[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-RubixML--ML":3,"tool-RubixML--ML":64},[4,17,27,35,43,56],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":16},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,3,"2026-04-05T11:01:52",[13,14,15],"开发框架","图像","Agent","ready",{"id":18,"name":19,"github_repo":20,"description_zh":21,"stars":22,"difficulty_score":23,"last_commit_at":24,"category_tags":25,"status":16},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",138956,2,"2026-04-05T11:33:21",[13,15,26],"语言模型",{"id":28,"name":29,"github_repo":30,"description_zh":31,"stars":32,"difficulty_score":23,"last_commit_at":33,"category_tags":34,"status":16},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",107662,"2026-04-03T11:11:01",[13,14,15],{"id":36,"name":37,"github_repo":38,"description_zh":39,"stars":40,"difficulty_score":23,"last_commit_at":41,"category_tags":42,"status":16},3704,"NextChat","ChatGPTNextWeb\u002FNextChat","NextChat 是一款轻量且极速的 AI 助手，旨在为用户提供流畅、跨平台的大模型交互体验。它完美解决了用户在多设备间切换时难以保持对话连续性，以及面对众多 AI 模型不知如何统一管理的痛点。无论是日常办公、学习辅助还是创意激发，NextChat 都能让用户随时随地通过网页、iOS、Android、Windows、MacOS 或 Linux 端无缝接入智能服务。\n\n这款工具非常适合普通用户、学生、职场人士以及需要私有化部署的企业团队使用。对于开发者而言，它也提供了便捷的自托管方案，支持一键部署到 Vercel 或 Zeabur 等平台。\n\nNextChat 的核心亮点在于其广泛的模型兼容性，原生支持 Claude、DeepSeek、GPT-4 及 Gemini Pro 等主流大模型，让用户在一个界面即可自由切换不同 AI 能力。此外，它还率先支持 MCP（Model Context Protocol）协议，增强了上下文处理能力。针对企业用户，NextChat 提供专业版解决方案，具备品牌定制、细粒度权限控制、内部知识库整合及安全审计等功能，满足公司对数据隐私和个性化管理的高标准要求。",87618,"2026-04-05T07:20:52",[13,26],{"id":44,"name":45,"github_repo":46,"description_zh":47,"stars":48,"difficulty_score":23,"last_commit_at":49,"category_tags":50,"status":16},2268,"ML-For-Beginners","microsoft\u002FML-For-Beginners","ML-For-Beginners 是由微软推出的一套系统化机器学习入门课程，旨在帮助零基础用户轻松掌握经典机器学习知识。这套课程将学习路径规划为 12 周，包含 26 节精炼课程和 52 道配套测验，内容涵盖从基础概念到实际应用的完整流程，有效解决了初学者面对庞大知识体系时无从下手、缺乏结构化指导的痛点。\n\n无论是希望转型的开发者、需要补充算法背景的研究人员，还是对人工智能充满好奇的普通爱好者，都能从中受益。课程不仅提供了清晰的理论讲解，还强调动手实践，让用户在循序渐进中建立扎实的技能基础。其独特的亮点在于强大的多语言支持，通过自动化机制提供了包括简体中文在内的 50 多种语言版本，极大地降低了全球不同背景用户的学习门槛。此外，项目采用开源协作模式，社区活跃且内容持续更新，确保学习者能获取前沿且准确的技术资讯。如果你正寻找一条清晰、友好且专业的机器学习入门之路，ML-For-Beginners 将是理想的起点。",84991,"2026-04-05T10:45:23",[14,51,52,53,15,54,26,13,55],"数据工具","视频","插件","其他","音频",{"id":57,"name":58,"github_repo":59,"description_zh":60,"stars":61,"difficulty_score":10,"last_commit_at":62,"category_tags":63,"status":16},3128,"ragflow","infiniflow\u002Fragflow","RAGFlow 是一款领先的开源检索增强生成（RAG）引擎，旨在为大语言模型构建更精准、可靠的上下文层。它巧妙地将前沿的 RAG 技术与智能体（Agent）能力相结合，不仅支持从各类文档中高效提取知识，还能让模型基于这些知识进行逻辑推理和任务执行。\n\n在大模型应用中，幻觉问题和知识滞后是常见痛点。RAGFlow 通过深度解析复杂文档结构（如表格、图表及混合排版），显著提升了信息检索的准确度，从而有效减少模型“胡编乱造”的现象，确保回答既有据可依又具备时效性。其内置的智能体机制更进一步，使系统不仅能回答问题，还能自主规划步骤解决复杂问题。\n\n这款工具特别适合开发者、企业技术团队以及 AI 研究人员使用。无论是希望快速搭建私有知识库问答系统，还是致力于探索大模型在垂直领域落地的创新者，都能从中受益。RAGFlow 提供了可视化的工作流编排界面和灵活的 API 接口，既降低了非算法背景用户的上手门槛，也满足了专业开发者对系统深度定制的需求。