{"id":1061179,"date":"2024-12-31T15:40:34","date_gmt":"2024-12-31T07:40:34","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1061179.html"},"modified":"2024-12-31T15:40:36","modified_gmt":"2024-12-31T07:40:36","slug":"python%e5%a6%82%e4%bd%95%e4%ba%a7%e7%94%9f%e5%9b%ba%e5%ae%9a%e7%9a%84%e9%9a%8f%e6%9c%ba%e6%95%b0","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1061179.html","title":{"rendered":"python\u5982\u4f55\u4ea7\u751f\u56fa\u5b9a\u7684\u968f\u673a\u6570"},"content":{"rendered":"

\"python\u5982\u4f55\u4ea7\u751f\u56fa\u5b9a\u7684\u968f\u673a\u6570\"<\/p>\n

\u5728Python\u4e2d\uff0c\u4ea7\u751f\u56fa\u5b9a\u7684\u968f\u673a\u6570\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u968f\u673a\u6570\u751f\u6210\u5668\u7684\u79cd\u5b50\u503c\u6765\u5b9e\u73b0\u3002<\/strong> \u8bbe\u7f6e\u79cd\u5b50\u503c\u53ef\u4ee5\u786e\u4fdd\u6bcf\u6b21\u8fd0\u884c\u4ee3\u7801\u65f6\u751f\u6210\u7684\u968f\u673a\u6570\u5e8f\u5217\u662f\u76f8\u540c\u7684\uff0c\u8fd9\u5bf9\u4e8e\u8c03\u8bd5\u548c\u6d4b\u8bd5\u975e\u5e38\u6709\u7528\u3002\u4f7f\u7528\u968f\u673a\u79cd\u5b50\u7684\u4e3b\u8981\u65b9\u6cd5\u5305\u62ec\uff1arandom.seed()\u3001numpy.random.seed()\u3001\u4f7f\u7528\u56fa\u5b9a\u79cd\u5b50\u503c\u521d\u59cb\u5316\u968f\u673a\u6570\u751f\u6210\u5668<\/code>\u7b49\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u5e76\u89e3\u91ca\u5b83\u4eec\u7684\u4f7f\u7528\u573a\u666f\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u4f7f\u7528random\u6a21\u5757\u8bbe\u7f6e\u79cd\u5b50\u503c<\/h2>\n<\/p>\n

Python\u7684random<\/code>\u6a21\u5757\u63d0\u4f9b\u4e86\u8bb8\u591a\u751f\u6210\u968f\u673a\u6570\u7684\u51fd\u6570\u3002\u901a\u8fc7\u8bbe\u7f6e\u79cd\u5b50\u503c\uff0c\u53ef\u4ee5\u786e\u4fdd\u6bcf\u6b21\u751f\u6210\u7684\u968f\u673a\u6570\u5e8f\u5217\u76f8\u540c\u3002<\/p>\n<\/p>\n

import random<\/p>\n
\u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50<\/strong><\/h2>\n
random.seed(42)<\/p>\n
\u751f\u6210\u968f\u673a\u6570<\/strong><\/h2>\n
print(random.random())<\/p>\n
print(random.randint(1, 10))<\/p>\n
print(random.choice([1, 2, 3, 4, 5]))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u901a\u8fc7random.seed(42)<\/code>\u8bbe\u7f6e\u4e86\u968f\u673a\u6570\u79cd\u5b50\u3002\u65e0\u8bba\u4f55\u65f6\u8fd0\u884c\u8fd9\u6bb5\u4ee3\u7801\uff0c\u751f\u6210\u7684\u968f\u673a\u6570\u5e8f\u5217\u90fd\u5c06\u662f\u76f8\u540c\u7684\u3002<\/p>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528numpy\u6a21\u5757\u8bbe\u7f6e\u79cd\u5b50\u503c<\/h2>\n<\/p>\n
\u5bf9\u4e8e\u9700\u8981\u5904\u7406\u5927\u89c4\u6a21\u6570\u636e\u6216\u8fdb\u884c\u79d1\u5b66\u8ba1\u7b97\u7684\u573a\u666f\uff0cnumpy<\/code>\u6a21\u5757\u662f\u4e00\u4e2a\u975e\u5e38\u5e38\u7528\u7684\u5de5\u5177\u3002numpy<\/code>\u4e5f\u63d0\u4f9b\u4e86\u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50\u7684\u65b9\u6cd5\u3002<\/p>\n<\/p>\n
