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)
• code: Code analysis and code2vec embeddings
• classification: Classification algorithms (Logistic Regression)
• tree: Decision tree classifiers
• metrics: Evaluation metrics
• mining: Pattern mining algorithms (Apriori for association rules)
• 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)
• bayesian: Bayesian inference (conjugate priors, MCMC, variational inference)
• glm: Generalized Linear Models (Poisson, Gamma, Binomial families)
• decomposition: Matrix decomposition (ICA, PCA)
• text: Text processing and NLP (tokenization, stop words, stemming)
• time_series: Time series analysis and forecasting (ARIMA)
• index: Approximate nearest neighbor search (HNSW)
• recommend: Recommendation systems (content-based, collaborative filtering)
• synthetic: Synthetic data generation for AutoML (EDA, back-translation, MixUp)
• bundle: Model bundling and memory paging for large models
• cache: Cache hierarchy and model registry for large model management
• chaos: Chaos engineering configuration (from renacer)
• inspect: Model inspection tooling (header analysis, diff, quality scoring)
• loading: Model loading subsystem with WCET and cryptographic agility
• scoring: 100-point model quality scoring system
• zoo: Model zoo protocol for sharing and discovery
• embed: Data embedding with test data and tiny model representations
• native: SIMD-native model format for zero-copy inference
• stack: Sovereign AI Stack integration types
• online: Online learning and dynamic retraining infrastructure

Re-exports

pub use error::AprenderError;

pub use error::Result;

pub use primitives::Matrix;

pub use primitives::Vector;

pub use traits::Estimator;

pub use traits::Transformer;

pub use traits::UnsupervisedEstimator;

Modules

active_learning

Active Learning strategies for label-efficient training.

autograd

Reverse-mode automatic differentiation engine for neural network training.

automl

Automated Machine Learning (AutoML) module.

bayesian

Bayesian inference and probability methods.

bench

Model evaluation and benchmarking framework (spec §7.10) Model Evaluation and Benchmarking Framework (aprender::bench)

bundle

Model Bundling and Memory Paging

cache

Model Cache and Registry

calibration

Model calibration for confidence estimation.

chaos

Chaos Engineering Configuration

citl

Compiler-in-the-Loop Learning (CITL) for transpiler support. Compiler-in-the-Loop Learning (CITL) module.

classification

Classification algorithms.

cluster

Clustering algorithms.

code

Code Analysis and Code2Vec Embeddings

data

DataFrame module for named column containers.

decomposition

Dimensionality reduction and matrix decomposition algorithms.

embed

Data embedding with test data and tiny model representations (spec §4) Data Embedding Module (spec §4)

ensemble

Mixture of Experts (MoE) ensemble learning (GH-101)

error

Error types for Aprender operations.

format

Aprender Model Format (.apr)

glm

Generalized Linear Models (GLM)

gnn

Graph Neural Network layers for learning on graph-structured data.

graph

Graph construction and analysis with cache-optimized CSR representation.

index

Indexing data structures for efficient nearest neighbor search.

inspect

Model inspection tooling (spec §7.2) Model Inspection Tooling

interpret

Model Interpretability and Explainability.

linear_model

Linear models for regression.

loading

Model loading subsystem with WCET and cryptographic agility (spec §7.1) APR Loading Subsystem

loss

Loss functions for training machine learning models.

metaheuristics

Derivative-free global optimization (metaheuristics).

metrics

Evaluation metrics for ML models.

mining

Pattern mining algorithms for association rule discovery.

model_selection

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

monte_carlo

Monte Carlo Simulation Framework

native

SIMD-native model format for zero-copy Trueno inference (spec §5) SIMD-Native Model Format (spec §5)

nn

Neural network modules for deep learning.

online

Online learning and dynamic retraining infrastructure Online Learning Infrastructure for Dynamic Model Retraining

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).

qa

Model Quality Assurance module (spec §7.9) Model Quality Assurance Module (aprender::qa)

recommend

Recommendation systems.

regularization

Regularization techniques for neural network training.

scoring

100-point model quality scoring system (spec §7) 100-Point Model Quality Scoring System (spec §7)

serialization

Model Serialization Module

stack

Sovereign AI Stack integration types (spec §9) Sovereign AI Stack Integration (spec §9)

stats

Traditional descriptive statistics for vector data.

synthetic

Synthetic Data Generation for AutoML.

text

Text processing and NLP utilities.

time_series

Time series analysis and forecasting.

traits

Core traits for ML estimators and transformers.

transfer

Transfer Learning module for cross-project knowledge sharing.

tree

Decision tree algorithms and ensemble methods.

weak_supervision

Weak Supervision and Label Model.

zoo

Model zoo protocol for sharing and discovery (spec §8) Model Zoo Protocol (spec §8)