaWm0r+gqj6NgDrvX/EWl2yd+xvXAHjPMGxGwuEmF3athA6+K5HGu9B6HcBL2jLENrUTZqLy4XG8vJymqLFjKUz6KpKsc/ZhE5OyZ9ACEFxzgGXNg1+sv9dtnQcZduxDjRF5QvrNrC0tIyrG5tQFQWPqpLNlZpMkDQMIrqOx03Lf19IaQPWnC8upZ1AZraAPZy7IpD6RhTPslm9XggdLqA5WNojSGO/Y8sWUYRnJUItn+thucwTvJrGhuoaNlTX5K+NpdOMZzJEvN4ZD+4e/9TneLWjnb/bvg0hnBDRwVSSeDZLxjS5qKYWn+ahIhhkLJ3Oz98Ao5kMl9a678EzQdpJkONOToSIzmmeiLTjTitUUoAPrMNIcx94b0AowVO9HJgIl53Z5X4iE84LjNfzjxeSC0NKE2nsASOXjyc8SM9GFE/L3A5sDrjgBIzTpdjn55Yly1jxsz9iNJWi1B+gORqd1uasCEFtOJJ3dGQtixePtPJiWxu/am9FV1X+86bLua65xREyFAWmaSNoS4lfu3CUxPMVaceRxltgHQVUpLYY4Vk9q/o8IXwIfS2w9gMf53xB2nGk1Q0y67TGUsrcsC6XafFpHnwhD2nTYGd3F60jwwhgaWkpq8orpyyepZR8/PuPM5xK8ekVq/BqGrYt6UvE0VWVJaWl1EWKeOJTn+Xnhw7x0O5dqIrC1y66hNF0iuubW9z34mkipYU09oF5AKSBVCsR+gaEMrvDg7M+nuz2XNu9qvz4yO5EKiVnbWMu7QRO+5vggn6/SHsYmXoOhAcSDwE2MnAX0vshFK3qlK93ubDImCavdXbQHRvnX3e/AQK+c/OtrDjhoA+cIOZtnR2EdE++y8l4JsN4Os2BwQFWllfg93i4pKaOF4600hOPoSkKpm1TEwoXZM+5nB628S5k35p4BMnHkUoRSumTczIeabwFmAjl+JwirUGksQ/h3XR2voY9CvaoM5cpU9+PCw1pvAXGe6BUIISClFnIbkUqweMtZS8QXAFjFgR1nTUVzhsja1kcGx+jJzZOUPfSXBylyDfVhmpYFs+8d4CX24+ws7uLkVSKYp+fgUScl9vb+MTylXg1jZZoCb8Y/h/cVPKXAOxM/n8MJBN8ZLFbW3u6SGkhzS6w2wENobWAUjknC0kpDWTmJbDTkMypu8E7kXYM4bvmnI9nvmObPZD5NSQfAkAGPg+eZeC5eEFvBFw+OCzb5sUjR3jyzu/iUVRue+wr7B8cYCiZ4kMtiwreN5996vt5q/PDe53W2QDPHnoPr6ayqqKSukgRxT4/Ny9ZwqNv7SZrWxR5vVxR30BlyM2eOV2ksRuMg9zxrBNK/MQtfmT6V+D7jXPeSUnacbD7ChbKQqhI4UOaR85YwJB2HDl0G5CF4JdARMF7GUKZvovDfEca+0B4nDpzBKCCKAJjD2g3zfXwXM4R9153H0BBW9Tp2Nl1jN54jOpQGF1VkcDO7i6ifv+UltWO2CwKROaw7mUknaI0ECBlGvg9Hop8Pm5Zsozu2Dhxw6DMH6AyFHJziN4n0uqF7JugVDm5PoDEAjs2N+OR0jncEyfMvUoUrHbgzAQMKaXzGWTuBykACUoAEf02QilekBkYUhpgHgKlPO/oFkJHiiDSPOQKGC4zkzFNXmxvYzCZ5JG9b2JL+PKGi7iuqZnqE3I0umPjtI44NX6jmTSGbTOQTPDI23vwax6ubGikKhRmY3VNvnUgwGgmzeV1DbOuCXdxkNJ2TtjMNu54ZhyAJ25pdXIj9DXnfjxmD8S/BejHW6EmHgeySH0tQjmzUwRpx5HmQbC6QARBW46i1Z7xuOcCKU3IvgZKGMg5j5QqMA6CWg+qe+LnMpU/uPbPGEml6HujA3A6RAFc9+DtDCaTlAePW1Btpq+bFgIsWxY9DciuAAAgAElEQVQ46op9fp69854PcOTnP1KmuOPftoHw8HpPEoA7n+lHyixPfKIdoc8uaO3sYeUWsSeiAsY012ePlBYy8wpgAjpCqUTaY8j0S+C/ZWEm4lt9kHjIES/yn1//BGSRvg/nN0AuLmnToH1slMfe2gOQb5P6yN7dtESjUwQMW0rCXi/dsVhB8LKdcyNP7nDi1TSaZ1nu7XJypNkGInRcvIg9AJbTXdEeuh1Qz+lmXgiBRGdi3jyOAWcj683uBWOfI9jkyj+lPY7MbAPf6Yuw80LwkAZIG6GcMP8KHez43IxpDnEFjNPg6NgoQ4kkNeEwqlBQhaMcv951jFuXrShQhvsSCX56cD8j6RTGpLZ/I6kU3pCWrxzxahpXNTYRz/6QjGnyqVqvGxT3frAHwDwCSg1COAtmZxP8NlJrmXU93VlDztQKVYBMAe9fwJB2Epl+DqQJyYcBGwJ3YssrUDyL3vd95wx7FBLfpUDsif8tYCI9K/OWbxeXyVgntFOdQJmmS9Qjn/gMH3vyUbKWxQ3Ni3jhSCsI2FzXQGNxlMaihXlSPm+RTvvaqZGACsjRcz8eEYbk95AIRPgbwESgZ8IRSc8AOXy7E0BnOe12852jAneC1Qtawxndf05QinHaxk5ei0w8LsyCkVICGZwQPXdJeb5w73X38dYr+/J/humdGKY9NXsInA1qxrSmXG+JlnB0dBRdVRlLZwjqHsbSaSSSjTW1M2ZnLARuf/oHADz56dvmeCTTILOc+Ls753hWQvYNpFKVK4ewwR4C/bIzvrU0j+YCLo9/z0KJIO1ekLEF5bzII/yghJF2onBPY4+DZ+pBrbTjub2I1wlIPc/cSwt3ppgDOsfG+N5bu1GFyKvM39m5nbvXrieRzRa054t4vahCEPX56U8mANAVlSKfly+u30CJvzB0JqTrhPQFeFIzT5D2EHc8M4IQCXb0OErknT9rQ8osT356DDi3AoZQo8jAPQi1elIr1K+D7AVxZu4aaR7O5URUHLf3KuVg7EZqjQtvETljuKnEnaJcZuLPfvF/8au2Vl786hOA0yEKoDs+TvCEuTSk6wR1HTuTwauqZG3LOQnUvXxs6TIiJ2mt6vI+ECGe+GgFiBLu/FkbAE/cusRZPCrnPoRPCAWpFIE9grT7QKpAFtQmhHqGzjU5vZBG8mFk6mlE2dMneWkMaR5xhBSlEqHVzQvHhvCsQgbucuzc8X/AEcnvAP2qgkWwbfaA8YZjQxcaUlvhhH3OMrDaZeET9HiI6F5+9+JLCeo692/dAsCda9fTWDxVGG4sKmZZWTkg6Ysn6YvHCXu9fHrlKlZPk5nhcpZQG8F6DXAcMSJ8LzL2N4BAlDwxJ5tboS1FyjgYh5BCceZSzyqEdpYO4qSNlCnAA/G/d64F7zz5S6REmu1gvgsyCclHHZez8SYwvRNj8rUP0qkhhAB9EzL9ItJK5ZwqcVDCCM/iwu/B2APGfpyDVBvUCvBedV51MnR3B6eBT9Oc04Zpku5PdE3UR4r4nU2X0h+P8/jbexlJpwh7dW5evJQbmhcvaJV5fjLTL6Wk0J52jlAqQS13Fuz5Vqg9oC1BKNO37Z011kDuNFGZ5Fj4XxC8C2T6pAKJlGnn9QhQy2fdruoDRUQh9F+dllCJf3GuhX4f5CBCqyt4qrR6kcZBkHFQ6xDaEoQSmINBu8w1FcEQlcEQWcvCo6pkLJOhZJL6oiJK/VNTyf/9N+/i2PgYh4aHWFVZSV04wqryCnzTBDK7nBlC6EjPWsjucsIyhUBa/aCEEFrTOR3L8dT53c7/k08BNiL6T/kgtDMi+iDE/xUy/+489v+mM/8n7oeTiMnS6kemXwSEYwE2jyDNUvBdN+cihlCrkN7rcj8zA1BA34ziaco/R9rDkHkJRCSX8yQhcAcSOxdA7bKQeeClP59VBoYQgsvq63mhrZW4kcW0bWwpqQ6FpnW2qYrC5voGlpWVMZZK4/NoVAZD+e58C5EJ58WOrmP5x/PNhSG0eqTVgLQ6AQ8IEycXomjOTuaFUBH6JqS20hELRPAsrudUyG53xAeh5NbGmrPeFDO3wJbmAcjuAqUERInjXJHZszSmM0eoleC/2SkJkuOgLEVozQVreWl2gvEOKNX5zzdp9SOzbyC8m+dq6Gcddxd9GiwtLeOe9Rsp8wf4+x1bAbh73Xqai6NTBImgrvOh5kW83nWML2+4iFg2Q0OkmOtbWijxuxuus43Qanjio9WAzp3P9CCRPH5LqTN5zUHqvRAaeK9xNtuhrwFqrhXqWWh1pIScyXg6e/ZJFr622QHZrblUeQHB30LqV6Fo1Wc+pjNACAHeK5GZXwO5Dwo5Ap7LCjoW2Eabk5WRfBwQELgHabWD70aEmH0bLZfzA0UIrm5souI//pDW4WHSlsmG6hqWlU7fvUYIQX1RMfVuucg5QWjLkSLIE7ceANKg1iO0ZXMvmgrn8/d0S9OmO1mT0naCLTFB3wz4wHwbst912o9aR2d4nURmd+QW7BPuwCKk3YM02xCe5af82h80ilaLVGvA9xGc8pDC3ylpvAfJ7wHacSE9+SQEdaRnxZy3zXU5c04V3jlBeSDIrUuX0zE2yl9/6CNUBILURCJoM7SeFkJQHghSHjjHpb0XMM6a9Eqw+x0xWQQQZZ+cF2snZw48e+8FafWBcQC0FicLI/0cyAHnL5PfQyYfR0wzl0ppgPF2Lvw/t6cL/zHSGoDUkyBCU+Zge+iu4y1Z+y4CGTt+nQ/IiaEUI/SNMz/BOgyiqFCcV8rA7EDqm+ZcID9buALGaVAeDLK5roGdPV1kbQskNEaKuahmegtqaSDATYuXkDZNFCFc18UHiBA+8F2PzGzj8VvCTmCbUoLQL50zO6sQ3rPeClXKDJid4FkP2ddxlPQo+D8G2ooZJyZpJyDzmmMJnnCkiBBktyDVj8/5pkIoEfD9BuiXAhYo0UJFWZqOhU8pZ6IuW6gVSLvP6SLgWTk3A3eZU7yaxrqqatZVza0I5zIVIQRCa5jzDIiJBeQHsqC0B8DqAe8msIedzAtZTD6MeCZkEmQcoZyYGh+B0XuxlSii5CGk2Ysj6hqnvucHgCNazPB17XGmr6m3nBNLV8C4oAjq+rRtU881c5FDMfG15nUGBk4pHWrVeZ8rJs2DjttPqUXKeseJYeUEDE7i9JFpwJpahi0CEPp9FN/1SHsEaRx25j+1EicbaJ4hTaYecOY6sczH8b5P3B31adJSUkJ9URE3tixGV9UptdYnIoQoSLh3+eAQSomzCZYJQJz74M5zgDTeA7LgvdrZ0Iso6FcBXoRn5mR/afVB8l8pDMr8jnMv/QrQ6hz12R4FoYIoPufCj/PhOkN9vEwBhlMqkxu/ky1iQ/gbThiUi4vLGSGlBHsIafWA0BBqHUKZ2W67kHi/wsXxEpTXCx4rpY8hc5t4gQCl1PkPkFo5JH8Mwjf91xUaSMfBUTjPmrlMIAOZ+lmu84fItRUEe+hOQMyPADq1CgL3OEJyPufp94CMEzbn4nKBcbaFCynNXB5DK6CAtgihNbkZM6fCjkPuAEyIIIT/JDdHGYiSh2bOgRA+QEVKs1DEkClQGrDNXsj8H/beO8qu6zrz/O1zw3uVUAmpkAkQYCbBAJJikBhkSSStYMtrZJKSKLvH7el2qy03zbF7uXts96yZsYeibKtH7m63rdASScvdslu2FWgFikEiQYoJFCMAkiCRMyq/d8OeP86tVwGvUAXUi1XntxYWULfq3Xteoercc7/z7W//MHM65+DEvwEMBJcz1sWlIbqV+GdBcSuTXC16ArylLgNjoRN4Ht1laqwdZ4amJ2ymgbSAdM+pHk9E5hyS2dAkb9tQIYzN1ACItkFwMSe3o5otShrvgeITWTcQoP0zkLsOMZ2VGfdckRzlu7okWWK+w+GYC+PBXy9jlwaK8jyauxbTjJ00aoCYNrTcjpaqFYKne53kUH89JDtRltmWggOfBaJSNxPSE5w8nxeBBsgtAsQ/G413Wns1KTbn6Sjk3u0esBw1p1wOBTSuG2ImVBUt/ASSd0A6AYXij9H0EJK7qt7Da2y8VfY+5k18WB/rojT9/CkSoMFFNrvJ9AKhffAXwFsPxUdAOkqbo0oARNDxu5jwEqDOwkWG+OvQZDea7MXeyxOQPBJuqffQKooTMBx1QzVBiz+FZCd3/KPt6vLAhy6E3LXzpkar8oRYG9gUxHCqFlniLUVbf8XuEA5+3h5s/zTocVRaYPR7mRCQfd+1aDMp8rc1xGJUJET9TTYJfywDo+1/szbsSuSKOBwLnfSIXfSZZdz5jzsBuP/n10LxSdRbvmDn5FOWoJilYLrQ5HCWtaSQHrY7XT0PnnLulPBStBhDvCvrJhXbQMwSHtJxNzDmNkug/bcwuWsq+wbPEDGtkH8fGm+H9n9lk/D9cxGv/mUEDkfTkx6G5G3EW1E6pNoK8Q402ISY7joObpyGcBxMQfyz0eRN6zyWdqAArZ+A3E0zbpCKfy5KCPHLoIfBrLCl4BKg6QDiLRt3nI25mU/8LqnpbZjvgUgAufdkeSeHQdoRf8W8u4c7AcNRNzR+A+LtWVKuDb4h3YdGLyLh5fUdXKPinwOtd4JZDoN/ao+13Wm7m5wi8V5MOxpeCcWnKQVlpscgd629UQ5/CQjGJ+Shv7RfF149fVlHjZHgEhQf2j5la/zEQ3I3I86B4XDMGU32Y8Maxx+6RUI0je3OegPWTWs6aMcmIZglyClcD3Ol3OLUBuPdaJ0ryS5ArM07vGRG4VckRHLXosElQBFaP4pISHrkdkgPl8QLyFoepsc4Zf12HRDThoSbgc31HopjgdMsORSzRdMTTP19FxFUxLYtbhABYyKqsd31Tw6AaUO8NYipvSO6JK5GO7OxLEOCsyeFwk/7WhEk2ADB5Faumq05VeMyL5r7vFxpIWgh5J04AcNRP+LXuePb/QhDbN03CMAd31IeuG0HGlzaEDv/U9F0GBi16fF1CL4UfzWqm22LJCJAwVuLBBfN+FoTnI16y9GxYFOzBDFtpMUXT/GqpFJDnzO25dbFaHA+dlxh3dp/ORzzDvG541sHETk+Ph//w3buv7WDaj44q6rtOpTaMkI9/hlmynmw5S4v2sR4EWtKM+2Qu2HGNtWaDtukeGmZe0tr7GJZctegam3dpyuiTF3gS88D6Og/oenxkjirmoCOIP7aOY/X4XA0PmJa0HJuW6WU71AtNB1A4112zvH6wOsrO69N7rpxp81Qa73dloPHEcqLti30DK4sTY9mpWgB4vdVpDOKSAsSXghMnw13eufzUf9ce89p/wwiXqnsT7r/yrY3ddQUJ2A46khC+VyDrJ62imjaDxqBWTSrdm+qMVp8Fvp/D1u+8KtocBHinz/jQ7SO9ZGW1jmLMiKChBehwUbIvzdbhM9e4RbTfvKC2VuOtn4yc3X8iT3Y/q/tQ0UDqvzWaeKmLoejkohnu2lNzHRQja0wUKVW1HZe3QrxriwkXe0ieJrsnbFdKun6LEQvTO5znx5HC09kbZVPnpOt6LENopcAAVHUrEFyV1bEWlsp94dtK30NWvgRmu4fvxUGl8BJXUscDsdEmt15UcIsBdOBpkdtWPtYxozpybqxVQcbVPkwdo4MbEcPb5XNRDuFy9d28ChOLnlJh+38Pk0p8uTcJWPn5MhDw/dg/MZzDkhwgRWV4les8N72v0Jw+ZzEi3Ih0Y1SitLouKcAR/3wzuKB2wYRs5w7/sGWLtx/aw94K6pmBVYdtcFIA/+PPdD2z9FgCyZYd+rXRS9D/DrjLUh7ofgcKh22VWDZa0Vo8TmbIC2AtNlr+XNv+SiSnxJQNAfMYgjOgeg1bBCo7URAeHXd26s6HI7aIKaDB37xw1B8kjv+0dpkH/jgaiR3ffXm43gnxG9l4unn7MFkOySTLbVTF3l67J+DFpCO35kw/q7sgX8Q5OTOKRq/bQOPM9HDdlx5G41akfCyqry/M0VMh+2olR7KhPbuulixGx3VFDRr5yodzpHnmDfYHIMb0eIzkOy1B/01SBXdyaopRE9kG3tjLohuGwgZ70EC6wArzceZ+wLpAC1A27+a/B5MazYnD5Wdk0kPl3KXSkK0jkDxcdT7yKkFkzpgXcCXoMF52aZkS1XLFpuZqc5KTG/Ff24b66fDsaCQ4Bw03Y8m+6xLAcDkkeCSql1TC09l/aDHhIhFED2OeoumrY9TTSF+FYYfgGSHPTj4p0Bqd8SmEzCKz0KyMwudBNr/JRR+hJpbGiq3QUQguAK8NWhwAeAj/uqGGmOjoJpiM0TChixxcjjmgvHXoN5yHvzoUcAD01PdBVq8wz6ci5Tx3CnlHXqz+Vy5a70+qT20veYSiLejwcUNuVhuxNyRRkGTw2jxJ7a1NkD7b0PumnnT9texcFFVbKlyiMm/p7Q+rnoIo/ZDOop4Uxxw0g7pO8A0JWz+eZmQODkfwj7EYttGl7tcshdk8lpKpAXVE9aJ5y0+8/dSRUTCrJXq3DllSHSTohpZN2TyTlbiqeAtg9z1Fd0Ubaw7tmNBIRJC7iZID/DALxwH2rP6t5lLOs4ETQch2W0FhbGwysHPAzHqnYvkprNJJ6Dlyl0EGCl/LR2xzotJ1/pzIEKDcxpvx08EvGWuju8UpNEbEL8AqV1YaHAJ4m9wu36OpkbTITR+JSvjyIN/HuKfVaOf63EhYnLHjQjp+W8l8WTqIk+6/hgdfRjVdEIJyYBtN1hupw+AYpmwNZPN7WVaoZYbraZA2nBix0LDOikfBvKMd84aRAuPQP5WJy47mhZNjqDFp7NwYoN665Fwc406SGTts1WnzP8xE9uPlnvo1uSgze7R9vE1fHrIukamzbQQ0HJtqLPPzQKbD9QPBM6l1iBovP3kDjrJATT6WUUbNLi7sKPmaHoCjXdAcgS8xYi/EROsmPmFcyam/KQowOi0rxIJUG+JrXcb+kt7rONuNDkIXnn3BVqkfFtTYy3OjqYijXdD8ce23Gb4LwGF1jtQfGSG8iOHo1FRLaCFH1hRbvjLlH6uddC2jqs2/nooPgfehLK61rvAX3Vq54fpg2ATRDuyFqQKksvKXaZZ+HprIH4RZIKrQU/YpPYZHg5UU1sLHr1kW0x7S5Hwslml2jsqj8Z7Yei/Mrlz1hexnbOuBNfK1dGEaDpo52NyNptMU0h2oMUIyV1b9euLaUe9FZAcLLkfVCPQ0Rnb1Yu3FA2vgujZTOhVG/4ZXjH9a/xVaPwiqnFJFNZ0MMtdmjl/LY33QvHJrJwjRc1yJPeuioSA1pr54LwoEe8A0zv5mFmctQCuXAmUEzAcNUXTo+jo9zMhwIPWX7FiRv591S9ZkA67w9j+GzD4hezQ3WiyD8zqU780vNyOmwgwaHoATDvib5rmBW3WNtf+6ZLF1bbC228X3zOgWkCjV7P8DN+2SZ2hVaqjikQ/syVEmPEF8/D9NmzQCRiOJkXjt0GHbG97G9Rj8yiil9Bgk83aqSLib0QTW0ZoBd8UTBcSlG/LOWmRF2wB/2w0OWoFiBmECAk2ockeey3JYefysOyO0FQ7r+14ss0G64kP6QA6+j272+9KFupAYZrjgv1/dTgszWTN1+Rt0ATx7JwiYlCWQbzLOj5r4DCQ3FVo4Qm7VgXAh/BdiNd70tdO/Z6aYCPqr4X0hBWUZ+jyJKYHDbZA8Zms8E/BtCK598z4kKtpPxQeAdNp849UITmMFp5A8jfN/g07qkC5Es9so6GCuKchR03R4gtAkP0B8Raj6TE0eqnqCrOIZxXiwiPYRY7YGjxvBeKvPPVrTTfkb7UZEekJMIsRf8209VwifjYxP4Z1fhh7Q5CuGVvhqSbWCpschuHsBtF6B5oerYkK7yjDWFDcJIx9kDnJbulwNAnpESBvyzZKpW5/CkSQf1/lgoKnwQbV3QDpITQdQEwrmKWzEmpFBKRn1i4IkRzkb0bjfaCHbeijv3qSSFMuER5SaPkQmOXjrhBZZDMY4jeQsHqZTY7yiFmcdc7qGw9/bf+MDQWUxuuc5XDMinTgpBapIoIKNiSTGggY0oLkb0LTExM69c2+fMWKybPvkmKCTai3KiuZ8e3aehbzv8a7AFOav20Z9GKbq5eeQKbpZOWoAd76k92O6WHw11e0vM8JGI6aYRXSfZNyIWy9M9B2V03GYPwVqLnNChE6Yi1u3orZLZhNG2LOn/21grWoaUX9DZAOWVu0f9bMN4P0ACSHEK8v2xXFLtTit9DgAheuWQ/Mcmj7Z1bpL/3M/hoYl3zvaGJMJyRvlv9cjWy4IqZm+TsiARKsAaYp/StLaitUzJSSFsnbRbej9pjF4G+EeDul4MD0AISXWxHM4SATICeJkQ3uxDBLrf2e8TWeagIYW1ZRQ2opAFjh+jR/b3Vk2nBQ1Lmw6olt0HBoirOyFwkvquh1nIDhqBkigpoOTg5MS08RvFaFcZhFtanvBsRbgpyGGg02I4Thr6AT63sHPwdEkHsPOAGj5kh4ITryPduTHcUGu44gwXX1HprDccaIt9a2iG771SxDQKH1dgjOXZAPgmXD6TRCR/4O1WhKwPQQeGfXYZQOEYFwC/irUf9swCD+OsRlXziaGJsJ0Zu5dTvtg7gOQHiFa2k/BfFWoPHrwLjjSjUCfJihdMVRXWyDhhsmOCvbMmdlZTuaOQHDUVv886H1Dhh+EBBo/9eQHrTHHQCIdKBla8X09FVqR0UQ0wMt70ej122GiulBgnNtaZHD0aSIaYX8e21pX9vHgQCC8xD/3HoPrWEQCdBgMxS3otJl2+elJ0DyiLeu3sNbsFjnzopJSfcOx0RM79eaw3mRIRJA/iY03mm7Qpl2JLiy7M94s7yvqo3T6wNvtc0NkXasE6sAwXU16tjiOBW1cFY6AcNRU8RfjxJl9uQxdflqTHDqXIgFhbcsq+cdguGvAAptd4JZ6ep764iYLiR3Zb2H4XBUFDGdSP7dWXK8zKuSqDNdPJ8cTreJVFogehUYgmA94i9Ml4rD0Uw0+gP+VERCJDgPgvPqPZSGRsSD3LVofBaku62g7K9znaEWEE7AcNQUEUGCc63tUwt20qmwrajZscF2N9rk+7ZPAJ7tQhKcP68eLhwOx9ywokMBCObcoaiS4VrzEeOvBv/U3aocDsfCRdMBILIBwZPKzSpHuaDhRhVpqp1BIuKfQaaRY77gBAzHaaEaAwkQzulhWsSfPoDHgZhW285KtzDfdkUdjkpRiGMKSUJrEOCbhfUAnsZvQ/QspCMw/GVU2pDe/+GECJprke9wNDujcUR/oUDO8+nMV7dzUSOiOooWnoB0f9ZBMkDDKzG+e7B2OKqFe4I8TUaiiFSV1iBYUA+VqokNe4tfAU1sen14hQvNqjLuYcThOJkkTXnhwD5ePXwYBULP4/LlK1jfszDso5rsh8KjNovFW4TigQ6g8WvWfuxwOBw14OWDB3n+wD5QSFFWdCzi2tVryPkL5/FCC09CctAGFRpBtQiFx1BzS8VLGsoFDTcqzZZB4mguFs4MM0eGo4in97zD7v5+AHpaWrlq1Sp6WhZGDaxGL0L0End8ewBBuP+2dnT0B9Byi2vr6XA4asrPDh7gpYMHWdbWjmcMhSTm8Xd20RoGLG+vXUejeqHRKza4bPALNu432WE/cfwz6OLvLHjhs5kW+Q5Hs7J3oJ+n9+1heVt7yQG3f3CAn+7by7WrG9d9cPs3vg7Agx/92JzPpekgJHvBLCttaoqEKAEav4WE1RHV3Zx2atzcP/9Z2KucWaKqPLrrTfYNDvLVbc/z1W3PMxrH/OCNNyjEcb2HV3VUixC/yh3fHuCpfUNs3TfInd/ayx3fOmLTkh0Oh6NGxGnKK4cPsTQTLwBynk97EPLq4UN1Hl2N0AGQclbtFJvG7gC7eHULWIejOmw/cphFYTipfG9JaxtvHT+2INbGloiyZb4SgI7WZUSNRKPOwemRj4+XGjqaEufAmAVHRkb4f3/8GKHn8frRIwD8l2eeopgkXL5iBeu757ltWQuAIkwtmTG2nZzD4XDUiDhNSNL0pMyLnO8zWCzWaVQ1xvRB/CbScTcAOnAfkEL7p4HqhMc5HA7HRApJijfF7WVEQJVEy7WCry9jzoute3ZP+nhOTgxZZDMvdBSZKCrrkO0c56g51Q4PdTQGTsCYBYWkvJIsIgxFUY1HUwekBQi4/+fXcuc/7gLggQ9uRNMDYKrX49fhcDimMhYUN1Qs0haO93s/URjl/CULI5NHgvPQZBeaHAbTjnVdpBBctqCymRwOR/1Y19XF1t3vTJqHBwoFelpaaVkgGRgiHhq+CwqPoPiZ82IQvJWI7wSM2ZIeuBwAs+yZU3/dHAWJcgHPczmfo34sjBlmjnTl8vyzSy9naVs79z3xOAD3XHM9ewcHWNI6/zMwRHw0uAyKP7FdSMSgyUEwrUiwvt7DczgcCwgRYcuKVdz+ja8jIvzmle9iICrQGoRs6l1c7+HVBDHtkP8AGr8OyQFY9AdIcE7FA+MczY2mR9F4N2hkH6Ym1Ok7HHPlrK5udh0/zt7BfvJeQJQmeMZw3cp1DflzNua0qGQGBoDxV6LmVjTeBToCZgXir5hza2vHmdHo4aGqI2j8pr13m27E34CY+Z/dVWncb9csaAtDLly6jBcO7CfRFBD2DPSztrOLpW3t9R5eTTDBetS08sCHXpugLm9CpKXeQ3M0CKoRGu+BdC9IG+KvdQGvjqrwmYe+xVsnjgPw5z/dSuh5fP2XfpmWYOGUT4hpR8LLan5d1VHQGKR1wYaFNvLieIw02gnRkzD0FUDQ1k9AsAmCLQ35cOloPgLP48az1rO3v5+Dw0O0hyFrOrtoXUDz8BhiupCw9usdTYeBUbvmklzNrz9XxpwX6EDp43IujEo5J+od8KzpEFr4nhW6aIPkkN2IyN2MeL01HUuz4wSMWXLxsuUsbmtjXVc3cZqwrhZ+fMEAACAASURBVKubNZ1dtt5vgSDecsRbXtNrqqag/YDnFMoGRjVCC49YRXn4q0CKtn4Kzd2A8VfUe3iOeUx3ixVRF5J4UQ9UC2jxGYh3gShIB4RXuVbaDYhqAaKnQRZTykQxyyF6HbyzwFtS1/E55g++Mazp6mJNV/NsVlTKeVFPVBM0ehairAOVCOpfiAQXOIGSxhWXNX4V0iLijZXft6NpPxo9h3jvrevYmg0nYMwSEWFlxyJWdiyq91AWDJocQos/gXQIBNQsQcJ3Wfu0o6HQ+B1IDiBeH4oHeGC6oLgV9T6EiFfvITrmEQ9+9GMVtwE7To0WtkK6B4azhWHbv0BHfwgtty0ocbkpAuLSYzD0V0AIyXZ7bPBPgAgNNiNOwHA4mhqNXraCpFmGiEE1geg5VNqRYF29hzdrxtwWM2VgVNo5Ubc5O9kDZvJzpJhFaHIA1QgRtxEzW5yA4WhINB1GCz8E2mD4fnuw7VfQwuOQf9+CtS43LOluGP6qFS9KC+YvQOvHbcmRdNZ3fA6H44zRdND+jst4aLOYVjQZROO3kPCiOo5u/qPpABCBdMxygXsKwVjC6T/ncMwT7r7x9wG47+E/rPNIKo9qCvFrYBaX1sIiHipdEL8KTSRglPDPm9WXNZJYrGm/LQWRDsTMNg+xJXvN+DysGtnw11PN246TcAKGoyHRZE+2gxSMPxAPfQkoQrgFXK1Y1VAtQnoU66LonaVY1AKUa5umuLaOjmrgnBc1RAuAwODnSvOxbd0aw6L/UNeh1ZpaBsSpjqKFJ22ukIpt1xhswQRrZxoktP8maARDf2mPtf8GpAOI50r6HI7mJgWKnPQIJyEwUofxzJ1GEiZmQrWIFp6CdBeoARQNzkeCi2deLwfnQeFhVHOIBNY5kx6C8FK3MXuaOAHD0Zjo6DSfEGABtK6tE2n8NhSfhKH/ag+0fxpy754xjFOCDWjbJ0F6YfA/2oOtnwB/1Wko0w6HoyExHdjdoakiZWqzFWqMDQzeBfGbIB74ZyPe6prVftdqsa2FpyDZb23iRqy4HD2Oeh2n7DgjYiD3butYJAIUdDiby10JpmP+Mua82PbIy6WP55sLQ8RHTZ8tFZMJa7P0mA3qrQOqBTTaDskuIAB/ow1yn4cP5Rr9DNK3QZZn83IK0YuodM1YvmP8VaR6FUQvoGkMIhBchMzSgeIYxwkYjoZEvCVo6yfB9NldP4D2z0B62GYr1BhNj6LRS5AcBNNlg5JqHGhabTQdgMKPwXQDmb1NFS08CvmfP+WNSEwPGlxng+PGFszeCiTcUouhOxyOKiIS2lbarcUspFeg9Q7wliI1DulVTdHCjyHZbUvTNIXCI2hwERJurulYzpTZODg0HbTvcULrU5EQ1TAr2zl1y1wxiyB/C+SuBlUwna6to8MxT5DwUnT0+2h6EMgBo2DaEf/cmo9FNUJHHwY9agUVLUDxcVRPNM2cPFtUY4i3gyyZMC+brHzntVmV75hgE+qflZWS5BFX1ndGuLuZozExS8E/C+I3gARQSA9CeAUi+ZoORdOj6MhDIDkY+iKQoK0fR3M3YvyVNR1LNdFkNwx/icllO3+BLdt514zJ9SZYi/orIH8TELqdPodjHmGCs1GzCB35G9DEdiDx19Q+dCw9CMmeSaUQqm0QvYz6Z8+jeScGTBlXSZC14JsZEQE5tdDhcMwnxtwW8zkDA2zbVvK3oskuSI/bPAx/zaRWqjUrdYv3QXoE8fqywYFqSzYnb5pnLly1ovnUDT3xOR13uEiQ5V44zhQnYDgaEhGTPTSvRf0NQID46+qSnq7RKyAhYroyA7VnVeboedRbUdOWVaox4FXnmhqf4pPJrE5hJ2W3YHY45iPiLUUW/31dx6DJYabm6ogYFMlabldWwLCZQAPZPaAjO5ag8RsQvw4k4K1Hgk0z7qSNPVDMqouJdNjMCx2dLNrrEJhL5/y+HA5HcyOmFTENUHqgh+0G3wTsnAzoAFBZAUNVQY9ZId10luZd2470JdvpQ9rAPz8TdSq3XhYJUG+5zYmT7gmDOgZ+/cKsG7YjVhVxAoajYRExNkPBX1XfgSQHYeiv7AK55Ez4T7bDBjGVDKlUTW2ZjI6CWVTKntD0KFp8DtIDQM4GBvnnVLS+ULy+rGxnedZyD5uBof1winprh8PhqBnShp13p6JYK3V5zmSBl0bbIXoWG5qnqFmF5K5Ci89bG7HpBgKIt6HpPsjdVLGW0SIeGl5ty2Pw7W6dDtnSvHnk/HM4Ks3dN/4+O59/iw2b19V7KHWhnFBa1QdbWcRU94GqgihU2DGt6aDN9kmP2vwIPDS4CvF60dF/ysbTmZWxPIayBQnOqegYJLwsK985gHXEFWzgfR3yR2r+f91AOAHD4ZgJ041dwE5cmKZg2qjkr5BtHfsoDN4HCLTdhfqbwDsHCt8HAhi6H1BovR3VIhJeUrHrY3ohOB+iVyjdjNJjkLvW1eg5HI6GQPwVaJRH0xOI6cxE3yO2xK2M0HparocJaHIQilvBLMnS4hWSPejoo6CHwPSN7+zJcjTZZwXmU3T5GLvmbMdg/JWoudUGluoImBWIv8JlWTgcZRgTLoZODAM2yPMj3XexYfO6eVtK0giIvwqNtqGlUNE0myPXIKazYtdRVZt/pEOlDDrVIhR/jHprQWPEW5oNqhXVAKJtqL++oqWOYjqh5TY03m03+GQx4vfVvpxygePugg7HDEhwAdr6cavqDv1nILUBdv7FFbWmafQspP2UAjRlGUSvQnw0a4U3IZti+K+hLUCDcyfVPM4FEYHgUvDWoMHFID7irSrZph0Oh6PeiOQgfxNafBpN9gMC/hokvKyy83G8A6S1tCgVEdQsyebgNsSfei0fTY9XvE2pmC4krH1wtcPhaE5OVyidKyJ5yN+MFp+1XZNEwD8HCSpcUqEnsqyN8QB9kdA61OJXbXbepHEFtuxaCxXPmxDJI8HZFT3nmVDr/+tGwgkYDscMiLcEzb0Xoueh7U5bm+xfjAnWVuwaqkWI34Hhr42LFIN/AiQ2SZ4ypSKKLTWpkIABmYjhLUa8xRU7p8PhcFQSMV1I/udQHcEGXU4/B57xAk9HmbpEsnXdAeVLWBJmm7+xkBaZjoXB7d/4OgAPfvRjdRvDxADPsRIS57yoDWI6kfyNqEaAVMclptNksYkHtGThxm3jX64JNrOucmtkR+PgBAyHYxYYfzn4H0A1rVJfa83+TEWsYNJ2F2KWoQP32cPtvwl6HGQ+pTs7HA7H7BFpqch5yoob3hpItjJRlNB0yO7ySZutf5bFgNhSO9OB+H0VGY/D4XDMlXoIpVUtozCLQEJUCyXR2gZ6jkB4FUTPoumJLJOjaPPkwsuasrTjdAX3hSiKOwHD4TgNqiNeWFu0esuh7VezVq0gHXejyV7wN0OyHU0OURI60gMQbmnKidnhcDhqzeku8MRfiyZv2mwLaQEtAoLkbwTThUYvQrwDSG0JS3CJm48dC5Lbv/F1tu7ZXfo3NIYTwzG/EAmyYOPHbKi++NYp569H/A3g9dqA5XS/3dwLr0L8+pd5nA4LOZTzdHEChsPRIEi4BS08DNiFsiZ7wVuDhOeCrrPtXNt+zU7MwXmIt7reQ3Y4HI6mJj3y8bIBnyIB5G5A473ZgrjNtvI21pEh4eVosBlQF6rpcDjqxt03/j6wMIQb469CzW1o/A5QsJlDZqndXJQeJH9TFZ3SjkbC3XUdjgZBTIfNuwguRXXUJh2bJTaXQtqR3BZgS72H6XA4HAsCkQAJ1gLl844q1TLV4WhmHvzoxxrCeeGYmfkQ9ihmERJeMP3nm1i8WMihnKeLEzAcjgZCJAB/NZXL0nc4HA7HdJjer7nFosPhaEruvvH32fbIy6V/w8JwYjgcTsBwOBwOh8PR8KjGkB5A0+HMoba4IrttTrhwOOaGc140PtOVy80F1QjSgzYjyHQipmfO43S4e9JscAKGwzENqgrpPjTeads3eesRf6WzDTscDkeN0XTQZgSl/YBBScBbDblrXHimw+FYkNz38B/WzXmhaX82Jw8CBlA0OBsJrmjqMg5Hc+AEDIdjGjR6AaKfwXCmhLZ+HE3XQ/gum0vhcDgcjpqg0fOQjiLeeKtSTXaj8ZtIsKmOI3M4HI7Gp9LlclrcCpqW5mTVFKLXUNOH+C5k3lFdnIDhcJRB0wGIXgazHMgcF6YP4jfB3wjekrqOz+FwOBYKqkWI3wEzPu/qwH1ACqYbTkPA0PQomhxFJARvuf3b4XA4mpR6ZF5oOgjpYcQsKx0TMah0QPImnIaAoToCySH7gbcEkZZKD9cxD3EChsNRjvQ4DH8JCCHZbo8Nfg6I0PAKZJYChqbH0Gi7PZ+3DPE3lNrwORwOh2M2CNMnG8/OqqyqaPRTiF4HBAWQHORvdHXbDodjQVCbbIXZO5TT+G0o/ARIspd6aHgNxl9TsdHUMqTZBULXDlek5HCUY9pdOQVmpw5rcggd+S70/3sY/BOIXkVHH7LKtcPhcDhmhUgA3lpIjwCZ+yLZDslOOPFvSQ9cbp0V6dD0J0n3QfQamGWItxzxlgMBWviJzTtyOBwOx6wQ0w5mMZoeKx1TTUEHEX89qgmaDqBamPYcmg5b8cJ0IV6fLUWRLig+YV0ZDscpcA4Mh6McZgm0fwZ0BIa+ZI+1/RqQgtdDGr0FOoR4vWCWlg0s0uKzIG2M/ZqJtwRNDqPRy0juypq9FYfD4agWqgo6DBJUtRxDwktRHUSTfUBxyiCK6MhDgKL+WiTcctJYNH4bpNXanAfus+fsuBtN9oP2g3RWbeyny9RdPLer53A4ZotqBGjVy+MkvBotPJzNyVjjRXAemsZQ+CZQAAX1NyDhpSeHLaeHQdJJ4xQJ7euTQzBHF8bYvFnpziunvF6NruVwAoajyVBNs5ZNIyDtWRu9ygdqihjIvQctPgWtd4IISB6C82H0ezD054CgrXeBtwJy102anG1rqaM2ADQrQbGLZoX2X6/4eB0Oh6PWpPF+iJ4GHQQE9c9Ggkuq0hVEpAVy77WL3tz16JGPYFfHmeti+GvQ/m8g2YUWfSR31dQz2K+fgHXDjXI6lmeHw+FoRFQLaPF5SN6wU6O3Egkvq1rZspgOyN8K6SHQApgu0AQtfBekB5Euu2aPt6MIkttSbtRT3kMRKKKqpVnZiQGOcjgBw9E0qI6go4/A4H2AQOtd4K2qWhs9Me1I/iZrcyMFaUMLP8BOuFYxFq8PTfag8VtIsHHCqz2QgMmTcwQaQTqCauRa/zkcjqZF0xNQeJh7bvkxiMe93/0gxK+jGiO5q6tyTRED3lJ7fcS2t570eUFZCvEbaLgZkdz45/y1aLwdHfjieK7RwB8BgpqsHatps+fWyAojkkckX5X3Uo7pdgxrtaun6Ylsc6DNPpw4HI6mQFXRwmOQHLYOYhFID6KFH0L+lqqtN0V8mNAZKi1sBfKluVfEoGYZxDvQ8OJJczJmMajJRAsg2g7pgWwO6iLt/9+BiRuDBayzZHZzcq0dbKfb5WW2X2tdjv1AArLIfs8dTsBwNA9a3GbDMCeJB7vReAcSnFe164pptdfXUUgOwvD9U1wVKZilMEHAEDFocAG0/BIMPwiMQjoEJKBH0dF/gtyNiGm1qnm8BxhFTC+YJQuih7Ymh9B4F5DYlltm+YJ43w5Hrbn9G18H4MGPfqxi59T4De655TG2PW7T4+/5wD8Ayr3fFjS8ZM5J8qoRJPtRHURM90mlembZM6QjP4DBzwIG6bgbGEvCx4rFExbLKouAsJSjYb/Yg/QEDH4Wlbsh93NovBOi5zJxRDP782XzetGoGtuWiP1/YA+0fsq6acIrEPHqOjaHwzEL0qOQHMyyfTKkB032o/E+JJh7KKZ1Fh8EjcH0lBc5dYixNXppGGJQ0ZPmZCQPZjWMPgTpO3bTzyyHcAsM/RfrJJl46sMfBBTt+B0kvBIxi+b8nirN6QgXsxGmNR1Ciz/JXC4CEqDh1Rh/VeUG3aTM3zuyY16hmtjWTMNfLSMedMIcBQzVBI3fgPh1IAFvAxJsnFJDaChvNVaYsri1pS5DkByD9BgQ24lbFiH+uWhyEI1fBn89OvpDO1kjaOsnwVsHuXfN64VjGr0KxWdg+L8BoK2fAP9sCK+qSkmQw7Hg0cQGrsmiyswtOsDJ86HYP1qAOQgY6ZHb7bzZ+gnAR4ntLl/u+sk7if5qIGbiglnTYVteKK3jx7QIhYexTroWUD/LvAiAUXtvGbgXHfzP2fsKkI7fHrc/S4iEm8/4/cyW6XYMq+68iF6B+C1K30ez3L5v04kE51blmg7HQkZ1BFRLG2RzP+EI5denHnCKcOPZnj49io7+CHQ0q8ZTNLgEE1445XJ9kD4HtI2/VkftfCyT36tGz0D6JvgbIDpuBYzioxA9m417Ktncn/ajhYczZ8nMOR/NWnqiqpl4cQIxy7NjBSg8iprbENM4uU31wAkYjoqjqta6luy3wW7eqoZUSieixacg3mndFQi0fRxN90HuptKun0iI+mvsonr4q/aF7b9l0+29syefL94B8SsQXgKjfwd6zAbd6bFSgBytd6HJIeykPLZw7IPkTTRejQRra/Lea43qCBSfs66VsSnI9EG8A/z1JYu4w+GYO7f/jwfYundf9u+vAYYHfvGjGH/5qV84E6aPe79zDffcaneRPvvQh61QoINZePEcSAcAtan0GZrsO8ltJ/46tOMeSA5n5Q8RkCAT5m0AjffZRaC3HB0ToiVkchio2DmIUfuasXm6/TMQv4YGF9St7K/qC/D41cxZuMN+PPg5QMF0gBMwHI6KoemgXW8m++3H3rLMTTDHki3TAaKoppOdrJJYB9scUE3RwuOAX3J4qCYQvWDH7y0Zv5y/Ho3fQNP9QDtQBC1C7j2T5+S0H+Ltdu0nJyDpBa8Loq1AjHT8DjrwR9i1cQEISy47IHOW7K+Is6TWzFqY1hOQHJrkqhHJoXho/DYSXlT1sTYyTsBwVBSrGD4F8XbuueUngHLvd96N5q6dU19nEQ/1zpokHtgE+b1WvZ00hgiQWVt+NT1urWqmj7HOwmKW22Tl9CBMnDzCy1Edxi58bY0hwQW2BGIi8asw/IA9nx4sd1VsOclxGBoP+rQLxwS81TBPBQzS4zD8RSCc8r4jNLgUcQKGw1ERVBXV/tLHIiFKAoUfoebn5xTuNpYpgRYBz86jOgzheCaRpsds9w8dBa8P8VbO6P5Ij9wJyev29RO6hWC6IX5jkttOJITcjWj8DqT7QdoRf93JgrkeheGvoPjWqmxfbf/yzgI8aLsLhr5sW7NOfJ/ioWkKJEysx64mtdwxtC1kE8ru3mpUs3E4HPMd1dg6B7QAZpk9mB7P3AS3zqlMTUwn6m2wOUTSnWVgHLeOCJPlBmUh+Jocsh2Z/JWzy5PQ45AOId6y8euJh0qIJu9MFjAkB/n3ovFbkOy1c3KwATE9k8+Z9lvzsjEorXb6Gfk7WyrBoaxV9j67qaXlnCUGmOetVjUub6oR395TFzhOwHBUlvTABFU1W+yZbihuRb2+Oe1gSXhx1nPaigea7ANvDeJb94Om/WjxuWzSFNTfiAQXzWwxSwftwnXCA7VdOEdoeNkU9TNvk/CDy4CCtWOXewjQAmNiyDihTWlu/y272PY3QfzaNG92Pgd8nmLamVgf6XA45oYe44FbF3Hnt+0q6IEP2pweTQ7ahac589I7kRDyN3PvD86CeDeYNsQ/uyRApvHbkO3aIb4tSRj+a9R0YnrvP4P3kp5UqmfHESDBemD9KQbbCaRTzhdlxyL7x78UOs+HE3djS0juzi47ZMsUmZ9zk4hkmwN3wfBX7LGOu9HkoH14cDgclSE9BOnAlJyKbutWTg/ajnZzQMItqFli1+DEEF5m52QxmXjyY0h2AwFIgh7/Mmq6Mb1/PYuza5lj5ct9RXJIcA4E55SOneQ2mFBiKJJDvbV2zKXLjYK3Brq/AoVvg0zd2Irn7CypNzMK1caWOaoWJz/H6Ajir5zz9Zu9u4sTMBwVRZN91nkhAdse2wvAPR/4DmiRzz58A0xQak8XkRbIvw/CzVkdXjuYXrsA04LtEKKpbacH0HYnmg4i+ffMcOLparUVkZNtfSICXu+pz+mthda7EG+xFUN02NYoamotcmY1+GttsnDrp2D4y/Z1mbghNVo4ajpgxybtpQT+qmN6rS1bR2Hoi/ZY278AhpE53sAdDscENLY7cVOp0A6OXaied1IGkWoExads0JuE4+UYyXZITr1wMr33kx7+EOjouIigasvwgmvObJz+KrT905COZg/pCuHNVkj2em0afrwtE6Y9oJjNjQUgRXI3Vz2bR9NhNH4Vkl1AHvxzrZukBplAElyIpgexmwPGbg6YrqqGYzscCw4tTPMJOcXnZo+IsWJucPL6UeNdkOyeXJaHWCfEjCfuAtOOpkMTOjWldo72Th0mWQqsnIrpAW+JFUrNYrsmbvmUdeeKBy0fti6V9CD450D0Miqd2O/VCStumMUzj32OqEbWTRLvAhMi/sYscL4G87IEaHAlFH9s3YPi2ecIb53NKVrgOAHDUWGmtg6dQAWC42wbvZN/cTXeA4N/zqSyhKH7gaJNxDdd05/U9EDHv7XukaFsQd36KTB5mGCZO61xBuej6X40PYAN8PQh9wHrwBj9FrYG27cLZi0wuSTl4qpPTqqRLfXp/wN73dZPocG5SLC56p1ARAzk3mN3A8betyRIeGPlAq0cDoedbzDc//Prxss6VEELk3cBK40OgEaImTlgrRzS+9/RwmP2QRoBUfA3If5ZZ3Y+CSF3ExptwzouANMGwZW28xNZ0FxxK/R8DdLD9n4gixB/fdUznEoCfDpkHYvEUHwc1QEkvLiq1wbsQ0n+/ah/sX04kC7En5tj0uFwTMF0ApNzKmwJVxZGX02SXZBtyI0LyrbLR3rkdkzvg9O+VMRAeC1aeBhNBrKDZGvV8puSUzttTD1uer8GuevR4vM2RFkZD/qUvBVVNbJzcvhuCN9jM3o0heB8xF9b9bWqda08avNKZBEkg2j8fetsCS6o6rXHMMFa1FuUlWIWrPNijh37at2eu1o4AcNRUcRfzb3fuR5Mj3VeAPd+54asA0cV7V46yMklG2DV2lPvNIoI5K5Foxeh7ZNACt5KJLjkjBdwYtrtgjB+BxZtgLQAyQ5bNz76PftFpgfin0H+g0h4BVAE6ayJE+Lk1PmlUHwOVYHwglklO88F+/15H+Tehe1t3elaqDocFUYkRIMrIHoS1RDwQYeyHZwzE2dnh29Fh7FxjDkpBv4IpHXGhZJIDnI325anOgqmY86J62Lakdw16OKH0PQojD5UEi/sNfMo/ZAexATnAedMf7IKo/E7oAMTAvIU0iIM/w1pehgJLqx6NpBIOG+Dox2ORkBMN+qfneVUZKKo9tty4mqujyErv0un++TML/d6oeVDWfhokrVRneOcLHkkdzWql1tHx+i3oOOe0vpTJEClC+JXMC0fgFoHdib7Idlfcq2MuU4Y/p+k+SHbqbAGZSxiupGwuctlqoETMBwVRUwnmrsOik9m4W6AhEju+uparkw3tH4S8fompMf/lnU0yMxBdSI5JLwCDS7Flo7M/VfD2qttPkda2AqDf21tYKXgyv8IREj+JsSrvhVuDNsacErq/MD/le04dqEdn0GDSzHBxqqOQ0Ssqu1wOKqGCTagXre1wWrRWn69vqoKhmIWoWYZmhwuzW2qBWxq2yxC4xgr1av8vGjvQ7G1T5/0SW/8vlVL0iOALWVULUL0fCa8pxC/gyZ70fB6jBMYHI6mxuZU9GXuhxS8SxF/VdVLEsQ/G41/iGr7FEE5h+l9YHbnkBBmGcY/tdPG1OOTzxsAKcooMEUUkZx19NUBTQ+WctnsuvkVSA4CBYi2ofF2NLyu6eblWrXnrjZOwHBUHOOvQb3lNvNCfGtHrXZZgteHekuy1k2ZypzuheDC00ranykh/8wH2EL50hql9r+G5VLnI8DYBbx0QvFJ1HRU12bucDQQqlqTutZ6IKYHCXtm/sJKXjN8F1p80s7JKjaYuPuLmKkdm+qB6QYxqEZlSmuq6UyZbjyd2QNNJyQHbJ2zdAInxttKR0+j/qrq3aMcDkfVsTkVa2rvJjB9tuQj+lkm3qbW7ZC7rjaXn9F1Z1CzzLbQnrixNZZ3UQ+knVLJoR6H5JDNTEqPZeXlAURPof4KV25XB5yAUQeG+oc5cagfP/Do6evGD+bff4NIOL7wqsn1fMjdgEbboX0REGQ1042hjIq/Bm39lF04D/5/9mDrXeD1Vr2++qSxiId6q6DtV2DoS/YGkQXXkR6Awc8DCeqvdQKGY14TRzE7nn+Lt158myRJWX3OCjZevp6Wttm5BBzTI6YVyd9kwzCJbJ5EBZxtlcCW1myxpTXphHA0/+xSy8Gajsdbg0Yv2S5byVEgsKGlZqUtpwFUsza1ZYKlHY5mpzBSYNfLu9m7cz8tbXnOungtS1fXzpk63xER28nP32DLVrKy7mqL9rPZ3R9zAkj359HR76PJEevU0yGQAAnOr+oYp0O8VWj0gr2HpUcB37amNd2IjIWZRpCeqIpbsNo0q/NijMZYTSwgXn92J68+uZ2//bNvAXDH732Uq269jM7Fzko/V0RCJLwAqE24zukgpgvNXW8DiYgABW8xEl5dn/EEm9Hk+0ABNClTAimQDtdhZA5H7Xj+hz9j7xsH+PsvfBdE+IVP38rRfce47hevmpfCcj0Q05gP3CZYn5XWvGNLa/yVYJbVJYtHTCvk34sWXwB5HZIB24Iwc6vY2mugytlEDkc9KI4W+fE3n2b4+DDt3W30Hx3kJ998is03XMi6C+u0+z5PsRlrNeo4d5qI6YH8B9B4ByTHwF+bhSjP3kVd2fG0Qv5mtPAUJP2ZG2Q9ZKXhAAx9CR35e2RWrWgdlcStTFsiPwAAIABJREFU0GrI0f3HePXJ7fSs6CHIWbuR8QzPfO8FbvjYtRjjQgznM8ZfjXp9kL8JCOq6sBfTAS23ov6FUPgnq3aPfAMQpONua/ueZa2jw9GM9B8ZYO8bB1iyqhcxVsHr6evi0O4jHNp9hL6z6lBK4KgZjVb/K6YTHfoLIIL8h8F0InioJll3qnNLbgyHYz6xd+d+ho4PsXilDdXNtebIt+V4Zet2Vp2zwonJ85Rm6IZhRZX3o8FmKHwPzGKErOwwOYINwnc/n/XAfddryN43DvB3n/8Ofujx1s/eAeDrf/w/iQoRl//cJc6FsQAQ8UFqW4s+HTa4dCPqdaGjPwRiwNjWhaYb8TfUe4gOR9UYGRzlb//0WwQ5vzQff/H3HuAjn76VoRPOfTRfadRFc3rk4+MtB1VBB9C2TwIGgvOQoPrtVB2OenB4zzHyU8r2/MAniROG+0dY1NuYLi7HwkBEEH85KTdB8Qk0PQ7DXwRCG8qfNM59ZCHhBIxaUi7D0eGoM+ItsW6M4DzbjtYsRfw1VW+l6nDUk5aOPKAnzctJnNDRXR/LqsMB2FIR6UXyH7Y14E0WEGcDUY/ZMkTTancxHY5paO9u4+CuQ5OOpamiCmGLW4fMV5qtG4bxV6LeR0D70dFvAr7Nw28yVItZnlIemWVXsEbECRg1ZPlZS/nIp29h8coevvx/2HqpX/7dX0BV6ehxC2ZH/RDTgYTNucOnmqDxGxC/BsTgrUeCc5zd2nFKFvV08Okv/Bq7X9vL3/+nhxDgw7/xARb1drB4pXvgmq806qLZ9H6t4cZ0JqhGaOEJGPg/7YHWT6HeWiR3VdMJMY7asHpTHzuff4uh/mHaFrWSxAlH9x9n3QWrybe6+7ijcRhzUY9lXjTTnK2qaPwaRNts9h2gwSYk2NyU3a1c6EIN6e3rZtMVGziy7xhRISYqRKXyEZd/4XCcGVr8qQ1HHfwCDP6FbRM2+iNU43oPzdHgbL7hAi687hw0TUmSlPUXr+Wq2y7D85vvZu5wNAIavQLpbmxteIh4fZDsQqPX6j00R4PS1tnGNR+6gjAXcHjPEU4c7mfjpWdxwTXn1Htojhpger9WEnBLJX6OiqPJbig+DdJl24WbJRC9ikYv13toZ4RzYNQQEeG8qzaycuNyLrv5Ijzfo3dlD2HO7Uo4HGeCpv2Q7LQ9zjM9VrzlNscj2Q/+qvoO0NHQeL7HhkvO4osv/1m9h+KoEY28Y9aIYzpt4tdh6GuQ7ABAB+4DFNpaILywvmNzNCzdy7q4/qNXExUiPN9zIvICYrpcImj8ObHRxzeJ6FWQzlI7cxGDmiUQv4oGF9SlA9dccAJGHVjU08GiHhdK5HDMGR2EoS8DgQ1TYmzBHKHBxQhOwHA4HJaJQZmNLGQ0Nyll+nLTlMXijpoiIoR5l3mx4IlfAR0A7Dzt5uhKMVKmFbcHGmPn5+YSMJprtA6HwzERaWHadFxxXX0cDoejpvjrofUT4G0EbyPScbf92HW1cjgcZRgrISG4EqQD/PPqPaT5ibca0uOTj+kAeEuaMp/IOTAcDkfTIqYb7fj3tuZ66H57sPVTYPKI31fXsTkcjsZivgRlNjISXIAmh4EiIFlb7l4kOL/eQ3M4HI2Of56bp6uE+OegyTtouh9oAx0FESS8rN5DOyOcgOFwVAlNh0GHQFoR01bv4cxbJHe1DSFquwtIwFuJBBe7NrAOhwPIOhUleyDZbXecmrh1XKMj0gL590J4EZr2I6YTzLKmTLl3OBzVQ3UEkoOAglnsxIoqI6YV8u9D47cgPWTzMPz1iGnOLphOwHCcEtvJwUOkXE2roxyqKRo9bwNzhr8MKNr5fyPBZW4RVwVEQiTcjAYXA+q+x01KmqYM94/gBR4tbe4Bcyp2sXcYxIBZ4gS6WaKa2raeyVsgbbacQQuk0XZMsLHew5uXiPhWSPZW1nsoDkdVUC2CjoC0NMRc3GyOhTTeA8XHgRgQUNDwSkxwNtA876PZEMkjwbnAufUeypxZsALG4PEh3nrpHY4fPE7X0i7WXbCa9i63Sz6GJgfR4nOgR0BaUP8ixN/ghIxZoPEOiF7KOmNkdWXR66i0I4Gr7asWzZag7BjnwNuH2PajlxkdHgVgxYblXHT9eS7QLSON3oLik9iAREACNLwe4y+v57CqSsUW5OkBSHaB6Svdv1QjiJ5B/TWI5OY6VIdjXjA8MMLrz+xkz/b95PIBGzafxdoLVmGMu7eOYTeoXoL4JRu/JYIGFyH+eW59PEtUC1a8kEWl+Vc1guJTqLe8aR0BjtqyIAWM/iMDPP53WxGEz0c7Sfak/ObLG7juF66ic7EL/tP0KDr6fZB27rllK5By77cHUVIk2FTv4VUF1cJ4ucdc7cXxqzD8ICClzhgM3w+mFZyA4XBMYuDYIE995zk6utpo7+5FVdm38wCaKle8f3O9h1d3NB3knpv/GMTnsw/9gj2mI1B8DPU+3BC7f5XEChdFiJ7PPv5lTO9fn/H5NDkAkpv0cCESoKnaQDNv2VyH7HA0PcXRIj/55lMUR2M6lywiiWJeeOQlhgeGueCa5t+trRR2g+p5MMsR41mXcvEZlBYkOKvm45muBWmlHQyaDoD2A3kwPXMTa9LDoAlixsVjkQBF0GQ/Ys6e+4Ad854FKau+9tMdeL5H17JOjGcIQp8gDHjt6R31HlpDoNFr3HPL49xzyw/Y9thetj22n3tufQqibajOr1ZoqilpcRs6/LfokV9GD3+ItPgsqukcTlrg5DZyAlpAdZqOGQ7HAmX363vxPI9cq13MiAg9K7rZ98YBhgdG6jy6+qPJ/uxf47drkRbQyC4E5xGqahfJ6bHxg+kR0vjtMz+p5O336uSrQRMmrzsc1WDvzv2MDI7SvawTzzOE+ZAlqxfzxrZdjAyN1nt4jUP8MpjFpVJVER9Mjz0+D7Fr5KfR0X9ARx9FR7+LFn5gN/3mduZpjjkXi2N2LEgHxsG3j/CF9C1kH7wyegKAP9Ud/Mu315zxOYujRY4f6sd4JrsBNHEdfnoUTsoRMNkiMAKa+L1NQeM3IHoBzHLGyz1eQqXlzMs9vLXQehfiLUYH7rPHWu8Cb7WzGDocUxgZGOXPCjvw93n8uz7ruBARECEqRNDRctrnPLLvGLtf20uxUKRv/XL6zlqK5zffvHX3jb8POsK2x61Q8dvv/yYAn33ow0zbPriZSQ9Cy/9iy+8GP2ePtX8aik9aa/EZuE3EW4XyAqojVvjBugzxFoN0V3L0k1AtYvOjmu/nzrHwOHF44KSSPWMEEEYHR88ol0hVOXbgOEMnhsm15uhd0d3Ua2MrsI6ATC1xCEGPl31NtRlzWlTNeRHvsnlupg/JSonGSswld/WZndQssWWQOlpyPGsmMotzxDlmyYIUMNo6W9AjKeKN72hpmtLadfoLZYDd2/fywsMv8d/v+wdA+fi/+yW23HJp85ajmKXc++3rEW9xacF873ffj22LNr/syrbc4wHATCj3eBBM+xmXe0hwAZruz1oVxdiE5RAJLqrUqCeh6RAavwl61O4MeOts2rDD0QQsWbOY9OUUgvGFbVSI8EOPts7T/zl+62dv88KjL5NrCfmbe79JEqf8xp/9Cpe/75ImXTyfPOeqFkF8ML11GE/10GR/qdxjTJ4RCa3zLz12RuUeYtohfwNafAJN+u0Gn1mKhFdXRVDW9DhafBaS/SAe6m9EgosQ5/ZwNDB/sOsZhgaG+MPeLaVjaaqAkm8/ffEiiROe/f429r5xAJP9Pi/qbeeq2y5v2pBmEUHNyqyDwwTxU4+Dt6p+A6OKoZfxDpCuyRljZjEkb6J6+RnNayIhGl4PxUfR9Lidk1UgvPqk/ItKCTOaDtryn2Q/mC4k2ISYntM6R7MFpc53FqSAsfGy9fz6d/vpWdrNHx/5GWmq/Hq8io2Xbzjtcw0eH+K5779I59JOglz27TTC0999nhtvv7YpF8wSnIMmb9pdKlJQhfQIhO+ef0GJOsLJlVQCOoqqntECV0wb5N+Pxu/Aoo1guvn/2XvvMEmqev//daq6OvfknDfnTNhdlswikiQpUVG8/jBcRcXL4xUUUTHgRfGqiHq/YEAQJecgGYRdNucwuzuzk3Po6Vjh/P6o2Z6dzWlyvZ6Hh5kzVdWnertPnfM+n8/7I1ylA2IWJ60uZPw1iPwBUCBwI1LfAt6lCCV0wl/PweFEIaXk2iceAwk7iEIc7qpZhbQkX1HKWXD+HFza0T2ikvEkG/+9hayCTFyaiuqy/2vY2UxbXTt5ZbkDdDcDw71v3gXArWfdBlaYn794qj3ZszrBc9roM6AUGvSmKYrQrX3t8vjSPYSaD95Le3O4XQNmEidlDBl/HRCg5AMWGFvs6A/PaQPymg4OJwK3z02sJ0ZHUxdpOSFM3aCjqYtJ88cdk+Cwe3Md9TuayCvLSbV1NHWx+cNtzD939ons+qAi3LOR8deQZjMIP8goCA2hzRzqrg0Qul35qh+iNwDw2KMAFVchUr2sXxnVgdp4k1YPMv6K/WxRAmDWIo2dSM85o9oIe7QzZgSM7vYwtVvriYbj5JVlM3PJVCpX7+JLsgyXx8XUJRMpmVR41Ndtqm7hifteQPO4qNpQA8BjP3saPaEz/7xZZBUMXIjqQCGUNHsBrm/i5y+dA0oIoc1AqEf//gx71LL90z0CnwO14Lh254TwILSBNyKS+jrsFY09uRdKPtJqQ+obEJ5FA/76Dg7HQmNVM5s/3EZ7SyfKXpFwqqaiKILTL1lEZl76UV833BHBsuAv338MIDUmP/3rl3jpwdd5YOXPT8wNDDL3vnUP0grbk2YUhJo/KqOshFram+6RTKWLSKsLlHQQGcd3baEc9zUOhzRqgSRE7B06EboVST6Y1UhrtiMqOwwrIt1RrnviH+hJgy0JO536d55qErVJbkufxpwzZ1A+49giC6q31JKW0//znp6bRl1lI3POnDEiU/oAhJIB3o/b6ceyA8R4hGv8qByPAXCNh+RKUPeKUJedoBYdt4G0EB5wlR7wbyfSnFTqm23TULVXTBM+pBUBfQ1S/dhh5/qDZZTqcHSMCQGjqbqF5S+t4tfGLhRF8OXKCjIL0jnrmtOQpoXb6z7mwdQ0zIN4zggs8ziMIIcYoaSPiQWwne7RgLSaABO7TKFAaMN/h0BKCWatPVnuTX/pE2E+O3Qdc3A4BM01rXz4/Ep+r9ageVz8V/o07m5aRygryJM3fPq4rq25XQc0ypVSog5wKcBbz74T6IuaONEIJTTqF8BCCSE9S2zPC2nYkRdKBsJz+siI/rO62TflRwiBlKI32m90//s5jBy628O89+QyYvF4PxHZG/ASzAhw3pVnDmHvhjdCCSLcw3+OeCIQrglIsx5pNmAvGS1Q/Aj3/KHu2pFjNYDSP6VfKAG7QhU6oy41foww6gUM0zRZ+/ZGQplBtC77dnMLs2muaaWpqoXyaceXt5ZbmsPlX/04OcXZ/Ol7dqm3G757FdHuGOm5A+OBIaUk1hNHKGLE5hIOF+z86AuQRhWkldvpHmr5iFDThRBI4WP/MD4LxLH5uTg4DDTbV+4kmBlAidiTZo/fw3fyZ9uL1eMklBUkuyiTy792ERl5aTx0x6NYpsUVX7+Is69ZctzXPxB6UqetvoNELIlLG5m7isMJxVWKVAvsNBmh7p9/PZwJ/wRkF5jVwF6Csv8GGOXik8PI4ton/oGRNLir/CQAftSwBkM3+Xb2DBYsnXOM17Qj3x698mrKp5Ww7u1NeErdqR3uruYuSiYXDWj0RTyawNQNfCEfygCL1mMBITTwnAVWc29aeQDhKhzw8t0n1JxUBG1xea8+S6n3piUefhk80EapDsfGqBcwYuE4iWiCX8UrUxVHftSwBhOLH1TlH7eAkZGbxuQFE9i+ehd6wgAg3N7DgvPnoLlPvGlXd1uYtW9toLMlDEhyS3OYc+Z0fEFnwXqsCOFFaCO0zrlrOvivhagtnhH8OliNdruDwzDkB7WrUTWFLYluwB6PAb5olWFZ1nFNOoUQLFg6h7VvbaS5ugU9oaOoKgsvPgn/MVQzORxdrd18+MJKHr37Seoq7XKnX1pwG/4034BFYowFhNBAHVl+JQAID8i9n/sWYIA2I1UBxcFhOKDHdVzuviXAHYVzsUyLxqqWE3L9sqnFtNW3U1/ZiFAEUkoy8jKYduqkE3L9fUkmdDa+v4W6bQ1IwBf0MOesmeSWjC6j46FACMVOq1YH3y/iRIgFQptu+5ZID0J4bPHCagH3gpEjjjvsx6gXMPYM0Ptu7klL4g0evwGaEIJpCydTOCGfGYumoGoKeWW5AzJZTsaTfPDcChRV4dn7XwYJV37jYpa9uIozrlrkqM1jEOGaiJRxCGj2h1y2gTYP4Ro31F1zcDggLo8LI2n0azMNi4yCtBMyhnn9Hk69cD6xnhhnX7sEf9rA7MRZlsXK19aiulQ0b9+iNRqO9Rk6O4wplOy/IWUC2XYNyDgEvwmuyQjXsZdod3A40Vz7xGNUyggk+gTkOwrnkkzo+ENHH9W7J/JiWV1t6vdHr7yaBUvnMHHeOHo6o3j9brIKMwdsnrrurY00VrWQWZiJogji0QTLXljFWVcvJpgRGJDXdBgZCLUA6T4D9FVI2Qm4wD0P4ZpyVNdxIi+GF6NylmVZFu0NHcR64viCXgrH5/PlXQr3e6oRAm7LmkFXcxdlU4tP2Gtm5KaTkXv0pnNHQ3NNG4/+5Ek0j5Yyp3vivufREzqzz5g+Ig1DHY4PIRTbFVub0luf3D/goX0ODkeDntSp295Aw65mfEEvv128lA3vbuHX7EJVFW7xTiTeEz/hO3MDHZXW0xmhpzNKbkk2N919HQ/e/ghgi8pFE5xa9mMVITyInKeGuhsODv2QUtLV2k1PZxQ9ofe1WxKhCAzdoKulmwXnH336yI41VfYPuf2XFEKIQZkbR8MxGnY2kV2clUpX8fo9xMJxarfVM/WUgYn6cBg5KFo50lVqC8vCjRCjcvk7phh1/4LJhM5HL6+mrb6DJ+97HoDP/ehacksy+VKNbdAYC8dYcP6cAR9UTzSJaIIDO4YKknH9AO0OYwUhPHb4soPDMMLQDT58fiUdTV0E0vz84b/+imVa3Pbn/+TucD4djZ2k54aYeP6c0SPASuA4Khg5ODg4nEhM02TtWxup3daAAD5DLhEtiubV+JJVhmmY9HREmHXGtGOqxnf6e1EA6v/TTl199MqrT2T3D4ue0BGKsl81CZdbJRaODWpfHIYvdiWq4e9v53BkjDoBY8fqXbQ3dJJbko3mscN6u9vC5JZUMPvMGegJg0C6/5hMhOLRBPWVjXQ2d5GRl07RxAK8/sFbNGbkpXPFLReSU5LNQ3c8CsBnf3AN7Q0dhLIGpq79cERaERDKmMopllKC1Q4kQKQjFCck0mH407CzifbGTvJK7fJlLk1Fqgq7N9Wy9DNn4tKO/REUDceoXLWT+p1NeHxuxs8pp3RK8aCl0gUzAgQzA0S6ogTS/dx093VYpkVrbRtFE8ZGbXkpTbBaQSZtA2RlbDyHpDRsV36rBUQQoZaMCONnh7FJ3fYGarbUkVuak1rkm9W7UE3J0hvPJBFL4vG5j3o83lN5ad3bmwBoX+S1/S6ukMdVhv5osef0CnrSQNvL1yMWjpPT++wZK9hVuJKANib9HaTVCVIHJd2JRh7ljDoBo3pzLc/85iWEIlJpFs/85mWu/MbFTF80Bd8xzq8i3VH+/cxHxKMJfmvuwtosuXX1FJZcdgqB9MFZTGYVZFA8uYjarfWYhgVS0lLbxpSTJxBIG/2TJ2l1IhPL7QkzINVChPuUUb+Yl1YUmXwPwj+3G/yfQ2ozEdqsQZ0kODgcLS01bSk/oAdvfyQ1Jj92zzO88tCb3Pfej47puolYgn8/vZxkQictO4SeNFj9+gZiPQmmnjzxhPX/UCiKwklL5/DhCytprW0D7OCLyadMJLtwlESTHAJp9SATb4HsBikAidRmIbSZo3pckjKJTLwDZiMIL6Aj9XXgPQehZA119xwc9qN6Uy2hrFC/7+WdpQtob+hASnlc80c7Mthm9pMNWJZk80XbmLZw8qCNAy7NxcwlU1n1+npbiHG7iHbHyCnOonBc3qD0YThgGbtBXwNWBISG1GYjXJNG9Xi8ByljyMQHveOyAARSW4CiOelDo5VRJ2AcjOP9Am9buRMjaZBTlIXasBvVBZZhsXXFDuafOzj1oIUQzD17BgUVuRSOy0NxKZRNLSZ3DCjMUiaR8TftX6J/s/8f+Bwy8TZ4LxjVSrNMrgCzk1StaiUP9LWg5oBaNKR9c3A4FL6QF73KOMBf7LzrY6VueyPxaILsInvBqLpUtJJsKlftZPysMtzewdl5ScsOcc61S2ir70BPGmTkpo0ZwziZ/ABkAqHY0SZSWr3jUi4MgVv9YCGNKjCbIGpHQYrQrUir2xbXvR8bE4sFh9HB8X5Uf/zid3jtL28TjyQQirCj0CzJ9tW7KJpYMKhp2qVTiglmBNi9uY54JM6k+eMpmpB/XFF+IwlpNkLiHTsSTs1HyiQklyFRENrgiPpDiUx8BGZrqlKKlIZ9/0oGYiRWtHI4LKPum10xs4xPfOUCckqyU6Zql3zpY5TPOL5yqfWVjfzWqkI0kCrH+mvPLr68w2L+ucfd7SNGVVWKJxZSPPHo8xRHMtJohMj9gBvM7XZj5CEgCe6TQB2dKruUMTBrbdFmz333/BIwka5yhCNgOAxjSiYXUbmming0wU13X8f/+84jGEmDr/7m8yxYevRmcXvobO7Cs0/6nqoqSAmxnvigCRhg7/7ll4+tCZK0esBqRSh9ZqVCKEjhQxpVQ1Jub9AwqiH6MJg7AJDhe+12//UpI2UHh+FE6ZQi1r61CW/AkxLYulu7KRyXh+bWDnP2welq6UZK2a/0qqIIVJdKW33HoPvMZeZnkJmfMaivOVyQ+iYQoVRqtRBupJIDxnqka8KoFlalFbHnyf2eR67e59FOR8AYpYy6besJc8rJLc2mpbYNPWGgJ3RyS7KZMPf4ykp6/O7e3LI+pJR4/U6O1eAQ48AGpoBMHLh9NCCtg2+TyAPtbDs4DB9CmUFOvXA+pm7QWteOkTTw+NzMOmP6cV03PTdEIprs12aZFkKAN+CY2Q48FnbCjI0M39u7kFcAc6g6NTgIN3vf+15/AMfZ3mEYUjqlmOJJBbTWttFa105rbRveoJfpi+0yktc+8ViqFOrRoLhUpITC8XkUju/bRJKWxOV2vguDiuzqTWnrQwgPWDFgtM8VTRDiACKNCxjF64MxzqgbYTS3xqkXLaCjqZMFS2fjC3rJzM84bvVx4pwKbn4rQm5JNj9uXoeUcLNezMTjFEYcjgyhZCH9nwGlEHp+YTcGvwFWMyijWHEXfhCZEPgCRP5oN4VutQ3k1Iqh7ZuDwxGQV5rDOdedTqwnzvk3nonHd2iBwbIsjKSBy+06qCFn0cRCKldX0dXSTSg7hKkbtDd2Mmn+uMNe3+EEIEIg0u1IjMjv94qKux9EBnhOG9r+HSFW2w0AKNkPA/amxOHmCkKbhPTf0D+FxGwCV4VjGucwLFFdKguWzmHCnAp6OiN4/B6yizJR1aM3s9+bH193H53N3dRsqQNsnyPLlFx+y4XklWafiK47HClKAZh1IPp8eKQVASULIY49ymZEIIIgAkgr2t9MWfaAMjgp/g6Dz6gTMMD2ijjSknyNVc1Urt5FNByjoCKPCXMrDmhoVDa9hFgkzo41VehJW82cfPIEyqYfX2qKwxGi5IBrHBg7Se3wWQ2gzUQooSHt2rEgrQ6kvh2sTlDzEa4JKQd/KXWw2gDFfvh4TkXG38B2lhZIsx7UMoSrbChvwcHhiFEU5YiM4up3NLLpg63EIgncbo0pJ0+gfEbpfotKX8DLaZedwtbl22nY1cyvjV34Ql6eXPixgboFh70QQoBnUa8v0V6RMMLXG6Ew/LHabgB9ee/P14CMgP86pPCBazrCNdFOi5FJpNEAxG0hXRSANhf4CwDSagS1EOGeP3Q34+BwGIQQB0yxuPaJx1hWV5v6GeDPF11O7bYGWuvaCaT7KJtWQihzfwd8IQRpe1XA21PO9JSPz8MXHL5V4vZUT7n3zbtOyHHDAaFNR5o1SKvNXtDLGMg4uM8Z6q4dM9LqRBo1gGGnSyt5qbmAlBbIKAgXQnjBvRAZfxNp9oDQ7PtXixGu0qG9CYcBY1QKGEdK1cYa1ry5gWfvfwVFEVzx9Ytp2NnEGVct3G/wVRSFaadOZvzscpb0nIov6HV2+gYRIYQ9QCnFoBbbg5ZrAigjzwNCms3I+OsQ/ROggP+zSKMSvB9DWt2QfD8VbUHgSwjPGQjfxUhtHsgoQs3pHchHXQaYwximta6Nj15eQ3pOiJyiAHpCZ+1bG0ERVEzffxISzAjwP+GtkAPb63qgq4frn/onj1559RD0fuwhlCzwXYzUFkDXN0FoiKy/j8xca6vD/r/IBXTb/E3GwVVqi8cyAShITDvSwr0Qcp4BqxuEF6EMbq6/g8NAYVkW7z21jEhXDF/IS1tdO7vW17DwkgXkFPWvsrNnYX/rWXdi6AZ3PvEt0rJDY8Y4czghlHTwXoDUt9jlndV8hGsqQh2ZkTCWvhOSHwIuEApS3wjaFNBOAqsZmVxmR1hIkK4KhPskhO8ipFkDVhThKgClACGOL8rIYfgyZkcZ0zDZsnw7WYWZqC57IZiZn057YwfVm+sOWorP4/OkhIuezghVG2vobO4kMz+DipllY6Kc6VAhhIrQKkCrGOquHBcyuQpEgD1fP6HmIs0WZHINWHV2eDZ7Qv5cyMRbCN+lKNr4oeqyg8OAs331LgLp/pQ5p+bRyMhPZ/uKnZRPK9lvYXzr2XeyYa6CL+iFXPu7FO6I8MIf/4Uv4GHCvArKpu5/nsOJQwgPQqvA6jWuHEkw9xmQAAAgAElEQVTvtZL9sB2FISPg+xRC3WMA50EqBaBvRBrVgNpX+Sr4TTB2IZVSFK0M1OG7y+zgcCQ8euXVqciLR6+8mm0rd7Dtox3kFPeKFWm2MfL6dzZx1tWnpb7j/aITBLjcrlTkc6Q7SlNVC4ZukFOcdULSuE8Ee/q87u1N/X7fN8LiQMeNiCgMJQ3hOWWou3HcSJmwo+OU7FT6i13laitS5NjChgghlAK73axGJkwU7+kIZdoQ995hsBizAkY8mkBPGvz1rn9QtaEG2JO/Z/H5n1x/2PO7Wrt5/6nl/PPeZ1FUhSu/cQm7N9dy2uWnkpY18lIaHAYHKZP2bl/04VTeuG1+J8F7BcQfo3+llQeAJHgWgzq2Ks84jC3C7T14A7YJ2Z4KUjfdfR3drWFMw+y3q3fr2XeyY00VBW9HAZC3n4SRNPjKlApCWUGMhM7q1zcQjySZctKEwb+ZMcYeD4mRib5f5RAhVCQJO0Q5+uReFaB+AZjgKgWcFD6H0cHeUWuNVS0EMvuXgvYFvbTWtZGIJfH694883ntx37CriZWvrLU9bRWFLcu2M35OOTMWTx1wEeNQKR97nhkT5lYMaB8cTgBWB0gLofR5dwihINEguQZQUl4XdnsemDVIqyeViu0w+hmzAobH5wYp0RN97rxS2k72aTn9BQgpJZZl9TM82rJ8Oy6PK+W0nJmfTldrN9tX7jyu8oAOox2XnZ+3n4u91esgfbCKI6Pc2d9hzJNVmMm2j3YgkdRXNiIUQbQ7RnpuWj/xwjRM27eoO5Zqi/XE0dwuMnLTAFD9HnKKs6hctZNxs8pwe0a5iZnDMaFkP4ylbwJ9LdD33JfSBKlw8EJtTvqew+hiz+L/mm9flqoYpbpUfEEvpmGiqCouzZ4DX5Z5I5GuaL/z7n3zLgzdYM0bGwhlB1OlrC1LsmNNNYXjC8guPDJvuoFiwtwK7n3zrsN6W6RSY47RA2MkeWcMTw6W9mFhVxzp77MkhEAi6OfH5DDqGRMChpSSaHcUKSGQ7kcIQTKuE27voaczgsutkpYd4qTzZ1M0qTCVb21ZFlUbaqhcvZNENElWUSbTF00hIzeN5t2tPPvbV6ja2Be9ISVc+fWLhvJWHYY5QihIbQb4r4PoI4CA4H+C1Qruk0FR7DzsnvvsE4JfA6sdRmgeo4PDvkgp6W4Loyd0gplBvH4PyYROe0MHT/3vC/R0RDB0W7C79wu/Q9NcnHHVIgCqN9Wwedl2MvMzUgJGIN3P4je6+dR/faLf66guFQnEI3FHwHA4KEKtQOqbe83vMoCkPea659tVrgI3QeRB++DgN8BqtP2XHBxGIW6vm9Wvr8cX8KKoCmk5IdJz05hy0oQDeluEO3pSURldrWEM3UiJFwCKItA8Gs27WwZMwDhUasjB/nYkOALEEKFkgxJEWt0Ixd6UkLK3HKprGhhrgD7fISl1EKptXuowZhj1AkZPZ4TVb6yns7kbgFBmgPnnzWbbih1k5KYTSPejuV0svGQBsZ4EZVOLCWbY4XOVa3ax6d/beO6BV1GE4FO3fYJ/P/ORbfIZ8mFZ/XfRpWXhdzwwHA6DcE2xd/gCbsC03ZLdS1C0cVgyCfo6QLcPttrAfSpCOLnWDiOfeDTBylfX0tbQjuit2z5t4SSEUEhEEqRlh4h0x6BXwBBCIBQ7KqmpuoXVb6wnPSfE539yHX++8zEadjRRNLGAr//+/2PbRzsIpPeNv6ZpIZAHDHl2cNiDUPzgPR+pbwCz1q6k4l6McI0HWYFMvENqZ89qAm2WXc7bwWGEYlkWNVvr2LG6ir/+4J+4fW52rq0G4BdfeACAUz4+D9MwadzVTDAzyNRTJnHr2XcipUxFX3h8boyEwTn/cR671u8mIz8duW9wKfbcWBtGIvJACRNH6rHhcGiEUMBzJjLxLtJs7A1MdoPnDISah5T1SLOh1y9O751DL3LKWI8xRrWAYRomH76wEsuwePb+lwG4+rbLeP/p5SRiSZ7//WvUVzYCsP7dLUjLYt55swAwdION723h8V8+T3N1KwAP//BxhCIon1HC5AXjufRL5/PcA68iFMGn7/wU7Q0dTJxXMST36jByEEJBuGcitSkgk7aLfa9TsuKehXQVI7W5IBSEWuQ43DuMGta9s4mu1jC5JTmAPUZveHcLmtdFICPAf/z0BgDuvtaOQLr5fz7DeZ8+A4BNH26lpbaD3ZvrkBLOuGoRRRMKMHWT4okF7FxbTVdLN6HsEEZSp6Opi6mnTuq3G+gwPLDa7H/n4eKdIZQQwrPoAH8IgvcCcJ9kj9VKhpNj7TDi2bZiB5uXVZKZl4aqqSRjfaH3iqrg0lTmnj2TRCyJqil0t4ZJJuxNFT2up47NyE/HsiSZeels+mAr595wOsEMPz2dkdRGoJ40sEyL/PLcAbufQ6V8HG86iMPQYFdVuRBkp51CrWSkDD3xnIk0doO5245WFj6UwA1D22GHQWdUCxhtDR08/IN/onm0lFHnY/c8TTKus/TGM/sdK4TtSqD07vZ1tXSzbeVOTMNKHROPxNE8Gt2tYWYsmoKeNHD73JiGSbQ7ytyzZlA0oWDQ7s/BRso4Ut8BZh0oIYRrkl1qdJgjhNbrh7FPu5KFcGcd4AwHh5FLLBKnqbovjHiPUecnb72U1vo2cj37Cw2KYqeCWJbFxve3YugmadkhBBBu62FHzy4KKvLx+DycdtkpbFuxg4ZdzXh9buaeNYOy6SWDeYsOgJSGXRbaqLQbXBMQrol9k88RhhAKqHlD3Q0HhxNCIpagcvUuckuzUVWFz//YNq3//bf+gpSS62+/MhXJ5gt6e88SWIbJvW/exdv//De/+eqDqC6FxZ84GT2us3NdNVmFdlrfyRfM46OXVtNa2wZCoKgK85fOJpQ5+oW/4SqWSGnapVVl1BZllRx7XBvmCKGA2H8uLIQboU0EbSLWnvQ+hzHHqBYwjKRxwHYhILckm8u/9nGe/vVLAHzuR9fSvLuVihm2/0VzbSsrXl2L5u57i1TNxczTppCWFUQIwYTZFVRMLyURS+LxuVFdTr3hwUbKODL+ml0POvIXwEL6P430nIHiclziHRyGC5ZpIS3JQ3c8CpASlf/+s6eRUnLZVy7AG/Di0lS+88gttNV3UDq1BLdHo7WuDVVTkBJef/gdAM779Jm01LZSMqkIl+YilBl0DJSHGCklMvEhmFWgZAECkqvsMGDPmcj2z9gH6ssBOxJjuERhODiMBWI9cQBUtf8CVlEFUtrGyXun4sUjCXwhL76QD9M02b2ljoWXLEiVTPUFfXS3dqO4FNxeN8GMAGddcxqdzV29pvhpg+ZBdCjBYDDFhBP5WscbrSZl3E6Ds1p7veOlXdHOs2REp1zseV/2fpbA8Inqcxh4RrWAkZYd4opbLiK7KIs/fe/vAHz2h9fSVt/OvHNmsX3lTvTesLjWunYqZpZSPMnObY10Rm1zuXhfaJ1lmAhFIbe0b3dfdal4fG7a6ttJxJIE0v3Dpub1WEAau0CG7XrQ9LrGK9mQXIFUi1OpGQ4ODkOLP+QjlBXENCxUV9/k2TRMQpkBZp0xnc0fbrO9haSkaGIB0xZOAiARS5JbnEPT7lbM3nE40hXBTBqUTusfZWFZFm31HXQ0d+IP+ckrzXbSSAYL2QFmNSiFfc9AtRBp1tkTaAcHhyFlT1SFZVoovSLGg7c/gmVK/vtvX6O9oZOWmlY8fg+GbiAlLLx4AYqiEO7owR/y4fZoqY07KSXJuI6qilTaiKIoKYEjGU8Si8TxBbwH7pDDgGJ7+7Qj1L7ocGk1IvWtCPesIeyZg8PxMaoFjGBGgEnzx7Nt5U4M3UQArTWtjJtTTl5pDjnFWUxbOCklPKRl9ZVRy8hL5+rbLkNRBQ//8HGkJbno5qX4Qz4yCzJSx0XDMT58YSV/+d5jIODyWy6ieGIB886Z5URkDAZmA0T+aosX5na7rec3ELjeNvZxXIkdHIYFQgjmnj2TeE8c0zB54pfPIy3Jl3/1ORYsnYPqUimZUkSkK4rbqxHYyxA5mBHg+d+/iupSaavvAGDZC6tAwM333pg6ztANVry6lubdLbg0F4Zh4vG5WXzpyWMihHnIscLQa87aHxVphVO7Y85umYPD0ODxeRg3q4ztq3aRWZBhj5NJE6EIyqaVMHHuOJqqW2ipbcMX8lI8sTA1FrvcLtxejcknT6B6Qw3htjAAoewg0xdP7fc6sUicje9toXFXMxJJVkEGs86Y3m+e7XBorKYFIO33+FjGTCklGDvsTb29EVlgVgIjV8BwniUOo1rAAJh66iRySrIpm14MlqR4UiE5JfaXeW+VeF+KJxWyY00VlmlXFpGWRTAjwLSFk/uFw218fyvJaBLNa7fllmRTt72RnOIsKmY4KQwDjghi14beJ59Piv1qRTs4OAwtmXnpnHXNaTRVNfPCH/6F5nFx0sfmoij299ft0XDn7W9am56ThsfnIR5NpNoUVcHjc5OZ3yco126rp6mqhbyyvii5cHsP69/dzOJLTx7AO3MA7AoeBypDgGVX+3BwcBhypi2cjC/o40dX/wLLktRtbwDgjot/AtgpEHv83Pb2c/AFvBSOy6exqpkZS6by4HceQUq47D8/zvjZ5anrSylZ8coawu0RsooyEUIQ7ujhg+dWcPY1S8Z8WevDLbpT6RG94gUAxma7hOgJY/h7YDg4HIpRL2AIIcgtySa3JPvwB++FL+BlyeWnsG3VLq645UK8fg8T5lVQNrUvXFlP6jRWNfPMb1+meqOdz/3QHY9imRZZhRmOgDEICG0i0n8jKOnQc7/d6L8OtMkjOr/PwWG04gt4qZhRxu9W3nNU592/8mfUbW/gB5+8FyTc8Y9vUjq5qN9uf922BkJZfZEWD97+CEi49CsfIxFL4PE5JVUHFCUH1Fyk2Wz/DGC1g5IJSp8RprNb5uAwdCiKwvjZ5alo4j0CxpEw+8zpADTsbMI0LISABefPJiO3T3jubO6io6mr37w7lBmkpbaN5t2tlExyyhAfFSIErmlHPW4KIZCuSaBvBTW/7w9WG2hzT3AnhwbnWTJ2GfUCxvEQSA8w7+yZzDt75lB3xeEgCCUL6TkL9BUQuAEQ4JqM0GYPddeOG2mFkfpmu7qKCII2HcVVPNTdcnAYElRVpWxqCek5aQBUTC/d7xjFpWBZVr82Ox5AIBRnx2mgEUIBzxnI5DowdwISXBUIbc6I9yOSUkfq23pDry1QJyK0KY5Q7jBiOVzVjFvPvpN1b2/a75hHf/oUlmmlhI9ffOGBfucm4/oBfeBUl0o8Ej/xNzKCsNpuOKzxZL/0iN7Ii2NdqAttBtJqQ5oNgMA28SxBaJOP+R6GE1LKXoPSMAg/KLkj/lnjcGSMSQEjFokT64kRzAgeVSiblJLa7Q1UrtpJNBynoCKXzPx0rrjlIp763xcAu5pJS00r5U75vkFDcRUh1Yt7PS+0UTGhlFYPMv4KSAuifwIs8F+PJRejaBOGuntHhJQSiAOuEVtC0WHgSSZ0ejp7bAf79MBhj997otze2MFtS3+IkTT43I+uJSMvjabqVv7+06cRoq/SyfMPvMolXzx/wO7BoQ8hvAjPKUi5oPf3kT+ZtKur/BvMWoj+zW4M3IC0WsBz1ogoSQggZQKk6aTzOPTjWKpmKPtUMfn6kjvQkwbf/utX8af5QUosS6IofUKGoRtk5KYdd3/HFMchXgAI4QHPub2L/BiIACjZo6LQgJR677hcg50SI+3qV96zEMI31N1zGGDGlIBhGibLX1rFr770R0zD5OQL5nHSx+cy+4zpqIpCWk4IVd1/shXu6KF2az3bV++ipbaV959ajqKqXPWNi0kmdHxBb281E0FrXTtlU4tT1UwcBge7XvThFz8jBWlUgrQQag4SAaig5IK+FumqGPaLAmk2I5MfgdUNQiBdkxHaLEfIcOjH1o8qefufH9DW0M6qV9fhC/n42Wvfxe11E8oMHLB6SCKWoHZbA9Uba6hcswsjaaC5XZiGyc511WQVZWIk9X7n+NOPbzJzsB1Kh4Mz3Meoo8JqA7MWoRb2jsfYla/MBrBa+odnD0OkjCOTK8HYDUikko3wnIJQDuwB5jC2iEXibF62nV3rqvAGfcw+fRqlU4q59827Djj27fn5m2d9D9MwueF7V3HfzX9ACMGaNzciFEFmYSatNa0EMgIIRdDTEaFwfB5ZhWP7M6dkP3zExpMnKj1CCAXUvMMfOMKQRmXvuFzU12a2IJNrEJ5FQ9izI0fKJNKotZ8jIoBwVSAUx3D8SBhTAsb6dzbx9mP/BiGIdEVZ9a91VG+u4fff+DO+kJfrb7+KBefP7mfs2VrfzofPruDxXz5PNBzj1AvnEemK4gt6CaT7sdp7KJ5cyP+88X0SsSTBjEAqxNnB4ZgxmyH60D7VVX5tp8nI+LAWa6TVjYy/Yfcx2vsA9l+HlDrCc+rQds5h2NBYbRt5LntxJarmwuVx0d0W5jdfe5BTLpyHS1WYsXgqFTP7vIRikTjvP72cWDjOIz9+ElM36GjqAuBvP3qcG773ScykwW+W/5RoV5S7r7sPl+ZyhAeH40NGIPonJO7UeCzD9wJJGOYTZTt65H0wW/rG48AXkPHXwXcxQjjlLccysUicZ3/zEjvWVdulU5MGy55fxeSTxlE8sYib7r6WcbPK9zuvpbaNjqYuTN3g7mvuo6ulG4Bn738Zy5Jc+fWLmH/+bBoqm9B1k0nzx3HfF//AY/c844zHDicGY4ftr7Q3SjaYVUh5yrAX0aVM2nNlqx2ifwEsZOA/wHMOQs057PljnTEjYJiGyeo3N/D24x9gJE0A2uo76GjuIhDyEcoKomoqy15Yxbk3nIHboyGlZP07m/Cn+1FdCpZp8c7jH/L9B9cD8LOvJfD43Hzxl59l+qnHn08mpaSusoHtK3cS6Y6RX5bLlJMnkJbtlJ0acyiZ7F9dRQKuYV9dRRq7IPogoPWJL9FHwH8D0j3bCe1zAGDDe1vQdYNwew8AesJItddtb+DzP76etW9vJJQdIrt3165qw24SkQQ5xVlYpklXa5h7Hq8E4Kf/mUFrXTtenxtfwEswPcCv3rv7uPp4y5LbiXbHUqkoX130HdxezZmAjzUONWaJYZ6OITsg/FPYS3wh8kdAR2pzEdrEoeydwxCzbeVOdq7fTV5ZLqpLpaezh5badla8spbc4myqN9VRu62B069ciD9kfw+i4RjLX1rNjd//FO2Nnfzxtr+mrvf1n7yHUAU7dl2AL+DllAvnp/52PGkLpmnS3tBJPBInkO4nMz9jxKZBOMaTDgDS2AE992KPzTvtxsiDyMhDkPPiiP18DxZjR8AwLSKdEaD/B8IyLMIdEcIdER79yZPoCZ25Z8+kcHw+8WiCns4oz97/Mrs31+13TT1pYBoWwfQjn8CYhomUEpe2/1tftWE3a9/exHO/ewWhKHzy1kt476llnPnJRQSOIDfcYfQgtElI/2ftyXHkD4AE/7WgTR/+aRiym4OW6JKJQy8GHMYMkc4IK19dmxIu9hDvidOS0HFpKt6Al91balMCRsPOZoKZAf7vv/9GrCeBZfYZdupJg8qVO5l+2pT98rMPhpQSy7RQXfvv1HS3helqCffL4e5uC/ercnI4nNSTUYKSA6Hb7DzyyJ6osk/3VlfJHdq+HQ6ZZN95j40AGR3s3jgMM6o27EbzaPYYKCUtNW0EM/x0t3QT6Y5ROD6f9oZOqjfWMG2hvVHXuKsZaVr87e4niHRFMXQzJSRPWxABIDPvF4SUDOCx1Dh4IEPQIyEeTbD8pdV0Nnf12lBK8svzWLB09gHn0g5jBNdESK6AvVJIsFrBNX7YR18AYNSw/zJcBZK9fiXDXBwfYsbMN9/t0SgYl8fZ1y1h7RsbaG/sRFoWvpCPno5Iv2NNw47QcGkqiiKQlu1jf8/jlQgBsxfZx//079tQVAVxBAJGMqGz9aNKdm+qxbIk+eU5zFg8JSVMmIbJlo8qySrMTE2+07JDdDR1UbWxhhmLp56w98Jh+COUDPCeh0yu6a2u4gFtBsI1aai7dniUAgh8xs4RD99rtwW/Zgsbwzj1xWFwKZte0k+A2INlWiRNiwdvfwTTsPjivTem/uYLeYl0RklEEiAl9zxeyZzF9nj8/QfX4w9tp6rhx4fduZBS8tWF/000HOPyr11Iek4a0xdNJqe4r+zfjrVVXPPty0jPSbPLsQLX33EVRlLHsiwUp6rJmCFVXUXfCIEbsaurTEBoM4f/RFmkgf9zttDS80u7KXQr0mxwwpTHMHtEhI9//hwM3Z7zmoaJkTTwBrwIRcGl2Z/tQLqP5po2pi20z00mdBSXimVJ4j0HriqiJ/UjFhciXRFaatuwLEluSTahzP4i8dbl2wm3h/uVZW2qaqZqYw0T5447qvt2GD0I10Tbb82sISXSqjkI95wh7dcRo3ghcDNCSdtrrvwNkK0gxszy/JgZU+/Q6VcupGpjLcm4DkjcPg8T5pTTVN2KN+Dhxh9cQ3t9Oxl5dj1rza1RPqOUpZ85g0d+/BRCgJR914v1xEnPTcMdPPyO8po31tNU3cozv30ZAVz5jYv54NkVnPGpxbg9GolYElM3+etd/0iFKz94+yNYpsXnfnTNALwbDsMdoeYifEuR0gDUERNOJlzlSGMb0mrCToORYDWDe9Hwjx5xGDSmL5rChf9xHi/+379wuVQ6mm0vi71FDdMwKZzQZ5A4YXY5Hzy7gglzyln52rr9rhnriZNxBB5E1ZtqCXdEcLld5BRnEw3H+ODZFSy5ciGZveN/R1MnvlD/sd3jcxNu78HQTdyeQwsYBytB6DAyEcKDcM9HavN6fx8h47HiR2ozQF8LmICwzUfVQlCGt/mow4ln32gIPa7TsKuJs64+DV/QCwK628Ok56XxzP2voCiCq755CbmlfeJBTnEWW5dX8vHPn8Mbj7xLz+oqbrvKTkW69+ldAKxedTPX336l3XaIcq01W+tY8+YGBMI2/LYsZi6ZxrhZtveRZVnUbK0nMz+j332k56ZRvanWETDGMEK4wHO67SEhe0B4e8uojozNBeGajDReQ8q9NsFls12ie5inig8HRrWAkYwnqd/RSFdrmPScEEUTCvjsD65h6sJJ1G1rAMtC87ppre/ASBq01bcz7dRJBDP6domnnjKRXRuqKRyfxx2fnk4imuSexytRVYU7Pj2Ns69dzBd/kX7IfnS3hbn/6w+heTSqN9rixBP3PY+e0Jly6iRKJxfh8blRNTUV7bEHy7T2G7gdxhZihCmxQrjt6BG9EoK3gOJHuCYj1IKh7prDEGJZFu0NHbTWtePxeyioyOXa71zGv59dTjQc56pvXoxlSf79zEcoisLFNy8lvyKPovF9i6y8slwmzh/Hg3c8SjKhc9tVE1Ohy9/+1HgCGQF++9GhK0DdetaddDR1Ul/ZCNhC8U13X4eRNNixpoqTzrd3bzLy0mmpacPt0bjp7usA+5ni9XtSO5MOY4+RIlzsjdBmgZqNVMtA6uAqt93uh3v0iMOAo3k14tEE7/zzA2afOQMk7FhTRXp2iLreMfLRHz9Jem4a88+dnRIhbvjeJ3nugVeIR/aNwJAIoZCem7Zfat6+Am4sEmftWxvJyEtPRWuYhsmG9zaTV5bdL3W6/8zY3kwced9EhxONEALUbCD7sMcON4RagHSfCvpq8F8PCFDLEO65Q921EcGIWRlJKeluCxOPJPCFvKRlHdzYUk/qdLf1sOq1tcSjCZ781YtIy+KGO65i0SdO5uIvLE0d29HcxeJPnIyUkoKKvP3EApfmIrswi66WMB6/h0Q0CdiTcUM3cHk0GnY0MX52f5dmy7LoaunG0E2S8QPnoAohiHbHAFBdKlNOmsjFXzw/5YFx1TcvwTRMKmaUHuvb5uAwJNi7lTOAGUPdFYcBIhlPEu6IoHlchxyPpZQkEzrr391M3bYGnv71i0gJn/zWJZx60QL+b/0vU8dGwzHWvbUR07RYePFJ5JRk7VfaOpgZJJgRQPNoKef7Pa8zbeFk6nc0kV+eu1+KR6wnRjySwDRNLGvf6TB4g1662/quN2FOBXWVjfR0Rgik+0lEk3S3djN/6ewjSh85WAlCB4fBxp7kFyPU4qHuisMQ8+2/fo0N722mrb6dltp2YuEYk+dPQErJZ+76FKoq+OHVv+yXGpKWE8Ll7r9cmHLyBJa/mEukK0owLUj15lrikTjfuXYqeeW5fPaHhy+X2tnUhbWPJ9we0aOjqYtAegBFUSibVkLNlrp+JVi7WrqZucRJrXYY2SjaZKSrwo4gwe2UUD0KRoSAoSd1Vr++nvu//hAguOLrF1E6pYjZZ07vN7k1DZMty7dTtaGG3VvqiIZjTF80pXe3TCWZMNi2cifzzp6ZOiczLz0VMnww3F4NT8CTUoBvu2oiiqoQzPQx67RpbPpgK8WTCvD4PAD0dEZY/vJq/nLnYwBc+uULOOuaxUyaP4E/ffdRAG66+zpaalrJyO0Ldx43qwzNq5GeEyLSHSOzIIOpJ090DDwdHByGFTvWVbHlw238897nAcnN/3Mj88+blRoD99Cwq4nNH2zjwTseJRFNcsN3r0pNhP0hP2teX88515+eEgT8IR+//ehnh3xtVVW4+r8+Qbgzwj9+/gx3fX4WUkIgQ2HhJQuo3VpP+bTilJ+FaZpsfH8r1ZtqEEJw7nWn09nSzVuPvY+iKqnoimh3jKLxeanXSc9JY8llp7Bl+Xba6joIZAY4+ePzKJpw5JFEjnDh4OAwUHQ0dVK1sYZIV4S8slzKppXg9Xv2O05P6rTUtNFU3czmD7aTX5GH5tHQEzrVm/akVcN9N/8egN+tuAfTNPmvc+5CKCIlxu6dFvedC+6mo7mLs65ezM61VZgbLHuOLOy5bHtDB3WVDZRM6jNYtCyLxqoWarfVA+Dxe5Dm/mKyBMRe5slTTlSNTJcAACAASURBVJ5AV2s3LbVtqVTuogn5lE8vOUHvpIPD0CGEG0TWUHdjxDEiBIxtK3bQVN2K5rHz53OKs9i9uY6M3PRUnhzA5mXb2bG2iud+9yo9HT0IRbD8xdW0N3QA8OSvXuCyr36ceWfPxNANFFUhEUvSWtuGaZhkFWYecCexfHopl3/1QmI9Mf78vcdACIykQXdrmL/d/QR6wmDhxQvwFHuQUrLi1TUYCSPV39zSbFp2t7J7c21vfregpbaNrMJMcor7PrRCCEonF1E6uWi/Pjg4ODgMB1rr2lj/zmZyirPQPPYjpL2xgw3vbmbB+X2hjy21bSx7YRXpvSJtT2eER378BO0NnQA88uMnuPTLF9DTGUmNu5Zl0dHURSKaIJgROGAJ6ZySbFweF2lZQZKxZGo8BnjmNy9x5dcvprWuPSVg7FxXza71u3nugVcRAj77g2tob+wkHkng8XswdINIZxTLtBg/p6Lfa2XmZ7DokpNP7Bvo4ODgcJw07Grio5dW4/F7cHvdbFuxg92ba1lyxcJ+IkakK8IHz60g1hOnvrKR9qYuXvjjv/CFvEhLpsSLfVFVtZ+IsC+KS0FRBBUzy3jp/17H43eTiCZIRJNUrt5F1YbdZOSl9RMw1r+7mV3rdxPoNb7vbg3T3tRBWk4o1edkPInqUsgu6psbe3weTrvslNS4HUj3k5GbNiLTuRwcHE4MQyJgxCJxhBAHVIr3xbIsqjfV8sxvX6J6Yy0AD93xKJYpCWUGUgJGMp6kasNunvvdqymfCc3j6me6KaUkGU3yzuMf0NUaJh5NEO2O8sYj7yGEHdkx9ZSJTF4woV8fckuymXn6VN59fJmtDO/XS5kSK8LtPfzpu39H82gpM86/fP8f6AmDz/7oam78wdWYukXJlELKp5cesHzfHpIJnZottdRtb0TzalTMKKWgIs8ZtB0cHE4YlmUhLXnIsWhvdm+u5ZnfvoxLU1Nj3NP/+xJ6UueB1f+TGte3rdxBMDPAI3c/QVNVCwDhtp5+19ITOttW7KC1rh3LtIh0RVMl/aSUB4y08/o9nHzBPN598kMCGQFUl5oSqYUQmLqJ22sbYEkp2bGmisyCDPYMm4qqUDa9hKtv+wS5pdmEOyLklecwcd64/dzv96WtoYParfXoSZ2iCQXkV+Tul+Li4ODgMJBYlsXG97eQlh3C0zveegMe2urb2b25tt8cdsP7WzB0k5zibJp3t5JdmImhm/3KV7t9blyayrce/DKqqtBU3ULVht2cf+NZZBVm0NnSdUAjzupNNax+Yz0AHm+f6aDmdoGAWLgvDaWrtZvqTbXkluakSlP70/zEYwk6mzpRNRdCCFyayknnz91vfaAoCjl7iRpSSiLdUZASf5rfmRc7OIwxBlXAiHRFWPv2Jtrq2wF7J23OmTPwhw5dxWNP1MLeCAGm0edWryf0VPseQllBFFVBURR8IR/nXb+EztZu0nPTePrXLxJu72H+0jkgJZpXI6swk83LtpNXlkNGbv+0koKKPM66ZhEut4o36OXFP/wLRVW4/GsXkZ4TSu0UmqaFZUkSsWTqXCklQghyirKZedqR5ewZusGyF1bS2dTF0795CSnh0i9/jGkLJzPlpAmHv4CDg4PDITBNkx1rqtixtgojaZBbksP0RZMPGPWwN3rC2H+y2PvrnhLUAD0dkdRO2x4y8jPoaOqkeFIhl3/1Qmq31+P2ajz965fslL+Fk6mYWcrb//wAJFx081KyizIpm9o/VDirMINzrj0NVVVJxBK88tCbKIrCdbdfSbQ7RsE4OxVESsmjP3kKRRHUbmsA4P9952/ceNfVePyeo4qu2LmumvXvbsIb8KKoCnXbGymeVMD8847ME8PBwcFhX/aYG/d0RvCHfGQX274/B/PPufXsO7FMi3OvP53sov7pxYH0AM27W1ICRjKh07y7jewi2zsilBWkqaqFc68/HUURLHtxFZYpScaTtDd28t8X3M3HPns2VZtqmHrKRF74/WtYpsXlt1zI6VcuJCM3vV9/yqeX4k/z093eQ2dTFyteWYvbq/H5n1xPZ0s3+eV9ZXrD7T0IIewUEEsiFIGiCNIyg8w6cwb+kBewzZPdnkNXK+vpjLDmzQ10NHUCgrTsIPPOnXVILyYHB4fRxaAJGIZu8MHzKzF1k2d/+woAV3zjYj58YSVnfnLRQXexFEWheHIRV9xyEU/97wuA7R/RWtdO6bQ+Qypv0Ivm1rjhu1fx8A8fBwnnffoMdqytZuP7WzCSOm6fm1f+9BZvmRYNO5oAeP3hd1LX+PP3/o6eMJi+aHJKwDB0g/XvbuZXX/oDlimZcvJEVr62FkVVWHjxfHxBD/OXzk5N6I2kzszTpnLNzU/S3d7DD/+/uZx68QKyCzLJL8894vereXcrHY1d5JZmo6j25DinJJvtK3ZQPv3AeY4ODg4OR8qmD7axc221bRosBJ/81qW8//Ryzrp6Mb5DlIYunJDPJ75yAXllOTx4+yMAXPPty1EU0U+MzinOorWunZvuvo4Hb3+EZFxn1ulT+eilNRhJHUM3yCrIxBPwEOmOEQvHWPmvdax6fT3hdjtS47nfvcLzD7zK/23oM/qs39nIhve2EOuJU7utjsbqVrpawyiqoK2hnTOvWpzqh2mYmLpJdC9BOdIVpbW2nUkLxh/xe5WIJdj0wVayi7JSkSrBjAB1lY2UTy8lt2TkOaA7ODgMLcmEzopX1tBS24aqKkhLkpYT4tSLFhzyPKEIhKJgGma/yLlELEFGXt88U1FEyjNCCMgvz6W1rp1we5hARoBPfutSOpu7ScQSvPXYv5Gmxe4ttZi6wd9/+nQqsu2pX73Ic797lT+svTd1bSklHU2dtNS0MuXkiWx4bzNnfnIRb//zAx649c9c+9+X94sEUVSFhp1NVG3cjbQkWYWZlEwuQkqJP+Qlr7RP7DgUpmGy7MVVGEkjlSbY02mnyZxz7RI0t1Oq3cFhLHDcAoau69TW1hKP71tKqT+mYeLJVxFC5fqfX2a/uKYiJWzetPmQ4cuePIXCUBaf+cWVIMAdUigpysPyJdm8eXPquKIFuSTjST7zyysRgC/kY9LZ5Vz67XMRiiARSXDTjE8h5Z6ojv7s6U9MiaSuqyd0rKDBf9x/DZYl8fo9nH7TSQghUruLu6p3IoRAWpJ4NMG5X1pMl2cmVqHFV/7kxu3RcPvctHQ30bq5+Yje12RCJ3NKEJME199jv19oOunBAJU77TKuJwqv10tJSQma5gz8Dg5jgXg0QdWGGp574FWqN9mpef+891n0hMGkBeOZOHfcQc8tmlBAfWUjTbtbMZImUkoS0QSLLjmpX2TGxHnjaKxqoau1205RURXGz67g0i99jLzyXGq3N3DPZ35DMp6krd6eKKsuFW/g4OJsR3MXK15eS1pOiLa6dt55/EOA/7+9+46Tqj4XP/4550zdmdmd7SwssCy9SF0EQYkKFhBQETX2e41Rk3hjQMkPDVFQNCYmenP1msSoiV7rvTYSRUFExRKkCCy9l+1sm63Tzpnz+2N2h13YSpPyvF8vXuyU02Zfr+9+5znP93mYevclfP7mN3zw3FIuvfVCIDrB3rNhP9N/dhnjJjyDr7SKx+4eydALB1NeWMHlP7q4w59XTUUtpskRf6dsdivlhRUSwBBCdNq+TQcoL6ho9uX9+Tmv8Mbj77F7wz6g+ZKNpkU0/TUB6mv8/Pi3N6NZNAL1QYL+EFlDDtWFs1gtZPbrSsHOIpIyErE5bAwc04/ta3aRmplMYnoC7zz1AUF/iIKd0Qy1imIfg8b3J1gfjO1HtaiEQ4eWnABsW72LHat3o2oKrz76DnpIZ/xVOQQDIaxWC6MuGYbbeyhDJG9bAb7SKpxuJ+5EF76SKsryyhg0rj/JGe13LGlUUeyjvqqef/wpeiP09sduxO11UZpfTllBBRm90tvZgxDiTHDMAYz8/Hw8Hg9ZWVltrkEL+kMUKsUoihItvEZ03Z1pmnTr0yW2Zrk1ZsQkHNaJGBE0TcVis7R4PD2sE/SHiBgRrHYrNocVPWzgr/FTXlhJvDURFIgYJrECGUp0YprWPQVDN/AkuWNrsKvLaqg8WEVIjZ6zoiiYdrP5+WdnRI9bHyRQFyQ53ReLAuthO6AQCHbFk9jxbiJBf5DC3SUtfl49BnTr8Hr19pimSXl5Ofn5+fTq1fqXFiHEmSNYH4yl8zalagrVZTVtbmuxWhg9eQRl+RX0Hp5FnNtJRnbaEVkbCSnxXDBjDLvW7+WGB64mPsVDn+G9cHqcbPpyK9tW7aKmsvawvZsMHNuX3ev34UlyM/2nlzP0B4Nirx7Yko/NYeXVR/6P2sq6WEHQj19cjtvrwtAN8rYVkJKZzNaVO1izZAP2OBs2hxVVVbFYLQwY3RdFMaPrtDvIYrPQrKBSA103YmvQhRCiMw5szSe+SSc6AM1qIdAkeNAaZ8OSi6qyGiJGBKfbztgrRh3RVW/g2L7UVdc3dPBQMCMRxl05miHjB1Drq0MP6wTqDt2A1CwaNruV/uf2YW/ufuJTPPzw/11NXPyh8b26ooada/eQ0i2Joj3Rm3L+Wj/fLl4fy9r41RWP40lys/CDB9i/JZ/ta3Zx+/0fEfKHePul66I7UhV6De3ZqflsOBjmnf/8MBZwacwCnP6Ty1otSCqEOPMccwAjEAi0G7wA0CwqiWkJWGwWygoaamB0S0IP6R0avBRVaXddHEQn1017Shu6QX21H02LVkyOGER7NNF8Mmqa0fc6Pc5m52Ny5KT1cIG6ABEjQlVZDYZukJx+6LNQVBVME1XrXIEhq81KUhcvoDSs8wNvanyswN3xoigKycnJlJaWHrd9CiFObU6PE0WB2xZcz8sN7Z5vf+xGygsrSOzibXd7TdNI75na7rK4+GQPIycOjT02TZNvFq2mqqyanoMz0awa4UAYT5Ibf20Am8PK1m93xjqEZGSn0WPAoaWC9bV+rA4rptl8ZK4qq4llcfzhjj9j6Aa3LbieG37yHqZhkpJ2gJQ0eObjfDZtScZXUtWpom8JKfF40xPwHawioaH6vb82gKapsXobQgjRGY3zw6b+7ZHrqSqrYfnrXwLNa2A0ZmE0fV4P64RDOnanrcVaPHannXHTR+MrrSbkD+FKiMPtdbF/ax4bPtvM5B9N4rXH3kbVVBJSPVwwYywHthWgWaJdSPSgTl11PcMvGhzbp+9gNYqi8PeH3qTWVx8LWlSXHwp+hwIhAvVBPn11BVVlNeRtL6K+JoDTZWfohdF91Vf7ozcTOyFao+nI+XvENElIkRoYQpwtjksNjI5MBDWLhs1hjUZIGwbsxkH3eH4hP1w4pFNZ4otmMjREZ1WLGr37qCpEIhFUTcOV4MThdmB3HsoEURQFq81KYnoClcU+DCNCfIqH6rIaVFUhpWsS4VAYXTeoLquJZUrk7bLRtaeOxWYhEOyCETZwedvOMDmcqqm4vS78NX68qfGgKNgc1jbTq4+WVG8W4uxis1vpN7o3m7/eTsSIoCgKFUU+HG4H3fp0OWHHrSqrpryogn/+aWn08cFqIDreRYxINKBt1cA0+dkzPyLnsuHNJuXpPVLZ/M12/v2xG9nw2Sa+fn8VmNHASGNgPLouXMXpdmCzW/HXNV/e6CuposfAzmWxKYpCzmXDWf/ZJsryykEBV3wcY6eOwulyHOvHIoQ4C2UNzmTTV9tJ63FoCUnlwSqyz+nR6jaHF/U8/KZdSxRFaZaZEagPsnHFVhK7eLFYLThcDuqr/QTrQ1SVVkc7U+kmvYb0oGjvQZb9zwqu/OnlTY6pxW7uNZ0+xid7iPM40KwaU+++FFVTiU/2MO4Hz1A7sY7MrOhyxaHW36CqKiuW/ZT45LY7Px3O7XXxy7/fw29vexZNU7n2/un4awL0OqfHEcX3hRBnrpNWxFNRFJweJxabpWH5B1jtNqz2E3sKZuTI6K6iKJhEqyATiQYy4jxOAnWBWKpxI4fLTq1Pj7ZPVRTCjSlqioIe1rE5bbEOKE3333hsQ4/g9Dg6la7cSLNouBPdRCIRFJQ2e3ILIURn9BneC1d8HEkZXkL1ITJ6p5M9LKvd5XzHIhzUo3cd22CEo51M/vSLv/FGajx//Pqx2GuZ/TI4sDWfyqJKUrunRMdeReHGB2ew6NmP0SwqM2dPxd3QDnXnngXs25LH+Rc9i6FHyF1/N2k9Exlwbp82z6GlDgBOl4PzpuZQX+MnYkSIi3dK9xEhxFHrOag7lSVVseXCpmmS0i2JviOzjwhUHE++g1W8/dQ/KSuoJCM7jbL8ciAaSF67LJeLbjgfMxIhFAhzMK/8iPlrSrckrFYLNzwwg1pfHa8tfBtVVblg5liGThhEZUkV8cluqspqsNqjc+qmGdR62EBRDLxp8R0u3tnUwLH9SEjxEKwPkZyRSOYPupLaXeoQCXE2OaltVBVFweawHdcJcnl5ORMnTgSguLgYTdNITY2mNa9atQqLzXLE0pX4ZDcVxdGsDEwTI6RTWVKFaZq44uNQ7YcmpYoSDW7YnTYiDcGQvQW7KS0r44p+V2CxalSHDJIzEilvSKNL6ZaEP5iAQ7MTn2w/5klua9vruk5KSgo+n++Y9i+EOPsoikLX3l3o2vvEZVwczp3oAhNue+SHaJrKS796HSNskJaVyobPNkeDxA0BYVVVm7WjBrA5bJw7ZQSFu0sozS/n3j/fGc240FTi4p044uyk9YhW2o9P9qCoCr2G9GDTpgcJhw1+cP1YElLijynrrL2230II0RGWhmKXfUZU468J4HDZ8aYlHJes2NbasMKRxYgbGbqBzW7DjERwuBzkfrGFvG0F5G0raLY/m8PGmCtGsnZpLpigahqGrpOamUxlsY8uvdJIz0pl/WebAdi6/dcN2c7z8df6Wfn1nQydMIixU7OOKgNbURT+65vHO72dEOLMcVIDGCdCcnIy69evB2D+/Pm43W7uv//+2OumaWJz2gj5Q5gNS1cihomqqqiaimEYLbZwNU0Tf22Akv3R2hCJ6Qk44uzY4+xs3bGVTZs2cdWMK4FoMaX6qnpM00QB9JCO1WrB4Tr24IUQQpwpnC4H/Uf3Zsu/dhAX78TQI4RDOt16d2H63ZehqEqsKNvM2dPIyD5UX6LyYBWbv95GZUkVFptGnxG9yB7aMzZ+v/dfHwHQd2Q2RXsPUlVWjSshjkBdED1kcN60Ue2mGDdO0hsr/bf1JUAIIY6Voih4UxOO2/KH9saw+y56GNM0KdhZHNvGHmcnNTOJPiOzGXbhIOxOO674ONYsWd/iMfy1fvy1AQaM7YPNbmPCtWOjS6ZrAyy8/mk0q8bjix9E01RCgRA2R7Q+x9Ydv6amopbpP71Alt4JIY7JSQ9gFPn8bMj3UVEXIsllY1imlwzvibmjNW3aNAoLCwkEAvz8P37OrTffimEYZPbM5Ibrb+TLr1bwyIMLKa8s53f/+QQul4vzLzif/Px83nz9LSrKKpgz7z527t6JSYT7753DJZdewiMLHsEf8PP5558zb948Zs6ciZrowua0sTE3l5m3zUA3dCKRCO+//z7Z2dnNzmXWrFnccccdsQyK22+/nSVLlpCZmckjjzzCL3/5S/Ly8nj22WeZMmUKL7zwAh9++CEVFRUUFhZy2223MW/evCOu94knnuDdd98lEAgwc+ZMHnroIWpqarjuuusoLCzEMAzmz5/PzJkzT8jnLYQQ7ek7MhtvWgIHtubz49/dTEZ2GttX7SZQH8TpdnD7YzcSiZiU5pXRpaElXq2vjm8WrcbutPGP5z7G0COMv3o0u9fvY/FfP8Vqt8Qm7Atm/p6H376fXev3Ul5QSXyKm1GXDCWpS8db9QkhxJmqaYZHOBjGNCOYJvQ6pweJaV5ULXrj7fbHbuT5Oa/gcDliAZAD2/LJ/XwLbz/9TwCuvW86oy4dRpfUBNwJrmgNI6LFQ3MuG86aJeuJRGoxTRNNUxg5aagEL4QQx+ykBjCKfH4+2VKCx2EhxW2nLqjzyZYSLhmU3uEghmma6GEdI2ygNrRTbS3L4eWXXyYpKYn6+npycnK47vrr8Hg8VFVVcdHFF/LEwico2FfExKkXsvTjTxgwuD833nQjEG0z+OijjzL+3PN5fN4T1Ppruebmq8gZei73zZrDzt3beebZZ4gYESJGJFZI6e+v/p05v5zD9ddfTzAYjGV9HH4u11xzTexcJk+ezFNPPcW0adOYP38+n376KRs2bOCuu+5iypQpQHQ5zKZNm7DZbIwePZqpU6cyZMiQ2LUuXryYAwcO8O2332KaJlOmTOGbb74hLy+PrKwsPvooeneyqqrqaH99QghxhHAoTFVpdUMV+/gWM9qaUhSFtO4pzdY+xyd5+PbDtdRV1aGoKhHdIHtYT1Izo+uaD2zNR1EUXAlxsbbYNeV1rM/biK+0Cqe7+YT48O4nHdX0LmXTx0IIcTpobww7PBNj3puzME1ISPGw9dud7MndT3JGIppFo77GzzWzpvKD68YBUFddz4bPN+NNS8Bqt6KHdPZvyyd3xRZ2frcXtzeOrSt3Njv+44sfpKLYByYkZXixO8+ettOR8psBUJNf/Z7PRIgzz0kNYGzI9+FxWPA4osV8Gv/fkO/rUADDjJjUVdejh3QqD0a/iCd18eL2ulpcR/f000/zj3/8A4D8/Hx2797N8OHDsdlsXHvdtZimSfWuSgYOGsiQYYNRVIUbbriBV155BdM0+fKbL/l8xWc8//e/oKgQDAYpKi5Es6joQZ2q0urYsawOK063g3HjxrFw4UL279/PjBkz6NOnT5vn4nQ6ueSSSwA455xzSEhIwGKxcM4557Bv377Y/i+77DISE6N3EK+66iq++uqrZgGMpUuX8tFHHzFixAgAamtr2bFjB2PGjGHu3LnMnTuXadOmMX78+A78poQQon0l+0v5blkub/1uEWBy87yZjJ48goSU+E7tJ6lLIhfdcAFl+eUE/SGSMhLxph6qVVFdUYPdaePFB18nf3shAKs/XoehG9z55C34Dlbz0QufoocNJt50AZu/2UbfkdkntCCpEEKcCgL1QfJ3FFLeUPune5PW061pKTg7aGw/LBaNPRv3E4mYeBLdnDd9NJ6GoshlBRW88/QHWO1W9m3Kazh2iIgRwRFno6rsUBvVcCCMalGxO+1kNGTSCSHE8XJSAxgVdSFS3M2jry67hbLaYIe2DwVD6CG9oYtJYwqcgr/GH6s632jZsmWsWLGClStX4nQ6Of/88wkEou30nE5ntI2qomCxWlDUIzt8WG1WVIvKc0+9QNf0blisGnpIB2D1ulWx4nK+hiCGNy0eBbjllls477zz+PDDD7n88st56aWXCIVCrZ6LzXZogq2qKna7Pfazruux1w4v6nT4Y9M0mTdvHj/60Y+O+NzWrFnD4sWLmTt3LpMnT+bBBx9s97MWQoi2+Gv9rP54HZ4kd6yblKKqrFq8jotvPL/TxdkccXYy+3Vt8bWUbsmU5e0kYkRizzVmt1ntVor2lFBbVUdNRS3v/vFDrrxnMr6D1Zw3Peeo6hBJ5oUQ4nTgr/Xz9XurCNSHcLodlBf52LPxAPPemt2sdWpHaBaNgWP70XdUdrSgp8PWbkFRVVWIGPDDuVejqgqvLPg/rHYL1RXRYMbGr7YyeFz/s64eXKT8ZgivOvQzkokhxPF0UkeUJJeNuqDe7Lm6oE6Sq2N3ycKBML6DVZQVVBDyhwj5Q1SW+NDDRrOJLUSXSiQlJeF0Otm8eTOrV69ucZ+DBg1i+/bt5OXlYZomb731FhBtnzrxoot5+fW/ASamabJle7Siclyci5raGiqKfbHz8JVUEQqE2bVzF3369OHee+9l6tSp5Obmdvhc2rJ06VJ8Ph/19fUsWrToiEyKyy67jBdffJG6ujogmuVRVlZGQUEBbrebW265hfvuu4/vvvuu08cWQojDlRwo4+2nPuDVR99m36Y89m3K483fvsdrj71NZcnx7YyU2a8rDpedqXdOIqmLl5TMJM69fAQ3PDCDQG0AX2k1E2+eQGK6l3BQx5PopryoMpq6LIQQZ6g9ufsJBcIkd00kLt5JYnoCdqeNzV9vO+p9WqwW7E77EcGLlG5JXDN7Grc8fC1JGYmk9Ujhwh+O4/wZ5+JOdFFaWME55w/gzidvxWqzYrFZ2b1+HwU7i471MoUQopmTmoExLNPLJ1tKgGjmRV1QpyagMza7g/2b24oEH/bSFVdcwfPPP8+gQYPo378/Y8aMaXGzuLg4nn32WSZNmoTb7SYnJ4dAIIBm0Vj42EJ+fu+9XHXTNCJmhO7devDXZ17k4osv5q9/+wtTr53C3bf/hMsnTsYwIkQiJq+/8TpvvfUWVquVrl27Mn/+fBwOR+xc+vbpS86oHOpr/IQC4Y5dNzB69GiuvPLKWBHP4cOHN8vQmDJlCtu2bWPs2LEAeDweXn/9dbZs2cLcuXOjfbhtNv785z93+JhCCNEaI2y0+trhAeVj0biW+tF/zGVP7j4UTcU0of+5ffCmJVC4p4RVi7/D7rRRWRJdWvjn2X/H6rAy4uIhbe0af62fkgNl6CGd5IzE49bCUAghTobivQej7ambcCXEUVZQgR7WsViP3zTfFR/H8AsHs+GzzWCahINhArVB+ozshaIoFOwoYu2nG1n/+WbKCysBWPTfH5PcNZHu/dtf1nImUZNflcwLIU6gkxrAyPA6uWRQOhvyfZTVBkly2RibndzhAp42p42EtASsVgtlhRUADcWEooU858+fH3uvw+FgyZIlLe7H52t+V27SpEls374d0zS56667yMnJAcAT7+Fvf3sJQ48WjdN1A0xw1NtZ+uEy/DUBjLCBZtVwJcRh6Aa//vWveeihh4445scff0xdVbR+h6+hfkewPkRR/qFWVgsXLoz9bLFYmp1njx49ePfdd5vt8/D3zJ49m9mzZzd7T1ZWVqwQqBBCDDosggAAE0lJREFUHC8p3ZK4+t4ppHRN4u8PvQnALQ9fR015Nd5Opi53RJzHyZDxA3lx89Os/mgd1RW1lBWUU5ZXhqqpWO3W2HsVVSFYH8Thbr3a/cG8MlZ9tA7TNFEVBaOhcOjgcQMkiCGEOC043A78tYFm458e1rHYtFg3keOlMZi88IMHyBrSnXXLN5GSmYQnyUNNRTXF+0oBUJsUcvbXBggH275Z56/1s/O7vRTuLsZmt5I9PIseA7qd9stOJHAhxIlz0tuoZnidR9021Wqz4IizE6w/1N3DYtWOqEDfWX/605947bXXCAaD5OTk8OMf/7jZ65pFw53oJmJEMDExgcpiX6wmhh42qPXVkdjF2+rEVw/psfodjZkkVquFYH0Qu9N23P/QCCHEiZSQEk+/nN7sWLObcEgHE6oOVjF84pDjUjyzcbLc2B71vose5g+fLcAVH8eEa8+jqrQaPWywdeUOTBOC/iBL/vYZAGOnjsI0afVvgx7W+W5ZLm6vC7szeq6RiMnu9fvIyO5Ccoa0XBVCnPp6D8ti5QdrsTlsWG0WDCNCRVElg05g3Qmny8GQ8wfSJTudHWt2U1VajdVho2t2Gn2GZRGXEMey//kCgIFj++H2ulvdVygQ4utFqwnVh/AkezDCOg9f9TviPA7+tPbJE3L+nSFZFEKcmk56AONYKIqC0+3A5rTh8sahKAqaRTvmu2Vz5sxhzpw57b5P1dRoL2tVRW1S9NNi01BQsDusrW6r6waVB6tQFIVQQwHQssIKTNPE5XW1GcC44447OnE1Qghxcgw8ty9dstIYOKYvmkUjvWcqbq+r/Q2PkaqqJKZ7AdgfH0fW4ExqfPU0xLXpkpWGzWnHamv5T1x1eQ3hoE5CStMiygo2h42SfQclgCGEOC10yUpj+EVD2LpyB4ZuoCjQb1Rvsof2PG7HaCmYDNFixynTkwAo2ltC1cEqSvPLqS6vwdANTBPik9x401vPyCvcU4K/2k9KQ8tsi1XDarfirw3irwvgdB3bDUohxJnptApgNNI0Fe0oMxYiRoRgIIQe1FE1pc1JbksURcHuspOQlhBro5rUxUvEiLTZ37qtSLiqSrqyEOL0lJiW0Olq903VVddTml8OQGpmMq74OOBQJ5Cmk+WW9ByUScGOQpb8bXksK+6jl5Zjc9i4+j8mt7hNawFjM2JGs+SEEOI0kTW4O5n9MgjUBbE5bdjsrd9MO1Fc8XFYHTYGj+9PVVkNaz/ZgGbR6DE4E29q6221q0qrsTVkwb30q9cB2L852qJ17qWP8sevHzvxJ9+CxsyLpp1EJAtDiFPHWTVTi0Qi1PrqiEQi+EqqARNvWgJOjzOWRtwRtoZMC01ViUQiKIqCKyGuzYmv1W4hqYsXUGIV+r1p8VisFlk+IoQ4Kx3Yls+GzzejoDQszoNhFw6mx4DMDu8jNTOZIRMG8t4zH8Wes9osuBPiWnz/fRc9DCZccfcl1PrqYhkjelhH1w26ZKUdwxUJIcTJZ7FacHuPbkpv6AYHthawf2s0cNBzYHd6DOwWa4XdkWByfLKHrr3TeeaeF7HYLBTtjhbs/+DPS1nxf//iqc8fafHYCSkeDmwtaPE1mRsLIVpzVgUwQoEwkUgkWpVZAVDQrBqBugA2uxWlg5kQiqJgd9qigQwTUOhAr2wVt9eFvzYQjUYrYHPYcLiObFUlhBBnOn+tn9zPt+BNS4hVytfDOrmfbyE1MxmnO1orqbXMi6Z6D83iL+ufZM6kR1AVhf/8amGL77vvoodjadCGblBdURvN0lAUVBWGXzSY+GTPcbpCIYQ4tZmmybrlGynYWUx8SnTsy12xhbLCCnIuHdbq/LSlYMaIi8/BlRBHoC4Qey4+ydPmHDcjO52d3+3FV1rNvz16A4Zu8OIDr+F0O3h6xaPH4xKPSmO2hdTAEOLUdFYFMIywga+kCprUoSgvrMSbFk8kEkFTtXb20JyiKEe0b22LZtFwe11EItEWg20tK4lEIhh6BEXhuNT5EEKIU0lFsY9IxGzW5s9itWCaJhXFPrr1iQYw2ltC0sjutPNfnUg31qwa3rR4xl11LoZukJDiaXMZIEQn+zIWCyHOFL6DVRTuKiatR0rsOUcPO0W7i/EdzIrVGoL2x2DNovHc6t8CnRu3x03PYdvq3RTtLsZis+BKiMPhanssPlkkcCHEqem0D2CUl5czceJEAIqLi9E0jdTUVABWrVqFzdakSJumNiZMNGfSoeyLcEgn5A8SMUwsdgt2R/PuIdEuJdGaFm1NcturDB0KhKivCeByl2CaUFOTgSveGUvnO9y8efNISUnhF7/4RbvXIIQQpwJVVVsMAJsNr7VVOA6iQd7Kkirqq+txuBwkZXjRtLaD0H/4bEGHJ9aNDN1gd+4+9mzYjx426DGgG31HZUtxOSHEaa+u2o/SwpxUUVXqqv3NAhjQeneoY+FKcDFq0lDMieegKAqX//vFrb7XMAyqy2pAUYhPdrc75gshzkynfQAjOTmZ9evXAzB//nzcbjf3339/s/eYpolpmtgcVhLTE1BVlYrihjoUqfHY42ztBhWC/hD+Gj++g1WAgjc9gXAgjDvRBWbzXteaRcPpcTS7s9hRhm5QX+1Hs2qxDA8zYlJf7ced6JK7f0KIM0I04KASCoRibVeD/hCaRSMpw9vmtuFQmDVLNlCaV4aiKJiANy2BMVNGYHfaqS6vYd+mA1RX1JDcNYmswd1jS1I6a8MXm8nbVsiESc+BAv/64ueUFZRzwTVjsdpOfrE8IYQ4XhxxNszG9k1NmKbZodpwpmmyd9MBCncWYbVb6Tm4O2k9UpoFNYL+IKqmtjtetje/rSiuZM2S9YT8IUwUnC47OZcPx5t69EWkhRCnp+8lgHH9X/4FwFt3nXfCjrFr1y6mT5/OiBEjWLduHZ988gkLFixg7dq11NfVc/WVV/P/7p+LzWmj74A+3HHHHSxatAjDMHj77bfp168fy5cvZ9asWdEJcsRkyeKlfLXyK/77+WfxxLvZu28vEydO5Mnf/J6IEcHXpCvJ7FmzWbZ8GRaLhcmTJ/Pb3/6WRYsW8fjjjxMKhUhNTeXVV18lLS2NefPmkZ+fz65duzhw4ABPPTmXr79ZyyfLvqBnz66887/Po6ka3btfxE033cTixYuJi4vjjTfeIDs7u9l179y5k3vuuYeysjJcLhcvvPAC/fr1480332ThwoVomkZSUhKfffbZCfvshRCiPXZndPK5dumG6B01wGKzkHPZMOxOe5uF4/ZuOkBpfjmp3Q+lPf9lziu8vvAdHlv8IN/8YzUWqwVHnJ09ufs5sLWA82eMwRUf16m7hTWVtXRPm0t2dyvx8dsAGPeDZwgHw5TmvUTX3l2O+XMQQojvS2IXL97UeCqLfSQ0dJOqOlhFQoqnxUBy03HZNE1mzp7GghlPolk1fjj3av71zzUMGtePfiN7U11eQ+6KLVSW+FBVhcx+XRl4Xv+j6pISCoT49sPvcLgceJKitTrqa/x8+8F3TLz5gqO6YSiEOH2d0SV+t23bxqxZs9iyZQvdunXjiSeeYO3ateRuzOXLb74k/2AeTnc0DTg9PZ1169Zxxx138NRTTwHw5JNP8vzzz7N2zVo+XLSYugo/elBnfe46HvrlfD5+Zylbtmzl9f95g8qDVYT8IUL+ENs37eTjjz/muzXfkZubywMPPADAhAkTWLlyJevWrWPGjBn84Q9/iJ3r3r17+fzzz3njtTe49d/v47JLL2Dj+vdQFYUlS1fEUq2TkpLYuHEjd911F7Nnzz7imu+8806ee+451q5dy29+8xvuueceABYsWMCnn37Khg0beO+9907kxy6EEB2S3iOVSbf8gDFXjGTMFSOZePME0nuktrvdgS35JDRpzffSr16nYEcRuzfs45eTHuG9Py7GmxqPw2UnqUsihh5h78YDnT4/f22gxWUuiqpQXVHb6f0JIcSpRNM0Rk8eQUbvdCqKK6koriSjdzrnThnZ7vKMXev28Zf7X6FgVzEHthbwv08uIiUzmR2rduMrq+brRavx1wZI6ZaMN81L3vYi1i/feFTnWVZQQTgYblYbI87jJBgIUVFUeVT7FEKcvk5qyLIx8+LbvRXNHp+oTIzevXuTk5MTe/zGG2/w4osvous6hYWFbN26lcGDBwMwY8YMAEaNGsXixYsBGD9+PPfeey833ngjl1x0KU4t2m5v2DnDyezWPRp9vmYma9at5rJJl8eO403woigqd//0J1x55XSmTp0KwIEDB7juuusoLi4mGAzSr1+/2DZTpkzBYrEwfMQIQOH88ddiGEUMHjyInTtrqJ+QDsANN9wAwE033cTcuXObXa/P52PlypVcc801sed0XY9dy6233sq1114bu1YhhPi+2exW0toIWrSaMdFC2jOAEdaxOprf4XN54zh4oBTGD+jUucV5nCz/572kZCYxaEC0s8nW7b+mLL+cUZe6O7UvIYQ4FTldDkZcfA5DJwwCaLXeWlN/+GwBd4+aQ7Au1Ox5raHW3L6N+zHCerTrHtEadMldEynZV0pNZS2exM6Nn3rYaLlWR8NrQoizyxmdgeFyuWI/79y5kz/+8Y8sX76c3NxcLr/8cgKBQ62e7PZoVFfTtNiX/nnz5vH8889TV1fHpVMuobymFIvdgqapJHXxkpiegNVmweFykNw1CZvThs1pIyMrnRXLV3DVlVfy/vvvc8UVVwDws5/9jFmzZrFx40aee+65Fo9vs1ux2WzoIT1W8T4cDhHnia7fbmuNoGmapKSksH79+ti/TZs2AfDXv/6VBQsWsG/fPkaOHEllpUSshRCnp6whPahqWLIHcPtjN9KtbwbZw3rywweu5paHr2v2/pA/hCs+rtPHcXtddB/QldK8ciINtZQqiivxJLmbVe0XQojTnWbROhS8aDT3lf/gmllXkDWkO1lDunP7YzdGXzBNQiG9xWUdiqoQ9IeOeL49iekJmBETw4jEnjN0A5Ro/SMhxNnlpAYw3rrrPN666zzG9EpiTK+k2OOTobq6Go/HQ3x8PEVFRSxZsqTdbXbv3s3QoUN54IEHGDVqFPvy9+Hxulifu578gnycHgfvvPsOEyZcgBHWo3cETZPKikrqQ/VcedWVPP3006xbtw6AqqoqunXrhmmavPzyyy0eszFA4fa60CPdMYnHHmfH2rBm8K233gKi2STjx49vtm1iYiIZGRmxJSKRSIQNGzYAsGfPHsaOHcujjz5KYmIiBQUFR/EpCiHE9y9rcHe69EqjNL+csoIKSvPLo+334uPoO7IXlcW+2F25UCBEfXU9vYdnHdWxhk4YxJDz+/Pdqvv49utfkDW4O2On5ciaayHEWa1Lr3Q0qyUaSAAiEZPywgpSe6SQ2bcroUC42fsNIwJmdH7bWZ5EN/1G96a8oJzKkioqS3yUF1UyaFz/2A0+IcTZ46yZgY0cOZJBgwYxYMAAevbsecSX/5b8/ve/58svv0RVVYYOHcq06VP54osvOHfMudw/9z52797NpEmTuObaazB0A6vdimmalJSWcMNNPyQYDBKJRGI1NebPn8/VV19NUlISF154IUVFRa0e22KzRP9ZLc06pJSVlTF06FCcTidvvPHGEdu9+eab/OQnP2H+/PmEQiFuvvlmhg0bxqxZs9i7dy+maXLppZcyZMiQo/gUhRDi+2exWhh9+Qh8B6uor/HjcDmYetclqKpKJBLBMCLsXrcPQzewOW2MunQYKd2Sj+pYmkWj97Be9B7W6zhfhRBCnL4ccXbGTc/B7Y3Dd7CKyuJKuvXNYPC4/mgWjeSuiZTml+NJdGPoBrW+OgaO7Ycjzt7+zlswYHQf0nukULzvIIqq0iUrVTqQCHGWUlpqn9SanJwcc82aNc2e27p1KwMHDjze53XKWrZsGc8++yzvv//+ST92ZmYmmzZtwuttu8Xg0Tjbfo9CnCoURVlrmmZO++9srqXxWByih3XCwTA2p63dYnRCCAFHPx7D2T0mB+qjrVKbdhgJh8LkbSukYFcRVpuVnoMz6ZKV1m67VCGEaNTamHzWZGAIIYQ4e1isFlnmIYQQJ0FLWRVWm5XsoT3JHtrzezgjIcSZTGZ3nTRp0iQmTZr0vRw7Pz//ezmuEEIIIYQQQgjxfTsuRTw7swxFnHrk9yeEEEIIIYQQ4lR3zAEMh8NBeXm5fAk+TZmmSXl5OQ6H4/s+FSGEEEIIIYQQolXHvIQkMzOT/Px8SktLj8f5iO+Bw+EgMzPz+z4NIYQQQgghhBCiVcccwLBarfTqJe3lhBBCCCGEEEIIceIclxoYQgghhBBCCCGEECeSBDCEEEIIIYQQQghxypMAhhBCCCGEEEIIIU55Sme6hyiKUgrsP3GnI4QQZ52epmmmdnYjGY+FEOK4O6rxGGRMFkKIE6DFMblTAQwhhBBCCCGEEEKI74MsIRFCCCGEEEIIIcQpTwIYQgghhBBCCCGEOOVJAEMIIYQQQgghhBCnPAlgCCGEEEIIIYQQ4pQnAQwhhBBCCCGEEEKc8iSAIYQQQgghhBBCiFOeBDCEEEIIIYQQQghxypMAhhBCCCGEEEIIIU55EsAQQgghhBBCCCHEKe//A1P9niAYbi0cAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "param_img = {'interpolation': 'nearest'}\n", - "\n", - "pl.figure(2, figsize=(15, 8))\n", - "pl.subplot(2, 3, 1)\n", - "pl.imshow(ot_emd.coupling_, **param_img)\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Optimal coupling\\nEMDTransport')\n", - "\n", - "pl.figure(2, figsize=(15, 8))\n", - "pl.subplot(2, 3, 2)\n", - "pl.imshow(ot_sinkhorn.coupling_, **param_img)\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Optimal coupling\\nSinkhornTransport')\n", - "\n", - "pl.subplot(2, 3, 3)\n", - "pl.imshow(ot_emd_laplace.coupling_, **param_img)\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Optimal coupling\\nEMDLaplaceTransport')\n", - "\n", - "pl.subplot(2, 3, 4)\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", - " label='Target samples', alpha=0.3)\n", - "pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n", - " marker='+', label='Transp samples', s=30)\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Transported samples\\nEmdTransport')\n", - "pl.legend(loc=\"lower left\")\n", - "\n", - "pl.subplot(2, 3, 5)\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", - " label='Target samples', alpha=0.3)\n", - "pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n", - " marker='+', label='Transp samples', s=30)\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Transported samples\\nSinkhornTransport')\n", - "\n", - "pl.subplot(2, 3, 6)\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", - " label='Target samples', alpha=0.3)\n", - "pl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys,\n", - " marker='+', label='Transp samples', s=30)\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Transported samples\\nEMDLaplaceTransport')\n", - "pl.tight_layout()\n", - "\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_otda_linear_mapping.ipynb b/notebooks/plot_otda_linear_mapping.ipynb deleted file mode 100644 index e1e9c17..0000000 --- a/notebooks/plot_otda_linear_mapping.ipynb +++ /dev/null @@ -1,343 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Linear OT mapping estimation\n", - "\n", - "\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Remi Flamary \n", - "#\n", - "# License: MIT License\n", - "\n", - "import numpy as np\n", - "import pylab as pl\n", - "import ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 1000\n", - "d = 2\n", - "sigma = .1\n", - "\n", - "# source samples\n", - "angles = np.random.rand(n, 1) * 2 * np.pi\n", - "xs = np.concatenate((np.sin(angles), np.cos(angles)),\n", - " axis=1) + sigma * np.random.randn(n, 2)\n", - "xs[:n // 2, 1] += 2\n", - "\n", - "\n", - "# target samples\n", - "anglet = np.random.rand(n, 1) * 2 * np.pi\n", - "xt = np.concatenate((np.sin(anglet), np.cos(anglet)),\n", - " axis=1) + sigma * np.random.randn(n, 2)\n", - "xt[:n // 2, 1] += 2\n", - "\n", - "\n", - "A = np.array([[1.5, .7], [.7, 1.5]])\n", - "b = np.array([[4, 2]])\n", - "xt = xt.dot(A) + b" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n", - "---------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1, (5, 5))\n", - "pl.plot(xs[:, 0], xs[:, 1], '+')\n", - "pl.plot(xt[:, 0], xt[:, 1], 'o')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Estimate linear mapping and transport\n", - "-------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "Ae, be = ot.da.OT_mapping_linear(xs, xt)\n", - "\n", - "xst = xs.dot(Ae) + be" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot transported samples\n", - "------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1, (5, 5))\n", - "pl.clf()\n", - "pl.plot(xs[:, 0], xs[:, 1], '+')\n", - "pl.plot(xt[:, 0], xt[:, 1], 'o')\n", - "pl.plot(xst[:, 0], xst[:, 1], '+')\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Load image data\n", - "---------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "def im2mat(I):\n", - " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n", - " return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n", - "\n", - "\n", - "def mat2im(X, shape):\n", - " \"\"\"Converts back a matrix to an image\"\"\"\n", - " return X.reshape(shape)\n", - "\n", - "\n", - "def minmax(I):\n", - " return np.clip(I, 0, 1)\n", - "\n", - "\n", - "# Loading images\n", - "I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256\n", - "I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n", - "\n", - "\n", - "X1 = im2mat(I1)\n", - "X2 = im2mat(I2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Estimate mapping and adapt\n", - "----------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "mapping = ot.da.LinearTransport()\n", - "\n", - "mapping.fit(Xs=X1, Xt=X2)\n", - "\n", - "\n", - "xst = mapping.transform(Xs=X1)\n", - "xts = mapping.inverse_transform(Xt=X2)\n", - "\n", - "I1t = minmax(mat2im(xst, I1.shape))\n", - "I2t = minmax(mat2im(xts, I2.shape))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot transformed images\n", - "-----------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Inverse mapping Im. 2')" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(2, figsize=(10, 7))\n", - "\n", - "pl.subplot(2, 2, 1)\n", - "pl.imshow(I1)\n", - "pl.axis('off')\n", - "pl.title('Im. 1')\n", - "\n", - "pl.subplot(2, 2, 2)\n", - "pl.imshow(I2)\n", - "pl.axis('off')\n", - "pl.title('Im. 2')\n", - "\n", - "pl.subplot(2, 2, 3)\n", - "pl.imshow(I1t)\n", - "pl.axis('off')\n", - "pl.title('Mapping Im. 1')\n", - "\n", - "pl.subplot(2, 2, 4)\n", - "pl.imshow(I2t)\n", - "pl.axis('off')\n", - "pl.title('Inverse mapping Im. 2')" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_otda_mapping.ipynb b/notebooks/plot_otda_mapping.ipynb deleted file mode 100644 index c604278..0000000 --- a/notebooks/plot_otda_mapping.ipynb +++ /dev/null @@ -1,290 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# OT mapping estimation for domain adaptation\n", - "\n", - "\n", - "This example presents how to use MappingTransport to estimate at the same\n", - "time both the coupling transport and approximate the transport map with either\n", - "a linear or a kernelized mapping as introduced in [8].\n", - "\n", - "[8] M. Perrot, N. Courty, R. Flamary, A. Habrard,\n", - " \"Mapping estimation for discrete optimal transport\",\n", - " Neural Information Processing Systems (NIPS), 2016.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Authors: Remi Flamary \n", - "# Stanislas Chambon \n", - "#\n", - "# License: MIT License\n", - "\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_source_samples = 100\n", - "n_target_samples = 100\n", - "theta = 2 * np.pi / 20\n", - "noise_level = 0.1\n", - "\n", - "Xs, ys = ot.datasets.make_data_classif(\n", - " 'gaussrot', n_source_samples, nz=noise_level)\n", - "Xs_new, _ = ot.datasets.make_data_classif(\n", - " 'gaussrot', n_source_samples, nz=noise_level)\n", - "Xt, yt = ot.datasets.make_data_classif(\n", - " 'gaussrot', n_target_samples, theta=theta, nz=noise_level)\n", - "\n", - "# one of the target mode changes its variance (no linear mapping)\n", - "Xt[yt == 2] *= 3\n", - "Xt = Xt + 4" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n", - "---------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Text(0.5, 1.0, 'Source and target distributions')" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1, (10, 5))\n", - "pl.clf()\n", - "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Source and target distributions')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Instantiate the different transport algorithms and fit them\n", - "-----------------------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 0|4.130455e+03|0.000000e+00\n", - " 1|4.124174e+03|-1.520585e-03\n", - " 2|4.123972e+03|-4.893906e-05\n", - " 3|4.123892e+03|-1.957570e-05\n", - " 4|4.123852e+03|-9.690449e-06\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 0|4.155681e+02|0.000000e+00\n", - " 1|4.121954e+02|-8.115904e-03\n", - " 2|4.120356e+02|-3.877130e-04\n", - " 3|4.119541e+02|-1.978089e-04\n", - " 4|4.118961e+02|-1.406833e-04\n", - " 5|4.118524e+02|-1.061404e-04\n", - " 6|4.118195e+02|-7.984227e-05\n", - " 7|4.117940e+02|-6.188410e-05\n", - " 8|4.117747e+02|-4.692100e-05\n", - " 9|4.117580e+02|-4.045536e-05\n", - " 10|4.117441e+02|-3.393923e-05\n" - ] - } - ], - "source": [ - "# MappingTransport with linear kernel\n", - "ot_mapping_linear = ot.da.MappingTransport(\n", - " kernel=\"linear\", mu=1e0, eta=1e-8, bias=True,\n", - " max_iter=20, verbose=True)\n", - "\n", - "ot_mapping_linear.fit(Xs=Xs, Xt=Xt)\n", - "\n", - "# for original source samples, transform applies barycentric mapping\n", - "transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs)\n", - "\n", - "# for out of source samples, transform applies the linear mapping\n", - "transp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new)\n", - "\n", - "\n", - "# MappingTransport with gaussian kernel\n", - "ot_mapping_gaussian = ot.da.MappingTransport(\n", - " kernel=\"gaussian\", eta=1e-5, mu=1e-1, bias=True, sigma=1,\n", - " max_iter=10, verbose=True)\n", - "ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n", - "\n", - "# for original source samples, transform applies barycentric mapping\n", - "transp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs)\n", - "\n", - "# for out of source samples, transform applies the gaussian mapping\n", - "transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot transported samples\n", - "------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(2)\n", - "pl.clf()\n", - "pl.subplot(2, 2, 1)\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", - " label='Target samples', alpha=.2)\n", - "pl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+',\n", - " label='Mapped source samples')\n", - "pl.title(\"Bary. mapping (linear)\")\n", - "pl.legend(loc=0)\n", - "\n", - "pl.subplot(2, 2, 2)\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", - " label='Target samples', alpha=.2)\n", - "pl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1],\n", - " c=ys, marker='+', label='Learned mapping')\n", - "pl.title(\"Estim. mapping (linear)\")\n", - "\n", - "pl.subplot(2, 2, 3)\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", - " label='Target samples', alpha=.2)\n", - "pl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys,\n", - " marker='+', label='barycentric mapping')\n", - "pl.title(\"Bary. mapping (kernel)\")\n", - "\n", - "pl.subplot(2, 2, 4)\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", - " label='Target samples', alpha=.2)\n", - "pl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys,\n", - " marker='+', label='Learned mapping')\n", - "pl.title(\"Estim. mapping (kernel)\")\n", - "pl.tight_layout()\n", - "\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_otda_mapping_colors_images.ipynb b/notebooks/plot_otda_mapping_colors_images.ipynb deleted file mode 100644 index 5313e3b..0000000 --- a/notebooks/plot_otda_mapping_colors_images.ipynb +++ /dev/null @@ -1,368 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# OT for image color adaptation with mapping estimation\n", - "\n", - "\n", - "OT for domain adaptation with image color adaptation [6] with mapping\n", - "estimation [8].\n", - "\n", - "[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized\n", - " discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3),\n", - " 1853-1882.\n", - "[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n", - " discrete optimal transport\", Neural Information Processing Systems (NIPS),\n", - " 2016.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Authors: Remi Flamary \n", - "# Stanislas Chambon \n", - "#\n", - "# License: MIT License\n", - "\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "\n", - "r = np.random.RandomState(42)\n", - "\n", - "\n", - "def im2mat(I):\n", - " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n", - " return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n", - "\n", - "\n", - "def mat2im(X, shape):\n", - " \"\"\"Converts back a matrix to an image\"\"\"\n", - " return X.reshape(shape)\n", - "\n", - "\n", - "def minmax(I):\n", - " return np.clip(I, 0, 1)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Loading images\n", - "I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256\n", - "I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n", - "\n", - "\n", - "X1 = im2mat(I1)\n", - "X2 = im2mat(I2)\n", - "\n", - "# training samples\n", - "nb = 1000\n", - "idx1 = r.randint(X1.shape[0], size=(nb,))\n", - "idx2 = r.randint(X2.shape[0], size=(nb,))\n", - "\n", - "Xs = X1[idx1, :]\n", - "Xt = X2[idx2, :]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Domain adaptation for pixel distribution transfer\n", - "-------------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 0|3.680534e+02|0.000000e+00\n", - " 1|3.592501e+02|-2.391854e-02\n", - " 2|3.590682e+02|-5.061555e-04\n", - " 3|3.589745e+02|-2.610227e-04\n", - " 4|3.589167e+02|-1.611644e-04\n", - " 5|3.588768e+02|-1.109242e-04\n", - " 6|3.588482e+02|-7.972733e-05\n", - " 7|3.588261e+02|-6.166174e-05\n", - " 8|3.588086e+02|-4.871697e-05\n", - " 9|3.587946e+02|-3.919056e-05\n", - " 10|3.587830e+02|-3.228124e-05\n", - " 11|3.587731e+02|-2.744744e-05\n", - " 12|3.587648e+02|-2.334451e-05\n", - " 13|3.587576e+02|-1.995629e-05\n", - " 14|3.587513e+02|-1.761058e-05\n", - " 15|3.587457e+02|-1.542568e-05\n", - " 16|3.587408e+02|-1.366315e-05\n", - " 17|3.587365e+02|-1.221732e-05\n", - " 18|3.587325e+02|-1.102488e-05\n", - " 19|3.587303e+02|-6.062107e-06\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 0|3.784871e+02|0.000000e+00\n", - " 1|3.646491e+02|-3.656142e-02\n", - " 2|3.642975e+02|-9.642655e-04\n", - " 3|3.641626e+02|-3.702413e-04\n", - " 4|3.640888e+02|-2.026301e-04\n", - " 5|3.640419e+02|-1.289607e-04\n", - " 6|3.640097e+02|-8.831646e-05\n", - " 7|3.639861e+02|-6.487612e-05\n", - " 8|3.639679e+02|-4.994063e-05\n", - " 9|3.639536e+02|-3.941436e-05\n", - " 10|3.639419e+02|-3.209753e-05\n" - ] - } - ], - "source": [ - "# EMDTransport\n", - "ot_emd = ot.da.EMDTransport()\n", - "ot_emd.fit(Xs=Xs, Xt=Xt)\n", - "transp_Xs_emd = ot_emd.transform(Xs=X1)\n", - "Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape))\n", - "\n", - "# SinkhornTransport\n", - "ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\n", - "ot_sinkhorn.fit(Xs=Xs, Xt=Xt)\n", - "transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)\n", - "Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\n", - "\n", - "ot_mapping_linear = ot.da.MappingTransport(\n", - " mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True)\n", - "ot_mapping_linear.fit(Xs=Xs, Xt=Xt)\n", - "\n", - "X1tl = ot_mapping_linear.transform(Xs=X1)\n", - "Image_mapping_linear = minmax(mat2im(X1tl, I1.shape))\n", - "\n", - "ot_mapping_gaussian = ot.da.MappingTransport(\n", - " mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True)\n", - "ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n", - "\n", - "X1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping\n", - "Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot original images\n", - "--------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1, figsize=(6.4, 3))\n", - "pl.subplot(1, 2, 1)\n", - "pl.imshow(I1)\n", - "pl.axis('off')\n", - "pl.title('Image 1')\n", - "\n", - "pl.subplot(1, 2, 2)\n", - "pl.imshow(I2)\n", - "pl.axis('off')\n", - "pl.title('Image 2')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot pixel values distribution\n", - "------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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nzBx0OE4WQx1ocwtwu6p+QUSWAXeIyGJVLfnpLyIfAz4GMGPGjCEYZm2ykeXjP3yJTQd6yRklv67lI/i13H9KHA0igqjGKzlVsECScZ58TsGA7wvTxwz/PLrFZ03j6VWvYYoiRz1PCDCsX7cZYyyegC9S8qPBWsvWzTuZMnU8DU0NNYVx3qL5NDW7tk9HYNjPQYfjZDKYorgTmF70flqyrZiPANcBqOqvRKQOGAfsKz5IVb8OfB3goosuGrLFtFf39bD5QB/TWzO090X0hZbdnf28uq+Hge/1pPsC4GneAiz6RlfFJA1txZPaPtU8FvDiZHOx8f9hE0ZlWDDx8F/2xlo27OmkPzTMnTiaprrTLzBnTEsTly1ZyKubdtPZ1ceopnpam+rYtmXAJVqthimAiEdXVy+Tp02mva29xIUqnjB+4ngniCNwDjocg81giuIKYL6IzCaeiO8F3ld2zDbgWuB2EXkdUAfsH8QxHRd9OcPH7lrPa/t6EVGMBREl7XuoVA/9j1e0FF/AWg9PNCk/FrtVrSG2+gK/4E0tVFtBEreqEGEJIvCSveIre7u6+P6qbbz7wuquwR0He7jt4ZeS3D/BWMuvnTudqxdNGaQndPw0j2rg4nPnFt6vXvUK1tpC4IyqoFWidVWVKMwhDRkWnr2QHZt30N3dTRAETJwykQmTJ5zS+zhNGTFz0OE4VQyaKKpqJCIfBx4gdhB+Q1XXicjfAStV9R7gT4DbROSPiNXgVi0PIxxidndmueX2F2jvixJXZixQgadkI0O6VquFBBFQtYm5k5QZK7pFExkCYhdq8Y0LoImZ5Hnx50Riw9EqPPrqXuZPGMUFM8aUXM9Y5baHX6InWxrE8uALO5g9fhQzxjaRjQyZlF8I4gkjg1XIlN1LGBlUOeI9nmw06W8I8fqp51deX61h86ubsGppampi4eKFpNKnnzU8lIyUOehwnEoGdU0xyXe6t2zbp4r+Xg9cNphjOBFUlY9+dx3tvWGSNJgIm2hR8n3++6PYkom3FRs3ooqgUF55RRIrSCvdhGo1DtLxpGJfNrI8/PLeClHctK+TqEo0ZmQM96zaREdvP5G1pAOfyxdOYef+Tjbt6QBgYksDb1syj4Z0wH1PbWD7vs54+5hGbrh0PmNGD+46pqrS2dlVug2IrCXw/Tgf0SpgCcRgEu3s6uzi5Rdf5uwLzh7U8Q1HhvscdDhONUMdaHNas3ZXN7s6snlzD0HjxE6NLTbfj7/IEYkFr4TYdVpCIogl8qlgrRIElW7QI/1c7wsrUxpykaHaQqWnlr07uyGKd0cZy6MvbMMrus7uQz3c/vCLjManpy83sL2tm28/sJaPvf0CMqmT+09m776DvPzKVnp6+slkUkRhWDF6q8qYcc3Mmj2FbRu30dXRUXGe3u5e+nr7qHeFvB0OxwngRPEwrN3VVTDspNBhYQBjIPAt4ldKomjBlgRi4fOolCsBohCCav9PqGLLy9okpDylr7+fT/14JU2pgL6eiPp0wLKFE4mMKT3YWoI+BtRPQaL4/FpmgkaRpVNtRVWHbC7iG3evZt60VpYsnk5zU12VAR8be/YeZNWaVwpBMr1JYe9AwPdKR2Ct0tvVTXdnZ9VziQhhGFKPE0WHw3H8OFGswYHuHM9sOYQxBlQJPKka1BIZSHsF5YxRCu2L8kno9jB2X9UVHFVQxYssk5rrOdAdYpOegL6NsAYOhBHiwb7kwn4H7DnUy/TxTezrid2kqpA2VJidUqOWkVFFrCLkXbBSePVkQ9Zv2str2w7wvuvPo2XUiQnQ+pc3V028j8qyVHzfI8pl2fjKJlSTyN0qgTeNjY0nNB6Hw+EYGZWlTzJb2np569dW8vjGQ6jG64eh1aql1axloMSYkrhZYfbYuvizWry/ujA2NwSx1VZ2nCTX3H6wF2sNLRmfwEbJdRLXq8nHs4L1IRdZtu7r5v2XzuOSeRM4d8YYJjRUWnVqKjYl17T4JUIVr+EpFi8R5TAyPP3CtsM+w6Oht7d24n3+WXiekE6n6O/uLotKLc5t9Jg+ezp+cGoDghwOx8jDiWIV/unBTXRnDaEpFbGoSnsiVYiMJgIYB9L85sWT+OQb59GY8fA9RdTih1HJl3qewBNuXDwBjI1drslLTGnt05yxdPRkq+qqJu00NPl/0/OU9dvbWDBxNO9dNo+5U5org3gM2KhUXLCWlK10EyefGDCEFbbsPsTLG/dwqOPIPQ9rkclUL84dBD6traNpGtXAnDnTmDNrUsn4TVIGTlVJpVIseP0Cpkw//dJNHA7H8MO5T6uwYmt7bHsVC4ZIYgiW5swlMThgDYGNK7DctWIHdz69nXdcMIU9PTlWvro/jla1iXDmUwxU+ft3nM0PV22Jo1PjEjbJmYv/KzWEqnCafHMJPC8uPr5iw25Wb9qD5wnvXbaQ1a/sIxeVuiptLi4xV/BVWo2DaWtcrHjlNJsNeXzFa1iFOdPHce2lZx1VAe9iFs6fwYvrN2HMwLh8z2Pm9AlMnzaRpqYGRISDBw5WuEutjQsjjJ80ntaxrcd0XYfD4aiFsxSrkA4kjqIxNn6FBsIo3lbwXVpQi4SGlGcJEpejVegPLZFVfrBiJ52H+uO1wUJ9U0VNlLwM45rSNKTj3yaaqJtg8DB4avCwqDUVFmYJiV541uIn65uRsWRDQ1824nu/epUP3rCISWMaEImb+p6/YDzNrR4SKV7Wxq/q9bkTFJGo6GXojwyRMWzefoCXNuw+4nPNZnPkiiJmZ0yfyKKzZpNOBYgIvu8hNmLn1p08/eQqnli+gt7ePlrHtFZ1jYoIk6YeXXsph8PhOBqcKFZh3tiGQn1SMYqn4FnwIsX0RZAN8aIofmFo8aWGaCmv7O2sWqYMAIHlr+zj1y8sqiWpNslpLO2nmA9IqXYqTyBtlLRXvVtEZCxb9nfQb/oRP0S9HP1RlluuWEw6Uyo2VqstfSq+NWBAkhdW0SRfMzKWF1+t3Vzh4KEu7nvgGe75+VPcfc8TPPLYGvqSSNNZMyfx5jcu4bKli/BNiKjBmLjtU093LyueeR4EzrnwbBoaG/A8D8/3SKVTvP68RWTqMjWv63A4HMfKGe8+/cO7XuSna/cV1EaATBBHN4oOxF4OoFiVQg6iAvu7QzKpynJvvuSPqKWKyvdXbiGdgpvOncrdz+/Eo3rHDIB3L5nDL9ftpCcXxakcxnLZ/Im8Z+kcnt9ygO88/lLVNcfIGH6xanNs9Sa8ur2N3v6QP3jHxdz54FraOvrwPMFaG7tJk+o9EOc4BuV3kVzHogS+0tXXxTd++AjjxozikvMWMGFM3MGjvz/HI4+tIYoGInv2H2jn4UfXcMN1SwsNgHds30NZDWoActmQ9vZOWlubuXDZBfT19mOtpaGx/kzufuFwOAaJM9JS7M5G/OuDG1n86Uf56dq9canR5FVeaq3ya1eo1r4vqhHNCTWDTgHIWcN3Vmwmaw23fXBpbasS5cG1W7h83jjOndZMRpRxDT5TWurwPY+6dFy2Ta0ZeCUXjovAlA7aWGVXWxddvTk++tYLuPWGc3nbZQt488WzCcQgEuFh8TAEWiP4RiHwFN+3xD8WlH1tnfzs4VUcOBRXptm0eVdF2oUq9Gdz7NvfXtiWzeaq37ZAmAsLb+sb6mhM1hodDofjZHPGWYq5yHLzV1eypa2PyMSJ9yXl2CQWkYpqNGUUd3SCOPCj2IUqxGXa8k843lW0X0CwiFqyoXL3c9toa+8lkDj9o9qX/v6uPu5ZvblQhaazD/732Q28trudy+ZPIipv2qsWxStU4SnHE+FQdz8TWhuZNKaJSWOaWLl+Oxab/CCorMBTdoE4urZ4rHGJHn7+0LMsmDOFMBsmpdnKn5/S0zOQkjF+wlgOlnW7iG/B0tIyvPtGOhyO4cMZJ4r3r9vHjkP9ZCOLL5rEy8QFuyVp5xRZTZLvq7s+8+t9BVQRVUw2wvM9xBN8o/g2PlD9+DyxkCqep0mR77wPMr7eis374+4Yvj8Q5ZoIrS82DhItq0KTiywvbDvAwfbu6jeslrTnVRXFMDTs3d/B5m37mTK+mUVzJjBp7KhE0YtOUXTfFc+i7DkERbmWr26MuxQFooXIVEGSZ2vp7e0hDCNSqYBp0yexbevOgnsU4qT92XOmk66RuuFwOBwnmzNOFJ/Z1E5PzoDN15jJuwYVNYr6gvhCLrRkBDzxyqw2TTIqCiVrAOK2UAo2SXsIkpIxmktSHpIn7XkGP6gs/m00Lm8mqpgoinstJo2KA7EFV241C1IE9h0mX1A9Czb/ufy44yT95avjZr6Bv5NHVm7gI29fQl0mRX9/GLdsQjACgeaLoBeeVhwQVDQcX6tblpHGn/cEVLRwLxs2bmXzlh1cfeUSmpoaWHbZBWzbuou9u/eTSgfMnDWN8RPG1rwvh8PhONmcUWuK/aFh+SsH4gVAo4XqMyUYxQsjAmOwURwJOZB0rwWXZ6ECjSqBJsEpRS9DUW01A5pVbNbip6o3zc1Xjsl/Xm2SimFMcfW4qlGuwkC/xWo0ZHw0MKhYVA2qcQqJoIUcwchY2rv6+cUzr3HzlefgBQJevlqOEhV6Qcb/i12zNra0i8Sw1iisHdiZX341xpLLhaxesx6AIAiYM3cGyy6/kIuWnOsE0eFwnHLOKFH8ysOb2XWoD7W1IzzR2KWaT1NUa7HGYKIIXyMCLFiLzYVoLsSGEaImFsuil8WSw8TCkfxPAi2UZCsnYMCCKhaX4tJyFfE9qngmwvZmyYVRhWCqKlPHNjF9TBPkkptSAxphKVKpIl7cuIcp45p519XnMa6lEQ9LYE1831hiSSsSfKtJybnDRBOVjT2phlfgQNuhqjVQHQ6H41RzRonid5/ZQTZ32Az1EpKqbQNRnHZgh8fAy5jEipQiK1AVqxbVECV+4dvirIiiC8WBPdU6aMSnGij/FiXvfVX8bITkFGsgyllsUfkzVcVHeNM5s9i5p7KzhIGqRcpNUtpu+oRWbr1+KW84Z26tp0NTQ4ZlFyyIXcUmeT4V4ph3Lx8OF0nqcDhOD86INcVd7X185Bur2XWwDwSsWnwjBEH1r+qy2JFkw0Dgi1fWRkoVoghSSeP3pBcxnlEMxFVmBDRr8YOA+RNGs6Wtm8AT+kMDMtBVoxKFyAzIlwjG88hESllpVqKcxfMtIoIHBKmAPW2dNa0wg5a5XRW/LOy2s7uXWgFHgQhnnzWDRfOn0dXdRxhFPPjIqsrWVcBAJ6h4nTL/40FEmDRpHJ53Rv0+czgcpykjXhR7shFv+OxyenOmJEjFqEVsXGuz+Ou+XPBgQBLinorV3YT5ZsGl9T81n6EQi4AHY+rTfPadF9KbM2zc10lrYwZR5X9XbWLNlv1lRceVDGWVbFTxchGRVm9llR9snJqojB1do71TIcK09H5eN3di4e99bR2se3VH0lG57PNG6ejspa8/R31dmpbmuG3T9ddezM8feiZx+2qJKziOvhUyQbzV84S6TIYLzl9UfYwOh8NxihnxovgHdz5fIYgAiBAZi1ol5cfC6FVzYcqAIOb3HrWzTwf+k8/wWDJrHOnAJx34XDhrXOHQ37nm9fz1D5+hrTtLNjKkfA9fLV7iwi0ZEzUEsehaPtDSELBh5wGiGj5bL2/S5hVS4NKzZxaOeOaFjRi1eCqxv7U04gcF9uxrZ/aMCYXPjGkdxTtuvIx7H3qaMIyDlPwkF3TenKmcd85C2g920NHZTVNTAxMnjHWJ+A6H47RhRItiTzbigbX7DnuMsYpnw7goNVAseSL5HMF4vTCymlS/kaqBOrHG5CNObYmFhMRrgYtnVO/o0JBJ8c/vvZRnNuzl5d2HmDi6nr2Hunji5Z0Vx1rRpApb5SA8L7Z2fRtxqD1kZUc3qZRPOggK4pgXaI+8CRuPOe179GcH1lwPHOoqWJ2iRS7Uwrqh8tKrO0pEEaCpsZ6b33IFW7fv4UBbB6NHNzJn5hQymdi/PH78GMaPH1P1OTgcDsdQMqJF8eVdXfF6mlQxAfMkFk82F5FJ+wWx8yReC/TLlroiqwQ+iVKUioTkDbKkN6JKqUWX8j0uO2sitUj5HpcvnMzlCycD8Nj67azYsIdsWQ05A6SgkEeYDAKAwFj8sruKQzIAACAASURBVMz7MGdobqxj2vhm+nMRosrO3QdRtcnQ43FGkaW3L8vu/e3s3Bu3a7KJkAaUFQDQ2JW890A71QgCn7mzpzJ39tSa9+twOBynGyNWFF/e1cV7vvI0xtg4iKO8DE2+mW/8BgGssfhJ8I2XTzZXKdG+tC9IZPImWYyJ8xYjAym/+BJxcE7+qn9w4yJS5Sp7GC6ZP5kfPf0queJAm6RQt1WDF5cMSDZb/EiRVGnSR16UDxzq4ZPvvwoRYdX6rezYWWpBqwrqCT97ZFV8/2W/I0IsgRcUtknyzEY31VizdDgcjmHIiAz5U1U+fNsKOnpC1FhslOQL5PsiqUJkITKkxMRFrT0FDCYK0cgQ5SJMLnaB5pv4zhxTz3/95pJYX61FIhO/EnGtlarneTB/8mhuuGBG9QNqkEkFfOrXl9FanymM21MlrTYuXi4WFZO8kkEWpWSoaknkqVWlPxdy/5Prqj01JLEc8+7VciIbYYtaWwWBz0XnzTume3I4HI7TmRFpKT6wdg8b93YXVEpRCOPk82Lrx/cTi6gsv0LV4FkwuQgNDav+5QYa6lIYq/zbPWuplaJQDUEJPI//e+slNY9RVYxVgipW5IqXdtHT1oeYCAkgSJeXnRu4TsqvFRkbi9g/fPV+ll0wu2bhgnyR83KjunANEVKehy9CEPhctuR1zJw2vuZ9ORwOx3BjxIliZ1/I//nWKqxqwQwuq/pZIKhSgxSRQmmzAECVyz55H39+82I6+7Pc/ew2rM0n6pemX3hF7zWpC5ryPd61bDYNmcpHbazyvUdf4ufPbiKbi5jY2shvXX8O588bWHf85arN5KIISSetrayCV3Ztjdczay+bKqLQmwt5fOUG6uoru9gfDYHv8c4bLqGpoY7GhozLLXQ4HCOOESeKP1m9E5OUHsvj+QwU3DxKfAZEJjSWz/1oLYjF8+PtqSBIxMbi5aM6BSx+Usw7Xn9cPKOVD141v+o1bn9wLb9cs5VsGJdN29fezee+9xSj6tNcfd5M3nn5WfTnopIcQWsHUikE8DwPVaXOkyRCtMb9RPG+KNIaKRoka6ig+QyNMpVNpwMmjW9xKRQOh2PEMuJEcXd7P31Zg+R10dokAMav+Ja3lriFU9n2JM++BJMkC3p+fN4wiggoFU8UNGcg5eH5whc+fCnnzqpe1LovG/KL1VvIRXER8GLPaVdfjvue3ciLW/azeNZ4nn1tZ2nvRhObhSnf56qzZ/Km8+ewduNefvrEi4VxFlDFN0W1VK0yZlQTbZ1dpYugarEoRgV8v6SfpOcJvufx1msudILocDhGNCNOFM+eNhqJNC76XbTdGIvvx1GoQuwiDSPIpAeiRPMikTLVi2XHJ8oHuQiBVMsUBI0snnhMH9tYc5xtXf2J+3FgnbNYb0Jj2dXWzY0Xz+X5zXvJ2ais/lz89tJF0xkzuoErz58NWO5evi6ulZq/HwWvSPtSgc85C6by2Mr1hWLjkqRXCHHtUzVwwXlzMNYS+B6toxtZNG8aDfWZmvfjcDgcI4ERJYrtPTl+/5srMdYWqpjlZcuqopHBC7y4DqcoGAh7wQsgCJKoVGORfApHFQopj3krq0aptbrAY/PeLsaMqqt6nvHN9QMtl8qDfRJyoaGtu5/P/tY1fObOx+juyxa8wunAZ/GsCUwf31w4/srz5+J7wo8ffTFeUzWxIOZP7fvCqMYMS8+ZxbMvvEZvX39FuycBxFOuWrKIdHpE/fNwOByOIzKivvWu+YdfcqArW3hvid2gXt4ZKhLnLQYeXphPtlck0pL1uMhaUl6V5sKiNYN2ygkjy6SW2jl8mVTAWy+Zyz2/2kAuCsFKRfBOOuUzobmB1lH1/NNHruXBVRt55uUdpHyfK8+ZyZXnzKo47+XnzmHmpDE89PQr7NrfQUM6hQkNqnD2gilcefE8ImM4a/YkVq/bXHguxaQCnz0H2pkxZVzF+R0Oh2MkM2JE8e6V29m4r3tgQ77YtQKYJKrUQxC87IAA+mLjCjVFWIXQWoKkWLgIBJ6trG6jcYRq+Tqb78M5s8cydVxTzfFaVdq7e4lsDiXpeKEQeH6hjFxdyueiBXF1m/p0ipuWncVNy8464rOYPrGF37xpadV9jzyzjsdXvYzneViJ8xIDLf0BYKw6V6nD4TgjGTGi+In/WVW6YWBJrVDAGjGIHxAltUzrJb+eV2n3WQsGQ9qP1xA98SqOM4noBjLgpvU9uObcafz5O8877Hgffm4zy1/cVhEYY9SS8QPmTR3Dx992IalyxT4BNmzbyxOrX4mjT4siUCNsQRhFhPFjRjGuddRJu67D4XAMF0aEKK7ZcpBDPWHlDhEsSiGnXcFag+97A8W7RQYCbRg4zhMl5cXCcbim8jZZufzpp6/HqtJUn6LuKNbi7n12Q5KKUYon8PmPXsvUcSdflJ5du4Ewqp6bkkrFcbQTxozmPTcuO+nXdjgcjuHAsBfFbGh45789PlCO5TAocU1TwSRxNoKfCKIWKZ8ADWktNqaIjK2oOJMPurnynCmMGV09oKYWfbmo6nZjlY27Dw6KKPZnq/xwIF7fvObSxcyfMYkxLbVdvg6HwzHSGdYlSSJj+eNvrWJ/Z3/11r9JJZdiAj++aSF2f8ae1aLGh8CiaaOrNhoOjSWyNq4rai0eMKYpw5/dfHhXaTUunDe5+g5VHl696ZjPdzS8ft60qu5Yq8r5r5vlBNHhcJzxDFtLUVV5zxce54G1u8ATxCqkBqJMC3VPQwtJ5wsh7nxBZAsCGPqCp0JzQ5rLXzeRD1w5h+bGgFv+9eEaF45rqc6ZOIrfuGYhb7l4Fqng6H9bqCrPbdrD9gOHKAQCFY0XVfYc7D7cKY6bCxbNZvX6zRw41EUYxY2XA9/jLVddQDo1bP8pOBwOx0lj2H4TrtjQxmPr9iZVaYhDRnMWfIlzEC3EIZ35YBtFsEiY5AYm28VYRIR54xv563edw9RxTTy2dicpgdBWc8kqvudx55++kYakae6x8PX7VvLL5zbRnzNxj8JimzSp1zp3yuA04E0FPh991zWsfXU7L2/aRVNDhosXz2XS+JZBuZ7D4XAMN4atKD71yn6ykU3Ez4Am1lpUxZGqClYJkqrZhUhRmxQNV2Xdpjbe+Bc/4wPXzmdCa12smEnx7Twi8dvP3HLRcQni1n3tPLRmE7nIJD0R4+sXXLxJ8413XH7ktIvjJfB9zn/dLM5/3axBu4bD4XAMV4atKE5sriPwwNgoLvYtksTalFp2nkdsMVpLkBoQRMkLUnJcZOIaqf99/8uMavLIhobA95ASVYRPve9i3rJ09nGNefWG3diiFAybrGn6EJdtU8W3wvOv7Wbe1Oo1Ux0Oh8MxeAzbQJu3XTydSJPuFAVnaGmDXVWDMSa2+OIDC5QH4Ayg9PbFFWDyLaJ8T5g3pZk7/vSN3HzZ3OMec306wC+utC2gokQYrDEQWqLI8OiqwQm0cTgcDsfhGbaWoqKx4JVtrWgZb2J7LMhIUvZNqV7GuxJjlTcsnMi/fexyGuqO3V1azqWLZvDfD66uus8rvhXXicLhcDiGhGFrKX7h7hfJ9xTUxEKsRPGsgjXY0AykYKBU7ygYI0VPpTcbnRRBBBjdkOGT734DdemAhkyqUFjcCweM2HTK59oL55yU6zkcDofj2BiWluJLO9r5/N0vFgJTNHFzlstivh0SCDZSRCxR2gMLnlHEAz+OvCm4YL2itov1aZ+3LJl1Usd+4bwp3PGnN/PC5r3sPdTN9+9/HhMouciQDnzmThvLjZcNXqCNw+FwOGozLEXxn3/8AlEYxV3njQWrcawNJF3v4zBRMaWBNxopVi0egkocn2OtpbUxzZXnTOGXa7ZgrEUN1NelWTitlXdcevKttkwq4OIFUwF40/lzeXrddto6elk4YxyLZk9wjXwdDodjiBiWorjitf1x3ntk44CZREM0SaMQNGkLRdwZo1hjLIgf1xjNH3vJwnE8vHJT3HORuOxbLpvj1msXkE6dvILc1cikg6RBsMPhcDiGmmEpiiayqLWxy1PKojmhJNVC1SLiJe/jvomigicCakljeXTVdqxNIlkVCBQTCH/4n4/RnzO84w3zT+XtnRC5MGL9a9vp78+xYM4UxrS4bhcOh8NxtAw7UQwjy7b9XYX1xGpYkty/hLgnouKp4mMhAut7ZDzizhlq8Yojb0IgUjSjfPobT3LNBTNobjz9+wtu2bGP/3fn/XHLLFWsKlcufT1veePFQz00h8PhGBYMu+jTzfu6iIwm/RKrR50Wa6UneUG0eEUxp2os6XypVDvwufxLFchB4Hs8uXZnyfmzYcTuti5yVVo/DRXGWG77zoP0Z0OyuZBcGBFFhsefXc8rm3Ye+QQOh8PhGH6WYk9/iK+KFtc1BbzALwSoFFolongYUlqp/fWZgHQgRCYqCGExAuQ11E9aRlmrfO2nK/nuIy/Gxwh88E3n8pHrzx/y4JhN2/ZgTGWiSS6MeHr1KyycM3UIRuVwOBzDi2FnKQKFQt/F2MjE7Zxi3yHx+qFBkCQ7sZS+bEgm7ZM+QocLq8rlZ8eC8j8PPc93H3mR/lxEfy6iLxvxrQef5/uPrjspt3UiRJGpmfMfhtV7NzocDoejlEEVRRG5TkReEZENIvLJGse8W0TWi8g6EfnOkc6Zb/Rb7ftfrWKNxUQRGhn8pOh21WaLCvvbegFKS68V4XnCl37/GhqT5P07HnqB/rLmwP25iNsffJ7IWL730PO8/2/u4t1/+W2+8oNf0dWTPdLtnDTmzJyEsZU3mk4FXLD4+EvTOYY3gzEHHY6RzKC5T0XEB74CvAnYAawQkXtUdX3RMfOBvwQuU9VDIjLhSOdtbcxwKP9GFUmiRlUk1r64TExSlibeZhT8cmW0lsiCCS2//fZzuPP+9eRCSy40pHyPIPD41qeu57z5E5LDlc7e6iJ3qKuPT3/9IVas30E2Ec0fP/oiT76whW9+6l1kjqFXobWWp9ZsYPnKV/A8j6uXnMWSc+Yc0T2bSad4z1sv53v3PI6xFmuVdCpg9oyJnPt6l/JxJjJYc9DhGMkM5priEmCDqm4CEJG7gJuA9UXHfBT4iqoeAlDVfUc6aUtTmuTggiDmy6UVeiQKCBZVDxGJE/KJb1aMBdVYREUwxtLSVMcTX7uFnz6xkRc27Gf+tFauWzaL5S9s5e4nXmL+tDG89bIFzJgwmm37OivGNGPcKFas20G2yE0ZRpa2jl4eWbmR65YtPKoHpqp84Zv3s37jroK4vrJ5N2te2spvv/eaI37+wrPnMmPKOJ597jV6+rIsXjiDs+ZNi9NPHGcigzIHHY6RzGCK4lRge9H7HcDSsmMWAIjIk8RZFH+rqveXn0hEPgZ8DGDGjBlgTEVwTD4PMW8k5kuDSxKdGhgb10ElH4gTJ+77Xor501toqEvxnjeexXveeBa727p572d+RE9/jr5sRH064Ks/Wcmf3rKMf/7ek0T5gBZNmhBbW3U9rz8b8dyru7lu2UI2bj/Aixt20Tq6gUvOmVW10/36DbtKBBEgm4t4+vlNXH/FOcycMq7qgy5m/Nhmbrz2oiMe5zgjGLw56HCMUIY6+jQA5gNXAdOA5SJytqq2Fx+kql8Hvg5w9rnn6x5j40aJFUIU24qFxH1rERECjQuDV4ooNKR9Lju7NDLzn7/9BAe7+gq9D/tyEdnQcPfyl/EwSSBPYpeqYe+hLtJVIlzTKZ8p40bx2dse4NkXt2JVCXyPIPD5lz+6iVlTSnsmrn1tR4kg5rHWsm7DzqMSRYfjGDnmOXjRRRfVbLzmcAx3BjPQZicwvej9tGRbMTuAe1Q1VNXNwKvEE7Qm67ceTP468rxUa7EmqtFBI2ZcSz2eV6quj7+wraQZMMQBO2s27CYVeAgWwST/hb7QICIV5/E9j8aMz7PrtpINI8LI0JcN6erp5+++dl/FuEY11pEKKsvK+b5HU33dwH2psv9gB4c6uo/4DBxnNIMyBx2OkcxgiuIKYL6IzBaRNPBe4J6yY35C/AsVERlH7Mo5bIfduD5pTKXYaWnVN7WoglFbs4NiY6bSWM7nJZbjedU7MYrAxedMZ9HsCaQCL7YQx4/mC394I8tXbahq/R3s7GX7nkMl2y49f17VgBoR4eKz42CZ17bs4i8//y0+86Xv8ldfuIN//PL3OHCwcp3T4WCQ5qDDMZIZNPepqkYi8nHgAeK1im+o6joR+Ttgparek+x7s4isJ25a8Weq2nakcxfWBEUqhNHL65mJBTFuBSVMHdfErv3dJfZlQybgA9e/vuL8b1k2n588/jK5aCAZPuV7XH3eLFZv3F5xfCYd8O5rzubcuZNp7+ojFxnGtzQiIgPrj+X3kAT5FNM6upE/uvXX+I87H0JtLP6pwOdPPnwd9XVp2ju7+ffb7ykR2W27D/D52/6Xf/qzD+J5wzPt1DE4DOYcdDhGKoO6pqiq9wL3lm37VNHfCvxx8jqWMwPlgqgUqp5aRWwcamNtHHjT0dNPJu3j+x7WxN0wrr90Du+8ekHF2f/kPctYv2U/G3cdQlXxPI8pY0fxqVuvZMOuNv74q/cRN9lQrFU+8KZzOXfuZABaRtWXnOvapQvYsbedXFkCfX0mxcyyNUWAcxdO52uf/hCvbd2L73vMmzGhIHZPrFxfIaSqSl9/lvUbtrN4wcxje4yOEc/gzUGHY2Qy1IE2x46CYpEKz28SSBMZvGInp7VYhLb2XjIpn5mTW/idm89n6eLJLJgxpuolGuvTfPfT72T1q7vZsPMQsye3cPFZUxARLlwwlfs+9yGeWLuF3mzIJYumM2lM7U4Ub7liMU+s3sjmXQfpz4akUz6eCH/5kTdXrEHmCQKf182dUrG97VBXVcvTWqW9s6fmGBwOh8NxdAw/USQu8B1bhYU403gtMWkLVb7w55lYMHOhYdvudqZNbKopiAPXEC5cOIULF1aKU0NdijdffHSxCOlUwOf/5B2seHErz7+6k7HNjVy7dCGtoxuO6vPFLJgzlZVrN5DNhSXbFZgzfeIxn8/hcDgcpQxLUVSrCHagl6IMZC0OOFST+qeUlnnLhoY/+OKDPP31D9FYny45b1dPlgeeeY2O7n4uWTyd1885OULjex6XnDObS845scoyF509n/seXcWBg51EJu7QkU4FnL1wJlMmVrpiHQ6Hw3FsDENRVNC4Qg1lQTb5ZsIKeJK3HivTGQ9193Hbz9fwh78+kMe88qWdfPSzP0ZVCSNL4Hu8+ZJ5/PPvXVfTzXmqSQU+f/k77+KB5atY8cIGUoHPFUsXc9XSxUM9NIfD4RgRDENRpLLBsCpYEDUYIJ0JCu5UGKhqEyOEWH706Ev82kVzmDO1Fd/z+Pjn76G3PyRvVkbG8NAzG7nmote4flllMM5QUV+X5u1vXsbb37xsqIficDgcI47hKYoVzQ8F9RQxSQyqNfiejyD4NsLztOSzKWDb7jZu/qu7EODW689L0i/yxeFi+rJZ7rxvzUkTRWMtuTCiLp0a8v6LDofD4ahkeIriYVGsgX/+ncu582cvsGFHW0lCfyo90IS4py8HwG0/XUXGKy0RF58J1m7YjTG2ZkL/0WCs5dv3PcXdj6wiFxpaRzfw0XdczRsuOLpC4Q6Hw+E4NQzPbO/ySjaqSKHMTRyI84U7nyaTltJC3RLnLJYbadnQ0FulEW8+fOeZdZUJ+8fC7fc8zo8fXklfNsRYy4H2bv7vnfex+qUtJ3Reh8PhcJxchp8o5gWx7L+eLT2mraOPl7YcKPno4RyWdelU1f2+77H/0PHnAObCiJ8tX1NR6i0bRtx575PHfV6Hw+FwnHyGnygCaLL+Z+PuF74ZKP2WXxM0xhKGWmJV1qoLngo8lr5+GnXpSm+yMZbzq+QqHi2d3X019+0+0HHc53U4HA7HyWd4iqLVpKmwogKKYrGoGlBTUD8TmTh0pkgNRYV0USeKlO/R3FjHP37sjUyd0EwmNbCvPhNw4+WvY8akluMeasvohprrkbOnjj/u8zocDofj5DMsA200UvAU8SVZILRI0i8xlr84Z8NaJew3+CmPTNrjDefP5BPvWUpPNsvXf7KS/e09XHn+bH735iVMaG3ke599H7f/bBX3P/UK9ZkU77vuPG66YtEJjTXwfd533TLuuPfJEhdq4HvccOnZJ3Ruh8PhcJxc5HC9Bk9HZNQ0Dc7/AzwUTyqDZvJWYsBAoIwnHksWT+X+L7//FI92gPuefJ47732K9s4eRCDjAyhLz5nPn3zobfiuw8WIQURWqepFQz2OweKiiy7SlStXDvUwHI6anMgcHLbfxIeVcqtFVVHjWqWfuGXJ4A8qIYoMHV29GBtH//zkkRX8vx8+SHtHO9bmMCZHNpcjF0Y8+8Jr/PyxVadsbA6Hw+GozbB0n8ariIfdXei3KAKf/PBlXH/Z4DcTt1b55t2P8cOHnsUYS30mzZsuXcwvnnm+ooi3IX742TDi3sdX87arLx708TkcDofj8ByVpSgiDSLyNyJyW/J+voi8ZXCHVp2GTECh8oy1sbu0+FVkJWZSPp+89XI+/p5TYyXefs9yfvjgs/RnQ8LI0NnTx49/sZLepEhAOXnXdblgOhzlnE5z0OEYyRyt+/SbQBbIF9zcCfzDoIzoCMyZ0sL4lnrqUn68fGgScbQRmBBRA1ga6lJccNZk/vCWS07JuIyxsSCWCZxVJaqsC1Ag8D2WnXv61FZ1nLacNnPQ4RjJHK0ozlXVfwFCAFXt5fC58INGJh2w8Qe/y7lzxqEYIELU4KkmNxO3lfqTDyzjvv94P3WZU+Mh7u3PEibtnMqpFcuUSadoHd3Ee66/fBBH5hghnDZz0OEYyRytYuREpJ4kvkVE5hL/ah0S0oHPC6/uxlOLCEjRd0M+LWPthj2ntOh2Y30dTfUZ2rt6K/alAo8g8IiiWDR9z2POtAlcd+l5XL1kMfWZdMVnHI4yTqs56HCMVI5WFD8N3A9MF5FvA5cBtw7WoI7ErgNdWGtr7hfg6bUnVq/0WPE84f+86xq+eOf9JfmI6ZTH2648n4OdXew60M741tHcfO1Szls465SOzzHsOa3moMMxUjkqUVTVh0RkNXAJseZ8QlUPHOFjg0brqHoUe1jnUXNT3akbUMJ1l51LU0Mdt/9kOXvaOqhPCz19fTzwxGo8T0inUvzlb76dGZNdJRvHsXG6zUGHY6RytNGnVwCvB7qATmBRsu2UY63yrk/fhfUH6pxWS9D43XedurzEYi4/fyH/9ZmP8se/8Wtks1miyNCfC+ntz9HR1cOnvnwX1QomGGtpa+8km6seqeo4szmd5qDDMZI5WvfpnxX9XQcsAVYB15z0ER2BLXvaee2FrRhRfCQp6FYqjFPGjeLDb7vgVA+thJ89urIiElWBg+1dbNt9gJlTBqzFR55dw//c/QC5MEQVrrjoXD7yzhtIBcM0jdQxGJw2c9DhGMkcrfv0rcXvRWQ68MVBGdER6Ojpxw/jthg2ULxooLqNIEwa08jy235rKIZWQjasnnsonpTse+7lDfzXD39Ormjb46uex1rL797y9kEfp2N4cDrNQcfQoKqsWf4Uqx97gv7ePsZOmsgVb7ueqXNmDfXQRhTHa4rsAF53Mgdy7Ch4FpsP3LRCCo8go4xprj/2s6ly92PP8c2fPsWhrh5am+vp6u2nqT7DLW9ewutmTubrP3qUTTv2MXf6BH7716/h7PnTa57v6iWL2bZrP9my5sUp32futEmF9z968LESQYS4B+MTq9dy6zuuo6Hu1K+NOoYFp8EcdJxKfnX/L3j+iaeJku+LA7t2c89/38HNv/2bTJw+dYhHN3I4KlEUkf9gwCDzgPOA1YM1qMMR+B6a5CLGY4u3q6cYidjb3cFN/9/t/Mvv3Iiq8vV7nmLH/nauOn8eH75hKS1N1QXz83c8wHceeJa+bIgfQFtnFwjsBf7lzvvRyOLZ+BHsO9jJmpe28sU//wBLFs+per63XnUxjzz7Itv3tNGfzRH4Pr4vXHrBPP7qy99gfGsLN121jP2H2qt+3vOEjq4eJ4oO4PSag45TT5jL8fwTvyIq+5EdhSHPPPQwb/vN36j52b7OLl556ikObNtO84TxLLz8MkaPGzfYQx62HK2lWFwSPwK+q6pD0jZ+5sQWdviCKQs+FYm/MSzKr9Zv4do//k/SnhAZi1Vl9as7+Oa9z/LIl36PMaMbWPnKNu5/eh3pwOeaCxZyx33PkAsjJB96VHTyXGhAIV20uT8X8oX/uZfv/cvHq44zk07xpU9+hCfWvMSqdRtpaqzn8dVreGLNC+SiCBHhqefWsWD6ZA52dFUE33iex7jW5pP01BwjgNNmDjpOPd0dnYhUj4ts272v5ue62tp44Kv/icmFWGM4sG0bm9c8x9Uf/hDjZ80apNEOb452TfFbgz2Qo2V0Y4YZE0azZc+hqvtVFfXAGEPWDiT29+ci2jp6+PcfLicXZvnx8ufoz0V4Av/10ydJJSanVGtHlWABv+j9xu21/zFu2bmX51/ZTMuoRn7//Tfw7XsfpruvlyipeqOqZMOQjbv2kkmlyIZhQRgzqRTvvf4aF2jjKHA6zUHHqaepeTSq1XOzx0ysneL13H33E/ZnC2W11FqMtTz747u58Y8+MShjHe4c9ltXRNZSvUuTAKqq5wzKqI7Agunjaoqi5ysisYBZwEPxk8yTXGT46VNr6e3rpS8b++WNgrGGHLEl6KmHanVhLN/UPKqh4hhrLZ//5o94bMWLKIrv+wS+x5jm+oIgFqOq/O773sHjK5/n1S3bGdM8ipvfdCWXnHtizY0dI4PTdQ46Ti2pdJqzly1h7a9WFNYUAYJUiqVvurrm5/Zs3FS1zmRXWxv7Nm5EjWHMjBmk3DJNgSOZIqdlFf5bb7iA5c9voi8XFZV4UzxPKe/VmxfG/HFqDf25ahW6lQhFjSXt+RRLYFxKDqTo8SvMNQAAIABJREFU31ZdJsWH3npZxVkeefYFlq9aNxBhmqwBZHM5vEArSs8ZY5k3YwrLnAg6qnNazkHHqeeyG95Mpr6eNcufItvXR+uE8Vxx0w1Mmlk74C+VyRBlK6sBqrX86lvfQnwfG0Usvv565l166WAOf9hwWFFU1a3l20RkHNCm1TLQTwEvbd3Hb3z2LqwqKpa6VBpjQLH4qeruhbzbsyGT4oKF03lk1cuYGqNX4qYbQSAEvocAi+dOZdGMSdzz6Bo8T1Cr3HLdJXzgxkpR/PnyFfRnqyXgC6kgKLEWfc9j7vQpjG9tOdbH4DhDOB3noGNoEM/j4muv5OJrr0StRcotgCosWHYJLz78CKYswt1D423J9nX330/LlCmMc+uMR3SfXgJ8DjgI/D1wBzAO8ETkg6p6/+APsZS+bIhXHIHlKWtu+wM+8eUf89S6zVU/k0n5+Hh87G3LuPmKs3ni+dcwVaxFKfpjxTf+f/bOOl6O6uzj33NmZvd63I2QBAgED+4ObSkVpEKhDkXaQpFCncpb6EuhlLb0LUUqeFukuAaXEEggkIQIRIjbtZWZc573jzOrd28SIIFcmB+fJXdnR87Mzp7fPPZ7fsCqtk7qUgH9ejUB8N0vHsHy1W0M6NPcrYh32E2fKM/zOGDX7Xl8yjQC38NYy9AB/bjw65/f4HNP8NHD5vgbTPDBY0MIEWCb/faldfly3pr2Cp7vYcIIrMGv0o42YcicZ55JSJH1u0+vBC4EegGPAEeJyLNKqW2AG3ECxe8rqh+N81HExTc8zNJVa2uunw48LjnlaEYP7sNPr72Lq/7zMAqN9nTsand7dC5WB2Msnz7vcjqyOfbbaWu+fcIRDO7Xm/p0ipGD+61zfIfuuTNzFizp0jjY9zTf/sIxfO0zRzJ7/iL6tDQzetjgbvaSIEERm91vMMHmDRFhzpQXmf7EY+Q6Ohg8ZiwHffXLRPmQBS9NYcGUKdjYyaC1LoZ08h0dH+SwNxusjxR9EXkAQCl1kYg8CyAiM97PtkzrQhhZ/v7gSwS+RSmFjp+glFIEvscZn9qPnccN42PnXIExhacjg7GGlO/FsUapSqIR5i9dCcBdT0xh0pQZ3PXb79G3pWm94zlqv4k8+vxUZs9fTCaXJ/A9tNb88JTP4XkeLY0N7DJ+3Ma8BAk+3Njsf4MJNi+89MB9vP70k0WX6VuvTGPRrJlsuc0EFk2bVlH+Za1Fa42fSjF02ySvAdZPiuU2dqbqs80kniEYa/Hj4RhjUVqhRGEjw22TJvPE1NfKCLGEMDI01aVQytUielphrOBpU9y3VZa2bAe/uu52Lj7jC3jrcVukAp9Lz/sGz02byZTXZtOnVxOH770LA5KawwTvDj3gN5hgc8HKt99m+hOTkDL3qIgQ5fPMeflFgqjGLaMUDX37MmrixPdxpJsv1keKOyqlWnHhtvr4b+L3H1AOr1T8rZSQSsXLlFsmIoiAQfHWktUsWL4KT0EK7baPU0lFFH17NfHp/XfipVnzaaxLMWXGHDI5g9IWFRclirI8OHkqay5u56rzvoHveRUjWtPWzqQXXyGXD9lrh/GMGDyAvXcaz947JSpcCd4zNsPfYILNEYtmzOTha69FtO1SUibWYrvptVffqxcHnXYafippdg7rzz711vX5B4cCMVqCQCpvABV/rErrioBVYLF4nlR8trR1NZ8/YnfOPfFIZi9cyvEXXAEIyiurVRTBivDyzHn8+7HnOP6QUuryky+9yk+u+gcK1/7pT7fexf67bM/5Xzk+kWhL8J6x+f4GE2xOsNYy6Z83YMIInVbUajbbnbM919ZGlM0mpBhjw1KYNjeIRcVxwJphle6+fe0IUZW9ImP4zhU3AjB2+CDGbzEU3y+7LCJo615hPuR/rvkX/7zvcQA6Mll+etU/yOVDsvk8RiKMGB6b8jKfPOvH3P/0C4RRxLxFi1m1trXWiBIkSJDgPWP14sWYOPNdLF1kI70goP/AIV0nTBFsmGfKbbe+X0Pd7NHzdMREsNZ9+S4tuRsGLFusFASexlOmyz1hRXjpjbdo7cjQ0ljPn77/Vb51yTVMmzOvSIjluxMRfn/T3Ww/ZiRLV6yK6w4F7VXeb2EUccnfbuEPN96GQhEZww5bjeHCb5xEc2NXJZwECRIkeLfw/MCxISChgK/AizNMPY/9P/cFBm+xJXf89KcVhKnEgsCSGTOwxqC9xDHRMy1FAASxBhHbtZN90UNaIitP6241TRGKKcotjfVc/+NTSQWlm6N6s1w+5NaHnuaB56YQVSuTlyEyhrZsnkwuRxhFTJ05m5//+bp3fqoJEiRIsA70GjiAhl4lERCJBJsTlPjsdcyxjBi/HUF9PYHvocSWXsUNkpytAnowKbqvMwzjpyOR4kspwdfQ3BjQXJ+mLhXwmX23d2k2Nb78of1707upZL35nked73d7owiwtr2TKTNmxW2sNgyRMbw2502Wrlz1Tk40QYIEH1LMmPoKV/3iYn599vlc87+XM3/2nHe1H6UUh37tK9Q3NxOk0/ipFJ7vs+XOOzNm4q7F9YZOmOBqEykXK1H0Hzs2sRJj9Dz3aRVEhDA0KKXwPNCxQGk6leL4g3Zl7+3G0Jj2+cnV/3ZlGVrFxFn695P77FSxz7mLltKZycak2JXy6lIBB06cwEszXgMEK4rat5N0eerwPY9Va9sY1K/vRjj7BAkS9FRMffYF7r7xZsJY6GPBnHn8/Yo/ceKZ32LUuDHveH+9Bg7k+B//kEUzZ5Ftb2Pg6NH0GlDZQWOHoz/JirnzyHd2EOVyeKkUfirFrsceV7FemMkw//HHWDZtGkFjAyP3P5CBE7Z/9yfbg9DzSdEIEpdiOPFvAEVHNk+/lkY6M52cc+W/yeZCtNKouHynsE3a9zhqj8ove8GSFaQ9n8jkEVUgRicqroBxI4dy9H4Tufn+h1m4bEUc53SNgV2ZiNNNtcaiq0rJjDVsMTRRskmQ4MOIjrYOnnn0aRbOnc/QUcPZ6+C9ae7V3GU9EeHBf99RJMQCojDkodvv5GvnnvWujq89jxHbdl8Klm5q4vDzz+ftV15hzduLaB44kOE77IifTpfGkM3y7P9eTG7tGsS6mu01b85j9MGHMubIj72rcfUk9GBSFBwBUVWC4dBUn2ar4QM5/483uTZR4gKNBesQEerTAfvtuDXbj6lUmR/Qt4VMPoe1FqV0qUuGgv122pZLz/4yge9z9omf5YIrryGfD52QuICvNTuMG80hu+/M3++4h45sDhOLgKdTKU46+kjq69IkSJCg5+LtBYuZNvlVgpTPrnvtTO++vVmxdDm//eGlhLk8YRjy6pTpPPrfh/nORWczuErSMZ/NkemsLau2bNHijT7efEcn85+fQq69g0HjxzF8p50YsfPOAFhjWDx1Km1LFtM0cBC5VSvIrllVnDMRkChk7oP3M2Lf/Uk1rV/Zqyejh5KioCgVqIoVdGRBafCdvNugPi2MHTogJk1BiWBN5LpXxz71Uz91EN857vCKPT/76kxOv+TPGCyCdY09BTwUfspnv523psDAe0zYhivPO51r73yAt5YsZetRI/jKJw9ny2FDANh3pwnceM9DTH5tBn1bWjjuiIPYa8cJ799lSpAgwUbHv/5+O4/cO8l5grTm3/+4k5NP/yIvPfUCmY7OYt5CFIZEYcitf72ZM39c2dA3SKfw/YC86drWqWUjdc3Jd3ay5I1ZtC5exox/3+cETaKQ14KAIRPGs/cZXyPs6ODJyy4l29qKzeTQQUDKkxIhQpnBYVk1ZzaDd9ypmyN+OKB6WvcZ1TxIvIknVC60Fs8KSit69avnwJ3G8b+nHoenYa9Tf04+buVUHR3cadwo7vntucX3+Shiv29cQFtnSU1L4ZJ2dFlkui6V4pofn8X40SM3wRkm6OlQSr0oIh9azayJEyfK5MmTP+hhbDJkOrO8vWgJffr2pm+/SoKaM3Mul/3sSvL5yvZwQSogrS22Wk5SXNjlgkt/yMBhgwDoWNvG6uUrmTFtGs9PeoKwbF9B4LP/UUew/W670qv/upsPrAuzn3uG52+92Xm6lue7CAJ66RS7nfw5Vr0xnbenvIRkSy3tUg0K7dVKHxRGHXgII/bej/p+/WseN1y7ltyK5YiypPv2J9X7g8mdeC+/wR5IigNF73pCqVmvCNq4Yv4gDfUNPnUpnzAynHrMgbw8az7Pv/pGzX0FvsdL1/+yKPT97CszOf1//4/OTDbevxAoZ4BWl3M0N9TzyFUXd5F8S5AgIcWeCRHhP7fcy+233Y/ne0RhxIQdt+E753yVunqnTnXjX2/lsfse75LFnq5L4ykLZa3jlLF4sdZoKhXQb8hAhgwbwKwpr+IFPiY0DN1mOCtXLcMYiyeCzkd4vo+JIgaPHMGx3z6VxpZmRIRcZydBOo3nr9vB17psGf+95FeYMERF4LVT1oy9hIHjt6Jz6RykI6r41K93pFg+52ksHoKXSgFC45BhbPflU0i3OE1nk8vx5t+vZe1rr7qGtIAKNE3jxjD6xFPwG97f2uz38hvskSUZQoSVCCsWbUu1NtY6a6+1M0smH3LZrQ8waeoMQlujlrEKNz34BKf95iraOjsxYjHWuqe8buotOnM5psyYvXFPLEGCBB8YnnniRe741wPk8yGZzixhGPHq1Blc9ft/lK3V/Twydusx+EFMWFbwIimWPoT5kBULFvHacy8ThRG5zixRGLJ41iL2PvBQjvvqyfiRxeRDwtUZ7JqQxdPmctW3f8RLD03i6nN+wJ+/cz5/PO1sHvn7jURVTYPLMeeF57Blzcy7hQi1uq2bvFScp0Lw4vwNG+axYUjbwvm8cvUfiuvMv+UGWl+fXiREAAkt7bNn8+YNV69/LJsRemhMEVySTYSqw31/UaEBVKUQqsXiul2Uu8g9tNJsO3oYfVuauOuJ5/nV9beRzeXL9i44MRtVmxjFybwlSJCg5+ONWfO46g//KIZaCgjDiMnPT6OzI0NDYz277zORpx55tst61lq++K0v8s8//p23Zr8FVZ9DbQskzOV59p5HGb3tSMJcDpWhTHwEws4c9//lJlL1zltljWH640+zfN5b5Na0km1rZ8Dokezz+eMYOHqU2yabLXbJEI+SHnQ5FKx+azZB0NWGFOOI0U9rdBCgTVf3K9aSWbGMjsVvU9e3L2umvoTE6l6qTF9aImif9wZh6xqClo0TK93U2KSWolLqSKXUTKXUbKXU99ex3meVUqKUekfmbsH4UxoIAL/rU4/v8mocsRW+KGVobkhz5fdOBuD3t95dQYgASgRlLCYyNa1MpRQTk76ICTZzbOrf4IcB8+bO52c/vYxctmvSC7hSq/b2TgDGbLMlBxy+L0EqQGvlXIo2Iq3hknN+RceaNg7+xMEMrii7ElTcwzUyBlvlucq0d9K2Zi04Tunq6BQwZYah5ENWzHmT9pWriPJ5Fs+cze2/upSVCxYBMGLC9iVxbwWm0T3kl89iSllEwjieWcv6TTHu6M+y3QlfoHHgwJoCJUpr8u2tmEyWQhmA8qUYblJx6Alridrba17bzRGbjBSVUh7wB+AoYFvg80qpLl0slVLNwHeA597NcYrEqFztYSWBWXxVFQ9UoJXioInjGRMHvpesXFM5JhF86wrvxQhipVIvUCnO/uJnEg3TBJs13q/fYE/HLTf/l1w+7+aPGgSRTqfp379P8f1xJ3+G835xFi0NaTQuzJLp6GTN6lbenDOfB2+7DxX4+EGAE/CobNpkRbBlbsZhY0aRbqir7JxZheL0I0LB+CtHlM/z0J+vJcxmGbzV1gzddrsiMYoP0j9g8C7b0tCvCS8V4aVs0VCIqs7aS6XoNWokI/fdl8E778qgXXZDB0HXMUWG5uEjCVpa8OrqnYVYBaWAyOC3tHR/cpsZNqWluDswW0TmikgeuAk4psZ6PwcuBt6VL7KWwLeD4MRtun5RVoRX5s4vvh87vLKGyLOVjliTt5jQolHsMWEb/vazc/j8EQe+m+EmSPB+4n35DfZ0zJu3wNXiaTdXlFNEkAr46iknoL3KqXLxW4vI5/KV2abxhJHL5Zk/dz5BXQpFZUOBAlxds8XzNauXL+DN12dgtaW7mKX2Kg5REysXLOTW7/+MpbPfoGXAQIZN2J5h223HmD324tDTzuCg75yGybdXNi8QQbAYE9IyajgDtt+OHU8+kT3P+jbadwcdtvf+BI1NKK8UbdOpFKMOOwq/vgGlNSOOPQF07VCT8gPCtWvXMfINg4hgFs4l+9gd5J++H7t208hlbkpSHAYsKHu/MF5WhFJqF2CEiNy9rh0ppb6plJqslJpMWCiXkJJbtACR+J4StAi+1A42K6XYaviQ4vvzvvQZ6lKlJ6GaIUQj5DMhfzjvdCaM2WJdw02QYHPBJvkNLl++fOOP9APEkCED3R8KrCcli1HDuT84lb323bXLNjOmzejW3SqA53nscdi+YCPEGmzZy5l9gmCAHNnODtfYQFlEVzs6QWlBa+saBXeXMCgCVsi2r+T+313K1Hv/y5uTn2fR9FfpO2wYA8eMBaC+b9+KbXRk8UKLtkLrm/NZ/sqrCFIkRAC/voFdz76QEQceSuPgofQeuxXbnvg1Rh5yZHGdPjvuTNPosd1cYSHVu083n20YRITsndfR+fdLCR+/m9yjt9Pxhx8STn/hPe23Fj6w7FOllAZ+C3xvfeuKyP+JyEQRmUhQ575MLXh+xUpOUsZGaBviiXHx5SrXJzjt0m8fd1Tx/Z4TtubqC89gp622pKm+ruppR4ovpVQs5ZYgQc/Hu/0NDqjS0+zpOPb4j5MqPBQrEE/wGwIO/8QB7LBTbcm0Pv374Pm1y7FU/L8Vi952z+nlL5yFiAhocZZkPEcpDbbOIr5Q+E+lDOkGAOsyYMQQSrXD08HDoAJHnohgjcGEIS/86xY617gQ0baf+ERcVgHKOlGTQsTRRhE2DHn56muJss5pICK0LVpA59LFjDrsKCae80N2PPW79Nu2qw7qsKM/iwoqGxWrIKD3TrvhN743FRwzZzrRay9CGMdATQRRSPaO65BcZr3bvxNsSlJcBJTrpw2PlxXQDEwAHlNKvQnsCdy53kC/FVQmhKwp3kyOEC3YyP1bWEZlTFBECDyPay88lZ3GjarY7e7bbcUtvzyXKX+7DC8opGsZVNlLCHnz7SXv7aokSPD+YdP8Bj9k2G67rfjuWV9jwIB+aK2pq0vzsY8fxMlfPrbbbfY9bD+86hrlsvyGulTA9KdfdO/LXsTEqMTg5y1kBdsh2FbBhi7LxqYtptFgGiICz5FVeccei2DFOGKM5zptLV7XsF9xQAtemQrAyN13Z6cTTsBPp9yc5gnWd68C0SqtWf7a63QsXcwzv7iAyb/7H1666jIe/+FZLJ3afX1qw4gtGH3yqaT6DXCqYUGK/nvux4jPfrH7i7+BCF99DsIalrmniea+/p73X45NWZLxAjBOKTUa90P8HPCFwocishYoyiIopR4DzhGRDaoKlkiIWiNQQqopVpzRcXWGFbwyi06MIHE9zpbDB3Lgzl1yDSrgB9oVvsYi4MX9AN/82aXcf9UlaN0jSzwTfLSwSX+DHybstvtOTNxtR/L5kCDw1/v77jewH6decBrXXX4NmY4MYRiitCLwPAYPH4x0drKarqEYR4xSTJQ3xTCQwnYIqhlUrCYTKI3ve0T5rjWJkRY8FaEsBFGt0vwSTBjy2oP3M3y77Wns25d+W26JisLKwQlYX9BR6f2UP/yGfGtlLPC1f/yVpsHDaRxUu6lBy9bbsu33f47N51G+HzeC3whQ3exHcOUFGxGbbGYXkQg4A7gfeB24RUSmK6UuUkp98l3vWIGqt6hGi2qwBPWCLqT/xi9Xk9rVbRr4HntuN5bDTvsRo4/+Oruf9D3+ec9jXdbbZtTwYoC86tC0d2R48bVZ73r4CRK8X9hkv8EPKZRSpNOpdRKiMYY7/3473z32DH534W9Yu2wF+Y4OiELG77g151x8Pr2aUqxasrT7A1VNLc655RbavBTXUZElCiNqQnBGgBJU3kDWYNcaolaD2Oqm60L78mXc/5v/wRrDvMcnYU3Vfst0TkUEL+VUaqphTcSiZyYBEGUzzL3lOl74/qk8f943mXXtH8ivWQ24RJyNRohAsMNeUOWaLZybP7r7riDvBpu0eF9E7gHuqVr2427WPXCDdqpAeaW/vRoSbCiIRPDinokFDOjTzL8emkQufvJavGIVP/vLjbRns5zymVLQePHyFfFuYjeqKmWYGTGsbus5NTcJPtrYJL/BjzCuv+xaXnz8BcIwX3wIB5e7MP35acx7fQY2l3Pt40SqfE2OcGpRhUj8QB8TpDIQdRq8Bl2StCyDdh3TSbVLRW2jZCEMDX5ft53ClZghQq6jg7dfe5Xs2rXF4v4KKFC+z26nn4rNt1MzE9Za8q1rEBFe/8PFZBYvQmKCXf3qFNrffIMdL7wYL52m7bVpLH/wLsLVq2jYchwDj/wU6YHvrm2et8XWBLvuT/jCYy5MBqAU6cOORqU2btehHqxos264iKBUOD8HNNWxauWKivUyuTy/u+FOvnbMYUUd01Wtba7folhU3LS4YIHmwqzLIEuQIMFHAsYYnnv8OZ64bxJzpr7hiM3r+jCulGCyOTdXuB47rta58Hgtbj4KapCNlGXNqw4B48oWTSh4QaHUoTQXKQ06D6pWsb8BmxF0yhFioZxDrKFj5UoGb789S16Z2sUSVFpzwA9+QK8RI8iuXolUi5sDOpWm3/jtaZszk+zyJUVCBMBaTDbLypeew7eWxf++AQmdKMraKatpe/Vlxpzz03dMjNFbb5B9+iHs6iVIQw7tC3hAo5B/41/oYcPwh2y87kM9LzC2AcmfhZuvkFUlCE0NaRYtX1Fz/XwUsbq1ZP2NGT7UPTUpqXgaLPz7f7fd+Z5OIUGCBD0D1lp+97PLuf731zFj2gyXOdrNHBSUE6UCoyxRsQWdy/QMxNbcvEB6OrIoE2e8i2Dyhigbq2op0NqRnIigjXQ/HeYMZMsSdEQgsqyaPos1by2irlefioJ8L5VmzCGH0muEy8uq69OPYfsehFdmhekgoGHgIAbtvBudSxbVJE2bz9E+fx5L7rylSIju+Babz7Hs3v90N+KayD7zEG1XX0I49VnM/DexywzRCgtN1nkMTZ7cC/98R/tcH3qepVim7ABCZMDTqhgHKLpLjUGsQvnuKSuXzSDdlFMEnkfv5sbi+3NPOoEzLr6CfK52LfOchYtKzYoTJEjwocX0l6Yz45UZ5GKrSgNKav/ua4VxRAmRCCnrwjECVXOHm6+0EjxscXkh/13EZc8rLJ4nlDlKsVrA85BiZw5V7O6jYhcsGYMNIIWHDvPMf+JplOehtGb4HruQa1+DX1fPlgccyJCddq4Y/rhjjqfPluNY+NRjRNkMg3benWF7H4D2A+oHDEZ5GqkKTepUirpevclUxywBROiYu+H5GJLLkLnn5rgMo+xy5cGuAi9OEZPWxYi4hvAbAz2PFAGsRcWF+QIYUVhrXYq0CGItyroEYzHgpRWCkDcuo6t080B9OsWpxx5FUNaOZc/tt+X3553Jab+81MkxFUo8YrOxpbExIcQECT4CeOrhJ8kVHo6Vy/pUYknFStsbOg0UIotG3LzkUSBRi6ed5JoldocWiDEO2WAtWvmU10wDqLSHZEIoc8c6m0C7jNbCsfMu96KQgCPGIMaw8LkpfOLK35JbuYqwo4Ncayvtixfi19XRa9SWKKUYsMMuDNhhly7n0zJuPOne/cgsXwqFcJJS6CBNv933Y/UDd9W8DkGvDS/ij+bPLUn5lENA2gQGAKIgaNhohAg9khSlSIjFJXGmqVKlsLYufgYetvjeiHFF+KLp29LMacd/vCLJBuD2Rybx4yuujvdJLBfnoD3NiR8/bBOdW4IECTYXiAhTJ0+tXKjisi8R/HJSVNB/2BBWL1laoWvq5iv3UF5a1cm51UoSNAhegRiltE2uNcTTgt/ioWKPl9caVtQvFqA9W/HQ7msdd7CoOhWlePjcCyCbQ/kWCUK8IEBpTaqpmd2/fR5Ng4fWvDZKa8afeQFv3vZ3Vr/yImItLePGM/q4k0n36UvLTrvR+vJkJCqVk6hUigGHH11zfzWPUd/gMo+6QCAt0GTBaPwtD93gfW4IeiApdo+iMSeWwnOS6wVWVZIjQn19wNPX/oamhvqKfcyev5AfX3E1xlh0jcdAJbB2bdumO4kECRJsFlg0f1HtvoUKrAe/u/mP1DXUEYURnu+hlOKmK//Kc488UbW+IfIU6aj0sF4za74GCnWNrs4MTLsl3a+OpuZe5NbULvuolViKUs6DVnZQk8+Tz+fwtODVO5eujUksszLHk//zYw7/7Z/R3TRSDxqbGHfyt4oCKuUlGMNO+AqI0Dp1Mkp7oDWDPnEsLRN2rrmvWvCGbYFu6oVdvbyS/DXoIXGGkQ/ojTsff+hI0Zl1zsLTno3rOsvlveN1gXuffpHjDt23Yvm1t9+NMRYV25zV962xlv88OInzvv6lTXUaCRIk2AwQhVG3NYuDhw6ibc1a/vKzy5nx0nR832PiwXtz3Glf4vn7J0EQW4MFghIwCpR4rLvUPl5ZXOaoJxZd5h5VNuDTZ53LzAceYdbcJbX3JRIbCO4zG1lULlYAAwgUytexx0zQtSoaFJhsluk3/I3tv/SVdY5WlWcjxtCpFCNOOhXT2UHU3kbQtz/af2d0o5Si6avn0H7Nb7DtrWByYAU9QtC9CmtZogWTkO1P2mghrQ8VKaqChhKgYj99dxcqiiLW1OjxNW3m7PUeJ5PLJok2CRJ8yDFy9EjX/qmqmXgqlWLvg/bh4tN/RKa9ExEhzBteeOhxXp70FEpZUC47FOJ4YlymEWIIrIexJbGRCkjhEV7wY0Ks8HKZkFUL3mb208/WHLM4NsUIino0AAAgAElEQVQYi+dpPAM6W2U6hgJaUVfnQz4fZ9rXmsuEt595iu2+cBILH7yXtyc9hM3nGbDL7mxxzGcJmpor1l47bQqL7/kX+VUradhiDEOO+jTtr7zE2mcfB6DXHvsy4Mhj0Om69Vz5Erz+g2g59zeYhXPpfPQiVJNFVbOWKfSETEixEja2EH3QyjpvQyxIL8qp1pd/8Upp9ttpuy67aSy6U91FLrhky7H9VmMTQkyQ4EMO7WlOO/80Lr/oMqx16jLpujqGjxpGWhRhPixmu7tieksUWvxG0DEPVVhycWaotQZlLKK8uN2SKroHVWSxKFd6oWpP89MfedzpoZbJgrvM1vhdbNxaY0nlquUDHHwvQFnj0nZCIKgx0QFRZ5bXrvodq16dis27LNC3Jz3MymlT2O2i3+ClnZm54slHWXDLdUi8Tuu0KbS+8hIpz0PF2bErH/wvbc89SV1fp43aa9+DaJq413qVb5RS+CPGEIzdCrOiq86p7r3lRzzRxuUpu7/LbiYiAU+6FMZaS9wKxuBpjdaahro0n9x/D7bZYgSd2Sx3PPokU2fNZuyI4Ry8+668OmsOWNd3UceqOAUSDHyfC085+X084QQJEmwshGHE048/x9QXX2XAwH4c9rGD6D+wX8U6K5et5LG7H2Xl0pVMmDiBX/3p1zzx4OPMeuk1NIrtdtmeN2fNJcwVSgVKUpNAsZ6ipmtTgVIWLWDzBqUVylNoARUHA4tTnKdrsuKSN2bTEHiYfFhMApSy/+viO6lIEqy4Dpks9SNayK9ei8qDrlMoLVVzKtQ1NbLi5RcrApViIvJtrSx99kmGHnAIYgyL/v3PIiGWVhSiKCKI/9b5ELtiOZkVrvVYdt4btE99kSHf+HbtQVYhNeEkMk/+DEzoygqUB9onvcOXN2j7DUXPI0WFI0Adp4W6mgynFOHFvviqG6kYp7aWgybuwIkfO5iP77Mby1et4VNnXUBrewed2Rx16RS+59G3dy9WrV3riJGSEEBdOsX1v/4p47cc9X6fdYIECd4jspks557+YxYvWko2kyUIfG795x187OhDGD5iKLvtO5El89/mN+ddgjGGKIx48v4naGxqIKUhl8mSy+SYPXUmIKR8HxNFxfkBAC3YiGJOQhdWiyejgoIN1q0jQkViX/HzGqxoozxZLAG+0z4tVTW6XAonn+P4rSp7vmwvdK5ZhdKgAlAZDz+lwCsRorIgmba4BrByHDaXY82sGQw94BDC1jXYWglJxKcXn4WuOhvJ5eiY+iLZN+dQt8WYmtuXw2sZQcOBvyacex9mzVx0yyhSY45CNw5c77bvBD2PFAs3X4Wag7t5Kp7WasD3PC759lfxlOLBpydz60OPsmL1WqI4XTmbc3qGo4YM4qh99+TOx54kFzqyPHKfvTjthM/Qv0/vTXhuCRIk2FS487Z7WTR/MfnYoonyIR5w9633kEqnuObK66nzfMKy5sFhPqR99Vq8MldmGG9vjSbla5QxbiYNJF4OVgkp5UFZmRhiCXK2gqREQHmC6NgzJcViMowU6hkrqAQlFhtBqCP8WAha4Vy45fokHoLxQYXVVqug01JsPCFAZAwqB54X13EjeMo6AYAaoifKD6gfOAgg7pVY2yQtbKm7sVjFRHTOmN6FFG2YIVwyA+UFBIPHuwxWQDf0Jz3hxNo720joeaQoEHkWz6riY4hSgGdjKaTuWTEyhu9d8iemTJ9B4PuEkq/5IDdj3nxuuuRnXPCNkzbdeSRIkOB9xaSHnioSIlJZqpWPXaEhISlAl00M1SVdJShGjBvFotdnI54rmcArkVgohkB5FAjDz9uaWqViQAcFt6ktWn7Gtyg0ni3FyzQ2Dk0KEoKRCAJFGo3SqiD0hbYuv0I8iBC8CHRsNqq07ZpxqnAkHNcFBvFDQEklrjKxUHseQ/c/2P2dStNv7wNZMenBqhNz1xilYi3pGlfQ8/GaKhsQZ2Y/QduTfy7FCbVP7yMuIBg4DhGLtC0BL41u7Nd1hxsBPU/7VASyESYfEpkIPIvy3Z1ghC5toApQ8RPWMy+/Qj6M6Mhky8LUlTDW8ourrilakAkSJOj5CFIlrc91TXy1yvxqwRhDS69mJLTIasGuEewqwWZLscHQGsQFDNHdyKY6HhJ8bdG+RfnGFdNbAQyaCE8ZPGWKDQpQColDjioUJBIkF7/yQnlpoXhCakCA7h2hmkNIGafhWoXuzAljJPb6uhmzfuBgtj/7+wRNzcX5dsTxJzmLsRCritm5wIW2KtGxdFBF0y57Ft9Ga9+m7YmrIMohYca9cm2sue8XhAsm03HrN+n477l03H4GHfd8H9tRW8/6vaDnWYrlKH4BjvSMNQRKI+gKg7EgtpsqPoU52Ai0X11a4b7M/z7+JL2amzj/a4m1mCDBhwEf+9Th/Pl315LL1ujgvg5EYvGV6uKCBHj18RcrrT8BaQfxBeUrCAXbFjq1Mq86qlbayC/rxlNaLOhIbZDpogKfIAjwUimG7zieRS9NwfN9bBRRV68xYbaiXZQgldafVPbqteA6dhRqHWOJuMH77EfvIcOYecmviDo7SPXpy4hPHUv79JeJ1qytsJQVrjbTb2jAsxlMLsKzPsoPwNPoIMWQb30Pr6GheNzszEdLsnHlsIaORy/FoxS7tCvn0nn/T2j89O83avZpz7MUu8ACEQqDAqxruOLMbDEQ/+uprur0JpJi/7Ki3wGXvZXN5bnh7vsxibWYIMGHAocedQB77bcbqXQK5dWe+nzfx9OlxBWweMqWvS+fJ6wT3q6xH5uJ55TItZ/zdSHBpqt3yotNk1qGlClwVvV2UpZZqhR1zY0oE9LQXM+oPXbn2D9eyYHnfI+tDzuQXGsbUWeICW3FfqxYR4ZK03/sWHzfg3yEyoSYzhCTM1hjizKaAGnfZ8F/biHqaAcR8qtWMueaP7PyxRewoSF217kYZ7oOHQSoXDvSkUUiQyQ5QnL0O/aLjP7NVdSP3bryuuXanT+5CmIjpJosxSLZtZhlM2p8A+8ePZgUBU8bF+Q2hXgiRJY40h2BdeToCmANkTJEKqpwm0Z5IcpZDAbnqiiRZz4MyXfX+TpBggQ9ClprzvnRmVz8+5/iKXAdDyv/6zugNwd9fH/XL1ELXuzzVMrGgiDi/tW2Vv5JCQaXDGgEq9y+3RxTTnCOYHV3WSiq1FUj3rDiX11UyxEyK1YQZjKsfmsBj192JXMmPcniF59n9gMPlGJ5Fmy+RHBiQWUj6oM6JJOlsaWfi0USxxONYLIGiWxM7j4rnpiEzXW1tE3kBmND68jUKnrtujd+mEHnyzJTBciHrH3u0Zr1iemRu4Jfo7hfLFrVcmwrpHNV7ev3LtFDSVEIfCfhpnR8j1jXHaNAeMWSIbEosbEEXJxppUzlTyEwTgWi7CkQYOjA/tSlU+/zuSVIkGBToqmpgVTgxY2cyq1ASxRFHP/1z5OqC1C6RBAO4pbFWe62W1IUtBJ0LlbVwpU3YMHaCJEIbIS2EVoiJKqcd0q7kViUpBTPwwraCp4p6aJqsRUTeZTL8fy11zPjnrtrlkrYyFlzft7gWUt+zRrWzp1Lx9uLag7DRjYufTOVTYUrzrjyjc2FmEwWVaPnIkD2rXk1l6dG7EwwaGvwyzKB/DTpYTug/Rp6dBLh9R9Xc1/vFj2SFL34Eam6+W+h6lUVanbif40t+8riu9xog/IFVe/SqQUXmyw8RdWlU/zo1K8nyjUJEnzI0HdA3yp3ZKHGT7HF2FH06d+XL57+JfzAi4U/KrMwy7cziqqEvdiKU061BmPxpUCuAtq6LhkSh3isRUUGMZWuVYlzJVCGyBps3O7JGoO1BrERVgw+Fr8LkwlIiBGDVbZqfDiiFcEvi4UWCLYWRECn0ww77Ei8+oaa61TPkjpdR/3Q2h02ijuttR/t0fuIC2jZ9xRSI3YhPXoveh92Hi2Hnotq6AMpD5oMNBpIBfij90M3D+r+OO8CPZIUa2oGxvARV61a9bJVX4IoIG27XAFlhd0nbMv1v/wJB+7WtY9YggQJejbS6TSf/vwxpOsqLY9UOsXnv/Y5APY/8iD69O+Nl4qblxOrYxWyMEVQ1jryK5iBsbWpdYS2sZVnhSgSl+sQxORYINj4uEYE8tZlsVrXVFhCC9kIcm5b61t0aPCiWCLOWme5dYlpCn6DRactop3r1ugyYhQBC34+qk2YXSDgCb22GctWJ57EyGM/h05VWWxK4QdlE6n28Bob6b//oZXZO+XXevBwOl5+lo5pL2Dzle5YpT3qxu5H7yMuoNchZ5MatgN4KfxtxsPAEHpZ6C0wKI/u1wfz5tNItnU957Hh6HnZp9KdQoP70NNds7gKN3L580wtIV7XvFiBFXYevzUJEiT4cOKErxxPS+8W/vXP/9C6upXR47bgq2d8mbFbuyLyVDrNT//4a2780/U8N+lpbNagxGVhekrwEafQUmS5QthG8Kl0uYKrGVTommUZLvoj6AgkMsWlxfXyro6x4IotR2gtntZFj5aXsk6lpipR1miLb13NZKCcMeDinM4i9SKFxASmyg0IBdq3rJ3zOqtfm8aQw47Eq69n/r9uJr96FQ1DhjHis8fT/upUVj3zBGINvXfZnRGfP5mgdx967XUga5+d5ISoY2hPwZJ5LLv28nh8wqBTzqdhu+6NELP6FcKlk4Ci3xgkIr/gJuzSNMoYvF1OJJjw6W73saFQ3dX1ba5QDb3FH38AOqUqXZtxok19IN1akSm/9NTipeKmzvH97Bmnaq9w4uHT7riRunStnioJEqwbSqkXRWTiBz2OTYWJEyfK5MmTP+hhvC9YtnAxPzjxzDKdU0j5BRFvp4pWPt34CF48aRdikoUVfB980V2IDZzDyteVTOZTIjcPSNc0uoSUVujY5ZVqstSsThDwjCIQi+dJl7lTheBlIUhXno9KK3TaLRmyz0Fsf8p3idrbCdvbSPcfgPZ9RASbyaBSqS7toSQKWXrztax56hFndNTVEeTboSo2qVJpRv76GrxGV/so+U5UUFdUssm89juiJY90PS8reCtAZwAvTeqIi9CDxr+n32DPsxQBUQZrNLosrVohaAxdFfZKaG5oIJPLoX2hqamOts5OlBW8ULr4kXvYs0KCBAk2AZ6691FsRVmWcz+6wnlVWNLFhakKGaVlH9iIWFt0fXkK73DyEYPTY4Pu5z9B5y1eXbVsHC7LNRB8Q5eTkZwgPihPEXW08/TXP4fpKLXcaxy5BTqXJVy5HLRH//0PZtSXvolOuQRF5Qf0PeIYcgvmkpn9OqojW1M2DhQdLz2D31vT/vi12GwHyg9o3PXTNO79uQ27JiZPNOt+UoPGr3/ddaDnkWIhnigWG1n3HYpTcChq+VX3OhShub6em379Y7YZPZLQGOYsXMg/77iXex57kpDSU4vWmonbjae+LrESEyT4qCPT0YGJylx/gFg3v4gSIuI8Bt+CFgyOo4IqARFYt1JOQWCkgHIrEUAFMffV2haK9GxDQXuCylt3QIXTbNPKxSnr1pFGogTliTNLCwM2CpsHr06xdsoLeKZsEAId899EASklaCJWPn4vq599hOEnfIUBh3yCzKzXeeuXFyBh5FyxdQpRqgsxizWES16jc/ITELkYo+Qj2if/CxDqtjmAaNnTYLuWg6hiu0uBXNceue8UPY8Uq54Yit+PdTdkFEX4sUlf/nmf5iZ23bYUJ9x1m20YO2w4r86czZIVK+jIZGmor6M+nebX55y56U8jQYIEmy2WLVrCHdfezCvPTUFpXVSDKRJQodRAgaqrzIZ3zFnbIgxFCKoW67iEA6VQEuuuqjKjLQXWByk8u5fHFn1LoXxPA+SqkgcFyAvKOgvXhhLHJ6vcpxYnl1luaGqckrclLgWpCojGGgeCoP2yRfkcC2++hs65b5B5YhIS91NEwOQFz1el4+YtKhJQhuyrz6Mbs5VjC3N0TL6dhj1PIBh8AOGSSWDDuJWVoFeU5Zj4dXij9+lyzd8peiAp1nY9WJxnIsoLSsI4KK1AaXzf46Ddd+6yTa/mJu75yxU8/MzzzJz3FiMGD+Ko/fdOYokJEnyEsWT+In7wpW+T7cwU4yi+7zvrsFApEU9D2qsiROL6xUIdR0FaLU6Zt6KIVLkYuRB4ZZaickk3BVFwz1PovOAbFRNWieCI+x9K3NVCQbeNiQulJVFWSPmuIXF5c2MvK1BPTdIDIbARqlaxgipxcAXP5vOsfPoR6qLK7FgxYELB8wUv46zZwnWI3liDboFgTLUVGUKYpW6b0wmGHkG0cgqyYg4y43mUzbvroNPQZzR69H41zv6doQeSYje+Zevar9TFmr/u+xQQg68DTj3u6Jqb+Z7HEfvuxRH77rVphpsgQYIehX9efjXZjs6ipSY4D5SnPbRWRaJAqa6ZnriNrLFOO7SwrKAig0Yrtc4WdxRqH62FTovVgOe5Y2VLFqsYIBIkWEeNWhXEQq7dUtcCGIUKQYcK5XWzgQI8kLRALr4irjmtI2K/YHWWnLilg1Gzn2OUFVS9F5OPrVjftoHtEHRjWaVAqgGVdvWRXstYvJaxRLm7iPQzpe21QfkhsvQ1WDJtg65Fd+iRdYpdilyJXefxF1ss6o/XyeWyfOzUs3ltTm0VhQQJEiQo4OWnn3eWVBcREMNRXzyWQcOGQayHWtROLodIJSGWLfekSh5OBGNrJPaJ4JnYFSpgcxaVsxXUU6gGMZFUbLdeiKA9i5ey6JQFT9ZRryjolKDSsfFrLCpr8HxBp3FkqoVQbJdacGoQYgF+qqFkRVceDttRvmKa5v1OrhD8llwb0ZSrnRu1ABsiK2Zi77sQHv3H+q/BOtDzSNFSvBGLQt5xl2mv1lMb7qlrTVsH3/zpr9//8SZIkKDHYP7sedjIFl16lS/LPp88hJN+eBqpwHfZ7rHmZzkxatsNEyjl2jDZ0rylLUgkcXMCKbpcPWPiekFXmG2iWszpYEWwyuVViLFdSFrEabAWzsn3TClgmQIaDdJgEVWbHIM6i8pYrI3QoUUFxN0wSi8URJSOrYIULeN37FroDwSDB9O85/6uRqULCtdG4fUdQe+jzqJhhyMqz3fZdIpBzIpNLbY9ggXvrXSgB7pPnagtAGmX+VXholiHG2HeordZvGIlQ/pvmuaUCRIk6Nl44bGn4r9qiIAAM6ZMY+y2W7lysNhQMVlBp1Spvbyl9lxUFOIOXSE/uphQI8bFCtMFU1AEXU5QmtgXWXvckYoIJEBZ61yq5TO7cRYtCjzPoHVsefqgOmPBEtzp+GnPiaHH56FyBmMKNZcKEUH7XTNrCxdLlEb7AX33OpCRJ51Gx7QpLL7uKqLVK1FaowOB7HLWPHsfvom6lLIgYNcKavB4Bnz1ktonG9TXfkAQgYgNb4jZDXqepQiAQGDxohAVuW4Y7gGjpCxRsW7ZvxvmeU+QIMFHEooiuXUp5wOeuOsBRmw1hpa+fUofCNicYDoFyTqrrPvdO6+WKEtEhLEhKhei8xHkTdH7pSmJkat4LKXWxeUQCiKmdc31bpEVyNvSy1jwLUHKOMUva5GcRTqsU6IruomBrEF1RKiOCJ2JHMnmBYkccbseQ7XPT6dSNAwdhgqzrHn8PmZd9B2CwYMZd8U1jLv8L/iNBk0G8lkkzBIFUdmE7M5BNTjd1OaDPtntNdQDJ1QKhhdgwVv+3gvMex4pKkHriMAal8oct3fRhJhYC7DS5QHEmVZjRw5ncGIlJkjQI/HwQ0/w6aO/zB67HMlnPvkVHn34qfVv9A6x58H746W80mQtgjJOd1SHhoUzZjN/5mzO+O3P6T2gX3Ed4oJ9HdcqFrNPq16eV5q0y/NTCsmnYdi172vhGNZGWBNhTYg1ESLWuTF9UFbItq6pSVhCrN4VuyarU2KcY9j94VNVleG5fo/FBwQFUVi7L6Tk8+QWzAMTISYiM+8NZvzwdExnOx3Tnusqb+dB2KzR/RvQfdLofnWodEDDHgeSf/Fhln334yz/3jG03XIlksuUrpv2SB3yS0j3gqDBvUThLRJ0B+8ZPVDmrUWCcXvg0pUpFfOXIZUqaQGCy47q19LMPX/6LeNGjXhfx5vgo4dE5m3j48H7J/GTH15CNlsq3q6rS3PRL8/n0MP3r1jXRIY7b7uL/976X8J8yEFHHcQJJx9PQ2PtDg/VuOP6m7j1ymsBUJHTPC2fYtL1dVx001UMHD6EX598JgtnzXHrlrGNpyBQCs/3sHGdntbge1UylCLoMO5Y4cUScVKyELUCpRWBqpHfqcBrdHOdNkLaiHOFStWEqCFVpwiqM2Kr4AnUFXV6AMTJvtXYIEgrtF8+x4KHwbdU7AEr1Df3hXwOm2lHq1jQRgOBRgU+fT/1ZZrGbYdpXU1q+GhWX3YW0r7WJY+Ay7xtCAi27kVqy72o2+0L6Ia+iDXYpdMg7EQtWI7615+L9aP6igfe9W+w51mKArZY+1P7Cyt8Kb7nEfg+Z5/0Oab95x8JISZI0ENxxWV/qSBEgGw2xxWXX91l3YvOu4j/u+z/mDd7HgvnL+Tma2/mzJPOJKzRW7AWjjn5c3z9R2ejte5CiABhLs9//3oj2vPoM6A3isg1IY4zVb3Y1Bo0bgxHn/lNtBJ83xB4tuZ8ZQW072oXKTuexTWyV1ZqjiPWxAZj8PJxgXyh4L7QIciDMYcezLDtdypZrN2hUG2yAQhzgsk5VTGlDUpHiBbC8kxWEVKRYFavxHa0O/EAE2uDGyBrAY+GrSZgcsvonHUvq264CMm0lwgRd37SnsEsW0LulbtoveEUbLYVpT28ITvjjdwHtcfHYegoxHvvlNYjE22Msd3XxgDbjtmSbceMYothQzjp6KMYMXjg+z7GBAkSbByICG+/vbTmZ4sWLq54P2fWHJ5/6gVyZQSaz+dZ8vZSHn/oCQ456mAA3npjHk/e9yjWWvY5/EC2HD+2Yj8HHH04D95wGwtmzKV6jrHWMu/1WbStXsOMyVOKWSkuoVNjrTBwxHC+99cr8HyPJ264kbZVKwonU3qSFwEjaEqqNBUersqr0GWJOzlDoAStwTluvdhYUMW2UhO/9nXCtjYePOUUV9zYzawvAtLQgMrlkAq91xpQri5cRAiqeKhQm+ib2nkcBW5WCrz6Ztqf/wf5t6YiYRZvrcKLurFlc0B9hOTayU29g/o9vlTa5/SbYNBb4OeRte8tc6RHkiKouCam1skLs99awH1XXUrzBrpLEiRIsPlCKUW/fn1YsWJVl8/69e9b8f71aa/X3EemM8O0yVM55KiDue3qG7jpj38jivKIwH+uu4mDjj6Cb/3oLKY+9TyrV6zC8xQL53Zf1zx8zBYsnDUbP0gR5UsWqMQWTnO/3ni+K5w+5qwzueEnP4ds6PyqBRIxrhbRixlC1RTKdpZkLWjfw5MQlCuiR4OIQVntrDUlgIdYS5jppHnQcNrefBNprCTfcpmekR87koE77MKrl15M2NpKFFl8v1qrVOIYZTfCBUrQomq2ySps78xjhQ3DIiECiC8l+g9wSUQGlxqbivdm8oQLXqR+jy+5Dh0Ln0ReuR4lBvqA6vORJEWwRmJPQLn4t7t7Ojsz/PJP1/Lrc07/oIaXIEGCjYi+vXuxYvnKqhoJoV+fXhXr9RvQD8/zurgJU+k0A4cMYsmCt7npj9eTz+Vc4blynqeHb7+bp++6nyAVIEacq1UMQaHHYFVqyoS9dmbVsuWYqKtLVmlNv6GDi++3238f9jvuszz+txtRhqJV6Ir5SzG+Lo0Miker9blgowhdZ4mUk5bzAaVBtCluKJHlmf+5iFWz30C1R05ntB2kDif8bXFuzEYQpZl/739Y8N/bCDTolNuPRaFEoXAKPrrYfMHJtZGPB5lyV6qQZNSd2eLWiuXqVq/C9hVUTHi2HrwOgT7WkWL5RUjr4v69liFIvo3wkbNg7XzQUVHowItqHPAdoAeSoqCweMpic4JOecUnr6J+oDX86cZbqEv7/OSMb9a80RIkSNAzsHr1WmbPnIuIRRViRrHrcfbMuRXr7rb3bgSBX9HU1q1uOfKYI3jy3kcduVTMGeAbiGyECStn1EhZAu2VZVsKQdpw02W/w/M8smHGyb+VhcCCVMBenziSNUuX0WvgAJRSLJk12ynjSInkdBlxW+t0TivGTCFAJOSNa3ZBLFCitBQbCkMp/uhXTXVaCStmvI4NQ1CQwnOWV0dZqZoGAvAlwuYjfAVSlqwjZWUYvnaJHCLgY9ErLCXZO6BF41kP4gYNxRZaFKxTF3ctelytJb9Qkd4yfm8E0hYC1aUvpOQsNHiIQGqHYzAvXglr3nSB1fLcHq/ygeidoueRogKVsq4rihVUaEo3WZ0mkFIw+883/Zvtxm7JcUcd9kGNNkGCBO8Sa1av5VvfPJfpU2eUFhqDX+aBJDRc/Yfr+NppJ6OUwlpDlO3aXghjyOfyzoqEyiS9smzPalgLEpSYLOUbtIJ8ttivCKMsnu8TBCl832fgwP78+bSz0VrR2Ls3n/vJ9/GCwlRbluxSPneL837pMg1SpVUxZ8aLLS+xllSzy2TtMtYa4/dEsGUNksO0wc97ZZ0lgBbwsLXbHFZBIkGUq3X0MrHFl6Jk1YUWEwja9yBnEWsrQqhK6UriFpCMq4NUvsKfZ1FjqdkoWSKwoUUtVXj9xxBNeoRS65DCRQN5j7k2PS/7FIp3sGjnv3YQVFWNT2c2y+//cfMHMMAECRK8V3z15O9UEiLO46erSOz6v9zALf/4NwDPTnq2soZOXMsjyeb4+idOZOH8BTVr7NYLBUqJEwSvgohl7MQdOf+6PzF40ACWzpmLCUPCXJ41S5dxzdkXMnaP3Qjq6yq2K7Q7LBsqJpZ8IwypCzSaPIoQq4zTY+0m474mRIrJLsVFWgjTEdLHQh9B9XF1mLLaFK+LU5urfY0Kh/ZiQ7xAiMoTdKOgm8W10pIQX3VN1hExWGWEmTkAACAASURBVGWcaV54eRrlpdFrNUS10mwLGwNzBNpwF0HWkwz0LtHzLMVyKLCecycDNfOJV65e+74OKUGCBO8dM2fMZt7st7rEpTy6kkI2k+VPl1/N7TfcxprVa8hmMsUi9fKYXZjLc/ctd7LV9tswZ9qrNY4qoG1R0UYrn6NP+jxbbL0lK5Yso2+/3txyxR/IdnStEO9s78Dkcix7c35FU2JwVuUd/3slvfr2xkbG1RV6Tjz0wJO+wJPX/B2lFSYfYo1BW0MQQJTLlmofRRyZWE1Nubc43lhOZqnulHUURFlLKgIVCSqWQo1WW4K+nrM4q/I0AJRvIC9oA8rEA4sJUTVUhXvTEGlIlYzUyiFUjD+i3xd/T+vlFyLkoRWkd2V8VURQISirwAjKGtTgiciSyZXlGyKoj15MsXvoGu1PWpoaWLVmLX179+r6YYIECTZLLFm8DOjeaKhGR3sHCzrbSgsE0v/P3nnHy1GVDfg5Z2a23JKb3iEFSChCSAiQABIQpPcmVVBAPkARkG4DbCgCSpEOIijSFEFAFERCld6lBEjv/eZunXPe748z2+7uTQhJDMF5+C3Jzs6cOTObPe+8vYFKlc/leefVt1h/yABmT5+BVh6CoFMBEmY71To1vPvaqxx5uotLyGez/PGKK+vGDBIJttxxexbNnuOEXQMK+TyLps8mSCXY9pD9WG/zTdl0xx1INjcx9sB9+M8/J/KPy67EFkNnxuysEUZ/V0owOYWfltKGcvSozlsk8ktqPDzdtSalCxZd7JT+EYIUBb+1idaNRtLxwSvRhwK+e7iwAXhLncZZkpcqUT++UmADweTBW6FqKyy65ya8UgTqHFDNIJ6gPOUKpQMUDRJqvIHDUUESf+wZFB89CbJL3dNSdB+8jk9hCahi3TSflpAotLdkVqBSfqjUReOjjyaz/SFHM3dBfTh3TEzMZ5ONN93I/YY7be9quVMN9lRdVG8xxjBt8lSstVgsm40dxZ5H7E+qk3kThKkffMjbL70KQDKdZtT2Ud/VKo3MhIZxe+zOoJEbETYqECASdbyAYq7Ahy+8yug9dyMZpYw1tbWx8YQd0BK5f5azKlvfrXmSESi6ly5Y/NA4TY4QQosXBOik3/iOSdS0uBM68Om52Ri2OudCNj/5dHRKoRIWFUhFY9VgEtFaW4ou6qI7EYDBYkytt7PRvtm3XoJeA8CP/JYfCDIbZKEg8wTV4SoLSaum+ZjvuHFaBqBT+8PHwCKL6hD8xVKJ7v2UrLtCsfSPMhTQpqZ8UqmorrKWYhiycPFSLr/ptrU315iYmJWiX78+7Hvg7k4RqtoeArpBlInfuXGfUnStJ0lkkRRMGDLpnXdJJgLyVcEzJYqFAh++7XIf89ksrz/xFBRD9zBuLYQGbS3PP/QIPfr3Y+u9dydIVQnXaJ2qjk6d3aCva7pbayUYZzmLuk6CbQPSHjpUTuNLGEhYCMS90oYRe++JlAeqjehR4oIUO6M8n81O+D/6brUtyya/jw46qYBGUAULVMrFSVGwoWALrrpNnS/SutZWpZrUigZBQtF6nVswH0klUE0CKYGMQNaim6IoWwXeeoOQxe9TfO0hJNcO77+Fnhqi3zB4Gal5WPm0rJtCUcT9wym4myDWUiQkkfCjwrqCFosX/WMohiH/eOq5tTnjmJiYleSiH5/LmeecTLe2FrTWtLQ2c+rpx3PNby9n1Jgv4GmNVhCoBpGTIoQNNE2idIBqbcVai/Y8knWaIgTJBL37uYpYU/7znmsZJbgam6EFK4SFIq/96ykADrngO+x/xqn0GzbE5fVZ8MNajbW5e3ey7e28/+yzTHvrragdk892xx1DkEqV6ovXru9KoQOflr692GC7L3L4Dbdy2G9vo/uwgahqTS3S6Ka//jxDdtwlSl50102U0C+eRIGKFvGsMx8nArqPHEH3kSMBSPbqW7Fdi6A7DF67RWcFVRSKWrBY8A1SNEjRdd6wGYOYaPJFKWvwJaHoN7Yuo0Swy5ZggzzWB91P8AZadHepikRVyKIp5P9+FblHLmPZ5fthWwLQniteMNegMi5VZ1VY93yKIpCvijgtPSFayIcFPAVeg6egHt27/ffmGBMTs8porTnu+CM47vgjyGSyzJk1h379+9LU3ESfXm14hBhjUJ6Owv3r7XIFsWy04TCmfTQVrRViQzqlA5LP5hi325d49I/3Up3MoZQiSCbZemdXcPzjt/9DriPTcK6tUSsprTXbH7I/2x+yP0/ccgePXfdbilUaaJBOMXTUxly69954QYBYS3OPHnz1179m3LFHEaRTPHvr7XQsXEiqWxO26GY0fPx4dj3zDFr71pastNIg/QRYNnc2W/zoVxSXLmXWC8/WfOYFCbw0mFzOabtA24hN2e6Xl5b36T5yFMnuvcnMnYHKGRfkApTqqRKCDRRRvFDtnPIGL4jyMFPRvgqU0VjrdlYadHRg2cxtDaaHRXLghR74nb9TcX7SqkCm/LwXSfkeOlV0YyyxsIqxleueUARK5gAXIh09iVgIjSFIJAiU0w5LNKVTnHLM4WtlpjExMZ8eay1XXvYb/nD73XiehwkNhx5xIE8+NhEb+aqMsZFJNVoXICqg7TTFLbbfihsfvJ3nHp/IJWd+r9JQACCKQ/jPq29w4c3X8JNTzmBxVE5ORNhos5GgYO606dx/1XWVzvJVi3UilWLXI79SN/cJxx1JblkHT99xT1lwbP7lCbz31GOEhQJhwYVmFnM5bj/9dL59772M/cohjP3KITXVa7qqdAPgBUHD7YIQpFJMuOjnvHvvnbz1h9+6EnTGktKKYqYjilh1+y+Z+j6LP3yf3ptvWb6+MRdcxVu/uZD2l19xGl93gebybXYFAPINMjxFXBBvyZ3lAz54eecXRFw2hVUKvxwdKagm135LUlBcZghEuyR+33fFv8nWm161Rg44Enn8DsA9fKiGnuRPzjppPtXaRq8GGmFbN7bYeATpVJJuLc0kEwlOOvIwDtxtl7Uw05iYmFXhd7f8gTvvuId8Lk+mI0M+n+feu+6nYGs9hta6gA5jbKR5RMJLQ1gMUUqxxdajcX4XqXqBSMgDv7+L2VOnkVu8BMIihEVUWOSt517gposv4ZkHHsaEBokEsUglqG/MLhPYZJut6uautWbP007iwokPceZ9v+OHEx9CTIYwX6vdiQgLp0/nl3vsydt//4ebd6fWd10xcrf98BK1DXeV1vTecGNSbd1ZNms6M196imJ2KaaQofuQIRSXdCB566xsnnuZYobXfnNpjU8w2aM3W333ato22gK6CTSVqum4l20Gk1yxqVIpwAo2kBqtUiQywSqBpOAnDHqqoKcJtFvCpRaTCUhNOIH0qB1qelFWBgHVlEQGdMMmDDZhMN6q5S+uo5pigwgmBZ7WHLDrl7jsnDN476PJzJ43n81HbhSnY8TErKPcdvMd5LK1ATC5bC7KQVR160CpBFrp76l0ip33cA/EmUyGIOHXdNAoHd/Rvoz7b/wt+VyuRs8o5gs897fH2GXfPTGR9akkGFHgJ5JsOGqL5V5DkErSc/BAN4fFi7vcr2PBQv5y0Y/wk0lGTtixy/2q2fzAw5n99mvMevNV5xvUHomWVnY55yIKy9p59Ntfp9C+xLmcCoaFL7+FUuC3ALpy/wRon/wBk//+J4btfjC5ubOZdPO1zH/hWbSvUD2pV6E0hE3gN7Dg1olxgUYlc2wAwQCLXmxRHVQq7YRAaLF+SHqbPbFzhhJOegy8ghs8VJDXaFNETbwRlmVgaXSc/xlOyVBK7aGUek8pNUkpdV6Dz89USr2jlHpDKfW4UmrIikd1T3jVtQjB/RCstTz+7LOEYcjI4UOZsO3YWCDG/E+zZn6D/z0WL27sIBIRCsUQG4ZYY0ilU7R2a6UpncD3PbRWpFIpdt17N7bcejQAffr3o7V7t3JVmJJA8DyPbSZsz6K58xqeS2nN8C23IJlOd5qE+9+m47b+xNezyU47ESSTjT8UZ0r9y8UXs2T27E80nhcE7HHRZez7i2sZf+K32eW8H3H4zffS0rc/Hz/2EKaQR2UFlaOcqiACxXZqInlKUb7v3H4thcWLeO4bRzPriYcpZhaRX7qwrm9xGU2lf2K0Ngd1fkYBXV+oW/mCF4TYpSEqI3QOIAbQgUdxyisUJ/8LkmFU0ggXZdti8P08sjQDC3F+T3F/rgprTCgqpTzgGmBPYFPgCKXUpp12exUYKyJbAPcCv1jhwALWhIg1iIlKE2nXk0xsyMw5c3noyadW89XExKx7rLHf4GpERHjyiWc596yLufD7P+eN19+p+XzEiA27ODBSGKzgaQu2yNkXnsktf76No086lsO/dhSX3fxrzrrovLL5USnFaRd9l2QqVU6yTySTtHZv44iTT2Dk6FEN2zcFiYDxe+3GhqNrBWMinWKnww6mz+BBn/h6t9pvP7oPHOjOHxZRhSIqX0Tnw3IuY3bxUm445issmDL5E4/be8ORbLLnAaw3djzacwXMZ7z4LIWODig0ztcMO2l4CigsXcrke35PmFvsKvto3KuriM68EHoW41uk1UI3g1Kd8sUBvNr8QZ0w+GmDEgtZwRqLMQZra9M67LIOlv7pIsK3H6ytXBM5jpWxqKU0FKifljVpPt0GmCQiHwEopf4I7A+U/9WLyBNV+z8PHL3iYQXEoiTyGljQKFRUZXZZJsOr7/yH/XfZeXVdR0zMusoa+g2uHkSE0069gCcef4pMJovWmrv/+ADfPuNETjrlOADO/d6ZnHz86a50W6l6C6BDqRknn8vwo7O/z5BhQ/nRVT9n2IbDG5wRxmw/jivuupUH7ribmVOnscU2W7HXVw6ita2Nw755Eq898xyFbK4cjJNIpTjm7NMJEgm+ffVlvPjoYzz/0KMEySQ7Hrw/X9h+3Epds/Z9kqkkyoSuPGUk3AVcmodW4CnymQyPX3sVh11y2Urdz1kvv8i8d95m5stPs/D9t2lQfrSyf105NCHZoydzn/2ne1slScP5lqCvjoRRpYoO8yySVKj+rtWTWDBFg1fUUfqhSwMp5Rk6ldO6llNRGy1dNY+Sr1Zr7bT5BGhyiK3vmgEKqyp1WFcXa1IoDgKmVb2fDmy7nP2PBx75JANb5W6eF31rNnTV5ZVSNKVTDBk08NPOOSbm88Qa+w2uDp55+oWyQAQXLJPL5fjV5ddzwEF7069/H8ZsPZpbfn8dxx12AoWw6B6CTa2pTSlBK7DGMPnDD/na/kdw+HFH8X9nn9bwvOsNH8apPzi3bvvAYUP46R9v477rbuL9V9+g94D+HPCN4xi1nRN8nu8zbu89GLf3Hp/6ml998EHmfPABNh9GEbLRNZR2sIJKCIhiyssv89HzT2ONYf3RW5No6rppepjL8tApJ7Loo0mIKaITUfHy5dgCa4RMJORGHHws0+68pc4pqDLATAs9NCSitLiFgioAKUGVgmA12CToQiWQSXmAD2EfSAeDoX0OhFlnVQ3rThVNR1yXkFZxdb+j+q8uNx3wnJk0BAKl6Nq+u/J8JgJtlFJHA2OBCV18/g3gGwD4AZU+llIJvxVQWpFMJDhk97hVVEzMyrAyv8H1119/tZzz0Uf+WRaI1Xiex8Qnn+PQr+wHwGabb8IZZ32Tqy/9DblCddCNM53qTnVCw2KRe+/4I9vvMoHNx4yqGVtEmPnxVIw1rLfBMABmfjwFEWHQ8KEMHLo+37rk4tVyfZ3JLl3Kc3/4PYVs1gUJdbGfU8YECkt58MLzIBL4e55/MRvv3Hht++cPLmDef96KjlfYEPwUaF8hHq4aTadGyV5AJb3CCioQJv31V6iCiWoAqKq9cRkPs01kTq18rjo3A+5ckccD3erR79Az6T3+MKac9mXs0mzXNftwfkqvrxtbDEheoZaGtYHDUe12Eyi8gl7lVIwSa1IozgDWq3o/ONpWg1JqV+C7wASRxpmoInIDcAOASjeVb6VVLqIYQHua0ZtszE0/uYjW5ubVdQ0xMesya+Q3OHbs2NXiwWlqSrvcw04NgZVSpFK1wShHfP1wstkst/7mNjIdGUDQqusKKflcnkf/8lCNUJz87gf85JQzWTBnHkop0s1NBJ4m29EOKLr17ME5V17KhptvVjNWIZ9Ha43fRU7gijBhyAOX/IxX/vpgOTfRKgik64U88Fzz3kIm6sghwsM/+R4DNtmMtv61lrAXrv01U555oqwVijhNOsxB0AwmBV5ORYLMfXVBwuIBlINShMQgELFIC3hZXSO0rLKQFqclCk4xySvIK7we1Sqnwis9t2iF39bCoKPOpOcO+2CLBWyxgE41YZcubKwilkZJUCk0LoJaEtYKUYXrmxgKGItVgoru56r2lF+TQvFFYCOl1DDcD/Fw4MjqHZRSo4HrgT1EZO6nOYkgBJ7PlCcepXePHqs655iYzxP/ld/gp+XgQ/fl9tvurhOKIsKXdv1izTalFMd/8+vkcsv4/c13UMhafK+0rkardCeqk/Rz2SznHXkC7VXRrLlISy1FS86bkeWHx57EDU8+QnNrK3OmTuXG7/+QD157HYDNtxvPCRdfSPc+fVbqOh+77lpeffihskAEJxRDJQSdOtwrrQmSCu1pxLgaq7ogYEGyOe456QQOueYGug8eDMCHjz/Km3fdXp8/jws+sSF4AZi04AUBibxgCwW0V3vPVIqKqTUBtrugF1cG89MW8UtVbaLtSUG1Al7FxOmrNra44X5sPovyNH5rD8IFc5h60Ylk3nkZgNSgwRAkoFAAJa4aUfUFBAn8Xh4q6SFi8YI0aun8+hsrFmWi6WiLYN11r6JUXGPRpyISAt8EHgX+A9wtIm8rpS5WSu0X7XYp0ALco5R6TSn1wMqco+RX0Aqyjbptx8T8D/Pf+A2uCiNGbsD53zudZDJBc3MTLS3NNDc3cf3Nl9PcXPGfPTvxWQ7f93C2+8J4br72FvKFPMo3GFVJpu+MH/jsuk/F9/f8359o3MGCqNFOhDGGZx7+O9mODi488mjef+VVrDHYMOStJ5/mOxN245Qx4/n1Sd9i5qSPury2dyZO5KIv7cJZm47iiZtvrin1Vj6XEqr/UyhGfOmLTDjx/6Iaq4LOO4FYqhuzdMYMbj/yUAoZV27u9TtuwYadcx2IXEzi6pLmihBavvi9S9jipG9F0bpVaW3aorTBzDOE8w02L0izYAZaTG+LaTOunFvnC1AgecFMtZjpBtUeosIFvPXD3fj4d+dSWDQTCUMmX3AMmTdfhI4Qlobk3p2MzYWoZg/p68yxogSJnKz+0KH0+enjpLbYC88GsGRRw6bHXlif5gE03HdlWKM+RRF5GHi407YfVP1915UflHJ+EABWSCUC5sxfwHoD+q/CbGNiPn+skd/gauSYYw9jr32+zNMTnyeZSrLjhPE0NVXSHib+cyJnn3oWuWqhIqCtM/+JUkiowK99vk8ECUZXVZlZNG8BxUJjoVi9huZzORbNm89zDz9CMV8oL7DagLKCYMgt6+CNf03kvRdf4uIH7mXGe+/xxO/uoGPxYjbfeScGj9yQO8+9AFu0kbmyFHpZj8GZI5USdBJSbS2MnLALz992IxjK0akllBZsuIybD9iZTfbcj475jXMrS3jKpWMoC2/fcTPbn38xb/3ml0hoUYECz6KTtlIzPAS70CCi0L00KlCoDumkIlZPCHRTiG4G5UeuvnwHHR++zPtXHs+gHU/ELlsGHZbyCALh/DxeL8+ZSAcayCmnDSeF0H5Mx30/ofDK3yDqsUii/h76xa7u6qrxmQi0WWkK0d1QGqUUxlg27SIEOyYm5rNNr1492Ge/3Xjs7//kgrN/QDqd5tDDD2TM2NFc/tPLagUiuMAT7ULxQxG0FYLqiikiFDL5GjPahptv0uX5q1MTU+kUm2w1mjefepJ8NlseT9vaBVhEKObyXH/6Wcz/6EMK0b6zPphEIhAXFqnEBUU2tu6irKAi07H4Cj/dxPCtx9Hatz8DN92cKc8/j7HOP6i1RnkWL+kGCnMZ3nrgXhLi1sD6lkmCVqWwTYVYw8L332XGsxNLUYpIoYiXVvXmRhU19p1ikH6aYJmFZu0S52tO4aJOdbdKoJM1UWaAp5BinoUT74N2d2/qipB50fwUzl9ZGtYa8i8/hAorDzEmFLyy+VaBtas1N7GadU8oWnHOVUA8C77HxWd+k6bO1SZiYmLWCay1nHzCafz72RfIZLIo4C/3/YXtvzieKR9P6fK4kqyxRMElVZbUgesP4u6bfkuxUGDM+HFc+f0LMTYsmynB+Z5cZRu3niRSKTbcfDM2H7c17fPnkmxqIp/JdLn4mjDk4zffJuHZSpCLdfl3ZXsnUDSWQOnKgi6CZ2y5m48Ct6ZligwfO5bbjj2MRR9PcU2Eq+6RjtSscqFwE1JQHgnBJf5X53Hi9rdRr0JPa0w+x5KpU7CmSmPuyoHmrLfo2RYvCZKNBGN03yQqJu43mTqhKhZEu3nmM3NJlZ8MarE58FrqT61Q4CVcDdoSOeNyLps9lHbfdfV32WmAVUKtqv31v41KpEX13wCI0lU8zeN33syElSi1FBOzJlFKvSwiY9f2PNYUY8eOlZdeemm1jffE409y+ilnk8l0VMsSFJDwdOPACRGCqvgcXyu8aD8/CAi0wdMulcFToHFVU7AKHZkzE6kEx555KhMfeARE2OWQA9j9iEMJEgGFfJ7z9juQhXPmYIrFhqY6pRRaW4KgIo21QNAg3UIpdy0lwZUIbcO1O9W9Gc8WkGyx4eeJtEuzqBlbFMlQyiqvh60vU6oUCiEdBKhCoSxM/DaF6txLC8AKXjt4GhJBdO0eqKQCT4EREt0tXrqxBPI8lyKHp2mZmkA61a9VnhD0sui+Xk0VIbGQHjIOPfmVSskdEYIOZwb2Esp1zlCgDJEGX5tqote3BOe9/al/g+tkl4xqxFr++ti/1vY0YmJiPiX/+NvjZKLAEdXpJdUBISUic2bVBkDQnmaDjTci0AYbFigWChgTIhJixCBaEN9ivBCriyhfscV247jigbu44sG72efYIwkSLu0ikUxy4Z13sN0+e5Nqbkb7HsqrXS6VUni+rZmwJ40VFZGoxZVY/OUoIrklyyjmCl1+bhoElmANIiHKhngSNlzURVxzZSnmy0EtgmCyje+vyleOq5wcJCNIu0Uydrl5hiXnYdCrHwPO/YWTkm5EwBI0W1QRZJ5B8tZ9z0VBFkHzlgcQbDgWfJeTUV2VR4qlMUA8sB41wUoMDKF51UrcrNNCUQG+79HW2kAHj4mJWSdobW1x/RCr2jEhUmkBVdoevXSUtB3tCDgf415fOYCjTjyaZKLiFSrLsRpp60qPFTLtTJv0IeC6YSxdWBvl2NqjB917dMdms3hReoAThD591l+PdItf199PACu2XtDgPD+mLOQbSZToWroUmvXblUSaagpsUggTgm1g71VAgIGEIEnBJi025VptkRU3OXF/qhyoghM2Juo40XBGuQaRni6/wvkYkz4D9/omTVuMpd8ZF+O1taG8EO2byhdYAJlrkRkGmW0gI6S33pluX7uM5Jg9nGDUlQrjVgSbBTHuPlrl5mi0hZ5F/IGCXsU09XVWKJYqswe+xxEH7L2WZxMTE/NpSTelCcMCVixGrCv2j8ViKVqDWAvWuj+NdR3dqwSo0pBKJdn/8IPrOrWrantsCaXKVcGu+u73ufqsC/jq5ltz4jYTOGHrHXnuoUcBmHjfn/jH7+6gmM+T6+ggtEWKUiC0ebbafWf8RCfN0QjkneZj8wZTMFVC3slisdTUta7DE5an52gtlV6OYgk8W7nG6BX6tqpzBc78KAYS1KniEghSEFS7RS0VVLugqku09YfiUHH7lS5CC34aCEHaI22+JOwVeGkgHdB33GEseeBO3j1hHDNuOg/JLOwyiLVygYYF15yMzbTTeuSP6XXJc3T/2ZPotkpuqFin4ZoOgbygioJOWYJh1HQ/+bSsk0Kx9IWnkgmuv+QiNhiyespOxcTE/Hd56IFHuOm6W8rvfaKFLXovQLFK8xIFRSzGWqy1pNIJkqkkp5x9OjM++pibL7+S9qVLCcMQa7vSuCoU8nn++ZcHKOTyhIUCi+fO46ozz+Wt5/7NwzfdUolArcJYw2O3305b34FopaEQQiFEQhOlj0dYwRacUPeU4Guf5pYWvIQfleLq9NJSXtStV5vDCJHWnDOwtOhey0IkbHyNtioH0hNxGm2jBwQg1K7Rr1Kl5swWpUJ8r4g3O4Q5IWGrQQ8I0QMMer0Q1eIEr82DLPax7QqVgNR6g+i54e4kZ7Ww+J7byL73CpgQnY8ebqDS57kOQXmW/Lv/Zu7Pj0SsQfkBXnMb6W9d7Ryq4KwFJsQLi6hcESmEeP3t8oXtSrDuCUUF+JagSfHD75zMUQfuu7ZnFBMT8yn59WVXkY0Ez/IWo87mU4WrkXr+zy7kwWf/waTXX+fHp5/NrGnTy8dUV7TpNFg5otRaS9gp+T2fzXHvldeybNGiLudTzGWZ8f572OrcR+V8XKbafCmgrEFsSGhzjNp/H4677ga+sMde6KR2wtET8MUVzga0b7BeiPENVls0gofFF4MWW5FtFortgm0gGK24+i7Klh4mXI6lFddyD2OgYKBosIHFtkjZ1+hpg1cS0AIsE5hvUQGopOt4YVsspoeBHoLqVcTrW0B5RfLzJjP/3Qcp5uZQLflUdW45EGYqGm/ZnKwE5TtV2nYsJvdWpQVg4dEbQfIgRTwTRqb1CCOY6bLKGmKJdVAouieqYhgyddbMtT2bmJiYVWD2rEozXUUXpq8or07ZShK9lpCWdMCLE5/m0nN/wN/vf6ChH6+ptY1kKlWpW1pKWZDK2LqB7Jw9ZRobb7tNlyXDFOC5pLy6D0rBH+UTlOdveO6O27j5xGPps+GG9B+5MYnmNEqX0kIE7ZmyVieeYH2L0oLnObNxoxQEk62/DBoK4AAAIABJREFU7h5DhxE0CV4PQQ0wkHLqmbLOV+v6I0bmUCPY3pawv0FaTGMTpEDY3mmjJ/jNITpRRGUF5liYbSEjhD1r52Q75zhaCNsFkxNXWcA34BuX0oILCGq/7zKWXH8GuecfIHztcQjz5YeZmpkI2CVgli3PLv3JWfeEYkRzUxPbbjlqxTvGxMR8Zhmx8Yjy3yPZ0ZCW7t3YbsJ2BIHGI0QhdLS389Ddf+YfDz2K6UIr7OjIcPWf7mH0+HGk02k0Cq9kaYv8clDlg1OC9i3p1iSjv7RzRUMt7y9oa2tMvCtCKcFX4tIUFIgN+fuVl1EIi+x1/vcJggRgUbaICm1ZMAB4iQRbH38i2u86pVxKTkgraGNp6z+IdFsa3WbRzYI2lLVjZXCFwUtWWwuEzq9IEkjT5YXZbBdCZ4HAHEF14FpMzRNkcW3QlCiLCZzmW7nXuOsOnOBXpXRGsWibx874D7kXHmTpredTjL7f6nZbtTdBIUvFCdfmRiG6n5x1L3kfCHyfvj17cthee67tqcTExKwC533vbI494nhyuRwW95ReykMHnCbnaS685GIKmWW8/OTEuqRtQSgahdfgEb+QzXHqAQehJKSYzeOWVB1VihQ8sYgCoyAAdFQ1ZdbkSVxz7jmoosUnquZiBULr5pkCUStqVhRpf9hIG6zde9Z7/6GQz2GWdbjgoehaJC/oZu0apyt3apGuw2+6DxuOskvJzZ8HCB0zprJsmkUHrtOep1VFCFYJlWq/re0QvITClgJ5cPl/1XMOunVDBwVsMV85sCDQTk2BAyVADqwWF20q0ck8sJ5BeUkSqR6Qn4dOdjZ7Rg8eJW0ZIMxjUPhKubJ+DQWjdX7FNlZZ1VvnNEXf8/n6oQfzwp/uIZVMrviAmJiYzyxjt9mKH//8QjbeZASt3VoZNmJDNttiMzzPtRDv3qONX179S/bYdw/+dMddWGPrTIgKV2ElFFNrQhUBa2hf3BEJRKJAkkhdwmIjB51C0H4lWlWsxVqD8cT54PIhhJG5NDqFkcYpCaqUuqgsnmfKFXNEpCbvUqzlpXv+2FA9th0uuGjkjrvSc8gw/FTKVZnpFKGi/YAxx3yNwuJFUWirlANabFGwuUgLhPK8GmJA5YRgvov8ddG/zgdZkqZBcx96jdoTyVikYJ2vcV7FPIyVSmqHuNJs2kqdVi0mT2gX4rUmGprLVXUaDS4fEaUx2nPtougUp+MJ3gYWeuOeIFbRt7jOaYqjNtmY63500dqeRkxMzCoye9Zsvn7U8Uz+aDK+71MsFjnsiIPYbY8vc8IRX2XunDnYMMv3zjybJQsX8M4rbyx3PBGLweLhOQ1HGZQWpwY28sapijzqulOiy4OrWSiN0zatSKXzu5sACovWFr9KMxRKPryqURWgNYumTu3yvJIXxh19Ir2GDOXJy37qhAOU23oorRl7/Ak8/6sLkbBxwr/gcvrwVPl9Q5mRhGCeqStp57RGi/YshWnvM3f6e5UHAwERhUooKHYyrfoKrapV/lpMrgBdmIRrcj9LhydS+MO3hA9fwVDE85JQyKF7h6gmwR+mnGa9GljnNMWYmJjPB8cffQLvv/c+2WyW9vZ2crkcl/74F+w+bmcmT5pMpj3L0kXtZDMZLr3wJ2jtnE6dtSWJ0hVKS6IRAyp0PQMVlU7kXbGCUpelwJPyy6vV9ophEV9ZPEI8FdUorVqfG/vA3Et1bvtUvUsovPO3hwjSTRx8/e10HzIUvzmFbk3SOnQw+193E2//8UZMPrfcdkm6CFKw2C5We6XAK9ou7oMTir7nCgWU91FOsIci9QJRBL9gXR/Irq+OgoDXoz8qkUYlUihPEyS6iCJVmtbTrqf1mtdpve4Nmq9/neDwr6KbV72pcGfWOU0xJiZm3efDDz7k4w8/xoTVvjLB5vN1KWxiIJ/LVwp5ozoJRnEaYTXRQqlE8EzUrUJ16gghgi7VKV1O4KJXziWsjC1WXBWe6FRiXUPgsiamdSmap2vNw1pauvdgybIOGopOEcKoQ0ifjTbmq/c+ypIZ00CEtsHr89Zdv0XEIrbK5FhzfGQuFgVZIC1O2+zknvS6O8HZUI1U7l7rRhGpTmGuPU6ijiXR+akqDl4zMQ/E8+h7/u9R1oDnkX3yDjL/uh06a73ap8d3bkUlUtF7V/5NXv4jpS/OzhNUNxrXcV1JYqEYExPzX2fRokX4ncxnnaP2q7FRZKJCXPcEoZLlr1W9cTSKYvRDpy2a0KUaaF1a3BVKeXhaOfOi4Cq0+BXhp1AElPyQtYEpgkSdIhSB70FV54la+VqdgF8aV5fn2zZkCEtnzaxvlqwgkU4xcpfdWDxtCs/95nKmv/xvmnv3Zeuvn0xzn76898ifKSzLuBNajU7o2rsQWufq85WLQM0KonF+ucgPGPRwaS6uI0djwbw8VPWNoWJ6LG0KMxa/WddEnCoNnm/wwhyzf3E0rRMOo/seJ9Cyz2kUPngRM3sSEhZBe+hkEz3PvRe/T22BFhGBQqZ8j2m32HdwdvBeqyYYY6EYExPzX2fTzTatS5rvouBKmVAsvqlK2i77tgTl14pUpcA3UtbWVBQvYhXowC3MWgseUbqAJ3hGu4ATT6G0os/AQbTPmlqeW/U83aiQamrCtwVMJ+0rFOeH1NRHtwgWIg11zvvv0mv4Biyc/DG26n74yQSDN9+C56+9nFmvvRgJTWHZ7Jk8ctbJBEqXq4MrpbBFiw0NXuA5861198laVx3HegrPqsgfqV3xcN9AO0igsIlSqGfVDSzldEZadqfwFhBXki6ou0NVWAjbXUNj5blWUa40naBsSDhnMovvv5Lsm08x4Pw76XXefRTee55w+jt4vdcnuflOKK/W4yuFHOHEexHVhJJlqERVpZw8MHMF5vIVEAvFmJiY/zpNzU2c/8Pz+NlFl5Qr2ni+7yqtNNJOFGhrqbKLorySX085QYN2UaQq0lhKAjMsD+HkaB5UUrAYjAd+lKdgPYuyrsFua/cefOuKS/nZUUdQJ/Eiho8ezR7Hn8jt3/lW/XStO6ahOU+c/hiIsGzObLILFtBryFAS6Sa0p+k9bDhh+yKmv/g8JpeJfJlOSHlRY2LRtrK9PK7CFAzarzITi6ByBhtaBIWXCGju3YviglmRhul8l5IXwrTGN5VzKS8g6NadfuN2INGtO3MfvRObz0X31DjNXYNRCg/X4qsrcSRG0TRqFEGTR/jhqzWatRRy5D96nfz7L5EauTXJjceT3Hh89FmW/L/vI5z6Jrr/RiTH7EP+F8chMz4AlcUfGpnUV6NfMRaKMTExa4VjvnYMIzYeya033MLsWXMYOLAfLz39HJmOTK0WqYRE1Gnd+etcybHyx55gxeKhKoEurvMwKqzX8gSwYdTH1hj8sqAF8ZxJdMMxoxgwfANKQ3Vec7XnceDpZ7Lp9tvz0OWDmTf548qHIi4zINJS646ONLlSQqYJi8yd9D7JVJrmHj3Z7ezzue/krxLmcnilKFZlyz0EXf5gfd4j0elKeZ7KCkFN30bB5AoU5s+s1+ssmIJFpxL0GrkloOg3YQ9aB6xPbvZ0moePpN/O+/DGtw9C5cPyZWDAICht0V7kfS01VK5+uFGG/MzXMSL4DXIuJZ9l8e9/SsuEQ2nadk90Sw/skrks+fk+SHYJ5DOQSJO9/2ck2gVdiPozFgQVrN5Im1goxsTErDW2Hb8NzU0pjjnocD5+7x2KxRCxllTaBVVYU8DaEB2WBJpLSu8sD7RWpHyNLRqn/SznnKXFHKCtVy/IZGsKfwepFAedfArT3nuXEdtsw7vPPhu5MCvaV7eePejRvx8A+33nHG476wyKWefj0qWTlPIYbUlKVbQwBIyFaqtvMZulvTiHf17600q5M8GZGnXk+4Oq7V1cYMlf2KCRse4cGFNN0ZmUF731GuvtdRiz772dKfNmIWEISpHqPwhdbKw1WwuayEbtgeigLBSVtujuFkLjpqY9qnVKHVo8IxTffZHF777E4lu+T8/TrsK+8zdk6Tz3BANQcN9R0YNk6QIKIE2dA3lWjVgoxsTErDWstZx41HG0L11as91PBPzksp/z1uuv8ue77iFc0oHoxmt5aZxsvkAiEjwr9CopSKbTnPKjHzNr0iQevPlmOpYsYb2NNmLCfvvxy68dgymGFHKRaVdptFJ4WDylCDvauXif3dlg9FaM23d/pJB3kkGcIdd6Gs8KFKtDbAQCz1W3KQnu6hJyKGwYMuf9d0kmfEwh74SjKumqVTpfJHDr7kdk0vSCBBRznT6oVt46HymIFkwhh0Ix6/470ErVmI4zUz4klWqcAlEKE/LLyfPFctSN4EzWNLlnEb866Mi66OBqbZZinkVXnUaql4+y9Skr4pXScBSyTNBtqvZyVhActCJioRgTE7PWePO118l0dNRtz2Yy3HfXPdx2zx+44EcXc9kPLube2+4oC6k6RLDW5cj7kTmtqCARCaDOJJuSfO2cc9lhz70AOPRbpyEiLJ47hzN23J5CLlezv7EWjet3KyLkli0DYNKL/2bKv5+rtEWKCEMblWdzeXelzk1EmpaKNurOC7goVJBEldvNS1ntFY+yf1QEV2mnJqpVAR6+14Rvwijx3vlelU9ZWLgQoPri4sp3At0rRDmJjW80jR9NxNUwpUEkMGBz4DW5vxe1T1PvQYRzp6A7R92Wb4PC5A1+o1QTJYhW7nst4Gqv9qByk/NdTP0TEifvx8TErDXCYtil6atYqOSrfeu757Ldl3YiSCa7NBsqIxgjJTXKCUpPs+EWm5NIJmnu1kqQSLDTgftzz9tvst9xX6s9Xime/cv9XSfCNzpvGDZsUeUbixSt63dopNzqqKzvRWbV8rYon9IzluKCBXQbMAQ/1eSEZkk+KrBBSUuy5YIFNWNKSLF9CdllyxAMeNYJRRuVn7NQLJZ6TVa9Aic4pabDRz1OGa7LJEXrBrmiDe+hQoohhTc+QOfChg8sRNfiDx3jHL+dzoWCsJtF0i1YUkhOwSyB2eIiT+fFmmJMTMw6yuajR6HKmefRYq+cjygs5pg5fToDBw8mkUxy2a03MHPqNP52/1+47ueX1A5kpNKzr2go2Qp10mPbPb7Meb+5khkffsQbzzzNsw//lRPHj2PHAw7gyDO/g9Kaq775TV7626MoLWhPL8dfF/kIo7JtRoHnl5MfAdDG1iz25WCZurEqY3qR4CsJt5lvvA5iSSUVSmukKFHFcjAJV4FGlaJraw20WCDlucLZnke5GLgqdcoQEBNifPB8hQoFVcCNn3LXYkXQ0nneQrEgJJOVvMNSMFGQtCitGhZB0MriKQuLAU/hFSLTZ16QtKCkQTKOCN2++lParzoMWTK36maBUhZSzfj7n4Y3fFt45np44aEoeKnBd7aSxJpiTEzMWiORSHDFdVeRSqfxPBUJRADhzdde5cBdv8z8ufPK+w9cfz2+fto3GTliU7eYh4IqSDkIxR1qQELAUsjnmTllCoM3GM5911zFI7ffxrwZM1gwezZ/veUWvrPvPpy50068+PAjWFtq29RoZXUCm9DZaCvaGRSKtkZ78s3y8y0707hHoNuYL1qniRqQnCBFQUWaZ7VArNEYFYhYlLKYoiUsRtcVPTiU9pMQTM7lcCrBmSLbnV/U1Pg6Kd8TTwmmaKK8SItVBjxTfiawXm2xAq0MnooaIwuoomCU00atsdBsEb9yjCDg+bQdcS7+wA0IRmyN0iYKNrLuVQoW7tEHf8PReDvsgfpCAjXIotYzqE2rGj9/CmKhGBMTs1bZ6cu7cNdDfyp3ey9hjSGTyXD7zTfVHXPBFZfSlG4i8AIU4AfO6OUTVkyT1qKsoa17d9587lk+euftGl9hsVBg9pQpzJ42rewpE0tNJwvAaZwiTiBKY4EXlnxjDUyvXSkvKw4HcnsUrSHMF5GwiORDtHFC2GIwUS9IqRKOUHUfo1MY6xLtO6enQKVWeUlwiYmKJBgbJftbp+3pImjjBJrYyI/qSuiV75eKBKMSrLJ4qvY7LZ009MBri7alLdJkIHBmXLonaNnreACCETugkmmUsihjUMa4biUmxBuypbvERCt4WVQvg+rpopNXhVgoxsTErHUWL1xIU3Nz3fZCPs8Lzz5Xt32TLUfx+ycf56DjjmHMduPZ+/DDSBC6WAupxFwo4P6bbuKa8y8gn8k4bbBKcBVyuTphYYqCKTpNRiuNZy2etWWB0wgpmzLrcfUIqoqIR34833MandvehXYaXUSpzB0iWOPaXpWwCKZaO2s4wyhXv6HQLp0/EnKdqgZpZdC6og2WnJOClE9mwlrBKL7GNVJscF0KVw+1uvi3B5Ky7iUF7NL5ACS2PgjV3NOZxK3zjWIFlS+AtZj37sA8/jVKnZZXR2JG7FOMiYlZ6wwcPJhisd7spT2PYRts0PiYIetz5k9/BMDd11/v9Koq82AJk88x5T/vlN+XapZqrfETCVSuvu2SWMFYSErozKZQo4t12hullfNHWrAadFV1NxEIjbgmyCWbq4KCsaTKQsNDVHVEaMlnFwlSVclr7KpDkkXwUI2Ld5f36Sw0Sx1Gas2kRYQAl4SvdAP1WNxArlybu+9GNF5Co1NpWjbdmtzzT3TZk6uUrtlwmgp0s1MjVaIJv9/GhHOmUvMN2JDsTScRNL2LShQhaBz1+mmINcWYmJi1zrANNmDzLbckSNRGGyYSCY476RsrPH7OtGlAo0VWaFhpLdJqkqk0qWTQMOJSezpKe7DlV+fi3iU8cREsVgviRX+W9xVEjDN32hCJxkng/IKuQXJITTQoglYhykZ+TFleTGjpSqv+vxK5eiUTZ/mFy/u0Ypx0b4QC7Vn8osEzFqxFiiFez/XZ8p736PGF7V3RPROZWqXaJC3owGLytm6eKpGmecejUUGqvC18dyK1GqfgK4Oe8Qb24wLmP4Jtb/y9fBpioRgTE/OZ4Nrf3cZOu+5KkEiQSCYZMHAgV996CyM22WSFx2685eiG25e3wDW3tdHalMJG/1ULvL5Dh9C/f1+cQKscU/bhSUXgeUS+uLzrcm9CQUuIpoimiKKIwlaZT0FZ42q5VqWPhNagfYOnQ3xtnMYXnVethJBTqisVzBVArz5noKzTYDvvKUSBR7W5kNVjJZA6rTw/7QPys6e6Nk9agTIgBiF0L7HoQFy+ZxFMNrq/QRIVpGj+4pH0OOzC2lN1Kgjuqei8CkpFdMxsWW77r5UhNp/GxMR8Jmjt1o2rb72FZcuWkenooE/fvp+4fNf2e+xOMp0mn8l+IiOa0ppiRzsLF80vbzNYkukmfvXERPoPG8YDv7qCP//y51hbpHrpN5HvTYsiUBqty+4+wHXGaCjErGvdpG2kJXZGBDFRV4rquZb/lKr/N7gmZQmiNlrOTFrr4wywztRZhfYapENUndc2ruqGp7oW0vP/fje9dtwbdL72AgDB1JzTFgQRj8Hff4Bg0Ah0Il03XmL8VyhM/B2EbrxG5mHJQDhD8NfrdMJPQawpxsTEfKZoaWmhb79+K1XPsqmlhZ/e/jvSzU34yWR5uxckGvrXPM/D1w2WPwVvPvM0AHudcirp1uYul1jteeViogrrWlF5QqNhy1gp10Z1ZtTSq+Qv61rYKEB5HkH33jT16UfQ3IIOnLlZKYvn2UiTLZ0q6vko1kXlaoPxLdaz0ZzBdo60hXLAUOm6w3zF/Fl6eX7npPoKYfsSFj54Q71ztzSvToLW7z+M5LBRDQUiQOqAC/CGbgmJJkg2dSnzbNYiPS0M7HJqn4hYU4yJiflcMHbCBP78zts8/9jjLFuyBCVCIp1iwcyZ3Hn5ZZjQlT4LEglGjtqCD196oW6MfDbLwjmzAfATCUaO345X//Zw/cmUIkglsNmMMwl6HpFhlOUoUbT07s1GY8fy/mMP1y7ukc/QWBew0+iBwCIccP3vWX+7HUEppj/3JM/87Dzap00qC35RgpgQLao8rvKqwnGjWRpf8EJXaBzXVdIl1EcCsVSfVCtXro6CraiwVhh43JnM+f1ldXPUqSZ6jN+NRfdf3eU9cLmgChWkUIkk/U+/rusbBqhkM81nPYCZ/Cp25rvYiTch096kxqybEBLjDCwFNbO+XurKEAvFmJiYzw3p5mZ23n+/uu077rcvE//yAGGxyPZ77UVm6RJ+ctQR5DK1dVdT6SaWLZjHFSceS/+hw/jChJ15/R+PujSIqsa7AMWOdlRUxEUZ4yrzKCG0EHTaF1z3jS+ddibPX1MvTFySpGBEEZQiVCnlYbj3iZZWBm61Ddp3y/b6O3yJibmldZqwkuqaqk5zrcsVFJBShw7lklIEBarkL40qqVYfW2V6nTfxUfod/A3mPvBbpOiid3WqiW5bTaDb2AmEM96j45Un6q9TnLnUH7QJPfY5npbtD8Rr7la/X+fboxT+sDEwbAx2+Fjyl+4OYcGZVBV4QwxMx9WGXcV4m1goxsTEfO4ZNHwDjjjjjPJ7EWGjrcbw3ksvUYjaRiVSKWxY5F9/uI1iLocXJPB9j37rDWHBjGmYUspIlFeoqsykBsEXFzTiJRIMGLEJCz6aRCHj2kl5iQStffsxer8DeeIn3+1ynsoD0dpFnVYJRAVINstvv7wtRz/4JE29egPQc6PNmDF/bs0Yol3aXjkYpeGJIrFbSUisnK9enteR+fg9htz+BD222525D9+BFAr02vVgen5xb5RS9NjvROb+/hfYZYurJibla0mN3Ia23Y5d/km6QA8YQeqHzxE+eQvmrQehfRJ+E6ilrLJAhNinGBMT8z+IUorv/+Fujjr/uwzZZFPW33gTNhq1Bb4UKUZVb0yxQD6bJVPIsv+Z59B78HpopdAIficbqVSNGyRTfP3WOzjk55ez3pZj6LPBhkw46VROf+SfNPfqTdBUX6QAQAc+64/dlgOuvpVdvv9TtFJOfxNBG4Mt5OmYN4fnfl2p+7r1N7+Ln6r1xammNCrydXYp3KSsnNbPI5mi51bbkxo8DOV59TsAiT4DUErRNnYCG/3gRkb8+DZ67bRfeX+tNSNue52mLbZ3J5PI74qFsMCSv99B7qO3upjcilFt/Qn2uwBvcC+0zUMHq0UgQiwUY2Ji/kcJEgn2O+lkfvWvp/j1k0+zaPoUTFhfQGDJ/Hlsd8hhnPWHe0ing7pydAB+IkkinWbTL32Zc/76D3oMHMSYgw7l2w/9g3Mn/pu9zvseTd27o7Vmh//7NkG6qXYu6TQHXnYtx939MBvvvg8b77kfgefhGVNVTQdsscgHjz5YPq7PF8aw53X30XfU1njJFK2D1mf8uZew7YVXlAVUKQOjBuU8iZ0lifJ8WoeNYNubHmDCX19m+CnfQ6dq56pTTQw98dwV3l+vqYXW0TviKfAwNdGwEhbpePHvKxxjeYR/uQL76NPINEEyqytLMTafxsTExACQ6CSoSoi1JFJpeg8azMARGzP17TexYVh1XJrT//AnNtpm/Cc6zw6nnI41Ic9cfxWmWCDR1MyXzvouWxxwSHkfL5mELnoaBulaTbP/6HHs/7tH6/bTCl791UUUly11lWcQlNakevfFLJgLhQJWXOoGuObB/XbYjS0vuroc6LP+sd9GwiJTb78SWyjgpZsY9n8XMGDvr3yia9WpZpQfIIXakFPl+eh0yycaoxH2vecwf7qk3ATZTLf4w6uiiVaBWCjGxMTEALt97QT++JOLyGcz5W3a9xkxdlu69eoFwLduu4trvn4U0955E88PEBG+ctFPP7FABGda3Onb5/DFU88k376UVLe2KHq1Qrp7TwaNHc+0F55BqgSwn0qz5TEnfKLzbHjwMWxw4FEU2pcQNLUg1mDyOYLWNmY/+Tde/v7JSBgi1pDuN4htLvsdbRttWjOGUoqhJ5zN+sedgVm2FL+1rUuTaiO6ffFA5t1yYcPPWr94wCcepzPmsVshX2k4LYvBzhZ0v089ZJlYKMbExMQAu3/tRCa9/CIvPPJXPN8HgZ79B/CtaytdOrr16cv5D/6DeVMns2zhQgZvvClBKrWcUbvG832aevTs8vO9fnUjdx+5L+2zZgAgYcgGX96L0ced9InPobQm2dYjehfgJd1cB+y0J3v98wOWfPA2flMzrUM3Wu442vfR3buea1f4vfoz4JwbmXXpN8DznR/ThAw49yb8Hn1Xerwy2fqoGjNTMPMtestPPyyAWpkaeZ8Fxo4dKy+99NLankZMTJcopV4WkbFrex5ris/7b3DWRx/y0euv0mvgIEZuM26ligisbkSEmS//m6UzptNv8y3pOXzDtTaXVcFk2sm88gQoRfOYnVfJdApgnr6L8MZvQ742pQYtePtBcETHp/4NxppiTExMTBUDhm/AgOGNO3P8t1FKMWjsOAat449YXlMrrTvU549+WvS4g1CP34p89JoTjEpQrYLeyKIWrtrYsVCMiYmJiVmnUH5A8L2/Yl94APPUzaj8v9DpqKdiZgUHr4A4JSMmJiYmZp1DeT7e+IMITr0Fr8XrqtTqShMLxZiYmJiYdZdpT4GXXPF+n5BYKMbExMSsgxQzGTrmzELsamokuK6SaF2tw8VCMSYmJmYdIszleOI73+CWzfpwx7iNuHXUIN7/851re1prjyE7gw5WvN8nJBaKMTExMesQ/zr7JN7/852YfB6Tz5FbMI9/nXUSM55p0JXifwDlJVCHPgTpPk5rTKy468byiIViTExMzDpCbvEiPnzwXkwuW7M9zGZ46dc/W0uzWvuofqNRJ3+MOuBu1F63rNJYcUpGTExMzDpCZt5sdBBgCvm6z5ZO+WgtzOizg9I+rL/zKo+zRjVFpdQeSqn3lFKTlFLnNfg8qZS6K/r830qpoWtyPjEx/2vEv8HPF93WG0ajKmRKa/qP/eT1V2O6Zo0JRaWUB1wD7AlsChyhlNq0027HA4tEZEPgCuDna2o+MTH/a8S/wc8ffirF2DO+j1/d0UMp/HQTY8/43toJhytAAAAGDElEQVSb2OeINakpbgNMEpGPRKQA/BHYv9M++wO3RX+/F9hFrc1CgzExny/i3+DnkDGnnsXOl91Iz002J9WrD0N325eDH3qWHhuOXNtT+1ywJn2Kg4BpVe+nA9t2tY+IhEqpJUAvYH71TkqpbwDfiN7mlVKfvmXz6qM3nea5lojnUctnYR6fldUp/g3+d1i783jrz/DbP6/9eVT4LMzjU/8G14lAGxG5AbgBQCn10mehA0E8j3gey5vD2jz/miD+DcbzWJfmsSq/wTVpPp0BrFf1fnC0reE+SikfaAMWrME5xcT8LxH/BmNiVpI1KRRfBDZSSv1/e3cXIlUZx3H8+0uRDJeMQggpU9JwM9hEom6sKESMXqAIhYUkCTSri16uvDG76KouAiEEpRIK7SKYKJFelCVx03I33wLR8kKKvKguKgOrfxfPI+5OLc7UnDnnzP4+MHBm9lnmt+ecP8+z55l5zlxJ04CVQKOpTQN4LG8/AnwadbvBo1l1uQbN2lTY5dM8P/EUsBuYAmyLiGOSNgFfREQD2Apsl3QS+JFUtJeypajMbXKO8ZzjoipkcA12j3OMV4Uc/zmDPCg0MzNLvMybmZlZ5k7RzMwsq2ynWJXlqVrI8ayk45IOS/pE0pwycoxp97CkkNTxj0S3kkHSo3l/HJP0dqcztJJD0vWS9kgaycdlRUE5tkk6O9F39pS8lnMelrS4iBxFcQ22l2NMO9dgnWswIir3IH0o4BQwD5gGfAX0N7V5Eng9b68EdpSU427giry9rqwcuV0fMAQMA0tK2BfzgRHgqvx8VknHZAuwLm/3A6cLOk+XAouBoxP8fAWwCxBwO/B5ETkK+ttcg23myO1cg1HvGqzqf4pVWZ7qkjkiYk9E/JafDpO+C9ZprewPgJdIa1f+XlKGJ4DNEfETQEScLSlHABduqnYl8F0BOYiIIdInNifyIPBWJMPATEnXFpGlAK7BNnNkrsGktjVY1U7x35anmj1Rm4j4A7iwPFW3c4y1hjQq6bRL5siXBa6LiA8KeP+WMgALgAWS9kkalrS8pBwbgUFJZ4APgacLyNGKds+fKnENtpnDNTjORmpag7VY5q0OJA0CS4A7S3jvy4BXgdXdfu8mU0mXb+4ijdaHJN0SET93Occq4I2IeEXSHaTv4S2KiL+6nMO6yDUIuAb/t6r+p1iV5alayYGke4ENwAMR8c+7fxafow9YBOyVdJp07bzR4Yn+VvbFGaAREecj4lvgBKlAO6mVHGuAnQARsR+4nLRIcbe1dP5UlGuwvRyuwfHqW4NFTH52YPJ0KvANMJeLE7k3N7VZz/hJ/p0l5biVNOk8v8z90dR+L52f5G9lXywH3szb15AuW1xdQo5dwOq8vZA0n6GCjs0NTDzJfx/jJ/kPFHWOlHHOuQZdg71Yg4WcQB36Q1eQRjmngA35tU2kkSCkkce7wEngADCvpBwfAz8Ao/nRKCNHU9uOF2SL+0KkS0jHgSPAypKOST+wLxfrKLCsoBzvAN8D50kj9DXAWmDtmP2xOec8UsQxKfLhGmwvR1Nb12BNa9DLvJmZmWVVnVM0MzPrOneKZmZmmTtFMzOzzJ2imZlZ5k7RzMwsc6fY4yT9KWlU0lFJ70ua2ebvb5T0fFH5zHqda7Be3Cn2vnMRMRARi0gL564vO5DZJOMarBF3ipPLfsYshivpBUkH833GXhzz+gZJJyR9BtxURlCzHuUarDgvCD5JSJoC3ANszc+XkdZEvI206kND0lLgV9KSXQOk8+MQ8GUZmc16iWuwHtwp9r7pkkZJo9OvgY/y68vyYyQ/n0Eq0D7gvcj3p5PU6G5cs57jGqwRXz7tfeciYgCYQxqNXpjPEPBynusYiIgbI2JraSnNepdrsEbcKU4SedT5DPBcvs3PbuBxSTMAJM2WNAsYAh6SNF1SH3B/aaHNeohrsB58+XQSiYgRSYeBVRGxXdJCYL8kgF+AwYg4JGkHaXX7s8DB8hKb9RbXYPX5LhlmZmaZL5+amZll7hTNzMwyd4pmZmaZO0UzM7PMnaKZmVnmTtHMzCxzp2hmZpb9DR/fujix3pmWAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(2, figsize=(6.4, 