def test_local_csm_properties_csm():
data = tensor.vector()
indices, indptr, shape = (tensor.ivector(), tensor.ivector(),
tensor.ivector())
mode = theano.compile.mode.get_default_mode()
mode = mode.including("specialize", "local_csm_properties_csm")
for CS, cast in [(sparse.CSC, sp.csc_matrix), (sparse.CSR, sp.csr_matrix)]:
f = theano.function([data, indices, indptr, shape],
sparse.csm_properties(
CS(data, indices, indptr, shape)),
mode=mode)
assert not any(
isinstance(node.op, (sparse.CSM, sparse.CSMProperties))
for node in f.maker.fgraph.toposort())
v = cast(random_lil((10, 40), config.floatX, 3))
f(v.data, v.indices, v.indptr, v.shape)
def test_local_csm_properties_csm():
data = tensor.vector()
indices, indptr, shape = (tensor.ivector(), tensor.ivector(),
tensor.ivector())
mode = theano.compile.mode.get_default_mode()
mode = mode.including("specialize", "local_csm_properties_csm")
for CS, cast in [(sparse.CSC, sp.csc_matrix),
(sparse.CSR, sp.csr_matrix)]:
f = theano.function([data, indices, indptr, shape],
sparse.csm_properties(
CS(data, indices, indptr, shape)),
mode=mode)
assert not any(
isinstance(node.op, (sparse.CSM, sparse.CSMProperties))
for node in f.maker.fgraph.toposort())
v = cast(random_lil((10, 40),
config.floatX, 3))
f(v.data, v.indices, v.indptr, v.shape)