更新 function.py

function.py

@@ -24,9 +24,15 @@ def train_logreg(X, y, learning_rate=0.01, iterations=1000, lambda_reg=0.01):
         z = np.dot(X, weights)
         predictions = 1 / (1 + np.exp(-z))
         gradient = np.dot(X.T, (predictions - y)) / m
-        gradient += (lambda_reg / m) * weights  # L2正则化
+        
+        # L2正则化：将正则项添加到梯度上
+        gradient += (lambda_reg / m) * weights
+
+        # 更新权重
         weights -= learning_rate * gradient
-        learning_rate *= 0.99  # 动态调整学习率
+
+        # 动态调整学习率
+        learning_rate *= 0.99  # 每次迭代将学习率乘以0.99
 
     return weights