更新 function.py

function.py

@@ -121,46 +121,18 @@ def load_data(filepath):
     
     return data, target_variable
 
-def accuracy(X, y, weights):
-    predictions = [predict_logreg(x, weights) for x in X]
-    accuracy = np.mean(predictions == y)
-    return accuracy
-
-
-def grid_search(X_train, y_train, X_test, y_test, param_grid):
-    best_accuracy = 0
-    best_params = None
-
-    for params in product(*param_grid.values()):
-        learning_rate, iterations, batch_size, regularization, lambda_reg = params
-
-        print(f"尝试参数组合: {params}")
-        weights = train_logreg(X_train, y_train, learning_rate, iterations, batch_size, regularization, lambda_reg)
-        acc = accuracy(X_test, y_test, weights)
-
-        print(f"当前准确率: {acc:.4f}")
-
-        if acc > best_accuracy:
-            best_accuracy = acc
-            best_params = params
-
-    return best_params, best_accuracy
-
 if __name__ == "__main__":
-    X_train, y_train = load_data("./horse-colic.data")
+    # 以下代码可用于测试，可自行随意修改
-    X_test, y_test = load_data("./horse-colic.test")
+    # 加载训练集
+    X_train, y_train = load_data("./data/horse-colic.data")
 
-    # 定义参数网格
+    # 加载测试集
-    param_grid = {
+    X_test, y_test = load_data("./data/horse-colic.test")
-        'learning_rate': [0.01, 0.05],
-        'iterations': [500, 1000],
-        'batch_size': [16, 32],
-        'regularization': [None, 'L1', 'L2'],
-        'lambda_reg': [0.1, 0.5]
-    }
 
-    # 进行网格搜索
+    # 训练对率回归模型
-    best_params, best_accuracy = grid_search(X_train, y_train, X_test, y_test, param_grid)
+    weights = train_logreg(X_train, y_train, learning_rate=0.01, iterations=1000, batch_size=16, regularization='L1',
+                           lambda_reg=0.1)
 
-    print(f"最佳参数组合: {best_params}")
+    # 测试单个数据
-    print(f"最佳准确率: {best_accuracy:.4f}")
+    y_pred = predict_logreg(X_test[11], weights)
+    print(f"预测结果: {y_pred}, 实际结果: {y_test[11]}")