From 5ea5e3cba10358a704376e01bed03b28cef4ddbb Mon Sep 17 00:00:00 2001 From: shenjianZ Date: Sun, 19 Jan 2025 17:11:00 +0800 Subject: [PATCH] add linear regression details --- db.json | 2 +- public/about/index.html | 6 +- public/archives/2024/08/index.html | 4 +- public/archives/2024/09/index.html | 4 +- public/archives/2024/index.html | 4 +- public/archives/2024/page/2/index.html | 4 +- public/archives/2025/01/index.html | 4 +- public/archives/2025/index.html | 4 +- public/archives/index.html | 4 +- public/archives/page/2/index.html | 4 +- public/categories/index.html | 2 +- public/categories/古文观止/index.html | 4 +- public/img/language/c-env-conf.png | Bin 0 -> 129515 bytes public/img/machinelearning/fitting.png | Bin 0 -> 101858 bytes public/img/machinelearning/linear.png | Bin 0 -> 126160 bytes public/index.html | 72 ++--- public/link/index.html | 6 +- public/movies/index.html | 4 +- public/music/index.html | 4 +- public/page/2/index.html | 36 ++- public/posts/12462.html | 172 ++++++++++ public/posts/14011.html | 4 +- public/posts/1441.html | 4 +- public/posts/16107.html | 4 +- public/posts/29139.html | 4 +- public/posts/33957.html | 4 +- public/posts/34849.html | 4 +- public/posts/41168.html | 2 +- public/posts/47807.html | 4 +- public/posts/52662.html | 222 +++++++++++++ public/posts/58638.html | 4 +- public/posts/58817.html | 4 +- public/posts/61251.html | 4 +- public/posts/61252.html | 4 +- public/posts/61253.html | 4 +- public/search.xml | 293 +++++++++++------- public/tags/C-C/index.html | 158 ++++++++++ public/tags/index.html | 4 +- public/tags/linear-regression/index.html | 158 ++++++++++ public/tags/machinelearning/index.html | 4 +- public/tags/uniapp/index.html | 4 +- public/tags/古文观止/index.html | 4 +- public/tags/网络代理/index.html | 4 +- source/_posts/language/C.md | 81 +++++ .../_posts/machinelearning/linearreression.md | 199 ++++++++++++ source/img/language/c-env-conf.png | Bin 0 -> 129515 bytes source/img/machinelearning/fitting.png | Bin 0 -> 101858 bytes source/img/machinelearning/linear.png | Bin 0 -> 126160 bytes 48 files changed, 1292 insertions(+), 229 deletions(-) create mode 100644 public/img/language/c-env-conf.png create mode 100644 public/img/machinelearning/fitting.png create mode 100644 public/img/machinelearning/linear.png create mode 100644 public/posts/12462.html create mode 100644 public/posts/52662.html create mode 100644 public/tags/C-C/index.html create mode 100644 public/tags/linear-regression/index.html create mode 100644 source/_posts/language/C.md create mode 100644 source/_posts/machinelearning/linearreression.md create mode 100644 source/img/language/c-env-conf.png create mode 100644 source/img/machinelearning/fitting.png create mode 100644 source/img/machinelearning/linear.png diff --git a/db.json b/db.json index 7e9769f..ccb6db6 100644 --- a/db.json +++ b/db.json @@ -1 +1 @@ -{"meta":{"version":1,"warehouse":"5.0.1"},"models":{"Asset":[{"_id":"themes/butterfly/source/css/index.styl","path":"css/index.styl","modified":1,"renderable":1},{"_id":"themes/butterfly/source/css/var.styl","path":"css/var.styl","modified":1,"renderable":1},{"_id":"themes/butterfly/source/img/404.jpg","path":"img/404.jpg","modified":1,"renderable":1},{"_id":"themes/butterfly/source/img/avatar.jpg","path":"img/avatar.jpg","modified":1,"renderable":1},{"_id":"themes/butterfly/source/img/friend_404.gif","path":"img/friend_404.gif","modified":1,"renderable":1},{"_id":"themes/butterfly/source/img/favicon.png","path":"img/favicon.png","modified":1,"renderable":1},{"_id":"themes/butterfly/source/js/main.js","path":"js/main.js","modified":1,"renderable":1},{"_id":"themes/butterfly/source/js/tw_cn.js","path":"js/tw_cn.js","modified":1,"renderable":1},{"_id":"themes/butterfly/source/js/utils.js","path":"js/utils.js","modified":1,"renderable":1},{"_id":"themes/butterfly/source/js/search/algolia.js","path":"js/search/algolia.js","modified":1,"renderable":1},{"_id":"themes/butterfly/source/js/search/local-search.js","path":"js/search/local-search.js","modified":1,"renderable":1},{"_id":"source/img/000001.png","path":"img/000001.png","modified":1,"renderable":0},{"_id":"source/img/dingyue.png","path":"img/dingyue.png","modified":1,"renderable":0},{"_id":"source/img/peiqian.png","path":"img/peiqian.png","modified":1,"renderable":0},{"_id":"source/img/site01.jpg","path":"img/site01.jpg","modified":1,"renderable":0},{"_id":"source/img/yiyuan.png","path":"img/yiyuan.png","modified":1,"renderable":0},{"_id":"source/img/machinelearning/cros-valid.png","path":"img/machinelearning/cros-valid.png","modified":1,"renderable":0},{"_id":"source/img/machinelearning/kfold-skfold.png","path":"img/machinelearning/kfold-skfold.png","modified":1,"renderable":0},{"_id":"source/img/machinelearning/knn-01.png","path":"img/machinelearning/knn-01.png","modified":1,"renderable":0}],"Cache":[{"_id":"source/about/index.md","hash":"31fdd093368e1c18c1592a8cad6f3c3fe6d6711a","modified":1723257494626},{"_id":"source/_data/link.yml","hash":"6aaf04b5c920e403bea8f82e4b3f4d719760e6df","modified":1723257824766},{"_id":"source/_posts/hello-world.md","hash":"40e804610ff712f079ace7012b862b4efecf82fb","modified":1723206110026},{"_id":"source/categories/index.md","hash":"49618dce0bee26dfc65f0de1794f01d2967cb7b8","modified":1722407843090},{"_id":"source/tags/index.md","hash":"1f27b735b6c7d629c8931b5bd3913bdd659f1981","modified":1722385980076},{"_id":"source/link/index.md","hash":"26e21fe46bf7c0fc5ae95b73d52bf516368d6dc7","modified":1723257768927},{"_id":"source/_posts/machinelearning/knn.md","hash":"6e6c444dc7be367d0cc131b29c02414e619e416e","modified":1736846489069},{"_id":"source/music/index.md","hash":"d8dcf467af235e0bac09805db3a4ab73ad782b83","modified":1723257619897},{"_id":"source/img/machinelearning/kfold-skfold.png","hash":"ab841f5412b7ea773468a3facff643df68a88c01","modified":1736846071435},{"_id":"source/img/machinelearning/knn-01.png","hash":"2a931981d1d5f72d6b42b39b8ef313569eab853e","modified":1736766866160},{"_id":"source/_posts/net/index.md","hash":"7eae8512c2a6bd937200487540b962d65a47ad9e","modified":1723206110028},{"_id":"source/_posts/frontend/frontend.md","hash":"3770215d35203b03d86d4f3a6ceee32f9849b584","modified":1723206110023},{"_id":"source/_posts/ancient/guwenguanzhi/1.md","hash":"629652f1d8e2a347e6b11c367d25b0a26698cd60","modified":1723257063678},{"_id":"source/movies/index.md","hash":"4abf7a6a712f712f22d80203f6004a89feca2014","modified":1723257633715},{"_id":"source/_posts/frontend/deploy/deploy.md","hash":"ba4a05741f30f92f9fbe7b815519142de09b13fb","modified":1723206110045},{"_id":"source/_posts/frontend/uniapp/uniapp.md","hash":"8c8594e3eb73d2c10c4c6dfc008b58c36763b054","modified":1723206110039},{"_id":"source/_posts/bigdata/hadoop/env.md","hash":"4b184c804e9c8083966b1360fe9d5aa539930005","modified":1726065928095},{"_id":"source/_posts/bigdata/hadoop/hdfs.md","hash":"3148149529354105eb301c70961b6f1b68030835","modified":1726066302712},{"_id":"source/_posts/frontend/uniapp/component1.md","hash":"b9b981d3903f5e57e7f327d930df4f04780211f9","modified":1723206110042},{"_id":"source/_posts/linux/script.md","hash":"2deda929ed0c81ddcd00945c673b29f1bd1353c0","modified":1723997145731},{"_id":"themes/butterfly/_config.yml","hash":"c6fab3700a6502b5790ca20d20113020544ea048","modified":1722500710088},{"_id":"source/_posts/net/jichang/jichang.md","hash":"ad0485f33d2f0a8eea342c815d869911433df370","modified":1723206110036},{"_id":"themes/butterfly/plugins.yml","hash":"7bb2c7350c0c57850aa30213cd0f26553a614702","modified":1722500710224},{"_id":"themes/butterfly/package.json","hash":"2b6fb6f62b9fa6a829311ffd532ae760fbd0a7db","modified":1722500710223},{"_id":"themes/butterfly/.github/FUNDING.yml","hash":"3b572099a992e30267f5fe4cd3c582ff7ac9f083","modified":1722500710075},{"_id":"themes/butterfly/languages/default.yml","hash":"90e9e2f36dc51aa77eb7804ae048b4876035b12d","modified":1722500710090},{"_id":"themes/butterfly/languages/en.yml","hash":"68127be0e6b44cfc5f31353d8b275c02939b3ff9","modified":1722500710092},{"_id":"themes/butterfly/languages/zh-CN.yml","hash":"2dcc70a011b37890215ae0fd6d8f8c78aa8af6b0","modified":1722500710093},{"_id":"themes/butterfly/languages/zh-TW.yml","hash":"1392e7b8c678cdfb54f55523693e66abc7d80538","modified":1722500710094},{"_id":"source/_posts/bigdata/hadoop/zookeper.md","hash":"96e58c86eba61accb620adc5e7b7dbc46e47b237","modified":1726066302706},{"_id":"themes/butterfly/layout/archive.pug","hash":"bc77220dfc269b8faad0930e1a4142ebf68165e5","modified":1722500710096},{"_id":"themes/butterfly/layout/category.pug","hash":"bf979aec88d78b644fc5d31518f8679ad7625792","modified":1722500710097},{"_id":"themes/butterfly/layout/page.pug","hash":"bf2d6c6d2d156777b55292e51be02b0b3acf0af8","modified":1722500710219},{"_id":"themes/butterfly/layout/tag.pug","hash":"4bb5efc6dabdf1626685bf6771aaa1467155ae86","modified":1722500710222},{"_id":"themes/butterfly/layout/index.pug","hash":"648dcbdb3d145a710de81c909e000e8664d2ac9c","modified":1722500710218},{"_id":"themes/butterfly/LICENSE","hash":"c8bc7df08db9dd3b39c2c2259a163a36cf2f6808","modified":1722500710084},{"_id":"themes/butterfly/layout/post.pug","hash":"fdbb508b5e6dec30fb8753c5a7fdd494410c4fc0","modified":1722500710220},{"_id":"themes/butterfly/.github/ISSUE_TEMPLATE/bug_report.yml","hash":"eed9190301095b35081aa2658204cc3f15b9f5e1","modified":1722500710076},{"_id":"themes/butterfly/.github/ISSUE_TEMPLATE/config.yml","hash":"63ad2249ad09fb3fe21bd5ff9adefb304a7ab24a","modified":1722500710077},{"_id":"themes/butterfly/layout/includes/404.pug","hash":"aace9ddff469de4226e47a52ede1c81e66d66d5c","modified":1722500710100},{"_id":"themes/butterfly/layout/includes/footer.pug","hash":"8715948b93e7508b84d913be1969b28c6b067b9b","modified":1722500710102},{"_id":"themes/butterfly/layout/includes/additional-js.pug","hash":"50eea5aa78cdeb6c72dd22f0aeabc407cc0f712e","modified":1722500710101},{"_id":"themes/butterfly/layout/includes/head.pug","hash":"ea8d4e8ac6af93cd268ba8f6ffcb80417bc2501e","modified":1722500710103},{"_id":"themes/butterfly/layout/includes/layout.pug","hash":"96df62e34661d8ca4a45267286127479e5178a79","modified":1722500710123},{"_id":"themes/butterfly/layout/includes/pagination.pug","hash":"c5c58714fb3cb839653e5c32e6094784c8662935","modified":1722500710135},{"_id":"themes/butterfly/.github/ISSUE_TEMPLATE/feature_request.yml","hash":"6e0f9470b18bd37d4891282ac73d61676b040e8c","modified":1722500710079},{"_id":"themes/butterfly/layout/includes/rightside.pug","hash":"f448bf73103b88de4443e52d600e871cf3de3e32","modified":1722500710138},{"_id":"themes/butterfly/layout/includes/sidebar.pug","hash":"9277fead4c29dbe93976f078adaa26e8f9253da3","modified":1722500710140},{"_id":"themes/butterfly/scripts/events/comment.js","hash":"95479790234c291b064d031577d71214cdd1d820","modified":1722500710229},{"_id":"themes/butterfly/scripts/events/cdn.js","hash":"7864ba45716c51aef8d8b04fd4bc212e0008ce3b","modified":1722500710226},{"_id":"themes/butterfly/scripts/events/init.js","hash":"ce68e84a9ccfcf91100befbaa9afc392a0cd93bb","modified":1722500710229},{"_id":"themes/butterfly/scripts/events/merge_config.js","hash":"b1dfc3c898b886eab1241b068fc27d7a26a3b7d2","modified":1722500710230},{"_id":"themes/butterfly/scripts/events/stylus.js","hash":"0a336dfe5ed08952fa0df1532421df38a74a20d6","modified":1722500710231},{"_id":"themes/butterfly/scripts/events/welcome.js","hash":"f59e10305fef59ea3e62a7395106c0927582879d","modified":1722500710232},{"_id":"themes/butterfly/scripts/filters/post_lazyload.js","hash":"5ed2d7ef240c927fe1b7a7fb5bf9e55e2bfd55a5","modified":1722500710233},{"_id":"themes/butterfly/scripts/filters/random_cover.js","hash":"0df22d7dbfa766a65cb6032a1f003348f4307cfe","modified":1722500710234},{"_id":"themes/butterfly/scripts/helpers/aside_categories.js","hash":"cdd992c8577d583c237b6aac9f5077d8200879b2","modified":1722500710237},{"_id":"themes/butterfly/scripts/helpers/aside_archives.js","hash":"4f712b4ea383b59a3122683db1d54c04a79ccc5d","modified":1722500710236},{"_id":"themes/butterfly/scripts/events/404.js","hash":"f1d1c378356b776e9b2a8411e6dca88dc8c3245c","modified":1722500710226},{"_id":"themes/butterfly/scripts/helpers/page.js","hash":"c74d6a9b8f71e69447f7847a5f5e81555d68b140","modified":1722500710240},{"_id":"themes/butterfly/scripts/helpers/findArchiveLength.js","hash":"b12895e0765d596494e5526d121de0dd5a7c23d3","modified":1722500710238},{"_id":"themes/butterfly/scripts/helpers/related_post.js","hash":"76343ac8422c9c8539082e77eda6ffee4b877eb2","modified":1722500710241},{"_id":"themes/butterfly/scripts/helpers/series.js","hash":"17c0095bc8d612a268cdcab000b1742dc4c6f811","modified":1722500710243},{"_id":"themes/butterfly/scripts/tag/button.js","hash":"164d5f1c2d1b4cb5a813a6fc574016743a53c019","modified":1722500710244},{"_id":"themes/butterfly/scripts/tag/flink.js","hash":"3ba7677969ff01fab06fc6713455ddc6861f0024","modified":1722500710245},{"_id":"themes/butterfly/scripts/tag/gallery.js","hash":"7ec77b3093f5de67e7032f40a5b12f1389f6f6ff","modified":1722500710247},{"_id":"themes/butterfly/scripts/tag/hide.js","hash":"5d08c3552f7d3c80a724ca628bff66321abe2e5a","modified":1722500710248},{"_id":"themes/butterfly/scripts/tag/inlineImg.js","hash":"c863d2732ce4bdc084f2d0db92f50f80328c1007","modified":1722500710248},{"_id":"themes/butterfly/scripts/tag/label.js","hash":"b013dc0a3d57d2caa18b89263f23871da9ec456d","modified":1722500710248},{"_id":"themes/butterfly/scripts/tag/mermaid.js","hash":"289f85847c58f0b2b7d98a68e370a2896edb8949","modified":1722500710250},{"_id":"themes/butterfly/README_CN.md","hash":"9d729ee2ffc5e5f703ccfbfbbb7b286d59071125","modified":1722500710087},{"_id":"themes/butterfly/scripts/tag/score.js","hash":"35d54adc92e717cc32e13515122b025fd1a98ea2","modified":1722500710252},{"_id":"themes/butterfly/scripts/tag/note.js","hash":"e68d8d21f3a86e3646907a3685550ee20e8d4a9f","modified":1722500710252},{"_id":"themes/butterfly/scripts/tag/timeline.js","hash":"e611074a5a7f489a8b04afac0a3f7f882ce26532","modified":1722500710255},{"_id":"themes/butterfly/scripts/tag/series.js","hash":"dc56e5182dd3813dc977c9bf8556dcc7615e467b","modified":1722500710252},{"_id":"themes/butterfly/scripts/tag/tabs.js","hash":"7c448886f230adb4f4a0208c88fff809abcb5637","modified":1722500710254},{"_id":"themes/butterfly/source/css/index.styl","hash":"b13d96924a5534bff91d75566b196ac87b4fac22","modified":1722500710313},{"_id":"themes/butterfly/scripts/helpers/inject_head_js.js","hash":"b55f71347d2ead097c7f98c0ec792b091433345c","modified":1722500710239},{"_id":"themes/butterfly/README.md","hash":"20a91bea7f7ada8b8195d2abff106a7ce21bba20","modified":1722500710085},{"_id":"themes/butterfly/source/css/var.styl","hash":"950250f66faeb611a67540e0fa6cedbcf5a7a321","modified":1722500710313},{"_id":"themes/butterfly/.github/workflows/stale.yml","hash":"4040c76547e270aaf184e9b219a44ca41bbb1b9f","modified":1722500710082},{"_id":"themes/butterfly/source/img/favicon.png","hash":"3cf89864b4f6c9b532522a4d260a2e887971c92d","modified":1722500710317},{"_id":"themes/butterfly/source/js/main.js","hash":"59cd756a94ecdf3ec7b18f50691a8a6305f7a65a","modified":1722500710321},{"_id":"themes/butterfly/.github/workflows/publish.yml","hash":"e320b40c051bae1549156cd5ea4a51383cf78598","modified":1722500710080},{"_id":"themes/butterfly/source/js/utils.js","hash":"7b871fe0c4456660cff4c7b9cc4ed089adac2caf","modified":1722500710327},{"_id":"themes/butterfly/layout/includes/head/analytics.pug","hash":"c7666a10448edd93f5ace37296051b7670495f1b","modified":1722500710106},{"_id":"themes/butterfly/source/img/avatar.jpg","hash":"cb0941101c6a6b8f762ce6ffc3c948641e7f642f","modified":1722500710316},{"_id":"themes/butterfly/layout/includes/head/config.pug","hash":"39e1ca0a54eb5fd3688a78737417a1aaa50914c9","modified":1722500710108},{"_id":"themes/butterfly/layout/includes/head/config_site.pug","hash":"bd5dd5452e28a4fe94c3241a758ec6f4fdb7a149","modified":1722500710109},{"_id":"themes/butterfly/layout/includes/head/google_adsense.pug","hash":"f29123e603cbbcc6ce277d4e8f600ba67498077c","modified":1722500710109},{"_id":"themes/butterfly/layout/includes/head/pwa.pug","hash":"6dc2c9b85df9ab4f5b554305339fd80a90a6cf43","modified":1722500710113},{"_id":"themes/butterfly/layout/includes/head/site_verification.pug","hash":"5168caadc4cf541f5d6676a9c5e8ae47a948f9ad","modified":1722500710114},{"_id":"themes/butterfly/layout/includes/head/preconnect.pug","hash":"a7c929b90ae52b78b39b1728e3ab0e3db1cb7b9a","modified":1722500710111},{"_id":"themes/butterfly/layout/includes/header/index.pug","hash":"1bef867c799ba158c5417272fb137539951aa120","modified":1722500710117},{"_id":"themes/butterfly/layout/includes/header/nav.pug","hash":"962ee70a35e60a13c31eea47d16b9f98069fe417","modified":1722500710119},{"_id":"themes/butterfly/layout/includes/header/menu_item.pug","hash":"ca8bcd90ad9467819330bfe7c02b76322754bccf","modified":1722500710118},{"_id":"themes/butterfly/layout/includes/header/social.pug","hash":"7a641b5dd45b970e1dafd1433eb32ea149e55cf2","modified":1722500710121},{"_id":"themes/butterfly/layout/includes/header/post-info.pug","hash":"cc99b2dc5c6b1f74391b0da609853ebc11de9610","modified":1722500710120},{"_id":"themes/butterfly/layout/includes/loading/pace.pug","hash":"a6fde4835d6460ce7baf792fd5e1977fad73db25","modified":1722500710127},{"_id":"themes/butterfly/layout/includes/loading/index.pug","hash":"00ae419f527d8225a2dc03d4f977cec737248423","modified":1722500710126},{"_id":"themes/butterfly/layout/includes/loading/fullpage-loading.pug","hash":"766baca6ddce49d1724a02312387b292ff2d0bdc","modified":1722500710125},{"_id":"themes/butterfly/layout/includes/mixins/article-sort.pug","hash":"9155f01d4c644a2e19b2b13b2d3c6d5e34dd0abf","modified":1722500710128},{"_id":"themes/butterfly/layout/includes/page/categories.pug","hash":"5276a8d2835e05bd535fedc9f593a0ce8c3e8437","modified":1722500710131},{"_id":"themes/butterfly/layout/includes/mixins/post-ui.pug","hash":"90eb453b14f6b5c25bfd8d28aa67783603a1411d","modified":1722500710129},{"_id":"themes/butterfly/layout/includes/page/default-page.pug","hash":"e9459f122af7b733398578f9f0f8ab3c5e12a217","modified":1722500710131},{"_id":"themes/butterfly/layout/includes/page/tags.pug","hash":"12be059c536490af216a397e8f2a7abbf6d4610e","modified":1722500710134},{"_id":"themes/butterfly/layout/includes/page/flink.pug","hash":"e37681bc9c169d4220f26ecda2b3d5c02b6b9a0f","modified":1722500710133},{"_id":"themes/butterfly/layout/includes/post/reward.pug","hash":"912df10a053db3135968e92b6fd1a707ee94c968","modified":1722500710138},{"_id":"themes/butterfly/layout/includes/post/post-copyright.pug","hash":"0abad416b1974a17e5be7817931d5fe799180170","modified":1722500710136},{"_id":"themes/butterfly/layout/includes/third-party/aplayer.pug","hash":"e939344fd389aeb11864ee697d5fd9b036d8325f","modified":1722500710146},{"_id":"themes/butterfly/layout/includes/head/Open_Graph.pug","hash":"c8dbdfe6145a0bc6f7691c9551be8169a2698f0a","modified":1722500710105},{"_id":"themes/butterfly/layout/includes/third-party/pangu.pug","hash":"f0898509da70388b5c532f19e762756d74080200","modified":1722500710191},{"_id":"themes/butterfly/layout/includes/widget/card_ad.pug","hash":"a8312b527493dabbadbb1280760168d3bc909a3b","modified":1722500710204},{"_id":"themes/butterfly/layout/includes/third-party/effect.pug","hash":"43014bfc63583d3ee8808d526dd165848c0ed52f","modified":1722500710177},{"_id":"themes/butterfly/layout/includes/third-party/prismjs.pug","hash":"08979afbfecb4476a5ae8e360947b92624d285b8","modified":1722500710194},{"_id":"themes/butterfly/layout/includes/third-party/subtitle.pug","hash":"dfb5e16a7e7106bb20b2ac2d0df1251d0fc79609","modified":1722500710202},{"_id":"themes/butterfly/layout/includes/widget/card_announcement.pug","hash":"21e019bdc3b1e796bb00976bb29af2d51f873624","modified":1722500710205},{"_id":"themes/butterfly/layout/includes/widget/card_bottom_self.pug","hash":"1dba77d250eeebfb6e293d504352c7e9ea31980b","modified":1722500710208},{"_id":"themes/butterfly/layout/includes/widget/card_archives.pug","hash":"73d33b6930e7944187a4b3403daf25d27077a2dd","modified":1722500710206},{"_id":"themes/butterfly/layout/includes/widget/card_author.pug","hash":"ab037bf5794638bd30da4cf7cf106e5d03b5f696","modified":1722500710207},{"_id":"themes/butterfly/layout/includes/widget/card_categories.pug","hash":"66e383b4ef374951eb87dd1bf4cdb7a667193fb5","modified":1722500710209},{"_id":"themes/butterfly/layout/includes/widget/card_newest_comment.pug","hash":"8e22f53886a57a68286970d8af8b4c950fd4a1d7","modified":1722500710210},{"_id":"themes/butterfly/layout/includes/widget/card_post_series.pug","hash":"e0bb72fa0ce15964b11b8fe421cae3432394e35f","modified":1722500710210},{"_id":"themes/butterfly/layout/includes/widget/card_post_toc.pug","hash":"d48d77af1670bd568d784794408bf524a448bfcc","modified":1722500710211},{"_id":"themes/butterfly/layout/includes/widget/card_recent_post.pug","hash":"bb842d2aa6469d65bf06af1372f0a19a9e4ef44c","modified":1722500710214},{"_id":"themes/butterfly/layout/includes/widget/card_tags.pug","hash":"842b772a387b576550fa127030e1c2e9bf65716d","modified":1722500710215},{"_id":"themes/butterfly/layout/includes/widget/index.pug","hash":"8df529f71e25f1c0a00e533de7944ed3d1ba7bd8","modified":1722500710217},{"_id":"themes/butterfly/layout/includes/widget/card_top_self.pug","hash":"7b5ae404a1205546b7de4be42291315cf918f2b3","modified":1722500710215},{"_id":"themes/butterfly/layout/includes/widget/card_webinfo.pug","hash":"12185713f9ca08984fc74e3b69d8cd6828d23da8","modified":1722500710216},{"_id":"themes/butterfly/source/css/_highlight/highlight.styl","hash":"41054740cfbd1357138785464f6859681ca58493","modified":1722500710260},{"_id":"themes/butterfly/source/css/_highlight/theme.styl","hash":"3c178608406c31d768af355ef1d7326da37cc75f","modified":1722500710268},{"_id":"themes/butterfly/source/css/_global/function.styl","hash":"e920dae9ce00177922468db49240f5aca0af4f64","modified":1722500710258},{"_id":"themes/butterfly/source/css/_global/index.styl","hash":"0421da07907b3d98df64239e073b23fbb3f04149","modified":1722500710259},{"_id":"themes/butterfly/source/css/_layout/comments.styl","hash":"fbfce4d67cacd1df22fb73d89d008693f59d9d91","modified":1722500710273},{"_id":"themes/butterfly/source/css/_layout/aside.styl","hash":"aae70ddd126b2e40158e45036abecbfa33cbfbba","modified":1722500710270},{"_id":"themes/butterfly/source/css/_layout/chat.styl","hash":"792a04d36de32f230ca3256ad87a90fe8392f333","modified":1722500710272},{"_id":"themes/butterfly/source/css/_layout/footer.styl","hash":"5e27f7842af82ff7498d4b59787ce9ca90fa9e6f","modified":1722500710275},{"_id":"themes/butterfly/source/css/_layout/loading.styl","hash":"f0b01bbf321c2c24fdccaee367dd9fd448031a72","modified":1722500710277},{"_id":"themes/butterfly/source/css/_layout/pagination.styl","hash":"bd099f7d3adef4b7edd24c0a25a07415b156e587","modified":1722500710278},{"_id":"themes/butterfly/source/css/_layout/head.styl","hash":"dd5d9a5631b682610ea699541b8246ceaa56fddb","modified":1723206391886},{"_id":"themes/butterfly/layout/includes/third-party/pjax.pug","hash":"9b734d99963f3e7f562597dcf60485ccbf6e961c","modified":1722500710192},{"_id":"themes/butterfly/source/css/_layout/post.styl","hash":"7ae27854a737a02eca89b0b92db94cb298fef59e","modified":1722500710280},{"_id":"themes/butterfly/source/css/_layout/relatedposts.styl","hash":"6dcf19c0933c8828a439f801b0f4b256447dec07","modified":1722500710281},{"_id":"themes/butterfly/source/css/_layout/reward.styl","hash":"c0b11a1a5f52e3a6af4e312a8134c93eda18a7dd","modified":1722500710281},{"_id":"themes/butterfly/source/css/_layout/third-party.styl","hash":"15ea7564b2e3bf46bc91fb6e49c94d057b37caaf","modified":1722500710283},{"_id":"themes/butterfly/source/css/_layout/sidebar.styl","hash":"80ee9d0bfe5d38aac1f0cdcea5fc88b71d310041","modified":1722500710283},{"_id":"themes/butterfly/source/css/_mode/readmode.styl","hash":"a22fd15048d21452f0015d0765d295d730203308","modified":1723078297080},{"_id":"themes/butterfly/source/css/_mode/darkmode.styl","hash":"dbc855795a881f8c805bf5c9c5c4d5d542a648ec","modified":1722500710286},{"_id":"themes/butterfly/source/css/_page/404.styl","hash":"a7223a8fcc4fa7b81e552c9a2554be7df9de312e","modified":1722500710289},{"_id":"themes/butterfly/source/css/_page/categories.styl","hash":"68bc8cbea25dbb3cdc170f09f9b43ce130547717","modified":1722500710292},{"_id":"themes/butterfly/source/css/_page/archives.styl","hash":"5dd1ba997741d02894ff846eda939ad8051c0bb2","modified":1722500710290},{"_id":"themes/butterfly/source/css/_page/flink.styl","hash":"ecc2b2e28c179eb9406fc2c6f00e141078249cdd","modified":1722500710294},{"_id":"themes/butterfly/source/css/_page/common.styl","hash":"df7a51fcabbadab5aa31770e3202a47c9599bbb7","modified":1722500710293},{"_id":"themes/butterfly/source/css/_page/tags.styl","hash":"9e35f91847773b915c74a78b8aa66c7bdb950ad0","modified":1722500710296},{"_id":"themes/butterfly/source/css/_page/homepage.styl","hash":"a977cd8161ef4d6ddd5293e81403519076657430","modified":1722500710295},{"_id":"themes/butterfly/source/img/404.jpg","hash":"fb4489bc1d30c93d28f7332158c1c6c1416148de","modified":1722500710315},{"_id":"themes/butterfly/source/css/_search/index.styl","hash":"0b23010154e19f37f0c4af0110f9f834d6d41a13","modified":1722500710298},{"_id":"themes/butterfly/source/css/_search/local-search.styl","hash":"8a53d7ba5ca2f5eb4124b684e7845b648583f658","modified":1722500710301},{"_id":"themes/butterfly/source/css/_tags/button.styl","hash":"62da1de0d5b8453fcecbfacddb16985265638ba5","modified":1722500710302},{"_id":"themes/butterfly/source/css/_tags/gallery.styl","hash":"3e9355b76f87e2ee90f652855282b37ab5ae0b3e","modified":1722500710304},{"_id":"themes/butterfly/source/css/_tags/hexo.styl","hash":"985b183db7b7bfd8f9bdb60494549fb7f850348b","modified":1722500710305},{"_id":"themes/butterfly/source/css/_tags/hide.styl","hash":"b7cf7753479fcf2fe07287ffdb0e568adbba4c18","modified":1722500710306},{"_id":"themes/butterfly/source/css/_tags/note.styl","hash":"4929382bd60788d34752a66e2fe764ef797a72a0","modified":1722500710308},{"_id":"themes/butterfly/source/css/_tags/inlineImg.styl","hash":"5a873d01fabebcf7ddf7a6b1c2e2e5e2714097f4","modified":1722500710307},{"_id":"themes/butterfly/source/css/_tags/label.styl","hash":"2f83bd145b870d80d4b18b0ac603235229a5694e","modified":1722500710307},{"_id":"themes/butterfly/source/css/_tags/tabs.styl","hash":"353b95f9a6c2c1e777d978118cb61f909ccbf89c","modified":1722500710309},{"_id":"themes/butterfly/source/img/friend_404.gif","hash":"8d2d0ebef70a8eb07329f57e645889b0e420fa48","modified":1722500710319},{"_id":"themes/butterfly/source/css/_tags/timeline.styl","hash":"07ea7134db7a66c87658116f089fb1a2a6906563","modified":1722500710310},{"_id":"themes/butterfly/source/css/_third-party/normalize.min.css","hash":"8549829fb7d3c21cd9e119884962e8c463a4a267","modified":1722500710312},{"_id":"themes/butterfly/source/js/search/local-search.js","hash":"ab3904451ae1d78903424b8b2ef815c8571e1749","modified":1722500710325},{"_id":"themes/butterfly/layout/includes/third-party/abcjs/abcjs.pug","hash":"8f95aca305b56ccd7c8c7367b03d26db816ebd5f","modified":1722500710143},{"_id":"themes/butterfly/layout/includes/third-party/card-post-count/disqus.pug","hash":"d6fff5a7f84c8b09f282f9ddc0020a68a8aac9ea","modified":1722500710148},{"_id":"themes/butterfly/layout/includes/third-party/abcjs/index.pug","hash":"58f37823f6cd9a194fb50f7ca7c2233e49939034","modified":1722500710144},{"_id":"themes/butterfly/source/css/_layout/rightside.styl","hash":"0322237e762db401d7b4aa33168d0b9334a9ec26","modified":1722500710282},{"_id":"themes/butterfly/layout/includes/third-party/card-post-count/index.pug","hash":"846cabae287ae31b3bbfac3da022475713dd5ecc","modified":1722500710151},{"_id":"themes/butterfly/layout/includes/third-party/card-post-count/artalk.pug","hash":"b03ee8625149191f9d5d057bbc9824b68d8dd0c4","modified":1722500710147},{"_id":"themes/butterfly/layout/includes/third-party/card-post-count/remark42.pug","hash":"716dc463fe4ef5112e7018ed60804125fdfa5cad","modified":1722500710151},{"_id":"themes/butterfly/layout/includes/third-party/card-post-count/valine.pug","hash":"cd4fc9c5a61608a5dedf645c1295430a1623040f","modified":1722500710153},{"_id":"themes/butterfly/layout/includes/third-party/card-post-count/fb.pug","hash":"4b98145d6584d586cabf033493282afc72ae816a","modified":1722500710149},{"_id":"themes/butterfly/layout/includes/third-party/card-post-count/twikoo.pug","hash":"7e233f872aea6fd6beccdc9efd86b1bf9ec9f12d","modified":1722500710152},{"_id":"themes/butterfly/layout/includes/third-party/card-post-count/waline.pug","hash":"fd2320ee25507bb8ef49f932c2d170586b44ea4d","modified":1722500710154},{"_id":"themes/butterfly/layout/includes/third-party/chat/chatra.pug","hash":"08a85e52fc800d3562df869e5e2613313e76fce6","modified":1722500710156},{"_id":"themes/butterfly/layout/includes/third-party/chat/crisp.pug","hash":"09d2ab2570b67e6f09244a898ccab5567cb82ace","modified":1722500710156},{"_id":"themes/butterfly/layout/includes/third-party/chat/index.pug","hash":"1157118db9f5d7c0c5a0fc7c346f6e934ca00d52","modified":1722500710159},{"_id":"themes/butterfly/layout/includes/third-party/chat/daovoice.pug","hash":"0d960849d5b05d27ec87627b983ca35f2411b9e8","modified":1722500710158},{"_id":"themes/butterfly/layout/includes/third-party/chat/messenger.pug","hash":"799da8f3015e6fe440681b21644bcb3810a5518c","modified":1722500710160},{"_id":"themes/butterfly/layout/includes/third-party/chat/tidio.pug","hash":"6d40b521eec4136f6742c548a4445ed593470b1b","modified":1722500710161},{"_id":"themes/butterfly/layout/includes/third-party/comments/artalk.pug","hash":"5373b822aa72ddb96f2f1f4baf6c058b40d705d6","modified":1722500710163},{"_id":"themes/butterfly/layout/includes/third-party/comments/disqus.pug","hash":"364d1fd655baca9132038ef1e312abde2c0bc7de","modified":1722500710164},{"_id":"themes/butterfly/layout/includes/third-party/comments/disqusjs.pug","hash":"f78c9c20c86d58c7cf099f6f8d6097103d7d43e5","modified":1722500710165},{"_id":"themes/butterfly/layout/includes/third-party/comments/giscus.pug","hash":"1eab7ca1cb16c6786f9c3ca0efef8cc15e444ab4","modified":1722500710167},{"_id":"themes/butterfly/layout/includes/third-party/comments/facebook_comments.pug","hash":"11f5dca1432e59f22955aaf4ac3e9de6b286d887","modified":1722500710166},{"_id":"themes/butterfly/layout/includes/third-party/comments/gitalk.pug","hash":"1c86c8fc1a28514a02a1f6a25ca9ec05eb3955b7","modified":1722500710168},{"_id":"themes/butterfly/layout/includes/third-party/comments/index.pug","hash":"db6713d2b90eb8183f86ac92c26761a8501c0ddb","modified":1722500710169},{"_id":"themes/butterfly/layout/includes/third-party/comments/js.pug","hash":"3abbaaa4ea575c45b3cebffd40bad1acc6ffce84","modified":1722500710170},{"_id":"themes/butterfly/layout/includes/third-party/comments/livere.pug","hash":"09c2ef4bc6d005f96dfa48b1d9af1ec095c5266d","modified":1722500710171},{"_id":"themes/butterfly/layout/includes/third-party/comments/remark42.pug","hash":"7f450664e6323a076ae59c393b0f22167cfa82e5","modified":1722500710172},{"_id":"themes/butterfly/layout/includes/third-party/comments/twikoo.pug","hash":"9942a903227350960c1d0716e59516ae79ac24a8","modified":1722500710173},{"_id":"themes/butterfly/layout/includes/third-party/comments/utterances.pug","hash":"b65a42167df5fb07e2a63f312a58c321d3112a90","modified":1722500710174},{"_id":"themes/butterfly/layout/includes/third-party/comments/valine.pug","hash":"4ed7c74087e81c6fcaf4fca7dced58b4e19f4cb1","modified":1722500710175},{"_id":"themes/butterfly/layout/includes/third-party/math/index.pug","hash":"2afa4c21dd19890f47fb568cfb0d90efb676a253","modified":1722500710179},{"_id":"themes/butterfly/layout/includes/third-party/math/katex.pug","hash":"f0d3eddd2bed68e5517274b3530bfe0fa5057d8e","modified":1722500710180},{"_id":"themes/butterfly/layout/includes/third-party/comments/waline.pug","hash":"efb72547fc2d470a124f5636391128dc59627498","modified":1722500710176},{"_id":"themes/butterfly/layout/includes/third-party/math/mathjax.pug","hash":"bb944185f4bb9f9a9b9d70ee215f66ccd6d4c6cf","modified":1722500710181},{"_id":"themes/butterfly/layout/includes/third-party/math/mermaid.pug","hash":"c682e4d61017fb0dd2e837bfcc242371f1a13364","modified":1722500710182},{"_id":"themes/butterfly/layout/includes/third-party/newest-comments/artalk.pug","hash":"2e36fac4791e99844cd56676898be0dbf5eb4e99","modified":1722500710184},{"_id":"themes/butterfly/layout/includes/third-party/newest-comments/index.pug","hash":"f8b65460c399973090c1fb7ab81e3708c252e7cc","modified":1722500710187},{"_id":"themes/butterfly/layout/includes/third-party/newest-comments/disqus-comment.pug","hash":"d8898e427acd91ceb97d6a7ee3acb011ca86b9fc","modified":1722500710184},{"_id":"themes/butterfly/layout/includes/third-party/newest-comments/remark42.pug","hash":"a4e52188b6effeee1df2a01dcbf4105de76a61a8","modified":1722500710188},{"_id":"themes/butterfly/layout/includes/third-party/newest-comments/twikoo-comment.pug","hash":"17520a86de12ae585289463c066d3ac91b78a2ff","modified":1722500710188},{"_id":"themes/butterfly/layout/includes/third-party/newest-comments/valine.pug","hash":"ecfff55b2c7f6d87ce4d5028fdf9f8c0bf155c73","modified":1722500710189},{"_id":"themes/butterfly/layout/includes/third-party/newest-comments/waline.pug","hash":"0544d91c0bc9e26e0fe1b5ff490f4a8540ed1ee1","modified":1722500710191},{"_id":"themes/butterfly/layout/includes/third-party/newest-comments/github-issues.pug","hash":"fc8814bd016d039874ec2fc24dcb78587892e2a6","modified":1722500710186},{"_id":"themes/butterfly/layout/includes/third-party/search/algolia.pug","hash":"90338ac4cd114d324fe1caaaeea8be9ca05d6a46","modified":1722500710195},{"_id":"themes/butterfly/layout/includes/third-party/search/index.pug","hash":"3adcf28a8d205ea3ee19828eda0e668702fac07a","modified":1722500710197},{"_id":"themes/butterfly/layout/includes/third-party/search/docsearch.pug","hash":"52a06a2e039f44383085333cac69f3f4e7d0ad3a","modified":1722500710196},{"_id":"themes/butterfly/layout/includes/third-party/search/local-search.pug","hash":"420a86e73d0d748ac234fd00d06d9e433ca5e3f2","modified":1722500710198},{"_id":"themes/butterfly/layout/includes/third-party/share/addtoany.pug","hash":"1f02a26730e5f36cc2dfec7ff4d5c93a099ed5ba","modified":1722500710199},{"_id":"themes/butterfly/layout/includes/third-party/share/index.pug","hash":"c16ee69b5ca8db016db0508d014ae0867c4ce929","modified":1722500710201},{"_id":"themes/butterfly/layout/includes/third-party/share/share-js.pug","hash":"8106bd031586f075a994956ee4438eb13be25d7b","modified":1722500710202},{"_id":"themes/butterfly/source/css/_highlight/highlight/diff.styl","hash":"6e77f1ca0cfb0db6b028f5c0238780e66d344f3d","modified":1722500710263},{"_id":"themes/butterfly/source/css/_highlight/highlight/index.styl","hash":"fc702a4614d0562a381907b083f71ba63d301d86","modified":1722500710264},{"_id":"themes/butterfly/source/css/_search/algolia.styl","hash":"37db99299af380e9111dce2a78a5049b301b13e0","modified":1722500710298},{"_id":"themes/butterfly/source/js/tw_cn.js","hash":"d776c670e4076ad6049dbb64cdee7a734b51d37f","modified":1722500710326},{"_id":"themes/butterfly/source/css/_highlight/prismjs/diff.styl","hash":"1309292f1c8c53d96cd7333507b106bcc24ca8fc","modified":1722500710265},{"_id":"themes/butterfly/source/js/search/algolia.js","hash":"a7c2fe73cc05ad3525909b86ad0ede1a9f2d3b48","modified":1722500710323},{"_id":"themes/butterfly/source/css/_highlight/prismjs/index.styl","hash":"01ff9e77eb1bd454bec65a6ff5972c8e219bc708","modified":1722500710267},{"_id":"source/img/000001.png","hash":"ad86c3b72174364d462bdab1d09540bd79eb123c","modified":1725979378674},{"_id":"source/img/machinelearning/cros-valid.png","hash":"8f9b204b651f93c17ad0856daa628d6abe985d97","modified":1736768886866},{"_id":"themes/butterfly/source/css/_highlight/prismjs/line-number.styl","hash":"7c9cc43e1d2577f7151039d58e603c30860fd281","modified":1722500710267},{"_id":"source/img/yiyuan.png","hash":"817a89509a8ebcddff6b369979d53ecf44a30a9f","modified":1722998234119},{"_id":"source/img/peiqian.png","hash":"2f077f1fff014ee448cd58b57ff83901702e2d88","modified":1723000686874},{"_id":"source/img/dingyue.png","hash":"c6afcd1124d84f07caeefcb895be3f3a5b301678","modified":1723001642809},{"_id":"source/img/site01.jpg","hash":"d93084432feb123fd5d781210c3a2c4db43c10ec","modified":1722524985872},{"_id":"public/search.xml","hash":"c74441d8486d2c420c4a9ec910968232d6c9734e","modified":1736846566584},{"_id":"public/categories/index.html","hash":"4cdbd3cb944cfabfbed8e61e3a37b5c9b83b7396","modified":1736846566584},{"_id":"public/archives/page/2/index.html","hash":"63e4f26007a93f524053a1e0cb6bc51bd6d8a238","modified":1736846566584},{"_id":"public/archives/2024/page/2/index.html","hash":"cef75c1ad6ecc3546d9c3b3d85d5fa5abc4496e4","modified":1736846566584},{"_id":"public/archives/2024/08/index.html","hash":"3a1fe1a2e2f8c7b522abb2288f03b6c860fcd961","modified":1736846566584},{"_id":"public/archives/2024/09/index.html","hash":"57f8c6aece22366a588aa014929ce938fc817834","modified":1736846566584},{"_id":"public/archives/2025/index.html","hash":"9570ac1a063dceff1c65cbb86c23d45d5ba13c16","modified":1736846566584},{"_id":"public/archives/2025/01/index.html","hash":"4992dcc3b763a79da912785b626af031dd0b51d2","modified":1736846566584},{"_id":"public/categories/古文观止/index.html","hash":"38a7c71204d6250eeb64835da114d81d29a96bd3","modified":1736846566584},{"_id":"public/tags/machinelearning/index.html","hash":"b61b88237972d9339ea47985786d95b867621ccd","modified":1736846566584},{"_id":"public/tags/uniapp/index.html","hash":"38560f6232dc61e37896693dac7f6fd62c442e17","modified":1736846566584},{"_id":"public/tags/古文观止/index.html","hash":"ac2e51c1948e633ea366d285d32bacd49a084be9","modified":1736846566584},{"_id":"public/tags/网络代理/index.html","hash":"30586ee6155b79d03b974461ec191617423ee9e9","modified":1736846566584},{"_id":"public/tags/index.html","hash":"993995c0a7fbb2c2fdac52a97c3c5cbb192666e1","modified":1736846566584},{"_id":"public/about/index.html","hash":"c9c4d571d48afaa58272256a09013db6fba5e325","modified":1736846566584},{"_id":"public/music/index.html","hash":"f976d20fe5d54263abb781b080d605cf1b9bdf12","modified":1736846566584},{"_id":"public/link/index.html","hash":"2516665d0cddc0f91ff97869fcad08a82a591c69","modified":1736846566584},{"_id":"public/movies/index.html","hash":"d99e71fca8a463d766356d8977d83d1b5d9eaa8c","modified":1736846566584},{"_id":"public/posts/29139.html","hash":"536395be9bf8e4760b17d09c676f6a77fe1fd976","modified":1736846566584},{"_id":"public/posts/61253.html","hash":"f923eb1311298fb34787e401568b2f5e041c7677","modified":1736846566584},{"_id":"public/posts/61251.html","hash":"b172067125d2ea1d72a67b045f098fbcde1b38e1","modified":1736846566584},{"_id":"public/posts/61252.html","hash":"3cbe1345d3737f098d0bebee66ec05d3b4a9555e","modified":1736846566584},{"_id":"public/posts/16107.html","hash":"34386a72d3dfd4c6b091f8aac2072e39eaed6feb","modified":1736846566584},{"_id":"public/posts/34849.html","hash":"7b7588271de905a37a1fcdf73424779cf3373a66","modified":1736846566584},{"_id":"public/posts/58638.html","hash":"a7f6b8cb183cfce43486303b778533e11bf85e7f","modified":1736846566584},{"_id":"public/posts/41168.html","hash":"d40b9a14d1e4b6ed6f7fbcad5b14cce52cb70b51","modified":1736846566584},{"_id":"public/posts/14011.html","hash":"a1dc0b992ff4c4c477b38d3c88c21f97225a0987","modified":1736846566584},{"_id":"public/posts/58817.html","hash":"fa05234a4f30849f8c1307ae8d3ba122215061e5","modified":1736846566584},{"_id":"public/posts/33957.html","hash":"9a5a5c409a390417117b576f178da8caa98f9220","modified":1736846566584},{"_id":"public/posts/47807.html","hash":"092726e6acee2bacc8f0f31382f914aba7459dc7","modified":1736846566584},{"_id":"public/posts/1441.html","hash":"0292564cd4084af24d2b19cd872897d4039cb9a5","modified":1736846566584},{"_id":"public/archives/index.html","hash":"f75ae491b1e1816d291da92a8993e0f21111ae85","modified":1736846566584},{"_id":"public/archives/2024/index.html","hash":"2a435300c19f4c09ebedf0e9a684cf8c4cb63d15","modified":1736846566584},{"_id":"public/index.html","hash":"b5a4f632368315255b3f8394b71fbb27ac0d3bae","modified":1736846566584},{"_id":"public/page/2/index.html","hash":"bb806417c4a65025c66264aab257b801dd09843b","modified":1736846566584},{"_id":"public/img/avatar.jpg","hash":"cb0941101c6a6b8f762ce6ffc3c948641e7f642f","modified":1736846566584},{"_id":"public/img/404.jpg","hash":"fb4489bc1d30c93d28f7332158c1c6c1416148de","modified":1736846566584},{"_id":"public/img/favicon.png","hash":"3cf89864b4f6c9b532522a4d260a2e887971c92d","modified":1736846566584},{"_id":"public/img/friend_404.gif","hash":"8d2d0ebef70a8eb07329f57e645889b0e420fa48","modified":1736846566584},{"_id":"public/img/machinelearning/kfold-skfold.png","hash":"ab841f5412b7ea773468a3facff643df68a88c01","modified":1736846566584},{"_id":"public/img/machinelearning/knn-01.png","hash":"2a931981d1d5f72d6b42b39b8ef313569eab853e","modified":1736846566584},{"_id":"public/css/var.css","hash":"da39a3ee5e6b4b0d3255bfef95601890afd80709","modified":1736846566584},{"_id":"public/js/search/local-search.js","hash":"e1f60ebac53a3f596fd0a4769b4f9275c48c6542","modified":1736846566584},{"_id":"public/js/utils.js","hash":"8e6b48d294e7aeaba8ff6348c43b2271cf865547","modified":1736846566584},{"_id":"public/js/search/algolia.js","hash":"108988d046da9a4716148df43b3975217c8ceaae","modified":1736846566584},{"_id":"public/css/index.css","hash":"a86592daf1fcadb01092b449a0eb5100fc2351fb","modified":1736846566584},{"_id":"public/js/main.js","hash":"0dac585446445e0c419b86eec5580bc9b0657dc6","modified":1736846566584},{"_id":"public/js/tw_cn.js","hash":"f8d2e3f31468991a7f5171cbfdb157dfb86d3372","modified":1736846566584},{"_id":"public/img/machinelearning/cros-valid.png","hash":"8f9b204b651f93c17ad0856daa628d6abe985d97","modified":1736846566584},{"_id":"public/img/000001.png","hash":"ad86c3b72174364d462bdab1d09540bd79eb123c","modified":1736846566584},{"_id":"public/img/yiyuan.png","hash":"817a89509a8ebcddff6b369979d53ecf44a30a9f","modified":1736846566584},{"_id":"public/img/peiqian.png","hash":"2f077f1fff014ee448cd58b57ff83901702e2d88","modified":1736846566584},{"_id":"public/img/dingyue.png","hash":"c6afcd1124d84f07caeefcb895be3f3a5b301678","modified":1736846566584},{"_id":"public/img/site01.jpg","hash":"d93084432feb123fd5d781210c3a2c4db43c10ec","modified":1736846566584}],"Category":[{"name":"古文观止","_id":"cm5w9lrdp000htgahcgif26p4"}],"Data":[{"_id":"link","data":[{"class_name":"友情鏈接","class_desc":"那些人，那些事","link_list":[{"name":"Hexo","link":"https://hexo.io/zh-tw/","avatar":"https://d33wubrfki0l68.cloudfront.net/6657ba50e702d84afb32fe846bed54fba1a77add/827ae/logo.svg","descr":"快速、簡單且強大的網誌框架"}]},{"class_name":"網站","class_desc":"值得推薦的網站","link_list":[{"name":"Youtube","link":"https://www.youtube.com/","avatar":"https://i.loli.net/2020/05/14/9ZkGg8v3azHJfM1.png","descr":"視頻網站"},{"name":"Weibo","link":"https://www.weibo.com/","avatar":"https://i.loli.net/2020/05/14/TLJBum386vcnI1P.png","descr":"中國最大社交分享平台"},{"name":"Twitter","link":"https://twitter.com/","avatar":"https://i.loli.net/2020/05/14/5VyHPQqR6LWF39a.png","descr":"社交分享平台"}]}]}],"Page":[{"title":"About me","date":"2024-08-10T02:35:41.000Z","_content":"\n落花飞舞，翩若惊鸿。\n","source":"about/index.md","raw":"---\ntitle: About me\ndate: 2024-08-10 10:35:41\n---\n\n落花飞舞，翩若惊鸿。\n","updated":"2024-08-10T02:38:14.626Z","path":"about/index.html","comments":1,"layout":"page","_id":"cm5w9lrd90000tgah6s394dm1","content":"

落花飞舞，翩若惊鸿。

\n","cover":false,"excerpt":"","more":"

落花飞舞，翩若惊鸿。

\n"},{"title":"categories","date":"2024-07-31T00:33:49.000Z","aside":false,"top_img":false,"type":"categories","_content":"\n### category","source":"categories/index.md","raw":"---\ntitle: categories\ndate: 2024-07-31 08:33:49\naside: false\ntop_img: false\ntype: \"categories\"\n---\n\n### category","updated":"2024-07-31T06:37:23.090Z","path":"categories/index.html","comments":1,"layout":"page","_id":"cm5w9lrdg0002tgahhfj95eri","content":"

category

","cover":false,"excerpt":"","more":"

category

"},{"title":"tags","date":"2024-07-31T00:32:38.000Z","type":"tags","comments":0,"top_img":false,"_content":"","source":"tags/index.md","raw":"---\ntitle: tags\ndate: 2024-07-31 08:32:38\ntype: \"tags\"\ncomments: false\ntop_img: false\n---\n","updated":"2024-07-31T00:33:00.076Z","path":"tags/index.html","layout":"page","_id":"cm5w9lrdh0004tgah1cwhcbiv","content":"","cover":false,"excerpt":"","more":""},{"title":"Music","date":"2024-08-10T02:40:19.000Z","_content":"","source":"music/index.md","raw":"---\ntitle: Music\ndate: 2024-08-10 10:40:19\n---\n","updated":"2024-08-10T02:40:19.897Z","path":"music/index.html","comments":1,"layout":"page","_id":"cm5w9lrdj0006tgaha0vu4yha","content":"","cover":false,"excerpt":"","more":""},{"title":"link","date":"2024-08-10T02:42:35.000Z","type":"link","_content":"","source":"link/index.md","raw":"---\ntitle: link\ndate: 2024-08-10 10:42:35\ntype: \"link\"\n---\n","updated":"2024-08-10T02:42:48.927Z","path":"link/index.html","comments":1,"layout":"page","_id":"cm5w9lrdl0009tgah5j9mfltg","content":"","cover":false,"excerpt":"","more":""},{"title":"Movies","date":"2024-08-10T02:40:33.000Z","_content":"","source":"movies/index.md","raw":"---\ntitle: Movies\ndate: 2024-08-10 10:40:33\n---\n","updated":"2024-08-10T02:40:33.715Z","path":"movies/index.html","comments":1,"layout":"page","_id":"cm5w9lrdm000btgah0epg77s3","content":"","cover":false,"excerpt":"","more":""}],"Post":[{"title":"script","abbrlink":34849,"date":"2024-08-17T03:09:24.000Z","_content":"\n### 查看CPU、内存使用率\n```bash\n#!/bin/bash\n\n# 定义颜色\nRED='\\033[0;31m'\nGREEN='\\033[0;32m'\nYELLOW='\\033[0;33m'\nBLUE='\\033[0;34m'\nNC='\\033[0m' # 无颜色\n\nwhile true; do\n # 获取所有进程的CPU使用率和内存使用率\n cpu_usage=$(ps aux | awk '{sum_cpu += $3} END {print sum_cpu}')\n mem_usage=$(ps aux | awk '{sum_mem += $4} END {print sum_mem}')\n \n # 打印结果，带有时间戳、分隔线和颜色高亮\n echo -e \"${BLUE}==============================${NC}\"\n echo -e \"${YELLOW}Timestamp: $(date)${NC}\"\n echo -e \"${BLUE}==============================${NC}\"\n echo -e \"${GREEN}Total CPU usage: ${RED}$cpu_usage%${NC}\"\n echo -e \"${GREEN}Total Memory usage: ${RED}$mem_usage%${NC}\"\n echo -e \"${BLUE}==============================${NC}\"\n \n # 等待5秒后再次执行\n sleep 5\ndone\n\n```\n**保存脚本到/usr/local/bin目录下**\n```bash\n mv usage.sh /usr/local/bin/usage\n```\n\n### Shell脚本编写的基本信息\n\n```bash\n#! /bin/bash\n# -------------------------------------------------\n# Filename: test.sh\n# Version: 1.0\n# Date: 2024/05/02\n# Author: shenjianZ\n# Email: shenjianZLT@gmail.com\n# Website: https://blog.shenjianl.cn\n# Description: this is a test shell\n# CopyRight: 2024 All rights reserved shenjianZ\n# License GPL\n# ------------------------------------------------\n\n\n# Your script logic goes here\n```","source":"_posts/linux/script.md","raw":"---\ntitle: script\nabbrlink: 34849\ndate: 2024-08-17 11:09:24\ntags:\n---\n\n### 查看CPU、内存使用率\n```bash\n#!/bin/bash\n\n# 定义颜色\nRED='\\033[0;31m'\nGREEN='\\033[0;32m'\nYELLOW='\\033[0;33m'\nBLUE='\\033[0;34m'\nNC='\\033[0m' # 无颜色\n\nwhile true; do\n # 获取所有进程的CPU使用率和内存使用率\n cpu_usage=$(ps aux | awk '{sum_cpu += $3} END {print sum_cpu}')\n mem_usage=$(ps aux | awk '{sum_mem += $4} END {print sum_mem}')\n \n # 打印结果，带有时间戳、分隔线和颜色高亮\n echo -e \"${BLUE}==============================${NC}\"\n echo -e \"${YELLOW}Timestamp: $(date)${NC}\"\n echo -e \"${BLUE}==============================${NC}\"\n echo -e \"${GREEN}Total CPU usage: ${RED}$cpu_usage%${NC}\"\n echo -e \"${GREEN}Total Memory usage: ${RED}$mem_usage%${NC}\"\n echo -e \"${BLUE}==============================${NC}\"\n \n # 等待5秒后再次执行\n sleep 5\ndone\n\n```\n**保存脚本到/usr/local/bin目录下**\n```bash\n mv usage.sh /usr/local/bin/usage\n```\n\n### Shell脚本编写的基本信息\n\n```bash\n#! /bin/bash\n# -------------------------------------------------\n# Filename: test.sh\n# Version: 1.0\n# Date: 2024/05/02\n# Author: shenjianZ\n# Email: shenjianZLT@gmail.com\n# Website: https://blog.shenjianl.cn\n# Description: this is a test shell\n# CopyRight: 2024 All rights reserved shenjianZ\n# License GPL\n# ------------------------------------------------\n\n\n# Your script logic goes here\n```","slug":"linux/script","published":1,"updated":"2024-08-18T16:05:45.731Z","comments":1,"layout":"post","photos":[],"_id":"cm5w9lrdc0001tgah0jou53lf","content":"

查看CPU、内存使用率

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
#!/bin/bash

# 定义颜色
RED='\\033[0;31m'
GREEN='\\033[0;32m'
YELLOW='\\033[0;33m'
BLUE='\\033[0;34m'
NC='\\033[0m' # 无颜色

while true; do
    # 获取所有进程的CPU使用率和内存使用率
    cpu_usage=$(ps aux | awk '{sum_cpu += $3} END {print sum_cpu}')
    mem_usage=$(ps aux | awk '{sum_mem += $4} END {print sum_mem}')
    
    # 打印结果，带有时间戳、分隔线和颜色高亮
    echo -e "${BLUE}==============================${NC}"
    echo -e "${YELLOW}Timestamp: $(date)${NC}"
    echo -e "${BLUE}==============================${NC}"
    echo -e "${GREEN}Total CPU usage: ${RED}$cpu_usage%${NC}"
    echo -e "${GREEN}Total Memory usage: ${RED}$mem_usage%${NC}"
    echo -e "${BLUE}==============================${NC}"
    
    # 等待5秒后再次执行
    sleep 5
done

\n

保存脚本到/usr/local/bin目录下

\n
1
mv usage.sh /usr/local/bin/usage
\n\n

Shell脚本编写的基本信息

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
#! /bin/bash
# -------------------------------------------------
# Filename:     test.sh
# Version:      1.0
# Date:         2024/05/02
# Author:       shenjianZ
# Email:        shenjianZLT@gmail.com
# Website:      https://blog.shenjianl.cn
# Description:  this is a test shell
# CopyRight:    2024 All rights reserved shenjianZ
# License       GPL
# ------------------------------------------------


# Your script logic goes here
","cover":false,"excerpt":"","more":"

查看CPU、内存使用率

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
#!/bin/bash

# 定义颜色
RED='\\033[0;31m'
GREEN='\\033[0;32m'
YELLOW='\\033[0;33m'
BLUE='\\033[0;34m'
NC='\\033[0m' # 无颜色

while true; do
    # 获取所有进程的CPU使用率和内存使用率
    cpu_usage=$(ps aux | awk '{sum_cpu += $3} END {print sum_cpu}')
    mem_usage=$(ps aux | awk '{sum_mem += $4} END {print sum_mem}')
    
    # 打印结果，带有时间戳、分隔线和颜色高亮
    echo -e "${BLUE}==============================${NC}"
    echo -e "${YELLOW}Timestamp: $(date)${NC}"
    echo -e "${BLUE}==============================${NC}"
    echo -e "${GREEN}Total CPU usage: ${RED}$cpu_usage%${NC}"
    echo -e "${GREEN}Total Memory usage: ${RED}$mem_usage%${NC}"
    echo -e "${BLUE}==============================${NC}"
    
    # 等待5秒后再次执行
    sleep 5
done

\n

保存脚本到/usr/local/bin目录下

\n
1
mv usage.sh /usr/local/bin/usage
\n\n

Shell脚本编写的基本信息

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
#! /bin/bash
# -------------------------------------------------
# Filename:     test.sh
# Version:      1.0
# Date:         2024/05/02
# Author:       shenjianZ
# Email:        shenjianZLT@gmail.com
# Website:      https://blog.shenjianl.cn
# Description:  this is a test shell
# CopyRight:    2024 All rights reserved shenjianZ
# License       GPL
# ------------------------------------------------


# Your script logic goes here
"},{"title":"Hello World","abbrlink":16107,"_content":"Welcome to [Hexo](https://hexo.io/)! This is your very first post. Check [documentation](https://hexo.io/docs/) for more info. If you get any problems when using Hexo, you can find the answer in [troubleshooting](https://hexo.io/docs/troubleshooting.html) or you can ask me on [GitHub](https://github.com/hexojs/hexo/issues).\n\n## Quick Start\n\n### Create a new post\n\n``` bash\n$ hexo new \"My New Post\"\n```\n\nMore info: [Writing](https://hexo.io/docs/writing.html)\n\n### Run server\n\n``` bash\n$ hexo server\n```\n\nMore info: [Server](https://hexo.io/docs/server.html)\n\n### Generate static files\n\n``` bash\n$ hexo generate\n```\n\nMore info: [Generating](https://hexo.io/docs/generating.html)\n\n### Deploy to remote sites\n\n``` bash\n$ hexo deploy\n```\n\nMore info: [Deployment](https://hexo.io/docs/one-command-deployment.html)\n","source":"_posts/hello-world.md","raw":"---\ntitle: Hello World\nabbrlink: 16107\n---\nWelcome to [Hexo](https://hexo.io/)! This is your very first post. Check [documentation](https://hexo.io/docs/) for more info. If you get any problems when using Hexo, you can find the answer in [troubleshooting](https://hexo.io/docs/troubleshooting.html) or you can ask me on [GitHub](https://github.com/hexojs/hexo/issues).\n\n## Quick Start\n\n### Create a new post\n\n``` bash\n$ hexo new \"My New Post\"\n```\n\nMore info: [Writing](https://hexo.io/docs/writing.html)\n\n### Run server\n\n``` bash\n$ hexo server\n```\n\nMore info: [Server](https://hexo.io/docs/server.html)\n\n### Generate static files\n\n``` bash\n$ hexo generate\n```\n\nMore info: [Generating](https://hexo.io/docs/generating.html)\n\n### Deploy to remote sites\n\n``` bash\n$ hexo deploy\n```\n\nMore info: [Deployment](https://hexo.io/docs/one-command-deployment.html)\n","slug":"hello-world","published":1,"date":"2024-09-11T00:01:10.419Z","updated":"2024-08-09T12:21:50.026Z","comments":1,"layout":"post","photos":[],"_id":"cm5w9lrdg0003tgah9d6e9nmn","content":"

Welcome to Hexo! This is your very first post. Check documentation for more info. If you get any problems when using Hexo, you can find the answer in troubleshooting or you can ask me on GitHub.