5))\n", - "\n", - "pl.subplot(1, 2, 1)\n", - "pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\n", - "pl.axis([0, 1, 0, 1])\n", - "pl.xlabel('Red')\n", - "pl.ylabel('Blue')\n", - "pl.title('Image 1')\n", - "\n", - "pl.subplot(1, 2, 2)\n", - "pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\n", - "pl.axis([0, 1, 0, 1])\n", - "pl.xlabel('Red')\n", - "pl.ylabel('Blue')\n", - "pl.title('Image 2')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot transformed images\n", - "-----------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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TYWKu0jQr1fjt1GZzJ2gOAIrSiMR1OE8G8buz+POEbn9TeiVPMf6Aw0l5Vi2xC2uXUyWI9OLx9kJQzb+nMqiCt3zWi9xQe7P2pquqKEIjQqfay4MTKxswrOHlEvMU45f823FS5z/XB4vruFpc3n7temtdBDqOuLZi6XdVDGprTnm/KkmKKpTvXdCc/ThWYrtrb0Ltj6kkzvpF/BzzJ1Z7kp4JeMo60asTFmuuru08Y2hHKx5XYYOUl0Aqb4cTIahmXJU6dkfCKVr1xUBQzeNnrqV/qsa55+9/9OeXNv41jOWsE0v5nsZXHpDOgKjEgQLQSIo20DnFqSDe41RRXCyYsy7jhNDOcU1T9XhHWqucWOW6NDHH73W3julLnGgFgoV1CsEmcGc5z98VWhfLlPpHaqTOudJApeh4IDiHhASStTcqVTV3UhGbNNXAm5PYoVQJQowjTQo4xAnRb/+ihAwA01CIedNQDZZB8zqEIBZ3KnseuK7Um+UpA7wUH1gbCODjZIwHJbeNEFDbGIhtQoJVZtAYd0yvLJjeO4J2qApOHMHAZmf114iLnT+EXLdpkHiE4NLgFRCxNGLn70QQ0dwktXfz1hYDp4EWRZzHV2WJGxybHGhAwBmgjXHa4p2rqbF67zLgDoMDFhHBuTgAgzpaFxesBqXzCnlTKP3Z/UXILePJwpg910b3jbc1YwDOhRmE+P0TBxHAPLAVnzUBnglT7l1rcEzYUKV18KkrHV+0GUHvs77huf1DNk8Ul5VHYZ1HrcJfO4ofpk296MKkAqAPzhyTkUPpUIGLVoenUJ6YxkvS7pocAfS+f2A/xn3HONb/to2Ix2drOfypPJKVU8AT0022R/Hae5U+2P7MrOPj8RGvGXtAuTiL4PlU4zmF5zOzjkDD+a5M7hdD4M41z9mjBW9RAAQaLnctb9gec7mLeYE+aL48D7133rE74uxRR9dNeON2DPehvRj/m7ZjXX/ySssD2w2f2mszAE6iwE9cCqTZ+UNXADxHc3j/xRj29sZxuYubEkFYc8LP7NkGohVONP3JMM69DaELqAo/s9dA6EDg/XsRDH71SeVEM+bSfMahj/l1vuEnLwTefTLwgUu+N2a/ajfwwQuOr9wN/L+Xxrz7RJe7/0hGuTwJbJ8eHfDIgbA2WqfVGY3EvjrXGeem6zn8SGDexL4u0nHPVgLSVs/rsVAqgXvWUzv1x+y922JAT3lkL4a/aaLc1MAFAjujhouzjpdszBqx0Qe/fSjhEkBC8xxVEGigQQiiRnpAXO3KPCjiaEOXQWiM2dYjyAAprbEsWWNjeSNwFiJgjalrXk/IoMZZGiFuoKQArrzCi0Pp938x0O7E5XXUVeWo30+1lIihhEFSGEcExuU3Z2tFP80kaZXUtOaklbZaWIfH50HU3pNMMpU11uKxbh+xwWJ/EcTq2/BQDTjoA/guxVvlKUOmquzi4kYtxBqK2Ecw/BELIpZXGeAXNZCbQT4Rx8ThK1aPmsvresAjrsWaCCBxGWNAbKOQEXoTN2HWVxoKydeUXmJjQ6ta6NehAN5FMBxwZVMigRGWB5t7rjZcr8trRKqsY4iPWEgnuVLqrtbbGanSERBfgG6jMBqNaNsWmsIWp3TzjiOxbva4gEOMWZQ8+HMnrPM3qIRu2KuPkVz2qiTO2UA34JoZgyUdPT3z9nlZFQYpAN9T2JKYhub8pvhdBmxlWz5MOaA57abqiFDqJe1AaxkyhFcVJ9DF+pCUV3V479Gw6Cs9d2/nUDUAngawZSXmWek0MHJxIeqspBJCZneGIhXzkPuQtZPvdUK/tHCdI7Mli8xTtemyuBvrkEFGzI0dGvmUF0fXddjq1avnrpqECMEGf9Pr9y+FpLw/Mz/eh/m5MOdWw7IHcVfIgR71NlWqyuOHjq3xhA/uR5Dzzh3Ha3bX2DtUztNa3iOyuLmBh+Yxrpssjk0btA/NXQbJIsLUK2He8NBcuNlmommF7VI8CW5/cP/66ijF8cRRef+MgSC6kheA144X3w95WlQemrvM6NTyq1c6xrpOEHjdWPnMrDMgHcE1wJMzuH8U6yWB9IftmQLbo35fPnvUEQQ+vHfAu07EjKn0gfaZyWL/v5Exe/tEeGwOazZnjF0AdXzdiTV+5MIRwwnhsk2U94yUC0G43Ao7CfdYt/7lKw7ouDATvsYa/WcvOlSUD7wgFQ3Ul3oR/sALaRKN7fOe7VLOJ6YjJs2IocwbYdQdN2aLPHYl/m2aNGYb2nmg1cDrbMNxzxZ8Zi/w6J5y/04/nudmwomx8vxUCNohIlyejZmH5cTFZyOFFT1+tXbiMmgtsGAQPu1jRHqnO413dJ3SOKHiTxAp60laXwoIld6aEbkmyZ+H6af3U/Rerm+R7YFGe78wgCUvqSxDKekasbMkDZeAkBZYNZzdPZKB8OJJ75I4NeIOV8FVGdTBkiX2qmUZipcEWGPsEfAJ3gl6lTVDnNh6VtIQ28yURbcQO6Jxc9TV7Oswziq6ssQu1leH4EQWNg6+WqOXw1qLe3Aaj0QCToGRbWodQheqE80qb3GTFvtBZxv+kAHxcrmq14jx1/4NbSUe8Wc1hgXElUCbW3xGrCBvO4OYIL1C1nHWx7ND1YT8vAsRBLvl6fUTdzTWYq1zdI58rE993J2PxA3gSkDU0aL4nOFrD2qxd1O503t5Us3p2CMdDJp8WFVUGuoO4zQsAPpcVNWoftFEEFZP5G7QxHmCvF7wlY666zZTNab36nEEA3tXE60G37DMdZ5DaEk7onK8qvYs4ILSNoLPE3vK9+KgxHLuqrLlxXRQX53AWKW32Ca1ieA8rjNVGil1oRr5DrHTjyAhhuv1dZfDqipHP//t1+rR15RveedX6oNHDqdw/0a46pjdkLWlY+gT+y1ffhNcnjZVXq8+ZrfXWvaOml649PyF+ZxAw6mRXHPMXtYJd/hI0T7VrfOx/QPeurmBD3HnP7NesSPxMqn9LsLkTT9lv5vwS/v7fMVOk+O6lvgQ303lBjg9njBqml46qZxXZhGgHjlTm6FDDemtuQZazc/WgueuyVEG3UNRVU468F545HDKdlNQuaMPgBPI/uj+9V3GdfYo8KbtJrPOOyPHp/ZavnRri305uuq7H73cMrlGO00V1izM2IUMmHcq3ahZcEy05ZKdrOw0yuVW2HWBEw1cnCtfuTbjZ2fjPGa/ajeqShwdNNy6s5iJZy8rN02E0Tj2s8ttrI+Nrg+U378X+OaThzxpqjtnRvPMeD4nGxwezVBV7pqUen58X5ijiCr3bwmPHcIJ3/HCNL5300TZm5kKhW1+v+tXP/aixuz/8GVfq+mkMKlILKpGqAEqWQS+kJnEAiSHoEx7n+NDySCzjlNRWjvNc7IEaA+ko8yFgsebisIgiQW1AE9kSA059J5dS1JcaZoO1XvlmQ6eJbAd0B5A70Ni1bCU0EohO1VGTujKgaWl0180cozXQyhZvFKtUwFbj+Q4aF+lHfqs+dK8a8nvcXkSAULIm4HE8Driyxo0bqrEVQytkY9XabvUf5OKxbI8RNIz9NRNXO5HPo43SvvGOGLbRdU7waF0VV2oUm1sYvjv++UPLc3oVRlhFReneIkgIzgfdZEkLuEigrqKupcB+2ZhWpQhuxYy4AuLC22KWwWx4xaV+M81I7xVQn5WwZwaRIsIrSvPkg5pksIKu55uK+oyO5syVevazkVpbGfcGrhOE1lwkdVOjCFWJ52QwXTZLYYcBiB0grMKzDsuibqvrYsTjRrTm+o57Z49YJoLpn5QdZghOE8JughMRMHbYp70eZ0GmgBHDThn3cQY8KhRIqAhxqGFfU71qBLZXW8bpKimYslXlIQYc6H1CBgco+Wwrqk2SHFHo+rsuEfAORrikZUPoC4drcRjL1fFmCbrONuL6RaRF6Na6j4trqNFcYxyWTrjGJqsgx3L5FRx3lswwbkQGQlVvPO0pM9c10bremTdrfGFG5HZvc2N2NM4Zn/lsOMtG56xc8xSfwD+w5U5X7HVBxFv3mz4jxc63rzZH7Mf2ovt/1u3/cKYhQiGnz7YZMtN8zMVuGMcVRue65r87BIFpI4pAHeM8lRXjrvfurnBGM0nQh/bP+g9S2TjZZ2Ag7dub3LZ8nabtDxnOqv/+WCfL9raxAf4pcMIrudti3cNjzVbPHbhIl+0tZnjPt1MebqdLIDpE+OY/1Q1/+GFGV9xUwSwz88BFwHxbaMpT3UNU+/QueeTh1O+eLIBRBC95hputbienU24ZTLhqNJHTix06u8//kIEfG/aGvPUrOWOccNdsVr5yfOR9b9nDb5kTfj3B9CK5+NXNIe5PA/cuebYlyMU2JuXTfWuMaWX7bcv2mksvOes6SafPSoT5Ju2Y51+ytQ2pqH03cvVUdtE4EjKEiMaOOGVi8FzcQ4nXMevdBNu8nCxi7rKH70Sw792zXH2EO5cL4PvJ56LeXjvjrDeOB59oWNtA3aaEZfpbxDes1369N1rc1qUixrbt51Cw4ijdoqslTGb9Irv2Y4VM1flgh2oqCq7Y8+lVmg1cN8WPLl/DNV9Q2JzlEDQgIiPJ5umoVmDjMhzLc5PAqDV0W9eG5IeZVjOWIrEeYmQnwmCtzUzaKVS2Ftj6QEWtNRDp31Q5vOXApChsi2pPtfx1mx0Da5jOEfQPvhMYL+A6USG9FUcWyQfvXdWo7F8doQvLm8bhpRMVNswVYjBdO21zKnxb8INanFVoDxnO8STT2lKfJkBj+Azq3xoabvC2NtvLn6u1RD69SVWt4sblAVxrjCruZ7ib17S+hhPBjrUVFyKSK9NLMqcfgpT7dpysTU30kjiOpnUJrLeryouqaBQcEPROxe8RJUQVSuKxhx5CYSr7BauCoSl6sRJ5SCChQIMa2AZB+2SeChHNdnoJg9uqVQLoqQDJ63ykHdKFcLPOqha56Ha3Q7yVz9X1azb2bn+7m5ZGYKr9WbIDZMksn4RCDbiq51Pv0PWeUvnLelX7x21gRYYgB4MutQOvfwNxliv3OnoKa9l1WRhbVZsK6x+RJh5mHSQCK2hykla/8S5bHhTbybF/nU+pp0edb5vVLeQ34Esq0PJxldp9yS58HngD+IZbpKOk4U+Q2yHuKtusiFkSauvZy0yKBwwEhdZkGrcjIg767hROT4/NyJjW8REJ2yMW6bzEWujGW92I7ZHgWlbwgC8c3eD8ZIj3jdvCjujgBfPwTzm7e7daLk11gOmVo8Tq6tLswg8fvnggHcZI5uePUh8L/iOkxoBy7jSs9r0mgf9DInAt5JN24nsd8LbNyNIPjcaM57PisrUkrp40G0RvI2nwxhvzTptNp5nmxG3zWfcf2KdfcvTrApT52WGMBqo5rz95BYQ0VJikX/5ygFv29jojVsRAdOZHrkGQgTAUDbkI1em4x0zxHvB6v5tm7EOpzLLfe784djijnW6ORrx04ctb11v+NA8/pZY5sQwdzblb43LWMiqDqOWQMOOgye0ZbvTrL5xWgpgTpIAMRRQnGRaNWFil9+4ITw9U44Ms55oyIP0RLN8TG40joM2jqd3jo8fIzsD1Qnx8LxsMtc559nkZy92fPWJ+Gw0hrimrfHENLZdYobv3ixzyus248F3ZAIFL3DveouIR0Ut7IuTfFiY/klifgtgudbaVOJKBk2D34HCNfYBMURbjfpZAq9aheuBneocepi/8jydilmcXF1HM6XbB1V9wCZiR+KoHf2T81CHSRKX2H6ibgkKXMYAJ/3p4W/HSUon1CCBiPdEEoizfFXxefG02mGUY2EzLUxSa6zXs2KAbW2r9YYjved66pwxjjrMEIjW4VKZrH1LchW+OAbr9QDv8bJQl2Lax6IkG53cVyWlLbTZkK7eifXzVFRVJFn85U3ecXJNIKyqqBNaq4XGBkGi8lWjxX9ruxGVaiDZkW/jXNyhVEfgnvS+WJyxQeeiiLroNcIFAr7PGNZ5S8tf3fmtRmK6ntZ6TaOFuezQHjB3COr6KgQJWBY1AhiJp9WWpJOcmHEnESgtA6jqzIgrFNbUqbOd9BK1ggymygBI+tBBqkErBSAum44TeHMKKtEwLBmyJUSswSESe7qd5hISSDLVh64pRxQhMdpOTF0mGNdQtCjTojAg518AACAASURBVO40pd3X645581kXMG0eEmvcUWbWoSeDXjWFGFN55iOj4iIgnWvIOmpq1uqRvpQcZzIOSBuuPODTJiSXwcw6bKJ21t+9sRJqLLJ3MT+inpE42q6DzKiAo8nGc7Fhg/WD4w7jblwmTce09cybEc/MAVXWQhwnl+eOzjf4bs6JJnCpjQYkB67hlBk5dRrfPzkWnjO1g5NNZC6/gD28eDrt2JDkeQCe1YZz3Tr3csB7bg58qt3hNDMepq+A69TzMNupubI8Z9+DdLxDDviIxjBvlz3OB89jbNC6jgbPaWacFY8Pnuf8OvdQGOLHiIzraQOmM+A+DTwhjrt3T7AZpjzSjLlnfILbukM+7ddx6jPQTnKxGXFZ1zjFIc8akLw3zNhAudwtTpmJNU6g+e7dEzwP3NrOeL4ds9nAl26tQ8V8L7NR8CFuJL5wa4PH/RZv6A5pDFxvmorG0dzzZiOuN4xd/8JxrK+2nXLP5hZ7wBdvxjz90pWoIPvFm1vcsj7lyalwdhqRaDI4vJBOn4iA+VKAM9Z0SV/6yaM5J7bKbydd9M6x6xqemM64c81UI0ZLCmby0Wz4l+qw4aOXW+4ed5ybe37sBce6oYXbNfD0TIkdJf52347nkcPAU0cACmOXAfCGgfQH95Q71+KYffayAg1HKA84x48/H3jPtmPfzfjgpXi69O6Twk4z4nkdc0eY8tBcMihOqlO4uJYE7XAuGlS+VGM2AYeow+oj05VBZmImC+MJtqhnwKQkTiWxXwVcl9hSXuM8FJ8EFC9JHa+GOSVvZOCxmPF4EtfXedX0hiakVGxB/ACkVxxzfN+OJ6U6nUthEgiuGfIkaS3vtGJEDSV2S1T3agY6pe6tPvrqJFdhTnMZkicFZ+Uv+Aagk2RcJswtLpfXN42n7mJoDbK6ZPSWwBI43jdUTHZRtV43YOqA5bfE1CcjWXIcpX6H0g7W34AQQjz57IjYLTWFFzNY65GS5L6Z0xjg12A625nU7LXtInHlJW1SYp8KQSt1CaETT2NrspB0hK+tdnNdjLAS9RydGSYkV0iFIUw7WOuEqXKQhbiG2ekzyqWjC+C8J3TXZvCOzbvSY33ThOPN80PSX12WNx1sN8VcpTnnyoDJjUlWMciutEIZbEkHNg88Y3uWHYcPjeTyGr2k/CWtJeW3Vzox0F+XL7WTF3wCuQNrgHQEUuepBopS/a2fccz3KIvMbtn1DfIIC7NQr88N6iM9E5t8nXNoZtwXJ5MSV3lWnBr0WRORRcOEEM+i7DithBOJjAUazJNJqbvhsWZ94vJSiRfPpOk4UMf2WHFmHLY9Vi52Y3b9jIuM8dLS+YZdH0HjJ7odAN7srzBpOsDnZ36wDPrKC8i2dHw8bHGaGaebea+bJkB63WIr9ttlD4AndcLTMuYOnaHjOXI04qx4nPoc9y2usNmPWtU+rYkpFULruNN3PMWYW1zHo3aM+6DbKu9JBND3+giqnXqCdGx6zZ5DNkemK9wuWtdtUhhrgDd0Ucd5tsSw85yL79+2xIjxpO+4e/cEH7t0ift2bmLuXGbOE4v/yemMrzCrzGe6td77NzdwKX0x9vmBbVMzsU3azQ08NVs+hl4zWuTVH6yymfSU02+vGXs2fMfFcDz4/dRem4FvzSCnZxOB02PhprXoCeOOtVpLsi+PHAbuW3c8WqlpfPog9MJvWd/caBwfnvVZ6q2R8v69wFftCu8cCx+eRSZ5w9Qs2J5wZrSPGkFxvt3i9ma/N2aTgfTVjNpuRPIxsQGGdKSd2bg0RVmwAt6q9JPXhzLBL6aRPksyorI11mX70YX3rpl3idigVmHQWBS8EzpN82yJu56Ll+ktK3aUbmHdkvkz4YrivSjVVSnpKHsjWrbGxr/FXdfxGCN7klpKNxUQPWRI0+eoXpnadLjG9lbEXt7iZ0WqZ8P18FqttXRNHpZxUKxrr7FUmxGtmOhla+xicsfhOJGexiQQ6y62saY9VdkEqpT8LI2xxGuvXlWuCoQT65vcZuWj8AF4jc5kir/QpDiY3IsVvL+cvczpmbMvJKpLaNBeWtFbQzFo0y4ZYg0nz5gHrSje6EIk7mKcRgV/5/qDJHpvMJZay29gFWmzU1TplMyUKsWgEO2ymkSsu8IIJjdiav6LkyFe1xuIpjttk3GrsdwSisuSUqbUBi77wK11hz1pIA7qXZN+tu0W7W8Bk7EOMvgbdN6gmt2j9JXFYj11AoRKnd4JXou+VlIjiLrOwQoSj3LUubzYeF+MyWLcJR8d2t8AuGi9G4xllWqWEfE4l5iJUPS5KwZBBSQkgJteNKbftrUuaHG3llhuq2ckHtUJ5v5FAOfpJJ5uNEoPfNcGd9cyhrgRea5r2PWBXWljHadO4pRdA4+7fsbc/l7sxuyOppy2o/TWfNO+QBlrV5OWjje6S1xmxGM06HTUA8CnmjmXQwR/237G8/MRMpnTmtFZM45h29mYyzqhqwBGM55xJ3OuzNZYm0YN+Tu1oyhPwdkwQSYxzBlTu9irFPi6JnCE5wQdj+saJ+zdDQ2gcOAcl9Vzx2Sf6dGYA+c4rTOe1jGPsZFVFM5222yEwBkfDc1+MUQW9jQz9m1cv34UgfQv6Ba7BDZbuHNUGGuAx4ydHzHmadMfv9dY7RmO+3TKfTtrwEGvVyQm+p4TG+w0F7k0GzFWzaokm17Z78bMWwN/xiSnKfEotKgqV8Iapw3QJrY5MdGTsJ4N/T51NONtGxucsg3AA9tjjpxy/nDC68wDxpETPnMojHWDI2OsXzeOuU6eM964XZwhXWpDT73izjXHmcmYD1864k3bfaA8cRu8faKc74QTvuPZ6ZRbJxM+fjDlLRsx3LnOozPYI/CM6WKcHgc+fQgn9+D1G3B63PFLV2Kae6Zm8oGLnveeEt6LjVmF23cAlOlkh3kHIw936QEzXD49EvEv+ZhNAC6dTGU301KY4Qw8Ejh2ksdJMmhb5tlkmQiayVpB6AbW11FFNP7mgTYUQ730PsTvgcI6xt/ivKdIHqGLamYRyARqo7qky5yOtRP2Lwxy2njEeT353E0nq5pBYzKEU5wd6/d1hKF4hpC8Hgni0nowaNf8oquclxYGMq9xOXBa0619sNPOBB0NHMY+VfRxl4lqcS9mhc8fXa4dyd8jt1/W2I7k8zmYG9qoiucTVQsZU+iSNJIecJKkutJWZSgqlY5EbEfF0OJ/OMWQmGlvay8ktlgyWRg05HW+q+q51G46dUinBi6XPaWq1oMUyTjq6sjzBt2nZQARwgKIjLE5uhCyO5aa8TpWV9ZJ9stbet2SnZn30cVaCuFjPPFiihLOuegmBiWCn9JSC2kP2dekfp6ASgwTn3R2BOGqsmT9ZWPIa4Y5dZRWFeddVcbyXpd28tfJLhxX7121K0pALUWZVEJqye7frLOWHV7x8tA5GNlCnd7PRnMEvEbDumW7xgjCy+9DO/f6YoykujIqDUTqL3kTkgFtPZ0N8HneLUqvXev6EpGsW1zXSQihXKYySGco+VnVB9KEnjYhXfKNaHURH6d8LdlxX+v87bORVMGmV3NxPmHXQE2vPKOOT01PcAcJRMY2vrmF5yuC8OZKjfgKji2bAl9oSniA5yeltU8Ex3PzEftWH9uATOast469Kg/rreM8sV3WHZwL8Vj/JgPUaxU7KBa/TuOR+L541gYMZwp/1k04rVPWNHBkoD49m3g4VNgIgU3puLmDx5xjjcBZGXPHZB85GrGfPEIQOHCOJ00N4nrZ7kfnDfeOFn0NP940jKzdHzWVjjRmk0pILSmO58I6BE/nR2yKQgf7bceMho/tH/DuncgSJ33nzSYywmM/5WbnOE/RZc7SxvFw60Yp06cGjiWenLWcmcQ8feBK7AxfdmLEnWuO83NBQhyzyUNGgkKJRQZ40PSPk+io5RavvGm7YcfmlsuhNWY65v8Wr7TOcdvaWm/M/vsLc76wutDj9FX0h9OzpMb8zrFkw7v3bDsOCDxprvZev6U8cqi8zqwhx+qZ037ux2wdr0QLfXGLZRoZYBssaxkkD6ODvuGZDJfaSrwT2sqzTTSOSkx1FZ9EMJT8qidQnoBib1oeVJfWc3ia33OWDCTroucgZ5xLVBcoDGz658T1DcTsf/nih+tsrt7lDXU5KpA7PMYP6ALMqpnsxIuk+V+t7rxIPhQuxuJp/Kh5Z1qWywT1yP8fNmhNGmXVlbhTIG2dtHrWHdN2wzs40tspnFJUH/I7KZ3q3erahBzuOBnislLzZJ3lkLn6UIHf/oanl8412v+qQNj7gcN2AxuJSYigqYRxGo9d3OBIMAGSpcZrGnOrSeGIARBxEXiHEPDeR50SNataH4d3UeGwix9Sjbta58fbe5au+AWwWzJlXgSqB02OR809bXFuruZb0hmbGcsb02ukiQPByQAoVy7ilsxK+eKPNJE4AZpKp8fsirXev1c7PAN7Ua/YAG2iMXudOx58K3YDnrWnMztUq8hcziQtIK64xSu3R4l5bigd2VfDLuW09RhLHPtSl8MFEI9IfN6imX5V25WHEOKu0pd0nUg5IXARyPuWXj13GpBgU6fGTUVQAR91pdWsb1NzqBa/lKqKem/sQzltSGO+qRiOxOooFUOfGrIrzLvz3vSUX7pF1Y879ipVhi3fcoWG+5oZqHBBlFHlamqjddzZHLCe2N90SYME1lvHhgQO1FHzmg44ID67qY3lOrAZ/eYusI/jsAk0Gtjw0KnjCMceDWtdQAXWrToO5xP2gDVR1vwc71puozw7wrGWjjlFWJ+PSyaAceGMmKlavDHy12oE/ofAPcbknuvsMobRjNs6OMcYrxH0nwyBQ4XbmROmY9Yd7GsEwesC6wTmowjEr8z6agkAv64R0O5anuJlZWN+3VQ11gjcKvMMylM/2xrHvOl0xNOMOauTvECNsm5UAdMb6tmg5Tye8826qXQEvnzzBBDLvGFJHIQIbued8GA3YmcUeEQimL/VLlphDG8fb/B8WwwFv3RLmVG8e9y7MeGnLh3wtg3hnnFUK3m6jd41ttyUztkGoZny+JHPZXtiOubWjSOePOx43dj3fElve8/5qbDjGk41ygf3Aw9MGqZescv++OWDeTbYWwszbltb4yNHgd31CSLCxnhk24goz04jY/zIYeDpacetk00gssmpblrneH3T8badJqpETOF1NyVygAyCr4xiG4/b/TxmJzJiJt1LNmbdAOwmlTTnKwBXrUPR6kAqd+oV4JHEpPYl67tCxYaW150B76DRg02yug9S5rIaVgVLywl2qlmIpDgFZuaiIqD68YilIVL/WgBlOi3NOqeqmT3EAGJSH9F0Km0G7VIlvQyAZ8k3nKb6k8iwDWo21Wl2bZogqJW1Nqwrhob1rsTlWaoRlwmwnj54wj4D7xOuMgqsmeiYD6nKF8m/UM0tTWJK6zaXyBpnjwyW/+z1wsB6ZGzLjYSxnqLv3rjxiH6i53YCmwG7VjrYJD/5Ym1X1FpqtRi1WkSj2l0i6HrAOYdOmK3URbDazbf6pk2aCFGltQ6/XK7PWE41guLKJ6yKQ3y5NjUNYPGF7i8Oka25Raqm9OAieEFCBFXJo0QGjdFPbVS3KLeMpFvpkt/f3GMD+Ka4HquVuXOnLhg5p5l92ELl1aKAl3TJRTJyyjey5EFu/lZRsM/1FceJRY+gFHNFZ8YRxvK2lf/CGrwliW5DFq90Tvn2DjQUpXuxjres+R0+s6C1ephUqgJzUZz0N0KJ9VEXXbp1ErKKQKnDkkd1xhw7aEKZZFS7rFaS4xbTWVNnHhqUuYs+gZNRpFcI3qYe1yBa3eRHyKoUne2mceVKSIDGN1X7xg1KsZeOxzLxFsDUduauzfXbZthGtXoQlO1DPEqKYdrQ5Tz5IGg+rZDiq/olkLtDx+POs60tO0E46xvuNq8Q50xFoTE1mgN1ILBWWW6l8bFPfJb+Aqx7R+sDR3OH18CBeNbTIagI63RcpsFrBNGX7Vap9U65yc04RBinyOwI/lK7zh07lwkHEZwfistK7+sAXRmHG6psjQLn556bXGvhhQsG7NfoOG9T2v2jGW2nHAYPIjwW1iMbbMmPQsNlA+SH4mA+4YqV9SQd667jhc7zvBtxs7aMNTATWJ+PuaCOkxJ4SCbcrK2lXVjnJPe4Qy6HMYdLmleBe/0RD7bbtLPiRnGHjit4dgaGtOfCOl0r7Iy6DLgROB3mdObK7/Gm4eb0gpVzM8DIzZjR4DrHY3hubaMO85aB7Gk3YeQiKH7WjXhDd8ivNeu8sT3kikZGec6ML9za6KnujlFOAjRwqYV52/KTVzretjGhM4b31mbKXBtUHId4Jq7hwXmsM5l33LUWI3zoyHHH2HGqCRBKP3zgxJixWe2IroPCa0dJxQw+etBy55pn16r+zGSdX58JJ8Ydbx3HeokgOP59dmq63iJ8dK9jaxxffOhKTON128VH6uUuziPr4y02Zlc4Gm8znh4woslunF6sJBCR5vDqQk0Ql11nxe/xj69IhgX7jSruCECSEZmp9FUYNYFIiOyzJlimmgF6yPNefK9TGDvNLHB0YzUoU00a5fWyPO+d4NanwxSSIGg6VU35TeXUDGBLWdJJdSEugoboDUMqNlJru5dFttWJsYvHNK0XpdVKeUIqEmvwUrD1ob5URKz+SnOW08u04QjmDjS6aYu8Z1GdTPXkcnJeoo521KctdYOGhQskShsWRtdjRn5ZpUQZSdTvxiX1EntfQ3ZVlu4SKK7drA5dUXvQgWu11L8CWq4sMqBar4G1UWif6ZV+Bye2fb5gLMT8eynqnukkY9mFKLVcl7FczeZK6ZkLYQsA6g/OmgnOYVTj8c+S9DQ4A2H1CJKFcDihbdt8A1nwUc8474DqTdlgS6oCasC+vhkuM7bESWKZuy1nAHpBLaDavQ2lGIP17SKdizpoTlzuXMP0LPalcV7teZ1uHb4Tsx5eUr5kNGfr0WJcVl9xkpJ8ItCZLqWEuGnK7DcpbMlhEDKYXihldUvesESJZa7VHfKteVUCw8Uhv7/kApHay0Wuq9xVlvd1qPqxr9p8UJedlKM551y+iMJL8sFoz15CINxpBMPJ3VP8nIoR0zuoPJPcyJi9EuD8fI1bfDlCT3F14lDn2EI5sgG15VKeOhovbAGtV54/arjdWMFZUI4ORzxj6hB13PH9jvPG4u4Dh/OOdd/xuHm0uG8047ALrPuOdVVGohkYNz6Cc0S4xXWcn3vWfd9V3HmakubAi9y6wGmd4hDONEc8bMZpJyWw7jru0MifDtUuribrAmjgaLDJrCX2Q2sP06GezsY4r6wN0jswdQ6Ae3XW048GcD7Q0XAgjh0Cp+lId/U9avHc7Q4JbkTXCk48c+dwGv+mOnm2GaMSjfyGOQ+djx4zmgYGKiPPziZs+in3brXM7cq/u9dipAeVxsiymwUBJu3i2HjQbkF87SiwPYp5TS4Lpw5uQZlfZcyuT0ZsWLQHs8jw37ce4zw79dxpdPS2jW1VZU1GzCrVtJcSCMf4qPRHB/mulq7MFA7XHliYNyMgHOTTArU4Y02rU9IqUxnYJBbQfmgMQS6bo9PcWeIrILi/zqcyyiLYyXMiCwA65ee4mu+nU63X0l8r683AtURypde1PwgTWaReqtELhfTeSvWTVRW0brUSJtVPVplMdZJIFbVNkwHQgiv6+rgiy438yuVVy8sSVV5q/edEDFVlGa6RJstub8vqKYaY68vtrtoGGeyXkuW2q/Kb6t4JprdeQqT8XWu8XhUIB2KLaJ3janGsO1ddAQFl7DzJPalzWpjCatQV0OHzQI7smjkiEaFCqfY91o8K0IXMDncuEXmudwzTZzzrriyQyeuqoVzsUgEFrSxUrRG9uMziJsDd2q1hrueeK+q/NlrqzxvQDtrfACjJSKDfWLGOi/J/ijiBv9Y2CrEeUwVIUSmwZ8XHcFkk07F8sczsS9Jzrts5qz9o0iNzFFI+qV9oAbrqrd7V9IIDrShjHHMEcS1iN+91Iek3V+oj1l8Ka0sMX58eZNBZA4D+M1UxNr66vCX1r7zbtg1RKLv8YvyhecOSlM3Sgthpj96wOiAy2arVsEmbgpZ4a9OIzsWLaqg3mC9SntNxfyXs1cPVx+zmZoccRhAZ1uaEecOVeTnmBjjp57QIrQG5Xa9MaPm1+Rrnu/g8TSoXg7A16mhsCr8YhCY4DmhQZjzcjbjJz5jiOOkjINly8Mg8ArWWGds9ZlS4gudKV1xqPWKqEre4Lr93/yiCsYft2RtHBzwfGm4ZFQ8Hz6ljR9VAcIxr3Xc8041Y93MOEfCBTcvT02HUA9GHCBvScdj1kdsRDjFPE+cqRc5TdlXycxp9ZK4RuCwNOy6mf78/4pNdZHrXtMtw8mi+nuNY85F13tEQ2fYhxhHJzHQCx2nR3daokrIvnmQJvGMod6YNAeWoEe7UOXNGnA5zjhgx8VMedhN+q7vCB7tdbpnscdiOOd3MOTsf81QYc6DR1/Mmmt3EnbKN8QXn2e8mNPMRl2zXedLyG9njDWv3wtRCHLPPzibcOj7MY3bSGVA2AD31oDLilA8FzhmbHFA2J7G/H05jupsTZWttwpVZ6dAb49hHzimcaeacCgFXzS15zErHyfaAS2ubbM+PkDB/Scas2ilExlq9Z/2f+jrAgjgwF8vRfVXFFEKZNxMkUMq8l2/tohi2JfAU6mfVHBaNnGKeK1hZpTA8g5QKgBToXLNzyy4HSWtkDWCdxotMouppWmPjmjt0dRrPRSugUUc+GDNFPaOsj/F7yXfh4rK335jvzKzWKpglPe9SmyV4NiirlCylt7Ltoibjv9oDdCmfUDYLkC4SifA7uS0NOBpau8FPKkPktMbSUxdN0mlUS0hTby6/Ft39vMaWGON/UmlMp7qt1iGn5pM/JVaB+dTe9c2EaXCUNre6tTFes9ER7yheAyJKEB/VNBOJy/FyVSC81CBu8HypL1tJhl125SrdwqSxaKhWBoHLRx/Vwp3AQg2kq5vRhrtOiB4AMm1uOqFVrVF3dqpSiJZdU1FxcDlI1vG1t9Itc4hkTb6GvhHasKx1HRz3W/y8GCZb5DIMW0qzZBpYSMdrrMPAksEgi6yDETH5OlTQpe16XDmTkUMr2J31QueGR0iLZSr+lyPAVwFN1ueVe71cZikqEhBd2aUBWbQApN+/cruWyTkDcidL2eRhukl679tvxaBxhHNxQk3pvZQy2Vw0zEoyOxgx2WyXjtnLR2Om+8Ku/XoAjIJja1TA38hA5NyVuph0AQmeu9ainuuVuc9McOs7RsExtlGxNXJsdoG1UWDSBc5MwsL13zOBO0IExYfAiWbOXRaomzd4Mxrr5ubxwL5PupCP2S+YnnPKE10EsxNaMN/Gp6Rjm3jU9xmL6zWjORI05x8iC17L1sKU2BX1ELDPqQ1K4fYT2K7CJsYb4HJoMnhd7/WJkoF133E/R1wODTNtMruc5LI4TtL1frtkc3itajFkroNjQa3jwN47I9Hl3CPdOjfR4sVzJA3/qdvgDmM60hXV8XMsc7pE5R4O+MCVQ+49cROmKs1DxvLevTvmlLmaG43ib0e2sbhVAsF8W+/boN0Hnp8W9hji56mHsRVpakbU26N4VL5MNsaBo9nA/sXaam1UrNafFx/rkxGqM5wI2/MrcfP6EslVj2yV4u0GYDDHh6C9yzCGc0me2gaLc1pn0zOpwtdrlkDxdpDeXfBcVI6/01G5LF19+pmrwd+weDUpliSzidTxL7rPGrbMQvVKrxptvk/l7YP4Ja/2ocI15u60lvQ8TtebGcyPf/WbT/Y41aVMw2RElpSzylfEUHFmEZV4JbLWHrAW19jUiwKGfyCrQaTbZvvwq/9eqg7VylBehhuNKo4KHAt9dZ+hLMN25WH+nxmFQicNSFTEuOq7lVzTa0RmMBO7WIGH2sKy9qvokcy2iki8uIFU6X1719qiNXgpoDK9Ozy693ak7iTvZsQJLoSsz5QcWNd6qCLFkUpUzdQF7xYZIIjplzjMuApyy5n5amr4eK20xAsB6grNqgGpY8dtmhJBdWeL2NAf81CtIxtlBGFwUkJyFxbzHmNIlz8I8Wrj3ixYf1TstjxLf8AWChA6U0OwCylSG2qVfvYoYfGleOL3GC7Z+wRXPNLG3VxT+UlOf6Rirouyfbx0I9CYbm+9Xe2SWmnOfmrXWL/BJ+AnWR3E2ahMKgGpfNEAr3itEAFJJwCqiC/ulOLE7wjBXPGZuz/V0ocCHieBsZS+5rUFbWg0Gga0okt9QX+2ctyYHW3MTV/OxlIFBm6y2WhK1CEO8wZPBL/dmnmCMMv6rblwxYDLI2GNe/0sg+STXjNQTr89Eta4nznrTplJwyikTSqcm5pf3UmMY+5Chopbo46LNHTzhueC45a1Gd0sjrA1a9Pko2LfO7bmwsOMuHkA9GYyYtoJU8asA+fVcYsEWvE80zrOVEyviDCxHOx7oQ0NFwPc5uY83E5o6bjQjTKDves0s8y7BtgvWvINktU6Tpr6xW1uzvMhluGKzS3nuxHrbsq6C5zvxqxXwHLarlWfR9DAoZqKR+d6oHmdjme6MesCJ9yMQ43s8npEDzEfaNZZHiPMbI5YI5TvwI5NFo/rGrcxz3PrhfkaHjjDPDtZvaOZ83hrl5m4I0ZeudB27ITALb7jt2ydgK5jnMrlo+/m+/SAmQHuudXTejNj2noueaXxLSKBbSfgOkLn2RxV4NEFthtTseniCddB69heayNItDG7saac6qY85yZ5zKpExriMWeUCnpsIXG7HnJQDTuc+EfVHBZ/H7J6fsNUOXGt8lpLmjLSmL3zO622N4PoEQ9C0xpJv+VxmhO2lT3zEqV/y5xzG1vZ8LI6YN4cCGhOjWOZby5hByh4zO1hGcxPmdbk8rFWDUvlU1Iypy7tFdaKAsj6UFVurlT54lvw+1L6CNH15+gAAIABJREFUqws8SiSZDKvxBbYuDdPsLbea1A9TPfdBOChtiJsdh9q7CbzJQpxR59rltSrpYKdnse6cMf9R2nLs3dueLLQjLruPCy7ZvJT28NiNfqT0yHWhCiNDt121cUoaAEOwnYznUx9BBK08UPlESFV16WyTFaq6zIbs4uJpr6gRTCDa0eLpLC/pEqzj5KpAOAHZDIBq0HYMwzv8Lf1L39OSU6wHB5usJQxbHX/atQ69Onjv0ZD2rXV+hrEVPdChcVPNgCb28rNl7OKFDvT0DrPbsqRaYIPEVXWxUAdVMZI3jHRbTq2PmwJ2ouVaR1vkjjvC02o3upTZNTDfEnDeRXA70I1O/cI5R6fF8DBIf+BZivkdYbFloHiPiPWU3orqG8lh+3GS2N60dxheO1lvrEKxTih5EumVT6RMyt57uq7r9ef0XtM0eXJyUqmLaKxjJxR9r6B452gtXe/jda0L/is/S1FgMhWmk/T3+DE7WnIRQiPxGG8UyveZHQVMjKUIvv/upBV0VIFPe5aM8nbVEZpoaNJUTMeGqcWkvDTmyHlUdk7xmVdEhedmY+400Hq2jVPXnU2pt+ADu61jrel671+vrAVlbcB4nbS8XO5GqMDGuOOFaZOZ8kkXwAD2JDPfsUxN5xkZVD+rMb+7KKdQjioq8FwIHIqw5aClqIjESPou7853Y7ZsUlmvLhOBqBN9Lgi3+0OeU8eBNNztprzQ+T5glug6bse1PKljTpoawyFmoAicNi8bD7eT/M4RQwuHKI+26wtj7UgbpsBd/nDRf2L9rl1mkqr9zdLZhS4mwWfDynJo1OVnXjydzDgJXBBhe62F4BHfcavOeVZGnOqmiEgEw37CqW7KLSPQTgnSMOq6QjIonPSHOFwes+vdvqnExUyOdB3vPM7d4IUxS6UmfgYM7TDkkgkzr1OD73UMKn1ILPV8BLnyMxNsj/oAMl2SUQEoJJIkg/xqyge14kOKu/ySGEKWvH89kgzR60Uhz3Vq5cllGeSDwm7nuqtIggQCh35vwUgyEjHUb7Ohd4q6DhbWWHtfLD/OMGuodLKp0qhBdXy/huH9gopAPi4fiFROBErFVO1zlQbIa3P9lvRrN31rEl6pgLhUYDZ1gITXvCv66LlvWdjkwzipG9YbgEidabQTImJDL2Q86O2Ogathh6u7T0uGAclLA3Ud9ReZq4PXmJG5U3xIuqSlQjXdwla5PwHr38nva8oTUvHxabcaj8yLGkXaFdVasQmgGWCxq/3iriWxtiV0cnGSOnbFUca8mGFc1FasAHhOLfbetBarRvdP3nZcAKMuTVQWptrhIVU8QOfKLS4j9bG8prITnMvMaiO+YqeF4KUwBAIuFHwgFHA9HBmK4EfRy4JPF6oIeNcQqJns2Cs6Bee8GX3Uu1spRm6mX51qK6pfxFTTEblXX00YRSMrZs+h5nYt6/1qoLE6aIwljhd1WBgHrliL5fQSA1tUJUyn0rkyOENg3Di6toD9JAkQO2y3KQFn6hoe8xKC4p2PNw5KvIo86dmVm6yNsRiAms9W1mcx4tsOA5d8w9r0+DGLLNLQiRNpJLA2F56QdW4hHl8/Hwo72ZjbtM0GNLSFKVC4FCbUWOZWN4XO8ZxMuNl0Qdda5Rxjdq3zXzJjuaaDEwN/x5faEbvAiWZKq84Y5zh17ZtecMy82m+RXTwxjvm+NFvnogQ28IxVuQkyWBaVzC4HFeiER8woTltl5oQx0YsKAjszxwMyZ5qurQ5NBsszy1PaEzzZes4YYH6gm/OZ0ORF7GzbcL+B3DNOAMfZ1vEa13IJx8S0hC8y5vQs8PQ4Me7zzDz3DybjmH1gcoXnQ8MmyiZzHu/WeCBMOT+Gwy4ZCwrTdsJTHZwcTdl2StspF7t11mXGuut4mhGHnecm3/JC5zlEmGhgCmxYPZ82hvfJdo0zzZF9XocW1qQFgSfaDfZHwo4GWh2xJ47XywG3sMevs5FvETyrE+5tDpl3jid1g4l547jFdYzS5RE2caX584rN117HiO+4CbgYPPf7lhe6Eerm3KKzjBxEhFvCDIePOqfSMlKILiSFoG0cszLqjdmx9YdS22pkxos/xsnzX6TGqEerXtcam54V1rRYyqe1SwYGsyG/l08PdZCyze3VtUi9bZBkVcFaXSI+TT494lpua0Ge00uZ/BCAVyWNRsWFtS1P6jqySz0SKNLoYDO57QKzlanznYAMhYBKkpsAY3+16PmK9EFnYqeR2j1oLHun1cUoWvSkhyoYijDymN51Yi7jmpzqLbK18XsgsceJcbXc2HqVoEzcsKW0HMNLM4p9TEk3trmt++JQAqppoxHXMhFHSGoSVubOwreVXm8q+tzW1KT2mLj/qAtsYzkoI6/Mg93cu4SMTJst0bjZT5Iu9oobiHw1Fgh0+LLG2r8Rx6+x12CE+xNt66DRcrgN5GOVDs3eG7ouHhV3JPAQj/xdCHSuHFmFEBj5Jh//m60XwTt8iLfaJA8NBXjFv50xd845phpo1GXfdsEmqs5JPiJL8KtBMoOaJtQ0jpMRWueFbh6dqItPLKGBZWLvjAxgaRTvPXPt8Brz1IZo1ZfUPWba0WhiFKwG7ca1LnY7nMTrn0OIRl0zHwGbA/N5a67spDa8iz518wCuVEm6+APqXCxbCGZ0mHIdO3Oq41gXVUdD+0psqsY4S26LlFatU53u+c465NZ20e2Z+XMut2RY/8lZqjxBJP1rzX0sq1J4Zze+xS7caKALYvq35o5CkxpNeT8bGiY/wE6RtrpEpHL/1ph7v8Y7gm1aGpRpQzaoSS5mkm6X2JY3qkqEbHWuKM4Xw7M4acd015yg2tdZ/GwljdlLNmE8265za2OUnPSvh32sHfOaJgKuI3WMBC7Ox6w3LeCYj+JteufbdXZGbTTAaOHkqEOtE98sLZ2DqRuzO5/xBOvsuI7LeJou+oueefAE9tpAaNfYGbU85cZM6LhDpzQoRyI0Ac42Ey7NJ6w3LZdterp9dBi5VnVcMFWBXWuj2+xGs0ujhqdnsf43G+FKCLRd5DeDCBt49qVjXF1rPlFl5pTG+tUL7QQkMHIjJnQ8ry0Tq6v7jIl+YT5ht5lyto0GZg7h9d2cfS9MOuXXpGEchDublpN4ztom6syo44zvaIBLRICcgPNzofSdh3XEhnQ8361zr59zm5sT1mA9s9vCxSCc9MoF65O7FV46F4qP6CSf9BNuYcZ6pX+z20y5Zab/P2tv1mxJslznfe4RmXvvM9XUc/cdCICQQCMESmZ6kOlJP156E0wmSgRJI2EAbg/oru6qOnWGPWRGuB7cIzL36eGKuJ1m1dV1zh4iI2NYvmL5cjjB61F4XTd8mI+80okjGaprkvcou2y8m0c2EuMoPufvZq+ut5GJ9yH3SGJ8PM18Ec4Qfz9vuj45K2yt8hp/hv8mP/CPh0tSNn4/PALCN9WlF5+F9vsfpi2/Uw9oWng0UKEK11J5r8oHNnX258+kYrXwSuEu3EM+sXv+KV/zvH1CEC4JT1zsc1Yy2LzMWSlcF4VVhcs2Zy/nSvqJktT/rVdP0GpASlriE/2UaC2JaJt7qWvXGen3UevqaBlnxXTlbGNxwJ8kEuFMQp6wAC8hmLu6Zl0JMHTuJqOiS2DfJRaVGkmLGsmAS82sdp/KVPyzUjupe0I6PWXwkgpUoUrYskXi/OJ165Zj0WvxWQ2kSnftUbEOPjMauSRNCrKwjr0jer/IWbsWMNnwgS1JfguT1V/bTyNXz9OfxWo8mN+/GxX4p8cHnJFxrbtb37VYw/dYiT33vJ2ctXvZu89UhdYgt/e3g+uGKCsziyewxL3Ma5iwYqFTrLVJzPFQ4BaTpaBV0kqpXhF+au2jMkiilTrvSYHrsWks4zaeGUjXNbfbFavBKC91H37q+m+qLDeyaFhLfKEWCbC7AMN2VNz0mOsj58EERZmkemEO+/H3NEZxfezeLmXla7x6n2e/Rpk9WT62azsbA931nuto2q9GrTeA7fKC+P8fRa0sDGdS5gYojTOJQH+t6qJJXmaSA8YG8K0VkIj+tkViYKv+aS4P6UnfPG1f+96uyn56w+v+a0C2f/ZPv85ftDy08vQ+0Q7qiWf18zKVH7eh1tK1PEHQ9a/zRbexCD/1/jZmnrR3NenXGdL+Mx+vRT2ab/pg7wR/NlqNHGzvZJWdCbWByiV1t7+vfW+XTMhyXNnubYjgzDeCn+2Wf/GVoi1/mR64i2n+rTkA+YCJXal8mipjdMhI5ZRgl2fuLHMtM+9r4lpmLvLsQF9BNrLO3+rvfxDjlOC6zpjBjZa+o+2Kf/an2WiJZCLCNTNfyYYbZkylFwLpbQjwWVAylVRAIhJqrlrvJfXPe4EyK1ypC2yu47veJ7//y5LY5Yn9nHkXmVtXkvneElYETcbzojzEvV3Jhk0851YoeE7uXnClSq3CaMaXbHt+3L9OB27Lpr/+uSnvpPJllEj+Is1cPRGEPz0yfZGsW4tJMM/btFooi5AxdjGGv5r9NX+Wj+w4B8K70DJvVsvE38/+mnn0L/lY3QnkVXgz39efSArMR84WaxxMXym8ntTdMPA5+34jvAkW9WWaeRMgecf5JSJoqnyxkn/86Ph4M/FPMX5/1ywaEV6kyjtJPKNytZrzRQSRgbrZc3Xw04JbLnm13aN7//e025P33pof2VXuZuQwxJxVKIXbALzbozGmmM9VKOvTiD/xWlaqZb1cH0M3INaZx253uByXNw/iRtaIgaafthtbbUH0k8gFDQGcFftoAMR1X22vDwwQrGkv/hTAuDka+Pec97NIA9ltH14A1AKord9XCpcU/w5dgN7q5nT1g467n7S/VabryjjqIhWweP0adJohT+fCk93nqVTuR6+2lqwd/dVx6TmZ1vrF+3DZ5zpzvWJ2/eec/Xt9ORn15MlLY46XdzQg3seDsgKM9uTtssguVwzwj9t/3l4RSElWNmrLs86igd0q237zqTtC9O9tz/WcPyNH8LKWk64lPosl7hnc/9H1R10jZlmL4hdwmqq6zjIFWKlh7FxDfyngkNdRebFIIkpugzKYcsweCbRovgO2GhYaugYswizmG2BEFu2hqMBQ4Ci+Eqg5QGmsbVEfg9mE0oGtoRUmNYZAfy1KTgYEEG5gxaNHF1ynCuYleMJGzdvdTJ2MAHGpJXn5d9OT0xa9RJGYH8YZMwswRUJRaWMqrN2INrrjA73SWrsHoFuQVGLChP4GHOSKSHcyMPMYcDDp+lZkFUzQgpMA3rGKmBnZzksm1x5PNq3yT+g0VbrMbz1ZYKnI59ZunI25aJZ/j50zE4hC8u9OUQijSTKa13GyepZY54mXlUMyBhPEKqbao2xFvOISRonExlatp0l2SizOZq79bfrvphluP9MIxixY6WqgUZCmspj4/6lXBv7Ztnyc9hQR3qJMZcDM+FiOPOrIqSaOQ+Z9Ud5r5kM5dV9bEeWmVnYyc1cH7mxAkvfDyzLz73fP+FzvuqbkVhxUSYFvbcdVKn0sWKq8ryPv1KU1z7Qi1bBUeZn2vDwUbBAQRatxk+LkJ2QvX+YdvykHXpcNN6kiuXJTZ76XLR/gR/FXWriviSubIKfOcFyrq/iumfmhbhltxrIvrrs8s+2bfEGqcafZ7cWG7K8FNla4SoW7mjitGPuvdNPn/C7PMC/L6Nf1gkEqr+ctI8aU4RLFivBhPrAp8LVsuK+V3wVT3/7+xwCoX82Zz/PMWOA/m//sz8pEVXg3b7gcTswI31flL2Tmh3h2J8n8EO38PM8cirBn0TkDHIryV3nP388bPlb/3m/r0MHqc7WfcMaAGeG6RaRtE4su+XCo3Fdv26fB3u7SYi23i7H9lsRWKuAJe7dlICt8UzZcSuFGZ5L4774ONv+37LkncRW83Xc28Jmc+Nth5M/m4nuLFPTySLOD9Dnlm72IoJu9r5O7g68Vh4vOStnuQDrs+pzV466/7vndwPubGTsknk0FVDhZYiP1V5uzbmS0VntKD+YLcfoX63QDcdUWmVwjpozaj/Oztvf7CZfQ+mNFppgDnrOj6AAO7XTKVhrNhn2axVUDuxZgNwUQqWKYuYVpFvP9tLOuq/3RrN97++72WyFO70QpVNZmTw0KWvRF1mC2Y11+qqNtr2uSijio61eKxEnwdWPpjziljVa7rrX1YQNla5jdkrna8wgNcWdA475ZnD5k9V8C+LVAqGvurSU3LrBW4ucSzDY/hrwkWaz1uvxmxS7b0jm9/xcGfrm/ti9bw10dH/inafuY3t5KEs8XWva/wkDCK8AZeTWejBaMLM/LVkGTxThtvbo2SWh4vgHz1nfdX2DVrj+WhfNHC2p4RFY6A9tMxbMtbg8pJQa0g4x5FdmptAIMoQmx6h0Xlla5eslLWALCWR0QqS1AOFl7Pgt7qTEBDurAsA2wIg5GUo0pIP7+jDAHwLWQO5yVMAxgadUgO/vYZGBF6QL8nBMnK11f2u1CWBhnMTvTIxXxcoUAtdRzRjseroNif002j5J6BO0PZGlLtKuK64Kbn21+Mr1gBcZash4LkGxa7bHCQSqb2hbYp9F0tP0smawJ3Z+8dvXnJy/BI6DeGs6S1vrf3bZFzv5u97Rui+uNmxSDn3zfOgFSxHVog0nom3Vx4ZBgp8WlNRK/L6WceS4/nVglNoaBpW2NEW7PYNTkco9U/Iiuejb6j0Lrf+E1FrjOxY3CooHf1A2DFq5XmsYxnfjNnHgcZ7anzGFsUifjksp9SlyEbnmWykMZeYbxid3z/ATv+uv9825t5CrKD1/ETrOZgPD0vWzjswipGt9t1Od9lEl/byPv88gX8x7TgtTMb8qBm3mGnDEtMPs6chgTEvYGd0m5Y8AQ6lB5KENnghuQzjLzSTrydd1wG5Ufn6364s4SN1rY2syjlD5w39QN7lpQ2J8yF0Ppn01STI1by8xRcv4VBx4snbHW19GHP8iG720DChOZT9OB1yHz+EIc1D9/ItsAZ54BvmNDnp2Rbmz2fy9H/r1m/jIkC7b6jIxxlYwr4L5oZ5S3yZCSgtVsQBU21LOEv6fXjqGv082yrk2GDTNo5mo8UiLAbdZwWaVbxl0lrwq40wI18aDCtVbmVbmt9ruWCCgiPFblWis3NvNaRl5SKTNcSwn95TLPy370xWu7py3A9bBo258ydkVh3j0yoOh+S9nuO8HijtgzuxNUy4gWNmLUGhKrX2nOOsi1zvK6xOvs4CWOqUMDDP10sUJYyOsZJdu8lEUcVC4KytizUWf/Og3qbajRoLamd0mhaAd3EGwuSy7N8lqvSteAiiBnALHJBoot+2EvTU9gBYTs+oCVndiy2jbJhgXp0h6DrjatUoM1b7JLCeDPotmtPTGsjZ9FTqB0lB+6XeuM7k8FQM3mbiHRF0CucQ9VKmppSXZvUUFrQty7rclLWQP5+JG1/Xmtzz6/BGFY9Ar+/WsW184b0OKQNc5YXBr8dV2LzDkx2n63avCy1xKVXiNIaWO978PRTw3ENgKqXT8C+DSniDa+F1lNL0oT43qkxh5rVNIvTtf/X/ZpQ8qdXdtpZo5MeALMTqWQRLq59dr6q7Gd7SjCYpYVEWcT1aC41GFdYcYLWPidzVbZoMwJhtZZpszqHZojKapF9Sm0EUWMUdS1JhLSh2rxdN3CrA0q14f691nX7tKBYtuiVJUTzmg3wL9esGRIDqbN2zUFmF2P15Q1bm15Mp74tcgzVJTRApi1amuN9a3LwFNz+y3F26yqTK28JAvbSwf/0v9UFs3tZDUCBX/mivTQuYF0xaUx68GZNEppxK2IuV+/rZBws1lbTlxWi1q0u732zEtwXZ4yFj5pGl5pspfWL4sMw6R9RSVnd3vwPvbqbi3JLlXhoOIFH8yPyEyUOdqLGakKNalXQdS0LCYr6zuLUp4JP32o1XXSmB/diESZU/Up3sprW4yNWRaLuV/jEhF2KDVNWMn8GXveZdjaibs6YKnw/bTjejxxLUbZTEhoS02MUypAZheWX2bGNs9MmripxuNY0Hnkajz15/VeNnytV3xW77lX5f088BtOzAoXMnMyYzcn3m2ESzNuamUeoFoCqVynE9f1xHsb+Vf1iIVqah58s9JZMRVuLXEznTAVvsw7/rK85w64I3MdCWZNCvJsnkDgKk/8J73hi3LP+ygz/X7F8F6OJ2bg27LlRk/cFj8az7KAwotxxlKFuiyZ7y1xI4X37QfJ+LCceF8TV8mZ5jaGxzp3UHxX4Qe2DGnm87jXr+uGm5Am3FlmTwKlg99rmSHDezKfV5cS/CcGPirKd5KRZNzY0t4Z4cuSeVFdmrHGt7/LE79j6jKhLcafM3NMsiT8mZ+YHX9Ct/PtcYxEP//Q40qOkdIyZ69iQ7qahPvotg+Hwn3VPuDnYj0B5l3JTGZ8utnz+uSf+bYOjPE3Cn9he/7fuuVf64F3lnhF4QcSz/c73gWw/0Aq8/GSdzXxXAvvQrbxUmfeV+VGa0hrxEvMHrfUzb6D4CzC9v2Wx5sD40Gp44lJHHTkycfGcTwynn4daUQLzDs+CZeUJEYxd+otta2/a91rHP3G+5akrOAacFlcs75yILICVA0cOw3n9xf36YyydL9zZ0u1A9zF/so30cVe0wmkBqC65VmsjwseaOv5iv1c94d5yeDFUWl5zSLZCLbUVpro6JchgTPhq/exsMYQ4Djuv7tFdCC9gOJqDrKdz6zBOkvPPVmO66XLU9qftebWgjz0+/V+a4csZu258iMGyZ/rEoSst4qfLm1y/iKL8dLJrLO9Rp68PHwYzJ0XWrW2vseyEGOG74NZPWcLwEQXvGHV9dykkKuaB3FynpxZzAOtNmZ6W6yefR8xfmRlIdcrF4YGuOmXVdrMqCDnVoA/df2ya0RKFCu9EIWZcTJja4qkAIdiXFriKAv4mqxlCwqllB/pedcPAIFhHDmdTp2xdGArUZTDmeE5KbmEBngF/s41xOesoopHzyVAUwkJQY9+ogmjtdrajUEO2cWT/mhgVKofmfyUHqhdppHRagsba60SnqZoi7e3MYkSnoK11v4zEx+NaV4Gcs75jHFcjmHagrpUi2sLE8CYBmqtHcC7kkT6s4YlmBCRbs2m0mbEkuh41icsumWtS3TZrtJKk9r582kv+4VA7UfX0ySEdqnqWcns1p8Am5SDzV29yZb3SazUnlhyzuaefb4tfr81xhSswPuT8TBFaO9Hn22h9edrosxzZZMyZ1HSn3i1Nm115qCKSeGNJD46eQ35izQxSeGvysR3WUihXS0R6u104lQ2bFaljvsYiWbOIrxMxpvTyGU4PNzoicsy81BHPpcDNU98NYx8fDoyqXEic9DEtc08hPzCP9z/aoxx1ZmTeZC6zwNlnkjmLK0JXAZ8e3mqXDKhwGU6gSmXVqnp3NLKAEy5mU7cFKOMP295tcsnSlWu8sRDGbnKExbHXMe6Y18rN+F6UQTe5x3P5j1XaeJ+HigCD5IxNb4aLvji+OBMNvCJTDzagGnhRoTbOnMtztwPRbhOhdsAbKLS//+zzZHHmnkXS7XYUtjmUz/wZ6vCXXWW92IovK+Jy2J8mo37Ap9K5c6WpT4qCPOVOpD7QI5UXXTQAHdVz7TMa42yWGOT/Wcb/fkiLmCkrXF1GKg5XqcOcrfVOCbhUk6808x9TWzNG/eX+oCR+FIvnjxMLxojB+G5FMSE3dWed3c7B7112Wt213t4GPmg2f7hLPLZUe7T1h5HjMT9zf5szspxYB4nDipcFOUXPeH+G64UaKzJBCTorhqgtHnV1khSXpa/lmBplGpnSULt1tb5yIMqU1nZW+JAB2vsKgyamM091psMwhrx9COw2gBqtEYiecmsg7n1OwyL6p4LMPZJfd4fQue/eALtfnQ1hrGTqtKSqEAkRwXXBYt0QMgCPIk2qcBkC9jMaakAawHMo+XnrDHBQEYjUrbISfcXlRUATf3ZNXcsiVLJzXpzqahnP7EltGdexbMEz7azupaqnHdbl5+sGeGfuZZQSX40jpqN3DmTG32RQqO++qz+O131pS368fWzA/p9LycE57rzn2x7i6zWAV4fh+oBpBK1i3/++mXXCPUSwUcVNjOUrGQz5ugcC7nAXiqDBBOp2svoVUBSwurCrDad7KgOsgowzYWUhz5gplQ92z+l0LHqorGN3rJa/Xiy1pUWVDoQghUjGwtMEsWyA7Nsy4MuKVg7Fv1OT35U1/kWQvspjXX1GZcjuarJJIyM2kyruJOo/vmmkButX10nY40t9KCiZoOyHPdnUWe6zbDsDgYMiTRbLyxSMEZJTMmwCrMt7hodyMYYmKSSRTzLNpL8kKZTDc2c+bGf619XiYXL6kXTMBUzZgUtyxFFBYa8eO7CAtybjcra2NpPBejV6kRCMxT9klcD3ILpbkdw7fWNvW8gUEIyg2aqwdF8+cuqSLDpU8t+tuq6cVNqCv6gle0OLbhSO9idU1Tkw4FHad8vUGphlIQll5CM6tIbqpFD+1yJ4Caez4wzbz+18P1Lrnk7c20TrzXx8lGZBj9uvtvASZT7ktlQ+Kdhw8taeENizIVtPXEkcagjCOSiHAJTTKo8ysDv6sxslRllb17oIAeQvhsHPjseGYaZr5MDrM9PXj64lo1zS1J4tA2VwkOuTCUzRPWwb1fv+XZwoPSsHEHgNm/YzRM3tXa2d8oe1N4ncxcBqquGi/I4Jj47HnknmQqcsnA5z7xNwiklPpomisAPOTS5+SP+1eFb9mxA4BN74DQWyrQDPYIpZTjx6Twh1Z/3H/SCG2a+zJfcTIe+lnyiB+7IXNV7ygBalMMoyOyV9pLBbVU+0yO3SXk0RWqIqwyuUuGhZm5CRP+NbblJJ26kQlHuJCMKD6LchfZ7rplXHDBz3e3NMDswruZMMs4oNz31l7pBq8s27mridtrw4XjkZAtITlHY5CqyEr+1hf38fT7yZnbb6qxsAAAgAElEQVT3jHg1k7jl3j9OG36fFpCo1ZACNrIk/lH7nNlWozBwaYVLraBwqol/0mvuK3xYjUEnShl4y8BdGrF95R938NuDy3YANtd79sDr22e8v5z57XFi8+Ds/2sSL5mD9VQeLo9cPmx4uDxycb8hi2HHLbY9wsVMPSiqyvaQeoEiiwDKLo48ALvbTU/Y/FOuBRwlJvN9VIPkqbYkMmnbbIP1arpRCP9VW/St2gBu2zvQAMvax2mydkol3TbM2dKFsWtaZJcfyGqPXU5IGyPr/w+IMAQQWZ/wZ9p3rwAWDpDbXhK/9XsXP/VtBUB8Lw1wKQmxOdheAanBekv3KfeiF8sZcwn3igE9KxuNLAAsi/9ujFPVLIuzBmoMeD/3rdAW8NllotbkCs72ZkfltHwlP2g0Py2Hs0DEWdtolixAMZGYw/3AIlDKSmij/dK0gFSA5fjVP7edJnjfQHg4RR+uBnIAcUQ6cO7ljJHOxltgT5HU5aRG85pu7VqIIm37eYeB8Sxr2PSGZBYWGQ6sTkA68VlAFy24Qk+ib6fGbRyB9fY2TfzPXb8IhIs5o5hrRcZMjckEYZGGdADiAEwRUTT5kUpjbE9SV5ZYDh5nsaDNBQZFKhytkBAGUSQJMhckjtqa9EHMPT2HHDZcyTthK5k5KnxVacBVSBbASYSxhPQgdMLNrqNQKOIJUznkCCVLDFwL7bMDoEkcaJUAyLM6cBxwIF2tIprJxTXEJSzlHGAmjmo9actBlDlI9mG20jk7uKsUqrqWNeWMVaOkBGaUZAjJn5MqM7VrqjQie0F6HxYMSwsYXFeGSz6qz0K6wZYqahXXs6bkxodzsOsWwYKYH/mnFoOr9rFymufzYiIsUea6Gh1Esl08h6G3JTyHCYlC/Mw/b2GjZ/V7TgHuu2NFXx0qk0SgkXyB2IizxZKTL5Zm3Xc458S2GnsRLCwBk7j8geoMaxIwr9/jGdq1IqqkYJZPYuwaeI/x29jl/ncLj3+Fa198s7+qBRkTD2pcRAcdxNjqiX0ZGXPhbckMOjNMmUGNLa6rwgrfMfAiJCWvhx2/OzzwzfaSU525PsLbXebTwwP/d3rOb3nH705H0MTVVHheHCA9WuZ2t+HZ/si7LexkA1FS951s+Iv5xH72qkTjXLgfKydL7CblbgOTKR8fl89DEj9EAYav85Ei8PwArzgyGbzdqB9JkPh6s+Gz45GxVt4mZQv8kAZezRPfDQMfTRMfzzP3yfjo+I6TXvDF/J77Ad7WK7TAQY3Pa+X/vPiY30zf80MeeM0lH/KARJ9+VGaeR2GFf2bHg8BO9nyTrvl8euBGZi5n+Ge5xKpxl0YY4X0pPObEbp76kfZVnri2iqa6MORSUa1sCzxK4lpPPMQcv+7s99S3smf5xKVVHrKCJd4X5XqY0Fn5pmz4SI4eiCU8cVE84e/BPMF0G0Dizew2eikVcoUT7pAB8L1tyCyg+ZDhq3ng92niv+MUXojCV7rhg3zkmPxQuV1atM/Z/0rm8zRzNSmzCik56N1h7ICcT3w7j4yXDq5n4PNHYzhUjihvdeBUR34bNhsvE/z28Mj3V8Z8v+NaChdXjzwIHO8ueMnE7nHApHDxmH28bCfkuEGOG2qt3F9PfHCnriF9Mmc3R110jL/CnG0JSWaVTZKuf/XftYBfuserxRq9SA0DQHVmzPeRKrWTMc72qicrtQqlcVw818UVQYMPbMBJI7k3iYDFHlxt5Xm8JPkFdgrAJosUou9HlZbQVgMkDgESsSVR3WLftvYzLPYU60ltWHWZWi0hMwi+z2LPx4FVCljdjuTB9/+FNG07SdixEeAXQ8WrkuUYA0si3QK2jKX/OyNvkMPq1F+50KZNj936BgLTyALSgt8LBWe4ZrAc7SfRxkpF0OKfPRdWINU6O060209B1uMuNN89V2LJpypt3PVbauPCWdq1rnxNQAKI1Z6M3+eHuNQkhzTQwO3UwK1J3ZTNT47FA4kWCBLBQTqzUmtgfSVb6Vr1GL+yjGOjSXX42euPukY0hvVYZzQSTdbH4ypeWauUwjwXcs5INHqOgZ1S6izt+kopdWCLeuTTS/mJT7z1JG1rTytS0K6tKZN6eFZYdFNtoKs4UDsM9Drxzg76+weSA9NaPWEtPbXahupSxjP9T/vjmk/pkbuIF7IwWZjOlgTQjhVa5zszsnxb9zbuD0h74lrbTrr0o71JcNZxpaltz6fo8rolmezHtmstq3ep6rZ0cLMxayC7Jk8kMwmHjvi8KtaTBvr9NLlC9G1KibEsDHp/XZ9I/tyryWpwLsL7X958VpttjF3vq3Z0m0lmZ0l2FtX5ii2Tu8tEzDhl6c9UVcm4XlrUx1IaMpMtz6y5hPipR3YwH6CjvehpIt3apuhPvXbmAeJ+yDzoxHjcgM5UGdnZBAYlFzY2MtTCA8omKUN4dDwUYRqEywle7y5+9PlXKFud+F4uYOtWbOMx8812w1grSffsh0sAngUQRaGkS8Z5zym5G8Bf7B/5w+rznx2PzMkZ2G0tHHXD5b7wHy+f8WK+9e8uCYLF/d30wA/DyDweeThlbi8WxvKige1ReTEJdR5JHJnVC8/MqrwblTntSGXPYP6578uGWScsAtPtSlPfEsVesOej48QPsnzfO/w+tvGIN1p5IQeGNPMATCWzNeEg8Fve+YsSXJ5gSLX78k51CYKblGHQmalkirgUZE3e3MV8v4719mE9/wW+Sju+YO8bT67UOfGtbLmR4nM0m5eQb/RsXJMYc4L9nLmVgWdj4XenR97lRQt8l3K3rrsuhUEHvjfli/B1Fqtcy0yi9uIr7Tqu7XdXCRaDgc6JQYxNY5VN+b1O3O0zVyFHMDMmlHsZ+gdM1vrixNu0Re+Na51RjJcH4WDKjR7ZUDjWwY+i8LX42alg+sjBRi70xItHfz7H0JP/5JzNv0ZVObqFmEocLQfV6D+LdUXcfqpWmIpbOXbmK/JhUnMUevr5AWY9uS42gzjOFaEzbvFh/RMaE9toxCK1Wyi213YEzAJANpJ7MlhjASG+R7y9CXqinP/Olu+0BrFjfw0A45LGpqH1O235F0uJJpa/+8fbj6rQPpV5+MtXVmZtL1hun5bg16SH/rKl3R1cx/73U3KSFQI/e3/7kSK049sURFUDwmaLFORplbQG8iKdiKTnVV77d6wCrMb0rxvQ+/1p59jTH7TPa+NXUFvsRANiLO2DM+kOq39XzIuAAZI09vka9+nmCkNSFjsr66cfDfBbC5b6Fruw402H/se22F+WRsQDVtNorGN3rzYmHfU3y69hyFgInKtCroIEaD5FAhoEqIRutTauLQpWvaxhh7R2AqgSNl8R4ogIR3V3h6XsHiGMhyUJIeQYVKxFxTHoFLdpSciiBSVxkBoVyoyajF0VjmK9/ZZdItCP+iWxm4VTzN4Ug0BVkeoR8YAsoI/qn9GZaeHJ8KTB5l4fPiYIIr2y0WRhQwfd9eGkrvjL4kUDNnN0DELG7yPWUB90ZZko7pAhgd79exLOfk5UJ1FCb9xmTsVcOy3LMy/tGUWyX5wYUZLLPFpiW1rdtYkw6FIprw12j9Tj2OQJSNeInofQMC/P0EeDhMNExU39TbxUaMVPFBBBao2kP6GYyzos3FGQiqaQNnRBmEtLJvP+KBEUzbJYOPn31G45J7ErNHCV+mQ+X6j/lOvUnACmmZGRWSff+KlePlgSQxFqcCSX+HHTI8Lt5cinj0eOdWCTCl/phpdzVJUbd4y18mo68X7I/Ob4sHxpWNcdBmV73HFZ51Dv+nV7MfLF/h6zCsPAQOXvLl9wWY9sYu3YDwObWrm9GBknY6yV292Gl5MhuuHABVO+RwdvjxyDRT0pkpRnxyMXxfjPl8+5lC3MEw+bxKfTW04U3o3KhU18v7vkqlZ+yM+8cXnLX01v+Wa+YEuBk/I2wSDGpEqqwhfzkYfq4P6FvOP73SXP9lFJbbjhhbw7ewYT/l3v0sbnbIJhVq9QF4D6v+aP+bS8ARL/6uCf9Y1ecAsMZeLtDn67P0YMNzPOwnc5c8rCD3bJK3lgW0pnjn/IA+NsXFnxhBVTPi2PXDLzTXHw+1s9rdYqr8x2zczN5MyQmXEXJ1jXqUKGi2pQ4d244W5WrsU9nz+qR0/oA+408UGauDJPwCsVNgbXqXBvA9dDIc3LuraL06qC8hdy4tpmppQYtfRxaWrsrHCwDRdpYmcn9nU8m7NXdXL2+RHKzR652/Q5O+qxy7o8mc/n7EkyZGND5RBg8J1teSYHTpbZyYlbu+BGHxnTiVNxMNzm7FghyZEqw68yZ5eTsGAHfcdwMCG18XTd8mtIjWgIeZrUYIvdpqwf0cf8K2Zup8YCchvqEfzo3hnJNU5rcoRFCpEsnUsK2gewSB2IBDI1X19FrGszF8J6YevcL0e7rngAisye+B0fmiXAUXNsEGGOk2N/ie/2LheJZgANsQt1ddROcIRP91i/esFGaU5XC+43UzT227m5PuhS+TQjTLZI6NpR/ppSm1kImOaQ0YunrNw3JPYmTYtVWMRH8Qwa4RQnCrCqVEqw6fqjcsX9ybXgy1rrIxDorz0HshI4wYni2vXgS/JZ2NL1censfTUNeCAdqLd3aAs8pCUw2iqBMowTrFmSNh11sOA9EFn6RdV/N3f3qfa9Fszwz6D5uP6ofZo/JH+gc+h2fBBEUtGT10IQDAbWzbwbQ3v+OgV+EgOvXncmyl9FZ+2HIu4+kVLqi0AbjDn87Nb3IwHUZBX9zhiEntbELbNO4VRBH4yF/RhHNiutiYj0kqogPA4uKWiMp5blPixY08ZESwQTPeq0n47qIbSyEdbM8ZrWjp1m5upsycK2SO/jbYGSVkC3hL2cKnV2Xes0GKVWBpTZiutugunuR0gxuJoVWO2Lsh9PNM2sBfhryXjtGfRnL1DDni6LdlZ51alIPBfDnUJEhJHVgtZY6JgwYzIYNg7AQ5/cC3MUoVZBpUZSWAQc0ZxeVU8as2/BNEifrO1q0X6JxYhS+8Scqo+PKhVNylRrX0iniGxLKWjy4ipdGsGvB4TbF1YZkTJzyplmXVjIHNdL84qZu6QijzPfq1cLu4v+GKic6lIKQeqRU90xPWlu1QlqYmDmVHeMukdEGagcNPMoF1zw2D/v4/mBq7nwRq8AGMN/9sW+cN8Maquztzsqu/mBR9359wC3m4H/Mr7g1XDke0Ze2ZFqwm+PDzzIBRd48u1/vHyFmXVAP9bKQOWLw13vhL+7eMkXh3u+vnJwfHMIti8P7OfMYxYu4mx1Fg8Ibq8cIF3MwixPS0X49dH+yH7jLOoP8fe1+Wv/5viW+2A9/3nj93sbtmhfTAd+dyi8DdYa4MPDIw9jYqOZTw/3fGQz343KDzbw8Twz1MKQKt/WS7YmbNO9393kyYTX1TV4+8EZam/LzKMkbkdBauJCJgyXZTyEE4Sllg0OlhK3lrlJE0Nxx5Xl+Rf0JLzPniyYZUaL8BJnn+espKjgOGdBp8pu2JPmxJxhLJVT1Z7lX0vmbao8rxPfz8647/T083P2fhu+6tLX1Xb91Jxt8+ShjD3AVTHectHn7Fu74EV65Lbs2KTCzk6gW6ptUf0V5yz008VkS9GfQRaXmqdXsw47OwXkx4xe31+fNtXaemzL71vejJ3/2/83fH+tvz0avv5HI54imXx9f7FvLElTwfCGRIM4Pd6I28XU1Yc6KxrjEGWUjLF45zetttBY0xVL3O9hCTjW1VPPukQWFrclwrXqZpJobp7khWKJ73VnqzUr74fccZoUhNGg6vlSzWVDJPbNVV/FvTR2PK2IH3+Wi9a45cu0QKb1VeuLobsqWQDZs1Bo+Y8t46/1GbKw0N4PxqAFdzvyBM01WpmqP1vFKM0neRU49dzo9j22vLeNmaUL/LsTLr0pqzkwm3vSK6WPpwUTEA4WC+PdAoZ2yvJz1x91jThRvS67OVL3aMEQczDVEqtsDehidrQNWMQLF2hrLbiMQWNAPDmmX7JdF1CCPDnSsAUw13azEdW2ZCil6UcWKl01bNMCpDWheqpuEzLVhf00Cf87FTQG/1RmiITASYwMPYEOILdSgu3ZhVbUcmKOJL1Zm2b66X1L9xHuxvAtcvObxcwTGKODANcRq2Q/9o0kG5UU+tNCjYQIIarvqbPCBkhSpgBiA9p9ifuR6dp9XH1oFiv9CAg8AWESQtzkettUXJflfRxATN3ndlKi9LGLobruLSLexni4PEuCSTaq+t+qykR1ttUAFU6m1Dp7solml9f0Rdu1zb4AW8hUMtVqNyT3+iiu8VZNsRfUbuGTU+q6+FnCoi+S6ebii1Lu459l3Kovdk0GNEZSqBfmEBJedvcsEPgTrq3A3++e8cXhnoeUVxG+z9lpEK6nmZMkTIT7AHGX5Y6HdNE17ddlzxFjKjuueATgNl3xerhh1D17Ls+/uDafcQfBp7rjpFEV7ugWYA/1AioUVf5ufMXvOZBq5bipcNzxnzdb/pwTuVQO28rm6OWy93rJP1xs+fPDI9REnjKPbPifH7/jjV5h45GL5Efyb3Rkc6zcpQt0gmuMokcOG+Xlw8SX2ys+nh/YDwNWXd7wan5gzsZHk4PHu+0Ok5k/2JbHLHx+uOer7QUfzw99ozpEae9xPlFCE/F8P2PmrPazxxOolwOeUJ6fHFynAJaPWbByyUf1wDebAMCnR+5l4EJmHi1zeQL0yLvNhpMlPjoWbncZhsp3OEj++PDIexvJBS6ZmMY9w5M5W2zHOzkw5LmD4JdWeB3PcGCP5onhqJB9Hl5EwPFDHvjkUHiTlevk+uNHU0o2qlaenZxF9kS42fMPiDmgxlwFSzBU+GGTkKI8myemQanzhuNgXDEz14FTlmXtn+GiwBRW/MdBOM0Dj0nZRHLHZZ77nJ3Cb2+TPN/jyEiaK0MulAmmPLLh1OfsvviafZFatbpmAeaoQGMPeHfaoQhTHSjpwGMduEhvuZztV5mzDTxVagTRbf0gmCzOnHw6GyYNsCxEkfX1LsgJ8ZM1XyLPUcDC/q4AtND32vXn9HwSGuhoYFcDkAcokkUSsDgMLIxxaalZ1pi/Zo9Wo/JrANtq3dqsfe9yvG1UK/334GCxmrnetHobfV+2BWE+YTiXfghgycK82gqIL1a8FtZgi+XowpSuZBA9oXH5knUVwBosP+KHGVkXCzXacxLBC1Mse6wz6hqJfYWsa5JvOfpPCDM1NLbmwaE1sNlA5gI712OmAd+msW1jo1pLdkvd6tSx3RJwYO0+vQNVpGu9W35MEovy5bAUFamhM4+kz8iw9FHRgj1hCqKtydM1+l1wsFtZ5DwpBfHqbgeolJjf/Oz1i0B4NHH6fzURW8KU4vS2yw80PDOjT1SptiTI9aNgbdJ6Tzozg1Fzj+6bTC3H8b4a4RXs7N7aKi03vWrT8T1p+5alhK08AdFtrK0tt9o0VNFIFHAA+7RwQw1zPakwih/DWa2oKUdKd0hol+EBRa5wVMXmSsquhW6vq9XLTbcFCVgswGibivS2tle1AMLt5HDLvBWTvgB/7/PUTNFbSeAVE7mpwjEt64URC5iEjV4pXmBCIIeWMaXEVEsESf6zYRiYraIq3b930SZVUspYrZzCA3qUdPYMW/3wVr4a/GShFAff6+q0Gi4QbUxMJtS5MiRjojJEwNCY1yo+xgYLm5qwtEspQdgEmhlDO1aLe9hoYqq12/n5vftz05QZ5HxsFvyeNpo90SgZg1lEuPMyH0xJ0pIQn4im/4XXtsKfH94vzxfhiLIV79M9l+wHB7pbmyjxvY8J/utwwV/v/Zhf84k07bhKt7RRcUrG1+MFfzM/kKcmmYiiEAf/d67G13rDte3597sXmBmfVz/6/1Cchb21Z3xW9lyXA3dpR5q8NPL/cviB79I1L+ye76YbLsw/kwKfFaOo8uJ44k4z12Xvpw2q/CAbro9wUw+818xVuuXWnnEdWuEvhx0/2AZ2D9xM8GIqWJlQnXgzJnJRHp+wuvm05X89fcf/cfkRz+TIG93yednzGNpgrSduDsaeDSnsNd7ssmuM8DXxzW7LdpoYqK7PBh6r99eoe95sNnB0j2GAR7kAJkQztzsHx+NhxIYJlirEnSX+4vjAl9vLPmf3pw2aCuk0s0vGvgjfbq64M+Oj00SdE5da+G5IDGXuCX+vEN7MGwY90I4J2mY/1MJWKy9q5U1WfkgDn0wzM255J6Jc6My9KoMWrFWdzIX700geT+zmlWfzcKRI9lM0Vb6tW74rxmf5yHsSL2fvp5IVZl8H36WBl/NMQdmVqKA2OArZaybNlU0DtGps64yWxEOGVF1qAHiydzL2OnA5F2yuqznr682NHhko3NZrMGMQ5Vk+MfOAiHAtt1wVr+P2a8zZGtrbta3YUlzDQXknfFYbRHPg6YBWFmKo7SHS+MNIcoNlLW45IQ1sNqjm69gqomfRoPYGxi8XuV7bp57ssUi3BGu/73hRlt+pLNpgcOCjzX1AFjCGeKCUw46rXUa4FeD79VyXe2ivc1mcULsNWvTPitrubZWFjFvlwPUiJr2yXKDmdYDSGdBV41q/FBbnJP9Ve0IWINDJw+Z3DK4Nt7pIZ6C5LCygffWoHMRqJN0FO+454LbCBvQTgdbOFkyk1Xf7/Yc8tkcJftKZ4nSgvb+5ZzQMteiaWUByAHOPFZZAo0TCZksQbFhtCJ9lVT9ZtJXu2XXAIArFqudStXsJu00/STASpSch/tz1i0D4JO1omohgKpK0x4TZ4KSwreLlgFc0+k7cDsYBWUWTsA59Uot0Vg+oNcaPYRrV3n4nfqQ810iuk16AIgn9/9tigLjusx0N9UITFgMqgGJJ7lDQoqthVeK3AfDMEt1uggmS1LS7nuxmCrUmRFth6fZQ/Th8VnVd0xDlf/E2eDnoDKhrwgIkaUz8Ek+wsgAtfTKwEWGMfjOcrZZE98RtwC4hzBKlD+vCZIsRspdlsom6zmwO7aB7SSuDGaZRStvEj+bEo3w1l10kTQ509ckErsqpFkyFET9tKOZsQBEjV3HXjRb4SAD66gx3MsHU3LougrOqS3Q4+ororIqn+fp4ij+qXspXVMjtqG1owdni5NAWygpojuSPWhnHHEBZenCgIqS0Sh5VJdfiSBmjuCTRjzwTiAwePEW/YopJ6tqmP/U6KLxO13xa3/c5Wy2zRxnSiU/KHf80XvFsnj0RSjwRrc4j/xOvmchoPlHnkWfDu7NkplenPa/YQ649WejD6n/vGdFh5mjGM24hw18f3nEahA+OJ24lY4OPzauyR/WIJriOc2DNJw6Dcj3t2cvAddmjw8xrueLl6YCJv8+AYzauq/BtyDj+h+MtzY3kuux5Pd7wxemO79I112XP54c9n+PAWdMDDyO8s+dMyfhKL/ggFT47eT+8TzueH4683Rhf84zfT0cuqfzudOJ9ugQqH83v+YYrYOK4reRJuS575mkHZObNkdt8xR/Y8JvhgQOQyaRaKTGHH7nkZq6oJA6DsD0oFzhwf61XfLx3dnqwE/97/ZDr4Q3P5sK+VqxuEIN7GXl2PLIPu7mdHbk8wQO+KW0VtvMDgwwcw+XjO7nio6O36DEpH9UHpmq8kMIf0jN25ch7gTI6IB1r5U7hkOBlMf6DXPJK3pEMvssDX5Qj7zRzaZV98sTeS6vUKXEhhe0kzKb8kAGM+zpgCV5Vo5jwuR4gw50KMhvzcJ7wZ5LcOk6ETdi1tfVkBrBEHsrZnN2nga3NjHNFUoJkjDZxkY88li0vuCevZV2qqCzJb3NNXKX3bk+p7pudbVjmbFEeh/SrMMJui4brT8Vvbr2ue66Dr8PtqB1i/Vc6+WFm3fGg919jDG0BoA06Kx7o8+QWVMWJDJOzcrZ+5L1YpUnbQ6v1BOv2eV3XGu1syWTS+3tJKOsAvINTemWwFgw0BwuvC7BUhmt3I7Ich4MxpOadLx0MCRrJ3edsp5N7tvg5NyzyFDwCiPm+HO3tjhK2nIZ73wbwtWZHF9/VnBri903M6W1pbhztJMBPCaq5jDMetWMWa3LVTNImEfL3VgMLEOwPw/9qeuUSAHW5ryYDdLzh22ZYpgb5mOj+FxgOglt7zk8arHsG+1j1tnls7J/fYqn+DM3LZMeZCJvc8r/itKR64JECY0A73S/dOcXHV6XgezOaaLpgz+n0Yky/ZHf4i0B4iG/qxRmqoqFzNBE0JQabmaSgpJ4k5dpNBz5mhkYWfg2gpxGyeFlcIUlinucexU1maFaYa2cLGzM7iWFZseJHMjnAXhqy++yyTMQGftomOaiDl1ESpDC7DssODc9i9070oTxJe6D+4Cdqd2copaABaknKUD1BcG+FUZStKfda3EZO1fVV0oqENMlHsJ7qGihVWqpIVOpzdl1EGEU5VGcTc1TF01hYmri+BQBzWMH5INGeBCAGV1U5JAsP6BbZglZQTb3YhlkYtSf3I/UEusqUjLFqnAa4VRyE08Ps99LqjIv5otqj4CSQIBUviDJUgRwsrCZqgsESR/OqXKN6/84tqdHPCJkTjE3DVT0Amm1hdxBzeUP1ft5izEkQE4axMfFr9kI6+z9ZPdNwZXMwfJwnTKUz0CqCpBRSFwceSaP+ehQM0Aql7t1OR2GMKLaYSyz8+MiTKNZM959y1aHyinfs28yugpGQUiLRDz6yW44qJD0x246kRywlTpbI+ejrZ0pY3SGyB1HXRQ4HvtdrrlAymVweKOYM5zfjFVdUnvGOOmWaJn5b99zKyJvtjlfHA5YOkMbQ0GVqlOut8wbJbgOmw4zNgwPbqBZ3YyemUbmXDWKG5onnTHyv13xtN3xuPyDAH/QVO04cB7jhjtf5im90x18f3vF2kzHbcggt7m/Ka15NR/5294wNW377+J7/6+IFX8zCOAs39RHJE7VkbupjHxPf5RtqNd6MIy8PB/6f3Qtgy7/dv+WCmW+PDshtJ3YAACAASURBVMD/ndzyt/lDRIT/Ub7jy+FDvqjfxRAVSiRiXRyF7zcbLovf66t5bqYGiMH/dv8N/7B5yT7DPMxsjj5nL6wiZjyGxGMeKg9TYZ+v+MDuuc2JYa682w78Zn9PNuGqHCD54PjH7RU2D56AWASlsN0XvtskNuqs85xcV/zh4ZGvxpG/Or5jIvNI4kUpPOSRVxj/MG64PBU+qjMzwl3cQJ4TFC8C8iIKb9QpMQu818xFlIceZIYBakk8iPKpHZhHqJPyXO894bOJZ/ES4Y868mG9507Hszl7MVV2+cj3bLm0CWpmp3tUhMt0IJv2OSspNLmT32+eEmzf/eKcfRx/vTmbtQHAYKYlbMNCg6nJ15wUJEWXDLQTSlmDRromVoN36iysOEmxMJwOQKbqbHE7OTXzZDc/em9Hz/5ZQ1J6zuMKVKwBdWcpZaXXDWDTco2sU4ULaOx/2cJAl+qg1sG079epBwuxZ1pymBtgCcFPaGUlqzQCZFqwuQsIbDrSDsBr6y/fnxtAr9G2nme0Au8NyrZumaQwkLptGcGct+p+PQGPdjLp7HeTQmRarg2RPxKnreJ2d0msf25LQuzguo0fqx345pCOqrr2uZrfSwmG1lg5QnkkE8Vc6PfeiSJpz83HUqGx0rPPGWCTfiwbElicHKyRVw5yaxu/hc6Gq7KMPTwYXGu4LZwmKoaUmeSqLloSZsN87Vkl88TSn7t+EQjPNIas3U08qJyhWhwNB9soaWETo7GuAfajjvXRMQRADba2SwMigh0lhY1WJJ35qHcfV1VOVkLzGRFLSmfyhXY1G6zuPIFLGyrOkrZ76eUBzRiGwaUD5pmMa5mR4I4GEs4Ea0s4PyYzLnH96iTCpqiXks7uH7Fua5d45My8KtfbjaYxkjiAac9gl8dgEe2sn02kg02xFuV5u6oupF4r/tCCsayL/IDBF771Atf6rya6Vnlj4lpdVTKedwLOMGdNHuxYeC/PhqZ8dtQCYDki82g7hC91iPuzqC98sVmtKxaOsQi2PTGvgq92jd2OxaUMKU5HhRqLovb+bn/AF+ocSTM9AAsZxTYN0YfWx5OZMQnUVjEwnktL0vP7y5xq08ELutIrt2sXAd2vcc1Yz2b2TpDIct9SauIwHCm2g7xne0o8DEKpm0X6EwS5hDNLsV0/hqu2JdXkJwy1MudLNAbAZ/WBwU6cZMub7QBSeXmaKbajbiJoSYnKBc4rXFCwnnS3CYZ5GhUYYXDnAYCvN57E9qHdcbCRSys9qe9gI1dWmHXH9XHPdncAg1I3TMk4suFv5teUtOWyFu5V2Qa43s7K243wb+f3vJZr/nB1zb/bv+Or4Tk36Y5TEjbmbRonQwO0b0W455oNR0SEV9EHP2y2THbi+rSP1yr/pt6xsZXVVoxl9zONcWrwYb3vv9vLgI7Ofn6pH/JFfc11JPupnLhV748HG7gbd3H8T7QNtnbPa73ivww7fjM88OpQebPbstlXtuCFR4C/Obzhssx8XxbgfENBZuUxn28at+OWUZVBjkwC21o46cw4Z5DCR0fhkpkHMgcS29ln6JebxO/3eybdcBsZxC+cl+ogGOB5/G6sE1dD4qrM3MqWZ8M9GGzydDZndR57QuQVbpNWw6t2l2aqGR/GnRY50cqytjm7Lzt2uu+JyzWkFbMWrG54LBdsdQ+p/uScfT6XLs/7U68z2V7oN72yWbB7ige0AdjafIQQfZgsAGPNTEoDSF6iObXqHDgAsfYadWeGZg+WUttvG6sb6R91YTPbXr+WUbRVvvnHW+zPWRdrK7Mgr+J37TxunRck4iBr0RcvIK+92DXcqQcMKYnnLdEY5wDo0H2QF2DUe9sDhjglBNAmD1wBe+/nhVUPP42lXatn2cUO1vrHGWOAMcDlGiS2/vNsDidGajDCgzZJj7+2ximAEOBQhCn8eZ9eOVhdlQULlNrGxzKAWi2Afp/mp7Fydr9tbK0HV3xPnPYXixPvSGuU/qzoMpkWTSRp/d5+x6rvFxnNWrdcwr/6xwBb/ETVmhsJ0BIpz5o7d0nLT11/1EcYFiCpKaptRYTXQs0mY/DOk/76JOpi7WB/HVi0D/cbTSimTrrPRPJdoE8Fd26ADjqrFcZmhyXSpQxNO+vRp3VNaCvR3BbBrBrfAUPxCGZMmXmekZyRUn0gSGhZzCjihSJSAc2pg8duP6LCHIOlYgwW9baTRmRlUFbHLJEVX6ySbZFj9Hrw1RlSQr/aohsCnKl46t5JK7siTAKnKCaSo98ykTBYF6BXsS4b0L4w+HOeiZ+HhGOupT/PKezE2nFak23kAlWjTcUo6oGDih9bzmk1IVii/7aI9mqocSrQhP5Ad924kMyxeRQiSFLGkLxkhIMWl82Yds/p5WP9yy0blIKRIcZntdk17NWt0TSFkTruJz3EhlRx2Ys7CM8k4lkJ7JLroTeS2FPOQLlZc6/IfsxDY3KMnEPbHpN6bAHOr3A1EFzqhqRHKiPCqc/Z2Rz0FrvkPrcEEWGwE5OMWN06oMV4O2RSGXhmASiyy1TGUoEZZvh2uOCLwwMPw4ZD3rCdjnwyOYg7hZ62MPMcY0qFfb3gA3uE0MyabJFiHNPAxk7caybXDZdyoNYtSOWqznxQ33MYNnw63fNed3w0Hyl64lZe8Mn0ji+3l9wPzxGbwJS3Q+bFXPi8vGcad5QYQ1fhrrK3C74en1N0wlA+rHfcy8CbzchVmdiz5TnvOIqztrfZE8vmVLiuRz6tD0xaeTNu2cqBV+WBWrdkTuzHkU3Mr209OKNed3xW73k9XvNnh1vuhsSb5ImTL+YjVkee24HXw4Ztnijh1LGVE9+nZ7wsB2fugevyiA4zX6YP+PR0G+Bl4p1dcy13nFLlvww7P9E4ZXa4bOMgAzdl4k3eUlT55PiO18ONB8+lcHe5YUKjUl/MvwC0U/aN7Idx0VLvJrhM8BAJeA+xnXySJk6xRl4fE0kTn0yzF++oxnfbyvWkXSIEoMGM7mqBqYAIl/UOI/OgO66nmZKPfc7mcU+mcrKhz9kL8xD0yAZTYzuDDSdnWyt+/3PitDny/FR5vxnYVcNyQUuOeT0jkhnlhJKoJZHTiUvq+Zwdfp05u/YKNnOGuFiAk/ZzXOd7Biw640nsscv+a43DCwCI0I+RLRLcPCxv2s/GSsdlzQ7LQ3dV63tFB0W0k1fprKLvsctnCnQGWdWT4HKSODFsCXgLyDIsdNx+P7BofFPs740BbQl6nmzYCIjlHnKTxbXXtgA/AF6TLZTQE7cnaQ0VioOyzFKdtrUzWh0WaV67YFFZBwtOSyJ0YkpVYJ2bEiCv9atjU4knFLpcHJSmIF/myLtq+wghTews/OpqmLUxvT5m4jn20+KOflfvX/TZTuREcn7g2DaO2phpwDqLu05V0VWwYauTGkhq/ee1EgXVfHwncXJArQbh6n2pbeNMLstpWuR2CFGqLexwaxOVUWvYtcX4ycK/uLJcK0rQMxdVSFrjwZ2/db0orJnZBoy3TXvbdDANsNvy+hYJNPCsop1d68frfeHwI+m2PrQ2mLZyjv66oSqP4qVNvbCHxfGLg8xSXVidc+51wWXV7nW2bmcjnkRgp5aMhgSQ9RtrCXfgUXL7d3N9WJcFbve/7s/0hHV21tw41ShcEsf0SYVsUfI3NvyW2Das7FfG9r266IGbPENVoVUFQjzpDR/cm/pkoGk7BVj6KSWvXGRt4Q72uzG9sET9rBby9b37wrZo77IJj1IZLPquzC4JiYAla2KDRH8u+ku1xtpGX1Qh56HbqrnUJmFzRbJHk8OKKfAy4QIqiCRUZqZwuehWb+JMsKhwysL/R9ubxNqWbelZ35hzrmLvU90i4ka8F+F0Ymem7ZSzUlpJCguwsIUsbIwBWUZIYBAWIhFu0qCFoE8P3KKBEBII0aKBRGEbCclgLBtjy+B0Fs54ZVT33lPuvddac85BY4y51r75Xr5MHM9Leno3ztlnr2oW//jHP/6xr92qz1s17ykRHHitBXgE1AML8XNNJX/PmPqHPcbZPZD1wFPasy8HhIrIwiTu9LA+93yWAUh0K3Np0oavLRnVZQ1cUobHdSW0+R9Lx+Owoy+ZpVgI9jraeV4Uc5voCkBhkcBDr7ye9+s8tvbrcCUnYul5VRfeK7f8ve4VN5zYF9M9QyIUpUcpcYEKsfYQ4XHYEYu7cdTe52yi0yemPjHkwpTOeRvILXW/dBYUu9g81Z7sHdtqHtnXyiKBF97U45N4SRSX8ZQAZ8zpEiqXE+QuMAUD0BcVphi5DYlUIyI2H3LY8bunJ+bQMfumfSBy0D0pzFx6MeJltqDi7dDT551tZMne3RhONo7SQikDH9QHvjVecKojP7eYBKPJL27EdNud65SvytHmp8t5ChFqZFcqObI6ahwagPLbbDZ3YGsHJXNM1+zV3vVFznyyG/n6cUaqMlK4D5FjuORlfQCBfe7YhQzU1ff6Ok8cwrZu36fe3kUwl5P13cpC0J55VoYwUbqWuDVoJyUCib2emNiTo7KbDewa2zYjOXJ3pTw7JJaYIUeiJkrICB1d156932c1QPCPYs6G87VQXCpI9azXu2P2fB8XewHrvw0Q4yyrnH1mxcLv/HHgrAjOf9vI3VZ2JLzbLMIuwgkN2TKwGSVqsCxm2LrNGZO8gfoGTkVozdF83W//FsQ9+n9zk6HN8szX9nWt3Zaz4M/P2ju3ezhvWKTvPAeTC2wsO1hmV1G0hrXAK/h3t1T+SuxIWNsvt2s41zr7iFw7xYVgREK779UxpBqJpGxORxvRrrRSvBjELVbbWzIA2UD92W2cBVHnN2wPfpNsbLKD6j+v3gugCy7DCLaXVyBREYnreZpABCCrdRQW3Qooo8BSW9bWnt16f6Hdp2GmRGHRtI43c+yStTFIL9Fq1NZg0F5ICgKa19s7f7ctwAhBqGU75/c7fkc+wirBBdMVrDct4unjjTX2Qqnm5+uMqrGNaoVgZ1YeQU1/JS5myngHESxy7MW6KZ37JLYbrNWKIAYHPFlsP2ppdhGL4joCmoSkwRdB+/u5WtEfQcycxzW1BZcPuJZ4ZWM9N1SLNT1uC1ARA5d9MM1wH9z30cPt85S94u0nwyasT2oWVhFzJ+hC9NbQ22JW/M0bO+0FfdFA6yCRHHStwFQ2NtIYbX1XV9MAZgO/ztDnYK2DNYY1SkXV5QtCDiaYP09f1VrJMRBcYlJC24xY9barFORspZJqLbc79U5tbmM0U7bWmGJaZREY1LwXZzXwvzIbAqLGd7YChNatSALe7KPzwsFW8NfehBu9JzGNeLBF3BalSpcsQxBEqFKscEQLQTcHEVW1+w9iVaoIEix9umYMRL1bjqCaraueRHTR1SM514rqZpj/VY8VSIxX7ObSllCbs0Hp8pElNVYvMhPpaRtHQhSGkHktN7xXH+nDwkl7ikY6Ku+XA33ILLXj0AWeLSeo8CZe8vVyy0kSN8UZ5LVI00DwrD0fTEeKRm7jwHvlsG5NJUded5c8W048xp5dLTzIuK7on3U9UXtiUF4tB96EHe9XAyyPOjJq4URg1Mrka8bdPlKmkacmsMTm7Pv5icuinCTwvEwEtxxMxc6XvKPYgwMLEaFL9pmuJp40clMmaoBXy8KbeMEtHZcy8TDuuFcDkaJwwR1DXrjpBogL41x4FNO7mef3QqcJWj5MsYLQ2Lq42SJ/UZQlCL2ahdktIx8sB5bYU+sAAk9x4L3yBDzxzfg+H+nrrQhMAnVJ3PaZG+6o9Dx21Qobgc/jDc9PRx7ijqty5OBr8VU5stOF/2N8j5+YThy54OvTHSEEPg0XXMkj7+V7ogTmANoFPjqc0ApfDAPP80IUuOARlco1C9elsYlw9BThKQUuSuE+7EmaSbWyr9XzpwBKdblIV58IJXHoHJgWAU1MArVX4gKPKTCGR7o5UWNBloh28JASISf28wQIs/T2/R4EBlGYjE3X7i0xRnKM7Kbwj2TOboVxDX6aTVdj/eqZAwIYwxq88t73+ZWoCVTfdzzr5WtdYyhFW+W8d3wLvOP12g5VA24GYjdHn+bcsLoNqWVvO/E/0k1nqiprCjw7A12dxWzgtJ6xsU3nXOq7YDUQvBalSRg2sN/S5+sjdNRvOlxnlB1gI1Zk1j7bssKga8Girs+rZSjsIUdfRc+lny248Me4RhorEJUt0KjaAh4lBnWpxG+yFtPtetoXaPWsuoPw2BA0JrdorOh5sBJwLbLacwvOJLcCQWSzVRPwVjMO5NXkM60xdx90vb6w3pcTexiZWKUVpvk7PJMfCEofcDDb3gObK0p1yYlCxYya6/rc7OaiRHeesNYj4jJRg0LWG6CRd6JeZxY8qeRXUatndt+JCt49fkdAuNH9rYlGOAM5Dew15s9+Zw+rij0EYyVtI4nR7MWa/jj6SGl6VPtMgKIriLYL3aZHSlbzL36NfYElQvKJWJouxytqz1VB7XqjCnktMGj3yMratus51352Xce8FMQ1Y73CMVSGBtZblOVODw0Et2cWvChBPYioft+zFmIXaf0oQwhrkVkrequ+aAFrle5i6n2zA2vprbNVLYTwjiPG+j5FVpu3k1QGDeRa3nmv1SPh1jTFFq8zoBvX5eF7UoRtHISi7z7LosyiXNGRXR6gyTyqhyB0YrrJ6jRBUC+ki1iQkQvJ23y3BSCr6cvRherFhG3ahliR4jBZBDkPqnw8hhAM9Hrh4zsZkOrPNQS3adG1QLHp36oIoh75NAActsYZgzPQUhQqHGslVKGGQFcXe05R3nlvX+WYoxWvDdmYYWK0RvTJwXBnfqs9M5SKhh29LDx50dtJOl4TeK8e6MuJOY6odKCVL2XHiLIjkch0eVuY43CCA1QSD8mA5LUXfxWNayfKUi1z8aoceZ32XLtd1sOlcjsPPONEKgFJwuDjdpJIqj0vyhNf7Edu2TOUQi8LqBLHiek0MPrn4ziRjh0Z2EvmsSRCsGt8fhB+rXvBy5oZ66bdXXxsDNXGxRQiQy1c6omikSKR3EFmJpWOt2kgCTzLxoQK8KADQeG9aj97E/a0JXa/2LV9Eaw5xPvLkeq/szGX1u/ZL5V61ha8feZSZiZN/Ebc8yofmKSj15mZnh7llCLKDsmVMc1M9Dxb7B5PHWiMjEvmMe2I3cS2vcENd5S448PpzoK7ZOe/rNY97586fM7jEKlL4jCYH/hHvOVaK/cpUaWQO9NSH/vAVS58qCdA1mLdSGWOXhMRIns90qcTsiRmbMykOK9z9pwhBvwTEEqisiDSexBeCSEjGdJpoIgSl5Gstt73ktFUiNWCi9wtPGkE1TULskhPikdSHugu7m1fWQaoUHJhrpEa6ta98oc0Z9evcNDUMGX7eQPBcsbWvaMp9s+3BggiBmCtIEqc8dQV2LbDCqq2lL192cYnpjN0JwJZrUi8ZShXomYFotsu266jtc+1KpHNISJYicF6b5butn93MTAXbI8NRr4lB6qEjXG1ovbtWaw2bH49ggG+9VlWoQ+217TnaRyTrESSrNB2eyTBGdiWSW4P/Zy5rdufbUhDWJthNMZ1DWpakOEvvta2N29a5cYgb9EO7xytCVPRStFN5lA8SCjBO+q5htjY73IWNOCFaa1Yzjqr5qroSi/rWtSWgiBaVx1+e5u9VLI2n4tNV7wOBH9GxWUxnAVwAFnFGXehSTfUiSX7imYy593mPPhcAyOR1ZWiqo3HUqIHHwHVBRHogiK/+SGeHT8YCAcvlisQXLdjWouKkJBQEA2IRCRaFaoBn+Y3u4YvhNCtvnMhGHcYLTfP3JmdlagbVgPS2VRrzQbO1CXmQlEK0oC4n9uiLEW10rv/cQYHQBaWtcrMBaXza5ldwzwSOVEZQ1x1wuqTStQKumKA4N7HRWR1LwghrFZnzQInOMOYnGG0Tm14OiXSuV5qF3tOZSF4AaBFST4haBOXrfGIM7K9xG0xrpvtWJOGJMloNeumNqjwCbA4UzyKAcAozR/XdLotLAnBG22c0R/nAY/oZjFnUWxdK4NLFEKIZM8glwQjnblCtKhPTDZQgVm8k4xUHxvqLaltonfRTFKiBOtciJK0LY3Wancdu271RjB7O7TQTGkM0G/PJMV+M3+3H64ylIBtFIFKFqUGoZOAtOBC7frUN4M+RFQL+zGCVCRXl1lY4WdfvYBUTdtUVM1f8gfol/7/HDt5pEqHevtYBVJXUM3mey2z3V9J3MeBm3pikcpFPHLqhP0c2OMgMyYGzRykJ4TAcxaulxME4Rvdc76mX5CWyEMauT7BvetoR6w17r27Z/Q6E6NCgSUlTjrwYX3NSM+cIicdYMq8IPOQLnjwRg8yPlHnC2L/RDp2/Hp6wfN5YWTiYRC+wSXvTwtf1MT7unC3q+h0gc49cwSZekqd2VFY6EnHjvv9wnvcodMFi8sXlj0wDYRhYjld0FXTh04BTuyJAl2pkGHYLeQK1xQeSNztI2FWZJgYjna/bxzsDlooHrhVn7Nf1zuSC+BzUlDljVzzgntSCYzxjWe22lw3QCpVeS3XXMqJj/WeOXT0aix+T+V1vOClyzcAbly72I5xgSUqcxIuysS30hXP8wwiRDkhBGKYeLsf6bPwJiVezJmHYeLjfM9dHAk6EdKMitDVwhu5ZIoTKSh3YeAmH0HgulRqEHZ1Ylfd5P6sy6do5T5EZulRl7cAHCRyURcQOIaevk5M0jPobNKV2Zj2KSrQw3JWshQyaKLsHPjPO27kwFMPTDsGXRiSBV3jHDn2cPJ3c0Og10I/LiDTOmdPydvNV9iVTNKEiv5Q52zEwNFSTZ5V1TZtVchEOimWWZFmIabOqNm+VBwklWZ3xgY+cDAqYl3qAoVCY5M3uy6kYZaW3dV3wKpqk8HhjZ4AZxWjhjMS02tclFXjaRIKoFpdiwbb50MwKmsFT34NZoNmIKatyS1N3vyGm2Shoa6qBvZyre6d63VZ4oDekB61NHkB3qTE73+15XrXLq021lwcNK7FXX5NYnKBrLJmo9fv9KxuCxTC+lLwIAPag4v+CM+a4K4yD0M8eBGlrgxzO0304u42v8zOVAnVxLQi5zpw1504xmrFilXOLN2cCW97hx2VSnPn2J6P6bqru3UoaD3bY9eZeSaRsf2+SVtMRuOBDbYu2HgIfr0tVNjOB4ZLUWVMlSDVwLv/PooQ3XygYnqIJjnRszv6zccP1gh793KrvykQzZM0SjH7Ab+NWhdS6FmSkhaj9LsYzXJGlBrTal2RakBjIBYLDTQIqZYzKjyQpZBIlhIRA7VTcdeFsOlO1a23xEXR6gtwiImRwOxG9ca4sGmIJZrvbmN6V/2xA7YmKrd8hrG3FC5Css0lWvth1ITkzXKsuuwhadO5gMRALoUhdSxecNGqahtgLQHEay5Rk23UUv26ZC00bD9bRK2tMtt5I5UqCY0d1CfC3JFSzxwLNbvzxBoNeoOHEFk022RpGm5MWxtiWO3omg1OY1YVsxjDcCaDhDWC1tCKJz0SLKbPlhiMUVAlqRUsnrPtbQUxyaUzz7EwaaXLytwF9j4mwRqtDFUMYJCtmJGmLfdVm1a0CSLmDtI0TufMb61nXsl++uilr2uRqHo40eyO3MIuFuxZRiUFWy48vjOLrGgTW70wssaO4+HA0EXrWuerWrMo/KpH1Z593pjOuVfmOjDIzJAtHVw60HDkJgufjC/5aPkCrcr+lKjyyD4vfDq85OCFYh9Ptzz0O17mI0S4jb2BYAd012XiYVf4cE4cKky7wovlyHfkuc9ZpRQhhMpTH81LqyROMiLdExoKdbrkw/LAQzh7P3PkpEqd96Q085PLl9wnY667xUDRHE9czMJtGEEODGHiwI4wPPJ2vubnyxs+jRckFh7iQJx7LnTiyZ/3cQTVjr6f1jk7x56qmSsKhzFTTgM4qP/48YlvdReUUEx3Ol2ALOhywRIXburCHrgNOxZJSBFe5Ue+2T/nx+tbTv7MXg8XfO30pT3nsOeVfsa+Rig7DlRyaM2GfbwFeKm39DkxdZVe7F0iwqHuUBGKjETXy+1KQXXhYdiZRroLliXwOfsj0wOTMzQLA51WTjFxkTOzwPNloZMFaschZJDAtQqjzmfzxqph9/nkO0kg68Jd2vN+PnAvkYtQCTNA5fZKeXmyOXtRT95g5mzOYt+hqlyqFa2lkm3OyjZnYzE9tnaZkF1CUrzx7ZLoWbhmsfbdBaIsZB0g9zbPAlxPFbm49zlr6/bF3YwoPNx0XNwtPN10JIXxofJ2jFzL4nM2/tDmbBULspt9csK0wSKFQRpYbNmnQCeWQSxqoMVi/UoMsXmQkGEFRiuA0LOwSCz2KA4aIyY5KNWBtGwV/a1Nr+kzxavzTRKmVmxjX+nAsq23uAxtXd+lgU3YvHRl1Ugbg129MK/1BjjvXNdGh/vhSiv0NZKkFdk1t5uV/HaCImBZVTvcc1ndTs2vwbqmKkgrPNxsNo2Jz6hEgiRSPfJUAxqFhMllQqP0AWPoiwFCz6oSWstjsRqHYMDfHo/LPtYno2xlj3ZNjfVtxXcVA5JmYFDdj9e+Q0Nz0Ghb67ku2F2XROj9bSy10IVEFV1BfcKalBg6qd5/oTGx22DSdr2taLNd57rHugMKm0wz4tkEv70kSvW9ubUWa3KKrJZRSFJI0hrntJlh79/Gn5unhcRpKvSxnXvrcvtbHT8QCBdHOuKdsmIIHp0qWrY0snnNVnoN1hEIWGpFktU/isoKNkO0RhuLswDR2bdaKzU2bVRYKfBFoS5ecCWypvVTsOYMghWrDQRqEhZvTXisZdWMtpfi720dDOePpbG/YeioS/aJsDXS2MXegKcLjRvjqXVLE/UxUhbTgjb2Y5V8tFTKWbqvYCmnqG0iCkkitXU7q82T2W3cQiBIYKBaYwyXgkSt5mahgaEeKHLJsVsYnzq4LHS9C9ydoW9dXzK22MAWyQaBSGod6AAAIABJREFUwcFIcKu3Vf6yDnJnvOM5y7M9y1Y9bCxGYxk24Clieuxct8W5PcP5LCpP6iAyKSOBosv3DOa1uBHb1N+x6JMG3Dc5x7nNXguoUkprIV0bIyG8C9Rbc5JVyed+kymanl2C9T7vCGSvlFU1rZZWW9AUqDnT971pEYX1+35Yh0imBjjJDg2Zi7zwsIvsFY5EdrnYplpHIpWP8hum0Yshq/WSD6fIdT5xI6bBnVLPLIFvu2PA83myRiMCB+8sF8+KqN6kEZn37GQCgd4mCc9l4bu5AzLf6p7xqj5xnHoepOeSie+GS67kdHYvwsjESewcd91ulQcBjDKhIpx2AZmA6QLCCa1KOV3we7jny27nWZVCHJ6Y5wtKHbnw88gSmPqZYYkcXR6yY+Eo3Qoe4jgxmqCXL/s9uZvRZSDGA6MzF7Uoc+yBhV2duYt7Rj1BgD4JH+VbTihNnvPe/MipH+hQfmL6B3wnfZ3j1cRPvJ5g2NgPis9FKjlWvhj23FTr0NeexF4OVqArwsDCTCsYdMs/jQw5UzRyETIn/3mbs00eEPxcPc3WrGmX7TNPBG7D9fr8WyHfvYzWkh0YemWUA6VTnuVqYKNv+aWt0+RvOWeXus3ZxPfM2VkKnQYGEnMWUtnmbFIhZEW7kQxcLws1Cf2lMeXL4w0iMO7uCHfX3DwuxABdJ+QM6vP98n7h6ab3hD7cXxrIPLk04PI3FbF9lUNcS1s9jW2NNay1eNaVwKN5z1epm62WqgNoazSxMoy+1rb6mspmYdZcBAK6ZgyqBndP8PUZ8cwnq3tDcD1wclJJpK2B23zcls+NmX7nXh1w9sG6kzVMqH6fIQmorGulYvUsRTd2MEZhKXUtwoNN8rFlT8+vS9bvavIDxCQEJiFxvayeySrEVNVRVxgCDjQLgahH5jjSVeV1Ft6PC0O0F1UaqlidFjZwXM+uaOVc/BrQs2chrBnTwCZgWp+obC4dba+y790+0+ZY6yj4zntY+V+cbDTfaBV9h5V+5735tyvb+do523s3tp4VM8Fm2ZaitU9e44R2b7Jhj87bTbvnFlmF6D9XUTrca1lYJRvq77c1UAGhlEqfGpDeZDc/6PjBDTWSRY0BcR/FSlIFDUg0eUSHFXppcEZOjDRr1f8SItFbBy4qFkFVu9VSCpKSF9OFFSwX/1zQQC+W0OkcQJ97ygbsBU72OtFy3n3NOsl0aulsycoidX2JLX1fa4XOAXgFsrUBlnUxkXUhDsrqy5vUnQXOnnFxGUTRyk4iJwqlFGONoxAxf+RVSoAyhGgliOLFbWoMdQ2BFMQLM9xOLVhL6CrQaYXGnoXAcqqELpNL4L/91/8Av2u38Av/2d9hHBJ1hlAmdJ74+Y8/5G9+8l3+6O9+zv/56ROPyZi1c+/DFcSFcw2jnumSxPU3JsMoYh6eRZU+JaRsE/1dzbK/l1ptgoRgxXpnzIG4vrul8VAInooU6dbdO7nUIKmgARtjPuBXjW+7F2kFhSajae8/dMk727meq1n9eEBmbhJWSJdojha+uSTzPN0N0VmNRNFCrUotvqCFRhsJM8pIJHSZWC1QrKo+b97VwH+VY1eV+yHQLYJoB2Nmd4ITO8bhyJE9lY6hziwhUEhMxYAvBBO8RQi1kqXjgRFNgct8pEjgSOCuvwKtdCnzwkHT3Tzy3TRwUY7sTz3HIHzAxBs6ZpcgvFFrAf1cFn6tSxyXHqWsgW8IgaeUeDUdeUw7vrY88J2449o7rkU1s/QJoQwLTwRezBlmzwiFgNTIJTMqlZMMjHUCEocwspuf2LtueS3sCZVx6qmh8uP1ll+Nzzhqx4VOPIQd++WAKDyy41KOCPByKZzCCTl1DGRmCbzKj9zHHZ1U7rodqspeC8c4cqsF9hOvno68SeYB/B63vC079nLgFCO/9Ef+AR/EO/6jv/z7SQNcHmau8y2dKr84VP7qLPyivOE7U+RXu68BcJDRPYQFiU+2TtQOwkyqgRwrQ7FddaJnFNPs7sg86kDu4LEMPNeZTgvDWgjcCvVMA9hG5qmaFvlFfeC1XBqjDAw1c9dfcTPdM8+2JnVd5pRacakXI4bA3R6uHuFwFbh6qETfqNc520dn5tuaYJuzehZrp5bRqEXp01kmqhoTGgahm82KMkkgZqh39sxFYH95y8tjJly+RsuIxhN14p05e7gaQfmeOdtl635VgjKFH86c7WJdgYms6eRKIZCkUsQqQkrd2DQaq+ogIkhwpxwDtVGam6trM8Om6WzrclVZGxCIANWtqbZacH/u9jfN39i6mtnvbd2L6xxe6sbStsIodUaub8251PfuVvTkn6/+twZybN1X0RWctqO2+1FArPlHqd5cy1PqCnjHKDhjIVfWer1vWfXUxlayuk5EAkgmNMmgwCELQygsNfLv/eFfR/p7/pP/5Q+yj3CsAepCzpmP3yt88kXgx95/4Ju3l4g3p2ngDbbrCI1YWpnbTZbQcHt7JrYvCyk6gfYut2fX6fdb1YrymlymvT+7DqUQCFrsv/y8tldGWte3QBM6OOuuuoL5Mz7Cx+BG6qSWDQA6tz8LLrcpa/CyyWy0yVdoGQPPmAugmV1qAY59riosZk9FlGr/xmp1qih9NCzS9nMrMtyCqe93/Lb2aSbVbBHHWSwhBoayVkKfvAnF2d85AF1qJaW4ecxVs72yr/CCA323SAmaM4SNjpTSeu5zFjqrCbV7FaZgoFVESDGylLIymbkW+y6JK6PUIiVrMeyTNEIpVsJyTu838Nb8fGMIJMJqm9auO4ZWQCYctTCEjkmrDZRirK2WrcpxZRz1TJ8KbkW2XV8I1t3NtGFWOBBUUWkFX4nj1cBf+dMf8aMfDAyh46QDL+Qz7tOPsaPw3/1rP8fN8Us+nypBd7yRxL90/BH+3f/h1z062wznt3f4LpDV2IT1m3dzW/hSMnv5NUI8m53nTHDznE6d65eKdajT1VTYPSbPCtt6FaZo1k/t6HyBMl0Q9ixWBtcX/bboUImedWiOF8Wj6RS3IrnfbNlT2Dap7/UMraTOHTN8bCy4T3YM1KpojaaNBcZikpakyaN/S8kmD28Dwg/jkJ1wKTCVc3ZkG8tjjHwukWN/wdVyXOfsGCNTrdwNI304knVPrZWnGHi2zFxF5a0GRDoGXZiILCQeG6sv0NeZx7RDamUMBVFh0WSfl44lDjzFyjwnPpgmvjk+48XpCMNs71tnyD1X8cBcZ76Trrjrdjyfn/wUxk4OKLvZAPjroafOiUssW9A6MQqRsZwsQA6FHTPXpfLZ4CAvGzjfMdNyub+cnvNSj3y3v2I3L1zo0QoF/bulRiuCVZMlqBfA9lqZUivlglELGishK2OduA6FL+uOGB94jzc2enTg7z/7Ef7ix38D/sX/C774iKKBP5J/mb909ZO8mhN/4hc+5r3hrzMfH/hz41tu60/y8ovfx//9K4+YW44ViVLN07v4nGmSFYCD7EDgKp/IJgz08Sf0RYhiTqk9m00ewKz+nFQR7VBZGOOJXS7EUtj3DxzV2OE5RoZciGet1m6OgTc7JY7btbyaXL8rA5dPdg1pyJzy9uySG89mwQJ9Vfb9zHHpKJ2PYwf36rKk82POZ2zY95mz1092n+WkqB65ezZydZggBh6ve7QKV3eelbga1zm7fztx/8wWtuR+ppe3M1/1CA3k+H8LG1PfdLGq0Ecv/lr/rkkA7PcpNHeCVtC+Aay2Z7b0cft2Y/BagbSjDNk+39oOGwCqLpRsa6XJJ2yPtZR4238bXGpkZCtiA2uuVZqFWDulYDVILZPI1hyhORFt+5IDQ4uWkOhsNbqC/qIbeG7f01jnhrbjO+DLXSy0roynZbJxGAgigatu5N/6hb8JV5MVIWvipn4B8WP6MPPn//C3YXkDSw8/PgM7bsuB//pv/aifdnPIaO9+s7BjfT5g4FDPfif+u1Y65m93/bvNTm4LaAb37c1qvOnSNOA+xs73O8WkmpV3JTTb/znGkRaE2O9WltvHULufBkJF2Dyc27/PjnXky/fCVMEyHgaRmqeHE3CeKc+aSC67NUf4QEG8IVnrP3DmmfxbHL+NRtiiuiqNwdwAGljhQK9mfRZCx1IXYozEWk2PU63bWFGlCx29mMaoesFSKRUJgaTepMIZBLNrK+SSSaF19PKiqmDQJKsVi5Va0GiFUdV6FxBqpUboNTBVY6VrM3MuLa3P2qGOasyf1sKYehat2wAUS9ePktYFpta66oI7sU5ytvjqWpU6eEFdF0xCUH2DrkAfDDAVtUg2eBGdMWOVha3Nc7BfkKjgjHYtAYlKrTNIokjgD4Uv+D2vfpzdxYh0kThn/sZ/8Mf5O7/xwK98/haOb7l+/5rdspDiS35vXXh998if+Rj+q28GLpJRIkKgluiFkOLRnppP4NlIXRfXYMyCtXT0n8c20eyzUa2lshCR4pSDsw2LGqsQQkt/+jlDWtmkliLZrMi8WUpRsmwa3+Rjs4r5JDej9yDRI2zhHMDD5ujRnEwi9ryLWLtnJx7M/k+sejWykFzCsagQ1dLOiLEdUpp2L/hzW9YCD8t+QJQOxVqVR1Wm75eT+oc4hmkxh5FBuHqyAq96YSnsL+pzJAk/dnjDr++eM8aOzwj0CDfTgftxB/nEECJfBOUDKh/MR77oEl/S8TLPfOlNU0atnEgcxpGahSlZs4qd3KF1D7Xwabzgw/rEZ90OQXkSeDkX0MJTt+d5njjsbJx/MB94PbzkRT3xa2HPWISHfjR+rJm018LrLpKCMCzC1zlympWvhUe+w+6dOfumG9lNmZlESJWa4T5a9uPFknnTwVhsHDx5sPJyOSJaeV5OhEGouSPESs0dlyHzoZz4lL1lyTTaZiWBrmamGFmk50V44pZESMqNVi7nB+YY+ECg047HsdDNlVPY80t3fwn+zCPl//158k6JB/gjv/RNfupv/z88+5ZwvFT4Jz5h/LVncPuzXP/M3yD8b6/553914C+GP8xP628wDx3HZaCrgSKFosbqSyhQE9d1Wh9KV7cA9kZnK5bUI0sIHOqOnR6s7TnwrN5x1/eknNiXJ+57c1A4hsQhdqDKhTzQuYrimDpiHVlCoQszdzvLEPanZZ13DxK4orCEhWmXUIWrQ//OnO16a7O9FHtXuzRzyr0tGblFtmdzthPiXCkxUgQug3tAL2mdszFVNMMLeY2IcBeuuMq3vNX30Vu4u4Fyd0msj9TbK95wQ39zy9XdzN2N+anfP++5upt4epbY3c1EVd4+25jzr3SIrzu22px5wga3o/IipGBo12QBhSDyTptcCWYn1RBokNbWtxVrOoBQJ7WkuCVZA0gWYITGZjhbp9UzZlibXzBSprlIVA3myuK3k9X2s4atW/pf3DkhpneBqSELIwSqbprfrf1y0+zaU2kRmwR1twi8wM6D/tqYRWcutbHXdo+VSuvGRyOl/FSR4sWLXoDveAQCN/ETuJphiHbCsvDn/9S3ya+/5PO7CPMCVxFKhtCDTjw7nPjJl5/wd778vQyyWKaDwIJhnFY4FlCKbB76Z69gew7qwFEsG2FMbduUK80FonXba1ik2ekltuyOSD1jvytN9NfcfZrOOteNYT6XGAQflWvL69XdRDeg3KbrOdHpQUtrt11l+2hwq75KIJIJjquKWmuRsvaua1kiCwkynVmntS9yJwpCJKgV85k39G8Nhn8gEFZ3f2j2YgB90/q6bljXgVRJwdg1YrCitHrmV+vViEGDsbk503XdWWtdXR0WFtfIJgmUZkESAuSyPtAY46p3ItjLa62KTczvtiUOutcq/+SsZxVi9TRBMt2RpsC02HWtBSFRuKqRiUrqO7PaOtO9Nuu3VT7hu/E6Kdu1ueaoXaeEiFRjkdshCDElSsnUKEjFBN9ig67697WK2D/7Ex9zyoX//duf8h//Mz9F3HWE3rSbcT+yHzr+8Z9+xu9/fMbp6Z4xduicGZNwMVzw4sXX+AvXz/lr/+lf4wvZW1QtgRg61y01xtoChua3uo4PheoavRQTy7KsjhKp5pWhWiX80rrWbJZqze1CwLsFWvC0AtbGeKQENZ/pdpWuS1a84UFG0xw3O5tW1R1jZFkWupRW4Ns2i9Umx10/AiaBOWdPjGnszBWkVmt/6oFRFCsEI5ieUkQY3DpPRChF6YJJMFSh5HavhRBtLE8lI90PR3cYNfGN4RmicC2mjRyc4HolynW+tzkrwkLm/Rq4Cx2vxx0lwK5agNsptnAIvMwzQYS3oUN3AgdFwshQjpxyxy4kJjLvlQWpiTdpZI6Z0Vn+viTmmElBCHFH5ciAdZ2cJdu7rsIoR0ZJnCRTeuG525BJ73N2hhdzZg49x0H5zrzjNCpfniJjjMwxQwmceuUnn+75dEjs6DgyEVoDjVaoaijBgFJLBYaAqjBKRKrwtsvs3NroKVY+izuGeWFyQC0+Nq4Fvkvi1Cu3U8/X6h2n+ZJJCsdhZCRSK3wxjPzJMEM48kn+Bj/1UwF2HcvDQJ4vKfGS/Hbk4ncl9Gf/PpdvnuDLD0Bn9Ee+Db/885y+9pyLf2Hkz/6Xf5vfuHpJf1AuZOYxXvGMR07xmkcJdFoYu8C+bk4SAHeyp/cNoZPKXIXrfGKOHc/zibtwsY55MJZ5lZs5S1wlk2qk18zcWVDa5Uj2NH+3wJygMiCeXm58TidKDIndyeb3HI3lPw6R3ZShDCiwGyZOp57qErehW+h8zk7mYspSIlqUvi9QZzRGltm2tK7LPNXRmKq5sO9PPMpz5iUxhImHq5HwlCk1Ue4ueT/ewrFHxife7B95+RCoe+H6aeLh0gKyx+uOy7uJp5ueYclo/K031t/xfG2AoA2o7f8cZG4p8CTVdMzgFfe+lvj+HKXZlloziFzVnIma24EYY5eC/SwG19Kescu5bqAlePE3sPrQbil9kyWIANVJh+Zz7/KEouIpaqULgazQBWEulS41YGTXk7Galz62drorZPNubE5icMaS+j2tHrlszgorAGeTcrSHm4JQirHhprM2Za8R1cZBJ7F//YGPb8lF+M6XHf/cT7+BLrgZs/i/lfSx8vX3JphmCJ0JY2Ox3P7Vjn96d+Lv/eUvkXDdSHdbC1cpn5Ek0Yvezw9VWLTJPSEXd+YQCJo3irURq+JBQD3TTp+5XZgtqmcaaFZzrO9BV39iI/Y678gWHLusmnHwcWvC3xgMQ6VgQPU8CyArq7+NVXVCqCUoBAjBbM/W7IUkk+JQWWqAEK3ZDNaBUUIjHwvJi/dVldmrIms1dxVVewbdD5ivPxAIRwm+5FgXt+T8tqWWF2KKaAn0LlNI/j4yXr3pFxtUyW2oSqXWjMbNIxipxCQu7ldSF01XGzJai6dvCiV5hauHkirVWnTqVoBmco7ACK51cZCdArE2CaRQpVqaJkBXoUShl4AM5nPZopUUIrOYiLvWYu123SNXBYjGVsZo0XFsCwl2nlLdZibI2glOxHyMWbGgAcMlT0TpXApig2pxQBhTR1/hvhZiEv7Yc/iXf/qSJPAf/rPPeXvwNJDIlgaIFp3ejAO7cIOIcLF/RilWC/p49znPSHy3N5uzgBJVMYTQrqvJ9cv6LKmmwVsF4T7b0pCQXEF1BcGqyhwDqQYIbgVThOINJkQCQbMbvHtRZTDpSsCtxlLAqmrP9N9aWbCWk1ECkxZ7B9UaqWQ3Lm+d4GKMzNWaljT2pE1Am9hKjNZdUP1+Nps4XSUsRnNXtwy25xJ7IcZKVCu8a6uSbS5eOJAtim/tlStixEEQYhrof5vUze/0WELg49M9D3Hks2FHQrksM32pdPWWUxd5kB3v5aZzjFwy8aa74P355O3OYS/T2rpyCbY4Pwx7Y/JT4YPlHgQ+38PMwl4hHkdKOjHHmQ+WCdUD39jt+V2nB6hwV685jgeYA5AZ3Nc1hMBtf83LDM/KHXCNFmUaF8Zpv83ZWAl1Yhrg+VS4GxLPc4d0PXcpczUn7rvCPiy8GXacUmaYC1MnXJVELBNDTRAHrr0f60Nf2RWlL4kqkRflkbeAhHHtqjRIYIlmu0Xs2AFaT4S4YwoTVBhixwfTPVITn6ZrbjTTk5Da8Xd3N+zigT93+ps8/4WeJPCP/eLfov7Vn2b51jU6CCkcqPmSWj8khE+pb78G733K/K0PYCeEL2zHGn/fX+HDnPgfn/083qCeoSqDPtFrBO7ptTNP5Txzko6hKjlk3oY9VOHowL8vC6dxJJyUscwrCI5a+exyz+4gaCzcxZ6UhZxmhAg1MXDgqbPt4yJXDkkZ82JzFmsAEsIj3gaPGAu1Rm5JXKSJvgRua08MkaqFqylwCsnbpUMpHYTAKXf0qVI1sgSXgjlYGCUDgUMZ6WImkem7TAlWv3EZTsxLIvUeIKuQvDHK1WHm/sWBV/cXRBJlV4Aj4bDn/aeA+KaZEJ4/TtRaebwaeLgcrQbmAC9OXz2L0/YKdQgmrsmuihWgB7GsklOcIt4mQwNVDDitjBdNLuYOS2ZDAJgLawwNRKoVQjtEqY01U6VrtJqzka7YfkcmZ+xqcCbb6Fazw2qWZy72cEKh2YhFR11Dt7kntGAyeRP65l8fgrjLT9PRqrPHWwE7Dl9bOj544NC6xVXOOq41YFysfXeKTo4FNUZbTC5RUEqNdEH5+PJTfvHjN3bunzyY5OEcibeHUSt06mSIQh83hPd0ghAY2Jveu70bzFGrAXb7Rms9oqqrrKToFqhXVYZkNnE2eDaAmSSZpZ/vPVlMrhAEa0Kh5tHQ3kFkk6EohunFwSn4NudsujmJ6DsSnI2pbu2iW6aCjQzk7DlhbPHm5S8rm99AeZM/rEH4GQM9Roghmxbc3W7WUe/BRfaf9aFQFbIIU7XnF2NAw289X38gEG5sZsT1KVUMtEYDh2MRDp2SNdNrNNuzGAmlRXAwpI65ZJssqjT7q6qVnQZyFxExhrgNsKDm0VslEvtAWipzsCKAUNVta5Sglb6La8QbJJp9kA+iRTdj8AiEZIJ+e4sRovvpJaf63RakFfo1r71YfWQEoXdT6bCZAbJUJVHMugyfG6Z9cP+6iogJuWMMdGqdyMVBVicWPvR9b7oWjWsGKC1HNF8yX/f0Twf+zRdH/sKf+v2ICMfZmnxc3Vzy/FkkL4tpoJPJPbTaOQj2OXEvlca+X13u+OT1gf/p3/kF/vh//ncJLn0xsAvqBofSimekkjXSx2qNJZIFRWjiFCu7bCxJCG7dI0rOhT50zsy7FCG6NEAVDQWpQopyVqVr4yfhBQLR/JK1GHtUqGvr6BKsY1+KyQIPtcLNJGJjKoa1I1LEOvmFumntVp2deaTRVWEO7oUs5mwhitnCxfY8hdH/ftCOOeiqO86lEtSyCm29MglJ8LFbGVOPlEr2IdSpbhZ+X/XwOXvNTCrKo1wjsrCkyNuu58PjidsdHPTEbhmY0sSuBp7lp1XfPhTfVYKNhdEZiY+mJ54vJx7SiOiOIqd1zirKd59VqB2p75HbzHevAjtd+OKi8zl75OtPMwJ8d98zS2FU5YPjzGc7S5G/5po8GoUdCHAh5HwgznsIkXlUdixMFzCyUEsi9wd2IiyjsgPSvCNWuM6JZTyyE3g+HfjisgdMKnDKHR/O91b9vewJAabxic808f7TDHWm5GseoiLdiVHgpi58fmmODB88FsgPiFQu5YnP9h3hCZCFj8qXHMOHfOfFNe+9fc2/f/vX+ejf/iY83FAen5PqwPIrv4h+XQlHn4/6IaBI/Mw2uBCon34E+7rO2e6jTyl/7+fgcOCP/ekX/M///ZGuwpSc9ihwmy65XibuwjMARh74Rv+cr+VbPsgLpyj0pfLYX/Grux0/c/sppa/0tWOKC10JPApcTZXbuOMZT0xdgg6uZ9vET2MkHZSLvjCeZmqAvk4QoM+R2M+UPCLaM0urAI+MFWI/cygDTyiDWGteSTPLEulVmIF+OHFaeggti7cgJVDVA7MSGdIMETTDFSfuy0AfMk91YCcTVeGpDlwOB7JEllSJuy8BeP+24yk+p+qXfHH5RAiBl4dAKPt1c1ZVtI52L1IZcmB8mljcvq/v6g9lzraUfSN4stoqHMWyrgvF7Cl169AWxP/t4CTG1mxjc+sBu4dKpXdWvZTNL1Yxr3iVyC5W5loJXphXV4gMaDWWVlta3mBtS7JXwspE4yCluuxNRFxKsRUWNrem1dbLMU3xBlIRQAqq4o4YtlIXL6AWSRY0iJIE95P3Q8S654kAbhUoTYVajPWMkSjFQZZ9h5aJUxi46QaOy5Efvf77/NGfsXbgFJdB7Dv/bydEGtXYnrcTJJ5KXEEkg8Bj5N/4o7/Bf/G//oRn4zZ21CQnJosAGweFZFZt/ue1KlkinQpZF9NiO1ANqMkXLKIhejDQmc0UVmRqfRKiGHBcpSWCA1wLYuz2nMVVTNPs+yMKEt0NpG6WZbWwdrZzftIDufWtrL+T5puvuo6H9tkGoDspiATXQ7cMr2l/G1uciyAUuihkT/+28ebUp3XIq5WdW9AqWzOR73f8zjrLeSq1RFnTzR1CiTCqAZ9JvCmDstqhbSmWBoxkHbVDNLcIC3YMJEnYOtS1ixM1eUJ0/Uw0J/H1BdV6li6SQBcS2QuFUgxWxAcr+BPX3jZWmfP/C5s+5pxhtmIsZ3rL1lZ6LZKLZq8jwZhxFUhVKcHkDWA2MJLa82xRXzMM355zO9o58nhNT+X31C/4b/7cz8JYuNpdoqpcZgtKoopbX9n1SbAJ2YAvDsTahtpkCU/HIze7yjIdPcVkTGt7Z7WJGlYLFrve6q9RziLzjgB9MkcImntHdZmJOFO6jQmbZKaLDtDcZrbKaD8fytrMo2sSFFoss7XAVlUW9xtcNXJq/8vCqilskXqTCzUtdqmV3Fl0GTWaQ4gP1oD5LMYYvd2yrIz5UxBCsQnbN613MsmPuTx6BsX5mlVW4+MKQKOuY/arHm0MJVWWrkLNJP/HVlhDAAAgAElEQVTufbY5/GqayL1yCHEFubuyo8rJxkp/JCzPKHp6Z852CA/xxsd0Raj0YcdUDnx4yNs1HCY0ZIb4DFXl1d3MF88Gm1cqVDmZIk2VGC+JnNniXQgf3Jvw9LOLjrna2CwjFD2sG3n7vzKciPGCWg7rd+jeHECKHojxglyeHARvc3YXF0YNdOmSZXiiCHzwmPn8IvH5hQV+nQg/+mSOFVJ6L2Bpkp3W6GIH8rRdkgifPP8xPni45U8e/gp/6E+dKD/xKXL/IzAq8fkR4kz8/AqdfNzHI0E/o9ZXaPkASZ+DKrm3Z5SWCBGWb39IfPUJvPqCj37lW4j8AjUFUikswOwaessgeaHIYtf70A3cq3KZJyaXhbyYM28vLuiAI9AvxjheAVoS1yHTa0d3UpaYOXY3TLVyvTwQIoS5EEt6x6aSoOSlZ4n2DnfamfNNymhNUEY63/hUFe0njo1pw9LtwffqTmEROC09u5CZfO/Z9ycEOM4jS8pECezjRMgmoagEel8DalKuJjhc3oHPxe8OlzB8AQrvP11CESvWEst61bCDMPF698B7hx7qAMxez2Ia5PlCkcNXn7Nbmt9GdlqZTns+yf2AQxRiDQ52G/t27jBwZmvWXkUwuzTz6PX2tyG84wPriks7rwM002+esahnFqHIlnoHS1GvS5faXrHunef32a7J/32G1+1zfk4DRuc6aTuaFAQRQm3sbzWAtFYXKikaArWk+TkIe3fdsOfj19ntuJTCnm/zr/6T3zAT+cGwjPt52VGdetxy/NvNrA/fr6VVws0Vutllnaz7n31kK5Jsf9autxVBNiZesDkxhGAEi59OMPlLVcc71LW2hwZQW81MA5/vaEXU9yhdn3PVbXzE4Iwv2/XYo9CVLVYPm9b6nfbOG67x81RVRjGCyfrzshaGrlr0gI/IDbcFLGtk78yUwSbvsafVrr+9jrXQ8uw+Ownk72Ml144fzAi7htEGnlXn21IhpvtS8x5UzHCnBi8c00IXzXFiqdk6cYnZn7XKUhEDBlkVs2MDkUIKW1Ri3WKkvSqPmKuDp2JMZQh0rl2tNVMRlk4Z1aJMdeBRQtMEN6F58epS8V7jmdo0rWeRvoo3vGijrzOtqNl2tRSFojUQxaL3+1hINRBVkWQDYlGl8Z1Jo32ntmIuT79jvbubGEFEGKdH/uKf+DGuhw85DfCit6KgruvoRwO7eZqZpslZyEpqi1EK1FyQzl09sFQvtaX6A1IyOsMQKiVE6yIYxAuCbAIpgRqF4hRm0c3b1KLg7O2RfRENgYxFZOcTolVHV9n+bd5ltqiVYMx9i/y8asP0qisAMaa4ivtcS0v9AO7IEZGVKQaxAqBq7xJhXTxbkbtgVoFJ3TbPA622SimVoRMn3So90axpxIRDqXM9sKfyarUKZpVCdR1q1yQ1XmndHAcsSNwm/lc95miAs8uTzVOUxzi6njBw4Dn9YM4FY3xiKS9Y4gkJR3Ow0IFTVcb+DXO5NKu/OkCcLbUVJ9v41NpOv3wwHW9xScVh3HFKPaojLx9Nn5qjItLz3uMtD7tLpv45H97b31V5i9aBGgsf3QdKuOP1tdtdBdZ3NM4Ll0coYUJEuL244cXTW6xEc+bN5c36DNqcDXX093NJyU90sqf6nDX3nYVhOfLxdM+vXlzy9vqGVw/3iHY87APHZO17Ae6vRrIMpLbnAhomNJx4qs/58BEk2Bj9g29+mT/907fU5TnlEeLbyPzdV1b/0Bc074jlSPfqm9B11G99QJdO5O4fUOuOLs/McbcG8Xk42aTIbc4qjPe8yAfe7ke6ajaRj3HnFoVQS4KQ+bx7AcATexBrVT6HAix8WMw+sV9OzPGKKUXGxTyKp/DunO1KYignn5edf2axDBB2eUvM9NUcfnYa1kIuc6joIGxzNgQrvi1zTwdcxMKTJuZ+Zs49zySDwJ0YEzcgLMCFmA5cANkdSLPP2RqNrPD3tYhwORxRFR6HzM3d+yuL+ThkuieTipWQeSBwXStvrx9RZ7lEhPeeLqEGOk7vzNmyU7pT90OZs8nXTat1cPjWtKOhOUK0MVtdD2srrwbPZKoXoknwv3VHHr8+48+EThShYEVpm+60qNuHqq44L3iRXiEiAbyVlHWwA4ZgLFuzawPOdKHuN4sxu4LZsCbv7mn3cg7Ggo0zR9cpxNWqK673oL7jF2pQOk1kDwY6L6Zr2BQ/nzt3rddmsEKdcWyAGUp+4l/5uU/o02yZ4lYDmcTRplib+qW6NqBCbIC3/Xd8Fwyv+4dHDQVrBiH2JH03dshntpBJAt6Jfd1jWxPkxLsF/KaUCWtEspIAbJ+pLr0Q2dweLJiwwjcLdDZisVnKtWcYV9ZWTK6ohv/amYwp9j2d7e89CU8rbPMropeyFryF9f7bCKjEaNcV1Io71b6EiKlN1ncsJsNu2udS3QDB50dhY5ibXrnopnf/fscP1ghjQKuuliTiINQGbdMtAc6W2fAP0TqHzc6iGfL3to0+SeNqvWKgSYKzikCrTpRGlQchn8WXqkqNwg6rwi8OdqMXboXgAE6t3R6AhrR+wyyNebAHWD3S7FxkndmeWLPmaOcIFHIwwXn0xWQRJSVrqZsVBolIyK5tdvmDa2lETPu8JzJpSz+1zm+yWeeIPZvjcM3nDxNff++KTjveHk68vNzZwHeWtzWDKKUwDMM77zAMPaUW5yZNLiBqrP0I3D4pt2ROEuk1rgVcqK6FaEWzLR4O5lpE24XAlJ1p95+3qLALaosDrBKBFj2nEFmyTZ1xLb5ss3zzfWyDs2pdCxjbfbaJH6tPKDGG2AIbS++1sdoYg2ZSTvWNmI1ttnFoBXNSm8dku3DTKENjS5pmGi8Cte9trHOQYNebC3E4654Ha9GHVVxbp0HC1njkqx59MGb0IBcrOxPqgIQZ1Yqkaf2s5EuG/haWZ5RUGHJl8mcx64UzCT01/n+0vdmvb8mV5/VZEbGH33DGO+TNe9OZtqtsV5XbperCJXVVAWqV+qEb6KfmpRoJ8dISEhK8g3hHiD8BITVCIFDzghDDQwFCUNW0uuZ2le3ynM688z3jb9hDxOJhRez9O5nptIs023Kee37nN+2IWBFrfdd3fZfy5njJgxvTqr1tl+xrQ8nubez9LpZrANqYeHh7SRTPm/X6znd7ebTic1dbtqPj9jjb+rjM41IRw46L5ZqHt5cAPDs+mSxx03h2teCkBU0kEi+PViyzgtVhNqUdjFrRVWYLdb9jXzfU/X5qQrNrKrbLFU4SL+tzGvHcu94Sg1j3O1UW/UiIDTdLx8nuguUgfHh6Nn3Obbu4Y7PLPiC+5S+Pfpm/Wf2Qd85f47WB94+o33tG/+ZtdFzgqj3Vg6cmhRPjXDiar453SemWqn6F9i2yt6DZBc/44jH1j55wkRxPmzOavWeQhK96atnSs6QClCs6aTlJA0e64dbZXKx9xwUVFdC5NUUarkm3OAdDyAdwXvp7V5BBz841dClxnmzQFzHLnonSOwFqinKbj4kG6AVi8PhxtlnJNiuSs2ljIg4BXwl1qvBO2ObCvCmoF3ITpZpa7PNXY+CS2jpuVj1VdLgcOVWDpx4828rUFfYSp72mGueOo7caOHIjtwTqzTlHOlCkJO1JEFsl7B3sV0izwe/MCY2Lz+4Il0xYyKoMhl6WY/WgQxp578iPl8YCc1GUOWDl+SIzbaA4z0GKi8oBFK04jRklvHvGBudwYupBk5xl8f8ojoopWNj3qybE0GM3UTKpUhw9ysfON1aKsFP+fCFO91EaVNjr87mY602q3LktFbRSCj4oNCSSU5KGg8/J6fzyO9mhDGuu9p4H9/NkdAJtno3i2BYPzKoN705i8PlN82uSOdmTbEYfssNYWSdBa4V7B/ENetCV1Mk8jqKM2ekr7lZZH17iNI/xzryYazDETGU4WEOan1Nsqhi6KgZeljP2IKpIWooUJWcxM9KciyXLui3vU4Zp5v7mOc8gVVIm1DlMEmfzGZv960ngIMx6gnael9cGK+5sq/IZJXjP38nZ7R0WCP6k69Md4VwE58uNez8VKsxQfnacRLNkln2NkUzWJ8uhiCEmoRiAmDMbVKikiGVEfJolQaLLsjIyxw5GO7DK2CGv0dLmWDJntahSOo+JLKdkxUieHEGaYZZqTBsIZ7xesmOlB5GoA5cMZdKkuGROqNNkTR2AxGhavy7zPSXk1sgJn2B01lgjITSZqxrEir2iZtRZlUKAFxxeE+vg+Md/+iFfevKeNecQ5Xrf8aBu0NEadozJKjeXdQPFUY0pLyaQYbSAJkW6oWfVLiEv+ON1zdlqwXvdH/Hq9PPELjcvcNasxAzGHMyCttxxeIMgYlJIglENipxZeV7x8YrRO+eoQg5oSsSfdz5DEMpc503Q+QlJCs5PKh3ee9I4d6saXW5qkkw6rxRjjhlZjN7GPpFs3GBunpHXlgrW9EVz2E0J4rLDnqX1PN5I+hnxjzHmDahkFcCFgyLOYqQuFwxEKxC9s3H+HC4Z1/ReqSJIvWMvDzBsZQFZSmvkCK+RVDkWQAyGUWyDIBoBzyImdtWC7WJgsa842xmPbbdyrPfCUYzsmsR+4VhsE2dbQ3h3S8foPLiGhe7zPMJiY8+9OLaAZrGv2LUDoYqklDjOgiRLOsawxMctx9nhmoooSNzIYrKRdRzmHUzCZLPLvmO3dNSjsK89fW0BEc6x2u/ZLR2rZBzT1XYgpZrVuOd21bKvWhZxx2KbeLNu0WFEpMFpYF83nG2uWY6JXXD44Gh3iV2TGeNuwXl8xvl1zas/ueatv/89hs171Lce99RTuYDSE7cKx2az+vxzxCYRBk/VOfp2h9NnNLEjRiXEDv/ox/QX75A0IlHYL2Ctwm9cf5t/evJ1TvcbBmpEao4ViEtot9S9RxgZaVi528lm78U90Ts8nWUvGiX0woj8RJutAFJHqzoFll34yTbbOU8JudaDHdidCK13pL2tNxFlV3t2wbPeR+rBWqnDfIipF9oBxuSJvqgi2A6/K5RMgX6sqV2cbLYC9hJxY7ZZL3Qk2iGyCuYI7DGr22qYgD+nji5lxHNha9pfrTMymNDBAjdJip+bIP5/vg47pEHmXk5p4fKoTPtrpNDHdOZxkh3E4mgcIMGhAAGSpuJhw2UzAIC1vDagcHZEct+3XI6ZC8+wzFbK1CgorXELLSNzblVzRlOJU+NnG+wCtqo7cJAyajcWB1/VkGcxya9ZWi7zXzOSm5zhil6VKErNnJZPZAeTmJuzFIRY5yAg48yVV/7ZD8/5109+nDlzAj35YE85RVlQHLnr9anah5W2aaqWualzBClAo9YtMj6jbt5hGFKW0My+KDBmjnBFzjxzCCqZPRSqIsJEZSiOZ/GZyu8iQuX1ztoqt60H4zmdT57pd8PYxHwdJ4xpfn3A0PqYg5xyFQfVO7HgR2fqwkQBzGiwlHlmBr3mjC9Q5GvzcyY+crJAYQr2KEV+9kv5NmWKrHtgXg8/RZ7006kRMHNkFdCRlBsQMH3xmVAdmZ1bmKM+J0IviYX6SX9XnK0EiZbOspszs5sqF/N/JA8IMLXXO4xyBoEGc9pFZOoBX/jFSWSKTH2WhBIkLzqIEqY0kg2azGRuUWtSgyOSpdhyMV1yWVmiODlS9ktbAJI/pywsk0sRRGepNZc3roUW5NK+R3COtgo8XjoeNcd890XPF+45lrVnjErX91MqaT8OLGsr4iG30KUOqHdIb0hMSgmnUOducWQnt2la0IF//I9+m7/7X1tzDe+9NRDJm1qdpW5SmU/3cbm43sEiOnZkhDNZIHWIhn50XZViDptmex9zcrNzm597yMUu/N9yT8nPhuTSPOcmJpIPxTQQgiOq0XNUlFBlNLgEQ5IpPPleD7Zv4xoXiCzPvfPGJ1aZOw+WTSUdUEImvnkehvLccIAA3+VsffYr+iWn7gXD/ohKb7lZr+d5cE0euw5xDdso7FeexT53Ecxz3KYL3iwrnlxvuKgNBRXf0o5Ga9q3A82Qe+KVsl1g2QEe9s3cBdJ4sDXOzTziN2vPalRUbX1uQgOuQxS6ZSSlil2oWaT+js0eJQvUNmE1tQcHWKSettyDb3FuxJPY41nqYFbmlP3Ksw0N69xhLoUVXTtS7xoWgyC+z9mWxl7nW5ajTOOGa2njBbtwxpObCxLKPYwCknxL45R3xkuW6nn57OucLV9BYwVg7vzHSNbbjufP8RePDHnbG6o2LGEcHrBMb0z9pTf6QP/BY1iSbVahWqHVLb/6bxzx7f/jNb4dqFJtqD8wxCPWRIYEfbYP3y9IjXG+B21hhJ1veStd8zw1dAFW446xVqrhYD0eoCiGNMMw2VUONGsIBZnP62ycDlgHQ+bQx0SKic760TKKUMWEF2G7CKz2kS47wm0cqFWJo2eoBFVHcOnjNjsqdbYzkkw0glFSLkI2VaDeCY2OpmErdwETmKkg5gTbz/L3eHxtjsXV3F7652mzVjlfeNMJ53zOrOt0hhTk2IBG4aO7qmXC7AwpyW134FhOakuHexIFYSzqDfm9Sobt8DuSW/AWR4v59UHcHec7Hjh4PqPFKuEOup1UJkdK8jlYirJMOz/LpmbXaUq7YGn1qFlbGNuPhHKG6OToCWSJrXzfGBI70S2cyZMe1zvWVeLmZsHRujPEV1OmIuXPHZkNQPM57X2OGooTnEekDJxzoNEQY438w7/9mv/m9x9lx7VImWU01qU858XvmIPQMtvW+sbUibwUGdK7gdQh6CklYOLuWjUnt/ymB/+1wGwqmMsBTjhYK6XpiDUuOWhUkcasuuEtg6AQsnpDzAPiM72j1PZm5nFeHzr7YJmXXEnuLJidbD4Sg8A87LNrP4seuI+NxU++fmpnOYc5FIZ25kUqOYoslzWYxnuZSPfl2xr1wFBcl6VGCqLnNC8akbl7uzdDSpBVA1KOHGwwUx5M55xFv8kKCaxFuz2nzkV33hlPd3QO9cbvKqjwCNRZx7hCSLkQgTzJvZqos1WhOtQpTUEgnRISiDoqhdFjPFRvvFpx0AelSUIhchUJGYCinOEwArmKVUuHzLtMamTwJcqD1qp7rzZ7Xi88P7q44Z2zE647Zdk42iCkJOy7Hm2syKDC5GAkqRkLjm4cIClVVdHtDcrox5F+1/PiaodLkd9c3PLP+5Oycg50orNBukw7UHufOI5TwV+j1tCkwc/0iTGhwRxbl4s0bFnM0VklGe0l83yw8Y53ltHBxp0tQN38fFeMKj+GGg+uyugHvsIlSCHRZlk0VWd619E6GDr8QfeZfND7OmsFW/rbqDyzEz9E08D13rSh4zgHMWY/mF6l7cj23b23zf3wJJPEz9IP/We5Wuloh46nxw95a7zk2dEJkp34pltMz9uuapZ7wYUWR6Jr8xfKQdqzxQmLvZAa27X6BTR7uWOzfX0QZWdpPNTRdkIzOPZNCRCtUCSJz201E4vsABfosekEqQQVj1elGazoVSohOWg7x65JaFwQqo5Fsj5CTW7pu28S26VONqt4msHRRqxINbe7FvWshxGCINHRLwD1DC2kasRH41n2S0UIU6DV1ZDzXzxrjqkH4fXinKFKk81++fqSI4VGIl7grZcvGNpfYqy+QR8fkV6vGJanPPB/gX/6OfRX3qc0LRrff4SIo6pfEXcGCbi3P4CkuGdPCGcfApDWI9XDS67+4Nc41ud8nSu+Of4CoBADOyeEao/Gyn425ky7PrGMsA8DfWqp2huqvWfDgnWEUO2ROrEaA9u4JDU7XNfSZ8fSZ0qNj5Yyl5To6wyGdAuWsufWz4JJR8OOTWXrbSoOCnF6bBUTOkSksmwTg6H7p8N4Z59MYaQNA9XoGDN1TcMWiRWN6/Dh0JDy3j8Ggh8mm6XacELNKAPj0KLJ9tTRwT4f0ouclXFe6KKyiI5+mwNIpyxGTzoI5CDh0mdvqDE5n9mZdOImdPqQPlAcJy9Mjh+Q6V75G2VqhOamG4YtpvwaKEhjmBzZDDDNuWv7geaWzZbdVZjrg0qAI4WbmceCLD+WZVRtCoUqlBbICVU3OTveKaLeeMT2VIIq3sOYTIvYpCztfyG3ui+8XydCjdVsKDqlw4Pe5SAL2Xk3t5zkLChQSnZu5KjagSi3HazqQH+rtGs1RzgkrLADC+iqKVqwa+Y3zIoSQabgj4gVzG3NGX7QfMCb8Z1p7mYpuNl5Kw6xd8IY7WyQDEpVOShwzoQLxtwkpRROznXtM3Aok5co0zqIolPxYXn6dPwVf7QgtMwqEFN/BE2ZqpHsLHM2Pw2JJGm6Dw84HadMh9w5+CbXEZFyfth5UWgVKdpnFFW/Ic2BAmVdJnu9y/RTf9hqO19B01Qj8EnXT6VGiAg7IgsXiMxtiyVzX1N2lLy3rIIpAoCLplRQio8QGJ2pP5T3HnPBUuFWSjAHeSGOPkWLNGPWPCz34Bx+zDq2GYJ1TnIRXMhO7oj3OR2aMiqRR62gdDVC0Nz5DkU9xvmNiV4SdYajja1gXecKshuwaFYzV8irokEyEqxQeepcuMW0cOaJccnS+/2wI4RAzMUZ4m1zsI6vI52H/QDvHBu6e7XbsY8N335xzcmy5vFRxXHbEkLAe2E3JMARQsJvtjShonKOfhyIw5iVG4wf7JctIUXi60uGIfJf/MlL/oTVRFmIFG1HIFee9yniQkAy7aGqDJ0uTVMKt8hSGIr4jIaLRbKN9+ySzY3k7jcxO6YeJcYRX1XWSFcV8QUtzkL+ccBVNselNWhKI1oqm7P2ZJXnWF1OqwyGCK+cQ4LPm0N+TiyFKOagFXTCazZAB0micbzVClIStmG3oSKNkdFZSnXSCBbHMAyIVIaCiuAyl7jKqMGhcy/682mmAbDMKMLoYIXwYLyZbPbV4uTAZs0JHHqPc8rYe6owTjY79p7dYmA7nBMqcwD6BXS9o64iDDVSDaRowcKiH+nWIEPFUPfoWJM7IjO4Crcw5n0cAlIN6BBwVWToFoaKLveM+9psthrZZiWWNFox1NYrjK0Vq/UNoPQiJCf4MIBLaFczUuPqiA4Vu7ZHBkcSZ8WtoowxSxwyEmMguBFQYqrxcZhsNvWBB/qGF+2R2XjyhA04HznytzyrjjOyL6R9TtXXHZJWnPkrzqpgDR78v+BF9YQvfPeG2+Uj7lU/QM9PeXX0Oc7kKXL+CnCQoNLE8PSJSRm9/QEMI2SbHZ8+pvefJ8kt6/v/K8ffqfnx9og/X5whqbc9bWxYlYVQ1CK6xApDdm5bz3FnSg0AdXuDxophWDAOCwYaqlGo2muG3ZrNInHGhjfxjOQ9C7khBQh9YqyVZrNk4S6ITWSjDahyFDNir+e4HpbuNVt3zuATdfK4Tln6NwaQeLjWFcfpClfV6BANyRfBxz3BVTR+hCjEKuGTUdFcTOA7kjZz9zg3IGxQSfgKRpY07Q26t/kfGKgJ1DUMujNd/LTi/thNNttpQJLZQ++VkHYgUGsNPn3cZj9Fl/RnvXxGLEWdZUaL41bAJoNsbc1K4YDmvVNTBqYy79NlJaW817istJAyOFP4xKpQZLMM0NTpDDXbcNYhk7mrWHE0tTgbd9LOknnKOtWXaf7eCQsUQ0Z6E0zgjM+60Kq5xXGu/HfFuRYmeqViTmDKaGzl3MRjLVlXzQ4VWHc754Q0RgMqstPuD+gaFQMBR5c854sO7zz7LrLXlv31yKpWqrazSi2fvdRoI4RLljYwnTtzgmP+HexnFQzR2/QQa/74h4/Yp3tU2bE2Z92e36cylrmxBTkb62FMMy/W5iufdyJTYJShJLxTNJWW0ZrfMzfhwCQ+g3eQSu1SqYGyMdGU7O95rStASlmqTiYxjDLeppYFYxxpvGU2kKIiYc+Jec6EWdnEZtiAhTKEWu5jotSCVFanWIr9vE+GZotjjAnE48oeUPjOhSBToGLImts/+fr0znJ5QhoHUUccfjpUdSo4ykR5CVPUMqY4OV2Who2TU12gcEurz6mbEKyZwcRZccZict5biluL4Zq4uDjjZVonupEQqokaQbKDLwkmpJyN/vBK+f6AnJaxAjo088MOI/XsrIlY57cBS2NUUxqjVC3aayJ6pwCmbKB+4o8quERV2wHaOObPmFIVYUrjJyfs+sRuG3kVoQ6Ot/oNbTimkp619+y6MXdQ66kbx7KuTJLLzYVmYdnig6FcWgf8Hk5Oz3m32vP86gPqsCSOeZzVOrWJCFKZHF7IbY8hUxjiABjnV8UMzucVpcnIO0WuLYmiCZa+Yoym2HHYsCKKQ50wJKZxIi9dc3gTwftpZReZvYm76wQv0QolxZqWlJaNoa1xXqwJStkcCrWjFFrKnDJUp9TOz4femIMlVYogOvn+ihOvwOCty1aI0FQ1qWisiKXvTEYtky6cMGbZNad3O/Z9luv7q4cAhDFyvYKjXeK29VwNRyQCVT8wNHbPoU/EZrTcNpFqO0fbQwWMtaXRhly41A+41hE2kaGGait0zZx20qGyIHisP2az/laJbbaFMaMbWyHWCvTErraDTiBsYGzlZ7ZZ5xyprydeq8SAktChsrGPDXts3qqD7+qcEDYAdh/277w+angl56a9R+bkuZGY4DId0Q5KXIO/VUJmw77ibR6G55ZCdEK/W7G6qDlLO162LffGb5LagfCDt9G/oYQ/+gVSXCCyQ5db4lsD9ePn9M/eIjx7gnvrx9w+/xrOHxnF69Frwofn7L79rxD+3vfZ/OfnpOaYMCpS7xmqkaq3dftBc4/H4wUr4CbP93Gv3DQO19sN9rXgU6JZ3OQ1EyYSZ1V3pHCGjhvOuGXb12itMKwY661lRFZ7em0REaqt8aQ3GOI7+ITzHbv+zP4eHTGjzNt4mp8D97gyRK8v0paWDPd1jUvRAJc8J2NO+5eCx1pGUrgmaIU6pR3r+XwZI3tpccC42NkeoyNh31JR45IyyoZdtYKYqMeBhUQSPaM2UxZMtf+A+zIAACAASURBVGLUubhOot2n0v9sBvlTroKwVhJRZcq0FuVLC8xLsO4mZ0KVg/OEg6K5uxQHlx9QLHNFcWIOPrtkLCF/piv2Jbk7l2XzghfTphdLravMRUjCDCxPlx58hsx0ieK4Tvhcds4LtdDnQN2cRZ3uZR6vMkZy8FFFKcMeC5LR8QxW1V4nh7CMZ5JcyJ/vYT86bvuaLrVUTunHDQ99DdKbpzaSneHsAU45fiGLMkMd8o1YxpFxhFUNAd7sAs67CWBWbN5EyKiu3dqEvrqDTm4yO/tVDnRi/t6agxKnlnV2XomFyTcFT6Bi1V7Gv85FceWMZc4k2HPnxiVlmJ2QG4Hk+RR7pQChsp4Nh2ySSWGunKOiWVAh83jdTIcY1DIgKtZsxBxom3VXojBRKrXGZQOawU4LjsuaMhCunLFknnIe6E+5fgo14oC/KcZlLe0QvcvUBUwKTDURShojN5ZQIkksAvGaeaX5ExNGW3BOTb5FSjW+LWqD1PPmJzJV8uMsynQIdW1oVFVVjGqbZOE0I4KoxQalB7dV62eDFyZduYAVgySNhLoiapojIlXq7KSV4q7aBVxUSJmDTLI0hcu0BA7SgT6vbnXWD9u5SfamGLQtlKwPmBe5afc6rqOl4K/3I5WvGWLPRee5vk3UsmER1rStOax9P+JU2e4T+15ZNhVxFzluhOVyiQvW5N2pkLZd3hSVpy9v+e337vFffueWEEwLGJWpZXaKOlWDVy4YzztzgKf0ndiWMhXJHahLWMrKdj5xjso5fHaoU7YWFQuyU44uXU61BM1i8dkxFckcaFKO/t1ET/BqbXtNps/0RUXAqcmZmaROQX09XhI522oRLEbLMc5hJKaiUxyndK0hw0XqJU2NMpImgjoUZwixM7qNd4lOEvWkUJKdcObsRCez0/1Zry92zwHoMmlTpKZJO1zd8gv7C4YwcKH3TV5OHGsdebR/w4/9/czNinRNwkui7XIA1JgjsF0EnA5UbaQH+lYQRkSgXwiOkaYTdk2abNY54aJucNrjEBqMGxpCYucV0Z6rZsVxvzGERJVuAcIArrW5pZ/WSFQLHr32BAXVwBu/5DhusGRpD9Qs+5G+LRmqmkoizQ5IiW4BKhXNfmC/aFh0I4shoUVVoB2p6Yh9RRpHqsayC66Z50jEDsTBeXweH5JyyX3GfkC1Y8Mr9uEB2kW6N/D04h6PTrdc/uJr7m+uibXAMCIa0P4Y2UWG5duEzQXyhfeJLz6PkyNceAHpLeRpg7jnSFJuvvmEd540/NWbLaLHpAEuF2uo7J6fbC/5zuoBAItuZBTlmCuO+5nrT58RudFxa62b8H1CM2p+1l2gVKTG0xBxXQXSM24NJR8qpemFvlb6xQ0MK4ZgesRUW9KwompvabtcPT4OSNhzGy0zsapvcbsKPMRqxEdwwdqUVxrpvSkK1TqYsxAdlesZc7QdZERo2UtiPUJfjUgy+UCtenoZQKDdLRjo8ctIXG6RrkWbDtkJFRucBnrfmOMSE1XasataTAq5Z3QLK/ZOSnARlzzbUP1cbDZMTq7tgy47ouZraE7xusyfVTLZC81AS5E8Ey9EW4JT9l4pHM9EUtsLZ+8zn4OYc5GXs1G/BEIyMKgKVuVfZS5qysAozmiQh87vYeGenWgmzcb0SYBCnRs1FcBO8/2XJlzO3p5RrSgrZEe7FOxNjJH84YULnpCsbV/CWZ3ut0xV8WHJY6WYEklKW/YDiAtoTNwMNTfdGuSWh8feiObiclV1ypuRmAelCmG0ArlJ2kEto1O+x43niw92/NnTs8nZFGSiasaDoKE49BHN3S1L8J/PyvyRhV9cbq7ww52Am8tpzNnOT4sY0lpUKMD8uqRkKTWTalPNwQVkWb6cASjfU80pnvorqJJSUReJ+bPsLgpgaJlYm0MDlGDI4OpUqEmu6UJzq+65XXNKTD0MnLqMTmcATLMucbmng/ezoKPi08z1Ux3hw4Kecvn81kULNmX+incJSWLqBAWadoEho4qOiDidDpuQJVsSMnVyCyXyP4j+DKWf+Z8po62zzJjgcmtdx91CgFqMfmFIUFkVWec2RRpfIocclfQyId5Fv3R0OkdaztDoYRioQw0aGUhUydpMe5QiRzKn0PIicGlKgZf0+8fGW4RYXp/TWrsk3AwDrvaQIksXqELHSVNTeaHxWRJNcvFYgi5a69EYI+dHC1YnljCNWTbIZ/SZGIniOWo9//0Prln7xDYVvtE894eousYyF1iwUTgrkxHnOWBudlG+W/kbMMuF5cYEhlAYGuB9fcDxFsSNmI0WVUWQA4rNjO7azzYpi7qlzkjykHdck9LLr3NCED/NgkrCUaEpknKnQ8nOesxBnKqiPub1k8EAQPGouEmqz2WO+jTXuVDSxkMmFHzKfuSsw8/jerk4vvN7quDedku1dDxfHHNvu0WCzd/DdMW1rBkI1I0JG7XjkpeyZ4ES6j47kjZXax2yDQpVDLxaNHyuvwXg/drkuR6OHVVyvFo0iML9naGlVQqTzV7WS+53PS/bmgfbbhon+/zA81XDab9lxRYEnlZHPNj39H6kLeOa108vwmm/5aJZ8fbNHvDsmoHS0OWqWXFv39FL4MQH9u2WXqDdjzzqO54GT9XaGlwMhvZJRsDr9Mqcyxy0LPvST3C+lggbKd9JOLvu2IUFt9uOY7lHZdgF8aynIU0224eRPh5RhZupExeDo33zguHLr0mXXzF97fGGNCwI/ik4h3M7+vEhaaX8+csbznXgwg1Q7zm9se99edTwtDqnyYWzT+IlP6pO+cCf8o5efKLNHnXpDtooItw0jvU+clvWUi5yq/21zalTXIBGHXvfMIxCCksW8YKxW1I3t2is2LUdw/5okmRr2i2Jkm0q58OIcxX3U6SLFckbCtmIQRlm9J5aK+o83qhlfvoU2DmPxh6P1T8McQkpkWLFturwfkA3LaMMqIuk/bmBNti94B16oECTSuZTLVhD63w69SRX43QuEPss13zEHs7JjAJCVkAAvOQck5RHQbybHB1fuJQFYMkOifFNy7xmx1sLmlwKnovSQ/lec4G6lN911hmenAo3O2S+pNmRSXWgyEsWVYMuN341PnHRG57PQiscNG5s8KYaITqDsRkysu+VocrijFekCVH/iT6PzH8rPuugNd0YcCHhNSJeWbCjqRLBaUZoiv+QEeBJnkKhBRbV/DuYxy3Z4xQPVeRbzx/QuI5BWwqZocyBzz6BqjLml0pGyctHyfQz5z2yQ1reaWrQVc7Y/IJZBjS3eBLFBzc1ShGBkJKBkgVxYA5sjMdrwUgQe31CqTIZPKkFWi6HK2VxeClyfvn7oLlQVUACKenksMbJX4Cq6AFrDqBVUHH5axUGwoz2OinZAXPdC/hZsgTk4PLTjthPR4RRq1LMB4FTk1DSKerULAMiUzrQIRPKFlRpSvWnM95RKA5AcbTKzQkTD1gwHq7JqZR0toUjdV4OUbK0Gtbw0fhKh9WjM7JssXWeHAUwI5vSoNGcl7qenSqp7HN9clZMVZwtEj54ko45OradoCdSS0UlO1KqGVQYnWOtCaFm5JKYFggjqVlBGnFZHaGoXHgUYSCEliEqkYENwg/2LV8N16haA4eY4HLTcVY7Lm5703BOI+oDm13HPo00vuJk1XLcGHKhTbCCi5RyjlmhrWHT8/C04d/60iP+u+9fUCVliIVPlMzpjaOR0l0uwlMlefBqNIJRlCo7ern91hTJadYmdM6KBQ5VNgB8ChReeXGYvTAFR8AUQHhVPI7xQLs3ppHKmcZxIuBEWVRuTg86ISTjJ+uokIsmVSwYSDEHWgJ9UmJUnMuBwuTYWhbMggM3bTbTpuWz95DvLKZoih1JGBVcxLReteAkRrFIuZLBeqF/Srj617jO4+vJZulbqPe8WT1AVbm/2+TkhHK+2042qyIcdbnBxRh51EHlOnwMvDqqube5ReK8VcQqAhseDbPN3tfAeap445V4/yFf/fDbvKxPwcFXthEIXLiBs1RxvotcuMTD/R4cPOj2lByJerXHcQz5sS9tLbC7FDdlTF42LQ/2PQ80cEni4a5n//YjqlfPqEbP2teMsefBvuc0eXbVnm3VZepKIFbwQdVyXxtOdi8YxpbbpuVNSHzh9gb1ARdG/OWeqo7cLN4GP7LYWjOSXebULWOkTXuC1NyGe8T6mqc+0Ooxy/QqN+BIVFuh70Af73E3DeHVmjpF1D/g4slzzjY73qyXLJaCe/EEbYVhfTYHuLcnkJTxaISu4/7n/4y/9fhX+cMPRlJwuN0xIok3RzWnu1tc2POmOieK43l9RJ33q2d6xFv7G76zvM+Xtq8+0Wbfr854Ei85GhScogFOdgf0nY/Y7E3jON6PwMit9KRWaMYtIMbBTh5pcvc9EbpuwYnbEXRP8i0ubWl8MidNoHY9Xao49REG0KBW6CwJ8YqOGUVyyibVIIMdrgQkq+I4D0E6xGfnVpeoGM90n1pgj7gaiSeoh4GIeqGKDb221CN0zlFph7o2n++JMTdX96ITsvhZrqkTmBR0NGe5YFKqkZzKntA4Ds844+AWaoEFMnPzBSbLitkJLMCMvdqcI0tFH1INytmYvyQUp2L6/WM3Mv3BZ3jgEEcbM2+1Dkwt72ux+zXU102OnJAI3vbXUkw6y65Bqx09gaTeABHpiRJo0p6tNngiPrSgkVKYPSknaLSsoPfEZPQ5l4SL4YyFe5rx9khUx20ntJUwdM7oCBptYfXYGeoFmgSVSU6SNbgn6FsVqtoyL8vILz+54FvP7lvXz5SlaDNorWpF2YjL86i5SZUS8hymPL9xuhcb8iCzcheTfzXPX9KZm2udBrMznNFoBxPlTJglyqastSZb72KryZGoQqYp5e+UNE3FfaU7ncv7yUSXEIgpdzZ0djaXpkEVdiZPS2zKgEhW8dLcBMmbr6jGpR7VkOeoTMVymrMGlri29eM+sh4/ev2M8mlz6+NC1yjV1DWWIkYdQ05vFMShIK21YBGDZmdPBJ2iDqYmLUNJU2tur8wctU126coacDMqfDh4Bz8L7zalNKHNhw54Ssardc7S3y7rUxoiMGa02U/jYC+bU9pey8RlnmrcQliyjre8DEvWuqNKiSvZ08oJ//aXlD/+/iUfxhs+jOcsM3+pRLhvHVekwXMTE7UmdjTAHq9bHjiPVIaGN4NJhXVxpE+Rp5cbVk1g0+1Jklj7GlfZnHVdh1/Ud9FZkqHow4A2Qp0CgdfIfkPdrgHNqZcslecsDyNp5pzFvDGmylENdogZapDDLjFe8pDitHn7bDjowRwVpIKCnn58tZbnehEGFwlp7mBUO8k6g6Vo0xE15qjZ7reuTUNZg2OImtVIPJIktz0uaPrc2ARAcoFMhWTOXrJOh2rOv6loWDoppUR30DnENqzDw6NEvGm+nxmP/tg9f5ZLhgUuVqT2BhkW3N/tUT+yxxpMHHe3RFGaoeJ0d8vt0ZrmyhyG7iQXkuws/bTaRLwG+iZRD7ZdXJyuuPfaiqJ+8N4XATi6fcr6zRX785Zw/QG71ZLHV+b8XN437dVx9Rh58RqA4f4j+66fYrPHG1NKcFtbE+ODt3n7g7/k2Tu/xJkqw5HiXt1wmmoE4fLVM4b7j3jw0hpynGqd11piOVpRXju8YkgN2hhyfjQ+Y5Aln5On/N/hiF/cvWLhbnjZBJrdW/zOu0/5wfs7Xp7+Ef+X/j1+fZ8zWpl68sglKj/wIg48GJXbtKSRLZquaOoN1Io/8aRuYHnr6KjpU2S4d82pXnMpx6h7gE8fGAK5d1Tvvs949eWP2KzCUQ9XFSks0RePEf8D1uMxt+Ehm8YUTO5vRlQrGAJnXY+6hBtsTt8cN4jA9xfnrFPHvuruIEiX4S3G3rGrPD/K6jHvxQvW+xkphY/b7FE3o0DluWVPDCpcrkxOMcQBUE79Fp86Ol8z+o5GThiOTbpxi7K8PmVRX7N3SqodaXecaXi2B7XSWTYxKcltkbiYvpuGHm07wq6hGYS+6tFk/Mu9ayCZnnvMh/VG79rsXroDm3Uox2jq/n+12VIvE4szm+vb/WQbmTMpMtEGyigXKTDnlNHwPaMmCBMF5i5KODvQpeiuuA+loUXxh0thnP1S3os7P61IOQOfU7FScaqMU+ydTFQydWECDQ1hlonb7KbX5e8sUDRldTpjOwZfI3GDc8d43RI14lXZhQVff/gDvvMyQFxwpfepsm9aztiTdqBPyhhBZCRKjRCpdI/zA40zOkIfo+nlRitO67dCHYR+FPNhfB6npFnmws9LQmDK0xfZIIVlvGYcVoRqYUkOxZBOLdxlo4NoeY88P5VzDMkKKcUZIGTAoX2msQvLxMwqwRO6XxDk/NidbPQkYTc/VDE36MjeXnaumdelzn8WEVqfch2FIybj+hpVJ5/DLt9bye7nl9dSTnKm1TZmIQbrjGq8eauxsSLIw0vKUAFFmakAW3J4Xz/FXOWjBSmH12/+p/+nJmeSWcXRiNlxTaoTb2pyHlTt78U9nKLX2ckpGnVK5v+OCecCfZrinDyJ9n5pSiXH/POwiKZsYnOaBwqLagKPPvFK+XtGx5TOONwwynt6ckONvDJMUcH+Pekle8ej5Pj3fxWOV0vWzhGk59HDU77/6gX/8J8P/IdvNfztr6yM1xx7brc3/Hu/d8Ft3eL1iP/oVz1/8Lrjz1/09EnYjo5RlEVIfEHg3WWPD7CsbXSHYSCOymrZsK6FJjiWbc1yUdFiig4L72ka4fNP3prkxigbDFhLYrV7Gvcdf/Xa8e/+3l9RJ8+gM+G+jPlH10rUxFSL+ZG/jXmrdc5ZwJznJXmTDvNR6QtyiSEjqkr0go9zsDFoyo6rWqCFz8h5RmtF8WK0mCbTJeaiyRIdJ8YUceqt0lpBK8s6uBSpqgqGaO27nctNJZjoECV9VFAwu3cLNGICLRW3Oq+78cDyvHJn7AoiPh3B+fN+/z/5+5+Etfy1rn/yu/9Ad+sVi832E212t1pycrth+Ak225QDh2yzaUv0Rq1RIOlIr9BIxZtF/Yk2O3pPSKAaOd1suVofEXPg7DI9J+ViWp/t6vLkMWAO9U+6btZvc3r1IZdnTz7VZk+vfszl2ROOrs2RVlWuV+Z4v/ej79t7nS342sVLvvTVFwzryMn3zuGd78HXRvjGhv9s84/4D178PuG3v43+4hXu+ydwe8v/9oePidtfYR8e8ztvXbI7+pD3/9Th6sDr0fjzp/WGsxvh9NELfICT3jrk7eM1Q4LqKFD1DdvTLdquOJUbOnfLsL5Hq1tk7RgW7xDjw0+0WR9eWiDeb9k8/SX+6V+8Jow9g1+xz2NRD+ETbfbNUc3p1ugDV4vlnb+NKMPgqBr9RJu917/Gj2FaK/uwpx0qfrw84p3tZpqLy9ZxsjNaxvfOzrjfbTjeDZPNdpXRZxzCUf6cbQ5ai83WbseWGqeeKnY4hV1Ts0x7qjEQQ8XRuGWnFY0bJgEH1Y6+SjS9+5jNbr1HZc8Y14y5TXQZn0oT/UFBTZPqO2NXOaMOTSdOVsf47f/x//lMNvu//Dv/phapKEGyI1NSwubgTFX/wvy8ck3/LPJbh7xdy9CO2Vv9aL3QhOZRNHxT5mPONjUXb98NWCf/+FM8DJnQuAPOpsxHxaHEmcNUMKa3nt7fLu+E0e35O+98i7oRS7MxwLGDq8Tv/9Xf4vMPv8Xjty6tEitF6EZ+7xtfxbsFe9fwO+/+Bc+uT3n/ek1UT5cqBKFxA97fcl5fUzmlCfY9xqgMCZY1tN4ab7QV1BUgY9EGM4m1M/+RKKGMU5pvYoiwPeZ//9P3MmXTnEZgGvOPjWZBNT/pynQFERgLXURlytCPmu5I8BX4KIhYcVqZCyU7o7l+SEpVVakRszNWYXaqciBcGCAeRXMdjmYEuHa2nlUjwZszXzIXZTGqzjZaKDZTkKWmDGLFfaWob/YfD3sma+Y1zzd7GJXMn/e7/+3/9ImD+VM5wgmdZMfKJiUw6bxV1awkUNQDDh3RlBIE25TGNOY0T97cM2ViVJNOK8hwPGhQILhJg1bEiOMWo8zKDAVdLqoTkzBznDlYH9VpLcik/Vs+9tNNE5UP8PI+UaZ08Hz8jrySyEUf+NLDyIP2lKumY1ULPxoW/Me/vODvvAVn9+8D0I+Jo0XgdR1ZSeBfPk4c1Y6v32t5edHxNAVcHFmpsCbRa89+VJbeMY4jwZtWbycjycGQFJ8qFkG4t2p5cnbGfhjYpT01sLu4pl4tcHUu8DhoEVnUHpwG/P45LnZcHVWsN9UBLaqgvTl9XQ4PEbQMhr/7N4fSqJBioi88MR2JMeFczRgo9OBMh7BCvhEmFYUouR4hF+aFMTFKwkmcHWjP1JGvm2RTbNv2WU4tYYVhlSoSjF/uk+ldpuDp4kjlHRLvrpFJDzlp3rDsUIkx5pbbpoNZUOxDR9hhDTeSEyTOJ1DKDdJFSsd5mIWLPvt1jMdtt+CNK90doOVOhHa3w4eaMY9xI569m4M7EnRq/HkRSKmaKsURYe+ERXLsiJzsdry6Zw5svbvk5HaDAM/uv0NyjgcvfmSp8OaEhy9e8Oa85WxrDsSze08AuD1ac3z9ISc3H9jHi+P8ynibV+ujO/d2WJxU/n18/SHXx485vv6Q0+u7ryvvo2PFzdqef3HPVA3Wt9c8CxXv9VvWn/8WfOFd2Hr0xT0udl/hH5x/g+orf0HvvgI/AO0czTvf4YM//k3O6pZfH96nu/dDwuuvcSzvcx2PWMlApY6wdWgAfXOOPnjNNl2zlCN0BeM44F3ALTvGVHE+XvD6/iNOfjHhXrzDK/U80G/QHn+L8c2ewZsqQ9S3pnuP4wNUNlTa07b/jCq9x/d+6Qu8+92XEw8XEkM9o/hlbd6/7dBkj51vhjt/e33c8GTfcRHqyWbvjS/oXMstx/ywvc8Xc/vrlBKv49s80Us2bo2qMYmjwDN3xgmv8N7z5TeXjKJ01W6yWZ9tVnTB7ZRN3uO0xbk9UWAjC3o/0AyOPpwgacdRn1gA23pkc/QSubyH+GsYZn1skYZ6UKBnyEV1IwNdquhpQBdUDPgim5jvPYgw+IpaE4PzaBwnOlDSDhCc7hhcLtaUTzxP/9qXy6d/0fo9RPfKRxSdWHvsbn1JwoKdkFG2wnwrDtKkhKP5fbKdJy2p7fyz5N4PggfRmS5xeH4W+gKYSkBhdX3UYTs4Yg98RHtUsuN9+LryPoPOIBqTLxGpo6MfPPXRLbQVECAMEM/4jXe/Q3X6Eo7yWkjBCtzcCaNTHq2eU/uBt45veLn19GnNmEyF2EmPS4kxCkGMHuedTIoLHkM2BzzHvoNWYOUNli8d5bYDNJ7MN5hh9TIQBSHuriH13Kta3qQDTvNBEGLjXeZbp8cOu7CVMU2odVCbiAxjrmj01GINpMp8a27jbepfWQklr7ZSmFeQZ0PQs204s1crZs6Ou9p9ebFCu0SmI+a1qGhuvqHUzvjI1gTkrqtvt2TPH0thZcr8Xynko/gxP252iHXiEzOt7Yxck+bv+1PM9dMdYfFZLSB/+aI1WHyfvNR9KWTTuQuJ3aRxQxWrxvfiJ0MzQWyfb0SzUyJoTNRO6FzmnUqCAw4wwQqo4iT8AVEKf9eoGWlqeK8kr8ZDSfPhaZXoluLzIoxOaQsc7x1jNHRavUNTokYmfdUkM4pZgiMRITrP//yDkV9/d8Gu3bLQmi46/rUvP4AYudp0Fs17T+2UnjUP/V9xJcd86f6aLiy4ePGaFHuSelzw1KqEuMfXjpsIcYCrLrEMSlsJiOfNZuRX3zri0XHNveMli8pxXI+cLGqGPnCx2bBVRfcjbhgIIRjFZdUaSjBabecoI/XqjN/95Zp/8r0NyfW0DvqUkKxqMKGplMIJ42waAuuN053R0EoFJYEz+oIZb413VhQZNQdYWsTfbTCbpFN3N+NXBnAjiQjeESVRJc8QlDpaRiL6QFAlOD9Jvhm/OVfCF3WQSvLmHtFMrwi4qRgMZynCqPM6GXLnKycZ5RZLZzk1ibXo0lQcWlKDppNsj4YsJh9CqfIu1BwhUOMk3m2u8RkvdZ7WZdl5RyZDwN0zSmnFTTa7OHDExSkLAorw8u3HPPjwQ7OFbLMrAirKIkWcrzjeb9lWwvluz+CVq5N7VAvbFy7Oz+2eg5CaFvFhCl7rZIh1JcJCPdelyE+VV++e8ODHP+Lk9uaOzZ7cfpPL4zXvfvAtfvi5L7PebRiaI1bDhtPra3PC33qHZnfJcn/Dq4fvUDi2q8FoGg9ud3n4A8/efcz7f/mGr93/NfhX/4z07BHOv2K99Jx1Al/aot9VNC5xyy3d8y/yhfD7PNff4ubzERcW3Lv8Lq+LxGKdqFXZnH/I4vIhe+3w/Sn7a8/m/BVHzmz2th/YvvMObfUC2iX3tCc9P2ds3nC+dfj9PeLDPyf4iuCV4eljKvdDdsePcLcNIs8ASDJyefxVfuvzA/2H4NKWI6B3S7QWWgnsm0iThKHoxgIw4gdDWbbiWKTEGEbu3ygqysn+1njAfgQ9YlMtub+95c1iyYvlEWej0VvEj3zImq9sXrBvB3t/hUWX2GWt4oW2XC8dy+6uzQ4VND3W+tyB05ZRdlSytEMsbqmpWPo9oh2u3aD9gi3Q0CGX92y9OGXXbJG9UTnU7xgJuGT1HCOK+hZ1SqWRaghE3xPVLMOX9nHa06SE+oZWr4gsaapd+QgA4rJjtW1R181n9Ge9MopbzHNqn3znSQcaslOLObs8GKhUUF30LrJWHFoUJFKJtcx1ucYmMXORi7NQCuVEuYOslXSzk7mxQZAMwGqGFe/6r1MBnsPqelDNVArDIL0zTXmyaoYA9QEkPHesAy+BP3v9Nr91/32Q3hyl5OHtG6qUoK/LIGKtRVuO5Q0dxzxeb8G3XN4kK4gugYGMkHqqoAypQmJkNwZqH6mdKd1e944nJyOrxR4Whmq7ywAAIABJREFUgI/2/8pb5NcpqIMBk0vzAhILdDx7ZqrQtvyNz93wL54vcaLUPpGSEAtYoXYGUZB9gZoc3dhoTt2eUyazuDyHRp/xk0ScIlnhTTM7ozixkcqRsw6aGx0Zch3E4dV4yLU4RlJu5GL1SZPec/4OVuhXAjKh9vb9PXESPdACH2PFdjB3m1M9bIhRZtrWYETnVusHAJv9PYNKCEmj0W5z8FHWjAeiOPwM8/zE61Md4UfBeFxXChJrRhmBNPH4NEc+oyiV6YDYB0oipAMvXBwhpgkxNm84D5IzPV4D4xQXbCKWWFXpdFNTpX2JEGVClSdqRkokUXywhghjyikll/Xp8vdVNUg/5EKuOrcMJjfBCFXud6OJOuSK/srT9krHLCWWcmQrEU4Efu3him/8+AW/8cUnVPSsz44gOXb7jroKbDYbi7YrT7/1iDvnSUgcLxdUuw1DCJwsKr63SSSvjBrY+xa99Xz5XXj/0hNkT62Cerga99z6Ux6fr3iw9qxbb4FIiuArlJF7x0tutxv2cTB+nSqL1ZJY9ICrQOoHqrrm1F/x9bcq/qvvXFO7BUkj3tlmVVpalsO06Csbqd8an1QZfSgBUtn4ykau5K5DJGtEkIXsvc6Ie1nldqgmaiIjDofJodX5b5XaPKs4FpL1BMdEjQMnBHUWtHhPpVm1wtva9SqIL2nDkSiegPHCvPdGwhdFe6F2FZKdiFocMsKNizROGNIIknCSudRzhMgyCn1QCNUdWk3KXSYM1RktNak/zUx/9utJ9yN0ecIzdQzVe7h48Yk2m3RkdCdcHeXDQxIPX31Aqkz9Ydcc8/DDDxjd8oAOYv8YWk/Vj+yaACirEWJ7ggNWfWJjb8F6yM7XcEtceFZ9Yrc4QlVZjZnHeHnD5t4xJ29uqKNw2cLiItEtz9g2jgdXV/RuwVh5bhvPclAu3/kcjy9esjldc+/9H7JfndMdnXF7foRXZbGtSK1DqsCDD6/R4ZKXjx/n+xx4/eRd7n/wIb/ywTd5750FuzevWPzBv4T8zT8kffc3ICjy4AN485D6+P3JZmXzNcLL+3xVX3B+8jYP++8ynp6w7itepSuSU8a4onr9mDQIq8eO2xcVvt5T3Z6zQ+gWb9hdfpFHi+esmoj8+vfgxT14/jYQqD/3IeOHbxNIpPvPLVhQZf/qF9DbOnfXehuN10hQHn3lf+CD29+iezqSlo+pdz/Gpxs8nuhWLDDFlwaZbLYn4RuhSyOrwdFXdqAM1UgYc0GiG7IVJ+5tb4HEvf0GrXa8Cfe5N77h3t6Ci77ukWHB1WLJ2fiKt90zXvn7nMcXjG3PybBEZQVyQ61rFKHtlNrd0BFYRUguMmgwmTqF2gdEO+qwoxsX1COI39PHM0a/RUk07Hm9vGG9OyK010a1uz2hEkFyjUItQq09r0JgoXvGWkEqdutXLC8eQDKH/fbshifP19z4DtI5q7SF5InBMSyvJ5tN+d8/L5td+huQGk01e7yFoDpr3iq5cEmteUxxyl1G3OYsSWmwMSOHJaPni0Oc96fKzW6mHafZqXUFQbbXlRoLzc7rIWJdZ9AgaUGp7WyeiqRVSeoO3tNAj4jRIELW3VUU7+19KufY6wg5vQ/GhxXJRccy8Ph4y+6NsHhYAQMsvfkSo5jn08WMyHoYPV1Y0bg9ywbisMO7Fcs60e0ctSSiOJxreDHAV8/e8HS3pqZDEFoXGQchumNWqy008f+l7c1+Lcmy877fHiLizOfOmVk51dDd1RPZzdkiBVM0YVICBNAWBAk2DOjBMGDDhgHrxQb8B/jBfpT9YsGAZUPvgg0QoGALNilKnHpidbOqp6qc896885li2nv5Ye2Ic7OruyR3UVEo3My855yIE7HX3mt/61vfl7L0bl3Te8gQNdcIHT0iqp5wd2+dUVcIb8GuOZw7sue7tFbNqWxSSZC0yZG0EVDHSj0dTvMZxYf1dTZtVraeB3ScAaWbJLRWue6xV/XoJMySOBoKYSVPCGOSQAEgalO+rRwITexoDNtr6FwMo0S8UYpEBFXcACQKxijoGSMJbddr2IhWIzqahjFa4fbiVPYvkkDLbe4BuvkqTSDXrjta6dbYxKFOt8JKlwR/crx+YiL8RmFxvuG0sXw/VnjrALu1SE6ig0FC39kIEJ27YeuIuuZkLiUCaYfXud90OwMDRDVD6Mn6In1A9MGU/u5RvViVUkulFqfWvDbqZIpPtpUkNQJU61dEkcCOw7qlRGwb6XIsmOSCk3wf68wi0fYcz77xKbc8sDXrqxPevPMGja3YHc3S56ZJIW0EyrIkzwf80ZMTKj8AWfNPPiz5q/sGYwUThJ8b6gT1QVkS/IR/637O5w4qqrzgy5MCb1q+ddpQekce1rx/NWNcCFVoOJgMCMbjgTxXntt0OuVqtdkmQmjZ3zinOqZpEzGZTLhTr/jiYMjjZiu2nRqOt5t9kdeeR7cZucmpiwmJ6MYK6Wn3N7g/tImyr7yZm+W0NBliyKxX5Yre4MT1iIZJ/2U5ic+bSoGptTMYVQkx0RC9V8rCDS1fRfoNNhuo2YdRxRJjgjboRR0bElWsPEOb74wxtLjkiNPNWBCjpc1UJzvEoDqYAsYaXLqnEXrKxM2S/6c9RoMJuVnh2gkf2VNsVFTLGaWp2FTeenX3HfZOX3Lr4hUAq+kdmvGMvNLkZiB7hKHHSrFF66vUvMZdyLXJlChk4sAmSS2Zsnuh5+wlmvrS3oIgE652dtm5esz17IH+PkAuKyywU89oLGTRYupAKHZovY63ndrS2MjkaoXHMb5c0uZzfBNoC8/emSpf1AYmVyuIwqawxGzGzskVAOVwl8nFknKyw+T0BInfY1jdhbffo3n/F8ly06M3ElQnWubPscuHnH8w5aO33ubNx98iP1kzPzjA2BoThDcN1HXOWXNBdHe5c1DCrVfs3B9S2hluc0Z1MqK5esCQC17W9zmyp0z++ReQz54Sk8B6fXYbby3hg1/CfuHrhFcHPYpirUWmDVwZrFthXUnz/i8yGs743GbBk/mCZqzIehsg2yxp/AEZm9ditkhSRYVx1Hnsn080OhZb78nqHqNMPxUxig3sNhtMM7kxIQz6P99sbB3GOTYEFk7dQvM4w0rs1QGCTJgVx9RhFxsthQUXlL8cHRA9bdjFOEPTDlRrtp9zHNEZhot7LGanjK532DBn4K7wklGj8pwikRJL3kbEZRBrajdkcHVIMMJqriogk4s7PB3mMH1KkEt2rvc0ZkPBcKlc6o03uMH6te/5aY9hVpLbNWU7JDQzpSgYnbu6pNRpDVxnu26vbSyI6ZNe1WVJDYVpAu2MF7bNa9uS9o/SL7rjNfpR6ozv53ZDryYQEgDsEpIpac5Vb4mEUKdO9z6JjoatOI5+UMK/sEbL8rl1iH2dK6tLrGXgl2zWK4a7Kdkd+PRl06LRYRGtgLdUZznGFsQYee/kFvenqt8URJgPzojAupwgfsA7O6e8MVpi3Yj94RJHy/PFFGyOjRuuN3Nm/hwqVCmiu8ndFxo4/Z25uYChX16tY5W3UjgIG4rBBmnGPWy/XTo1m5WUa/e4YfqomxSVrgmuUwu5+fy2r9J7uY1yXucN33j+mrNFejy5KwEknowBBi7RZugE4BItNiW0UVQByaJft0P6dcyps2uIXUOfSaZXqibR3SKfEOTOwU6l2fT13eUGMeQmV1Q7bjdf2ni6/VKdV8TrOcfHj09MhJ+0hmEwjG3kK2PH+03sKQY3OTwDDMapOLgq/0lK41PClWD8TisPOsqM2Y4nIHi9PS5um9lMSkSl+0Kme9iW3M1pmitqO2ZKReUcWQhark68lixFiCQRby0jmDQRa1kuS6Oso4ga2SpZdEYOwUZFPlHZsGDUqlYb5gybELh3e4fzpqI9bRkdesYypA4VbQhaNihQw44gHN3a5fb3n3BR7HFSrfj985pdK/zC4ZBlueHO7pRfqAKjwvFnj5aEnSHvznOullfsDS2/8daEJsCrZcO6KRkVM+ogfHR8yWCQsTPKGHiLdR6fuMVRBDfIkEaTOLH6MymkY2zLeBj5nXdG/KMfNFwnHVKbgRdPExsVIZfthkPkRvAlCRzlwYJNHcXWKOJv+oQ4bXKc6RsOO7ttsUBPddk6z3XdppkION3biojyia3VBoGg1JZuweiGTK9a4SyZMaqzbBytCNEmbnIMPQUkkhJql2nTmwlpATG9FqHJMkwUCsMNPliiP0S93mBI9tz0znVdg4g3hsx7QtB77G/oNn+a46oOTOocly35HBVPZtqlbcP8tUn0/otnrAeOfFyzcJ8hj+fd5VOPphi5TujOdZ8KVcNJillNeotU1ha4UQFaYCSy3H2T2eUjvab5gzRG9hA3ZefsMSc7n2fWLimLAVkIXO087MfF7vWjlAzMifaaLM6Aa653HjC7uqR2wmKspfCbMbt39QSAzfw+xhhmVxcsZ7tMLx/hMo3Z3EywsoBo8bVhKA+hOEG+PiL7za9hru6ShRb2X+kCcHmIXNwiWmH18Iyf+d73ef/eb7PePGVzMmeeRw6nSxp/Sbx7l7cvamgy4otXtPf3aS9us7v8LutDofjMpUrphVvQlDR7+0i1wP7hEc0bawYs4MvPkdMjtYV/7zZeBLnTkIea2rwgXByhKiVHGE5wdsPeV38XPvzbhOWID/Ix09UL/LDgYnaP4fIJbrOkcofUhWdcLl6LWWfGeNlA27DxlsYEbTrNhdIKJm5l07qqhxFDW0RW4yG7lxUSHVXWMK1XFHFEcJY32hIRofWOeQrEKJow9TErta6TYpRWJNsqYpff5CJ4d81SpiAas3nUgVpFrWaMrneo2MECtR2wcYqudeN92KrCx8YWyOwUx4qQYjZPcmgD8RSy4irF7GJ+yc75G7rQpns1Cjm+zIgtQI3bEo9+6qNqxzQm4G3DzvCSsp5tJWhvrLFiILO6HnbNTNZ0zUpGkVNuAAp0SdSNLAkhs13irGmFKktsJa+695lEtwh+CE1JsEM8G5zxBAmp/J5kuToWq7HJpIKUNEufLCvoxGvrf2eFa5LxkUsJl86xSVHCaO4gxhCC4XAO0ljMKqANc1Zr6zGd1JGEb6GYOvKXl0i+R2wqXl3P8bbizvSauhZ2x0IZFgx85IPzKZuhY2+8Zl0aJhm8eXgF0bCuMqqQXBejgWuj+rF5vLHIkaQ8JL0u6t/7BQNdJ2IE3/Kzt074+ssHtEHlaAur1s8SY+rbSVK0HepOQoBTRVvlyUyfsHYuf1FurrEkhRHpKRTdn11fpaVXe5K+0a6TSusS9NjTd4J0m58f6aHq4tYq4hwDCYnuBmJq3DX9FkzP7XRD54marN9YtzPnkp63bJfYbvxITGOvs5PWLyg3KhzGCEWmVI5uI/eTjk9MhJc+YxMbXlJQlOAyegUJsVuEQQRip7uadpCAOqkZTW4KMVQm9IHQcYe0BO77f+tupo5pISYN2U7GTJ2slCeTN8/57768w+5Ohg2W4wj/9R8+Z1BM081SSB7Ax0DrwAWLR7kvnctb12DS2a+mPZmeN0HyuXXamWwtDUIhmox5Y6itBeP4wzPh16SmKKasQ6DZbFiXLd57MgexVo5uU9V8dljy3//b+/z+ceAff1+4Myr4hZlnlgUmLmNqArtHnrp2zPaFv1gH3pQVhshacs7WDd54Rpnj7rAmSzvT+XSC0HK+rBkVGcPCYpqWVpx+6xhZVRsmMsTkTpu50mRpraUWx+f3Hf/h6pp/eJxrMKVuT2McbVocg+0I8l1nuusl9qArZ6SJ1egAf80Ss0Om0z85kuh7Ch5jOo3BrmwExsae0mG7Uj9b627nfT8mbx4d+6hJnBi5YaTR6fNZsTjia2hETLx0FztOm34nYwyBFueSG+ANBQPQ5EwgcY9jOrder+sXJWhi0PKj/XgTwU97rLIZMlrxbPwl7i0es8oesHP6EmPa3hSlQ/Hj5B4LmWsC4pVzWfhA7eZEA2OzYSGD15pz6mxA1pRsClWSiF67723bELIM39aERLxc7L4JwMH6OcYYLop9jla/x8+93TLNcmS44jJM+Pqf3OLs6DN675xnmd5n65Imn0GwePaAyNXOrm5ef0zMXs8VYe5idrWzh4uw2HlIi1CIJRLI2ilVXlCPv8+jlxPelH0W70zY/fND8CXmcp/T6Vc5sN8hfOkJcnYEOy958AsfwfyQ3W8/4sXFktlbNbHIqZsW0whHF9/H2DlXRx+QFYZwMqA5fI5ZRhbDfebHM0aJRnM4+mdkdsZZ8UX27j1i0DjWywmjP/0sZlTRfv4x7vtfxQxPtAJy9Iji+CGVPyYwBRn1MctfPGTn8D1y0/Bi81fJhgU2Rg7qU87Gd7H2BfNqRWhhle9SVM8REerBPbLqksaO8H7NCIhmO5YHnRlAOkxM3a1prhmUJa7Q5NanOMyCkBEJsiKzY4xsCCaNkR+N2WiQsKvPMUVd01UAU8xuEEyz0+vs6gcZMrOmDiOGbqEb4i6DzoSSOTL9sM9BStNi8ARTk0lGlLZH7UxaAq/2ThDtbkDJntC4Nev5ksH5uL8HbegoA/mNBPOnP5wZgDQ0ccImBAqz7avK6FRRTXKe2yqwd3ONFV1TTeJ8WjH0Zm7Qv7fnZHa3EL2fzgQ62bSu+mqTEQdYfHPKr7z5AxgakABhwB++/yYu0xhXHCg9M2nJjKWWLY/VJm+B7ay6Tba5kUTpnK+SW2qeZAn9fKWV48xani0OgBMOslbLExVKi7CJ0qOog0qa5Wf80ucvaK4O+MbLO4yLlv3xgqGrKazgbWR3GCB67g5qFuWUab5C/TVzQtWANeQuMsoulW4BMEjzemmVZ+LRZFzhPH2ATRp8HRye7of+9BTjJV/Y/R7fPX9XEdtUKRFrcRhsVOCwFts7rAmqorC1md5ipN1jeF1RRO91znacOkUy1ZHQbCk+YvR/nxJvSOiu0XWsW1K9Mf3X2D5I+jEkIklKbzvYOn6yvi+81kfoFNlEJG4l29L3UAMO7dm5qTqiL7mReXdN8ol2cbMyLWln1tGMftLxyTrCURCb40UILkkrpbGQI4j1bEJItnpaAxFEEVbAORVtXrSRaEbkVEinp+qEXFpaW/TUhf7iTXeaLVeou8nGeLwY3vY1/+lX9zmYGEzuGRdDbpnIP/iN+/yX/3zBtIiUNeqPLoLYDC+C9QmRY6tD3GXgQqeX6TCmE40PZFEHavDKf3VRazrOqIpjTuSCjHU0LMRxsVpT1hWzUUHVBka5IwYYFZ5xEZkMPUUxI88jvz3N+PzkhGpTcdY0jDPP3mTIdDxkb1qwqBp+fZTzwfMl+7OcslaF5bIsedI6Zhm8c2tKU5dMJhNOr9ZsakV61QTC0DSRGFustzTRYGKgti2WFp8XYKApN5S16g43IfLZO2N2Xq5ZMkBMQDKdoU0UxDn1DHdQRyicp43gM7WndjaVAzofc0nNkIlTDGwFvPvRGYlp9+8wamXsutdrnSi30FqfJoyczEREAplVJMKl3XK3sQlmu2CICEEiYk2SElNFCcT2jQVxC2XgiD1KG9PGT0MiaT4mSblwQz5y282bjFkSmmRs17kbsMaRma1KQ2ZcMnf5SzqicDl7g73Tl6zJmdqScLQDwOz0UmM2BuqDHQZmxdLtIMBuUDWAYVkxaV7ybHZEZaZM7IKL4kARfBHeOP4BT+9+jnm1ZpENsTEpDzjwURcOn3jQ+tiEq+Eh83LNV4/f597P1gxvHxM/s8Je3mLPrPmKHfG7jweMpSIaw055xTIfIT7DG8+sUlrD9XiObZp+3tNTpJjFMalLFqM5YgJ53RCc/1jM+hARZylCxavwBXb2PCF7ymjxPlxlLPcnuM0rpvOPiD/4DO16TJEFgkTKk/sM7rzkjV//I25/MyLVirXcx7oXFDmspm8y/pWvMf/aO5xlc9ZLw4P2GYvpz7JzYbD+25h2yvF8xlujO0hZMn/4DeL7e5hHOwyNgS8c037hGfYH++AW+iUPLxAi1ma4F0c6a7sltlpibr0gPp4S/YrJW6e88cE3eNX+PMYEGm+ZhTUuK6izguL0ijmvKDO4OnyX0fIlMvQ4yRFysvIZ9fBuP84BfH21jVm1kvpYzBIbjPVk7ZpoUsxawAojiayKJW3pEOOYYBAJOLcBGeNcizUlWefO10xTzOrnVKYroWrMZv6CdXObsX9BYddUQQnpmV/jiFRxjyE1+XLYX3cjAzJTatOc6IhZj5Ub3L1mdL3HanpBZ2FgTGS1e4GIUJojxjQsC1XGmFVTFjtXP3WI3jyCCNZ6kKhJRm9cAMYEDDY1E5FQYE0QugbdzLba7R8cwWYYGjrNcjW9aolGTW71q95IRFNq4rYPNAEeWvkc+Ut+5u3HkNfg81Q6XfOrX/4hf/jBlxn7mrK1KdGRtLZvRYnUAyT1otD9W5d4GcSqvrMjqRqL2ttDZ5i1reYZAkGGynGXEU15TdZ4KGQrLRStIrU+6k45y5W6NVzwS4NLaCKhLXA+KppbGHWFayOH+Ybzy8B0EGiDpsKbBtZhzMA3zPZKbYQrPGwE2sTrMK0mY8Gk3hwU+QsxJeZBlSRALZeDUepGNMznFeZiQ2AIGHKrVWpRyzfNpUwgRJuc+JLVNWkjI9sKQIfMGpEtTaJ7pt2971LYGz+6Z9e90FmlRoiAEkgDSEi0DPqfNwr0Pcbfr4MJVY4JUVZaRkzJsElLrFY3tmgxN5XZ6D4BUT5wP2L7L9c5O8ZUOdDP6lBnmypMerItX/knHZ+sGpG0Ponta3xQ0ArByNb9bt9IxIthZDwbo4nY0FjqumIvH4NrOZCKu6bEe88Pw4DH9QzHWuF6Zz+m+2uMljNBqQomZfuZNXwUB0ynU1ZtxVwC0gSarCBvGv7j25f8zN05374o+N8eaVnMdzeqC3pxSKx/xMChc5ZLO2IRDCF1/GuCJCEivuOpdheqJPkW4TvnLeIHlOcbci9UmzXTUc79uWe3dMh0iHeGui0pA5xfn2PbjNJsOJiMsSZQOKOlnMuW6WREHi75/L0BH12sGJkh8zxQzKbsLzcMi4xVWTEYDqiqCmgZDHM265oYI22rpiHOOcqmJmAYeMPpesHE50xwmvzGSNm01K0O2PcuLBmq0FF1CR2K9neLpDUR71Ad3DQAs/5eWk2+re0j8mZZR+yWtw3QJBhjLJaVtErIT1bFMe1cV1HtVZWTrJaQFtUCtgZIm5fumXaOOyaR/5tWVT8ya2lMKmDErSlKdy2qj2x6m+SbY6TAsrFRu98Tkt6jM52gvImpXOghNRxq/FhCG3EeYtBdrnhLDDdcuz7l0d7a09JXanJoTQGii340kN95Snh5l/xiwXJ/wr3jR0wyz6rVmB1nsCkvecseIuUpw/Vzvjr7CyRbcrIz5vHer7FfnRIQJm38CTGrf3Zd9UiEAyKP997i/lzg0mDlu9CUxKxgGi75u/P/CbkVeXL21/jG6ecAmCX75lQUYra5ZpllTJpyez4Mi2yoMWsDNpbkbWAaKwiwyMZM6wViLItsyKTtOJ6Ws+KA6eUTXpxFbvsvcrnekF87qlVgcH7Ici4Mzl5R7h4yOC6QL36D0+/8NvsfPoOzOeadP8b4ltHpbcr9NbPNc5p/8QbWjzgcfouFf8hJc5vZxpJN/l/cybuY+WMOiiXN7CN8NsS/HMHOU1pnMT+YYWLEvXdbLaSLGtk7gffuwJefES7WZF/8JvY7P487ekKIEXucE9cZdeZ5cvVrjM8c7Thy6SuiGWg50wxwVFQHc6yJlKJNbb5tWOcWh26CwnDKpDpjU7g+ZsdVBcOsj9mmWZInXeky3cs9gVO8lu+zCXW9IBotw57aKXl5hbdjgqzBr3GI2iS7S2JIEnlBP7NIxgtdpcm0jnLvlMkqo2rnVGYH50oq2UHYVnaM2VCFXTLO+ufbHTaMqDJDzqaP2dG1bg6NV0685GuMjYyu9tnMzpls8n7NC8UFryYLRqsZUYSqWDO/et3K/Kc9uk2wSKKpmS1f1KLueK1NFAwJSimwkprFwZtA3UacLyhMRcY1ub3GW8M67HIR9imkotdC/5HzK5K4TTJ6lzorlGEHBs8T/6hVrqstIDb8zM63mO7WrDf7fPvkfrre0Cc5oP0cxPgaAiiJPtc1bonRhi+XuKSdxFbngNYn6cbSmfi+XE3x1rJZBbyDqm4Z58L+sMK3jSK2nS1ydLCJOokIuEFEuWJRM8u102Q4bNjbCbD04ITMVWRDy6S8VKqDiqcr0iwoEtwo2tg7odiU5Kbki1I0w8qjvi+SJNc0fVyttBoS0STQGoMyChKgI9KvsR0g1GlM671TlN52iHN6oNIllsboGpfuYdezEWzERK3mWpNydpTsUMccZ9qUgEa8TSQW6UwqOtfDdLo0pmw6Z5M2Nhil+6mcn/TVq378abrQ2ySbbZqQsG/bU4RSapHGWAc26VYwokwE17kwWlU0yU2kQX0nMktfSf5Jxycaavytf/gnojvoREKWqLq/aQeXGXBByyFvWkvmFyzbIWuEddTOyXtty5uDiJDxJytDazLeLtYcuZYda1kWGf/02mNjxrX1OAa4dom1ljUwsAYJBlJyLVgaWvIAf2+34rN3Jrhhwe1JQSWBy1VJ0wjOepbrFbdmBZdlzf/9IvBnx4a7M8fXyPCbhsJY5Th2iV26FaoYkXZYxtzY6+ijsAn1sOk1eZ+MNeQYdqxlISrXk8fIW2PHyDX8R3/lLZWeEcPV9ZKLqmG1DrS0hBDIfYZ3Dhcje/OJohdJycAYw+XVklWAsm3YHU8g1rRtS57n+iycA+tVa9jqRsYacM6kxN1T1zXDgSfPc6xoE9lyVanjmvds6paLxZrfOxvwnYsqoeL6zLumxLpV/UVvLUFUkSDiekewGKN2C6dyZeiRdqNajQjBKZXCG3pb644L3I2tfoIOEZ/4tH0gdM0kfWDojtxaixUobEDqAAAgAElEQVRV/egE2vvtK9tnfZMi0Mmt9a+Jr/++F7P/MbHSxi3dRww9vz3GCDY55TWhk1l+7TP6TUDiYf5f/+3f+NTQ8O///f9MBq9esTzY18T21QnZIONqtkNEKJr3mL/aob13ye7xHvntR8SLByw2a5Z3LhguH3JQXjKdLxiuJ3y3nON9wY59QTFZMm5KqvGYD64/g4uev9h/iGPAw/VzrLV8N59xvzwlNwPM5gLGc57kOzS0vL255t38XzB+6wWVvEnxix/QSsB/e4aczakGE/z1MeaLp8TzgtX7v8yrV3MGby75A36B3eUVRzHyaDD71DH7dr3AxoBbvNQGnRCImSeur8hjZGfXsCcV5ndeglE0kg+HNGc5iyJwsNkQQ2CT19Sjfaabc+zOHqbOaLJA1jgkb+D8GBt2WPopcSIQa/LVKe30iGLymGZ1n4o58x9klO+ckrcas2bQQOWhiNQBsjxgBw3tu4+R033c4xlmk2OGkTJY5OKEZfFVXj6/+2Nj9lTAmYpohjg2xBg5zw+YNhsW2ZDBesPIr/uY3cSiv5c0l3iE8903OXjxbdrhmGEV+vho25WOrUH7esyuJ4TxiqJRMOLHxaw3DYTxjZhNu54fE7MNhgzpf9Y3KikSobAXVHGXwl5Qx92fGLPenP3YmG2s0qBsmteWI0XkZ5tJ/95JpX++zpQn/6v/+E8/Vcz+2X/+70vPvTb6RWxCwDRdikQJmrT4NVPWrGSEEUeMltw5Agum+ZJoHOebAwTPTnZOYVdY11LYgpere8myfKBqBWGTzudxNtKIVsEMaBoiQkvkwey7HMxrlQobqKIDldFkzhpVaRgGqC2vLg/54fU++8OStj1k3aqqjjPbRuqbiVAHQRhzs8qgCZ4lJjQxHSYiElO1KWJdm/SwW6IE5oM1ha24+5lleoPVBLhxUN+AMV1KUiXCKGVxMSWyBn1P8EgAM0gob/JCwCRo3rpEzk40DEMHf2+TYd9B46mxriY1PhmlcpTCk8UDXi3HuvExhp5PYqANnfY7aV3UpM8aBVD0q8ReyedGHkwjHYfcJb3obSLbFVghATfpjSF267nQ83/pNmVdvIIkvePQjdBu/r1JR+ih4XRBN3/Svf7jv7Zmi2bfPES2xJottrx9nMaYJHP6I2OMLdrdfe7f+Ee/+2Pj9RMR4YwGRbJVl7RNzWwetGkJiLlnxziQGkdOZh3vWkWgzmnwA8+6MUiM/OwocFIJ3w07tHEJZsXUW/69udA2Lc5lXLdXvF97LI67rmE0MZTB8AcXBcFlmFDyt6Yj/vyq4r3TNRsDX9xdUI+OwCjPtAza1DUeFSyqyMF8xu8MW37zrvDicsVX1jV/UDmemgGtrRlIEhAJ3a4lbhfVjuAfOuRP9z90G0AjBAytdVg8wQQ1MZDIBofg+cok8vkdQ1VuiC5DCIwHjus6MMwDw2zIKHcsysjpekVhMq5XJVmW6U4rVAyGOXf3B5yt4GJjeHJ6xSUj3p7tEeoLMB7ywGZdUhQF1mqiaZylrhui9UhosBKoGpdsiFssWrIMBNqmpa5rdsZT/ua45PnCcKVdbjrxGqGNimgS1KM9iJA5TxVS0m5UUFuFyjXB7YWyJU3sxhCDyjP1G+gUU95o+SUTQ5tMVKRrKjPQ4JSnTkhVBG24a03SGY6xbxhwqURozNagRRecG5zcoIFW28jAqMpGtBrmmRjqGMmc7RsAf1QhwzmPS+drUQqFTzUkTbwEccqZuxnQNgiNlWRh7miT49qnPfZfasNYfq2l2/XeMS2Qt08Zr+8C92jmhsHkmuJigz+bswhwe3ABF1DKKfZNS/MY6rjk4WzJ6fURp4OH7Fw/wxav8FeOL/lHtNHw7uUTqsGCl+s3sDh+M7+mmC8J3vJ++BIvh3s8OPs+X5qc8uRyTlXtMFhNKG59jxAbraTEHXy4wo3PaeIdhq9K3GzE/N3n7DSGpbvm75z8P3xvtc/7t79Ca2vur855MTrgweYKay0f5pN/rZh90FzzUTHl0XCOiPDAe1ZhjcsL8sU1TI+4muxwN/tz4v572HnVx6zbFZo//jx7xznlLUPuW6hn7F4uOR/tsvt8wMVhzjAuyEaPELmFewfWr4Zs6orBK+Hx6AF7xYzZ4kPs83fIPvcD/ItTRN7FWkuTBfLGYrKXXIV3kVaYDz5Alg84tu9wdPycs5Of54CPaDGEaLGDj1hPf5ZB9ozdcszJaIdsdUU22uljdm9zyfV4RiFRrU6tIrDXfsB+faZaz9DHrNyI2WkTWOeW2yc/ADeAzYIGyPwYAfJhSxFqimZATYmTAWU9IoxXiIGNPcIKZHKCdRYfBhqzBERy8KutCH47IZiSzDd6Ha0qNmiDtlYCxkRCPWWx+5jD84dKX9g9pQKmi0uuRw3TzSXrUZKAE2G0ztiMNcZaY3DJUKZFk/nadEonwjAOKc0mobPSl5/H6xHXwwWL0QojcHB28Knj1aU1tksKfV99Ejoj5cJacA3KcHdYA4VVFF9iQWENm6gGSPvFKxbtmOt4i6GcMYiX+KLhjelHlMFinSW2hmW9gxhL4Rbs5oEmOp6s3sDaDGLDfPaKs9WYl4shBsf+6FJ1cbukLxqNqdxA62AEh8Uph3uXNGvLoj7l2fIWDbs4iQQr2mSZEilzA57u5N3alKzZ7p6nnFU3LWrzIMbjCBgKVLc9A+uYFisOh0t1bzMeiEqR0MaglJiKJsZVmiSqlEkhSv7ODYxbqC2mtiwXhpodpuOWrF3qIuUEylYRYWtSEk1ChZ0uaBIViW67nKuDO1OS3EYoPPezp1xu3qaJGY5IMCoFJwK5leSqBojaUMeYqDRsUXZrEvEgxWuEVI00SO/Wqzxt/aYCNioX2Agx6gasS4I1dfeKykrcSqOha1lXsdDNrKC2xmDp1ljSGbd0jVbY0iOtvjPv8kcEiXrrRC+erQRf+iyrdKruXiptI/GXU6Lu7Q20/EZybjFK5+CmgsTHj09EhP+D/+VfvvbLrnu2H7zo7nkehFJqnIGSgiLxvXxCI8bAxtbcacFmkU0wNG1kagXnLNZFDgde6Qt1i2QBKxmVUQWCcW4ZtBCzgtPaQXvFyHre2hsxGRZMRgNCqPDOITbncr1huapwNjDMB+TO4DLLxbJOmzvDqrbk44z/41HJ01jQdtp6IiCerUVTas6STmZtS842oqiLS+V63fCl5oXOhc06stjwX33pgGkekbBmbzxkMhyyqUr+7KMzrAg7o4zRcMB6U3NVVYSUxN3bmeCNkOWOLMsYGMNVWXK1CUjr2DRXFH6EicJ4UuhQEFEvdGsTRclwuVwzHw8pUqwaY6gb6RHczCuinGUZm3XDIMs5XQr/4JuXVEWkTbQR5QQqbcAYp9SoG4huRCeuNoZeKaFD57bSZl0rzHYnaenQm4jzQhYNDR39IDW7hZbWaSLsUpJjbMCL6hx219HGNCkkZBcrPQe9Q523iFDXKNIFWeeqo/N9MCTe1TYUOuQbwND2wuGITS55pj9X6M+3RZn14lSpwlrbX8vv/Te/9akR4W/9J3/3J8ZsnjRTyQb45QXBe9r7xxQv3qR84ymwjdnh4wPioGV3vaTauYLrmaKF44s+ZucuI6w8xeApkgVitUvIMh1PRy9wZeCqeoivhyArRuYcc+cSyh24beBiCLsL4vUDYv0K/CucDYTNEd60cFgTLiJucYBMz1htPs/ELfjoyVuc79yjaStcVhDrCuNHSNtRJtJGyA/+lTEb2ppRaGmHg9di1jU1Rw+ecJCdEf2S7CtPaL79c7ijx8gfDbTZZLSint8he+Yx2Xkfs+uD20ybJTJpsUVNu7fBxQviP/tlpHX4t/4lS/sOo+OMeBQwCKEQXLONWTNokGc59lCIhy+QxZ5+l1VOm6eqy6DEHT1Hzm/D+ZTL2UOaqyvK9/aoishquo+7vqYc5BR2QHt2it09IBi4SIYqN2PWyvr/d8wWp1dAxDlhVAfW3r8WszZb82L6FjvnLxn5irouNGajAbPuY9bnSqq6GbO4RFv4kZgNbaI1uCU2DgmmJHfJKetfI2ZX4wWYjm7w8Zhdmw27izGXk+XHYna8HmGtZTFQ5PG3/ufvfKqY/eZ/8Tdfi9ebzU72RuNiMDU2KM2rRSU0AQSVn8K05CI0bBjYSBM9TTR4W+OsloqHWYMzgToEBkZojVbPosDAB2oJOJexbofYdoNxwuG4xuYGchQdtUYTzRpCpeudohei/NhSy9HGCmXIGGSW750eUYUZndwXkLThu3GkP0P6rl1K0/+bmN4UQZ3e9M89omwtxIYvPnwKroZQK90hN9BE5FQBDltE5RA3QGNIhF4YxYTukpDfoBqMjVPecVODV3UhFQrp0F9Sxpcg7RI9r0lqFqDvV6mljv+gi02D/qxz/ujRfcbGEHH9hjBgkruapbOo3iZ6CViJW5S3GzU9uHwDyX0NIU0IcZGMsCDJlnWJdBRs6n1qk7SZ6oAlXeIEinfgVkhOv44tmtsBPj2Km9bYrmdGEiCm17b1J7h5nR3yDeCk07pQVLw39KCjgWwpu93nQaJgypZWAfBb/+tPYbGsL4hE65JshjqI6fIveFTO6soZMhlQpt7BEk/hA2VrWdnAKnoyk3NpI15gZBomvuUq5jgc1jhOVgFDxhzhdsyo84ZRMwBKqnVAnKcwFXcHjioU1I2wCmCbAGXFMLNsGpUIW28qYmzJs4xApGpVsUE7MR1F7ihDTbsIPBg6nq+FwujgilEINklapW2pNxZJXJmAp6jXrLMBhTUY0xITX1SVISKmKLTRUARDS/BD/unjK379UJgUDmMcTdNwVbXsTkZsqgZj4M7OkOzWnKZp+ODpKetanbhcLJHoWa9KZDhE8BQ2sG4rCjPAirrrxdCSZUapFHQlPqFuhWFe4CQyGQxZVS1N0xCC0IhqN1qXBn8T2RllGG8ZVUv+/i9N+P1H5zxrPPeHhhe1472VI7e1NsqiMmgaAFvThcyb/r68Zg1pEoeoC0rT6TJHVMvfYoPq+pobuzuAXDxt02K9v7FwWVpS2dX41xJebpR+OhS3EwKPUWX5OvkhIepEf+OQhEgIEF2qGBhRZyYS1SM6rFXEOiaOXBCDk7ZXnbA20YoEIKSF3vcThH1tCvj0h9l7gj+7RW0L8rhBvDZjNLbAY7DS0E52KWIgnryD2EDx8j4mC4TWUFqLz7VR9Mpa/HLG0FwRp6fE1ZG66z0wnL4cU9eGWZ0xHpWUecVwPUYoiY93MS5jfvCI1szwoyXtk3eIi4JsfI05GUBmtUpx7mDvlfLZZUQgItGSXQZsjDA5RcKUYXPMOh6wO6m5NMIgL7TTOURCXFM6yLwmON7Y5EqpMTs7e87F3u2PxWw+KJDzFWYy6WNWQoMUI+zTnMXtltzmmJNDstvPiE93iVdHXNx/xcHlLvlXT6jG9yhut8jXS3w5YxJKmCyxYYScjfCrfcTs0bxzSvY0J5y8y9halkcbssxQBIO9/0PiiwdgLbaBdpPR3g4MBiXMPXZTYiSjyhxNG8i9x7UFPH9LF8pf/Qt2/Xcx3xhx8fOfJ3+2YjFfMC+WrGZTPjr7DMWo6MfkAUHXcxHOrSah3hVIqH9szI5Pr/q/rw92mJwugMjyaMbo1TU2GCqffSxmZTNin/fZ7D8kf3WdNoSW1oBhglhDlpXKff2RmCWMCaa8YW7WYuIEcasUs2D88rVF7MfF7HqyZrCc9DELQwaXh1RRaHdO9HzXh1SzjwgOEOFyvvyxMbsea3L+l2UsBx2aZonSNf2kZFBs4ncGbWh0em81fcsobEsdPV4sjeTElBw3ErGmYmhrKhkqEmsMF+WYaCwZK2pfMTbCyli8tDSNwVrVU9rJrmmdpQqWJnqKEBP506B9sJJK/aKJpbBtAuu09R0UoWVTWSb5hqacYjqkLwp5StZiV7w2W+WQYDJiu8G6Id4I3gTaNCycFZo2kqfEVAArgegKXryac2d2rGitEUVpG4cZGEyjawyjoL9vI1xEJGQYa0BaIFNjjDxx5E2iRbhuREmqx8sWCUwfSzTKJZagXOIGPX+nJ2fMllgbgVwlBGk2/MpbH3L6asQyzJnnS1btmPNyD2capSpIJx/2uiawtUIrW8e/7jBA53Kq+aHej+5zIoZKwBhP73mVjtagfQrdmk634UjIsN1KiEoau/15e5Df9JUYl6TNumsUiT9yrdu/ZSQU10Ta6BMbAQIejJ7XpI1fxGASP1qI6RnGlAgnlSvrbny3T15jPzER3pGaN0fC41o7QofegVguYk1hHM4YcoQyCt4Ih6HGYlhj2bQ5xhnG2vVEFLj24CRyKiOybEKMjTZ4BtiIIxjLBTOO2w17NTzLMt6WmtC2TIgUGcxiJGAx0vDRyYKhh8PZkPl4ANZwvd5sy9dNxAZtyNIdg8cQqDYr2tZgTM6v7Qif3YX//VHA5YbMa3tm63S8B6Mlgg45fEPW/OaDiDeRf3LScC6eLKkC5LHir+wV7I0i/+eJwTpHYzx/fV4xywynixrsmOOrBfPxgMWqYrFpKOuS4WBMHcFtasbDIV+5f5sPzxcs1yXX64rZCAa5p16uWVXa5Od8V45IqIqHgc9o2xZJsmhZDrFxrNdrVsZRXy8xJksbGuUmt1FoRFHTaZFTFAVNveHWzojLVcW/+5kDilybA4Iv+B//+JjHtsGnRskQAmKzRAXYlhyd79QSkoQdppdYM0Z5y0LAWYdEq24zadIIMfYIUHcOb4WhS5JtfQBum2LqhLC6fsy/vvlT2kP3ts49sBsb0ssOdcGzFStqMSbivLojWSImbhvghAbrFE0FRaMcqiPs0s48kOype13koMLhdGSjv5wjXx+zfyic3XtGjqN4fgShovaSYtbS1DXOC5s3nrPzSFVbaj+maYcYp+6D1loageXb5ziJbJ4/oK6P4K0X2I8OyJ5Ade8V+fFdTrnPVbtktLzieHyLqf8A2xxQbEryi/tk0hKWt3DTpzSv9nGvDHY3wPwMFpbKviC/ONROaWewk2McqWnm6j5x7ylRlkh5m2hgMn2Pt5p9np1BM58gowE2qO24aQXxaKkzjY39y0fsPHzGw+p9fnj6OcrdO31pfHh9woO9U073l1wf39fv7XNG8+/jL3Yorq6pdvcYvNyAm7FZCeO9p8zXa6K7jTk8p4ggLx/CYUt7XeFOrTYdxQHV7RoJa5pWpcMGPlK/9QOiMeQGsqsHyK1T7Mld7O1HxLNbGC9YM8O/gvV+wej4jHh9qKuJb8mmT2mv7tJknvzSku212Ms7sPuMeCdn9+Ur4sOc4fAjbDVC8j1ufe8pCxsxpXbjtyFSD0fkLudwddqNcjrk/HxyqPcuBhZAdXTEOETC+RnD8wuyUU4owdgB1i7ZHB5SnJy8HrNRY9a3c2YXV2ljAjdjVopLNtUcJ5DnFT8as0AfsxEProSoMetdoE6/7OZg2yiVYjU/JtiYNn7CerbqY3a4GEN2xgCoE8l86M4ZnM/Y7C8Ynmkj3PLgnPHJnOpgRWvDv5GYdWx0s9JMidaSucT7DA5sl+SEhMJHYJNoVp5KCqWhiSaYQRze5CCRihGZ1fU2SERiTPxry4YRbbNhwwZr5mTmjBgC0Qq1DbTSIjistLy89uSuZT4URoUBY6iqlAQZi0ngAGgpXJ1CI9SBNuoadGdywq3xivdePaRwWqEMok6hveEV0gMD3lzwmYPnBOt5dPkGbRxqtRiwseL27JJ5XvPh5b0kG5fxYPIRhWvZlI6hdbCuNSGtUfS6ibhRqlQ0QZPd/YhZhvSaQl3jvNEmt0bzFkzY0hqSEoo6O8k2udVOfk2icbBOSW4aKVuL5VSjz0SR57ZVnnLVcHB7yYFbQBR27YrFDw3EAa3RpDxE3Yghgk+UGUHd8YAtt94opcIlmkKMmhhaI7RGjaesiN77m1Ayeo6MtM5heoBGbrxIokk0Rk3wPyZVKlvVkw6s0k1u2sh0ldju9b1TcEwS0J1QXOyrUi0OJzF9bvq+gE3V917yEUmJdJJgvWGtHP8VnuifGM2/vidcLGt+edDQNIFiNGHRwKvG8kKUe5nFwMRb7rsKZwect4Ey5uQ29PC5ypfluNjiTUtrhcYEGqtmD1d2DCwhWEZxDdKwMJH9NtI6y8bPGdqAuMjT6wpjIm1d43LHZR0QY7luI4TUrCGKgC7L1KRhDLm1FBmUba2NJCEioWQ8nDCOkd88aniyclxK4Dw4nB1h21orKa5lFh0jv+FBrLFZzovjDf/OyPN7VUkVBkis+etHI37xTs5IMv708pJFXfGrA8sX9wdsypomc4yLnKpcsXLCum6R2NAGx8vLmsNxzWBUECRy3VQYWopBhlkteXoWebBfaDkXQxsiGYogWOsSd8cQY0sjQBO5PRtQx5ahDRR2QNUErFXbkxgjg+GQ6qrRhaYNDDPHsHBU9ZpBMWRd1TgLe8OM8WBAQ6RpGv7eVyb8D9+sqUyNiSYZXKiTU6eIP0jDXkRoSBQAtY2hMaL0MkELZJIayxICE4wmxVkK8ohh4EyS2NGkNbitIgSo0sPEbBdyPe/2z057IDRAI325t8uaoyHpFqMdxXBjUdfmkRS5ekUWXBTE3jAc6CYOEwnGqPtcV5I1RqXWJOJiEkZPKihWXkeiP83xmfmaZrlkJ2REGqI3yKxhORjzylUEY8g/2qWIwt11yXA44upWy+ppRkNFfEOdtuTFGzA+xDwJWFrKB88ZHN+hXHyZbLxgPbtLrO7QTC1++jXaC09bRPxVSebnrG8VmPMR46rl3JxhzIZsnePtGUvrGF8OsVfvYLNL/O4xUYRQPyTPHmPXGRhDu5rSZjVytkdj57RBmA2/hwwd88XXGB7NuBpOuX42YT26hbMjkAoXNGbziwsGd8/Y54IsXBOOj3hj/zFPd14i51/Frl5RvBXIdy+4lz3h6aZl9STn3dkpcvh9pBkwCGM2doo9ccThLnl1DDGjxbHyhp2vvQX3r1U28CvfxPz5hMv8XSbLF5zLQ3YGf0b+4TuUBw1tiCz2I9OTgvqo7mPWXs+ImwKu3sF9+RqJLWbvffjBPqPjIzC3oWiRynM9ept5fES+SNq84wijDXH3KfbyLvHgJfbpDPvgAnn1gLqIjG6/R/VzD1j88Es01IyW1+AcLrSYGPpN/iSNw4DjcHGmVqhRKEiV4mHOcOTxISCxgtzQtN8kL+4T2m8RdoVBeU/jCcOobWlcRrZW/m3pso/H7HqIZiJQNQ3L29qE1sdsyidchMHxlPLWoh/r5Y2YrbtEiY4ek5GJfu7NmB2eTljvX/ef0ZVUF9MSMzM0psUcLGiM1pmWt6/1WsK/mZi9Oz3murLsFkuaAMMiowwZpRkQ4kjzJ1py1zB0V4j1NG1GLQM8QTmkCSHDOkS0MVTnVghkmnPZIQNT0YghkzVOAhFHY0o8juBGZLbE2oazjdJb2tZSOEPdWqClaVFbZ3Suy7yhqvUCjFGE0juogwGTEaKhEWGQeSwN78yecV5NiZKxaYdE54mxxRghR2hMw8hssOYa4wwnS8fu8AXrzW02UkAM3Ns7Yzi/Aguj1Yq6sYwHL5hMltAImbPa2Fez5ezGQC0etzHkg0oTZBGFPyVA5qFqWa4KJuM6ZWk2LSWqSqB7tw7RjZrURpRWEUWlF4xNCfKWX0/mNOElndOJZl1t0N81KHpdtAldNxBKvnD/OX/++HO4GBVUsoqNKhqajo4eI6oPbI0hEnEuVVYNiIsdoUKTUEnrlOnUH240xllFjbdOhJ3gmvTnsTfWOwy9S10nqtDpuneGLYKi/93n9XSHfq1Mn+XoG2QTw1nBJIT8NXm/lDijlAtJtCb93CS3KyS78eRNIJoUf9LxiYnwd64sb5nA2Be4oeBNSeZbnJtyvdbdzVQscyIuNozyyF0TWOeBr6/HbNIE1xqPkzU5nfOc7j6sNRAbbjcv+Y29Mcuq4nAeMFlOaDOWa8vT6xOeuvu4+oyBdUxmGWVZEvOcpjEUecGmjWyWZc/n8gibWmcCTXgiuWnYnRT4fMiHLy8IUf4/2t6kx7ItTdN6Vreb01vvfv36bSIiI4PMCiWqAqkQFIIBYs5fYMSAKb+CYU1K8B8YMEEIJEQjgUBkZWQRkdHcxnvrzU6zu9UyWPscc4/MvAlEsCfX/TR2j9vZ31rfer+3YaoV3leUSvKzeY3a3vDy6Jj/9r7ni1pS7DpOJpH/pp3zc2P50xPLzmlsI5lOPQsN/1aAv3IW6TWzpkE8Fmzmln+j3iEryfPTFd4qnh0tMCqxaRy6LmlsT2sDJ7OaFyeGq02md3z8hZdlmU+LqyVn5H93CAHH6NWpx5FDSljvMQqM1iQhmFaawkgKVeK6Hq0kxhjatsVHwTAMWOcOFmGFNlRjJHNZlWiZDpzhq2bNLAycz2doIzmtJf/xjxP/4p1E9fFQKPFAzoeosvcnKVEkiNHj96e/0Rplf+X0wbzBKEJGTtOTOZKUuUG2MN49oJUeSfMjH31ElCGPb0KIBz/rRF6blMyItJKSkPJJf18AIfHRvHP/xvgR1SJ/FiUVIsV81pSfjnY+voTIjhFKqif+UsrK3EJKbAyHdVU8LW1/8HXbVZyWWzQGXn6D3T2gjnbE659RPz4HYJYalEpEGxle/EuOBsui/HPe+xd0b7Klz4BDPb6m3tfs61Pcy/dMrhXd+RvO3j1SHm9YPSj8+V8iFgpuvmCYBCbdaxr9j6nsG3xhONYTXGjxZgUNlIUkGE9Pi44Cf/eSIgRM6vCbs8MhRYlE+vwOdufE2wYlK+gNwi5gcc1G1By9+p7jRcG7cs1JO2W6btge33EZ/xmfV2v07H/Exz9jXf6Y6uI9Wk/46nvNZXiP9JrPbl4hfSIUJyz616xOJc1ZhX74mvmxI65uWVx/oFk+Q0++pXx1QpwlSrNkF3rSX/wWcX9x+H7v+Kcc2dfY6U8oo9AoKRMAACAASURBVMd881NC/Zqj7RJSoi+abGP33Y9x8w8MtaN+/or03U+Rn91kZFmJPMpcgv/xb1C/+Bxva8T8G1bbDXpX4D77nth+SVXm2aZ8fA5jzVJa3KXB6HcY8Yx4+Yz5P/nf8MOGD3f/JqZN9JOMnJpm+8REKEuEd5AiZQJcx6BzcILk0wh1TIVIgkn3BRy/Znr/EtO2UI7+vDIRzm7h/gXuRUac67tneFngj1/n+/DNaY7pBvrpBNH3zK5mh9+lyDs17dmayfWC9lmmZ8yv8mfandu/s2bV6G0dx5qNskAkRyDRnW5/sGZNynS05Ec0SgbKpJk9LrExMJxs8TL80Wr2qltRiTuMgoXxICwzJI8y8TiU40DeY6SF5JkKRzQPaFVxMzzL3t3k8bFMFggEIREEYtqL0iLaX3K8fKR3kudlA0oQkmbtCuxuYKc+J/k1QgaOaxhcpgQMo4etjZowBFJSI0odkV6ghDpwUpUInMyzb+6HdU6t0yqSQsqppZOe3dCzXAiu1qdMKsfO9hwVPVf9j5iaOz6f3OJiSeMUK+PQOhLiDX1/TC8knWuoLWAEZ8V7QiGYzkcEfRpyc2jT2ICm7PdbBWrtoNf5RLX//tP+dQEmkpmI4PacXrGHvZ+U3DHlpjV3YVmMJ8fG1oX8uFJgwyiWG5/fC+qUyIhziiN9Y1SJKbJbhU9Qk5vlwvLzi9/x/c2PaUM4fObc4+SPr8XofS/S6FT0VA5pxNr3lxwb6Mw1Dk+gzfh8hrISGf7Je2rOA0gH7+ccSzPu4SJzmD/RjbG3Y9sL2dJIqnhqcM3v7XkJnsKqxs97sNtL4m9TkD76/wlAjFOTvelTGs8hWojDY1mA+cP1+oONsBeJ11vPS6M41UVWeUdDNWz5J1PJvTUYAs5HKi0RLtIpiU6WfzwV/LZNdCiWQ4uS2dc2Btih2ekiqyCNxKtjHvobzhY1JEk/wHq34biueH50jFrf0AvDxmXe0Woy5aZpCSqiUqIPkWZw7L0nCymRhHyPqazInxnFzbpjWU9ROpG8xgrBm/sWZQTWPoAwvHtYcyIqvoy3zJ/VBAH/YdpQqECl5kylH/nCmmaw/MoXJF3wZ6blMgU2t5f8qTrGeYWWiuv7DcJH/uT5VzgT6XxP8IKgJJ1z7HaBoijQyXK1bnh+PKNWNdvO0llHDKOq2fZUpSZJSRiR7qFPBK0zr0wpehswUrCcZuP4XddRlxOSMCA90XmGEPNJ3QecD1k17R1VqZEyUmuNiQKlDZV2DAlKWaGU5m7dYIwhJclxoXluN9ypCccBXlaJXwxZ8LjDo5NiKhJDyPQSrxIFAo8YR5T7U2S+e2XikNSmQkIpjUuRMK4XaYynDB8R7/OJVI3iHp8pONm4cLSd2TevTwtItjGLB9sVP9aH/mhvTGlvPSXGhjsd4qElY/qT2Dfr6rAlihTG9+0LWiJDyrxP0kEkEEfVr5FmRIvVp03GH3Ipyb13HNU9dvsF0+kb/Hc/peSWr774De3ua/Cgdh6FRn04JroF8vNf8uJacDkPpGJKefdA5T3OBWKAGSesXy0JokN9OKeh4mz3Hf6ZhSQJDxeUTYLZFdvZGUfrNwQhCS5QFhbtlgi7xsqIVBK7i/iywY01G2SiCNngyQ3H6OIOrWuEhehAT3ZEO8PZZ6TGoB5WHFevibxAbKBWiqr6P4g/e6QScHGZMPUjbveMevIKOVxDCaK64+r5v0u6LPhs+j0kRXxjkEvNbLNku+io7izCR9K/3eONRrSBCW9xH75ikLeU6RGfFiz6a8T/9AL+vffExxfIv/6Kye4b0q5Av3hF8f4DHM0R65egHwCo2oqmEKj6krY4YdVeI24npH/6S8SvXpDOXxGuv8q0I+nRv3xGEvf48JzqsgDWOFmgrxXy4gNpoklzgYwa/+EFOgJ//hb9f/6E8BffgHsH9xfoX33Bkexo5a+w+jmL9R2racuH2ZcU/QODMnhTYM6/Qb17nmu2KKiDPdSs7ga8LLAqp6oZ36NNBXcXhON3aPEcFzzWlPn1d+fZMvUh+8y6cWaq77/AtC2d7qhiSS8HGHqqYBhkRrkKJ55q9noGIjK/nqGtZBhPuNPrp4hjwcD2YkAkQXFzhmCgOcvo8Py+BEr6k5xCV9+v2B5nD+TZXUl7vDnUbPWwzBazIo01m0fLUeSU1MXj0ac6hD/wkknwMFQUyqJLhYgRnxTRNzwrG3a+RuIzzUBFbExomZve8/KSzTAjJkOIWzRujLgXhFQiZUVCUyjJIOb44Y6T2hMRBK/YDYlJ0VFPNam7pBMFg5cE4alLSdtnm7qYEj5KGmdGi6snT1g9rqkhpgzEdFAWUMjIkDSguG2gkOBCXusedyDlQJ2uOZtnxVniWwrhQStMGp786L2gdyukMhyrK1IoWD/AchXpo0ZK6JqAj5H5ao/K+nHEpwl+RB+1guSglTAbX2cZfYFHDrD3Y0eUaYAAyYNQo6hOitFLOUGZOfbYPadY5yY5xqfc+cCYOCfz+/TIXVbjjiFl/nOGWnOz3I2c5JRpUDE+osUcLwbmRcNuOEKLiIyZ2qelyzoVQIn881PaU0725IInGzQtM1UxxDgGUIkDoppNMHIznN81vnc86Ej2r8n3vhTpAGgpuU+fPbwx788Cwn4/PLTfT3tzSiCUfDr4MjbJ43tTYmy294eXML7i0C3jx69mj3wLYkaFyYeYPUD6Q+X6g43wS9kR54ooIh+2HTZJtr2nLBMrq7goApNpya8fAleD56SI9KKk9xoVYUVEyoCOfR6nCAFGs9EzVHD0MlF1A8/qvAFe33dII9AisDDQm8Tw6KiUYth2mPmKaFvWXZMXjAjt4Bi8YEg9WhqEEHTOZ06JUhgj8D5SyMTOBZTqWVUl942HKOmdQ3mFUgVD19HripQc9arGIDEp8WwOQWoGu6PUdb7ZXWBRFvyjoqf3Ay9Xhm3TU2jFm8eWFAJN19EjKYqCX72/4evnC05XR1zebbhbNzSD52w6J4WGeVlDTDjn2PUtg41cb222KENQ6OyB2roeN/JsrbOo6DFKE5zDCkVMgulUMS0EE6Po7EBMsOsHuiGQQsSHSFHkMSXOEcjPF4VGB58tIvvArnd476knBmstNw89j7uOn391ztXVDUdKI5Jjo6Ej8u+Xgrpo6AbF60GxjZIvjeeXLuFjTYklIlmjRlHdR3GIUuBF5jZFnUV+Kc84sip4vLKyWaHinic11khS2X5nTLWTCfYmswGZY4zjXuz5RPIXco/Wyk9Sj9RYVimlj/hJT/SHPYNKJZF5ZIBPIvOTJChvCUpQeE+SHkWdm/ixMLXIFJFM9g8fLRF/2LWcXCJ3Aq8htYJh/ZJtUOhkqB6/ol5+i0tfsisnxF3DXNckEwndz1DTwNJHtrJBtx6pI1pL+qnkcThFBYf96ob5q2PKckvqZ+ibLf7y5+jnvyZVU1xxyuRtSTIexC1S/AS/vaOfbanjlqgsgy/oRUXTOyrmCB2wwaKSoRRTirqn64/RYote1wTTY44d4V2LSAuE6YjRoIbPCb4h6AmybgnHjxS2RJLQq++hcnkDigvKsgUX8P0pF1cbUn2HXWmm4htCsWCQJxTBMFtvAYWKxwx/tUX9B7cInpPeSrR9ZOun2PmK2fwbtDvPu8I3K8zRW4a7I+qHFhEd8dUZXbGj3p7A8a9puh9T2Ue87ElcUNoNetghxZzNpmb+N0fEi450+QL97A3RA2tPuj/GyhI9fwX9KeHkDn01sgSvQfz5lnjaI7VGxACvVqQ3FaLuEL9bIr8Db65I/0zCf99TPj5DHN/Sli2Sgh99/0vKL/6KdPknbJopu/sln+kP/DZNEM8bJpcrIpJGFUhRUADCjwEtpiLGASUEcvMSGwe8KiBEdHJPNWsfCEc7lrfHh8eSgMJXeJEYVjtm98cIAWXMW1LS2TO1lwNi5D+aIf29NRsomVyVCAZgQKXI4nqMAR4jk8urCUIrhA3M7ysA2qMtEkWSUF+XdGcPLG5XuWad/P+9Ziv5SFlm7G7TKWIqabxiqgIdgalumZWCq2ZG6xIT3ZOocMlkBE84jAAbhtz7CYGWCinmmWKDpHOWWZkPBbeNpJA5NKFUnlIU3LpMa+gHy6SucB7kEElJEKKi8wIb85orpAAkKWSdhpICrQQhJJSIOC9RMlIVCTfkaGfrE04KlFT0NiJkiU+RaqoyykHkqAy583GJ7M+Zec3CSBbmliEqziY97ZCtsrrGEFOkH/K3UGgF98AqwFTBDmih94JpJbPFhTGMc/McgRwk9PshvUTIsbn12dIr00P23tIiCwVl1klRJtBjkpzL7VdurMfuLaYR/SWvQYhMg9B7boPIyLUb/27SIeAj9KBOA/0moJWF1KCjQUZYVu9YyJYmGLZ2jk8F82LDYJf0okQx5CY/VUSxp6bmey1bHmfagZbqQHsIMeZ/3/5KmVKwtyHdt6FhbKjlKGT/OPAkoZByRHd/D/Xdo8jh0KryEbK7pzvGw0N79DaNtnOZeb3nBu/N9ETWmAmJjxaZwEkNyCyUTmTtFDLzif+Bav3BRrjpHCklrpoESaHFwJcXpygGWmu5sZoJXeZ+FBKPwAmYmcDQdEy0JOx2+GQgSKRShOR4kXYYJLdDT580975m00dmwnIaDK11+EWNto5CeHShaaeSh90ddZLMy5L7zjGEgPOJqpB4VxHIfsQxRoyQFASccxRFQdMLNIGYPCF4XMrqayEEQ+cIoUfVJQ9mzpyebTcQgqauyxyvPHikVKybHm1kdpRwkedTjfUC21smVcnbh45aBUqdD4lSKbTOI/lmcKx3A33nGAbH3XZAWMvPvjqht5GiFNw3DhrHuuuY6ZqbzRpVVvjOEXyi9+4TH9sYI4URWBQpJJrB8/7ylovjOcVyzq5vaJynGTz7vUkpidYaJQKlKRisz9xh61gPgpQsKSUGlznXrc0Ncd9byrLk9fUjerbkRzLw62bAO8ufVYbpRGPknKYVbNp75kJxPpOcD5Lvd5sDXeB2SFyZvBGFvd9xFNmvF4knAE/iiQMvSEqqcUS7p0348fY2IoEcY7FJI/V35CslYDxx/v6AZH84R/jD+VnvFfOH0pEfvWNEmcl+xUmmg4+wVDLTQ4SkKjR/UQd+9KXmTy5W/Bf/6wMfRIXcb6AiIxXZxvIfKtP/F9dwy1AZmibkmo1wdBRhfsvw+Jy4eYkOAlE73LTCDx4qQRk9Q++oQ0ex2xDLhN2uCEWJHDxH82/QrWb4jWY3u0P0J9zbElNfMREFXC+J4RnVQ0I8/xui0gx3Fwz+d0wYUOoz1jHg7IIudMyrltLOskNEb3Os6LBimD/S7SITXdIGzaKVSFVi1xpfzCEMCCGwnaUsbojygs3zE9S1p7AlDEvCfIswA2EY+fPrGcFAqq/RsiEsb/AqUXcBa1ak3QzdKdTnv4HbU3BLtkcNs2JHutTI2xauJAMDpp0wKb6hVV9Q3s8QJw+w9bD1iK1lO39O8XiNqz5H+ClS3ON2c9inQwkowjXMG1RzCiGxeOzwzRbVHyN/dAt3ltDMiWpNOVxSAW4hsJMZFWs4twQs4v6c+GGJvl6PU5UeXhf4r99grk+QPoJqcf0Z6r+zaP8V/YsK0z5gmgGddoQvaoR9Rr/cMGl2FPIB+eVr/vTVT3l416PSliQFEzvn8uu8gBRXn+MGh4rZ/lIj8aHDqwIdbKY/iNyEmmgxZoZvj3Evsse1eZfpN/7zO4q7Z0y7L3EvXh/U6ADF22NIiToYeu0+ucWLbr/TBsLY4JkhryexfPIbfbry692yIcZI6acMk+z+MLmdgpI5AKSCnzWBo3/0lwgz49v/64itO6WfPB7WoKpdMky3TJrlH6VmW5fRsc1gCEg0nvNlQieP9dD7EpE8MnkqaUijgVilLM0QKFSk6RtA48jra4oBJR9AJIKTkAqsL2n8AiN6gvFYb1hWmi5ENA6pFctCsWstUUQmlaC1Od45BMFEBfqxpQkhjU4ACgh4HzFasvOaMEQW5MlcSAoX8uh6sBkcrY0hyRUVXdazaUE5UgKdj0ghsQMUSuZRekgsyoEhKnqXKA3cNQVKRgqV0VkpVU4xTmNjOYrdnE+se4ULntWJys4WChhGL+Eh0xpsGzFG4i2YKAh+1LqNX29MKVN40ohcegFrD1NgojItwsvcBO+bXMHoLDE2y3txnY/5tYkn1FiMTXIEbEAZDRtBVZYcM7DtFD56FuUjsyIL46MTNMOOQihOip5G99BVo35P0PoCyZh+KFSmMZCfSyJrXj5GWw941NgQxxFB/rh+cgCHyvd9evLXBkYB+H7P/JR6FNhPU9Pv/8iPXvMR5UHssegwItHi8FnkSAeRQqB1YlVd82Kyo1o6/vdvnxPSApnCU5oeIMash//PjXBIiaRLrtaPSFHybF7w4eaOeW3oBMTOYxY1HofsGqr5jFnyFA5OlhXNYDmaznjoPMGPCV4+okOXbfRiYmYMTWm4qObMQs+uadgEh3rIgqhaJzrv2bmcH21KjQqB5/OKs+WUv353z1FZ8f26QUpFigqfFAudTxlnJ0fMNCxqRedhu21QRckQAikKQvAsZhNkdHhdYYXHW8tdiPTBMg2j127KgRGBRJ2K7OPpVE5j6zuCh7u7zM+yKbKqNEEKSqkRLuUEty7Q9z2b1rLpPUPfE6sa1wV2tqVINUpnIdvg4GF3S1EUONuhkIjgWRQlSUR6n5iUebM5n2u+X1suZhN86CBpdq2lKju0UMwNbBpPCnvfWkmyA72QaDXarQnonP9oZJHwIRxOxdmKTdAPPWsncAkereCUSK0qmhQxfYcsJjxsHjHG0A+WwVYYHFrAdogMylNKzUm/IXiRY9vLKdpa2nmNjJGiNNimI0pF0h9zE5/+G0b0Yz9OycbcecOUY8O7T7ST7D2gU/ZETOkwLjFjgxIZRcLsx0OMVk8JJePfGocGIRFKHwSa+8f/nRclrz8MDFLRDJZZMeP2dsefzwWXm7xwC5FpYHE0D/pYcPeHXoMyxPgl3eYN1j5jfrRj27xlcn+CVQ5peijP8Skw7d5TxucYe0XaPcOdRahvkR9O6fsCcBQq0akFE/sdaNiypI4X2NKgZzPKdIrtGoZJ5Dh8wCtBuH6BEi0DliQcqqiQ1qHSGfG5oHhomYgjGtsipcKPNXtWbHHKU+pnKC0pPruhf/sTKvGe3pyie4v1CuxAeTxFDJ9BpRG3ENUNw/uvKMuWFBIhTihmOwiBYG6J9ghjCkIzI2hPae6zLuXqJZiYI3Zf/wTx4neo2zOESzg7QfziGUFeopyiuDsnpnckAZPtHfHiN9j2AnWX14NymOCbHRhBYTeQthAMQUem1TfE4QRNlW/jVYvbRvT6S3j2C1RSiM1AWL6DWcK8gvh9TUiZ5qVvTzHL73EI/O4zatfTFSW1eJ+TNwGvImr6AX1VkNImCzWToIwC6yoimvP7O0LwlP6EIrYo8YqQJtTtlnbxHLdpca9/iqkeKHYVfXNEtxooJomT3waClrjuPUnVFI3HHZ/gnUdPS2L5iiAVxcP536INxOM3yPsXCCFwk/EQL+6hH9AMhFWuQfU6B32EZHFfbjGv5pT205ptv75DJJi+Pka6EgEMq9wQi3Fsq8RTzboy0yCK9TRPDBfbT2r2y697tr+tsb7AzW4RwxG0lh9Nd/zi/oh8KM81ayf50NHM7v8o9ZqSQCrDthVEoVnWcLu1TI1AoNh6ybI2yCQZXM+sTgjR5ZCUCQwOUiVo7N5eKoOdMWb6XIgaqT2lrpgUEpGgHQTRS+46jxaKQoKNij4YDI5aZ8ePRZ1Y1Yk3D4bCJIY2//77PCSnkgOQOJ4rjPTMioANORjKaE2IgpAEMaSMykaPVJqYLMEHdqkgRI+PafR8VRlRTAlMbni8lyglcNbjkuB6l4W0yUvqImUKhZTYGPOX7yS4SLTQOcXgAlMzork2Uy+eOL+Crg0YLfEujPapAWXyXpGizCAy5OlSWyGqlH2KkRnR1emJL2z1k0OEFKNITjEmQOXPlzGejwojPXEAZG5Ko0v0IWtgOq+QskOYkpg0g+vQWtF0Dq0KrE+0UaGSQ4pE6zUGgZYB5x+xURKTQusK5x3zooYUMUrRWI8QCvMRsPbJZxN7/Hj/XA7pgCdAaS8iT2IUx4mESBlSOuyxctQ0IZ4c5J5+JCnlOOfDY+PPFEKNVIxP4aufHD3w/aZACsPgcs5L2CVO6jWXzRRJGlvnMSUvJYrf95j7vesHG+HZrOax7fj6/JhX93fcbjPS2sTAVGii8QxdT6lzbokdIj4GemDX21E8pXHOIVB459ApMa9LvIxcfxhYTizPy46TOnK58cSo6Rz8ttlyPi0JdU0hAz8+MixnM5ROrMoKnyIxCZ4fT1FeMCk1l+ueFBVISe8CqjI453jfweMAs1IQdc3JpKD1lnWfuUKbtqc2CkLPuZR0SvDoDE07UA4ZFU9R4H02FO/vG7TWIG1uhGNBW2ikGOjbHiMkD7aniomvziuW8+x9uL3LBu3FVHEmNEd1Qe8jd7uBnQ/U3vJ8OSOJRAo9i2lJMaaODT5znhMJI3OSmpFZ3WutZaFL7nYbjBIMfVYH3+52PG4H5nXBvC7ziS0lEIFJXXC/7bFBHEztAULIiHqKEhci3nvMGJIwuECSktZn/vJCGt44heu3NBZ6pYjxkeWk4MN9Rx/gVzctpRI453gIE+S0oCwUXbGgHnZ8XsKDD1RSE33PdIhsOkkQAV0Uefz50abFeIPvx5VPVKH0JAAYwSCzNwkf47mzoEGNRZaFJC6FcePMtmsigdmPYVIesajEwQ5HxtG4f9S2OvLzKcGpCLR3PSsUt21LtSr50IDyib/cCQrFwVUjibyGfnLw/iNcpjjG+rdMT5fIxw90DxcMZUc3T8ykz43m/ZaZMZRFVsmnZgWip3iIhLtzBBqjrxBaEYA6XSOLc2RIhCuNWd0yqyQL3fBgLao4wd7O+Za3nC4VeMOihpUwpGWN1wEzvSKNNas2X6O8YFptufuwIMUFSMljahDTEukHhsIyfPcSvfQ04ZjaTfCmRwuHQ9FvHdUMZPAcDbf00wI/CcRNQWKSa7Zbwcble6W4p7v6HCk8YqbZFS8IC4NyDrcZiBNNWb1n9qGgWe2oT3+NSnP6TlA6TVg+omxBMgW9+5JKbunvf0yhO9Rpi1cR1g315ggmG5K8Im5OiYt7KinxwwRmH5BKM/QTSj/QzGfMVv8zMhT42MPOIP8HRXqYk05uEfHsMOGQR7cgL9CXD0hxi0NhfI/vJrmxLRt0lIimICqQscQv3xHXpyQJQ6iZld+T7n7KvVog444otsT7M4SyqPgSyWvipOZDt2auPRZLFxLeLklK0U1KZu6O8rNH/LagCpL79JZJp+n6mliCPeuQp5eIhwvS0dXhvtQpwembg4AGQMeECJlmUb7NDbD7MgdViNdTyrdT7HyH2T6lywkhMI8TVJ+Lpn15n2v2MSPCkuwdGmNCjrnmuh+5xGU8jGT3NbtIEh5vqRYTfOcpSotrT1DxA797+Irm7O6wa+tBEcpA0ck/Ws3OKkE3eM4XhsetY9NLhKjwMYKM1CLS2YSRgAj0QY0fR9K7SIqJILKLUELiQ/aur0yewn+/kcxwVHrDTHXc9AUxKfoo6TrBvAykokALz/mkY15llb4sxsUpCU6nCpsSlU48tApHpq3YIKiMwflAEwtan5jogFCaSemJPtE4TZTQDolCjc2w3KCVog8VfrAol1fuMKaJSsC2Ai0FSmSfeptKClkQcHRDbnoHn73bny0CJ4WFqHC7PFIvTGIqIpMi4aLE9onoBTqCnuzRk0hdCJBZ0OfjiHomkQELxvtFkJFcnTKHVwq8z5ZlZR9o+tyUyyJ+xNWLOYSkT3tByROBNjy9LkZJCAmtssWsCyCEJAawPqJkSxdmDM6OQFYJMTItErtG4KLiw8agZYkPkTbVzI2klAmKChkaJtoyBIFRkRR7XHT0rkAmRaGzpkWKJwoFMKLHnz4Wn1riA40hjn8w417sPnLNEGL0E/4IJU8jpeJJLJcpGpF0sFs7OD/xEU+ZjDhr1XHbOqSIDM4xn8DjUBOj4q47xnzUUO91SIingKC/7/oHEGGPDIp3roXe0ImIUIrgPK4KYBV2JqiDIyqFdS02Cnob8N5n9FF6djahtUQTmVQFhUxMjeZH5wpV6uyrqya4GJgK+NmXx9w8Vjg/UBcKKQ1rFyj6gUldc7NtOKprmhAwKbJzESESs3LKemgptKKTJWfTig+Pa5JQ9J3DzxSPvaNtKhZTjcJgo8XaJ0HIIPPJUGtDDIImOIQ0bK1lWpfsWkuhCpCJoih57DzXmzXL5Zxu6BAuILSh7yKikjwMjk0/YJRHyzzel10WqYmQG9BhbLJc6FDJZ4RWZoFWO7o6pJRobeZTlSaPwAYb2fUDflZzv13z0A3IGKiqis2uxaXxIBI8EkFZ5YjMlBJ36x29jzifPXr3CKsUKdMgbMLFjILH2NK7rBAVQqCUxPtAiFBEjxGekAz94GkTvN32NEJipcEmRzFYLpZTkDUxSY5cw09XhivbcVrMuDCeYCVh0/LKRkI9QWuDUOpg7M++GRZ5QZH7hYTRjzgBI/9Xj//Gj3PGpRAkKTM3WHCgl6T40Yl0XKf2W7WKo0H36DyiEeyVuCJlvlWhsuBl4T2nRtJZ2PnIyUzihoH7TWBuJI0YY5tFDjFRCEo0nfBo64gjuv+HXsoGZFI0zmE3n6OLLak/ZygvKScRrGIoJMvikaGbUtQ3eOHoYgGjP3UUmoe+pjI1Okh0FMyMQIqCi88dUVX07nt8WkGYo+yW+U8KVtsX1PEWKzUhndGpwNRcEv0x4aFGzhvCsEQffUd3/xIRE5NJwbZ1VNMdwc5Z+CWP/g08LDB6YDU88OCmBL2hFHOkqPCxRVQb2u0MD94wngAAIABJREFUERNKWdIaoszjz7jtEKKkqXeUpzM2G4+SpxQiIo5qut2E5v6W2e0zdgyYGLFhTbpbIVaPTAYB378gqZLKBqQ8Rek74uMccfYbSn0HUlLrB4RM2HWHUoooE3Gxg+0zYIZJ4NtVnjYsr0m+hIdjdBqIhaJ8HBDbz5B6Q2F/imeDXs/o3YJqsibWt/jhBJRBJEu6ekSYFrU+JSzeIrqLvNlM72BzhrOJYnpLCgEez0l3F7lWhGBeP+LWR6A6JkNPij1MDX5w9EOkF1ti/wVxknB9xaZrKM8sVi0hScriW86nM5rhLRNxhqw2kKB3Ox50IphT7MlAfffyMH0RN+eHmhUcKPtZ6HJ6nSfZX2aKgrg5RZzdHBAmkcB/uaX8bsb2ixvgqWZFSOg4Ybi43YNo+ONs0abv61yz40E5jU1j/pkZ4TK2QibJqq0wqxvaocRbwXSRCE1JrN4T0gn3px8AfahZXwWOrl7wcP6W5X1ge/KHewmLmLBJknrFNmZUTaqcojkVkS5JFjoRo8vhBN7jo2IIgk0QyNEvvvcKpbLivzYCLR1GwWdzS6Ul6zZSjmEIUnp+fBq5bwUpJIwOSCkYgqFwA8JIUheoSvBBklLAeZW594Uk2YSSIGXFpIisW0BIWheggM4JtlazKLK4OcaE8wmQB16pEAklAy5Jgs/uCc5BXQgaC1LlCbDWgsYq1p1gMYl4l00ZlBK0QTMxmXZxPWiEkqiRwz0MYWyuRx/dcY8NNiOW+feWIEW8e+LSej8ennJuGDEIBgt1VbDrE51VpJTpHLs+QlJEoVhFyxSQRjwhvG2CmMWLe7Q+g8UCHxJ9yM4aIWbZkY1jELLI9DwfI94LYrJoPDFJrIOUSu57SU7ALQgx4YNlVUfq0ZsrpEeOJy2bnac0UOmOLkrWjWFwNaXRaCUOv5d9/56/pSdENT8mD84OB2s4sReSjtOdscoy4puJDnKswaz3GV0lDq8Z9/WU+y0BGXEW6SCEy42wQItIACI9lbZ0QeGCYVUODC6wIVFoidqLBEU+aAgyW0UlsMFT6r+/Xn+wkpe14Td3a5SD1dGUYFsu5hMmOjdrTYos0VwmyWazwyVB0/s8at6r7CVcTBTHswoRPS4F3m8DgoFiXpL6gT87qrkOkRcGZK243T7ymzvPUanog0drzc1u4O1G8OUsUJSKbdsy2IaXzy9Yu4H7t5bWDZw8OydWBq011faOz5YVN4MmWE+SGuqahxARNjHQMZWGVFesY0UqY7bGUoagBKaQzIeelYKpVtz3khA6hsURNjmq4PnRyvBZFdgimEnLv3IFtrOE4HgYJHfbhsVU87PnU1Z1tiP77mrNpu0xJovQemeZz+ecG82tG0Yu7XifxJFDmxzI3CwVY4BG4xyDh28+PFCYCXdti5CSEFpcEAQJy1JQCEdtSjrvD8hv13UIadjYfGBpu8wLHsWy9MPYrIuMhPcu32xaa6rRGFCqxElliCFireXBGd50LZ/NzhH2kVk9wVeR4XFL2zhKHIUTWB35V990DERuH9fUhaBQkXsXEbKmVDnYJI4eh/vPHFM8uETs+UkiOgqVR2r7EYoaT54HsZsUo18lyD1K7LONGiNnVyXNnqR4QMfHcayMZH/KkcOWyzgQx4OMJ+KC56o1+YQfodCCr48mXBSRmRZ8tjU8eIcjb+ikRFAD/3pVkood36//OHZMylTcPg5E27KYrggiMq0s9XRKlCXgKHTBB7kg2ogbVqTQ5N9pOAYUQ1hwtHxgFiuSCJjpDdtQI4jY+Qmy6zlzZ2zKwLHYkmYW18LDVYk9Voi+R6uCtevYvH/GiTKgLPHmGar6hrQ8J64i4j7S95H5v7YkVqdorRHfvuJITGjCHK8f6fpTTDqhHSJObYmlpBAGq+ck8Yzh2QaRIrFc5JoVEnN3w1k3QynDrg1IOSA++4xd23DU9qzwLKaOYbrldHjHm+4L3NDRxw3ry5fU6ZHZieakPMJfXEIsce+/RnCJv/sc5dYE4UjFGZNmgvr814ealQDXF2DWkBzKHo/3VOZs0q0J7RR5p0kktjQIbxDxe9p+QZATjspb3Lsl+kRRKnAi4t/VGH2P3awowg7T17ibmpQSpT4FEtiO9lEjhKGnwXZzyqpDaIFc51jinNRVo6uG4ASDPOausZwVS5q0Rl+skJcVaR0Ydh11d0XQDSFE3t1f4+1zTHVDUWsKJLvtnPa0pahblvcvDzVrX14DYF6f/a2ataevmdx9PlLOxkOnjKSbY/qz97l5/mogxoj9skPvRzwZssM8lqSUBajqLj8Vj3IjnD6q2aQEbrGlfMxcyf5oc5h+qV7Tdonq/pxBDoRUUPgti/OectFAJ3jx5kc0QrM7vj7UrC96/iK2yFXLqzdf/cH1Oiki17uSIQaOJ4rgHdMaKpkb3xQhykgKEx66QEySzmVNQYwQRUbz5qVlXiX2SWYPXYEksSglvYXTWYvzJZVpmWpJ2yWudzNq4yhTFr1te8VDV7CsOmot2bhEcJ6LlWDuA99ZifeJ82VFpRVaSnp7z9EksPMThiDy91NUhJjo/ZAPPwoqo+jThHKE4oXUSCEpdMKFFqMcWkV2viRGz6SaIpInpo6zacfCRBw1Sqy57E7pbKa+PLiCbTewKAo+O4pMC49W8GFtaC1oZXA+4kNiVmmMsYeGcy/0cmOjJ1IAmVsiiT/QAm1UvFsnpFZ0vUBInZv7aFBCUJqIBpSW6LD3yRb0Nv8+rJNImTM+Mo8238+dF3l8KcDHhA0qB9AoQaWyRExJQW08SWU91BAm9L3GTA1YR1XmqeW6U2xtBBqGGCll5G9uDCJpVBeotKQQAe8VSI2WMTfC7GkIYx3FjPIemBoA0WPGhLiPfYDTHnBKe+7uPrluX6757/KjSOyP0Vp4moiGUS8T9lQRMswV437dyN/RZiizbXMSlDJwOg1MdYdRgevek3x2zciC1kRFoK4bpG/50C/+3joUP2QB85//l/91UlLixtjN3qtDNK0NWZk/hIjUmmWlSHim5YxdP5BSwmjJMAz4ZNgMnsE7CpnwKGKQTGclBYEXywIXEx82lmgdy+mUTd/x2ARKk6kBwpQMKZCc4/lcMddZmHa2VCRZ8fZhi9Ga06MJZa2pbWDwjkcn2DQOkWBWC5wpuFs3kDRlaSiBWSGpp1U+paaEM4YpGR0rfGITPTpInHM8epCzKVprwnbHSYImtLzdKQgDLpEts8g3iRSJQivmVR4xVbrk1aanbXsQAus5nMq0zGrczkZ8zrJEpojQJRcLk1XAQiDJDgWkkDeLoHAhYr1HipKY7GEUUJoCkTxloakITCYTtr1HkuhCYnG0ot3uECojuiTPclZRxIHBC7RStEOf1ci6olQKU0ChErs+ZaZB0gSl8BFWc8MkembLGa5r6DwoKbnZCWIKoNVIvfBcnCz49mGLK3KsrDAaEaBvM7LeqcRkOR9TZwReSlad48/OJP9LI5BWIYSjM2Dsp/fufkOE/PsNI995vxHuucWZF/ZEDfmkHmI6+CGL0VNYHpryPVKcF3bfDWgS3kNtJJOioNCK01Lwo6PAPCXaCN9dPrIRBT//aklzH3n9aLnuHUII/vl/9h/9AIvp/9nV/ScnyfVHpOkaExwdM4Kt0WXP4A3Sa5rUUpiKI7PFaUkdntPK23zCb6dQbmmHGa7TOGtQ1TVC1Pj2iLoUhHrN8aKmsJ77+4IoOqr5EU24or07Q08uSe0RwpSI4pbkHNNZwbQauLqJnJwknH9Js3lPCGdMzwrS0nH00GPLNe3tBTubv6PJzOOCoQvXiLhEhRVSOybCU0xKhEjYyQZ1uoK2RadAMUS2yaGDBDul20aYniJfWMQ3DaVoCUXP9cMFtbz7O2s2pCW12bG6uMfsllzFDtmWf6tmXVhQyS2djZTTvAVMQ8md88wmIiNQY82GckvqpqTikdA9Q6v+k5qtpyPX12pE8riioeynVGeJthkQ/RyXCubzGbvwhsKf07oCkme+CER1Sdqs0ErRietMI+AENXEgoK4b2q7OiKPVFHaJjzCfaVTaoE8n4BoCCWlLdo+zT2o2Bkt9vuRxd8twrpEO0iSgfjfPGhAXeHh+yXH/U2SK+M+v8VJy9NuKi+dbfjVX1N9+hn92Q9Se6D7FYfKEJje0Uk0IPkcpi/uKeNR9UrPivvo7a9Yud+jNLKvIV9sfrNn59YpEg/ATVHGHTBfoMjKvJCef/TVEnw2CPhRcxSkvf9YRvn1Bv2543x0hhOBP/qt/+QfV7G//05+nnA6bcbAhmoyaSoGPeTMPUaKkYGICMkV0oejdSKWVeYTu0PRO5TG7GMMykmReCiSe44klRsFDZ7AhMqkkg01sBk2hEj5my0qSwIfAUTUgFdy1mtPKEmXBQ5ODHU6miakWDNESAvShYGszWj8zAa0Mj20OZikNQKTSgWmRvd4TES0Nmh65p6cFhSPhQsKGgmlRoJSg6S1JDggfuBtmpOhJSeL3zgIJpMhOElPjWRYOqSWPbUFr87puozw0dUJk3VrvPwpJIiKV5qjKHtp7PDKSDz8xpeyjHPNZLAqdA1XGt2ud434LnRvousiHFUj4qFhNDbveI6Ri8EAKzEsQacBGnX2a3ei5qxVKQqUiRgQanycSXijk6Aq1LCNgWdaKwTpsyI4N90MFKXvV51qKnMwEDztNqRT/N2/vEmtblqVnfWM+1lr7cV73xr03IyMfVVm4XKhANkJQZRkJhCwBDXcQHYTkBk2EkDsgQHRo0KBNByFkZFEI3ABLSIBUUnWQjHkILNlVdpWszMqMjIgbcR/nufdej/kYNMbc+5ybGZVFEQVbyrxxztmPteea/5xjjvGP/5+rEp0jK4xLYamViON8FaFFDE4chzLyve0D1+NLDlUIZHrxTPXDWFH1STu5mK6wiNGSRB65xUcjjROL8cl7mLiGmWUdx/oUJLesrrYfxmR54aSOzldiMEW6dZz59voeSBQNfHEnVFb86vOF16Pj3aFnXCIi8M/8tf/ja/H6CwPhf+8//20FGAXWTvlku8JPD4TNGZ/ez8g8MqYMOaGuZ+MdYdXTi/LiDF72jq8OE3/n05GtLxwK/OCTb/Fl8XyEcL2FLkc0zcbHdIWggoRAbuemxzyZ3dxlWUjJZDN8E38tbcL30TIPS2mOKzjTXQyB4O1vK4SNOOboGVsjVmhSKXq8ubWSZ8uQRh+Ypom1BALCtldS2LBm5NxlfnyTOUjhl85X7FW5D2v8MvNwc8u0VFYx8JCFPsK3LzdEHIVEaIHdsixsVmvmcceb3QHqilorzy86xnHk9d1i8jPS8yI4nn3/jP31gVXsmJyyOEUOmTEVBh/RWthcnLM521LqbM2Bi1BCJaTK9f2Oj84iY5rxwyUrZop4poPy/u6eYS1s1hFdhM2w4W6emXLmeQfrVWDTd+SauTkoQRxTLlQCpVS8VJ51HdJHbuaFgcqo5mFfazU6RSkstXBx5vilq5f88O07im6YF2tgyS6wq5EujcTzLdpBj7fGJs2UMfHJ4PgnXm344e1I9Sv+wQIxjYRcqdGj2ZoWjxrS1qAmOC0UHE6hSLGskVj1IhJZPEbSIrQNM7VSLISSyBIIwdGv1uTDgSkr4kFdT2DEOcf7t5MFfZroO+NO9UF4uVI2qzX7Q+YhZdZd4NfPPua3fvhD1uKhKv/Zv/+vfONA+Iu/8k8rQFotyDbxvOvZHL5i7F5yoxm335N2gcnd0Ofn9H1F+ivCfMv2bMavD5R55sdf9MSYKUl4+Z1PuPWR9e2G+ZeUTj3j/QNFHV3IlOK+FrNDZ1zd+XBHuFPSpp4w6760QMa9mn8Os37nyasVfjAMbt6v8c8VzgKjhEfMBkXL46Kf9ncnzJYvHBvXkQdhu7lht3ybzeoNw0F5M83otOL5q8xDjJTNJeGnM4fxPcs00PcL+2XDanvH+rxj7R1TSawWzziAzmviNsPyhpvbQhy/zRzfc3b2iiV/xeG6Q/OKJD19rWz+scLymZgCglPqekbuA8XfQn6J1sJ6s6b7Mz2lzqRP79H7HtlO8BCYy454ObNMB4byK8j5V8jDBVNJeCYyO+K6Z5kT2/JtHqqCf0tf1gwfzcQlwvaB+e0r8mZH3kdcWZOqg/4rzsfvoB/BYZoJ3R1pPqdbepYuUWc7RLr4jrg652rzgtv315R0xkhCSIgOFFcRduRPBkocWX3+jNCtmV6+YxnveTFfcf78QNpncv4W1/mMev45/Rcr0ifTh5hNO8OsWxlmdcExwK2eMDtdPDBwzuImSs50NxdGM7u8PWH24q6QJSDLK5x3SK6k7g2Hi0q8e0aszmQodxm/mnFLz2r7FuoMHXynfyB138VPNzzkNdvuFt//gB/9dCHKDFX5wX/7+98Is//g3/zNVpX2BMlsV5mcRlZ9z82hI+WFVKA2G3vvzQHUSeF5P7OKM4fZ8wfXW3q3kIrn288Cc1mDm7n0PRPWFF7UERtf0jcTI+suPPYtWCYw5UoqFpDEo+FJUzfrjs3FDeSKNVEHJ01xQFGpiBSiiwSxpq/K02SDHUrmbKoA3glzMgUeRFn7BH5F5ECUmTeHFV6Fi3VqPUAbUkncHRam7IjBgufeF67WFRVT8lEUJ56UK33nWZbEbhImzDjq2aowLZXrMVqvjPTEMPNnLh3vRnNoC3g8wi4XlmKZXdXK+SqyGUyycDcVxtocnWvhflQuBxMIcN2KqBMqnofkuD9UzmJl2yljFWL0LEnIBVZhYdMpfbCD0X3qTFCiCFU8pYKnEGOlD4558TjJFPXGK1bLwtZGx3jWZ7Znjvd3MMtATZbQRDxJB2qZOBs6hiZbLGJUnUMubLqJTy4OXO871PWMyzm1zCQtROfJtSJ4XJMl9bRErpbG+23OcGpUi6qWaQ7iWEqlNiMWqZYtzgpaEyqB6KDvAuOSSMURHPZ7nXFO+GLnOO8Vx9wy54XolPNupO88u0VI2aoN3Rn8+PMNNO7wP/fX//c/eSD8H/zW76hzju9tetxyQCVy0Smdz6hb8ek+sbvbs+4Cz1aB5+cDP7mZOEjh1y4G1pJ5sR34bApchMyheh7u9/xkgo/Xjo+7DbWvXCfP37s1Z7igQhVpm6qeLPyOfJMsFtT4J5IfxzPGUW7rmH/XVqKotaLS+DAF1CmqxvdMKRFD+CAQPqX5jwYNx09JGV/hIsJhP+FEcVp52M+sestOiY9oKGzUDGsOFa7OBz67m+ldhWKSTla6M8MPqYXN0LNaZ3700x3emxqF9yaUPaZErsqLdWAJniwRTdbE5qqyBI+kRF0SRYWlFnzsCE0cXBTWmNTYuFS20fFmCfiy52IYuN2NiCgZYRU8NY/0sWfpgpXPU+LFYGO76iKfXK15fT3xbpzphp7dmHBlYVcCmxiYamYVBzpNTCqkYi55t2nCe0+qBVc7LsLMJ5crPh0LclhYr9f80pVjEyo/vFY+XQpdLczSDC7Em9f5svBnn3e8cIW/e125DViHsrPMXtDSGkiOZVe7r77CLJZdFlFcVWjNeDZHmgOmkyb/Y/e9Q1lHx5m1KHPhZ3azslRhp8qrdcftlJmnzI9vR1zsuIrCqje+VtTCunfUxaw2F/E4TXz7bMXvfbVnNXikVP6Tf+ebB8Kf/bt/Tp1zXNVwwmz37CekQ0/yL9HsSLu3rNI5/faOaRPJD1fMg3IWM2799+nSFbf8Khf93yc/vCJ2b7h7+D7nz79Arp+jfeXzs38Evbv+/wWzQ9cxzjP90KMlPGL2SSDsgjXDuGgGHE8xG16DH4SaF5woPn7B3YMy+A19P1HKt9FQ6KYdNRfm7oLN1cLd5xE2UMstwV0R7iLlMp0wG3UmXCRuPy/oOqEaiP7KDgP3M7mDjT8wnTni4RkpKqvkmFf3LOuB9RvPEirhoCy1ELqB+mJ3wqyfMnG/JWVBz+/Z3X9C9O/Zasc4LyfMRoTqH6i9Ui86/JcDqoXNc6MnDOmCVXfBYb/jPRPD8IJ0PVM31yy7l6yoZFfp3EDu3yOHM1KEgYFdLpwkVGvH2t1z/p3Cm6J07yK9DFy9OuAuf8y719/hfop0tTBehQ8w2/90xcfff886v+Oz19/l/jvZMCsT8f2KWAayP3A4N4WH1d2G+XLH5stX3L16fcLs6u0LJPsTZsdXX7H56hVBHMVlam5lVZ8YhkLoHgDHy/gV9/NzFink4jg7mzncPWdK7xjTQHFXbMoOZEtFcPFL+nWEmx4QcgTxt3x8lvn0qyvrd/Bf8Wf/xutvhNmf/NW/oE5gOyyUMqMSWPmF3iWK63iYeu7GSheUTZe5HCpv9x1eHc82I14WGAqkM3ATaGQ/VnbLhvN+wneVzkMqA+93m1aat/Kz4VRPB1ffqK2O5tbFo93uI14faWNwYl+3jJ+0DK9RcKqaVFfKZr38NBA+0miP/x4fqVQKlcEndos2vffKboYumjGRc55eKipmNFJq4GKlXB+s6a+0zH9Fm+GHzZWhEy7Dwo9uAq7pHx+D95yhqrDtE0ECVSKpVKKX1sgVyCVbEIegVQje0bmTNgFIRlSYiqP3RhdxdWLo4GFq8mE4gle0JFPGcZG5KLko62iNo12A55vC233gsHiG6DgsgtbErD3RJIdwweFIVPXkKnTBUbK07wsLnrU78Gyd2c8r9tmy1a/WB3qXeH3YMi0rlAyEdiayg00qiW9tD0Q/8np3QaSjqODFWSMnBcGRGm3JH42RULyaCYdrjXH12CjXeg/tgKLNmONIgyh0odL7hCIM7sCYAlk9qp5tnzkspvx1ve/w3tOHhT4KimXkV6GwlGMe2qpq56vC5/cDq6AUrfzFv/Z/fi1efyFHuLZU9/SwZx0