RustyML

A comprehensive machine learning and deep learning library written in pure Rust.
一个用纯Rust编写的全面机器学习和深度学习库。

Overview | 概述

RustyML aims to be a feature-rich machine learning and deep learning framework that leverages Rust's performance, memory safety, and concurrency features. While currently in early development stages, the project's long-term vision is to provide a complete ecosystem for machine learning, deep learning, and transformer-based models.
RustyML 旨在成为一个功能丰富的机器学习和深度学习框架，充分利用Rust的性能、内存安全性和并发特性。虽然目前处于早期开发阶段，但项目的长期愿景是提供一个完整的机器学习、深度学习和基于transformer架构的模型生态系统。

Key Features | 核心特性

• Pure Rust Implementation | 纯Rust实现: No external C/C++ dependencies, ensuring memory safety and portability | 无需外部C/C++依赖，确保内存安全性和可移植性
• Parallel Processing | 并行处理: Leverages Rayon for efficient multi-threaded computation | 利用Rayon进行高效多线程计算
• Rich Algorithm Collection | 丰富的算法集合: Supervised, unsupervised learning, and neural networks | 监督学习、无监督学习和神经网络
• Comprehensive Metrics | 全面的评估指标: Evaluation tools for regression, classification, and clustering | 回归、分类和聚类的评估工具
• Model Persistence | 模型持久化: Save and load trained models with JSON serialization | 通过JSON序列化保存和加载训练好的模型

Architecture | 架构

Machine Learning | 机器学习 (features = ["machine_learning"])

Classical machine learning algorithms for supervised and unsupervised learning:
经典机器学习算法，用于监督和无监督学习：

• Regression | 回归:

  • Linear Regression with L1/L2 regularization | 带L1/L2正则化的线性回归

• Classification | 分类:

  • Logistic Regression | 逻辑回归
  • KNN (K-Nearest Neighbors) | K近邻
  • Decision Tree (ID3, C4.5, CART) | 决策树（ID3、C4.5、CART）
  • SVC (Support Vector Classification) | 支持向量分类
  • Linear SVC | 线性支持向量分类
  • LDA (Linear Discriminant Analysis) | 线性判别分析

• Clustering | 聚类:

  • KMeans with K-means++ initialization | K均值（带K-means++初始化）
  • DBSCAN (Density-based clustering) | DBSCAN（基于密度的聚类）
  • MeanShift | 均值漂移

• Anomaly Detection | 异常检测:

  • Isolation Forest | 隔离森林

Neural Network | 神经网络 (features = ["neural_network"])

Complete neural network framework with flexible architecture design:
完整的神经网络框架，具有灵活的架构设计：

• Layers | 层:

  • Dense: Fully connected layers with customizable activation functions | Dense层: 具有可定制激活功能的全连接层
  • Activation: Standalone activation functions (ReLU, Sigmoid, Tanh, Softmax, etc.) | 激活函数: 独立激活函数（ReLU, Sigmoid, Tanh， Softmax等）
  • Pooling Layers: Max pooling and average pooling operations for 1D, 2D, and 3D data | 池化层: 对1D、2D和3D数据进行最大池化和平均池化操作
  • Global Pooling: Global max pooling and global average pooling for 1D, 2D, and 3D tensors | 全局池化层: 对1D、2D和3D张量进行全局最大池化和全局平均池化
  • Recurrent Layers: Sequential Modeling Layers like RNN, LSTM, and GRU | 循环层: 序列建模层，如RNN, LSTM和GRU
  • Dropout: Regularization layer to prevent overfitting during training | Dropout层：正则化层，防止训练过程中的过拟合

• Optimizers | 优化器:

  • SGD (Stochastic Gradient Descent) | 随机梯度下降
  • Adam | Adam优化器
  • RMSProp | RMSProp优化器
  • AdaGrad | AdaGrad优化器

• Loss Functions | 损失函数:

  • MSE (Mean Squared Error) | 均方误差
  • MAE (Mean Absolute Error) | 平均绝对误差
  • Binary Cross-Entropy | 二元交叉熵
  • Categorical Cross-Entropy | 分类交叉熵
  • Sparse Categorical Cross-Entropy | 稀疏分类交叉熵

• Models | 模型:

