{"id":1409,"date":"2024-05-10T03:33:44","date_gmt":"2024-05-10T03:33:44","guid":{"rendered":"https:\/\/www.nicekj.com\/?p=1409"},"modified":"2024-05-10T03:36:46","modified_gmt":"2024-05-10T03:36:46","slug":"pythonshangshoujiqixuexisuanfa-shengchengshiaishiyongde","status":"publish","type":"post","link":"https:\/\/www.nicekj.com\/pythonshangshoujiqixuexisuanfa-shengchengshiaishiyongde.html","title":{"rendered":"python\u4e0a\u624b\u673a\u5668\u5b66\u4e60\u7b97\u6cd5 & \u751f\u6210\u5f0fAI\u4f7f\u7528\u7684"},"content":{"rendered":"

AI\u53d1\u5c55\u4e0e\u73b0\u72b6<\/h1>\n

<\/h3>\n

\u4eba\u5de5\u667a\u80fd\uff08AI\uff09\u7684\u53d1\u5c55\u5386\u53f2\u53ef\u4ee5\u6982\u62ec\u5982\u4e0b\uff1a<\/h3>\n
    \n
  1. \u8d77\u6b65\u9636\u6bb5\uff0820\u4e16\u7eaa\u4e2d\u53f6\uff09\uff1aAI\u7684\u6982\u5ff5\u9996\u6b21\u63d0\u51fa\uff0c\u65e9\u671f\u5173\u6ce8\u903b\u8f91\u548c\u7b26\u53f7\u5904\u7406\u3002<\/li>\n
  2. \u63a8\u7406\u7cfb\u7edf\uff081950s-1960s\uff09\uff1a\u5f00\u59cb\u7814\u7a76\u63a8\u7406\u7cfb\u7edf\uff0c\u5305\u62ec\u65e9\u671fAI\u9879\u76ee\u548cDartmouth\u4f1a\u8bae\uff08Wiki\u5730\u5740<\/a>\uff09\u3002
    \n\n
    \"image.png\"<\/figure>\n
    \n\u5927\u5bb6\u5bf9\u8fd9\u4e2a\u6e38\u620f\u662f\u5426\u6709\u5370\u8c61\uff1f\u4eba\u673a\u8c61\u68cb\u6e38\u620f\u4f7f\u7528\u5c31\u662f\u8be5\u63a8\u7406\u7cfb\u7edf<\/li>\n
  3. \u77e5\u8bc6\u8868\u793a\u548c\u4e13\u5bb6\u7cfb\u7edf\uff081970s-1980s\uff09\uff1a\u7814\u7a76\u77e5\u8bc6\u8868\u793a\u548c\u4e13\u5bb6\u7cfb\u7edf\uff0c\u6a21\u4eff\u4e13\u5bb6\u51b3\u7b56\u3002<\/li>\n
  4. \u673a\u5668\u5b66\u4e60\u5174\u8d77\uff081980s-1990s\uff09\uff1a \u673a\u5668\u5b66\u4e60\u6210\u4e3a\u7126\u70b9\uff0c\u5305\u62ec\u76d1\u7763\u5b66\u4e60\u548c\u65e0\u76d1\u7763\u5b66\u4e60\u3002<\/li>\n
  5. \u795e\u7ecf\u7f51\u7edc\u590d\u5174\uff082000s-\u73b0\u5728\uff09\uff1a \u6df1\u5ea6\u5b66\u4e60\u548c\u795e\u7ecf\u7f51\u7edc\u5728\u8ba1\u7b97\u673a\u89c6\u89c9\u548c\u81ea\u7136\u8bed\u8a00\u5904\u7406\u4e2d\u6210\u529f\u3002\n\u4e3a\u4ec0\u4e48\u73b0\u5728\u5404\u5382\u90fd\u5728\u6784\u5efa\u5de8\u5927\u7684\u795e\u7ecf\u7f51\u7edc\u8ba1\u7b97\u8d44\u6e90\u5462\uff1f\n\n
    \"image.png\"<\/figure>\n\n(\u4e0a\u56fe\u6570\u636e\u6765\u81ea\u7f8e\u56fd\u534e\u88d4\u79d1\u5b66\u5bb6\u5434\u6069\u8fbe\uff08\u524d\u767e\u5ea6\u9996\u5e2d\u79d1\u5b66\u5bb6\uff09\u5728\u65af\u5766\u798f\u5927\u5b66\u6700\u8fd1\u7684\u6f14\u8bb2\u6570\u636e)<\/li>\n<\/ol>\n

    \u7531\u4e0b\u56fe\u53ef\u89c1\u5728\u4e00\u4e2a\u6027\u80fd\u4e0d\u662f\u5f88\u5f3a\u5927\u7684\u8ba1\u7b97\u8d44\u6e90\u4e0b\u53bb\u6295\u5582\u6570\u636e\uff0c\u6570\u636e\u5230\u4e00\u5b9a\u89c4\u6a21\u4e4b\u540eAI\u6027\u80fd\u4f1a\u8fbe\u5230\u4e00\u4e2a\u74f6\u9888\uff0c\u4f46\u5982\u679c\u6295\u5165\u5927\u91cf\u7684\u8ba1\u7b97\u8d44\u6e90\u548c\u6570\u636e\uff0cAI\u6027\u80fd\u5c06\u8d8a\u6765\u8d8a\u597d<\/p>\n

    \u673a\u5668\u5b66\u4e60<\/h2>\n

    \u4e0a\u6587\u63d0\u5230\u7684\u673a\u5668\u5b66\u4e60\uff0c\u90a3\u4e48\u4ec0\u4e48\u4e8b\u673a\u5668\u5b66\u4e60\u5462\uff1f<\/p>\n

    \u673a\u5668\u5b66\u4e60\u662f\u4eba\u5de5\u667a\u80fd\u7684\u4e00\u4e2a\u91cd\u8981\u5206\u652f\uff0c\u5b83\u5173\u6ce8\u5982\u4f55\u8bbe\u8ba1\u548c\u5f00\u53d1\u7b97\u6cd5\u548c\u6a21\u578b\uff0c\u4f7f\u8ba1\u7b97\u673a\u80fd\u591f\u4ece\u6570\u636e\u4e2d\u5b66\u4e60\uff0c\u6539\u8fdb\u5176\u6027\u80fd\u548c\u6267\u884c\u4efb\u52a1\uff0c\u800c\u4e0d\u9700\u8981\u660e\u786e\u7684\u7f16\u7a0b\u6307\u5bfc\u3002\u5728AI\u4e2d\uff0c\u673a\u5668\u5b66\u4e60\u662f\u5b9e\u73b0\u667a\u80fd\u884c\u4e3a\u548c\u51b3\u7b56\u7684\u5173\u952e\u6280\u672f\u4e4b\u4e00\u3002<\/p>\n

