更新 function.py

function.py

@@ -1,3 +1,7 @@
+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。
@@ -23,46 +27,118 @@ id = "202210423211"
 name = "梅晓东"
 
 
-import pandas as pd
-from sklearn.linear_model import LogisticRegression
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import accuracy_score
+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 load_data(file_path):
-    """
-    加载数据
-    :param file_path: 数据文件路径
-    :return: 特征和标签
-    """
-    data = pd.read_csv(file_path)
-    X = data.drop('death', axis=1)  # 假设 'death' 列为标签
-    y = data['death']
-    return X, y
+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")
 
-def train_logreg(X, y):
-    """
-    训练对率回归模型
-    :param X: 特征
-    :param y: 标签
-    :return: 训练好的模型
-    """
-    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
-    model = LogisticRegression()
-    model.fit(X_train, y_train)
-    y_pred = model.predict(X_test)
-    accuracy = accuracy_score(y_test, y_pred)
-    print(f'Accuracy: {accuracy:.2f}')
-    return model
+    for i in range(iterations):
+        indices = np.random.permutation(m)
+        X_shuffled = X[indices]
+        y_shuffled = y[indices]
 
-def predict_logreg(model, X_new):
-    """
-    使用训练好的模型进行预测
-    :param model: 训练好的对率回归模型
-    :param X_new: 新的特征数据
-    :return: 预测结果
-    """
-    predictions = model.predict(X_new)
-    return predictions
+        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]}")