def test_col_scale():
x = theano.sparse.csc_dmatrix()
s = theano.tensor.dvector()
def d(x, s):
return sp.sp_sum(sp.col_scale(x, s), sparse_grad=True)
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))
if 0:
tensor.verify_grad(None,
d, [x_val, s_val],
mode=theano.Mode(linker='py',
optimizer='fast_compile'))
else:
print >> sys.stderr, "WARNING: skipping gradient test because verify_grad doesn't support sparse arguments"
def test_col_scale():
x = theano.sparse.csc_dmatrix()
s = theano.tensor.dvector()
def d(x, s):
return sp.sp_sum(sp.col_scale(x, s), sparse_grad=True)
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))
if 0:
tensor.verify_grad(None, d, [x_val, s_val], mode=theano.Mode(linker="py", optimizer="fast_compile"))
else:
print >> sys.stderr, "WARNING: skipping gradient test because verify_grad doesn't support sparse arguments"
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)
def d(x, s):
return sp.sp_sum(sp.col_scale(x, s), sparse_grad=True)
def d(x, s):
return sp.sp_sum(sp.col_scale(x, s), sparse_grad=True)
