更新 function.py

function.py

@@ -26,17 +26,23 @@ name = "陈垣豪"
 
 
 
-
 def train_logreg(X, y, learning_rate=0.01, iterations=1000):
-    """训练逻辑回归模型."""
+    """训练逻辑回归模型，使用随机梯度下降."""
     n_samples, n_features = X.shape
     weights = np.zeros(n_features + 1)  # 增加一位用于偏置
     X = np.c_[X, np.ones((n_samples, 1))]  # 增加偏置项
 
     for _ in range(iterations):
-        linear_model = np.dot(X, weights)
+        # 随机选择一个样本
-        # 直接使用线性模型的输出进行梯度更新
+        random_index = np.random.randint(n_samples)
-        dw = (1 / n_samples) * np.dot(X.T, (linear_model - y))
+        X_i = X[random_index]
+        y_i = y[random_index]
+        
+        # 计算线性模型输出
+        linear_model = np.dot(X_i, weights)
+        
+        # 更新权重
+        dw = (linear_model - y_i) * X_i
         weights -= learning_rate * dw
 
     return weights