/usr/share/pyshared/mvpa2/measures/rsm.py is in python-mvpa2 2.1.0-1.
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#
# See COPYING file distributed along with the PyMVPA package for the
# copyright and license terms.
#
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"""Similarity measure correlation computed over all
subjects
"""
__docformat__ = 'restructuredtext'
import numpy as np
from mvpa2.measures.base import Measure
from mvpa2.misc.stats import DSMatrix
from mvpa2.datasets.base import Dataset
import copy
class RSMMeasure(Measure):
"""RSMMeasure creates a DatasetMeasure object
where metric can be one of 'euclidean', 'spearman', 'pearson'
or 'confusion' and nsubjs has to be number of subjects
and compare_ave flag determines whether to correlate with
average of all other subjects or just one-to-one
k should be 0 to n 0-to use DSM including diagonal
"""
def __init__(self, dset_metric, nsubjs, compare_ave, k, **kwargs):
Measure.__init__(self, **kwargs)
self.dset_metric = dset_metric
self.dset_dsm = []
self.nsubjs = nsubjs
self.compare_ave = compare_ave
self.k = k
def __call__(self, dataset):
dsm_all = []
rsm_all = []
nsubjs = self.nsubjs
# create the dissimilarity matrix for each subject's data in the input dataset
''' TODO: How to handle Nan? should we uncomment the workarounds?
'''
for i in xrange(nsubjs):
if self.dset_metric == 'pearson':
self.dset_dsm = np.corrcoef(dataset.samples[i*dataset.nsamples/nsubjs:((i+1)*dataset.nsamples/nsubjs),:])
else:
self.dset_dsm = DSMatrix(dataset.samples[i*dataset.nsamples/nsubjs:((i+1)*dataset.nsamples/nsubjs),:], self.dset_metric)
self.dset_dsm = self.dset_dsm.full_matrix
orig_dsmatrix = copy.deepcopy(np.matrix(self.dset_dsm))
#orig_dsmatrix[np.isnan(orig_dsmatrix)] = 0
#orig_dsmatrix[orig_dsmatrix == 0] = -2
#orig_tri = np.triu(orig_dsmatrix, k=self.k)
#vector_form = orig_tri[abs(orig_tri) > 0]
#vector_form[vector_form == -2] = 0
vector_form = orig_dsmatrix[np.tri(len(orig_dsmatrix),k=-1*self.k,dtype=bool)]
vector_form = np.asarray(vector_form)
dset_vec = vector_form[0]
dsm_all.append(dset_vec)
dsm_all = np.vstack(dsm_all)
#print dsm_all.shape
if self.compare_ave:
for i in xrange(nsubjs):
dsm_temp = nsubjs*np.mean(dsm_all, axis=0) - dsm_all[i,:]
rsm = np.corrcoef(dsm_temp, dsm_all[i,:])
rsm_all.append(rsm[0,1])
else:
rsm = np.corrcoef(dsm_all)
rsm = np.matrix(rsm)
#rsm[np.isnan(rsm)] = 0
#rsm[rsm == 0] = -2
#rsm = np.triu(rsm, k=1)
#rsm = rsm[abs(rsm) > 0]
#rsm[rsm == -2] = 0
rsm = rsm[np.tri(len(rsm),k=-1,dtype=bool)]
rsm = np.asarray(rsm)
rsm_all = rsm[0]
return Dataset(rsm_all)
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