作为基于 Apache 2.0 协议开源的项目，它正成为连接通用大模型与行业专有知识之间的重要桥梁。",77062,"2026-04-04T04:44:48",[15,14,13,26,54],{"id":65,"github_repo":66,"name":67,"description_en":68,"description_zh":69,"ai_summary_zh":69,"readme_en":70,"readme_zh":71,"quickstart_zh":72,"use_case_zh":73,"hero_image_url":74,"owner_login":75,"owner_name":76,"owner_avatar_url":77,"owner_bio":78,"owner_company":79,"owner_location":79,"owner_email":79,"owner_twitter":79,"owner_website":79,"owner_url":80,"languages":81,"stars":86,"forks":87,"last_commit_at":88,"license":89,"difficulty_score":23,"env_os":90,"env_gpu":90,"env_ram":90,"env_deps":91,"category_tags":94,"github_topics":95,"view_count":23,"oss_zip_url":79,"oss_zip_packed_at":79,"status":16,"created_at":105,"updated_at":106,"faqs":107,"releases":128},1974,"RubixML\u002FML","ML","A high-level machine learning and deep learning library for the PHP language.","Rubix ML 是专为 PHP 开发者打造的开源机器学习库，让 PHP 项目无缝集成 AI 能力。它提供 40 多种监督与非监督算法，覆盖数据预处理、模型训练、交叉验证到部署的全流程，支持 Tensor 扩展加速矩阵运算。开发者无需切换语言，即可在 PHP 环境中快速构建图像识别、客户流失预测、文本分析等应用。适合熟悉 PHP 的后端工程师、数据科学家及需要将机器学习嵌入现有系统的团队。文档详实，附带多个实战案例，开源免费且商业可用，上手门槛低。","# Rubix ML\n\n[![PHP from Packagist](https:\u002F\u002Fimg.shields.io\u002Fpackagist\u002Fphp-v\u002Frubix\u002Fml.svg?style=flat&colorB=8892BF)](https:\u002F\u002Fwww.php.net\u002F) [![Latest Stable Version](https:\u002F\u002Fimg.shields.io\u002Fpackagist\u002Fv\u002Frubix\u002Fml.svg?style=flat&colorB=orange)](https:\u002F\u002Fpackagist.org\u002Fpackages\u002Frubix\u002Fml) [![Downloads from Packagist](https:\u002F\u002Fimg.shields.io\u002Fpackagist\u002Fdt\u002Frubix\u002Fml.svg?style=flat&colorB=red)](https:\u002F\u002Fpackagist.org\u002Fpackages\u002Frubix\u002Fml) [![Code Checks](https:\u002F\u002Fgithub.com\u002FRubixML\u002FML\u002Factions\u002Fworkflows\u002Fci.yml\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002FRubixML\u002FML\u002Factions\u002Fworkflows\u002Fci.yml) [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002FRubixML\u002FRubixML)](https:\u002F\u002Fgithub.com\u002FRubixML\u002FML\u002Fblob\u002Fmaster\u002FLICENSE.md)\n\nA high-level machine learning and deep learning library for the [PHP](https:\u002F\u002Fphp.net) language.\n\n- **Developer-friendly** API is delightful to use\n- **40+** supervised and unsupervised learning algorithms\n- **Support** for ETL, preprocessing, and cross-validation\n- **Open source** and free to use commercially\n\n## Installation\nInstall Rubix ML into your project using [Composer](https:\u002F\u002Fgetcomposer.org\u002F):\n```sh\ncomposer require rubix\u002Fml\n```\n\n### Requirements\n- [PHP](https:\u002F\u002Fphp.net\u002Fmanual\u002Fen\u002Finstall.php) 7.4 or above\n\n#### Recommended\n- [Tensor extension](https:\u002F\u002Fgithub.com\u002FRubixML\u002FTensor) for fast Matrix\u002FVector computing\n\n#### Optional\n\n- [GD