def testMakeLowRankMatrix(self):
X = make_low_rank_matrix(n_samples=50, n_features=25, effective_rank=5,
tail_strength=0.01, random_state=0)
self.assertEqual(X.shape, (50, 25), "X shape mismatch")
_, s, _ = svd(X)
self.assertLess((s.sum() - 5).execute(n_parallel=1), 0.1, "X rank is not approximately 5")
def test_svd_execution(setup):
rs = np.random.RandomState()
data = rs.randn(18, 6) + 1j * rs.randn(18, 6)
a = tensor(data, chunk_size=(9, 6))
U, s, V = svd(a)
t = U.dot(diag(s).dot(V))
res = t.execute().fetch()
np.testing.assert_array_almost_equal(res, data)
a = tensor(data, chunk_size=(18, 6))
U, s, V = svd(a)
t = U.dot(diag(s).dot(V))
res = t.execute().fetch()
np.testing.assert_array_almost_equal(res, data)
a = tensor(data, chunk_size=(2, 6))
U, s, V = svd(a)
t = U.dot(diag(s).dot(V))
res = t.execute().fetch()
np.testing.assert_array_almost_equal(res, data)
data = rs.randn(6, 18) + 1j * rs.randn(6, 18)
a = tensor(data)
U, s, V = svd(a)
t = U.dot(diag(s).dot(V))
res = t.execute().fetch()
np.testing.assert_array_almost_equal(res, data)
# test for matrix of ones
data = np.ones((20, 10))
a = tensor(data, chunk_size=10)
s = svd(a)[1]
res = s.execute().fetch()
expected = np.linalg.svd(a)[1]
np.testing.assert_array_almost_equal(res, expected)
def test_make_low_rank_matrix(setup):
X = make_low_rank_matrix(n_samples=50,
n_features=25,
effective_rank=5,
tail_strength=0.01,
random_state=0)
assert X.shape == (50, 25)
_, s, _ = svd(X)
assert (s.sum() - 5).to_numpy() < 0.1
def testSVDExecution(self):
data = np.random.randn(18, 6) + 1j * np.random.randn(18, 6)
a = tensor(data, chunk_size=(9, 6))
U, s, V = svd(a)
t = U.dot(diag(s).dot(V))
res = self.executor.execute_tensor(t, concat=True)[0]
self.assertTrue(np.allclose(res, data))
a = tensor(data, chunk_size=(18, 6))
U, s, V = svd(a)
t = U.dot(diag(s).dot(V))
res = self.executor.execute_tensor(t, concat=True)[0]
self.assertTrue(np.allclose(res, data))
a = tensor(data, chunk_size=(2, 6))
U, s, V = svd(a)
t = U.dot(diag(s).dot(V))
res = self.executor.execute_tensor(t, concat=True)[0]
self.assertTrue(np.allclose(res, data))
data = np.random.randn(6, 18) + 1j * np.random.randn(6, 18)
a = tensor(data)
U, s, V = svd(a)
t = U.dot(diag(s).dot(V))
res = self.executor.execute_tensor(t, concat=True)[0]
self.assertTrue(np.allclose(res, data))
# test for matrix of ones
data = np.ones((20, 10))
a = tensor(data, chunk_size=10)
s = svd(a)[1]
res = self.executor.execute_tensor(s, concat=True)[0]
expected = np.linalg.svd(a)[1]
np.testing.assert_array_almost_equal(res, expected)