    \u4e3b\u8981\u6709\u4ee5\u4e0b\u7279\u5f81\uff1a<\/p>\n

      \n
    1. \u6a21\u578b\uff1a\u673a\u5668\u5b66\u4e60\u6a21\u578b\u662f\u6570\u5b66\u548c\u7edf\u8ba1\u7b97\u6cd5\u7684\u8868\u793a\uff0c\u7528\u4e8e\u6355\u83b7\u6570\u636e\u4e2d\u7684\u6a21\u5f0f\u548c\u5173\u7cfb\u3002\u5e38\u89c1\u7684\u673a\u5668\u5b66\u4e60\u6a21\u578b\u5305\u62ec\u7ebf\u6027\u56de\u5f52\u3001\u51b3\u7b56\u6811\u3001\u652f\u6301\u5411\u91cf\u673a\u3001\u795e\u7ecf\u7f51\u7edc\u7b49\u3002\u8fd9\u4e9b\u6a21\u578b\u53ef\u4ee5\u7528\u4e8e\u5206\u7c7b\u3001\u56de\u5f52\u3001\u805a\u7c7b\u3001\u964d\u7ef4\u3001\u5f02\u5e38\u68c0\u6d4b\u7b49\u4efb\u52a1\u3002<\/li>\n
    2. \u8bad\u7ec3\u548c\u8bc4\u4f30\uff1a\u673a\u5668\u5b66\u4e60\u6a21\u578b\u9700\u8981\u901a\u8fc7\u8bad\u7ec3\u6570\u636e\u6765\u5b66\u4e60\uff0c\u7136\u540e\u4f7f\u7528\u6d4b\u8bd5\u6570\u636e\u6765\u8bc4\u4f30\u5176\u6027\u80fd\u3002\u5e38\u89c1\u7684\u6027\u80fd\u6307\u6807\u5305\u62ec\u51c6\u786e\u5ea6\u3001\u5747\u65b9\u8bef\u5dee\u3001\u7cbe\u786e\u5ea6\u3001\u53ec\u56de\u7387\u7b49\uff0c\u8fd9\u4e9b\u6307\u6807\u7528\u4e8e\u8861\u91cf\u6a21\u578b\u7684\u8d28\u91cf\u3002<\/li>\n<\/ol>\n

      KNN\u7b97\u6cd5<\/h3>\n

      \u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u5206\u4e3a\u591a\u4e2a\u7c7b\u522b\uff0c\u5305\u62ec\u76d1\u7763\u5b66\u4e60\u3001\u65e0\u76d1\u7763\u5b66\u4e60\u3001\u534a\u76d1\u7763\u5b66\u4e60\u548c\u5f3a\u5316\u5b66\u4e60\u3002\u6211\u4eec\u901a\u8fc7\u76d1\u7763\u5b66\u4e60\u4e2dK\u6700\u8fd1\u90bb\u7b97\u6cd5 (K-Nearest Neighbors, KNN)\u6765\u4e86\u89e3\u673a\u5668\u5b66\u4e60\nKNN\u90bb\u8fd1\u7b97\u6cd5\u6982\u8981\uff1a\n\u76ee\u7684\uff1a\u786e\u5b9a\u6d4b\u8bd5\u6837\u672c\u5c5e\u4e8e\u54ea\u4e00\u7c7b\n\u65b9\u5f0f\uff1a\u5bfb\u627e\u6240\u6709\u8bad\u7ec3\u6837\u672c\u4e2d\u4e0e\u8be5\u6d4b\u8bd5\u6837\u672c\u201c\u8ddd\u79bb\u201d\u6700\u8fd1\u7684\u524dK\u4e2a\u6837\u672c\uff0c\u7136\u540e\u770b\u8fd9K\u4e2a\u6837\u672c\u5927\u90e8\u5206\u5c5e\u4e8e\u54ea\u4e00\u7c7b\uff0c\u90a3\u4e48\u5c31\u8ba4\u4e3a\u8fd9\u4e2a\u6d4b\u8bd5\u6837\u672c\u4e5f\u5c5e\u4e8e\u54ea\u4e00\u7c7b\u3002<\/p>\n

      KNN\u7b97\u6cd5\u5224\u5b9a<\/strong> \u4e2a\u4f53\u4f53\u91cd\u662f\u5426\u6b63\u5e38<\/strong><\/p>\n

      3\u4e2a\u7ef4\u5ea6\uff1a\u8eab\u9ad8\uff0c\u4f53\u91cd\uff0c\u662f\u5426\u6b63\u5e38\n\n

      \"image.png\"<\/figure>\n\n\u5750\u6807\u662f\u8eab\u9ad8\uff0c\u7eb5\u5750\u6807\u4f53\u91cd\uff0c\u84dd\u8272\u8868\u793a\u6b63\u5e38\uff0c\u7ea2\u8272\u8868\u793a\u504f\u80d6\u6216\u8005\u504f\u7626\n\u73b0\u5728\u6709\u4e24\u4e2a\u6837\u672c\uff1a
      \n\u8001\u738b\uff1a165cm\uff0c85kg\uff0c\u770b\u6700\u8fd1\u7684\u8ddd\u79bb\u4e3ak=1\u7684\u662f\u8c01,\u53d1\u73b0\u662f165\/90\u8fd9\u4e2a\u70b9\uff0c\u90a3\u4e48\u6211\u4eec\u5c31\u8ba4\u4e3a\u8001\u738b\u548c\u8ddd\u79bb\u4ed6\u6700\u8fd1\u7684\u70b9\u662f\u4e00\u7c7b\u4eba\uff0c\u628a\u4ed6\u5212\u5206\u4e3a\u80d6\u7684<\/strong>
      \n\u8001\u674e\uff1a170cm\uff0c65kg\uff0c\u540c\u7406\u8ba1\u7b97\u8ddd\u79bb\u4e3ak=1\u7684\u70b9\u662f\u8c01,\u53d1\u73b0\u662f170\/69\u8fd9\u4e2a\u70b9,\u90a3\u4e48\u6211\u4eec\u5c31\u8ba4\u4e3a\u8001\u674e\u548c\u8ddd\u79bb\u4ed6\u6700\u8fd1\u7684\u70b9\u662f\u4e00\u7c7b\u4eba\uff0c\u628a\u4ed6\u5212\u5206\u4e3a\u6b63\u5e38\u7684<\/strong>
      <\/p>\n

