def _testSS(
self, op, array1=numpy.array([[1.0, 0], [3, 0], [0, 6]]), array2=numpy.asarray([[0, 2.0], [0, 4], [5, 0]])
):
for mtype in _mtypes:
a = mtype(array1)
aR = as_sparse_variable(a)
self.assertFalse(aR.data is a)
self.assertTrue(_is_sparse(a))
self.assertTrue(_is_sparse_variable(aR))
b = mtype(array2)
bR = as_sparse_variable(b)
self.assertFalse(bR.data is b)
self.assertTrue(_is_sparse(b))
self.assertTrue(_is_sparse_variable(bR))
apb = op(aR, bR)
self.assertTrue(_is_sparse_variable(apb))
self.assertTrue(apb.type.dtype == aR.type.dtype, apb.type.dtype)
self.assertTrue(apb.type.dtype == bR.type.dtype, apb.type.dtype)
self.assertTrue(apb.type.format == aR.type.format, apb.type.format)
self.assertTrue(apb.type.format == bR.type.format, apb.type.format)
val = eval_outputs([apb])
self.assertTrue(val.shape == (3, 2))
if op is add:
self.assertTrue(numpy.all(val.todense() == (a + b).todense()))
ans = numpy.array([[1.0, 2], [3, 4], [5, 6]])
self.assertTrue(numpy.all(val.todense() == ans))
verify_grad_sparse(op, [a, b], structured=False)
elif op is mul:
self.assertTrue(numpy.all(val.todense() == (a.multiply(b)).todense()))
ans = numpy.array([[1, 0], [9, 0], [0, 36]])
self.assertTrue(numpy.all(val.todense() == ans))
def test_structureddot_csr_grad(self):
# shortcut: testing csc in float32, testing csr in float64
# allocate a random sparse matrix
spmat = sp.csr_matrix(random_lil((4, 3), "float64", 3))
mat = numpy.asarray(numpy.random.randn(3, 2), "float64")
verify_grad_sparse(structured_dot, [spmat, mat], structured=True)
def buildgraph_T(spmat, mat):
return structured_dot(mat.T, spmat.T)
verify_grad_sparse(buildgraph_T, [spmat, mat], structured=True)
