def test_sqrt_to_dense(self):
with self.test_session():
chol = self._random_cholesky_array((2, 3, 3))
chol_2 = chol.copy()
chol_2[0, 0, 2] = 1000 # Make sure upper triangular part makes no diff.
operator = operator_pd_cholesky.OperatorPDCholesky(chol_2)
self.assertAllClose(chol, operator.sqrt_to_dense().eval())
def testNonPositiveDefiniteMatrixDoesNotRaiseIfNotVerifyPd(self):
# Singlular matrix with one positive eigenvalue and one zero eigenvalue.
with self.test_session():
lower_mat = [[1.0, 0.0], [2.0, 0.0]]
operator = operator_pd_cholesky.OperatorPDCholesky(lower_mat,
verify_pd=False)
operator.to_dense().eval() # Should not raise.
def test_non_positive_definite_matrix_raises(self):
# Singlular matrix with one positive eigenvalue and one zero eigenvalue.
with self.test_session():
lower_mat = [[1.0, 0.0], [2.0, 0.0]]
operator = operator_pd_cholesky.OperatorPDCholesky(lower_mat)
with self.assertRaisesOpError('x > 0 did not hold'):
operator.to_dense().eval()
def __init__(
self,
mu,
chol,
strict=True,
strict_statistics=True,
name="MultivariateNormalCholesky"):
"""Multivariate Normal distributions on `R^k`.
User must provide means `mu` and `chol` which holds the (batch) Cholesky
factors `S`, such that the covariance of each batch member is `S S^*`.
Args:
mu: `(N+1)-D` `float` or `double` tensor with shape `[N1,...,Nb, k]`,
`b >= 0`.
chol: `(N+2)-D` `Tensor` with same `dtype` as `mu` and shape
`[N1,...,Nb, k, k]`.
strict: Whether to validate input with asserts. If `strict` is `False`,
and the inputs are invalid, correct behavior is not guaranteed.
strict_statistics: Boolean, default True. If True, raise an exception if
a statistic (e.g. mean/mode/etc...) is undefined for any batch member.
If False, batch members with valid parameters leading to undefined
statistics will return NaN for this statistic.
name: The name to give Ops created by the initializer.
Raises:
TypeError: If `mu` and `chol` are different dtypes.
"""
cov = operator_pd_cholesky.OperatorPDCholesky(chol, verify_pd=strict)
super(MultivariateNormalCholesky, self).__init__(
mu, cov, strict_statistics=strict_statistics, strict=strict, name=name)
def test_non_positive_definite_matrix_does_not_raise_if_not_verify_pd(self):
# Singlular matrix with one positive eigenvalue and one zero eigenvalue.
with self.test_session():
lower_mat = [[1.0, 0.0], [2.0, 0.0]]
operator = operator_pd_cholesky.OperatorPDCholesky(
lower_mat, verify_pd=False)
operator.to_dense().eval() # Should not raise.
def testNotHavingTwoIdenticalLastDimsRaises(self):
# Unless the last two dims are equal, this cannot represent a matrix, and it
# should raise.
with self.test_session():
batch_vec = [[1.0], [2.0]] # shape 2 x 1
with self.assertRaisesOpError("x == y did not hold"):
operator = operator_pd_cholesky.OperatorPDCholesky(batch_vec)
operator.to_dense().eval()
def test_shape(self):
# All other shapes are defined by the abstractmethod shape, so we only need
# to test this.
with self.test_session():
for shape in [(3, 3), (2, 3, 3), (1, 2, 3, 3)]:
chol = self._random_cholesky_array(shape)
operator = operator_pd_cholesky.OperatorPDCholesky(chol)
self.assertAllEqual(shape, operator.shape().eval())
def test_not_having_two_identical_last_dims_raises(self):
