{"id":13595501,"url":"https://github.com/Dod-o/Statistical-Learning-Method_Code","last_synced_at":"2025-04-09T13:32:23.701Z","repository":{"id":37678444,"uuid":"157733401","full_name":"Dod-o/Statistical-Learning-Method_Code","owner":"Dod-o","description":"手写实现李航《统计学习方法》书中全部算法","archived":false,"fork":false,"pushed_at":"2025-03-30T15:08:23.000Z","size":100255,"stargazers_count":11297,"open_issues_count":19,"forks_count":2893,"subscribers_count":310,"default_branch":"master","last_synced_at":"2025-04-02T20:01:19.183Z","etag":null,"topics":["code","machine-learning-algorithms","statistical-learning-method"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/Dod-o.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2018-11-15T15:33:17.000Z","updated_at":"2025-04-02T11:57:50.000Z","dependencies_parsed_at":"2022-07-14T08:49:31.186Z","dependency_job_id":"67b9e458-e51e-45d5-9e93-07fe59d3ca25","html_url":"https://github.com/Dod-o/Statistical-Learning-Method_Code","commit_stats":{"total_commits":125,"total_committers":4,"mean_commits":31.25,"dds":"0.20799999999999996","last_synced_commit":"d86abd830fe9455ae128205413063989714a683e"},"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Dod-o%2FStatistical-Learning-Method_Code","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Dod-o%2FStatistical-Learning-Method_Code/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Dod-o%2FStatistical-Learning-Method_Code/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/Dod-o%2FStatistical-Learning-Method_Code/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/Dod-o","download_url":"https://codeload.github.com/Dod-o/Statistical-Learning-Method_Code/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248049441,"owners_count":21039219,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["code","machine-learning-algorithms","statistical-learning-method"],"created_at":"2024-08-01T16:01:51.250Z","updated_at":"2025-04-09T13:32:18.684Z","avatar_url":"https://github.com/Dod-o.png","language":"Python","funding_links":[],"categories":["Python","A01_机器学习教程","Math / Algorithm"],"sub_categories":[],"readme":"## 【广告】每日Arxiv（中文版）\n每日Arxiv（中文版）立志paper**汉化**，目前翻译目前涵盖**标题**和**摘要**，AI学科近期支持论文**全文汉化**\n\n一天阅读百篇paper不是梦！\n\n链接： [学术巷子(xueshuxiangzi.com)](https://www.xueshuxiangzi.com/)\n\n\n前言\n====\n\n力求每行代码都有注释，重要部分注明公式来源。具体会追求下方这样的代码，学习者可以照着公式看程序，让代码有据可查。\n\n![image](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/CodePic.png)\n\n    \n如果时间充沛的话，可能会试着给每一章写一篇博客。先放个博客链接吧：[传送门](http://www.pkudodo.com/)。    \n\n##### 注：其中Mnist数据集已转换为csv格式，由于体积为107M超过限制，改为压缩包形式。下载后务必先将Mnist文件内压缩包直接解压。  \n\n### 【Updates】\n**书籍出版**：目前已与**人民邮电出版社**签订合同，未来将结合该repo整理出版机器学习实践相关书籍。同时会在book分支中对代码进行重构，欢迎在issue中提建议！同时issue中现有的问题也会考虑进去。（Feb 12 2022）\n\n**线下培训**：女朋友计划近期开办**ML/MLP/CV线下培训班**，地点**北上广深杭**，目标各方向**快速入门**，正在筹备。这里帮她打个广告，可以添加微信15324951814（备注线下培训）。本人也会被拉过去义务评估课程质量。。。（Feb 12 2022）\n\n**无监督部分更新**：部分**无监督**算法已更新！！！ 该部分由[Harold-Ran](https://github.com/Harold-Ran)提供，在此感谢！ 有其他算法补充的同学也欢迎添加我微信并pr！