import numpy as np<\/p>\n
\u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50<\/strong><\/h2>\n
np.random.seed(42)<\/p>\n
\u751f\u6210\u968f\u673a\u6570<\/strong><\/h2>\n
print(np.random.rand())<\/p>\n
print(np.random.randint(1, 10))<\/p>\n
print(np.random.choice([1, 2, 3, 4, 5]))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e0erandom<\/code>\u6a21\u5757\u7c7b\u4f3c\uff0c\u901a\u8fc7np.random.seed(42)<\/code>\u8bbe\u7f6e\u79cd\u5b50\u503c\uff0c\u53ef\u4ee5\u786e\u4fdd\u751f\u6210\u7684\u968f\u673a\u6570\u5e8f\u5217\u662f\u4e00\u81f4\u7684\u3002<\/p>\n<\/p>\n
\u4e09\u3001\u4f7f\u7528\u56fa\u5b9a\u79cd\u5b50\u503c\u521d\u59cb\u5316\u968f\u673a\u6570\u751f\u6210\u5668<\/h2>\n<\/p>\n
\u6709\u65f6\u6211\u4eec\u53ef\u80fd\u9700\u8981\u521b\u5efa\u72ec\u7acb\u7684\u968f\u673a\u6570\u751f\u6210\u5668\u5b9e\u4f8b\uff0c\u5e76\u4f7f\u7528\u56fa\u5b9a\u7684\u79cd\u5b50\u503c\u8fdb\u884c\u521d\u59cb\u5316\u3002\u8fd9\u5728\u9700\u8981\u591a\u4e2a\u72ec\u7acb\u7684\u968f\u673a\u6570\u5e8f\u5217\u65f6\u975e\u5e38\u6709\u7528\u3002<\/p>\n<\/p>\n
import random<\/p>\n
\u521b\u5efa\u968f\u673a\u6570\u751f\u6210\u5668\u5b9e\u4f8b<\/strong><\/h2>\n
rng = random.Random(42)<\/p>\n
\u751f\u6210\u968f\u673a\u6570<\/strong><\/h2>\n
print(rng.random())<\/p>\n
print(rng.randint(1, 10))<\/p>\n
print(rng.choice([1, 2, 3, 4, 5]))<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u6211\u4eec\u901a\u8fc7random.Random(42)<\/code>\u521b\u5efa\u4e86\u4e00\u4e2a\u72ec\u7acb\u7684\u968f\u673a\u6570\u751f\u6210\u5668\u5b9e\u4f8brng<\/code>\uff0c\u5e76\u4f7f\u7528\u56fa\u5b9a\u7684\u79cd\u5b50\u503c\u8fdb\u884c\u521d\u59cb\u5316\u3002\u8fd9\u6837\u751f\u6210\u7684\u968f\u673a\u6570\u5e8f\u5217\u5c06\u4e0e\u5168\u5c40\u968f\u673a\u6570\u751f\u6210\u5668\u65e0\u5173\u3002<\/p>\n<\/p>\n
\u56db\u3001\u5728\u673a\u5668\u5b66\u4e60<\/a>\u4e2d\u4f7f\u7528\u56fa\u5b9a\u79cd\u5b50\u503c<\/h2>\n<\/p>\n
\u5728\u673a\u5668\u5b66\u4e60\u4e2d\uff0c\u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50\u503c\u975e\u5e38\u91cd\u8981\uff0c\u56e0\u4e3a\u5b83\u53ef\u4ee5\u786e\u4fdd\u5b9e\u9a8c\u7ed3\u679c\u7684\u53ef\u91cd\u590d\u6027\u3002\u4ee5\u4e0b\u662f\u5982\u4f55\u5728\u5e38\u7528\u7684\u673a\u5668\u5b66\u4e60\u5e93\u4e2d\u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50\u503c\u7684\u793a\u4f8b\u3002<\/p>\n<\/p>\n
    \n
  1. \u4f7f\u7528Scikit-learn\u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50<\/li>\n<\/ol>\n
    from sklearn.model_selection import trAI<\/a>n_test_split<\/p>\n
    from sklearn.ensemble import RandomForestClassifier<\/p>\n
    import numpy as np<\/p>\n
    \u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50<\/strong><\/h2>\n
    np.random.seed(42)<\/p>\n