\n

Quick Start

Create a new post

1
$ hexo new "My New Post"
\n\n

More info: Writing

\n

Run server

1
$ hexo server
\n\n

More info: Server

\n

Generate static files

1
$ hexo generate
\n\n

More info: Generating

\n

Deploy to remote sites

1
$ hexo deploy
\n\n

More info: Deployment

\n","cover":false,"excerpt":"","more":"

Welcome to Hexo! This is your very first post. Check documentation for more info. If you get any problems when using Hexo, you can find the answer in troubleshooting or you can ask me on GitHub.

\n

Quick Start

Create a new post

1
$ hexo new "My New Post"
\n\n

More info: Writing

\n

Run server

1
$ hexo server
\n\n

More info: Server

\n

Generate static files

1
$ hexo generate
\n\n

More info: Generating

\n

Deploy to remote sites

1
$ hexo deploy
\n\n

More info: Deployment

\n"},{"title":"k近邻算法（K-Nearest Neighbors）KNN","abbrlink":29139,"mathjax":true,"date":"2025-01-13T09:20:59.000Z","_content":"## **k近邻算法（K-Nearest Neighbors）KNN**\n将当前样本的类别归类于距离最近的**k**个样本的类别\n\n#### **距离公式(2维)**\n\n- 欧式距离\n$$\nd = \\sqrt{(x_1-y_1)^2 + (x_2 - y_2)^2}\n$$\n- 曼哈顿距离\n$$\nd = |x_1 - x_2| + |y_1 - y_2|\n$$\n- 切比雪夫距离\n$$\nd = \\max\\left(|x_1 - x_2|, |y_1 - y_2|\\right)\n$$\n#### k值选择问题\n\n| k值 | 影响 |\n| --- | ------------------ |\n| 越大 | 模型过拟合，准确率波动较大 |\n| 越小 | 模型欠拟合，准确率趋于稳定但可能较低 |\n### 特征预处理\n> 通过一些转换函数将特征数据转换成更加适合算法模型的特征数据过程 \n- 归一化\n 将数据变换到指定区间（默认是\\[0,1\\]）\n $$ x' = \\frac{x- x_{\\text {min}}}{x_{\\text{max}} - x_{\\text{min}}} $$\n 若需要缩放到任意区间 \\(\\[a, b\\]\\)，公式为： $$ x' = a + \\frac{(x - x_{\\text{min}}) \\cdot (b - a)}{x_{\\text{max}} - x_{\\text{min}}} $$\n 其中：\\( \\[a, b\\] \\)：目标区间的范围\n 归一化受到数据集的异常值的影响，需要进行标准化处理（更加合理）\n ``` python\n\t from sklearn.preprocessing import MinMaxScaler # 归一化\n\t```\n- 标准化\n 将数据调整为均值为 0，标准差为 1 的标准正态分布\n $$ z = \\frac{x - \\mu}{\\sigma} $$\n \\( z \\)：标准化后的值 \\( x \\)：原始数据值 \\( $\\mu$ \\)：数据的均值 \\( $\\sigma$\\)：数据的标准差\n \n ``` python\n \t from sklearn.preprocessing import StandardScaler # 标准化\n ```\n\n### KNN代码实现\n```python\nimport seaborn as sns\nimport matplotlib.pyplot as plt \nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.datasets import load_iris\nfrom sklearn.preprocessing import MinMaxScaler,StandardScaler\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import accuracy_score\n\n# 1 数据集获取\niris = load_iris()\n# print(iris.feature_names)\niris_data = pd.DataFrame(iris.data,columns=['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width'])\niris_data['target'] = iris.target\n\ndef iris_plot(data,col1,col2):\n sns.lmplot(x=col1,y=col2,data=data,hue=\"target\",fit_reg=False)\n plt.show()\n# 2 数据集可视化\n# iris_plot(iris_data, 'Sepal_Width', 'Petal_Length')\n\n# 3 数据集的划分\nX_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=0.2,random_state=44)\n# print(\"训练集的特征值：\\n\",X_train)\n# print(\"训练集的目标值：\\n\",y_train)\n# print(\"测试集的特征值：\\n\",X_test)\n# print(\"测试集的特征值：\\n\",y_test)\n\n# 4 归一化\ntransfer = StandardScaler()\nX_train = transfer.fit_transform(X_train)\nX_test = transfer.transform(X_test)\n# print(\"归一化的,X_train：\\n\",X_train)\n# print(\"归一化的X_test：\\n\",X_test)\n\n# 5 机器学习 KNN\n# 5.1 实例化估计器\nestimator = KNeighborsClassifier(n_neighbors=9)\n# 5.2 进行训练\nestimator.fit(X_train,y_train)\n\n# 6 模型评估\ny_pred = estimator.predict(X_test)\nprint(\"预测值：\\n\",y_pre)\nprint(\"预测值与真实值是否相等：\\n\",y_pred==y_test)\naccuracy = accuracy_score(y_test, y_pred)\nprint(f\"\\nKNN 模型的准确率: {accuracy:.4f}\")\n```\n\n![](/img/machinelearning/knn-01.png)\n### 交叉验证与网格搜索\n```python\nimport seaborn as sns\nimport matplotlib.pyplot as plt \nimport pandas as pd\nfrom sklearn.model_selection import train_test_split,GridSearchCV\nfrom sklearn.datasets import load_iris\nfrom sklearn.preprocessing import MinMaxScaler,StandardScaler\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import accuracy_score\n\n# 1 数据集获取\niris = load_iris()\niris_data = pd.DataFrame(iris.data,columns=['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width'])\niris_data['target'] = iris.target\n\n# 3 数据集的划分\nX_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=0.2)\n\n# 4 归一化\ntransfer = StandardScaler()\nX_train = transfer.fit_transform(X_train)\nX_test = transfer.transform(X_test)\n\n# 5 机器学习 KNN\n# 5.1 实例化估计器\n#\n#不指定 n_neighbors ,使用网格搜索进行循环训练\nestimator = KNeighborsClassifier()\n# 5.2 模型调优 -- 交叉验证，网格搜素\nestimator = GridSearchCV(estimator,param_grid={\"n_neighbors\":[1,3,5,7]},cv=5) # 5 折\n# 5.2 进行训练\nestimator.fit(X_train,y_train)\n \n# 6 模型评估\ny_pred = estimator.predict(X_test)\nprint(\"预测值：\\n\",y_pred)\nprint(\"预测值与真实值是否相等：\\n\",y_pred==y_test)\naccuracy = accuracy_score(y_test, y_pred)\nprint(f\"\\nKNN 模型的准确率: {accuracy:.4f}\")\n\n# 交叉验证的相关参数\nprint(f\"最好结果：{estimator.best_score_}\")\nprint(f\"最好模型：{estimator.best_estimator_}\")\nprint(f\"最好模型结果：{estimator.cv_results_}\")\n```\n![](/img/machinelearning/cros-valid.png)\n\n### 机器学习的基本步骤\n- 获取数据集\n- 数据集基本处理\n - 去重去空、填充等操作 \n - 确定特征值和目标值\n - 分割数据集\n- 特征工程（特征预处理 标准化等）\n- 机器学习\n- 模型评估\n\n### 数据分割的方法\n- 留出法\n 训练/测试集的划分要尽可能保持数据分布的一致性，避免因数据划分过程引入额外的偏差而对最终结果产生影响。\n 单次使用留出法得到的估计结果往往不够稳定可靠，在使用留出法时，一般要采用若干次随机划分、重复进行实验评估后取平均值作为留出法的评估结果。\n ``` python\n\tfrom sklearn.model_selection import KFold,StratifiedKFold\n\timport pandas as pd\n\tX = np.array([\n\t[1,2,3,4],\n\t[11,12,13,14],\n\t[21,22,23,24],\n\t[31,32,33,34],\n\t[41,42,43,44],\n\t[51,52,53,54],\n\t[61,62,63,64],\n\t[71,72,73,74]\n\t])\n\ty=np.array([1,1,0,0,1,1,0,0])\n\tfolder = KFold(n_splits=4)\n\tsfloder = StratifiedKFold(n_splits=4)\n\tprint(\"KFOLD:\")\n\tfor train,test in folder.split(X,y):\n\t print(f\"train:{train},test:{test}\")\n\tprint(\"SKFOLD:\")\n\tfor train,test in sfloder.split(X,y):\n print(f\"train:{train},test:{test}\")\n\t```\n\t![](/img/machinelearning/kfold-skfold.png)\n- 自助法\n- 交叉验证法","source":"_posts/machinelearning/knn.md","raw":"---\ntitle: k近邻算法（K-Nearest Neighbors）KNN\ntags: machinelearning\nabbrlink: 29139\nmathjax: true\ndate: 2025-01-13 17:20:59\n---\n## **k近邻算法（K-Nearest Neighbors）KNN**\n将当前样本的类别归类于距离最近的**k**个样本的类别\n\n#### **距离公式(2维)**\n\n- 欧式距离\n$$\nd = \\sqrt{(x_1-y_1)^2 + (x_2 - y_2)^2}\n$$\n- 曼哈顿距离\n$$\nd = |x_1 - x_2| + |y_1 - y_2|\n$$\n- 切比雪夫距离\n$$\nd = \\max\\left(|x_1 - x_2|, |y_1 - y_2|\\right)\n$$\n#### k值选择问题\n\n| k值 | 影响 |\n| --- | ------------------ |\n| 越大 | 模型过拟合，准确率波动较大 |\n| 越小 | 模型欠拟合，准确率趋于稳定但可能较低 |\n### 特征预处理\n> 通过一些转换函数将特征数据转换成更加适合算法模型的特征数据过程 \n- 归一化\n 将数据变换到指定区间（默认是\\[0,1\\]）\n $$ x' = \\frac{x- x_{\\text {min}}}{x_{\\text{max}} - x_{\\text{min}}} $$\n 若需要缩放到任意区间 \\(\\[a, b\\]\\)，公式为： $$ x' = a + \\frac{(x - x_{\\text{min}}) \\cdot (b - a)}{x_{\\text{max}} - x_{\\text{min}}} $$\n 其中：\\( \\[a, b\\] \\)：目标区间的范围\n 归一化受到数据集的异常值的影响，需要进行标准化处理（更加合理）\n ``` python\n\t from sklearn.preprocessing import MinMaxScaler # 归一化\n\t```\n- 标准化\n 将数据调整为均值为 0，标准差为 1 的标准正态分布\n $$ z = \\frac{x - \\mu}{\\sigma} $$\n \\( z \\)：标准化后的值 \\( x \\)：原始数据值 \\( $\\mu$ \\)：数据的均值 \\( $\\sigma$\\)：数据的标准差\n \n ``` python\n \t from sklearn.preprocessing import StandardScaler # 标准化\n ```\n\n### KNN代码实现\n```python\nimport seaborn as sns\nimport matplotlib.pyplot as plt \nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.datasets import load_iris\nfrom sklearn.preprocessing import MinMaxScaler,StandardScaler\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import accuracy_score\n\n# 1 数据集获取\niris = load_iris()\n# print(iris.feature_names)\niris_data = pd.DataFrame(iris.data,columns=['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width'])\niris_data['target'] = iris.target\n\ndef iris_plot(data,col1,col2):\n sns.lmplot(x=col1,y=col2,data=data,hue=\"target\",fit_reg=False)\n plt.show()\n# 2 数据集可视化\n# iris_plot(iris_data, 'Sepal_Width', 'Petal_Length')\n\n# 3 数据集的划分\nX_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=0.2,random_state=44)\n# print(\"训练集的特征值：\\n\",X_train)\n# print(\"训练集的目标值：\\n\",y_train)\n# print(\"测试集的特征值：\\n\",X_test)\n# print(\"测试集的特征值：\\n\",y_test)\n\n# 4 归一化\ntransfer = StandardScaler()\nX_train = transfer.fit_transform(X_train)\nX_test = transfer.transform(X_test)\n# print(\"归一化的,X_train：\\n\",X_train)\n# print(\"归一化的X_test：\\n\",X_test)\n\n# 5 机器学习 KNN\n# 5.1 实例化估计器\nestimator = KNeighborsClassifier(n_neighbors=9)\n# 5.2 进行训练\nestimator.fit(X_train,y_train)\n\n# 6 模型评估\ny_pred = estimator.predict(X_test)\nprint(\"预测值：\\n\",y_pre)\nprint(\"预测值与真实值是否相等：\\n\",y_pred==y_test)\naccuracy = accuracy_score(y_test, y_pred)\nprint(f\"\\nKNN 模型的准确率: {accuracy:.4f}\")\n```\n\n![](/img/machinelearning/knn-01.png)\n### 交叉验证与网格搜索\n```python\nimport seaborn as sns\nimport matplotlib.pyplot as plt \nimport pandas as pd\nfrom sklearn.model_selection import train_test_split,GridSearchCV\nfrom sklearn.datasets import load_iris\nfrom sklearn.preprocessing import MinMaxScaler,StandardScaler\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import accuracy_score\n\n# 1 数据集获取\niris = load_iris()\niris_data = pd.DataFrame(iris.data,columns=['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width'])\niris_data['target'] = iris.target\n\n# 3 数据集的划分\nX_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=0.2)\n\n# 4 归一化\ntransfer = StandardScaler()\nX_train = transfer.fit_transform(X_train)\nX_test = transfer.transform(X_test)\n\n# 5 机器学习 KNN\n# 5.1 实例化估计器\n#\n#不指定 n_neighbors ,使用网格搜索进行循环训练\nestimator = KNeighborsClassifier()\n# 5.2 模型调优 -- 交叉验证，网格搜素\nestimator = GridSearchCV(estimator,param_grid={\"n_neighbors\":[1,3,5,7]},cv=5) # 5 折\n# 5.2 进行训练\nestimator.fit(X_train,y_train)\n \n# 6 模型评估\ny_pred = estimator.predict(X_test)\nprint(\"预测值：\\n\",y_pred)\nprint(\"预测值与真实值是否相等：\\n\",y_pred==y_test)\naccuracy = accuracy_score(y_test, y_pred)\nprint(f\"\\nKNN 模型的准确率: {accuracy:.4f}\")\n\n# 交叉验证的相关参数\nprint(f\"最好结果：{estimator.best_score_}\")\nprint(f\"最好模型：{estimator.best_estimator_}\")\nprint(f\"最好模型结果：{estimator.cv_results_}\")\n```\n![](/img/machinelearning/cros-valid.png)\n\n### 机器学习的基本步骤\n- 获取数据集\n- 数据集基本处理\n - 去重去空、填充等操作 \n - 确定特征值和目标值\n - 分割数据集\n- 特征工程（特征预处理 标准化等）\n- 机器学习\n- 模型评估\n\n### 数据分割的方法\n- 留出法\n 训练/测试集的划分要尽可能保持数据分布的一致性，避免因数据划分过程引入额外的偏差而对最终结果产生影响。\n 单次使用留出法得到的估计结果往往不够稳定可靠，在使用留出法时，一般要采用若干次随机划分、重复进行实验评估后取平均值作为留出法的评估结果。\n ``` python\n\tfrom sklearn.model_selection import KFold,StratifiedKFold\n\timport pandas as pd\n\tX = np.array([\n\t[1,2,3,4],\n\t[11,12,13,14],\n\t[21,22,23,24],\n\t[31,32,33,34],\n\t[41,42,43,44],\n\t[51,52,53,54],\n\t[61,62,63,64],\n\t[71,72,73,74]\n\t])\n\ty=np.array([1,1,0,0,1,1,0,0])\n\tfolder = KFold(n_splits=4)\n\tsfloder = StratifiedKFold(n_splits=4)\n\tprint(\"KFOLD:\")\n\tfor train,test in folder.split(X,y):\n\t print(f\"train:{train},test:{test}\")\n\tprint(\"SKFOLD:\")\n\tfor train,test in sfloder.split(X,y):\n print(f\"train:{train},test:{test}\")\n\t```\n\t![](/img/machinelearning/kfold-skfold.png)\n- 自助法\n- 交叉验证法","slug":"machinelearning/knn","published":1,"updated":"2025-01-14T09:21:29.069Z","comments":1,"layout":"post","photos":[],"_id":"cm5w9lrdi0005tgah797v3otl","content":"

k近邻算法（K-Nearest Neighbors）KNN

将当前样本的类别归类于距离最近的k个样本的类别

\n

距离公式(2维)

\n

k值选择问题

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
k值影响
越大模型过拟合，准确率波动较大
越小模型欠拟合，准确率趋于稳定但可能较低
\n

特征预处理

\n

通过一些转换函数将特征数据转换成更加适合算法模型的特征数据过程

\n
\n\n

KNN代码实现

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
import seaborn as sns
import matplotlib.pyplot as plt  
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from sklearn.preprocessing import MinMaxScaler,StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# 1 数据集获取
iris = load_iris()
# print(iris.feature_names)
iris_data = pd.DataFrame(iris.data,columns=['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width'])
iris_data['target'] = iris.target

def iris_plot(data,col1,col2):
    sns.lmplot(x=col1,y=col2,data=data,hue="target",fit_reg=False)
    plt.show()
# 2 数据集可视化
# iris_plot(iris_data, 'Sepal_Width', 'Petal_Length')

# 3 数据集的划分
X_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=0.2,random_state=44)
# print("训练集的特征值：\\n",X_train)
# print("训练集的目标值：\\n",y_train)
# print("测试集的特征值：\\n",X_test)
# print("测试集的特征值：\\n",y_test)

# 4 归一化
transfer = StandardScaler()
X_train = transfer.fit_transform(X_train)
X_test = transfer.transform(X_test)
# print("归一化的,X_train：\\n",X_train)
# print("归一化的X_test：\\n",X_test)

# 5 机器学习 KNN
# 5.1 实例化估计器
estimator = KNeighborsClassifier(n_neighbors=9)
# 5.2 进行训练
estimator.fit(X_train,y_train)

# 6 模型评估
y_pred = estimator.predict(X_test)
print("预测值：\\n",y_pre)
print("预测值与真实值是否相等：\\n",y_pred==y_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"\\nKNN 模型的准确率: {accuracy:.4f}")
\n\n

\n

交叉验证与网格搜索

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
import seaborn as sns
import matplotlib.pyplot as plt  
import pandas as pd
from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.datasets import load_iris
from sklearn.preprocessing import MinMaxScaler,StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# 1 数据集获取
iris = load_iris()
iris_data = pd.DataFrame(iris.data,columns=['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width'])
iris_data['target'] = iris.target

# 3 数据集的划分
X_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=0.2)

# 4 归一化
transfer = StandardScaler()
X_train = transfer.fit_transform(X_train)
X_test = transfer.transform(X_test)

# 5 机器学习 KNN
# 5.1 实例化估计器
#
#不指定 <code> n_neighbors </code> ,使用网格搜索进行循环训练
estimator = KNeighborsClassifier()
# 5.2 模型调优 -- 交叉验证，网格搜素
estimator = GridSearchCV(estimator,param_grid={"n_neighbors":[1,3,5,7]},cv=5) # 5 折
# 5.2 进行训练
estimator.fit(X_train,y_train)
 
# 6 模型评估
y_pred = estimator.predict(X_test)
print("预测值：\\n",y_pred)
print("预测值与真实值是否相等：\\n",y_pred==y_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"\\nKNN 模型的准确率: {accuracy:.4f}")

# 交叉验证的相关参数
print(f"最好结果：{estimator.best_score_}")
print(f"最好模型：{estimator.best_estimator_}")
print(f"最好模型结果：{estimator.cv_results_}")
\n

\n

机器学习的基本步骤

\n

数据分割的方法

\n","cover":false,"excerpt":"","more":"

k近邻算法（K-Nearest Neighbors）KNN

将当前样本的类别归类于距离最近的k个样本的类别

\n

距离公式(2维)

\n

k值选择问题

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
k值影响
越大模型过拟合，准确率波动较大
越小模型欠拟合，准确率趋于稳定但可能较低
\n

特征预处理

\n

通过一些转换函数将特征数据转换成更加适合算法模型的特征数据过程

\n
\n\n

KNN代码实现

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
import seaborn as sns
import matplotlib.pyplot as plt  
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from sklearn.preprocessing import MinMaxScaler,StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# 1 数据集获取
iris = load_iris()
# print(iris.feature_names)
iris_data = pd.DataFrame(iris.data,columns=['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width'])
iris_data['target'] = iris.target

def iris_plot(data,col1,col2):
    sns.lmplot(x=col1,y=col2,data=data,hue="target",fit_reg=False)
    plt.show()
# 2 数据集可视化
# iris_plot(iris_data, 'Sepal_Width', 'Petal_Length')

# 3 数据集的划分
X_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=0.2,random_state=44)
# print("训练集的特征值：\\n",X_train)
# print("训练集的目标值：\\n",y_train)
# print("测试集的特征值：\\n",X_test)
# print("测试集的特征值：\\n",y_test)

# 4 归一化
transfer = StandardScaler()
X_train = transfer.fit_transform(X_train)
X_test = transfer.transform(X_test)
# print("归一化的,X_train：\\n",X_train)
# print("归一化的X_test：\\n",X_test)

# 5 机器学习 KNN
# 5.1 实例化估计器
estimator = KNeighborsClassifier(n_neighbors=9)
# 5.2 进行训练
estimator.fit(X_train,y_train)

# 6 模型评估
y_pred = estimator.predict(X_test)
print("预测值：\\n",y_pre)
print("预测值与真实值是否相等：\\n",y_pred==y_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"\\nKNN 模型的准确率: {accuracy:.4f}")
\n\n

\n

交叉验证与网格搜索

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
import seaborn as sns
import matplotlib.pyplot as plt  
import pandas as pd
from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.datasets import load_iris
from sklearn.preprocessing import MinMaxScaler,StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# 1 数据集获取
iris = load_iris()
iris_data = pd.DataFrame(iris.data,columns=['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width'])
iris_data['target'] = iris.target

# 3 数据集的划分
X_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=0.2)

# 4 归一化
transfer = StandardScaler()
X_train = transfer.fit_transform(X_train)
X_test = transfer.transform(X_test)

# 5 机器学习 KNN
# 5.1 实例化估计器
#
#不指定 <code> n_neighbors </code> ,使用网格搜索进行循环训练
estimator = KNeighborsClassifier()
# 5.2 模型调优 -- 交叉验证，网格搜素
estimator = GridSearchCV(estimator,param_grid={"n_neighbors":[1,3,5,7]},cv=5) # 5 折
# 5.2 进行训练
estimator.fit(X_train,y_train)
 
# 6 模型评估
y_pred = estimator.predict(X_test)
print("预测值：\\n",y_pred)
print("预测值与真实值是否相等：\\n",y_pred==y_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"\\nKNN 模型的准确率: {accuracy:.4f}")

# 交叉验证的相关参数
print(f"最好结果：{estimator.best_score_}")
print(f"最好模型：{estimator.best_estimator_}")
print(f"最好模型结果：{estimator.cv_results_}")
\n

\n

机器学习的基本步骤

\n

数据分割的方法

\n"},{"title":"page","abbrlink":1441,"date":"2024-08-01T01:00:10.000Z","_content":"\n- [deploy](./deploy)\n- ","source":"_posts/frontend/frontend.md","raw":"---\ntitle: page\nabbrlink: 1441\ndate: 2024-08-01 09:00:10\ntags:\n---\n\n- [deploy](./deploy)\n- ","slug":"frontend/frontend","published":1,"updated":"2024-08-09T12:21:50.023Z","comments":1,"layout":"post","photos":[],"_id":"cm5w9lrdj0007tgah975l4qm5","content":"\n","cover":false,"excerpt":"","more":"\n"},{"title":"网络相关","abbrlink":41168,"date":"2024-08-07T02:06:08.000Z","_content":"\n","source":"_posts/net/index.md","raw":"---\ntitle: 网络相关\nabbrlink: 41168\ndate: 2024-08-07 10:06:08\ntags:\n---\n\n","slug":"net/index","published":1,"updated":"2024-08-09T12:21:50.028Z","comments":1,"layout":"post","photos":[],"_id":"cm5w9lrdm000atgahetofhexy","content":"","cover":false,"excerpt":"","more":""},{"title":"uniapp 开发","abbrlink":58817,"date":"2024-08-05T06:07:01.000Z","_content":"- [uniapp component](../component1)","source":"_posts/frontend/uniapp/uniapp.md","raw":"---\ntitle: uniapp 开发\ntags: uniapp\nabbrlink: 58817\ndate: 2024-08-05 14:07:01\n---\n- [uniapp component](../component1)","slug":"frontend/uniapp/uniapp","published":1,"updated":"2024-08-09T12:21:50.039Z","comments":1,"layout":"post","photos":[],"_id":"cm5w9lrdn000ctgah8ll3dor6","content":"\n","cover":false,"excerpt":"","more":"\n"},{"title":"郑伯克段于鄢","abbrlink":58638,"date":"2024-08-09T12:00:13.000Z","_content":"原文如下：\n\n      初，郑武公娶于申【申国】，曰武姜【武为武公谥号，姜为其宗族之性】。生庄公及共叔段【共表示其曾出逃到共，叔为老三，段为名】。庄公寤生【出生时头先出，难产】，惊姜氏，故名曰“寤生”， 遂恶之，爱【喜爱】共叔段，欲立【立为储君】之，亟（qì）【多次】请于武公，及庄公即位，为之【共叔段】请制【一个叫制的封地，虎牢关所在】。公曰：“制，岩邑【险要的城邑】也，虢叔死焉，佗【通“他”，其他】邑唯命（是听）。”请京，使居之，谓之“京城大叔”。","source":"_posts/ancient/guwenguanzhi/1.md","raw":"---\ntitle: 郑伯克段于鄢\ntags: 古文观止\ncategories:\n - 古文观止\nabbrlink: 58638\ndate: 2024-08-09 20:00:13\n---\n原文如下：\n\n      初，郑武公娶于申【申国】，曰武姜【武为武公谥号，姜为其宗族之性】。生庄公及共叔段【共表示其曾出逃到共，叔为老三，段为名】。庄公寤生【出生时头先出，难产】，惊姜氏，故名曰“寤生”， 遂恶之，爱【喜爱】共叔段，欲立【立为储君】之，亟（qì）【多次】请于武公，及庄公即位，为之【共叔段】请制【一个叫制的封地，虎牢关所在】。公曰：“制，岩邑【险要的城邑】也，虢叔死焉，佗【通“他”，其他】邑唯命（是听）。”请京，使居之，谓之“京城大叔”。","slug":"ancient/guwenguanzhi/1","published":1,"updated":"2024-08-10T02:31:03.678Z","comments":1,"layout":"post","photos":[],"_id":"cm5w9lrdn000dtgah04audthw","content":"

原文如下：

\n

      初，郑武公娶于申【申国】，曰武姜【武为武公谥号，姜为其宗族之性】。生庄公及共叔段【共表示其曾出逃到共，叔为老三，段为名】。庄公寤生【出生时头先出，难产】，惊姜氏，故名曰“寤生”， 遂恶之，爱【喜爱】共叔段，欲立【立为储君】之，亟（qì）【多次】请于武公，及庄公即位，为之【共叔段】请制【一个叫制的封地，虎牢关所在】。公曰：“制，岩邑【险要的城邑】也，虢叔死焉，佗【通“他”，其他】邑唯命（是听）。”请京，使居之，谓之“京城大叔”。

\n","cover":false,"excerpt":"","more":"

原文如下：

\n

      初，郑武公娶于申【申国】，曰武姜【武为武公谥号，姜为其宗族之性】。生庄公及共叔段【共表示其曾出逃到共，叔为老三，段为名】。庄公寤生【出生时头先出，难产】，惊姜氏，故名曰“寤生”， 遂恶之，爱【喜爱】共叔段，欲立【立为储君】之，亟（qì）【多次】请于武公，及庄公即位，为之【共叔段】请制【一个叫制的封地，虎牢关所在】。公曰：“制，岩邑【险要的城邑】也，虢叔死焉，佗【通“他”，其他】邑唯命（是听）。”请京，使居之，谓之“京城大叔”。

\n"},{"title":"组件使用","abbrlink":33957,"date":"2024-08-05T06:07:01.000Z","_content":"\n### 组件自动导入\n```json\n\t\"easycom\":{\n\t \"autoscan\": true,\n\t \"custom\": {\n\t \"^tui-(.*)\": \"@/components/thorui/tui-$1/tui-$1.vue\" // 匹配components目录内的vue文件\n\t }\n\t}\n```\n\n### `tui-sticky 吸顶容器` \n\n> 包含 以下 `tui` 组件 :\n> - tui-sticky\n> - tui-list-view\n> - tui-list-cell\n> \n\n```html\n\n \n \n \n \n \n \n\n\n\n```\n","source":"_posts/frontend/uniapp/component1.md","raw":"---\ntitle: 组件使用\ntags: uniapp\nabbrlink: 33957\ndate: 2024-08-05 14:07:01\n---\n\n### 组件自动导入\n```json\n\t\"easycom\":{\n\t \"autoscan\": true,\n\t \"custom\": {\n\t \"^tui-(.*)\": \"@/components/thorui/tui-$1/tui-$1.vue\" // 匹配components目录内的vue文件\n\t }\n\t}\n```\n\n### `tui-sticky 吸顶容器` \n\n> 包含 以下 `tui` 组件 :\n> - tui-sticky\n> - tui-list-view\n> - tui-list-cell\n> \n\n```html\n\n \n \n \n \n \n \n\n\n\n```\n","slug":"frontend/uniapp/component1","published":1,"updated":"2024-08-09T12:21:50.042Z","comments":1,"layout":"post","photos":[],"_id":"cm5w9lrdo000gtgah38tl6q9j","content":"

组件自动导入

1
2
3
4
5
6
"easycom":{
  "autoscan": true,
  "custom": {
    "^tui-(.*)": "@/components/thorui/tui-$1/tui-$1.vue"  // 匹配components目录内的vue文件
  }
}
\n\n

tui-sticky 吸顶容器

\n

包含 以下 tui 组件 :

\n\n
\n
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
<tui-sticky :scrollTop="scrollTop" stickyHeight="104rpx" container>
    <!-- header start -->
    <template v-slot:header>
        <view class="sticky-item">
            <view class="setting">设置</view>
        </view>
    </template>
    <!-- header end -->
    <!--内容 start-->
    <template v-slot:content>
        <tui-list-view class="content">
            <tui-list-cell  :arrow="false">
                <switch class='switch' checked color="#FFCC33" />
            </tui-list-cell>
        </tui-list-view>
    </template>
    <!--内容 end-->
</tui-sticky>

<script setup>
    import { ref } from 'vue'
    import { onPageScroll } from '@dcloudio/uni-app'

    // 定义 scrollTop 响应式变量
    const scrollTop = ref(0)
    // 监听页面滚动事件
    onPageScroll((e) => {
        scrollTop.value = e.scrollTop
    })
</script>
\n","cover":false,"excerpt":"","more":"

组件自动导入

1
2
3
4
5
6
"easycom":{
  "autoscan": true,
  "custom": {
    "^tui-(.*)": "@/components/thorui/tui-$1/tui-$1.vue"  // 匹配components目录内的vue文件
  }
}
\n\n

tui-sticky 吸顶容器

\n

包含 以下 tui 组件 :

\n\n
\n
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
<tui-sticky :scrollTop="scrollTop" stickyHeight="104rpx" container>
    <!-- header start -->
    <template v-slot:header>
        <view class="sticky-item">
            <view class="setting">设置</view>
        </view>
    </template>
    <!-- header end -->
    <!--内容 start-->
    <template v-slot:content>
        <tui-list-view class="content">
            <tui-list-cell  :arrow="false">
                <switch class='switch' checked color="#FFCC33" />
            </tui-list-cell>
        </tui-list-view>
    </template>
    <!--内容 end-->
</tui-sticky>

<script setup>
    import { ref } from 'vue'
    import { onPageScroll } from '@dcloudio/uni-app'

    // 定义 scrollTop 响应式变量
    const scrollTop = ref(0)
    // 监听页面滚动事件
    onPageScroll((e) => {
        scrollTop.value = e.scrollTop
    })
</script>
\n"},{"title":"Docker被墙，如何继续使用？","top_img":"/img/site01.jpg","top_img_height":"800px","abbrlink":47807,"date":"2024-08-01T01:10:40.000Z","_content":"\n## Docker Download\n> 自从docker官方仓库在中国大陆被墙后，docker的部署方式也发生了改变。\n> 解决docker安装问题：https://github.com/shenjianZ/docker_installer\n\n1. 安装docker \n ```shell\n sudo curl -fsSL https://gitee.com/tech-shrimp/docker_installer/releases/download/latest/linux.sh| bash -s docker --mirror Aliyun\n ```\n \n2. 启动docker\n ```shell\n sudo systemctl start docker\n ```\n \n3. 设置开机自启\n ```shell\n sudo systemctl enable docker\n ```\n \n4. Docker pull images\n > 将image下载到阿里云镜像仓库中\n > 解决docker pull 镜像问题:https://github.com/shenjianZ/docker_image_pusher\n \n 1. **登录阿里云镜像服务** https://cr.console.aliyun.com/,\n\n 启用个人实例，创建一个命名空间（`ALIYUN_NAME_SPACE`）\n\n 2. 在**访问凭证** 可以看到账号 用户名（`ALIYUN_REGISTRY_USER`)\n\n 密码（`ALIYUN_REGISTRY_PASSWORD`)\n\n 仓库地址（`ALIYUN_REGISTRY`）\n\n 3. **启动Action**\n 进入您自己的项目，点击`Action`，启用`Github Action`功能\n \n 4. **配置环境变量**\n 进入Settings->Secret and variables->Actions->New Repository secret\n 将上一步的四个值\n `ALIYUN_NAME_SPACE`,\n \n ` ALIYUN_REGISTRY_USER`，\n \n `ALIYUN_REGISTRY_PASSWORD`，\n \n `ALIYUN_REGISTRY`\n 配置成环境变量\n \n 5. **添加镜像**\n 打开`images.txt`文件，添加你想要的镜像 可以加tag\n \n 6. 使用镜像\n 回到阿里云，镜像仓库，点击任意镜像，可查看镜像状态。(可以改成公开，拉取镜像免登录)","source":"_posts/frontend/deploy/deploy.md","raw":"---\ntitle: Docker被墙，如何继续使用？\ntop_img: /img/site01.jpg\ntop_img_height: 800px\nabbrlink: 47807\ndate: 2024-08-01 09:10:40\ntags:\n---\n\n## Docker Download\n> 自从docker官方仓库在中国大陆被墙后，docker的部署方式也发生了改变。\n> 解决docker安装问题：https://github.com/shenjianZ/docker_installer\n\n1. 安装docker \n ```shell\n sudo curl -fsSL https://gitee.com/tech-shrimp/docker_installer/releases/download/latest/linux.sh| bash -s docker --mirror Aliyun\n ```\n \n2. 启动docker\n ```shell\n sudo systemctl start docker\n ```\n \n3. 设置开机自启\n ```shell\n sudo systemctl enable docker\n ```\n \n4. Docker pull images\n > 将image下载到阿里云镜像仓库中\n > 解决docker pull 镜像问题:https://github.com/shenjianZ/docker_image_pusher\n \n 1. **登录阿里云镜像服务** https://cr.console.aliyun.com/,\n\n 启用个人实例，创建一个命名空间（`ALIYUN_NAME_SPACE`）\n\n 2. 在**访问凭证** 可以看到账号 用户名（`ALIYUN_REGISTRY_USER`)\n\n 密码（`ALIYUN_REGISTRY_PASSWORD`)\n\n 仓库地址（`ALIYUN_REGISTRY`）\n\n 3. **启动Action**\n 进入您自己的项目，点击`Action`，启用`Github Action`功能\n \n 4. **配置环境变量**\n 进入Settings->Secret and variables->Actions->New Repository secret\n 将上一步的四个值\n `ALIYUN_NAME_SPACE`,\n \n ` ALIYUN_REGISTRY_USER`，\n \n `ALIYUN_REGISTRY_PASSWORD`，\n \n `ALIYUN_REGISTRY`\n 配置成环境变量\n \n 5. **添加镜像**\n 打开`images.txt`文件，添加你想要的镜像 可以加tag\n \n 6. 使用镜像\n 回到阿里云，镜像仓库，点击任意镜像，可查看镜像状态。(可以改成公开，拉取镜像免登录)","slug":"frontend/deploy/deploy","published":1,"updated":"2024-08-09T12:21:50.045Z","comments":1,"layout":"post","photos":[],"_id":"cm5w9lrdt000otgah0ogb4vdp","content":"

Docker Download

\n

自从docker官方仓库在中国大陆被墙后，docker的部署方式也发生了改变。
解决docker安装问题：https://github.com/shenjianZ/docker_installer

\n
\n
    \n

  1. 安装docker

    \n 
    1
    sudo curl -fsSL https://gitee.com/tech-shrimp/docker_installer/releases/download/latest/linux.sh| bash -s docker --mirror Aliyun
    \n
    2. \n

  2. 启动docker

    \n 
    1
    sudo systemctl start docker
    \n
    3. \n

  3. 设置开机自启

    \n 
    1
    sudo systemctl enable docker
    \n
    4. \n

  4. Docker pull images

    \n
    \n

    将image下载到阿里云镜像仓库中
    解决docker pull 镜像问题:https://github.com/shenjianZ/docker_image_pusher

    \n
    \n
      \n

    1. 登录阿里云镜像服务 https://cr.console.aliyun.com/,

      \n

      启用个人实例，创建一个命名空间（ALIYUN_NAME_SPACE）

      \n
      2. \n

    2. 在访问凭证 可以看到账号 用户名（ALIYUN_REGISTRY_USER)

      \n

      密码（ALIYUN_REGISTRY_PASSWORD)

      \n

      仓库地址（ALIYUN_REGISTRY）

      \n
      3. \n

    3. 启动Action
      进入您自己的项目，点击Action，启用Github Action功能

      \n
      4. \n

    4. 配置环境变量
      进入Settings->Secret and variables->Actions->New Repository secret
      将上一步的四个值
      ALIYUN_NAME_SPACE,

      \n

      ALIYUN_REGISTRY_USER，

      \n

      ALIYUN_REGISTRY_PASSWORD，

      \n

      ALIYUN_REGISTRY
      配置成环境变量

      \n
      5. \n

    5. 添加镜像
      打开images.txt文件，添加你想要的镜像 可以加tag

      \n
      6. \n

    6. 使用镜像
      回到阿里云，镜像仓库，点击任意镜像，可查看镜像状态。(可以改成公开，拉取镜像免登录)

      \n
      7. \n
    \n
    5. \n
\n","cover":false,"excerpt":"","more":"

Docker Download

\n

自从docker官方仓库在中国大陆被墙后，docker的部署方式也发生了改变。
解决docker安装问题：https://github.com/shenjianZ/docker_installer

\n
\n
    \n

  1. 安装docker

    \n 
    1
    sudo curl -fsSL https://gitee.com/tech-shrimp/docker_installer/releases/download/latest/linux.sh| bash -s docker --mirror Aliyun
    \n
    2. \n

  2. 启动docker

    \n 
    1
    sudo systemctl start docker
    \n
    3. \n

  3. 设置开机自启

    \n 
    1
    sudo systemctl enable docker
    \n
    4. \n

  4. Docker pull images

    \n
    \n

    将image下载到阿里云镜像仓库中
    解决docker pull 镜像问题:https://github.com/shenjianZ/docker_image_pusher

    \n
    \n
      \n

    1. 登录阿里云镜像服务 https://cr.console.aliyun.com/,

      \n

      启用个人实例，创建一个命名空间（ALIYUN_NAME_SPACE）

      \n
      2. \n

    2. 在访问凭证 可以看到账号 用户名（ALIYUN_REGISTRY_USER)

      \n

      密码（ALIYUN_REGISTRY_PASSWORD)

      \n

      仓库地址（ALIYUN_REGISTRY）

      \n
      3. \n

    3. 启动Action
      进入您自己的项目，点击Action，启用Github Action功能

      \n
      4. \n

    4. 配置环境变量
      进入Settings->Secret and variables->Actions->New Repository secret
      将上一步的四个值
      ALIYUN_NAME_SPACE,

      \n

      ALIYUN_REGISTRY_USER，

      \n

      ALIYUN_REGISTRY_PASSWORD，

      \n

      ALIYUN_REGISTRY
      配置成环境变量

      \n
      5. \n

    5. 添加镜像
      打开images.txt文件，添加你想要的镜像 可以加tag

      \n
      6. \n

    6. 使用镜像
      回到阿里云，镜像仓库，点击任意镜像，可查看镜像状态。(可以改成公开，拉取镜像免登录)

      \n
      7. \n
    \n
    5. \n
\n"},{"title":"Hadoop集群搭建基础环境","top_img":"/img/site01.jpg","top_img_height":"800px","abbrlink":61253,"date":"2024-09-11T14:45:40.000Z","_content":"\n### 防火墙关闭\n```bash\n# 在 6 台主机执行\nsystemctl stop firewalld\nsystemctl disable firewalld\n```\n### 配置yum源\n- 下载 repo 文件：\n [Centos-7.repo](http://mirrors.aliyun.com/repo/Centos-7.repo)\n 并上传到`/tmp`，进入到`/tmp`\n- 备份并且替换系统的repo文件\n ``` bash\n \tcp Centos-7.repo /etc/yum.repos.d/ \n\tcd /etc/yum.repos.d/ \n\tmv CentOS-Base.repo CentOS-Base.repo.bak \n\tmv Centos-7.repo CentOS-Base.repo\n\t ```\n- 将`nn1`上的`CentOS-Base.repo`拷贝到其他主机\n ```bash\n scp /etc/yum.repos.d/CentOS-Base.repo root@nn2:/etc/yum.repos.d\n scp /etc/yum.repos.d/CentOS-Base.repo root@nn3:/etc/yum.repos.d\n scp /etc/yum.repos.d/CentOS-Base.repo root@s1:/etc/yum.repos.d\n scp /etc/yum.repos.d/CentOS-Base.repo root@s2:/etc/yum.repos.d\n scp /etc/yum.repos.d/CentOS-Base.repo root@s3:/etc/yum.repos.d\n ```\n- 执行yum源更新命令\n ```bash\n\t yum clean all\n\t yum makecache \n\t yum update -y \n\t```\n- 安装常用软件\n ```bash\n yum install -y openssh-server vim gcc gcc-c++ glibc-headers bzip2-devel lzo-devel curl wget openssh-clients zlib-devel autoconf automake cmake libtool openssl-devel fuse-devel snappy-devel telnet unzip zip net-tools.x86_64 firewalld systemd ntp unrar bzip2\n ```\n### JDK安装\n>注意需要在六台机器依次执行\n- 上传到`/tmp`目录下，安装\n ```bash\n cd /tmp\n rpm -ivh jdk-8u144-linux-x64.rpm\n ```\n- 配置环境变量\n ```bash\n ln -s /usr/java/jdk1.8.0_144/ /usr/java/jdk1.8\n echo 'export JAVA_HOME=/usr/java/jdk1.8' >> /etc/profile.d/myEnv.sh \n echo 'export PATH=$PATH:$JAVA_HOME/bin' >> /etc/profile.d/myEnv.sh \n source /etc/profile \n java -version\n ```\n### 修改主机名和主机名映射\n\n```bash\nvim /etc/hostname\n```\n6台机器分别为nn1、nn2、nn3、s1、s2、s3\n\n```bash\nvim /etc/hosts\n```\n\n修改为\n```text\n192.168.1.30 nn1\n192.168.1.31 nn2\n192.168.1.32 nn3\n192.168.1.33 s1\n192.168.1.34 s2\n192.168.1.35 s3\n```\n### 创建hadoop用户\n```bash\n#创建hadoop用户 \nuseradd hadoop \n#给hadoop用户设置密码: 12345678 \npasswd hadoop\n```\n### 禁止非 wheel 组用户切换到root，配置免密切换root\n- 修改/etc/pam.d/su配置\n ```bash\n sed -i 's/#auth\\t\\trequired\\tpam_wheel.so/auth\\t\\trequired\\tpam_wheel.so/g' '/etc/pam.d/su' \n sed -i 's/#auth\\t\\tsufficient\\tpam_wheel.so/auth\\t\\tsufficient\\tpam_wheel.so/g' '/etc/pam.d/su'\n ```\n- 修改/etc/login.defs文件\n ```bash\n echo \"SU_WHEEL_ONLY yes\" >> /etc/login.defs\n ```\n- 添加用户到管理员，禁止普通用户su 到 root\n ```bash\n #把hadoop用户加到wheel组里\n gpasswd -a hadoop wheel\n #查看wheel组里是否有hadoop用户\n cat /etc/group | grep wheel\n ```\n### 给hadoop用户，配置SSH密钥\n#### 配置hadoop用户ssh免密码登录到hadoop\n- 仅在`nn1`执行这段脚本命令即可\n 但是 `su - hadoop ` ,` mkdir ~/.ssh` 需要在其他主机执行一下\n ```bash\n #切换到hadoop用户 \n su - hadoop\n #生成ssh公私钥 \n ssh-keygen -t rsa -f ~/.ssh/id_rsa -P ''\n ssh-copy-id nn1\n ssh-copy-id nn2\n ssh-copy-id nn3\n ssh-copy-id s1\n ssh-copy-id s2\n ssh-copy-id s3\n scp /home/hadoop/.ssh/id_rsa hadoop@nn2:/home/hadoop/.ssh\n scp /home/hadoop/.ssh/id_rsa hadoop@nn3:/home/hadoop/.ssh\n scp /home/hadoop/.ssh/id_rsa hadoop@s1:/home/hadoop/.ssh\n scp /home/hadoop/.ssh/id_rsa hadoop@s2:/home/hadoop/.ssh\n scp /home/hadoop/.ssh/id_rsa hadoop@s3:/home/hadoop/.ssh\n ```\n#### 配置hadoop用户ssh免密码登录到root\n- 同上\n ```bash\n ssh-copy-id root@nn1\n ssh-copy-id root@ nn2\n ssh-copy-id root@nn3\n ssh-copy-id root@s1\n ssh-copy-id root@s2\n ssh-copy-id root@s3\n scp /home/hadoop/.ssh/id_rsa root@nn2:/root/.ssh\n scp /home/hadoop/.ssh/id_rsa root@nn3:/root/.ssh\n scp /home/hadoop/.ssh/id_rsa root@s1:/root/.ssh\n scp /home/hadoop/.ssh/id_rsa root@s2:/root/.ssh\n scp /home/hadoop/.ssh/id_rsa root@s3:/root/.ssh\n ```\n### 脚本配置\n- **ips**\n ```bash\n vim /home/hadoop/bin/ips\n ```\n \n ```bash\n nn1 \n nn2\n nn3\n s1 \n s2 \n s3\n ```\n- **ssh_all.sh**\n ```bash\n vim /home/hadoop/bin/ssh_all.sh\n ```\n\n ```bash\n #! /bin/bash\n # 进入到当前脚本所在目录\n cd `dirname $0`\n # 获取当前脚本所在目录\n dir_path=`pwd`\n #echo $dir_path\n # 读ips文件得到数组(里面是一堆主机名)\n ip_arr=(`cat $dir_path/ips`)\n # 遍历数组里的主机名\n for ip in ${ip_arr[*]}\n do\n # 拼接ssh命令: ssh hadoop@nn1.hadoop ls\n cmd_=\"ssh hadoop@${ip} \\\"$*\\\" \"\n echo $cmd_\n # 通过eval命令 执行 拼接的ssh 命令\n if eval ${cmd_} ; then\n echo \"OK\"\n else\n echo \"FAIL\"\n fi\n done\n ```\n- **ssh_root.sh**\n ```bash\n #! /bin/bash\n # 进入到当前脚本所在目录\n cd `dirname $0`\n # 获取当前脚本所在目录\n dir_path=`pwd`\n #echo $dir_path\n # 读ips文件得到数组(里面是一堆主机名)\n ip_arr=(`cat $dir_path/ips`)\n # 遍历数组里的主机名\n for ip in ${ip_arr[*]}\n do\n # 拼接ssh命令: ssh hadoop@nn1.hadoop ls\n cmd_=\"ssh hadoop@${ip} ~/bin/exe.sh \\\"$*\\\"\"\n echo $cmd_\n # 通过eval命令 执行 拼接的ssh 命令\n if eval ${cmd_} ; then\n echo \"OK\"\n else\n echo \"FAIL\"\n fi\n done\n ```\n- **scp_all.sh**\n ```bash\n #! /bin/bash\n # 进入到当前脚本所在目录\n cd `dirname $0`\n # 获取当前脚本所在目录\n dir_path=`pwd`\n #echo $dir_path\n # 读ips文件得到数组(里面是一堆主机名)\n ip_arr=(`cat $dir_path/ips`)\n # 源\n source_=$1\n # 目标\n target=$2\n # 遍历数组里的主机名\n for ip in ${ip_arr[*]}\n do\n # 拼接scp命令: scp 源 hadoop@nn1.hadoop:目标\n cmd_=\"scp -r ${source_} hadoop@${ip}:${target}\"\n echo $cmd_\n # 通过eval命令 执行 拼接的scp 命令\n if eval ${cmd_} ; then\n echo \"OK\"\n else\n echo \"FAIL\"\n fi\n done\n ```\n- **exe.sh**\n ```bash\n #切换到root用户执行cmd命令\n cmd=$*\n su - << EOF\n $cmd\n EOF\n ```\n- 赋予执行权限\n ```bash\n chmod +x ssh_all.sh \n chmod +x scp_all.sh\n chmod +x ssh_root.sh\n chmod +x exe.sh\n ```\n- 分发到其他主机\n ```bash\n ./ssh_all.sh mkdir /home/hadoop/bin\n ./scp_all.sh /home/hadoop/bin/ips /home/hadoop/bin/\n ./scp_all.sh /home/hadoop/bin/exe.sh /home/hadoop/bin/\n ./scp_all.sh /home/hadoop/bin/ssh_all.sh /home/hadoop/bin/\n ./scp_all.sh /home/hadoop/bin/scp_all.sh /home/hadoop/bin/\n ./scp_all.sh /home/hadoop/bin/ssh_root.sh /home/hadoop/bin/\n ```\n\n- 将 `/home/hadoop/bin`添加到hadoop用户的环境变量，需要切换到`hadoop`用户\n\n ```bash\n echo 'export PATH=$PATH:/home/hadoop/bin' >> ~/.bashrc && source ~/.bashrc\n scp_all.sh /home/hadoop/.bashrc /home/hadoop/\n ssh_all.sh source ~/.bashrc \n ```","source":"_posts/bigdata/hadoop/env.md","raw":"---\ntitle: Hadoop集群搭建基础环境\ntop_img: /img/site01.jpg\ntop_img_height: 800px\nabbrlink: 61253\ndate: 2024-09-011 22:45:40\n---\n\n### 防火墙关闭\n```bash\n# 在 6 台主机执行\nsystemctl stop firewalld\nsystemctl disable firewalld\n```\n### 配置yum源\n- 下载 repo 文件：\n [Centos-7.repo](http://mirrors.aliyun.com/repo/Centos-7.repo)\n 并上传到`/tmp`，进入到`/tmp`\n- 备份并且替换系统的repo文件\n ``` bash\n \tcp Centos-7.repo /etc/yum.repos.d/ \n\tcd /etc/yum.repos.d/ \n\tmv CentOS-Base.repo CentOS-Base.repo.bak \n\tmv Centos-7.repo CentOS-Base.repo\n\t ```\n- 将`nn1`上的`CentOS-Base.repo`拷贝到其他主机\n ```bash\n scp /etc/yum.repos.d/CentOS-Base.repo root@nn2:/etc/yum.repos.d\n scp /etc/yum.repos.d/CentOS-Base.repo root@nn3:/etc/yum.repos.d\n scp /etc/yum.repos.d/CentOS-Base.repo root@s1:/etc/yum.repos.d\n scp /etc/yum.repos.d/CentOS-Base.repo root@s2:/etc/yum.repos.d\n scp /etc/yum.repos.d/CentOS-Base.repo root@s3:/etc/yum.repos.d\n ```\n- 执行yum源更新命令\n ```bash\n\t yum clean all\n\t yum makecache \n\t yum update -y \n\t```\n- 安装常用软件\n ```bash\n yum install -y openssh-server vim gcc gcc-c++ glibc-headers bzip2-devel lzo-devel curl wget openssh-clients zlib-devel autoconf automake cmake libtool openssl-devel fuse-devel snappy-devel telnet unzip zip net-tools.x86_64 firewalld systemd ntp unrar bzip2\n ```\n### JDK安装\n>注意需要在六台机器依次执行\n- 上传到`/tmp`目录下，安装\n ```bash\n cd /tmp\n rpm -ivh jdk-8u144-linux-x64.rpm\n ```\n- 配置环境变量\n ```bash\n ln -s /usr/java/jdk1.8.0_144/ /usr/java/jdk1.8\n echo 'export JAVA_HOME=/usr/java/jdk1.8' >> /etc/profile.d/myEnv.sh \n echo 'export PATH=$PATH:$JAVA_HOME/bin' >> /etc/profile.d/myEnv.sh \n source /etc/profile \n java -version\n ```\n### 修改主机名和主机名映射\n\n```bash\nvim /etc/hostname\n```\n6台机器分别为nn1、nn2、nn3、s1、s2、s3\n\n```bash\nvim /etc/hosts\n```\n\n修改为\n```text\n192.168.1.30 nn1\n192.168.1.31 nn2\n192.168.1.32 nn3\n192.168.1.33 s1\n192.168.1.34 s2\n192.168.1.35 s3\n```\n### 创建hadoop用户\n```bash\n#创建hadoop用户 \nuseradd hadoop \n#给hadoop用户设置密码: 12345678 \npasswd hadoop\n```\n### 禁止非 wheel 组用户切换到root，配置免密切换root\n- 修改/etc/pam.d/su配置\n ```bash\n sed -i 's/#auth\\t\\trequired\\tpam_wheel.so/auth\\t\\trequired\\tpam_wheel.so/g' '/etc/pam.d/su' \n sed -i 's/#auth\\t\\tsufficient\\tpam_wheel.so/auth\\t\\tsufficient\\tpam_wheel.so/g' '/etc/pam.d/su'\n ```\n- 修改/etc/login.defs文件\n ```bash\n echo \"SU_WHEEL_ONLY yes\" >> /etc/login.defs\n ```\n- 添加用户到管理员，禁止普通用户su 到 root\n ```bash\n #把hadoop用户加到wheel组里\n gpasswd -a hadoop wheel\n #查看wheel组里是否有hadoop用户\n cat /etc/group | grep wheel\n ```\n### 给hadoop用户，配置SSH密钥\n#### 配置hadoop用户ssh免密码登录到hadoop\n- 仅在`nn1`执行这段脚本命令即可\n 但是 `su - hadoop ` ,` mkdir ~/.ssh` 需要在其他主机执行一下\n ```bash\n #切换到hadoop用户 \n su - hadoop\n #生成ssh公私钥 \n ssh-keygen -t rsa -f ~/.ssh/id_rsa -P ''\n ssh-copy-id nn1\n ssh-copy-id nn2\n ssh-copy-id nn3\n ssh-copy-id s1\n ssh-copy-id s2\n ssh-copy-id s3\n scp /home/hadoop/.ssh/id_rsa hadoop@nn2:/home/hadoop/.ssh\n scp /home/hadoop/.ssh/id_rsa hadoop@nn3:/home/hadoop/.ssh\n scp /home/hadoop/.ssh/id_rsa hadoop@s1:/home/hadoop/.ssh\n scp /home/hadoop/.ssh/id_rsa hadoop@s2:/home/hadoop/.ssh\n scp /home/hadoop/.ssh/id_rsa hadoop@s3:/home/hadoop/.ssh\n ```\n#### 配置hadoop用户ssh免密码登录到root\n- 同上\n ```bash\n ssh-copy-id root@nn1\n ssh-copy-id root@ nn2\n ssh-copy-id root@nn3\n ssh-copy-id root@s1\n ssh-copy-id root@s2\n ssh-copy-id root@s3\n scp /home/hadoop/.ssh/id_rsa root@nn2:/root/.ssh\n scp /home/hadoop/.ssh/id_rsa root@nn3:/root/.ssh\n scp /home/hadoop/.ssh/id_rsa root@s1:/root/.ssh\n scp /home/hadoop/.ssh/id_rsa root@s2:/root/.ssh\n scp /home/hadoop/.ssh/id_rsa root@s3:/root/.ssh\n ```\n### 脚本配置\n- **ips**\n ```bash\n vim /home/hadoop/bin/ips\n ```\n \n ```bash\n nn1 \n nn2\n nn3\n s1 \n s2 \n s3\n ```\n- **ssh_all.sh**\n ```bash\n vim /home/hadoop/bin/ssh_all.sh\n ```\n\n ```bash\n #! /bin/bash\n # 进入到当前脚本所在目录\n cd `dirname $0`\n # 获取当前脚本所在目录\n dir_path=`pwd`\n #echo $dir_path\n # 读ips文件得到数组(里面是一堆主机名)\n ip_arr=(`cat $dir_path/ips`)\n # 遍历数组里的主机名\n for ip in ${ip_arr[*]}\n do\n # 拼接ssh命令: ssh hadoop@nn1.hadoop ls\n cmd_=\"ssh hadoop@${ip} \\\"$*\\\" \"\n echo $cmd_\n # 通过eval命令 执行 拼接的ssh 命令\n if eval ${cmd_} ; then\n echo \"OK\"\n else\n echo \"FAIL\"\n fi\n done\n ```\n- **ssh_root.sh**\n ```bash\n #! /bin/bash\n # 进入到当前脚本所在目录\n cd `dirname $0`\n # 获取当前脚本所在目录\n dir_path=`pwd`\n #echo $dir_path\n # 读ips文件得到数组(里面是一堆主机名)\n ip_arr=(`cat $dir_path/ips`)\n # 遍历数组里的主机名\n for ip in ${ip_arr[*]}\n do\n # 拼接ssh命令: ssh hadoop@nn1.hadoop ls\n cmd_=\"ssh hadoop@${ip} ~/bin/exe.sh \\\"$*\\\"\"\n echo $cmd_\n # 通过eval命令 执行 拼接的ssh 命令\n if eval ${cmd_} ; then\n echo \"OK\"\n else\n echo \"FAIL\"\n fi\n done\n ```\n- **scp_all.sh**\n ```bash\n #! /bin/bash\n # 进入到当前脚本所在目录\n cd `dirname $0`\n # 获取当前脚本所在目录\n dir_path=`pwd`\n #echo $dir_path\n # 读ips文件得到数组(里面是一堆主机名)\n ip_arr=(`cat $dir_path/ips`)\n # 源\n source_=$1\n # 目标\n target=$2\n # 遍历数组里的主机名\n for ip in ${ip_arr[*]}\n do\n # 拼接scp命令: scp 源 hadoop@nn1.hadoop:目标\n cmd_=\"scp -r ${source_} hadoop@${ip}:${target}\"\n echo $cmd_\n # 通过eval命令 执行 拼接的scp 命令\n if eval ${cmd_} ; then\n echo \"OK\"\n else\n echo \"FAIL\"\n fi\n done\n ```\n- **exe.sh**\n ```bash\n #切换到root用户执行cmd命令\n cmd=$*\n su - << EOF\n $cmd\n EOF\n ```\n- 赋予执行权限\n ```bash\n chmod +x ssh_all.sh \n chmod +x scp_all.sh\n chmod +x ssh_root.sh\n chmod +x exe.sh\n ```\n- 分发到其他主机\n ```bash\n ./ssh_all.sh mkdir /home/hadoop/bin\n ./scp_all.sh /home/hadoop/bin/ips /home/hadoop/bin/\n ./scp_all.sh /home/hadoop/bin/exe.sh /home/hadoop/bin/\n ./scp_all.sh /home/hadoop/bin/ssh_all.sh /home/hadoop/bin/\n ./scp_all.sh /home/hadoop/bin/scp_all.sh /home/hadoop/bin/\n ./scp_all.sh /home/hadoop/bin/ssh_root.sh /home/hadoop/bin/\n ```\n\n- 将 `/home/hadoop/bin`添加到hadoop用户的环境变量，需要切换到`hadoop`用户\n\n ```bash\n echo 'export PATH=$PATH:/home/hadoop/bin' >> ~/.bashrc && source ~/.bashrc\n scp_all.sh /home/hadoop/.bashrc /home/hadoop/\n ssh_all.sh source ~/.bashrc \n ```","slug":"bigdata/hadoop/env","published":1,"updated":"2024-09-11T14:45:28.095Z","comments":1,"layout":"post","photos":[],"_id":"cm5w9lrdu000ptgah6cdk1e7e","content":"