extension](https:\u002F\u002Fphp.net\u002Fmanual\u002Fen\u002Fbook.image.php) for image support\n- [Mbstring extension](https:\u002F\u002Fwww.php.net\u002Fmanual\u002Fen\u002Fbook.mbstring.php) for fast multibyte string manipulation\n- [SVM extension](https:\u002F\u002Fphp.net\u002Fmanual\u002Fen\u002Fbook.svm.php) for Support Vector Machine engine (libsvm)\n- [PDO extension](https:\u002F\u002Fwww.php.net\u002Fmanual\u002Fen\u002Fbook.pdo.php) for relational database support\n- [GraphViz](https:\u002F\u002Fgraphviz.org\u002F) for graph visualization\n\n## Documentation\nRead the latest docs [here](https:\u002F\u002Frubixml.github.io\u002FML\u002Flatest\u002F).\n\n## What is Rubix ML?\nRubix ML is a free open-source machine learning (ML) library that allows you to build programs that learn from your data using the PHP language. We provide tools for the entire machine learning life cycle from ETL to training, cross-validation, and production with over 40 supervised and unsupervised learning algorithms. In addition, we provide tutorials and other educational content to help you get started using ML in your projects.\n\n## Getting Started\nIf you are new to machine learning, we recommend taking a look at the [What is Machine Learning?](https:\u002F\u002Frubixml.github.io\u002FML\u002Flatest\u002Fwhat-is-machine-learning.html) section to get started. If you are already familiar with basic ML concepts, you can browse the [basic introduction](https:\u002F\u002Frubixml.github.io\u002FML\u002Flatest\u002Fbasic-introduction.html) for a brief look at a typical Rubix ML project. From there, you can browse the official tutorials below which range from beginner to advanced skill level.\n\n### Tutorials & Example Projects\nCheck out these example projects using the Rubix ML library. Many come with instructions and a pre-cleaned dataset.\n\n- [CIFAR-10 Image Recognizer](https:\u002F\u002Fgithub.com\u002FRubixML\u002FCIFAR-10)\n- [Color Clusterer](https:\u002F\u002Fgithub.com\u002FRubixML\u002FColors)\n- [Credit Default Risk Predictor](https:\u002F\u002Fgithub.com\u002FRubixML\u002FCredit)\n- [Customer Churn Predictor](https:\u002F\u002Fgithub.com\u002FRubixML\u002FChurn)\n- [Divorce Predictor](https:\u002F\u002Fgithub.com\u002FRubixML\u002FDivorce)\n- [DNA Taxonomer](https:\u002F\u002Fgithub.com\u002FRubixML\u002FDNA)\n- [Dota 2 Game Outcome Predictor](https:\u002F\u002Fgithub.com\u002FRubixML\u002FDota2)\n- [Human Activity Recognizer](https:\u002F\u002Fgithub.com\u002FRubixML\u002FHAR)\n- [Housing Price Predictor](https:\u002F\u002Fgithub.com\u002FRubixML\u002FHousing)\n- [Iris Flower Classifier](https:\u002F\u002Fgithub.com\u002FRubixML\u002FIris)\n- [MNIST Handwritten Digit Recognizer](https:\u002F\u002Fgithub.com\u002FRubixML\u002FMNIST)\n- [Text Sentiment Analyzer](https:\u002F\u002Fgithub.com\u002FRubixML\u002FSentiment)\n- [Titanic Survival Predictor](https:\u002F\u002Fgithub.com\u002FJenutka\u002Ftitanic_php)\n\n## Interact With The Community\n\n- [Join Our Telegram Channel](https:\u002F\u002Ft.me\u002FRubixML)\n\n## Contributing\nSee [CONTRIBUTING.md](CONTRIBUTING.md) for guidelines.