diff --git a/function.py b/function.py
index 606ca90..346980b 100644
--- a/function.py
+++ b/function.py
@@ -1,4 +1,3 @@
-# function.py
 import numpy as np
 
 """
@@ -28,6 +27,7 @@ name = "雷武"
 # 这里写自己的代码实现
 def sigmoid(z):
     return 1 / (1 + np.exp(-z))
+
 def train_logreg(X, y, learning_rate=0.01, iterations=1000):
     """
     对率回归训练函数。
@@ -43,7 +43,7 @@ def train_logreg(X, y, learning_rate=0.01, iterations=1000):
     m, n = X.shape
     weights = np.zeros(n)
     bias = 0
-     for i in range(iterations):
+    for i in range(iterations):
         linear_model = np.dot(X, weights) + bias
         y_pred = sigmoid(linear_model)
 
@@ -96,8 +96,8 @@ if __name__ == "__main__":
     X_test, y_test = load_data("./data/horse-colic.test")
 
     # 训练对率回归模型
-    weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000)
+    weights, bias = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000)
 
     # 测试单个数据
-    y_pred = predict_logreg(X_test[11], weights)
+    y_pred = predict_logreg(X_test[11], weights, bias)
     print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")