lsst.scarlet.lite ge1c02a5578+afb4790f61
 
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lsst.scarlet.lite.parameters.FixedParameter Class Reference
Inheritance diagram for lsst.scarlet.lite.parameters.FixedParameter:
lsst.scarlet.lite.parameters.Parameter

Public Member Functions

 __init__ (self, np.ndarray x)
 
 update (self, int it, np.ndarray input_grad, *args)
 
FixedParameter __copy__ (self)
 
FixedParameter __deepcopy__ (self, dict[int, Any]|None memo=None)
 
- Public Member Functions inherited from lsst.scarlet.lite.parameters.Parameter
 __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)
 
 resize (self, Box old_box, Box new_box)
 

Additional Inherited Members

- Public Attributes inherited from lsst.scarlet.lite.parameters.Parameter
 x = x
 
 helpers = helpers
 
 grad = grad
 
 prox = prox
 
- Protected Attributes inherited from lsst.scarlet.lite.parameters.Parameter
 _step = _step
 

Detailed Description

A parameter that is not updated

Member Function Documentation

◆ __copy__()

FixedParameter lsst.scarlet.lite.parameters.FixedParameter.__copy__ ( self)
Create a shallow copy of this parameter.

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

◆ __deepcopy__()

FixedParameter lsst.scarlet.lite.parameters.FixedParameter.__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.

◆ update()

lsst.scarlet.lite.parameters.FixedParameter.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 from lsst.scarlet.lite.parameters.Parameter.


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