gwinferno.numpyro_distributions.Powerlaw#

class Powerlaw(alpha, minimum=0.0, maximum=1.0, low=0.0, high=1.0, validate_args=None)[source]#

Bases: Distribution

Methods

`cdf`(value)	The cumulative distribution function of this distribution.
`entropy`()	Returns the entropy of the distribution.
`enumerate_support`([expand])	Returns an array with shape len(support) x batch_shape containing all values in the support.
`expand`(batch_shape)	Returns a new `ExpandedDistribution` instance with batch dimensions expanded to batch_shape.
`expand_by`(sample_shape)	Expands a distribution by adding `sample_shape` to the left side of its `batch_shape`.
`gather_pytree_aux_fields`()
`gather_pytree_data_fields`()
`get_args`()	Get arguments of the distribution.
`icdf`(q)	The inverse cumulative distribution function of this distribution.
`infer_shapes`(args, *kwargs)	Infers `batch_shape` and `event_shape` given shapes of args to `__init__()`.
`log_prob`(args, *kwargs)	Evaluates the log probability density for a batch of samples given by value.
`mask`(mask)	Masks a distribution by a boolean or boolean-valued array that is broadcastable to the distributions `Distribution.batch_shape` .
`rsample`(key[, sample_shape])
`sample`(key[, sample_shape])	Returns a sample from the distribution having shape given by sample_shape + batch_shape + event_shape.
`sample_with_intermediates`(key[, sample_shape])	Same as `sample` except that any intermediate computations are returned (useful for TransformedDistribution).
`set_default_validate_args`(value)
`shape`([sample_shape])	The tensor shape of samples from this distribution.
`to_event`([reinterpreted_batch_ndims])	Interpret the rightmost reinterpreted_batch_ndims batch dimensions as dependent event dimensions.
`tree_flatten`()
`tree_unflatten`(aux_data, params)
`validate_args`([strict])	Validate the arguments of the distribution.

Attributes

`arg_constraints`
`batch_shape`	Returns the shape over which the distribution parameters are batched.
`event_dim`
`event_shape`	Returns the shape of a single sample from the distribution without batching.
`has_enumerate_support`
`has_rsample`
`is_discrete`
`mean`	Mean of the distribution.
`pytree_aux_fields`
`pytree_data_fields`
`reparametrized_params`
`support`
`variance`	Variance of the distribution.

__call__(*args, **kwargs)#: Call self as a function.

__init__(alpha, minimum=0.0, maximum=1.0, low=0.0, high=1.0, validate_args=None)[source]#

classmethod __init_subclass__(**kwargs)#

This method is called when a class is subclassed.

The default implementation does nothing. It may be overridden to extend subclasses.

__repr__()#

Return repr(self).

Return type:: str

property batch_shape: tuple[int, ...]#

Returns the shape over which the distribution parameters are batched.

Returns:: batch shape of the distribution.
Return type:: tuple

cdf(value)[source]#

The cumulative distribution function of this distribution.

Parameters:: value – samples from this distribution.
Returns:: output of the cumulative distribution function evaluated at value.

entropy()#: Returns the entropy of the distribution.

enumerate_support(expand=True)#: Returns an array with shape len(support) x batch_shape containing all values in the support.

property event_dim: int#

Returns:: Number of dimensions of individual events.
Return type:: int

property event_shape: tuple[int, ...]#

Returns the shape of a single sample from the distribution without batching.

Returns:: event shape of the distribution.
Return type:: tuple

expand(batch_shape)#

Returns a new ExpandedDistribution instance with batch dimensions expanded to batch_shape.

Parameters:: batch_shape (tuple) – batch shape to expand to.
Returns:: an instance of ExpandedDistribution.
Return type:: ExpandedDistribution

expand_by(sample_shape)#

Expands a distribution by adding sample_shape to the left side of its batch_shape. To expand internal dims of self.batch_shape from 1 to something larger, use expand() instead.

Parameters:: sample_shape (tuple) – The size of the iid batch to be drawn from the distribution.
Returns:: An expanded version of this distribution.
Return type:: ExpandedDistribution

get_args()#

Get arguments of the distribution.

Return type:: dict

icdf(q)[source]#

The inverse cumulative distribution function of this distribution.

Parameters:: q – quantile values, should belong to [0, 1].
Returns:: the samples whose cdf values equals to q.

classmethod infer_shapes(*args, **kwargs)#

Infers batch_shape and event_shape given shapes of args to __init__().

Note

This assumes distribution shape depends only on the shapes of tensor inputs, not in the data contained in those inputs.

Parameters:

*args – Positional args replacing each input arg with a tuple representing the sizes of each tensor input.
**kwargs – Keywords mapping name of input arg to tuple representing the sizes of each tensor input.

Returns:

A pair (batch_shape, event_shape) of the shapes of a distribution that would be created with input args of the given shapes.

Return type:

tuple

log_prob(*args, **kwargs)#

Evaluates the log probability density for a batch of samples given by value.

Parameters:: value – A batch of samples from the distribution.
Returns:: an array with shape value.shape[:-self.event_shape]
Return type:: numpy.ndarray

mask(mask)#

Masks a distribution by a boolean or boolean-valued array that is broadcastable to the distributions Distribution.batch_shape .

Parameters:: mask (bool or jnp.ndarray) – A boolean or boolean valued array (True includes a site, False excludes a site).
Returns:: A masked copy of this distribution.
Return type:: MaskedDistribution

Example:

property mean: Array | ndarray | bool_ | number | bool | int | float | complex#: Mean of the distribution.

sample(key, sample_shape=())[source]#

Returns a sample from the distribution having shape given by sample_shape + batch_shape + event_shape. Note that when sample_shape is non-empty, leading dimensions (of size sample_shape) of the returned sample will be filled with iid draws from the distribution instance.

Parameters:

key (jax.random.PRNGKey) – the rng_key key to be used for the distribution.
sample_shape (tuple) – the sample shape for the distribution.

Returns:

an array of shape sample_shape + batch_shape + event_shape

Return type:

numpy.ndarray

sample_with_intermediates(key, sample_shape=())#

Same as sample except that any intermediate computations are returned (useful for TransformedDistribution).

Parameters:

key (jax.random.PRNGKey) – the rng_key key to be used for the distribution.
sample_shape (tuple) – the sample shape for the distribution.

Returns:

an array of shape sample_shape + batch_shape + event_shape

Return type:

numpy.ndarray

shape(sample_shape=())#

The tensor shape of samples from this distribution.

Samples are of shape:

d.shape(sample_shape) == sample_shape + d.batch_shape + d.event_shape

Parameters:: sample_shape (tuple) – the size of the iid batch to be drawn from the distribution.
Returns:: shape of samples.
Return type:: tuple

to_event(reinterpreted_batch_ndims=None)#

Interpret the rightmost reinterpreted_batch_ndims batch dimensions as dependent event dimensions.

Parameters:: reinterpreted_batch_ndims – Number of rightmost batch dims to interpret as event dims.
Returns:: An instance of Independent distribution.
Return type:: numpyro.distributions.distribution.Independent

validate_args(strict=True)#

Validate the arguments of the distribution.

Parameters:: strict (bool) – Require strict validation, raising an error if the function is called inside jitted code.
Return type:: None

property variance: Array | ndarray | bool_ | number | bool | int | float | complex#: Variance of the distribution.