aprender 0.12.0

Aprender provides implementations of classical machine learning algorithms optimized for performance and safety. The library requires no external dependencies beyond the Rust standard library and offers seamless compilation to WebAssembly.

Table of Contents

• Features
• Installation
• Quick Start
• Algorithms
• Model Persistence
• Documentation
• Contributing
• License

Features

• Pure Rust — Zero C/C++ dependencies, memory-safe, thread-safe by default
• SIMD Acceleration — Vectorized operations via trueno backend
• WebAssembly Ready — Compile to WASM for browser and edge deployment
• Native Model Format — .apr format with encryption, signatures, and zero-copy loading
• Interoperability — Export to SafeTensors and GGUF formats

Installation

Add aprender to your Cargo.toml:

[dependencies]
aprender = "0.11"

Optional Features

[dependencies]
aprender = { version = "0.11", features = ["format-encryption", "hf-hub-integration"] }

Quick Start

use aprender::prelude::*;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Training data
    let x = Matrix::from_vec(4, 2, vec![
        1.0, 2.0,
        2.0, 3.0,
        3.0, 4.0,
        4.0, 5.0,
    ])?;
    let y = Vector::from_slice(&[3.0, 5.0, 7.0, 9.0]);

    // Train model
    let mut model = LinearRegression::new();
    model.fit(&x, &y)?;

    // Evaluate
    println!("R² = {:.4}", model.score(&x, &y));

    Ok(())
}

Algorithms

Supervised Learning

Unsupervised Learning

Additional Modules

• Graph Analysis — PageRank, betweenness centrality, community detection
• Time Series — ARIMA forecasting
• Text Processing — Tokenization, TF-IDF, stemming
• Neural Networks — Sequential models, transformers, mixture of experts

Model Persistence

The .apr format provides secure, efficient model serialization:

use aprender::format::{save, load, ModelType, SaveOptions};

// Save with encryption
save(&model, ModelType::LinearRegression, "model.apr",
    SaveOptions::default()
        .with_encryption("password")
        .with_compression(true))?;

// Load
let model: LinearRegression = load("model.apr", ModelType::LinearRegression)?;

Format Capabilities

• Security — AES-256-GCM encryption, Ed25519 signatures, X25519 key exchange
• Performance — Memory-mapped loading, 600x faster than standard deserialization
• Integrity — CRC32 checksums with automatic corruption detection
• Commercial — License blocks, watermarking, buyer-specific encryption

Documentation

Contributing

We welcome contributions. Please ensure your changes pass the test suite:

cargo test --all-features
cargo clippy --all-targets -- -D warnings
cargo fmt --check

See CONTRIBUTING.md for guidelines.

License

Aprender is distributed under the MIT License. See LICENSE for details.