From ab3c74b6573b542c1b64fb1c635d060ef8c547e0 Mon Sep 17 00:00:00 2001
From: dong <2938690875@qq.com>
Date: Fri, 1 Nov 2024 10:21:26 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 function.py | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/function.py b/function.py
index 09da6ad..a89a693 100644
--- a/function.py
+++ b/function.py
@@ -4,6 +4,11 @@ from sklearn.preprocessing import StandardScaler
 from sklearn.model_selection import train_test_split, GridSearchCV
 from sklearn.linear_model import LogisticRegression
 
+
+id = "202210423130"
+name = "董秋"
+
+
 def load_data(filepath):
     data = []
     labels = []
@@ -32,12 +37,24 @@ def load_data(filepath):
 
     return np.array(data), np.array(labels)
 
+
 def preprocess_data(X):
     # 特征标准化
     scaler = StandardScaler()
     return scaler.fit_transform(X)
 
+
 def train_logreg(X_train, y_train):
+    """
+    逻辑回归训练函数，使用网格搜索优化超参数。
+
+    Args:
+    - X_train (numpy array): 特征数据。
+    - y_train (numpy array): 标签数据。
+
+    Returns:
+    - LogisticRegression: 训练得到的最佳模型。
+    """
     # 定义要调优的超参数范围
     param_grid = {
         'C': [0.001, 0.01, 0.1, 1, 10, 100],  # 正则化参数
@@ -56,12 +73,15 @@ def train_logreg(X_train, y_train):
 
     return grid_search.best_estimator_
 
+
 def predict_logreg(inX, weights):
     """
     逻辑回归预测函数。
+
     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。
     """
@@ -69,6 +89,7 @@ def predict_logreg(inX, weights):
     prediction_prob = 1 / (1 + np.exp(-z))
     return 1 if prediction_prob >= 0.5 else 0
 
+
 if __name__ == "__main__":
     # 加载数据集
     X, y = load_data("./data/horse-colic.data")