def test_validate_cp_tensor():
rng = tl.check_random_state(12345)
true_shape = (3, 4, 5)
true_rank = 3
cp_tensor = random_cp(true_shape, true_rank)
(weights, factors) = cp_normalize(cp_tensor)
# Check correct rank and shapes are returned
shape, rank = _validate_cp_tensor((weights, factors))
assert_equal(
shape,
true_shape,
err_msg='Returned incorrect shape (got {}, expected {})'.format(
shape, true_shape))
assert_equal(
rank,
true_rank,
err_msg='Returned incorrect rank (got {}, expected {})'.format(
rank, true_rank))
# One of the factors has the wrong rank
factors[0], copy = tl.tensor(rng.random_sample((4, 4))), factors[0]
with assert_raises(ValueError):
_validate_cp_tensor((weights, factors))
# Not the correct amount of weights
factors[0] = copy
wrong_weights = weights[1:]
with assert_raises(ValueError):
_validate_cp_tensor((wrong_weights, factors))
# Not enough factors
with assert_raises(ValueError):
_validate_cp_tensor((weights[:1], factors[:1]))
def test_cp_flip_sign():
shape = (3, 4, 5)
rank = 4
cp_tensor = random_cp(shape, rank)
weights, factors = cp_flip_sign(cp_tensor)
assert_(tl.all(tl.mean(factors[1], axis=0) > 0))
assert_(tl.all(tl.mean(factors[2], axis=0) > 0))
assert_equal(cp_tensor.rank, cp_tensor.rank)
assert_array_equal(cp_tensor.weights, weights)
assert_array_almost_equal(cp_to_tensor((weights, factors)), cp_to_tensor(cp_tensor))
def test_cp_mode_dot():
"""Test for cp_mode_dot
We will compare cp_mode_dot
(which operates directly on decomposed tensors)
with mode_dot (which operates on full tensors)
and check that the results are the same.
"""
rng = tl.check_random_state(12345)
shape = (5, 4, 6)
rank = 3
cp_ten = random_cp(shape, rank=rank, orthogonal=True, full=False)
full_tensor = tl.cp_to_tensor(cp_ten)
# matrix for mode 1
matrix = tl.tensor(rng.random_sample((7, shape[1])))
# vec for mode 2
vec = tl.tensor(rng.random_sample(shape[2]))
# Test cp_mode_dot with matrix
res = cp_mode_dot(cp_ten, matrix, mode=1, copy=True)
# Note that if copy=True is not respected, factors will be changes
# And the next test will fail
res = tl.cp_to_tensor(res)
true_res = mode_dot(full_tensor, matrix, mode=1)
assert_array_almost_equal(true_res, res)
# Check that the data was indeed copied
rec = tl.cp_to_tensor(cp_ten)
assert_array_almost_equal(full_tensor, rec)
# Test cp_mode_dot with vec
res = cp_mode_dot(cp_ten, vec, mode=2, copy=True)
res = tl.cp_to_tensor(res)
true_res = mode_dot(full_tensor, vec, mode=2)
assert_equal(res.shape, true_res.shape)
assert_array_almost_equal(true_res, res)
