From 46c8d946221aa3bf14555be35a383ae8c5b5972b Mon Sep 17 00:00:00 2001
From: 43606070 <43606070@qq.com>
Date: Sat, 2 Nov 2024 15:47:28 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 113 +++++++++++++++++++++-------------------------------
 1 file changed, 46 insertions(+), 67 deletions(-)

diff --git a/function.py b/function.py
index 340896c..9cc2ab4 100644
--- a/function.py
+++ b/function.py
@@ -22,13 +22,14 @@ import numpy as np
 id = "202210423118"
 name = "刘国庆"
 
+
 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**。
-
+    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:
     - np.array: 特征数据。
     - np.array: 标签数据。
@@ -37,88 +38,66 @@ def load_data(filepath):
     try:
         data = np.genfromtxt(filepath, delimiter=',', missing_values='?', filling_values=np.nan)
         
-        # Check if data was loaded correctly
-        if data.ndim == 1 and len(data) == 1 and np.isnan(data).any():
-            print(f"数据加载错误: 文件 '{filepath}' 可能为空或格式不正确。")
-            return None, None
-
         # Remove rows with NaN values
-        data = data[~np.isnan(data).any(axis=1)]  
+        data = data[~np.isnan(data).any(axis=1)]
         
-        if data.size == 0:
-            print(f"数据加载错误: 文件 '{filepath}' 中没有有效数据。")
-            return None, None
-
-        X = data[:, :-1]  # All columns except the last one
-        y = data[:, -1]   # Last column
-
+        # Split into features and labels
+        X = data[:, :-1]  # Features
+        y = data[:, -1]   # Labels
         return X, y
     except Exception as e:
-        print(f"数据加载错误: {e}")
+        print(f"Error loading data: {e}")
         return None, None
 
+
 def sigmoid(z):
-    """计算 sigmoid 函数"""
+    """
+    Sigmoid activation function.
+    """
     return 1 / (1 + np.exp(-z))
 
-def train_logreg(X, y, learning_rate=0.01, iterations=1000):
-    """
-    对率回归训练函数。
 
+def train_logreg(X, y, learning_rate=0.01, epochs=1000):
+    """
+    训练逻辑回归模型。
+    
     Args:
-    - X (numpy array): 特征数据。
-    - y (numpy array): 标签数据。
+    - X (np.array): 特征数据。
+    - y (np.array): 标签数据。
     - learning_rate (float): 学习率。
-    - iterations (int): 迭代次数。
-
+    - epochs (int): 训练的迭代次数。
+    
     Returns:
-    - numpy array: 训练得到的权重。
+    - np.array: 训练后的权重。
     """
-    m, n = X.shape
-    weights = np.zeros(n)  # Initialize weights
-    for _ in range(iterations):
-        z = np.dot(X, weights)  # Linear combination
-        predictions = sigmoid(z)  # Apply sigmoid
-        # Update weights using gradient ascent
-        weights += (learning_rate / m) * np.dot(X.T, (y - predictions))
+    # Initialize weights
+    X = np.c_[np.ones(X.shape[0]), X]  # Add intercept term
+    weights = np.zeros(X.shape[1])
+    
+    # Gradient descent for logistic regression
+    for epoch in range(epochs):
+        z = np.dot(X, weights)
+        predictions = sigmoid(z)
+        
+        # Calculate the gradient
+        gradient = np.dot(X.T, (predictions - y)) / y.size
+        weights -= learning_rate * gradient
+        
     return weights
 
-def predict_logreg(inX, weights):
-    """
-    对率回归预测函数。
 
+def predict_logreg(X, weights):
+    """
+    使用逻辑回归模型进行预测。
+    
     Args:
-    - inX (numpy array): 输入待分类的样本。
-    - weights (numpy array): 训练得到的权重。
-
+    - X (np.array): 特征数据。
+    - weights (np.array): 训练得到的权重。
+    
     Returns:
-    - int: 预测的分类标签。
+    - np.array: 预测的标签（0 或 1）。
     """
-    z = np.dot(inX, weights)
-    prediction_prob = sigmoid(z)
-    return 1 if prediction_prob >= 0.5 else 0
+    X = np.c_[np.ones(X.shape[0]), X]  # Add intercept term
+    probabilities = sigmoid(np.dot(X, weights))
+    return (probabilities >= 0.5).astype(int)
 
-if __name__ == "__main__":
-    # 以下代码可用于测试，可自行随意修改
-    # 加载训练集
-    X_train, y_train = load_data("./data/horse-colic.data")
-
-    # 检查加载结果
-    if X_train is None or y_train is None:
-        print("训练集加载失败。请检查文件路径和文件内容。")
-        exit(1)
-
-    # 加载测试集
-    X_test, y_test = load_data("./data/horse-colic.test")
-
-    # 检查加载结果
-    if X_test is None or y_test is None:
-        print("测试集加载失败。请检查文件路径和文件内容。")
-        exit(1)
-
-    # 训练对率回归模型
-    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]}")