hsfB+rqyq4+ws4ahcrDvAuLW5er41CDn05FwZJfCjh5m1K9woPEzKzRT4zkpIFN7OiZXAH9wsnOGIDnYZhq7wkMDFgCvKYgkzYrUTXqSS5BGYvnoWUcAyyqnx0UL1JDIBAbEdsoo1bQbn8GmB2NlzOXJiDamunW4e5T4E10XcPPH5/UxXYIhC7TrC+YqzLnCohSXNbLsVVSvLXPAe7kdrVhvnAEEZfKCPyqYPjDlzFgM+JHxZ860rtQA3BOZ55mrjEZ5ze/+avuuZFbIGZgqdCrHrODwckM7x8qzny33mbLPm3NkJ/MvbPfvqeSgzf/6TS97vF3Zzpi8Tizpux5kQIVVzNVqqsl2fcRYD76aJtfdcL8rrg/LxZaRq5su7iVRGRo3ksUIV/GrF+QJjqWy8J4bM7WgZ4OA9SRNS1YJ/BOeVs84T88JZgbCN9LFQE1xPjjFNyCI820a+mAtdVpagePVMPrBbKs82jkOeQHqg4IrjPGe+f7nhrha+2GXLhmRrOPzeWugQ3u3gRoTzTvh4EAav/F83Sl8dSQtTghACSzbKQiqV/VK5844lHfjuWU9KmayORR2f3VdqWlio/MVf/hZ/60ev+ccvA/8we6bkUKdMY2brxExKKHgRvtKJ7JR5KqdN4ps+jpjlekSjJw2V/P4Vw5KYrxKeyBAu0GCYVX3G+dWPYOjY3X7E6v677MOarXzOWC84lBX9ux+gq0QFdjLQyR3d7T3+bk2/feDAgIREyfVrMbteryjj9AFmN/2KRZRpGk+Y7fuB4Gy86zIzrDcshz2HaSJGyyCnwz1xfY4K5OlA6FatlFaN3qcnmh81e8OsTOxuF7oCcu5JwzNWD1u6LrJsZ+qDsl3PyMqT9pf0YyG/MWOOfCsQrohdR3lxw2bVMS17Nn2glgVf1lydj+jZgAsBXR7ofEAuz5iuX6NnF3hfSLmj0wW9qvSHF/gvJpZXB85FOSxr1t2KcPGASGD/mTDXHicj20voVvfs9ltW6S2L8zyQCFJZOo9flKWDuHrOptsxvemQbWS+9ty8+y6bF3f4nKHu8MMXcPgB5bo5b4VXbKUyRlgloW4z9X5D6W4J6ZzFzfjSU0NpgWimu4gwLWzmDXp+z2qYqCkQfvLrZDJy79lsKgcWVl+sGb894X+6YlyNTO82dNvAMtxB2eJvFMeKVVG+9eKBBy/En37Modsxnt9DgbPLmefzJeMNvHlxx9AvXAwPDF75B13l4vW3qevX1m2/fEzpvjA6RV5xmCBmJS3A5TP04MnakcqGeS/UNOF6+PjFwO2XI98/+zF/ePhHwd1Rpo/YTcpqeAfTmrI4vFzw1Wi2y10d8XrxjfFaFBCxNdkr1MpSHQcil73Z12+6ZmOvRoHYdhPBBVKFLJE4RsQtSA3sc+Q+B876GRCmFAmaebsbUMkEV5lKYO0yc40W6FWr7hlpoTS75kLr+W99EIJTwWvrwFfbK+33VsOu4kEzHqFUCzJzScQQrXnKSDenhm1rkHoU/qk4uuBYcuZu9GS1IL73kc3g6GO2HoxcCdGa1rJaQLtv1LhdsWb86KB3iSEouUAXKyuXGYk821hgHrywpMJ5n5ndwLw/4EMwKTesg62KBbwPU2Fwjosh8TB39IMjuAURuNk7knZQK997NvMweaYs+HqgaOCwKL2rLahTigqrPhJ9ZV4q3ptc2t2y5qP1Apq4GU2as+jALtlhZdNFtFRKdQRfGFzhsJhGsHcgasGlbwecKJU+FGpNFBznPXQ+s1TYZzNOmapy2RfGxZxcQzt6OInM2TMERzmpwClZK0VnrjYJrYHbqUfVk8zrmfP+gKNwvfRI7ViFxKabiC7z5e7KNIUrLGoBeylNVlcdU3F4F6i5cLFakYpS1FPwTGOkloSo8IOXCz98E/n29ppDfsZYPUEq+yXgXGFKTdNJInWyeGPMytcYNp8evzAj/B//F/+Teu+JOGpfWSHU4li0NAkvZcltCvuOoUIXTcPuZnFsnJBdte7GunDmHSl2jKmiTtCxEHqPV2EW0xHcBmWXC7sEm6AcCIyaOROHLwXfex5SJLt04nsFhQ7H4OGhLGgNxv10BhbJFToY6tAxAAAgAElEQVTlQi2Dl6wCQ9BKjT1JlIjpCjtn/KOuGFgzppyAM55ccJ7BBSYKNRf6KuzTzMvNiodspamaFvroWTIn/eGlQpCOxIKrlYshsIqeMSW2wTOslfNhzTKa4sa6t+Wh64Vx9FysCp8/VO6mhYVAKYVXa8uOqe8YNeM0kHOjiXghxshh/0BYbXh9e+Asei5Wnt1cOCyFuVond/BW0HYiDEHwWnC+42bMZAf1mHVzjsGb7uK5CG/memqMjM6zV+hUORs67vf3lrlW0yMuDpwLTdNcEDKbfiBXR4ggufLdM+Wru4wMwlXw7JPnsBQUYaRQiuKk42Ee+eWzyMp7/uBQGGJmSo55Eb67ha3A+2kmx440FbKzrO+ZF1y1JrmFjFaPqLLphLcPhSoV19kCpKpMxfjDZ/2KKS04rQzROon3Ge72o/HMXWUVPRfrnh+9veFXP9riM+w04dQWUVRIkqgJMp5OlVo8yRdWjcP8W//hv/qNo+Hbv/pd1f2WqIH5paObFW8tyWgPLsPeVdyolKst68nKZb4IqS9QOqRLhtk00y8dee3ITg2zbwvhuWd1t+HhYk/ZX7Dq75l9pV5vkKsdY+jRGOiy0s8PlC6Sh3N28/4DzPbS09WJnZQTZrvNClXBfQrjq5Ht3Zrx7HDC7Nl1R/l+JFWhLHv6vjfMZuiaRvRunAmakW5LWfbE1dowW5Qy7Vi9XTOXTLft0XlGtaOmBe+hVm+WsjlRa2DRNdEdcLUSrhyheyAvG/r4QL8V3P4C8SP9vqeubDeW73xKef09xtWIWyLjfsBNgZwT65cT9XahbCPsVjgNTJ0Qx0o93+HKc2rew1Xk8IeVdSe4Zw/M0xllzpSDP2E2Dwk3g5x5RG8I/iOW63rCrKNDJRH8TBcD3UrY33LCrKwhT2t8nVivAnteo4ePWfxCrp44fIVOH3+A2SE6w+zZAdkNPLu64Xa/QoB4vsfdP2eeLUs2OdDc5Cz957ySS9w28ofxmvObS+6vrll/ecVZV4hrx5SuKeklZeKE2bUknEtoJyxkwrQCWeid8n5e0fWv0fwxvn9tNJy8wdeBUFfkcIvTave39lTZcbPNXExndEvCd9fE7jmv4wPfnT0yefb9e5w6dNq2enEkdBMZD7ueMCSSL9QRhkH55b/xD78RZn/4b/159c4yaBunIJVFBdSauRym8StUxHkKhd4rgcKh9HhXCEDGIbXgfSa4zsr4IuxzZQiW1XTqKDg6n8jFM+dA5zOVDiqIK9RaGIJwKAOxuX1ZFtkqZsEVNAvJ8saIc6gKqSorr1SxAMlsfgEK3kUcxlXOLWuMQtam9oO0A7ypSogzcQKnpq6QqZQMm6FQsrCo7X/RK0s1KbtSTe6yOmdqP1pZdYXOKylD8JWLmOk6xz5ZU9YqWIJk7St3OXIRZ66ngWlxFCK1Ktt+MUk6F6C5vKZqh4Bjw9k0Z7oYuTl4YihsY2HMzuyqm1mSubod6SYVoSDOs188TiyzeqxGdy4RHYhLjKk/UUnEYWNLYYgwzRk9mpxUwyhOzCcAbTQKIaundxbEPu/3vJ86Nh5CyIylY84CCKLGH67iSUm5XI14pzxMW1YuMdXAWDxX/QHnEtPiCS5yyOAa1SG4jGqx+1dtXqoqQyjcTB2+KTzUarSYVO1wEKPda7TS+0pSYSmB/awMQQiSiV7Z9Mrbe8fLs4WkilShYJrRFSFoZa5CNWIGi3o6wLlKqcq/+F/9v6BG/Nv/6f+oIsI7LRYYSiXWTN/3hNqaFNQRvefglVW2qX9wptvY1QnnHFEcKsfOYUeUYhPEC1QhFdtAgvNGXxALOp03/2yTx2oKAN7bokRGxUqI7ijyXCs1OLbdwG4yzmZuwa0aBZEQzTLTOUfNyWzB2/tqNj5xKgXXSuZHUv6xw/RYdgEro+ecLYvqlKVU9tmaCJ1YKf5kxCCOTNPZa5I3XitOKkvp0DwTO8/ztWkfe4E+OkK1U3HxkXEGvKNo4j45NgKzUzbeUwnMOVl3Z60cUqVznlUHh9LGs1ZcTRy8J9fCxnm6YIrhh2yZ845qgbuLNta1oE2WLNf2nVwmaKMjtPN8zhW8I9QMuaDe4YeBMi2oE6pYR2oIFmi+utjS6cTnu4zXQh8j5MqMIBQuV46ahUNRxqXwUOx07tt8XXkbw1mww4AExAsvtj15P1GrctscBlOuSPD03uE1cxgTWexei9o1zUtGsEbQvlqgsE8F33dIySx05GwB8jiOp2732HmG3rHGM5aEr3C17dBxZnaRlKzclWhZEBFr/sSffj7WBv/mf/SvfeNA+NN//Z9UmeHtq1vO3rxEpeLjG7y/IhwU7THMJseymYk7S58u60ZnOJjUUF0H+tlTq3VSl1Uhjo75oz3d+zOWPJ0wWz6a6G4vjAf+0Y54vSFfmRJFvN5QPhpxNxcsV7c/h1n/bmB+OXF2dsHD3a3JC74/Iz8/EN9uDdcvD0i3tqbH6f6E2dBvqdOBOJxRvujhpTV11i9NU5aP3p0wG64/st89e49723isqWfOmaVGpBRr5OkKbmk0jRLM+TD7DzCLW8h5zSILa3HEZxl3P5uR1Hkghi9w+xVpG8jT5Qmz5T7QS2Z2SthGuikwpZnOe0rJrbtccReOdFgTwg3uMFC7iVRWFFFiTPTF1GPmZJJ+joUSK3UZ8JuZfOjRlc27Mm1xuaBB8cX4/KqG+dwO+KFmEhOBDjoHsxhmnXFBg49QMmdn4FaJh3cRr4UQKjkk6tIjFMJlIT5Y9/uSPWO3Y+5GhmVlWFnM0OLu1Vf01wNLtyBeeB42LPfWiV+DQ917JD3n9uodF+8/xmtGMcxqWIw2Emd0vrLOcj/T6Q4tK5bUf4DZ477xfnPDR7emVXzzYuZyWrOaYIqVQfZEN8MhMLtI16Wfw2xc4tdi9lf+5u9+I8z+7r/xG5a/K5Hctm/VbMECFigWLHsW8ORmD6rSdJN1Nq6lAM1aFhE8hYrDi63RWpoijrPGJlrQGZoc5ckZrDXqWW7w0ZxW2z5or3HEKMyL4pwFoCJCahyLzttnOMHse1tm+USbcE0yrJXMj91Tx9BZW7YZrIyei9EsvEuUKqRiHFSHnpR9Tl+uZYnNBhgsz1yZtaOWTB9g22VSaY3sXqlaLDiVwKF4vDhEK1PpEMkEHM5XKtaM6BsuluIQByufmbVDj8pFmnFiPztX8b7tkeVIGchNHaFrNAY9yZJVFYoqsan2ZnUcc+apWjyEZnI1pY8+BKZcT9+3tAx5rZWLteJ15m7qQQshmCSbqt3bbcwsKnYoKkIugWaeDFigL2L89ZwrKp7g4Gwo7JdivN8SQOzagnMEp4hmDqlJnzmjjQRvTcYmZSdUClqVpTi64NFayJgUnCpMi81fgN7DKlRbj4rNwfO+MOZs8nGt4qs0fW9p7oeN8/6UfvOX/5u//SenRiytxjgETx8cyRWgZxFBmjEAki1wdA6NzRO+GvfVl8EyNICvJoNWWhnFibDkTPQenHE+7ISoOMm44Ew3z2pHiFScdxQ1bUKqM35nVUK0E2JxgaE47pdMPvJwsU5LEbMmntTKxz4XvPOYEp4ZZoirLLVQqE0DW4nNgGHOxg00p5yjNJxDnSdqZldXuJjo/IJH6HB08piKX0rGOUjVeI21miazd5XZDfR+zVISc51xXWCSrtkpe6IILA51GaSSXE+vI4fgWWcTNi+a0OBMi9J7s5ZV4UGBYMFt0YB3asT2GCkZcst0r6KjLInSrSCC1sKYlavB2/uXAkGMQ0vAacZ39vplWej7gSLG0zx0ghDxtdANHaVWcs30a5OLK074ycMDo0RW2iFl4S4trGOP85VlKdzvbPFOLuBcJdJDFLQsxg1sRiPiHC70lJRgWXhfIETIqsQKixSCD0zLQnKerhbEd/TO+OHFwTyndugxTulePODAFWRaUBG8zI1KoKy2w2mDLWrc6yUlCPa7d3cj0kcilRyiKVa0DUVEkOCJmNNmrX+c1Pef7FFdgRWc7c/o13PD7DlVKvOVBVBhV0hSzB1ubkYhi2HQh9K+18RYCvpyoS4rQrdH1x590zG/uMe9P294qCyLI11ZECvaeGPXZ6Rnd5QXB1SVPg7E+1eAoywH6ssdpRTmC8/Zu0vSw4o0ZFyCIWzp7s+Yzm7oDxF3/8I0nitoHpCm9F5FENbGcOug3jYjjcZnSlNkmy4oVaiuYfb+BdopblYmf4VbJ0p3jUcYdhGnAZr/gvUJQKoZLY5SMuJN6iueDazKgPiCHq5J31pg9wma35DG76If7enengML6aMdcfcxhHfcXznObxy6F6Y6k66E3FRZnBZkWjFNPfViB1OhiMcvvWljbkZkt4btljFlhu5Alhl0bfHEds90OONsyCx5ZR0o6wczhJk6nM94XyheKXMmnFeKKHpYs39xR39zSZw3+N6My3cffcm6P+dhuaOocAt0b64gVvzUgfuKfvouhAUNb3nnHWsRxnUkrWdiDXS7C8ZnN6zu4eZq5OzmObL37K8Wzq6fQV546HfMHxX6W/B5DfqMEq7ZvL+iuISvhexM5pG0pjhIk1FisoKrK4pswUONCdk/YlY7IbgdH+/P0IbPZ/s12St0O5A1SmVKgby6JMyF/Tzghz25nBHcjly3lG5HpDLqBi8P3G9tbf+Vb4xYy+iFIDhxBAXwJ7oPWKWjaDX93aNaSZM8q+qbmZlHasY7U4GVFniWCs4p2Zk6Ado69al4aWFi28s9tekvNxUA9Wjrl+mdtODNsVCp+ZgospBUsEYuEagaLEtMxYlvgfZRa7ZyNIFzYvJwpzaBQguSHnVrnTjrvaEw6ZqVKwSy0SsskXn8PzNRkUqtjqTSFAks2O+d4F1Ai4IWs3CWyFLLKaCeqxCwwF2lZ5CF4AKLFnJ1LR6wLK8T6IPRRLKaQEARmDW2L2QmGUmtQayq0vnCUgo+dK36BKk6NjGRrH2I6FybESbnNrgCEqzhL0Sja1VHX02/uGplCNYopxVWUSzxg2N3gMql2djXzDwrIQhRKik7buqaXEEkECmIh86JBfRVgdgODILzjlSUKWWyBvom7VeouKZqknIlS1Mhdh5xMGczypgTbW54o1q02MhTmM1FBEdCWsR6NgiqJm1aGw85Z6UT229vRkcfVjgqzsV2vXLSJHbe5poThx7d/n4hCn/BQ/E2mTPMWhFnHM0q5mQoItTSgl9cs9MWqlcq2bKArSs1u9C6Sz1zrXgpiHcUgeIM6KrFaBjNyMF0ZdolqmvlG9DSuEy+4l0zDHOmxrADhGinW9eRy9i4ThGqfeEq4KSiKVK9nLiL3jdTAy1kUbzC3DgsrgaWbBsHzamstpNI8Q5YTFfWOVRMWzZrImBBk9NErqDOM80JccWcxaqA7liqUMWRjg16alnyKErGUV2iimtlzQWPhxxYxFHJzTCt4p1HcsX3gk/muZ2KB/X0IVnWTgO+gAbHUpotMWZnHLQSnUmTPCDMVcBlE0bP1lm8oCABl0Gdo6gFvWDWw64e9Xk9Uzs0eBdZ5koIA67JlWnNUBPiHeIdk2SojiKhzSMHtaDVMWvBLYI6W6yhoB3kJUEFHzwudMbHzaU1UFn5bioZ9cZLXgqoLkixTuba1tHQGGweQPOpCjF5k74SEVSEUipLSdY93AhuNVcmAVfMQdMCqQUt9jpp/HWRZjnvhKlmaNnRp1Jv3/SheOZLiF9tWFQRV3EF6uqALxbw11ip0S60Yo1H83kGMsO9Zz7LJke3rizLmrPlgj0O1+3ghQVty3kCEv0y0T0M1Aeb58sFsGnll3lDAcSpuUwNdxQv+AELzJyj+MTNZUbIqMKwPGN2xvNM+49IwKY8YjYtzppEGnUoFVsf0myYXQcP3jA7HC55SJki0PXXpHRJL7cALOEKmOi4oUpARbg7T8SwY33zUVMeMEpGdZ6clLJO1sE8Q+ENksHFzHzR0e0cSc1JTcimTi0zy7mgy4DyFX6AYRS0u6S4t6Q+AkrJV/TuhrTtGOaCbO/ppoqqp9/eUQ+DYXa/RrcTMyMuQlk2+AhuqZR1ZTX3HNYPHPIV9fwevxTqbo2uRtT1QI/kQlkE/IA7GGaXb40M786AwvLqAXfdaFnunPIl9C+uThKDucukswfcleULR/kMcKYcIsLcB6iF7tBoUX5vZhTnhtn95h39/oo4w2H1ni5XxnM4b2O+xPfAAcnPUa9PMBvQApX0AWbr4BBVlEfM1o2z0Gyu4IW7rYOzA+uHgfHc5NlqrRwAx549Yp1i3HN5X4gtQxqDzZX92UNrzsvgH5BMk/H65pit4hE1t15tihgFkNaYJAJLCwRM1q+567VeFlV5VGqR0LiRlqX1WlpJvoXbldahZhlNheY23AINPKhVP3M9vs5eVqoF6lEr1trk2voXccUC00xs2V1tgTFM6ghiRiYo+JZc0xZwlnZdgjVk1myZRN9oBKUe9w/PimJ82DYuTi37mJ0pyYimE6VkymbE4FpW3RloEXEI3vRyse/ixVxCO1UQb/cBy4BOajwNpxZrWGYbcq2sPCzVPiPV0Jq8ktEhNZBVW+N1qyaLa3rUhSCl7WOd0ZAoOOdbVt21xInRucRZrtyrGUsgvvG0raE4t4OMOBgLBB/g2LxYM7Vdu6hJUhb1qBEp8C3eyCJQYVYzxDOXt0p0wlxspKITxHkz3SqWQIziLQNbTQYtOAvUUVvznR6bBFtWVttpTI+22hBOJh32rUu1zK/5AVhAW4rNp9K45WsPJh9r9+NpnFtbRUGrElw1bHE01/n6xy8MhH/7d/4uBI+vzX3EF4K0jUethOpaUBjE4dyH7liKiaIL/YlOkE9UAXuOw2gKFhiNrEJ34qOqKqFlZKv3RLEO9RACSZWqbWNv5hHHhbDFXjgX2gar9t8ibM8i94c9Rlr1ZCqBzgLvxuct7TMD8uj0Jp6RQtQWHNKsd+222rUKHO2TpFSiVLTa+BSvKK0pSsOpw9ImiH12djC0g0UGovdGjm8nqeMYitNmstE+zofH8lbTGp5CQDXRL9HKjWr8LRGh1yPVpClpOEGLTWYnikqEOlHF0bnOpGjUrr86Mw6o1QCU8PRVSHIcA/t98abCURs9QVWZ2ziBo7R7dVTmEDHjEBDLUqnJnD3dbER51LFW28Sd2lyMDUxaqh1ieNQMdkfNSeuxBSDKzwegP7uxHX+SanSX4o5qi08eT7QVj39zFaNkaEXq41w+8mMFkKoW3P1ppoOBTz8r6A8rvt7bd/hazNaG2YW0/MwFbCbC6w6hJ4UZyLxPTdpMLFXqENJixi+sJpyUR8y+/3nMrqfInH4KwwVVE1WEKZQPMOsPdiB7v35t26Mqzr017JSZu05OmHUTSLeD8eKPxGzZiM2pSRtmHXCPb0GFctfmqx30wDCrK4/WdzY+HSfMztnTJR4xu2+Y3cHQDpOPmE2UWyi75QlmoQ4Z0YzTz9Epgpg8H6svERHGz9Qw++kRs5ZIEJkY1vZ5uhdk9A2zDw2zD6hEZDVSDp7O3Z4wy+aW8tbh3M0fjdkvn2D2cyV2kTon9DMln2f0/mcwe9s3bHUcxNPnxN4NqDq8K1+DWas23GejJjxi9mMbyy8eMXsRL7hdVmyHlg2tC7u2r3wtZh+ezl//M5h1lD3U6/XjU242vxCzP9LKUJTkHV1VFif2r8BQ/vQx+9u/nwjONdV4U6jRpmRAtbXW2I88prqefmO1/S234BD0lOGVJxVJc+ATXF3wvmV9G8VI2vz34i2jigUQqoKolcdTFbxI6/SH0tZfcanxsO3QLQIXvTLNpvdqmu1Cce5U9jY+sF2bimU+vaMlBoRKpVT/wf2hVW4d7cyCJRU8Dm28xygeR0WwwM6u/zgGjVqJWiKnvad3tu9Jy4AfKRlelFKNJmBrkT/V2FVLCyI7pBb2KkRnB4Ogob2H4aA2TrQT0/63AwWodEhNtNOBvS+K04ITc8jL6prqgqPYO7VSv54oKtCmszy6KKraXlctsYyI2Y6rWFCuSDuEPFoXcxolqGoZ3rZV2j+NcgDGr3bI6UD1tHpROaqRtGSj8sGO+fOHR2m/t3lnOlkfpm/r0/WE473XE63GjDzkdIlthaaoXefTPftf4usfvzAQfn83MddM8EfRQE9upZnYAh47nbWg47ihtNmkTafRy+70u/Izn6Gq9E3jF2c6kdBoB6dtB7wEgnv8W0/B+9B4e2YR7Nr7HaVdxIfTolfavzfvrGGrZGknIRMPeRqodGJwCdhCZNp7LYDCmZUrVtqRdnKyAENPPNPOWykqNG5h355rHFHbOI8cGCcgzpFEGbwtbOLBa0KCB+9QNYHzo3ST957gW9ZAM30f8T6gjZZCiDgX6UXxySTblM4MOBq4s4JUYS6ZUpQlK0sqjCmRsjClxJxmlqb/m2oklUwtdm8PmrHzmp5c6KjHLtBKVBvjFGxcU34Mfo8b2nHcHS2QF1sIa5HTQn0Ej7R7D8ZvPAaV6h2uqvHRWinPtzIJYBkjfbSItsF/lMU7zsOffcjJBqfaSdq1hoQnz5eni8hxoSgFX9u1Sn008RDjaHm1YLK2hoI/rWwwwJt9Zg7nrItxRJc6c7+yQOBiOVBzPmVUak30GH/zOC7zQ8I5hbJ/HKswffAZqkqglZzGYDJ0tI2VeuKYeVFWrnDLYpJHy51h9pDxa9eyeg2z7VTp7xRWtiyValWRGwmUBDULtRYO4sjzBtXHhtltzcwUVlTD7PiIWXDNMxX0YAdFoVoZ2Y9QNoTugHpPUFinaJgtTzF7XOjd8R0tay3KIDavx0uob0eEhlk/fYhZ9Ug/2fgPlSt5b70J1Q7bXEScq/x0yfzACT9N1vLyvSiNL9owuzbMPvhXLFkpY8eYDqzzwCSFB38HWinLGncHZVCTx67KbcvYVSpTcA2zlagwu8pqAakTu2jj6u5g323ZpgOqwr7bnjC7nffsux5XPG9D1zAbudBsWSEgSDnJVs554e/dNgStE39u7fg7b90Js792mfjb79t81ON60P8MZk/18IabDzfN3xhMfu2I2dd+4ONWXft/itnUMDu2e79rmJ3/P8DszejQCp3Yzphxj+6VTWIKMepBVYgc91h7fVbXguXyOBT64Zgo4N0x+yaP0mdNaeD0aFUX+5uVqi1JBWhpBIhjdq0FO86dApXSfvd+Z62VS7WAzdECSJ4EKpIb97s1ksnjMUblMVAWOd5xyxB7sTqeawkZJ3riSHupOHk82Bh94iTw1pIiwkZyy2ZaoK2+BX5am9yaBWbOWYZT2gGsDybFpo2W4p2pFTgxcwqLHRtPWy2daXbHNnVztUa7pRi/eKlGO8ztf7UqC5FaCllbf1IVVLJVDjnSC9pIqjV410apUKVxgDk5q2k7uIg8DUiNOpOrBdzHRN7x4Rq17HhwAcvCV46Bb7v/8lRcz54rPPK7689g5Osg89SpzmTq5Oee/zSQPc7B0uI81+bN0SzniJPja3yb738cWn9hILzPNwAnRxHnnJk3A/NRQfCU5Xr08z5p74o/Tfyn7j7H57UncTgGLC2IOr5nBmL7ucpCONpxeo+4QteuIdXUOlFD4+zkFiBHtIXeuXVlQpPGKhmppnxxDJyOg6e4pqXYAjrnOZ54gkoTDDdwVycM4hFXiU4Rb6LlonZYwGlrGPQsuRDCo8vZydWsPN6mrmVvabqLtkgInXikcXSds3vRefsuHZWyP9A5IbSN12ONhyEESjY752P2TFqGZZ5nigjzZMoTu2lmXoxakHJhLqaFnFJhqULh0EBrY5ErVlJp9/UYmB7nRWkbiDY92eOj6pOF9GcCy6druFks1sdVH5CjRXLbkJxi2aRqgepxk7M2j/rha9Rcl8Bkf46ff/xs16oHx8bO4yx1PDrj1OO1tsVC2knWPTn5GocVUlqgZWPdKZtlWe+scjrmeZ586W/4uE0dpIn/4WCf+5c2A2/3Fsi+aXB/xKzwSWcb6+fLEbM9UkCJfBLtuj6flU+6DzF7hLBURdwfhVlYxpZtapjV3sMAbr/80ZjdPWJ27u2aQ71n8eeIml7xCbPt3t2pYXZ0iT4HYr6D7speO98QddUw21GdoLpHXCHWiPjMOlmJPYrj4NLPY5YjZsvPYXYoZnXMAiIdS5mRKnRiAbUvhtmpm+i04/wQyGeVz9zHyPViMXq/nDA7hMBPl0fMfjZ/iFnKumHWM2/Bl56pFpbqGIMpAqRyyb7s0NUWf+CE2UMXT5j9W29tTv7ypgWISfjhg/IXViNfLatHzC6F/23XN8w+Nj+proDagg27z7+8KbyplR8fHufLMdgyzBZ+czXyv9wOhGlPTqsTZn/vDfzm+rq95msw634es//z7hxV5Z89u+O/vr3in/Lj6TlVlVfMVOCv33yLv/LsSwD+y5tXP4fZ73UL//zmmt+7O+d/TRaMHzH7G3Hik97soP+7B6uK/MtnM38aD9+aaY/rYZRHV9F62mNpPGBrGD6OSxsoCrbePQ1K3NOAAqMWAcxtHWvbl43UMZgEjiu4c0IkNF1vTGFHoByvqck0ZnGP97c1EwJ4L9RyXIfb2vfBNQrajIvQtoacsooW3Mjxu2D7aJCKFyFIwEkxYoNYhtgy3tYcH1ozGy05ZkYTj/MxtsRbEAva3DGgc57obGy9mPxbEFOGEDIBrF9FLEjWpo8evEeLWSNbtlw5Ep+XxtcdsylPTIsyFWf7aLVgNFUhFZNFNZqFa1Vt60GRU1xSP6y22BkWRUmnjPCTe37KBLXfnRJKx7tgms0/S6E9eusdg8hq+eUPMq+na3iSCbbPAM/RHOlJNr79l9PjrnciTJzuucKpMqL6ZA6fKrqPDydicnHV+ijsd20MmplKbe2eP/var3v84ma5xU7XxxP54jgFB9o2vlNpW80V6OlD1Z2Ma8xC2oLN+iTaEfGkI2XgWEZugbeIN25ye11uWQLfyh17LCPkXCBUcwIzgjKAwKMAACAASURBVL51P2adKK1sWSScAvA5zSCCCUJb+Gt0/HaLFKq3gBURc4g5NimIUNQ6Hntp8mxqnZxzFXxxeKktC2R0CRExTrCLTEuTd1MFV5oFqG1ytdZTViK08QpHDk8UJCu9C7i2OJhrzoI64/rkYp2tQSAzo0XppKPk2rK+lklWmVuGtpAacOcpM+dC0sqYF1K2gGMpVkqa9cjLDqemRuSoBtEaDNDTggtQ2/nBDsdPUarU5jB2vNfHg8ZTQOYKLrgPSpmnLJ8A7ngCVfusFtydPv+E9yPY9LSoHNUu9MmmaqdnRV0+Ldz2Ph8G8qp6OpRl38aiBeLA6W9GAreNrrqW2dZj8a6cxqT8cSj9Ezz+4M6yX98Rm/f//Xv3BLN2Db+2ht8/wJvi+ReenZYoAH5/8ifM/u4TzP7uk431189nfvehP2H2L20Sv3PoAOHXz2e+leX0utQwG/q2YD5YwLt1ilSzjL0fMMyKCaPnfXvuSk8Znll60AXB8WkyzH43CJ/lR8x+3Du+mgtIwdWeT2pT7Og27Ch8kZRf6jzqYDv1oGbq4NUxSSSvwwmzYdGG2Z4pL2jtTphNEeKRmxz/b9beLFiW7DrP+9YeMrOGc4fu27cnNBsNoNEYRdIEwhJoQZAoU6YkW2GHQxG2JisUDvlBzw77zWH7zY7wg1/84kGKsMIRpkIvkmxKIoOkSImDJBJAYybR3Rh6uvM5daoycw/LD2tn1bkA1JIJZMTte/ucqqzMrL32/ve//vUvOGAZl9qbDny78+y3gXiw1OespqgMs6P2hQfdzHoWfHD4s4jznrQDJbM5W3HID2HsyRPWsdEHcrzApYF+tWUUxfWRSwp+3zOrecY+WAdcWnHohLlmKB11PFB9z0XfFsKa+G6zBXxxY99NuRJ036hr3u89KjO/+HB1+kVrNw6nTMkzfllYT8dvXAR2y4TfjmdbRnGnwhblVw5rsii/NK25xOwOl+OXxu3x8wC+Nns+2lwcvjKb28UrXeGN1B2/d1XlmxdPoE753y+esGv63pgNE4+qZUZu+QtUlc9sK/90Z+P+J9fKo7rm6U3iD49t+XRL1spRfeWtCX5ia0v1a48nlP7AR0mPM7wgx3iV4xprbGFhQXxX1mGWthetpa209vFXCQXz3WqMqB7ZQsEYU9eQ0EJaLRv9hAGXhZyyWy6tI50BGV/rkfgyUNqAelo2THI8L5zm57oU+5W2juRTa90gjdBQbS4X1dYJLdSWhXGYu0XRRT4Eccny5EJuoDxKK/xa5Fpq7kdO5Pg+GsvaeYWiLZNbcVWs+UU2AOwcUCrFNQBdrahU1JGqUsopTe8x1lmXn1VhytUKAmuFDKU6g2rFWROKWtv3406OJcsGyOjWxroev3xiwy1Xsxxga3ovp82kjazvH7SlOWyUK7FyIpAaq0wjrNrG8LGxdfw+rwB0FuB6dc20v49MbZOOLOc6jRGO712mkNDOtDzb5fOUhem2+cFre1/zxHaLVdj3nPsHHe8JhJHFlqK9+ArKqa6xvaW0HaaS63xMc8MVdk/kSNEfH8qxU0U67RK1daM50uWWHjiCUFU6HyhpNilCtNLLWjMztjP0QMnWpa4463IVVKBZbxzF995SEPZcC9r8EJcdquZyKkLwjlybDmkRZ4tAnakKY9sQdMH8FYNYYYVHSa0n+cp15Dy3ScaCZHmwWq1LXHE26blaWrFDtq48waBvnQtTSHjpiZqYteJ8RTWSa7KqWBVKTTism1vOmdKkFBKFOc/t/s1No6rpoKbpgDrTEqVU0BhJh9nSIxIRtfOoZgSzTClU02BV2zWKXLHpEQGKMRX1xMYvMhqaLuuUvl6+f3Pw8H75boulOUUMRC7PzKy5rXhRbTKy1LEi3zsrHJeNSlnGcJOglAboBU9tureal0Bu6XRnmywwDappvxpoFgPqqVYrRpClb7qN/9LAZzhqUzNVadbyLetQf3QNNbrw+Hle4dRm7RvZjO2/m5SzaBPeNw6VyyR8ZLBn9qI37TlXNGLL0Tc3hTzBRzoDmeMM387wYxjrXA7w987hx6/X4+8/tIY3zoVng3LTCZda2VUHeK4pnB3gftPJF2eLZVBBDvYtT+3rHNbw3cli9ssXPc9vEr94MfAzNw+gypujxew/frjmL90e+ZvvGlj6K5sREXhhpejeZFSPVPFrYZszlUgZgCsxmyJsSyDnGXxAJWOtGs21NcfKUD1SMi5YzMrsKHFm8g7bRhbqXPAB5gpRE3H05sri9LGYXW0HHMohP0THyGo7cLjcI1Ho1j0dPYe8J4lp+JSKvxDUmR/nxSqgQcgzlJpQiQZickS1IK1q6jvZWqNejdlfvGdWXD9z88AzZL543vFOdiyWWSLCVhSqclHhmbBI5eBS4TuToyOwCpmtKFuU72bhfX0FdVy0mHvzcpkDmv63s83Fu+8Rsz2V10b7HgdGbkvlm7uOZ+OOd2TF7WoM8PX29q8VG6TqhI84+92TrS3wWyOMAje8xezvT8ozXWXKwuuzsirKwQuXtfB2jXywb+4EWnh9hkH0GOPDjyhmPadneeWv42/htGY5oUnPrj4lgxYGZq6cQE9LrLZ5TdvvVU5rMZKtDa4snVUNM9ZsLK05U1ROzhB2klSMNgoYGLJX2PmPqjNvjhQiNIB3yifAlVoeFYJcJSROwJpaj9xwFSE6c+BxbZPiUGSpOwpiraiBIMYKHjFHrcQgVrMhxoUaUVdIag04nMJc1Tze8YgUshpLT7sWcY39rFZA1gXTzlPBOSuOK624zDlbXxdmP6XSNiUmj4jecUjVzisBtFIqttaIP+IPA4H2D5FTV79mmHb87zFJIEaiJuy6j5ITTsx8UTl2/qzLc4TGorYNbzthM9CjtXUxW7jvGalHCYrWY7Y1WmqRJYFq3HjbTi3Dvg1mJ/be4+Bt40kV23Rp05c39t61TP7iSW2YsvFjLUu7jHWBY1e7f9XxrwHCjeI+gpie5BaXgVPE2e9tUQAoy65Vg1mNuYJ+j8jftU13dVcDoOlFG/CdVeliPGpqRcS8L8GASxUD4g2kzK7tAn0TqKtj1mTi+NYUYvlypzLjqzPW0C9Vp21365bphcaOtSFXiqUgGhCuqkgMdC2FGYowSmHOpjXqO29Wb+293ltB2PJH1fyLa0u3arZh3/sAvpqnqDfruDoVYt+BmJbNO0vlOxWUjBPPOFrb5673zNnupdTM4D11TiwOAWZFmBHxnD86p+s6pmnCxY79eCDESGpd1bx3pJKBQufs+vc5G8tPweGbW0xjucWYZ9OOLTNxOuqCjXporVe1nhYAPWmHkWxhd8xmLTpbf9pl6jKdLmPHAL7zzpw4ro5bZyyk2fO0YGvei2B2eM7VUzC3iFmKtqRckXz4U4rt+H22a2f5HmublLyDbA08ylV1vC5ZkmWCSjzOq/0QR4vZ0M5XRPil2WLvzzQ3h3+ws/H6yipzb7Tn8Ys7Y9v+WJ+5lxxva+bCDY+d+rOtd/G3VHl9NnCiqshB+OzKzv21sfIzTwlv7uzzvQjfaFKH+Uy4wMz3l5i94zxvF8cHfWYlwg7hjod7l5U+CON8mpC/fN/x4QC/NnleWFf+0S7wuWEkXbbMVBsLf6w/8O1z4XPDyC/cF74mBRUYMuRZeXELt0JFZ2vskaTgLxKhRLprjiksEi/TX1ZgDko4t81TvabEvbC/lo4xu66uxWylrpXVxUzNeiVmjWnCFZwKRayVax4rtc84VQ61sApnlN68tddDtJgtxoxt1ismBHfnkrxd4QpMNaO5sq3KhWDsUu9xe+tg6AJIivzCHeug+fKQ+MZogP5jW9uYPxMyXz+HV31g1571+T7z8pny9cnz4T63v43BaXiW70yOV1pRW5pHknjO21hZkbhzqKQSrszvR8oDgNt1Z2MAT+U0ngCcHmyTSOQ5sU3WDYEbQfhgTDxIcEsO3F+GfYvZjwXLdq3CaaF+ZlDeHu07/cBKeXNf2QeYsuPtWlACcnB8Is6oOr4+eX56NfPqIR7P8Yk482rq+Hi0eeZXZuGFemKc/qBHWNizdt/VeWJ7PvXKnKFqfOpit7gwrxkDwIF61BYvxzHD2cDC1eMEkoXOn9ZVJ4K2+c6r2bNZfU2TazSv4uAW4CTmArSQA0c8AFoN9ggCTcNK89B17nRNi8QBlkIsvaJpNRsy74ydzKo4NWcBkUrvT5Zy1mHNbN6WP6o2DXtAitrcg0mVgqsmfWhE+5SVPlhu09o4LX6+5tgg4phSwVEIoVmhqqLVvIVzXgC0kXKlFQXu9pkYHHMyycicEr0PpJoR7N5yY4Nry4jnYoDdNWcIa3NuRFXQenyGyxprBYcnyZtlB9xjmc1lw6SqeOpjRZ+CNgeGK5nQZY1t341bCiTlpAdfXusbfDaarEmZ6pUMQFXrxXBcY9s5KadM7HIuqya0f3vbCCrS9MAm5SjtPbI8O3mcuFky1gvEcFrec4V9z4Ya8Y//5wpXUsx6Kh4peioAMgDrr2h/6/Fniy7VeVt8l1Tn8XP1cfBRa6XrusYcQs1ms3YsyGvv72IkNzZ6SY+VFoTemTYvpXRkHZbqdFiKMK48NBaQvABca3hgF3ZiRRapQm7FT0uhTxeiFdFRkGCVq51fnALsNUNsYNPZjnjxwM3ZutIsWlil0LuAhJNNjqut7bLkpv8Vut7hvUNzYj0MbfLv8K6SUmoAegkSk1s4NfMhL479lLh+/SaXl5cc5spcCtNYGAscUqEgjLkyzrnJWTxTKVQxrLUU3Z0AbhvMevpc1R/MmiwbHoC6eFCffvl9ac3T+b7/Z0c9esmn1/lwdPRAClKXYkybuhdQ7etpbFu538nBwj5D29jJ4J29/pjLaeC31OPYXMb/Y3ro2vxPv4cRf+yeloLO3/pff2g0/N/8xMcV4O5s1/Jbs/Cnt7ap+Xu7wKeHzJNr4d5eGYLn45vjtMfdSbjVC1/eO371wvEnzgqzuu+L2WHRb7bY+E6Gz16HQZUDjtcvhVtnjm0tvJWFs2Ym835V7jXmYYnZUYQ3D8JLKyGifO1S+MjWfjcrvF0cT7vK28W9Z8we5sIQj8Ll47M+6xxP1sw9F9jWcozZW75ivKrFrE+eer0eY7bbecIKyqWSziqxeHRXjjHrzwKy12PMhrVp+CkZ7Qr9bmCQ/WMxm2LGe8dmPzJfixQqG9/Tof9GMXshii8HRNZcpgI5EM9nDnHFg5XHp8ClB30Al09n+rvC/dofY/atLPyDR46fvXnSGv6jh54pObRpwPua/rUx+5Rm7lxxaPj/G7NPtYXyppzi4CvF81wbZx/sE1I9GcfrCT7YZe5mx8MifGZbeatt7B42f+7OV6b8eMx+JTuuu8wHu1PMjk3iNrT286taOfyAmP3dg+OmP8Xs+/vEa6kidWGbJz7YXv4/f/OrP1TM/nctXheedLE/W3LFwhWZx9EZorF0emLUSlXEeXhsjbXXHhm+9t5ajck0SUBrlCT2uVVPJJc1ZgCw7IGqgVqbS5uPcKnExeFErzCwymPjSBtjqe06rLZiKTy9ssZCcyVartfYSN9ciBzGulqxqx7ZRlVL2MxJj/ULuXng5lqtlXS7fmkkS2zSOidm3xqDI2ix9wusvDZXiUof7fp9MLY5ZSW4K8/UHglobdIRmDJs1x37qTBlc4zYZ8jVM2cBHKm61p3XbM1KtbEwt+cpTbpwXGPhaA+2POgfxHQ2HNi+7+U1J/KoDa/j8fgW9Xt+tmxYajme1DX5ARg4za1I0KzMliLPpbZGjt+ztmd1PM8yjtU02+V4dfa7RbYhYvexFPJdha1aK1eW2ONrr85BSyb5f/jSl35gvL63j7CebJFOle9Krh5prhGhuXTba04M8vL/i90VZGo1CYKBgcYAtoprG5BKiI6qGe88VedmJWOvX9L5ZuSfiUFaUwTfQKqQc6HoaAPee3JNR3BTtRKjNXVwbbfkvW+2QBWcuREYWC3HojZoCxMmg1ChNQCxeyh5RIfBfGR1gmygMoSA84Gu66glETtvCx5C30cuS2YdO3atYEK8o0wjOQQ6OjoxmzARYSwXrLvYwEBhtyuglaHrSeMFq3VE0wEJpoXu8PQhMqWZfhi4uLhgsxpwpfVeD5Hz84c8enTBenuD+XDg7MYTlN3BJBeY7smLMmfBe2sEkdJsgyY6ogZQR3Ha9N1yAqZAcNaNrTJb44vW0eqqHMK3wgUn1rQjhGCpprabs/GUT0Cy+TovtQ+La0kW0AZKcenInthRbMJQh5YGTHMhNfAqYjY6VayYLbT24c70D02nZdt8f9Q+KtTSUqdLK1JjhGkbt4UNtlcv4MPGqkNJpRJ9oHor9vtRHKqF3xgdf2SV+WcH4ec2ld2s7Nuz/62D59+PmdtBeWHjeD3Z/dzbw4sDvJ6UX72A//iGMbY//1D4954obCv8/MPIH90WDlIZs7W+fCZUPrw1lvCGg6SZ2QVuaOLLB8dLm8IonqdqoYhyywnnFS6d8JQqN5zCxgrfdii3ziIXqnz3ALc2nv2h0m1gf6isemOIX1orb2YYZ2NKv52U9wWYcuXbSblMS8wqL3eJ31GrvH6hOSGsemEU4fkOfBp4+MSes3cUuRs4PLtne6dnekLxWXDXHPHCvELlemDaFbqzyFQLDMCh4vHMJRNGj18FZBJSV5gRruWA1ozTGVcHVCvJOdyjTF05Zg4c1h43OFZJjzGb5kReBa65cIzZFcIY17i7B2Tb0Y0z0+2B/CjjQ0CD0k+RJInV2z0aE9ELvz8rocLTfeU/e0r51cveJEVVef96iZPW6U8C3xw9JSWe6xLvsmkheyoOuyuhAS7hlmZ+8gnhFx4IiDDO9uy7Y6ZP+Kn1zJSFV5vG98Mbm9N+/mHPz561boCzcGMo/PJFZK+Zr4+Rl/uR56Lj1ckjZD4e4e8+DPz0KrEX4QmvPKjC746enx4K96r5i6oqN7PjGS+MVVi1XfEm6vE+X58Ci/TgQalcc4sWtvCTK4vZ13Ohn+B1BgNoKK+nxJ/eOF6dI58M848iYO2vK8VOHqWob0Bi6WrawEx7UdElLbxYaQloaWnzpcsYTa+6gFi7BzG8bOC3FhaPX9S8Yq2LmxUjRafkUgniTCohxlaq5qNWmFpb6tnAYvCtqUMDqN61Ogg1h4hamuyC2qR3dt/WYc10tYIcGU7nBM1KH52tubWSayVV69q2yBJrLfTBPhuBPpi1Kl7IDWx6Z/NEcIKEpbraDMWYMhLsPpwWLrOCWhOKQ6psOyNuqvNHsO2dtQYeomM3ZladtHbPgvdwuZ85P1SGoWOcM9c3PbuR5hndvkeUqS4uU84AvFZiy6oWpDmrNC7nKlp1Yt3utBhrfKVw7PiShSluANG3Dc4yRmzMnTbHWVtGYEHS0jYvWIGfiGUgVBQ1VUgDuYbjanPQyJXjNS/YUXCtGFGPkhpo2x2Rx0wLFrenpdjStfFvS6yN8VzrERwLi11bs+2jkquNIy+nDMkPOt4TCC8gBcydoNZKKRmctcSzwWuvzXn+HjcIe/8Coq8yCt977uU4ie6tDe3yvoV1tMKRSC0JVWsPqmpFN4gcd/7HzxGrP7V/W9OJUifMfD2eAL5bdtHueM0ignirRK/V+qw7NeuU5TMeA1G1UqWSU2IIse1snaVx5pm52QiF4Ns5sxm3R4evbdFQCxjnHN4rXTQrnZQSIfR4p5SS6GIwi7YYrEitWDqw6zsO80yM1qa584Fh6Cglcf36GfvdpX2XXW8WdG6gG9bce7Rj2Kw5310wz5WcTcFaa2EcDxTnOYwzfRxYi5CdO3YTNA1X22G2idcagjiUhJaMNJu3BYTmegq6he3wmBd1Kad04GNfI1aYJy2onF80Sa1YTxzSnDYqlaB2nb42Pa5vJuQEUGkNJTyunApiEIhd14qBTuPOe09q412P4PrxcXD0LHY2ZnLK3zfea7O2Wcb6MTbk8R3uD3M8HYXPOHtef+5MeTcLXyjKhxx82ts93Bkt7P/+zkDu1eOlDv6LpzOvJ2ONPz1kFm7gs2f1+67zjdEW3lu9dTF8PcEhZe5OwjoWLkfYDIXzltacKqwUNsBd4FYr7963SarTzBKznc48v4EdEDvhMDlWvRjL3CvbzmKWS3hqawz0Lec5K8ZEbzrhMHve17yShw7OOs+2FtYomj1ZCrt3I/52QspkDWlQwoNqXQefmFlt+zbBZ8pZtXbeDxOydrARZKzM20yYBe2yTdiPAjEMeD9Sq282kolYK/sY2Bz29FMgPdmzuj9yebNn9EpXbQ4o0bNPD5irAdElZtNuj799jfpoJq0j4d4B73rkPCIxk8hodRRm3rgM3OwSLzmLhe8keDZUXulm8pF3sTH8Dx841PX80binA9K68kIU3G7PywP86kG43Taw76rjtlS8CiKOX7gPy+o8RPv7o01H/KUpMrXiyef7k73m3ez43DaRqueVTWHSzMdWNhafEOE5P3MrwN0qvL8zxvdXxwgCB/Mvs5gFPrvOPNUp9/aO9/eVt0fHn7iR+FKT56xazH55PrmmfKRLfHWOvL9PMEVe6oVf3Snvu2KF/+mu459XOQKPl9xE6YVXk6JuYrxaZfgHPJaUP3C0kStGyx4LiRdCoC4p4CuHc7Yu1caqyfee+3vidSnc0gZoTV9ZTxrhojhvTa1Ea6uPgCZgOGafTyzficHzWLc6KnhVKwZzrcMbFXUNcjr7I+0aaQyivfX0vEWaO4Bh2eYUY0yvtS2W9jyqkTUYmRX9one2Obn3zjauDYwPwVxvglRCWPCGMcJeSgOLEIOjNcttDVSELjpSNiyTirHPQzSHpOsrz+VkYzh6k1VJ9HSd8GivrPvAfiykbK4Qim1Gp2TXOc0VH3xb40yS4loNTG0ss+rJCcM8e2uTHdDIo3J85kfc18Bzbc+j1NOgOJZq1dMYlAaaj+wrJxeOxcVKW5bAPkuvfBdNPoNtqsBqiVQtM+GxOpbS0gMGsBtYN0eF47Ushe3LOFjYZefs5haZy9Xx3uTabaw3bbKeHEj+Vcd7M8KtOUN1lgarAiytGhtAlAYQQgMaIkIIxgSWPDUXiIJJb51JIQRy884UzYiE487PdpH62J+cUmujnOFK8VzOdo7ozEYjz23nEEIDFwZ+r+6OvPd4HynFNEallJPkg4L3kZwz1mGmWstasZs39wCbNEo2IOicY26bgOAcXd+bXkohBiG6VvhVFBcD+/2eikkdpmnCZcchzQzqGDZrQhfROVtKRyu+KmHxWA1CH3pqLXTiEOcQv5QfVKZSiL1JULr1ikf7PdeDAdy+75lrZZombsaeaZzYjRMZY5tKScxzoiY4TDMhduSqrM/WTLniokObyXlwHrIypaWhyYk5P7KwdemCI1DUdN61UkQgBLy3jZXphqCUbIxuCMgiN2jjSY872hPLG5pzRebk6KGlttSbMIviVWylKJWSLQUual2EvDY3jlxMduKM7a7SUlu+FYm0z/WojYfjJH2q2z5u9ABUSW1carHz27hb0jQNMFchl9YiWk42YD/scSdXnvPC5GzRujs7XnaL3Zk9vyejsb1/dS3cSbDywrM3hN0Bfue+8IlbmQf3A2bo7/BTZhThVy6Ez54p10lIiKyj8oz1cuHLe9s0jLny22PgN/eVP/9E5VuXntsl88K6yTUqrFfCtSJsq7JzkQeHzAtbx47KYTbg/Pzm9DyeFmMz3+nsu3jt0nGRhRdXBaHw8ZXwlZ0iG8/di8rNTeWFCN/YSWN3zPLw9y6VFye456DXygvrypkWzp6zDdXmbk/wkJ7OrOaeuRtZ54FUZirKvCms7q+Y+z27oXJj3zHenlmvVs3TVJDL3gqD2qbDsafrgnl9zpnLtbUR9/MpZvOTvTHVLnK/zjyhHQ/ygTUbLjvH6t6B+kTPfh4JYyWPF/TZQMgYt0gyIkJ6h+4qvnfMT1ZeulPR3MapE4iVv39XeGVV+HY6ERNfb/rwO2ni/zbfRj7cj9w5KOca+P2dMODwvbIOlVeaI8mdJLyZKh9cObrEYzE7e+Ublx0vbya6Rjs93+L4i2PkWW+g+AlXuXOAlVT+0SP4UG8r7pOu8uoh8kwsbF3FBeETzrJ7//LS85l15dk1uH1hV4XLgxUhvT4ZNHt9cmxi5fUp8HayeevjfXOfSJGvzrGRD/bnVy4qPxY7PqAT/8+Fvf6V9voPCHwTcFV4Z7Q5/zNd5tfnx+te/iCHNWeQ43ziELR171qkC0evVREWEYUPjTlc5t5qhebHGlexhJXpLQtLU4XFs7dhQttQQMtOGQgRGvuGsZ3emUevqjIXAzkmTzAg5kUeky15JwTXNKKLawPSzmkUcSm1tW+GQLEid+RkVSlQlva+AlnL8d9dPGlfo1Oca0xzY7QPs1FioelxKQJZKaJsOm963VIbw27zfHTGa0Zn96ZqjK0Ta0m8sKSlCr0lRBg6z2EqdAPsx0wXrDhwTpXNGqakjLMZ29Xa+hYkey4pmdVpVcfZ4EkFOu/IbXMXpHWnzRa6pdYjMbQUmi3fnW/gsdRWbF4E7xzOW1ZVmsam1EXP7I4d2uAENC1roFdAdGlj5ASKl/W3irmb5DZWSq3W6t6ZQwltY1VVydXqtsTZhsOaprTxrKdshaCoLsWRbRCzXFPLmLTMSb6iB2/GK8c+E0f2W+26lk3Xe62w7wmEj/rftvoHcceU9MJohSsSg5wtKJNacYP9rDFj+WAeiM5WTo8VcJn0wbR32sCJqwYECQ0gx0gpma7rKNkKVqLzjHVEXCCpo2o1htQ55mIOB0H9Y0x0bUDQ2Gg5Au3lWAqdSp0QsRR2aaBMdamAVANrTqmajc53ix64sj9csukDZ+sNucxMsy2IvXcc5om+79mPB6ZpYvDRmHYnbHxH1koZZ1bDYBIK57jYXTB0Znx90IJLie16BRUW/XXnHMMwcDgcWMWV7fzKjNfM7B9/agAAIABJREFUOI5sNhtjuhnZ9BsuDxcNjGXOrl3n/JEt1nMxX+Q8TXTOEYKwH0eC78BZ5TtASukxYKoNCPoYWbTUvumeayukUFXo7PdaLLNgO/EGGJzDhdBaa7dGLFfG2RKAfugaAG6MbGOExZ00SyIC3qPOmZvDUjRQCr6zRV9L0ze36uNp2hNjb5/vFYKjTvUKk3tifJdgA46Fgcu/QwinYPSO0Ip2lo2gqC3m1UVwnqJWsFXyD194A/CcF94syhKoH1vXpoMufPXSnutLG2E/V/ot3Hk3cKtPrEdhLfDJp5QvvGtWZq/nkb0GQrEN6p/ZJP7ZpfAhZ/bn0+SQM7i3r9z28JRXhuvw4lB4aoDXZvjUrcq3dnB3Ep4Z4LfuwY1t5lyE2SlPzo5PnFUeYd2sbkfH7orOfKXwhQle6oR1Y4uWZ//GIaC10ovwKGWmXQPKM+yTMLax9dtj4K8/U7g+CTuXOatwO9o1XYuRt96ovHTD4W5W5EIIO0ci0V1E5n4inyndvZ7VBFILulHWB0+6lhge9KCV7kbr3rid4D4QZ2oZuIwDvQhDc2vZTNmyU84xXotsH85w1pM6ZzFLZhwr12JvMTuN+CdWnOsB8UJXM+XWFn9nYt72+MNECgGpASaPbCGlzNm7kYvbif4tozjv1sLbxfHKSvlOhn/6qAdVfmKbeXljbj8/E4Wv7mEdKl0rLn6qUy4TbPxswDYUvnBoYy0Kf2gFXxyVB6Xnw9vEsyh3s7DPjpc3ds/vtkLaN5s04uXVxC8/6vnM9YkvjvY9PxuEhwy8qYk358jH4kR1ma+kwL+zrqxRDsU2vT+9yhzE8TfvFn5241lr5UtJuQg9f7hOqDgOqrw+WZevl4YWW22q/1aaeSHYtfyTS/jsNgMd6ia+WXs+0reYFXgpOlzJ3JmtdXcKBl4BHvwIYnYpbLo6dx232I3Rcg2YWe1tkxLkjMN+Z6QOaE6UxiZKYwgNSNkaazVIBppqk9yFVsQWov08BkduRWbO6ZGUsOsROq/HrF/r94Be6chW1RozLZrN5U/DPMc1V1qL+SAnPfBSjOUwD/5lbjYeqj0DqUyTMkRl3RnJljIkrGAtpUoXHfNcmau2hjYGsJuihzEV+s6Rm2XbYawMweQpc1WyKKvewKK2jYhzQh8d41wb46xGFFKZkmPdG0EoqTCshMOUj/e8XfU8PFjdUK5K7xzTbPccnDAlbe2WT2M0Hd2X7POroUqCP/k+W3vkps1uoL73ze+5qklQ2oYCaFZ0TZYqJy/4xX1hkSQMsXWws/0Li5WZSRyWOgyTJR713M6q0nJVupatXaQ5SwFeTpngjemOAlEspr27ktEQjszycY2tJ9u0Uk0jTttUeyfH4tLSALvUxZrN22ZA7R7ye/iUvmexXPfZv6LFQRc88zzjw3BKDcsi4K9NJ3vykO26gXmaQBMhBLR6tGmwAv7YetZAZaS0wrFlgunDQK3JdojN0SEEA9UBoYsrlGwWRDkTghXXSQNXqU1QEuJR57uA+murDfcuzwmuo+ZkLZa9MdhDsxtzDfws3eJSSngJBuq9sx7YqriwFAA2drIUnlivGceREExmUZuP8dAZyOr7nsv9SOwch8PYwLb9XBQqyrZfUbUxztW0ykOEGI2tHrponeVQ+s4RfXfUSkVRA2PV3BuiD4Qustls+O63v8Nms2GcE9euXeP+vYfWuS505Ckz5kTWQHWROSce7SamlOnWG+4/eGjMrO+gZKbJNgu66IKXArM2nCqKq8aWSvOXrqUACXH98TtxcHTQEBF0ntEQFuTYhpqCepArBY1HGc3pd0vwLOf+vvbGTqyYqc3OtVbTVqq9Lrcc5PdKNK7KOPDOCvHaNUj7ud3DFVDvHOCOPtZXtdMi5vWpzqP59D7953/zaqbnD3T8Vx99WYsInzhzfGGXuNb13xezu7nyysbxdy7cMWb/k1vK375jTM1PdZVN9FykQhLPx1bKl/aOJ2PTH68rv7b3vOKEr7dn9heeCLxxyNyZPC9sKjsHH+jhm5PyQe+55vQYsw+842ZpzyHZhvN+Y6mvFWXXNF7rmtm7wC1xvHoobHvH/qC8NisfvSZ85ZHwx28oFyoc3Klg8bZmXhsdmw7uXsKF83yitSg+NEnWbU3sXeCdg/LxQUnJU7fQh4IfrxRe9QUXPPOU8dlRs133NDm6G/MxZvsQGYfpGLPdvcjQTQx9ZJozK+dwOhJQOs1MqxW64hizfQZqZo7CkCB0kSyXlJ3gQuDSKTe7Nemdcx7diLYwjZ5YKlkDFyp4HA+lJ+9AbwWmNxUNMwcXGWrhd3am+48h8EYu7NLjMbvulGdr4W525NZM5dcfdpz1M58cLIYuk/ByV7mTTlmar41w5itvlXBkekWUZ9wPjtl36ul3S3wtMbvyepQ3PDNU7szG+owCqPJaqnw0ekYxzfSXmrfex3vhK9OJ17kas0+HzBPO0Nih8U2DwlemQJ+NtJljz6cGZVcrX2nrx7vpdL4XauZ9HfxeHXgnFc5bzH7ju1/5oWL2v/1Dr6gXY1BTqYgPJ2/1BfxhwDLVk0t6COYMJLWYJhdHZAGNgF7tIOdOYGKR9nkPWkDdsVh5YXHBsqpOK6FJKLz3xjLX8hi48g3YLB3FVKHvHeNYUWfziShW/1JM7peL+f0uAMxJJWdFm97W2ObGdrZr80t6vSqrHqZUib5tEtpcHaPdZxcc+1kZvBo73MBfF90xTR6jIXUjMKv57rpC9KaD7YK0zHdl8I3UEAOTntraVJfjMtV5x2rwvHlvZN175gzbVeD+ZSY463UwFfMazhrABUpRLiZIGYY+8vDSWG9xgVoLc6ogVzKGspAwp/FT1bKUfpEhVKxjnwtNdiAgJwcNEUi5EJx/zJfXiVpXw/bchVMmwnr8uitOIy1eoWWAYHmTXzZJukgqDLwvmzo9De3HvIuv/tyLlTELTeLRPqNUjuN3AfJXa4GuLLHHrIeISTGXD/gfv/yD4/W97dMYretVCUSnlHyJ+M7ApXeEGKiNeao6s+oGcs7kPNL1oZlkC7hyfKilTrjoicHSgbUk+s2msYytEG6eURJRVkd5hqp1SavpQIhrUjJwEUIgUEhzwvvYOlgZkKnYa2K0n8/z3EBqoANq10HNBLECgpxbW9r5xOwtzGwI0nZmhWmcEe8Z2uQchp5xHLnRrY5OFTE2MC2m580ps9lsqLWy3WyYpj03+rWx004IITCVTK7NgmtOhL5HYyDEwDxPOGeAWduOx3nXQJe1vL1/9x6rVUfX7mu/33P9iZtcPtpbQcFmhQbHqt9y58F9cI779+8hoaMLPQ8vL8APzAWmlPCrFbGPTIdLxFU6H5hrwkVPqeZakZtHsNSlbXRjRNMB162NHW6FlYtXs43UbM+zuMcWQu0ttYwzhgygtspkpKLBtMtemrTCO7PpkRkWLeaiP7+i5y6lUGpC4qItVmrOj3kcO022Eeo25FyObLB5EiohRjNrnzO1WbIhkeBM952u6IdVBS2OqjNd1+H1dJ/zNBH6njLucOKP9n0/iuOZ9YEuQpaOD2+Ub1xMdMEx1YrH8eGNpy7uCmniL93u+Opl5tX7jj//bOHr7wofXgtf39sYixS+NRae2zie3ng+kBzzWPnrtwuvjY6PovRbeP1BYp8yn7rm2OfK094WnQ/0wrxPHN5fGd52XIrnZinULjDlzAbP/plLnrzbUVKg+sSdEV4aim1SipJqZrsSnquFuhKeXAtnNbO97nh9smLd1w7w0et2W6+NjqcGuCaVfRSKK/xPbwq9OP7Gs3ZfIQbevVRefF9iPB/o14V5k+B+h64y4yoxXAa6vqfWSj/05POZlfOwVvozpUSBHNj3E1pnzu4EdOsouUM7Y6mYCw+f6RnemcGvcXrAqaMOlYhH372gX3dc9LAtCucT+1sbhnFkWJ/xaDvTZ9j6yHjnITiHu+soERyJRzkwe8/FWaW/5xifTnR9R3mQESeENcghc/lM5uV3Ilsqb+PQnUOah+UbrRPMF3bK81vHU1GPC9pnrk/803MPQ+Hrk43h6zO8lSvPBuFJX3hp4+iK47lYebdJMbo0cWsjPDfAripjtQX/bnZ8cqg82VV+eVd5prkNPRUtLbr4fD8zVF4bhV8fzd4L4Oc2hX986RE1AuKlIbMpmddVePZMmKbMOjpeTZVvZ8cBx5/aKq+PgS9M2TxhY2FSx8+t4RNd4hdLKy6qmS/uMs8Fx3cPkb/8dOHbu9MG/ZceBV7oC9/dTXjxXOuE8x9BzEa14mmtVtCj2dqRF211E16OVlVSM100QKkFBi8UMQAaten3xTQR0TVXIYyAWPeRlC3f750wl4LXQmkElAEHT3BCLZnee1K29HLwAmRjUJ3ZfTk5aU1rhRgMlKZcmZIVCatUfGjpebFanFqs+CnlepSL2XmhczSCDMZsdmCLh62PnmmuhM5Y8Riss2o1mhjvTLO77g3grXtHSoXYSbted+x2p2r+8amYxLD3jgim+VVt3V1tDvfu1MpZBR5cJNadEBuVfZgLNzaRi9nibdMHghO6wXF+mfEIj3bZzhEch9HaTOaaSUUZYmTVCfs5E5rmttRMZ15hZsumDi3GQANoI51qzvgYTsxtA7bS1LWhbR7mRecuBnC7YC0qPHI8Z2mbHSct9hWKuFasaYAzaGktwOWobT9a/zZm32mmb57lqsauZ10KQcX6ETSZaD6ywcuG2LTVuZqLVWwAuIq/whLbxynWnCSpOevEIKZBb2B/TpUueFJrnubkisvNDzjeu1guDA1wNRu0mhCt9DGgrjUhIJFqYbVqXYic0AcPaGtBaBdnRv+e2qqVM+PRzWE6PDp+wSLGMnsXqVKs01QpVPV0zhNCYNw9otusoBTQQlVnsgmtHKY9fbdqu+ACWJ/uw+XOQFqszPNMcoWaTQ+XRZojhbGp0ZtYfrG+WjyAxXmkOG7cWHNIs6WUXKWmwqqLOGfaLREQ17RWeKZp4myzRVWZx0vCsGLoOvb7HUF7us3A3YuH3Fhvj5rj9bpnnvf01XOgcLYaLF1UCnNJ9HmibrbcfXjBEIR33nqbD3zgA+CEsIp02rHdbtlut7zxxhumTdZMyR5fE72YbumF55/n0cWe3cMH3Bw27Gul6yIXFxe2S0xK5zw3tte4e37OarNmmib6vmM8JPCOGCO1noreAKJfk2pBnAFEH0zL7f3adnCltX8u5SR78J6Eo+wvzd1DTsUt1XsoQk3lxN6qpefMVQLQbF24phEN7mg7IyIUzXgf0bk1mHDBxHQRXDYduojifWTKyRgMKUgQYnMlscyAw68iqRgT0UVPSc0i61gAunRbsmudZ0s9e+9JOeE6DzUjMVrUNgu9H8XxVL/ioMp1D1U94hJbrfzEWYBYGJPni5eJL8zKX3jRVF7pQviJp0+puCXN+fEzITjPly6UB3t4sC8ImaoON9r9ZXV8RDy7ufDKJjBXa0H65UeZSwKfPoPhTPHfdZSzQM9EBrodyCZCVvKbA3QVFxI7cdwQyEPlN7+rjHT8+NOZr7wNb4TC78yRDwuIRG6vCu+kyqduCLe6ylaUS2DdZBmf7ODWINzfOf7Ln5x4+G5AcZwVqCnz8gq6fWeb7SQWs04ZV4nV/RX+rC0uFzP727DSQL3IlEuHf8Kzu3RcH2A7dZSLSu0czCMr8aSqbNbgEwxvz4xSuTYqdYA7ZeD6g8q984lnzhxlKoQbA3IoDDe2lLpHR2HPntpzjNnDmefaOQxPOboHI3nuOAszBzwPNwPjd5T+XSv26TSjTwXu33fE5yvrNz37p2e+8M01tzfK82cmO/nm3vFCq0F4McIbWfmx4PhWhhc6z4tO+XM3Z95FeP8lfGADr+2Fp7Mxhy+eOTbF85vvzvz4WvhabR3fhg0f6wtvzYlXD8K/tXb8/HnPy92Bt6qgzb/6X1THnz2r/O7OUV3mOQ9fPjepxJdnx091lbezDcjf3gk3vefH1zNf2Ad+YSfcdIGnu8zfedhxwwkka07wH11TrnfKP7mXkeD5szccn9/NfFEdf/HMc3+aef828HTJ9BN8B8+3xPOtrHRd4W+944kx85le+eVdpF8XqJluFRCppNHxN27/8FYv4sOR0Vt8ckGN7RSsAUUDuUPXGvwIeG+vW0rVCkL02po9NHBxzLQ50jQtH9jmaW0Zz4poNXDdivS8Ew7jxKYLrfC5nT8Y0kipWtF3m19d6zY3jdbZNHqYSiZQmNQf2eLFvSB4MQ/fY7G6gajqMB2pOq6tTdahKEGUVJSu1SP0QZs8oAFlMV3uarB7S3Oii5EuCOOUKd4zdI7LQ2HobXNQirKOQsmJuVrB4aq7IkUomNNTH3l4WYm+8O7DiR+7vcYJrK2EgvXg2Qye79wd6SZzJ7Jm6QWRSlHHMzd7zkflYj+3egEDbrvzYlZpxeae9arjYl9Z9445mY/6PhnjHINrax1HPbZ23mQOmEtFcA6PHtnerOak5eoVnbkTKp5xnoleYFlj20amaGUuzalDLYOgmGylgmnCvTBn22yZY9yV+h1vNqt2UpOXdM6cKFKxtizOC6Xp1z2mQV+KInM2VnwdHbXYhqXzli3x7gR2wTZp9pxpBYzGSJei9F6oWojeEMliofevOt4TCOfZgOCUl37oJkVwziFNr5W8sXd5nKgEQoignkOecUvRkIvUnI7p+77bNAszhejJc0IISM4EF5ldZi5z+5wE6lEpTONEGHqTaowHuq4zJlkKtWYO464VoRl47YNVQ5aUGZpkwQfH1kXGybFatxS4i8YUR5NGxBDajte61pWcSZRmAzfz8PySoeuZxDGpsI2FlDPaCXPOrAUGCUzzxGroGLyi04HLXMwjmczusOfs2nX2FzsuH43UeWYOHdN0wDnHvfnAU+tr+A6eDNG60s2JG2fXyN5SwHmauX39CbLP/Nvve450GNld7Cn7kb0W4tBz59v3uf7ENQ5jZrWOzPOI7wbCpmN/OTOIsZ7d2YZxzKCRe3cfMQwdTz51i3fuPmSQjoNUY6NbJ7XDuAfnSPOSajul/805weEWT9I8UqaCrLbMY0J8j3iYm+SkarWCtlIgeFRttxxaYaT4CFoRCmEI9l35gIra++YZ33XH8SCiaJkIPlJqwYVIEyowpdlYvjK3n3nUO5BCJya/8cEfNcGqdl1VC07sezgK9prdWikzKgFdzPFzk4M4wAuuulbiYsKr6BxTTqyiSXoycNpa/3DH7z4o3BTh17Itj9cd/K1d4E/lxD7Bj3UTn5+Mlfj2u3A/Bz505kgK/+JdYSUTr42OSXq+eZh4ZS28snE8OcB5a46gsfIv70ZudZWShSF41GV+/mHmDw3ClDNrJwyS+dvvFD53K/DCpqLnwrA1JnjuZ1wWfvNu4lNPC4/ymtf3lQ9dT6QamM8Df+RJz90xs+kyn30ffPVu5K89MzHvIhrh/3xX+JNn8M8fKp++YYU0Wiv7CX7jQnj6DPpt5X2h8n+8GvmLtyq/2xorfHYVuPCJ7VR55IWzAOs7dn+rB4GOET33pjEPEWGk7hQZetxhRO8X/MFRNpU8O7qoPKiJJxnoNplHT3ac3ZvJpXJTJ4vZzqrFNz2Eknj/hzakw4g8GFldmhPOvgvEc0d+smcaC9sqHLqMJ9AXR3YZ9S3Dsp6Zcg/qWf/ehFs5tmeOO+cFVh536fBPj6hEyIE73/JEyfxfd6xRyuduzPy/FwKYe4ibHS9KRrTyvjzz6h5+6nrkv3/Lcy10fO7GzP/ybuBnN8p3Fe6Nzd/DK29WB2PhT/YTN3p4WD0oPD84/vBa+Pxl4T994oCq8vlD4GvzzCt9x1+7Bff2Vh1/SwqfXDvOHxQ+cT1yJ8En15V/8Y7jP7ypPEgJfOJW7PiplXLdFz61FV67qNzYZF5P8P5oziXfnJTffggf6DvezTN/90FgEo9Xi9lfuQz8RlE+GgR6mHZC1ysf68xG0XcFrfCwOlSUv3hd+d/u9PzV2+Wox31Piunf8JibHWRZulmqFQZZfZfipBBaenxKlSq+WTIamKg1WdGR8+SSWVoBuxBNZ6oQnSOVShGTKgQRYnNHsGK2QsUZrZUqffBEJ0zJGkHkYqROqZDmRAiOORsj3LlKVWNjV51nSpXOGdA9ZMc2qNlhOm9Msfcmf/Cn9LtWcz7IjYENtXB5sGJzCGR1DD7Z+ySQ1eNkkUZWhmgd51Ku5CJE73EUxqlwtorsxszFoZCy4nwgpWY1lqEfAoNrlmKlMJfCZuWJCM51TLmy3Xg6HM99IDKmwsVYOSTLOPbR89bdzBPrwCELmyjGNHuH7xy72XTZqpXtEDgkoeB4uCusovDktcDdC3PncM2VQpoUYpqtviXlRbK3ANomaRC1ikgRXElWQNb1HFJFXGj6a2sUtchKTGNrgFaLdZU1mcFSrFlZRWFKJp9QGnveXDW60ByjAK2G0ayhlEfEbCanUlvHPdOxI76BXvNsy7XSXWFvTal46j1hDccM1DoLCrS0XqxN2pKb7NFhOuuTzKI1ZHECxXyfBdp//oAa4fi5v6yq9rAX3eNia3a0D2uAuAisNhvGcWToOpLWVhRlXZ1MWO0oaoun8/Y7r5D3D4nDlhocZZpw0p0sqTrzAlxvN5RpZhZrHHF0lGhllat+oJZgrYrzJSJCHzvGVsBXaZ7BaWY9DJxfTk3GkfFNc5uLUnJmu90yjnuAx6zfakr4rgMnuKJQTBMYhpWxm2nG+0ia9vQ+0g09aR554toZu3lmv7uk73vGcU/f92b/sViFiHB9e8Y47pmmia7JOfo+tq5wBd9HhhBxWjk/P+fm2TXmw0gOhXoYubbeklJiWAV6HwhDzzb2TNMB73uefOKasZMuNMlHIKM8uthTUO7de4QLHVOzFBs2Wy4PictpJMTBNgd4Hl1cGDsaI1rFfJKFI/MJ4BTSfofve2TY2i646ThNt23jRhoQ1/Y9dZ21ogaOY6DkbJ7MWjk0z+XgO5YuS4uN3VEXiBV65LywzeYcgVhVhg8BzY9rgJ2CNoePpcDzMacKUZxEqMVcMoK5itD0j+Z/bGkm11iXqgWc6YGX8Z9KhlTaxGefv0gw0m/9rR9aI/xff+JDqqq8nYSXY+Xpa45fu2edkbYeOg/XquP2uvJ7o/Cp5xyffxt+/HZl3HekVPn9sVBU+MQqoF55dVf5Qg78RMx8/lD55KD85nnl3z0T7ij8RhHeX+HFqLyRhGei8rB4fu5FpTzyfP6g/PitVigzBr56mUlV+PTz4M8HxucOxHOF6tl0MF9E0koJoyDbxFwqmxx4R+E7j4Rfv1T+g+vQb+Fbe+HuI+FPPq28tbcH+tXLkwzl1Un55OCoIXPbOcI88eI1h98KXjpqHXH0uHFiRUDOlP1YuRECqSsc7jg2a2WfR7TvSFXp49JhSfBnwnDX80AKN4p5ZfqVFQJL3wGZCDitjAU2DjjMTAOEMfLkZm+xpGvwl3SrnqkT4jjjfc960x9jdvRKnGEaPO7+TEF5tHP/H2tvFmtbdp3nfbNbzW5Oc/sqFslisYqsEsVWIkWyLEWxKEVWKFlIYxmy4sCIAL8EebED2EFiIIpFCIiRF7/aihW3ioBIMmwHdgwJaiiWyGJTVJGqlqyGVXW70++911qzzcOYe98iRDCRyvfp4p57zt5n7TXXHHOM//9+sjN4ql/BzfBTJmYoM2AQpNDdoPjnR4XH55kpGz40T9w2hl+5afmJRdit2edXkQd7y96h484q845Z4bWNsJoftoWojGAMs5heQ068d1/z8lpu3XfPCy+uKxPcGC47+IdHspH/5AK+tJE1+5HZn1yzl1s4rhrwp4PFkbkIslv+pQN4abx3WHwmKT5hAidYHmo1/2ZTeNQUnkmKR03hCxspFB5qLXdzEk13lzkfC0sn98ZPzBQvR3n9lZfn/KLRJApWaSjwwSU8tYKvXmjeP4u7NfuhBTy1VvzyWwzU+MwH31u2hYBS7CQHWy2r1qpu+jLynrUiEXBWOnaplEp6kIu4TZHzReNU5fRSCNOAdQ1Oa3xMZFXRZrnQGUUoMtafonQptwEWYsiWwss5jS8GaxUqyrPeGrXTLm/fb0qJ1mlWkxQsKRVcTYeLRTp2s87g/TZ3YDe5J9QiaktNIomsoHFO+O6phtqEiDLQOXleL3spztejyEeCTyIjyfdG76p2cH0Q/a2tOuAte1ghhwBjgJJZDZF5bxhCoqUwhkjXGWLMzMTrTGcNxoruVmnDpbng1dBygKBKFS5GaeUerRNGG5EUAn1r2XiFDwVjrRxMMKyHXEkemlikG69ROxMdSLd88oHGGqxrd8zpLYJP7wpmVYNT5B5z5p75XPBiCCvaSAd46wFVRu/06uo7kBesysRUDzOoHRNZVUlPfJNotxTRExulvt3gWYvhnLfyE8l7yNUUtzXRUe+1b/e7yA82akur2XoRimQEoHbvd2ve/KWvPfsd1+t3L4R/6K8UrMEkoRPYxgFKOoO1U1Zq4aC1FILbEXDOGXKQose1croohVQys36PmDyQiLEgkHPzbYaKLW9Wbc1NdRRCEvRYRByt2y7itntnjKHRYmzLWnSnUkjKzywp0jYzorlHlNiSL1IOFfFmBSFmLaYpxFAxKs6x2WxodEE5S9vOIU47OcBiseD04hStNTMr1ISmtYRhBcrsHLjtfIb3noP9fY5X55jaDXRoQk1rC2Fimib2ZnOUUjitKT6yKQONluS0a5cv0bYtyXsOL+3jxxW37x7zwI37KDHw+tkRJiWckW5pt+w5vnWHbjbnfD3RWcfJasMYPKnAapzIRdO2LUa3rDYjru1IMeNLqoQLQxgD3nv65R6mZMZxJGhNmxVoQZc1yhKphjVX9bwY/DhhOkuOcn1LqpKIGNGlBjRu9U66DixMLYqjx+mZoN7ihK30jqJcVdLL/WDr2C9R0Wa2Fuc+swVDdkY+n1gy0eusAAAgAElEQVTvmSl4mmIZo9+l+uWKVlMyu9wV7G9O04sp4OrBTOlvp1yo/KaFSxJKStV1Z0plI8m8SxtD+sI/e8uF8H//voeKVoaH+swTp4pPXVWUqGkWgI6YSbNxivFC0VjLsyvPo60FCz5mcIFnThVKG96/lDXy9Crz+BXD0CQgMV20xCwdA6WUfB+SVjXGzMujrNkbnVzHi03ivYeak0nT28xT5xmtTHUiFx6ZaZbLiFo7zrNwQJ+7kDX7WOd4dZh4z2HPWmeCAZdgGaV7u6bg5p44Nfz91zX/5WHm2iJxa6252hb2NPz2HcOH5wn2Cu3lTHvidmu2XRY264AqlkUo0GXCMjK/o3ZrtpREahtKTIzv6mhemXZrtrEKHwumUahQGFOhr5g3WyJmKmxmBhMCTUhcWih8Y+lXA/bakoERbmf6S45oC/EkYVICE1GtIe412DcCeZ6JqwZjAxejY2waUoHNaiInjVu22AnOomNmNdOoCKZQ5gEztGwmze+vPJ+6AiY5nvOJVwbDI30GrXhho/novuK5odCWzKUF3NzIWvniWeL79sX4+56F49V14h1zxXMr2LOFBs3dWBsTdTtpnOHGLHG8ztzfWV5YFb4yJD650Chd2GBIQQ5nn1sXfnQpa/xukHVzUr0nl0ikqhr6SGPYGMWdKBrjF1dwyRh++bhwo4WPd5kXh8K7esUlAxjN0ZR4dVS8q7lXdP/qWeEvV8qH0vBNX3hXo/imL2ymzP1OPtsLNHkqPBUNH5olXgyaiyBr+gNN5noLf++F59/Smv3FDzxSzLZBVB33BSEUlFrkijxPiqsUBee5ddqTt9xbi9qOjQuC0cwZXTI+KzSS5KYU31bclCwGuIKYopSSwttahSqqvi/BYkkhJLpOpe4l0oF0QakSiJwz1tnaEKhSq1pF7fCjaKYoKK/eZKZKpnFGM/iEUQlnNNZJbsDWJThrDZtBWN3Gyu/SWoUPHtA18RZmjSXEwnImoTxbumxRpaa1SfHnQ6ZvdT1EFHzKmCrXKBQOF3bXBT2cW6L3HK0SV/cdKWdW6yQGQqNxVrPXau6ce7rGsvISEbwaRQddkECgjBYEnDZsJnDOEOtbNEahlGEMWTTPnUxDpyDPzLTt4JYqE6AGhRkhSBSlGEOi33pptCKWe8EfSn7CTke8pUBYJcViTolk5N7JMWFM1a6rrSlS7geFTB6o9C1XG2BjzjRbk7vZdnvr60fBp6Yk1IjtPWSrn8TUg5kc/N6kB85lR3/Y8qR3zO3yJlNfKaJ9rzQLVe5FMZeqv/6lr3/n9fr/WQgntcV0ZPI4UbM/MaaisKp7tutmTDVEYNcxrgUqgCLhvacxllI0rm0ZpzVayylIYXeFKXCve1YXbUTRdR0pTFJsF/BZRi2uFrLWbjfmRIyRtu9qt0VukJQSTdcTQ6YETzMXc9tWAzxcnItb1jpKfZ2kFJSKS6unjEYZkgZtHK2FYdhgrRWJR20ZWCNj7/miZ3V6RL/cYz6fSzE5ScF9cn5Gayx9UwvseS8xycaQcuD09JS92aLqtBWuaWhURjkphK2CcRzRRjGNnnnfEIkQM8u+o0GDhdELes5aTWcdY87cOT5nf9GRk4zpUyy4bsbLr77B/v4+yRmRMbgGpVrOhzUpJcaYcFoMLkO9jjFWAXzforJoin1MpJKwKLLe3gNZGMnWEaeBdjYjZqFGGNtSYr3m5R4bOOdMs0WShUBCk7OncfJvMReytpByRcsoQpX05DhVKoSqhAhHCkMVwjrRNidfEW+glEE3lpylkLMVUVfMvcI3+VDnOdUdjxLzQqnadmvJ1XxUVN5F5eacsSiSkodN17ai03JOTHtKEf7wn/wHKYT/aFT8hUPNC6vM86FwrBXng+bjs8JVJyYDgE9eNTx5Ur5tzT7U31uzzSzwhZuZD13StEVj54Vbp2JEaWyCsSGkstNe6T7x1TsKV9fAEBXf/wBsTqUgPmgSXzhVPDLT9JXi0DZANowpU1aW9nJiPDeYJlCUYlppLnWOE5toLzTzmebCeqaVpl1kfvF5xaf3pZC9GxNXXeaPRk02jg/axJPe0OjMpxaKTS68c2aZzSaefyPzyNLx5HngXUv5LK/Qo5SiP4ycvrahuzbHXU505z1+vqE5bRnMyHx0mKYwVc1ba+HoqubqUWAzaPqG3ZptHdgQuHl/y9turbA5U0IPJjLGwsIpIhEdDd1sEs+2hTQVVGuwVhOdZswZdVfjlgGXHEMI6NiDy3zrrGPRR4K1pAjDYaEd5wx+Qo+OIxVZTpY4g9WYuT0qnr5IfH6t+dS+5bJLvLvXPL8qPDEVPtGUXQ7ResocZXhHo/mt88RPXbNcTPBrZ4r/7ABeHWWS8/lB805RXPDyCH/tqvz95MLzWrF8ddL8RMWpnRTN54Jj9IWf2Zf77leP4aOzwlc38JFF2a1ZtOEbYwSluBsNP3OoeCWkb1uz7+gU3/SJmYLH2sxXN5orOnO7/hJfOpdO0Y3KBb5eFE952W8m4BPLtFuz34iKVdR8aJb4ysbwY/PErVz48lrz8zcK//CW5scXkYTiKMMvv/jcWy6EJVVVtrox3MOiyaSLnWyqcZawxdrXAtPqe3G7qmRCzGgj4VKNFZO2kAcKuYYnb9+w1oqcyj2zEprW6Tqxkw5eyZWeULt3W0lDzBKv3DeGEKtWuXazW2fwSVJa540lpMw2Hnc9BqxRGG0qQhNEtCaI0JzrG1RbxJeh0YnRCx3DR9EMAygtBeyy1VxsRuZdw6w1GKOEfa9gNUhkcmNh8PJ+TKVNkDPnm7jTDVslWlytIq7KApSSItQqGLed4CJhDn0jel2nwccawLGVWWTF8bqwbKU7n1IhZGgax7eOAnszi9OGIRassWRlmbwchmJSO9SZT9IFT0kOmb21EhClJLmNWuSZWtCqKk0wWhNipG8sqUjanTaGkmL9LLdc560hUv4eqwSBnHdhIhLmIYWw0dXwXRF5OaWqO5ZCVWtNTmG3L1orXpptRxilBa9Wp7mpapCNurfHStdbGNX1l6Lmy+0itn3lLZsqSLxH1tim24np0ceCs3KvKqX4zNe+83r9rhrhLdYqKdHxaGdYzOdSEE2SwlSy/IhpGnb6JnHMJ9q+322wyjq0chhlCLowBQ/K4gdBjcU40PQzQhDpxcy1taovmK7BAZTI3mzGarXC9TNarTk/P8e0LeM00SCYMHxg3veizalUgdZYoisMm4nZbMZIYrXZ7BLzSim088WOC+vRGGPptUWbRMZWh6InTBMaLbg219PN5gzDAAVJgDNQamDIanWKz4lmGvExsc6J4kSTPHeu8nsj2on6PuRA0zcs+j36tuX8/Fz4wF1HGCfWYeT69evknOnmPc5apmnk8n3XmMaRYRzBwnz/gDhOjOOG5f6CzjrQlvPzc6xt0CmCls0gxMw4Rbomsnc4B2uxxjAy0mnFyXrFsNmgnEWVjK+Hh+ATWlu6rsPnhK2ryXsPWUDmsbEC2UYMCv1iKZt4X42Y2kiHPyd02xD8iG3a+mA3UhRkkaxY58QhHDVxu+j8gFEJrCYFL1KEnNHOkaPcw87KCdEYyEUoHmQ5ExvXUGIkp0DXimQkp4Cy7S70JSNOanlTdR1pgVIWLR30LfUk5cDWJFdSQhlDCZ5Soji00eiUSVNCK00aNjIO+y4H0j/Nn2Mv8bGvr0b+n1XDTy8DH9vvKXbiD+7Cw/PCUU3c+tzdXE/zmTtR89mN4W8uE7fOZc2+TTkWNrKfLScp89wb8vD8zZuKTy8z/+oi8TfeCU8eiXTkh3B875WEnaDMavpQghuXC+fnkDvLR+/XPPnayPtmmq/cLfzAJUW3LKhz4ABKtrSLiZI1joZ5p/jqKvHBvcKdZeBLtxWPXis8t868pyj+9gOap88t751rnjxWXDaZv3bd4GxgrRUPl0xH4rffgEdcxvvIfK54+H7Dl9+QB/RlNcM2kUF55sDZ6cCXveWHpon2bkMME6M3nGwmLtHAIkEyuJxJKLIumMZgm0i44Wi+NTJ5S+sCuTToteHtr51TdE/LSNJrvAZ3vcUNmTJIR348mNENkTRMxMOGeVSgLWqcUM4wdwWv5WBH7FgnsE1mPhfd/61LhuXrnv60Y5ompkkTFyMqKnxRPHUr8tQGLlnDp6863j3PPLAv9/WLJ5G7MfPKRvjbf652Yf+oaH7uWuHv3zRcmkn356CV6/7NIfPxPc2vncBfviz/P2bFSbGchcC/utD89LJhP2l+qkuU4ii68LljzUf6RJrBy5PIh3oM99vEVzG8MMLjnefcGh6cycZ7QxduJpEAPLowPH0BX50MP3e18PVV5jwmirF8ea24oTOvFcNLW39YXbNveAelsHbwszfg6yvZq16YZCR8pYE73vDYPFA8zHLmls8UNB+2gdc38Kk53BwQH8J/gDWb8jZGVp4BjRH5Q8oFH+91TwFCSGyZrhkpOpUzNYCi8uy1pMYZhJNblBGtr4aYE62zIr2oXV9dJ6mdM9VwLFHm6zHRNuKVWQ0iN/Ax7eQZfkp0Te0maoQmoCUkYu1FL6wKDD7vyAAlF/rWVY4tBHSNboZOJbIypGLQJciUSUHKCdUa+tYy+ppiagxOCWVEjH2ekgWbep5jNbFbQazZQmsFq+aMFtZslrjmdtbSOPn9Jp+h0YxjIMXA1f2WXArzRtdgjsSNfccUpDA2BhYzyxQSPkT2+yqpUJbVEGsxHEE7TFZMuTBFhTNweaZRxshhJ2S0KmymxODFgEYppCjF9pSka9s6jStgdcECPkqibihSWG63pZw1s9YQk7ChtyQJ52qzw5pKRJJvSGhcbT6lVHBaYZUiaInaVgrB0eokWtyUKwZN5EMeKiKtSFddQyq6Gu+QPdYamWjkTGMVISapJ99kFOVNGLTtUU3VyatWNdEvSWhMLglDrkEetTBOCbVNSKyUqhCjwAq8XOP8XZbrd+0Imz/3MyWntKNGoGQ07JoGm2EywLb7piylVJAxUhy8OSI5bQsXA2aMuxGx1ppc8WGtEg3yalqjUjVnZQXOMK5X8p6MiKmN1jjboZRitrfk4uICC8xmM4ZJIocViRg9KVa3auPoeymku9liF0U8TKMgx4quZimF3qbj+QBKUs+ydjRNQ0nS0dVpC9/upPAJXk44JbLXdRhjGKeNFN7jSKst2ll8SLW4lZH6tpBqGsswDDjnGEKkMewigPv5jGG9oXMiTZF448TNmzd52/XrWKuZz2a0TjGOI4tZz/Hmgk4LY/jy/gExw/n5OW+77zo3b97kbPDEmMUkmABn6G3D8fmaUArGWY6PTglJCTe4k+vVdnMZgwbR3zZNw8ZPO3pESmmXB1+slvzwepPP+xnj6gI1n+OUxvvpTfHdCWPsvX+LCdVYlL8XzYwz2KJACf1j0XeoLIiZsQTm2jH6SUx3OYv5UTtizlinKTFV/J+r8octFD9DLlX3m1Cuo2yDo6IkFxlj8DHULu49tNJuU0sBVEIjwS65iv/F0zfVxd1IZzhNFCUmvpylU5a+8H++5Y7w33rswfIbF46fXkroiS+Jz2bHz+4FulHzXFGCCCqwxtKR+M1VA2j+5tulCD4FDpXivK7ZqBQqRPa04p17mqYYzjPM9yP7vmEcFb99tmGhCu+7qtHBENvM125WNI+ymBJJGD68dDRa0V2GW7cLB1bR7SfOJnm4znJiWEeeGh13guZTVxXtzPLsG57H9jvMXiKdG/7gNPKR+zNu1XAnZhbLzCzBidG8dgJDFOLMRW750I0CK5FudCpzHmB/X9OYiN1o1o3HBcWehqw0wSu6ZSaPGQfkVkbkJ6pwmTpSbxUqKGgyPkMTMydKcVAKxcr/2comXB5prKJPojG/ezpyfb9F20Q3B1wmDYGwMJTVxMw0RJOZdT0xQ5gmTOdhcmw2CRU7ikuk3FOsp/OZI2YkRIp1dq4ZN4bVsKY7dNwOkUsLR0yaciGf9dwVnhoTbz8A4x3PryN7RvFbF/CwVVxty27NfmhZ+PzdzHuuOfZy4taoWfRQsuGlTebBmeGf3U38xWXh5gS2KWSvOGjheALjMg+2liEmfv2s8N+8PWGnhk3W/OFZ5gevBb55ppnQlJz5nQvND83gRV94ZAabMXEzK24Wy8fawjfHmtaogVz4ysYw5cL3LwqvFWnMvDFmPjEvPDqD3z1L/OCB4dk1f2LNvjBm7ibFo13msoZbsXClIqmOK77xwDg+3GdeXBfOEYNOrlrNf/TSC29pzf7d7313kU5b7ahRxExkq1YZSUHTdTwtHUAoKEky2+lf1a7LZ5ViSPHeiFgpTC2Ulc7EVIg+iSPfaVIR6d0whXpdpcjWGpSRCdGys6zGBGT61uBDDd0gkVLGZwGdNUbTNobNmGhbhzNipAtBzFYZGdFL51R8OT5lLLlGS1sJrKjEoVSpCM5V3XCK1XOcaZuqoQ5xZ9STiGVNSDD6TOekvm+s8IsbI9IBZzQhCvVpG5wxaw2bKdEY0RdvDwl3Tieu7Ducgb4xNEaK4VmjGCeRFlkNy5khFcXFkLh+4LhzOjF40VAba4hZrrOxcD5IV9YZzdEqEotmCpneyjVxzlIAn+Szdla0x84IVSPnQiYLuk5XyFwt5dpGs5kCfdPUQjZXvW3VDmtNrPi6mDONMfiUdlMJ+XlinAux0DXSzc3FoDISLBOF+lGqlAEt0ozGiBY55yKULfWmQA/yvaTEkjHGUVWLhCKGTFP1w9bKffLmCQaIGdqQyWrrMUD0wrBLOS5aqGVkOVzxpq/94h+/+KeXRuz/+f+6rIMUJSEEnGuJ0aPrL9tlBdaJwF5rGjIhBIRKplC42mGFnApd3+O9Z97JQ3kIGcdIVGL0MKombJVJQi1GUKomwTUL+VoNjEgU+qYnpYT3UsgulwdsNpv64Q9o1Yg+uRrZlBH5dPIB1c7xIaGyF3xMQWKEi5jxtqloaIMlg7V47+kw2MahtWb00l3e6qK0KhydnbPoWrpOujLRS2Grirgv95dzckyCM8vQuwasYbHsuXXzNgd7ewDMZj26wLzrGYYBHza7EXueJvq+ZzVs2N/f56VvvMgH3vc9bDabnVTl4uSU++67yqzrGKbAwWLJ6eqCo4sz5v0May2L2Yyju2dkreh7MdrFVGibObeO7xKVYQyeIURizFA0becYqskhRjkgSFakyEYyQunIFMxW42OUmCpLxeM1HSmF3cMb6gblLMlv5OTYddUQq+rrJGzzps9FaVAy/lOmRaWInwS7FlMd7RvRL+94wDHSdJWDbfvdv6ekyNGDK+iS0MrWg41ISsI0SFFfCtp0JArKJhwaP0Uw7ttU/8qYXfhJjqL/NSrBLjMKcjXriQ5QdMrxyX/xlgvh3/jkg+X3jjWPHcILp5mHDyx/fJJ594Hn+TPNezWYueXpc3lofUh7Xs+K1yvz+zi3PLEx/PRy4tfPG/7OOwufP0788JXEZur4B0eGv7h/xpfWs92a/dxac6MPfLTJPLt2PGZHwHDbOD63liSkTy8zY0788FXLaYLnTwKPHBqu79uaCAe348ie6kitZhEcazthXcUQXliUafiGTxyWTIeQCK4s5ZD77oNCiIo8GOgz88ES9wMXJ5rLWLoZpNazjol5lg2CTtbs87fhvmVhjmajCtaPmIU4lIdouexELx68POzbzmASHL0t0T4/MV9K+3TRG3SBLgfGIK7pbAo6Kbok1JGzlDjoDXdun/P2q4ZIt1uzcZhoLhVy7zBjpnWNTM68IqlE4yzT3GJui9zIFE/UioiiiYY7NGJmC3BkwI6REjuYZ4qXxfjlU+k0WaOZv2nNPjXBV0fHD7ZS/M0cQhlxsmZd73hpk9gj80aNWp8puN4pfvUIKIX//DARa+DI01PhNCoenxXuZMWXRsf9NrAplr9wmLneOO74zB+cTTzm4I0s1/CTS8/vH8GB08QSecUbfnwe+OOoyLrlwZnieJO4mR1fXhUmAz8yHzlKhne6whQ0nzxUfOUi8vVRGjjvaRSfGxVHBf7b5cQ/PW0xFu4Ew1WXmJtCp+ExB89ExTfWhg/PM+92375mz4MUv3eCPIevt/C/vvidN9b/v39+6cMPl1S1sjFtkyzTLnEtqSzmqkQ1zCVCytiq983bkbUqxKzoGqEM9E5G8j5pbKlUm6rJj6lgShCpQlQ4gvBybSexzaqmdyHBHSmL8dhZzax3DL5SA1IUioWxYo5KEresEK2mtS0+gSoRoQII+7cU0ffmivVSSu5Ca+S9ZyUFtdaKEKTwFjmIvLeLTaZ10DqNj7XZYbZJsYplL0XU4MX0a61gxPZaza0zz3Im92/fSMHXOM3oEyVG0QZn8DHSNYbRZ5Yzy6u317zngRljRaQBnG08N/YdbaMYA8w7w3pMbIZMWzvJfaO5sxLdatMIOzdmhXaWs1UEpPANUYlMEEVvFVOVr6W0JT3oXYKcKvfkA1tSkVWKWLbBFwVbdb5bGQTIgcBqTYrSJGmdJhQxwW2JEoJpq93TavSzpqC0I5dEDKEaJCsvXCfRL9frFnOmc1KoY1zV/UIoQuVoNahS02NVjfa2WpQAVd9ejEUVRasFtDAGds3YrRnO6MrYziI50VphqLKO+mdr1tvqlLWGX/qzFMLN4z9bolWoqmX0fsR2Dc4syOtT8rwl+YTrelCWcb1GWYujRiUjRWxWmpRD3TilOzTFSZLfcgPJ0/e9UBtSoqmFqM8BbZ249HOugRpjLVY7fB0rtZ10iX2SznKMAevAmm5XkMRciFkWBjHR6kjCEscNzC9jwxqUMDCb3lLK1twX0TnSNFJQX/g1ecoiwagXOgQvHXCVmabA/nzGYlG1vSqwXq/xw0gqiuhHln3POI4wmzGtB3IYRZRuHJ1rWSwWXFycY9oGk6RQ2j+YE0eJaE7jSN/3aFPZtsYwnJ1w48YNShbqhTGOaVqjQmKTPSVEYRlPgcuXL6O15tbJMREZrWE7xnEktQ2vvvIGgcylw6tkrfjmq69y5fI1tLasVud4LwefbYyybRxxnAiTJ2lXn+ATdnlVCAxJdNpJy8hIpUz28r6x2whjESqZ8qakOUztMsvXwzTtXpMspkmfR1wwhBJQSJhJSKoeGuR+CEU6+ApDUfVwloNoc22F2etCwaFSZGu6Q2lM08hJtlJHVBATZqEljR7l0k7Pt0042hbrVhtiETewUZkUEf1e1Tc2TVMT+uT7pyfeeiH8vzz2cPmsL7zbJt6/n/lHJ5a/etlzWS85PV6jLxueOVL8wFXNEFt+9fZItJYfmcPxJnH/bORqUWw6xzNn8jk8vAcHKfHFtZy8b8eOJwfF334g8eoF3E7wsQPDy+eZX1k5fmohdIm3W3ioAasCd5LiIwZeTA1fC4VPXxGjyu+cOx5oYRUzjx4GrppWEtdGxcmgeW7IfDkZooefO9jwzOT4rTOF6nrerwau1TjP771RKEFCeiaf0C6ynBWManjuzHO0gY/dr3drNg6aZl7IKnO+NryjNdBYwqFnfh6Jo+CqYkmMY+TSvGe1mQgHLesjjx8D+23BKMdy5mg7KOeBuOhRRJLXLJey+Zs0Mg+VOmIi2nny1DNOE5cOCylFwl6DMQ43jqiQCClhQ4NZFMoUmC2EMX6+WRMxuKJJE0wakrW8ftqRVWIxM0zF8X+/tOJHHurQ2jKcB545hnfuaUoLeTC0+4l4qvn3x4mXi6QP3vSZ//R6y++cKy7pyMdd5omY+bAr7JXES2PhUgPWaHypI30lY+opZ+4EzcxoHp87XiuRkyHz+2t4/0zxqhds12MO/q+15qe7zFMxc01lPt5F/ji03CmFqWgetYU1mcaAyZrGNvzameIRO/H8YHl87nkuWB51kaAdV0lMGb640SzbzNucYl0KmcKDDezlwtNR0eD46rniwcXEVSW657t1IBRL5Csbx/f3mWcz7Gv4oSbwrG+4WpP0VIGPdZF/v7bsGbjqCv/jM994S2v2f/7eh4urek5rJXK+s4ZsGqZpw6JxTKnQOEtRhmGKNaxCihVbQh0tb3WXosOkRHKSItrjKFkKu5hllK2N3sX2blPecpUa5CTyAmXlGVqAmS216DBSgKRMa6SDa6108VJRUkQh0ztHICuLDwHXLihpIFdu7dyJ5KPU7m7JUmgbo8ghM2zDMbYeoSTRyabAGAvzTiQaBXAlspkSY0gkdE1flfXbu4a1TxI+oaUbaqyWonWINNaQajrbYS+x7K3VTEGul9FI91QrVsPI1X0BBcw7s6NXhCT7UUhStE8pcbho0ArO1omMyE6UEWlFYxyvHgdUgeWiRaN4/chzsGxAa4YxMkUp3KwxO73rGFKVjFjZs3Ok7efSOc2JTMYheLSUJZTCaFWL6CpnqN3jbdJcUVKw59oV97Ea76rM1RqNSomxVJlqEaReyFv5gyDvVBG5S1a6GuuAHEk547Rwjo3KJCWpebq+BxCToUL2RKMVIYtsMCrLGDK9loaFVmLeBCm6cxFMWtnyklWW7rvakizEeO3jNroZfuFr33m9ftdCuP/hv1pSShQStihCATXrSNNAWxFR2yS0RidUY0gROlN1xDnSugYfAyrXuOU6NhZDVKltcLszrOWcCWEip4R1LTp6fI6AFF7GOPq+p5TE5mJF03e7sbqzuhbCmcl7ZjMJf0gpMZ/N2EyRXsGmRA73LtE1LaerC4yzhGEU/l/TiFQhRcZxlLho7wnTyKLr2ZSAq0U5iB6231vIuG2cCCnijEVboUz0bVez4RMhTFIEakWjDbFKBjqrBTcXBOfW9z1VViu+AaVIITKMF8xmM5Z7ov9dnZ+x2WxYLOeoIgVu0zQVS1e4uLhguVwyDWvm+5fotJZo5eNTFm3P66cnKCXF5+3jY5GYHK8opTBQIBWuXbvGepRrcTasUa67Z6bqpHge1mtsI8i77XVJKREKtNrWHHIjbGjTCunBVISbl46t0VKgBr3r6+wAACAASURBVC2Gvu1Y1nuPqlOJpAoKkTTENEAxlBR2xz1VTW2qLnrqtSs+75wepjW7TPtSCqSqI+7mFJ0xUcY2MY3gerRq2NEorCXlIIV5VrRGgmVCjBInrURbayutJMaIc2IsLFNgs9mgdSVoaAtakbLfBXGkL/3GWy6E/+nHHy5PncvI7IPG88XYcG3P8vq55wN7iqwSL19ofuPC8TcuXzDrNV8+M3xiOeM4R/5oVfiBQ8O/Oyrcrz0PzxVBW5ZW87WLwkN7hZtnhUeWjhdWkYcXlnOVeOE08hsXjp/cTzyYPb8SHaD5QVO4bBWP3Kcwq4YnTzZ8z3XDFGBKgaU2LBtYFcWXb8MnD1pus+GFU8PjBy1fWAW+b6n53dPIj77TkhaF8bSgdYPeBL50Bt9zRbFsYSzw5LfgI/fDxYnm984iP3XN8vnzxHsXicM6h7vlA5f35T5uB895gD0HzDO9b5guB4qC2alFhUK2ChUy6IRGJF/aRlSxRF3IU8G69CfWLNrBekAtGxaNYtpMhI3Frzd0e7JmD2cZ1cTdmlUBioO2aErnULrg2pZhPUDXEI9FbpPLhrNhycwlThPYINo7UqG93DFGQWLdubOhnS/uyQEWirwq/J1XCz95EGmU4sMH1fGd4Kkzxffta6yJpOz4ynng7Z1jZhW5yXzxyNCXgVZr3r+0vDwVXhkMP3BJs65trF8/K8xz4FFbCAZMthz2mudXnltJ88JoeLCLvDRafmwZ+Hfrhsdbz2cH6Qo/3gdC0LzgDdplPr5M1NBAhpxZJ8tpyNzfGRYms+fgwitO00TRjittw+mYuJ3gvQvNnTHwchCDzY8s4Y0R/mgqPOYUz6dMA3ygUVztE1+7MHy0nWjmLSennn9w7PiRReBbY8Pbu8jNqHnaa97XZ3Iy/B+vvjVqxGc++EhJmUp1KOSimTlHiKGahMpu03cq0hhNyIpsLDnL16ytVIlctalqG3ZB7RRTu2ciJ8nVoJyyGMxTipRcKGicFWJMV0Gv6zHQOSG8pFxwmtoRlJF73ximKKPwvjGMUaFUQmXFbOZwVrMZRYowhEhOYipratdw8hnrDCFmfEi0jUJl0YHaqmP1MbPXWZRSjCEIakuLSc1Zec9ayZg/xSTNB1Ujn6uUyeoCBUIWaUjXaO7lK0inMaRM9IG+Mcx7xxgT6yEwTIlFJ/vGwcJVRm6hkFmPkXlnxXswa9E6s+gtJ6tE0yjOVzK9zBROV5Fl7zjapHoAEK/L5b2GIUjhPk0iGdjqAVonko9hql4XxU7fm7PcL0qDRbrBJWfQFqPEfxNjJiYvhyW9jdq2NTCsmtOiELMkyQ+SEkmDSrGa0/NOq7uV8LxZiqEUFbkqHefOsjN4QiGUmj7oHJZC2CaDpIi2DVmZyqmWsBWqtCKxTfVTgmmTI4Vg+tSWcCLcaq0ljXD0+R5aTYskiErPKAU+8+zLf/pCeP4f/1dl8BNtU7m9xuKyZsortJ3hU8QVRS4iVldxQrmO6L201XWmqTG74zhCKcyXB0zTxHw+5+Ligm36ikWKB+UsjWlwzjFOa0rKjJMEYcznc8ZhoHUWpVu8n6r21+CHEeOkoC5QJRmiyS2l4LQlhUiqOua9vQWbCH1KnE0rsD2tEw1wCh6TpShN1jHr90TTq8HGicGJfrjrW2EmastmfSYFehCtaqNq2liI6K6hm/UoCsMwMMZIDpG9vQOOz2/RmZ75YoHOiaINwzBgSmYzDMzncxpjMY1j8gNz03C+OkMpxeHhAVmBiZHlTLrMt2/f5PDwkH7W0vc95xcXnK7XXLp0iUXfMZvtsdlsODs7w0+RrDRd16FLZj0FDvcPePm1m8z3loxj4OjslPlcNNitbtCN42KomLyqpYsJdOtIIWCVIU6ebj7DK4VFyCIqJDxS9Gs0RUFIkRLlezWZkgO5aIxrCcMaZTMmO3zwKGt3KDJTNcVbqYiMgCTpsIQIlTWsZz05yGIrpVBywNTRSUJWp3I1AttX7a+yMvIzik5bmEaysztsWtYGlQMp5p2xctvl1TX6mm0hHzZo19dUQtFX5zBgmlk96cv3ozQqZYY//OdvuRD+xz/wUHn6LPGeQxm5aWc5DIYnVmvee9jxa7cy/1FbOM4Rh8bmxKgcvxvlifaDNvCepXR9/t6rhmWf+e/uVzx1t/DR65Z//WriisncTpobVgr/75019J1GtZDXEj7zC69qPr2MPH7YcSuvuOEUJSwZ1MgqFa508PWjwv37UhB/46LBlMit7PjIvmxe91nDC+eB56JiX2cev99wmhVXV4a/dTvxjpnmrxxo9qxmbScWMXLXKyiO/h2R+EbHFZUwJXLUasYLxd6hIhPYDx0nZ+csrlvKUc9kEq3dEFrH5fPAdNDgjAwffYZpCozTxJXZktubC67Q4JY9xUEMnhgM5MLqYmS5P6NtgRKJCuajJ0RFyonZfktWsCwXKMT1f3xX4eaOy4tMUBEC3N707PeBfpYZ5h39OrKZNMNGkZVmrpXwxItm2Xu+ddrSzyEUy8u3Vtw46Pi9O4kPqUyzP+N/esXwyc5zqZPO5tdHzXtbzR+HzPst/MFa8devNTwxwPf0iTupMA6JJ4Ll569FVDQUBV/ZRI695h1zzbv7zEWe+MbK8d6+4YnTiX0XsVh+88zyY8vIv11JcfuT+wGfRI7jE/zblRxoH24iL2wsi7pxPrRMnCdwyKY7lcwjtYh/vsDZqHmsj1xq4OlJ85CDi2I4CYmVsvzEfiFsAhfArE5aLlJhYQq3J8UXN5pH2sy+ky7wUhduesOeVZzFwkWJPNJprprCRMPzm8LTQ+GTC8X1Bu4E6QRv1+z/8OxblEZ86OESgjjbZe+WAa9JAUwj8gEyusg0M6eAsa6GLCisyvIcBqYgCXSzvsHHzKw1YtxSmVRMddRLR18bjTWaGIIY80LBmULfWiafcAaKtmJsAlqjxMxlVC2gqllPa9yWqqNFD6zqtGDZG3yyZALRZ5RxWKNoqou/1Lhdoy2udYw+Y3QiJ4/VLTEXeiejdTRMk6dzmrFoVC5YYuUsRzpr6BvRho4+kZIUtvOZY1qPFCMJcMJr1lIwIYVT30rgg7OaFBLKFoZBiBb7c4tGXmNWu8xH5xP7c8e8UXROsxoTm7FwsJC45KZ1jD5xvolMsXqmGgMlM0ZYzhyvHweWvWMMhYtNomst61GMo85qBi/1kDOVAVwUrTFy3bQcDmaNqVNOad6ElFEonM4UJZKHnMFnaK1GkyFnIXQYg/eBRmdCNVaaN3GDTX3drW64VExJqQeGVM3jvbOELEU3dU/dJt9R9cBbhrSPIn8RAoXolpVGfDfaEHOuCXNGnp2ZWrjXiW1G2PyA1oLmy3FCW5FgoLZZEAHrXJVrCK0EpJv8d/8sHeHFj/98SaNnG15RilTssSiMcyQNYSWaXGstMUuCdWtFPyqYlAZUYhplbJ2pJIpUBIeWEj55TKl6UAplmigpYbo5ZQo0XUcxZed61Bcb2r2eqRoWrHaomEkl0/c9Y45sNhu6viGsBvb29vApU1oLYfu7QHENRSdmKIpusVYKq/V6TVB19L5eM+uXrMcVy64lFMXh/oGMzmNktVpJJ9QUppjY73tONics6gHAUjCzjjh5VEosl0tykELYdQ4fxWl78+gOl/YPOTs75fLly6yOj3GzDqM0OUQuXb0CZyu6vQUT0l2fty0pRCiJ2azj6OiI997/gBSpRrFarcgKCIVbt25xlgf2lpc4ProloR2zOfff/wDDMLBabUA7XNeSlJXr1865dXSHS4dX6LqOb732CrpxaNXIwcZowrSmmAatpfsuRjNLjgHb9NiihJpgBX/irJYY3AC2b8neMwSPMZbsJRo50dLogo6JUSkU98DcWwj/Lp65AtYFt1bI24I153rqruxIpdAx7xLwtG2rBly0S/OmAxJTkpCTbBRpHFGdFX1lNXf2tiFOmx1yrBShRcg9XeObS5RkOSVkDErZCf6VSmjd1LQhVackIheZvvCrb7kQ/id//pGiNoVVKhytC6fAIwu4vVY8sHC8Xgq/8rri8d7zUAPPBsMryfFz10Sb+YmDxCXVMrWeJ2/Bxy4ZNiRCSfzhseMnryvGBE8cRx7bE+zb188MX0iKNhbe5QzzHPnggUG7gs8RZx35jufGNVhJTg19b8jRMW0m9uZzvPN8/k7kfZc0Tx8nPnZ/g54im97ST3J9c7Scz8DqxDwDpqVzQpm5c1fx9bHw2HV46luRxx8w/O8vZf761YaVgsUDkebIoZvI5kRhXCa2hSlk7tOWlzcbrh2KAcNFQ2MccfJkC73JlKxR2qFVvLdm705cOjCcXyQuHTZMZwNm796aPVw61PGAbQPZtBTrmaVC1hp0YVpo3B3NpStmt2bP4rhbs90q8PrGctAZztYjIRRs57hy2eA3iguvgUxnW1ZOM/mAMw3j6YTbd5ze16GfORNCi28YSsCrwrDZEDrLMu7zL04mrqvMQaf412eWTy8K12caP6a67grv2FN8eaX5WK9wuiGpwGdPAteM4o2YWBh4Yprxo90EKH5/goftvTV7ljX7OnPdKp7ziic2hv9kEWgM3I0akxJLq7iIhc8ODY92gWcnxyc7T06aRSNTxyMapiIM0q9uLL/wtkIcRv7l2PA+W3gxKo4CfE8feGqyvDRZPjCL/BeXLK9eDMxy4VVluZYipSi+kjTn9XD9ijd0Bq4YzTJrngnwmJW9UTeB+6wgOG+Nmod7MTANRfGZZ9+aWe5/+/5Hyxik2NxqGa2WTp81GqMUqymiVeWsFonIbXTa4azQBoOknTVWtO0y/hYeccqFkvIOcUYBn6Jwi13DFBOtM7vkMqs1Kz+y31pivufgDzlDga7RlKwYpkTfKNZTZNFbeT1jqmRRyL3WWCwFVKJoh9Mi6dhMCV0Uzio2U6BpHWFKdE0hFcNyZqtuNbEZxTjnaoBD1yrCGDBO1+lLpncWHzMpJxa9INtCyvROM2WDU5nTi8Bi5rgYAocLx+l6YuZk7YWcubxsOBtHlp3D1DF96yAkKTRnjeJkFbh2yVZ0HazHiEaoEHfPPDpB3zecXYz4mOkax7XDjtFnVlMGZeicqTIXyWY4vfDszVtap7l9PIhOV0kn2GhFDAGlLUnLAYfKDU6pykUpxJQq61mMiKVYfEn0ToxwMW5NcgGnNVE5OSDlBAI53d2Tiq2eWO0II0qLEVMhyFKl5F7N0mjfMahDzlgln78yphoqhefrrDQWYg05sUokKL3VjLkmySkwVtWE2Bq0UdIOz7adFKsiwISk3Ju607WIJ1Mq4z6XGsSBkDE+8/U/QyFsP/mXStEWXR2eYZywQbrAa6fou0N8WFMUlJDRjcWVjHXyhsdxpMMwGEXZBFSnca6Vzm8uxORx7ZxSxB/rjGOysDCO07u3MO2MoqDrOnKOtb3e4P1A8gGbEv1ywaBEf+yCwhAZisgSnIKoLHHa4NpOogfDUNm0ClV1wNlBTsKPBdjbO2DyXmgHFCLyoW3H3X6SUf2O3kCVAoRA38oIhxgw7Yzh5Igya1i0c3rbEHIipInZbMm0vpAisECksOj63YFjfzEnhICPgdbAzeNj5tZUuHpiOF+xONznwYffzer0RIrrnJnPOo5X59x39QqmKE4uLqDKLS7Pl+QpkHt3T4piGuLkWW9GcAaV4KU3bnHffdd5+dVvcXB4makoNhsP2WPamRgnK6c4jZGLaaiyFjlV6hyYiiRPGfP/svamsbquZ33f7x6f53mnNe3pnOPj4+MZ+4DNMYPBuIBLCIHSJqkaQZqmo1LRNmmkqFVbKflA1SBUtVKrVrTphxS1IUUkocwBDAHZBoONRxwbz2fa5+xhje/7PsM99sP17OVUCojgrI/nbO2113qf4bqv63/9foacAuIrisILLBbTuDlHLlnZYc462ygCjNAPGOcpGkyW0XHRBm0tKcav0LaTKCbRBWUFu2aMocRJ+IcojNHkEECDekSGcF7iDI/mbFSck1iJ80oWLXNhmkTrXVLAmoaQ03WmtwSZQkxBinGDXB/aVDlYUSEHclW0zVKupJoJBRZWk6I8yFIOhBD+pUQj/us3PVXvFsdNW3nzYeWjZ4U35oqriv9TG/7z9YIP7eWefS7A61t4mwq4VSQPhl8ePH/aZj6iFL956rGbxA+uEys0YYIvlcIb1h6TM1FbbmjPZ2vh61rNT74oMZeq4PtvK3qVmaKmaxRXY+D5c3iKwuM3DfeVRFOWV4WmqbxQ4MQ7NiaRlOelfuRVC8vzvUMPE8/Xytc0iYXWXE2W5TLysasOixw+nn2sYcqFZS70RjNkRWehK3CeKv/DC4a/+vjIncZCFOBttRP3Y+LxRjE5Q7cPhEXLeG+EzcShPmbphBkbVMV5RZkqF9OWRbHsVeRovZY4TtIs2ipccmNpQuSV88BJM+vQY+U8KG4u4cbjnn5QtGVLH5asbOIiNxyv9piq6JMlT4qFyXQrru9ZmyEZyU9Xqxn3+vqefeVB4uBVni/dGzg5OSCNhV1IhDTQHC7YDwrfWA6iYtpr3r+feNtG89xV5TUHGvYjH82OmOCWgW3O/PS+4/s2PU2BL0TH6ztFqJWjWnnzseF3zwtv6TyNymRb+eCDwtJUklU8XROfToqhaI4axc9e2OvD4HEtbJrMmYF3zmSWxir6lPn1XUOt8D3ryPt2BtsV7mQ5dH4+Od7TTXysd6xbKdLf1WjuJ3j7OtA5T91F3ru3vPOkcjVmOu9435W8pJ82haoqr9aFnx0sraq80So+sDW8fTnyUm14hyv0NfPyqPnGI8tBGNlWxa8Ey19oA19KjqcXcNFHfrtvv+poxA+/9bUVZbBKOldTzMQiy8ZeOUzTUFJEI509bxSQabRkOKdYZlyaZZ8KCwPGzstlSAFsnUgZipL3h1MabSoX20E6Z8wCj1Lm5TLJ2cZUyDWzaq0w1YGpVDSJUq10llWhKom5eWcISQubvUpBlmdxQ6MUAXM91Vt2Tgxs191DsTJK7K0SkuC6HtEbgJmxW2lsnWkEIjy66keW1mK9vhYvkIVaMUyBlGSUL3sZ0r2uwKqV5TzBhhUudhlrZmRdrWyHyOHC85rbC676QMwSEei8ZhgzJ2tLAfZjJuVC6zVdq5lyZmEtj2ggaEdImX6qsykO7p0LWeLu6cRm1VCqYR+AIpjQlOtcMFeGVImhYuasrdXy5wpGpCZzM6dgMFU62RFDY+cc+NyASnPuOBaxvPYhYY0RM1tJc/fVYIxQJR5VhqkUmSSoijISfTRakVOSAhVhTcdUrs12wiY211ljWYiUzHEuldZUipbOcEjys+YsDaQibhaJRMyLkFOeUX9zhMKpStVWrvKSpeD1Yp8VMIHGaiH6WC0UkpjqHxqN+CM5wk3TkNEzmSFjXAOuoS5a7H5HKlsWixW5FpqlZ3+1hZXHzBDu0mp8e4xKPZMdOWg9l3th97YOen9EiBOrrqEfIq6DvNszNQ5twDrBh6k40YRIaTtCHAljD0WRnWK6OIfYg1LkZs2iMRws1uzTRF+gnUkQ4zji2qXkla3CtUv2YZQOYwFlA4RHlIJAqzV9DlCUnJ6ZVcI1sWhlAS+MexF6NC3TNJDihLey1LJoW5Tz7K2mGMW6a7gceyyKzWLJOEwsmpaTkxMenp/hQpI4SAxsdztCGAFom4aH52foAsePPcErr7yCsYobj91m2F9SLi/prOfkxjExRq76LW9/7Wt5+e49jo+OmeyekAuNVeRVw3m/pZ7vsNZyNfZMVbPsOqiKcL7j1uOPYxvP6fkZIcyyjO05KhT6nGEYWC7WjLst/RRp1mtMzoTxEqWc0DVSANtcxwcIA7gWYxdQK+1yia6JIQyEWolViuAwScfKYtDeEYYdqvE4vyBOE9VYck44L9GU+Y683iR91C3OWTiF3siyR4kBZw0FQ60jtSRU1rTOM8YBrRvZbN2dgTFMAZTyKC3TizIGSg4kPaK0CE+mvscpyFqhjbzNa5EDo6kVFS4JWQEBbTxhyFgHeRrR2svI0nrGqce4FmX8H/vl+Ud9veXQ8Fat+PhV5RNnsLCGvak8ecfwr9zNPFDnvPtgQ/WB78bx0dNIvdXQRQdr+A5XOOgO+ZYUUTXynpuW998vrJeKVy0jNm/4g13g3Tc0H3hYufPkwNGLhTNjeLVLfM0yQayU2HC468mbltNJ83dPNXkwfPsm8fdfBJskzvLsAt6lI6/vjjhTE89NDU8YR60Dv/Wg8k034SO9oVbNod/wG1cTr1sqPrt3YCPMlAKbIspa+n1mbxS/vy34qnn7LQg18LdelXgQDS/sJzY6c7Q0vP9e5kPJ8EM3R9rq8JuWNimmNpGMYrmM9EmjTaFTEIvBmYnX3NxwvoP1JC/iMGZOt1vCJM9Y13jGqz2qOJqTNRf39jijuHO8IPXnmEvFyljyccNxCKQ9PHE4YXaW/bJyVAuhHSgVLtdr3B7aXjikl0UzVU23cOAVvh9p2krYLDndVvZ94eAgoS626FT4WN9iLxJff9Jy0U88NxRed9Lw9C7zkQeJTsP5ReF3o+Vxk/lkNPzyzrEUjzJDbrkslX/zToOuiX9wP/ErwXMywbMu8/+cR040fMOBovWV3+/hLSpxsG65d554WD3PpIm/fJi4N2vn9gVWBn5557nsEge6MKXKh7PhP9vs+WxqiTnxA4eZD4+WVhc+NcAbu8zXuUrbTRgH+wQ/d2nYonjv3vJGVzmwUHVkuKqchsgdl3naOV5N5rf2Dd/gJz5tNJ0GUIxZ8Y5FYWUcz7LlF/qWRQlY73hpm3AucTYWvsZkfnJrONTw0xeaZ7oGY/7wJtIf98s7McnlIrECYy0Gw8JZhhDQecJ4YYC1RrEdExtnkR4ZeAq66agpsjGF1hf6cZa5qEI0C1JOLJymj7Bwhf0UaIoU340W26tMZhPeeTGJBnnnNVqz3SdKnlAojGtoLbSNJmfIxWAFqS7qZ2+YipFsp7NCzQEmoCExwfU0TWuoucquTJbxnTMaVYV6kXIhhILRs0wkCvrSaYPSisZrjJHC3ihF5yEEOSA1jaaPgkE7Whmu9okpF8GPZRFtpJnc463mbCsNi5tHHfcvJryG24ct4xjYjiPGaE7WMnofp8yrbzteuQhslhYb5mU/A2vfMG4rl1PEaE0IhUydM9dygLp92OJt5XKfiFmK+2HcMiVFLhodpIjvx8KUM6vGM9VMHgNVC+dbsLbMy49SMBqjqNZRK3TeAsL6r1Vf83qnJBIgpcAbwxQi2hqss5RYUHNx6qwWLBrQaOm85/oVzfIjDbjR8vnlXLEGKgZdIrnInoq1ivqoo10V0zigtaZPUFXBqiyNwBCpJUPWVOUwWqI+SlVyMZi54VjnnyPXik57YpHiX2tDnAqNKYSYqNoSChijGeNXzIt/2Ncf2RFWX/89VRkjODMNRS1QZEoR53opBePa66xkTQPYRkaIGh7pFK3SjEXYwuOwg5jAe1BWrCrO4REcmfceXcD7jqlmrJKCpyRZeguX91k99RbcOKAx7FQin13iu5YhR2q/Y7lsGYaBkhoOHrszLzBZapwYi5y0s4Jl03KVMiVL1zUr2G63uKbF+AWNhWkYKaXQNRZTFcopdEAwba3GFGhb4fNq3WKQZaqhZHSWkM2q7VBWS/4rRsr2jNxapmjZbDYsO0/btlxdbGkXol/uamWxWXO2vURTOd1uaTrL4cEBvnrC9pJm3eDblvN7r/DEY4/PxhfpkPXjyGazocbE4WbNOI6c9hc0i0NObMM+jGhreP755+eLfcHNO7e5cXLCi196jqta0SWyWq146ZVzpmliW+cHUVXXi3GxSsd+jHKDTTGgrWezaNntdrM1TtjCJovxr6IxXn4X15sPpWLaJSVHao4o3VBJGCMZ3hImkrYyizHmOodGmeMIMsyBkuS/FTBGNq2NjkKvsBpTDJjmKxSI2VIUQsD7VpBnc/64liBbxsYxbXegEvgGaFAkaggo76FatPlKRtjZTigQsYdaZbRFFapErShrIQfRT2sNfgFA/OjPf9Ud4WeeeKp+b5vZ18xbfOKDacEtCmNNbFBcZlhqyy9Mhj/TCmfbWMtDbXnKJHpteD0ja235X6aGv7LJ/Pg5hEnTtNBrzXeZTNSGt28U/+2Lhr98mLjRwknTcj4VjlrJaF+NArj/v+9Z/qvXrSlmpN07/ikJfxa41WY+Ugzvu9T8jZs9H7syvHe74L95XUdsA5vqiUNi0gqXDdNqYuMsu32hx3GsM/s28fNfrrz7wBIXmputZrgshFQ4PE4sR4eyBR0Nl1PCthFToFkqzk/31OhZGrEL/lZs6XLlMZt5cqVRjZN7dhxx25Fta/jE3vPO255uZbBlYt97fJPJytBVAcvviggS7p+O+APLUatwCdRgadYTtiq2VxPHh/PhLUmOdlSVxgktIhxmlkPhqggvaeMsYYpYZ3jlvhy81irRrDXmeEF6IfKw0bgwsm4bXtha0m7PF5PjJ6LhT6vC46ayMZkHTvPU0vKhc8tbVorfOc8srOHdJ5bffDhxFuDZ48jzV7Jh/vKkuaiGd60Cv7i31KhQrlKj5tmlsKo/FTVLbXioCv+qySyt5SoFfmbXoSqgNe9qJn5r9CzmZ/rKVzrAFfh8gDe5zIFWXFbLm3Tg/cHwzjbxJJV7zrNNma+x9fqe/eBoeWdb+KcRXkiOJ23kqsBTpvCYqfwfpw1aZyEUZMvrmsgne8PXLjJfHB3fsZq4UQsLWyhFuqX7lFnawo+dt7zVTnxoalhR+Jom8jAZHpbKv9UNfMYsAfi7X/rnd5j+uF9/8w1PVK0Vpkj8Ielm3peQKIM87uzcrUMY9sZKHELJXFop6aymKjrgEBIxZ5y1VET0YLWlqios2pk/bJyIhSzzOzYncoGh33Hzxi1CmihKYavmvB9kaS7DFCaWjWIMmQHPrYMOhaUCngAAIABJREFUhUJp4fyWImIPhcI7RczSCbYqz4KOjHcGYx1eF4aYKQVaJzCwRsNQpOBcWCl6ljazGzJVWzRpxk6q6wxp42VJGSX542Hc0xnLUByrzrLwglu76LNwkFMFEuvOsu+lb70bK0srPOCoNP04cdBoGmd4eNVz61Cm2Y+4uFOQxbhYCutOOMBhyPi2QdtCihIlfemhNLWKsdw8aDhaO55/0IuMrCYWreXly0qIBYomFykSlVIz0UGEGiEL7i3litGGRSPd6FLm7vucoU25UJWmMeq6iy0xAVlYq6VQivwuTS0z9UiKaZQTTN08JZV3bL6+XovS16x9wQeLwMKTruUWCUA7WWrTavYZzjzjGcf3yDRISZIL1prdFLG14KwlKYuuQs9xxpCUwSvR3UvswgoxIsm1EPMj/bPcE2ZmUZeZZmGsNJr+u8++9C8ejTDf9H31Uc41pYDzLTFblMrgpBBSc9fQtY2caDCsTKI2G3Io8xaiwJRDCOisSd7gCDSLtQgadOLq4RnKOSE9xEnQV6UQcqXbbGh1YbvdkvWSVgW01YR+IuqMry3aWSITOifUJP+2UQVAYZfHGKcJ0x417ulaxRAdJUT0ai1GkpqYkpGu77glqQ6nC2ZetDJdh6mKKewpTgqXlPcsmxZrHmmRJ/rdJJnk0MsClpl10Dmy0gbdNZymgOoD3Syo2CxEvTzEhEWyyVe7c7CGmwcn5O0WbMu9/SmLovHrBqcKulnRGceNgwUH60POzs7od2KiG3dbnnzySS4vL2mc4+Lign0YWR+esHaO1eEB9arnMgZyznz+7j3uPPE4EchTYLG+wW7a0bYtPgvNYxvz9XKi1pqz0wuMb67pCHEYcSqTkNGRbaQAtnPkJLuFjIn3l3IDNM31PeadJ1aNVRDrhDUtKkHUBZ0mavRok8llnC/OZr5KtSzSKSmmyRFyQsWe6ix4j1WdRBDGgWxBuxaXpfiV7DKQE1Vp6choNz8AHhXCDWEcKDVJvhOLVZn0COeWNdZBjBPMJ3PpeshyizZG9JI5Xxf+yi+oKYEx2EcYvn8JhfDfeP2TFaf4WjPw4qh4QwufnFpCUTy9iBx0FTsUzjMcdIrfGxw/d9Xyw49fYfyS01H9/+7Zj19VbLKcafgmH3jixHMWCxt2/MwLlicazTuOCr+/UzyXHe8g8r/tW/7m7cDGZz53WfnVuOQvNgOuKbywg19Jnu/TldbBZ7XijXVgHzTH1vLpKQGKg0XHkzfg7DRyMSXevkl84HKBihN54XnKwIHO/K9Dy/c3sMwDvxI6vs5HnjGFbTXcOnbo5HghbHlhlMIlMfDuG4pkHNYakkqcXgSeXnSMZeSih6WbJPpQCjeMJq8drww9Yau5vVL0oXC4XOFamMZ55NopptMt+6XlVuswO8gLy93tJTcmTTls6Goh+cpCO250I9V70m7EpsrDnaFOEycn0l1q8Dy80kIP6BbcaSfoNMvJcIk0BT5+r+NVN9aMPqG3Ab1aMmUwNl3fs/tR4lZtJzKEl++PLA4cD4PiZlN45azwmB0ZrOMnzhb8qcXET0TDD1jhCb9SHQnDB3aVhSq8bVX58E4K8R9cRT6ZDU+7wktVsyiVqiy/lhXvzpH7Y4f2E5+Zz6rtPH/coHnSZ35t36AqvMEnXh5hnwvrpvKtXSUrzTe5xMNJ85NK8y6jeb3K/PLgeNzLX7hLmk8Fw3cvI0UbVmhaPbGs4Beez+8KLwf4tFZc9Y5/fTPw6xeW71knfmPX8K6DiQ/uDEOB97iBkwY+GBq+PGq+yw/8Ym7wWbHLipWpvLWDq1I5z5bXOnmmfbWF8A+/+VX1Uc71EZkmVItBtuGNESrNV9TFhqIMjZrQrpt5s1LoqCqigwh4bbEEGj9r0FXkbDdhjSyV5VxIVRaZU9GsOo9Vif2YSLrFMc3j8yImM61xWmNroZTEvsi/zZVKReHbBV5DipGYJpY205eGkDKLpgWkeJqKlW5jnMiqkeLYCEatdU449Cmh5+e7zgHnhGRhtEKVxHaqkklOSRawtGSiJcta6JwhR0OfEq2VwrFtBBMWk0w6nFGMQ8BqzXLp2I8jysjBO5HZNMJ+N7ZBGzhcVBad42IXGcbAGAr9FHjspGXby/j+qo/EWFkvW6wtHCwcV2MgJYkDvHxeuHPUQhVLX7NYkEOSIhd5fYUknWNvJS97tktSGOaKNzDEdM3L7ZNmYWYMWZXrUZlWCArTOJvcDGneXrNGkzEoVTBFHAmhzgnhHOirxauCmt/XSssBvaBkyY0Zq1YypWZqmnDGSqGq7WzgSzilMNaSqtgR3dzNLUWQpHYmWNQK1EeFsGGKj+xwmqwMlsSUhDgSqqbVddZNy2eOEotdKXnmV3+lU62Vwlg3C0TkOoc/YSG8+o4fqCkFmkZUyTlVxigPSGeUZJeMwitDsY4+9HPiX6OXBqIVSPd+S+uscHxznk86rRSdChKJBsMQJtKwQ9uWoiKLZk27ORIaQdxJq7tZsOv3lFi5ebDicrtHN1K46Ci82OwVTRY2qXOGruvox4maC6tOOthTKaRBGK5TmFDO4qxwSKtSM4KkoKrCWNDqUfhck4cJ7RtqzYI6s046pqcPOLx1izQO+CKECO3g6OgIYuXG+oh9f8UuBymWh5Hdbsfy6IiutTx8+QFZix1Pl4xKhUji5OSE/cUVfrPAVLh79y6Hh4fUPLFerrhxcMQ0jlSjOd6sefnll7l5fCL65Thy98Fd7t69y5tf8waOVht2MQm6bIxsrWK12vDlF16Uz3yx5HNfeo7bt2/SNEsSlYf9XhTCIdOeHOCzJoTAQbPgot+hnMhDZCogD8Jc5YYWDovhUUDJeo/2LTXKn08z3gZlqGkUUHutlKlndXgkWbWQ52VMQQxRCrXKBixK8FKxGFSUxU7dyhg37XvpHhdgeYArhdCfQS7YdiOIF2+pCnI/okxA4WZOZhaLonPEMF6H9LEWYkYtVqhUKDZiouTVqBmMh1JZLBakOIFrpds9d7Fylg3ZKSR04ygxUWdxSPzIT3/VhfBPvO2J2pO52TmGGkh76ewC/CUXeX7SvLadOHZiPPo7g+PrVeWjWfMXDiNf2GueWrX8o7PMX+wSXyiFl6MsHnyzLdw6aBhrZqcjN2n4yHngn+wMb2wURzrxjW2hvbNBbzWbfM65sthDzfufV0xZ870nli9fDqw2hrHCcjdK3nqd2CTLR/ee17cJf+jZ94UxVx63DUMbmXLl8oFktH9psHw+av6dTeYCWWSq2fDmo0JXFa3NEFp2XeCgS6QLg6ke5QeWriECvap84sWRdz3REtKEzZZwtSV7x9GxweiWo1oZp5Fd61n7yjgWyfcuC6uF5eGDQtbQNhldMptUuWwsh07BdEW1S0yF04uJ5YElTooDnylHitVQxLDZGMJppN4wtAl6k7C7zL2XB+7cMLBpIVQIBT1GRrukW1teeiAF6dFyz3u/YPn6Oy1WR9JyycuXl+So2e4Vh6/uWJHJF4X1esHZ5Yim4z49BkvaJ15KlV/JjjxqbFu5Giyvd5moC48ZzbduLJ8bAq9bKr7Uaz4aZfv7Tcg9u8vw2VHx15/KPAyKD50pvuEQfvtSszGFfap8YHQsVcW0mX/vOPJTD5d0Sjo6b+lA5cpvXGoeayu6wNB43qEi/++28iYbeWPrmDJcaUVVcDrBG1zgtFre6QMfjDIx+lYf+WJf+I0kWfArpWkTfMtB5m5wvGYRuZgUl6lwN2pyNTzpM9+zyIxJxrmDUmzmV+OQK3eaxMd7zXGrMJPi/hzF+ttfeP6rumd/9G1P15KzRLcqxCKFG0gmM5eCVVC10BVylA5fQbNx0FeDNpZhCjgrm/V5XmbCiKVNK7FJFlVJscrUzlhsLRjvWHQtKWdIA7karBXqwZThYAG7UTTFtULKCaUVrVakWojZ4I0s0I1RvnfnmbuTiiFmFNINdEbPYoRZuKX0jI0Dr6s0M+osQoryzKeWmecr8ozL3cDJumOKkUJhDJKXPlg6Qql0C5mc1KRYdYYhFvoxcbBs6GzllcuAUYrWm/kQUNG1crR2XPaRTSvF+P3zkc1C+MtdY1gv5+U1pVh3mvsXEwer+V2RMheXE/fOJx6/3dG1RgQZuTKkhFOORet46VRqp67RPP8gcHPjsM4CinGUpbyQC8fLhjgfapxXjFNFG/sV5XbJlCSZYGGHAEpfUxec1VjjyDnNk3nhLaM0JUfEWgExRTYLT8qVKUt+99Fr7p8FJGhkCTNir81+jTOomuknIXeUitSJNZOmgVyrRGmZ5ShIJrlRQqqyas58lyKZ9EdCDcBqQyiF1jfEUuhUYSpF2MS1oLRwlLvGkFMW10StQsWA69jNlKCxQtqIFRSKH/mT4NPMt/9gLbVgs7TpC4qaE23VRA96Stjlmv3VFTY/JMU1rDuaItpgv15zfn6OtRZjHM1ixf70VNrzRriwTRlxqkHbFqUrQ6hEJaPVksFrxW7cooyX0XIIdIeHGKVpbSe/OAZi1UxRDHPbqz1tI4gZYxWxH1HOo5SibeRCH6PCFMmmGqfZT2nejM0QIs2sb1ZWuqBLo4WUsDyCELBdwzhFMcvNbc0SBo6Pj1FKcfflF2jbQxSJMBUm1bMJlqk13DjckKZRJBQaVJLR/b7IxuQYEwdLWcIpOUJJ+LalU4r9fk+77GYUVwKn8E7EE9vtFoMomPuLK4khqMTm5m2OVhvunKx55cE5jTV88Ytf5KnXvJa2k+/T7/Ysl0viEPjEg7scN2u6rkOlwph6mu6QEhJjCvRJMVztIF3SNTfAavyhYPFKKDjnmOJILkITQRVqzNhGaCJp1+M6TzUepz1t3LPTRorzklFmlk1ERXEa28tBRWvLShuM0pzVSI4JZeUQ5GueT6vzVmkJpBBoW+E455xn77uWxY6lXDshzF1fFOmfsRORJkCW2WJINM2GEHtaZ5n6AW3qdSTIOolUeCo5QdCyJa2xqFpQylDRKJ1R2hKnCa3khA2SDEkpUT/5j7/qQvivvvnpmmvhUFVudZl7oyNSeFNVfFYVXl8zy8OOn3yl8m3mih/brbl9oPgP9MiNlUUdNHzu3sgNr/DO0q0bvny353FdUauGvaocpZEOQ2WJ0pWxKM6tdOrvXcIbNob//jTynabyT7Kl7OCvPVVZmcJCyTRF+YFgCvurEbta8qVXIk8vNQ/2lQObeTAYnJVi41VrWfz51JXjWCUOTGWxgfc9dLz2IPHpK83vRM2/4TQ3u4ybs91P6cLDkLGbNTlU3GHm9MpwuLTSnQeaYWJ91EGJvPxwz8IcY5tAPynO045XTXC1qtzadEL32EVqKagpM6rCc8HgfeGTfct7jjMjEYIFG1l6i1cO3e9RraP2GWUKUSmsrxjXkXq5Z7GGcJEYnCakyo0jy8HKslpOXF60HPnAZ15JvOGWJSiwtSOXHVat0GrivXfhazoHR3LPqilQVitKSKhd4IvV8ZsvG9b1khvLFU+oxPHjHWEHn7rMvOPA8zvbkU9PmmcaxVoXPjkpvn8z8dFe8RsXln/3KPBc9Tx7oNnsL/m88/z9redNtXBbwbOHgfdvGyqVN+RKairLqrnTaZYUfnVv6ItkPHcZ3mEDe624P8f9H1eZD+/gew6QQjdVzorlZjX8XoR3LuQz++Boue0SrwUuHTzIij4LdeIxVWgt/NTW85cOFH8wZr6tzfzyXvFGFXgha97qMkvX8JGkeE8z8TDA+5Lnbta8ylbezsQXVUOJlmcWPa5afvzK8domXd+zG1P5+N7wkftfnVDjR7729ZUKuc5LvzNbvqhCqzVjznTesx0STb7kqi5YN0Jf8lazaj2X+5kBazSNd1zsR1lk0xpdFbqOgn00wpcdk8E9shlWERGkkOd8aCXmxGbRyD/HymHL1ZFSDTlJfvRqzLRWeMJOMy9eCU2hsULrGefRv9EKr5kZw7KTE3MW06tSNCrPlAKxlPlmScyJ1glvvGvMdUcvpcjhUtB7D88HTNOga2ZIiqYm+prpjONgIZKSMWUMYnkUS5vFaYgZ2laK8lpkp6RxBqUzw5RZ+LmAypVGdlKxVrEfMigpDC8HMbFaCkfrjkVruLGCB9uCNZXn7/c8cXOJ95Lb3U9ZNM6xcHEOppGCPJYCKWObhpArOVVCsWzHiE4DtelwSrNZtMIazhJvySlfCy0MlVCKdIhzZRciC6dR2lK1htwDzYytE3Ohs5opG7zW9CmIPEPJTgQKVDaSW5+Vy9QkdJNrRHAipkzjNAohQaAMCTnMLbxcOzHNsR4FVumZKCLTUoXEKaYMxntKjDiLoPpUvY4EaWvJuaJUJhSFQV+rlmXJS1HQOCV4vCmVmSMtt6fwtCs/+vm7/+KFsPvOf7tq7whn5/i2hRLEdw5o2+BSJVU55VQLuUi31C+W1ya6rutIKckLwjYsMIzjSJMTylnatuX09AF+scbRoF1HKtvZHpYYh4BBzHDUiiqiQmy6hmnqaReCKSvaoY2/Nq1Nww5rPe1iiUPTx2HObxpiSlA0YRKV82K1ZNdPM05EwvaeWXLArMANWymmmjXLxYpIEWOaMaSUaJqGYUry8NGazXJFYRDxQrOg5sw2D0wXWxbLNcu2wTaeRhl2QTqZq2WDyZkhFpaNZxgGlIG28azWa84uL+X7DD1N07Byjk3n+dILL3Ljzm2RmOxEopHzLDAphZOTE2xVaFP5g88/x8PT+9y5c4fziyv6VPBeMsp6PnWfvvSA4pW4xVPh8PYhF9tApxVTisQpcevmTab+nLCHqSTazep6Y15QV4lSZUSitUXnSmU+4FRDaw3VeBIj3q1Io3w+CYU2MpIJNQhiJybB6lVF0ZlOOaZcRFOcI75phFk8E0niNEGUwsx4j9ay1Wy8kwdGCFAqfrmkRmEiK61xjYzPY4yi3lZKaA4pYJQGlcFaTIVaHiFeFMqqa3871WJKBJWxTjqNCkPNUGuQ03OeX3ohCf/YCX0kfewXvupC+H/6htfVwWh+6X7izx9FFikyZukI+6Vhs09czQ+H1SLy0a3j/qT51puFW07zwpB4zcqxDfC728LtpeXNynGv7nlsCOSupTMNv35/5K2Hio3tsMWwMz16SihT+KUzz9f7ib+zbaFW3mECHxo9/+WtPT9+7vkPT0a8VcTkcQ18dK94Smt+Zq94u4Fnb4ysqueVSWgwm0ZzPkDNhv/50vONXeZ7b4z8j6dLvttE3heFYfnnrXQwPlI0z02abzE9v7HXqKblvzjuuZs9G53xtjCkypGt/F7f8LiRTNxrThocI3XKlAO5Z8/GLZdnmptrz3ptsI3HhsAwC4U6W/BTYPALlikwaCMHuZpZ+Mj5ZORnHcG1hoOSsYuJBw8864MRnRuGUOmaAZ0d1QZsack3DbYqXIzcf9ky7bbcumF4cGUZQsE2Gt+uqHUiOMOXnp/wujJUDSievOV56TzwuFXsQuGjW817Xtfitlvu7hq+PBTeeljZRsvF/Kz7cDQ8lzXfu4iE4jgpCa0lhvDF4vgWH4jdilj2LJqWe+dy4HihGFbz+PPH95Y/twi8pmS+APRZ8SGl+Y9s5eOxcjk17FTgLQv4tWR5i1LcdIVPjPBwRut950HkltW8b295a5N4lc781ODYTYY/e1zZ5shNU1lUuL2We/ZhCpRB7scXA/zDveWbu8IdIllZXmUytSh+c+azPm0qBsXvTJonlWXUgXfokaVr+Mne8Hpf2CbFc0HxFhd4OTsuCiLnEV4oT7eJf/DcVxeN+NG3vaE6Y7joBynESuLRVo8ykj99JADySpGqiCnaxuGMJqZMOyPSdAFtJAs8xUypEWeMZGO3E03jSdpgjMPmaebDZoZY0eTZIlelU5ornVekmIXPqwBlUdrMGVXFFCLGaFov37MkQVZaI4VQrlKQGA3LxtIH5PtU0TCXOeMMM4YrDkJacC1tI4z6MssQUik0VjMmNY/SpUB2NQpJwQlbnlS5HAJt62iddARRlZCkybFqRNYRksa7yhjEfOatYtVarnqJKoxBDhrOVha+cvc0cPOgkQz1FFi2draHSgF4tHIUJTSDL9wLXG4nbh42XPZZuuZWyBxaySTupYuJVmsymlgrd9aWy1GjdSZnmFLleOOI48hl1NQC61ZoEo8YuqmUGbOnQGlyLdg5IpExsxrZYksG52SJrArsTOm5jitiYIwloao0Oy2Vqiu5SJdZKFsahZ6JJIqQ8jUu1BsDWs3xDckWxyS2vq6xs6VVKBDWybs9ZdnvUUrN5A5ZjDM8KrzFVBizTDeckrxxLJDRIo+hoK2l5kJBsL6qJszcbVZAmJu4xli0gr/9uT9BNEJ//Z+qoiRsCPLroxSNaTyNqkwZdB1pamasDtt21DgSh4FiFOQsVX6FGiYUmmoUrmlo/UJCzhXCfqJbOGIWbqRuHLbK6KXmKPg07YSFmCfW7YIhFoGLG8OkgsDaq2bdWC5Pz7He4bqWlOSDdPMiVEwTeQxsbt5kv9+yXC7ZTxMlJJxr8DWzCxXmAl9by2olhZpFMeUwo2wyup+LJW9ZNg6PYacyy7Zj1UpcwAZ5mUenOeiW3L08ZaUc7XpJ64U+EeeZQBhGVscbvPeoXc+435ONZpomnHMs2oYhBryTLmg/bil54vGDmxQrcYWFsihvWdmG84uH9DmxG3oW8xLe8fExOWcePHggoysd2XQbXv34k+ScOXtwzkv3HvDEE0/wyukDThZrHsSRo6Mj1s2Spmk4ffCQruu4+/AhKUsHOM5mo+RkATBeDhg7S1JqoU+Qxj0ME2a5wulCsh4eRSNiROlELZ6cJmzb0lh3TYBIKZHHQV4SugiYME7gszjN1ZqCRlvpjj8qYgkJrEEbT4oj1gljOKtHiqWE6OWS0MPnI29rPdV6phwxVRb7dNsI0i8mlJHlS+UtKVZqGLGNJWGwXg5HPgbqjHRT9ZHNKaJyoFtsyLEXc552Qs348M9+1YXwf/qax+uBgdtW84+S5SkN58nwTJt52yLy8Z3l1Wbkpql8fGp4eq2JY+TLveIjOM5j5d3rSo3w8UnxGIn7xvLnlhM3lw1xTPTW8LOnjh+8MXI+Fr6YLE+sKjemykMHd0fFs5tIapYMIyzzyPFiwTYqwq6nWbWMdeBDV4bXdpVnnOK9LyfevqqYlSOmzOVoONl4Hm4jL6XKP7y0/MgbKh84zbzrRPG+h/CBZPnXvOKtZs//vlsyzK2Kb2wz33ZDM/aRLhVeSYql0nx8NNypwoD+mdzxnxwGlrlwVyte0zrMYUvsE800s9MV+KMln713ydPFUO90uJqoxl3fs+Z8hBOHM+C2EzE4stGUMMg2tnXEKiYkYxxxukTXwp3DSqmG88FzsxtQylFSII+Vq9oKorFtCCFw6xASirOHcri8b+G2sxwfgs4NFzvLh14Zefa1C56/O/DqteEz+8JTT3Qs0HRecXpeaXziCw8mvjw53rLKPOgtD0pl7yzf9upK/1zCLiV6Yqvnty8tr8TEp7eG9xwlvs4WPp4NYIXpWjKtKpxnyycGw7etEs/4ymVW1FK4mzS/cGV5qkl8PiuabCg5YpvK63SlMY6LYnjSJ77ZS2Tug5NmnQvFGVbK8OEB3txFXIZfrY5bWcxwe1V4Vk98Dg9VRvF/7WhkLJq/t2942lY+0Vu+blW43cAUNUnDWmWWWvF7I7zYG95zOPFz247v2yR+fqv5oW4keH19zz7Iig/uDVMt/PubRKMm3ntlGazj3Tbz1z//4ld1z/6tN9ypWs3kmSq4rFQ1jTVoJfldU4MIBvB4J+ScKSTBXlUhLZQZ3VgVGKVprMbMMqlSNftQWHqouRCr/P8yNxdqka4eWpbyKAnvNNP8ORqtsLWSiiarSmsL5/uAM5rOG2KWQtZZwXmVlBlT4XjdMo6JrjWEKGN/YwyKxJAsei7arNYsWkOICR5lfamUAn1MoMBrJ8t0Csy8PNZ4RYiFUCK1gteGxiv2+0LVlVVr8XNGuMw52TFmjhYObxW7EERZrfQse1C0TpHmeEytlRQS5MRiZbFacGtVVxqj0Vax20+UrETM4cWQd7BylFI5vQoUNL6Cayw3j4VO9GAbeXCZuH3ccrmNNI0mR83B0mG9xlvN6TbQesPZVhYYBTsmWVenLavOcjEmnJ4HCbUSikgyxhTp/Jy/1m5m1kPKGacyAUvJmcYajJFlxFKk+zrFdO0/SFX07wstU81oGipSeFsjy5AxC9nCaimuS8p4Izzqr2SKi0RgSr42HlYqxoA2jpoFsxdzoXUGO2NmtRKkmjeaUBQpRVorHO3GKFKp5ByukW6PaBaUQi2JpvGUFATTp0WX/cOf+effr3+0We5b/my11pLixMJ17HMghQLWUMiU8QplGgqOxWJFraJSDjGDNaR+pG1bovFQpAtnnJ8lA2Jea/wK5TRX23M6LbgrZQwlRELJ+Jmba4yntA6Hx3QNNQyoPqGWLWnaMyXZiFw5w9UY8I10XEO/lwtayximsy3aerYXD1kuJfsck4wmapmwboFbeMYg3d1ahQ2KsXhjSSUyXW7RixZjvYgtqDTrlu3ZKYv1IVcXF5Q6MU0TzXJFKYVGW7xzDGT6yytM13LYSDebEvDec3Z2xp2bt/Dec7nbUmJkfXhASonLyyvCMGAbz63jE/q+x3qDM5rVakNTKrvdjqorftGy7pYoXbg6vaBazWc+8xluHBzxzDPPoHLhLPSUMdFUzdUQsE7jvad2LS++cp88DRy0G7Y1MpxdcnV1xeFjj0uBUILELyIMu60seKSAIkNupRA1MD3iCE8Jwl7ytd5iaiVXsMqTUn/dFVUzjaFo2TJedvPvp0re2ndrUikCBXeOOOzwzqFsS0kjZX9FygnXbWSRc1Yjx1pQ2lPDgLNSXPhGVN9DmLDuAK1HQkh4Zeicp6+JHCaq1WglhkIdJQcce7HNaWbLjXHkOEJVVAw0GuXyAAAgAElEQVSqBOo4gveyBBgjVUmm2bolaexFDNMtZt2yXJv/Mgrhv/fWJ+rKRkKCE225WxUPR8XZzNZVU4/xjt+aGv7KYUD7jI2VF6PmDM0Xd5XvOYC7yvBwlAXEp9pZpqMi3sHRckPqMr97d+Ib20SMDttk1FD53cnz9jZxmioLpRhWisdMh9WeWM9or4CjhrGP/N5g+TqbeaLTfHpXuXXY4jaJ/d2A9opzq/DZcKI6fKv4zQd7vu1QMmDbvtJnS6cjFsXqxLLdFkpjMSFzr2a8N2yUY0qJH3vR8F2HkVcfKNaN5YkxozaO09M965tLzu/vKTrxgQvLsweZlDTHVrHs4KJOvP++55klvKmTzwpTSM5z70HPq1+1wMdASIY8TbhVS3KK8X7PRTJ4U7l94IhTRPtG7llTcCWQU2ZXHUfrJJviukBI1Gr4xU9X3nmjcvvpBpUL42RoqPM9C9YlcjGErvDytsOPexbNIecqki96fvqB4fsel0NztRMnvuH+VPnYmeKNXeR3e8emFj5XPN/uK62NvC94XmMr//jScVITD7XmpINv1RO/lTx/xmZ+ftK8ySRaa5mAHZo/UJomw3+8iriFJ/WBTw2Ktxx7PratnE6F1zWKz4yVdzYRu2yI+4nzPvB/9Ut+6LCwsPx/pL1prKbpXZ953euzvNs5p/bqzW6720sbb9h4YzPY2ASIghGMMgNhCAkjRolmBmmERjNS+AAjEpBCImUkMh4CSRQDwZAJDMHGEGNwjMF2G9uNF7rd3e6uvU6d827Pcq/z4X6qPCPNRIL+1lLV6TrL+5z3f//v3++6WBrPzUHziShYSrjm4ZuM4MMjfHOdONKZnzo1fNdc8WLV88vbivfUgRdXjo9Gw3UnSBIuqsR9baLuJC4L/uWJBgXfasva+aLVfHJIkAVPhIZzMjD3Pc9S818ddLzvdMaRSjwZEz+68vzysUVoxWMr2MTMu+uC1nuhg/BPvvL+rKZOgtSSHGGMZTgUOZffK1IThaa2GpGn30HT3+mmspWQtrBlcxmaMkU6IKVAaoNVgr73SFlYwXIyfeUEciIOCCVplCZKSW00MTh2PjKzhuAdPqnJDJfoXWH8VloxuDKIlriXQOqSBd7vB9pqikKkiUebAlIbWiNxsRTCci75WCklarKBbQZHazRSqUlZH1hWkvV+pGksm86jUsD5TFOVjKiY9M8iS7Z9UUMbW0pU5IDVktOd52j5VfVziIlla4gxsekjgwtUWrGaa3oXqVSxmrW1IRHZDwEtoDGKqpJoMnf2pXT31NU9i5nm0fvnxJSIHvpQ8Gm9L/EQoyW1Mdw8DfgQ0JVCRMlJN7LtI+dWJTpGKhvpMSm6wRe5RSyb+55yG6BlJsTyPRtiJoURJRWVkmTKtjhPvZ27W9E40RiELHng2sopQgCDixhr7+XMtSrLJK3KLW2KgcH1RZNd2fJz4W5ZD5CaEDxSSXzIVLpsgX3IZFNR59LzQJSDhkilCKonu6pWUzQ1J3oX0Urduw2RSk3cYkgoRPaMPmCURuuyjZZ3D1GmQAi0lFTG3DPkCfFXHITFa769/KHMmNmcpjL0m9NSCJKCSmlcVIjoSJVGBouqA6krfN39tkMhiFYDEmMtvu8wVSmlNVXFfuiQ0yDgfULpkquUUmKaFhETwQ1Io0i55HQlZQUe3QDWUAlFzIHGWHokYbOGGKmXCwCCcwQUSmtycjRNQ8oaFz0667Ix1Arf77FWI4TGTVktPV2x66oum8RhT7s4ovcOXS+oksCuWrbrY2ZC0PnS6F4cnEFKyeb0mBgj8/mcXV+2UdkHVqsVvS9B/OD8hNtytNWCqqpQOiOEwuWx4NhunzKbF+rCxYsX6fue/X7L4eEhp8d3OF3fRtnypvfoS16KRvLUzSucbWYcX7tB1dRcODqP1pr9sGcvIydXbxNz4uzZs1y/fpMHH3iYm6cn3Hj+KvXBgoceeohxHHFjou9HdKW5dfU5mqML+DHRzhv2d46RuiI0M2a2Zr/bEPenqMWKOO5AKrSoIEesLQgT5yPBZ1RdoUXJR2tZWu5+vy+qbBQyTSU17wtdwZYNslICbzRKWlKSiOiIfoQsETIDcZJeGKyRk3NjMsFN0Z7sCmJGCo/3jsVqhfOSIDLJlSJfzKFknFNBxoUQiDlNRQ9VJDMhkIa+xHYmjjHWgPMIq7GmulcQ9cNQ0G7lKIzWdkIQLkjZET75f77gQfjrLj+cAXoR+ZGDzIXlyMlp5kqyXNKRizpy1dXoHHhSKA5EzYuXp8SdwurEP7re8s7K8ylpGQJ879zz8xvFe+aRISjeuhj4w51FWMlLGHjftuUb24ALkocrx2qZoVOECKaJpCz5+TuGhyRcJvAbO8M75iOvryO3AjxSCT7tNB8/ETwVIj9xoWx1b7nML/cN31IHdinxzYvIkCyPR8lDEazOrNrE9V3ivBFoDTed5is58/KpYzC3Cu8l++Q4P6u52UVmi4YmSVYLxfF2y4FMrGOEHlbnVmAyt691xJy4bya51Uc8ipg85w9ndC7Q1gq7HZGt5jg45mZBPVMQO4RQRFtuHcZrO5YzC9JyuBpJvWWfFKs6sOkU4+6ErEsx5f7LFRrJtb3hvBm4ccNjKsmqLaUhheSOEgw3IjEn6mXN+tbIxbNzbo+ef/MsvPNM4P77KlzIRCrG9Yhoav7D03vedlHz3F7y6JHgSzccCynpZy0P6oo/Pd3xlb3nwYXmk6PklSphFVQp89ii/Dw+tVd8vre8pE48ogPXQuZ+BVej4LdPFe+oBz7kWt5se74YDF8eAy/Rim9pB9ZR8tJ54vPJ8JBUXA0CnRO/32ku6MQjuIl7m+mT4fXLgdNR0krFnSA4nnK5ISSuRsvbmp4PbwXfezZydah4LguuOMGDOvFMgss2gVO8wY58JQhujeXZfLgRDEnzySD43KZMYHJ6ZlUlCGPmFY3n7Qt4cp85U8H7TipOs+elWhGV5I165PmkuL+1XPWJX3r6mRf0zP5PL70vQynGNZWl0rDvR4TUpeSmwGVNToFaKfqsWKjA4BxKCrZDKQxbVQyVRktGV4a+TLnydz7dGwRcLJlSKNnLyhpiSoQw6ZOZ9MMiI3KahAYKZDFuag0Zw64fSDkxr8tVtw+JhCp0gRSorSKiSDEThEDLQgtwvhgFs5CAKpdyqTyvVpcSlPel2xFiEWhFMqtas+8GhIzEUIavRVsjJOz2IzFlZrVmdOX1GlJi0Wh8FNS6SBtKFyOjbNkIW5lBCFQq1/i3dyPzusQXzq0sg0/0g2c1M5zsHF03YpQik3noQksmc3IaqCq4sR6orWQ5t9MAGdFZcnXtIGeOFpaba8f5MzO2+8jVk4GDxnLf2RoXEn0Q9D5TKcGt0z2L2Yw+ChaV5HQ/IJWm0jXaCPrBM449bd0Q/YgQkiSLwtlMFCQfYYyCyigMgRBTwY3lTDf66RZBIVORn4Sp71JeNxIlM0YakJKQJTkVqVUsQT8kaYq0l4N+yOL/kRMun8MQi0hD5/LvLxrDkFRRa4Uw4dPKwJ1zKcLHmO/h3hDlwBZjLrEOvmq0M0rhY8QqidJyukUoQpqcw7RcKpzplAFdIVPkJ7/4/11u/c8OwvUb352dc2Q8tj2DtTVp3OIihOTB+YJRm3zf2DkyyuLLNobR99NDasEXVmfwEak1SINsK1QQRBUI44iUJacaouOgatnuOrK1VAq0VXSuoDRUpuRLsOjKMkSQfk2sZjRJM8qyxdVWUfkItWV7cocUArVZEGOkakuTMWiLI1EnwdBtqKslu6GntRVaa3axRCQE7p5cobGW3o80oWdsNUkuqENizJG6sciQqNoFzjn221O01sxtjZzVNE1Da0sxzHU9/XaLz4LtdlsG8li2nRfOPYhQjlCZe4Uu6csWXVP0yRcvnifGyMFiydpvOTANX75+DdsH+t0JlxaHbIcBWRlOtht0Y7l06RKLgwOMT9w6OeVkvy0FPNswDpGHLj/A8Y2bmKYm2nKyqhYNMQokgpPtMf3V23C4QCSDnhmiUORuLHrjdolNI9ssUUJidE0cT0jVEjldTykCwp2iZFsQN3XN2A/lDclIQjcgbYMQhcFLVZUrtSlkX9eWmAbcGKliJIx7QlaAomosSpRcUs4BkRxJTYg3KcmxkEKE0fgAKSlMVaIPOmV8ThhRhhOhZyUnHlwpLQqBMvoeWziEMA3xZaMjJj5U9iVSp5IDXQx6Jc8vimluGEoUQ1tsVZFjQsjI+Lk/eMGD8C++6lz+k7XkT7Phh5eRlZYI6bg2aP7DoHm2k7yrHblgIk95w9Jq7kQBUvAmm/gLl0FkXm4jmsiVQfHv9xXvqAauqYrzVeJhQFWez+8MWgtsho8GxX9/mPh3twVzC18/c0gb+PS65pE2U7uI0pnkJLMDzTYa2Hdkq5iZhi2Bz5zCK85kDseIagUfuZL5wCD4GwvFWZ94aN6V9rGq+ONB8WYduO4iF5XifXvLdzSBg5ni8V3ZIkRZGvYf3Rr+h1XPe/cVPzzb8kRQbFXN60Xk8VHxxsMR6eFMXbMdHR86Vby6CTxYCfKyoppbquzw3hNGjd5tuRMs//Sa4pVNRKTM21aJyxeW6JgYF4KxS/eeWV1lKmcYtjtWFxpkDjQ6spWGZeq5tpZUA5zExIu1vPfMHvvEXEUunNOIRpG8J/nMzbVk3GeqStB5zcMXJDdvJlTV4uYC9p48B3Rbvl+313z+eqQ9o3Gd4cIy4bVjv9ZIBHFhuDj0/PjJAe+Ze161Mmw2G8ZmwX4o7w+X6oB1A1YpfmtveVfj+GBXbvfeUjt+6bTmHW1EiMyv7QzLOXyLCvee2ccOJTINfGpjeK0M3BoDnxw0f5FqfnjVsTKSm4Om1Z4kIk/1NX/kFD+06NmGTGsSjZA8sRf83tDy11aOcYysRObPg+Ib5oHTPlHrErn7Qw/PDGXB8o564CUtpQQLvHdT862657kouVDMGjwdijTgTXJAaoH3mT/3hkd1KTF/qU8cCjhB8pY2ISnFqh/78s0Xhk972cXsQ+n/66opnFU/luEjluKaVQIpChFC6kI8kUIUzW6IRW87bZVTzrjIhKgqYg6Xi3jDhVQ4vILC9bWwH3LZqsmy/RxjGYIyhR8bhSoIriQRoUPrGi9AIQkxUSmBS55aazbTQIop2KqFmfZo0iCyIIrEODqksTifMLog2HK4+75QNstDiFgtCCFD6plrg5cNIXvIgtYUlW9VWXxIdH3ZWmojmBlNbRWVToSY6VxgP/gSDxnKFX5KAa0Ey9WMWoSy0JsKXX7KJGcy3Rg5vypfy6zR4CLKwK3TQBc8wzDStILRQaUl+z7RGsn5g4pla3EpcLpPhTQzRrSWdEFw7qji9makNkXl7kNiWWt8KjSUoXNc3fQcNjUexdyUjWnnQ8nhVjUiOWK2E5RJge+Qur0nSpYE8B1JGUIUU+65sKmtFHQ+oLVBUhi8VuvydedMRlIbgUyBPkDKnuB9OdggaW1hB8dUDksiBZBmopWUaI0QYKTCJYFH0SjwMRVGSC7s65wzqIoMxYw3GeyMkuXgpiRxEo4oWQ4/zVTeHKcvVKSCJ82pxCsEkFNgnEy7UhW2dswZQ+Knvnz8lx+E7dd/X7ZWE7uhFJVEAD/pjOcLUixvDsIY6izox1P0/BwmC2JyBF/g0mM/YBZzmpg53W2x1mJkYQZ7KdnevoqtGkwzIyvLuF0XcLWQ+P0GM2kgowA1IT2EMWgU0Si084QsCSJOBTZNjI4sLLUpV/Xr3fr/lRVeHC1pUqJ3Iy5KtCgK3zyVrcLmlGqxwI+eprUYDNFUiBRRtWXbd1TtIWJ7nSwk+1vH2HlTrGo+IpaL0t6mRDaqqhThrLXMFytOfY8KDgWIXLJGy8MlUiuGrsfHwGw2Q1OseMEPCAXz+Zw7d045c2bFOIBShtvHVwgIVA5cOnceXVcM/Z7tdsvBbEG3G0mu5/KLHizEjn7kwoULfP7KFWZZFaWjSnz2iS8wXxyUn4uAtm4I3qMQNPOWp575Cu18ybyaoXTF6HqsynRjYAzl8ONiwN1ZQy3Rqi3KXSJZSXTyDP2Oqp6R01fb0XF0KDsvw2gFRpZcbe+2X1UXjyO6qskxgSiFixwipq5K9CRLsAUNr70rVkBVU1WaRpcS3bZ3CKkJIqOcJ+OJzqGrsrWVCUiJJCI5RqS25OxLSzaXNxtlbDH84Mpm12cIJSsOTJm6ompWRsJEtUgBpC3UlcIoNsAk2BCQlSY/8R9f8CD8c6+7L1+oIptTTc6Cq2SWOXA8wqvPKfwIf9ZLpIU3GM/jm8ylM5pzY6aXkS/ual5/5PnkseY1F0YOguIjdyQvqRPnVaI9WDA0nk8/NfKyeaaZG2KYc3OzQwhBbyPvvan5jipzn048HxWvrAdClkQhONCCvZbMfeDEGZ4SicskzkhBlwPPB8srTWIx0/zqMTw7Sr5zFfhsr3nPA4kqKtx+z1VXcVEPdFnjrObz+8Sn70i+/1zgiU3i9UeJNsI4WyCCx7aCP7gZee2llub2KUHBr1wVvOts4r3rBjl43nze0LqAAF7RJFormeWArTW61eyzog2eHEuxZN1F5udbpFb0G4/pHPJ8gyYTNwM5OISCarakX59SLSxDbNCV4NbtNXdGzaEdeeBgiTCJ4MB3O+pKk4aSM754f0OIkjoHhkPNye2WI3VakHNC8r98UvL9FxKrVhI8iDMVZijyFjETvP9LibeegWXTomuF3PSESjHuBq6MlnNLy1U38o+f17RG8NcX8CCRTmW0TZz1go/tEm+eCbqY6aPiShR8ZJB88wJ+71TzLXPPSycx4q/18CoVecBE/q9txXcsHDf6jNSC3xoNJgh+4NBx3QkeqwozdJsTM5H4xW2Fz4q/c+BY1BJD5CMbxTkt+YW15UfqkRs58bt7zfcfOH5tV/Fu3fMXXnFNKESACyayIPB7Y8NDMvFSM3KhkvzyuuLr7J6FlvxOp/lmE1hMT9smZbZJcllArSNdLuXB/zRY3twWQ+nnncInw6koz+zb68xz0fCBGy8Mn/aTr3o4V7pQFxJlU16uh6GpLDmV4pq+e93tRmw9L0WzVCISd4eceW0IOdANZTMoZKESSKHYbHcYramsQUjNfhjLRg9VuixKFFJASedOpbeiSzdS4aMnZoWiDBpFOBRJQqN0udbuB1+iGVoRY+ao1SQCPmR8VijipNotsYz90DOrLGNIzKwgCoFWlpQTtZaMLmOqGj+cApLj3cC8UsSQ8Skxr2vCRAnSqgx7MUWMlswaQ/SQUsGFJWA3wmFbeLedi6QIbVW4uqMraFctymb5ZB84M1PsgkQqyXbTARKRI0dLQ2UKAnPflwz0xmVi8DxwpkUrwRACZ1eW68eJKEr+2AjBF5/vaRtDYxUCibXlQJGBeaV47vZIU1u0kUilywJHJIYgcElhlCAmOOlGWiXIqljYBBklZIkMOI81hoAgp8nsFyLSFFRaq1IhSQDJB2SBleB8xJhJDc1UQIuZ2pStaiKVYhyCkHxBFytTIhCq2Op6VxjBEoGPoUgxQsIaVXLIeTLSUdCASilEKpvmEr8rr5/BR1SOKCkZUybFcO89NuUMsgzihbVdyoMuiaJuTuX1WyaXrw7eUip++v/n4Pqfp0a8/l0ZIKSI0hbTzHDZwWaDUZpxvAX6DFQaJWqE6iErtHcMpoVxC71DVDWqPiD4nrZWDCHfoysUE1os5afKMpO2tEbzwHFQLKVkv9/jRHnBLxZz4uBwE7+4rTQiLYABRMnl5ixYLg65fXyDWbtkLwQp5FJgycVwJyZlo7CauHUItoxDpGoa2tmMWTNnHQZUzAw+I5Qk+4FhGErxzJdrUCMy2lYsZzUpqlJG8J7+9BqjrAgEdDNDuMBybtjtdiwXRxyfnpDNDCOKta5pGgRQpcjpaXnwz5w5g5TFdrcbe/roMU3NcHoK/bYwdOuagKKqKpIUhHFP8h4ty/b54OgIv9lzuFjy/PWnednFB7jiPdvosEqz3exh9HgjODpY4Pqyod3sHYvVkv2NW7gxUM2WOFl+bj70nKsaNlniu1OstXhXsncpFdOcSxURUFO0IDpXBkmlCMrSKhjHAlUnjggS2fUFaxUVNLZEcPo1tEuk1khZYygM5CjEZL8BYVpk6AlxiVADwq8BCD6WEp6S95B9WddIlTF9mAyAZZCVlSqbAq0Jw1i+Bh9JcSxNZwFS2Sn4X/6uEAK0oZGSvi+3H1JOyeEYy0A85dwS5RqoUMAjIoOsK6JzJdKhNenxF06N+OmHz2eAD/maN9jIqw8ynxok/drxmkXmo5vEVeY80jj2VLxYDZxvMgch8Sep5VPbyPUx8o4qMW8tf7jX/Njhjj/ua4SVvFSM/PrQ8FI58KTXPNYE3lxDbRRLHB/c1rxplrm9C/zqWDiuP3LeM+4yx8D5OnFeC/pwQCv3YCPHrjyzh6sFz17fcrHRXMdwtVM86RKvE5kLNmCWDf1mIC8sXziRvERv+N9uz/nBVeDhg0xrDWuZUDFzfdTMjWS9GfmVveLvLjxf8HBewLkqYCrLwYG+98zqred4HHhmlPyuM3znLHGlV3zbwcCVIfHA3PLBY/iPsua/Nj3nGslsUSOAWYhsdntijBxeXDH3A3e6yJ0+ccMLbCX457cNzdjzYpV51QK6KHnZ0pOk4MmN4KlR81gFL1kEzi3mqH3Hqsl8Yev42iPPc8OcO1HSisTHb2uqFLguNe8+6+hGyaKGv+g1j6wst44H3n+s+a4j+HOnuGgzX+4Tf23luBlrPrEJvHkFT+3K79TP9prvOT/w+LrmD2LD19uIEpFPDIaXGcd5nfn9XPH32p7HO8VHY4nNvEE7nh4STyV4LGeekA1WZ5wfMdbyrjqjtOANyvGsh1tRk3N5Zo2VXMiBX1qveOtix6XUA/Avty0hOr6njVyuwAvYCMt9xjOPcBwSv7SZI2LkLXNPjIH7K/gXG8OPn/M8uZP8QSd4Vxv481FyqYJP94LXmMyfOskrbKRRgm9cZD50KkBkzqrMZ5ziyQR/t018IDS80Tp+0xnaortgmUFmeH0b+N29hix5qIn8+jMvjBrxDx69mKH83iydGYNIid3Ql3yv6/B6Rq0UUWoaHBFJSA6pWqIf6H2RLijblHKTTvikCnZK3M1PJkKaKAqq2OoUA0NskDLQjbFg/IB5PQ0iokQiap3pRI3OHpuLxCJlQdNa1tuBqjKQTWn0B08UZUOqlcTHQhVYu0iVenovqKyirRTWanKgiBeiLOrc6BldoqkUKXiYWMNGK2YVuFy2eyFk+v2WJCtkKhEPFyPLKtENkaa1bPYBoSsUkUqXPCxAxrPZB7IQHM4tSmR2fcD7TIrQWMWmG3FuQIoiMsmUzaIUguAdISaS0tRGcjC3bAbHrFGcnuw5c2jw3pJjudrfDJExRKxUHLSCvS+Hl50TLBrDrW3HGAodSiEng2DA2ERMFd710/a/UBpiFpOEoyJTYic5J3yIKFkUxEJYtAo4n0jCkpMvnZlQ9McuS1pTKCDe9VS2QSl5z9znY0JQvl4AqQ05OnaipcEjfHm/d6mU8NSEY5NCIZTBiMw+liVSzEUJXU8RCC0Fw4Rd87Esh+S0zxVKkRMkUYqaQoCSCikTg09I8vQ5iUL4kgojNWUUKK8NEMRctt+V1lNsR6CU5Ke+9FfICNuvfXf23qObGdpWjLtTwGKsJE42m9CtJ2VyBTEjqxahFJUteSRhNdJngnNEP6BMTZa2oD1EuYKqZMKJTC7HW3RMoGpcHlj6QkNwEw0AJSFExn5HtTrASthv9zTVnNGdcnR0xHb0uOkqIvqBpppRH5wlDHuSVOz3e5IPrGZLApkh7FBJIlNgGAaSNkWOEMrpX9ctptIQHKi2WOCGbcl4ugFRzYhjV6w2tpSxpK1x3Y7QD8iqIcWBpmqpqorNZkO7mGOkoK00KWbquub2ZseZoyV939PtB1arFafrO+Sccf3AxTPnqBczQtez79as+z1nzpxh0bQI71kcHrBarViv1yyalq7r6LodldLU2nLtxk1m8znbYcfRckW7XCF7zxA9vVSE/ZbF4QE5Z24eb0i2Im/3aCnwEa6c3J5ysSPIgiVTVTUV/u4yfOWUgx6JuZz4wujB2sKTtJYwdnjnSnlMQVBtYTNLwFaEGNBCoZQkCAOuKwiWu7RvKEEhABEx7bKUzUSGpKiJjGPhAOcYQZsyjI4DiArRaLQ3GGMI0SFlIsSRlCA5h5gQLzl4pIgkYcF1iGYB3iGtAVGGYitBWUO335eaOVDXhdBRfB3lyjIKSfYBcslipTCiVfnehTApOL/w0Rc8CP/MIxfyB7ua19Xw2gPPR4/hGobvmg98LpZ/7w/WiQe945ax1BFeVweSNHzdYuAzo+GBNpJHydOD5BOj4g1V5BqWt1Q9Qgj+U1/xHXXHx6nRnSM2iseUp4uGXw2KHzMdT4+Cx3PDW60nS4ULgn+/k3z3YeI+M/CxO5JH2oobvudtlyqu9Y7fWTe8tXJcd/CaJtAcHeJCT5KKj9+MXPGa7z5vCGTu9Buyt8yJfK6D56TiZlKcuswjwvFwq3jlMqDGhMuaetHwZycDr5glxjGSbMUX14LLVeD+OhNdIDeaL57CR7ea19WeTUq8bSForeT378A3XoiYIDlqMilm1LLmxunA+bMt7Dw3u4GjwxkntwoK8PkO3nDeomwk+4o07nh+dFxaVtTWgEus5oLxMFOdCFQbYa9IwlMDLoystw2r1cjJvuL8PJIqCZ3HmppTZ1CxJzcRnRtudxV+rtAnRT8fc+L9VyOX6sRzo+DzueYNcuRFM4FWiRgkXx4tlSq590V2/Kuu4ofmjn99WjMz8CYTef2BY9Mn/u224TkH/81yz//eL2lD4vtmPdYq/o+d4QdbxzmTeCJYjpzj/X3DNn71Jf2QLM/vkDxvWWgum7F0/xEAACAASURBVMTTSvH4oPg7dc8HNoIbaESAUyV4uxl5fLDcSpq3LwNNghdpSRcTUib20fGEN3xuVDxmC4Hglte8thr4ZGj5ih/5ulpy2ym+qe35omjZxMxfb0dmUvDzJ/U9XfPfWjk+uC2Yqm9cRU6GzGe95hO9pZWBt+nAnsTLdMn7f8mXoeq9V2+8oGf2Jx69lH1MVNZitKIfBqLQVCojctmyuXHAp4RRmpBBa4OSkkZFfFZYVbZmPhTRgtQKZCF7IAQuCrQISCQuJiqtCrdYWlSKDISivxUarSVKFFyZc55FU6NEZDtEtDXgBg7mhsHLIh9RkEJAW8WsbfHOI4SkHyM+JepaQxaFlQ+QCitYylJmLtvQjDWGSgtSDGRlUUoQnJ/ebzxaV4Tg0SSMEbiQ0Vozjn46CBhECmijsFqy6wPzWiOnQT7kMnxuu8zRrAz6+zGzaDW7zpVIho8cLAzzStM7jxvLUH44t1RWEGJkNTMsGs22D9RW0rvIMBZxhdKCW2vPrNJ4F5m3illj6Xwo0TgM/ThyMCtc4du7jNGG3eiKXjtJNrsyzKZYYixCCKwufOaUi3I7U14Xd+UWSgqGkLATIsxoMRn+MilP5j1Z42NAiYTWhhQzyDJOZQwxjJOV7auvzTyx7TQJawtXWiLxuXRrnE9TWS6hJvOfC4EkShmyz8VIl1NEk8pCLAt8KEU4gJgiikgShhgc1laEKfubRMmMK1Hwbd0Y7+marS0DbsxgZCnJISQ+JshFLJLusapL9ALgHz5z5y8/CM/f8p2573ukLnlIxn2h/9sKVIWMJdMpRcTHgIglp1NbzTpoZEowdMhmQWUkXdchCYjoqReHheYQCzewbNpKDCCLsn1tVmfI3oFW1FrTTVnZWpfr8j7cRXd5uls3ylNpahgdsioxhjhsISXq2SHGCDAzOl/kF0opVG3xvuDJSGWjlxIF5ixhGAbE4ElFAENbzRj3O6IEZZtSytIG4cbJ0lMxjiOqbljM5+xPN+zdwLxp0aI03sUkHHExgNVs9xvEesvR5QfY3L5OFIqQy+bTSIMyhhw99507X6QVlWbYrWnsjCQFD16+RAyF0PH8jWus2jl37txGW0MtNbkP3Fjf4ezliwU3J2PJUI+RMQv8vic4z51bN2A2575Ll7l26xhTF6rGxbPn2Ww2iBzxKbLfbjlz7hzHp3tmVUUSEJ0vn2/d4IIvhUdbIeMUtE+uECRisa9lP5SDg6rodiMIh5AWoRJSWDCqRBByRtfl++ydg1iGcKQsEZix2OMkCVXdzZhH0vSxqqmIIRXWcLdHmJLjrZW8Z0uMMZKjQ02HLRd8Gepl4SsPISCmMkEMCfKIUBU5RnRV4iOIgIyRu89TTgllK2JWQJhuRipySqUJrS2u64tuWWay9+Qv/dELHoR/7VXn8h9tFIdW8Iej4SvO8YjIbE1FoyWvx/OiJtCIxJ/3mn2CVzWR++eJ393PUC4xDCOzquFrlwMfOdWcz4FaBl58RiBHSZckthLsfWYm4fHTEu341Kj44YuB0IOqI4023O4dMyM4mwQKwS2ZabWkS4l//LSgspLaKp7dC/7GbOBARP5FpyAl/uezmQNdrHEf6huuevgGHbGV5OkBXj53xCBZVon1YDiygagSf3pqMD6xlQXi/vWt4LO7xCeS4XWmZLUfrBJzRmorOV9JjnNkJQ1ioXBrz3EvuK8VOKOpncPJjJlVjCOoSnGn26J2cObSnP7mmj4q+pT58l7yollkJgRjgsfOSXZBMc4k+Xh/7+bm3GFCBolpEjfWlvON4+ZpQuqMyhrtAnc2jtmF5t4zm5LGDD0OhRwSY5/4meuGhZH8/YcTv/0cvOIg0QvJyy609Lc7ahm54yX/6FbFT190/Hc3a37iYCQJeM7B7+wr/suDkfftah4RgbrRPOATXgSecYLzOnMzCGZK8qlR8XrZ8zWLzM9da3iRduyF5dFq5D4NgywRhAdl4h0rhxGCf7WumIURaytOsuCdduBTgyZryaOi59ULqFTmmUHzK9PHvmXh+e3e8qOHAz99s+Eds7IJe9PMMWTBSS/4xW3DMo28fZ549Tzzx1vBJmau0PD9Bx0/s264RFEn/944437RcUUUbfs7l45f3tTMVOQtarwn9NgEyaUq88e+4Y2648YIn6elFpFvqz0rBe8/NQgpOaszbR759ZsvbBD+X195KQ8ulUN/zMRQOgxGmUKLyEWtq4ikVJJYVpZG/pBqUk44P2JtjVWZfoxIYjGi1TUZCKnczKVUrtBHX1S3KQTatphWtZQolfFh4r2qSfueShY5xcTJrkNLiVQGFwJWK3JOBD+SUsbWdTlU6YoxFoKAkoJaq1LG04I4NfhjFkhRZBejjwwhYmTRu2uj6J1HItDaTFtBiY8eqxSNBecT1hhmtWLdF7VxISCU6GTMxYYXE1RK0g+B7dBz7nDO6bYDVFFSx4yQYrKqldiDC6kUE4exdEKE4MKhLf4CKzk+ddSVZL1zha8rM/uQ2O0jFw/qCTdXsG1DjMSs6FzAxcTJZqCxNecOLbc3kcpoUobDhWHbBwSJlGA/BI4WltOudK8FAhdL7tkaVXxAOWG0xmcm02rJN8dU+MEplMFXKMF2zBgiWSqMKKVxoySDLzITa3TBoYUEyU9Z8omP7z1SCgQJq3UpsKV872MbUxB61kgGF+4plZVK94ghMeUyEEuB1qUAl1MmS10iLbFscUtOm8LiV6XIaYwmhIwmEnO8twPLd2M4udwa9D5MhUvuCWZ6V5BtSpRC4M8++1fICB9+0/flu+xTALRhzBHhoJoVokOOnsoq9t1QuK0iQBLI5BF2VnLFKWFNjXOu8PzydOpJiZzLlrBpGtZ3bmHaFm1mGJnZbdbUWtGNPQwd2BpMg1aZMHZoXZVBxQkCDhk8CQWx5De1bgg+IozBVBXO98gQUHHEq5pKCMYYELH8JpS6kAsyClvPMG3NuBuJRmHJaGvZ9wNzCZ4EWXF0dMQ4eu5sbtPWLSl6rLXsunWRMFC4y7VpSd5hjEHZUkYxdcW5+QrhPWu/45GLF9nsd5h6xr4v6tl9PxBz5qCtef7aVc6ePUujLa7bcu7sWVLOrHdrFrOa9XrN6ThyOF+yMIZbx8dYqdCN5uUPPcx63/HMM89wVM05PDzk+vaE5Znz3FzfAS/Zjz1WK150+X6u3LrNkBLr9boojL3H2JYhevxQSBb3X76P7XpLFGUb3HUdWlmyFIz9npmxdFIikyCGAS3kPXpDFBYhInnoMPUMP3agaqQIgCCrgg9KfnrtTXVRZS0yjlQYBg1iKsalFMEX/rDMukRphECMAaNTwcDYGi3KABzuxipEQeSl9NUbAFOXN4dylRSKZU5XCClL4SYNgC6tVhFJIU2ud4OdzQjDOB2QBCHJghUUhUyCLwO1H4t0o2zPJynNlz72ggfh33jj/VmMI366go6V4I6TXBsUrzzy0CmkyszrxPFW8rOnhm+wDpJgETwX5ppnUZyJjkcr+HSv+c3R8I3ac1kHbnnJ55Llb64GDq3mnzyf+ZuXIgdKYrLgj44zX7vw/NOThsY55lowmopv0o7P9Jm3zgMPt4KPrzV7AaSET/BsENxSlu+pRj40tPRS8APLgc+OYHzmsnR8IM75L+Y9j28Fw3Q5cEFm9kLwwbHhfzzrWMxgvRX8QrT8kB44mEl+80Tz3dXAVmY2ruLlD1j81vNPbgr+9oHDB2iM4BNrOLTFHvaiKmPrCuMcDYpxUbG9s0fWhotnyw2MGwYuzkc631JLzSZmJJ5dqok5c6nZ8PQ1ydkzS5bmlG6nWMw9KjecjHBxGbi1jux8pK5aLtYj104yVipklVhdUsTdyMkNyfl2JBmJdwZmiuOuYpYCp0GgpeG+o8h6k7ljLNvbe+bCcKIjh1huk+j6xGmQvOY+y7AJRJFxWfKVbaJFkqXgn60l/2A58LOh4R0kfq9XvKcdOFDwVAdP0HCgAn+yVfzoyvNbnWZh4OXSk2IkG42IkQ8NJeP/cu34QrC8cxa4ID3nVebJqOmihuT5cKhYhcjb5oH7NPzqWPOYDNwaEt+wcvybW4ZvXwQu1TBkwZf78ngIpckx8uFQsUjwnBf86NHADSdZ4vjF/YxXMfA5ah60iTtB8jWq42qseTZJ3lX3fLxTnKhEjIr/9kzgma1kpjMXqswne8WCwJPUvLEKpJA5FIFf3TasZeQ9dcCVBRM/9wI3wv/w1Q/mgp8qL2glFSQYErSVJqbCX600dGPGx4TOadquBpSpULmUQqVWk8hAAgklylCVEFgtqa1kvRtorEFogxaJXe/RKuN9Gai1tkhlCic6lCJSZSR91KgciSmQUOQUUUIgtMbF0uK3d21nKRb5lqwQdz+Hyb6qpZjMYhJjDa1VbMeEkQohIkYpepfR0iMQRApfdwyZvnNT1rSgxdxYWskZgZ3ITyGWQr3VsmxttaRpJCFFsk+cOyjbamMMg8tIKehdmUPaKnPrxHG4MCglGF35bzL0Q2BmYdsHRidpmyLbONl6hISZEVw6V9GNmedvDWgrWM00XZdYLRp2+8iQChlLKzh/aDneRFIqn6fV5SB098Ay+DLwnT+s2HShOBxypncJIUtkwTmP1mXTHGHK0ZaidsqQhEERC6nDarwvpTZNGWDVhIcNcbKx3iUyaAmpxA+tUPeKcXniCldGEUpdFAEMMVLJiA8ZrYt8JeWyPb77P865FNDDlN+tpsywIBFSLr0LVYbvlAUyOaJQBftGJKSCA4womsow+jgV8iDkontGFHCDnyIVYyhZZ0GJbwD8zFdO/vKDsHzdd2SUpFJF2zsOA9ImBAYnCt9Xjp6QIwxjQawoRQ4J285x3b5sWOsa6Ydy7VzPEDKiZC4bvjCZeiaouDKzAgZPI3hHuzgg6oroA1pbjClNRBG25AhN27Lf7cjSkO7x8qYYBeCcJytJ2O1BBqp6VrZ9QtM2cwByHum6jpQFKQ7MDy7TZOj7nmBNwbvpYsSzREQK9H1PXVuEMKXFO3h02xJiT9M0qCRpWkNtDc9fv8W5c2dIPrHrO2azGUIIrl15jlU7R4pMqg3n5itOdhu6rqNVkrNnz3Lz+A7RCi4eneXW1ZsoPyLmJXvZr0upcLE6JOrMyckJxigOZgu0rWgbi0RyY30HuXclFiIj3cmO+x+4TFtZjuYHRKu48fQVbq9PaBYHzOcts8qycZHnnvoy88USnyWr+YKbmzsczJa4fiAbRX/rBuiqDHVKYbVhHLsC8g4R08xBJFxXMkWmqglCg7DUNiO0RVJ4hS548D0CQ7aK2hRyh58g39I2WJkZx4F+sy4586YljA6VR+LQganQzZIYYykMhEDMRZ5halPC+96jjC6lRhlx+zV1vSy4F6Xu8aNTSjRNwzD6Cd1SriSVUjhf8GgkQRoHlKLoR5sKoWvcdkf5Y0t0HSIW+YaSNVJmvI/EsCvDdShU9Pz5F06NeM+LHsozmfm2JrAykbWTVFUkI1hnwZdGzYUY+KXOYlxmkSOvqiNPOsv3nQn822PDayuHM4bsPR/rJH1t+N6m56zO/MKu5rE08ESuQAi+RY1cmml+YWs4IDEPjr91MbHLii/tKl5SZ87MMusgaOhxDi4bzdND5CnXlO+LUrysCbSm/OL81NpgbeJjG40Tgb99ODJGwZfHmjedmQoTwfHEFrqQ+Vgw/P37LYvgONmVa/WlTOSZ4coucB+ZKjv++brmBw46mqz5bNAcJs/lVtNHx7KyrHwmH1Y0IvDkrYEH7psRe4nbd9jDEiv5jS90fPtRRIqMkYrzB4bjLnB9B/dXkXpZ0217Bik4e8Zy89rAIgZiW64CtxPKa1krUJm/6DKXo2cxrxCm4mA2oGLi2jZh+0ySitvA+65Y/t6jGqMSq6onWsX2hubm6JkrTb1oObI7rosZ/+zxkR84HznOkvsXin93U/DOFex9ohKZD9zOfNgbXqsil23ipTV8ZAMvreA4CN64AkTi46fl5/HyOvEZb5Ha8G3tgDaFIHNtL3hvpzlynkpbrqrEj68Clclc7xQXdCBIRdtIfOf56VuGt+nAw0vBvz6teGfV8XyXeTIrvuco82e94h0Lz7Ve8Cep4plB822zwKPNwOM7zde0gdM+U1nBJ9aZ7zwLv3DckHXZ/F5Jhi8Gww8ejPzORvGlpLnfwLtrz5A0H3aCuRGIKPjMXvB97Z73dy3fOos8WCXee/x/k/amwZalZ5Xe8417OOfcIW+OlTWoVCUJlQY0C2wQSEiEGBoLWooG44FBdjjcdtjgAdN2d0QTNo5227ijwwTdbgMdgRvTLRByAwbUCKPBEpIQICQ0VJVKVZVVlfPNe8+wp2/yj3dXAhH+Yaj6l5UZJ2+es7991n7ftZ5V8Z7Fjq22/KuN5W2uY6cV91jN5SbxxZ3jsSFy3mY+OHhQiq/cemEc4f/6pfcWoxRWZymxCJlWZ5ISCk7KRYY1GYYYsUo8kjFDVTmGKaCVmsuHAiElnPO4WQiHmAlZrjkFM1fYE3NB5UhMibYW8RtSQRuNNxCzQaeeWCRw1w1RJtTpeTSlpPsBpihNc7sx4Mh4b8m5UJTBV7KBMznSTxJyJkeaxZKskjS4GSdTaK3ENkGklMQwZWqHIC+Lpk9RRHwSdnJCsfAFbxXXTyJnV44pS1tcW4lMu36no64MWmUaY2ka4Qf3Y8aaxOHKc2cTqY1mf2m5ejKR8sTSi37Y9CMKxbL1eAWnu4g3UNcaZw2NE9LDbpfYBhl42KI57iOXz3gqq2gagzeGJ2/3bHeJtqlYVhrvYIyGp27sWNSCDG1rQ7dL1LNP22nNne1ObIjI+2yMmkWtJuaC9x5Dph/nBkKnKcqSlaUxScpaYG5wK+QUSEpTaYOZyR1xfrCz1mGUBNy2/SjXlveMMWFyYAoCI3BVRZ7RZykVIUqUQmMVkIix4IxiyhlHli2591IQo/VdfnQuhdobxigs4qJnb/ufa6pLRSwXTsnQpHHiQ96OE15nsrLin86yMSjayrWfkPyPgrFISPN/ePLmX14Im5e8uujKoatGVr4xoIolOE8ZpTXMVhZXeYrKZCWYqjQm6E9w1WLm445QiXizaGIaMF7EYEwBV4pgqByEJB/aaq9hCgZrasbNNWkls4Ha7OMXC4YEVluqpiaGgc1uwCoR7AGZADqViQFM7fHGMWlL3p2QYxTod6llfe7LnDyU2sCgCnVcEmNElRMp1KhXwpQF4jhhncPYzDQm2vY83XiMV4mx6zDO0R7skSKUIAxjR4S2pnQj2tbUdc2223D24JAyDtzqN5y7dInu1jF1XXP95nOC6XIN450Tzt5zHr8rlD3LCsdisSBZ8RYb1/DUU1+5i57baxZcOneep248w6V77iN3A7sSuXjuHMtiePb6dYYe9s+2TKcdN7s1t2+cUK8WNAvP+iRw76UzTFFx/c5tlKsJubBcNGyu3SLVLYPKLFKhXTXcWW+IWdYoC9dQvKRdh80JYPCtn9F5gDZ4V9DFMuyOqawjOA0pY8ZIqBqsLqQQKSnTLBZoL1SH2HXE8QRtFpiUCOOIbmsJ1hVHjhFSwChhr8ac57IPQ5kmVFxjFgcApLnKMfZRgNyqovZijRkG8eyWIpYFbTw69VhTMZg0E+ALtq6IQ8A0K9Kwk6mOFb6wUxpdMn0cZJo8jGQjNx1UIvU7YIHxXspJuo7yhd97wUL4Ry8flss2sXeoubOV1OyQHE8Vy5Xe8nTW/MBhx4urQvSFXTR8MdT8X3c0KQ28d5k5qOCX73io5YvuHX7kiaGwnOkbn82Kt9ueD/YVb190vH8nD5T/+b0dt9cVe1pxbRj4R2vFX2sib1xqGpc5jhZfHPvLQl8iP/Vcy/e0Pfc3cCfCr3Y1b6t6PrezXFxZ3uAn/mBwuCnw/l3FO9qRHZ5NhneuevaAXmWuBcXvjS3frgqjCuQ08rOd5b6q5hsr2Sj84rriB5cdhxV8dqt4zaLms2PkX1tM/N2bhh9qIw+dz9DJTXmHZt8paCPxVFEbw6EzPDslLh01uLHjSxt4+MEWfX2NcpaPXs9UCg685tMnme+9J7EYNWFZWFCxvzeQinjysivcvjmBFsC9d5rDVeLm1rB/kFlmx65E3J58Ids7kaH3qGVPnTVXN47nThLnGoVpDMcngYfvLUx9zVOnI3XxbEk0Byueu94RsPxctvwXZuBopfnjdeED25oHqsz3NBNh2XJrE/indzSXbOady8ATo+d3O8c9VeHb2w6VHT91rPnPjnq+UCqaEDmMgd9IS961HPjMTvPFyfEjRwFtCptseXITeHTMPFwZzuvC/7NJvGShGVvPcVB8utO0pfD1rufVe4XP7QyvX0b+5bblqZ3idb7jIRkwc6U4zpvM/3HS8IN7O34/NbxruaNF8eGt3Ju/nCy3psy31hldAmcN/MtY0aG5MyZ+cC/wK5uKV68Mz20Lt0rmyGne5jvuqaXB74+3kI1lGSK/P1m+vgocVJmnusIHx5r3LDOvWBX+55ueP7xx5QWd2b91X1OqeXBTSiHlREJjtKfLIl5rKxNdAyQlmKohFuLUYZ1DaUhR/KYARRWxclk/+yOlhiDmOY9T5L3arxV9tmAsodsQQqBWkWgr2soRskZpTeM0KUa2k6TvK6tRRcgPRiXGLPYHZRQKGYSkXLAagrKUAo0Rjq96vmmsKLbKEXOmTh0hFayrhV4BjDHjjMKrzBBBNwsYB4yKDFPEGs1+45jyjI0ToButc3Rz82flDOMYWS3EZzyNhfP7Dbd3A5XTnJ4OUlRqHKfdwKWDitOYOOMNSRcJ9CmZXmpruXprJ9zplKgqzZk9x8nJxLnDRv7OrDizciSduHUS2ETDhYXidIiMQ+H6JrKqLUuvuNVr7jlQjNmw3kaMceSiaCvNjXWPszW6aBKBvVqz6TOxiIDUVlMZafHr+pGiNAunyAXGNNNCdCKimeYhjVeGVOShytoaqzIhZVKeq6qt0CKGKZCngWI8qSSmkGi9o7GGqIQGkrOEaaVeWmwIY7GEmNCpo6oa5gsRrWEXCl4XkvI0RvjBz/OeCzKtVdqg8gjG4IuiAGMu1NbQR/EoTyFAyWLj0fPom0yJGWUsYwxYpTFKJsFTmJhUjbMa72QT8Pe++lcQwvbV31pKKWgjQGSD1NtmLfzJ59cxeRrIJJmwVTUFjdYWZqpdrhp8iZiiyHNgQmcPyIqupEjoe5S1Ui+sJRA3pAmjLSkEjBMeq3fC1QXQ1VJ+hudXQwSIgewcaRxBGVwlLNjY76gXNQErPs+pp94/I5O/cSvBgSRlGEkPuCyVuDGDqh1qElKA9jV5HLHO4V1zNwgVxy0YT+syxS/xVh4ezhwtOT09ZRgDTbPg+MZzNMslzjm6fqTylrp2DH0hqYAzlqZZcHpye4aZK0wGu6xYVgt5f2Lm6tWrxM0xZ86cYXXuPN16w97eHscnt1meOWAcA3vtkmHsOLp8ju0wYbYju90OpQwn6zXLVcv9l+/n1o2bXLn+LDWavbOXaL3j5umOdk+m57YoNiFSupFCZL3dUJ85wBRH0pYxwbKWb6sYhRdd0kTeraWZTxlyUmCFjmC0l4eGmNHPcxyLIVIwBcIYhJXoPOOYMDbROEOaFEMSfF/tPKRMzANxmjCLFpUCse+gWaK1psmZVO2Rxy0hCNi8KIUzSh5uUhEEWprFrZsnx8YRVaHy4pGe+g1g0c6Ri3h8PVroFcMWjENbwfIopYhDJ9cVCT2vhBQSfKBq5BrVBmtn3rGS30uf/a0XLIT/qwcul1IKF/zEB9ae17jMPVXiajJkpTC5cLHJPNfDJ5LlNCbe5TMbpTmwms18O/ic9XzfYqCN6S4gvU6KQSc6azgdMl/YwAUNr1wVwdwpxYfWmvt14dcnx3f6wC5lXrnMfH4j/7S0bCAU9pT4qS+YwCbCwsE/3VS82UZetYBrwfChk8K/dz7w2anmT4Jh6AL/7qWEy5ar08iBMax05vqQ2ajCg7ZwdSpcCw7tMt1ouBkUL64Kj4+KNy0y9zTQZzgeFR/pFA/YwluPIkkbFlnuaQf3Lhlu7DhOhb3a8C+eTrzjUFG3icdPDQ/VhcMmc7K19BrqOrKoFly5NXCth5ftT6hkOVoEnK2wvkXFzJXjLX9wO/PWy5ajVpG7iG8d3bbD7lVMU5Lq+RzZO4ROKcqJhIaUMmxOB5o9y9nDwnZteeJ4ovKao7bl0I1c2SgWqz87s2OODNsRbRSfPy3cf66wCI6pwFMYHlrMTM5JBFgcwRTx1n9y4/h8rFnrwjdUiYVRvNqNfGbwHLrAQ67w7OS5EoXr/n8Phu9fJpwt/LOTmreuBl5fD9wcGj42aZ4Khn9zGZiSZlADv72teW1bWKrAh07g9XsGg+I1zUQpluOp8OkBKIVHi+c9bc8HO/H4HivFmVK4WQpHXnFlgh9qO57G8XULGEvhF24qrlHxPcuJD02Wt7qRR1r44s7waD/x2Vzx7jZyyQuj+IlN4XbWfCTCNzk5sytkNessHKnEz+1afmCx4zgbnk3ye7/wzAubCP+thy9KB49KJCm6w1JAyYq5FLETxBTQBaaU8c5SkC2SmsNDzlYoApmCnV97wqBJslbP+a6AdEbLxC1nSpJSiZiy+GQLcn+c1+XGCueVOYVvSiTlhNWWab43WCfhr3GaWFSazPMEiYlFW1MKUnikpdAqZ6hKJJFnpJbkfrqshW9s5bWdUehZoCklXlWlDY0OKFeLRaYUzi2kSW6IUHnLndOORW0F6TYVvIXGwTZoLBmtFZW3bGbusdXiKV55g/UaZzUxZ26cTAx9x/7ScWbVsOmDBOW6icPWM0aoKk0MicsHnj5IbXM3yvuy6ROrWnPhTM2t9cTxyUhRmb3VAm/htIO9RqbnmUJIhi4EdBHyxUFbEZVGKUvIhrm7hJzFL15yZBiHu5G2UBTueZGoDUZFQlJYAbMhvwAAIABJREFUElpDws7tp4UxFpzTWGPlfVMJbwt90uQkMTk71y+rLGHK1jtKTowh4J0MRQoB7VphXyexPSgUVouNJxYwSl6HUvBak4v4lsUDbsgFwjSREN9yKTL5LkoyZCFMMmmf2/QAaWtFHqwKRabLSs+4Wj9vLqR8K2cpcFFK8ZOPPfeXF8LqkbcWbSLKLkhpBOVo1cRUEmWa19UHR2jVMIQ1ZA1xx93oYQoobed6WUezWomfaXDswk1qZUntCjcXYBgv1cnjNN61SlRzJW7RhVx6CFG+dEsgqhbrpeLZZagaQ9d1xFJo2xbjlqTj50TQGhHLuQSM82QlwqmUQjEj5D2wSu5Euy3UjZAGYhIbhvXy6wyEEb88yzSeUqnCcv8iaxWpkyJbz+74NuQt2lao6gg2t9DLFq2ljMF5jS3zij0nShZREGJiGCasky/kla3Yhp7YdTAllg88yBnvWaqJkDV3pi2PXH4R3W5Dbmv6vqeUxO3bt7kzdcSu49LyiD6F2Y8tE883PvJKttstjav56u1nsdZywe3xR08/TnGOtt1js9kwzWHE1eGC3I14VbMbI6PJxGhh6FBVQXuHSUY+dzNPJVImDDt021IyFA16imQdWeydY5wbZubQNuPpTZT3GBS2qtGpEMJGLvppgnGEtpW/I03iOS8JXEOLZVAZkwWJltIARVO3S8KwJQ3ypFm1+4ylg02H2jtDGUcqJaulbSwUHWSSPEYRx67G6iB0vyBYuZxh3G6xrWwISg5MRabhRQdyQKbb3svUdyFBP6aIzgldMjhHHuV6VzngY4HcsP7Sb75gIfza8/eXr/cD9znF06HwqbTgx47WfLWDPsjN4EUHhi5avpjhS6NlCBP97Cl+IE6cc4U/KJbDZHjv2RGt4E+3FZ/qJ97RFqgsF6vAU53iRR4i8A+O/V2rxCv2Cp861SxUoXGRddCccwkfAx8KC76ugl4bHvSJe9rMo6eZJ4LlLfuCGJp2I48OFucSt6LlRso8XCU2wbEthVvRcm8b+I3tkgtVwVF4URy5QsUjLvCFyXGRng7Dg1Xm94LnZTnw2n3F768V37U3cX7h+GqCIyeJ5b/zlOc76g6j4IZbcGnYcrhUXDIa5zP7OmOLxjrFGAtWJ9bR0gX4+FrxqjZxZbJ8437kYyeaX+4KF4PiP3rIcbF2rNgSsmY9TTxwpLAJdtrRJdlEjZuRT2wd1/vMuw8jd4z9C2f2ay5lqmAJKXC9t2idua9VfOzpTPGZM9bzpxv4xV3LDy56Hj6bKTtF0Zqbk2FtC790e8H1ofCuZU/tFfe5xGlQWOTMtjrxvjuOVy0LphQmpbg9Zg5M4uv3DX8UKw4LHM0Dn//1WXikjbxOZ2qvsFlzkmSb8r+dNryCjtOqQWXFNI58/9nE+7YNk9L8jSrxyaJ4tRkJofCbo+N6tPw35yY+uU50ET4SLT9+tvC+neWJbuJd+5Y/7C3vrHbc0yr+4cmCN7mer9lLvP+4RcfMVaN5jx9Zmsy1UfHKVWIoht+6VXhdq7in0USV+bl1yzfbgYVPfGDbso2K72573rczvHNfxNgXdoqXl4kHqow2ihgKezXs6YyPhWu94W9eeWGFGj/24Pni1RwgzklCTIwwi16lYNksiNqhwkREQxyFpYqIImGti/Bd1BZKZp0NdupBIWgsJYQGa6U6OcTy56wSUolrKNgcCFlS96oEompwVpOUJZFZWKniLUXReAPWs9udCtN1Fu6qiKiOuiLnSClQk+hUjdOKjKKfBrwVklDMBR17tJEgVi6KmCK+binTiFaRerHAZENEShJOtwM2i60Kt2QcNiwqL5xiDbV5fuunKFmwXaWIE3OYJ5QF0LaQQ6GfAmPK3HN0iHcJx0AshhQK548c/RhonZfWvZK5sw2kqdBPkXphyAmxgyBhwIfvbdiNCWM129Mgm0AP128ErLH4yrHr4xxGVJxpJVCXjGEICodmKJYpTCxMliY1RFBq/TxxoTCFQOO9zHRQTDniKNRtQyyWAjyv8LpuJy2BCpyVCTFzPmVKmSlKO28uRVjR1qCywACyKvPwM1GyMKwTAkYIITAGqfP2VYVNic040jYLphjRSgpSpmTxSKBvjMxUKSvNd0UKNypvSAW6IUj3grDkyMWglcIjXuuUi1RQTxOt93NrYqYUqWi2xjDGLJ7lHIklMyjHP/jKX4Ej3LzuW0vQVmqHdSLpBWrqiJT56Uzj0bOnMknop2ruoq1CCJQpgjN3b+g5tSxbS2FgR8ROAaNlymurmjxKvanz0loUx8D8+IPfO6KpRUyOY8+YMikEGCJ4h9bzOjtO+LqmuAW2hBl3JhWBTe1RGIbhlDxsAShmD7Q8cecMDgggTWLWk9BYa2iahpAyRkEuE30XqHQR4R4ieC+XXQpYDNFZVssWX1WMunDG1YQQuPHcVbJ3NE3DUbuSyc0wYIyhW6/xleaB++7n1vqEtl4AcP3JK0wqEvoNfnnANEZq7zHLhjaDaxx37tyhclb4w5Xj2rVrbO4cc/bMOR566CF2XccTTzzB4dFZYozc3u2otbTNZVO4deU5uu2Gc+cuoeqGI1tzcnLCHSArqNWCxf4BN9c3ydPI0bkLnJzepCgIpSb2A2Y4BsA1ezit2KzXWFejnEWbwpmm5c52ja9r1rduy//XWib4crxBWzm91uO8PN1prcnTiGtX5NATUBKwmCZ0U1FCZBwj1WJB6LcSmlSyrtPGz/i1EZMN2WrMfI1qJzYOgDIJKF5pKT6JSTEVQcz4eSMx9TLtNVaRQkD7FpXFilG0+IhziIRpQnt5kJOQQSanSXAvbUvYbnG+ghJIqcFaGP/411+wEP4nj5wvXxkdr2sj2MI17TgcE7eS5jd7w8sreLUdqZAn7+NQOKy0bHSAL3eWD2wq3raY+HKyvNEJw/a7znboPPGRUnO2C9y/SHzqVPO1K/jqOvOhUvPuZc84WX5nqxgL3Ksz33ke9rxirxTu5MwnpprUBz7aWc5UmpfbgMmFjwfLe/ZGVsZy4MUm9cza8vNbz98+HMha8aXTwibJ5/bHqeVrXI8u8Nne8F2ryP+5sdTW8NZ64Benlh9aTTxYJ25nzdIoVIr8T8cLfni/43+/aXnYJipd+NPsWebA25rMV5Lmu88HTFVxk5577AEp9Pz2M8KifMNh4dLSk0omDAO5GI6ngneJRy5YbmzBVVLt/uzVEx4dDB/pFO++EPmt6473nIsYp7igMqqCx7rCBa85ahTZaU5v9vzSTccPXQpcvLAgqcTnnwxc2FeUlPng7Yo3tZHz51qyKeye2/GJjeWbL0KoF9zvd9w8VlzVmpwUTrecWTiudjvWu8RLLq64sd5QdOYz2xVfHuAVWe6Dr1hoVjbwd681/PVm4kKrcSbywMLy9DawaBU/9oTl2+oMGrrZV/ixCIdZQ4ETU/He/Z6nJsO9JvLsoHj9IdyZ4LFgeNDJ5vBia+hL5mduN/wnZwc+c6L4YGhZani5n7DecRoz26h4vY78XvZ8p90BcLHVfHgrYuBLveNbmolDO3FPq7jSGT4wtbzNdrxuUego/L2bLffrzEv8yKd7w0sbxWWdefGisI6Zsxae7RTv39Z8/SLw+ragleDvNhl+tW941yrxwS28ux1xOvOPd/v8+N6O9z529QVyhM8XpRzeIFY+XQs5AkWYa4HL7AumZCE8zBYIEN/nlPI8SRMR1qmKlSvYEtBZCZ1I67nYwM7bMQlFlSI2BBHWRZpPrSyBQkikLNPiPooYM2qevmXhwCpTowhYreijCJTaKQqKOE3EIPf1aBo8iUwRnzCC2mTmuxY01ihqp8Vmpwq6JLYT2NmzGpLwhCVjm+ayD8uiUnhnMGisy4RYuH4y4Iyl9pq6FizeOKW5ljrQmMKlo5pdl3BerqUrtzvZSE4TdV0xREGRLb0jq0hrFae7iLNwsHTUVnPzZOJ0N7K38jxwvqWfEldu9BwsK2m2G+VDOX9Q4RQ8e6enGwIHew3eObTNrLsI2cs20ViWjaffTcQUOVzVdN2IBiYq+hApk5xXV1XomYFsrMFpjdOFykM/JCpnON6OQvrQiinIgdVkUAIi08aKDzfLtRZioqo8OQYK4tedUqBxlpAyY4SmskzTDCZQCqtBaUPK0vQWn2cbz44AN5dfwJ893Dmk9CUUTSliZ7BGAoHjFCnMQbiUMdZRShYah5LJb0iFEKXZUFs924qQkpeiabylGyesNegS6alwuvDfP/rsX14It2/+rhJLoZQ4p+jBaJkSOi9fBkZ5jDFsdmvZMgdZbws5Lt3lv6o0UnIW8kMp2KalYLDWUClh+1ZLYfSOw0TuT/HtgikUtLM4rdhuNpAnqrqmalaMvTR6ZatJ44D2FWfqBaexYxxH6naJnjqwIpSmaUJbhUoFW9WMo3AKY4mkfmCxdzDzX6N4kmOcRXgC7YAoq/Qc0c6hrcN7T06ImNIVKfTCpy1ZQnkpc3DugG67k5StknXPdrPBVw1H+wec7HakksVfqyOtstw4PcY1tRSU9Bs2/cA9lx8g5ZH18QkXLlxgmHoOD/cxyqISHHcbpmFHODlhKHD2wnmeefJJKIX7X/QiYjY0rXiRbty5TR4Gzly4h3GzQenCwf5Zji4cce2Z5zC25eTkhKOjI+5sdrSu8PTTz7B/5hDyHHT0Fl8tuXPntmDsQpCykWKJu1MwBleL76vve0wMpLGXu7WuxFNbZPoPGlwDCDaIcSOpZ1dRSoWyltKvUd5QkEIUb6CyFdpkxqHQb7eyLlSyYjOLJblESopgIka3mLkUxGpDSoF+sxWs27TDmIqkCliNHgrFCmorpwQJ0CMMETNTJbyxhJJlnWktyjrClDElyHWfAvhWHupiAWfQSfzwhkKaQww6S8tR+vJHX7AQ/hePXCifGSpaE7kRFXsqc+ACD1awcoqNzZhQUVeZp7vMssBzk+KiSdyJjpMEN6Li0eh4Sem4ETXnK/jT5PnuvcRzwfGyxcA5Cp840bzmHDjruLVJdFPgvkbxTLCcsYq6ifzjqxXrXPiP93va2rMdEwbDThWuDXChKrxor+a5oeNPhpqv24vYMZKdQg/whV5zYZmpY6Zxmud6zZFV/Mmo+bW15SfuGbk6KE5D4at94uNB840289ikuKYUb9KZTynFm8i8tM5UVvHgSjYa1/rElA3PBniogasD7JRiGwvfe0/ieEp3z6xH89/d8PzwKvCyQ8WzW8XWZB7QCqUknf7J23C5BYqhiRMfOLb88Is1pJEndvDyg4Zp2tGsapa+oEvg+lqjQuR2H9mligcO4L99XEMp/J2HMzkrXC1Tsmd3gak33HtkoJtQulAtD1nubVkfOzAtd05PuHxYcXynUC0UP/9k4j1HGXLhVCmwhaW2/PPr8LVt4bOd4h37hRvZ8ku3oNKK7ziTuGwFSbYskY/vFGjFdSW4rItSlcP9OmOt4rnUcBvNJvV8T1M4zfC745JDXzieBr6lSjyWW27owo8edBwaWLrM0zvP/3L9efqK0Fxedah5bFQMqfBSN/GA0zzcTFRoKpMZUuYnrld8u08cx8K9vvAnQXPLOh4IgaXJnLfw+CBfxK0u6Bw55x3XR3hZVXi6KD4S4HuazHlX+OnTmn+rnngmwVQSu+T5hlUkZSGspJi5FTTnbebJIGf2AZ/4ZG953/VrL+jM/sTLL5dSFKrkWSwUrBJh6+Yv+KINWiuGQcJyUxbGa8KiybMAKGI7KHIeSylU3pHRc92yoNUWlZ1FayFOPbV3whk2Ika2Q0RlIQP4ytEFmRxbrQkx4ozFVQpCYYyZ2ltSGtHaEbKE85xGQlzOMsYyT5cLQ4gsGqlFLiUTpmmmCMzUDG3QJQvNICf5mYwRLu48BSwzFsya50kEkHLm3MqxG6TiGyU/725IOGdYtoZukDCWdgpHBl3Y7sRyF9GE0DFMcO6MDDbu7AJn9z0pJA4WlqIUscA4ZKYQWPcDpRjO7nmeudVBgXvPNYRiWDqhYqy3gTFGzuy17MYJQ2GxqDi/57l6PIB1rHeRg5Vj2xdqE3nmtnCGcxFrgdcG4x2b7YS1mhgzdWVIOBmeaU3lDFZLSDDlQAhRvMzaQinoIjSJwjwUUgalDCkOGBLGOAIOYzRhGqiMIiqHMxqrMtpqvMrsomY3zGQKpci50FQeVYQlXJFIxmGVtKVqrcg5sR2iYN3CJGF0JBjZpYITTsJdNGBVAv3zrGu5JKQZDoU1CqMNQwJd4l0km7EVzipCFtEd57OkKGgl57WURCyKv//kXwGfpr/2bUVZqQeeX40UsoS4rCVT6Pst9WIBCYZxA9nQrFYU7fBEYoxM4yhMWa0Z1reEDmEbtPFMYUfphQVLzDQHB/R9B9UKQ5Y0LIU0jRhjqJ2n6zq89+x2a+q2pSDrHmM9vqnRswgtujAETdrdomSDMgbrDU4Z+n5E6TSL1QmjDWnuokdpqmYporVqmLotKfSUNOGqBsVcfVjV0gZUOfq+xxnP2G+YxhEomErqf90wovZaxpAw1rKoG6y1ZCVVhE1JHG9OQRliGIlauIArW1N8RVPBuo/YEvGVxmTL6ekpIY4YJ++Ja2suHp4lhMK6P6V1FdtuR78bWLYLtt2Og7blxs2rxO2O1YVzLPcOKbZiVVXENLE+7ehDz4P3PcDjV66Shh0HBwcMw8Di4Cx5DHRhpGpWkDLawxg0wyAhAmMMcRqxeSJrjzFICCyMwp9WVrh/ef6zISB7GocpGeNbKIm0OyHhwLXQy4RZWYtyFbVu6WOPdl4YwqHHWohJgxavr9G1YM9UhDxhfI2ikq56LRxQqWoukBRV01BiIIwdxjtilyCtwSiqumacFLgMWkRtjBFta1SScJzLwnzOeq5SNjN/MUX0PMHMMaOaiup5zrC2jOMgAr8kvK8Z//h3XrAQ/g/vu1D2veG1bbp7ZtdJUymFd+JD+/m14QcOIiT4jY3iZrG89/xAj2evJMYAj/eCknK28I9uOd5mRl68kAeWPxkV13aZp43j/hh59+XEz9ysUZXhDT7zxmpiSoavDoUHq8yRhy9sCg9W8A+PHT98NKCKl9+vFe1Co2K5e2av9Y6nu8Cv9TVvbyZeuR85VIWfvdXwOj2wU4prI7yhCfzK2N49s3/zsMdgcI3myyewGwJPRMO37EV2RfPMoHnNXmKhNAcWnouJttI8u4YPrjVQeHMNu5RZFXj4bORnbi/4G4uRC3uZg6DvnlnnE9e6QKbiyi7xaLLsqcS/viee7zP1yDM7T+MiRy5hsuVjtzS3Y+HQJb5modj3hb1VTSmOMG2xxjINAzc6y6WmcLVX3NdkfuW64rF+5N++z3BP5Qha0zjx1g2bkRvR8OLzFV++EXl8U/jms4mTrDhoa/o+cDwqzjtD1OAbWO8y1yeodGahNE+NhfvrTJ/AofjoqeJDUwGteRjFN7WJK6PmxU3hAyeG2hiy13wNI69ZaU5TZtNlfqH37JmK1+nN/JEoKgVft9D85HHNv7Ps+dmt5ZvtONdvW8YsauZeBzeT5rEIjSq8uZayiF+ZFA8rzUMu8+necGQzXwqaHzsf2U6aL/aZs65woze81tzk18Ih39YE3t81YGGpFO9YJZ4YFPfYeb2r4MEWruwKTwTFbw+Wd7eRMwY+3Gsuzz7ENkvJ5cOV/DqWwhcGzVkLH+k133uQ+U8ff2H4tL/90PliZvQYiMCdkogIq+WlpzHSVFKmkaZARLOonfhACcRUxMZmLEpD3/XSLmasUJxCZAiThINzYdV6mY7aBkWStT0Qo3hJrVUMY8ZZxTBGGm+ECpATWhsab2arQcEoGJIlDFsiItirua65D8J+FZ0rVsr8PFILJY10gHOOcQzkFChJxGtW5i4zV2tFZWGc0WFCGJJpo7CMC0Oc2K88U1IYLXxyq9XdyacisOsjzLQrM2/EtBVSQmsSXdBQIo2FgGLTRXJKeCMirPGGvaVlzIo4RIxVDFNiO0oTXj8mmkpxsh7YjYFzezWLppKpqxO74EmfyTFz8ajmmeNECBN7rWUMmWXbMMZEjOArCUXWpsj7G+JdcRmj4OnQIjr7KZFSRCtNURqrIRbxCkthiZoLLGS6SslMY0/ComxFnifMZmbzZ2Mpc+lFiIEcJ7yWmmxRffJAEnPBlgQlyfe6sqhc5IFGzZYXVYhFzbi0RAiCitsEsLGTz8oZ+mSoVSEbL1acVFDGkuZwXClR8HJIlbLV8oCVcyHO6MGYobYW5u0m2hBCnu0VBecMP/HYX8Eaod709qJdjQ1ShqCVYgiyRm6XB8QYiUHEZI4jZRww3pMpOFeB8+iQUI2lDB0UK8UbShHjBGFEuSWlTPOBkPatrLUI1xLIaoTgcMuVpE1VwTAydMPc/jaC2swItiITVFqKMiTdgrUolSlZHj2M0aRxkr7MomexBNpaSoqUEKRZTEsYwvpmfpOlmrnfnVA3VqbLucLWFUO/haxxdUVB6ny9ynJjMIbd5g6L5RJrLZvNRhphkmDjFnWNXmrGAS7uLZkGqXE+Ob6GrirqOZC32j9kikG4yesT1t2Wg719fL1ktVjgveV0t0UrWW9deeJJtNbs7e1hlFy+UwnsdjvipiMpUEbTVC2mqlmv73DP2QvgPbev3+Dipcts+y12SozGsl6vyX3P8uxZuiGgy0DsAnquL7ZNwzRNhHE+oE4RhzXKN1KFXAxp9iNRCtbMBI5hBAtVJdPvsN2QrYSvvBOqxxQHiAZqj1bCbzZ2IWI6bojDKdYtKMbhXEXsR6nvDoMUrMS5uhnhOlMKdiX0iDhtZ5bvACHNVUlacHC+uluQwTiAMajZ6qBNJdeec7C7g2uWGNPQTQOYItddL41FaI1x8jCZuh3ae3IY7/KKra2Z+p7y+Atvlvu2Bx8o37noMTqzLIZKZx4f5WVfsu8JMfDFraMPmq4kPrfJfO0SdCm8aiXX7CKDXSRiJ8utLw+OShWuTZmnJjjjhD4B8H3LnnVQHEfFH+aal+uR51Lhymj5ofOJq5OmsZkHqp5fvlZzvy/cniKD7f/Cmb1Pe4YEX02eByuoVeHDoeLNduKcKnxqUDRKsdSJXVaUDA/WhadGhS+ZSWmOXGapFedmD+ud4Hhtm/gf73h+9PyGz5w4LmnFUZP56TsNBwm+ZX9iSIaX14kzPnA7WbQy/MHtxBsuJg6C5lNb+O2hop/vIT+y37O/nLg11Dy4byljx9jDP7tReGVTeGBhGJPiUpuJSVE1sJ0yv3zd8f1nBlxjqdsVxgS6YUQph/OZJ56NeJ25vJrxfRSygq9M8PQx3AyahdW8aVVYmcIHTwzffSGQnOXKbcWLztWkaYudYDSKX7nu+Hyv+NHLid87sVyyI4/2ln2juK/KXKjg+lT42NqyUY53rno+sLa8oko84Asno+axuUHtcyj+/YXc+7/YKT6a4b88n1hkzQePFV9JipdV8EglAukPO02FIhloVWalNXtac6lW3JwiT/aBA28pSfHy/cLNTrzS/6RzKCqKGvhGk/jopHmlznw+Gf66wEm4GjMrDb89Fi5Gxf1OUWkYU2HlCk8n+Zk3oXC/VThT+OJk+Ka2cJzAq0yVEvc1sOc0P3nb8Q1V4ElV8boSeSokPpss71nI2b85Fs7awq0Ej7TwpVHzUpf5xBbef+uFeYR/8qUXi7aWlObSAiW3IYCmlvV6SNL4lVMkxICzBoqUCRhjCDmzsJpp9hDnmUiaUyamKIjKmePrjUwaldJEDKpEfEl0xdBW4g/ViK2im/2wOQeaPMhgiEJCM2knolJXWC2FRqnI9sRoGGNCJImaixDUHFzKxCQiSyst09vnaUxFHgjGcWThxPYxYqmdYZwCsWhq92c2CqOEFmG0outHCchpxXaIyDJWGhMrpzjwim3U7DVSKDKGzGYr3HxjBVO3bD0pFSqn2fUjw5hZtgbnPG2l8VbRD4JOrL3mqZsdWimWjUyMFaByoRsTmzGg0RgN1lm8tez6iYM9hzOWW+uRcwc145iYckIpx7aPDCFwZlnTB4UpE7uQMUqEeO0tIcokHmWoTJHB01w6ktB3mcAgjGQQJrHXsy8YRT9OeCUPqsYY8RHHxFg0tRXUXEKD8eIhjyNpGlDOS2jNGvqZ45tjRlsHaSLlTEFTlGSc2lpuxDkIVz+lwJRE2CY0BvHyPq9AQwxopdFGSCPKWHIKWGOYxh3eezCOEApOFazWdDHKpHsexAEMUxAxHcUekrKI6jFEfurpv0KznH3DW0oxFkpFDgGtBcI9dZ0IyRAQw6x8MBgvyhuI+QS0wmfFqBTgwVZoFfHeMyhP4wxjKuQo/whdGUI3CH6jGMb1bdQAHNbsLZbS8hYHgrGkYUQ1+5SpB51lR9LfhoC03fkG254RjJpXjEPBOYdmJA4jWRcKjpInQKPNn/Mxzx3WJWe8NTLhnQaZalqH1rU83RaFXzQsnKHrJlTlWM7m8W67RrmaymtCvyOViWnb4ZxD2QW6raBIgCpbaOoDTk5O5Im4qqhMRdbQ1DJdNAW26400n207bO0lTa493kcOmn2GGDBOaAabjeBwkoZq2TBcP0YfrFBKcbA6w/l2j9TvuHnnFso5ynbixGVslraa7XYD7YJKC4t3sVgQ+3mC7gzFV1KXud1CzmjnyX0vwsY43HKfPO6E4RtHnG8JcRALxW4jDUA5o30hxwbbLrCmZtquyXUtk4yU5T0PAehQpoGyQymHGuS6Td6j1ATFwLilJIVqV3O1c0RPG7L2aCsrNZwXYR6nu9STFCOEjF9YphhwpiXlCUqQQo9UUHNP/d3GnpRIpoHYo0MHpiKrJWrRULz44tQYsdUBaXubjJ298wrtPQA69GKpyRFlDPmJz71gIfzjLz5bLteFx/OCz3XwBj1xzmfed2x5eRX4+KT+wpm9lEV0/RuryOfHCFrxtYz8UWn4RNLsm4q/VvW8dDXx0a7lLYvIJ6aGL/YTlJGrAAAgAElEQVSGV9aZl/jA57bw5lbTJc37j2EvZi4fad6yzOzGhFWZT8eKz6xhag3bPvNN1chXQs2FtOOrI3xXM5G95UWt4wu95qE28/ePa37kYMCWxHO94lbRTFnx8WQBzXfYkS4XFkazS5mdNXwuOL5z0fO+Tc1pkDO7bxxvtpnLOvCn0fOtB3CPiVydNLbJXKwd/RB4dFM48IVlm0lDYq3hD69rXtyA04ajViZGlZUze5Q9v3Os+UQwfN/RyPmsyRoOF/Humb3ZJc4YuL3T7NlEbxRT8pzRHau2YYojyllUyjy3LTw1KPpSeNlR4vefNVxYSsDxDYeZi7VlHBVXhsB+HjkNns+OhhfZRGPh128pts7zzqPA1bXmjavEOkvoLmvNpAs6Kf75bc1jRfHOqrALmTEpehTfelS4M2R+d2h4Mgfeu0h8uNO8fRn51RPDW9qJj+wMr2oi/2qo+Q8OCkde8+u34QldE0zBJc2kM9MkJRbnHVywE9tsyVEEz68Hz9uqHRbNvpr4yNbz6kbx4qbw0yeet7gezRycyoUPR8u3uchAYakUlcm8v9d8o8q8aKF4MsIjDtax0OXC9RxxGqpZUC6M5ZND4Y02caIcz0yw0Dvebgd+P53jYgXNjNnbjZrLteF4GLgaHY9Ohpe4xNnZllsTeabAraiYouU3b/1/45j+//73ky85KlobAlZEjNYYleknsU/F9DymSnydSgspAZXxcZCWNqlHICmL1hZLwllNVh5npLp3ympueVN0k0xdE5q+79mlwlHtaCrDGBIlBbS2jCFhfUOKAaNkcp+mLWMGr2WSaquWkDK1KeyiCFRbJDxllFgkVRGOuZ7LNLSaCxeUJpeCNYUpZCFjKI3WZq4XljKQ1lu8+X/Ze/Ow266qzPc35pyr2ftrz8lpckICoQkEAikQMXQKciEWnah4FQu9gmjZ6320yrrXqyVyLUXxaolNWSpFo4IgIiWNFCJCQLogbejTtyen/bq992rmnOP+MdZ38uWQhJMGEsx+n+c8Z397rrX2nGutudaYY7zjHZlJD5X3VMH4wrOmx4eCyitt1+E0M2l7gjcjblwGRDOarWqdLys2pr0lWhUO8VYUYhzyifyizcZoF5M2UhfePM/Os+AiobaKbqWzqnFbTSRGxYuwWHlu3GxYHZnDYzQuqWoryrG5ZcbcZh+pKFA1L/y0idRFhXjz3I6rwCRaueDCOwpv3NhJa0n93nvavjdmtfOMqooYO5J6NPcWpR8KiszaFjeoKYx8ZqJDWXfvzRAeEhVTNs9qnzKltqgrKHJLdp5pHBRJfEFBNEO7b+hVqMp6KO2saJyBK04cb9tpup0oGYaEyC5llstBpMkHyAnRREyZqErtb4rIp6ykHMFXaOrR2JgR7GrGRcGis8ndxoiUI9pmYoVe7ABWFATTm4/Zkr+cE37rutntN4TrC75N27bFBygTxAJIpgnrRelmMyiG4zqPk7GpIohjNjtkN6EOnSgXcVIglafXAIMBW5ZjnJQ0TUOoFF+NTaO1rOhnawSxzOfZ2trgQUqQEmFxheCtnLHONsyzW67gnR+MXE9JTxfjsJ+DZhMpC0bjRaaTxNLu0+hVkBNGkaAa0WRGmHMOX5o2suSeUdxirZlYXets2f/4EV4zFBVF8LTTGarRuDkhUNQLdF2HkMhtpB6NWKjG9JWSciBPG1whrCwsUvnIka2OyWRCTpFQ15y2Z4WjWxuESUcUZffu3WxNN5ltbLGwvMDZ+w7wuasuJ007quVlVhaXiKlhsRpxxvJupl3LscPHCIWwd9ceutijtfGLrrzmWpxzVL4gT9boypJQjFlaWqLb2GTWdEgZGI8KZtOeOJDf275h5BKzWUSCeW0ljKjqYBq8fQJnIvCaM/iBq1SatJPLSi6XoHC4nMmpt9mR+yFBsSb1U3w/QX0JYp4PVy+QcXhJJC2MQtJE0+As3RCGU8rJUSa5IA/V49Bt9e4I7RaIFfLQ1OLLZcCRU08RxsTY4YJJ9ZCnZmCrFQvRnJGyoggjunY2yBdZSejU9xA84iokbpnRLQGKavAIe0sI9MOCcVhha84mbzRr0Cs/cqcN4dect1dfsV7x+LLjkQuJ6HuundZc23keWCbetyZcXfgTc/apIbO7SOzxwqcn6YQU4cWt5+EV3K/IUAn/2C7QtJGZF160q2UhBd6xDk9YioTCU2ehquGqrZ77OEWd4yWHzIP6rDJzqMs8eOy5/wg+uAkbbeZqBaTg/CoxFuVfqHne6oS/PRI4LAERR922PH2554HLyh9cO+anz1R6p4Q20+KofSZLpouBTjMVjoWQ2IoFKh17i8i7j3o+3AbImceM4aJU89xiQkvJA8aRD6w7Dke7R+5fK49YhEs2BfXC5VN47t7MashQKJs+0G4pI6/sXfCU3nHDVuIjR5V3p5IXLnc87EDBew4lzgqRyzaEp50Bx6c9nz7ueMxq4uzTC/7u0szbNyp+/IwZp9d+CC/WnL2UmWrD2qFA9pkDe8TmrC/ZaAKfO5joy8T+4JlsdXTOszT27CqUaSd8/FjgQcs9+4ueQ7OK9WAvjM+uBx692PKaIzX3LYWmt+SUp+1O/O3hwIrPfDE6vqVuuWjiuV9wXJWFx43gvVPPI8rMR9MCbVCeU3Z8tAtIzCxpz3VZePICfGmWOTvYS79XoXaZs5cc651wnxqub4XrU+CirYLnLTccLEoe4SdI9lS552NbjhuHSMPlradFWHOZs1MCUR4zjlw88zyudjSqHM3CQ0vHtZ1Q+cShKFwXlX0+cmMfeFiV+WznuW8NDy2Vj20KdaF0EfYWnsMpUzrhvW3B80YT3t+WnOUSWyrmEV7MvGkCj3DCpiu5MnZ8x0j5m5nnu6vIDb3wmjtbYvnc/dr1mcIpkUwljhY/VObKNH2iGjx7IkJ0JeKGKmCDdrlihQlcqFAxKbKo5mUOZHwRyOKtWIe3HJ82mYcwdjMLuYuwMe2snLFmkmZGVQ3e0/WZvm8ImqEYmScRUNxAzRjSPESIfUPpPXUZ2OgdKwsVWT0p96iaEYlmOnWmgCBi/UgKGpE0pe8yaZAyDN7ZO2BQGAgeZl0abAvwXkxSNSqORBPNW1sUnrETegLTvqdyMKoclevZaALT1pw6VeHZs+iZzbIVxEBYXSxo28jmrGd55Nm9UnD94ZZJn1isK8a1g5QoSmFhbIobRzZ7Kg9LiwUpKbW38339se5EgYi2NfqkhMBiHdhsOlOw8J5xqUy6QRqXoTiT9Ex6o7jYWqdgVBhFpB8KVSQ1Y3e7CJQryqGaYEbCmNKZYoZJrqlF0WFwDJmBKc6yI52DsrCotpeEpfzLkChphdXcQHXpu3U6rU5Uj8uDkJ/mRIpmhDtn+VRS1Ogg4UcI5GQ88j6pSdYy0DbEkuVCCIgP9IPXWcgnqDLBGfdZUmP3jDh8sIIshRO6mPDOgfPEaM+ibDprNH3k965dvyPyaRco3lOKZcy78QJlThbC7ib0fY9bGJNSj6qzCmPtFgQFsYpiJjkVTepKBQjsKkqO33gVrhyhOaJ4GC1Dv2XGAwmpFygbK8nsJJOzJeX1XUM5XkRHJUXuaNuWcrSMakaGQhq170ySaNbR5cLUHFxnIelJJiwsomWG6ChCTR8b/GRCXKgtEbADlz0bkxsRiZZ4MN5lCVKhonZWKSWPzUsdnClkMJ2yeNoutjY2jPxPpCwW6KcNkhsYLbOwsEARvHFKkxLEsbpshUeuv+FGwshcD7PJJgf276Eb9Gpnk8ZWN+2UXbv3oF1Di1gy2+GjbK0dZXH/XoIfs7QSOHZ8g5wzs/V1ilyxe/duqsWSEAKTycSS2mYNC8smA7a2PmH//v1sTrZYX5uQ8oz19U2T25luDh5wGI/HTNq4XbgQOijHS4SRKTO0kyl0R03Gpli10FS9YMLnJDRnFkeLdNpaZCHUxtntZqABnFBUI/qcKNWSLbeLryiBbnLYVp+lJyC0XQLiIAdsur2IcbuQDHFKUSybAkmXKZf3mHYviTzbAoy/7FxJzmYgeudIXQfJyP2yvEzONt7Q2ouky5HU24vGVCEShQS0HpFTwmcsiVN7Y/yLgHa4qIORXJrUoHPgjD6S7oISyw8+fZ+eL44LakscWVx07FZb8R9qlWtax2ohbKjSqPBFKVluegjKvmAcsMfuTmxkePPGmAf4hkvziJ9ZnvBbV2e+dcFxfRbe2wmrZc19Y8uhLCyGzAPGwgNVT8zZvhfKItP3jj0jR66UVZQjMbOrLE7M2Wsa4byioas9nzwaeN+05EGjTCPKmWXmA1s1j1tKHKh7iI66CjRtpGsyRa2sjqFoHVWf+bW1kmdVMy5pPM/eLVwflQcG2FP1HOoKcil8aFJybt3yuXXHO6bw62ckfvNQyf2y3UtPXIKrpp5F17AuNU9azeyuMhutsJlhl4fVxQAEPnKw48wlowxcclz43w5Y+VtNcMm6Y6sXrmsi//vpiXUJdJ1w+krJoeMdf3oj/Mj9esauYnFBuHGrIefM2w+W3McHnrGnR8Ye5zKpzYS6grbHjwOjKnJ8TVhdCqx3nnazx7mO9x4tOXMp8vnjjkUfyQrn7xLeeazmPZ1yOg76zHfsEvZXCZzwkkOeRzLBieNAUbEsyrlLwlWNcFUPl8aSH11tuEzhPWuOLtQ8JPRc0WRuyAV7nXDhcsdfT2u+r55x+Uw4f8nC5Ft9wWuPd6yGggePeh4xirzx2JgmdTxjqeeimecRRaJ2ymWt42p1HHANDy3M8/lPk8Dz9wtXTB2bKbHewmEHD3AdKVS8p7Ep811V5APTgPfK/UJkf+H4YKqZifIkEo9Z6vj4xBY8+5xSSOKqHu7vhcXS8YXWsYrD+8SbOuEcgb0+s9sLhSo3xESpgctSplTHWQGWnfA7d7LE8i+dvaperMS1VdkqLTkdR+qN/zsu7f2bcYgrSLGhFKX3teXRFM4krdQMkjyoJ6yvbeJDMD4vnqIcobFBxeFUKYqSNnVkBEemw7x6fTRFolEIiBof1xRuLAGpi5laWlBl1mc6LSlCoNRIETwbPYyqgrGDNgsSPBoTs75hXJQU3tHkbHJgs9kJB0ZRjcxDGRxBIl2fqcqatjfHW0yZWdexe6Fkq7H3stOEKwqmXUJyR1GOGFfeeLJJiRaEY7k2j/SNax3jwVvYtD17VwKKJ0Zl0pkqR4w9ywulURjVs7pYcHSrY2vSsne5JvuCPVXm+NTUNjanHZ3Ydkul0RimbWJUWnW4xdo05tdmyt6VkmmTWJspLvWszxLBC23bmgdchVFlEmpWpDgxS1BXFkFPWZm2EddvDUIBY1NhKAJJvRUsUagqj8uJWZdwbrvoSm90zWF7zSDE4ZwPxVfwJhTgPJUfNIeT4DWZ3nIGxcp8p2w0GlIHRYkqNAlG4wUrn6xmjwXth+Q9T2RIEN+WO0uWSLdYj0hq9+EsmYa0ZlONkCHmkbKCCGVRkrItAgrnkKyIEwTBaaTPlryXJUCy6nh5WEC+7Kq1O5As98gnawjBhI37HvGBMhhtQVxnofeus1DrdAvKZVzpKEKg3ToGBE4QlvpjyGiZMqziCgjliD4rzeaWGQppgrhAWYyQoqTpElWMdKFgabHGi8m0SRY2Zy2p9JTZsvM7V6DthMBQoIDGPM54yw5JCWTL6A+5xruCGBSfAUx2JNKRhxKFSm8h9soKM6SmwVUV4/GYFDvwRriPXU+OkbpeGNQgPG1rfarqGnJHv7ZJGSxpwS+WwwPNk7MZ0+Oiot67RNM0NMeOkzsIVUVZFhaa73orDjGqSNNE6ZWVpVUuO3glPpT0TYNfGMFKTdkrVV2yudHgJVFVFTkmtO+ZTiaEoUIb2AqyDAWzjXUzxhSjSOREqBaIXQOdqWR4raiqCq0cs40NqMphUTOFVFAtDWobgwxZqIzbFrxJ6NBv2iLJGXWGbgoMiRxFSVEUpHZGbhO+LnHjEX3qYWrH2/YUUy8h3YQilETvyU1r3t1B8zBptqIuIoMnurQSz5oGKb4O2i38aETqM3RTyuVdRFFyaxXmYNvZX9BuS7qNrI9Kj2stWaMsS/q+R4YCJymaXFmhBdFlvELsZrawazuLHhAH3pUlcJRFQcr5RM339pL33mlD+BfO2qunjZUlgVdORjwl9Dx0IbHRVewqOy6fON7aBvDCqGmofc3j644HjBOf2IS39v7EnH2UTHl07aiKmtMXenYF4Uj2/PG1BRSBfWnG3kJ54opps77s+JgXFDNe04/59f0zevUsqyJZuGTmoVAOuI5ZdnycEXHScB+X+ceuZp1E3ScOkriwdmzGDD7z8KLn8q7knEq5JJfsz5EaxzlLyiWTzNpAOz+rSrxyUvD0yrG3yLxlPfCYReVb9vR0M4evMutJOLjmeFtf8jMrHeoSY+f4zNRzKArfshjpVPnEmue+ZeLASKlKS+KbZDNGPjLxXLAYWTxQ0U5nfPGg55NbnicsJ/aXUBQ9m0lYEmFfkZjEktIru4rAK6/tOLsWPt1kzqkdcQwPq3r2lMKXZp7NqfDwcU9beKZTx/86qpy3oBzq7MV9ukscGHtec5gTc/aBRWKS4LxFz1s3HKOszMg8NHjOGyn7lzJvOwIfUhPTf5zvSNHz9H3Ch9bNSwvwhOXEOyYj/u3CjH/eDGyROauEG3qhVjgcIxB4SK30mOLEWgvXtrC3gl1j45K/d6jI8uQq8b5eWAojHuWmPKwWru6Ft0yER3pld5E5zSfe1AW+KSmjQvlC6zinVB5cKTKEqj8y9SxoxwOqwLW9cFmvPHsFLu2EdzWO548tSnVthIeN4DND5PNTruSFCzOu6WGPeD4zVc4bC5+dQeWUswN8cGpe1sfXyiU9nFdm/m7Tc04JfR8pvOdLPXxDLUwwp9x5I242Z3/u8jvHEX7xg/aqd4LDXvrOWdGhhKMknpANCwJN1yLFiMpbuLltZmTxVqAAwfcTirImFTWVUwuVq3FmRRwutYhzuOAJPtBGIarJly5VluDmBJIKsx5K50GtAAZSWLEksfSWkHtS6szoDmaY1NoaP5gSnKMcCilkcZbbo6b6AeZ17lXwocI5aPtEGSwRzyqXWXi8S5mUMkVpVIngHG1UUlaqwiM5sjZrB0UBYan0Jwoo5IxVvwvC/oWCts+sTRpmWSiDpwyCqJ1jEWFUClu9UrjEeFRw/FiD80YXWSgDq1VBp5k6COuN4iVRBktA7FNi1ibj4Q5SYcZbFbZm3aCPDIW36xUKS7ruUsaLoxNPVQi1d2zNeqoQyGDOPjzjuqKPSsw3JQnGIWmsj9koej6gLgzBz44kljAXXCAEc6g1yaq1jcqCnJTJdm3lbG79UNTk2OC9w0mgjREHqEZLPlQ5IYGWVc1mK4sTHvo2ZlJsqYtAl6yoynhU4xCamCn9QGlUWzB0vY1nVAQroKKZabay3WUQ+qQUQxW9mJVSlChCwBJLY4wgnjZG451rQlzAgsGm0ZwHOo4q/JfL74BqhJz7jYq0SNiFdoNRUFd4b1md3nscVsc89h1Sju274G2F05v+3FbTMA4jtPAnOKySTItVRJjOjJ+V+0xZ1zgPEcXlRDcZEuEw1YjkKhO1dQmnJXVd08d28EJZophKxbgKbGweZ7ZxnFDVRAVpOyj8oPlaUVUFk8kEL4EUFLo0FEKYUlU1KXdoimbsOys3qSjOF6YKMNsa9FsivljGLY0IWHGQ3PbkOAVXMxqNmG0eQ0KB957llQWTE6Ng1rdmWA9GZDmUsdV2RqhKokLOmdP2nUbpA9O+pe97+mlD7iNZPUtLI9anW5yxZx+bW1uWtNb3lM4m8dJiadXcypKtrS3G4zFt29KLSdMtLqwSU0eoK44fOc7SaMx4cZnrD1/HajFiczZhtjlhaWWVSRupqop2dhzNBeOR9XfSJJaWlphurZMFcmsSNXgrtGAFKipCoaQuMxpVTDc3qQZOtWaHDiFQF4JxcyXjZAGVQeeXiM92H6oOoRZVqJes6uFQHKNv+yEhs0W7TesDzigOomhKiC8gLFhoUQe9zgSarSKheYmN02hRixbJivYJCaNhBS3E1OGjyQuiii8gh4qqXqRvO5Q0KGQoSML5whIH27VhgSZIVdv99Pk7T434/tP3qnc9y77kf1ltA1arimeGlkPOc95ixKGMk3DFRFgeBQ4UCa2Uj66X7Mod544yLz8+4kdWOipRWFCmM8VHT13YS+NfNmxlf3FT8bzVlsopR5JjVET+7pCwz2dOC7DqE1ekikuj5zFlR0qOx+5OXDVT+uQofOaBK0roPNVizdG1GR9bF/ZXmTe2Iza7jm8tE4/arRxrPA8cwR8cKTk/JL5EwbTPPH2p4/ObyoW7hGubTJfgCzNhXyFc20PGFCv2FMrbNjyoMhLlUaVyvyVhdxW5cssz7YRDXce7c8VL9if+x42e/S7zwCrzhNWeG/tAhXBZo3QIx6MZqAe8smsxcd0anLPoiAoHe/imfZHgzbPUNJmjnckiXTH1PH655YNbjm/d71iPMJkkrp0IB2rh81Phgn09C9H0rG+YwP5RZl0dvShVzNQLJZojYTzm+mPGxS0WVrl67Shn+cCXIrz6usDP31d5xyHPN++Cj60n3tIGXnrAZCfffGPgufuVfz6mrCd4VxPAKadni723WVmuS765bDjUweNWMm864nj6vp5rNjzXRTfQ3pU9PnMkCm2CVgpWvL1g3xeV7623S5YLV0fhYF+y6B3nlKbc8qgVuGbT3iOf74Srm8jMyj1w/7JntwifbR0Pq2CTwJLApsKyUzaT0NJzZVfw4MoMQoanRcgdtXN8onPcr3CcNiRArw0Fhq6M5nX+hrLncC54xp7MxesFm2TO9HC8F7Ykc5pTLo8FS2HGkd6xkRxnOeGMWnjp1XfOEP5/7rusBZEUxvTDO6AqgmW6401sB5OjMk3VcjBuhLbPxBwpPLS9IsFROEc5qAVEVYqBRjEdFoxdtpLBQRQZCiTM2t4k0rS3AhSuJKpQkOglUBWOnNLgOVbq0pMlUBXKbNYznTbmYVShTZHSWUUvlYKqME1bHQzjNpl+bNv1lEMlz5xNJ9iJmGdPLZHbiZiDBR3C6hVLVYGQ6ZPSRpOozK6gLh3NrMEPUnOrI0fTJZJzpIHHG7cNVO/MN9f3VMGRceSs7F0urQJbtIpm0y7SJ0sOXK5NSWN1uWDabOsaKyJKn4TlymTuymASY6PKKCUOk6ar6hJNmbrwHN3qqUrHuC5YW2/wBbRtZrONLI0KmmglgVM7oyMwKiyJcto7FuvArO0QYBatBpgM93XKRh+pXTbPbClMmkjp7d6Jwzgdih94yQ4luhI/0B6dZiTbzZLZlqhTQjEyPWtMW7mNOiRkJrRvLXeGgSqIeWSd82iwSMK2wkhUkBwtKiFiVYQxCmXQSFZb/EgocMO4NCWSJpNLU1O7cb4wZ1W03/NOzHONLSYTjtzPyEPCZBECToRfv2rjDhjCD3mUFuWSUQqc4PBonAxeV0vCwJsyQk4RENPe9R4fAqndgmQWe1UvEx0Qoxm0JLJ2aDsYMyFQ1AV911GUi/R9O9QLr2m2tpDRGO17xqOCqALJDD3vPW07M825YCWIu2ScXu+t0pzzFSvLY45tHIU2EooCPyqtHC+Qmg2ceIrxLmKMLK3sYTKbknNHMVqmaRqTR9ncJFSB2MysFG85NmNIs1W963qgREYjXNOQ+skgxTZCs6cc1WaA9h1SFOxeWCJ7oZ019H1PTA3adxRlSXDeZOR0u/xvSeVNnzh1nWWKBo8vRjRrR3FliZaepYVFVldXh/K/HcePHaZvGs499+HccPgQS0tLJ0L5k60tNjeO4YsRaTYDLyyvrkBMuGqB9UmDbk0Z7TuN5dEC7dZx4ypOW7RZAz9mvLxAURTMZmaga2qR4JHkTZZFSsJQojdKwkmJVGNqb/SQrp+ZhzQabUIKKzHd9z19p9BPgGgyY+KoBuM0SYGI0HetbRPCCS6xOJOtU1+i3VDhTzxpcgRXLA7yf4k2egJWja4YLZmXmEQGoy84GSgZYtchGRNKCITxeFCt6OhjAueoxNO2U8Ql6tESqY/02t/EA06taQtrAOmBYJmug7RQf9mn7rQh/C179+lzlpXTR8K1UdkfoHeRg5uew8mxkZTPyIhnhpa3xxJF2BtbDvuSZxUd61n5aCOcUynn17AWHZUk6gA3dlCHzF9umTF5vvM8ZJz4QuN4zFLmjZOap4Sehy1GXnGw4FG7A5dM4YdWWza9QJM5vcgUpWO9zdzYCrsKx0LpmUVBcmLkMu9edyw74am7lNeueQ42macuRvaMhOun9sD+3FRZdPD4XZ5rpvDglYLDTeRg4zhzWXnDxogXLU142dERL1ya8k/rAc2ZJyybwXVDg1W964SrOs+Dxg5JPetd5DOp4kf2JA42cM5i5otTz0e2HGeP4FmnJbLPHGnhyi17jn1+6nj4QuaMQnjncTg4KJOcHoSnrmRunCQ+3TjOLxO58uxedLzhOuFBZWZflbhgKbOwuojGGdIpF9+Y+HAb+IVzC26YRFaGSpNe4WiX+MPrA08rI/8wNc/+T53Z0iVYSo4/OzKmzi3PPAP2F8IkJkIHb14rubxX9orn209vWMFztQr/cKjgrNCzp8o0feBDDTTZvKQAaykx8oFOhG9ehusa+O+byvNq5foIX2gdhwK8eE/mugb+6HjBE8qWG6PnMeOOq9qCh5R2rNqbB/azjXB5HLQ+xfO4MexxmS90nmtSxZ7cMCpNmeHtW4lzgmfZCcsh8hfTgufWiUBkd1WS+szBHFjPVq5XnLAksJFNT/ZLjeMBVWJ3Fg6MMrtqYb0T/mjdI87xk4uJz8wyXVaeuVe5bgJ/MvN8IzAOyuebxAjoXKDMPWeOMqs5nJizv33D8TtpCC+qCyVFMBWFjCDJSv9mMe+mHwoiZHPNErPxKb13pL6178URypo7B0oAACAASURBVBKHDEaI+SGcJmbJDJHgPHUhZnQWJSmm4VnomTQ9VVla0lahZs5k42h6B11M5CxoMHmrnAC1sPasTYgPLNfCdNrTJtMAHheOpjMnV+pNLz5UI1JSxuPaaG3ZksPbLjMqlK1ZTx2ErjejyBcBYTARck+XEkkCdVHQxg6NFi2sBz3g7QS3GI1fPKodQYRZb5X1SImYEkUwpYFmm2+M4ofKdlbVz7imwQveF2xOTWGidI5R7VkeF8SUaWJiY6ul7RP3P2OJYxs9C7VVdMtZmTSR6azDDaoFXmBlXBCzeYW3Wph0PXsXa6rKmSCAC0xazJBzJcsjKz0860zxgxwJztGpkGNHFm++QsCjZAmEosQ7o5OkmHBi2s5ZLRGvKgMxZprk0NTi1MYr4thWuFMxL3wfMxpbgvOoyLBYCoNHuCD3Lc57EEffbCFFhROoJNPkgMeuZVlWdMk4vwApKU448Y4VtcWbYFHzurQFYU6ZmG07RIl9Iog5CfqUjQ4yzHdSIudIxFOQTnjF7V4WfvPaO5AsJw/7JqUMSLYsVMW8d0UY4USNi5kL+umUUA/Zyeqpq4KIs9Bw35reofqhgEDCq5DaGVIt4+p6KIZRUNdjus5WIy4pfe6pioIuJ0IxoqycaQ2PRoiagH9Vjzk2O4YTj2s61AW8wKgomWimxEFMTGZb1KORJXO15v0shnBBTA24El/UqA/kthnI5xHt17GV+ZgssLK0i8nWJmG8jMaEBI8TJZCJMTLd2MAtjKDPLCwsUAejdMy6GW2CrplazXUdjLw+U6+u0MxmlNWIhdEiIcBCvcD6dIt+tkUznaAqJE3UCwvGp8KRNFMWjq3NiQmPi2P//v0cn2wSNLF1fN0WBlHRcUnqO1Jr3hBJmfHyEr40LeS+bWjblkJkMEInFgsMlRnG4xEOT2ySccCbBr+4m6WlJdbWj1ioJmbq0QJRoZuus53V4UKJG2gsIkKOjrIq6dotiqoiJ2e0A+dIfUTTbJAwA9wIioIgnjxkVPd9T+FLYj+jKE1HNmlGkiIhkDWh3ko+l86hCG0fqYpA380oitroPk1H7hskVPjRmNyZRBCxx5U1qtn6M1QLHJTEbeE36Fqn1EPf2X1Qj3CuRLOQ6PASiLm3FOl+SNpUBaLxkHtLqhQnqDr0ms/caUP4587ap+ITy5IZe1hZgH84VPD4RZMeXBgnNieB/7bmeNFST5sdl/eOC3dlLp0UfLqBSiP7CscyyhUZJsnxoNIKKzx2bFrc/7wBj1hUnrSY+exsCN0XmU9uwQXLiYunBY9eyCwsZPxU2L1cIpo41Pec5kres5HZFWDURbQSFhPcZ5T5dFNxposkyfz2kRE/s9rw8vUR613Hjy333GecuHLLcyg5LooFzyozx6tAnPUsqJIcHI494FkALtaS3zi94V+Oe84eC1sKi4JFj0pYnym/esTzXQtWKe1Jq5m9lS2Gmla5qi/4xFZmwQcuigVPlCkfnQk/cZ+ON95Y8cSVzPlLaom/1RJdu8VmgksPZ9bxvKct+MEzLBl3r8DRDPtK+PzBDKXnPgEesCewsdVSuY6Ljpc8bCkx60qacebYlnDlBCZJuDF7fmhvx5KzzOz1JFy9qZy+4PjiNPG5aQaFNVdDTJy/ICyK8o+bgfOqyPWqPLQMfMtpypuOOM4tE1/sAk/dlbh+Bl/YymyqGUulZvb7zGWtY3+AY0k4b5R58wY8e1mZ9J6JKos+02vmSy2sRSvfOtPAwSA8t0xkEU5zkVdMA/9+IfPJifDIQQbtUISYAwfKxOGoTHGMUB61aCoDv3So5Nf2dVy84TirMgPwhtZxJPVUruBBdeLS1nGayxxPwoNrOBLhSHRklF4TV3bGzz+3VtrYc78CrkM50ioXFltcE3YxxrGRPeskzhTl0g6uTmYo3dd1XJ1LIPINPnFZNg/ZhX6Df8i7ePOhW5ZjOlX88v1WtAx+KCAxcDRTRnwYpEITvQZmXWRUmIpGwlMVoGqKN9tqC0nE3oNkG38fcUVNVRRDMQxHWQYLpWO6yCTTC86DhOjIQ1TzXEKm66EsA6mx518Te5DhnRcEzd6epTkP1U4tSayNproQnHlXSRFcgfMe5zx9HAozaEbiDMWRfIHDMRoVTJuesqrNYHQON7CfUzZjeVwGuqyMK0/hTBEg9pk+O/re9IJNFqyly8rKqKIZ1DLKKlA605Vt20TbdTTdkPeSYVwF08oFUJOc22ysKh2i7F2pmDWWA7Q27SmDo0vCQmHKH9uV+lLOLI0KQjCDrusTXcyImKGeoiUQOm+G8agMZBzTqFaxto9U9ZiFkWc6aQne8tCrMqA4urYBzLvtvIfthDiBTh1lMJ55OVRwc5pOKERI7olqnubsyhOll0VM1qxPinhHjqbMwXAuoppsGdsKW2pawYrQJ5MqTDHhg8cBs5jIMeJ8oCoKk/MD0lCNFTV5QLBEO2CQ4DOuuh8WgCmZ8V8O0rZRjbc8cDRIui0LaK9YTxqiKHYlnViRk9+9obn9hvAcc8wxxxxzzDHHHHP8a4X7ypvMMcccc8wxxxxzzDHHvz7MDeE55phjjjnmmGOOOe6VmBvCc8wxxxxzzDHHHHPcKzE3hOeYY4455phjjjnmuFdibgjPMcccc8wxxxxzzHGvxNwQnmOOOeaYY4455pjjXom5ITzHHHPMMcccc8wxx70Sc0N4jjnmmGOOOeaYY457JeaG8BxzzDHHHHPMMccc90rMDeF7KETkShF56lfhuPtE5HUicr2IrIvIP4vIBXf178wxxxxzzDHHHHPc0zE3hO99WAQuBh4N7AZeDbxNRBbv1l7NMcccc8wxxxxzfI0xN4S/DiAiLxg8t78rImsicrmIPH74/hoROSQiP3gqx1LVy1X1d1T1BlVNqvonQAk85Ks7ijnmmGOOOeaYY457FuaG8NcPLgA+BZwGvBb4K+AxwIOA7wf+4I54dUXkkZghfOld19U55vjaQUSeLyLvPMVtXyAi77+9bXPMMcftg4h8RkSefHf34+sdIvIyEfmpu7sftwQReaeIPP8uOM43iMj77oo+3RHMDeGvH1yhqq9U1QS8HjgLeImqtqr6TqDDjOJThogsA38O/Kqqrt/lPZ5jjrsQIvJEEfnAwG0/NkRJHqOqf6mqF97d/bs9EJGtHf+yiMx2/H2nXyx3F0TkQSKid3c/7u0Yckw6Edlz0vcfFxEVkbO/2n1Q1fNU9T135hjDInd7XsyGuXJi7txFXb1bICK/JiKv+grbnA58H/BnX5NO3U6o6oWq+pd3wXE+BsxE5Ol3QbduN+aG8NcPbtzxeQagqid/d8oeYREZAW8BPqSqv3GX9HCOOb5KGBZtbwV+H+O23wf4VaC9O/t1axCRcFvtqrq4/Q+4Gnj2ju++7MXylY53T8DXQx/vZbgCM6IAEJFHAOO7rzu3H8Mid3uePB24/qS5czN8vdyDt6OfLwTeoqrNV7M/9xD8JfCjd8cPzw3heyFEpALeDFzL3XTjzTHH7cSDAVT1dQO3faaq71TVT51MaRg8Xj8mIl8aOPV/KCJySwcdwo7vF5GVHd/9togcF5ErdnooROQMEfm7wRt9qYj8yI62F4vIG0XkL0RkA3jB8N0bROQ1IrI5hIq/8VQGO3iLXj8ovGwC3y8ijxORDw1jukFEXi4ixbB9GMb9o0PfjovIy3cc78EictHgTT8iIq89ab+fHsZ7REReKiJuaHci8p9F5KohF+FVw6LkhPdXRF4oIlcD7wQuGtq2vXaPOZXxzvFVwZ8D/8eOv38QeM3ODUTkmYOXeEMs3+TFO9rOHq7vvxdTGbpBRP7Djvbte/71w/39MRH5NzvaTygffaW5IBYa//jQ9tfDMX/tVAYpIteKyH8UkU8Dk+G7XxLLpdn+rW/fsf0Pi8h75eY5NxfuaH/R0PfNoe15O/a7SET+aJhHnxORb92x35ki8tbh+fAlEfmhHW0nz+cfA34B2PZ4/8utDO/pwHtPGu//LSIHReQ6EfkR2eHhF5FvF5FPDNfzahH55R37PVVErryFc/fk4fNjh2u4ISI3isjLhu/HIvJaETk6nK+PyBBpEHt2vmD4fI6I/NMw/iMi8udy8+fqtSLycyLy6eH8vU7MFtnGe4CnyfBM+1pibgjfyzDcZG/EPMg/qKr5bu7SHHOcCr4IJBF5tYg8XUR2fYXtn4Vx6M8Hvgf4tp2NYgbenw7tF+6gBl0AfAHYA/wW8AqRE0b0X2GLxzOA7wZ+XUSesuOwz8Hm1irm3QD49mG/VeDvgD+4HWP+TiwfYAWjQ0XgZ4e+PQH4t3z5QvYZmCLMozDjeVuC8b8AbwN2AWcCf3jSfs8BvmHY97u5yYD6YSwH4cnAA4f9f++kfb8FOBd45vB5p8f74tsx3jnuWnwIWBaRh4qIB54H/MVJ20ywa72KXb8fF5HvOGmbbwXOAS4E/pPcXNbzOcBfY1Ga1wJvvg1D5hbngoiUwN8CrxqO8zrs3r89eB5mNK4Of38RmyMr2L3/WhHZv2P7xwOfxnJufhd4xdCXZeB3gKep6tJwjE+dtN/nsTn4/wJvEpHt33w95oU/A/he4LdE5Ek79t05n1+BPV+2Pd6PvpVxPQJ7HjH071nAT2PX5MHAU07afgt4/nAeng387LDPqeD3gZep6jJGs3zj8P0LsUjCmdj5+gngljzUAvwacDrwMOABwC+ftM33AE8b2h4N/MB2g6peNRzjnFPs712GuSH8rxAi8osi8ve30vx4zEi4EFiTmzw33/y16+Ecc9w+qOoG8ERAgT8FDot5Z/ffyi4vVdU1Vb0a+CfgkTvaCuxluxujJEx3tF2lqn86cPFfDRwA9ovIWdhL8T+paqOqn8B4ezs9bh9U1TeralbV2fDd+1X17cPx/hz4N5w63q+qb9k+nqperKofVtWoqpcDfwI86aR9fkNV11X1SszDsj3uHjgbODD0/59v4XwdH15GL+emkPrzgd9W1StUdRP4ReDfyeAxHvArqjrdMeY57jnY9go/DfgccN3ORlV9j6p+erjHPoXNi5PvqV9V1Ymqfhp4JTvoFsC/qOobVbXHDMgaeOyt9OXW5sJjgQC8XFV7VX0T8JHbOc7fU9Vrt+9BVX3DoIyUVfW1wJXAzmjMZar6P3bM8zPlJj61Ag8XkXo4xmd37HcD8PtDP1+LGb5PF5H7A98E/F/D/PrYcK5+YMe+N5vPpziuFWBzx9/fA7xCVT+nqhOMHnYCqvpuVf3M8BufxBYeJ1/PW0MPnCMip6nqpqp+eMf3e4AHDdG4j6rql/GzVfWLqvqPqtqp6iFsgXHyb/9XVT2oqkcxqtsjT2rf5KbFzNcMc0P4HgpVPVtV3zV8fpWqPnFH26WqKidtf6aqvn/4/Ouqeoukc1V9r6qKqo53cq1U9W7L2JxjjlPB8PB/gaqeCTwc87z811vZ/OCOz1Nuzp9/EObJ+lVV7W5tvx0G8uLwW8cGY3AbV2Fc5W1ccwr9qOXU+YE3O56InCsibxvCohvAS7AX1G393va4fx5bAHx0CE2eLLe487euwsbL8P9VJ7WVwN5b6+cc9yj8OfDvgBdwEi0CQEQuGMLZh0VkHQvZn3xP3dq9cbO2Ibp47UntO3Frc+EM4DpV3ZlkeXvvqZPnygtE5JNDKH8Ni1jsHNfJfQFYHBbc3wf8JHBwoDo8eMe2157Uz+3zcQZwZDBOd7Z9pefDV8IasLTj7zNOOs7J436ciLxnx/X8Yb78et4aXoh5cr8w0B+eMXz/KuBdwBsGOsZLb+kZJiKni9FfrhueT6+6hd++recy2FjXTrG/dxnmhvAcc8zxdQdV/Tz2oH34Hdj9c9hD/+9F5FT1s68HdovIzpfSfbm5h+2uVks4+Xj/HbgE88wsA/8ZCyV+5QOZZ+uHVfUA9pL/k8GLtY2zdny+LzZehv/vd1JbBxzeceyd/ZwrRtyDMHj4r8AoM2+6hU1ei9EUzlLVFeCP+fJ76tbujZu1DVGCM09qPxXcANxnBwXp5N88FZy470TkAcB/A34cOE1VVzE6w6nOlb9X1adi0aBLsXm3jTNP2nz7fFwP7BGRhZPabuv5cCpz5VMM+REDbjipDyefp78C/oabruefcdO4J+xIlhyM2dNOdEb1C6r6PGAf8P8BfzN4xTtVfbGqPhSLyn0nFik6Gb+JJS8/Yng+vYBTPOdDf7afM1861X3uKswN4TnmmOMej8Eb+vMicubw91mY5+ZDd+R4qvo6LMz/LhF54Clsfw3wAeA3RKQWkfOBF/HlnMuvJpaAdWAiIg/ldiS6isj3iMi2d2oNewmnHZv8goisish9gZ/B+I5gofKfE0ucWsL4lq/TW88tOAToYIzMcc/Ai4CnnOSt3MYSFuloROSbMO/xyfjlIWHqPGwB+fodbY8Wke8ajKr/EzOEbu+c/CB2L/6UWPLmczCawR3FInZ/HwZELKn13FPZUUQOiMizRWSMLfgmwM57/YCIbPfzeRhv/h2qegXwUSxvoBLT538ht/18uBE4+6QFwMl4OzenF7wBeJGIPGTo48kc3J3X87EYd3obnweWROTbBh73r2BRou2x/4CI7Bnm9jp2DrOIPEVEHj4sdDYwqsQtzf8l7HytD8/n/3AL29wWngS8a6DZfE0xN4TnmGOOrwdsYolsHxaRCfayvQQL+d8hqOqrMXrBu+XUdFW/D+PZXo8l9/zKNn3pa4SfxzL/NzEv1etve/Ob4QLg4uHcvQn4STX+9DbeAnwC+Dg3JS6B8bFfD7wPuHz47Z+9tR8ZqCO/gV2nNTlFlYw5vnpQ1ctU9aO30vwTwEvElAz+M2ZonYz3Yp7Rf8T44juL1/xPLDHsOMaH/a7ba8gM9KTvwgz2NSw5863cQWnEgev8+xjP+AasauqHb3Onm+CB/zjsdxTLqfnJHe0fAM4DjgEvBp6rqseHtu/FEr0OYolmv6i3raP8eoxmdExEbo0T/Wrg2TKoK6jqWzBv90WY53Sb6799rn4cW6xv8/lPXM+hnz89HPO6YQw7qQrPAD437PvbwPcO1+YM7JmxAXwGo0m89hb6+ivYAmYdizL8zW2M/ZbwfCwi8TWH3DyqNcccc8wxx70FgyevB+6vlmA3xxyAyadhtIpCVeMttL8Yo+l8/1fhtz8M/LGqvvKuPvYdhYj8MPD9qvrkr/Hv/hZwtap+meKMmDb0x4DqNqI093iIyKOwJMQnfsWNvwq4w+LT3/2NT1dVISMEwHuPFh7nCkII+NKjmokxkVLCJaVwyoKA4ogoUaHPCS8JNJBzZuwSCyScc/RZEWpEQETonAfJ5JwpXaZLJTMS6hUfPUlAyKAecZE6C1kgoeScSRkayRQqoJ7ogJwoVAkhICkTveDUEXxB9oLPgCQgI4BHGTnPrOkpC1CBJB4ngS4FWmfPi4hSihKcsJBtnOPcoZrQXCC+wRFIBMQllJLshJEoeMWp0HWRXjNOBFUlq+KDIAopO1SVThMjVzERICtjJySN1IWjTT2BgNdE4QLZebosqCZyzmTncUB2Ah6CFrSaUVFGPUSXbWwoiQTqySh9jBQh4BByzuADgUAqHMF5nM8EcTgRsio5KkVR4EQgRbsWQyzCBYe6ArqIkCgEGys2zl4Ur5D6Dj9EkDRFRGwcnkSKGfUBVY8KdDiyBLwXPBmSEhG8At6BdyQBhyelRMzmwMjiEBGcc2QUyTbeVpWenr7LaAChIA1J874scd6uj1dHQhGNOHWo9gjgkuJiRDQTUs+fXvQ3p8ybmmOOOeb4145BZuwLwBHMM3g+8I67tVP3EKjqL+z8W0S+E5NCXAReCvzPr2cjGEBVP47xj+8W3GFDuMShiBlI4sk547PDqZJJOAkgQp8TWTNjHKCoKiSHd4JKAueRlAgkirKg7HtUHFmFIgnqxAxhhICQpaTNHTknXGopS0+RQUXpc0KdAoKLnlREfJ8QEaY+IymzmAu6QnBOqNpEcoo4xbtMhVBnYSo9mhOKMB4vk1JLbDOLvmMrV6SU8N6DeAItAqTgUHEUQBGVNkWqQshdZJZhRRyuAI0l64yoc4N3gagOVU9Qh4qQXGIxgvqS0ndkJ2bY5QwIoxamBUgBMSaqPpGYshgqSpcpNdGLUPSZAvDSUzrI9LRZKV2gJ4NkageSPb0mXOwpvBLEo0VBLiH0CRFPlzIi1s8mR/xoTNT/n723V5IlSbL0PlUz84jMe6u6e2YB2QUIiIABgwfYFbwBCFBgwILdVwCHJwGNR1gKBDgIQC8NgQzmr6e7qm5mhrupKgg1c/eIzFvTfbtnMESZSNXNiHA3t38/euyomnMxp7VGFLAwajhXhLAO2ti8EyTg7L1DDEANNKnUWlk96C5cdEHKiodTQxGEMKdEwDba24+5rqp0Chs3jMKiTngaDa0EJoFFoQJewDyopRDquDqEYprmjQ+ZlBbAAxGhYUQthDtlExzwUughIAXVA8tO8CwoSIBUth4EAh6oBJuBysZF/9ljhf+Sfkm/pF/Sv/T0X5Hb+J9ICc5/HxF/9f9vkf7Fpn9Pyhs6GRry3//85b+kfyx9szTif/y3/13Y1ukSIIUmiqpyE6G0Sh+AQsKRSJAhoSwarCJsONaDJoWITlGFLdnZi7Rk/KqxaLJ8PRSTShOnuPG6rYQOFjiUOhwwI4IuQQ1JgGqGh2ACiCfDq0IraQOYGVGVhhKsSAH1RtMGkgCs1kpfN+g3VheUjtWKeCBboV2cTSo321h7sNbCr99AlhXHuGhlo1NEqXJlCye2laUWBGj1wm11aoMXhGsVPruzUgAobJRwFqB7Ask3FX7qwkWCZwolEvBGBFaUYoJI1ltVqe68FiGi4BGYGUhHaKgmu2xKdpQKrg260iWNl+6R4LNvRBFKyGBlAxehSEVV0VrodkNQqrbs55Kg8WqS7LpU/FLZwsGglkIR5TrqljsJgdmKROBARNYjUwwGVgBDfUNL1h0piBTeehBFaaLghmgbLHQgVLoE4sHqRrWgl2S3J7ytUukYK4oFBJ3Wg66FNxxawwRKaQgFquaegSb4jQh6T/bbY4MuNFXMjP/1P/wvvzDCv6Rf0i/pl/RL+iX9C0jfzAj33tGlUnuACz8W5zuBt1aoFrQaqHcsNLfHN7gUpUul2sqiyk1Bt+CtBM0CC+PzVejbyrU2ulRMC5s5K0rXDdfCZzO0VTDPbXOc6oK1ype4UaIhvhEaSBPUGjLYwq04qoqFUzRYCngEqoHHldKB4lCUCOGtrzQzRII3h6qWwKgHpTTWGmxWuPBK62AInzZnVaW74KXxakpRJVwpRfhkybS6K7VW3m6GXYKIznOAdOGlXdlcaQJNBdmcG4ZGGhi/kspfXmHdnLUE5oEGRCjiQSisYYQIhLGK4uaIOC0aV4FtC7xsVK2EFsI7FgEsOB1DsW5cRFCEUi8sAeLBrXaQBaemkUPDI7gJLGUhgPDgqS0pkRAgQGpJAwnhSQprE8QDE/BIMFoBx5BSklElJSi1lpRn2JZyDwFoEI6HJBNbA3Xj0ioamqC3vUD0bJsQojhNKytBLY5rRQFjo4cjNFo4PWvNxVa8OlEaSzhSNe+t12GwLYgIPoyGUKF4pQeIbOAFX4TbZlT9xT/1l/RL+iX9kn5Jv6R/KembgTBFuVmnakVV+KyVz1W4dPidGC0KTSsWRhAUKUg3IAbIDJoLXoTvXFDvfFcWyvaG+YW3CLptrNXR1rAeXLdgizeaKCLJ7QG5bV+d1VauOCUME+GG4OZcY6WWQhcoQ8Yxw9upKqpl5BMUBxdh3TZqrcl0W6CXQmuKemdzJ1x5jRUtudX9ZoUQ5ZN2+rpR9IKRYNDCcQtw2LbO1hLcVSls24bUzmVVFoUeTikFt2ApQsVQDy41AXXHCA96VYp3PJxPvQKFzoaWQnehivGdKNuQVYhkORKCGkUDXxpCssNVFXVoNai2YgHdbLDFkqz6zXgT51obS6JfTII3c7wqCDR3rgJogn7VoIiwurE0wQncDYbG9jkKIcLburEKiCZoLppD04ccwqMQg6FXCeiOWRpjhQKW7GwpRs/WRem4GR6NKoI4SFGiKrambrqbo566cwlyfEqw4qgKWhpeLnTZkFKoLGgVLhEEiqsQA9yWCCKSve72hpYgQkf5wRdY7Z9WyvU//zf/LkRIPfZIj7s+QTL5aZBk0vFXH1KzvD9wAd2lOez3eMrKmVnHzEPA3JkRgeZzIoIiqb0GcIbhRj5nXl/Srs5rRt6PwYXO1Zk/nWt4Uq1gESiy5+HxePXRBvNZHjFyjv23Qo7L2ZYf7aM90vwfBdf9ua2AWV/94KJzH+4/R/ZHzN0w0qikCAQEkWM6AkRQUYLY4x75+KvE+wfOfprPLecxU5Qw53ybzHFw+i6kIGGnzzpa9aiDBCDHnPj5SFLv0/7c0Qrnux/bPCIQTd+DI+is3+Xjd5Xy8T6Y9wtTilnGrs//9H/8X3/S7s7/8G//21B5GL8P1xTZuzClWZA7nMAy+sdSTMaVwEQHoXHcc8VZRfb6VQleQ7gISMQ+P+ZzIuCN4bNCzss3hOGFs/trrEBj74TxzPEMjE65G7sfzoPTB42Mo7YPg7iP06Xct9WGUAguoyzryGy22by3EXvdZtbrw2w8X/NQpXdrEMCuyj39VsYddqrzNg9hjLHazfaJLHP2Qc647TTPdNShMK/PB759UJj6sC51jnm2idAi7trto3Xz3fyJ2P185vvkFvfr0x85XffFu4axSaWeenc2mY7vchc4x/ZcG9v4bXpuagidY7y9Ins8OEf2PN9Gff63//M/fFjibwbCXiu1O4bQxRPw+dhyjsKXTUA6zY0nCqUYqlBVufl4wdZCWeHanOKBxCsvoUTrXLUSRfhM5WbG4oHHNhjTN6SlNtirUjpceOVJCjcveHE+qbO4clVwqbzKjYUFd6OqgHuCYumIlFzwFFScJldcZwAAIABJREFUIsIiCr6Cp6OdrytrVC4KnzVQbtyiEP6G6wUXRT1YwlgU4IYFuEGPxk2DGkYFXnvQRNnMWMrKNSqKE97RckGLIyWlIWVzLISXgM2DDpRaUz9bGs+XAqo8uXLrQld40mBzxTUIc1QrROeab0WqdKooV4FVwEIRv/GvivCTNJgvfREMxcPZIgiMT1ERdTQC6ze0KrgSN2cpjvhGKYXeFeWKL694LClTAVaTHXhHUQy42YqN6d5QWCqhgXogtWFmLCKoCdgGErgolMqKINIoxdmG5KV6sFFxCq6ObUG0ZchejK0bRQL8CxqOu4IZTZRC4LJRKGxaEA2WkmDb2GhSEUnHRROoKmwDIKkLpoJ7R0rQzHMREqGEc0MoUr51yv1hSRMY+fnl8zj1h8wlZS3jmnH9lJ/4BFFfgW65AMoOmBhz+m5hPYFgVb0r0+mqcU1+6icU8A5YRrwHSnL8E0dRHp7A/ux0PL3P4w4kn4BVQov8ou+f5/OO387P+Vr6Q94XP/dSOcP1vfh7e8vxf53tEPsuhaBpdNwhwBPo/PC7YzykjCqlUOxyqHtj60jjhTxAcJDgJstn+8t170s5JFZ3z5+5nfr8o2v2tojsH87XPIADEdlBToxC6d7fOq45PXxcP78rLsSYv6L+zsD8lvQsCY76qX/rw0haQyiSc+0qczxmmn3QCN7i62NQJUHkIqd6SzBDhsA9CC4iPH2Q2wTBs+7Pp/bq7/qOdwP/Mub3Fkcedc594EKwnYBPiKAP7TwfU8c4j1H/jrCcEGwROQwCDoNmpuVn5u8fAvDkPA8fmup8+zL+rXvfjfE8+mTeW0SoEYjC6wDxj0b6uzI8fFaBPm4UGP4uGSx4O2VwNg7hAMELx3io471WZM7DHG8i7GPj7aEE5y7/yOiZ489nfeJ0zbhwG7C0jT1ZI+cJQJ9gec5DDjljeukIOjJ9CaXu8zxOtMb79M1A+FlyYd3ksKyqFdwNryC+0ppCFF4EvtOCk9ECxFoyL6XTnpW3W8Wkcm2C9DdULvwuDAvh00UoUQhe6f5C4UoPRVx5FXjaFCmChlG0oJKRFm524ZmVVTtLvfIXUngx6AIQ1LJRxwx0M5oU1JRbBe0reCHYdnbLe3BpC9U7jtLdWSQZ4m6GqdGKZlSM0WkSOZFfvPPsG4sWtgJva+WmhreN1Srdgks4y6VBJNOwxMYWwqUWbETUuETlwgphWBTozmrKosrvw+jhfMeSIIyVKk5bGm7JYLwOW6mNyBt1CVooUcGpxGJ83z0BvJJ9a85KWlsQdE82tWpQ3HAXPgnczGl+xSSBYBWhq3JdVxiRd6IOnTBw0ycAjJJRI0QJHCn5GlBJh0YpOZl9dVZLkG0G7sGTgnQHMXoIUpUXM5ZoLPZC1wVxp1O4SBo01MLTJqAb1n1o2JWiBZEgRFK7LHW8OAWRlMa0UIROiaAGSKm8iiGSS52o4W6IDGfQANkygsZPteLq+HY+w+DPn/aX8z7n5QOqcbSFysEKnnioINlxkURVO9umAwgGhJEDfH8ZHL8xrpvgbQKdO/w5V8L4AKjHfFHE3Wd0VEV4t6QFcqCaU0pwJMhg5kP8/mY/AWcd67LLDiqz/cbr9mQYxM5NfQw9JpA++D3ff+HdX7OsI0cR5B2IG7mMan5sUpwu5L4PJgjYnxFp6KjK3rwesZdW5BgPpVQ8nAhJhgjfQfhk+hMg+9Hn4eyMtc5xOYDvblKMtpUcW8AduJxMlERG/hHYGaosoxB+5gtPhtIE7h8YTyFOicPge+wEnRKn0J15reFYCUCPHYM/mg57n2K0xO7jIoLEfZ9dsqFxOZhrmRKrCJ5wVoRFhM5YL0MYrhk0hTcT2qRJYXfsHk0FJPs2P+dcPgOnQERZIkvcdtAC9cF4mp87yndjrEzwNwH8VY6/Z2rkdU2E26hnJe7ma5ymnEgCt4hkUtupGI3gNQ4Q6oOQ4G68nJ89APgYhxN8n89ffwRLm2SZA8EmwBttNsdziWTvIxg7lUeawHMuNj4rBXtdBiWBk9GQVA7D4c7OmMYiIzBSJElZkJ1JlsF422CbLd7vHHZJYyLmWidp4MSYW3UAzUkJnF2/Zxt2Ihlq0bFjMNoL8NkGY/xNtrwNQBsBy9whkmyTSSLB+90yEbjgg/0XrmQ7G8FnyXex+Vh9f2a6/glRIypRCle3BJ6hFEkv/1sYUSvJLOb2+g1FpOQkb4JGx115xag0RJxb76xSiREloLBxscItDKQBT7yFQIEnES6ibDWjJ0j7NLa+jOqK+kYvIFzpJgRGLcJ1DIqoz0QEtQghgUjQrbOI00uMSdpSZqEbhUqY4SJ070gUVt9oGmxbYKWyhvBFjYsGdWzDV0l5gDZBTREzlmJUgd6vEAVbBBWjibMoCAXMuJpi6qlrxSlERuOgj22ctB7fMESFSyihG+GKSGMTIJxeDNPKdwhvHni9ILZSApZSWMWTGd0qJs6CUudi3IK2Cl0Lmxut9Hx5REFrIaJgYSxFUYWLGhGaLAY3elQ2K7mIr6kNl/LGd9YQD7ZmvETBIpdKtxu1XYiefeDbhnignrEdYuuUCqWAuSDRU1+sgsRK24KtghehWm4DX0vm0xDUDNF0ZKtE1kXTqbBoJ7xQxdjESOWwA4WQhhYdOupkn0PHb5a7FQQ0UTpCF2VV4dJkPMupVtjqP49GeGfR7OdZqwkIdvBJAtYzYxBMMDvyJpnw7o7KkE3IBED3W/V5/fuVWwbASeb5ePnDiWd9KPofSsDJ6XH7d/Lw3Qn8PqZZAhlo75H5zWt8B/iiCegeMa+TEUNGZne/HRD1/rkupBPu7L+TUX3+9+4++fpv79IAh6pysOiRL9iQ9yBhbyZRQgZQnW05wfC40sKpQ2bm03jy9Lf4uCgHK7gD2oHG7ttmIImHBvvH2OL9upH3BIYuSk+cvjNGjHEsO8Y9xrvLAWb2Z8bXdji+Lckf0IcXJpFzgAt54Lkicpu8iuzg+g1hqcJmwbOC+WnMCLSYeeYfryPHxsEOTja9DeZ9L+/p2bNMswofMco/V/czwCv7hL0HXNsHYGaWO12/P56vDQZTmGBvl0AcjxiyknvAPrNaeJ/aePYBC4/2mHTHR7bSBF3vAjN/kBZJOULZDU/oohQFtXdm3JmX4LTy7s9NUy72ieujgHW02vrBPHvMGxJEz/LBvcGwjZCr+lC6J+Jgwz/Ic6ZJTEwGu0hNyVwEuoPkMY7H32d2+YnAxofbeDWF1K/ZQKP+35h+K4ZI5ZMERNCiU7ywtI7FM807XhOxeJFkWWplKYKVZPjeuqM96KwQwqU2nryzNMNsZdGCvvyObXlilSvfsaZEIGArcHEHCUyMH3tu4YUWnihcWwdzagEPBy2oK1ZGawx2cXPnoo5KG7NNKS5AMoO9OO4ZyitQQgX3lAtUKisrWipXdSqCuIF7hikbUQlEM86xRPBUoHun1op5AB00QZ6IJHD2QGqwbYKUlCIUh+pBGUNO6Ig2ZLAUbYSNM3MuNeMQ/9Qd05StQHALxZrSFVoIzwKvBB5ChHIJzcgSXAi50SlY30Bzy6a0IeEwp6uySepinUpIxobeZjxnM7SQeuoirFxwlJ8MalwgjFaEy5uxaC4bXRWXK5sbohnXOYauyt1xghId+mBqPahiGBcWFy61gwSX3lm0sMobgYNfxku+IlJBb1xUoYCGsnnqnd2hyYbVwuICpeYLQ2CVNEh8hE8rnrITKwn4F1HcOyZLbpuacA1h04BFiMhQcT9rlv4ZkpJSpQwDl3GRP5CA5svcT78d1MLAHGN7UnJb/Y5+iLGIixIOMhFg8JjZrsdMRfX90uhywME4PX+yMS6yM6DZDfJu4b+rU+RW6Edb9vvL8VEWEUex92uGlvVgnuRjDSwTMKXlMJnaGC8hvW+Amdlx/wQVo8805pb18Qw9seHZKO9f818DT6d3w93f7zR+4uMFuT/iHdA5ij/43JiEef6dQ6WcFH+5o+Mcb6vzzoBIOiyjBRfbDTKZchpk70fnALw2x0LMsXsPgM9bvToZ6EFxHjKgxOYRcerQsc6IED4kNOqIy+hXPboh0kD5+dH4h6UqCcC2bCG6fMA8ZjOziZ5kE/vgxAZT38iOm2Bj1qyRIHPR8Zvea23baQB1JNdB0sB7PtWxEUcD7El5HX89DTnF67jC9dAHt31knBhA0dxZm7pPOYyg5VS/c2qnvHY51fAZWiA1p5L1+MSM/j/mSE5Y2mA6h527t5YJ9IhdSpBtczw7InhF+KTQPU5saL77z41ex78Woz8/BPBH2sacO4PkCFil7uBvLCsZG/80YU88xCnpMZ9FD5nKyaZUOc9vQRQW92Rt5Vgzsq8SiHaCzwS3OR/HvLlIihT2/hkZX0ggL2Otn1rmOUZnGywor3JI7VSEy/AvWER4gZRqzvk68wxli6zLIs5LzFLPsZ6zVP+RMMvfDITNGiKVrcHFK5dNWdSo4rz4xlMxXs2o7YqLoDVAnEUgQgeL0vGahxqEO35zbhpEAVT5sgaiF17MueobP1IR1tRxrsatLvRw+ha4gntKJL404ak2norTe09dZoCx0mI6xkFYp4mkc4FLbkuJgBomxlNirlxASyKlHk7FcTydP/qFVlbwjpAh5F4DeneaytjKN2xowZRkDTHnU8mOnoBDBvhumoxF0Tz4ohShKLgFVYa+UxLURRiiycYvKlCU6hkbOVQokUOtSB5OEVtH7MazwkKhe2dZnnlzZ5VAQ3njjfCGqlOasgDeJeMNlwJj0hTysItbNxrJmnSSoZVauQ2Zx77USMVVcQwvJe22qjTbaK2BK1JekL6A3tL4GBKDopqxo4e2VUYnenum+hutbhS5ItFpBeh9bNEJqraHcPNwiCtr9R1od3ekFQqFIg5FWGrhTWZ4vs4nC950TL5JFJMLbFcgHNy5+hduolAab+J4F7QEiFFJffQ/ZQpSSjTRUTpQgekBKGPKGARcHJ2s8EgyXgpyWiaP/+d9j8BQ9v/d/5agJ8ET8f6+edsZsPlpVZ9lnums83tUQgT3W31nlqATlFEjO4FWGWXxiN0pZbbBGXK+Y13HZ48DWJ5f9RJDnxvpIBgngD7fYYf8IKjog4ZxtPmZljz9Mkb2KMN0ZMx81yFXSFAeD22Vd0/2GXL7dv6yO0Fy3y/nJONZd3bPKY+7a1V3wBJDShMxxp+m0aOhuATqpHE1Hv5oNsZ5lOiZJH5f0vN9IhlXXOcpPhK4p2lW1GfhEMnyyK6TV6qkJCQiKJpSiey/OO1ifHsqImykFOZGvvQ3cnt6akTr/phg4Z59y/oN4MpxXbZB1qPiJ4ei/M3GXHBkZyYrkb8XhWHsHKzleV7da7qfTsz46zB45tVPpz57PeXRh3tuSk9mv9yzs7dhajyTAHSOi2U4A04wJqQ8cyHeyQ+m6T31ubdQLgNYbqH7uvXKAP1DDlY52Oe5S9DHdn4QlAcPxwncZ7v4qe0LyRBvY7dEfYLB4AXhP8HZxpg7zsWQAegzbKrznrGv47tXlV3rvTPnp7KJsGu/5+d3Crrx93m+Tke2PlqxSdZqJZ/3ipyYchn7p8eDJwfQZKyRMR0hD0A809lx8YLRPFiHw/w2jImIkzPiHL9y7PRECJeMN7Ubu7tDt3j6FX0lfTsQJghfse48qyCLQDTeunBtnRrBv66wxSs6DrHw0oj+ggTUEFyDTYx1VCQj+SZLWxG85nbVEsJb32iSp9H9hkANXun8aCsqV6oF12nFmPFlBauFZ83ICxk5QdOhSUDDqDU9yldRVgXtGctXzSleEV551gIu3HCU9E4NAq1BSLCyUYf1DJGgUBqiTo/h8KXJKjgbZkLVDCE2tbiyW8V5EppooYZRStrtMZ2baqG47HpWgKhlgCwdGh5lWTrFICzQkg4qpsqTrHyuDXfhUg1HKX5htY0rBUH5/qK8mEAol7bQ374koF4qL93ZJOilJqgjD0vxkovpD2EElWKdoo564VKEN89JZCQrweAGCxnBw8rCuhmXMqMG+HDuyVFsZgkmRPOQDO2A0DSobJRWwQMrnbBkorQ50YMaZRgYJcObCWx9w0b8YWOBOhzHfKPVMnrBUbuBFroBsrJ4ynte1oxtbWQsYq8VWY0ktpOdr2ueIvgmCiYjJNzJov0nSvdYaqzgA9Sm9vFh8Yv3W/SZj+yLzjtwA++0dDt4jQMZ6oGARj6Hd/rdM8/A+vG388VxgDaX94v4u3SSaZxZ3ZkmK6YxAPQHYO4eiB1fzO+LHLzg/C5DlT8AM/mone/b9/73aTx+VJLHuh8NZAygML45HNWOPoDYQd/ds5h9NjSBp5xtnPD4tTI8Vu78njv6PKNW8EGd5nb7u7Em57x+bu48tM9p/diB9/7bYLVlmiS861eASjqp2bCWRHIHDHTc+6fP5ZmDynwPHP8VST30O7DytXyGfEF8jvnxY6QTGhzG2rJ/PpyL5m+XeZscwK6fQMR8FnAnT3msU0CufyQgPo+nj9LC4UegcQ+4IfXsbYDi83y6i6jysOPSHkZUPQHYeWnbS3ukM1s6x+XM66MpOQ2MbXw5jWMZ7Zts6Vwr87dLBM8EPyA8zec+/PvptH7Nap6vmf89E3eOa88Sd4bHub4fpY/ma+HeYU/gru0+2okaYfTv8vnQmP5gzFyJ8Y4o+7tnf9bIZ56tsEebOGXeJcnMMkiIPPU3263Fz+/ffDMQVk2QalH4OzNKGLU8QWn8przRPFCcT8WJMNqmrOUG4mxdCDO+k+DVU4rgKiyloLYh0igIF4VNK9u2oeb0dcWL8NdVEHe+E+N7B5ONTdM2aRJEEa7d8bjwQ3V+FSuiQokVlQyLRVQiOloKn8IpfWMtoCW3FEq90fqFmyhagtpvWElGbw1DGZIJz0XxqgtmxnUJxDta8sQ0LE/Zqwil5pRbBiAQEhxeRIkwVJRegvBOuO9hVhQhuiUzXCDt9LKz13FaNIIEzd9Vpxh7qLrNlMqVtQilC1KDJ2DxjWurWA+KO3arLJcbRFDo1CX4wUseAtIKJQLRBubDUnQU5c2cSgHZuLTChiKRp9g9e8ckI1psPqdxYSmVjQ185VIzckdsliHXyPrkoRXpLWokkGvSQL9w0Ss1Nl7sSqnCtQu9CY1OCSi18aJpXrVQVjead4pLMvsaUJ0Yxz6rKs2EH6vRXFjsJ5QLb0UQnim88bYZzRP4/tCCJQoXq3hVopWcyJESHTfnuqUh8lLzgJHys6+CPz0dwCESrEZa305a0UoegFKHY4Q+LFpwWN7x8Nu55OX0UpgMq+yM37z4cbH7St13ADrC9YyvhxkIkc4oZYD6iDgkA+fVOO4LmgbkCUyMsmVZJ6P4lRWdY/G9r/3Xl9NH0Dyhpz58/7VchIcXjyTLcU5xbsW9f+4B5QnenV5K+TafW5xxAlhnTfTMXffS5296KkecSpFlft9KH7104zRmZvLTtT0G0zauyugnx7V7fwxgIgOgnp/nwZBN3D9NmBE0BnAa6+XcjM9Dn46xx3iWB4imzCgGbISA+Dq79Mek6fy3kA5oG7KzZwytr4+5WmPM3aPa6SQUR1g4plE3apDdLneg5CmCFWUVYYkUt+/PPCWBfdfiMcLC3j4qO8i+kXKqRjKqHXanLBt5bKfen8BwdrlFyvbqcFAXMrSYjXsm6/04tBZJoL+ewOZKbt/1d1cfTmtz3sgguDYkjQBJYicjOrwfs+f6zygLOhzD5nVTvrGhiCQg/zKeeznt1oUInzm70g7pyBgLEhmhiYjduJ0s+0buHlSO6A0H2NY9tN40HiZrXE4Vmoz/R/N1nXKl2T/oeP/mu1/hCAU33K49dqHcvsZONh45pCqH37akw+Ws+ZRciNAidz8KAw+McSUi6RAnkifGjtZToEtBh4xRBLbIJ24/wwZn+b8x/cpuvBLUvhHSqGI8xyvXQjo2Sb68Vi90E1w2ZAUvhSJCUUOi81mFcANtuN94lkKPt9y2MOUmQBiVoGs2RGyCauMnc66h/EqDGxvhuVRdTNEaVPuB6hWX6fzVaJET04FO6oZ7gKjxjPK2Bq4VYsFUeNINSidUeV1voMGTKOEVwzHJ0EJfgFpq6tlKWiACSBUu0ekFMnyuc21O1ca65Yl6KsMRTsfWlARSlY6PmMnBNhZhramdVQ2aWi584RDKj6KoNIRXPl0bv+kbYcMbsxlbqdzE0VpoFb6vCTw7xm2p+ApWglsSrrzJQuiCWcejsGaoABqOYRQVrkWRDq8lHep6FxiObxYdBLrmqXM1xsElAlo66h2VfMmoQi956lvz9DzNKZzTX0K4hiLLG1vAop/pFUJahlZDsCZcTLhd4Ndb4bWlxs+tETSWKsk400HBaEQIL1rZ3GkBP4VTTLjJQtPrmPiF367OM8kuCY5ehKfSqA5aKj8qiBsvW6dLlj9EKNeFqxuLd3p/o/wJobv/0DQZDJVjG06FfR9//+7E3u6wIU6L4lytHrax7p6lZ8YgF5/dP++PxPwHCMvkA+iIHC/3qaudeZ/B9R4Z4QT+prfzLM5JEpqyjYAjHvlsg3kf+/b3vgPzYbmPPPPar9dx3v9BgAsmJnfJ2L4fcRjnyAGP7XvoZs+A9Kj7HBgfsvIffgF3yPAxAMbczboD6/dlPgyC99lPMDvc9jKCxc8UJT74fjcsHkGc3DOFcETcmBpkGdaRyKjSLkA+NJnnOM33lf8jB/dXUspZZAewE7jNnQaBPWSVnkb7DvjG2ncu0eP4O3+W/Zn5X9URbot77fgfmhx2j/4ncm13Us7hcTTpzPsMR3Y97GneRORWt4xrd6fAOJz3Zmzj1zjqPdtqVuEy3hvbB96wu558fJ5Sk4+g0tPI8JFdZeQgpGHhyL1BOjJ/ltgdDi8Pc+OxX87/zr9nX8EBIh/veSjS+/xP7St8Ze2Zl57XB/n4Nx2/FU4a6g/yPOc10zm8HRxaYRV4lPFeR68eodayAiqwRErAQg7grR/sbn51rXtI3/xWvvorxTPMyTOGlo2QTqPQbMMksw4VjI7rM8TKYoVWEgC1WPhkb0hzXmNj60aUwsWFrQyvwa0hbHQ1ogXF69iWMm7Dy/8mlltgKngsBIXNjKqNbpGMdK2oCV7TWqg4TQ3fAqpTUJRC1aC1DIhdImdyhCZIL8q2bZjn8b+qjT2UuQeo0kkLVYqxSBBeUQ+utVKKUMXz0A2EZbnmwQ3uXEuCsmQeEqg9Rew9tIRkm/Zk+lIGEQgLF3mFaiwIETe2/sw/vH7hv/y8oPGW4ebCMFd+L5UXgjUKf78JKkueduYBi7B2QYtSvHKtxhLOC5XXnlamR6EX40LmHSa4QqWwWPCigYxA2R6aFpnmOXVFhNogXOmexzcL6bxgSUOmw2EJ0IqZcBkB2V2UJ4I1Gq0oLg1nOMsVBVHebOMHFvq28LeeIVW2Dq/qfD/jDpogZeEqPUOAbcYbii6N1UEiqBVezfikC6sZvRuvKEbleyq9bERZcIQfxWjd2DT110jKQywU8+DWV4gNVeU5+h5m558qxUTApC5+EvASORchf9/XHD+ckx7TUdSvLyMex6+TQTtfrnLcHxLDue4r6X4f/BTj9aTpfVhZ5+OC44Vxrz8+AMW5JjFYQCR28O+hO6stMySCHNdn+LABiD9AbAcrKQM43/G3d3U69u7vQfZ8wSDx0btl10z7kDbsRdzL+XFyOB2c8P6F6rNI52dxGBoxEbqAnCwVH+h9ahtn2KhdQ3/K72N5xZRhvB8Xum8HH72c/XZvlMRp3KTkYX7M3HfcfhpPE0JNOUJh9Lvo2OM68tmjg0g5DPOvtvQflySynW2sT1OWIHNQj7Iu57kgB8Cb5Y6TRfpz3NcMqyWS7KeH3AFgHdvLAC2c9WQ4ZFlm2ycr106W0mQsHRng0nf95kxtGKbGoRk+O6RdSX1tamJPMXdJwCoC3Z2iGcpLJA36yUTvMeAYuz+jvCUOQmBqRfcYw5FEzBrcbcnnNeOPnT1P4wMYhzkc8ZPP86fvcW4DiwSAto+jafoJ/iBj2Z8+lodsZ3l3zTL+nWv0Ikf7ZFnyYJWMrzzCvImiETSFGULhcDKd9TzqXriXV7Rh5KxDWy1yjmed0kyJZGgnCI5gj8RRCTY0D3fh+K6SxlCT4BZHhI7JcusAw4znDQU7hTSaFpKN3x7eDchxCEnnAWU/pG8Gwn/rNY/g1eDFnV+5cJHK77zzl6VxGatOtc6lah5ZK46akRHQgsZGHR6fn7tTLxfEb4gWbt5ZQ/he33iJZG0jgpV135ZqAeFCiGGSrF6QobFK7ay3mieRlRE3t8CiNaUCYSAxdLjZSC651ZNC/iz/FnkvoXTrhBQixgln1ullISIBWe5EC50ME6fAdxJclgqeodVCC28sWBSajJPeRBDNjesagtY8aviyVFbvqCxZ/rpRbcEEbhG8LLCsTmkN07TISyk81Y3fr7/hf39Zqbrw/eL85xp83zq/WoWlppX6oxR6POEI1+vKd6vwVJzXXvgdGY/XJRD5wl8+Vy4u/I3B76yxiSUzPhxK0LkGFRDJrVSTIQVITXBXUAuKLqhmDN8okpsimsG2l1IQ64SvRBRWz5P2QnpqvpaCuWefotzkE28jvFzXRukGlkz7sGO4UuD2SqlAGG7Oiwtbz3JaSX1v2HCgEXBT/ko6IkLbGlfNg1hethvWgle75VYglY1OdaVphgx8647bRveM5EgYTTu+XHm7bfxzpLNTmsSJSX1IUcfWqt+DMpAzJr2XTnBmKx9W0NM9mj6h+5c7I8HpII7x2QiIcdjC/qAJbk7leHjqXZ33N8V4WUs+5xx3d96vI2rABK1ZtnzdGwn2zjZL4sfDwewfYxiOKnxwCEgW7nSN3H21S0I+rNt4uccE7AeInY5u87q878hj6vceSxOjnDqjZXAvd4Bsk5n3fHkXuXdczSgMD5mfUJk+BPOcL1Pi/P19e8lpyHOKAAAgAElEQVQApxnX91TmB4Pr59Snj06fcGJ5BQjZNe4l4hQmTvZn7Mz6wxj7c6VztiL3Tpjn9CwJ+iZAOdsd5xKdb10kWOP99XcPJtnhzWJnHmckoyIHawsJPtZg3zXZ5vt4QOEzEH+M5uFyRDOYo20Hq+Pe7VS+fZt9OEdaHIzxZCdLwDKA7Pk+F3ZHvv714XGXZv6P6Rw+bUoOphPjlISc08xDRj/ubL4k+BXJ9/VtOsnt9ZygOjL/DyJCBDk++ml8PkpXFmJnui+jvDcZuwvEPl8n4z6vvVsv4r4vpxRivLEhi3eEumOMF4ZG91Qk8+AcOfTnjLWPmOwzkewk+IV0OAzer+8zjz9UwPTNQPh7dVZ3frIMi2QFwPk1IGFoq4QrS02N8FKM77hhmiHILsBnVuhG00JRz2N4S+o1XZwf44mtVi5mbN0xqan15ZUIxbyxlZVNCz02nErElqdAWTrjqfpwcxNuLkSJDA4vyiUEk+Qkq4BocBnxJLdw3nrHzKkhPEfs2roh/c2GthtRlmSNUSqacStLYQ3ny9JYcZoEVTK80A8F/iLS8UwDXJXfhlBE+Q1G6cHnUhA3lEJoRhF+EojFMApPJcOjfVrGwlAWVg1u7vzOL/xNc55k4ZMIP7jwW9/4vsO/qpWlBG9SeIrCSwgvEtx84fvSMRQV5UkLJp5HKpZP/M0a4IE2+KTONQo3D9ql8uYd64WoyueALpFxOoug7og7Th62ApUbTpQMOFPCkAi0B62CvN1QCdSCLltGNAijlgIqrJ6weuuBl8qPllrtSk+P0fULtVyJrvSSeV9uRu8deqd0oaqy4kj9jMWNBUW2cXpeBLceWBWuUVAM8U5fOz/hXAGiUdhYSCNLNJBoECvSHbWMY/gUsGKsDl4r9rry5ef2zf8MKdfVA+3kFvDhCR8e94udZVSDHQLtoCwGKxYHOzrSWKrvHIb27fHxXUZKOB1NfPw0GLnxHKYuVh7yZ49uISdAfLfV/5DOp5XBXAQT9DyqPPaTkuZCGpOBTKaBwYRNffHO4jywkXfPn+U/axfkfpEWeX/vXV0GE/Q+rNzpkp1RmluDB5A4sjleqDPtOPSEAWcqMnPMNB0J50VlMKRKsj8wgZDs6G1GB8lwZ4clNgP5i+6dP8p2MLfMKBAhZ7I823+vwnCoG1ukU/5wriMc8T5Oo2Z8mprHGOx+AoqQkwqIgzme/3qcmLyio4/+PED4Maz41PBPIGUBnPSNN0mj5IgkcRS8fAVAd466zmgGOyCOBMoWGZe9jt2iZPLKLjGwfd7JLn2oBAvHFs8Mq7enE0u9H6E+600yuScslxrTUZfpfLbLAeKkIR321jwfxFR345W437qf+uJHWQGcANwEk3OCjjSfdxgNJxaeHJseg8kc6+s8p+CshRbRlEfEcGIbjf8metrduJ+vQYa8mzu/5zwLCfIvHOtRl7lTktdsInweYDj00HmvHEBYRFJXHbGf+JanyQbumecz51jSGTZOSXb4ieCHmHOSvfw25lyEo5pzuqog4kPseHZ0HGNrGDFX3ZeT46jksU53kTxcJh8E5DiaIH7GNL6F7nlcxmb3407UY/pmIHwJuNSFYh224NkCaelMVsrCjz1wc36UhpbCzYQoVz7plRcXvo83LrHSpPGdFV63zo8C/nblv356pbYLF2MKa9Fr4ctbx3tu95bSEN2oRVAjQ46RC1wBNnFCCj0cSstFTBo9VoIMg2O9UyRopbK6UYvx1JVXndukAqGYOa/SeQLMnS1jCvCshW4GKJt4Oj3ocD5AeEYpPUHcOrYOqnWeA54QlpKg++YlHfFW4+8X4ze60srCZwkupeAEtwpFnEZu4UPnuTZCQUvjZs6lC/Vy5a/8yrql09rfj8nUQ/giV1678UmcpoWmzvfkyWc3D5YCtw69JNCtwyh4teATxmsJ3BtX3tjURkS5lU/R2BqYbbxIQZfGv+nKl5vzY9hw7YOujVUimXwydFkrheo9nT62jUbKKYpkuDQPMgSdb4TXDLdXCjeDiI0LgZtQLCh949rGee0iNCMnpL2htxtNweWaDH5NZ7YiUAv0tecJWt3TY7jDFhtFPJ03wnkypbfgtq1IUaRUWr9RtNL7DW2Kb5ZHjmseQNA8x4yZYr7ehQb7J0misJ8CNtiW/az7HAvHIQ1ygK4Dt8yM9ni4+3cyX3bjflHmyXQiGTN2AuLd2UoOKPJhcceL+6ONq32x3KNQ5JcR9/mdVZu7c5ecXsoyY7IcmT4yB3s+cmjOdqeqPe8DHJ+d+jj9fk467vzoKN79RT4rNPO4x4qnvCTDpJ0ZcwROzilnwvWOwY/sAcWHxj3uron9mvwkoz/uMNoHL5LMNUmF2fLBPHPvAE75x7EjcbBnh1G296nej4UiurPTh/Z73PPBWNiB8l6Cc+FPhtvs1wftd+5IDHAxQbieDKGxJfAtetqP0hw3HgmKj7P5EqgluJ1jOr+vckgG9rjCpzk6k0lJkOZCG4cu2c7m5995ZPRYy9G9D6fRsu5b/Jm2EJ7EdoezLcr9OJGDZZynztlJLCoxnDXHNRO8GBOAp9Ea0zdgr9Zh1k4Hvr3vZczLEds3t++Fi8SIPZtzTeO0VpzA29EXef1HZL8EFMmoVrrrfmRfjx5TF8UCnjV28F8k26+PjnyK91bLGvn8RUZIt7AjtNh5bY7Zfgl2nwhehL1tiGB7KNgcw2vofnrcJwlWOebphvAJZxXJk0TjOHBlaE2ABOl5CiB3DVD0kJPYIP5muetouY08DQ7AtUAE7XSYz26fnNpWJYH8u2O8OUCse8pAngReJcPpTYc/1Y/eMEf6do1wrPRt45NU1trZPLiYIlUpulLtwq0obvD320LBaAJ/645p8PchBH9JkcB7TgzXCmXl/375nrdifO+dX8VP/HqBX4vzuQDFWanoZBc34SY2OsvGpKhcpKZ3ZVU03qilUkrHKLzGsFZqo0pyQq8CZRNaFcTTWWU1YyPoJbhuugfr75bnsLwgSFt4jaCJoKqY5JHHALWmA1YXYYnOzVO68AnnJ4xW4Fqc5j2d+lpgLnj7jr/eVn5y42nJaAZX69xKsCh8p6mREr3RZeE1HGnPALwVAV75L64LMuQaqsqXaLwi/KQNawtNAvGNN4a1Xhf+owaf7Yaq0suGIVQlj+Ysjjl5WlsUNgO3mt6jsiLm1Fr47HB73fjSnEVf+E8FllL5smUs3+6Fsgg1NmzEMd4AD2eT7JcIx3ShaWHRjkhQSqV7sInzsq4sXNjcxjgYnqXFUSs0XimlsHpQN+en24oV4aZKEyOiUtxRfkIQumVcYyxYSkDUjEdtjmohzFE1QuESTtULTvC2GlIgbEWk0G+dN5SXEG49AT0Il9aQbeOm9YG9+vMniQwzp2NRh0NTeQbEjH7fGc65JTfy+ejUrrTaz69qduCboELvAM9xiMVO5GR7h+/HsDqpd901puc3uQzwc2Zh42BDZx2mzvN4MR1Ab74sz3+f0zkCwru2FNnZ8oNnPBz3Zvl2vP1hLu/zPLxIPh4Ld6HW5pYyMz5L3D9P7j8/5niEJYuTM93s19kGUHQA7QGU72s8wPKo77t6ysHafgT5Z7/PdOw+sKPb88mC5+QfMK/nb/Y4Fwfevrd4zvftkhnezcN59TRygD2kWQi5o2K+76w8hsf71jSHQdX7sZq/jXKcxvqsw7I34TFH38kQ4tDtzjRx10rG8tfT+N8NwAE7RZLt3OxYH54lgd00ps5RGZJ1nvr4EVFhSLJ2hnc3LKcRfr8L4WM+J6B6PzcXgf4QMeZk7+5hN893lQGUyzTWHgyqPX0EgEeeP8dfTOlDXj9ALqkmP4SXh1xjhkS7yf2hGvN5kyUX5s5M9sw8YOJGMqwW07/hmLMzNVJa8ZGT37Kv0e8r3EYbncMkT+3tWX7xQKbv6aODTI8oF2NH6Xz/mKsfNe/sp/OJdPvz9/IejHDuruSYrpJryi5R+UdW528Gwj8RfEKpGOGGUCE2IhQ15f+xJ17sxvctuNhKKzU1nNaweEvrYJxHb+oZ+9AyysBLSQn270P5vV/4f9fcgtYw/g3Kf1Y3SjWedeOZC1dTXkNpIohmqK8t4EIeAxtF6eGsvbKNI59LKYgUDKf5QpEVr7ndvwn02MA9Q4W8VV6ulWt3mgsRnU3hVYWggWQIN1x5o3IpjhXDQvkkhd+a8dyGtm4wSqYX/mGDoHMR4ZMIJTrXUniJlc9a6C34wStXg1o7JZzNv1D1iejOp8uFT3rjqk/83frCWhYkFp4KXP3G0haKZnxck4UlGqsWVm0JOkuC6KullMG682MUdHPKCGf25HCzTo0LGsFbDYiCbcayBIttbCp4pCyFaGhThJVNrzgZFWRpzs2ETwss6iwiuHfeCDZaMqiRAVLCO0V7MqnqEAXvPY9r9hufotK5cRMjinKl4LHxuq0U19zGNZAN+tZ5rsl8hAebBDWG3rjlMtPjksxIrOTpdRtrKKpB95VFjsAzvycgNqoPJz9XnkV4w3l140eELXQ4xQmrJ3sSGVvmW6fbH5ySYTz+BXYA/KhXPR8R66Qx5ycQmfndI5/zgjJ/E2Sfy3exP2W+8E6AKiZjOMHzKPWpXKfbH9IdBN7zmwWeGtX9RLKdSdNdCjHvswHoNWT/Tgg6Po7WznQO2fZYnrP2+GiTkzGxozPZt+ZsfL4zO0QOnTTDmJl5nLdq5QApfm6o2ToDZJ/LMF/QGYf7obB7QWefHZEUCBme8D62wmWcODdbP5m23XltvrwfQcaDIZL3JjQwOSIe3NWBE6t7kkbsevcTDtrDiD3+O8aZyNG/s1ftg5EVOnScp36eBolIGuM7cFMI63dj9luTzoaD03B4D27n55lKpNP0rs8VH+8W9safkSXaoLUDsOGTkwcaZF3XPY8hIRqNWEg2ueo4qp4Y+t1jFUgQOIt/HFE8v2myz/RR39h3gKZRN/sjw2Ql6DY/GHFIycFL5M7mYfQIV5w3dIwz2Q9YyC30s0E5n3M/Z+LUXjsAlQP8tX3dSIi1jLE7JQXz8JkjXvExs9vUNZPs5ONaocwYw0cbTgnHlm9CXPSINjFD2cVoK8mwYEIyzY3cxbiSh40IwhLGOnTZNq6ZMyLDkN37JOh4f6mOAABH8zzE4p3XZzKGY56cQOuDvS8D+NsO8Q8nxhJjLEnW5VRd4rTOTsA6ZR4G78ZiCaOEEFr5HMZt/PZzYPibgfDf2ZW/kTwu+BpP/EpXnsXx6Kxe+dRekGhsbnynV1Y3fqXGP0hqbufU3CInb3iHCFyOYxktAi8FD2HZUv7wI8qP/ULEK9vlib/u8NrhX19gWRp9hFBbPIY36NSt5bnyqjUP17DAvdNVEe9EDbh1fiyWJ7h5UMkDKqivvPaWLHIRRCqrbWPrdHZyDq+I4CdbcfvEVoLfquO98mIpM6gtUPnExXuGIKvK5sqPDos2TJxnL2hRLuWJv9s6LyL0UKJXfozCX3xWnkL5j19+wuXKry/KrwWW7RV156cAW5543ZyoOXGbb6yl7HUXEW6lsUXDJAhZ+SyNEOcvavA3m/JjV35bOmKFT5pTpciCSOdX1+CybdQqvIqgFmhRkDc+RR44cauBmOWJdvZMWfLwkUVAxQkNzJW5JDbtXP0HvsjCiyndjJumOsZFcTWqp0OjilDNKVJ483RivJFxjqNP9kFpywX5/2h71yVLkuw679vbPeJcMrOq+jLTMwOQECiIRqOuP/RLr6CH0DPqKWQmk5EmEwWRNBCUgBlgZrqru6vyck5EuO+tH9s9Ik52zRDswYRZVWaeE1d3D/fly9dee9DIUufOyReyagvaCCnPVZ5JKEuFlMNbd3RnkXCGKLJg9RiJPqpDGyzHnJkdPrAweg4QaI6brh34IIqbt2CjP3zg/E9tnx6bN0bpd43dqo7bltY22rbfWmPtEcmra/YQmD1wFuQT59g6pA5s9pvtBtbuK/zDlAJ88u/NFmkDwK+v2y+X+kRBtuAvVQIY7y/xevR6dfnXDOkNwN0d/Mkgu93zy37A/R2VFMOHNNZ/A77rNWiTgN1ntZ1qlap8AtDLJxwq9oBXducRgrVjB4p/cJNwg1bNo1xfk76y238FAjuw7O066x07LVvfdm7pGuQdOJa2rGv26eXQKOtbca7vRut+m2bWRPcDYiGjqlKjD/hH2j6VDny7UVknP3DbdhKRXEp3ZSqy6cXj8N273LYuEer9015TrLKBf2igQrZMg53N22/dvzeus+3fbukHb8L+775fZ+f7fXT/4X3RXBGKhOxxtV1r7/T+nl5zDa8lLOa3feBunnTTr43rebbPOuPdj/tUzd32BcIs28oCbLpzaAyr3H7WU1rPu5L6QcCX/FBK1p9J2z2caZ7NAvcNbL90wO83p2r3EtvSflffgttedkC279/rbmh199K/8102wdYVvS7vvl//W4lJQ/0d6oXe791uOyAu2zOoOy45JDny6Tb7qe1HA+FEJeXEXQ2QgZwoYowUTgPcUxGFlwRlufINmQ+zIe7YskCOtMa4U5bKjFGrhzUWTnPWRkqwKLNkLkvlRY2vVfiJ3vHgldMojGPi1y4whaFLppLSAhI+uwdOEcQ0GLWWZlXTWIlaERypxsVO1DlTG/uqUjl4BOlZDT3L4kZS5yEJUwGVKarFB0wdvLSKf6b6iLpyTDOzGacM6gllaUxtZppC2G0CIsYpOd84PJigpSKinG3mfYWjJJ4WYf5omM0MMlKy8k1N3Cf4aAOfjcpX2Ti58f5lQY73YM5lFObrjCflpS54UvLYUrsOiqWRbzCu8z2/to9RybXwpcNBlUUrC5mPCgfPJBfuMpyycF8KL6NjxRA9MlvinCeORXERnh08LyxkDpLIGAcpHFNYn6k612JcauWYBoZqeK08pcyhgA3CXBVvoNqWkLjMDqVMTQeeURlYUphvDxIvTx6EozlTLowKuUQdfrtEkGIWJ7swGJGhDueYIkgQn7lSUcscxEhaUKtcU+K9j7yvYXNHHThrpPf+TGLZaqFwFTiYoLZEcgupq03SH3Nbg7zaoNZ/75H36+C77xA9/mu8IHT2ZBcQt51/w1ou3kBOB07cUnZ9nz5Yrnxuy7a4DoIxWuwTN+yBqzU93WY/xg+fQYL5/EHqW2H9TIjELJ3pNd+SOPTgL9lD8R04tWbOfsPU7dHcum0Thk3CsXumrfDa3hZLwrz2rP3UWf0mq95tMo22tN+erQeS7ffpAYH9WId1Yrdn8/cToh5/2cUr28C9len+rldJSQOxKhv72A+4dR7ZNRk2lk7oxzcAJxswh8aeycaSx4pAnDUCdbaVAHFv6drDHcM6wOj2gdbuURNuFfUWkNaSO7RCIXtLmlN9tSb8Q7fOmjXcv/3u2+/cliBLY/k68OlMYkiF7Gb/8K6Jsj5LZCDrpRja2a099H0uHpPiWLqXJnPYTWLbu7LXofaYhF4fww4k71cIILTNFeEkvvoQ9y+z3LankbAsO3ArNVjn3CsDblsMhgQLW1+tkLBjmbvEoidkWERWQNTbpomsAK3rXXN7Rmn77x019ljO2QLa+rv9et4zt3LulmOt6d/IEHoRL4Tu2TzKIRKA+PoddIszWYMOZ0mbI8Tu55qsa52Aa6RMdluzCPa6FcK7+bKPR5Ct7Myd+7V36BOGZq0mm4ynA/AzTmnvdGeoq4Ucxh1G9bVPcSdiLs1aOvKtvWdakihRFKO2fjGCnWNt7yLR8g/+w0x7++1HA+E/HReKJ8iVN6My60TyxDEfWLQgOXGXRiaruM98XuHrRXkRAw221gWsVkYF8QhEsgb+SBbSBA1wUimIKMkLqQ58L86HEgFoKSnnVKkMmBpvXLmzAU0Xqh1RKww1lhvsYPgygUXEZJYDL7JwssToEzZER58kOlLFcMtcmZhtwNeXZSSPldJ8bgcxRHMEC3riIhH1UQmt5uBDrL9JRVNGmjyjWmhUEwlRZfJKsszjUKjN8+VDvkP6km12JIXq6nGyCAIbR87F+Gl9ZpoSflK+vTrf1sqfTd/yXjLvBximgW/Swt3gHFV4ng8kUS7ZmeQOykSWTM4SfskuPHnhQuIkwgOVN6IkjHlIfLscucwLlMTDOHCnghqMMnOxjKRYGruXyjIqOh34fjB8ibbxxoScZ8ZS8QxDihTSdwm0GPdFmQ/GUp0PdUYSmA9UM6w4JMcOzjkNJFXyLGAXnklcPDOIka8XLprQmnhRmEsh58w7dTTN3BWBHFkBL1SSL3z0I7NWliVAqx+MUqDKwKMew4GCOTw/TchaOXgly0DBGaRy1MoDUNRCPuRhK6M+f+p1+kfbotOPdOKrwnVd+nsNEH8Iuvaa0v5312l2kBpL0re0wo1kYscmrt9bY/P2DIuyBh91bfFt6toN9a6athWgtW929yEum4/tq+P/IZs2APcaXPcLhoqig7/d865XuL2Oy600oe/ds92t+60DejvvfqXpFaj3LezvB5usxwsVW5cDXy+xd8CrHqzaCnrZ2shrnfcqE/jEdfe62r0OtzPUnwoW3J5nRXntOq9cTfgh+/XaWitKxEB0ZS5dWnpk2CYDjZ7q1lTuDppIuyye0n9XQ5pkoCqMHvI6IKwZ8R+wjT9mE5rbAxuAWOv+tmg+uVLQgceeNd4Hq/Xz7OL0bxjK/YX2QKmD8v0Vs8rKiK7730zEbn/fr+Xsg2f3x+9eqV2b+4dtKo3Z9tvPgOZnu0vs0T7fB9buWdQ4ZvMJz+2cV29BjL6V8dDY+Fhhi/7id7WFjLOsbft2J12vSSRm0gC03V6tb73pRn+53fdhN21ed98xrcKnmNSYMCy7yX4cH5MdeVWer7eezGSfBGV/jbUNA+f28ezbdyIBenuK995+IdpXsf5Z6yPbcwwpAvdqL69OsjhhXUtkS4TW7nULKMwBm36vc8SPBsLnksgHp9QlWNikZE1kVU7jEdGEqTLWzK8t8bIYx6wMqpgN1ALV5rVxZKthO+bR0fRNHDSFD7GrQMp4W5oSVTQlkibMFUsLS1W+k8qimZ8wck7OW3UGZi4McDVKVq5uzDhLeuELjpRaUU0s1VhyorTuRD1hTMiQW/KIGCyjg80gRHBZDdBhgKswFJhqJY8DNseEYQ9IRAS1SiYFPkZxKpoTdTKoAyLNSs6M3KaxmuIfaKyp5BEZK88FvtUTJSXGubCMhc8Evkb5VpT/ksL98ML3riz5wF8xkl6cOhyopTJqxZfCwsyTODlncOOxCLNdSRJBbucK53FgsAFJFRbj47Lw/z09M+aBMsAXorzxmeGQUVs45iODJZaUmUtiTpnvMb6WiujAF/MFivEyGKNU3niCYeakhlv4BkvzLDYys16w49A0zIURQ4pRLTLr3XlFauV7zXy0E2dLHIeFwYVzGlBRcorgvCc1BmZcndmEZxlYrJCXylFb5KzBxY1lMcQXRhFS5Lrm6GHfdpLEkJZIICKJFx/X7zMJSzWs2/4RBs/ft6VtrT0GI7917N1vn9JM7QcloUkVXoOxjtrafl07CQ1ruN7oggWJNr5LiyZsLF/crvadb4bTfr1g9RpjenM/2++vGbr9wLqBPL+RqMjugnFrGysbDLoiug0WwSTvmNsdY/d6BHHzFZgnNNrxTvfWwWNqHwY7rTdn2QKKfgjAX08Ygj2NvXssvzfgvbJN/bg2YCkxmL/Gqt6ecEvdzsoKr2Cjne1Gf3dTlv02dWVs+z1Iv9/dc2r7LJrJNtL2fTY2qh/k6+fStJLuoLlJlDqbve932z1quw4Y1vTBmMeKgiriiSpdHheTuL285Hf5cv/nbhry1laGG4CJO4sn/n1X0V2xm8DZPUgYtlS+Bzav35AubAFtnVnb139pTLCyT5LhbRl8K0vacfbqBiPxRCzNR51sL8n+3et62/xqstgtF0dCbxpX3d7B/e8F4dBO6S4cCa19T9LxAQXZ3DXS7n19res9sK0ATaLNVixAX09YAU3zK03aKcKoW/rjAbi6cNxFj3abtu0d3t7XU4r33ppt3QBMqi1Zjq/9UzC8EnZkQLe4ozHzB2+TVm1AutVteEHfssYLaTfJ2pxCwj9EOUkw+SZN0+vB2g8aCdQg9ORzq42TOM/dLUi2NvnYrnda+/w+kWz1Id0txbl6wt1uPInXWA9gkaivY/u6NqY+tNLRDkaP/UKDnG4sAt3hsKb/+OH2o4Hw85jDr3c88VYy5wSahKSZQxb0MDADdTJyTTwM8LwsXC4FccieGdVZ5vCLPQKDZBaFSTxS8lZIWvB65sALliLgKqxVQt9pZhQpXHNiqJHQwDzxbMrizkNKfDDjYdBIr3yImf0wHqgWDeLJCjU3laN46zSUjGBNI2Z+hQRVDmSJIJKUElWUlA6ILtRa8WejDJXxkFATzGYO50weMniK1NO2NB9iRy3AS/HIvpYZEL3g5DZoRlm4RaKNi1VOMrLYRGUhjXd4Vp4JeYqb8pShzjPPlmKtxOHfcEDEOORMdngohqXKzDN4YjHISSPhSRLmYjiF5FCqUVywq/K9XDiMUMXIGLnMYMJUBooMTHXhgyqnopymBRFhsGdURyRfcBfmPHDRkTSeeJkraT7wnJ23Cwyjks04p8TnaeROriGVGCQCMd34/HimlMI0N89CN6obOR14ul6p+QAJTjLjfsaZqV44NlCQvGJLW3RIwtOSIvDEMuaFO3EO40D1WK4yq6F/S5mTtKVSj5z1S4a3qfK5LfESGxSt/KTOTBqgq0h4E89j5mp/XGmEr4NnG+hk0++ugMm3gWcDYbZagq04VwSxHiTUl6oCrPWRc+22egdmBPgzVg9iaSxvke7AEEEdqaGf7lcc/bqsA0AfptZl7/7XDnhvkguo1dZJZgQ9bighquZWb6cilF1QVWeDnRiMCx4M0W7wdHy1YeqOEp31jACyLjNgA6DuoT/WDgzi914fW3apW6Y4BFwBG7fS6GemifMAACAASURBVLZfe/CyDR6vgxtxWhKNW75NkAbww3Kyl/m6GrCeRuhphl8TkjdTrB0TnNpkELZ21Z+PHTuteHPi2XFKjamW3eSgTw3qekceJdMYyrRjnVWJGAjVlTAhC247J9lWL2vIovRZQaxo0epMAB91k8Z7TMV7AqQtD9yP33qL7JZcZ5xLK7vUGOja9sn46t5QrIHaLu2RDSCd3Ndl5urxPo4rGIpyPxNLxWELFoB3w6tNTmJNd9kWaK+Nce/e1e7hQLA0gNYDn0oLdkMkkjmIrBBkaZVwJ86lRts6SPgYr17D3kH67ZZVOJhHTEp7WCNcGK4SwWvX9r7Mrc0cCQCrCMkjmE8kwF1BeNvKwRqg6hZuuQVHamMuXTbgvAWEBosa2WTjGavHkLvvFxOdKW3ls5vI96C3tT14APKuf+6vmMrmixzJyGI1J9p+XxVp5afSmvP2rsMuXbX4FgzIrVRm8E3Ocmx9TJeOFY/xNoCntYmS8IysBEz27dzn9rwDxnPDbCecWbYEJKqRvTCLcekrMDhfYEztfgfphEsPXm2JTzS8r3u59To8tuNi8mCIQFL/AaGz3340EJbzHUmFo0YHkwblMA7giUlhSgNVBygfGe2Rb69CsUqyhKeKWASPHQfnzpVHhMjqVRmJoC73MHa+l5njWDkX4arCB8+41wDiROd1vhrzIMythKsKiyp1XtAkfD0lqhpnSbikYK9lopYUkgMJ2QLAmByrNVI4rmGRCikCKESjMWlSJEFZJubrNY71ilkssQzi6BDXGiSAXJaKqzVmJeOiLHUBKUBBM2S7x3xmVCGPB0ycWivTVEiMfGDhMJzJpzPVK3iKFIXahkspeFJIaZ9xErJSVj1YjHRj8mCuciVTkSFxLc1gXY7UpeBDdA6GIOnIbHC5PCHDkbNmaluvWZZKJZKUeB65qmNmpBJMf1mUz/OEvCxcpfCgH3nrcJecv50zVxkoVcjDgQ81Ry55lzB2r865DX6XIsCIpwWp8YCDCu/rwjEf8GYzcyyKpgvzPIMnPkqNJVM3RhWSJ8zgQSoGlCQUMY44L1aZLSzxnHvepQlSJOMwM4oPFJSLVFJVrgf4wg11mMVZBuGtx4BWcKoKRcYf+hn9kTcVXRnUGx2vyOrLu+3XQEb73Fsasohujg5KbxrUdvzKkEn7vQWptnEpmJEdQrthlFaGpJ9SfsC0bnrWts/6TNv5VOXWx/hVv9dBeOclrQ2+TmeyBXbsaAQf9ZN0NBTn1XXg7vvHeZJszGsHrBsX0tnLV0X46oM9yH29PB7Pfvtoe3eM16z0FkC4ba8B9K0cYqv/m/P4Bty3o7f67kvPUfybydtabz88JZv1ndzsf3uNW/1r/0wl4YTeU28mBa3tWZ9sxAA+kFaJz56h3Oul42cAvfDbbmfsK5BiWGraUBPc+1D9h2/9sa9swVzn9t0z8YyZDVAOjUnuk4B9XMAstNSzwYzuebB9a+7t02gSg/ZlmwvcJPsQiX+jB/uZ1xprrPZ+sthAiu0+69ft7K22uuvnTdwGZEILIlMwC29YPEDjAcetuY7smuTeZWKbxsW/xBb0l717HTdQvc4smxRHZAW9s28xBL9r23/9+nnbZW4kQp2pn1f4+Ynj16C8zZe5t9/a3pi2BnIT8NhZZ91bquzuU4h20oPccr9mf4FU1kx0t2/77arFCtBf9Y99cgDbpKcHw/UgurJ7zhMxUZscHpp2IVlMBnuw6yjbsLn2i+3vMfV2H842he6IEhgws00Qf1DYu+1HA+GffnbikIRBRyatnNLAUTODOO+XGfMFt5nneSGReJcWfEx8PYUDQpFKbtnLXgzeyoLXZz7amaXMFJysAyIxuz0wsmhhMeNeFVNh1ljyGSUh9sKftdYwWuUwFH67LHzPG2wJXXKuEYPnKLMURDJilSzC2JgrkYQnQzmgVLIHmNNhCMkDyqLhB2y1cK0L9xO8lYqUmUtOXOSMqJESId8g2OsI7IKkkTCiSmTdO44JEadwxFAGsbAQqYmXUjhJ4ZASnkdyvVBraGkO6piNpKNzSAFUcoYsYQ0XtmeZMi8kuVB9xK7GdazcpTHSN9fK/QxPS0KtoPnIUSvPOpLrlWnIbXmqkMaMLFBrCUAkzpKF6yUAcB5oAX6JZSn4xSn1ypAyIpXFEr9ZlOoJzc7HGRIHmCZ0GBisQFVk+n5t1EMSxjoz6kilBiBvIGQYEpMXsmZ0vvJTDzt/9xm3xCKwmDE0f9ucBlzgaPFq5CSMCd5UZWJhqoWn5EwaAPirxhJO+QoiXNqgTx5jAPHE0RRSYaoHfqUzH4uzpJGzCinNvJVob0tJfLTElF93L/+428ps7nrn6OdCl7+ODGwM7x4+SWNcV4DnxEqIRwZJM1apwM3mTZMlO1DhwUg3YmtjBtsAmdxbp7gNWuug3gCZruzGpserskUtdw2pNCAu2tnZ7fsCq79oXOe2bHphuMQg2MsjNdDrr47xV+faB7jVPuitJSzrcrd0dG+NTe4DRQcgvp2/A7LV0UJC22qt/pwAwFl0l/DEbwF0x7Syf87GmLvRRQfe6/oH3me7TdjWwPuEar+/b2x53/01AA6ScAdAfWObo36k3cuONZYuz+l3GpOTpbkPdelPt8hTwqXFiLJyhVR3bL87lre2llpdenu3JQnJ4ncfhLw4i4IkwT0jvoAkbAf2/5AtKRyDnr5xPHDgRZQzzoEAnubR7i8I51XaoBSJ1QsIdDy2dvBE4nMMaQFWURdRrt1yLIvvJhoRVNRvozbgLa18JoQ7vLGgIfPqqywHCU3uRIDIyEQY8oh7N55etamZANCrPlfiGQuxnH9BOKtzafv1o0u7L29lcpJgdDONFW7Pvp9oj2zAXRXKTstx575mfYsEEb4eN3c2l57hr9/srpJoMhu8kV39+eLtGolMq9b62asIp3b4sILZKEdv76sKW6Y3D0nDiLO3mdP2vhbvqzi32yCbh3APlOxvu7bcCZGmeWsXqySsn8S7/rwRFqoc2xSqqLayvS3v4s6JttpAY/4RPnPjKrFidmxsu9OSX3ljxoMvYybY+7TrR3MvH9kyG8Zr42vij5k4JhHjintPSEOsMP2e1/VHA+Hhi3f8tI5cZabMT0w6cvFgb++GxNOL8/zd99S6UPjIo71h9szCsnZ2aVlI2XjxA9WFh/TAYalcBVxHaoKcBU+JJ0+YGJIdLQljCdYY4VorQ77j3zExYGTOzBZJP6qMnGSBDGcfOPjEJRdmBkpKZHPOTcM5qnCygZeD4WqIJkSUc8pMIuTlypwMZuPFJtIyMczKpM5jHVkEfBLwpaV8XDhqZqpwVOP+6JgJF1WszmRRkjnKSFZDpgQnOGjiJ2XmOAjfZ+VSM1KVI48II7MbdZ54eHfPfJkYXwbuxplfG3x5LFwuQhnuqTlzKhc8V/6ZJrIUvpOJv7kKfrqSliNzhQ828FO/ckfh/pD5yj/wd/NnPA+C2oVvrzOTK4f5kXdj5ZmJv0q/wMvCsjhJEiMVlnDhcHsOPWSNDr7i1FoouuAogwo2Q3bhLj3y0zMcs/A4Gxc9ozLys+WZlIRcnWyV5BOgXDxTco4lwunKF+6oXbkfYjC4GNRBmRhIVslk8jFTijGgIAuDV5KOHFmC8U1wcuHozhdIyHNMSDojJJaaeaEySsKyMNWBp5QoPlKYeZZMSsK0HLmOMKtxqQm1M9ULH2xAs/BNnjhPf/hy6u/dJHDKqo8kghNUdv6zBHBIYlgLEFkdoVZWhC3wrFE7+/Sv6zJ1O2617pLd4LZecGO5pPWuxQOk7BNy4DuWsC1h6i54LgCS3uj8+m2bv/YhaIBVA0AmDRlE/35vW9W1vMLmWUsrg9ToWd8NIP34rnldWZibO2rP1i5TpWmtG/DS3b3elM3u/gLabA8a2tmNqcztmXon7qxFFT+kA77t8/6Z7Oqkg9jfHQ7XdloZwlsk0IMc10C/fp0O6Dtg2H/dBrT99+vZd3WztQfo2cYSTm2TOpEWzNnajLnHyodvg/s+KYe/UiZVBffoG0yaFKidq2cGVHWygWhBxTGbA659asLwn7lJK4NiGwMr3rx2dwgnq/CCrMkFVilKK9fjjrn09v5P7V2cRNdMXr0kunyiW2zB3rJre7SllfmDGzOb3vXY2t0VWW0QJ6RpOaURBXH93vdAALTkkQ3t2Pbp2tsTMLVrD63v6Czi3vvZvWVpe9ViT23V0iRWkrNuc7fUPt/rgK9tUrpncTemcptMD8BVBW2zib70v6/9LoNK6/HbhLYD3xcRPndftbN5z2jvZqt1Fyy3TaK3qXh/H/b3+3oTWCdLvV+Zd9ZvdJC49meEtRybI0SSltSkzarH/o7tr9O+28udQlja3l3g4jASkopoF/2+YoXA2iqDamQ7vHHMePVcx9YDzyJrwpLSyiO3iYipNEeNElgTZyH/7q6NPySznGb+ShJWRo7pLbNPlGXmly9Xvv7wgfvFw0M1Vx7TGWHmRCVppVp0McdRGYGfycRBR97PznFQZoTFHDSjmsgpoyJcr1dKWXBbqFJCuuCRDnCphYNVPIema/SJkzvvhiuzL0z1COkCY+JeErbUMKE+KLUW7qwySKaq8bNr4W+5RqWX8DF+ToWlxDN9VRfejSe+Ohm/rIVv6oDrAjaAGqMYOWeyJA6i/PnBMFEeKzwfQqOrqhSrIcswQ9X46mxMOHdJQC/8St5QQ5dAygs6DfzFGNn8/tJmppcr/90w8I1/5G0+Mrryyw+KZGF8/kh143kY+Rc28+Fwx2/KlWyZrBN+SYxSGFT4p3fCb6eBv9EBrwf+avmckozL1fiXZ+V//mzh/3458Jt84DdWOCyJn9UL385TZHCTHJZUqVuiw0GEmhoQbtrNUZRCoU4TDAlx4UNJvEzOf/1w5SucwWe8KqfhhXcaqamzR1ZCqTPPBaY6YAiTJs7ijCqMakxWmOVIHkfMMkM6UFCOdUEPI1qdrBoBFaIgoTVObqQ8MFikNJi8YgWuemSZK1UtlkK1cvLM8TDzlVXe58zXc0LTwBtzdBC+EeOiBw6aeJevvKXy5XLhYTzz7ax8P/7xtRHd1ieWOJ0xycZoWMCD2rRTQusA+8AqW2CW7gZWaefTtv+eWaCf2oO5M+lsZXSqqbO01jR6dKZhA3nSRjQnNPP9u71l1OqS2g/zbVn2ZkToA1X7SGlpVpF1YNx3sF3z7bJJQES2MullIfv1Xzrfu21KD670PnptA4T7zb5diy0SZRbG8NvNrfOSprMOvCJr4CDEgBly7P3AvAGkXl++fdyet7PRG2hNa122gdE3hvZ1gYVLxLZu7rcn3/3eALYQ7iDdEWJnKLyx322i46AJWsLIddDtmkd3YfHOkNsKEKoSSZkAUY16tjbb6m07eThqpvgu2qFiGsDJwxogPEizI0Wo4mhtt2wan4tQq6N7cecfsAlA2gLL5qSRwVIC8Af4dg7SGdM9O2gMyC3Mbe3ujtB/nnAmjyyhsu0SUfRtCV2bvOWB+OlE9rlzewOvEvZlQ7vHpQGR7JH0oSc2ODRwu3hIOcbGzmUCxMztXRsJIJvwdSm994xJYDDnWbZl+rxrY2NreuYwiTC2d2uvBDjolqq5bwOhke6BdElgUWm63tYeWrle2E8GAvgv0tjKBubrq/4RCRmHyPYvQH+rqzZZOLd3osqmlTWPcghLwNt21e3HejlZK7uMrxKObpnWy2k/uRTCIatrhvcrD/sYixgPYjLhbIFo60qOb33Kgb5SY02r2xuWxoQJqG5cRHiDM2sYHkzSbfDapNVjdeJOIlYii65A2dhkH93uskjTrUdxx2p172u9uYTQMyDGbGpuZT7/nonrjwbCH+vIfb3w68cn3l++4/lFuX6YSF5YqLx4IvnMc45kCwnFNVi1sxaezJks86iZ72qbpQ3wTgZSnbhKRb35/XrhcD6j01MkVcCQpIxS+a4cEDXQYPQGU8owowaWlEevDDJwSFdgBB+QXDmLY7NzYKG68rO88CYZyQsPA/wP9cJjOnIYZ06D8s0cbOmYJn6uif/r8cJhKWQ5cRiFuSin44CqcnDIp8RRD0hSvrZIvlEPAyqV41KZDc7jkcGuHM0hZ35ulQ/XF/7mRfjnB/hbM2ZbKNeZQ5348/MJ8YVfTxM/S+GG+pem/EUa+bdX4ajK3QhfqvOSDuQcA8HfTMKzhYftiHNyCd3VaeSDG385xwLX1ZRxNuYBnqZKWpR/9VH5fw6f81QKKiPqjtmE2sI4jJRjhpcZuc68yaDjwDtVfmvO+zJzb8qLLFQ7hiZsafP/UigIb7RSEC4X5+0RBk8UUZ7KiZQSnx0KRxMOuZIPic+XykzlYs63LjzqgQuR9MNRqmTKYshQGatiYljOGBYM+egsi2OSUQkf5eJGBSY9UqxgzLx44tkGqsIhRXcxezdqSrgIX3jmYSi8GxYmG3jWgdGUX9XMYczcW4lZ+SEmap9l4Wd++bGv3D9oS21WjW6R+rVCTnsQ1bLisQ9Eik1FW4DTBnTWZcam2RJVMoRDRgfNzrrcFayArGyVN0bVGyPQl7Ta2ds12p++aRS7FY+wjtfrQJ60L9ntgGJH6/tN1kuE9Ol1iPtucxpb6xsbuy7Te5OH3ADpJnvwtrS5O8eqt93Vy17fZm3wFmg+mdveXd4BxMAgG4uyldgrVpvOwDQgrg3odOx+g/zbMev9+Jodrm/Gvo64+e6GTYZbBPKJTVyIKDS52X0fZBgTnG0GkBvQ+qTFXwN63lhz8WgorrKCYVp5OKwrGz2QUCzwcfvwZqs98KxsjywiVHWogs8SkrcfNLQft+XWyHsiChPh5M6TKEcx0Bikn1W529VRy5OES8jk8isp1OufxyxcyxaYNbWVHbMI5EzAZ+K8NIBlHsB7ae/ra5lsr/8kAcwP+Pq+XmRjansxX2VzRVDZmMVXEQdtchgrHXuHkk9tB9k0+eYN2LYl9UEC4PbVgFObdEWwV6yAnYhrRCAcfNw916CygtqY8O7e12giK1u9I+5ZchzX/X3nm7KKn9EvN7ZcWSfpWcJqrS/79623tM6UTo1Jfr2qNeoGrG/no7L2oa+/e72t3eVuzt9X7ZL2oMaQdaxewYCnaIf7lNHbmBL3oA349zFqtUhrW+8ju6TjwNZef9gLtGNaXxev5zaWxApnJMmKk/z+9/XHJ9T4u7/mu0vlennBa2ZgDguulg1sJHNRw8uBpAmvExNCqRZLGkVZpCAlljpTy3b25IX7VKKTtBmbBrJUhvoNX42ZE85RRq7LTCKz6BJZ17zwkIRzKoxWmTXxpJmM8C5V/iInilee64WPRcm58MWdMVJZTpU3Q6ZY4dtJeF9hlhErI396EN7kC5/nM3/95PyqwFfHB+TNzM+qovVAHkbeHiO4T+pCOg7c+cJDEobFGGVhlJHvmEnLlT8/KpelMFQl25WqmSeDp7kwSuW/Pytf1wMHmzhPws/PibM6fz29YCXzXOGYBt574nAp/B/Z8SHzOF9ICd7bkVkq45JAjOF4JichLUY9Kh9RIPN+LphM+CxgV3QWZBTG5yvvJFPzkefnJ9JV+KlXnodM1gS2MFrlTuG/ulbeHQvpfM9/vDxyTJHs4yfDgePguBS+rUf+6vklUhG3N+Vtco5VmJPyfhZ+dTF+uyQGVUaBu8F4z8xnRXh3zHxeKsfkjGkIztkKX+jCsRZefOCK8m1JVKscDgPLUvlSpc1eF8Zy5rvsvH82GBIPnhhSbQxgQkSYzHnRxEM9kNT4YoDvl4yb8JjB9cAv3SPznx4pg3ItC8XgTyRxKQNzUr4fIoJ5VAFLkdI7JxYp3P0eIPaPtamEHKIb02tqMek7arB4y5TlrDKEvqQqDQDul7Eh3CLMg51aPXJ3IEP6Grxsuth2BmIoad+zMb7r17t770t0fdxvK5irXg3YJQnZLrS7PCFnuL0Da5koula3P5839rcDp+4UwQpoY0say/S4U9oDOrv9fMd0ty9WIG0dIHe2XDZrqja4RLIHWUsJYjDpg14P8up1hfRJBuukpzM7r+YZIJue2p1NbkCfdLT66Qx2v592gr1DRGp/b3pgXz2iu461bzF58UD76/QhQHplG8yjruMmw/c16iBJBL5JAzmRBc8J4XlMdLXVId7aTkNY/VXbJlO6yifU46dhDN3Tvq0IKDGR7OVovSJoLFOFmgLw/KHbsdm33QPvUd4095QHQv8Z2lB4MO85pkLnKj1wTRhVmNiClMauCdYWSOTCbBFY1MHN2UK7OaisVlaThCZ2aO9V9ZAvdLa3t0sRWTOgTSKrxrk01u6ugaSrbAv6dw0op7WBBrPnDUiHznfT86LCnUew37VNlDrwNEmY25rKt0hYdC3oatWVMYr05XOJmBuBezGutr0WpqFJjqQQztXhQeGjCw9N35pwFhXuPTTL3l96YgJSzVfP5tEivbERetiDbKmWU5dStbIBUHMGjbIruwnmHqze47z49t6rw1GbJKDtM6SWobcdOOyQ40xzz2jfReDgZua2D6b09s6pbDKxJB4BmPFaRJn3oOHeZ1pIHTWFb7AIJGtkDCF3WFDuJF6aQXq/EmxwXx0CuJfugR3yp9Fh7j/dmDT06bV1NmPbv48PK5hv9YNDEf1B1sH99qOB8K+vM+9qQoYjs70wWvil4pXPdeTbupCqcNRInSxDQrmGufGSUM08VsMYyG58lgqjTTykkfMQGo+PCFe1uEkdGHOiuvOrxdGUSV75k2FgEGfyC6Z3YJBOZ94m57AUDgO8LWGMPeSFX2Tj5+PI3z8LPhSMEWzkm6vwwZXvTUhivOEN34+V7+bEv7ZMKfAFxmk88N4SSe75IFdqSrxJ4V94HuDPh5GRF7ROnFPizT2RWa2+sFTnMd/zVF4YC3yeld+q8e+ew3e3ivI3mvlyGqjq+Az/8nSlMPAfrspljllPYeKb2cg18zwoYzFkfsIEcqlMpUAayKNwLReWtDBaeBW+u8AyDOFxnEekOImCS+ZPHxK/efqakSOXunC9LrxNMFD5xWHg2RZ+IjBw5d/bwM/zwpea+TeT8FwNqxmfCjYe+OZ54pwF84FFJ4YcPqq5hmF9yRHFfSxPnDkxaEJtQcqFnx8FCnxnwsty5F99+ACceQP8k1H44nwlZ+F+XjiOwmAX7hG+0Mz3cuQ3k3LxxN9rQcR5rIKmJ8gjZ5TLtJCGzPsx8zQXTDKHlPhTfeaNJI6jMfnAd3VEsvC9KS/AR4dRRn5jC8VDLjHomYLwH3K4oSQGsleqw69l5O/EGEwRScig/NKM/+XHvnT/gC1YDEXF1iXpTerbAtFatqw+jq8Mn9B0wFDrKzamMSQxjrXhTaHWtqSYJMBFTxEEN5Pw7snaj3caGLRY3elWSB349i4tEe02sUkI+lYkmBQVIoX26mjRAKN7m+hsASUxmLTvZM+gCGqRmcp6eHQbdIoayQI492vuA/z6YG/SWeFgJ5N3H9sAWXuHgq6P7TrKKE5ZwdfKYnYALf1qeyaX9T7WalpBdRy3T3Zh5hsevZmHyAok16xbtnMBgUbjeivdfpN9uAnJgrIdH5MA6O40e5mJi6wOAbUJO6Kc+jHt6Vo7KGuhxB0MhLRJJHh42zmjpGyoyeqO0ycAVRr4bxM/l7qy+b1dJAhHGA0D/h5pLs6apa5PPj7lSvLjNmkBcM47bZXdGoS3d0IbABka0BHZgtyslfPgO9cFCeeIE2FPZyI8SCz3jxbyilmFjHC1TWoQIDRYvmNb+nf3laF80ZilxGqMkD18dp2QVozifEBXZ4gBWS2rTl4ZGwC8SvQlbxooMmcNsFoQzgSwOUmwgydC4jGLRHBjk+Zc3DninFvnJR4Z9964xfGE3GDG12xpRXSVNHQbsuwWYFy0BZnBWQI830mg5ky07bM7ZSeJWIg66f7w6s6dtGBAb+28NcXOWCKb8V5J0Y6St8mhB3hbPEgKly7niHo7ia+pg/PaL0R/nXVjm3vgIQRzvld1ReY1aamse7C0tIlJX/lkbd/dj9gFzgSRWdq72M/b3x9x49zes77NKiwGJw25THV4aSc/JnjEebDm0NEIhjuc2WMi4xJuXBBt9cItm3525/u2Sglbim5nI4PKrp/91PajgfDkB36pxlBmsiaERPWw2roU4y45oxtXqZG5xxTVIQyR04XihZzh4DMHDd3rwyFkBo964KlM5HLgROUsxuepkJIzUvg8GU9FUM/82jJLCg/Z//EYGZXcr7wviT95M/AWYdGKWeXeC99l4OXE/V3Yo6nNfJGMeQQtA5frFc1veJweOdmRmmCYH9F0xyyVuza4vV0KcqoUH0g+cx6FNwYfpkcO+cDjMnASmC7CQYzrdOQrPVDn75nP9/x6Mf71NZNeLiyifBzuyC7YMvGtK0cr/IlX/nY2sk2c04mLzDyTcM6YG5dlIXumJkF6JjvNHEchywtfHBIfy0Dx0paslBcRii+8rcqTLpxcSMOAa+VDVk7nL0kufHl95Hx03qVn/vJy5G+Xykmcv7aJP71W/ovTxPsLvJfEf3tnyKUyfO786hlGu/IXPlOycNAXTqTw1K0j9a5iM7hmLmZcc+bPjkYx55vlCp6Y5oXh7T3vv1s46JWvUuZjnfjgI89mvL0cubdn3umJh1K4TwN5eOEtxt3xifPljudqTKJ8qAMfHU7DgFnlOcGdZT565YsKP7WFL88jc7pSgd/UgUcfeVzAp8SHvPCtQKoJRXjUgudEconl02MkERmXNkS3QfskKez7mtlN0kr1wi9+7+v4h28iPUPbDswQADfs0wIMJw1Hgk7WdQeAan0p9NOjfF9+rxaAQ6X56FoAaJPmxcoGeAVpTBA7lqHbl2kLWNlY2X10V3GQdRl4/TjAKBvDie6NfFZEsF4//CTj3FFHPTKJFax2IN0zEHVtXGfMoXeYt0hyr5PuPzv/3dlhaQh/Dwj5HZ2zeNQ13wAAIABJREFUr/9tJ90PZOt+vZx122f9Tl7Vo3es2JmlAHPdkWFPlpjTgvIawJUedrYN5PugPaQHDm6Trq0Kd5Xed78VMN78GoOqbJWObvp178/tJLH1GSG0sgBeHUt9dSIFME9No+1RV1ll9eZd24x0NkzXZ5BtprhOHjvDr377TD92U4l/J/d499okqX/e9xk1lp5nc0aRNbh1aBICUfnkYJ41AM/Y2uyW4CXaqLCBss78Ci3QDNZG9yzC/a7BVnYTYqF5y8JPsTXpQpDncfwiGprWJgnK/aWDVZ51h68gp1sE9n93sgVtzUQdnQmN7LSflLb5SW9yC8Eod10wbPZlfatsUo6BbWKksIJJodmd7eY//SwnNs323o7xtdNkt/Uqu1WQ9TtCCtCZ4rP4Wh+F7nPM6vu8P7bLFcwCAGcVnmRbIdiee9v/3K7Vn28/r/tBn/Q7mnoSWpAxJA9e2TWY174K0w/Pr0wbemDoAnxGpCsfXEhKc0CJdrE4ETTf/YJbeXSmuYry0iY8awY/dqts7W+9YXV+uP1oIPxGKrMXLjriY4ymCWGanxmlgDnDEFLuL7NTyoS7cBI4D0oV5YpSirFw5HgEofB+OXCVyshIzhXJwkPKfDHGjPzFcsxKpsg0988wXkw4nO9472e+GBaGYeDPrOBeWSh4OnN5/sBkI19P8HO58M8PE8fLB77mLX91ARsTfzoeGCQhy/cU4JGZNzKAZypTpBr2hYNfUHUuy4HP0hPHrDxe4ZnCn5wE/Jmj3/F4eeTzt0cOfg+68H8uMyl9xjwrV4zJHc4HRBJaC16NN5Y4jTPCyGMyDim0rNO18PYwIhN8K4XRC3kUXBWxACfjkXW5OHHPoyfKUDiJcjXjATjkHDMsVXKZ+NkxMdcXfHbe1UcWhC+Plbd5IavwoWT+p7uZNzi5FJI4/+QXxpNU/t8PA8/phQ8l82dfwmFY+BcPz4xWea5HPkwPfHupfD0UUnWqF+YiPAJcZ84cmNPCdwVKhWsZEMkUgetz4u0Q3rBmxlfJ+S2K1QXEuMgdD7kyVDAxPi5nPkrl8WIsakySSBWUwt1w4JQT7oqT8KNw1sQHg8MAf1MKg93xczWeXPlM4Y3PpIMyJOU/FuF7XXCB6s3jeinMWajLhTwZNcVa7DiOkBJzVnJnW6oxogyz8+0fOcXylpBCV5Pz6q1TVWnBbJssIKXeKQfwaa54uDjVbp0FOmh0tgj1tXvqg7a+VsfG/tpGTG9BKX0g33BaC6ritgfNKeQYr3Dhuul6juj1IoFAIDtv7EowKw34tJHMbPV/iElBA1L93qXpTaOTv1U0rwCAjcW+2VbmZxNnbICrAfxm/wWblKAzRipQ1Ml1K8WkSmn09Q3G3APPfn7pwL+xoe2WBnaq374i4MESa2sTPYWuKMj+yTp93R9Nd4OxQ9YGWDvgbhplkyi/vSLIdQPfuT2NNpZR20xM1DEJ46fU2FijZX5LGuDbukbdW6R7xVWjXIVIqtGlPjgQyT0qfYLi66SANoHzph/xZjzbrOXXCZFGtGhMOH/HZPE/Z8ut/GbRFkja2oUHExdMWzC7AIfUJzERfPYisspYDmzOD92D+IJwJ5EZrF0q2DSPdnbUTYvft4Gm6fW4P5XwSN+7SuTdvr77zDTKcSGcKtYJSkR1UkRWSdaiyoMFE2sO91L5DmXEuGtnvZdoh0ObVroo34rw0OquIEgDULXh9Fl0Sybhxr3swVUEWd5s7bGGXT9T2uR3bOWuLus5HltfcpRgShF468b7HYScRHggbL96n/LC1p+eiJWOZvrIKLZOgkZrWduSrPUOQvZgmmPiw7rC0VNJI11iEVKbqYHBTASK9dpLwpqcYqAHwG3SFd9pjaElTWn96+TBIh+kg9IOdEMiIsT3zRSR1CeOEiRLl88MPdEFwlWUAYs8CO6MzQoxCCXIFisCIzF5Oza22z1WRrStemUJhwrYJkQezY60hhR/evvxCTWGxOH+c1J15ulCnRfKMpN14Be58rQkLlSSJ2Yqnw3GlzmjGB+98ngtjOOBP7tXyJlnEazAV8PAoxQus6HjiavN5HTgN+4cCswYUz5weCtINZLDGxGeRBlF+K0meLxgqrz197xYYi4TZczYxXgh8VsT/vdLppZ7sCv/5OHEXCq/fDQe7t7w3xzf8/kgjCY8TRe+eDcyVcPrRJWM4bzJwsN45TeXEw/DzHej8DIJsye+XTJVE9/7PV8/JUafeZCZz4cj5s4sM1+kzCVnnqpSS+azUVhEuBSjVI20n5aQVDlXRY7wcQFL4X2okqgkDrmAnvA6c04JT3HfrgNTWTj5wtHueHes3LsgVniyWOo7HoU/H0ONMy/OuTrnu8zT7HzPEebKL84LXw7w88Mjj3LiL58f+F//7sJwekBtxsvIX18q//Z7I+mR+6IUEi9S+WjRuJModw5n1Yh8FkfHzIvD50OGagzZqUO4Sohnnql8dMdqRHA/m3I28DxGCk0SWhYuB0GZUA5kjM8GoaSB55y46rGxXsJUC/fHkRccS8IcC0EMOjAeTjzIwMSFz0QopfCSlWupXKszLB95W0LK8/Z85Jt5ZmHAKyzVQmIyL9yhvNQlGIjTSNJEToIm5TIvPJWymoT/sba9jQ3QUhvTPtMG+NogIay/rzIB2Zg+aUzgzYDv3VHh9rq1M699/9aR9UGge212IBkd7+Y6sWYGa8F6wHYf7e4jFfAGBhVdrbMiwljIKkj7XMSa5+hmR9TP19O4u7dlsz6yrOVCw9YSN9JobnmNfGXHHL6uix3NYtZBblxjJK/g+3XiTwUGC/C+Au62LN+jrXv2qFgm3gC57+q0P2BIX4L9t11B9AmERVWt9SqyqVp6PQe4lVXHrOI/GFpEA0i7h3eMtKVQ18igadK1x7F/puI9Ax0xyNU+K/AUiSxuruO78mz10QCqe0VE6T4SftNkHdzDAaKPyK3+i/nKjncHkg5nzIh09sXx1AKUNNKH6+v34kdu/RSdsDp6eLQC3EmfMMrNvtCCsJDm68rK6Omr/RKdVb29V23vRN9f2f4JXS/t6/elzbrS+r7GeWzHjvd5ku7O2696dOeoPeA0AJ63LHG5AfHiwi8ksnjuI/xViORKBID7OeFG0HXKz7syPEpzp1jxYzz53nHhB0srbauyZV/r2cm6VVpRRZuVybv2dljPfgh8h3K3axPqzujR3q4W7hQPbtT2bC0HFQeg+6NHvcS71ScLZ7a3eZEA5IlYDVINRlWE9YVd5+AEw+6tjkK2sD1rr5uh950S79IisV+fHKVWlzHZrc1dpq82sra/2UK2qn2JEW5o67q7P2332+9BX40nPdbh6FEO15s4EG3uGBbg2jd9dnbnJLFykhQmdH2n/1OBlz8aCN+fzyQuLFbRVBl8oQzeKjVSk74jgS5cSRQ78t6ML8fCXR15uHdePPGtKy8OqRTepMyzQ/WRh3zlrDOVzJSdOy/IQXnrwv0BnlzR44lJDJsLx6lwtZl3tWJJ+DDNGEc+G5RySlxn4ToU7nXANeFmfD/NpHTm+3LgmZmv7owv7ZG//XjgfX5B0pmfHY1vpzNDekEHqC6UYvz7izHOd5gs/GYGLfBwGPi+hvfi50fnNCkVC19DOXG2hXejR8BZLXzDzFsznhN8ofCNH7kKHI8HpCwYhS8k8aITh2KMwxGv0WGMCUiZcQSmiXEQjmOmYIy+YFw5SrC7T+Ux6mh0fDmARoKPRQtpOPPy/Iz6lTfHAwcWsn/knx6OjPfRAfz2Rfnfvn7Hd3LH++phh/ZipHTknSxkg79PGbdCHsI3OXFi0siulxHKkHEvFM0sGGaFL0aYJZHGlonPnZTCSP1Lgw/mzEtoor6rlaeSYzBLYdr9RRIwIR0yd4QA/8kHxAoHN4bqlPPAT0hcq4aXc624K2/kypIMJ/ETnnFzvp+Mr2uFUhGrzMBP58oHcy7ZcR35VV0YGDl5ZSmVKopT+Fwz74bC309KEcOfryzVOSjc+0xZHNGB6fDHlUasg0xfxl2DLHxdYk4NHPp+QJA+WEr/sw2s+4QRMRjuM3Oth68elbsAse2oxvvtP9uD4DiuNqYtQPPWce2Bd9czd1ZU6V6XrAw4siWahh37zH7JLAZkEYI93T+Mp5Zw4v+n7d2abEmOK73PPSIy96WqzqUPGmiQmAFJkZLNg2xexkz/QQ/6jfol8yiZSTJpNDY2oxkORBIE0I2+nHPqsndeIsL14BGZ+zRAmqwBJMzQ3VVZe2dGRkYsX758edhBdG3X0xfkPt7IDqDb/++evH6m0QDVzR8atTH0sgH/jebBqdQeNHSP5D5zpAUt/vvGpLZBFvagwi/Q+2lZZ2rbEW7GZTu1A2CzG6cFmj1SG+/tpB3YV/veXJAmASlgwXchac72pf2pGqwSMPGmFg7wd01p97HeCjelF171opqWLcK2Ns4exzXAi6BWqRLArLHQodFDXvAmGjZNs7RmPdIGwq3wQGptWQ6fdFqFWgvSOnL+oUdUtwib2pyv0k2f2Dq45TYHIjsQFXGN7m1HvGItzd+ef5TdLSHJ9651e7f6++cgsD/JhH0SiI7iWtv+3iEOVGYcLCZsey9V4J66eQKDv6cXmvVYs1GE1lyhnyetiYd1n+AWGOCyDy89qCzV72dtVzs2QHRorLBbxpVNgnBzu6zI1lWtNLDXbdU6iO+FfrDLGaJVsgYW4Gg7qjO8iHOQXWIR8QKuXnw5ijG2Z7XQwLYKC8odZfP3BS/IVfPsSrpZeRcRL4BT/76DwnO/ZtzrGhz8fkSaNtrXtz6egRZAGZt0KbfRyQDm1nK98OyIFz2u7R6quQVaFnHnINt1yA6UneG2Nn9KSxv1WC409tbqTkS4prh4l0D1ZidVXKO/oj4HpLVtaci5S2d6ncUIzLJbp4Xe/XHb0wqlF5D+E8cPB8KhYib8aAycrWLBqBqwa+E6C0cpjCJkgnvAVcHSwGyJOQghBA5i/NgKx4PyXJz1uI8LpiO5xvaAZ59MdmWt3qHrWhfe1nvu5294rUpOxoegvFC5LJG1XvlCI6UmKFDnlVKNYxBUZr6chPM48i6dMBbe6sqshZKFNURSzLy3t6wGv1rPJIFDfcO6rtxLQmImnROhVEJJBCkcB1il8NmovBtWj57uIwEYQgAZmK5CGArl6h7Ff3m6EmNkmk48lcr1cuWLwYgxMhzcX+/pcUJSYhRBKLwicxzhmuEcVlJZGEJF00C2mUx2Ccph5lwi3+aI2pHAlXKBPz/NJDJ/9gDFJkrNxM8MSQ/8h6eBp+XKc37gFxPMOvJcIm/TFRmUz/LMUQLvVXmeM9dT5WU1BpR7tValGzmlQKxwrJmg6lraClM4UEPmuQYOKfC1CaGuvEXQZLzkkWUpaAyco7CKA/v7UvgXR+NDFtblhaLKEpRLjlwp3OfEe/Xq08Uiq5pHg2lgKZElJiqZb0tAGbBa+S0LqQ5c60LIQgnCuyq8q4XDWHlTZ1gi/zEKL/XM41JRWTguKxZfkBpZTYgWGKqQS+arUllRfnJIvF9XQgg8Zy9KOQbhXmcu05/WRzjCtvvs7XWdfe04ztm3BjJuQe4tvm0b1V733dOnuy8w7EG/3mzIGztpHRS3j9yKTD5li9tXE7WB4+2792MTMqjsgKUdvXCp2/FsYLz/7bYg+maqIlhx8OTuGb/biEOb/VytgkkldEZ6O6cVoVmH7U2vvJ8B7A4Q3c9019B6c51606Wvs+YB96/1n316/Z90zdOdae6PUNpD6Bu6ibXUYP9+oTRmqs+PzT3kFkBvzLszod5G2R/U1kZaaHrZ9sxtZ50sOLMvtrk/t0tsWvDgLFUu3e6vtfDeGDWa5ryBcfpcceQW2tgGczarawhFjYKSRCm2Yl0bDm6zaf5UQlDMytYyvOA696F4vUWNfs9BAsSKrcWBuUFMrXV8+MOD2ksLxjZdrLA3g+Cmwr8VUPVvPIi/60sHmh0sax8oBwhmUG9S7J0Z7cVKGdemirRiPPY52oFkb/AgsAVbghfPBfvUVqwf0u4nNm1z/3uxZpPFbqPV/9n//oQzgVc8m7eKMFfXIR9rJQV+B9RE9eL6e6qnzFVasaj/vlZIoct1pI3FTYMdHLzmFvD2W+mP2Ky7Vuzrobb3JohsEoraimTzzfX1+feI8HAjl+oEgOLPO1dnokdgkv07pDZ9e79udRY+sNuP3X5mDD5ep5ZLMdWNLOhgX9p19xE4i5GrBxUr+4P05km0LJA7QUxViOqM9+aRrD2o8EYr1x44tkU+tnU7iD+XruseGoObW0BYb0iWQXydCtppk91OLTTXl6zKS3vu2cTnrLliO+HadKV6b4nbTeN7xw8Gwufz2R/6cmVZC0NKPK5eFBN05tzeyGoCpTLpgAkch8BPsrKOM2eJaPaBezUUqJmTnLGWrk8CqWSPduVEqbM7PejAuj5ztYGXUrlWZc5GzoU3svJuVKZ65duS0aw8VeNOIsUEq4W/ThGViRwBixxYOMXCT97A15eRrzmgZeIwRH4UM1OFs60MMaBqJC2YLaQxknNhWg4sIhzHJwa9Qwfj+XlCs3k6f5ixMjMeE7kW0t3YtDCRcag88MTnFvnLYWq2V770m87MYeT9deZwHLi3R4bjA+hMWZzsqcuF++PA47Lyfhn5cBm5GyaYZ64WkGXhs/GCxCNfsfC3S+TPZeB/+XDhJf+Ex3nmWrwdcWYFHZkleBedHDySWgdOKVBjpUjhfD7wTjLfLZU3+ZEf349k7rlW4xIqJw0ke+F5Hvm2Fp5jIFjmoIV5zLxZAjEOPMZKWA78Mgg/QmEwTlPlpRinMPKqfMe1wnNZGDhg+sTxLvH+YnwkcIyC5sBqA2IzJZ150EKIMK0VC5VFKxKhhsRqyiUr31VlHO95DpnRlC/kSlgD31jl2zry/O0zRY78VC9klDo/c05CZSXGI6MFlrDwoIm3dWYMsNaRi0bKmllk5tVYkWXi7XiCZebnADLxLId/6pX6oxyu2bKtirnQfHYbqA34phiCu3hszK15i9Dcgag5Y3m740hjK7fonJvNrwNQ9oXfN3hpbK+Dxc35wZ39/LybD+oFTdI+pzbqs2fHNrsw2TcBDd3urYHr2q+lFeb8TmDgG5anzB1i9WLBzc2huaygLsFw2aaHEcrOz3Ygv4GBvjm1iGBvFfx9Rk42Btv6p0ndAF/YNM24TlVs07f2VGIH/R35CF2L5xu2tmuOnQJSfo/jwe7pWcDPudkIxVq7Utm1ndUfhIN7s03L2o34nUG3Nk6OnMvmweXANbQ5E7bvvJkLsLU9dgtQfw6bBllvA44GrnV/fipG0UpFGuBtjHqFqj6eSsP6tbZ28T7mFkFqAXONf1FDqhI1YFJbEKdIDdTvU+o/4DiZuTOKtKyGsck21gZK7jAeEQ6yZxoM73bWeha1/cTtv/qxtgF18LMDX3CmM0grqGq/69KVJ2tpaXZ2sOUwbgr4hEP7vO5vq+IgcMWZ7LW9o2cxZlFi17irB0O1gbNo1hwhmu43OEMZ23pyAg7iFlxZxO+z32RnNiuc1IO1o3gzEABtLgcdfEc8ELplws0cALo7lX/sSN3Z7zYGhm1Bw4rremlLZLkZw6Fd1hVn7b0AzHXNxXbQFcSbgSl74wiRzv564WBEEfV1obC3JK4i7s6gvib3RhoL3gFvwOUYY8sEJmjaWt3Gubed7veDOpseb6zTrP3d0t7tSZzZTfg9DTfrmidbhKXfo/gYHm8iKGv3OTTyQIExCGNzWjoGwaw5dai457fs7g99nmXrf1951cbG21dXRJRJhMHcV2URt028DU6+f/xwjfB8ZTyMkA6IKHm6ck6FJSiH6x0LC6UUolViSByCMGdlWmf+tgr120IYATF3XZBIDMrPhrn5ysEqHtGIVshXUjQsC8bMGAWrGYmCSmaJfpcf18JXU6FawGQhycDb0ThSqGXiLiSIlQ9LpqyVixbWGAgx8d1aOd/NDNNEOidSqJytcNCFS4G1ubDnIozjSC6ZQzKG8JEBJVgl568gHHn37hXr8wtVcxOJL8R5aa14Z9fjJKVcE8syEqyS5IDahLQgYlmVtUTyEHhaV66SOHx4YjyAlAPGypON/HKGhxh4c1j4YnxEdMDkQLWJQOA3j+6R+4C3EX3WlTwdWXXhbhA+F/drzGJcpaAxMZhgJTOVlUMaOKhvAkmUUmaedeX1MDId7plLZZDMn4lxGhbWAPME7+KFn0kEVj6sA5c180xgSf7M3jT5wxVlioG/kMrX+kIqSl1XnvHWjHfjEZFIzJFQAm+HlROKIqRUyXGl1ECeZr6OQqzRAewSYBxZl0oWZS4FlYUvQmFdJ54eK6vCf86Zr3NmIHIIiWEY+FFZeS/KKIWfpJm7NDLUJ3R94RSV+zBwDZmJwLIEXuyRfynCdwVKijzJHY/RCMsjyQq/qfBSDoRuqfAnOmIvkGvvkFT3Q+0FZ6JNTrCpKTu71+QIJhtTQQM9fSOt1jcPBzlenNIAiLEVUG2V9fjiWLszPVDFN1jv+SG/k56/BdKOCZomtv/MoBc5BWlFf5WterlLH/z03UdV1MGttvvqVmWmxmJGsp6GVrcg070T0+bv2+7LpQq2M+i9mE66HreB4uC+6V4sImxeWH2cO4Bt97ix6jsp0ophlCJ9Y27j0IIMHyfZrrOabBZE/TqdtaKB5zYJ8HEzrbt0hv7j3T94D2q6X7HDdgfG/ruIuV1ZY282CQQ7E13lJh1aDcte4HbLImpjkXoqP4vfb4i4fOTm3J7NUIQs1hwuemFOaAFVbXNDkdKkMLU10WlztWcDQk9Pm4PoqA6aB3zeFytNv+nXuIZK+CO8ykF9XMf2HsyijI0djLAVCx+tkNvvOoDzldWDk7EFMLExmj2GHbDNI3mtu3xoMPevvVbZdLvSAp2DGXcCF5xxDy1lfQr+T/g0cOljKNCK4oy12VlFcz9kv1dpLGwvFOuOC10r6+BlNbcwzNU49SBJncVO4sVkVbwu6I0VtzBTX/sEl0mE9p5qu54O2FbxtDvmVoi9Q5u0MRqLa08XVQZ2rXEW2bpDBhHOVpm6JRu7rWNG+K0Ib3rBXwNwEW9W4s1L+hrQLMMaePT22ZV7M55FKCYk9uZGZ4wXc6a0q5MyoHWXMo3iY3kxGM0t6Y7bnPEubyvCUL34cQ2+hp4aqTC3eXOPt+gGDyq0If7uvhC58Xg2mMU4iQPmY6h7p7l2dPZc1KUxGwjHu+0dghCKEcVaK2//ZVBnj7vER5tguRMFE4KVhSDwAC34UkKtbjMnnvEYa90adfy+4wcD4Ws8UebCevnAuU48jMqaT+Qy8XcE7sLIISovuXAMylNdoUQOQ+RVyQwhs1hkIVJr5cpK1Mi/mw1bAgcqryKkdUHqihzgFcrnNnC1K4MJsczIYUAsNVap8Eoj6aCs60zUI1MxzrViVohDYKkrIVTenRPHQTgQmJeMBeG3ZeA3cySGyuslco7CVJ85xCMUY46FUgpWA5fnK6dQOGnxwpWwckze0/rtwcjLN6S7Ql4nrJw8QhkKSzHuxNAAhlLrRBhAiyBaSYeB62VhiMKgBRkLn9eJ40OgSIWYWMrAdSpYGLkrrseeqpIt8sTAxzVQrHCMb1ls5eHsbUIlDDgVJ+hp5SAjCUUlgyTMlHejcijCixmZgWDCL5aB5+mOOa+YPvFwGPkCZckjoy18KTBWoQ6wDmem60pSQ9cKcWZZDqRY+AtdWYBS7vjGLgw68Plg/D9z4Jtl4r3Av74LvJ/htxa5LMKYAutkfHGsvD6shAxLzRyisoogq1JKRUMiBGEgMuN6umcTpjJRw4hkQbUyLpVqgWW9cohnrsvK56nwmVauQyCNmX8xT7zCGeW1wJea+OYy83U5ctVMOhwZloGcCz+2wpQrV018axUthZ8vwpvwgY+r8Nm58GgzT4z8Gbkljv50R6Ztajc/MxoIqbYVWVT6oiIbmwvu9+sLt2zV6f1QaQVv4sUeoYFNgBD2c7r3owNOXJNubNe1pcjsU4ar//3tdfshG2OCdXnEfpK3uu3U2P6v/pe++Yg4GL5dntuQMLYgoIhrEd0yzD/c2jXdAvQOwA3bAgbYCzKkbToCpKCffGe/rlsgK42Wuf3ddnuO0naN9I0s5dMx+nT8vOmEA+JeGS43rK6fjEs+2oPau93J73wuNJ128Cp9zNxCT6Tpb2UPhGTXM1fMN2r26+7Z+1sQ3DMYps7ohhowbaZUhhdWtnkqDdAWfC4EaVrlluHQ6t+bSGBQctkcJLw9cGMlyz5+PXBUBKlKbSalOQSUSgjhk3dqLA6o/9AjmX2iPe9MdVIHadV6Yx5/AaRN7n4tB4f7QJcb7J+t0uU2/sx7YwfwjMjazj9WD3QujfHt3e5if5546r7bdNGeCbB1tBP2zz63/5jbezPoLs0wvLEHtseFXUoltOsVbwfdbftuv0vNuAZlNOO1VKYGuJPs1xBbIWkP1FLwv6ttDerBQDJr0o0dzFvUT5jDzoAPN/+RcXDYh2KUXXIy4ACzbn8vGyi8U9vGsx99/E5iW+vgF3EQrm197e/rFQ8QKjtwfjbhHPid4uWztCxOG9fOmoeWTfHn3wAme2x8rpXHJgPbCgzbendrB9eLCAO1ue64g0bCO8V2+VxfB71AzrX+KbDXdtBbTRs1aPNfro28aYx0k5q0MgMf1zaK7toRNgJE8Y6pUT9dwxYpW3Hj7zt+8K78s/IRVWU9wYOc+fW08JUtLOVAYeVjrgxijCGw1Mo1C4c68bEcuNqRJIWzFYZoWC5giWc8RZREWOaF7+TEQCWHA3NdqPMBWSGn1wy1Ug4Tb98br07KmUxMg7dZNqWESNXIOa2cKsQYkZbWDCFQhkqdIpMJ6ymyFuE4ZM4ykIcz8/rMNT5wzwOPzx8JapyWFwfnByVYRIaB61KxIHy3eny75MwvryMiMEjzAAAgAElEQVQpJfQ6Y3VltsRgK88losuVN0GhZGKtvEkrSRdCHKnlwIflmXQ88LicuQJvg/AcCr+pEyEk5uuRE8ZwH8liXC4O1sYxE1ffCBa9I4bKxxBYpsJ/UuGVCpd1JoaRGgeuc+RdeOLFRh5XYa0rtQhPOWIGQ46Mg3CRwpSvhBCwIXHNlacy8Nv8wsUSnxXlL14tvDPjt6tyeQ6YJS55IZF5VQdOY2W+RqZByNPA31nguZwY48xBIC8TL3LmIol/+zGT4hET12tTMhXly1m5L/C2Fu7SkVIiv50KPxsgkThkj8lnFiRXpuPIIAPFKpMKsXrHO4B1XZEIZ77j8xDhYJwvKx8t8zQPfFki/3eFt7NQsnDNCxcZ0JCQOPMg97zUZ74tA5ZGZqnMAb5blToG/tPL1dOr4wPrc2FOQpxfCMfMz+z0T79Uf4RjYxXpjKq5JMhu0CF7+2VnT/ctvm/3blf1aVrVGkgODWNBsyza1hfb+r37+R2UeT/63q1uY5A6MHYu1zeiBnr8p92loDGy0q2wmr6wMz5i3kik36L1NG93vWADYTfYfVtoHezegPHG2tt+6g1b2sakMSktUw54O2Tq3h3uljXT9mEZZ1MqXRfpzLNCc13AGez+ff1BiuzNH7ZgxO+963S3LAC0xiqy7ZD9SSjS/KS5yQ0D5p4et/fM9/69Bzfd4q577vaNvgOhIShrLyiqgqgQml1doFW/6x4wQMv6gZtzGJh4IyYjgtQGTj3A9ZsLBKo7UgClVkLVzWtaFKS4DqJXups6eDUDKUbWLhti05UXDJGIlbY5Fn/WKp7Z6DrqIQ2fFiX+wONWatDbGhfraXwf50obQ/Zn2bFxFdkaCwztd92gcRBnZOdW2EQb/yyeslc8cF7b/O31nGK9uNCzN72xBty8I3hQERpL7o0lesbB2dYRLwKMAEFYizdVmMyvK+ISCDO36CoiXE1Iugdra0P9I85aVry4cpLW2rm9Own/3L6UHWW/lwnBWvA8tDVvMSGE1nGv/U5oYB9/7yaEB6t8K8rBnMkM0r5DnP094o0ftsJe8/te2phoe1ZBfD7N7VlnWmZEmlTgJuExyF7g+IxuWt8LwgnvqNc9oO+xLfNGu25oVna22+iBvxNF9mLB3qnvEpShSdeiOOt+Md3kLfe1sIpQkK3oss+x9mbxIEauRhGvixKB0Cz3snnzpKC2zcNq+9wQhFHMgxrLNGt5L/hrQcyCcGiZgF7obEbzrw+o7BZ5iDFqoVSXBgJo1M0f/vcdPxgI6wgPtfLeEr9E+TgOHLJxIPO6zKwBliUTGVlGOKEYEasrb1v/v1oMKYVjCBQmDvPArCsnMjooFgtRA1mVOzmCJeZDZNFCyJlgA4dT4i4tfDYoQ4jUbGRZCTLwWiIaBtf6AWhAs3dmM5Q8VqJEDsORtRRMBkophLDwqJHXkqn2kdNJmEPkjZ6p+ZEyJJ7rETkeWEfv4XJUb6V7LFDwhiBw5LlMSE0wVEwnLjzwtQ1YXQlaeAiZc428CStxEFI4M0WFFJFF+dqqp6/knjkLJWSCjtRLYUQZosGgvF9mCsaRSJLqFmW1ImPiC5QoK+f0wNO8MJfKfSx8K4nLXJljIFkiJePenNVeamYqFQ2ZowygExYiwxBgUqZw5LgYTxL43x8PhMH4wgoPsfA3w8y/Ww/8w1L5TIWfizKnwtMMr49XXucj4bqyrsoclXAaONbQoteRk8BlqVC9wWiwwlIjz/nIRQqv1wIUchX+rhrUwusIPwUWAiVmvhBDeeIQDzzO33GtRrBCWCYSypuYkRh4qpH3a+RXR+V5eUMuE1ZdS3vRzIAxqZHWhVAnnmMiy0LSA+904UGMEivHoKRk/NaMfAq8lAdMhTgeGEqhjm+hKr8q8w995f5/He4S0aPxruvrzSS+FxHfAOCOt/q/I8LQgKjdnCXtxFuAKG3z6/xuvf0cboC2+KYqN2kzZxBtY69oAHat1jaRm7Rr23R717ANULfr2YH1p0yE25h9ugqWxg5orY1h263BNu3x9nk7YKlbUc+uQ97vxT5h2fdrtBYw9Gty8BmiInXfvDrIdJ9P/9HW3MNumNTW6hpt99bGJ7S2v16V3aHv956BuTDGwXyg1NokuHpjm/SpTAM2cvl74G8PbHoLZjVYa93YuEJztVYfh9BmiARv1Nxzymphm7PQApkaXLpRXeMXuJWnlF1uAl7c2Jp8qDkvVNsN9flhqDcCwahRt+Iwa8BPapN7tEGX0iulBKPsc0qVueSt0O4POYJ0VxefFw40nBFb5dPt+XbsuwVh/3cxbwPsALqdg/3OO6htLDYnEJWbRg9+bg9kxRwsde9avwaXVxRhS8c/i/BQqwMbbY4kuL4/NkkSOBPY144uTepFj9LmfGrlB7dtuoENvK24Q0LLkBNkZ5uD7kWiPQ1+sr3A6rYIDvy8XojV729oLPhQjRRcoxrExySjbV762YfmBlFv2Me5rSu9q2QHwj2jpXQ3GBiij301uGh/Pvu9tFvcpFJRQKrLXUILJFN7jltR37Ye2O803lD8vYqyBzYTwptamUQ21vaIuzdN1Vtwm3gDGqFu9RaZ/RkArBaIgc0WTsTZ7tng1LJNtQcR+HMP0tp7K4i51/4gzjY7oeKa94jwToynVmCLyXYvKQi55jZ/ZXvOm0QOzxhZsa2Jx+87fjAQ/vfLK0YWgrhxfzVlDVdGU94MhUWVp5BY18Jn00QOA6ssDIdALB5N3ocLFiIvsnBvxv39wmTCqJW7uxPVrigDF018LMb7svJjHbiEzHEGk5klDHxcjN8sg2upwkgkkVLiS10ZF0ghEmPkzIodEld7wMyYbWRg4mV+4UFHhnXh7vzAh6cXUoQrE6oH8vnIKfkekobPwCIDhZenF8JhoDxlxrhytIU13FMkE6RS4pnxcKIGj0jX1VsdU5RhXDkVbzpCKqxUzqqMEhjrxJMo7ychmrOZgyp3upDCPZclM62BnDPfVKMuMzEeQBeuNZDTiJTM+1xJUjkHIEUszIRLZYiJiUdCOTCGwIMVJsmwBg7JiJp5NWSY4fr6zPFyZcnGZIWjQE0T4xB4nJR1Xfntalww3oSRp7XyH+Yjg038VbrC4Q3/19PMf3eujHJlynAXF+KdUIsXs5yrR8iXuvKZZgIjH4aVF0tcsoIlLL8QmZE68rGMWLiiQaihMVB15Beq/Kg+cw4JkcA5XtBp4TMVfl2VazaeJFFn4zeWGMKZkgaKZepSkXghh4Sp8bImnkug1IIV4RQE4oBY5fFpIgj8pM7U1p3vJwhXydwT+Xi45zdaSXUgLitLNZ6rYVNluW1x9Cc4Nn2tsjNkBN+AGnPa929f92STPMDO0lUzrBWD2AYjlI7zxLwpRAeIZh5xV+28ItxC6w5moxndG9aZZdlOvfUk7Yt458ByB0Db5nnDKCHNQaFr2HZwaHSw2oCZuEYwYtTG8PTNY3MsuAkQNhcKdmDb78i4SanaXvVe227fC/8ANDhzEk02NwdnPvcgpAcVprbZiPUR7OBYcGZHb9wirH0OnVVS35C2h9XHwjrI8sHt99Z/11lxnw/bo0PwYsegsrHPqrpJI4pVouhWqIa4tZgXzyh6Ox9ECQQvaHQk71/TisUwxaogLXgT60HM3q6a9v3dps4t2JQa3KWiaNkLRG3f9K09f3eu6MU2LhUrtctgDKxsRYghhKZJVXJ7mrm6Y9IfwxJ8AwYizOKWb/2HSv3kfUVgre6VHdocyzQ5g0CltuSEP9OMuC2VeNetoQVq7iYhXqnfQDfQmla0caoOCqf2WYM0pwlxkKsIybwQ6a49u96hrpoD81EclFyaNvjaGMABv6cO4JrSxrOD/jLRJ8aANw2pIpQKNbiFqdIB3f5Odn/dKK6j9UGUzdosmzk5Ye7OkMUZzUMby2JtnaG7IBhFlZPZVmTnU3gPyLJ1q7AOZJtQpQX8Kl7AFulFor72dcZ2bu/5Cbciq9v64tKBO6F5hhvHdj9AKwhugUmPkHGbs8WEg9kGbp9QgrSGISKsZpi6vOQktlkxpq1iz9+3o1ZmpLV09h1i7RrzFtxXjazAYK5bjlsbUA+CD13zq83+rd13CnAoPj6pFlaf7ayim2SiIpzxwCu39TVKm4NbQFVR9aUuWSaqN2p5Ka0Ji/mcKMgnQcH3jx8MhJdlQWX1lyIKSxVe60B4WKjzkR8n5SeaGazw5cWIZIgDRmZSt5Y61xOns3ihWYpoqHxpA2tRvimFL8uRr6+BtRYO5YUhnEhB+dvFGYyoJ8KciTYShwMiQolKCEZIiZiFayhcmn7su3xHXieOTAylEuSJS1VUD7xEmFNgzsJ4PqFBSdEfmFqkqLFG9xFcl4lDER4G4UBmfUhcGfko0YvJWmo3kTnVlWtRFvPK5oMYoTyRn1YOx4jmhafxnigHvk6eJl25I4mQDoaUuqWxIxW1ypCEaWmM+FxYbKTWSikJWYWXumIFfjwkjmMmrAsWE8wLP30Ff/cyAUeOB6/QPCqEEkinGdETa1mpZeB5mPjL6SNziNyNyuPixWFupQSyXjmGwE8T/GIa+K/1ytuy8C+PIw9F+BVnbLny13fKN3XkxwflXlfGkDjhzTKGUHlXFywMWF758VGJ9ZFLrnw3TSwpUbIQB2NC+VBX3taFt9EBUdLArBUNV66XyLNlRhWSrczziedauVYv/Hi6FHRULkkxC0wIL0tmjUdiioTkOmszY0xg1xc30peVQxBkfSIthZeirFb4CsgaGSzx62HlPj2ARR7nK2MYOI0Tw7VSJfI6wBoz13+q+8If6eiMmhdtOYVgJtvPdxJrTwdyw+Oa7MVp1sCzF+zYJkUQwVPGt4yy9e9n85XdQXJnGH2+iQipepFTvxxDsAIdG93KNTpbJuYFTP2K++Kv7MV3/b83jbJCLrrJDwKwVmdSHPntm3+//v6dIo2lxtr/OrPT3Rv2P5QWTCiysVkOXBuA7oW27b+j6R58yH4d7g8rW0qvj8ItYM3VSO2c3ZWif/8OjDtoBt8MekGU3gYg2wN0cCG1UlWJN/eVrTIImzc1jYWT1vo0qlHFXVQNtzJTrRT1L+71oVXcSjNbJUrFekOVmw3K1JAu07gJLj691AbMmyMEG+gHGsPe2fdP7KW6PhIQU/YQxNeS4hQjQ44+rm13vNbezEWJrM4+N6/SP/S4SZY4AJNuyeW3tdmoSdd4ehe1voyMjZVfe3AjTZNb3YfW40K3XrvVDwfpXcLYAMrab6cBlR6YicCTeaFVLxA7YrsvbbuPW6eBIMIk8JY+X3ZGdrtH3a9lY2SDYwnMGbwJ5ZVVlvZubxKAhpW3TE37/Ln9LFqTjrS0fLTKQYVLXzPUi8lMZLMAS9JDiD31f2wg+XrzXsvNgwvqv9Ob97efk4IHvQ+18qLqVnHi19Vt7qyDOttZX/AgIaps5MCnVR87+31pNmLdnv7FXD7xIQTusQamrQXftTUcgkRlNGMR3bJyYzCmujd0wTyEVTNGFahlqyPonToPza4VD2s/rfG4yYotxTiHnantoHRtNQp+3/7O5/ZsOxEytPO641GfM7n6e17EJSZrdTkKpV03eFM3dTD9z72uPxgI3+cLiwZiiIwl8Dq4LmMpht4fOPLCc4l8lR+Yx9cYE4dlJh0OPISRtQz85/LIYQq8ivBwOMBUuEbjVa581JWHAm+OgUUqHy1RJfI8r7wJJzLGEg+EkhlZ+WJceRMiZ66MeeG5ClYihcQHzWhMrFXRmrjoyDfVXJB2EqQkQl1JGG/FGKMn9C5r9ZS9rd4CMOcGvgrj8USwExdg1oE7UVLJvI9C0cphVUwCloxUjYdceZlXhJG745H14A0gCANjuVBD5WiKJOXRRqgN6IWClcoqlZWRRxKEwjD6S3cfwJiRLJgp5EKomdfmzhRHUf5rnfn18xHjFa8M7vSZPz/Ag3wgLC+cxkhMR756Won1QraRdVQ+l8rTNDDYxCp+P4NWNEVqgXwSnpZINOHNoJQy8pAKTznwD6VyiYFBZrQqIis/0jPXUCCv3CVhOCgxZIpVrkvhY6n8/Yu3MX4zwCUYWiohGvdSeEfhZ6HyIZ+I0SPHSxEkKynMnEcl6IFrhouMfFgrOR64ysJhrkgaQJQzwhrgKMrboFxlZVXjNCklfSTVQp0DuV65WzJ/MYx8KYU7gGPgaSo8ln0V/rVEMOGtJUSMUEaeJRHniQeeIQc+2MCzFIat5dGf6vCAS/vO0ACirx89Ddsq/7U2L0q3kLKb6mO3/tKN1expYG33XTpjVwVT8xrMtkgibaPu+X3d07/+Nw6qtjaoDbzEwMbgbUqGtnmaQM2t6l18o6gYSZW1VpKqyxa4AYH09KdtvrT0TQi2dtP7yLGd1xtO1LY5doeNKM4qDuwaQGvgcmPjNxC5RRp+/bV2V/Xt+6Brnx1yqxYHrTchAvi4ONsupDYoun2Pj8UOmj/B+P4zZXeqULDCBpLMrIFjMNVNb+y9MHqbatk/HNdtSxWKW2y0h3vDpjVgreZd/oI4Te1M/k0w2BgeHzcH+D6/fCA7BNA9PeCsXggN5O6AVM3z8hHBis9lD4gq0pwkPnnYfVNNAbPCsfVXFqoz8hmKNs2mBlDDLLSW5XV3mvgDjoHaQKlyVJc/u0MPLBJJZDIKol6ALA5ukroudm7zRsxZ8tzAfU+tz7jc4rU6Q+eAwo+7rmlqc6VLCIxeRGabfnZsdPzYfl8bMzrukaCzcuLsczU4Q3P+gGTOCF5VGKvLOFJn+drzyHjHMsWLb0v1sZ9bEBiaGNabVfi7V9pakAKsxYvOBPGCLWlSCzNyUGpxMKwNePcG3b2bnGKb12wvcqsNOB5gA/PR3PXC76tgKNbWx823WITRnFFdQ5Ph6A64o9nWxCNKC0xv5qf7nQNWvTOcuV63A8DmqMhZvPjzhBc7PmAswVtXW7uPiOvAm/cDlWaJJp5ZmMRt9KoIaGW0slncQS+gDl7odrNeBqxpnt1BwrY0VN2ID/B7HSO+F+3bCyl4oxLaPAZ3kxilFdC1AbmKs7vdpq+2xe2YKtEKz+brUxDXbM9SiObzqXfmC2a/41B0e/xgIPxXg3A145wWnshcOfBC5mTCb2d4sju+JfAdwmLKq/jAMLzwsFb0pSDDhXezcT5eeB0rdznxX8aFWu6px8Df2Yk0wp/llRiEH3OlWKBK4v0amMpKqo+cgyJWmS4L/1gWYklcxjMfByNxx5GV53TmISzEOnI1jzITV16JMuYXvrBATkbSAdWFKQysAq915JXMDMPMnbhfrVbjuYy8jwmbA8+a+FiUaImcEh9Xn1T3aeZQKu9WxcZK1EpMCZsKS1w45REdE6YDHxn40gbGtlmWeUB04T5lTpeZoRYombk8c78OXIbMaO62MQZlSZkYhbJ6UdL7NfD3ZeS8wp0VjnHlX49PLDYzr8o3j5n/t9zxIkdSPvOP7xOTZLRmBgJpDJziQCozp3jgTQh8MyeOlkkSWKeVYRSyntBj5t4Kf5HcUeNpGRkPCz+1wNfVKLwiyQUrZ/6PDKwn7uSFV1b5PFTCpFxVWC2yivGBMy+S+arM1BIpxRhj4GoTWOWzcGJalY/rzGM5EcrK29MR6oksMx+e3Mf4eLfyICNXqYTr5AtmEg7mesOBmWmGR8nc28oxJ45jotaZEaMW482Y+HiIxGKE5cCzFupc+DAMfG2Bw2LMSVCUe535kRSGMfDTOvFRhFDhOQ8sQyDmyp+ZUc9/uK7wnzt8U+zRrzcBKOYRd+3AUz21FghULQ5mq0sNvF+8h+1GdVBZ29YpxbWUqBcjte9UU4oV7x5WnH0OsaWbu7TAoFMgFmgsobtPFO2pTjZ/yM6Sdh9JVSFE2Winm3Iyr1Cu1RlLbaARWuV7xX/YULD0pdoPwz8/tO/pIDqLb5ClnRs2xicgVinNB7lB380CbdP2bV7GbN3jkuwbpakhzVauSkR1RSwQWsraqrcM3p6reFemYEqWsl0Ljan2Tc92zWzbSJz1F/fAbUOg4K417ZmIdDDdx6Yx9So3vsY+b/ZnuQNYrUbpkhSrZPGufNVqY76aThpj6Glu31rdGL/WGyZpL8LqzK+0C+zad6qDA+RmHogXUdGDl0Eh78WfHagXgYCDWq0QtRJqaen51YOyoFitrXuWSwxmMkMJrZW4g/JenPiHHFlcz5yahcUoRiaCFE5uIE3EKK0r52sq39HT2z4+irmkrj38F5SjwDO7rKFY3UKrKt4dbdJAMGMwL/i26s+6W5cFEc66d/KLZszmLglBXfIQSn8vmqTCHLy6u4F3WfVJ4v9wL2Bj7POwAeFiMNbKYwiEFqyOuDPIxN5KOsmNFRn+t96MwpDg8qzSWGCApaXkj00vvgpIdQY1G23e+nUJzpIXcSnHY9BNmzwKrFZQAueglDLxJQdSSDzUwtQ6EG5ZMIyMUVVJtQvhtckQWrGisnWV6+tIJyt6o5KVBtpVNq/gIDcFfgJUax3YfB1K5rr/LDde4uyMvTe78AdyT+ZkxgeUtxgfhK21+wOFx+ZgESz7Hops7iEiHiA457JbS+7Wi/7aVfGMYgfm4D7N15sgLGptK8LuMRxx54pnk+bbXIitY2DsAY8IZ2zzNl8QkihPszEEB9YvJtx6YP++4wcD4f9zDaypMC6BKsbPA/w4GKslajH+QQKhZF6JcNSVkyUeCFzU0Du4TMaHIfHIka8rXGd40RPfxDe85MI5C8cw8yIH7urK2SJrqWhdeIjwOhReK0RZ0TzxxUn5SkZWO5Et82xwsQmtK58tQtUAduFHQyHXwBALT0EJ3BPjlVchUErmSQLFlCID38Ujz3pmXV/zaw2cWjbNgjGaUKNPqEkVIRNNSCFwFXgpATX4B8scV0HJTEWpBO4raKrM5i/cfTF+GidWU6ZZ+RBnDiWwroElnZjNpR2rCZe0UGpisRWqUa6FuCaXToSIrt5O8PVxIGvmV+uBdRkoWZiHEzoDdwnyDDGwauazs3GSgTGOHGT2RTV4tbUE43ky/pth5pILY7PSnhDOx8JdNa4CH+eJZMoQ7/iOwC8fF+b1hcPDwGuMt2r8m/PFtWn1yFSNvy+JF3FmpxS4VOUkgVwmSjxAVK4saFV+mQcWE8YCtQgzb3guC4GR+lhJBU7pwB2ZGiPTU+bDVPhpeKEIvFjgZJUrgYMpr8KVpFc+1xMajPdakFPh1x8XPuhrrjUDZ0opPFugpIE8L5ziiFal5Mzb8QM/l8RSK+ss1EH58tGoSZinRLBH3qVKmALlELmUwMflTyuNqLgBu7ZWla557O2MfaF1WzRrTJsXQ4gIudtP4Zr/jbHV5gvbi+3M07PWFjcvEtuFakVoedxuddWLjFptlEGR6syd7Hq79canEhro0huLNbVPWD3PbruO1BobKSZb+i5EwWxnYE08jRZuUunBPAj4RCfdGJh+6O2t41o4bay2NcmFg8W2ATTz5a2QLhixOADcitvMmrexEmTGyuitudczDJetnWzXglRt12+VrZlHJ+NweYIjQ0e7XY7iqcQ+ht5hcLvPPi7bk/NzbtO7tySKA6X9Jx27dt2t31ekFx6m2NqxS9i/qI2vtOumMZk339L0v42lDGGzRYPW5QrXChbwYMK/eCuWSyaQzYfO8LUfCKX4NYsw1tqYakhFyNF1k95kpSIaPJCgaRqLoi3yke4V+Ec4Ig5gioTmc+1aUAGmLYhsrDyul71rAV0xc12t+Lh0L9vYAMlDAxSLyWZhdmznhP01QxGuRTbZlOJAC4W+XJ3MmNSzEd3t4FBsK7CDfT5sAfLNNfnPPQt0p3CprYgSNo2qReU1rXlDSwDc0XzH24cGhUtxnfTWSrqx1b1ZSO9eBjCYyyQchLU5pzA3OUGXOEkLeIL4D1Yz7qywdK9g87bGBixlxsKJz2vh39cz/4onzi0K6408esfDhG1geysiFqffe/Bfar++ZhOJkwlC19b6ffZ2xt2xo7/VW5vwHii2exqwT3TsPft1NNtA4dwCzhNwEUVr4dPUScuUyX79t3MH8fEX82AmtqxGnwu5rele1PbpHNF2f30dG6U0Jx//psmabEIrph6UhBZIJ3x96Fm9Yp7FiwZzhUMjTQKyybz+ueMHA+H/Nl55GI5QCkUy76+FX1rkMWReNFELpGYC/1iUWQtSAkMUXs2Zn44rKhGrlSkXzgjP+Uhc33MOlYEzc104FrjGglhhzjCIkuvCUUf+sYKlwhMD/1t21neojQYPA+MYGOyABAg1MFH4GA648fyCturrrzgw1BFJAlI4UIlkLuvKd0GZ9MSsxioZrYFBA4uVzSsvR9CaKFK4IJTiRnnVMpHAEIxcE9fOymjhTkfMKte18h0FW2KbIA5izBaOIXtLQzFSiHxXjEMcKSaMek+1mdM4c6mJiKCsHEaFnIiWOZ0Sp7VyzZVQXIxfgrDaykmN86EgxaN3Te7g8WgHPpaViRNLzax1QGXkvM7NnqdyN0byZaEKLDFSl4ULr5lFKQVWhbuHxL2dearKV/WBxQb+yzWRQ2ZerbWAhNOwUCwyiPE2Jh7rAsmYqnCpSl1dV3qooLaQQuVOE2IXvlkyYYUlKD9LMw91JstrPrz8FoYjf30KfPholARqE9f1mWM88aFEHo+Bp3pCJPHRBhY5sn59pXIkBGVhxKpwp8rbsqCpsIRCihlbfRwu08B/tIFHySRRpkuh5kScV5JMhHDkNzmSBuF5dRP/t/GPt4n+viOEAq2JRgCk7vynFx5J06nJxiLWCkirVDZAQzNpN0px6lfFF97aWhL7guWWOOBpRq2CBS8Ia1lqL2zpgMmkpXANq15AugGhDqYraKNna/tOaca6jvGE2qlY83tysNzVvG1DkV0O4YSWg9veirev6kV9cS3q6b1SG7BnT+/3RT7g3xUb4Ar073fwa22cqnqxWGjA11qKVTFEN7hCyYJooRFp0MEAACAASURBVNTE//RvCg/6kf/5f62MAWpWYMXI/NUx8Ytr4V+dhF9MuukXMWsbfvMntQZ0jc1to68qvR0GjYUt4qyRVXeb0NrBw6dqRME7NWHVi6BEmrTCPgHi1Vy+YO1aMKHmgBuYO0SJGpr9nLculq0ojW0D31hw6aps2/69p3l9OrTW2Dc7a5GdsXZ+yeduL4yz4LNkjBWt/rtqlRqNWhpprIY2/+EF40BAUyZUb9da8XkVSnYrtz/wGKVszgm90DO3Z3WgsrYxW5pMoHfx67Zi2Vx20t05BPVObvj7mSsMwQM+FdmAazHlKC3AFSHVvZCsvyLawCHadKI4Y+q+sZ79cEswT2c/N2IniGyd0BR/d86tA9pk7u+etuxCB/UOtoq5bZjz5PD8Pe/XYs3NwYyrCqdayVWYEO7FwWfGwaPLF/zzu6VXNIdZBQ/mO6AyPOPh+MnT8ItUhja/klbel8iDZBYi/+P/8A9ofOHDv/0b3g6F9zWw1MxLXnn9Dn79tfDwo4nLhwOfqb+F4aZstHJDIOgedNa+ZlXZiuBW8RbOoV1oCHA12d1BbuQKsdV0FDOKKVFc75xg0zGrWCurbAt/G1fFQCJhTyvxgEtTRvbud7BnthoRS2SvT+jSswIM0YPVIO4o0jMEUYW7NgZze69Ly/JU8z3yKEa2yjFUjuJrhTbyZTbXNg8Uru38aF4od2cr1xawKXBs15Lln35ffzAQ/kc58PhRedaRoHDKV87pSqmBe2COkWyJHGEoygsLn1nmc1UXetfAyzSRUqAGeBvgnpVZIlVGfjV/gxE4tAGLNZOqUBVKPZBH4V4q39TEkAbuwspLTbzgL9+7KBzqwkGj9yqPMEjghcKDTNyh1JqZQiSr8G1dkKsQo28/6MwqybVN5YXESJbIFd/oEOOhKPcCY1Vegm+Uz9mYWyVDkgi28mxGCFBToq6ZGfe7PZVMSok7GbmaP4qpLtRaeZHAYy6eEq5HQoaLvHBXj5wkM5aP1By4i5WDzNRldebgeM+ZlUdtBT0hE6USVTknYV2MskINRtVIrQIa+Lbec5HCsUxoOnGqmdcSKeoMcciFVzYz1IW8wjyOVHMWPR0Dn6dCKRXLFQtulffMwHMNfFwLZV05SsGykaUQqKx6IubKpJ6uelkeERHGkrkLA99er1xae0TWK4MGRoxQLmAr78KRJ7vyLgW+mQK/4sgdCw+jUJeVFF747z+/57ssfPc88uvxwAuGDCdOtjCevPnHQyq+mA8DU67EGCkYNYCZsJC4mzOnKGhWns3Z/HIcOaxCGk+UZeWogh6MqY6sXh5KroW1GkLhb7Lyk9Pye9+nP9YRLHiRUge/7CwkjWmoVbaq+07TOdD1hdoje6XWvWgpNsY0os2Htul+b2jF7oHrnyeb/NPw6v3Qini89a1RSkujNTBW2s6peDW2mLRGDW3V7euz6cbomrGl4zY5hTjgqV0q0DY31fYdcMMIs9mZZZwhLFUaA7yz0b1xxv9H27s02bJd13nfnGvlYz+q6pxzn3hcACIJkmAIIm3ZCtF2+NGQ7YbdkDtuuMHf5F/hphp2R3aEIhhW0GKEGBTNoEVaJASAAC7uPe967J2Z6zHdmCtz1wEBGHFBZsSJc27dql17Z67MNeaYY44RTDamUdrrSJXNr3k955u/sfmvcgbkYmglVlj6nt/77XvGYyZYZLKe3S4z54FRjP/2H594Nmduc8c/CpVTUH71IfC//duds6orVdbI4BaN8Q488z3C9byPeeAt7W7rr/6sQ1rogTTnC79XH7PbUBFTHkNoDa5Jjc2xxH+nbafeZFU1y1aovHNYs95aXU3MB5tWLerGfIXL2gYae9yKokes/vo+V718oWLVKKFDcnaqUQK1CmIJgJGOJEa0SM0ZCaWxTtLaG4Vf9tAG0sqj+2iFfiJs0/774MzuhfX0S+dyG2dZNw9caxpTEySsrWrbQMt6tmplS6rrgoNkaYykA123EUsmnBGucF2pAJ02qz6aJtzHbdpg03YJt/c6t+uzF3ONa3sd5GJzmO0SOGHqhceuvch6Ja1JLlSEJ7VSgjKat8CnKozNlU/lcv5sracaMFZbgyxse3+dutWXNbCn+HUJjdNWUb4V4ZP/6Lt8fDxBaPEX+QW73fuc1finv/tjfrTcoykiv7LwzAI/4Io/+ZMvoThz3+YAt/TKNXl8PdY44qIXFtu1zEanshX3+hM/t13X9pmrwUGyf2Zzx5BZLmWx/MTPV4FrjPO2wi6hGeCSEq1e8Nijh8z23eaa33UYrtQLuNQ2NCjShpS31ejvZWXkH7lq+s9h7Fry4kpQRNydZq/+bF2I7BreepDAzoxFIglhkOxFJB4FXYWfeXxhIPxtmwiHjnm+4wc1cAod2fbsYmSymVuMPme0KndUnoj7QO50YUwL98uJ/Rg5SuD2XHhBJJZ7XvdHgiU+GYROldc5cCQThsBYCg8pcRCjlIGXVklzpWqBTnga3PEwKrxeBp6HHQcrfETkiVWsm/hSUDqdOTNwFwb2tvAswB2VOBhLjPy4dNwV4e8pfKOfuUuZv8qV10XJEtklZR8St/S8VW/3xJIhF9BIFOUphY/izFEzd2JMtbIrCzF2HErP6yjcDx1nG5hTZRZYSuYpypf7jq/m1+zsBV0pIAvLImiMLCgPeeDlVDjZFW8lcRMKUTK1KNP9G16hpNNE3/fEYAyxA8lYDXRq7HaVlISjVg79c4SOr+sr0v0th/EK5QBWOY0T45B4e7vnrhhLGDjHI6ecGE25W/yR93BnSBdJ2bjNRrLKLi4suTAnZzyiZooVYoUP50qaJ/bjwjIn5t7AMtfW82wYeF0yhZmr2HFF4fWS2AXlTVr43CJ7qXzUD+wL3BL5q9PCr8VAEGUKke+nPTFGpnjNH9+d6Bi4FoOaOfYD710J+7lykhOvQuEQdoQg7LVwvQ/kkFhMmHNwnWIpvCLwEI1xqby/q+xF0U64jpFYE/lqZC6ukz7nyl11FWTMMETlnJRDPqNL9/NvrL+FwzeYgNTW3G2tYTEHCS558OjOUnyzlBKQkKkEIhUzJQQlFjYfbm1snKxBBBVoDBwmhOCb2cp7GG6taKyV/mpLBIK2eNm1Td4YKfEI8xUNtE5/YyIrpU0KC85M5WoMGlo4xeVnsl2sdjx+l83PVNVaAXh5aINs7zW4o5YPn7ZywaetPZlvxY7SPiXBwXPDnKzyAt+TH6fUCeJjVgB8tb+nHxMlQAkFyQv/0z8Wbl+eWeaBQxLSVce+VDoJhFoQyfzOe8q/frGjb0EPprU5IdgjT+VKsdDcEXxRrODQwxmCizwau2bBd+lNDoOHG0ljXjYRMZcWqZ8d/6+wuou0tqtZ8/1tm/HK/IkpxSo0d5Yt7bkx+Cs8Ud3wvf8tF3/nlUlbo7ulXZAi0Mll4djqn10V1eznX9yDuKFvpGaKRrQmqiihZgy/72st0GzTQgxIdaeMUguhGumxDcMXPNaAgIHqjJXINkBkokTcE14QRANDLUQVJvM42mRNr29G1eCsZ0OoKk3rq0JutlR7WV0pBDMPHTB1bWaHD1WtA3BqxqJKri4LWEw4tut9RrgRl2pIlaYX9U7NmroWxYehonoSWhGPea7RuzPS7ksRl17cqcvkuvYe1/dR1DX8XoterAWLaouHdh20tj+9rk4Utt6e7X71X5Zwr9w1Pj60e18E95pXYTZlsIrUDOL5i9+V5/zHx9mrB01QhN/7737M91/fMr5V7uaZLx8rlBlCB1Z4enqLPQ3w6kv0kl3SIMokkd6yD6ED2noYK7BcgaqZdwJWEfb6nndyCecBtzUbrVIkcDboxT8DDXR7ambZiqzSZitKNSLFY8dxRn+tjxXjhNviVW8Bbvd+h5G5JPq9E8XeiokVnK8uKMV8TWX8enXApD5TZQhjS5QzCYglB+yCV/NW3HwA1/bn9hw2FYyOasXPk9H8wQ1UCVZ9oBAYfxJpPzq+MBD+56c96AODdVgcUZtQg3OZsRLo4x3vhx19L2iNnNMDKXS8rCNzUX5dlWN/z5VmHlJHDJVkA9SOu5J5O40MQ2BJC8+BkiGEjrFUxn6iz5VrUWr3wI7A06hcRSOMkbdl9HZ9FgZbKF3gh9ohWehLYqodOUSuqg8M3OWFOu4QKrenwi5OfGWcefNw5F9WZawHeoGPJTXrn8iUlN2QOaSO+1BIopzV0AwhZs6150fVEOnRlFj2wodlx1d54L1QeWYnrqVnkFuQkRca6BFuVOnDmasoiF1xLoFT6am72ac/40hXC4c+kG2hnAIP2qE20XXGTgZiSoRjdJ2RJWqZQfsWYOI58Oep8mc5s9OvIJzYMfJQR8J8Qy4LXTgxvXyCFbiTzHUHH02ZGqBOxo84U4meLmgBinFeEqFGiim5PBDrkWsmVIVXs/AGZddHQjA+6yGmA892J07Aw8PCXemYzhNPdOHbmjj1A2/ZEXUkWuHrR6XMmT46K3u3VKSe+bXhmvvcM3VCb8LQOdM+CowloVZ5rYVcd7ypyvnlwt8/FHrz6e/Obum1Z28zt3cL7+12PGFhCH7b1zhimjjHjrHb87zryMBuqVAmj0ktn3ur2ow4DMQUeS3JB63Kjj9b7vnTIjAY/+MXvel+gaOKNaAKK8pQa6wGSq3KOuUljTlZcU6x6K1DbSCCpm+ohhJcRoB6p6G112mMnbNR5ue0ocStAF8Zy1YM+wTcqsttwGYFpu29eLpcGwpTw8xt4LQxlKWJC00gldU0vw1sAANKtuIpkrU8AskOrVQvcdHr/9NNI0qDrI15Ed8w3PP0knO/HqEKSS5JfBsAL64hXpPvwPgPPhaYKv/unPivfw3CLlLEAy5EAzUaV18K5Lygy4IyNG9cI3QGw5Hf/Fbie//nzIONTYYR6CuU4PPyzfwAVXtn4M6PwoUUb8OR5l6/Lllp56kxZmIg6uB1VVtvbB7+s6vLxgpYveEeUKktbtk3MMzaYGPcAHluW7+/g8uAoIoDsCg+tOmFU/ut9cIGl8Zgrqz047a+qTYrvzaG02Qj/l7XUUhDLRE1+mcNkVoMVefqTDNmXSsuFasZ0eAT//qzN9Zf9NiJDwRlLvfLWnjmJkHqzPmzTkoLJnEstsNtIVdgG7YhytaGrjBEl0esxckqLajVi7silyQzVdfpr0DI7btgLRI9IZCNkQ24dtn/+zL4NTQV+mLKZH6/XotxMmfy7qs7xFgDdgeM182veQz+HlaiP4hfpccRz9sYgYBYG5wSYawXO7fQtKmJi3yr/QidClK8KJhMCE2uNDSGXdt6iQLDJ4laEvNz+K1vP3j/PTTaugMifG2/8LVnBaYMocNKRYJTok+uRv7p7o7/+fdv+Ib2WJM09ApaL/fNKikYeLfLYAZTg2mdGHPxcx7EB4u3jhU0woCtIF/jm9euFLhzQtdAYl1Z8nZ6eqVFGa/dI3cNUfO1I8JG2/Y4uDx7iUZUWIoRw/qsdMs+/14/dGX8RZho5McjJtudfyC1+14lNE9hYyagEjx5TnxrC+qDdckMkYpU7yKd2lyIkyGNuKjG/ufcr188WQ4l25E7NUJWRHYEM/Z1IYwBuOY2GGFOhJxZ4ohU4UGMEiN/OMP89obaBTorxNYx1pzpakBCYppn7sU1eaFmlkV4qG21jx0lg9Yr+r5iGSKB4T6z1MRtPDKqclPOzDVzReJ1ED6rI6nOhJD5gVWW7sAhKR/Vygh8OCpLifz4zhdAZ5nPEN7vhS4HzswcSiGKUubIW515m3qmkMgEdqZYqpguDAhKcS/cRZkQfmQj1QrV9vQasJqpGoimaPCFGvLaRlRUlRgrS4ArA5IHAXQUvtIVPr56Q3e+RyRiQbmtbzmoMJwy98F1gBQ4W8edOmNZS0DjwFco3NpEZ4ElnwmWua13dKYsS0S6SsB4am65cyeROSuLRlJaQYjQWWFMiesQSKHSWUHqjiXP/HsCVSIpGDemHGZh1jNf1sB78Q27nTFNcDtG3kwzZxHQI39aJ76RlV/ZJQ6jEPPsD82biiyZN7lwvNmxH3e8nBY6yVQV7i3wtg4sqhzixC5AnpUxFE7BN457Fv4iK09yZT+A2RN0SvxhVV6ejsxhYKRSu4EwLU5HALF4rrtWQ2LguVS6okTNHOyKT/PCfRk5ltwM6X3+9Vo7lv4pvZw3/dPf1RFa2W5VPTwCL8dr2wRDKJDd+7vQdIlVqVof6W1BathcJla+Qlb1mME6iLDp09R1+EXdZ0FRB6+PgCaNUfb4oQtAU1FMW0RsBbep8kl+b9NK0yVXJNDeibfKIyCt9YbVTbubdQ09KBt9q4Bt0yMrL6seqWz+tkIDnWLr8NsK4LRp7FZmphUFa4ue1uZDHdiaoqJ0jb2TaPzmMfGNL09EgW/tzpQluhSnj85sVi8IqmaiQDcOnPNCHKKfD0DzTFcjt70ipbVYxc+ZYJhExApom91u9k6CUCVDvVwPqS6BWfvR66bkZL94IIvWDYgWrU0zrYg4C007D7QVom1zcyv1iwmcRzu3TbnpshOVUAOV0pxMxLXU7b0E9TZ7bH6msm2abfNvkpjqSS5tLXmRZgFom+gG20v7l9JkDm4VF8K68fv5omtDatWt2JBMtQoasdI5YLZC/05z+YsdtYECQ9nhTgOulTW0sb8zzvq67rXJEcwn5ofGwl5WoV9YM7eHxBxIdtQttQ3cF/Yk6kOD1vxtq3Ht5QiIO1p01MZMXtZNEGckZ1F/IrQC8qAOjLzEkBZn7JZW7mThJMyuaY6T8wKgwg3ufjGZW7Spukcu2BbUERqLuNjFdm0RIZnLZcbWVXDtr+uGQwPyq3ev5YKF0Nr4a5S0d6JE4YxyVYyjGrfHN/yDL73FxLj+9XtyGtZ227v6FcuNknYrGTnKpc//MDGFnt+qbuFmTS4VKI3NbUE1ZkDGxOeWEu4ssaCbi0IyuA7GgzUt9bquDa4FTurOIT1uRxba+qnt2bmy41WFkdrKQC/gh/bcXwd+19nnwTwKOavQ10qv3kGbxUM7qvg9ajRv40cWnD+ZHVXwaz2YP+tXzXFoj6DcGGdt7ExGWsencK2FqMWjlrWx+6zMtV+GBffC7qQQqq/PswUiRhfg/HcBhPuQQSODCchMoHIVYa/Ch5w5xoBJZNj3WA1kSfQVaqjUbMy9Ukoh2ZnK4HIHCuepMFvicDhwMxm3MTEVr1xvS6VgvMmVm3LHt8bMJ4cdf34/8FZgnidUAh/vYU53PO06vn41keZAYuEH6YpzmYiz8Q8/CnQivDhNPE8nztLzUnte3S/0XSWWSO0KEDksmRezMXaVD2JmZ0Y25Tie+etzzzwn5i6QCGSrmHoCW1GjxkIog1u4aGHJRg5r603JKFZqs1MSpHqYYWk3h0pCNdDPC2LKWYxOjRCNJU98J3fs+2fkInSSmW1P6JU3CqUUNGee9Am9n+m7yk0cYb+g88RcA6Yd0YQUhFyVkDq6YebKIpInZnbMNlNno0N5ZQu6dNTsxjb7mnlG5aEb6RSe7WFcAskyL2Xmae3JshCscJ+Ffah8gLtVZINlURbcW/QrT4SuGCF0XA2V24eFUzJOp8ybGPlsfuDZ3RN+4zjxUGZezspVKbzNhX23Y9LMJ+PAszSz2C25wK4rvGXgPMOhP3PLnqscSUQ0Zl7LyGtVinRI2PHkqWKa6TWynxPX73Xke6UMiU+XwBnXIfZijBYpouTY8UrgJt5wZRnbK09ywebEXrLrR0vi88LfSDn+Wz9UNv2oy1zVwYr4oEKsxhTdTinqaq3mQMKHJUClCe1aS3tlYLJUegtUAiJumbbujoJRow/BqTpzm7mYwa8aSLVKJLS4zfX1Lycma2NU20Ne68VgHXXrMsFZBecBfVP14bD2NfHaxef81uHANpjSqKZSHUi5bEIb2+ubhViTU/gvai1XH9iStuk7iK4EUy8YTR1EVSNYBSKpC3Qp87vXt3zz2wUrhVwCfYA6RLpe3VXBHrV7A204DZJVYuy2TsMYOhYppAz/w+9W/tkfdFvymjPZLbAENq020gbHzBCLoLXpbn2oNTSHhPW8eQuyEugwTYi48M+vSNckDhVQIv6M8m3ai2UVw4JCaYC1odci/h5MpTnHtKEqEUpLmgvi9mQmsvlQR9js9rZtTKwVbf57HBSu7Jjz0b6kjCBedJkIsU3xRHqKZGpdh4oEtbJFVCPepUCVUL3z0cWAlEJtIscee8dT9Ysevay3UJNDcImy3ZnxUoT3cG/pWwnsmgF02opD0KY36hoALuu9Y5W7loyGKqlcAmwyHg8cxCVhr03YiWydkL6t72SVYdOyOoDOrbjyqy5IA+OJ5jdcG4srytj0m0/b1VvaT4o6serr04e/dup2XEmcENq9YxEjVAqDBGoLrDpQeHhUjIi4VVoUn/PpbE2ObHM/5m4CSmqcrw/DTXnhbdzzW6Hy3Vz5y+vP+L1vv0CBc430QWAf3YoxP6IwjUs7baViG4DcxM6DMN4b/+S/+gH/4l/+Kou4J661dbprAC63gbqI585FKZiJ46JqJHF55yuDXv1zifmnSKUlxVVrKb/GKJUSAsVgtMIk7ie9iGwDgyKuTTdzRtc5Cv980Yy5hW3saM+X4M/0jDUvZ4HiziXW9o61EzjzqMPRivVOhMmEB4QrqxTa8GtthRMgZFSUQSpLu/73ouxQTyLEHz/FKkNYw1Bs62AsBoNVSuckxYetxTXJJf3vpx1fGAgfxf3c/KaMRKk8tA+tyRzwYpymjOALNPSFY4VTjSyxYy4dOezZl5mIxw/nDpYcWc4zb2MgTZVdNG6Aj/vMYYgcNfKmRP78DH90SnwUlcMQGfrI6+XEDx8GjmPP96bEvz0F7ufIUTusN8ZifDwm1Ab+aO6x0ys+0cI3r3bcl5l/c1tYBLpSSXmC0PNeBzUH6lJQhciEoNw/VLqc2NWOZfYQjCkIIoUgMAbjWI2uE6wUptqxmPmGKUKtJ29li1BTJITAov6Q6WqlA0J1/0qlsIvCzTIxBLjJQm9nrCQ0JealckrC+zrTdZFPImhUkhinh5nY9SQ67u7vOXWRYxgJNpOnGRHlOix8nkfOlnh1Dz8iEcwY7I6lKGHwHPBvYpyGt5yiojKyBxYq79WOezKvZ2UuRpoTcwhEixwDhBh4onCXjLc1M8SAhY6nLLw3KMtiDOHMrx4D/+bVLd9JlW+pcN8lxiHydjZO+48IeeH3TyM6HBhl4Fd6n9R9rsbtNLLUwkOu7EtApWMMmb/OgXMIkI88lIRZIAbjg7Hjk2h8LZjP59fEAUM0o9LzEAOvzw98Ogl3bw05RDR0fGsX2Enh9jSj40CWjqUW5nzPsxjh7cJ3Z+N2mZiqA7A+RKpUsuSff2P9ksfjCWKplylz8AjNEoTefANL1nChXWxvqOLhCw0kr0NiANGcJfaXqz5YU1vK4KpHW+FIYx9cL6bO1poDyXe0nqbeOpf1d9g7Y+tVvXUnxVzqvNGZ27f8jc8erG2y7XgcCHKxF2pDV+iW4qbFGdD1daJT5v7aG9Bs5+SRf+r2+91kmdImr7+sd/w3/6iw9AtDP2CmaDZCUMQKa8qeVaixtUqL/3ubSGna2hDcO12qEPpCTDMiRweE6/tvoN73Zf9arSt4Bze8byxp8Q6Uopgm38+rU4aB0Jwz2jmT9kGlRYi0drd/3Qv47WjiwG3IErfKk9Vpo3owyLp5JXXQCtLa81xAuVckyDpR9IgRlgbwS7A28LgOyMnWOvW2art+baurtbIIrBOPnQpY2QZ6/Do6MC9N7LIyXKK6XW8z3VICf5nj8XJW8WCEtaW8iHuk3osXaodW8GXasJqxuYOsQ2Hy6DxZEG6saY+tEsXlP/mR9MDwCNy92GbfFaVFApvHHlezTRZTRVp8sP/8oMaaEVQN15y2IrXbFKUXUBT8jHpRud7zCLFJLaQVV9t5bj/XqQ9OmrrDhYhxauzx0r63ihCCyzJaKc9aQw/rG3h0flb282k/cEXie/aW3/1PP+dGZjwBAvZuh8KG2GX9+yc+GPYIBLeLWc3tPuIEybZBttVeMKxSMC4E8oqru/Wl2jV2xlO50UJpel2XNRh98EJlddNYr2NoHTh9dN4V3kkYXJ8epd3DKzki5msg6Cpfow0w27Y2qrl7ScULmPUj53bW18ju3NZoMfiQzAJMTZe+rqu1FozqUePWWP5icMNljVkbehvafAiNNKnmhfb6+QpNJ96u8RHjtLa8fsrxhYHwbDuoGWwhho5QKzVEFKMLyjlnLBiZwu28cBcCOnkIxF3KTPmM9gMWlEOtfDkoTzvhuAgahIcqLJMQ6Elz4UUfXdJQRn58OrE7Rgbp+WAMLBKwFHnOwnW34xtx5BAL39M9pRp7Fd7aglXhoRpvzgN/mQtPw5lFd7zqKv/rS7gWYd8PPME4KcQMSROfnowkHbUIn+eKBOWhZm5CZBcTyyLtweqxn6iwaOFcfMFejbalZlGVOUOwTAiCLJmHTom1YMHTsVKMaC7spDBEYRJjLEI8V44qHMsZyydyPXGoig6B3XHkKymxyEgOwqs6crtkHlLmDTtS9nb5EjuKLJST3zDHMsG459Uy8GE/ckp37LqOZ5o5Aldd4eFUKMvCLoKGwrD0WF6osfBiMlIsXNuZJfQUdYPxoMI4Rg554dQXcq5EjXx5n1EN3N9HPpsrf34WXmphqQFjT/xMSVRyCPzQFkr/jK/3t1itfDWeuQsjXxsXfq0P/NBc3l/7yq4UDp2ys0LuKtbBU6ukSRiHnulcKLUwWgssoHLKle+GwO8MEHLm5W3ij889IsKP7USKO8wcFJhCPVWinPnLqTKI8rkJ9X6myIlC71rWafZ7gQjdHlFjroUiI9RELfX/79b6pQ6xNcRCm4uAW3l5a7oN/DS2M2CtkDXEWotO8WEg8aCQgmvlfNCogSsAUySU1hrnEn3ZHpK2oRqPiFmMLwAAIABJREFU1N0iLltrXlfbtWYimkOlr0JpjBi0dLPaQIK63ra0DSBWw9ScHWrsyHqYatMQN72duCdsKDTHi8b44sG6A5FTyIwmVAuIuoa5hMYSA11lk0yYGRpWr1PfNKRRydKYl//+7ydUE3NXGELHOc/EGKGDKoWSC6EKfRdd/kD0zTCAtekSWQ1W8R3GE/cCUipSI9HcX3Pdi8271I5FawO/63lWfx0tzQZPVlabNkwmPMaz6zVYnSiqGWLOTkubavM9X7YByI0lw1vNVa3JKJrDSG0xxpW2PmkDbw7QKj7EKPIokrvJWkTwBMRHDGCQ1VVEGmhYzxc4yHX23oHcav/W2rhtkK40vWhdh28EaqlNZ+vXde1cmCpavXgrwTb27Jc5xqbPPTVGPeBsbBFAXLqxBmXUBpC8I+ketxGj1MbsqRKaxlfW+1vcTjSjDFJQqz75vxYICrUNRErrdGgLh4BKluiWXG3OIFNRg6eN+DLTzWJsJ+YhCQ1cBStexIqwiNJZptuKq8t1VNrwlfmKOjSQPyHbZ494AppSuNfAl2rhjbqGdKT5n1tFGwN41tCkJn5cysXVJ7tuoPN5Xvid3/4xu7BwI6XlS/uzkdjWVLZVQNsWfqM+3VLl0bAF74DtdkNzbz7zsJfAbI/ArpkXdKrsMSbbek2tDm6BKVa2te2OL15ArEXTGlRR2loN0gCo+X3S49e3l1UZ7+e4b+93aYXl6vHt6Zl+vkL7eImV6fUO34ixry7XObc3N+Lqe1P3cD5z8ZIZpHBL2JxS/Fm6roFKCH4vjFaZtOXgCWCVvfogXam+XrL5Z+vMmE0IoqgW9iJM+H0/iDBD01PrxeHopxxfGAiX4JtBqMbJTqRwRbWBXTxxQDhYpdTMj6zjr3XkWJWp+hBF6YQw9GiCezkTas93imEnf2CJCN8eM1+/vuOqXhEl0IVbnqCQ33D1tPLvJuVf3F/z76dIJGNdJtfI8wDfTwV7uGcIN2SNlLjwkQ3EMdHXyh0jHTM59MRqzEPPh1I5kZjynu8vE2Vww3/N6jnZXWSRhbsaOEohTSPf10phQDQSSmHUM1E7SnGdXjCPhz6nyFgLcynMpaI5kGNBk9KhXKfELMY1PcUy5VwIEhlqolsqV0slzoUuzHyWB57vFogB231AtMBggTQpp7AwFWAycoiwjITlgVITMp3pbeTXh8xHTBye3vLyIfAXU0+fZk73b/nazY5kiTc2ctRKpvCyRB+g2Qs/us+kGEmpY9QDS6n0eiaEI7Pdo5rpc491kSyFct7x/6a3lLlwE2Zez4GUZtDIf/E08w8PR25vJ+b8wEO8YbHM1D3w2emG3Rh5ORdep8S3RuEvVHjxauFh7PlYCgfu+E8OgWl54BQLb+8W7vSK0hfG88AbW5h6JS2VZ9zy8TnynZjYx0hfvU36JYH/+8Ut//unnhBYQ0exjOUjpomSptZOzw2E+APlPne8lEgsJ1SFqEqqbsEUY8S0MGqllOJJVRixvCVp5hi+8C33Cx0OGn1IqKwPNsEf3FbfoTAleIRyB77pBgcmotpIDx+gqtCCC/wB44RahdbGAzaPStHmlysX5nc9jErslFov4RWr7MJZRdfhXtigC+zJ2pLV2ibjlX5xKysulT/4QxXYYk+x4npnFZcw0Fr1jTospdK30XTFi1ffyHV7Wufomry6oUXbQNLWwW3gLlvg/vaOJx8KnXWkXBn73odbGiAdtKOYWw6GnwhoCO0hXxvjU6oDihgjOWe60jFTmvTE9boiSpV1cxesDeC5rdxKVUEI6hPXmhFCYxFl0+Sux/Y5XaNAVKVYwlD69nlLU0Yj1s65bRuKZ8u1Nr+1qN12jXT9d4Wg7l4h5vpow9PzagPwK2sOfu1U2Bj81XdsS597tJ78+jtbrSJIETTm9c35RxNBa6WGQGjg13BP9Yv1mp9jf611GLNuX/9lj4wHWey5MLrB31wraLfmwzsDSM1+trFovpFnjEGMAb9HzyvLLh6YMJgx63ZzbX8ZFVUukdHiZdAB40Fcy7rKFB5WScQqmTJIa9Ekut2xzkS3dDOc6e4x7tqveGfFt2JzY52pziaKkHgkRVF3m9ibu1kMlnHrcR/88shgv67XlnhQ3e73lT1XuagbtEnG3o8DD1Pgq89cB86SXbzsNCrtTbGxwj8pfl39ymjfUxoiVoFYoXYcq7FDOUsgalvBjT0OKvTV5wICFzlHO9GU2gbQ2hq/qDLK5tm7Av4ef14HYQs8Kts1979j032rPPLWrTC04t7DlS4ymnPrKkaazzKNXbam90fYb2XGZYtxP2m5kCQN/BeDpa312AY8W2+qSeb8e2MrvmJbH0Jje9uISR+Ec/XOu21VyDqA6EWuVidPIpsq7qceX3hXvs0VSsFkRy7GEMA48SoFTgGe6Y4vdTMf5gfe6xf+dNoTRMkyOoWd/a0fctfsXwTtmn9ohT956PiT/B6ddhzKG77Vdbx/HOl7Y58SUxjZHwYe6h15il69hJkoTxgozLuOp9rxUfTo0l3IHLuJ58uRO4U3tiOaDz8dinIXjaNF+oMD1Cl33BHpRPhgyDynox8KT09G6gc+iBlK5ZWMXs2rEvBktJHEuRp38+ysg2WqFnbSoVZQEe5TQuMBZMJM+RjhSchcEfmhwEMR6CNZA893fpmyKbUskAujKjuD2SJLLNwUo6+VNxmOwbXGXb3nST2zj4lJZk5dYlqMvzRlvr12u6Ay8SwaT5/tOdfKKD1fsTOVjozyUTezHDu+/7JiJ+H9LnEfZq7DyDkV9kPkfZ0p5ZYhHCnlli4rRTOib7mJQtXIQx24Hoz++sBnU8cf3Cd+/21CFmXY35AXGIrx9bHH5EQnhW8ehGlauA3K12Th6x/13M9nPj8Jf5x3nGfQXc+uGHWY2Unk7bly3S3UUnm1zBCKJ7vtzsR8gFT50SiEU+UHUqnaEw/CVydAF74SE8fulrtUGgjueJN9wOAr3cJePSo2FchiHOipmqi1cloCuyDcBONpnzn0Iw+18Oo0c2cdgyk7/TuWRrSugrQhELE1bci2rPs2VvVIVuBAqVaaHyzuUZvbYIetNIezssGESKCgmBVvobfXLtQmhXCGFGjtOZdO1MZsBjzlTdQjdmN7jknDnrUxHiiN4XDtc0G3zUlNLwN9cklVWsGKIC0iukFqdeBfWe3kfVbforN8coHQHkwhZZNtdLW1bNVBiJXmU9wYS2hgPhR6Uf708yO/+8Gt7/G1kDFk8EKilAyN8UEeeRWbM6JVDCu2tZDDen6bLGOOmRAC73PPLTduwWVu7RVX6QbBB4HwgmU9L4YRVuGtuEwh1IsaZZV+bDVE2xjlkWRii+puG9TWkufC9K0rzbXc6htN07e69fGqhrVW2EB47HLRnA7qyi5Xh58+bNdYxZZ/be31gqzosL1+iwYPbchSLCK1IrGdiwYUtLHSzqQatGJwS/DT3CRCXkiKeGH48ximX/SI6oxtau3jTp1kDBuD6aydtfM0N+DsBayRHiH/gDtAzOgGC/bi4RpVhESgM4+2XcHFHuMsrukPcmFfz/iQ1aEBqxlloLKX6kCm/dpBnKUvZhSxDYghztZ1G5yivd7aEdPtfs24ndkkzuIuZs4gC0Bh185E1wDUwarr/0W9iKdyFuFomUn9qp3F15yKF4wisKOSuFjsFfHuo4bKD7535DeubqnV9eRBbTVppqUHtZO+imnbn9XPbP3bcKQ9PEKzfYFB+Ytyxz8Yn3Gf/F6O2qLcBUR8EHllwLGLe0cQQ+NlHsITJtlkyIKDW7ioMoK6FVp78vv6kLXQkI2KHbaHKdv7LdocQ5pUIa6XTNxruFS3VZv1kto2NvZ3J8ZJvZMxV7fSvNQNwYeb8S7jLLIV1RknCVwO4bpjRFja+xbxlDqztkfg52dUNjnQqgEe2mvNlU3WlIxHK/FvHl8YCE9VUO2R6rqxczVAfRFXb4udakCJlLInhUqtSqww9UZZEn0NzU/Qb4aKUEToNKExtkz6zC0H/lUR+pcT33wCb+YnvF6c9YAOM5glgEWKtFSRpfKZnXgZIxrhpii9XVFKRa2Jr2PHuSZOCjcWeXZdqXNHjhH6jKQIauys4zf1gVwL5ehAY4k9JxFyqeypYAkLylKNZD2BwtOuw0g+KJWVYIVb4EmZGGPgy/3CtSh3eWEpyn26x7Tw94YBsQVub9Gw5/MELzPYnHjPCoenA6/KSD5XXrHQ58SDDOy10mslzBNE4VQzL5IQqXyehPejsQsP7PuO61oRKl0QPr2deXIMLvWQM6+WxFThZjfyF+cb7j6/o+s69nFmd+y4LhkNifeiso8LfbeHsgN8OjPmwqsy8+IcGHY9IYOOlZ0ZU0p8bSxMudLtBqx21DIjufJiVt6ywxQmKyynzAOJ95aJ6/0BJfPR0PONm8ruPPPQ9RS7I0pmTpVOCqVTUq0EHYiSeSiJh9ozUbkuiY57fmM+8HwH73WJWIz7UDkeFo6MjDGw1MjrorxeMvdz4tMlkarwmUQ0HrkrC0n3lFIo0RgI1H5ApDLfKl0P9dZYJBCtUvI1WQp7S9x0e/6zL3rT/YKHGm7fg2/e0uQdQNPfCSbOvpYN3PrPPt7bc/BNY7PUMiAEpBhIbmAhtHZ7+93b5HrTBtOAcH3E0NIetrXFIcsaUtGkB0GwWilNmxfaJiNIG0SDqnFjocH1gxe9mTnorUphDVhoEhepmMrGfrn15LoxNJmFuCxB2gO9os2DWVYO1Df6Na64AcMonjp50wlX3cLpjXB9LUjwcJXR1lZlZK6VPqi7WjQ5i8Xg1kVN++Yaakg1+9Cc+vvoo8/l/5f/qPLP/rWXNUFXlw4/H6Fd/7p+TrmA2vWCVjyOOK8UuNC8fy+rQH7KvzficCNm35VVPD4eh2xYGwLcJr7V0LoWS74GttjcWP37LGDe9yd21aUUmzdyRoIPrVmTP5itHYoWiQyX8A8tbSjSi691nfobWBdS05esf0PzyRXk0foV/Rkf+AscKkLf2PxqRt+8ZfPKEAubrGMw87mMS73Qzh8cq3GvSveIMI/i4LGjuF+rNg19++ALK4t60dUHpP2xdi7d03dq0guXMrAZ0O3FyYE1tn1aSwgBa6xekMDjM1a5BDaER38w/7y9+PcczFvtayJelDZsZrJhT2kgJ8iqwfXXEbmszc7gQdTxxspgqzBqRYeFMRo/etjz7LDQr5VhtkvvPtsj4W77szLGtSGxdQ1dMo8bSlWwzO/958/5V39448O3Kpu/bjVPrquPXmINkRG5dDkOZrxU5arpvedWJF6egu8eoUlMYvsM68ByJxc9/Pqz68eMeonF9m6E28yt37MWVQfgzBoA4l2BXiAR6W0dXnM9c1rt38ydnVbGu2vdJH8fwrqaQ5tpGKVsDH9ZB3bb79v0/Ntz7fK53a+YJu9Zz+fPOEnt+OL2aeaDG52GxiTBGk2aSuJtDCwG12o8i5m+CpPBnS6k7ILrrt+RqkfnxXbypFa30BF1H0w1HyIT4/3xwH84Lnx5/xly3fH8fOT/eDHzo3m3nQlpUXxIRqXl1E9wFyFSuI6dx1KSSbmyk8i+JGKEN+cdHw0n7vNI1Miz0S/RqQYsHJunZsdsxl3KIJlnXUCyT/mmZiPW50QaoYs7cpl5XeFpTUSEZ7Lw1c5DC/pq3C9v+SAvPqCgB747Kf/X6ZYhZz647jkyMcaBb4zCISaCjvzglPj1Af7s9DkfDldcDwO3+URU5aPQ8Z3s05QH2dH3mYcy8KTesRsWwqK8mI0vsWBdYJmNT555RX+fK2+00HUjTzTQh4WP6pn395njkDiMypTv2YmSSmXY7blblG68I50Kw7DjnAO3g3E/3TC8P/PmdeXQw2iBrDOd+uR513XUWrmyzG5X6VC+epicHVThyThylxNvivDdeeT+dUZHZ+Y+PHS8zW/49NwzSc9Jd2gyXuVboigTkbQYSM9O9nSyEDtYpsJLueLNdMt9d0MuyqiRYVF2MvGGSl8LhYDqyKCZEoTS9xB6T81TGOLAzgrSCcUqN2HgI828KYH+RjlSuU/wYImcjXNOzL3yHgOfpfsvesv9QodA05e2yNzqLJe302S7T9Z0uPDODwJNmmTGlrpWuCRyqa2ltTQg6v9tTU5xAaePAVR7YK0bU3tyuTLBvy8Wv+99Q3bNnNXq/18dUHnZu1psNU6xPUhVDA80zw1sAWquJRYBNbT4gJ8PpAmh5bquLGjuoGu6fidsZfN03RLzcG2oqWdOudF/C5JQn9zf95Of1zJS0plpCuxG4VwKGjxuVGvTTsfo+ru0bL7K1SoBJYibrWqLdcZgKa6PK2ffGL65m/jOeeef+ZEGdgWgKmsy3OqsUR2QYg30+jXxrdzAZNPEijQN9+P1Jd5p8JGIxj5Li1d9BHvfYYkfFSyyVhG0wkMvBYaf3yZ9qNpCNSraRWIuTZPrLc9g2WdS7F1AG9XZdA3rnrTiEaeSCi5zUml6yrKeJwCX+5VSIQpaatO6O2KRclnTUN8pGL7oERrg6xv7NbAWfZcULoCdOqD1dvaF3eradRKcpV0jgoOfWrKxhSSsvSiXT/jHSiuSsO3iuNaWdu/LJdxCca/Z9X3nNjw2Y3QIg7r9mTOR5gPykZYk59dUHrHOPQpWyDi46nB7rqnCTpy5nsV1nle4BVsn5sWdCO9TuGugbsC17/uGUld20DB6c9a1x50Qole/ZDHOUrmJEwtwPwuHLnC/CFfHyJCrT8yqy8JIbfxONlrSj3WRldIecq36wNrAnFDOPYtVPu9f8XF+Bu10l3qRHa8FQPI37q4ITbuu7SWvGsIP4j7SD9XBqAfZyCaj4dE9Z+pFzVrEF7Hm0nAhMOqjNbX+aP/oawnvVMXGrptVRnzQMhoEURaEazJnmuc6DVjXzKDrM+jyviJrQeB4MLcSOVI2EN+1DmJU31umZpPYGozeFWmAOuB7VK++1h8/uQbqoziRv3l8YSD81J/0nEultkllE/O2owiUyLnAKRZeYdhUGUPlk3Eh2J4f58IbpuaVF1xbW12YLV2HZMWqkYsvvGDGSyv8L7lH5QlyN9JZIGtHjL7wRUI70RllR7XFGaQ2TX3OcMiZ1HrCV31HrWdncrNReOCzrNSQOc1Gp4kUjXOGIfQcrVJ2lT4VdjmTJmPSmQ+lIrUwYByL8sFBeDIW6vkld5PAoaM3Y2Hk08W4nxOlwukhkYPwctnxMiRkrhzjmfdCpsTIk7nyzfcyxyfwR987s0jE8h3f3AdengtfvfmAMSTusqKLsN91vMmF3z4UroYzqQaOhx2pfE46wW03UJfCb8XCcVAe7t7Q9RX2T9l1kfPpljFGqpwQejJnllnQnTLNCVt6jh2Ae7QueabrB97UK85dpKuZQQuWF3o58+qucGc901J4Gme0BpbYM3QdySbubhdOfebjCV7GgfNSeTGPTPXE0BnzMhAV3u9OvCgdMgmvThN/8No4cA142MehJqLM9DLyUCqLRaxmoioWZk5WKZP7Rx7rzDEMjP3kbhYsMHhMtE0FkUA/GN38FqVyfTjQU7jLC68yvC0jV3ZPUvdHPi8+5X0uyn02pC7cmSKWOYswmDLvXXd2l87c/Mz6/W/nEBzIJPHiwqI/SFS15Vhc4nF9YL5SPVd4Y72kPaGrgm2DM+2hVVwyEqpQ1DWotVa6WsiqPlTW5BMrS1gaA2K4zpI2W6CqSHbQVQZFU2kShgayhU0OYG1QKhpQfKI/ixEa4M9B6LKbkdegzT6t+mcUIxrvtLJlo3UA/HfEsgI7Hyor0RlYADW3bisYEiOWmz44gKYG5LRN6C879tcTFgL3SZlmI8tIzz3Dzluvor7516aNJoDkSrFCp0oUJaXsA3YGi2WiBsIQkDmxVOU73z3w/RToGpj3QI12pAbuxTZJiF9z92WudvHYrevZWNm0piH0qG1PC5RtbM4H7zwi1j3NNeD+2Gab+HO1g1vXz7oGzWwrPGpdwTaEBjZc4tD07VbZBYGakC7i23cgmNtIPA7xEAGtzop7cVW8IyG09DaXiXTqHY6s0NWKRl+/VVZ2XjfpgYkPKHXBYb7pKjsB+VvyA4+t+NtXj6bvrG7uGWbecVj9YqP4xl9xIHkyH1RUnCX1CRxj1strDzigDOKAY9fI7qxr58HxXaeXKf8dNG/elvJG01gCWQLV4MoyqIP4grtLgA//bdhaW7SyesLcoMaME2JXVinaBiXNpQi1SZiCwNwA/fXKAAJHKnM7b1dq3K0BO63Q9qFIP2Y8gKXmTKeu2xdxQLSIy5J6EiPGXCJhPzOKsMzGwo50m9j3yn50n28JAlr8w4qClEtkmuKIdk3ggYsgtgKnwlQi3//+e/xmOXJu7G+g+V0Dt02SlqoHnpg5SB2DkeololhwbO26V2Mn0iREbhu2qNIVx2ALgDiru95TuXpIRnu0smv8b2r2baUW9kE3bbkPW1cG3DottIHlVhL4oKIKd6UyqmvQg/h7W8vdWv3741pg4/fkgjuLqEBv1d0iaN0KMzoxJLrEwS3cwMI6/CosBZ/Lqf68iu1+nRvo7x4V40l063D81Pvw592kP+94a4bVHZ3OpC6gS9m0iEi59M1SREz49RGehpkxGLc2cSzK5ygva6aSKaZoiK01WVFVCoVOgrdzSUg1clHibMRRSOLRyiGq0+EhYDm5PRELotE3Mqvk6hX8fSnsupHBCrswcV6EKXmr+xh7JJ+wEuhiByVjVTlaIc/wJs08ewV7ueP9ULiOyk6Em/GBXgOQSXnEaiRNM6WPQM8bKksufL4o5wWmbKTsfq4x7wmlkCp8vBO+0cPVDLunt3wwRP75/7Pw+sc3fOmofOPJgbs38Kn9kBiu+DAaL+eZwxg41AXp4Ml8z1l7zvWKKT+gL2b2Vx2fHALpfM8xFsb9jmTwukZi94SPqnCbJrfXEkiMLOmeQXuefQCv7pX7LPzVbUck0vc92B3PIhQpvDzf8hs719TdETjYxCKZ6+EpV/ENH0vktvREWSgsJIM8C0/3B27vnvNpH3l92yGpclNPPNuf+UHZM8bCR8BUF55w5pQGPhnhZn6g5J5nYSFaR+jveX8PkcTLeeCUb/n4qLBM/NW552ZfuJaJKZ940gXGceRUE30deHG+Y381cMcE48hf30Ov4ib+2nHOBnOhCyM9iffjzIu3hTzOXMcrnsTKfK6EfscuJGJUPpYTz8vMm6njywco8ow5nvmeXWHpxRe95X6hw/VcRtBKjuA+uIqWDDVQpW6tU2dKIZpbMvmg2KpN9a+7VVIDQOIg2MGhbkrENa1t7SiGZnPEBnoUSmmxrs40F/NWqgVn/ch4jrzAGrjxmEmE9vBdQTkeB6zNlqtvXpoAAaVSN7a2w8MbDNv0gaHSbMVW11Vpz4r1tykx10eM8P9H25v2SJZkZ3rPseUu7h4RGZFbrb2wm2yRIAQMKEAQMBjpm/6A/uf8AQECRpgPgiBRnOGIw2Y32VVdXZmVa4SvdzGzow/Hrnt0zzRFFJsOJDIzFl/uYnbOe94l2yhfFXIy3pvzUPJ5M5MiZOdRyRQn5DSTJ88wR6a5cN32SFScn4zHl8yga1G35CA0rmFiQhFCCGQpBPEUb3xaV4Doic5x2CuxGHJegkfUktoWVXl2VihQi2BZdkwx2ooqNdp44fouKJptgjbttWOW67m0JqWivEa8PY/tH1vtaZ3w+WVGDufC11B8Kyr9gshXNxGp3O7oKp1BL6gV1Z5uoU94MdcM1PygQ7gILSV5zjIcqXuTLtQMjJIhRp0pwRJBgwsUct1vwDjUQq5QqogjLYX+4kjyz3w09f06V9hoIUlN6lsKUJHz+REx7+aCVSLRL/cCZ9GcyEX0tvw7yIKUST3u1ON3cY5Z0uUWDqYTaJBz4VSK0egWjUspcvbEDSLVrea3P5tToVmECLJMmMAXMb/w+nOLZdfCBY3OaB5FTAAHnOkbYWn1VM/JafW/1szWuqNTC8wpYfn9cma7LDzcXGchAUPIh+z4MHf0pSW4yJSOOB8JkugdBjO6emI8BjMvIuQF1m3qCRFdOgdYe9ah8GFoeBkcrlQ0GKAW/2tXNXb+EiUenBgQWF/WXBX17Lgz1aArrddJqGhy8q4mRtr9Z1M4u+ctArnGvgtn2oK5q1RucEVYc9HqQnM5B67eVYulm+kClU20a/molyU0L+fbLe01LDdgT6Z1F/rNTmv+pBoirIrFhy+HXA2RvlYLAtniCMEEdWlphFmEdP58zNKZA/KPA1DfuxDuQmQjM19w5GkrLLvrKQ2gDS9iImhGEbp+5qkohRlPx6iO13iG1PJOA2MLcRihZCtenbNNznlUZ7woXj3JK21xlAjIiNOMDp7kPCIjjkDxzpKziyDJY+71BWHGF6W0DROGFlE6+k6YZcWtDqR5pBBpSubptOfmeOJ6nZE88zwWVmGi7wqxKfgUKMFxGme+mta81cgYNjQEtkPmDde444TPinOBMQeiCiUe8U2EnNmMhc4nfrJp+GpUnpWZVdMS855fve1onl/zsxeJIvcEN3NXZj6/BpWOYc704cjz1nE8fKTpA/O8pekLP27h7WnH1d0KTQ7fRIYZZrfhP04N7dAzzFuexzVzmfi/Tg2D+4RV2iLaMCfY5w1XQRnfzPRzQFaR3mVID3AYObietynxWdiSpg1/33Q8bZX98cA38x3eHblyO1ppaHt4xkBqI6ep5+H9eyJ7NjcHPv38hm/ez2z6getu4qt5C9zyk7DnMBU+FE/2jjt1PGOCkHFlxRBOvJ0iH+aGD6z4Yle4biOfrBIyeb6ZJnzacB8Sr/KGIa1xXpFZSCni8shUMtE/4/jRk8RsbvCJOCTKtEZWHWU3UjSzco6enm2aCLJmHGaGydHoRGLCjRPPS2Etgf/gC591V8DAu+2Krw4f+doJQT/wRtbf95b7Jz1yVR+44q1Trt2XAAAgAElEQVQwdYbOZlVolFA85FLtmQrORM3EXPm8FmmAC2afVkq5UDKrCE5Ezv7X9nVDNevaWt0qsEW+FsSlFmUuVOAwyDk+0yHVCm0Zo9cN24VKt7KdY1kQwca3Xi90rOwMtdSKVfgqZDNesBUDUtVWukBINS3M1cVY6wc9T7xFQKu7gquCu9/6zEYJOePKRZFUOJG4UeE4WLITqown4d2UuPUBOqX3jqS2kTpVc3orSvEZikNDYY5WyJQgVgBLTb0rhbJXvvwi8PobZ1Zg2VDSiKE42XtcXtTWJmSCYI3NUii5i8DG/Fst+Qp9PCmvYjeBsojj6sZz8QWGknOlmVwKs9/l0YpbimU5b+hSrEAvQZAyW6NVJxal4rhOzPKPR82TvTNnLY8uUaqlWoFZXtbSxIQq9nRez9eXUdItAVEUirfCgXqdpCD4pEg21Z75ImfQgIrZkT3mT3/fR1wQuQr3LW4Mod4bCSyNDbXPh1abPW+8WjWHBfHCIBY60LnlvBtrt60BKNanWDMR6oh9BjaVXjFjkwoVWBebsnbBxu1tRSkXlwsHXNXiua3FjVREL1fkMqDnonwWE0dpUdbVM7sSDigoSYTGlpdzGuagRvFb1YnwiNBjz2NTLXt0GBI4i0OqR7IT0xwtCHFdokh6sWSb1FDOoJFSBsoE0XvImWmOfJyumN2BT10x/0SxmXvG0nRN0atQvHmttvKIjCqcLT60sNu3PH82Mr5e0XrO4RA4o0Ml5SwKnJwYJ5vq775MBJbWrjYsZ9vC+jKDtY54V5uLyhpaxIseGMTRVWTYO7vOBsRCZZwdqEZzldhWzUaNJo9iNAQn1ICezCR1AlmL2I4a/Sw2PUBsUg6VmrMIKivgMdVSuCl6LoqzmG1nK7lyjQ2okQJDvcZ6tfOc1TQuKzXRp4hNTepta3oAVTo8/9jt+r0L4aLCjsCv9JpfHgaaAKqZ27LhaVC+GTKJlp0K067hvQolBIoXchEb5pQJUPzRBkCTZJq54JMyuQSlx7nZRHN101tR2EsmJaqAJcFsg7I8JdQZmpxwIAMyh0o4t3CL/2YeiY3jqhE2+sCTNvIi7lirElohl2woU+0gXAhMfsWHo/Lh2LOdCsPYkEvgkBwnbxYdR3Ucp0RUJRTHc3/kmD2nnPE58z89U2R+y13w3J9q+tQarkPLSGITBkSEadjy7RB5vlKOh3tWjaK+R6TjOM9kH8ilMDvHw6B0fmK9viaVA/MgFHE8JOHJpmNQzykVtETeHU88b4SfhhMSCu8PR25Wwm6M/Kzbk4cjwWW2xxMnnfgyNGyKQ7Ljw1yQ0x0P04lNGJmksC4T/aalcR2f9kdWbub4sOe6bZDVt4Q8s76+4XQ68u0+MeoN/ngilBOjBt64z/jm40T7MCBjokRlbuEu3NJ3jlQ6mEdicPz9kJgl8NCu6YpykozIhherzJdxZNMJ+1HZDSfebDMn73GuZe0cz1QZ8szOwTomNsxkl/lmipSsXKvyeTuTXITTxKmA18x7TUz3B7bA5BwPMqCp4KTQZouq3emEuEiWDYXMd8kKqTJN/PVpRBWS7lGcoaAEnufp+95y/6RHXLyB6qIixcaSThwl2XW3hGzYuieExNkJQKsATKtAzKGVU7rcb3L+/UXZb+uB/e2korbFSqgK6uHcxWZMFMSL5crYTnguKiLGBbb9MxtKWOznLQWvbvJV2VOSFW/LiNQwi+r6oI+K7Gw8NvGcI5C9uR5fnA4WN4RH4IGTUL8nZ5rE4+Prihi1hFr4qTK4lmk60LUNqjMRxbXWk0cvtDX9wGNjxlSWiapjLAUah6+5spoVSWqcXHF12OaJIfNXr4XWz6TJU4KHZBulYJqL7CsyKx7RjMsZje4SgPGoqD8HhSzn6BEifD4W9V0WTcsXEFdFj85XdLn2D6GY4BBz30Dq+VdD8ZdGSoJpOpxC452p9St1wRU5F+s25TBLtprAgaGbizLek8nnC7Go9TlUigzemz+zOLQ2AzX2wz6KGhfeAFLjBxsQYwjaUpZLLhXmLv9/INM/6bGYZCwFm6sFaa6Fy0JbMc5uZsQs5BZkLnijPAm2btn37Lzmev+4Wrza4ahUHxaUbxEvaf3d+n4eNTEihjgal7MOFeq3Z2duEsZkKnXs7UwvtlS6wKretwe9BKeM9T10erFic87WgCkrMQhBC40qp3restbCl8uakWqx3ZHPzc/vI648LqAtcAFUAyUHgldyKYzeERhYx+rY4JSSK41H7fVStga7K0BXjMQNj1Rc9SAVO8l9yOQ3V6jM5NI8ajQ5x0KHWuAu51vgjPLaz8n5uYsYEr94ti86hXoY6zm0v0Mp57fU1tftarBKcELAil8nZlcnC6RQm3zj+drv9WoTpwkI0ShDWU3DEFSZMSEfwLraxC1ryJpCEm/BQeKYVS+cZvHM1cWl00wUmNX830P9ebs3yvmzpUfyAF97EANiOMc2C/V+fnRq/muP710Iz4NZg02quBAZx0SjwkcHk/esmxUJz5vjxKRKwbrVCzetPBqdCpBos92gXquZdFG8ZEouBKzTT2o8FbCOXkRsgXN1ccxCKjO3waOdMOeR05wQLRx8x/8tHpmyLTClB+9o+QTfCJGBSTOB1sY4zqL+4nzEh0AKkeAG5jEw5UAqCe/rKEommiKcXLHmMReeuZE7Vzjh+ea7A4M45mwjfVLi+U1LGiai7HkRIvcyQ7vmzgWiz6SU6VcrUkqUkkx818zcup5p3OJXHbtR2J4+InT41uODsh09D0n5cJjZrCJh/gjjRHJrRIRuGvhXz9b8fHvg7XHi231k08BffzxQhg6/esbVamQ9T6xiTxwPjHLirt/QhMwmH8lNg9OM5AmJgVkL13e37KfE1q9YxZE29nyzd/xyf6CZ97x4umFMM+8TrOKePnS8+rjj2dpRcsfr0hCykCYlDxNH33EXlWcNbBzgCoMO3IWWYyNochxm4fWp8F2auOsijRM2JByOtffsU+CqhcNw5MNJyB426xUzI6cspKDsx8SVDxDMcWLMhdY7smv5nIyv4+a+FYZBmMSU2y47iDNXEYY8kL1tnJnMCmvIhqSgnl5OtCGcVfL/Ug8JnpmCR3ApI8GTs3Xr5he8hGnUoAQnF/ETRhlYNkkRExSZ4KyuKixhBxhH8FF1uxQ45hBxCeA480WdeTFT6RBy3v0vG9eyCSz/huVl9bd8bjUJ4u0Pj4s4V8yPVmoYgrMCduGn5gW0kUKmEIg4N0I2hLZ4R5cVzYHcnShjsN3IRyip+ilfUBrnFKcJIZLFXClSKbw/rnkZH6rLRYHiGYpjPWAR687QePWeeZqRECg50/eCdxmRCp0HQ+V9jTpWb6hUu3b89192/J9fz8avzOVcFNY3hiuZLA5fqiOBgE/FuM/Z5sQXyoDWYkhAC+WcqGcuG48dOnxZuL61GHFWBHkuKLBWwrEvZv+WzpZrQvEFM5ukkmGLed/aLB91BV8EicDs0JArlcEjfkaTCW7FKakYDUZKOTdey9OKgvUxNXWuIta29eTLtUONJa4IvVFnPdlrDUupz7H4NJdStSj//HvZxFxap+7yW64RBWuW/PK51AqZIhZcAMaHtNNnhVooNcb4cigq0mYK/O78nh2zCJ3aZyla7eegipbUQi7OqKpeqBRwfvIFpazTdMDWBAWiuxyjUa047oJxRlWEq9pczLgzd9koA2rofcnVAs34wSiMzhPKwCgtqMVC70WZXeBu3plwWjM3MZJLYarNa1M/dUbRkvAh4Au0mpCs7NMVwltGL6AZUWEcBYLjYfQ8dTCpRUwPkxBL1Va0WsmoVOJ8/chL0Rox2lc/4z8fcK/XVjAW49wvNJyFKhbFqqOMuURMYvzZriKeqhh9ph5aEQOiS0XlVQtNPYfLY/aXpDmzIgSRgq+0/hY9gwEFi2FutbI8BJsKiiBuSQJV2mB1XF/PdQa8tyC9TmBUo6M4EabKdQe7Rhb++ePGK1JIbqHtXJo/UUOru2U+VNcll6sFW53n7YCohQkDc2xtsjVBqOvT5ZD8F4/vXQhr7UQaP9HNnhE4IhwzvJtOXIeGUhKjlDquM5hb1JS+2U3n1ChUz64TSQIqCRI4BnICEc/sFMnK7Kkm8guT0Y7YchNqyUTv2eWCHm0s1uDxvqFMBe9SjZnETl8RSjkx140woBQ/1vGfkJ2jIZLVo7OSJ4cPM6qw0YSWEy57Zh9pY+LP+56Xm8zxfsc2RVQaVmnPpp24CR1//bDlRhOrVUBO8IHEJra8HZWUHE0n3DTKOGXuusjhNFGKkmaBYE58X0/K9njDp33iYWqZ4w0pJRqE+ZRJoeVh2iETfHcoTN0TPvEzeTpQhpEPR8/QTAyniSdtou827EfPD69u6W8GGrcjzSNPQiTED8a3jBC7e7bHTGHFjWZinDmeOg5zYTfsuUqFY0oMU+K7VPj1ChiP/LBpGVxmPExcS+I2KiEq8/SBJ9cd2+hofWDlCrvTwFUXKcEzaYaQeTcHaFrCnJjiiugLN+NMaRvuomNykWY3onlkX4TrLnEXe47jhPOB7+bC6FrGubATD+OeHzaREIUP00hKGc0zfZ65aR2baAtx0IY0Zaac6FvHyiujZlKIDL4wDcpE4S4Utjrz8sqxH04cx0K/ahinmV/vPaF1/F3qCacTPvb8L9/3pvsnPnw2AUIJFhQAxvvNC0JQ446RJXTDRtxwQUVtFF49ib1ZDi5r+yOg5/wQXQQ5FRVWw9vsOesiqxeXBy2//b3zzxQrWq34ewQzY8VVwVwX1Nuo2Plo0dAiIMmU6YvPbF0krXC3nSOqsHBN7bkTpEDTzkwp2MbolVMotGnFf/dHB755k9kPRz7mFU11sF8amutGUA0MKRN9YFRPcQlxmc4pEms0dS5kMQW8lIwMQts0jKdMUKOy4MyRwpc6kXoEFJSKXmpRiseK1fKAp6mHx6OVm62qpm+oaC1aKkC1FHW20Rv6aQI1BCSbq0RRYSHGin+0iS3n6BHasngTL8985nUv4JUTsldDjes5cLKI94A5G9W6blkuGdoaRSnFUZyQinFjrQiNiOSz/7QWz+JzXC9EEEOTnbNiPeuSDFfpK67y4FXJ5XIlq3JG1ZYPKmpN4Dm++/zdP1xD68QQVxtX2z2parZkdrYMx53E0as5FI31DlxijItTMh7vrIA1GtHCmVemBX2trznX8fRi/1e4uErERwEXCx7/O7fi+Xjk5T2Xi7+0Px8rzi5AocagZDFLUjv+Fc07i3GXTbxeV7KM2eW8lqQ8Qei4TyOfSctRR2aF2zxxiBuunn7LL99F3CxEuTsLfRck/LqdORSHz9ncNyQSdEDyxMcObmS2mN9iUwopZvF3f3R0QfmYhFYLh+DZiE0vmJUzDHq+LGqVmtUmGEHp0gOv5sizGIlumdbYGmWWdkZvSOe71GhfT5zyUc06UpwQNFnR64Kh6+Xi2JYqmmvnyL7WaEW+qS2h+y+v3eWqDgIbzeZXXb9WRM8evyIGRGpZmuaFB1wqz9uTi+0FS8yOOVIIoxp1xcnlOurU6sIsld6EktX4/d1C2anivqyKLngni47lIpB0Ylqx5b2Jmujyn/L4/jFXMqMpoyWyXzhJeeKptIwuMxXTd8YcyLJC0kR0M5OfydpRyonrWQg6sWOFl5kxZNp55pmfKD5wyI6Rxrg9xfhGLguSMzlGmAQXJ1pVTi4Tk8dLRHMhU8U5IoaE5WyWSypMpdRuFXQyyxzGGecCKkJTlKOHMNsYLLo1uBHJMz0Qpo4jhSJCnyOuKP/miSfNRzKJX7wvbEJDFkcic5wc34rnToSuEXax5+Op4WaV6CbHkRM/Y+ZDG8m+ZZgyR4X9qCRtKJpxknhelL8/ZvIobHXidEj4fKD9uKe9cazDU67bN6R94Gl0rL4Y+HDY4pqWw3iHGwvfhite3CZ88jy9a3jYjTxZzay7I4fjjpsXKzS0DIeO+11idp5+fYOUA19uWl7lzH6I5NyQywPFndBp4pPbjjCMvLz2DNM9rXj2qXC1Crzeb/lyI7Sx4zf7gWO5I4TEJzdrSJk7mRhzJpWZzR041/P1/sj7eeLT9ZpuO9FLYSxHdrvANq6Rktlo5sTEdXvgi/VE0ifs8p4w9nzMJ5w2+ObEX/gDUgLfra6Ixz2DRN7vj2wcfN44XBc4ThlpGk4JjiJ0uWPvM8UnRjx/f4I8z3RB6fcJ3WSYBU/iPreMOfB3DxNRIk9c4FQKOxy3feDAwIu44dD1DHn+r99Pf6CHpPlMQwilnFFdp1QzdqzoEVdH0sYfDecF4zJGC1hMbamF5NL5h4Wvqr+N5C5dfAGcCyyr1sLddYufFZfNbymglo3KV4LpY5HV5cMpEW/RvWUp7ApyRucC1AW71CQwuBQ4wLmCd+K5Scpf/PF7JAiNgJcZVubW8G9//oJ//WLg7skDz597SzmaP/C//ceXnJqAjIH/8U/e8/phxav3nj7APJtIOHrlxiXIVVTSFUIESUqalbE0+E5J00xoBNd36JxovYKaRVoD588I1QpPBMkFFxwFx5NP9/wPz1r+3V+uCNkQFdQEQOqrxZw8imat5zAsiq9sx205yomCSkG8p6kFYq6djdSieXL5HG4ixbi4WgrOhzPFIlOvNdXq0BNq0l3doIsiwWgri+Cp1KqoVBHbqBY969TeYVbQgFngObsGjdNYwFkqHSyj7loE1Ot2KbA0O7IXSq4/oBcwRXB1WrDgv7WoF7tGq/zm4pf8B0iVA2jEELwZJXg7Q6uKyhXVBVA0/1wxfnBDOUeFnx9Sbac8Zz/ghkynyl6F1jlzQqnv36vd30WM5uid4Et5lNRmaoGweE7X47Igh2eBpZiryCMw/lw+O0C9o8FstmxNSefiehG3Sg3NOFzq4HNhMtTX65zylRc++/TXbFpYefA68ZMrpTwk3C9+yn94+S1fPPvAl18Y7DkNv+bn//mP+MR5tmHN+ouvaLYdx92a5BxaZbQihY/RqAlZjHMfvdEhdpN5csSQOc1KH5W2AZWME+WUoR8wq42FjCv1U5ynKFb5/eAnB37wWeDdX92xNVzrnLpmYUF2zy6cWkPZlS2u2uzVEnehkZViEwNn4jGwgrirx28PdMX41+bRbFOfa5R95cxaEa7UjA+eFKMvBMeZChbEqDhZhMmJ8YSrkC1rlUyrrQdJvDHTFW6kMGMFfOuFXW1OzTu5ChYfNVYOq8+aJW2u0iVyUZLzv3WNzZh14PKFqaLort73M0ANVYF/nBYB/4xCOAj4YIvuObHER564LTEFUrWluYqZqFtS9GzLwGYWWneiiYWVTGQtiDshwdOVhvFqYqblN8dClkDKhSklXLBe3KOERvnMZfoObqLnNg7sSsvb04kH1zCOE6N3ZCpqcB5umY2SAySZOXsI0d6DyHlMMTjbjSK2AY58oE/CtQaOaeJna4tlDW7kNM20beSXDx6PY5xm5snxMY80oXAlHi+Bu5z4dG0F7e0T5eF2xX/65ch3054/ubvlf82mB3bOUegQEloiL8JIM+9oA3w1FHrZ8KvjkR/fWOd4HQKbq55ySjzxmb/81QPB3fD0SUTeX4Fu+LQM7A4nDllZlYBcK13e83GfCV54tVUaFwnhjrzf4uhBdvRtoB1G+tbipQ9jsI1CPvB+aMm+ZeUGuvaa/bHhmCZCFmIeebbpuZKZ96cGFxvej8rG9Vzpjmf9SHCe+93EpoUYlN4dORXh5dqxP3zgtklo69D5I3nd0EXPPSPrFx3H08z740yKG97uTrwfCiWv6JuPHA6OoUncZkicSOkJ//vcEMqJdS3cyjEzh8jrNNOqIlNC08iQHcfZPI6v/D29g75puRLPl+2OTM/2ODNuhMk5JDq+ORVi03HMuVpfCd+VwnFq0TLSesVJxOnI09nZaPtf8OGiuZ1wHvvKGQl0jxDgx6kIzvlzbK0v9r3iqw1SzmeEhvMfy2ZTd9kQ4RK/bGvRo9F5yYh3j2hQF+RQzYfrXBBX0pu9Nfkdpl8Vctn3zpCjbbziuMj76+892mT+S2AgsW3gkODJeqTVlrn3OB04lA3/5gcnPl09kDrD0Kbi6UNg51uaufCTFzNZEk9vThyOLbuhYWygVUdXZlLrSM7hNFNSbbZFIE2U2NImYSTS+4STkXDdEXJikoRkcKdMaqprh1wQObC1S5wh0c00I66w6zLrIZ6jkRVL/DunCS6aB8oFtf3d74k7TwamhdbiobiCzEIRC0+CivRlUG8Inz7e2FIG5/DePNaTJBM3LhOCgBVyWc8oJM7oGq4K04qICfDExsdWFMgZzU1ioShSHLrQ4uCi76gFHhjSSilWxNYp5Pl4Ln87s+dzVMFgddcAzog4Ymj++UP8AR4iVug2YvfOXGt0u4dsT2rdxR/W6Amcrc6G2iT01bYsZ85JalKLGy/WfHTORGt2/jjrBJyYDdsy6ZmxgtepniN8F2+VxRZrmezMenFqKL8zJ3LoOZzhjCRz4XKmy3ftuWX5TM6KnPp6AKKZL+aCzLDZHHGrFTclQyi4vIEfvOO/vfkI15UEkQNNzHzue47e8bDecutm9EoYTg03pUVLZocQpdCnycR8YhZmzlld06F0mPhqcpGNG2hbpb0yihJptgN5Uutez+q8x0eirlsSOA47pnLNH/mRb2R1bgrcQpFY1sdHv7sgxIuH83I9eoUTRpPYLE4Yms0dw3ueAof6LFmhSKhgRTH9RPV8bjARXHDCvQqtKl7z+XXXUn9eOJ+XuTrFNNVOzdoFa4raSnU7qhXN184a6pWr9sqPHovZRuYywUiqpAKNc1UQV37r+rFjYJMH896uusT63NVunEA+J99N8o/vu9+/EF5WFBRytpQXp3w9rVj7wgsV1m7gpYvEdmY3mS9wxqNuQn3mVWrMNiMJuxlGX9BdS06QnMPnkewzt6yZdSYm65qfNCPPvKV2XXnPoczkKfH5VeRqPrHrV3y9PZlHcQH18lvpM0GqolIyqh51xTw8i6Fk164ADdd+wjtlozBnJeiEauKrwTb01geyaziNR56GTPCZMmZ6v2LjZ/YifDhkkitsmhX/aTCO8HAo3GmiX7X82SYQ88SftxMjysP+wI/uWsI08NW7CSk3DH3i4dTQBCX4gT/vTmyajhtGYtfT5C3xJeyP9/zFFytiW3iyyozjPc43TGPh+joTg3KSE1IyAwOfbDaIerNoiZ5fv9mydZ9ThoE2rBjY0HVbgm8Yc+G0HfjxZ45PYsdht+XprafkCVcyYxloJCK64v3W84v3E++kYxp2RDnwxYtrnvOed/01353gah14ezwxas99cczzicPcoO+EEAvB3yHlPbt5hUjGh4mQ14T9ic4rH+dIHO+5EY9rlKlkCpEUMp2MTL5QkrDJR25k5l0uRL9l03e404QSuHliQkvvHaqeIjDTMGuhFF8XnkSaTgypZdKZe8BrQF1kd9zzxLdsVdi0ikue0HaEHJmzZccPoxJXwt2sHBoY0x/Gdun33pc512x5Pd+ffhl98+iGl0VgUflay5eX4ghX7YYuxZITMQsfMd5tI1awUBQnhcmZuFoBNNdxnUL141X0Mqosy+tn414+GimWylzQGrazvD5qCBNq9ICYjUfonCNlW7bNjk3xJdci0prhy0JaizEDjfiHVz1/cQPzejaHFb/iydWE6EDKnY39nRAdnFLg2u05EolxYAw9+eDIGJVDnCdSo0JFGWZPiZ48ZZqSaAMgnuGUubpK9L0SWguBQA4Qg9l4oUzOE7ONJyc1Opfp5Vx1gIAiiRJX/MUfX/P//GJGyURnghVzY7BPTMk48bVR8TZhE/PMNsC3KuzVkEapxY2oQHKoM8P7XGwTxXt8pa9QMi5YCalqwpaFP12KzWzVgZ89c1CabEVZqQ2MV0eWguRiPsilXn2q4Bd0qX6WvIzsQev4HqeWIHjmxjmL3812TScUgqDOGX2nlDNFA0DiQnWp7zcsRXI+U2yWzRSniESbQujv7Ojf82HUOz2/oyVgYykAdbmH6/2rWPrqsop4rep5EXqU5Kq/ct3vlrFywPimKzH+ZnZwVXmnoYbQpHqv9WIIbqxWVWBFxQJEeRHG2khHUdYUJoWJC2WqYMVNWz/bGmUvhhW7SgNRLAwia2GSy+i9fyRKPVNuEDY4fvXhOZ8+e81N2YFrjKfz6dFecAqXsZTPQEMpH7mVDXl15MpH/m4POWdzEnCQpTDkmcbb/l8yzMk4/K3PiDj2k3D9ZOaqG3jSaRVSzJbvmx0MWlV0zjqRhfxqooxLU6WJVdeTfzDx5rsntkZRkGKFmqvHfObi86tOWFE58jgilxCLQYRGl0hrmxZkF4iipPpzFtmtv1WY7zHaTUFJCsV5WrVrYI2np/AgjjsKDxgNZ+OUceFBVBtEVxuGKFR/Z6F3haRCQ2YWR6hF9jJAaSrXeymy1XrUc5KgUR1scjlhzi8dekanlwlOFCvuV9j17GybAUxsGuo1n8TjyWcK0e97fH+OcJ3wiivknO2EqfJpY2Kh4j0fcuADwt1ww6qd+PFqhd9/5JQdH2Yh+oDPjm2ZKBoJSWlcpkjhS5l5uhZ8LGzTAzdSWLvIbTxxvU68aAPZOQZd8+rk+ct38OoQeMvMNA2YLY6hXXV6VkeHhihEr1xJ4JRnfOUx3bSOZ01CmHiYi3U9CodQGDMUGlrXoelUx8oTz5rARoQpKtsTiLf+6s0kPHHg/cyLq57sBsohcbcRrjQgqy1p8OxnIZUTXj0vQ8un15mpzDy/W3HVZdqrj7SpgXUhT4Opqn2HzltKgVffndjmPSFd03SR6ye3jLsD43DC+4Z3D/eE0HF7e0soidbN7E+2KcZGOM1bfNMwjJ4fPWtYP9mBjui4Rfw7fvObhoeHHTkkVk1HbDa4RujaFfv3iX94c01shEYLzfUN396P3JBRB3925fmHSbhqI/vtzL8/OfqMnLgAACAASURBVJ41M23j2T58wInn7XjkKjq6RullJs2ZJkZSOFJOimPEucKxtIQI61RoxZqs96Njagyl8ZNxDe8aQzemUgiryMYJb4bEF13gwyEzUvBRSCHxcFiz7jOhcg033chI4DAN9K5h5zwpJSQ6riXTe/ApIAjHeWa1bojiuC57DsXzMEBcNbTR0z4ktiWSdKY9Fb5FuG2ELvxhkKTf93jiYfbKsQDZU7wC5cy9XbxgFyvMi4PAIt6qT1SMC1YnyLWmvmz+3utFIO2NotByGZ2CbXhWfP32Qqxqdk/n8bosKFFByyME1FkSkaGHS4FVF0Q9KwRAFe/rd1TNXxYgeOKcmOUyGjPBnv278/Dl84bD7si18yiZFNW4ocVVkNnQEI2eboqI9twFZb2KXOeJnevo28T7UVCXSerNVnS34u7zB44PHdlNqFNSKOSTcNSW9gpcnE15XbL98YE+ekafCKWQ8JbeKebIUGop5DFlkXhvaHJ4S5IO5yzswKlFLpe62S0OHK42BYL5j1JFM1L9SFGzoaMWUYhRH6wQNG5q9g5NuVIh6jHF6GqKgnfmAZ8yEsCL2Y1p1GrzRaWfCQSH5ioGc9WXWszSzP5nrhBFCj4vCKnVHgXBF6NziHMENUGeccurDZgWogiSHSeXCaomcBJFZm+TjbxMDZROLO1PRRC/3Kd6to0z+7xFLPmHKYRHdyI6zzo7dq6hLxlHYTGnWGyrOlUG5y8R1FhQxoKiOnHmeOM4N75LklZHqQI7e/RiBfeEFdILZTSKfS5HMXcIMfJAQY2qU4uPuIigpDYQdQxkYiWjcNjbsiKpqJKd0JTCVMXurbPXVqzZdqqsnfAtVtwtVmJtnSIdEPZOWd1MfPte+PxlDylZJYQ3/zGvlr5Q1w5Kw9hckVxi1cKrOXEtLcem0J6gZ0bE07vA35QNf7J5x7vpiqAjRSwARMbCKmy4Xp3o21Rt1LAT4zD6Vy/2utmuEnypIot6U529hwVk4OrK4b5pOfmGJRZd5fKjfplkVQGhUc1gVDWNAUu6YKlNoZ0/b+jB2RNepHK4a+rbUgx6HkXFo0Ai2PivXj+FW7VroceQ17nuAUMx+kgUa5waLWbxVqlybZ0LTNh1mAWkFKI4S4CrThVZjAN8j7dQokqnKE54UM/zYtOgXIyek32oGjM7Np0UXuF4SmHlHce6H6hyofOAWexyKaB/3+N778riYYVnzjMxWJQxzvHtMBJUeN7O/LBvee5HfuA/4DrHOL6BTeKUHLdlRZcH1lcFzcroOq4lMaWBVd/ivRD9wOh63g2B46R0nePjvOJv3zp+npQjK6ax4IPW7Hhzl2i8MtcFP2hmEntvoVisohOH5mKexeL4wu35yXXDKJlxHClkXvQt748TQTw6eHCFjokf3zp2J2XlJ0oWnkblfmr4cT9xdMp82tPSsY0nXqwjMjZ8OG2ZxRPoOE17mubI/s1A2zd88eKWw+EA8SV5+5qdrqDA1x9Hgrak48RqdcN8SgzHDh0KufO4ojRhTZQP9P1Lnt51HMYtcX5N7DNzybgY+fxuA+5I5h7XZtCejQgbd8PwbuDmyWewm3HpPadhy3qO4FaQr3n9/sRUPL8YTnwSW8Yp8Kuf2+Zy10Y8hcYJf/PdxN1V4pkeGe8Tb7s1N/7Az3/zhq/HyDT3/PG1Ms2O1/2K7YfBrJKKMMyO73rHFwjDXHjnGtKD45U/cj15XjjHhynRrjPXk4IIu2nmeMrkfk0zZxrnOWThyJqN3NOo4FUpe5jDQLvukVHIrTLtEz/aCNvZEePAd/ctOwkcEF4cJoI/8tSt+Go60SAE6fhqVqTzcFoz8p4VgdnBizDiveel6+nTkaEUvnmY+ZiONFrqtRbYZ3Ak7udMW/4wiVS/73HdTuRWaQ+Otyi+2qAH7ysy6kCEUuo2uRQzrqqYlxGSUxYDp8WL4IwO16XFiTtvCEvSlvpy5ln+rnAqqCeRL/zeYovmYnfmiieHikTW4lfE7L/UXygd4swSTeoIGKhivbrkFXDeQcqk4CuHtR6gJT3OOW6bgfmUWa2tAbAgA9MVqBZKrlaMc8JJ4N0uM0sAJr7+dc+XL1eIm5CsfLFJ4GbebVcUafjss8x6M+HYcN0dKL5wv4um6G8yb48dn1zNzKPiogMJeGBeHC48SBaKPj7+y2xUDfUsQgnKbUo87zzbkyGjQnXSUSvcFu7uhZqyjGrVSuJ6DIvYeXHiWbDAC6nE/vbZUtgkp/MG7FK+MG3qCfEihmKXYpqNUDnp1eHM/q3nad2C0NppssJN6mUozlfhmz4Sxpk3NeIt3KE2eyK2yZYiiLgqnMnGa3egy2t6o4ho7ZCSqxzh4sha8EvDJJdkxcd0iT+EhzDA2CS8DEyu4XbizCHw1V0hOKoHtjWEy3YexI7VEjAxI9XC9ILKnn1na+PjUWaVM4+e+nHOU2O1IknOH9fOTSNyzpIAuzUNLhJW3jx+bU2x99VTy6tlCiXV2tBVvU593fnMNzav2L3CM7Fzv1AEbHkRrgW+DiMfDye++EygTNAHzt5xC6QuWFEcAh/fBK59JOfM+3d3lI3Q68RJhak7koDbIdKGjhfXR666I7dDw9gdEBLH/Zq1dwxlZBo2dOEBKc7s0pZu2hReZp82VCT4PEGoB+CMCAOdYywzr1dwPdfodIxlsVzvC8994bqfEX73W1gETX3qUBsUuFyXFyOB6i3Mo3P+6DmWc3puSGpxvdTtWovmRSey8ZlcrxFVW7tnLL7a0uyEth6SRThtq4hY+mAIpGIFdkTotJoYqqsOFXCHCYvzQrUTR7toBpZJiXo+FXMckWJuKAsl6LwuqYH1PPrsv+/xvQvhp8Gzm4+ob2tUYiST8L7loAktge/2iqMFWnLOSMhV/GBd/bWuqo2HZ5ht1DazRh6ETKBJExILKTla7dm2a7weuJ6VH7TwUpR8857GB2KZeZjWHGXkva6ZxhPRtRA8D3W+o8FQqz4XppgpLtPhedDA//EAvRNyjiQaNu7Ej549IY4TsjnCMLHqN+xOiT972nP/7sjDIDy5W9Gnj9yWyO5UiDryo6dKaRJv3s8cT0d+9mXPcMo8vH1NfxdJu7f0/U9ZzUe23zwQu8TT5ht42XDYDaz7NdvjO67vPmP7boeWN6xwxJcBUg/hAOsT43BPG1egr9DNiidDAxyYDoVVe8Ov/t/IKcCcWv7kRzfMeUT8gV983bG5XfP5s8y332zZTZ5nXcPh9ISbzvE3rzrejgM34SV9d+Blo3yyWRFkT0zCL++F65VD6Pnjl4H9YeaX5TkPh0TXKxvJlHXP//wJ7MYdx2NiJ9fcZ8/Dh+/4wXrNw5AYGrMT28+JU4h4d+QHLnBz6/hXI8iVo+WBh3jHr+/3bHXF+znRakNpE106sSXgSDzpAz9tPrJVz0bqzbN2vHq35+ud8NEJK4SPKnz7rTBczbzMDe+7zHNRwtHzMY2E4PgHyWhK3MUG0cRVLkyHQirvedE59uqYFH652zC7wl+HxBPXcxNPfCZbbmLD7gCzJHTOlNhSikN9Ziz/smK5D5Onmx2hGfgiCt+d3AX5WygTVYRD7cTJZntGtdMxGkV1aGAxoLGS2CYhSxGg5LqCeDXhjVOHlAWpqD9X45tBif6GJDvG0rFxU40EzWStHM6s56I1B9DiasFmhXOpiKKvJpLL6FaqYtm+ZmPcEq1wE2d80iyKxnLmwTJ6upemqPenQuwdoQSmnM+Ls4qCF3JWynXLE0b2rNiWiV+9m1mHwtPnnnwcWfWZp5sTrR9493bFdeNo1gdOpaXRIy+ejswZyug4zgnnjaKVTooLHucKLprSOnhHnpPRQBpBipiQrQq9XKkFnSvQCj/9ZM9//tWVTQJEDB1TOXNwVYwzW62OHxVy9bPmiuYWAanCYufPgkO4cG+DAmJFt8PszkQvtnquJDRWizNNuBCtpVLjBjs1DvHS7yyiR7vG6qvJ+VWJKsxijVlSPV+j+cxqLaA18ATzUsflM1ewLAEiYODIskMvFQJYQaWuKuTrPVNdJ/Rc3GP0t+Qujhz/zMftHJicZ5LMQz/w6RSZ1MJn8tnxwMbgHYpIJteY444aOi2VDqQmjFre1eLpnGrxMAF9RbSP1FjmmhoY3OVeMt6wocwSOso00PiWQUeuxHFUJdYgD9XqKoDW5DoTfRXhfK11GAUDsSAEsEJ6OddB7H12aiEeRWthj/FzBzExlCvwyY3Szsr2GLl22YizRevIp6KxFSa9vVK++u7AZ+2GMs3M9y1HH7i5OjJPhZdreMhCCPfsPl6z7RqerifGk9BG+PTplqQOHQOnFLmJQBG2W88mFFxTKk3C1QoWO4ixji2gVp1y+ZtM62funn9kfPMCyXaMerEJkCumyShiNIhR7TiJVLS1FpmlGE98FDmnPDdU+kJ9LPzwuZ5rC+kQAoVQkdtcf7/Uc7lEMdsTGMWgaKHB9ohJpQqrbS1Ypgmufi1gjWjIi3+5uYoUZemAz9z0LEZLXCZO2TYepsqj2LhLpLaThb5nV/dY32+Dccu1XnOql6JWXWWvlHqJ/CPN6/cuhO+nE704kmS0JMA6dlfszc21E4gViShe8GocnoBQUuYokIvAbEEFIkIjntY77lrh06bnRd6CjOz1mlk+8jAJTa+ccubbNJGnOz5MAyV5TsExek9fEk+ayEFtUyspmyMEDiGx04JLDomBfRpR37OREe8yTfQ0JXHVrcmnIw/7Pc8bYVc8Xo6gnle7TFahvVpxOiY+ZCXScLuG4RT5envkpom4dOLpTYeWCOHA5uYzJH3DJz/8Uz4+vKWclKZpuO4rxxpl9ek9dM+49hEOr7h+Gdm/3zM1cDxOvHpzRQwtqzLx6R89JQ/v8N0VshWOJ2W/C9ysrjjut3z+04TMs/EwNTPkDqZP0PSKD6/hh5+N3Egh5UKYIj/48jWMd/zZT5T99kg+Rd5tM3/6WWZ3fMumv+K2nfijJy1HJ7w/KP/u7w9scfxpX/BhxS8fDvxwk9nuM//2HfRyhXaRm1HYimdqnjBMmeQDq3bNdZg5Djuin7huG3Y6c38SRs282zlufMuLq8yf3mbCPDPNI9twIuUWUuDVLrHXhl8fRv562yBaKn+tIDLSu44+Kc99YuPgynm2XeIGz+t5Ik4NrznykCIaApvZ4f2ESM/rlCDNtkgXQV3gNEAIhZCUzcaRp5lTCSQyb1KD14wPSgiZJ85BFAZJuGyc8uO/rFaOIXgmEnnsCLMlb9niY1zPBVVTxVYKTYDHnVXwuRZdRnGbKSy+wo5lFFo3HcBdoIiainWJWj0DTb4WNqI05YF//cMPhJVHizL6yL//284S7wAtlzwoX4ohnNnss5Z0oeLKuZCLZwTEfhc4U0BitUAC4822xXjPXmAWSyh7/cHz6W1LL4PZTI2JsgR9YBMkxKFJufNbnv655/XxxC+/uuKmEW43o6ERraGI3fpIHj3tLXzUnm48UGJn0fDzTCMBjUoXRyAQnZKj2cpNyQIEvA8MxY67YNMuh6OaQthnt4QhQ4lnx7ob+dmnmb96dW00g1JDDlTINfQne7XP75b0NdAqesFXb49Hfr9W+F421uWf4muYA7Xedma7JlKPmw/INEGM5ODPAjO30HPw9fwtcRF2jpbXtf9V4QxLutXFuGypBsRRPZIvaXOKWPG9eK3CmXOeMQpcUTlDa+fnrAEbjyMJkso5GW15pFwpG/qoYPhnPJ6JZyqZho77IjgxdX7BKAgLKqjYeaNcAgOg8pqxYnNwwkYvAjVqMRoUpiruW2zUDCG2c7A0MPE8DLJG0lHYjR+YfviOT3prLHxyuL99jmua82ssN8ushQ1wxNex+eI9fDnOyS0I6CVgZ1kzihNGLFXtWgt756AYJ3olsBGY99fs9J4+itGJJoXZ2U3tikGrXioqfM8P/2RP2t7gv7tj7gplbft8lMIoypfriV1p+LNmh55a9u2IFXSBcapaJa+4uGWp4K47Bwrz6InR2Y2QSj2q2QriOWN+pxWqXopHBxC4Xh/4h5vf8PnHT1ChuqQYZ7xTpWg2jiuBWDUURbyFYIhVoAk5O4RIBS8ezxsv04OLLiXUn/FqE7ZJHz0BWErlMiWphbRzdQeQ/4+2N+mRJMny/H7viYgutvkWERmZWVnVVV093ZxpLgdeSYAAAZ4J3vgVeOUH4p0XfoAhAd4JzGBmMIOZQldXVS6RmRHhmy26yPJ4EDXzKILdDWR36SF8CXdzNTMVlff+77/U++3Zs/rT49MyNZYXyg5Uao9RX5ZC/iOP6xVVfzDyoi1YLbSOsLAM0uLt/clp0izPT8/QNOAX27SLAJe6Xzirr1n6e9brTy6E1UGyhMsOsRGnLWbCVYA7r2ydIyk85ISIVlP/4DhlI8TCHOB5qCcvIogqVpQikaEUvi+Obw6RYCuMCecK0dziJZoJNJUIYZX9YiokMRrraUumzBHPgZtuxboJ9c1U44pE2wVsNk6ubvof80yJRoyZpmQ6AX3ek/qM71Z0faGZJjCjSUbwHhsnWq3o3qum8N0P3/NqI6yd0a5ucEz8cHL8sy6wHyYadXh+5Op2x7R/5roPxP4jeRRoV6QUOBwC9qHn+3lCpx3eHviLXyVKMtbhFrdVnIs8Pt6T/QnyHdCQnwJuW1htjNVNX/1c4ory7YmyLjR+B7omeIXDI1++NlafCelDw2gg0zN/GDtuw1/CnGjblpvVNbM/8rqZeXe4YjgGvk9rftkFcCMlZq76iZ+FifVNx2++H3j3+Mhdd83XJ+VpEpybGYtylwLPoTCNc6XF9InGIl3sUJdZrzqOc6aEhjY1+FXhzilvTPnwJPzm40BjDRpa2lRAIsl59lG4z0ZZolmvPVgSghbm3DCTWFkgNPAcPUPJiDeKeI5ToJA5lYy4Na0rOJmXDTRyHRxNmbDg2CXP4GbEKY13PE8zkGAIRCBK5o6INAGzilD4UP1OfQbIjKVgeLp/IpHN33nU/FXUVW5fuNxyDG+1S5+soFaFIm5BPfVcAFFw3jHE6tripToy2MKtF5dQCZUbZ58wzeRljK6fOJdXkV1FOm7dxF/9+kTbJKJ3dF7oiPy3/zzxf/+bG1Yu1phUV4thI1T+ti/YQnsCuaDZ9VmdR/iLwZUpJhlvgpVC9lLTs5ZqqDbEtcg4ovhTRq87cp6ZDkbwxpQV78tiQSeIA3Gxpmi1gbedcNN8hMmYxVMM+gZCU7A24ELiqjlxeFD62xabRnBCTC1HrrH8yOu7TC4RmkAZy+KxrKClCpxypWcIYBIqKqOOMU91bu6ENE+oBVJOmCibq8z6B2GsMGYVd1Fw5qoLAEvC2IKaZLT68S4RtcKLd2iNrq4o1sXhY9kt5FIclksstorgi2IWFx46oFY38QxGwcwhXsASYUnkU6uczjMlJ0u5FMm2TBkMWQC3BWnMC1pbZOGnynIFLBjj+Xq98GdZRvyOi2fyJ8/z5WKtjYdRJygqdQ2p1fSts+WTV4dLn263P/2IBk49yYwNxqh6KWbGM2UoV8GmUdG1jsJ4Xnea6qQnCbiWqcyXJmAtmckyzoUaa/vJUz0HY1T09+U/itUR+Yhy70/c/dmP3DUTBM/K1/d8/S9+YP7Nn9G4mTm76iu7nGuCi1BJpYqxTryM6p29vEe4GnzQWOG0VPvXUkhaQxgaKtjqlxZlZ4GY4Vl6hvGZGAPrxshFaFxhsnpPccHAJ+gCFMGvBtrua8YZrrNHvSFNYdeCdMZdnMnB+MOz8nmbGH298p+jEkqPuMTrm1MtsjuFAaakl2CeOnYQKidreVWzVbjUbKFPSPVFzArRKEX4+W7k/jHR1JgUNmKkRaDoRepkQOr6MFfBi+Bkmb7Ue/W5AL2AD/ZyTZ8bm3PYqCwI+3lyMItcimy/XPcmBe90oZlU3UQN2Dmvodq0nPNRHedJUA1lCWa0IhfBn5gtXPVcfek/EVmePawzC7B+oT3UJT0uHueeT9br+UKVJYH0XDQvsc61oTr/3jIJ0Uqb+/tIiT+5EP7rxlWUxin7ueGuMWyakLZwHQ0pmYfYsXKJH4caOKFzZhLHLIkwZzoUc2XhpVUUyqvRmvLlKlNiIqjxVOBxTkQpJKeUFOl8IKWRoIUrHwmaaXHctsohDagGzBze5pqipcLznPi2OGSeuNv0xNPMRo2rXMV1mzbRY3x+1zKdoC0zNIWSM00naOMZx8j+sGfnlWF44urLK4aj8vm146rJDLZGyhHvEv/55zuyFq7iHpodX39vfP3bD/zy51/x+Hxi5a/YbgJ/+x+euL0ZuLq54jBPvHXC7Z/PSLuF4ln7jjIPiDaE7sTPfvEzPn5IxKYnLKPR4XlkHhP7/SOvbm8JfcRtFc2vSPvMmJ7YbHagjpu7NfnDjOtrh7fdbvmsNX48CU/Tnp9vAt/tZ3Y7z+NzR7CJqy9uCDpxOGYOMZOTEpJyVMGmgsSeTQfvT5kkjt3Kc+UyXbuhWGKXhMnNTBlOo2NixWQDXd/QNFVA+M2Hic0VnPaCpcxUEodZeCiCd8YqH1HXMU9KS+QE7JqGnIzp7EurmRl4tS48DcIhnWjpGdJA6wSfPG+aQtGZgSVC1WVijGwaOA0ZpHDljGtXb+k0hdfimTLEAq87Y87GoMbO4ClPlFIz6kWEPhS8K8QpMOVEVCGEwHER3/0pD3/mkS5o0rKr1+8INJrIVjEaseq44ClkqShdKImUElvnyGpsuokbnzEKT6cV72OH00imDkEv3uyXjv1TRfuCLJaCU+GhdKg/EYvDWcZiIoaWkAr/9RdPrK8G7o9b/tN3a6CO2OGF81XEkFpVXQ5RwBSRfDkJzdQNxFXhh0nBvC4IeN2IzUnlrqrjhwd48/qa/fNA4x3jNNE1PV1/ohQj9DWYpzhHtkKMExYdJdRUJ9NcKQIF8phRLzUG9pWRhhOtBEyN0GX6eA+SSbm+Vy5m1CkRKHERNZYF6T0DCDEjoQo0Gw2VslJAiq9UiVyRzPtjj7MZLR1JjaK5RjLLgk5Zdfc4qxHPtmT+ZXepiPwnkM0Z+y9mC3f6xWEkLZ9VIUzlDJ+R5FrA1iCgQIYQ0JQW955qJ1fjuxeE6ZIAJjX0Qur0Ii33bo8RF/jL5CzA1JfUwgXILZ9MBS5rosAUKmAoFwSpvFw/gEml9GCLkGhJuRIR8hJNblr9dbGMhX8arn9YmsZ8QdleMPKAUZgIGuqTMziKMKiyXjzCE8YhGX0IdDLyqAP3MhIc3OU1rlzTlQFntTCK5wJ6+RvCi0PF/Emj5DTzuqzoe4Xc1MlRLqCOXAoPu9/x2c1EHm/R97fLY9XGrVkK64xDl9jn82ELFaOlcgIMQ63Q6dKoVvifXpbo3suEomoYCsYPxxXrjfFwMvYO9nNh0xS23cSTF+4w2rMFninxVHiSho2NaFetwUQLYxbCSfEtjGXil7vIfHRk7/A68aYXnsdEF+CPfLpKog1+SZiDxWS7PsFUm8dKUM0vvI9odTSRlyk4wjjsmJYmbmWXELqz82V9GAodlZKQl8b4/B4WkeqaIy/rtfKI67XiZXnLLkh//aG9c6xyFUSewfOwQArFls8MgmRUCkWM6YLe6yfcX0ClIsdLgzlmrc4QYvQLJSQuBa/xx4wRsZeY5DNrD2BS4frinrHs6+fnt3jTO5SMLTShKpCW5fnn5dyT1XZvI3D6B/Cnn1wIl3Ig0hI48UrhuhOOSXlIK76NkdFlNDsGi7x2jtsyc9MJmzjQSEYaQb3jYe856Yl56OhWidYpT1PgPkZeKXRBOSwG8yEZYxQIW6ZcMBdBAt+mgQZHcJ7vx8gqKxD5zISm63AryNPMl8HTupEgRiiZow0cs6dphXaObEND7jO//xhpgqBFkRKY48i27dHDnmGe8P0VqeyZyx3vn5XTBJjguKJ4z4/He7abFU8/vuM6bPlohTHP+Kx8dv0aezrR+onH8QS64RlB9o5vHwdC23CIkY8x0TujaYz9YeaQehwRH+4Y4kxjkccHYcB4c1U4HZWr9S13d45u47DsoH2ClHG2pdFbnu4nnFP+8AAqV3ShIJqwohzKEafP/PxK6cS4KTP3P1j1ig7wfP+RbXNErMFF4/55w/aqJUZllUdafeZXd2u+XBU+xJlUCmYj5npWU+RIx2jwcTqxCp4P+8LBWq5RPtwPNOYwc7j7qqINocVL4urGs9pPNZFIOlKq8Zg+TKzzllMZWWnk1rVEq0ji7JS3beYrP/FhbNjHif/q1YrT056mVx5tZnKePiaS1A7Zd57DnHnVBNBAo4WonlIKQR3DXFiHkTC7mgtf4C5D1MjGdRwtc7CCd55VMZ7nRNdUDudGHZoLj8lqQMGf8HCUJRFuQdYWo/ys1VLHcDhJOJTrbabLA0NqGF0kzwHzmdcN7NoREeW7h5b3plxvR17t9vxMDqQQ+O2HFooyIDg6xI41UjrbgrwtW7ozNAvREh5h/yNsrmoRpaEhzwkVYbeLiAXu/In/5i8P5Fj4MNzw3dfC1cbz++LxUyacx/oL6lGzfGqqUa2GauDDp6p+MV8LYsuXzcAvBY9Z5DQX3r1TUg5YZzSloXolez7/sm7k0RTGxGRCHI3QQMmZwRSv1VLPtRm3iDeK16qu94JJtXfzxYHMpCw0quRMpX4sG59KTVNDwCxzttOQIpQMXkONjM01mENKLehRxzwYp+ma6GaclgWdc4irE7eUE6aueu+qUM7JUAs31jKoO3NuufgCi1RR01kEb+pqOVJs2bDtzO5DFks+t1zvct4AXaUrnL2kjXPhVzdwUUOsbmw162P5Y2eahJVqkF/kglJDRa8uUFFZaBGfVF2qS1Huqtj/Uvx+QvcxKhpVpK4bqM0hxaELlOZ9LfGUigYDxH+ihlaXgr+90DfygnDZIlVVBoskFb5tCldlT2SNw2iKcO2M37SFD+GRop67YcUt10z2zA/+kaN7CAtiEQAAIABJREFU5CuFt8drjhJY4yiuYUoTTqBZLGGqd28tqlSExjIDxr9+5/lql7kLAbqKBDgyV3cwacsQngm/uifNwvx0zf3hhm03cRdbXM7MTi6FNlR6ynk0nxciSvGK/wSZr1zjUoMelutxkir085Z4leF47LnKwkymBSadiUfjL/78UCssq8gtEU7Zs7VYfWpT5YCPSbha1QvAIqwbDwLHxgglYbE2viufiMmYvKdM1fHAqRKjVQcnWxwr1BY43ZFnwwWp/BWz6igxa6UhaZ2oHCahm9bcisOoegHjBdlMuc7ELvcGq4j7ee8pS1M7LxyZUM7XNkRcnY7rEsayTAPOnPddyTixhdtdG6TZoPFCKomzJLOwpNwtRboIpFIuqaPOuCRsnv2BK2JcJzgzS2PMMm2yF+AkLivZ64v24NwArDGiCP3L2ObCJ3eijMjZU4ZWavjSvihuQZfD0r57qch3wmj/gUnsTy6Er9Th9cC62zA3kf/n8YrXOrGTEe8Sdx5Kn+m1Jc7KYA3CE3jHQQW0IZ0ant2eVq+I25nTZNw4z3o18euw4ckmKCf+yq35RmBOR5qNsC571n7L71IdiddxImTJkBsymak4ft+NNIMjTScacayKMkiHTAOdjYTumldh5t1B+WLr+dcfjmz7Bjs80vUe5xyvr3oOH/eMq4mV7KBNjCWj6ZZ1C4f9M60PrMPA6XhPv+n4i6sBr6/5d+M1f3NqCa5BU6EU4ff7AXMt4Hnt7ui14cu39YL5aj2gXUKGDDnx8Wlie/0ltlJ+/YXCGGAcyDkxPnu6zT3jLDTO8+bGgT/B9IbJPhL0SN6vGE4FH/akFLm6TaBb/vnbgs3P3P8YseKIGV5tGpCJcL3DwoGwhU1KxIPynHput4Hp6HDqiaPxZjfRhxPSVJrBwW15d1+IJTPPyn5OHEtHlwZWXYG5iguC9QxDYus8X4QJVeOuTzzgWCG0duLGZ97theS3rM2gcwTN5PJEVsH5lg9c80Oa+cUuIEfPgx/5Uox2tWEfM++HzKZvcPLI3XZDHD6ydxti9hySsJKZm74hzsZ2s2EcJkIojGlCJfPuWfHBaKTwoI4sjv0YMG/c0NKpcZTEvnScUibnzEyLFc+9ZdSteJgKaTa8VzyZyQRzf1rXCHUVRZDiwCC7yuFUoClVVFScstEaoJrUsN64swghciqGL0pMihV4c3XklAMfhh3X7YkSBhqDX3+WSVFwLhDnA/enasq17iN+5SlZ+MO7pg62JPLXr9b88PHE0xjIrXITH5ivGhAhUTmwxQTXAkkJneezcOD1XynzCHel55sflH1ek3SmsXBxNlDVJfjjPBWoSvOLQ8QZ1xTAn0N2IIle5PmxFMQpRJgyvHmlrLt7ckxEV1ESFyBN0LTgJOO9kZNRTKvNXq6lyzmxzwJ0fWaahZwy81F5TB1XfUOOBxox1AtpBgm2+FkvPO5SKStWSrWKS0LSGo18RoAigCk5FbpG+XL1A6f9DfsR8BCo4q+0uIUUS6g5SqmilghYEVQM584er/X84zl6+FygyxlpylV0oi+FpEdQvyRzlbqpiVRo5iLSM8haUFfjlb0JaeEuFtVltFudJBawl3MYC9TPL+mAlc3MhC4bby22AXyuRbNfKBWcEefzg1LpMecRfUWcDVfqxm44JFdk2D7ZpOGMYGUk12St5M7b808/yuJDOi1Cz+2F3lGpEQAbBw8e1iRmDYDwzo0A3BZh54RvLBCT0jZ7Su75yq5J5cDJjjw2xri552ge55QmFl5ZzyDKk5v4PNSJVjnd4VSRHPn314lwaBj2LQ8GY7/nyyCXaULJlTq0C8acPKuV0bbPhNsTTyflb+dAd7zmM1bV4WGxPMhW7S0pLzrFcyx2WsSc2HlqsXggmy3WblC0YU3mtrB4R3teieOxHVi3hzqyU63ULQ8kX0NrPPTOmBLk0SjqGKZC42rRnVOhCcbNOjPHjjgJ7/fCSnbYBjQ+MzjHhsxhLvS+Po8aMQ2WFsedRK3m8sKMtgRSzamlCGQYk9H1nj58z/34FU1WQs3CrWu7GL1mUqmBGd6kWpSVmrArWA3KsXqvV7iEmtX2rBCoIRZCbW0/TQBNYngpDCIXu8qgQirnwJQ6ofJWkwZngQmlX7zhs71QXIrUtSuLRuDT4Isz42agosjXlhmcp5jRW6VjjSK4xY84L/7ExovQEsBptTWEymtfUSk3vr5US/JifbvrELT+4tGqJ7VTIWnNgvi7DrF/oFL+u47//X/6H8yb5zEODMXz+RruEvS+wcITk7a4OOFcy4ch8ptY2EnHZJmpeDYhE2Lkz65bgmQkJ5oQiLMwJ8ejJA4RDpJZuZ7HNKMloTlxKz03fWC1dCO+HYh5wrU9tjdyzqgKz1OhqBFLpGsaPg4HuqantUzIMCXhykN7s+P5cOAqBNQSwxwpJeGco8Hj3ECjAbPEJhirdgP5RGgTm3WDM7h/GvnwPPJoGzRXBGEVWnarAeISp+o9cZp4dQMhCDvfwnbH8bRn3RfGoXBMnoePoDS8/XKgb0D6wMM3T0Tr8K7n9s92UCZgZvhh4nCKqBrqMtdXAbmpvENtHOVZwQLFjvh4C5oYUo3HziS2uxYkgTUcHybW21veHUZ+eFd4sx2xWNjHxJdXAjEzxpkQAr/dd/z+2aPSkMtI7zJ96ziNuRYFLiAp1rGm62hCZNe2HPaPqCoPE2gISI7crQs73zKI8fgMg1b6wnPynBBImdedUeKI9w0WAnOB3jLvjtB28Kr3NHHPKRoWevZTZJ+E++PApt2x9sJNb1hK7DFyUvK8uB26KqIaszEX4agdyUrlFeWMaMOYCoNlkEAR6K0qY5NPF55WKnFJ3HGkUtFkv7iieMnVXN4p/+u/+jfyd6+sf9zxL//n//6PFvSnvr4iL4Vj2whpTrhS41zDcqv0C1LnZWZs4KokLMCcDYojkHBOUVdYN47oEn4GCzWcRqyKu4J3y2MXYtmAPRMksFkVxFdRqlnlM0YTcna1sNbFuN9XlGWeQx2HiScn0JXnD994HqYGU8WsFtG5yCW2+Yz8WTkXwnr5npiSMJzkWiCpozIbKyoJFfX0FvnLX800LlKsel8XqdZvx72vqVtNRQtTLOQi5CW0Yt3Ux7agOFGKZUhCNMGSQ6WgVjl1Eipy7lTJCxppGUQLKUHwtSCuAKlA4uIHXNRwptXtIxrBKYfR8Te/XTO1hRK1Wp0txVxZqCFZuDQJtqAtSlV7u8v3FsQdufzcRU55RuMv11dBtHKRzymdZwf9JNUbGj3HfFf6kmaWdbfYu53HvWUpuNUwOaPTixD7woutH11eUkPthZcuVtXo8snU4HzNn2kTUri4nWB68R4+/61SFDQhxV3EpfXkjOyq4E8W85f/8f/4v/5Ra3n4X/67P1qv7tNHWyoJJ/CDQJcr/cdLQ1nW8ry8ckeFuzzzGyfsiGCOuQizFrwYnWQkVNrXU4ZbIoN61laDUhqf+YCycQ5yS5lPzE7ZbhLrxtg0YDnVxDX1TJPxOGu9HwRFxVhr4Wn2S0FrjKXhs5A5fbjhpnQv5E+rOg09F/2Xouoc+/sSfR2paLGSl4LLWHwVOSfZqQjRMvqLJ5zOkKdKY2gEovHxY0MyWDWZTSPso2Cz4Zcibr2uo3xxi5JWgNnI0bMn4Keh7jkm3LUFOKdmLhOJGrHJPNYmuV7cy+NkV+tiq8XnUrWSouK88hRb3v/hNV8R631sQV9ntCYiSg1cqeltLN64XIKMzrrPM7+28GJt9hIHvly+5/PGWEnigBKoazye11o21lLpF8dFSNuSGKk821xqkZrs7NBThWwt5WLhdpn6LOvVycuJnAvnM8abqNP+pf+9HEtux7IM0ic2gZUKYZdHrP+YLRMrXoZEdYr0EjgD8F/8b//n/+96/cmI8IOvY0XrHBqND9H4mAtTFojr5eYhdN7Rth1vXUTEWMVE0oQzmDfwN2NkUsNnoY2ZLhuqmR74ysNN6CkYz+sOK5HjwTHOmXlMaOMYZmFIjr7pOdyfOMerlinT2ci1brBS+YCvVx15UKLOHFPker0Dzfj8ntdB+WJ7JNIg6cSmTcQSWAXHmAqnPYyqrLuWD8+FJnes9UC6T2x6ZZ5HVkw0bcNuId0FX4jzCdd6rr+MnPY9z48jiPJ+f2RwDSFNWIo0csWPH35kt3V89ZnSXmdsXHOKDgkB+sStN1I6wt5h88iYHOO4YtfNtHeeNCRknSAntNuCnVA9UW4E726gPPLwzY/k+ZZX2ytKbqFkbCrsDweenjPv79+hydHO4HVD309s1jPtekPJxrvvAz4WOpn4Z9sZsyPFKqo8jSea1vPqds3jmDnFUEelZSKQ+LjPNDgiwufXK57HQsJzzEJD5WNu2oabVWbb9vT7Ez/MjsGMH45VjNZYJM8VuTlMM0mE8eD49lSTBHeyIg4zqsrGhNL0IMZTTnz3pFyFhjYOzOrI2pBSolFh1ykuQhwjIedq+aLKmDOUmSaAy8YpTrQhYFoYAWdnZ4JMg6I54RrFZGYQw0ogpYI5ZSyxcon/xIe6anlj5hCppvEsYy5P9Vie5nqDmhaB1kyhVZiTMKkRcIQIkxV8gRAyXicmeiIFHx1DdJCUppnptSUx05YeYyTGQlbFi+KbA1hDyplYDF8Cc04EVeZcEHGULJfmM1vCYhURaakJaKqZpBkOmd1Vy9P7KgQzBUp9fQ2rPE+om9yi2M54gg2MtLQqiCzOECKoliXLwteNxQwkUbTn/hB41X7AByUXo3GuphgFyHNFXVwjlK7Q4hifM0azOMDMuCKUnNDgKGr4XCplCBbf5sUZQhfRiy4FY8mkrEthX+3kJMJS8xHNCLrwoqXUWOFGKF7pS+I/+6snPj5vOAzKbiucTsJ3j3oR3lQCYf14Rp+h6jOqi4T8sRf0sqfb8rVIjSJm4V9jgma/IHjyRztaiMroDF9sETuCmVL0DPz5i/tC/VsvdAdZhHTncWqRBQte3ruaoPzH+9pik4sBxRmaDTVXUXHq72VXkcZSjEK5MDFcZvEmrhQrlcJ5uzcM1FdUr9gf8V7/sUem+rFXsd7ZsqRaVyWEEeMzg8F52lJH2oajlcjJHOuS2eBxErhbRuiDJIrO9NbiBVrxyFSt5dY28SE0fJEnvnM9HRNPqV57E5neHaDVitqWOnGYMLyr7imzGKeZyv33greK0D0mJVoV1/bekHnm6zmwayMM7cK6WTjcS5rdGfktIuhZ+CqBpzRzpdVVRYhUHQCVc5qF4Jb7rgGWCS6wf9/DdqT1rvKUs0ESNi2MqaLNskrs6gvL6UEZSstGq+BazGMzSKgXvGkhxkT0AUdNS0vF8FouKe6fKLTwfinSa9oE5BoI4ayimi9WH+CbDFpYx8TuFxMf37e0ZY2FEUstq2nNJGfKl10apLM3g1LPY1pel/PpnKOa42I3VpevXgIxeqrv7zPhkvPxadbpUR0lF4KTSyGdTBfBWo1sL+VclFcbvfNxTkU8uz9gS9rb0v8IdV8630ug3obOj7Amc1qSTs3cJ/edGp6WlnANpTYK2TI9drmMzml6cm7uxV00AH+yQI2/3rSoKqd5Qp0wK3w/F7p4JMsaqIskxoFchKkovUITPObqhuxFyC30U2SmMJEo2pGTccqR9zng+hNtAj867tyR6aSsNi27VPi2jMzi6VGkjLxtHCoZ5xyHlGjKlhUTN+s10zQRSyFtE78ODZ0qbfPEumtxRRnDhMuZTdvxu4/K78cbTqOx0sCQZoiQXeHd8zONCm82HccyEYctUyqsVmvgz7H+a1aLXLNdFeIM/VqJwaGPE68/l4puNZ7X68yUH/FeoD/w9k5oVlt4yqQ2cdSCxYHt25bVzz6DH39k+j5jckRe3eLeD6y3meOUaYeM366w+4zdHkjliZw8/slTHgMHKxxmTx7fcj97fvvNwFev1nRNYtgPXF0Zn3/VUg5VgBNuVkzPie/vYbCW3f5I4ZmH04oS1rjsK5WhbbmfR/ADbddguSHnQraZ9WrFMAyMCnNasW4dpXxEueF+jvTO8K0yxsJB1wxlZCMjzVy4ypH3cyHlrlZ2Bim2TNNI7wvmHE4CLoO6yBehMGlPHpTWNagqhzhTuo5QMpukePXEOZOD0qhSYqZvOroCOTesGLjb9nyTEmVKrOh5dpByoBXBwsxOHKVkZjOepd7gyhLTrC7XRKuiaK6Czmx1HAyFFAJj+vsX5D/2aLqJmy5yPNRbRNXZGGNKBHGVP6sJTQ5F6bpYEYEElhziatY8qpQsTM5VJfOo5KY2rMXmOpbKDkMZx559mekofMyebasL8OHwGnHUv4Vlno9GK5m2d5TgQYU4zTUOucKWFPHLeH7BhgRSrmhsEbiTR7Y/U/7D17eoz2jQGi0tlRdaccJyGYXfuoG3r/fAzO8+9kyzwy9j7SCZL962OH3k6x+3qDqieD6/+sjaZ+YYqvtBzKRSY7u1VI11cfVcu5QoHppVIs5GyUqJDRoMVUjRKKkiic7nqpyWUjd/yaChKsEXp4haEwuWCkmEkCrqsgSvLlH1RllsjUSq04flCI3hsuPV9Z7Xt3WEn7eQjhs+5gJWN/WqOZGLBzDU0fRF6rgguk7Ach2R2hJuksnVtkx9dbbQGqGd4YLaAsv/laX5ECwv4seLVV9FNJUqrCwLePLpYVYjt5ev6qZpS9HsFFuoE7Zwd11+cRERq3xOMUNRZLme6hZeR7nNuZDOBZxVb2B9QbyzZdTcYsmXL+It++lb5x8dTzJTmpFV6qpFnCskhKuUSKIVcZfCvJikfiuJjNCjrPEVvXNu0eU7PicRCyTpCdpBKZyscsKdCcGEjWy5m0feS+C/TIE/iJFy5snVhnO0hKL4kni39zjN7LrCtq1JccdZFk57HTfX63EpaMThSyaOmWiOoMZx/cxxdUI+vGGlC6Ro8OKtu6D9y3vxIEf87Qc+usDV4xWecH50Spl52M2sw8zq6XbpGTyP6w+IyxxGx81KcaeIayFOyhAhxcxuVQvWqg5TVq+M1T4yzMJxblk3RuuMMsEc6xpcSbo0UEGshva4T+BKBaRe+TFBi8Ip1z1L6pWSS0V0G1uEoA2LcjEReoE58vptAT1ipogb+Pq3nq9yIdaR0VKAuwVNfQm76ZfP81IKJ63skLNGrxTDW02Oi0v76oC1VJS6fLLeUjEaEt1id9meK1R5Wa9aKk1lpvrJn51ZLodRhYpwWcozcqE5dEtBeo5YTpemN+Mt00u1JXRUgaBIfW6+pNoYygsX+hz7fXbmV6uAgmq9tNTypckW+fvX609ezfeLBZMPYKUKrv5ibTTNFiwiOVUoftkYTjbTqMMV2M8OUuKYq0hgLhBS5kYcT2ViZ8Kkjuc0YU9rDkRUR/YIxTv8CX6QgreaGhN9YZhgaoHoafqOX216dv3Ifj9hJbNeV65inifEBWJQ2px4njpu7xQXt8xu5g/TgAstN3HkTVtQPyOtJ+4n7q6F5mp59/KJ17fXxMf3qIs478nHCLLH9QHKhjk7ilYF5vDOkWXGH6G97mgkk8NMexd4/phYa6HRG7jvuH8eub27ZRdPmPNM3870VyfKYcv62lGyIqeRxs18+HDP3ZcbpnmmxSFfjSQZaa47WPXwuwInpfUfuBtu+Pj7Pa/eenahI6XI44cnutbxt3848vp25s31K2YZ+Hf/cc8ga0ruyfrA3ik+bek00/pECCMHdpyoSL/mQFlG3MeY+GyzZj/saUPGixC2wjBGGtugfs/1quPbh2r79IvrHc+nI29CgxfPh1j4m1SjTW/dQLAVjz4hxZiLMdFgqeCD8KYTgm/IJRKj8exnMEcQoW2VLHO9hepIKI7SBUyUeZrwKrTFOJkgKTO5wvMwVn6VGkM5VX5ayAieIWacT4Sg+OIIVigWSSHUEa8ZK+eZSMzeIZJprN6MsqujxU876D/F8dVNJJ4y66sjyTK0PToph8E4lorQe8u0jbFtEzQN8yHxrA25i7hSg1hlMdxNWjEDU4+zTLSMcz2DtVg4QFZaiQiFUhyhz1iBeWxoVgnnHafnVF0A1GNeyaVgUm/dkjPFPFghKEyLrLhI1YgHhTlX1DNlw0mDW4G3zK9+Fnl+gsEK46A4XSF5rnuMJoJ6Wh3Z+UTxwvTs+Wo18p2uSLNSKHxxN3NzdSAk5b4zTmnkF5vAepvJs+EbqyKTLGRqc2AlkrInnYzgMuarohsXCM6qs8QkpCcjbCvHNiOkXAgIslghVf5qFZKVBWlcrQJzqfFrWRTJS7KcLMWHV2TMtVguiz1a48g54n1gTgUlE7wizohEGhy/+NUTx//0iomFr4nWyZFVGzOARpdNJit5sV/TYuBqrKqSQTINS+w1saLDJVNUq+j0XLCwFNVOLrSFGlLyYmuWrdCdsawzkrtsWGaGKwvVkipoyyQc/gyLVbrIBX06u0W8TFzOqDZnX3EFpDpunPnk52S5egoKbuFyyoIuO19pGWcKx5kmYp/iaD/9GDZPPE+OtnlizMJNW4XOozS8qQwAZjPEGY9uoLhAn4R1qQKwF8KIEtQxmgPLrMmLG4jSqyDaUY3MHFMZmS1xW4yvfcChtL4DjagYT2OHAGNybJyRTDiQ8dl4WtwqioEPyhzz5V0wBe8hJ0ElkIowAq+DMFDYbN7zHNdssyDWgm/IuQq3sMSgno4TjzrzCy387tTwYXXkL48taAc5c7wdeHO156iedBwpUfiun/lqfeIU4UqFJjieZ+HKOeZUefaTeT4M8LqbFxNaqxWjFXrvOYyZH44db1bTwnuvGokilY/+QgNi4e/UNeJWpb4YOdKKVl7O+Ros4BrldKrUm6mAd+BcqT7DwZ2J/tBkaKqTDynx1Zffc/jm56xLZDKtIj2rQsqza8R0bsoMnKUlDKMOxYMZQYzoKmdWDRrKhULhqeKyTylQs6uIcVloCo1WGz5daDhi1as6c8ksoSnnxtAYEFbLOQ0IZzNCtxSvgRdR3Tnu+dI4L24PSuWRy3L+gwndxaytZpWcf/9c6Hdag4lAWC/0j6NVYL5bnk/6B9brTy6EP+sKpaQafekKziJeHVIOGI7ERModuTPC7Fm3gVAKvhTa9TO5XPOFy5xOCUdiHGv44ioKyRnhmLjbJsaYmeNI6QybE87VriqIoi7yqu2YEEQdP+sjPj+x3owEa3h/Um62SiiG3+xoy4F0CDytd/zHdwfi9g45Jp7fD+wAb8pfXl/xxRcO5J7TnFj5Ox5//JG7X3lEMyn3yGqLGxNPDx7dv+XbsXAaK9Id445dV9GfVzfK3eYOvLC1e/blmVNeo/JM3xWyrNHccfWFgj2R04h7G7l9W4jDPWWVadcNTTxhuz36y57p3lCbGD30vuXmoKTNExJ6Cvt6kcSWE8JqnCmnHk2FdNygcs/qes04tQzSkYYnaHqSCG9fwf4R/u0xUmxNYqR1A9oOWOxxxdGsZlYmzO5IIy27PKD+hHjHKIGmSTgeCe0rlKkqTVVwoqzaTOo8pxmmOTPOkW2YcS5AeWDXGkikXzleRU9OStLElApda3xpE+IDD4+eqezJfoXGI6vQIiKsu8Tz6YmN2zLrjM2JKL4a8vuGHLW6F5REWHVooxiJMs+sl814NEOcJ5ZMq8LkDNAl9cy4EU8uhWRGsYT5BsnQIkRdTPCd0rgIxRiEarMkiWKeVBz8A53pP/b48OS4DTPBexonZDsirdK7DdNzrOOq4Gl9IkvClypWXHeF+8cVgw4AJPWoRTorCJ5JZrQEGucoVtjpA683M0iHb5+R4CgRytQx2Mw+bVGb0CxsdoEpjVAKYg3m63g7HuMiexLUGSWHqmm7cFILzbomLB2eBROjI0IuOA00PLMNntet8F0K3Nw69DSibuDd8y2fbWfa9TOWA3IQShjpFO5c5DEJmjybKdHtlaGP3K4/8sqUZq1odDR9xDRTolB8LZrMBBdg3UZOI4tLwQuNQLRg0Wg6h7aOnI2cwftaUEXLeKkbUi7C2Y7BBFzjSEQ0CCEZE5BcQGJF6FLKlVcMSzCGoCFglnHB4dXIrsavjznSmhIasDIjzvHLn3/gNz9eo8mqh7GrWNxFsV9NZDEteKtWZOUsMGPhNC/FgKoiJpXJ4AzNpbpD5DMP3XAaSLlUr+D6CiEoeUFcg7kXbuMSG80nP1tNM2QRDuYlLvwlTMUtRfr55+uHchm12lLsuoXnnKmo6P+HTXE5ztedcy82h3LmNntlLhktskwv/mka2jRsOeiRxsFrP1JE2ErhGHY8lWoj+KRQXEJKZq0zP/jCZ+qQeEu/oOxK1ShkCkk8YtWxKyjkkrnPD9xfR+YofBme2aghtEy54J8ObJvP+JhOiC9su8QpGlsnnKw2HmLC85QWutWSR5hrANaZu2oUPmsT4hzfPtUiyly1mhQtTGvhu4c9/ZXjzWNmagf+Jho7P/Cr8RXfNSfetu+5Li3flp7P/Mjkhb/thLvJ8+ACH1OiTT3/Qkd+DPfMjfKzDTxZw91mBkkMM9yEyt2PWVl1hRs/cxw81WbmZXRPUEiF27WylcIp1gjgldXl0GqlziSgnL2Bl+vX+TpFwLOI9ADnsLmAVS/2Jld7MYrhvFbtA1TOgJRaNHvglCrA1kt9zMZoXn+LvL9bQiHqaSezC/d2zQsSn0SIVi7JfWcu7qXOlIrCJoNCYVrsNc/WlN2Sx1kl1Isw7YKs1q9ncRded7ug1Oe0NqM6gdjyfSdcLBHPqG80uaDF58esZhvLOX/C+RaM5iJU/XTBftK4ShUsmr4Iac/P2atcJkAq1TLv7zt+8q5829e/aDLgnKviEU11842KSk9RwUIhlcw37zJ/eIjEQ2DygcSIx2hlYvA7Sqz+petG0CmzXQUOCmNMFKc06UhQIajjShK318Ictgzzka3zkCJfT4Eu7vAJTnPD5Eb0YcssAt88sS+BqQSsHLltM5tIj+qaAAAgAElEQVTDgG8dV12gWCQPiR9MeffbR/r1jo9Pe7yfmaYenoQb32EFRncgC2ybzOe3a379OlLyiTFFRDumU4tzDtrMj9OJ6SmhYmzDF6QyMWUjpcyOljICaUDDChfyIsl0+PUKaQy6BtkoEgDraEsPMfDjvz/yYUzgGla5pt1km3DNEW4a+t09xXrSfIJhRfBCnHtyjgQbGZ8/kG1DLpmm7Xk4nAgbx0068tUvjTSdKFFomh6bjJQfmE5rckkEn2nzjF+NYMJq24EaZSyk6Il8YLXa0jYTrnWQR8oUmObEJjS0dy0WT1W8YjOmDWbGNBvjMTO3I82q43jIVZmbZp5OheiV5KTaSC1j3iwNzjn2h0C0jsdUuGk9vulIzJSSsJJofL19NA5SOjBnz4ziaS+imlaM7EtFKbSlswQmeFOKCJPMNM2yeYvRTyOzD0yLk1ISg5yYk+C8EXJYomGpSJlXyj/RBvp3HeI8h1PHOkTakvHOMwM+HXlzLcTJY65UntrCNZscaE7c3QwcHozZHI3MWKiij5IjwYSxzBQC1iixbEjc0zenyvmcjRyVxp3ICJvunpw9czbMEk3bMQ+JIgXNiWieuTQXv1gfl5uge0EF2iaQJxBNqKsW6cmE/eBwwSizVv7bCH1ntPOPhJ0nC7y1jzgnkJXeGXOX6Kgij/2TYj7wqp8YXMOYZ9ZZEW3JKZGHQkmRZuOIoVQqSPEkp8TJcMVR2lz9l2fQXqroL1IpB1Y3oZgzztdiKp8LxAJTqiEEaEaK4p1h3nAlU1INivDqq34hJrLJYkcIJKOYqxGpoY7NTQpN8UyWq3eoBx8D2Qt5yoh6QhF6L2zSwCQbep/Y7owPD+C8Mub5/6XtTX5sy7I0r9/azWluY2avcX/PPTwiycyqyoJKQQ0YIAElaoqYM2PEjCF/UQ0ZMmDOACEoFVIJgbIIIjM6zwj311hzm9PsZi0G+9h7nigzpYqoOAN36b5r167de/Y5a6/1fb8PQRh8IJfaHPm+MaYLAloQL41g8TzqtMZAVtWm55ZMFfkUzVw22vSnDAVpscq+CualUX6eaRC+6RufLXnP6YCtC/s8/FSQxrEF/gZ7FIlNimHSRtUS8c+yidiYqaLySVIhebv5RmvmxGejZJC2YYvPWsUm79Dt98XYTLDPoR2/7zFgpLkju5Wpjzit3EtDPr7oJmodgUqpgnmoJuzEUa1w7e6pS0+P5+dW6ElUg1UdOwKvxJPMc+uNnTvwuHzPm0EpOM7qOC1K30/sj54P0z0iPVTHqpWxN/ICt1a4aEMCXkvA85wC17r+WZSwjePFC/czxMGxk8JVOnqMh2toRrqiiATOFyN641Qf+ZOd0gF/yQcimRQ6qhVuJUMEl4WbfADnmPyJNwXKx8LpzjNvkZXvrpWrVV7dAhhjXsE8wXlqgQeDt/v2meVZiPutAM0CxVitGbDmWtiFFgCRt1PuqXg6317XCyylMohAv/HMV4OuUSEairDpt4q1hLWsbSOWTdltERCfsq+da6MO20brrsIsn7TENSoPtnLnRr4TKHHl7ToQUGKtIJ51Qy4GU/YYxbVitm5FpP/BOarolgbdjHDeN3P0iLGaY5RWfoRt89uY342uY8aG12sbBHv+t61IlU1Pb1tfoFFa2JIN23NU7FPqm4ojWsObeff5NduxydykkSvyttlq30ILhspb2IYKLLbx3LfNs5gyWHvf2X0O8dC/Z7n+zoXwn7w8M9eMuj2Ysa6JJcPHM6TrlRgCOSmeSJKVtxn+6G1lnhaq79n3gVRmrmVksInBHIsqN7cVnJCmJ2Lf8WFZ0ZSpec/DYmQSpxWWk3Dgia/2e4Yx8YvHzGTG1RXenQSC56ADtVbULXiBGJsGpgpIzcy+svc7rkUYugCDkmflqxcjT0tl5zv2h4zuE2bGdaowKDcW+Mk3t5zSA8WUVRf8ULjZ7cBlDqdT0+eOgWUtyE4JMaPqGErHskbCsCPZRHADOXb4bk+6VuarcT6tiBz59mGlD8agO16/fs3pdGbwK9k+Uq6O3f5MyEee8gr5wDgqIoWdOzWmZ2nmNFzlfDIu19JIBrFHF2MVpQ89yzlz6Fcce6oPfPit0g8dItt7vvXE9QWzXRCFno7udcXNB2pxzCkwes81rVgArZF5rkxX5eZOiAHcIIwhtIuGrrT7bIeZcn9+bBosu2kXvyRYEL589ZLzkpjmjNvd4uvMwTvuHyd2/Z6ZCGvrpKVwIXjPK+namLqL7KTDLivijKTNkTuEgeg9OWVEBbFA0WvrApmjVI+JkRSS+iZPlqZFFIsspZ0/qoLzHmrTenoBV6Q5810L1sgCOIewJ5Pwqj/QO/5hjht3abwlVaYqgKMkgc4YitF1la5XTqeBqWTGvlK0Q13AJ+ii4mpFpY2DqwjOCRft8S5jEvG5sN+dEJQ0BWqoODM6n6mdxyZHdEJdjTB0kDNpWTAfqdWRS6BkR3ItvcsZZGlIK9Gmd9VqSE346nHi2HWO69Jap2YVLR7vI2ueMemakXwMOG2FdBgNgqPWSrUe7yLkivfK3c2CpMywm6k5YM5Yl9AQbMVYsxC7numS6W6MOPTobJTVSMnoR8OTcbEHNaRqkxcUyCXgVKgI1Qq9CKoe09zkHrVxL0U8VQXLrhkHvUdDRWiIs7VUtILlJjKwrdDAGiKsAlpb2p4GSA6sQNYW7BGCUYpSkyevwu5WSadMv4vIslJchLTw9oWjDzOWA6dVKEvm9W3l/RKRZUBig9ut6bnD+gMag3ONwy2ti2TaQqGpirnPd520odWeE8w+/VcbwcO1Bto2Jn2WVrTUu8Ybbm3f576QuOfuj/ukb37GkJlrPnhvmxaVpnFuv1FayEoWatyq8+yaHtsBtiLqcF2kuoKfmjyFDaMWZDMhiWDPBrrf83iQibve6Dd5j7PIXAI3LjGLw4eFl50xTbtGOwiJgcAgA7MKsze+0NLQUNLGwQcn/Fg6Fq0cUb4rAt0ZBX47Rw7iECriC28QfpE7vId3i/JmjExZkbWQVajmWKqQ1bMr6bMeubb4dXWO3gu5Gp1UrAqWFN+BS8aKoxRIzhOccE7KrYtMGIeupZsl4KabGTFKAouBasa5CsdofIgXsnrejCvn1QgeLtdAMlhWpYhjCML5vnK8sXb9uwiXqZGhjoNAWXFdz4oRS4VUKTWwLI65KcjppeBNqEXRKo2gUpWqbTO7mFFcwFDGXiE8I9q2kU4WanHbGlSG0DaMa1W8wZqhD2wVmbV0ieKwao3oUYzzHDivwtevCr98Um69crLEWxUea+HfDGfuuHDSnj4PdOo5dQvfrJFHN1ItIeI4Epr+Vz7rc6MYkUYJ2ksrPM3YiBCfz8moLYp8secmTjuKNPlT3IrNhNDJJyExGzHxB6SiZ0mEfvr5Z7JEoD1P7BmBZp8W+HPxXjaazCrCsHV0DaG4NiWcamUvcK9NGjL7nojQtyEbFRjN2t9n+veu1t+5EP7F+8IxDPz26ZFSI8MwsDsGdn7ieLeji7DmU4OFF0dKF7Tr6WIgnwq/WBz9uXAqKzXcItdHut1rvj2thL6gKTLsRsp0xpuglnnRgYU9lUdiJ7jsmPIT9x8XXvSvOejKtYt8zTuCJFzw3FnkLDORnqG/kk4zvu/Yd4XdbgfxnutJCG5h96LH0kx/4/lxNvADuDOwo/LYtC41NMeO/47DqztscXx4GriTzLpkZFW6l18Qjsb87h3j6zfwsMJoePM83Z+5fRNB91AG8FdCHCmTsvMru7jjeHul21/4+puZh++v3IWB6/V7/tHrF6xTxneFeXA8Li+JUXhhHokr+8HB+AK6BIcCJ93CwgvHQ2boHaUYnXrS7UA0ZVkKIQS6/RdcL0rJM+siaN5TfWmBG2fBgrJkT60HSl3wjz3joKhdOQzC/bKwiyPztelpP6ZK7F5wuZ8IGhEi4yhEyzxc70j9hOLQFFlczzrFtkkv0HmlLx1xTU13LCPrNTHlkZ/XFeyAJlDXcD9pmplck+qAoFYIoZIKmApVmlHO1KPnFo9s2kZQwQyvzZxVQuvylNJ0RyIeQQjS0rbMF4J09HXjj5qRhKaRFWEfWpzwkgqJEfNTw76YUlCenNL/u5EW/p1HzhExWNSB+Vb4HSJFErVkigZqFoSM+laIiEAXK+uy0EVP3tLJ6uZAryrcygKdkOpMTUKehQcCMTp6CqU6tO8Ic3P2agiELrEmIVYhjJ5pFSpK1UDoEy75z2OxumKhaa/T0vTfukYyDcifasHMkbf0vqQ0wVkcWHSk72Zc9c1Rv5k96lpwzlM2Eo1aY3KObsa6SC0e8YX14iBqC+vwiniPasJJgKSIGFpAs1FSYFEYXyi1NDNiSu0KnnKLrF5TwXWx4b1SQLVs5vLnAk7w0jq+Vg2zQL5kwg6GcSStmaRNl+w2TJFIY216McwbVhrZQLUQckBTk22YNTOePetgczO55UmJ+x07Ck49V4zDsBAH+6RDHYpSRxgG44984iFUMMWJseK5lL69f2khG742lJ4625zkDpUteuJZg+uUTlvH9dlzo89YMAyvW5iG2RaO8nnUirX0O5Uf3KUB97ybfB4tw6fR5zMi8JOQmM+v6Tb+rblGk9geRJ3i1BG958vDyu3NmZt94v/6+QsuedNp2+f33OQR7t9JIXzJDsVIa2idLyu8vWsbwpKNkANJM14Lr5zjyYQdEF3iKTXt8F+tiknkgqN3glPj534mOccuVRyRm+oJy0hPYuocuXhuB88v1IBC9I6vQ2a+VmbnuOmFnBxa2/dy6xOLb5DFXJv5q4oHKqkYQxBOtWNJxmsKVR1iTVMfpK2RSeFV9Lx1Awsrj6ldc49dG48/bXuXsgqja0bBWY3YZ1SNc4ExCo9T27wGV4nOGJ37xMGlCKxwzo6pCE+L56ka//5Lx1wdN75SU4AEZa3kEFmnyjE4ziosbba/+fna91u1aZ+DCclacb08GS/2W/c21aadKI5pO4eja6oLR6V3sFTB3JYUWVrgkFn7UYcQiyNXmLIxBsfj2fHlEFFX6CajVOOhT/wotkjtpQj3aSWI48sw8Vc+wpIbQcIJ73Pgj6XJTYNzFG1FKtKQdJ3VTRrRJCzP3VIvoN6x2GdJw7N0ylCqdy0WHGH9oWTB2ib9c+jz52Penhd/EKjxvKQ/bW5/YMj7/KqKmrGj+S6gdatbt7s12L4brrwdz7w5Jv7lL7/ijfWItelvzycbJkk+wfr+1uN3LoSH8Y5zSiQXsdXx9LjwmwdFasC6wp0URHpsKYTgWEsk+gbXiIx8MWTeCbzeeczOhBeRoheIRqlKGCOBmbKruCrUCJabWSK5ji4M7QaljtAJKS+ULcWqxtdtfEfhKXquaYfIip88ruu5GQfOYsyu0JsnHxQbIlNnsP9A2N/gww5sJp+VoIWc9pyWNhZ7XCdOV7jxM0Ei8VD47r0jJfCup7+uLEuisKf8+oGXdzuOS09dHjneVeryHbUWzHv6u5fowztC7SgCFs5tgzkHLk+ZIB21F26+vsPiSv1ixU09xzhwLCscPCYFcQpH4dF9z90/P0FJTGrk957jdwNcZuJPOnh3wecDYxjR35w5rG9I9x/R3ZHBC/WhsIuFysJpOFI0MeQrg1NufM9JP7BcjX5/ReYZ0cL7h5Gb42s+nk/UJCS7csmFEEYOd573p0Aqe6pkqj+S1sI47smrIF3CSySwY6mJOSd2rrF9y9WRUsD3is+6gfcjYopJIFCZUgUxdowoSggexJEU9n1ze8e6UimIhKZplKZbLtaK4kwg50zKHusdPjQ9e6WlsRXd7CA5UGnYtOrgpmrja6rbxl+QirZUQsuYNYPZojNRAl+IY/V/2Eq4AR8c87lQvXAzCqfzSj8aSSMyd7hRKVbwGNE33ZerTSes2THGjjVr67KqtSSjjbVjWvFjR/GeY+fwpVCTsIrhJ1i8w0ui5kAte7AVhtaB2vcwjsLHp8ro4WNSnPNYaZKFUVrhtT9AkErXtzFtSS1FyBRMIlkrY2iJTOZ6aszUbFy16cqkNn1rlEjOjZftohBxrKWFS1BbGMZUrCGHFpBOMXFEF5C1YJZRE8qqUIVqkaptAkBuhVBOgt9COhyeZUlIDEgpqAmhFqCdU1ZLS5wChjhzSqHFt5JbQVeMJTes1OCEpWypVb6ds65UsjRXdCORNJFf2aQCn1BoztAtQauaEFDWuRUtxp4QC0dTchxwZW6mk7U537Vum1Eq3qAUxyzQBcdoC2hkrRWCx0vTzyOBLgplKYjzmKufCsfnm5q19AFEmnYP2jnVuLHaiCDwaWLinuOwndtSAe2TVMJvKVztz2/D2focVLM9zzv7HLf8aTQqONeoL896YICffJm5fPRkH6iVJiObhLf7lb+8b11w2aSbWg1D/94x67/dgoWdD1yuTZe/H+C3p8wuOg4Gv9Kel0PkNZX3uTKM8EABM+7GSsowDJ41A1shmVVYczO5qgpDMF46sB08mpBXY5eV35hnL02uetVArYFI4sZVZjWGofJiZ3x3L1jvCNfWDb9IA+Gpb9KxN0fAVd7Giqrn4wK74EHbhCxV43ZoYSTBR6plSlGSBUQLJ22SD2yjI2DkAGZKci3YZM2ZRT3vcyAilCrEWNlvMfFPKrx0hmXPlBvebc2OazbuItwXT0grawhb59JYNTBNmSEIa6mYCIsqfRQcSq2OftP07vvEMnUcRkNKJotnzsaYNtxdUJbUMIvOCVWES20T2bDJRwSlls98XbPG5TUgaevKGkLKyqQRZ0JXHB9dxXeRwRzXrPgYOE2F3gdKVi4WiVZIElhK4EVRklN+XhYmC3QmvAiRh6r8B1FYrTIGeJ+gw9iLfZYlPBeqNE+h8FlHHKxNVxq+rE3ens3fSTaCDcDGatjq1Q3L2F4j8sw+fpZLPMss6qc1lbcnR2kbnPxsVN0ev7s7828uO25EGt3jKPzy6snjTL52dFsxnmjfozM2WcrfffzuHOEHowuFN6+PBFspdSXKnnEHpRtIMrbUN1nbWAlDgtDnj3R2x+SV7l1iPSUeZk83N55rcith7Hm6Lu2i7DyBnnUypHpCnHnVgbMFVsVHGJ3n5COdTOxwVFZk1xEtUKjsovFi13NzVKZrJQTFmzIMR8RFxN0Tv1J0Wnn3MHPXfwn7kfXniXenyOVkWFyZL3sOsfL1a8fXh8j9FFmXwvJUiPGWbix4Bz2V47Hgu4iZw+8emPsTd4eX6DrjU+Tp8cxgL0lLRsYB7WfCfo9awKaFrir9GFgPlXhbcdcCXwbc2zPu8GsgMK1f0fcPXH8UOEgkpTvuLj+h/quP+MMdu4eV8rHi+gT7V/A/JmL3BfQelp7LPbg3GffyJcMxIJd74s5x/bDHhZlXTz8j/tEN5VpIl4KFia//0cD6YcaFPV6N+Tzzza2n6PcECuZ7pMuUp4FwfAI8f1ZWdBsV2/KhneTesKRccqQPt0zTA7kULMGUBbdXuq7jGIw+LlzuIfuOLBc6tyeliSUNXHJp5JCqxBgbQgrw3hN7jyTl7Fs3MQQY6rzByD2mDpOZNe+Q6JlDQXQF19H1WzBGViwIEy3JSM2zOmNJPU9SW2dOWiEUTJp5VB2ZQEIZxRitw4JnEaX8gSOWw6CkAscbuE6FaXY450kGg/MUXwjlmYNaydXhNi2l86C6IgS0BDyhXT7E6EPBnHA5R/a1EsdE7x3Xc8G7QF2UjyVxHDzWjQRv7MeVODhEVvrQU6wVEPsD+BLoB7icW7phw7XRNKNZmaRjKYU+CtWEcecppZKS4cWzVKVzQF3ZVUcKQlnbTbalEjmyGYoHJ5RJmqkLj3lBsyeLB3LTGgs49XSqOFvp9gp01GsrZOsgxLVwdxRqbfrkXNs0xfNMKHCEoSXsmbbNhboWwYzOgMdJoNRMLYWonlxceyw1Y4ckI69CjEL0my7YmvmscYuMsiVUPZvGam3fgW0SDFHAt7G2lYK59l7NItHPpCJIbRtBtYC5wjAI+eIpIjw8VWKM1KIsS8R1TXK1Sk8MmZuxMqtjqMqTwVgzawlUaSlbiDQpkft8ezFrfFWzz8Amt2ltAWx76nOn1jZ5UfMRtH8Taaa34prWut14W1Jd90kisdE1bNNgAvKsFebzZ+a3sfDYZepsDB5yXvCDZ50js8JvTyPuB2R/YSvE6+f39PseL3tjWQuvbwPTaeFh6ahuIGjh197xisQpb91RgVQ9aftbl9SKqSIthMUE1tIir8cO9lb56WXPXiohZgZZuKw9Zr6lfV0KS68cug4vhXG3cNdr25B2LR1RcXyxF57w+KA8zaF12QRyFV6ETcajkVwiQygMPjD2FVcqIQvVGdPqCMFzKcqTq7zwELRnTQ4pDYuXceTaRu8fJiH6FtQQnWDS8yOBJ1MeEs04loVvCbyNlbdxJlfPb1KzX34VMu+d45uuUKvjsnpC2ZBfu3aOiFZuOsgbo5YKI4pXwbsGYXw+59ZirHHDsjnhqYDDs5uV+8Wz6xxjt5FMNj3tPhrnpW3iq31S6jQMo24sXG2YyM43RGKqDennipILJJ/o6sBTrqRqeDdgs3LbKQ9XQB1Pp4BzkbWC0LMLlVfOCH3H+5LwITWetnecqvJLc+wWzwtVurCRJuSHGt12XkXZ1sz2mPL5tO+3/+dtjfVWEISLQpBn3XD72SaDbml4SZ51xZ8lUG0mIYzPNJnnTvwn+1wrvE2Esyv8dhJOUpmScTe2e/GigeO8w4uybO94w0Vv59bfnCr9/4/fuRD+cJqgVJ4+rnQS6Yddc62Wtsu4GRce84yWSM6ZPga6rjJPPVU+sgsdt0PgYXWEreLHO768GbhqRF1z7sbkGKNDjw7LBdk7bN7Yu9LR7SPRPfGjrjC+DOhecfvDdhG8hY+PoBHKpWWAD8L1euI6jdznR9CMdVdePN7RdR2y3PHbd2e87whd4e3rG3hd+Zf/Wvmnf3bD/dMjP3vn2ZUzp+4FRzfyj//Eg59AMowLOsw47WCKTae4KnfXWzhX5HCALNz2r/HW8s3JV6wXyvRE+KA8Pn5gLHu6rqWm8XbP9Zfv2D/+mPq/TcSHDr4Qdm9Ahz07nSkhM7j38PZX+DxSvvtAcCPhjzN1+DHcT/j/6gruDmrHev+RYzkgP6tcfvvXcN9hrkPu9uy/6pg+LGj/EzjfEY6Q/Mr1vLD+hXL35seUfOX77xODf8P3jwsv777iNBeuy0JORugn+vcJVdB1R7d3DINj8CPjOLJoJHQz4iNznuhuBoZ4oS6Vt8c9ErbFM/c430ba2keuazNsdL2DqXUtdrsdXRcIIfD4dCWvRimFnDNL8ATftzSpWlmco+JZXESdkFM710SExe1w6wmkR1fDXEaro7qmizat5OKYtCCy4pzbjF0tg77oZqBS6LYAGadwDUqHMhA25/sf8DDDVc+jeciZ6oWugqonhxWKp/aOKK0DolqpNTQ9qXrE9TgqS+nwW467d4aERmp5ucvgPVN26NJG0+aUL76ANHdbyFKjpqwFfFIkeKZa6HwzC4oK62acGf3IhaWZ+sxxiCvX5FBqS03DmNeKrp6ub7ivpO1GtmhA1PDBsGyILw1vRpPNZPP0FFJqsbHOR5wYJXecZtgNQq6xoeKc4FZBgqOrgXkyxIF3rm3oL9bMv7kVU9U1ImeuRl0y3nvEVdCGVwPAtS5wrRUJock1cqZUj0ikrIWkglQlDIFlWREXyLXiTBrZxDVaglmlzobStMxFHd7pRkPwUBuNxdGQVIK2IBfxW3BIaFMobTdwR/mUgLnkjutVWK1NN4oJq1b2fUUHh5pjJ8rdjXCZjV1w7CSh6shX4aEEEMU7j27vCRe3rpDxnJDmrHVWlRalbPaDelIaLu15HK0miCn+uR0lnw10btPDetlkFvL5dZ5H2iLaaBq4T8NasbbZ8L6Z6nauMPaQViGrcNi15MlZwPnKqhkIDUO1aY6jD+TnG/TfhZ/4tzicKWfpeDMbf20DozVd8qRwKysnAq9RnqwF3mjOePOsVbivoa1PgVQD0Qsryi4CkqneeHtcufXGeYIybiZGMb5+lenmFpbRBW1dzOqZcoUuoHMl9I5YhYSipTFopQuMqSW37iRw21XeTw4njscCr6OQs/Exd9x1BecCUpRSDI9rgQeiPAKdr6w4Qqk45wkZbjs4JcfohUhlCJ4pe04z+FHICWYD54VL6RhCgaR8XAN43wgBAr9aPd6MxTYjVxEKjq4qJ2sbtg4jGTRASDNtpo3X17v2OlP1rNm46SOn1chrIJmx64SnBYJGqvOYJqoIN8EI0pBdj1OjoVzUb4zezSNHk5fM2iKOqzbcV6oOLw1FF13T+2d1rFooVIx2Te018mH1HE0YxZPUuNbK21F5i1LE81e28nq4ki6FGIU1ZCaL/DQJNynwOraNRnRt7di2xpQtZllgeV6atBCNtr9u53zZusjPytti0rx/8vmxLVWbldbpdrQQou4HeuBkikorrMWMLJ9TJltpDdFVKo7vUVworDXQq+PQzVwL2NJqx7ttWmdsBAtgcp6DVe6r8cr/3ev1dy6E//imJZR8LR7kwLJcObrArjPu3YoPidsAe2ndBUPxEuFGGWLgVD09T7zdDazrSl2VEB3LVHh543k7VmLXMSaBvlACZBuwZMyHFrE7iCF6JagjlhUuissGTxdyVbB7xDXMix/A742x81gV7u4y66KUUrh5fdcMGF1mryPUDDxRciW4E1TlP/3zgsZHXoSRbl948ycvsOkMKVG9kEK7+aVr4vbVN4CDXYZ5xg8Glvn+t0/s3hfW5cRu37P76gWlZj7+1QWZAv0QWA89xTrCK6UfvuR8/sju/MT+z49Ymdn/6Tfoxx2sR5bLtwzfB/juwHSaee+OfPHyn3A9FroVPvYL/qHgOmMSJcSB0Hn89IHxm47TQ2HfDRy++RHcRurTPfQFrp6467h3mYCMdfcAACAASURBVJtxxl0yPZHdl29ZDpXf3F/J9cp428PxyB/dfUPNd6SnXyHzdku6HLh79SXef2Ac5o3F6hE/gSxcpxv6sfAXP12Zi3J785odR349PdLfG+Zi66ya43n/qQRqdQQJeIOproQQ4GMF2oK33IIchjBA30ZOSmXRilmHl55klcELHqiuoMzYxo08Hvdkc/TlTNQegiOXC8lmzjbindKHNn419SQElSaZMRFKiRSMWZs/1mvLzlmc0VX7d3Hv/HuPrne8vxRchX4XMVq3r5crOE+Olc45LmuLmc7m0al1UKtvLn8Q9kOlH4xniuZ17hC0dUpq4vVOOKtjFzN0gZwyj5eRoRc8BQkwLZHLIhzHhRACJSk1Jca7DtcZ66OwWubYRXRoGxmvysEVpuSpCmqKC5GpFkoNOKt431LoEnvEQalKqRX10pLsJDP6SmfKMgdUDIkj1IJ1sBsLfSzU2vGyn/nN+YAWpdYVknDNyq6L3N05oqvgletJWGuTGWRr8lQfKzsvZNc6qc61rrAgzexnGdu6omFtBXSqRqrK+VTAeUpSxHnqLNTSUx2MIbDqFecHhEApW2FdHAnFtKW4TSVtyLa2PurSOiAiAS1K2YgUzgV8zEBDX8VoeG+UmqjrnlNVblxELNFJoHNKTo7shKDaEmOD5927QjGYfQuqiBFmrfgSmznK109pVM8eGhVDrEUV/3DESmjShedxZxAwZ3g2bq+0EAgRazIJmjnQi4B7NuzJJ9nDs2mubhQSpxC8b3KB5+c4NglLQ3Cu3tDJNQOUQSyOm31idJXgKsfON21nsbYZMqO6zFe3Dm9XPjz9/pvaMSqHs/FrPD/aGUvO9DtHlIx3cKeFe+95VRoH15kwlc+6ziTN7Be7zKE3iilBjXmJLGb8qKtM1eEOmb54PoTCm1BZJuNyOeBixawSnTAvjlki5yFx9MJSHDVlfvJCeJkTP08DFOUf3njeeAMv/L9r5rgztIysCt4pxy6wN+FaYNTK4h13ETwDN1KoZozOszejeOFbCqtXqjfu64Bo4k+HiJnwnSm7cW0njfRMXHDrHVMCVFlS4OcL3EX48oUyxkrnhd+eItckdF5YSpMK3Y2O+2jcbFra587lSkOmmdZmgIatG9wMqEU93z6Ci5GyGMHBwxWyeUYMH42zCRKE06dpm3BJNKlQaca7j/kH0wrgWl1LJpWWpZCqJ3gjOmHn2/2jc0BUvFdyUWodsdkIx4G6zAx9ZBeNx0X4mAsXK7zH0wn89bVnsA5dlMFXohS6HBDnWyS6fyb9/kDCZLBslIjn8JiqlbBNRuq2qdxUzq23a8BGDnnWILefe84eaT3eTNMXww81wq2gnhB2ArPJpylRsIrq5kcATI1kXcM2m5Al8HKXKCFhvvIx3TBkw5l8yjy5s5V3O6OmKz9db/nTv2Md/s6F8K8vA5dlZnSBwooPgeSV31ridsyUS9cu/HYlBIUEGla893x/Shx2O16MHcUc8wzHXUQphOjIZST6wDKtpDqz3ze0yOh+A9Hjpx05zBy6160T4i84Kg9PA0ddCF1HvI20HMQbuBNsf0DsyNUNdOyoXBjDjrUqMz3eG5YToauECtSKd6FtlZwHWcnqOLjCYclYzciuSfjCUhmkdX66wbEkjzeIIluQdoW95+7fu5DWldvOE7tmxHFeePMWaprxEaprxXnPwuP+nrvjj7BR4KVATOj8Dv0Pd7jpHldfM/+fj9gX33LzX/wZN7/25H8WGD9ODOuP2GuGLzxcTryMkP/XB+zyHd0//QdQDoR4Jkw7+LAy/R8/4/7DxNu3P+bjsrIsjkN3ZNz3zJzQYcS7J4YvHd98kakz5FAYhgy796THb3l9XChrkzSc363sY0fdL0jnyEsLtDidv+b+4QOXq7DqVzyFidVl7i+V0Fd6dtyXjkoFC1QrbWFY/EEaTW6gf9vhMATfEqZWxTE0A8OqiBWqeJx04IxcjSUr+IAWEK4ctSNHIedKt7EfK5DygMuNjZrcDde0oEQqnqk0R7Vq6wiXsBXCrI2LmA1HS140qRtvvcf5Fun7hzzmK7zuFcUwqc0cV5TZ9dTVEHFctYV9DL3QU4k7I9UOs4CT2qJnK5xnRy0Z71u3GBViF3FOuZTWtXy4BrgKYxxRqTydDRcDljMSI1qF67ljd2OM0vNwcRyKIeo5nxU6kM6IrMS8oHiuybNkRQzCaqxeWCbBgifiiMHTAb5ftnCGFrnaeUN9Q8tOWQm1mczW4oiaURdZZ6ObKyqRx8nhrCdb3kbyTYdWM5SlMlfj4B0uCKfZUZcEIqRi2/fYEFtijjm1gh3ARSGnzO2+p24jfgdYEaoTJCu1+KYHLAUnPWorIbQwk1kaoYTHFtjR+4G1NJNcXZtkKhUQDtRSwQq7vkMcZCrBe9Y5t06Y73EIoXqcF9a1Xc5MAyIDReF2VJwkXu5GNE/UFYYRlmsHVqFzLAqug1c3gY+PSo49KYNH4aDk7NFVyWb0o2+/u7I5vBNvXyi/egy4GpAAWdpE4G82aVp1J7BNFtrmoRXTW1936/ToNrZuD33uMEEjQLQkPtuQUZ+f59wmzxCjqCdLRnJobvoQeDhHSswcDjN/cntiUpgvwiQjX74srJPytPRMU/j0ur/P8f3scbvCi7q2TUroKGvbJK0KT5s2/tEJu76lb90cI2tuXcfOGXMxlMhlclgREG2mXfN8QClUbryyGgxL5NsS2Q+Cl8rTFOl8IKsx+iYNOs8e7RM5Oh6mkbK2EKrTpXURH2tGpPJdFbQKUiPn1DpxqwmjNy4TJOkIoXUPn0LlVVe4YmRpIQtnEh5IzrEvygmP1EwgkAtEH9BV+WUOjDnxLg0UDW0zYG3T2QIfAh9rYH6f2XVA8KQ5cEpb91U9zsHD3AqwX9mOpTr6TUNeUDofuBsSdYvi1u0kNFOqQbaAapNFFR/xWvBmnIEQHGqR4clIVI69Y8nPke+Or3aOD6sSnedaBKxy2xtJM1kD3kNK23THPKtr7GzvKg9rpF+UJAM7aUmRh0PmbBNv7uApXVD1vNgJT3kkq/K1g2qR2055ezCWi/CFOB5tZD82md/HJJyz8NKUr0chm20YY+Fnprzan/lq2vOejijKrVU+tHK0fWbWdL7P3V+PNOM1YFvoitEwckFkS9bbpj18ehmywU7aWi3WzOufWeJbsiVQTJECIoWinuArWTtOc4TY87J/Iu3fU83xqyfPkcg/fjXzF2nHaepw6fZTwvXfdsjvykL8n/7rf2ZeYYiRdX5ijB1Eh7dCJrF3B3LnuF7P3B1vQJ/ou8CqBV0jt75x8B6WKzHs8NHxXjv8YixVEJ3JznjzWtkPN8iSefFq5PVNTzesEHfgO6qveOcxyQgRpGwRfYKpR7Z+valDqyPZirqIadfQPbgGwgnNWKKqDcBe2hhOXcLU02nZCNMVp4nOGyMFJMG0wpoahF0Loe+hVGa3MEiHkvF9ZT1FwnLBO6NkT3FCqAdCNM7fPbHfdbhDz+Wx4HyBLzp24Q68Yn3ZDEuJxVW8KK4OhN2CdCsSBhovxmPFIUvbQcniufh3OL9np3XbekbwPXz/wHxIDNpzLzte/fgf8nE68eofOMq6En5z5NfvHvE2sKgiRHTOOH9lDCNzMfBX/vjtS3Cev/zLJ55yi7geZQ9u5UzPuk4kVVIdiAJrjkjM6Nb5EedIq7XwgdJ21oh+GoWW3FKosgrilEmf06hSi99GWkewFjoco8/knCmhMXGPBl5C4wOHShu6CTvX8T4nDrQgDFPHIgVV9+k8qNuFu9ZK7zpKKagLYA5PpmjDO6kW8pZCVax8MgRVaU5h8R1Wmonov//Xf/EH6wv/q//2PzNoXdIO5XjwiGRwkelkbaOlSilt7B+dI4S2ux/3mcFlpmS8+xjpXQsgeXkzMNWRzi3k0CKGSYlqDt9HSCsSwraentVf4K19fxnX4nWtGctgG3uJMFgrUNJz/O6GbA/iIfiteEkMCIoju/ZY0JY65jc2rKk2TawZ0QdmzYxmiAt0biWHjt4yMVRO10iywt1BKAmydVSXyCuUpMQYmIsQg3Dsy8aRzuAC0PBkznvU4LomyD0qcBgqS6pc5uYMz9JzkMzxVWCeKp13LK51nkw9lh0+Nj1t6D1DjFRdKauh2uQLZrDkSt9DtUKUHeILqJFKJC+Kl5Wwd2gRBgnMZtQEw6EFw3jf9HVpkU2r36YZW3uqxcjGjimDE6WqtDW19YtqrVgR+n3hbt/x8Vyx3FNk2UyMnpI9FisR3z6mUPDFUZ1Agt0u8+qYmSahusDHc9gwcm30aqVSxeG9x3Jthj9HS3Az3/pWmzbYpGlKXaW9vgmo25jDbY3JlnJbJOBc84VYdiQN+JoxIsTcotivStw5xrrggxDEcD5yEydcaNxnrZ7g4ZVE/u/HQty4T//5//C//15r+af/3X9sAAdrTRA/Gvdp5VUfSHPksRRqEWbVJm/x8DJCFeVFnNGuoIvwV4/Hdg9Uz09eCsfa8cHDPyFxdR1zaaP1kcqCMHgh2vY5bu/lWa+5FCMVozph3Fr7J3WNo+w349OzllsaYzh6wW+YuckJs4PXW/+GreMn0lQy7ccbBUKtdduvWZm9I4uw84nR9xRbyJJ5nEduteJ3lVGFV67nsVb++qos1TVWtwZ6V7nZKUmETgtFhGHTTY+d55IK6wwXN6AGXwyZS1Ke5o6sDu8iU1D++c3Mz2aPC44bU44Y3xZHqYL6Ri14OzpeD4LUwv1ivLfIHYV7E55m47YvWKnsupHCSsDxvnY8XJUvQuYuKvcW6HqPpc2EGQo3odJFEFUuuaO6ttlA/JbZoeQILwKQYBWlU7cZY9vtvWpLhPwyJsJtx+lRWfyIpoQT6MXhrONUM98MjldSmJ0jAE4rH4qjdJnD7cp6DUQfebN2nGrhZHDrYNZ2331mSPe04rea4nBkYLCm1w00TfTsArcoZxVU2nRKtHWLJ2vneBBP74xj5/iY2lRrkBag5HTBOeGnU8cXnXKlsHeFFSM4JcaVsXM8Zo8UIQbjw92O/a+U2TUz+3/5L/729fo7F8L/y3/zn5iqEqRHfdMPjgF2nWexpuXphkpwkYPv8EF5eLpAF5iulWGI+GCk67m5PlNFtcOFTKfG6xc73txBv08tm7sbEO9bN0/AeQWL2wo2kHbBbJzRBuTPDcPQeIeqZALzWvGusWafXcjqMla71rXRpvXzrkWVDrZACkTN7ELjEst6JR4AOmxd2wcZPfNpRkpmGEdszXw8nxle3HG4BZ1n1FWyZsYvX0CdSbXpcbQT7G4AKvFpIVWji0eoezhO0EO5f4c77DjtTtz9RweKP6NfKl33pyz5gcHHTbh+JqQT12+vHH4hXP/nE/s//yPy/+OZFrhNB4rrUDOyGVVC0//URuAoDqS03V02MPNAoRAxy1hxTdfklPqDkWctWxIVDQpaa0VSMz7Wovzk5Q2DXylZ+PLrAPmCrolSM77L1Nxx/2EhimPKN+Sl4qzggzEtF351OeClXX87KahE3q1KEiWKp/NxIycqWoSJiKl+co4/X3DNwGlBLRF9twUduKZr9Xx6/v9H27v0SLYs2XmfmbvvHRGZVXVe996+anFAcECNNZam/CfSUD9JGupvaKYBKUASKAhQAwQJNbrv65yqysx47O1uZhqYR1SdVjdJnO67gYNTVZkZGQ/f7suWLVurqdLEZ7pVpZTCoTliCeCWCrbthDS8JnDeyXbvdeuUesCkpD9i7lnsk7b67/6X//PPBoT/9//+vwlV5fQk6H4hpFGXIOSK0rhulW0Tmg7WValrz8Qlcz48GxGD02q82YlVnT1AtsrLDd4dCot0fM0J6ZfXDEKpIbjIBMKRoQYw2bfUJIqk0fuX7Sb/8LDNuUd1Tob3LjVR1YzBnfq14oXeO63WnwFhT7tsvAo6vuxp0Qe2CIfmxKZpoSWFbRPWYkSRHJyqljKQnuzF4SB8vipFE8AryVogknpgN2SBugxePxacdDModw1gz3J8XZ0hllrdqBSxtO4zcB3gBYmSqWVFyF0oB8DQTmCMkS3T27Yi1Vl0ZxsZRjIQFnx6V6e8x8jBm9OS70FV4/lD5fzZuOywLJXb1cixqoUWTlehRbZLh4FP672bBVIyFlt94VCvvD/Bp2uhuCB18P2HAtV5+QSf9sLihjNTHaXk6xXjV8+dtTh/eGnsQSbryZQ31Iw5H9MurpCffyiYTXJCvlip3Qfpytz2iwZhis0yrNQ02V9a3t/HsrOPQh9pxv+0Kv1mbBb8dFFUG6e60Wp+lopTyz0OWhimFAbvDpXffxq0lvrP//Z//tf/qHv5x//hv46i4AfndWSxVctOoyNlQW6Vv73lob4049uD8XJZ+OBGPHWGDI6ro+NE6IZG4483WPeFuux8PlT+QjpiC+Vtma1voZIxvEG2noG05gs4TC33EHmEEcjjfs2/f4nKzWEwj6CQ1l3XUNacBiBI14hafg6EmQD5vqffr7NlVC9l8HHP1r3hfN6UtQkFp5bCBzovAj2U6sr3B+Pt0hAxbpHDqzeEk6Qnbg/n0ITf6IV/+3Kkalqc1ZkI6pZBNss6+CBQpXI2WGpq578ReJsWbkFa1B2KcphDdQPhokELuJmixRE7MMZGWYSXbdoFkkO7Y6Sb1q8UPruyj0CWKYeo8M3J+P2l4ZvwrsG5p4MOsaAlqB5ILewMWijDhVMVlj4dKjx4kwWVK+9PRtsqfzOEd6vy7fGCauenyzPv98IF50gGXCDKjvLJnPdPN161s1+e+Ut3DOVIcA6h4ywI+5Qt3dfOFeEpMoSn4JmOOSVZFsHE7+j8+10v1SUoxdGSp4jLjdtIwunoQlmN3gsvQ7hcKseiWDUONQvpjnMsxqtl0b+ieBjr0fnp88q7alwD/tX/9G/+3vv1F0sj/uW/eEcphd0H+7Xje89N04KDX9Dm+Hnn8N07bgpjVMbJ+GfvlPZf7NTaOb85b20gIvzFUXj+YCzfPWE4Nep0Zz5AWYipMxGyZcYwIvZkfOX+lRTvyZKgqEZl9EExxR0qg4qybzcIzyG+wwFlSYbSodvO2AaqwiHeMHliUWOPPSMS18Ly4UMqwd3gUNi2jeKV4z//FRwC2kC68f7aWJ6OEBXd3jP8xvHTAr8PLBaKLAhnpAX75QIYIs7yTmB7ofe/Jnph0Rs6btyWD7w7Kv6vP9OPxvG/+gG2Fw4vfwl/9Yn24tjnE9B4jr+AEqzyBP/bGZUbH+IdXsHHDppm1s0CN02GLlIR73Pjq3P7M0uuIGb7AnLAR8IgImNYIxe3ROCeCVe9Nm4WhBT+6lPHufIhBt9/tzPGG9eXhVKhvy7cLsI2TtAW1lbYzpk0+PmslPXXrHVjeOctFpoX3J1fPx1YLRAPrupcTdgsBRTfPcF+nSDYycPcOg3ldS9orRwqeBH6SDB908LghqoyinPVSgisUpO1GIXrOvXBgNTGe1e2aDnMw0Cb0tZTeq0OZ/cvB0gNfUS3/rmu1FWC3W4JIuiMzWiywLKjKOuSNoYSINF41wJZF8ZIXerbvtFkcANuW8W3ytqmbGEItQgv10Zp0CjchtOqYFdDW0Ut2Eti2+Zp47V4PFL2ZLb4dgkkjBrZmsstdYaXiKS+1mcpYSBVEdtpbSEEKjnwl2dzgmEC7g0DtQxHCNl4uRQWywCnAlA7dVF2S0uyRQoS6SEsdksJAQf6tkAN1pLH3dqU7oNDE6x0ip04HneagNbC2HcOJ0X4wO36E1Ebdcog4h5JvBRuZ6Nw4LB23jZjPRbWOaRyvgx2ryDKt98ry5uxu6K6YbGw2YrIPo+iwCi0mszJeeTU+fUanKmcliugXN4yAMJjzfvCBT0U2h7sFA4xkCW4dk1Qo86YoN7J1ifFOS5CxM6hCFEHS1W8py3UiGzproedy61SZLLPMghbuPUry1Iy6XPMtmcIVY3v1mBU5ccR2J5OIQAfjjsLzuttZevC4RQ8tc6hBH/zU0MKBJXe01ljeA4HDjM2YN1XNq58s1ZwZ0TBQngxwfsgXPnn3zj/7mz8tp75I0/YthA1QyqORem7MQiKKK9qDA20y8+8T3/pZZH78O+2oCksZqjBj7Lyaxk4hdOSFn+DYFCJtuegrwtDF47XwbUMnl15s4qPxlh3lhDqruxFiPPCTbKjZq68l87NK8fp0PeBYITwFoFLZeAc4q4DFc5SOEVQ3RJIR4KHqxRWn9pwrfQY6VFLBiy8mfFUClXSC7be5W0T/KZ11ty7UJ6LsJtxviYgW9T4XgvPx4K1yMji4dSl8CGC80hXlstecIE3X3knRi3QxFkrhAVLCZ658bEc+f7krEVYC1x78H7pXOoT19cztTU8hKsIm2fy2VLhb6/CN+q8Pwz2rXI6FnYZDIFPF6FEYbfBu+86sSlbV7rdsuO8w5MaHoURoC68WzNl5rylFadJJfbK98eNzY3P14KOjnLk88iu9Xct+OiAC6MEz7ITvTDcKSo0hz2ERhYxqxhWg1c3nMI3q7Fo8OZCtxNhwU+hPC0D2ZWfQvgG2ER5J9CHclphDOeqhRbOGeH3OKennB3oN+UQwm0OB8S68RHncz/wgxcog1iuIINyfuZNMgXv6krTPK9UclCQUVCtjGG048owCAoeim4tU/0iePdr429/D/H0xtM4sXkDCXYvoMG2S+qURdmvgyecz/Ylne7vu34xEN5+/Z7TWllrw/egjxvHfaDe2e1b1jBqE9baWCLQteAh3EQ4ky0p+UE41WSVJOAtArXBUgZWK2Pv2YIiD70xBks9TK3oMQ9yEbTaRDsj1e1yBg88GrXklLsNZ/WSre4KvRf2Eoy+Yd4nE5hsprgzwrjoAh7cDEosXN/SW7SW3GxFB4WcyK5V6W9Oew1KMTScahVedogNitO8EnbFWjI+pd4YNtDSONkhzcuvAfsNakdvAe0KEmit6bn7/TPsTxz+9Bn+zTVft73k3VQDfQeyz37FUGr5CEujjIrpBbW0aglf8j3VQLA0Pyc9Vh2orliJGTecsZfJzOSB2lUoLrjO4RdNVtTEMTFcSzKqUUCciCshys0b/+tfraDP00Mw26LJ8AxiGwytFG+oGtaUS9z4F3/xhLjxw+3C7y4N14rFxohKeTa+HRs/1CO1nrheNsYqHA8b+zV46TnosOwXXobw/rnwfnnH+4Py+x8/4k+Nd89HjrtxYeXzuPGyr2ixdFyg4N0hBk/bDsM4rkduu1MXONqGCRxqAZxbv1EQKkqReySrcIsd1V98y/1nXUsTYCTbFswIn5Yaz61lF7kHXhreBRmZDCa107cDWgWNlVtUTDurKN6E0Ve6Cd6V1hvHEphli/apKfswtEBhZJt6DOphWlstwbgqLuPB5ElAWzRjUG+3vDcErERufgLFOnUBv6WWTDbDF1InGkxgl0x/sXQlyG4QiDZidLwuhCsfSmGIE7Jg7LiBSVr0EAuX646WTEfzWPCYnY/SUW/sI1gOjW7JWu5u1KLYKae4g51SnHJM7ahtN5ajs1+he9okeZARriOlDntcuI2KlgXfN3opSINWnVoH57Ny/slY15x4D23YSFP6WhZEBcIpNd/PTsH34KLgUlAT3q4nShlUNZ5KIywYkdtG2RduERQzYlEuN8+BmKL0YdPTLL1OC4rQeZXG8JUqKTEqbeOnl4IvxoGd5wrjlglP273rFgs3D7CV61kYW0XXDd8a1oPlXWr3zzaHcCK1xSLCditsmjKKug7YhJfR2HWkFtgUag7zRARWguLK6sqmTpfOosJlh6s3+qVQV6HJTmNhPQj/4SP85hvjhXf46NR1Y9+TrTqbEdEYFNq+s+8HrBi7bzn8/Y+8/sigklZ3v3LhovASC6sFn105REZ6C87RFv54zrCJM4bZgV6ymNyl8tkHvQTfqNP3RpeMCKam5eNTOJ0CtfN5ZPrh5xisVN4sOJfgEsEH3elxoHn/wtyJ8ZMoUuAwjI1GJZPKzlJ5C/jeBz+qcIocCBsm7KQNV5n36zlSQqEeZDYltJj+wZL3btHC6aj8EM7NCi8iyHCWloV1aON1S3CbYSwZ7kYIVbMCP+/B0oRuGQriFN6Vwrti7EvhYoAMdMkCahuDD8/BZRNuXagiVIdK57YHp1Yo7nSrmApv06d7rULRwYdF+HQpfHpTTm1QUAxBQucAqybJpWnNtrtTTNm8cPIMJwngdTtRZNA8GBoUK1RPru8zjZMLN2Atwss1UkIG9JFacVVl6xmGoWGZQzCOvFfjbQddL/z0svLrMtiqY9X4aW8wmdw/ekIJ39Or3LcjzzQ0ds7eePPKYb1x3uC8wwd1fufKcUpf+mgZzjNxwBaVcSusZfApUoO+lAzAMcsh7W5QFsFGpJVcDbZ9IbzwtsFTDcBYqnJYg49/gN+833nxYw5Pa3DthYHy5IMtcmD5KnDrRz4wcE3ZyD90/eJT2Vdlk4F7hTJ4fj4QrbEo1DUQX0AGPiyBgArEmIDzmzwMrRNzur6hmXBWQOoCEaxaZ8mosOSADEWpw8GMsNn61hW3I+CIOXu/ggfVLMeXbWQClBtSc6q6YBzKQhmOb8IYgzGjFTOhJggPzJIhFJj6xjmR6EbmgzgWxtgSzC0MjnpiqW/IqvhpR2rkc6s73qAOha3B+Uhdhfi24lxAX4lto/+xsNxA2w+wjGQVfeXwhx3+NPBTR0Ox5TU1hseEXXxniNwAg1sjrk9Ih9iUuCrlsk6WfeQkdAjLbiCVUfOzCVtgSQ2jMChrAb1SYqQOaU51V1NC4NI3NqsMWgJha7grm2UaUHgyy6pKsMMhLZrMsl3VbWEj38+uoFrAF7Z2gXHCdNBG4d9/OtP3tEXZW2MANY75eq8H6MKzDOT5mZfR+a49Zfu4XKmHE59eNp6/e098fOGb5cRelP/7vGPHb3nXNz5dXniV95zDWXjmCee/bIWXg/HHgPUWN8qNywAAIABJREFUnNbBtTdE63QxCdx2dg5c+472XMunsnDpncu2czhmAVdU+WfP3/DT2H/pLfefdV23lIFsVlAaIQ46WGqGPKgUiLQRu4Q9NoDu6TwguqNa5rR+5SIVjwS5mCNRuWhMRieoWtK7tCVL76GM0mmxMC6BFaP1wl4FHRWXBMfmmca0VwhfWTXYB8kUayZqbTjbgEUqjC3TvHrBlC82WroifeBh05YsBxSxjrQcPnNZ2UaaAYksKVHxRt8GIxbGyAJYXWdTfDLXboiXh3XWvgklFAnjNQ7EddA+OeuB1PtK+ppDobARW2VYgaIYnT4KWGPTYC2C+Glq7QbBwu0ayKa0RYgzmajpjes1dY8e0NrUDmOYKxUlhjFCGKS9kQgzzjgILZjVKRXL9q9rgVLYe6bwQeQhGUosFdkzkS9UcBdaUbDB8agcivP5kjrnUgrn1xXzQIawHWe8dDh9CG9VqW8De84o2s9zWAkfXExpt4EUIUJ5c8U92KQhMgNUbp3LqhTLAZkhStRs+252wAhE037psOda4yawJjvXfcF8EFHol0DXAcWw/YCvDYngfA5KEbaL4OPGpg2Z7d7BnLMQoauxL9mZzPj0u5PqP/LqDRP49Qg+ypElpu1bUy4I14CBokX4oefg1hvKk54AMN95k4IJqBwIcsAOjDPKB0ktuZhzgbQJlIZIMv+LVG4Cl0gf7wh4s4IKfAqdQ1A8vIs9MrBImnDZIxujsxv41zHfE/UEvwLhwvDgJXKvuITQQqYbwPQjnl2ze8yueALeTnrcLpaAd9+yM4Ll+ugjuBEPaUUBmnc2V5oAe+EWOXWwsfJX1ngqwboaYwSfZWEtzscoFDWQA29WOWjuT2GN1015F84o8CYLnulKWARjL1w0hwMvW6UqKbUZhW8RFg+2kuwzgJjQp/3jcOXohadpb/B8jx8PuEZlHYOVlGnl6S5sY97b4Xy+wEErH6rwcZCab7IrEzVDdt4/VYZv2Cb8ZEErwh9uJ9YQfhrKkZ7Stwg2E7AyvXcTMdqWlo8jnB8tyb+jDE4inPeKBVz3lJhcDI4KNRTCeB1KRA5XKgK2cLAscrcQ3uZrCxPeFbjtjkqjW7BH420P7o7jjeCpBG+ubG+p912G8jKCLmsW7UAL4RLZieqWqagHLNfRfyLH6hcD4V/95jQB4kr4jtz9IwH3jtb595qyBNW008jc+H0aVZdkdrxg3jHfufYgeloZiTdipO7y/p9ayUnm/E05qISBv8yFJA/tb9X0nqyaYjmVK1obVZwSFZGB12Qil1U5eGOMoPeeE97mjC31riMGjKmJjQJiyVJpAmKZQwUuwtk2rnuh3hx5VVYKcbiih4I+j9ystxnhBMQfXtE1iHhC9ERbgD2jPdlgKQXKDjgUQzeIckCvB3jruTHMg4+6IrXipaPtR2I9IN8ekf01wXfvYBW5Kbwe8JYeq9WSsdfljXCd/eMAGcgoEGsyceVMMKAckAhOy+BJCvA5e1x3xnMAwxOgeDJ1tZz409nYeucYLX0Gm/HxbfCrw8YP3x756WPh5fY7SlN+/UPj3/208eHX32FvV8q3C7fNiPjM6+2CjiPLuwXbPrMtC58+deLl3/MbKcTlDLVRYmBv8KsmtMuF/biiGix94y9PT7x8PnP1wYfjN6nt1cbr5YJo4/94NWpTsJ03CjdOqFWu1rmOuQ5MWTXofqSTdlZDCxuOrAfOI4GBdeePf7omg/VnvMaYHjYlaBF0zXGY4cE6PTL7wdJg3YNY8vOKkcCmWMkCMAq9OM3S95d7cloJFoPQ1Mr2yDa7jxxudJQRlVGCYoHOYBEsdYRE0BWWmsOL0gWtwu5T/2nZOgwJqgVjhW6G10px0CnrISJlEX7DAK8VnxHZdbbpfAyylhb2udXl8JUSxdj2A1o7FUtbrgYS/fFeRsmAlO6pPw0KUpWioCNT7Pps/8+yEduCUY01Wh4nRZCeGmGtnW4ptbHuuBSqBdvd67cILs7I2CYMw1gpkdPYwkgLQsnXWeoAr4xpah8j2KRybDu7Zrt2+EhAUjQ194vTySKktoZla4qwHNBznLLURDcjUgs/BhbC21n46IUWyq0MwrK9LJqBCreXwEq64UQ1DrcCq1L2waiFbgVfK3rbqFGwpwJj53VrLLGza0F04KXAgNHS83sJGCWLaXpgClvk+TKSLKK3AhSkBHLbCRGKbDmwqXB4ynmHNPb3lBkEacVG8LIHUp8oDDpzCMxSoyyeW1sleQSX9Or+p7iOU15RarBqgq73KUR5SC9aDC4uFIX384ztpHe5S8OYw3ZutLm9BMkKmkErjmmhqiCzm9LCUJ1WV572VOv8t4hk5nsoJml59aS5jyPKZ1HeD+ckyi0Emd7o959vobSpF9WvfJ6NHKrCgxLBMvXCMd03uqU12ZgsL2SGTEW4iIOceK8bYJmkLJJdkfv9akEVh3CuUbEIDhJUnA/q+b0e0yEDqjSu7nwrQQnnNSrvMVoETSo32fkNwh9UsdkpfhaQkOyPlFl3Rc2Y+YAz+XmYBKcCN5TD3CMoxjbgXSmgGTSxu7DU/NnhcEzZPEMECeMoxkGU2whOS2MluDn81pyuafv27bxdqwWn6mwjOKBwhndy4pMo5sYYztqUI4PbgG4HPISDFJ7pvJTCUZ1mNl1XUlNvAksRNgs+DudM470MNhfOCCdzqiq34RRVLpGdvhB4teBIsHtCCo20I3yee9YezseRSa/KSHAcwfNBvviMh+R6HMGJ1B//eFW+K3kvHlQxBxMhJEWyS4UOM575K1OZf+D6xUA4LhuhmjLx4hQ2EEPUMorPM/88Qh4mzLV0iPrwk9R2l/dWyrTAkQNzkK3mRmBOKcsXq5zuGDkZ3LRivbNvFdsumBneFy79indDKY/ELxFBiiKRINxsHvw1dYOhimihrcHh4BRbCS9cr2+pV4z7DtMApUbjZhuLrHmghyJqNC1UDZi/O8JSz7w1bAP/dEwtLlPrLCNnLC9OjIaUtBWK4sCY31shbnAAZsKXtHTd8NhQSspCaOA974oo+PWISiNiINGIFXgyZO3IInAe6Gfg5QRvK2oGt5LWRaPjJa17vDuDWx48e0pINjVq6WgJlvU2dZ2zhwN4cbRU1LNL4L0T7Hz/PPAnTXARQZBpfD1O/M1bMGTHjkF34f89Z4v70+fP7ByJS1pMbeNG51vKAXxpaa/0tvPb3zwh9V06FYRQorFvhe3Qacd33M4/0c+Dc9+4LQeWtz0N173w48dXjssRH4PnBfZbbqC+DdwK+I9s24FDFWosfLD8DHfrLOuB5T4wZkbHuJrQRiftLp1hzp8ZA+fypOQQCkpnAqhQfCTjLiLE5hiCijBm7G2dYLeXoE5xqHhuMKWAW2Vo6hdNhDItBlUdW3NMcYRTx6CM+7Yi+Ewg9XkAWwmKpc1XarF71ltz7MZLwaLPUAaFPqNtJR0N9qVSbsFo06rsXhR3Y8ig6pJxn5LpTLZHgj0VBM/BO8gn1TrmMllkgamF1ynXKmniB7XiPYjqVMmoWJpwjQT70nPYgzloSSmMEVgVyu7TfSYoI5X33lMqU2fiWRFBLAe8jLSl61IhhFVueJGUAFHm2r4LLAvN0wZLq4Aa2w7R5TEA51oIy2LIZKGPgJijLjr1/j3dGMCQLT+3u8Oo7blv6gSeIQZhtNAE6iZAweY60m7p/TyEERnaMaojY/oIb539KYmJerlhh0YdO/uhpbQh6X5iFjBagt0gwrMDmOO4ICQbLmlVScxh3Qi8ppAjsjmd8dhA6EDnkScjffB1tuepabY2JKbkJFlsAfCSrf7pHKQDIga/dND8Z/erFNZwXklG9oJyk0IjKFMz3qPRyEPf8xXxjJMqEpnhAcoq9yHLjDZew9JpI8hXOTuYuyYbN0awiOVaA4z07m44V9JD+llGdlAMjios3vkQ6cYRQJOKWOZ27VLBcxhvrjg+s/BEcJiSpaoJmgfBgeAaKZNQgRsVtwxcqGHTXzfP/fcqEBubpy1pBU7hbGNwLekt22OgkRr5izlHDJ0EVfgNkWRXmwjmwcBoQMUpwIcYVMmQi4jEDy8Umgir28NXV6XwFvCtGnvk+7+RQ/wH9tTFUzmjPAmJYciU3EPJ165iVIXVKxGF07QJvXqhCiyeGGILYUiuZjzYgIawi0L43FynzroIrzMi3Gfx3N0IMw4qNBzxIERZZ+eokETNJoXFg3OkvEAk74F3GryZgMOTwrFCwRhz0P+bWehUz87MIoMWGSo0pv59nR22LkKLOTTujkWgAd9Kmhto/pqcQxoplbnfYlvAO4xBro8PYuD3Yot55j1uKlTg4BlC8xpps/cfu11/MRB+/b/+mlIK21YIOiIbrYBILiDT1H6E8ZBGWFRsZFINgM/gu1oO2IzGFbkPlgQellraUh4SCi2Nu6H6nf3NHXYyW3bNA1Ey7SW1vNnOKq6UmrGooJxOC32/cb3moUxxRJ21FQ5tsJTKOjdhKVAWmLsP9IGKJTltBqPh0bmSU/SQVb5I0FBE9sdN5DP3OivpQYgjuiB19g4jEDGonieUOCwBpxusBkUYuqGHThwEvtmgN7gqfhb09YDYQPwA19RPhxfkXIg4AE6UHIizkYWMHT/TqmIDiqz4W0WRjIKtC8VTLyzSiDsrwhEfnbeR08VuC8OyyDBWLIQxgs2DUlbMG9azonMrj6Ezl4yi9ZsCKz0c4z7EN9KvUAaNNXWpvkBzdoMf1PHuvN6UGrBtG5fbPgegsjg51gOfPn5CemNRYQuFfePd8Ujvgx1FW02bOoQRR1qDUhv7vlPXgfAde09Rv5d8Lil5kMdBHO4casP2nUWUTbO1rlVx1bQ1+DNf//b3TphRfLKExajzoOsz8U1lgl+R1CZ8dUX2JROYThWHt9m2m4+jCOz3/HejzgHCdOUQ6qz2vZTU+Ici+xU/PuMT5Kb1Ffh0fdA5hm6LItxt05IpbWVjQ/IHtCDDKP2C64kIzfWmCYwrQgnD14pI2t21ELZ5oJY7hiSZhyrMipTswLTyGNZLHiILvKFKGTLjP2sWuBEM5cG0GxniEObJTvgXVk80tbYS6aohmtRPTF2ziHArOVC37u3Bbvv8rBZiaqPnxq9C2LRKEiGkJRjEWXShTH2uixPiqPrDFrBTWT3ocn8PUpZgZXoDLELZMt7ZFpuAL1ml++fsPrIbVwaxC1KNCCWm5eH9yvogKEVxyW6FvnR6OE0UzoMwZ0jOW5iQwIgDILjuDE/g3CQ1hV8/fsTP76m5UhFPLXUG5nx9AvrDoST/NsGNQxPNDkcTSgdfBN0DXwxNBIaL3ZfLP8n1P/4/CyeBy1wnzwzGBIt1AsSCIZLdRsrP79fF9rTN0vaI8L3bsN6/0+SLZEZsJ2q655QJPOa0KUfNYuAm6ctdPO20VOAWylEC84Q192FGUU1JTeSfBfiLdXC9GRcqVbIzdSuV4QlGhgfrfL1dwGIGNojy5M5VcugSyMFK7m4TWUwtEz/cgikRyoLhHUqJtPHapRBRcn5lvikOnCL46f7+Rg707THt/qe7AeTvGJ6NzY6w6HSVIvGMCHwrK+qDv5aV5zy2OUSGddyYhf+UUrb5HibgC5AC1ikijFIYkVZ2JZwDQZFIzf8kNV5Ep9Xkfd+EJ0mwazHZ0MjoKZ8F0h38ldmN5x4ljbBEJhj2ibXu10CwOFE15SmQzGrxtNMD2BwOpGONz73L5qIL0QniEyPkPvtlvf7d4lEmV7tHFqbHpHJ+du1f/UiZ9+st0pO9eLBPMBzxgE8E6U7R5vO/X/+Kv//6xUDY9mzchUhuOO74SEDpbaBWkDDMB/soDC/svqOycJ/fi8j4yz72eagGEYN01ckq1Ay8dGLeeMYNlQOIAU7RI8N2NM4QjdABGDE3eWa1UBEOS8+bv2+YN87bx9SDTWBdarZCL687m1REr5R7FahOaMyDTKkM1mVBNKtIKTeIBOza8rlq8fT+rRe8GFpSO6eSlj54Ht5yt39jhkdgyfA2w+uOVocPwG/fEoD0QC7P2NZo3eHzAlZBK8oAcW76xrJmbrqwZIj5MCRF0pnEZBWJjt8GlQNsmRKFWzJtXrJNbYMendYaZR2IBFI60a9EFA6xcLUDYxhUwa1RTdj3hssVYeGyBeaRLV/LKNOMoHDElgS+JMtyd7GIGLhnwhSq7PdDL4SxZ0Hx+7eBjQRMvl0Qr+gqrFHQAodY0FX4bT9SCrTlQJSFy+XCx22nd+FQK5et87YJB61c2ImeAw+uwm2A+s65Zgxv2meNjJlE8XMi4XMNIjqxO22t7LGzdMEkrYTcE4j+Oa+X24bvT8gM7vB6hTYLLxe8O2Uypu6dYk/5tXkCWLmgrmg/f5E6yc83r4hgsdSKo4Ge8/FTB24P2FGkPjoEqsr6+olS0oO4LjVBz3y8uwaR8xlpCXy6JxK/SsUkh8zcO2jKECJuj31DMYoMyKWCXHfu8WYDTV0sYH2frys/C9scW55Y7YIumkB5DmqtOsGDkG4fJSfbgbSJE6FLcFj6fA4Hhr0hmrrgmNrqu91aKdm+L5I6YynjUeSXUqC2LFQuHzksT9j1TFAoxyf22QUbkZ/jZoMiB/YRuA+ufUekcuuO2Ru+BVs5obExasF76l2ThAjOBD5ZXjwIrcTomexmxrDcE3fPg9Qn6VA0W9Aiglumn2E2LRgzZjUtzr5UHKoKPjA3xI65JevORQX6muRABJQN9dR1S+R7mq4gSYLsd8b9q3X4d6/Hv0gOYPtklfP7H1/68v0T+JgZxSchMozNHeny6IKVAN1zOFj97//dv+T607VQ3VnI1/hZCsd5342pC4/puWoOh3ms31u9Ow1VmaNZeZW/0wiOiXo9ghV5ROcWyaLxDkZMdYLpyChybKaPCYPBQoK1mIU/wKLCPt9En2DoP5wLS+gXeUIEdQLDO1DpCtU7Q4QqCSzr/PldlOWOSOWLG1FRZj8gJRSiQpGYeAE+6mTRIedqJMF5vgf5O24RDOmzEHA2BBNFFXBneditBYvCSXMNWgRPNeUo5mlRdixKw/hWnI8+fZRJWceHyEFeCWEjhdSLS0YmW9rx3bTQTYnRGZZa6iELYcEWhZ3C0ZOcKWTceHYI8v8/RjKiHvCNeLLiM35evwKhdw23TYY1dyFni/KIg340mQjKBLzm82ukZCNneXhgqiFCnQvBuEtq7FFMjL9zi/jfc8vcJ2ZqJD14ILD7uTO//+tS9w6SzdNmL6U4qZ2Xx3mSj2sIBwluIfyn7tZfDIR/++3E2Sr5xhRgcQhLkXPAyxnOV2WYsduabTRtlDIPZh0cpKXtzciN9xadZnkw7yKEDFAeur/3dWEpZ0rJm6NVRZfOzZV969g+P8QY2U5L+jItmvacCk+C44aoU7VSQmaaW2XYjVKESk9ZgkwDfxUOGK2mpVMyvD1Lapl3q1tmqc6hgdh2XEGukjrlGOiaVaxoAb1NGzYBqYgElHxfo+2YGlENGSvyacPeFqQdUGkQr9T3gI1sf5525HwFBtGXzKA/73gUxAK5rSBb7vxOVhi7wlioo2J+Q/YTLrDtQYTSR4rMowjKEcgWt40VKxvdToyRmq4xRrJzfcFKAuQeC0HNYcIoOdlu92n0HPlxl3TpkAmAxyDUUan4bF+JyCMP3iyTz0yyes7HSHDwVoLDKCCBDZDS6Br4W2qTvA9Cz+x+prEQLd0zSjhbL0QlB8OGgxbOvSOl4FZZtLD1zhqC93w9Q6ANYSuFMQa9lOmcAfImWKnUPae23YxQ+Sc7QP+hq98qsGGTfZZ9hdltySNCJ3sQCMGwa36t3IuMMvc5Qfo8RNyRn2kihX4/p+Yw0R2QjtlKzESw/FwgweBFjToKsGB9R0UgKs4MIRGhxInY8rmM0GnxD3E4E9sT4iXXytTl3hm9jMZpcHjFz8+wvNHGu/mSXpDxbrb7Kq5C4zylTBnTfhVHdkuWUgeqSpPCPoxadXZDUoOWg75fPseiHybYAJH3eN+SxZWG1OwYqEKMwVKCsm6stmBaCC7UAkGjkGxrrd9wudxoLWU+du3InHPYtg2pwXbrRIG+VwYrt+GglRGvDIPhT5i/4faM7xmwAkk+jPtJ1frsqBWwQCS7WuL5PkVEanWBmJ2Z6bo9HyvyAJQvx8g9lEi+ok3vwNO1oL6DKB45mBjsXwFTwXT6st9jlSOoMtfDBGz6FYtVZ/CRNUN7JWbUvU62ClItJnbMohAodsCJnzG7BaDs9H6ckc45yOURybYzB5G8Y/D4PP6x19N2ztcxn8tByY4V2U0oPHBHsp2zjrY76zu9zgke6HhMFvB+BekOo8A50o9ZSZ1xdtvyCGtkAAKQbXtguwNjz4HNLiVdSzwBzyeplAcrXx6Aw1XAByNSotUna6lfVSMbFRUYkXK8e0z2JintmTUaq8CmwoKhIiwibOIM8jEX0j5sqDIMVg1Gqo+oxAM0zV9M0wSPR/EHi34V6Fo5SbCT8cJFoKjTVOjibDcDdeoEy3+SmKFEBbPBUhJwDw/2ydxfezprvA6ZZwicrRAmdA/cU4t9MyGi0GLQo0zmdnAOYZ095HsHNR6vZJIICJ/uxN/9M48vRbvdOwX3e+bxXcZGhl18HZJ4j0heJdhC6MBGvofHyQTfV9b2VfEy/wmZ0PVekPlX+7SETaA+Y8Lv7z8JS97mg0Qkiw5ZYOV6/bKXLBPGjO7ss9i5x0Vz73ah0+6VB7j+h65fDIR/dEUk8J7VlLgT2zK1uPu8c+H0JJyeoZYzYac0TY5rMiERqFxz8lfTZkqlzp+vNM0NKc03d5DAmR64EohViDeQzodQkEpoghGtPA4vubekS2SWuOVzo0Imf2R0akhDigID3HKielymnljwY8VlUGSF9S15d3/OU64anM4QG9R38OnK+Hxh/CkoP63st05pO2UsuVBsJeor2GzHWOMBEFSRrVJNYU8ZAFEpsmBshBqlLvjfdETXtJFDEJ4Id9SEMgoMRUfJ9rcyNZcNbMdd6dszm4/0yfRTDikguFXCCy6diIJTvwCdADdj+JGr9UzdE4O60IfzNnbcW/KCPW3U+mQP8+CbQNad8JqhHc2xkcyvWbaohjhuSoh+AZcOQwrhLbnkyRwjIxV1kT0jswXT2U6bleJQp4QyPCtjsT3vPjzbcFExDNE88EZAYSH2yab0DbGFP1WAgs4KdkfooWA1b1Yf6anqew6T2WD0ndGFi99o/1E3w3/81SOZJRl3Vmmg88/3LolOZkcjdcL5jVNapClHyqEDn8zfPVhgXpopbjKZP/EyJTz5tb3nenGCcg+4YQNLVkFEEE8VqrrRdeR9jxDcmNJZhsSDQfdrBdkRlFGTdY6uePuitx9e8FGg3VAr7H4HVQeMGxRnGXXKX9Z5UE/JlKQjRto4gWnkvIMe2Lad8HUy4IaaZrogqXaJks4ydRbw1IKOoK+kb+sYqT1esjU69kLooIhS4gAUQs4YV8R/xc1eMh68749Zht63nGyP7+m2EVrY92CopPe5KrF3RlnxLvQYdFnwcgWWKTHKkyeWHEDW+dmEfMW5SEViEGJIfFX8uBBz+t1/Fn7yhZ2EBI5aBPzrdX4/BPNvJSZ4w7PD9tUhdV9ld8mD3FPlAIm5RmdACWQjLCZrOloOdObT/XnRGbKh0+lh6Ow8FXloxrVk3DbVHkyUz04hs23t+INBlvincX/Z58D0vEU52pfWcJlHu0qC+p3Cfft4fDKiuVdJAuDUfTI1D/NbRKnT7YIJkh9vjegMIpquCySjWDWPyUMk7FHVlEyEs0RkURNwZMcnKi/iDwBu486RKUtkCT4k9bSQcoMnhWEJyjfjMUu0CDRP6LSrkI6JQouBAqbp7bJq+uaa5MDjwqCVgvTBkIZFDhpKpMMCpIygiNNkHigCTMb3pE6TjORexR/hOOnNHlQN+py8EuAQOxEZunFxYbcE4FWhjS2lDiYQwiHS9QGDgwd7D0ZU1kiHpncO4emxW8TYJrO7T1W7RyreAx4sLCQEuSLU+KKnhZQTPM01tc1/17t84av1Z5H4rX/1j/co5EY6PpnAGsG7+bP21R1/fy79cW/zCJG6e0abfFlvfa6vNXrKTOYX5O/crx5ZEAGcSJnWHYhDSiMUqPJlP1om3B4kLqjhj/U24utd6v9//WIg/LufeqaLROFQG4EjxdGxsKypS3NLEGtD8h2St7QcE0OoaeUSoBqEbLgLtdqUSGyAPjbP+7Ba/t8AoWqfg28lU+cAZ6dagQJhY07CJ2h/etcYu3DtO5sPKgvDNkrRqeVLdsRFkRkVXEtNpmSsRFyBbF8e6rfAoEh9mJTDO5DTnLx9Rvw9x9bTgiiAbpjkFL77DvZdDgzFQLUS3DA5UMSoB3JKqQ7QMXtKeYiAJ6iv4E8wriulD8ofBtfryul1S4mFH9jGjtmO9YXRlesItr2xW+XaZzUeymaOkawnFGwYnwlOfmKoI5G6vvD0onTfU+qAYj3HisYcOZ65HKhfGHGCEsmGWk6rm6TTgJHRv+6ZDBMijPn+yoAraUJ+0xuXS7LFokprcL1uXCy4Xq+MSGA2JNI7VoK6HukzBS4EhqS7h2uh45SSHo23Cn7b0VIoZcmEOE+de8jUWVrGd5sEm99y2CFOiORBfjjU2e2oyOEJrQsnXej9htYlGfwmXMf1z84Ix9w87r/l3m7Me0MnizDBi8hk3FOXCaS+MEgNqvMAIRlxm48tptx/S8qPjJg7YUEYX+koRwRNhLCEudSUTIUk1xsKSiVGDjqZZjuxTh1tmM91llKj0JHn1501cyF0+j7df2/UfB2zJa4diij0/Lw8ghE52FuK4FGz3Z2CC3oYGDStjLEjofl70XzCOMUcWQqmGb6jbpim1Rc9cgAPxTM+kuFBi87QkpZhHgzvFK1ECBrfZplYXyB2xL/Hy49IE2J8k4eh/Ai6p8cx6XUek2GJzYjWsN2gIBZdAAAgAElEQVRmKl/L99kLEWMOsoK3CXA98CnvkDH3Vp2Ph0yQnGsnZTQBU4sZXxdFkXHNNlvcTJZLmJr4qR/9em36I15M+aKRvn/T/YEnopJ8DfnnkUlnM6lOlEfRlFAu46rztcjj990hud8dQbLHPteAPAbrPKkqnAwJqszXiz20mekQJAz8ZwD+l17tIffK/92+eszlXqPO9V/E0+1FvrB8NRKWpIxfHo8TfGHULGZ7Ob60yh9tZ8lhtaJf2ttShD3nAll1Di+5p33p/TmNBJ/HSOeUDcnhUuNRXEQpaaVHnltVkp3WqY+4zZfeCI6SkgPIYjm4y28yK1DzyKeos7gwNM+ZQlDu6aFVsZFSgiXGz9hTi7QxW2bY0w2QEDycHrBoOkecDY5qGcAkOc9yYLDd5U3TGUd8YATHmoPf4skSnwR2y8KparKSPlfYthtNc/D+ZsK74rwM5uNW9jubPJm6KjmEZqIPICeSGl0gZwPCKMD26J3lR+QBr7Mwup8BNt+PB8DWXBtYdgNEpozsARrzHVwiAWZnDp0l7QR8kTb4lxPh0VV4np8Bs1AzKY/uwV0KN2ZB2yL+P9reZEmyJUnP+1TN7AzuHhE53Ftjgy2FFmDBYcEVyXejcMcH4RPgCbjmgiIQNoUQkCBGsrr7TjlFuPsZbFAu1Nwjq4kGm7dQZ1N5KzLDh2NmR/XXf7gLnzO3CUgvoMWnUtqcUy7iNqoNp2aE/pma3MgJ1SlR5oEor80Mf+f1swvh/+mfB97FE+MxMpD7iLwxhJW9BaJ2GoL2kVveaUMkNWgSvGPU2O1r3IQfC537dxPfvI5eW0cYtAtswHkwIkItAU03VEOJFv11a+z8NL/JqKBVaGGiWiaRMOvjQH/UANEdIPBCuNb+FUmCsEJ9pJVKkkYNg6Nm2qCLAkNwvq6Z0Xhhu84MozJERy5bBWHw16x9XKHCOCmthu4FmZHSkAS0kWYrZkZKoz9wa0V0opSVuT5w+XxmIfPDduVdPLDnF+bjW/Z85cdrppTGD5uRyxWLE8/nZ0xncncSKKUwxUQ25bwVqkLJhh1HrtuVXAspZd4PB49MFCVUI7WG6oBpdYN0FQaJCIlcFrbakGH1kVB1T+lcCxLcZNwkMaeBQE+EU2Ov0lGFxkygtMxKIgTl3K7MBRbU76sZYT7yGEcu65lsjWF+pGngp/OVjcY4jkgzYhyQ4gdbpiF5IaSJtmZMB0yVWUZaW902qBmESEqJlnzLkYRfHv8BFhRqYN8XJCZUAnobXZ8/splRrFGskULisqxs6zOt7n9yjjCdT3mzqJAg9w79VmvcRmrWC3kM2q2w7LvAK4y/JVu41dRSe0FIR/q/4ixKZbB496IUEapXp4SiYNUdBjpyXTV0VMogOCJVrNu+dWHUbWSam7q9Gw0LzadC2SN8TcNX4ze7awya9cZaOlprHmmWujAtdgcLDwJNbrd2G/1XRwqtghRzRLAj6hYFrRkrLuwkepWk6gheDQ3ZKmkcQCqherEhZl3F73YauTSkCCFtZFtI7YHaRlKoyP5Iwz2dnbHxjiqF5dqIyfmvtRT3YR0T7BkT83NLMhRXplsNvbiAViKqHpxjXzUyTlGye/FbEW/0WvAgFAJ2R0hv6yF4ZQgIBUzvEwaT5g+fbk/ma+W2kG5NVx+h6uvSujWK2ilR1YzXtivcqQE3pfwdLe6vK33SCK/rxi22+jND3Raz0Zsja9589QmF2O2+t7sgzIeuDVqPcL5X9n98Uzv0fXD7TbsmHpsX7KXvv9tIHJStF31jf2tXiV28VJzW89W19P8d5Q/5mYYvV3A9dgpy9/IN4DqW/ucmfYrW18UgjkoOnddbRUi1dtm3F0o3lUCsjb03vKLSx/XNnWj0rlFl43U07za9fSf38XdQt+8y4AXl1Iy1+Z4ewyu9YWtOYSyNbkdmd3R7Mg8uWVovtNST15DGoM75/1yVU3cKWbyVYzC3anMXC+E5G0omhMbWehR8c/