更新 function.py

function.py

@@ -83,14 +83,20 @@ def preprocess_data(X):
     return scaler.fit_transform(X)
 
 if __name__ == "__main__":
-    # 加载数据集
+    # 加载训练集
-    X, y = load_data("./data/horse-colic.data")
+    X_train, y_train = load_data("./data/horse-colic.data")
 
-    # 归一化数据
+    # 归一化训练集
-    X = preprocess_data(X)
+    X_train = preprocess_data(X_train)
+
+    # 加载测试集
+    X_test, y_test = load_data("./data/horse-colic.test")
+
+    # 归一化测试集
+    X_test = preprocess_data(X_test)
 
     # 使用 train_test_split 打乱并随机抽取数据
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+    X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2, random_state=42)
 
     # K折交叉验证
     k = 5
@@ -114,6 +120,9 @@ if __name__ == "__main__":
     print(f"K折交叉验证得分: {accuracies}")
     print(f"平均准确率: {np.mean(accuracies)}")
 
+    # 训练逻辑回归模型
+    weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000)
+
     # 测试单个数据
     y_pred = predict_logreg(X_test[11], weights)
     print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")