防火墙关闭

1
2
3
# 在 6 台主机执行
systemctl stop firewalld
systemctl disable firewalld
\n

配置yum源

\n

JDK安装

\n

注意需要在六台机器依次执行

\n
\n\n

修改主机名和主机名映射

1
vim /etc/hostname
\n

6台机器分别为nn1、nn2、nn3、s1、s2、s3

\n
1
vim /etc/hosts
\n\n

修改为

\n
1
2
3
4
5
6
192.168.1.30 nn1
192.168.1.31 nn2
192.168.1.32 nn3
192.168.1.33 s1
192.168.1.34 s2
192.168.1.35 s3
\n

创建hadoop用户

1
2
3
4
#创建hadoop用户 
useradd hadoop 
#给hadoop用户设置密码: 12345678 
passwd hadoop
\n

禁止非 wheel 组用户切换到root，配置免密切换root

\n

给hadoop用户，配置SSH密钥

配置hadoop用户ssh免密码登录到hadoop

\n

配置hadoop用户ssh免密码登录到root

\n

脚本配置

\n","cover":false,"excerpt":"","more":"

防火墙关闭

1
2
3
# 在 6 台主机执行
systemctl stop firewalld
systemctl disable firewalld
\n

配置yum源

\n

JDK安装

\n

注意需要在六台机器依次执行

\n
\n\n

修改主机名和主机名映射

1
vim /etc/hostname
\n

6台机器分别为nn1、nn2、nn3、s1、s2、s3

\n
1
vim /etc/hosts
\n\n

修改为

\n
1
2
3
4
5
6
192.168.1.30 nn1
192.168.1.31 nn2
192.168.1.32 nn3
192.168.1.33 s1
192.168.1.34 s2
192.168.1.35 s3
\n

创建hadoop用户

1
2
3
4
#创建hadoop用户 
useradd hadoop 
#给hadoop用户设置密码: 12345678 
passwd hadoop
\n

禁止非 wheel 组用户切换到root，配置免密切换root

\n

给hadoop用户，配置SSH密钥

配置hadoop用户ssh免密码登录到hadoop

\n

配置hadoop用户ssh免密码登录到root

\n

脚本配置

\n"},{"title":"Hadoop集群HDFS配置","top_img":"/img/site01.jpg","top_img_height":"800px","abbrlink":61252,"date":"2024-09-11T14:45:40.000Z","_content":"\n### 上传`hadoop-3.1.4.tar.gz`到`/tmp`，解压\n>注意在六台机器均上传到`/tmp`\n```bash\n# 在6台机器执行\nsudo tar -zxvf /tmp/hadoop-3.1.4.tar.gz -C /usr/local/\n# 分发到其他主机\nssh_root.sh chown -R hadoop:hadoop /usr/local/hadoop-3.1.4\nssh_root.sh ln -s /usr/local/hadoop-3.1.4/ /usr/local/hadoop\n```\n### 配置环境变量\n```bash\necho 'export HADOOP_HOME=/usr/local/hadoop' >> /etc/profile.d/myEnv.sh\necho 'export PATH=$PATH:$HADOOP_HOME/bin' >> /etc/profile.d/myEnv.sh\necho 'export PATH=$PATH:$HADOOP_HOME/sbin' >> /etc/profile.d/myEnv.sh\n```\n\n```bash\n# 分发到nn2、nn3、s1、s2、s3\nscp_all.sh /etc/profile.d/myEnv.sh /etc/profile.d/\n# source 环境变量\nssh_root.sh source /etc/profile\n```\n>还需要创建 `/data`这个目录，由于nn1、nn2、nn3已经创建`/data`，其他三台需要创建一下\n```bash\n### 在s1、s2、s3执行\nsudo mkdir /data\nsudo chown -R hadoop:hadoop /data\n```\n\n### 修改core-site.xml\n```bash\nvim /usr/local/hadoop/etc/hadoop/core-site.xml \n```\n\n```xml\n\n\t\n\t fs.defaultFS\n\t hdfs://ns1\n\t 默认文件服务的协议和NS逻辑名称，和hdfs-site.xml里的对应此配置替代了1.0里的fs.default.name\n\t\n\t\n\t\n\t hadoop.tmp.dir\n\t /data/tmp\n\t 数据存储目录\n\t\n\t\n\t\n\t hadoop.proxyuser.root.groups\n\t hadoop\n\t \n\t hdfs dfsadmin –refreshSuperUserGroupsConfiguration,\n\t yarn rmadmin –refreshSuperUserGroupsConfiguration\n\t 使用这两个命令不用重启就能刷新\n\t \n\t\n\t\n\t\n\t hadoop.proxyuser.root.hosts\n\t localhost\n\t 本地代理\n\t\n\t\n\t\n\t \n\t\tha.zookeeper.quorum \n\t\tnn1:2181,nn2:2181,nn3:2181 \n\t\tHA使用的zookeeper地址 \n\t\n\n```\n### 修改`hdfs-site.xml`\n```bash\nvim /usr/local/hadoop/etc/hadoop/hdfs-site.xml \n```\n\n```xml\n\n \n dfs.namenode.name.dir\n /data/namenode\n namenode本地文件存放地址\n \n \n \n dfs.nameservices\n ns1\n 提供服务的NS逻辑名称，与core-site.xml里的对应\n \n \n \n \n \n dfs.ha.namenodes.ns1\n nn1,nn2,nn3\n 列出该逻辑名称下的NameNode逻辑名称\n \n \n \n dfs.namenode.rpc-address.ns1.nn1\n nn1:9000\n 指定NameNode的RPC位置\n \n \n \n dfs.namenode.http-address.ns1.nn1\n nn1:50070\n 指定NameNode的Web Server位置\n \n \n \n dfs.namenode.rpc-address.ns1.nn2\n nn2:9000\n 指定NameNode的RPC位置\n \n \n \n dfs.namenode.http-address.ns1.nn2\n nn2:50070\n 指定NameNode的Web Server位置\n \n \n \n dfs.namenode.rpc-address.ns1.nn3\n nn3:9000\n 指定NameNode的RPC位置\n \n \n \n dfs.namenode.http-address.ns1.nn3\n nn3:50070\n 指定NameNode的Web Server位置\n \n \n \n dfs.namenode.handler.count\n 77\n namenode的工作线程数\n \n\n \n \n dfs.namenode.shared.edits.dir\n qjournal://nn1:8485;nn2:8485;nn3:8485/ns1\n 指定用于HA存放edits的共享存储，通常是namenode的所在机器\n \n \n \n dfs.journalnode.edits.dir\n /data/journaldata/\n journaldata服务存放文件的地址\n \n \n \n ipc.client.connect.max.retries\n 10\n namenode和journalnode的链接重试次数10次\n \n \n \n ipc.client.connect.retry.interval\n 10000\n 重试的间隔时间10s\n \n\n \n \n dfs.ha.fencing.methods\n sshfence\n 指定HA做隔离的方法，缺省是ssh，可设为shell，稍后详述\n \n \n \n dfs.ha.fencing.ssh.private-key-files\n /home/hadoop/.ssh/id_rsa\n 杀死命令脚本的免密配置秘钥\n \n \n \n dfs.client.failover.proxy.provider.ns1\n org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider\n 指定客户端用于HA切换的代理类，不同的NS可以用不同的代理类以上示例为Hadoop 2.0自带的缺省代理类\n \n \n \n dfs.client.failover.proxy.provider.auto-ha\n org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider\n \n \n \n dfs.ha.automatic-failover.enabled\n true\n \n\t\n\t\n\t dfs.datanode.data.dir\n\t /data/datanode\n\t datanode本地文件存放地址\n\t\n\t\n\t dfs.replication\n\t 3\n\t 文件复本数\n\t\n\t\n\t dfs.namenode.datanode.registration.ip-hostname-check\n\t false\n\t\n\t\n\t dfs.client.use.datanode.hostname\n\t true\n\t\n\t\n\t dfs.datanode.use.datanode.hostname\n\t true\n\t\n\n```\n### 修改`hadoop-env.sh`\n```bash\nvim /usr/local/hadoop/etc/hadoop/hadoop-env.sh\n```\n\n```bash\n# 添加这两行\nsource /etc/profile \nexport HADOOP_HEAPSIZE_MAX=512\n```\n### 分发这些配置文件\n```bash\nscp_all.sh /usr/local/hadoop/etc/hadoop/core-site.xml /usr/local/hadoop/etc/hadoop/\nscp_all.sh /usr/local/hadoop/etc/hadoop/hdfs-site.xml /usr/local/hadoop/etc/hadoop/\nscp_all.sh /usr/local/hadoop/etc/hadoop/hadoop-env.sh /usr/local/hadoop/etc/hadoop/\n```\n### 集群初始化\n- 需要先启动zookeeper集群\n ```bash\n ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh start\n ```\n\n ```bash\n # 第一次启动先启动journalnode，便于3个namenode的元数据同步\n ssh_all_zookeeper.sh hadoop-daemon.sh start journalnode\n ```\n- `zkfc`搭建\n ```bash\n #在nn1节点执行 \n hdfs zkfc -formatZK\n #nn1 nn2 nn3启动zkfc \n hadoop-daemon.sh start zkfc\n ```\n- 初始化nn1的namenode，在nn1执行\n ```bash\n hdfs namenode -format \n hadoop-daemon.sh start namenode\n ```\n- 格式化第二台和第三台namenode，并且启动namenode,在nn2、nn3执行\n ```bash\n hdfs namenode -bootstrapStandby \n hadoop-daemon.sh start namenode\n ```\n- 修改**workers**\n ```bash\n vim /usr/local/hadoop/etc/hadoop/workers\n ```\n 修改为\n ```text\n s1\n s2\n s3\n ```\n 分发给其他机器\n ```bash\n scp_all.sh /usr/local/hadoop/etc/hadoop/workers /usr/local/hadoop/etc/hadoop\n ```\n- 启动datanode节点，在s1、s2、s3执行\n\n ```bash\n #启动各个节点的datanode\n hadoop-daemons.sh start datanode\n ```\n### 集群启动\n```bash\nstart-dfs.sh\n```","source":"_posts/bigdata/hadoop/hdfs.md","raw":"---\ntitle: Hadoop集群HDFS配置\ntop_img: /img/site01.jpg\ntop_img_height: 800px\nabbrlink: 61252\ndate: 2024-09-011 22:45:40\n---\n\n### 上传`hadoop-3.1.4.tar.gz`到`/tmp`，解压\n>注意在六台机器均上传到`/tmp`\n```bash\n# 在6台机器执行\nsudo tar -zxvf /tmp/hadoop-3.1.4.tar.gz -C /usr/local/\n# 分发到其他主机\nssh_root.sh chown -R hadoop:hadoop /usr/local/hadoop-3.1.4\nssh_root.sh ln -s /usr/local/hadoop-3.1.4/ /usr/local/hadoop\n```\n### 配置环境变量\n```bash\necho 'export HADOOP_HOME=/usr/local/hadoop' >> /etc/profile.d/myEnv.sh\necho 'export PATH=$PATH:$HADOOP_HOME/bin' >> /etc/profile.d/myEnv.sh\necho 'export PATH=$PATH:$HADOOP_HOME/sbin' >> /etc/profile.d/myEnv.sh\n```\n\n```bash\n# 分发到nn2、nn3、s1、s2、s3\nscp_all.sh /etc/profile.d/myEnv.sh /etc/profile.d/\n# source 环境变量\nssh_root.sh source /etc/profile\n```\n>还需要创建 `/data`这个目录，由于nn1、nn2、nn3已经创建`/data`，其他三台需要创建一下\n```bash\n### 在s1、s2、s3执行\nsudo mkdir /data\nsudo chown -R hadoop:hadoop /data\n```\n\n### 修改core-site.xml\n```bash\nvim /usr/local/hadoop/etc/hadoop/core-site.xml \n```\n\n```xml\n\n\t\n\t fs.defaultFS\n\t hdfs://ns1\n\t 默认文件服务的协议和NS逻辑名称，和hdfs-site.xml里的对应此配置替代了1.0里的fs.default.name\n\t\n\t\n\t\n\t hadoop.tmp.dir\n\t /data/tmp\n\t 数据存储目录\n\t\n\t\n\t\n\t hadoop.proxyuser.root.groups\n\t hadoop\n\t \n\t hdfs dfsadmin –refreshSuperUserGroupsConfiguration,\n\t yarn rmadmin –refreshSuperUserGroupsConfiguration\n\t 使用这两个命令不用重启就能刷新\n\t \n\t\n\t\n\t\n\t hadoop.proxyuser.root.hosts\n\t localhost\n\t 本地代理\n\t\n\t\n\t\n\t \n\t\tha.zookeeper.quorum \n\t\tnn1:2181,nn2:2181,nn3:2181 \n\t\tHA使用的zookeeper地址 \n\t\n\n```\n### 修改`hdfs-site.xml`\n```bash\nvim /usr/local/hadoop/etc/hadoop/hdfs-site.xml \n```\n\n```xml\n\n \n dfs.namenode.name.dir\n /data/namenode\n namenode本地文件存放地址\n \n \n \n dfs.nameservices\n ns1\n 提供服务的NS逻辑名称，与core-site.xml里的对应\n \n \n \n \n \n dfs.ha.namenodes.ns1\n nn1,nn2,nn3\n 列出该逻辑名称下的NameNode逻辑名称\n \n \n \n dfs.namenode.rpc-address.ns1.nn1\n nn1:9000\n 指定NameNode的RPC位置\n \n \n \n dfs.namenode.http-address.ns1.nn1\n nn1:50070\n 指定NameNode的Web Server位置\n \n \n \n dfs.namenode.rpc-address.ns1.nn2\n nn2:9000\n 指定NameNode的RPC位置\n \n \n \n dfs.namenode.http-address.ns1.nn2\n nn2:50070\n 指定NameNode的Web Server位置\n \n \n \n dfs.namenode.rpc-address.ns1.nn3\n nn3:9000\n 指定NameNode的RPC位置\n \n \n \n dfs.namenode.http-address.ns1.nn3\n nn3:50070\n 指定NameNode的Web Server位置\n \n \n \n dfs.namenode.handler.count\n 77\n namenode的工作线程数\n \n\n \n \n dfs.namenode.shared.edits.dir\n qjournal://nn1:8485;nn2:8485;nn3:8485/ns1\n 指定用于HA存放edits的共享存储，通常是namenode的所在机器\n \n \n \n dfs.journalnode.edits.dir\n /data/journaldata/\n journaldata服务存放文件的地址\n \n \n \n ipc.client.connect.max.retries\n 10\n namenode和journalnode的链接重试次数10次\n \n \n \n ipc.client.connect.retry.interval\n 10000\n 重试的间隔时间10s\n \n\n \n \n dfs.ha.fencing.methods\n sshfence\n 指定HA做隔离的方法，缺省是ssh，可设为shell，稍后详述\n \n \n \n dfs.ha.fencing.ssh.private-key-files\n /home/hadoop/.ssh/id_rsa\n 杀死命令脚本的免密配置秘钥\n \n \n \n dfs.client.failover.proxy.provider.ns1\n org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider\n 指定客户端用于HA切换的代理类，不同的NS可以用不同的代理类以上示例为Hadoop 2.0自带的缺省代理类\n \n \n \n dfs.client.failover.proxy.provider.auto-ha\n org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider\n \n \n \n dfs.ha.automatic-failover.enabled\n true\n \n\t\n\t\n\t dfs.datanode.data.dir\n\t /data/datanode\n\t datanode本地文件存放地址\n\t\n\t\n\t dfs.replication\n\t 3\n\t 文件复本数\n\t\n\t\n\t dfs.namenode.datanode.registration.ip-hostname-check\n\t false\n\t\n\t\n\t dfs.client.use.datanode.hostname\n\t true\n\t\n\t\n\t dfs.datanode.use.datanode.hostname\n\t true\n\t\n\n```\n### 修改`hadoop-env.sh`\n```bash\nvim /usr/local/hadoop/etc/hadoop/hadoop-env.sh\n```\n\n```bash\n# 添加这两行\nsource /etc/profile \nexport HADOOP_HEAPSIZE_MAX=512\n```\n### 分发这些配置文件\n```bash\nscp_all.sh /usr/local/hadoop/etc/hadoop/core-site.xml /usr/local/hadoop/etc/hadoop/\nscp_all.sh /usr/local/hadoop/etc/hadoop/hdfs-site.xml /usr/local/hadoop/etc/hadoop/\nscp_all.sh /usr/local/hadoop/etc/hadoop/hadoop-env.sh /usr/local/hadoop/etc/hadoop/\n```\n### 集群初始化\n- 需要先启动zookeeper集群\n ```bash\n ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh start\n ```\n\n ```bash\n # 第一次启动先启动journalnode，便于3个namenode的元数据同步\n ssh_all_zookeeper.sh hadoop-daemon.sh start journalnode\n ```\n- `zkfc`搭建\n ```bash\n #在nn1节点执行 \n hdfs zkfc -formatZK\n #nn1 nn2 nn3启动zkfc \n hadoop-daemon.sh start zkfc\n ```\n- 初始化nn1的namenode，在nn1执行\n ```bash\n hdfs namenode -format \n hadoop-daemon.sh start namenode\n ```\n- 格式化第二台和第三台namenode，并且启动namenode,在nn2、nn3执行\n ```bash\n hdfs namenode -bootstrapStandby \n hadoop-daemon.sh start namenode\n ```\n- 修改**workers**\n ```bash\n vim /usr/local/hadoop/etc/hadoop/workers\n ```\n 修改为\n ```text\n s1\n s2\n s3\n ```\n 分发给其他机器\n ```bash\n scp_all.sh /usr/local/hadoop/etc/hadoop/workers /usr/local/hadoop/etc/hadoop\n ```\n- 启动datanode节点，在s1、s2、s3执行\n\n ```bash\n #启动各个节点的datanode\n hadoop-daemons.sh start datanode\n ```\n### 集群启动\n```bash\nstart-dfs.sh\n```","slug":"bigdata/hadoop/hdfs","published":1,"updated":"2024-09-11T14:51:42.712Z","comments":1,"layout":"post","photos":[],"_id":"cm5w9lrdv000qtgah29015x3b","content":"

上传hadoop-3.1.4.tar.gz到/tmp，解压

\n

注意在六台机器均上传到/tmp

\n
\n
1
2
3
4
5
# 在6台机器执行
sudo tar -zxvf /tmp/hadoop-3.1.4.tar.gz -C /usr/local/
# 分发到其他主机
ssh_root.sh chown -R hadoop:hadoop /usr/local/hadoop-3.1.4
ssh_root.sh ln -s /usr/local/hadoop-3.1.4/ /usr/local/hadoop
\n

配置环境变量

1
2
3
echo 'export HADOOP_HOME=/usr/local/hadoop' >> /etc/profile.d/myEnv.sh
echo 'export PATH=$PATH:$HADOOP_HOME/bin' >> /etc/profile.d/myEnv.sh
echo 'export PATH=$PATH:$HADOOP_HOME/sbin' >> /etc/profile.d/myEnv.sh
\n\n
1
2
3
4
# 分发到nn2、nn3、s1、s2、s3
scp_all.sh /etc/profile.d/myEnv.sh /etc/profile.d/
# source 环境变量
ssh_root.sh source /etc/profile
\n
\n

还需要创建 /data这个目录，由于nn1、nn2、nn3已经创建/data，其他三台需要创建一下

\n
\n
1
2
3
### 在s1、s2、s3执行
sudo mkdir /data
sudo chown -R hadoop:hadoop /data
\n\n

修改core-site.xml

1
vim /usr/local/hadoop/etc/hadoop/core-site.xml
\n\n
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
<configuration>
\t<property>
\t    <name>fs.defaultFS</name>
\t    <value>hdfs://ns1</value>
\t    <description>默认文件服务的协议和NS逻辑名称，和hdfs-site.xml里的对应此配置替代了1.0里的fs.default.name</description>
\t</property>
\t
\t<property>
\t    <name>hadoop.tmp.dir</name>
\t    <value>/data/tmp</value>
\t    <description>数据存储目录</description>
\t</property>
\t
\t<property>
\t    <name>hadoop.proxyuser.root.groups</name>
\t    <value>hadoop</value>
\t    <description>
\t        hdfs dfsadmin –refreshSuperUserGroupsConfiguration,
\t        yarn rmadmin –refreshSuperUserGroupsConfiguration
\t        使用这两个命令不用重启就能刷新
\t    </description>
\t</property>
\t
\t<property>
\t    <name>hadoop.proxyuser.root.hosts</name>
\t    <value>localhost</value>
\t    <description>本地代理</description>
\t</property>
\t
\t<!-- zkfc的配置 -->
\t<property> 
\t\t<name>ha.zookeeper.quorum</name> 
\t\t<value>nn1:2181,nn2:2181,nn3:2181</value> 
\t\t<description>HA使用的zookeeper地址</description> 
\t</property>
</configuration>
\n

修改hdfs-site.xml

1
vim /usr/local/hadoop/etc/hadoop/hdfs-site.xml
\n\n
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
<configuration>
    <property>
        <name>dfs.namenode.name.dir</name>
        <value>/data/namenode</value>
        <description>namenode本地文件存放地址</description>
    </property>
    
    <property>
        <name>dfs.nameservices</name>
        <value>ns1</value>
        <description>提供服务的NS逻辑名称，与core-site.xml里的对应</description>
    </property>
    
    <!-- namenode的配置 -->
    <!-- 主要的 -->
    <property>
        <name>dfs.ha.namenodes.ns1</name>
        <value>nn1,nn2,nn3</value>
        <description>列出该逻辑名称下的NameNode逻辑名称</description>
    </property>
    
    <property>
        <name>dfs.namenode.rpc-address.ns1.nn1</name>
        <value>nn1:9000</value>
        <description>指定NameNode的RPC位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.http-address.ns1.nn1</name>
        <value>nn1:50070</value>
        <description>指定NameNode的Web Server位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.rpc-address.ns1.nn2</name>
        <value>nn2:9000</value>
        <description>指定NameNode的RPC位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.http-address.ns1.nn2</name>
        <value>nn2:50070</value>
        <description>指定NameNode的Web Server位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.rpc-address.ns1.nn3</name>
        <value>nn3:9000</value>
        <description>指定NameNode的RPC位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.http-address.ns1.nn3</name>
        <value>nn3:50070</value>
        <description>指定NameNode的Web Server位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.handler.count</name>
        <value>77</value>
        <description>namenode的工作线程数</description>
    </property>

    <!-- journaldata配置,使得其他两个namenode同步第一个namenode数据 --> 
    <property>
        <name>dfs.namenode.shared.edits.dir</name>
        <value>qjournal://nn1:8485;nn2:8485;nn3:8485/ns1</value>
        <description>指定用于HA存放edits的共享存储，通常是namenode的所在机器</description>
    </property>
    
    <property>
        <name>dfs.journalnode.edits.dir</name>
        <value>/data/journaldata/</value>
        <description>journaldata服务存放文件的地址</description>
    </property>
    
    <property>
        <name>ipc.client.connect.max.retries</name>
        <value>10</value>
        <description>namenode和journalnode的链接重试次数10次</description>
    </property>
    
    <property>
        <name>ipc.client.connect.retry.interval</name>
        <value>10000</value>
        <description>重试的间隔时间10s</description>
    </property>

    <!-- zkfc的配置 -->
    <property>
        <name>dfs.ha.fencing.methods</name>
        <value>sshfence</value>
        <description>指定HA做隔离的方法，缺省是ssh，可设为shell，稍后详述</description>
    </property>
    
    <property>
        <name>dfs.ha.fencing.ssh.private-key-files</name>
        <value>/home/hadoop/.ssh/id_rsa</value>
        <description>杀死命令脚本的免密配置秘钥</description>
    </property>
    
    <property>
        <name>dfs.client.failover.proxy.provider.ns1</name>
        <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value>
        <description>指定客户端用于HA切换的代理类，不同的NS可以用不同的代理类以上示例为Hadoop 2.0自带的缺省代理类</description>
    </property>
    
    <property>
        <name>dfs.client.failover.proxy.provider.auto-ha</name>
        <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value>
    </property> 
    
    <property>
        <name>dfs.ha.automatic-failover.enabled</name>
        <value>true</value>
    </property>
\t<!-- datanode配置 -->
\t<property>
\t    <name>dfs.datanode.data.dir</name>
\t    <value>/data/datanode</value>
\t    <description>datanode本地文件存放地址</description>
\t</property>
\t<property>
\t    <name>dfs.replication</name>
\t    <value>3</value>
\t    <description>文件复本数</description>
\t</property>
\t<property>
\t    <name>dfs.namenode.datanode.registration.ip-hostname-check</name>
\t    <value>false</value>
\t</property>
\t<property>
\t    <name>dfs.client.use.datanode.hostname</name>
\t    <value>true</value>
\t</property>
\t<property>
\t    <name>dfs.datanode.use.datanode.hostname</name>
\t    <value>true</value>
\t</property>
</configuration>
\n

修改hadoop-env.sh

1
vim /usr/local/hadoop/etc/hadoop/hadoop-env.sh
\n\n
1
2
3
# 添加这两行
source /etc/profile 
export HADOOP_HEAPSIZE_MAX=512
\n

分发这些配置文件

1
2
3
scp_all.sh /usr/local/hadoop/etc/hadoop/core-site.xml /usr/local/hadoop/etc/hadoop/
scp_all.sh /usr/local/hadoop/etc/hadoop/hdfs-site.xml /usr/local/hadoop/etc/hadoop/
scp_all.sh /usr/local/hadoop/etc/hadoop/hadoop-env.sh /usr/local/hadoop/etc/hadoop/
\n

集群初始化

\n

集群启动

1
start-dfs.sh
","cover":false,"excerpt":"","more":"

上传hadoop-3.1.4.tar.gz到/tmp，解压

\n

注意在六台机器均上传到/tmp

\n
\n
1
2
3
4
5
# 在6台机器执行
sudo tar -zxvf /tmp/hadoop-3.1.4.tar.gz -C /usr/local/
# 分发到其他主机
ssh_root.sh chown -R hadoop:hadoop /usr/local/hadoop-3.1.4
ssh_root.sh ln -s /usr/local/hadoop-3.1.4/ /usr/local/hadoop
\n

配置环境变量

1
2
3
echo 'export HADOOP_HOME=/usr/local/hadoop' >> /etc/profile.d/myEnv.sh
echo 'export PATH=$PATH:$HADOOP_HOME/bin' >> /etc/profile.d/myEnv.sh
echo 'export PATH=$PATH:$HADOOP_HOME/sbin' >> /etc/profile.d/myEnv.sh
\n\n
1
2
3
4
# 分发到nn2、nn3、s1、s2、s3
scp_all.sh /etc/profile.d/myEnv.sh /etc/profile.d/
# source 环境变量
ssh_root.sh source /etc/profile
\n
\n

还需要创建 /data这个目录，由于nn1、nn2、nn3已经创建/data，其他三台需要创建一下

\n
\n
1
2
3
### 在s1、s2、s3执行
sudo mkdir /data
sudo chown -R hadoop:hadoop /data
\n\n

修改core-site.xml

1
vim /usr/local/hadoop/etc/hadoop/core-site.xml
\n\n
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
<configuration>
\t<property>
\t    <name>fs.defaultFS</name>
\t    <value>hdfs://ns1</value>
\t    <description>默认文件服务的协议和NS逻辑名称，和hdfs-site.xml里的对应此配置替代了1.0里的fs.default.name</description>
\t</property>
\t
\t<property>
\t    <name>hadoop.tmp.dir</name>
\t    <value>/data/tmp</value>
\t    <description>数据存储目录</description>
\t</property>
\t
\t<property>
\t    <name>hadoop.proxyuser.root.groups</name>
\t    <value>hadoop</value>
\t    <description>
\t        hdfs dfsadmin –refreshSuperUserGroupsConfiguration,
\t        yarn rmadmin –refreshSuperUserGroupsConfiguration
\t        使用这两个命令不用重启就能刷新
\t    </description>
\t</property>
\t
\t<property>
\t    <name>hadoop.proxyuser.root.hosts</name>
\t    <value>localhost</value>
\t    <description>本地代理</description>
\t</property>
\t
\t<!-- zkfc的配置 -->
\t<property> 
\t\t<name>ha.zookeeper.quorum</name> 
\t\t<value>nn1:2181,nn2:2181,nn3:2181</value> 
\t\t<description>HA使用的zookeeper地址</description> 
\t</property>
</configuration>
\n

修改hdfs-site.xml

1
vim /usr/local/hadoop/etc/hadoop/hdfs-site.xml
\n\n
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
<configuration>
    <property>
        <name>dfs.namenode.name.dir</name>
        <value>/data/namenode</value>
        <description>namenode本地文件存放地址</description>
    </property>
    
    <property>
        <name>dfs.nameservices</name>
        <value>ns1</value>
        <description>提供服务的NS逻辑名称，与core-site.xml里的对应</description>
    </property>
    
    <!-- namenode的配置 -->
    <!-- 主要的 -->
    <property>
        <name>dfs.ha.namenodes.ns1</name>
        <value>nn1,nn2,nn3</value>
        <description>列出该逻辑名称下的NameNode逻辑名称</description>
    </property>
    
    <property>
        <name>dfs.namenode.rpc-address.ns1.nn1</name>
        <value>nn1:9000</value>
        <description>指定NameNode的RPC位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.http-address.ns1.nn1</name>
        <value>nn1:50070</value>
        <description>指定NameNode的Web Server位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.rpc-address.ns1.nn2</name>
        <value>nn2:9000</value>
        <description>指定NameNode的RPC位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.http-address.ns1.nn2</name>
        <value>nn2:50070</value>
        <description>指定NameNode的Web Server位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.rpc-address.ns1.nn3</name>
        <value>nn3:9000</value>
        <description>指定NameNode的RPC位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.http-address.ns1.nn3</name>
        <value>nn3:50070</value>
        <description>指定NameNode的Web Server位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.handler.count</name>
        <value>77</value>
        <description>namenode的工作线程数</description>
    </property>

    <!-- journaldata配置,使得其他两个namenode同步第一个namenode数据 --> 
    <property>
        <name>dfs.namenode.shared.edits.dir</name>
        <value>qjournal://nn1:8485;nn2:8485;nn3:8485/ns1</value>
        <description>指定用于HA存放edits的共享存储，通常是namenode的所在机器</description>
    </property>
    
    <property>
        <name>dfs.journalnode.edits.dir</name>
        <value>/data/journaldata/</value>
        <description>journaldata服务存放文件的地址</description>
    </property>
    
    <property>
        <name>ipc.client.connect.max.retries</name>
        <value>10</value>
        <description>namenode和journalnode的链接重试次数10次</description>
    </property>
    
    <property>
        <name>ipc.client.connect.retry.interval</name>
        <value>10000</value>
        <description>重试的间隔时间10s</description>
    </property>

    <!-- zkfc的配置 -->
    <property>
        <name>dfs.ha.fencing.methods</name>
        <value>sshfence</value>
        <description>指定HA做隔离的方法，缺省是ssh，可设为shell，稍后详述</description>
    </property>
    
    <property>
        <name>dfs.ha.fencing.ssh.private-key-files</name>
        <value>/home/hadoop/.ssh/id_rsa</value>
        <description>杀死命令脚本的免密配置秘钥</description>
    </property>
    
    <property>
        <name>dfs.client.failover.proxy.provider.ns1</name>
        <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value>
        <description>指定客户端用于HA切换的代理类，不同的NS可以用不同的代理类以上示例为Hadoop 2.0自带的缺省代理类</description>
    </property>
    
    <property>
        <name>dfs.client.failover.proxy.provider.auto-ha</name>
        <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value>
    </property> 
    
    <property>
        <name>dfs.ha.automatic-failover.enabled</name>
        <value>true</value>
    </property>
\t<!-- datanode配置 -->
\t<property>
\t    <name>dfs.datanode.data.dir</name>
\t    <value>/data/datanode</value>
\t    <description>datanode本地文件存放地址</description>
\t</property>
\t<property>
\t    <name>dfs.replication</name>
\t    <value>3</value>
\t    <description>文件复本数</description>
\t</property>
\t<property>
\t    <name>dfs.namenode.datanode.registration.ip-hostname-check</name>
\t    <value>false</value>
\t</property>
\t<property>
\t    <name>dfs.client.use.datanode.hostname</name>
\t    <value>true</value>
\t</property>
\t<property>
\t    <name>dfs.datanode.use.datanode.hostname</name>
\t    <value>true</value>
\t</property>
</configuration>
\n

修改hadoop-env.sh

1
vim /usr/local/hadoop/etc/hadoop/hadoop-env.sh
\n\n
1
2
3
# 添加这两行
source /etc/profile 
export HADOOP_HEAPSIZE_MAX=512
\n

分发这些配置文件

1
2
3
scp_all.sh /usr/local/hadoop/etc/hadoop/core-site.xml /usr/local/hadoop/etc/hadoop/
scp_all.sh /usr/local/hadoop/etc/hadoop/hdfs-site.xml /usr/local/hadoop/etc/hadoop/
scp_all.sh /usr/local/hadoop/etc/hadoop/hadoop-env.sh /usr/local/hadoop/etc/hadoop/
\n

集群初始化

\n

集群启动

1
start-dfs.sh
"},{"title":"Hadoop集群Zookeeper配置","top_img":"/img/site01.jpg","top_img_height":"800px","abbrlink":61251,"date":"2024-09-11T14:45:40.000Z","_content":"\n### Zookeeper脚本配置\n- 拷贝ips\n ```bash\n cp ips ips_zookeeper\n ```\n 修改为\n ```bash\n nn1\n nn2\n nn3\n ```\n- 拷贝三个脚本\n ```bash\n cp scp_all.sh scp_all_zookeeper.sh \n cp ssh_all.sh ssh_all_zookeeper.sh \n cp ssh_root.sh ssh_root_zookeeper.sh\n ```\n- 修改脚本\n ```shell\n vim scp_all_zookeeper.sh \n vim ssh_all_zookeeper.sh \n vim ssh_root_zookeeper.sh \n ```\n\n 将三个脚本中的ips改为ips_zookeeper\n### Zookeeper安装\n- 上传到`/tmp`目录下，解压\n ```bash\n sudo tar -zxvf /tmp/zookeeper-3.4.8.tar.gz -C /usr/local/\n scp -r /usr/local/zookeeper-3.4.8/ root@nn2:/usr/local/\n scp -r /usr/local/zookeeper-3.4.8/ root@nn3:/usr/local/\n ssh_root_zookeeper.sh chown -R hadoop:hadoop /usr/local/zookeeper-3.4.8\n ssh_root_zookeeper.s ln -s /usr/local/zookeeper-3.4.8/ /usr/local/zookeeper\n ```\n### Zookeeper配置\n - zoo.cfg配置\n ```bash\n cd /usr/local/zookeeper/conf/\n cp zoo_sample.cfg zoo.cfg\n ```\n 然后`vim zoo.cfg`,修改如下：\n ```properties\n # 修改dataDir\n dataDir=/data/zookeeper\n # 添加一下内容\n server.1=nn1:2888:3888 \n server.2=nn2:2888:3888 \n server.3=nn3:2888:3888\n ```\n 分发给nn2、nn3\n `scp_all_zookeeper.sh /usr/local/zookeeper/conf/zoo.cfg /usr/local/zookeeper/conf/`\n\n- `zkEnv.sh`配置\n `vim /usr/local/zookeeper/bin/zkEnv.sh`\n ![000001.png](..%2F..%2F..%2Fimg%2F000001.png)\n 分发到nn2、nn3\n```bash\nscp_all_zookeeper.sh /usr/local/zookeeper/bin/zkEnv.sh /usr/local/zookeeper/bin/\n```\n- 创建zookeeper数据目录\n```bash\nssh_root_zookeeper.sh mkdir -p /data/zookeeper\nssh_root_zookeeper.sh chown -R hadoop:hadoop /data\n```\n- 创建myid文件\n ```bash\n ssh nn1 'echo \"1\" > /data/zookeeper/myid'\n ssh nn2 'echo \"2\" > /data/zookeeper/myid'\n ssh nn3 'echo \"3\" > /data/zookeeper/myid'\n ```\n- 配置Zookeeper环境变量\n ```bash\n # 在其他所有主机也执行\n sudo chown -R hadoop:hadoop /etc/profile.d/myEnv.sh\n ```\n\n ```bash\n echo 'export ZOOKEEPER_HOME=/usr/local/zookeeper' >> /etc/profile.d/myEnv.sh\n echo 'export PATH=$PATH:$ZOOKEEPER_HOME/bin' >> /etc/profile.d/myEnv.sh\n ```\n\n ```bash\n # 分发到nn2、nn3\n scp_all_zookeeper.sh /etc/profile.d/myEnv.sh /etc/profile.d/\n # source 环境变量\n ssh_all_zookeeper.sh source /etc/profile\n ```\n### Zookeeper的命令\n```bash\n#启动zk服务\nssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh start\n#查看每个机器ZK运行的状态\nssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh status\n#整体停止服务\nssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh stop \n#重启zk服务\nssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh restart\n```\n\n```shell\n#启动zookeeper客户端，并连接zookeeper集群\n/usr/local/zookeeper/bin/zkCli.sh -server nn1:2181\n# 可以简化为:\nzkCli.sh\n```\n\n","source":"_posts/bigdata/hadoop/zookeper.md","raw":"---\ntitle: Hadoop集群Zookeeper配置\ntop_img: /img/site01.jpg\ntop_img_height: 800px\nabbrlink: 61251\ndate: 2024-09-011 22:45:40\n---\n\n### Zookeeper脚本配置\n- 拷贝ips\n ```bash\n cp ips ips_zookeeper\n ```\n 修改为\n ```bash\n nn1\n nn2\n nn3\n ```\n- 拷贝三个脚本\n ```bash\n cp scp_all.sh scp_all_zookeeper.sh \n cp ssh_all.sh ssh_all_zookeeper.sh \n cp ssh_root.sh ssh_root_zookeeper.sh\n ```\n- 修改脚本\n ```shell\n vim scp_all_zookeeper.sh \n vim ssh_all_zookeeper.sh \n vim ssh_root_zookeeper.sh \n ```\n\n 将三个脚本中的ips改为ips_zookeeper\n### Zookeeper安装\n- 上传到`/tmp`目录下，解压\n ```bash\n sudo tar -zxvf /tmp/zookeeper-3.4.8.tar.gz -C /usr/local/\n scp -r /usr/local/zookeeper-3.4.8/ root@nn2:/usr/local/\n scp -r /usr/local/zookeeper-3.4.8/ root@nn3:/usr/local/\n ssh_root_zookeeper.sh chown -R hadoop:hadoop /usr/local/zookeeper-3.4.8\n ssh_root_zookeeper.s ln -s /usr/local/zookeeper-3.4.8/ /usr/local/zookeeper\n ```\n### Zookeeper配置\n - zoo.cfg配置\n ```bash\n cd /usr/local/zookeeper/conf/\n cp zoo_sample.cfg zoo.cfg\n ```\n 然后`vim zoo.cfg`,修改如下：\n ```properties\n # 修改dataDir\n dataDir=/data/zookeeper\n # 添加一下内容\n server.1=nn1:2888:3888 \n server.2=nn2:2888:3888 \n server.3=nn3:2888:3888\n ```\n 分发给nn2、nn3\n `scp_all_zookeeper.sh /usr/local/zookeeper/conf/zoo.cfg /usr/local/zookeeper/conf/`\n\n- `zkEnv.sh`配置\n `vim /usr/local/zookeeper/bin/zkEnv.sh`\n ![000001.png](..%2F..%2F..%2Fimg%2F000001.png)\n 分发到nn2、nn3\n```bash\nscp_all_zookeeper.sh /usr/local/zookeeper/bin/zkEnv.sh /usr/local/zookeeper/bin/\n```\n- 创建zookeeper数据目录\n```bash\nssh_root_zookeeper.sh mkdir -p /data/zookeeper\nssh_root_zookeeper.sh chown -R hadoop:hadoop /data\n```\n- 创建myid文件\n ```bash\n ssh nn1 'echo \"1\" > /data/zookeeper/myid'\n ssh nn2 'echo \"2\" > /data/zookeeper/myid'\n ssh nn3 'echo \"3\" > /data/zookeeper/myid'\n ```\n- 配置Zookeeper环境变量\n ```bash\n # 在其他所有主机也执行\n sudo chown -R hadoop:hadoop /etc/profile.d/myEnv.sh\n ```\n\n ```bash\n echo 'export ZOOKEEPER_HOME=/usr/local/zookeeper' >> /etc/profile.d/myEnv.sh\n echo 'export PATH=$PATH:$ZOOKEEPER_HOME/bin' >> /etc/profile.d/myEnv.sh\n ```\n\n ```bash\n # 分发到nn2、nn3\n scp_all_zookeeper.sh /etc/profile.d/myEnv.sh /etc/profile.d/\n # source 环境变量\n ssh_all_zookeeper.sh source /etc/profile\n ```\n### Zookeeper的命令\n```bash\n#启动zk服务\nssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh start\n#查看每个机器ZK运行的状态\nssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh status\n#整体停止服务\nssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh stop \n#重启zk服务\nssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh restart\n```\n\n```shell\n#启动zookeeper客户端，并连接zookeeper集群\n/usr/local/zookeeper/bin/zkCli.sh -server nn1:2181\n# 可以简化为:\nzkCli.sh\n```\n\n","slug":"bigdata/hadoop/zookeper","published":1,"updated":"2024-09-11T14:51:42.706Z","comments":1,"layout":"post","photos":[],"_id":"cm5w9lrdv000rtgah4ojt276r","content":"

Zookeeper脚本配置

\n

Zookeeper安装

\n

Zookeeper配置

\n

Zookeeper的命令

1
2
3
4
5
6
7
8
#启动zk服务
ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh start
#查看每个机器ZK运行的状态
ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh status
#整体停止服务
ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh stop 
#重启zk服务
ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh restart
\n\n
1
2
3
4
#启动zookeeper客户端，并连接zookeeper集群
/usr/local/zookeeper/bin/zkCli.sh -server nn1:2181
# 可以简化为:
zkCli.sh
\n\n","cover":false,"excerpt":"","more":"

Zookeeper脚本配置

\n

Zookeeper安装

\n

Zookeeper配置

\n

Zookeeper的命令

1
2
3
4
5
6
7
8
#启动zk服务
ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh start
#查看每个机器ZK运行的状态
ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh status
#整体停止服务
ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh stop 
#重启zk服务
ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh restart
\n\n
1
2
3
4
#启动zookeeper客户端，并连接zookeeper集群
/usr/local/zookeeper/bin/zkCli.sh -server nn1:2181
# 可以简化为:
zkCli.sh
\n\n"},{"title":"无法访问外网？需要订阅代理服务？","abbrlink":14011,"date":"2024-08-07T02:06:08.000Z","_content":"\n{% note info %}\n**由于中国大陆的GFW（防火墙）限制，无法访问外网网络，因此需要访问像GitHub、YouTube这样的\n的网站将被屏蔽拦截，接下来我将给出一种使用`VPN`服务的可行的方案来保证服务的可靠性。**\n{% endnote %}\n\n### 介绍\n> 根据测试，许多提供服务的提供商所在的网站需要使用`外部网络`才能打开，仅有少部分的网站(**比较贵**)可以直接使用\n> 国内网络环境打开直接购买订阅服务。\n>\n\n那么你现在可以有两个选择：\n1. **方案一**：使用无需`外部网络`便能开通订阅服务的VPN，费用高，如果你选择此方案，那么你可自行搜索解决，此处仅仅讨论方案二。\n2. **方案二**：如果使用此方案，详见下方。\n\n\n### 解决方案\n> 采用**方案二**方式\n> \n> 这是一些订阅服务推广的链接： https://9.234456.xyz/abc.html?t=638586217737356738 (此链接打开无需使用VPN，但进入对应的机场页面却仍无法打开)\n> \n> 此教程中我们使用的机场是 \n> 1. `一元机场`: https://xn--4gq62f52gdss.com/\n> 2. `赔钱机场`：https://xn--mes358aby2apfg.com/\n\n### 机场选择的建议：\n\n- `一元机场`\n ![](/img/yiyuan.png)\n 可以看到\n - `12元/年`,每月50GB的可用额度，下个月重置流量额度\n - `15元/季`，即为`60元/年`，每月有4000GB的可用额度，下个月重置流量额度\n - `7元/月`，即为`84元/年`，每个月8000GB的可用额度，下个月重置流量额度\n 根据我个人的使用情况，大多数情况下我每月的流量使用未超过50GB，如果没有频繁的流量使用，\n 建议选择`12元/年`，否则可以选择`15元/季`，这意味着每月将有4000GB的可用额度\n\n- `赔钱机场`\n\n ![](/img/peiqian.png)\n `赔钱机场`的订阅共有9种方案，这里我仅显示自己正在使用的，个人认为十分优惠：\n - `34.99元/年`,每月500GB的可用额度，根据我观察和使用，这个订阅方案比`一元机场`的性价比更高，且流量使用额度也不用担心\n \n### 如何订阅？\n{% note success %}\n由于需要外部网络才能完成订阅服务的购买，你可以向我的邮箱`15202078626@163.com`发送你的订阅计划方案，\n扫描付款二维码，我将为你开通订阅（您只需要付款对应的订阅金额即可）\n{% endnote %}\n\n\n### 完成订阅后如何使用？\n> 你可以在 `Windows`、`Mac`、`Android`等平台使用此服务\n> 使用订阅的对应链接： https://flowus.cn/shenjian/22f76d4f-e7b3-4b8a-8a89-561566f6eb60\n\n\n ","source":"_posts/net/jichang/jichang.md","raw":"---\ntitle: 无法访问外网？需要订阅代理服务？\ntags: 网络代理\nabbrlink: 14011\ndate: 2024-08-07 10:06:08\n---\n\n{% note info %}\n**由于中国大陆的GFW（防火墙）限制，无法访问外网网络，因此需要访问像GitHub、YouTube这样的\n的网站将被屏蔽拦截，接下来我将给出一种使用`VPN`服务的可行的方案来保证服务的可靠性。**\n{% endnote %}\n\n### 介绍\n> 根据测试，许多提供服务的提供商所在的网站需要使用`外部网络`才能打开，仅有少部分的网站(**比较贵**)可以直接使用\n> 国内网络环境打开直接购买订阅服务。\n>\n\n那么你现在可以有两个选择：\n1. **方案一**：使用无需`外部网络`便能开通订阅服务的VPN，费用高，如果你选择此方案，那么你可自行搜索解决，此处仅仅讨论方案二。\n2. **方案二**：如果使用此方案，详见下方。\n\n\n### 解决方案\n> 采用**方案二**方式\n> \n> 这是一些订阅服务推广的链接： https://9.234456.xyz/abc.html?t=638586217737356738 (此链接打开无需使用VPN，但进入对应的机场页面却仍无法打开)\n> \n> 此教程中我们使用的机场是 \n> 1. `一元机场`: https://xn--4gq62f52gdss.com/\n> 2. `赔钱机场`：https://xn--mes358aby2apfg.com/\n\n### 机场选择的建议：\n\n- `一元机场`\n ![](/img/yiyuan.png)\n 可以看到\n - `12元/年`,每月50GB的可用额度，下个月重置流量额度\n - `15元/季`，即为`60元/年`，每月有4000GB的可用额度，下个月重置流量额度\n - `7元/月`，即为`84元/年`，每个月8000GB的可用额度，下个月重置流量额度\n 根据我个人的使用情况，大多数情况下我每月的流量使用未超过50GB，如果没有频繁的流量使用，\n 建议选择`12元/年`，否则可以选择`15元/季`，这意味着每月将有4000GB的可用额度\n\n- `赔钱机场`\n\n ![](/img/peiqian.png)\n `赔钱机场`的订阅共有9种方案，这里我仅显示自己正在使用的，个人认为十分优惠：\n - `34.99元/年`,每月500GB的可用额度，根据我观察和使用，这个订阅方案比`一元机场`的性价比更高，且流量使用额度也不用担心\n \n### 如何订阅？\n{% note success %}\n由于需要外部网络才能完成订阅服务的购买，你可以向我的邮箱`15202078626@163.com`发送你的订阅计划方案，\n扫描付款二维码，我将为你开通订阅（您只需要付款对应的订阅金额即可）\n{% endnote %}\n\n\n### 完成订阅后如何使用？\n> 你可以在 `Windows`、`Mac`、`Android`等平台使用此服务\n> 使用订阅的对应链接： https://flowus.cn/shenjian/22f76d4f-e7b3-4b8a-8a89-561566f6eb60\n\n\n ","slug":"net/jichang/jichang","published":1,"updated":"2024-08-09T12:21:50.036Z","comments":1,"layout":"post","photos":[],"_id":"cm5w9lrdv000stgah26ty73mb","content":"

由于中国大陆的GFW（防火墙）限制，无法访问外网网络，因此需要访问像GitHub、YouTube这样的
的网站将被屏蔽拦截，接下来我将给出一种使用VPN服务的可行的方案来保证服务的可靠性。

\n
\n\n

介绍

\n

根据测试，许多提供服务的提供商所在的网站需要使用外部网络才能打开，仅有少部分的网站(比较贵)可以直接使用
国内网络环境打开直接购买订阅服务。

\n
\n

那么你现在可以有两个选择：

\n
    \n
  1. 方案一：使用无需外部网络便能开通订阅服务的VPN，费用高，如果你选择此方案，那么你可自行搜索解决，此处仅仅讨论方案二。
    2. \n
  2. 方案二：如果使用此方案，详见下方。
    3. \n
\n

解决方案

\n

采用方案二方式

\n

这是一些订阅服务推广的链接： https://9.234456.xyz/abc.html?t=638586217737356738 (此链接打开无需使用VPN，但进入对应的机场页面却仍无法打开)

\n

此教程中我们使用的机场是

\n
    \n
  1. 一元机场: https://xn--4gq62f52gdss.com/
    2. \n
  2. 赔钱机场：https://xn--mes358aby2apfg.com/
    3. \n
\n
\n

机场选择的建议：

\n

如何订阅？

由于需要外部网络才能完成订阅服务的购买，你可以向我的邮箱15202078626@163.com发送你的订阅计划方案，
扫描付款二维码，我将为你开通订阅（您只需要付款对应的订阅金额即可）

\n
\n\n\n

完成订阅后如何使用？

\n

你可以在 Windows、Mac、Android等平台使用此服务
使用订阅的对应链接： https://flowus.cn/shenjian/22f76d4f-e7b3-4b8a-8a89-561566f6eb60

\n
\n","cover":false,"excerpt":"","more":"

由于中国大陆的GFW（防火墙）限制，无法访问外网网络，因此需要访问像GitHub、YouTube这样的
的网站将被屏蔽拦截，接下来我将给出一种使用VPN服务的可行的方案来保证服务的可靠性。

\n
\n\n

介绍

\n

根据测试，许多提供服务的提供商所在的网站需要使用外部网络才能打开，仅有少部分的网站(比较贵)可以直接使用
国内网络环境打开直接购买订阅服务。

\n
\n

那么你现在可以有两个选择：

\n
    \n
  1. 方案一：使用无需外部网络便能开通订阅服务的VPN，费用高，如果你选择此方案，那么你可自行搜索解决，此处仅仅讨论方案二。
    2. \n
  2. 方案二：如果使用此方案，详见下方。
    3. \n
\n

解决方案

\n

采用方案二方式

\n

这是一些订阅服务推广的链接： https://9.234456.xyz/abc.html?t=638586217737356738 (此链接打开无需使用VPN，但进入对应的机场页面却仍无法打开)

\n

此教程中我们使用的机场是

\n
    \n
  1. 一元机场: https://xn--4gq62f52gdss.com/
    2. \n
  2. 赔钱机场：https://xn--mes358aby2apfg.com/
    3. \n
\n
\n

机场选择的建议：

\n

如何订阅？

由于需要外部网络才能完成订阅服务的购买，你可以向我的邮箱15202078626@163.com发送你的订阅计划方案，
扫描付款二维码，我将为你开通订阅（您只需要付款对应的订阅金额即可）

\n
\n\n\n

完成订阅后如何使用？

\n

你可以在 Windows、Mac、Android等平台使用此服务
使用订阅的对应链接： https://flowus.cn/shenjian/22f76d4f-e7b3-4b8a-8a89-561566f6eb60

