def test_kruskal_to_tensor_with_weights():
A = tl.reshape(tl.arange(1, 5), (2, 2))
B = tl.reshape(tl.arange(5, 9), (2, 2))
weights = tl.tensor([2, -1])
out = kruskal_to_tensor([A, B], weights=weights)
expected = tl.tensor([[-2, -2], [6, 10]]) # computed by hand
tl.assert_array_equal(out, expected)
def test_kruskal_to_vec():
"""Test for kruskal_to_vec"""
U1 = np.reshape(np.arange(1, 10), (3, 3))
U2 = np.reshape(np.arange(10, 22), (4, 3))
U3 = np.reshape(np.arange(22, 28), (2, 3))
U4 = np.reshape(np.arange(28, 34), (2, 3))
U = [tl.tensor(t) for t in [U1, U2, U3, U4]]
full_tensor = kruskal_to_tensor(U)
true_res = tensor_to_vec(full_tensor)
res = kruskal_to_vec(U)
tl.assert_array_equal(
true_res,
res,
err_msg='khatri_rao product converted incorrectly to vec.')
def test_kruskal_to_unfolded():
"""Test for kruskal_to_unfolded.
!!Assumes that kruskal_to_tensor and unfold are properly tested and work!!
"""
U1 = np.reshape(np.arange(1, 10), (3, 3))
U2 = np.reshape(np.arange(10, 22), (4, 3))
U3 = np.reshape(np.arange(22, 28), (2, 3))
U4 = np.reshape(np.arange(28, 34), (2, 3))
U = [tl.tensor(t) for t in [U1, U2, U3, U4]]
full_tensor = kruskal_to_tensor(U)
for mode in range(4):
true_res = unfold(full_tensor, mode)
res = kruskal_to_unfolded(U, mode)
tl.assert_array_equal(
true_res,
res,
err_msg='khatri_rao product unfolded incorrectly for mode {}.'.
format(mode))
def test_kruskal_to_tensor():
"""Test for kruskal_to_tensor."""
U1 = np.reshape(np.arange(1, 10), (3, 3))
U2 = np.reshape(np.arange(10, 22), (4, 3))
U3 = np.reshape(np.arange(22, 28), (2, 3))
U4 = np.reshape(np.arange(28, 34), (2, 3))
U = [tl.tensor(t) for t in [U1, U2, U3, U4]]
true_res = tl.tensor([[[[46754., 51524.], [52748., 58130.]],
[[59084., 65114.], [66662., 73466.]],
[[71414., 78704.], [80576., 88802.]],
[[83744., 92294.], [94490., 104138.]]],
[[[113165., 124784.], [127790., 140912.]],
[[143522., 158264.], [162080., 178730.]],
[[173879., 191744.], [196370., 216548.]],
[[204236., 225224.], [230660., 254366.]]],
[[[179576., 198044.], [202832., 223694.]],
[[227960., 251414.], [257498., 283994.]],
[[276344., 304784.], [312164., 344294.]],
[[324728., 358154.], [366830., 404594.]]]])
res = kruskal_to_tensor(U)
tl.assert_array_equal(
res,
true_res,
err_msg='Khatri-rao incorrectly transformed into full tensor.')
columns = 4
rows = [3, 4, 2]
matrices = [
tl.tensor(np.arange(k * columns).reshape((k, columns))) for k in rows
]
tensor = kruskal_to_tensor(matrices)
for i in range(len(rows)):
unfolded = unfold(tensor, mode=i)
U_i = matrices.pop(i)
reconstructed = tl.dot(U_i, tl.transpose(khatri_rao(matrices)))
tl.assert_array_almost_equal(reconstructed, unfolded)
matrices.insert(i, U_i)