更新 function.py

function.py

@@ -50,15 +50,18 @@ def train_logreg(X, y, learning_rate=0.01, iterations=1000, batch_size=32, lambd
     weights = np.zeros(n)
     
     for _ in range(iterations):
+        # 确保 batch_size 不超过 m
+        effective_batch_size = min(batch_size, m)
+        
         # 随机选择一个小批量
-        indices = np.random.choice(m, batch_size, replace=False)
+        indices = np.random.choice(m, effective_batch_size, replace=False)
         X_batch = X[indices]
         y_batch = y[indices]
         
         # 计算预测和梯度
         z = np.dot(X_batch, weights)
         h = sigmoid(z)
-        gradient = np.dot(X_batch.T, (h - y_batch)) / batch_size + lambda_ * weights
+        gradient = np.dot(X_batch.T, (h - y_batch)) / effective_batch_size + lambda_ * weights
         weights -= learning_rate * gradient
     
     return weights