apply_model#

t3toolbox.fitting.apply_model(geometry, x, ww, residual, regularizer=None)#
def apply_model(
        geometry,                            # MANIFOLD / COREWISE (or the UNIFORM_* twin for a uniform x)
        x:          typ.Union[t3.TuckerTensorTrain, ut3.UniformTuckerTensorTrain],   # the current point
        ww:         typ.Sequence[NDArray],   # sample vectors, len=d, elm_shape=W+(Ni,)
        residual:   NDArray,                 # r = apply(x) − y, shape W+C
        regularizer: typ.Any = None,         # optional regularizer, e.g. optimizers.IdentityRegularizer(λ)
) -> typ.Union[GaussNewtonModel, UniformGaussNewtonModel]:

The Gauss-Newton model of an all-modes apply least-squares objective at x, on geometry.

Accepts a ragged TuckerTensorTrain (-> GaussNewtonModel) or a uniform UniformTuckerTensorTrain (-> UniformGaussNewtonModel); the representation is inferred from x and the geometry must match. regularizer adds ρ(x) to the model (ragged only).

Parameters:
Return type:

t3toolbox.backend.common.typ.Union[GaussNewtonModel, UniformGaussNewtonModel]