lsst.scarlet.lite gee10cc3b42+772f6ae910
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Public Member Functions | Public Attributes | Protected Attributes | List of all members
lsst.scarlet.lite.parameters.Parameter Class Reference
Inheritance diagram for lsst.scarlet.lite.parameters.Parameter:
lsst.scarlet.lite.parameters.AdaproxParameter lsst.scarlet.lite.parameters.FistaParameter lsst.scarlet.lite.parameters.FixedParameter

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)
 
 update (self, int it, np.ndarray input_grad, *args)
 
 resize (self, Box old_box, Box new_box)
 

Public Attributes

 x
 
 helpers
 
 grad
 
 prox
 

Protected Attributes

 _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)
Copy this parameter, including all of the helper arrays.

◆ 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.FistaParameter, lsst.scarlet.lite.parameters.AdaproxParameter, and lsst.scarlet.lite.parameters.FixedParameter.


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