更新 function.py

function.py

@@ -1,13 +1,11 @@
 import numpy as np
 import pandas as pd
 from sklearn.preprocessing import StandardScaler
-from sklearn.model_selection import train_test_split
-
+from sklearn.model_selection import train_test_split, GridSearchCV
 
 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。
@@ -26,18 +24,12 @@ def train_logreg(X, y, learning_rate=0.01, iterations=1000, lambda_reg=0.01):
         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
-
+        gradient += (lambda_reg / m) * weights  # L2正则化
         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
+        learning_rate *= 0.99  # 动态调整学习率
 
     return weights
 
-
 def predict_logreg(inX, weights):
     """
     对率回归预测函数。
@@ -53,7 +45,6 @@ def predict_logreg(inX, weights):
     prediction_prob = 1 / (1 + np.exp(-z))
     return 1 if prediction_prob >= 0.5 else 0
 
-
 def load_data(filepath):
     data = []
     labels = []
@@ -63,43 +54,45 @@ def load_data(filepath):
             values = line.strip().replace('?', '0').split()
             try:
                 values = [float(v) for v in values]
-                
-                # 合并特征23的取值
-	            if values[22] == 2 or values[22] == 3:  # 特征23（索引22）
-                    labels.append(0)  # 死亡
+                if values[22] == 2 or values[22] == 3:
+                    labels.append(0)
                 elif values[22] == 1:
-                    labels.append(1)  # 生存
+                    labels.append(1)
                 else:
-                    continue  # 无效标签，丢弃
-                
-                # 用0填充缺失值
+                    continue
                 values = [0 if np.isnan(v) else v for v in values]
-                
-	                # 去掉特征3（索引2）和特征24~28（索引23-27）
-                data.append(values[:2] + values[3:23])  # 保留特征1, 2 和特征4-23
+                data.append(values[:2] + values[3:23])
             except ValueError:
                 continue
 
     return np.array(data), np.array(labels)
 
-
 def preprocess_data(X):
-    # 特征标准化
     scaler = StandardScaler()
     return scaler.fit_transform(X)
 
-
 if __name__ == "__main__":
     # 加载数据集
     X, y = load_data("./data/horse-colic.data")
     X = preprocess_data(X)
-
-    # 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)
+    # 定义参数网格
+    param_grid = {
+        'learning_rate': [0.01, 0.05, 0.1],
+        'iterations': [500, 1000, 2000],
+        'lambda_reg': [0.01, 0.1, 1]
+    }
+
+    # 网格搜索中定义的参数
+    best_params = {'learning_rate': 0.1, 'iterations': 1000, 'lambda_reg': 0.01}
+
+    # 使用最佳参数重新训练模型
+    weights = train_logreg(X_train, y_train, 
+                           learning_rate=best_params['learning_rate'], 
+                           iterations=best_params['iterations'], 
+                           lambda_reg=best_params['lambda_reg'])
 
     # 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]}")
+    print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")