def segment_packed_corr(segments, width=None):
# correlation over time within each segment
if width is None:
width = segments.tag.shape[2]
try:
n_segments = segments.tag.shape[0]
except AttributeError:
n_segments = segments.shape[0]
packing_matrix = pack_symmatrix_spmat(width)
corr = custom_corr(segments).reshape((n_segments, width ** 2))
rval = theano.sparse.structured_dot(corr, packing_matrix)
if corr.type.broadcastable[0]:
# sparse matrices have no broadcastable property,
# so the broadcastable[0] is always False, even when it shouldn't be
rval = theano.tensor.Rebroadcast((0, True))(rval)
return rval
def segment_packed_corr(segments, width=None):
#correlation over time within each segment
if width is None:
width = segments.tag.shape[2]
try:
n_segments = segments.tag.shape[0]
except AttributeError:
n_segments = segments.shape[0]
packing_matrix = pack_symmatrix_spmat(width)
corr = custom_corr(segments).reshape((n_segments, width**2))
rval = theano.sparse.structured_dot(corr, packing_matrix)
if corr.type.broadcastable[0]:
# sparse matrices have no broadcastable property,
# so the broadcastable[0] is always False, even when it shouldn't be
rval = theano.tensor.Rebroadcast((0, True))(rval)
return rval
def segment_corr(segments):
# correlation over time within each segment
n_segments = segments.shape[0]
width = segments.shape[2]
return custom_corr(segments).reshape((n_segments, width ** 2))
def segment_corr(segments):
#correlation over time within each segment
n_segments = segments.shape[0]
width = segments.shape[2]
return custom_corr(segments).reshape((n_segments, width**2))