[英]How can I use a 3d array of indices for a 2d array slicing in Numpy
I have 2 arrays as input.我有 2 arrays 作为输入。 On array as output. Array
a
holds the data and is of shape (N,M)
, while array b
holds the indices and is of shape (N,X,2)
.在数组上为 output。数组
a
保存数据并且形状为(N,M)
,而数组b
保存索引并且形状为(N,X,2)
。 The resulting array should be of shape (N,X)
, with the values taken from a
.结果数组的形状应为
(N,X)
,其值取自a
。
Right now it only works with a for loop.现在它只适用于 for 循环。 How could I vectorize it since I have huge arrays as input?
由于我有巨大的 arrays 作为输入,我该如何对其进行矢量化?
Below is a sample code to demonstrate what I have right now:下面是一个示例代码,用于演示我现在拥有的内容:
import numpy as np
# a of shape (N,M)
# b of shape (N,X,2)
# t_result of shape (N, X)
a = np.random.randint(0, 10, size=(10, 10))
b = np.random.randint(0, 2, size=(10, 9, 2))
t_result = np.empty((10, 9))
for i in range(b.shape[0]):
t_result[i] = a[i, b[i, :, 0]]
print(t_result)
print(t_result.shape)
Ok so I adapted a bit the answer to another post from scleronomic :好的,所以我对scleronomic的另一篇文章的答案进行了一些调整:
import numpy as np
# a of shape (N,M)
# b of shape (N,X,2)
# t_result of shape (N, X)
a = np.random.randint(0, 10, size=(10, 10))
b = np.random.randint(0, 2, size=(10, 9, 2))
t_result = np.empty((10, 9))
t_result = a[np.arange(a.shape[0])[:,None],b[np.arange(b.shape[0]),:,0]]
print(t_result)
print(t_result.shape)
I am not sure whether or not it is the best solution but it works.我不确定它是否是最佳解决方案,但它确实有效。
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