    \u751f\u6210\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
    X, y = np.random.rand(100, 10), np.random.randint(0, 2, 100)<\/p>\n
    \u62c6\u5206\u6570\u636e\u96c6<\/strong><\/h2>\n
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)<\/p>\n
    \u521b\u5efa\u5e76\u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
    clf = RandomForestClassifier(random_state=42)<\/p>\n
    clf.fit(X_train, y_train)<\/p>\n
    \u8fdb\u884c\u9884\u6d4b<\/strong><\/h2>\n
    y_pred = clf.predict(X_test)<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u901a\u8fc7\u8bbe\u7f6enp.random.seed(42)<\/code>\u3001train_test_split<\/code>\u4e2d\u7684random_state=42<\/code>\u548cRandomForestClassifier<\/code>\u4e2d\u7684random_state=42<\/code>\uff0c\u53ef\u4ee5\u786e\u4fdd\u6570\u636e\u62c6\u5206\u548c\u6a21\u578b\u8bad\u7ec3\u7684\u8fc7\u7a0b\u662f\u53ef\u91cd\u590d\u7684\u3002<\/p>\n<\/p>\n
      \n
    1. \u4f7f\u7528TensorFlow\u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50<\/li>\n<\/ol>\n
      import tensorflow as tf<\/p>\n
      import numpy as np<\/p>\n
      \u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50<\/strong><\/h2>\n
      tf.random.set_seed(42)<\/p>\n
      np.random.seed(42)<\/p>\n
      \u751f\u6210\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
      X = np.random.rand(100, 10)<\/p>\n
      y = np.random.randint(0, 2, 100)<\/p>\n
      \u521b\u5efa\u6a21\u578b<\/strong><\/h2>\n
      model = tf.keras.models.Sequential([<\/p>\n
          tf.keras.layers.Dense(64, activation='relu', input_shape=(10,)),<\/p>\n
          tf.keras.layers.Dense(1, activation='sigmoid')<\/p>\n
      ])<\/p>\n
      \u7f16\u8bd1\u6a21\u578b<\/strong><\/h2>\n
      model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])<\/p>\n
      \u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
      model.fit(X, y, epochs=10, batch_size=32)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u901a\u8fc7\u8bbe\u7f6etf.random.set_seed(42)<\/code>\u548cnp.random.seed(42)<\/code>\uff0c\u53ef\u4ee5\u786e\u4fddTensorFlow\u6a21\u578b\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u7684\u968f\u673a\u6027\u662f\u53ef\u63a7\u7684\u3002<\/p>\n<\/p>\n
      \u4e94\u3001\u5728PyTorch\u4e2d\u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50<\/h2>\n<\/p>\n
      import torch<\/p>\n
      import numpy as np<\/p>\n
      \u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50<\/strong><\/h2>\n
      torch.manual_seed(42)<\/p>\n
      np.random.seed(42)<\/p>\n
      \u751f\u6210\u793a\u4f8b\u6570\u636e<\/strong><\/h2>\n
      X = torch.randn(100, 10)<\/p>\n
      y = torch.randint(0, 2, (100,))<\/p>\n
      \u521b\u5efa\u6a21\u578b<\/strong><\/h2>\n
      model = torch.nn.Sequential(<\/p>\n
          torch.nn.Linear(10, 64),<\/p>\n