      \n

      \"image.png\"<\/figure>\n<\/p>\n

      KNN\u8bad\u7ec3\u9e22\u5c3e\u82b1iris\u6570\u636e\u96c6<\/h3>\n

      \n

      \"image.png\"<\/figure>\n
      \nTalk is cheap. Show me the code. \u4ee3\u7801\u662f\u4e00\u95e8\u624b\u827a\uff0c\u5177\u8c61\u7406\u8bba\u662f\u4e00\u95e8\u827a\u672f \uff0c\u8fd9\u91cc\u901a\u8fc7python \u7ed3\u5408\u4e0a\u6587\u63d0\u5230\u7684KNN\u7b97\u6cd5\u6765\u505a\u4e2a\u8bc6\u522b\u9e22\u5c3e\u82b1\u7684\u4f8b\u5b50
      \n\u9e22\u5c3e\u82b1\u6570\u636e\uff1a
      \n\u5c5e\u6027\uff1a4 \u4e2a\u8f93\u5165\u7279\u5f81\uff1a\u82b1\u843c\u957f\u3001\u82b1\u843c\u5bbd\u3001\u82b1\u74e3\u957f\u3001\u82b1\u74e3\u5bbd \uff1b
      \n\u7c7b\u522b\uff1a\u4f5c\u4e3a\u6807\u7b7e 0 \u4ee3\u8868\u72d7\u5c3e\u8349\u9e22\u5c3e\uff0c1 \u4ee3\u8868\u6742\u8272\u9e22\u5c3e\uff0c2 \u4ee3\u8868\u5f17\u5409\u5c3c\u4e9a\u9e22\u5c3e
      \n\n
      \"image.png\"<\/figure>\n<\/p>\n
        \n
      1. \u5b89\u88c5\u7528\u4e8e\u673a\u5668\u5b66\u4e60\u548c\u6570\u636e\u6316\u6398\u7684\u5f00\u6e90Python\u5e93\uff1asklearn<\/li>\n<\/ol>\n\n
        <\/div>
        <\/span>
        \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div> pip3 install scikit-learn<\/span>\n<\/code><\/pre>\n
        2.  \u8f93\u51fasklearn\u5e93\u4e2d\u7684iris\u6570\u636e\u96c6<\/p>\n\n
        <\/div>
        python<\/span>
        \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>from<\/span> sklearn.datasets import<\/span> load_iris<\/span>\nfrom<\/span> pandas import<\/span> DataFrame<\/span>\nimport<\/span> pandas as<\/span> pd<\/span>\nimport<\/span> matplotlib.pyplot as<\/span> plt<\/span>\n# \u8f7d\u5165sklearn\u5e93\u4e2d\u7684iris\u6570\u636e\u96c6 <\/span><\/span>\niris = load_iris()<\/span>\nx_data = iris.data  # .data\u8fd4\u56deiris\u6570\u636e\u96c6\u6240\u6709\u8f93\u5165\u7279\u5f81<\/span><\/span>\ny_data = iris.target  # .target\u8fd4\u56deiris\u6570\u636e\u96c6\u6240\u6709\u6807\u7b7e<\/span><\/span>\nprint<\/span>(\"x_data from datasets: n\"<\/span>, x_data)<\/span>\nprint<\/span>(\"y_data from datasets: n\"<\/span>, y_data)<\/span>\n<\/span>\n<\/span>\nx_data = DataFrame(x_data, columns=['\u82b1\u843c\u957f\u5ea6'<\/span>, '\u82b1\u843c\u5bbd\u5ea6'<\/span>, '\u82b1\u74e3\u957f\u5ea6'<\/span>, '\u82b1\u74e3\u5bbd\u5ea6'<\/span>]) # \u4e3a\u8868\u683c\u589e\u52a0\u884c\u7d22\u5f15\uff08\u5de6\u4fa7\uff09\u548c\u5217\u6807\u7b7e\uff08\u4e0a\u65b9\uff09<\/span><\/span>\npd.set_option('display.unicode.east_asian_width'<\/span>, True<\/span>)  # \u8bbe\u7f6e\u5217\u540d\u5bf9\u9f50<\/span><\/span>\nprint<\/span>(\"x_data add index: n\"<\/span>, x_data)<\/span>\n<\/span>\n<\/span>\nx_data['\u7c7b\u522b'<\/span>] = y_data  # \u65b0\u52a0\u4e00\u5217\uff0c\u5217\u6807\u7b7e\u4e3a\u2018\u7c7b\u522b\u2019\uff0c\u6570\u636e\u4e3ay_data<\/span><\/span>\nprint<\/span>(\"x_data add a column: n\"<\/span>, x_data<\/span>\n<\/code><\/pre>\n
        \u8f93\u51fa\uff1a\u603b\u6709150\u6761\u6570\u636e\u96c6\uff0c\u7c7b\u522b\u6709\uff1a0\uff08\u72d7\u5c3e\u8349\u9e22\u5c3e\uff09,1\uff08\u6742\u8272\u9e22\u5c3e\uff09,2\uff08\u5f17\u5409\u5c3c\u4e9a\u9e22\u5c3e\uff09 \u6bcf\u4e2a\u7c7b\u522b50\u6761<\/p>\n
        <\/div>
        sql<\/span>
        \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>[150<\/span> rows<\/span> x 4<\/span> columns]<\/span>\nx_data add<\/span> a column<\/span>: <\/span>\n      \u82b1\u843c\u957f\u5ea6  \u82b1\u843c\u5bbd\u5ea6  \u82b1\u74e3\u957f\u5ea6  \u82b1\u74e3\u5bbd\u5ea6  \u7c7b\u522b<\/span>\n0<\/span>         5.1<\/span>       3.5<\/span>       1.4<\/span>       0.2<\/span>     0<\/span><\/span>\n1<\/span>         4.9<\/span>       3.0<\/span>       1.4<\/span>       0.2<\/span>     0<\/span><\/span>\n2<\/span>         4.7<\/span>       3.2<\/span>       1.3<\/span>       0.2<\/span>     0<\/span><\/span>\n3<\/span>         4.6<\/span>       3.1<\/span>       1.5<\/span>       0.2<\/span>     0<\/span><\/span>\n4<\/span>         5.0<\/span>       3.6<\/span>       1.4<\/span>       0.2<\/span>     0<\/span><\/span>\n..        ...       ...       ...       ...   ...<\/span>\n145<\/span>       6.7<\/span>       3.0<\/span>       5.2<\/span>       2.3<\/span>     2<\/span><\/span>\n146<\/span>       6.3<\/span>       2.5<\/span>       5.0<\/span>       1.9<\/span>     2<\/span><\/span>\n147<\/span>       6.5<\/span>       3.0<\/span>       5.2<\/span>       2.0<\/span>     2<\/span><\/span>\n148<\/span>       6.2<\/span>       3.4<\/span>       5.4<\/span>       2.3<\/span>     2<\/span><\/span>\n149<\/span>       5.9<\/span>       3.0<\/span>       5.1<\/span>       1.8<\/span>     2<\/span><\/span>\n<\/code><\/pre>\n
