更新 function.py

function.py

@@ -56,4 +56,80 @@ def load_data(filepath, k_features=20):
 
     return data, labels
 
-# 其余代码保持不变
+def sigmoid(z):
+    """
+    Sigmoid激活函数，用于对率回归模型。
+
+    Args:
+    - z (numpy array): 输入数据。
+
+    Returns:
+    - numpy array: Sigmoid函数输出值。
+    """
+    return 1 / (1 + np.exp(-z))
+
+def train_logreg(X, y, initial_lr=0.005, iterations=5000, regularization=0.01, decay=0.0001):
+    """
+    对率回归训练函数，包含学习率衰减策略。
+
+    Args:
+    - X (numpy array): 特征数据。
+    - y (numpy array): 标签数据。
+    - initial_lr (float): 初始学习率。
+    - iterations (int): 迭代次数。
+    - regularization (float): 正则化强度。
+    - decay (float): 学习率衰减系数。
+
+    Returns:
+    - numpy array: 训练得到的权重。
+    """
+    m, n = X.shape
+    weights = np.zeros(n)
+    learning_rate = initial_lr
+
+    for i in range(iterations):
+        z = np.dot(X, weights)
+        predictions = sigmoid(z)
+        errors = y - predictions
+        gradient = np.dot(X.T, errors) / m - regularization * weights / m
+        weights += learning_rate * gradient
+
+        # 学习率衰减
+        learning_rate = initial_lr / (1 + decay * i)
+
+    return weights
+
+def predict_logreg(inX, weights, threshold=0.5):
+    """
+    对率回归预测函数，包含可调阈值。
+
+    Args:
+    - inX (numpy array): 输入待分类的样本。
+    - weights (numpy array): 训练得到的权重。
+    - threshold (float): 判定阈值，默认为0.5。
+
+    Returns:
+    - int: 预测的分类标签。
+    """
+    probability = sigmoid(np.dot(inX, weights))
+    return 1 if probability >= threshold else 2
+
+# 测试代码
+if __name__ == "__main__":
+    # 加载数据并选择前20个最重要的特征
+    X_train, y_train = load_data("./data/horse-colic.data", k_features=20)
+    X_test, y_test = load_data("./data/horse-colic.test", k_features=20)
+
+    # 训练模型
+    weights = train_logreg(X_train, y_train, initial_lr=0.005, iterations=5000, regularization=0.01, decay=0.0001)
+
+    # 模型评估
+    correct_predictions = 0
+    for i in range(len(X_test)):
+        y_pred = predict_logreg(X_test[i], weights, threshold=0.5)
+        if y_pred == y_test[i]:
+            correct_predictions += 1
+        print(f"预测结果: {y_pred}, 实际结果: {y_test[i]}")
+
+    accuracy = correct_predictions / len(X_test)
+    print(f"测试集准确率: {accuracy:.2f}")