aprender

Next Generation Machine Learning, Statistics and Deep Learning in PURE Rust

Overview

Aprender is a lightweight, pure Rust machine learning library designed for efficiency and ease of use. Built with EXTREME TDD methodology, it provides reliable implementations of core ML algorithms with comprehensive test coverage.

Features (v0.1.0)

Core Primitives

• Vector - 1D numerical array with statistical operations (mean, sum, dot, norm, variance)
• Matrix - 2D numerical array with linear algebra (matmul, transpose, Cholesky decomposition)
• DataFrame - Named column container for ML data preparation workflows

Machine Learning Models

• LinearRegression - Ordinary Least Squares via normal equations
• KMeans - K-means++ initialization with Lloyd's algorithm

Metrics

• Regression: r_squared, mse, rmse, mae
• Clustering: silhouette_score, inertia

Installation

Add to your Cargo.toml:

[dependencies]
aprender = "0.1.0"

Quick Start

Linear Regression

use aprender::prelude::*;

fn main() {
    // Features: [sqft, bedrooms]
    let x = Matrix::from_vec(5, 2, vec![
        1500.0, 3.0,
        2000.0, 4.0,
        1200.0, 2.0,
        1800.0, 3.0,
        2500.0, 5.0,
    ]).unwrap();

    // Target: price
    let y = Vector::from_slice(&[250.0, 350.0, 180.0, 280.0, 450.0]);

    let mut model = LinearRegression::new();
    model.fit(&x, &y).expect("Failed to fit");

    let predictions = model.predict(&x);
    let r2 = model.score(&x, &y);

    println!("R² Score: {:.4}", r2);
}

K-Means Clustering

use aprender::prelude::*;

fn main() {
    let x = Matrix::from_vec(6, 2, vec![
        1.0, 2.0,
        1.5, 1.8,
        5.0, 8.0,
        6.0, 8.0,
        1.0, 0.6,
        9.0, 11.0,
    ]).unwrap();

    let mut kmeans = KMeans::new(2)
        .with_max_iter(100)
        .with_random_state(42);

    kmeans.fit(&x).expect("Failed to fit");

    let labels = kmeans.predict(&x);
    let score = silhouette_score(&x, &labels);

    println!("Silhouette Score: {:.4}", score);
}

DataFrame Operations

use aprender::prelude::*;

fn main() {
    let columns = vec![
        ("age".to_string(), Vector::from_slice(&[25.0, 30.0, 35.0])),
        ("income".to_string(), Vector::from_slice(&[50000.0, 60000.0, 75000.0])),
    ];

    let df = DataFrame::new(columns).expect("Failed to create DataFrame");

    println!("Shape: {:?}", df.shape());
    println!("Mean age: {:.1}", df.column("age").unwrap().mean());

    // Convert to Matrix for ML
    let matrix = df.to_matrix();
}

Examples

Run the included examples:

cargo run --example boston_housing    # Linear regression demo
cargo run --example iris_clustering   # K-Means clustering demo
cargo run --example dataframe_basics  # DataFrame operations demo

Quality Metrics

• TDG Score: 94.1/100 (A grade)
• Unit Tests: 120
• Property Tests: 19 (proptest)
• Doc Tests: 13
• Max Cyclomatic Complexity: 5

API Reference

Generate documentation:

cargo doc --open

License

MIT License - see LICENSE for details.

Contributing

Contributions welcome! Please ensure:

• All tests pass: cargo test --all
• No clippy warnings: cargo clippy --all-targets
• Code is formatted: cargo fmt