更新 function.py

function.py

@@ -26,11 +26,13 @@ name = "周佳成"
 
 
 # 这里写自己的代码实现
+import numpy as np
+
 # Sigmoid函数
 def sigmoid(z):
+    """计算Sigmoid函数值"""
     return 1 / (1 + np.exp(-z))
 
-
 # 对率回归训练函数
 def train_logreg(X, y, learning_rate=0.01, iterations=1000):
     """
@@ -52,7 +54,7 @@ def train_logreg(X, y, learning_rate=0.01, iterations=1000):
     X = np.c_[X, np.ones((n_samples, 1))]
 
     # 梯度下降法
-    for i in range(iterations):
+    for _ in range(iterations):
         linear_model = np.dot(X, weights)
         y_pred = sigmoid(linear_model)
 
@@ -64,7 +66,6 @@ def train_logreg(X, y, learning_rate=0.01, iterations=1000):
 
     return weights
 
-
 # 对率回归预测函数
 def predict_logreg(inX, weights):
     """
@@ -83,7 +84,6 @@ def predict_logreg(inX, weights):
     y_pred = sigmoid(linear_model)
     return 1 if y_pred >= 0.5 else 0
 
-
 # 数据加载函数
 def load_data(filepath):
     """
@@ -109,7 +109,6 @@ def load_data(filepath):
     y = data[:, -1]   # 最后一列是标签
     return X, y
 
-
 if __name__ == "__main__":
     # 加载训练集
     X_train, y_train = load_data("./data/horse-colic.data")
@@ -123,4 +122,3 @@ if __name__ == "__main__":
     # 测试单个数据
     y_pred = predict_logreg(X_test[11], weights)
     print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")
-