更新 function.py

function.py

@@ -37,8 +37,8 @@ def train_logreg(X, y):
     return model  # 返回训练好的模型
 
 def predict_logreg(model, X):
-    X_processed = preprocess_data(X.reshape(1, -1))  # 预处理单个测试样本
+    X_processed = preprocess_data(X)  # 预处理输入数据
-    return model.predict(X_processed)[0]  # 返回预测结果
+    return model.predict(X_processed)  # 返回预测结果
 
 if __name__ == "__main__":
     X, y = load_data("./data/horse-colic.data")
@@ -50,9 +50,7 @@ if __name__ == "__main__":
     model = train_logreg(X_train, y_train)
 
     # 预测
-    y_pred = []
+    y_pred = model.predict(preprocess_data(X_test))  # 直接在测试集上预测
-    for sample in X_test:
-        y_pred.append(predict_logreg(model, sample))  # 逐个样本预测
 
     # 计算准确率
     accuracy = np.mean(y_pred == y_test)