更新 function.py

function.py

@@ -1,5 +1,7 @@
 # function.py
 import numpy as np
+from collections import Counter
+from scipy.spatial import distance
 
 """
 挑战说明:
@@ -53,14 +55,13 @@ def classify0(inX, dataSet, labels, k=3):
     """
 
     # 计算输入样本与训练集中每个样本的欧几里得距离
-    distances = [np.sqrt(np.sum((x_train - inX) ** 2)) for x_train in dataSet]  
+    distances = distance.cdist(dataSet, [inX], metric='euclidean').flatten()
 
     # 按距离排序并选择前k个最小的距离
     k_indices = np.argsort(distances)[:k]
     k_nearest_labels = [labels[i] for i in k_indices]
 
     # 计算多数标签
-    from collections import Counter  
     label_counts = Counter(k_nearest_labels)
     most_common_label = label_counts.most_common(1)[0][0]
 
@@ -90,7 +91,7 @@ if __name__ == "__main__":
     predicted_label = classify0(test_sample, train_data, train_labels)
     print(f"Predicted label: {predicted_label}, Actual label: {test_labels[0]}")
 
-    # 你也可以遍历整个测试集来评估分类器的性能  
+    # 遍历整个测试集来评估分类器的性能
     correct_count = 0
     for i in range(len(test_data)):
         predicted_label = classify0(test_data[i], train_data, train_labels)