        \u901a\u8fc7\u82b1\u843c\u957f\u5ea6\uff0c\u82b1\u843c\u5bbd\u5ea6\u533a\u5206\u51fa\u7279\u5f81<\/p>\n
        <\/div>
        ini<\/span>
        \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div># \u83b7\u53d6\u82b1\u5349\u4e09\u56db\u5217\u7279\u5f81\u6570\u636e\u96c6  \uff08\u82b1\u74e3\u7279\u5f81\uff09<\/span><\/span>\nDD<\/span> = iris.data  <\/span>\nX<\/span> = [x[2<\/span>] for x in DD]  <\/span>\nprint (X)  <\/span>\nY<\/span> = [x[3<\/span>] for x in DD]  <\/span>\nprint (Y)  <\/span>\n  <\/span>\n#plt.scatter(X, Y, c=iris.target, marker='x')<\/span><\/span>\n# \u7b2c\u4e00\u7c7b \u524d50\u4e2a\u6837\u672c<\/span><\/span>\nplt.scatter(X[:50]<\/span>, Y[:50]<\/span>, color<\/span>='red'<\/span>, marker='o'<\/span>, label='setosa'<\/span>)<\/span>\n# \u7b2c\u4e8c\u7c7b \u4e2d\u95f450\u4e2a\u6837\u672c<\/span><\/span>\nplt.scatter(X[50:100]<\/span>, Y[50:100]<\/span>, color<\/span>='blue'<\/span>, marker='x'<\/span>, label='versicolor'<\/span>) <\/span>\n# \u7b2c\u4e09\u7c7b \u540e50\u4e2a\u6837\u672c<\/span><\/span>\nplt.scatter(X[100:]<\/span>, Y[100:]<\/span>,color<\/span>='green'<\/span>, marker='+'<\/span>, label='Virginica'<\/span>)<\/span>\n# \u56fe\u4f8b<\/span><\/span>\nplt.legend(loc<\/span>=2<\/span>) #\u5de6\u4e0a\u89d2<\/span><\/span>\nplt.show()<\/span>\n<\/code><\/pre>\n
        \n
        \"image.png\"<\/figure>\n 
        \n\u901a\u8fc7\u82b1\u843c\u957f\u5ea6\uff0c\u82b1\u843c\u5bbd\u5ea6\u80fd\u6bd4\u8f83\u76f4\u89c2\u7684\u5206\u8fa8\u51fa\u9e22\u5c3e\u82b1\u7c7b\u522b
        \n\u4f7f\u7528 KNN\u6700\u8fd1\u90bb\u5206\u7c7b\u5668\u6a21\u578b\uff0c\u8f93\u51fa \u9e22\u5c3e\u82b1\u7279\u5f81\u6570\u503c\u9a8c\u8bc1\u6d4b\u8bd5\u7ed3\u679c
        <\/p>\n
        <\/div>
        ini<\/span>
        \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>from sklearn import datasets<\/span>\nfrom sklearn.neighbors import KNeighborsClassifier<\/span>\n<\/span>\n<\/span>\n# \u52a0\u8f7d\u9e22\u5c3e\u82b1\u6570\u636e\u96c6<\/span><\/span>\niris<\/span> = datasets.load_iris()<\/span>\nX, y<\/span> = iris.data, iris.target<\/span>\n<\/span>\n<\/span>\n# \u521b\u5efaK\u6700\u8fd1\u90bb\u5206\u7c7b\u5668\u6a21\u578b<\/span><\/span>\nmodel<\/span> = KNeighborsClassifier(n_neighbors=3<\/span>)  # \u4f7f\u75283\u4e2a\u6700\u8fd1\u90bb\u5c45<\/span><\/span>\n<\/span>\n<\/span>\n# \u5728\u6574\u4e2a\u6570\u636e\u96c6\u4e0a\u62df\u5408\u6a21\u578b<\/span><\/span>\nmodel.fit(X, y)<\/span>\n<\/span>\n<\/span>\n# \u7528\u6237\u8f93\u5165\u7279\u5f81\u503c<\/span><\/span>\nsepal_length<\/span> = float(input(\"\u8bf7\u8f93\u5165\u82b1\u843c\u957f\u5ea6 (cm): \"<\/span>))<\/span>\nsepal_width<\/span> = float(input(\"\u8bf7\u8f93\u5165\u82b1\u843c\u5bbd\u5ea6 (cm): \"<\/span>))<\/span>\npetal_length<\/span> = float(input(\"\u8bf7\u8f93\u5165\u82b1\u74e3\u957f\u5ea6 (cm): \"<\/span>))<\/span>\npetal_width<\/span> = float(input(\"\u8bf7\u8f93\u5165\u82b1\u74e3\u5bbd\u5ea6 (cm): \"<\/span>))<\/span>\n<\/span>\n<\/span>\n# \u6784\u5efa\u8f93\u5165\u7279\u5f81\u7684\u6570\u7ec4<\/span><\/span>\ninput_features<\/span> = [[sepal_length, sepal_width, petal_length, petal_width]]<\/span>\n<\/span>\n<\/span>\n# \u4f7f\u7528\u6a21\u578b\u8fdb\u884c\u9884\u6d4b<\/span><\/span>\npredicted_class<\/span> = model.predict(input_features)[0<\/span>]<\/span>\npredicted_species<\/span> = iris.target_names[predicted_class]<\/span>\n<\/span>\n<\/span>\n# \u8f93\u51fa\u9884\u6d4b\u7ed3\u679c<\/span><\/span>\nprint(f\"\u9884\u6d4b\u7684\u9e22\u5c3e\u82b1\u7c7b\u522b\u4e3a: {predicted_speci<\/span>\n<\/code><\/pre>\n
