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#
Returns the shape over which the distribution parameters are batched. |
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Number of dimensions of individual events. |
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Returns the shape of a single sample from the distribution without batching. |
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Mean of the distribution. |
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Variance of the distribution. |
Methods table#
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The cumulative distribution function of this distribution. |
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Returns the entropy of the distribution. |
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Returns an array with shape len(support) x batch_shape containing all values in the support. |
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Returns a new |
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Expands a distribution by adding |
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The inverse cumulative distribution function of this distribution. |
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Infers |
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Evaluates the log probability density for a batch of samples given by value. |
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Masks a distribution by a boolean or boolean-valued array that is broadcastable to the distributions |
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Returns a sample from the distribution having shape given by sample_shape + batch_shape + event_shape. |
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Same as |
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The tensor shape of samples from this distribution. |
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Interpret the rightmost reinterpreted_batch_ndims batch dimensions as dependent event dimensions. |
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Attributes#
- JaxNegativeBinomialMeanDisp.arg_constraints = {'inverse_dispersion': Positive(lower_bound=0.0), 'mean': Positive(lower_bound=0.0)}#
- JaxNegativeBinomialMeanDisp.batch_shape[source]#
Returns the shape over which the distribution parameters are batched.
- Returns:
batch shape of the distribution.
- Return type:
- JaxNegativeBinomialMeanDisp.event_dim[source]#
Number of dimensions of individual events. :rtype: int
- Type:
return
- JaxNegativeBinomialMeanDisp.event_shape[source]#
Returns the shape of a single sample from the distribution without batching.
- Returns:
event shape of the distribution.
- Return type:
- JaxNegativeBinomialMeanDisp.has_enumerate_support = False#
- JaxNegativeBinomialMeanDisp.pytree_aux_fields = ('_batch_shape', '_event_shape')#
- JaxNegativeBinomialMeanDisp.pytree_data_fields = ('concentration',)#
- JaxNegativeBinomialMeanDisp.reparametrized_params = []#
- JaxNegativeBinomialMeanDisp.support = IntegerNonnegative(lower_bound=0)#
Methods#
- JaxNegativeBinomialMeanDisp.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.
- JaxNegativeBinomialMeanDisp.enumerate_support(expand=True)[source]#
Returns an array with shape len(support) x batch_shape containing all values in the support.
- 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
- JaxNegativeBinomialMeanDisp.expand_by(sample_shape)[source]#
Expands a distribution by adding
sample_shape
to the left side of itsbatch_shape
. To expand internal dims ofself.batch_shape
from 1 to something larger, useexpand()
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
- 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.
- classmethod JaxNegativeBinomialMeanDisp.infer_shapes(*args, **kwargs)[source]#
Infers
batch_shape
andevent_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:
- 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:
- 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:
- 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:
- 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:
- 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
- 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