From 17ac81a1d241248b6c345fc1f06cc9e82edf5e01 Mon Sep 17 00:00:00 2001
From: meixiaodong <3081674342@qq.com>
Date: Sun, 3 Nov 2024 22:27:58 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 145 +++++++++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 143 insertions(+), 2 deletions(-)

diff --git a/function.py b/function.py
index 750a541..fc327e9 100644
--- a/function.py
+++ b/function.py
@@ -23,8 +23,8 @@ import numpy as np
 姓名请直接**填写中文**
 例如: name = "张三"
 """
-id = "202210423xxx"
-name = "test_name"
+id = "202210423211"
+name = "梅晓东"
 
 
 # 这里写自己的代码实现
@@ -40,7 +40,148 @@ 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。
     """
+# function.py
+import numpy as np
+import os
+from sklearn.preprocessing import StandardScaler
 
+"""
+实验说明:
+- 自行编写实现数据加载与预测病马死亡的对率回归算法。
+
+注意事项:
+- 在 function.py 中实现对率回归算法。
+- 可以自行导入其他库，需要在 requirements.txt 添加依赖，若未在 requirements.txt 中添加使用到的依赖，则无法自动评测，成绩为零。
+- 自动评测脚本仅会加载 train_logreg、predict_logreg 和 load_data 函数，请勿修改函数名。
+
+测评环境:
+- CPU: Intel Xeon(R) CPU E3-1231 v3 @ 3.40GHz
+- OS: Ubuntu 22.04.4 LTS
+- Python: 3.10.12
+- numpy: 2.1.1
+"""
+
+
+"""
+请在这里填写学号和姓名，不填写即成绩无效。
+姓名请直接**填写中文**
+例如: name = "张三"
+"""
+id = "202210423110"
+name = "罗浩"
+
+
+import numpy as np
+
+
+def sigmoid(z):
+    """计算sigmoid函数"""
+    return 1.0 / (1 + np.exp(-z))
+
+def compute_gradient(X_batch, y_batch, weights, lambda_reg):
+    """计算梯度，包含L2正则化"""
+    z = np.dot(X_batch, weights)
+    h = sigmoid(z)
+    gradient = np.dot(X_batch.T, (h - y_batch)) / len(y_batch)
+    gradient += (lambda_reg / len(y_batch)) * weights  # L2正则化
+    return gradient
+
+def train_logreg(X, y, learning_rate=0.001, iterations=10000, batch_size=64, lambda_reg=0.1):
+    """逻辑回归训练函数。"""
+    m, n = X.shape
+    weights = np.random.randn(n) * np.sqrt(2. / n)  # He初始化
+    print("开始训练逻辑回归模型")
+
+    for i in range(iterations):
+        indices = np.random.permutation(m)
+        X_shuffled = X[indices]
+        y_shuffled = y[indices]
+
+        for j in range(0, m, batch_size):
+            X_batch = X_shuffled[j:j + batch_size]
+            y_batch = y_shuffled[j:j + batch_size]
+            gradient = compute_gradient(X_batch, y_batch, weights, lambda_reg)
+            weights -= learning_rate * gradient
+            
+        if i % 1000 == 0:  # 每1000次迭代打印一次信息
+            print(f"迭代 {i}: 当前权重形状: {weights.shape}")
+
+    return weights
+
+def predict_logreg(inX, weights):
+    """逻辑回归预测函数。"""
+    z = np.dot(inX, weights)
+    h = sigmoid(z)
+    return 1 if h >= 0.5 else 0
+
+def load_data(filepath):
+    """从文件中加载数据。"""
+    if not os.path.exists(filepath):
+        raise FileNotFoundError(f"文件 {filepath} 不存在！")
+
+    data, labels = [], []
+    print(f"加载数据中: {filepath}")  
+ with open(filepath, 'r') as f:  
+ for line in f:  
+ line = line.strip().split()  
+ if len(line) >= 1 and line[-1].isdigit():  
+ line_data = [0 if x == '?' else float(x) for x in line[:-1]]  
+ data.append(line_data)  
+ labels.append(int(line[-1]))  
+   data = np.array(data)  
+ labels = np.array(labels)  
+   num_features = data.shape[1]  
+ indices_to_remove = [2]  
+   if num_features > 23:  
+ indices_to_remove.extend(range(23, min(28, num_features)))  
+   data = np.delete(data, indices_to_remove, axis=1)  
+ target_variable = np.where((labels == 2) | (labels == 3), 0, 1)  
+   print(f"数据加载成功，数据形状: {data.shape}, 标签形状: {target_variable.shape}")  
+ return data, target_variable  
+   def k_fold_cross_validation(X, y, k=5, learning_rate=0.001, iterations=10000, batch_size=64, lambda_reg=0.1):  
+ """K折交叉验证。"""  
+ m = X.shape[0]  
+ fold_size = m // k  
+ accuracies = []  
+   for i in range(k):  
+ val_start = i * fold_size  
+ val_end = (i + 1) * fold_size if i != k - 1 else m  
+   X_val = X[val_start:val_end]  
+ y_val = y[val_start:val_end]  
+ X_train = np.concatenate((X[:val_start], X[val_end:]))  
+ y_train = np.concatenate((y[:val_start], y[val_end:]))  
+   weights = train_logreg(X_train, y_train, learning_rate, iterations, batch_size, lambda_reg)  
+   val_accuracy = np.mean([predict_logreg(X_val[j], weights) == y_val[j] for j in range(len(y_val))])  
+ accuracies.append(val_accuracy)  
+   print(f"Fold {i + 1}/{k} - 验证准确率: {val_accuracy:.4f}")  
+   return np.mean(accuracies)  
+   if __name__ == "__main__":  
+ # 加载训练集  
+ try:  
+ X_train, y_train = load_data("./data/horse-colic.data")  
+ except FileNotFoundError as e:  
+ print(e)  
+ exit()  
+   # 数据归一化处理  
+ scaler = StandardScaler()  
+ X_train = scaler.fit_transform(X_train) # 使用训练集进行标准化  
+   # k折交叉验证  
+ avg_accuracy = k_fold_cross_validation(X_train, y_train, k=5, learning_rate=0.01, iterations=1000)  
+ print(f"K折交叉验证的平均准确率: {avg_accuracy:.4f}")  
+   # 加载测试集  
+ try:  
+ X_test, y_test = load_data("./data/horse-colic.test")  
+ except FileNotFoundError
+ as e:  
+ print(e)  
+ exit()  
+   # 测试集也需要进行相同的归一化  
+ X_test = scaler.transform(X_test) # 使用相同的Scaler进行测试集的标准化  
+   # 训练逻辑回归模型  
+ weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000)  
+   # 测试单个数据  
+ y_pred = predict_logreg(X_test[11], weights)  
+ print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}") 
 
 def predict_logreg(inX, weights):
     """