        cmd\u4e2d\u8f93\u5165\uff1a\n\n
        \"image.png\"<\/figure>\n\n\u8f93\u51fa\u4e3a\u5f17\u5409\u5c3c\u4e9a\u9e22\u5c3e \u7b26\u5408\u9884\u671f\uff0c\u5176\u4e2d n_neighbors\u4e3a\u90bb\u8fd1\u503c\uff0c\u8be5\u503c\u8d8a\u5c0f\u51c6\u786e\u5ea6\u8d8a\u9ad8<\/p>\n
        \u91d1\u878d\u7c7b\u6295\u8d44\u7ba1\u7406\u7c7b\u5f00\u6e90\u5e93<\/h3>\n
        \u5f53\u6d89\u53ca\u5230\u6295\u8d44\u7ba1\u7406\u7b97\u6cd5\u7684\u5b66\u4e60\u548c\u5f00\u53d1\uff0c\u4ee5\u4e0b\u662f\u4e00\u4e9b\u5f00\u6e90\u5e93\uff0c\u5b83\u4eec\u63d0\u4f9b\u4e86\u5f3a\u5927\u7684\u5de5\u5177\u548c\u8d44\u6e90\uff1a<\/p>\n
          \n
        1. QuantLib\u662f\u4e00\u4e2a\u5e7f\u6cdb\u7528\u4e8e\u91d1\u878d\u5de5\u7a0b\u7684\u5f00\u6e90\u5e93\uff0c\u63d0\u4f9b\u4e86\u7528\u4e8e\u5b9a\u4ef7\u3001\u98ce\u9669\u7ba1\u7406\u548c\u8d44\u4ea7\u4f30\u503c\u7684\u5de5\u5177\u3002<\/li>\n
        2. PyAlgoTrade\u662f\u4e00\u4e2a\u7528Python\u7f16\u5199\u7684\u5f00\u6e90\u7b97\u6cd5\u4ea4\u6613\u5e93\uff0c\u652f\u6301\u4e8b\u4ef6\u9a71\u52a8\u7684\u4ea4\u6613\u548c\u6280\u672f\u6307\u6807\u5206\u6790\u3002<\/li>\n
        3. Zipline\u662fQuantopian\u5f00\u53d1\u7684Python\u5e93\uff0c\u7528\u4e8e\u7b97\u6cd5\u4ea4\u6613\u548c\u91cf\u5316\u91d1\u878d\u7814\u7a76\uff0c\u5305\u62ec\u56de\u6d4b\u548c\u5b9e\u65f6\u4ea4\u6613\u529f\u80fd\u3002<\/li>\n
        4. Backtrader\u662f\u53e6\u4e00\u4e2aPython\u5e93\uff0c\u7528\u4e8e\u5f00\u53d1\u548c\u6d4b\u8bd5\u7b97\u6cd5\u4ea4\u6613\u7b56\u7565\uff0c\u652f\u6301\u591a\u79cd\u6570\u636e\u6e90\u3001\u6280\u672f\u6307\u6807\u548c\u98ce\u9669\u7ba1\u7406\u65b9\u6cd5\u3002<\/li>\n<\/ol>\n
          \u76d1\u7763\u5b66\u4e60\u4e0e\u751f\u6210\u5f0fAI<\/h2>\n
          \u4e86\u89e3\u7b80\u5355\u7684\u673a\u5668\u5b66\u4e60\u7b97\u6cd5\u540e\uff0c\u6211\u4eec\u6765\u770b\u5f53\u524d\u4e0d\u540c\u673a\u5668\u5b66\u4e60\u65b9\u6cd5\u7684\u6bd4\u4f8b\n\n
          \"image.png\"<\/figure>\n 
          \n\u4ece\u56fe\u4e2d \u76d1\u7763\u5b66\u4e60\uff08Supervised Learning\uff09\u5728\u4eba\u5de5\u667a\u80fd\u9886\u57df\u662f\u4e3b\u529b\uff0c\u800c\u6700\u8fd1\u706b\u70ed\u7684\u751f\u6210\u5f0fAI\u4e5f\u5728\u4e0d\u65ad\u589e\u957f \uff0c\u5c31\u6b64\u4e24\u79cd\u65b9\u5f0f\u5c55\u5f00<\/p>\n
          \u76d1\u7763\u5b66\u4e60<\/h3>\n
          \u76d1\u7763\u5b66\u4e60\u5173\u6ce8\u5982\u4f55\u4ece\u6709\u6807\u7b7e\u7684\u6570\u636e\u4e2d\u5b66\u4f1a\u8fdb\u884c\u9884\u6d4b\uff0c\u4f8b\u5982\u6839\u636e\u56fe\u50cf\u6765\u5206\u7c7b\u7269\u4f53\u3002\u6211\u4eec\u4ee5\u9910\u5385\u8bc4\u4ef7\u8bad\u7ec3\u6a21\u578b\u4e3a\u4f8b\uff1a
          \n\n
          \"image.png\"<\/figure>\n<\/p>\n
            \n
          1. \u4eba\u5de5\u533a\u5206\u8bc4\u4ef7\u6570\u636e\uff0c\u6807\u8bc6 \u8be5\u6570\u636e\u201cPositive\u201d \u6216\u8005 \u201cNegative\u201d<\/li>\n
          2. \u4f7f\u7528\u6807\u8bc6\u6570\u636e\u8bad\u7ec3 AI\u6a21\u578b<\/li>\n
          3. \u90e8\u7f72AI\u6a21\u578b\uff0c\u5bf9\u6a21\u578b\u8fdb\u884c\u9a8c\u8bc1\uff0c\u6bd4\u5982\u8f93\u5165\u201c\u8fd9\u4e2a\u725b\u6392\u771f\u597d\u5403\u201d \uff0c\u9a8c\u8bc1\u7ed3\u679c\u662f\u5426\u4e3a\u79ef\u6781\u8bc4\u8bba<\/li>\n<\/ol>\n
            \u751f\u6210\u5f0fAI<\/h3>\n
            \u901a\u8fc7\u4e92\u8054\u7f51\u83b7\u53d6\u7684\u6587\u672c\u548c\u8f93\u5165\u8f93\u51fa\u7684\u76d1\u7763\u5b66\u4e60\u6765\u53cd\u590d\u9884\u6d4b\u4e0b\u4e00\u4e2a\u5355\u8bcd\uff0c\u901a\u8fc7\u4e07\u4ebf\u5de8\u91cf\u7684\u5355\u8bcd\u80fd\u5f97\u5230\u4e00\u4e2a\u5927\u8bed\u8a00\u6a21\u578b\u6bd4\u5982\uff1achatGPT 3 
            \n\u5927\u8bed\u8a00\u6a21\u578b\u4e0d\u4ec5\u53ef\u4ee5\u7528\u5728\u5bf9\u8bdd\u8fd8\u53ef\u4ee5\u7528\u4e8e\u5f00\u53d1\uff08\u540e\u6587\u5c06\u9610\u8ff0\uff09
            \n\u4e13\u6ce8\u4e8e\u5b66\u4e60\u6570\u636e\u7684\u6a21\u5f0f\uff0c\u4ee5\u521b\u9020\u65b0\u7684\u6570\u636e\uff0c\u5982\u751f\u6210\u56fe\u50cf\u6216\u6587\u672c\uff0c\u4e0b\u56fe\u4e3a\u7b80\u5355\u7684\u9610\u8ff0\n\n
            \"image.png\"<\/figure>\n<\/p>\n
            \u751f\u6210\u5f0fAI\u4f7f\u7528<\/h1>\n
            chatGPT<\/h2>\n
