lsst.scarlet.lite.parameters.Parameter Class Reference

Inheritance diagram for lsst.scarlet.lite.parameters.Parameter:

Public Member Functions
	__init__ (self, np.ndarray x, dict[str, np.ndarray] helpers, Callable|float step, Callable|None grad=None, Callable|None prox=None)
float	step (self)
tuple[int,...]	shape (self)
npt.DTypeLike	dtype (self)
Parameter	__copy__ (self)
Parameter	__deepcopy__ (self, dict[int, Any]|None memo=None)
Parameter	copy (self, bool deep=False)
	update (self, int it, np.ndarray input_grad, *args)
	resize (self, Box old_box, Box new_box)

Public Attributes
	x = x
	helpers = helpers
	grad = grad
	prox = prox

Protected Attributes
	_step = _step

Detailed Description

A parameter in a `Component`

Parameters
----------
x:
    The array of values that is being fit.
helpers:
    A dictionary of helper arrays that are used by an optimizer to
    persist values like the gradient of `x`, the Hessian of `x`, etc.
step:
    A numerical step value or function to calculate the step for a
    given `x``.
grad:
    A function to calculate the gradient of `x`.
prox:
    A function to take the proximal operator of `x`.

Member Function Documentation

__copy__()

Parameter lsst.scarlet.lite.parameters.Parameter.__copy__

(

self

)

Create a shallow copy of this parameter.

Returns
-------
parameter:
    A shallow copy of this parameter.

__deepcopy__()

Parameter lsst.scarlet.lite.parameters.Parameter.__deepcopy__	(		self,
		dict[int, Any] | None	memo = None )

Create a deep copy of this parameter.

Parameters
----------
memo:
    A memoization dictionary used by `copy.deepcopy`.
Returns
-------
parameter:
    A deep copy of this parameter.

copy()

Parameter lsst.scarlet.lite.parameters.Parameter.copy	(		self,
		bool	deep = False )

Copy this parameter, including all of the helper arrays.

Parameters
----------
deep:
    If `True`, a deep copy is made.
    If `False`, a shallow copy is made.

Returns
-------
parameter:
    A copy of this parameter.

dtype()

npt.DTypeLike lsst.scarlet.lite.parameters.Parameter.dtype

(

self

)

The numpy dtype of the array that is being fit.

resize()

lsst.scarlet.lite.parameters.Parameter.resize	(		self,
		Box	old_box,
		Box	new_box )

Grow the parameter and all of the helper parameters

Parameters
----------
old_box:
    The old bounding box for the parameter.
new_box:
    The new bounding box for the parameter.

shape()

tuple[int, ...] lsst.scarlet.lite.parameters.Parameter.shape

(

self

)

The shape of the array that is being fit.

step()

float lsst.scarlet.lite.parameters.Parameter.step

(

self

)

Calculate the step

Return
------
step:
    The numerical step if no iteration is given.

update()

lsst.scarlet.lite.parameters.Parameter.update	(		self,
		int	it,
		np.ndarray	input_grad,
		*	args )

Update the parameter in one iteration.

This includes the gradient update, proximal update,
and any meta parameters that are stored as class
attributes to update the parameter.

Parameters
----------
it:
    The current iteration
input_grad:
    The gradient from the full model, passed to the parameter.

Reimplemented in lsst.scarlet.lite.parameters.AdaproxParameter, lsst.scarlet.lite.parameters.FistaParameter, and lsst.scarlet.lite.parameters.FixedParameter.

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The documentation for this class was generated from the following file:

• python/lsst/scarlet/lite/parameters.py