Numpy 是否给出了意想不到的结果？

Question

我有一个数据集X，这样X.shape 会产生(10000, 9)。我想使用以下代码选择X 的子集：

X = np.asarray(np.random.normal(size = (10000,9)))
train_fraction = 0.7 # fraction of X that will be marked as train data
train_size = int(X.shape[0]*train_fraction) # fraction converted to number
test_size = X.shape[0] - train_size # remaining rows will be marked as test data
train_ind = np.asarray([False]*X.shape[0])     
train_ind[np.random.randint(low = X.shape[0], size = (train_size,))] = True # mark True at 70% of the places

问题是np.sum(train_ind) 不是 7000 的预期值。相反，它给出了 5033 等随机值。

我最初认为np.random.randint(low = X.shape[0], size = (train_size,)) 可能是罪魁祸首。但是当我做np.random.randint(low = X.shape[0], size = (train_size,)).shape 时，我得到(7000,)。

我哪里出错了？

Answer 1

以np.random.choice(np.arange(0,X.shape[0]), size = train_size, replace = False)

问题是，np.random.randint 不会被注入，基本上数字 1 可能会出现两次。这意味着索引 1 将被设置为 True 两次，而另一个不会被设置为 True。

np.random.choice 函数确保每个数字最多出现一次（如果您设置了replace = False