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Reservoir Chaos Pocket
Reservoir Chaos Pocket fits an Echo State Network to the Lorenz-63 attractor. The recurrent reservoir is fixed after spectral-radius scaling; only the linear readout is trained, in one ridge-regression solve. Linear autoregression and persistence are explicit controls.
Evaluation measures teacher-forced one-step RMSE and the first closed-loop step where normalized vector error exceeds 0.4. The Space visualizes how rapidly each forecast diverges on a chaotic trajectory.
Verified local result
The fixed 128-unit reservoir stores 17,292 values but trains only its 396-value readout. It reached 0.0165 one-step RMSE and a 38-step closed-loop horizon, versus 0.299/2 for 12-parameter linear autoregression and 0.358/2 for persistence.
uv run python projects/reservoir-chaos-pocket/train.py
uv run pytest tests/test_reservoir_chaos_pocket.py
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