在任何情况下,您是否更喜欢O(log n)时间复杂度和O(1)时间复杂度？或O(n)到O(log n)？
你有什么例子吗？
我试图矢量化以下代码:
for i in xrange(s.shape[0]):
a[i] = np.argmax(np.random.multinomial(1,s[i,:]))
s.shape = 400 x 100 [给定].
a.shape = 400 [预期].
s是2D矩阵,包含对的概率.期望多项式从s矩阵的每一行中抽取随机样本并将结果存储在向量a中.
Numpy 具有该random.choice功能，可让您从分类分布中进行采样。你会如何在一个轴上重复这个？为了说明我的意思，这是我当前的代码：
categorical_distributions = np.array([
[.1, .3, .6],
[.2, .4, .4],
])
_, n = categorical_distributions.shape
np.array([np.random.choice(n, p=row)
for row in categorical_distributions])
理想情况下，我想消除 for 循环。
I regularly find myself in the position of needing a random index to an array or a list, where the probabilities of indices are not uniformly distributed, but according to certain positive weights. What's a fast way to obtain them? I know I can pass weights to numpy.random.choice as optional argument p, but the function seems quite slow, and building an arange to pass it is not ideal either. The sum of weights can be an arbitrary positive number …