Source Code for Module mvpa.tests.test_icamapper

 1  # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- 
 2  # vi: set ft=python sts=4 ts=4 sw=4 et: 
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 4  # 
 5  #   See COPYING file distributed along with the PyMVPA package for the 
 6  #   copyright and license terms. 
 7  # 
 8  ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## 
 9  """Unit tests for PyMVPA ICA mapper""" 
10   
11   
12  import unittest 
13  from mvpa.support.copy import deepcopy 
14  import numpy as N 
15  from mvpa.mappers.ica import ICAMapper 
16   
17  from mvpa.datasets import Dataset 
18   
19 -class ICAMapperTests(unittest.TestCase): 
20   
21 -    def setUp(self): 
22          # data: 40 sample feature line in 2d space (40x2; samples x features) 
23          samples = N.vstack([N.arange(40.) for i in range(2)]).T 
24          samples -= samples.mean() 
25          samples +=  N.random.normal(size=samples.shape, scale=0.1) 
26          self.ndlin = Dataset(samples=samples, labels=1, chunks=1) 
27   
28          # data: 40 sample feature line in 50d space (40x50; samples x features) 
29          samples = N.vstack([N.arange(40.) for i in range(50)]).T 
30          samples -= samples.mean() 
31          samples +=  N.random.normal(size=samples.shape, scale=0.1) 
32          self.largefeat = Dataset(samples=samples, labels=1, chunks=1) 
33   
34          self.pm = ICAMapper() 
35   
36   
37 -    def testSimpleICA(self): 
38          # train 
39          self.pm.train(self.ndlin) 
40   
41          self.failUnlessEqual(self.pm.proj.shape, (2, 2)) 
42   
43          # now project data into ICA space 
44          p = self.pm.forward(self.ndlin.samples) 
45   
46          self.failUnlessEqual(p.shape, (40, 2)) 
47   
48          # check that the mapped data can be fully recovered by 'reverse()' 
49          self.failUnless(N.abs(self.pm.reverse(p) - self.ndlin.samples).mean() \ 
50                          < 0.0001) 
51   
52   
53  #    def testAutoOptimzeICA(self): 
54  #        # train 
55  #        self.pm.train(self.largefeat) 
56  # 
57   #       self.failUnlessEqual(self.pm.proj.shape, (50, 40)) 
58  # 
59   #       # now project data into ICA space 
60   ##       p = self.pm.forward(self.largefeat.samples) 
61  # 
62  #        self.failUnless(p.shape[1] == 40) 
63  #        print self.pm.proj 
64  #        print self.pm.recon 
65  #        print p 
66   
67  #        P.scatter(p[:20,0], p[:20,1],color='green') 
68  #        P.scatter(p[20:,0], p[20:,1], color='red') 
69  #        P.show() 
70  #        self.failUnless(N.abs(self.pm.reverse(p) - self.largefeat.samples).mean() \ 
71   #                       < 0.0001) 
72   
73 -def suite(): 
74      return unittest.makeSuite(ICAMapperTests) 
75   
76   
77  if __name__ == '__main__': 
78      import runner 
79