From c408fbace3edaec2c1a78d3eeb3127498a0846e7 Mon Sep 17 00:00:00 2001
From: wujunjie <297081150@qq.com>
Date: Tue, 29 Oct 2024 07:48:23 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 39 +++++++++++++++++++++++++++++++++------
 1 file changed, 33 insertions(+), 6 deletions(-)

diff --git a/function.py b/function.py
index 409b1ac..b532259 100644
--- a/function.py
+++ b/function.py
@@ -21,12 +21,12 @@ import numpy as np
 """
 请在这里填写学号和姓名，不填写即成绩无效。
 """
-id = "202210423xxx"
-name = "test_name"
+id = "202210423217"
+name = "吴俊煯"
 
 
 # 这里写自己的代码实现
-def train_logreg(X, y, other_params="你自己设置一些其它参数"):
+def train_logreg(X, y, learning_rate=0.01, iterations=1000):
     """
     对率回归训练函数。
 
@@ -38,7 +38,23 @@ def train_logreg(X, y, other_params="你自己设置一些其它参数"):
     Returns:
     - numpy array: 训练得到的权重。
     """
-
+    m, n = X.shape  
+    weights = np.zeros(n)  
+    bias = 0  
+      
+    for _ in range(iterations):  
+        for idx, x_i in enumerate(X):  
+            linear_output = np.dot(x_i, weights) + bias  
+            y_predicted = 1 / (1 + np.exp(-linear_output))  
+              
+            # 计算误差  
+            error = y[idx] - y_predicted  
+              
+            # 更新权重和偏置  
+            weights += learning_rate * error * x_i  
+            bias += learning_rate * error  
+      
+    return weights  
 
 def predict_logreg(inX, weights):
     """
@@ -51,7 +67,11 @@ def predict_logreg(inX, weights):
     Returns:
     - int: 预测的分类标签。
     """
-
+    linear_output = np.dot(inX, weights)  
+    y_predicted = 1 / (1 + np.exp(-linear_output))  
+      
+    # 将输出转换为分类标签  
+    return 1 if y_predicted >= 0.5 else 0
 
 # 这里也写自己的代码实现
 def load_data(filepath):
@@ -65,7 +85,14 @@ def load_data(filepath):
     - np.array: 特征数据。
     - np.array: 标签数据。
     """
-
+    data = np.loadtxt(filepath, delimiter=',')  
+    X = data[:, :-1]  
+    y = data[:, -1]  
+      
+    # 将标签转换为0和1（假设原始标签为1和2）  
+    y = np.where(y == 1, 0, 1)  
+      
+    return X, y 
 
 if __name__ == "__main__":
     # 以下代码可用于测试，可自行随意修改