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rusty-machine\n\n**This library is no longer actively maintained.**\n\n[![Join the chat at https://gitter.im/AtheMathmo/rusty-machine](https://badges.gitter.im/AtheMathmo/rusty-machine.svg)](https://gitter.im/AtheMathmo/rusty-machine?utm_source=badge\u0026utm_medium=badge\u0026utm_campaign=pr-badge\u0026utm_content=badge) [![Build Status](https://travis-ci.org/AtheMathmo/rusty-machine.svg?branch=master)](https://travis-ci.org/AtheMathmo/rusty-machine)\n\nThe crate is currently on version [0.5.4](https://crates.io/crates/rusty-machine/).\n\nRead the [API Documentation](https://AtheMathmo.github.io/rusty-machine/) to learn more.\n\nAnd here is a document detailing development efforts. Including a projected timeline for immediate features.\nPlease feel free to give feedback and let me know if there any features you believe should take precedence.\n\n- [Development](DEVELOPMENT.md)\n\n---\n\n## Summary\n\nRusty-machine is a general purpose machine learning library implemented entirely in Rust.\nIt aims to combine speed and ease of use - without requiring a huge number of external dependencies.\n\nThis project began as a way for me to learn Rust and brush up on some less familiar machine learning algorithms and techniques.\nNow the project aims to provide a complete, easy to use, machine learning library for Rust.\n\nThis library is still very much in early stages of development. Although there are a good number of algorithms many other\nthings are missing. Rusty-machine is probably not the best choice for any serious projects - but hopefully that can change in the near future!\n\n#### Contributing\n\nThis project is currently looking for contributors of all capacities!\n\nI have now created a dedicated page for [contributing](CONTRIBUTING.md). If you're interested please take a look.\n\n---\n\n## Implementation\n\nThis project is implemented using [Rust](https://www.rust-lang.org/). Currently there are no other dependencies!\nThough, we are planning on introducing optional BLAS/LAPACK dependencies soon.\n\n---\n\n## Current Progress\n\nRusty-machine uses [rulinalg](https://github.com/AtheMathmo/rulinalg) for its linear algebra back end.\nThis is fairly complete but there is still lots of room for optimization and we should provide BLAS/LAPACK support.\n\n### Machine Learning\n\n- Linear Regression\n- Logistic Regression\n- Generalized Linear Models\n- K-Means Clustering\n- Neural Networks\n- Gaussian Process Regression\n- Support Vector Machines\n- Gaussian Mixture Models\n- Naive Bayes Classifiers\n- DBSCAN\n- k-Nearest Neighbor Classifiers\n- Principal Component Analysis\n\nThere is also a basic `stats` module behind a feature flag.\n\n---\n\n## Usage\n\nThe library usage is described well in the [API documentation](https://AtheMathmo.github.io/rusty-machine/) - including example code.\nI will provide a brief overview of the library in it's current state and intended usage.\n\n### Installation\n\nThe library is most easily used with [cargo](http://doc.crates.io/guide.html). Simply include the following in your Cargo.toml file:\n\n```toml\n[dependencies]\nrusty-machine=\"0.5.4\"\n```\n\nAnd then import the library using:\n\n```rust\nextern crate rusty_machine as rm;\n```\n\nThe library consists of two core components. The linear algebra module and the learning module.\n\n#### Linalg\n\nThe linear algebra module contains reexports from the [rulinalg](https://github.com/AtheMathmo/rulinalg) crate. This is to\nprovide easy access to components which are used frequently within rusty-machine.\n\nMore detailed coverage can be found in the [API documentation](https://AtheMathmo.github.io/rusty-machine/).\n\n#### Learning\n\nThe learning module contains machine learning models. The machine learning implementations are designed with\nsimpicity and customization in mind. This means you can control the optimization algorithms but still retain\nthe ease of using default values. This is an area I am actively trying to improve on!\n\nThe models all provide `predict` and `train` methods enforced by the `SupModel` and `UnSupModel` traits.\n\nThere are some examples within this repository that can help you familiarize yourself with the library.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fathemathmo%2Frusty-machine","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fathemathmo%2Frusty-machine","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fathemathmo%2Frusty-machine/lists"}