{"id":4062,"date":"2023-07-13T20:58:15","date_gmt":"2023-07-13T20:58:15","guid":{"rendered":"https:\/\/statorials.org\/cn\/python%e4%b8%ad%e7%9a%84%e8%82%98%e9%83%a8%e6%96%b9%e6%b3%95\/"},"modified":"2023-07-13T20:58:15","modified_gmt":"2023-07-13T20:58:15","slug":"python%e4%b8%ad%e7%9a%84%e8%82%98%e9%83%a8%e6%96%b9%e6%b3%95","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/python%e4%b8%ad%e7%9a%84%e8%82%98%e9%83%a8%e6%96%b9%e6%b3%95\/","title":{"rendered":"\u5982\u4f55\u5728 python \u4e2d\u4f7f\u7528 elbow \u65b9\u6cd5\u67e5\u627e\u6700\u4f73\u7c07"},"content":{"rendered":"

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\u673a\u5668\u5b66\u4e60<\/a>\u4e2d\u6700\u5e38\u89c1\u7684\u805a\u7c7b\u7b97\u6cd5\u4e4b\u4e00\u79f0\u4e3ak \u5747\u503c\u805a\u7c7b<\/strong>\u3002<\/span><\/p>\n

K \u5747\u503c\u805a\u7c7b\u662f\u4e00\u79cd\u5c06\u6570\u636e\u96c6\u4e2d\u7684\u6bcf\u4e2a\u89c2\u5bdf\u7ed3\u679c\u653e\u5165K<\/em>\u4e2a\u805a\u7c7b\u4e2d\u7684\u4e00\u4e2a\u6280\u672f\u3002<\/span><\/p>\n

\u6700\u7ec8\u76ee\u6807\u662f\u62e5\u6709K \u4e2a<\/em>\u7c07\uff0c\u5176\u4e2d\u6bcf\u4e2a\u7c07\u5185\u7684\u89c2\u5bdf\u7ed3\u679c\u5f7c\u6b64\u975e\u5e38\u76f8\u4f3c\uff0c\u800c\u4e0d\u540c\u7c07\u4e2d\u7684\u89c2\u5bdf\u7ed3\u679c\u5f7c\u6b64\u975e\u5e38\u4e0d\u540c\u3002<\/span><\/p>\n

\u5728\u8fdb\u884c k \u5747\u503c\u805a\u7c7b\u65f6\uff0c\u7b2c\u4e00\u6b65\u662f\u9009\u62e9K<\/em>\u503c\u2014\u2014\u6211\u4eec\u60f3\u8981\u5c06\u89c2\u6d4b\u503c\u653e\u5165\u7684\u805a\u7c7b\u6570\u91cf\u3002<\/span><\/p>\n

\u9009\u62e9K<\/em>\u503c\u7684\u6700\u5e38\u89c1\u65b9\u6cd5\u4e4b\u4e00\u79f0\u4e3a\u8098\u6cd5<\/strong>\uff0c\u8be5\u65b9\u6cd5\u6d89\u53ca\u521b\u5efa\u4e00\u4e2a\u56fe\uff0c\u5176\u4e2d x \u8f74\u4e3a\u7c07\u6570\uff0cy \u8f74\u4e3a\u5e73\u65b9\u548c\u603b\u6570\uff0c\u7136\u540e\u786e\u5b9a\u56fe\u4e2d\u51fa\u73b0\u201c\u819d\u76d6\u201d\u6216\u8f6c\u5f2f\u7684\u5730\u65b9\u3002<\/span><\/p>\n

x \u8f74\u4e0a\u51fa\u73b0\u201c\u62d0\u70b9\u201d\u7684\u70b9\u544a\u8bc9\u6211\u4eec\u5728 k \u5747\u503c\u805a\u7c7b\u7b97\u6cd5\u4e2d\u4f7f\u7528\u7684\u6700\u4f73\u805a\u7c7b\u6570\u91cf\u3002<\/span><\/p>\n

\u4ee5\u4e0b\u793a\u4f8b\u5c55\u793a\u4e86\u5982\u4f55\u5728 Python \u4e2d\u4f7f\u7528\u8098\u90e8\u65b9\u6cd5\u3002<\/span><\/p>\n

\u7b2c1\u6b65\uff1a\u5bfc\u5165\u5fc5\u8981\u7684\u6a21\u5757<\/strong><\/span><\/h2>\n

