Merge branch 'master' of github.com:rflamary/POT

7 files changed, 39 insertions, 24 deletions

diff --git a/examples/plot_OT_1D.py b/examples/plot_OT_1D.py
index 719058f..90325c9 100644
--- a/examples/plot_OT_1D.py
+++ b/examples/plot_OT_1D.py
@@ -16,6 +16,7 @@ and their visualization.
 import numpy as np
 import matplotlib.pylab as pl
 import ot
+import ot.plot
 from ot.datasets import get_1D_gauss as gauss
 
 ##############################################################################
diff --git a/examples/plot_OT_L1_vs_L2.py b/examples/plot_OT_L1_vs_L2.py
index 090e809..37b429f 100644
--- a/examples/plot_OT_L1_vs_L2.py
+++ b/examples/plot_OT_L1_vs_L2.py
@@ -19,6 +19,7 @@ https://arxiv.org/pdf/1706.07650.pdf
 import numpy as np
 import matplotlib.pylab as pl
 import ot
+import ot.plot
 
 ##############################################################################
 # Dataset 1 : uniform sampling
@@ -52,7 +53,7 @@ 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')
+pl.title('Source and target distributions')
 
 
 # Cost matrices
diff --git a/examples/plot_barycenter_1D.py b/examples/plot_barycenter_1D.py
index 620936b..ecf640c 100644
--- a/examples/plot_barycenter_1D.py
+++ b/examples/plot_barycenter_1D.py
@@ -25,7 +25,7 @@ import ot
 from mpl_toolkits.mplot3d import Axes3D  # noqa
 from matplotlib.collections import PolyCollection
 
-##############################################################################
+#
 # Generate data
 # -------------
 
@@ -48,7 +48,7 @@ n_distributions = A.shape[1]
 M = ot.utils.dist0(n)
 M /= M.max()
 
-##############################################################################
+#
 # Plot data
 # ---------
 
@@ -60,7 +60,7 @@ for i in range(n_distributions):
 pl.title('Distributions')
 pl.tight_layout()
 
-##############################################################################
+#
 # Barycenter computation
 # ----------------------
 
@@ -90,7 +90,7 @@ pl.legend()
 pl.title('Barycenters')
 pl.tight_layout()
 
-##############################################################################
+#
 # Barycentric interpolation
 # -------------------------
 
diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py
index d3f724c..5cd40f6 100644
--- a/examples/plot_gromov.py
+++ b/examples/plot_gromov.py
@@ -19,8 +19,8 @@ import matplotlib.pylab as pl
 from mpl_toolkits.mplot3d import Axes3D  # noqa

 import ot

 

-

-##############################################################################

+#############################################################################

+#

 # Sample two Gaussian distributions (2D and 3D)

 # ---------------------------------------------

 #

@@ -42,8 +42,8 @@ xs = ot.datasets.get_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

 # --------------------------

 

@@ -55,8 +55,8 @@ 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

 # ---------------------------------------------------------

 

@@ -74,20 +74,33 @@ pl.subplot(122)
 pl.imshow(C2)

 pl.show()

 

-##############################################################################

+#############################################################################

+#

 # Compute Gromov-Wasserstein plans and distance

 # ---------------------------------------------

 

-

 p = ot.unif(n_samples)

 q = ot.unif(n_samples)

 

-gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4)

-gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4)

+gw0, log0 = ot.gromov.gromov_wasserstein(

+    C1, C2, p, q, 'square_loss', verbose=True, log=True)

 

-print('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist))

+gw, log = ot.gromov.entropic_gromov_wasserstein(

+    C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)

 

-pl.figure()

+

+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.colorbar()

+pl.title('Entropic Gromov Wasserstein')

+

 pl.show()

diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py
index 180b0cf..58fc51a 100755
--- a/examples/plot_gromov_barycenter.py
+++ b/examples/plot_gromov_barycenter.py
@@ -132,28 +132,28 @@ 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,

+                                            ], 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,

+                                            ], 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,

+                                            ], 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,

+                                            ], p, lambdast[i], 'square_loss',  # 5e-4,

                                            max_iter=100, tol=1e-3)

 

 

diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py
index e1a737e..92df016 100644
--- a/examples/plot_optim_OTreg.py
+++ b/examples/plot_optim_OTreg.py
@@ -28,7 +28,7 @@ arXiv preprint arXiv:1510.06567.
 import numpy as np
 import matplotlib.pylab as pl
 import ot
-
+import ot.plot
 
 ##############################################################################
 # Generate data
diff --git a/examples/plot_otda_d2.py b/examples/plot_otda_d2.py
index e53d7d6..70beb35 100644
--- a/examples/plot_otda_d2.py
+++ b/examples/plot_otda_d2.py
@@ -20,7 +20,7 @@ of what the transport methods are doing.
 
 import matplotlib.pylab as pl
 import ot
-
+import ot.plot
 
 ##############################################################################
 # generate data