reservoir op• Data kinds: conn_graph × matrix → matrix
• Call: import fullseye as fs; fs.ledger.reservoir_encode(W: 'Any', X: 'Any', steps: 'int' = 6, in_scale: 'float' = 0.1, leak: 'float' = 0.3, nonlinearity: 'str' = 'tanh', seed: 'int' = 0) -> 'np.ndarray' (to call the implementation directly, import conngraph; conngraph.reservoir_encode(W: 'Any', X: 'Any', steps: 'int' = 6, in_scale: 'float' = 0.1, leak: 'float' = 0.3, nonlinearity: 'str' = 'tanh', seed: 'int' = 0) -> 'np.ndarray'; from the registry, opsconngraph.get("reservoir_encode"))
Batch reservoir encoding of static inputs (for classification): the final state (N, n) after running each row of X for steps iterations from the zero state.
For each row x, x_{t+1} = (1−leak) x_t + leak · f(Wᵀ x_t + W_in x), repeated steps times. W_in is the same seeded uniform (−in_scale, in_scale) (n, d) as in reservoir_states. No loop over rows is written; 1 step = 1 matrix product (N, n) @ (n, n), advancing all rows at once. With linear, leak 1 and steps 1 it equals X W_inᵀ exactly; with steps 2, (X W_inᵀ) W + X W_inᵀ exactly.
• 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.
• poc_larval_connectome_reservoir — py -3.11 examples/poc_larval_connectome_reservoir.py
matrix as input)reservoir_states · ridge_readout · ridge_predict · graph_activation_latency · graph_activity_spread · points_activity_video · graph_layer_propagate · states_participation_ratio
reservoir)reservoir_from_graph · reservoir_states · ridge_readout · ridge_predict
*Provenance: conngraph.py — CONNGRAPH 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.