更新 function.py

function.py

@@ -18,6 +18,7 @@ from sklearn.impute import SimpleImputer
 - Python: 3.10.12
 - numpy: 2.1.1
 """
+
 # Sigmoid 函数
 def sigmoid(z):
     return 1 / (1 + np.exp(-z))
@@ -29,7 +30,7 @@ id = "202210423223"
 name = "李顺潮"
 
 # 对率回归训练函数
-def train_logreg(X, y, learning_rate=0.01, iterations=1000):
+def train_logreg(X, y, learning_rate=0.01, iterations=10000):
     X = np.hstack((np.ones((X.shape[0], 1)), X))  # 在特征前添加一列全为1的列
     m, n = X.shape
     weights = np.zeros(n)
@@ -58,7 +59,6 @@ def load_data(filepath):
     imputer = SimpleImputer(strategy='mean') # 使用均值填充
     data = pd.DataFrame(imputer.fit_transform(data))
 
-
     X = data.iloc[:, :-1].to_numpy()  # 特征
     y = data.iloc[:, -1].to_numpy().astype(int)    # 标签,确保y是整数类型
     return X, y
@@ -81,7 +81,7 @@ if __name__ == "__main__":
 
 
     # 训练对率回归模型
-    weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=10000) #增加迭代次数
+    weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=10000)
 
     # 测试单个数据
     y_pred = predict_logreg(X_test[11], weights)