# 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。
[](https://www.rust-lang.org/)
[](https://github.com/SomeB1oody/RustyML/blob/master/LICENSE)
[](https://crates.io/crates/ndarray)
[](https://crates.io/crates/rand)
[](https://crates.io/crates/nalgebra)
[](https://crates.io/crates/statrs)
[](https://crates.io/crates/rand_distr)
[](https://crates.io/crates/rayon)
[](https://crates.io/crates/ndarray-rand)
[](https://crates.io/crates/ahash)
## 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。
## Current Features | 当前功能
- **Mathematical Utilities (use `features = ["math"]` to enable) | 数学工具 (使用`features = ["math"]`来开启)**:
- Sum of square total (SST) for measuring data variability | 总平方和(SST)，用于衡量数据变异性
- Sum of squared errors (SSE) for evaluating prediction errors | 误差平方和(SSE)，用于评估预测误差
- Sigmoid function for logistic regression and neural networks | Sigmoid函数，用于逻辑回归和神经网络
- Logistic loss (log loss) for binary classification models | 逻辑损失函数(对数损失)，用于二元分类模型
- Accuracy score for classification model evaluation | 准确率分数，用于分类模型评估
- Calculate the squared Euclidean distance between two points | 计算两点之间的欧几里得距离平方
- Calculate the Manhattan distance between two points ｜ 计算两点间的曼哈顿距离
- Calculate the Minkowski distance between two points ｜ 计算两点间的闵可夫斯基距离
- Calculate the variance | 计算方差
- Calculates the entropy of a label set | 计算标签集的熵
- Calculates the Gini impurity of a label set | 计算标签集的基尼不纯度
- Calculates the information gain when splitting a dataset | 计算数据集分割时的信息增益
- Calculates the gain ratio for a dataset split | 计算数据集分割的增益率
- Calculates the leaf node adjustment factor c(n) | 计算叶节点调整因子 c(n)
- Calculates the standard deviation of a set of values | 计算标准差
- **Metric Utilities (use `features = ["metric"]` to enable) | (使用`features = ["metric"]`来开启)**:
- R-squared (R²) score for assessing model fit quality | R平方(R²)分数，用于评估模型拟合质量
- Calculates the Root Mean Squared Error (RMSE) between predicted and actual values | 计算均方误差根
- Calculates the Mean Squared Error (MSE) of a set of values | 计算一组值的均方误差(MSE)
- Calculates the Mean Absolute Error (MAE) between predicted and actual values | 计算平均绝对误差
- Confusion Matrix for binary classification evaluation | 混淆矩阵及相关计算公式
- Calculates the Normalized Mutual Information (NMI) between two cluster label assignments.(Unit: nat) | 计算NMI值(单位: nat)
- Calculates the Adjusted Mutual Information (AMI) between two cluster label assignments.(Unit: nat) | 计算AMI值(单位: nat)
- Calculates the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) | 计算AUC-ROC值
- Calculates the Mean Squared Error (MSE) of a set of values | 计算一组值的均方误差(MSE)
- **Machine Learning Models (use `features = ["machine_learning"]` to enable) | 机器学习模型 (使用`features = ["machine_learning"]`来开启)**:
- Supervised Learning | 监督式学习:
- Linear Regression | 线性回归
- Logistic Regression | 逻辑回归
- KNN | K邻近值聚类
- Decision Tree | 决策树
- SVC(Support Vector Classification) | 支持向量机分类
- Linear SVC | 线性向量机分类
- LDA(Linear Discriminant Analysis) | 线性判别分析
- Kernel PCA | 核主成分分析
- Unsupervised Learning | 无监督学习:
- KMeans | K均值聚类
- MeanShift | MeanShift聚类
- DBSCAN | DBSCAN聚类
- Isolation Forest | 隔离森林
- **Utilities (use `features = ["utility"]` to enable) | 工具 (使用`features = ["utility"]`来开启)**:
