ed.models.ConditionalTransformedDistribution

Class ConditionalTransformedDistribution

Inherits From: RandomVariable

A TransformedDistribution that allows intrinsic conditioning.

Properties

allow_nan_stats

Python bool describing behavior when a stat is undefined.

Stats return +/- infinity when it makes sense. E.g., the variance of a Cauchy distribution is infinity. However, sometimes the statistic is undefined, e.g., if a distribution's pdf does not achieve a maximum within the support of the distribution, the mode is undefined. If the mean is undefined, then by definition the variance is undefined. E.g. the mean for Student's T for df = 1 is undefined (no clear way to say it is either + or - infinity), so the variance = E[(X - mean)**2] is also undefined.

Returns:

  • allow_nan_stats: Python bool.

batch_shape

Shape of a single sample from a single event index as a TensorShape.

May be partially defined or unknown.

The batch dimensions are indexes into independent, non-identical parameterizations of this distribution.

Returns:

  • batch_shape: TensorShape, possibly unknown.

bijector

Function transforming x => y.

distribution

Base distribution, p(x).

dtype

The DType of Tensors handled by this Distribution.

event_shape

Shape of a single sample from a single batch as a TensorShape.

May be partially defined or unknown.

Returns:

  • event_shape: TensorShape, possibly unknown.

name

Name prepended to all ops created by this Distribution.

parameters

Dictionary of parameters used to instantiate this Distribution.

reparameterization_type

Describes how samples from the distribution are reparameterized.

Currently this is one of the static instances distributions.FULLY_REPARAMETERIZED or distributions.NOT_REPARAMETERIZED.

Returns:

An instance of ReparameterizationType.

sample_shape

Sample shape of random variable.

shape

Shape of random variable.

unique_name

Name of random variable with its unique scoping name. Use name to just get the name of the random variable.

validate_args

Python bool indicating possibly expensive checks are enabled.

Methods

init

__init__(
    *args,
    **kwargs
)

Construct a Transformed Distribution.

Args:

  • distribution: The base distribution instance to transform. Typically an instance of Distribution.
  • bijector: The object responsible for calculating the transformation. Typically an instance of Bijector. None means Identity().
  • batch_shape: integer vector Tensor which overrides distribution batch_shape; valid only if distribution.is_scalar_batch().
  • event_shape: integer vector Tensor which overrides distribution event_shape; valid only if distribution.is_scalar_event().
  • 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.
  • name: Python str name prefixed to Ops created by this class. Default: bijector.name + distribution.name.

abs

__abs__()

add

__add__(other)

and

__and__(other)

bool

__bool__()

div

__div__(other)

eq

__eq__(other)

floordiv

__floordiv__(other)

ge

__ge__(other)

getitem

__getitem__(key)

Subset the tensor associated to the random variable, not the random variable itself.

gt

__gt__(other)

invert

__invert__()

iter

__iter__()

le

__le__(other)

lt

__lt__(other)

mod

__mod__(other)

mul

__mul__(other)

neg

__neg__()

nonzero

__nonzero__()

or

__or__(other)

pow

__pow__(other)

radd

__radd__(other)

rand

__rand__(other)

rdiv

__rdiv__(other)

rfloordiv

__rfloordiv__(other)

rmod

__rmod__(other)

rmul

__rmul__(other)

ror

__ror__(other)

rpow

__rpow__(other)

rsub

__rsub__(other)

rtruediv

__rtruediv__(other)

rxor

__rxor__(other)

sub

__sub__(other)

truediv

__truediv__(other)

xor

__xor__(other)

batch_shape_tensor

batch_shape_tensor(name='batch_shape_tensor')

Shape of a single sample from a single event index as a 1-D Tensor.

The batch dimensions are indexes into independent, non-identical parameterizations of this distribution.

