def testLUExecution(self):
np.random.seed(1)
# square matrix
data = np.random.randint(1, 10, (6, 6))
a = tensor(data)
P, L, U = lu(a)
# check lower and upper triangular matrix
result_l = self.executor.execute_tensor(L, concat=True)[0]
result_u = self.executor.execute_tensor(U, concat=True)[0]
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
t = P.dot(L).dot(U)
res = self.executor.execute_tensor(t, concat=True)[0]
np.testing.assert_allclose(res, data)
a = tensor(data, chunk_size=2)
P, L, U = lu(a)
# check lower and upper triangular matrix
result_l = self.executor.execute_tensor(L, concat=True)[0]
result_u = self.executor.execute_tensor(U, concat=True)[0]
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
t = P.dot(L).dot(U)
res = self.executor.execute_tensor(t, concat=True)[0]
np.testing.assert_allclose(res, data)
a = tensor(data, chunk_size=(2, 3))
P, L, U = lu(a)
# check lower and upper triangular matrix
result_l = self.executor.execute_tensor(L, concat=True)[0]
result_u = self.executor.execute_tensor(U, concat=True)[0]
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
t = P.dot(L).dot(U)
res = self.executor.execute_tensor(t, concat=True)[0]
np.testing.assert_allclose(res, data)
a = tensor(data, chunk_size=4)
P, L, U = lu(a)
# check lower and upper triangular matrix
result_l = self.executor.execute_tensor(L, concat=True)[0]
result_u = self.executor.execute_tensor(U, concat=True)[0]
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
t = P.dot(L).dot(U)
res = self.executor.execute_tensor(t, concat=True)[0]
np.testing.assert_allclose(res, data)
# shape[0] > shape[1]
data = np.random.randint(1, 10, (10, 6))
a = tensor(data)
P, L, U = lu(a)
# check lower and upper triangular matrix
result_l = self.executor.execute_tensor(L, concat=True)[0]
result_u = self.executor.execute_tensor(U, concat=True)[0]
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
t = P.dot(L).dot(U)
res = self.executor.execute_tensor(t, concat=True)[0]
np.testing.assert_allclose(res, data)
a = tensor(data, chunk_size=2)
P, L, U = lu(a)
# check lower and upper triangular matrix
result_l = self.executor.execute_tensor(L, concat=True)[0]
result_u = self.executor.execute_tensor(U, concat=True)[0]
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
t = P.dot(L).dot(U)
res = self.executor.execute_tensor(t, concat=True)[0]
np.testing.assert_allclose(res, data)
a = tensor(data, chunk_size=(2, 3))
P, L, U = lu(a)
# check lower and upper triangular matrix
result_l = self.executor.execute_tensor(L, concat=True)[0]
result_u = self.executor.execute_tensor(U, concat=True)[0]
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
t = P.dot(L).dot(U)
res = self.executor.execute_tensor(t, concat=True)[0]
np.testing.assert_allclose(res, data)
a = tensor(data, chunk_size=4)
P, L, U = lu(a)
# check lower and upper triangular matrix
result_l, result_u = self.executor.execute_tensors([L, U])
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
t = P.dot(L).dot(U)
res = self.executor.execute_tensor(t, concat=True)[0]
np.testing.assert_allclose(res, data)
# shape[0] < shape[1]
data = np.random.randint(1, 10, (6, 10))
a = tensor(data)
P, L, U = lu(a)
# check lower and upper triangular matrix
result_l = self.executor.execute_tensor(L, concat=True)[0]
result_u = self.executor.execute_tensor(U, concat=True)[0]
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
t = P.dot(L).dot(U)
res = self.executor.execute_tensor(t, concat=True)[0]
np.testing.assert_allclose(res, data)
a = tensor(data, chunk_size=2)
P, L, U = lu(a)
# check lower and upper triangular matrix
result_l = self.executor.execute_tensor(L, concat=True)[0]
result_u = self.executor.execute_tensor(U, concat=True)[0]
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
t = P.dot(L).dot(U)
res = self.executor.execute_tensor(t, concat=True)[0]
np.testing.assert_allclose(res, data)
a = tensor(data, chunk_size=(2, 3))
P, L, U = lu(a)
# check lower and upper triangular matrix
result_l = self.executor.execute_tensor(L, concat=True)[0]
result_u = self.executor.execute_tensor(U, concat=True)[0]
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
t = P.dot(L).dot(U)
res = self.executor.execute_tensor(t, concat=True)[0]
np.testing.assert_allclose(res, data)
a = tensor(data, chunk_size=4)
P, L, U = lu(a)
# check lower and upper triangular matrix
result_l, result_u = self.executor.execute_tensors([L, U])
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
t = P.dot(L).dot(U)
res = self.executor.execute_tensor(t, concat=True)[0]
np.testing.assert_allclose(res, data)
# test for sparse
data = sps.csr_matrix([[2, 0, 0, 0, 5, 2], [0, 6, 1, 0, 0, 6],
[8, 0, 9, 0, 0, 2], [0, 6, 0, 8, 7, 3],
[7, 0, 6, 1, 7, 0], [0, 0, 0, 7, 0, 8]])
a = tensor(data)
P, L, U = lu(a)
result_l = self.executor.execute_tensor(L, concat=True)[0]
result_u = self.executor.execute_tensor(U, concat=True)[0]
# check lower and upper triangular matrix
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
self.assertIsInstance(result_l, SparseNDArray)
self.assertIsInstance(result_u, SparseNDArray)
