def test_structured_add_s_v_grad(self):
sp_types = {'csc': sp.csc_matrix,
'csr': sp.csr_matrix}
for format in ['csr', 'csc']:
for dtype in ['float32', 'float64']:
spmat = sp_types[format](random_lil((4, 3), dtype, 3))
mat = numpy.asarray(numpy.random.rand(3), dtype=dtype)
S.verify_grad_sparse(S2.mul_s_v,
[spmat, mat], structured=True)
def test_structured_add_s_v_grad(self):
sp_types = {'csc': sp.csc_matrix,
'csr': sp.csr_matrix}
for format in ['csr', 'csc']:
for dtype in ['float32', 'float64']:
spmat = sp_types[format](random_lil((4, 3), dtype, 3))
mat = numpy.ones(3, dtype=dtype)
S.verify_grad_sparse(S2.structured_add_s_v,
[spmat, mat], structured=True)
def test_col_scale():
x = theano.sparse.csc_dmatrix()
s = theano.tensor.dvector()
rng = numpy.random.RandomState(8723)
R = 5
C = 8
x_val_dense = numpy.zeros((R, C), dtype="d")
for idx in [(0, 0), (4, 1), (2, 1), (3, 3), (4, 4), (3, 7), (2, 7)]:
x_val_dense.__setitem__(idx, rng.randn())
x_val = scipy.sparse.csc_matrix(x_val_dense)
s_val = rng.randn(C)
f = theano.function([x, s], sp.col_scale(x, s))
# print 'A', f(x_val, s_val).toarray()
# print 'B', (x_val_dense * s_val)
assert numpy.all(f(x_val, s_val).toarray() == (x_val_dense * s_val))
verify_grad_sparse(sp.col_scale, [x_val, s_val], structured=False)
def test_col_scale():
x = theano.sparse.csc_dmatrix()
s = theano.tensor.dvector()
rng = numpy.random.RandomState(8723)
R = 5
C = 8
x_val_dense = numpy.zeros((R, C), dtype='d')
for idx in [(0, 0), (4, 1), (2, 1), (3, 3), (4, 4), (3, 7), (2, 7)]:
x_val_dense.__setitem__(idx, rng.randn())
x_val = scipy.sparse.csc_matrix(x_val_dense)
s_val = rng.randn(C)
f = theano.function([x, s], sp.col_scale(x, s))
# print 'A', f(x_val, s_val).toarray()
# print 'B', (x_val_dense * s_val)
assert numpy.all(f(x_val, s_val).toarray() == (x_val_dense * s_val))
verify_grad_sparse(sp.col_scale, [x_val, s_val], structured=False)