\n
\n"}],"PostAsset":[],"PostCategory":[{"post_id":"cm5w9lrdn000dtgah04audthw","category_id":"cm5w9lrdp000htgahcgif26p4","_id":"cm5w9lrds000ltgahfnvufs59"}],"PostTag":[{"post_id":"cm5w9lrdi0005tgah797v3otl","tag_id":"cm5w9lrdk0008tgahcjy0gsmw","_id":"cm5w9lrdo000ftgahcer4h9z8"},{"post_id":"cm5w9lrdn000ctgah8ll3dor6","tag_id":"cm5w9lrdo000etgahbm4uhrtq","_id":"cm5w9lrdq000jtgah8bewbri1"},{"post_id":"cm5w9lrdn000dtgah04audthw","tag_id":"cm5w9lrdq000itgah0ue9cysm","_id":"cm5w9lrds000mtgahhjn02wyq"},{"post_id":"cm5w9lrdo000gtgah38tl6q9j","tag_id":"cm5w9lrdo000etgahbm4uhrtq","_id":"cm5w9lrds000ntgah8fy86a3s"},{"post_id":"cm5w9lrdv000stgah26ty73mb","tag_id":"cm5w9lrdw000ttgah4ix0bjqs","_id":"cm5w9lrdw000utgah31j22idn"}],"Tag":[{"name":"machinelearning","_id":"cm5w9lrdk0008tgahcjy0gsmw"},{"name":"uniapp","_id":"cm5w9lrdo000etgahbm4uhrtq"},{"name":"古文观止","_id":"cm5w9lrdq000itgah0ue9cysm"},{"name":"网络代理","_id":"cm5w9lrdw000ttgah4ix0bjqs"}]}} \ No newline at end of file +{"meta":{"version":1,"warehouse":"5.0.1"},"models":{"Asset":[{"_id":"themes/butterfly/source/css/index.styl","path":"css/index.styl","modified":1,"renderable":1},{"_id":"themes/butterfly/source/css/var.styl","path":"css/var.styl","modified":1,"renderable":1},{"_id":"themes/butterfly/source/img/404.jpg","path":"img/404.jpg","modified":1,"renderable":1},{"_id":"themes/butterfly/source/img/avatar.jpg","path":"img/avatar.jpg","modified":1,"renderable":1},{"_id":"themes/butterfly/source/img/favicon.png","path":"img/favicon.png","modified":1,"renderable":1},{"_id":"themes/butterfly/source/img/friend_404.gif","path":"img/friend_404.gif","modified":1,"renderable":1},{"_id":"themes/butterfly/source/js/main.js","path":"js/main.js","modified":1,"renderable":1},{"_id":"themes/butterfly/source/js/tw_cn.js","path":"js/tw_cn.js","modified":1,"renderable":1},{"_id":"themes/butterfly/source/js/utils.js","path":"js/utils.js","modified":1,"renderable":1},{"_id":"themes/butterfly/source/js/search/algolia.js","path":"js/search/algolia.js","modified":1,"renderable":1},{"_id":"themes/butterfly/source/js/search/local-search.js","path":"js/search/local-search.js","modified":1,"renderable":1},{"_id":"source/img/000001.png","path":"img/000001.png","modified":1,"renderable":0},{"_id":"source/img/dingyue.png","path":"img/dingyue.png","modified":1,"renderable":0},{"_id":"source/img/peiqian.png","path":"img/peiqian.png","modified":1,"renderable":0},{"_id":"source/img/site01.jpg","path":"img/site01.jpg","modified":1,"renderable":0},{"_id":"source/img/yiyuan.png","path":"img/yiyuan.png","modified":1,"renderable":0},{"_id":"source/img/language/c-env-conf.png","path":"img/language/c-env-conf.png","modified":1,"renderable":0},{"_id":"source/img/machinelearning/cros-valid.png","path":"img/machinelearning/cros-valid.png","modified":1,"renderable":0},{"_id":"source/img/machinelearning/fitting.png","path":"img/machinelearning/fitting.png","modified":1,"renderable":0},{"_id":"source/img/machinelearning/kfold-skfold.png","path":"img/machinelearning/kfold-skfold.png","modified":1,"renderable":0},{"_id":"source/img/machinelearning/knn-01.png","path":"img/machinelearning/knn-01.png","modified":1,"renderable":0},{"_id":"source/img/machinelearning/linear.png","path":"img/machinelearning/linear.png","modified":1,"renderable":0}],"Cache":[{"_id":"source/_data/link.yml","hash":"6aaf04b5c920e403bea8f82e4b3f4d719760e6df","modified":1723257824766},{"_id":"source/_posts/hello-world.md","hash":"40e804610ff712f079ace7012b862b4efecf82fb","modified":1723206110026},{"_id":"source/categories/index.md","hash":"49618dce0bee26dfc65f0de1794f01d2967cb7b8","modified":1722407843090},{"_id":"source/about/index.md","hash":"31fdd093368e1c18c1592a8cad6f3c3fe6d6711a","modified":1723257494626},{"_id":"source/link/index.md","hash":"26e21fe46bf7c0fc5ae95b73d52bf516368d6dc7","modified":1723257768927},{"_id":"source/movies/index.md","hash":"4abf7a6a712f712f22d80203f6004a89feca2014","modified":1723257633715},{"_id":"source/music/index.md","hash":"d8dcf467af235e0bac09805db3a4ab73ad782b83","modified":1723257619897},{"_id":"source/tags/index.md","hash":"1f27b735b6c7d629c8931b5bd3913bdd659f1981","modified":1722385980076},{"_id":"source/_posts/frontend/frontend.md","hash":"3770215d35203b03d86d4f3a6ceee32f9849b584","modified":1723206110023},{"_id":"source/_posts/language/C.md","hash":"df561079ee6169482dc11c86d13ce64795420201","modified":1736945074907},{"_id":"source/_posts/linux/script.md","hash":"2deda929ed0c81ddcd00945c673b29f1bd1353c0","modified":1723997145731},{"_id":"source/_posts/machinelearning/linearreression.md","hash":"3095935215b99cd38615e52dba5d3f1c2f86a177","modified":1737277809051},{"_id":"source/_posts/machinelearning/knn.md","hash":"6e6c444dc7be367d0cc131b29c02414e619e416e","modified":1736846489069},{"_id":"source/_posts/net/index.md","hash":"7eae8512c2a6bd937200487540b962d65a47ad9e","modified":1723206110028},{"_id":"source/img/machinelearning/kfold-skfold.png","hash":"ab841f5412b7ea773468a3facff643df68a88c01","modified":1736846071435},{"_id":"source/img/machinelearning/knn-01.png","hash":"2a931981d1d5f72d6b42b39b8ef313569eab853e","modified":1736766866160},{"_id":"source/_posts/ancient/guwenguanzhi/1.md","hash":"629652f1d8e2a347e6b11c367d25b0a26698cd60","modified":1723257063678},{"_id":"source/_posts/bigdata/hadoop/env.md","hash":"4b184c804e9c8083966b1360fe9d5aa539930005","modified":1726065928095},{"_id":"source/_posts/bigdata/hadoop/zookeper.md","hash":"96e58c86eba61accb620adc5e7b7dbc46e47b237","modified":1726066302706},{"_id":"source/_posts/bigdata/hadoop/hdfs.md","hash":"3148149529354105eb301c70961b6f1b68030835","modified":1726066302712},{"_id":"source/_posts/frontend/deploy/deploy.md","hash":"ba4a05741f30f92f9fbe7b815519142de09b13fb","modified":1723206110045},{"_id":"source/_posts/frontend/uniapp/component1.md","hash":"b9b981d3903f5e57e7f327d930df4f04780211f9","modified":1723206110042},{"_id":"source/_posts/frontend/uniapp/uniapp.md","hash":"8c8594e3eb73d2c10c4c6dfc008b58c36763b054","modified":1723206110039},{"_id":"source/_posts/net/jichang/jichang.md","hash":"ad0485f33d2f0a8eea342c815d869911433df370","modified":1723206110036},{"_id":"source/img/language/c-env-conf.png","hash":"09de801caef07f177c829e889464442f535b189c","modified":1736906329621},{"_id":"source/img/machinelearning/fitting.png","hash":"89c0cf6301f0b42de93ac41e5bac33d99acfd530","modified":1737261253822},{"_id":"source/img/machinelearning/linear.png","hash":"4a54dbcc028e2385bd50bf8b022adde0a0d9fbcf","modified":1737254479607},{"_id":"source/img/000001.png","hash":"ad86c3b72174364d462bdab1d09540bd79eb123c","modified":1725979378674},{"_id":"source/img/machinelearning/cros-valid.png","hash":"8f9b204b651f93c17ad0856daa628d6abe985d97","modified":1736768886866},{"_id":"source/img/yiyuan.png","hash":"817a89509a8ebcddff6b369979d53ecf44a30a9f","modified":1722998234119},{"_id":"source/img/peiqian.png","hash":"2f077f1fff014ee448cd58b57ff83901702e2d88","modified":1723000686874},{"_id":"themes/butterfly/LICENSE","hash":"c8bc7df08db9dd3b39c2c2259a163a36cf2f6808","modified":1722500710084},{"_id":"themes/butterfly/README.md","hash":"20a91bea7f7ada8b8195d2abff106a7ce21bba20","modified":1722500710085},{"_id":"themes/butterfly/README_CN.md","hash":"9d729ee2ffc5e5f703ccfbfbbb7b286d59071125","modified":1722500710087},{"_id":"themes/butterfly/_config.yml","hash":"c6fab3700a6502b5790ca20d20113020544ea048","modified":1722500710088},{"_id":"themes/butterfly/package.json","hash":"2b6fb6f62b9fa6a829311ffd532ae760fbd0a7db","modified":1722500710223},{"_id":"themes/butterfly/plugins.yml","hash":"7bb2c7350c0c57850aa30213cd0f26553a614702","modified":1722500710224},{"_id":"themes/butterfly/.github/FUNDING.yml","hash":"3b572099a992e30267f5fe4cd3c582ff7ac9f083","modified":1722500710075},{"_id":"themes/butterfly/languages/default.yml","hash":"90e9e2f36dc51aa77eb7804ae048b4876035b12d","modified":1722500710090},{"_id":"themes/butterfly/languages/en.yml","hash":"68127be0e6b44cfc5f31353d8b275c02939b3ff9","modified":1722500710092},{"_id":"themes/butterfly/languages/zh-CN.yml","hash":"2dcc70a011b37890215ae0fd6d8f8c78aa8af6b0","modified":1722500710093},{"_id":"themes/butterfly/languages/zh-TW.yml","hash":"1392e7b8c678cdfb54f55523693e66abc7d80538","modified":1722500710094},{"_id":"themes/butterfly/layout/archive.pug","hash":"bc77220dfc269b8faad0930e1a4142ebf68165e5","modified":1722500710096},{"_id":"themes/butterfly/layout/category.pug","hash":"bf979aec88d78b644fc5d31518f8679ad7625792","modified":1722500710097},{"_id":"themes/butterfly/layout/index.pug","hash":"648dcbdb3d145a710de81c909e000e8664d2ac9c","modified":1722500710218},{"_id":"themes/butterfly/layout/page.pug","hash":"bf2d6c6d2d156777b55292e51be02b0b3acf0af8","modified":1722500710219},{"_id":"themes/butterfly/layout/post.pug","hash":"fdbb508b5e6dec30fb8753c5a7fdd494410c4fc0","modified":1722500710220},{"_id":"themes/butterfly/layout/tag.pug","hash":"4bb5efc6dabdf1626685bf6771aaa1467155ae86","modified":1722500710222},{"_id":"themes/butterfly/.github/ISSUE_TEMPLATE/bug_report.yml","hash":"eed9190301095b35081aa2658204cc3f15b9f5e1","modified":1722500710076},{"_id":"themes/butterfly/.github/ISSUE_TEMPLATE/config.yml","hash":"63ad2249ad09fb3fe21bd5ff9adefb304a7ab24a","modified":1722500710077},{"_id":"themes/butterfly/.github/ISSUE_TEMPLATE/feature_request.yml","hash":"6e0f9470b18bd37d4891282ac73d61676b040e8c","modified":1722500710079},{"_id":"themes/butterfly/.github/workflows/publish.yml","hash":"e320b40c051bae1549156cd5ea4a51383cf78598","modified":1722500710080},{"_id":"themes/butterfly/.github/workflows/stale.yml","hash":"4040c76547e270aaf184e9b219a44ca41bbb1b9f","modified":1722500710082},{"_id":"themes/butterfly/layout/includes/404.pug","hash":"aace9ddff469de4226e47a52ede1c81e66d66d5c","modified":1722500710100},{"_id":"themes/butterfly/layout/includes/additional-js.pug","hash":"50eea5aa78cdeb6c72dd22f0aeabc407cc0f712e","modified":1722500710101},{"_id":"themes/butterfly/layout/includes/footer.pug","hash":"8715948b93e7508b84d913be1969b28c6b067b9b","modified":1722500710102},{"_id":"themes/butterfly/layout/includes/head.pug","hash":"ea8d4e8ac6af93cd268ba8f6ffcb80417bc2501e","modified":1722500710103},{"_id":"themes/butterfly/layout/includes/layout.pug","hash":"96df62e34661d8ca4a45267286127479e5178a79","modified":1722500710123},{"_id":"themes/butterfly/layout/includes/pagination.pug","hash":"c5c58714fb3cb839653e5c32e6094784c8662935","modified":1722500710135},{"_id":"themes/butterfly/layout/includes/rightside.pug","hash":"f448bf73103b88de4443e52d600e871cf3de3e32","modified":1722500710138},{"_id":"themes/butterfly/layout/includes/sidebar.pug","hash":"9277fead4c29dbe93976f078adaa26e8f9253da3","modified":1722500710140},{"_id":"themes/butterfly/scripts/events/404.js","hash":"f1d1c378356b776e9b2a8411e6dca88dc8c3245c","modified":1722500710226},{"_id":"themes/butterfly/scripts/events/cdn.js","hash":"7864ba45716c51aef8d8b04fd4bc212e0008ce3b","modified":1722500710226},{"_id":"themes/butterfly/scripts/events/comment.js","hash":"95479790234c291b064d031577d71214cdd1d820","modified":1722500710229},{"_id":"themes/butterfly/scripts/events/init.js","hash":"ce68e84a9ccfcf91100befbaa9afc392a0cd93bb","modified":1722500710229},{"_id":"themes/butterfly/scripts/events/merge_config.js","hash":"b1dfc3c898b886eab1241b068fc27d7a26a3b7d2","modified":1722500710230},{"_id":"themes/butterfly/scripts/events/stylus.js","hash":"0a336dfe5ed08952fa0df1532421df38a74a20d6","modified":1722500710231},{"_id":"themes/butterfly/scripts/events/welcome.js","hash":"f59e10305fef59ea3e62a7395106c0927582879d","modified":1722500710232},{"_id":"themes/butterfly/scripts/filters/post_lazyload.js","hash":"5ed2d7ef240c927fe1b7a7fb5bf9e55e2bfd55a5","modified":1722500710233},{"_id":"themes/butterfly/scripts/filters/random_cover.js","hash":"0df22d7dbfa766a65cb6032a1f003348f4307cfe","modified":1722500710234},{"_id":"themes/butterfly/scripts/helpers/aside_archives.js","hash":"4f712b4ea383b59a3122683db1d54c04a79ccc5d","modified":1722500710236},{"_id":"themes/butterfly/scripts/helpers/aside_categories.js","hash":"cdd992c8577d583c237b6aac9f5077d8200879b2","modified":1722500710237},{"_id":"themes/butterfly/scripts/helpers/findArchiveLength.js","hash":"b12895e0765d596494e5526d121de0dd5a7c23d3","modified":1722500710238},{"_id":"themes/butterfly/scripts/helpers/inject_head_js.js","hash":"b55f71347d2ead097c7f98c0ec792b091433345c","modified":1722500710239},{"_id":"themes/butterfly/scripts/helpers/page.js","hash":"c74d6a9b8f71e69447f7847a5f5e81555d68b140","modified":1722500710240},{"_id":"themes/butterfly/scripts/helpers/related_post.js","hash":"76343ac8422c9c8539082e77eda6ffee4b877eb2","modified":1722500710241},{"_id":"themes/butterfly/scripts/helpers/series.js","hash":"17c0095bc8d612a268cdcab000b1742dc4c6f811","modified":1722500710243},{"_id":"themes/butterfly/scripts/tag/button.js","hash":"164d5f1c2d1b4cb5a813a6fc574016743a53c019","modified":1722500710244},{"_id":"themes/butterfly/scripts/tag/flink.js","hash":"3ba7677969ff01fab06fc6713455ddc6861f0024","modified":1722500710245},{"_id":"themes/butterfly/scripts/tag/gallery.js","hash":"7ec77b3093f5de67e7032f40a5b12f1389f6f6ff","modified":1722500710247},{"_id":"themes/butterfly/scripts/tag/hide.js","hash":"5d08c3552f7d3c80a724ca628bff66321abe2e5a","modified":1722500710248},{"_id":"themes/butterfly/scripts/tag/inlineImg.js","hash":"c863d2732ce4bdc084f2d0db92f50f80328c1007","modified":1722500710248},{"_id":"themes/butterfly/scripts/tag/mermaid.js","hash":"289f85847c58f0b2b7d98a68e370a2896edb8949","modified":1722500710250},{"_id":"themes/butterfly/scripts/tag/label.js","hash":"b013dc0a3d57d2caa18b89263f23871da9ec456d","modified":1722500710248},{"_id":"themes/butterfly/scripts/tag/note.js","hash":"e68d8d21f3a86e3646907a3685550ee20e8d4a9f","modified":1722500710252},{"_id":"themes/butterfly/scripts/tag/score.js","hash":"35d54adc92e717cc32e13515122b025fd1a98ea2","modified":1722500710252},{"_id":"themes/butterfly/scripts/tag/series.js","hash":"dc56e5182dd3813dc977c9bf8556dcc7615e467b","modified":1722500710252},{"_id":"themes/butterfly/scripts/tag/tabs.js","hash":"7c448886f230adb4f4a0208c88fff809abcb5637","modified":1722500710254},{"_id":"themes/butterfly/scripts/tag/timeline.js","hash":"e611074a5a7f489a8b04afac0a3f7f882ce26532","modified":1722500710255},{"_id":"themes/butterfly/source/css/index.styl","hash":"b13d96924a5534bff91d75566b196ac87b4fac22","modified":1722500710313},{"_id":"themes/butterfly/source/css/var.styl","hash":"950250f66faeb611a67540e0fa6cedbcf5a7a321","modified":1722500710313},{"_id":"themes/butterfly/source/img/404.jpg","hash":"fb4489bc1d30c93d28f7332158c1c6c1416148de","modified":1722500710315},{"_id":"themes/butterfly/source/img/avatar.jpg","hash":"cb0941101c6a6b8f762ce6ffc3c948641e7f642f","modified":1722500710316},{"_id":"themes/butterfly/source/img/favicon.png","hash":"3cf89864b4f6c9b532522a4d260a2e887971c92d","modified":1722500710317},{"_id":"themes/butterfly/source/img/friend_404.gif","hash":"8d2d0ebef70a8eb07329f57e645889b0e420fa48","modified":1722500710319},{"_id":"themes/butterfly/source/js/main.js","hash":"59cd756a94ecdf3ec7b18f50691a8a6305f7a65a","modified":1722500710321},{"_id":"themes/butterfly/source/js/tw_cn.js","hash":"d776c670e4076ad6049dbb64cdee7a734b51d37f","modified":1722500710326},{"_id":"themes/butterfly/source/js/utils.js","hash":"7b871fe0c4456660cff4c7b9cc4ed089adac2caf","modified":1722500710327},{"_id":"themes/butterfly/layout/includes/head/Open_Graph.pug","hash":"c8dbdfe6145a0bc6f7691c9551be8169a2698f0a","modified":1722500710105},{"_id":"themes/butterfly/layout/includes/head/analytics.pug","hash":"c7666a10448edd93f5ace37296051b7670495f1b","modified":1722500710106},{"_id":"themes/butterfly/layout/includes/head/config.pug","hash":"39e1ca0a54eb5fd3688a78737417a1aaa50914c9","modified":1722500710108},{"_id":"themes/butterfly/layout/includes/head/config_site.pug","hash":"bd5dd5452e28a4fe94c3241a758ec6f4fdb7a149","modified":1722500710109},{"_id":"themes/butterfly/layout/includes/head/google_adsense.pug","hash":"f29123e603cbbcc6ce277d4e8f600ba67498077c","modified":1722500710109},{"_id":"themes/butterfly/layout/includes/head/preconnect.pug","hash":"a7c929b90ae52b78b39b1728e3ab0e3db1cb7b9a","modified":1722500710111},{"_id":"themes/butterfly/layout/includes/head/pwa.pug","hash":"6dc2c9b85df9ab4f5b554305339fd80a90a6cf43","modified":1722500710113},{"_id":"themes/butterfly/layout/includes/header/index.pug","hash":"1bef867c799ba158c5417272fb137539951aa120","modified":1722500710117},{"_id":"themes/butterfly/layout/includes/head/site_verification.pug","hash":"5168caadc4cf541f5d6676a9c5e8ae47a948f9ad","modified":1722500710114},{"_id":"themes/butterfly/layout/includes/header/menu_item.pug","hash":"ca8bcd90ad9467819330bfe7c02b76322754bccf","modified":1722500710118},{"_id":"themes/butterfly/layout/includes/header/nav.pug","hash":"962ee70a35e60a13c31eea47d16b9f98069fe417","modified":1722500710119},{"_id":"themes/butterfly/layout/includes/header/post-info.pug","hash":"cc99b2dc5c6b1f74391b0da609853ebc11de9610","modified":1722500710120},{"_id":"themes/butterfly/layout/includes/header/social.pug","hash":"7a641b5dd45b970e1dafd1433eb32ea149e55cf2","modified":1722500710121},{"_id":"themes/butterfly/layout/includes/loading/fullpage-loading.pug","hash":"766baca6ddce49d1724a02312387b292ff2d0bdc","modified":1722500710125},{"_id":"themes/butterfly/layout/includes/loading/index.pug","hash":"00ae419f527d8225a2dc03d4f977cec737248423","modified":1722500710126},{"_id":"themes/butterfly/layout/includes/mixins/article-sort.pug","hash":"9155f01d4c644a2e19b2b13b2d3c6d5e34dd0abf","modified":1722500710128},{"_id":"themes/butterfly/layout/includes/loading/pace.pug","hash":"a6fde4835d6460ce7baf792fd5e1977fad73db25","modified":1722500710127},{"_id":"themes/butterfly/layout/includes/mixins/post-ui.pug","hash":"90eb453b14f6b5c25bfd8d28aa67783603a1411d","modified":1722500710129},{"_id":"themes/butterfly/layout/includes/page/categories.pug","hash":"5276a8d2835e05bd535fedc9f593a0ce8c3e8437","modified":1722500710131},{"_id":"themes/butterfly/layout/includes/page/default-page.pug","hash":"e9459f122af7b733398578f9f0f8ab3c5e12a217","modified":1722500710131},{"_id":"themes/butterfly/layout/includes/page/flink.pug","hash":"e37681bc9c169d4220f26ecda2b3d5c02b6b9a0f","modified":1722500710133},{"_id":"themes/butterfly/layout/includes/page/tags.pug","hash":"12be059c536490af216a397e8f2a7abbf6d4610e","modified":1722500710134},{"_id":"themes/butterfly/layout/includes/post/post-copyright.pug","hash":"0abad416b1974a17e5be7817931d5fe799180170","modified":1722500710136},{"_id":"themes/butterfly/layout/includes/post/reward.pug","hash":"912df10a053db3135968e92b6fd1a707ee94c968","modified":1722500710138},{"_id":"themes/butterfly/layout/includes/third-party/aplayer.pug","hash":"e939344fd389aeb11864ee697d5fd9b036d8325f","modified":1722500710146},{"_id":"themes/butterfly/layout/includes/third-party/effect.pug","hash":"43014bfc63583d3ee8808d526dd165848c0ed52f","modified":1722500710177},{"_id":"themes/butterfly/layout/includes/third-party/pangu.pug","hash":"f0898509da70388b5c532f19e762756d74080200","modified":1722500710191},{"_id":"themes/butterfly/layout/includes/third-party/pjax.pug","hash":"9b734d99963f3e7f562597dcf60485ccbf6e961c","modified":1722500710192},{"_id":"themes/butterfly/layout/includes/third-party/prismjs.pug","hash":"08979afbfecb4476a5ae8e360947b92624d285b8","modified":1722500710194},{"_id":"themes/butterfly/layout/includes/third-party/subtitle.pug","hash":"dfb5e16a7e7106bb20b2ac2d0df1251d0fc79609","modified":1722500710202},{"_id":"themes/butterfly/layout/includes/widget/card_ad.pug","hash":"a8312b527493dabbadbb1280760168d3bc909a3b","modified":1722500710204},{"_id":"themes/butterfly/layout/includes/widget/card_announcement.pug","hash":"21e019bdc3b1e796bb00976bb29af2d51f873624","modified":1722500710205},{"_id":"themes/butterfly/layout/includes/widget/card_archives.pug","hash":"73d33b6930e7944187a4b3403daf25d27077a2dd","modified":1722500710206},{"_id":"themes/butterfly/layout/includes/widget/card_author.pug","hash":"ab037bf5794638bd30da4cf7cf106e5d03b5f696","modified":1722500710207},{"_id":"themes/butterfly/layout/includes/widget/card_bottom_self.pug","hash":"1dba77d250eeebfb6e293d504352c7e9ea31980b","modified":1722500710208},{"_id":"themes/butterfly/layout/includes/widget/card_categories.pug","hash":"66e383b4ef374951eb87dd1bf4cdb7a667193fb5","modified":1722500710209},{"_id":"themes/butterfly/layout/includes/widget/card_newest_comment.pug","hash":"8e22f53886a57a68286970d8af8b4c950fd4a1d7","modified":1722500710210},{"_id":"themes/butterfly/layout/includes/widget/card_post_series.pug","hash":"e0bb72fa0ce15964b11b8fe421cae3432394e35f","modified":1722500710210},{"_id":"themes/butterfly/layout/includes/widget/card_post_toc.pug","hash":"d48d77af1670bd568d784794408bf524a448bfcc","modified":1722500710211},{"_id":"themes/butterfly/layout/includes/widget/card_recent_post.pug","hash":"bb842d2aa6469d65bf06af1372f0a19a9e4ef44c","modified":1722500710214},{"_id":"themes/butterfly/layout/includes/widget/card_tags.pug","hash":"842b772a387b576550fa127030e1c2e9bf65716d","modified":1722500710215},{"_id":"themes/butterfly/layout/includes/widget/card_top_self.pug","hash":"7b5ae404a1205546b7de4be42291315cf918f2b3","modified":1722500710215},{"_id":"themes/butterfly/layout/includes/widget/card_webinfo.pug","hash":"12185713f9ca08984fc74e3b69d8cd6828d23da8","modified":1722500710216},{"_id":"themes/butterfly/layout/includes/widget/index.pug","hash":"8df529f71e25f1c0a00e533de7944ed3d1ba7bd8","modified":1722500710217},{"_id":"themes/butterfly/source/css/_global/function.styl","hash":"e920dae9ce00177922468db49240f5aca0af4f64","modified":1722500710258},{"_id":"themes/butterfly/source/css/_global/index.styl","hash":"0421da07907b3d98df64239e073b23fbb3f04149","modified":1722500710259},{"_id":"themes/butterfly/source/css/_highlight/highlight.styl","hash":"41054740cfbd1357138785464f6859681ca58493","modified":1722500710260},{"_id":"themes/butterfly/source/css/_highlight/theme.styl","hash":"3c178608406c31d768af355ef1d7326da37cc75f","modified":1722500710268},{"_id":"themes/butterfly/source/css/_layout/aside.styl","hash":"aae70ddd126b2e40158e45036abecbfa33cbfbba","modified":1722500710270},{"_id":"themes/butterfly/source/css/_layout/chat.styl","hash":"792a04d36de32f230ca3256ad87a90fe8392f333","modified":1722500710272},{"_id":"themes/butterfly/source/css/_layout/comments.styl","hash":"fbfce4d67cacd1df22fb73d89d008693f59d9d91","modified":1722500710273},{"_id":"themes/butterfly/source/css/_layout/footer.styl","hash":"5e27f7842af82ff7498d4b59787ce9ca90fa9e6f","modified":1722500710275},{"_id":"themes/butterfly/source/css/_layout/head.styl","hash":"dd5d9a5631b682610ea699541b8246ceaa56fddb","modified":1723206391886},{"_id":"themes/butterfly/source/css/_layout/loading.styl","hash":"f0b01bbf321c2c24fdccaee367dd9fd448031a72","modified":1722500710277},{"_id":"themes/butterfly/source/css/_layout/pagination.styl","hash":"bd099f7d3adef4b7edd24c0a25a07415b156e587","modified":1722500710278},{"_id":"themes/butterfly/source/css/_layout/post.styl","hash":"7ae27854a737a02eca89b0b92db94cb298fef59e","modified":1722500710280},{"_id":"themes/butterfly/source/css/_layout/relatedposts.styl","hash":"6dcf19c0933c8828a439f801b0f4b256447dec07","modified":1722500710281},{"_id":"themes/butterfly/source/css/_layout/reward.styl","hash":"c0b11a1a5f52e3a6af4e312a8134c93eda18a7dd","modified":1722500710281},{"_id":"themes/butterfly/source/css/_layout/sidebar.styl","hash":"80ee9d0bfe5d38aac1f0cdcea5fc88b71d310041","modified":1722500710283},{"_id":"themes/butterfly/source/css/_layout/rightside.styl","hash":"0322237e762db401d7b4aa33168d0b9334a9ec26","modified":1722500710282},{"_id":"themes/butterfly/source/css/_layout/third-party.styl","hash":"15ea7564b2e3bf46bc91fb6e49c94d057b37caaf","modified":1722500710283},{"_id":"themes/butterfly/source/css/_mode/darkmode.styl","hash":"dbc855795a881f8c805bf5c9c5c4d5d542a648ec","modified":1722500710286},{"_id":"themes/butterfly/source/css/_mode/readmode.styl","hash":"a22fd15048d21452f0015d0765d295d730203308","modified":1723078297080},{"_id":"themes/butterfly/source/css/_page/404.styl","hash":"a7223a8fcc4fa7b81e552c9a2554be7df9de312e","modified":1722500710289},{"_id":"themes/butterfly/source/css/_page/archives.styl","hash":"5dd1ba997741d02894ff846eda939ad8051c0bb2","modified":1722500710290},{"_id":"themes/butterfly/source/css/_page/categories.styl","hash":"68bc8cbea25dbb3cdc170f09f9b43ce130547717","modified":1722500710292},{"_id":"themes/butterfly/source/css/_page/common.styl","hash":"df7a51fcabbadab5aa31770e3202a47c9599bbb7","modified":1722500710293},{"_id":"themes/butterfly/source/css/_page/flink.styl","hash":"ecc2b2e28c179eb9406fc2c6f00e141078249cdd","modified":1722500710294},{"_id":"themes/butterfly/source/css/_page/homepage.styl","hash":"a977cd8161ef4d6ddd5293e81403519076657430","modified":1722500710295},{"_id":"themes/butterfly/source/css/_page/tags.styl","hash":"9e35f91847773b915c74a78b8aa66c7bdb950ad0","modified":1722500710296},{"_id":"themes/butterfly/source/css/_search/algolia.styl","hash":"37db99299af380e9111dce2a78a5049b301b13e0","modified":1722500710298},{"_id":"themes/butterfly/source/css/_search/index.styl","hash":"0b23010154e19f37f0c4af0110f9f834d6d41a13","modified":1722500710298},{"_id":"themes/butterfly/source/css/_search/local-search.styl","hash":"8a53d7ba5ca2f5eb4124b684e7845b648583f658","modified":1722500710301},{"_id":"themes/butterfly/source/css/_tags/button.styl","hash":"62da1de0d5b8453fcecbfacddb16985265638ba5","modified":1722500710302},{"_id":"themes/butterfly/source/css/_tags/gallery.styl","hash":"3e9355b76f87e2ee90f652855282b37ab5ae0b3e","modified":1722500710304},{"_id":"themes/butterfly/source/css/_tags/hexo.styl","hash":"985b183db7b7bfd8f9bdb60494549fb7f850348b","modified":1722500710305},{"_id":"themes/butterfly/source/css/_tags/hide.styl","hash":"b7cf7753479fcf2fe07287ffdb0e568adbba4c18","modified":1722500710306},{"_id":"themes/butterfly/source/css/_tags/inlineImg.styl","hash":"5a873d01fabebcf7ddf7a6b1c2e2e5e2714097f4","modified":1722500710307},{"_id":"themes/butterfly/source/css/_tags/label.styl","hash":"2f83bd145b870d80d4b18b0ac603235229a5694e","modified":1722500710307},{"_id":"themes/butterfly/source/css/_tags/note.styl","hash":"4929382bd60788d34752a66e2fe764ef797a72a0","modified":1722500710308},{"_id":"themes/butterfly/source/css/_tags/tabs.styl","hash":"353b95f9a6c2c1e777d978118cb61f909ccbf89c","modified":1722500710309},{"_id":"themes/butterfly/source/css/_tags/timeline.styl","hash":"07ea7134db7a66c87658116f089fb1a2a6906563","modified":1722500710310},{"_id":"themes/butterfly/source/css/_third-party/normalize.min.css","hash":"8549829fb7d3c21cd9e119884962e8c463a4a267","modified":1722500710312},{"_id":"themes/butterfly/source/js/search/algolia.js","hash":"a7c2fe73cc05ad3525909b86ad0ede1a9f2d3b48","modified":1722500710323},{"_id":"themes/butterfly/source/js/search/local-search.js","hash":"ab3904451ae1d78903424b8b2ef815c8571e1749","modified":1722500710325},{"_id":"themes/butterfly/layout/includes/third-party/abcjs/abcjs.pug","hash":"8f95aca305b56ccd7c8c7367b03d26db816ebd5f","modified":1722500710143},{"_id":"themes/butterfly/layout/includes/third-party/abcjs/index.pug","hash":"58f37823f6cd9a194fb50f7ca7c2233e49939034","modified":1722500710144},{"_id":"themes/butterfly/layout/includes/third-party/card-post-count/artalk.pug","hash":"b03ee8625149191f9d5d057bbc9824b68d8dd0c4","modified":1722500710147},{"_id":"themes/butterfly/layout/includes/third-party/card-post-count/disqus.pug","hash":"d6fff5a7f84c8b09f282f9ddc0020a68a8aac9ea","modified":1722500710148},{"_id":"themes/butterfly/layout/includes/third-party/card-post-count/fb.pug","hash":"4b98145d6584d586cabf033493282afc72ae816a","modified":1722500710149},{"_id":"themes/butterfly/layout/includes/third-party/card-post-count/index.pug","hash":"846cabae287ae31b3bbfac3da022475713dd5ecc","modified":1722500710151},{"_id":"themes/butterfly/layout/includes/third-party/card-post-count/remark42.pug","hash":"716dc463fe4ef5112e7018ed60804125fdfa5cad","modified":1722500710151},{"_id":"themes/butterfly/layout/includes/third-party/card-post-count/twikoo.pug","hash":"7e233f872aea6fd6beccdc9efd86b1bf9ec9f12d","modified":1722500710152},{"_id":"themes/butterfly/layout/includes/third-party/card-post-count/valine.pug","hash":"cd4fc9c5a61608a5dedf645c1295430a1623040f","modified":1722500710153},{"_id":"themes/butterfly/layout/includes/third-party/card-post-count/waline.pug","hash":"fd2320ee25507bb8ef49f932c2d170586b44ea4d","modified":1722500710154},{"_id":"themes/butterfly/layout/includes/third-party/chat/chatra.pug","hash":"08a85e52fc800d3562df869e5e2613313e76fce6","modified":1722500710156},{"_id":"themes/butterfly/layout/includes/third-party/chat/crisp.pug","hash":"09d2ab2570b67e6f09244a898ccab5567cb82ace","modified":1722500710156},{"_id":"themes/butterfly/layout/includes/third-party/chat/daovoice.pug","hash":"0d960849d5b05d27ec87627b983ca35f2411b9e8","modified":1722500710158},{"_id":"themes/butterfly/layout/includes/third-party/chat/index.pug","hash":"1157118db9f5d7c0c5a0fc7c346f6e934ca00d52","modified":1722500710159},{"_id":"themes/butterfly/layout/includes/third-party/chat/messenger.pug","hash":"799da8f3015e6fe440681b21644bcb3810a5518c","modified":1722500710160},{"_id":"themes/butterfly/layout/includes/third-party/chat/tidio.pug","hash":"6d40b521eec4136f6742c548a4445ed593470b1b","modified":1722500710161},{"_id":"themes/butterfly/layout/includes/third-party/comments/artalk.pug","hash":"5373b822aa72ddb96f2f1f4baf6c058b40d705d6","modified":1722500710163},{"_id":"themes/butterfly/layout/includes/third-party/comments/disqus.pug","hash":"364d1fd655baca9132038ef1e312abde2c0bc7de","modified":1722500710164},{"_id":"themes/butterfly/layout/includes/third-party/comments/disqusjs.pug","hash":"f78c9c20c86d58c7cf099f6f8d6097103d7d43e5","modified":1722500710165},{"_id":"themes/butterfly/layout/includes/third-party/comments/facebook_comments.pug","hash":"11f5dca1432e59f22955aaf4ac3e9de6b286d887","modified":1722500710166},{"_id":"themes/butterfly/layout/includes/third-party/comments/giscus.pug","hash":"1eab7ca1cb16c6786f9c3ca0efef8cc15e444ab4","modified":1722500710167},{"_id":"themes/butterfly/layout/includes/third-party/comments/gitalk.pug","hash":"1c86c8fc1a28514a02a1f6a25ca9ec05eb3955b7","modified":1722500710168},{"_id":"themes/butterfly/layout/includes/third-party/comments/index.pug","hash":"db6713d2b90eb8183f86ac92c26761a8501c0ddb","modified":1722500710169},{"_id":"themes/butterfly/layout/includes/third-party/comments/js.pug","hash":"3abbaaa4ea575c45b3cebffd40bad1acc6ffce84","modified":1722500710170},{"_id":"themes/butterfly/layout/includes/third-party/comments/livere.pug","hash":"09c2ef4bc6d005f96dfa48b1d9af1ec095c5266d","modified":1722500710171},{"_id":"themes/butterfly/layout/includes/third-party/comments/remark42.pug","hash":"7f450664e6323a076ae59c393b0f22167cfa82e5","modified":1722500710172},{"_id":"themes/butterfly/layout/includes/third-party/comments/twikoo.pug","hash":"9942a903227350960c1d0716e59516ae79ac24a8","modified":1722500710173},{"_id":"themes/butterfly/layout/includes/third-party/comments/utterances.pug","hash":"b65a42167df5fb07e2a63f312a58c321d3112a90","modified":1722500710174},{"_id":"themes/butterfly/layout/includes/third-party/comments/valine.pug","hash":"4ed7c74087e81c6fcaf4fca7dced58b4e19f4cb1","modified":1722500710175},{"_id":"themes/butterfly/layout/includes/third-party/comments/waline.pug","hash":"efb72547fc2d470a124f5636391128dc59627498","modified":1722500710176},{"_id":"themes/butterfly/layout/includes/third-party/math/index.pug","hash":"2afa4c21dd19890f47fb568cfb0d90efb676a253","modified":1722500710179},{"_id":"themes/butterfly/layout/includes/third-party/math/katex.pug","hash":"f0d3eddd2bed68e5517274b3530bfe0fa5057d8e","modified":1722500710180},{"_id":"themes/butterfly/layout/includes/third-party/math/mathjax.pug","hash":"bb944185f4bb9f9a9b9d70ee215f66ccd6d4c6cf","modified":1722500710181},{"_id":"themes/butterfly/layout/includes/third-party/math/mermaid.pug","hash":"c682e4d61017fb0dd2e837bfcc242371f1a13364","modified":1722500710182},{"_id":"themes/butterfly/layout/includes/third-party/newest-comments/artalk.pug","hash":"2e36fac4791e99844cd56676898be0dbf5eb4e99","modified":1722500710184},{"_id":"themes/butterfly/layout/includes/third-party/newest-comments/disqus-comment.pug","hash":"d8898e427acd91ceb97d6a7ee3acb011ca86b9fc","modified":1722500710184},{"_id":"themes/butterfly/layout/includes/third-party/newest-comments/github-issues.pug","hash":"fc8814bd016d039874ec2fc24dcb78587892e2a6","modified":1722500710186},{"_id":"themes/butterfly/layout/includes/third-party/newest-comments/index.pug","hash":"f8b65460c399973090c1fb7ab81e3708c252e7cc","modified":1722500710187},{"_id":"themes/butterfly/layout/includes/third-party/newest-comments/remark42.pug","hash":"a4e52188b6effeee1df2a01dcbf4105de76a61a8","modified":1722500710188},{"_id":"themes/butterfly/layout/includes/third-party/newest-comments/twikoo-comment.pug","hash":"17520a86de12ae585289463c066d3ac91b78a2ff","modified":1722500710188},{"_id":"themes/butterfly/layout/includes/third-party/newest-comments/valine.pug","hash":"ecfff55b2c7f6d87ce4d5028fdf9f8c0bf155c73","modified":1722500710189},{"_id":"themes/butterfly/layout/includes/third-party/newest-comments/waline.pug","hash":"0544d91c0bc9e26e0fe1b5ff490f4a8540ed1ee1","modified":1722500710191},{"_id":"themes/butterfly/layout/includes/third-party/search/algolia.pug","hash":"90338ac4cd114d324fe1caaaeea8be9ca05d6a46","modified":1722500710195},{"_id":"themes/butterfly/layout/includes/third-party/search/docsearch.pug","hash":"52a06a2e039f44383085333cac69f3f4e7d0ad3a","modified":1722500710196},{"_id":"themes/butterfly/layout/includes/third-party/search/index.pug","hash":"3adcf28a8d205ea3ee19828eda0e668702fac07a","modified":1722500710197},{"_id":"themes/butterfly/layout/includes/third-party/search/local-search.pug","hash":"420a86e73d0d748ac234fd00d06d9e433ca5e3f2","modified":1722500710198},{"_id":"themes/butterfly/layout/includes/third-party/share/addtoany.pug","hash":"1f02a26730e5f36cc2dfec7ff4d5c93a099ed5ba","modified":1722500710199},{"_id":"themes/butterfly/layout/includes/third-party/share/index.pug","hash":"c16ee69b5ca8db016db0508d014ae0867c4ce929","modified":1722500710201},{"_id":"themes/butterfly/layout/includes/third-party/share/share-js.pug","hash":"8106bd031586f075a994956ee4438eb13be25d7b","modified":1722500710202},{"_id":"themes/butterfly/source/css/_highlight/highlight/diff.styl","hash":"6e77f1ca0cfb0db6b028f5c0238780e66d344f3d","modified":1722500710263},{"_id":"themes/butterfly/source/css/_highlight/highlight/index.styl","hash":"fc702a4614d0562a381907b083f71ba63d301d86","modified":1722500710264},{"_id":"themes/butterfly/source/css/_highlight/prismjs/diff.styl","hash":"1309292f1c8c53d96cd7333507b106bcc24ca8fc","modified":1722500710265},{"_id":"themes/butterfly/source/css/_highlight/prismjs/index.styl","hash":"01ff9e77eb1bd454bec65a6ff5972c8e219bc708","modified":1722500710267},{"_id":"themes/butterfly/source/css/_highlight/prismjs/line-number.styl","hash":"7c9cc43e1d2577f7151039d58e603c30860fd281","modified":1722500710267},{"_id":"source/img/dingyue.png","hash":"c6afcd1124d84f07caeefcb895be3f3a5b301678","modified":1723001642809},{"_id":"source/img/site01.jpg","hash":"d93084432feb123fd5d781210c3a2c4db43c10ec","modified":1722524985872},{"_id":"public/search.xml","hash":"d7c54ea0ee570cdba7de0576cfd9b605cc136799","modified":1737277817398},{"_id":"public/categories/index.html","hash":"9b5e9827d76711bfba7b0800a5bd02b1970ad5e3","modified":1737277817398},{"_id":"public/categories/古文观止/index.html","hash":"cb7a40c9394e7f8d907ef5e3570d9a1348eba405","modified":1737277817398},{"_id":"public/archives/page/2/index.html","hash":"22a305ef698fef1145107be3a58f0d13fff22c8e","modified":1737277817398},{"_id":"public/archives/2024/08/index.html","hash":"576759b1ab4d367b1352fb4b1b7784faf41d51e5","modified":1737277817398},{"_id":"public/about/index.html","hash":"0479c13f35329cba2a12f621e1db1cc593bbc88f","modified":1737277817398},{"_id":"public/music/index.html","hash":"ef3ddd2ead996e2dbbdb4f4927c4f9cd28ebfc33","modified":1737277817398},{"_id":"public/link/index.html","hash":"6b235c04f1c9a4d19742e34150faa14225fb202c","modified":1737277817398},{"_id":"public/tags/index.html","hash":"4b8999e99eae0cf86e95700f23e29e3906379d41","modified":1737277817398},{"_id":"public/movies/index.html","hash":"207d0d15f3855cdc1a4fe0fede085d0a32a5d0bb","modified":1737277817398},{"_id":"public/posts/52662.html","hash":"c1868da491de983635f7652ccbe10d09d88e8679","modified":1737277817398},{"_id":"public/posts/12462.html","hash":"3beb0e397623fe166bd30ba32eef699cd8609976","modified":1737277817398},{"_id":"public/posts/29139.html","hash":"cf48b6aba62da500a4a06e7684cd7ac0d4983096","modified":1737277817398},{"_id":"public/posts/61253.html","hash":"b10f19480a9d850c683b7ab3b29c8fe6a51cce38","modified":1737277817398},{"_id":"public/posts/61251.html","hash":"64a1f1288ed0552d68c51b425f5bae46dbd78ebf","modified":1737277817398},{"_id":"public/posts/61252.html","hash":"243322e5d0a9ab179f7bdacd35aefb6b4a7ff458","modified":1737277817398},{"_id":"public/posts/16107.html","hash":"81f54efce7e7017aa449375bbb12d3bde2ad6b7d","modified":1737277817398},{"_id":"public/posts/34849.html","hash":"7f5717de25caee1bbfa52b00f885bcf90802f3bd","modified":1737277817398},{"_id":"public/posts/41168.html","hash":"1106c6c73d14dff8942bf955ac99e81f3a73d266","modified":1737277817398},{"_id":"public/posts/58638.html","hash":"6c02c9c50e4d0f8106695c4a9446ede0cfd5823a","modified":1737277817398},{"_id":"public/posts/14011.html","hash":"ec33bfc37da0da9da39e0c351f7d7d72462235eb","modified":1737277817398},{"_id":"public/posts/33957.html","hash":"3fdca326c005af03ff8bf675d1c905d5af21b395","modified":1737277817398},{"_id":"public/posts/58817.html","hash":"1c1c87e43fef8d412efb0e07eb7e6bb3fbc136ee","modified":1737277817398},{"_id":"public/posts/47807.html","hash":"1416e269c09d37c71f2890a3dc2f1420428407f2","modified":1737277817398},{"_id":"public/posts/1441.html","hash":"8d090a7f1cb6a245cd1763f9cd56aa63ed68e789","modified":1737277817398},{"_id":"public/archives/index.html","hash":"56887c99de1fc22a8e6b32f85ebf78c98577acb9","modified":1737277817398},{"_id":"public/archives/2024/index.html","hash":"fc9d9a29b14c9fd1a9ac45fe3db641ffae8b0a87","modified":1737277817398},{"_id":"public/archives/2024/page/2/index.html","hash":"b93c901af79b676e4156287c5bb4fcb382b5c886","modified":1737277817398},{"_id":"public/archives/2024/09/index.html","hash":"6920d158b21f3031963176ac7eee30f299f81284","modified":1737277817398},{"_id":"public/archives/2025/index.html","hash":"f9db7ea00ed775a37b4a431abf715f68aacb1775","modified":1737277817398},{"_id":"public/archives/2025/01/index.html","hash":"18521b6aaebea48db3128c28ff68375f16e88646","modified":1737277817398},{"_id":"public/tags/machinelearning/index.html","hash":"153e1cfb3263cf5168a53dda6f74d4d5a7e2d2fd","modified":1737277817398},{"_id":"public/tags/C-C/index.html","hash":"41e6fdbef6551fc07e774db43d207c1201bb75fe","modified":1737277817398},{"_id":"public/tags/linear-regression/index.html","hash":"38b1d34bcb40341ead8c959c572adb9581b28189","modified":1737277817398},{"_id":"public/tags/古文观止/index.html","hash":"2b7d1151fa5785f51b60d596fe015590d5f740b6","modified":1737277817398},{"_id":"public/tags/uniapp/index.html","hash":"6cacb80dcc2d1b9f0bef693220a64c80af34d6c6","modified":1737277817398},{"_id":"public/tags/网络代理/index.html","hash":"003207b48df5689926009ae9d145825c68c3f08d","modified":1737277817398},{"_id":"public/index.html","hash":"87af80453385854d84af55ea56ddb72bb958a334","modified":1737277817398},{"_id":"public/page/2/index.html","hash":"b2289447cb06ec7b81ff3b82c7999191e0a3cf70","modified":1737277817398},{"_id":"public/img/404.jpg","hash":"fb4489bc1d30c93d28f7332158c1c6c1416148de","modified":1737277817398},{"_id":"public/img/favicon.png","hash":"3cf89864b4f6c9b532522a4d260a2e887971c92d","modified":1737277817398},{"_id":"public/img/avatar.jpg","hash":"cb0941101c6a6b8f762ce6ffc3c948641e7f642f","modified":1737277817398},{"_id":"public/img/friend_404.gif","hash":"8d2d0ebef70a8eb07329f57e645889b0e420fa48","modified":1737277817398},{"_id":"public/img/machinelearning/knn-01.png","hash":"2a931981d1d5f72d6b42b39b8ef313569eab853e","modified":1737277817398},{"_id":"public/img/machinelearning/kfold-skfold.png","hash":"ab841f5412b7ea773468a3facff643df68a88c01","modified":1737277817398},{"_id":"public/img/machinelearning/fitting.png","hash":"89c0cf6301f0b42de93ac41e5bac33d99acfd530","modified":1737277817398},{"_id":"public/img/language/c-env-conf.png","hash":"09de801caef07f177c829e889464442f535b189c","modified":1737277817398},{"_id":"public/img/machinelearning/linear.png","hash":"4a54dbcc028e2385bd50bf8b022adde0a0d9fbcf","modified":1737277817398},{"_id":"public/js/utils.js","hash":"8e6b48d294e7aeaba8ff6348c43b2271cf865547","modified":1737277817398},{"_id":"public/css/var.css","hash":"da39a3ee5e6b4b0d3255bfef95601890afd80709","modified":1737277817398},{"_id":"public/js/search/algolia.js","hash":"108988d046da9a4716148df43b3975217c8ceaae","modified":1737277817398},{"_id":"public/js/search/local-search.js","hash":"e1f60ebac53a3f596fd0a4769b4f9275c48c6542","modified":1737277817398},{"_id":"public/css/index.css","hash":"a86592daf1fcadb01092b449a0eb5100fc2351fb","modified":1737277817398},{"_id":"public/js/tw_cn.js","hash":"f8d2e3f31468991a7f5171cbfdb157dfb86d3372","modified":1737277817398},{"_id":"public/js/main.js","hash":"0dac585446445e0c419b86eec5580bc9b0657dc6","modified":1737277817398},{"_id":"public/img/000001.png","hash":"ad86c3b72174364d462bdab1d09540bd79eb123c","modified":1737277817398},{"_id":"public/img/machinelearning/cros-valid.png","hash":"8f9b204b651f93c17ad0856daa628d6abe985d97","modified":1737277817398},{"_id":"public/img/yiyuan.png","hash":"817a89509a8ebcddff6b369979d53ecf44a30a9f","modified":1737277817398},{"_id":"public/img/peiqian.png","hash":"2f077f1fff014ee448cd58b57ff83901702e2d88","modified":1737277817398},{"_id":"public/img/dingyue.png","hash":"c6afcd1124d84f07caeefcb895be3f3a5b301678","modified":1737277817398},{"_id":"public/img/site01.jpg","hash":"d93084432feb123fd5d781210c3a2c4db43c10ec","modified":1737277817398}],"Category":[{"name":"古文观止","_id":"cm63ecyk8000jrcah4oekbwlh"}],"Data":[{"_id":"link","data":[{"class_name":"友情鏈接","class_desc":"那些人，那些事","link_list":[{"name":"Hexo","link":"https://hexo.io/zh-tw/","avatar":"https://d33wubrfki0l68.cloudfront.net/6657ba50e702d84afb32fe846bed54fba1a77add/827ae/logo.svg","descr":"快速、簡單且強大的網誌框架"}]},{"class_name":"網站","class_desc":"值得推薦的網站","link_list":[{"name":"Youtube","link":"https://www.youtube.com/","avatar":"https://i.loli.net/2020/05/14/9ZkGg8v3azHJfM1.png","descr":"視頻網站"},{"name":"Weibo","link":"https://www.weibo.com/","avatar":"https://i.loli.net/2020/05/14/TLJBum386vcnI1P.png","descr":"中國最大社交分享平台"},{"name":"Twitter","link":"https://twitter.com/","avatar":"https://i.loli.net/2020/05/14/5VyHPQqR6LWF39a.png","descr":"社交分享平台"}]}]}],"Page":[{"title":"About me","date":"2024-08-10T02:35:41.000Z","_content":"\n落花飞舞，翩若惊鸿。\n","source":"about/index.md","raw":"---\ntitle: About me\ndate: 2024-08-10 10:35:41\n---\n\n落花飞舞，翩若惊鸿。\n","updated":"2024-08-10T02:38:14.626Z","path":"about/index.html","comments":1,"layout":"page","_id":"cm63ecyjq0000rcah9qbp1uo6","content":"

落花飞舞，翩若惊鸿。

\n","cover":false,"excerpt":"","more":"

落花飞舞，翩若惊鸿。

\n"},{"title":"Music","date":"2024-08-10T02:40:19.000Z","_content":"","source":"music/index.md","raw":"---\ntitle: Music\ndate: 2024-08-10 10:40:19\n---\n","updated":"2024-08-10T02:40:19.897Z","path":"music/index.html","comments":1,"layout":"page","_id":"cm63ecyjw0002rcah7n8c3e69","content":"","cover":false,"excerpt":"","more":""},{"title":"categories","date":"2024-07-31T00:33:49.000Z","aside":false,"top_img":false,"type":"categories","_content":"\n### category","source":"categories/index.md","raw":"---\ntitle: categories\ndate: 2024-07-31 08:33:49\naside: false\ntop_img: false\ntype: \"categories\"\n---\n\n### category","updated":"2024-07-31T06:37:23.090Z","path":"categories/index.html","comments":1,"layout":"page","_id":"cm63ecyjy0004rcahbc01fmln","content":"

category

","cover":false,"excerpt":"","more":"

category

"},{"title":"link","date":"2024-08-10T02:42:35.000Z","type":"link","_content":"","source":"link/index.md","raw":"---\ntitle: link\ndate: 2024-08-10 10:42:35\ntype: \"link\"\n---\n","updated":"2024-08-10T02:42:48.927Z","path":"link/index.html","comments":1,"layout":"page","_id":"cm63ecyk10007rcah7tex1r3q","content":"","cover":false,"excerpt":"","more":""},{"title":"tags","date":"2024-07-31T00:32:38.000Z","type":"tags","comments":0,"top_img":false,"_content":"","source":"tags/index.md","raw":"---\ntitle: tags\ndate: 2024-07-31 08:32:38\ntype: \"tags\"\ncomments: false\ntop_img: false\n---\n","updated":"2024-07-31T00:33:00.076Z","path":"tags/index.html","layout":"page","_id":"cm63ecyk30009rcahcvxqfniy","content":"","cover":false,"excerpt":"","more":""},{"title":"Movies","date":"2024-08-10T02:40:33.000Z","_content":"","source":"movies/index.md","raw":"---\ntitle: Movies\ndate: 2024-08-10 10:40:33\n---\n","updated":"2024-08-10T02:40:33.715Z","path":"movies/index.html","comments":1,"layout":"page","_id":"cm63ecyk4000brcahgz5ifv5d","content":"","cover":false,"excerpt":"","more":""}],"Post":[{"title":"Hello World","abbrlink":16107,"_content":"Welcome to [Hexo](https://hexo.io/)! This is your very first post. Check [documentation](https://hexo.io/docs/) for more info. If you get any problems when using Hexo, you can find the answer in [troubleshooting](https://hexo.io/docs/troubleshooting.html) or you can ask me on [GitHub](https://github.com/hexojs/hexo/issues).\n\n## Quick Start\n\n### Create a new post\n\n``` bash\n$ hexo new \"My New Post\"\n```\n\nMore info: [Writing](https://hexo.io/docs/writing.html)\n\n### Run server\n\n``` bash\n$ hexo server\n```\n\nMore info: [Server](https://hexo.io/docs/server.html)\n\n### Generate static files\n\n``` bash\n$ hexo generate\n```\n\nMore info: [Generating](https://hexo.io/docs/generating.html)\n\n### Deploy to remote sites\n\n``` bash\n$ hexo deploy\n```\n\nMore info: [Deployment](https://hexo.io/docs/one-command-deployment.html)\n","source":"_posts/hello-world.md","raw":"---\ntitle: Hello World\nabbrlink: 16107\n---\nWelcome to [Hexo](https://hexo.io/)! This is your very first post. Check [documentation](https://hexo.io/docs/) for more info. If you get any problems when using Hexo, you can find the answer in [troubleshooting](https://hexo.io/docs/troubleshooting.html) or you can ask me on [GitHub](https://github.com/hexojs/hexo/issues).\n\n## Quick Start\n\n### Create a new post\n\n``` bash\n$ hexo new \"My New Post\"\n```\n\nMore info: [Writing](https://hexo.io/docs/writing.html)\n\n### Run server\n\n``` bash\n$ hexo server\n```\n\nMore info: [Server](https://hexo.io/docs/server.html)\n\n### Generate static files\n\n``` bash\n$ hexo generate\n```\n\nMore info: [Generating](https://hexo.io/docs/generating.html)\n\n### Deploy to remote sites\n\n``` bash\n$ hexo deploy\n```\n\nMore info: [Deployment](https://hexo.io/docs/one-command-deployment.html)\n","slug":"hello-world","published":1,"date":"2024-09-11T00:01:10.419Z","updated":"2024-08-09T12:21:50.026Z","comments":1,"layout":"post","photos":[],"_id":"cm63ecyjs0001rcahb307abr9","content":"

Welcome to Hexo! This is your very first post. Check documentation for more info. If you get any problems when using Hexo, you can find the answer in troubleshooting or you can ask me on GitHub.

\n

Quick Start

Create a new post

1
$ hexo new "My New Post"
\n\n

More info: Writing

\n

Run server

1
$ hexo server
\n\n

More info: Server

\n

Generate static files

1
$ hexo generate
\n\n

More info: Generating

\n

Deploy to remote sites

1
$ hexo deploy
\n\n

More info: Deployment

\n","cover":false,"excerpt":"","more":"

Welcome to Hexo! This is your very first post. Check documentation for more info. If you get any problems when using Hexo, you can find the answer in troubleshooting or you can ask me on GitHub.

