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 --- docs/source/auto_examples/plot_WDA.rst | 244 --------------------------------- 1 file changed, 244 deletions(-) delete mode 100644 docs/source/auto_examples/plot_WDA.rst (limited to 'docs/source/auto_examples/plot_WDA.rst') 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 `_ -- cgit v1.2.3