From ed11c9c57bc17def93c4d074f0f0012b003e4401 Mon Sep 17 00:00:00 2001
From: dong <2938690875@qq.com>
Date: Thu, 31 Oct 2024 11:40:37 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 32 ++++++++++++++++++++++++++------
 1 file changed, 26 insertions(+), 6 deletions(-)

diff --git a/function.py b/function.py
index 409b1ac..d7cf809 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 = "202210423130"
+name = "董秋"
 
 
 # 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 +38,21 @@ 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)
+    
+    for _ in range(iterations):
+        # 计算预测值
+        z = np.dot(X, weights)
+        predictions = 1 / (1 + np.exp(-z))
+        
+        # 计算损失的梯度
+        gradient = np.dot(X.T, (predictions - y)) / m
+        
+        # 更新权重
+        weights -= learning_rate * gradient
+        
+    return weights
 
 def predict_logreg(inX, weights):
     """
@@ -51,7 +65,10 @@ def predict_logreg(inX, weights):
     Returns:
     - int: 预测的分类标签。
     """
-
+    z = np.dot(inX, weights)
+    prediction_prob = 1 / (1 + np.exp(-z))
+    
+    return 1 if prediction_prob >= 0.5 else 0
 
 # 这里也写自己的代码实现
 def load_data(filepath):
@@ -65,7 +82,10 @@ def load_data(filepath):
     - np.array: 特征数据。
     - np.array: 标签数据。
     """
-
+    data = np.loadtxt(filepath, delimiter=',', dtype=float)
+    X = data[:, :-1]  # 特征数据
+    y = data[:, -1]   # 标签数据
+    return X, y
 
 if __name__ == "__main__":
     # 以下代码可用于测试，可自行随意修改