\n\n## License\nThe code is licensed [MIT](LICENSE) and the documentation is licensed [CC BY-NC 4.0](https:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby-nc\u002F4.0\u002F).\n","# Rubix ML\n\n[![PHP from Packagist](https:\u002F\u002Fimg.shields.io\u002Fpackagist\u002Fphp-v\u002Frubix\u002Fml.svg?style=flat&colorB=8892BF)](https:\u002F\u002Fwww.php.net\u002F) [![Latest Stable Version](https:\u002F\u002Fimg.shields.io\u002Fpackagist\u002Fv\u002Frubix\u002Fml.svg?style=flat&colorB=orange)](https:\u002F\u002Fpackagist.org\u002Fpackages\u002Frubix\u002Fml) [![Downloads from Packagist](https:\u002F\u002Fimg.shields.io\u002Fpackagist\u002Fdt\u002Frubix\u002Fml.svg?style=flat&colorB=red)](https:\u002F\u002Fpackagist.org\u002Fpackages\u002Frubix\u002Fml) [![Code Checks](https:\u002F\u002Fgithub.com\u002FRubixML\u002FML\u002Factions\u002Fworkflows\u002Fci.yml\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002FRubixML\u002FML\u002Factions\u002Fworkflows\u002Fci.yml) [![GitHub](https:\u002F\u002Fimg.shields.io\u002Fgithub\u002Flicense\u002FRubixML\u002FRubixML)](https:\u002F\u002Fgithub.com\u002FRubixML\u002FML\u002Fblob\u002Fmaster\u002FLICENSE.md)\n\n一个面向[PHP](https:\u002F\u002Fphp.net)语言的高级机器学习与深度学习库。\n\n- **开发者友好** 的API使用起来令人愉悦\n- **40多种** 监督与非监督学习算法\n- **支持** ETL、预处理与交叉验证\n- **开源** 并可免费用于商业用途\n\n## 安装\n使用[Composer](https:\u002F\u002Fgetcomposer.org\u002F)将Rubix ML安装到您的项目中：\n```sh\ncomposer require rubix\u002Fml\n```\n\n### 要求\n- [PHP](https:\u002F\u002Fphp.net\u002Fmanual\u002Fen\u002Finstall.php) 7.4或更高版本\n\n#### 推荐\n- [Tensor扩展](https:\u002F\u002Fgithub.com\u002FRubixML\u002FTensor)以实现快速矩阵\u002F向量计算\n\n#### 可选\n\n- [GD扩展](https:\u002F\u002Fphp.net\u002Fmanual\u002Fen\u002Fbook.image.php)以支持图像处理\n- [Mbstring扩展](https:\u002F\u002Fwww.php.net\u002Fmanual\u002Fen\u002Fbook.mbstring.php)以实现快速多字节字符串操作\n- [SVM扩展](https:\u002F\u002Fphp.net\u002Fmanual\u002Fen\u002Fbook.svm.php)以支持向量机引擎（libsvm）\n- [PDO扩展](https:\u002F\u002Fwww.php.net\u002Fmanual\u002Fen\u002Fbook.pdo.php)以支持关系型数据库\n- [GraphViz](https:\u002F\u002Fgraphviz.org\u002F)以进行图可视化\n\n## 文档\n请阅读最新的文档[这里](https:\u002F\u002Frubixml.github.io\u002FML\u002Flatest\u002F)。\n\n## Rubix ML是什么？\nRubix ML是一个免费的开源机器学习（ML）库，允许您使用PHP语言构建从数据中学习的程序。我们提供了从ETL到训练、交叉验证以及生产的整个机器学习生命周期所需的工具，涵盖超过40种监督与非监督学习算法。此外，我们还提供教程和其他教育内容，帮助您在项目中快速上手ML。\n\n## 开始使用\n如果您是机器学习新手，我们建议您先查看[什么是机器学习？](https:\u002F\u002Frubixml.github.io\u002FML\u002Flatest\u002Fwhat-is-machine-learning.html)部分以入门。