Args:

  • name: name to give to the op

Returns:

  • batch_shape: Tensor.

cdf

cdf(
    *args,
    **kwargs
)

Additional documentation from ConditionalTransformedDistribution:

kwargs:
  • bijector_kwargs: Python dictionary of arg names/values forwarded to the bijector.
  • distribution_kwargs: Python dictionary of arg names/values forwarded to the distribution.

copy

copy(**override_parameters_kwargs)

Creates a deep copy of the distribution.

Note: the copy distribution may continue to depend on the original initialization arguments.

Args:

**override_parameters_kwargs: String/value dictionary of initialization arguments to override with new values.

Returns:

  • distribution: A new instance of type(self) initialized from the union of self.parameters and override_parameters_kwargs, i.e., dict(self.parameters, **override_parameters_kwargs).

covariance

covariance(name='covariance')

Covariance.

Covariance is (possibly) defined only for non-scalar-event distributions.

For example, for a length-k, vector-valued distribution, it is calculated as,

Cov[i, j] = Covariance(X_i, X_j) = E[(X_i - E[X_i]) (X_j - E[X_j])]

where Cov is a (batch of) k x k matrix, 0 <= (i, j) < k, and E denotes expectation.

Alternatively, for non-vector, multivariate distributions (e.g., matrix-valued, Wishart), Covariance shall return a (batch of) matrices under some vectorization of the events, i.e.,

Cov[i, j] = Covariance(Vec(X)_i, Vec(X)_j) = [as above]

where Cov is a (batch of) k' x k' matrices, 0 <= (i, j) < k' = reduce_prod(event_shape), and Vec is some function mapping indices of this distribution's event dimensions to indices of a length-k' vector.

Args:

  • name: The name to give this op.

Returns:

  • covariance: Floating-point Tensor with shape [B1, ..., Bn, k', k'] where the first n dimensions are batch coordinates and k' = reduce_prod(self.event_shape).

entropy

entropy(name='entropy')

Shannon entropy in nats.

eval

eval(
    session=None,
    feed_dict=None
)

In a session, computes and returns the value of this random variable.

This is not a graph construction method, it does not add ops to the graph.

This convenience method requires a session where the graph containing this variable has been launched. If no session is passed, the default session is used.

Args:

  • session: tf.BaseSession, optional. The tf.Session to use to evaluate this random variable. If none, the default session is used.
  • feed_dict: dict, optional. A dictionary that maps tf.Tensor objects to feed values. See tf.Session.run() for a description of the valid feed values.

Examples

x = Normal(0.0, 1.0)
with tf.Session() as sess:
  # Usage passing the session explicitly.
  print(x.eval(sess))
  # Usage with the default session.  The 'with' block
  # above makes 'sess' the default session.
  print(x.eval())

event_shape_tensor

event_shape_tensor(name='event_shape_tensor')

Shape of a single sample from a single batch as a 1-D int32 Tensor.

Args:

  • name: name to give to the op

Returns:

  • event_shape: Tensor.

get_ancestors

get_ancestors(collection=None)

Get ancestor random variables.

get_blanket

get_blanket(collection=None)

Get the random variable's Markov blanket.

get_children

get_children(collection=None)

Get child random variables.

get_descendants

get_descendants(collection=None)

Get descendant random variables.

get_parents

get_parents(collection=None)

Get parent random variables.

get_shape

get_shape()

Get shape of random variable.

get_siblings

get_siblings(collection=None)

Get sibling random variables.

get_variables

get_variables(collection=None)

Get TensorFlow variables that the random variable depends on.

is_scalar_batch

is_scalar_batch(name='is_scalar_batch')

Indicates that batch_shape == [].

Args:

  • name: The name to give this op.

Returns:

  • is_scalar_batch: bool scalar Tensor.

is_scalar_event

is_scalar_event(name='is_scalar_event')

Indicates that event_shape == [].

Args:

  • name: The name to give this op.