\u9996\u5148\uff0c\u6211\u4eec\u5c06\u5bfc\u5165\u6267\u884c k \u5747\u503c\u805a\u7c7b\u6240\u9700\u7684\u6240\u6709\u6a21\u5757\uff1a<\/span><\/p>\n

 import<\/span> pandas as<\/span> pd\nimport<\/span> numpy as<\/span> np\nimport<\/span> matplotlib. pyplot<\/span> as<\/span> plt\nfrom<\/span> sklearn. cluster<\/span> import<\/span> KMeans\nfrom<\/span> sklearn. preprocessing<\/span> import<\/span> StandardScaler<\/strong><\/span><\/pre>\n
\u7b2c 2 \u6b65\uff1a\u521b\u5efa\u6570\u636e\u6846<\/strong><\/span><\/h2>\n
\u63a5\u4e0b\u6765\uff0c\u6211\u4eec\u5c06\u521b\u5efa\u4e00\u4e2a\u5305\u542b 20 \u540d\u4e0d\u540c\u7bee\u7403\u8fd0\u52a8\u5458\u7684\u4e09\u4e2a\u53d8\u91cf\u7684 DataFrame\uff1a<\/span><\/p>\n
 #createDataFrame\n<\/span>df = pd. DataFrame<\/span> ({' points<\/span> ': [18, np.nan, 19, 14, 14, 11, 20, 28, 30, 31,\n                              35, 33, 29, 25, 25, 27, 29, 30, 19, 23],\n                   ' assists<\/span> ': [3, 3, 4, 5, 4, 7, 8, 7, 6, 9, 12, 14,\n                               np.nan, 9, 4, 3, 4, 12, 15, 11],\n                   ' rebounds<\/span> ': [15, 14, 14, 10, 8, 14, 13, 9, 5, 4,\n                                11, 6, 5, 5, 3, 8, 12, 7, 6, 5]})\n\n#drop rows with NA values in any columns\ndf = df. dropna<\/span> ()<\/span>\n\n#create scaled DataFrame where each variable has mean of 0 and standard dev of 1\nscaled_df = StandardScaler(). fit_transform<\/span> (df)\n<\/span><\/span><\/strong><\/span><\/pre>\n
\u6b65\u9aa4 3\uff1a\u4f7f\u7528\u8098\u90e8\u6cd5\u627e\u5230\u6700\u4f73\u7c07\u6570<\/strong><\/span><\/h2>\n
\u5047\u8bbe\u6211\u4eec\u60f3\u8981\u4f7f\u7528 k \u5747\u503c\u805a\u7c7b\u6839\u636e\u8fd9\u4e09\u4e2a\u6307\u6807\u5c06\u76f8\u4f3c\u7684\u53c2\u4e0e\u8005\u5206\u7ec4\u5728\u4e00\u8d77\u3002<\/span><\/p>\n
\u8981\u5728 Python \u4e2d\u6267\u884c k \u5747\u503c\u805a\u7c7b\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528sklearn<\/strong>\u6a21\u5757\u4e2d\u7684KMeans<\/a>\u51fd\u6570\u3002<\/span><\/p>\n
\u8be5\u51fd\u6570\u6700\u91cd\u8981\u7684\u53c2\u6570\u662fn_clusters<\/strong> \uff0c\u5b83\u6307\u5b9a\u8981\u5728\u5176\u4e2d\u653e\u7f6e\u89c2\u6d4b\u503c\u7684\u7c07\u6570\u3002<\/span><\/p>\n
\u4e3a\u4e86\u786e\u5b9a\u6700\u4f73\u7c07\u6570\uff0c\u6211\u4eec\u5c06\u521b\u5efa\u4e00\u4e2a\u56fe\u8868\uff0c\u663e\u793a\u7c07\u6570\u4ee5\u53ca\u6a21\u578b\u7684 SSE\uff08\u8bef\u5dee\u5e73\u65b9\u548c\uff09\u3002<\/span><\/p>\n
\u7136\u540e\uff0c\u6211\u4eec\u5c06\u5bfb\u627e\u5e73\u65b9\u548c\u5f00\u59cb\u201c\u5f2f\u66f2\u201d\u6216\u7a33\u5b9a\u7684\u201c\u819d\u76d6\u201d\u3002\u8be5\u70b9\u4ee3\u8868\u6700\u4f73\u7c07\u6570\u3002<\/span><\/p>\n
\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u521b\u5efa\u8fd9\u79cd\u7c7b\u578b\u7684\u56fe\uff0c\u8be5\u56fe\u5728 x \u8f74\u4e0a\u663e\u793a\u7c07\u6570\uff0c\u5728 y \u8f74\u4e0a\u663e\u793a SSE\uff1a<\/span> <\/p>\n
 #initialize kmeans parameters\n<\/span>kmeans_kwargs = {\n\" init<\/span> \": \" random<\/span> \",\n\" n_init<\/span> \": 10,\n\" random_state<\/span> \": 1,\n}\n\n#create list to hold SSE values for each k\n<\/span>sse = []\nfor<\/span> k in<\/span> range(1, 11):\n    kmeans = KMeans(n_clusters=k, **<\/span> kmeans_kwargs)\n    kmeans. fit<\/span> (scaled_df)\n    sse. append<\/span> (kmeans.inertia_)\n\n#visualize results\n<\/span>plt. plot<\/span> (range(1, 11), sse)\nplt. xticks<\/span> (range(1, 11))\nplt. xlabel<\/span> (\" Number of Clusters<\/span> \")\nplt. ylabel<\/span> (\u201c SSE<\/span> \u201d)\nplt. show<\/span> ()<\/strong> <\/pre>\n
