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+
+
+.. _sphx_glr_auto_examples_plot_WDA.py:
+
+
+=================================
+WAsserstein Discriminant Analysis
+=================================
+
+@author: rflamary
+
+
+
+
+.. image:: /auto_examples/images/sphx_glr_plot_WDA_001.png
+    :align: center
+
+
+.. rst-class:: sphx-glr-script-out
+
+ Out::
+
+    Compiling cost function...
+    Computing gradient of cost function...
+     iter              cost val         grad. norm
+        1   +7.5272200933021116e-01 8.85804426e-01
+        2   +2.5764223980223788e-01 3.04501586e-01
+        3   +1.6018169776696620e-01 1.78298483e-01
+        4   +1.4560944642106255e-01 1.42133298e-01
+        5   +1.0243843483991794e-01 1.23342675e-01
+        6   +7.8856617504010643e-02 1.05379766e-01
+        7   +7.7620851864404483e-02 1.04044062e-01
+        8   +7.3160520861018416e-02 8.33770034e-02
+        9   +6.6999294576662857e-02 2.87368977e-02
+       10   +6.6250206928793964e-02 1.72155066e-03
+       11   +6.6247631521353170e-02 2.43806911e-04
+       12   +6.6247596955965438e-02 1.40066459e-04
+       13   +6.6247580176638649e-02 4.77471577e-06
+       14   +6.6247580163923028e-02 3.00484279e-06
+       15   +6.6247580159235792e-02 1.91039983e-06
+       16   +6.6247580156889613e-02 9.56038747e-07
+    Terminated - min grad norm reached after 16 iterations, 7.78 seconds.
+
+
+
+
+|
+
+
+.. code-block:: python
+
+
+    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
+
+
+    #%% 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)
+
+    nbnoise=8
+
+    xs=np.hstack((xs,np.random.randn(n,nbnoise)))
+    xt=np.hstack((xt,np.random.randn(n,nbnoise)))
+
+    #%% plot samples
+
+    pl.figure(1)
+
+
+    pl.scatter(xt[:,0],xt[:,1],c=ys,marker='+',label='Source samples')
+    pl.legend(loc=0)
+    pl.title('Discriminant dimensions')
+
+
+    #%% plot distributions and loss matrix
+    p=2
+    reg=1
+    k=10
+    maxiter=100
+
+    P,proj = wda(xs,ys,p,reg,k,maxiter=maxiter)
+
+    #%% plot samples
+
+    xsp=proj(xs)
+    xtp=proj(xt)
+
+    pl.figure(1,(10,5))
+
+    pl.subplot(1,2,1)
+    pl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples')
+    pl.legend(loc=0)
+    pl.title('Projected training samples')
+
+
+    pl.subplot(1,2,2)
+    pl.scatter(xtp[:,0],xtp[:,1],c=ys,marker='+',label='Projected samples')
+    pl.legend(loc=0)
+    pl.title('Projected test samples')
+
+**Total running time of the script:** ( 0 minutes  14.134 seconds)
+
+
+
+.. container:: sphx-glr-footer
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Python source code: plot_WDA.py <plot_WDA.py>`
+
+
+
+  .. container:: sphx-glr-download
+
+     :download:`Download Jupyter notebook: plot_WDA.ipynb <plot_WDA.ipynb>`
+
+.. rst-class:: sphx-glr-signature
+
+    `Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_