更新 function.py

function.py

@@ -19,16 +19,17 @@ from itertools import product
 - numpy: 2.1.1
 """
 
-
 """
 请在这里填写学号和姓名，不填写即成绩无效。
 """
 id = "202210423117"
 name = "程悦"
 
+
 def sigmoid(z):
     return 1.0 / (1 + np.exp(-z))
 
+
 def train_logreg(X, y, learning_rate=0.01, iterations=1000, batch_size=32, regularization=None, lambda_reg=0.1):
     """
     对率回归训练函数。
@@ -68,6 +69,7 @@ def train_logreg(X, y, learning_rate=0.01, iterations=1000, batch_size=32, regul
 
     return weights
 
+
 def predict_logreg(inX, weights):
     """
     对率回归预测函数。
@@ -83,6 +85,7 @@ def predict_logreg(inX, weights):
     h = sigmoid(z)
     return 1 if h > 0.5 else 0
 
+
 def load_data(filepath):
     """
     从文件中加载数据，填充缺失值，并进行特征处理。
@@ -118,20 +121,27 @@ def load_data(filepath):
 
     scaler = StandardScaler()
     data = scaler.fit_transform(data)
-    
+
     return data, target_variable
 
+def accuracy(X, y, weights):
+    predictions = [predict_logreg(sample, weights) for sample in X]
+    return np.mean(predictions == y)  # 计算准确率
+
 if __name__ == "__main__":
     # 以下代码可用于测试，可自行随意修改
     # 加载训练集
-    X_train, y_train = load_data("./data/horse-colic.data")
+    X_train, y_train = load_data("./horse-colic.data")
 
     # 加载测试集
-    X_test, y_test = load_data("./data/horse-colic.test")
+    X_test, y_test = load_data("./horse-colic.test")
 
     # 训练对率回归模型
-    weights = train_logreg(X_train, y_train, learning_rate=0.15, iterations=500, batch_size=32, regularization='L1',
+    weights = train_logreg(X_train, y_train, learning_rate=0.2, iterations=200, batch_size=32, regularization='L1',
                            lambda_reg=0.15)
+    # 计算并输出准确率
+    acc = accuracy(X_test, y_test, weights)
+    print(f"模型的准确率: {acc:.2f}")
 
     # 测试单个数据
     y_pred = predict_logreg(X_test[11], weights)