diff --git a/function.py b/function.py
index 6f89dc5..567963e 100644
--- a/function.py
+++ b/function.py
@@ -5,15 +5,15 @@ import numpy as np
 """
 id = "202210423224"
 name = "吴杨洋"
- 
- 
- 
+
+
 def sigmoid(z):
     """
     Sigmoid 激活函数。
     """
     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。
@@ -37,7 +37,6 @@ def train_logreg(X, y, learning_rate=0.01, iterations=1000):
     return weights
 
 
-
 def predict_logreg(inX, weights):
     """
     对率回归预测函数。
@@ -53,6 +52,7 @@ def predict_logreg(inX, weights):
     y_predicted = sigmoid(linear_model)
     return 1 if y_predicted > 0.5 else 0
 
+
 def load_data(filepath):
     """
     从文件中加载数据，若不自行正确实现，则无法通过评测，不记录成绩。
@@ -64,11 +64,12 @@ def load_data(filepath):
     - X (np.array): 特征数据。
     - y (np.array): 标签数据。
     """
-    data = np.loadtxt(filepath, delimiter=',')
+    data = np.genfromtxt(filepath, delimiter=',', dtype=float, filling_values=0)
     X = data[:, :-1]
     y = data[:, -1]
     return X, y
 
+
 if __name__ == "__main__":
     # 加载训练集
     X_train, y_train = load_data("./data/horse-colic.data")
@@ -76,7 +77,6 @@ if __name__ == "__main__":
     # 加载测试集
     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)