{"id":22014143,"url":"https://github.com/laqudee/pca","last_synced_at":"2025-03-23T08:27:09.646Z","repository":{"id":146793057,"uuid":"126585881","full_name":"laqudee/PCA","owner":"laqudee","description":"模式识别中的PCA实验，python","archived":false,"fork":false,"pushed_at":"2018-06-06T14:45:14.000Z","size":1192,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"master","last_synced_at":"2025-01-28T15:18:53.324Z","etag":null,"topics":["pca","python3","tutorial"],"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/laqudee.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":"2018-03-24T09:48:59.000Z","updated_at":"2023-03-17T01:52:03.000Z","dependencies_parsed_at":"2023-04-30T19:17:07.002Z","dependency_job_id":null,"html_url":"https://github.com/laqudee/PCA","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/laqudee%2FPCA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/laqudee%2FPCA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/laqudee%2FPCA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/laqudee%2FPCA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/laqudee","download_url":"https://codeload.github.com/laqudee/PCA/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245075320,"owners_count":20556907,"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":["pca","python3","tutorial"],"created_at":"2024-11-30T03:26:28.465Z","updated_at":"2025-03-23T08:27:09.618Z","avatar_url":"https://github.com/laqudee.png","language":"Python","readme":"# PCA\n模式识别中的PCA实验，python\n\npython_codes保存的是实验代码\n\n\n运行结果图像集保存的是实验中代码运行的结果截图和图片，每运行一次结果都是不一样的，因为训练集是随机产生的！注意\n\n\n\n参考教程保存的是实验中参考的书，有教程还有实验指导。\n\n\n\n参考只作为练习实验了Lindsay I Smith的《A tutorial on Principal Components Analysis》，邓世文老师的《模式识别教案》第6章和Sebastian Raschka的Implementing a Principal Component Analysis (PCA)\n\n\n\n动手胜过一切！\n\n\n\n以上三位的书和教案只作练习实验使用，没有任何商业目的！\n\n\n\n\n\n附Sebastian Raschka的Implementing a Principal Component Analysis (PCA)的网址：\nhttp://sebastianraschka.com/Articles/2014_pca_step_by_step.html#generating-some-3-dimensional-sample-data\n\n\n\n刚加入了数据的标准化代码，自己编写，代码简单、粗糙，主要利用均差和方差来标准数据。另外，采用softmax()标准数据，代码编写貌似出现错误。代码：数据标准化_java文件夹。\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flaqudee%2Fpca","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flaqudee%2Fpca","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flaqudee%2Fpca/lists"}