            chatGPT \u65e0\u7591\u662f\u73b0\u5728\u751f\u6210\u5f0fAI\u4e2d\u7126\u70b9\uff0c\u90a3\u4e48\u5728\u7a0b\u5e8f\u5f00\u53d1\u4e2d\u5982\u4f55\u63d0\u9ad8\u6548\u7387\u5462\uff1f<\/p>\n
            chatGPT \u6ce8\u518c\uff1a<\/h3>\n
            \u7531\u4e8echatGPT \u4e0d\u652f\u6301\u4e2d\u56fd\u5730\u533a\uff0c\u9700\u8981\u4f7f\u7528 \u201c\u5c0f\u98de\u673a\u201d\u5207\u6362\u7f51\u7edc\u8282\u70b9\u5230\u56fd\u5916\u6bd4\u5982\uff1a\u7f8e\u56fd\u8282\u70b9<\/p>\n
              \n
            1. \u5e38\u89c4\u90ae\u7bb1\u3001apple\u6ce8\u518c<\/li>\n<\/ol>\n
              \n
              \"image.png\"<\/figure>\n<\/p>\n
                \n
              1. \u624b\u673a\u53f7\u7801\u9a8c\u8bc1\uff0c\u8fd9\u91cc\u53ef\u4ee5\u53bb\u7b2c\u4e09\u65b9\u77ed\u4fe1\u5e73\u53f0\u8d2d\u4e70 \uff0c\u4ef7\u683c\u4e00\u822c\u57282\u7f8e\u5206\u5de6\u53f3\uff0c\u4e0d\u540c\u56fd\u5bb6\u4ef7\u683c\u7565\u6709\u4e0d\u540c<\/li>\n<\/ol>\n
                \n
                \"image.png\"<\/figure>\n<\/p>\n
                \u8f85\u52a9\u5f00\u53d1\u751f\u6210\u9700\u8981\u7684\u4ee3\u7801<\/h3>\n
                \u8f93\u5165\uff1a\u751f\u6210\u4e2anode \u51fd\u6570\uff0c\u8f93\u5165\u6587\u4ef6\u8def\u5f84\uff0c\u8f93\u51fa\u5305\u62ec\u6539\u6587\u4ef6\u5185\u6240\u6709\u6587\u4ef6\u7684size<\/p>\n
                chatgpt \u8f93\u51fa\u7684\u4ee3\u7801\uff1a<\/p>\n
                <\/div>
                ini<\/span>
                \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>const fs<\/span> = require(\"fs\"<\/span>);<\/span><\/span>\nconst path<\/span> = require(\"path\"<\/span>);<\/span><\/span>\n<\/span>\n<\/span>\n\/\/ \u9012\u5f52\u8ba1\u7b97\u6587\u4ef6\u5939\u5927\u5c0f\u7684\u51fd\u6570<\/span>\nfunction calculateFolderSize(folderPath) {<\/span>\n  let totalSize<\/span> = 0<\/span>;<\/span><\/span>\n<\/span>\n<\/span>\n  \/\/ \u8bfb\u53d6\u6587\u4ef6\u5939\u5185\u7684\u6240\u6709\u6587\u4ef6\u548c\u5b50\u6587\u4ef6\u5939<\/span>\n  const items<\/span> = fs.readdirSync(folderPath);<\/span><\/span>\n<\/span>\n<\/span>\n  items.forEach((item) => {<\/span>\n    const itemPath<\/span> = path.join(folderPath, item);<\/span><\/span>\n    const stats<\/span> = fs.statSync(itemPath);<\/span><\/span>\n<\/span>\n<\/span>\n    if (stats.isFile()) {<\/span>\n      \/\/ \u5982\u679c\u662f\u6587\u4ef6\uff0c\u7d2f\u52a0\u6587\u4ef6\u5927\u5c0f<\/span>\n      totalSize += stats.size;<\/span><\/span>\n    } else if (stats.isDirectory()) {<\/span>\n      \/\/ \u5982\u679c\u662f\u6587\u4ef6\u5939\uff0c\u9012\u5f52\u8ba1\u7b97\u6587\u4ef6\u5939\u5927\u5c0f<\/span>\n      totalSize += calculateFolderSize(itemPath);<\/span><\/span>\n    }<\/span>\n  });<\/span><\/span>\n<\/span>\n<\/span>\n  return totalSize;<\/span><\/span>\n}<\/span>\n<\/span>\n<\/span>\n\/\/ \u4f7f\u7528\u793a\u4f8b<\/span>\nconst folderPath<\/span> = \"..\/public\/pdf\"<\/span>; \/\/ \u66ff\u6362\u4e3a\u4f60\u7684\u6587\u4ef6\u5939\u8def\u5f84<\/span><\/span>\nconst totalSize<\/span> = calculateFolderSize(folderPath);<\/span><\/span>\nconsole.log(<\/span>\n  `\u6587\u4ef6\u5939 ${folderPath} \u5185\u6240\u6709\u6587\u4ef6\u7684\u603b\u5927\u5c0f\u4e3a ${<\/span>\n    totalSize \/ 1024 \/ 1024<\/span>\n  } MB<\/span>\n<\/code><\/pre>\n
                \u8fd9\u65f6\u5019\u518d\u8981\u6c42\u4e0a\u8ff0\u529f\u80fd\u8f93\u51fapython\u7684\u5199\u6cd5<\/p>\n
                chatGPT\u8f93\u51fa\uff1a<\/p>\n
                <\/div>
                ini<\/span>
                \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>import os<\/span>\n<\/span>\n<\/span>\n# \u9012\u5f52\u8ba1\u7b97\u6587\u4ef6\u5939\u5927\u5c0f\u7684\u51fd\u6570<\/span><\/span>\ndef calculate_folder_size(folder_path):<\/span>\n    total_size<\/span> = 0<\/span><\/span>\n<\/span>\n<\/span>\n    # \u904d\u5386\u6587\u4ef6\u5939\u5185\u7684\u6240\u6709\u6587\u4ef6\u548c\u5b50\u6587\u4ef6\u5939<\/span><\/span>\n    for root, dirs, files in os.walk(folder_path):<\/span>\n        for file in files:<\/span>\n            file_path<\/span> = os.path.join(root, file)<\/span>\n            total_size += os.path.getsize(file_path)<\/span>\n<\/span>\n<\/span>\n    return total_size<\/span>\n<\/span>\n<\/span>\n# \u4f7f\u7528\u793a\u4f8b<\/span><\/span>\nfolder_path<\/span> = \"..\/public\/pdf\"<\/span>  # \u66ff\u6362\u4e3a\u4f60\u7684\u6587\u4ef6\u5939\u8def\u5f84<\/span><\/span>\ntotal_size<\/span> = calculate_folder_size(folder_path)<\/span>\nprint(f\"\u6587\u4ef6\u5939 {folder_path} \u5185\u6240\u6709\u6587\u4ef6\u7684\u603b\u5927\u5c0f\u4e3a {total_size \/ 1024 \/ 1024:.2f} MB<\/span>\n<\/code><\/pre>\n
