更新 function.py

function.py

@@ -44,12 +44,13 @@ def load_data(filepath):
     # 分离特征和标签
     X = data[:, :-1]
     y = data[:, -1]
+    
+    # 标准化特征数据
+    X = (X - np.mean(X, axis=0)) / np.std(X, axis=0)
+    
     return X, y
-
-
-
 # 这里实现对率回归训练函数
-def train_logreg(X, y, learning_rate=0.01, num_iterations=1000, regularization_strength=0.01):
+def train_logreg(X, y, learning_rate=0.001, num_iterations=5000, regularization_strength=0.001):
     """
     对率回归训练函数。
 
@@ -70,8 +71,6 @@ def train_logreg(X, y, learning_rate=0.01, num_iterations=1000, regularization_s
         gradient = (1 / m) * np.dot(X.T, (predictions - y)) + regularization_strength * weights
         weights -= learning_rate * gradient
     return weights
-
-
 # 这里实现对率回归预测函数
 def predict_logreg(inX, weights):
     """
@@ -86,7 +85,6 @@ def predict_logreg(inX, weights):
     """
     probability = 1 / (1 + np.exp(-np.dot(inX, weights)))
     return 1 if probability >= 0.5 else 0
-
 if __name__ == "__main__":
     # 以下代码可用于测试，可自行随意修改
     # 加载训练集