          torch.nn.ReLU(),<\/p>\n
          torch.nn.Linear(64, 1),<\/p>\n
          torch.nn.Sigmoid()<\/p>\n
      )<\/p>\n
      \u5b9a\u4e49\u635f\u5931\u51fd\u6570\u548c\u4f18\u5316\u5668<\/strong><\/h2>\n
      criterion = torch.nn.BCELoss()<\/p>\n
      optimizer = torch.optim.Adam(model.parameters())<\/p>\n
      \u8bad\u7ec3\u6a21\u578b<\/strong><\/h2>\n
      for epoch in range(10):<\/p>\n
          optimizer.zero_grad()<\/p>\n
          output = model(X)<\/p>\n
          loss = criterion(output, y.float().unsqueeze(1))<\/p>\n
          loss.backward()<\/p>\n
          optimizer.step()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u901a\u8fc7\u8bbe\u7f6etorch.manual_seed(42)<\/code>\u548cnp.random.seed(42)<\/code>\uff0c\u53ef\u4ee5\u786e\u4fddPyTorch\u6a21\u578b\u8bad\u7ec3\u8fc7\u7a0b\u4e2d\u7684\u968f\u673a\u6027\u662f\u53ef\u63a7\u7684\u3002<\/p>\n<\/p>\n
      \u516d\u3001\u4f7f\u7528\u56fa\u5b9a\u968f\u673a\u6570\u79cd\u5b50\u8fdb\u884c\u6570\u636e\u589e\u5f3a<\/h2>\n<\/p>\n
      \u5728\u56fe\u50cf\u5904\u7406\u548c\u8ba1\u7b97\u673a\u89c6\u89c9\u4efb\u52a1\u4e2d\uff0c\u6570\u636e\u589e\u5f3a\u662f\u4e00\u79cd\u5e38\u7528\u7684\u6280\u672f\u3002\u901a\u8fc7\u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50\uff0c\u53ef\u4ee5\u786e\u4fdd\u6570\u636e\u589e\u5f3a\u8fc7\u7a0b\u7684\u4e00\u81f4\u6027\u3002<\/p>\n<\/p>\n
      import numpy as np<\/p>\n
      from tensorflow.keras.preprocessing.image import ImageDataGenerator<\/p>\n
      \u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50<\/strong><\/h2>\n
      np.random.seed(42)<\/p>\n
      \u521b\u5efa\u6570\u636e\u589e\u5f3a\u751f\u6210\u5668<\/strong><\/h2>\n
      datagen = ImageDataGenerator(<\/p>\n
          rotation_range=40,<\/p>\n
          width_shift_range=0.2,<\/p>\n
          height_shift_range=0.2,<\/p>\n
          shear_range=0.2,<\/p>\n
          zoom_range=0.2,<\/p>\n
          horizontal_flip=True,<\/p>\n
          fill_mode='nearest'<\/p>\n
      )<\/p>\n
      \u751f\u6210\u589e\u5f3a\u540e\u7684\u56fe\u50cf<\/strong><\/h2>\n
      for batch in datagen.flow(X, batch_size=32, seed=42):<\/p>\n
          # \u5904\u7406\u589e\u5f3a\u540e\u7684\u56fe\u50cf<\/p>\n
          pass<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u901a\u8fc7\u8bbe\u7f6enp.random.seed(42)<\/code>\u548cdatagen.flow<\/code>\u4e2d\u7684seed=42<\/code>\uff0c\u53ef\u4ee5\u786e\u4fdd\u6570\u636e\u589e\u5f3a\u8fc7\u7a0b\u4e2d\u7684\u968f\u673a\u6027\u662f\u4e00\u81f4\u7684\u3002<\/p>\n<\/p>\n
      \u4e03\u3001\u5728\u5206\u5e03\u5f0f\u73af\u5883\u4e2d\u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50<\/h2>\n<\/p>\n