                <\/h3>\n
                copt api \u6587\u6863\u5b57\u6bb5\u751f\u6210ts\u6587\u4ef6<\/strong><\/h3>\n
                \u524d\u7aef\u5f88\u591a\u65f6\u5019\u9700\u8981\u628aAPI<\/code>\u63a5\u53e3\u6587\u6863\u4e2dresponse<\/code>\u6570\u636e\u7ed3\u6784\u8f6c\u6362\u6210.ts<\/code>\u6587\u4ef6,\u5199\u8d77\u6765\u7b80\u5355\u4f46\u662f\u7e41\u7410\uff0c\u53ef\u4ee5\u901a\u8fc7chatgpt<\/code>\u5e2e\u5fd9\u5b8c\u6210\u3002\n\u9700\u8981\u751f\u6210typescript<\/code>\u7684API<\/code>\u63a5\u53e3\u6587\u6863\u7ed3\u6784\uff1a\n\n
                \"image.png\"<\/figure>\n\n\u7528\u9f20\u6807\u9009\u4e2dcopy api\u6587\u6863\u540e\u7684\u7ed3\u6784sources<\/code>\uff1a<\/p>\n
                <\/div>
                c<\/span>
                \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>  companyId string<\/span> \u5fc5\u987b \u4e3b\u4f53id <\/span>\n  entityName string<\/span> \u5fc5\u987b \u4e3b\u4f53\u540d\u79f0<\/span>\n  ....<\/span>\n<\/code><\/pre>\n
                \u8f93\u5165\u8981\u6c42chatgpt\u8bb0\u5f55\u4e00\u6bb5 “template 1” \u4ee3\u7801\uff1a<\/p>\n
                <\/div>
                csharp<\/span>
                \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>Remember this<\/span> code template 1<\/span>:<\/span>\n sources\uff1a<\/span>\n <\/span>\n entityName string<\/span> \u5fc5\u987b \u4e3b\u4f53\u540d\u79f0<\/span>\n <\/span>\n <\/span>\n Ignore \u201c\u5fc5\u987b\u201d and<\/span> output typescript code\uff1a<\/span>\n interface<\/span> RowInfo<\/span>{ <\/span>\n    \/**<\/span><\/span>\n    * \u4e3b\u4f53\u540d\u79f0 <\/span>\n    *\/ <\/span>\n    entityName?: string<\/span><\/span>\n    ....<\/span>\n    (The rest of the code is<\/span> write here) <\/span>\n } <\/span>\n<\/code><\/pre>\n
                \u8f93\u5165”sources”\u8981\u6c42chatgpt\u6309\u7167”template 1″ \u751f\u6210\u4ee3\u7801\uff1a<\/p>\n
                <\/div>
                csharp<\/span>
                \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>  sources\uff1a <\/span>\n  <\/span>\n  companyId string<\/span> \u5fc5\u987b \u4e3b\u4f53id <\/span>\n  entityName string<\/span> \u5fc5\u987b \u4e3b\u4f53\u540d\u79f0<\/span>\n  <\/span>\n  Create a TypeScript definition based on<\/span> these sources by<\/span> template 1<\/span><\/span>\n<\/code><\/pre>\n
                chatgpt\u8f93\u51fa\uff1a
                \n\n
                \"image.png\"<\/figure>\n\ncopy code \u5f97\u5230<\/p>\n
                <\/div>
                csharp<\/span>
                \u590d\u5236\u4ee3\u7801<\/div><\/div><\/div>interface<\/span> RowInfo<\/span> {<\/span>\n  \/**<\/span><\/span>\n   * \u4e3b\u4f53id<\/span>\n   *\/<\/span>\n  companyId: string<\/span>;<\/span>\n  <\/span>\n  \/**<\/span><\/span>\n   * \u4e3b\u4f53\u540d\u79f0<\/span>\n   *\/<\/span>\n  entityName: string<\/span>;<\/span>\n<\/span>\n<\/span>\n  \/\/ Add other properties here as needed<\/span><\/span>\n}<\/span>\n<\/code><\/pre>\n
                chatGPT\u6781\u5927\u63d0\u9ad8\u4e86\u5f00\u53d1\u4eba\u5458\u7684\u6548\u7387\u3002<\/p>\n
                \u6628\u5929\u4e3e\u529e\u7684\u9996\u5c4aOpenAI\u5f00\u53d1\u8005\u5927\u4f1a\u53d1\u8868\u4e86GPT-4 Turbo, \u81ea\u5b9a\u4e49chatGPT, GPT Store,\u548c\u66f4\u4f4e\u7684api\u8c03\u7528\u8d39\u7528\u80fd\u8017\u7b49\uff0c\u80fd\u770b\u51fa\u6765chatGPT\u5728\u52a0\u901f\u4e86\u5546\u4e1a\u5316\u548c\u5927\u4f17\u5316<\/p>\n
                Midjourney<\/strong><\/h2>\n
                AI \u4e0d\u662f\u73b0\u5b9e\u4e16\u754c\u7684\u590d\u523b\uff0c\u800c\u662f\u4eba\u7c7b\u60f3\u8c61\u529b\u7684\u5ef6\u4f38<\/strong>\uff0cMidjourney \u80fd\u8ba9\u6211\u4eec\u7684\u60f3\u8c61\u529b\u5ef6\u4f38\u5230AI\u4e2d 