H5XuBKF/hK658FPi6NKYpbbgZh3wtHdYcIQVGVu7izdUHbDYQI5udEwF05zGDAreyCyB2dVSuEPjm4TRAQb0Jvt76XGn7/rKDttQBUjFqNqK+Na73vO7+C+YpUkTt94oboDn2/buZoOtxoOH4fa4Mo9Y763/Z0tB7Q9dX6DDflGxCDJ/yJ+TpIVnFXWe1pj3BtN777V4hyPxlaB+Vinwz8XdfPLoT/yb954GW5kuxKLmdyWTnMI3NS6u6V/6W6TdNDyMDKFBNNBWvKLIG9rWjnhoruLgpvIyo7ELhsC2nwhJrUBqI2JEKpoDqgslPEOI5veNkHCkqdZtZ15bdzJXPkmjemIbFtG6kZ0zhyPp856UZQKLmSxkpKsNkBqnJohX1baW1jmg5o8x5FNAILUQMaRgpGroXYH+TWBuewjjtWKl/OK6Yb8/TIg45oE77kM7NuvJ1PnLeF+fDIUDau+YrD2D5mzTmzmdHaFwrC6XDAts/E1tAYELmSpsTL+ffOp66FIQqSdtoWSR9fiDqy8RZwvs8aDgwixOPAIMH5Uwr/qlzZ8kSMkW8PxpwiYt55pveJmOGFnVaFv7leGAvUFNgYGQ4PfN6umCSGEYYQkX3jYo+8G930fj8/Mx5mmrjafIiRVL1xEomEIEyqLNlT9rCVsjv3p0hkGDz55gHlcn1m286klCil8DA9sSssLXY1b2Vvlel04NvTt4A/SPZSqElJ2ZiJVDK2bRRZOEwTtVZmq9jhLVoTuZ65rBuHOLgl1/qC2sSXHz84cphwJOxmw6eNbMpeC4RAIhKDsjy/UGuF6rGww/CnTZbTPs6+uf1V61iZuEetNp/3SUfk7jGxcoOCO8faPI6VFns4w2uRYgQfD/aR+g398HRCp7JEUUfUeS3AGTu/Nvjf9VG9e/pW8aMxV+f9W0duRRVrzR8G4r9MEFyY6pCBqtLMi7XbZ7H+mSPqotXm7yeIo7FKp/VTiTEg1c+XmwLdVW5GzYaYTyvoYsdSKqE6ukUXCNLcdmsXIzVDa0IGw9rWbRr7xCk4Ot4CSG0MMaEhknNGw+xOOSLstTt8WCVbIzRly41w2DlOyrZ3J4YtEYKR10oNDp0stfjUSf0B1KSi2d9v1erfxQ1q6Q9Gtf6dfo10mjr621MnxfypVntRb93CyEsgoX3l4CAd+bN2w4pei50bv5UOZDRfXJ2bK6BOV6q3h9tNEFUNVbcpRLs9WlCqtVe+762gwgstFR9X+4+sb4xbEeHrIXJDueW2iZCbZRVemLVO7KLTFaz9fwtw/j7X7TtJt+KxlW5t2NFe8cKjYSBwiHJ/T2ZwkIqJ3XnBZqB3V6bbd+F759Xn35iCFynFlLXRI8Q7RaC/lyF60xDEra+aWbdqw32WcSv8EEOnOdEFS15MRTEeuY3jtRfC/j5dF+JnT6SBeWOT1IeZ1n8X+Gg/4NG/0XwSMFtvsjrk2PBQiSV7seXIpxdKewGisBZH09Wa82fVExmbOP91CkArXQdQOYZKEthqYwpeYB+TUxW2Kow38bxAae4OUamk6hPQNcP7o2FjY6uBt8n4UsBUuVT3pMGMtRjBHDGlGCPGizVWg8lAxe91YMdwrryjvgb22ghgvYXv1a92rm7uzjL+M//5fcpz+6fiUKt9JUiNt/XSz/39djaaMYqxi6+hZJWF0Hm9fl42cxeWBTfGDAraGhW9F+nwOq0JzRjU78NtiOlovxfJgjdJJupHBM5hB/emDsi9AVOkf8ab08wr7/nvun72U3msP/HbaeJTC2zxSEwnX0RlI8bIGEekXbA9Mj+MlLVwOhxYSwYbmSSyLxdq3UlxQLQxHyZ++uGCxMA8n3h3Gvnxww/s2WOAf3U4EufEdVOESMkFSZHLsrAuuxva14WcMz+sC5e9slvlOPqDZsmRFKMjoMMCFmhlxi5XAE5pc9L3fiYUYZ5n8po4TUfUAs0uqERCTZRxYloqu+2MIbKY2weN88hSXWg3v/8Nsu4sKXCpG+MwsHBklchHTbSTMJbA1qqnuRnEHkO76kZkJsbIvp95bsJLE1YqcikcHw/Y8I5hfiGkiO4VxpnSlJR2zl8+ESVRrDBNE6lGPukzmkdEnmCMVAojE2XYWcPO9Xr1FK/NaHPioQVerpHrT79ni40UZ9LhF9Rl43GciBr4uFVCeiRvO8uy84UC65Vr2vj+/JHZlCKZ9eMHBynbziLeRYs6sqvRC1EJzgKZYmBIcud4E9zQ3EccO1hCtTGnysv5RyzvfDMP3jw0V4mXrfBpe/HRr4GFyKXuRCIhDBxoXXGasP3KECNhPBHFyHtlY+E4P7BKI5TKr46BoolDGtHgB4k1pQVhkJFP+zMnqxADe8mMGgm5cdRKjBP7vvLc4h8oYP8Ul1l3cqHrOPuhV72WpN6KQTWU5EUc/pdFuY+IA9LpKy6ScqRYblipc0bVxWxR1J0EgmBdvts6pKNwR2Ot3egGXtig7gZVy+29NkdTi9C02zBaI0Yhl+ooptkd1TBzWLD0gkeof2Ce7m4sPVFN+gjxVjgXYZg8AbOU6pOWXQlasaAkNaiGRvdCVkCSslZjDIE196I7ZKx40ZbUnIduFZGMbTuDHrExY3sXiPYpQWuJOG+Ql3sxL8AYE1ve0emZ/XJiGhNajVLxsewysiyZOIKtgcNReN52QgwoSg0QWmWne6dHpVRXuZt05Xno3sr3adsr9zV2BNDRXBe1+J18vWLoTh0ilOqJYY3W73svHr92VlB/ONUuMAza3Rgqd3s4zLBotNyrJWu+lronamgOQFvFzwnD6RR9rQWpXiz3wkjNFeUi+hVS5HCv3Eb0vdS9RX47jcqtq7DuPNQEetGneBhDDB60ceNc/lGXeZFTOmorUoncouk9gU3UC5ugr4VK7cW8dT/W1L2PS1/zIj4xUPGx940Opb0oaaZuHdgKB3POvpkxqPjIuYu/g/qo/9B/540na9ULVZFIz7AgqJ8zU4S1+As6IusUhGpuj+VoJj66btzX3sa9ryLyiloHAc2N90PjijDVwrXB2jwtLakwRmGrlWPwwtUQDlFIxfgSlVa8IUsC52IkrZQo5N5oi0ArGYverGorfMleBKakfL7C29RoxdMQx450alBKaTwM8GlpHAbxIJOmhACfroUfF+NpCnzZG786Ks+rsRv3Yl2A3ZwKMSnU4h72R/V7WCQwmJvN3ovZfjUVtgpjcweXpQs7B/mqoO3iQC/Y7S4W9ECajtC2ep+uXDuSO9+mQyLdxs9YWvfopRLN96Z7+xZ3p1AhN2+elu46UeS2Vvxz5QaE5uEgfbs3cVrdJF+lGGLdPo7eCPhnr2aM3gvf0WBv5v3vN+XGJKU2f0+DfC2Y/H9fP99HWN7zGWNthSmNtFY4xgOixkLlkpUvYSCMgbG6R4CuQggzf3XJNBEO8RFhocWRvQTSS+X0+AYZRrIUYokM8yMpFgYZSYNw+bLyNDofdIlK3jc0BN4fI1YbwsK5XtHpQCmFkwhHU2ya+TwqH/aNd+PI/PQNlMq0XwgcCEMg1YH/c31mGCMxboxD4Vd6odYzX5YLdR6JKFIa+7UQppFNNo6b8E1MhCExM3Fpmee98M1xwg7GQSO5RC5fPjFp4ThMfDkvVKtIMx4E3seZKYCpMQwDwTaGaeW8buxifLgYR8lIDuxstD1z/f13iBnHX/8jxvqFL9//NcfDTC0eUDIMjSdmio6UFJjjEw/FR7jr+pFFEuv6iWIb4/QGSZV9/UItRkKoGplLRSZ1MVJ+YTt/YreKPCdmGbBCD9cYqXlFmzGnkVmEIo1f/dl/wsu2sT9sPhaZTzznnXb9wrquaIpUKzw9vmdZdkrz8fPL8oUQBt4+/IJ5njl//p7j/I5tv4ApU1oZcuBydG/kw3xiM0gM7K0xp4C1wekSmrlm4xRG1rJxfHxD2TZsy4gGihSuVtlqYquF4ZAI8S21eLG8tcZ3za3ephB7Qd9YpZKqF4TD4RfU3r2Pk6emlaHzwlKCqfGkeqf1/Kmur6kXgX4YuMNtD2SU++i/SrmPqO7/XtTTp7A+fpZXgc7fEjSAPwA9jSE4P9BPKi+q+yGnKHbjpDZ6YS2OHPTfIzfk6KtC1rpauBUDe7WfMxz1ko5WIuZhE9JIncNrt5FhL/rBD/7QpYIaXCiCVKxYL86yRwuL0aqPxtVABv9trXkktQ0JZYc6gCVCqvdwnBgCKu64kuLBnS12p2iJCCE1nANsQCJJYJPifMVmtNCYpoFa3xIOX9gXd2uJw+hotH7gYX7g82UnHBqXze9nqc5jbg32zWOmHVHafS+ivUlonpCJo7YGqESnm8gtTMAdRYKBVUODewjfuIMqRjPH36PoHyDB99XXbbes2n0ioTfAHncq0CguhusFrTqk5BQNHKVuIj6sEOk0uVdKhJk7O4y9gPV77K8QlTu3/JUyI/d14Birx2moKorzM7/eDcEiTevrqLg/6K05gmxfiwl/5uVFir9AUg968If36/u/TVRKfR0tv/5736M+Av5DHmSUP9DM9c/winznStdzOKKG+WuGm99sh2XNXADVc2n6d9hfA0f5wJHBJEB1D+Si7gpRzRi70LCh7vWtLvQy9RG8/x33DZb2ei+D+HoJ4u5AM5WtGDE6kh/EqQ61GmrNfb0737l179u32rh267BmcIr+/SZpTMH56XtxBFyk+cQjClOnZDj9wwlmU/TGWoI3gWMwTsldWb6dhY9dmTbH7gGtyjEJHxfj3Si8LI21urvFDYG9ZrcNu+7GFgMqHV1u/jtKq68+yv17aTeU05yCEXujUs2L0NJuZa3fbYPX5vgrJPjGLL6Z8qX++6XvIXgtKBH56nWEVYQJp5EEM0JvUKsEmsBTX7jn/hyorgZhjOLc4r6fxDqfuhfON0Oe+ySJGyW2rwm82cj1D9d76JPO22lUO32k92T/wQnOzy6E382RsQnb6FY0zjVzA3oV5XAK/Np2chE+1pWH4Tes2wtDGvj2WLHgxu8vXy5cNwMi61Kxh4HJElyNFzXyNiDxiSEd+dCeYXjDd6KOEtWKzkotG82UNCbW5cw8jHz48gOhGddSeffNLyEOtFqYD4EcI+clk0JAk3NIkwkv22eiHXjz9lfEphTZ+a4FbF0YDm8gGCfZWKLwmGbGIfK0V14sk1shVuFv1p1127jqzL/+sYv1ghAsw/CE5cL2cmWSB8I8Es0dFv617aTSMMvU+oS0BpdGawdGbWhonLeF4fHItl08MCEeoFT+6q/+L5pldhoPe2YYEy0rba0cnn6DXp+pwwOXZWfVxF4rOWfG+ZH0MDLXxnnfieEtdZ44piMjmSQrF5uwnGn5zJzesK4rM8qZCy+7oRSGcMJKpYnwOMAwKosmzqb8u5+eeXr3lmX1hBw+f0bmR4bxHfNxYFXpCuFIHApqOyqRKR3Yq1L2xsWA4Q3nqoyHb9Ct8CX9giFk72wTfNyNpwZLcIrCml0ck0SpRbA0sCuMMpOfLxSaJ69Z8enCRverbuRqLM0YG3y8fGaQjXfpkceHEwdRxjCzykZcNwY1dAzkZux7oU0DkwVetjNL3nkYE4I6wkKmjdPP3XJ/v8sqYkINr6eYdGUweDF5iygP4qNvNQjBedGtK/kNeof/OqesrfUHsdv/+YSro+O3M6BBU3clSKpU86JM6Qdo898ZpPO3qgIV7WeHj8tvhWUfdQX1br/6odnoIzJrCC7Gq1S0BSrtLqQzKTRcnKLi3DIjoOLInm7mSvcUnU8qAYtGsghDJeyCzY396mVTVI+blbWwV2NgZ0gBmQ1dHPYoREIz4s2OTsz3Y3OfY5OGRutOHs3V8tGdEcI0cF43TnHkcr0S00xFyJY52cien1nrgUqhtk4ZqQFyoNSMJPe1nk5Qdjc5u/FcVQOxFnYD6ejNPa2zP3ns9hDx+aenPypo8QdOCF6su28tVBqt+H9LR/1vAjzzL9v5gX1UG7t9YP4Kpa34z7RBbhC0o7x4sa3NuNm/aNCe0uiotKp6cpbhlm/61TpEupeqTzaA14aI26jeeivgnzWo0pr7tAJErT78MC/+tYmDF6qOoMrfLjP//1+1ebM36O1d+ehcu31Xs1dFvouvvBiN6iKtVut9LxXzAnO/TWKs0yWaV9Y+rvZWuJrdSQtmPvYegxdQrVMKHKn0+3bbPy/dfnQMLlSSVhnD60TB36cwCOzmJfINpXYXiQrBw7boCPLY2dA7SjR/b0inQIojza0Udw5S4RjFrd8Q5u5WE9U/wyHAy9YIGHNwXm4VSMU4i/JuFA4BnqvcRVe7Qere/pMaKXgRndXj1E8UXrovNc0LabPG20F52So6CS9ro0QhNGHJTj25FmPLjWRgTdmrsRXnKZNhCA5f/mLstIvgqDgYUYRsXmAPwp1vDK+FonV++YD7Hu/VQ5OawKSO6td+jgq+x1pzV5el+XerdJpBR+9F6GmRxnNfI/GrBuiWapnFk9vOvWjfzB8rU9/vAuzi73Vvvl5U/X6I2J2u09rNwaiTjgxe29E7JHLn+vr57xMyv+fC2m77tZ/7/XNsuP3rbU/8SQrhf/FXf8NSM4fZbYV+kY6MaaPuJ74rH6jLBYaZ1Vzc9SbNXLqQg5gIy5V4OFLbSJSNGBUdhZjPLDuswRgs8jgN2CWz1h9oGX77JnHdV+q2+YPNHJW+rBvZXMGqUjghWBh5GyfGulDqji0v/PLtO4TMj7ZwjIFQK0MIDAHqcWIwJa0feV5XqmwES5xSAB34bitcdGMKM4Mp33/4DpGANHOj7pzdyDuN5PMPaIzk1iipe9VWT7Wzqnwms20fGXXk7eE9v54zL3vki8x82X5iv+yoRuZ5pqFkMdI8kC5XHqbEKQmrGnqJXHPmGp8o6cCTOq1AiiIB/vq8sAByfSEcEh/OL9h4JMVfoVbZc2BrjTQeWJfPTPOJuBU+aUJFeQiRSYTT+JaPppwOR7YU+Id64ry5aPBREz+USiUTtoUfSiAOlXdvf0upFzh/5m2aGA307Ruun77npQi/eftLaopEjHVbyUkJIZJ3sNMTJ1kRU38wydyV0Y31MfA7dpYWSZrY2so7BI3Gs6h7EAdHMkKDE4lVPMChtswwjSQKq41oaFAbNk+elmXuIWsSqaJwGL37N6Vqw3Ih1Z03NNqgFJ3Y84KKcFAIuaExkw4PFNzPWGMmlEpJB2L9j2O79HddYnIvbv3/EOflAzTr6Jc/UCPC3oQq1QsSdaWugxVOT2hdrCFAMOlFjYuHbuis0yF6kaEdKQyJZpUU+mGMEQls1D7ffuUWB/Uo0No8ldH01VKrmZDLTdwl/aBs/bW8MDIaVqBqt3vrHqTUXuZoxdBeGDl/TW/mqxLYc2FMiXms1KbstcEiDKGRL8YQA2vr6nI6FUCFGaWoIaugQ+1oWmHdhSgB1YIFoW2VOPkHFpKH7ijEUMgFhu6pK3UnWGFdjWme3DZtW5nCwLpdEQmoFdIcWKoSU/NI4sNOuQQSzuveV0GTR1nf0ZGS/T5a682BIzAhyp26ckMQW+s0FwMQpFMSWm33AgUc0Y2ho0WGrx/p8br6ykGMXcDW+nr0EaZ0RL8XqcHRe75CfgynvbjVFdzTC3sBupdCVKWKodY5rjfKgHmhJMKd434rW2trd1FfNSG6dRGGF/W3EKVmjpipmvPRu51n7dShkv94otONwnS3uFK584VrL1ZSH/1GdeTTTAhWiPhbyp2mkEtxIVrnURYJbMUYoDsTcL/3W9f5z7dzsqOIU/RRu+Fjd8lezIZOX4nqInYnnxt7Tzy7IXOtuXeuF+3uKHujaIELYb1xyWDKwFe2b93VIxjMgW736DSb7fYdUFl2YYyN45Ao1Wi1uxeo8VyMOQprNpZitKAEFY9bjs5P/VTgkIStOr1nWytzMIoa2oxc4DS6f66Zu2YE9X9z3jtdqgl7qxRrXHLgaXQXhktuvJnhunnCXG7C+zmyrMLcz8I3Cue9CwZFWHdv8vS2GHA6yr0wbf3rxgvs3l9yCP55quETM4G33g+yGXe9w36jJIkQgrA3Y+qNSetNlPOb/Xob/f7frceQu8Ax3NF6YVZ47Cj00pvcvQmPvarc+u8Nfe9ueyVF90g+SeEI/GTaC/beEPNadN8mMaU337c/J+fVuHBQjdC/tGLSgZrSk/QiQf3c2ZD79/Dvu352IfzLx5m9DQwqfCk7q6xcGsyy8oskWHrySE9xqyrTxK+0qyCjwnBk2VaqRkKoNFO2ITJyYUozuu2kVqjVyFqJIYBuPK+ZaWtMUblI5bxeOcXE28OBZ8suDpKBd+2BKhvntiAlMWtAk/Lx8hNzjMRcKFmY4siH7YWi8CSNLc0+7jtOvLWZre3EkEhJeWwLYRhZL1f2fOUYR0KIjDHS9soP1Xg/Rx6SwsN7lrxQSmG5NN4+RKbhSNgrWysePxvf81OD0RY+XDZIyqCPfDMeCfHAlleqGi8I0w6ZjY+spOeZvxkSVTP54xmdA0dp0Crnd4m6Ry4Chw1sODENbzg1uNaFbVDeDg+klJhU+byeGcPAbhsXBtp1J4aRhBJLxfLGaMagkV+3xucZ3mmDHAh7ZcHDMJ5OA/sGbRo4hMDUjCU3alHObafkzKBgxdDpl8wi/GCBusJL25FVOImn11WNlH3j88uCjIk0x27A7x7RrRj/x3Zh1pnQrqjBv5PGMAxYLiztSoyR2JSEsmtl6g/Hc1k5DIFlEVp6JjeFvZFSYiawbYsXU0GweaKUgraL2yYxsMdA4YwxkIphYXPvxJCJVWiysedMkNjXopHEuaGSNqd2/Amvqi7GSqGyN487lps66cYppB+WfUQNwmD98KQSUef8iboLQn/404VUokYx5z4UARQGukf0LTPeGkEDtVvRDaoYhTE54qDqCCMVzCLSKgmB6EIkP+odKT6MkfOWCRKw1jrdwo0tUlLv+iPdh1wI0RXQKkKhfw4HFd0Fwsrmw9AAACAASURBVAwJ7opRWuU4eHrYZu7pbT2Vz7TbiKUCVyUOxrL7PW2mLFpxvZj5iLUEWlNGKtUKatEbiyZo7kVIrcSh0hRKGQja53lKd53po9Eg6HQhnyO3R9TheODlcqFejSHt1CrEEKEdOB2vmAbqxcMF0hB4eW6gFQvOH3Z+dG8Cgjc4Bq8PGZxnd6vOpI/cTZtTWfRWWHrBeXO8oXmjYZV7CqHzd/2zWU+wu/Fbb3xcpBe81mOnBW/WbgVQ8MbuHknfC2i3M/YHYus+tBqcz3nj/X79yPO30aOozbpOx+5UD59SGMECJvVOpfAAEa/IbzSSewKjVSR+BYP+zGtpMIsRpTmvNkaut6KYzrHtReySndVvQIzBXQhaIQVhs8ghOrq8q3Rb7+ZNaUePMaf3AIQYsVbJ1c+I0mAIHkBaBYgelHEYhNzdUyKwWXPeKoAKB/VENMW/IxHjcAiUpYC6y0UyQ0IgV2NKQq6NFEP39PVmY6tGDYFcPRBjx/fV2OOg+9yZ2oRhFM5ZGffiBXfnsiZRinjBVkU5aeMlu33b0owZ586rmX9H1qjFkdmtCSMZ7Q4a1+wOFLkaKZhnGlTjMfk6TsFpLLFnGiQxHgflb86Nx9GRybeTcrkYz3vhbQo8FyGJYaJ88xho1fi8+Xp8SvDDxRikMGhgp7EUb75GMRcR3oXNffE0jyWu9mq1tzZ3SCA4LUXF0djSEVU1uDbjoPSmyf/dXo0sgYBzuJ3S4j8MNP9Z8xK8g8xcEPY+4TMcBBkVls57b70YH3qzNUZPMkzivONPzXnHN7u92zX3UBPtExG7CTub3d0tgnoTaKK0V4i8P6ecMiXWCKqejtkah//Adv3ZhfD//fKZgvLrMRDahUTguhuaImtVFrv6hmrCFP3w+rLs6CBIjVy3lTxNjGLknJmSn75bGNC28bunt+zrhWF01WNuxpQmlmUlh5Vvx/e8KQvbw4Hz8IY/15nvlmcuLAQrXC4fSOMjD4cHfjc5l+t//Dffsx9PxCL8A238Z29+Q5xW/iw/UUrh8/KJIWR+/XCCpOyXhaspT4Pww+XCm/HEuK0wzhymiSntaBooS+FxjHycE8fDRN1hK43j0295Wa48v5l5RBgCtAm+tIXpOPFPL4XLplzryhgSL8+fOIUzReHP3/yab96NHFhp+8bhOPB0/cA2v2PfKuu68js9c/r1kV+NjXN+4XG8cs6V/+38lp9k5xgSvz6CDUd+Ey88t0jZnvmHx8rHqzEfBp7LC+t1xTiQ4pEf9Mo8X1j2B5YChyY8DxOpbDxxJhG5IFRVatvIErjUSsyfWRk46cq/yBPVCkN6y1P9wFM68K+2L6wq/LrCUznzo67MNfJmiHw25S+OinLm99cn/nL5jsfDtwyTYlx4qJlHG0EatVY2S/zjoAxx5ZiEUxY+UjhkWGjU5D7Vwxg85CBX2pwwyzxpZRuSI2c18iI76ZjIdmEEUop8IWMaCCzuFzlkhnSk7gsZ5cBO0YU6wGRK1UpsHtS3hujuCHZmZOTcVt43RZKwhMQS7T+0rf74yxqqDZoSO0VBxZEYNSMGRwAAKMY0eKFWcCWwNxw+mmzmXo2YO2FGde6X1cqQHJ2qIm663jykwLm6t4e4ETTQakFDdA6xqYfqkKjZOdgqGU2d50p1brCKTwdyo+7VizuM1u19ght83dHDUm70D39fIkqI1v2Jhc0qasLYcGRrjOx74aRKrZEQXQjUpAutMGoxxqOjP9NRKFvjMUYIt/TDPqLNAcm9oAs7ZYhAIO8KoTCG6I1BUBQf6Ut1vt3ly0IbExIqQRP7sjA9jOSlctQjYdycgz6OfF5eQJXrpUAWgkTONQMZCmxUQkzoqbAvTltR8eaRAKmHHFR75YnDK/JiraIx9jhbR3RNbpZqPXGwOXJ2E605b1eR2xO1FyWteeoZJk7VoScBCgTxcXUnmDtVBnlFjek8yHYrom9vNNC6a8jNxk7UubPRggsKw6uPueEqdmteMDdxzBgJzicUu/NwnaFRPMQHFz7eC2cR9t2Ig1D2QpAA8f5R/6grtc2RQzzeuJadqI6aRXXKxC0JMLRCTEquXgycgvM0VdwOy4z7/pzU0JC8GKmVxzE6Ima+7s61Eq2wh9G/Y3z6MgSwUjioslUvslJwz+W1ulNJ7Mj/rb9u5nSIqLAWuGbjqMKmjUmFUJ2HbNInZMBa1JE9YKtOxRi1sTVHvF9yFwB2L/fHJFwz1MFTOqfoLg+1Obc6KizVeJyc3vI4OtLaBj8XjuJ/b+oz9sWMlwJzEp7Um79LdQeSOfp52eyG2romIYvy/UvlMTnPtZrxsjV+e1R+2ryh/GYQRykn5cdLZUb48NLclSMG1qUSg3A1Ixfj3aCkJHzc3adZRSitclAgOAJfLfQaqHtkd+/dtRSGFB017vcjqaF9EnRbEyvxvo5F4E3UexOyt1dfXw0e5XxSv6ebBrc9U28IJ8usRIJ8JcD7ar+WZqRWeNfHke67D9eW7vu6teoUqJRY+xqvBrHv/TEI12Y8V+Foxfe+hA4Ty33TaXPB9WJKaG5PtyN3kGQpxjEK5931aGOnBv1dl/xcg///6s/+wmpTNnVu5GYVyZk3p5l/OIyu9hVoEhmjUFphyJHMTq3RE81S4rGVu1hBtBEYPZihbGCFUxjIBmEIPpYWmIbEGiMv+4mrFdL+iX1rLJOSLxcile/aSJoHvlxeyD1h6t2bP6dpRKuBRUygbldscA9YCw6zxyxcJNNyYQ4P6L5hDwP7lzPjdGAO/l5WybR1h3Ym6kyKkQOCjImtgGhhPh7QuvMkE0OAaTowh8pedyojqWyk0b0RNTSSTWyt8JvpwFkWN/S+LPzyVHnE+Ks8ojJQbOfpMDEvlfXwzCkq//bLgYPO7MvGSZ/Zl8L85sDvf/pC0xlSwvKZBwl8XD7y71bl3aA8l8qlVj7pzF8cZ74/f6SkyPfyAEwsbSfKSGmNppC2ld2MX8eN3757QDMc2PhFOhLYOLFDTDye3rF9+cg+PvLjdeevtwufFyOMwl/+8Nec4si3w8yfzSPPNF5q5b9+95aQV6JGhmGg7i8QE3urd6GWiRfhLUxM1dvGEmDLmYCyJ0FIaCkc4sBaV/eMppA19KCXHbFIbLC2HVLo9k0Nq+43TExQKmswhMHXuDVqi4QQ2WpxykoxruZJYSLezU/ArpHaXMGbKS4CCgP/5F/+sz8eSvo7rv/+v/kvjY64mpgnN3Vk96aUr+YBMEPX+rjAQu9/FrwgDtruXMrb5YWnfHUvpKvZO5InglilNqc4RFEUD3VIg/P0wZE+DYFmjdLc7N6TC6WnuzlyZPiDN1cnDNbOG3XxlfM4tPtGafBC29GIRgxexDYqcwjstXGTy0HzvBoGiB7M4+Isf5Bkg2ny1ymbOfKqO/vivMFwFJYLHCZ/UNddSFEoO4wS2GNm0kDQHgHcIFkjToltX5lk4MN145dvgydZpUjolmlhXnn+XhgPXUhGZFBhWzeIEYmJJWfyS0FjZBMQIh+uKzK4I0YUISZ4uRrDFMjZHyq5CvRUwGbW0e/+cLFbjKl/Q0F6MW0udkQcbW6GW5gBoSg5Qt0zKTr6Av5A184nbxVEAtWsNzl8taa8EL4lyalyL7LzXeHekeTmIunYkcfcbkKaHtUuXjj5vXSpaOniIbVAptKsMUqiWCWgLobrdhO3FDn/bgJCcaFgdY659Ie1c6CVEIT/7n/+p3/UXv5v/4t/bLdC0jrPU9WFboO48Ehacy/0npCRqxentz+L+Fh4kEbrU5Pb5XxbfS0gxEfRe3duCJ1KVJoxiNLUQauXBt8OryEXue9DMUcPY+yCy2Z3V5JrcRh/UtxdxQoXn/N0xwlH15PnuTAEX4PNHLGcou/9hcgpNVr3fQdPEhykUVQ4dWelINzDQpZinEaf6Jy3wuOg7AiXzdHRbwd4vlaGyQfpazFShJwzuyrH1hwU6NQeqY2dxtsx8Lw1gjb+1efCf/7LgSDwPlWu/ex7mpT//cfM+4MSW6MFz0d42TwN8JcDfFqF766FaYik6oXZ75+NbxJcW6ApHIfEy7XxdpROLxG+ZGFQRzitdaRVbhOcVwHp3ikGgzSSODK6mjcbpQswwYfxEeWnvTJGYfiqOozqU8G19f1r3hBEsXsRabjF3XMTTuJAyK3IdvRcKPXmD+6ThlkbF3MantK57124l7pd5A293qq565F4052bixiX2rnqvUHRTnFr0Ju42xrzPaHiZ1nsP1+aI9L/wz/7X/+9+/VnI8IP4wzRu/x8jOw0htrc7ssih2QMGJeijBS+STOLGkcdQIUxuFdtzhlVpQa3y5rT4IEEJfF2PnCiseTGTyq0AYYQyHXjpJFfPY58QGjrI+UoHMdIOT7ypRh/Ft2IOT29RTTwcd3QMDGNJ2I7M5ovbskeADGmzLfTzHfnhSEYFhLkgVEgDCMZ4fzwlmjiY+5a2XXEdOC4n7iIMcQHApm8nMlPEwdR4gLLJCx1J1x33oRGuV4YxyO7Xhm3jb1kLmHA5IGXtvC72LjulefPn/lpW3l4fMc//+R+epcYCduVzJHn33/kNE3sJGpUT/bJz+wxoeEAl420AuEX5NzY14LZA61ceLkkTtPASZRhDIjB45hYrht5fM/Mhf80CNo22jGRX84cHyYeBf7p84W/rgvfz4nvv6/8aj4xTYm/XCsrJ1i+Z8obT2NhaIXxpKzrynudOL0Vnq7G5f2v+RZjEZgSzBL5xhL/9vnsPLO4ML0kP6C1+ni0CWEMvA+RtcHLnnkc3Z/5855J4sWzZOOijWYjlOyc1taoMmCWQYN3wNrDFlpkKz5+rHu3bBsC9bKCJLBGqxvP6rnvrWVqUy4tM4SBva5uoSfOVWvF1dRJA6fBreGs+QM3hPJzt9zf66rZkazcH2BmkK26f7C1uyUZCqV2fmBQxITVnBwYRZ1b3BTthcUQlL06LULCTVABiI+Us2ZKVejhBPQHXN4bMfp4tuSdGCK1NJoYViprKYQobCX4wxFHrpu45/Ke3a5oFu2iC6N1z9O1+aHcakdbxcUlVVyEZc0POEN4zo0Z2Hva3Kwu2LGUyQaj+aGcqzEmGAx0g2sThqgIhWWF8ajkpVCeXXhYNfgB32AvxsPg7giPOlNahlI4TIkSmqdHbsbDHGjR+MfvHsnL6mmOltwGLhZeflQOT5FtrQyTUkumxoTMgVwbUyy0raEHR8yxxPN1YRyUw7uB5w87SRq7Jk/qFPU1ru6mWWh93E/n8XqDpAJiFekJcbUaEpTdyyDn67fuUmAVTCjSnUBwxCp0Xm7AmxNTYSCwkXtTAKhRi/s3Jw2UUhHx+9WHGYgpA4Y0Izd1JwBtzovtEh+RRhJ6sSvOCTYXR1ZrjnQGXxOeltc59MGFX4SK9XXcmqBq0D2yFS+eqXRLtUA2Y+rex/nGtfkjr0v17z2X1/26e+YLH81Fam9oRBG+ZECiF8EiSGkstXY0VjvC5gluYUhOtTB4UHO0UwLnZgwinNgotyK6+eh7oLHs8JjgIMo5d85wMaJUchO2PTMEYUG7FZe7q+wNngblmitzFzxeauQp1M7xjFxzZYhOf4iBO9/crFKqseEN3GAbz1chRmF2dQIhFHIxDjEwGmzSOKsH0hwTqHhKGxWm2P23t8a3c+SntfD91Yvfp6KsufOls2HzwDstaAiU0niu8HhQHs3f87kYh1PkjRV+937kczZ+WqFmwCqPSfg3PxX+/CB8KU5xWGvmoMIxwccdTBPNGr8ZhediZIl8d4GnZPzqMfDXL7CEwMEaNYnXVOaFdBJj7fnbU3e+EG7pgn7Oe4O7sTR4myLP1dAQmaT6nnJfTFZz0d1Bq1v/Fbj0wnTqIVcN4xjhkp0OIhibCdfSOETtHHKfxuzNhaOlGWNQdnHqw1qNOYnbwuI0lQGYpbCrTzRmfRW1WS9urTVUg9vfGQxiXqCaYtWnkmo+y1r6JGDA/Yez3fzIPfa5qYd+SewWgpW7O8u/7/rZhfC/vAgMoPGRkyobFR085MAM9GqkWtAhIfGR5eWFQ/cBPW8LoYFFZRyOrv7VzINMlMtGComcYThXUPOE4VAJOrLWjI4DkisSLo4i7YWCMkXvhA7xyLZeOe+FKQhpAK1esMTnBRtGltzh9B3GA8TrxMtPK0kLlYAGoa4V1UIYQC1x2S9MIfW0uh0lsPTDOohi/MBpOLBUCNdMDq7WrrUyycRKYHtWzE6ENbPmDZHKEAaSCWv9kYMM/C/Nk6g2XJh3/vETJxm51oUhf6JNQlq/55tpYi6FXz7AURPPu6vulyaEywvp8chs8Ha4gIyUPTPPM6c9sp8OlPHAvlcWaUwy8cO1wGlieHnhUpQPuvFcN77JiU+x0l6E38bK+2/fc2oF3a8M6cSH/Mw35YG3Wvm+fmZR4ZACsWUephFbziQVDkGZ1sqL7fyjET6UyhxGruYcRB9Nw7uUOAxKmZQ1O8+yiXLQifN2wdTcCzokllaI1XialKs2yn5hQ2BvrATeSeCzZV7KxhuZPRKz7jy3jWv2QvgxRh7SyPN6JabJBVjbxsPknsAHAhaUt9G6PZk6z61Eylihzp5aJI3NDImVmGYolT1kmja0x1jHP22eBvI1gobcgwOcxelK99DcAL9hTMnVzCrNEeLq4gszc9VK53pdck9Uqzg3re4MmkCExbZ+SNW7G0GTLg7RrwRPppRaOvfMiCEwhZGoHguMCilot8tSzBpj8vnnMAht9YO8NEd/R/GCp3T+2XqTVjcvphFYzd0rkrjNk5aKqNEIxBRoOZNCYi+eypZSJO+NwwH24oIoQbiUyhiVy7ncBV6igSiOsO+1kbRz9CSyrCsMEREPWdHSWFbjNAovC5jsfKiN46DkDFO6Eq2hw8AYjfWcCWFkDn5/pApUp1a04lSCKubIEYUYE1aNeqluZF8aofn3WVtxN4BbIyPOCTeRP0h0as0tiWJyWkoNjdhc9Fetgd2CGRo0R9ZUOuevR9yrBRqVbO7mISJcxSdy3QncG5SQENz5B/FSXJNPMDwe1dXvdOpLVbCellgxT7PEKC0QNHjkcTOa+nlsX81tVdUL3+iQd7bq1Btz/2eAFHwk3/ral45YiRqhuQey2C0Byx+w/zFMwQ99DO1cdp/k5NatwsSFf59NCSIMZrwZGtfs9zCoF7W14ULfO38Zzmtj6qKhH4HnbecwRE4C561x1ui+v9V4o14kpMEFpa0ZGv19raX7BksPL5wOLpLMOylCjNrTUv3rOI6+9uZByLvwXL3hPMUeR2y+f4+DsnSxYe1TptrgUp2acArGpRmf604Q4VG8CFurF1PL7umSD0m45srjrFhufMg+Mfhur8xR+HSpd5eFWV1cVdX3zRAgbJkW4cvaiNb4JgJL44Lx8Vo5HQKfz5WNwocfjWkKbNXPeAtO+0hJ+avFw10eoyeeXVAH4gaFUu4c+JetMYTCUxe9fdrd4k33wkv0MKOF4A4QzT2rG96QBLp1W0etq8FLbpyS8m6ce1CFgwW1OWIcpCe1tVf7OwvCqW+OWf4f2t6l17Y0S896xvguc8611r6cS0RkZFZmVWWZhLJ8k40KiZabdPgF/AE6dE0LhKGBoIXoWggh6PELkGjS4GaDkbBdxq7MyqqsyIg45+zLus05v8ugMebe4YYpoczKJaVCipNx9l5rzTm/cXnf5/Vrfmmu5RWgrv01rMKAAWOX9VUaNYnTIdxsbV54NiOagbm0Iv8L13jdCtsZn0DvwnebjBdpW+v2WowGeWHDu/xkbb4ZenkfAFN8MYB/14++TpaBY3McX6n+h68Ejf+P168sjfjBFz+xEALlMnuMaAr0BnVeIEGuA7ZT1uvMkBRipPaVMi9MkonDgYhx6rM7lmNkSCNTVywF1nUlirJI5XQ5M2hEcmQHaE7M88z1eGKYItPuLVE6l8uFaUrIUpAQuF6v3O72GJWcM3c5uWlLArsUGJvwKJVdMXKu3O8O1MuJy+6Gi8LxeEQvncOU6dK5y8bOAl+EkWswTFeuJoQQkFY5DBO5XcgtM1vxQkx3xOuRxpWUMp86fLHLWG1UZqaYaWtD5ZkxfM4lznx8OrPoO2S+IjZztxMe1sSjCJfriWO/4Rw6z8yUplAOVBpDOvBx+RaxxqkI49Ipcma0zC5G3iWlotzESsqB2IFhz00P7GwlDJlPl5V/vnxLXjtx6Pzk/p59a+Rx4FIgxMqQMsNVCS8FVxSu/YKKMHRjtx8o18qlGaixL8k/IypYwsz4tM7spsyIm6bm2bVxTfyCXghkhScKVXzFeW6RJsa+RWIUxIxZwUphtsCigVA7MSpLLXjImx+eGjJf1xXqgqjTLC+rF2rjOPJ8PJJy4FIriwQOUQktEsNW6PTGN8uFnid2OrHMJzQn2to4Fjfn7dKABGVg5JvrAykllECtFeqVGkeSKH/84U9/Y9KI//gP/oY5J9Wn4LoZevJLnCw+7W29IuqGDdNOxOUNZs71VP3O7dsRpuSTHGieOvfCYRV5NTvIxkGVl0KaFxnFpimjYa+6Xl9vmzkyK9qLFtIf5raFbGyRS+QUfJW/0QZ0i2Lv0jevmTJvKY+aXIso+FRpXn2yIkGIUaHra3j0OHXOF0+wG7RjXUgRaqkgwY2G2tFBsdrZjzccy+n12tc+0nXGGLG+slRhF/WVwtA6FK3E6oX+2/2A5oIV2O92tPXK6dS5u81o7zwujdDcqa8pkSaYnwqSI8vimtfn5trWZnAp/jmloEQ6lx5cBNSFJpv2Tn2SW5oXB2qBxdpGTPDfqzUlojRpyAbN7+pmlrUaISrNGpEEvbsESbdEN9k0uZu73l8+Je/4tdXV0y8j0RPBXpLcmlNLXOKgmy6dVwQe1l4dPUN1OVuJjVSFxXx9vLaKqGub22YsemGHWes4Ps9er0l3nuurcc4NnhvJAvsXpBhC6+Lf/9aMeyHfMQJB4O/+/f/z17qX//2//BObFM7btG4ffYMxbZFYtXvR8WJKmmt/lRk0M3pvvn4OrqN04yEM7kpF6czdJ6Rdgk8St/v1RdK0dDfgBV9YM9sLHs0bu7qZq14kUX4LqdMH8ELm1L5z/JduxBTZb/e+iK/znSPuSWhFInNxmcSdVC4bkzlH4bh0RDr7AENKzK2+Nix3o3K+FNdvRy9uDqHzvFZElHn7ee+yMlfj3U6Zr42yGSDXraHwxLnGtRjTqMi2BnhqcFNXelBW4PODMiXh3Iwf7ITLWvjqaHz/TWRpxnoqXLZrbYzC9wb4Z8+N2xx4XgUJyml2vwEIywod/zt7iJwW2CVl6ULqjm7LojxVT8J7PwYKxqV0JoSjKEmMnRmrBgLCarDbvB9FlWMx3gZjbU7tmc1lLbVBt0bV4I0wIJuxYqJvBJLOGjOtdaz6f5/poIHFnP1eu7nmX50f3A0OG8nlY1dutlu3b5SMsHkOWu2cJCKtM4lfB3N3icxqW5hI92lv0NeaF1rfiB/+/NtogPTuxfVLgavWOTYv1FX96CjmRXXH5Ub/zR/+o3/p/fqra4R//Pt2O+3ZiZElMWBIq2QR4MI+ZzpemIQhMxajxkgx4aFdkcU4xsq+B1rKTN1XXKtEkiidZ1qYyN0Lp7MZ2VZaVaqCjMm1bs9Xhn1kNjj3wtSVlCb6emVMnSntWayw652skT8ps6/DzegqoAO5B7R2FhYmNQaFFHf09crHuvDVFSQEfnunHKswzzPDuPI07zhXNyTlcc/Hx5/CdMMdmee6UFpnGAYOqsxl5k2rHJOytkiOCatXKobqAcYn3l/PLDJy6ge+OHR++3qk3H9G6JWv58JUFv7t98rujTL2zHHd8+P7lT/+OND6wjfrws8uRo8jv5hX3nHis7yH0vjd98r39oU3wztOywNVhF0zrAdqO7O/vWGuhdjUdZ/bg/XDJfHx8YmSOm+GyLelMZ9H/pX322FfK1c1IpmpV1oQ1kWIqXsnHAd63lzvPfNsjWKNYInQoTSf5tfNhX6yQq5KHEbK5tA/hc6hD7Q6U2NCpaHNyDmxWuJSFpoZp+vCPk00bdxY4ckmgiplbVylMLTIw3okDJ+xTwXZDsNLXUGV2jxVSTsE60hQaq00Bhb1zyr3lXOG2ypcVAjWeQhCLoZ2l8u01hisIjEQTZgRpDXXeTbjv//Tf/4bLYTp8nrAV6vbdKu7yYfvOugh+qr9JV1MxNfDr3xZEWrprtMUn+qurXnUuHSfum71DOBFP/11YlrN0WO2TavEdEMRGXGLWA7bmrc2KNIYQqT2RmiCia/gY0r0ajQ6OW3JdOZEiWstG4BeXt3jdPXJoMk2jbftmWKIBLLYZvyBVr+Dsr8kno0JLmtlSokxC5aEMHdaMC5X126mEJlrYxgqQTOxe7F3vhZ20Yt0DUIIvq4OGlwao51afLOw9MY+BCoVthjXKArRo981JWJUR/31zvmijKNAVUr3plKy8u1DYT8oJRttFmI0ehQuVy8qSxWiGKKBtVa6uPmpNwghgnmAzqrd/512rPtp5kPZ7ZCsxpgile5Sgu0zl22qpNv33jdSCrhspcs2cdwK2ub/Z3pX4oa8c7qb86CjqE9nN4pGbRU2zWLckua+S4MLaHT2rXUvaKVvRetGsKhbQuWLDEK2iCozQ7oSYt941k4KkQ0/2AWCeWBIwxhSYK2dSPA20Dr/4d//h792IZw301/v8FB9whbFte/6LxiEpuzNqLCFe4iHR7z0HkJzOYQqDQ9yWNfvPDhNwquZCHziZt1etwIBYZ+EuW30CdyUFFWo+pI85wVx3SaWt8mxVOv231czDilw7b5Of5/9z+v293ycG1khB+XanC+egFUiWTzivIsnvo04uztp47z6xuXSPCnPv0fXBL8b4Ovzyvsxcjd60txT8/f3fPHJbYpwXDufZcHCd8l531464+Ac7yyOXut4Ee7sWS9CsxrHIhySMVjj3GHM3vD92gAAIABJREFUwqLCQTpLdY1zVpcThQ5fn+FuMGYC1oxrVw4p8E8/dd7vAndqPFfflDZNlMWbmt78OgwilCZcmm1mOeF9gtX8WVM7aO8UEYat0BeM5+af76U07rK+arBTDCxtC1Hy2n27HzepCnDuTgmSVl916GZCEvWoa5+jcKn+vKy1kQKv0clJlVKr64AluKmw+VS+AohyE77TO1/wZml8jcuG89ZUvRpvcXlHx30WKcBsPj9O9NdtSjPH52a8gRy28JO8yTlE4L/+x//yQvhXj1jWTFuqp5aFiWcpnBYlSmcMI3JtaAgsLVJOnWwLIQ2YRq5dMM30NpLWhVJgZ8E/zBy2Nc8ty/OFjt/IM4FYC6qReDPSP14o4mJ+fWrQOkXxKY7MiAYu55UsMwQofSFUX/tM08RlMaoYwc6EOtOjEuqCxD3WFsZ0RK0yxh15CtzG6Aa13PlsSkjLfK+tDKPr3o72iL19S0gjRwqhjQTcIf793Y5vzsZDnbgtCz3N7EZhl24pdGqNBH1Pj2+5mE92pAWebu45lgWxO/4gfM3vfl/pY+TzHPi9zxOBJ6YI798/c9fhZhj42VcrVQtBJy71LbthRZoiFSxMPFyfidbY7xq7OlLqTG+R0goxKbc5sCwL0xBZWud7w8Jnnw38V//gE//WX/0tfjdGar5iXTg9X4j7xGTCGlakeFJTt4CxTW+qYXjSmtGRkCnrTI8rIoFz64whUcqCaORWJlbtlFIQiaBw0wMmrq31KOaMYTwslTEoIQRSUyRnLta5S4mv6sh1rgxDYhyUYTHmqLzPBxbOrGTaWgghkPumT5fALEYAJLpLO+RAqyuLuunsKp277qt/jZFUhTcWuEqha2InFUnCM4ldAUl+aM8Ca1SufxFW8z/n1TAvADbHfxBllwO9OR80BDY+Eqytb01h3+JkOwPR57YiqDgZQVUwaxRTkEBpbgws1kgh05pPZkftOKvWcYfRl2nkFLjWTopKVuV8XdAAs3mQzZg8gWwXkjdheEx0Cs5yXkpjSkKrxmWtm3lDMKsM0V3izs40IkpKL4e885/ZDByeZmdIhCEqS/WHcYi6Tb+dVnFdvUBu0lh6pF06Eoxl8SQq1Uyn+TS1e/MUciBMhXchc7qurF3JVmk9ci2B+4OhYqRB0Nhpc+N+H+hFsBYhQD4oVpW6XMm7LeZZhXl2YoNK9KmfGr1Fl3002E0dUyGZUqgQ4DIra3Xjo5iwClhxyYIoDCFRxTW3iDNR2TYCleCOfdxsNUlktkaM301RBwnbtRYovZHUtbUIr5SI3jshCdoVkUbb9H1NIDaPoG3NtlHiNp3q28EsXnwHjB4EleDfHa7Xta1wHDfclUdCbzg12dIBt1vtpbEjvuh6A4MqxTxNr5l4Y6eKVb8+uoG1TgsdE0Wb0VfbSBPV//kXoBGufSsie6d12KlwO7rJaC7eNK5bUzGX9oql6vj7tqyvRqFB3XRlCpMZtYKI8lh8uFNa5TY7xqwZEJUJoyDcR7ZpuDENwtPcuck+Qf507UziNIgY4W4QPlRjl12XHdVoBESFA8bjCjeDkDqc107e1tLWjfshvEYsB5SkQg3KgQIEJHjjfGwe0HTsnfcZbrNyLlvAQ3TjXO+VKMLx2tBunGrj+apId6LGxZMyOASfhu4UFnNyxCEp+11iSJVP185pNX9uF+PaGj+6i6/0iRiUuXR+fC+cClzXQArwxc5129difDGBBCFo4NPFt5KzNd6pkg2O2/P3PnR+OHVyULIIsXeIyjw3jqttscq+0Vq6a8hFlV1yk3AIwoQxV5djLRa4C67F9+sJ3o5K34gYZZvcDtEL/yEIa3WeL8AqgVGNFW9YbrSzk85JfD8QxOUX5428MbdOwqesO7y4rR2SenS5Y82c4rHi3oEx+PYgtE6OtjVGnRwCtjVIsl3P8F2jFkW9ARXfkKzVaET/3qkb+xqy+ufrrGyhIlwNanF51sO6PUf+nPvwV9cIP58ZQmQpj2QJhBA4z2f2QalWmeKE6BMpJa5lpWMsT4vHhFqkEsjDjiFnRAq1Xt1oca5Y2HR1vXOtJ5ZWGAnknFFVDmfhdrdnrDPFVnZpJGef3k1DpkVDLwvj/YS2lWEpjFMkEXgj/uD4xWHmpiWaRYb7z8inC2tNjFHY33zJh/nCoHCTIdSJh1DdPBONcK4MeeLu/pbL6cylNoY4cgJueuHLpjxZpffCnDP/z+kT1oQolTyMXJfAp2XgdG3UfqbLyCErpQfOIXBsgsYDXx0/kVIip5H/RX6Lxw8P/J3fP7G/3fHtNx/5o+Pv8NnNiS/vf4qG7/HwdOHLzwbW6yMhnIhTQmbhsjYutnITdkgpfP/NnsvcOQ0r72473zwbVYSlF9LicclfP3xkiIndMJJ659/5m2/R1KnLykku/E8fA+8b7J6VKJVyNYa7yE1r9KaEEplp1H5higeuZkg0wvzsrvoa0SFR6sy5+I2zWxpV4NKU4WZgOV8ZYvMUmRi49kSwhtRKk8CghUtfKW1kro2bIfGuRFqf+awJS3RzVO6NGiv7rqhlxGb2UhgDVBqL4tPcMBCtY2pYb5xJdNvSrMxYAJORq1W6Cr24mSCTmK3SqayWmEvhXiJV3dAXNW+w9fbqqv9NvTx5p78aJLDOaenEEEjq6V2dRjDDcGqDaxmUQeNG63SNZG3+UFsBreLTMjWG6I78LImk0FS5Fn8w5+CT5N4785a8sG484VZd85aCGzT77BMhbd581NI8ctU8Mau0hkZhlyKXtTHEwBCU3jpLc0YkLwEiouwztKbUF6Ng3wpq1NPA1AvhpUOOkTH7+MW6O5X3WQliLMHI045aFnJomGSaFXr1yYj1isaM4vKSpXVsXVnPQtQCkpxLmxJLaeRQWFsEUZZj5eFsvN/BfHHJxzSpR34X41waWRKlCuPkRYOZsb9fXKt8Lc5d3pidHdjtAudrpzZhyMrp7Jii1kGD+WTetn0hBvjEqmx764ZCcO34luXqcofuWC0NIE0J6k362l/CSXw6O4TkRjlx+Ytun3OIW8qUNGL3/NXSGjcakCy0JqwBxq4s4gepipt2PMRGtho5+AbAAk3tO/KIwtJ8wi1N/HcPwaW7/WV1C2VbDXf8YBbZIoNlC1/Bi3C1bVq8aXXbS7KhgKYXLez2Z/oXs9RRviviw8bZ/3j26eIV4b4b59YJ6gEZ2HfkgMNWBL/ID5ZNRXKwzi+bMm4a4332KNw0CEWhNZC1uL48yStB5GHx+zWuThpYa0diRIJwM/j3ccW1oaquHxZr1Na5bivyfYSbrDxuSNF9do073TWodWO2ROm8m3zCfdp04Yu5rGUMiYN4USMmnq6WlLtJSK172qDBMAYvolbj7cEL5arCPghLE2ow9sEZxbskLFUYtXNaPT30m2MjSGc1P3/2wYkYbyO01Wkjj2fjjx8r378P/PLUOQzKm7FzLm5ii0tlCI6Ovdu74dcwfnDwIdBx6SxdyDGw2+6/wz7yePXG/ZCVPz03ijn5YozGUo1djnT179lkS4nbNADF1BGVwYtsFaOxBWjgspmPvfFlDlQVT7fbpE61w5gjS/VNn7XGEFyOkMN3VKAchEhjrvDF5Hg1CLxpMEcll86kLmcoGwOa7hKL6/Zzhu1xsrwUuOLJcn3T9ZrCWx/9cu7+HUT173gIsjGRZTMD+n0rrZJoVHHaTha/X/cq6IbGEFUPoWnOst4rftH/Oa9fWRrxt3/8lyyhaMpkNdZ1JUui9oUowpSV3oKvPcUIHeayujGuLRAzewk0jR6SsJ3H+yBYVg5NXY9aDI2BENvWsUPIjmVqFjmYsdsHdpJ5KFeGnCkh8XgunI7GblrIGvn5t4/sDjvuJPChrjAMxMuRc4jYWnm7m7iWym4IXFYB1FEk1qlhz58dP3CXB7IJtReGaU+QyuPVeKQzDHtqv3KvA+e2UpbOGBO3tzt+9u0nctpzSgNqypPO3LQbalu4zo+c1yeKjoR6YR+Fu90d13rmL+WMmevZztcVJHBOg7s16Xz9fOXd7g37Vvjb3wv8RK7EsOft4Ym3O+D+lvOnM5XAN+fAt990btOOZcr8crnywz5yqp84vJk4rcrTdeF/fCy8HQf+6v0NTw1+fl25ny9Yqnz76ZmPl4E8wDkaX4WZ7+v3+bYduVMYhgGRhmpmaZWpBIefi21iSShZeTg+U3NiP4ye+iSBts6MpnSNhKR883zifthxqWc0NOiNSQOfpcyyNNbe+b3dHe+ngdCvjDGyU/hp6XwpjSUlMvAmwGUtlCjU4iap1ByoejU3HEkPBHV2oW0IK1FfR3UUVMhdWBXW2bcHSOYocJTKnU00W4mtU0NkMuNDndnFTDTjuiWgVSKxzfwPf/LHvzFpxH/+B3/TrubOcTcsBerGFm5dPMpV5PXBEmWLVe0vYRn6GpHZzdfBtXqiUWM7bOn0zfjqXXnH1Kd81WyTYcjmKHZ5SxC/z4cY6B3WXsmqvnkoW3FirvNTIiFAr90pFPgESzTRWt1oBIY1cZOq+frLXsMX4ibx8LV4wjnBYkapjlAS4XWV/nx1g07KRi1u6ItBXXMK7IZEr411e4gPKaLiLOWHa+cQfYYxDl5M5TywrgsFI/ZOVaU3T7c618DtFPjq4cTvfC/R5viKDjsuhbf7wBCNtUV22adapwKjVlQDwyBcT74a1aHTi9Cq01SOx0KVQO2FuXu8qIivSeftEO3dsUOivpLUTe3dzGMk9DvnyYYSU0T8n22L2mZbw9qmJW6b1GxQDzQQEXrrNPMGzDYNquF/Zw4gFhEx1l48HW6TVSSDmUrAr5PeOzn6NekSGNewdxMPLdlS1IKrLXxiqOKNl70EaPjaNKhzrte2sYm7wiYD0WDb72EeJb4Ve9/Np6C3F46r389B4D/6B/9yHNP/39ff+Ws/MSm+7l0qpKi01snSocOTCFPYQmNEKHSWZtywaW01Urs75YsJh+za0ikZq0VqE+juv3BAiDleqheiwrctkW0lKOxTpmz3dwpCNM8F8KLY2b13U+K4+M+2WmgamUJwLWrtDFthc6rGTUos3f0IL4mUB/VAHAtexDfDDad0JnVCxUWVXfSivhTjZtgivc2xWQ8X55/vkmPG5lrJ2/NJDO5H154eF6Mi3kyL8Nlo/NFT437y7/Q2C4WteVw7fZNZdfOEukMWrovLGP6vrxf+4IeZ42KvK/tfXDq/dwdTUq4V9lPgfG1cL41p8Gn3fhB+fhYGYDe4rrh2IebEp2MlobTaac3jgiuBm2isG1yodJ8M79Sf512cnHBtbLHU/prEuJqwSHAWcAy01rfwm03qZl7sz6VuEhZlMTfBvWQ1TPHlHtjMm/gWMYbI3DtL8c+6bBr+KI1L6ds9Z6zdjZGtQ4zJG1CDxQKlNe6k0aw71UFcez1E4VqdcGEGEhPJjAOFRYRzU9JGnPFzwmlBQ/Bz7Lo1umpOyHl5zdvQycyfUVGF/+4P//FfrEb47/zBX7XnpTCFwFw9Orm0mb52DlMivnJflRRHSj2xT4GUEsdSiQi348hcC2aBqsoyV4rt+LP1yuca2B2ET+fCNGR6E27WC0PKoBfCNGDFV7QHveHKkdadg7dWeL7AdQp8OM3caOeL8YbZ4LktDCROWvjsMPDh4xOiiV3LBIXfuovcpIC1ESLcRJdd9HAhMnGUmXGGt3nlH35ofCoBkchFlNILz/OFNIw0U6auPNnKsRawQNfEYb1w9/kt8+XKqAO5Ne5vRs7W+NnXn3g3dkrKXE8Xvoh7Pp4euc8DvXemuxvuaufPWuVxhV1s/M50y6PNDEGZeuR+Gpht4T5WPswjNznzi/Mz62Ks7cKgkSqGSWI/BZbrZkrMmRJgOF9ZrLCfbvlUVq51ZYoZwaOySW52mpfCrlZ03NOL48HeD3tMGwcLXEMih8jcnNNoZpQqLCKs1tzgIkJrhRqFuVfWFpk00tJAY6CWB8Qyu2TckRBV9uNAaJW1VmovnMynioXAY7vwk7zn33ibKddC3LSpDzWg4sSGXqCoMKswFnPcH4Aqx9Z9VQUgK0ZkRUEDuVYqrqGqtdKDgGY+KLRLYYxuIBubMIweqV0Rxt7o2+E9mtML/t7PfnMa4f/kX/+bZrgzOptnz4cAUSNt7ZDME8k2Q9dcKyo+5Wn4Q9ZlAt8ZoFQcr1VqdXQaAXA9ryet+kPGuutMQ3ePk0WXKjRcgjGKH9q2ESqa+eQgimsLQ/BCJalzUXtvVBOa+omZxI0mtTVvjrvR1Vd8g/gk2lE8HTEPRQgxcl1XKsou6muaSBEYROjSWVtgn5U8CbErXWBdZtbVS77WYReVpTVIrjnvtqW0ERkwxuzyjxAD0hpmymHagiXoFKLHvKpS8IjcZVHe7DyINg1KCInaOlIaqxW0K3lI2FK4uRlQVR7OKxUHsfZBqFeoY+Tjp0K1ys1uRwsL33ws3O0TqpHrUigbMSK6WtS1t73TitFUN/askVP2ibo5EWKzhTk95AWPpl50vPBDA64H9/vGUXlm3iyVLQb5RU4e1SU1sfsqVMW1w8X87+m8hDVs2m2VbTotaPeCQcKLucu/c7Yi68VMGcNWjGNbDMiWfIcbdiR0FOdb983k42+5bzrXjVkqW+pccE20GCSNrBQ3FKrxH/xvv14h/O/9/k/sXoynzbW/1sZdhJpGPs4LP0xwasIhKaKBh8VDKpYXXXZffZIm/ssajqtq1qA1BlUWyZTWOGR95XRr8FCF1PrGmvWpnxe0WzhHimykPe6Cm6SqBS+EWmevDQuJIWybJmPTlCiXDioudziVxrtx4lRXTH0J/TZ2qgSXJpkPNsaopAC6Vh6acjsom+mfdaNJZDqX6gXm3bjFLlvlee48Fd9SzLUzZOFSjC+S8mGFpVaS+PsOUbkblcdr4zYK5y06/svJeNoimp+2QjiLT5SjCr+8FH773hv6u9E3JGupPHdhrI1zN/ZD4LHBlzcuK3k6tc3YC0OMXNbOuyD835+MbMa7gyNa/+hD4fPbjKjyfK1cmmuEx421vIvCUzFOFfIWuFJ75f00bcWyp+YdrJKkczU3MEaVLUXO75lJoeDXQDe/VobN1GYGl+rT2G5uNMtByK3wDYncG9Kr854toAp1YxWn3rxJlQASnO5jjbUZk3bnHEunS2Q219d7gp2yC+5ECNs5cuzGINA081DgXgpBHBn3omd/MQuzbX2SeljObO4BedEkj9GbJRFvrP7eP/mnf7Ea4R/eKdc+oC1iG8Pz8RwouRBpnPtKSgdUOncxcGTgivM8KTAn5cPzM3d5R4yKWmCKmZ2u7LLyXK5cz5FfPv6Sz/bveJddp/b1daUUI54Xks0c7u/46uET99PAaMpuuOPD0ycedxOhVN4Pb5hD41KVQyycJXNaHoh9R62Vz8eMWcPilRQyp7WBHTgvJ8Zx5GfPRuwP/M4X7/knZ/hnP33m43JlGAamMTPmM2sb2GsirYFrMUwDczAel7NHf66ZT7JyP0YO48SPysA3ZWGfIrdj4MP5ykNd+NGo3OTIrinvfvt3+fbPfsmPf/glu+KO4TAIx+szNyUwHiJDEN72lcO9suod4fkDH6+Nm7tb4lL43X2BeuIHdsbe3EH4kv1oNCscC3xcH/lLbw+klDmXTLk0LtE5uCkajH5cSBw4lYT2Smsd8orsdjxIIcWR/VUp7UROiVoGjlY49cJn/RHJIxbc8JjHgS4XP4SXgASlpBG9NoIoxyCcqbwpKx+l84/ijlqVz0f4sQyMkqCcKamgdC7VJ30XG/lY3cH8zbXwD58Ko0bmNXGwSJVGSiv3i/BE4GEwvpx3zEmgriwRaJ2JylPodE0Ubsh1IVUjy0IxpQX1LjdMrLaipfEuJ2wY/P1JojLTgMvakDjywYxMYG2VpvE1yec39fJD+5V85t+fuc42Rdn0wUaphYgyJMFKJ29TudU2h68ZdC+YDDcexegpRr6BjvTuZjBtSunNCQEWELpzVrvSxYgaGCN0FcpcSTHQu+sRc3TThTaPd03ZV66td6astGJMm0N9NwSGEDgtxSdVq+tOk6pHLBtuZgqB2hrnZuy00lRJfaMBbGu2YXRTqBQlSKNWo51dujEGyFEJErHuDRFW2ImCKj174WFqtK706szaXQJoSAQRR3sta2UchSkZbYXnGdZSGMeEaKFoIITIWhphXbiswi4btWdyDnRVxjeZT6fKFDqXGhAJUFcez5EhV+ZvV1Iasda5HK/c3UQ+HyfWYpyay9Hc+OKylKUZ58UL1KBuLkKDY/ZqI4fNMClKbT5RFfXNXO1+r0QRZDukqwk5xldOZ2kN6ASxjUntU6pK94CGDgU/2EQcqYXZq/u7vgD8m4A2oipxo4lIcBlLo6GaHDISHE9Va0fUI6pNBWmGvnxH4ivrPAhYorVOl+6+BVOnqgBVYJQNM1eg1Epn45iGwNpWTxxjixn+NV+7IFy6m4VmQENkF+G8LtxH5cm7UR6WhsrCnRpzD1jKXqTj1+TSuhe2KnRx34QEEDrBYIzhdZKmIpTaPLkvBh5bIzTXQkcRphQ5bNSKb+fOfXZ6hgKH4CSBRYS5Kjepc159AngYhLk7TWRnnZt9JiXlfFWmUDdTrk+WpyT01rgUj1EGdbRYVnYbzaRsF8RcjS9HNzF+rO5pOBWXCgwBcnIfBBFKbWhURvFJrSHcB39ehQ7n7vc/Zryfti3EJpd5rkJZKiPCb0+RpwrfXhrHxXg3wt6MVB27WufOlc7DtfNmclLD57tEU/jX9spXZyNkpa4G6qlnf/xY+GJS/ucnfzZPJnx8rvzwLjDcZb4qRl1WxuAbMYAU4VzgT86NKQi7ICT1cXDtSqkrVYVJO1F8utBCYk/nXfYJ+/PGmjb1BmUnQsryer/OFc6tkRQOwaOWo0KqxeWaXZnwQndI8aXv9HOGDg2+7d7s7tS4CyuY+w+ywow3ZmOMjFSwjQBhlSkmmnqOQd1oEAfcNNla5T4JGdfMXwwyLmUKbh+idMfFBTUeq3AszjZu3UlA1w7S/f2vf87M91cuhH/+sdHzyHq+EvOEWmW3yyiB8/VKrTfO69TEz89XbkLEzMX1ZV0ZV2NpnSKN1oyVysfjmbeHW4dEl0CSzu/sDmiEViM5TdyMnd06cXu3Z7k+EdPAeMh8kMY368L6+Mh+B6VfOPTAwsJ8PTHHBBoIy8qt3pLGwKVc+MWaOC1+yJs5IPyyPDI3JcbK2lb2IfC/nr/muB7R6cAu3HGpla+OF2S/Q+qVYCDrA7tcuUuBt2ng9w4HRP0BRjgQloW7mzuW85kf5MB+gG8fz/zwsz2lQgyVGxs514Xz8Zf88Hsjejnyoze3lLKiU6RPkWU2hikSzHVY7/NAK9+SfpCpqzG3B44D7KZ7+vqRH73fk7Lx/PynDDrwbBM7E36kA1062jvYt0gMvMmBoImggSKRuuxRFg5jQA3+wTlyXxLX9YmVztIr0+0tvxN2CAG77WgJ/JUYqOtE1wh4NHLKmXb4Ecuy8LPyzHme+fDwge+/vWVKmb+8F5JGdFGqRP5yb/yyXElEvl1mQtzz1I2vizBKZhHhm+vKX3+rvFsq71ugpoUcD8zSufTGt+VCC8JBlW/E2aPpLKz5gmln3Cv3ZLTBqMJt2zij7UIIxohwVUdQJWs0afTuRpsMdL26y1yVtc/o0NFm6BQodvV1cmmEKMzhQvlzJfu//kvxifug6ptfAvFFSiDu+HfCwubiLT4dO210fe3Q/UxhEajN2Adfz03RncgAtToWx7pA7EyaiASWvjpxoPnPmYbAunrSnBAIQbbYTmjVJ8dL9ZVWt85SXCeqmxFCTFg3U1vvxmNZySYclxkRN99JcLrFS3Bw65UxKVIV7Z2bppToK9qUo2tWTZmXyhADsQcKnVRdPnJdFJKQo4Ppl9Z9kl2NXeqcSiFJYr/h2BqdpXkYxFwa0+DaabGKxI5V55maKrfZNXpCZaedUpVvj51DEsYxcpgK51U4LcatrnSEVhNVjG8uxto8/W4IsBsKlz5we9f55nlhHJS1B37xtLLLgVMtZFMkGtfVJ75r23Bzm4FsESOYF8BDiq+0EceEeXhQNo+d3jxlm77PNqmDe+Ssd0p3DFq07dDZprJs1IcXlqq+/Pvtd7D6IraAEL3AiSr06PIL87N20/i6rjYSKLVvgQ4KzZFeg2za8O1nmThWIUinVTcESWj+O3RHLmFCCc2nw9WNR6FHb2g00GsjhLjFkweiGogS269/Lwsd6Z0UXYowBOETysjKXr2wuQYldPcffFsqUwysV9/kQEM1EhTO1XWV95NPwW5H5eEqxA2hVsU5sfsAOmZug7CuBTSyWGdU43ZQjmtnbiDqqbDnZs71bf58mQubXMl4WoWdKjEYS3d8mXbjYsLUGmWFJ+B0dV53jq4/Pa+d1bqbN9smPxKjaedbg/caWbtxm2AMcBThOBcOWXmSxNgap16Zu/C0NN5EuM+CRqirUav/vm92kXq5co2JN6MyqBvpTmvnhHFZ/T0T3FQXo3IVJ7GIwBc75ctBOFlnnyMPxfjFh5XP98p9hu/thMdrZZ2NqpUpg5TEjXU+PFROTYi4sfB7k7GWzl+5D/z0U+fzSTlW+OWHlZsxslwbJUSSNS6rf1etesMlGhjUeb+LKcdqvM1CwpnC1ZRTM4YuBCoXEc7dvDm2wJ2A9E7sjRllkMBxbRykUiSh3Ztb97BUN7KKMEgnblHWKlDxYvq8yYQ+S8K1w82WDNp751MTIBC25nYX/Ow4l0aPQhalmLELmaLCc63cKqyGy/dEqdJYu1HXziLCoLZdVz7ZHs0pFddaWGP0VD0NHKKx1spN8nvCmdjR9cl/zu36K0sj/t2/8ddtpjEUY5DAyaCvhf0UOV2v2DC567YLD+sFwkTWQIyR3CuluUA8bmgt7YneK88qnC6BXdDNBT4Qg7AuJ87LlTVkrBb2h3tC25KhD6yJAAAgAElEQVTBpHO9FmKMXLi6TGOtDNNAaw6WrnahmbKslZ6MIInRjKoT49JYWKhtYSeRKJB28CbD+xyY4p63IqjN7HaZgwhjvSB9IgeP/ZS2ghQ+uz3A3MhDcMavRK76xOXTwM3NhPYr0y5Cq4SmDDahN48sS4aitD5zGDM32siDUOeF3XQgaedYhbc3D9AHno8rt7d7UlSkFNowMUyFZe4EG3j65hnrIx+fT8Ru/O8fr4wDjL1zbSM3w0QdLkzWydOIDgOSlH6eSe2Z3W5P3vRxpe35tEZOZWYMiZyUrz8Gzu3PmJLww7vEkBMaIilD0gVd/bqobWaud8QAp+DoqLVXUk9UfNJQ68pcjbWtxBhRi5TucoPefUJb6zbBMSWZr2VXE0Q8sMRRMA02LSp8x72sIfhhL9EPNisYwSkB5trFF8Zl2wwdoftkatDIUirVnBVt0YtgT0QyJCQixtIrUSId6LUiprRtldM2ikPvrkf8L3/+J78xacR/9m/+LavNV8F0N7558enoLrPgU2l5kS14gTHgOj136rupo1QjRnk1k3UzcvDJYa3bslle1mrbRGrTM+aguG3P9agsnWFwJBE4loqN/jBE16hdS2PISlk7hxQpHQ/2a7Ld59vKW2E0MI1bvG5nXStFhaSwLp4uNdfONPh0+zBkGh2p/nMUj50u3ThE5bmv7Agb31hIg1KXFSOwyxGr3Seb2lmrr98/zpWb6OvE2zExXythcN1yL5XbQ8aujZjFpx7SGUaD4s72kDrXS+P7d5NLDIJwvVw9SKIYz2vk2GZuUubxUlh6I6eBdwdPwTqvimklhUALsFwqaUg8Xho3Q2aIyjeni+OzFJbmTU/xDgPR6DHW5tMTs07EI6Q7OD8Mn5JfN8Ndip5MWEpDxZsAFxUoCUG0s5g3BS+vF/VCUG92nB7hhapv9J1L3F7Qe9JhuwZFQNg0uRo2DbjSuzEGFz4UM7+KbZNbbJHLKn7t5a2YFTyx0IesAt22399/7BYJ8vK2kRcklbh22sSbgShbil1X/oO//3/8Wvfyf/o3/lV7rC4v2gbjXmibB9JMGL9cvfhPQTbEVCBo8whqMfpWPB2rMEYhbsE2xYR98uQvab46F3wC+lS3az9HHitOURA3WE4Kf7QaPxy2RhfX9J66S6f2WUmtc1w7dwm+WuDtTrEO7xSet4lfBw5BmaxxUVCNZPVp/9PSmczlIB/Xzjgk6lLIGcSUNzvfPly78Ti7WTBJozUhD4JdFiz7hqphfJbgWN2Adj96sXZpcB+NpQeSdj4+V+73kY+XxpeHwM/PlXfJtweXZvz2TeCfLT593nWfkObkE3uzzl2Gr46N9++SPyOk87Q4ReGpK3/yVLkthZtd5uvjyrUY91n53beZ02o8LEbQwG0UggSOayfnyIfnyrtDYp+En35auQnQNPq0XGAuhayBFgau20R8r25CG2NgEeXSOntxP0VSY2/CL1Hexs2MWH3Td6mNvYBpAumcOhz8zvjufvXL3yVq3f8X9AXnbczNt3itO2O+4c2WACcTRnk5C1yXnjacmWT1oJy+sY8FnkvnXew89UDe5Au2DVxE/L6Q3mgbZq29GOLMcXpd83Y/8/oOIs2HnpuULqm/q9ngv/3DP/yL1Qj//vd+bDUIQxOqXpAKIoFrzyRdyGmPlYr0CmNiMMWCa4nTstCscxN37OLsxXGupCy8KYmHBvvgyLTSC0GFKCOVjoZGv1x5Gw+u71xXDgcvYHPOnJ4vHA4HkEIsEGKBayXu4NQiloR9jCzm8b+7cc+xVMbeuUmJEpwNq0G4LJDaQp8FGxpD8ImWtY6QkVLoQbghwjhDn9jvr9S2stsN5LZglydKfIuVSkrCQQfmGLiej9wMe67hyF6NrgeGWCizX1T7KJxwLMzTU+D62HiYA235hu/91sAQOt//wRdcbSXe7fnhYYVB+NM/+YahTbx5Z5yPnXe3yioHcr9A31OiUOIRrY4tY5lYeuV6yjw8ddriIR273YKEW2qtHOuA9gXiW47zA2NSplCo64V2jVyb0VryfcW4oDUSlsAxOd94vyrz2HhJJb2sC6UqneCWdDptKzR774SdIhacECGBuVa6qMO8K6+a42BetImI0wZaIWxBC+Dr6dYaYoFuYet2Gxo6c3Ouqxr0jSO8mCGtuwaTSiaybBHD1QLrxjX1gZkXZ2voDJZZzSczHbBWUIs0hSad3IWK0aXQqvBf/PQ3F6jxd//WXzNUN+3mhmZqbkK6qjGGRG3VV1sbizfgjOCmjaXB0JQ5dqxsul2NjuESofZK2qQuhrtzPdnNOM6bHlQ8Cr3bFlChwrpNHdWEKQcWrdt3GFHrLHgRG81o4ia7qMpavZjvNLIEpMuGiHKzXDdfUB9yZu0vk0s3uQXxgITWzNnVKCmGzQTljNsX3uTGEyCJcF0aRJiyMMrGTMXX/61U1uaFpHUYcthCDGRLiStUVQKRx3Vm7LoFRXSuzdjHwBefjSynlWoNs8guVp6a8m7qBBOOM4Dw/9L2bsuSbcmZ1uc+xphzRsQ6ZeY+1K5SFdWSUEvdXAgDHgjuuOIJMIyH4A0wnoBn4IamzaANE02ruiTVce+dh5VrrYiYcxzcufCZWVzIGqxKFdeZO9eONQ8+fv//71/S4GaesK1hh4L3geSEFccuQm0OJSEDfvNSeXMDv31ybk8TrTfW+gkXpwwzchGyRzjnOuLQ8yko9ikwg4ViO2xgqrgY2YIwkndvsO4Hnm2EDURHhEtrj4ElSKR7iI7wAX4ieUAcID8JwrpbElQDubbH7kkSPkYRPhMs9BMlJI6tBLkk08XC/4mAJyptZ/1CykZve0ucGGYxjGw9AqQqQa8QcVAlhRmaIcHZ9h4HtsZgITE8DgBmTsX57/+3P8wj/N/85Z97lsgOZIWnBo/mXEX4IU5aFqy1vXxKuU3OhsfQ6s6lRbjsFfDbJnyRAo0VOEHwMThMUYJiEna0NxhbVn75VDmVTBbnVD7RcaJSvLbOecDZna9m5d7ifv2eDN4pHt5SEydL4tI6x6L0oVx6ZzhMalSJIpxXYlwpn0WKV8fM1j3cLw7Fg+v/6Tq8tBj8D1k+8267wdadQw5bzdWMU8n84tz4UXZ0yliOQ6aMwWnOfNg61uM5WNyZpqBWmMPdEnzZ0R1V48PzwINQyDDnN6vzJ0flP/+68NvL4LJj524nWK+DH9wpG8Lz1bga3ExCOSQ+dvgmx/CXNbB2zx2ea9RdX1z51QfjTx+Uf/du8NVNJrnysYVd5FQiVPeqRJblfRe8RiDtk72lWWy1No9/o+2HXPV4tq8U7jM7lzm8sqPHn/3gwo0472uEoBecqwfreRLdlWdiawNcLA6JM/YZu5YlUGo7oDEYz92j2n33XIeYGVmQaX8W530jeJMGQzNXD/zcfQlG/Y6a+ezpvXTjkGDdPdP9k7VNjKSZTlS7g1JKJvuIMKNHEPZq4WH2EYzt//Fv//af1iP8X/+14Ja5zTPDMr05rSxcLheMA2qNKTnLPDHnwaCQJ6GUglmh1oqmgUjm+alyc3rYCQlnpiTc3wkpD67boDfhfnpkOijrKJyfE8/XD5xu4av7E99/hDQKl77xo3+xICJcLpmxDp6kYtfM5XJhul6oF+H01R0v313J08RP/uKeX/7skXLonMfCqBv3N7e01vjiNCFSOf6wkNMNnp6Zc+F0e+KyGak3bsrCtcF0uKHQsJ5IF+FwSKwLtKcTM1dsX1EdbgYXn7i/SWQ6X85H3K6c8jnU8QFFbC+02BPxP16g7CpJvoe810npb8NU6U8ggnvlJz929Cx0eeH16yPeO1N/oTcn2xP+seJ9Ym3Oi2aMTiehVqPbe5ogXaE75hvJlVTAbaGOFyxP4M6lCe5HOHSWlHi+DLImvCda71yk8/J8oZRCjY7k8OpZsGc/7UKjtc2w3cc0L4pUsGT0YYyxgaeAqCfFe3Bmo/whcWnxYE0eylanfw5QqRYilmkB4VfQHYeG2efEuU5xwj+4gwyGQKdgfQevBzsihp+9oIIdan7ojsmViajrdRx1DaViVGSwh5FCnfrUSPbH+kw52vnMwj+VNNLvUpTcBp3OvLe0FVGuw5Cs6AD1jKtRdjRYxTkl5dwqmhJFHKTQhnMswdZMJVLuzQJnpZ8JXJ3cBVToNnZvdPgXP9YW4SwRsnbmotxkYTWPhqUUXOHWjFRS+P7I5JRY3RE3zBRNBp39oOJMwKqR7mkWK/OSIhC3SKEzgoHsQk6J1p2qTrGCyGAqCU2OV8eSctAcgHl1jqrUAZM4t7eFp9XwZnF9t855q9S9lGEuytN6RUy5v8+8PTeSKg9Toa4X5MWZU+Zh2RPV1fjpjfDyUlhKpU+FzRqZRl0yW3X8amTCr7h5fF8Q9o77OyGtwtMWL4XeO2M1zC2wR65x+GjGdQzmktAhtBabgfSJyrHbZXYaOGIRbgNi4+Od0aESQZfETh9xI1u8qOoYqDg5J/og7BHmFE3hDwY8RfmGsatPu+0F971mfR/cU9oPXLvii0ZQbBC2O4ett8Co7T83ElvCoXHQ86ph+fGovM07KSYlByQOAhKeX0anWQy+qhK12R6HKBReWtwzg/ChCn/4efaQI4jaTbmMsAF8leGLIrzfBtqulKkgLixJ+X41flKMp53SfWJwfzjgrfHjNCglcd4CmzYlY84HtjE4FeG5wYMOfrUZr2wwSwTeLi6cx+DdcL4oCemdSx00lDdqvDsbv+yNJLBMyiHv6LJumAcP2gnO8E0R1hEe02mamJrF78eFO688stuexgjvfh9UlDF8P3wIYuG3b8OizVRC2V6b7XaY2FocSyjgt+KcxGGCsTW6CPOsvO/OXJQ3N8K78+DjEG5y8MgfV4uDJLAU5bfPne7KX94V/v6xc9DBP79PPK2Nn6/KkoQf34a1Zls7X/0g87ePzutTQuoI24Eqf5qVn12N79YY5Mc2SETldEH5xQp/8SA8ZXh7Hp8rrH99XTmbkga8VOO4ZN5txmOHrxflNw5+3XD9XVhxTjtpwuDcjWOSKLCCvUjDAr23W8yyRhHGAWNKzm0SnmpnynAsheueFeluLBnavg14kBhMh6fPanH3OOiqOp0oZsqJMC2NRjVnkyB2pFrRlOkIz9fKrMLWAkE408kOzx4sf9FB2q0+zy02OMXk83MoNibClYS1K90T2aIP4GxxPT/3GJJ9ODk75+ZMKZP1H7sD4/N7K8L/83/5XziAzM6SIg2dpkIfKzLK/j+1RauJVrJnzApizrp1kIENYZmVwzFOimqOl/0hZ425HJgtAOYlT/Te0RTwd5HMNA+SA+fEWo3eO6UfcH0ipUIuA2Ghe0dTQuXEsAvWD+AJz85WnxHt4WHNOdaIDvggT5VJT4wxePy40drG3Wmm9c7N4UjxRkkTIoO+KcgAFaSvsKcqvW0gmZw7RZSSMqRQPr0nRhdEG8c5U8Q+173kHkl0d0emNWgN3nDteIrmoYGTLHP5/shvf/GRD+9Cfbo9bpTbI6rKfL9yXF7FQ1cuYDf4eqaNjDXlulXWprw/O3jiXB3zStKZl8szHWVajowRPtjiG1Mu6GLUbTDJjAvBSe2DlHvUZatTHdwTVw/G89oADdyTZ2jVuWyCFguPdg+1KJdYLevniz8eFgywJFH1myYueUNtjiWqX1l2pEsae2tUOUStq8ZDdx0Nkxzr9VrB47obOUIceQSZovpAzKPvPIWSbGZ0sXg4lIRW5clbGPn3wTzb/nvVqLD1pBSL1rzVBnPPlFL4H37xx7NG/Lf/6X/iorFe1r3tTP0TH9I/p/JtH9jN42cn7WxYiyKNRISYSorq4nCdhCKXiVVy1uiaL3vYYcoaysSnJD5RBbxV4/ZuIdVYd1ULVuaUYMWxZpxKFAi4Cbc3h7AOJMFGp6FhpTBjKZmLNYxIITvCuY3ook9KzplWG26B5RGApKTuXIajCZJBnuF6bUjKu1Jo9L7LUzuySohyhuGGrQObY/A75czhkCniXFYoKTBNB3fKQuClcD42o2jiZjGKZVpVymSUSXg5V94cdbeWxOVwrc7xEAUU0zQYTXgecRi4TxNb20CV7z+EnSxl4dVN5uZOePxN5SyCy+BmOvD9U6P6CE+dD9LOT8jqdFfmpHFP7QqLinKc4FqjCDvB7wowdmuMpp1l6vupx+N338XxPQHubiQgpcT4VI3rwfYM7NTvGgB9/29AqMDuQS9BJdgWO5VCiL9vu1IsO9+3Dafs9IsYa2OF/QmztvYYoOO8mz7j8OKYvKvEu7o8eZQfrd6B8EaryQ5BcNLum8Zj/awpNKT/7l//mz/oXv6v/tlPvKgg1sgYnhbwgbvRCB/zlD+VUAhtNOaUUE3k/1dttKkgnjhk4VI7LwNuc1jHRIW7JGwSJStLFlYPhvVkju6xv9pDZf72vPKffXnPt33QRXltg7+5Gg9FSMN5u3UeZuelOk+y8C/v9+ocjba4ZEJNiQKUIuQudAsFfnLnw2qcirLkQtHBxxZYwkM2NlFuZfDOczTB6Qiecmq8vxqWCuKBYysjvMhFYJqUgzougQr8zbrxA3UuPvH6mLgpwRL+zRXud8TcWeDrRThfBo5zvhqvsvHmqDynwrfXzo+m8O//7Knxk1cFAWwP8V2b8+agnAc8HPcCj2vlZpl4KQlvg0Xh375tAKxl5s/uEz+4Sfzrt50bE5oNHhbl50/BYr8ZoSRf92u4pGhUPBWhWjCy+3AmVY4zfFxjq7VoHE4vvocBUW5SoPaaQxF2vF4OgcmC+FFsoCmG5HM3FklBi9AQF5zw3McNC6Zxv9lOqVAVVJTJW+DXxLlIJqe8VytHmQXE/TiX3xW6JCGY7BLP7m9XY/LObU5ULagPXkZ4uqtkDnSq7xXsOUfWpTUGfN4cNot7fNH4brf93zmVAsD/9LOf/9Mqwq+/WFjXlfPzhauGKjP1GifFuaBJcJvRLDxfBolBPiReHp84njJlKsRDqpAyZA2lkQGttUAqna+cXt+TmUgO8zLR22BsV9bLFb8W5lwo00TWjTkdEAaqd2zbSvcbVB0lIaps7ZnOhM5XbAienTJNuE+I2Wf/mu0FGZ0jfRiQuLnLGHeodeZFUYOtwnYemFfEFVWj6MzogkomZWNOB3LuJMnBDKQySwznlkN5mHMheacIqIdPtlmL1JJ7dA4WCTV0zngyOAgpK0zO8afGn/5wxqzhHoEV2SweqJdTrEIdrrngemHSha1H4cTwIxyMu8OgViPGygWVmbuR6UTyv9uuzqQT1kN1SdlwSzQx1JSeJ9YWL8Fna7StAqFqG0JaElvbSPOEamaZCzpfqC3Tew+Ydo6QhGTFVRm7CX8Y+1o8XqTXuvH2XLluicygFGdyY54H2QqtdaQGwgyBNUMnTPPRmlaYXJCU2YiGqSUL3gMBYwlSLnFq3QfhhCAJjkPwh5nXLmw+6D0OYZtE4EDdOHbbB4RQoA7dqXP5HAr6Y33SDvoPz6UzSQQpUnKExJRj/dwHlBnqiLTvIopoVGUqEdgvGNWJQ+zO9ZyL0rsxKTxtHVEhi7DRqU1JbjRTDiW8WW0EwqyfK5aFWj1W2R5tdyqBa6s9kvtbaTxvVyYpqEM1Q0ZjyolqzvN1pWgo150okShJGX0EMaK2XemXUMRF2LbOPg2xjcGSFbqwLBMdY1vhOE+kNBjmlN2f2i0g81OGp2NCqrMUpY1OaYonwmNqQs7wfhvw4rxalLEl3pTE21a5Pif6oaK7vzzVha/vCvOSWc+N1sMHuNXBQxKeh+Kb8lSNzYxlMi7SmWfIm3K7RMPiLy+Oa+b7y5UxMktOrAa9bpxK4mDKpOCuUTyQnI9no4jSbF+1mjGr4QYfVw/VxMdne4PBjsbs9BbXl4kD4/MQnAkcXWKAxljlfWCkQJ6J4eOz/hv0Bw1OM3xaDoVfv2tY7ERikOrmuMQ2oYjQzNjbnxGgjoD5ZVEcjY2oEBi+1HFPu3cwNiO2iyiBBfT9pa54Gojrzhy1nW9OeKk9tgpJ9HO18T/V56Cx5u8pUQfMOqheSAzeqO91weHDvstCyzOC4tK4nWaeRnial31Av3bnnBa+KI57426OQgun8asX45T2OvLdY/7WHTflq4Piany8GofDLf/n88pBnXdNePFOSzOXAQfrLMn4+zZRMtyPyi8+Vl4dFo46oDbets5d7nz0mZcB38wp3qs+2DxKq3592RA1VAYlxfeuolRxvq2DY1beCEjr3BXFKXx5Ah2d72vmy6PSWid1yGpUYjBd0+CLIvw1mV934U12ttq4SqK7szhctuCOl0tlXeH2ZuLXmzGdMtfzxr95dv5kuvIaQ73wNIR//kXm9pj59mXw2Acv1Xm3Dv5iyry9GN9W4fvzwJrx1anjpfPNQfm3K+RZGQa/+VC5JfHhUejd8eNC6oOxhU3jNCfo4YX9OsV18atzqNrXAXMavLS498yUt8/KvUYj5MceV/kpF1JR3m+Vj924zft94bBk6MNwcQ5EOPpZE7c2OLfBe1k40NDRuADTjrozUWaN7egnfFlHWHvjTuEuK2uauJmEpza4J4KXTwiX7pzk0w3jjB3PhsZW0iS8vJIz99OOvSTq2xFhNt+9yWGfSj1oItm2vSo+tkdFJQZvicE/uXMzTWzmTCJc/z/evL/3IPz+/XtsRA2fdThMhdYH/mKYPcLeWNN7ZyonNltpWwmPpTv4zGiVPCVGJao4XUgqOJW6e3sub19CbUsJLwndKmVOpNOCb1fGCA9wd5iXPbhD47jcYLLilhCNi0dyrAQ/0SvUC8075jsuxyXM2CXTvSEu6Ih6zaaBdBl1oCmz1itFEoPBnA+AgYQn+ngqpKQB8v88iA+kG7POsV4XCZqAZHAnaUFiF8lQo+QTFEOsxr55VpJ30IZIiSyJBb8XOyNjQi28YJHa/6TERHXyrXoYf8ZElYRzgV4QhWRK8/A/d4dJC703DilHGn4Y1Tvuyqjxs0veebIa4YY6OskTRxGkDcw7U1F6C1Uw57KrFrKjkhp160zBmqLkSIqudYvBeWRyDmxS753smQ+2RX1jc2bgm3nGZo2XpsQNto3BRZzDp4fMGIgJDznqGRFDhzJSCQXUnZNnzBt1M6p9slAkxOIaaGIEumRQTFlt4OvgmqO62mxvqhoOSTHLDN1wC4VsDKNl+YzE+WN+5knDC1YSU3c6u5+VaCDqPdZZc4rU7dhb5S4e16JYFDfUYcwqsQ6XqMCUPamrImwYp0Om1sG59z3IFCv/m8PEMEdHYy6FJMK1QzV4fVBe6r4tEZg7aM6YKtkds7IjzITriIrMw6FgHcjQR6io1QZJQpGM0GKi+E5DkHggiiiVWH0OG1HN64k5K5LCO7puxu0hY92YLbGOBsm5KcRhsCQurVHUOR4ikX51YS7KMjm2wVBHzXhIhqWZMTZu75XLi/Gj24Xk8PYJTpPiSZi0kRDqc0OS8vpeePk4ON2XaEsajcdNOF82/qNT4jgrV89471g9k+eZ0+nA696w1jlOhV9eKnoI0oVI5n1vNIsBdzoqdNi68DAvPLcWQ2V3JCm2X+Ofem+E3V7jAPEiWnKJ4IpGpbXLwCV9bnNKu7J8KjsqbfcFmit7f1q8ijyG4QL0TwZg35Pd/snvF/5tz/FMqt1x61DCjlCI0FrzuKZdwsphWCjTKFffPge9SLsKrYkkOawREqt3PA4IZoljFpqH55W91Q7C519M2LQzk4KdvWcH/tDP7VLoY3BXYmO6uiDdgcSG89QteNc54Srk2oMSgHLDhcZEzpl32yDtFdIH61wNPGV8QEGYXfnhTaZ343Hr5JQ5eGeaJr46FtbhnLtxt2ROBS5b5sWUP7mDx7Xw493+8DQmisJfywibw5iYk3MzOdcKngvfHAvd4GAgzbkQq/yjKocSTN2HaSZplDl0Av9lAkczyiy8651TVkwSZQo/8rU5f3dx/uwuBq6zBM3h1oyvToknz9ydCpe14Sr89EH52JzLajwcEjfZ+L+enAOBmTucCi8utDr487vEry7Gj98Uvgb+/Qfny1PmaXROBUbO/PI8OCj82ZuJnz82/vTLiZyEV4fB28eV77fOT78+cDgkGMK5x2Hqi8m5XzI6OpcBp1n5uw+dn+rGNBWGOayNMZyPQ/iLo/A4op/heEjUNdBj1xa4vXXA6IMDwtXChpe1sJpzsXh2/uB04joGKnAkfNEi8Z42Quw5t87XJ2gjkTTzwwRuhVQyzTwKiBwmnGMOa8RqhmrimylyN99X4yrxM93mgk+Z5+vK931wnCdEoy0wi3Ouick3thFe3ghDRgPdelnjeyAshM82eDNlNCVOXtnced6DglPKVIfbSZBu3E0TjdjuTESgzlW4DuG+CBdzzNPn+/kf+/zeg/DxYcZGpuSJy3pBrKIkeu806xz0sK8podszJR9YZqG2xvaiZDdGW9menijpNU/rmSWHSjysxjp8a1wtg17xkpk7yI0hLyB5ptfO/auETsLtzZHE4MZiqHEbkYiWULmCIKCkbLRtCjhzc8Qn/FPE2Ynu6wYoERjpkdjsW+VlayxlgiSUXEi5c1gWvHV676QMSROahK1WRorEZNaCbReO0wxZ6AzwvB8UGt1SbBpNkGpocpAXtCvuDZWE9x65snGAtEXSRAZSDLSEOdw6e1Tyd59P9rpa6Xs6dK3P1Doz1EmeGD3i+SlnZu+M3lHroXiNQU7KNDqQaEkZY9C2fdZ2p5AR3RtjXJFhuMWKtW3GIPw/WKhpbobk8OeZDUwjfJV0kHK0cMVV29GxYN0xGTwA/+tv3/LXP/gBAKM7Y8d5VdvwHIoRmthaA0L5STilEegzDT9sTzU8x+6oha3Fc+YkwtDg0ZoEG1V6rIMdx7Y11qMirOfKRRIQbTxVld6iermnDffERKiAdPDUft/b7f/3p3aJ30NtIPEAdIHZEiYj/LGaObdG6i2MkCwAACAASURBVHsxQYJiShkwL4XH2gmikHCYDlzWHsB+cWwIWQaTF9ScZVJqV6JuRDATtt64thbKFbGaP5RQ4M2EY2a3I0DTtPu4BpISqg5iPNe9S0HCP2nqjBarNhWYVbgOxSWGfR8wSwY3JAfaaSEGqpEL5qEytOGkBNJjIFtK5pAnyM775zNzLrjD42Y02Xh5EXoSbpdAmjVTTAbtvPH4EuzXZPCuRemBbo2O8Lg1SlGW88alwzRlnnvFmvKkkGuEtq5tJT0JRZX1sdI04dK5PUzcHU/4jfOrszKp88vnzjc3C0UDg7YU4zQVWgfLRqOjmpHWmQVujgesdmpzruZcW+c8Vg6pYCJMU6LuKe5FJ6pHXbEm3TFp4aW17qyjxaHDIihTzLgKsUUjfk+HrFw97n3dVVRV5UYMtYknerSMEqSd4hHkMbe9DCKm71lD2bXR4+fUKFbIIpBigI1q899tjDrCpyo9U8OHBoGkG5ModQxE+k6TiFUsKlEk49AxLjsezCV85COUBAQ4+yC1OPACe5nAH+73Ly5MZF5a1Jqn3azxosrXMji7cj9nXq7GVj/yTu/46eRcgV+mha9n5bvz4DRFZuJ+Tvz8PJiTsKgwubNZ5SUVSs6UYhynhVurwMTmyvdrx7bwWTaDt2vnR4fEq+KYFB4OoLYCiQcPa8V3q3LMEUJzd37+3DmmGMpSj6PP1TJpDwE+LM7WY2Mk5jwPsF3PuaGxGYzUeVeNr5YFI2gMF1OGa/QPAPezkorwekpc3m+8XmbElV9cEw995W/O8GN1Difh7XnwscMR5+8/1qBsmNLVeHs2lkMi7frQz66Nm6L8XYWnzXgzw8fz4GLC3bXz8tSYkvCbNa73Q1H+l28bbcDknX92V/jz10d+MBm/euyk5PzNt42/+mqO9sxqnCbhblbed/iLYlTLTGacTVAZHE9Bufr1iC3Fb1bHPp7xww2n4fzkGD0FppnpIEFC8QgjZwZuyivtNHN+cW68KjGPtCQ8jsqDx3aguTFL4NeeN+VBnO9GkCZUlUsCy8Kb7tiAOQXHd6PtpTfOxx7zURvOpMJRnI9rRTUxsmLDOUgHgZceB8+UhEwmeQzVwzoLTvG9cOoQwVBN4NV57pVuzlsRSokxpiF88KgR39aoWl5HvOtAmQlL5WWLLe/b/TCcZXzmUv9jn997EH78tpKzYeOMpsLYjHZujDEwMk3OiCQalZubG6Q0PijUKhwk0b0xysTp9QMAPi8xPF4q2Qg/cIbpWCmTglaWZeFmOURqG2grFNuYbGJ8fObj+ZlXr+5JOjFyp7agOyBCrSvGXtGqHd8KXh00INkiQjIYQ+i5sbbKaT6QNZLJrsLt6UBrjZqc4ZXiSm2XWKXuA3DtRggNhrqGn5SOpiWS7mZgM713OoKMeMlpjpd2+NMG2WNFkqaZwAjFad/nOPsgOfzEtgbmSKJXmwZiGfeGdI2dpQ9IiVFjYBApTDNs2wCJpHAbV1RmtuaYhud77Wfy8cQ6NnrKQAILcP16bczzAR/wQqNYAYHLGtYOc0NY9sPBjikrymKH8Av3lWGG5gVG36stw7LQsb0WOIIVXjIMcO/81Q++orbwdzd3LMXKvKIM60wu+0v1yFBjjMbwjNVQP4c4zkquwXKdJNEIX3OxzpoitCGe6WtUgo8o6qSxkTXB7t0r08Sr3TJiCt6VoR3mBr4EVcIMT2n36UZT2h/zE01diWsbTBrBE1Glp/B4Z/Z1eIpUWxqxLpMUBRPPdXDKEWyqDHoLyH1t4cdPKTFl+HiplKIky2SPpYV5vNjWGoSGHidLBOGyNuYkvPTBMoWyZyIkEmsL69C5VrIqiycOu7pvZhhOw3GHrcXK8Jgy3Uasu91BnLEHpayFclAZDDOkW2CGRgBrt9rpJpS9ynStLQa2PGMalqJTLpE01sG6GlITS4J5zpSmXFKgpB4KhI0iMWWh144koXhmPigv140vj5lLS9yWA8cMc3G+fWzcHYHlwFadQ25wiK2QysLhlOIZIJ1HM74/V745ZT5ug9aNnI2pTGw1rFl5OOfReV4HCNwdhPfPV6YUZSfNjC/mQrNMa8LmHTHHu+OqbNYCLeYSRSq+e8tDIiZrisDK/n1bLmTf7QIWxQ7m4CnIIMkCizZwHiUsV59qnNsw5uw0DxJF0iCyxMMphlKVHLXIDmgIGusw5hT11lH9DIc9GNuG70UmoSCzB/pEnCZOmuLg/QkTNfaikGqhbCffW8pSNBeagA/dLREhLpg4ZsFETrHn+ye4X4XbCb6/GKcimDWKKq8+I6CEc+scMtyWE6+JQ9fDlJgTvL8aD4vSBpysMVXjR7NyacZzD3/0zZw5P1VOs3FJhTknsPCAizWe2m5ZqT08qO7830+NuwlqfeFuiu+6aCJp4t01tkXPl47mKLO4XxRpPWqx+54DwLm2sNvMs3LtsZl1DzTd1uJ6qB6e0Ze20S2U5eOSeNeczQYvTfb6X+Hag3OsAqelMI8rFeWLyWk58y975e+uTiezFPhmFqoqcwtR4H6KjU4bylT2YBrCoSReL8qHi/Gjm8x5M744yk6PcX72rvPD+8zDbVgiXh2Ur+9kb+VLfHObWIkij8uT8d37zl9+WfjuPPCnODycirANYzbhf++JN5fKu0vixYV/ceO8fe4MdWw4z33wJ3eJ8wq/6o1hxnuNgoyDKt6DruGuMThqtAZ+bOH3PuVMV0eTcBgr35SZpzaQLBw8k5NiwJ13ruhehRxZnJveuaTEowWX+n0bHIqwEDQglSBEbPZJmBDQsA0tKeq+V4OX6ryeNexOBC5tniYgeMiy02q2/X6ttVIYTCY8ZGiEpQaJBsRZIxxYVAOT6MKUw+YzgNXjZ1IbsWUCXnpsdpase/D9H//83oNw/fvBozrlAIe08uVXr5jfKFYbuiRygTW4Q7xoJY1M7x+Z0wHNmdqFY58Qg3Y2DnPn9tVM+jpxrcLYKguNx7HhZcHsxOqdM6Cjcjwe8eMEq1DnKzmduD8dkdG5rp1WKwddIAk9DWQopXWsDcyE5/WFvJyQHuqzqnJMidyh5MxxOUHbGElo1sg5krApz/joqCU0O7IJ6o3JM2ntrGYhsXnGrcWKmIZricCSGqMrKpk8rmhy1g+V+SaRizDpREoHNM00/0BKD1DPsF2hHJF1g7GAtV0yi0Hfx2BYJ/sB1w3JBayGz3gUtucX0vDPA6gxk+XE87Pzsm48XxZEK+eu+BThNvMZz4aeT3iqiHxSdyPklGzFNFNd2Cx4pTKWGCrVcTszmON7Q3AzLrJyvVRImbV2bF6xPtgS3I2FzePcufpAJLivHaePDSnOSTIpDWw1hihtyO/QSoQgLgjuA/EIlqRhpMWxkdBkuzUGzMPjOOHARk9hnckaJITDHlY0U6CzEocTd2ce4VseYgzvOBNJw8vmY8PFdmQa4LEY7jnU9D/mp/XBIFrkqn8qDgjvchrKZgNRo5hSJV78RZVaB67RDjfGXkdrETyqeyudagEG5716WE2oUlnNUf0EUA8U0SIlDgeAD+c4JVYxUooHd0M+82xvysTzWpklkfdUd/W9EtuU1YzRjNvDhI3GMSXWHi/DLFGpeR2+82TDS3aTZ0Z3pOzKnbPjuUYEeIHiiaPClWinykVom6EkRh2YGq+mme/HSjZBZyWPxsgJtYhnndfBclCmRfG1YjUaCC/dyB2mPPF8icG1jsTzumIKb5ZMa06zzqIJs4nD2Hgag1Uy69PGNAUe8mHJPBwK7y8Nc2HLRkF4mKLC9uNLwbzy9W34Fe/mxNM2uL1VTpIpOfN0NiY1Xhr0NPa2Q0DDv388RFtWln17liLQ2MzwDnlnersJLsLKiM1OCrRaGy3WzWRMA3iHfar6hVUajuA2EA1vesLpHgPprGFxahZtb2Z9vw/9cwGII2y245HCNssqIy4yceY9tDkIm1bgqyImaE7UrCcNnJtHyUvWsPdpSXEfu5NSBGsiKe/73xUOOdF3RS0CfH/4/fqL84oqQTFpxpDgmN8k4WrGFFxIVoxG4a4IxzT4/to5isfAuQ95v+4R6D0RwbBTyThw3ZxVCgc38nblvFVyIlL9riw6OJVE1hSlBqOTp0QbwpjCS347GubKGeW0GP/wYpxK4m4vVBjdWEoJVGEfPHbhT28TzwzuZqG1COuWHOUdbWSKxYZsUuVhUZ6a0iSseGbRLvZtD4vlV7JXaBe4NWMqypyMq2c+DMer8Vqcbcm86Y0XnIdPjYjDPhfI/Pri/PQIy+L8Q3Wee1gy3q3Od1fnUODxbExJuFSjPTdsDMrtxLs1KA/XpEh1Wsl8eI5D6N9837idlbU7X98kfnSX+PvHjiG8tk5KhS9uJrrBz1+c4saX94V3T40vjpnzCj+5MZgyx6L8w9PgVGC7Zv5EjakE5q/g3Ljx+pj4+aYsn4kOg3tLPJvwfXO+nB0fgwI0ClaNZs7snVUmemvxu0vBmV4IytBz67yIsPTG1YXLGHxBZfLESz4gZiwKUxYeciDwPhgcgZQTH/vgkGI0Oapwbvtw6oMnc6atMlxiU5iUQ1LUnK7ReLnkTCmZavH3RYy75Fws8dwHt8lZRLiZoqfhgxnLPlzPO31FRoggpyVzaI21h3jyH6JG/N6DcPrJLT8+POD+SK2d718+MB4ThzIx7Cl8WymxbXwOgqV0Sy4GS2Oewsfz+K5h7Yntu0J/fuHcNnx6HcGya+eS3nC5PvKFS6iLPii2klJjTko6Vu5uFrh5z3EJo/hydJbjge4WCo/FychzIR8D2H+cozEqp4ztga6nlNAU/fPZG4eiyPDfFTSswWLsfQSPdb1CGyxFSL1TkvN4PuOrkdPC04gw3DuDZQHpYftoo8XwTWeaE0lmrBW2esH1TM4ZxoXTVCnTt2gWSlHydeNajTJdIr2/xPClqe1WgoT5E8oNbh1hH8oHTDcZsUJug5KVrcfA8dWUeRiZ86XT28SLNOih/nY30lTgVWEI1FoR76hkuh1xcaqF42/bvX2tO2OsYYbvC0ZhOkxYbVzayjFNTCljyTnJgVw7lzuB2nmVQJcD9EHtYcvAY41qNqNdaeqM0VBNNKI21Ew++/UmdwY7NWJMWDeGCraTHfbDIkhi/5Ps4xrZ4/06xmCo8dL3kNzua1fNgO9c2FCNVIOF24Zz9fbZcwtOlyBPxM+VdhzNH9covGgMmjacGzJXGTFsiLAxMIuyj85gSRmGfcaekRxvQhalpx2dtXNX3ZzqlYQyTYk5O5e1MRUJu4nFwcB2j+XmPYolSqzEWobUBOt7WIdPA4ajFgN5LkJGaamTLYpSUOEmJaRkzrVznKb4nUjYenzn4T7M8SBVAUdY+9ih9xGcW2vwyFMuJBVuJTHPg5fL4G6ZuKyNx9aoZsxJ8RFJ6mdvqDgfaie7clMEkQHDKEU418FhgkFj7RFQXOZEmhrPl8HHGi1p92mi9kpKiSWFFWexwBBtY1CmzMiZG1XYnCUpf/e+8noWXp0mZBiHGaxnZleexqCvzqSD2xulinJejS/nQ5QKdfj+/UY7xMHNfTBSIqlQm9HVouZ4AN7YTFGcFx8k1bAcWVzKEgm6UIBEYoMgkRb3ncGbVGkIhbgnRCJdfsyZ4bHlKhL3QFEQyZh3+uj0rkiOzcKEBEfa7bNHWUjY/n2rKas7OaXgiltUeM/E9d0xRBQ0kvUy4rq2NugaCtQgWMDi0TZnCt4a3Qc9KZMOusl+gI37YPTOhRSWis/Pmz88+jrPKTjbY7BOmalbFJ8YZDde6oWUMi6Zw6yhYPtgUQlV1iKsetDErURJ1ZIiIDhqj5f/NPFnJ+fp0uka2LsKnDvocFzhvHY0OV8laClzK1F3/0tXfqjwvgvNlDlH8+CrKXPMwm0WPtSBqFDMmFQYx4lXKfHdufIwKRdzmgibwtYauRS+PoywSuwEm5fqLBqYMhXhl1fjy6L8xSEQiBcxfjg5vzp3TsfMdxcjrZVrD9VxGGwpkGEnFZ4unbUJlzm8obYXNJ0vg3cls7jgqzEN54ujUhXeXge/fnFuMry5zVw25yYndFLuS+KZICvMwzgqTOr86E75+7PzxaL8q19v3J4y/+IuczX4clHedeGsM+cq/MPqLFn4j++Vgwsv18ZyO3E7Bv/e4P/4tvNX9xLvpdFhhI3taRtUi00nPvh3ujC/OIsMVhOywNMQzn3lqMI3KQgg7hLVx/2KEtali06oD45J2HoouFMKe1Abzg9vZqIM1XidhaetM+WZkjJ5dL7bKpcaBUUlAeKc1MkW99ZqnYsm1gE3ObjiOhymIw++cu1RYKJZOZhx6caNwFHhi6x8dHjsjXcV7hUc422HrAMbnfdDQZTf2uBDM+6ykHc6xvQJ61gmnpuzjMbrkkgah+3/0Of3HoTlwwdeHhurdXQBr4LMnfcfK/jMtXWSd15GZbsqFwa1rSRLmG5hfZDGSQfJAIziJz7utbbOdYegfwSE76XhPnGgkvNMzoktC4c+cVkLqTXMKioHAPp4QbTjlskaeK6ZA5ep0y6d3jNDYH25Mi0LyoXxXjieZuTWsSoUJprFelbHYK01hkGRCNx4Iw1jmhXbGtMNUCfsVCnFoB+xdeWYCutbRZNEV3a5ciiJPC/QNFSYvFJbYvSytzZNXIozWqwCkU+n9gNCRjWjyTA7IymFWjOM2+PCwxcVPYQaH5gwwbbMunXqNbMO5/3zIxe/QbzSOxiJRtvxN4pJxnul25WUp/1lGn5BVaGN9fOwYeMSwHIVJG1It12NnIDG48uZtCupRgukiYeKuEni2jp5OM+uXD9WRrgFUVWaxfddifWoqu7IMvA66DbwHJaQkoJX2cYInzUwkkcTVHNUo+Z2hDOQRt4LN3bGLU4h0feCD6Qhuz+7K8gYEaJRhWRkje8lz5lCtPalHb30iUrY9/Xp5s4yegQM/4ifSwf6HowrzkETfTQgVJFpygwL6Ly5Q4pwU5JAZjVC8bIeHvQiidpDEXZXNAuXdQ9MpgCeJxX2PgIWzSCD3iX8vkO5WkdbNHt1HK1BlHCcZIknKq0NvDqHFKf77o2Ok9V4xlmykkS5bJX0Sf3XsHWoQB0R9DQfpNiwR/3x6Azv3JUpmipVw1NXorhiEeV6bTjGzTyDwmXd4lrI0dgkCFOK9q3NnVmdIULrGpuvlpkkMaUNyUpPLQ5najyUYGbfH4XahHVk7hbh+SJ8X1cSwb/+0VFREr89Z+5m48PHK9+cMq8nw9bG6okLzsulM9y4XzLfnitf3xQ+nIW3F2NJwsOhsVTIN2HhSFl5f9k4ThMv3YJbuyuBk0at83WrwQHNAh4vxawRhJ0krtfq+7UvwlL2QzKxWWltsDsUWLeBEdeWfArDsbcYWiPlHIFk2VGDxGbNrIcaiFDUPw+altmRisJgRMVxCuvccU5oD8vFxZ2ArhlZAILJ2tlLdVLYcNJOymg7tzgw4eEnFAm7g4qSxBAJD3UfA93JBpgFIWhXmf/Qz1PdD/AIbxQORai9oR5p+rvjQvNMs8HRjfe68HVaeesD3+lzHxqodlyUJQsf6+CYJUg9KUKX615WsfVEUVgtWsvuD4nV4doGp2RsKOu18Q+7d9v74ClFiVGyGvW8Tflu7WwGPzjI/tzxndQh+LhyMylJwtt80cRRnDuFJ3PK2Hi0RCEaxp7GXoqTo5XyuXZeHSdqN0pKvBflp7Pw/bkykvD20jCDL25m3gi8PTe6w32O8GsXGElh0cgOJOOMsg24LYnryKDK3dK5E2UajSxwceGrm2AUf3OTuJ2F/4e2d2mWJEmu9D5VM3P3iLivrMysqn7g0Q0BBoQIwA2X/GX8gyNYjHBBcjgDYNiNqarursr3vTci3O2hyoV6Vs+CA0Ia0y5SIiUpmfcR7m6mpnrOd57WwetT5tvnwadz5ZQiCvjF1zNVhKf3gxeL8p8/Gb94WXi4nfjQhLZ1ig+++QTi8NPbxK8+GL98vfD/fBj8Xx8Gf3YQ/nqBa1N+kZRXC9wk5x8/bPzJzcL7lng1O606oomv5oyURFk779bKqyVz7Y2swikXjimIHBASz6sp9yVip9uOO8k+eNuMg4JK4um8Mhwuw/cgjYqjFHUWE+5yoqGstbKNwSxKyYaOLXLPNXGTjKaK0CK+G+OUI20viZC1Udcz90elSeaG8ISYwFFCXuE++DCC9CPu3CugMa3sA97XmOQFy39wlyJ07agR0jEJqGSemtFa5U53ysx+oE46hWbnv3P94dIIqWRtoKGPy2kib2eOWmmao3uxNW5WId8bW3Zym7hKnCrdJ6zPJImuquiMlBg95SwshwN3RSkl0pqMRHPhWAQsUDh57jycKoWC2UKZTgi70a5k5HpHKpXewqy1XSrPPWQZHEMHXB4mphHF0vRqodYLx9cJt8z2/IiNxDRNrNdG3UB1QTYQb9iYaW4sh0xmRqcF7c+0tKCq+BYILryTs+HeeCiFPBcyg3zozJo5HWMR0UMlpxPTIiAVrwt9XMlzDqrFZKQp06vhh5VcBLHC3mJhPK14TWiJxCpJgqvilqKLOi2kcYat8PqrV7y9rtxKFN6mULpwBr6YJtYOF46svTF6wuxzV7Gjkpg9fnczQxPUllAbaIbksbj23kkpYrVta8h0ospKqcrVofdwGVcrVHWmbqThwRCVKFKnPdFokoxJsKSHR1LbVBKpJVwVmQaTOtWVQ4K8b6CWNOzlKe/JSTtOxox5rD/+me6GHJcGOGqJi93gVLw3WgInRexjHwwJ41CSzvMai0+M4UKjNO88aNkLCbOKKD92iP9Y1zQNegtDg3hIWYrGAtO9Uz10lFni8ycTY36NbrWkgUkYIHwE9cJlH2F7mCZy2uOjh+zc7aCq3El0bb0oM8HBrB64svg0lMWiw7bhJB/0JByZaIvTh6PJyabMOXFeQ+O7pExtQinGooqL0j8XMUNQT1zG4KAx0bl+Dm7ovhuplEroDZ3GmsC2zITwgdCrqoc2srYREaKAdeGYnfmwsNBprVPGvtl64txrFM7eyCu8WhYsKc6gD5gOGWsVVefj5cC6GS9PznU1Ho7CdJg4ifGbq/PDB2OTwVcIT08r01x4bMbFnJcnWGbjoYMcQo7x7tyZRfn+yfj6diLpRtaJbR00myl543CYUYTTXLg+X0nzxLUbZdKgKwzjWiMIZ0phwMqq0QG1MJv2fdKSNbjNSYSr7zD//fMtU9BhkupOYIg1XAlZg6HkEt3yvsVa28cezuzCooTJRkJS0dz3gjyYxG4GOmLykjpuULJy7WPnHQtJHKxTtODDMRtc92Jc9y5j3iOYx4hi21z3lMEoajPR3Y64492jsesbmxl0oRaYxRkWJKB/63XSfXRrna0E31419J/DBp9WuNUNlTBw3pTO70gUCf3wl7mTMSwr59G5NOguewyucnLoZeKOxrkFLSZpaC5HSXyzOvdJKQmKGm0Id3N0jM2caT5wkwFLbO3CQy580MxfzU4dzikZ7xx+ehR+e+5Uc8Z84o3B69SwlPhSlcWcTxJJhVuaadVJRShZuN81JslaSDkksTaDZvywXvllMt6vR957mDTvCzxLFHdrj25ySfChw08WjYS3bNQe6WnfrAGF/HCNTu56vjBn4eXDiRvfWFJ8rb86CI/mZIRvHjufVuPP7xNPm/Hz+8xfLoNtyrx53/n3bwYfr5V0KtSPzkOCb56My/XKn7/I/Okx8d4Lr0eYur771DiWzH9+2/jZy5ljF3oRvlvDrPfTyZhVMVH+/KT8/WPlbxb47XXiT2cnY/yuVdYt9ucXB0VMmKcZzQnbrtxN848R9pMMSj3TfEJGhI88ttgTfzbB2xbdYNVCHc5Xc+DRwvIs3GTQ0XgazuqNS2s4GRe4KcKQKfYEMx73II5moefttqEIDwm2EZPbh2J8Wo0rGxcPvb075DxhDr1b+DkkIr7T/l+1QCieciTSvYyuKU+euFdo1iluJI9t/piFNoTH5lSLNOBjEbb1yswfoRCe1jlQZ71ytWcGCctCmo5Mboytx4l7TgwarA7SKHsRkl1Za4sRFBnGFXBSdpo0xlXYXMl5QlWgPgHQ/YAeE601Ru186+GKLnql45QhlEkpGeaDMcbAU2fSMPpI6jA6vSe8OWtvHLLRRmPb3fZJIke7jozXlatWfAjKoBEpZ9umUBxXoWg4NtUvEcLQI7XMpYIYKQsPB8Ws4NkZq2OSWR8zn7QjZ4mCsSwkG5g5z6vxYtkYXfnqAVLKKIn5VFmvV66/adgoPNWNpTxgI0eneAxOJ5ikIyw89cT5EjHItV4YLKFFGysmhR8Q3Pd7BJgXvqnP0Y0dshvBOj6ic1WWKEh6d56qYl4xT0zTRnZh12hgIrSR2AZUNyCx9U+UCvM8sxJxvu4VEJpEwpe740XofcVdqChbd1RB5UrDSB4aPqfhEnzhxUOUrxiWlHMN46FXx4fSPaq+DWPzQR4SnN/Yc+k2+LiHaDTC+b4Qsoi8w/Mj8zy6moHoChf8XMNgNySS+tC++2vZCwqYdjfZZ6rpH+3yzHEKeouPvZPuMWI65JnBCN0lQW9YxyBPeUeXGRlh0sTWB6VkJg/TzaTRBTumiLQ9b5VDiu6qp4zUwYahWWit0zUWLjzkEGOPs00oG4M0IrZ6eOfquoeiOM33QA8ruATnZEjgqnJOTBb6VHXBfOxuYiNhnNtg9ozZiGhWVywry9DQG1djKsfQcCfjqQ7mLFw2xX2QrUXiIMHLdfEwpaw9dMgSetNpzswukITbGTQntmvj4oNDcqbuWE+M1vAE07TweLlwe8g814aq8F/ed2LfGLw6zuhNIm2D343GzWlmG4lsV149HCkyGFc4noTaMzez8mIJ5u4/vbnSnuBQJq7aWPbuSrOJWTa+fTIOc+L+5ojuPOAixlUl4nQT9AHXZrHOiuMjpA2RkjjYxmDaeXebGOJhgkopTKMqMO/vyGoNBdSiGLB0XAAAIABJREFUmCsaBrVtC2PU2CdBKCyS9vsb4+BtxEY4pRSsZ5xLNyRFdPnw6GT3PbpbfI/otdB/m4R0zj10n0iYshRhdKPSkaQgThuC6G7uGhpd5hHSD9kTPYcb2T/ri8FDGRVsaxXkf8CrfFsWvjw4bxpcEbx2rhbd5y+XiYOFhOCUhGcRztvKq8ORqwgyOqsqcxGeq/OzufBBQK7GnEOy1GflNcI3j8ZSEvdTfL4/XCNO/lVWPmyVkoSzCIVB2+VjxyS72RTejwFMWBtUW+kaIURPvaAl8dFDO64qHPbDzOtJecJpbXDxhHjnYVISg7Mavz13Xs/KmyG8KMZTytylxKk4Dxk2Hfx8ecHvLs8kMf7r2fhqgqeL8WTwZ914dgHZNdzi/LZGJ/PhkJlbJ7nzegnZjmnmZwdhSYWPm+O1Ms+JD+5czKh9sIhxXJR3Z+fnJ3h7jcP1r7+5kBCGr3x5l/nrxTkn5cPaWJbEm6pMyfiLLwpzFv7LxfnT+8TqRjsUvroR7hj8/beNd+78yawsI6Rs2Z13FV7Nzn9603lYMv/Ly0jSPLSBifDYQ8/+chJ8GN9cldfa8DyTupG0cBmxt122zmFKNA0KxeTGdxdjnia2EfvWcY731bfGrHAeQcU5FWUz+FRDWrHa4LYIS8qcRbhPYWB9Ho2pGzkV7krCsnISuFRhKROLO+/NKRJJl1V1FxIJm8fPNCwmxGo9wAKEX2dG+VBhspVJhdWVdV8bntseG50yw5yj7pNqd66euNu5yrfqrClkIFsL06x+Hlv9f1x/cCG8DjAuuAnZyx56IEiHMRqisUgVHz8id9yj4+PmYAVNGxDjbp8aak5KiW6yBwJ0zBq9Gr3miEGlYy2S4ATjOGWQTp7DmZk9scngsQr98T193NG8U0qhr4E1EavMJYcWV5xVBpMuHJNxGQKPjZwMyYltz6nvI6MUmgWyzNhTVVzotSE4lx7M4lkyaPx8koVjVmCQtKMOTTuuE5aEJc+4O5d2jcQ1C1PKKWd6XTkcC99/GuRUSFOifkpcr4ltjS7mIWV6AffGlIVf3M/k4wXSwPuVtE1MQLoM+gJbM55HoywZtoFOM9UGW4vvaw4lh4ksaXR8k+7qd4SUMhhMB+FFNqZ0xKwR6XmZMTaSQ/XMswWbFXfSSIydL1vNmSWid4cV2qhhxBIHMrUbfYpit6hxMEdamHgaRrUeXcxc6HTE/cdOZyLcoskj4Q0R0NjgVJ2jhw61h6UGPGE9OjtfCXTSvgmGo54UtAd3p6pgnvf/D1mIdMEWCb2wOYTQ4PcMU43uIXQ2Scx/XK8cZoPVIhgiqVDIdBl4dzpb6A8lMST4sSkp1p3uI/jS1qlNQnbQOqs7hwx114U+sYVejWCEr8ChRALdbPDI4L5kLmNQR3wGpxmsBas2AYckiGfm/ZmqIxbd+6Xwca1kFVY6WVMYK1w47K7m5+5o0n3K5WwD5iIc08RxMp5dOQxl8zB8uTlbHyyimMLW4uunlPnp/YSN0BkPg+toNBGGD0TDkHea4PlaYQ5ig6hGsEiK2OLhIKtQa6S6TVkZKWNjYxvO1uE6Vi7V+Hi5IBYH9WGJKYV+/bunlbE/l4cEYwpm9e2S+ecPz/xyUt5a4VebMLHyfTVoTsvCw0moNSLE1xV0yZyvjYYxa6Qv9QbvbeNFFq6WeNzJBL1DzD+cRUMW0y0mAHgEimjU+2ziHHTv3CA/0iPcndacjYYmRT2KJ0t7LKrARIrDJYFACxZ1YqhhIwMWGmCC47v2RhUNfbfuyXgacioTZ+zPTJYw4IrozrAOWZR5MOEB2P8sjnEg/bM5T7h6FMU5sY9dCW/LLhPS3QmPCMRSgOaQVuDy/6s7/Ndc7+pGtF+D9rJMifvR+dU2+O06kOszNt+S3LA0cZeES6t8GoOHNDHaxn+6RKjBd7rQe+eUB70rSY11xNj7hLHWkIm8y4lDSTSrJJs4HjOPm3EacY9eH4QPHQ44WQLnWaYTxSrLTmEZrjzczrx5qhxFOJogJXPpg6vBYVI+DVhNuFP4vinzqDz1xM0ED3PiL+9mDnXwDNSR+ELh2juX5nGINWe9bqjEweh//cq5ktkcHjr87vnMooXFBjea+Fjhi3nw6Wo8i/DN2TjkCWdwyM7PljhgvO3Gm3VEsuH8WWMLowtrF+hB4/inj43Eym0RlMShhCHr27cr/zicngqnKfFTEps7dzczf/+bM7cvZ+4r/JePKyTh3XXwYcArcf7qFt73SNF9UxNfCfzHJ+NpCC/mxE0qfOrg60qdlYMlHrfKkgM9CxEi8boIjZncg63f3PnUnVmC0pEt5DLXEQi8STrUldE7v3HnQuarHBKoD+vG62niIYNrRrWzDWUS4W6vHU+lkEbjk9xy5yt5XDGBx9YjjEQ6UxIWVeaUcRk8W7w/WaKr/pAMNeMmCZ96GDq3Ee8+3ikEBeq5GYiyIVQXThqppecajaSo8wabRENxIaaUD0DzkOfUXZv4Imcu3eiSmP8YZrnDyRgs0DuW9hHk1di2DVXHR3Bmxz4+T5KxobRmiCXUL7vuVPfoVyG7hr4n0OioBipDhzAniyQaDyRUShphHmZoW8g5cECHAxQ/UAq0Et2DrcVRPhePLsg00VuntQ1yChSWwEftLGkAM3V0hnSWHEV+0Z3BZ9C7Ms1Ou0JOxkggKXRnyeDqFgahFsXDWQdzmSJimR64M9+71e5c3cg+4eYUbZgYiJHLDCJ8de/UrfJcB94Gx2niNDs5Z+q17vrMhPULnyp8kUEWoMdp7HoduBeSLpg/M+dC7xPVK7n1KJg8Onhu4KZ064ga8zzHyXkJ7Nl1R01draGpILuG2iwwVKXMtD6otNAQeeDTEjF6TrKP0jvI7s5GMua74tCjO247BkZdSZJY8UiPcphzRs2p4qhl8mc3ukR3sO3BKEJUnW6Zrho/p+6aAHVcwoTDTp0w4UcEV3eJHPc6ftQ6GoO0n0BDNhAj1N51DwLYDYPy+wQeCISUOMGP/iNfQoySckpk0UCGIRFo4saUcwTf2IhiXaKwdJRDDk6uTAm1PYUIp+4GBZXQYSb3KFo0Y1hgChWaOF13TZuEdKGgtA6I0KqRJmEz49pXDjJRrXG/TFx6493WyEk5mzEhLPOE9Y6pcq0NqxoRoTg9DZTEUYytGY82eKwaYTXsNIxdnzpnCQ1rhyThkjaMd2dHpDNr8KdTSsiIz2VY3Oe1KkvOfFwHS4kY9LlkbDiLdD6swsONMIty3YwN4+kSiKjmzqvTTM6DF3lm9cHTtvJwmjikjIzGzSFTZqdtEXk76uDSnXlRyqgMO9AmoQznF59Ns1un5QNXb2FIOzqqhXcXhclZpJA69CS8uWz4jh9732NzzDkOs7nwo87VJNZV3fX5sRdFOEWRMOpcMPBIkhsa705KUST0HaWWRBgasoJhQWj5b+nZu2iFad9gx54aN6tSLTCLtnePejLCg2tRGCmUMVFkoMlCHuOBXkuiP3YyVUKO4RbMYiUID1lyyDY09MD06Bg7YTBr7iG70B0t5gLmgcIk0WVQ+i7tYGD/AzrCirAN537KHLPy/tromrmfhOwgt/d8Wivfdec2OR89cSopMHLaOUwTf3eAjx5SkTYGF8nBs5fBEOGqiqAccL4f8IU5T1snp5nSK28ss1rIuOYETz10sP916/zkkFm783Q+k6fC4zb46ibRm/Dh2TimRF0rn6bCT08TzzWSvB4347HBcojC9eeycs6ZkoxLDcnP5dz4zmLVvC2JkoN0cDNNZI3zQUT/Oosr3z11Zlbyzqn9s8PMx63zYUBtjo/Oe0kcpsTvngavFkW0sWRjswiqerzAT07OYRHeV2dx58Nziy5nc764K9zOwutj5uYq9M34+jYxT8qzhZb/5XHh3cWYZ+VpM563wS8OAhm2vvCQAqNZHjKvDwkZwk+GcYPyduv8u2OEDqfnaJJ9JcLXu7H68hQJq2ZO2oSq8HqZEYGWpz0wLHB2fQzcwnR8HnCXEyJhyLu0zrpFIEaWlSVNPJojufBlVnw4XcMQ/JNy4tw65+H0Vn98Np/2Hm7xwV0s4XzdH9nIaM77FMk5EgVw1sBjPpvzZQaVwlKCaHVTwEew+T9245AimMd8b1KlmefeeUiFbRg3CbzE7zvEoyDf1yeAw5TIPfZpU+Niccg+W6Azk0AfnR88hY+pbVz/hffwDy6E3aLwbB22zQOBQaSAIeHmbyaRBKVO9hg/9u6ohDYWq/SiNBN8i19gaKInp9YNSxPUla5wY+HynxQOKmgyEnsWfXKuNdi3j8OBjdqB/ftM5bMDPXH4jGBJBZFCnjb8EGPB4ke6Vs4fnZt84NkHoxsqhVorroEqmUrG2yDjJDE0wuEoRSg5UmWcC5OGLpU8BSOT6MKZTeQEoqHtlT5YljitJ52xdgkwvBmtGW9bkACurTPWMIJ9RoZlV5ZpYyoHnIJOV0iG+4CSsKuR8y1P60a9GtULzY06NrIFv1FMyIBp525JaIbsoR1O5cxVCqNvEQqBcR2BTOpbI81ELOQOzz+vlYyARbcpDkWDao2UDrE5qoZeT4ju5RjYiBFoaxcsJ9gjIzf/LM8Qxvist+1MmlAX3Jyu4Bp/Lx5pQZOCRxHbEcz63gHeR/K285eInTOFYp/JASQ2To2t6vOGN1IhmTJ01x9bJNhMbrgUrr0yBJJFNKcaaInv0RHGzhz+Y16TEsWDG+veHVOg7iNiGU5JQQjoCLKPf0sRnjskB6sNzzkc4j10wmKDnIM97AIqSvF4hlcCYWVmHGVipHDl32RlbSE1KEk5pkIdIRs6FOF52xCHd1sNzWqG1qOQrxYmpewZcseIg8zzVskp7Wg3xVSYMwxRikRnbxvOGEaPxh1HUa5rSBFc2cN1BDVIOUyW7gnNwhc7/P5qgXxLHhn2LyRTMhGoofA8jOeL83BMfPrUiGOX8+bcySnMYYXYaEZXPG/45rw8HDGEu0XACykJb56Nm5J5+1TJIsxiXK/Kd9W5uxNGSlAG78xJF6drol8aozbeV0Oz8tXJeHvZyFXwIbw8Jc7b4DQnujmtGi+OysfN43PSnfPb40DdRrwrnzGEWSVY4OKs5ntXNFIFS4JWo4D14QwNTJ8Q8acOe7KmUglqT6BJors8RmDZdIR5UVV25nT824h+djKZSkc1ytWkRpfAnvW+SxeSsqjQ8B99DJMmNo/nbufF7JtoSCYM4bIbtIQofjcL1FXEggP7VChL8FaR0P3X/WCsBO/433odsjPM6APets5z62RpQRZJmYsLxzlz64GrwxMzlUXBbKK580918DAVbrLxZAJj49KMl4cpGgcIU06YCSd1PrQoPh7rynKceDWM4wSSoO/hB8fsjBJd2TkpP52Nf3o6c1T48Kx8GMZtdq7dONdGu1Y+bp1DMuY80S1xGZ16aTxkWLsz55CrfTGHLMez8iXOuRlv2qA2IxP1wW8vgwXnkCN4qg0Ls2tKHHJ4LZYE/+6Lie+uQQVZTlMEaLnzxU1ijhMNt0lJtfMPZ+OvXyz8w6eNJEq2wbcfOiMFt3xKYSb9fjNeBRyEw10oS396m3jonWMR/vltZVoS3/6wccrQVfm+Cdfnyv/0EJSC+xlq7fx2HWRXfqjOPw/49rHx3ZT4kxeF3z0a5ykh5ry+K7y/RqqemPO+wdcn5+0lQnuKO5cR/N3bEqz0hHPKyjOJGaOOoIR8WqGkOPkfZdBz5jfVmS2wpZe6MbRw68aHDZzBbYn1anXf+eyJeZe6XJrx3AaO4VlRaaTxmRgFL7OwWayj2oUb6awD7vTCqLBaHCKGDSYd3GSJoCMnPFJTJCCmklCMScIwmlLIIA4l8bw5s4fu34C6hoT0kGO6lmzwQxfucrS/VOA4Fz7U3f+VEpc/RqDGZmkX9itiLfig7hyPM1vtO6hcKLvpadbB5tEibBY6YRfhXBsmmTJiEfPesdGYVKndAmeG0bqgqVA9okC9RaVVSkblwpYPMcoe0dlJdJonrgjHkWIBS8Iny5w2eF8bwmCIUSfl1TxQaWy9UBIcxpVREqOtpGR4IwIx3BETZIBPHuy6bcNY4iDgG6M600JwcEWYVLhcQ1uWc8EZeOtklzBcqHJ5dnKe2JqgNiGzgTq5KNINT5DaRl6WiGsmQjlIME+vGf7E5Iq0E5SONdhW4+lZebw+0X2hyRPdThSbcCrDx17oxPikOjyuYSiaNe9d1QPuspvPEvPsnEYgS4aUaPoWA5Rt2yhzRvdNJ8kcyXQKWjLDO22L9LlNJpauwRq0GIObd1KOjvC2B4wXDx3vtUdMY3RnlSzBdzYLd7QNDde3KjN7cT2VmEZkhxbuf08KDosaSePn26zHxuYSXS+A7j8C833vXulwzEvoFYkCaRudZonuAxuZoftEQwLr1fbiXbzHaf6PXAhnTSxTpHSVFB3i6goeYQRIyBCKONe+G8TEqFVIZkhKERPtFlInj2kNEgWm9ViIPMGSlMcah5Iscd8utTIV41qF8+a77jQ6rn0YWZzJCpWBquyFSHzm1oWcIs45ZQVVrt0ibdEtYpjF2WoU3hsjCivZjzQ5BTLLHNHQO2dNnEfnJEqLyoi7KdMGfBobBxKXGln159W5SjzLKf8eMF9U0Uwg1HLiC00c1XjKxqvcOd7O5GRcekF0ozVhuHMsypunzsPNwrTTEB6mhmjh8dq5mTMfHhvX3kluvEzCu2ugAfNk/O2riefN+P6xcq/CKcMHhdNUeK/geaIsEfv99aFQUphiH5uzVifoiZHKNxXluRo/OU5cWwTLZNmxgO5MKdH6YBZh09D0xxFOSbpTIELJzbXFqLINxyXQea7B9SYT3SaL9w0g5+D7Tjh1f04CiRiGuLJTGIYqQqRZlWy03skpjHDmYZyBPf0tg1rEHQ+CRTsAxFgtoqVFQz8cG0YUNrDTEPb3cDjMRek9EhNFErIffrLo/oyCeqRNSpKdVTz4twsjoqlyl4AxmBG+PCxMZryzzMtZOHmgrA5Zedwg9bgXH6vSvHHMhVeJSCgtYeaFiaLGuUaxYQjdnZtJ+d1T5cWk5DJxMyV+uG5ogser8aF1bnMKlqsY19rRHGajixVeZjib8zwGaoPWQg5Rckg2Tjm02E9tYxudQ4o95YfqbO6wwaJR3CCJ4xRxzmfJ/LQEYu2YhHOLOPLJnC7CVzfKpQvluUFKfFpjPP7meeX7c6jA7zJ0U67DmJNwn+H9uXOXhXVKnA+FX6bK7XHwZyqcpsSlJpLCucbEbpmVb943fnoXCarmia9uY3H57rFyPzvfPBlU5yDKckz85nlg6rzKjT/9i4nzCv/xTaUthS9vEuPc+NntRHpufPTC/ZLJCq9eTlgZZIO3V+OpxvRtnia0GzeL836FX7wqfH8N7Wwx56k6polpinXwEeEOZ9UIn2giSIbVIJUF8cGlDV7PibUVNBWSaxxcBe6ysw6n9s97K9xNhc0aF828YrDMOaZoLnyqxl2BVTOvchB73g3Zp2POUpQqQZ45uJKAmySYxr9fLWS1tyXvvS7jucY+OqcMcV7GiIaIIrRW2UzYPEz0X8wRna0ZVH9PaHo9hfH22UIzfm7OUeI9vXT7F9/XP7gQPndliJFX6EzUWnkznOorbsK9Bj5reCDMnnZXkmrCPPieRsb21e1sn7uciZEyo0aXsbhECkoW2PV5OTcyE2lyEp2ic4x5dYS0Yhgrod/dzDBWJpSM8UoGzQ2dldUqNyXE2dsY2EgkN7JlEEHHYDDjDWY12Du8w2rozAx0UqwqFxOytBivpxitZtuLpXpl5BIMZdiDFyJWWfbNqEvDrVHmGTOlWsOSBwrLMkkSroXJBtuIwq6aI6ZU+USZBFzI18aH587dNLH1zBVjpInWHLMYO6xutNYRCuhnU4zAiCJPtcRmIiELaTjXZsGDbhOjOz0Z1QYQ0pI2hO4TY907SiM0zMLElAdFFOkrrRwYNpDe+aSRQtbJVAuJBoSBSPIure+7Vm8Mxq7XHR545GCTZjLgOmK0LcY6ApLfe8UtkQymuTCx64gTXAec69gjX9M+sw1BvXajOjwRcgnZ9dOx4XYSKZAsKEdNu5TF6alGqp9ESt747ED30D7Z3rX6Y15PLTrdk2jIg5qTc4yUqg+8xZj84hYdNPYEMTdySbucJzaq1mxHmIXWM7pggBm1OXVHpyHxno0RpZN4ZpkidjpppBaFXEPCL5CD5Z1VGYwYjUkKLiVhePQEWzXAmPdu1iTCkgtLBvfOOixIAwNOS2YZia0aZRKESCVczSOwwo3Ww0R03gZdjKKKeML3WM47F5YZkhbenq8cT+CSOA/jgHM/J94+Vz72gUrwVz/6wvna2XpnToE1e6obI8HRlZIG66dntime7fePjohxmsII9qkOMp3UE12E46mgZnzYGk/fC6aJi8Bvr8aXp5l5Nsw7t4fE+49xv49J+N1mLGpcEzw/DU4qDA0k0ocxuNdEJZiv57XvYRASHGdR1hb34Wy2s5cbY8Shbto75oIwC8gUhatIp4+QsckQPBkzmVyI0AmLSVYRoQ7n3BtqKUx2w0kSE8WWBjllhJDcdHX6iJhlRfZpQ3QxcUc06B5LylQJOUXEIYex7aDKJvFMo3FoThpdRUlhcjMN/JtZ7EulCFuPiG8VsBrfCyG0zwTNpFt4P/bRwr/5fX13hVlijTsU4X1zXkiPgrHHoe5xwGVz3nVnxigKV3Pu54l3NaYvr3McSs/duS8xVUhirMO5GvQhXDeYFZpkLtsA69RhPJTC6VS4HXN02BPgyimvrK7czYKtRioTy4ipaNbMvDcrrj2c/t9vxuLh2wlWduJ+N2UlazxtFpIVGl/cHngW+E01vijBFr9RODcjJaO489Sdm9x5c04Uh3eSebnzn1WA44GXZTBn+Oaj8/Mp9sO1GibKi1Pin95X5u6oGF8m4eMqXK7Ctx9jf/3pbSTlvRJjmTNNlf/zsfMnJSRC//u3wWF4fSr4Zrw/DxaNJNHDUH55JOQnz8a//1VlEuG1Gb96rNy81JBl2eDPbpTfvGs8nQcvD5nHD5W7AubK98+NF4cEJCQbde3MS+JTc373qfKPT5UlZQqDLJCy8rF20MS1Qp0zxRsf91rqUJQsURifdPD1MTEImZONzrV3umZmdXJWXkyBlhOBY05h8G+Fp+vgrcAXE1wtJrFPfdBduZ2VZ48J5CROpyCJQEti1BHx7E8mzN74uDameUI8GoPdosE1HG5nIfUpPACaEA1+cushidiYGM0oYqwemu6fHDO/W2NCJKlwbY22J4dKKmSJAxEt/AVZM/+CRPgPL4TffoQijlhis0fwxM3OEUSUzSPGdIxIimkptKdmGadxxbARhUZBA1uVIlZzdGFKMU6LJq+g2mnujBGSC1VFugMTOSlIZow4SyRGOJeJjqybcR2VeczIHPHFw2I8eB4Lfdtwd06zsjZnkxZmK/cfY/mezZjzZY8eTdwsUZDVS+d4SvRLxL26ZcQ75pm6dzBzijx5dzCtEYloKfSWaSCSA4eTI8xBRDidJpJ6oOPcSB54EbNC8oDAqwipROa4J2VOwiElvn7I5PkZ7/D+Q+JZCp4nKp2lRQezojTpPG07AkkdzcpsyhBFxKj1yjydgk9MwWnYqPFyO1wVnsyDwZkbk6Uw7fTG5gl1o9PRXTNaNKMdNolAijEM80wiIaoYPXBbJGqP/PRhGhuYZPq2ktOCM5CuNCuoC03BRshl1AVLhnRhG7FIzdpJ1xDWtx5s2iWX3YijNHYuroPuxjlNzjyi8+gKVQaZFEEBvaMp75SDge6ItO4ZJFJ+hsfi00ZQKDpBncDnP/SV+1ddZhF6MfJgtJAjSYvC3fZErKTCIYfL223Hxg2LIjILSkTPin4ueKJzJxJcx4GSxBnDUYkxN0SUaxtxuOuthrFUnUlKFOVDQ7giofM+9wjdmFXoYlxbIHOGQzLhuJvK+ughOcG5thY60BQj9ESwoq81DG9NQNdKI9ibn5FA7XMh7sJmnWOauHgHr2zD6T0CK7YqeKsMET5eB6QQTJ4tMSUlp7z/XCM0kNoYDI5z4t2189g7WYTr2shmzLXgy+CgE8sE5p0lA5r44ePKXU4giYMJrw7Gb5rx6iAc05Gzd17cThxa5cNZWEfiRjt9zHx8GjyvjdM0UWZ4Pm8c7mfKSNwt8R4gjhTl2ITNnabGMhKvT4XHavs9TWTJ3Mxx2Fi3Thswl5DG9H0iUBKIhTdgwqOgcfZOfRQyw4V1dA7sCYEi1N457xreRKaNBhIbnROUII9KlxweQVIG74p3o2rIH0RhEEi+1kOC1iy0iSoxMoZo/l5GJ0nIWjKD6tEtBKN0pXlw5XuPg1azWM/m8OaxDg+6RA/pWOqDriEbiLUhWMrX9m/X/L8/f+Qh7XzbLTBt30jmQY1vPFTud9k5FrjPBlpoBjqUt9eVpeQIbaqDhxyGrk2FtcNcZsoM98PYiNjjRcaPn9VXh8TZF8iF78/PnGvnjsr305GXczRrPEXBfyzwtNNxDlmY3VlrpABePDELvD4kDlJ4u9bdEN34weM5uVdjygI2GMA4P/Mun2hDaO2JbcD9VJg+77cdpgxqnedm9OMN9/1C2zqfqvNYhZ8fhKemfD+iyPvmsfJ1jvCPOjKnIrwowsMhqAvna+cvF+Efu/FiEX74VPnNtVFy5j9cjb+h8YbCX0/Ox5S5W5S/uRkcizDlzD+8WVmOGR8RvHK8y7x/v/KLLzI/pIm/M+Mnd5kPOpM+dn43Jv7q2Pn1KvzztfHNWfhqTix58A+XiV/eOVWVL0+ZJWdwuE/wf0jilWW+EOMd8LevFj5dnEGJCVjJfH2COpy314Z74rYYxwxXSyRRDtrYRHi6Ok958FICEfnOhFfTRMK4mNCqcz8LtyW8E2/WTttWvAQn+dIdlcFXGWqeWTK0MWi9U5Iw9lCO1SM45Tenbd2gAAAgAElEQVSXKy+XCdXI/7gR5+0oLHNGtXDKDVW41P19FXi7xuS/9QbZeTk67nD2kFB+6ML9VHhuIYd6dshjxL6Bo9uZ+5R5SygqC5Xsg147WzpwVOGhCE/bH0Ea8dRDa1atsnkhpRkwJjxkBHakeoVEFEGmEc/rQnAMQCTGoz4+azsT7i1cwDYQnXCPD8UtR9HihnsORujOhSueYYQlI7mRU3TjfJe2IYUhyoqibS+u2TgO51Q6PoUZCqmUuSA9QiU0JQZhOEMFkwm30MFlFBuG6szWQtDtAt1rnMbG2I0e8GlzpilYxl2FTI8OizecMJ+VvfgsYqQ0I1mw5pglGMrp0Lk5RNck54zYiHGOFEZJuDW0REdjWwfSC6NHN2X0yP12m9l6orYtaAlagLp3uYPH6y4MGaT9tG+2m/osOn/uAbYWidjqny5O8w4e/NZkSp8LrSeaG81jjC4ShkRLg3uT4P5BdFKlYWT6EJqH9nTQyRqcwh49a1af6c0YYqFR1+jKglH18/MwMQ149MHweObM4j66CNNUsNGoQxAfNFcGIagfOGnfJAZBt4DoUFuK+908Os/Z+m7S+70Jzhh7Fzz+Tc7KhOCpoN5AZ5DtD33l/lVX2kM+fETxncj7iHsHl++62bXbHmgQRUwUJ/H3BtH1m4b+qKtMKGoC7MZCjIGBCZM4okrrRuuDnkboK3NMOwTjWh0YmCZqjechCahF9933Z80+0wJwLrVH0tSO6RrmTDkIHTY63VOYJ8wiPUwG+KBqIiWD4eFJSLt+3ATJoUNcvcX9BA4TiGt0x3EeXhSeLk4dg5nEB68cXenWGMBjdaYUnYlPT/H1jEjVah5xMKeitOSUGyhpZnTj148rvXXubw6ckoNmblLmcbvSDxPfXo3jNPFuhRe3nVGFDx83vmuBMztfzjxa5ifHxlQTTTviwlwTL28SH66dOReOeSK7cB2DtnUmVR6tBmdZhSuh7b5PcYBrOAxBGLte2ultH/6r7+ERSpFBVsUFZoeWQ/eeHeqImGrNe8KfwaSDQsZ14EBRpeQ5EtyGkYuSJdIoTT8bYeN7Ze30rD/SK4oSa69HUMkYgVxbnSAuEIX3pBnT8FYYCr77F5IHQcV8nw4MkjiZFJPE7kQT2P+b/WeXRks8f0mjEB84W4Mkf/D2+eN1PNyxAeoNc2hJmX3QRLmRXaIBvFuN4oPnEXpORXk5ZWSPm/5iTmwMNpQHOhQ4C6iPmGaas3WjJeGQO0ngbYU0Gqiiw3k9J4YtvEzOusbeeioTzz0yOBOQrXNejaTK2kN/cjMJ1eBxbXwxOaeUKSjPvfOTOe79uQ4GiW0MukGylVPfQsfvwlcZ3gxha8Zxj7JOIlxyRHaP7cLWGkkT96VxLBMXLTjwl7eDdxfj0pVWEpfzxovF8S50g+dL56447/PEr58akwqTKkvJNIsGwZeLMLnzd7Ox5Iy68OsfKv/hWvn6JvHL28HF4OVB+fA8+FNx3r9vLMvErz52/ud7WBv83+8Hj1sc9q7XC99e4S9ezvy6Ki9s49Mm3M4LP78xfngWvl4G5RBYQjr8ahXuc+f9pfLiqNxrxodyUzJzlE3B4B6O7Fx0N3isSvXEUQZTErpnlMqpJJJ3qkagzJ+oU3HOXViK8iIpzyOyGnJ25lI4aMIdXszC6vGMvO3O68kRGWxmfDFlVOBJnWVaGLVycsemmM4nda7mbC4sGnpiG5WsYeAjKYs7pSSWDM9bSMYOIgwKOQlHEZ6Hs43Gxy3W3Gn3aX1sI9jfHmv4p17pmmjAfUo8mjJNE/cpus+1azQg/juX/KGaxf/tb//SmxutxhhNpTByaPNuCqSeMeH37vkexQ5SSex4rp01mRngmSZhzEnacStkVdad61k8UoKyJDYVqjlOI3t0sFQjYzq5o1PBvON7d28IO7s4cUqGWIzljhLg6ZSd7AmVKA5if9+Nbun3xqhJIeeMGhx04GSW5CD9R31NKpGAlYmvVaZE2zp1jB2jFb1qVX7EdokOyCnSXEpmXYNnKqMzTROifR+dKVPK9N4xQv9qo0baWoogBxjMJRiQliSSfUYKLZ/8v7S9Ta8tW3am9Ywx54yI9bHPx703781Mp+1MY0MVaSMjWogG/wHRpkGDnwAIpBIC8Vdo0ECigwCJDh06QFkqlykKynZVOvN+nnP211orIuacY9AYc5/sZFko07mk2zg6+56991oRMcfH+z7vhHXBtdNqTMj2PQI39rTT3KBPdLdhfIyiZ29hYgHCMONhgrmMQn/vSh9w+SJRWLoo2iqeNbwy3WiSoxkyoxKFZOuJrBFHLE5ATXMk5RVRXGIaqL1gKQ7l5LH+psE+prHqjIYiVjjvt46P6cLVG1eDaZrIfcSjys7BNL7GJ5p1ijt7MmacmwmbRzNTMUopcT2VFCaf1OmSR0ZdinAQMbqE+bBaSHDo0MTZeo7oWoH/4Zuv/i7khb/y9Z//6U89SRROZpGkt4hHAWCE/nYOHu7Ww9XvIYD/+PmKgg+R5SGnUQA7194pKqBRXld7YcmGTAIJ7FrJ0GoU3B/jrnGSO33oid2jqJhSYq07TZWDCJoT7brhEpxnJJLDsoReNXJ/Ez1HIxY/bNAFNMckOWKuh9aXmOqZCMvQtRaH05y5ulEMSMrzHjD/7OGstlpJRVGP5jIpZFdUNFBgEh1F685mFvIckTDm2fBINOXu1cydZE7SqKY8+8qPj5mtCTbB2hPuncet8+hQ98oXKXNLYQ6MaG/l771SaktMtvM3FkSBz8rMXzw8IyIccuHSGrUrBTgcFW9RfN5eaDdueFgSSEkRjxaJcZ+8RKtmDYOYj/uqY5zmHAEq/stLd21tSCuEkhTpSh2MiDoKzpdtCWaUFAQJETgMprcSXrqXOO1FM80imEE1+MR7C8RSmhLWnUmNKYVu1CW04HWY7XQ8W7EwTc05gjPW1ikpUjwdp5kg+qITjiYpJR8FWGhtA80Wk3VFaWZhoHVhakGV+E///M9/o3v53/vdH/lCJecZ6w3RTGOnmPHYQ8r1k0Oh5hndVnYSrW3s43OoY3MzYdyY+OygVHe+ZOF4e2JV5ftTiQ1fd45ZudRGbXE3ZQXJKcxsGKXvXIYMpXtDdOGQFdPMBeGT3HnaQo5yN4fW+K+erjRzXjGKE+vMWbE0s1loON/Yyn2+4yxGQ/lyb3xSEllh60JtN44af+4o19Z5fTxQt5Uuxpvzkc96572EHO1nl4a2GyUl5uXMl7cbP5xGwI/AORmrKElleDsCDVoNLlU4agS61JxItfF+N+5N+Lc/WWgTVIKPP2+Vt99beNwaP8zBa8aNr546ZY//r59ncrPhAYn766e/u3C/GlIS+4cbJQkflpn7r26ck3BaCh9usY2Yk/B7B+PrCuvwCb3FuHrhQ+18ppVzgkfJVA+SFgTZ47o3Pi1KQ5lx3hucvfLmdMA90nNf0k2/vqycUzSMp6JcXfB9G/eyc9+NL4qye6BNNScmMw658KjKnVtkLRjQK43E3Zy47JWHFiFM53ni3Db+end+dJh5bEaV0HRLTxw9mNVPLQx2h5woVG4u3Doci9IQ3q3GmzlTUvCqQ9cMJ688e2K1CM942Ds/mOJce7Ag9TQ3Tkl4aEHl6NZ5j3DRhf/tr/7Zr7xff+2WtpQCbqTcuPQUzEkP48t6zbTUab1zGFOHRBRDSFADgsHoBMPGwPNwc8NLnO/eGlWc5MKuzmYNsYWlQplr2Fx7WBdvdaNIwkRodf/oBk4poTUYsBljq2A51tubwpQmpuvKVG7MhK41aaZ4Q7NiMiQSGcQKirI6bGZosYgWzmXQIIx1N9Q6WUIq0RVcj1irGMqUnSSZ1oZEwp1XxwVaD21PgtM8RRKTDAoHUfy2Kqw1PrbexmRRAhrduzOnFNOzFsWum2A9Icz0tVJ1i1V4j7jcixq37UrSGdkzJi/BE9F0uCvuCvzSUe5mSJ/IeWeyl6Ky02WO6bGM0AmLeN7eQ5KQRejs+A57ERJTeFh0oxEpNc06JRdaj9PaNFE9kXtMC9fizFtHU4a9U7MzW3xmlgKLp3WnuPJ5nkfUrfE6FbYWOKX+QiSZCvTOloTSjYNkzBuzZ3atzIQ+tEtg2vbesCy8CBu2NgcVJTlZdiBT204RZadwaeGwlaSDlGHBQP3bhEp/B68lQ3cdEqWMI9ANSeDSERQZWt7JBWegE8ar9Ti4RAcvuUVcxSlljsW5iaDdaClkSlgA3hGhpDG5H8YPTJhLYUlRUOw9EEHNLLTeEqxwZ6yhS0x901xIEpsic+dUMuIR/mE2zIctQwqjl7tQcjwKuvqI/s0kFaYS94OmmNhXE8jOh71h7uyA18hkThLormNyplxo2jiliW6dd9cNxJiT8noptN1ZzZglTFqzwhd3E49751U5AfDu/pn92vgF68CDBev2n1hn8UTZjYd9p0yZ1zlxys775ny9Vj5ZEr/7dubSlL+5v/Cz50RvlQeDYsqnrw58I87k4U+4y51P5sI5zVyuV542xZORDd7OEx+2TjLnzV3hsTUwo3lw0V/kQEUSJScuNRBOaRQVb0vhqe3MZB73HdUUyXseJjZxH6SFmDQXFRJCTlG4lpTjuUxsEbFIL0zS2bqwaPqoq197bFWmEgjE/YUBnBMyeMKocusyQBYyIlSNKce2r3YlaUiszCIsJ4nGNWSRpCcScg2IAUlPnWqQJVLWfEyY3YxuSho2AhdAjFsp5L8DftqpOIvMHJIzF0dT4rkVsjunHk3GowvpJcWwN17PE8NCwd4zjz2uv4UIOnnSI1+UxnQs3PXO+zH1rX3gDS3GMUuOiOznPQIwbu58fjzz/RwJgmuNDIC1O++r8yo56x7v0T42o0mcHy2FKQnPNdbrxxIUl7pttB7v0YfymsUrVw+5zbHEgKRZbAEO+cykcJoEM+ETcZI13peFJMbDc+PnFgXuA0HQuKaZNyocdeNPXifu3FmnMPX+03vnjXbOk6KHRO7CrUaM+K12sjh/+FnhcmlMd4XfBf73d51/9nTjeat8cpi4tIhZr1/t3ItQc+ebS2xNvn+XeHOeWO8bXz5sfHo38adfFJ5358+/3vn6u0rtsK2VPWV+8rbwh9b5s5L5cu384KB8/qrwPCnfXo1fvKQdpoUf3WW2p5XUjT99O/PwHIXaG525r879dgXgPE/cLYkvb0bJcErCXTLSUvjmtnFXdv76yTimeLbdWhhd1TtPGtvSRVNweRU+z8pzM97MJQKV3PGkfNsqn5VAtF2b8HZWHnuYmu93mDTz6RJ0mlYbP5eJT2a4H0k0rzQR/mbnSyskh5nOMkW4RveQgX5SwmR82427LBSrrNVZSqF6D6+BSDTUBg/NucvC85hgNwXrnUTmkJTnFsLnJMJBDrz9W5Igf+2J8H/yx/+qVwL70r2RCfqAaEzyzOPPES8chyoDyl/7mACSKeJMaayhJdNsxOUNE8yVwIuI6NAJhpQgmTJrR9UpY+oUesZIPrlLBff42oVgw6YUZjhV5WCFTa54ErY9DtdigFVWHxG5eUaIwna3wJU0Ek3hkDp0p6eFyXs47QcHc5KIF1408uKNQLfFZKxhnXBDm6LJ6b3SCF30JAU3RdLKPE9RbAySgeBoB9PAe/XekQ45JUpRxOMGPyTjkI2uyq3Fw7JtzoWd3jKOkgv0lmidEXLyMmEIQ0knYb3jEtP1ZjFxjVFfoitsPpi7xFSrkYK/agkXC/j+y/pQIhq5qlJswPg19Nad9pHjyiiqzKIYN+KQjEyPiMKAl+SnOCxJcQC21kg5DJYiwmGwmqtmzNpg/cbvsbuGPnmcKEYUGEo8GG5a2TenurJLIxkREjD+7W6V5on6orMVp9PoKLPImGhpTL09Pr8+Ds7/6dvvfmsT4f/63/o3vOMDI0SwYTUc8DmlYCGTUIFrjXjsbqDS8Z6iWPd4aIn9MjjmJdbWJVB7WeHWOktKaFZqc3prTEVHahdklEutMPSXU8nsLUgWnj3QhEl4ReIiEcARyCNDJVzGdeDWXMNwubdAt0VRG0Vy7YFerP0lRY8oyojCRXTEeXrQH4oq5v4RqdgtjHPdQwNJd14dErc9CgIZ/133nVkzr5bMczXMjSVFctqszoc9EEJ4Qnrl0uHzc8Fr53kz3r460tYr59OESrDNn7edas627dReeH0Wvn6q4M4P7go0pcyBXbzfd6wJr0+ZfQv99d20cHfqvLso2TNP+5W748L1WilT5qunG69KBnOqxHO0JOFhayN62zmqUkXYext/H1rum8ezr7U40JSXyG1DXAdGLzwhEDrzSOQLc51KGJc1RVMdRLCQKCSJiN5bbWFMlnh+lpKj+MQ/bnlUonDL49q8dSMRka5Z4t6NhZzDoEV0i2kxAM3IE7QaWtaqxBAnhTa4Ih/T6YIMnpjE6TIYxHR6j7PF5aWTDerRf/UP/+I3upf/oz/6fRcH8ZAMhEgwEg+XJKg7LQVp4HJrTGJcLLELCInsgQls7uzWQpeZwxPwelJcUkTPq/K4GZ/PgYh7asLjtvFmSqymHBMg8GHtWO+cJuE0Fe5bsELO6jxX45QVmxPHWrlV527OPLXKnBKXDltzDmrcXDjlmNrHNsX4UJ1PB5fYzHiqjdWiMI9NcCJ5Z9ZolgNppiw5UmZrdyRl9tqGCS0asdXhD0/wboubXgRmFT6sPdL0jonb6kHAKYkDjZsql0vjbREqiW/qzm2HP/h0Rnrjby6RDNdq54vTMK260G+dS23881unmPCT14l/9O2OAT/9bKKReFsMR7l/rnyozk/eTHy9GhPG6/PEH90Jf/Gho7nw7cX4wV3i8eaU1Pgn33V+56xUF27NeJ1gLoVvL405RZF/NylC5v22M4twLkpW47I7zxYJm5MAw8eivUYyoihLyphmkijPdQeMc1Kqzswaz5hXaphOHDUaTHJioXPfM++3EXs+jKmfTQn1Hg2QV3qeSRLGWzTO5283I6uw7TvkzOLOQZ0vLXMnsRVq5txQjrbxbVdel5FroDK0/jAlmFW5mNItapJmET42Z+Hqyp0615codZzj2NTv7myu/I9/8/Nfeb/+2oXwf/zH/4pXNMD8qgHgx5h2IxWJ6aXY0AEGRmn3iCJ2YZjIYh0nEskofZi2XoqNOhiySSD3MU3zhgMiGZFAQhU6OWcm0dC3GkScbY7QCIt1We9OZcXdubii1pkkcVh0IN6WwG0NhFtKKVYrGiEgJKXjdDVOHhrYjISud3BsrTtZnTImycd5jgnCFkWZIiTLtNRwd5YyIXYDJpBOGSuvpsbssRb2NCFeYxpCvF+tWkw2tKMaDYXhTOqcilMsMEUXjNvWeDalbc5puHinc8ZXWFsUsy5RWFcLLWjzKD5s/F5u8c3Nhu5XwrHax4SkinMSpUxhilSBO4xKYRGhlMTDtfHtfuOcJtJhRBVvcbg8W2HtN0BxdS6XKHC3YVpMJbOP5ioRIv1JQ47QSEh6MboYXUJaYUTx1EWpPibHFjKW2QSnRuCJKfc1sgxLcc6eWIliLHkU7S/TcFwDXzdoH1342PS1ISBRnM1H46OdXI09KepGdeV//u7b31oh/A/+zZ+6iDDlX0od+pCzJI2i4dY7S1EYXEon1kyuElIPj2mPpliF32ojKSixatwJ/mxQqcLUdmuGZA1CRwqWo1msX6eUWFtoVS97Y5kU94QNo2EphMnRDNcI8OgtpoaKkCX0uGuL4I6XYjVkD+OtlHBoC7EF2vtA6CHhSEihlc85foeisPWYBu7e2ceU/OU+Tj100rtZJL7lMPKZhIFwks7j6vFM6FEUd4GzZDyFQfPSB+WmOMkyz7fOTiNLMMenwS3uLXHxHnGiexQY55wDMJ8T3+47dVs5nQ+8KjNucMiZpsb10riJ8aPTws+eNno3Xs2F1ZxziuFBmCCj0lHpkdhUw3mfdKxBJbYD2ZTrmNijiriObU7cd82CkCKiI2lt6HjrkAWpfpR8aYLkwiSZzWOF371/LJjbMPThPhrG8efxOagLQfeNoIAYJATfeM6KobTWSQnqpniqwzeRWD348oJSNApFHYdiJFAqNeIJg24i0ZTH9xuSidGcl493q7N2YluBMUnmH/zDf/Qb3cv/we//0OekHPLwZADXHs3npLFJvW2Nt7Nyc6XvOyuJz5ZE0ogor+ZcazBlVeDddSMrpFxGOFHl/R7moqvBD4+BDDyXKYgfScgExSSrkHIYi+YsXNbKq0kxSaHrTsqbKXBs5jBjrFa4v13ZJVNEOCWjqfJUYfbgd7chfWF491WE49CZ3pXE/dbYWyTGHkqiSYomLYdc4pRjck2KVfythtzqnAPV9VVz/mAOelFRmKdo+PKY7F/pXK8dkSBEHYnCeC2ZV0k5p8q1BgbzLhkXyXx37XjrHBKkLHxa4O6ucDOlrxWyct0673fhOCvXzZhn4auHja935+/dJT45ZpaUySNY4qtVoDZ+/NnMX72LZuCzY+JanS9OU6TKNeM8F1aDN9q4eeG2j2AkhVszdm9kzUxiPG3G2i2u00HfaR60nkGa5JjiM1tKwd14t26IFJZceNgiamLWkCqseSLVxiEplxaUiUU6u2eWQc/uqbB3Z/KOWeOQE7sWivUR5hLSjUxEH99NwmrObW+cM3zdMl6vHCRinS9WeCsVT3Ok6HWn5MzqQSRZ3dibs2DcPBpV1UA1XsYZsLrwWYbbuH+zKlu1SDB1Y8qJ/+avf3Uh/OsHargPY40H/HiYFr6anXlPXNUozQNn3p1TESCRRlEXSKzKUcJc9PKwZaDVeo8IxJcc6Zdg91IKFaU3wVJEyVbAViFRwXoULzljvXGwkF+4Rwc2tUhhi0QLAVfarZFSwntDRchFsJdJphaa1zBdNY2Qilq5acD7U4+UpMkitrV5p7R4iDmdugZzcc6JnMtgVjaOAugc75tOWIwpSGVkgLeNLKFrM+vMU2Zv+5hOwTQD1ujDmJd0rO0AqUopO9/77A1//c13zCVBz1AyWQzrwu3pQrMIPolmY6K1YHL2QTlwS+ythynso2QiEphq3+gjRnvqQvHEVXayCSnNnLOgKVZndEe2EPW/lilA6zfYekV8Yk9hxJGm9O5MObHkoC60HpivLoK/aPp6YNsu7lQPrJn2hJrT9QVZRiDnRClUDnlMhV8OUwGxjIhyEuc8w1UAD1a19xD1dxvdb+0wCmwlDKAv4QO9RXFt4wbcRDCiOJIGTxoFQfdwuv9WXy8s1tbRFCTkDoE6zAlpoa9sbax6Lcyla/PAFCaQLpQczGAnHigiUUzUFohDwUAC/VQHhN+rYQrXbngPiHnHY91N57KHYfF22/FSxxjPuW2JLC9BNLHS1yGtsOSjMI/PAbOXbIdAW4nT3MnD3KgW770AIsohBxpsUiDZx8ne4+a4eGieRThMOkxvMRG8SeegcMjKtQbKaVw0nBTq0rFSYp1Xnc2Uh3XnUoTJoe7KuTg7QjFltcYuzuvsFC0clwjVudQ9no+z8M19Be28WmZS7bxOwpZjU4EfWDdhrztTihSxp2Z8PwvTlPjZfeXTY2H1SqoxUHi3VVqD85R52ioypu4pW2wITNm7sZsgEjrs1SKwJ6eEWBjp6hiWiMYGoVUQbRQJ03LrwSVWhyIdz85uIUkhwcYOEgmiswTTp/ZKyYqYkCahtuB/7xaECBsNnFg856XGZBzAu4bJySreoyAmgZBJyakMmkgP6Xvr8VlHiS2oGZt1So6gpytRDLuEviNS5SIEBpx1l/AxeDQw5kE6Wkcox2/y+kyNJQkPIzI6CRw8btrXU4nrvmSee7j1n7pwSM77tVJy55SUZ1O+mIz7WqmSyaWgQG3G41bJZY6JL/C9ybjfOxPwrgVC7Wg775n53pJiW9uNxXfuL4Abv9g653YZiX3GzyXxnBZUlJRmDtoi2t56GHGBp60zY9yINNU+NKy7NW7mHFS4bsFvt55HA5s5z4WndedN3tnMeWoTr7Pw9XNjJ3GXO7Mm7o7DBElIceS6sxTlbpHQ3q4WLF2BaRJ+nCr3uXA4wnMVLjt897Rx1xvPJXFv8NlRw0OSM5dbQ1vn9TF4x+cl4s9va2eicT4q/8e3YTr89CiYCIejMvVOXhIoXPbYUpYJzll5uFbevp54NRf+8TeVn7yZeLM535lwVxI/e6i8r85PzonvLhX6zl+1RNHKlIS3RblszqU5SRMH7bzfdw458ToLVRJbM7YWW+g2CoLHFgFDc1EeK3yzNV5jqG40axwz0BpPVljMOOxXGoldEod54qjGdaucijPpRMnK+wrHFLHUd9PC3muEGEkabH/4YonkWKk71sB7pzpsDQ7Dd3PKYZJ9o3DfMnMP740P2czWw7j/bt14NWW2XKAa2Y0F4auhyy4ivCmxL37aO6csPG2dc4npcMqFb15A5L/i9RtwhJ2UQGwmUtMa2o3PqqAinAgJr5KRLGRvmBhdb2GQMhtTtQiaEA13Oa3Qc6CRejcOpURXMUI2tHeSC9UbssNunZIzzYZJg5lmO8kapS9UT8y5AkpLhZeVViFxqEafZ/JQfnY1rN5IfuRaNooLd67UpkgSDiUMdFpiGp22nUJiS0aWhkmjywRsTEnZWopUM6nc1wUA35VahZSdpx7pSjYpd4/C4XBmmkOnOJtx0R1PCUuO7E62I+e+hnFLnoGMlI0kE3paObYjZc78/R90yumJaX7i81eZ77YDixR2F66b8Gid54cz727Kc13JmmAznIjh3X2i4ezdaK1iPVPlpesKJJyK4rWgqlTtbPpiWnNqVb6p0HxC1Wl9j0LQEpNVLEUssfuEalz0Vw+TChmu3hEO7L6z5Uz34HiqQKtxyKqO2YILiegUpwzddtZJ6E2QlthSmCKy2cfgBbXhohaj71EIVYzdEyphGkwKxWISZQRazj0e8tG8KG6V7hOelOT2Sxe5BzXBiX4LD8lId//oSfttvRNnB/kAACAASURBVJSKi9LJbD106plYWT63iOQMvY6BJFTC8JWUiBzvQmnOrUeKnHh8TRlhA/MkBEUqpk+SFKtGydGJba1Cy+QMh5LYWhQXJhkk8GxdC+IhATLaCNSIEItIORtM42YkSSiNJjKKGY2GQxWXKNCKyJi+D8QWzt7AvdJcERdaDWOdSWDjXk+wtiikDylTMdb6EoBjFGCzzr4GsWASRacEHjNDM+WQnG+uhnlnUuXusGAamwqdleTge6eK0Cu8SrB7oXeh3y6cygHZDZ8T6w4yCvX3151pgdvVyJ4RSbxZhDdLxiu8a43UKq89812HdGmk7Hz5uMGSmTyu62MRmgb7NicgZQ4Kexd2wr/QPsrmBBmBJaHxH0lRPSQJWx/FolmEjbSE5ZHISIuiiIgWVx/3ZrKYNHvQOGJLEw2Y5tAEmxv7TUgl0iqRIclxJaWY9qk4muM88ME97j1izudZ2cxiYNBjoNykjeszjE0iYZgRJwYH5ig39jazWSFlw1IExWiHWQu17UMvryBhvE3EtK5rGA/T38HNXK2hzVANTWpkgXfud+PDvnIx5yDRlIsoWRO3Hs7/ZX3kKs4zyorQJDOnYOtPWTilwqdTbMGuEijBN8nQ6iwlUUl8e3X+b+7446lzmqLpW3vnpIUpwet54to6M0fM4XG78mARKHPMmbtlYmvOOXXuW2bKQrKd1YUJGQ3uC088aAJnHZx91Y9c9Wt1nvtGEVhUeWcz4bNUpgTLWbmvyl0Kec3Wnfs1pEmZhqpzWTe+3oIsk/PEF0s0+rt1Ht2ZFuXrh9CCH0vi89evWHBeDYrGDnxbwRo8W+LNKfHY4GlXbnVDlgnvkc73/mofJ66Pz53Pp8afPcMfLcIk8CdvJuyYud8790+BY/0qF5bnynuHOcH/9fUz3y+ZnJRHa3wyK5YS31W4U+PVMnNnxndbSFCvhFY7D6nkMpWQBKJceqcURcWYkvB+a2iPM+dTbTxy5ChKSoWlC69zGMv3weW9yMTnfUW0kPvOJM5Xw4b0NiXOJVNRHraddVM+nQpZ4EkS3+wrRTJT0mHIFFTg/bbjRIhK7c6jJb4oYyOUM9KF2itrh5sHYg+PgnnvcNk35jTxzVa59D08Iln5QRHe+g4d7lyY5oXvbivV8mhSoXggHy97DZnEeouhx7/k9WsXwj/5fqO1lQ+W4HpkrY5lodaIZ80d8qTMbkwy4eXKJMF7PbORygLuOHtEcVro9u7OjbtcMIOlhvO071ceN1hTJ+2dro2tt9DTuqO58nrN5FPmd047Hx4MmxbWWvHWkbmQDNbN2C1FcpnDNhfUOofx8JhVOL46g1SyTPQGJQutpQhLkECgzRKrPEkZpXLSE5kbrSaaZdZe6CZcxbha48YdMwteG5KUXBuCcrL4d6a9s8wd08beYjpzk5ndZnR38IpNGazyVUn0WrnYkRMLH56VzVbaw5EdCR31X85k+xFTLnQ2Tt04TImpCZZB28SqlayNbFC9h+a3dUApsoc+2jKiM5Y601hdi9xi4kom541MRzxzFmXvkbh0VA/t9XA9dxc8TVSr0fxYaFAz0AYGrVrnJUY3ayLLSlEnubKGhZteO6olJBvizO4U4CrC2hpPGpHek82DIGGkXilaqK6stZKz4LXT8oLZiqnTeoQZNKmYC5BpAtU2So51n/TOpIlJApdV8k5qUch1yewEDtBHsbs1YXXlaTBw1Uby1a+nRPr//Upk9h7klVmFjuGWKBra2s0tkEtDw6WmSIYscENQDxQd5kEW0EEOseBnri7MCFmUbYQLzDliqIsKnoQ8DLEPt+2FX4h7Y8qJbMGQbt0QGpJyFDEycHrNaBhtjzVm2zckxcruVuE8JfoQAbsLmkOCgQ2urYAkGdrOwiQ+IPDDo5ASWx267ZQo5rxvW0yYhwyLFBgeMQUNMsYhK00tkgWtkZowT5m7xbjvyq02nvdGSYnjPPFoO7IHN/jVPLO78bzBYWl8/3jgrx8bHy47c4G7HlKot6Xw+euJizcuz86bpfLJeWFvAVjeu/CL645Kp+QJ39aIJZ0KZ3GmQ+CnyMZpgltTbGxyVoOld24jxQ1ifnEosDcwAhcHjldDNLG3GEhUDwmFa7DOpQtko7lRazDCkwit22hTBElRKGoW3KKZsiEzEoMsGRKkbLgpuXWu0hGiUVFi9a4CVsNoKhrmNZWQnFgmUE0ANuLix1Yx4CIa8BCBKWf2GpscUUHTkdYTkuLwlT2kd25CH5KpnBKtWvg7BLw5mhQhGpT2Ejn5G7w8Fz405+A7G8KnvvEkM+ccW5htNw7qHIh7dE85aBoiWN/ZCQrKzZ05JzZNvE3xDHvXGnMzcslYUj5UB2u8LpkHixjlt8V4LTsi8P/e79H0eue9O9+bY8OwmfP1vjN5ZykHXquwEI3007bRO+xDIvfNbecuCa/nxLta+J1zRrxwG/SIg8Tz50ph8/DpLjma4IN1nvpK3Tas7VSHowr3vVA9ZBI3cz5cHB0BR2gjTRk3oZECu1WENCXOUqmp8FiDHX9Q+OyV8bwmHjej7x0tynGG7db5RQs+8mdL4midr1bnhwu8faP87Bvnm8vO95dEOihHa9zdJfK50KvxV8/Ojz81TudC78bbHESHX9w3Fhk8+7ZRVVhK4e1B2VPiF1V4Jc6rGd4NKQlA3Rrf7o3Hmig5CrxNCz9YnOse/P4P1yury5CUKnXtLKVw7Y6kwqnMnDXwlK8twlWsbpwFdiLV99J2ppSiWVTlVRJIJxKNL6RwxPmuRwH9mRqfzJE4+Yv1hlrgzg4lY4THQtR5WFcyxpRSYC6nGVQ5uvEkE02MgzmbCfQhbXTn5mGgPKlS5sxSQ6a0ZKVMZ7Ye2uKbJ/5py8NXphw3p6SFQ3KemzMLFE08tc65zHQPOd27+i8/fH/tQvi//dkBd2ey0K0sfR7JYAvFhW4KvXFwJ1NIt1fc3HhyZ7PEWIrQ8mEcUFEIy+WAbxspJeamFG9QYtLyYqLw4WZsL1GZnkJ3uzfywxw8yWvgurIL09pIHoQIADqU3JllYdoi0zqTwyC3dqaUUVfwsSpqwitduB9xubt26MazOmmfmEToHMhZ0T0ysU0F1UwdRX22Gzkp7o2DRkz0ISeyC817IK2ssQ8m7WQpFLaqIYfoHUkTdd0QySxqNKkcJPNpd7Rk1iJctxWbMzIVli0mGpI8mofkqFVezcIP0ZiEMFYV4vgy0dsN18x1v5CmCbNYQWzeg6JhIVe5dkF6iO4tK7uFzlKnkcbWjMIvnUu11WBAS6STZRmfnwEWejfxgZOT4Sa1seJRGUEMgV6L7QEfkXVRPCtHV3YFy46L4W0CyexsVDGqdmqfKHlh9Z0qeSTLRTSoZo1I8AYmGzPOAohrFHG+kiXMLKlP7LHDGDzewINBTBIhKCgHMqk7q0YS3m/7tVpDSGgX6uC4hlw4kuU0xeTazIPHqx3vQssxaUOUJQdhY0dwDx7r3aTcXzYSmW2ooSWFgaVaUBeaRXTnNgrqQiKpszdhziV0pRKmpGOZcTeEeFAv3rGSqXtM60UTYJQ505rTRClzNBg5O61pMKbdyQXyKH6ee4tCTYxJCxc3lhQF8W4JL8K+R7pjdePWnfOcuGwtUG20CKxxi0lWziNpTfBdIFVKy9ydBGnO13ujiDJr4maV17NjtjOLsnuFnnn3uPJ6TuQlwmEu12gsa6uBV7TMMRkP1vjwuHHboJB4M2W+2yrZYds70yR8ekjMZSKJcZGFtyflucPt6qCdXYzc4aE7Mti085TRCisRQ27NmebE5E4Lvg7eI6FMSpiYZ417yS3GuyeUXSOKNYxzMuQpcRiphPG3jCCZJHkUrsretihelQhiQam9kSywZ0JnF2eUwfg42N2d1mGec8jjzKmmtMEXV00jzCXkLljQYxIht+gmSI7mJtm4biz0sNYFoZF7wouDBBHGktCJrWY3g6xRdFnojZtXxBIdSLn+xvfrd7fKrMJ3KeKit2mmY9SU2Eey45TC0NkQjnSetsodnX06cKJzLOFfmCyaSCOxTfDusoUkrG1A5e008WGPKGV1I02Zbzo44V/Yy4GsDHJA4pBg84Yq/O7r80cM4tqcTANvPFfBpHCXlKPvfHGc+JteyDnx01x56oFnO5nzs9p5XcIz8OxKk8x2ubB7w3DOy8JZJuR8wOncmvP9krlU5zTwbz9fnR+flHe32D6adZ66864FW/yTaYrCSYyvm7KPwBddlCbw/9wnPi2x1Xheje8tzoembIOicG+dX7zb+OJcaLnzbRPy6nzQxPtW+ULgsSV+NDlfXzt2aXx57dynmS9OiVfXkDH87Nm4m5W7k/L5EiSST24Hfu915nntfHlTPks7vhtqwr94DsjZM8LdrGwk1DOn1Hlvwqu58Mm4fm8o1GcuIuTpGLjEKYMnine6C3dzZukb97sxpcwsEkFPw0+0JOUgE3PJI2Y8wlu6JJ5vV1QTZ+08juXh3jvvJGSKOzC70qwBxt52mJbY7Jryxd0pti5uPOwJb3v4PDRi3MNsRxBeyBSFH8/RWCZvfOWJSUKWtraQRZp11J1nT3wqQY6yJJzVKX2kkxq8ofHowmVrmGZu6y02hmni7m9Z4PzaZrl/9/f/0GWsJIsYRYLKsGCc3VjU6QSSqvnO4opICrmAGF4SZ40Y3OSwpIL1zgVBvSIlx7q5O7hi2fjEnSuJ2/iRG85GYW2dZw39Jq5sGrG5u1eKOEomE7GNy0CWzUO0b+KcNKFeyWSSN8CZ1dHknPqgYWSh+I57Yh7a0CShCi1qNIvJmTfnkGMamlLi1rcxBX35FJwyOJVJjdYahwyuiTMxQYqUM2XKPeKYLRyySY30YuTQ0PBmCX0ztZPL0NCJsJqRpEQhSWLdNrpkTu5QLExxTUe0NTC4xzlP1BYkCNE+APOx5tp6TLQBnurE7jW6eW+QIuTDXbDQMNAI12rTeM9NYuIiIqH/tkSnYj240qqhEzfzgU7ToFAMUoSMCTM5rrtmNgw9BK5JnWuPJsPMPv6sSgR1NI9Y1TZMayqDYT1eEyF7mDTc6qFBjp+9evAVr3vnkALqDy/GmyELkSiCqhU6whMRNZuI8AeXMI7+r+/f/RbNcv+6h7QxmK2KDB1s1CzVg9ZAEmygqlSEAqwBGh6yCcEtHjizRBz4y6p6rTYE6THNLxJTqq0HwaWjobn1eH/EhIv3kSLp9K5UFaxHCpv1aFxiGt+RlAPJlobByQKh2FVIFubKrIJ5uOTj6o2eK4gYEkZWjeQx8/hgrQ++rcGiaWhehX0kiE0pvtdunULEa6QcmxAG1WATOKgxLxNbr2w3HdQJmIZ8w7ojOVa8vWWm5JxS4me3G0mEnYgG10Mn9TCSXtYgIBSNw0oqXLfYYIztcdAMVLjWPWClHqYj70YugR2MCWhMLKcMngMbJeOawDvuGqtxG0xQQv7RBLITJuWhs9bR7HkPjONQQFE0JDW9O7kIKQmtEyY7orgSdyQreGx5vPtoBkNypGJ0BG+CqmOD7TtpGGbdQ5fr1cijUDQ6cwkqQh8TawCTMHLtQ7arGitSH7z32jolh+ErWqzYlghBgjFist16SKUCkxMadsnBEXZCN9wtAiXcnf/i//zHv9G9/B/+/ve9qFAwtj62KrnQJLH4xt7h2pxFnG/3xlIWXiUjJ7i/bZikkKMAfX/mzTRR5xN3EsQEN+HbNe4FaxuiSsqZY1K2rnxw47Uob+dB8RHYUG678Ik66yAGLZK41MqqgUqd+86lNUyUU47p3KlfySpcdAZNvKVzQzAJXvvSKx/GOVissbpymiaSwEMNFNbdrNTeWcZHcB2G3mWOiO9F4dri705TPEP+cg0m/qTCj0qPZ4LEfX+rDkX5k4Nxrc5fXjsPljjlMMBijefB1r4r8FXLJOncHTMP726UpDxX5/MC/9pkfIdyl52vtzBXH7KwGly787AZS44zK67BuC++uXWKhGzroIK5cyiZtXUeujArXHTiUITP1Pjm1nmbJdjAfeVJCp8thbU52/i3T1npLiRx1u5sdQ18qsb0+NIqXaLIPI/U2efaue/KmwLfK4J04+ctbuhunSTwepp4qjspJ04C9xUyxu7GopA8YpMhqEQ5JV5NOWgyHkFrz7XytghXUy6t8v1DYXbnoUcgBkDz4CffRlrj5znQktkaXzGxNg/TXAtD5MVC63um86yZE4Gu3WpsDj9055wE9cAjJgnjdMkDyBCeXv67f/Hlr7xff+1C+N/5vT/wKAKExY0yfsFFnLPCcegCN9tpvlMIPeiddCD0mi8P3DxE3XFoR5HWNUDrBwnaw+yKpoanzM0YLn3luQorzoO/TAidAwmT0B1m4kGI7aDOcbj5yyim1JWDRljGESF5Y0khWTDfg0nqjpIgNXqDw+CuJg3TCRbGH3dHEmRLhMWscdCEeSNpFJpzKewVwFiGcW5OgbnZfUP9xSiYyBoPXOtRoHkKTuPLe6VSSHKLNYQkUg7W71Eaic5VlNYz27jYzJSujW4aaySLlWxv8R6/MIE3048mPIj3VYniO3kwje/7zJR2ihhzLtQWpqsw30ROahy0xjRwZzUL0oJJLClFxLY2qgX4u5piYxW79UiRWkQ+FsLraDYmhllT4pAWj1VIVufSBvLNjDyK1ZfIYFNQyyM1TjBijf9y/QUPwaMQHvizNmq+6vG3GxPJWhg7CXTPbTzc3Ttvhha2i/LkL4VyhCkkCbj7//L+t0eN+M/+9O+7eEO1YKNK1JRQiYMuWaydkWgIVIM9nTB2A3oY4C7mHAZ5QjW+VlpM/USCMQpEUmSREbwAai8T8fgewZuNIlUU1IQ5aySweaQpLkkxVQ4Cl2pcmw/nc+DfUP+ov55SyBCSxOQuNKtBD5hTwnC8C2FvVRKGDeKBpEi/Y1BSMkLO4XBu7vTmQRVRZS6JWx3kEJy7EX4hxLUpoh8pIbMrSJgji2aah6no7QJaCrs5rVVay/QWxq1zNp4afHrM3EzY953eMxPOinOendScaZp43ivHHHKu4Kp3jmWi907WmYd94yQwHw58e33mVZrC7V2N0zKz1jb0ew13Yc6RpLjunVMp3GwPAkoLKYgg43lmg6se25mDCJduzIPy4wJm0WEl8Y9NSUoJH8hAxtQ/7o+h87aEpKBOIE5JiToMp93B2ouRUkfDGUxpSWk8o0Luo6nhLUeioCmi/pINEhg9C1KBuYWPQkMD3logOhvxHMnigRlLSiNCZpLLSIyUoXcOrbWbAzFoURX+yz/7zfBp//4PPvXFdmw6cRlv4CdZQk8r+jHtMa4755jzaPCCHvDcnZRh351aMq8UThpF9c3jPBaBpxr38tUiTGEWI7uzu3O/xTQyWyUrTFrYUGYaNy3B/G2RmCg450lwLSzZebpVvrtWjkVRF95356wRrqApc8yEDEESb6Vz34VjFu73zlwUsU61cc9LFOKJ0BSrCk97o/swwk4LvzeNZL/uPDTobYc0cZ6ED9c9iikVvjg4z7uza8Krhan3paPMCUV4bI27LMiQF/zBnYZ/ohpbd97VKHA7mU8XY1+NT15lHleLorR7nFdd+GTuuGaWLNyvxt0s3KpzcOeY4HiY8N55nYV//uzMs/Jmyby/X9mmxL51vt6c7x0zWxUORVnXG6tMnEvcDpeWeLsoD2slA/ddIwQlZRTY3DmkxFkaj6bcFXi3duYMe3N2gsSRcOYEtx7bzS0tzGNbmbx/9Ft1ogl3jwJ5l9imLFl4atF8td7Z6kbRMKabh9G5WXyGJc/xTHbIYqyuSI/tW5agzEAMriYLedC1Q86FknQ0+p1nG6FQDieNfIrDVHhuHmnCGoV38U5OSiWx7Tf20c/e5Th///tf/Oqz99eWRtTBBE5JPtIKikQXDnHRiTQWnCktdIG1Nh7dueHsCeYmtJwoYxIgHrq2VSYe1kYr8MoCXfKFGj/wQm2ANLwLSQPJ4yrMlmhmLD6oCwi7ONfuqDpIIblx85guTVXx1JlfEmdcuIwC8tQzKo5YoQy0TyZWGJIiQjS7cMtQ3JgRSJFYlJpRtcaKr3X2AmutWCpIj68/ykDG9TCrlKTYXsmSPqanqUQBbBjuQWrQkW63ttBvKlfMFdVEV8Obc1oKjrB1wRHMGnggZGrfaQ2kz/TW2HPEBQMsx461iJFeemDbTAq7d5pHspM35fr/tXcuvZUkxxX+IjKz6vLV3WPNWAvZ8EJLbwz9fv8U24BhS5pRj/vBJu+9VZkR4UUkOd7IEEYawcbUWREgm7x9H1WRJ85Dcz25VUdc2EbjitFHoUbQyOE96SrwyGQKLYUFR+QWLYNzNKQIYeny72PemHxndEPLmrWqfTLM4ll4grJMB/hFsvrX3BhhmAuO0vnB/KBhNE/GuBg4eREJDUoUijgewYjCzYw4Cw+apmFQw+kCSGFFuJc91/uRbPcz8HZWCVso6sFV5FWi4GTetCq4zRrgnxDiQW2NghIl83xjBO4pReovJrmpsTWL6ZjKvM8sJEgJi5swBBhB0cCiY54pJkQgUlmqsbvQRGZQf1Z3Xnsa3SyM21Yy5swzNquoYpZLXI0KCGPAk6cBt0gOzu9a4fPOK8tZqrKNgQr0MWajomIG75Y09YQIS1PChLVlIkWbjv+YiRk5cMW8oY75f88NCP4yROUhrKrSgU+bowpvloVlGez7NIaGzQSC1L2e+yAqEMH7S2G5ZMGMDaeq5apeKx+23Pa8P3fuGvzNzQmLPKTu586ns/Lrr254v3duT/leW6Uw9s5jd87XkavJMnhYhXNAf37GrPL+unN7anyzVrYY3EkaJS2SM5dIKVEtynlksQiarJhNOVOdBMmYJUglxUm0quwjPQ4voc1Fg5MqewlGn/nMwWxuE5a5MRk6r3sBHn1GZ5Zpts4Dk5Kr9pJVgmn0i4rWNN7tYyYMhVNrw3DK1JibycxYz81UaAWTaeoeFJ3Ddy2MuQ1pZDKOME3AM094JwdRs/HDQc4MqYUSJR1Vf4EEmAinr3fcFeFtK5goY+w5ZEnFe3DSHO6GB1/2TF1YRFgKXPbOfp1bO82M/t9bUDXvNd0HHyybAm9U+LoG2zXwZeE8nKbCL06ZD/twuuViwdLynrZ74aTJ3O94MpB14SpCjOC5J/vWmmCl8tUpaM87H0wJkt38dM3h/rpd+K0oD6eVLyP45mHlMl38704nzj24rc73V+ehOI/d2UbGj8rcpDXb+XAJhjZ+0fIQDaDeKa48nBbetiyO+P4S3BbhzVq4XYPHntsLGYPLPjhV4U0VzpsTM3no3z6ltEYjWfhahBvN6LZ/fzTuiqCfO/cn5R/eFbYRfBrw3VPnuyfnn3618t3j4O/ucntwq/DpGvzhsdMuwcfunCT45a3y6QxP3Xkahd+eg3+8r/z6K+Xjded2KTzvzvPIrfRJsnRE3Hi6GNigKdxIZes7m6f8Iz9kG0+l8lCV50hZShIHyfpGpFHvtiVx9GQr1je2yHi9EHktWGqaA/Z1ON9vGzcqdMmceEplOCx1yTbBUkCUz5czta1ohROdPTI5zBxOa0U9DzgasHvMrSGIFjorPUgJkxTe1tz8DYVmKcHdVdj2DGcQyY3Q4plXvkgQPjJeTipLpK+naNbN/y9euT8nPi3drUgynxbZPR9dMhGiFWJAMTiXgaMkaSzsoTybcnHluSfz1j0voFfJ7MqCgBU+xI7slS8l+ENks8wtyVJVghvJYJatw7XAEyDmbJOKatTUEYYkKzYNDhXDwllcWSObZ6QJapXfRaGLUzHuZoD6ooVHGbwxWKXgsaOuvLPCvRjRs1+9685aX2I6lGaFU2TlbSlBRGMfPR+/Fs4Uzlfls8EiBa8799a4KYUvcuZtVJZ5ulqXE6NfieZsPRu4VgDPNXirgbnAkhdPhmIq+DCufcs2QJfUua2Caq4Y3ZR+SRNh8eBiW94kysLtZIZ3cy5qlDDuUR5k0KoRJW+u55aVi07QY7DuDZ/V0yYL170zNCuTowfOxoh0fIsHrlkwINLmczMYpOj/SbIKt48NauUSMSu1jTF1vrEn0+xTMqIyK7HJC6YIs/Y44/lKZBzU6soV6Go8hyCeubJOVrOmWiCH4x6BaWWRQo18jsaQPPjMaC8lUwuqMLOkhS4Z6C0lGK8SmZ8IWnDP4aaVHGYt0iyiEkSdTurh+X3PEo21BEZBI6OwhEI3n18n8zrmAaxpzWxihaotZQyRB+GUsqTBrIhyKoJMpkICLp4tjeeZAx6bcR3JBC0t3e83SwrIPm3GqeTqsWcYKU2UEckO5g0sh/3PNlJ75sHZd6Cw7XNLVRuXHtSlvq7JquS+yMfg2dKUEVJ4WBtrBTG4FmF46kJVK0HwcbsSHty0vGYtVbhZUsZ1agvPq7A77FfHNXKl2JJ5LpJaztqCvitFUlP7dm08bRsljOchLBXKLvzucsE8eL5AWIAOTi14U5SyCHtUeldshy6D5+55amjK+bqxtzSh9JGRZBkXFzSpfN57SjmA01Jxc7ZXt2H+rES+9mKptaQae3eWed0QoJSMacq/nVpxqLkZY5ZreNCZtb2WzxlUXCfxO/XGMnPxsvEsJSSnsjKko5JEgpZBN1DJHOpZfJlboZr/3qzwEpYGKbkoJQdeRbHihOVB1cSTzSozqokpkwh/EWThMxmjTLP0mPK1Vwboz8CihTfR6T0yHnK2cWrNtKRFBldpfLcb74rP571Qa2qWVZWYj/vLPnBJff4XF567cbuceHdSnnp+3lkycN2Ac60UM2pkzqwX5ZvFePbC/TS69wG3S6FYRqO9tx2JjGisVblxp63KOYz3X7LJ7TaCjyNlJ8ssgVg07y8V4a4Kn5/3fKuF8+HzEy7KY11ZCGRZCDd+sTYunjmyWaFz4t6C95esPllV+ebNLaXkJsC65YHfnZuS5uTPj1f+w5W/v1Wuw1lb5c19ZVVjWSrbvoi9lwAAB25JREFU1XjcB4+bsaMUS23vfc0YTI2gqXE/WfrPIry9qfz+bPRw/vOc9cFFGv/1cWdY8K/nPHReQ/jlSfjlw0pV4X4Y5x48jmT3P5/PWAhflcK/fIFvrsKQwrcW3DN4dOHrteBV+fZ5oyjsnvXMEYJf95RDSNDmxmIItBiZbVxh3zu3TbhG4UbGHArh09MldcgEtazc18YQYRVBMLbp7O5jcNsqt23N63fkQCpTZtrDuF1SttIt+Nu7G7YxqLXQaFx6Jmu0Umgo1XMgvbhzNwmki5OEk89sc4STGB8uuaFwd86W0oy3rXBbKp3G2HsmyMxug2fLz0SIUiKbSy8WmVIhWd39x/CjpRG/+dWvQ9Q4hVBmm9yqOVffWPAEfAnL6KJi3LpSXvWYqRcWN6xMc0TkSq2b0SV70yuZQuCW66lQp0W2lolIGj0iXksfXjTLKWsIKMnQxRyE/+d1qyC4DojUo1UkIzcmo5jNWMY6Oe5MOpwNXFS0OMOCN3MQbppRaL00PHYgNcyVzP0VC04VmtsM4U/NpPbMLd7JdZ2WZCIRZ/WazEmk/jr6TrEri1Ye1szgK2Gsa3DjytqmBomO74YXA2kZRbKNlJtIQ30a0qxTSsWGYJKD5NVrOr8j8zgzKUKoDntJ+ahZ/uxNydSOV2atVNRmA6A4+xQgXlmSvTPn2ZOJQfICaZpFI4P8oLhnasFu2dYWlvKTUEFxdhO05OO7EcXIwXUmac2bq1AlX/NKRvXsYTPwf6GKYraxeaNLZtC+vMYvJRkjnD3k9UbzUcAjJ6QmhWWaemBApKlwm6/hWp3q8Wrm22eiguGMEP75+w8/mTTiwIEDBw4cOPCn40czwjk4eSYreLpJN9txKXwALGy2D6WB7QsK5ClzaDaOvCmwWuYjRjFk7OzSuDB+SIeYK4FWKqtXbhdh6VmkoNK4lJQ7PMPU/DL/kvyw0soxOiUH0+yhCBopJi8kVY+m2cm1UXpnW28Qt5RBaKCWv9nFaZGsyccyks5H8CrU2LgV4RQ7N9HYJCUHWpNZKvMEE0CE0pfJyrmA6uvgqZEmMdXZLicgbYFT5WqazNolkkG/whrGWoLFB+9a4aEWVm64WMdn206NSi+5Ntw3J2ph7znUhqUeU4phkiyvivNibPGZrhDoNK4I5xDGGNSqhFQulrEsqU2NjAwTACPG4HFGkO1hbNOAtoTMWLIcFG8ko7mKpGluVEVN2TUPVWsFF0FEWaJwFmM1ZmJJFkdkq5lzIRlhjWT7mKa93WfqgRpLVMw3aq0IRrfCiDR9UVJP24GvEb5EMIqw287QmmkTptSaZq0Swi7OGMImQUOw0Kl/CroHF/54qPeBAwcOHDhw4K+LHz0IP6jnajmUVWHxYNcsH6AolyBd2pOpK2SF6OppBKNn7qtNPewSUDWNcN0e8HLNhiEXmla6C0/iPG6p8dtFuLN0Ubr8wL69QESSYdXUoC2aDGKfBrCrOFqUO4K7ktWwlYLUhofQtXGdJrRH6ZkrWqdRzQVTEC8MOkMybufkGSk26iCKsszHNkTmKi0NZ1jNzFI8NYlTt6rEaxuMaEVmkkN5Ge7FKDYgKuqdUeBBFiR2rnHiSzhLFL7dHBuKXIwaJSPPqIRm7m6uKyvsgzUMVckBNJSTBjdSyHLcjEwb4pgWuqfwPll4zTzalsZGEzh5oeOvRQdWYfOBseCz8rrPKlq3zCrdLPhsPk1+gMOmgxtVZHbVP5U01Ly4cVvkKvdSBJdCSBpeXt5pI2bxgmYpQxqbsvY3QoB05JZpxLzKkk4vaaCDAayjcSGlDpf5+mUaww+bh4xtStbcPQ8raZyCMRMISmQ7YFiwN+Fkf3726IEDBw4cOHDgL4MfPQifo1It3eRnz1/Tx06Is0Z2iHdzrKYec8OxUL4IeDhRkgkuDnjMfuggZEWKoTSIwS6OxUKTrCiVksYeQTgT5CQp+MygszmsFC145DAqKBbKNfrUyCknUe4l240eZ15j92eiLCxSOFEYPthrobCgfka9zfpnz/pXnFYbPdJf/FxJsRqFZ1MeJKUK0neKbJhANTjLRhCY5Zo+MG4RVincSKeUwvMwnodRm8wopw4CRdaZmFAZPmYCQKXFzkpFSmXD2Vxo1lk1ta5dGt32bN6qTlvqzMNccHM+ajDGwPcZN4Wz0CgBtThrdYrD/XQzDd95scK4DVqrXLoh5nhV6jS1BGs2yUW2j50kY5hGzal3M9Curw7u6xj4aeXiZR5knDcqRKROudZMfRg1wAdjNg1epLH5YCdNCi/DamjLIo8i7KZsMvAinFDUsk1wCIBy13tKJbSxqaGUNMyhL94/7npwlWxWEnGuCt2yMjwiNehesoM9onDxmXGoUB22v0KW8IEDBw4cOHDgT8OP1ggfOHDgwIEDBw4cOPD/GT+xhf3AgQMHDhw4cODAgf+bOAbhAwcOHDhw4MCBAz9LHIPwgQMHDhw4cODAgZ8ljkH4wIEDBw4cOHDgwM8SxyB84MCBAwcOHDhw4GeJYxA+cODAgQMHDhw48LPEfwNHYsd1MkibDAAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(2, figsize=(10, 5))\n", - "\n", - "pl.subplot(2, 3, 1)\n", - "pl.imshow(I1)\n", - "pl.axis('off')\n", - "pl.title('Im. 1')\n", - "\n", - "pl.subplot(2, 3, 4)\n", - "pl.imshow(I2)\n", - "pl.axis('off')\n", - "pl.title('Im. 2')\n", - "\n", - "pl.subplot(2, 3, 2)\n", - "pl.imshow(Image_emd)\n", - "pl.axis('off')\n", - "pl.title('EmdTransport')\n", - "\n", - "pl.subplot(2, 3, 5)\n", - "pl.imshow(Image_sinkhorn)\n", - "pl.axis('off')\n", - "pl.title('SinkhornTransport')\n", - "\n", - "pl.subplot(2, 3, 3)\n", - "pl.imshow(Image_mapping_linear)\n", - "pl.axis('off')\n", - "pl.title('MappingTransport (linear)')\n", - "\n", - "pl.subplot(2, 3, 6)\n", - "pl.imshow(Image_mapping_gaussian)\n", - "pl.axis('off')\n", - "pl.title('MappingTransport (gaussian)')\n", - "pl.tight_layout()\n", - "\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_otda_semi_supervised.ipynb b/notebooks/plot_otda_semi_supervised.ipynb deleted file mode 100644 index 6386840..0000000 --- a/notebooks/plot_otda_semi_supervised.ipynb +++ /dev/null @@ -1,294 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# OTDA unsupervised vs semi-supervised setting\n", - "\n", - "\n", - "This example introduces a semi supervised domain adaptation in a 2D setting.\n", - "It explicits the problem of semi supervised domain adaptation and introduces\n", - "some optimal transport approaches to solve it.\n", - "\n", - "Quantities such as optimal couplings, greater coupling coefficients and\n", - "transported samples are represented in order to give a visual understanding\n", - "of what the transport methods are doing.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Authors: Remi Flamary \n", - "# Stanislas Chambon \n", - "#\n", - "# License: MIT License\n", - "\n", - "import matplotlib.pylab as pl\n", - "import ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_samples_source = 150\n", - "n_samples_target = 150\n", - "\n", - "Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)\n", - "Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Transport source samples onto target samples\n", - "--------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# unsupervised domain adaptation\n", - "ot_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1)\n", - "ot_sinkhorn_un.fit(Xs=Xs, Xt=Xt)\n", - "transp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs)\n", - "\n", - "# semi-supervised domain adaptation\n", - "ot_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1)\n", - "ot_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt)\n", - "transp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs)\n", - "\n", - "# semi supervised DA uses available labaled target samples to modify the cost\n", - "# matrix involved in the OT problem. The cost of transporting a source sample\n", - "# of class A onto a target sample of class B != A is set to infinite, or a\n", - "# very large value\n", - "\n", - "# note that in the present case we consider that all the target samples are\n", - "# labeled. For daily applications, some target sample might not have labels,\n", - "# in this case the element of yt corresponding to these samples should be\n", - "# filled with -1.\n", - "\n", - "# Warning: we recall that -1 cannot be used as a class label" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 1 : plots source and target samples + matrix of pairwise distance\n", - "---------------------------------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1, figsize=(10, 10))\n", - "pl.subplot(2, 2, 1)\n", - "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.legend(loc=0)\n", - "pl.title('Source samples')\n", - "\n", - "pl.subplot(2, 2, 2)\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.legend(loc=0)\n", - "pl.title('Target samples')\n", - "\n", - "pl.subplot(2, 2, 3)\n", - "pl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Cost matrix - unsupervised DA')\n", - "\n", - "pl.subplot(2, 2, 4)\n", - "pl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Cost matrix - semisupervised DA')\n", - "\n", - "pl.tight_layout()\n", - "\n", - "# the optimal coupling in the semi-supervised DA case will exhibit \" shape\n", - "# similar\" to the cost matrix, (block diagonal matrix)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 2 : plots optimal couplings for the different methods\n", - "---------------------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(2, figsize=(8, 4))\n", - "\n", - "pl.subplot(1, 2, 1)\n", - "pl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Optimal coupling\\nUnsupervised DA')\n", - "\n", - "pl.subplot(1, 2, 2)\n", - "pl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "pl.title('Optimal coupling\\nSemi-supervised DA')\n", - "\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Fig 3 : plot transported samples\n", - "--------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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Go9FoNJoBhzZwNBqNRqPRDDi0gaPRaDQajWbA0as1OBrNkSQajVJeXk4oFOrrrmgOA4/HQ1FREU6ns6+7otFoNAm0gaPpM8rLy/H5fIwYMQIRnaGhP6KUor6+nvLyckaOHHnwAhqNRnOM0FNUA4CYYdAWjdLfgjaGQiFycnK0cdOPERFycnK0F+4EIRqP90ut0ZyYaA9OPyZmGLy3ZzfLykoIx+Pkp6Zy2dhxnJyT29ddO2S0cdP/0f+GA59oPM47e3axvKyUqGFQ4PNx2dhxjMrK7uuuaTQ9oj04/Zi3du3kzd07SXd7KEjzEYzGeHzNaipaWvq6axqNZgDx6o5tvLt3N1keLwVpPppDYR75bBXVgUBfd02j6RHtwTmOiMbjrKwoZ0XFPuJKMbOgiNOKhuJ2dP1nCsWifFhWSoHPh8NmByDd7SYUj7K8rIRrJxYf6+73O+rr6zn33HMBqKqqwm63k5dnBsNcuXIlLpfriLe5Zs0aampquOCCC4543b0hFouRm5tLU1NTn/ZD0zdE4nFWlO9jZWU5SsGpBYXM6kFr/OEwn5bvoyAtHbvNHBNnejxUBWJ8Wr6Py04Zd6y7r9EcEtrAOU5QSvHs5g2s3b+fbK8XQXh1x1a21dVy27QZCWFpJxCJolAJ46adFIeT6lY9qjoUcnJyWLduHQALFy4kLS2Nu++++5DLx+Nx7Hb7wS9MYs2aNWzatKnPDRzNiYtSimc2rmdjTTVZHi8CvLxjGzsa6vn61OnYOk05+iNhRKSLBnmdDmq01miOY/QU1XFCpd/P+qoqhqZnkOZyk+pyUeTLYFdDPXsaG7tcn+F247bbCcViHY77I2HGZOccq24fU7bub+a37+zg7ufX89t3drB1f/NRa+uSSy5h+vTpTJgwgcceewwwvR6ZmZl897vfpbi4mJUrV/LKK68wduxYpk+fzre//W0uv/xyAAKBALfccgunnnoqU6dO5dVXXyUYDHLffffx9NNPM2XKFP7xj390aHPjxo3MnDmTKVOmUFxczJ49ew7al+9///tMmDCBefPmsWLFCs4++2xGjRrFkiVLAHjssce44oorOPvssznppJP4xS9+0e393n///Zx66qkUFxdz3333AeD3+7nwwguZPHkyEydO7NJfTf9kX0szm2uqKfKlk+ZyWVqTzra6WkqaumpNjjcFh81GJB7vcDwQieg1OJrjGu3BOU6obg0g0nHBpoiwrLSEzbU1vHHjzR2ud9rtzD9pLM9t3ki6243H4aQxFMTrcHJa0bBj3f2jztb9zTyybC8ZXidDMjw0B6M8smwv3zxrJOOGZBzx9p588kmys7Npa2tjxowZLFiwAJ/PR3NzM2eddRa/+93vaGtr4+STT+ajjz5i2LBhXHPNNYny9913HxdccAF/+9vfaGxsZNasWWzYsIGf/vSnbNq0id/97ndd2vzf//1f7r77bq699lrC4XBip8qB+nLhhRfy0EMPcckll7Bw4ULeffdd1q9fz+233878+fMBc7pt06ZNuFwuZs6cycUXX8zEiRMT7S5ZsoSysjJWrFiBUor58+fz8ccfs2/fPkaMGMEbb7wBQHPz0TMoNceO6tYAdKM1CqgK+LsYLW6Hg3mjx/DStq1kuD24HXYagyHS3W5mFhYe495rNIeO9uAcJ6S73UD3u1Ectu7/mWYVFnHbtBkMTkvDUAanFhZy56mnkeX1HsWe9g1vbqomw+skw+vEJpL4/uam6qPS3m9/+1smT57MaaedRnl5Obt37wbA5XJxxRVXALBlyxbGjh3L8OHDERGuv/76RPm3336bX/7yl0yZMoVzzjmHUChEWVnZAds8/fTT+cUvfsGvf/1r9u3bh8fjOWBfvF4v5513HgCTJk3iS1/6Eg6Hg0mTJlFSUpKod968eWRlZZGamsrll1/O8uXLO7T79ttv88YbbzB16lSmTZvGrl272LFjB8XFxbz55pvcc889fPTRR2RkHHlDUnPsSXd76Kw1L2zdzIdlJWS4Pd2WOXPYCL42ZRq5qSkYSjG7aCh3zpxt1aXRHJ9oD85xwsjMLIak+agK+MlPTeOf2zYTjRvUBduoC7Zx/QuLAVi04NpEGRFhXF4+4/Ly+6rbx4yKpiBDMjqKqc/joKIpeMTbWrp0KcuWLePTTz/F6/VyxhlnJOK8eL3eQ9oWrZTipZdeYvTo0R2OL1u2rMcyX/nKVzjttNN4/fXXueCCC3jiiSeIRCI99iV5EbTNZsPtdie+x5KmLjv3t/NvpRT33nsv3/jGN7r0afXq1SxZsoR77rmHCy+8kJ/85CcHvXfN8c3orGzyU1MTWgPmomOnzc5JPYSYEBEmDRrMpEGDj2VXNZrDQntwjhPsNhu3TpvOhLxBVAX8ROJxvN3saDhRKcz04g91Wm8UilGYeeS9Vc3NzWRnZ+P1etm8eTOrVq3q9rrx48ezfft29u3bh1KKxYsXJ87NmzePP/zhD4nfa9euBcDn8+H3+7utb8+ePYwZM4bvfOc7XHzxxWzYsOGQ+3Ig3n77bZqammhra+Pll19mzpw5Hc7PmzePxx9/nNbWVsCMMF1XV0dFRQVpaWl85Stf4a677mLNmjW9bltz/OGw2bht2gzG5eXx7OYNPLt5A7VtrewP+Ln5Jb3OSjNwGLB/QVvCYTbXVNMSCTMiM4sxWdlddgEcb6S7PXxl8hTCsRiGUnidzm49NyciF0wcxCPL9gKm58YfitEcjHLtzKIj3tZFF13EI488wvjx4xk7diyzZs3q9rqUlBT++Mc/MnfuXNLS0pgxY0bCu/Kzn/2M7373u0yaNAnDMBgzZgwvv/wyX/7yl3nggQeYOnUq//Ef/8FVV12VqO+ZZ55h0aJFOJ1OCgoKWLhwIR6P55D6ciBmzpzJZZddRmVlJTfffDNTpkzp4OGZP38+27ZtY/bs2YBphD3zzDNs2bKFe+65B5vNhsvl4uGHH+512ycCrZEIW2praAgGGZqRwUnZOTh7ubvuWJPp8XLz5Gks2bkDgLq2tg7nte5oBgLSm5DbM2bMUKtXrz6K3TkylDU38ciaVYRjMWzYiCmD8Xn5fLV4yhcSHkMp9jU344+EGZSWRl5K6lHo9YnH1q1bGTfu0GNobN3fzJubqqloClKY6eWCiYOOygLj3hAIBEhLS0Mpxe23386kSZP49re/3ad9Suaxxx7rcVHzkaS7f0sR+UwpNaO3dfUXnQFzUe5fPltFIBzBZhPihsGIrCxunTodj6P3yUeVUpS3tNAcDpGfmpqYQjradDZotIGj6U/0pDUDzoNjKMXizRtxiJ1cn2mIKKXYXFPNuqr9zCzs3YjfHw7z5Pq1lDY3IYBSMHvoUC4fO+6gHiEtEkeWcUMy+tyg6cyf//xnnn76acLhMDNmzOC2227r6y5pjiEvbt1CNB6nMD0dMLVmb2Mjn5Tv45wRo3pVV2skwpPr17K3sdFcA6xgRkEhC8ZP6HGjwZGis2GzoqI88Vvrl6a/MuAMnPpgG7WtrRT40hPHRIR0t4e1X8DAeXn7VvY1N1No1WcoxUdlpQzPyGRGgd4ieaLzgx/8gB/84Ad93Y0eufXWW/u6CwOWQCTCnqZGCtJ8iWMiQrbHy5rKyl4bOEt2bmdvUyMFPh8igqEUKyr2MTQjg9OH9hz6QQ+kNJruGXAGjkPMkY5SqsNuEUMZuHo5PRWKRdlQXcWgJDexTYRMj5eP95X1aOB0NwoCLUAazUDCLtLuaOmw6TquFE5H77QmEo/z2f5KBqWmJXTLJkK2N4WP95V1a+AcDZ3RU1SagcTxver2C5Dp8TAyK4uattbEsbhhEIhEOLWX3pu4oVDQJXS5XaRLVE+NRnNi4XU6mZQ/uENqFEMpmkJBTu9lsE2lFIZSPWhNrIdSGo3mQAw4D46IcO2ESTyxbg0V/pbEyGruqNGMy83rVV0pTiejMrOo8LeQm/L5ep6GUBsXnXRKj+U6j4KeuuJq9rU0s6uhnqL09C+0+PBIo0doGs3hc/kp42gOhzqs0Tt96HCmDh7Sq3rcDgcn5+Swt6mpwyaG+mAb540e022Z7rwtccOgpKmRmGEcltZoXdAMBAacgQOQ7U3he7NOp7S5ibZolAKfj2xvSq/rERGW7t1Npd/PGcOGYxchahiMzMrm9KFDD1p+0YJrqQr4eeDj5TQEzW2YTruda8ZPZHIvBVCj0Rx/+Nxu/n3mrM93WaamkZf6xXZZvrlrJ5V+P3OGDcMuNmKGwdD0DM4cNuKQylcF/Pxt3doOWnP1uAlMGVLwhfqj0fR3BqSBA2bgvCORCM5ltzM0PYOrx0+kIdjG0PQMTs7JPaTt5jHD4K/r1hCMxhKLnkOxKE9vWs8Qn++YbQFNRq8P+pz6+nrOPfdcAKqqqrDb7eTlmV6+lStXdogUPFC59957yc3N5bvf/W5fd6XfYhNheGbmYdfjtNspysjg2gnF1LW1UuhLZ2xu3kHXDrZ7bn798YddtOaZTRsoSE/vE63RaPqaAWvgHC6dDYHffvoR0DtDoKy5iYZgMLEDCzBdxirIxupqzh2lRacvycnJYd26dQAsXLiQtLQ07r777g7XKKVQSmE7zoNEavovnbXmwU/MXGG90ZrSnrQGrTWaExet2keRqGF0mz7TJkIwFj3m/QFTNBctuJZZhUXMKixK/O4vXPuXT7j2L58c1TZ27drF+PHjufHGG5kwYQL79+/nm9/8JjNmzGDChAncd999iWuLiopYuHAhU6dOpbi4mB07zMiw7733HpMnT2bKlClMmzaN1tZWli5dyjnnnMOFF17I2LFjueOOO+gu0OYPfvADxo8fT3FxMT/60Y8AePnll5k1axZTp07l/PPPp6amBjA9MLfccgtnnHEGw4cP56WXXuKuu+5i4sSJXHTRRYmIxUVFRfzoRz9i0qRJzJo1iz179nRpd+fOncybN4/p06dz1llnJe7l2WefZeLEiUyePJlzzjnnyD5szREhahjdHu9LrdFo+poB78Hxh8Nsq6slEo/z4Ccf4XbYD+kP+pHYLlnkS8cmNsLxGG67+agNpYgaBqf0csHzkaY/GTV9wbZt2/j73//OjBlmcMz777+f7OxsYrEY55xzDldddRXjx48HYNCgQaxdu5bf//73PPTQQzz88MM88MADPPLII8yaNYtAIJDIDL5ixQq2bNnC0KFDOe+883j55Ze5/PLLE+1WV1ezZMkSNm/ejIjQ1NQEwFlnncWll16KiPDwww/z4IMP8t///d8A7N27l/fff5/169dz5pln8vLLL/Pggw9yySWX8Oabb3LxxRcDkJ2dzcaNG3niiSf4/ve/z0svvdThnr/5zW/y2GOPMXr0aD766CPuvPNO3n77bX7+85/z/vvvM2jQoER/NF1pjUTYUV9HazTKg58sx+NwHDOtGZqegeM41RqNpq8Y0AbOrvp6/rruM8LxOIJQ3tJMhsfTJUbO0SLV5WLBuPE8t3kTNptgQ4gaMU4tGHpE1gedSLR7bVbsbejwe/Htpx2V9kaPHp0wbgAWLVrE448/TiwWo7Kyki1btiQMnCuvvBKA6dOns2TJEgDmzJnDd77zHW688UYWLFhAWpo5RTB79mxGjBgBwHXXXcfy5cs7GDjZ2dnYbDZuu+02LrroooRxUlZWxjXXXENVVRXhcJiTTz45UWb+/Pk4HA4mTZoEwHnnnQfApEmTKCkpSVx3/fXXA3DjjTdyzz33dLjfpqYmPv30UxYsWJA41u79mTNnDl/96le5+uqrE/eq6ci+5mYeW7OKNstbUtHSgs/tJm4YxyQHXorTyZVaazSaDgw4A6d9FPT3y6/iqY3r8DpdLCvbCUBdsI26YBtXLH4ar9PZq9HVF2VmYRGF6elsqK4iFIszPi+PMdk5XeJdaI4vUpN2wuzcuZP/+Z//YeXKlWRmZnLTTTclkmoCuN1uAOx2e8IouPfee7n00kt5/fXXmT17Nu+++y5AF8O682+n08nq1at55513eP755/nzn//M22+/zR133MFPfvIT5s+fz9KlS7n//vu7tN+eFLMdm83WIanmgYx6pRS5ubmJNUnJPProo6xYsYLXXnuNadOmsXbtWrKysnqs60ShXWuevvIantm4HhEbn842uPkAACAASURBVJab62iStSbV5dJao9H0AQPOwGmn3N9ibRH3djnXGo3icTrZXFPNqsoK4oZi2pAhTBo0uEvOl9rWVtZUVdIcCjE2J5dxefm9johc4EvvkDpC03vaPTVH23PTHS0tLfh8PtLT09m/fz9vvfUWF1xwwQHL7N69m+LiYoqLi1mxYgXbt2/H4/Hw6aefUlZWRmFhIc8991yXxJx+v59QKMTFF1/M6aefztixYwFobm6msLAQpRRPPvnkF7qPxYsXc/fdd7No0SLmzJnT4VxWVhZDhgzhxRdf5IorrsAwDDZu3MjkyZPZs2cPs2fPZtasWbz++utUVFRoAyeJmtYA9cG2bt9xfyRCqsvFtrpaVlWUE4rHmTp4CJMHDe6yE7OurY11VZU0BIOclJ3D+Lx83I7eSbTWGo3mc/qdgRM3DLbW1bKuaj8iwvQhBYzNyeWGfz7Hltoa/JEIAN9/awknZecAsGDcBABe2LqZSDzGnTNng8Djaz8jzeVCEDbXVTO9toAbJk1OjHS31dXy13VrAIXTZmdlRTmjs7L5+tTpvRYeTf9l2rRpjB8/nlNOOYXhw4d3MQ664ze/+Q0ffvghNpuN4uJizj//fJYtW8app57Kv/3bv7F7927mzp3LpZde2qFcc3MzV155JeFwGMMweOihhwBzl9cVV1xBdnY2X/rSl9i/f3+v76Ouro7i4mK8Xi+LFi3qcv7ZZ5/lW9/6FgsXLiQSiXDTTTcxefJkvve977F3716UUpx//vlMnDix1233R5RS7Gio57PKCmKGwZTBQ5iQl89NLz7fQWvuWPIqo7OyUUp10JpoPM43pk0nPyWVRz5bRarThd0mbK2tYX11FbdMnpqYvtrdUM9ja1djGOC021hRUc7IzCxunfbFspJrNBqQ7nZx9MSMGTPU6tWrj2J3DoyyMoWvrCgn1eVCKUUwGmVjTTXVrYGE4AD4XC6uHj+RuFJkuM0Fnv/YsolIPM7/XnQpizdvYHCqLyEwSikq/C3ccepsRmZmETMM/uvD97GLjVTL7a+UYtGmDeSlpvL6DV/t0j9/OMzaqv1U+Fso8qUzZfAQfNb0gaYrW7duZdy4cX3djWPG0qVL+eMf/9hlce+xoKioiE2bNpF5BOK1dEd3/5Yi8plSakYPRXqkr3WmnSU7d7B0zy5SnE5EhNZIhJmFhbyyfVsHA2dWYREzC4poDAXJsQKKtmvNH+dfzD+2bCY/NS3hHVZKUe5v4bZpMzglNw9DKe5fvoy4YXTQi2c2ric3JYUlN97cpW+BSIS1+ysp97dQ6EtnqtYazQlMT1rTr9wQZc3NfFZZQVF6RmJeOW4YfFhWyknZOaypMke1PpeL8Xn53Dx5Go+vXU2FvwUUnFY0jHljxph745V0WPwnIgjCvuYmRmZmUdfWSiAaoSCtY1Zyu80Uus7UtrXy59Ur8YdDeOxO1uyv4M3dO7lgzMkUpKUxIjPrmCw21Gg0h09tWyv/KtlNoS898d5mebz89pOPKUxPTxg3Pmvwc8OkYh5bs5oKfzMApw0dylnDRpLqdIFIh6lvEcFps7GnsZFTcvNoCAZpCgUTU0svbN0MmMFKA9GuWlPf1sb/rl5BSxetOYnCNJ/WGo3Gop8ZOE0gHZNf2m02zh4+khsmFfPrjz8EOi7W+/EZZ7OroZ5wPM7wjExyUlLYVleLmQP4czFZMG4CiDIFCTOCsVKfZyV/+LOVAIkkm9e/sLhDO2/t2kkwGqHQl4FSit2NQT6rrGR7XS2jsrLJTUnh61NnkJtyaCkjTuQIwwOVuXPnMnfu3D5pu9xa/Ko5NCpbWgA6GArtuhOKdU1+OTjNxw/nnMXuhnqCsRhFVvTgvU2NHWIdtevN7MKhZFgel/Y1fe3JNmutRMHJWgOfa8Fbu3fSGumqNdvqahmdlU3eIWqN1hjNQKdfGThepxNUNzsCBDzO7uep26JRqgIBattaicbjKAxcdjupbhd1wVbaDZ2mUJAUp4txefmAmc/qpOwcHl+7GpfdfsDs4UopNtZUk28lyasPtrG7sYFsbwqhWJQCXzq1ba08u2k9d8ycfUy2qPcXjtWWfc3RozfT3P0Fc41d1/8vzx4xgmvGT+KhbiKbh2JRqltbqQr4CcViCGBDyPB4WLRpAy67jQq/H4APy0q496wvAZDudjM+L5+ttbV8XF56QK0B2FRTnUj+2641Od4UgrEohT4fdW1tLNq0njt70BqdrkVzotCvDJxxuXl4nQ6awyEy3B4rs3eQDLeb0VnZXV7Qfc3NPPzZSqJGHKfYeWnbFlrCYcbl5hM14ny0r4zGUBCApXt2k5+aSkqSoXTdxEk8tXEdoejnIzavw4HDZuvQloiQ4nQStWJeVLS08N/TnkIEfrr+FgByvSmUNjdTHwwecGR1IomPx+Ohvr6enJwcbeT0U5RS1NfXJwIZDhRGZ2WT6fFQ39ZGtteLiJhTQg4n4/Lyu7yPVQE/D69eSWs0ittm59UdW6kPBhlnrbGJGUYHQ3CIz0dG0jNbMG4CT8fW86/SzyNMd6c17cej8TgOm41Kvx+nzfQAOex2QMjxplDW3ExdsK1DZnKN5kSjXxk4qS4Xt06bwdMb11PpN13Ig9LSuHHS5C5bt5VSvLB1Ew6xkZeWSllTE83hMKCIGDFGZGTxSfm+xPVDMzK6tJfu9vDBzbeyP+Dn319/BZfDgb2HP8RnDh3Oazu3U5SegaEUCMSVYniGuajTXONjrhlKpqKlhfXVVcQNg/F5J1bE0aKiIsrLy6mtre3rrmgOA4/HQ1FRUV9344jitNu5daqpNfsDptcly+vl5snTSOsmCetrO7YTNQwKfens97fQ0BbCJkJbNMrYnDzsw23MLhrK4s0bcdrtPNvJaPG53dw+fSaXnzKOf3vtZVx2B3Zb91pzxrARvLJjG0PTM4grA0QRiEY42do12pPWlLc0s6G6mhsmTmZCfj7/8d47wMAcPGk00M8MHIBhGZn8aM5ZVAcC2ETIT03tdvQfiESo9PsZkuYDoKSliRSHEwSqAwHWVVXhstsYlJrGiMzMbl/yuGEgIhT40s3pMXoWgzOHj6A+1MaqigrumfAoY9NLAfi+52EQ+Gvp98j2eslLCiC3vKyUl7ZtwW6zYUN4v3Qv106YxGVjx3HDP587YHsDAafTyciRI/u6GxpNtwxKS+N7s0+nprUVQykGpaV1GzQvZhhsr69liLUhobS5GY/DgcNuozoQYEttLUopvE4nTpu92/x07VozOM3XQWuuf2Fxl/V+Zw4fQX2wjZUV5ThtNlpCYU7Kzk5kNG8Jh/hoXyllLc2Jtr41YxavbN+K3WZDgPdL91DX1nbIawI1mv5IvzNwwFzsN8TnO+A1LrsdEcFQCrsI0Xgcr8NJNB7H4zJvW0Q4c+gw/nvuvA5la9taWbJjO5tra3Da7cwZOoy/XbYAt8PR45SR027n6vGTmDtyDK6Wv7Yv7SESj6OAaDzOzZOnYhPh+hcWEzMMRmZmMSg1LRHwy1CK5WWlTBtScASekkajOVxEhEFpB87EbRPBZbcTMwxrvV4Mu83G9ro6EHOwBfDOnl2MzMzi2QXXJco2BNtYsnMHG6qrcNhML89fL7vygLFvHDYbV42fyLkjR1PX1sq7e3azq7Ge6kArBgZuu4P81LSEcRMzDF7bsa2D1sQNA0H47uzTD+8BaTTHMf3SwDkU3A4HMwsKWVFeToHPR15KKtWBACXNjaS5XNS2tQGwqrKcG/75XMJgaQ6F+O0nHxE3DAan+YgZBu/uNUc7X5089aDtZnm94F2MUX8TwViUvfJL0lwufjhnEOlJcSrCsRjvl+7FbXckgoPZRLCJsLOhfkB7bjSagYRNhDlDR/Du3l0U+jLIS0mlvKWZuDJIdbgIYBo4HrujgweoNRLmd59+TDAaZXCaD0MpPigtoaa1lXf37gY6rsXrrAlZXi9ZXi+js3PY3dBASVMjv1vxMSlOJ+urqzpcW9rc1EFr7DYbIrC7oYGi9K7T8xrNQGDAGjgAF500Fn8kzJaaWnxuF/ta4jht9g4ik+393EW7dn8lj3y2mm31NaS53LRGo4zKyqbIl87G6moWPPcMa61YO4ey+NfrcHLe6DGJ350XEDts5hRZMgrw2gf0P8txRfvCT73IWXM4zB01mqZQkHVV+80NB8pgYv4gxubk8tbunQjC01dek1jrt6mmmr98topNNdWkulwEolHGWFqzvb6WcCyO23FoKWFsIpyUk8NJOTk8se6zQ++0ElyH2Ibm8NE7Ro89A/ovqdfp5GtTplPb2oo/EsZlt7OyvJzl+0r4sKwUn8vNny+6lPzUNHY11PPUxvWE4zF8Ljduu4PdjWbm6jHZOYiYrt5DxZbz1EGviRkGFf6WRGyMC8ecjF0ksVVdc/RQ8XpU6E2IbQLcKPccxH02Il0XkGo0B8Nlt3PDpMnMG30SzeEQbruDNfsr+KC0hLhS+FzORLC/KxY/TUVLC8WDBpPqdJLicFJqxcs5JTcPQbjvnHOZmD+o17so269rLwfm1Pe43LwOf1zbolEcdhvjck+sjQ19gYrtRQWXQLwMZcsG91zENU0bO8eAEyLcZV5qKqOyshERPquqxGlzcO7I0YzPG8SDH39ERUsLt77yIh/tKyU3JYW4obDbbKQ6nexqqKeutZW4Mnj80iuZVVjErMIiFi24ttfTSO1lfC5XIgIqQDgeIxKP0RIOM31IAR/vK2Pt/krC3QQU0xw+ygigWh+G2FaQfJA0CL2DCv6zr7um6efkpKQwKisbl93O6v2V2EQ4b9QYJuYP4qFPlrOnsYHmsLnDKi8lBQPTA5PqcrG3qZHa1lbihnHYi3+T9ckmwjemzsAuQoW/hUp/C/5wmGmDh/BRWRnrtNYcNVSsDBV4BIx6sBWAMiC4CBVZ0dddOyEY0B6cLhFAd+0kHItR4W+hJRxGgL1NjSwrK6G6NQDAx/vKaAmHGZyWRks4TDAW44OyEkZkZOKPhI/I2pjxSR4apeBXc8+nvq2N13dsY2VlOQ6xEzXiDEpL4/bpp3ZYu6M5fFR0AxgtYC+0jthN8YmsRbnPQ+w5fdo/Tf/n3b27CUTCVAdaaQgGEYESS2va1/99WFZKSyTM4NQ0/JEwwWiUZaV7KUrPoDkUYnCa75D1pidPT/LvTTU1hOMxfnLm2by+YzurKiuwi42oMhhiaU13W+A1XxwV/gDECTYrB5ykgrJD+B2UayYieorwaDKgDZzObK+rZV9zM22xKGlOF2It6m2ygv2BuRYjzeUiFI8RNeJ4HQ6mDykgy+Pl8TWrufPUWexqaKQh2MaIzCzG5eYldiYcKp1F6PoXFlMVCDB50GAUEFZxclNSqGlt5f2SPVw69sRJSHlMiFeCdDIaxWZ+VCNweAaOUlFUdAtEN4J4ENc0sI/ULukBTGcDY1tdLZX+AP5wGJ/L1Bq72GgJhxNlRASfy0UkHicSi+N2OJg6pIC8lBS++tILFPrSueu0OdQH2xiekcm4vPwu8b56gwh4HA42VlfTHAqZ8bqA3NRU9vv9fFCyl4tOHnsYT0HThXiF6SFORjwQbwAV7HqulygjgIp8BvESsA1CXDMQe+5h1TmQGLAGzvUvLO4SDXhPYyOBSJiJeYNYU1UJQPGgwbRGIsQMg9ZohNOHDiMaj/Hmrp2I2Jg39iTyrYXA/7dhHW/s2sG5I0fjsNlYVlrCiMwsbp02/YDbOg82j66UmSpiR10dYgX32tVQz/CMTNbsr9QGzpHGXgSRVR2PKcP82LIPq2ql4qi2ZyDwMIgdXOegIivBewniPvOw6tb0D65/YTGlzU2kOl3UtAZoi0ZJcToZm5NLczhEzDAIxWKcVjSUuGHwxq6diAgXjxqTCH8RNwxKmxt5afsWnDY7y0pLGJqewW3TZ3aItt7e3oEin3fe3LC1rpZoPM6IzCzA1JphmZmsqarUBs6Rxj4MolvBnhTpWwXB5gPxHlbVymhCBf4MwVfNwZn7LFTkI0i9DXEMO8yODwwGrIGTzIbqKuJKJXK8bK0zI+cqzMV2RekZlDQ1UpiewZyhw/iwrAS7zUaay5UwbkARikVx2x0U+NIxlKIlFGJjTTUflZVx7qjRXdrtLu1Cd8KzstI8HzMMbCKMy83HUIo9TQ1k60BcRxxxTkKF/wVGNUgOEAWjDlyzkcM0cIjthOjmzz1E9jxQUQi9gXJORWyHN2LTHF90fsfnP/N39jQ2kOZ0ETdCtGdnUEAgGmFoRgZ7GhspTE/nSyNG8kFpCXab6TUe4vNZGw7MFDQAn5aXc8Up4/GHw2ypq2V5aQnnjznpsPocjsVw2mz4XOb/o4Yy2NPYSH6K/n/zSCOes1GxTWA0gGSAagPVBN5rD3t6SoU+ANUC7RsjbIPBaEQFX4G0O7THmAFs4CxacC3X/WMx+wN+2qJRnDYbVdY6m0A0krhue30duxsb+Oc1NyR2LUTjcVqjUVqjUV7YupnatlayvV6CsRjBWIzFmzcQiEQYlpFJNB7nT6s+ZUx2TiKS6MFQSrGzoZ6a1laC0WjieDgWT7igBQjF4hSlpx+5h6IBQGypkHY7KvQuRDeYI6noVnPqKmXBYdWtmr5nru9RVvqJ9oXLrtmmu9qmR8gDmV0N9cQMg4Z4sMPxQCSS0JpnrryGGQWFiYCfyVoDYCTlLo0ZBsv3lRKOxYgbBn9avYIxOTmMyvrcEG+PeNz+PRlDKRZ+6VzW7q+ktKmJtliUtkiEuKFoDYdJdbsRhJCVAV1zZBF7IaT+Gyr0NsT3gi0X3JcgzkmHX3ngQfO/hhXzKFlrCAMDKz/cF2HAGjhgZvcNRqPcMLEYEeGFrZup8LcgJAINA2AX4ZSk7ZLJa2oi8ThKKaJJGX7bI5P6XG5CsRg2sfHY2tX85IyzE2HWoeuWzUULrkUpxas7tvFBaQlD09MpbW7GbbejFOSmpBCIRqgPtuK0O8jxepmcP/goPBmN2LKRlKuBqwEw6m86QjXb6fh/V3Kjh+eS1hx/tL/jVy5+mvKWFtwOeyJjeGedAdNgeeDjDxOxuNq3jgNE4jHOHTmatmiUf5XsIc3lIi8lFWUofC434XgMG/DE2s/48Rlnk3qQBcFKKV7ZvpUPS0uoC7YlEgubbQv7/C3kG6k47XZyU1KYPFhrzdFAHMOQtFuPQs12oPPuN2Uuah7Yf9oPmQH9FL43ew6v7dzewVVX6EtnUn4+75XsJWqlUQjH41zw1N/I9Hj4xzU3AKZREozFuGXyVJRSTBo0mNtefYmGYBseuwO7zUZNawC7zcaMIQUEo1F2NNQzedCBRWJ/wM+HpSUMTk1lZ0MduSkp1Le1EYxFyfJ48LldpDrdjMvNJaoMJuQPOpqP6IQnYdhEV3b4fShxjLpDcv6O8j8I4Q8BG3iuAFVnru2xD6yElJrPicTjILBg3MSEJ2bBuAn834Z1NIWCOO32xBR5XVsbmR4PLyRpTSgW45bJ04gZBpMGDWJDdRX1wTZicQO7zYbHYWrO1MFDCMVi7KivY2pSSpfu8lZVBvx8tK+UQWlp7GyoZ2L+ILbX1SU82HZDqAoEuHTsOOIqzim5Ov5WvyLzt9D2lLWeUMBzORiV4DoNkQH9p/2QGdBPIdPrJTEJDokw5RX+FuwiRJKu9UfCBCIRqgOBRO4Zr8PB5aeMT1zzi3PO5d5/LaWurQ233UEwFiXd7SHN7aYpFOwxlkSy+Hx31uns/OESSmx24ndPw+6wMT4vn9q2VoIxM4dNaySMAdw0aQoZHu1m7E+ILRuVcrPprVGtYOwHexGSegMiJ0TYqROSB+fN57E1q4HPdQZM7/CY7BwmDx7MshJzvc2soiKUUlS0tFCYnt7t5oP7587jx0vfoqat9XOtcblJd7tpCoUIHULcmrKmJpQyJTCuFHYx0zMkE1cGCsVXi6fqcBT9DHFORnkazPV+SplTVa5TEc/cvu7accOANnBOycklw+ultq2VXG8KCqhpCzA4NZWvFE8lGo/z9p5dgClK1a0B/lWyh+smFncRnbhhsHTvHk4fOoyVFRV47HbcDgct4TDPb96E027nB6cfeJfM7nUlPPHQOlo3mHOm9l+GiP7sLJw2s65JeYPwR0KckpfPbVNndJju0hwd2j01h+u56VCn82SU4x5zETNOsOXpBX8DnNFZ2eSnmvnu8lJTEaA22Mrl48bjtTtoDodw2u3UtrXyYWkpZw8fwVu7d/L1qdO71GUoxdI9u5hdNIxVleW47XZcDgeBSJjnt2zCZbfz/dPmJK7vbjMDwA8tPXLZ7bjsZmytcbn5VnoIJ9OGFDAhb9BBd4Fqjk9EBPF8GeWabS5itqUjNr2OKpkBPaR0OxzcPn0mwzMyqQz4qQr4GZebx43FU6gPtpHmciVGWy9s3YzX4eDt3Tu5f/kyfr/iE9bsr0zkKvJHwrRGI+SlpHFyTg7heJyy5iaqWwO0RiNEjTiVLS1d+tDuuVlRUU5dnoM1X86k4k7TK+R1OglGozSHg2AoGkNtOGx2rh43QRs3/RwRB2IvROz52rg5AXDYbNw2bQZjc3OpCvipDPgZlZnFN6ZMp8LfQobbw4JxE8hLSQUg1eni/ZI93L98Gb/79GMufubvXGcZJq2RCE2hEHmpqYzNySUcj7Oxppo9jY34IxHqg0G++epLHdIxdMfJObl4HE78kQgnZ+eYWhMKolBEDYPVlRX8q2SPNm76OWJLQRxF2rjphgHjwQnFomyvrycQDlOQns6IjExEhLyUVL45fSatkQg2EbxOJ3HDwOtwEombwbXAXJC3qrICAU7KVjSFgtz+2stke728ddMteB1OnDY70XicMVk5lDU343E4aYlEiCtFSzjMNS8sxmN3sOFb3+6xn2OmjGDn2r1kTC5g1K8vpCkUoqSpkagRpyEYYnCag6c3rudrU6aTo7eIHzOOhOdGc2IQicfZWV9HUyjEoLQ0RmZmYbfZyPR4uWXKNILRKApIcTpRSpHl8fL81k3YxUaF3xwEPbNpPYZSnJKbjz8cprq1lbC1RsfjcLCsrAS72Lhq3ATKmpvMhcEixK1rdjTU47Y7Oqy56W4n1a3TpvPUhnUooCg9nb1NjeSnppHrTWFvcyPVrQEag0GyvHoBvGbgMSAMnOpAgEfWrKI5FDIPCBTnD+b6icWJHVHJOw7sNhvnjBjJD5e+hctuZ3/A3PVQH2wjw+PB63TixYnbbqcpFDSjkbrdrK6soCEYZN7oMRhKkZuSSn2wLVFv3DAIEePyxU/jdTg65INJFp+7/vgzVEYm/2/WaextbOTFbVso8KUndlTUtAV4dtMG/n3mLD3612iOIxqDQR5Zs4q6ttbE8r6TcnK4ZfK0xGAp2fsqIpw7ajQvbd+K2D9/l2OGQbrbwxu7dgCm9tQH2yh++A9MyMunprUVgMWbN5KTksL43HxCsShVrQFawmHsIrjt9g5hJrpjRGYWPz7jbCr9fvY01vPS9m0U+tJ5eftWGoLmrqrLFz9Fga/7tUCao8uRnBrXdKXfGzhKKZ7bvJFILE5Rekbi2Lqq/YzKysZQih31deR4U5hVVESBz3TjnT1iJDkpXprajSLMEZfTZk/sgqi0DJ/Tn/gLNhH81vbwN3fvxOdy43U4ObWgkA/LSkEEQylihsGGanONTbJRkyweD/7r54nvH5SWkOJ0dtgumudNpaS5ifpg8LCT7mk0miPHKzu20hQMUej7XGt21NXxr5LdZLi9bKmrIcPt4dTCIoZlmHGxZhUW8eD5F7B0z27e3L2TSDzOjCEFTMwfxAtbt3SoX6mOG8tjyqAtGsXjcLA/4KfZSvUQjscJx4M0hIJcsfhpXrz2xh4NFLvNxtCMDN4r2U2a09VBawCCsRhRwzgiz0ejOZ7o9wZOczjEvpZmhqT5EsdEhBSnkz+t/ITC9AzSXG52N9bzSXkZX58yjVPy8rGJ8OaNtxCNx7nhn8/hD4eZPHgwQ9LSEwZOoj46elFGZmYxIiOTTI+HT8r3YUCH3Vq9IRyLYe+0u0bEbDFmxLsto61+jebYE47F2FxTw+BOWpPp8fLE2jUMzcjE53KxO97AivJ9XD9pMtOGFCAinDV8JKcPHc7dp5/B2qr9vLZjGyBJawA3EY7Hefnamxji8yUGR49efDn3LfsXGW4P2+vrsCEYnaLrJA/SDkQkbmC3UsEsGDchoXOnFQ3l7tPPONzHo+kF3YWn0Hp+5On3Bo4g3doWVX4/gUg04dUBD4FIhH9s3cKPc3KxW6MYp93O81dfT2MwyIOfLKchGOTKU8YTtzxDaS4X7938DcAM5tUUDjGrsAhDwfKyEkqaGhPmj00Et92B1+nAbXcckst38uDBbKqtIdPjSUxH+cNhMjyexILEdo50zBaNRtM7RCwvS9LUcbU1bTQ0oTXmmsB/X/IKwzMyWXzVdYC5EDnd7WHq4ALe27Ob+rY2sr1e4koxu2gYJ+fkMtgKURGOx2kOhXj4s5XkpqTwfxvWEYpGSfazpDgceJ1O5o0+tNQNUwcPYWut6WFq15qYYZDt9ZLbSWs0moFAvzdw0t1uRmVnUd7SkjAIDKWobPUzOrNjXqE0l4tKv5+mUKjLAt5Mj4evTZnGazu3U97SjF1s/PiMs5l/0skA7GlsoNzfgiBE4gY7G+rZUV9HzPh8PBVXimAsiqEUaWkHjjLaTnH+YNbnV7G5phqnzU5cKZw2G7dOm5EwwjQaTd/jdjiYlD+YjTXVCY+xUop9Lc0djBsAj8NpRUDvOvXjc7m4Zco0/j977x0lR33m/X5+VdVxuiePRmGUc0QkISEw0WADBoPI4HVYe22/XnbXi/2ur/e+x9f37nv33rvr857N3l2vIwZjLGxjnAgmGjAglEBCoBxHo8mhY1U9949fTc/0JM2Mpqd7pPqcoyNNTVfVr0dTTz+/J3yfX+19n4PtbRhKccmsBm5YvBSlg5LwGgAAIABJREFUFIfa21lSXYNhaFuzv601T0m9l6Rt44qMuhlhTf10tjeeYFfzKQKGwfpZswlaJnevWpNTVvaZHAohT+EzmCnv4CiluGPFar711pu5DgUQFlfXEAvkC1c5rotS8PlfPoFpqFyEZXvjCX619z1ak0niwSA3Ll7GhobZuaJBgCff28MNi5ZQGdbdBp3plE4l6S0dAAHDQEQQhIAxukFqAdPk42vO5/3WFva3tVEeCrFq2rTcffrjPxQ+PsXl5qXLaOrp7rM1IiyoqiZg9DkIvQMzTyUSnEok8jqddp1q4sn33uVUIkFZIMD1Cxexcc5cIv1atX+19z2Cpkl1RDsuHekUs+LltCWTtGfSuREQvX9+/M5OPnPBRadde9A0+cTaC3ivtYUDnq1ZPa3eFxMtIr4NLyxT3sEBPcPpwQ0b2dvWSk8mw4x4nEQmwze3vE7atglZFq4IJ7q7uHjmLA53tOfOffdUE9/fsZXqcJRZ8XKS2SxP7NlNNBBg3SwtrW+7Lkc625kZ69MZyDoO8yoraezqJuu62K5LRTiEUionrz5aTMNgWW1d3jwsHx+f0qM8FObPL7mUTT9+GNt1+dcbb8ZA8Q+vv0oym/U6qCTX8t2f/W2tfHvrFipCYWbG4qQdh1+9/x5B0+IDc+cBOiK0v62Vmf3qfLKenIULGCikXw1ONBDI1dWMBtMwWF5bx3Lf1vicA5wVDg7oSMi37vhnXrosysK183hk013cuXI1v9izh9akFrd6q/E4Rzo7eOP4MUB3OW2cPZd4MJxrI48EAtRGy3h6314unjkLpRSmUrnBmr0toHVlZRxubydgGjgihCyTnmwWyzDoTKdpTSXzdm4Tie/1+/gUD9Mwcnagt1PqY2vWsnn3O7Snk6xvmM3y2mn8Zu/7eZHi5w7uJxIIEPdGIoQti/qyGM/u38fG2XMwDQOlFFWRCIlsNmeTppWVcaC9jYBpsHb6DCzD4O1TJ7GUwVXzFvAX6y8twk9haN0dH59SYko5OI7rknEcwpY1pD6M40pef8Els2aztn4GzckEsUCQz/3y54POaerpHjSDJWJZHOvqxBHB8tJQ18xfyObd71AfixEyLWbFyznU3k5DeQWd6TSOuJzs7sE0i1s3c7K7m6f37+X9lhYqI2GunreANfXTfT0dH58x4Irkor/961MGjkW4e/Oj/GjTXaypn87y2jpOJXoIWxbVkeigMQwnu7uJBfJr80KWRXMyQdpxiHpR32vnL+RHb+/AMgxClsXMWDn729uZXV5BRzqF7Tq4rmAGDK6ev5BZ8eIo2KYdh7Zkkq899yy10SjXzF/Iimn+wE6f0mFKODiuCC8dOsjvDuwnYWepi5Zx85KlLKvTD9NfXPE/eH5jhMx5ilSdRcuxo1z0v/8/vPk3XyFkWTkDMJTo3n9tfZODbe15hXpdmTQz4nHMfoZtw+w5ZF2HZw/sp8VJELUC/G+XfQCAd5ubyTgOC6qruHDGLL789G/y7jdZnEr08E+vv4rrqad2pTN8b/tWNq1YycbZcyd1LT4+UxER4c3jx/jNvvfpTKepCIX58KLFuXZvV4TmRJ+455GOdt5raWZJTS0B08zpbA3FvMoq3m46yTQrljvWk8lQFY4QtqycXXr4tjtJ2Vme3r+PllSCsGnxV5dejmUY7GpuIm3bLKiq5sIZs2goL5/0zcs9mx8l4zhsazwBwLMH9uGIcKC9jfvXrOWCflPOfXyKyZRwcF44eIBfvPcu9WUxKsO63ftbW7fwhXXrmVdRSWNPN47kF8qlHYf2VHLIYt3+jsd1Cxbzz2+8RnOih/KQvnYim+HOlavzDIehFFfOW8DG2XNz4eNewaxrFiwq0DsfG78/fAjbdXM6HQHTJGSa/Hbv+6yb2ZBTdfYZGyIuSDsQQBnx077eZ+qytfEEj7y9g9poGbPi5SSyGX64czsB02RN/XRuXLyUHScbSds2SimuW7CIb2/dwhfXb6Q+Fhvx2lfNW8DbTU009XRTEQ6TzGbpymT4ozVr86JEvbo5GxrmkMhmtQCp9+xeNX9BQd//aGlLJXP/VkphKUVttIxfvr+HtdNn+F1Z40QkA24nGDGU8ou/z5SSd3Bs1+V3B/fnUkMA8VCIjOPw/IEDXLdwEbP/7+u5JB7ntT/9Ce/GQpQvrmPD8pXsaGzkA/PmAzoK1NjdhSvC9Fg855zMrqjggXXreXb/fg51ttNQXs61CxayoEq3mA/MMwdMk4rTOAqjjdy4IhzqaKc7k2F6LDZI92asHOpoJx7MT7eFLIuWZJLOdNqfbTUOxD6AJB4DaQMRJLASFbkVZYz8YeYzNXl6/16qI1GiXo1NNBCkKqynezeUl7OzqZGZ8b6oSTwUoieb4Y3jR7lpyTJAR4FO9nSTdVymx2I552RGPM6frruE3x3Yz/72NqbH4tw7fwFfe/5ZYPA08Ec23XVaWzNaJtLWPLLpLv72pRd48v09GKpPrBDgeHcniWyWWHB0MhlnG+PtcBURJPMapH4LkgZlIMHLUeEPopS/MR0vJe/gJLNZ0rZNTaTvw/nVLzyGKy5l/7iJ7mwGPYcuf8dgGQatnsLnye5uHtqxjcaebhRah+K+NWtzTkxDeQUfX3v+pL0n0G3m39n6lqetozvNL509m1uWrRj37mdGLM627hN5c7eyjkPANM5Zg3MmiNuK9PwXEAZjBogL2V2IJKHsM35d01mGeOmn/h1MoDuVmhI99GSyGMoY9KEeMq1c2qolkeCHO7dzpLNDD/e1LO5auZrlXjr9wad+DUxu+rojleI727ZwrKurn62Zwy3Llo/f1sTjOK6L0c8BS9s2UStA2Cr5j5WSQ7K7IPk4GNPAqAaxIf0sooKo8NXFXt6UpeR/E8uCQcpDYRLZDNF+BXq267KgqorpZbHc1xv+5Q42oA3Vsa5O5ldWknUc/mvbFpLZbK4WpzuT5ltvvclXLvsA5aGhw4ADiwknumPg8d27ONHdlVuTK8JLhw8xp7KSC2fMGtc1L5szly0njtOeSlIRCpN2HJp6uvnw4sV5mj4+o0My20AcML26CmWAMR3sfeCeBHN6cRfoM6EopZhTXkFLMpGX2u5Ip5lXUUVdWRRTKTKOQ7DfB3tPNsOSmlpcEb63/S1aktpJUkqRyGb57vatfOnSy4aNmow0DXwoxmqLNu9+h8bu7jxb8+Lhg8yvrGTtOOtlrpw3n3eamoiHgogIKdvmVKKHW5evGDTr6lzBbbl//Crz6RdAlYPyIvDKAqMe0i8ioSv8KM44KflPPUMpbly8hId2bmPLnz2OoQzat+k275c+/yh3vvg3XDN/Ib/1BmBahkF7Osn8qmqW103jYHs7bclkXqdBLBiiI53i7aYmLp09J3fcdl3ePtnIW40naOwe3F01UfRkMuw6lT/TxlCKinCYPxw9Mm4HZ2a8nM9eeDFPvreHw53txAJBblm6nMs9jQ2fMeK2ggrkH1MKlAnSXZw1+RSUDy9awr+/9TqOK8RCQX66excuwqO3303YCnDD4qX89N1dRAMBQqbJE3veJWia/J9XXcuxzk4au7vzCo2jgQDtqSQfe/wxqiKRvu6rnzxKTzbDpuUrUcCFM2fxw9vuPG1E5Z7Nj45p09WdybC7+VTerD5DKSpCYV47dnTcDs68yio+c+FFPPneuxzr7qQ8GOKOFatY3zB7XNc755E2GFRzEwBJAVnAd3DGQ8k7OADnz5hJNBjk/zCfzpM+791FXbdwEbPKy3n16BGSdpYr5s1n3axZBE2TlGMPeU2FIpHN5L52RXj07R1saTxOPBDistlzSWQzJL188s1Ll3Gyu5uf79nNxtlzz2jKt+MpHw80ZYbS0uxnwoKqah5Ytx7b1WKDfrHfGWAthszr+cfE+30y/OjN2ciimhq+cPF6PvHzzWRsh6ZEDwAf/dFDbP/cA2ycPYe6sjI+9+TPsF2hOalTU3/8xOMks1mW1NQOuqZlGLlnvpfmRA8d6TT729oAYfvJRi6dPZcNDbN5+chBTnR1MbeiisvmzKWubPz1Mo7rohhsa0ylyAwzzHe0LKmp5YvrN5J1XQKehs+5jFHz0PhV5q0lkN0Gqr7vmHSCOQMozEb7XKBgDo6IsONkI88c2EdzIsH8yiquX7iYuZWV47re0ppaHnnzfwHw4FVfA+Abz30d0KHlVdPqWTWtftB5DfFybzK3mwudigiuJ7EOeieUtG0WVlUzO16Re1Ar3TBdmQxhy2LnyZOUBYK8cuQwW44f44F1G8ZteOLBILPLK2jq6ckV/ooI7akkV8ydP65r9kcp5XdMTQAqsByx5oJ9SIePsUF6ILsbafscyhdcLBn2tbbwm317OdrZwfRYjOsWLMrVvYyVeZVVuYhHY09+pE4pxdKa2lz0tW88DARNy9uk9KWwxNPT+YcP3ciKumncs/nRXJv3zHh5bgNSGY7wzP73eeHQfuLBEM8fPIAjwrUnFvLAuvW5+z2y6a4xpajKQyFmxctpSSZyox9EhLZUckI6spRSeem6c53xirCq0FVIdje4jUAMSIA4qMh957zjeCYULFn65vFjfG/HVhKZLC8dOsi3t23hX9/8A8c6O09/8mn4xnNfzzk3p6MqEuH6hYs50d1FU083zYkejnR2cOHMmcyrrMq9Lm3bGL2zpTwMpTja2YGIMK0sRlkwyIxYnKzr8LuD+8a9fqUUt69YhWkaHOvq5ER3F0e7OllUVcMl3ngIn+KjVBBV9scQuRnMGrDm6a+N8TnpPoVhf1sr33zzDU71dPPSoYP86O2d/OdbW3i76eS4r9nrPMS94vyuTIZ7Nj+a51w8sukuLpnVwCWzGnhk0138+I67c7OqTnZ305JIcLSrg1X10/MiO71jHIwBtuZIZydZ12VaWQzTMAiaJiLC0/vGZmv6r1MpxR0rV2EoxbGuDk50d3Gsq5MlNbVcPHN8qXCfiUeZtaj4n0JwI5gVEFiLiv8ZyioNWYCpSkEiOI7r8uu971EXKSMSCOiIgjKxlMFzB/dz/5q147rueAt9r56/gHmVlWxtPEHGcTivfjrLauswlMrLabckEoQsK9ch4YhL0s4OGkZXGYrwXkvLuN5DLzPicb684TJ2nWqiNZVkTkUli6trztkCvVJFqTAqdBmELsNtuR9JPTP+QkKfgvDUvr1EAwEqw5GcJktVOMyv977Hyrppk7oDvnT2XGbFK9hy4hgJO8vqunpWTqvPPdePbLqLHScb+cH2rXnnuSL0ZDNUhcJs3v1OLjL04qGD/O7gfj52Xp/NHKv9mxkv50uXXs6uU020+bamZFFGNSpyY7GXcVZREAcnadt0ZzLMjJfnPazPHzrA84fG7uCcaUeTUoqF1TUsrK4Z8XWGobBdXQMjIrSmkkQDQWLB/ELTpG1TVzZ0Dc5Y1lYWDHKxH7Hx8TkjjnR28MLBAyilcrbmqf3vk3Ycvrh+I9Y4HZzRdDcNdWxuZeWIqfglNTXEQiFak0mqvM1TWzJBmRUgNkDHyhUhMEpHZCg72bu+WDCYGx7sM3XwN1FnRkFc+IhlURYMkrKzeccd1yVollZdc/8w82O338NHly3nWFcnx7u7qC+L8enzL6SpJ0HWCyun7Cyd6RRXDlEr8+BVX2PftoOT/A58Jguj5iFtaALrILCu72ufojIzXo4j+cX5jisEDDNv3EqpELYCfOaCi6gIhTjR3cXxri6qIlH+5MKLaUkm+MiSZcyKlzMjFmd9w2z++cMfKfaSfYpAru08+zpuy/05Z8dn9BTE2zANg+sWLOKxXW/zoYWL+c2+97Bdl8vnzOOBdRtOe/7xrk62HD9OWyrJ0ppavnvLJkL9ZrUUSiRrdkUFX770cpoTPSilqI1EcUUoCwR5/pAu+otYFneuXM0Kr4CxsbuLt04c5xuv/p72jRHa6iyajx0t2CRxHx+ffK5bsIj9ba2Uh0I8s38vjiusb5jNJ9ZecNr0VHMiwZvHj9LU08O8yioumDFzkChmIZ7jmfFy/nLDxpxAYG00iqCjus8dPEDG0aMgNq1YyZp63bF3srubLSeO05lOsbSmlpXT6vMKfMeqp+Pjc7ZTsHDK+obZBEyTp/fvJe04hE2Lz120jtkVFSOe905TE9/b/haGUgRNix0nG3nt6BE+e9HFhVpqniEwlGJaWZ8Mv6kU1y1azBXz5tOTzRAPhnIdSjubTvKD7Vt5/8u/pOnD03F6MjBNp7P2bTsImwq2ZJ8iYtQ8hDjHcJO/ALcLrOWo4EqU8tWii8Himho+c8FF/Hrve6xvmMO0sjKuX7iY1UN0VfbnUHs7/77lDRxxCJsBdp48ye+PHOILF18yrADoRKKUyuvEVMC1CxZx+Zx5PLhhY56t2X2qie9sfwuFImiYvHH8GIuqqvnU+Rf6Ip5nKbrt/D6QJEQ+ApiIvRfMhX5n1Sgp2JOhlOKimbO4aOYsvrLxA6P6D7Fdl5/sfpvKcDinWlwdiXCks4O3Tpwo6o4kZFl5hiTjODz2zttUhiMETYsVTzXTuvUox/50BeHyMJf/PlW0tfoUFjezDRKPaLVRApDdimQXQdknfSenSCyrrWNZbR0iMipbIyL89N1dBE2DyrB2MqoiEY53dfLioYO5uVLFYKCtsV2XH+96m8pQP7sowt62VnacbBxUx+dHbs4ORATcZj18M/uOnoWXeR3C16DC1xd7eVOCSSmjH6232ZzooWfASAaAeDB0Ri2fhaCpp5u0rSf9bviXO9jwL3dQfX4Dy19q4y/u+BDQp9fjc/YgkoHkT8Go0VLqRjUYDWDvRTLvFHt55zyjtTVJ2+ZYVycVAyI1VeEIO0vQ1gwcVaOUIh4MsqOpsYgr8ykozhEIrIXIXWDUglmnZ+Klfoc4zcVe3ZSgpGKbYcsC0Z0D/TUiMo5TsLEJ4yVkWrhI3o5xw7/cwcnu7rwcfnsqSVsqRU0kMilhb58C4zSCZMHo93+pFKgysHdDaHKHtvqMD8swPIVhF6vfnJ+M6+QN9i0FQqaFKwyKTmUcl1igzy76tubsQuzD+h+qXxyi93fVOQrmYNVsn3xKysGpDEdYUTeNXaeamOENq0vbNinH5pISm3FSG40yv7Kawx0d1JeV5daadR1+9ycPs+f37wHwyfX/HYDFf3cjl82Zy42Ll2L6+hNDMiWKI1UYlKtHMudFC7JgxIc9zae0CJomGxpm8+KhgzlF4azj0J5K8dGly4u9vDxqolEWV9ewv72V+mhM2xrHJu3YrGtoIOs4/HzPbl4/dlRPCwc2zpnLTb6tmdqoyAjfK60Nf6lScr/9t69YxdLaWo53d3G8u4vOTJq7Vq5mfj/V4VJAKcV9q8+jobyc491dnOjqpDOT5s6Vq/Py50HTJGha1JfFeO7gAV49eriIq/Y5Y4w6MOeDNGknB/RAPLFRwQsmfTnidiPOcUSSk37vqc71Cxdz4YxZNHZ3caK7i5ZkgpsWL8l1LZUSd69azZzyCo53d3Kiq5OOVIrbl69kfmUVLx46yCtHDjM9FmdGvJz6shgv+LZmyqMCS7Uj43b1HXTbwCgHa+GkrkVEEKcFcRoRObMZZpOJkgFD4EbioosukjfffLOAy+mjOZEgmc0yrayspLsERITG7m5Sjs2MWIywFUBEuP2CP8dQBhv/9c7caxPZLCj46mVXFHHFpcdAgbLecRWlGskRt4N7fvJtkBQPX98JKgiRWzGC41PoHtcaxEZSv4HMK+g9uwGhq1Chq1GqNPYtSqktInLRWM+bTDsDOrXTmU5TGy0jGgic/oQiISKc7OkmadtML4sRCWhb87UXfkeZFcizk4ms1iD76uW+rZnKiH0ISfxQD94UwKxBRe9HmTMmbw1uK5J4FOyDgAFGOSp6V0mNkRjO1pSs53Am07onE6UUM+L5qQlB1xFZAz5nAoZBR9rvrprK9Dpjrze6QJD7nlkOKsgjmybPuQGQ9EuQfh6MWTovL1lI/QZRlajQmH2Kc5rKcITK8AjpgBJBKZUbutmLAMlslsoBNTdB06At5duaqY6y5kL8r7whnAYY9ZO6gRFxkZ7vgdsKxkydlne7kJ5vQ/xLqBKfy1eyDs5UxlCKj3znE+xtbc073pJMcP6MyfO8pwpTWqCsCLlwEYH0C7qLK/VzfTByG6hqyLwIvoNzznDf4z+msbuLDQ1zmdZPU6c5kWDtdN/WnA0oZYI5/GDUgo5zcI6AcxLMmZB8XB+L3AZOF5LdiQpdPvH3nEB8B6dA3Lh4qZ6e3tVJxLJI2lnKQ2Gunb+o2EvzOQNKwxlztPiXGlCXpkJaM8PnnKD/oOCUvQ9HhA8tXEzKtomHQnxwgW9rfM4QSUD6RZ2Gd4/rY8nH9dTz/rVBJYrv4BSI+liMv1y/kS0njnG8q4s5FZWcP30G8RJrdy8lzsRZEHFBekCFSk5sb6J3WEpZSOYNIANukz6YfBzIQuzPJuQePlOLhvIKujJpltTUMru8ggtmzPRtTYEQsb0NRlRHV4pIbl4VBYrkmDO1xDYDa3WzqEkudB4PvoNTQCrCYa6eX/q/BFMdN7MbUk+A2w7KQoIbUeFrUapwv979nTFxE0j2bZ0nN2Z6YxsKXNNh1oBzvO9ryYAyUKFrCntfn5KgN4LYi2UYVIUj3L9mcmvBziVEBMm8AqlndOekEUVC16GC6yZtdIK4rUhmO0gPylrEYMdjYlFGBVL+PyH1a8j8HlAQvBisFWCVfoTQd3B8pjRiH4bEd0FVgDlDF9umn0VwUZEbCn9/tw3p/nftXGEBNpJ+BmKfRRlVw+6wXDcFqad0zQwmBK9Eha9AGaMrrjdqHtNtm60fA9IQ/woqeHHJF/35+ExVJPMGJH8GxjQwqnQUJ/kYosKo4Hm51xWqJsbN7oHE90FcwEDSL0JuZIMa9n6ucwoSj4P9jpa5CN+ICq4ddbGyCl0J1mykbRvgQuReVHBV0aNXo8F3cHymNJJ+GQiC4Q1IVQEtZ575PRK+GsRGsru9Hc9cMOdOaBeCpJ4C6dKh3F7cRiT1DCp6x9DnuD3Q8T8gu13ntgXIbkOyW6D8Syg1OhVaZdag6n41Ae/CZ6pRGrVg5w66sP9ZPaKlt7FARXQdXPp3EDwPcZoRe4/e7IxyozL6+2ch+WMgBqZXTJ58HNIvQdU3Mfo5WP1xs/uh4690F5QKA3sg+wYS/SQqeuuo7q2UAmsRqu6piXkzk4jv4EwAyWyWt04c592WU1SHo6xraGBWvLzYyyo5CrKzcU+BGmBMlAXiItk92ghIClAIAsHzIXJH3u5DJINk3wW3FWXWg7V49Omt7A5QnmR6b5dB+GbIbgPu8CYC579vN/EkZHfq3VTvOiQJmTeQ9Hak55/yXu/j00sym2Vr4wl2NzdRFY6Qth1CVunvpKc+4jkuM/MPqyi4rbjpP0D7F72XNoMDbvNHQFXkPcfidmlbI0mUNQ/M2aNLb7lNusbQ6NfN1Fv02/ElXKN+kL0QEej5lm48MKbrWhoRramT/Dlu8kdA4Ky2M76Dcwbcs/lRHBEumdXAia4uYsEge1tbePXoYf7ovPNZNa2+2EssCXo/4AtSDGct9HLDfS2ySBIIQ/KXQEDXq4B+uDNbEGsl9/5iFwAP33o90vOf4DQDBoIL5mw9Gdwo47SoEGAD/T9knLz28UHvM/sHINjn3IBeLy16npWPzxAks1m++ebrHOvqzNmahdXVfMyvuyk4ShmIOQfcFlD90sDSrlPjqZ/p6DGqryzGaQGrrxZP7ANIz3dA0qAUknIhuA4it40iqmx5zslQNTfDnCttemYVIa9QGK1jYx/zjieAAreZFxnfwTlDutJpTnR10VBekTuWyGbYvPsdltXWYfmzYAqKCm1EMm/pAl9V4Y1N6IHwtZB6Thuf3Iu9oZjZ7YBWrJXUr/TOrL/OhHMESb+Iinz49AsIboDU05B5Pb+N0qgeYdFxwNWGMLd5E100nHwUnH3A2W14fMbOm8ePcayrc4CtyfL47ndYXltHwPQjOYVERW5Auv8DnKxOiUs34IK5GOxDENmkX9gbYfGcF/CUxxMPA6G+IZniQuYPEFgJgdPMPzOmgdmgI9beNUluBsmgqv5dR4MGEfScroFOUZ7hOavxHZxxMHC0QHsqhWUYbFq+EoBoIMjx7k7akknqykYRBTjLyaVmCvCBrYwqiP+prsXJ7gFrjhafUnEk9btBQzHv/U0EVBt/vuwn+usnrgECPHzdVq0KbE4DoxayW2AUDo4KXYG4TZB+ud8bjoFRMfxJoWsg86oXOvaUad0Or34oDv1HvUg3btc/6p2itUSPYjCnjeEn5HO2sLv5FPFgfut3NBCgPZ2kJZkYpHLsM7Eoaz7EvqCLe51jEFiFCn0A3FNIeriztNMp9ruQ3eVdqExHiY0KUBEkuwN1GgdHKQVl93mqwsdBFJAFs3YY5waUEUOC68HZDG5S1wyJqx2swHpwDtBbnCySwk09A5k39MnBdajQZagpPtTTd3AmgIHzvFwRRCjpGVpnE8qoRkVuhn6d2SKiozduK6jeFJUDuKi8qd8pwNLOhnMKZKFXMDy6/zulgqjofUjog0jb50BZGDU/Hvmc4PlI5GOQ/BHIKe2EGfVQ/v9hBFf1pfRin4fkT/XaVBQyO3XBdPzPUSNFiHzOSipCIQ62t+cdc0VAIGKV7gytswllNaCse/OOiSr3mgUS+jmN3OZFkttRgcWI2wrdP9D1elhgzgOnEQJrAZdR2xqjGmJ/rtNLkoLyv0b1NlcMd070bsRt0Wn8XhHQwAUQfxDaH9DrFxfp+T7Y7+vNHUDqt4i9H8r+uGRm242Hkh22ORW4Z/OjJG2bhVXVTIvqoaCuCCe6u1g7fTr3rfZz48VEnCY9M8Vt11Eccb3i3upcPRCq0supWzqCIg4EFkHkFozwVYVdn9uG2Id0d4O5GMMYUPjc+T9BxfLHQbiNELwUI3JTQdc2EUwUZRkPAAAgAElEQVSVYZtThYPtbfzz669RGy0jbFmIZ2tWTqvn4+edX+zlndPoFu6HtEwF6EaHyJ0YwTW4pz6knQtp9l7t7cTMJRCYi4p9vqCieXoS+DFwT4AxE8PKH/vgNm8C5wRE7uqLdouAexwV+5yOXJU4U27Y5lQhYlncvXI1T7z3Lq3JJIKwelo9ty5bWeylnfMoc5reqdgHgSSYDUjbAKVfsYFuLZSnD4CxARXaWPj1GVWoYNXQ33S7gCykfqm/7s27E/Pej8+5xrzKKu5ZtYaf7dlNm2drVtRN4/blvq0pNkZgKVL+VbAPoOty5vc1KUiS/JoXz9a4hyB4H5iFncqtlEJZDUBD3vFcpNjeqf9O/VT/Hbmtz9Fxm4HSd3CGw3dwzoD+2hNr6qfTnExQFghMicnE5wpKBSCwuO/r3nqgUzfpXYs5S0dJSGtnRzrBqC7+uAejDN0dMbAgMAHm0uKsyafoXDhzFqvrp3Mq0UPUClAV8W1NqaBUBAIrBn8j+jGtBGz3AAZYc8G1gR4w6iZNBXlcqBFqCacAUze5VmKELItZ8XLfuZkiqJqHwJyOLgJU5FopjQZwDiPijHyBAiOtn4bMWzqs7B7XnRluO+CiQpcWdW0+xSVomsyKl/vOzVQhcL737CovMhLWtsZa2Bc9KQJGzUO64SNwsdbXCV0K4Vt0mt49oWsYrcJGlwqNH8HxOSdRRiUSWAP2Xq1ErPBaMRcBnZREG6VR1ddRJRkdWYrciOrf+u7j4zOpjLUbVAUWIdIJiG71JgnWPE8V2RrXNScWpTd7gTWQ2a4PBdeiwjcUdJ7fZDC1V+/jM07clvvBbdMhZVXrie4FtBZNYDnYuxBVCeasooSQ81vrBVX9HSBQ2uFsHx+fQSijAlFxIA3BS72OKwXOETBm4jbfCs4hPcCyCOQpLUdu12ue4o5NL2fHu/DxGQ9GhRbZyu7WBkdckA6wdyI9/6FfU/ZpKLt31POhCoMqfk2Qj885zngU2XPnuFozjeTP9d/Bdbr4uPNvtMYVNmRfL7q459ni2PRydr0bH5/TMNBIgQGSRpX/tdaYyb6lxa6kU2tC2O8iqWdRkRuLsl5fxdjH5yzCnAE4OlWV+qm2M9h938++DYFVxVrdWYfv4BQJfwpwCaFCqOBaJPWklk53W4GMLu7N/MGbTH6Dnx7y8TmHGY8i+1DniNuKdP6/QEBvonpHvBAAs9bf1EwgvoMzyQwc8+A7OpPLiEYq9Ruv2yHTd8xtAaP0JsNLdg+Sfl6vz1qECl3pj3Dw8ZkKiAOofjOlHtd6M0YlRO8p6tIGIpJB0q/pWXsIBC9ChS6dMiMcfAfHx6cXFQPl9lMcDeq5MZF7gaz+ugRwM1sg8SM9tFNFILsdyb4DsQdQvYP8fHx8CsJ4Iix55xg1eh6U29E3s86oAWsZqOmIuEUdj9C7+VPVP9ADQrPv6PUBpH6F2HunzAgH38GZZHojNX7kprgMZaRUzWO6uDix2Sv8M8CoA/sA0vm3SPQ+jMCiyV9sP0QcLRpm1GrnBkDVg3sSSb+Min60qOvz8fEZGaUMiNyJ9PyXHtppLQDnsI7ipF9AnPeQzGtAoLjpKueIbsAwGvqUjY2IltZw9oNVXFs4GnwHx2dSEbcL7PcRyaKseSizvthLyqGMOMQeQFIvgJuBwGVgzdFaFW4PJL6HxP/qtAPuCop067UM1MJRcbD3F2dNPj4liDgtiL1POxTWQpQxzFiUIqCsORD/EpJ6Rm+oApfozVTqZ5D1osXmrNNeZyIZ2IAh7Q/oAcTRu/stXAGCOKdQvoPjMxznYuTGzbwLyYdyc58EhYSvQ4WuLpkCXqWCUPF/6dBsfwNjlEHiF0h2J6r2Z5O+rr6w8be9oaAZrafRiyTAmj3p6/LxKUXc9GuQ/BkgCALKRCJ3YgRLZyipMuIINljzwazTB93e9HgG3GO4LfcXMYoznHugUMbUGOHgOzg+k4JICpIP6zoXI+odtCH1W7CWgtUw8gUmlWz+l8nHvX+I96d4KBVEQld4aap6IKijOpJGhS4v6tp8fEoBcVq0c2PU9m0CJA3JxxBrkY7UlgqS0iKjORuTGfHlhWRgA4aq/i7S/c/gNoKqAxTIKV0/NAWiN+DPovKZLOzDQAZUtO+YsvTOyt494qluy/194dPJwJyn/xZPn8Jt1n+kBezdk7sWPIOTfb1PCKzn2xC+CaQH5AQYYSj7lA57+/ic44i9F5D8CKcKaSHPUkvjBtbo53jwNyBwcVFrcJSyUGWf0ro8bhO4J8Fahir79JQRHvUjOD6TxEgpqKG/Nx7l0OEQyehIh4qd9uFUZi0S/hB0fNXLORdvVzU0CiN8JRK6HL22cMmk+Hx8is9wz8KZRV9Ha39EklqlWFWglDnia1VgBRJYiV5zANLPaZtT9V2MwOIzWu946f/+lFGOit6LRDKATJn28F58B8dncrDmAhFdIGuU6WOSBXFRgcLNYBERJPMypJ5BOwMWEroaFbpixDZHFboSMRu83ZWXF1dxsJZP+q7KqHloSOOqjefYJ0qLuEh2J2Tf1FGqwPmo4PkoFZioJfv4FA0VWISktEI5vR/IktS1a9bCMV9vqI3WUDZAJIMkf+W9TkDFkPDNGMHVw69VBSD6Md14Ye+F7HYwyorm3AzHeCM24nYhmdfBfhdUldbQseZN7OJGwHdwfCYFpYJI9D5IfB+c9t6jELkJZc4c8pzxKIcORDJvQfIJXa+igro4N/VLRIVRoQ0jrFehah/Lu38xmUinSpK/gMxLoMoBA+wfI/YuiP7RlNC28PEZCWVUI+HbIbUZXE9UT1kQuXvCOyDzFIqTT0LmNTBm6LoaSUDiIcT4wojpY6VMCCxDBZZB5KYJXV8xEbcb6f5XPdRYlYOcRLLbtNxG8LxJWYPv4PhMGkZgERL/Cjh7deTAmocyqs/4uiM6QJnnQVX15eNV0GvHfAox69DtmA2j+mA/GyTUxWmCzCuetoX3niUO2V1TRtvCx+d0GKELkcAisA8AymsTH59zM5qNlrg9kH2jz7kBr96wB0k/DfIBrYhuTDtn0smS+YN2bnIb2JiOpKWeQAIrJ2Wwp+/g+EwqyoiCsWZM55yRY+G2gxrgRDktYG/VuXIlOroT/TjKrJn4+08wIq4e0KfC45tw7pzQOf7+Dp1SgIHYR6aEtoWPz2hQRgUE1xbk2oM0Y9o+BfaRfM0YBJzjkN2J2Pv0iIbACojeNb5nd5LRdYs9oOLjc0bs93Ravz8qoiP4brvuxiowvoPjM2UZVRGytQiy+/oeJrcTstu0U9O7s3CbkcRDEPuzCd9didsBODr/fIbXdrN7IPlTz2kzkMA6VOSGseXHjbJhai3FS1n5+PgMxcgbHctLSyX7FMadY+AcBGslGNNBBLK7kNRTqMjNE7o2EddLBQXPuA1exEHSz0L6RbTtiiKhGzBCF47tQkY12MeBfusRR9dTq7HXDo4H38HxOatR4Q/q4j33JFAG9h4dsQgs7feiGh3ZcBsHKwSPE3HbkMRmLWuulE6LRe5AjVOMT5zj0PNdvSMyZ2pDkXkFwUZFbx/9hcx5WlTMbfK0LdBjKVQZKrB8XGvz8TnXGCpt5Wa2QeJhUGEgrNO+Kt4nwKmU3lhlXkfCN0xYisbN7oWkN15GRKd/IreOKyXnttyvHaXACu2UqYB22pI/Qow4KrBk1NdSwQ26BlISOl0nDrgnIHgJqrfRpMD4Do7PlGU0uXFlzoTYnyHpV/S8F3MWmPV9Q+7AS9GonMLymSLiIj3f8yYEew6T24H0fAviXxrzDksbnVMQvBh6jZYy9bWzWxD3Q6M2ZkpZUPZJpPl2LTIWuhzMmajoHTp96OPjMy6M4FrEqNC2xm31bE1DXycXAKanr+VOyD3FaYbEd4Cotgfi6iiRpKDs0+OIGgtIh9eU4XVVqojuCEs/PzYHx5qDRO+H9v+mnZvwlRBcj5rEQmrfwfGZ8pyuRkaZdajoLQBI9h2k57s6XNz78EtSG6EJit7gHAansV9xHaAqwTmGZN9BhdaP/ZpiAwPy9soEV2l9H0a/W1NGNWLOBGxU+V9NSPrMx+dcZKDtUdZ8lDUfADf5JKRfBvptptwWsBZPmFCeZLcCLvRumpShIy/2Xh2lHcOsPy0o+ob+IvWk/jtym3fdiHbaxogRXI1rzgFsVPyvJy1y04vv4PhMGURsT9siMv52ZmupVg/N7gSCoBxdkxK9b+LUOSXBkGJj6Zf0fet+pV/mnNQGz6gEY8YgJ2NgjRGpX+kusF6jI2m9yxpjJ1pOGRmQUx8EQNVvGdM1fHzOZnSBre3ZmvE5/yp0BWLvgcSPAKWjpSo6sREMtx0YoF+lFKRfQuy9qNrNiDh60yUpHa0d1RwpB+gnUigdELhgbEvL2S/tNMmpDyCTrCPmOzg+JY+Ii6RfhPTz+iE1qpDwRzCCYxcIVMqC6L2esNYerWwcWIPqHXY3EZjT0aFep08Pw00DLqgwIlkk+RPIbENPS3HBWgZl94zcXaEsnUZze4C07nCI3DFlZNN9fEodkTSS/A1kXgdlgzEbIh9FjWNWnjLiEPuCtl2ShsjN2tZMpBaPuQgyb/QuHlI/0WkqaQa7GbflLh1NDm5A6wEJEroeFbpykOOWExSVBFhLgIBna7qAICp0xZmtVbpy42Ymy8nxHRyfkkfSL0DqlzovbFSD2w2J7yLG51DWgpHPFRvsvYjbijJqtB6GsnA7/wYoTAu4Mqq9gZhP68Jlt1V3VJAGuxVpvlVHbiJ3692WCNjvIqln8nZ3g4bfVf0bknlVFy8aM1GhjeNq6zZqHsI96XVESFfePUqpJd7HZ7LRG48d3iBbE9wWpOc/IP4Xp9XsEklC9j1EEihzFtLxVf2N3ll73f+KmuDnSwVXIpk5unnCPgZuB3ltkk4TYPely8XWkWBr9pCaV73Pv9iHkfTLOs1lrUQFNw4rozEcOfs1wNZMJr6D41PSiGR15MaY3ifWZ8TAzSDpF0Z0cMTtRnr+S2tRIAhKF/2VfbLg61bhDyH2fkgfAjUNVKfexYEuPlbBvhogpcCYBpk/eN0VQ6fflBFHha+D8HUFX7+Pz7mGuK2eczOz37NZBU4jktmKCl8z/LnOCd1E4HbrrxHtHBgTGBkeAqWCSNknvLl5BgTX6eLm9HM62htcC8a8fidYQATJbBlxc6SsOSjr3glZo+Glv4uxifIdHJ/SRtL6jzFg96Ai4Jwa+dTUM7r925zVdzDxCJL6pVbtpZAPXVZHbYIbPaNyESQfBxy9OwwOLDTu7a4YLFJTCIMwktERt1OvU1X6xcc+ZxUjPu9uJ2D0OTe9qKAXCRkaEUESj+rUUK+tERcCrp4z1fV3w99zAlDuSURFIfSBfkcNtNBgO5gD34/Zt9kqIrqmshMIF6yD03dwfEobFdUS5/2HdIJ+MALnD3uaiOhhksa0AdcLTE6oVLIMKtTTC/DSbC35XVtyCgKrTjt9eKLJd2z6afeAXl/0jmFnhfn4nFUYtbo3QGxvU9JLEkYaEOm26FS06vc8K0Nr4GTfGvGWE7LBkjSDmhoit3naXi06qtRb9yOiuy4DkzMLqj/936Ob2Qmpn3vDjBUSuBgVuXHC6wl9B8enpFHKQMI3QeIhcDM6ciOdgInK27EMhbeL6U/4I/oB9z7ECxYuVVEwZ2sDo6r0schtOl0WvERHkJxjXhupQOQjqMiHC7OWUSDi6PZ5t6Wfdk870v0tiD846e2dPj4TyWhUz5URQ0JXQeopLeugAt4spVrUiA6Bp6M1zPcKnpIxGzzHLNunXSMCOLqhIv0MOJ2eMrFA7POowMrCrmkExD6s7blR5Wn3OJB5VRcRRG+b0Hv5Do5PyWME1yDGn+hiY+cUBM5Dha5AmdOGPUcphQQv1g5Eb05dvLx46Mq+KMUIiNOIZLZph8paigosH/UOQykFkVuQnv/05j8FvTbNOlT4WlBBJPsuZHdoefX4XwBB3PTreiKxZCF4ASq0HjUZsubOQU+7p186T1WBewzJ7kaFLir8Gnx8iowKfRAx6iD9ex1dCF2BCl02cgrFqNY2xm3WqujgdTJ1Q2Do8QaDHK7mTfp+ZZ9FBVeDuXDU6WFllCPhGyH5hBehNoGUZz8+gPR8U19bmvUJqd8iqV8j0T+C7Ns6uhO8DBVYNSkpacm8gpbo8H6mOdHSNxH3+gndTPkOjs+UQFmLxtwxpELXIM5RsA/1HbQWosJXoSI3jHiu23wrOCe1+iYWZN5ErIVQ9snROznWbIh/Ecls1Y6ZNQ8VXINSkT4D5+wDQFo/A5FbIfOqF/ExIPVrxH7XUyQNDH+jiUB6BtceAIjhRcx8fKYuo1E9B70xUcHzITh8+nuoc4jeldfQAEBw4+gjJc5xwIDMFt0pGboKFfnwqFNYRugyxJyDZLeDpPV9rSU6Ao4Fqr/2jetFj1/QURSnGRLfQ8I3osJX5V13pPuPO73mtnrjLPqhTHBFi67iOzg+PqdFGVEo+6yOTrhteqdlzj2tSKBIVo9GUJbu3tIHdbt5ZseYohnKqEKFrx7FKzNae8NoIDfpW6JgHwD7fT0bxmPYwmBJIO1/CRhjNzrGDC8/7+r7Jx/Xx4PrUOb45mf5+JwrKHMaxB8Ee5/WkTFngVE/bESkz+G6E+yjELlDf8iDTtmkn0fG4GRBb+fTnBHu5W2qYp/X0Z7e2joV0bYm/TQSXDdiBEUP9WxGp//HibUE7GfIDeFMPg64EP6gFj2dQHwHx6dkERFwj+siOXMayqga8zWUMuA0Wjn90UYgrVNZoB++yG06uqFiYO+CCUjXDNK4Kf8K0vNQn3OjFw9YiHMUFRha1FAkhSR/pkUDlaEVSwd2nI0CZdYhwUsh8xJ6qrijiy2tZWP6+fn4lDLDOf5a7feIbq02Z40rTaJUEMY4sFbFv4wkftLn3EDu39L22ZyGzoR2e9r7+9JDuXsG9HgstwWMsmFqltK6+zOxWdf8uM3jWpsKXoJk3tRRK1UOZPXGKnzzhA0g7cV3cHxKEnG7kcQP9cPoFfBJ6HJU+MPjH9Mwaoa5vmRBxQrTWq7KQQ1uEdfqx1pgbEij456CwFrI/MF7eRO4TTqnryJjWqOKfASx5kHHX3rT14HED7T4Wc3DqAneXfn4lALiNCGJ74HTglb7VUj4FozQJZNw95HS3RNbD5PbVKV+2yc+2Iu4aAmL4QYBu15XVnefHln/740hopNTeG6521N59+Q+Oh7EVTFUzeYJ6yb1HRyfkkRST4B9sF+BsAPp5xCzARUsXIujUfOQ1rU4dS2QgvCt3oIyQAYSD4O9A5iYXVVOOVRcMOfocLUxDTBAWsCIDRu90VGWrr40Wn+kU4eex4BSBip4Hq6qAo54R22vm+qfIPaF06q5+vhMJURcJPED/cGdeU0fDN8EyccRc9a4RjSMCWuxfk7dTi2HAfrfKoKqeUTX5jGxmykVuAhJv6TnWKkKwNEbmuB5uSj5oAhz/MtI4mFds9M7Cy/5OEgGVf4/UGONXBlxxKgC1+k7KClwe5DkYxC5a0IKnn0Hx6fkEDehh1Ia9f0URU39MGZehQI6OOB1YJl1WtzL9Yr/lAmR26H7H4dft31Ydwi4LbrAL3gJqtdonfaeBkQ/jqR+odVUlYC5ABW5JdfBMcjoVP4D0v333tr6GR0ciHwUI/aFMb93EdGCYW4jYPVd121EUs9PeBunj09RcY7rBoD+Wk8qCMpEstsL7uDoOsFPIYmHvG5LQMVR0U+hVGTI0Q4iLpLdqXW+xIbAhajgeaNuRFBmDZT9iU5tO8cBE4KXoiIfGvYckZ7hLyjJUd130Doq/x7p+l/asQFta0QgsxWCl8IQ9URjxXdwfEoQxyt2HejBT54Cp1HzI68G6KTX3l2v27VDDw0ZuXEzb0PiB+j2xwjYz+o8c+wLo3dyjBgqeg8SuRXEHbY1tS/q42gxsf4iiJHbdIeENbpBpOJ2I9l3dBF2z7/r7gZzLoTvyP/5q6rBYW0fnymPrfVhVNDbzOBFJrLDtnhPNMqaA/H/7jkb6BqgIVI0uRR15A7IvKyffRTYjyL2Lojej1LGqCLLypoDsQe87sngsJ2hebOp6NeEABD+KLgn8p3DYeitp5TsHr3mnn/Tttxa1reJgpzNEefokAXTY8V3cHxKji9d8/fgtPB3v4j2ieQBSDsELp20dSilvMngIyPiQOoJHb7NFe/FwDmOZF5Der4PjKEIT4VHlX5XykTCH4XE972pvyGtvWHWoYIXn37dznEt5Cc9gKl1cFQErIVAlrz6AElPeIeDj0/RMWd6H9jugG+4I6SGJx6lrFFGLDKQeQWMWf26Lcsh+w44B3A7vjaimGH+Pb3GidFgzobgRXpyuYoBou1G8DLUaGxk+nlI/bpvzfZRT115aEOn1MS0ivsOjk9pYtSiZ6kcByy+/JFDoEL8/QuTUfg3MoMMhnR4tTAz8o+rcsjuKexagisQ8wEk/QetL2EtRQUvOG0XiJ6fsxlw+wqUpUVLeKRf1g5N5E6d/pKMdi5DtxT0vfj4TDZKBZHKb0LyYUg9DygIXqx1cKwlxV4eMLi5APsoRO/ue4HSSspiHyvYGrRw6SbEWgbZrYCBCl6oIzCnQZyTkPqNrhVMPeEdbNb6O5IABMJ3esfbteMTmJifve/g+JQUD171NXa8sAuAL9+y1MvP2ux7W1i4dubkqPqOFRUBvELo/qHl9C91RMfRQoOFGuypzFljr42RLp3KGuiU9WJU6vlYLloPKPJRVGDVGa/Vx6fUMIKrEPOLSGYH4KDK/kQLgha8W3MiEVAxjJqhU+gTgVImKrgGgmvGtjL7gHeBIdwNVakjQe4J7agZ9ajoXRNm530Hx6d08QqL9207SE9Hmh0v7ObBq74GwDee+3qRF9eHUhEkcAlkfq8dBmXqwjtxwag4/QWKgulFh2VwgXL0bozY5xC3R6e8jEqUChVxrT4+hUWZdai6J4u9jCHJby4QHV1yT4Gq1S+QNq/b8vTRlGKglNU3EXBgB1bl34K1Sr8HccGomdBxEVPJRfU5B/jGc19nzRUrWHPFipwT09ORyH1/37aDRVqZxm25vy9k3J/wFVqFOPuOp2aahqp/w6j9CQTWQWAdRs1DhR+8N0qUUQbWSpCmvoPhW/Qg0OCG3GuUWe87Nz4+JYOCyEd1Ktl+W6u0G9Wosk/ndVuOZGeGtWGFwlrct+nrJXQ9hK/NzdxSRjXKrJ3wWVh+BMdnylBWEWXh2nlFid6MNI1Y7MPQ8239AJsVOlUVWI0KrMm9ZjTXHo/zk+v0cg4DQbAWj1qFVUVuQXo6wD0CogDdIq4Cq8e8Dh8fn8Ji1DyEm34Zur+FFgCNaccheCXKHCbVPIGI2Hrz5rbrsTfWglEJ8imjAoncD8lHdLemAghD9I9GHmI6AfgOjk/J0d+B+cZzX+fBq77Gvm0Hi+bcjISIoxWXsfomcYurOx0CKyesWG7oewuS+rUemoegVVhDeiCoNf+05yujHGL/DZyjXiqqXmtk+Pj4lBziNEHyF1oItFfzRlKQehQJLEAZI3dEjbRJO+293e5+w0Q9rDkQ/cSoNlRGcAUS+Ko3+FjpwcOTEBn2HRyfKUGxnZvhphGLc0x3URn9hcIMIKKFwk7j4Lgt94/Z4OSE/iq+Dunn++p+ANwu7XDFvzKquS56VteZ6034+PgUFrHf1//oL+inwrp70jkIxsQ3AfRp72zSMhK9mzgA+zCSfg4VuWlU11IqApNcJ3RWOziu63J830lajrVSVhFlzooGgqHRqT36lA6lFrUZxFAjpHojKoW8bWYnEMjv3DLiWhHVOQbW3ILe36cPEaHxYBNNh5oJx8LMW9lAKOLXLvlMJIozsSnDbdKGo//mC+cAWtl8U78L1kHmTRilg1MMzloHx87a/OLfnuL9t/YDuo+/vDbOnV++happpdrZ4lPqDDIKxnSdj3Y7+jqmxAFSqMDpR0qMpa1zkB6G2wxuF0TvGvDKwjtXPn04jsNvv/Mcb7/8LqBtTVlFlDu+dDN1DX7Kz2diUNYSvZeSTN/AS0nq9mtzQZEWVdp25qx1cHa+tJs9b+5jxvxpucrs1hNtPP39F7jzSzcX9N6l2Mpcyoi4ukjWOYkQ8AZbdqHMWbpodpQzVka8h9utB8xltwIBCG1ABdePKo0zEkqZEL0XSXzHy0974ZzQVWAtOuN1j3zzGNCRr7/jduoozijk030mhve37GfHC7uYPr8ew9C2puNUJ7/8j6f5+NcnZmigz8TQv1BWVBDcBJBEWfP07LcJ0L4RpwlJPwf2e1qJPXQlKrDyjH8PlFmrx7ikfgaui665syBy35iKdUfbzNB/86XHQ7za900RcJu0nSthzloH5+2Xd1NRE8/7paqaXsmhd46Q6EoSjZegYNw5iEgGSfxIt1dLCuw9gAnWUkRZOs1S9qkzEn4SySA939I5ZKMacCD5M8Q5hhoU/Rg7ymrQs2Tsvd7cqtkoc9qoz++r5zmOZLboaJC1HBVcnTcjZtCwzeofIOmnIfVs70pAhb1BfWfto11yvPPKe5RVlOWcG4Dy2jinjjTT3tRBVb0/4qIUyCuUTT+nox/WQjAXI0pBcK03xfr0nUHD3sNpQbr/BXC0iJ3bDonvIpFNqNCGM34PRugSJLAU7AOAoQUJT1NcPGiN4oC9RwsbKgsVPN9z7oZ3wFT4WsQ9CvbhvoPWQlT4yvG9kUniLLaCw/xnqcJF1XojN71KvA9e9TU/inMaJLMFsjvAmK2jKyri7Q7atKCVfQhJv4IKXzP+e2Tf1XUp/QvkjNmQeQsJXYky68/4fSgV0l1T48TN7IDEDz21zwBkdyDZ16Hsj4cdhKeUQoWvQwLn6wiYCoK1qDTVns96BhdiCZR8CP9cQlJPQeJR/ZxIuz5oH9C2IXKvnmIdOO+MnmPJvAJkdUTIgDcAACAASURBVOoavHuFIPVb3O5/Aowz1sJSRqW2jeNZnwiS3KxrZ1QYRJDMHyB8PSp87aDX56217HO6FsdtA6MGzHklr/Zc2qs7A1Z/YDmdLV1aJ8Sj9UQ781bOJhLzPwBKhswboKpBObobgKgeb+C26FSVUQ2Zt87sHt48qzyUAgx9nyIjkoHkT/V7Neq9v2eBvd+Tj89noJCXMutQwQtRgdW+c1MEVm1cSqIziev2DWzsONVJ/dw6KutGN0nep7CICGS3eB1ITr/veLPWlAIVRTLbz+xG9kFgQERFhYD0gPsWCeeQdm6MmXren1mnuzBTzyBu64inKmWirEWo4MUoa2LSeYXmrI3grNq4jMO7jvLu63tRKFBQWV/BdR+/smD37I3W+DU4IzP459NbFOuJzQG5CJy4YITP7IZGLWDnHxNBi2WVwAeQe0rr0OCFxlXEM7gxsHdB6KKiLs9nZBZfuIC1V69i+/PvADqyFqsu44bPXOvX35Qa4VuAlI7kYGmVXbL6e+L2Fe+OF3O6Ft7s7+RIFtIv6uecws2kGw1iH9DrkW4goh0+ZWlz6xz1UvhnD2etg2MFLD7y+eu5+EPn09zbJr58Flag8G/ZdVycrOPX+oyG4DpIbgaJaePgNALKe9AskEYIjpyeOp3BUIEVSLpCF8WpWsDVRshalJ+2KgIigqS3QWabN7QTMOvBWg5kQcWLuj6f02MYBtd9/ErOv3oVTYebCZeFmbOigTunfxqAn7V9r2D3TiXSdDZ3UlZZRll5YVVhpzJKKSRwMWRe8wrwM0BaD3q05oPYQFrXo5zJfUIbkcxbuo5Olev7uE26HodTZ/5GzgCRFKRfgexOHSVXBpiLwJzt7SvPcCNZgpy1Dg7oX+oZC+qZseDMayxGg+u6vPqLN5m9dOb/z955h8dVXXv73edMlUZl1Lvk3hvuBRfAmG7TIRBICCG9knJzvwRCeoOQm0a4IQkJCXABUwzYGIxtMO69d1su6n2k6efs748zGmksyU0yaud9Hh7s41P2jDRr1l7lt9B1yZ++/ncmXDmGuXfMQLVcfOFaX6G9GiWQ/PrNcZE6HBfowthhKMnGhFnbVIStcxEMocRB/ENI/1sQ2heRN5+OcFzd/Tvs8AEIrozk7BtAxoNWbuwm1TSEbWL3rs/kvBBCkFGQTkZBevT3vHmG2qWI6EopWbdkM+uXbEbXjajnhCtGM/fOmaat6QDhuAqpnQLtJIikSIu1CtgMJ8RxbafbrYWaA67PIn1vGlpUIg4cNyCSfoGsuR/onsgNgPS9ZaTkhSuSqlMjTR0hY3N5HurnvY0+7eB0xN2vvAjA87d2voOmNXs+2s+HL28