From 07daa3d4ddc65fcea7b159928a5773def919a326 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=E5=90=B4=E6=9D=A8=E6=B4=8B?= <2445091977@qq.com>
Date: Sat, 2 Nov 2024 21:34:52 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 function.py | 67 +++++++++++++++--------------------------------------
 1 file changed, 19 insertions(+), 48 deletions(-)

diff --git a/function.py b/function.py
index f189af5..5efa462 100644
--- a/function.py
+++ b/function.py
@@ -1,61 +1,40 @@
 import numpy as np
 
-"""
-请在这里填写学号和姓名，不填写即成绩无效。
-"""
 id = "202210423224"
 name = "吴杨洋"
 
+def sigmoid(z, epsilon=1e-15):
+    """Sigmoid 激活函数，添加 epsilon 防止溢出"""
+    s = 1 / (1 + np.exp(-z))
+    s = np.clip(s, epsilon, 1 - epsilon)  # 防止溢出
+    return s
 
-def sigmoid(z):
-    """
-    Sigmoid 激活函数。
-    """
-    return 1 / (1 + np.exp(-z))
-
-
-def train_logreg(X, y, numIter=1000, learning_rate=0.01, lambda_reg=0.01):
+def train_logreg(X, y, numIter=1000, learning_rate=0.01, lambda_reg=0.01, batch_size=64):
     m, n = X.shape
     weights = np.zeros(n)
     for j in range(numIter):
-        for i in range(m):
-            z = np.dot(X[i], weights)
+        indices = np.random.permutation(m)
+        X_shuffled = X[indices]
+        y_shuffled = y[indices]
+        for i in range(0, m, batch_size):
+            end = min(i + batch_size, m)
+            batch_X = X_shuffled[i:end]
+            batch_y = y_shuffled[i:end]
+            z = np.dot(batch_X, weights)
             h = sigmoid(z)
-            error = y[i] - h
-            gradient = X[i] * error
+            error = batch_y - h
+            gradient = np.dot(batch_X.T, error) / len(batch_y)
             reg_term = lambda_reg * weights
-            gradient += reg_term
-            weights += learning_rate * gradient
+            gradient += reg_term / len(batch_y)
+            weights -= learning_rate * gradient
     return weights
 
-
 def predict_logreg(inX, weights):
-    """
-    对率回归预测函数。
-
-    Args:
-    - inX (numpy array): 输入待分类的样本。
-    - weights (numpy array): 训练得到的权重。
-
-    Returns:
-    - int: 预测的分类标签。
-    """
     z = np.dot(inX, weights)
     prob = sigmoid(z)
     return 1 if prob >= 0.5 else 0
 
-
 def load_data(filepath):
-    """
-    从文件中加载数据，若不自行正确实现，则无法通过评测，不记录成绩。
-
-    Args:
-    - filepath (str): 数据文件路径。
-
-    Returns:
-    - X (np.array): 特征数据。
-    - y (np.array): 标签数据。
-    """
     file = open(filepath)
     fr = file.readlines()
     data_list = []
@@ -81,31 +60,23 @@ def load_data(filepath):
     X_train = np.insert(X, 0, 1, axis=1)  # 添加偏置项
     return X_train, y_train
 
-
 def standardize(X):
     mean = X.mean(axis=0)
     std = X.std(axis=0)
     return (X - mean) / std, mean, std
 
-
 def main():
-    # 加载训练集
     X_train, y_train = load_data("./data/horse-colic.data")
-    # 加载测试集
     X_test, y_test = load_data("./data/horse-colic.test")
 
-    # 标准化特征数据
     X_train, mean, std = standardize(X_train)
     X_test = (X_test - mean) / std
 
-    # 训练对率回归模型
-    weights = train_logreg(X_train, y_train, numIter=1000, learning_rate=0.01, lambda_reg=0.1)
+    weights = train_logreg(X_train, y_train, numIter=1000, learning_rate=0.01, lambda_reg=0.1, batch_size=64)
 
-    # 测试单个数据
     y_pred_test = [predict_logreg(x, weights) for x in X_test]
     accuracy_test = np.mean(y_pred_test == y_test)
     print(f"Test Accuracy: {accuracy_test:.2f}")
 
-
 if __name__ == "__main__":
     main()
\ No newline at end of file