def test_remove0(self):
configs = [
# structure type, numpy matching class
('csc', scipy.sparse.csc_matrix),
('csr', scipy.sparse.csr_matrix), ]
for format, matrix_class in configs:
for zero, unsor in [(True, True), (True, False),
(False, True), (False, False)]:
(x,), (mat,) = sparse_random_inputs(format, (6, 8),
out_dtype=config.floatX,
explicit_zero=zero,
unsorted_indices=unsor)
assert 0 in mat.data or not zero
assert not mat.has_sorted_indices or not unsor
# the In thingy has to be there because theano has as rule not
# to optimize inputs
f = theano.function([theano.In(x, borrow=True, mutable=True)],
Remove0()(x))
# assert optimization local_inplace_remove0 is applied in
# modes with optimization
if theano.config.mode not in ['FAST_COMPILE']:
# list of apply nodes in the optimized graph.
nodes = f.maker.fgraph.toposort()
# Check there isn't any Remove0 instance not inplace.
assert not any([isinstance(node.op, Remove0) and
not node.op.inplace for node in nodes]), (
'Inplace optimization should have been applied')
# Check there is at least one Remove0 inplace.
assert any([isinstance(node.op, Remove0) and node.op.inplace
for node in nodes])
# checking
# makes sense to change its name
target = mat
result = f(mat)
mat.eliminate_zeros()
msg = 'Matrices sizes differ. Have zeros been removed ?'
assert result.size == target.size, msg
if unsor:
assert not result.has_sorted_indices
assert not target.has_sorted_indices
else:
assert result.has_sorted_indices
assert target.has_sorted_indices
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