From 852312e7cae413e31358a194e3b846d3a082e417 Mon Sep 17 00:00:00 2001
From: Dengddd <3149942468@qq.com>
Date: Sun, 10 Nov 2024 15:56:43 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 25 +------------------------
 1 file changed, 1 insertion(+), 24 deletions(-)

diff --git a/function.py b/function.py
index 65088b1..934c758 100644
--- a/function.py
+++ b/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")
@@ -143,15 +131,4 @@ 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}")
\ No newline at end of file
+    print(f"最终测试集的准确率: {final_accuracy:.4f}")
\ No newline at end of file