def __init__(self,
df,
validate_args=False,
allow_nan_stats=True,
name='Chi'):
"""Construct Chi distributions with parameter `df`.
Args:
df: Floating point tensor, the degrees of freedom of the
distribution(s). `df` must contain only positive values.
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.
Default value: `'Chi'`.
"""
parameters = dict(locals())
with tf.name_scope(name) as name:
dtype = dtype_util.common_dtype([df], dtype_hint=tf.float32)
self._df = tensor_util.convert_nonref_to_tensor(
df, name='df', dtype=dtype)
super(Chi, self).__init__(
distribution=chi2.Chi2(df=self._df,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats),
bijector=invert_bijector.Invert(
square_bijector.Square(validate_args=validate_args)),
validate_args=validate_args,
parameters=parameters,
name=name)
def _sample_n(self, n, seed=None):
# Like with the univariate Student's t, sampling can be implemented as a
# ratio of samples from a multivariate gaussian with the appropriate
# covariance matrix and a sample from the chi-squared distribution.
seed = seed_stream.SeedStream(seed, salt="multivariate t")
loc = tf.broadcast_to(self.loc, self._sample_shape())
mvn = mvn_linear_operator.MultivariateNormalLinearOperator(
loc=tf.zeros_like(loc), scale=self.scale)
normal_samp = mvn.sample(n, seed=seed())
df = tf.broadcast_to(self.df, self.batch_shape_tensor())
chi2 = chi2_lib.Chi2(df=df)
chi2_samp = chi2.sample(n, seed=seed())
return (self._loc +
normal_samp * tf.math.rsqrt(chi2_samp / self._df)[..., tf.newaxis])
def __init__(self,
df,
validate_args=False,
allow_nan_stats=True,
name="Chi"):
"""Construct Chi distributions with parameter `df`.
Args:
df: Floating point tensor, the degrees of freedom of the
distribution(s). `df` must contain only positive values.
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.
Default value: `'Chi'`.
"""
with tf.compat.v1.name_scope(name, values=[df]) as name:
df = tf.convert_to_tensor(
value=df,
name="df",
dtype=dtype_util.common_dtype([df], preferred_dtype=tf.float32))
validation_assertions = [tf.compat.v1.assert_positive(df)
] if validate_args else []
with tf.control_dependencies(validation_assertions):
self._df = tf.identity(df, name="df")
super(Chi, self).__init__(
distribution=chi2.Chi2(df=self._df,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
name=name),
bijector=invert_bijector.Invert(square_bijector.Square()))
