X, y = load_data(return_X_y=True)X_train, X_test, y_train, y_test = train_test_split(X,y,                                                test_size=0.3, random_state=123,                                            shuffle=True)def sample(X,y):# 尝试的代码 train_index = sorted(np.random.permutation(len(X))) test_index = [i for i in range(0,len(X)) if i not in train_index]for i in range(0,len(X)):# 训练集和验证集的分割    x_trn, x_val = X[train_index], X[test_index]    y_trn, y_val = y[train_inex], y[test_index]return x_trn , x_val, y_trn, y_val

然后调用它如下

sample(X_train, y_train)

填写代码以从训练数据中均匀地进行有放回抽样。抽样数据集的大小应等于训练数据集的大小。

回答：

根据作业的描述，这就是你所需要的。你随机选择索引，然后返回这些索引处的点。

def sample(X,y):    picks = np.random.randint(0,len(X),len(X))    return X[picks], y[picks]