/usr/share/pyshared/mvpa/mappers/samplegroup.py is in python-mvpa 0.4.8-3.
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#
# See COPYING file distributed along with the PyMVPA package for the
# copyright and license terms.
#
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"""Data mapper"""
__docformat__ = 'restructuredtext'
import numpy as N
from mvpa.base.dochelpers import enhancedDocString
from mvpa.mappers.base import Mapper
from mvpa.misc.transformers import FirstAxisMean
if __debug__:
from mvpa.base import debug
class SampleGroupMapper(Mapper):
# name is ugly, please help!
"""Mapper to apply a mapping function to samples of the same type.
A customimzable function is applied individually to all samples with the
same unique label from the same chunk. This mapper is somewhat
unconventional since it doesn't preserve number of samples (ie the size of
0-th dimension...)
"""
def __init__(self, fx=FirstAxisMean):
"""Initialize the PCAMapper
Parameters:
startpoints: A sequence of index value along the first axis of
'data'.
boxlength: The number of elements after 'startpoint' along the
first axis of 'data' to be considered for averaging.
offset: The offset between the starting point and the
averaging window (boxcar).
collision_resolution : string
if a sample belonged to multiple output samples, then on reverse,
how to resolve the value (choices: 'mean')
"""
Mapper.__init__(self)
self.__fx = fx
self.__uniquechunks = None
self.__uniquelabels = None
self.__chunks = None
self.__labels = None
self.__datashape = None
__doc__ = enhancedDocString('SampleGroupMapper', locals(), Mapper)
def train(self, dataset):
"""
"""
# just store the relevant information
self.__uniquechunks = dataset.uniquechunks
self.__uniquelabels = dataset.uniquelabels
self.__chunks = dataset.chunks
self.__labels = dataset.labels
self.__datashape = (dataset.nfeatures, )
def forward(self, data):
"""
"""
if self.__datashape is None:
raise RuntimeError, \
"SampleGroupMapper needs to be trained before it can be used"
mdata = []
# for each label in each chunk
for c in self.__uniquechunks:
for l in self.__uniquelabels:
mdata.append(self.__fx(data[N.logical_and(self.__labels == l,
self.__chunks == c)]))
return N.array(mdata)
def reverse(self, data):
"""This is not implemented."""
raise NotImplementedError
def getInSize(self):
"""Returns the number of original samples which were combined.
"""
return self.__datashape[0]
def getOutSize(self):
"""Returns the number of output samples.
"""
return self.__datashape[0]
def selectOut(self, outIds):
"""Just complain for now"""
raise NotImplementedError, \
"For feature selection use MaskMapper on output of the %s mapper" \
% self.__class__.__name__
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