  • Sequential architecture for feed-forward networks | 用于前馈网络的顺序架构

• Activation layers | 激活层:

  • ReLU, Tanh, Sigmoid, Softmax

Utility | 工具 (features = ["utility"])

Data preprocessing and dimensionality reduction utilities:
数据预处理和降维工具：

• Dimensionality Reduction | 降维:

  • PCA (Principal Component Analysis) | 主成分分析
  • Kernel PCA (with RBF, Linear, Polynomial, Sigmoid kernels) | 核主成分分析（支持RBF、线性、多项式、Sigmoid核）
  • LDA (Linear Discriminant Analysis) | 线性判别分析
  • t-SNE (t-Distributed Stochastic Neighbor Embedding) | t-分布随机邻域嵌入

• Preprocessing | 预处理:

  • Standardization (z-score normalization) | 标准化（z分数归一化）
  • Train-test splitting | 训练测试集分割

• Kernel Functions | 核函数:

  • RBF, Linear, Polynomial, Sigmoid | RBF、线性、多项式、Sigmoid

Metric | 评估指标 (features = ["metric"])

Comprehensive evaluation metrics for model performance assessment:
用于模型性能评估的全面评估指标：

• Regression Metrics | 回归指标:

  • MSE (Mean Squared Error) | 均方误差
  • RMSE (Root Mean Squared Error) | 均方根误差
  • MAE (Mean Absolute Error) | 平均绝对误差
  • R² score | R²分数

• Classification Metrics | 分类指标:

  • Accuracy | 准确率
  • Confusion Matrix (with TP, FP, TN, FN, precision, recall, F1-score) | 混淆矩阵（包含TP、FP、TN、FN、精确率、召回率、F1分数）
  • AUC-ROC | AUC-ROC曲线下面积

• Clustering Metrics | 聚类指标:

  • Adjusted Rand Index (ARI) | 调整兰德指数
  • Normalized Mutual Information (NMI) | 标准化互信息
  • Adjusted Mutual Information (AMI) | 调整互信息
  • Silhouette Score | 轮廓系数

Math | 数学工具 (features = ["math"])

Mathematical utilities and statistical functions:
数学工具和统计函数：

• Distance Metrics | 距离度量:

  • Euclidean distance | 欧几里得距离
  • Manhattan distance | 曼哈顿距离
  • Minkowski distance | 闵可夫斯基距离

• Impurity Measures | 不纯度度量:

  • Entropy | 熵
  • Gini impurity | 基尼不纯度
  • Information Gain | 信息增益
  • Gain Ratio | 增益率

• Statistical Functions | 统计函数:

  • Variance | 方差
  • Standard deviation | 标准差
  • SST (Sum of Squares Total) | 总平方和
  • SSE (Sum of Squared Errors) | 误差平方和

• Activation Functions | 激活函数:

  • Sigmoid | Sigmoid函数
  • Logistic loss | 逻辑损失

Dataset | 数据集 (features = ["dataset"])

Access to standardized datasets for experimentation:
用于实验的标准化数据集：

• Iris (150 samples, 4 features, 3 classes) | 鸢尾花数据集（150个样本，4个特征，3个类别）
• Diabetes (442 samples, 10 features) | 糖尿病数据集（442个样本，10个特征）
• Boston Housing | 波士顿房价数据集
• Wine Quality (red and white wines) | 葡萄酒质量数据集（红葡萄酒和白葡萄酒）
• Titanic | 泰坦尼克号数据集

Getting Started | 开始使用

Machine Learning Example | 机器学习示例

Add the library to your Cargo.toml:
将库添加到您的Cargo.toml文件中：

[dependencies]
rustyml = {version = "0.9.1", features = ["machine_learning"]} 
# or use `features = ["full"]` to enable all features
# Or use features = ["default"] to enable default modules (`machine_learning` and `neural_network`)

In your Rust code, write: 在你的Rust代码里写：

use rustyml::machine_learning::linear_regression::*; // or `use rustyml::prelude::*`
use ndarray::{Array1, Array2};
    
// Create a linear regression model
let mut model = LinearRegression::new(true, 0.01, 1000, 1e-6, None).unwrap();

// Prepare training data
let raw_x = vec![vec![1.0, 2.0], vec![2.0, 3.0], vec![3.0, 4.0]];
let raw_y = vec![6.0, 9.0, 12.0];

