diff --git a/function.py b/function.py
index 7353415..bea1bd9 100644
--- a/function.py
+++ b/function.py
@@ -6,24 +6,18 @@ name = "吴杨洋"
 def sigmoid(z):
     return 1 / (1 + np.exp(-np.clip(z, -500, 500)))
 
-def train_logreg(X, y, numIter=1000, learning_rate=0.01, lambda_reg=0.01, batch_size=64):
+def train_logreg(X, y, numIter=1000, learning_rate=0.01, lambda_reg=0.01):
     m, n = X.shape
     weights = np.zeros(n)
     for j in range(numIter):
-        indices = np.random.permutation(m)
-        X_shuffled = X[indices]
-        y_shuffled = y[indices]
-        for i in range(0, m, batch_size):
-            end = min(i + batch_size, m)
-            batch_X = X_shuffled[i:end]
-            batch_y = y_shuffled[i:end]
-            z = np.dot(batch_X, weights)
+        for i in range(m):
+            z = np.dot(X[i], weights)
             h = sigmoid(z)
-            error = batch_y - h
-            gradient = np.dot(batch_X.T, error) / len(batch_y)
+            error = y[i] - h
+            gradient = X[i] * error
             reg_term = lambda_reg * weights
-            gradient += reg_term / len(batch_y)
-            weights -= learning_rate * gradient
+            gradient += reg_term
+            weights += learning_rate * gradient
     return weights
 
 def predict_logreg(inX, weights):
@@ -68,11 +62,22 @@ def main():
     X_train, mean, std = standardize(X_train)
     X_test = (X_test - mean) / std
 
-    weights = train_logreg(X_train, y_train, numIter=1000, learning_rate=0.01, lambda_reg=0.1, batch_size=64)
+    # 尝试不同的学习率和正则化参数
+    learning_rates = [0.01, 0.001, 0.0001]
+    lambda_regs = [0.1, 0.01, 0.001]
 
-    y_pred_test = [predict_logreg(x, weights) for x in X_test]
-    accuracy_test = np.mean(y_pred_test == y_test)
-    print(f"Test Accuracy: {accuracy_test:.2f}")
+    best_accuracy = 0
+    best_weights = None
+    for lr in learning_rates:
+        for reg in lambda_regs:
+            weights = train_logreg(X_train, y_train, numIter=1000, learning_rate=lr, lambda_reg=reg)
+            y_pred_test = [predict_logreg(x, weights) for x in X_test]
+            accuracy_test = np.mean(y_pred_test == y_test)
+            if accuracy_test > best_accuracy:
+                best_accuracy = accuracy_test
+                best_weights = weights
+
+    print(f"Best Test Accuracy: {best_accuracy:.2f}")
 
 if __name__ == "__main__":
     main()
\ No newline at end of file