（Jan 27 2021）\n       \n实现\n======\n\n## 监督部分\n\n### 第二章 感知机：\n博客：[统计学习方法|感知机原理剖析及实现](http://www.pkudodo.com/2018/11/18/1-4/)      \n实现：[perceptron/perceptron_dichotomy.py](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/perceptron/perceptron_dichotomy.py)\n      \n### 第三章 K近邻：\n博客：[统计学习方法|K近邻原理剖析及实现](http://www.pkudodo.com/2018/11/19/1-2/)      \n实现：[KNN/KNN.py](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/KNN/KNN.py)\n      \n### 第四章 朴素贝叶斯：\n博客：[统计学习方法|朴素贝叶斯原理剖析及实现](http://www.pkudodo.com/2018/11/21/1-3/)      \n实现：[NaiveBayes/NaiveBayes.py](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/NaiveBayes/NaiveBayes.py)    \n      \n### 第五章 决策树：\n博客：[统计学习方法|决策树原理剖析及实现](http://www.pkudodo.com/2018/11/30/1-5/)      \n实现：[DecisionTree/DecisionTree.py](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/DecisionTree/DecisionTree.py)    \n      \n### 第六章 逻辑斯蒂回归与最大熵模型：       \n博客：逻辑斯蒂回归：[统计学习方法|逻辑斯蒂原理剖析及实现](http://www.pkudodo.com/2018/12/03/1-6/)        \n博客：最大熵：[统计学习方法|最大熵原理剖析及实现](http://www.pkudodo.com/2018/12/05/1-7/)        \n\n实现：逻辑斯蒂回归：[Logistic_and_maximum_entropy_models/logisticRegression.py](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/Logistic_and_maximum_entropy_models/logisticRegression.py)    \n实现：最大熵：[Logistic_and_maximum_entropy_models/maxEntropy.py](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/Logistic_and_maximum_entropy_models/maxEntropy.py)       \n      \n### 第七章 支持向量机：    \n博客：[统计学习方法|支持向量机(SVM)原理剖析及实现](http://www.pkudodo.com/2018/12/16/1-8/)      \n实现：[SVM/SVM.py](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/SVM/SVM.py)    \n      \n### 第八章 提升方法：\n实现：[AdaBoost/AdaBoost.py](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/AdaBoost/AdaBoost.py)    \n      \n### 第九章 EM算法及其推广：\n实现：[EM/EM.py](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/EM/EM.py)    \n      \n### 第十章 隐马尔可夫模型：\n实现：[HMM/HMM.py](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/HMM/HMM.py)    \n\n## 无监督部分\n\n### 第十四章 聚类方法\n实现：[K-means_Clustering.py](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/Clustering/K-means_Clustering/K-means_Clustering.py)\n\n实现：[Hierachical_Clustering.py](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/Clustering/Hierachical_Clustering/Hierachical_Clustering.py)\n\n### 第十六章 主成分分析\n实现：[PCA.py](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/PCA/PCA.py)\n\n### 第十七章 潜在语意分析\n实现：[LSA.py](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/LSA/LSA.py)\n\n### 第十八章 概率潜在语意分析\n实现：[PLSA.py](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/PLSA/PLSA.py)\n\n### 第二十章 潜在狄利克雷分配\n实现：[LDA.py](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/LDA/LDA.py)\n\n### 第二十一章 PageRank算法\n实现：[Page_Rank.py](https://github.com/Dod-o/Statistical-Learning-Method_Code/blob/master/Page_Rank/Page_Rank.py)\n\n\n\n\n## 许可 / License\n本项目内容许可遵循[Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)。\n\nThe content of this project itself is licensed under the [Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/)\n\n\n联系\n======\n欢迎pr，有疑问也可通过issue、微信或邮件联系。      \n此外如果有需要**MSRA**实习内推的同学，欢迎骚扰。             \n**Wechat:** lvtengchao（备注“blog-学校/单位-姓名”）      \n**Email:** lvtengchao@pku.edu.cn      \n      \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FDod-o%2FStatistical-Learning-Method_Code","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FDod-o%2FStatistical-Learning-Method_Code","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FDod-o%2FStatistical-Learning-Method_Code/lists"}