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@SomeB1oody SomeB1oody released this 31 Mar 00:22
· 548 commits to master since this release
v0.1.0
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RustyML v0.1.0 Release Announcement | RustyML v0.1.0 发布公告

Project Introduction | 项目简介

RustyML is a machine learning and deep learning library written in pure Rust, designed to provide high-performance, memory-safe, and concurrency-friendly machine learning tools and algorithms. This project leverages Rust's performance and safety features to offer a reliable alternative for data science and machine learning practitioners.

RustyML 是一个用纯 Rust 编写的机器学习和深度学习库，旨在提供高性能、内存安全、并发友好的机器学习工具和算法。该项目利用 Rust 的性能和安全特性，为数据科学和机器学习从业者提供可靠的替代方案。

v0.1.0 Highlights | v0.1.0 版本亮点

This is the first public release of RustyML, featuring the following core functionalities:

这是 RustyML 的首个公开发布版本，包含了以下核心功能：

Mathematical Utilities | 数学工具

  • Core mathematical functions for statistical operations (SST, SSE, R-squared) ｜ 统计操作核心数学函数 (总平方和、误差平方和、R平方评分)
  • Sigmoid function and logistic loss function ｜ Sigmoid 函数和逻辑损失函数
  • Classification model evaluation metrics (Accuracy) ｜ 分类模型评估指标 (准确率)
  • Distance calculations (Euclidean, Manhattan, Minkowski) ｜ 距离计算 (欧几里得距离、曼哈顿距离、闵可夫斯基距离)
  • Feature engineering tools (Gaussian/RBF kernel) ｜ 特征工程工具 (高斯核/RBF 核)
  • Decision tree related calculations (Entropy, Gini impurity, Information gain, Gain ratio) ｜ 决策树相关计算 (熵、基尼不纯度、信息增益、增益率)
  • Basic statistical functions (MSE, standard deviation) ｜ 基础统计函数 (均方误差、标准差)

Machine Learning Models | 机器学习模型

Supervised Learning | 监督学习:

  • Linear Regression | 线性回归
  • Logistic Regression | 逻辑回归
  • KNN | K近邻算法
  • Decision Tree | 决策树

Unsupervised Learning | 无监督学习:

  • KMeans | K均值聚类
  • MeanShift | 均值漂移聚类
  • DBSCAN | DBSCAN 聚类
  • Isolation Forest | 隔离森林

Utilities | 工具

  • Principal Component Analysis (PCA) | 主成分分析

Installation | 安装使用

Add the library to your Cargo.toml:

将库添加到您的 Cargo.toml 中:

[dependencies]
rustyml = "0.1.0"

Future Plans | 未来规划

This is the initial version of RustyML. Future versions will add | 这是 RustyML 的初始版本，后续版本将增加:

  • Deep learning building blocks | 深度学习基础构建模块
  • Data preprocessing tools | 数据预处理工具
  • More comprehensive performance metrics | 更全面的性能指标

Dependencies | 依赖

  • ndarray (0.16.1)
  • rand (0.9.0)
  • ndarray-linalg (0.17.0)

Contributing | 贡献

We welcome community contributions! Please check the repository for more information.

我们欢迎社区贡献! 请查看项目仓库以获取更多信息。

License | 许可

RustyML is licensed under MIT - License.

RustyML 使用 MIT - License。

Thank you for your interest in and support of RustyML!

感谢您对 RustyML 的兴趣和支持!

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