Source Code for Module mvpa.algorithms.hyperalignment

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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  """Hyperalignment of functional data to the common space 
 10   
 11  References: TODO... 
 12   
 13  see SMLR code for example on how to embed the reference so in future 
 14  it gets properly referenced... 
 15  """ 
 16   
 17  __docformat__ = 'restructuredtext' 
 18   
 19  from mvpa.support.copy import deepcopy 
 20   
 21  from mvpa.base import warning 
 22  from mvpa.misc.state import StateVariable, ClassWithCollections 
 23  from mvpa.misc.param import Parameter 
 24  from mvpa.misc.transformers import GrandMean 
 25   
 26  if __debug__: 
 27      from mvpa.base import debug 
 28   
 29   
 30 -class Hyperalignment(ClassWithCollections): 
 31      """ ... 
 32   
 33      Given a set of datasets (may be just data) provide mapping of 
 34      features into a common space 
 35      """ 
 36   
 37      # May be something we might store optionally upon user request 
 38      who_knows_maybe_something_to_store_optionally = \ 
 39         StateVariable(enabled=False, doc= """....""") 
 40   
 41      # Lets use built-in facilities to specify parameters which 
 42      # constructor should accept 
 43      alignment = Parameter(None, # might provide allowedtype later on 
 44              doc="""... XXX If `None` (default) an instance of 
 45              :class:`~mvpa.mappers.procrustean.ProcrusteanMapper` is 
 46              used.""") 
 47   
 48      levels = Parameter(3, allowedtype='int', min=1, 
 49              doc="Number of levels ....XXX ") 
 50   
 51      combiner1 = Parameter('mean', # 
 52              doc="XXX ") 
 53   
 54      combiner2 = Parameter('mean', # 
 55              doc="XXX ") 
 56   
 57   
 58 -    def __init__(self, 
 59                   alignment=None, 
 60                   levels=3, 
 61                   combiner1='mean', 
 62                   combiner2='mean', 
 63                   **kwargs): 
 64   
 65          ClassWithCollections.__init__(self, **kwargs) 
 66   
 67          if self.params.alignment == None: 
 68              self.params.alignment = ProcrusteanMapper() 
 69   
 70          raise NotImlementedError, "WiP!  Use development branch of version >= 0.5.0" 
 71   
 72   
 73 -    def __call__(self, data): 
 74          """Estimate mappers for each data(set) 
 75   
 76          Parameters 
 77          ---------- 
 78            data : list or tuple of dataset of data 
 79              XXX 
 80   
 81          Returns 
 82          ------- 
 83          A list of trained Mappers ... of length equal to len(data) 
 84          """ 
 85          params = self.params            # for quicker access ;) 
 86          nelements = len(data) 
 87   
 88          # might prefer some other way to initialize... later 
 89          result = [deepcopy(params.alignment) for i in xrange(nelements)] 
 90   
 91          # Level 1 
 92          commonspace = data[0] 
 93          for m, d in zip(mappers[1:], data[1:]): 
 94              # XXX For now lets just call this way: 
 95              m.train(d, commonspace) 
 96              commonspace = mean(m.forward(d), commonspace)# here yarik stopped ;) 
 97   
 98   
 99          # Level 2 to params.levels 
100   
101           
102          return result 
103