      \u5728\u5206\u5e03\u5f0f\u8ba1\u7b97\u73af\u5883\u4e2d\uff0c\u786e\u4fdd\u6240\u6709\u8282\u70b9\u4f7f\u7528\u76f8\u540c\u7684\u968f\u673a\u6570\u79cd\u5b50\u53ef\u4ee5\u786e\u4fdd\u5b9e\u9a8c\u7ed3\u679c\u7684\u4e00\u81f4\u6027\u3002\u4ee5\u4e0b\u662f\u5982\u4f55\u5728\u5206\u5e03\u5f0f\u73af\u5883\u4e2d\u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50\u7684\u793a\u4f8b\u3002<\/p>\n<\/p>\n
      import numpy as np<\/p>\n
      import tensorflow as tf<\/p>\n
      \u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50<\/strong><\/h2>\n
      np.random.seed(42)<\/p>\n
      tf.random.set_seed(42)<\/p>\n
      \u521b\u5efa\u5206\u5e03\u5f0f\u7b56\u7565<\/strong><\/h2>\n
      strategy = tf.distribute.MirroredStrategy()<\/p>\n
      with strategy.scope():<\/p>\n
          # \u751f\u6210\u793a\u4f8b\u6570\u636e<\/p>\n
          X = np.random.rand(100, 10)<\/p>\n
          y = np.random.randint(0, 2, 100)<\/p>\n
          # \u521b\u5efa\u6a21\u578b<\/p>\n
          model = tf.keras.models.Sequential([<\/p>\n
              tf.keras.layers.Dense(64, activation='relu', input_shape=(10,)),<\/p>\n
              tf.keras.layers.Dense(1, activation='sigmoid')<\/p>\n
          ])<\/p>\n
          # \u7f16\u8bd1\u6a21\u578b<\/p>\n
          model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])<\/p>\n
          # \u8bad\u7ec3\u6a21\u578b<\/p>\n
          model.fit(X, y, epochs=10, batch_size=32)<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u901a\u8fc7\u8bbe\u7f6enp.random.seed(42)<\/code>\u548ctf.random.set_seed(42)<\/code>\uff0c\u53ef\u4ee5\u786e\u4fdd\u5728\u5206\u5e03\u5f0f\u73af\u5883\u4e2d\uff0c\u6240\u6709\u8282\u70b9\u4f7f\u7528\u76f8\u540c\u7684\u968f\u673a\u6570\u79cd\u5b50\uff0c\u4ece\u800c\u786e\u4fdd\u5b9e\u9a8c\u7ed3\u679c\u7684\u4e00\u81f4\u6027\u3002<\/p>\n<\/p>\n
      \u516b\u3001\u5728\u591a\u7ebf\u7a0b\u73af\u5883\u4e2d\u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50<\/h2>\n<\/p>\n
      \u5728\u591a\u7ebf\u7a0b\u73af\u5883\u4e2d\uff0c\u6bcf\u4e2a\u7ebf\u7a0b\u9700\u8981\u72ec\u7acb\u7684\u968f\u673a\u6570\u751f\u6210\u5668\u5b9e\u4f8b\uff0c\u4ee5\u786e\u4fdd\u968f\u673a\u6570\u5e8f\u5217\u7684\u72ec\u7acb\u6027\u548c\u53ef\u91cd\u590d\u6027\u3002<\/p>\n<\/p>\n
      import threading<\/p>\n
      import random<\/p>\n
      def worker(seed):<\/p>\n
          rng = random.Random(seed)<\/p>\n
          print(rng.random())<\/p>\n
      \u521b\u5efa\u7ebf\u7a0b<\/strong><\/h2>\n
      threads = []<\/p>\n
      for i in range(5):<\/p>\n
          t = threading.Thread(target=worker, args=(42 + i,))<\/p>\n
          threads.append(t)<\/p>\n
          t.start()<\/p>\n
      \u7b49\u5f85\u6240\u6709\u7ebf\u7a0b\u5b8c\u6210<\/strong><\/h2>\n
      for t in threads:<\/p>\n
          t.join()<\/p>\n
      <\/code><\/pre>\n<\/p>\n
