This repository contains implementations of basic machine learning algorithms in plain Rust. It is a fork of, and follows the spirit of, the original machine learning basics in plain Python. All algorithms are implemented from scratch without using additional machine learning libraries. The intention is to provide a basic understanding of the algorithms and their underlying structure, and how to port ML algorithms to Rust, not to provide the most efficient implementations.
- Logistic Regression e.g.
cargo run -- lgr --n_iters 600 --learning_rate 0.009 - K Nearest Neighbor e.g.
cargo run -- knn -k 5 - K Means Clustering e.g.
cargo run -- kmc -k 4 - Decision tree for regression e.g.
cargo run -- dtr --max_depth 4 --min_samples 2
Still missing:
- Linear Regression
- Perceptron
- Simple neural network with one hidden layer
- Multinomial Logistic Regression
- Decision tree for classification
- Reinforcement learning (e.g. Q-learning with a linear neural network)
- Support Vector Machine
If you have a favorite algorithm that should be included or spot a mistake, please let me know by creating a new issue.
See the LICENSE file for license rights and limitations (MIT).