unuran_sampler

UNU.RAN Default Sampler

This module provides the default UNU.RAN sampler implementation that uses the UNU.RAN library for efficient random variate generation from probability distributions. The sampler automatically selects appropriate sampling methods based on available distribution characteristics.

class pysatl_core.sampling.unuran.core.unuran_sampler.DefaultUnuranSampler(distr, config=None)[source]

Bases: object

Default UNU.RAN sampler implementation.

This sampler provides a default implementation for generating random variates from probability distributions using the UNU.RAN library. It automatically selects appropriate sampling methods based on available distribution characteristics (PDF, CDF, PPF, PMF).

The sampler supports both continuous and discrete univariate distributions and uses various UNU.RAN methods (PINV, HINV, NINV, DGT, etc.) depending on the available characteristics and configuration.

Parameters:

distr (Distribution)
config (UnuranMethodConfig | None)

distr

The probability distribution to sample from.

Type:: Distribution

config

Configuration for method selection and parameters.

Type:: UnuranMethodConfig

method

The sampling method currently used by this sampler.

Type:: UnuranMethod

Notes

Only univariate Euclidean distributions are supported
The sampler automatically selects the best method if method=AUTO
Callbacks are kept alive to prevent garbage collection issues with CFFI
Resources are automatically cleaned up when the object is deleted

__init__(distr, config=None)[source]

Initialize the UNU.RAN sampler.

Parameters:

distr (Distribution) – The probability distribution to sample from. Must be a univariate Euclidean distribution (dimension=1).
config (UnuranMethodConfig, optional) – Configuration for method selection and parameters. If None, uses default configuration.

Raises:

RuntimeError – If the distribution type is not supported (not Euclidean or dimension != 1), or if initialization fails.

Notes

The initialization process: 1. Validates distribution type and dimension 2. Determines available characteristics 3. Selects appropriate sampling method (if AUTO) 4. Creates UNURAN distribution object 5. Sets up callbacks for available characteristics 6. Initializes the UNURAN generator

__del__()[source]

Cleanup on object deletion.

Automatically calls _cleanup() when the object is garbage collected. All exceptions during finalization are silently ignored.

Return type:: None

sample(n)[source]

Generate random variates from the distribution.

Parameters:

n (int) – Number of samples to generate. Must be non-negative.

Returns:

1D array of shape (n,) containing the generated samples.

Return type:

npt.NDArray[np.float64]

Raises:

RuntimeError – If the sampler is not initialized.
ValueError – If n < 0.

Notes

Uses UNURAN’s sampling functions: - unur_sample_cont() for continuous distributions - unur_sample_discr() for discrete distributions

Samples are generated sequentially in a loop. For large n, this may be slower than vectorized operations, but it’s necessary due to UNURAN’s C API design.

property method: UnuranMethod: The sampling method used by this sampler.