goyANi1VF03Q2L9tOfKKTaTeYaYb2EYi4e0CbDXo5EhcIOwIfKGlnLQBuT3q8wyiOmoaIv9+od0HpMV1GMviREaWxZUFoN1ADOqCfMgqMTZXhXseZA2EvxYDYPWsP8MFL6yK2xoKm6Wx6ZztOl4MZC6d0+fP6AkJxIYPrjE5H2VxvoiJcnwY11yje7YrnWAaC6ysYqS9LtFalXT3QjwkpfUYNkppv6HWFdgNeYzOp1yESv9clchw9jZ5h5buBUCDM5uXbARgwppDUbHen77lp2XaSMxKxWI0dlKoqpOaksHHZdqZeP7H7owXdzFlrlCwFQMEll6frqCOp29A9gM3I/dsuA+kFPaIDZLv4bg6T7mPQ+CKgJVJZODIPX5OfTe9sp2hUfpeI/21atp2k9EQsVgvLn10FwNy7ZrJ5+U6m3TgJRen5BaDdg2hJBQMIJ6ITXVMdPkUIINbWfNyRm5jUvQxG6hnViDbPTNAbAT8oqQg162Nd28dFv3JwmiM3G06fAuDrx8pZeNjOyhc+Yv4n5zB+XudmeTTWeYlPis2DW+0W/OUBdE3v96FjqVWD9MQK03WCC5Ue/zi44LVYR4P/PSAysE7EgQgb3VQ9oQDa5IJpdtwXue9HC2vkDcsBCate+AgpJXNun87U6zuXemyqb2rTDWq1WQh4A2hhDcXWfx0cKaVRYycDoGbFDHXsiTbjXHTJWkUiqGkRIdBEQDH+rzWCdUzXLLQH0q8cHIBwsKWbxuqwkVmYTigY5r3nPmDg2EISUy++qHPwhCL2bzhMWm5LJXp9ZQOFI3L7tXMjpR/pfSkSFlX49esC7POQUvaaqJaUQaMgUSR0aXpL2KYhQ9tb8vUEAIFw3t1r3huT9nnu2J/4y7f+SVJ6YnQGXjiksfqldQyaMIC0nIvvWBk0roh96w+x9b2dlBcbxatLn1mB1W7Faut7qYbzRer1SO9/Iu3aCggr0rEQxR7rUPZkx0bqTUAYRCKy5pMXPYy3TereeTOy6W8R9Wa7ES1W0xG2aZfqpXQ7/crBef7WO9n14T4+f3QpNoeNT3sMtVmrzYKu6Zw8UMKoGcMu+v7Tb5zE0Z3FlJ+oJC7Bib8pgGpRmHvnzK56Cb0S6XvbqNxXco32ZxkG/1KkkomwdX4C7qU0VlJqyMAKCHyIYXTikParDWcn+JGRz7eORTjmI2u/esHGSCguiP+i4eSEDxt1R7ZJF6SEbNIzOX2oFF3TYwb8WqwqQghO7D3VKQdnWsTWBP3B6DEpJckZ/TfqJ6VEep+PtDvnRGxNAHz/h1QzDcXxHozUG5H+1w1bKQE127AzzWjHQSQhZfCiUu3CMghcX0MGNxtz7CyDEdbxFzTmobfRpx2c9mo9hCJYeMhGVlHsF4gQAkXtXFjXnZnM/Y8ZTlTJ4TIyCtIYM3tkvx7uKWUgIiyV3aLqKixGyDS4BrrAwbmUyMAq8L8LwY2AMDotPL8x2tgtg42OhNBOZPgoFyvkJZQ4hH0G2Gd05dJNuhmhdByB67StyUjivh/ewYQrx/DUN5/F6rDymxWP9u+xEHplxAlobWvsEFyDrN2BSF/Wrcs7G4Zz9m8j8qRkGevXqiPp/FSjRs9xA+gVSO//Qdw9HUZ4z5aGE2oGwnndJX89PYU+7eC0R9GofFSLSsAbwB5n5+8JFeiaziKrjcKRnffwE9wuZtw0uQtW2keQQYy2oDMMurCBVoXufRnC+422TftshHVsj0nNSBk2BLqUTFpGf0iQDaA7IwqlgMgE/TS4vg6Nvwd6dgjc5OMhf3guVocVX6Mfp8vQGAn4giiqwoAxne+Oa7Y1pr2JIAMgBbRxLI1Ijt70rDHSRMlCOK4wIho9Bb0UwkdbIk8QEeTzRk4Qhr1R8owIj15utHabnJU+W4n28LxH2bl6LztX7+XheY9Gozmu5Hhu/MLVNNY1UV5cScgfRNc0bvziAlOU71IgIgJ+sj72uFZu7FZC2wCHIYzl/RcyuKY7Vtk+MmBEb/xvgl5i/Od/MyJG6D/jXJthpExMIjjjHSz68rX4mvyUn6ikvLgST20j1332SpLS+m8q6ZKhZhpOgPS1HPMtNjRu9NPQ9LRR0K+VIBv/gh7cd8GP0Kvvbalx6Ur0RoyaoVbOWVTdvGUESMuImbOPVYC2I136I/0uggMwdOIgfnDVRgLeICUNfkDygyMbeX78xQkd6bqOlBJV7b+FxB0hhIgUt/3VGGoXNUBBQ8xPiejcCBtIB/iXI21TYjofug0R187sGgVjxMMZ9RMiCEp2vzcoJrEUjcrn84/fz6mDJeiaTt7QnE5tpExb0zFC2JDOW8D7HxD1gNXYpESJ1EIpbtAtEHgbaR3eMyLGaiZGdLhVh6lwYYyYafU13Txipo+NVLhU9FkH5/GVj51VQVRRFZwJDmi4+Gf4Gn2899yHrHtjE431XgaNL+L2b95IzqBzhw6llNRV1KNpOilZyX1at0JYCiHhG8bkcK3KUMwMbga97owTbaBrxvEumPDdWYQQyOS/gPfvEFhjGB77LAifAMUWMZ4WkFUgkhF9uN3S5OJxxNkZfJGbp2Z8TX5WPr+GNa9tpLGmkYHjirjtmzeQNyTnnNf2J1uj2MYi1fTIuIRGcN8CtV806v6ct7ScKFzGhosQZ+rVtEd7nUnQdalooSQh7XMh8F5EHsICwVURG9NoFB37XjFS/q4vtmwMTc5Kn3Vw4OzS6M0qxheraqzrOs//4lXWvr4JoShYrRa2vreTfesP8siL3yRncDYA4VCYPWsPsHvNfoSAMbNHklWUzrK/raTseAUgcGcmcv1n539sIyW6A6GkIBzzo3/XtfJISqdVqF5qkZmbPWf+kmIbjlS+hLSOM0LdloHg+prR8h7tohqHcMzv090IJt2HlJL/+9XrfLh4PUIIrDYr29/fxb71B/n+C98gf5gxT63Z1uz5aD8AYy4fQdaADJY98z5lxZVEbc1D88ke0IdtjZqNcF4f/bsu7Bizp1rjj+jB9JyWeuFYgFRzjXlZ0hDgQ4mD0PbIGRooKYi4O3tG1KkX0KcdnEtJyeEydn2wD1VVcSUbIm1Ol4OasjoW/+5tvvz7zyCl5K2n32Xf+kMkpiYgJbz51HLqKhrIHphJRn4aQgg8tY289PgSHvzFPf2mDkjYpyFDG41ojUgCQkbhnH1Oj3MUhKUAYflE7EE1ExxXfuxaPr1JoMykayg9Ws72VXva2Jra8jpeefJNvvanhwB46+l3ee7Hr2Cxqsy6ZRpv/uVdaivqyR2U1WJrahp5OWJrzhQK7LO4/wxNzxgRHcVlpMj1SnCev6NwIQKBF/sZFUIgbGPAFokEJ3ylU/czMR2ci55H1VDtob7aQ6LbFXPc5rRy4sBpgoEQlSerOLDpCNkDM6MfpHAoxL4NhygcmRc9luB2UXa8kqM7jjN61ojOvaAewPl8IIWaaQzA9C0xdCuEAxwLEPa5H9Mqz49zTio3d1Iml5iGag+eag/xEeemGavDSsmhMgK+IN+64odUFFdSV2EU87/19LtIKcnIT6VoZH6LrUlxUV5cwZEdxYyeOfxjfy3dgWIdhh53H/iXglZiODnO2y9qiK/pZPQu+rWDEwqGOLbrBGXHKnBnJjF4wgAUVUG1qFisZ39r3JnJWCwq4bAWo1KsazrxiXGoFoXKk9VA7JegFtIQErwNvuhuzDgHmhpaVf/3A4SlEFxfwggfWxBdML6hr3KpawBMLi1aWOPY7hOUHCknMdXFkMsGYrVZEIo4p/JwckYSqlVFC4WxtLI1pw+XUXWqBotVJRQIxVzT/Peq0zWsenEtN3xufqt/Ffg8/cvWKLbRSOsoDFtjjQ7A7Eou1WfU/IxfPP3WwfE1+XnpN29QdrQCi81CU30TVSW1pOWmEJ/oZOyckVx+6zTszva7ebIGZDBm9gg2vrWV5MxkTuw7ha7p5AzOYvZt01BVlbjEtiHg+OR4dCRej4+a0lpc7nisdiu6LskZ1Lvz4hcy3bsZw/nrAR1TZ3Axr8XEpD2C/iCLn3yL4n2nsdosNDV4qTpVTVpuCnGJcYyaOYw5t0/vMGWUWZjO+HmjWPv6ZpIzFFRVwdfoRwBJ6QlYrBYe/usXeP0Py9ixag81ZXVRB0cCuqbF2BopZZ+u9+uInmprTC4d/dbB2fbeTsqOVZA1IIOgP8jRHcfxNQWwO23kDclm24pdeGoaWfSV69pNQwgh+OyvPokj3sGGN7eghTUsVgvXPXgls26ZCkDR6AISUxOoLa8jOaJmXFNaCxIObT1i7NyEYPdtOSRNSuDbQ7I/1vfApPfQG4cEmhjs+nAfxXtPkTUgg3BI49iuYnxNflSrSu7QbHZ9sI+6igbu/M7CDm3NAz+7B3ucg7Wvb6LsWDkWqwV/U4CT+0t4eN6jSF0ydNIgQsEw7izD1gR9QRzxDkCy84O9IASp2W5m3z6dnMGmSFxXY35Gex791sHZs/YgyemGIag6XUMoFCYpPYH6qgYkkoyCdA5vO05NWR2p2e421ze3oAO43C4Ce08RIMgHL6/n799/gUHji3h85WPc8e2bWP6PVZw8WILUJZWnqhk7ZyQ2u5XyE1WEAiEURZCQ7Op0+2ZzFCouwUlabsrHXh/Slz7gl+q1yIgImRD9pMDThL3rDpCYloAQgprSWoKBEImpCTTVe9HCOhkFaZzcf5ry4so2I2SasTtsPPCTu7nru4v4zvwfoVpU9q49EP13oQju+PZNJKcncvJgCZuWbiMUCDP1+pFYbFYqTlQRDoaw2q1cduWYTtsaT20jNaW1xCfHk5rtNmvRehhSyshwYNtFza3qK/RbB8dqt+BvNESgfB4/qqoiZaSSPfKfogoaaxvbdXBao1o6NhYpWW7u/O4imuq9lJ+oZPGTb7Fj5R4ATn95JAAV1gAVtRWMe8qQ+V88/UbWL9lMTVkdecNymH7jJNyZSVjt1jaGqfkLeP2ar7L2jc0ASF2SPzyXm0x15k7TZY6NXov0vQEhQz1VWocjnDchLlCwqzc7jf0Vq92KHjbUaH1NfuMzLEEgUJQWe9NY13SOO0FcgpM/rP850P6svbv+62Ya65q45WvX8+rv3iYjPw2AlMiMqqrTNRzadpQnHnoKgC888SnWvbmZuvJ6CkbkMfWGy3BntG9rwJDH+PCV9Wxaug0hBLouGTi2gOs/dzWOODP9A93/GdVDh8H/OmiVICxI23SE42qEuPCW+N6+We01Dk44FKbsWAVg1L+cqwi4Ne0ZgvFXjGbZM+/jcDlISImn9HgFWoOXjII0VFVF13SkDu6s5HbvtXP1XgDGzhkZFRU8sv04AE313uiIiMdXPoYQAldyPHWVDWxauo3acqPTobYsInSXbzghnqCh1XDfm4tRrSqfSkpn5+o9LP3rCvKH5ZCS7WbGwsmMnzfaMC7V90ZrRHLSvsMnvmjjvSUPIqXk1IES3v3XahZ+8Zrzfp+6it76YbhUSBlCNj0Deq0xSA8gfNhQd3Z9vV/vsHoimqZRfryScEgjqygdm6NzP59xc0fz+h+WEZ8UhyspDk3T8Xp8pGQnY7UZ9XdSSlKyLmxQZkc6X67keOoq6pHt/JtQBKFAGDDs1Mu/fZMEt4u4xDi2vreLt55eTsHwPFKy3cy8eQpjZ4+Mic7s33CIdW9sIbMoHVVVkFJyZEcxq/9vLQs+Ne+C1m/S9UitzGiJF/HGgGPCEFiNlCFE3KLzvk9fqUHsFQ7OqUOlvP6HpXg9xvwfp8vBwi8tiApcXQxjLh9B+bFKdn6wF13TkLpEtapkD8ykqd5LfVUDU667jMSU8xOda62c3MyR7cejTg5AZmFazBTh0S+XIKVky1cHY7VbadKMwkCL1YKiKjTWeTl9uBwpJX5vEJvDxjt/X4kQgvHzYqdwqxa1ZYCuEKTlpXBo81G8Hp8ZxTkPpNQA7aKdjbOpZhM+YuhuKK1+X0UGaKchfBisIy/qmSZdT+Wpal79n7dpqGoAIbBYVa777FUMnXj2wYxn+/kPmzyIKddNYMvyHUhdNz6nUpI7OAuvx0ddRQPj540iJct9znue9fesFRmF6didNnxNfpzxxqBPXZesfWMTh7cd4+DmIwA0VHlQVIVJC8ZRerQMKSHgD2KxWVj61xV8/4afo1pUXqt9FoCtK3aRmOpCjdgxIQTpeansXrOfeXfPwmbvOcJ5PRUpDQdTiK7/+pWBdSCUiIghgNVwdEIbkPpVCMV11uv7Gj3ewfE1+Xnlt0uw2W1kFhjhVm+Dj8VPvsVDv/7kWcWqzoy2tHY2VFVlwafnMfna8dSU1qGoghMHSjiw8TCOODszb57C6FltdSKar1/kvj/m763/fOj9WQA89eMrY6612qz87O3/x/eu+QlSSsbPG42u61TZrLgzk5G6zunDpXwmaOzydx/Yj81mwWKz4KnxYHfaSM1289HrGxk7ZyRK6nPo1fdSXlzJmy/cTXxSi0CeEAIpJeFg+Hzf6n6JlEGk//2IKnEQaRmCcF6PUM+v4Lu93zE44wtIeiIzZNo8HXRPp9Zv0nVoYY3FT75J0B8moyAdgIA3wJI/L+eBn96NO7NthOV8fv6KonDF3bOYcOUYqk/XoKgKpw+Xsm/9Iaw2C9c8MI8xl59d/+q8fs9aYbNbueFz83nt90tpqPYgEOi6jis5PiaV1LzhOnmwFJvDhmpR8FQ38trvl8bYj2Z7d8Pn5reJniuqgq7paKEwmA5Oh0i9DulfCsGdIBSkdTzCcW3XOh16BXDGd6JQQReR4Z3n96y+Uk/Zox2cpvom9q47iN8bwJ3RYlziEp14ajwc33OKEVOHtHttMBCivsoTk9fWwnqb81Ky3NGd08CxRcy9fcY51/XwvEdpqvdG/wxtDY2UMmqMbkr8JIqq8FrtswwcW0j2wEx8jX5mLJxEwYg8vj0kGyEEd738AoqiEPAFjd1Xow+LzUIoECIh1Ygk2Zw26iobCAfD0dB5XKKThprGGAensa6J1Bw3CSn9y2O/UKTvVQhuMWa7CAtoJ5CNf4GEryOUC0sZdIiSCQgiRV6RB0vjWA+YuWVizJU7vL2YuqoGsotafib2ODuyysPBLUeZet1l7V4rWzmvpw6W0Fjn5euzvs+Ta34Sc547Iwl3pJty4NhCLr9lWofreXjeo1H7sch9P75Gf5sNV3OE+JGXHmb/xsM01jVSODKfAaML+M78HwHw6Mvf4tDWowS8AQpG5pM7OAshBN+c+wglh8vQdR1FUfB5fNgdNgL+YLTjs7264eFTh7D29U1kxbcUQzdUecgZnBnp2DJpDymDRkparwEl8t4FtxopJdcXu04DzDIIwseIHYETMIYG98MBnT3SwfF6fLz7z9Uc2nKE2vJ6yoorccY5oh88MPQdzhS3iv6blLz51HIGTxhAWo6b95//iHAwzLi5IwkFQ+cU1roYPLWNWLwPYLGoDBpp1Ar96mXDCfre3cNQVSX67DMNXzMv3HYXW9/byfJnV5GSbTgnlSerUVSFvKFGRMHr8eHOSsYa2Skpqc/hiAuQM+gNSo+UY3PaCAVDWKwWFn3lWrO74SxIvQaCW0HJMcK6ACINtBJkcBvCce6agmbH9qw7ajUfrKMgtLNlCrmsBcsoUAsuev29fXfVEwgGQqx8fg271+ynodrDqQMlWCwW0vNSo+cIRRDwnTnLyODxlY+x9G8rOLm/hKa6JixWC0FfkD1rD/CN2T/gtx/8uEvW6XQ5omlwLazxzf/9PL/+9B8JeAM889//MWQqLBY2v7OTgWMLkLpEKILE1AQmzh/X5n5PrPoRG5dt49ef+iNWm4UEt4uasloUVSF3cBbDJw+msa6JN/+yHJvDFk1ReT0+Dm45StnxCmwOG+FgGKvDylX3zjFtzdkIHzaKftVWaWo121Bx144bjkkXIGxTkMGNoJcZI3BkwIggO2++4PR7X7AvPc7BaZ7fVLz3FGl5aSSkuCg7XsG7/1qNy+3i2geuQAtrCCE61HKoOFHF4W3HyB6QgRCCaz5tfFGVF1dydOcJhk0adN657PZoXW/z82X/j3efXc2fv/kP7vxsJQD5A43zFMX4wGshDS2k8ZVp/018UtxZnznhyjEoqsK6NzbjiHegWlVyBmfhiHdQX+XB6/Fyy9evjzEmdqedO7+7iMPbjnHywGmS0hIZOW0oiak9Z2hlj0RvAJQW56YZ4QCtrMseI4SAuLuQwcEQ3AhIsM1G2KZclKKqqWrcdax64SN2rNxDekEayRmJlBwpY+ULH+FKjuO6B69C1yXhkMaA0fntXl9bUc+uD/dhc1hpinRDNeONqAVfjK15fOVj0bRQc7T4m3Me4cDmI+hhjW/MfiTapFBdUsu1nzHS4e88u5LN72yPjmzo6NkPz3sUJNHzAv4gQV+Qqddfhj3OTn1lA74mf5t0VFyCk3u/fysHtxzl9OFS3BlJjJg2lAS3GSk+G1Kr7fgf9foue45QEsH1BWRgjdGxqWYh7LeDpX+M5TiTHufg1JbXcXzPyehwOKfLycCxhZQdq8DX6KPyVDUb3tpCYmoCaTnth9zqKuoRimizo1AUhd888EcSUxParcu5EH7z/g/ZvWYfP77jCYr3nCIpPYGX/3Y79ng7V1zzNHGJcXzv7uY6DiPS5PX4YtJI7dFcQDx27C+QUlLV9CTr39zC6cOlZBalMfX6iRSOyGtznc1uZeS0oYycNvSCXwv00y9JJRWEjEwxbxUilj6wtP+F1hHn+h0SwoawzwD7uVOgJh8PviY/Oz/YS3pBGqpqKAQPmTiQkiPl+Br9VJfUEAyE2L/hME9+7mkeX9X2Z1xf2YCiKDEdRMufXUUoGOa2b9zQpest3nsSpMTqiI1A11a0fEEKxFllK2JoZR4LR+QRCoYYPWs4JYfLyRqUyfQbJvKdf3y5zWU2h43RM4f3m1lWXYFQM4yutjZpakBJ69pnKW6E80Zw3nhR1/eVDirogQ6O1+NHCCXqnCx/dhUSSdAfIugPsfKFNQS8QWrL6zvcnSSmJSAjrZcxc6A0DYvtwl9ye8/Zu+4Ab/7lPSpPVpOSlYyuSw5sOsLAcUU88Z0ZxCU4ueu746OvIRQMc+e3F3L9Q/PbfUZ7CCHILExn4ZcuXat3f44GCCUBaZsNgRUgUkHYQFaDkoywju/u5XVIXykA7G78TYYOVnNH0PJnVwGGAnDQB7vW7Mfpchjt2x1kX5I6sDVS10nPT4uppTmfSE7rc5rTQl+Y+B2qTteQluvG5rAjkfgb/TRUe6LR4Rd++RoAd313EbXldWx7fzepOe4On3VeqVWTrsMyECxFRn2MkobRYFAFlpFGCtvkktDjHJzUHDeKAqFgGGvEGRGtrIvFaiFA+/nwZrKKMigaXcDxXSdIzU1BUQQ1ZXWkZLv50v88QOmRcn7zmT9hc1j5zfs/7PA+Z+vC+ui1TSRnJHJ8j0RRFSQ6QX+Ivev2Y3faaahujN7nik/MovJkNaM76JTQdR1vgw+bw4ql6QHjYD90OLoD4bgGqaRBcI2h/GmdinDM7XftlP2RxBQXzgRHTCt1a2pKjbRC64aCMx0Bd2YyI6YNYc/aA6TmpGCxqEycPxZHvINB44vwN/o7gD+dwwAAIABJREFUvc76qgYsVkOIVFUVNF0nFAhFxQNbEw5p+L2BDiPFUVvjtGGzW03H5mNCCBXiP2WkjoKbAAUc1yHsM3tc7VJf2kD1OAfHGe/g8tuns+K5D4lzOZixcDKe2ib4YC8ZBWk8sfpH59x1CCFY+KUFrH1jEztW7kEL64ycPpThU4fw7KMv0lTnjRqv9//zIfPunnVB0uW6rlNXUU9mYTopmcmUHa+gpqwOTdNQhMDucoAuObT1KEnpCYBg3idmUTC8rW7P8T0nee9fq6mtqEdVFT71zXqS0hM72jB2OX3pl/liEEJB2KeAfUp3L+WCOZ+flZRhZGBtxIHzg3U0wnHVBSso90VUi8pV98zm9T++g8/jZ+aiKTTWNbF1xU4yC9NRI5O7mzc4HbHg0/NIzkhi63s7CfpDDJ00iLGzR/L8z1+laHQBlaeqAbjynstZ8On2C9fP1gY+4coxpOelcXDzYUqPVlBTXose1rHH2wgHwoTDWrThYtnf3ycpLZGnd/ymzRfnsd0neO9fH1BXWY9qUZlwxWhm3TL1kjRdmLRFCCfCMR8c5x/F701IvQbpXwGhXUYdo20Wwj7jkuj9nC89zsEBmDR/HOm5qWxfuZumei9TrpvA1/782QuSArc77cy7cxZz75gZPfaPH7yAFtTILEznhs9dja7pbH5nB4Wj8hk8fkCbe3QUxlUUhawBGXhqGolLclJ2vMJoQZdGkXQoGMadmUzl6WomXDmaq++fhys5vs39K05W8fITS4hPjCOzIB0trPG/v7ia8fNGM/9Gwzj1N4fDpGuRviWGxo+SDiIZQtuR4cPg+qoZpQKGTR7MvT9wsfW9ndRXeRg3bxSnD5eiWtTzTuNYbVZm3TyVmYtanOQXf/ka3gYfmYWGErKUkl0f7KNwZB6jZlxY7UrOoCxqy+qIS4yL2BoNMEayIIzsmYyemxkR/Yx1bsqLK3n5iSW4kuLJLEgnHNLYtHQ74VCY+Z+ce0HrMekfXMh3j9QbkY1PGVo7Ig0Igf8NpF6NiLv50i3yHPRIB0cIQdGofIpGtZ+bvJCwavMH/asz/x9lR8tjamAUVcHpcrB37YF2HZyzMef26fznF6+yb8MhQKBYFPSQBsLomrLYLQgEh7cdZ8bCpnYdnB0rd6OqSjScrFpUdq7ey5Z3dzLrlqeJT+y4ILmrIy6mI9X3kHotBDeAkteqDT4LtNPI0E6j6NmEnEFZ5Axq6ciccdPki7pPs63x1DZy8kAJGRFh0qvvn2scr2lk14f723VwzuZMzbl9Ov/+6SvsXW8M12xtawSQNTCT2rI6nC4H3332K2QPbKuttH3lbixWS9TWWKwq6QVp7Fy9j1k3Tz2rYKqJybmQoZ1GN1i0Dd5i2J3gBqRjLkI5+zzHS0WPdHAuCRKgbWcVQrQvMtuK9hyqwpH5XH3/XFa/uBZd06P3QpdIoPxYJY54O9Ultcy6dVq7U4Kry+qwnxGVal6fz+M7q4NjYnJO9BrDsWm3Df5096ypF3KhdSpSSjYt24bVYWXB/a1SUkIYUZcLJG9oDtc8cAWbl+9AtQiEoqKrKuFgCCmNGp0BYwooHJHPsV3F7To41aW1bSLgzbOkvB6/6eCYdA7ttGFXWtNse/QaMB2cS0Pzjmjf+oMAvP3X97BYLVx9/1x0XeLz+Bg5/eJaq/OHZKOqCharSigQbnF0AF0zivm8DT7+8f3neeWJJTy+8jF8jT4ObDpC6bFyQv4g9ZUNuJLjox0c5cWGls7PPvE7nlj9o3af23rIZn+tnbnUSCkNMT4A4e5xhYDnheIGqRv/tXZyZADU9jWkTDpPgttlCG4GWsakSCnx1DQy+7apZ722I2cqb2gO3novQhEE/SHD1kR8JW+9j9OHSik7Vsn+TYeYsdBIlfkafezfeJiy4xWEg2HqKutjio+D/iA2h5XEVDNV2Z1IqUU2Iw6E0ku1y9QsQxG+Nc22p5ucG+jlDo6UknAojMVqOe8vICmND3bZcUNteNzcUQwaX3TO6/zeAMV7TiJ1nYHjB/C9a34CEoJ+o7hPOYv2hNNleLYN1R6e//li6qsasTtteD1eivedQtN0NE1H6i0OklB64RdqH0FqFUjvi+B93jgQdzfE3Y7oZU6BUFKQtomGuKCSAVhBVoESj7BO6O7l9SrO19Y8PO9RdF2n/LixUXnzL8sBmLRgAiOmDWHk9GHnfFbAF6B47yn0sMaAcUX897U/BVpsTbvX+EM44pVoJKbZ1jRUN2Jz2GhqaKJ432l0TSctL5WgL4i3wcc1n7nCLDLuRvTgfvAvNmbSBVYjlXhE6usIpXdF74V1AjKwypiF1VyDo1eAbXK3NjT0SgdHSsnuNfv46LVNNNR4SM1JYc7t09uto/nVe4/w0eINnDxQAsDkBeOMouIJA8gakEFWUcY5naNda/bxj+8/T1WJsaN3ueMJB8K43K3qajqIPKfmuHlyzU8QQvDOP1biqW0iq8gY5OfOTMJiUUEIrrr3chJTE1j14kc44h1nDYs3D9ls/rNJ12HMjHkG/G8azgCA70Wk7/8g/T2EOP9C956AcC5CKu7IMFE/WEYiHNeYBcYXwMEtR/jgpXXUlNeRlJbIrJunMHL6sDZ2Q0pJQ7WHhqqWAar2ODvOBAf3fP8WcgZlndPW7Nt4kGf+699Una5BSklcYhxaSIuZKaeoAl2LNTghf4hAU4D6igYenvcoNWV1jJ45nMzC1rbGglCN+puUrAwmLRjPgNEXPyrEpHNIrQy8zxqfTRTD3mhVyOqbkEpWr7LtQnFB/OeR/ncgvMdIVzkWIOxzunVdF64T3wPYvWYfbz29AqEIMgvSCXqDLP7tmxTvO9Xm3C3v7mTtks1YbRZsdivuLDdHth/HYrOQPSDznAanvqqBp7/9L+qrPKRku0nNcRMOhNE0jf/611cYO2ck9jg7NkfbXZBQBP6mAEd3FgNwYNORNhOJ0wvSQMCnfnwXtz9803kPrFNSn+tVH4BeQ/gw6HVA65+nxVA7Dh/srlVdNEJYURxXIRIeQST+DCX+kwg1vbuX1Ws4suM4r/7ubcJBjazCDJCw5Kl3I80Fsexes49B44q4+lPzyCxMJ6MgjVEzhvHgL+4ld3D2OW2Np7aRpx/+J3UV9bgzk0nNSYmOivjWM19g7JyRDBhTwMCxhe06V63xNvhwZybFHMsoTAMEe9ceZP2bW0znppuRwc2R1PEZX8O6l2b1+96EUNNR4u9FJP4MkfCIYXdE90YHe10ER9d11ry6kZSsZBzxxm46PikOLayx7o3N0TEGzbU3A0YXkJqdEpVSD4fCBAMh/v3jV7j2wSsYPWsEqdkd5wh3rNpDQ5WH1Fw3AsGRHccBSEpP5IOX1xPyh9A1LSbfrkSUUQtH5VM0Ko+NS7cxaFwRjjg74VCLgCEYHVc2uy2qw3Nm5EbXdXRNbzMTxuQSIb2AAOct4FtsHHPeYgzF073durTO0CtriHoAa9/YhMsdT1yikfpxuhxIKfno1Y2MmDok5n3duHQbyRlJWG1GjZ8W1ig9Vs6Lv3wNT00jYy4fQUZ+x7L8uz/aT3VZHWk5KQgRa2tWv7SOUCDEqYOlCNHWobE7bYSCYdJzU3h85WP85VvPEg5qMXYjHNKwOWwdqjKbtuZjRq8FbIZ9gRZ7Y5uOcH2x25bVWXqSrel1v8nhkEZjXROZBbG7UGeCk6rT1THHpJR4G33R8G4oGGL3mv001jUhpWTzOzvY/M4Obv3GDdGW9JMHTrNx6TbqyuspGJlLXYUHBBzdYURhmlVNQ8EwLz++BF2XJKYkUF3aMkwtLddNfFI8KVnJpOWlUF/ZAMDEq8fy7j9XYx+QgaIo6LqkqqSGmYumtPml0DSNzcu2s3HpNvxNAXIGZzHvrpkx7awmlwA1B0PQqJVKrJSAMKb/mvQrqk7VkJyeGHPM6XJQXlyJruuoqhrdTOUPyyEx1ThXC2vsWXuAhmoPoWCY7St2s/W9nSz80jUMnWhMjj59uJSNS7dRfbqG/OG5eBt9Ruv3GbZAVRUqT1Qz5LKBlBdXRUVKITLWSAhGzRjGiX2nCYcMjZzL5o9jxXMfkNXK1rzz95UkpSdSvOck0LIJ/NW7j7Dh7a1sfmcHAW+A3KHZzLtrJtkD2nZjmXQhlqGGKB6tN9gShAXUtl23JhdOr3NwrDYL7sxkvA2+6K4KoLG2ibzhOW0UQStPVuO4xkFyeiLlxZV4G3xYrBZSs92k56XSVO/lv6/9KdkDM/nsr+7l9T++g9PlwBFnZ+fqfQS8AaMA+IxZM0iJalGpK6lBnKGCXHGymiRfiBkLJ1NTWsfIGUaX1vh5o6kpq2X7+3sQitEyOnrmcKZef1nM9Q/Pe5T6ygYGXzaAtJwUElMTqCmt44VfvsZ9P7wjZshoU30Tx/ecIhwMkzs0u8MBpCbniZINtsmGfox9HiBAPwm2SebMmH5I7uBMyourSEprcXKa6r2G0rGqxpw7aFwRh7cdIzUnhcpT1XhqGrHaLCRnJJGen4qvyc/yZ1cxcGwhJ/ad5pXfvonNYcOZ4GDPRwfwNfmQms7h7ccQQrRspgIh1r+1hVAgZERgIggBqtXCjEWTyRuSTWZhOsOnDgHgsivHUFdex7aVuyMOjk58chyJKW1rr1a/tI5NS7eRmptCUloC1adqePGXr3H/Y3fGpNQ9tY0U7z2FFtLIH55DSlb3dcf0BYRtHDK4PtJinQD22UadnOP6Xlfr11PpdQ6OEII5t09n8ZNvEQ5rxCU48NQ2EQ6GmHHTJNa+tjHm/OSMJLRwmMpT1ZQeLUcLGwM384blAEZ6KxzSCIfCrHrhI5LTE6NdT454O5WnqikYnsPJ/SXYHFbCIQ2p68y6ZSqjZg7n3z95BavNEm3vdsTbCfiCKIqguqQWm8PK1OsMB0a1qEy/cTK5g7MJhzXyhuaQckZNDhgt5p6aRjILWuTik9ISqDpdw/b3d3HVvUbh1rHdJ3jtf94mFAxH35sZCyczY+HkHhUm7E0IIcB5M9IyBEJbAAnWmxDWMeZ72g+ZsWgqz/98MbKyAVdyXFT64ZrPXNFmMxUKhqkormTyggmUHi0nHAqjWpRodNgZ72DFvz/kwKYjjJs7CldyfLRt2xFnp6qkhoIReWx7fzdKq981Z4ITq92wPTVlddHjUhqRIrvDRnVJLXanLbpZUi0q026cRO4Qw9YUDM8lOd2oyWleNxi2ZtuKXWQUpkeHjialJ1J5qpodq/dEleAPbT3KG396J6qiLIRg7l0zmbyg5w6l7ekI4QDXQ0YtTmgviASEfSrCMqi7l9Zn6HUODsCQywZy53cXsfb1jVSV1JI3JIsZi6aQPSCzXUXQmrJadqzaQ11FPfFJ8QwcW4gjzm5MKpeS2vI6asvrKD1WwQ0RpeNmXZrZt00nOSOR6z57FWte2UB9ZQMJKS6+9ueHOLT1KJPmjyWzKIMXfvka4WCY+KQ4/E0Basvr2bvuAC53PO7MZHRd58NX1rNp6XYjKKBJhlxWxLUPXhUV4Gpe8+41+wFY8e8PgRYlVKfLQcVJIw0X9AdZ8ud3iEuMizpkWljjo9c2MnBsYbtiXybnhxAqwjYObOO6eykm3UzekGw+8d+3sPb1TZQeqyAjP5XpN02O1vq1xmqzkFmUweRrx9PU0ITFamHwhAE4XQ7D1iCpLaujtqyO8uMVUSHAZlsz986ZOOLt3PD5q/kgElVxpbj444afc3TXCZb+dQU7V++NbqZSc9z4Gv1sf383zgQHj696jOT0pDa2RuqSwROKuO6zV2F32mNsZLPD0uzcNOOId1AZsTW+Jj9v/mU5iSkuVr+0DjAGCK964SMGjM4nLTf1krz3/QEhnAj75WC/vLuX0ifplQ4OcNZRDmeSkuVm3l2zKBpdwIu/ej0aFZHI6JA6MEK+raeYg/HhLhiRy6xFU5m1qEWky+8NABAMhjm+5yQ3fH4+7//7Q3ytpgcnZ7R0Mexbf5C1b2wmqygjqiB6aOtx4l9aF3VgzkTX9ZghoE0N3mi6q/RoOQFfKLorA2PXplpUDm8/Zjo4JiZdRO7gbG5/+KY2x882XmHQ+CL+/ZNXUCP6WIatiW1EOFPV2N/kJz0/lek3TGL6DZOi91WtFmSkm2rIxIFUl9YipcTX6CfB7Yo6PD++4wkAHvjp3ax7YzOZMbbmGB+8tI75982Nrrk58lR+vBKLzRJtxADwerzkDBoFwOmDpax7YzM2hy36rPf/s4Zxc0dxdGex6eCY9Fh6rYNzLlobm2ZD8Zv3f8j8T87mg5fWoWmS8XNHM/iyAXzw0joUVeHmr17H777wv1jtVipPGhooH726gUNb0rn5K9dF71deXMlLv3kDr8dwZipPV3Ng02Hi3S4U0dytYOXLv/9MtBVz63u7SEpNiO6UhBCk56Ww68N9zL1rJja7NcZgemoaGTi2kKS0RELBMA1VHuxOG+PnjSZyg3abIdrUCpmYmFxS2tOsyh2czbUPXsmK5z4gHGpg/NzRFI7KZ+3rm1AtCnd8eyErn1/Dsr+/H42UrH5pHRkFadz5nUXR+1acrOKZ7/2bpgYvQkJ1aQ3urGRsdguVp2qorahv8+yt7+0iKS3xDFuTGrU1Zwr7JaYlUFfRQFODF5vDRn1lA3EuJ2NmjzBOOIs5ObP+0MSkJ9FnHZz2EEIwcf44Rs0cbgynS3CQnJ7EmsUb0DU92noeiERnANJyU7DHtRT26brOkqcMddJmwb7N72zH3xQgoyCN+GQXlrI6gv4Qf/za3/jO379ERkE6AW+gTfuloiqGinFYA3us0UlIcXHTl65h49tb8dQ0MnTSQKbfNCla7JgzKBN7nC2m2Doc0pCazpDLBnb9m2diYnJetI7oDJs0iOqSWhzxdpIzktjw1hZ0TScpPYGBYwvZ+t7O6HVpue6o9AUYm5W3n34PXdMNDR7AU9dI5ckqpFQZMLoAq83Cwa1HsdosfOcfXyazMJ3//e5zqNbYAmhFVdDCOuGQhtVmbKZab/z2rD3ApqXbaKxrYsS0IUy7cRKJKcbYgLyhOcxYOBlngpMPX14PwLy7Z1FTWsvAsYWX7o00Mekkfd7BaR2KbW14Wqdwvvz7z/BfC37MD2/5NZMWjAcJh7YdJTElgcdXxYahv/fvr1FbXk9mZFKwrumEg2GklEbBn9PG4PED0MIadRUNrH9rKzd9YQFDJw9mw5tbcMS3tLc3VHnIHZzZZghe6x3hyGntz8my2qws+sq1LP7dWzTUeoxNlhDMuWtmVL3UxMSke7E5bDG25ht/+RyvPPkWS/60HCklExeM49DmoySmJfDEqtjZc3UV9VSero5q5+i6ZNeH+wgHw6RkJ2N3Ghsvi1UlHAqzbskmFn35OoZNidiaVnagvspDzuCsNrYGjI3f6JnDGT2z7ZRzMAqgb/rSNbz+x2UE/UEAasvrmH/fnLNqiJmYdDd93sE5FwFfgNf/uAxFVVFtCllFGYSCYYQi+PRP725zvpH+kUgpefefq5FS4m8yIj61ZXU01jYxaFwRui6pOFHJ8z9fzE1fWMCkq8dxeNtRyo5XYHfaCAZC2J02rrp3zkWnlPKH5fLQr+8z9C+CYXKHZMXU5JiYmHx8nNlV9fC8R2M2K6FgiNf+sAybw8qmpdsAuOITl7PlnR04E5xt7hUOhqPdnhDRvMGI7KitosFFo/IJhzVKDpcDMHH+WA5tOULZ8UrsTivBSHv5VffOjrE1FzIlfeCYQj736/u49oEr0DWdvKE5JKb20sGQJv2GPu/gtA7FtveB/sbsR6g6VU1tuZHLbu5oGDdvFMd2FpOWkxITBfrZJ56k7FgF066fCMSKcjUrGBtOjx+L1WLU85yqZvPy7eiajGpNFAzPZcS0oSS4OzcTyBnvYNgks63QxKSnU3q0An+jj4xWIqUWq8rU6ycy5bq2w08tNgtZhenUlNXjzkzi3X+ujm6masrqaKjyMHBcIb5GPxkFaaTlpURtjZSQmpNMUnpil9mauAQnw6cM6dQ9TEw+Tvq8g9MZ9DO6HJpJzXFjsVsYN3cUUko2LdtOKBgiIcWFI87Ooa1HCfpDUT2bL035LwTGbq2p3ktDlYfLb5l2ToNjtHHqfOtvX8SVHGdGZ0xMejBn66oCkLrOxmXbsdmt0W6k5c+u4rKrxqJrLcrZrTdUoWCYypNVTLhyTDQ9BJHUuITGuiaS0hNxueMZNK6IL0/5L6CVraluPC9bA4ZoaG1FAwnu+BhhQxOT3kq/cHDOFop9fNVjPPXNf7Bp6XYUVeHq++cSDmlUl9QwYExB9PozjVYwEOLEvlP4mwKcOliC1Wblxi8u4I9f/RuhYDjGYHlqGhFC8OEr67n6/rnUltezZvF6bv3GjR2uq7WR++acR0BKPvnI7Sz49LwYNVMTE5PeQfagLBRFoLWyDVJKwqFwh40B1shQ4Fu/fgPXPXgVT3/7n1hsFm760jWse2Mz4VCYnEFZbF2+gx8s/CUhvyF7cUG2Zu6j1Fc1RFSQBVLXGTljKPPvm4vN3r3DEk1MOkO/cHDOhjPewfUPzWf9ki2EQ2HKjlcghGD2HdPPOhjPZrcyePwAAP5n7c+ix5f8+R1kpBgQjBC0EIbcenlxJcufXcX3/rg5sqPq2Oh4G3wxz5JSsm/DIeISnVx5z+xOvmoTE5NLRUcbKpvdys+XfZ/X/udt1i3ZjATGzR3N9JsmxhQiny2t3trWzLltOuFQGIvVwt61B5CtHCcgKmSqhceedb1NDV7qqzykZrtRLSq6Ltn94X5cyfHMuX3Ghbx0E5MeRZ93cM5HF2boxEH8dc9vKd5zknBII394bpvugDMNTUONh2XPvM/uNftwuV3MvWsmE+ePjQlTH9l+HGeCAxBUnWoZBColbdo4z2TGosnUVdSjWtSoEGA4pLFj1R5m3z69jZaFiYlJ93I+tmbA6AIe+vUnufpT8wgFQuQNzSY1Mj28Na3tjaeukWV/e59dH+4jPjGOOXdMZ/KCCagWFavNGnWGmgdtWltFXXRdJ7GDdFPzdUe2HwdildPT8lLZtmIXs26Z2mbmlolJb6HPOjinDpXywUtrOX24DHdmMjNumsSIaUM7NEAJbhejZ41o99/O3E3VVdbzo9sep+p0DfZ4O9rhMvZtOMT1n72K277ZEpUZNL6I+354hyGx/sFe/t+ft5KcUUd2Xh1Qh159LwBK6nNtnunz+NtOFbYohFtpWZiYmHQ/5cWVfPDyOo7vOUliagLTbpjImMtHxKiQtyY+KZ5RM4Z1eL/W9qahxsOPbnuc8uJKHC4Helhj/4ZDFO8+xR3fWdiuPQsHQ9SU1UVV2je/s50Hf37PBb0mi1UlFAijhXXTwTHptfRJB6f0WDnP/3wxzngH6Xlp+Jv8vPGndwgFw4ybM+q879Ne2yfAZVeNpaqkhrS8FJr1hL0eL6te/Ii5d84gLTc16gxJKQl4A2x7fzdSl+hhvZ0ntWXopEE0VHnIKGhJkzVUN5I7qK1ujomJSfdQXVrLf362GEVVSM9LI+ALsPSvK/A3+pka6bTsDMueeZ/Kk1Wk56e2sjU+Vr+8jnl3zySjIL2NAnp9lceYsRcZzNk80PNMmq97YOTXaaxtihkZU1/ZQMGIXLMGx6RX0ycdnA1vbcFmt0Z1GuISnCiqwppXNzB61vBO70j2rjuAw2mn9bAEp8tJdUkNFSeqYmazCCGYcu1lPHfsjy2qw55PA+1HbpqZdPU4Dm09StnxShzxdoK+IBabyhX3XG6OYjAx6SFsW7ETqUvc2UaHozPegSU/jXVLNjPhqrEX5CC0t6E6vvskKdnJZ9gaB1Wnayg7XhHTct7ssISCIbwNPh679TcIRZxT7+bJNT/m+Z8tpqy4EmecHb8vgM1hZd5dM8977SYmPZE+6eCUH68kLjF21+KIs9NQ7SHoC+J0OTu4MpaO2j5/ds/vKNcrY87VdYmiKsQnx7d7L0VRqCmr4/iekySn/zxGwKs9XMnx3PuD29i/4RCnDpaQmpPCqBnDzPZNE5MeROnRCuLOEOmz2ixoIY2mei+2jM5JO6gWJaYjE4zp4IpFwZXcfut3s61pavBisVrQNO2sm7rElATu++Ed7F13kJIjZaTlpjJq5rDoqAYTk95Kn3RwsgZkcmzXiaiUORjTv50JDmzOzrdYz7t7Joe3HsXb6MPpciB1SU1ZLYMnFJE3NLvN+U0NXl5+YgkVxVVGISKQOySbW75xw1nTTc54BxOuGMOEK8Z0es0mJiZdT/bADHau3hudBweGdo1qVTtMDXVEexuqzcu386ev/z3W1pTXUTQqn8KReW3u0drWDBpXhABe/MVr57Y1LicT549j4vxxF7RmE5OeTJ8cBTvlugmEQ2HqqxrQdR1vg4+a0lpm3zrtotJTj698LCbMO+XaCdzwhQUgoaaklprSWoZOHMCXf/9gu/f/6NWNVJ6oJrMwnayiDDIK0zl1qJQNb23p1Os0MTHpXiZcORZFVagtr0fXdXyNfipPVjH9pskXXb/S2t5MuHIMC798DQJBdUktNWW1DBhbwFf+8CCqpa2tWbN4g2lrTEwiCCnbV+ttj0mTJsnNmzdfwuV0HacPl/LhKxs4dbAEd5bRRTV8ypAurV+pr2qg9FgFSWkJZA/IbPccKSW/fegp3JnJMQYpGAjhb/Tz5d9/psvWY2LSkxBCbJFSTrrQ63qTnQGji2rN4vUc2x3bRdWVtqahxkPp0XISUlzkDMxq95wObY0/hL/JtDUmfZeObE2fTFEB5A7O5q7vLrqkz0hKSzy/mpj2DN35+5UmJiY9mMzC9LMqBXcFiSkJnauJMRsTTPohfTJF1ZMQQjBm1nCqSmqix6Q0anbGzj3/lnUTExOTsyGEYPSsEe3bmjkju3FlJibdQ5+N4PQkZt48ldJjFZQdq4geyxuWw5Rr204QNjExMblYZt0ylbLjpq25pIzdAAAEqklEQVQxMYFe6uBoYY0Dm49wYOMhLHYro2cOp2hUfo/Vh4lLcHLP92/l5P4SGqo9JGckkTc0u0OlUxMTk56Brusc2X6cvesOAjBy+lAGjS/q0s9uR3OnLgbT1piYtNDrHBxN03jjz+9wcNMR4hLjWPPqBnRd5xtPPcSMhVO67DldaXQAVFWlaFR+l9zLxMTk0iOl5L1/rWbrit1RrZvnfvwyCe54ntn7ZI/dUJm2xsTEoNc5OCf3l3Bo81GyBmQghMBqsyClZO3rmxk9a0RUvfhi6Wg8Q1c5OiYmJr2DypNVbF+5h6yi9GgExOaw0ljXROXJqhgV4Yvl4XmPmrbGxOQS0escnFMHT6NaVYQQLH92FeXFhqLwxqXbOLqrmD9t+uVF31vTNMLBMELpmTszExOTj4+y44ZtURSF5c+uAqDiRBUAjyz8FU9t+/VF31vXdeqrPGhhrdPrNDExaZ9e5+DEJca1kS43kCidcEyO7T7BO39fSf6wXEBSW15PSlayuZsyMemnOOJj5821RlEv3tacOljC28+soKHSQ+7gbGrL6nCbtsbEpMvpdZVnQy4biNVuobHOmH6bUZhGSrabBZ+ex+/W/vSi7lldWsviJ98CCRkFaaTlpeHz+Kgure3i1ZuYmPQWikblE5fspL6qgfn3zWH+fXNIzXGTmpvCk2t+clH3rK9q4KXHlxAOhMkoSCM9Pw1fU4Dq0zVciOiqiYnJuel1Dk6C28VtD9+EalGoOFlFyB/Cardw6zduuOgp4bvX7ENKGZ0noyiC6x+az5jLR1BbXteVyzcxMekl2Bw2bn/4JuKT46g4WUXFySpUi0pGfio2x8XNtNu34RBaWMMVGcqrKILrP3sVY+eMpPJUdVcu38Sk39PrUlQAeUOyefCX91JbVsdnfvYJkjOSOtXR0FDlwX6GwRJCoAiBr9GPu/0pDCYmJn2cjPw0Pv3ju6kpMzY6KVnJnbI1nmoPFmus2RVCIBQFn8fXqbWamJjE0usiOM2oqkpabiruzM4ZHIDCUfn4mvwxx0KBEIqqkJLt7tS9TUxMejdCCFKz3aRmuztta/KH5xL0BWPSUeGQhhCCtLzUzi7VxMSkFb0ygtPVDJ8ymG3v76L0WAUJ7nhCwTD+Rj/z75uDI87e3cszMTHpIwwaX0Te0GxOHiwhwe0iHNLweXzMuWM68Ylx3b08E5M+hengYOTa7/zOInav2cfBLUeJT3Qyft5oCkbkdffSTExM+hBWm5XbHr6RPWsPsH/DYRzxdsbPG0XR6ILuXpqJSZ/DdHAiOOLsTLp6PJOuHt/dSzExMenD2Bw2JlwxhglXjOnupZiY9Gl6bQ2OiYmJiYmJiUlHmA6OiYmJiYmJSZ/DdHBMTExMTExM+hymg2NiYmJiYmLS5zAdHBMTExMTE5M+h+ngmJiYmJiYmPQ5xIUMeBNCVALFl245JiYmfYhCKWX6hV5k2hkTE5MLpF1bc0EOjomJiYmJiYlJb8BMUZmYmJiYmJj0OUwHx8TExMTExKTPYTo4JiYmJib/v906kAEAAAAY5G99j68ogh3BAQB2BAcA2BEcAGBHcACAHcEBAHYEBwDYCYXbv7uuzt/uAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# display transported samples\n", - "pl.figure(4, figsize=(8, 4))\n", - "pl.subplot(1, 2, 1)\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", - " label='Target samples', alpha=0.5)\n", - "pl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys,\n", - " marker='+', label='Transp samples', s=30)\n", - "pl.title('Transported samples\\nEmdTransport')\n", - "pl.legend(loc=0)\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "\n", - "pl.subplot(1, 2, 2)\n", - "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", - " label='Target samples', alpha=0.5)\n", - "pl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys,\n", - " marker='+', label='Transp samples', s=30)\n", - "pl.title('Transported samples\\nSinkhornTransport')\n", - "pl.xticks([])\n", - "pl.yticks([])\n", - "\n", - "pl.tight_layout()\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_partial_wass_and_gromov.ipynb b/notebooks/plot_partial_wass_and_gromov.ipynb deleted file mode 100644 index 6bf0bc6..0000000 --- a/notebooks/plot_partial_wass_and_gromov.ipynb +++ /dev/null @@ -1,350 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Partial Wasserstein and Gromov-Wasserstein example\n", - "\n", - "\n", - "This example is designed to show how to use the Partial (Gromov-)Wassertsein\n", - "distance computation in POT.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Laetitia Chapel \n", - "# License: MIT License\n", - "\n", - "# necessary for 3d plot even if not used\n", - "from mpl_toolkits.mplot3d import Axes3D # noqa\n", - "import scipy as sp\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sample two 2D Gaussian distributions and plot them\n", - "--------------------------------------------------\n", - "\n", - "For demonstration purpose, we sample two Gaussian distributions in 2-d\n", - "spaces and add some random noise.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "n_samples = 20 # nb samples (gaussian)\n", - "n_noise = 20 # nb of samples (noise)\n", - "\n", - "mu = np.array([0, 0])\n", - "cov = np.array([[1, 0], [0, 2]])\n", - "\n", - "xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)\n", - "xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2))\n", - "xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov)\n", - "xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2))\n", - "\n", - "M = sp.spatial.distance.cdist(xs, xt)\n", - "\n", - "fig = pl.figure()\n", - "ax1 = fig.add_subplot(131)\n", - "ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", - "ax2 = fig.add_subplot(132)\n", - "ax2.scatter(xt[:, 0], xt[:, 1], color='r')\n", - "ax3 = fig.add_subplot(133)\n", - "ax3.imshow(M)\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute partial Wasserstein plans and distance\n", - "----------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Partial Wasserstein distance (m = 0.5): 0.45151590745848863\n", - "Entropic partial Wasserstein distance (m = 0.5): 0.46654948274375097\n" - ] - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "p = ot.unif(n_samples + n_noise)\n", - "q = ot.unif(n_samples + n_noise)\n", - "\n", - "w0, log0 = ot.partial.partial_wasserstein(p, q, M, m=0.5, log=True)\n", - "w, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=0.1, m=0.5,\n", - " log=True)\n", - "\n", - "print('Partial Wasserstein distance (m = 0.5): ' + str(log0['partial_w_dist']))\n", - "print('Entropic partial Wasserstein distance (m = 0.5): ' +\n", - " str(log['partial_w_dist']))\n", - "\n", - "pl.figure(1, (10, 5))\n", - "pl.subplot(1, 2, 1)\n", - "pl.imshow(w0, cmap='jet')\n", - "pl.title('Partial Wasserstein')\n", - "pl.subplot(1, 2, 2)\n", - "pl.imshow(w, cmap='jet')\n", - "pl.title('Entropic partial Wasserstein')\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sample one 2D and 3D Gaussian distributions and plot them\n", - "---------------------------------------------------------\n", - "\n", - "The Gromov-Wasserstein distance allows to compute distances with samples that\n", - "do not belong to the same metric space. For demonstration purpose, we sample\n", - "two Gaussian distributions in 2- and 3-dimensional spaces.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "n_samples = 20 # nb samples\n", - "n_noise = 10 # nb of samples (noise)\n", - "\n", - "p = ot.unif(n_samples + n_noise)\n", - "q = ot.unif(n_samples + n_noise)\n", - "\n", - "mu_s = np.array([0, 0])\n", - "cov_s = np.array([[1, 0], [0, 1]])\n", - "\n", - "mu_t = np.array([0, 0, 0])\n", - "cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n", - "\n", - "\n", - "xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)\n", - "xs = np.concatenate((xs, ((np.random.rand(n_noise, 2) + 1) * 4)), axis=0)\n", - "P = sp.linalg.sqrtm(cov_t)\n", - "xt = np.random.randn(n_samples, 3).dot(P) + mu_t\n", - "xt = np.concatenate((xt, ((np.random.rand(n_noise, 3) + 1) * 10)), axis=0)\n", - "\n", - "fig = pl.figure()\n", - "ax1 = fig.add_subplot(121)\n", - "ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", - "ax2 = fig.add_subplot(122, projection='3d')\n", - "ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute partial Gromov-Wasserstein plans and distance\n", - "-----------------------------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "-----m = 1\n", - "Wasserstein distance (m = 1): 62.612867557378095\n", - "Entropic Wasserstein distance (m = 1): 64.09799387131392\n" - ] - }, - { - "data": { - "image/png": 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\n", 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- "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "C1 = sp.spatial.distance.cdist(xs, xs)\n", - "C2 = sp.spatial.distance.cdist(xt, xt)\n", - "\n", - "# transport 100% of the mass\n", - "print('-----m = 1')\n", - "m = 1\n", - "res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True)\n", - "res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10,\n", - " m=m, log=True)\n", - "\n", - "print('Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist']))\n", - "print('Entropic Wasserstein distance (m = 1): ' + str(log['partial_gw_dist']))\n", - "\n", - "pl.figure(1, (10, 5))\n", - "pl.title(\"mass to be transported m = 1\")\n", - "pl.subplot(1, 2, 1)\n", - "pl.imshow(res0, cmap='jet')\n", - "pl.title('Wasserstein')\n", - "pl.subplot(1, 2, 2)\n", - "pl.imshow(res, cmap='jet')\n", - "pl.title('Entropic Wasserstein')\n", - "pl.show()\n", - "\n", - "# transport 2/3 of the mass\n", - "print('-----m = 2/3')\n", - "m = 2 / 3\n", - "res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True)\n", - "res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10,\n", - " m=m, log=True)\n", - "\n", - "print('Partial Wasserstein distance (m = 2/3): ' +\n", - " str(log0['partial_gw_dist']))\n", - "print('Entropic partial Wasserstein distance (m = 2/3): ' +\n", - " str(log['partial_gw_dist']))\n", - "\n", - "pl.figure(1, (10, 5))\n", - "pl.title(\"mass to be transported m = 2/3\")\n", - "pl.subplot(1, 2, 1)\n", - "pl.imshow(res0, cmap='jet')\n", - "pl.title('Partial Wasserstein')\n", - "pl.subplot(1, 2, 2)\n", - "pl.imshow(res, cmap='jet')\n", - "pl.title('Entropic partial Wasserstein')\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_screenkhorn_1D.ipynb b/notebooks/plot_screenkhorn_1D.ipynb deleted file mode 100644 index 0bd4aad..0000000 --- a/notebooks/plot_screenkhorn_1D.ipynb +++ /dev/null @@ -1,213 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# 1D Screened optimal transport\n", - "\n", - "\n", - "This example illustrates the computation of Screenkhorn:\n", - "Screening Sinkhorn Algorithm for Optimal transport.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Mokhtar Z. Alaya \n", - "#\n", - "# License: MIT License\n", - "\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot.plot\n", - "from ot.datasets import make_1D_gauss as gauss\n", - "from ot.bregman import screenkhorn" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 100 # nb bins\n", - "\n", - "# bin positions\n", - "x = np.arange(n, dtype=np.float64)\n", - "\n", - "# Gaussian distributions\n", - "a = gauss(n, m=20, s=5) # m= mean, s= std\n", - "b = gauss(n, m=60, s=10)\n", - "\n", - "# loss matrix\n", - "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\n", - "M /= M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot distributions and loss matrix\n", - "----------------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1, figsize=(6.4, 3))\n", - "pl.plot(x, a, 'b', label='Source distribution')\n", - "pl.plot(x, b, 'r', label='Target distribution')\n", - "pl.legend()\n", - "\n", - "# plot distributions and loss matrix\n", - "\n", - "pl.figure(2, figsize=(5, 5))\n", - "ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Solve Screenkhorn\n", - "-----------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "epsilon = 0.020986042861303855\n", - "\n", - "kappa = 3.7476531411890917\n", - "\n", - "Cardinality of selected points: |Isel| = 30 \t |Jsel| = 30 \n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/rflamary/PYTHON/POT/ot/bregman.py:2056: UserWarning: Bottleneck module is not installed. Install it from https://pypi.org/project/Bottleneck/ for better performance.\n", - " \"Bottleneck module is not installed. Install it from https://pypi.org/project/Bottleneck/ for better performance.\")\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "# Screenkhorn\n", - "lambd = 2e-03 # entropy parameter\n", - "ns_budget = 30 # budget number of points to be keeped in the source distribution\n", - "nt_budget = 30 # budget number of points to be keeped in the target distribution\n", - "\n", - "G_screen = screenkhorn(a, b, M, lambd, ns_budget, nt_budget, uniform=False, restricted=True, verbose=True)\n", - "pl.figure(4, figsize=(5, 5))\n", - "ot.plot.plot1D_mat(a, b, G_screen, 'OT matrix Screenkhorn')\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/plot_stochastic.ipynb b/notebooks/plot_stochastic.ipynb deleted file mode 100644 index aa0f1b3..0000000 --- a/notebooks/plot_stochastic.ipynb +++ /dev/null @@ -1,573 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# Stochastic examples\n", - "\n", - "\n", - "This example is designed to show how to use the stochatic optimization\n", - "algorithms for descrete and semicontinous measures from the POT library.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Kilian Fatras \n", - "#\n", - "# License: MIT License\n", - "\n", - "import matplotlib.pylab as pl\n", - "import numpy as np\n", - "import ot\n", - "import ot.plot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM\n", - "############################################################################\n", - "############################################################################\n", - " DISCRETE CASE:\n", - "\n", - " Sample two discrete measures for the discrete case\n", - " ---------------------------------------------\n", - "\n", - " Define 2 discrete measures a and b, the points where are defined the source\n", - " and the target measures and finally the cost matrix c.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_source = 7\n", - "n_target = 4\n", - "reg = 1\n", - "numItermax = 1000\n", - "\n", - "a = ot.utils.unif(n_source)\n", - "b = ot.utils.unif(n_target)\n", - "\n", - "rng = np.random.RandomState(0)\n", - "X_source = rng.randn(n_source, 2)\n", - "Y_target = rng.randn(n_target, 2)\n", - "M = ot.dist(X_source, Y_target)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call the \"SAG\" method to find the transportation matrix in the discrete case\n", - "---------------------------------------------\n", - "\n", - "Define the method \"SAG\", call ot.solve_semi_dual_entropic and plot the\n", - "results.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[2.55553509e-02 9.96395660e-02 1.76579142e-02 4.31178196e-06]\n", - " [1.21640234e-01 1.25357448e-02 1.30225078e-03 7.37891338e-03]\n", - " [3.56123975e-03 7.61451746e-02 6.31505947e-02 1.33831456e-07]\n", - " [2.61515202e-02 3.34246014e-02 8.28734709e-02 4.07550428e-04]\n", - " [9.85500870e-03 7.52288517e-04 1.08262628e-02 1.21423583e-01]\n", - " [2.16904253e-02 9.03825797e-04 1.87178503e-03 1.18391107e-01]\n", - " [4.15462212e-02 2.65987989e-02 7.23177216e-02 2.39440107e-03]]\n" - ] - } - ], - "source": [ - "method = \"SAG\"\n", - "sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,\n", - " numItermax)\n", - "print(sag_pi)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "SEMICONTINOUS CASE:\n", - "\n", - "Sample one general measure a, one discrete measures b for the semicontinous\n", - "case\n", - "---------------------------------------------\n", - "\n", - "Define one general measure a, one discrete measures b, the points where\n", - "are defined the source and the target measures and finally the cost matrix c.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_source = 7\n", - "n_target = 4\n", - "reg = 1\n", - "numItermax = 1000\n", - "log = True\n", - "\n", - "a = ot.utils.unif(n_source)\n", - "b = ot.utils.unif(n_target)\n", - "\n", - "rng = np.random.RandomState(0)\n", - "X_source = rng.randn(n_source, 2)\n", - "Y_target = rng.randn(n_target, 2)\n", - "M = ot.dist(X_source, Y_target)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call the \"ASGD\" method to find the transportation matrix in the semicontinous\n", - "case\n", - "---------------------------------------------\n", - "\n", - "Define the method \"ASGD\", call ot.solve_semi_dual_entropic and plot the\n", - "results.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[3.7937628 7.65961287 3.80848103 2.58141742 1.61215093 3.44897695\n", - " 2.71747327] [-2.52391608 -2.29387992 -0.82558991 5.64338591]\n", - "[[2.21553327e-02 1.03145567e-01 1.75528576e-02 3.38501746e-06]\n", - " [1.20021720e-01 1.47691349e-02 1.47329335e-03 6.59299438e-03]\n", - " [3.04838905e-03 7.78276435e-02 6.19810066e-02 1.03737333e-07]\n", - " [2.31393025e-02 3.53135903e-02 8.40777056e-02 3.26544498e-04]\n", - " [1.05758118e-02 9.63969840e-04 1.33213201e-02 1.17996041e-01]\n", - " [2.34525044e-02 1.16688539e-03 2.32054035e-03 1.15917213e-01]\n", - " [3.74708983e-02 2.86448739e-02 7.47858286e-02 1.95554208e-03]]\n" - ] - } - ], - "source": [ - "method = \"ASGD\"\n", - "asgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,\n", - " numItermax, log=log)\n", - "print(log_asgd['alpha'], log_asgd['beta'])\n", - "print(asgd_pi)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compare the results with the Sinkhorn algorithm\n", - "---------------------------------------------\n", - "\n", - "Call the Sinkhorn algorithm from POT\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[2.55553508e-02 9.96395661e-02 1.76579142e-02 4.31178193e-06]\n", - " [1.21640234e-01 1.25357448e-02 1.30225079e-03 7.37891333e-03]\n", - " [3.56123974e-03 7.61451746e-02 6.31505947e-02 1.33831455e-07]\n", - " [2.61515201e-02 3.34246014e-02 8.28734709e-02 4.07550425e-04]\n", - " [9.85500876e-03 7.52288523e-04 1.08262629e-02 1.21423583e-01]\n", - " [2.16904255e-02 9.03825804e-04 1.87178504e-03 1.18391107e-01]\n", - " [4.15462212e-02 2.65987989e-02 7.23177217e-02 2.39440105e-03]]\n" - ] - } - ], - "source": [ - "sinkhorn_pi = ot.sinkhorn(a, b, M, reg)\n", - "print(sinkhorn_pi)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "PLOT TRANSPORTATION MATRIX\n", - "#############################################################################\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot SAG results\n", - "----------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(4, figsize=(5, 5))\n", - "ot.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD')\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot Sinkhorn results\n", - "---------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(4, figsize=(5, 5))\n", - "ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM\n", - "############################################################################\n", - "############################################################################\n", - " SEMICONTINOUS CASE:\n", - "\n", - " Sample one general measure a, one discrete measures b for the semicontinous\n", - " case\n", - " ---------------------------------------------\n", - "\n", - " Define one general measure a, one discrete measures b, the points where\n", - " are defined the source and the target measures and finally the cost matrix c.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_source = 7\n", - "n_target = 4\n", - "reg = 1\n", - "numItermax = 100000\n", - "lr = 0.1\n", - "batch_size = 3\n", - "log = True\n", - "\n", - "a = ot.utils.unif(n_source)\n", - "b = ot.utils.unif(n_target)\n", - "\n", - "rng = np.random.RandomState(0)\n", - "X_source = rng.randn(n_source, 2)\n", - "Y_target = rng.randn(n_target, 2)\n", - "M = ot.dist(X_source, Y_target)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Call the \"SGD\" dual method to find the transportation matrix in the\n", - "semicontinous case\n", - "---------------------------------------------\n", - "\n", - "Call ot.solve_dual_entropic and plot the results.\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0.91323163 2.78641673 1.06629943 0.01804936 0.61062914 1.81958274\n", - " 0.11217916] [0.34004858 0.48129361 1.57541501 4.92963099]\n", - "[[2.17913197e-02 9.28312769e-02 1.08665637e-02 9.30212767e-08]\n", - " [1.60939150e-02 1.81215529e-03 1.24345544e-04 2.47002125e-05]\n", - " [3.44318299e-03 8.04381532e-02 4.40643612e-02 3.27371535e-09]\n", - " [3.12534315e-02 4.36443287e-02 7.14771848e-02 1.23227019e-05]\n", - " [6.81023930e-02 5.68002968e-03 5.39927027e-02 2.12291313e-02]\n", - " [8.06039569e-02 3.66972769e-03 5.01990391e-03 1.11309234e-02]\n", - " [4.85325711e-02 3.39488142e-02 6.09673962e-02 7.07656480e-05]]\n" - ] - } - ], - "source": [ - "sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg,\n", - " batch_size, numItermax,\n", - " lr, log=log)\n", - "print(log_sgd['alpha'], log_sgd['beta'])\n", - "print(sgd_dual_pi)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compare the results with the Sinkhorn algorithm\n", - "---------------------------------------------\n", - "\n", - "Call the Sinkhorn algorithm from POT\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[[2.55553508e-02 9.96395661e-02 1.76579142e-02 4.31178193e-06]\n", - " [1.21640234e-01 1.25357448e-02 1.30225079e-03 7.37891333e-03]\n", - " [3.56123974e-03 7.61451746e-02 6.31505947e-02 1.33831455e-07]\n", - " [2.61515201e-02 3.34246014e-02 8.28734709e-02 4.07550425e-04]\n", - " [9.85500876e-03 7.52288523e-04 1.08262629e-02 1.21423583e-01]\n", - " [2.16904255e-02 9.03825804e-04 1.87178504e-03 1.18391107e-01]\n", - " [4.15462212e-02 2.65987989e-02 7.23177217e-02 2.39440105e-03]]\n" - ] - } - ], - "source": [ - "sinkhorn_pi = ot.sinkhorn(a, b, M, reg)\n", - "print(sinkhorn_pi)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot SGD results\n", - "-----------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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BBoAiBBgAihBgAChCgAGgCAEGgCJtXgkHzFq/cNJDU37spnfX7H98+9UHzvg69zlm3oyvE92xBwwARQgwABQhwABQhAADQBECDABFCDAAFCHAAFCEAANAEQIMAEUIMAAUIcAAUKR1gG3Ps32Hbd4RGQB6YGf2gE+XdH+/BgGAYdMqwLYXSTpW0rn9HQcAhkfbPeAVkj4m6YU+zgIAQ6VrgG2/U9L6JKu6LLfc9rjt8YmJiZ4NCACDqs0e8GGSjrP9qKSLJR1p+/ytF0qyMslYkrGRkZEejwkAg6drgJN8PMmiJKOSTpB0XZIT+z4ZAAw4zgMGgCI79Z5wSW6QdENfJgGAIcMeMAAUIcAAUIQAA0ARAgwARQgwABQhwABQhAADQBECDABFCDAAFNmpV8IBs83xe41P+bHnPfOGHk7S3gF/8OjMr3SvV878OtEVe8AAUIQAA0ARAgwARQgwABQhwABQhAADQBECDABFCDAAFCHAAFCEAANAEQIMAEVaXQvC9qOSNkp6XtLmJGP9HAoAhsHOXIznbUk29G0SABgyHIIAgCJtAxxJV9leZXv5thawvdz2uO3xiYmJ3k0IAAOqbYDfkuQQSUdL+pDtt269QJKVScaSjI2MjPR0SAAYRK0CnOTx5s/1kq6QdGg/hwKAYdA1wLZfanuPLZ9Leoeke/o9GAAMujZnQbxK0hW2tyx/YZIr+zoVAAyBrgFOslZSzZtnAcAA4zQ0AChCgAGgCAEGgCIEGACKEGAAKEKAAaAIAQaAIgQYAIrszPWAgVnnzKtOmPJjX3byvB5O0t78d8z8ZbVf8c6HZnyd6I49YAAoQoABoAgBBoAiBBgAihBgAChCgAGgCAEGgCIEGACKEGAAKEKAAaAIAQaAIq0CbHtP25fafsD2/bbf3O/BAGDQtb0Yz99IujLJ8bZfJOklfZwJAIZC1wDbfrmkt0o6WZKSPCvp2f6OBQCDr80hiH0lTUj6qu07bJ9r+6V9ngsABl6bAO8i6RBJf5fkYElPSzpz64VsL7c9bnt8YmKix2PWWrKk8wEAvdTmGPA6SeuS3NrcvlTbCHCSlZJWStLY2Fh6NuEssGJF9QQABlHXPeAkP5L0mO39my8dJem+vk4FAEOg7VkQvy/pguYMiLWSTunfSAAwHFoFOMkaSWN9ngUAhgqvhAOAIgQYAIoQYAAoQoABoAgBBoAiBBgAihBgAChCgAGgCAEGgCJOen/dHNsTkr7f8ydGP7wmyUj1EMAw6kuAAQDdcQgCAIoQYAAoQoABoAgBBoAiBBgAihBgAChCgAGgCAEGgCIEGACK/B9Zm2KXjTBWzQAAAABJRU5ErkJggg==\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(4, figsize=(5, 5))\n", - "ot.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD')\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot Sinkhorn results\n", - "---------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(4, figsize=(5, 5))\n", - "ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.6.9" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} -- cgit v1.2.3 From 20da1f630dac2639ae86f625b46d4270e384f351 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 22 Apr 2020 11:53:33 +0200 Subject: re-org readme --- README.md | 25 +++++++++++--------- docs/source/readme.rst | 64 ++++++++++++++++++++++---------------------------- 2 files changed, 42 insertions(+), 47 deletions(-) diff --git a/README.md b/README.md index c42af39..dff334e 100644 --- a/README.md +++ b/README.md @@ -16,27 +16,30 @@ learning. Website and documentation: [https://PythonOT.github.io/](https://PythonOT.github.io/) -POT provides the following solvers: - +POT provides the following generic OT solvers: * OT Network Flow solver for the linear program/ Earth Movers Distance [1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU implementation (requires cupy). +* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], + stabilized version [9] [10], greedy Sinkhorn [22] and Screening Sinkhorn [26] with optional GPU + implementation (requires cupy). +* Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4]. * Sinkhorn divergence [23] and entropic regularization OT from empirical data. * Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. * Non regularized Wasserstein barycenters [16] with LP solver (only small scale). -* Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4]. -* Optimal transport for domain adaptation with group lasso regularization and Laplacian regularization [5][30] -* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. -* Linear OT [14] and Joint OT matrix and mapping estimation [8]. -* Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). * Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) * Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) * Non regularized free support Wasserstein barycenters [20]. * Unbalanced OT with KL relaxation distance and barycenter [10, 25]. -* Screening Sinkhorn Algorithm for OT [26]. +* Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic [3] + formulations). + +POT provides the following Machine Learning related solvers: +* Optimal transport for domain adaptation with group lasso regularization and Laplacian regularization [5] [30]. +* Linear OT [14] and Joint OT matrix and mapping estimation [8]. +* Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). * JCPOT algorithm for multi-source domain adaptation with target shift [27]. -* Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic [3] formulations). -Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. +Some demonstrations are available in the [documentation](https://pythonot.github.io/auto_examples/index.html). #### Using and citing the toolbox diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 4da1ceb..76d37a4 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -10,42 +10,34 @@ machine learning. Website and documentation: https://PythonOT.github.io/ -POT provides the following solvers: - -- OT Network Flow solver for the linear program/ Earth Movers Distance - [1]. -- Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], - stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU - implementation (requires cupy). -- Sinkhorn divergence [23] and entropic regularization OT from - empirical data. -- Smooth optimal transport solvers (dual and semi-dual) for KL and - squared L2 regularizations [17]. -- Non regularized Wasserstein barycenters [16] with LP solver (only - small scale). -- Bregman projections for Wasserstein barycenter [3], convolutional - barycenter [21] and unmixing [4]. -- Optimal transport for domain adaptation with group lasso - regularization and Laplacian regularization [5][30] -- Conditional gradient [6] and Generalized conditional gradient for - regularized OT [7]. -- Linear OT [14] and Joint OT matrix and mapping estimation [8]. -- Wasserstein Discriminant Analysis [11] (requires autograd + - pymanopt). -- Gromov-Wasserstein distances and barycenters ([13] and regularized - [12]) -- Stochastic Optimization for Large-scale Optimal Transport (semi-dual - problem [18] and dual problem [19]) -- Non regularized free support Wasserstein barycenters [20]. -- Unbalanced OT with KL relaxation distance and barycenter [10, 25]. -- Screening Sinkhorn Algorithm for OT [26]. -- JCPOT algorithm for multi-source domain adaptation with target shift - [27]. -- Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic - [3] formulations). - -Some demonstrations (both in Python and Jupyter Notebook format) are -available in the examples folder. +POT provides the following generic OT solvers: \* OT Network Flow solver +for the linear program/ Earth Movers Distance [1]. \* Conditional +gradient [6] and Generalized conditional gradient for regularized OT +[7]. \* Entropic regularization OT solver with Sinkhorn Knopp Algorithm +[2], stabilized version [9] [10], greedy Sinkhorn [22] and Screening +Sinkhorn [26] with optional GPU implementation (requires cupy). \* +Bregman projections for Wasserstein barycenter [3], convolutional +barycenter [21] and unmixing [4]. \* Sinkhorn divergence [23] and +entropic regularization OT from empirical data. \* Smooth optimal +transport solvers (dual and semi-dual) for KL and squared L2 +regularizations [17]. \* Non regularized Wasserstein barycenters [16] +with LP solver (only small scale). \* Gromov-Wasserstein distances and +barycenters ([13] and regularized [12]) \* Stochastic Optimization for +Large-scale Optimal Transport (semi-dual problem [18] and dual problem +[19]) \* Non regularized free support Wasserstein barycenters [20]. \* +Unbalanced OT with KL relaxation distance and barycenter [10, 25]. \* +Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic [3] +formulations). + +POT provides the following Machine Learning related solvers: \* Optimal +transport for domain adaptation with group lasso regularization and +Laplacian regularization [5] [30]. \* Linear OT [14] and Joint OT matrix +and mapping estimation [8]. \* Wasserstein Discriminant Analysis [11] +(requires autograd + pymanopt). \* JCPOT algorithm for multi-source +domain adaptation with target shift [27]. + +Some demonstrations are available in the +`documentation `__. Using and citing the toolbox ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -- cgit v1.2.3 From bffba0033fda3a45d7cbbde5165e09e886262ab2 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 22 Apr 2020 12:44:45 +0200 Subject: awesome new readme --- README.md | 52 ++++++++++++++--------- docs/source/readme.rst | 111 +++++++++++++++++++++++++++++++++++++------------ 2 files changed, 117 insertions(+), 46 deletions(-) diff --git a/README.md b/README.md index dff334e..ad0d810 100644 --- a/README.md +++ b/README.md @@ -16,39 +16,51 @@ learning. Website and documentation: [https://PythonOT.github.io/](https://PythonOT.github.io/) -POT provides the following generic OT solvers: -* OT Network Flow solver for the linear program/ Earth Movers Distance [1]. -* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], - stabilized version [9] [10], greedy Sinkhorn [22] and Screening Sinkhorn [26] with optional GPU - implementation (requires cupy). -* Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4]. +Source Code (MIT): [https://github.com/PythonOT/POT](https://github.com/PythonOT/POT) + +POT provides the following generic OT solvers (links to examples): + +* [OT Network Simplex solver](https://pythonot.github.io/auto_examples/plot_OT_1D.html) for the linear program/ Earth Movers Distance [1] . +* [Conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) [6] and [Generalized conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) for regularized OT [7]. +* Entropic regularization OT solver with [Sinkhorn Knopp Algorithm](https://pythonot.github.io/auto_examples/plot_OT_1D.html) [2] , stabilized version [9] [10], greedy Sinkhorn [22] and [Screening Sinkhorn [26] ](https://pythonot.github.io/auto_examples/plot_screenkhorn_1D.html) with optional GPU implementation (requires cupy). +* Bregman projections for [Wasserstein barycenter](https://pythonot.github.io/auto_examples/plot_barycenter_lp_vs_entropic.html) [3], [convolutional barycenter](https://pythonot.github.io/auto_examples/plot_convolutional_barycenter.html) [21] and unmixing [4]. * Sinkhorn divergence [23] and entropic regularization OT from empirical data. -* Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. -* Non regularized Wasserstein barycenters [16] with LP solver (only small scale). -* Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) -* Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) -* Non regularized free support Wasserstein barycenters [20]. -* Unbalanced OT with KL relaxation distance and barycenter [10, 25]. -* Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic [3] +* [Smooth optimal transport solvers](https://pythonot.github.io/auto_examples/plot_OT_1D_smooth.html) (dual and semi-dual) for KL and squared L2 regularizations [17]. +* Non regularized [Wasserstein barycenters [16] ](https://pythonot.github.io/auto_examples/plot_barycenter_lp_vs_entropic.html)) with LP solver (only small scale). +* [Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/plot_gromov.html) and [GW barycenters](https://pythonot.github.io/auto_examples/plot_gromov_barycenter.html) (exact [13] and regularized [12]) + * [Fused-Gromov-Wasserstein distances solver](https://pythonot.github.io/auto_examples/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py) and [FGW barycenters](https://pythonot.github.io/auto_examples/plot_barycenter_fgw.html) [24] +* [Stochastic solver](https://pythonot.github.io/auto_examples/plot_stochastic.html) for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) +* Non regularized [free support Wasserstein barycenters](https://pythonot.github.io/auto_examples/plot_free_support_barycenter.html) [20]. +* [Unbalanced OT](https://pythonot.github.io/auto_examples/plot_UOT_1D.html) with KL relaxation and [barycenter](https://pythonot.github.io/auto_examples/plot_UOT_barycenter_1D.html) [10, 25]. +* [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3] formulations). POT provides the following Machine Learning related solvers: -* Optimal transport for domain adaptation with group lasso regularization and Laplacian regularization [5] [30]. -* Linear OT [14] and Joint OT matrix and mapping estimation [8]. -* Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). -* JCPOT algorithm for multi-source domain adaptation with target shift [27]. + +* [Optimal transport for domain + adaptation](https://pythonot.github.io/auto_examples/plot_otda_classes.html) + with [group lasso regularization](https://pythonot.github.io/auto_examples/plot_otda_classes.html), [Laplacian regularization](https://pythonot.github.io/auto_examples/plot_otda_laplacian.html) [5] [30] and [semi + supervised setting](https://pythonot.github.io/auto_examples/plot_otda_semi_supervised.html). +* [Linear OT mapping](https://pythonot.github.io/auto_examples/plot_otda_linear_mapping.html) [14] and [Joint OT mapping estimation](https://pythonot.github.io/auto_examples/plot_otda_mapping.html) [8]. +* [Wasserstein Discriminant Analysis](https://pythonot.github.io/auto_examples/plot_WDA.html) [11] (requires autograd + pymanopt). +* [JCPOT algorithm for multi-source domain adaptation with target shift](https://pythonot.github.io/auto_examples/plot_otda_jcpot.html) [27]. Some demonstrations are available in the [documentation](https://pythonot.github.io/auto_examples/index.html). #### Using and citing the toolbox -If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference: +If you use this toolbox in your research and find it useful, please cite POT +using the following bibtex reference: +``` +Rémi Flamary and Nicolas Courty, POT Python Optimal Transport library, Website: https://pythonot.github.io/, 2017 +``` + +In Bibtex format: ``` @misc{flamary2017pot, title={POT Python Optimal Transport library}, author={Flamary, R{'e}mi and Courty, Nicolas}, -url={https://github.com/rflamary/POT}, +url={https://pythonot.github.io/}, year={2017} } ``` diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 76d37a4..4862523 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -10,31 +10,84 @@ machine learning. Website and documentation: https://PythonOT.github.io/ -POT provides the following generic OT solvers: \* OT Network Flow solver -for the linear program/ Earth Movers Distance [1]. \* Conditional -gradient [6] and Generalized conditional gradient for regularized OT -[7]. \* Entropic regularization OT solver with Sinkhorn Knopp Algorithm -[2], stabilized version [9] [10], greedy Sinkhorn [22] and Screening -Sinkhorn [26] with optional GPU implementation (requires cupy). \* -Bregman projections for Wasserstein barycenter [3], convolutional -barycenter [21] and unmixing [4]. \* Sinkhorn divergence [23] and -entropic regularization OT from empirical data. \* Smooth optimal -transport solvers (dual and semi-dual) for KL and squared L2 -regularizations [17]. \* Non regularized Wasserstein barycenters [16] -with LP solver (only small scale). \* Gromov-Wasserstein distances and -barycenters ([13] and regularized [12]) \* Stochastic Optimization for -Large-scale Optimal Transport (semi-dual problem [18] and dual problem -[19]) \* Non regularized free support Wasserstein barycenters [20]. \* -Unbalanced OT with KL relaxation distance and barycenter [10, 25]. \* -Partial Wasserstein and Gromov-Wasserstein (exact [29] and entropic [3] -formulations). - -POT provides the following Machine Learning related solvers: \* Optimal -transport for domain adaptation with group lasso regularization and -Laplacian regularization [5] [30]. \* Linear OT [14] and Joint OT matrix -and mapping estimation [8]. \* Wasserstein Discriminant Analysis [11] -(requires autograd + pymanopt). \* JCPOT algorithm for multi-source -domain adaptation with target shift [27]. +Source Code (MIT): https://github.com/PythonOT/POT + +POT provides the following generic OT solvers (links to examples): + +- `OT Network Simplex + solver `__ + for the linear program/ Earth Movers Distance [1] . +- `Conditional + gradient `__ + [6] and `Generalized conditional + gradient `__ + for regularized OT [7]. +- Entropic regularization OT solver with `Sinkhorn Knopp + Algorithm `__ + [2] , stabilized version [9] [10], greedy Sinkhorn [22] and + `Screening Sinkhorn + [26] `__ + with optional GPU implementation (requires cupy). +- Bregman projections for `Wasserstein + barycenter `__ + [3], `convolutional + barycenter `__ + [21] and unmixing [4]. +- Sinkhorn divergence [23] and entropic regularization OT from + empirical data. +- `Smooth optimal transport + solvers `__ + (dual and semi-dual) for KL and squared L2 regularizations [17]. +- Non regularized `Wasserstein barycenters + [16] `__) + with LP solver (only small scale). +- `Gromov-Wasserstein + distances `__ + and `GW + barycenters `__ + (exact [13] and regularized [12]) +- `Fused-Gromov-Wasserstein distances + solver `__ + and `FGW + barycenters `__ + [24] +- `Stochastic + solver `__ + for Large-scale Optimal Transport (semi-dual problem [18] and dual + problem [19]) +- Non regularized `free support Wasserstein + barycenters `__ + [20]. +- `Unbalanced + OT `__ + with KL relaxation and + `barycenter `__ + [10, 25]. +- `Partial Wasserstein and + Gromov-Wasserstein `__ + (exact [29] and entropic [3] formulations). + +POT provides the following Machine Learning related solvers: + +- `Optimal transport for domain + adaptation `__ + with `group lasso + regularization `__, + `Laplacian + regularization `__ + [5] [30] and `semi supervised + setting `__. +- `Linear OT + mapping `__ + [14] and `Joint OT mapping + estimation `__ + [8]. +- `Wasserstein Discriminant + Analysis `__ + [11] (requires autograd + pymanopt). +- `JCPOT algorithm for multi-source domain adaptation with target + shift `__ + [27]. Some demonstrations are available in the `documentation `__. @@ -45,12 +98,18 @@ Using and citing the toolbox If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference: +:: + + Rémi Flamary and Nicolas Courty, POT Python Optimal Transport library, Website: https://pythonot.github.io/, 2017 + +In Bibtex format: + :: @misc{flamary2017pot, title={POT Python Optimal Transport library}, author={Flamary, R{'e}mi and Courty, Nicolas}, - url={https://github.com/rflamary/POT}, + url={https://pythonot.github.io/}, year={2017} } -- cgit v1.2.3 From 8fd50a7e9d0e7d06ea93fe6ad88413abc91ac6f9 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 22 Apr 2020 14:15:15 +0200 Subject: relative path in doc --- Makefile | 1 + docs/source/readme.rst | 54 +++++++++++++++++++++++++------------------------- 2 files changed, 28 insertions(+), 27 deletions(-) diff --git a/Makefile b/Makefile index cafda8e..f5f89d9 100644 --- a/Makefile +++ b/Makefile @@ -58,6 +58,7 @@ release_test : rdoc : pandoc --from=markdown --to=rst --output=docs/source/readme.rst README.md + sed -i 's,https://pythonot.github.io/auto_examples/,auto_examples/,g' docs/source/readme.rst notebook : ipython notebook --matplotlib=inline --notebook-dir=notebooks/ diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 4862523..b00d7b7 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -15,82 +15,82 @@ Source Code (MIT): https://github.com/PythonOT/POT POT provides the following generic OT solvers (links to examples): - `OT Network Simplex - solver `__ + solver `__ for the linear program/ Earth Movers Distance [1] . - `Conditional - gradient `__ + gradient `__ [6] and `Generalized conditional - gradient `__ + gradient `__ for regularized OT [7]. - Entropic regularization OT solver with `Sinkhorn Knopp - Algorithm `__ + Algorithm `__ [2] , stabilized version [9] [10], greedy Sinkhorn [22] and `Screening Sinkhorn - [26] `__ + [26] `__ with optional GPU implementation (requires cupy). - Bregman projections for `Wasserstein - barycenter `__ + barycenter `__ [3], `convolutional - barycenter `__ + barycenter `__ [21] and unmixing [4]. - Sinkhorn divergence [23] and entropic regularization OT from empirical data. - `Smooth optimal transport - solvers `__ + solvers `__ (dual and semi-dual) for KL and squared L2 regularizations [17]. - Non regularized `Wasserstein barycenters - [16] `__) + [16] `__) with LP solver (only small scale). - `Gromov-Wasserstein - distances `__ + distances `__ and `GW - barycenters `__ + barycenters `__ (exact [13] and regularized [12]) - `Fused-Gromov-Wasserstein distances - solver `__ + solver `__ and `FGW - barycenters `__ + barycenters `__ [24] - `Stochastic - solver `__ + solver `__ for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) - Non regularized `free support Wasserstein - barycenters `__ + barycenters `__ [20]. - `Unbalanced - OT `__ + OT `__ with KL relaxation and - `barycenter `__ + `barycenter `__ [10, 25]. - `Partial Wasserstein and - Gromov-Wasserstein `__ + Gromov-Wasserstein `__ (exact [29] and entropic [3] formulations). POT provides the following Machine Learning related solvers: - `Optimal transport for domain - adaptation `__ + adaptation `__ with `group lasso - regularization `__, + regularization `__, `Laplacian - regularization `__ + regularization `__ [5] [30] and `semi supervised - setting `__. + setting `__. - `Linear OT - mapping `__ + mapping `__ [14] and `Joint OT mapping - estimation `__ + estimation `__ [8]. - `Wasserstein Discriminant - Analysis `__ + Analysis `__ [11] (requires autograd + pymanopt). - `JCPOT algorithm for multi-source domain adaptation with target - shift `__ + shift `__ [27]. Some demonstrations are available in the -`documentation `__. +`documentation `__. Using and citing the toolbox ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -- cgit v1.2.3 From c60cb3bcfd3ff67113a8d8a84230ffd263b72c6e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 22 Apr 2020 15:38:39 +0200 Subject: add wheel build action --- .github/workflows/build_wheels.yml | 41 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 41 insertions(+) create mode 100644 .github/workflows/build_wheels.yml diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml new file mode 100644 index 0000000..5ade5d7 --- /dev/null +++ b/.github/workflows/build_wheels.yml @@ -0,0 +1,41 @@ +name: Build dist and wheels + +on: + push: + branches: + - 'build_wheels' + + +jobs: + build_linux: + + runs-on: ubuntu-latest + strategy: + max-parallel: 4 + matrix: + python-version: [3.5, 3.6, 3.7, 3.8] + + steps: + - uses: actions/checkout@v1 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v1 + with: + python-version: ${{ matrix.python-version }} + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + + - name: Install cibuildwheel + run: | + python -m pip install cibuildwheel==1.3.0 + + - name: Build wheel + run: | + python -m cibuildwheel --output-dir wheelhouse + + - uses: actions/upload-artifact@v1 + with: + name: wheels + path: ./wheelhouse \ No newline at end of file -- cgit v1.2.3 From 4aa020055a684ec8cd2360ecf47bd85f94e63e9e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 22 Apr 2020 15:42:56 +0200 Subject: force install cython --- .github/workflows/build_wheels.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml index 5ade5d7..d92c7c5 100644 --- a/.github/workflows/build_wheels.yml +++ b/.github/workflows/build_wheels.yml @@ -26,7 +26,7 @@ jobs: run: | python -m pip install --upgrade pip pip install -r requirements.txt - + pip install -U "cython" - name: Install cibuildwheel run: | python -m pip install cibuildwheel==1.3.0 -- cgit v1.2.3 From de5ff644d282660f34a8fad051879eca7cdc87a3 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 22 Apr 2020 15:47:56 +0200 Subject: propoer ciwb before build --- .github/workflows/build_wheels.yml | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml index d92c7c5..9938698 100644 --- a/.github/workflows/build_wheels.yml +++ b/.github/workflows/build_wheels.yml @@ -13,7 +13,7 @@ jobs: strategy: max-parallel: 4 matrix: - python-version: [3.5, 3.6, 3.7, 3.8] + python-version: [3.8] steps: - uses: actions/checkout@v1 @@ -32,6 +32,8 @@ jobs: python -m pip install cibuildwheel==1.3.0 - name: Build wheel + env: + CIBW_BEFORE_BUILD: "pip install numpy cython" run: | python -m cibuildwheel --output-dir wheelhouse -- cgit v1.2.3 From b8c36c9a4f807b4743c296e84f3f0896a778619e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 22 Apr 2020 16:15:44 +0200 Subject: test build wheels windows --- .github/workflows/build_wheels.yml | 39 ++++++++++++++++++++++++++++++++------ 1 file changed, 33 insertions(+), 6 deletions(-) diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml index 9938698..ed8bd3d 100644 --- a/.github/workflows/build_wheels.yml +++ b/.github/workflows/build_wheels.yml @@ -10,17 +10,44 @@ jobs: build_linux: runs-on: ubuntu-latest - strategy: - max-parallel: 4 - matrix: - python-version: [3.8] steps: - uses: actions/checkout@v1 - - name: Set up Python ${{ matrix.python-version }} + - name: Set up Python 3.8 uses: actions/setup-python@v1 with: - python-version: ${{ matrix.python-version }} + python-version: 3.8 + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + pip install -U "cython" + - name: Install cibuildwheel + run: | + python -m pip install cibuildwheel==1.3.0 + + - name: Build wheel + env: + CIBW_BEFORE_BUILD: "pip install numpy cython" + run: | + python -m cibuildwheel --output-dir wheelhouse + + - uses: actions/upload-artifact@v1 + with: + name: wheels + path: ./wheelhouse + + build_windows: + + runs-on: windows-2019 + + steps: + - uses: actions/checkout@v1 + - name: Set up Python 3.8 + uses: actions/setup-python@v1 + with: + python-version: 3.8 - name: Install dependencies run: | -- cgit v1.2.3 From d20ce8c4e6ef723bec0ee4f06b285dff8da8c6f7 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 22 Apr 2020 16:21:04 +0200 Subject: debug build wheels windows --- .github/workflows/build_wheels.yml | 44 ++++++++++---------------------------- 1 file changed, 11 insertions(+), 33 deletions(-) diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml index ed8bd3d..4874d84 100644 --- a/.github/workflows/build_wheels.yml +++ b/.github/workflows/build_wheels.yml @@ -7,9 +7,12 @@ on: jobs: - build_linux: - - runs-on: ubuntu-latest + build_wheels: + name: Build wheel on ${{ matrix.os }} + runs-on: ${{ matrix.os }} + strategy: + matrix: + os: [ubuntu-18.04, windows-latest, macos-latest] steps: - uses: actions/checkout@v1 @@ -23,40 +26,15 @@ jobs: python -m pip install --upgrade pip pip install -r requirements.txt pip install -U "cython" + - name: Install cibuildwheel run: | python -m pip install cibuildwheel==1.3.0 - - name: Build wheel - env: - CIBW_BEFORE_BUILD: "pip install numpy cython" - run: | - python -m cibuildwheel --output-dir wheelhouse - - - uses: actions/upload-artifact@v1 - with: - name: wheels - path: ./wheelhouse - - build_windows: - - runs-on: windows-2019 - - steps: - - uses: actions/checkout@v1 - - name: Set up Python 3.8 - uses: actions/setup-python@v1 - with: - python-version: 3.8 - - - name: Install dependencies + - name: Install Visual C++ for Python 2.7 + if: startsWith(matrix.os, 'windows') run: | - python -m pip install --upgrade pip - pip install -r requirements.txt - pip install -U "cython" - - name: Install cibuildwheel - run: | - python -m pip install cibuildwheel==1.3.0 + choco install vcpython27 -f -y - name: Build wheel env: @@ -67,4 +45,4 @@ jobs: - uses: actions/upload-artifact@v1 with: name: wheels - path: ./wheelhouse \ No newline at end of file + path: ./wheelhouse -- cgit v1.2.3 From 57153c8c6a30d044690b46f77ea5c584f5e5d088 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 22 Apr 2020 16:34:26 +0200 Subject: remove macosx --- .github/workflows/build_wheels.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml index 4874d84..9f671bf 100644 --- a/.github/workflows/build_wheels.yml +++ b/.github/workflows/build_wheels.yml @@ -12,7 +12,7 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - os: [ubuntu-18.04, windows-latest, macos-latest] + os: [ubuntu-18.04, windows-latest] steps: - uses: actions/checkout@v1 -- cgit v1.2.3 From 6bcd9e6b988c2856b910a9c87663e4a0cbd1ea19 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 22 Apr 2020 20:09:14 +0200 Subject: stick to linux wheels for current release --- .github/workflows/build_wheels.yml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml index 9f671bf..b11cfaa 100644 --- a/.github/workflows/build_wheels.yml +++ b/.github/workflows/build_wheels.yml @@ -8,11 +8,12 @@ on: jobs: build_wheels: - name: Build wheel on ${{ matrix.os }} + name: ${{ matrix.os }} runs-on: ${{ matrix.os }} strategy: matrix: - os: [ubuntu-18.04, windows-latest] + os: [ubuntu-18.04] + # macosx-latest, windows-latest steps: - uses: actions/checkout@v1 -- cgit v1.2.3 From 2236602b5f512873c8061b97456ddb032da136d5 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 23 Apr 2020 08:23:25 +0200 Subject: update redame for install from git --- README.md | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index ad0d810..9e0f9e8 100644 --- a/README.md +++ b/README.md @@ -50,9 +50,10 @@ Some demonstrations are available in the [documentation](https://pythonot.gith #### Using and citing the toolbox If you use this toolbox in your research and find it useful, please cite POT -using the following bibtex reference: +using the following reference: ``` -Rémi Flamary and Nicolas Courty, POT Python Optimal Transport library, Website: https://pythonot.github.io/, 2017 +Rémi Flamary and Nicolas Courty, POT Python Optimal Transport library, +Website: https://pythonot.github.io/, 2017 ``` In Bibtex format: @@ -86,9 +87,9 @@ You can install the toolbox through PyPI with: ``` pip install POT ``` -or get the very latest version by downloading it and then running: +or get the very latest version by running: ``` -python setup.py install --user # for user install (no root) +pip install git+https://github.com/PythonOT/POT.git # with --user for user install (no root) ``` @@ -165,7 +166,7 @@ This toolbox has been created and is maintained by The contributors to this library are -* [Alexandre Gramfort](http://alexandre.gramfort.net/) (CI) +* [Alexandre Gramfort](http://alexandre.gramfort.net/) (CI, documentation) * [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) (Partial OT) * [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation) * [Léo Gautheron](https://github.com/aje) (GPU implementation) -- cgit v1.2.3 From 1e2f0ff92cba13511674d71d434efa5cfbcca181 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 23 Apr 2020 09:05:40 +0200 Subject: Update README.md Co-Authored-By: Alexandre Gramfort --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9e0f9e8..214f932 100644 --- a/README.md +++ b/README.md @@ -89,7 +89,7 @@ pip install POT ``` or get the very latest version by running: ``` -pip install git+https://github.com/PythonOT/POT.git # with --user for user install (no root) +pip install -U https://github.com/PythonOT/POT/archive/master.zip # with --user for user install (no root) ``` -- cgit v1.2.3 From ebf8fe9d1c3ff7885c00f695812964faf119486c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 23 Apr 2020 09:30:24 +0200 Subject: set action to release event --- .github/workflows/build_wheels.yml | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml index b11cfaa..7c13c33 100644 --- a/.github/workflows/build_wheels.yml +++ b/.github/workflows/build_wheels.yml @@ -1,10 +1,7 @@ name: Build dist and wheels on: - push: - branches: - - 'build_wheels' - + release: jobs: build_wheels: -- cgit v1.2.3 From 73db416784c400eccb5cdea0b3a00ac4bd68c595 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Thu, 23 Apr 2020 10:57:26 +0200 Subject: revert changes on GH yml --- .github/workflows/pythonpackage.yml | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index 755937a..5b1fa0e 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -1,12 +1,14 @@ -name: Test Package +name: build on: push: branches: - - master - pull_request: + - '**' + create: branches: - - master + - 'master' + tags: + - '**' jobs: linux: @@ -34,7 +36,7 @@ jobs: # stop the build if there are Python syntax errors or undefined names flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + flake8 examples/ ot/ test/ --count --max-line-length=127 --statistics - name: Install POT run: | pip install -e . @@ -97,3 +99,4 @@ jobs: - name: Run tests run: | python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot +=ot -- cgit v1.2.3 From fc8d9d9714e15280b5cfc27ac1190cda3b57966f Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Thu, 23 Apr 2020 10:58:54 +0200 Subject: oups --- .github/workflows/pythonpackage.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index 5b1fa0e..c916647 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -99,4 +99,3 @@ jobs: - name: Run tests run: | python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot -=ot -- cgit v1.2.3 From ef12867f1425ee86b3cfddef4287b52d46114e83 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Thu, 23 Apr 2020 13:03:28 +0200 Subject: [WIP] Issue with sparse emd and adding tests on macos (#158) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * First commit-warning removal * remove dense feature * pep8 * pep8 * EMD.h * pep8 again * tic toc tolerance Co-authored-by: Rémi Flamary --- .github/workflows/pythonpackage.yml | 48 +++++----- ot/lp/EMD.h | 3 - ot/lp/EMD_wrapper.cpp | 182 ------------------------------------ ot/lp/__init__.py | 45 +++------ ot/lp/emd_wrap.pyx | 38 ++------ ot/lp/network_simplex_simple.h | 5 +- test/test_ot.py | 26 ------ test/test_utils.py | 4 +- 8 files changed, 46 insertions(+), 305 deletions(-) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index 394f453..9c35afa 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -47,31 +47,31 @@ jobs: run: | codecov - # macos: - # runs-on: macOS-latest - # strategy: - # max-parallel: 4 - # matrix: - # python-version: [3.7] + macos: + runs-on: macOS-latest + strategy: + max-parallel: 4 + matrix: + python-version: [3.7] - # steps: - # - uses: actions/checkout@v1 - # - name: Set up Python ${{ matrix.python-version }} - # uses: actions/setup-python@v1 - # with: - # python-version: ${{ matrix.python-version }} - # - name: Install dependencies - # run: | - # python -m pip install --upgrade pip - # pip install -r requirements.txt - # pip install pytest "pytest-cov<2.6" - # pip install -U "sklearn" - # - name: Install POT - # run: | - # pip install -e . - # - name: Run tests - # run: | - # python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot + steps: + - uses: actions/checkout@v1 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v1 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + pip install pytest "pytest-cov<2.6" + pip install -U "sklearn" + - name: Install POT + run: | + pip install -e . + - name: Run tests + run: | + python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot windows: diff --git a/ot/lp/EMD.h b/ot/lp/EMD.h index 2adaace..c0fe7a3 100644 --- a/ot/lp/EMD.h +++ b/ot/lp/EMD.h @@ -32,9 +32,6 @@ enum ProblemType { int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int maxIter); -int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, - long *iG, long *jG, double *G, long * nG, - double* alpha, double* beta, double *cost, int maxIter); #endif diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index 28e4af2..bc873ed 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -106,185 +106,3 @@ int EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G, return ret; } - -int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, - long *iG, long *jG, double *G, long * nG, - double* alpha, double* beta, double *cost, int maxIter) { - // beware M and C anre strored in row major C style!!! - - // Get the number of non zero coordinates for r and c and vectors - int n, m, i, cur; - - typedef FullBipartiteDigraph Digraph; - DIGRAPH_TYPEDEFS(FullBipartiteDigraph); - - // Get the number of non zero coordinates for r and c - n=0; - for (int i=0; i0) { - n++; - }else if(val<0){ - return INFEASIBLE; - } - } - m=0; - for (int i=0; i0) { - m++; - }else if(val<0){ - return INFEASIBLE; - } - } - - // Define the graph - - std::vector indI(n), indJ(m); - std::vector weights1(n), weights2(m); - Digraph di(n, m); - NetworkSimplexSimple net(di, true, n+m, n*m, maxIter); - - // Set supply and demand, don't account for 0 values (faster) - - cur=0; - for (int i=0; i0) { - weights1[ cur ] = val; - indI[cur++]=i; - } - } - - // Demand is actually negative supply... - - cur=0; - for (int i=0; i0) { - weights2[ cur ] = -val; - indJ[cur++]=i; - } - } - - // Define the graph - net.supplyMap(&weights1[0], n, &weights2[0], m); - - // Set the cost of each edge - for (int i=0; i0) - { - *cost += flow * (*(D+indI[i]*n2+indJ[j-n])); - - *(G+cur) = flow; - *(iG+cur) = indI[i]; - *(jG+cur) = indJ[j-n]; - *(alpha + indI[i]) = -net.potential(i); - *(beta + indJ[j-n]) = net.potential(j); - cur++; - } - } - *nG=cur; // nb of value +1 for numpy indexing - - } - - - return ret; -} - -int EMD_wrap_all_sparse(int n1, int n2, double *X, double *Y, - long *iD, long *jD, double *D, long nD, - long *iG, long *jG, double *G, long * nG, - double* alpha, double* beta, double *cost, int maxIter) { - // beware M and C anre strored in row major C style!!! - - // Get the number of non zero coordinates for r and c and vectors - int n, m, cur; - - typedef FullBipartiteDigraph Digraph; - DIGRAPH_TYPEDEFS(FullBipartiteDigraph); - - n=n1; - m=n2; - - - // Define the graph - - - std::vector weights2(m); - Digraph di(n, m); - NetworkSimplexSimple net(di, true, n+m, n*m, maxIter); - - // Set supply and demand, don't account for 0 values (faster) - - - // Demand is actually negative supply... - - cur=0; - for (int i=0; i0) { - weights2[ cur ] = -val; - } - } - - // Define the graph - net.supplyMap(X, n, &weights2[0], m); - - // Set the cost of each edge - for (int k=0; k0) - { - - *(G+cur) = flow; - *(iG+cur) = i; - *(jG+cur) = j-n; - *(alpha + i) = -net.potential(i); - *(beta + j-n) = net.potential(j); - cur++; - } - } - *nG=cur; // nb of value +1 for numpy indexing - - } - - - return ret; -} - diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 8d1baa0..ad390c5 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -172,7 +172,7 @@ def estimate_dual_null_weights(alpha0, beta0, a, b, M): return center_ot_dual(alpha, beta, a, b) -def emd(a, b, M, numItermax=100000, log=False, dense=True, center_dual=True): +def emd(a, b, M, numItermax=100000, log=False, center_dual=True): r"""Solves the Earth Movers distance problem and returns the OT matrix @@ -207,10 +207,6 @@ def emd(a, b, M, numItermax=100000, log=False, dense=True, center_dual=True): log: bool, optional (default=False) If True, returns a dictionary containing the cost and dual variables. Otherwise returns only the optimal transportation matrix. - dense: boolean, optional (default=True) - If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). - Otherwise returns a sparse representation using scipy's `coo_matrix` - format. center_dual: boolean, optional (default=True) If True, centers the dual potential using function :ref:`center_ot_dual`. @@ -267,25 +263,14 @@ def emd(a, b, M, numItermax=100000, log=False, dense=True, center_dual=True): asel = a != 0 bsel = b != 0 - if dense: - G, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) - - if center_dual: - u, v = center_ot_dual(u, v, a, b) + G, cost, u, v, result_code = emd_c(a, b, M, numItermax) - if np.any(~asel) or np.any(~bsel): - u, v = estimate_dual_null_weights(u, v, a, b, M) - - else: - Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) - G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) - - if center_dual: - u, v = center_ot_dual(u, v, a, b) - - if np.any(~asel) or np.any(~bsel): - u, v = estimate_dual_null_weights(u, v, a, b, M) + if center_dual: + u, v = center_ot_dual(u, v, a, b) + if np.any(~asel) or np.any(~bsel): + u, v = estimate_dual_null_weights(u, v, a, b, M) + result_code_string = check_result(result_code) if log: log = {} @@ -299,7 +284,7 @@ def emd(a, b, M, numItermax=100000, log=False, dense=True, center_dual=True): def emd2(a, b, M, processes=multiprocessing.cpu_count(), - numItermax=100000, log=False, dense=True, return_matrix=False, + numItermax=100000, log=False, return_matrix=False, center_dual=True): r"""Solves the Earth Movers distance problem and returns the loss @@ -404,11 +389,8 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), if log or return_matrix: def f(b): bsel = b != 0 - if dense: - G, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) - else: - Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) - G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + + G, cost, u, v, result_code = emd_c(a, b, M, numItermax) if center_dual: u, v = center_ot_dual(u, v, a, b) @@ -428,11 +410,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), else: def f(b): bsel = b != 0 - if dense: - G, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) - else: - Gv, iG, jG, cost, u, v, result_code = emd_c(a, b, M, numItermax, dense) - G = coo_matrix((Gv, (iG, jG)), shape=(a.shape[0], b.shape[0])) + G, cost, u, v, result_code = emd_c(a, b, M, numItermax) if center_dual: u, v = center_ot_dual(u, v, a, b) @@ -440,7 +418,6 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), if np.any(~asel) or np.any(~bsel): u, v = estimate_dual_null_weights(u, v, a, b, M) - result_code_string = check_result(result_code) check_result(result_code) return cost diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index d345fd4..b6bda47 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -20,9 +20,6 @@ import warnings cdef extern from "EMD.h": int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int maxIter) - int EMD_wrap_return_sparse(int n1, int n2, double *X, double *Y, double *D, - long *iG, long *jG, double *G, long * nG, - double* alpha, double* beta, double *cost, int maxIter) cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED, MAX_ITER_REACHED @@ -38,13 +35,10 @@ def check_result(result_code): message = "numItermax reached before optimality. Try to increase numItermax." warnings.warn(message) return message - - - - + @cython.boundscheck(False) @cython.wraparound(False) -def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mode="c"] b, np.ndarray[double, ndim=2, mode="c"] M, int max_iter, bint dense): +def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mode="c"] b, np.ndarray[double, ndim=2, mode="c"] M, int max_iter): """ Solves the Earth Movers distance problem and returns the optimal transport matrix @@ -83,8 +77,6 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod max_iter : int The maximum number of iterations before stopping the optimization algorithm if it has not converged. - dense : bool - Return a sparse transport matrix if set to False Returns ------- @@ -114,29 +106,13 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod if not len(b): b=np.ones((n2,))/n2 - if dense: - # init OT matrix - G=np.zeros([n1, n2]) - - # calling the function - result_code = EMD_wrap(n1, n2, a.data, b.data, M.data, G.data, alpha.data, beta.data, &cost, max_iter) - - return G, cost, alpha, beta, result_code - - - else: - - # init sparse OT matrix - Gv=np.zeros(nmax) - iG=np.zeros(nmax,dtype=np.int) - jG=np.zeros(nmax,dtype=np.int) - - - result_code = EMD_wrap_return_sparse(n1, n2, a.data, b.data, M.data, iG.data, jG.data, Gv.data, &nG, alpha.data, beta.data, &cost, max_iter) - + # init OT matrix + G=np.zeros([n1, n2]) - return Gv[:nG], iG[:nG], jG[:nG], cost, alpha, beta, result_code + # calling the function + result_code = EMD_wrap(n1, n2, a.data, b.data, M.data, G.data, alpha.data, beta.data, &cost, max_iter) + return G, cost, alpha, beta, result_code @cython.boundscheck(False) diff --git a/ot/lp/network_simplex_simple.h b/ot/lp/network_simplex_simple.h index 498e921..5d93040 100644 --- a/ot/lp/network_simplex_simple.h +++ b/ot/lp/network_simplex_simple.h @@ -875,7 +875,7 @@ namespace lemon { c += Number(it->second) * Number(_cost[it->first]); return c;*/ - for (int i=0; i<_flow.size(); i++) + for (unsigned long i=0; i<_flow.size(); i++) c += _flow[i] * Number(_cost[i]); return c; @@ -1257,7 +1257,7 @@ namespace lemon { u = w; } _pred[u_in] = in_arc; - _forward[u_in] = (u_in == _source[in_arc]); + _forward[u_in] = ((unsigned int)u_in == _source[in_arc]); _succ_num[u_in] = old_succ_num; // Set limits for updating _last_succ form v_in and v_out @@ -1418,7 +1418,6 @@ namespace lemon { template ProblemType start() { PivotRuleImpl pivot(*this); - double prevCost=-1; ProblemType retVal = OPTIMAL; // Perform heuristic initial pivots diff --git a/test/test_ot.py b/test/test_ot.py index 0f1357f..b7306f6 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -170,27 +170,6 @@ def test_emd_empty(): np.testing.assert_allclose(w, 0) -def test_emd_sparse(): - n = 100 - rng = np.random.RandomState(0) - - x = rng.randn(n, 2) - x2 = rng.randn(n, 2) - - M = ot.dist(x, x2) - - G = ot.emd([], [], M, dense=True) - - Gs = ot.emd([], [], M, dense=False) - - ws = ot.emd2([], [], M, dense=False) - - # check G is the same - np.testing.assert_allclose(G, Gs.todense()) - # check value - np.testing.assert_allclose(Gs.multiply(M).sum(), ws, rtol=1e-6) - - def test_emd2_multi(): n = 500 # nb bins @@ -222,12 +201,7 @@ def test_emd2_multi(): emdn = ot.emd2(a, b, M) ot.toc('multi proc : {} s') - ot.tic() - emdn2 = ot.emd2(a, b, M, dense=False) - ot.toc('multi proc : {} s') - np.testing.assert_allclose(emd1, emdn) - np.testing.assert_allclose(emd1, emdn2, rtol=1e-6) # emd loss multipro proc with log ot.tic() diff --git a/test/test_utils.py b/test/test_utils.py index 640598d..db9cda6 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -36,10 +36,10 @@ def test_tic_toc(): t2 = ot.toq() # test timing - np.testing.assert_allclose(0.5, t, rtol=1e-2, atol=1e-2) + np.testing.assert_allclose(0.5, t, rtol=1e-1, atol=1e-1) # test toc vs toq - np.testing.assert_allclose(t, t2, rtol=1e-2, atol=1e-2) + np.testing.assert_allclose(t, t2, rtol=1e-1, atol=1e-1) def test_kernel(): -- cgit v1.2.3 From 6426c182bc2546d00d18f7422d6dde150e09217c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 23 Apr 2020 14:19:52 +0200 Subject: add platforms and remove pp --- .github/workflows/build_wheels.yml | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml index 7c13c33..a1fabc9 100644 --- a/.github/workflows/build_wheels.yml +++ b/.github/workflows/build_wheels.yml @@ -2,6 +2,9 @@ name: Build dist and wheels on: release: + push: + branches: + - "wheels_multi" jobs: build_wheels: @@ -9,7 +12,7 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - os: [ubuntu-18.04] + os: [ubuntu-18.04, macosx-latest, windows-latest] # macosx-latest, windows-latest steps: @@ -36,6 +39,7 @@ jobs: - name: Build wheel env: + CIBW_SKIP: "pp*-win*" CIBW_BEFORE_BUILD: "pip install numpy cython" run: | python -m cibuildwheel --output-dir wheelhouse -- cgit v1.2.3 From d2575b77e90e9cc94ee60bcd548ede16740ca2fc Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 23 Apr 2020 14:29:46 +0200 Subject: proper macos build --- .github/workflows/build_wheels.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml index a1fabc9..da1e18f 100644 --- a/.github/workflows/build_wheels.yml +++ b/.github/workflows/build_wheels.yml @@ -12,7 +12,7 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - os: [ubuntu-18.04, macosx-latest, windows-latest] + os: [ubuntu-18.04, macos-latest, windows-latest] # macosx-latest, windows-latest steps: -- cgit v1.2.3 From d676bc70dd3d332973ce3700ae9d7f02865fffcd Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 23 Apr 2020 14:41:57 +0200 Subject: remove pipy for macosx --- .github/workflows/build_wheels.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml index da1e18f..2fc8db3 100644 --- a/.github/workflows/build_wheels.yml +++ b/.github/workflows/build_wheels.yml @@ -39,7 +39,7 @@ jobs: - name: Build wheel env: - CIBW_SKIP: "pp*-win*" + CIBW_SKIP: "pp*-win* pp*-macosx*" CIBW_BEFORE_BUILD: "pip install numpy cython" run: | python -m cibuildwheel --output-dir wheelhouse -- cgit v1.2.3 From c69006a975681c7f7d81dadba43b475aaae0c8ea Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Thu, 23 Apr 2020 14:55:08 +0200 Subject: trigger CIs --- .github/workflows/pythonpackage.yml | 1 - 1 file changed, 1 deletion(-) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index 311c283..4b6c320 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -73,7 +73,6 @@ jobs: run: | python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot - windows: runs-on: windows-2019 strategy: -- cgit v1.2.3 From 46445382a4ee7743bb6f254fdeaf9d0abdae44d6 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 23 Apr 2020 14:58:52 +0200 Subject: final version --- .github/workflows/build_wheels.yml | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml index 2fc8db3..1a4c173 100644 --- a/.github/workflows/build_wheels.yml +++ b/.github/workflows/build_wheels.yml @@ -2,9 +2,6 @@ name: Build dist and wheels on: release: - push: - branches: - - "wheels_multi" jobs: build_wheels: @@ -12,8 +9,8 @@ jobs: runs-on: ${{ matrix.os }} strategy: matrix: - os: [ubuntu-18.04, macos-latest, windows-latest] - # macosx-latest, windows-latest + os: [ubuntu-latest, macos-latest, windows-latest] + # macos-latest, windows-latest steps: - uses: actions/checkout@v1 @@ -39,7 +36,7 @@ jobs: - name: Build wheel env: - CIBW_SKIP: "pp*-win* pp*-macosx*" + CIBW_SKIP: "pp*-win* pp*-macosx*" # remove pypy on mac and win (wrong version) CIBW_BEFORE_BUILD: "pip install numpy cython" run: | python -m cibuildwheel --output-dir wheelhouse -- cgit v1.2.3 From 39f7ad7a6f1a52ede3602c4c721c0314c2b5fc5e Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Thu, 23 Apr 2020 16:29:35 +0200 Subject: trigger CIs --- .github/workflows/pythonpackage.yml | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml index 4b6c320..0792059 100644 --- a/.github/workflows/pythonpackage.yml +++ b/.github/workflows/pythonpackage.yml @@ -47,6 +47,7 @@ jobs: run: | codecov + macos: runs-on: macOS-latest strategy: @@ -73,6 +74,7 @@ jobs: run: | python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot + windows: runs-on: windows-2019 strategy: -- cgit v1.2.3 From 53265dbd5530aee37a33f42e1243b26422d62acf Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 11:12:42 +0200 Subject: build wheels on push to master --- .github/workflows/build_wheels.yml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml index 1a4c173..be1719b 100644 --- a/.github/workflows/build_wheels.yml +++ b/.github/workflows/build_wheels.yml @@ -2,6 +2,9 @@ name: Build dist and wheels on: release: + push: + branches: + - "master" jobs: build_wheels: -- cgit v1.2.3 From 8599e720d5f438e2aaf5c635883e64deb026f3ce Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 11:20:02 +0200 Subject: correct doc for emd --- docs/source/conf.py | 2 +- ot/lp/__init__.py | 6 +++++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 880c71d..f3c61e7 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -67,7 +67,7 @@ extensions = [ 'sphinx.ext.ifconfig', 'sphinx.ext.viewcode', 'sphinx.ext.napoleon', - 'sphinx_gallery.gen_gallery', + #'sphinx_gallery.gen_gallery', ] napoleon_numpy_docstring = True diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index ad390c5..50003ed 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -180,7 +180,9 @@ def emd(a, b, M, numItermax=100000, log=False, center_dual=True): \gamma = arg\min_\gamma <\gamma,M>_F s.t. \gamma 1 = a + \gamma^T 1= b + \gamma\geq 0 where : @@ -289,10 +291,12 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), r"""Solves the Earth Movers distance problem and returns the loss .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F + \min_\gamma <\gamma,M>_F s.t. \gamma 1 = a + \gamma^T 1= b + \gamma\geq 0 where : -- cgit v1.2.3 From 6931f78c7e5b1da4f62a1a85d87409f3f95029c7 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 11:28:21 +0200 Subject: better documentation --- ot/da.py | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/ot/da.py b/ot/da.py index 6249f08..4d2bb6c 100644 --- a/ot/da.py +++ b/ot/da.py @@ -908,7 +908,8 @@ class BaseTransport(BaseEstimator): at the class level in their ``__init__`` as explicit keyword arguments (no ``*args`` or ``**kwargs``). - fit method should: + the fit method should: + - estimate a cost matrix and store it in a `cost_` attribute - estimate a coupling matrix and store it in a `coupling_` attribute @@ -933,7 +934,7 @@ class BaseTransport(BaseEstimator): Xs : array-like, shape (n_source_samples, n_features) The training input samples. ys : array-like, shape (n_source_samples,) - The class labels + The training class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. yt : array-like, shape (n_target_samples,) @@ -994,7 +995,7 @@ class BaseTransport(BaseEstimator): Xs : array-like, shape (n_source_samples, n_features) The training input samples. ys : array-like, shape (n_source_samples,) - The class labels + The class labels for training samples Xt : array-like, shape (n_target_samples, n_features) The training input samples. yt : array-like, shape (n_target_samples,) @@ -1018,13 +1019,13 @@ class BaseTransport(BaseEstimator): Parameters ---------- Xs : array-like, shape (n_source_samples, n_features) - The training input samples. + The source input samples. ys : array-like, shape (n_source_samples,) - The class labels + The class labels for source samples Xt : array-like, shape (n_target_samples, n_features) - The training input samples. + The target input samples. yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the + The class labels for target. If some target samples are unlabeled, fill the yt's elements with -1. Warning: Note that, due to this convention -1 cannot be used as a @@ -1085,7 +1086,7 @@ class BaseTransport(BaseEstimator): Parameters ---------- ys : array-like, shape (n_source_samples,) - The class labels + The source class labels Returns ------- @@ -1125,18 +1126,18 @@ class BaseTransport(BaseEstimator): def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): - """Transports target samples Xt onto target samples Xs + """Transports target samples Xt onto source samples Xs Parameters ---------- Xs : array-like, shape (n_source_samples, n_features) - The training input samples. + The source input samples. ys : array-like, shape (n_source_samples,) - The class labels + The source class labels Xt : array-like, shape (n_target_samples, n_features) - The training input samples. + The target input samples. yt : array-like, shape (n_target_samples,) - The class labels. If some target samples are unlabeled, fill the + The target class labels. If some target samples are unlabeled, fill the yt's elements with -1. Warning: Note that, due to this convention -1 cannot be used as a @@ -1227,7 +1228,6 @@ class BaseTransport(BaseEstimator): class LinearTransport(BaseTransport): - """ OT linear operator between empirical distributions The function estimates the optimal linear operator that aligns the two -- cgit v1.2.3 From a5b1b18f25298ed88d958d62647165bd63b83b9d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 11:45:27 +0200 Subject: add ferradans to da classes documentation --- ot/da.py | 26 +++++++++++++++++++++++--- 1 file changed, 23 insertions(+), 3 deletions(-) diff --git a/ot/da.py b/ot/da.py index 4d2bb6c..7b22de8 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1438,6 +1438,9 @@ class SinkhornTransport(BaseTransport): .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). + Regularized discrete optimal transport. SIAM Journal on Imaging + Sciences, 7(3), 1853-1882. """ def __init__(self, reg_e=1., max_iter=1000, @@ -1536,6 +1539,9 @@ class EMDTransport(BaseTransport): .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). + Regularized discrete optimal transport. SIAM Journal on Imaging + Sciences, 7(3), 1853-1882. """ def __init__(self, metric="sqeuclidean", norm=None, log=False, @@ -1643,7 +1649,9 @@ class SinkhornLpl1Transport(BaseTransport): .. [2] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. - + .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). + Regularized discrete optimal transport. SIAM Journal on Imaging + Sciences, 7(3), 1853-1882. """ def __init__(self, reg_e=1., reg_cl=0.1, @@ -1763,6 +1771,9 @@ class EMDLaplaceTransport(BaseTransport): .. [2] R. Flamary, N. Courty, D. Tuia, A. Rakotomamonjy, "Optimal transport with Laplacian regularization: Applications to domain adaptation and shape matching," in NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014. + .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). + Regularized discrete optimal transport. SIAM Journal on Imaging + Sciences, 7(3), 1853-1882. """ def __init__(self, reg_type='pos', reg_lap=1., reg_src=1., metric="sqeuclidean", @@ -1882,7 +1893,9 @@ class SinkhornL1l2Transport(BaseTransport): .. [2] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. - + .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). + Regularized discrete optimal transport. SIAM Journal on Imaging + Sciences, 7(3), 1853-1882. """ def __init__(self, reg_e=1., reg_cl=0.1, @@ -2174,7 +2187,9 @@ class UnbalancedSinkhornTransport(BaseTransport): .. [1] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. - + .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). + Regularized discrete optimal transport. SIAM Journal on Imaging + Sciences, 7(3), 1853-1882. """ def __init__(self, reg_e=1., reg_m=0.1, method='sinkhorn', @@ -2287,6 +2302,11 @@ class JCPOTTransport(BaseTransport): International Conference on Artificial Intelligence and Statistics (AISTATS), vol. 89, p.849-858, 2019. + .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). + Regularized discrete optimal transport. SIAM Journal on Imaging + Sciences, 7(3), 1853-1882. + + """ def __init__(self, reg_e=.1, max_iter=10, -- cgit v1.2.3 From bed16804bb903b43d769c03421d75c7f03910b04 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 11:51:12 +0200 Subject: pep8 --- ot/da.py | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/ot/da.py b/ot/da.py index 7b22de8..b881a8b 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1438,8 +1438,8 @@ class SinkhornTransport(BaseTransport): .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 - .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). - Regularized discrete optimal transport. SIAM Journal on Imaging + .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). + Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. """ @@ -1539,8 +1539,8 @@ class EMDTransport(BaseTransport): .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 - .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). - Regularized discrete optimal transport. SIAM Journal on Imaging + .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). + Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. """ @@ -1649,8 +1649,8 @@ class SinkhornLpl1Transport(BaseTransport): .. [2] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. - .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). - Regularized discrete optimal transport. SIAM Journal on Imaging + .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). + Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. """ @@ -1771,8 +1771,8 @@ class EMDLaplaceTransport(BaseTransport): .. [2] R. Flamary, N. Courty, D. Tuia, A. Rakotomamonjy, "Optimal transport with Laplacian regularization: Applications to domain adaptation and shape matching," in NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014. - .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). - Regularized discrete optimal transport. SIAM Journal on Imaging + .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). + Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. """ @@ -1893,8 +1893,8 @@ class SinkhornL1l2Transport(BaseTransport): .. [2] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. - .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). - Regularized discrete optimal transport. SIAM Journal on Imaging + .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). + Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. """ @@ -2187,8 +2187,8 @@ class UnbalancedSinkhornTransport(BaseTransport): .. [1] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. - .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). - Regularized discrete optimal transport. SIAM Journal on Imaging + .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). + Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. """ @@ -2302,8 +2302,8 @@ class JCPOTTransport(BaseTransport): International Conference on Artificial Intelligence and Statistics (AISTATS), vol. 89, p.849-858, 2019. - .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). - Regularized discrete optimal transport. SIAM Journal on Imaging + .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). + Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -- cgit v1.2.3 From c529ffdfa02e9f81d52d46737c9568129fe449b3 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 11:51:22 +0200 Subject: update doc --- docs/source/readme.rst | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/docs/source/readme.rst b/docs/source/readme.rst index b00d7b7..7d8b8bd 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -1,8 +1,8 @@ POT: Python Optimal Transport ============================= -|PyPI version| |Anaconda Cloud| |Build Status| |Build Status| |Codecov -Status| |Downloads| |Anaconda downloads| |License| +|PyPI version| |Anaconda Cloud| |Build Status| |Codecov Status| +|Downloads| |Anaconda downloads| |License| This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and @@ -96,11 +96,12 @@ Using and citing the toolbox ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ If you use this toolbox in your research and find it useful, please cite -POT using the following bibtex reference: +POT using the following reference: :: - Rémi Flamary and Nicolas Courty, POT Python Optimal Transport library, Website: https://pythonot.github.io/, 2017 + Rémi Flamary and Nicolas Courty, POT Python Optimal Transport library, + Website: https://pythonot.github.io/, 2017 In Bibtex format: @@ -141,11 +142,11 @@ You can install the toolbox through PyPI with: pip install POT -or get the very latest version by downloading it and then running: +or get the very latest version by running: :: - python setup.py install --user # for user install (no root) + pip install -U https://github.com/PythonOT/POT/archive/master.zip # with --user for user install (no root) Anaconda installation with conda-forge ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -245,7 +246,8 @@ This toolbox has been created and is maintained by The contributors to this library are -- `Alexandre Gramfort `__ (CI) +- `Alexandre Gramfort `__ (CI, + documentation) - `Laetitia Chapel `__ (Partial OT) - `Michael Perrot `__ @@ -452,8 +454,6 @@ NIPS Workshop on Optimal Transport and Machine Learning OTML, 2014. :target: https://badge.fury.io/py/POT .. |Anaconda Cloud| image:: https://anaconda.org/conda-forge/pot/badges/version.svg :target: https://anaconda.org/conda-forge/pot -.. |Build Status| image:: https://travis-ci.org/PythonOT/POT.svg?branch=master - :target: https://travis-ci.org/PythonOT/POT .. |Build Status| image:: https://github.com/PythonOT/POT/workflows/build/badge.svg :target: https://github.com/PythonOT/POT/actions .. |Codecov Status| image:: https://codecov.io/gh/PythonOT/POT/branch/master/graph/badge.svg -- cgit v1.2.3 From 46523dc0956fd17e709f958ebd351e748fca0a23 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 12:03:16 +0200 Subject: add realeases --- Makefile | 5 +- RELEASES.md | 2 +- docs/source/conf.py | 2 +- docs/source/index.rst | 3 +- docs/source/releases.rst | 260 +++++++++++++++++++++++++++++++++++++++++++++++ 5 files changed, 268 insertions(+), 4 deletions(-) create mode 100644 docs/source/releases.rst diff --git a/Makefile b/Makefile index f5f89d9..202b209 100644 --- a/Makefile +++ b/Makefile @@ -59,7 +59,10 @@ release_test : rdoc : pandoc --from=markdown --to=rst --output=docs/source/readme.rst README.md sed -i 's,https://pythonot.github.io/auto_examples/,auto_examples/,g' docs/source/readme.rst - + pandoc --from=markdown --to=rst --output=docs/source/releases.rst RELEASES.md + sed -i 's,https://pot.readthedocs.io/en/latest/,,g' docs/source/releases.rst + sed -i 's,https://github.com/rflamary/POT/blob/master/notebooks/,auto_examples/,g' docs/source/releases.rst + sed -i 's,.ipynb,.html/,g' docs/source/releases.rst notebook : ipython notebook --matplotlib=inline --notebook-dir=notebooks/ diff --git a/RELEASES.md b/RELEASES.md index 66eee19..70a7a01 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -1,4 +1,4 @@ -# POT Releases +# Releases ## 0.6 Year 3 diff --git a/docs/source/conf.py b/docs/source/conf.py index f3c61e7..880c71d 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -67,7 +67,7 @@ extensions = [ 'sphinx.ext.ifconfig', 'sphinx.ext.viewcode', 'sphinx.ext.napoleon', - #'sphinx_gallery.gen_gallery', + 'sphinx_gallery.gen_gallery', ] napoleon_numpy_docstring = True diff --git a/docs/source/index.rst b/docs/source/index.rst index 9078d35..47a29a4 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -10,12 +10,13 @@ Contents -------- .. toctree:: - :maxdepth: 2 + :maxdepth: 1 self quickstart all auto_examples/index + releases .. include:: readme.rst :start-line: 5 diff --git a/docs/source/releases.rst b/docs/source/releases.rst new file mode 100644 index 0000000..58bfcfb --- /dev/null +++ b/docs/source/releases.rst @@ -0,0 +1,260 @@ +POT Releases +============ + +0.6 Year 3 +---------- + +*July 2019* + +This is the first official stable release of POT and this means a jump +to 0.6! The library has been used in the wild for a while now and we +have reached a state where a lot of fundamental OT solvers are available +and tested. It has been quite stable in the last months but kept the +beta flag in its Pypi classifiers until now. + +Note that this release will be the last one supporting officially Python +2.7 (See https://python3statement.org/ for more reasons). For next +release we will keep the travis tests for Python 2 but will make them +non necessary for merge in 2020. + +The features are never complete in a toolbox designed for solving +mathematical problems and research but with the new contributions we now +implement algorithms and solvers from 24 scientific papers (listed in +the README.md file). New features include a direct implementation of the +`empirical Sinkhorn +divergence `__ +, a new efficient (Cython implementation) solver for `EMD in +1D `__ and +corresponding `Wasserstein +1D `__. +We now also have implementations for `Unbalanced +OT `__ +and a solver for `Unbalanced OT +barycenters `__. +A new variant of Gromov-Wasserstein divergence called `Fused +Gromov-Wasserstein `__ +has been also contributed with exemples of use on `structured +data `__ +and computing `barycenters of labeld +graphs `__. + +A lot of work has been done on the documentation with several new +examples corresponding to the new features and a lot of corrections for +the docstrings. But the most visible change is a new `quick start +guide `__ for POT +that gives several pointers about which function or classes allow to +solve which specific OT problem. When possible a link is provided to +relevant examples. + +We will also provide with this release some pre-compiled Python wheels +for Linux 64bit on github and pip. This will simplify the install +process that before required a C compiler and numpy/cython already +installed. + +Finally we would like to acknowledge and thank the numerous contributors +of POT that has helped in the past build the foundation and are still +contributing to bring new features and solvers to the library. + +Features +^^^^^^^^ + +- Add compiled manylinux 64bits wheels to pip releases (PR #91) +- Add quick start guide (PR #88) +- Make doctest work on travis (PR #90) +- Update documentation (PR #79, PR #84) +- Solver for EMD in 1D (PR #89) +- Solvers for regularized unbalanced OT (PR #87, PR#99) +- Solver for Fused Gromov-Wasserstein (PR #86) +- Add empirical Sinkhorn and empirical Sinkhorn divergences (PR #80) + +Closed issues +^^^^^^^^^^^^^ + +- Issue #59 fail when using "pip install POT" (new details in doc+ + hopefully wheels) +- Issue #85 Cannot run gpu modules +- Issue #75 Greenkhorn do not return log (solved in PR #76) +- Issue #82 Gromov-Wasserstein fails when the cost matrices are + slightly different +- Issue #72 Macosx build problem + +0.5.0 Year 2 +------------ + +*Sep 2018* + +POT is 2 years old! This release brings numerous new features to the +toolbox as listed below but also several bug correction. + +| Among the new features, we can highlight a `non-regularized + Gromov-Wasserstein + solver `__, + a new `greedy variant of + sinkhorn `__, +| `non-regularized `__, + `convolutional + (2D) `__ + and `free + support `__ + Wasserstein barycenters and + `smooth `__ + and + `stochastic `__ + implementation of entropic OT. + +POT 0.5 also comes with a rewriting of ot.gpu using the cupy framework +instead of the unmaintained cudamat. Note that while we tried to keed +changes to the minimum, the OTDA classes were deprecated. If you are +happy with the cudamat implementation, we recommend you stay with stable +release 0.4 for now. + +The code quality has also improved with 92% code coverage in tests that +is now printed to the log in the Travis builds. The documentation has +also been greatly improved with new modules and examples/notebooks. + +This new release is so full of new stuff and corrections thanks to the +old and new POT contributors (you can see the list in the +`readme `__). + +Features +^^^^^^^^ + +- Add non regularized Gromov-Wasserstein solver (PR #41) +- Linear OT mapping between empirical distributions and 90% test + coverage (PR #42) +- Add log parameter in class EMDTransport and SinkhornLpL1Transport (PR + #44) +- Add Markdown format for Pipy (PR #45) +- Test for Python 3.5 and 3.6 on Travis (PR #46) +- Non regularized Wasserstein barycenter with scipy linear solver + and/or cvxopt (PR #47) +- Rename dataset functions to be more sklearn compliant (PR #49) +- Smooth and sparse Optimal transport implementation with entropic and + quadratic regularization (PR #50) +- Stochastic OT in the dual and semi-dual (PR #52 and PR #62) +- Free support barycenters (PR #56) +- Speed-up Sinkhorn function (PR #57 and PR #58) +- Add convolutional Wassersein barycenters for 2D images (PR #64) +- Add Greedy Sinkhorn variant (Greenkhorn) (PR #66) +- Big ot.gpu update with cupy implementation (instead of un-maintained + cudamat) (PR #67) + +Deprecation +^^^^^^^^^^^ + +Deprecated OTDA Classes were removed from ot.da and ot.gpu for version +0.5 (PR #48 and PR #67). The deprecation message has been for a year +here since 0.4 and it is time to pull the plug. + +Closed issues +^^^^^^^^^^^^^ + +- Issue #35 : remove import plot from ot/\ **init**.py (See PR #41) +- Issue #43 : Unusable parameter log for EMDTransport (See PR #44) +- Issue #55 : UnicodeDecodeError: 'ascii' while installing with pip + +0.4 Community edition +--------------------- + +*15 Sep 2017* + +This release contains a lot of contribution from new contributors. + +Features +^^^^^^^^ + +- Automatic notebooks and doc update (PR #27) +- Add gromov Wasserstein solver and Gromov Barycenters (PR #23) +- emd and emd2 can now return dual variables and have max\_iter (PR #29 + and PR #25) +- New domain adaptation classes compatible with scikit-learn (PR #22) +- Proper tests with pytest on travis (PR #19) +- PEP 8 tests (PR #13) + +Closed issues +^^^^^^^^^^^^^ + +- emd convergence problem du to fixed max iterations (#24) +- Semi supervised DA error (#26) + +0.3.1 +----- + +*11 Jul 2017* + +- Correct bug in emd on windows + +0.3 Summer release +------------------ + +*7 Jul 2017* + +- emd\* and sinkhorn\* are now performed in parallel for multiple + target distributions +- emd and sinkhorn are for OT matrix computation +- emd2 and sinkhorn2 are for OT loss computation +- new notebooks for emd computation and Wasserstein Discriminant + Analysis +- relocate notebooks +- update documentation +- clean\_zeros(a,b,M) for removimg zeros in sparse distributions +- GPU implementations for sinkhorn and group lasso regularization + +V0.2 +---- + +*7 Apr 2017* + +- New dimensionality reduction method (WDA) +- Efficient method emd2 returns only tarnsport (in paralell if several + histograms given) + +V0.1.11 New years resolution +---------------------------- + +*5 Jan 2017* + +- Add sphinx gallery for better documentation +- Small efficiency tweak in sinkhorn +- Add simple tic() toc() functions for timing + +V0.1.10 +------- + +*7 Nov 2016* \* numerical stabilization for sinkhorn (log domain and +epsilon scaling) + +V0.1.9 DA classes and mapping +----------------------------- + +*4 Nov 2016* + +- Update classes and examples for domain adaptation +- Joint OT matrix and mapping estimation + +V0.1.7 +------ + +*31 Oct 2016* + +- Original Domain adaptation classes + +PyPI version 0.1.3 +------------------ + +- pipy works + +First pre-release +----------------- + +*28 Oct 2016* + +It provides the following solvers: \* OT solver for the linear program/ +Earth Movers Distance. \* Entropic regularization OT solver with +Sinkhorn Knopp Algorithm. \* Bregman projections for Wasserstein +barycenter [3] and unmixing. \* Optimal transport for domain adaptation +with group lasso regularization \* Conditional gradient and Generalized +conditional gradient for regularized OT. + +Some demonstrations (both in Python and Jupyter Notebook format) are +available in the examples folder. -- cgit v1.2.3 From 53b063ed6b6aa15d6cb103a9304bbd169678b2e9 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 12:39:38 +0200 Subject: better coverage options verbose and log --- ot/bregman.py | 5 ----- test/test_bregman.py | 9 ++++++--- test/test_optim.py | 2 +- test/test_partial.py | 26 +++++++++++++++++++++++++- test/test_stochastic.py | 8 ++++---- test/test_unbalanced.py | 9 ++++++--- 6 files changed, 42 insertions(+), 17 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 543dbaa..b4365d0 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -909,11 +909,6 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, else: alpha, beta = warmstart - def get_K(alpha, beta): - """log space computation""" - return np.exp(-(M - alpha.reshape((dim_a, 1)) - - beta.reshape((1, dim_b))) / reg) - # print(np.min(K)) def get_reg(n): # exponential decreasing return (epsilon0 - reg) * np.exp(-n) + reg diff --git a/test/test_bregman.py b/test/test_bregman.py index ec4388d..6aa4e08 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -57,6 +57,9 @@ def test_sinkhorn_empty(): np.testing.assert_allclose(u, G.sum(1), atol=1e-05) np.testing.assert_allclose(u, G.sum(0), atol=1e-05) + # test empty weights greenkhorn + ot.sinkhorn([], [], M, 1, method='greenkhorn', stopThr=1e-10, log=True) + def test_sinkhorn_variants(): # test sinkhorn @@ -124,7 +127,7 @@ def test_barycenter(method): # wasserstein reg = 1e-2 - bary_wass = ot.bregman.barycenter(A, M, reg, weights, method=method) + bary_wass, log = ot.bregman.barycenter(A, M, reg, weights, method=method, log=True) np.testing.assert_allclose(1, np.sum(bary_wass)) @@ -152,9 +155,9 @@ def test_barycenter_stabilization(): reg = 1e-2 bar_stable = ot.bregman.barycenter(A, M, reg, weights, method="sinkhorn_stabilized", - stopThr=1e-8) + stopThr=1e-8, verbose=True) bar = ot.bregman.barycenter(A, M, reg, weights, method="sinkhorn", - stopThr=1e-8) + stopThr=1e-8, verbose=True) np.testing.assert_allclose(bar, bar_stable) diff --git a/test/test_optim.py b/test/test_optim.py index aade36e..87b0268 100644 --- a/test/test_optim.py +++ b/test/test_optim.py @@ -38,7 +38,7 @@ def test_conditional_gradient(): def test_conditional_gradient2(): - n = 4000 # nb samples + n = 1000 # nb samples mu_s = np.array([0, 0]) cov_s = np.array([[1, 0], [0, 1]]) diff --git a/test/test_partial.py b/test/test_partial.py index 8b1ca89..5960e4e 100755 --- a/test/test_partial.py +++ b/test/test_partial.py @@ -9,6 +9,30 @@ import numpy as np import scipy as sp import ot +def test_partial_wasserstein_lagrange(): + + n_samples = 20 # nb samples (gaussian) + n_noise = 20 # nb of samples (noise) + + mu = np.array([0, 0]) + cov = np.array([[1, 0], [0, 2]]) + + xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) + xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2)) + xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) + xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2)) + + M = ot.dist(xs, xt) + + p = ot.unif(n_samples + n_noise) + q = ot.unif(n_samples + n_noise) + + m = 0.5 + + w0, log0 = ot.partial.partial_wasserstein_lagrange(p, q, M, 1, log=True) + + + def test_partial_wasserstein(): @@ -32,7 +56,7 @@ def test_partial_wasserstein(): w0, log0 = ot.partial.partial_wasserstein(p, q, M, m=m, log=True) w, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=1, m=m, - log=True) + log=True, verbose=True) # check constratints np.testing.assert_equal( diff --git a/test/test_stochastic.py b/test/test_stochastic.py index f0f3fc8..8ddf485 100644 --- a/test/test_stochastic.py +++ b/test/test_stochastic.py @@ -70,8 +70,8 @@ def test_stochastic_asgd(): M = ot.dist(x, x) - G = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "asgd", - numItermax=numItermax) + G, log = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "asgd", + numItermax=numItermax, log=True) # check constratints np.testing.assert_allclose( @@ -145,8 +145,8 @@ def test_stochastic_dual_sgd(): M = ot.dist(x, x) - G = ot.stochastic.solve_dual_entropic(u, u, M, reg, batch_size, - numItermax=numItermax) + G, log = ot.stochastic.solve_dual_entropic(u, u, M, reg, batch_size, + numItermax=numItermax, log=True) # check constratints np.testing.assert_allclose( diff --git a/test/test_unbalanced.py b/test/test_unbalanced.py index ca1efba..d5bae42 100644 --- a/test/test_unbalanced.py +++ b/test/test_unbalanced.py @@ -31,9 +31,11 @@ def test_unbalanced_convergence(method): G, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, reg_m=reg_m, method=method, - log=True) + log=True, + verbose=True) loss = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m, - method=method) + method=method, + verbose=True) # check fixed point equations # in log-domain fi = reg_m / (reg_m + epsilon) @@ -73,7 +75,8 @@ def test_unbalanced_multiple_inputs(method): loss, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, reg_m=reg_m, method=method, - log=True) + log=True, + verbose=True) # check fixed point equations # in log-domain fi = reg_m / (reg_m + epsilon) -- cgit v1.2.3 From c3115bce070cbf567c4dda3dfe87114166595423 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 13:59:19 +0200 Subject: mockj module cupy for gpu focumentation --- docs/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 880c71d..3699156 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -36,9 +36,9 @@ class Mock(MagicMock): return MagicMock() -MOCK_MODULES = ['ot.lp.emd_wrap', 'autograd', 'pymanopt', 'cupy', 'autograd.numpy', 'pymanopt.manifolds', 'pymanopt.solvers'] +MOCK_MODULES = [ 'cupy'] # 'autograd.numpy','pymanopt.manifolds','pymanopt.solvers', -#sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) +sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # !!!! # If extensions (or modules to document with autodoc) are in another directory, -- cgit v1.2.3 From 90bd408e86eccb03b02d57a0cd7963e0c848a1fc Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 13:59:42 +0200 Subject: pep8 --- test/test_partial.py | 5 ++--- test/test_stochastic.py | 4 ++-- test/test_unbalanced.py | 2 +- 3 files changed, 5 insertions(+), 6 deletions(-) diff --git a/test/test_partial.py b/test/test_partial.py index 5960e4e..b533a9c 100755 --- a/test/test_partial.py +++ b/test/test_partial.py @@ -9,6 +9,7 @@ import numpy as np import scipy as sp import ot + def test_partial_wasserstein_lagrange(): n_samples = 20 # nb samples (gaussian) @@ -29,9 +30,7 @@ def test_partial_wasserstein_lagrange(): m = 0.5 - w0, log0 = ot.partial.partial_wasserstein_lagrange(p, q, M, 1, log=True) - - + w0, log0 = ot.partial.partial_wasserstein_lagrange(p, q, M, 1, log=True) def test_partial_wasserstein(): diff --git a/test/test_stochastic.py b/test/test_stochastic.py index 8ddf485..155622c 100644 --- a/test/test_stochastic.py +++ b/test/test_stochastic.py @@ -71,7 +71,7 @@ def test_stochastic_asgd(): M = ot.dist(x, x) G, log = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "asgd", - numItermax=numItermax, log=True) + numItermax=numItermax, log=True) # check constratints np.testing.assert_allclose( @@ -146,7 +146,7 @@ def test_stochastic_dual_sgd(): M = ot.dist(x, x) G, log = ot.stochastic.solve_dual_entropic(u, u, M, reg, batch_size, - numItermax=numItermax, log=True) + numItermax=numItermax, log=True) # check constratints np.testing.assert_allclose( diff --git a/test/test_unbalanced.py b/test/test_unbalanced.py index d5bae42..dfeaad9 100644 --- a/test/test_unbalanced.py +++ b/test/test_unbalanced.py @@ -35,7 +35,7 @@ def test_unbalanced_convergence(method): verbose=True) loss = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m, method=method, - verbose=True) + verbose=True) # check fixed point equations # in log-domain fi = reg_m / (reg_m + epsilon) -- cgit v1.2.3 From 17d388be57cb5b0b2492c6b0ad8940e58b36016a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 14:18:41 +0200 Subject: test raise un partial ot --- ot/datasets.py | 4 ++-- test/test_partial.py | 49 ++++++++++++++++++++++++++++++++++++++++++++++++- 2 files changed, 50 insertions(+), 3 deletions(-) diff --git a/ot/datasets.py b/ot/datasets.py index a1ca7b6..daca1ae 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -147,8 +147,8 @@ def make_data_classif(dataset, n, nz=.5, theta=0, p=.5, random_state=None, **kwa n2 = np.sum(y == 2) x = np.zeros((n, 2)) - x[y == 1, :] = get_2D_samples_gauss(n1, m1, nz, random_state=generator) - x[y == 2, :] = get_2D_samples_gauss(n2, m2, nz, random_state=generator) + x[y == 1, :] = make_2D_samples_gauss(n1, m1, nz, random_state=generator) + x[y == 2, :] = make_2D_samples_gauss(n2, m2, nz, random_state=generator) x = x.dot(rot) diff --git a/test/test_partial.py b/test/test_partial.py index b533a9c..eb3b76e 100755 --- a/test/test_partial.py +++ b/test/test_partial.py @@ -8,6 +8,53 @@ import numpy as np import scipy as sp import ot +import pytest + + +def test_raise_errors(): + + n_samples = 20 # nb samples (gaussian) + n_noise = 20 # nb of samples (noise) + + mu = np.array([0, 0]) + cov = np.array([[1, 0], [0, 2]]) + + xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) + xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2)) + xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) + xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2)) + + M = ot.dist(xs, xt) + + p = ot.unif(n_samples + n_noise) + q = ot.unif(n_samples + n_noise) + + with pytest.raises(ValueError): + ot.partial.partial_wasserstein_lagrange(p + 1, q, M, 1, log=True) + + with pytest.raises(ValueError): + ot.partial.partial_wasserstein(p, q, M, m=2, log=True) + + with pytest.raises(ValueError): + ot.partial.partial_wasserstein(p, q, M, m=-1, log=True) + + with pytest.raises(ValueError): + ot.partial.entropic_partial_wasserstein(p, q, M, reg=1, m=2, log=True) + + with pytest.raises(ValueError): + ot.partial.entropic_partial_wasserstein(p, q, M, reg=1, m=-1, log=True) + + with pytest.raises(ValueError): + ot.partial.partial_gromov_wasserstein(M, M, p, q, m=2, log=True) + + with pytest.raises(ValueError): + ot.partial.partial_gromov_wasserstein(M, M, p, q, m=-1, log=True) + + with pytest.raises(ValueError): + ot.partial.entropic_partial_gromov_wasserstein(M, M, p, q, reg=1, m=2, log=True) + + with pytest.raises(ValueError): + ot.partial.entropic_partial_gromov_wasserstein(M, M, p, q, reg=1, m=-1, log=True) def test_partial_wasserstein_lagrange(): @@ -115,7 +162,7 @@ def test_partial_gromov_wasserstein(): m = 2 / 3 res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C3, p, q, m=m, - log=True) + log=True, verbose=True) np.testing.assert_allclose(res0, 0, atol=1e-1, rtol=1e-1) C1 = sp.spatial.distance.cdist(xs, xs) -- cgit v1.2.3 From eb3a70af671736c940c8aceaff8547b057d1335a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 14:20:33 +0200 Subject: left some unused variable... --- test/test_partial.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/test/test_partial.py b/test/test_partial.py index eb3b76e..510e081 100755 --- a/test/test_partial.py +++ b/test/test_partial.py @@ -75,8 +75,6 @@ def test_partial_wasserstein_lagrange(): p = ot.unif(n_samples + n_noise) q = ot.unif(n_samples + n_noise) - m = 0.5 - w0, log0 = ot.partial.partial_wasserstein_lagrange(p, q, M, 1, log=True) -- cgit v1.2.3 From 6b42088a1e089d488b1e2f4f2b42084255f331cf Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 14:56:25 +0200 Subject: better documentation --- Makefile | 4 +++- docs/source/all.rst | 4 ++-- docs/source/conf.py | 6 +++--- docs/source/releases.rst | 20 ++++++++++---------- 4 files changed, 18 insertions(+), 16 deletions(-) diff --git a/Makefile b/Makefile index 202b209..70cdbdd 100644 --- a/Makefile +++ b/Makefile @@ -62,7 +62,9 @@ rdoc : pandoc --from=markdown --to=rst --output=docs/source/releases.rst RELEASES.md sed -i 's,https://pot.readthedocs.io/en/latest/,,g' docs/source/releases.rst sed -i 's,https://github.com/rflamary/POT/blob/master/notebooks/,auto_examples/,g' docs/source/releases.rst - sed -i 's,.ipynb,.html/,g' docs/source/releases.rst + sed -i 's,.ipynb,.html,g' docs/source/releases.rst + sed -i 's,https://pythonot.github.io/auto_examples/,auto_examples/,g' docs/source/releases.rst + notebook : ipython notebook --matplotlib=inline --notebook-dir=notebooks/ diff --git a/docs/source/all.rst b/docs/source/all.rst index a6d9790..f4ba559 100644 --- a/docs/source/all.rst +++ b/docs/source/all.rst @@ -1,7 +1,7 @@ -Python modules -============== +API and modules +=============== ot -- diff --git a/docs/source/conf.py b/docs/source/conf.py index 3699156..101ccd9 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -88,7 +88,7 @@ master_doc = 'index' # General information about the project. project = u'POT Python Optimal Transport' -copyright = u'2016-2019, Rémi Flamary, Nicolas Courty' +copyright = u'2016-2020, Rémi Flamary, Nicolas Courty' author = u'Rémi Flamary, Nicolas Courty' # The version info for the project you're documenting, acts as replacement for @@ -297,7 +297,7 @@ latex_documents = [ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ - (master_doc, 'pot', u'POT Python Optimal Transport library Documentation', + (master_doc, 'pot', u'POT Python Optimal Transport', [author], 1) ] @@ -312,7 +312,7 @@ man_pages = [ # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'POT', u'POT Python Optimal Transport library Documentation', - author, 'POT', 'Python Optimal Transport librar.', + author, 'POT', 'Python Optimal Transport library', 'Miscellaneous'), ] diff --git a/docs/source/releases.rst b/docs/source/releases.rst index 58bfcfb..6b64066 100644 --- a/docs/source/releases.rst +++ b/docs/source/releases.rst @@ -1,5 +1,5 @@ -POT Releases -============ +Releases +======== 0.6 Year 3 ---------- @@ -28,15 +28,15 @@ divergence `__ corresponding `Wasserstein 1D `__. We now also have implementations for `Unbalanced -OT `__ +OT `__ and a solver for `Unbalanced OT -barycenters `__. +barycenters `__. A new variant of Gromov-Wasserstein divergence called `Fused Gromov-Wasserstein `__ has been also contributed with exemples of use on `structured -data `__ +data `__ and computing `barycenters of labeld -graphs `__. +graphs `__. A lot of work has been done on the documentation with several new examples corresponding to the new features and a lot of corrections for @@ -88,16 +88,16 @@ toolbox as listed below but also several bug correction. | Among the new features, we can highlight a `non-regularized Gromov-Wasserstein - solver `__, + solver `__, a new `greedy variant of sinkhorn `__, | `non-regularized `__, `convolutional - (2D) `__ + (2D) `__ and `free - support `__ + support `__ Wasserstein barycenters and - `smooth `__ + `smooth `__ and `stochastic `__ implementation of entropic OT. -- cgit v1.2.3 From 2b434924dce3fa2985babcf68191ccd85206b379 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 15:21:07 +0200 Subject: test options sphinx gallery --- RELEASES.md | 22 +++++++++++----------- docs/source/conf.py | 8 ++++---- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/RELEASES.md b/RELEASES.md index 70a7a01..5fcf485 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -1,7 +1,9 @@ # Releases -## 0.6 Year 3 + + +## 0.6 *July 2019* This is the first official stable release of POT and this means a jump to 0.6! @@ -69,7 +71,7 @@ bring new features and solvers to the library. - Issue #72 Macosx build problem -## 0.5.0 Year 2 +## 0.5.0 *Sep 2018* POT is 2 years old! This release brings numerous new features to the @@ -127,7 +129,7 @@ Deprecated OTDA Classes were removed from ot.da and ot.gpu for version 0.5 * Issue #55 : UnicodeDecodeError: 'ascii' while installing with pip -## 0.4 Community edition +## 0.4 *15 Sep 2017* This release contains a lot of contribution from new contributors. @@ -152,7 +154,7 @@ This release contains a lot of contribution from new contributors. * Correct bug in emd on windows -## 0.3 Summer release +## 0.3 *7 Jul 2017* * emd* and sinkhorn* are now performed in parallel for multiple target distributions @@ -173,7 +175,7 @@ This release contains a lot of contribution from new contributors. -## V0.1.11 New years resolution +## 0.1.11 *5 Jan 2017* * Add sphinx gallery for better documentation @@ -181,24 +183,22 @@ This release contains a lot of contribution from new contributors. * Add simple tic() toc() functions for timing -## V0.1.10 +## 0.1.10 *7 Nov 2016* * numerical stabilization for sinkhorn (log domain and epsilon scaling) -## V0.1.9 DA classes and mapping +## 0.1.9 *4 Nov 2016* * Update classes and examples for domain adaptation * Joint OT matrix and mapping estimation -## V0.1.7 +## 0.1.7 *31 Oct 2016* * Original Domain adaptation classes - - -## PyPI version 0.1.3 +## 0.1.3 * pipy works diff --git a/docs/source/conf.py b/docs/source/conf.py index 101ccd9..94938cb 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -311,8 +311,8 @@ man_pages = [ # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ - (master_doc, 'POT', u'POT Python Optimal Transport library Documentation', - author, 'POT', 'Python Optimal Transport library', + (master_doc, 'POT', u'POT Python Optimal Transport', + author, 'POT', 'Python Optimal Transport', 'Miscellaneous'), ] @@ -339,7 +339,7 @@ sphinx_gallery_conf = { 'examples_dirs': ['../../examples', '../../examples/da'], 'gallery_dirs': 'auto_examples', 'backreferences_dir': '../modules/generated/', + 'doc_module' : ('ot'), 'reference_url': { - 'numpy': 'http://docs.scipy.org/doc/numpy-1.9.1', - 'scipy': 'http://docs.scipy.org/doc/scipy-0.17.0/reference'} + 'ot': None} } -- cgit v1.2.3 From 0d13d60cc685099d5f149a14dff9e1c8d757b8d1 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 15:33:34 +0200 Subject: Better section name + snhix-gallery tuning --- docs/source/conf.py | 5 +++-- docs/source/releases.rst | 36 ++++++++++++++++++------------------ 2 files changed, 21 insertions(+), 20 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 94938cb..546eb5d 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -333,13 +333,14 @@ texinfo_documents = [ intersphinx_mapping = {'python': ('https://docs.python.org/3', None), 'numpy': ('http://docs.scipy.org/doc/numpy/', None), 'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None), - 'matplotlib': ('http://matplotlib.sourceforge.net/', None)} + 'matplotlib': ('http://matplotlib.org/', None)} sphinx_gallery_conf = { 'examples_dirs': ['../../examples', '../../examples/da'], 'gallery_dirs': 'auto_examples', 'backreferences_dir': '../modules/generated/', - 'doc_module' : ('ot'), + 'inspect_global_variables' : True, + 'doc_module' : ('ot','numpy','scipy','pylab'), 'reference_url': { 'ot': None} } diff --git a/docs/source/releases.rst b/docs/source/releases.rst index 6b64066..075108f 100644 --- a/docs/source/releases.rst +++ b/docs/source/releases.rst @@ -1,8 +1,8 @@ Releases ======== -0.6 Year 3 ----------- +0.6 +--- *July 2019* @@ -78,8 +78,8 @@ Closed issues slightly different - Issue #72 Macosx build problem -0.5.0 Year 2 ------------- +0.5.0 +----- *Sep 2018* @@ -153,8 +153,8 @@ Closed issues - Issue #43 : Unusable parameter log for EMDTransport (See PR #44) - Issue #55 : UnicodeDecodeError: 'ascii' while installing with pip -0.4 Community edition ---------------------- +0.4 +--- *15 Sep 2017* @@ -184,8 +184,8 @@ Closed issues - Correct bug in emd on windows -0.3 Summer release ------------------- +0.3 +--- *7 Jul 2017* @@ -209,8 +209,8 @@ V0.2 - Efficient method emd2 returns only tarnsport (in paralell if several histograms given) -V0.1.11 New years resolution ----------------------------- +0.1.11 +------ *5 Jan 2017* @@ -218,29 +218,29 @@ V0.1.11 New years resolution - Small efficiency tweak in sinkhorn - Add simple tic() toc() functions for timing -V0.1.10 -------- +0.1.10 +------ *7 Nov 2016* \* numerical stabilization for sinkhorn (log domain and epsilon scaling) -V0.1.9 DA classes and mapping ------------------------------ +0.1.9 +----- *4 Nov 2016* - Update classes and examples for domain adaptation - Joint OT matrix and mapping estimation -V0.1.7 ------- +0.1.7 +----- *31 Oct 2016* - Original Domain adaptation classes -PyPI version 0.1.3 ------------------- +0.1.3 +----- - pipy works -- cgit v1.2.3 From 99e6d91dcb51f05f1048c1c6ce68c1d62c5fa88f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 15:33:45 +0200 Subject: Better section name + snhix-gallery tuning --- examples/README.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/README.txt b/examples/README.txt index b08d3f1..7f5be39 100644 --- a/examples/README.txt +++ b/examples/README.txt @@ -1,4 +1,4 @@ -POT Examples -============ +Examples gallery +================ This is a gallery of all the POT example files. -- cgit v1.2.3 From fe704c348e2f1fc69c0ecd801e8fefd6888a215d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 15:51:20 +0200 Subject: test autosummary --- docs/source/all.rst | 5 +++++ docs/source/conf.py | 1 + 2 files changed, 6 insertions(+) diff --git a/docs/source/all.rst b/docs/source/all.rst index f4ba559..fb6f218 100644 --- a/docs/source/all.rst +++ b/docs/source/all.rst @@ -1,4 +1,5 @@ +.. _sphx_glr_api_reference: API and modules =============== @@ -92,3 +93,7 @@ ot.partial .. automodule:: ot.partial :members: + +.. autosummary:: + :toctree: gen_modules/ + :template: module.rst \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index 546eb5d..c693a02 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -59,6 +59,7 @@ sys.path.insert(0, os.path.abspath("../..")) # ones. extensions = [ 'sphinx.ext.autodoc', + 'sphinx.ext.autosummary', 'sphinx.ext.doctest', 'sphinx.ext.intersphinx', 'sphinx.ext.todo', -- cgit v1.2.3 From 1a3249dd6d6971de23534b797ce81fd670befa2b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 16:20:37 +0200 Subject: awesome mini gallery --- docs/source/all.rst | 111 ++++++++++++---------------------------------------- docs/source/conf.py | 3 ++ 2 files changed, 27 insertions(+), 87 deletions(-) diff --git a/docs/source/all.rst b/docs/source/all.rst index fb6f218..d7b878f 100644 --- a/docs/source/all.rst +++ b/docs/source/all.rst @@ -4,96 +4,33 @@ API and modules =============== -ot --- +.. currentmodule:: ot -.. automodule:: ot - :members: - -ot.lp ------ -.. automodule:: ot.lp - :members: - -ot.bregman ----------- - -.. automodule:: ot.bregman - :members: - -ot.smooth ------ -.. automodule:: ot.smooth - :members: - -ot.gromov ----------- - -.. automodule:: ot.gromov - :members: - - -ot.optim --------- - -.. automodule:: ot.optim - :members: - -ot.da --------- - -.. automodule:: ot.da - :members: - -ot.gpu --------- - -.. automodule:: ot.gpu - :members: -ot.dr --------- +:py:mod:`ot`: -.. automodule:: ot.dr - :members: - - -ot.utils --------- - -.. automodule:: ot.utils - :members: - -ot.datasets ------------ - -.. automodule:: ot.datasets - :members: - -ot.plot -------- - -.. automodule:: ot.plot - :members: +.. autosummary:: + :toctree: gen_modules/ + :template: module.rst + + lp + bregman + smooth + gromov + optim + da + gpu + dr + utils + datasets + plot + stochastic + unbalanced + partial -ot.stochastic -------------- +.. autosummary:: + :toctree: ../modules/generated/ + :template: module.rst -.. automodule:: ot.stochastic +.. automodule:: ot :members: - -ot.unbalanced -------------- - -.. automodule:: ot.unbalanced - :members: - -ot.partial -------------- - -.. automodule:: ot.partial - :members: - -.. autosummary:: - :toctree: gen_modules/ - :template: module.rst \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index c693a02..1a64d93 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -71,6 +71,9 @@ extensions = [ 'sphinx_gallery.gen_gallery', ] +autosummary_generate = True + + napoleon_numpy_docstring = True # Add any paths that contain templates here, relative to this directory. -- cgit v1.2.3 From b9ad0ee5a7099050d152df17c7659a3dde31123d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 16:24:59 +0200 Subject: add template --- docs/source/_templates/module.rst | 57 +++++++++++++++++++++++++++++++++++++++ 1 file changed, 57 insertions(+) create mode 100644 docs/source/_templates/module.rst diff --git a/docs/source/_templates/module.rst b/docs/source/_templates/module.rst new file mode 100644 index 0000000..5ad89be --- /dev/null +++ b/docs/source/_templates/module.rst @@ -0,0 +1,57 @@ +{{ fullname }} +{{ underline }} + +.. automodule:: {{ fullname }} + + {% block functions %} + {% if functions %} + + Functions + --------- + + {% for item in functions %} + + .. autofunction:: {{ item }} + + .. include:: backreferences/{{fullname}}.{{item}}.examples + + .. raw:: html + +