Returns:

  • is_scalar_event: bool scalar Tensor.

log_cdf

log_cdf(
    *args,
    **kwargs
)

Additional documentation from ConditionalTransformedDistribution:

kwargs:
  • bijector_kwargs: Python dictionary of arg names/values forwarded to the bijector.
  • distribution_kwargs: Python dictionary of arg names/values forwarded to the distribution.

log_prob

log_prob(
    *args,
    **kwargs
)

Additional documentation from ConditionalTransformedDistribution:

kwargs:
  • bijector_kwargs: Python dictionary of arg names/values forwarded to the bijector.
  • distribution_kwargs: Python dictionary of arg names/values forwarded to the distribution.

log_survival_function

log_survival_function(
    *args,
    **kwargs
)

Additional documentation from ConditionalTransformedDistribution:

kwargs:
  • bijector_kwargs: Python dictionary of arg names/values forwarded to the bijector.
  • distribution_kwargs: Python dictionary of arg names/values forwarded to the distribution.

mean

mean(name='mean')

Mean.

mode

mode(name='mode')

Mode.

param_shapes

param_shapes(
    cls,
    sample_shape,
    name='DistributionParamShapes'
)

Shapes of parameters given the desired shape of a call to sample().

This is a class method that describes what key/value arguments are required to instantiate the given Distribution so that a particular shape is returned for that instance's call to sample().

Subclasses should override class method _param_shapes.

Args:

  • sample_shape: Tensor or python list/tuple. Desired shape of a call to sample().
  • name: name to prepend ops with.

Returns:

dict of parameter name to Tensor shapes.

param_static_shapes

param_static_shapes(
    cls,
    sample_shape
)

param_shapes with static (i.e. TensorShape) shapes.

This is a class method that describes what key/value arguments are required to instantiate the given Distribution so that a particular shape is returned for that instance's call to sample(). Assumes that the sample's shape is known statically.

Subclasses should override class method _param_shapes to return constant-valued tensors when constant values are fed.

Args:

  • sample_shape: TensorShape or python list/tuple. Desired shape of a call to sample().

Returns:

dict of parameter name to TensorShape.

Raises:

  • ValueError: if sample_shape is a TensorShape and is not fully defined.

prob

prob(
    *args,
    **kwargs
)

Additional documentation from ConditionalTransformedDistribution:

kwargs:
  • bijector_kwargs: Python dictionary of arg names/values forwarded to the bijector.
  • distribution_kwargs: Python dictionary of arg names/values forwarded to the distribution.

quantile

quantile(
    value,
    name='quantile'
)

Quantile function. Aka "inverse cdf" or "percent point function".

Given random variable X and p in [0, 1], the quantile is:

quantile(p) := x such that P[X <= x] == p

Args:

  • value: float or double Tensor.
  • name: The name to give this op.

Returns:

  • quantile: a Tensor of shape sample_shape(x) + self.batch_shape with values of type self.dtype.

sample

sample(
    *args,
    **kwargs
)
kwargs:
  • **condition_kwargs: Named arguments forwarded to subclass implementation.

stddev

stddev(name='stddev')

Standard deviation.

Standard deviation is defined as,

stddev = E[(X - E[X])**2]**0.5

where X is the random variable associated with this distribution, E denotes expectation, and stddev.shape = batch_shape + event_shape.

Args:

  • name: The name to give this op.

Returns:

  • stddev: Floating-point Tensor with shape identical to batch_shape + event_shape, i.e., the same shape as self.mean().

survival_function

survival_function(
    *args,
    **kwargs
)

Additional documentation from ConditionalTransformedDistribution:

kwargs:
  • bijector_kwargs: Python dictionary of arg names/values forwarded to the bijector.
  • distribution_kwargs: Python dictionary of arg names/values forwarded to the distribution.

value

value()

Get tensor that the random variable corresponds to.

variance

variance(name='variance')

Variance.

Variance is defined as,

Var = E[(X - E[X])**2]

where X is the random variable associated with this distribution, E denotes expectation, and Var.shape = batch_shape + event_shape.

Args:

  • name: The name to give this op.

Returns:

  • variance: Floating-point Tensor with shape identical to batch_shape + event_shape, i.e., the same shape as self.mean().