pmrf.sampling.BaseSampler

class pmrf.sampling.BaseSampler(model)

Bases: ABC

Parameters:

model (ModelT)

__init__(model)

Parameters:

model (ModelT)

Methods

__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(value, /)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattribute__(name, /)	Return getattr(self, name).
__getstate__()	Helper for pickle.
__gt__(value, /)	Return self>value.
__hash__()	Return hash(self).
__init__(model)
__init_subclass__	This method is called when a class is subclassed.
__iter__()
__le__(value, /)	Return self<=value.
__len__()
__lt__(value, /)	Return self<value.
__ne__(value, /)	Return self!=value.
__new__(*args, **kwargs)
__reduce__()	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value, /)	Implement setattr(self, name, value).
__sizeof__()	Size of object in memory, in bytes.
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
_generate_hypercube_samples(N, D)
_generate_params(N)
generate_features(N, features, frequency[, ...])
generate_models(N)	Generates N random models using the sampler's engine.
range(N)	Allows the CircuitSampler to be used as an iterable. To use, call e.g:

Attributes

__abstractmethods__
__annotations__
__dict__
__doc__
__module__
__slots__
__weakref__	list of weak references to the object
_abc_impl

range(N)

Allows the CircuitSampler to be used as an iterable. To use, call e.g:

for i, system in enumerate(sampler.range(10)).

Args:

n (int): The number of samples to generate

Returns:

Model: self

Return type:

ModelT

generate_models(N)

Generates N random models using the sampler’s engine.

Note that, if you want to generate samples one-by-one, you can use this class in iterator mode by passing N to the constructor or by using Sampler.range(..).

Args:

n (int, optional): The number of samples to generate. Defaults to 10.

Returns:

_type_: Model | None

Return type:

list[ModelT]

generate_features(N, features, frequency, dont_jit=False, **kwargs)

Parameters:

• features (str | tuple[str, str] | tuple[str, str, tuple[int, int]] | Sequence[str | tuple[str, str] | tuple[str, str, tuple[int, int]]] | dict[str, str | tuple[str, str] | tuple[str, str, tuple[int, int]] | Sequence[str | tuple[str, str] | tuple[str, str, tuple[int, int]]]])

• frequency (Frequency | Frequency)

Return type:

array