\n

Quick Start

Create a new post

1
$ hexo new "My New Post"
\n\n

More info: Writing

\n

Run server

1
$ hexo server
\n\n

More info: Server

\n

Generate static files

1
$ hexo generate
\n\n

More info: Generating

\n

Deploy to remote sites

1
$ hexo deploy
\n\n

More info: Deployment

\n"},{"title":"C lang","abbrlink":12462,"date":"2025-01-15T12:41:26.000Z","_content":"\n### c lang在windows下的开发（VS code）\n[WinLibs - GCC+MinGW-w64 compiler for Windows](https://winlibs.com/#download-release)下载你需要的版本\n解压到`D:\\ProgramModule`，并将 `bin\\`加入环境变量`PATH`\n打开新的`Terminal`输入`gcc -v`，查看`gcc`是否安装成功\n在`VS code` 的插件管理下载`Code Runner`、`C\\C++`这两个插件\n在`*.c`源文件的内容区，右键点击`Run Code` ，即可运行成功\n![](/img/language/c-env-conf.png)\n\n### 数据类型\n- 整数类型\n ```c\n    short a = 12;\n    int b = 100;\n    long c = 1000L;\n    long long d = 1000000LL;\n    unsigned int e = 10;\n    printf(\"a: %hd\\n\",a);\n    printf(\"b: %d\\n\",b);\n    printf(\"c: %ld\\n\",c);\n    printf(\"d: %lld\\n\",d);\n    printf(\"e: %u\\n\",e);\n    printf(\"f: %.3f\\n\",f);\n ```\n- 小数类型\n ```c\n float f = 3.14F;\n printf(\"f: %.3f\\n\",f);\n double g = 5.65;\n printf(\"g: %.2lf\\n\",g);\n ```\n- 字符类型\n ```c\n char h = 'x';\n printf(\"x: %c\\n\",x);\n ```\n### 类型转换\n- 隐式转换\n- 强制转换\n ```c\n int b = 23;\n short c = (short) b;\n ```\n### 数组\n```c\n#include \n\nint main(){\n    int arr [10] = {2,3,4,5,6,7,8,9,10,11};\n    arr[0] = 1525;\n    *(arr+1) = 25;\n    int len = sizeof(arr)/sizeof(arr[0]);\n    void printArr(int arr[], int len){\n        for (int i = 0; i < len;i++){\n            printf(\"%d\\t\",arr[i]);\n        }\n    }\n    printArr(arr,len);\n    return 0;\n}\n```\n### 指针\n```c\n // swap the value of a and b\n    void swap(int* x, int* y){\n        int temp = *x;\n        *x = *y;\n        *y = temp;\n\n    }\n    int a = 5;\n    int b = 10;\n    swap(&a, &b);\n    printf(\"a = %d b = %d\\n\", a, b);\n```\n","source":"_posts/language/C.md","raw":"---\ntitle: C lang\ntags: C C++\nabbrlink: 12462\ndate: 2025-01-15 20:41:26\n---\n\n### c lang在windows下的开发（VS code）\n[WinLibs - GCC+MinGW-w64 compiler for Windows](https://winlibs.com/#download-release)下载你需要的版本\n解压到`D:\\ProgramModule`，并将 `bin\\`加入环境变量`PATH`\n打开新的`Terminal`输入`gcc -v`，查看`gcc`是否安装成功\n在`VS code` 的插件管理下载`Code Runner`、`C\\C++`这两个插件\n在`*.c`源文件的内容区，右键点击`Run Code` ，即可运行成功\n![](/img/language/c-env-conf.png)\n\n### 数据类型\n- 整数类型\n ```c\n    short a = 12;\n    int b = 100;\n    long c = 1000L;\n    long long d = 1000000LL;\n    unsigned int e = 10;\n    printf(\"a: %hd\\n\",a);\n    printf(\"b: %d\\n\",b);\n    printf(\"c: %ld\\n\",c);\n    printf(\"d: %lld\\n\",d);\n    printf(\"e: %u\\n\",e);\n    printf(\"f: %.3f\\n\",f);\n ```\n- 小数类型\n ```c\n float f = 3.14F;\n printf(\"f: %.3f\\n\",f);\n double g = 5.65;\n printf(\"g: %.2lf\\n\",g);\n ```\n- 字符类型\n ```c\n char h = 'x';\n printf(\"x: %c\\n\",x);\n ```\n### 类型转换\n- 隐式转换\n- 强制转换\n ```c\n int b = 23;\n short c = (short) b;\n ```\n### 数组\n```c\n#include \n\nint main(){\n    int arr [10] = {2,3,4,5,6,7,8,9,10,11};\n    arr[0] = 1525;\n    *(arr+1) = 25;\n    int len = sizeof(arr)/sizeof(arr[0]);\n    void printArr(int arr[], int len){\n        for (int i = 0; i < len;i++){\n            printf(\"%d\\t\",arr[i]);\n        }\n    }\n    printArr(arr,len);\n    return 0;\n}\n```\n### 指针\n```c\n // swap the value of a and b\n    void swap(int* x, int* y){\n        int temp = *x;\n        *x = *y;\n        *y = temp;\n\n    }\n    int a = 5;\n    int b = 10;\n    swap(&a, &b);\n    printf(\"a = %d b = %d\\n\", a, b);\n```\n","slug":"language/C","published":1,"updated":"2025-01-15T12:44:34.907Z","comments":1,"layout":"post","photos":[],"_id":"cm63ecyjx0003rcaheuf03fft","content":"

c lang在windows下的开发（VS code）

WinLibs - GCC+MinGW-w64 compiler for Windows下载你需要的版本
解压到D:\\ProgramModule，并将 bin\\加入环境变量PATH
打开新的Terminal输入gcc -v，查看gcc是否安装成功
在VS code 的插件管理下载Code Runner、C\\C++这两个插件
在*.c源文件的内容区，右键点击Run Code ，即可运行成功

\n

数据类型

    \n
  • 整数类型
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
        short a = 12;
        int b = 100;
        long c = 1000L;
        long long d = 1000000LL;
        unsigned int e = 10;
        printf("a: %hd\\n",a);
        printf("b: %d\\n",b);
        printf("c: %ld\\n",c);
        printf("d: %lld\\n",d);
        printf("e: %u\\n",e);
        printf("f: %.3f\\n",f);
    • \n
  • 小数类型
    1
    2
    3
    4
    float f = 3.14F;
    printf("f: %.3f\\n",f);
    double g = 5.65;
    printf("g: %.2lf\\n",g);
    • \n
  • 字符类型
    1
    2
    char h = 'x';
    printf("x: %c\\n",x);
    • \n
\n

类型转换

    \n
  • 隐式转换
    • \n
  • 强制转换
    1
    2
    int b = 23;
    short c = (short) b;
    • \n
\n

数组

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
#include <stdio.h>

int main(){
    int arr [10] = {2,3,4,5,6,7,8,9,10,11};
    arr[0] = 1525;
    *(arr+1) = 25;
    int len = sizeof(arr)/sizeof(arr[0]);
    void printArr(int arr[], int len){
        for (int i = 0; i < len;i++){
            printf("%d\\t",arr[i]);
        }
    }
    printArr(arr,len);
    return 0;
}
\n

指针

1
2
3
4
5
6
7
8
9
10
11
// swap the value of a and b
    void swap(int* x, int* y){
        int temp = *x;
        *x = *y;
        *y = temp;

    }
    int a = 5;
    int b = 10;
    swap(&a, &b);
    printf("a = %d b = %d\\n", a, b);
\n","cover":false,"excerpt":"","more":"

c lang在windows下的开发（VS code）

WinLibs - GCC+MinGW-w64 compiler for Windows下载你需要的版本
解压到D:\\ProgramModule，并将 bin\\加入环境变量PATH
打开新的Terminal输入gcc -v，查看gcc是否安装成功
在VS code 的插件管理下载Code Runner、C\\C++这两个插件
在*.c源文件的内容区，右键点击Run Code ，即可运行成功

\n

数据类型

    \n
  • 整数类型
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
        short a = 12;
        int b = 100;
        long c = 1000L;
        long long d = 1000000LL;
        unsigned int e = 10;
        printf("a: %hd\\n",a);
        printf("b: %d\\n",b);
        printf("c: %ld\\n",c);
        printf("d: %lld\\n",d);
        printf("e: %u\\n",e);
        printf("f: %.3f\\n",f);
    • \n
  • 小数类型
    1
    2
    3
    4
    float f = 3.14F;
    printf("f: %.3f\\n",f);
    double g = 5.65;
    printf("g: %.2lf\\n",g);
    • \n
  • 字符类型
    1
    2
    char h = 'x';
    printf("x: %c\\n",x);
    • \n
\n

类型转换

    \n
  • 隐式转换
    • \n
  • 强制转换
    1
    2
    int b = 23;
    short c = (short) b;
    • \n
\n

数组

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
#include <stdio.h>

int main(){
    int arr [10] = {2,3,4,5,6,7,8,9,10,11};
    arr[0] = 1525;
    *(arr+1) = 25;
    int len = sizeof(arr)/sizeof(arr[0]);
    void printArr(int arr[], int len){
        for (int i = 0; i < len;i++){
            printf("%d\\t",arr[i]);
        }
    }
    printArr(arr,len);
    return 0;
}
\n

指针

1
2
3
4
5
6
7
8
9
10
11
// swap the value of a and b
    void swap(int* x, int* y){
        int temp = *x;
        *x = *y;
        *y = temp;

    }
    int a = 5;
    int b = 10;
    swap(&a, &b);
    printf("a = %d b = %d\\n", a, b);
\n"},{"title":"page","abbrlink":1441,"date":"2024-08-01T01:00:10.000Z","_content":"\n- [deploy](./deploy)\n- ","source":"_posts/frontend/frontend.md","raw":"---\ntitle: page\nabbrlink: 1441\ndate: 2024-08-01 09:00:10\ntags:\n---\n\n- [deploy](./deploy)\n- ","slug":"frontend/frontend","published":1,"updated":"2024-08-09T12:21:50.023Z","comments":1,"layout":"post","photos":[],"_id":"cm63ecyjz0005rcah41j97v64","content":"\n","cover":false,"excerpt":"","more":"\n"},{"title":"script","abbrlink":34849,"date":"2024-08-17T03:09:24.000Z","_content":"\n### 查看CPU、内存使用率\n```bash\n#!/bin/bash\n\n# 定义颜色\nRED='\\033[0;31m'\nGREEN='\\033[0;32m'\nYELLOW='\\033[0;33m'\nBLUE='\\033[0;34m'\nNC='\\033[0m' # 无颜色\n\nwhile true; do\n # 获取所有进程的CPU使用率和内存使用率\n cpu_usage=$(ps aux | awk '{sum_cpu += $3} END {print sum_cpu}')\n mem_usage=$(ps aux | awk '{sum_mem += $4} END {print sum_mem}')\n \n # 打印结果，带有时间戳、分隔线和颜色高亮\n echo -e \"${BLUE}==============================${NC}\"\n echo -e \"${YELLOW}Timestamp: $(date)${NC}\"\n echo -e \"${BLUE}==============================${NC}\"\n echo -e \"${GREEN}Total CPU usage: ${RED}$cpu_usage%${NC}\"\n echo -e \"${GREEN}Total Memory usage: ${RED}$mem_usage%${NC}\"\n echo -e \"${BLUE}==============================${NC}\"\n \n # 等待5秒后再次执行\n sleep 5\ndone\n\n```\n**保存脚本到/usr/local/bin目录下**\n```bash\n mv usage.sh /usr/local/bin/usage\n```\n\n### Shell脚本编写的基本信息\n\n```bash\n#! /bin/bash\n# -------------------------------------------------\n# Filename: test.sh\n# Version: 1.0\n# Date: 2024/05/02\n# Author: shenjianZ\n# Email: shenjianZLT@gmail.com\n# Website: https://blog.shenjianl.cn\n# Description: this is a test shell\n# CopyRight: 2024 All rights reserved shenjianZ\n# License GPL\n# ------------------------------------------------\n\n\n# Your script logic goes here\n```","source":"_posts/linux/script.md","raw":"---\ntitle: script\nabbrlink: 34849\ndate: 2024-08-17 11:09:24\ntags:\n---\n\n### 查看CPU、内存使用率\n```bash\n#!/bin/bash\n\n# 定义颜色\nRED='\\033[0;31m'\nGREEN='\\033[0;32m'\nYELLOW='\\033[0;33m'\nBLUE='\\033[0;34m'\nNC='\\033[0m' # 无颜色\n\nwhile true; do\n # 获取所有进程的CPU使用率和内存使用率\n cpu_usage=$(ps aux | awk '{sum_cpu += $3} END {print sum_cpu}')\n mem_usage=$(ps aux | awk '{sum_mem += $4} END {print sum_mem}')\n \n # 打印结果，带有时间戳、分隔线和颜色高亮\n echo -e \"${BLUE}==============================${NC}\"\n echo -e \"${YELLOW}Timestamp: $(date)${NC}\"\n echo -e \"${BLUE}==============================${NC}\"\n echo -e \"${GREEN}Total CPU usage: ${RED}$cpu_usage%${NC}\"\n echo -e \"${GREEN}Total Memory usage: ${RED}$mem_usage%${NC}\"\n echo -e \"${BLUE}==============================${NC}\"\n \n # 等待5秒后再次执行\n sleep 5\ndone\n\n```\n**保存脚本到/usr/local/bin目录下**\n```bash\n mv usage.sh /usr/local/bin/usage\n```\n\n### Shell脚本编写的基本信息\n\n```bash\n#! /bin/bash\n# -------------------------------------------------\n# Filename: test.sh\n# Version: 1.0\n# Date: 2024/05/02\n# Author: shenjianZ\n# Email: shenjianZLT@gmail.com\n# Website: https://blog.shenjianl.cn\n# Description: this is a test shell\n# CopyRight: 2024 All rights reserved shenjianZ\n# License GPL\n# ------------------------------------------------\n\n\n# Your script logic goes here\n```","slug":"linux/script","published":1,"updated":"2024-08-18T16:05:45.731Z","comments":1,"layout":"post","photos":[],"_id":"cm63ecyk20008rcahdev799fn","content":"

查看CPU、内存使用率

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
#!/bin/bash

# 定义颜色
RED='\\033[0;31m'
GREEN='\\033[0;32m'
YELLOW='\\033[0;33m'
BLUE='\\033[0;34m'
NC='\\033[0m' # 无颜色

while true; do
    # 获取所有进程的CPU使用率和内存使用率
    cpu_usage=$(ps aux | awk '{sum_cpu += $3} END {print sum_cpu}')
    mem_usage=$(ps aux | awk '{sum_mem += $4} END {print sum_mem}')
    
    # 打印结果，带有时间戳、分隔线和颜色高亮
    echo -e "${BLUE}==============================${NC}"
    echo -e "${YELLOW}Timestamp: $(date)${NC}"
    echo -e "${BLUE}==============================${NC}"
    echo -e "${GREEN}Total CPU usage: ${RED}$cpu_usage%${NC}"
    echo -e "${GREEN}Total Memory usage: ${RED}$mem_usage%${NC}"
    echo -e "${BLUE}==============================${NC}"
    
    # 等待5秒后再次执行
    sleep 5
done

\n

保存脚本到/usr/local/bin目录下

\n
1
mv usage.sh /usr/local/bin/usage
\n\n

Shell脚本编写的基本信息

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
#! /bin/bash
# -------------------------------------------------
# Filename:     test.sh
# Version:      1.0
# Date:         2024/05/02
# Author:       shenjianZ
# Email:        shenjianZLT@gmail.com
# Website:      https://blog.shenjianl.cn
# Description:  this is a test shell
# CopyRight:    2024 All rights reserved shenjianZ
# License       GPL
# ------------------------------------------------


# Your script logic goes here
","cover":false,"excerpt":"","more":"

查看CPU、内存使用率

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
#!/bin/bash

# 定义颜色
RED='\\033[0;31m'
GREEN='\\033[0;32m'
YELLOW='\\033[0;33m'
BLUE='\\033[0;34m'
NC='\\033[0m' # 无颜色

while true; do
    # 获取所有进程的CPU使用率和内存使用率
    cpu_usage=$(ps aux | awk '{sum_cpu += $3} END {print sum_cpu}')
    mem_usage=$(ps aux | awk '{sum_mem += $4} END {print sum_mem}')
    
    # 打印结果，带有时间戳、分隔线和颜色高亮
    echo -e "${BLUE}==============================${NC}"
    echo -e "${YELLOW}Timestamp: $(date)${NC}"
    echo -e "${BLUE}==============================${NC}"
    echo -e "${GREEN}Total CPU usage: ${RED}$cpu_usage%${NC}"
    echo -e "${GREEN}Total Memory usage: ${RED}$mem_usage%${NC}"
    echo -e "${BLUE}==============================${NC}"
    
    # 等待5秒后再次执行
    sleep 5
done

\n

保存脚本到/usr/local/bin目录下

\n
1
mv usage.sh /usr/local/bin/usage
\n\n

Shell脚本编写的基本信息

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
#! /bin/bash
# -------------------------------------------------
# Filename:     test.sh
# Version:      1.0
# Date:         2024/05/02
# Author:       shenjianZ
# Email:        shenjianZLT@gmail.com
# Website:      https://blog.shenjianl.cn
# Description:  this is a test shell
# CopyRight:    2024 All rights reserved shenjianZ
# License       GPL
# ------------------------------------------------


# Your script logic goes here
"},{"title":"k近邻算法（K-Nearest Neighbors）KNN","abbrlink":29139,"mathjax":true,"date":"2025-01-13T09:20:59.000Z","_content":"## **k近邻算法（K-Nearest Neighbors）KNN**\n将当前样本的类别归类于距离最近的**k**个样本的类别\n\n#### **距离公式(2维)**\n\n- 欧式距离\n$$\nd = \\sqrt{(x_1-y_1)^2 + (x_2 - y_2)^2}\n$$\n- 曼哈顿距离\n$$\nd = |x_1 - x_2| + |y_1 - y_2|\n$$\n- 切比雪夫距离\n$$\nd = \\max\\left(|x_1 - x_2|, |y_1 - y_2|\\right)\n$$\n#### k值选择问题\n\n| k值 | 影响 |\n| --- | ------------------ |\n| 越大 | 模型过拟合，准确率波动较大 |\n| 越小 | 模型欠拟合，准确率趋于稳定但可能较低 |\n### 特征预处理\n> 通过一些转换函数将特征数据转换成更加适合算法模型的特征数据过程 \n- 归一化\n 将数据变换到指定区间（默认是\\[0,1\\]）\n $$ x' = \\frac{x- x_{\\text {min}}}{x_{\\text{max}} - x_{\\text{min}}} $$\n 若需要缩放到任意区间 \\(\\[a, b\\]\\)，公式为： $$ x' = a + \\frac{(x - x_{\\text{min}}) \\cdot (b - a)}{x_{\\text{max}} - x_{\\text{min}}} $$\n 其中：\\( \\[a, b\\] \\)：目标区间的范围\n 归一化受到数据集的异常值的影响，需要进行标准化处理（更加合理）\n ``` python\n\t from sklearn.preprocessing import MinMaxScaler # 归一化\n\t```\n- 标准化\n 将数据调整为均值为 0，标准差为 1 的标准正态分布\n $$ z = \\frac{x - \\mu}{\\sigma} $$\n \\( z \\)：标准化后的值 \\( x \\)：原始数据值 \\( $\\mu$ \\)：数据的均值 \\( $\\sigma$\\)：数据的标准差\n \n ``` python\n \t from sklearn.preprocessing import StandardScaler # 标准化\n ```\n\n### KNN代码实现\n```python\nimport seaborn as sns\nimport matplotlib.pyplot as plt \nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.datasets import load_iris\nfrom sklearn.preprocessing import MinMaxScaler,StandardScaler\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import accuracy_score\n\n# 1 数据集获取\niris = load_iris()\n# print(iris.feature_names)\niris_data = pd.DataFrame(iris.data,columns=['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width'])\niris_data['target'] = iris.target\n\ndef iris_plot(data,col1,col2):\n sns.lmplot(x=col1,y=col2,data=data,hue=\"target\",fit_reg=False)\n plt.show()\n# 2 数据集可视化\n# iris_plot(iris_data, 'Sepal_Width', 'Petal_Length')\n\n# 3 数据集的划分\nX_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=0.2,random_state=44)\n# print(\"训练集的特征值：\\n\",X_train)\n# print(\"训练集的目标值：\\n\",y_train)\n# print(\"测试集的特征值：\\n\",X_test)\n# print(\"测试集的特征值：\\n\",y_test)\n\n# 4 归一化\ntransfer = StandardScaler()\nX_train = transfer.fit_transform(X_train)\nX_test = transfer.transform(X_test)\n# print(\"归一化的,X_train：\\n\",X_train)\n# print(\"归一化的X_test：\\n\",X_test)\n\n# 5 机器学习 KNN\n# 5.1 实例化估计器\nestimator = KNeighborsClassifier(n_neighbors=9)\n# 5.2 进行训练\nestimator.fit(X_train,y_train)\n\n# 6 模型评估\ny_pred = estimator.predict(X_test)\nprint(\"预测值：\\n\",y_pre)\nprint(\"预测值与真实值是否相等：\\n\",y_pred==y_test)\naccuracy = accuracy_score(y_test, y_pred)\nprint(f\"\\nKNN 模型的准确率: {accuracy:.4f}\")\n```\n\n![](/img/machinelearning/knn-01.png)\n### 交叉验证与网格搜索\n```python\nimport seaborn as sns\nimport matplotlib.pyplot as plt \nimport pandas as pd\nfrom sklearn.model_selection import train_test_split,GridSearchCV\nfrom sklearn.datasets import load_iris\nfrom sklearn.preprocessing import MinMaxScaler,StandardScaler\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import accuracy_score\n\n# 1 数据集获取\niris = load_iris()\niris_data = pd.DataFrame(iris.data,columns=['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width'])\niris_data['target'] = iris.target\n\n# 3 数据集的划分\nX_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=0.2)\n\n# 4 归一化\ntransfer = StandardScaler()\nX_train = transfer.fit_transform(X_train)\nX_test = transfer.transform(X_test)\n\n# 5 机器学习 KNN\n# 5.1 实例化估计器\n#\n#不指定 n_neighbors ,使用网格搜索进行循环训练\nestimator = KNeighborsClassifier()\n# 5.2 模型调优 -- 交叉验证，网格搜素\nestimator = GridSearchCV(estimator,param_grid={\"n_neighbors\":[1,3,5,7]},cv=5) # 5 折\n# 5.2 进行训练\nestimator.fit(X_train,y_train)\n \n# 6 模型评估\ny_pred = estimator.predict(X_test)\nprint(\"预测值：\\n\",y_pred)\nprint(\"预测值与真实值是否相等：\\n\",y_pred==y_test)\naccuracy = accuracy_score(y_test, y_pred)\nprint(f\"\\nKNN 模型的准确率: {accuracy:.4f}\")\n\n# 交叉验证的相关参数\nprint(f\"最好结果：{estimator.best_score_}\")\nprint(f\"最好模型：{estimator.best_estimator_}\")\nprint(f\"最好模型结果：{estimator.cv_results_}\")\n```\n![](/img/machinelearning/cros-valid.png)\n\n### 机器学习的基本步骤\n- 获取数据集\n- 数据集基本处理\n - 去重去空、填充等操作 \n - 确定特征值和目标值\n - 分割数据集\n- 特征工程（特征预处理 标准化等）\n- 机器学习\n- 模型评估\n\n### 数据分割的方法\n- 留出法\n 训练/测试集的划分要尽可能保持数据分布的一致性，避免因数据划分过程引入额外的偏差而对最终结果产生影响。\n 单次使用留出法得到的估计结果往往不够稳定可靠，在使用留出法时，一般要采用若干次随机划分、重复进行实验评估后取平均值作为留出法的评估结果。\n ``` python\n\tfrom sklearn.model_selection import KFold,StratifiedKFold\n\timport pandas as pd\n\tX = np.array([\n\t[1,2,3,4],\n\t[11,12,13,14],\n\t[21,22,23,24],\n\t[31,32,33,34],\n\t[41,42,43,44],\n\t[51,52,53,54],\n\t[61,62,63,64],\n\t[71,72,73,74]\n\t])\n\ty=np.array([1,1,0,0,1,1,0,0])\n\tfolder = KFold(n_splits=4)\n\tsfloder = StratifiedKFold(n_splits=4)\n\tprint(\"KFOLD:\")\n\tfor train,test in folder.split(X,y):\n\t print(f\"train:{train},test:{test}\")\n\tprint(\"SKFOLD:\")\n\tfor train,test in sfloder.split(X,y):\n print(f\"train:{train},test:{test}\")\n\t```\n\t![](/img/machinelearning/kfold-skfold.png)\n- 自助法\n- 交叉验证法","source":"_posts/machinelearning/knn.md","raw":"---\ntitle: k近邻算法（K-Nearest Neighbors）KNN\ntags: machinelearning\nabbrlink: 29139\nmathjax: true\ndate: 2025-01-13 17:20:59\n---\n## **k近邻算法（K-Nearest Neighbors）KNN**\n将当前样本的类别归类于距离最近的**k**个样本的类别\n\n#### **距离公式(2维)**\n\n- 欧式距离\n$$\nd = \\sqrt{(x_1-y_1)^2 + (x_2 - y_2)^2}\n$$\n- 曼哈顿距离\n$$\nd = |x_1 - x_2| + |y_1 - y_2|\n$$\n- 切比雪夫距离\n$$\nd = \\max\\left(|x_1 - x_2|, |y_1 - y_2|\\right)\n$$\n#### k值选择问题\n\n| k值 | 影响 |\n| --- | ------------------ |\n| 越大 | 模型过拟合，准确率波动较大 |\n| 越小 | 模型欠拟合，准确率趋于稳定但可能较低 |\n### 特征预处理\n> 通过一些转换函数将特征数据转换成更加适合算法模型的特征数据过程 \n- 归一化\n 将数据变换到指定区间（默认是\\[0,1\\]）\n $$ x' = \\frac{x- x_{\\text {min}}}{x_{\\text{max}} - x_{\\text{min}}} $$\n 若需要缩放到任意区间 \\(\\[a, b\\]\\)，公式为： $$ x' = a + \\frac{(x - x_{\\text{min}}) \\cdot (b - a)}{x_{\\text{max}} - x_{\\text{min}}} $$\n 其中：\\( \\[a, b\\] \\)：目标区间的范围\n 归一化受到数据集的异常值的影响，需要进行标准化处理（更加合理）\n ``` python\n\t from sklearn.preprocessing import MinMaxScaler # 归一化\n\t```\n- 标准化\n 将数据调整为均值为 0，标准差为 1 的标准正态分布\n $$ z = \\frac{x - \\mu}{\\sigma} $$\n \\( z \\)：标准化后的值 \\( x \\)：原始数据值 \\( $\\mu$ \\)：数据的均值 \\( $\\sigma$\\)：数据的标准差\n \n ``` python\n \t from sklearn.preprocessing import StandardScaler # 标准化\n ```\n\n### KNN代码实现\n```python\nimport seaborn as sns\nimport matplotlib.pyplot as plt \nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.datasets import load_iris\nfrom sklearn.preprocessing import MinMaxScaler,StandardScaler\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import accuracy_score\n\n# 1 数据集获取\niris = load_iris()\n# print(iris.feature_names)\niris_data = pd.DataFrame(iris.data,columns=['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width'])\niris_data['target'] = iris.target\n\ndef iris_plot(data,col1,col2):\n sns.lmplot(x=col1,y=col2,data=data,hue=\"target\",fit_reg=False)\n plt.show()\n# 2 数据集可视化\n# iris_plot(iris_data, 'Sepal_Width', 'Petal_Length')\n\n# 3 数据集的划分\nX_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=0.2,random_state=44)\n# print(\"训练集的特征值：\\n\",X_train)\n# print(\"训练集的目标值：\\n\",y_train)\n# print(\"测试集的特征值：\\n\",X_test)\n# print(\"测试集的特征值：\\n\",y_test)\n\n# 4 归一化\ntransfer = StandardScaler()\nX_train = transfer.fit_transform(X_train)\nX_test = transfer.transform(X_test)\n# print(\"归一化的,X_train：\\n\",X_train)\n# print(\"归一化的X_test：\\n\",X_test)\n\n# 5 机器学习 KNN\n# 5.1 实例化估计器\nestimator = KNeighborsClassifier(n_neighbors=9)\n# 5.2 进行训练\nestimator.fit(X_train,y_train)\n\n# 6 模型评估\ny_pred = estimator.predict(X_test)\nprint(\"预测值：\\n\",y_pre)\nprint(\"预测值与真实值是否相等：\\n\",y_pred==y_test)\naccuracy = accuracy_score(y_test, y_pred)\nprint(f\"\\nKNN 模型的准确率: {accuracy:.4f}\")\n```\n\n![](/img/machinelearning/knn-01.png)\n### 交叉验证与网格搜索\n```python\nimport seaborn as sns\nimport matplotlib.pyplot as plt \nimport pandas as pd\nfrom sklearn.model_selection import train_test_split,GridSearchCV\nfrom sklearn.datasets import load_iris\nfrom sklearn.preprocessing import MinMaxScaler,StandardScaler\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import accuracy_score\n\n# 1 数据集获取\niris = load_iris()\niris_data = pd.DataFrame(iris.data,columns=['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width'])\niris_data['target'] = iris.target\n\n# 3 数据集的划分\nX_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=0.2)\n\n# 4 归一化\ntransfer = StandardScaler()\nX_train = transfer.fit_transform(X_train)\nX_test = transfer.transform(X_test)\n\n# 5 机器学习 KNN\n# 5.1 实例化估计器\n#\n#不指定 n_neighbors ,使用网格搜索进行循环训练\nestimator = KNeighborsClassifier()\n# 5.2 模型调优 -- 交叉验证，网格搜素\nestimator = GridSearchCV(estimator,param_grid={\"n_neighbors\":[1,3,5,7]},cv=5) # 5 折\n# 5.2 进行训练\nestimator.fit(X_train,y_train)\n \n# 6 模型评估\ny_pred = estimator.predict(X_test)\nprint(\"预测值：\\n\",y_pred)\nprint(\"预测值与真实值是否相等：\\n\",y_pred==y_test)\naccuracy = accuracy_score(y_test, y_pred)\nprint(f\"\\nKNN 模型的准确率: {accuracy:.4f}\")\n\n# 交叉验证的相关参数\nprint(f\"最好结果：{estimator.best_score_}\")\nprint(f\"最好模型：{estimator.best_estimator_}\")\nprint(f\"最好模型结果：{estimator.cv_results_}\")\n```\n![](/img/machinelearning/cros-valid.png)\n\n### 机器学习的基本步骤\n- 获取数据集\n- 数据集基本处理\n - 去重去空、填充等操作 \n - 确定特征值和目标值\n - 分割数据集\n- 特征工程（特征预处理 标准化等）\n- 机器学习\n- 模型评估\n\n### 数据分割的方法\n- 留出法\n 训练/测试集的划分要尽可能保持数据分布的一致性，避免因数据划分过程引入额外的偏差而对最终结果产生影响。\n 单次使用留出法得到的估计结果往往不够稳定可靠，在使用留出法时，一般要采用若干次随机划分、重复进行实验评估后取平均值作为留出法的评估结果。\n ``` python\n\tfrom sklearn.model_selection import KFold,StratifiedKFold\n\timport pandas as pd\n\tX = np.array([\n\t[1,2,3,4],\n\t[11,12,13,14],\n\t[21,22,23,24],\n\t[31,32,33,34],\n\t[41,42,43,44],\n\t[51,52,53,54],\n\t[61,62,63,64],\n\t[71,72,73,74]\n\t])\n\ty=np.array([1,1,0,0,1,1,0,0])\n\tfolder = KFold(n_splits=4)\n\tsfloder = StratifiedKFold(n_splits=4)\n\tprint(\"KFOLD:\")\n\tfor train,test in folder.split(X,y):\n\t print(f\"train:{train},test:{test}\")\n\tprint(\"SKFOLD:\")\n\tfor train,test in sfloder.split(X,y):\n print(f\"train:{train},test:{test}\")\n\t```\n\t![](/img/machinelearning/kfold-skfold.png)\n- 自助法\n- 交叉验证法","slug":"machinelearning/knn","published":1,"updated":"2025-01-14T09:21:29.069Z","comments":1,"layout":"post","photos":[],"_id":"cm63ecyk3000arcah2gpi320x","content":"

k近邻算法（K-Nearest Neighbors）KNN

将当前样本的类别归类于距离最近的k个样本的类别

\n

距离公式(2维)

    \n
  • 欧式距离
    $$
    d = \\sqrt{(x_1-y_1)^2 + (x_2 - y_2)^2}
    $$
    • \n
  • 曼哈顿距离
    $$
    d = |x_1 - x_2| + |y_1 - y_2|
    $$
    • \n
  • 切比雪夫距离
    $$
    d = \\max\\left(|x_1 - x_2|, |y_1 - y_2|\\right)
    $$
    • \n
\n

k值选择问题

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
k值影响
越大模型过拟合，准确率波动较大
越小模型欠拟合，准确率趋于稳定但可能较低
\n

特征预处理

\n

通过一些转换函数将特征数据转换成更加适合算法模型的特征数据过程

\n
\n
    \n

  • 归一化
    将数据变换到指定区间（默认是[0,1]）
    $$ x’ = \\frac{x- x_{\\text {min}}}{x_{\\text{max}} - x_{\\text{min}}} $$
    若需要缩放到任意区间 ([a, b])，公式为： $$ x’ = a + \\frac{(x - x_{\\text{min}}) \\cdot (b - a)}{x_{\\text{max}} - x_{\\text{min}}} $$
    其中：( [a, b] )：目标区间的范围
    归一化受到数据集的异常值的影响，需要进行标准化处理（更加合理）

    \n
    1
    from sklearn.preprocessing import MinMaxScaler # 归一化
    • \n

  • 标准化
    将数据调整为均值为 0，标准差为 1 的标准正态分布
    $$ z = \\frac{x - \\mu}{\\sigma} $$
    ( z )：标准化后的值 ( x )：原始数据值 ( $\\mu$ )：数据的均值 ( $\\sigma$)：数据的标准差

    \n
    1
    from sklearn.preprocessing import StandardScaler # 标准化
    • \n
\n

KNN代码实现

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
import seaborn as sns
import matplotlib.pyplot as plt  
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from sklearn.preprocessing import MinMaxScaler,StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# 1 数据集获取
iris = load_iris()
# print(iris.feature_names)
iris_data = pd.DataFrame(iris.data,columns=['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width'])
iris_data['target'] = iris.target

def iris_plot(data,col1,col2):
    sns.lmplot(x=col1,y=col2,data=data,hue="target",fit_reg=False)
    plt.show()
# 2 数据集可视化
# iris_plot(iris_data, 'Sepal_Width', 'Petal_Length')

# 3 数据集的划分
X_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=0.2,random_state=44)
# print("训练集的特征值：\\n",X_train)
# print("训练集的目标值：\\n",y_train)
# print("测试集的特征值：\\n",X_test)
# print("测试集的特征值：\\n",y_test)

# 4 归一化
transfer = StandardScaler()
X_train = transfer.fit_transform(X_train)
X_test = transfer.transform(X_test)
# print("归一化的,X_train：\\n",X_train)
# print("归一化的X_test：\\n",X_test)

# 5 机器学习 KNN
# 5.1 实例化估计器
estimator = KNeighborsClassifier(n_neighbors=9)
# 5.2 进行训练
estimator.fit(X_train,y_train)

# 6 模型评估
y_pred = estimator.predict(X_test)
print("预测值：\\n",y_pre)
print("预测值与真实值是否相等：\\n",y_pred==y_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"\\nKNN 模型的准确率: {accuracy:.4f}")
\n\n

\n

交叉验证与网格搜索

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
import seaborn as sns
import matplotlib.pyplot as plt  
import pandas as pd
from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.datasets import load_iris
from sklearn.preprocessing import MinMaxScaler,StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# 1 数据集获取
iris = load_iris()
iris_data = pd.DataFrame(iris.data,columns=['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width'])
iris_data['target'] = iris.target

# 3 数据集的划分
X_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=0.2)

# 4 归一化
transfer = StandardScaler()
X_train = transfer.fit_transform(X_train)
X_test = transfer.transform(X_test)

# 5 机器学习 KNN
# 5.1 实例化估计器
#
#不指定 <code> n_neighbors </code> ,使用网格搜索进行循环训练
estimator = KNeighborsClassifier()
# 5.2 模型调优 -- 交叉验证，网格搜素
estimator = GridSearchCV(estimator,param_grid={"n_neighbors":[1,3,5,7]},cv=5) # 5 折
# 5.2 进行训练
estimator.fit(X_train,y_train)
 
# 6 模型评估
y_pred = estimator.predict(X_test)
print("预测值：\\n",y_pred)
print("预测值与真实值是否相等：\\n",y_pred==y_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"\\nKNN 模型的准确率: {accuracy:.4f}")

# 交叉验证的相关参数
print(f"最好结果：{estimator.best_score_}")
print(f"最好模型：{estimator.best_estimator_}")
print(f"最好模型结果：{estimator.cv_results_}")
\n

\n

机器学习的基本步骤

    \n
  • 获取数据集
    • \n
  • 数据集基本处理
      \n
    • 去重去空、填充等操作
      • \n
    • 确定特征值和目标值
      • \n
    • 分割数据集
      • \n
    \n
    • \n
  • 特征工程（特征预处理 标准化等）
    • \n
  • 机器学习
    • \n
  • 模型评估
    • \n
\n

数据分割的方法

    \n
  • 留出法
    训练/测试集的划分要尽可能保持数据分布的一致性，避免因数据划分过程引入额外的偏差而对最终结果产生影响。
    单次使用留出法得到的估计结果往往不够稳定可靠，在使用留出法时，一般要采用若干次随机划分、重复进行实验评估后取平均值作为留出法的评估结果。
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    from sklearn.model_selection import KFold,StratifiedKFold
    import pandas as pd
    X = np.array([
    [1,2,3,4],
    [11,12,13,14],
    [21,22,23,24],
    [31,32,33,34],
    [41,42,43,44],
    [51,52,53,54],
    [61,62,63,64],
    [71,72,73,74]
    ])
    y=np.array([1,1,0,0,1,1,0,0])
    folder = KFold(n_splits=4)
    sfloder = StratifiedKFold(n_splits=4)
    print("KFOLD:")
    for train,test in folder.split(X,y):
        print(f"train:{train},test:{test}")
    print("SKFOLD:")
    for train,test in sfloder.split(X,y):
       print(f"train:{train},test:{test}")
    \n
    • \n
  • 自助法
    • \n
  • 交叉验证法
    • \n
\n","cover":false,"excerpt":"","more":"

k近邻算法（K-Nearest Neighbors）KNN

将当前样本的类别归类于距离最近的k个样本的类别

\n

距离公式(2维)

    \n
  • 欧式距离
    $$
    d = \\sqrt{(x_1-y_1)^2 + (x_2 - y_2)^2}
    $$
    • \n
  • 曼哈顿距离
    $$
    d = |x_1 - x_2| + |y_1 - y_2|
    $$
    • \n
  • 切比雪夫距离
    $$
    d = \\max\\left(|x_1 - x_2|, |y_1 - y_2|\\right)
    $$
    • \n
\n

k值选择问题

\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
k值影响
越大模型过拟合，准确率波动较大
越小模型欠拟合，准确率趋于稳定但可能较低
\n

特征预处理

\n

通过一些转换函数将特征数据转换成更加适合算法模型的特征数据过程

\n
\n
    \n

  • 归一化
    将数据变换到指定区间（默认是[0,1]）
    $$ x’ = \\frac{x- x_{\\text {min}}}{x_{\\text{max}} - x_{\\text{min}}} $$
    若需要缩放到任意区间 ([a, b])，公式为： $$ x’ = a + \\frac{(x - x_{\\text{min}}) \\cdot (b - a)}{x_{\\text{max}} - x_{\\text{min}}} $$
    其中：( [a, b] )：目标区间的范围
    归一化受到数据集的异常值的影响，需要进行标准化处理（更加合理）

    \n
    1
    from sklearn.preprocessing import MinMaxScaler # 归一化
    • \n

  • 标准化
    将数据调整为均值为 0，标准差为 1 的标准正态分布
    $$ z = \\frac{x - \\mu}{\\sigma} $$
    ( z )：标准化后的值 ( x )：原始数据值 ( $\\mu$ )：数据的均值 ( $\\sigma$)：数据的标准差

    \n
    1
    from sklearn.preprocessing import StandardScaler # 标准化
    • \n
\n

KNN代码实现

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
import seaborn as sns
import matplotlib.pyplot as plt  
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_iris
from sklearn.preprocessing import MinMaxScaler,StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# 1 数据集获取
iris = load_iris()
# print(iris.feature_names)
iris_data = pd.DataFrame(iris.data,columns=['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width'])
iris_data['target'] = iris.target

def iris_plot(data,col1,col2):
    sns.lmplot(x=col1,y=col2,data=data,hue="target",fit_reg=False)
    plt.show()
# 2 数据集可视化
# iris_plot(iris_data, 'Sepal_Width', 'Petal_Length')

# 3 数据集的划分
X_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=0.2,random_state=44)
# print("训练集的特征值：\\n",X_train)
# print("训练集的目标值：\\n",y_train)
# print("测试集的特征值：\\n",X_test)
# print("测试集的特征值：\\n",y_test)

# 4 归一化
transfer = StandardScaler()
X_train = transfer.fit_transform(X_train)
X_test = transfer.transform(X_test)
# print("归一化的,X_train：\\n",X_train)
# print("归一化的X_test：\\n",X_test)

# 5 机器学习 KNN
# 5.1 实例化估计器
estimator = KNeighborsClassifier(n_neighbors=9)
# 5.2 进行训练
estimator.fit(X_train,y_train)

# 6 模型评估
y_pred = estimator.predict(X_test)
print("预测值：\\n",y_pre)
print("预测值与真实值是否相等：\\n",y_pred==y_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"\\nKNN 模型的准确率: {accuracy:.4f}")
\n\n

\n

交叉验证与网格搜索

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
import seaborn as sns
import matplotlib.pyplot as plt  
import pandas as pd
from sklearn.model_selection import train_test_split,GridSearchCV
from sklearn.datasets import load_iris
from sklearn.preprocessing import MinMaxScaler,StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

# 1 数据集获取
iris = load_iris()
iris_data = pd.DataFrame(iris.data,columns=['Sepal_Length', 'Sepal_Width', 'Petal_Length', 'Petal_Width'])
iris_data['target'] = iris.target

# 3 数据集的划分
X_train,X_test,y_train,y_test = train_test_split(iris.data,iris.target,test_size=0.2)

# 4 归一化
transfer = StandardScaler()
X_train = transfer.fit_transform(X_train)
X_test = transfer.transform(X_test)

# 5 机器学习 KNN
# 5.1 实例化估计器
#
#不指定 <code> n_neighbors </code> ,使用网格搜索进行循环训练
estimator = KNeighborsClassifier()
# 5.2 模型调优 -- 交叉验证，网格搜素
estimator = GridSearchCV(estimator,param_grid={"n_neighbors":[1,3,5,7]},cv=5) # 5 折
# 5.2 进行训练
estimator.fit(X_train,y_train)
 
# 6 模型评估
y_pred = estimator.predict(X_test)
print("预测值：\\n",y_pred)
print("预测值与真实值是否相等：\\n",y_pred==y_test)
accuracy = accuracy_score(y_test, y_pred)
print(f"\\nKNN 模型的准确率: {accuracy:.4f}")

# 交叉验证的相关参数
print(f"最好结果：{estimator.best_score_}")
print(f"最好模型：{estimator.best_estimator_}")
print(f"最好模型结果：{estimator.cv_results_}")
\n

\n

机器学习的基本步骤

    \n
  • 获取数据集
    • \n
  • 数据集基本处理
      \n
    • 去重去空、填充等操作
      • \n
    • 确定特征值和目标值
      • \n
    • 分割数据集
      • \n
    \n
    • \n
  • 特征工程（特征预处理 标准化等）
    • \n
  • 机器学习
    • \n
  • 模型评估
    • \n
\n

数据分割的方法

    \n
  • 留出法
    训练/测试集的划分要尽可能保持数据分布的一致性，避免因数据划分过程引入额外的偏差而对最终结果产生影响。
    单次使用留出法得到的估计结果往往不够稳定可靠，在使用留出法时，一般要采用若干次随机划分、重复进行实验评估后取平均值作为留出法的评估结果。
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    from sklearn.model_selection import KFold,StratifiedKFold
    import pandas as pd
    X = np.array([
    [1,2,3,4],
    [11,12,13,14],
    [21,22,23,24],
    [31,32,33,34],
    [41,42,43,44],
    [51,52,53,54],
    [61,62,63,64],
    [71,72,73,74]
    ])
    y=np.array([1,1,0,0,1,1,0,0])
    folder = KFold(n_splits=4)
    sfloder = StratifiedKFold(n_splits=4)
    print("KFOLD:")
    for train,test in folder.split(X,y):
        print(f"train:{train},test:{test}")
    print("SKFOLD:")
    for train,test in sfloder.split(X,y):
       print(f"train:{train},test:{test}")
    \n
    • \n
  • 自助法
    • \n
  • 交叉验证法
    • \n
\n"},{"title":"线性回归","mathjax":true,"abbrlink":52662,"date":"2025-01-19T08:46:51.000Z","_content":"\n### 线性回归简介\n>用于预测一个连续的目标变量（因变量），与一个或多个特征（自变量）之间存在线性关系。\n\n假设函数： \n$$y = w_1x_1 + w_2x_2 + \\cdot\\cdot\\cdot+w_nx_n$$\n- $y$ 是目标变量（因变量），即我们希望预测的值。\n- $x1​,x2​,…,xn$​ 是特征变量（自变量），即输入的值。\n### 损失函数\n\n为了找到最佳的线性模型，我们需要通过最小化损失函数来优化模型参数。在线性回归中，常用的损失函数是 **均方误差（MSE）**：\n$$MSE = \\frac{1}{m} \\sum_{i=1}^{m} (y_i - \\hat{y}_i)^2$$\n- m 是样本的数量。\n- $y_i$​ 是第 i 个样本的真实值。\n- $\\hat{y}_i​$ 是模型预测的第 i 个样本的值。\n\n### 线性回归优化\n\n- 梯度下降法\n ```python\n from sklearn.datasets import fetch_california_housing\n from sklearn.model_selection import train_test_split\n from sklearn.preprocessing import StandardScaler\n from sklearn.linear_model import SGDRegressor\n from sklearn.metrics import mean_squared_error\n\n # 1. 获取数据集\n housing = fetch_california_housing()\n\n # 2. 数据集处理\n # 2.1 分割数据集\n X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)\n\n # 3. 特征工程\n # 3.1 标准化\n transfer = StandardScaler()\n X_train = transfer.fit_transform(X_train)\n X_test = transfer.transform(X_test) # 使用 transform() 而不是 fit_transform()\n\n # 4.机器学习- 梯度下降法\n estimater = SGDRegressor(max_iter=1000, eta0=0.01)\n estimater.fit(X_train, y_train)\n print(f\"SGD模型的偏置是：{estimater.intercept_}\")\n print(f\"SGD模型的系数是：{estimater.coef_}\")\n\n # 5. 模型评估\n y_pred = estimater.predict(X_test)\n print(f\"SGD模型预测值：{y_pred}\")\n mse = mean_squared_error(y_test, y_pred)\n print(f\"SGD模型均方误差:{mse}\")\n ```\n\n- 正规方程\n ```python\n from sklearn.datasets import fetch_california_housing\n from sklearn.model_selection import train_test_split\n from sklearn.preprocessing import StandardScaler\n from sklearn.linear_model import LinearRegression\n from sklearn.metrics import mean_squared_error\n\n # 1. 获取数据集\n housing = fetch_california_housing()\n\n # 2. 数据集处理\n # 2.1 分割数据集\n X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)\n\n # 3. 特征工程\n # 3.1 标准化\n transfer = StandardScaler()\n X_train = transfer.fit_transform(X_train)\n X_test = transfer.fit_transform(X_test)\n\n # 4.机器学习- 线性回归\n estimater = LinearRegression()\n estimater.fit(X_train, y_train)\n print(f\"模型的偏置是：{estimater.intercept_}\")\n print(f\"模型的系数是：{estimater.coef_}\")\n\n # 5. 模型评估\n y_pred = estimater.predict(X_test)\n print(f\"模型预测值：{y_pred}\")\n mse = mean_squared_error(y_test, y_pred)\n print(f\"模型均方误差:{mse}\")\n ```\n\n- 岭回归\n ```python\n from sklearn.datasets import fetch_california_housing\n from sklearn.model_selection import train_test_split\n from sklearn.preprocessing import StandardScaler\n from sklearn.linear_model import Ridge, RidgeCV\n from sklearn.metrics import mean_squared_error\n\n # 1. 获取数据集\n housing = fetch_california_housing()\n\n # 2. 数据集处理\n # 2.1 分割数据集\n X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)\n\n # 3. 特征工程\n # 3.1 标准化\n transfer = StandardScaler()\n X_train = transfer.fit_transform(X_train)\n X_test = transfer.transform(X_test) # 使用 transform() 而不是 fit_transform()\n\n # 4.机器学习- 岭回归 使用了Ridge的alpha的搜索\n # estimater = Ridge(alpha=1.0)\n estimater = RidgeCV(alphas=[0.001, 0.01, 0.1, 1, 10, 100])\n estimater.fit(X_train, y_train)\n print(f\"Ridge模型的偏置是：{estimater.intercept_}\")\n print(f\"Ridge模型的系数是：{estimater.coef_}\")\n\n # 查看最佳 alpha\n print(f\"最佳 alpha 值是：{estimater.alpha_}\")\n\n # 5. 模型评估\n y_pred = estimater.predict(X_test)\n print(f\"Ridge模型预测值：{y_pred}\")\n mse = mean_squared_error(y_test, y_pred)\n print(f\"Ridge模型均方误差:{mse}\")\n ```\n\n这样每个代码块的缩进保持一致，便于阅读和理解。如果有其他优化需求，随时告诉我！\n\n\n![](/img/machinelearning/linear.png)\n\n![](/img/machinelearning/fitting.png)\n### 模型保存和加载\n```python\nfrom sklearn.datasets import fetch_california_housing\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import Ridge, RidgeCV\nfrom sklearn.metrics import mean_squared_error\nimport joblib\n\ndef save_model():\n # 1. 获取数据集\n housing = fetch_california_housing()\n # 2. 数据集处理\n # 2.1 分割数据集\n X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)\n # 3. 特征工程\n # 3.1 标准化\n transfer = StandardScaler()\n X_train = transfer.fit_transform(X_train)\n X_test = transfer.transform(X_test) # 使用 transform() 而不是 fit_transform()\n # 4. 机器学习 - 岭回归 使用了Ridge的alpha的搜索\n estimater = RidgeCV(alphas=[0.001, 0.01, 0.1, 1, 10, 100])\n estimater.fit(X_train, y_train)\n print(f\"Ridge模型的偏置是：{estimater.intercept_}\")\n print(f\"Ridge模型的系数是：{estimater.coef_}\")\n # 保存模型\n joblib.dump(estimater, 'ridge_model.pkl')\n # 查看最佳 alpha\n print(f\"最佳 alpha 值是：{estimater.alpha_}\")\n # 5. 模型评估\n y_pred = estimater.predict(X_test)\n mse = mean_squared_error(y_test, y_pred)\n print(f\"Ridge模型均方误差:{mse}\")\n\ndef load_model():\n # 1. 获取数据集\n housing = fetch_california_housing()\n # 2. 数据集处理\n # 2.1 分割数据集\n X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)\n # 3. 特征工程\n # 3.1 标准化\n transfer = StandardScaler()\n X_train = transfer.fit_transform(X_train)\n X_test = transfer.transform(X_test) # 使用 transform() 而不是 fit_transform()\n # 加载模型\n estimater = joblib.load('ridge_model.pkl')\n print(f\"Ridge模型的偏置是：{estimater.intercept_}\")\n print(f\"Ridge模型的系数是：{estimater.coef_}\")\n # 查看最佳 alpha\n print(f\"最佳 alpha 值是：{estimater.alpha_}\")\n # 5. 模型评估\n y_pred = estimater.predict(X_test)\n mse = mean_squared_error(y_test, y_pred)\n print(f\"Ridge模型预测值：{y_pred}\")\n print(f\"Ridge模型均方误差:{mse}\")\n\nprint(\"训练并保存模型：\")\nsave_model()\nprint(\"加载模型\")\nload_model()\n```","source":"_posts/machinelearning/linearreression.md","raw":"---\ntitle: 线性回归\ntags: linear-regression\nmathjax: true\nabbrlink: 52662\ndate: 2025-01-19 16:46:51\n---\n\n### 线性回归简介\n>用于预测一个连续的目标变量（因变量），与一个或多个特征（自变量）之间存在线性关系。\n\n假设函数： \n$$y = w_1x_1 + w_2x_2 + \\cdot\\cdot\\cdot+w_nx_n$$\n- $y$ 是目标变量（因变量），即我们希望预测的值。\n- $x1​,x2​,…,xn$​ 是特征变量（自变量），即输入的值。\n### 损失函数\n\n为了找到最佳的线性模型，我们需要通过最小化损失函数来优化模型参数。在线性回归中，常用的损失函数是 **均方误差（MSE）**：\n$$MSE = \\frac{1}{m} \\sum_{i=1}^{m} (y_i - \\hat{y}_i)^2$$\n- m 是样本的数量。\n- $y_i$​ 是第 i 个样本的真实值。\n- $\\hat{y}_i​$ 是模型预测的第 i 个样本的值。\n\n### 线性回归优化\n\n- 梯度下降法\n ```python\n from sklearn.datasets import fetch_california_housing\n from sklearn.model_selection import train_test_split\n from sklearn.preprocessing import StandardScaler\n from sklearn.linear_model import SGDRegressor\n from sklearn.metrics import mean_squared_error\n\n # 1. 获取数据集\n housing = fetch_california_housing()\n\n # 2. 数据集处理\n # 2.1 分割数据集\n X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)\n\n # 3. 特征工程\n # 3.1 标准化\n transfer = StandardScaler()\n X_train = transfer.fit_transform(X_train)\n X_test = transfer.transform(X_test) # 使用 transform() 而不是 fit_transform()\n\n # 4.机器学习- 梯度下降法\n estimater = SGDRegressor(max_iter=1000, eta0=0.01)\n estimater.fit(X_train, y_train)\n print(f\"SGD模型的偏置是：{estimater.intercept_}\")\n print(f\"SGD模型的系数是：{estimater.coef_}\")\n\n # 5. 模型评估\n y_pred = estimater.predict(X_test)\n print(f\"SGD模型预测值：{y_pred}\")\n mse = mean_squared_error(y_test, y_pred)\n print(f\"SGD模型均方误差:{mse}\")\n ```\n\n- 正规方程\n ```python\n from sklearn.datasets import fetch_california_housing\n from sklearn.model_selection import train_test_split\n from sklearn.preprocessing import StandardScaler\n from sklearn.linear_model import LinearRegression\n from sklearn.metrics import mean_squared_error\n\n # 1. 获取数据集\n housing = fetch_california_housing()\n\n # 2. 数据集处理\n # 2.1 分割数据集\n X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)\n\n # 3. 特征工程\n # 3.1 标准化\n transfer = StandardScaler()\n X_train = transfer.fit_transform(X_train)\n X_test = transfer.fit_transform(X_test)\n\n # 4.机器学习- 线性回归\n estimater = LinearRegression()\n estimater.fit(X_train, y_train)\n print(f\"模型的偏置是：{estimater.intercept_}\")\n print(f\"模型的系数是：{estimater.coef_}\")\n\n # 5. 模型评估\n y_pred = estimater.predict(X_test)\n print(f\"模型预测值：{y_pred}\")\n mse = mean_squared_error(y_test, y_pred)\n print(f\"模型均方误差:{mse}\")\n ```\n\n- 岭回归\n ```python\n from sklearn.datasets import fetch_california_housing\n from sklearn.model_selection import train_test_split\n from sklearn.preprocessing import StandardScaler\n from sklearn.linear_model import Ridge, RidgeCV\n from sklearn.metrics import mean_squared_error\n\n # 1. 获取数据集\n housing = fetch_california_housing()\n\n # 2. 数据集处理\n # 2.1 分割数据集\n X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)\n\n # 3. 特征工程\n # 3.1 标准化\n transfer = StandardScaler()\n X_train = transfer.fit_transform(X_train)\n X_test = transfer.transform(X_test) # 使用 transform() 而不是 fit_transform()\n\n # 4.机器学习- 岭回归 使用了Ridge的alpha的搜索\n # estimater = Ridge(alpha=1.0)\n estimater = RidgeCV(alphas=[0.001, 0.01, 0.1, 1, 10, 100])\n estimater.fit(X_train, y_train)\n print(f\"Ridge模型的偏置是：{estimater.intercept_}\")\n print(f\"Ridge模型的系数是：{estimater.coef_}\")\n\n # 查看最佳 alpha\n print(f\"最佳 alpha 值是：{estimater.alpha_}\")\n\n # 5. 模型评估\n y_pred = estimater.predict(X_test)\n print(f\"Ridge模型预测值：{y_pred}\")\n mse = mean_squared_error(y_test, y_pred)\n print(f\"Ridge模型均方误差:{mse}\")\n ```\n\n这样每个代码块的缩进保持一致，便于阅读和理解。如果有其他优化需求，随时告诉我！\n\n\n![](/img/machinelearning/linear.png)\n\n![](/img/machinelearning/fitting.png)\n### 模型保存和加载\n```python\nfrom sklearn.datasets import fetch_california_housing\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import Ridge, RidgeCV\nfrom sklearn.metrics import mean_squared_error\nimport joblib\n\ndef save_model():\n # 1. 获取数据集\n housing = fetch_california_housing()\n # 2. 数据集处理\n # 2.1 分割数据集\n X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)\n # 3. 特征工程\n # 3.1 标准化\n transfer = StandardScaler()\n X_train = transfer.fit_transform(X_train)\n X_test = transfer.transform(X_test) # 使用 transform() 而不是 fit_transform()\n # 4. 机器学习 - 岭回归 使用了Ridge的alpha的搜索\n estimater = RidgeCV(alphas=[0.001, 0.01, 0.1, 1, 10, 100])\n estimater.fit(X_train, y_train)\n print(f\"Ridge模型的偏置是：{estimater.intercept_}\")\n print(f\"Ridge模型的系数是：{estimater.coef_}\")\n # 保存模型\n joblib.dump(estimater, 'ridge_model.pkl')\n # 查看最佳 alpha\n print(f\"最佳 alpha 值是：{estimater.alpha_}\")\n # 5. 模型评估\n y_pred = estimater.predict(X_test)\n mse = mean_squared_error(y_test, y_pred)\n print(f\"Ridge模型均方误差:{mse}\")\n\ndef load_model():\n # 1. 获取数据集\n housing = fetch_california_housing()\n # 2. 数据集处理\n # 2.1 分割数据集\n X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)\n # 3. 特征工程\n # 3.1 标准化\n transfer = StandardScaler()\n X_train = transfer.fit_transform(X_train)\n X_test = transfer.transform(X_test) # 使用 transform() 而不是 fit_transform()\n # 加载模型\n estimater = joblib.load('ridge_model.pkl')\n print(f\"Ridge模型的偏置是：{estimater.intercept_}\")\n print(f\"Ridge模型的系数是：{estimater.coef_}\")\n # 查看最佳 alpha\n print(f\"最佳 alpha 值是：{estimater.alpha_}\")\n # 5. 模型评估\n y_pred = estimater.predict(X_test)\n mse = mean_squared_error(y_test, y_pred)\n print(f\"Ridge模型预测值：{y_pred}\")\n print(f\"Ridge模型均方误差:{mse}\")\n\nprint(\"训练并保存模型：\")\nsave_model()\nprint(\"加载模型\")\nload_model()\n```","slug":"machinelearning/linearreression","published":1,"updated":"2025-01-19T09:10:09.051Z","comments":1,"layout":"post","photos":[],"_id":"cm63ecyk4000crcah1m2udz0g","content":"

线性回归简介

\n

用于预测一个连续的目标变量（因变量），与一个或多个特征（自变量）之间存在线性关系。

\n
\n

假设函数：
$$y = w_1x_1 + w_2x_2 + \\cdot\\cdot\\cdot+w_nx_n$$

\n
    \n
  • $y$ 是目标变量（因变量），即我们希望预测的值。
    • \n
  • $x1​,x2​,…,xn$​ 是特征变量（自变量），即输入的值。
    • \n
\n

损失函数

为了找到最佳的线性模型，我们需要通过最小化损失函数来优化模型参数。在线性回归中，常用的损失函数是 均方误差（MSE）：
$$MSE = \\frac{1}{m} \\sum_{i=1}^{m} (y_i - \\hat{y}_i)^2$$

\n
    \n
  • m 是样本的数量。
    • \n
  • $y_i$​ 是第 i 个样本的真实值。
    • \n
  • $\\hat{y}_i​$ 是模型预测的第 i 个样本的值。
    • \n
\n

线性回归优化

    \n

  • 梯度下降法

    \n 
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    22
    23
    24
    25
    26
    27
    28
    29
    30
    from sklearn.datasets import fetch_california_housing
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler
    from sklearn.linear_model import SGDRegressor
    from sklearn.metrics import mean_squared_error

    # 1. 获取数据集
    housing = fetch_california_housing()

    # 2. 数据集处理
    # 2.1 分割数据集
    X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)

    # 3. 特征工程
    # 3.1 标准化
    transfer = StandardScaler()
    X_train = transfer.fit_transform(X_train)
    X_test = transfer.transform(X_test)  # 使用 transform() 而不是 fit_transform()

    # 4.机器学习- 梯度下降法
    estimater = SGDRegressor(max_iter=1000, eta0=0.01)
    estimater.fit(X_train, y_train)
    print(f"SGD模型的偏置是：{estimater.intercept_}")
    print(f"SGD模型的系数是：{estimater.coef_}")

    # 5. 模型评估
    y_pred = estimater.predict(X_test)
    print(f"SGD模型预测值：{y_pred}")
    mse = mean_squared_error(y_test, y_pred)
    print(f"SGD模型均方误差:{mse}")
    \n
    • \n

  • 正规方程

    \n 
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    22
    23
    24
    25
    26
    27
    28
    29
    30
    from sklearn.datasets import fetch_california_housing
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler
    from sklearn.linear_model import LinearRegression
    from sklearn.metrics import mean_squared_error

    # 1. 获取数据集
    housing = fetch_california_housing()

    # 2. 数据集处理
    # 2.1 分割数据集
    X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)

    # 3. 特征工程
    # 3.1 标准化
    transfer = StandardScaler()
    X_train = transfer.fit_transform(X_train)
    X_test = transfer.fit_transform(X_test)

    # 4.机器学习- 线性回归
    estimater = LinearRegression()
    estimater.fit(X_train, y_train)
    print(f"模型的偏置是：{estimater.intercept_}")
    print(f"模型的系数是：{estimater.coef_}")

    # 5. 模型评估
    y_pred = estimater.predict(X_test)
    print(f"模型预测值：{y_pred}")
    mse = mean_squared_error(y_test, y_pred)
    print(f"模型均方误差:{mse}")
    \n
    • \n

  • 岭回归

    \n 
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    22
    23
    24
    25
    26
    27
    28
    29
    30
    31
    32
    33
    34
    from sklearn.datasets import fetch_california_housing
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler
    from sklearn.linear_model import Ridge, RidgeCV
    from sklearn.metrics import mean_squared_error

