# scvi.distributions.NegativeBinomialMixture#

class scvi.distributions.NegativeBinomialMixture(mu1, mu2, theta1, mixture_logits, theta2=None, validate_args=False)[source]#

Negative binomial mixture distribution.

See NegativeBinomial for further description of parameters.

Parameters:

## Attributes table#

 arg_constraints batch_shape Returns the shape over which parameters are batched. event_shape Returns the shape of a single sample (without batching). has_enumerate_support has_rsample mean Returns the mean of the distribution. mixture_probs rtype: Tensor mode Returns the mode of the distribution. stddev Returns the standard deviation of the distribution. support variance Returns the variance of the distribution.

## Methods table#

 cdf(value) Returns the cumulative density/mass function evaluated at value. Returns entropy of distribution, batched over batch_shape. enumerate_support([expand]) Returns tensor containing all values supported by a discrete distribution. expand(batch_shape[, _instance]) Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to batch_shape. icdf(value) Returns the inverse cumulative density/mass function evaluated at value. log_prob(value) Log probability. Returns perplexity of distribution, batched over batch_shape. rsample([sample_shape]) Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched. sample([sample_shape]) Sample from the distribution. Generates n samples or n batches of samples if the distribution parameters are batched. Sets whether validation is enabled or disabled.

## Attributes#

arg_constraints

NegativeBinomialMixture.arg_constraints = {'mixture_logits': Real(), 'mixture_probs': HalfOpenInterval(lower_bound=0.0, upper_bound=1.0), 'mu1': GreaterThanEq(lower_bound=0), 'mu2': GreaterThanEq(lower_bound=0), 'theta1': GreaterThanEq(lower_bound=0)}#

batch_shape

NegativeBinomialMixture.batch_shape[source]#

Returns the shape over which parameters are batched.

event_shape

NegativeBinomialMixture.event_shape[source]#

Returns the shape of a single sample (without batching).

has_enumerate_support

NegativeBinomialMixture.has_enumerate_support = False#

has_rsample

NegativeBinomialMixture.has_rsample = False#

mean

NegativeBinomialMixture.mean[source]#

mixture_probs

NegativeBinomialMixture.mixture_probs[source]#
Return type:

Tensor

mode

NegativeBinomialMixture.mode[source]#

Returns the mode of the distribution.

stddev

NegativeBinomialMixture.stddev[source]#

Returns the standard deviation of the distribution.

support

NegativeBinomialMixture.support = IntegerGreaterThan(lower_bound=0)#

variance

NegativeBinomialMixture.variance[source]#

Returns the variance of the distribution.

## Methods#

cdf

NegativeBinomialMixture.cdf(value)[source]#

Returns the cumulative density/mass function evaluated at value.

Parameters:

value (Tensor) –

entropy

NegativeBinomialMixture.entropy()[source]#

Returns entropy of distribution, batched over batch_shape.

Returns:

Tensor of shape batch_shape.

enumerate_support

NegativeBinomialMixture.enumerate_support(expand=True)[source]#

Returns tensor containing all values supported by a discrete distribution. The result will enumerate over dimension 0, so the shape of the result will be (cardinality,) + batch_shape + event_shape (where event_shape = () for univariate distributions).

Note that this enumerates over all batched tensors in lock-step [[0, 0], [1, 1], …]. With expand=False, enumeration happens along dim 0, but with the remaining batch dimensions being singleton dimensions, [[0], [1], ...

To iterate over the full Cartesian product use itertools.product(m.enumerate_support()).

Parameters:

expand (bool) – whether to expand the support over the batch dims to match the distribution’s batch_shape.

Returns:

Tensor iterating over dimension 0.

expand

NegativeBinomialMixture.expand(batch_shape, _instance=None)[source]#

Returns a new distribution instance (or populates an existing instance provided by a derived class) with batch dimensions expanded to batch_shape. This method calls expand on the distribution’s parameters. As such, this does not allocate new memory for the expanded distribution instance. Additionally, this does not repeat any args checking or parameter broadcasting in __init__.py, when an instance is first created.

Parameters:
• batch_shape (torch.Size) – the desired expanded size.

• _instance – new instance provided by subclasses that need to override .expand.

Returns:

New distribution instance with batch dimensions expanded to batch_size.

icdf

NegativeBinomialMixture.icdf(value)[source]#

Returns the inverse cumulative density/mass function evaluated at value.

Parameters:

value (Tensor) –

log_prob

NegativeBinomialMixture.log_prob(value)[source]#

Log probability.

Return type:

Tensor

perplexity

NegativeBinomialMixture.perplexity()[source]#

Returns perplexity of distribution, batched over batch_shape.

Returns:

Tensor of shape batch_shape.

rsample

NegativeBinomialMixture.rsample(sample_shape=torch.Size([]))[source]#

Generates a sample_shape shaped reparameterized sample or sample_shape shaped batch of reparameterized samples if the distribution parameters are batched.

sample

NegativeBinomialMixture.sample(sample_shape=None)[source]#

Sample from the distribution.

Return type:

Tensor

sample_n

NegativeBinomialMixture.sample_n(n)[source]#

Generates n samples or n batches of samples if the distribution parameters are batched.

set_default_validate_args

static NegativeBinomialMixture.set_default_validate_args(value)[source]#

Sets whether validation is enabled or disabled.

The default behavior mimics Python’s assert statement: validation is on by default, but is disabled if Python is run in optimized mode (via python -O). Validation may be expensive, so you may want to disable it once a model is working.

Parameters:

value (bool) – Whether to enable validation.