更新 function.py

function.py

@@ -1,82 +1,46 @@
-import numpy as np
+	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 load_data(filepath):
+def train_logreg(X, y, learning_rate=0.01, iterations=1000, lambda_reg=0.01):
-    data = []
-    labels = []
-
-    with open(filepath, 'r') as f:
-        for line in f:
-            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
-                elif values[22] == 1:
-                    labels.append(1)  # 生存
-                else:
-                    continue  # 无效标签，丢弃
-                
-                # 用0填充缺失值
-                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
-            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)
-
-
-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。
+    对率回归训练函数。
 
     Args:
-    - X_train (numpy array): 特征数据。
+    - X (numpy array): 特征数据。
-    - y_train (numpy array): 标签数据。
+    - y (numpy array): 标签数据。
 
     Returns:
-    - LogisticRegression: 训练得到的最佳模型。
+    - numpy array: 训练得到的权重。
     """
-    # 定义要调优的超参数范围
+    m, n = X.shape
-    param_grid = {
+    weights = np.zeros(n)
-        '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
+    for i in range(iterations):
-    log_reg = LogisticRegression()
+        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
 
-    # 创建网格搜索对象
+        weights -= learning_rate * gradient
-    grid_search = GridSearchCV(estimator=log_reg, param_grid=param_grid, cv=5, scoring='accuracy')
 
-    # 进行网格搜索
+        # 动态调整学习率，逐渐减小
-    grid_search.fit(X_train, y_train)
+        learning_rate *= 0.99  # 每次迭代将学习率乘以0.99
 
-    return grid_search.best_estimator_
+    return weights
 
 
 def predict_logreg(inX, weights):
     """
-    逻辑回归预测函数。
+    对率回归预测函数。
 
     Args:
     - inX (numpy array): 输入待分类的样本。
@@ -90,6 +54,41 @@ def predict_logreg(inX, weights):
     return 1 if prediction_prob >= 0.5 else 0
 
 
+def load_data(filepath):
+    data = []
+    labels = []
+
+    with open(filepath, 'r') as f:
+        for line in f:
+            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
+                elif values[22] == 1:
+                    labels.append(1)  # 生存
+                else:
+                    continue  # 无效标签，丢弃
+                
+                # 用0填充缺失值
+                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
+            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")
@@ -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]}")