From 236265dbe5ce3d15411273808e6ecc1db0f868c4 Mon Sep 17 00:00:00 2001
From: dong <2938690875@qq.com>
Date: Fri, 1 Nov 2024 10:18:24 +0800
Subject: [PATCH] =?UTF-8?q?=E6=9B=B4=E6=96=B0=20function.py?=
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---
 function.py | 94 +++++++++++++++++++++--------------------------------
 1 file changed, 37 insertions(+), 57 deletions(-)

diff --git a/function.py b/function.py
index 8c1ba8b..09da6ad 100644
--- a/function.py
+++ b/function.py
@@ -1,58 +1,8 @@
 import numpy as np
 import pandas as pd
 from sklearn.preprocessing import StandardScaler
-from sklearn.model_selection import train_test_split
-
-
-id = "202210423130"
-name = "董秋"
-
-
-def train_logreg(X, y, learning_rate=0.01, iterations=1000, lambda_reg=0.01):
-    """
-    对率回归训练函数。
-
-    Args:
-    - X (numpy array): 特征数据。
-    - y (numpy array): 标签数据。
-
-    Returns:
-    - numpy array: 训练得到的权重。
-    """
-    m, n = X.shape
-    weights = np.zeros(n)
-
-    for i in range(iterations):
-        z = np.dot(X, weights)
-        predictions = 1 / (1 + np.exp(-z))
-        gradient = np.dot(X.T, (predictions - y)) / m
-        
-        # L2正则化
-        gradient += (lambda_reg / m) * weights
-
-        weights -= learning_rate * gradient
-
-        # 动态调整学习率，逐渐减小
-        learning_rate *= 0.99  # 每次迭代将学习率乘以0.99
-
-    return weights
-
-
-def predict_logreg(inX, weights):
-    """
-    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。
-    """
-    z = np.dot(inX, weights)
-    prediction_prob = 1 / (1 + np.exp(-z))
-    return 1 if prediction_prob >= 0.5 else 0
-
+from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.linear_model import LogisticRegression
 
 def load_data(filepath):
     data = []
@@ -82,12 +32,42 @@ 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):
+    # 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
+        'solver': ['liblinear', 'lbfgs'],     # 求解器
+        'max_iter': [100, 500, 1000]          # 最大迭代次数
+    }
+
+    # 创建逻辑回归模型
+    log_reg = LogisticRegression()
+
+    # 创建网格搜索对象
+    grid_search = GridSearchCV(estimator=log_reg, param_grid=param_grid, cv=5, scoring='accuracy')
+
+    # 进行网格搜索
+    grid_search.fit(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。
+    """
+    z = np.dot(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
@@ -97,9 +77,9 @@ if __name__ == "__main__":
     # 使用 train_test_split 打乱并随机抽取数据
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
 
-    # 训练对率回归模型
-    weights = train_logreg(X_train, y_train, learning_rate=0.1, iterations=1000, lambda_reg=0.01)
+    # 训练逻辑回归模型
+    best_model = train_logreg(X_train, y_train)
 
     # 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]}")
+    y_pred = best_model.predict(X_test[11].reshape(1, -1))  # 进行预测时需要调整输入形状
+    print(f"预测结果: {y_pred[0]}, 实际结果: {y_test[11]}")