reservoir-computing-benedictchen 1.1.0

Echo State Networks and Liquid State Machines - Revolutionary temporal processing without training recurrent weights

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Reservoir Computing

🌊 Echo State Networks & Liquid State Machines

Jaeger, H. (2001) & Maass, W. (2002) - Reservoir Computing foundations

📦 Installation

pip install reservoir-computing-benedictchen

🚀 Quick Start

import reservoir_computing_benedictchen
import numpy as np

# Create Echo State Network
esn = reservoir_computing_benedictchen.EchoStateNetwork(
    reservoir_size=100,
    input_size=3,
    output_size=1,
    spectral_radius=0.95
)

# Generate sample temporal data
time_steps = 1000
X = np.random.randn(time_steps, 3)  # Input sequences
y = np.sin(np.arange(time_steps) * 0.1)[:, np.newaxis]  # Target

# Train the network
esn.train(X, y)

# Make predictions
predictions = esn.predict(X[:100])
print(f"✅ ESN prediction shape: {predictions.shape}")

# Create Liquid State Machine  
lsm = reservoir_computing_benedictchen.create_lsm_with_presets(
    'temporal_pattern_recognition'
)

# Process spike trains
spike_train = np.random.poisson(0.1, (100, 50))  # 100 time steps, 50 inputs
liquid_states = lsm.process(spike_train)
print(f"✅ LSM liquid states: {liquid_states.shape}")

🎓 About the Implementation

Implemented by Benedict Chen - bringing foundational AI research to modern Python.

📧 Contact: [e-mail]

📖 Citation

If you use this implementation in your research, please cite the original paper:

Jaeger, H. (2001) & Maass, W. (2002) - Reservoir Computing foundations

📜 License

Custom Non-Commercial License with Donation Requirements - See LICENSE file for details.

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💰 Support This Work - Donation Appreciated!

This implementation represents hundreds of hours of research and development. If you find it valuable, please consider donating:

💳 DONATE VIA PAYPAL - CLICK HERE

Your support helps maintain and expand these research implementations! 🙏