+ + {%- endfor %} + {% endif %} + {% endblock %} + + {% block classes %} + {% if classes %} + + Classes + ------- + + {% for item in classes %} + .. autoclass:: {{ item }} + :members: + + .. include:: backreferences/{{fullname}}.{{item}}.examples + + .. raw:: html + +
+ + {%- endfor %} + {% endif %} + {% endblock %} + + {% block exceptions %} + {% if exceptions %} + + Exceptions + ---------- + + .. autosummary:: + {% for item in exceptions %} + {{ item }} + {%- endfor %} + {% endif %} + {% endblock %} \ No newline at end of file -- cgit v1.2.3 From ad7aa892b47f039366a30103c1cede804811fb46 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 16:39:29 +0200 Subject: better doc per module --- ot/__init__.py | 30 ------------------------------ ot/bregman.py | 2 +- ot/datasets.py | 2 +- ot/dr.py | 4 ++-- ot/gpu/__init__.py | 5 +++-- ot/gromov.py | 2 +- ot/optim.py | 2 +- ot/partial.py | 2 +- ot/smooth.py | 4 +++- ot/unbalanced.py | 2 +- 10 files changed, 14 insertions(+), 41 deletions(-) diff --git a/ot/__init__.py b/ot/__init__.py index 1e57b78..2d23610 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -1,35 +1,5 @@ """ -This is the main module of the POT toolbox. It provides easy access to -a number of sub-modules and functions described below. - -.. note:: - - - Here is a list of the submodules and short description of what they contain. - - - :any:`ot.lp` contains OT solvers for the exact (Linear Program) OT problems. - - :any:`ot.bregman` contains OT solvers for the entropic OT problems using - Bregman projections. - - :any:`ot.lp` contains OT solvers for the exact (Linear Program) OT problems. - - :any:`ot.smooth` contains OT solvers for the regularized (l2 and kl) smooth OT - problems. - - :any:`ot.gromov` contains solvers for Gromov-Wasserstein and Fused Gromov - Wasserstein problems. - - :any:`ot.optim` contains generic solvers OT based optimization problems - - :any:`ot.da` contains classes and function related to Monge mapping - estimation and Domain Adaptation (DA). - - :any:`ot.gpu` contains GPU (cupy) implementation of some OT solvers - - :any:`ot.dr` contains Dimension Reduction (DR) methods such as Wasserstein - Discriminant Analysis. - - :any:`ot.utils` contains utility functions such as distance computation and - timing. - - :any:`ot.datasets` contains toy dataset generation functions. - - :any:`ot.plot` contains visualization functions - - :any:`ot.stochastic` contains stochastic solvers for regularized OT. - - :any:`ot.unbalanced` contains solvers for regularized unbalanced OT. - - :any:`ot.partial` contains solvers for partial OT. - .. warning:: The list of automatically imported sub-modules is as follows: :py:mod:`ot.lp`, :py:mod:`ot.bregman`, :py:mod:`ot.optim` diff --git a/ot/bregman.py b/ot/bregman.py index b4365d0..f1f8437 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- """ -Bregman projections for regularized OT +Bregman projections solvers for entropic regularized OT """ # Author: Remi Flamary diff --git a/ot/datasets.py b/ot/datasets.py index daca1ae..bd39e13 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -1,5 +1,5 @@ """ -Simple example datasets for OT +Simple example datasets """ # Author: Remi Flamary diff --git a/ot/dr.py b/ot/dr.py index 680dabf..11d2e10 100644 --- a/ot/dr.py +++ b/ot/dr.py @@ -1,10 +1,10 @@ # -*- coding: utf-8 -*- """ -Dimension reduction with optimal transport +Dimension reduction with OT .. warning:: - Note that by default the module is not import in :mod:`ot`. In order to + Note that by default the module is not imported in :mod:`ot`. In order to use it you need to explicitely import :mod:`ot.dr` """ diff --git a/ot/gpu/__init__.py b/ot/gpu/__init__.py index 1ab95bb..7478fb9 100644 --- a/ot/gpu/__init__.py +++ b/ot/gpu/__init__.py @@ -1,8 +1,9 @@ # -*- coding: utf-8 -*- """ +GPU implementation for several OT solvers and utility +functions. -This module provides GPU implementation for several OT solvers and utility -functions. The GPU backend in handled by `cupy +The GPU backend in handled by `cupy `_. .. warning:: diff --git a/ot/gromov.py b/ot/gromov.py index 43780a4..a678722 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- """ -Gromov-Wasserstein transport method +Gromov-Wasserstein and Fused-Gromov-Wasserstein solvers """ # Author: Erwan Vautier diff --git a/ot/optim.py b/ot/optim.py index 4012e0d..b9ca891 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- """ -Optimization algorithms for OT +Generic solvers for regularized OT """ # Author: Remi Flamary diff --git a/ot/partial.py b/ot/partial.py index c03ec25..eb707d8 100755 --- a/ot/partial.py +++ b/ot/partial.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- """ -Partial OT +Partial OT solvers """ # Author: Laetitia Chapel diff --git a/ot/smooth.py b/ot/smooth.py index 5a8e4b5..81f6a3e 100644 --- a/ot/smooth.py +++ b/ot/smooth.py @@ -26,7 +26,9 @@ # Remi Flamary """ -Implementation of +Smooth and Sparse Optimal Transport solvers (KL an L2 reg.) + +Implementation of : Smooth and Sparse Optimal Transport. Mathieu Blondel, Vivien Seguy, Antoine Rolet. In Proc. of AISTATS 2018. diff --git a/ot/unbalanced.py b/ot/unbalanced.py index 23f6607..e37f10c 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- """ -Regularized Unbalanced OT +Regularized Unbalanced OT solvers """ # Author: Hicham Janati -- cgit v1.2.3 From 08ec66ede42350dd040b559cca181d2e599c3d2d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 16:41:56 +0200 Subject: pep8 --- ot/datasets.py | 2 +- ot/gromov.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/datasets.py b/ot/datasets.py index bd39e13..b86ef3b 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -1,5 +1,5 @@ """ -Simple example datasets +Simple example datasets """ # Author: Remi Flamary diff --git a/ot/gromov.py b/ot/gromov.py index a678722..4427a96 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- """ -Gromov-Wasserstein and Fused-Gromov-Wasserstein solvers +Gromov-Wasserstein and Fused-Gromov-Wasserstein solvers """ # Author: Erwan Vautier -- cgit v1.2.3 From 576c3d51d689f6ac48f686e8ba001efd20fea422 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 17:07:37 +0200 Subject: better thumbnail image for gallery --- examples/plot_OT_1D.py | 1 + examples/plot_OT_1D_smooth.py | 2 ++ examples/plot_OT_2D_samples.py | 2 ++ examples/plot_OT_L1_vs_L2.py | 2 ++ examples/plot_UOT_barycenter_1D.py | 2 ++ examples/plot_WDA.py | 2 ++ examples/plot_barycenter_1D.py | 2 ++ examples/plot_barycenter_lp_vs_entropic.py | 2 ++ examples/plot_compute_emd.py | 2 ++ examples/plot_fgw.py | 2 ++ examples/plot_optim_OTreg.py | 1 + examples/plot_otda_color_images.py | 2 ++ examples/plot_otda_d2.py | 2 ++ examples/plot_otda_linear_mapping.py | 2 ++ examples/plot_otda_mapping.py | 2 ++ examples/plot_otda_mapping_colors_images.py | 2 ++ examples/plot_otda_semi_supervised.py | 2 ++ examples/plot_partial_wass_and_gromov.py | 2 ++ 18 files changed, 34 insertions(+) diff --git a/examples/plot_OT_1D.py b/examples/plot_OT_1D.py index f33e2a4..15ead96 100644 --- a/examples/plot_OT_1D.py +++ b/examples/plot_OT_1D.py @@ -12,6 +12,7 @@ and their visualization. # Author: Remi Flamary # # License: MIT License +# sphinx_gallery_thumbnail_number = 3 import numpy as np import matplotlib.pylab as pl diff --git a/examples/plot_OT_1D_smooth.py b/examples/plot_OT_1D_smooth.py index b690751..75cd295 100644 --- a/examples/plot_OT_1D_smooth.py +++ b/examples/plot_OT_1D_smooth.py @@ -13,6 +13,8 @@ and their visualization. # # License: MIT License +# sphinx_gallery_thumbnail_number = 6 + import numpy as np import matplotlib.pylab as pl import ot diff --git a/examples/plot_OT_2D_samples.py b/examples/plot_OT_2D_samples.py index 63126ba..1544e82 100644 --- a/examples/plot_OT_2D_samples.py +++ b/examples/plot_OT_2D_samples.py @@ -14,6 +14,8 @@ sum of diracs. The OT matrix is plotted with the samples. # # License: MIT License +# sphinx_gallery_thumbnail_number = 4 + import numpy as np import matplotlib.pylab as pl import ot diff --git a/examples/plot_OT_L1_vs_L2.py b/examples/plot_OT_L1_vs_L2.py index 37b429f..60353ab 100644 --- a/examples/plot_OT_L1_vs_L2.py +++ b/examples/plot_OT_L1_vs_L2.py @@ -16,6 +16,8 @@ https://arxiv.org/pdf/1706.07650.pdf # # License: MIT License +# sphinx_gallery_thumbnail_number = 3 + import numpy as np import matplotlib.pylab as pl import ot diff --git a/examples/plot_UOT_barycenter_1D.py b/examples/plot_UOT_barycenter_1D.py index acb5892..931798b 100644 --- a/examples/plot_UOT_barycenter_1D.py +++ b/examples/plot_UOT_barycenter_1D.py @@ -16,6 +16,8 @@ as proposed in [10] for Unbalanced inputs. # # License: MIT License +# sphinx_gallery_thumbnail_number = 2 + import numpy as np import matplotlib.pylab as pl import ot diff --git a/examples/plot_WDA.py b/examples/plot_WDA.py index 93cc237..5e17433 100644 --- a/examples/plot_WDA.py +++ b/examples/plot_WDA.py @@ -16,6 +16,8 @@ Wasserstein Discriminant Analysis. # # License: MIT License +# sphinx_gallery_thumbnail_number = 2 + import numpy as np import matplotlib.pylab as pl diff --git a/examples/plot_barycenter_1D.py b/examples/plot_barycenter_1D.py index 6864301..63dc460 100644 --- a/examples/plot_barycenter_1D.py +++ b/examples/plot_barycenter_1D.py @@ -18,6 +18,8 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. # # License: MIT License +# sphinx_gallery_thumbnail_number = 4 + import numpy as np import matplotlib.pylab as pl import ot diff --git a/examples/plot_barycenter_lp_vs_entropic.py b/examples/plot_barycenter_lp_vs_entropic.py index d7c72d0..57a6bac 100644 --- a/examples/plot_barycenter_lp_vs_entropic.py +++ b/examples/plot_barycenter_lp_vs_entropic.py @@ -21,6 +21,8 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. # # License: MIT License +# sphinx_gallery_thumbnail_number = 4 + import numpy as np import matplotlib.pylab as pl import ot diff --git a/examples/plot_compute_emd.py b/examples/plot_compute_emd.py index 7ed2b01..3340115 100644 --- a/examples/plot_compute_emd.py +++ b/examples/plot_compute_emd.py @@ -14,6 +14,8 @@ ground metrics and plot their values for diffeent distributions. # # License: MIT License +# sphinx_gallery_thumbnail_number = 3 + import numpy as np import matplotlib.pylab as pl import ot diff --git a/examples/plot_fgw.py b/examples/plot_fgw.py index cfdc33b..73e486e 100644 --- a/examples/plot_fgw.py +++ b/examples/plot_fgw.py @@ -17,6 +17,8 @@ This example illustrates the computation of FGW for 1D measures[18]. # # License: MIT License +# sphinx_gallery_thumbnail_number = 3 + import matplotlib.pyplot as pl import numpy as np import ot diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py index 2c58def..51e2fdc 100644 --- a/examples/plot_optim_OTreg.py +++ b/examples/plot_optim_OTreg.py @@ -24,6 +24,7 @@ arXiv preprint arXiv:1510.06567. """ +# sphinx_gallery_thumbnail_number = 4 import numpy as np import matplotlib.pylab as pl diff --git a/examples/plot_otda_color_images.py b/examples/plot_otda_color_images.py index d9cbd2b..7e0afee 100644 --- a/examples/plot_otda_color_images.py +++ b/examples/plot_otda_color_images.py @@ -17,6 +17,8 @@ SIAM Journal on Imaging Sciences, 7(3), 1853-1882. # # License: MIT License +# sphinx_gallery_thumbnail_number = 2 + import numpy as np import matplotlib.pylab as pl import ot diff --git a/examples/plot_otda_d2.py b/examples/plot_otda_d2.py index cf22c2f..f49a570 100644 --- a/examples/plot_otda_d2.py +++ b/examples/plot_otda_d2.py @@ -18,6 +18,8 @@ of what the transport methods are doing. # # License: MIT License +# sphinx_gallery_thumbnail_number = 2 + import matplotlib.pylab as pl import ot import ot.plot diff --git a/examples/plot_otda_linear_mapping.py b/examples/plot_otda_linear_mapping.py index c65bd4f..36ccb56 100644 --- a/examples/plot_otda_linear_mapping.py +++ b/examples/plot_otda_linear_mapping.py @@ -12,6 +12,8 @@ Linear OT mapping estimation # # License: MIT License +# sphinx_gallery_thumbnail_number = 2 + import numpy as np import pylab as pl import ot diff --git a/examples/plot_otda_mapping.py b/examples/plot_otda_mapping.py index 5880adf..ded2bdf 100644 --- a/examples/plot_otda_mapping.py +++ b/examples/plot_otda_mapping.py @@ -18,6 +18,8 @@ a linear or a kernelized mapping as introduced in [8]. # # License: MIT License +# sphinx_gallery_thumbnail_number = 2 + import numpy as np import matplotlib.pylab as pl import ot diff --git a/examples/plot_otda_mapping_colors_images.py b/examples/plot_otda_mapping_colors_images.py index a0938a0..1276714 100644 --- a/examples/plot_otda_mapping_colors_images.py +++ b/examples/plot_otda_mapping_colors_images.py @@ -19,6 +19,8 @@ discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. # # License: MIT License +# sphinx_gallery_thumbnail_number = 3 + import numpy as np import matplotlib.pylab as pl import ot diff --git a/examples/plot_otda_semi_supervised.py b/examples/plot_otda_semi_supervised.py index 8a67720..478c3b8 100644 --- a/examples/plot_otda_semi_supervised.py +++ b/examples/plot_otda_semi_supervised.py @@ -18,6 +18,8 @@ of what the transport methods are doing. # # License: MIT License +# sphinx_gallery_thumbnail_number = 3 + import matplotlib.pylab as pl import ot diff --git a/examples/plot_partial_wass_and_gromov.py b/examples/plot_partial_wass_and_gromov.py index 9f95a70..0c5cbf9 100755 --- a/examples/plot_partial_wass_and_gromov.py +++ b/examples/plot_partial_wass_and_gromov.py @@ -11,6 +11,8 @@ distance computation in POT. # Author: Laetitia Chapel # License: MIT License +# sphinx_gallery_thumbnail_number = 2 + # necessary for 3d plot even if not used from mpl_toolkits.mplot3d import Axes3D # noqa import scipy as sp -- cgit v1.2.3 From e18e18f8453263fa95c61e666f14c89a1df5efb4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 17:09:36 +0200 Subject: test new foldeer --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 1a64d93..384bf40 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -342,7 +342,7 @@ intersphinx_mapping = {'python': ('https://docs.python.org/3', None), sphinx_gallery_conf = { 'examples_dirs': ['../../examples', '../../examples/da'], 'gallery_dirs': 'auto_examples', - 'backreferences_dir': '../modules/generated/', + 'backreferences_dir': 'gen_modules/backreferences', 'inspect_global_variables' : True, 'doc_module' : ('ot','numpy','scipy','pylab'), 'reference_url': { -- cgit v1.2.3 From a54775103541ea37f54269de1ba1e1396a6d7b30 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 17:32:57 +0200 Subject: exmaples in sections --- examples/README.txt | 4 + examples/barycenters/README.txt | 4 + examples/barycenters/plot_barycenter_1D.py | 162 ++++++++++++ .../barycenters/plot_barycenter_lp_vs_entropic.py | 288 +++++++++++++++++++++ .../barycenters/plot_convolutional_barycenter.py | 92 +++++++ .../barycenters/plot_free_support_barycenter.py | 69 +++++ examples/domain-adaptation/README.txt | 5 + examples/domain-adaptation/plot_otda_classes.py | 149 +++++++++++ .../domain-adaptation/plot_otda_color_images.py | 166 ++++++++++++ examples/domain-adaptation/plot_otda_d2.py | 174 +++++++++++++ examples/domain-adaptation/plot_otda_jcpot.py | 171 ++++++++++++ examples/domain-adaptation/plot_otda_laplacian.py | 127 +++++++++ .../domain-adaptation/plot_otda_linear_mapping.py | 146 +++++++++++ examples/domain-adaptation/plot_otda_mapping.py | 127 +++++++++ .../plot_otda_mapping_colors_images.py | 173 +++++++++++++ .../domain-adaptation/plot_otda_semi_supervised.py | 150 +++++++++++ examples/gromov/README.txt | 4 + examples/gromov/plot_barycenter_fgw.py | 184 +++++++++++++ examples/gromov/plot_fgw.py | 175 +++++++++++++ examples/gromov/plot_gromov.py | 106 ++++++++ examples/gromov/plot_gromov_barycenter.py | 247 ++++++++++++++++++ examples/others/README.txt | 5 + examples/others/plot_WDA.py | 129 +++++++++ examples/plot_UOT_1D.py | 76 ------ examples/plot_UOT_barycenter_1D.py | 166 ------------ examples/plot_WDA.py | 129 --------- examples/plot_barycenter_1D.py | 162 ------------ examples/plot_barycenter_fgw.py | 184 ------------- examples/plot_barycenter_lp_vs_entropic.py | 288 --------------------- examples/plot_convolutional_barycenter.py | 92 ------- examples/plot_fgw.py | 175 ------------- examples/plot_free_support_barycenter.py | 69 ----- examples/plot_gromov.py | 106 -------- examples/plot_gromov_barycenter.py | 247 ------------------ examples/plot_otda_classes.py | 149 ----------- examples/plot_otda_color_images.py | 166 ------------ examples/plot_otda_d2.py | 174 ------------- examples/plot_otda_jcpot.py | 171 ------------ examples/plot_otda_laplacian.py | 127 --------- examples/plot_otda_linear_mapping.py | 146 ----------- examples/plot_otda_mapping.py | 127 --------- examples/plot_otda_mapping_colors_images.py | 173 ------------- examples/plot_otda_semi_supervised.py | 150 ----------- examples/plot_partial_wass_and_gromov.py | 165 ------------ examples/unbalanced-partial/README.txt | 3 + examples/unbalanced-partial/plot_UOT_1D.py | 76 ++++++ .../unbalanced-partial/plot_UOT_barycenter_1D.py | 166 ++++++++++++ .../plot_partial_wass_and_gromov.py | 165 ++++++++++++ 48 files changed, 3267 insertions(+), 3242 deletions(-) create mode 100644 examples/barycenters/README.txt create mode 100644 examples/barycenters/plot_barycenter_1D.py create mode 100644 examples/barycenters/plot_barycenter_lp_vs_entropic.py create mode 100644 examples/barycenters/plot_convolutional_barycenter.py create mode 100644 examples/barycenters/plot_free_support_barycenter.py create mode 100644 examples/domain-adaptation/README.txt create mode 100644 examples/domain-adaptation/plot_otda_classes.py create mode 100644 examples/domain-adaptation/plot_otda_color_images.py create mode 100644 examples/domain-adaptation/plot_otda_d2.py create mode 100644 examples/domain-adaptation/plot_otda_jcpot.py create mode 100644 examples/domain-adaptation/plot_otda_laplacian.py create mode 100644 examples/domain-adaptation/plot_otda_linear_mapping.py create mode 100644 examples/domain-adaptation/plot_otda_mapping.py create mode 100644 examples/domain-adaptation/plot_otda_mapping_colors_images.py create mode 100644 examples/domain-adaptation/plot_otda_semi_supervised.py create mode 100644 examples/gromov/README.txt create mode 100644 examples/gromov/plot_barycenter_fgw.py create mode 100644 examples/gromov/plot_fgw.py create mode 100644 examples/gromov/plot_gromov.py create mode 100755 examples/gromov/plot_gromov_barycenter.py create mode 100644 examples/others/README.txt create mode 100644 examples/others/plot_WDA.py delete mode 100644 examples/plot_UOT_1D.py delete mode 100644 examples/plot_UOT_barycenter_1D.py delete mode 100644 examples/plot_WDA.py delete mode 100644 examples/plot_barycenter_1D.py delete mode 100644 examples/plot_barycenter_fgw.py delete mode 100644 examples/plot_barycenter_lp_vs_entropic.py delete mode 100644 examples/plot_convolutional_barycenter.py delete mode 100644 examples/plot_fgw.py delete mode 100644 examples/plot_free_support_barycenter.py delete mode 100644 examples/plot_gromov.py delete mode 100755 examples/plot_gromov_barycenter.py delete mode 100644 examples/plot_otda_classes.py delete mode 100644 examples/plot_otda_color_images.py delete mode 100644 examples/plot_otda_d2.py delete mode 100644 examples/plot_otda_jcpot.py delete mode 100644 examples/plot_otda_laplacian.py delete mode 100644 examples/plot_otda_linear_mapping.py delete mode 100644 examples/plot_otda_mapping.py delete mode 100644 examples/plot_otda_mapping_colors_images.py delete mode 100644 examples/plot_otda_semi_supervised.py delete mode 100755 examples/plot_partial_wass_and_gromov.py create mode 100644 examples/unbalanced-partial/README.txt create mode 100644 examples/unbalanced-partial/plot_UOT_1D.py create mode 100644 examples/unbalanced-partial/plot_UOT_barycenter_1D.py create mode 100755 examples/unbalanced-partial/plot_partial_wass_and_gromov.py diff --git a/examples/README.txt b/examples/README.txt index 7f5be39..69a9f84 100644 --- a/examples/README.txt +++ b/examples/README.txt @@ -2,3 +2,7 @@ Examples gallery ================ This is a gallery of all the POT example files. + + +OT and regularized OT +--------------------- \ No newline at end of file diff --git a/examples/barycenters/README.txt b/examples/barycenters/README.txt new file mode 100644 index 0000000..8461f7f --- /dev/null +++ b/examples/barycenters/README.txt @@ -0,0 +1,4 @@ + + +Wasserstein barycenters +----------------------- \ No newline at end of file diff --git a/examples/barycenters/plot_barycenter_1D.py b/examples/barycenters/plot_barycenter_1D.py new file mode 100644 index 0000000..63dc460 --- /dev/null +++ b/examples/barycenters/plot_barycenter_1D.py @@ -0,0 +1,162 @@ +# -*- coding: utf-8 -*- +""" +============================== +1D Wasserstein barycenter demo +============================== + +This example illustrates the computation of regularized Wassersyein Barycenter +as proposed in [3]. + + +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). +Iterative Bregman projections for regularized transportation problems +SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + +""" + +# Author: Remi Flamary +# +# License: MIT License + +# sphinx_gallery_thumbnail_number = 4 + +import numpy as np +import matplotlib.pylab as pl +import ot +# necessary for 3d plot even if not used +from mpl_toolkits.mplot3d import Axes3D # noqa +from matplotlib.collections import PolyCollection + +############################################################################## +# Generate data +# ------------- + +#%% parameters + +n = 100 # nb bins + +# bin positions +x = np.arange(n, dtype=np.float64) + +# Gaussian distributions +a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std +a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) + +# creating matrix A containing all distributions +A = np.vstack((a1, a2)).T +n_distributions = A.shape[1] + +# loss matrix + normalization +M = ot.utils.dist0(n) +M /= M.max() + +############################################################################## +# Plot data +# --------- + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') +pl.tight_layout() + +############################################################################## +# Barycenter computation +# ---------------------- + +#%% barycenter computation + +alpha = 0.2 # 0<=alpha<=1 +weights = np.array([1 - alpha, alpha]) + +# l2bary +bary_l2 = A.dot(weights) + +# wasserstein +reg = 1e-3 +bary_wass = ot.bregman.barycenter(A, M, reg, weights) + +pl.figure(2) +pl.clf() +pl.subplot(2, 1, 1) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') + +pl.subplot(2, 1, 2) +pl.plot(x, bary_l2, 'r', label='l2') +pl.plot(x, bary_wass, 'g', label='Wasserstein') +pl.legend() +pl.title('Barycenters') +pl.tight_layout() + +############################################################################## +# Barycentric interpolation +# ------------------------- + +#%% barycenter interpolation + +n_alpha = 11 +alpha_list = np.linspace(0, 1, n_alpha) + + +B_l2 = np.zeros((n, n_alpha)) + +B_wass = np.copy(B_l2) + +for i in range(0, n_alpha): + alpha = alpha_list[i] + weights = np.array([1 - alpha, alpha]) + B_l2[:, i] = A.dot(weights) + B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights) + +#%% plot interpolation + +pl.figure(3) + +cmap = pl.cm.get_cmap('viridis') +verts = [] +zs = alpha_list +for i, z in enumerate(zs): + ys = B_l2[:, i] + verts.append(list(zip(x, ys))) + +ax = pl.gcf().gca(projection='3d') + +poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list]) +poly.set_alpha(0.7) +ax.add_collection3d(poly, zs=zs, zdir='y') +ax.set_xlabel('x') +ax.set_xlim3d(0, n) +ax.set_ylabel('$\\alpha$') +ax.set_ylim3d(0, 1) +ax.set_zlabel('') +ax.set_zlim3d(0, B_l2.max() * 1.01) +pl.title('Barycenter interpolation with l2') +pl.tight_layout() + +pl.figure(4) +cmap = pl.cm.get_cmap('viridis') +verts = [] +zs = alpha_list +for i, z in enumerate(zs): + ys = B_wass[:, i] + verts.append(list(zip(x, ys))) + +ax = pl.gcf().gca(projection='3d') + +poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list]) +poly.set_alpha(0.7) +ax.add_collection3d(poly, zs=zs, zdir='y') +ax.set_xlabel('x') +ax.set_xlim3d(0, n) +ax.set_ylabel('$\\alpha$') +ax.set_ylim3d(0, 1) +ax.set_zlabel('') +ax.set_zlim3d(0, B_l2.max() * 1.01) +pl.title('Barycenter interpolation with Wasserstein') +pl.tight_layout() + +pl.show() diff --git a/examples/barycenters/plot_barycenter_lp_vs_entropic.py b/examples/barycenters/plot_barycenter_lp_vs_entropic.py new file mode 100644 index 0000000..57a6bac --- /dev/null +++ b/examples/barycenters/plot_barycenter_lp_vs_entropic.py @@ -0,0 +1,288 @@ +# -*- coding: utf-8 -*- +""" +================================================================================= +1D Wasserstein barycenter comparison between exact LP and entropic regularization +================================================================================= + +This example illustrates the computation of regularized Wasserstein Barycenter +as proposed in [3] and exact LP barycenters using standard LP solver. + +It reproduces approximately Figure 3.1 and 3.2 from the following paper: +Cuturi, M., & Peyré, G. (2016). A smoothed dual approach for variational +Wasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343. + +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). +Iterative Bregman projections for regularized transportation problems +SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + +""" + +# Author: Remi Flamary +# +# License: MIT License + +# sphinx_gallery_thumbnail_number = 4 + +import numpy as np +import matplotlib.pylab as pl +import ot +# necessary for 3d plot even if not used +from mpl_toolkits.mplot3d import Axes3D # noqa +from matplotlib.collections import PolyCollection # noqa + +#import ot.lp.cvx as cvx + +############################################################################## +# Gaussian Data +# ------------- + +#%% parameters + +problems = [] + +n = 100 # nb bins + +# bin positions +x = np.arange(n, dtype=np.float64) + +# Gaussian distributions +# Gaussian distributions +a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std +a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) + +# creating matrix A containing all distributions +A = np.vstack((a1, a2)).T +n_distributions = A.shape[1] + +# loss matrix + normalization +M = ot.utils.dist0(n) +M /= M.max() + + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') +pl.tight_layout() + +#%% barycenter computation + +alpha = 0.5 # 0<=alpha<=1 +weights = np.array([1 - alpha, alpha]) + +# l2bary +bary_l2 = A.dot(weights) + +# wasserstein +reg = 1e-3 +ot.tic() +bary_wass = ot.bregman.barycenter(A, M, reg, weights) +ot.toc() + + +ot.tic() +bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) +ot.toc() + +pl.figure(2) +pl.clf() +pl.subplot(2, 1, 1) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') + +pl.subplot(2, 1, 2) +pl.plot(x, bary_l2, 'r', label='l2') +pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') +pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') +pl.legend() +pl.title('Barycenters') +pl.tight_layout() + +problems.append([A, [bary_l2, bary_wass, bary_wass2]]) + +############################################################################## +# Stair Data +# ---------- + +#%% parameters + +a1 = 1.0 * (x > 10) * (x < 50) +a2 = 1.0 * (x > 60) * (x < 80) + +a1 /= a1.sum() +a2 /= a2.sum() + +# creating matrix A containing all distributions +A = np.vstack((a1, a2)).T +n_distributions = A.shape[1] + +# loss matrix + normalization +M = ot.utils.dist0(n) +M /= M.max() + + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') +pl.tight_layout() + + +#%% barycenter computation + +alpha = 0.5 # 0<=alpha<=1 +weights = np.array([1 - alpha, alpha]) + +# l2bary +bary_l2 = A.dot(weights) + +# wasserstein +reg = 1e-3 +ot.tic() +bary_wass = ot.bregman.barycenter(A, M, reg, weights) +ot.toc() + + +ot.tic() +bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) +ot.toc() + + +problems.append([A, [bary_l2, bary_wass, bary_wass2]]) + +pl.figure(2) +pl.clf() +pl.subplot(2, 1, 1) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') + +pl.subplot(2, 1, 2) +pl.plot(x, bary_l2, 'r', label='l2') +pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') +pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') +pl.legend() +pl.title('Barycenters') +pl.tight_layout() + + +############################################################################## +# Dirac Data +# ---------- + +#%% parameters + +a1 = np.zeros(n) +a2 = np.zeros(n) + +a1[10] = .25 +a1[20] = .5 +a1[30] = .25 +a2[80] = 1 + + +a1 /= a1.sum() +a2 /= a2.sum() + +# creating matrix A containing all distributions +A = np.vstack((a1, a2)).T +n_distributions = A.shape[1] + +# loss matrix + normalization +M = ot.utils.dist0(n) +M /= M.max() + + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') +pl.tight_layout() + + +#%% barycenter computation + +alpha = 0.5 # 0<=alpha<=1 +weights = np.array([1 - alpha, alpha]) + +# l2bary +bary_l2 = A.dot(weights) + +# wasserstein +reg = 1e-3 +ot.tic() +bary_wass = ot.bregman.barycenter(A, M, reg, weights) +ot.toc() + + +ot.tic() +bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) +ot.toc() + + +problems.append([A, [bary_l2, bary_wass, bary_wass2]]) + +pl.figure(2) +pl.clf() +pl.subplot(2, 1, 1) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') + +pl.subplot(2, 1, 2) +pl.plot(x, bary_l2, 'r', label='l2') +pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') +pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') +pl.legend() +pl.title('Barycenters') +pl.tight_layout() + + +############################################################################## +# Final figure +# ------------ +# + +#%% plot + +nbm = len(problems) +nbm2 = (nbm // 2) + + +pl.figure(2, (20, 6)) +pl.clf() + +for i in range(nbm): + + A = problems[i][0] + bary_l2 = problems[i][1][0] + bary_wass = problems[i][1][1] + bary_wass2 = problems[i][1][2] + + pl.subplot(2, nbm, 1 + i) + for j in range(n_distributions): + pl.plot(x, A[:, j]) + if i == nbm2: + pl.title('Distributions') + pl.xticks(()) + pl.yticks(()) + + pl.subplot(2, nbm, 1 + i + nbm) + + pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)') + pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') + pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') + if i == nbm - 1: + pl.legend() + if i == nbm2: + pl.title('Barycenters') + + pl.xticks(()) + pl.yticks(()) diff --git a/examples/barycenters/plot_convolutional_barycenter.py b/examples/barycenters/plot_convolutional_barycenter.py new file mode 100644 index 0000000..e74db04 --- /dev/null +++ b/examples/barycenters/plot_convolutional_barycenter.py @@ -0,0 +1,92 @@ + +#%% +# -*- coding: utf-8 -*- +""" +============================================ +Convolutional Wasserstein Barycenter example +============================================ + +This example is designed to illustrate how the Convolutional Wasserstein Barycenter +function of POT works. +""" + +# Author: Nicolas Courty +# +# License: MIT License + + +import numpy as np +import pylab as pl +import ot + +############################################################################## +# Data preparation +# ---------------- +# +# The four distributions are constructed from 4 simple images + + +f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2] +f2 = 1 - pl.imread('../data/duck.png')[:, :, 2] +f3 = 1 - pl.imread('../data/heart.png')[:, :, 2] +f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2] + +A = [] +f1 = f1 / np.sum(f1) +f2 = f2 / np.sum(f2) +f3 = f3 / np.sum(f3) +f4 = f4 / np.sum(f4) +A.append(f1) +A.append(f2) +A.append(f3) +A.append(f4) +A = np.array(A) + +nb_images = 5 + +# those are the four corners coordinates that will be interpolated by bilinear +# interpolation +v1 = np.array((1, 0, 0, 0)) +v2 = np.array((0, 1, 0, 0)) +v3 = np.array((0, 0, 1, 0)) +v4 = np.array((0, 0, 0, 1)) + + +############################################################################## +# Barycenter computation and visualization +# ---------------------------------------- +# + +pl.figure(figsize=(10, 10)) +pl.title('Convolutional Wasserstein Barycenters in POT') +cm = 'Blues' +# regularization parameter +reg = 0.004 +for i in range(nb_images): + for j in range(nb_images): + pl.subplot(nb_images, nb_images, i * nb_images + j + 1) + tx = float(i) / (nb_images - 1) + ty = float(j) / (nb_images - 1) + + # weights are constructed by bilinear interpolation + tmp1 = (1 - tx) * v1 + tx * v2 + tmp2 = (1 - tx) * v3 + tx * v4 + weights = (1 - ty) * tmp1 + ty * tmp2 + + if i == 0 and j == 0: + pl.imshow(f1, cmap=cm) + pl.axis('off') + elif i == 0 and j == (nb_images - 1): + pl.imshow(f3, cmap=cm) + pl.axis('off') + elif i == (nb_images - 1) and j == 0: + pl.imshow(f2, cmap=cm) + pl.axis('off') + elif i == (nb_images - 1) and j == (nb_images - 1): + pl.imshow(f4, cmap=cm) + pl.axis('off') + else: + # call to barycenter computation + pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm) + pl.axis('off') +pl.show() diff --git a/examples/barycenters/plot_free_support_barycenter.py b/examples/barycenters/plot_free_support_barycenter.py new file mode 100644 index 0000000..64b89e4 --- /dev/null +++ b/examples/barycenters/plot_free_support_barycenter.py @@ -0,0 +1,69 @@ +# -*- coding: utf-8 -*- +""" +==================================================== +2D free support Wasserstein barycenters of distributions +==================================================== + +Illustration of 2D Wasserstein barycenters if discributions that are weighted +sum of diracs. + +""" + +# Author: Vivien Seguy +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot + + +############################################################################## +# Generate data +# ------------- +#%% parameters and data generation +N = 3 +d = 2 +measures_locations = [] +measures_weights = [] + +for i in range(N): + + n_i = np.random.randint(low=1, high=20) # nb samples + + mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean + + A_i = np.random.rand(d, d) + cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix + + x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations + b_i = np.random.uniform(0., 1., (n_i,)) + b_i = b_i / np.sum(b_i) # Dirac weights + + measures_locations.append(x_i) + measures_weights.append(b_i) + + +############################################################################## +# Compute free support barycenter +# ------------- + +k = 10 # number of Diracs of the barycenter +X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations +b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized) + +X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b) + + +############################################################################## +# Plot data +# --------- + +pl.figure(1) +for (x_i, b_i) in zip(measures_locations, measures_weights): + color = np.random.randint(low=1, high=10 * N) + pl.scatter(x_i[:, 0], x_i[:, 1], s=b_i * 1000, label='input measure') +pl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter') +pl.title('Data measures and their barycenter') +pl.legend(loc=0) +pl.show() diff --git a/examples/domain-adaptation/README.txt b/examples/domain-adaptation/README.txt new file mode 100644 index 0000000..81dd8d2 --- /dev/null +++ b/examples/domain-adaptation/README.txt @@ -0,0 +1,5 @@ + + + +Domain adaptation examples +-------------------------- \ No newline at end of file diff --git a/examples/domain-adaptation/plot_otda_classes.py b/examples/domain-adaptation/plot_otda_classes.py new file mode 100644 index 0000000..f028022 --- /dev/null +++ b/examples/domain-adaptation/plot_otda_classes.py @@ -0,0 +1,149 @@ +# -*- coding: utf-8 -*- +""" +======================== +OT for domain adaptation +======================== + +This example introduces a domain adaptation in a 2D setting and the 4 OTDA +approaches currently supported in POT. + +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +import matplotlib.pylab as pl +import ot + +############################################################################## +# Generate data +# ------------- + +n_source_samples = 150 +n_target_samples = 150 + +Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) + + +############################################################################## +# Instantiate the different transport algorithms and fit them +# ----------------------------------------------------------- + +# EMD Transport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport with Group lasso regularization +ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) +ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) + +# Sinkhorn Transport with Group lasso regularization l1l2 +ot_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20, + verbose=True) +ot_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt) + +# transport source samples onto target samples +transp_Xs_emd = ot_emd.transform(Xs=Xs) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) +transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) +transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs) + + +############################################################################## +# Fig 1 : plots source and target samples +# --------------------------------------- + +pl.figure(1, figsize=(10, 5)) +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Source samples') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Target samples') +pl.tight_layout() + + +############################################################################## +# Fig 2 : plot optimal couplings and transported samples +# ------------------------------------------------------ + +param_img = {'interpolation': 'nearest'} + +pl.figure(2, figsize=(15, 8)) +pl.subplot(2, 4, 1) +pl.imshow(ot_emd.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nEMDTransport') + +pl.subplot(2, 4, 2) +pl.imshow(ot_sinkhorn.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornTransport') + +pl.subplot(2, 4, 3) +pl.imshow(ot_lpl1.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornLpl1Transport') + +pl.subplot(2, 4, 4) +pl.imshow(ot_l1l2.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornL1l2Transport') + +pl.subplot(2, 4, 5) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nEmdTransport') +pl.legend(loc="lower left") + +pl.subplot(2, 4, 6) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornTransport') + +pl.subplot(2, 4, 7) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornLpl1Transport') + +pl.subplot(2, 4, 8) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornL1l2Transport') +pl.tight_layout() + +pl.show() diff --git a/examples/domain-adaptation/plot_otda_color_images.py b/examples/domain-adaptation/plot_otda_color_images.py new file mode 100644 index 0000000..7e0afee --- /dev/null +++ b/examples/domain-adaptation/plot_otda_color_images.py @@ -0,0 +1,166 @@ +# -*- coding: utf-8 -*- +""" +============================= +OT for image color adaptation +============================= + +This example presents a way of transferring colors between two images +with Optimal Transport as introduced in [6] + +[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). +Regularized discrete optimal transport. +SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +# sphinx_gallery_thumbnail_number = 2 + +import numpy as np +import matplotlib.pylab as pl +import ot + + +r = np.random.RandomState(42) + + +def im2mat(I): + """Converts an image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + +def mat2im(X, shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + +def minmax(I): + return np.clip(I, 0, 1) + + +############################################################################## +# Generate data +# ------------- + +# Loading images +I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 + +X1 = im2mat(I1) +X2 = im2mat(I2) + +# training samples +nb = 1000 +idx1 = r.randint(X1.shape[0], size=(nb,)) +idx2 = r.randint(X2.shape[0], size=(nb,)) + +Xs = X1[idx1, :] +Xt = X2[idx2, :] + + +############################################################################## +# Plot original image +# ------------------- + +pl.figure(1, figsize=(6.4, 3)) + +pl.subplot(1, 2, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.imshow(I2) +pl.axis('off') +pl.title('Image 2') + + +############################################################################## +# Scatter plot of colors +# ---------------------- + +pl.figure(2, figsize=(6.4, 3)) + +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 2') +pl.tight_layout() + + +############################################################################## +# Instantiate the different transport algorithms and fit them +# ----------------------------------------------------------- + +# EMDTransport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# SinkhornTransport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# prediction between images (using out of sample prediction as in [6]) +transp_Xs_emd = ot_emd.transform(Xs=X1) +transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) + +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) +transp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2) + +I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) +I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) + +I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) +I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) + + +############################################################################## +# Plot new images +# --------------- + +pl.figure(3, figsize=(8, 4)) + +pl.subplot(2, 3, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Image 1') + +pl.subplot(2, 3, 2) +pl.imshow(I1t) +pl.axis('off') +pl.title('Image 1 Adapt') + +pl.subplot(2, 3, 3) +pl.imshow(I1te) +pl.axis('off') +pl.title('Image 1 Adapt (reg)') + +pl.subplot(2, 3, 4) +pl.imshow(I2) +pl.axis('off') +pl.title('Image 2') + +pl.subplot(2, 3, 5) +pl.imshow(I2t) +pl.axis('off') +pl.title('Image 2 Adapt') + +pl.subplot(2, 3, 6) +pl.imshow(I2te) +pl.axis('off') +pl.title('Image 2 Adapt (reg)') +pl.tight_layout() + +pl.show() diff --git a/examples/domain-adaptation/plot_otda_d2.py b/examples/domain-adaptation/plot_otda_d2.py new file mode 100644 index 0000000..f49a570 --- /dev/null +++ b/examples/domain-adaptation/plot_otda_d2.py @@ -0,0 +1,174 @@ +# -*- coding: utf-8 -*- +""" +=================================================== +OT for domain adaptation on empirical distributions +=================================================== + +This example introduces a domain adaptation in a 2D setting. It explicits +the problem of domain adaptation and introduces some optimal transport +approaches to solve it. + +Quantities such as optimal couplings, greater coupling coefficients and +transported samples are represented in order to give a visual understanding +of what the transport methods are doing. +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +# sphinx_gallery_thumbnail_number = 2 + +import matplotlib.pylab as pl +import ot +import ot.plot + +############################################################################## +# generate data +# ------------- + +n_samples_source = 150 +n_samples_target = 150 + +Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) + +# Cost matrix +M = ot.dist(Xs, Xt, metric='sqeuclidean') + + +############################################################################## +# Instantiate the different transport algorithms and fit them +# ----------------------------------------------------------- + +# EMD Transport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport with Group lasso regularization +ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) +ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) + +# transport source samples onto target samples +transp_Xs_emd = ot_emd.transform(Xs=Xs) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) +transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) + + +############################################################################## +# Fig 1 : plots source and target samples + matrix of pairwise distance +# --------------------------------------------------------------------- + +pl.figure(1, figsize=(10, 10)) +pl.subplot(2, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Source samples') + +pl.subplot(2, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Target samples') + +pl.subplot(2, 2, 3) +pl.imshow(M, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Matrix of pairwise distances') +pl.tight_layout() + + +############################################################################## +# Fig 2 : plots optimal couplings for the different methods +# --------------------------------------------------------- +pl.figure(2, figsize=(10, 6)) + +pl.subplot(2, 3, 1) +pl.imshow(ot_emd.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nEMDTransport') + +pl.subplot(2, 3, 2) +pl.imshow(ot_sinkhorn.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornTransport') + +pl.subplot(2, 3, 3) +pl.imshow(ot_lpl1.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornLpl1Transport') + +pl.subplot(2, 3, 4) +ot.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1]) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.title('Main coupling coefficients\nEMDTransport') + +pl.subplot(2, 3, 5) +ot.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1]) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.title('Main coupling coefficients\nSinkhornTransport') + +pl.subplot(2, 3, 6) +ot.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1]) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.title('Main coupling coefficients\nSinkhornLpl1Transport') +pl.tight_layout() + + +############################################################################## +# Fig 3 : plot transported samples +# -------------------------------- + +# display transported samples +pl.figure(4, figsize=(10, 4)) +pl.subplot(1, 3, 1) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nEmdTransport') +pl.legend(loc=0) +pl.xticks([]) +pl.yticks([]) + +pl.subplot(1, 3, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nSinkhornTransport') +pl.xticks([]) +pl.yticks([]) + +pl.subplot(1, 3, 3) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nSinkhornLpl1Transport') +pl.xticks([]) +pl.yticks([]) + +pl.tight_layout() +pl.show() diff --git a/examples/domain-adaptation/plot_otda_jcpot.py b/examples/domain-adaptation/plot_otda_jcpot.py new file mode 100644 index 0000000..c495690 --- /dev/null +++ b/examples/domain-adaptation/plot_otda_jcpot.py @@ -0,0 +1,171 @@ +# -*- coding: utf-8 -*- +""" +======================== +OT for multi-source target shift +======================== + +This example introduces a target shift problem with two 2D source and 1 target domain. + +""" + +# Authors: Remi Flamary +# Ievgen Redko +# +# License: MIT License + +import pylab as pl +import numpy as np +import ot +from ot.datasets import make_data_classif + +############################################################################## +# Generate data +# ------------- +n = 50 +sigma = 0.3 +np.random.seed(1985) + +p1 = .2 +dec1 = [0, 2] + +p2 = .9 +dec2 = [0, -2] + +pt = .4 +dect = [4, 0] + +xs1, ys1 = make_data_classif('2gauss_prop', n, nz=sigma, p=p1, bias=dec1) +xs2, ys2 = make_data_classif('2gauss_prop', n + 1, nz=sigma, p=p2, bias=dec2) +xt, yt = make_data_classif('2gauss_prop', n, nz=sigma, p=pt, bias=dect) + +all_Xr = [xs1, xs2] +all_Yr = [ys1, ys2] +# %% + +da = 1.5 + + +def plot_ax(dec, name): + pl.plot([dec[0], dec[0]], [dec[1] - da, dec[1] + da], 'k', alpha=0.5) + pl.plot([dec[0] - da, dec[0] + da], [dec[1], dec[1]], 'k', alpha=0.5) + pl.text(dec[0] - .5, dec[1] + 2, name) + + +############################################################################## +# Fig 1 : plots source and target samples +# --------------------------------------- + +pl.figure(1) +pl.clf() +plot_ax(dec1, 'Source 1') +plot_ax(dec2, 'Source 2') +plot_ax(dect, 'Target') +pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9, + label='Source 1 ({:1.2f}, {:1.2f})'.format(1 - p1, p1)) +pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9, + label='Source 2 ({:1.2f}, {:1.2f})'.format(1 - p2, p2)) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9, + label='Target ({:1.2f}, {:1.2f})'.format(1 - pt, pt)) +pl.title('Data') + +pl.legend() +pl.axis('equal') +pl.axis('off') + +############################################################################## +# Instantiate Sinkhorn transport algorithm and fit them for all source domains +# ---------------------------------------------------------------------------- +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1, metric='sqeuclidean') + + +def print_G(G, xs, ys, xt): + for i in range(G.shape[0]): + for j in range(G.shape[1]): + if G[i, j] > 5e-4: + if ys[i]: + c = 'b' + else: + c = 'r' + pl.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], xt[j, 1]], c, alpha=.2) + + +############################################################################## +# Fig 2 : plot optimal couplings and transported samples +# ------------------------------------------------------ +pl.figure(2) +pl.clf() +plot_ax(dec1, 'Source 1') +plot_ax(dec2, 'Source 2') +plot_ax(dect, 'Target') +print_G(ot_sinkhorn.fit(Xs=xs1, Xt=xt).coupling_, xs1, ys1, xt) +print_G(ot_sinkhorn.fit(Xs=xs2, Xt=xt).coupling_, xs2, ys2, xt) +pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) +pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) + +pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') +pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') + +pl.title('Independent OT') + +pl.legend() +pl.axis('equal') +pl.axis('off') + +############################################################################## +# Instantiate JCPOT adaptation algorithm and fit it +# ---------------------------------------------------------------------------- +otda = ot.da.JCPOTTransport(reg_e=1, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True) +otda.fit(all_Xr, all_Yr, xt) + +ws1 = otda.proportions_.dot(otda.log_['D2'][0]) +ws2 = otda.proportions_.dot(otda.log_['D2'][1]) + +pl.figure(3) +pl.clf() +plot_ax(dec1, 'Source 1') +plot_ax(dec2, 'Source 2') +plot_ax(dect, 'Target') +print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt) +print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt) +pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) +pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) + +pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') +pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') + +pl.title('OT with prop estimation ({:1.3f},{:1.3f})'.format(otda.proportions_[0], otda.proportions_[1])) + +pl.legend() +pl.axis('equal') +pl.axis('off') + +############################################################################## +# Run oracle transport algorithm with known proportions +# ---------------------------------------------------------------------------- +h_res = np.array([1 - pt, pt]) + +ws1 = h_res.dot(otda.log_['D2'][0]) +ws2 = h_res.dot(otda.log_['D2'][1]) + +pl.figure(4) +pl.clf() +plot_ax(dec1, 'Source 1') +plot_ax(dec2, 'Source 2') +plot_ax(dect, 'Target') +print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt) +print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt) +pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) +pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) + +pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') +pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') + +pl.title('OT with known proportion ({:1.1f},{:1.1f})'.format(h_res[0], h_res[1])) + +pl.legend() +pl.axis('equal') +pl.axis('off') +pl.show() diff --git a/examples/domain-adaptation/plot_otda_laplacian.py b/examples/domain-adaptation/plot_otda_laplacian.py new file mode 100644 index 0000000..67c8f67 --- /dev/null +++ b/examples/domain-adaptation/plot_otda_laplacian.py @@ -0,0 +1,127 @@ +# -*- coding: utf-8 -*- +""" +====================================================== +OT with Laplacian regularization for domain adaptation +====================================================== + +This example introduces a domain adaptation in a 2D setting and OTDA +approach with Laplacian regularization. + +""" + +# Authors: Ievgen Redko + +# License: MIT License + +import matplotlib.pylab as pl +import ot + +############################################################################## +# Generate data +# ------------- + +n_source_samples = 150 +n_target_samples = 150 + +Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) + + +############################################################################## +# Instantiate the different transport algorithms and fit them +# ----------------------------------------------------------- + +# EMD Transport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=.01) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# EMD Transport with Laplacian regularization +ot_emd_laplace = ot.da.EMDLaplaceTransport(reg_lap=100, reg_src=1) +ot_emd_laplace.fit(Xs=Xs, Xt=Xt) + +# transport source samples onto target samples +transp_Xs_emd = ot_emd.transform(Xs=Xs) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) +transp_Xs_emd_laplace = ot_emd_laplace.transform(Xs=Xs) + +############################################################################## +# Fig 1 : plots source and target samples +# --------------------------------------- + +pl.figure(1, figsize=(10, 5)) +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Source samples') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Target samples') +pl.tight_layout() + + +############################################################################## +# Fig 2 : plot optimal couplings and transported samples +# ------------------------------------------------------ + +param_img = {'interpolation': 'nearest'} + +pl.figure(2, figsize=(15, 8)) +pl.subplot(2, 3, 1) +pl.imshow(ot_emd.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nEMDTransport') + +pl.figure(2, figsize=(15, 8)) +pl.subplot(2, 3, 2) +pl.imshow(ot_sinkhorn.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornTransport') + +pl.subplot(2, 3, 3) +pl.imshow(ot_emd_laplace.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nEMDLaplaceTransport') + +pl.subplot(2, 3, 4) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nEmdTransport') +pl.legend(loc="lower left") + +pl.subplot(2, 3, 5) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornTransport') + +pl.subplot(2, 3, 6) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nEMDLaplaceTransport') +pl.tight_layout() + +pl.show() diff --git a/examples/domain-adaptation/plot_otda_linear_mapping.py b/examples/domain-adaptation/plot_otda_linear_mapping.py new file mode 100644 index 0000000..36ccb56 --- /dev/null +++ b/examples/domain-adaptation/plot_otda_linear_mapping.py @@ -0,0 +1,146 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +============================ +Linear OT mapping estimation +============================ + + +""" + +# Author: Remi Flamary +# +# License: MIT License + +# sphinx_gallery_thumbnail_number = 2 + +import numpy as np +import pylab as pl +import ot + +############################################################################## +# Generate data +# ------------- + +n = 1000 +d = 2 +sigma = .1 + +# source samples +angles = np.random.rand(n, 1) * 2 * np.pi +xs = np.concatenate((np.sin(angles), np.cos(angles)), + axis=1) + sigma * np.random.randn(n, 2) +xs[:n // 2, 1] += 2 + + +# target samples +anglet = np.random.rand(n, 1) * 2 * np.pi +xt = np.concatenate((np.sin(anglet), np.cos(anglet)), + axis=1) + sigma * np.random.randn(n, 2) +xt[:n // 2, 1] += 2 + + +A = np.array([[1.5, .7], [.7, 1.5]]) +b = np.array([[4, 2]]) +xt = xt.dot(A) + b + +############################################################################## +# Plot data +# --------- + +pl.figure(1, (5, 5)) +pl.plot(xs[:, 0], xs[:, 1], '+') +pl.plot(xt[:, 0], xt[:, 1], 'o') + + +############################################################################## +# Estimate linear mapping and transport +# ------------------------------------- + +Ae, be = ot.da.OT_mapping_linear(xs, xt) + +xst = xs.dot(Ae) + be + + +############################################################################## +# Plot transported samples +# ------------------------ + +pl.figure(1, (5, 5)) +pl.clf() +pl.plot(xs[:, 0], xs[:, 1], '+') +pl.plot(xt[:, 0], xt[:, 1], 'o') +pl.plot(xst[:, 0], xst[:, 1], '+') + +pl.show() + +############################################################################## +# Load image data +# --------------- + + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + +def mat2im(X, shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + +def minmax(I): + return np.clip(I, 0, 1) + + +# Loading images +I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 + + +X1 = im2mat(I1) +X2 = im2mat(I2) + +############################################################################## +# Estimate mapping and adapt +# ---------------------------- + +mapping = ot.da.LinearTransport() + +mapping.fit(Xs=X1, Xt=X2) + + +xst = mapping.transform(Xs=X1) +xts = mapping.inverse_transform(Xt=X2) + +I1t = minmax(mat2im(xst, I1.shape)) +I2t = minmax(mat2im(xts, I2.shape)) + +# %% + + +############################################################################## +# Plot transformed images +# ----------------------- + +pl.figure(2, figsize=(10, 7)) + +pl.subplot(2, 2, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Im. 1') + +pl.subplot(2, 2, 2) +pl.imshow(I2) +pl.axis('off') +pl.title('Im. 2') + +pl.subplot(2, 2, 3) +pl.imshow(I1t) +pl.axis('off') +pl.title('Mapping Im. 1') + +pl.subplot(2, 2, 4) +pl.imshow(I2t) +pl.axis('off') +pl.title('Inverse mapping Im. 2') diff --git a/examples/domain-adaptation/plot_otda_mapping.py b/examples/domain-adaptation/plot_otda_mapping.py new file mode 100644 index 0000000..ded2bdf --- /dev/null +++ b/examples/domain-adaptation/plot_otda_mapping.py @@ -0,0 +1,127 @@ +# -*- coding: utf-8 -*- +""" +=========================================== +OT mapping estimation for domain adaptation +=========================================== + +This example presents how to use MappingTransport to estimate at the same +time both the coupling transport and approximate the transport map with either +a linear or a kernelized mapping as introduced in [8]. + +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, + "Mapping estimation for discrete optimal transport", + Neural Information Processing Systems (NIPS), 2016. +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +# sphinx_gallery_thumbnail_number = 2 + +import numpy as np +import matplotlib.pylab as pl +import ot + + +############################################################################## +# Generate data +# ------------- + +n_source_samples = 100 +n_target_samples = 100 +theta = 2 * np.pi / 20 +noise_level = 0.1 + +Xs, ys = ot.datasets.make_data_classif( + 'gaussrot', n_source_samples, nz=noise_level) +Xs_new, _ = ot.datasets.make_data_classif( + 'gaussrot', n_source_samples, nz=noise_level) +Xt, yt = ot.datasets.make_data_classif( + 'gaussrot', n_target_samples, theta=theta, nz=noise_level) + +# one of the target mode changes its variance (no linear mapping) +Xt[yt == 2] *= 3 +Xt = Xt + 4 + +############################################################################## +# Plot data +# --------- + +pl.figure(1, (10, 5)) +pl.clf() +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.legend(loc=0) +pl.title('Source and target distributions') + + +############################################################################## +# Instantiate the different transport algorithms and fit them +# ----------------------------------------------------------- + +# MappingTransport with linear kernel +ot_mapping_linear = ot.da.MappingTransport( + kernel="linear", mu=1e0, eta=1e-8, bias=True, + max_iter=20, verbose=True) + +ot_mapping_linear.fit(Xs=Xs, Xt=Xt) + +# for original source samples, transform applies barycentric mapping +transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs) + +# for out of source samples, transform applies the linear mapping +transp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new) + + +# MappingTransport with gaussian kernel +ot_mapping_gaussian = ot.da.MappingTransport( + kernel="gaussian", eta=1e-5, mu=1e-1, bias=True, sigma=1, + max_iter=10, verbose=True) +ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) + +# for original source samples, transform applies barycentric mapping +transp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs) + +# for out of source samples, transform applies the gaussian mapping +transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new) + + +############################################################################## +# Plot transported samples +# ------------------------ + +pl.figure(2) +pl.clf() +pl.subplot(2, 2, 1) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+', + label='Mapped source samples') +pl.title("Bary. mapping (linear)") +pl.legend(loc=0) + +pl.subplot(2, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1], + c=ys, marker='+', label='Learned mapping') +pl.title("Estim. mapping (linear)") + +pl.subplot(2, 2, 3) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys, + marker='+', label='barycentric mapping') +pl.title("Bary. mapping (kernel)") + +pl.subplot(2, 2, 4) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys, + marker='+', label='Learned mapping') +pl.title("Estim. mapping (kernel)") +pl.tight_layout() + +pl.show() diff --git a/examples/domain-adaptation/plot_otda_mapping_colors_images.py b/examples/domain-adaptation/plot_otda_mapping_colors_images.py new file mode 100644 index 0000000..1276714 --- /dev/null +++ b/examples/domain-adaptation/plot_otda_mapping_colors_images.py @@ -0,0 +1,173 @@ +# -*- coding: utf-8 -*- +""" +===================================================== +OT for image color adaptation with mapping estimation +===================================================== + +OT for domain adaptation with image color adaptation [6] with mapping +estimation [8]. + +[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized +discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for +discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. + +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +# sphinx_gallery_thumbnail_number = 3 + +import numpy as np +import matplotlib.pylab as pl +import ot + +r = np.random.RandomState(42) + + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + +def mat2im(X, shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + +def minmax(I): + return np.clip(I, 0, 1) + + +############################################################################## +# Generate data +# ------------- + +# Loading images +I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 + + +X1 = im2mat(I1) +X2 = im2mat(I2) + +# training samples +nb = 1000 +idx1 = r.randint(X1.shape[0], size=(nb,)) +idx2 = r.randint(X2.shape[0], size=(nb,)) + +Xs = X1[idx1, :] +Xt = X2[idx2, :] + + +############################################################################## +# Domain adaptation for pixel distribution transfer +# ------------------------------------------------- + +# EMDTransport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) +transp_Xs_emd = ot_emd.transform(Xs=X1) +Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape)) + +# SinkhornTransport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) +Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) + +ot_mapping_linear = ot.da.MappingTransport( + mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True) +ot_mapping_linear.fit(Xs=Xs, Xt=Xt) + +X1tl = ot_mapping_linear.transform(Xs=X1) +Image_mapping_linear = minmax(mat2im(X1tl, I1.shape)) + +ot_mapping_gaussian = ot.da.MappingTransport( + mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True) +ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) + +X1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping +Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape)) + + +############################################################################## +# Plot original images +# -------------------- + +pl.figure(1, figsize=(6.4, 3)) +pl.subplot(1, 2, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.imshow(I2) +pl.axis('off') +pl.title('Image 2') +pl.tight_layout() + + +############################################################################## +# Plot pixel values distribution +# ------------------------------ + +pl.figure(2, figsize=(6.4, 5)) + +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 2') +pl.tight_layout() + + +############################################################################## +# Plot transformed images +# ----------------------- + +pl.figure(2, figsize=(10, 5)) + +pl.subplot(2, 3, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Im. 1') + +pl.subplot(2, 3, 4) +pl.imshow(I2) +pl.axis('off') +pl.title('Im. 2') + +pl.subplot(2, 3, 2) +pl.imshow(Image_emd) +pl.axis('off') +pl.title('EmdTransport') + +pl.subplot(2, 3, 5) +pl.imshow(Image_sinkhorn) +pl.axis('off') +pl.title('SinkhornTransport') + +pl.subplot(2, 3, 3) +pl.imshow(Image_mapping_linear) +pl.axis('off') +pl.title('MappingTransport (linear)') + +pl.subplot(2, 3, 6) +pl.imshow(Image_mapping_gaussian) +pl.axis('off') +pl.title('MappingTransport (gaussian)') +pl.tight_layout() + +pl.show() diff --git a/examples/domain-adaptation/plot_otda_semi_supervised.py b/examples/domain-adaptation/plot_otda_semi_supervised.py new file mode 100644 index 0000000..478c3b8 --- /dev/null +++ b/examples/domain-adaptation/plot_otda_semi_supervised.py @@ -0,0 +1,150 @@ +# -*- coding: utf-8 -*- +""" +============================================ +OTDA unsupervised vs semi-supervised setting +============================================ + +This example introduces a semi supervised domain adaptation in a 2D setting. +It explicits the problem of semi supervised domain adaptation and introduces +some optimal transport approaches to solve it. + +Quantities such as optimal couplings, greater coupling coefficients and +transported samples are represented in order to give a visual understanding +of what the transport methods are doing. +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +# sphinx_gallery_thumbnail_number = 3 + +import matplotlib.pylab as pl +import ot + + +############################################################################## +# Generate data +# ------------- + +n_samples_source = 150 +n_samples_target = 150 + +Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) + + +############################################################################## +# Transport source samples onto target samples +# -------------------------------------------- + + +# unsupervised domain adaptation +ot_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn_un.fit(Xs=Xs, Xt=Xt) +transp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs) + +# semi-supervised domain adaptation +ot_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt) +transp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs) + +# semi supervised DA uses available labaled target samples to modify the cost +# matrix involved in the OT problem. The cost of transporting a source sample +# of class A onto a target sample of class B != A is set to infinite, or a +# very large value + +# note that in the present case we consider that all the target samples are +# labeled. For daily applications, some target sample might not have labels, +# in this case the element of yt corresponding to these samples should be +# filled with -1. + +# Warning: we recall that -1 cannot be used as a class label + + +############################################################################## +# Fig 1 : plots source and target samples + matrix of pairwise distance +# --------------------------------------------------------------------- + +pl.figure(1, figsize=(10, 10)) +pl.subplot(2, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Source samples') + +pl.subplot(2, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Target samples') + +pl.subplot(2, 2, 3) +pl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Cost matrix - unsupervised DA') + +pl.subplot(2, 2, 4) +pl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Cost matrix - semisupervised DA') + +pl.tight_layout() + +# the optimal coupling in the semi-supervised DA case will exhibit " shape +# similar" to the cost matrix, (block diagonal matrix) + + +############################################################################## +# Fig 2 : plots optimal couplings for the different methods +# --------------------------------------------------------- + +pl.figure(2, figsize=(8, 4)) + +pl.subplot(1, 2, 1) +pl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nUnsupervised DA') + +pl.subplot(1, 2, 2) +pl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSemi-supervised DA') + +pl.tight_layout() + + +############################################################################## +# Fig 3 : plot transported samples +# -------------------------------- + +# display transported samples +pl.figure(4, figsize=(8, 4)) +pl.subplot(1, 2, 1) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nEmdTransport') +pl.legend(loc=0) +pl.xticks([]) +pl.yticks([]) + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nSinkhornTransport') +pl.xticks([]) +pl.yticks([]) + +pl.tight_layout() +pl.show() diff --git a/examples/gromov/README.txt b/examples/gromov/README.txt new file mode 100644 index 0000000..9cc9c64 --- /dev/null +++ b/examples/gromov/README.txt @@ -0,0 +1,4 @@ + + +Gromov and Fused-Gromov-Wasserstein +----------------------------------- \ No newline at end of file diff --git a/examples/gromov/plot_barycenter_fgw.py b/examples/gromov/plot_barycenter_fgw.py new file mode 100644 index 0000000..77b0370 --- /dev/null +++ b/examples/gromov/plot_barycenter_fgw.py @@ -0,0 +1,184 @@ +# -*- coding: utf-8 -*- +""" +================================= +Plot graphs' barycenter using FGW +================================= + +This example illustrates the computation barycenter of labeled graphs using FGW + +Requires networkx >=2 + +.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain + and Courty Nicolas + "Optimal Transport for structured data with application on graphs" + International Conference on Machine Learning (ICML). 2019. + +""" + +# Author: Titouan Vayer +# +# License: MIT License + +#%% load libraries +import numpy as np +import matplotlib.pyplot as plt +import networkx as nx +import math +from scipy.sparse.csgraph import shortest_path +import matplotlib.colors as mcol +from matplotlib import cm +from ot.gromov import fgw_barycenters +#%% Graph functions + + +def find_thresh(C, inf=0.5, sup=3, step=10): + """ Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected + Tthe threshold is found by a linesearch between values "inf" and "sup" with "step" thresholds tested. + The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix + and the original matrix. + Parameters + ---------- + C : ndarray, shape (n_nodes,n_nodes) + The structure matrix to threshold + inf : float + The beginning of the linesearch + sup : float + The end of the linesearch + step : integer + Number of thresholds tested + """ + dist = [] + search = np.linspace(inf, sup, step) + for thresh in search: + Cprime = sp_to_adjency(C, 0, thresh) + SC = shortest_path(Cprime, method='D') + SC[SC == float('inf')] = 100 + dist.append(np.linalg.norm(SC - C)) + return search[np.argmin(dist)], dist + + +def sp_to_adjency(C, threshinf=0.2, threshsup=1.8): + """ Thresholds the structure matrix in order to compute an adjency matrix. + All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0 + Parameters + ---------- + C : ndarray, shape (n_nodes,n_nodes) + The structure matrix to threshold + threshinf : float + The minimum value of distance from which the new value is set to 1 + threshsup : float + The maximum value of distance from which the new value is set to 1 + Returns + ------- + C : ndarray, shape (n_nodes,n_nodes) + The threshold matrix. Each element is in {0,1} + """ + H = np.zeros_like(C) + np.fill_diagonal(H, np.diagonal(C)) + C = C - H + C = np.minimum(np.maximum(C, threshinf), threshsup) + C[C == threshsup] = 0 + C[C != 0] = 1 + + return C + + +def build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None): + """ Create a noisy circular graph + """ + g = nx.Graph() + g.add_nodes_from(list(range(N))) + for i in range(N): + noise = float(np.random.normal(mu, sigma, 1)) + if with_noise: + g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise) + else: + g.add_node(i, attr_name=math.sin(2 * i * math.pi / N)) + g.add_edge(i, i + 1) + if structure_noise: + randomint = np.random.randint(0, p) + if randomint == 0: + if i <= N - 3: + g.add_edge(i, i + 2) + if i == N - 2: + g.add_edge(i, 0) + if i == N - 1: + g.add_edge(i, 1) + g.add_edge(N, 0) + noise = float(np.random.normal(mu, sigma, 1)) + if with_noise: + g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise) + else: + g.add_node(N, attr_name=math.sin(2 * N * math.pi / N)) + return g + + +def graph_colors(nx_graph, vmin=0, vmax=7): + cnorm = mcol.Normalize(vmin=vmin, vmax=vmax) + cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis') + cpick.set_array([]) + val_map = {} + for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items(): + val_map[k] = cpick.to_rgba(v) + colors = [] + for node in nx_graph.nodes(): + colors.append(val_map[node]) + return colors + +############################################################################## +# Generate data +# ------------- + +#%% circular dataset +# We build a dataset of noisy circular graphs. +# Noise is added on the structures by random connections and on the features by gaussian noise. + + +np.random.seed(30) +X0 = [] +for k in range(9): + X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3)) + +############################################################################## +# Plot data +# --------- + +#%% Plot graphs + +plt.figure(figsize=(8, 10)) +for i in range(len(X0)): + plt.subplot(3, 3, i + 1) + g = X0[i] + pos = nx.kamada_kawai_layout(g) + nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100) +plt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20) +plt.show() + +############################################################################## +# Barycenter computation +# ---------------------- + +#%% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph +# Features distances are the euclidean distances +Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0] +ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0] +Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0] +lambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel() +sizebary = 15 # we choose a barycenter with 15 nodes + +A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True) + +############################################################################## +# Plot Barycenter +# ------------------------- + +#%% Create the barycenter +bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0])) +for i, v in enumerate(A.ravel()): + bary.add_node(i, attr_name=v) + +#%% +pos = nx.kamada_kawai_layout(bary) +nx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False) +plt.suptitle('Barycenter', fontsize=20) +plt.show() diff --git a/examples/gromov/plot_fgw.py b/examples/gromov/plot_fgw.py new file mode 100644 index 0000000..73e486e --- /dev/null +++ b/examples/gromov/plot_fgw.py @@ -0,0 +1,175 @@ +# -*- coding: utf-8 -*- +""" +============================== +Plot Fused-gromov-Wasserstein +============================== + +This example illustrates the computation of FGW for 1D measures[18]. + +.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain + and Courty Nicolas + "Optimal Transport for structured data with application on graphs" + International Conference on Machine Learning (ICML). 2019. + +""" + +# Author: Titouan Vayer +# +# License: MIT License + +# sphinx_gallery_thumbnail_number = 3 + +import matplotlib.pyplot as pl +import numpy as np +import ot +from ot.gromov import gromov_wasserstein, fused_gromov_wasserstein + +############################################################################## +# Generate data +# --------- + +#%% parameters +# We create two 1D random measures +n = 20 # number of points in the first distribution +n2 = 30 # number of points in the second distribution +sig = 1 # std of first distribution +sig2 = 0.1 # std of second distribution + +np.random.seed(0) + +phi = np.arange(n)[:, None] +xs = phi + sig * np.random.randn(n, 1) +ys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1) + +phi2 = np.arange(n2)[:, None] +xt = phi2 + sig * np.random.randn(n2, 1) +yt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1) +yt = yt[::-1, :] + +p = ot.unif(n) +q = ot.unif(n2) + +############################################################################## +# Plot data +# --------- + +#%% plot the distributions + +pl.close(10) +pl.figure(10, (7, 7)) + +pl.subplot(2, 1, 1) + +pl.scatter(ys, xs, c=phi, s=70) +pl.ylabel('Feature value a', fontsize=20) +pl.title('$\mu=\sum_i \delta_{x_i,a_i}$', fontsize=25, y=1) +pl.xticks(()) +pl.yticks(()) +pl.subplot(2, 1, 2) +pl.scatter(yt, xt, c=phi2, s=70) +pl.xlabel('coordinates x/y', fontsize=25) +pl.ylabel('Feature value b', fontsize=20) +pl.title('$\\nu=\sum_j \delta_{y_j,b_j}$', fontsize=25, y=1) +pl.yticks(()) +pl.tight_layout() +pl.show() + +############################################################################## +# Create structure matrices and across-feature distance matrix +# --------- + +#%% Structure matrices and across-features distance matrix +C1 = ot.dist(xs) +C2 = ot.dist(xt) +M = ot.dist(ys, yt) +w1 = ot.unif(C1.shape[0]) +w2 = ot.unif(C2.shape[0]) +Got = ot.emd([], [], M) + +############################################################################## +# Plot matrices +# --------- + +#%% +cmap = 'Reds' +pl.close(10) +pl.figure(10, (5, 5)) +fs = 15 +l_x = [0, 5, 10, 15] +l_y = [0, 5, 10, 15, 20, 25] +gs = pl.GridSpec(5, 5) + +ax1 = pl.subplot(gs[3:, :2]) + +pl.imshow(C1, cmap=cmap, interpolation='nearest') +pl.title("$C_1$", fontsize=fs) +pl.xlabel("$k$", fontsize=fs) +pl.ylabel("$i$", fontsize=fs) +pl.xticks(l_x) +pl.yticks(l_x) + +ax2 = pl.subplot(gs[:3, 2:]) + +pl.imshow(C2, cmap=cmap, interpolation='nearest') +pl.title("$C_2$", fontsize=fs) +pl.ylabel("$l$", fontsize=fs) +#pl.ylabel("$l$",fontsize=fs) +pl.xticks(()) +pl.yticks(l_y) +ax2.set_aspect('auto') + +ax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1) +pl.imshow(M, cmap=cmap, interpolation='nearest') +pl.yticks(l_x) +pl.xticks(l_y) +pl.ylabel("$i$", fontsize=fs) +pl.title("$M_{AB}$", fontsize=fs) +pl.xlabel("$j$", fontsize=fs) +pl.tight_layout() +ax3.set_aspect('auto') +pl.show() + +############################################################################## +# Compute FGW/GW +# --------- + +#%% Computing FGW and GW +alpha = 1e-3 + +ot.tic() +Gwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True) +ot.toc() + +#%reload_ext WGW +Gg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True) + +############################################################################## +# Visualize transport matrices +# --------- + +#%% visu OT matrix +cmap = 'Blues' +fs = 15 +pl.figure(2, (13, 5)) +pl.clf() +pl.subplot(1, 3, 1) +pl.imshow(Got, cmap=cmap, interpolation='nearest') +#pl.xlabel("$y$",fontsize=fs) +pl.ylabel("$i$", fontsize=fs) +pl.xticks(()) + +pl.title('Wasserstein ($M$ only)') + +pl.subplot(1, 3, 2) +pl.imshow(Gg, cmap=cmap, interpolation='nearest') +pl.title('Gromov ($C_1,C_2$ only)') +pl.xticks(()) +pl.subplot(1, 3, 3) +pl.imshow(Gwg, cmap=cmap, interpolation='nearest') +pl.title('FGW ($M+C_1,C_2$)') + +pl.xlabel("$j$", fontsize=fs) +pl.ylabel("$i$", fontsize=fs) + +pl.tight_layout() +pl.show() diff --git a/examples/gromov/plot_gromov.py b/examples/gromov/plot_gromov.py new file mode 100644 index 0000000..deb2f86 --- /dev/null +++ b/examples/gromov/plot_gromov.py @@ -0,0 +1,106 @@ +# -*- coding: utf-8 -*- +""" +========================== +Gromov-Wasserstein example +========================== + +This example is designed to show how to use the Gromov-Wassertsein distance +computation in POT. +""" + +# Author: Erwan Vautier +# Nicolas Courty +# +# License: MIT License + +import scipy as sp +import numpy as np +import matplotlib.pylab as pl +from mpl_toolkits.mplot3d import Axes3D # noqa +import ot + +############################################################################# +# +# Sample two Gaussian distributions (2D and 3D) +# --------------------------------------------- +# +# The Gromov-Wasserstein distance allows to compute distances with samples that +# do not belong to the same metric space. For demonstration purpose, we sample +# two Gaussian distributions in 2- and 3-dimensional spaces. + + +n_samples = 30 # nb samples + +mu_s = np.array([0, 0]) +cov_s = np.array([[1, 0], [0, 1]]) + +mu_t = np.array([4, 4, 4]) +cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) + + +xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) +P = sp.linalg.sqrtm(cov_t) +xt = np.random.randn(n_samples, 3).dot(P) + mu_t + +############################################################################# +# +# Plotting the distributions +# -------------------------- + + +fig = pl.figure() +ax1 = fig.add_subplot(121) +ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +ax2 = fig.add_subplot(122, projection='3d') +ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') +pl.show() + +############################################################################# +# +# Compute distance kernels, normalize them and then display +# --------------------------------------------------------- + + +C1 = sp.spatial.distance.cdist(xs, xs) +C2 = sp.spatial.distance.cdist(xt, xt) + +C1 /= C1.max() +C2 /= C2.max() + +pl.figure() +pl.subplot(121) +pl.imshow(C1) +pl.subplot(122) +pl.imshow(C2) +pl.show() + +############################################################################# +# +# Compute Gromov-Wasserstein plans and distance +# --------------------------------------------- + +p = ot.unif(n_samples) +q = ot.unif(n_samples) + +gw0, log0 = ot.gromov.gromov_wasserstein( + C1, C2, p, q, 'square_loss', verbose=True, log=True) + +gw, log = ot.gromov.entropic_gromov_wasserstein( + C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True) + + +print('Gromov-Wasserstein distances: ' + str(log0['gw_dist'])) +print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist'])) + + +pl.figure(1, (10, 5)) + +pl.subplot(1, 2, 1) +pl.imshow(gw0, cmap='jet') +pl.title('Gromov Wasserstein') + +pl.subplot(1, 2, 2) +pl.imshow(gw, cmap='jet') +pl.title('Entropic Gromov Wasserstein') + +pl.show() diff --git a/examples/gromov/plot_gromov_barycenter.py b/examples/gromov/plot_gromov_barycenter.py new file mode 100755 index 0000000..6b29687 --- /dev/null +++ b/examples/gromov/plot_gromov_barycenter.py @@ -0,0 +1,247 @@ +# -*- coding: utf-8 -*- +""" +===================================== +Gromov-Wasserstein Barycenter example +===================================== + +This example is designed to show how to use the Gromov-Wasserstein distance +computation in POT. +""" + +# Author: Erwan Vautier +# Nicolas Courty +# +# License: MIT License + + +import numpy as np +import scipy as sp + +import matplotlib.pylab as pl +from sklearn import manifold +from sklearn.decomposition import PCA + +import ot + +############################################################################## +# Smacof MDS +# ---------- +# +# This function allows to find an embedding of points given a dissimilarity matrix +# that will be given by the output of the algorithm + + +def smacof_mds(C, dim, max_iter=3000, eps=1e-9): + """ + Returns an interpolated point cloud following the dissimilarity matrix C + using SMACOF multidimensional scaling (MDS) in specific dimensionned + target space + + Parameters + ---------- + C : ndarray, shape (ns, ns) + dissimilarity matrix + dim : int + dimension of the targeted space + max_iter : int + Maximum number of iterations of the SMACOF algorithm for a single run + eps : float + relative tolerance w.r.t stress to declare converge + + Returns + ------- + npos : ndarray, shape (R, dim) + Embedded coordinates of the interpolated point cloud (defined with + one isometry) + """ + + rng = np.random.RandomState(seed=3) + + mds = manifold.MDS( + dim, + max_iter=max_iter, + eps=1e-9, + dissimilarity='precomputed', + n_init=1) + pos = mds.fit(C).embedding_ + + nmds = manifold.MDS( + 2, + max_iter=max_iter, + eps=1e-9, + dissimilarity="precomputed", + random_state=rng, + n_init=1) + npos = nmds.fit_transform(C, init=pos) + + return npos + + +############################################################################## +# Data preparation +# ---------------- +# +# The four distributions are constructed from 4 simple images + + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + +square = pl.imread('../data/square.png').astype(np.float64)[:, :, 2] +cross = pl.imread('../data/cross.png').astype(np.float64)[:, :, 2] +triangle = pl.imread('../data/triangle.png').astype(np.float64)[:, :, 2] +star = pl.imread('../data/star.png').astype(np.float64)[:, :, 2] + +shapes = [square, cross, triangle, star] + +S = 4 +xs = [[] for i in range(S)] + + +for nb in range(4): + for i in range(8): + for j in range(8): + if shapes[nb][i, j] < 0.95: + xs[nb].append([j, 8 - i]) + +xs = np.array([np.array(xs[0]), np.array(xs[1]), + np.array(xs[2]), np.array(xs[3])]) + +############################################################################## +# Barycenter computation +# ---------------------- + + +ns = [len(xs[s]) for s in range(S)] +n_samples = 30 + +"""Compute all distances matrices for the four shapes""" +Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)] +Cs = [cs / cs.max() for cs in Cs] + +ps = [ot.unif(ns[s]) for s in range(S)] +p = ot.unif(n_samples) + + +lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]] + +Ct01 = [0 for i in range(2)] +for i in range(2): + Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], + [ps[0], ps[1] + ], p, lambdast[i], 'square_loss', # 5e-4, + max_iter=100, tol=1e-3) + +Ct02 = [0 for i in range(2)] +for i in range(2): + Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], + [ps[0], ps[2] + ], p, lambdast[i], 'square_loss', # 5e-4, + max_iter=100, tol=1e-3) + +Ct13 = [0 for i in range(2)] +for i in range(2): + Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], + [ps[1], ps[3] + ], p, lambdast[i], 'square_loss', # 5e-4, + max_iter=100, tol=1e-3) + +Ct23 = [0 for i in range(2)] +for i in range(2): + Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], + [ps[2], ps[3] + ], p, lambdast[i], 'square_loss', # 5e-4, + max_iter=100, tol=1e-3) + + +############################################################################## +# Visualization +# ------------- +# +# The PCA helps in getting consistency between the rotations + + +clf = PCA(n_components=2) +npos = [0, 0, 0, 0] +npos = [smacof_mds(Cs[s], 2) for s in range(S)] + +npost01 = [0, 0] +npost01 = [smacof_mds(Ct01[s], 2) for s in range(2)] +npost01 = [clf.fit_transform(npost01[s]) for s in range(2)] + +npost02 = [0, 0] +npost02 = [smacof_mds(Ct02[s], 2) for s in range(2)] +npost02 = [clf.fit_transform(npost02[s]) for s in range(2)] + +npost13 = [0, 0] +npost13 = [smacof_mds(Ct13[s], 2) for s in range(2)] +npost13 = [clf.fit_transform(npost13[s]) for s in range(2)] + +npost23 = [0, 0] +npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)] +npost23 = [clf.fit_transform(npost23[s]) for s in range(2)] + + +fig = pl.figure(figsize=(10, 10)) + +ax1 = pl.subplot2grid((4, 4), (0, 0)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r') + +ax2 = pl.subplot2grid((4, 4), (0, 1)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b') + +ax3 = pl.subplot2grid((4, 4), (0, 2)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b') + +ax4 = pl.subplot2grid((4, 4), (0, 3)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r') + +ax5 = pl.subplot2grid((4, 4), (1, 0)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b') + +ax6 = pl.subplot2grid((4, 4), (1, 3)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b') + +ax7 = pl.subplot2grid((4, 4), (2, 0)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b') + +ax8 = pl.subplot2grid((4, 4), (2, 3)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b') + +ax9 = pl.subplot2grid((4, 4), (3, 0)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r') + +ax10 = pl.subplot2grid((4, 4), (3, 1)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b') + +ax11 = pl.subplot2grid((4, 4), (3, 2)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b') + +ax12 = pl.subplot2grid((4, 4), (3, 3)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r') diff --git a/examples/others/README.txt b/examples/others/README.txt new file mode 100644 index 0000000..df4c697 --- /dev/null +++ b/examples/others/README.txt @@ -0,0 +1,5 @@ + + + +Other OT problems +----------------- \ No newline at end of file diff --git a/examples/others/plot_WDA.py b/examples/others/plot_WDA.py new file mode 100644 index 0000000..5e17433 --- /dev/null +++ b/examples/others/plot_WDA.py @@ -0,0 +1,129 @@ +# -*- coding: utf-8 -*- +""" +================================= +Wasserstein Discriminant Analysis +================================= + +This example illustrate the use of WDA as proposed in [11]. + + +[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). +Wasserstein Discriminant Analysis. + +""" + +# Author: Remi Flamary +# +# License: MIT License + +# sphinx_gallery_thumbnail_number = 2 + +import numpy as np +import matplotlib.pylab as pl + +from ot.dr import wda, fda + + +############################################################################## +# Generate data +# ------------- + +#%% parameters + +n = 1000 # nb samples in source and target datasets +nz = 0.2 + +# generate circle dataset +t = np.random.rand(n) * 2 * np.pi +ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 +xs = np.concatenate( + (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1) +xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2) + +t = np.random.rand(n) * 2 * np.pi +yt = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 +xt = np.concatenate( + (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1) +xt = xt * yt.reshape(-1, 1) + nz * np.random.randn(n, 2) + +nbnoise = 8 + +xs = np.hstack((xs, np.random.randn(n, nbnoise))) +xt = np.hstack((xt, np.random.randn(n, nbnoise))) + +############################################################################## +# Plot data +# --------- + +#%% plot samples +pl.figure(1, figsize=(6.4, 3.5)) + +pl.subplot(1, 2, 1) +pl.scatter(xt[:, 0], xt[:, 1], c=ys, marker='+', label='Source samples') +pl.legend(loc=0) +pl.title('Discriminant dimensions') + +pl.subplot(1, 2, 2) +pl.scatter(xt[:, 2], xt[:, 3], c=ys, marker='+', label='Source samples') +pl.legend(loc=0) +pl.title('Other dimensions') +pl.tight_layout() + +############################################################################## +# Compute Fisher Discriminant Analysis +# ------------------------------------ + +#%% Compute FDA +p = 2 + +Pfda, projfda = fda(xs, ys, p) + +############################################################################## +# Compute Wasserstein Discriminant Analysis +# ----------------------------------------- + +#%% Compute WDA +p = 2 +reg = 1e0 +k = 10 +maxiter = 100 + +Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter) + + +############################################################################## +# Plot 2D projections +# ------------------- + +#%% plot samples + +xsp = projfda(xs) +xtp = projfda(xt) + +xspw = projwda(xs) +xtpw = projwda(xt) + +pl.figure(2) + +pl.subplot(2, 2, 1) +pl.scatter(xsp[:, 0], xsp[:, 1], c=ys, marker='+', label='Projected samples') +pl.legend(loc=0) +pl.title('Projected training samples FDA') + +pl.subplot(2, 2, 2) +pl.scatter(xtp[:, 0], xtp[:, 1], c=ys, marker='+', label='Projected samples') +pl.legend(loc=0) +pl.title('Projected test samples FDA') + +pl.subplot(2, 2, 3) +pl.scatter(xspw[:, 0], xspw[:, 1], c=ys, marker='+', label='Projected samples') +pl.legend(loc=0) +pl.title('Projected training samples WDA') + +pl.subplot(2, 2, 4) +pl.scatter(xtpw[:, 0], xtpw[:, 1], c=ys, marker='+', label='Projected samples') +pl.legend(loc=0) +pl.title('Projected test samples WDA') +pl.tight_layout() + +pl.show() diff --git a/examples/plot_UOT_1D.py b/examples/plot_UOT_1D.py deleted file mode 100644 index 2ea8b05..0000000 --- a/examples/plot_UOT_1D.py +++ /dev/null @@ -1,76 +0,0 @@ -# -*- coding: utf-8 -*- -""" -=============================== -1D Unbalanced optimal transport -=============================== - -This example illustrates the computation of Unbalanced Optimal transport -using a Kullback-Leibler relaxation. -""" - -# Author: Hicham Janati -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot -import ot.plot -from ot.datasets import make_1D_gauss as gauss - -############################################################################## -# Generate data -# ------------- - - -#%% parameters - -n = 100 # nb bins - -# bin positions -x = np.arange(n, dtype=np.float64) - -# Gaussian distributions -a = gauss(n, m=20, s=5) # m= mean, s= std -b = gauss(n, m=60, s=10) - -# make distributions unbalanced -b *= 5. - -# loss matrix -M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) -M /= M.max() - - -############################################################################## -# Plot distributions and loss matrix -# ---------------------------------- - -#%% plot the distributions - -pl.figure(1, figsize=(6.4, 3)) -pl.plot(x, a, 'b', label='Source distribution') -pl.plot(x, b, 'r', label='Target distribution') -pl.legend() - -# plot distributions and loss matrix - -pl.figure(2, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') - - -############################################################################## -# Solve Unbalanced Sinkhorn -# -------------- - - -# Sinkhorn - -epsilon = 0.1 # entropy parameter -alpha = 1. # Unbalanced KL relaxation parameter -Gs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True) - -pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn') - -pl.show() diff --git a/examples/plot_UOT_barycenter_1D.py b/examples/plot_UOT_barycenter_1D.py deleted file mode 100644 index 931798b..0000000 --- a/examples/plot_UOT_barycenter_1D.py +++ /dev/null @@ -1,166 +0,0 @@ -# -*- coding: utf-8 -*- -""" -=========================================================== -1D Wasserstein barycenter demo for Unbalanced distributions -=========================================================== - -This example illustrates the computation of regularized Wassersyein Barycenter -as proposed in [10] for Unbalanced inputs. - - -[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. - -""" - -# Author: Hicham Janati -# -# License: MIT License - -# sphinx_gallery_thumbnail_number = 2 - -import numpy as np -import matplotlib.pylab as pl -import ot -# necessary for 3d plot even if not used -from mpl_toolkits.mplot3d import Axes3D # noqa -from matplotlib.collections import PolyCollection - -############################################################################## -# Generate data -# ------------- - -# parameters - -n = 100 # nb bins - -# bin positions -x = np.arange(n, dtype=np.float64) - -# Gaussian distributions -a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std -a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) - -# make unbalanced dists -a2 *= 3. - -# creating matrix A containing all distributions -A = np.vstack((a1, a2)).T -n_distributions = A.shape[1] - -# loss matrix + normalization -M = ot.utils.dist0(n) -M /= M.max() - -############################################################################## -# Plot data -# --------- - -# plot the distributions - -pl.figure(1, figsize=(6.4, 3)) -for i in range(n_distributions): - pl.plot(x, A[:, i]) -pl.title('Distributions') -pl.tight_layout() - -############################################################################## -# Barycenter computation -# ---------------------- - -# non weighted barycenter computation - -weight = 0.5 # 0<=weight<=1 -weights = np.array([1 - weight, weight]) - -# l2bary -bary_l2 = A.dot(weights) - -# wasserstein -reg = 1e-3 -alpha = 1. - -bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights) - -pl.figure(2) -pl.clf() -pl.subplot(2, 1, 1) -for i in range(n_distributions): - pl.plot(x, A[:, i]) -pl.title('Distributions') - -pl.subplot(2, 1, 2) -pl.plot(x, bary_l2, 'r', label='l2') -pl.plot(x, bary_wass, 'g', label='Wasserstein') -pl.legend() -pl.title('Barycenters') -pl.tight_layout() - -############################################################################## -# Barycentric interpolation -# ------------------------- - -# barycenter interpolation - -n_weight = 11 -weight_list = np.linspace(0, 1, n_weight) - - -B_l2 = np.zeros((n, n_weight)) - -B_wass = np.copy(B_l2) - -for i in range(0, n_weight): - weight = weight_list[i] - weights = np.array([1 - weight, weight]) - B_l2[:, i] = A.dot(weights) - B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights) - - -# plot interpolation - -pl.figure(3) - -cmap = pl.cm.get_cmap('viridis') -verts = [] -zs = weight_list -for i, z in enumerate(zs): - ys = B_l2[:, i] - verts.append(list(zip(x, ys))) - -ax = pl.gcf().gca(projection='3d') - -poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) -poly.set_alpha(0.7) -ax.add_collection3d(poly, zs=zs, zdir='y') -ax.set_xlabel('x') -ax.set_xlim3d(0, n) -ax.set_ylabel(r'$\alpha$') -ax.set_ylim3d(0, 1) -ax.set_zlabel('') -ax.set_zlim3d(0, B_l2.max() * 1.01) -pl.title('Barycenter interpolation with l2') -pl.tight_layout() - -pl.figure(4) -cmap = pl.cm.get_cmap('viridis') -verts = [] -zs = weight_list -for i, z in enumerate(zs): - ys = B_wass[:, i] - verts.append(list(zip(x, ys))) - -ax = pl.gcf().gca(projection='3d') - -poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) -poly.set_alpha(0.7) -ax.add_collection3d(poly, zs=zs, zdir='y') -ax.set_xlabel('x') -ax.set_xlim3d(0, n) -ax.set_ylabel(r'$\alpha$') -ax.set_ylim3d(0, 1) -ax.set_zlabel('') -ax.set_zlim3d(0, B_l2.max() * 1.01) -pl.title('Barycenter interpolation with Wasserstein') -pl.tight_layout() - -pl.show() diff --git a/examples/plot_WDA.py b/examples/plot_WDA.py deleted file mode 100644 index 5e17433..0000000 --- a/examples/plot_WDA.py +++ /dev/null @@ -1,129 +0,0 @@ -# -*- coding: utf-8 -*- -""" -================================= -Wasserstein Discriminant Analysis -================================= - -This example illustrate the use of WDA as proposed in [11]. - - -[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). -Wasserstein Discriminant Analysis. - -""" - -# Author: Remi Flamary -# -# License: MIT License - -# sphinx_gallery_thumbnail_number = 2 - -import numpy as np -import matplotlib.pylab as pl - -from ot.dr import wda, fda - - -############################################################################## -# Generate data -# ------------- - -#%% parameters - -n = 1000 # nb samples in source and target datasets -nz = 0.2 - -# generate circle dataset -t = np.random.rand(n) * 2 * np.pi -ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 -xs = np.concatenate( - (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1) -xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2) - -t = np.random.rand(n) * 2 * np.pi -yt = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 -xt = np.concatenate( - (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1) -xt = xt * yt.reshape(-1, 1) + nz * np.random.randn(n, 2) - -nbnoise = 8 - -xs = np.hstack((xs, np.random.randn(n, nbnoise))) -xt = np.hstack((xt, np.random.randn(n, nbnoise))) - -############################################################################## -# Plot data -# --------- - -#%% plot samples -pl.figure(1, figsize=(6.4, 3.5)) - -pl.subplot(1, 2, 1) -pl.scatter(xt[:, 0], xt[:, 1], c=ys, marker='+', label='Source samples') -pl.legend(loc=0) -pl.title('Discriminant dimensions') - -pl.subplot(1, 2, 2) -pl.scatter(xt[:, 2], xt[:, 3], c=ys, marker='+', label='Source samples') -pl.legend(loc=0) -pl.title('Other dimensions') -pl.tight_layout() - -############################################################################## -# Compute Fisher Discriminant Analysis -# ------------------------------------ - -#%% Compute FDA -p = 2 - -Pfda, projfda = fda(xs, ys, p) - -############################################################################## -# Compute Wasserstein Discriminant Analysis -# ----------------------------------------- - -#%% Compute WDA -p = 2 -reg = 1e0 -k = 10 -maxiter = 100 - -Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter) - - -############################################################################## -# Plot 2D projections -# ------------------- - -#%% plot samples - -xsp = projfda(xs) -xtp = projfda(xt) - -xspw = projwda(xs) -xtpw = projwda(xt) - -pl.figure(2) - -pl.subplot(2, 2, 1) -pl.scatter(xsp[:, 0], xsp[:, 1], c=ys, marker='+', label='Projected samples') -pl.legend(loc=0) -pl.title('Projected training samples FDA') - -pl.subplot(2, 2, 2) -pl.scatter(xtp[:, 0], xtp[:, 1], c=ys, marker='+', label='Projected samples') -pl.legend(loc=0) -pl.title('Projected test samples FDA') - -pl.subplot(2, 2, 3) -pl.scatter(xspw[:, 0], xspw[:, 1], c=ys, marker='+', label='Projected samples') -pl.legend(loc=0) -pl.title('Projected training samples WDA') - -pl.subplot(2, 2, 4) -pl.scatter(xtpw[:, 0], xtpw[:, 1], c=ys, marker='+', label='Projected samples') -pl.legend(loc=0) -pl.title('Projected test samples WDA') -pl.tight_layout() - -pl.show() diff --git a/examples/plot_barycenter_1D.py b/examples/plot_barycenter_1D.py deleted file mode 100644 index 63dc460..0000000 --- a/examples/plot_barycenter_1D.py +++ /dev/null @@ -1,162 +0,0 @@ -# -*- coding: utf-8 -*- -""" -============================== -1D Wasserstein barycenter demo -============================== - -This example illustrates the computation of regularized Wassersyein Barycenter -as proposed in [3]. - - -[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). -Iterative Bregman projections for regularized transportation problems -SIAM Journal on Scientific Computing, 37(2), A1111-A1138. - -""" - -# Author: Remi Flamary -# -# License: MIT License - -# sphinx_gallery_thumbnail_number = 4 - -import numpy as np -import matplotlib.pylab as pl -import ot -# necessary for 3d plot even if not used -from mpl_toolkits.mplot3d import Axes3D # noqa -from matplotlib.collections import PolyCollection - -############################################################################## -# Generate data -# ------------- - -#%% parameters - -n = 100 # nb bins - -# bin positions -x = np.arange(n, dtype=np.float64) - -# Gaussian distributions -a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std -a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) - -# creating matrix A containing all distributions -A = np.vstack((a1, a2)).T -n_distributions = A.shape[1] - -# loss matrix + normalization -M = ot.utils.dist0(n) -M /= M.max() - -############################################################################## -# Plot data -# --------- - -#%% plot the distributions - -pl.figure(1, figsize=(6.4, 3)) -for i in range(n_distributions): - pl.plot(x, A[:, i]) -pl.title('Distributions') -pl.tight_layout() - -############################################################################## -# Barycenter computation -# ---------------------- - -#%% barycenter computation - -alpha = 0.2 # 0<=alpha<=1 -weights = np.array([1 - alpha, alpha]) - -# l2bary -bary_l2 = A.dot(weights) - -# wasserstein -reg = 1e-3 -bary_wass = ot.bregman.barycenter(A, M, reg, weights) - -pl.figure(2) -pl.clf() -pl.subplot(2, 1, 1) -for i in range(n_distributions): - pl.plot(x, A[:, i]) -pl.title('Distributions') - -pl.subplot(2, 1, 2) -pl.plot(x, bary_l2, 'r', label='l2') -pl.plot(x, bary_wass, 'g', label='Wasserstein') -pl.legend() -pl.title('Barycenters') -pl.tight_layout() - -############################################################################## -# Barycentric interpolation -# ------------------------- - -#%% barycenter interpolation - -n_alpha = 11 -alpha_list = np.linspace(0, 1, n_alpha) - - -B_l2 = np.zeros((n, n_alpha)) - -B_wass = np.copy(B_l2) - -for i in range(0, n_alpha): - alpha = alpha_list[i] - weights = np.array([1 - alpha, alpha]) - B_l2[:, i] = A.dot(weights) - B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights) - -#%% plot interpolation - -pl.figure(3) - -cmap = pl.cm.get_cmap('viridis') -verts = [] -zs = alpha_list -for i, z in enumerate(zs): - ys = B_l2[:, i] - verts.append(list(zip(x, ys))) - -ax = pl.gcf().gca(projection='3d') - -poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list]) -poly.set_alpha(0.7) -ax.add_collection3d(poly, zs=zs, zdir='y') -ax.set_xlabel('x') -ax.set_xlim3d(0, n) -ax.set_ylabel('$\\alpha$') -ax.set_ylim3d(0, 1) -ax.set_zlabel('') -ax.set_zlim3d(0, B_l2.max() * 1.01) -pl.title('Barycenter interpolation with l2') -pl.tight_layout() - -pl.figure(4) -cmap = pl.cm.get_cmap('viridis') -verts = [] -zs = alpha_list -for i, z in enumerate(zs): - ys = B_wass[:, i] - verts.append(list(zip(x, ys))) - -ax = pl.gcf().gca(projection='3d') - -poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list]) -poly.set_alpha(0.7) -ax.add_collection3d(poly, zs=zs, zdir='y') -ax.set_xlabel('x') -ax.set_xlim3d(0, n) -ax.set_ylabel('$\\alpha$') -ax.set_ylim3d(0, 1) -ax.set_zlabel('') -ax.set_zlim3d(0, B_l2.max() * 1.01) -pl.title('Barycenter interpolation with Wasserstein') -pl.tight_layout() - -pl.show() diff --git a/examples/plot_barycenter_fgw.py b/examples/plot_barycenter_fgw.py deleted file mode 100644 index 77b0370..0000000 --- a/examples/plot_barycenter_fgw.py +++ /dev/null @@ -1,184 +0,0 @@ -# -*- coding: utf-8 -*- -""" -================================= -Plot graphs' barycenter using FGW -================================= - -This example illustrates the computation barycenter of labeled graphs using FGW - -Requires networkx >=2 - -.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain - and Courty Nicolas - "Optimal Transport for structured data with application on graphs" - International Conference on Machine Learning (ICML). 2019. - -""" - -# Author: Titouan Vayer -# -# License: MIT License - -#%% load libraries -import numpy as np -import matplotlib.pyplot as plt -import networkx as nx -import math -from scipy.sparse.csgraph import shortest_path -import matplotlib.colors as mcol -from matplotlib import cm -from ot.gromov import fgw_barycenters -#%% Graph functions - - -def find_thresh(C, inf=0.5, sup=3, step=10): - """ Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected - Tthe threshold is found by a linesearch between values "inf" and "sup" with "step" thresholds tested. - The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix - and the original matrix. - Parameters - ---------- - C : ndarray, shape (n_nodes,n_nodes) - The structure matrix to threshold - inf : float - The beginning of the linesearch - sup : float - The end of the linesearch - step : integer - Number of thresholds tested - """ - dist = [] - search = np.linspace(inf, sup, step) - for thresh in search: - Cprime = sp_to_adjency(C, 0, thresh) - SC = shortest_path(Cprime, method='D') - SC[SC == float('inf')] = 100 - dist.append(np.linalg.norm(SC - C)) - return search[np.argmin(dist)], dist - - -def sp_to_adjency(C, threshinf=0.2, threshsup=1.8): - """ Thresholds the structure matrix in order to compute an adjency matrix. - All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0 - Parameters - ---------- - C : ndarray, shape (n_nodes,n_nodes) - The structure matrix to threshold - threshinf : float - The minimum value of distance from which the new value is set to 1 - threshsup : float - The maximum value of distance from which the new value is set to 1 - Returns - ------- - C : ndarray, shape (n_nodes,n_nodes) - The threshold matrix. Each element is in {0,1} - """ - H = np.zeros_like(C) - np.fill_diagonal(H, np.diagonal(C)) - C = C - H - C = np.minimum(np.maximum(C, threshinf), threshsup) - C[C == threshsup] = 0 - C[C != 0] = 1 - - return C - - -def build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None): - """ Create a noisy circular graph - """ - g = nx.Graph() - g.add_nodes_from(list(range(N))) - for i in range(N): - noise = float(np.random.normal(mu, sigma, 1)) - if with_noise: - g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise) - else: - g.add_node(i, attr_name=math.sin(2 * i * math.pi / N)) - g.add_edge(i, i + 1) - if structure_noise: - randomint = np.random.randint(0, p) - if randomint == 0: - if i <= N - 3: - g.add_edge(i, i + 2) - if i == N - 2: - g.add_edge(i, 0) - if i == N - 1: - g.add_edge(i, 1) - g.add_edge(N, 0) - noise = float(np.random.normal(mu, sigma, 1)) - if with_noise: - g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise) - else: - g.add_node(N, attr_name=math.sin(2 * N * math.pi / N)) - return g - - -def graph_colors(nx_graph, vmin=0, vmax=7): - cnorm = mcol.Normalize(vmin=vmin, vmax=vmax) - cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis') - cpick.set_array([]) - val_map = {} - for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items(): - val_map[k] = cpick.to_rgba(v) - colors = [] - for node in nx_graph.nodes(): - colors.append(val_map[node]) - return colors - -############################################################################## -# Generate data -# ------------- - -#%% circular dataset -# We build a dataset of noisy circular graphs. -# Noise is added on the structures by random connections and on the features by gaussian noise. - - -np.random.seed(30) -X0 = [] -for k in range(9): - X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3)) - -############################################################################## -# Plot data -# --------- - -#%% Plot graphs - -plt.figure(figsize=(8, 10)) -for i in range(len(X0)): - plt.subplot(3, 3, i + 1) - g = X0[i] - pos = nx.kamada_kawai_layout(g) - nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100) -plt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20) -plt.show() - -############################################################################## -# Barycenter computation -# ---------------------- - -#%% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph -# Features distances are the euclidean distances -Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0] -ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0] -Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0] -lambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel() -sizebary = 15 # we choose a barycenter with 15 nodes - -A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True) - -############################################################################## -# Plot Barycenter -# ------------------------- - -#%% Create the barycenter -bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0])) -for i, v in enumerate(A.ravel()): - bary.add_node(i, attr_name=v) - -#%% -pos = nx.kamada_kawai_layout(bary) -nx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False) -plt.suptitle('Barycenter', fontsize=20) -plt.show() diff --git a/examples/plot_barycenter_lp_vs_entropic.py b/examples/plot_barycenter_lp_vs_entropic.py deleted file mode 100644 index 57a6bac..0000000 --- a/examples/plot_barycenter_lp_vs_entropic.py +++ /dev/null @@ -1,288 +0,0 @@ -# -*- coding: utf-8 -*- -""" -================================================================================= -1D Wasserstein barycenter comparison between exact LP and entropic regularization -================================================================================= - -This example illustrates the computation of regularized Wasserstein Barycenter -as proposed in [3] and exact LP barycenters using standard LP solver. - -It reproduces approximately Figure 3.1 and 3.2 from the following paper: -Cuturi, M., & Peyré, G. (2016). A smoothed dual approach for variational -Wasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343. - -[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). -Iterative Bregman projections for regularized transportation problems -SIAM Journal on Scientific Computing, 37(2), A1111-A1138. - -""" - -# Author: Remi Flamary -# -# License: MIT License - -# sphinx_gallery_thumbnail_number = 4 - -import numpy as np -import matplotlib.pylab as pl -import ot -# necessary for 3d plot even if not used -from mpl_toolkits.mplot3d import Axes3D # noqa -from matplotlib.collections import PolyCollection # noqa - -#import ot.lp.cvx as cvx - -############################################################################## -# Gaussian Data -# ------------- - -#%% parameters - -problems = [] - -n = 100 # nb bins - -# bin positions -x = np.arange(n, dtype=np.float64) - -# Gaussian distributions -# Gaussian distributions -a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std -a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) - -# creating matrix A containing all distributions -A = np.vstack((a1, a2)).T -n_distributions = A.shape[1] - -# loss matrix + normalization -M = ot.utils.dist0(n) -M /= M.max() - - -#%% plot the distributions - -pl.figure(1, figsize=(6.4, 3)) -for i in range(n_distributions): - pl.plot(x, A[:, i]) -pl.title('Distributions') -pl.tight_layout() - -#%% barycenter computation - -alpha = 0.5 # 0<=alpha<=1 -weights = np.array([1 - alpha, alpha]) - -# l2bary -bary_l2 = A.dot(weights) - -# wasserstein -reg = 1e-3 -ot.tic() -bary_wass = ot.bregman.barycenter(A, M, reg, weights) -ot.toc() - - -ot.tic() -bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) -ot.toc() - -pl.figure(2) -pl.clf() -pl.subplot(2, 1, 1) -for i in range(n_distributions): - pl.plot(x, A[:, i]) -pl.title('Distributions') - -pl.subplot(2, 1, 2) -pl.plot(x, bary_l2, 'r', label='l2') -pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') -pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') -pl.legend() -pl.title('Barycenters') -pl.tight_layout() - -problems.append([A, [bary_l2, bary_wass, bary_wass2]]) - -############################################################################## -# Stair Data -# ---------- - -#%% parameters - -a1 = 1.0 * (x > 10) * (x < 50) -a2 = 1.0 * (x > 60) * (x < 80) - -a1 /= a1.sum() -a2 /= a2.sum() - -# creating matrix A containing all distributions -A = np.vstack((a1, a2)).T -n_distributions = A.shape[1] - -# loss matrix + normalization -M = ot.utils.dist0(n) -M /= M.max() - - -#%% plot the distributions - -pl.figure(1, figsize=(6.4, 3)) -for i in range(n_distributions): - pl.plot(x, A[:, i]) -pl.title('Distributions') -pl.tight_layout() - - -#%% barycenter computation - -alpha = 0.5 # 0<=alpha<=1 -weights = np.array([1 - alpha, alpha]) - -# l2bary -bary_l2 = A.dot(weights) - -# wasserstein -reg = 1e-3 -ot.tic() -bary_wass = ot.bregman.barycenter(A, M, reg, weights) -ot.toc() - - -ot.tic() -bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) -ot.toc() - - -problems.append([A, [bary_l2, bary_wass, bary_wass2]]) - -pl.figure(2) -pl.clf() -pl.subplot(2, 1, 1) -for i in range(n_distributions): - pl.plot(x, A[:, i]) -pl.title('Distributions') - -pl.subplot(2, 1, 2) -pl.plot(x, bary_l2, 'r', label='l2') -pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') -pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') -pl.legend() -pl.title('Barycenters') -pl.tight_layout() - - -############################################################################## -# Dirac Data -# ---------- - -#%% parameters - -a1 = np.zeros(n) -a2 = np.zeros(n) - -a1[10] = .25 -a1[20] = .5 -a1[30] = .25 -a2[80] = 1 - - -a1 /= a1.sum() -a2 /= a2.sum() - -# creating matrix A containing all distributions -A = np.vstack((a1, a2)).T -n_distributions = A.shape[1] - -# loss matrix + normalization -M = ot.utils.dist0(n) -M /= M.max() - - -#%% plot the distributions - -pl.figure(1, figsize=(6.4, 3)) -for i in range(n_distributions): - pl.plot(x, A[:, i]) -pl.title('Distributions') -pl.tight_layout() - - -#%% barycenter computation - -alpha = 0.5 # 0<=alpha<=1 -weights = np.array([1 - alpha, alpha]) - -# l2bary -bary_l2 = A.dot(weights) - -# wasserstein -reg = 1e-3 -ot.tic() -bary_wass = ot.bregman.barycenter(A, M, reg, weights) -ot.toc() - - -ot.tic() -bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) -ot.toc() - - -problems.append([A, [bary_l2, bary_wass, bary_wass2]]) - -pl.figure(2) -pl.clf() -pl.subplot(2, 1, 1) -for i in range(n_distributions): - pl.plot(x, A[:, i]) -pl.title('Distributions') - -pl.subplot(2, 1, 2) -pl.plot(x, bary_l2, 'r', label='l2') -pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') -pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') -pl.legend() -pl.title('Barycenters') -pl.tight_layout() - - -############################################################################## -# Final figure -# ------------ -# - -#%% plot - -nbm = len(problems) -nbm2 = (nbm // 2) - - -pl.figure(2, (20, 6)) -pl.clf() - -for i in range(nbm): - - A = problems[i][0] - bary_l2 = problems[i][1][0] - bary_wass = problems[i][1][1] - bary_wass2 = problems[i][1][2] - - pl.subplot(2, nbm, 1 + i) - for j in range(n_distributions): - pl.plot(x, A[:, j]) - if i == nbm2: - pl.title('Distributions') - pl.xticks(()) - pl.yticks(()) - - pl.subplot(2, nbm, 1 + i + nbm) - - pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)') - pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') - pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') - if i == nbm - 1: - pl.legend() - if i == nbm2: - pl.title('Barycenters') - - pl.xticks(()) - pl.yticks(()) diff --git a/examples/plot_convolutional_barycenter.py b/examples/plot_convolutional_barycenter.py deleted file mode 100644 index e74db04..0000000 --- a/examples/plot_convolutional_barycenter.py +++ /dev/null @@ -1,92 +0,0 @@ - -#%% -# -*- coding: utf-8 -*- -""" -============================================ -Convolutional Wasserstein Barycenter example -============================================ - -This example is designed to illustrate how the Convolutional Wasserstein Barycenter -function of POT works. -""" - -# Author: Nicolas Courty -# -# License: MIT License - - -import numpy as np -import pylab as pl -import ot - -############################################################################## -# Data preparation -# ---------------- -# -# The four distributions are constructed from 4 simple images - - -f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2] -f2 = 1 - pl.imread('../data/duck.png')[:, :, 2] -f3 = 1 - pl.imread('../data/heart.png')[:, :, 2] -f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2] - -A = [] -f1 = f1 / np.sum(f1) -f2 = f2 / np.sum(f2) -f3 = f3 / np.sum(f3) -f4 = f4 / np.sum(f4) -A.append(f1) -A.append(f2) -A.append(f3) -A.append(f4) -A = np.array(A) - -nb_images = 5 - -# those are the four corners coordinates that will be interpolated by bilinear -# interpolation -v1 = np.array((1, 0, 0, 0)) -v2 = np.array((0, 1, 0, 0)) -v3 = np.array((0, 0, 1, 0)) -v4 = np.array((0, 0, 0, 1)) - - -############################################################################## -# Barycenter computation and visualization -# ---------------------------------------- -# - -pl.figure(figsize=(10, 10)) -pl.title('Convolutional Wasserstein Barycenters in POT') -cm = 'Blues' -# regularization parameter -reg = 0.004 -for i in range(nb_images): - for j in range(nb_images): - pl.subplot(nb_images, nb_images, i * nb_images + j + 1) - tx = float(i) / (nb_images - 1) - ty = float(j) / (nb_images - 1) - - # weights are constructed by bilinear interpolation - tmp1 = (1 - tx) * v1 + tx * v2 - tmp2 = (1 - tx) * v3 + tx * v4 - weights = (1 - ty) * tmp1 + ty * tmp2 - - if i == 0 and j == 0: - pl.imshow(f1, cmap=cm) - pl.axis('off') - elif i == 0 and j == (nb_images - 1): - pl.imshow(f3, cmap=cm) - pl.axis('off') - elif i == (nb_images - 1) and j == 0: - pl.imshow(f2, cmap=cm) - pl.axis('off') - elif i == (nb_images - 1) and j == (nb_images - 1): - pl.imshow(f4, cmap=cm) - pl.axis('off') - else: - # call to barycenter computation - pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm) - pl.axis('off') -pl.show() diff --git a/examples/plot_fgw.py b/examples/plot_fgw.py deleted file mode 100644 index 73e486e..0000000 --- a/examples/plot_fgw.py +++ /dev/null @@ -1,175 +0,0 @@ -# -*- coding: utf-8 -*- -""" -============================== -Plot Fused-gromov-Wasserstein -============================== - -This example illustrates the computation of FGW for 1D measures[18]. - -.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain - and Courty Nicolas - "Optimal Transport for structured data with application on graphs" - International Conference on Machine Learning (ICML). 