states_participation_ratio — CONNGRAPH `dimension` op

• Data kinds: matrix → measurement

• Call: import fullseye as fs; fs.ledger.states_participation_ratio(X: 'Any') -> 'float' (to call the implementation directly, import conngraph; conngraph.states_participation_ratio(X: 'Any') -> 'float'; from the registry, opsconngraph.get("states_participation_ratio"))

Usage

> This operator's description has not been translated yet. The original text follows as it is.

状態列 `X (N, n) の**実効次元** = participation ratio PR = (Σλ)² / Σλ²`(λ は共分散の固有値、

Gao et al. 2017)。全列が独立で同じ分散なら n、1 本の方向に乗っていれば 1。

標本数 N に依存する(N が小さいと PR ≤ N に頭打ち)ので、比べるときは N を揃える。定数(分散 0)なら 0。

Detailed usage guide

• conngraph family guide

References (sample data, literature)

• 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.

Runnable examples (verified samples that actually call this op)

• poc_connectome_motor_bottleneck — py -3.11 examples/poc_connectome_motor_bottleneck.py

Ops the type connects to (they accept `measurement` as input)

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Same category (`dimension`)

graph_block_shuffle · graph_layer_propagate · states_layer_dimension

*Provenance: conngraph.py — CONNGRAPH operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

states_participation_ratio — CONNGRAPH dimension op