更新 function.py

function.py

@@ -113,21 +113,9 @@ def loadDataSet(filename='dataSet.txt'):
         lineList = line.strip().split()
         # 修改前
         dataList.append([1.0, float(lineList[0]), float(lineList[1])])
-        # 修改后
-        # dataList.append([float(lineList[0]), float(lineList[1])])
         labelList.append(int(lineList[2]))
     return dataList, labelList
 
-# 假设这里有一个简单的线性回归模型训练和评估的函数
-def trainAndEvaluate(dataList, labelList):
-    # 这里只是一个简单的示例，实际中可能需要使用更复杂的模型和评估方法
-    import numpy as np
-    X = np.array(dataList)
-    y = np.array(labelList)
-    w = np.linalg.inv(X.T @ X) @ X.T @ y
-    predictions = X @ w
-    error = np.mean((predictions - y)**2)
-    return error
 
 if __name__ == "__main__":
     X_train, y_train = load_data("./horse-colic.data")
@@ -144,14 +132,3 @@ if __name__ == "__main__":
     # 计算测试集准确率
     final_accuracy = accuracy(X_test, y_test, final_weights)
     print(f"最终测试集的准确率: {final_accuracy:.4f}")
-
-    # 读取原始数据
-    dataList1, labelList1 = loadDataSet()
-    error1 = trainAndEvaluate(dataList1, labelList1)
-
-    # 修改代码后读取数据
-    dataList2, labelList2 = loadDataSet()
-    error2 = trainAndEvaluate(dataList2, labelList2)
-
-    print(f"有常数项时的误差：{error1}")
-    print(f"无常数项时的误差：{error2}")