如果您已经熟悉基本的ML概念，可以浏览[基础介绍](https:\u002F\u002Frubixml.github.io\u002FML\u002Flatest\u002Fbasic-introduction.html)，了解典型的Rubix ML项目。之后，您可以继续浏览以下官方教程，这些教程涵盖了从初级到高级的不同水平。\n\n### 教程与示例项目\n看看这些使用Rubix ML库的示例项目。许多项目都附带了说明和预先清理好的数据集。\n\n- [CIFAR-10图像识别器](https:\u002F\u002Fgithub.com\u002FRubixML\u002FCIFAR-10)\n- [颜色聚类器](https:\u002F\u002Fgithub.com\u002FRubixML\u002FColors)\n- [信用违约风险预测器](https:\u002F\u002Fgithub.com\u002FRubixML\u002FCredit)\n- [客户流失预测器](https:\u002F\u002Fgithub.com\u002FRubixML\u002FChurn)\n- [离婚预测器](https:\u002F\u002Fgithub.com\u002FRubixML\u002FDivorce)\n- [DNA分类器](https:\u002F\u002Fgithub.com\u002FRubixML\u002FDNA)\n- [Dota 2游戏结果预测器](https:\u002F\u002Fgithub.com\u002FRubixML\u002FDota2)\n- [人体活动识别器](https:\u002F\u002Fgithub.com\u002FRubixML\u002FHAR)\n- [房价预测器](https:\u002F\u002Fgithub.com\u002FRubixML\u002FHousing)\n- [鸢尾花分类器](https:\u002F\u002Fgithub.com\u002FRubixML\u002FIris)\n- [MNIST手写数字识别器](https:\u002F\u002Fgithub.com\u002FRubixML\u002FMNIST)\n- [文本情感分析器](https:\u002F\u002Fgithub.com\u002FRubixML\u002FSentiment)\n- [泰坦尼克号生存预测器](https:\u002F\u002Fgithub.com\u002FJenutka\u002Ftitanic_php)\n\n## 与社区互动\n\n- [加入我们的Telegram频道](https:\u002F\u002Ft.me\u002FRubixML)\n\n## 贡献\n请参阅[CONTRIBUTING.md](CONTRIBUTING.md)以获取指南。\n\n## 许可\n代码采用[MIT](LICENSE)许可，文档采用[CC BY-NC 4.0](https:\u002F\u002Fcreativecommons.org\u002Flicenses\u002Fby-nc\u002F4.0\u002F)许可。","# Rubix ML 快速上手指南\n\n## 环境准备\n- **系统要求**：PHP 7.4 或更高版本  \n- **推荐依赖**：  \n  - [Tensor 扩展](https:\u002F\u002Fgithub.com\u002FRubixML\u002FTensor)（加速矩阵运算）  \n- **可选依赖**（按需安装）：  \n  - GD（图像处理）  \n  - Mbstring（多字节字符串处理）  \n  - SVM（支持向量机引擎）  \n  - PDO（数据库支持）  \n  - GraphViz（图表可视化）  \n\n## 安装步骤\n```sh\n# 安装 Rubix ML\ncomposer require rubix\u002Fml\n\n# 国内用户推荐使用阿里云镜像加速\ncomposer config -g repo.packagist composer https:\u002F\u002Fmirrors.aliyun.com\u002Fcomposer\u002F\n```\n\n## 基本使用\n```php\n\u003C?php\nrequire 'vendor\u002Fautoload.php';\n\nuse Rubix\\ML\\Classifiers\\KNearestNeighbors;\nuse Rubix\\ML\\Datasets\\Generators\\RandomClassification;\n\n\u002F\u002F 生成随机数据集（100条样本，3个特征，2个类别）\n$generator = new RandomClassification(100, 3, 2);\n$dataset = $generator->generate();\n\n\u002F\u002F 创建并训练 KNN 模型\n$estimator = new KNearestNeighbors(3);\n$estimator->train($dataset);\n\n\u002F\u002F 预测第一条数据\n$prediction = $estimator->predict($dataset->slice(0, 1));\nprint_r($prediction);\n```","某电商公司的产品经理小李，负责分析客户行为数据以识别高流失风险客户，每月处理数万条记录，需快速制定挽留策略。\n\n### 没有 Rubix ML 时  \n- 手动处理数据效率低下：每次分析需导出CSV用Excel处理，耗时数小时，且Excel易崩溃，数据清洗错误频发。  \n- 缺乏精准预测模型：仅依赖简单规则（如“30天未登录”），误报率高达40%，无法捕捉复杂行为模式。  \n- 集成困难：想将预测功能嵌入PHP网站，但PHP无原生ML支持，需调用Python服务，增加系统复杂度和维护成本。  \n- 模型更新繁琐：重新训练模型时需手动操作，无法自动化，导致预测结果滞后，影响决策时效。  \n\n### 使用 Rubix ML 后  \n- 数据处理自动化：使用Rubix ML的ETL工具，一键清洗和转换数据，处理时间缩短至10分钟，错误率降至1%以下。  \n- 高精度预测：应用随机森林算法，准确率从60%提升至85%，精准识别流失风险客户。  \n- 原生PHP集成：直接在PHP代码中训练和调用模型，无需外部依赖，系统部署更简单，开发效率提升50%。  \n- 定期自动训练：通过定时脚本每周自动更新模型，确保预测实时有效，支持快速业务响应。  \n\nRubix ML让PHP开发者轻松构建智能预测系统，显著提升客户留存率和运营效率。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FRubixML_ML_4c8c770f.png","RubixML","Rubix ML","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002FRubixML_c3b7efd1.png","Machine Learning and Deep Learning for the PHP language.",null,"https:\u002F\u002Fgithub.com\u002FRubixML",[82],{"name":83,"color":84,"percentage":85},"PHP","#4F5D95",100,2193,195,"2026-04-03T11:32:31","MIT","未说明",{"notes":92,"python":90,"dependencies":93},"该工具为PHP机器学习库，无需Python环境，需安装PHP 7.4+及相应扩展（如Tensor、GD、Mbstring等），具体依赖见Requirements部分。",[],[26,13],[96,97,98,99,100,101,102,103,104],"machine-learning","analytics","php","classification","clustering","regression","anomaly-detection","deep-learning","natural-language-processing","2026-03-27T02:49:30.150509","2026-04-06T06:44:02.870349",[108,113,118,123],{"id":109,"question_zh":110,"answer_zh":111,"source_url":112},8902,"训练多层感知器分类器时出现'Undefined offset'错误，如何解决？","该错误由学习率过高导致数值下溢\u002F上溢，产生NaN值。解决方案：降低学习率（例如，Adam优化器学习率降低10倍），并确保捕获NaN值。具体步骤：在优化器配置中减小学习率，如从0.001降至0.0001。","https:\u002F\u002Fgithub.com\u002FRubixML\u002FML\u002Fissues\u002F64",{"id":114,"question_zh":115,"answer_zh":116,"source_url":117},8903,"部分训练高斯朴素贝叶斯模型时出现'Undefined offset'错误，如何解决？","该错误通常由词汇量过大引起。解决方案：将词汇量限制为2的幂次方（如1024、2048）以减少内存浪费。具体步骤：在WordCountVectorizer中设置词汇大小，例如new WordCountVectorizer(2048, 1, new NGram(1, 2, new WordStemmer('romanian')));","https:\u002F\u002Fgithub.com\u002FRubixML\u002FML\u002Fissues\u002F83",{"id":119,"question_zh":120,"answer_zh":121,"source_url":122},8904,"彩票号码数据是否适合机器学习训练？","彩票号码是随机的，不可预测，不适合机器学习训练。应避免使用随机特征，选择有明确模式的问题，如分类或回归任务，确保特征与标签有逻辑关联。","https:\u002F\u002Fgithub.com\u002FRubixML\u002FML\u002Fissues\u002F28",{"id":124,"question_zh":125,"answer_zh":126,"source_url":127},8905,"安装RubixML时出现'could not find a version matching minimum stability'错误，如何解决？","在composer.json中设置'minimum-stability'为'dev'，或直接使用命令`composer require rubix\u002Fml:dev-master`。具体步骤：编辑composer.json，添加\"minimum-stability\": \"dev\"，然后运行composer update；或直接执行`composer require rubix\u002Fml:dev-master`。","https:\u002F\u002Fgithub.com\u002FRubixML\u002FML\u002Fissues\u002F93",[129,134,139,144,149,154,159,164,169,174,178,183,188,193,198,203,208,212,217,222],{"id":130,"version":131,"summary_zh":132,"released_at":133},106305,"2.5.3","- Update PHP Stemmer to version 4.0","2025-10-01T17:56:53",{"id":135,"version":136,"summary_zh":137,"released_at":138},106306,"2.5.2","- Fix bug in One-class SVM inferencing","2024-11-09T23:07:34",{"id":140,"version":141,"summary_zh":142,"released_at":143},106307,"2.5.1","- Fix bug in SVM (SVC and SVR) inferencing","2024-08-31T22:58:23",{"id":145,"version":146,"summary_zh":147,"released_at":148},106308,"2.5.0","- Added Vantage Point Spatial tree\r\n- Blob Generator can now `simulate()` a