fullseye

doe_morris — MULTIVAR `sensitivity` op

データ種: なし → design(引数だけで決まる op —— 画像やデータの入力を取らない)
呼び出し: import fullseye as fs; fs.ledger.doe_morris(r=10, factors=4, levels=4, seed=0, n_candidates=0) -> 'dict' (実装を直接呼ぶなら import sensitivity; sensitivity.doe_morris(r=10, factors=4, levels=4, seed=0, n_candidates=0) -> 'dict'、台帳から引くなら opsmultivar.get("doe_morris"))

使い方

Morris one-at-a-time screening plan: r trajectories of k+1 runs -> design.

Each trajectory starts at a random point of the levels-level grid on the unit cube and moves one factor at a time by delta = levels / (2 (levels - 1)) (Morris 1991; with an even levels every grid level is equally likely to be visited), so r (k+1) runs give r elementary effects per factor. n_candidates > r draws that many trajectories and keeps r of them that are spread out: the trajectory distance of Campolongo et al. 2007 (sum over point pairs of their Euclidean distances), chosen greedily — a pair of the farthest trajectories first, then repeatedly the trajectory that adds the most distance (an approximation of their exhaustive search, stated in meta['selection']).

The coded matrix is 2u - 1 of the unit-cube points; meta keeps the trajectory bookkeeping that :func:sens_morris needs (changed factor and sign per step).

詳しい使い方ガイド

design_of_experiments ファミリガイド

参考(サンプルデータ・文献)

サンプルデータカタログ(DL URL / ライセンス) — 2-D は skimage.data(BSD/public)+ 合成、3-D は実データ源(Stanford/PDS 等)の DL URL。
演算子の来歴・参考文献 — この op 族の元になった研究/手法の出典。
アルゴリズムの正典(著者・年)と用途は上記ファミリ使い方ガイドに記載。

実行できる例(この op を実際に呼ぶ検証済みサンプル)

sensitivity_tour — py -3.11 examples/sensitivity_tour.py

型が繋がる次の op(`design` を入力に取れる)

doe_decode · rsm_fit · doe_main_effects · sens_morris · sens_sobol

同カテゴリ(`sensitivity`)

sens_morris · doe_saltelli · sens_sobol

Provenance: sensitivity.py — MULTIVAR operator registry. この per-op ノートは tools/opdocs.py md が自動生成(手編集しない)。

This site is open source. Improve this page.