def __init__(self,
concentration1=1.,
concentration0=1.,
validate_args=False,
name="kumaraswamy"):
"""Instantiates the `Kumaraswamy` bijector.
Args:
concentration1: Python `float` scalar indicating the transform power,
i.e., `Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)` where `a` is
`concentration1`.
concentration0: Python `float` scalar indicating the transform power,
i.e., `Y = g(X) = (1 - (1 - X)**(1 / b))**(1 / a)` where `b` is
`concentration0`.
validate_args: Python `bool` indicating whether arguments should be
checked for correctness.
name: Python `str` name given to ops managed by this object.
"""
with tf.name_scope(name) as name:
dtype = dtype_util.common_dtype([concentration0, concentration1],
dtype_hint=tf.float32)
self._concentration0 = tensor_util.convert_nonref_to_tensor(
concentration0, dtype=dtype, name="concentration0")
self._concentration1 = tensor_util.convert_nonref_to_tensor(
concentration1, dtype=dtype, name="concentration1")
super(Kumaraswamy, self).__init__(forward_min_event_ndims=0,
validate_args=validate_args,
name=name)
def __init__(self,
low=0.,
high=1.,
peak=0.5,
validate_args=False,
allow_nan_stats=True,
name='Triangular'):
"""Initialize a batch of Triangular distributions.
Args:
low: Floating point tensor, lower boundary of the output interval. Must
have `low < high`.
Default value: `0`.
high: Floating point tensor, upper boundary of the output interval. Must
have `low < high`.
Default value: `1`.
peak: Floating point tensor, mode of the output interval. Must have
`low <= peak` and `peak <= high`.
Default value: `0.5`.
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.
Default value: `False`.
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.
Default value: `True`.
name: Python `str` name prefixed to Ops created by this class.
Default value: `'Triangular'`.
Raises:
InvalidArgumentError: if `validate_args=True` and one of the following is
True:
* `low >= high`.
* `peak > high`.
* `low > peak`.
"""
parameters = dict(locals())
with tf.name_scope(name) as name:
dtype = dtype_util.common_dtype([low, high, peak], tf.float32)
self._low = tensor_util.convert_nonref_to_tensor(low,
name='low',
dtype=dtype)
self._high = tensor_util.convert_nonref_to_tensor(high,
name='high',
dtype=dtype)
self._peak = tensor_util.convert_nonref_to_tensor(peak,
name='peak',
dtype=dtype)
super(Triangular,
self).__init__(dtype=self._low.dtype,
reparameterization_type=reparameterization.
FULLY_REPARAMETERIZED,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
parameters=parameters,
name=name)
def __init__(self,
dimension,
concentration,
input_output_cholesky=False,
validate_args=False,
allow_nan_stats=True,
name='LKJ'):
"""Construct LKJ distributions.
Args:
dimension: Python `int`. The dimension of the correlation matrices
to sample.
concentration: `float` or `double` `Tensor`. The positive concentration
parameter of the LKJ distributions. The pdf of a sample matrix `X` is
proportional to `det(X) ** (concentration - 1)`.
input_output_cholesky: Python `bool`. If `True`, functions whose input or
output have the semantics of samples assume inputs are in Cholesky form
and return outputs in Cholesky form. In particular, if this flag is
`True`, input to `log_prob` is presumed of Cholesky form and output from
`sample` is of Cholesky form. Setting this argument to `True` is purely
a computational optimization and does not change the underlying
distribution. Additionally, validation checks which are only defined on
the multiplied-out form are omitted, even if `validate_args` is `True`.
Default value: `False` (i.e., input/output does not have Cholesky
semantics).
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.
Raises:
ValueError: If `dimension` is negative.
"""
if dimension < 0:
raise ValueError(
'There are no negative-dimension correlation matrices.')
parameters = dict(locals())
self._input_output_cholesky = input_output_cholesky
with tf.name_scope(name):
dtype = dtype_util.common_dtype([concentration], tf.float32)
self._concentration = tensor_util.convert_nonref_to_tensor(
concentration, name='concentration', dtype=dtype)
self._dimension = dimension
super(LKJ, self).__init__(
dtype=self._concentration.dtype,
validate_args=validate_args,
allow_nan_stats=allow_nan_stats,
reparameterization_type=reparameterization.NOT_REPARAMETERIZED,
parameters=parameters,
name=name)
