Numpy 3D数组索引 [英] Numpy 3d array indexing

问题描述

在下面的示例中，我有一个3d numpy数组( n_samples x num_components x 2 )， n_samples = 5， num_components =7.

I have a 3d numpy array (n_samples x num_components x 2) in the example below n_samples = 5 and num_components = 7.

我还有另一个数组( indices )，它是每个形状为( n_samples ，)的样本的选定组件.

I have another array (indices) which is the selected component for each sample which is of shape (n_samples,).

我想从给定索引的数据数组中进行选择，以使结果数组为 n_samples x 2 .

I want to select from the data array given the indices so that the resulting array is n_samples x 2.

代码如下:

import numpy as np
np.random.seed(77)
data=np.random.randint(low=0, high=10, size=(5, 7, 2))
indices = np.array([0, 1, 6, 4, 5])
#how can I select indices from the data array?

例如，对于数据0，所选组件应为0，对于数据1，所选组件应为1.

For example for data 0, the selected component should be the 0th and for data 1 the selected component should be 1.

请注意，我无法在forano中使用任何循环，因为我在Theano中使用了它，因此解决方案应完全基于numpy.

Note that I can't use any for loops because I'm using it in Theano and the solution should be solely based on numpy.

推荐答案

这是您要寻找的吗?

In [36]: data[np.arange(data.shape[0]),indices,:]
Out[36]: 
array([[7, 4],
       [7, 3],
       [4, 5],
       [8, 2],
       [5, 8]])

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