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Diffstat (limited to 'docs/source/auto_examples/plot_WDA.rst')
| -rw-r--r-- | docs/source/auto_examples/plot_WDA.rst | 172 |
1 files changed, 109 insertions, 63 deletions
diff --git a/docs/source/auto_examples/plot_WDA.rst b/docs/source/auto_examples/plot_WDA.rst index 76ebaf5..8c9ee29 100644 --- a/docs/source/auto_examples/plot_WDA.rst +++ b/docs/source/auto_examples/plot_WDA.rst @@ -7,86 +7,40 @@ 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. -.. rst-class:: sphx-glr-horizontal - * +.. code-block:: python - .. image:: /auto_examples/images/sphx_glr_plot_WDA_001.png - :scale: 47 - * + # Author: Remi Flamary <remi.flamary@unice.fr> + # + # License: MIT License - .. image:: /auto_examples/images/sphx_glr_plot_WDA_002.png - :scale: 47 + import numpy as np + import matplotlib.pylab as pl + from ot.dr import wda, fda -.. rst-class:: sphx-glr-script-out - Out:: - Compiling cost function... - Computing gradient of cost function... - iter cost val grad. norm - 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. - - - - -| -.. code-block:: python - # Author: Remi Flamary <remi.flamary@unice.fr> - # - # License: MIT License - import numpy as np - import matplotlib.pylab as pl +Generate data +############################################################################# - from ot.dr import wda, fda + + +.. code-block:: python #%% parameters @@ -112,6 +66,20 @@ Wasserstein Discriminant Analysis 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)) @@ -126,11 +94,42 @@ Wasserstein Discriminant Analysis 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 @@ -139,6 +138,45 @@ Wasserstein Discriminant Analysis 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 +7.7038877420882157e-01 6.30647522e-01 + 2 +3.3969600919721271e-01 2.83791849e-01 + 3 +3.0014000762425608e-01 2.56139137e-01 + 4 +2.3397191702411621e-01 6.41134216e-02 + 5 +2.3107227220070231e-01 2.24837190e-02 + 6 +2.3072327156158298e-01 1.71334761e-03 + 7 +2.3072143589220098e-01 6.30059431e-04 + 8 +2.3072133109125159e-01 4.88673790e-04 + 9 +2.3072119579341774e-01 1.74129117e-04 + 10 +2.3072118662364521e-01 1.27046386e-04 + 11 +2.3072118228917746e-01 9.70877451e-05 + 12 +2.3072117734120351e-01 4.17292699e-05 + 13 +2.3072117623493599e-01 4.46062100e-06 + 14 +2.3072117622383431e-01 1.59801454e-06 + 15 +2.3072117622300498e-01 1.12117391e-06 + 16 +2.3072117622220378e-01 4.14581994e-08 + Terminated - min grad norm reached after 16 iterations, 7.77 seconds. + + +Plot 2D projections +############################################################################# + + + +.. code-block:: python + + #%% plot samples xsp = projfda(xs) @@ -172,7 +210,15 @@ Wasserstein Discriminant Analysis pl.show() -**Total running time of the script:** ( 0 minutes 19.853 seconds) + + +.. image:: /auto_examples/images/sphx_glr_plot_WDA_003.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 8.568 seconds) @@ -191,4 +237,4 @@ Wasserstein Discriminant Analysis .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_ + `Generated by Sphinx-Gallery <https://sphinx-gallery.readthedocs.io>`_ |