# Unless the last two dims are equal, this cannot represent a matrix, and it
# should raise.
with self.test_session():
batch_vec = [[1.0], [2.0]] # shape 2 x 1
with self.assertRaisesRegexp(ValueError, '.*Dimensions.*'):
operator = operator_pd_cholesky.OperatorPDCholesky(batch_vec)
operator.to_dense().eval()
def test_log_det(self):
with self.test_session():
batch_shape = ()
for k in [1, 4]:
chol_shape = batch_shape + (k, k)
chol = self._random_cholesky_array(chol_shape)
operator = operator_pd_cholesky.OperatorPDCholesky(chol)
log_det = operator.log_det()
expected_log_det = np.log(np.prod(np.diag(chol))**2)
self.assertEqual(batch_shape, log_det.get_shape())
self.assertAllClose(expected_log_det, log_det.eval())
def test_log_det_batch_matrix(self):
with self.test_session():
batch_shape = (2, 3)
for k in [1, 4]:
chol_shape = batch_shape + (k, k)
chol = self._random_cholesky_array(chol_shape)
operator = operator_pd_cholesky.OperatorPDCholesky(chol)
log_det = operator.log_det()
self.assertEqual(batch_shape, log_det.get_shape())
# Test the log-determinant of the [1, 1] matrix.
chol_11 = chol[1, 1, :, :]
expected_log_det = np.log(np.prod(np.diag(chol_11))**2)
self.assertAllClose(expected_log_det, log_det.eval()[1, 1])
def test_matmul_batch_matrix(self):
with self.test_session():
batch_shape = (2, 3)
for k in [1, 4]:
x_shape = batch_shape + (k, 5)
x = self._rng.rand(*x_shape)
chol_shape = batch_shape + (k, k)
chol = self._random_cholesky_array(chol_shape)
matrix = tf.batch_matmul(chol, chol, adj_y=True)
operator = operator_pd_cholesky.OperatorPDCholesky(chol)
expected = tf.batch_matmul(matrix, x)
self.assertEqual(expected.get_shape(), operator.matmul(x).get_shape())
self.assertAllClose(expected.eval(), operator.matmul(x).eval())
def test_sqrt_matmul_single_matrix(self):
with self.test_session():
batch_shape = ()
for k in [1, 4]:
x_shape = batch_shape + (k, 3)
x = self._rng.rand(*x_shape)
chol_shape = batch_shape + (k, k)
chol = self._random_cholesky_array(chol_shape)
operator = operator_pd_cholesky.OperatorPDCholesky(chol)
sqrt_operator_times_x = operator.sqrt_matmul(x)
expected = tf.batch_matmul(chol, x)
self.assertEqual(expected.get_shape(),
sqrt_operator_times_x.get_shape())
self.assertAllClose(expected.eval(), sqrt_operator_times_x.eval())
def testSqrtMatmulBatchMatrixWithTranspose(self):
with self.test_session():
batch_shape = (2, 3)
for k in [1, 4]:
x_shape = batch_shape + (5, k)
x = self._rng.rand(*x_shape)
chol_shape = batch_shape + (k, k)
chol = self._random_cholesky_array(chol_shape)
operator = operator_pd_cholesky.OperatorPDCholesky(chol)
sqrt_operator_times_x = operator.sqrt_matmul(x, transpose_x=True)
# tf.batch_matmul is defined x * y, so "y" is on the right, not "x".
expected = math_ops.matmul(chol, x, adjoint_b=True)
self.assertEqual(expected.get_shape(),
sqrt_operator_times_x.get_shape())
self.assertAllClose(expected.eval(), sqrt_operator_times_x.eval())
def test_matmul_batch_matrix_with_transpose(self):
with self.test_session():
batch_shape = (2, 3)
for k in [1, 4]:
x_shape = batch_shape + (5, k)
x = self._rng.rand(*x_shape)
chol_shape = batch_shape + (k, k)
chol = self._random_cholesky_array(chol_shape)
matrix = tf.batch_matmul(chol, chol, adj_y=True)
operator = operator_pd_cholesky.OperatorPDCholesky(chol)
operator_times_x = operator.matmul(x, transpose_x=True)
# tf.batch_matmul is defined x * y, so "y" is on the right, not "x".
expected = tf.batch_matmul(matrix, x, adj_y=True)
self.assertEqual(expected.get_shape(), operator_times_x.get_shape())
self.assertAllClose(expected.eval(), operator_times_x.eval())
def __init__(self,
mu,
chol,
validate_args=False,
allow_nan_stats=True,
name="MultivariateNormalCholesky"):
"""Multivariate Normal distributions on `R^k`.
