.. _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   +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.




|


.. 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  16.902 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

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