probe_derivatives_kind

t3toolbox.backend.fitting.probe_derivatives_kind(order, weight=None, chunk_size=100)

def probe_derivatives_kind(
        order:      int,
        weight:     typ.Optional[typ.Any] = None,   # residual weight ω[mode,order], (d,order+1) broadcast; None = 1
        chunk_size: typ.Optional[int] = 100,        # W-chunk size for the 𝒥ᵀ gradient assembly (docs/chunking.md)
) -> SamplingKind:                                  # sample = (ww, pp); data = list of d, (order+1)+W+(Ni,)

The probe-derivatives sampling kind: vector-valued (one free mode per probe), so the residual / output is a list of d arrays. sample = (ww, pp). Probe has both a mode and an order axis, so weight is the full ω[mode, order] matrix (d, order+1) (a row (order+1,) = per-order, a column (d, 1) = per-mode, a matrix = both).

Parameters:

• order (int)

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

• chunk_size (t3toolbox.backend.common.typ.Optional[int])

Return type:

SamplingKind