def _call(self, dataset):
# compute rsm
dsm = DSMatrix(dataset.samples, metric=self.metric)
vec_form = samples = dsm.get_vector_form()
n = dsm.full_matrix.shape[0]
# mask for upper diagonal
msk = np.asarray([[i > j for i in xrange(n)] for j in xrange(n)])
# compute z scores
arr = np.reshape(np.asarray(dsm.full_matrix[msk]), (-1, ))
arr_z = (arr - np.mean(arr)) / np.std(arr)
# set the space
ds = Dataset(samples=arr_z)
if not self.space is None:
space_vals = dataset.sa[self.space].value
labels = [
'%s-%s' % (space_vals[i], space_vals[j]) for i in xrange(n)
for j in xrange(n) if msk[i, j]
]
ds.sa[self.space] = labels
return ds
def __call__(self, dataset):
# create the dissimilarity matrix for the data in the input dataset
self.dset_dsm = DSMatrix(dataset.samples, self.dset_metric)
in_vec = self.dsmatrix.get_vector_form()
dset_vec = self.dset_dsm.get_vector_form()
# concatenate the two vectors, send to dissimlarity function
test_mat = np.asarray([in_vec, dset_vec])
test_dsmatrix = DSMatrix(test_mat, self.output_metric)
# return correct dissimilarity value
return test_dsmatrix.get_full_matrix()[0, 1]
def __call__(self, dataset):
# create the dissimilarity matrix for the data in the input dataset
self.dset_dsm = DSMatrix(dataset.samples, self.dset_metric)
in_vec = self.dsmatrix.get_vector_form()
dset_vec = self.dset_dsm.get_vector_form()
# concatenate the two vectors, send to dissimlarity function
test_mat = np.asarray([in_vec, dset_vec])
test_dsmatrix = DSMatrix(test_mat, self.output_metric)
# return correct dissimilarity value
return test_dsmatrix.get_full_matrix()[0, 1]
class DSMMeasure(Measure):
"""DSMMeasure creates a Measure object
where metric can be one of 'euclidean', 'spearman', 'pearson'
or 'confusion'"""
def __init__(self, dsmatrix, dset_metric, output_metric='spearman'):
Measure.__init__(self)
self.dsmatrix = dsmatrix
self.dset_metric = dset_metric
self.output_metric = output_metric
self.dset_dsm = []
def __call__(self, dataset):
# create the dissimilarity matrix for the data in the input dataset
self.dset_dsm = DSMatrix(dataset.samples, self.dset_metric)
in_vec = self.dsmatrix.get_vector_form()
dset_vec = self.dset_dsm.get_vector_form()
# concatenate the two vectors, send to dissimlarity function
test_mat = np.asarray([in_vec, dset_vec])
test_dsmatrix = DSMatrix(test_mat, self.output_metric)
# return correct dissimilarity value
return test_dsmatrix.get_full_matrix()[0, 1]
class DSMMeasure(Measure):
"""DSMMeasure creates a Measure object
where metric can be one of 'euclidean', 'spearman', 'pearson'
or 'confusion'"""
def __init__(self, dsmatrix, dset_metric, output_metric="spearman"):
Measure.__init__(self)
self.dsmatrix = dsmatrix
self.dset_metric = dset_metric
self.output_metric = output_metric
self.dset_dsm = []
def __call__(self, dataset):
# create the dissimilarity matrix for the data in the input dataset
self.dset_dsm = DSMatrix(dataset.samples, self.dset_metric)
in_vec = self.dsmatrix.get_vector_form()
dset_vec = self.dset_dsm.get_vector_form()
# concatenate the two vectors, send to dissimlarity function
test_mat = np.asarray([in_vec, dset_vec])
test_dsmatrix = DSMatrix(test_mat, self.output_metric)
# return correct dissimilarity value
return test_dsmatrix.get_full_matrix()[0, 1]
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)
def _call(self, dataset):
# compute rsm
dsm = DSMatrix(dataset.samples, metric=self.metric)
vec_form = samples = dsm.get_vector_form()
n = dsm.full_matrix.shape[0]
# mask for upper diagonal
msk = np.asarray([[i > j for i in xrange(n)] for j in xrange(n)])
# compute z scores
arr = np.reshape(np.asarray(dsm.full_matrix[msk]), (-1,))
arr_z = (arr - np.mean(arr)) / np.std(arr)
# set the space
ds = Dataset(samples=arr_z)
if not self.space is None:
space_vals = dataset.sa[self.space].value
labels = ['%s-%s' % (space_vals[i], space_vals[j])
for i in xrange(n) for j in xrange(n) if msk[i, j]]
ds.sa[self.space] = labels
return ds
