LuasNumPyro

class luas.numpyro_ext.LuasNumPyro(gp=None, var_dict=None, likelihood_fn=None, validate_args=None)

Bases: Distribution

Custom NumPyro distribution which allows a luas.GPClass.GP object to be used with NumPyro.

Parameters:

• gp (object) – The GP object used for log likelihood calculations.

• var_dict (PyTree) – A PyTree of parameter values for calculating the log likelihood

• likelihood_fn (Callable, optional) – Can specify a different log likelihood function than the default of GP.logP i.e. GP.logP_hessianable may be used which is more numerically stable if performing hessian/second order derivative calculations.

property batch_shape

Returns the shape over which the distribution parameters are batched.

Returns:

batch shape of the distribution.

Return type:

tuple

cdf(value)

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(expand=True)

Returns an array with shape len(support) x batch_shape containing all values in the support.

property event_dim

Number of dimensions of individual events. :rtype: int

Type:

return

property event_shape

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

icdf(q)

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

Mean of the distribution.

sample(key, sample_shape=())

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

property variance

Variance of the distribution.