From b32dc0220d74141e3db2647bc8703312d0695ce8 Mon Sep 17 00:00:00 2001
From: 202210423105 <3374617300@qq.com>
Date: Sun, 3 Nov 2024 16:15:01 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 36 ++++++++++++++++++++++++++++++------
 1 file changed, 30 insertions(+), 6 deletions(-)

diff --git a/function.py b/function.py
index 750a541..a0aff78 100644
--- a/function.py
+++ b/function.py
@@ -23,12 +23,12 @@ import numpy as np
 姓名请直接**填写中文**
 例如: name = "张三"
 """
-id = "202210423xxx"
-name = "test_name"
+id = "202210423105"
+name = "魏明帅"
 
 
 # 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。
 
@@ -40,7 +40,19 @@ 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):
+        for i in range(m):
+            x_i = X[i]
+            y_i = y[i]
+            z = np.dot(x_i, weights)
+            h = 1 / (1 + np.exp(-z))
+            error = y_i - h
+            weights += learning_rate * error * x_i
+    
+    return weights
 
 def predict_logreg(inX, weights):
     """
@@ -53,7 +65,9 @@ def predict_logreg(inX, weights):
     Returns:
     - int: 预测的分类标签。
     """
-
+z = np.dot(inX, weights)
+    h = 1 / (1 + np.exp(-z))
+    return 1 if h >= 0.5 else 0
 
 # 这里也写自己的代码实现
 def load_data(filepath):
@@ -67,7 +81,17 @@ def load_data(filepath):
     - np.array: 特征数据。
     - np.array: 标签数据。
     """
-
+with open(filepath, 'r') as f:
+        lines = f.readlines()
+    
+    X = []
+    y = []
+    for line in lines:
+        values = line.strip().split()
+        y.append(int(values[-1]))
+        X.append([float(x) for x in values[:-1]])
+    
+    return np.array(X), np.array(y)
 
 if __name__ == "__main__":
     # 以下代码可用于测试，可自行随意修改