model op• Data kinds: shapemodel → signal
• Call: import fullseye as fs; fs.ledger.shape_explained_variance(model) (to call the implementation directly, import shapestats; shapestats.shape_explained_variance(model); from the registry, opsshapestat.get("shape_explained_variance"))
> This operator's description has not been translated yet. The original text follows as it is.
各主成分の寄与率(合計 1)。→ `(k,)`。
式: `variance / total_variance。分母は model["total_variance"](shape_pca`
が全特異値から計算した総分散)で、無ければ `variance.sum()`。
• `model: shape_pca の返り値。必須キーが無ければ ValueError`。
• 返り値: `(k,) float64、各要素は [0, 1]。shape_pca(n_components=0)`
(全成分)なら合計は 1。**`n_components` で打ち切ったモデルでは合計が 1 未満**
になる(切り捨てた成分の分だけ足りない)―― 「上位 k 本でどれだけ説明できるか」
を読むにはむしろその方が正しい。
• 総分散が 0(全個体が同じ形)なら全要素 0 を返す(0 除算にしない)。
`shape_pca の分散は標本分散(K-1` で割る)なので、個体数が少ないと
固有値の絶対値は膨らむが、比であるこの量は影響を受けにくい。累積和
(`cumsum)で「99 % に何本要るか」を決め、shape_mahalanobis の cumulative`
や `shape_synthesize の n_modes` に渡す。
• shape_statistics family guide
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• shapestat_landmark_tour — py -3.11 examples/shapestat_landmark_tour.py
signal as input)model)shape_pca · shape_project · shape_reconstruct · shape_mahalanobis · shape_synthesize
*Provenance: shapestats.py — SHAPESTAT operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.