      \u5728\u4e0a\u8ff0\u4ee3\u7801\u4e2d\uff0c\u901a\u8fc7\u4e3a\u6bcf\u4e2a\u7ebf\u7a0b\u521b\u5efa\u72ec\u7acb\u7684\u968f\u673a\u6570\u751f\u6210\u5668\u5b9e\u4f8brng<\/code>\uff0c\u5e76\u4f7f\u7528\u4e0d\u540c\u7684\u79cd\u5b50\u503c\u8fdb\u884c\u521d\u59cb\u5316\uff0c\u53ef\u4ee5\u786e\u4fdd\u6bcf\u4e2a\u7ebf\u7a0b\u751f\u6210\u7684\u968f\u673a\u6570\u5e8f\u5217\u662f\u72ec\u7acb\u4e14\u53ef\u91cd\u590d\u7684\u3002<\/p>\n<\/p>\n
      \u603b\u7ed3<\/h2>\n<\/p>\n
      \u5728Python\u4e2d\uff0c\u901a\u8fc7\u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50\u503c\uff0c\u53ef\u4ee5\u786e\u4fdd\u6bcf\u6b21\u8fd0\u884c\u4ee3\u7801\u65f6\u751f\u6210\u7684\u968f\u673a\u6570\u5e8f\u5217\u662f\u76f8\u540c\u7684<\/strong>\u3002\u8fd9\u5bf9\u4e8e\u8c03\u8bd5\u548c\u6d4b\u8bd5\u975e\u5e38\u6709\u7528\u3002\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528random.seed()<\/code>\u3001numpy.random.seed()<\/code>\u3001\u521b\u5efa\u72ec\u7acb\u7684\u968f\u673a\u6570\u751f\u6210\u5668\u5b9e\u4f8b\u7b49\u65b9\u6cd5\u6765\u5b9e\u73b0\u8fd9\u4e00\u70b9\u3002\u5728\u673a\u5668\u5b66\u4e60\u3001\u6570\u636e\u589e\u5f3a\u3001\u5206\u5e03\u5f0f\u8ba1\u7b97\u548c\u591a\u7ebf\u7a0b\u73af\u5883\u4e2d\uff0c\u8bbe\u7f6e\u968f\u673a\u6570\u79cd\u5b50\u503c\u5c24\u4e3a\u91cd\u8981\uff0c\u56e0\u4e3a\u5b83\u53ef\u4ee5\u786e\u4fdd\u5b9e\u9a8c\u7ed3\u679c\u7684\u4e00\u81f4\u6027\u548c\u53ef\u91cd\u590d\u6027\u3002\u901a\u8fc7\u638c\u63e1\u8fd9\u4e9b\u65b9\u6cd5\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u597d\u5730\u63a7\u5236\u968f\u673a\u6027\uff0c\u63d0\u9ad8\u4ee3\u7801\u7684\u53ef\u9884\u6d4b\u6027\u548c\u7a33\u5b9a\u6027\u3002<\/p>\n<\/p>\n
      \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
       \u5982\u4f55\u5728Python\u4e2d\u751f\u6210\u53ef\u91cd\u590d\u7684\u968f\u673a\u6570\uff1f<\/strong>
      \u5728Python\u4e2d\uff0c\u4f7f\u7528random<\/code>\u6a21\u5757\u53ef\u4ee5\u751f\u6210\u968f\u673a\u6570\u3002\u5982\u679c\u5e0c\u671b\u751f\u6210\u53ef\u91cd\u590d\u7684\u968f\u673a\u6570\uff0c\u53ef\u4ee5\u5728\u751f\u6210\u968f\u673a\u6570\u4e4b\u524d\u8bbe\u7f6e\u968f\u673a\u79cd\u5b50\u3002\u901a\u8fc7\u8c03\u7528random.seed()<\/code>\u51fd\u6570\u5e76\u4f20\u5165\u4e00\u4e2a\u6574\u6570\u503c\uff0c\u540e\u7eed\u751f\u6210\u7684\u968f\u673a\u6570\u5e8f\u5217\u5c06\u4fdd\u6301\u4e00\u81f4\u3002\u4f8b\u5982\uff1a<\/p>\n
      import random\n\nrandom.seed(42)  # \u8bbe\u7f6e\u79cd\u5b50\nprint(random.randint(1, 100))  # \u751f\u6210\u4e00\u4e2a1\u5230100\u4e4b\u95f4\u7684\u968f\u673a\u6570\n<\/code><\/pre>\n
      \u6bcf\u6b21\u8fd0\u884c\u8fd9\u6bb5\u4ee3\u7801\u65f6\uff0c\u90fd\u4f1a\u5f97\u5230\u76f8\u540c\u7684\u7ed3\u679c\u3002<\/p>\n
      \u4f7f\u7528\u54ea\u4e9b\u65b9\u6cd5\u53ef\u4ee5\u751f\u6210\u968f\u673a\u6d6e\u70b9\u6570\uff1f<\/strong>
