更新 function.py

function.py

@@ -54,25 +54,24 @@ def predict_logreg(model, scaler, inX):
     inX_scaled = scaler.transform(inX.reshape(1, -1))  # 标准化输入数据
     return model.predict(inX_scaled)[0]
 
-if __name__ == "__main__":
+if __name__ == "__main__":  
-    # 示例：加载数据并训练模型
+    # 加载训练集  
-    X_train, y_train = load_data("./data/horse-colic.data")
+    X_train, y_train = load_data("./data/horse-colic.data")  
-    model, scaler = train_logreg(X_train, y_train)
-
-    # 测试单个数据
-    X_test, y_test = load_data("./data/horse-colic.test")
-    y_pred = predict_logreg(model, scaler, X_test[11])
     
+    # 数据标准化  
+    scaler = StandardScaler()  
+    X_train = scaler.fit_transform(X_train)  
+    
+    # 训练逻辑回归模型  
+    model, _ = train_logreg(X_train, y_train)  # 只需要模型和缩放器
+
+    # 测试单个数据  
+    X_test, y_test = load_data("./data/horse-colic.test")  
+    X_test = scaler.transform(X_test)  # 同样标准化测试集  
+
+    # 进行预测
+    y_pred = predict_logreg(model, scaler, X_test[11])  # 传递模型和缩放器
+
     print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")
 
 
-
-    # 训练逻辑回归模型
-    weights = train_logreg(X_train, y_train, learning_rate=0.001, iterations=5000)
-
-    # 测试单个数据
-    X_test, y_test = load_data("./data/horse-colic.test")
-    X_test = scaler.transform(X_test)  # 同样标准化测试集
-    y_pred = predict_logreg(X_test[11], weights)
-    print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")
-