完整的replit在这里：https://repl.it/@JacksonEnnis/KNNPercentage

我正在尝试使用sci-kit learn中的KNN工具进行一些预测。

我有两个函数，recurse()和predict()。recurse()旨在遍历所有可能的特征组合，而predict()则负责实际的

  def predict(self, data, answers):    from sklearn.neighbors import KNeighborsClassifier    from sklearn.model_selection import train_test_split as tts    import numpy as np    if len(data) > 1:      print("转置前的长度 {}".format(len(data)))      #n_data = np.transpose(data)      #print("转置后的长度 {}".format(len(n_data)))      knn = KNeighborsClassifier(n_neighbors=1)      xTrain, xTest, yTrain, yTest = tts(data, answers)      print("xTrain数据: {}".format(len(xTrain)))      knn.fit(xTrain, yTrain)      print(knn.score(xTest, yTest))  def recurse(self, data):    self.predict(data, self.y)    if len(data) > 0:      self.recurse(self.rLeft(data))    if len(data) > 1:      self.recurse(self.rMid(data))    if len(data) > 2:      self.recurse(self.rRight(data))

然而，当我运行程序时，它指出在训练/测试行上出现了问题。我检查了每个特征中的样本以及答案，发现它们长度都相同，所以我不知道为什么会发生这种情况。

Traceback (most recent call last):  File "main.py", line 12, in <module>    best = Config(apple)  File "/home/runner/Config.py", line 13, in __init__    self.predict(self.features, self.y)  File "/home/runner/Config.py", line 45, in predict    xTrain, xTest, yTrain, yTest = tts(data, answers)  File "/home/runner/.local/lib/python3.6/site-packages/sklearn/model_selection/_split.py", line 2096, in train_test_split    arrays = indexable(*arrays)  File "/home/runner/.local/lib/python3.6/site-packages/sklearn/utils/validation.py", line 230, in indexable    check_consistent_length(*result)  File "/home/runner/.local/lib/python3.6/site-packages/sklearn/utils/validation.py", line 205, in check_consistent_length    " samples: %r" % [int(l) for l in lengths])ValueError: 发现输入变量的样本数量不一致：[20, 499]

回答：

你的轴向反了。对于你的每个数组，array.shape[0]必须是相同的大小。我建议你查看scikit文档以获取更多示例。

tts(np.array(data).T, answers)