t = P.dot(L).dot(U)
res = self.executor.execute_tensor(t, concat=True)[0]
np.testing.assert_array_almost_equal(data.A, res)
a = tensor(data, chunk_size=2)
P, L, U = lu(a)
result_l = self.executor.execute_tensor(L, concat=True)[0]
result_u = self.executor.execute_tensor(U, concat=True)[0]
# check lower and upper triangular matrix
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
self.assertIsInstance(result_l, SparseNDArray)
self.assertIsInstance(result_u, SparseNDArray)
t = P.dot(L).dot(U)
res = self.executor.execute_tensor(t, concat=True)[0]
np.testing.assert_array_almost_equal(data.A, res)
a = tensor(data, chunk_size=(2, 3))
P, L, U = lu(a)
result_l = self.executor.execute_tensor(L, concat=True)[0]
result_u = self.executor.execute_tensor(U, concat=True)[0]
# check lower and upper triangular matrix
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
self.assertIsInstance(result_l, SparseNDArray)
self.assertIsInstance(result_u, SparseNDArray)
t = P.dot(L).dot(U)
res = self.executor.execute_tensor(t, concat=True)[0]
np.testing.assert_array_almost_equal(data.A, res)
a = tensor(data, chunk_size=4)
P, L, U = lu(a)
result_l = self.executor.execute_tensor(L, concat=True)[0]
result_u = self.executor.execute_tensor(U, concat=True)[0]
# check lower and upper triangular matrix
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
self.assertIsInstance(result_l, SparseNDArray)
self.assertIsInstance(result_u, SparseNDArray)
t = P.dot(L).dot(U)
res = self.executor.execute_tensor(t, concat=True)[0]
np.testing.assert_array_almost_equal(data.A, res)
def test_lu_execution(setup):
rs = np.random.RandomState(0)
# square matrix
data = rs.randint(1, 10, (6, 6))
a = tensor(data)
P, L, U = lu(a)
# check lower and upper triangular matrix
result_l = L.execute().fetch()
result_u = U.execute().fetch()
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
t = P.dot(L).dot(U)
res = t.execute().fetch()
np.testing.assert_allclose(res, data)
a = tensor(data, chunk_size=(3, 4))
P, L, U = lu(a)
# check lower and upper triangular matrix
result_l = L.execute().fetch()
result_u = U.execute().fetch()
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
t = P.dot(L).dot(U)
res = t.execute().fetch()
np.testing.assert_allclose(res, data)
# shape[0] > shape[1]
data = rs.randint(1, 10, (10, 6))
a = tensor(data)
P, L, U = lu(a)
# check lower and upper triangular matrix
result_l = L.execute().fetch()
result_u = U.execute().fetch()
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
t = P.dot(L).dot(U)
res = t.execute().fetch()
np.testing.assert_allclose(res, data)
a = tensor(data, chunk_size=5)
P, L, U = lu(a)
# check lower and upper triangular matrix
result_l = L.execute().fetch()
result_u = U.execute().fetch()
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
t = P.dot(L).dot(U)
res = t.execute().fetch()
np.testing.assert_allclose(res, data)
a = tensor(data, chunk_size=(4, 5))
P, L, U = lu(a)
# check lower and upper triangular matrix
result_l = L.execute().fetch()
result_u = U.execute().fetch()
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
t = P.dot(L).dot(U)
res = t.execute().fetch()
np.testing.assert_allclose(res, data)
# shape[0] < shape[1]
data = rs.randint(1, 10, (6, 10))
a = tensor(data)
P, L, U = lu(a)
# check lower and upper triangular matrix
result_l = L.execute().fetch()
result_u = U.execute().fetch()
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
t = P.dot(L).dot(U)
res = t.execute().fetch()
np.testing.assert_allclose(res, data)
a = tensor(data, chunk_size=5)
P, L, U = lu(a)
# check lower and upper triangular matrix
result_l = L.execute().fetch()
result_u = U.execute().fetch()
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
t = P.dot(L).dot(U)
res = t.execute().fetch()
np.testing.assert_allclose(res, data)
a = tensor(data, chunk_size=(4, 5))
P, L, U = lu(a)
# check lower and upper triangular matrix
result_l = L.execute().fetch()
result_u = U.execute().fetch()
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
t = P.dot(L).dot(U)
res = t.execute().fetch()
np.testing.assert_allclose(res, data)
# test for sparse
data = sps.csr_matrix([[2, 0, 0, 0, 5, 2], [0, 6, 1, 0, 0, 6],
[8, 0, 9, 0, 0, 2], [0, 6, 0, 8, 7, 3],
[7, 0, 6, 1, 7, 0], [0, 0, 0, 7, 0, 8]])
a = tensor(data)
P, L, U = lu(a)
result_l = L.execute().fetch()
result_u = U.execute().fetch()
# check lower and upper triangular matrix
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
assert isinstance(result_l, SparseNDArray)
assert isinstance(result_u, SparseNDArray)
t = P.dot(L).dot(U)
res = t.execute().fetch()
np.testing.assert_array_almost_equal(data.A, res)
a = tensor(data, chunk_size=5)
P, L, U = lu(a)
result_l = L.execute().fetch()
result_u = U.execute().fetch()
# check lower and upper triangular matrix
np.testing.assert_allclose(np.tril(result_l), result_l)
np.testing.assert_allclose(np.triu(result_u), result_u)
assert isinstance(result_l, SparseNDArray)
assert isinstance(result_u, SparseNDArray)
t = P.dot(L).dot(U)
res = t.execute().fetch()
np.testing.assert_array_almost_equal(data.A, res)