From c04a62e60c92896b77c98111f5b9d769a3cbe7d1 Mon Sep 17 00:00:00 2001
From: Ultraman <2900311815@qq.com>
Date: Sun, 3 Nov 2024 17:25:48 +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 | 58 ++++++++++++++++++++++++++---------------------------
 1 file changed, 29 insertions(+), 29 deletions(-)

diff --git a/function.py b/function.py
index bfd96c4..d7a9064 100644
--- a/function.py
+++ b/function.py
@@ -1,28 +1,28 @@
 import numpy as np
 import os
-from sklearn.preprocessing import StandardScaler  # 导入标准化库
+from sklearn.preprocessing import StandardScaler  # Import standardization library
 
-# 学号和姓名
+# Student ID and Name
 id = "202210423220"
 name = "方子涵"
 
 def sigmoid(z):
-    """计算sigmoid函数"""
+    """Calculate the sigmoid function"""
     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"""
+    """Calculate the gradient, including L2 regularization"""
     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
+    gradient += (lambda_reg / len(y_batch)) * weights  # L2 regularization
     return gradient
 
 def train_logreg(X, y, learning_rate=0.001, iterations=10000, batch_size=64, lambda_reg=0.1):
-    """对率回归训练函数。"""
+    """Logistic regression training function."""
     m, n = X.shape
-    weights = np.random.randn(n) * np.sqrt(2. / n)  # He初始化
-    print("开始训练对率回归模型")
+    weights = np.random.randn(n) * np.sqrt(2. / n)  # He initialization
+    print("Starting to train the logistic regression model")
     
     for i in range(iterations):
         indices = np.random.permutation(m)
@@ -35,24 +35,24 @@ def train_logreg(X, y, learning_rate=0.001, iterations=10000, batch_size=64, lam
             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}")
+        if i % 1000 == 0:  # Print information every 1000 iterations
+            print(f"Iteration {i}: Current weight shape: {weights.shape}")
 
     return weights
 
 def predict_logreg(inX, weights):
-    """对率回归预测函数。"""
+    """Logistic regression prediction function."""
     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。"""
+    """Load data from a file."""
     if not os.path.exists(filepath):
-        raise FileNotFoundError(f"文件 {filepath} 不存在！")
+        raise FileNotFoundError(f"File {filepath} does not exist!")
     
     data, labels = [], []
-    print(f"加载数据中: {filepath}")
+    print(f"Loading data: {filepath}")
     with open(filepath, 'r') as f:
         for line in f:
             line = line.strip().split()
@@ -65,7 +65,7 @@ def load_data(filepath):
     labels = np.array(labels)
     
     num_features = data.shape[1]
-        indices_to_remove = [2]  # 去掉特征3（索引2）
+    indices_to_remove = [2]  # Remove feature 3 (index 2)
     
     if num_features > 23:
         indices_to_remove.extend(range(23, min(28, num_features)))
@@ -73,11 +73,11 @@ def load_data(filepath):
     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}")
+    print(f"Data loaded successfully, data shape: {data.shape}, label shape: {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。"""
+    """K-fold cross-validation."""
     m = X.shape[0]
     fold_size = m // k
     accuracies = []
@@ -96,39 +96,39 @@ def k_fold_cross_validation(X, y, k=5, learning_rate=0.001, iterations=10000, ba
         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}")
+        print(f"Fold {i + 1}/{k} - Validation accuracy: {val_accuracy:.4f}")
 
     return np.mean(accuracies)
 
 if __name__ == "__main__":
-    # 加载训练集
+    # Load training set
     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
+    # Data normalization
     scaler = StandardScaler()
-    X_train = scaler.fit_transform(X_train)  # 使用训练集进行标准化
+    X_train = scaler.fit_transform(X_train)  # Standardize using the training set
 
-    # k折交叉验证
+    # K-fold cross-validation
     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}")
+    print(f"Average accuracy of K-fold cross-validation: {avg_accuracy:.4f}")
 
-    # 加载测试集
+    # Load test set
     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
+    # Test set also needs the same normalization
+    X_test = scaler.transform(X_test)  # Standardize using the same Scaler
 
-    # 训练对率回归模型
+    # Train logistic regression model
     weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000)
 
-    # 测试单个数据
+    # Test a single data point
     y_pred = predict_logreg(X_test[11], weights)
-    print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")
\ No newline at end of file
+    print(f"Predicted result: {y_pred}, Actual result: {y_test[11]}")
\ No newline at end of file