- PCA(Principal Component Analysis) | 主成分分析
- standardize data($\mu = 0$, $\sigma = 1$) | 标准化数据(确保均值 $\mu = 0$ , 协方差 $\sigma = 1$ )
- Split dataset for training and dataset for test | 分离训练集和测试集
- LDA(Linear Discriminant Analysis) | 线性判别分析
- t-Distributed Stochastic Neighbor Embedding (t-SNE) | t分布随机邻域嵌入
- **Datasets (use `features = ["dataset"]` to enable) | 数据集 (使用`features = ["dataset"]`来开启)**:
- Iris dataset | 鸢尾花分类数据集
- Diabetes dataset | 糖尿病回归数据集
- Wine quality dataset | 酒质量数据集
- **Neural Network (use `features = ["neural_network"]` to enable) | 神经网络 (使用`features = [["neural_network"]]`来开启)**:
- model | 模型:
- Sequential | 顺序模型
- Layer | 层:
- Dense | 全连接层
- SimpleRNN(without LSTM) | 简单循环神经网络(没有LSTM)
- LSTM | 长短期记忆循环神经网络
- Feature Extraction Layers (Convolution, Pooling) | 特征提取层(卷积, 池化)
- Loss function | 损失计算函数:
- Mean Squared Error | 均方误差
- 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
- Optimizer | 优化器:
- SGD | 随机梯度下降
- Adam | 自适应矩估计
- RMSprop | 均方根传播
- Activation Function | 激活函数:
- ReLU | 修正线性单元（线性整流函数）
- Tanh | 双曲正切函数
- Sigmoid | 逻辑函数
- Softmax | 归一化指数函数
## Vision | 愿景
While the library is in its early stages, Rust AI aims to evolve into a comprehensive crate that includes:
虽然该库处于早期阶段，但Rust AI旨在发展成为一个包含以下内容的综合性crate：
- **Classical Machine Learning Algorithms | 经典机器学习算法**:
- Linear and Logistic Regression | 线性和逻辑回归
- Decision Trees | 决策树
- Clustering algorithms (K-means, MeanShift, KNN) | 聚类算法(K均值, MeanShift, KNN)
- Dimensionality reduction technique (PCA) | 降维技术(PCA)
- **Deep Learning | 深度学习**:
- Neural network building blocks | 神经网络构建模块
- Convolutional neural networks | 卷积神经网络
- Recurrent neural networks | 循环神经网络
- Optimization algorithms | 优化算法
- **Transformer Architecture | Transformer架构**:
- Self-attention mechanisms | 自注意力机制
- Multi-head attention | 多头注意力
- Encoder-decoder architectures | 编码器-解码器架构
- Pre-training and fine-tuning capabilities | 预训练和微调能力
- **Utilities | 实用工具**:
- Data preprocessing | 数据预处理
## Getting Started | 开始使用
Add the library to your `Cargo.toml`:
将库添加到您的`Cargo.toml`文件中：
``` toml
[dependencies]
rustyml = {version = "0.7.0", features = ["machine_learning"]} # or use `features = ["full"]` to enable all features
```
use rustyml::machine_learning::linear_regression::*;
use ndarray::{Array1, Array2, array};
// Create a linear regression model
let mut model = LinearRegression::new(true, 0.01, 1000, 1e-6, None);
// 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());
// 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);
```
For Chinese mainland users, a mandarin video tutorial is provided on Bilibili, follow [@SomeB1oody](https://space.bilibili.com/1349872478)
对于中国大陆用户，B站有视频教程，关注[@SomeB1oody](https://space.bilibili.com/1349872478)
## Project Status | 项目状态
This project is in the **early development stage**. The API is unstable and subject to significant changes as the project evolves.
该项目处于**早期开发阶段**。API不稳定，随着项目的发展可能会有重大变化。
## 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：
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)
_RustyML - Bringing the power and safety of Rust to machine learning and AI._
_RustyML - 将Rust的强大性能和安全特性引入机器学习和人工智能领域。_