\"\"<\/p>\n
\u5728\u6b64\u56fe\u4e2d\uff0c\u4f3c\u4e4e\u5728 k = 3 \u4e2a\u7c07<\/strong>\u5904\u5b58\u5728\u626d\u7ed3\u6216\u201c\u62d0\u70b9\u201d\u3002<\/span><\/p>\n
\u56e0\u6b64\uff0c\u5728\u4e0b\u4e00\u6b65\u62df\u5408 k \u5747\u503c\u805a\u7c7b\u6a21\u578b\u65f6\uff0c\u6211\u4eec\u5c06\u4f7f\u7528 3 \u4e2a\u805a\u7c7b\u3002<\/span><\/p>\n
\u6b65\u9aa4 4\uff1a\u4f7f\u7528\u6700\u4f73K<\/em>\u6267\u884c K \u5747\u503c\u805a\u7c7b<\/strong><\/span><\/h2>\n
\u4ee5\u4e0b\u4ee3\u7801\u5c55\u793a\u4e86\u5982\u4f55\u4f7f\u7528k<\/em>\u7684\u6700\u4f73\u503c 3 \u5bf9\u6570\u636e\u96c6\u6267\u884c k \u5747\u503c\u805a\u7c7b\uff1a<\/span><\/p>\n
 #instantiate the k-means class, using optimal number of clusters\n<\/span>kmeans = KMeans(init=\" random<\/span> \", n_clusters= 3<\/span> , n_init= 10<\/span> , random_state= 1<\/span> )\n\n#fit k-means algorithm to data\n<\/span>kmeans. fit<\/span> (scaled_df)\n\n#view cluster assignments for each observation\n<\/span>kmeans. labels_\n\n<\/span>array([1, 1, 1, 1, 1, 1, 2, 2, 0, 0, 0, 0, 2, 2, 2, 0, 0, 0]) \n<\/span><\/span><\/strong><\/pre>\n
\u7ed3\u679c\u8868\u663e\u793a\u4e86 DataFrame \u4e2d\u6bcf\u4e2a\u89c2\u5bdf\u503c\u7684\u805a\u7c7b\u5206\u914d\u3002<\/span><\/p>\n
\u4e3a\u4e86\u4f7f\u8fd9\u4e9b\u7ed3\u679c\u66f4\u6613\u4e8e\u89e3\u91ca\uff0c\u6211\u4eec\u53ef\u4ee5\u5728 DataFrame \u4e2d\u6dfb\u52a0\u4e00\u5217\u6765\u663e\u793a\u6bcf\u4e2a\u73a9\u5bb6\u7684\u96c6\u7fa4\u5206\u914d\uff1a<\/span><\/p>\n
 #append cluster assingments to original DataFrame\n<\/span>df[' cluster<\/span> '] = kmeans. labels_<\/span>\n\n#view updated DataFrame\n<\/span>print<\/span> (df)\n\n<\/span><\/span>points assists rebounds cluster\n0 18.0 3.0 15 1\n2 19.0 4.0 14 1\n3 14.0 5.0 10 1\n4 14.0 4.0 8 1\n5 11.0 7.0 14 1\n6 20.0 8.0 13 1\n7 28.0 7.0 9 2\n8 30.0 6.0 5 2\n9 31.0 9.0 4 0\n10 35.0 12.0 11 0\n11 33.0 14.0 6 0\n13 25.0 9.0 5 0\n14 25.0 4.0 3 2\n15 27.0 3.0 8 2\n16 29.0 4.0 12 2\n17 30.0 12.0 7 0\n18 19.0 15.0 6 0\n19 23.0 11.0 5 0\n<\/strong><\/pre>\n
\u96c6\u7fa4<\/strong>\u5217\u5305\u542b\u6bcf\u4e2a\u73a9\u5bb6\u5206\u914d\u5230\u7684\u96c6\u7fa4\u7f16\u53f7\uff080\u30011 \u6216 2\uff09\u3002<\/span><\/p>\n
\u5c5e\u4e8e\u540c\u4e00\u7c07\u7684\u7403\u5458\u7684\u5f97\u5206<\/strong>\u3001\u52a9\u653b<\/strong>\u548c\u7bee\u677f<\/strong>\u6570\u5217\u7684\u503c\u5927\u81f4\u76f8\u4f3c\u3002<\/span><\/p>\n
\u6ce8\u610f<\/strong>\uff1a\u60a8\u53ef\u4ee5\u5728\u6b64\u5904<\/a>\u627e\u5230sklearn<\/strong>\u7684KMeans<\/strong>\u51fd\u6570\u7684\u5b8c\u6574\u6587\u6863\u3002<\/span><\/p>\n
\u5176\u4ed6\u8d44\u6e90<\/strong><\/span><\/h2>\n
\u4ee5\u4e0b\u6559\u7a0b\u89e3\u91ca\u4e86\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u5176\u4ed6\u5e38\u89c1\u4efb\u52a1\uff1a<\/span><\/p>\n
\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u7ebf\u6027\u56de\u5f52<\/a>
\u5982\u4f55\u5728 Python \u4e2d\u6267\u884c\u903b\u8f91\u56de\u5f52<\/a>
\u5982\u4f55\u5728Python\u4e2d\u6267\u884cK-Fold\u4ea4\u53c9\u9a8c\u8bc1<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"
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