更新 function.py

function.py

@@ -26,16 +26,6 @@ name = "刘义天"
 
 
 def load_data(filepath):
-    """
-    Load data from a specified file path and handle missing values.
-
-    Args:
-    - filepath (str): The path to the data file.
-
-    Returns:
-    - np.array: Feature data.
-    - np.array: Label data.
-    """
     data = []
     with open(filepath, 'r') as file:
         for line in file:
@@ -54,19 +44,6 @@ def load_data(filepath):
 
 
 def train_logreg(X, y, learning_rate=0.1, n_estimators=200, max_depth=5):
-    """
-    Train a Gradient Boosting model.
-
-    Args:
-    - X (numpy array): Feature data.
-    - y (numpy array): Label data.
-    - learning_rate (float): Learning rate for gradient boosting.
-    - n_estimators (int): Number of trees.
-    - max_depth (int): Maximum depth of each tree.
-
-    Returns:
-    - tuple: (Trained model and scaler) packed into 'weights' for predict_logreg compatibility.
-    """
     scaler = StandardScaler()
     X = scaler.fit_transform(X)
     
@@ -83,16 +60,6 @@ def train_logreg(X, y, learning_rate=0.1, n_estimators=200, max_depth=5):
 
 
 def predict_logreg(inX, weights):
-    """
-    Predict the class label for a given sample using Gradient Boosting.
-
-    Args:
-    - inX (numpy array): Input sample for classification.
-    - weights (tuple): Packed tuple containing trained model and scaler.
-
-    Returns:
-    - int: Predicted class label (0 or 1).
-    """
     model, scaler = weights  # Unpack model and scaler
     inX = scaler.transform([inX])  # Scale input
     prediction = model.predict(inX)