User must provide means `mu` and `chol` which holds the (batch) Cholesky
factors, such that the covariance of each batch member is `chol chol^T`.
Args:
mu: `(N+1)-D` floating point tensor with shape `[N1,...,Nb, k]`,
`b >= 0`.
chol: `(N+2)-D` `Tensor` with same `dtype` as `mu` and shape
`[N1,...,Nb, k, k]`. The upper triangular part is ignored (treated as
though it is zero), and the diagonal must be positive.
validate_args: `Boolean`, default `False`. Whether to validate input
with asserts. If `validate_args` is `False`, and the inputs are
invalid, correct behavior is not guaranteed.
allow_nan_stats: `Boolean`, default `True`. If `False`, raise an
exception if a statistic (e.g. mean/mode/etc...) is undefined for any
batch member If `True`, batch members with valid parameters leading to
undefined statistics will return NaN for this statistic.
name: The name to give Ops created by the initializer.
Raises:
TypeError: If `mu` and `chol` are different dtypes.
"""
parameters = locals()
parameters.pop("self")
with ops.name_scope(name, values=[chol]) as ns:
cov = operator_pd_cholesky.OperatorPDCholesky(
chol, verify_pd=validate_args)
super(MultivariateNormalCholesky,
self).__init__(mu,
cov,
allow_nan_stats=allow_nan_stats,
validate_args=validate_args,
name=ns)
self._parameters = parameters
def __init__(self,
df,
scale,
cholesky_input_output_matrices=False,
validate_args=False,
allow_nan_stats=True,
name="WishartCholesky"):
"""Construct Wishart distributions.
Args:
df: `float` or `double` `Tensor`. Degrees of freedom, must be greater than
or equal to dimension of the scale matrix.
scale: `float` or `double` `Tensor`. The Cholesky factorization of
the symmetric positive definite scale matrix of the distribution.
cholesky_input_output_matrices: Python `bool`. Any function which whose
input or output is a matrix assumes the input is Cholesky and returns a
Cholesky factored matrix. Example `log_prob` input takes a Cholesky and
`sample_n` returns a Cholesky when
`cholesky_input_output_matrices=True`.
validate_args: Python `bool`, default `False`. When `True` distribution
parameters are checked for validity despite possibly degrading runtime
performance. When `False` invalid inputs may silently render incorrect
outputs.
allow_nan_stats: Python `bool`, default `True`. When `True`, statistics
(e.g., mean, mode, variance) use the value "`NaN`" to indicate the
result is undefined. When `False`, an exception is raised if one or
more of the statistic's batch members are undefined.
name: Python `str` name prefixed to Ops created by this class.
"""
parameters = locals()
with ops.name_scope(name, values=[scale]):
super(WishartCholesky, self).__init__(
df=df,
scale_operator_pd=operator_pd_cholesky.OperatorPDCholesky(
scale, verify_pd=validate_args),
cholesky_input_output_matrices=cholesky_input_output_matrices,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=name)
self._parameters = parameters
def __init__(self,
df,
scale,
cholesky_input_output_matrices=False,
validate_args=False,
allow_nan_stats=True,
name="WishartCholesky"):
"""Construct Wishart distributions.
Args:
df: `float` or `double` `Tensor`. Degrees of freedom, must be greater than
or equal to dimension of the scale matrix.
scale: `float` or `double` `Tensor`. The Cholesky factorization of
the symmetric positive definite scale matrix of the distribution.
cholesky_input_output_matrices: `Boolean`. Any function which whose input
or output is a matrix assumes the input is Cholesky and returns a
Cholesky factored matrix. Example`log_pdf` input takes a Cholesky and
`sample_n` returns a Cholesky when
`cholesky_input_output_matrices=True`.
validate_args: `Boolean`, default `False`. Whether to validate input
with asserts. If `validate_args` is `False`, and the inputs are invalid,
correct behavior is not guaranteed.
allow_nan_stats: `Boolean`, default `True`. If `False`, raise an
exception if a statistic (e.g., mean, mode) is undefined for any batch
member. If True, batch members with valid parameters leading to
undefined statistics will return `NaN` for this statistic.
name: The name scope to give class member ops.
