Source Code for Module mvpa.mappers.samplegroup

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  4  # 
  5  #   See COPYING file distributed along with the PyMVPA package for the 
  6  #   copyright and license terms. 
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  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 Mapper 
 17  from mvpa.misc.transformers import FirstAxisMean 
 18   
 19  if __debug__: 
 20      from mvpa.base import debug 
 21   
 22   
 23 -class SampleGroupMapper(Mapper): 
 24      # name is ugly, please help! 
 25      """Mapper to apply a mapping function to samples of the same type. 
 26   
 27      A customimzable function is applied individually to all samples with the 
 28      same unique label from the same chunk. This mapper is somewhat 
 29      unconventional since it doesn't preserve number of samples (ie the size of 
 30      0-th dimension...) 
 31      """ 
 32   
 33 -    def __init__(self, fx=FirstAxisMean): 
 34          """Initialize the PCAMapper 
 35   
 36          Parameters: 
 37            startpoints:    A sequence of index value along the first axis of 
 38                            'data'. 
 39            boxlength:      The number of elements after 'startpoint' along the 
 40                            first axis of 'data' to be considered for averaging. 
 41            offset:         The offset between the starting point and the 
 42                            averaging window (boxcar). 
 43            collision_resolution : string 
 44              if a sample belonged to multiple output samples, then on reverse, 
 45              how to resolve the value (choices: 'mean') 
 46          """ 
 47          Mapper.__init__(self) 
 48   
 49          self.__fx = fx 
 50          self.__uniquechunks = None 
 51          self.__uniquelabels = None 
 52          self.__chunks = None 
 53          self.__labels = None 
 54          self.__datashape = None 
 55   
 56   
 57      __doc__ = enhancedDocString('SampleGroupMapper', locals(), Mapper) 
 58   
 59   
 60 -    def train(self, dataset): 
 61          """ 
 62          """ 
 63          # just store the relevant information 
 64          self.__uniquechunks = dataset.uniquechunks 
 65          self.__uniquelabels = dataset.uniquelabels 
 66          self.__chunks = dataset.chunks 
 67          self.__labels = dataset.labels 
 68          self.__datashape = (dataset.nfeatures, ) 
 69   
 70   
 71 -    def forward(self, data): 
 72          """ 
 73          """ 
 74          if self.__datashape is None: 
 75              raise RuntimeError, \ 
 76                    "SampleGroupMapper needs to be trained before it can be used" 
 77   
 78          mdata = [] 
 79   
 80          # for each label in each chunk 
 81          for c in self.__uniquechunks: 
 82              for l in self.__uniquelabels: 
 83                  mdata.append(self.__fx(data[N.logical_and(self.__labels == l, 
 84                                                            self.__chunks == c)])) 
 85   
 86          return N.array(mdata) 
 87   
 88   
 89 -    def reverse(self, data): 
 90          """This is not implemented.""" 
 91          raise NotImplementedError 
 92   
 93   
 94 -    def getInSize(self): 
 95          """Returns the number of original samples which were combined. 
 96          """ 
 97          return self.__datashape[0] 
 98   
 99   
100 -    def getOutSize(self): 
101          """Returns the number of output samples. 
102          """ 
103          return self.__datashape[0] 
104   
105   
106 -    def selectOut(self, outIds): 
107          """Just complain for now""" 
108          raise NotImplementedError, \ 
109              "For feature selection use MaskMapper on output of the %s mapper" \ 
110              % self.__class__.__name__ 
111