使用 numpy bool 数组作为另一个 arry 的索引

Question

编辑我的问题以使其更加清晰，因为我无法获得第一个工作答案。我正在尝试制作一个 N 维查找表，根据 N 个 True/False 值的输入列表从中查找一个值。为此，我创建了一个 N 维数组，其中每个维度的长度为 2 - 一个值表示 False (=0)，一个值表示 True (=1)import numpy as npimport randomdims = 3values = range(pow(2,dims))lookup=np.reshape(values,np.tile(2,dims)) #this makes a 2x2x2x... arraycondition=random.choices([True,False],k=dims) #this makes a 1d list of bools条件中的布尔值现在应该指定要查找的索引。以 N=3 为例：如果条件 =(True,False,True)，我想要查找[1,0,1]。只需使用lookup[condition.astype(int)]不起作用，因为 numpy 不会将 1x3 数组解释为索引。lookup[condition[0],condition[1],condition[2]]作品。但我还没弄清楚如何在 N 维上写这个

Answer 1

手动将其转换为 int 元组可以实现您所寻求的索引。测试下面的代码。

import numpy as np

# your example had d=3, but you may have something else.

# this is just creation of some random data...

d=3

answers = np.random.normal(size=[2] * d)

truth = tuple(np.random.choice([True, False], replace=True, size=d))

# manual conversion to int-tuple is needed, for numpy to understand that it

# is a indexing tuple.

# the box at the top of https://numpy.org/doc/stable/reference/arrays.indexing.html 

# says that tuple indexing is just like normal indexing, and in that case, we 

# need to have normal ints.

idx = tuple(int(b) for b in truth)

answer = answers[idx]

print(answer)