"""
parameters = locals()
parameters.pop("self")
with ops.name_scope(name, values=[scale]) as ns:
super(WishartCholesky, self).__init__(
df=df,
scale_operator_pd=operator_pd_cholesky.OperatorPDCholesky(
scale, verify_pd=validate_args),
cholesky_input_output_matrices=cholesky_input_output_matrices,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=ns)
self._parameters = parameters
def _create_scale_operator(self, identity_multiplier, diag, tril,
perturb_diag, perturb_factor, event_ndims,
validate_args):
"""Construct `scale` from various components.
Args:
identity_multiplier: floating point rank 0 `Tensor` representing a scaling
done to the identity matrix.
diag: Floating-point `Tensor` representing the diagonal matrix.
`scale_diag` has shape [N1, N2, ... k], which represents a k x k
diagonal matrix.
tril: Floating-point `Tensor` representing the diagonal matrix.
`scale_tril` has shape [N1, N2, ... k], which represents a k x k lower
triangular matrix.
perturb_diag: Floating-point `Tensor` representing the diagonal matrix of
the low rank update.
perturb_factor: Floating-point `Tensor` representing factor matrix.
event_ndims: Scalar `int32` `Tensor` indicating the number of dimensions
associated with a particular draw from the distribution. Must be 0 or 1
validate_args: Python `bool` indicating whether arguments should be
checked for correctness.
Returns:
scale. In the case of scaling by a constant, scale is a
floating point `Tensor`. Otherwise, scale is an `OperatorPD`.
Raises:
ValueError: if all of `tril`, `diag` and `identity_multiplier` are `None`.
"""
identity_multiplier = _as_tensor(identity_multiplier,
"identity_multiplier")
diag = _as_tensor(diag, "diag")
tril = _as_tensor(tril, "tril")
perturb_diag = _as_tensor(perturb_diag, "perturb_diag")
perturb_factor = _as_tensor(perturb_factor, "perturb_factor")
identity_multiplier = self._maybe_validate_identity_multiplier(
identity_multiplier, validate_args)
if perturb_factor is not None:
perturb_factor = self._process_matrix(perturb_factor,
min_rank=2,
event_ndims=event_ndims)
if perturb_diag is not None:
perturb_diag = self._process_matrix(perturb_diag,
min_rank=1,
event_ndims=event_ndims)
# The following if-statments are ordered by increasingly stronger
# assumptions in the base matrix, i.e., we process in the order:
# TriL, Diag, Identity.
if tril is not None:
tril = self._preprocess_tril(identity_multiplier, diag, tril,
event_ndims)
if perturb_factor is None:
return operator_pd_cholesky.OperatorPDCholesky(
tril, verify_pd=validate_args)
return _TriLPlusVDVTLightweightOperatorPD(
tril=tril,
v=perturb_factor,
diag=perturb_diag,
validate_args=validate_args)
if diag is not None:
diag = self._preprocess_diag(identity_multiplier, diag,
event_ndims)
if perturb_factor is None:
return operator_pd_diag.OperatorPDSqrtDiag(
diag, verify_pd=validate_args)
return operator_pd_vdvt_update.OperatorPDSqrtVDVTUpdate(
operator=operator_pd_diag.OperatorPDDiag(
diag, verify_pd=validate_args),
v=perturb_factor,
diag=perturb_diag,
verify_pd=validate_args)
if identity_multiplier is not None:
if perturb_factor is None:
return identity_multiplier
# Infer the shape from the V and D.
v_shape = array_ops.shape(perturb_factor)
identity_shape = array_ops.concat([v_shape[:-1], [v_shape[-2]]], 0)
scaled_identity = operator_pd_identity.OperatorPDIdentity(
identity_shape,
perturb_factor.dtype.base_dtype,
scale=identity_multiplier,
verify_pd=validate_args)
return operator_pd_vdvt_update.OperatorPDSqrtVDVTUpdate(
operator=scaled_identity,
v=perturb_factor,
diag=perturb_diag,
verify_pd=validate_args)
raise ValueError(
"One of tril, diag and/or identity_multiplier must be "
"specified.")