Biological and Machine Intelligence Library

A Python library written in Rust. Provides various tools for biologically-inspired machine learning.

NEAT genetic algorithms

Implemented features:

• Compositional Pattern Producing Networks (CPPN) with crossover
• Picbreeder
• HyperNEAT
• numpy integration
• GPU acceleration (with OpenCL)

Planned:

• ES-HyperNEAT
• Plastic Hyper-HEAT
• Continous-time recurrent neural networks (CTRNN)
• Novelty Search
• L-systems producing deep fractal neural networks
• L-systems producing plastic neural networks

HTM biologically-constrained neural networks

Implemented features:

• Sparse distributed representation (SDR)
• SDR encoders
• Spacial Poolers
• Pattern separation with negative spacial poolers
• GPU acceleration with OpenCL
• Higher order memory with the temporal memory algorithm

Planned:

Building

cargo build --release

Then you can find produced artifacts in target/release.

While developing, you can symlink (or copy) and rename the shared library from the target folder: On MacOS, rename librusty_neat.dylib to rusty_neat.so, on Windows librusty_neat.dll to rusty_neat.pyd, and on Linux librusty_neat.so to rusty_neat.so. Then open a Python shell in the same folder and you'll be able to import rusty_neat.

Usage:

Check out this tutorial for details.