更新 function.py

function.py

@@ -114,16 +114,22 @@ if __name__ == "__main__":
     # 数据预处理
     X, y = preprocess_data(X, y)
 
-     # 训练模型
+    # 尝试不同的正则化强度
-    weights = train_logreg(X, y, learning_rate=0.001, l2_penalty=0.01)
+    l2_penalties = [0.01, 0.1, 1.0]
-
-    # 加载测试数据并进行预处理
-    X_test, y_test = load_data("./data/horse-colic.test")
-    X_test, y_test = preprocess_data(X_test, y_test)
     
-    # 预测
+    for l2_penalty in l2_penalties:
-    predictions = predict_logreg(X_test, weights)
+        # 训练模型
+        weights = train_logreg(X, y, learning_rate=0.01, l2_penalty=l2_penalty)
 
-    # 打印预测结果和实际结果
+        # 加载测试数据并进行预处理
+        X_test, y_test = load_data("./data/horse-colic.test")
+        X_test, y_test = preprocess_data(X_test, y_test)
+        
+        # 评估模型准确率
+        accuracy = evaluate_model(X_test, y_test, weights)
+        print(f"L2正则化强度: {l2_penalty}, 准确率: {accuracy:.4f}")
+
+    # 打印预测结果和实际结果（以最后一个正则化强度为例）
+    predictions = predict_logreg(X_test, weights)
     for i in range(min(10, len(y_test))):
         print(f"预测结果: {predictions[i]}, 实际结果: {y_test[i]}")