    # 1. 获取数据集
    housing = fetch_california_housing()

    # 2. 数据集处理
    # 2.1 分割数据集
    X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)

    # 3. 特征工程
    # 3.1 标准化
    transfer = StandardScaler()
    X_train = transfer.fit_transform(X_train)
    X_test = transfer.transform(X_test)  # 使用 transform() 而不是 fit_transform()

    # 4.机器学习- 岭回归 使用了Ridge的alpha的搜索
    # estimater = Ridge(alpha=1.0)
    estimater = RidgeCV(alphas=[0.001, 0.01, 0.1, 1, 10, 100])
    estimater.fit(X_train, y_train)
    print(f"Ridge模型的偏置是：{estimater.intercept_}")
    print(f"Ridge模型的系数是：{estimater.coef_}")

    # 查看最佳 alpha
    print(f"最佳 alpha 值是：{estimater.alpha_}")

    # 5. 模型评估
    y_pred = estimater.predict(X_test)
    print(f"Ridge模型预测值：{y_pred}")
    mse = mean_squared_error(y_test, y_pred)
    print(f"Ridge模型均方误差:{mse}")
    • \n
\n

这样每个代码块的缩进保持一致，便于阅读和理解。如果有其他优化需求，随时告诉我！

\n

\n

\n

模型保存和加载

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Ridge, RidgeCV
from sklearn.metrics import mean_squared_error
import joblib

def save_model():
    # 1. 获取数据集
    housing = fetch_california_housing()
    # 2. 数据集处理
    # 2.1 分割数据集
    X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)
    # 3. 特征工程
    # 3.1 标准化
    transfer = StandardScaler()
    X_train = transfer.fit_transform(X_train)
    X_test = transfer.transform(X_test)  # 使用 transform() 而不是 fit_transform()
    # 4. 机器学习 - 岭回归 使用了Ridge的alpha的搜索
    estimater = RidgeCV(alphas=[0.001, 0.01, 0.1, 1, 10, 100])
    estimater.fit(X_train, y_train)
    print(f"Ridge模型的偏置是：{estimater.intercept_}")
    print(f"Ridge模型的系数是：{estimater.coef_}")
    # 保存模型
    joblib.dump(estimater, 'ridge_model.pkl')
    # 查看最佳 alpha
    print(f"最佳 alpha 值是：{estimater.alpha_}")
    # 5. 模型评估
    y_pred = estimater.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    print(f"Ridge模型均方误差:{mse}")

def load_model():
    # 1. 获取数据集
    housing = fetch_california_housing()
    # 2. 数据集处理
    # 2.1 分割数据集
    X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)
    # 3. 特征工程
    # 3.1 标准化
    transfer = StandardScaler()
    X_train = transfer.fit_transform(X_train)
    X_test = transfer.transform(X_test)  # 使用 transform() 而不是 fit_transform()
    # 加载模型
    estimater = joblib.load('ridge_model.pkl')
    print(f"Ridge模型的偏置是：{estimater.intercept_}")
    print(f"Ridge模型的系数是：{estimater.coef_}")
    # 查看最佳 alpha
    print(f"最佳 alpha 值是：{estimater.alpha_}")
    # 5. 模型评估
    y_pred = estimater.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    print(f"Ridge模型预测值：{y_pred}")
    print(f"Ridge模型均方误差:{mse}")

print("训练并保存模型：")
save_model()
print("加载模型")
load_model()
","cover":false,"excerpt":"","more":"

线性回归简介

\n

用于预测一个连续的目标变量（因变量），与一个或多个特征（自变量）之间存在线性关系。

\n
\n

假设函数：
$$y = w_1x_1 + w_2x_2 + \\cdot\\cdot\\cdot+w_nx_n$$

\n
    \n
  • $y$ 是目标变量（因变量），即我们希望预测的值。
    • \n
  • $x1​,x2​,…,xn$​ 是特征变量（自变量），即输入的值。
    • \n
\n

损失函数

为了找到最佳的线性模型，我们需要通过最小化损失函数来优化模型参数。在线性回归中，常用的损失函数是 均方误差（MSE）：
$$MSE = \\frac{1}{m} \\sum_{i=1}^{m} (y_i - \\hat{y}_i)^2$$

\n
    \n
  • m 是样本的数量。
    • \n
  • $y_i$​ 是第 i 个样本的真实值。
    • \n
  • $\\hat{y}_i​$ 是模型预测的第 i 个样本的值。
    • \n
\n

线性回归优化

    \n

  • 梯度下降法

    \n 
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    22
    23
    24
    25
    26
    27
    28
    29
    30
    from sklearn.datasets import fetch_california_housing
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler
    from sklearn.linear_model import SGDRegressor
    from sklearn.metrics import mean_squared_error

    # 1. 获取数据集
    housing = fetch_california_housing()

    # 2. 数据集处理
    # 2.1 分割数据集
    X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)

    # 3. 特征工程
    # 3.1 标准化
    transfer = StandardScaler()
    X_train = transfer.fit_transform(X_train)
    X_test = transfer.transform(X_test)  # 使用 transform() 而不是 fit_transform()

    # 4.机器学习- 梯度下降法
    estimater = SGDRegressor(max_iter=1000, eta0=0.01)
    estimater.fit(X_train, y_train)
    print(f"SGD模型的偏置是：{estimater.intercept_}")
    print(f"SGD模型的系数是：{estimater.coef_}")

    # 5. 模型评估
    y_pred = estimater.predict(X_test)
    print(f"SGD模型预测值：{y_pred}")
    mse = mean_squared_error(y_test, y_pred)
    print(f"SGD模型均方误差:{mse}")
    \n
    • \n

  • 正规方程

    \n 
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    22
    23
    24
    25
    26
    27
    28
    29
    30
    from sklearn.datasets import fetch_california_housing
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler
    from sklearn.linear_model import LinearRegression
    from sklearn.metrics import mean_squared_error

    # 1. 获取数据集
    housing = fetch_california_housing()

    # 2. 数据集处理
    # 2.1 分割数据集
    X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)

    # 3. 特征工程
    # 3.1 标准化
    transfer = StandardScaler()
    X_train = transfer.fit_transform(X_train)
    X_test = transfer.fit_transform(X_test)

    # 4.机器学习- 线性回归
    estimater = LinearRegression()
    estimater.fit(X_train, y_train)
    print(f"模型的偏置是：{estimater.intercept_}")
    print(f"模型的系数是：{estimater.coef_}")

    # 5. 模型评估
    y_pred = estimater.predict(X_test)
    print(f"模型预测值：{y_pred}")
    mse = mean_squared_error(y_test, y_pred)
    print(f"模型均方误差:{mse}")
    \n
    • \n

  • 岭回归

    \n 
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    22
    23
    24
    25
    26
    27
    28
    29
    30
    31
    32
    33
    34
    from sklearn.datasets import fetch_california_housing
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler
    from sklearn.linear_model import Ridge, RidgeCV
    from sklearn.metrics import mean_squared_error

    # 1. 获取数据集
    housing = fetch_california_housing()

    # 2. 数据集处理
    # 2.1 分割数据集
    X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)

    # 3. 特征工程
    # 3.1 标准化
    transfer = StandardScaler()
    X_train = transfer.fit_transform(X_train)
    X_test = transfer.transform(X_test)  # 使用 transform() 而不是 fit_transform()

    # 4.机器学习- 岭回归 使用了Ridge的alpha的搜索
    # estimater = Ridge(alpha=1.0)
    estimater = RidgeCV(alphas=[0.001, 0.01, 0.1, 1, 10, 100])
    estimater.fit(X_train, y_train)
    print(f"Ridge模型的偏置是：{estimater.intercept_}")
    print(f"Ridge模型的系数是：{estimater.coef_}")

    # 查看最佳 alpha
    print(f"最佳 alpha 值是：{estimater.alpha_}")

    # 5. 模型评估
    y_pred = estimater.predict(X_test)
    print(f"Ridge模型预测值：{y_pred}")
    mse = mean_squared_error(y_test, y_pred)
    print(f"Ridge模型均方误差:{mse}")
    • \n
\n

这样每个代码块的缩进保持一致，便于阅读和理解。如果有其他优化需求，随时告诉我！

\n

\n

\n

模型保存和加载

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Ridge, RidgeCV
from sklearn.metrics import mean_squared_error
import joblib

def save_model():
    # 1. 获取数据集
    housing = fetch_california_housing()
    # 2. 数据集处理
    # 2.1 分割数据集
    X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)
    # 3. 特征工程
    # 3.1 标准化
    transfer = StandardScaler()
    X_train = transfer.fit_transform(X_train)
    X_test = transfer.transform(X_test)  # 使用 transform() 而不是 fit_transform()
    # 4. 机器学习 - 岭回归 使用了Ridge的alpha的搜索
    estimater = RidgeCV(alphas=[0.001, 0.01, 0.1, 1, 10, 100])
    estimater.fit(X_train, y_train)
    print(f"Ridge模型的偏置是：{estimater.intercept_}")
    print(f"Ridge模型的系数是：{estimater.coef_}")
    # 保存模型
    joblib.dump(estimater, 'ridge_model.pkl')
    # 查看最佳 alpha
    print(f"最佳 alpha 值是：{estimater.alpha_}")
    # 5. 模型评估
    y_pred = estimater.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    print(f"Ridge模型均方误差:{mse}")

def load_model():
    # 1. 获取数据集
    housing = fetch_california_housing()
    # 2. 数据集处理
    # 2.1 分割数据集
    X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)
    # 3. 特征工程
    # 3.1 标准化
    transfer = StandardScaler()
    X_train = transfer.fit_transform(X_train)
    X_test = transfer.transform(X_test)  # 使用 transform() 而不是 fit_transform()
    # 加载模型
    estimater = joblib.load('ridge_model.pkl')
    print(f"Ridge模型的偏置是：{estimater.intercept_}")
    print(f"Ridge模型的系数是：{estimater.coef_}")
    # 查看最佳 alpha
    print(f"最佳 alpha 值是：{estimater.alpha_}")
    # 5. 模型评估
    y_pred = estimater.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    print(f"Ridge模型预测值：{y_pred}")
    print(f"Ridge模型均方误差:{mse}")

print("训练并保存模型：")
save_model()
print("加载模型")
load_model()
"},{"title":"网络相关","abbrlink":41168,"date":"2024-08-07T02:06:08.000Z","_content":"\n","source":"_posts/net/index.md","raw":"---\ntitle: 网络相关\nabbrlink: 41168\ndate: 2024-08-07 10:06:08\ntags:\n---\n\n","slug":"net/index","published":1,"updated":"2024-08-09T12:21:50.028Z","comments":1,"layout":"post","photos":[],"_id":"cm63ecyk6000frcahfjjpggc3","content":"","cover":false,"excerpt":"","more":""},{"title":"郑伯克段于鄢","abbrlink":58638,"date":"2024-08-09T12:00:13.000Z","_content":"原文如下：\n\n      初，郑武公娶于申【申国】，曰武姜【武为武公谥号，姜为其宗族之性】。生庄公及共叔段【共表示其曾出逃到共，叔为老三，段为名】。庄公寤生【出生时头先出，难产】，惊姜氏，故名曰“寤生”， 遂恶之，爱【喜爱】共叔段，欲立【立为储君】之，亟（qì）【多次】请于武公，及庄公即位，为之【共叔段】请制【一个叫制的封地，虎牢关所在】。公曰：“制，岩邑【险要的城邑】也，虢叔死焉，佗【通“他”，其他】邑唯命（是听）。”请京，使居之，谓之“京城大叔”。","source":"_posts/ancient/guwenguanzhi/1.md","raw":"---\ntitle: 郑伯克段于鄢\ntags: 古文观止\ncategories:\n - 古文观止\nabbrlink: 58638\ndate: 2024-08-09 20:00:13\n---\n原文如下：\n\n      初，郑武公娶于申【申国】，曰武姜【武为武公谥号，姜为其宗族之性】。生庄公及共叔段【共表示其曾出逃到共，叔为老三，段为名】。庄公寤生【出生时头先出，难产】，惊姜氏，故名曰“寤生”， 遂恶之，爱【喜爱】共叔段，欲立【立为储君】之，亟（qì）【多次】请于武公，及庄公即位，为之【共叔段】请制【一个叫制的封地，虎牢关所在】。公曰：“制，岩邑【险要的城邑】也，虢叔死焉，佗【通“他”，其他】邑唯命（是听）。”请京，使居之，谓之“京城大叔”。","slug":"ancient/guwenguanzhi/1","published":1,"updated":"2024-08-10T02:31:03.678Z","comments":1,"layout":"post","photos":[],"_id":"cm63ecyk6000grcah3k7sfd16","content":"

原文如下：

\n

      初，郑武公娶于申【申国】，曰武姜【武为武公谥号，姜为其宗族之性】。生庄公及共叔段【共表示其曾出逃到共，叔为老三，段为名】。庄公寤生【出生时头先出，难产】，惊姜氏，故名曰“寤生”， 遂恶之，爱【喜爱】共叔段，欲立【立为储君】之，亟（qì）【多次】请于武公，及庄公即位，为之【共叔段】请制【一个叫制的封地，虎牢关所在】。公曰：“制，岩邑【险要的城邑】也，虢叔死焉，佗【通“他”，其他】邑唯命（是听）。”请京，使居之，谓之“京城大叔”。

\n","cover":false,"excerpt":"","more":"

原文如下：

\n

      初，郑武公娶于申【申国】，曰武姜【武为武公谥号，姜为其宗族之性】。生庄公及共叔段【共表示其曾出逃到共，叔为老三，段为名】。庄公寤生【出生时头先出，难产】，惊姜氏，故名曰“寤生”， 遂恶之，爱【喜爱】共叔段，欲立【立为储君】之，亟（qì）【多次】请于武公，及庄公即位，为之【共叔段】请制【一个叫制的封地，虎牢关所在】。公曰：“制，岩邑【险要的城邑】也，虢叔死焉，佗【通“他”，其他】邑唯命（是听）。”请京，使居之，谓之“京城大叔”。

\n"},{"title":"Hadoop集群Zookeeper配置","top_img":"/img/site01.jpg","top_img_height":"800px","abbrlink":61251,"date":"2024-09-11T14:45:40.000Z","_content":"\n### Zookeeper脚本配置\n- 拷贝ips\n ```bash\n cp ips ips_zookeeper\n ```\n 修改为\n ```bash\n nn1\n nn2\n nn3\n ```\n- 拷贝三个脚本\n ```bash\n cp scp_all.sh scp_all_zookeeper.sh \n cp ssh_all.sh ssh_all_zookeeper.sh \n cp ssh_root.sh ssh_root_zookeeper.sh\n ```\n- 修改脚本\n ```shell\n vim scp_all_zookeeper.sh \n vim ssh_all_zookeeper.sh \n vim ssh_root_zookeeper.sh \n ```\n\n 将三个脚本中的ips改为ips_zookeeper\n### Zookeeper安装\n- 上传到`/tmp`目录下，解压\n ```bash\n sudo tar -zxvf /tmp/zookeeper-3.4.8.tar.gz -C /usr/local/\n scp -r /usr/local/zookeeper-3.4.8/ root@nn2:/usr/local/\n scp -r /usr/local/zookeeper-3.4.8/ root@nn3:/usr/local/\n ssh_root_zookeeper.sh chown -R hadoop:hadoop /usr/local/zookeeper-3.4.8\n ssh_root_zookeeper.s ln -s /usr/local/zookeeper-3.4.8/ /usr/local/zookeeper\n ```\n### Zookeeper配置\n - zoo.cfg配置\n ```bash\n cd /usr/local/zookeeper/conf/\n cp zoo_sample.cfg zoo.cfg\n ```\n 然后`vim zoo.cfg`,修改如下：\n ```properties\n # 修改dataDir\n dataDir=/data/zookeeper\n # 添加一下内容\n server.1=nn1:2888:3888 \n server.2=nn2:2888:3888 \n server.3=nn3:2888:3888\n ```\n 分发给nn2、nn3\n `scp_all_zookeeper.sh /usr/local/zookeeper/conf/zoo.cfg /usr/local/zookeeper/conf/`\n\n- `zkEnv.sh`配置\n `vim /usr/local/zookeeper/bin/zkEnv.sh`\n ![000001.png](..%2F..%2F..%2Fimg%2F000001.png)\n 分发到nn2、nn3\n```bash\nscp_all_zookeeper.sh /usr/local/zookeeper/bin/zkEnv.sh /usr/local/zookeeper/bin/\n```\n- 创建zookeeper数据目录\n```bash\nssh_root_zookeeper.sh mkdir -p /data/zookeeper\nssh_root_zookeeper.sh chown -R hadoop:hadoop /data\n```\n- 创建myid文件\n ```bash\n ssh nn1 'echo \"1\" > /data/zookeeper/myid'\n ssh nn2 'echo \"2\" > /data/zookeeper/myid'\n ssh nn3 'echo \"3\" > /data/zookeeper/myid'\n ```\n- 配置Zookeeper环境变量\n ```bash\n # 在其他所有主机也执行\n sudo chown -R hadoop:hadoop /etc/profile.d/myEnv.sh\n ```\n\n ```bash\n echo 'export ZOOKEEPER_HOME=/usr/local/zookeeper' >> /etc/profile.d/myEnv.sh\n echo 'export PATH=$PATH:$ZOOKEEPER_HOME/bin' >> /etc/profile.d/myEnv.sh\n ```\n\n ```bash\n # 分发到nn2、nn3\n scp_all_zookeeper.sh /etc/profile.d/myEnv.sh /etc/profile.d/\n # source 环境变量\n ssh_all_zookeeper.sh source /etc/profile\n ```\n### Zookeeper的命令\n```bash\n#启动zk服务\nssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh start\n#查看每个机器ZK运行的状态\nssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh status\n#整体停止服务\nssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh stop \n#重启zk服务\nssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh restart\n```\n\n```shell\n#启动zookeeper客户端，并连接zookeeper集群\n/usr/local/zookeeper/bin/zkCli.sh -server nn1:2181\n# 可以简化为:\nzkCli.sh\n```\n\n","source":"_posts/bigdata/hadoop/zookeper.md","raw":"---\ntitle: Hadoop集群Zookeeper配置\ntop_img: /img/site01.jpg\ntop_img_height: 800px\nabbrlink: 61251\ndate: 2024-09-011 22:45:40\n---\n\n### Zookeeper脚本配置\n- 拷贝ips\n ```bash\n cp ips ips_zookeeper\n ```\n 修改为\n ```bash\n nn1\n nn2\n nn3\n ```\n- 拷贝三个脚本\n ```bash\n cp scp_all.sh scp_all_zookeeper.sh \n cp ssh_all.sh ssh_all_zookeeper.sh \n cp ssh_root.sh ssh_root_zookeeper.sh\n ```\n- 修改脚本\n ```shell\n vim scp_all_zookeeper.sh \n vim ssh_all_zookeeper.sh \n vim ssh_root_zookeeper.sh \n ```\n\n 将三个脚本中的ips改为ips_zookeeper\n### Zookeeper安装\n- 上传到`/tmp`目录下，解压\n ```bash\n sudo tar -zxvf /tmp/zookeeper-3.4.8.tar.gz -C /usr/local/\n scp -r /usr/local/zookeeper-3.4.8/ root@nn2:/usr/local/\n scp -r /usr/local/zookeeper-3.4.8/ root@nn3:/usr/local/\n ssh_root_zookeeper.sh chown -R hadoop:hadoop /usr/local/zookeeper-3.4.8\n ssh_root_zookeeper.s ln -s /usr/local/zookeeper-3.4.8/ /usr/local/zookeeper\n ```\n### Zookeeper配置\n - zoo.cfg配置\n ```bash\n cd /usr/local/zookeeper/conf/\n cp zoo_sample.cfg zoo.cfg\n ```\n 然后`vim zoo.cfg`,修改如下：\n ```properties\n # 修改dataDir\n dataDir=/data/zookeeper\n # 添加一下内容\n server.1=nn1:2888:3888 \n server.2=nn2:2888:3888 \n server.3=nn3:2888:3888\n ```\n 分发给nn2、nn3\n `scp_all_zookeeper.sh /usr/local/zookeeper/conf/zoo.cfg /usr/local/zookeeper/conf/`\n\n- `zkEnv.sh`配置\n `vim /usr/local/zookeeper/bin/zkEnv.sh`\n ![000001.png](..%2F..%2F..%2Fimg%2F000001.png)\n 分发到nn2、nn3\n```bash\nscp_all_zookeeper.sh /usr/local/zookeeper/bin/zkEnv.sh /usr/local/zookeeper/bin/\n```\n- 创建zookeeper数据目录\n```bash\nssh_root_zookeeper.sh mkdir -p /data/zookeeper\nssh_root_zookeeper.sh chown -R hadoop:hadoop /data\n```\n- 创建myid文件\n ```bash\n ssh nn1 'echo \"1\" > /data/zookeeper/myid'\n ssh nn2 'echo \"2\" > /data/zookeeper/myid'\n ssh nn3 'echo \"3\" > /data/zookeeper/myid'\n ```\n- 配置Zookeeper环境变量\n ```bash\n # 在其他所有主机也执行\n sudo chown -R hadoop:hadoop /etc/profile.d/myEnv.sh\n ```\n\n ```bash\n echo 'export ZOOKEEPER_HOME=/usr/local/zookeeper' >> /etc/profile.d/myEnv.sh\n echo 'export PATH=$PATH:$ZOOKEEPER_HOME/bin' >> /etc/profile.d/myEnv.sh\n ```\n\n ```bash\n # 分发到nn2、nn3\n scp_all_zookeeper.sh /etc/profile.d/myEnv.sh /etc/profile.d/\n # source 环境变量\n ssh_all_zookeeper.sh source /etc/profile\n ```\n### Zookeeper的命令\n```bash\n#启动zk服务\nssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh start\n#查看每个机器ZK运行的状态\nssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh status\n#整体停止服务\nssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh stop \n#重启zk服务\nssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh restart\n```\n\n```shell\n#启动zookeeper客户端，并连接zookeeper集群\n/usr/local/zookeeper/bin/zkCli.sh -server nn1:2181\n# 可以简化为:\nzkCli.sh\n```\n\n","slug":"bigdata/hadoop/zookeper","published":1,"updated":"2024-09-11T14:51:42.706Z","comments":1,"layout":"post","photos":[],"_id":"cm63ecyk7000hrcah7vt111sf","content":"

Zookeeper脚本配置

    \n
  • 拷贝ips
    1
    cp ips ips_zookeeper
    \n修改为
    1
    2
    3
    nn1
    nn2
    nn3
    • \n
  • 拷贝三个脚本
    1
    2
    3
    cp scp_all.sh scp_all_zookeeper.sh 
    cp ssh_all.sh ssh_all_zookeeper.sh 
    cp ssh_root.sh ssh_root_zookeeper.sh
    • \n
  • 修改脚本
    1
    2
    3
    vim scp_all_zookeeper.sh 
    vim ssh_all_zookeeper.sh 
    vim ssh_root_zookeeper.sh
    \n\n将三个脚本中的ips改为ips_zookeeper
    • \n
\n

Zookeeper安装

    \n
  • 上传到/tmp目录下，解压 
    1
    2
    3
    4
    5
    sudo tar -zxvf /tmp/zookeeper-3.4.8.tar.gz -C /usr/local/
    scp -r /usr/local/zookeeper-3.4.8/ root@nn2:/usr/local/
    scp -r /usr/local/zookeeper-3.4.8/ root@nn3:/usr/local/
    ssh_root_zookeeper.sh chown -R hadoop:hadoop /usr/local/zookeeper-3.4.8
    ssh_root_zookeeper.s ln -s /usr/local/zookeeper-3.4.8/ /usr/local/zookeeper
    • \n
\n

Zookeeper配置

    \n

  • zoo.cfg配置

    \n
    1
    2
    cd /usr/local/zookeeper/conf/
    cp zoo_sample.cfg zoo.cfg
    \n

    然后vim zoo.cfg,修改如下：

    \n
    1
    2
    3
    4
    5
    6
    # 修改dataDir
    dataDir=/data/zookeeper
    # 添加一下内容
    server.1=nn1:2888:3888 
    server.2=nn2:2888:3888 
    server.3=nn3:2888:3888
    \n

    分发给nn2、nn3
    scp_all_zookeeper.sh /usr/local/zookeeper/conf/zoo.cfg /usr/local/zookeeper/conf/

    \n
    • \n

  • zkEnv.sh配置
    vim /usr/local/zookeeper/bin/zkEnv.sh
    \"000001.png\"
    分发到nn2、nn3

    \n
    1
    scp_all_zookeeper.sh /usr/local/zookeeper/bin/zkEnv.sh /usr/local/zookeeper/bin/
    • \n

  • 创建zookeeper数据目录

    \n
    1
    2
    ssh_root_zookeeper.sh mkdir -p /data/zookeeper
    ssh_root_zookeeper.sh chown -R hadoop:hadoop /data
    • \n

  • 创建myid文件

    \n
    1
    2
    3
    ssh nn1 'echo "1" > /data/zookeeper/myid'
    ssh nn2 'echo "2" > /data/zookeeper/myid'
    ssh nn3 'echo "3" > /data/zookeeper/myid'
    • \n

  • 配置Zookeeper环境变量

    \n
    1
    2
    # 在其他所有主机也执行
    sudo chown -R hadoop:hadoop /etc/profile.d/myEnv.sh
    \n\n
    1
    2
    echo 'export ZOOKEEPER_HOME=/usr/local/zookeeper' >> /etc/profile.d/myEnv.sh
    echo 'export PATH=$PATH:$ZOOKEEPER_HOME/bin' >> /etc/profile.d/myEnv.sh
    \n\n
    1
    2
    3
    4
    # 分发到nn2、nn3
    scp_all_zookeeper.sh /etc/profile.d/myEnv.sh /etc/profile.d/
    # source 环境变量
    ssh_all_zookeeper.sh source /etc/profile
    • \n
\n

Zookeeper的命令

1
2
3
4
5
6
7
8
#启动zk服务
ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh start
#查看每个机器ZK运行的状态
ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh status
#整体停止服务
ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh stop 
#重启zk服务
ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh restart
\n\n
1
2
3
4
#启动zookeeper客户端，并连接zookeeper集群
/usr/local/zookeeper/bin/zkCli.sh -server nn1:2181
# 可以简化为:
zkCli.sh
\n\n","cover":false,"excerpt":"","more":"

Zookeeper脚本配置

    \n
  • 拷贝ips
    1
    cp ips ips_zookeeper
    \n修改为
    1
    2
    3
    nn1
    nn2
    nn3
    • \n
  • 拷贝三个脚本
    1
    2
    3
    cp scp_all.sh scp_all_zookeeper.sh 
    cp ssh_all.sh ssh_all_zookeeper.sh 
    cp ssh_root.sh ssh_root_zookeeper.sh
    • \n
  • 修改脚本
    1
    2
    3
    vim scp_all_zookeeper.sh 
    vim ssh_all_zookeeper.sh 
    vim ssh_root_zookeeper.sh
    \n\n将三个脚本中的ips改为ips_zookeeper
    • \n
\n

Zookeeper安装

    \n
  • 上传到/tmp目录下，解压 
    1
    2
    3
    4
    5
    sudo tar -zxvf /tmp/zookeeper-3.4.8.tar.gz -C /usr/local/
    scp -r /usr/local/zookeeper-3.4.8/ root@nn2:/usr/local/
    scp -r /usr/local/zookeeper-3.4.8/ root@nn3:/usr/local/
    ssh_root_zookeeper.sh chown -R hadoop:hadoop /usr/local/zookeeper-3.4.8
    ssh_root_zookeeper.s ln -s /usr/local/zookeeper-3.4.8/ /usr/local/zookeeper
    • \n
\n

Zookeeper配置

    \n

  • zoo.cfg配置

    \n
    1
    2
    cd /usr/local/zookeeper/conf/
    cp zoo_sample.cfg zoo.cfg
    \n

    然后vim zoo.cfg,修改如下：

    \n
    1
    2
    3
    4
    5
    6
    # 修改dataDir
    dataDir=/data/zookeeper
    # 添加一下内容
    server.1=nn1:2888:3888 
    server.2=nn2:2888:3888 
    server.3=nn3:2888:3888
    \n

    分发给nn2、nn3
    scp_all_zookeeper.sh /usr/local/zookeeper/conf/zoo.cfg /usr/local/zookeeper/conf/

    \n
    • \n

  • zkEnv.sh配置
    vim /usr/local/zookeeper/bin/zkEnv.sh
    \"000001.png\"
    分发到nn2、nn3

    \n
    1
    scp_all_zookeeper.sh /usr/local/zookeeper/bin/zkEnv.sh /usr/local/zookeeper/bin/
    • \n

  • 创建zookeeper数据目录

    \n
    1
    2
    ssh_root_zookeeper.sh mkdir -p /data/zookeeper
    ssh_root_zookeeper.sh chown -R hadoop:hadoop /data
    • \n

  • 创建myid文件

    \n
    1
    2
    3
    ssh nn1 'echo "1" > /data/zookeeper/myid'
    ssh nn2 'echo "2" > /data/zookeeper/myid'
    ssh nn3 'echo "3" > /data/zookeeper/myid'
    • \n

  • 配置Zookeeper环境变量

    \n
    1
    2
    # 在其他所有主机也执行
    sudo chown -R hadoop:hadoop /etc/profile.d/myEnv.sh
    \n\n
    1
    2
    echo 'export ZOOKEEPER_HOME=/usr/local/zookeeper' >> /etc/profile.d/myEnv.sh
    echo 'export PATH=$PATH:$ZOOKEEPER_HOME/bin' >> /etc/profile.d/myEnv.sh
    \n\n
    1
    2
    3
    4
    # 分发到nn2、nn3
    scp_all_zookeeper.sh /etc/profile.d/myEnv.sh /etc/profile.d/
    # source 环境变量
    ssh_all_zookeeper.sh source /etc/profile
    • \n
\n

Zookeeper的命令

1
2
3
4
5
6
7
8
#启动zk服务
ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh start
#查看每个机器ZK运行的状态
ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh status
#整体停止服务
ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh stop 
#重启zk服务
ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh restart
\n\n
1
2
3
4
#启动zookeeper客户端，并连接zookeeper集群
/usr/local/zookeeper/bin/zkCli.sh -server nn1:2181
# 可以简化为:
zkCli.sh
\n\n"},{"title":"Hadoop集群搭建基础环境","top_img":"/img/site01.jpg","top_img_height":"800px","abbrlink":61253,"date":"2024-09-11T14:45:40.000Z","_content":"\n### 防火墙关闭\n```bash\n# 在 6 台主机执行\nsystemctl stop firewalld\nsystemctl disable firewalld\n```\n### 配置yum源\n- 下载 repo 文件：\n [Centos-7.repo](http://mirrors.aliyun.com/repo/Centos-7.repo)\n 并上传到`/tmp`，进入到`/tmp`\n- 备份并且替换系统的repo文件\n ``` bash\n \tcp Centos-7.repo /etc/yum.repos.d/ \n\tcd /etc/yum.repos.d/ \n\tmv CentOS-Base.repo CentOS-Base.repo.bak \n\tmv Centos-7.repo CentOS-Base.repo\n\t ```\n- 将`nn1`上的`CentOS-Base.repo`拷贝到其他主机\n ```bash\n scp /etc/yum.repos.d/CentOS-Base.repo root@nn2:/etc/yum.repos.d\n scp /etc/yum.repos.d/CentOS-Base.repo root@nn3:/etc/yum.repos.d\n scp /etc/yum.repos.d/CentOS-Base.repo root@s1:/etc/yum.repos.d\n scp /etc/yum.repos.d/CentOS-Base.repo root@s2:/etc/yum.repos.d\n scp /etc/yum.repos.d/CentOS-Base.repo root@s3:/etc/yum.repos.d\n ```\n- 执行yum源更新命令\n ```bash\n\t yum clean all\n\t yum makecache \n\t yum update -y \n\t```\n- 安装常用软件\n ```bash\n yum install -y openssh-server vim gcc gcc-c++ glibc-headers bzip2-devel lzo-devel curl wget openssh-clients zlib-devel autoconf automake cmake libtool openssl-devel fuse-devel snappy-devel telnet unzip zip net-tools.x86_64 firewalld systemd ntp unrar bzip2\n ```\n### JDK安装\n>注意需要在六台机器依次执行\n- 上传到`/tmp`目录下，安装\n ```bash\n cd /tmp\n rpm -ivh jdk-8u144-linux-x64.rpm\n ```\n- 配置环境变量\n ```bash\n ln -s /usr/java/jdk1.8.0_144/ /usr/java/jdk1.8\n echo 'export JAVA_HOME=/usr/java/jdk1.8' >> /etc/profile.d/myEnv.sh \n echo 'export PATH=$PATH:$JAVA_HOME/bin' >> /etc/profile.d/myEnv.sh \n source /etc/profile \n java -version\n ```\n### 修改主机名和主机名映射\n\n```bash\nvim /etc/hostname\n```\n6台机器分别为nn1、nn2、nn3、s1、s2、s3\n\n```bash\nvim /etc/hosts\n```\n\n修改为\n```text\n192.168.1.30 nn1\n192.168.1.31 nn2\n192.168.1.32 nn3\n192.168.1.33 s1\n192.168.1.34 s2\n192.168.1.35 s3\n```\n### 创建hadoop用户\n```bash\n#创建hadoop用户 \nuseradd hadoop \n#给hadoop用户设置密码: 12345678 \npasswd hadoop\n```\n### 禁止非 wheel 组用户切换到root，配置免密切换root\n- 修改/etc/pam.d/su配置\n ```bash\n sed -i 's/#auth\\t\\trequired\\tpam_wheel.so/auth\\t\\trequired\\tpam_wheel.so/g' '/etc/pam.d/su' \n sed -i 's/#auth\\t\\tsufficient\\tpam_wheel.so/auth\\t\\tsufficient\\tpam_wheel.so/g' '/etc/pam.d/su'\n ```\n- 修改/etc/login.defs文件\n ```bash\n echo \"SU_WHEEL_ONLY yes\" >> /etc/login.defs\n ```\n- 添加用户到管理员，禁止普通用户su 到 root\n ```bash\n #把hadoop用户加到wheel组里\n gpasswd -a hadoop wheel\n #查看wheel组里是否有hadoop用户\n cat /etc/group | grep wheel\n ```\n### 给hadoop用户，配置SSH密钥\n#### 配置hadoop用户ssh免密码登录到hadoop\n- 仅在`nn1`执行这段脚本命令即可\n 但是 `su - hadoop ` ,` mkdir ~/.ssh` 需要在其他主机执行一下\n ```bash\n #切换到hadoop用户 \n su - hadoop\n #生成ssh公私钥 \n ssh-keygen -t rsa -f ~/.ssh/id_rsa -P ''\n ssh-copy-id nn1\n ssh-copy-id nn2\n ssh-copy-id nn3\n ssh-copy-id s1\n ssh-copy-id s2\n ssh-copy-id s3\n scp /home/hadoop/.ssh/id_rsa hadoop@nn2:/home/hadoop/.ssh\n scp /home/hadoop/.ssh/id_rsa hadoop@nn3:/home/hadoop/.ssh\n scp /home/hadoop/.ssh/id_rsa hadoop@s1:/home/hadoop/.ssh\n scp /home/hadoop/.ssh/id_rsa hadoop@s2:/home/hadoop/.ssh\n scp /home/hadoop/.ssh/id_rsa hadoop@s3:/home/hadoop/.ssh\n ```\n#### 配置hadoop用户ssh免密码登录到root\n- 同上\n ```bash\n ssh-copy-id root@nn1\n ssh-copy-id root@ nn2\n ssh-copy-id root@nn3\n ssh-copy-id root@s1\n ssh-copy-id root@s2\n ssh-copy-id root@s3\n scp /home/hadoop/.ssh/id_rsa root@nn2:/root/.ssh\n scp /home/hadoop/.ssh/id_rsa root@nn3:/root/.ssh\n scp /home/hadoop/.ssh/id_rsa root@s1:/root/.ssh\n scp /home/hadoop/.ssh/id_rsa root@s2:/root/.ssh\n scp /home/hadoop/.ssh/id_rsa root@s3:/root/.ssh\n ```\n### 脚本配置\n- **ips**\n ```bash\n vim /home/hadoop/bin/ips\n ```\n \n ```bash\n nn1 \n nn2\n nn3\n s1 \n s2 \n s3\n ```\n- **ssh_all.sh**\n ```bash\n vim /home/hadoop/bin/ssh_all.sh\n ```\n\n ```bash\n #! /bin/bash\n # 进入到当前脚本所在目录\n cd `dirname $0`\n # 获取当前脚本所在目录\n dir_path=`pwd`\n #echo $dir_path\n # 读ips文件得到数组(里面是一堆主机名)\n ip_arr=(`cat $dir_path/ips`)\n # 遍历数组里的主机名\n for ip in ${ip_arr[*]}\n do\n # 拼接ssh命令: ssh hadoop@nn1.hadoop ls\n cmd_=\"ssh hadoop@${ip} \\\"$*\\\" \"\n echo $cmd_\n # 通过eval命令 执行 拼接的ssh 命令\n if eval ${cmd_} ; then\n echo \"OK\"\n else\n echo \"FAIL\"\n fi\n done\n ```\n- **ssh_root.sh**\n ```bash\n #! /bin/bash\n # 进入到当前脚本所在目录\n cd `dirname $0`\n # 获取当前脚本所在目录\n dir_path=`pwd`\n #echo $dir_path\n # 读ips文件得到数组(里面是一堆主机名)\n ip_arr=(`cat $dir_path/ips`)\n # 遍历数组里的主机名\n for ip in ${ip_arr[*]}\n do\n # 拼接ssh命令: ssh hadoop@nn1.hadoop ls\n cmd_=\"ssh hadoop@${ip} ~/bin/exe.sh \\\"$*\\\"\"\n echo $cmd_\n # 通过eval命令 执行 拼接的ssh 命令\n if eval ${cmd_} ; then\n echo \"OK\"\n else\n echo \"FAIL\"\n fi\n done\n ```\n- **scp_all.sh**\n ```bash\n #! /bin/bash\n # 进入到当前脚本所在目录\n cd `dirname $0`\n # 获取当前脚本所在目录\n dir_path=`pwd`\n #echo $dir_path\n # 读ips文件得到数组(里面是一堆主机名)\n ip_arr=(`cat $dir_path/ips`)\n # 源\n source_=$1\n # 目标\n target=$2\n # 遍历数组里的主机名\n for ip in ${ip_arr[*]}\n do\n # 拼接scp命令: scp 源 hadoop@nn1.hadoop:目标\n cmd_=\"scp -r ${source_} hadoop@${ip}:${target}\"\n echo $cmd_\n # 通过eval命令 执行 拼接的scp 命令\n if eval ${cmd_} ; then\n echo \"OK\"\n else\n echo \"FAIL\"\n fi\n done\n ```\n- **exe.sh**\n ```bash\n #切换到root用户执行cmd命令\n cmd=$*\n su - << EOF\n $cmd\n EOF\n ```\n- 赋予执行权限\n ```bash\n chmod +x ssh_all.sh \n chmod +x scp_all.sh\n chmod +x ssh_root.sh\n chmod +x exe.sh\n ```\n- 分发到其他主机\n ```bash\n ./ssh_all.sh mkdir /home/hadoop/bin\n ./scp_all.sh /home/hadoop/bin/ips /home/hadoop/bin/\n ./scp_all.sh /home/hadoop/bin/exe.sh /home/hadoop/bin/\n ./scp_all.sh /home/hadoop/bin/ssh_all.sh /home/hadoop/bin/\n ./scp_all.sh /home/hadoop/bin/scp_all.sh /home/hadoop/bin/\n ./scp_all.sh /home/hadoop/bin/ssh_root.sh /home/hadoop/bin/\n ```\n\n- 将 `/home/hadoop/bin`添加到hadoop用户的环境变量，需要切换到`hadoop`用户\n\n ```bash\n echo 'export PATH=$PATH:/home/hadoop/bin' >> ~/.bashrc && source ~/.bashrc\n scp_all.sh /home/hadoop/.bashrc /home/hadoop/\n ssh_all.sh source ~/.bashrc \n ```","source":"_posts/bigdata/hadoop/env.md","raw":"---\ntitle: Hadoop集群搭建基础环境\ntop_img: /img/site01.jpg\ntop_img_height: 800px\nabbrlink: 61253\ndate: 2024-09-011 22:45:40\n---\n\n### 防火墙关闭\n```bash\n# 在 6 台主机执行\nsystemctl stop firewalld\nsystemctl disable firewalld\n```\n### 配置yum源\n- 下载 repo 文件：\n [Centos-7.repo](http://mirrors.aliyun.com/repo/Centos-7.repo)\n 并上传到`/tmp`，进入到`/tmp`\n- 备份并且替换系统的repo文件\n ``` bash\n \tcp Centos-7.repo /etc/yum.repos.d/ \n\tcd /etc/yum.repos.d/ \n\tmv CentOS-Base.repo CentOS-Base.repo.bak \n\tmv Centos-7.repo CentOS-Base.repo\n\t ```\n- 将`nn1`上的`CentOS-Base.repo`拷贝到其他主机\n ```bash\n scp /etc/yum.repos.d/CentOS-Base.repo root@nn2:/etc/yum.repos.d\n scp /etc/yum.repos.d/CentOS-Base.repo root@nn3:/etc/yum.repos.d\n scp /etc/yum.repos.d/CentOS-Base.repo root@s1:/etc/yum.repos.d\n scp /etc/yum.repos.d/CentOS-Base.repo root@s2:/etc/yum.repos.d\n scp /etc/yum.repos.d/CentOS-Base.repo root@s3:/etc/yum.repos.d\n ```\n- 执行yum源更新命令\n ```bash\n\t yum clean all\n\t yum makecache \n\t yum update -y \n\t```\n- 安装常用软件\n ```bash\n yum install -y openssh-server vim gcc gcc-c++ glibc-headers bzip2-devel lzo-devel curl wget openssh-clients zlib-devel autoconf automake cmake libtool openssl-devel fuse-devel snappy-devel telnet unzip zip net-tools.x86_64 firewalld systemd ntp unrar bzip2\n ```\n### JDK安装\n>注意需要在六台机器依次执行\n- 上传到`/tmp`目录下，安装\n ```bash\n cd /tmp\n rpm -ivh jdk-8u144-linux-x64.rpm\n ```\n- 配置环境变量\n ```bash\n ln -s /usr/java/jdk1.8.0_144/ /usr/java/jdk1.8\n echo 'export JAVA_HOME=/usr/java/jdk1.8' >> /etc/profile.d/myEnv.sh \n echo 'export PATH=$PATH:$JAVA_HOME/bin' >> /etc/profile.d/myEnv.sh \n source /etc/profile \n java -version\n ```\n### 修改主机名和主机名映射\n\n```bash\nvim /etc/hostname\n```\n6台机器分别为nn1、nn2、nn3、s1、s2、s3\n\n```bash\nvim /etc/hosts\n```\n\n修改为\n```text\n192.168.1.30 nn1\n192.168.1.31 nn2\n192.168.1.32 nn3\n192.168.1.33 s1\n192.168.1.34 s2\n192.168.1.35 s3\n```\n### 创建hadoop用户\n```bash\n#创建hadoop用户 \nuseradd hadoop \n#给hadoop用户设置密码: 12345678 \npasswd hadoop\n```\n### 禁止非 wheel 组用户切换到root，配置免密切换root\n- 修改/etc/pam.d/su配置\n ```bash\n sed -i 's/#auth\\t\\trequired\\tpam_wheel.so/auth\\t\\trequired\\tpam_wheel.so/g' '/etc/pam.d/su' \n sed -i 's/#auth\\t\\tsufficient\\tpam_wheel.so/auth\\t\\tsufficient\\tpam_wheel.so/g' '/etc/pam.d/su'\n ```\n- 修改/etc/login.defs文件\n ```bash\n echo \"SU_WHEEL_ONLY yes\" >> /etc/login.defs\n ```\n- 添加用户到管理员，禁止普通用户su 到 root\n ```bash\n #把hadoop用户加到wheel组里\n gpasswd -a hadoop wheel\n #查看wheel组里是否有hadoop用户\n cat /etc/group | grep wheel\n ```\n### 给hadoop用户，配置SSH密钥\n#### 配置hadoop用户ssh免密码登录到hadoop\n- 仅在`nn1`执行这段脚本命令即可\n 但是 `su - hadoop ` ,` mkdir ~/.ssh` 需要在其他主机执行一下\n ```bash\n #切换到hadoop用户 \n su - hadoop\n #生成ssh公私钥 \n ssh-keygen -t rsa -f ~/.ssh/id_rsa -P ''\n ssh-copy-id nn1\n ssh-copy-id nn2\n ssh-copy-id nn3\n ssh-copy-id s1\n ssh-copy-id s2\n ssh-copy-id s3\n scp /home/hadoop/.ssh/id_rsa hadoop@nn2:/home/hadoop/.ssh\n scp /home/hadoop/.ssh/id_rsa hadoop@nn3:/home/hadoop/.ssh\n scp /home/hadoop/.ssh/id_rsa hadoop@s1:/home/hadoop/.ssh\n scp /home/hadoop/.ssh/id_rsa hadoop@s2:/home/hadoop/.ssh\n scp /home/hadoop/.ssh/id_rsa hadoop@s3:/home/hadoop/.ssh\n ```\n#### 配置hadoop用户ssh免密码登录到root\n- 同上\n ```bash\n ssh-copy-id root@nn1\n ssh-copy-id root@ nn2\n ssh-copy-id root@nn3\n ssh-copy-id root@s1\n ssh-copy-id root@s2\n ssh-copy-id root@s3\n scp /home/hadoop/.ssh/id_rsa root@nn2:/root/.ssh\n scp /home/hadoop/.ssh/id_rsa root@nn3:/root/.ssh\n scp /home/hadoop/.ssh/id_rsa root@s1:/root/.ssh\n scp /home/hadoop/.ssh/id_rsa root@s2:/root/.ssh\n scp /home/hadoop/.ssh/id_rsa root@s3:/root/.ssh\n ```\n### 脚本配置\n- **ips**\n ```bash\n vim /home/hadoop/bin/ips\n ```\n \n ```bash\n nn1 \n nn2\n nn3\n s1 \n s2 \n s3\n ```\n- **ssh_all.sh**\n ```bash\n vim /home/hadoop/bin/ssh_all.sh\n ```\n\n ```bash\n #! /bin/bash\n # 进入到当前脚本所在目录\n cd `dirname $0`\n # 获取当前脚本所在目录\n dir_path=`pwd`\n #echo $dir_path\n # 读ips文件得到数组(里面是一堆主机名)\n ip_arr=(`cat $dir_path/ips`)\n # 遍历数组里的主机名\n for ip in ${ip_arr[*]}\n do\n # 拼接ssh命令: ssh hadoop@nn1.hadoop ls\n cmd_=\"ssh hadoop@${ip} \\\"$*\\\" \"\n echo $cmd_\n # 通过eval命令 执行 拼接的ssh 命令\n if eval ${cmd_} ; then\n echo \"OK\"\n else\n echo \"FAIL\"\n fi\n done\n ```\n- **ssh_root.sh**\n ```bash\n #! /bin/bash\n # 进入到当前脚本所在目录\n cd `dirname $0`\n # 获取当前脚本所在目录\n dir_path=`pwd`\n #echo $dir_path\n # 读ips文件得到数组(里面是一堆主机名)\n ip_arr=(`cat $dir_path/ips`)\n # 遍历数组里的主机名\n for ip in ${ip_arr[*]}\n do\n # 拼接ssh命令: ssh hadoop@nn1.hadoop ls\n cmd_=\"ssh hadoop@${ip} ~/bin/exe.sh \\\"$*\\\"\"\n echo $cmd_\n # 通过eval命令 执行 拼接的ssh 命令\n if eval ${cmd_} ; then\n echo \"OK\"\n else\n echo \"FAIL\"\n fi\n done\n ```\n- **scp_all.sh**\n ```bash\n #! /bin/bash\n # 进入到当前脚本所在目录\n cd `dirname $0`\n # 获取当前脚本所在目录\n dir_path=`pwd`\n #echo $dir_path\n # 读ips文件得到数组(里面是一堆主机名)\n ip_arr=(`cat $dir_path/ips`)\n # 源\n source_=$1\n # 目标\n target=$2\n # 遍历数组里的主机名\n for ip in ${ip_arr[*]}\n do\n # 拼接scp命令: scp 源 hadoop@nn1.hadoop:目标\n cmd_=\"scp -r ${source_} hadoop@${ip}:${target}\"\n echo $cmd_\n # 通过eval命令 执行 拼接的scp 命令\n if eval ${cmd_} ; then\n echo \"OK\"\n else\n echo \"FAIL\"\n fi\n done\n ```\n- **exe.sh**\n ```bash\n #切换到root用户执行cmd命令\n cmd=$*\n su - << EOF\n $cmd\n EOF\n ```\n- 赋予执行权限\n ```bash\n chmod +x ssh_all.sh \n chmod +x scp_all.sh\n chmod +x ssh_root.sh\n chmod +x exe.sh\n ```\n- 分发到其他主机\n ```bash\n ./ssh_all.sh mkdir /home/hadoop/bin\n ./scp_all.sh /home/hadoop/bin/ips /home/hadoop/bin/\n ./scp_all.sh /home/hadoop/bin/exe.sh /home/hadoop/bin/\n ./scp_all.sh /home/hadoop/bin/ssh_all.sh /home/hadoop/bin/\n ./scp_all.sh /home/hadoop/bin/scp_all.sh /home/hadoop/bin/\n ./scp_all.sh /home/hadoop/bin/ssh_root.sh /home/hadoop/bin/\n ```\n\n- 将 `/home/hadoop/bin`添加到hadoop用户的环境变量，需要切换到`hadoop`用户\n\n ```bash\n echo 'export PATH=$PATH:/home/hadoop/bin' >> ~/.bashrc && source ~/.bashrc\n scp_all.sh /home/hadoop/.bashrc /home/hadoop/\n ssh_all.sh source ~/.bashrc \n ```","slug":"bigdata/hadoop/env","published":1,"updated":"2024-09-11T14:45:28.095Z","comments":1,"layout":"post","photos":[],"_id":"cm63ecykc000prcah11ke5r05","content":"

防火墙关闭

1
2
3
# 在 6 台主机执行
systemctl stop firewalld
systemctl disable firewalld
\n

配置yum源

    \n
  • 下载 repo 文件：
    Centos-7.repo
    并上传到/tmp，进入到/tmp
    • \n
  • 备份并且替换系统的repo文件
    1
    2
    3
    4
     \tcp Centos-7.repo /etc/yum.repos.d/ 
    cd /etc/yum.repos.d/ 
    mv CentOS-Base.repo CentOS-Base.repo.bak 
    mv Centos-7.repo CentOS-Base.repo
    • \n
  • 将nn1上的CentOS-Base.repo拷贝到其他主机
    1
    2
    3
    4
    5
    scp /etc/yum.repos.d/CentOS-Base.repo root@nn2:/etc/yum.repos.d
    scp /etc/yum.repos.d/CentOS-Base.repo root@nn3:/etc/yum.repos.d
    scp /etc/yum.repos.d/CentOS-Base.repo root@s1:/etc/yum.repos.d
    scp /etc/yum.repos.d/CentOS-Base.repo root@s2:/etc/yum.repos.d
    scp /etc/yum.repos.d/CentOS-Base.repo root@s3:/etc/yum.repos.d
    • \n
  • 执行yum源更新命令
    1
    2
    3
    yum clean all
    yum makecache 
    yum update -y
    • \n
  • 安装常用软件 
    1
    yum install -y openssh-server vim gcc gcc-c++ glibc-headers bzip2-devel lzo-devel curl wget openssh-clients zlib-devel autoconf automake cmake libtool openssl-devel fuse-devel snappy-devel telnet unzip zip net-tools.x86_64 firewalld systemd ntp unrar bzip2
    • \n
\n

JDK安装

\n

注意需要在六台机器依次执行

\n
\n
    \n
  • 上传到/tmp目录下，安装 
    1
    2
    cd /tmp
    rpm -ivh jdk-8u144-linux-x64.rpm
    • \n
  • 配置环境变量 
    1
    2
    3
    4
    5
    ln -s /usr/java/jdk1.8.0_144/ /usr/java/jdk1.8
    echo 'export JAVA_HOME=/usr/java/jdk1.8' >> /etc/profile.d/myEnv.sh 
    echo 'export PATH=$PATH:$JAVA_HOME/bin' >> /etc/profile.d/myEnv.sh 
    source /etc/profile 
    java -version
    • \n
\n

修改主机名和主机名映射

1
vim /etc/hostname
\n

6台机器分别为nn1、nn2、nn3、s1、s2、s3

\n
1
vim /etc/hosts
\n\n

修改为

\n
1
2
3
4
5
6
192.168.1.30 nn1
192.168.1.31 nn2
192.168.1.32 nn3
192.168.1.33 s1
192.168.1.34 s2
192.168.1.35 s3
\n

创建hadoop用户

1
2
3
4
#创建hadoop用户 
useradd hadoop 
#给hadoop用户设置密码: 12345678 
passwd hadoop
\n

禁止非 wheel 组用户切换到root，配置免密切换root

    \n
  • 修改/etc/pam.d/su配置 
    1
    2
    sed -i 's/#auth\\t\\trequired\\tpam_wheel.so/auth\\t\\trequired\\tpam_wheel.so/g' '/etc/pam.d/su' 
    sed -i 's/#auth\\t\\tsufficient\\tpam_wheel.so/auth\\t\\tsufficient\\tpam_wheel.so/g' '/etc/pam.d/su'
    • \n
  • 修改/etc/login.defs文件 
    1
    echo "SU_WHEEL_ONLY yes" >> /etc/login.defs
    • \n
  • 添加用户到管理员，禁止普通用户su 到 root 
    1
    2
    3
    4
    #把hadoop用户加到wheel组里
    gpasswd -a hadoop wheel
    #查看wheel组里是否有hadoop用户
    cat /etc/group | grep wheel
    • \n
\n

给hadoop用户，配置SSH密钥

配置hadoop用户ssh免密码登录到hadoop

    \n
  • 仅在nn1执行这段脚本命令即可
    但是 su - hadoop , mkdir ~/.ssh 需要在其他主机执行一下 
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    #切换到hadoop用户 
    su - hadoop
    #生成ssh公私钥 
    ssh-keygen -t rsa -f ~/.ssh/id_rsa -P ''
    ssh-copy-id nn1
    ssh-copy-id nn2
    ssh-copy-id nn3
    ssh-copy-id s1
    ssh-copy-id s2
    ssh-copy-id s3
    scp /home/hadoop/.ssh/id_rsa hadoop@nn2:/home/hadoop/.ssh
    scp /home/hadoop/.ssh/id_rsa hadoop@nn3:/home/hadoop/.ssh
    scp /home/hadoop/.ssh/id_rsa hadoop@s1:/home/hadoop/.ssh
    scp /home/hadoop/.ssh/id_rsa hadoop@s2:/home/hadoop/.ssh
    scp /home/hadoop/.ssh/id_rsa hadoop@s3:/home/hadoop/.ssh
    • \n
\n

配置hadoop用户ssh免密码登录到root

    \n
  • 同上 
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    ssh-copy-id root@nn1
    ssh-copy-id root@ nn2
    ssh-copy-id root@nn3
    ssh-copy-id root@s1
    ssh-copy-id root@s2
    ssh-copy-id root@s3
    scp /home/hadoop/.ssh/id_rsa root@nn2:/root/.ssh
    scp /home/hadoop/.ssh/id_rsa root@nn3:/root/.ssh
    scp /home/hadoop/.ssh/id_rsa root@s1:/root/.ssh
    scp /home/hadoop/.ssh/id_rsa root@s2:/root/.ssh
    scp /home/hadoop/.ssh/id_rsa root@s3:/root/.ssh
    • \n
\n

脚本配置

    \n

  • ips

    \n 
    1
    vim /home/hadoop/bin/ips
    \n 
    1
    2
    3
    4
    5
    6
    nn1 
    nn2
    nn3
    s1 
    s2 
    s3
    • \n

  • ssh_all.sh

    \n 
    1
    vim /home/hadoop/bin/ssh_all.sh
    \n\n 
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    #! /bin/bash
    # 进入到当前脚本所在目录
    cd `dirname $0`
    # 获取当前脚本所在目录
    dir_path=`pwd`
    #echo $dir_path
    # 读ips文件得到数组(里面是一堆主机名)
    ip_arr=(`cat $dir_path/ips`)
    # 遍历数组里的主机名
    for ip in ${ip_arr[*]}
    do
            # 拼接ssh命令: ssh hadoop@nn1.hadoop ls
            cmd_="ssh hadoop@${ip} \\"$*\\"  "
            echo $cmd_
            # 通过eval命令 执行 拼接的ssh 命令
            if eval ${cmd_} ; then
                    echo "OK"
            else
                    echo "FAIL"
            fi
    done
    • \n

  • ssh_root.sh

    \n 
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    #! /bin/bash
    # 进入到当前脚本所在目录
    cd `dirname $0`
    # 获取当前脚本所在目录
    dir_path=`pwd`
    #echo $dir_path
    # 读ips文件得到数组(里面是一堆主机名)
    ip_arr=(`cat $dir_path/ips`)
    # 遍历数组里的主机名
    for ip in ${ip_arr[*]}
    do
            # 拼接ssh命令: ssh hadoop@nn1.hadoop ls
            cmd_="ssh  hadoop@${ip} ~/bin/exe.sh \\"$*\\""
            echo $cmd_
            # 通过eval命令 执行 拼接的ssh 命令
            if eval ${cmd_} ; then
                    echo "OK"
            else
                    echo "FAIL"
            fi
    done
    • \n

  • scp_all.sh

    \n 
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    22
    23
    24
    25
    #! /bin/bash
    # 进入到当前脚本所在目录
    cd `dirname $0`
    # 获取当前脚本所在目录
    dir_path=`pwd`
    #echo $dir_path
    # 读ips文件得到数组(里面是一堆主机名)
    ip_arr=(`cat $dir_path/ips`)
    # 源
    source_=$1
    # 目标
    target=$2
    # 遍历数组里的主机名
    for ip in ${ip_arr[*]}
    do
            # 拼接scp命令: scp 源 hadoop@nn1.hadoop:目标
            cmd_="scp -r ${source_} hadoop@${ip}:${target}"
            echo $cmd_
            # 通过eval命令 执行 拼接的scp 命令
            if eval ${cmd_} ; then
                    echo "OK"
            else
                    echo "FAIL"
            fi
    done
    • \n

  • exe.sh

    \n 
    1
    2
    3
    4
    5
    #切换到root用户执行cmd命令
    cmd=$*
    su - << EOF
    $cmd
    EOF
    • \n

  • 赋予执行权限

    \n 
    1
    2
    3
    4
    chmod +x ssh_all.sh 
    chmod +x scp_all.sh
    chmod +x ssh_root.sh
    chmod +x exe.sh
    • \n

  • 分发到其他主机

    \n 
    1
    2
    3
    4
    5
    6
    ./ssh_all.sh mkdir /home/hadoop/bin
    ./scp_all.sh /home/hadoop/bin/ips /home/hadoop/bin/
    ./scp_all.sh /home/hadoop/bin/exe.sh /home/hadoop/bin/
    ./scp_all.sh /home/hadoop/bin/ssh_all.sh /home/hadoop/bin/
    ./scp_all.sh /home/hadoop/bin/scp_all.sh /home/hadoop/bin/
    ./scp_all.sh /home/hadoop/bin/ssh_root.sh /home/hadoop/bin/
    \n
    • \n

  • 将 /home/hadoop/bin添加到hadoop用户的环境变量，需要切换到hadoop用户

    \n 
    1
    2
    3
    echo 'export PATH=$PATH:/home/hadoop/bin' >> ~/.bashrc && source ~/.bashrc
    scp_all.sh /home/hadoop/.bashrc /home/hadoop/
    ssh_all.sh source ~/.bashrc
    • \n
\n","cover":false,"excerpt":"","more":"

