更新 function.py

function.py

@@ -45,7 +45,7 @@ name = "陈宇杰"
 
 # 这里写自己的代码实现
 
-def knn_classify(inX, dataSet, labels, k):
+def classify0(inX, dataSet, labels, k):
     """
     KNN 分类函数
     :param inX: 待分类的样本
@@ -60,7 +60,6 @@ def knn_classify(inX, dataSet, labels, k):
     predicted_label = Counter(nearest_labels).most_common(1)[0][0]  # 多数投票
     return predicted_label
 
-
 def load_data(file_path):
     """
     加载 TSV 格式数据文件
@@ -72,7 +71,6 @@ def load_data(file_path):
     labels = data[:, -1].astype(int)  # 标签
     return features, labels
 
-
 def main():
     # 加载训练集
     train_file = "datingTrainSet2.tsv"  # 训练集文件路径
@@ -97,7 +95,7 @@ def main():
             train_labels_subset = np.delete(train_labels, i)
 
             # 使用 KNN 分类器预测
-            predicted_label = knn_classify(test_sample, train_subset, train_labels_subset, k)
+            predicted_label = classify0(test_sample, train_subset, train_labels_subset, k)
             if predicted_label == train_labels[i]:
                 correct_predictions += 1
 
@@ -111,10 +109,9 @@ def main():
     # 测试分类
     test_sample = [65915, 3.868359, 0.128078]  # 测试样本
     test_sample = scaler.transform([test_sample])  # 对测试样本进行相同的缩放
-    predicted_label = knn_classify(test_sample[0], train_data, train_labels, best_k)  # 分类
+    predicted_label = classify0(test_sample[0], train_data, train_labels, best_k)  # 分类
 
     print(f"测试样本 {test_sample[0]} 的预测分类标签为: {predicted_label}")
 
-
 if __name__ == "__main__":
     main()