更新 function.py

function.py

@@ -37,8 +37,15 @@ def train_logreg(X, y, learning_rate=0.01, iterations=1000, regularization=0.0):
         y_predicted = 1 / (1 + np.exp(-linear_output))  
         error = y - y_predicted  
   
-        # 更新权重和偏置，添加正则化项  
+
-        weights += learning_rate * (error * X.T - regularization * weights) / m  
+        # 计算梯度（不包括正则化项）  
+        gradient = np.dot(X.T, error) / m  
+  
+        # 加上正则化项的梯度（注意这里是对weights的每个元素都加上-regularization*weight[i]）  
+        gradient += -regularization * weights  
+  
+        # 更新权重  
+        weights += learning_rate * gradient 
         bias += learning_rate * np.sum(error) / m  
   
     return weights, bias