def _ttm_compute(self, V, mode, transp):
Z = self.unfold(mode, transp=True).tocsr()
if transp:
V = V.T
Z = Z.dot(V.T)
shape = copy(self.shape)
shape[mode] = V.shape[0]
if issparse_mat(Z):
newT = unfolded_sptensor((Z.data, (Z.row, Z.col)), [mode], None, shape=shape).fold()
else:
newT = unfolded_dtensor(Z.T, mode, shape).fold()
return newT
def disabled_test_factorization_sparse():
I, J, K, rank = 10, 20, 75, 5
Tmat = sprand(I, J * K, 0.1).tocoo()
T = unfolded_sptensor((Tmat.data, (Tmat.row, Tmat.col)), None, 0, [], (I, J, K)).fold()
core, U = tucker.hooi(T, rank, maxIter=20)
Tmat = Tmat.toarray()
T = unfolded_dtensor(Tmat, 0, (I, J, K)).fold()
core2, U2 = tucker.hooi(T, rank, maxIter=20)
assert allclose(core2, core)
for i in range(len(U)):
assert allclose(U2[i], U[i])
def test_factorization_sparse():
I, J, K, rank = 10, 20, 75, 5
Tmat = sprand(I, J * K, 0.1).tocoo()
T = unfolded_sptensor((Tmat.data, (Tmat.row, Tmat.col)), None, 0, [], (I, J, K)).fold()
core, U = tucker_hooi.tucker_hooi(T, rank, maxIter=20)
Tmat = Tmat.toarray()
T = unfolded_dtensor(Tmat, 0, (I, J, K)).fold()
core2, U2 = tucker_hooi.tucker_hooi(T, rank, maxIter=20)
assert_true(allclose(core2, core))
for i in range(len(U)):
assert_true(allclose(U2[i], U[i]))
def _ttm_compute(self, V, mode, transp):
Z = self.unfold(mode, transp=True).tocsr()
if transp:
V = V.T
Z = Z.dot(V.T)
shape = np.copy(self.shape)
shape[mode] = V.shape[0]
if issparse_mat(Z):
newT = unfolded_sptensor((Z.data, (Z.row, Z.col)), [mode],
None,
shape=shape).fold()
else:
newT = unfolded_dtensor(Z.T, mode, shape).fold()
return newT
