From 5c774e66d31de1c8b70e9b81762f3afed1882e8d Mon Sep 17 00:00:00 2001
From: mogu-king <1547976000@qq.com>
Date: Sun, 3 Nov 2024 05:28:43 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 33 ---------------------------------
 1 file changed, 33 deletions(-)

diff --git a/function.py b/function.py
index 74d1aa8..029d052 100644
--- a/function.py
+++ b/function.py
@@ -26,16 +26,6 @@ name = "刘义天"
 
 
 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**
-
-    Args:
-    - filepath (str): 数据文件路径
-
-    Returns:
-    - X (numpy array): 特征数据
-    - y (numpy array): 标签数据
-    """
     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):
-     """
-    对率回归训练函数。
-
-    Args:
-    - X (numpy array): 特征数据。
-    - y (numpy array): 标签数据。
-    - learning_rate (float): 学习率。
-    - num_iterations (int): 迭代次数。
-    - regularization_strength (float): 正则化强度。
-
-    Returns:
-    - weights (numpy array): 训练得到的权重。
-    """
     scaler = StandardScaler()
     X = scaler.fit_transform(X)
     
@@ -81,16 +58,6 @@ def train_logreg(X, y, learning_rate=0.1, n_estimators=200, max_depth=5):
 
 
 def predict_logreg(inX, model, scaler):
-    """
-    对率回归预测函数。
-
-    Args:
-    - inX (numpy array): 输入待分类的样本。
-    - weights (numpy array): 训练得到的权重。
-
-    Returns:
-    - int: 预测的分类标签。
-    """
     inX = scaler.transform([inX])
     prediction = model.predict(inX)
     return int(prediction[0])