更新 function.py

function.py

@@ -43,6 +43,8 @@ name = "章程鸿"
 import numpy as np
 from sklearn.model_selection import GridSearchCV
 from sklearn.neighbors import KNeighborsClassifier
+from sklearn.preprocessing import MinMaxScaler
+from sklearn.metrics import accuracy_score
 
 def load_data(file_path):
     """
@@ -53,25 +55,6 @@ def load_data(file_path):
     labels = data[:, -1].astype(int)
     return features, labels
 
-def normalize_min_max(data):  
-    """  
-    对数据进行最小-最大缩放归一化。  
-  
-    Args:  
-    - data (numpy array): 待归一化的数据。  
-  
-    Returns:  
-    - numpy array: 归一化后的数据。  
-    - numpy array: 最小值。  
-    - numpy array: 最大值。  
-    """  
-    min_vals = data.min(0)  
-    max_vals = data.max(0)  
-    ranges = max_vals - min_vals  
-    ranges[ranges == 0.0] = 1.0  # 避免除以零  
-    norm_data = (data - min_vals) / ranges  
-    return norm_data, min_vals, max_vals  
-  
 if __name__ == "__main__":
     train_file = "datingTrainSet2.tsv"
     test_file = "datingTestSet2.tsv"  # 假设您有一个测试集文件
@@ -80,9 +63,10 @@ if __name__ == "__main__":
     train_data, train_labels = load_data(train_file)
     test_data, test_labels = load_data(test_file)
 
-    # 归一化训练和测试数据  
-    train_data_normalized, min_vals, max_vals = normalize_min_max(train_data)  
-    test_data_normalized = (test_data - min_vals) / (max_vals - min_vals)  
+    # 使用MinMaxScaler进行归一化
+    scaler = MinMaxScaler()
+    train_data_normalized = scaler.fit_transform(train_data)
+    test_data_normalized = scaler.transform(test_data)
 
     # 定义KNN分类器
     knn = KNeighborsClassifier()
@@ -104,4 +88,12 @@ if __name__ == "__main__":
 
     # 对测试数据进行预测
     predicted_labels = best_knn.predict(test_data_normalized)
-    print("预测的分类标签是:", predicted_label[0])
+
+    # 输出预测结果
+    print("预测的分类标签是:")
+    for i, (predicted, actual) in enumerate(zip(predicted_labels, test_labels)):
+        print(f"样本 {i+1}: 预测标签: {predicted}, 实际标签: {actual}")
+
+    # 计算并输出准确率
+    accuracy = accuracy_score(test_labels, predicted_labels)
+    print(f"模型在测试集上的准确率为: {accuracy * 100:.2f}%")