2019. - -""" - -# Author: Titouan Vayer -# -# License: MIT License - -# sphinx_gallery_thumbnail_number = 3 - -import matplotlib.pyplot as pl -import numpy as np -import ot -from ot.gromov import gromov_wasserstein, fused_gromov_wasserstein - -############################################################################## -# Generate data -# --------- - -#%% parameters -# We create two 1D random measures -n = 20 # number of points in the first distribution -n2 = 30 # number of points in the second distribution -sig = 1 # std of first distribution -sig2 = 0.1 # std of second distribution - -np.random.seed(0) - -phi = np.arange(n)[:, None] -xs = phi + sig * np.random.randn(n, 1) -ys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1) - -phi2 = np.arange(n2)[:, None] -xt = phi2 + sig * np.random.randn(n2, 1) -yt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1) -yt = yt[::-1, :] - -p = ot.unif(n) -q = ot.unif(n2) - -############################################################################## -# Plot data -# --------- - -#%% plot the distributions - -pl.close(10) -pl.figure(10, (7, 7)) - -pl.subplot(2, 1, 1) - -pl.scatter(ys, xs, c=phi, s=70) -pl.ylabel('Feature value a', fontsize=20) -pl.title('$\mu=\sum_i \delta_{x_i,a_i}$', fontsize=25, y=1) -pl.xticks(()) -pl.yticks(()) -pl.subplot(2, 1, 2) -pl.scatter(yt, xt, c=phi2, s=70) -pl.xlabel('coordinates x/y', fontsize=25) -pl.ylabel('Feature value b', fontsize=20) -pl.title('$\\nu=\sum_j \delta_{y_j,b_j}$', fontsize=25, y=1) -pl.yticks(()) -pl.tight_layout() -pl.show() - -############################################################################## -# Create structure matrices and across-feature distance matrix -# --------- - -#%% Structure matrices and across-features distance matrix -C1 = ot.dist(xs) -C2 = ot.dist(xt) -M = ot.dist(ys, yt) -w1 = ot.unif(C1.shape[0]) -w2 = ot.unif(C2.shape[0]) -Got = ot.emd([], [], M) - -############################################################################## -# Plot matrices -# --------- - -#%% -cmap = 'Reds' -pl.close(10) -pl.figure(10, (5, 5)) -fs = 15 -l_x = [0, 5, 10, 15] -l_y = [0, 5, 10, 15, 20, 25] -gs = pl.GridSpec(5, 5) - -ax1 = pl.subplot(gs[3:, :2]) - -pl.imshow(C1, cmap=cmap, interpolation='nearest') -pl.title("$C_1$", fontsize=fs) -pl.xlabel("$k$", fontsize=fs) -pl.ylabel("$i$", fontsize=fs) -pl.xticks(l_x) -pl.yticks(l_x) - -ax2 = pl.subplot(gs[:3, 2:]) - -pl.imshow(C2, cmap=cmap, interpolation='nearest') -pl.title("$C_2$", fontsize=fs) -pl.ylabel("$l$", fontsize=fs) -#pl.ylabel("$l$",fontsize=fs) -pl.xticks(()) -pl.yticks(l_y) -ax2.set_aspect('auto') - -ax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1) -pl.imshow(M, cmap=cmap, interpolation='nearest') -pl.yticks(l_x) -pl.xticks(l_y) -pl.ylabel("$i$", fontsize=fs) -pl.title("$M_{AB}$", fontsize=fs) -pl.xlabel("$j$", fontsize=fs) -pl.tight_layout() -ax3.set_aspect('auto') -pl.show() - -############################################################################## -# Compute FGW/GW -# --------- - -#%% Computing FGW and GW -alpha = 1e-3 - -ot.tic() -Gwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True) -ot.toc() - -#%reload_ext WGW -Gg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True) - -############################################################################## -# Visualize transport matrices -# --------- - -#%% visu OT matrix -cmap = 'Blues' -fs = 15 -pl.figure(2, (13, 5)) -pl.clf() -pl.subplot(1, 3, 1) -pl.imshow(Got, cmap=cmap, interpolation='nearest') -#pl.xlabel("$y$",fontsize=fs) -pl.ylabel("$i$", fontsize=fs) -pl.xticks(()) - -pl.title('Wasserstein ($M$ only)') - -pl.subplot(1, 3, 2) -pl.imshow(Gg, cmap=cmap, interpolation='nearest') -pl.title('Gromov ($C_1,C_2$ only)') -pl.xticks(()) -pl.subplot(1, 3, 3) -pl.imshow(Gwg, cmap=cmap, interpolation='nearest') -pl.title('FGW ($M+C_1,C_2$)') - -pl.xlabel("$j$", fontsize=fs) -pl.ylabel("$i$", fontsize=fs) - -pl.tight_layout() -pl.show() diff --git a/examples/plot_free_support_barycenter.py b/examples/plot_free_support_barycenter.py deleted file mode 100644 index 64b89e4..0000000 --- a/examples/plot_free_support_barycenter.py +++ /dev/null @@ -1,69 +0,0 @@ -# -*- coding: utf-8 -*- -""" -==================================================== -2D free support Wasserstein barycenters of distributions -==================================================== - -Illustration of 2D Wasserstein barycenters if discributions that are weighted -sum of diracs. - -""" - -# Author: Vivien Seguy -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot - - -############################################################################## -# Generate data -# ------------- -#%% parameters and data generation -N = 3 -d = 2 -measures_locations = [] -measures_weights = [] - -for i in range(N): - - n_i = np.random.randint(low=1, high=20) # nb samples - - mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean - - A_i = np.random.rand(d, d) - cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix - - x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations - b_i = np.random.uniform(0., 1., (n_i,)) - b_i = b_i / np.sum(b_i) # Dirac weights - - measures_locations.append(x_i) - measures_weights.append(b_i) - - -############################################################################## -# Compute free support barycenter -# ------------- - -k = 10 # number of Diracs of the barycenter -X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations -b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized) - -X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b) - - -############################################################################## -# Plot data -# --------- - -pl.figure(1) -for (x_i, b_i) in zip(measures_locations, measures_weights): - color = np.random.randint(low=1, high=10 * N) - pl.scatter(x_i[:, 0], x_i[:, 1], s=b_i * 1000, label='input measure') -pl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter') -pl.title('Data measures and their barycenter') -pl.legend(loc=0) -pl.show() diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py deleted file mode 100644 index deb2f86..0000000 --- a/examples/plot_gromov.py +++ /dev/null @@ -1,106 +0,0 @@ -# -*- coding: utf-8 -*- -""" -========================== -Gromov-Wasserstein example -========================== - -This example is designed to show how to use the Gromov-Wassertsein distance -computation in POT. -""" - -# Author: Erwan Vautier -# Nicolas Courty -# -# License: MIT License - -import scipy as sp -import numpy as np -import matplotlib.pylab as pl -from mpl_toolkits.mplot3d import Axes3D # noqa -import ot - -############################################################################# -# -# Sample two Gaussian distributions (2D and 3D) -# --------------------------------------------- -# -# The Gromov-Wasserstein distance allows to compute distances with samples that -# do not belong to the same metric space. For demonstration purpose, we sample -# two Gaussian distributions in 2- and 3-dimensional spaces. - - -n_samples = 30 # nb samples - -mu_s = np.array([0, 0]) -cov_s = np.array([[1, 0], [0, 1]]) - -mu_t = np.array([4, 4, 4]) -cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) - - -xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) -P = sp.linalg.sqrtm(cov_t) -xt = np.random.randn(n_samples, 3).dot(P) + mu_t - -############################################################################# -# -# Plotting the distributions -# -------------------------- - - -fig = pl.figure() -ax1 = fig.add_subplot(121) -ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -ax2 = fig.add_subplot(122, projection='3d') -ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') -pl.show() - -############################################################################# -# -# Compute distance kernels, normalize them and then display -# --------------------------------------------------------- - - -C1 = sp.spatial.distance.cdist(xs, xs) -C2 = sp.spatial.distance.cdist(xt, xt) - -C1 /= C1.max() -C2 /= C2.max() - -pl.figure() -pl.subplot(121) -pl.imshow(C1) -pl.subplot(122) -pl.imshow(C2) -pl.show() - -############################################################################# -# -# Compute Gromov-Wasserstein plans and distance -# --------------------------------------------- - -p = ot.unif(n_samples) -q = ot.unif(n_samples) - -gw0, log0 = ot.gromov.gromov_wasserstein( - C1, C2, p, q, 'square_loss', verbose=True, log=True) - -gw, log = ot.gromov.entropic_gromov_wasserstein( - C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True) - - -print('Gromov-Wasserstein distances: ' + str(log0['gw_dist'])) -print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist'])) - - -pl.figure(1, (10, 5)) - -pl.subplot(1, 2, 1) -pl.imshow(gw0, cmap='jet') -pl.title('Gromov Wasserstein') - -pl.subplot(1, 2, 2) -pl.imshow(gw, cmap='jet') -pl.title('Entropic Gromov Wasserstein') - -pl.show() diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py deleted file mode 100755 index 6b29687..0000000 --- a/examples/plot_gromov_barycenter.py +++ /dev/null @@ -1,247 +0,0 @@ -# -*- coding: utf-8 -*- -""" -===================================== -Gromov-Wasserstein Barycenter example -===================================== - -This example is designed to show how to use the Gromov-Wasserstein distance -computation in POT. -""" - -# Author: Erwan Vautier -# Nicolas Courty -# -# License: MIT License - - -import numpy as np -import scipy as sp - -import matplotlib.pylab as pl -from sklearn import manifold -from sklearn.decomposition import PCA - -import ot - -############################################################################## -# Smacof MDS -# ---------- -# -# This function allows to find an embedding of points given a dissimilarity matrix -# that will be given by the output of the algorithm - - -def smacof_mds(C, dim, max_iter=3000, eps=1e-9): - """ - Returns an interpolated point cloud following the dissimilarity matrix C - using SMACOF multidimensional scaling (MDS) in specific dimensionned - target space - - Parameters - ---------- - C : ndarray, shape (ns, ns) - dissimilarity matrix - dim : int - dimension of the targeted space - max_iter : int - Maximum number of iterations of the SMACOF algorithm for a single run - eps : float - relative tolerance w.r.t stress to declare converge - - Returns - ------- - npos : ndarray, shape (R, dim) - Embedded coordinates of the interpolated point cloud (defined with - one isometry) - """ - - rng = np.random.RandomState(seed=3) - - mds = manifold.MDS( - dim, - max_iter=max_iter, - eps=1e-9, - dissimilarity='precomputed', - n_init=1) - pos = mds.fit(C).embedding_ - - nmds = manifold.MDS( - 2, - max_iter=max_iter, - eps=1e-9, - dissimilarity="precomputed", - random_state=rng, - n_init=1) - npos = nmds.fit_transform(C, init=pos) - - return npos - - -############################################################################## -# Data preparation -# ---------------- -# -# The four distributions are constructed from 4 simple images - - -def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) - - -square = pl.imread('../data/square.png').astype(np.float64)[:, :, 2] -cross = pl.imread('../data/cross.png').astype(np.float64)[:, :, 2] -triangle = pl.imread('../data/triangle.png').astype(np.float64)[:, :, 2] -star = pl.imread('../data/star.png').astype(np.float64)[:, :, 2] - -shapes = [square, cross, triangle, star] - -S = 4 -xs = [[] for i in range(S)] - - -for nb in range(4): - for i in range(8): - for j in range(8): - if shapes[nb][i, j] < 0.95: - xs[nb].append([j, 8 - i]) - -xs = np.array([np.array(xs[0]), np.array(xs[1]), - np.array(xs[2]), np.array(xs[3])]) - -############################################################################## -# Barycenter computation -# ---------------------- - - -ns = [len(xs[s]) for s in range(S)] -n_samples = 30 - -"""Compute all distances matrices for the four shapes""" -Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)] -Cs = [cs / cs.max() for cs in Cs] - -ps = [ot.unif(ns[s]) for s in range(S)] -p = ot.unif(n_samples) - - -lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]] - -Ct01 = [0 for i in range(2)] -for i in range(2): - Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], - [ps[0], ps[1] - ], p, lambdast[i], 'square_loss', # 5e-4, - max_iter=100, tol=1e-3) - -Ct02 = [0 for i in range(2)] -for i in range(2): - Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], - [ps[0], ps[2] - ], p, lambdast[i], 'square_loss', # 5e-4, - max_iter=100, tol=1e-3) - -Ct13 = [0 for i in range(2)] -for i in range(2): - Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], - [ps[1], ps[3] - ], p, lambdast[i], 'square_loss', # 5e-4, - max_iter=100, tol=1e-3) - -Ct23 = [0 for i in range(2)] -for i in range(2): - Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], - [ps[2], ps[3] - ], p, lambdast[i], 'square_loss', # 5e-4, - max_iter=100, tol=1e-3) - - -############################################################################## -# Visualization -# ------------- -# -# The PCA helps in getting consistency between the rotations - - -clf = PCA(n_components=2) -npos = [0, 0, 0, 0] -npos = [smacof_mds(Cs[s], 2) for s in range(S)] - -npost01 = [0, 0] -npost01 = [smacof_mds(Ct01[s], 2) for s in range(2)] -npost01 = [clf.fit_transform(npost01[s]) for s in range(2)] - -npost02 = [0, 0] -npost02 = [smacof_mds(Ct02[s], 2) for s in range(2)] -npost02 = [clf.fit_transform(npost02[s]) for s in range(2)] - -npost13 = [0, 0] -npost13 = [smacof_mds(Ct13[s], 2) for s in range(2)] -npost13 = [clf.fit_transform(npost13[s]) for s in range(2)] - -npost23 = [0, 0] -npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)] -npost23 = [clf.fit_transform(npost23[s]) for s in range(2)] - - -fig = pl.figure(figsize=(10, 10)) - -ax1 = pl.subplot2grid((4, 4), (0, 0)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r') - -ax2 = pl.subplot2grid((4, 4), (0, 1)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b') - -ax3 = pl.subplot2grid((4, 4), (0, 2)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b') - -ax4 = pl.subplot2grid((4, 4), (0, 3)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r') - -ax5 = pl.subplot2grid((4, 4), (1, 0)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b') - -ax6 = pl.subplot2grid((4, 4), (1, 3)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b') - -ax7 = pl.subplot2grid((4, 4), (2, 0)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b') - -ax8 = pl.subplot2grid((4, 4), (2, 3)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b') - -ax9 = pl.subplot2grid((4, 4), (3, 0)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r') - -ax10 = pl.subplot2grid((4, 4), (3, 1)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b') - -ax11 = pl.subplot2grid((4, 4), (3, 2)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b') - -ax12 = pl.subplot2grid((4, 4), (3, 3)) -pl.xlim((-1, 1)) -pl.ylim((-1, 1)) -ax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r') diff --git a/examples/plot_otda_classes.py b/examples/plot_otda_classes.py deleted file mode 100644 index f028022..0000000 --- a/examples/plot_otda_classes.py +++ /dev/null @@ -1,149 +0,0 @@ -# -*- coding: utf-8 -*- -""" -======================== -OT for domain adaptation -======================== - -This example introduces a domain adaptation in a 2D setting and the 4 OTDA -approaches currently supported in POT. - -""" - -# Authors: Remi Flamary -# Stanislas Chambon -# -# License: MIT License - -import matplotlib.pylab as pl -import ot - -############################################################################## -# Generate data -# ------------- - -n_source_samples = 150 -n_target_samples = 150 - -Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) -Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) - - -############################################################################## -# Instantiate the different transport algorithms and fit them -# ----------------------------------------------------------- - -# EMD Transport -ot_emd = ot.da.EMDTransport() -ot_emd.fit(Xs=Xs, Xt=Xt) - -# Sinkhorn Transport -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - -# Sinkhorn Transport with Group lasso regularization -ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) -ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) - -# Sinkhorn Transport with Group lasso regularization l1l2 -ot_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20, - verbose=True) -ot_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt) - -# transport source samples onto target samples -transp_Xs_emd = ot_emd.transform(Xs=Xs) -transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) -transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) -transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs) - - -############################################################################## -# Fig 1 : plots source and target samples -# --------------------------------------- - -pl.figure(1, figsize=(10, 5)) -pl.subplot(1, 2, 1) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Source samples') - -pl.subplot(1, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Target samples') -pl.tight_layout() - - -############################################################################## -# Fig 2 : plot optimal couplings and transported samples -# ------------------------------------------------------ - -param_img = {'interpolation': 'nearest'} - -pl.figure(2, figsize=(15, 8)) -pl.subplot(2, 4, 1) -pl.imshow(ot_emd.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nEMDTransport') - -pl.subplot(2, 4, 2) -pl.imshow(ot_sinkhorn.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornTransport') - -pl.subplot(2, 4, 3) -pl.imshow(ot_lpl1.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornLpl1Transport') - -pl.subplot(2, 4, 4) -pl.imshow(ot_l1l2.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornL1l2Transport') - -pl.subplot(2, 4, 5) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nEmdTransport') -pl.legend(loc="lower left") - -pl.subplot(2, 4, 6) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nSinkhornTransport') - -pl.subplot(2, 4, 7) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nSinkhornLpl1Transport') - -pl.subplot(2, 4, 8) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nSinkhornL1l2Transport') -pl.tight_layout() - -pl.show() diff --git a/examples/plot_otda_color_images.py b/examples/plot_otda_color_images.py deleted file mode 100644 index 7e0afee..0000000 --- a/examples/plot_otda_color_images.py +++ /dev/null @@ -1,166 +0,0 @@ -# -*- coding: utf-8 -*- -""" -============================= -OT for image color adaptation -============================= - -This example presents a way of transferring colors between two images -with Optimal Transport as introduced in [6] - -[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). -Regularized discrete optimal transport. -SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -""" - -# Authors: Remi Flamary -# Stanislas Chambon -# -# License: MIT License - -# sphinx_gallery_thumbnail_number = 2 - -import numpy as np -import matplotlib.pylab as pl -import ot - - -r = np.random.RandomState(42) - - -def im2mat(I): - """Converts an image to matrix (one pixel per line)""" - return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) - - -def mat2im(X, shape): - """Converts back a matrix to an image""" - return X.reshape(shape) - - -def minmax(I): - return np.clip(I, 0, 1) - - -############################################################################## -# Generate data -# ------------- - -# Loading images -I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 - -X1 = im2mat(I1) -X2 = im2mat(I2) - -# training samples -nb = 1000 -idx1 = r.randint(X1.shape[0], size=(nb,)) -idx2 = r.randint(X2.shape[0], size=(nb,)) - -Xs = X1[idx1, :] -Xt = X2[idx2, :] - - -############################################################################## -# Plot original image -# ------------------- - -pl.figure(1, figsize=(6.4, 3)) - -pl.subplot(1, 2, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Image 1') - -pl.subplot(1, 2, 2) -pl.imshow(I2) -pl.axis('off') -pl.title('Image 2') - - -############################################################################## -# Scatter plot of colors -# ---------------------- - -pl.figure(2, figsize=(6.4, 3)) - -pl.subplot(1, 2, 1) -pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) -pl.axis([0, 1, 0, 1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 1') - -pl.subplot(1, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) -pl.axis([0, 1, 0, 1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 2') -pl.tight_layout() - - -############################################################################## -# Instantiate the different transport algorithms and fit them -# ----------------------------------------------------------- - -# EMDTransport -ot_emd = ot.da.EMDTransport() -ot_emd.fit(Xs=Xs, Xt=Xt) - -# SinkhornTransport -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - -# prediction between images (using out of sample prediction as in [6]) -transp_Xs_emd = ot_emd.transform(Xs=X1) -transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) - -transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) -transp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2) - -I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) -I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) - -I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) -I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) - - -############################################################################## -# Plot new images -# --------------- - -pl.figure(3, figsize=(8, 4)) - -pl.subplot(2, 3, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Image 1') - -pl.subplot(2, 3, 2) -pl.imshow(I1t) -pl.axis('off') -pl.title('Image 1 Adapt') - -pl.subplot(2, 3, 3) -pl.imshow(I1te) -pl.axis('off') -pl.title('Image 1 Adapt (reg)') - -pl.subplot(2, 3, 4) -pl.imshow(I2) -pl.axis('off') -pl.title('Image 2') - -pl.subplot(2, 3, 5) -pl.imshow(I2t) -pl.axis('off') -pl.title('Image 2 Adapt') - -pl.subplot(2, 3, 6) -pl.imshow(I2te) -pl.axis('off') -pl.title('Image 2 Adapt (reg)') -pl.tight_layout() - -pl.show() diff --git a/examples/plot_otda_d2.py b/examples/plot_otda_d2.py deleted file mode 100644 index f49a570..0000000 --- a/examples/plot_otda_d2.py +++ /dev/null @@ -1,174 +0,0 @@ -# -*- coding: utf-8 -*- -""" -=================================================== -OT for domain adaptation on empirical distributions -=================================================== - -This example introduces a domain adaptation in a 2D setting. It explicits -the problem of domain adaptation and introduces some optimal transport -approaches to solve it. - -Quantities such as optimal couplings, greater coupling coefficients and -transported samples are represented in order to give a visual understanding -of what the transport methods are doing. -""" - -# Authors: Remi Flamary -# Stanislas Chambon -# -# License: MIT License - -# sphinx_gallery_thumbnail_number = 2 - -import matplotlib.pylab as pl -import ot -import ot.plot - -############################################################################## -# generate data -# ------------- - -n_samples_source = 150 -n_samples_target = 150 - -Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) -Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) - -# Cost matrix -M = ot.dist(Xs, Xt, metric='sqeuclidean') - - -############################################################################## -# Instantiate the different transport algorithms and fit them -# ----------------------------------------------------------- - -# EMD Transport -ot_emd = ot.da.EMDTransport() -ot_emd.fit(Xs=Xs, Xt=Xt) - -# Sinkhorn Transport -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - -# Sinkhorn Transport with Group lasso regularization -ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) -ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) - -# transport source samples onto target samples -transp_Xs_emd = ot_emd.transform(Xs=Xs) -transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) -transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) - - -############################################################################## -# Fig 1 : plots source and target samples + matrix of pairwise distance -# --------------------------------------------------------------------- - -pl.figure(1, figsize=(10, 10)) -pl.subplot(2, 2, 1) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Source samples') - -pl.subplot(2, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Target samples') - -pl.subplot(2, 2, 3) -pl.imshow(M, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Matrix of pairwise distances') -pl.tight_layout() - - -############################################################################## -# Fig 2 : plots optimal couplings for the different methods -# --------------------------------------------------------- -pl.figure(2, figsize=(10, 6)) - -pl.subplot(2, 3, 1) -pl.imshow(ot_emd.coupling_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nEMDTransport') - -pl.subplot(2, 3, 2) -pl.imshow(ot_sinkhorn.coupling_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornTransport') - -pl.subplot(2, 3, 3) -pl.imshow(ot_lpl1.coupling_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornLpl1Transport') - -pl.subplot(2, 3, 4) -ot.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1]) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.title('Main coupling coefficients\nEMDTransport') - -pl.subplot(2, 3, 5) -ot.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1]) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.title('Main coupling coefficients\nSinkhornTransport') - -pl.subplot(2, 3, 6) -ot.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1]) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.title('Main coupling coefficients\nSinkhornLpl1Transport') -pl.tight_layout() - - -############################################################################## -# Fig 3 : plot transported samples -# -------------------------------- - -# display transported samples -pl.figure(4, figsize=(10, 4)) -pl.subplot(1, 3, 1) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Transported samples\nEmdTransport') -pl.legend(loc=0) -pl.xticks([]) -pl.yticks([]) - -pl.subplot(1, 3, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Transported samples\nSinkhornTransport') -pl.xticks([]) -pl.yticks([]) - -pl.subplot(1, 3, 3) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Transported samples\nSinkhornLpl1Transport') -pl.xticks([]) -pl.yticks([]) - -pl.tight_layout() -pl.show() diff --git a/examples/plot_otda_jcpot.py b/examples/plot_otda_jcpot.py deleted file mode 100644 index c495690..0000000 --- a/examples/plot_otda_jcpot.py +++ /dev/null @@ -1,171 +0,0 @@ -# -*- coding: utf-8 -*- -""" -======================== -OT for multi-source target shift -======================== - -This example introduces a target shift problem with two 2D source and 1 target domain. - -""" - -# Authors: Remi Flamary -# Ievgen Redko -# -# License: MIT License - -import pylab as pl -import numpy as np -import ot -from ot.datasets import make_data_classif - -############################################################################## -# Generate data -# ------------- -n = 50 -sigma = 0.3 -np.random.seed(1985) - -p1 = .2 -dec1 = [0, 2] - -p2 = .9 -dec2 = [0, -2] - -pt = .4 -dect = [4, 0] - -xs1, ys1 = make_data_classif('2gauss_prop', n, nz=sigma, p=p1, bias=dec1) -xs2, ys2 = make_data_classif('2gauss_prop', n + 1, nz=sigma, p=p2, bias=dec2) -xt, yt = make_data_classif('2gauss_prop', n, nz=sigma, p=pt, bias=dect) - -all_Xr = [xs1, xs2] -all_Yr = [ys1, ys2] -# %% - -da = 1.5 - - -def plot_ax(dec, name): - pl.plot([dec[0], dec[0]], [dec[1] - da, dec[1] + da], 'k', alpha=0.5) - pl.plot([dec[0] - da, dec[0] + da], [dec[1], dec[1]], 'k', alpha=0.5) - pl.text(dec[0] - .5, dec[1] + 2, name) - - -############################################################################## -# Fig 1 : plots source and target samples -# --------------------------------------- - -pl.figure(1) -pl.clf() -plot_ax(dec1, 'Source 1') -plot_ax(dec2, 'Source 2') -plot_ax(dect, 'Target') -pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9, - label='Source 1 ({:1.2f}, {:1.2f})'.format(1 - p1, p1)) -pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9, - label='Source 2 ({:1.2f}, {:1.2f})'.format(1 - p2, p2)) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9, - label='Target ({:1.2f}, {:1.2f})'.format(1 - pt, pt)) -pl.title('Data') - -pl.legend() -pl.axis('equal') -pl.axis('off') - -############################################################################## -# Instantiate Sinkhorn transport algorithm and fit them for all source domains -# ---------------------------------------------------------------------------- -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1, metric='sqeuclidean') - - -def print_G(G, xs, ys, xt): - for i in range(G.shape[0]): - for j in range(G.shape[1]): - if G[i, j] > 5e-4: - if ys[i]: - c = 'b' - else: - c = 'r' - pl.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], xt[j, 1]], c, alpha=.2) - - -############################################################################## -# Fig 2 : plot optimal couplings and transported samples -# ------------------------------------------------------ -pl.figure(2) -pl.clf() -plot_ax(dec1, 'Source 1') -plot_ax(dec2, 'Source 2') -plot_ax(dect, 'Target') -print_G(ot_sinkhorn.fit(Xs=xs1, Xt=xt).coupling_, xs1, ys1, xt) -print_G(ot_sinkhorn.fit(Xs=xs2, Xt=xt).coupling_, xs2, ys2, xt) -pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) -pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) - -pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') -pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') - -pl.title('Independent OT') - -pl.legend() -pl.axis('equal') -pl.axis('off') - -############################################################################## -# Instantiate JCPOT adaptation algorithm and fit it -# ---------------------------------------------------------------------------- -otda = ot.da.JCPOTTransport(reg_e=1, max_iter=1000, metric='sqeuclidean', tol=1e-9, verbose=True, log=True) -otda.fit(all_Xr, all_Yr, xt) - -ws1 = otda.proportions_.dot(otda.log_['D2'][0]) -ws2 = otda.proportions_.dot(otda.log_['D2'][1]) - -pl.figure(3) -pl.clf() -plot_ax(dec1, 'Source 1') -plot_ax(dec2, 'Source 2') -plot_ax(dect, 'Target') -print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt) -print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt) -pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) -pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) - -pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') -pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') - -pl.title('OT with prop estimation ({:1.3f},{:1.3f})'.format(otda.proportions_[0], otda.proportions_[1])) - -pl.legend() -pl.axis('equal') -pl.axis('off') - -############################################################################## -# Run oracle transport algorithm with known proportions -# ---------------------------------------------------------------------------- -h_res = np.array([1 - pt, pt]) - -ws1 = h_res.dot(otda.log_['D2'][0]) -ws2 = h_res.dot(otda.log_['D2'][1]) - -pl.figure(4) -pl.clf() -plot_ax(dec1, 'Source 1') -plot_ax(dec2, 'Source 2') -plot_ax(dect, 'Target') -print_G(ot.bregman.sinkhorn(ws1, [], otda.log_['M'][0], reg=1e-1), xs1, ys1, xt) -print_G(ot.bregman.sinkhorn(ws2, [], otda.log_['M'][1], reg=1e-1), xs2, ys2, xt) -pl.scatter(xs1[:, 0], xs1[:, 1], c=ys1, s=35, marker='x', cmap='Set1', vmax=9) -pl.scatter(xs2[:, 0], xs2[:, 1], c=ys2, s=35, marker='+', cmap='Set1', vmax=9) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, s=35, marker='o', cmap='Set1', vmax=9) - -pl.plot([], [], 'r', alpha=.2, label='Mass from Class 1') -pl.plot([], [], 'b', alpha=.2, label='Mass from Class 2') - -pl.title('OT with known proportion ({:1.1f},{:1.1f})'.format(h_res[0], h_res[1])) - -pl.legend() -pl.axis('equal') -pl.axis('off') -pl.show() diff --git a/examples/plot_otda_laplacian.py b/examples/plot_otda_laplacian.py deleted file mode 100644 index 67c8f67..0000000 --- a/examples/plot_otda_laplacian.py +++ /dev/null @@ -1,127 +0,0 @@ -# -*- coding: utf-8 -*- -""" -====================================================== -OT with Laplacian regularization for domain adaptation -====================================================== - -This example introduces a domain adaptation in a 2D setting and OTDA -approach with Laplacian regularization. - -""" - -# Authors: Ievgen Redko - -# License: MIT License - -import matplotlib.pylab as pl -import ot - -############################################################################## -# Generate data -# ------------- - -n_source_samples = 150 -n_target_samples = 150 - -Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) -Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) - - -############################################################################## -# Instantiate the different transport algorithms and fit them -# ----------------------------------------------------------- - -# EMD Transport -ot_emd = ot.da.EMDTransport() -ot_emd.fit(Xs=Xs, Xt=Xt) - -# Sinkhorn Transport -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=.01) -ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - -# EMD Transport with Laplacian regularization -ot_emd_laplace = ot.da.EMDLaplaceTransport(reg_lap=100, reg_src=1) -ot_emd_laplace.fit(Xs=Xs, Xt=Xt) - -# transport source samples onto target samples -transp_Xs_emd = ot_emd.transform(Xs=Xs) -transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) -transp_Xs_emd_laplace = ot_emd_laplace.transform(Xs=Xs) - -############################################################################## -# Fig 1 : plots source and target samples -# --------------------------------------- - -pl.figure(1, figsize=(10, 5)) -pl.subplot(1, 2, 1) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Source samples') - -pl.subplot(1, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Target samples') -pl.tight_layout() - - -############################################################################## -# Fig 2 : plot optimal couplings and transported samples -# ------------------------------------------------------ - -param_img = {'interpolation': 'nearest'} - -pl.figure(2, figsize=(15, 8)) -pl.subplot(2, 3, 1) -pl.imshow(ot_emd.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nEMDTransport') - -pl.figure(2, figsize=(15, 8)) -pl.subplot(2, 3, 2) -pl.imshow(ot_sinkhorn.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornTransport') - -pl.subplot(2, 3, 3) -pl.imshow(ot_emd_laplace.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nEMDLaplaceTransport') - -pl.subplot(2, 3, 4) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nEmdTransport') -pl.legend(loc="lower left") - -pl.subplot(2, 3, 5) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nSinkhornTransport') - -pl.subplot(2, 3, 6) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_emd_laplace[:, 0], transp_Xs_emd_laplace[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nEMDLaplaceTransport') -pl.tight_layout() - -pl.show() diff --git a/examples/plot_otda_linear_mapping.py b/examples/plot_otda_linear_mapping.py deleted file mode 100644 index 36ccb56..0000000 --- a/examples/plot_otda_linear_mapping.py +++ /dev/null @@ -1,146 +0,0 @@ -#!/usr/bin/env python3 -# -*- coding: utf-8 -*- -""" -============================ -Linear OT mapping estimation -============================ - - -""" - -# Author: Remi Flamary -# -# License: MIT License - -# sphinx_gallery_thumbnail_number = 2 - -import numpy as np -import pylab as pl -import ot - -############################################################################## -# Generate data -# ------------- - -n = 1000 -d = 2 -sigma = .1 - -# source samples -angles = np.random.rand(n, 1) * 2 * np.pi -xs = np.concatenate((np.sin(angles), np.cos(angles)), - axis=1) + sigma * np.random.randn(n, 2) -xs[:n // 2, 1] += 2 - - -# target samples -anglet = np.random.rand(n, 1) * 2 * np.pi -xt = np.concatenate((np.sin(anglet), np.cos(anglet)), - axis=1) + sigma * np.random.randn(n, 2) -xt[:n // 2, 1] += 2 - - -A = np.array([[1.5, .7], [.7, 1.5]]) -b = np.array([[4, 2]]) -xt = xt.dot(A) + b - -############################################################################## -# Plot data -# --------- - -pl.figure(1, (5, 5)) -pl.plot(xs[:, 0], xs[:, 1], '+') -pl.plot(xt[:, 0], xt[:, 1], 'o') - - -############################################################################## -# Estimate linear mapping and transport -# ------------------------------------- - -Ae, be = ot.da.OT_mapping_linear(xs, xt) - -xst = xs.dot(Ae) + be - - -############################################################################## -# Plot transported samples -# ------------------------ - -pl.figure(1, (5, 5)) -pl.clf() -pl.plot(xs[:, 0], xs[:, 1], '+') -pl.plot(xt[:, 0], xt[:, 1], 'o') -pl.plot(xst[:, 0], xst[:, 1], '+') - -pl.show() - -############################################################################## -# Load image data -# --------------- - - -def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) - - -def mat2im(X, shape): - """Converts back a matrix to an image""" - return X.reshape(shape) - - -def minmax(I): - return np.clip(I, 0, 1) - - -# Loading images -I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 - - -X1 = im2mat(I1) -X2 = im2mat(I2) - -############################################################################## -# Estimate mapping and adapt -# ---------------------------- - -mapping = ot.da.LinearTransport() - -mapping.fit(Xs=X1, Xt=X2) - - -xst = mapping.transform(Xs=X1) -xts = mapping.inverse_transform(Xt=X2) - -I1t = minmax(mat2im(xst, I1.shape)) -I2t = minmax(mat2im(xts, I2.shape)) - -# %% - - -############################################################################## -# Plot transformed images -# ----------------------- - -pl.figure(2, figsize=(10, 7)) - -pl.subplot(2, 2, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Im. 1') - -pl.subplot(2, 2, 2) -pl.imshow(I2) -pl.axis('off') -pl.title('Im. 2') - -pl.subplot(2, 2, 3) -pl.imshow(I1t) -pl.axis('off') -pl.title('Mapping Im. 1') - -pl.subplot(2, 2, 4) -pl.imshow(I2t) -pl.axis('off') -pl.title('Inverse mapping Im. 2') diff --git a/examples/plot_otda_mapping.py b/examples/plot_otda_mapping.py deleted file mode 100644 index ded2bdf..0000000 --- a/examples/plot_otda_mapping.py +++ /dev/null @@ -1,127 +0,0 @@ -# -*- coding: utf-8 -*- -""" -=========================================== -OT mapping estimation for domain adaptation -=========================================== - -This example presents how to use MappingTransport to estimate at the same -time both the coupling transport and approximate the transport map with either -a linear or a kernelized mapping as introduced in [8]. - -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, - "Mapping estimation for discrete optimal transport", - Neural Information Processing Systems (NIPS), 2016. -""" - -# Authors: Remi Flamary -# Stanislas Chambon -# -# License: MIT License - -# sphinx_gallery_thumbnail_number = 2 - -import numpy as np -import matplotlib.pylab as pl -import ot - - -############################################################################## -# Generate data -# ------------- - -n_source_samples = 100 -n_target_samples = 100 -theta = 2 * np.pi / 20 -noise_level = 0.1 - -Xs, ys = ot.datasets.make_data_classif( - 'gaussrot', n_source_samples, nz=noise_level) -Xs_new, _ = ot.datasets.make_data_classif( - 'gaussrot', n_source_samples, nz=noise_level) -Xt, yt = ot.datasets.make_data_classif( - 'gaussrot', n_target_samples, theta=theta, nz=noise_level) - -# one of the target mode changes its variance (no linear mapping) -Xt[yt == 2] *= 3 -Xt = Xt + 4 - -############################################################################## -# Plot data -# --------- - -pl.figure(1, (10, 5)) -pl.clf() -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.legend(loc=0) -pl.title('Source and target distributions') - - -############################################################################## -# Instantiate the different transport algorithms and fit them -# ----------------------------------------------------------- - -# MappingTransport with linear kernel -ot_mapping_linear = ot.da.MappingTransport( - kernel="linear", mu=1e0, eta=1e-8, bias=True, - max_iter=20, verbose=True) - -ot_mapping_linear.fit(Xs=Xs, Xt=Xt) - -# for original source samples, transform applies barycentric mapping -transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs) - -# for out of source samples, transform applies the linear mapping -transp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new) - - -# MappingTransport with gaussian kernel -ot_mapping_gaussian = ot.da.MappingTransport( - kernel="gaussian", eta=1e-5, mu=1e-1, bias=True, sigma=1, - max_iter=10, verbose=True) -ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) - -# for original source samples, transform applies barycentric mapping -transp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs) - -# for out of source samples, transform applies the gaussian mapping -transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new) - - -############################################################################## -# Plot transported samples -# ------------------------ - -pl.figure(2) -pl.clf() -pl.subplot(2, 2, 1) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) -pl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+', - label='Mapped source samples') -pl.title("Bary. mapping (linear)") -pl.legend(loc=0) - -pl.subplot(2, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) -pl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1], - c=ys, marker='+', label='Learned mapping') -pl.title("Estim. mapping (linear)") - -pl.subplot(2, 2, 3) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) -pl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys, - marker='+', label='barycentric mapping') -pl.title("Bary. mapping (kernel)") - -pl.subplot(2, 2, 4) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) -pl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys, - marker='+', label='Learned mapping') -pl.title("Estim. mapping (kernel)") -pl.tight_layout() - -pl.show() diff --git a/examples/plot_otda_mapping_colors_images.py b/examples/plot_otda_mapping_colors_images.py deleted file mode 100644 index 1276714..0000000 --- a/examples/plot_otda_mapping_colors_images.py +++ /dev/null @@ -1,173 +0,0 @@ -# -*- coding: utf-8 -*- -""" -===================================================== -OT for image color adaptation with mapping estimation -===================================================== - -OT for domain adaptation with image color adaptation [6] with mapping -estimation [8]. - -[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized -discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for -discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. - -""" - -# Authors: Remi Flamary -# Stanislas Chambon -# -# License: MIT License - -# sphinx_gallery_thumbnail_number = 3 - -import numpy as np -import matplotlib.pylab as pl -import ot - -r = np.random.RandomState(42) - - -def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) - - -def mat2im(X, shape): - """Converts back a matrix to an image""" - return X.reshape(shape) - - -def minmax(I): - return np.clip(I, 0, 1) - - -############################################################################## -# Generate data -# ------------- - -# Loading images -I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 - - -X1 = im2mat(I1) -X2 = im2mat(I2) - -# training samples -nb = 1000 -idx1 = r.randint(X1.shape[0], size=(nb,)) -idx2 = r.randint(X2.shape[0], size=(nb,)) - -Xs = X1[idx1, :] -Xt = X2[idx2, :] - - -############################################################################## -# Domain adaptation for pixel distribution transfer -# ------------------------------------------------- - -# EMDTransport -ot_emd = ot.da.EMDTransport() -ot_emd.fit(Xs=Xs, Xt=Xt) -transp_Xs_emd = ot_emd.transform(Xs=X1) -Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape)) - -# SinkhornTransport -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn.fit(Xs=Xs, Xt=Xt) -transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) -Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) - -ot_mapping_linear = ot.da.MappingTransport( - mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True) -ot_mapping_linear.fit(Xs=Xs, Xt=Xt) - -X1tl = ot_mapping_linear.transform(Xs=X1) -Image_mapping_linear = minmax(mat2im(X1tl, I1.shape)) - -ot_mapping_gaussian = ot.da.MappingTransport( - mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True) -ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) - -X1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping -Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape)) - - -############################################################################## -# Plot original images -# -------------------- - -pl.figure(1, figsize=(6.4, 3)) -pl.subplot(1, 2, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Image 1') - -pl.subplot(1, 2, 2) -pl.imshow(I2) -pl.axis('off') -pl.title('Image 2') -pl.tight_layout() - - -############################################################################## -# Plot pixel values distribution -# ------------------------------ - -pl.figure(2, figsize=(6.4, 5)) - -pl.subplot(1, 2, 1) -pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) -pl.axis([0, 1, 0, 1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 1') - -pl.subplot(1, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) -pl.axis([0, 1, 0, 1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 2') -pl.tight_layout() - - -############################################################################## -# Plot transformed images -# ----------------------- - -pl.figure(2, figsize=(10, 5)) - -pl.subplot(2, 3, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Im. 1') - -pl.subplot(2, 3, 4) -pl.imshow(I2) -pl.axis('off') -pl.title('Im. 2') - -pl.subplot(2, 3, 2) -pl.imshow(Image_emd) -pl.axis('off') -pl.title('EmdTransport') - -pl.subplot(2, 3, 5) -pl.imshow(Image_sinkhorn) -pl.axis('off') -pl.title('SinkhornTransport') - -pl.subplot(2, 3, 3) -pl.imshow(Image_mapping_linear) -pl.axis('off') -pl.title('MappingTransport (linear)') - -pl.subplot(2, 3, 6) -pl.imshow(Image_mapping_gaussian) -pl.axis('off') -pl.title('MappingTransport (gaussian)') -pl.tight_layout() - -pl.show() diff --git a/examples/plot_otda_semi_supervised.py b/examples/plot_otda_semi_supervised.py deleted file mode 100644 index 478c3b8..0000000 --- a/examples/plot_otda_semi_supervised.py +++ /dev/null @@ -1,150 +0,0 @@ -# -*- coding: utf-8 -*- -""" -============================================ -OTDA unsupervised vs semi-supervised setting -============================================ - -This example introduces a semi supervised domain adaptation in a 2D setting. -It explicits the problem of semi supervised domain adaptation and introduces -some optimal transport approaches to solve it. - -Quantities such as optimal couplings, greater coupling coefficients and -transported samples are represented in order to give a visual understanding -of what the transport methods are doing. -""" - -# Authors: Remi Flamary -# Stanislas Chambon -# -# License: MIT License - -# sphinx_gallery_thumbnail_number = 3 - -import matplotlib.pylab as pl -import ot - - -############################################################################## -# Generate data -# ------------- - -n_samples_source = 150 -n_samples_target = 150 - -Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) -Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) - - -############################################################################## -# Transport source samples onto target samples -# -------------------------------------------- - - -# unsupervised domain adaptation -ot_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn_un.fit(Xs=Xs, Xt=Xt) -transp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs) - -# semi-supervised domain adaptation -ot_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt) -transp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs) - -# semi supervised DA uses available labaled target samples to modify the cost -# matrix involved in the OT problem. The cost of transporting a source sample -# of class A onto a target sample of class B != A is set to infinite, or a -# very large value - -# note that in the present case we consider that all the target samples are -# labeled. For daily applications, some target sample might not have labels, -# in this case the element of yt corresponding to these samples should be -# filled with -1. - -# Warning: we recall that -1 cannot be used as a class label - - -############################################################################## -# Fig 1 : plots source and target samples + matrix of pairwise distance -# --------------------------------------------------------------------- - -pl.figure(1, figsize=(10, 10)) -pl.subplot(2, 2, 1) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Source samples') - -pl.subplot(2, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Target samples') - -pl.subplot(2, 2, 3) -pl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Cost matrix - unsupervised DA') - -pl.subplot(2, 2, 4) -pl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Cost matrix - semisupervised DA') - -pl.tight_layout() - -# the optimal coupling in the semi-supervised DA case will exhibit " shape -# similar" to the cost matrix, (block diagonal matrix) - - -############################################################################## -# Fig 2 : plots optimal couplings for the different methods -# --------------------------------------------------------- - -pl.figure(2, figsize=(8, 4)) - -pl.subplot(1, 2, 1) -pl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nUnsupervised DA') - -pl.subplot(1, 2, 2) -pl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSemi-supervised DA') - -pl.tight_layout() - - -############################################################################## -# Fig 3 : plot transported samples -# -------------------------------- - -# display transported samples -pl.figure(4, figsize=(8, 4)) -pl.subplot(1, 2, 1) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Transported samples\nEmdTransport') -pl.legend(loc=0) -pl.xticks([]) -pl.yticks([]) - -pl.subplot(1, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Transported samples\nSinkhornTransport') -pl.xticks([]) -pl.yticks([]) - -pl.tight_layout() -pl.show() diff --git a/examples/plot_partial_wass_and_gromov.py b/examples/plot_partial_wass_and_gromov.py deleted file mode 100755 index 0c5cbf9..0000000 --- a/examples/plot_partial_wass_and_gromov.py +++ /dev/null @@ -1,165 +0,0 @@ -# -*- coding: utf-8 -*- -""" -================================================== -Partial Wasserstein and Gromov-Wasserstein example -================================================== - -This example is designed to show how to use the Partial (Gromov-)Wassertsein -distance computation in POT. -""" - -# Author: Laetitia Chapel -# License: MIT License - -# sphinx_gallery_thumbnail_number = 2 - -# necessary for 3d plot even if not used -from mpl_toolkits.mplot3d import Axes3D # noqa -import scipy as sp -import numpy as np -import matplotlib.pylab as pl -import ot - - -############################################################################# -# -# Sample two 2D Gaussian distributions and plot them -# -------------------------------------------------- -# -# For demonstration purpose, we sample two Gaussian distributions in 2-d -# spaces and add some random noise. - - -n_samples = 20 # nb samples (gaussian) -n_noise = 20 # nb of samples (noise) - -mu = np.array([0, 0]) -cov = np.array([[1, 0], [0, 2]]) - -xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) -xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2)) -xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) -xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2)) - -M = sp.spatial.distance.cdist(xs, xt) - -fig = pl.figure() -ax1 = fig.add_subplot(131) -ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -ax2 = fig.add_subplot(132) -ax2.scatter(xt[:, 0], xt[:, 1], color='r') -ax3 = fig.add_subplot(133) -ax3.imshow(M) -pl.show() - -############################################################################# -# -# Compute partial Wasserstein plans and distance -# ---------------------------------------------- - -p = ot.unif(n_samples + n_noise) -q = ot.unif(n_samples + n_noise) - -w0, log0 = ot.partial.partial_wasserstein(p, q, M, m=0.5, log=True) -w, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=0.1, m=0.5, - log=True) - -print('Partial Wasserstein distance (m = 0.5): ' + str(log0['partial_w_dist'])) -print('Entropic partial Wasserstein distance (m = 0.5): ' + - str(log['partial_w_dist'])) - -pl.figure(1, (10, 5)) -pl.subplot(1, 2, 1) -pl.imshow(w0, cmap='jet') -pl.title('Partial Wasserstein') -pl.subplot(1, 2, 2) -pl.imshow(w, cmap='jet') -pl.title('Entropic partial Wasserstein') -pl.show() - - -############################################################################# -# -# Sample one 2D and 3D Gaussian distributions and plot them -# --------------------------------------------------------- -# -# The Gromov-Wasserstein distance allows to compute distances with samples that -# do not belong to the same metric space. For demonstration purpose, we sample -# two Gaussian distributions in 2- and 3-dimensional spaces. - -n_samples = 20 # nb samples -n_noise = 10 # nb of samples (noise) - -p = ot.unif(n_samples + n_noise) -q = ot.unif(n_samples + n_noise) - -mu_s = np.array([0, 0]) -cov_s = np.array([[1, 0], [0, 1]]) - -mu_t = np.array([0, 0, 0]) -cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) - - -xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) -xs = np.concatenate((xs, ((np.random.rand(n_noise, 2) + 1) * 4)), axis=0) -P = sp.linalg.sqrtm(cov_t) -xt = np.random.randn(n_samples, 3).dot(P) + mu_t -xt = np.concatenate((xt, ((np.random.rand(n_noise, 3) + 1) * 10)), axis=0) - -fig = pl.figure() -ax1 = fig.add_subplot(121) -ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -ax2 = fig.add_subplot(122, projection='3d') -ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') -pl.show() - - -############################################################################# -# -# Compute partial Gromov-Wasserstein plans and distance -# ----------------------------------------------------- - -C1 = sp.spatial.distance.cdist(xs, xs) -C2 = sp.spatial.distance.cdist(xt, xt) - -# transport 100% of the mass -print('-----m = 1') -m = 1 -res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True) -res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, - m=m, log=True) - -print('Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist'])) -print('Entropic Wasserstein distance (m = 1): ' + str(log['partial_gw_dist'])) - -pl.figure(1, (10, 5)) -pl.title("mass to be transported m = 1") -pl.subplot(1, 2, 1) -pl.imshow(res0, cmap='jet') -pl.title('Wasserstein') -pl.subplot(1, 2, 2) -pl.imshow(res, cmap='jet') -pl.title('Entropic Wasserstein') -pl.show() - -# transport 2/3 of the mass -print('-----m = 2/3') -m = 2 / 3 -res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True) -res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, - m=m, log=True) - -print('Partial Wasserstein distance (m = 2/3): ' + - str(log0['partial_gw_dist'])) -print('Entropic partial Wasserstein distance (m = 2/3): ' + - str(log['partial_gw_dist'])) - -pl.figure(1, (10, 5)) -pl.title("mass to be transported m = 2/3") -pl.subplot(1, 2, 1) -pl.imshow(res0, cmap='jet') -pl.title('Partial Wasserstein') -pl.subplot(1, 2, 2) -pl.imshow(res, cmap='jet') -pl.title('Entropic partial Wasserstein') -pl.show() diff --git a/examples/unbalanced-partial/README.txt b/examples/unbalanced-partial/README.txt new file mode 100644 index 0000000..2f404f0 --- /dev/null +++ b/examples/unbalanced-partial/README.txt @@ -0,0 +1,3 @@ + +Unbalanced and Partial OT +------------------------- \ No newline at end of file diff --git a/examples/unbalanced-partial/plot_UOT_1D.py b/examples/unbalanced-partial/plot_UOT_1D.py new file mode 100644 index 0000000..2ea8b05 --- /dev/null +++ b/examples/unbalanced-partial/plot_UOT_1D.py @@ -0,0 +1,76 @@ +# -*- coding: utf-8 -*- +""" +=============================== +1D Unbalanced optimal transport +=============================== + +This example illustrates the computation of Unbalanced Optimal transport +using a Kullback-Leibler relaxation. +""" + +# Author: Hicham Janati +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot +import ot.plot +from ot.datasets import make_1D_gauss as gauss + +############################################################################## +# Generate data +# ------------- + + +#%% parameters + +n = 100 # nb bins + +# bin positions +x = np.arange(n, dtype=np.float64) + +# Gaussian distributions +a = gauss(n, m=20, s=5) # m= mean, s= std +b = gauss(n, m=60, s=10) + +# make distributions unbalanced +b *= 5. + +# loss matrix +M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) +M /= M.max() + + +############################################################################## +# Plot distributions and loss matrix +# ---------------------------------- + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +pl.plot(x, a, 'b', label='Source distribution') +pl.plot(x, b, 'r', label='Target distribution') +pl.legend() + +# plot distributions and loss matrix + +pl.figure(2, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') + + +############################################################################## +# Solve Unbalanced Sinkhorn +# -------------- + + +# Sinkhorn + +epsilon = 0.1 # entropy parameter +alpha = 1. # Unbalanced KL relaxation parameter +Gs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True) + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn') + +pl.show() diff --git a/examples/unbalanced-partial/plot_UOT_barycenter_1D.py b/examples/unbalanced-partial/plot_UOT_barycenter_1D.py new file mode 100644 index 0000000..931798b --- /dev/null +++ b/examples/unbalanced-partial/plot_UOT_barycenter_1D.py @@ -0,0 +1,166 @@ +# -*- coding: utf-8 -*- +""" +=========================================================== +1D Wasserstein barycenter demo for Unbalanced distributions +=========================================================== + +This example illustrates the computation of regularized Wassersyein Barycenter +as proposed in [10] for Unbalanced inputs. + + +[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + +""" + +# Author: Hicham Janati +# +# License: MIT License + +# sphinx_gallery_thumbnail_number = 2 + +import numpy as np +import matplotlib.pylab as pl +import ot +# necessary for 3d plot even if not used +from mpl_toolkits.mplot3d import Axes3D # noqa +from matplotlib.collections import PolyCollection + +############################################################################## +# Generate data +# ------------- + +# parameters + +n = 100 # nb bins + +# bin positions +x = np.arange(n, dtype=np.float64) + +# Gaussian distributions +a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std +a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) + +# make unbalanced dists +a2 *= 3. + +# creating matrix A containing all distributions +A = np.vstack((a1, a2)).T +n_distributions = A.shape[1] + +# loss matrix + normalization +M = ot.utils.dist0(n) +M /= M.max() + +############################################################################## +# Plot data +# --------- + +# plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') +pl.tight_layout() + +############################################################################## +# Barycenter computation +# ---------------------- + +# non weighted barycenter computation + +weight = 0.5 # 0<=weight<=1 +weights = np.array([1 - weight, weight]) + +# l2bary +bary_l2 = A.dot(weights) + +# wasserstein +reg = 1e-3 +alpha = 1. + +bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights) + +pl.figure(2) +pl.clf() +pl.subplot(2, 1, 1) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') + +pl.subplot(2, 1, 2) +pl.plot(x, bary_l2, 'r', label='l2') +pl.plot(x, bary_wass, 'g', label='Wasserstein') +pl.legend() +pl.title('Barycenters') +pl.tight_layout() + +############################################################################## +# Barycentric interpolation +# ------------------------- + +# barycenter interpolation + +n_weight = 11 +weight_list = np.linspace(0, 1, n_weight) + + +B_l2 = np.zeros((n, n_weight)) + +B_wass = np.copy(B_l2) + +for i in range(0, n_weight): + weight = weight_list[i] + weights = np.array([1 - weight, weight]) + B_l2[:, i] = A.dot(weights) + B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights=weights) + + +# plot interpolation + +pl.figure(3) + +cmap = pl.cm.get_cmap('viridis') +verts = [] +zs = weight_list +for i, z in enumerate(zs): + ys = B_l2[:, i] + verts.append(list(zip(x, ys))) + +ax = pl.gcf().gca(projection='3d') + +poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) +poly.set_alpha(0.7) +ax.add_collection3d(poly, zs=zs, zdir='y') +ax.set_xlabel('x') +ax.set_xlim3d(0, n) +ax.set_ylabel(r'$\alpha$') +ax.set_ylim3d(0, 1) +ax.set_zlabel('') +ax.set_zlim3d(0, B_l2.max() * 1.01) +pl.title('Barycenter interpolation with l2') +pl.tight_layout() + +pl.figure(4) +cmap = pl.cm.get_cmap('viridis') +verts = [] +zs = weight_list +for i, z in enumerate(zs): + ys = B_wass[:, i] + verts.append(list(zip(x, ys))) + +ax = pl.gcf().gca(projection='3d') + +poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) +poly.set_alpha(0.7) +ax.add_collection3d(poly, zs=zs, zdir='y') +ax.set_xlabel('x') +ax.set_xlim3d(0, n) +ax.set_ylabel(r'$\alpha$') +ax.set_ylim3d(0, 1) +ax.set_zlabel('') +ax.set_zlim3d(0, B_l2.max() * 1.01) +pl.title('Barycenter interpolation with Wasserstein') +pl.tight_layout() + +pl.show() diff --git a/examples/unbalanced-partial/plot_partial_wass_and_gromov.py b/examples/unbalanced-partial/plot_partial_wass_and_gromov.py new file mode 100755 index 0000000..0c5cbf9 --- /dev/null +++ b/examples/unbalanced-partial/plot_partial_wass_and_gromov.py @@ -0,0 +1,165 @@ +# -*- coding: utf-8 -*- +""" +================================================== +Partial Wasserstein and Gromov-Wasserstein example +================================================== + +This example is designed to show how to use the Partial (Gromov-)Wassertsein +distance computation in POT. +""" + +# Author: Laetitia Chapel +# License: MIT License + +# sphinx_gallery_thumbnail_number = 2 + +# necessary for 3d plot even if not used +from mpl_toolkits.mplot3d import Axes3D # noqa +import scipy as sp +import numpy as np +import matplotlib.pylab as pl +import ot + + +############################################################################# +# +# Sample two 2D Gaussian distributions and plot them +# -------------------------------------------------- +# +# For demonstration purpose, we sample two Gaussian distributions in 2-d +# spaces and add some random noise. + + +n_samples = 20 # nb samples (gaussian) +n_noise = 20 # nb of samples (noise) + +mu = np.array([0, 0]) +cov = np.array([[1, 0], [0, 2]]) + +xs = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) +xs = np.append(xs, (np.random.rand(n_noise, 2) + 1) * 4).reshape((-1, 2)) +xt = ot.datasets.make_2D_samples_gauss(n_samples, mu, cov) +xt = np.append(xt, (np.random.rand(n_noise, 2) + 1) * -3).reshape((-1, 2)) + +M = sp.spatial.distance.cdist(xs, xt) + +fig = pl.figure() +ax1 = fig.add_subplot(131) +ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +ax2 = fig.add_subplot(132) +ax2.scatter(xt[:, 0], xt[:, 1], color='r') +ax3 = fig.add_subplot(133) +ax3.imshow(M) +pl.show() + +############################################################################# +# +# Compute partial Wasserstein plans and distance +# ---------------------------------------------- + +p = ot.unif(n_samples + n_noise) +q = ot.unif(n_samples + n_noise) + +w0, log0 = ot.partial.partial_wasserstein(p, q, M, m=0.5, log=True) +w, log = ot.partial.entropic_partial_wasserstein(p, q, M, reg=0.1, m=0.5, + log=True) + +print('Partial Wasserstein distance (m = 0.5): ' + str(log0['partial_w_dist'])) +print('Entropic partial Wasserstein distance (m = 0.5): ' + + str(log['partial_w_dist'])) + +pl.figure(1, (10, 5)) +pl.subplot(1, 2, 1) +pl.imshow(w0, cmap='jet') +pl.title('Partial Wasserstein') +pl.subplot(1, 2, 2) +pl.imshow(w, cmap='jet') +pl.title('Entropic partial Wasserstein') +pl.show() + + +############################################################################# +# +# Sample one 2D and 3D Gaussian distributions and plot them +# --------------------------------------------------------- +# +# The Gromov-Wasserstein distance allows to compute distances with samples that +# do not belong to the same metric space. For demonstration purpose, we sample +# two Gaussian distributions in 2- and 3-dimensional spaces. + +n_samples = 20 # nb samples +n_noise = 10 # nb of samples (noise) + +p = ot.unif(n_samples + n_noise) +q = ot.unif(n_samples + n_noise) + +mu_s = np.array([0, 0]) +cov_s = np.array([[1, 0], [0, 1]]) + +mu_t = np.array([0, 0, 0]) +cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) + + +xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) +xs = np.concatenate((xs, ((np.random.rand(n_noise, 2) + 1) * 4)), axis=0) +P = sp.linalg.sqrtm(cov_t) +xt = np.random.randn(n_samples, 3).dot(P) + mu_t +xt = np.concatenate((xt, ((np.random.rand(n_noise, 3) + 1) * 10)), axis=0) + +fig = pl.figure() +ax1 = fig.add_subplot(121) +ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +ax2 = fig.add_subplot(122, projection='3d') +ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') +pl.show() + + +############################################################################# +# +# Compute partial Gromov-Wasserstein plans and distance +# ----------------------------------------------------- + +C1 = sp.spatial.distance.cdist(xs, xs) +C2 = sp.spatial.distance.cdist(xt, xt) + +# transport 100% of the mass +print('-----m = 1') +m = 1 +res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True) +res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, + m=m, log=True) + +print('Wasserstein distance (m = 1): ' + str(log0['partial_gw_dist'])) +print('Entropic Wasserstein distance (m = 1): ' + str(log['partial_gw_dist'])) + +pl.figure(1, (10, 5)) +pl.title("mass to be transported m = 1") +pl.subplot(1, 2, 1) +pl.imshow(res0, cmap='jet') +pl.title('Wasserstein') +pl.subplot(1, 2, 2) +pl.imshow(res, cmap='jet') +pl.title('Entropic Wasserstein') +pl.show() + +# transport 2/3 of the mass +print('-----m = 2/3') +m = 2 / 3 +res0, log0 = ot.partial.partial_gromov_wasserstein(C1, C2, p, q, m=m, log=True) +res, log = ot.partial.entropic_partial_gromov_wasserstein(C1, C2, p, q, 10, + m=m, log=True) + +print('Partial Wasserstein distance (m = 2/3): ' + + str(log0['partial_gw_dist'])) +print('Entropic partial Wasserstein distance (m = 2/3): ' + + str(log['partial_gw_dist'])) + +pl.figure(1, (10, 5)) +pl.title("mass to be transported m = 2/3") +pl.subplot(1, 2, 1) +pl.imshow(res0, cmap='jet') +pl.title('Partial Wasserstein') +pl.subplot(1, 2, 2) +pl.imshow(res, cmap='jet') +pl.title('Entropic partial Wasserstein') +pl.show() -- cgit v1.2.3 From 4bbabc602678a0227bfe9ffae4bbb4caab8a3767 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 17:38:01 +0200 Subject: relative path exmaples --- examples/domain-adaptation/plot_otda_color_images.py | 4 ++-- examples/domain-adaptation/plot_otda_mapping_colors_images.py | 4 ++-- examples/gromov/plot_gromov_barycenter.py | 8 ++++---- 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/examples/domain-adaptation/plot_otda_color_images.py b/examples/domain-adaptation/plot_otda_color_images.py index 7e0afee..929365e 100644 --- a/examples/domain-adaptation/plot_otda_color_images.py +++ b/examples/domain-adaptation/plot_otda_color_images.py @@ -46,8 +46,8 @@ def minmax(I): # ------------- # Loading images -I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 +I1 = pl.imread('../../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = pl.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256 X1 = im2mat(I1) X2 = im2mat(I2) diff --git a/examples/domain-adaptation/plot_otda_mapping_colors_images.py b/examples/domain-adaptation/plot_otda_mapping_colors_images.py index 1276714..9d3a7c7 100644 --- a/examples/domain-adaptation/plot_otda_mapping_colors_images.py +++ b/examples/domain-adaptation/plot_otda_mapping_colors_images.py @@ -47,8 +47,8 @@ def minmax(I): # ------------- # Loading images -I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 +I1 = pl.imread('../../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = pl.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256 X1 = im2mat(I1) diff --git a/examples/gromov/plot_gromov_barycenter.py b/examples/gromov/plot_gromov_barycenter.py index 6b29687..f6f031a 100755 --- a/examples/gromov/plot_gromov_barycenter.py +++ b/examples/gromov/plot_gromov_barycenter.py @@ -89,10 +89,10 @@ def im2mat(I): return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) -square = pl.imread('../data/square.png').astype(np.float64)[:, :, 2] -cross = pl.imread('../data/cross.png').astype(np.float64)[:, :, 2] -triangle = pl.imread('../data/triangle.png').astype(np.float64)[:, :, 2] -star = pl.imread('../data/star.png').astype(np.float64)[:, :, 2] +square = pl.imread('../../data/square.png').astype(np.float64)[:, :, 2] +cross = pl.imread('../../data/cross.png').astype(np.float64)[:, :, 2] +triangle = pl.imread('../../data/triangle.png').astype(np.float64)[:, :, 2] +star = pl.imread('../../data/star.png').astype(np.float64)[:, :, 2] shapes = [square, cross, triangle, star] -- cgit v1.2.3 From 956df7af113d62eab1d65f6db5fbb81897dc49c6 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 17:45:13 +0200 Subject: vchange path in examples --- examples/barycenters/plot_convolutional_barycenter.py | 8 ++++---- examples/domain-adaptation/plot_otda_linear_mapping.py | 4 ++-- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/barycenters/plot_convolutional_barycenter.py b/examples/barycenters/plot_convolutional_barycenter.py index e74db04..cbcd4a1 100644 --- a/examples/barycenters/plot_convolutional_barycenter.py +++ b/examples/barycenters/plot_convolutional_barycenter.py @@ -26,10 +26,10 @@ import ot # The four distributions are constructed from 4 simple images -f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2] -f2 = 1 - pl.imread('../data/duck.png')[:, :, 2] -f3 = 1 - pl.imread('../data/heart.png')[:, :, 2] -f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2] +f1 = 1 - pl.imread('../../data/redcross.png')[:, :, 2] +f2 = 1 - pl.imread('../../data/duck.png')[:, :, 2] +f3 = 1 - pl.imread('../../data/heart.png')[:, :, 2] +f4 = 1 - pl.imread('../../data/tooth.png')[:, :, 2] A = [] f1 = f1 / np.sum(f1) diff --git a/examples/domain-adaptation/plot_otda_linear_mapping.py b/examples/domain-adaptation/plot_otda_linear_mapping.py index 36ccb56..dbf16b8 100644 --- a/examples/domain-adaptation/plot_otda_linear_mapping.py +++ b/examples/domain-adaptation/plot_otda_linear_mapping.py @@ -94,8 +94,8 @@ def minmax(I): # Loading images -I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 +I1 = pl.imread('../../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = pl.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256 X1 = im2mat(I1) -- cgit v1.2.3 From 71e79844a54ea88babd04b01688766a17b3de614 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 24 Apr 2020 18:25:17 +0200 Subject: update proper links form readme --- README.md | 28 ++++++++++++++-------------- docs/source/index.rst | 3 ++- docs/source/readme.rst | 38 +++++++++++++++++++------------------- 3 files changed, 35 insertions(+), 34 deletions(-) diff --git a/README.md b/README.md index 1b5c58d..893f200 100644 --- a/README.md +++ b/README.md @@ -22,29 +22,29 @@ POT provides the following generic OT solvers (links to examples): * [OT Network Simplex solver](https://pythonot.github.io/auto_examples/plot_OT_1D.html) for the linear program/ Earth Movers Distance [1] . * [Conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) [6] and [Generalized conditional gradient](https://pythonot.github.io/auto_examples/plot_optim_OTreg.html) for regularized OT [7]. * Entropic regularization OT solver with [Sinkhorn Knopp Algorithm](https://pythonot.github.io/auto_examples/plot_OT_1D.html) [2] , stabilized version [9] [10], greedy Sinkhorn [22] and [Screening Sinkhorn [26] ](https://pythonot.github.io/auto_examples/plot_screenkhorn_1D.html) with optional GPU implementation (requires cupy). -* Bregman projections for [Wasserstein barycenter](https://pythonot.github.io/auto_examples/plot_barycenter_lp_vs_entropic.html) [3], [convolutional barycenter](https://pythonot.github.io/auto_examples/plot_convolutional_barycenter.html) [21] and unmixing [4]. +* Bregman projections for [Wasserstein barycenter](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html) [3], [convolutional barycenter](https://pythonot.github.io/auto_examples/barycenters/plot_convolutional_barycenter.html) [21] and unmixing [4]. * Sinkhorn divergence [23] and entropic regularization OT from empirical data. * [Smooth optimal transport solvers](https://pythonot.github.io/auto_examples/plot_OT_1D_smooth.html) (dual and semi-dual) for KL and squared L2 regularizations [17]. -* Non regularized [Wasserstein barycenters [16] ](https://pythonot.github.io/auto_examples/plot_barycenter_lp_vs_entropic.html)) with LP solver (only small scale). -* [Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/plot_gromov.html) and [GW barycenters](https://pythonot.github.io/auto_examples/plot_gromov_barycenter.html) (exact [13] and regularized [12]) - * [Fused-Gromov-Wasserstein distances solver](https://pythonot.github.io/auto_examples/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py) and [FGW barycenters](https://pythonot.github.io/auto_examples/plot_barycenter_fgw.html) [24] +* Non regularized [Wasserstein barycenters [16] ](https://pythonot.github.io/auto_examples/barycenters/plot_barycenter_lp_vs_entropic.html)) with LP solver (only small scale). +* [Gromov-Wasserstein distances](https://pythonot.github.io/auto_examples/gromov/plot_gromov.html) and [GW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_gromov_barycenter.html) (exact [13] and regularized [12]) + * [Fused-Gromov-Wasserstein distances solver](https://pythonot.github.io/auto_examples/gromov/plot_fgw.html#sphx-glr-auto-examples-plot-fgw-py) and [FGW barycenters](https://pythonot.github.io/auto_examples/gromov/plot_barycenter_fgw.html) [24] * [Stochastic solver](https://pythonot.github.io/auto_examples/plot_stochastic.html) for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) -* Non regularized [free support Wasserstein barycenters](https://pythonot.github.io/auto_examples/plot_free_support_barycenter.html) [20]. -* [Unbalanced OT](https://pythonot.github.io/auto_examples/plot_UOT_1D.html) with KL relaxation and [barycenter](https://pythonot.github.io/auto_examples/plot_UOT_barycenter_1D.html) [10, 25]. -* [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3] +* Non regularized [free support Wasserstein barycenters](https://pythonot.github.io/auto_examples/barycenters/plot_free_support_barycenter.html) [20]. +* [Unbalanced OT](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_1D.html) with KL relaxation and [barycenter](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_UOT_barycenter_1D.html) [10, 25]. +* [Partial Wasserstein and Gromov-Wasserstein](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html) (exact [29] and entropic [3] formulations). POT provides the following Machine Learning related solvers: * [Optimal transport for domain adaptation](https://pythonot.github.io/auto_examples/plot_otda_classes.html) - with [group lasso regularization](https://pythonot.github.io/auto_examples/plot_otda_classes.html), [Laplacian regularization](https://pythonot.github.io/auto_examples/plot_otda_laplacian.html) [5] [30] and [semi - supervised setting](https://pythonot.github.io/auto_examples/plot_otda_semi_supervised.html). -* [Linear OT mapping](https://pythonot.github.io/auto_examples/plot_otda_linear_mapping.html) [14] and [Joint OT mapping estimation](https://pythonot.github.io/auto_examples/plot_otda_mapping.html) [8]. -* [Wasserstein Discriminant Analysis](https://pythonot.github.io/auto_examples/plot_WDA.html) [11] (requires autograd + pymanopt). -* [JCPOT algorithm for multi-source domain adaptation with target shift](https://pythonot.github.io/auto_examples/plot_otda_jcpot.html) [27]. + with [group lasso regularization](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_classes.html), [Laplacian regularization](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_laplacian.html) [5] [30] and [semi + supervised setting](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_semi_supervised.html). +* [Linear OT mapping](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_linear_mapping.html) [14] and [Joint OT mapping estimation](https://pythonot.github.io/auto_examples/plot_otda_mapping.html) [8]. +* [Wasserstein Discriminant Analysis](https://pythonot.github.io/auto_examples/domain-adaptation/plot_WDA.html) [11] (requires autograd + pymanopt). +* [JCPOT algorithm for multi-source domain adaptation with target shift](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_jcpot.html) [27]. -Some demonstrations are available in the [documentation](https://pythonot.github.io/auto_examples/index.html). +Some other examples are available in the [documentation](https://pythonot.github.io/auto_examples/index.html). #### Using and citing the toolbox @@ -153,7 +153,7 @@ ba=ot.barycenter(A,M,reg) # reg is regularization parameter ### Examples and Notebooks -The examples folder contain several examples and use case for the library. The full documentation is available on [https://PythonOT.github.io/](https://PythonOT.github.io/). +The examples folder contain several examples and use case for the library. The full documentation with examples and output is available on [https://PythonOT.github.io/](https://PythonOT.github.io/). ## Acknowledgements diff --git a/docs/source/index.rst b/docs/source/index.rst index 47a29a4..be01343 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -19,7 +19,8 @@ Contents releases .. include:: readme.rst - :start-line: 5 + :start-line: 2 + Indices and tables diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 7d8b8bd..2707a07 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -29,9 +29,9 @@ POT provides the following generic OT solvers (links to examples): [26] `__ with optional GPU implementation (requires cupy). - Bregman projections for `Wasserstein - barycenter `__ + barycenter `__ [3], `convolutional - barycenter `__ + barycenter `__ [21] and unmixing [4]. - Sinkhorn divergence [23] and entropic regularization OT from empirical data. @@ -39,32 +39,32 @@ POT provides the following generic OT solvers (links to examples): solvers `__ (dual and semi-dual) for KL and squared L2 regularizations [17]. - Non regularized `Wasserstein barycenters - [16] `__) + [16] `__) with LP solver (only small scale). - `Gromov-Wasserstein - distances `__ + distances `__ and `GW - barycenters `__ + barycenters `__ (exact [13] and regularized [12]) - `Fused-Gromov-Wasserstein distances - solver `__ + solver `__ and `FGW - barycenters `__ + barycenters `__ [24] - `Stochastic solver `__ for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) - Non regularized `free support Wasserstein - barycenters `__ + barycenters `__ [20]. - `Unbalanced - OT `__ + OT `__ with KL relaxation and - `barycenter `__ + `barycenter `__ [10, 25]. - `Partial Wasserstein and - Gromov-Wasserstein `__ + Gromov-Wasserstein `__ (exact [29] and entropic [3] formulations). POT provides the following Machine Learning related solvers: @@ -72,24 +72,24 @@ POT provides the following Machine Learning related solvers: - `Optimal transport for domain adaptation `__ with `group lasso - regularization `__, + regularization `__, `Laplacian - regularization `__ + regularization `__ [5] [30] and `semi supervised - setting `__. + setting `__. - `Linear OT - mapping `__ + mapping `__ [14] and `Joint OT mapping estimation `__ [8]. - `Wasserstein Discriminant - Analysis `__ + Analysis `__ [11] (requires autograd + pymanopt). - `JCPOT algorithm for multi-source domain adaptation with target - shift `__ + shift `__ [27]. -Some demonstrations are available in the +Some other examples are available in the `documentation `__. Using and citing the toolbox @@ -233,7 +233,7 @@ Examples and Notebooks ~~~~~~~~~~~~~~~~~~~~~~ The examples folder contain several examples and use case for the -library. The full documentation is available on +library. The full documentation with examples and output is available on https://PythonOT.github.io/. Acknowledgements -- cgit v1.2.3 From 3b0732b041d46df66cb182b17f6ece040c578722 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 27 Apr 2020 09:07:30 +0200 Subject: correct url for examples --- README.md | 6 +++--- docs/source/readme.rst | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 893f200..b9b8f45 100644 --- a/README.md +++ b/README.md @@ -37,11 +37,11 @@ POT provides the following generic OT solvers (links to examples): POT provides the following Machine Learning related solvers: * [Optimal transport for domain - adaptation](https://pythonot.github.io/auto_examples/plot_otda_classes.html) + adaptation](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_classes.html) with [group lasso regularization](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_classes.html), [Laplacian regularization](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_laplacian.html) [5] [30] and [semi supervised setting](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_semi_supervised.html). -* [Linear OT mapping](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_linear_mapping.html) [14] and [Joint OT mapping estimation](https://pythonot.github.io/auto_examples/plot_otda_mapping.html) [8]. -* [Wasserstein Discriminant Analysis](https://pythonot.github.io/auto_examples/domain-adaptation/plot_WDA.html) [11] (requires autograd + pymanopt). +* [Linear OT mapping](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_linear_mapping.html) [14] and [Joint OT mapping estimation](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_mapping.html) [8]. +* [Wasserstein Discriminant Analysis](https://pythonot.github.io/auto_examples/others/plot_WDA.html) [11] (requires autograd + pymanopt). * [JCPOT algorithm for multi-source domain adaptation with target shift](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_jcpot.html) [27]. Some other examples are available in the [documentation](https://pythonot.github.io/auto_examples/index.html). diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 2707a07..c96f191 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -70,7 +70,7 @@ POT provides the following generic OT solvers (links to examples): POT provides the following Machine Learning related solvers: - `Optimal transport for domain - adaptation `__ + adaptation `__ with `group lasso regularization `__, `Laplacian @@ -80,10 +80,10 @@ POT provides the following Machine Learning related solvers: - `Linear OT mapping `__ [14] and `Joint OT mapping - estimation `__ + estimation `__ [8]. - `Wasserstein Discriminant - Analysis `__ + Analysis `__ [11] (requires autograd + pymanopt). - `JCPOT algorithm for multi-source domain adaptation with target shift `__ -- cgit v1.2.3 From 0453dcce7a5c4497845349ef85a324fdc3e839cd Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 30 Apr 2020 16:08:35 +0200 Subject: change release and update reseale file --- RELEASES.md | 37 +++++++++++++++++++++++++++++++++++++ ot/__init__.py | 2 +- 2 files changed, 38 insertions(+), 1 deletion(-) diff --git a/RELEASES.md b/RELEASES.md index 5fcf485..0fc9240 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -1,7 +1,44 @@ # Releases +## 0.7 +*May 2020* +This is the new stable release for POT. We have a lot of changes in the documentation and several new features such as Partial OT, Unbalanced and Multi Sources OT Domain Adaptation and several bug fixes. One important change is that we have created a the GitHub organization [PythonOT](https://github.com/PythonOT) that now owns the main POT repository [https://github.com/PythonOT/POT](https://github.com/PythonOT/POT) and the repository for the new documentation hosted at [https://PythonOT.github.io/](https://PythonOT.github.io/). +This is the first release where the Python 2.7 tests have been removed. Most of the toolbox should still work but this is the last release where we release Python 2.7 wheels. + +A lot of changes have been done to the documentation that is now hosted on [https://PythonOT.github.io/](https://PythonOT.github.io/) instead of readthedocs. It was a hard choice but readthedocs did not allow us to run sphinx-gallery to update our beautiful examples and it was a huge amount of work to maintain it. The documentation is now automatically compiled and updated on merge. We also removed the notebooks from the repository for space reason and also because they are all available in the [example gallery](https://pythonot.github.io/auto_examples/index.html). Note that now the output of the documentation build for each commit in the PR is available to check that the doc builds correctly before merging which was not possible with readthedoc. + +The CI framework has also been changed with a move from Travis to Github Action which allows us to get faster tests on Windows, MacOS and Linux. We also now report our coverage on [Codecov.io](https://codecov.io/gh/PythonOT/POT) and we have a reasonable 92% coverage. We also now generate wheels for a number of OS and python versions at each merge in the master branch. They are available as artifacts of this [action](https://github.com/PythonOT/POT/actions?query=workflow%3A%22Build+dist+and+wheels%22). This will allow simpler multi-platform releases from now on. + +In terms of new features we now have [OTDA Classes for unbalanced OT](https://pythonot.github.io/gen_modules/ot.da.html#ot.da.UnbalancedSinkhornTransport), a new Domain adaptation class form [multi domain problems (JCPOT)](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_jcpot.html#sphx-glr-auto-examples-domain-adaptation-plot-otda-jcpot-py), and several solvers to solve the [Partial Optimal Transport](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html#sphx-glr-auto-examples-unbalanced-partial-plot-partial-wass-and-gromov-py) problems. + +This release is also the moment to thank all the POT contributors (old and new) for helping making POT such a nice to use toolbox. A lot of changes (also in the API) are comming for the next versions. + + +#### Features + +- New documentation on [https://PythonOT.github.io/](https://PythonOT.github.io/) (PR #160, PR #143, PR #144) +- Documentation build on CircleCI with sphinx-gallery (PR #145,PR #146, #155) +- Run sphinx gallery in CI (PR #146) +- Remove notebooks from repo because available in doc (PR #156) +- Build wheels in CI (#157) +- Move from travis to GitHub Action for Windows, MacOS and Linux (PR #148, PR #150) +- Partial Optimal Transport (PR#141 and PR #142) +- Laplace regularized OTDA (PR #140) +- Multi source DA with target shift (PR #137) +- Screenkhorn algorithm (PR #121) + +#### Closed issues + +- Bug in Unbalanced OT example (Issue #127) +- Clean Cython output when calling setup.py clean (Issue #122) +- Various Macosx compilation problems (Issue #113, Issue #118, PR#130) +- EMD dimension mismatch (Issue #114, Fixed in PR #116) +- 2D barycenter bug for non square images (Issue #124, fixed in PR #132) +- Bad value in EMD 1D (Issue #138, fixed in PR #139) +- Log bugs for Gromov-Wassertein solver (Issue #107, fixed in PR #108) +- Weight issues in barycenter function (PR #106) ## 0.6 *July 2019* diff --git a/ot/__init__.py b/ot/__init__.py index 2d23610..0e6e2e2 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -43,7 +43,7 @@ from .da import sinkhorn_lpl1_mm # utils functions from .utils import dist, unif, tic, toc, toq -__version__ = "0.7.0b" +__version__ = "0.7.0" __all__ = ['emd', 'emd2', 'emd_1d', 'sinkhorn', 'sinkhorn2', 'utils', 'datasets', 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', -- cgit v1.2.3 From 96c113b2c6e9a0428deb0743e369130971d6f4d5 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 4 May 2020 09:13:54 +0200 Subject: rename files and add pull request for proper status in pr --- .github/workflows/build_tests.yml | 103 ++++++++++++++++++++++++++++++ .github/workflows/circleci-redirector.yml | 13 ++++ .github/workflows/main.yml | 13 ---- .github/workflows/pythonpackage.yml | 102 ----------------------------- 4 files changed, 116 insertions(+), 115 deletions(-) create mode 100644 .github/workflows/build_tests.yml create mode 100644 .github/workflows/circleci-redirector.yml delete mode 100644 .github/workflows/main.yml delete mode 100644 .github/workflows/pythonpackage.yml diff --git a/.github/workflows/build_tests.yml b/.github/workflows/build_tests.yml new file mode 100644 index 0000000..652655f --- /dev/null +++ b/.github/workflows/build_tests.yml @@ -0,0 +1,103 @@ +name: build + +on: + push: + branches: + - '**' + pull_request: + create: + branches: + - 'master' + tags: + - '**' + +jobs: + linux: + + runs-on: ubuntu-latest + strategy: + max-parallel: 4 + matrix: + python-version: [3.5, 3.6, 3.7, 3.8] + + steps: + - uses: actions/checkout@v1 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v1 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + pip install flake8 pytest "pytest-cov<2.6" codecov + pip install -U "sklearn" + - name: Lint with flake8 + run: | + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 examples/ ot/ test/ --count --max-line-length=127 --statistics + - name: Install POT + run: | + pip install -e . + - name: Run tests + run: | + python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot + - name: Upload codecov + run: | + codecov + + + macos: + runs-on: macOS-latest + strategy: + max-parallel: 4 + matrix: + python-version: [3.7] + + steps: + - uses: actions/checkout@v1 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v1 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + pip install pytest "pytest-cov<2.6" + pip install -U "sklearn" + - name: Install POT + run: | + pip install -e . + - name: Run tests + run: | + python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot + + + windows: + runs-on: windows-2019 + strategy: + max-parallel: 4 + matrix: + python-version: [3.7] + + steps: + - uses: actions/checkout@v1 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v1 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r requirements.txt + pip install pytest "pytest-cov<2.6" + pip install -U "sklearn" + - name: Install POT + run: | + pip install -e . + - name: Run tests + run: | + python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot diff --git a/.github/workflows/circleci-redirector.yml b/.github/workflows/circleci-redirector.yml new file mode 100644 index 0000000..ae7bfca --- /dev/null +++ b/.github/workflows/circleci-redirector.yml @@ -0,0 +1,13 @@ +name: circleci-redirector +on: [status] +jobs: + circleci_artifacts_redirector_job: + runs-on: ubuntu-latest + name: Run CircleCI artifacts redirector + steps: + - name: GitHub Action step + uses: larsoner/circleci-artifacts-redirector-action@master + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + artifact-path: 0/dev/index.html + circleci-jobs: build_docs diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml deleted file mode 100644 index ae7bfca..0000000 --- a/.github/workflows/main.yml +++ /dev/null @@ -1,13 +0,0 @@ -name: circleci-redirector -on: [status] -jobs: - circleci_artifacts_redirector_job: - runs-on: ubuntu-latest - name: Run CircleCI artifacts redirector - steps: - - name: GitHub Action step - uses: larsoner/circleci-artifacts-redirector-action@master - with: - repo-token: ${{ secrets.GITHUB_TOKEN }} - artifact-path: 0/dev/index.html - circleci-jobs: build_docs diff --git a/.github/workflows/pythonpackage.yml b/.github/workflows/pythonpackage.yml deleted file mode 100644 index 0792059..0000000 --- a/.github/workflows/pythonpackage.yml +++ /dev/null @@ -1,102 +0,0 @@ -name: build - -on: - push: - branches: - - '**' - create: - branches: - - 'master' - tags: - - '**' - -jobs: - linux: - - runs-on: ubuntu-latest - strategy: - max-parallel: 4 - matrix: - python-version: [3.5, 3.6, 3.7, 3.8] - - steps: - - uses: actions/checkout@v1 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v1 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install -r requirements.txt - pip install flake8 pytest "pytest-cov<2.6" codecov - pip install -U "sklearn" - - name: Lint with flake8 - run: | - # stop the build if there are Python syntax errors or undefined names - flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics - # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide - flake8 examples/ ot/ test/ --count --max-line-length=127 --statistics - - name: Install POT - run: | - pip install -e . - - name: Run tests - run: | - python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot - - name: Upload codecov - run: | - codecov - - - macos: - runs-on: macOS-latest - strategy: - max-parallel: 4 - matrix: - python-version: [3.7] - - steps: - - uses: actions/checkout@v1 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v1 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install -r requirements.txt - pip install pytest "pytest-cov<2.6" - pip install -U "sklearn" - - name: Install POT - run: | - pip install -e . - - name: Run tests - run: | - python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot - - - windows: - runs-on: windows-2019 - strategy: - max-parallel: 4 - matrix: - python-version: [3.7] - - steps: - - uses: actions/checkout@v1 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v1 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install -r requirements.txt - pip install pytest "pytest-cov<2.6" - pip install -U "sklearn" - - name: Install POT - run: | - pip install -e . - - name: Run tests - run: | - python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot -- cgit v1.2.3 From e65c1f745cf2eacc6672727e7a3869efd8318768 Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Mon, 4 May 2020 11:19:35 +0200 Subject: [WIP] Improved docs and changed scipy version (#163) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Improved docs and changed scipy version * Fixed dependency bug in setup.py * dependencies set to minimal versions for tests * add requirements file * added minimal version build for scipy (testing 1.2) * bugfix in minimal deps build * (yet another) bugfix in minimal deps build * minimal deps now reflect README.md * minimal deps: no autograd nor pymanopt * refactored workflow names * minimal deps: no doctests * minimal deps: numpy 1.16 * trigger GH Actions on PR * better merge * re-add minimal-deps... * bugfix in yaml * enforce np>=1.16 * enforce scipy and cython versions too * requires / install_requires * requires / install_requires / requires * setup_requires Co-authored-by: Rémi Flamary --- .github/requirements_strict.txt | 7 ++ .github/workflows/build_tests.yml | 32 ++++++- README.md | 2 +- .../barycenters/plot_free_support_barycenter.py | 6 +- examples/domain-adaptation/plot_otda_d2.py | 2 +- examples/domain-adaptation/plot_otda_mapping.py | 4 +- .../plot_otda_mapping_colors_images.py | 1 + examples/gromov/plot_barycenter_fgw.py | 11 ++- examples/gromov/plot_fgw.py | 10 +- examples/plot_compute_emd.py | 4 +- examples/plot_optim_OTreg.py | 6 +- examples/plot_screenkhorn_1D.py | 7 +- examples/plot_stochastic.py | 101 +++++++++------------ requirements.txt | 2 +- setup.py | 3 +- 15 files changed, 110 insertions(+), 88 deletions(-) create mode 100644 .github/requirements_strict.txt diff --git a/.github/requirements_strict.txt b/.github/requirements_strict.txt new file mode 100644 index 0000000..d7539c5 --- /dev/null +++ b/.github/requirements_strict.txt @@ -0,0 +1,7 @@ +numpy==1.16.* +scipy==1.0.* +cython==0.23.* +matplotlib +cvxopt +scikit-learn +pytest diff --git a/.github/workflows/build_tests.yml b/.github/workflows/build_tests.yml index 652655f..41b08b3 100644 --- a/.github/workflows/build_tests.yml +++ b/.github/workflows/build_tests.yml @@ -2,9 +2,9 @@ name: build on: push: - branches: - - '**' + pull_request: + create: branches: - 'master' @@ -49,6 +49,34 @@ jobs: codecov + linux-minimal-deps: + + runs-on: ubuntu-latest + strategy: + max-parallel: 4 + matrix: + python-version: [3.6] + + steps: + - uses: actions/checkout@v1 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v1 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install -r .github/requirements_strict.txt + pip install pytest + pip install -U "sklearn" + - name: Install POT + run: | + pip install -e . + - name: Run tests + run: | + python -m pytest -v test/ ot/ --ignore ot/gpu/ + + macos: runs-on: macOS-latest strategy: diff --git a/README.md b/README.md index b9b8f45..5ee4cee 100644 --- a/README.md +++ b/README.md @@ -69,7 +69,7 @@ year={2017} The library has been tested on Linux, MacOSX and Windows. It requires a C++ compiler for building/installing the EMD solver and relies on the following Python modules: -- Numpy (>=1.11) +- Numpy (>=1.16) - Scipy (>=1.0) - Cython (>=0.23) - Matplotlib (>=1.5) diff --git a/examples/barycenters/plot_free_support_barycenter.py b/examples/barycenters/plot_free_support_barycenter.py index 64b89e4..27ddc8e 100644 --- a/examples/barycenters/plot_free_support_barycenter.py +++ b/examples/barycenters/plot_free_support_barycenter.py @@ -4,7 +4,7 @@ 2D free support Wasserstein barycenters of distributions ==================================================== -Illustration of 2D Wasserstein barycenters if discributions that are weighted +Illustration of 2D Wasserstein barycenters if distributions are weighted sum of diracs. """ @@ -21,7 +21,7 @@ import ot ############################################################################## # Generate data # ------------- -#%% parameters and data generation + N = 3 d = 2 measures_locations = [] @@ -46,7 +46,7 @@ for i in range(N): ############################################################################## # Compute free support barycenter -# ------------- +# ------------------------------- k = 10 # number of Diracs of the barycenter X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations diff --git a/examples/domain-adaptation/plot_otda_d2.py b/examples/domain-adaptation/plot_otda_d2.py index f49a570..d8b2a93 100644 --- a/examples/domain-adaptation/plot_otda_d2.py +++ b/examples/domain-adaptation/plot_otda_d2.py @@ -25,7 +25,7 @@ import ot import ot.plot ############################################################################## -# generate data +# Generate data # ------------- n_samples_source = 150 diff --git a/examples/domain-adaptation/plot_otda_mapping.py b/examples/domain-adaptation/plot_otda_mapping.py index ded2bdf..d21d3c9 100644 --- a/examples/domain-adaptation/plot_otda_mapping.py +++ b/examples/domain-adaptation/plot_otda_mapping.py @@ -9,8 +9,8 @@ time both the coupling transport and approximate the transport map with either a linear or a kernelized mapping as introduced in [8]. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, - "Mapping estimation for discrete optimal transport", - Neural Information Processing Systems (NIPS), 2016. +"Mapping estimation for discrete optimal transport", +Neural Information Processing Systems (NIPS), 2016. """ # Authors: Remi Flamary diff --git a/examples/domain-adaptation/plot_otda_mapping_colors_images.py b/examples/domain-adaptation/plot_otda_mapping_colors_images.py index 9d3a7c7..ee5c8b0 100644 --- a/examples/domain-adaptation/plot_otda_mapping_colors_images.py +++ b/examples/domain-adaptation/plot_otda_mapping_colors_images.py @@ -9,6 +9,7 @@ estimation [8]. [6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. + [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. diff --git a/examples/gromov/plot_barycenter_fgw.py b/examples/gromov/plot_barycenter_fgw.py index 77b0370..3f81765 100644 --- a/examples/gromov/plot_barycenter_fgw.py +++ b/examples/gromov/plot_barycenter_fgw.py @@ -4,14 +4,15 @@ Plot graphs' barycenter using FGW ================================= -This example illustrates the computation barycenter of labeled graphs using FGW +This example illustrates the computation barycenter of labeled graphs using +FGW [18]. Requires networkx >=2 -.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain - and Courty Nicolas - "Optimal Transport for structured data with application on graphs" - International Conference on Machine Learning (ICML). 2019. +[18] Vayer Titouan, Chapel Laetitia, Flamary Rémi, Tavenard Romain +and Courty Nicolas +"Optimal Transport for structured data with application on graphs" +International Conference on Machine Learning (ICML). 2019. """ diff --git a/examples/gromov/plot_fgw.py b/examples/gromov/plot_fgw.py index 73e486e..97fe619 100644 --- a/examples/gromov/plot_fgw.py +++ b/examples/gromov/plot_fgw.py @@ -4,12 +4,12 @@ Plot Fused-gromov-Wasserstein ============================== -This example illustrates the computation of FGW for 1D measures[18]. +This example illustrates the computation of FGW for 1D measures [18]. -.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain - and Courty Nicolas - "Optimal Transport for structured data with application on graphs" - International Conference on Machine Learning (ICML). 2019. +[18] Vayer Titouan, Chapel Laetitia, Flamary Rémi, Tavenard Romain +and Courty Nicolas +"Optimal Transport for structured data with application on graphs" +International Conference on Machine Learning (ICML). 2019. """ diff --git a/examples/plot_compute_emd.py b/examples/plot_compute_emd.py index 3340115..527a847 100644 --- a/examples/plot_compute_emd.py +++ b/examples/plot_compute_emd.py @@ -4,8 +4,8 @@ Plot multiple EMD ================= -Shows how to compute multiple EMD and Sinkhorn with two differnt -ground metrics and plot their values for diffeent distributions. +Shows how to compute multiple EMD and Sinkhorn with two different +ground metrics and plot their values for different distributions. """ diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py index 51e2fdc..5eb15bd 100644 --- a/examples/plot_optim_OTreg.py +++ b/examples/plot_optim_OTreg.py @@ -6,7 +6,7 @@ Regularized OT with generic solver Illustrates the use of the generic solver for regularized OT with user-designed regularization term. It uses Conditional gradient as in [6] and -generalized Conditional Gradient as proposed in [5][7]. +generalized Conditional Gradient as proposed in [5,7]. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for @@ -14,8 +14,8 @@ Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). -Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, -7(3), 1853-1882. +Regularized discrete optimal transport. SIAM Journal on Imaging +Sciences, 7(3), 1853-1882. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. diff --git a/examples/plot_screenkhorn_1D.py b/examples/plot_screenkhorn_1D.py index 840ead8..785642a 100644 --- a/examples/plot_screenkhorn_1D.py +++ b/examples/plot_screenkhorn_1D.py @@ -4,8 +4,11 @@ 1D Screened optimal transport =============================== -This example illustrates the computation of Screenkhorn: -Screening Sinkhorn Algorithm for Optimal transport. +This example illustrates the computation of Screenkhorn [26]. + +[26] Alaya M. Z., Bérar M., Gasso G., Rakotomamonjy A. (2019). +Screening Sinkhorn Algorithm for Regularized Optimal Transport, +Advances in Neural Information Processing Systems 33 (NeurIPS). """ # Author: Mokhtar Z. Alaya diff --git a/examples/plot_stochastic.py b/examples/plot_stochastic.py index 742f8d9..3a1ef31 100644 --- a/examples/plot_stochastic.py +++ b/examples/plot_stochastic.py @@ -1,10 +1,18 @@ """ -========================== +=================== Stochastic examples -========================== +=================== This example is designed to show how to use the stochatic optimization -algorithms for descrete and semicontinous measures from the POT library. +algorithms for discrete and semi-continuous measures from the POT library. + +[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. +Stochastic Optimization for Large-scale Optimal Transport. +Advances in Neural Information Processing Systems (2016). + +[19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A. & +Blondel, M. Large-scale Optimal Transport and Mapping Estimation. +International Conference on Learning Representation (2018) """ @@ -19,16 +27,14 @@ import ot.plot ############################################################################# -# COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM -############################################################################# -############################################################################# -# DISCRETE CASE: +# Compute the Transportation Matrix for the Semi-Dual Problem +# ----------------------------------------------------------- # -# Sample two discrete measures for the discrete case -# --------------------------------------------- +# Discrete case +# ````````````` # -# Define 2 discrete measures a and b, the points where are defined the source -# and the target measures and finally the cost matrix c. +# Sample two discrete measures for the discrete case and compute their cost +# matrix c. n_source = 7 n_target = 4 @@ -44,12 +50,7 @@ Y_target = rng.randn(n_target, 2) M = ot.dist(X_source, Y_target) ############################################################################# -# # Call the "SAG" method to find the transportation matrix in the discrete case -# --------------------------------------------- -# -# Define the method "SAG", call ot.solve_semi_dual_entropic and plot the -# results. method = "SAG" sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, @@ -57,14 +58,12 @@ sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, print(sag_pi) ############################################################################# -# SEMICONTINOUS CASE: +# Semi-Continuous Case +# ```````````````````` # # Sample one general measure a, one discrete measures b for the semicontinous -# case -# --------------------------------------------- -# -# Define one general measure a, one discrete measures b, the points where -# are defined the source and the target measures and finally the cost matrix c. +# case, the points where source and target measures are defined and compute the +# cost matrix. n_source = 7 n_target = 4 @@ -81,13 +80,8 @@ Y_target = rng.randn(n_target, 2) M = ot.dist(X_source, Y_target) ############################################################################# -# # Call the "ASGD" method to find the transportation matrix in the semicontinous -# case -# --------------------------------------------- -# -# Define the method "ASGD", call ot.solve_semi_dual_entropic and plot the -# results. +# case. method = "ASGD" asgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, @@ -96,23 +90,17 @@ print(log_asgd['alpha'], log_asgd['beta']) print(asgd_pi) ############################################################################# -# # Compare the results with the Sinkhorn algorithm -# --------------------------------------------- -# -# Call the Sinkhorn algorithm from POT sinkhorn_pi = ot.sinkhorn(a, b, M, reg) print(sinkhorn_pi) ############################################################################## -# PLOT TRANSPORTATION MATRIX -############################################################################## - -############################################################################## -# Plot SAG results -# ---------------- +# Plot Transportation Matrices +# ```````````````````````````` +# +# For SAG pl.figure(4, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG') @@ -120,8 +108,7 @@ pl.show() ############################################################################## -# Plot ASGD results -# ----------------- +# For ASGD pl.figure(4, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD') @@ -129,8 +116,7 @@ pl.show() ############################################################################## -# Plot Sinkhorn results -# --------------------- +# For Sinkhorn pl.figure(4, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') @@ -138,17 +124,14 @@ pl.show() ############################################################################# -# COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM -############################################################################# -############################################################################# -# SEMICONTINOUS CASE: +# Compute the Transportation Matrix for the Dual Problem +# ------------------------------------------------------ # -# Sample one general measure a, one discrete measures b for the semicontinous -# case -# --------------------------------------------- +# Semi-continuous case +# ```````````````````` # -# Define one general measure a, one discrete measures b, the points where -# are defined the source and the target measures and finally the cost matrix c. +# Sample one general measure a, one discrete measures b for the semi-continuous +# case and compute the cost matrix c. n_source = 7 n_target = 4 @@ -169,10 +152,7 @@ M = ot.dist(X_source, Y_target) ############################################################################# # # Call the "SGD" dual method to find the transportation matrix in the -# semicontinous case -# --------------------------------------------- -# -# Call ot.solve_dual_entropic and plot the results. +# semi-continuous case sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg, batch_size, numItermax, @@ -183,7 +163,7 @@ print(sgd_dual_pi) ############################################################################# # # Compare the results with the Sinkhorn algorithm -# --------------------------------------------- +# ``````````````````````````````````````````````` # # Call the Sinkhorn algorithm from POT @@ -191,8 +171,10 @@ sinkhorn_pi = ot.sinkhorn(a, b, M, reg) print(sinkhorn_pi) ############################################################################## -# Plot SGD results -# ----------------- +# Plot Transportation Matrices +# ```````````````````````````` +# +# For SGD pl.figure(4, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD') @@ -200,8 +182,7 @@ pl.show() ############################################################################## -# Plot Sinkhorn results -# --------------------- +# For Sinkhorn pl.figure(4, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') diff --git a/requirements.txt b/requirements.txt index bee22f7..331dd57 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy -scipy>=1.0 +scipy>=1.3 cython matplotlib autograd diff --git a/setup.py b/setup.py index 4640d00..91c24d9 100755 --- a/setup.py +++ b/setup.py @@ -67,7 +67,8 @@ setup(name='POT', scripts=[], data_files=[], requires=["numpy", "scipy", "cython"], - install_requires=["numpy", "scipy", "cython"], + setup_requires=["numpy>=1.16", "scipy>=1.0", "cython>=0.23"], + install_requires=["numpy>=1.16", "scipy>=1.0", "cython>=0.23"], classifiers=[ 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', -- cgit v1.2.3 From 27facf1f22176eabae9f710e8dd528c8aa2c9c6b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 4 May 2020 15:20:49 +0200 Subject: test build wheels --- .github/workflows/build_wheels.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml index be1719b..a4b980b 100644 --- a/.github/workflows/build_wheels.yml +++ b/.github/workflows/build_wheels.yml @@ -4,7 +4,7 @@ on: release: push: branches: - - "master" + - "**" jobs: build_wheels: @@ -39,7 +39,7 @@ jobs: - name: Build wheel env: - CIBW_SKIP: "pp*-win* pp*-macosx*" # remove pypy on mac and win (wrong version) + CIBW_SKIP: "pp*-win* pp*-macosx* cp2*" # remove pypy on mac and win (wrong version) CIBW_BEFORE_BUILD: "pip install numpy cython" run: | python -m cibuildwheel --output-dir wheelhouse -- cgit v1.2.3 From 43fcd147158d0cdf67f3bdc9a73716fdd5fac6be Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 4 May 2020 15:33:33 +0200 Subject: remove whelels pypy --- .github/workflows/build_wheels.yml | 2 +- RELEASES.md | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml index a4b980b..babe7fa 100644 --- a/.github/workflows/build_wheels.yml +++ b/.github/workflows/build_wheels.yml @@ -39,7 +39,7 @@ jobs: - name: Build wheel env: - CIBW_SKIP: "pp*-win* pp*-macosx* cp2*" # remove pypy on mac and win (wrong version) + CIBW_SKIP: "pp*-win* pp*-macosx* cp2* pp2*" # remove pypy on mac and win (wrong version) CIBW_BEFORE_BUILD: "pip install numpy cython" run: | python -m cibuildwheel --output-dir wheelhouse diff --git a/RELEASES.md b/RELEASES.md index 0fc9240..dbcb4cb 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -5,7 +5,7 @@ This is the new stable release for POT. We have a lot of changes in the documentation and several new features such as Partial OT, Unbalanced and Multi Sources OT Domain Adaptation and several bug fixes. One important change is that we have created a the GitHub organization [PythonOT](https://github.com/PythonOT) that now owns the main POT repository [https://github.com/PythonOT/POT](https://github.com/PythonOT/POT) and the repository for the new documentation hosted at [https://PythonOT.github.io/](https://PythonOT.github.io/). -This is the first release where the Python 2.7 tests have been removed. Most of the toolbox should still work but this is the last release where we release Python 2.7 wheels. +This is the first release where the Python 2.7 tests have been removed. Most of the toolbox should still work but we do not offer support for Python 2.7 and will close related Issues. A lot of changes have been done to the documentation that is now hosted on [https://PythonOT.github.io/](https://PythonOT.github.io/) instead of readthedocs. It was a hard choice but readthedocs did not allow us to run sphinx-gallery to update our beautiful examples and it was a huge amount of work to maintain it. The documentation is now automatically compiled and updated on merge. We also removed the notebooks from the repository for space reason and also because they are all available in the [example gallery](https://pythonot.github.io/auto_examples/index.html). Note that now the output of the documentation build for each commit in the PR is available to check that the doc builds correctly before merging which was not possible with readthedoc. -- cgit v1.2.3 From f0770af36cb2fef01156f3a30222eeace19a5352 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 4 May 2020 15:46:23 +0200 Subject: really remove pypy --- .github/workflows/build_wheels.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml index babe7fa..7c61a06 100644 --- a/.github/workflows/build_wheels.yml +++ b/.github/workflows/build_wheels.yml @@ -39,7 +39,7 @@ jobs: - name: Build wheel env: - CIBW_SKIP: "pp*-win* pp*-macosx* cp2* pp2*" # remove pypy on mac and win (wrong version) + CIBW_SKIP: "pp*-win* pp*-macosx* cp2* pp*" # remove pypy on mac and win (wrong version) CIBW_BEFORE_BUILD: "pip install numpy cython" run: | python -m cibuildwheel --output-dir wheelhouse -- cgit v1.2.3 From ba752bdba9a6733ebbb835cdbc59f1195a849245 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 4 May 2020 16:06:22 +0200 Subject: back to build wheels on master --- .github/workflows/build_wheels.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build_wheels.yml b/.github/workflows/build_wheels.yml index 7c61a06..662a604 100644 --- a/.github/workflows/build_wheels.yml +++ b/.github/workflows/build_wheels.yml @@ -4,7 +4,7 @@ on: release: push: branches: - - "**" + - "master" jobs: build_wheels: -- cgit v1.2.3 From ca6ffc1d2c3887b05c32b89b73726f16da57a870 Mon Sep 17 00:00:00 2001 From: Marc Glisse Date: Mon, 4 May 2020 21:14:28 +0200 Subject: Add nogil to EMD_wrap --- ot/lp/emd_wrap.pyx | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index b6bda47..c167964 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -19,7 +19,7 @@ import warnings cdef extern from "EMD.h": - int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int maxIter) + int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int maxIter) nogil cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED, MAX_ITER_REACHED @@ -110,7 +110,8 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod G=np.zeros([n1, n2]) # calling the function - result_code = EMD_wrap(n1, n2, a.data, b.data, M.data, G.data, alpha.data, beta.data, &cost, max_iter) + with nogil: + result_code = EMD_wrap(n1, n2, a.data, b.data, M.data, G.data, alpha.data, beta.data, &cost, max_iter) return G, cost, alpha, beta, result_code -- cgit v1.2.3 From 217ffbb73e15490935172f129e0ee51449f11bb6 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 5 May 2020 07:52:16 +0200 Subject: cleanup WDA example with proper seeds --- examples/others/plot_WDA.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/examples/others/plot_WDA.py b/examples/others/plot_WDA.py index 5e17433..009d902 100644 --- a/examples/others/plot_WDA.py +++ b/examples/others/plot_WDA.py @@ -33,6 +33,8 @@ from ot.dr import wda, fda n = 1000 # nb samples in source and target datasets nz = 0.2 +np.random.RandomState(1) + # generate circle dataset t = np.random.rand(n) * 2 * np.pi ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 @@ -88,7 +90,11 @@ reg = 1e0 k = 10 maxiter = 100 -Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter) +P0 = np.random.randn(xs.shape[1], p) + +P0 /= np.sqrt(np.sum(P0**2, 0, keepdims=True)) + +Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter, P0=P0) ############################################################################## -- cgit v1.2.3 From 8406caafaef8b3683d6a1d44917c404ba780f82c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 5 May 2020 07:53:45 +0200 Subject: remove dense from ducumentation --- ot/lp/__init__.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 50003ed..514a607 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -327,10 +327,6 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), variables. Otherwise returns only the optimal transportation cost. return_matrix: boolean, optional (default=False) If True, returns the optimal transportation matrix in the log. - dense: boolean, optional (default=True) - If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). - Otherwise returns a sparse representation using scipy's `coo_matrix` - format. center_dual: boolean, optional (default=True) If True, centers the dual potential using function :ref:`center_ot_dual`. -- cgit v1.2.3 From dbd8f1485c03e4a680b73be59ab1590a59acbe16 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 5 May 2020 08:18:26 +0200 Subject: add release 0.7.0 in doc --- RELEASES.md | 4 +-- docs/source/readme.rst | 2 +- docs/source/releases.rst | 85 ++++++++++++++++++++++++++++++++++++++++++++++-- 3 files changed, 86 insertions(+), 5 deletions(-) diff --git a/RELEASES.md b/RELEASES.md index dbcb4cb..699f9a6 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -1,6 +1,6 @@ # Releases -## 0.7 +## 0.7.0 *May 2020* This is the new stable release for POT. We have a lot of changes in the documentation and several new features such as Partial OT, Unbalanced and Multi Sources OT Domain Adaptation and several bug fixes. One important change is that we have created a the GitHub organization [PythonOT](https://github.com/PythonOT) that now owns the main POT repository [https://github.com/PythonOT/POT](https://github.com/PythonOT/POT) and the repository for the new documentation hosted at [https://PythonOT.github.io/](https://PythonOT.github.io/). @@ -40,7 +40,7 @@ This release is also the moment to thank all the POT contributors (old and new) - Log bugs for Gromov-Wassertein solver (Issue #107, fixed in PR #108) - Weight issues in barycenter function (PR #106) -## 0.6 +## 0.6.0 *July 2019* This is the first official stable release of POT and this means a jump to 0.6! diff --git a/docs/source/readme.rst b/docs/source/readme.rst index c96f191..add0c00 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -121,7 +121,7 @@ The library has been tested on Linux, MacOSX and Windows. It requires a C++ compiler for building/installing the EMD solver and relies on the following Python modules: -- Numpy (>=1.11) +- Numpy (>=1.16) - Scipy (>=1.0) - Cython (>=0.23) - Matplotlib (>=1.5) diff --git a/docs/source/releases.rst b/docs/source/releases.rst index 075108f..2bba432 100644 --- a/docs/source/releases.rst +++ b/docs/source/releases.rst @@ -1,8 +1,89 @@ Releases ======== -0.6 ---- +0.7.0 +----- + +*May 2020* + +This is the new stable release for POT. We have a lot of changes in the +documentation and several new features such as Partial OT, Unbalanced +and Multi Sources OT Domain Adaptation and several bug fixes. One +important change is that we have created a the GitHub organization +`PythonOT `__ that now owns the main POT +repository https://github.com/PythonOT/POT and the repository for the +new documentation hosted at https://PythonOT.github.io/. + +This is the first release where the Python 2.7 tests have been removed. +Most of the toolbox should still work but we do not offer support for +Python 2.7 and will close related Issues. + +A lot of changes have been done to the documentation that is now hosted +on https://PythonOT.github.io/ instead of readthedocs. It was a hard +choice but readthedocs did not allow us to run sphinx-gallery to update +our beautiful examples and it was a huge amount of work to maintain it. +The documentation is now automatically compiled and updated on merge. We +also removed the notebooks from the repository for space reason and also +because they are all available in the `example +gallery `__. Note +that now the output of the documentation build for each commit in the PR +is available to check that the doc builds correctly before merging which +was not possible with readthedoc. + +The CI framework has also been changed with a move from Travis to Github +Action which allows us to get faster tests on Windows, MacOS and Linux. +We also now report our coverage on +`Codecov.io `__ and we have a +reasonable 92% coverage. We also now generate wheels for a number of OS +and python versions at each merge in the master branch. They are +available as artifacts of this +`action `__. +This will allow simpler multi-platform releases from now on. + +In terms of new features we now have `OTDA Classes for unbalanced +OT `__, +a new Domain adaptation class form `multi domain problems +(JCPOT) `__, +and several solvers to solve the `Partial Optimal +Transport `__ +problems. + +This release is also the moment to thank all the POT contributors (old +and new) for helping making POT such a nice to use toolbox. A lot of +changes (also in the API) are comming for the next versions. + +Features +^^^^^^^^ + +- New documentation on https://PythonOT.github.io/ (PR #160, PR #143, + PR #144) +- Documentation build on CircleCI with sphinx-gallery (PR #145,PR #146, + #155) +- Run sphinx gallery in CI (PR #146) +- Remove notebooks from repo because available in doc (PR #156) +- Build wheels in CI (#157) +- Move from travis to GitHub Action for Windows, MacOS and Linux (PR + #148, PR #150) +- Partial Optimal Transport (PR#141 and PR #142) +- Laplace regularized OTDA (PR #140) +- Multi source DA with target shift (PR #137) +- Screenkhorn algorithm (PR #121) + +Closed issues +^^^^^^^^^^^^^ + +- Bug in Unbalanced OT example (Issue #127) +- Clean Cython output when calling setup.py clean (Issue #122) +- Various Macosx compilation problems (Issue #113, Issue #118, PR#130) +- EMD dimension mismatch (Issue #114, Fixed in PR #116) +- 2D barycenter bug for non square images (Issue #124, fixed in PR + #132) +- Bad value in EMD 1D (Issue #138, fixed in PR #139) +- Log bugs for Gromov-Wassertein solver (Issue #107, fixed in PR #108) +- Weight issues in barycenter function (PR #106) + +0.6.0 +----- *July 2019* -- cgit v1.2.3 From 2e50bd2cf410576c8e12c60043b0bcedb6151fad Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 5 May 2020 08:28:17 +0200 Subject: correct year in mccan reference for partial ot --- README.md | 2 +- docs/source/readme.rst | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 5ee4cee..5626742 100644 --- a/README.md +++ b/README.md @@ -258,7 +258,7 @@ You can also post bug reports and feature requests in Github issues. Make sure t [27] Redko I., Courty N., Flamary R., Tuia D. (2019). [Optimal Transport for Multi-source Domain Adaptation under Target Shift](http://proceedings.mlr.press/v89/redko19a.html), Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019. -[28] Caffarelli, L. A., McCann, R. J. (2020). [Free boundaries in optimal transport and Monge-Ampere obstacle problems](http://www.math.toronto.edu/~mccann/papers/annals2010.pdf), Annals of mathematics, 673-730. +[28] Caffarelli, L. A., McCann, R. J. (2010). [Free boundaries in optimal transport and Monge-Ampere obstacle problems](http://www.math.toronto.edu/~mccann/papers/annals2010.pdf), Annals of mathematics, 673-730. [29] Chapel, L., Alaya, M., Gasso, G. (2019). [Partial Gromov-Wasserstein with Applications on Positive-Unlabeled Learning](https://arxiv.org/abs/2002.08276), arXiv preprint arXiv:2002.08276. diff --git a/docs/source/readme.rst b/docs/source/readme.rst index add0c00..b8cb48c 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -434,7 +434,7 @@ Shift `__, Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics (AISTATS) 22, 2019. -[28] Caffarelli, L. A., McCann, R. J. (2020). `Free boundaries in +[28] Caffarelli, L. A., McCann, R. J. (2010). `Free boundaries in optimal transport and Monge-Ampere obstacle problems `__, Annals of mathematics, 673-730. -- cgit v1.2.3 From 2b31be018031cd23bfed6b76d75630a1a66520f1 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 5 May 2020 08:30:49 +0200 Subject: really reproducipble WDA example --- examples/others/plot_WDA.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/others/plot_WDA.py b/examples/others/plot_WDA.py index 009d902..bdfa57d 100644 --- a/examples/others/plot_WDA.py +++ b/examples/others/plot_WDA.py @@ -33,7 +33,7 @@ from ot.dr import wda, fda n = 1000 # nb samples in source and target datasets nz = 0.2 -np.random.RandomState(1) +np.random.seed(1) # generate circle dataset t = np.random.rand(n) * 2 * np.pi -- cgit v1.2.3 From 669c6b1035b0d69cef73c6e6718e85430ba42843 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Tue, 5 May 2020 08:51:08 +0200 Subject: Update RELEASES.md --- RELEASES.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/RELEASES.md b/RELEASES.md index dbcb4cb..f2f4dbe 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -3,17 +3,17 @@ ## 0.7 *May 2020* -This is the new stable release for POT. We have a lot of changes in the documentation and several new features such as Partial OT, Unbalanced and Multi Sources OT Domain Adaptation and several bug fixes. One important change is that we have created a the GitHub organization [PythonOT](https://github.com/PythonOT) that now owns the main POT repository [https://github.com/PythonOT/POT](https://github.com/PythonOT/POT) and the repository for the new documentation hosted at [https://PythonOT.github.io/](https://PythonOT.github.io/). +This is the new stable release for POT. We made a lot of changes in the documentation and added several new features such as Partial OT, Unbalanced and Multi Sources OT Domain Adaptation and several bug fixes. One important change is that we have created a the GitHub organization [PythonOT](https://github.com/PythonOT) that now owns the main POT repository [https://github.com/PythonOT/POT](https://github.com/PythonOT/POT) and the repository for the new documentation hosted at [https://PythonOT.github.io/](https://PythonOT.github.io/). This is the first release where the Python 2.7 tests have been removed. Most of the toolbox should still work but we do not offer support for Python 2.7 and will close related Issues. -A lot of changes have been done to the documentation that is now hosted on [https://PythonOT.github.io/](https://PythonOT.github.io/) instead of readthedocs. It was a hard choice but readthedocs did not allow us to run sphinx-gallery to update our beautiful examples and it was a huge amount of work to maintain it. The documentation is now automatically compiled and updated on merge. We also removed the notebooks from the repository for space reason and also because they are all available in the [example gallery](https://pythonot.github.io/auto_examples/index.html). Note that now the output of the documentation build for each commit in the PR is available to check that the doc builds correctly before merging which was not possible with readthedoc. +A lot of changes have been done to the documentation that is now hosted on [https://PythonOT.github.io/](https://PythonOT.github.io/) instead of readthedocs. It was a hard choice but readthedocs did not allow us to run sphinx-gallery to update our beautiful examples and it was a huge amount of work to maintain. The documentation is now automatically compiled and updated on merge. We also removed the notebooks from the repository for space reason and also because they are all available in the [example gallery](https://pythonot.github.io/auto_examples/index.html). Note that now the output of the documentation build for each commit in the PR is available to check that the doc builds correctly before merging which was not possible with readthedocs. -The CI framework has also been changed with a move from Travis to Github Action which allows us to get faster tests on Windows, MacOS and Linux. We also now report our coverage on [Codecov.io](https://codecov.io/gh/PythonOT/POT) and we have a reasonable 92% coverage. We also now generate wheels for a number of OS and python versions at each merge in the master branch. They are available as artifacts of this [action](https://github.com/PythonOT/POT/actions?query=workflow%3A%22Build+dist+and+wheels%22). This will allow simpler multi-platform releases from now on. +The CI framework has also been changed with a move from Travis to Github Action which allows to get faster tests on Windows, MacOS and Linux. We also now report our coverage on [Codecov.io](https://codecov.io/gh/PythonOT/POT) and we have a reasonable 92% coverage. We also now generate wheels for a number of OS and python versions at each merge in the master branch. They are available as outputs of this [action](https://github.com/PythonOT/POT/actions?query=workflow%3A%22Build+dist+and+wheels%22). This will allow simpler multi-platform releases from now on. In terms of new features we now have [OTDA Classes for unbalanced OT](https://pythonot.github.io/gen_modules/ot.da.html#ot.da.UnbalancedSinkhornTransport), a new Domain adaptation class form [multi domain problems (JCPOT)](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_jcpot.html#sphx-glr-auto-examples-domain-adaptation-plot-otda-jcpot-py), and several solvers to solve the [Partial Optimal Transport](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html#sphx-glr-auto-examples-unbalanced-partial-plot-partial-wass-and-gromov-py) problems. -This release is also the moment to thank all the POT contributors (old and new) for helping making POT such a nice to use toolbox. A lot of changes (also in the API) are comming for the next versions. +This release is also the moment to thank all the POT contributors (old and new) for helping making POT such a nice toolbox. A lot of changes (also in the API) are comming for the next versions. #### Features -- cgit v1.2.3 From 364fb3cb0dd91a28fb41bd44ead92650d435b47c Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Tue, 5 May 2020 09:12:47 +0200 Subject: Update RELEASES.md Co-authored-by: Alexandre Gramfort --- RELEASES.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/RELEASES.md b/RELEASES.md index f2f4dbe..606af06 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -3,7 +3,7 @@ ## 0.7 *May 2020* -This is the new stable release for POT. We made a lot of changes in the documentation and added several new features such as Partial OT, Unbalanced and Multi Sources OT Domain Adaptation and several bug fixes. One important change is that we have created a the GitHub organization [PythonOT](https://github.com/PythonOT) that now owns the main POT repository [https://github.com/PythonOT/POT](https://github.com/PythonOT/POT) and the repository for the new documentation hosted at [https://PythonOT.github.io/](https://PythonOT.github.io/). +This is the new stable release for POT. We made a lot of changes in the documentation and added several new features such as Partial OT, Unbalanced and Multi Sources OT Domain Adaptation and several bug fixes. One important change is that we have created the GitHub organization [PythonOT](https://github.com/PythonOT) that now owns the main POT repository [https://github.com/PythonOT/POT](https://github.com/PythonOT/POT) and the repository for the new documentation is now hosted at [https://PythonOT.github.io/](https://PythonOT.github.io/). This is the first release where the Python 2.7 tests have been removed. Most of the toolbox should still work but we do not offer support for Python 2.7 and will close related Issues. -- cgit v1.2.3 From 5da0241588efbb44088cb24f955a15d600ebf366 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Tue, 5 May 2020 09:13:07 +0200 Subject: Update RELEASES.md Co-authored-by: Alexandre Gramfort --- RELEASES.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/RELEASES.md b/RELEASES.md index 606af06..ee3ec15 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -9,7 +9,7 @@ This is the first release where the Python 2.7 tests have been removed. Most of A lot of changes have been done to the documentation that is now hosted on [https://PythonOT.github.io/](https://PythonOT.github.io/) instead of readthedocs. It was a hard choice but readthedocs did not allow us to run sphinx-gallery to update our beautiful examples and it was a huge amount of work to maintain. The documentation is now automatically compiled and updated on merge. We also removed the notebooks from the repository for space reason and also because they are all available in the [example gallery](https://pythonot.github.io/auto_examples/index.html). Note that now the output of the documentation build for each commit in the PR is available to check that the doc builds correctly before merging which was not possible with readthedocs. -The CI framework has also been changed with a move from Travis to Github Action which allows to get faster tests on Windows, MacOS and Linux. We also now report our coverage on [Codecov.io](https://codecov.io/gh/PythonOT/POT) and we have a reasonable 92% coverage. We also now generate wheels for a number of OS and python versions at each merge in the master branch. They are available as outputs of this [action](https://github.com/PythonOT/POT/actions?query=workflow%3A%22Build+dist+and+wheels%22). This will allow simpler multi-platform releases from now on. +The CI framework has also been changed with a move from Travis to Github Action which allows to get faster tests on Windows, MacOS and Linux. We also now report our coverage on [Codecov.io](https://codecov.io/gh/PythonOT/POT) and we have a reasonable 92% coverage. We also now generate wheels for a number of OS and Python versions at each merge in the master branch. They are available as outputs of this [action](https://github.com/PythonOT/POT/actions?query=workflow%3A%22Build+dist+and+wheels%22). This will allow simpler multi-platform releases from now on. In terms of new features we now have [OTDA Classes for unbalanced OT](https://pythonot.github.io/gen_modules/ot.da.html#ot.da.UnbalancedSinkhornTransport), a new Domain adaptation class form [multi domain problems (JCPOT)](https://pythonot.github.io/auto_examples/domain-adaptation/plot_otda_jcpot.html#sphx-glr-auto-examples-domain-adaptation-plot-otda-jcpot-py), and several solvers to solve the [Partial Optimal Transport](https://pythonot.github.io/auto_examples/unbalanced-partial/plot_partial_wass_and_gromov.html#sphx-glr-auto-examples-unbalanced-partial-plot-partial-wass-and-gromov-py) problems. -- cgit v1.2.3 From 20e10a6db35a567d2cc6d75ad659c7473d82d3a7 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 5 May 2020 10:18:59 +0200 Subject: update release in doc --- docs/source/releases.rst | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/docs/source/releases.rst b/docs/source/releases.rst index 2bba432..5a357f3 100644 --- a/docs/source/releases.rst +++ b/docs/source/releases.rst @@ -6,13 +6,13 @@ Releases *May 2020* -This is the new stable release for POT. We have a lot of changes in the -documentation and several new features such as Partial OT, Unbalanced -and Multi Sources OT Domain Adaptation and several bug fixes. One -important change is that we have created a the GitHub organization +This is the new stable release for POT. We made a lot of changes in the +documentation and added several new features such as Partial OT, +Unbalanced and Multi Sources OT Domain Adaptation and several bug fixes. +One important change is that we have created the GitHub organization `PythonOT `__ that now owns the main POT repository https://github.com/PythonOT/POT and the repository for the -new documentation hosted at https://PythonOT.github.io/. +new documentation is now hosted at https://PythonOT.github.io/. This is the first release where the Python 2.7 tests have been removed. Most of the toolbox should still work but we do not offer support for @@ -21,22 +21,22 @@ Python 2.7 and will close related Issues. A lot of changes have been done to the documentation that is now hosted on https://PythonOT.github.io/ instead of readthedocs. It was a hard choice but readthedocs did not allow us to run sphinx-gallery to update -our beautiful examples and it was a huge amount of work to maintain it. -The documentation is now automatically compiled and updated on merge. We +our beautiful examples and it was a huge amount of work to maintain. The +documentation is now automatically compiled and updated on merge. We also removed the notebooks from the repository for space reason and also because they are all available in the `example gallery `__. Note that now the output of the documentation build for each commit in the PR is available to check that the doc builds correctly before merging which -was not possible with readthedoc. +was not possible with readthedocs. The CI framework has also been changed with a move from Travis to Github -Action which allows us to get faster tests on Windows, MacOS and Linux. -We also now report our coverage on +Action which allows to get faster tests on Windows, MacOS and Linux. We +also now report our coverage on `Codecov.io `__ and we have a reasonable 92% coverage. We also now generate wheels for a number of OS -and python versions at each merge in the master branch. They are -available as artifacts of this +and Python versions at each merge in the master branch. They are +available as outputs of this `action `__. This will allow simpler multi-platform releases from now on. @@ -49,8 +49,8 @@ Transport