更新 function.py

function.py

@@ -1,5 +1,6 @@
 import numpy as np
 import os
+import time
 from sklearn.preprocessing import StandardScaler  # 导入标准化库
 
 # 学号和姓名
@@ -100,6 +101,18 @@ def k_fold_cross_validation(X, y, k=5, learning_rate=0.001, iterations=10000, ba
 
     return np.mean(accuracies)
 
+def evaluate_model(X_test, y_test, weights):
+    """基于测试集计算模型的分类精度和执行时间"""
+    start_time = time.time()  # 记录开始时间
+    y_pred = [predict_logreg(X_test[i], weights) for i in range(len(y_test))]  # 对所有测试样本进行预测
+    end_time = time.time()  # 记录结束时间
+
+    # 计算分类精度
+    accuracy = np.mean(np.array(y_pred) == y_test)  # 分类精度
+    execution_time = end_time - start_time  # 执行时间
+    
+    return accuracy, execution_time
+
 if __name__ == "__main__":
     # 加载训练集
     try:
@@ -129,6 +142,13 @@ if __name__ == "__main__":
     # 训练对率回归模型
     weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1500)
 
+    # 基于测试集进行性能评估
+    accuracy, execution_time = evaluate_model(X_test, y_test, weights)
+
+    # 输出分类精度和执行时间
+    print(f"基于测试集的分类精度: {accuracy * 100:.2f}%")
+    print(f"基于测试集的执行时间: {execution_time:.4f} 秒")
+
     # 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]}")
+    print(f"测试样本 11 的预测结果: {y_pred}, 实际结果: {y_test[11]}")