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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.
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.. 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>`_
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