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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 +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
`Generated by Sphinx-Gallery <http://sphinx-gallery.readthedocs.io>`_
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