759b67a9e1
CI / Format (ruff format) (push) Successful in 27s
CI / Lint (ruff check) (push) Successful in 27s
CI / Sync project version with tag (push) Has been skipped
CI / Lint (ruff check) (pull_request) Successful in 35s
CI / Type check (ty) (push) Successful in 38s
CI / Format (ruff format) (pull_request) Successful in 33s
CI / Sync project version with tag (pull_request) Has been skipped
CI / Type check (ty) (pull_request) Successful in 33s
CI / Tests (pull_request) Successful in 1m21s
CI / Tests (push) Successful in 1m27s
The streaming update re-derived two full (B, F) arrays from the running mean (once before updating it, once after) plus an elementwise product — five passes over each chunk and three temporary arrays, to maintain a mean/variance that's tiny in width (COND_DIM=15 at most). Reformulate as Chan/Golub/LeVeque's parallel-variance algorithm: compute the chunk's own local mean/M2 (independent of the running state) and merge it in with an O(F) combination formula. Same streaming interface and output (identical to ~1e-14, float64 rounding noise), ~40% faster per update() call on a benchmark chunk. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>