pmrf.fitting.FrequentistFitter

class pmrf.fitting.FrequentistFitter(model, *, cost_kind=None, cost_function=None, **kwargs)

Bases: BaseFitter

A base class for frequentist (classical) optimization methods.

This class extends BaseFitter by adding the concept of a cost function, which takes the difference between model features and measured features and computes a single scalar value representing the “cost” or “error”.

Parameters:

• model (Model)

• cost_kind (str | None)

• cost_function (Callable[[Array], Array] | list[Callable[[Array], Array]] | Module | None)

__init__(model, *, cost_kind=None, cost_function=None, **kwargs)

Initializes the FrequentistFitter.

Parameters:

• model (Model) – The parametric pmrf Model to be fitted.

• cost_kind (str, optional) – A cost ‘kind’ alias to initialize the features and cost function from. Can be one of ‘convolutional’, ‘complex’, ‘magnitude’ or None.

• cost_function (ArrayFuncT, list[ArrayFuncT] or eqx.Module, optional) – A function or sequence of functions defining the cost metric. If a list of functions is provided, they are composed sequentially. If None, then cost_kind defines the cost function. Defaults to None.

• **kwargs – Additional arguments forwarded to BaseFitter.

Return type:

None

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).
__gt__(value, /)	Return self>value.
__hash__()	Return hash(self).
__init__(model, *[, cost_kind, cost_function])	Initializes the FrequentistFitter.
__init_subclass__	This method is called when a class is subclassed.
__le__(value, /)	Return self<=value.
__lt__(value, /)	Return self<value.
__ne__(value, /)	Return self!=value.
__new__(**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().
_create_context(measured, *[, cost_kind, ...])	Create a FrequentistContext for the fitting process.
_run_algorithm(context, **kwargs)	Executes the fitting algorithm.
_run_context(context, *[, load_previous, ...])	Executes the fitting context.
fit(measured, **kwargs)	Fits the model to measured data.
fit_submodels(measured, **kwargs)	Fits the submodels.

Attributes

__abstractmethods__
__annotations__
__dict__
__doc__
__module__
__slots__
__weakref__	list of weak references to the object (if defined)
_abc_impl

__init__(model, *, cost_kind=None, cost_function=None, **kwargs)

Initializes the FrequentistFitter.

Parameters:

• model (Model) – The parametric pmrf Model to be fitted.

• cost_kind (str, optional) – A cost ‘kind’ alias to initialize the features and cost function from. Can be one of ‘convolutional’, ‘complex’, ‘magnitude’ or None.

• cost_function (ArrayFuncT, list[ArrayFuncT] or eqx.Module, optional) – A function or sequence of functions defining the cost metric. If a list of functions is provided, they are composed sequentially. If None, then cost_kind defines the cost function. Defaults to None.

• **kwargs – Additional arguments forwarded to BaseFitter.

Return type:

None