def __init__(self,
df,
scale,
cholesky_input_output_matrices=False,
validate_args=False,
allow_nan_stats=True,
name="WishartFull"):
"""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 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 = dict(locals())
with ops.name_scope(name) as name:
with ops.name_scope("init", values=[scale]):
scale = ops.convert_to_tensor(scale)
if validate_args:
scale = distribution_util.assert_symmetric(scale)
chol = linalg_ops.cholesky(scale)
chol = control_flow_ops.with_dependencies([
check_ops.assert_positive(array_ops.matrix_diag_part(chol))
] if validate_args else [], chol)
super(WishartFull, self).__init__(
df=df,
scale_operator=linalg.LinearOperatorLowerTriangular(
tril=chol,
is_non_singular=True,
is_positive_definite=True,
is_square=True),
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="WishartFull"):
"""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 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 = dict(locals())
with ops.name_scope(name) as name:
with ops.name_scope("init", values=[scale]):
scale = ops.convert_to_tensor(scale)
if validate_args:
scale = distribution_util.assert_symmetric(scale)
chol = linalg_ops.cholesky(scale)
chol = control_flow_ops.with_dependencies([
check_ops.assert_positive(array_ops.matrix_diag_part(chol))
] if validate_args else [], chol)
super(WishartFull, self).__init__(
df=df,
scale_operator=linalg.LinearOperatorLowerTriangular(
tril=chol,
is_non_singular=True,
is_positive_definite=True,
is_square=True),
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, matrix, verify_pd=True, name="OperatorPDFull"):
"""Initialize an OperatorPDFull.
Args:
matrix: Shape `[N1,...,Nb, k, k]` tensor with `b >= 0`, `k >= 1`. The
last two dimensions should be `k x k` symmetric positive definite
matrices.
verify_pd: Whether to check that `matrix` is symmetric positive definite.
If `verify_pd` is `False`, correct behavior is not guaranteed.
name: A name to prepend to all ops created by this class.
"""
with ops.name_scope(name):
with ops.name_scope("init", values=[matrix]):
matrix = ops.convert_to_tensor(matrix)
# Check symmetric here. Positivity will be verified by checking the
# diagonal of the Cholesky factor inside the parent class. The Cholesky
# factorization linalg_ops.cholesky() does not always fail for non PSD
# matrices, so don't rely on that.
if verify_pd:
matrix = distribution_util.assert_symmetric(matrix)
chol = linalg_ops.cholesky(matrix)
super(OperatorPDFull, self).__init__(chol, verify_pd=verify_pd)
def __init__(self, matrix, verify_pd=True, name="OperatorPDFull"):
"""Initialize an OperatorPDFull.
Args:
matrix: Shape `[N1,...,Nb, k, k]` tensor with `b >= 0`, `k >= 1`. The
last two dimensions should be `k x k` symmetric positive definite
matrices.
verify_pd: Whether to check that `matrix` is symmetric positive definite.
If `verify_pd` is `False`, correct behavior is not guaranteed.
name: A name to prepend to all ops created by this class.
"""
with ops.name_scope(name):
with ops.name_scope("init", values=[matrix]):
matrix = ops.convert_to_tensor(matrix)
# Check symmetric here. Positivity will be verified by checking the
# diagonal of the Cholesky factor inside the parent class. The Cholesky
# factorization .batch_cholesky() does not always fail for non PSD
# matrices, so don't rely on that.
if verify_pd:
matrix = distribution_util.assert_symmetric(matrix)
chol = linalg_ops.batch_cholesky(matrix)
super(OperatorPDFull, self).__init__(chol, verify_pd=verify_pd)