防火墙关闭

1
2
3
# 在 6 台主机执行
systemctl stop firewalld
systemctl disable firewalld
\n

配置yum源

    \n
  • 下载 repo 文件：
    Centos-7.repo
    并上传到/tmp，进入到/tmp
    • \n
  • 备份并且替换系统的repo文件
    1
    2
    3
    4
     \tcp Centos-7.repo /etc/yum.repos.d/ 
    cd /etc/yum.repos.d/ 
    mv CentOS-Base.repo CentOS-Base.repo.bak 
    mv Centos-7.repo CentOS-Base.repo
    • \n
  • 将nn1上的CentOS-Base.repo拷贝到其他主机
    1
    2
    3
    4
    5
    scp /etc/yum.repos.d/CentOS-Base.repo root@nn2:/etc/yum.repos.d
    scp /etc/yum.repos.d/CentOS-Base.repo root@nn3:/etc/yum.repos.d
    scp /etc/yum.repos.d/CentOS-Base.repo root@s1:/etc/yum.repos.d
    scp /etc/yum.repos.d/CentOS-Base.repo root@s2:/etc/yum.repos.d
    scp /etc/yum.repos.d/CentOS-Base.repo root@s3:/etc/yum.repos.d
    • \n
  • 执行yum源更新命令
    1
    2
    3
    yum clean all
    yum makecache 
    yum update -y
    • \n
  • 安装常用软件 
    1
    yum install -y openssh-server vim gcc gcc-c++ glibc-headers bzip2-devel lzo-devel curl wget openssh-clients zlib-devel autoconf automake cmake libtool openssl-devel fuse-devel snappy-devel telnet unzip zip net-tools.x86_64 firewalld systemd ntp unrar bzip2
    • \n
\n

JDK安装

\n

注意需要在六台机器依次执行

\n
\n
    \n
  • 上传到/tmp目录下，安装 
    1
    2
    cd /tmp
    rpm -ivh jdk-8u144-linux-x64.rpm
    • \n
  • 配置环境变量 
    1
    2
    3
    4
    5
    ln -s /usr/java/jdk1.8.0_144/ /usr/java/jdk1.8
    echo 'export JAVA_HOME=/usr/java/jdk1.8' >> /etc/profile.d/myEnv.sh 
    echo 'export PATH=$PATH:$JAVA_HOME/bin' >> /etc/profile.d/myEnv.sh 
    source /etc/profile 
    java -version
    • \n
\n

修改主机名和主机名映射

1
vim /etc/hostname
\n

6台机器分别为nn1、nn2、nn3、s1、s2、s3

\n
1
vim /etc/hosts
\n\n

修改为

\n
1
2
3
4
5
6
192.168.1.30 nn1
192.168.1.31 nn2
192.168.1.32 nn3
192.168.1.33 s1
192.168.1.34 s2
192.168.1.35 s3
\n

创建hadoop用户

1
2
3
4
#创建hadoop用户 
useradd hadoop 
#给hadoop用户设置密码: 12345678 
passwd hadoop
\n

禁止非 wheel 组用户切换到root，配置免密切换root

    \n
  • 修改/etc/pam.d/su配置 
    1
    2
    sed -i 's/#auth\\t\\trequired\\tpam_wheel.so/auth\\t\\trequired\\tpam_wheel.so/g' '/etc/pam.d/su' 
    sed -i 's/#auth\\t\\tsufficient\\tpam_wheel.so/auth\\t\\tsufficient\\tpam_wheel.so/g' '/etc/pam.d/su'
    • \n
  • 修改/etc/login.defs文件 
    1
    echo "SU_WHEEL_ONLY yes" >> /etc/login.defs
    • \n
  • 添加用户到管理员，禁止普通用户su 到 root 
    1
    2
    3
    4
    #把hadoop用户加到wheel组里
    gpasswd -a hadoop wheel
    #查看wheel组里是否有hadoop用户
    cat /etc/group | grep wheel
    • \n
\n

给hadoop用户，配置SSH密钥

配置hadoop用户ssh免密码登录到hadoop

    \n
  • 仅在nn1执行这段脚本命令即可
    但是 su - hadoop , mkdir ~/.ssh 需要在其他主机执行一下 
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    #切换到hadoop用户 
    su - hadoop
    #生成ssh公私钥 
    ssh-keygen -t rsa -f ~/.ssh/id_rsa -P ''
    ssh-copy-id nn1
    ssh-copy-id nn2
    ssh-copy-id nn3
    ssh-copy-id s1
    ssh-copy-id s2
    ssh-copy-id s3
    scp /home/hadoop/.ssh/id_rsa hadoop@nn2:/home/hadoop/.ssh
    scp /home/hadoop/.ssh/id_rsa hadoop@nn3:/home/hadoop/.ssh
    scp /home/hadoop/.ssh/id_rsa hadoop@s1:/home/hadoop/.ssh
    scp /home/hadoop/.ssh/id_rsa hadoop@s2:/home/hadoop/.ssh
    scp /home/hadoop/.ssh/id_rsa hadoop@s3:/home/hadoop/.ssh
    • \n
\n

配置hadoop用户ssh免密码登录到root

    \n
  • 同上 
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    ssh-copy-id root@nn1
    ssh-copy-id root@ nn2
    ssh-copy-id root@nn3
    ssh-copy-id root@s1
    ssh-copy-id root@s2
    ssh-copy-id root@s3
    scp /home/hadoop/.ssh/id_rsa root@nn2:/root/.ssh
    scp /home/hadoop/.ssh/id_rsa root@nn3:/root/.ssh
    scp /home/hadoop/.ssh/id_rsa root@s1:/root/.ssh
    scp /home/hadoop/.ssh/id_rsa root@s2:/root/.ssh
    scp /home/hadoop/.ssh/id_rsa root@s3:/root/.ssh
    • \n
\n

脚本配置

    \n

  • ips

    \n 
    1
    vim /home/hadoop/bin/ips
    \n 
    1
    2
    3
    4
    5
    6
    nn1 
    nn2
    nn3
    s1 
    s2 
    s3
    • \n

  • ssh_all.sh

    \n 
    1
    vim /home/hadoop/bin/ssh_all.sh
    \n\n 
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    #! /bin/bash
    # 进入到当前脚本所在目录
    cd `dirname $0`
    # 获取当前脚本所在目录
    dir_path=`pwd`
    #echo $dir_path
    # 读ips文件得到数组(里面是一堆主机名)
    ip_arr=(`cat $dir_path/ips`)
    # 遍历数组里的主机名
    for ip in ${ip_arr[*]}
    do
            # 拼接ssh命令: ssh hadoop@nn1.hadoop ls
            cmd_="ssh hadoop@${ip} \\"$*\\"  "
            echo $cmd_
            # 通过eval命令 执行 拼接的ssh 命令
            if eval ${cmd_} ; then
                    echo "OK"
            else
                    echo "FAIL"
            fi
    done
    • \n

  • ssh_root.sh

    \n 
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    #! /bin/bash
    # 进入到当前脚本所在目录
    cd `dirname $0`
    # 获取当前脚本所在目录
    dir_path=`pwd`
    #echo $dir_path
    # 读ips文件得到数组(里面是一堆主机名)
    ip_arr=(`cat $dir_path/ips`)
    # 遍历数组里的主机名
    for ip in ${ip_arr[*]}
    do
            # 拼接ssh命令: ssh hadoop@nn1.hadoop ls
            cmd_="ssh  hadoop@${ip} ~/bin/exe.sh \\"$*\\""
            echo $cmd_
            # 通过eval命令 执行 拼接的ssh 命令
            if eval ${cmd_} ; then
                    echo "OK"
            else
                    echo "FAIL"
            fi
    done
    • \n

  • scp_all.sh

    \n 
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    22
    23
    24
    25
    #! /bin/bash
    # 进入到当前脚本所在目录
    cd `dirname $0`
    # 获取当前脚本所在目录
    dir_path=`pwd`
    #echo $dir_path
    # 读ips文件得到数组(里面是一堆主机名)
    ip_arr=(`cat $dir_path/ips`)
    # 源
    source_=$1
    # 目标
    target=$2
    # 遍历数组里的主机名
    for ip in ${ip_arr[*]}
    do
            # 拼接scp命令: scp 源 hadoop@nn1.hadoop:目标
            cmd_="scp -r ${source_} hadoop@${ip}:${target}"
            echo $cmd_
            # 通过eval命令 执行 拼接的scp 命令
            if eval ${cmd_} ; then
                    echo "OK"
            else
                    echo "FAIL"
            fi
    done
    • \n

  • exe.sh

    \n 
    1
    2
    3
    4
    5
    #切换到root用户执行cmd命令
    cmd=$*
    su - << EOF
    $cmd
    EOF
    • \n

  • 赋予执行权限

    \n 
    1
    2
    3
    4
    chmod +x ssh_all.sh 
    chmod +x scp_all.sh
    chmod +x ssh_root.sh
    chmod +x exe.sh
    • \n

  • 分发到其他主机

    \n 
    1
    2
    3
    4
    5
    6
    ./ssh_all.sh mkdir /home/hadoop/bin
    ./scp_all.sh /home/hadoop/bin/ips /home/hadoop/bin/
    ./scp_all.sh /home/hadoop/bin/exe.sh /home/hadoop/bin/
    ./scp_all.sh /home/hadoop/bin/ssh_all.sh /home/hadoop/bin/
    ./scp_all.sh /home/hadoop/bin/scp_all.sh /home/hadoop/bin/
    ./scp_all.sh /home/hadoop/bin/ssh_root.sh /home/hadoop/bin/
    \n
    • \n

  • 将 /home/hadoop/bin添加到hadoop用户的环境变量，需要切换到hadoop用户

    \n 
    1
    2
    3
    echo 'export PATH=$PATH:/home/hadoop/bin' >> ~/.bashrc && source ~/.bashrc
    scp_all.sh /home/hadoop/.bashrc /home/hadoop/
    ssh_all.sh source ~/.bashrc
    • \n
\n"},{"title":"Hadoop集群HDFS配置","top_img":"/img/site01.jpg","top_img_height":"800px","abbrlink":61252,"date":"2024-09-11T14:45:40.000Z","_content":"\n### 上传`hadoop-3.1.4.tar.gz`到`/tmp`，解压\n>注意在六台机器均上传到`/tmp`\n```bash\n# 在6台机器执行\nsudo tar -zxvf /tmp/hadoop-3.1.4.tar.gz -C /usr/local/\n# 分发到其他主机\nssh_root.sh chown -R hadoop:hadoop /usr/local/hadoop-3.1.4\nssh_root.sh ln -s /usr/local/hadoop-3.1.4/ /usr/local/hadoop\n```\n### 配置环境变量\n```bash\necho 'export HADOOP_HOME=/usr/local/hadoop' >> /etc/profile.d/myEnv.sh\necho 'export PATH=$PATH:$HADOOP_HOME/bin' >> /etc/profile.d/myEnv.sh\necho 'export PATH=$PATH:$HADOOP_HOME/sbin' >> /etc/profile.d/myEnv.sh\n```\n\n```bash\n# 分发到nn2、nn3、s1、s2、s3\nscp_all.sh /etc/profile.d/myEnv.sh /etc/profile.d/\n# source 环境变量\nssh_root.sh source /etc/profile\n```\n>还需要创建 `/data`这个目录，由于nn1、nn2、nn3已经创建`/data`，其他三台需要创建一下\n```bash\n### 在s1、s2、s3执行\nsudo mkdir /data\nsudo chown -R hadoop:hadoop /data\n```\n\n### 修改core-site.xml\n```bash\nvim /usr/local/hadoop/etc/hadoop/core-site.xml \n```\n\n```xml\n\n\t\n\t fs.defaultFS\n\t hdfs://ns1\n\t 默认文件服务的协议和NS逻辑名称，和hdfs-site.xml里的对应此配置替代了1.0里的fs.default.name\n\t\n\t\n\t\n\t hadoop.tmp.dir\n\t /data/tmp\n\t 数据存储目录\n\t\n\t\n\t\n\t hadoop.proxyuser.root.groups\n\t hadoop\n\t \n\t hdfs dfsadmin –refreshSuperUserGroupsConfiguration,\n\t yarn rmadmin –refreshSuperUserGroupsConfiguration\n\t 使用这两个命令不用重启就能刷新\n\t \n\t\n\t\n\t\n\t hadoop.proxyuser.root.hosts\n\t localhost\n\t 本地代理\n\t\n\t\n\t\n\t \n\t\tha.zookeeper.quorum \n\t\tnn1:2181,nn2:2181,nn3:2181 \n\t\tHA使用的zookeeper地址 \n\t\n\n```\n### 修改`hdfs-site.xml`\n```bash\nvim /usr/local/hadoop/etc/hadoop/hdfs-site.xml \n```\n\n```xml\n\n \n dfs.namenode.name.dir\n /data/namenode\n namenode本地文件存放地址\n \n \n \n dfs.nameservices\n ns1\n 提供服务的NS逻辑名称，与core-site.xml里的对应\n \n \n \n \n \n dfs.ha.namenodes.ns1\n nn1,nn2,nn3\n 列出该逻辑名称下的NameNode逻辑名称\n \n \n \n dfs.namenode.rpc-address.ns1.nn1\n nn1:9000\n 指定NameNode的RPC位置\n \n \n \n dfs.namenode.http-address.ns1.nn1\n nn1:50070\n 指定NameNode的Web Server位置\n \n \n \n dfs.namenode.rpc-address.ns1.nn2\n nn2:9000\n 指定NameNode的RPC位置\n \n \n \n dfs.namenode.http-address.ns1.nn2\n nn2:50070\n 指定NameNode的Web Server位置\n \n \n \n dfs.namenode.rpc-address.ns1.nn3\n nn3:9000\n 指定NameNode的RPC位置\n \n \n \n dfs.namenode.http-address.ns1.nn3\n nn3:50070\n 指定NameNode的Web Server位置\n \n \n \n dfs.namenode.handler.count\n 77\n namenode的工作线程数\n \n\n \n \n dfs.namenode.shared.edits.dir\n qjournal://nn1:8485;nn2:8485;nn3:8485/ns1\n 指定用于HA存放edits的共享存储，通常是namenode的所在机器\n \n \n \n dfs.journalnode.edits.dir\n /data/journaldata/\n journaldata服务存放文件的地址\n \n \n \n ipc.client.connect.max.retries\n 10\n namenode和journalnode的链接重试次数10次\n \n \n \n ipc.client.connect.retry.interval\n 10000\n 重试的间隔时间10s\n \n\n \n \n dfs.ha.fencing.methods\n sshfence\n 指定HA做隔离的方法，缺省是ssh，可设为shell，稍后详述\n \n \n \n dfs.ha.fencing.ssh.private-key-files\n /home/hadoop/.ssh/id_rsa\n 杀死命令脚本的免密配置秘钥\n \n \n \n dfs.client.failover.proxy.provider.ns1\n org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider\n 指定客户端用于HA切换的代理类，不同的NS可以用不同的代理类以上示例为Hadoop 2.0自带的缺省代理类\n \n \n \n dfs.client.failover.proxy.provider.auto-ha\n org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider\n \n \n \n dfs.ha.automatic-failover.enabled\n true\n \n\t\n\t\n\t dfs.datanode.data.dir\n\t /data/datanode\n\t datanode本地文件存放地址\n\t\n\t\n\t dfs.replication\n\t 3\n\t 文件复本数\n\t\n\t\n\t dfs.namenode.datanode.registration.ip-hostname-check\n\t false\n\t\n\t\n\t dfs.client.use.datanode.hostname\n\t true\n\t\n\t\n\t dfs.datanode.use.datanode.hostname\n\t true\n\t\n\n```\n### 修改`hadoop-env.sh`\n```bash\nvim /usr/local/hadoop/etc/hadoop/hadoop-env.sh\n```\n\n```bash\n# 添加这两行\nsource /etc/profile \nexport HADOOP_HEAPSIZE_MAX=512\n```\n### 分发这些配置文件\n```bash\nscp_all.sh /usr/local/hadoop/etc/hadoop/core-site.xml /usr/local/hadoop/etc/hadoop/\nscp_all.sh /usr/local/hadoop/etc/hadoop/hdfs-site.xml /usr/local/hadoop/etc/hadoop/\nscp_all.sh /usr/local/hadoop/etc/hadoop/hadoop-env.sh /usr/local/hadoop/etc/hadoop/\n```\n### 集群初始化\n- 需要先启动zookeeper集群\n ```bash\n ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh start\n ```\n\n ```bash\n # 第一次启动先启动journalnode，便于3个namenode的元数据同步\n ssh_all_zookeeper.sh hadoop-daemon.sh start journalnode\n ```\n- `zkfc`搭建\n ```bash\n #在nn1节点执行 \n hdfs zkfc -formatZK\n #nn1 nn2 nn3启动zkfc \n hadoop-daemon.sh start zkfc\n ```\n- 初始化nn1的namenode，在nn1执行\n ```bash\n hdfs namenode -format \n hadoop-daemon.sh start namenode\n ```\n- 格式化第二台和第三台namenode，并且启动namenode,在nn2、nn3执行\n ```bash\n hdfs namenode -bootstrapStandby \n hadoop-daemon.sh start namenode\n ```\n- 修改**workers**\n ```bash\n vim /usr/local/hadoop/etc/hadoop/workers\n ```\n 修改为\n ```text\n s1\n s2\n s3\n ```\n 分发给其他机器\n ```bash\n scp_all.sh /usr/local/hadoop/etc/hadoop/workers /usr/local/hadoop/etc/hadoop\n ```\n- 启动datanode节点，在s1、s2、s3执行\n\n ```bash\n #启动各个节点的datanode\n hadoop-daemons.sh start datanode\n ```\n### 集群启动\n```bash\nstart-dfs.sh\n```","source":"_posts/bigdata/hadoop/hdfs.md","raw":"---\ntitle: Hadoop集群HDFS配置\ntop_img: /img/site01.jpg\ntop_img_height: 800px\nabbrlink: 61252\ndate: 2024-09-011 22:45:40\n---\n\n### 上传`hadoop-3.1.4.tar.gz`到`/tmp`，解压\n>注意在六台机器均上传到`/tmp`\n```bash\n# 在6台机器执行\nsudo tar -zxvf /tmp/hadoop-3.1.4.tar.gz -C /usr/local/\n# 分发到其他主机\nssh_root.sh chown -R hadoop:hadoop /usr/local/hadoop-3.1.4\nssh_root.sh ln -s /usr/local/hadoop-3.1.4/ /usr/local/hadoop\n```\n### 配置环境变量\n```bash\necho 'export HADOOP_HOME=/usr/local/hadoop' >> /etc/profile.d/myEnv.sh\necho 'export PATH=$PATH:$HADOOP_HOME/bin' >> /etc/profile.d/myEnv.sh\necho 'export PATH=$PATH:$HADOOP_HOME/sbin' >> /etc/profile.d/myEnv.sh\n```\n\n```bash\n# 分发到nn2、nn3、s1、s2、s3\nscp_all.sh /etc/profile.d/myEnv.sh /etc/profile.d/\n# source 环境变量\nssh_root.sh source /etc/profile\n```\n>还需要创建 `/data`这个目录，由于nn1、nn2、nn3已经创建`/data`，其他三台需要创建一下\n```bash\n### 在s1、s2、s3执行\nsudo mkdir /data\nsudo chown -R hadoop:hadoop /data\n```\n\n### 修改core-site.xml\n```bash\nvim /usr/local/hadoop/etc/hadoop/core-site.xml \n```\n\n```xml\n\n\t\n\t fs.defaultFS\n\t hdfs://ns1\n\t 默认文件服务的协议和NS逻辑名称，和hdfs-site.xml里的对应此配置替代了1.0里的fs.default.name\n\t\n\t\n\t\n\t hadoop.tmp.dir\n\t /data/tmp\n\t 数据存储目录\n\t\n\t\n\t\n\t hadoop.proxyuser.root.groups\n\t hadoop\n\t \n\t hdfs dfsadmin –refreshSuperUserGroupsConfiguration,\n\t yarn rmadmin –refreshSuperUserGroupsConfiguration\n\t 使用这两个命令不用重启就能刷新\n\t \n\t\n\t\n\t\n\t hadoop.proxyuser.root.hosts\n\t localhost\n\t 本地代理\n\t\n\t\n\t\n\t \n\t\tha.zookeeper.quorum \n\t\tnn1:2181,nn2:2181,nn3:2181 \n\t\tHA使用的zookeeper地址 \n\t\n\n```\n### 修改`hdfs-site.xml`\n```bash\nvim /usr/local/hadoop/etc/hadoop/hdfs-site.xml \n```\n\n```xml\n\n \n dfs.namenode.name.dir\n /data/namenode\n namenode本地文件存放地址\n \n \n \n dfs.nameservices\n ns1\n 提供服务的NS逻辑名称，与core-site.xml里的对应\n \n \n \n \n \n dfs.ha.namenodes.ns1\n nn1,nn2,nn3\n 列出该逻辑名称下的NameNode逻辑名称\n \n \n \n dfs.namenode.rpc-address.ns1.nn1\n nn1:9000\n 指定NameNode的RPC位置\n \n \n \n dfs.namenode.http-address.ns1.nn1\n nn1:50070\n 指定NameNode的Web Server位置\n \n \n \n dfs.namenode.rpc-address.ns1.nn2\n nn2:9000\n 指定NameNode的RPC位置\n \n \n \n dfs.namenode.http-address.ns1.nn2\n nn2:50070\n 指定NameNode的Web Server位置\n \n \n \n dfs.namenode.rpc-address.ns1.nn3\n nn3:9000\n 指定NameNode的RPC位置\n \n \n \n dfs.namenode.http-address.ns1.nn3\n nn3:50070\n 指定NameNode的Web Server位置\n \n \n \n dfs.namenode.handler.count\n 77\n namenode的工作线程数\n \n\n \n \n dfs.namenode.shared.edits.dir\n qjournal://nn1:8485;nn2:8485;nn3:8485/ns1\n 指定用于HA存放edits的共享存储，通常是namenode的所在机器\n \n \n \n dfs.journalnode.edits.dir\n /data/journaldata/\n journaldata服务存放文件的地址\n \n \n \n ipc.client.connect.max.retries\n 10\n namenode和journalnode的链接重试次数10次\n \n \n \n ipc.client.connect.retry.interval\n 10000\n 重试的间隔时间10s\n \n\n \n \n dfs.ha.fencing.methods\n sshfence\n 指定HA做隔离的方法，缺省是ssh，可设为shell，稍后详述\n \n \n \n dfs.ha.fencing.ssh.private-key-files\n /home/hadoop/.ssh/id_rsa\n 杀死命令脚本的免密配置秘钥\n \n \n \n dfs.client.failover.proxy.provider.ns1\n org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider\n 指定客户端用于HA切换的代理类，不同的NS可以用不同的代理类以上示例为Hadoop 2.0自带的缺省代理类\n \n \n \n dfs.client.failover.proxy.provider.auto-ha\n org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider\n \n \n \n dfs.ha.automatic-failover.enabled\n true\n \n\t\n\t\n\t dfs.datanode.data.dir\n\t /data/datanode\n\t datanode本地文件存放地址\n\t\n\t\n\t dfs.replication\n\t 3\n\t 文件复本数\n\t\n\t\n\t dfs.namenode.datanode.registration.ip-hostname-check\n\t false\n\t\n\t\n\t dfs.client.use.datanode.hostname\n\t true\n\t\n\t\n\t dfs.datanode.use.datanode.hostname\n\t true\n\t\n\n```\n### 修改`hadoop-env.sh`\n```bash\nvim /usr/local/hadoop/etc/hadoop/hadoop-env.sh\n```\n\n```bash\n# 添加这两行\nsource /etc/profile \nexport HADOOP_HEAPSIZE_MAX=512\n```\n### 分发这些配置文件\n```bash\nscp_all.sh /usr/local/hadoop/etc/hadoop/core-site.xml /usr/local/hadoop/etc/hadoop/\nscp_all.sh /usr/local/hadoop/etc/hadoop/hdfs-site.xml /usr/local/hadoop/etc/hadoop/\nscp_all.sh /usr/local/hadoop/etc/hadoop/hadoop-env.sh /usr/local/hadoop/etc/hadoop/\n```\n### 集群初始化\n- 需要先启动zookeeper集群\n ```bash\n ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh start\n ```\n\n ```bash\n # 第一次启动先启动journalnode，便于3个namenode的元数据同步\n ssh_all_zookeeper.sh hadoop-daemon.sh start journalnode\n ```\n- `zkfc`搭建\n ```bash\n #在nn1节点执行 \n hdfs zkfc -formatZK\n #nn1 nn2 nn3启动zkfc \n hadoop-daemon.sh start zkfc\n ```\n- 初始化nn1的namenode，在nn1执行\n ```bash\n hdfs namenode -format \n hadoop-daemon.sh start namenode\n ```\n- 格式化第二台和第三台namenode，并且启动namenode,在nn2、nn3执行\n ```bash\n hdfs namenode -bootstrapStandby \n hadoop-daemon.sh start namenode\n ```\n- 修改**workers**\n ```bash\n vim /usr/local/hadoop/etc/hadoop/workers\n ```\n 修改为\n ```text\n s1\n s2\n s3\n ```\n 分发给其他机器\n ```bash\n scp_all.sh /usr/local/hadoop/etc/hadoop/workers /usr/local/hadoop/etc/hadoop\n ```\n- 启动datanode节点，在s1、s2、s3执行\n\n ```bash\n #启动各个节点的datanode\n hadoop-daemons.sh start datanode\n ```\n### 集群启动\n```bash\nstart-dfs.sh\n```","slug":"bigdata/hadoop/hdfs","published":1,"updated":"2024-09-11T14:51:42.712Z","comments":1,"layout":"post","photos":[],"_id":"cm63ecykc000qrcah5e1o2819","content":"

上传hadoop-3.1.4.tar.gz到/tmp，解压

\n

注意在六台机器均上传到/tmp

\n
\n
1
2
3
4
5
# 在6台机器执行
sudo tar -zxvf /tmp/hadoop-3.1.4.tar.gz -C /usr/local/
# 分发到其他主机
ssh_root.sh chown -R hadoop:hadoop /usr/local/hadoop-3.1.4
ssh_root.sh ln -s /usr/local/hadoop-3.1.4/ /usr/local/hadoop
\n

配置环境变量

1
2
3
echo 'export HADOOP_HOME=/usr/local/hadoop' >> /etc/profile.d/myEnv.sh
echo 'export PATH=$PATH:$HADOOP_HOME/bin' >> /etc/profile.d/myEnv.sh
echo 'export PATH=$PATH:$HADOOP_HOME/sbin' >> /etc/profile.d/myEnv.sh
\n\n
1
2
3
4
# 分发到nn2、nn3、s1、s2、s3
scp_all.sh /etc/profile.d/myEnv.sh /etc/profile.d/
# source 环境变量
ssh_root.sh source /etc/profile
\n
\n

还需要创建 /data这个目录，由于nn1、nn2、nn3已经创建/data，其他三台需要创建一下

\n
\n
1
2
3
### 在s1、s2、s3执行
sudo mkdir /data
sudo chown -R hadoop:hadoop /data
\n\n

修改core-site.xml

1
vim /usr/local/hadoop/etc/hadoop/core-site.xml
\n\n
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
<configuration>
\t<property>
\t    <name>fs.defaultFS</name>
\t    <value>hdfs://ns1</value>
\t    <description>默认文件服务的协议和NS逻辑名称，和hdfs-site.xml里的对应此配置替代了1.0里的fs.default.name</description>
\t</property>
\t
\t<property>
\t    <name>hadoop.tmp.dir</name>
\t    <value>/data/tmp</value>
\t    <description>数据存储目录</description>
\t</property>
\t
\t<property>
\t    <name>hadoop.proxyuser.root.groups</name>
\t    <value>hadoop</value>
\t    <description>
\t        hdfs dfsadmin –refreshSuperUserGroupsConfiguration,
\t        yarn rmadmin –refreshSuperUserGroupsConfiguration
\t        使用这两个命令不用重启就能刷新
\t    </description>
\t</property>
\t
\t<property>
\t    <name>hadoop.proxyuser.root.hosts</name>
\t    <value>localhost</value>
\t    <description>本地代理</description>
\t</property>
\t
\t<!-- zkfc的配置 -->
\t<property> 
\t\t<name>ha.zookeeper.quorum</name> 
\t\t<value>nn1:2181,nn2:2181,nn3:2181</value> 
\t\t<description>HA使用的zookeeper地址</description> 
\t</property>
</configuration>
\n

修改hdfs-site.xml

1
vim /usr/local/hadoop/etc/hadoop/hdfs-site.xml
\n\n
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
<configuration>
    <property>
        <name>dfs.namenode.name.dir</name>
        <value>/data/namenode</value>
        <description>namenode本地文件存放地址</description>
    </property>
    
    <property>
        <name>dfs.nameservices</name>
        <value>ns1</value>
        <description>提供服务的NS逻辑名称，与core-site.xml里的对应</description>
    </property>
    
    <!-- namenode的配置 -->
    <!-- 主要的 -->
    <property>
        <name>dfs.ha.namenodes.ns1</name>
        <value>nn1,nn2,nn3</value>
        <description>列出该逻辑名称下的NameNode逻辑名称</description>
    </property>
    
    <property>
        <name>dfs.namenode.rpc-address.ns1.nn1</name>
        <value>nn1:9000</value>
        <description>指定NameNode的RPC位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.http-address.ns1.nn1</name>
        <value>nn1:50070</value>
        <description>指定NameNode的Web Server位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.rpc-address.ns1.nn2</name>
        <value>nn2:9000</value>
        <description>指定NameNode的RPC位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.http-address.ns1.nn2</name>
        <value>nn2:50070</value>
        <description>指定NameNode的Web Server位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.rpc-address.ns1.nn3</name>
        <value>nn3:9000</value>
        <description>指定NameNode的RPC位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.http-address.ns1.nn3</name>
        <value>nn3:50070</value>
        <description>指定NameNode的Web Server位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.handler.count</name>
        <value>77</value>
        <description>namenode的工作线程数</description>
    </property>

    <!-- journaldata配置,使得其他两个namenode同步第一个namenode数据 --> 
    <property>
        <name>dfs.namenode.shared.edits.dir</name>
        <value>qjournal://nn1:8485;nn2:8485;nn3:8485/ns1</value>
        <description>指定用于HA存放edits的共享存储，通常是namenode的所在机器</description>
    </property>
    
    <property>
        <name>dfs.journalnode.edits.dir</name>
        <value>/data/journaldata/</value>
        <description>journaldata服务存放文件的地址</description>
    </property>
    
    <property>
        <name>ipc.client.connect.max.retries</name>
        <value>10</value>
        <description>namenode和journalnode的链接重试次数10次</description>
    </property>
    
    <property>
        <name>ipc.client.connect.retry.interval</name>
        <value>10000</value>
        <description>重试的间隔时间10s</description>
    </property>

    <!-- zkfc的配置 -->
    <property>
        <name>dfs.ha.fencing.methods</name>
        <value>sshfence</value>
        <description>指定HA做隔离的方法，缺省是ssh，可设为shell，稍后详述</description>
    </property>
    
    <property>
        <name>dfs.ha.fencing.ssh.private-key-files</name>
        <value>/home/hadoop/.ssh/id_rsa</value>
        <description>杀死命令脚本的免密配置秘钥</description>
    </property>
    
    <property>
        <name>dfs.client.failover.proxy.provider.ns1</name>
        <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value>
        <description>指定客户端用于HA切换的代理类，不同的NS可以用不同的代理类以上示例为Hadoop 2.0自带的缺省代理类</description>
    </property>
    
    <property>
        <name>dfs.client.failover.proxy.provider.auto-ha</name>
        <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value>
    </property> 
    
    <property>
        <name>dfs.ha.automatic-failover.enabled</name>
        <value>true</value>
    </property>
\t<!-- datanode配置 -->
\t<property>
\t    <name>dfs.datanode.data.dir</name>
\t    <value>/data/datanode</value>
\t    <description>datanode本地文件存放地址</description>
\t</property>
\t<property>
\t    <name>dfs.replication</name>
\t    <value>3</value>
\t    <description>文件复本数</description>
\t</property>
\t<property>
\t    <name>dfs.namenode.datanode.registration.ip-hostname-check</name>
\t    <value>false</value>
\t</property>
\t<property>
\t    <name>dfs.client.use.datanode.hostname</name>
\t    <value>true</value>
\t</property>
\t<property>
\t    <name>dfs.datanode.use.datanode.hostname</name>
\t    <value>true</value>
\t</property>
</configuration>
\n

修改hadoop-env.sh

1
vim /usr/local/hadoop/etc/hadoop/hadoop-env.sh
\n\n
1
2
3
# 添加这两行
source /etc/profile 
export HADOOP_HEAPSIZE_MAX=512
\n

分发这些配置文件

1
2
3
scp_all.sh /usr/local/hadoop/etc/hadoop/core-site.xml /usr/local/hadoop/etc/hadoop/
scp_all.sh /usr/local/hadoop/etc/hadoop/hdfs-site.xml /usr/local/hadoop/etc/hadoop/
scp_all.sh /usr/local/hadoop/etc/hadoop/hadoop-env.sh /usr/local/hadoop/etc/hadoop/
\n

集群初始化

    \n

  • 需要先启动zookeeper集群

    \n 
    1
    ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh start
    \n\n 
    1
    2
    # 第一次启动先启动journalnode，便于3个namenode的元数据同步
    ssh_all_zookeeper.sh hadoop-daemon.sh start journalnode
    • \n

  • zkfc搭建

    \n 
    1
    2
    3
    4
    #在nn1节点执行 
    hdfs zkfc -formatZK
    #nn1 nn2 nn3启动zkfc 
    hadoop-daemon.sh start zkfc
    • \n

  • 初始化nn1的namenode，在nn1执行

    \n 
    1
    2
    hdfs namenode -format 
    hadoop-daemon.sh start namenode
    • \n

  • 格式化第二台和第三台namenode，并且启动namenode,在nn2、nn3执行

    \n 
    1
    2
    hdfs namenode -bootstrapStandby 
    hadoop-daemon.sh start namenode
    • \n

  • 修改workers

    \n 
    1
    vim /usr/local/hadoop/etc/hadoop/workers
    \n

    修改为

    \n 
    1
    2
    3
    s1
    s2
    s3
    \n

    分发给其他机器

    \n 
    1
    scp_all.sh /usr/local/hadoop/etc/hadoop/workers /usr/local/hadoop/etc/hadoop
    • \n

  • 启动datanode节点，在s1、s2、s3执行

    \n 
    1
    2
    #启动各个节点的datanode
    hadoop-daemons.sh start datanode
    • \n
\n

集群启动

1
start-dfs.sh
","cover":false,"excerpt":"","more":"

上传hadoop-3.1.4.tar.gz到/tmp，解压

\n

注意在六台机器均上传到/tmp

\n
\n
1
2
3
4
5
# 在6台机器执行
sudo tar -zxvf /tmp/hadoop-3.1.4.tar.gz -C /usr/local/
# 分发到其他主机
ssh_root.sh chown -R hadoop:hadoop /usr/local/hadoop-3.1.4
ssh_root.sh ln -s /usr/local/hadoop-3.1.4/ /usr/local/hadoop
\n

配置环境变量

1
2
3
echo 'export HADOOP_HOME=/usr/local/hadoop' >> /etc/profile.d/myEnv.sh
echo 'export PATH=$PATH:$HADOOP_HOME/bin' >> /etc/profile.d/myEnv.sh
echo 'export PATH=$PATH:$HADOOP_HOME/sbin' >> /etc/profile.d/myEnv.sh
\n\n
1
2
3
4
# 分发到nn2、nn3、s1、s2、s3
scp_all.sh /etc/profile.d/myEnv.sh /etc/profile.d/
# source 环境变量
ssh_root.sh source /etc/profile
\n
\n

还需要创建 /data这个目录，由于nn1、nn2、nn3已经创建/data，其他三台需要创建一下

\n
\n
1
2
3
### 在s1、s2、s3执行
sudo mkdir /data
sudo chown -R hadoop:hadoop /data
\n\n

修改core-site.xml

1
vim /usr/local/hadoop/etc/hadoop/core-site.xml
\n\n
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
<configuration>
\t<property>
\t    <name>fs.defaultFS</name>
\t    <value>hdfs://ns1</value>
\t    <description>默认文件服务的协议和NS逻辑名称，和hdfs-site.xml里的对应此配置替代了1.0里的fs.default.name</description>
\t</property>
\t
\t<property>
\t    <name>hadoop.tmp.dir</name>
\t    <value>/data/tmp</value>
\t    <description>数据存储目录</description>
\t</property>
\t
\t<property>
\t    <name>hadoop.proxyuser.root.groups</name>
\t    <value>hadoop</value>
\t    <description>
\t        hdfs dfsadmin –refreshSuperUserGroupsConfiguration,
\t        yarn rmadmin –refreshSuperUserGroupsConfiguration
\t        使用这两个命令不用重启就能刷新
\t    </description>
\t</property>
\t
\t<property>
\t    <name>hadoop.proxyuser.root.hosts</name>
\t    <value>localhost</value>
\t    <description>本地代理</description>
\t</property>
\t
\t<!-- zkfc的配置 -->
\t<property> 
\t\t<name>ha.zookeeper.quorum</name> 
\t\t<value>nn1:2181,nn2:2181,nn3:2181</value> 
\t\t<description>HA使用的zookeeper地址</description> 
\t</property>
</configuration>
\n

修改hdfs-site.xml

1
vim /usr/local/hadoop/etc/hadoop/hdfs-site.xml
\n\n
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
<configuration>
    <property>
        <name>dfs.namenode.name.dir</name>
        <value>/data/namenode</value>
        <description>namenode本地文件存放地址</description>
    </property>
    
    <property>
        <name>dfs.nameservices</name>
        <value>ns1</value>
        <description>提供服务的NS逻辑名称，与core-site.xml里的对应</description>
    </property>
    
    <!-- namenode的配置 -->
    <!-- 主要的 -->
    <property>
        <name>dfs.ha.namenodes.ns1</name>
        <value>nn1,nn2,nn3</value>
        <description>列出该逻辑名称下的NameNode逻辑名称</description>
    </property>
    
    <property>
        <name>dfs.namenode.rpc-address.ns1.nn1</name>
        <value>nn1:9000</value>
        <description>指定NameNode的RPC位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.http-address.ns1.nn1</name>
        <value>nn1:50070</value>
        <description>指定NameNode的Web Server位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.rpc-address.ns1.nn2</name>
        <value>nn2:9000</value>
        <description>指定NameNode的RPC位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.http-address.ns1.nn2</name>
        <value>nn2:50070</value>
        <description>指定NameNode的Web Server位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.rpc-address.ns1.nn3</name>
        <value>nn3:9000</value>
        <description>指定NameNode的RPC位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.http-address.ns1.nn3</name>
        <value>nn3:50070</value>
        <description>指定NameNode的Web Server位置</description>
    </property>
    
    <property>
        <name>dfs.namenode.handler.count</name>
        <value>77</value>
        <description>namenode的工作线程数</description>
    </property>

    <!-- journaldata配置,使得其他两个namenode同步第一个namenode数据 --> 
    <property>
        <name>dfs.namenode.shared.edits.dir</name>
        <value>qjournal://nn1:8485;nn2:8485;nn3:8485/ns1</value>
        <description>指定用于HA存放edits的共享存储，通常是namenode的所在机器</description>
    </property>
    
    <property>
        <name>dfs.journalnode.edits.dir</name>
        <value>/data/journaldata/</value>
        <description>journaldata服务存放文件的地址</description>
    </property>
    
    <property>
        <name>ipc.client.connect.max.retries</name>
        <value>10</value>
        <description>namenode和journalnode的链接重试次数10次</description>
    </property>
    
    <property>
        <name>ipc.client.connect.retry.interval</name>
        <value>10000</value>
        <description>重试的间隔时间10s</description>
    </property>

    <!-- zkfc的配置 -->
    <property>
        <name>dfs.ha.fencing.methods</name>
        <value>sshfence</value>
        <description>指定HA做隔离的方法，缺省是ssh，可设为shell，稍后详述</description>
    </property>
    
    <property>
        <name>dfs.ha.fencing.ssh.private-key-files</name>
        <value>/home/hadoop/.ssh/id_rsa</value>
        <description>杀死命令脚本的免密配置秘钥</description>
    </property>
    
    <property>
        <name>dfs.client.failover.proxy.provider.ns1</name>
        <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value>
        <description>指定客户端用于HA切换的代理类，不同的NS可以用不同的代理类以上示例为Hadoop 2.0自带的缺省代理类</description>
    </property>
    
    <property>
        <name>dfs.client.failover.proxy.provider.auto-ha</name>
        <value>org.apache.hadoop.hdfs.server.namenode.ha.ConfiguredFailoverProxyProvider</value>
    </property> 
    
    <property>
        <name>dfs.ha.automatic-failover.enabled</name>
        <value>true</value>
    </property>
\t<!-- datanode配置 -->
\t<property>
\t    <name>dfs.datanode.data.dir</name>
\t    <value>/data/datanode</value>
\t    <description>datanode本地文件存放地址</description>
\t</property>
\t<property>
\t    <name>dfs.replication</name>
\t    <value>3</value>
\t    <description>文件复本数</description>
\t</property>
\t<property>
\t    <name>dfs.namenode.datanode.registration.ip-hostname-check</name>
\t    <value>false</value>
\t</property>
\t<property>
\t    <name>dfs.client.use.datanode.hostname</name>
\t    <value>true</value>
\t</property>
\t<property>
\t    <name>dfs.datanode.use.datanode.hostname</name>
\t    <value>true</value>
\t</property>
</configuration>
\n

修改hadoop-env.sh

1
vim /usr/local/hadoop/etc/hadoop/hadoop-env.sh
\n\n
1
2
3
# 添加这两行
source /etc/profile 
export HADOOP_HEAPSIZE_MAX=512
\n

分发这些配置文件

1
2
3
scp_all.sh /usr/local/hadoop/etc/hadoop/core-site.xml /usr/local/hadoop/etc/hadoop/
scp_all.sh /usr/local/hadoop/etc/hadoop/hdfs-site.xml /usr/local/hadoop/etc/hadoop/
scp_all.sh /usr/local/hadoop/etc/hadoop/hadoop-env.sh /usr/local/hadoop/etc/hadoop/
\n

集群初始化

    \n

  • 需要先启动zookeeper集群

    \n 
    1
    ssh_all_zookeeper.sh /usr/local/zookeeper/bin/zkServer.sh start
    \n\n 
    1
    2
    # 第一次启动先启动journalnode，便于3个namenode的元数据同步
    ssh_all_zookeeper.sh hadoop-daemon.sh start journalnode
    • \n

  • zkfc搭建

    \n 
    1
    2
    3
    4
    #在nn1节点执行 
    hdfs zkfc -formatZK
    #nn1 nn2 nn3启动zkfc 
    hadoop-daemon.sh start zkfc
    • \n

  • 初始化nn1的namenode，在nn1执行

    \n 
    1
    2
    hdfs namenode -format 
    hadoop-daemon.sh start namenode
    • \n

  • 格式化第二台和第三台namenode，并且启动namenode,在nn2、nn3执行

    \n 
    1
    2
    hdfs namenode -bootstrapStandby 
    hadoop-daemon.sh start namenode
    • \n

  • 修改workers

    \n 
    1
    vim /usr/local/hadoop/etc/hadoop/workers
    \n

    修改为

    \n 
    1
    2
    3
    s1
    s2
    s3
    \n

    分发给其他机器

    \n 
    1
    scp_all.sh /usr/local/hadoop/etc/hadoop/workers /usr/local/hadoop/etc/hadoop
    • \n

  • 启动datanode节点，在s1、s2、s3执行

    \n 
    1
    2
    #启动各个节点的datanode
    hadoop-daemons.sh start datanode
    • \n
\n

集群启动

1
start-dfs.sh
"},{"title":"Docker被墙，如何继续使用？","top_img":"/img/site01.jpg","top_img_height":"800px","abbrlink":47807,"date":"2024-08-01T01:10:40.000Z","_content":"\n## Docker Download\n> 自从docker官方仓库在中国大陆被墙后，docker的部署方式也发生了改变。\n> 解决docker安装问题：https://github.com/shenjianZ/docker_installer\n\n1. 安装docker \n ```shell\n sudo curl -fsSL https://gitee.com/tech-shrimp/docker_installer/releases/download/latest/linux.sh| bash -s docker --mirror Aliyun\n ```\n \n2. 启动docker\n ```shell\n sudo systemctl start docker\n ```\n \n3. 设置开机自启\n ```shell\n sudo systemctl enable docker\n ```\n \n4. Docker pull images\n > 将image下载到阿里云镜像仓库中\n > 解决docker pull 镜像问题:https://github.com/shenjianZ/docker_image_pusher\n \n 1. **登录阿里云镜像服务** https://cr.console.aliyun.com/,\n\n 启用个人实例，创建一个命名空间（`ALIYUN_NAME_SPACE`）\n\n 2. 在**访问凭证** 可以看到账号 用户名（`ALIYUN_REGISTRY_USER`)\n\n 密码（`ALIYUN_REGISTRY_PASSWORD`)\n\n 仓库地址（`ALIYUN_REGISTRY`）\n\n 3. **启动Action**\n 进入您自己的项目，点击`Action`，启用`Github Action`功能\n \n 4. **配置环境变量**\n 进入Settings->Secret and variables->Actions->New Repository secret\n 将上一步的四个值\n `ALIYUN_NAME_SPACE`,\n \n ` ALIYUN_REGISTRY_USER`，\n \n `ALIYUN_REGISTRY_PASSWORD`，\n \n `ALIYUN_REGISTRY`\n 配置成环境变量\n \n 5. **添加镜像**\n 打开`images.txt`文件，添加你想要的镜像 可以加tag\n \n 6. 使用镜像\n 回到阿里云，镜像仓库，点击任意镜像，可查看镜像状态。(可以改成公开，拉取镜像免登录)","source":"_posts/frontend/deploy/deploy.md","raw":"---\ntitle: Docker被墙，如何继续使用？\ntop_img: /img/site01.jpg\ntop_img_height: 800px\nabbrlink: 47807\ndate: 2024-08-01 09:10:40\ntags:\n---\n\n## Docker Download\n> 自从docker官方仓库在中国大陆被墙后，docker的部署方式也发生了改变。\n> 解决docker安装问题：https://github.com/shenjianZ/docker_installer\n\n1. 安装docker \n ```shell\n sudo curl -fsSL https://gitee.com/tech-shrimp/docker_installer/releases/download/latest/linux.sh| bash -s docker --mirror Aliyun\n ```\n \n2. 启动docker\n ```shell\n sudo systemctl start docker\n ```\n \n3. 设置开机自启\n ```shell\n sudo systemctl enable docker\n ```\n \n4. Docker pull images\n > 将image下载到阿里云镜像仓库中\n > 解决docker pull 镜像问题:https://github.com/shenjianZ/docker_image_pusher\n \n 1. **登录阿里云镜像服务** https://cr.console.aliyun.com/,\n\n 启用个人实例，创建一个命名空间（`ALIYUN_NAME_SPACE`）\n\n 2. 在**访问凭证** 可以看到账号 用户名（`ALIYUN_REGISTRY_USER`)\n\n 密码（`ALIYUN_REGISTRY_PASSWORD`)\n\n 仓库地址（`ALIYUN_REGISTRY`）\n\n 3. **启动Action**\n 进入您自己的项目，点击`Action`，启用`Github Action`功能\n \n 4. **配置环境变量**\n 进入Settings->Secret and variables->Actions->New Repository secret\n 将上一步的四个值\n `ALIYUN_NAME_SPACE`,\n \n ` ALIYUN_REGISTRY_USER`，\n \n `ALIYUN_REGISTRY_PASSWORD`，\n \n `ALIYUN_REGISTRY`\n 配置成环境变量\n \n 5. **添加镜像**\n 打开`images.txt`文件，添加你想要的镜像 可以加tag\n \n 6. 使用镜像\n 回到阿里云，镜像仓库，点击任意镜像，可查看镜像状态。(可以改成公开，拉取镜像免登录)","slug":"frontend/deploy/deploy","published":1,"updated":"2024-08-09T12:21:50.045Z","comments":1,"layout":"post","photos":[],"_id":"cm63ecykd000rrcah9t70fbfd","content":"

Docker Download

\n

自从docker官方仓库在中国大陆被墙后，docker的部署方式也发生了改变。
解决docker安装问题：https://github.com/shenjianZ/docker_installer

\n
\n
    \n

  1. 安装docker

    \n 
    1
    sudo curl -fsSL https://gitee.com/tech-shrimp/docker_installer/releases/download/latest/linux.sh| bash -s docker --mirror Aliyun
    \n
    2. \n

  2. 启动docker

    \n 
    1
    sudo systemctl start docker
    \n
    3. \n

  3. 设置开机自启

    \n 
    1
    sudo systemctl enable docker
    \n
    4. \n

  4. Docker pull images

    \n
    \n

    将image下载到阿里云镜像仓库中
    解决docker pull 镜像问题:https://github.com/shenjianZ/docker_image_pusher

    \n
    \n
      \n

    1. 登录阿里云镜像服务 https://cr.console.aliyun.com/,

      \n

      启用个人实例，创建一个命名空间（ALIYUN_NAME_SPACE）

      \n
      2. \n

    2. 在访问凭证 可以看到账号 用户名（ALIYUN_REGISTRY_USER)

      \n

      密码（ALIYUN_REGISTRY_PASSWORD)

      \n

      仓库地址（ALIYUN_REGISTRY）

      \n
      3. \n

    3. 启动Action
      进入您自己的项目，点击Action，启用Github Action功能

      \n
      4. \n

    4. 配置环境变量
      进入Settings->Secret and variables->Actions->New Repository secret
      将上一步的四个值
      ALIYUN_NAME_SPACE,

      \n

      ALIYUN_REGISTRY_USER，

      \n

      ALIYUN_REGISTRY_PASSWORD，

      \n

      ALIYUN_REGISTRY
      配置成环境变量

      \n
      5. \n

    5. 添加镜像
      打开images.txt文件，添加你想要的镜像 可以加tag

      \n
      6. \n

    6. 使用镜像
      回到阿里云，镜像仓库，点击任意镜像，可查看镜像状态。(可以改成公开，拉取镜像免登录)

      \n
      7. \n
    \n
    5. \n
\n","cover":false,"excerpt":"","more":"

Docker Download

\n

自从docker官方仓库在中国大陆被墙后，docker的部署方式也发生了改变。
解决docker安装问题：https://github.com/shenjianZ/docker_installer

\n
\n
    \n

  1. 安装docker

    \n 
    1
    sudo curl -fsSL https://gitee.com/tech-shrimp/docker_installer/releases/download/latest/linux.sh| bash -s docker --mirror Aliyun
    \n
    2. \n

  2. 启动docker

    \n 
    1
    sudo systemctl start docker
    \n
    3. \n

  3. 设置开机自启

    \n 
    1
    sudo systemctl enable docker
    \n
    4. \n

  4. Docker pull images

    \n
    \n

    将image下载到阿里云镜像仓库中
    解决docker pull 镜像问题:https://github.com/shenjianZ/docker_image_pusher

    \n
    \n
      \n

    1. 登录阿里云镜像服务 https://cr.console.aliyun.com/,

      \n

      启用个人实例，创建一个命名空间（ALIYUN_NAME_SPACE）

      \n
      2. \n

    2. 在访问凭证 可以看到账号 用户名（ALIYUN_REGISTRY_USER)

      \n

      密码（ALIYUN_REGISTRY_PASSWORD)

      \n

      仓库地址（ALIYUN_REGISTRY）

      \n
      3. \n

    3. 启动Action
      进入您自己的项目，点击Action，启用Github Action功能

      \n
      4. \n

    4. 配置环境变量
      进入Settings->Secret and variables->Actions->New Repository secret
      将上一步的四个值
      ALIYUN_NAME_SPACE,

      \n

      ALIYUN_REGISTRY_USER，

      \n

      ALIYUN_REGISTRY_PASSWORD，

      \n

      ALIYUN_REGISTRY
      配置成环境变量

      \n
      5. \n

    5. 添加镜像
      打开images.txt文件，添加你想要的镜像 可以加tag

      \n
      6. \n

    6. 使用镜像
      回到阿里云，镜像仓库，点击任意镜像，可查看镜像状态。(可以改成公开，拉取镜像免登录)

      \n
      7. \n
    \n
    5. \n
\n"},{"title":"组件使用","abbrlink":33957,"date":"2024-08-05T06:07:01.000Z","_content":"\n### 组件自动导入\n```json\n\t\"easycom\":{\n\t \"autoscan\": true,\n\t \"custom\": {\n\t \"^tui-(.*)\": \"@/components/thorui/tui-$1/tui-$1.vue\" // 匹配components目录内的vue文件\n\t }\n\t}\n```\n\n### `tui-sticky 吸顶容器` \n\n> 包含 以下 `tui` 组件 :\n> - tui-sticky\n> - tui-list-view\n> - tui-list-cell\n> \n\n```html\n\n \n \n \n \n \n \n\n\n\n```\n","source":"_posts/frontend/uniapp/component1.md","raw":"---\ntitle: 组件使用\ntags: uniapp\nabbrlink: 33957\ndate: 2024-08-05 14:07:01\n---\n\n### 组件自动导入\n```json\n\t\"easycom\":{\n\t \"autoscan\": true,\n\t \"custom\": {\n\t \"^tui-(.*)\": \"@/components/thorui/tui-$1/tui-$1.vue\" // 匹配components目录内的vue文件\n\t }\n\t}\n```\n\n### `tui-sticky 吸顶容器` \n\n> 包含 以下 `tui` 组件 :\n> - tui-sticky\n> - tui-list-view\n> - tui-list-cell\n> \n\n```html\n\n \n \n \n \n \n \n\n\n\n```\n","slug":"frontend/uniapp/component1","published":1,"updated":"2024-08-09T12:21:50.042Z","comments":1,"layout":"post","photos":[],"_id":"cm63ecykd000srcahe04kfgm3","content":"

组件自动导入

1
2
3
4
5
6
"easycom":{
  "autoscan": true,
  "custom": {
    "^tui-(.*)": "@/components/thorui/tui-$1/tui-$1.vue"  // 匹配components目录内的vue文件
  }
}
\n\n

tui-sticky 吸顶容器

\n

包含 以下 tui 组件 :

\n
    \n
  • tui-sticky
    • \n
  • tui-list-view
    • \n
  • tui-list-cell
    • \n
\n
\n
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
<tui-sticky :scrollTop="scrollTop" stickyHeight="104rpx" container>
    <!-- header start -->
    <template v-slot:header>
        <view class="sticky-item">
            <view class="setting">设置</view>
        </view>
    </template>
    <!-- header end -->
    <!--内容 start-->
    <template v-slot:content>
        <tui-list-view class="content">
            <tui-list-cell  :arrow="false">
                <switch class='switch' checked color="#FFCC33" />
            </tui-list-cell>
        </tui-list-view>
    </template>
    <!--内容 end-->
</tui-sticky>

<script setup>
    import { ref } from 'vue'
    import { onPageScroll } from '@dcloudio/uni-app'

    // 定义 scrollTop 响应式变量
    const scrollTop = ref(0)
    // 监听页面滚动事件
    onPageScroll((e) => {
        scrollTop.value = e.scrollTop
    })
</script>
\n","cover":false,"excerpt":"","more":"

组件自动导入

1
2
3
4
5
6
"easycom":{
  "autoscan": true,
  "custom": {
    "^tui-(.*)": "@/components/thorui/tui-$1/tui-$1.vue"  // 匹配components目录内的vue文件
  }
}
\n\n

tui-sticky 吸顶容器

\n

包含 以下 tui 组件 :

\n
    \n
  • tui-sticky
    • \n
  • tui-list-view
    • \n
  • tui-list-cell
    • \n
\n
\n
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
<tui-sticky :scrollTop="scrollTop" stickyHeight="104rpx" container>
    <!-- header start -->
    <template v-slot:header>
        <view class="sticky-item">
            <view class="setting">设置</view>
        </view>
    </template>
    <!-- header end -->
    <!--内容 start-->
    <template v-slot:content>
        <tui-list-view class="content">
            <tui-list-cell  :arrow="false">
                <switch class='switch' checked color="#FFCC33" />
            </tui-list-cell>
        </tui-list-view>
    </template>
    <!--内容 end-->
</tui-sticky>

<script setup>
    import { ref } from 'vue'
    import { onPageScroll } from '@dcloudio/uni-app'

    // 定义 scrollTop 响应式变量
    const scrollTop = ref(0)
    // 监听页面滚动事件
    onPageScroll((e) => {
        scrollTop.value = e.scrollTop
    })
</script>
\n"},{"title":"uniapp 开发","abbrlink":58817,"date":"2024-08-05T06:07:01.000Z","_content":"- [uniapp component](../component1)","source":"_posts/frontend/uniapp/uniapp.md","raw":"---\ntitle: uniapp 开发\ntags: uniapp\nabbrlink: 58817\ndate: 2024-08-05 14:07:01\n---\n- [uniapp component](../component1)","slug":"frontend/uniapp/uniapp","published":1,"updated":"2024-08-09T12:21:50.039Z","comments":1,"layout":"post","photos":[],"_id":"cm63ecykd000trcah96geh51h","content":"\n","cover":false,"excerpt":"","more":"\n"},{"title":"无法访问外网？需要订阅代理服务？","abbrlink":14011,"date":"2024-08-07T02:06:08.000Z","_content":"\n{% note info %}\n**由于中国大陆的GFW（防火墙）限制，无法访问外网网络，因此需要访问像GitHub、YouTube这样的\n的网站将被屏蔽拦截，接下来我将给出一种使用`VPN`服务的可行的方案来保证服务的可靠性。**\n{% endnote %}\n\n### 介绍\n> 根据测试，许多提供服务的提供商所在的网站需要使用`外部网络`才能打开，仅有少部分的网站(**比较贵**)可以直接使用\n> 国内网络环境打开直接购买订阅服务。\n>\n\n那么你现在可以有两个选择：\n1. **方案一**：使用无需`外部网络`便能开通订阅服务的VPN，费用高，如果你选择此方案，那么你可自行搜索解决，此处仅仅讨论方案二。\n2. **方案二**：如果使用此方案，详见下方。\n\n\n### 解决方案\n> 采用**方案二**方式\n> \n> 这是一些订阅服务推广的链接： https://9.234456.xyz/abc.html?t=638586217737356738 (此链接打开无需使用VPN，但进入对应的机场页面却仍无法打开)\n> \n> 此教程中我们使用的机场是 \n> 1. `一元机场`: https://xn--4gq62f52gdss.com/\n> 2. `赔钱机场`：https://xn--mes358aby2apfg.com/\n\n### 机场选择的建议：\n\n- `一元机场`\n ![](/img/yiyuan.png)\n 可以看到\n - `12元/年`,每月50GB的可用额度，下个月重置流量额度\n - `15元/季`，即为`60元/年`，每月有4000GB的可用额度，下个月重置流量额度\n - `7元/月`，即为`84元/年`，每个月8000GB的可用额度，下个月重置流量额度\n 根据我个人的使用情况，大多数情况下我每月的流量使用未超过50GB，如果没有频繁的流量使用，\n 建议选择`12元/年`，否则可以选择`15元/季`，这意味着每月将有4000GB的可用额度\n\n- `赔钱机场`\n\n ![](/img/peiqian.png)\n `赔钱机场`的订阅共有9种方案，这里我仅显示自己正在使用的，个人认为十分优惠：\n - `34.99元/年`,每月500GB的可用额度，根据我观察和使用，这个订阅方案比`一元机场`的性价比更高，且流量使用额度也不用担心\n \n### 如何订阅？\n{% note success %}\n由于需要外部网络才能完成订阅服务的购买，你可以向我的邮箱`15202078626@163.com`发送你的订阅计划方案，\n扫描付款二维码，我将为你开通订阅（您只需要付款对应的订阅金额即可）\n{% endnote %}\n\n\n### 完成订阅后如何使用？\n> 你可以在 `Windows`、`Mac`、`Android`等平台使用此服务\n> 使用订阅的对应链接： https://flowus.cn/shenjian/22f76d4f-e7b3-4b8a-8a89-561566f6eb60\n\n\n ","source":"_posts/net/jichang/jichang.md","raw":"---\ntitle: 无法访问外网？需要订阅代理服务？\ntags: 网络代理\nabbrlink: 14011\ndate: 2024-08-07 10:06:08\n---\n\n{% note info %}\n**由于中国大陆的GFW（防火墙）限制，无法访问外网网络，因此需要访问像GitHub、YouTube这样的\n的网站将被屏蔽拦截，接下来我将给出一种使用`VPN`服务的可行的方案来保证服务的可靠性。**\n{% endnote %}\n\n### 介绍\n> 根据测试，许多提供服务的提供商所在的网站需要使用`外部网络`才能打开，仅有少部分的网站(**比较贵**)可以直接使用\n> 国内网络环境打开直接购买订阅服务。\n>\n\n那么你现在可以有两个选择：\n1. **方案一**：使用无需`外部网络`便能开通订阅服务的VPN，费用高，如果你选择此方案，那么你可自行搜索解决，此处仅仅讨论方案二。\n2. **方案二**：如果使用此方案，详见下方。\n\n\n### 解决方案\n> 采用**方案二**方式\n> \n> 这是一些订阅服务推广的链接： https://9.234456.xyz/abc.html?t=638586217737356738 (此链接打开无需使用VPN，但进入对应的机场页面却仍无法打开)\n> \n> 此教程中我们使用的机场是 \n> 1. `一元机场`: https://xn--4gq62f52gdss.com/\n> 2. `赔钱机场`：https://xn--mes358aby2apfg.com/\n\n### 机场选择的建议：\n\n- `一元机场`\n ![](/img/yiyuan.png)\n 可以看到\n - `12元/年`,每月50GB的可用额度，下个月重置流量额度\n - `15元/季`，即为`60元/年`，每月有4000GB的可用额度，下个月重置流量额度\n - `7元/月`，即为`84元/年`，每个月8000GB的可用额度，下个月重置流量额度\n 根据我个人的使用情况，大多数情况下我每月的流量使用未超过50GB，如果没有频繁的流量使用，\n 建议选择`12元/年`，否则可以选择`15元/季`，这意味着每月将有4000GB的可用额度\n\n- `赔钱机场`\n\n ![](/img/peiqian.png)\n `赔钱机场`的订阅共有9种方案，这里我仅显示自己正在使用的，个人认为十分优惠：\n - `34.99元/年`,每月500GB的可用额度，根据我观察和使用，这个订阅方案比`一元机场`的性价比更高，且流量使用额度也不用担心\n \n### 如何订阅？\n{% note success %}\n由于需要外部网络才能完成订阅服务的购买，你可以向我的邮箱`15202078626@163.com`发送你的订阅计划方案，\n扫描付款二维码，我将为你开通订阅（您只需要付款对应的订阅金额即可）\n{% endnote %}\n\n\n### 完成订阅后如何使用？\n> 你可以在 `Windows`、`Mac`、`Android`等平台使用此服务\n> 使用订阅的对应链接： https://flowus.cn/shenjian/22f76d4f-e7b3-4b8a-8a89-561566f6eb60\n\n\n ","slug":"net/jichang/jichang","published":1,"updated":"2024-08-09T12:21:50.036Z","comments":1,"layout":"post","photos":[],"_id":"cm63ecyke000vrcah4jrfbs1x","content":"