Dataset object\r\n- Added Wrapper interface\r\n- Plus Plus added check for min number of sample seeds\r\n- LOF prevent div by 0 local reachability density","2024-05-23T17:45:35",{"id":150,"version":151,"summary_zh":152,"released_at":153},106309,"2.4.0","- Add GELU activation function\r\n- Add `numParams()` method to Network\r\n- Neural Network Learners now report number of trainable parameters\r\n- Regex Filter added pattern to match unicode emojis\r\n- Custom escape character for CSV Extractor","2023-05-26T16:55:41",{"id":155,"version":156,"summary_zh":157,"released_at":158},106310,"2.3.2","- Optimize Adam and AdaMax Optimizers","2023-05-13T19:14:15",{"id":160,"version":161,"summary_zh":162,"released_at":163},106311,"2.3.1","- Fix PSR-3 log version compatibility issue\r\n- Check for the correct version of RBX format","2023-03-07T01:56:37",{"id":165,"version":166,"summary_zh":167,"released_at":168},106312,"2.3.0","- Added BM25 Transformer\r\n- Add `dropFeature()` method to the dataset object API\r\n- Add neural network architecture visualization via GraphViz","2022-12-31T01:33:49",{"id":170,"version":171,"summary_zh":172,"released_at":173},106313,"2.2.2","- Fix Grid Search best model selection","2022-12-06T22:56:24",{"id":175,"version":176,"summary_zh":172,"released_at":177},106314,"1.3.5","2022-12-06T23:01:38",{"id":179,"version":180,"summary_zh":181,"released_at":182},106315,"2.2.1","- Fix Extra Tree divide by zero when split finding","2022-10-15T20:15:21",{"id":184,"version":185,"summary_zh":186,"released_at":187},106316,"0.4.3","- Update to Flysystem 2.1 and above","2022-10-06T20:29:28",{"id":189,"version":190,"summary_zh":191,"released_at":192},106317,"2.2.0","- Added Image Rotator transformer\r\n- Added One Vs Rest ensemble classifier\r\n- Add variance and range to the Dataset `describe()` report\r\n- Added Gower distance kernel\r\n- Added `types()` method to Dataset\r\n- Concatenator now accepts an iterator of iterators","2022-10-01T17:58:24",{"id":194,"version":195,"summary_zh":196,"released_at":197},106318,"2.1.1","- Do not consider unset properties when determining revision","2022-09-13T00:10:06",{"id":199,"version":200,"summary_zh":201,"released_at":202},106319,"2.1.0","Big thanks to @torchello and @DrDub for their huge contributions to this release!\r\n\r\n- Added Probabilistic Metric interface\r\n- Added Probabilistic and Top K Accuracy\r\n- Added Brier Score Probabilistic Metric\r\n- Export Decision Tree-based models in Graphviz \"dot\" format\r\n- Added Graphviz helper class\r\n- Graph subsystem memory and storage optimizations\r\n\r\n**Warning**: This release contains changes to the Graph subsystem which breaks backward compatibility for all Decision tree-based learners that were saved with a previous version. Classification Tree, Extra Tree Classifiers, Random Forests, LogitBoost, Adaboost, Regression Tree, Extra Tree Regressor, and Gradient Boost are all affected.