scvi.distributions.JaxNegativeBinomialMeanDisp#

class scvi.distributions.JaxNegativeBinomialMeanDisp(mean, inverse_dispersion, validate_args=None, eps=1e-08)[source]#: Negative binomial parameterized by mean and inverse dispersion.

Attributes table#

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

Methods table#

`cdf`(value)	The cummulative distribution function of this 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`.
`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)

Attributes#

arg_constraints

JaxNegativeBinomialMeanDisp.arg_constraints = {'inverse_dispersion': Positive(lower_bound=0.0), 'mean': Positive(lower_bound=0.0)}#

batch_shape

JaxNegativeBinomialMeanDisp.batch_shape[source]#

Returns the shape over which the distribution parameters are batched.

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

event_dim

JaxNegativeBinomialMeanDisp.event_dim[source]#

Number of dimensions of individual events. :rtype: int

Type:: return

event_shape

JaxNegativeBinomialMeanDisp.event_shape[source]#

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

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

has_enumerate_support

JaxNegativeBinomialMeanDisp.has_enumerate_support = False#

has_rsample

JaxNegativeBinomialMeanDisp.has_rsample[source]#

inverse_dispersion

JaxNegativeBinomialMeanDisp.inverse_dispersion[source]#

is_discrete

JaxNegativeBinomialMeanDisp.is_discrete[source]#

mean

JaxNegativeBinomialMeanDisp.mean[source]#

reparametrized_params

JaxNegativeBinomialMeanDisp.reparametrized_params = []#

support

JaxNegativeBinomialMeanDisp.support = IntegerNonnegative(lower_bound=0)#

variance

JaxNegativeBinomialMeanDisp.variance[source]#

Methods#

cdf

JaxNegativeBinomialMeanDisp.cdf(value)[source]#

The cummulative distribution function of this distribution.

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

enumerate_support

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

expand

JaxNegativeBinomialMeanDisp.expand(batch_shape)[source]#

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

JaxNegativeBinomialMeanDisp.expand_by(sample_shape)[source]#

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

icdf

JaxNegativeBinomialMeanDisp.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.

infer_shapes

classmethod JaxNegativeBinomialMeanDisp.infer_shapes(*args, **kwargs)[source]#

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

JaxNegativeBinomialMeanDisp.log_prob(*args, **kwargs)[source]#

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

JaxNegativeBinomialMeanDisp.mask(mask)[source]#

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:

rsample

JaxNegativeBinomialMeanDisp.rsample(key, sample_shape=())[source]#

sample

JaxNegativeBinomialMeanDisp.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

JaxNegativeBinomialMeanDisp.sample_with_intermediates(key, sample_shape=())[source]#

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

set_default_validate_args

static JaxNegativeBinomialMeanDisp.set_default_validate_args(value)[source]#

shape

JaxNegativeBinomialMeanDisp.shape(sample_shape=())[source]#

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

JaxNegativeBinomialMeanDisp.to_event(reinterpreted_batch_ndims=None)[source]#

Interpret the rightmost reinterpreted_batch_ndims batch dimensions as dependent event dimensions.

Parameters:: reinterpreted_batch_ndims (default: None) – Number of rightmost batch dims to interpret as event dims.
Returns:: An instance of Independent distribution.
Return type:: numpyro.distributions.distribution.Independent

tree_flatten

JaxNegativeBinomialMeanDisp.tree_flatten()[source]#

tree_unflatten

classmethod JaxNegativeBinomialMeanDisp.tree_unflatten(aux_data, params)[source]#