更新 function.py

function.py

@@ -71,12 +71,12 @@ def classify0(inX, dataSet, labels, k=3):
     Returns:
     - int: 预测的分类标签。
     """
-    dists = np.sum(np.abs(dataSet - inX), axis=1)
+    dists = np.sqrt(((dataSet - inX) ** 2).sum(axis=1))
     sorted_index = np.argsort(dists)
     class_count = {}
     for i in range(k):
         vote_label = labels[sorted_index[i]]
-        class_count[vote_label] = class_count.get(vote_label, 0) + 1
+        class_count[vote_label] = class_count.get(vote_label, 0) + 1 / (dists[sorted_index[i]] + 1e-5)  # 加权，防止除零
     sorted_class_count = sorted(class_count.items(), key=lambda item: item[1], reverse=True)
     if sorted_class_count:
         return sorted_class_count[0][0]
@@ -84,10 +84,19 @@ def classify0(inX, dataSet, labels, k=3):
         return -1
 
 
+def normalize(features):
+    min_vals = features.min(axis=0)
+    max_vals = features.max(axis=0)
+    ranges = max_vals - min_vals
+    normalized = (features - min_vals) / ranges
+    normalized[ranges == 0] = 0  # 防止除以零
+    return normalized
+
 if __name__ == "__main__":
-    # 假设 load_data 和 normalize 函数已经定义
     train_features, train_labels = load_data("datingTrainSet2.tsv")
     test_features, test_labels = load_data("datingTestSet2.tsv")
+
+    # 归一化特征
     train_features = normalize(train_features)
     test_features = normalize(test_features)
 
@@ -109,4 +118,4 @@ if __name__ == "__main__":
             best_accuracy = accuracy
             best_k = k
 
-    print(f"Best K: {best_k}, Best Accuracy: {best_accuracy:.2f}")
+    print(f"Best K: {best_k}, Best Accuracy: {best_accuracy:.2f}")