block_sumsq_over_samples#
- t3toolbox.backend.fitting.block_sumsq_over_samples(out, n_w, has_order)#
def block_sumsq_over_samples( out: NDArray, # scalar-output residual: (order+1)+W+C (has_order) or W+C n_w: int, # number of leading W axes (unused -- W + C are summed wholesale) has_order: bool, # True for the derivative kinds (a leading order axis at axis 0) ) -> NDArray: # (1, n_order) -- per-order sum of squares (no mode axis: apply/entries)
Per-
(mode, order)sum-of-squares for the scalar-output apply/entries kinds -> a 2-D(1, n_order)matrix (no mode axis – they contract every mode). Keeps only a leading order axis, sums the rest (W+C); UNWEIGHTED (raw‖r_·j‖²).