更新 function.py

function.py

@@ -111,6 +111,9 @@ def evaluate_model(X_test, y_test, weights):
     accuracy = np.mean(np.array(y_pred) == y_test)  # 分类精度
     execution_time = end_time - start_time  # 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")
+
     return accuracy, execution_time
 
 if __name__ == "__main__":
@@ -142,13 +145,5 @@ 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"测试样本 11 的预测结果: {y_pred}, 实际结果: {y_test[11]}")
+    # 在测试集上评估模型
+    evaluate_model(X_test, y_test, weights)