EchoStateNetwork
Echo state networks / reservoir computing in pure numpy. One class,
EchoStateNetwork: ridge-regression training with chaotic recycle validation
(contiguous runs or ragged dwell segments), parametric inputs, optional Bayesian
hyperparameter search (scikit-optimize), closed-loop prediction, and Jacobians
for data assimilation.
This is the single shared reservoir core behind
qlrom (quantized-local ESN families) and
romda (real-time ESN forecasting and
bias-aware data assimilation).
Documentation: https://andreanovoa.github.io/EchoStateNetwork/
Tutorials
tutorials/01_echo_state_network.ipynb— train an ESN on the Lorenz 63 system, with full and partial observability.tutorials/02_parametric_esn.ipynb— one reservoir conditioned on a physical parameter, forecasting at unseen parameter values.
Install
pip install echostatenetwork # core (numpy/scipy/matplotlib)
pip install "echostatenetwork[opt]" # + scikit-optimize for Bayesian hyperparameter search
pip install -e ".[dev]" # development
Quickstart
import numpy as np
from echostatenetwork import EchoStateNetwork
y = my_time_series # (Nt, N_dim)
esn = EchoStateNetwork(y, dt=0.01, N_units=200, t_train=40.0, t_val=4.0)
esn.train([y])
u_wash, r = ... # see docstrings: washout then closed loop
Acknowledgements
This repository is based on alberacca/Echo-State-Networks, the reference implementation of the recycle-validation ESN:
Racca, A., & Magri, L. (2021). Robust optimization and validation of echo state networks for learning chaotic dynamics. Neural Networks, 142, 252-268. doi:10.1016/j.neunet.2021.05.004
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