\r\n\r\n**Note**: Moving forward, we will only release changes that break the backward compatibility of saved objects in a major release unless they are part of a bug fix. See https:\u002F\u002Fdocs.rubixml.com\u002F2.0\u002Fmodel-persistence.html#caveats for an explanation as to why saved objects are not as straightforward to maintain backward compatibility as the API.","2022-07-30T19:52:38",{"id":204,"version":205,"summary_zh":206,"released_at":207},106320,"2.0.2","- Fix Decision Tree max height terminating condition","2022-06-03T23:14:08",{"id":209,"version":210,"summary_zh":206,"released_at":211},106321,"1.3.4","2022-06-03T23:11:45",{"id":213,"version":214,"summary_zh":215,"released_at":216},106322,"2.0.1","- Compensate for PHP 8.1 backward compatibility issues","2022-04-03T07:15:03",{"id":218,"version":219,"summary_zh":220,"released_at":221},106323,"2.0.0","- Gradient Boost now uses gradient-based subsampling\r\n- Allow Token Hashing Vectorizer custom hash functions\r\n- Gradient Boost base estimator no longer configurable\r\n- Move dummy estimators to the Extras package\r\n- Increase default MLP window from 3 to 5\r\n- Decrease default Gradient Boost window from 10 to 5\r\n- Rename alpha regularization parameter to L2 penalty\r\n- Added RBX serializer class property type change detection\r\n- Rename boosting estimators param to epochs\r\n- Neural net-based learners can now train for 0 epochs\r\n- Rename Labeled stratify() to stratifyByLabel()\r\n- Added Sparse Cosine distance kernel\r\n- Cosine distance now optimized for dense and sparse vectors\r\n- Word Count Vectorizer now uses min count and max ratio DFs\r\n- Numeric String Converter now handles NAN and INFs\r\n- Numeric String Converter is now Reversible\r\n- Removed Numeric String Converter NAN_PLACEHOLDER constant\r\n- Added MurmurHash3 and FNV1a 32-bit hashing functions to Token Hashing Vectorizer\r\n- Changed Token Hashing Vectorizer max dimensions to 2,147,483,647\r\n- Increase SQL Table Extractor batch size from 100 to 256\r\n- Ranks Features interface no longer extends Stringable\r\n- Verbose Learners now log change in loss\r\n- Numerical instability logged as a warning instead of info\r\n- Added header() method to CSV and SQL Table Extractors\r\n- Argmax() now throws an exception when undefined\r\n- MLP Learners recover from numerical instability with a snapshot\r\n- Rename Gzip serializer to Gzip Native\r\n- Change RBX serializer constructor argument from base to level\r\n- Rename Writeable extractor interface to Exporter","2022-03-30T01:59:55",{"id":223,"version":224,"summary_zh":225,"released_at":226},106324,"1.3.2","- Forego unnecessary logistic computation in Logit Boost","2022-02-22T01:50:39"]