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Diffstat (limited to 'docs/source/auto_examples/plot_WDA.rst')
| -rw-r--r-- | docs/source/auto_examples/plot_WDA.rst | 168 |
1 files changed, 116 insertions, 52 deletions
diff --git a/docs/source/auto_examples/plot_WDA.rst b/docs/source/auto_examples/plot_WDA.rst index 540555d..76ebaf5 100644 --- a/docs/source/auto_examples/plot_WDA.rst +++ b/docs/source/auto_examples/plot_WDA.rst @@ -7,13 +7,22 @@ Wasserstein Discriminant Analysis ================================= -@author: rflamary -.. image:: /auto_examples/images/sphx_glr_plot_WDA_001.png - :align: center +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_WDA_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_WDA_002.png + :scale: 47 .. rst-class:: sphx-glr-script-out @@ -23,26 +32,43 @@ Wasserstein Discriminant Analysis Compiling cost function... Computing gradient of cost function... iter cost val grad. norm - 1 +5.2427396265941129e-01 8.16627951e-01 - 2 +1.7904850059627236e-01 1.91366819e-01 - 3 +1.6985797253002377e-01 1.70940682e-01 - 4 +1.3903474972292729e-01 1.28606342e-01 - 5 +7.4961734618782416e-02 6.41973980e-02 - 6 +7.1900245222486239e-02 4.25693592e-02 - 7 +7.0472023318269614e-02 2.34599232e-02 - 8 +6.9917568641317152e-02 5.66542766e-03 - 9 +6.9885086242452696e-02 4.05756115e-04 - 10 +6.9884967432653489e-02 2.16836017e-04 - 11 +6.9884923649884148e-02 5.74961622e-05 - 12 +6.9884921818258436e-02 3.83257203e-05 - 13 +6.9884920459612282e-02 9.97486224e-06 - 14 +6.9884920414414409e-02 7.33567875e-06 - 15 +6.9884920388431387e-02 5.23889187e-06 - 16 +6.9884920385183902e-02 4.91959084e-06 - 17 +6.9884920373983223e-02 3.56451669e-06 - 18 +6.9884920369701245e-02 2.88858709e-06 - 19 +6.9884920361621208e-02 1.82294279e-07 - Terminated - min grad norm reached after 19 iterations, 9.65 seconds. + 1 +8.9741888001949222e-01 3.71269078e-01 + 2 +4.9103998133976140e-01 3.46687543e-01 + 3 +4.2142651893148553e-01 1.04789602e-01 + 4 +4.1573609749588841e-01 5.21726648e-02 + 5 +4.1486046805261961e-01 5.35335513e-02 + 6 +4.1315953904635105e-01 2.17803599e-02 + 7 +4.1313030162717523e-01 6.06901182e-02 + 8 +4.1301511591963386e-01 5.88598758e-02 + 9 +4.1258349404769817e-01 5.14307874e-02 + 10 +4.1139242901051226e-01 2.03198793e-02 + 11 +4.1113798965164017e-01 1.18944721e-02 + 12 +4.1103446820878486e-01 2.21783648e-02 + 13 +4.1076586830791861e-01 9.51495863e-03 + 14 +4.1036935287519144e-01 3.74973214e-02 + 15 +4.0958729714575060e-01 1.23810902e-02 + 16 +4.0898266309095005e-01 4.01999918e-02 + 17 +4.0816076944357715e-01 2.27240277e-02 + 18 +4.0788116701894767e-01 4.42815945e-02 + 19 +4.0695443744952403e-01 3.28464304e-02 + 20 +4.0293834480911150e-01 7.76000681e-02 + 21 +3.8488003705202750e-01 1.49378022e-01 + 22 +3.0767344927282614e-01 2.15432117e-01 + 23 +2.3849425361868334e-01 1.07942382e-01 + 24 +2.3845125762548214e-01 1.08953278e-01 + 25 +2.3828007730494005e-01 1.07934830e-01 + 26 +2.3760839060570119e-01 1.03822134e-01 + 27 +2.3514215179705886e-01 8.67263481e-02 + 28 +2.2978886197588613e-01 9.26609306e-03 + 29 +2.2972671019495342e-01 2.59476089e-03 + 30 +2.2972355865247496e-01 1.57205146e-03 + 31 +2.2972296662351968e-01 1.29300760e-03 + 32 +2.2972181557051569e-01 8.82375756e-05 + 33 +2.2972181277025336e-01 6.20536544e-05 + 34 +2.2972181023486152e-01 7.01884014e-06 + 35 +2.2972181020400181e-01 1.60415765e-06 + 36 +2.2972181020236590e-01 2.44290966e-07 + Terminated - min grad norm reached after 36 iterations, 13.41 seconds. @@ -53,62 +79,100 @@ Wasserstein Discriminant Analysis .. code-block:: python + # Author: Remi Flamary <remi.flamary@unice.fr> + # + # License: MIT License + import numpy as np import matplotlib.pylab as pl - import ot - from ot.datasets import get_1D_gauss as gauss - from ot.dr import wda + + from ot.dr import wda, fda #%% parameters - n=1000 # nb samples in source and target datasets - nz=0.2 - xs,ys=ot.datasets.get_data_classif('3gauss',n,nz) - xt,yt=ot.datasets.get_data_classif('3gauss',n,nz) + n = 1000 # nb samples in source and target datasets + nz = 0.2 - nbnoise=8 + # 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) - xs=np.hstack((xs,np.random.randn(n,nbnoise))) - xt=np.hstack((xt,np.random.randn(n,nbnoise))) + 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) - #%% plot samples + nbnoise = 8 - pl.figure(1) + xs = np.hstack((xs, np.random.randn(n, nbnoise))) + xt = np.hstack((xt, np.random.randn(n, nbnoise))) + #%% plot samples + pl.figure(1, figsize=(6.4, 3.5)) - pl.scatter(xt[:,0],xt[:,1],c=ys,marker='+',label='Source samples') + 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 FDA + p = 2 - #%% plot distributions and loss matrix - p=2 - reg=1 - k=10 - maxiter=100 + Pfda, projfda = fda(xs, ys, p) - P,proj = wda(xs,ys,p,reg,k,maxiter=maxiter) + #%% Compute WDA + p = 2 + reg = 1e0 + k = 10 + maxiter = 100 + + Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter) #%% plot samples - xsp=proj(xs) - xtp=proj(xt) + xsp = projfda(xs) + xtp = projfda(xt) + + xspw = projwda(xs) + xtpw = projwda(xt) - pl.figure(1,(10,5)) + pl.figure(2) - pl.subplot(1,2,1) - pl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples') + 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') + 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(1,2,2) - pl.scatter(xtp[:,0],xtp[:,1],c=ys,marker='+',label='Projected samples') + 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') + pl.title('Projected test samples WDA') + pl.tight_layout() + + pl.show() -**Total running time of the script:** ( 0 minutes 16.902 seconds) +**Total running time of the script:** ( 0 minutes 19.853 seconds) |