def test_shapes(self):
"""Tests the sample shapes."""
sample_no_batch = self.evaluate(
random_ops.multivariate_normal([2, 4], mean=[0.2, 0.1]))
self.assertEqual(sample_no_batch.shape, (2, 4, 2))
sample_batch = self.evaluate(
random_ops.multivariate_normal([2, 4],
mean=[[0.2, 0.1], [0., -0.1],
[0., 0.1]]))
self.assertEqual(sample_batch.shape, (2, 4, 3, 2))
def test_general_mean_covariance(self):
"""Tests that the sample is correctly generated for general params."""
mean = np.array([[1.0, 0.1], [0.1, 1.0]])
covar = np.array([
[[0.9, -0.1], [-0.1, 1.0]],
[[1.1, -0.3], [-0.3, 0.6]],
])
size = 30000
sample = self.evaluate(
random_ops.multivariate_normal([size],
mean=mean,
covariance_matrix=covar,
seed=4567))
np.testing.assert_array_equal(sample.shape, [size, 2, 2])
np.testing.assert_array_almost_equal(np.mean(sample, axis=0),
mean,
decimal=1)
np.testing.assert_array_almost_equal(np.cov(sample[:, 0, :],
rowvar=False),
covar[0],
decimal=1)
np.testing.assert_array_almost_equal(np.cov(sample[:, 1, :],
rowvar=False),
covar[1],
decimal=1)
def test_mean_and_scale(self):
"""Tests sample for scale specification."""
mean = np.array([[1.0, 0.1], [0.1, 1.0]])
scale = np.array([[0.4, -0.1], [0.22, 1.38]])
covariance = np.matmul(scale, scale.transpose())
size = 30000
sample = self.evaluate(
random_ops.multivariate_normal([size],
mean=mean,
scale_matrix=scale,
seed=7534))
np.testing.assert_array_equal(sample.shape, [size, 2, 2])
np.testing.assert_array_almost_equal(np.mean(sample, axis=0),
mean,
decimal=1)
np.testing.assert_array_almost_equal(np.cov(sample[:, 0, :],
rowvar=False),
covariance,
decimal=1)
np.testing.assert_array_almost_equal(np.cov(sample[:, 1, :],
rowvar=False),
covariance,
decimal=1)
def test_mean_default(self):
"""Tests that the default value of mean is 0."""
covar = np.array([[1.0, 0.1], [0.1, 1.0]])
sample = self.evaluate(
random_ops.multivariate_normal([40000],
covariance_matrix=covar,
seed=1234))
np.testing.assert_array_equal(sample.shape, [40000, 2])
self.assertArrayNear(np.mean(sample, axis=0), [0.0, 0.0], 1e-2)
self.assertArrayNear(
np.cov(sample, rowvar=False).reshape([-1]), covar.reshape([-1]),
2e-2)
def test_covariance_default(self):
"""Tests that the default value of the covariance matrix is identity."""
mean = np.array([[1.0, 0.1], [0.1, 1.0]])
sample = self.evaluate(
random_ops.multivariate_normal([10000], mean=mean))
np.testing.assert_array_equal(sample.shape, [10000, 2, 2])
np.testing.assert_array_almost_equal(np.mean(sample, axis=0),
mean,
decimal=1)
np.testing.assert_array_almost_equal(np.cov(sample[:, 0, :],
rowvar=False),
np.eye(2),
decimal=1)
np.testing.assert_array_almost_equal(np.cov(sample[:, 1, :],
rowvar=False),
np.eye(2),
decimal=1)