更新 function.py

function.py

@@ -1,14 +1,59 @@
 	import numpy as np
 import pandas as pd
 from sklearn.preprocessing import StandardScaler
-from sklearn.model_selection import train_test_split, GridSearchCV
+from sklearn.model_selection import train_test_split
-from sklearn.linear_model import LogisticRegression
 
 
 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
+
+
 def load_data(filepath):
     data = []
     labels = []
@@ -44,52 +89,6 @@ def preprocess_data(X):
     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
-        '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
     X, y = load_data("./data/horse-colic.data")
@@ -98,9 +97,9 @@ 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)
 
-    # 训练逻辑回归模型
+    # 训练对率回归模型
-    best_model = train_logreg(X_train, y_train)
+    weights = train_logreg(X_train, y_train, learning_rate=0.1, iterations=1000, lambda_reg=0.01)
 
     # 测试单个数据
-    y_pred = best_model.predict(X_test[11].reshape(1, -1))  # 进行预测时需要调整输入形状
+    y_pred = predict_logreg(X_test[11], weights)
-    print(f"预测结果: {y_pred[0]}, 实际结果: {y_test[11]}")
+    print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")