// Convert Vec to ndarray types
let x = Array2::from_shape_vec((3, 2), raw_x.into_iter().flatten().collect()).unwrap();
let y = Array1::from_vec(raw_y);

// Train the model
model.fit(&x.view(), &y.view()).unwrap();

// Make predictions
let new_data = Array2::from_shape_vec((1, 2), vec![4.0, 5.0]).unwrap();
let _predictions = model.predict(&new_data.view());

// Save the trained model to a file
model.save_to_path("linear_regression_model.json").unwrap();

// Load the model from the file
let loaded_model = LinearRegression::load_from_path("linear_regression_model.json").unwrap();

// Use the loaded model for predictions
let _loaded_predictions = loaded_model.predict(&new_data.view());

// Since Clone is implemented, the model can be easily cloned
let _model_copy = model.clone();

// Since Debug is implemented, detailed model information can be printed
println!("{:?}", model);

Neural Network Example | 神经网络示例

Add the library to your Cargo.toml:
将库添加到您的Cargo.toml文件中：

[dependencies]
rustyml = {version = "0.9.1", features = ["neural_network"]} 
# or use `features = ["full"]` to enable all features
# Or use `features = ["default"]` to enable default modules (`machine_learning` and `neural_network`)

In your Rust code, write: 在你的Rust代码里写：

use rustyml::prelude::*;  
use ndarray::Array;  
  
// Create training data   
let x = Array::ones((32, 784)).into_dyn(); // 32 samples, 784 features 
let y = Array::ones((32, 10)).into_dyn();  // 32 samples, 10 classes
  
// Build a neural network   
let mut model = Sequential::new();  
model  
    .add(Dense::new(784, 128, ReLU::new()).unwrap())    
    .add(Dense::new(128, 64, ReLU::new()).unwrap())    
    .add(Dense::new(64, 10, Softmax::new()).unwrap())    
    .compile(Adam::new(0.001, 0.9, 0.999, 1e-8).unwrap(), CategoricalCrossEntropy::new());  
// Display model structure   
model.summary();  
  
// Train the model  
model.fit(&x, &y, 10).unwrap();  
  
// Save model weights to file  
model.save_to_path("model.json").unwrap();  
  
// Create a new model with the same architecture  
let mut new_model = Sequential::new();
new_model  
    .add(Dense::new(784, 128, ReLU::new()).unwrap())    
    .add(Dense::new(128, 64, ReLU::new()).unwrap())    
    .add(Dense::new(64, 10, Softmax::new()).unwrap());
  
// Load weights from file  
new_model.load_from_path("model.json").unwrap();  
  
// Compile before using (required for training, optional for prediction)    
new_model.compile(Adam::new(0.001, 0.9, 0.999, 1e-8).unwrap(), CategoricalCrossEntropy::new());  
  
// Make predictions with loaded model  
let predictions = new_model.predict(&x);  
println!("Predictions shape: {:?}", predictions.shape());  

For Chinese mainland users, a mandarin video tutorial is provided on Bilibili, follow @SomeB1oody 对于中国大陆用户，B站有视频教程，关注@SomeB1oody

Feature Flags | 特性标志

The crate uses feature flags for modular compilation:
该crate使用特性标志进行模块化编译：

Project Status | 项目状态

RustyML is in active development. While the API is stabilizing, breaking changes may occur in minor version updates until version 1.0.0. RustyML正在积极开发中。虽然API正在稳定，但在1.0.0版本之前，次要版本更新中可能会出现破坏性更改。

Contribution | 贡献

Contributions are welcome! If you're interested in helping build a robust machine learning ecosystem in Rust, please feel free to:
欢迎贡献！如果您有兴趣帮助构建Rust中的强大机器学习生态系统，请随时：

1. Submit issues for bugs or feature requests | 提交bug或功能请求
2. Create pull requests for improvements | 创建改进的拉取请求
3. Provide feedback on the API design | 提供API设计的反馈意见
4. Help with documentation and examples | 帮助完善文档和示例

Authors | 作者

• SomeB1oody (stanyin64@gmail.com)

License | 许可证

Licensed under the MIT License. See LICENSE file for details.
根据MIT许可证授权。有关详细信息，请参阅LICENSE文件。