Summary

rustic_ml is a machine learning library designed to be easy to use, and give the developer a flexible API to work with. This library is built of first principles, and the goal is to avoid any dependencies.

⚠️ This library is in the prototype stage. Breaking changes can happen.

Table of content

• Summary

• Feature list

• Usage

• Use Cases

  • Binary classification

• Macros

• Feature Flags

• Deeper Reading

Feature list

The library includes the following key features:

• Matrix implementation
• Dataframe implementation
• Perceptron binary classifier

Usage

rustic_ml has documentation on docs.rs. It will be very useful to read it through https://docs.rs/rustic_ml/latest/rustic_ml/

Run the following Cargo command in your project directory:

cargo add rustic_ml

Or add it to the Cargo manifest. Make sure to pick the newest version:

[dependencies]
rustic_ml = "0.0.2"

Also see the ./examples/ folder for different examples. See also the specific use cases in the next section of the README file.

Use Cases

Binary classification

rustic_ml has implemented the Perceptron. It works well when you know your data is linearly separable. In the example below, we use a Jupyter Notebook with Rust kernel. This makes it easy to build up models with Rust:

(See the full demo examples/notebook_binary_classification.ipynb

Macros

Coming soon!

Feature Flags

Coming soon!

Deeper Reading

Coming soon!