      random<\/code>\u6a21\u5757\u63d0\u4f9b\u4e86\u591a\u4e2a\u65b9\u6cd5\u6765\u751f\u6210\u968f\u673a\u6d6e\u70b9\u6570\u3002random.random()<\/code>\u53ef\u4ee5\u751f\u6210\u4e00\u4e2a\u57280\u52301\u4e4b\u95f4\u7684\u6d6e\u70b9\u6570\uff0c\u800crandom.uniform(a, b)<\/code>\u5219\u80fd\u751f\u6210\u4e00\u4e2a\u5728\u6307\u5b9a\u8303\u56f4[a, b]\u5185\u7684\u6d6e\u70b9\u6570\u3002\u793a\u4f8b\u4ee3\u7801\u5982\u4e0b\uff1a<\/p>\n
      import random\n\nprint(random.random())  # \u751f\u6210\u4e00\u4e2a0\u52301\u4e4b\u95f4\u7684\u968f\u673a\u6d6e\u70b9\u6570\nprint(random.uniform(1.0, 10.0))  # \u751f\u6210\u4e00\u4e2a1\u523010\u4e4b\u95f4\u7684\u968f\u673a\u6d6e\u70b9\u6570\n<\/code><\/pre>\n
      \u5982\u4f55\u751f\u6210\u7279\u5b9a\u8303\u56f4\u5185\u7684\u968f\u673a\u6574\u6570\uff1f<\/strong>
      \u8981\u751f\u6210\u7279\u5b9a\u8303\u56f4\u5185\u7684\u968f\u673a\u6574\u6570\uff0c\u53ef\u4ee5\u4f7f\u7528random.randint(a, b)<\/code>\u51fd\u6570\uff0c\u5b83\u4f1a\u8fd4\u56de\u4e00\u4e2a\u5728a\u548cb\u4e4b\u95f4\uff08\u5305\u62eca\u548cb\uff09\u7684\u968f\u673a\u6574\u6570\u3002\u4f8b\u5982\uff0c\u5982\u679c\u60f3\u751f\u6210\u4e00\u4e2a1\u523010\u4e4b\u95f4\u7684\u968f\u673a\u6574\u6570\uff0c\u53ef\u4ee5\u5982\u4e0b\u5b9e\u73b0\uff1a<\/p>\n
      import random\n\nrandom_integer = random.randint(1, 10)\nprint(random_integer)  # \u8f93\u51fa1\u523010\u4e4b\u95f4\u7684\u968f\u673a\u6574\u6570\n<\/code><\/pre>\n
      \u8fd9\u79cd\u65b9\u6cd5\u975e\u5e38\u7b80\u5355\u4e14\u6709\u6548\uff0c\u9002\u5408\u9700\u8981\u6574\u6570\u7ed3\u679c\u7684\u573a\u666f\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"\u5728Python\u4e2d\uff0c\u4ea7\u751f\u56fa\u5b9a\u7684\u968f\u673a\u6570\u53ef\u4ee5\u901a\u8fc7\u8bbe\u7f6e\u968f\u673a\u6570\u751f\u6210\u5668\u7684\u79cd\u5b50\u503c\u6765\u5b9e\u73b0\u3002 \u8bbe\u7f6e\u79cd\u5b50\u503c\u53ef\u4ee5\u786e\u4fdd\u6bcf\u6b21\u8fd0\u884c\u4ee3\u7801\u65f6\u751f […]","protected":false},"author":3,"featured_media":1061190,"comment_status":"closed","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[37],"tags":[],"acf":[],"_links":{"self":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1061179"}],"collection":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/comments?post=1061179"}],"version-history":[{"count":"1","href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1061179\/revisions"}],"predecessor-version":[{"id":1061193,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/posts\/1061179\/revisions\/1061193"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media\/1061190"}],"wp:attachment":[{"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/media?parent=1061179"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/categories?post=1061179"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/docs.pingcode.com\/wp-json\/wp\/v2\/tags?post=1061179"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}