                \n\u901a\u8fc7\u5728\u5bf9\u8bdd\u7684\u5f62\u5f0f\u63d0\u793aMidjourney \u5f00\u59cb\u6211\u4eec\u7684\u60f3\u8c61\u4e4b\u65c5\u5427
                \n\u8f93\u5165\uff1a\/imagine The Terra-cotta warriors typing code \uff08<\/strong> \u6572\u4ee3\u7801\u7684\u5175\u9a6c\u4fd1 \uff09<\/strong> 
                \n\n
                \"image.png\"<\/figure>\n
                \n\u8f93\u5165\uff1a\/imagine Playing DJ’s Terra-cotta warriors More lively more colorful (\u4f1adj\u7684\u5175\u9a6c\u4fd1\u751f\u52a8\u591a\u5f69)<\/strong>\n\n
                \"image.png\"<\/figure>\n\n\u4e5f\u53ef\u4ee5\u67e5\u770bmidjourney explore www.midjourney.com\/explore<\/a> \u627e\u5230\u611f\u5174\u8da3\u7684\u56fe\u7247 \u590d\u5236\u5176\u4e2d\u7684\u63d0\u793a\u8bcd
                \n\n
                \"image.png\"<\/figure>\n
                \n\u518d\u5230 \u8f93\u5165\u6846\u9ecf\u8d34\u590d\u5236\u5185\u5bb9\u5f97\u5230\uff1a
                \n\n
                \"image.png\"<\/figure>\n
                \n\u5982\u679c\u6709\u81ea\u5df1\u4e2d\u610f\u7684\u56fe\u7247\u4e5f\u53ef\u4ee5\u4e0a\u4f20\u5e76\u8ba9 midjourney\u751f\u6210\u5bf9\u5e94\u753b\u98ce\u7684\u56fe\u7247 \u6b65\u9aa4\uff1a
                <\/p>\n
                  \n
                1. \u4e0a\u4f20\u56fe\u7247
                  \n\n
                  \"image.png\"<\/figure>\n
                  \n\u4e0a\u4f20\u6210\u529f\u540e\u70b9\u51fb\u5927\u56fe \u590d\u5236\u56fe\u7247\u5730\u5740
                  \n\n
                  \"image.png\"<\/figure>\n
                  \n\u518d\u6b21\u8f93\u5165\uff1a\/imagine \u590d\u5236\u7684\u56fe\u7247\u5730\u5740 + Beautiful illustration, a lotus in front of Buddha, commercial photography –ar 1:1 –v 5.2 –s 250 \uff08\u5728explore \u590d\u5236\u7684\u63d0\u793a\u8bcd\uff09<\/strong>
                  <\/li>\n<\/ol>\n
                  \u518d\u6b21\u751f\u6210\u7684\u56fe\u7247\u5982\u4e0b \u8fd8\u662f\u86ee\u9177\u7684\uff1a<\/strong>\n\n
                  \"image.png\"<\/figure>\n\n\n
                  \"image.png\"<\/figure>\n<\/p>\n
                  \u6280\u672f\u4e0d\u4f1a\u56e0\u4e3a\u4eba\u7684\u610f\u5fd7\u800c\u505c\u6ede\uff0c\u800c\u662f\u4e0d\u65ad\u8fed\u4ee3\u524d\u8fdb\uff0c \u5982\u4f55\u62e5\u62b1\u548c\u4f7f\u7528\u8fd9\u4e9b\u4eba\u5de5\u667a\u80fd\u4f5c\u4e3a\u65e5\u5e38\u5f00\u53d1\u5de5\u5177\uff0c\u5c06\u66f4\u5927\u5316\u4eab\u53d7\u8fd9\u4e00\u6ce2AI\u7ea2\u5229<\/h2>","protected":false},"excerpt":{"rendered":"
                  AI\u53d1\u5c55\u4e0e\u73b0\u72b6 \u4eba\u5de5\u667a\u80fd\uff08AI\uff09\u7684\u53d1\u5c55\u5386\u53f2\u53ef\u4ee5\u6982\u62ec\u5982\u4e0b\uff1a \u8d77\u6b65\u9636\u6bb5\uff0820\u4e16\u7eaa\u4e2d\u53f6\uff09\uff1aAI\u7684\u6982\u5ff5\u9996\u6b21\u63d0\u51fa\uff0c\u65e9\u671f\u5173\u6ce8\u903b\u8f91\u548c\u7b26\u53f7\u5904\u7406\u3002 \u63a8\u7406\u7cfb\u7edf\uff081950s-1960s\uff09\uff1a\u5f00\u59cb\u7814\u7a76\u63a8\u7406\u7cfb\u7edf\uff0c\u5305\u62ec\u65e9\u671fAI\u9879\u76ee<\/p>\n","protected":false},"author":1,"featured_media":8327,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"rank_math_title":"","rank_math_description":"","rank_math_focus_keyword":"","views":"2","footnotes":""},"categories":[3],"tags":[126,127,128,129,136],"collection":[],"class_list":["post-1409","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-fenlei2","tag-gpt","tag-ai","tag-128","tag-129","tag-136"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/posts\/1409","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/comments?post=1409"}],"version-history":[{"count":0,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/posts\/1409\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/media\/8327"}],"wp:attachment":[{"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/media?parent=1409"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/categories?post=1409"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/tags?post=1409"},{"taxonomy":"collection","embeddable":true,"href":"https:\/\/www.nicekj.com\/nicekj2024\/wp\/v2\/collection?post=1409"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}