mrpro.operators.ElementaryFunctional

class mrpro.operators.ElementaryFunctional(target: Tensor | None | complex = None, weight: Tensor | complex = 1.0, dim: int | Sequence[int] | None = None, divide_by_n: bool = False, keepdim: bool = False)

Bases: Functional

Elementary functional base class.

Here, an ‘elementary’ functional is a functional that can be written as \(f(x) = \phi ( weight ( x - target))\), returning a real value. It does not require another functional for initialization.

__init__(target: Tensor | None | complex = None, weight: Tensor | complex = 1.0, dim: int | Sequence[int] | None = None, divide_by_n: bool = False, keepdim: bool = False) → None

Initialize a Functional.

We assume that functionals are given in the form \(f(x) = \phi ( weight ( x - target))\) for some functional \(\phi\).

Parameters:

• target – target element - often data tensor (see above)

• weight – weight parameter (see above)

• dim – dimension(s) over which functional is reduced. All other dimensions of weight ( x - target) will be treated as batch dimensions.

• divide_by_n – if true, the result is scaled by the number of elements of the dimensions index by dim in the tensor weight ( x - target). If true, the functional is thus calculated as the mean, else the sum.

• keepdim – if true, the dimension(s) of the input indexed by dim are maintained and collapsed to singeltons, else they are removed from the result.

abstract forward(*args: Unpack) → Tout

Apply forward operator.