Source Code for Module mvpa.mappers.ica

 1  # emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*- 
 2  # vi: set ft=python sts=4 ts=4 sw=4 et: 
 3  ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## 
 4  # 
 5  #   See COPYING file distributed along with the PyMVPA package for the 
 6  #   copyright and license terms. 
 7  # 
 8  ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ## 
 9  """Data mapper""" 
10   
11  __docformat__ = 'restructuredtext' 
12   
13  import numpy as N 
14   
15  from mvpa.base.dochelpers import enhancedDocString 
16  from mvpa.mappers.base import ProjectionMapper 
17   
18  import mvpa.base.externals as externals 
19  if externals.exists('mdp', raiseException=True): 
20      from mdp.nodes import FastICANode, CuBICANode 
21   
22   
23 -class ICAMapper(ProjectionMapper): 
24      """Mapper to project data onto ICA components estimated from some dataset. 
25   
26      After the mapper has been instantiated, it has to be train first. The ICA 
27      mapper only handles 2D data matrices. 
28      """ 
29 -    def __init__(self, algorithm='cubica', transpose=False, **kwargs): 
30          ProjectionMapper.__init__(self, **kwargs) 
31   
32          self._algorithm = algorithm 
33          self._transpose = transpose 
34   
35      __doc__ = enhancedDocString('ICAMapper', locals(), ProjectionMapper) 
36   
37   
38 -    def _train(self, dataset): 
39          """Determine the projection matrix onto the components from 
40          a 2D samples x feature data matrix. 
41          """ 
42          white_param = {} 
43   
44          # more features than samples? -> rank deficiancy 
45          # if not tranposing the data, MDP has to do SVD prior to ICA 
46          if dataset.samples.shape[1] > dataset.samples.shape[0] \ 
47             and not self._transpose: 
48              white_param['svd'] = True 
49   
50          if self._algorithm == 'fastica': 
51              node = FastICANode(white_parm=white_param, 
52                                 dtype=dataset.samples.dtype) 
53          elif self._algorithm == 'cubica': 
54              node = CuBICANode(white_parm=white_param, 
55                                dtype=dataset.samples.dtype) 
56          else: 
57              raise NotImplementedError 
58   
59  #            node.train(dataset.samples.T) 
60  #            self._proj = dataset.samples.T * N.asmatrix(node.get_projmatrix()) 
61  #            print self._proj.shape 
62  #        else: 
63          node.train(dataset.samples) 
64          self._proj = N.asmatrix(node.get_projmatrix()) 
65          self._recon = N.asmatrix(node.get_recmatrix()) 
66