由于中国大陆的GFW（防火墙）限制，无法访问外网网络，因此需要访问像GitHub、YouTube这样的
的网站将被屏蔽拦截，接下来我将给出一种使用VPN服务的可行的方案来保证服务的可靠性。

\n
\n\n

介绍

\n

根据测试，许多提供服务的提供商所在的网站需要使用外部网络才能打开，仅有少部分的网站(比较贵)可以直接使用
国内网络环境打开直接购买订阅服务。

\n
\n

那么你现在可以有两个选择：

\n
    \n
  1. 方案一：使用无需外部网络便能开通订阅服务的VPN，费用高，如果你选择此方案，那么你可自行搜索解决，此处仅仅讨论方案二。
    2. \n
  2. 方案二：如果使用此方案，详见下方。
    3. \n
\n

解决方案

\n

采用方案二方式

\n

这是一些订阅服务推广的链接： https://9.234456.xyz/abc.html?t=638586217737356738 (此链接打开无需使用VPN，但进入对应的机场页面却仍无法打开)

\n

此教程中我们使用的机场是

\n
    \n
  1. 一元机场: https://xn--4gq62f52gdss.com/
    2. \n
  2. 赔钱机场：https://xn--mes358aby2apfg.com/
    3. \n
\n
\n

机场选择的建议：

    \n

  • 一元机场

    可以看到

    \n
      \n
    • 12元/年,每月50GB的可用额度，下个月重置流量额度
      • \n
    • 15元/季，即为60元/年，每月有4000GB的可用额度，下个月重置流量额度
      • \n
    • 7元/月，即为84元/年，每个月8000GB的可用额度，下个月重置流量额度
      根据我个人的使用情况，大多数情况下我每月的流量使用未超过50GB，如果没有频繁的流量使用，
      建议选择12元/年，否则可以选择15元/季，这意味着每月将有4000GB的可用额度
      • \n
    \n
    • \n

  • 赔钱机场

    \n


    赔钱机场的订阅共有9种方案，这里我仅显示自己正在使用的，个人认为十分优惠：

    \n
      \n
    • 34.99元/年,每月500GB的可用额度，根据我观察和使用，这个订阅方案比一元机场的性价比更高，且流量使用额度也不用担心
      • \n
    \n
    • \n
\n

如何订阅？

由于需要外部网络才能完成订阅服务的购买，你可以向我的邮箱15202078626@163.com发送你的订阅计划方案，
扫描付款二维码，我将为你开通订阅（您只需要付款对应的订阅金额即可）

\n
\n\n\n

完成订阅后如何使用？

\n

你可以在 Windows、Mac、Android等平台使用此服务
使用订阅的对应链接： https://flowus.cn/shenjian/22f76d4f-e7b3-4b8a-8a89-561566f6eb60

\n
\n","cover":false,"excerpt":"","more":"

由于中国大陆的GFW（防火墙）限制，无法访问外网网络，因此需要访问像GitHub、YouTube这样的
的网站将被屏蔽拦截，接下来我将给出一种使用VPN服务的可行的方案来保证服务的可靠性。

\n
\n\n

介绍

\n

根据测试，许多提供服务的提供商所在的网站需要使用外部网络才能打开，仅有少部分的网站(比较贵)可以直接使用
国内网络环境打开直接购买订阅服务。

\n
\n

那么你现在可以有两个选择：

\n
    \n
  1. 方案一：使用无需外部网络便能开通订阅服务的VPN，费用高，如果你选择此方案，那么你可自行搜索解决，此处仅仅讨论方案二。
    2. \n
  2. 方案二：如果使用此方案，详见下方。
    3. \n
\n

解决方案

\n

采用方案二方式

\n

这是一些订阅服务推广的链接： https://9.234456.xyz/abc.html?t=638586217737356738 (此链接打开无需使用VPN，但进入对应的机场页面却仍无法打开)

\n

此教程中我们使用的机场是

\n
    \n
  1. 一元机场: https://xn--4gq62f52gdss.com/
    2. \n
  2. 赔钱机场：https://xn--mes358aby2apfg.com/
    3. \n
\n
\n

机场选择的建议：

    \n

  • 一元机场

    可以看到

    \n
      \n
    • 12元/年,每月50GB的可用额度，下个月重置流量额度
      • \n
    • 15元/季，即为60元/年，每月有4000GB的可用额度，下个月重置流量额度
      • \n
    • 7元/月，即为84元/年，每个月8000GB的可用额度，下个月重置流量额度
      根据我个人的使用情况，大多数情况下我每月的流量使用未超过50GB，如果没有频繁的流量使用，
      建议选择12元/年，否则可以选择15元/季，这意味着每月将有4000GB的可用额度
      • \n
    \n
    • \n

  • 赔钱机场

    \n


    赔钱机场的订阅共有9种方案，这里我仅显示自己正在使用的，个人认为十分优惠：

    \n
      \n
    • 34.99元/年,每月500GB的可用额度，根据我观察和使用，这个订阅方案比一元机场的性价比更高，且流量使用额度也不用担心
      • \n
    \n
    • \n
\n

如何订阅？

由于需要外部网络才能完成订阅服务的购买，你可以向我的邮箱15202078626@163.com发送你的订阅计划方案，
扫描付款二维码，我将为你开通订阅（您只需要付款对应的订阅金额即可）

\n
\n\n\n

完成订阅后如何使用？

\n

你可以在 Windows、Mac、Android等平台使用此服务
使用订阅的对应链接： https://flowus.cn/shenjian/22f76d4f-e7b3-4b8a-8a89-561566f6eb60

\n
\n"}],"PostAsset":[],"PostCategory":[{"post_id":"cm63ecyk6000grcah3k7sfd16","category_id":"cm63ecyk8000jrcah4oekbwlh","_id":"cm63ecyka000nrcahgjw5az1h"}],"PostTag":[{"post_id":"cm63ecyjx0003rcaheuf03fft","tag_id":"cm63ecyk00006rcah6wge7022","_id":"cm63ecyk5000ercah2of09czv"},{"post_id":"cm63ecyk3000arcah2gpi320x","tag_id":"cm63ecyk5000drcah6g2t37bu","_id":"cm63ecyk9000krcahgch46z8m"},{"post_id":"cm63ecyk4000crcah1m2udz0g","tag_id":"cm63ecyk8000ircah2jqu6oke","_id":"cm63ecyka000mrcahhhar4ci2"},{"post_id":"cm63ecyk6000grcah3k7sfd16","tag_id":"cm63ecyk9000lrcaheytpbuc4","_id":"cm63ecyka000orcahg86icugy"},{"post_id":"cm63ecykd000srcahe04kfgm3","tag_id":"cm63ecyke000urcah4ci9ai61","_id":"cm63ecykg000xrcahfddr5yw0"},{"post_id":"cm63ecykd000trcah96geh51h","tag_id":"cm63ecyke000urcah4ci9ai61","_id":"cm63ecykg000zrcah3reqagqe"},{"post_id":"cm63ecyke000vrcah4jrfbs1x","tag_id":"cm63ecykg000yrcah2ojxbj9i","_id":"cm63ecykg0010rcah12kagdb6"}],"Tag":[{"name":"C C++","_id":"cm63ecyk00006rcah6wge7022"},{"name":"machinelearning","_id":"cm63ecyk5000drcah6g2t37bu"},{"name":"linear-regression","_id":"cm63ecyk8000ircah2jqu6oke"},{"name":"古文观止","_id":"cm63ecyk9000lrcaheytpbuc4"},{"name":"uniapp","_id":"cm63ecyke000urcah4ci9ai61"},{"name":"网络代理","_id":"cm63ecykg000yrcah2ojxbj9i"}]}} \ No newline at end of file diff --git a/public/about/index.html b/public/about/index.html index ba43eb0..ff666ce 100644 --- a/public/about/index.html +++ b/public/about/index.html @@ -153,15 +153,15 @@ } } detectApple() - })(window)

About me

落花飞舞，翩若惊鸿。

-
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
13
标签
4
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群搭建基础环境
Hadoop集群HDFS配置
Hadoop集群Zookeeper配置
Hello World
+ })(window)

About me

落花飞舞，翩若惊鸿。

+
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
15
标签
6
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群Zookeeper配置
Hadoop集群搭建基础环境
分类
标签
machinelearning uniapp 网络代理 古文观止
归档
网站资讯
文章数目 :
13
已运行时间 :
本站访客数 :
本站总访问量 :
最后更新时间 :

八月 2024

文章总览 - 8
2024
script
郑伯克段于鄢
网络相关
无法访问外网？需要订阅代理服务？
uniapp 开发
组件使用
Docker被墙，如何继续使用？
page

八月 2024

文章总览 - 8
2024
script
郑伯克段于鄢
网络相关
无法访问外网？需要订阅代理服务？
组件使用
uniapp 开发
Docker被墙，如何继续使用？
page

九月 2024

文章总览 - 4
2024
Hadoop集群搭建基础环境
Hadoop集群HDFS配置
Hadoop集群Zookeeper配置
Hello World

九月 2024

文章总览 - 4
2024
Hadoop集群Zookeeper配置
Hadoop集群搭建基础环境
Hadoop集群HDFS配置
Hello World

2024

2024

2024

文章总览 - 12
2024
Docker被墙，如何继续使用？
page

2024

文章总览 - 12
2024
Docker被墙，如何继续使用？
page

一月 2025

文章总览 - 1
2025
k近邻算法（K-Nearest Neighbors）KNN

一月 2025

文章总览 - 3
2025
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN

2025

文章总览 - 1
2025
k近邻算法（K-Nearest Neighbors）KNN

2025

文章总览 - 3
2025
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN

Archives

Archives

文章总览 - 15
2025
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN
2024
Hadoop集群Zookeeper配置
Hadoop集群搭建基础环境
Hadoop集群HDFS配置
Hello World
script
郑伯克段于鄢
网络相关

Archives

文章总览 - 13
2024
组件使用
Docker被墙，如何继续使用？
page

Archives

categories

categories

古文观止

分类 - 古文观止
2024
郑伯克段于鄢

古文观止

分类 - 古文观止
2024
郑伯克段于鄢

QuickReference

k近邻算法（K-Nearest Neighbors）KNN
发表于
k近邻算法（K-Nearest Neighbors）KNN将当前样本的类别归类于距离最近的k个样本的类别 + })(window)

QuickReference

线性回归
发表于
线性回归简介 +用于预测一个连续的目标变量（因变量），与一个或多个特征（自变量）之间存在线性关系。 + +假设函数：$$y = w_1x_1 + w_2x_2 + \cdot\cdot\cdot+w_nx_n$$ + +$y$ 是目标变量（因变量），即我们希望预测的值。 +$x1​,x2​,…,xn$​ 是特征变量（自变量），即输入的值。 + +损失函数为了找到最佳的线性模型，我们需要通过最小化损失函数来优化模型参数。在线性回归中，常用的损失函数是 均方误差（MSE）：$$MSE = \frac{1}{m} \sum_{i=1}^{m} (y_i - \hat{y}_i)^2$$ + +m 是样本的数量。 +$y_i$​ 是第 i 个样本的真实值。 +$\hat{y}_i​$ 是模型预测的第 i 个样本的值。 + +线性回归优化 +梯度下降法 + 123456789101112131415161718192021222324252627282930from sklearn.datasets import fetch_california_housingfrom sklearn.mode ...
C lang
发表于
c lang在windows下的开发（VS code）WinLibs - GCC+MinGW-w64 compiler for Windows下载你需要的版本解压到D:\ProgramModule，并将 bin\加入环境变量PATH打开新的Terminal输入gcc -v，查看gcc是否安装成功在VS code 的插件管理下载Code Runner、C\C++这两个插件在*.c源文件的内容区，右键点击Run Code ，即可运行成功 +数据类型 +整数类型1234567891011    short a = 12;    int b = 100;    long c = 1000L;    long long d = 1000000LL;    unsigned int e = 10;    printf("a: %hd\n",a);    printf("b: %d\n",b);    printf("c: %ld\n",c);    printf("d: %lld\n",d);    printf(&quo ...
k近邻算法（K-Nearest Neighbors）KNN
发表于
k近邻算法（K-Nearest Neighbors）KNN将当前样本的类别归类于距离最近的k个样本的类别 距离公式(2维) 欧式距离$$d = \sqrt{(x_1-y_1)^2 + (x_2 - y_2)^2}$$ 曼哈顿距离$$d = |x_1 - x_2| + |y_1 - y_2|$$ @@ -177,15 +195,7 @@ k值 通过一些转换函数将特征数据转换成更加适合算法模型的特征数据过程 -归一化将数据变换到指定区间（默认是[0,1]）$$ x’ = \frac{x- x_{\text {min}}}{x_{\text{max}} - x_{\text{min}}} $$若需要缩放到任意区间 ([a, b])，公式为： $$ x’ = a + \frac{(x - x_{\text{min}}) \cdot (b - a ...
Hadoop集群搭建基础环境
发表于
防火墙关闭123# 在 6 台主机执行systemctl stop firewalldsystemctl disable firewalld -配置yum源 -下载 repo 文件：Centos-7.repo并上传到/tmp，进入到/tmp -备份并且替换系统的repo文件1234 cp Centos-7.repo /etc/yum.repos.d/ cd /etc/yum.repos.d/ mv CentOS-Base.repo CentOS-Base.repo.bak mv Centos-7.repo CentOS-Base.repo -将nn1上的CentOS-Base.repo拷贝到其他主机12345scp /etc/yum.repos.d/CentOS-Base.repo root@nn2:/etc/yum.repos.dscp /etc/yum.repos.d/CentOS-Base.repo root@nn3:/etc/yum.repos.dscp /etc/yum.repos.d/CentOS-Base.repo root@s1:/etc/yum.repos.dscp /et ...
Hadoop集群HDFS配置
发表于
上传hadoop-3.1.4.tar.gz到/tmp，解压 -注意在六台机器均上传到/tmp - -12345# 在6台机器执行sudo tar -zxvf /tmp/hadoop-3.1.4.tar.gz -C /usr/local/# 分发到其他主机ssh_root.sh chown -R hadoop:hadoop /usr/local/hadoop-3.1.4ssh_root.sh ln -s /usr/local/hadoop-3.1.4/ /usr/local/hadoop -配置环境变量123echo 'export HADOOP_HOME=/usr/local/hadoop' >> /etc/profile.d/myEnv.shecho 'export PATH=$PATH:$HADOOP_HOME/bin' >> /etc/profile.d/myEnv.shecho 'export PATH=$PATH:$HADOOP_HOME/sbin' >> /etc/profile.d/m ...
Hadoop集群Zookeeper配置
发表于
Zookeeper脚本配置 +归一化将数据变换到指定区间（默认是[0,1]）$$ x’ = \frac{x- x_{\text {min}}}{x_{\text{max}} - x_{\text{min}}} $$若需要缩放到任意区间 ([a, b])，公式为： $$ x’ = a + \frac{(x - x_{\text{min}}) \cdot (b - a ...
Hadoop集群Zookeeper配置
发表于
Zookeeper脚本配置 拷贝ips1cp ips ips_zookeeper 修改为123nn1nn2nn3 拷贝三个脚本123cp scp_all.sh scp_all_zookeeper.sh cp ssh_all.sh ssh_all_zookeeper.sh cp ssh_root.sh ssh_root_zookeeper.sh @@ -194,7 +204,15 @@ k值 将三个脚本中的ips改为ips_zookeeper Zookeeper安装 -上传到/tmp目录下，解压 12345sudo tar -zxvf /tmp/zookeeper-3.4.8.tar.gz -C /usr/local/scp -r /usr/local/zookeeper-3.4.8/ root@nn2:/usr/local/scp -r /usr/local/zookeeper-3.4.8/ root@nn3:/usr/local/ssh_root_zookeepe ...
Hello World
发表于
Welcome to Hexo! This is your very first post. Check documentation for more info. If you get any problems when using Hexo, you can find the answer in troubleshooting or you can ask me on GitHub. +上传到/tmp目录下，解压 12345sudo tar -zxvf /tmp/zookeeper-3.4.8.tar.gz -C /usr/local/scp -r /usr/local/zookeeper-3.4.8/ root@nn2:/usr/local/scp -r /usr/local/zookeeper-3.4.8/ root@nn3:/usr/local/ssh_root_zookeepe ...
Hadoop集群搭建基础环境
发表于
防火墙关闭123# 在 6 台主机执行systemctl stop firewalldsystemctl disable firewalld +配置yum源 +下载 repo 文件：Centos-7.repo并上传到/tmp，进入到/tmp +备份并且替换系统的repo文件1234 cp Centos-7.repo /etc/yum.repos.d/ cd /etc/yum.repos.d/ mv CentOS-Base.repo CentOS-Base.repo.bak mv Centos-7.repo CentOS-Base.repo +将nn1上的CentOS-Base.repo拷贝到其他主机12345scp /etc/yum.repos.d/CentOS-Base.repo root@nn2:/etc/yum.repos.dscp /etc/yum.repos.d/CentOS-Base.repo root@nn3:/etc/yum.repos.dscp /etc/yum.repos.d/CentOS-Base.repo root@s1:/etc/yum.repos.dscp /et ...
Hadoop集群HDFS配置
发表于
上传hadoop-3.1.4.tar.gz到/tmp，解压 +注意在六台机器均上传到/tmp + +12345# 在6台机器执行sudo tar -zxvf /tmp/hadoop-3.1.4.tar.gz -C /usr/local/# 分发到其他主机ssh_root.sh chown -R hadoop:hadoop /usr/local/hadoop-3.1.4ssh_root.sh ln -s /usr/local/hadoop-3.1.4/ /usr/local/hadoop +配置环境变量123echo 'export HADOOP_HOME=/usr/local/hadoop' >> /etc/profile.d/myEnv.shecho 'export PATH=$PATH:$HADOOP_HOME/bin' >> /etc/profile.d/myEnv.shecho 'export PATH=$PATH:$HADOOP_HOME/sbin' >> /etc/profile.d/m ...
Hello World
发表于
Welcome to Hexo! This is your very first post. Check documentation for more info. If you get any problems when using Hexo, you can find the answer in troubleshooting or you can ask me on GitHub. Quick StartCreate a new post1$ hexo new "My New Post" More info: Writing @@ -209,40 +227,14 @@ Deploy to remote sites1$ hexo deploy More info: Deployment
script
发表于
查看CPU、内存使用率1234567891011121314151617181920212223242526#!/bin/bash# 定义颜色RED='\033[0;31m'GREEN='\033[0;32m'YELLOW='\033[0;33m'BLUE='\033[0;34m'NC='\033[0m' # 无颜色while true; do # 获取所有进程的CPU使用率和内存使用率 cpu_usage=$(ps aux | awk '{sum_cpu += $3} END {print sum_cpu}') mem_usage=$(ps aux | awk '{sum_mem += $4} END {print sum_mem}') # 打印结果，带有时间戳、分隔线和颜色高亮 echo -e "${BLUE ...
郑伯克段于鄢
发表于|古文观止
原文如下：       初，郑武公娶于申【申国】，曰武姜【武为武公谥号，姜为其宗族之性】。生庄公及共叔段【共表示其曾出逃到共，叔为老三，段为名】。庄公寤生【出生时头先出，难产】，惊姜氏，故名曰“寤生”， 遂恶之，爱【喜爱】共叔段，欲立【立为储君】之，亟（qì）【多次】请于武公，及庄公即位，为之【共叔段】请制【一个叫制的封地，虎牢关所在】。公曰：“制，岩邑【险要的城邑】也，虢叔死焉，佗【通“他”，其他】邑唯命（是听）。”请京，使居之，谓之“京城大叔”。 -
网络相关
发表于
无法访问外网？需要订阅代理服务？
发表于
由于中国大陆的GFW（防火墙）限制，无法访问外网网络，因此需要访问像GitHub、YouTube这样的的网站将被屏蔽拦截，接下来我将给出一种使用VPN服务的可行的方案来保证服务的可靠性。 - - -介绍 -根据测试，许多提供服务的提供商所在的网站需要使用外部网络才能打开，仅有少部分的网站(比较贵)可以直接使用国内网络环境打开直接购买订阅服务。 - -那么你现在可以有两个选择： - -方案一：使用无需外部网络便能开通订阅服务的VPN，费用高，如果你选择此方案，那么你可自行搜索解决，此处仅仅讨论方案二。 -方案二：如果使用此方案，详见下方。 - -解决方案 -采用方案二方式 -这是一些订阅服务推广的链接： https://9.234456.xyz/abc.html?t=638586217737356738 (此链接打开无需使用VPN，但进入对应的机场页面却仍无法打开) -此教程中我们使用的机场是 - -一元机场: https://xn--4gq62f52gdss.com/ -赔钱机场：https://xn--mes358aby2apfg.com/ - - -机场选择的建议： -一元机场可以看到 - -12元/年,每月50GB的可用额 ...
uniapp 开发
发表于
-uniapp component - -
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
13
标签
4
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群搭建基础环境
Hadoop集群HDFS配置
Hadoop集群Zookeeper配置
Hello World
+
网络相关
发表于
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
15
标签
6
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群Zookeeper配置
Hadoop集群搭建基础环境
分类
标签
machinelearning uniapp 网络代理 古文观止
归档
网站资讯
文章数目 :
13
已运行时间 :
本站访客数 :
本站总访问量 :
最后更新时间 :

link

友情鏈接

那些人，那些事
+ })(window)

link

友情鏈接

那些人，那些事
@@ -189,14 +189,14 @@
Twitter
社交分享平台
 -
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
13
标签
4
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群搭建基础环境
Hadoop集群HDFS配置
Hadoop集群Zookeeper配置
Hello World
+
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
15
标签
6
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群Zookeeper配置
Hadoop集群搭建基础环境
分类
标签
machinelearning uniapp 网络代理 古文观止
归档
网站资讯
文章数目 :
13
已运行时间 :
本站访客数 :
本站总访问量 :
最后更新时间 :

Movies

avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
13
标签
4
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群搭建基础环境
Hadoop集群HDFS配置
Hadoop集群Zookeeper配置
Hello World
+ })(window)

Movies

avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
15
标签
6
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群Zookeeper配置
Hadoop集群搭建基础环境
分类
标签
machinelearning uniapp 网络代理 古文观止
归档
网站资讯
文章数目 :
13
已运行时间 :
本站访客数 :
本站总访问量 :
最后更新时间 :

Music

avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
13
标签
4
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群搭建基础环境
Hadoop集群HDFS配置
Hadoop集群Zookeeper配置
Hello World
+ })(window)

Music

avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
15
标签
6
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群Zookeeper配置
Hadoop集群搭建基础环境
分类
标签
machinelearning uniapp 网络代理 古文观止
归档
网站资讯
文章数目 :
13
已运行时间 :
本站访客数 :
本站总访问量 :
最后更新时间 :

QuickReference

组件使用
发表于
组件自动导入123456"easycom":{ "autoscan": true, "custom": { "^tui-(.*)": "@/components/thorui/tui-$1/tui-$1.vue" // 匹配components目录内的vue文件 }} + })(window)

QuickReference

无法访问外网？需要订阅代理服务？
发表于
由于中国大陆的GFW（防火墙）限制，无法访问外网网络，因此需要访问像GitHub、YouTube这样的的网站将被屏蔽拦截，接下来我将给出一种使用VPN服务的可行的方案来保证服务的可靠性。 + + +介绍 +根据测试，许多提供服务的提供商所在的网站需要使用外部网络才能打开，仅有少部分的网站(比较贵)可以直接使用国内网络环境打开直接购买订阅服务。 + +那么你现在可以有两个选择： + +方案一：使用无需外部网络便能开通订阅服务的VPN，费用高，如果你选择此方案，那么你可自行搜索解决，此处仅仅讨论方案二。 +方案二：如果使用此方案，详见下方。 + +解决方案 +采用方案二方式 +这是一些订阅服务推广的链接： https://9.234456.xyz/abc.html?t=638586217737356738 (此链接打开无需使用VPN，但进入对应的机场页面却仍无法打开) +此教程中我们使用的机场是 + +一元机场: https://xn--4gq62f52gdss.com/ +赔钱机场：https://xn--mes358aby2apfg.com/ + + +机场选择的建议： +一元机场可以看到 + +12元/年,每月50GB的可用额 ...
组件使用
发表于
组件自动导入123456"easycom":{ "autoscan": true, "custom": { "^tui-(.*)": "@/components/thorui/tui-$1/tui-$1.vue" // 匹配components目录内的vue文件 }} tui-sticky 吸顶容器 包含 以下 tui 组件 : @@ -161,7 +184,10 @@ tui-list-view tui-list-cell -123456789101112131415161718192021222324252627282930<tui-sticky :scrollTop="scrollTop" stickyHeight="104rpx" container> <!-- header start --> <template v-slot:header> ...
Docker被墙，如何继续使用？
发表于
Docker Download +123456789101112131415161718192021222324252627282930<tui-sticky :scrollTop="scrollTop" stickyHeight="104rpx" container> <!-- header start --> <template v-slot:header> ...
uniapp 开发
发表于
+uniapp component + +
Docker被墙，如何继续使用？
发表于
Docker Download 自从docker官方仓库在中国大陆被墙后，docker的部署方式也发生了改变。解决docker安装问题：https://github.com/shenjianZ/docker_installer @@ -184,14 +210,14 @@ Docker pull images deploy -
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
13
标签
4
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群搭建基础环境
Hadoop集群HDFS配置
Hadoop集群Zookeeper配置
Hello World
+
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
15
标签
6
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群Zookeeper配置
Hadoop集群搭建基础环境
分类
标签
machinelearning uniapp 网络代理 古文观止
归档
网站资讯
文章数目 :
13
已运行时间 :
本站访客数 :
本站总访问量 :
最后更新时间 :

C lang

发表于|更新于
|阅读量:

c lang在windows下的开发（VS code）

WinLibs - GCC+MinGW-w64 compiler for Windows下载你需要的版本
解压到D:\ProgramModule，并将 bin\加入环境变量PATH
打开新的Terminal输入gcc -v，查看gcc是否安装成功
在VS code 的插件管理下载Code Runner、C\C++这两个插件
在*.c源文件的内容区，右键点击Run Code ，即可运行成功

+

数据类型

    +
  • 整数类型
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
        short a = 12;
        int b = 100;
        long c = 1000L;
        long long d = 1000000LL;
        unsigned int e = 10;
        printf("a: %hd\n",a);
        printf("b: %d\n",b);
        printf("c: %ld\n",c);
        printf("d: %lld\n",d);
        printf("e: %u\n",e);
        printf("f: %.3f\n",f);
    • +
  • 小数类型
    1
    2
    3
    4
    float f = 3.14F;
    printf("f: %.3f\n",f);
    double g = 5.65;
    printf("g: %.2lf\n",g);
    • +
  • 字符类型
    1
    2
    char h = 'x';
    printf("x: %c\n",x);
    • +
+

类型转换

    +
  • 隐式转换
    • +
  • 强制转换
    1
    2
    int b = 23;
    short c = (short) b;
    • +
+

数组

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
#include <stdio.h>

int main(){
    int arr [10] = {2,3,4,5,6,7,8,9,10,11};
    arr[0] = 1525;
    *(arr+1) = 25;
    int len = sizeof(arr)/sizeof(arr[0]);
    void printArr(int arr[], int len){
        for (int i = 0; i < len;i++){
            printf("%d\t",arr[i]);
        }
    }
    printArr(arr,len);
    return 0;
}
+

指针

1
2
3
4
5
6
7
8
9
10
11
// swap the value of a and b
    void swap(int* x, int* y){
        int temp = *x;
        *x = *y;
        *y = temp;

    }
    int a = 5;
    int b = 10;
    swap(&a, &b);
    printf("a = %d b = %d\n", a, b);
+
文章作者: shenjianZ
文章链接: https://rq.shenjianl.cn/posts/12462.html
版权声明: 本博客所有文章除特别声明外，均采用 CC BY-NC-SA 4.0 许可协议。转载请注明来自 QuickReference！
C C++
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
15
标签
6
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群Zookeeper配置
Hadoop集群搭建基础环境
\ No newline at end of file diff --git a/public/posts/14011.html b/public/posts/14011.html index 331380b..af7e2ba 100644 --- a/public/posts/14011.html +++ b/public/posts/14011.html @@ -153,7 +153,7 @@ } } detectApple() - })(window)

无法访问外网？需要订阅代理服务？

发表于|更新于
|阅读量:

由于中国大陆的GFW（防火墙）限制，无法访问外网网络，因此需要访问像GitHub、YouTube这样的
的网站将被屏蔽拦截，接下来我将给出一种使用VPN服务的可行的方案来保证服务的可靠性。

+ })(window)

无法访问外网？需要订阅代理服务？

发表于|更新于
|阅读量:

由于中国大陆的GFW（防火墙）限制，无法访问外网网络，因此需要访问像GitHub、YouTube这样的
的网站将被屏蔽拦截，接下来我将给出一种使用VPN服务的可行的方案来保证服务的可靠性。

介绍

@@ -195,7 +195,7 @@

完成订阅后如何使用？

你可以在 Windows、Mac、Android等平台使用此服务
使用订阅的对应链接： https://flowus.cn/shenjian/22f76d4f-e7b3-4b8a-8a89-561566f6eb60

-
文章作者: shenjianZ
文章链接: https://rq.shenjianl.cn/posts/14011.html
版权声明: 本博客所有文章除特别声明外，均采用 CC BY-NC-SA 4.0 许可协议。转载请注明来自 QuickReference！
网络代理
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
13
标签
4
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群搭建基础环境
Hadoop集群HDFS配置
Hadoop集群Zookeeper配置
Hello World
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
15
标签
6
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群Zookeeper配置
Hadoop集群搭建基础环境

page

发表于|更新于
|阅读量:
    + })(window)

    page

    发表于|更新于
    |阅读量:
    文章作者: shenjianZ
    文章链接: https://rq.shenjianl.cn/posts/1441.html
    版权声明: 本博客所有文章除特别声明外，均采用 CC BY-NC-SA 4.0 许可协议。转载请注明来自 QuickReference！
    avatar
    shenjianZ
    一份快捷简便的文档，便于查阅编程的细节
    文章
    13
    标签
    4
    分类
    1
    Follow Me
    公告
    一个简单快捷的文档知识点查阅网站
    最新文章
    k近邻算法（K-Nearest Neighbors）KNN
    Hadoop集群搭建基础环境
    Hadoop集群HDFS配置
    Hadoop集群Zookeeper配置
    Hello World
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
15
标签
6
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群Zookeeper配置
Hadoop集群搭建基础环境

Hello World

发表于|更新于
|阅读量:

Welcome to Hexo! This is your very first post. Check documentation for more info. If you get any problems when using Hexo, you can find the answer in troubleshooting or you can ask me on GitHub.

+ })(window)

Hello World

发表于|更新于
|阅读量:

Welcome to Hexo! This is your very first post. Check documentation for more info. If you get any problems when using Hexo, you can find the answer in troubleshooting or you can ask me on GitHub.

Quick Start

Create a new post

1
$ hexo new "My New Post"

More info: Writing

@@ -166,7 +166,7 @@

Deploy to remote sites

1
$ hexo deploy

More info: Deployment

-
文章作者: shenjianZ
文章链接: https://rq.shenjianl.cn/posts/16107.html
版权声明: 本博客所有文章除特别声明外，均采用 CC BY-NC-SA 4.0 许可协议。转载请注明来自 QuickReference！
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
13
标签
4
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群搭建基础环境
Hadoop集群HDFS配置
Hadoop集群Zookeeper配置
Hello World
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
15
标签
6
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群Zookeeper配置
Hadoop集群搭建基础环境

k近邻算法（K-Nearest Neighbors）KNN

发表于|更新于
|阅读量:

k近邻算法（K-Nearest Neighbors）KNN

将当前样本的类别归类于距离最近的k个样本的类别

+ })(window)

k近邻算法（K-Nearest Neighbors）KNN

发表于|更新于
|阅读量:

k近邻算法（K-Nearest Neighbors）KNN

将当前样本的类别归类于距离最近的k个样本的类别

距离公式(2维)

  • 欧式距离
    $$
    d = \sqrt{(x_1-y_1)^2 + (x_2 - y_2)^2}
    $$
  • 曼哈顿距离
    $$
    d = |x_1 - x_2| + |y_1 - y_2|
    $$
    • @@ -207,7 +207,7 @@
  • 自助法
  • 交叉验证法
-
文章作者: shenjianZ
文章链接: https://rq.shenjianl.cn/posts/29139.html
版权声明: 本博客所有文章除特别声明外，均采用 CC BY-NC-SA 4.0 许可协议。转载请注明来自 QuickReference！
machinelearning
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
13
标签
4
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群搭建基础环境
Hadoop集群HDFS配置
Hadoop集群Zookeeper配置
Hello World
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
15
标签
6
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群Zookeeper配置
Hadoop集群搭建基础环境

组件使用

发表于|更新于
|阅读量:

组件自动导入

1
2
3
4
5
6
"easycom":{
  "autoscan": true,
  "custom": {
    "^tui-(.*)": "@/components/thorui/tui-$1/tui-$1.vue"  // 匹配components目录内的vue文件
  }
}
+ })(window)

组件使用

发表于|更新于
|阅读量:

组件自动导入

1
2
3
4
5
6
"easycom":{
  "autoscan": true,
  "custom": {
    "^tui-(.*)": "@/components/thorui/tui-$1/tui-$1.vue"  // 匹配components目录内的vue文件
  }
}

tui-sticky 吸顶容器

包含 以下 tui 组件 :

@@ -164,7 +164,7 @@ 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
<tui-sticky :scrollTop="scrollTop" stickyHeight="104rpx" container>
    <!-- header start -->
    <template v-slot:header>
        <view class="sticky-item">
            <view class="setting">设置</view>
        </view>
    </template>
    <!-- header end -->
    <!--内容 start-->
    <template v-slot:content>
        <tui-list-view class="content">
            <tui-list-cell  :arrow="false">
                <switch class='switch' checked color="#FFCC33" />
            </tui-list-cell>
        </tui-list-view>
    </template>
    <!--内容 end-->
</tui-sticky>

<script setup>
    import { ref } from 'vue'
    import { onPageScroll } from '@dcloudio/uni-app'

    // 定义 scrollTop 响应式变量
    const scrollTop = ref(0)
    // 监听页面滚动事件
    onPageScroll((e) => {
        scrollTop.value = e.scrollTop
    })
</script>
-
文章作者: shenjianZ
文章链接: https://rq.shenjianl.cn/posts/33957.html
版权声明: 本博客所有文章除特别声明外，均采用 CC BY-NC-SA 4.0 许可协议。转载请注明来自 QuickReference！
uniapp
相关推荐
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
13
标签
4
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群搭建基础环境
Hadoop集群HDFS配置
Hadoop集群Zookeeper配置
Hello World
相关推荐
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
15
标签
6
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群Zookeeper配置
Hadoop集群搭建基础环境

script

发表于|更新于
|阅读量:

查看CPU、内存使用率

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
#!/bin/bash

# 定义颜色
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[0;33m'
BLUE='\033[0;34m'
NC='\033[0m' # 无颜色

while true; do
    # 获取所有进程的CPU使用率和内存使用率
    cpu_usage=$(ps aux | awk '{sum_cpu += $3} END {print sum_cpu}')
    mem_usage=$(ps aux | awk '{sum_mem += $4} END {print sum_mem}')
    
    # 打印结果，带有时间戳、分隔线和颜色高亮
    echo -e "${BLUE}==============================${NC}"
    echo -e "${YELLOW}Timestamp: $(date)${NC}"
    echo -e "${BLUE}==============================${NC}"
    echo -e "${GREEN}Total CPU usage: ${RED}$cpu_usage%${NC}"
    echo -e "${GREEN}Total Memory usage: ${RED}$mem_usage%${NC}"
    echo -e "${BLUE}==============================${NC}"
    
    # 等待5秒后再次执行
    sleep 5
done

+ })(window)

script

发表于|更新于
|阅读量:

查看CPU、内存使用率

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
#!/bin/bash

# 定义颜色
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[0;33m'
BLUE='\033[0;34m'
NC='\033[0m' # 无颜色

while true; do
    # 获取所有进程的CPU使用率和内存使用率
    cpu_usage=$(ps aux | awk '{sum_cpu += $3} END {print sum_cpu}')
    mem_usage=$(ps aux | awk '{sum_mem += $4} END {print sum_mem}')
    
    # 打印结果，带有时间戳、分隔线和颜色高亮
    echo -e "${BLUE}==============================${NC}"
    echo -e "${YELLOW}Timestamp: $(date)${NC}"
    echo -e "${BLUE}==============================${NC}"
    echo -e "${GREEN}Total CPU usage: ${RED}$cpu_usage%${NC}"
    echo -e "${GREEN}Total Memory usage: ${RED}$mem_usage%${NC}"
    echo -e "${BLUE}==============================${NC}"
    
    # 等待5秒后再次执行
    sleep 5
done

保存脚本到/usr/local/bin目录下

1
mv usage.sh /usr/local/bin/usage
-

Shell脚本编写的基本信息

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
#! /bin/bash
# -------------------------------------------------
# Filename:     test.sh
# Version:      1.0
# Date:         2024/05/02
# Author:       shenjianZ
# Email:        shenjianZLT@gmail.com
# Website:      https://blog.shenjianl.cn
# Description:  this is a test shell
# CopyRight:    2024 All rights reserved shenjianZ
# License       GPL
# ------------------------------------------------


# Your script logic goes here
文章作者: shenjianZ
文章链接: https://rq.shenjianl.cn/posts/34849.html
版权声明: 本博客所有文章除特别声明外，均采用 CC BY-NC-SA 4.0 许可协议。转载请注明来自 QuickReference！
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
13
标签
4
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群搭建基础环境
Hadoop集群HDFS配置
Hadoop集群Zookeeper配置
Hello World
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
15
标签
6
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群Zookeeper配置
Hadoop集群搭建基础环境

网络相关

发表于|更新于
|阅读量:
文章作者: shenjianZ
文章链接: https://rq.shenjianl.cn/posts/41168.html
版权声明: 本博客所有文章除特别声明外，均采用 CC BY-NC-SA 4.0 许可协议。转载请注明来自 QuickReference！
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
13
标签
4
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群搭建基础环境
Hadoop集群HDFS配置
Hadoop集群Zookeeper配置
Hello World

网络相关

发表于|更新于
|阅读量:
文章作者: shenjianZ
文章链接: https://rq.shenjianl.cn/posts/41168.html
版权声明: 本博客所有文章除特别声明外，均采用 CC BY-NC-SA 4.0 许可协议。转载请注明来自 QuickReference！
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
15
标签
6
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群Zookeeper配置
Hadoop集群搭建基础环境

Docker被墙，如何继续使用？

发表于|更新于
|阅读量:

Docker Download

+ })(window)

Docker被墙，如何继续使用？

发表于|更新于
|阅读量:
文章作者: shenjianZ
文章链接: https://rq.shenjianl.cn/posts/47807.html
版权声明: 本博客所有文章除特别声明外，均采用 CC BY-NC-SA 4.0 许可协议。转载请注明来自 QuickReference！
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
13
标签
4
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群搭建基础环境
Hadoop集群HDFS配置
Hadoop集群Zookeeper配置
Hello World
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
15
标签
6
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群Zookeeper配置
Hadoop集群搭建基础环境

线性回归

发表于|更新于
|阅读量:

线性回归简介

+

用于预测一个连续的目标变量（因变量），与一个或多个特征（自变量）之间存在线性关系。

+
+

假设函数：
$$y = w_1x_1 + w_2x_2 + \cdot\cdot\cdot+w_nx_n$$

+
    +
  • $y$ 是目标变量（因变量），即我们希望预测的值。
    • +
  • $x1​,x2​,…,xn$​ 是特征变量（自变量），即输入的值。
    • +
+

损失函数

为了找到最佳的线性模型，我们需要通过最小化损失函数来优化模型参数。在线性回归中，常用的损失函数是 均方误差（MSE）：
$$MSE = \frac{1}{m} \sum_{i=1}^{m} (y_i - \hat{y}_i)^2$$

+
    +
  • m 是样本的数量。
    • +
  • $y_i$​ 是第 i 个样本的真实值。
    • +
  • $\hat{y}_i​$ 是模型预测的第 i 个样本的值。
    • +
+

线性回归优化

    +

  • 梯度下降法

    + 
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    22
    23
    24
    25
    26
    27
    28
    29
    30
    from sklearn.datasets import fetch_california_housing
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler
    from sklearn.linear_model import SGDRegressor
    from sklearn.metrics import mean_squared_error

    # 1. 获取数据集
    housing = fetch_california_housing()

    # 2. 数据集处理
    # 2.1 分割数据集
    X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)

    # 3. 特征工程
    # 3.1 标准化
    transfer = StandardScaler()
    X_train = transfer.fit_transform(X_train)
    X_test = transfer.transform(X_test)  # 使用 transform() 而不是 fit_transform()

    # 4.机器学习- 梯度下降法
    estimater = SGDRegressor(max_iter=1000, eta0=0.01)
    estimater.fit(X_train, y_train)
    print(f"SGD模型的偏置是：{estimater.intercept_}")
    print(f"SGD模型的系数是：{estimater.coef_}")

    # 5. 模型评估
    y_pred = estimater.predict(X_test)
    print(f"SGD模型预测值：{y_pred}")
    mse = mean_squared_error(y_test, y_pred)
    print(f"SGD模型均方误差:{mse}")
    +
    • +

  • 正规方程

    + 
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    22
    23
    24
    25
    26
    27
    28
    29
    30
    from sklearn.datasets import fetch_california_housing
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler
    from sklearn.linear_model import LinearRegression
    from sklearn.metrics import mean_squared_error

    # 1. 获取数据集
    housing = fetch_california_housing()

    # 2. 数据集处理
    # 2.1 分割数据集
    X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)

    # 3. 特征工程
    # 3.1 标准化
    transfer = StandardScaler()
    X_train = transfer.fit_transform(X_train)
    X_test = transfer.fit_transform(X_test)

    # 4.机器学习- 线性回归
    estimater = LinearRegression()
    estimater.fit(X_train, y_train)
    print(f"模型的偏置是：{estimater.intercept_}")
    print(f"模型的系数是：{estimater.coef_}")

    # 5. 模型评估
    y_pred = estimater.predict(X_test)
    print(f"模型预测值：{y_pred}")
    mse = mean_squared_error(y_test, y_pred)
    print(f"模型均方误差:{mse}")
    +
    • +

  • 岭回归

    + 
    1
    2
    3
    4
    5
    6
    7
    8
    9
    10
    11
    12
    13
    14
    15
    16
    17
    18
    19
    20
    21
    22
    23
    24
    25
    26
    27
    28
    29
    30
    31
    32
    33
    34
    from sklearn.datasets import fetch_california_housing
    from sklearn.model_selection import train_test_split
    from sklearn.preprocessing import StandardScaler
    from sklearn.linear_model import Ridge, RidgeCV
    from sklearn.metrics import mean_squared_error

    # 1. 获取数据集
    housing = fetch_california_housing()

    # 2. 数据集处理
    # 2.1 分割数据集
    X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)

    # 3. 特征工程
    # 3.1 标准化
    transfer = StandardScaler()
    X_train = transfer.fit_transform(X_train)
    X_test = transfer.transform(X_test)  # 使用 transform() 而不是 fit_transform()

    # 4.机器学习- 岭回归 使用了Ridge的alpha的搜索
    # estimater = Ridge(alpha=1.0)
    estimater = RidgeCV(alphas=[0.001, 0.01, 0.1, 1, 10, 100])
    estimater.fit(X_train, y_train)
    print(f"Ridge模型的偏置是：{estimater.intercept_}")
    print(f"Ridge模型的系数是：{estimater.coef_}")

    # 查看最佳 alpha
    print(f"最佳 alpha 值是：{estimater.alpha_}")

    # 5. 模型评估
    y_pred = estimater.predict(X_test)
    print(f"Ridge模型预测值：{y_pred}")
    mse = mean_squared_error(y_test, y_pred)
    print(f"Ridge模型均方误差:{mse}")
    • +
+

这样每个代码块的缩进保持一致，便于阅读和理解。如果有其他优化需求，随时告诉我！

+

+

+

模型保存和加载

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import Ridge, RidgeCV
from sklearn.metrics import mean_squared_error
import joblib

def save_model():
    # 1. 获取数据集
    housing = fetch_california_housing()
    # 2. 数据集处理
    # 2.1 分割数据集
    X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)
    # 3. 特征工程
    # 3.1 标准化
    transfer = StandardScaler()
    X_train = transfer.fit_transform(X_train)
    X_test = transfer.transform(X_test)  # 使用 transform() 而不是 fit_transform()
    # 4. 机器学习 - 岭回归 使用了Ridge的alpha的搜索
    estimater = RidgeCV(alphas=[0.001, 0.01, 0.1, 1, 10, 100])
    estimater.fit(X_train, y_train)
    print(f"Ridge模型的偏置是：{estimater.intercept_}")
    print(f"Ridge模型的系数是：{estimater.coef_}")
    # 保存模型
    joblib.dump(estimater, 'ridge_model.pkl')
    # 查看最佳 alpha
    print(f"最佳 alpha 值是：{estimater.alpha_}")
    # 5. 模型评估
    y_pred = estimater.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    print(f"Ridge模型均方误差:{mse}")

def load_model():
    # 1. 获取数据集
    housing = fetch_california_housing()
    # 2. 数据集处理
    # 2.1 分割数据集
    X_train, X_test, y_train, y_test = train_test_split(housing.data, housing.target, test_size=0.25)
    # 3. 特征工程
    # 3.1 标准化
    transfer = StandardScaler()
    X_train = transfer.fit_transform(X_train)
    X_test = transfer.transform(X_test)  # 使用 transform() 而不是 fit_transform()
    # 加载模型
    estimater = joblib.load('ridge_model.pkl')
    print(f"Ridge模型的偏置是：{estimater.intercept_}")
    print(f"Ridge模型的系数是：{estimater.coef_}")
    # 查看最佳 alpha
    print(f"最佳 alpha 值是：{estimater.alpha_}")
    # 5. 模型评估
    y_pred = estimater.predict(X_test)
    mse = mean_squared_error(y_test, y_pred)
    print(f"Ridge模型预测值：{y_pred}")
    print(f"Ridge模型均方误差:{mse}")

print("训练并保存模型：")
save_model()
print("加载模型")
load_model()
文章作者: shenjianZ
文章链接: https://rq.shenjianl.cn/posts/52662.html
版权声明: 本博客所有文章除特别声明外，均采用 CC BY-NC-SA 4.0 许可协议。转载请注明来自 QuickReference！
linear-regression
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
15
标签
6
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群Zookeeper配置
Hadoop集群搭建基础环境
\ No newline at end of file diff --git a/public/posts/58638.html b/public/posts/58638.html index 4eab51f..a160951 100644 --- a/public/posts/58638.html +++ b/public/posts/58638.html @@ -153,9 +153,9 @@ } } detectApple() - })(window)

郑伯克段于鄢

发表于|更新于|古文观止
|阅读量:

原文如下：

+ })(window)

郑伯克段于鄢

发表于|更新于|古文观止
|阅读量:

原文如下：

      初，郑武公娶于申【申国】，曰武姜【武为武公谥号，姜为其宗族之性】。生庄公及共叔段【共表示其曾出逃到共，叔为老三，段为名】。庄公寤生【出生时头先出，难产】，惊姜氏，故名曰“寤生”， 遂恶之，爱【喜爱】共叔段，欲立【立为储君】之，亟（qì）【多次】请于武公，及庄公即位，为之【共叔段】请制【一个叫制的封地，虎牢关所在】。公曰：“制，岩邑【险要的城邑】也，虢叔死焉，佗【通“他”，其他】邑唯命（是听）。”请京，使居之，谓之“京城大叔”。

-
文章作者: shenjianZ
文章链接: https://rq.shenjianl.cn/posts/58638.html
版权声明: 本博客所有文章除特别声明外，均采用 CC BY-NC-SA 4.0 许可协议。转载请注明来自 QuickReference！
古文观止
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
13
标签
4
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群搭建基础环境
Hadoop集群HDFS配置
Hadoop集群Zookeeper配置
Hello World
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
15
标签
6
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群Zookeeper配置
Hadoop集群搭建基础环境

uniapp 开发

发表于|更新于
|阅读量:
相关推荐
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
15
标签
6
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群Zookeeper配置
Hadoop集群搭建基础环境

Hadoop集群Zookeeper配置

发表于|更新于
|阅读量:

Zookeeper脚本配置

    + })(window)

    Hadoop集群Zookeeper配置

    发表于|更新于
    |阅读量:

    Zookeeper脚本配置

    • 拷贝ips
      1
      cp ips ips_zookeeper
      修改为
      1
      2
      3
      nn1
      nn2
      nn3
    • 拷贝三个脚本
      1
      2
      3
      cp scp_all.sh scp_all_zookeeper.sh 
      cp ssh_all.sh ssh_all_zookeeper.sh 
      cp ssh_root.sh ssh_root_zookeeper.sh
      • @@ -188,7 +188,7 @@ 
      1
      2
      3
      4
      #启动zookeeper客户端，并连接zookeeper集群
      /usr/local/zookeeper/bin/zkCli.sh -server nn1:2181
      # 可以简化为:
      zkCli.sh
      -
    文章作者: shenjianZ
    文章链接: https://rq.shenjianl.cn/posts/61251.html
    版权声明: 本博客所有文章除特别声明外，均采用 CC BY-NC-SA 4.0 许可协议。转载请注明来自 QuickReference！
    avatar
    shenjianZ
    一份快捷简便的文档，便于查阅编程的细节
    文章
    13
    标签
    4
    分类
    1
    Follow Me
    公告
    一个简单快捷的文档知识点查阅网站
    最新文章
    k近邻算法（K-Nearest Neighbors）KNN
    Hadoop集群搭建基础环境
    Hadoop集群HDFS配置
    Hadoop集群Zookeeper配置
    Hello World
avatar
shenjianZ
一份快捷简便的文档，便于查阅编程的细节
文章
15
标签
6
分类
1
Follow Me
公告
一个简单快捷的文档知识点查阅网站
最新文章
线性回归
C lang
k近邻算法（K-Nearest Neighbors）KNN
Hadoop集群Zookeeper配置
Hadoop集群搭建基础环境

Hadoop集群HDFS配置

发表于|更新于
|阅读量:

上传hadoop-3.1.4.tar.gz到/tmp，解压

+ })(window)

Hadoop集群HDFS配置

发表于|更新于
|阅读量:

上传hadoop-3.1.4.tar.gz到/tmp，解压

注意在六台机器均上传到/tmp

1
2
3
4
5
# 在6台机器执行
sudo tar -zxvf /tmp/hadoop-3.1.4.tar.gz -C /usr/local/
# 分发到其他主机
ssh_root.sh chown -R hadoop:hadoop /usr/local/hadoop-3.1.4
ssh_root.sh ln -s /usr/local/hadoop-3.1.4/ /usr/local/hadoop
@@ -195,7 +195,7 @@

  • 启动datanode节点，在s1、s2、s3执行

    1
    2
    #启动各个节点的datanode
    hadoop-daemons.sh start datanode
    • -

    集群启动

    1
    start-dfs.sh
    文章作者: shenjianZ
    文章链接: https://rq.shenjianl.cn/posts/61252.html
    版权声明: 本博客所有文章除特别声明外，均采用 CC BY-NC-SA 4.0 许可协议。转载请注明来自 QuickReference！
    avatar
    shenjianZ
    一份快捷简便的文档，便于查阅编程的细节
    文章
    13
    标签
    4
    分类
    1
    Follow Me
    公告
    一个简单快捷的文档知识点查阅网站
    最新文章
    k近邻算法（K-Nearest Neighbors）KNN
    Hadoop集群搭建基础环境
    Hadoop集群HDFS配置
    Hadoop集群Zookeeper配置
    Hello World
    avatar
    shenjianZ
    一份快捷简便的文档，便于查阅编程的细节
    文章
    15
    标签
    6
    分类
    1
    Follow Me
    公告
    一个简单快捷的文档知识点查阅网站
    最新文章
    线性回归
    C lang
    k近邻算法（K-Nearest Neighbors）KNN
    Hadoop集群Zookeeper配置
    Hadoop集群搭建基础环境

    Hadoop集群搭建基础环境

    发表于|更新于
    |阅读量:

    防火墙关闭

    1
    2
    3
    # 在 6 台主机执行
    systemctl stop firewalld
    systemctl disable firewalld
    + })(window)

    Hadoop集群搭建基础环境

    发表于|更新于
    |阅读量:

    防火墙关闭

    1
    2
    3
    # 在 6 台主机执行
    systemctl stop firewalld
    systemctl disable firewalld

    配置yum源

    • 下载 repo 文件：
      Centos-7.repo
      并上传到/tmp，进入到/tmp
    • 备份并且替换系统的repo文件
      1
      2
      3
      4
       	cp Centos-7.repo /etc/yum.repos.d/ 
      cd /etc/yum.repos.d/ 
      mv CentOS-Base.repo CentOS-Base.repo.bak 
      mv Centos-7.repo CentOS-Base.repo
      • @@ -208,7 +208,7 @@

    • 将 /home/hadoop/bin添加到hadoop用户的环境变量，需要切换到hadoop用户

      1
      2
      3
      echo 'export PATH=$PATH:/home/hadoop/bin' >> ~/.bashrc && source ~/.bashrc
      scp_all.sh /home/hadoop/.bashrc /home/hadoop/
      ssh_all.sh source ~/.bashrc
    -
    文章作者: shenjianZ
    文章链接: https://rq.shenjianl.cn/posts/61253.html
    版权声明: 本博客所有文章除特别声明外，均采用 CC BY-NC-SA 4.0 许可协议。转载请注明来自 QuickReference！
    avatar
    shenjianZ
    一份快捷简便的文档，便于查阅编程的细节
    文章
    13
    标签
    4
    分类
    1
    Follow Me
    公告
    一个简单快捷的文档知识点查阅网站
    最新文章
    k近邻算法（K-Nearest Neighbors）KNN
    Hadoop集群搭建基础环境
    Hadoop集群HDFS配置
    Hadoop集群Zookeeper配置
    Hello World
    avatar
    shenjianZ
    一份快捷简便的文档，便于查阅编程的细节
    文章
    15
    标签
    6
    分类
    1
    Follow Me
    公告
    一个简单快捷的文档知识点查阅网站
    最新文章
    线性回归
    C lang
    k近邻算法（K-Nearest Neighbors）KNN
    Hadoop集群Zookeeper配置
    Hadoop集群搭建基础环境

    C C++

    标签 - C C++
    2025
    C lang
    \ No newline at end of file diff --git a/public/tags/index.html b/public/tags/index.html index 654e3e0..582ee8e 100644 --- a/public/tags/index.html +++ b/public/tags/index.html @@ -153,14 +153,14 @@ } } detectApple() - })(window)

    tags

    machinelearninguniapp古文观止网络代理
    avatar
    shenjianZ
    一份快捷简便的文档，便于查阅编程的细节
    文章
    13
    标签
    4
    分类
    1
    Follow Me
    公告
    一个简单快捷的文档知识点查阅网站
    最新文章
    k近邻算法（K-Nearest Neighbors）KNN
    Hadoop集群搭建基础环境
    Hadoop集群HDFS配置
    Hadoop集群Zookeeper配置
    Hello World
    + })(window)

    tags

    C C++machinelearninglinear-regression古文观止uniapp网络代理
    avatar
    shenjianZ
    一份快捷简便的文档，便于查阅编程的细节
    文章
    15
    标签
    6
    分类
    1
    Follow Me
    公告
    一个简单快捷的文档知识点查阅网站
    最新文章
    线性回归
    C lang
    k近邻算法（K-Nearest Neighbors）KNN
    Hadoop集群Zookeeper配置
    Hadoop集群搭建基础环境
    分类
    标签
    machinelearning uniapp 网络代理 古文观止
    归档
    网站资讯
    文章数目 :
    13
    已运行时间 :
    本站访客数 :
    本站总访问量 :
    最后更新时间 :

    linear-regression

    标签 - linear-regression
    2025
    线性回归
    \ No newline at end of file diff --git a/public/tags/machinelearning/index.html b/public/tags/machinelearning/index.html index 099ca58..18b099a 100644 --- a/public/tags/machinelearning/index.html +++ b/public/tags/machinelearning/index.html @@ -54,7 +54,7 @@ isHome: false, isHighlightShrink: undefined, isToc: false, - postUpdate: '2025-01-14 17:22:51' + postUpdate: '2025-01-19 17:10:22' }

    machinelearning

    标签 - machinelearning
    2025
    k近邻算法（K-Nearest Neighbors）KNN

    machinelearning

    标签 - machinelearning
    2025
    k近邻算法（K-Nearest Neighbors）KNN

    uniapp

    标签 - uniapp
    2024
    uniapp 开发
    组件使用

    uniapp

    标签 - uniapp
    2024
    组件使用
    uniapp 开发

    古文观止

    标签 - 古文观止
    2024
    郑伯克段于鄢

    古文观止

    标签 - 古文观止
    2024
    郑伯克段于鄢

    网络代理

    标签 - 网络代理
    2024
    无法访问外网？需要订阅代理服务？

    网络代理

    标签 - 网络代理
    2024
    无法访问外网？需要订阅代理服务？