`leaspy.models.noise_models`.DistributionFamily

class DistributionFamily(parameters: Dict[str, Tensor] | None = None)

Bases: object

Base class for a distribution family being able to sample “around” user-provided values.

Parameters:

parametersdict [ str, torch.Tensor ] or None: Values for all the free parameters of the distribution family. All of them must have values before using the sampling methods.

Attributes:

free_parametersfrozenset of str: Name of all the free parameters (but loc) needed to characterize the distribution. Nota: for each parameter, if a method named “validate_xxx” exists (torch.Tensor -> torch.Tensor), then it will be used for user-input validation of parameter “xxx”.
factoryNone or function(free parameters values) -> torch.distributions.distribution.Distribution: The factory for the distribution family.
parametersdict [ str, torch.Tensor ] or None: Values for all the free parameters of the distribution family. All of them must have values before using the sampling methods.

Methods

`move_to_device`(device)	Move all torch tensors stored in this instance to the provided device (parameters & hyperparameters).
`raise_if_partially_defined`()	Raise an error if some of the free parameters are not defined.
`raise_if_unknown_parameters`(params)	Raise an error if the provided parameters are not part of the free parameters.
`rv_around`(loc)	Return the torch distribution centred around values (only if noise is not None).
`sample_around`(loc)	Realization around loc with respect to partially defined distribution.
`sampler_around`(loc)	Return the sampling function around input values.
`to_dict`()	Serialize instance as dictionary.
`update_parameters`(*[, validate])	(Partial) update of the free parameters of the distribution family.
`validate`(**params)	Validation function for parameters (based on 'validate_xxx' methods).

move_to_device(device: device) → None

Move all torch tensors stored in this instance to the provided device (parameters & hyperparameters).

Parameters:

devicetorch.device: Torch device on which to move the tensors.

raise_if_partially_defined() → None: Raise an error if some of the free parameters are not defined.

classmethod raise_if_unknown_parameters(params: Iterable | None) → None

Raise an error if the provided parameters are not part of the free parameters.

Parameters:

paramsIterable, optional: The list of parameters to analyze.

rv_around(loc: Tensor) → Distribution

Return the torch distribution centred around values (only if noise is not None).

Parameters:

loctorch.Tensor: The loc around which to sample.

Returns:

torch.distributions.distribution.Distribution: The torch distribution centered around the loc.

sample_around(loc: Tensor) → Tensor

Realization around loc with respect to partially defined distribution.

Parameters:

loctorch.Tensor: The loc around which to sample.

Returns:

torch.Tensor: The requested sample.

sampler_around(loc: Tensor) → Callable[[], Tensor]

Return the sampling function around input values.

Parameters:

loctorch.Tensor: The loc around which to sample.

Returns:

Callable: The sampler.

to_dict() → Dict[str, Any]

Serialize instance as dictionary.

Returns:

KwargsType: The instance serialized as a dictionary.

update_parameters(*, validate: bool = False, **parameters: Tensor) → None

(Partial) update of the free parameters of the distribution family.

Parameters:

validatebool, optional: If True, the provided parameters are validated before being updated. Default=False.
**parameterstorch.Tensor: The new parameters.

validate(**params: Any) → Dict[str, Tensor]

Validation function for parameters (based on ‘validate_xxx’ methods).

Parameters:

**paramsAny: The parameters to validate.

Returns:

DictParamsTorch: The validated parameters.

leaspy.models.noise_models.DistributionFamily

`leaspy.models.noise_models`.DistributionFamily