entries_derivatives_model#

t3toolbox.fitting.entries_derivatives_model(geometry, x, index, pp, order, residual, weight=None, regularizer=None)#
def entries_derivatives_model(
        geometry,                            # MANIFOLD / COREWISE (or the UNIFORM_* twin for a uniform x)
        x:          typ.Union[t3.TuckerTensorTrain, ut3.UniformTuckerTensorTrain],   # the current point
        index:      NDArray,                 # int, shape=(d,)+W -- the grid points
        pp:         typ.Sequence[NDArray],   # perturbation vectors P, len=d, elm_shape=W+(Ni,)
        order:      int,
        residual:   NDArray,                 # RAW r = entries_derivatives(x) − y, shape (order+1)+W+C
        weight:     typ.Optional[typ.Any] = None,  # ORDER-only residual weight ω, (order+1,); None = 1
        regularizer: typ.Any = None,         # optional regularizer, e.g. optimizers.IdentityRegularizer(λ)
) -> typ.Union[GaussNewtonModel, UniformGaussNewtonModel]:

The entries-derivatives Gauss-Newton model – like apply_derivatives_model() at integer grid points index. Order-only weight (no mode axis – mode weighting is probe-only).

Parameters:

  • x (t3toolbox.backend.common.typ.Union[TuckerTensorTrain, UniformTuckerTensorTrain])

  • index (NDArray)

  • pp (t3toolbox.backend.common.typ.Sequence[NDArray])

  • order (int)

  • residual (NDArray)

  • weight (t3toolbox.backend.common.typ.Optional[t3toolbox.backend.common.typ.Any])

  • regularizer (t3toolbox.backend.common.typ.Any)

Return type:

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