def _get_sigma_ls(self):
"""Initialize covariance matrix of var distribution over unary potentials.
"""
cov = self.cov
inputs_dists = self.inputs_dists
K_mm = cov.kron_cov(inputs_dists)
return t3f.get_variable('sigma_ls', initializer=kron.cholesky(K_mm))
def _get_sigma_l(self):
"""Initializes latent inputs covariance Sigma_l.
"""
shapes = self.inputs.npoints
cov = self.cov
inputs_dists = self.inputs_dists
K_mm = cov.kron_cov(inputs_dists)
return t3f.get_variable('sigma_l', initializer=kron.cholesky(K_mm))
def testCholesky(self):
# Tests the cholesky function
np.random.seed(8)
# generating two symmetric positive-definite tt-cores
L_1 = np.tril(np.random.normal(scale=2., size=(2, 2)))
L_2 = np.tril(np.random.normal(scale=2., size=(3, 3)))
K_1 = L_1.dot(L_1.T)
K_2 = L_2.dot(L_2.T)
K = np.kron(K_1, K_2)
initializer = TensorTrain(
[K_1[None, :, :, None], K_2[None, :, :, None]], tt_ranks=7 * [1])
kron_mat = variables.get_variable('kron_mat', initializer=initializer)
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
desired = np.linalg.cholesky(K)
actual = ops.full(kr.cholesky(kron_mat)).eval()
self.assertAllClose(desired, actual)
def testCholesky(self):
# Tests the cholesky function
np.random.seed(8)
# generating two symmetric positive-definite tt-cores
L_1 = np.tril(np.random.normal(scale=2., size=(2, 2)))
L_1 = L_1.astype(self.dtype.as_numpy_dtype)
L_2 = np.tril(np.random.normal(scale=2., size=(3, 3)))
L_2 = L_2.astype(self.dtype.as_numpy_dtype)
K_1 = L_1.dot(L_1.T)
K_2 = L_2.dot(L_2.T)
K = np.kron(K_1, K_2)
initializer = TensorTrain(
[K_1[None, :, :, None], K_2[None, :, :, None]], tt_ranks=7 * [1])
kron_mat = variables.get_variable('kron_mat', initializer=initializer)
init_op = tf.compat.v1.global_variables_initializer()
self.evaluate(init_op)
desired = np.linalg.cholesky(K)
actual = self.evaluate(ops.full(kr.cholesky(kron_mat)))
self.assertAllClose(desired, actual, atol=1e-5, rtol=1e-5)
def testCholesky(self):
# Tests the cholesky function
np.random.seed(8)
# generating two symmetric positive-definite tt-cores
L_1 = np.tril(np.random.normal(scale=2., size=(4, 2, 2)))
L_2 = np.tril(np.random.normal(scale=2., size=(4, 3, 3)))
K_1 = np.einsum('ijk,ilk->ijl', L_1, L_1)
K_2 = np.einsum('ijk,ilk->ijl', L_2, L_2)
initializer = TensorTrainBatch(
[K_1[:, None, :, :, None], K_2[:, None, :, :, None]],
tt_ranks=7 * [1])
kron_mat_batch = variables.get_variable('kron_mat_batch',
initializer=initializer)
init_op = tf.global_variables_initializer()
with self.test_session() as sess:
sess.run(init_op)
desired = np.linalg.cholesky(ops.full(kron_mat_batch).eval())
actual = ops.full(kr.cholesky(kron_mat_batch)).eval()
self.assertAllClose(desired, actual)
def _get_sigma_ls(self):
cov = self.cov
inputs_dists = self.inputs_dists
K_mm = cov.kron_cov(inputs_dists)
return t3f.get_variable('sigma_ls', initializer=kron.cholesky(K_mm))