def test_non_square_matrix(self):
rng = np.random.RandomState(utt.fetch_seed())
A = tensor.matrix("A", dtype=theano.config.floatX)
Q = matrix_power(A, 3)
f = function([A], [Q])
a = rng.rand(4, 3).astype(theano.config.floatX)
self.assertRaises(ValueError, f, a)
def test_non_square_matrix(self):
rng = np.random.RandomState(utt.fetch_seed())
A = tensor.matrix("A", dtype=theano.config.floatX)
Q = matrix_power(A, 3)
f = function([A], [Q])
a = rng.rand(4, 3).astype(theano.config.floatX)
self.assertRaises(ValueError, f, a)
def test_numpy_compare(self, n):
a = np.array([[0.1231101, 0.72381381],
[0.28748201, 0.43036511]]).astype(config.floatX)
A = tensor.matrix("A", dtype=config.floatX)
A.tag.test_value = a
Q = matrix_power(A, n)
n_p = np.linalg.matrix_power(a, n)
assert np.allclose(n_p, Q.get_test_value())
def test_numpy_compare(self):
rng = np.random.RandomState(utt.fetch_seed())
A = tensor.matrix("A", dtype=theano.config.floatX)
Q = matrix_power(A, 3)
fn = function([A], [Q])
a = rng.rand(4, 4).astype(theano.config.floatX)
n_p = np.linalg.matrix_power(a, 3)
t_p = fn(a)
assert np.allclose(n_p, t_p)
def test_numpy_compare(self):
rng = np.random.RandomState(utt.fetch_seed())
A = tensor.matrix("A", dtype=theano.config.floatX)
Q = matrix_power(A, 3)
fn = function([A], [Q])
a = rng.rand(4, 4).astype(theano.config.floatX)
n_p = np.linalg.matrix_power(a, 3)
t_p = fn(a)
assert np.allclose(n_p, t_p)
def test_non_square_matrix(self):
A = tensor.matrix("A", dtype=config.floatX)
Q = matrix_power(A, 3)
f = function([A], [Q])
a = np.array([
[0.47497769, 0.81869379],
[0.74387558, 0.31780172],
[0.54381007, 0.28153101],
]).astype(config.floatX)
with pytest.raises(ValueError):
f(a)
