Crate aprender 

Aprender: Next-generation machine learning library in pure Rust.

Aprender provides production-grade ML algorithms with a focus on ergonomic APIs, comprehensive testing, and backend-agnostic compute.

Quick Start

use aprender::prelude::*;

// Create training data (y = 2*x + 1)
let x = Matrix::from_vec(4, 1, vec![
    1.0,
    2.0,
    3.0,
    4.0,
]).unwrap();
let y = Vector::from_slice(&[3.0, 5.0, 7.0, 9.0]);

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

// Make predictions
let predictions = model.predict(&x);
let r2 = model.score(&x, &y);
assert!(r2 > 0.99);

Modules

• primitives: Core Vector and Matrix types
• data: DataFrame for named columns
• linear_model: Linear regression algorithms
• cluster: Clustering algorithms (K-Means)
• classification: Classification algorithms (Logistic Regression)
• tree: Decision tree classifiers
• metrics: Evaluation metrics
• model_selection: Cross-validation and train/test splitting
• preprocessing: Data transformers (scalers, encoders)
• optim: Optimization algorithms (SGD, Adam)
• loss: Loss functions for training (MSE, MAE, Huber)
• serialization: Model serialization (SafeTensors format)
• stats: Traditional descriptive statistics (quantiles, histograms)
• graph: Graph construction and analysis (centrality, community detection)
• chaos: Chaos engineering configuration (from renacer)

Re-exports

pub use primitives::Matrix;

pub use primitives::Vector;

pub use traits::Estimator;

pub use traits::Transformer;

pub use traits::UnsupervisedEstimator;

Modules

chaos

Chaos Engineering Configuration

classification

Classification algorithms.

cluster

Clustering algorithms.

data

DataFrame module for named column containers.

graph

Graph construction and analysis with cache-optimized CSR representation.

linear_model

Linear models for regression.

loss

Loss functions for training machine learning models.

metrics

Evaluation metrics for ML models.

model_selection

Model selection utilities for cross-validation and train/test splitting.

optim

Optimization algorithms for gradient-based learning.

prelude

Convenience re-exports for common usage.

preprocessing

Preprocessing transformers for data standardization and normalization.

primitives

Core compute primitives (Vector, Matrix).

serialization

Model Serialization Module

stats

Traditional descriptive statistics for vector data.

traits

Core traits for ML estimators and transformers.

tree

Decision tree algorithms and ensemble methods.