删除numpy中的循环以进行简单的矩阵分配 [英] Removing loops in numpy for a simple matrix assignment

问题描述

如何在这种简单的矩阵分配中删除循环以提高性能?

How can I remove loops in this simple matrix assignment in order to increase performance?

nk,ncol,nrow=index.shape
for kk in range(0,nk):
   for ii in range(0,nrow):
       for jj in range(0,ncol):
           idx=index[kk][ii][jj]
           counter[idx][ii][jj]+=1

我来自C ++，发现很难适应numpy的函数来进行一些非常基本的矩阵操作，例如这样.我想我已经将其简化为一个一维的循环，但是对于我所需要的来说，这仍然太慢了，在我看来，必须有一种更直接的方法来完成它.有什么建议么?谢谢

I come from C++ and I am finding it difficult to adapt to numpy's functions to do some very basic matrix manipulation like this one. I think I have simplified it to a one dimensional loop, but this is still too slow for what I need and it seems to me that there is got to be a more direct way of doing it. Any suggestions? thanks

for kk in range(0,nk):
    xx,yy = np.meshgrid(np.arange(ncol),np.arange(nrow))
    counter[index[kk,:,:].flatten(),yy.flatten(),xx.flatten()]+=1    

推荐答案

如果我理解正确，那么您正在寻找:

If I understand it correctly, you are looking for this:

uniq, counter = np.unique(index, return_counts=True, axis=0)

uniq 应该为您提供一组唯一的 x，y s( x，y 将被展平为单个数组)，并且< index

The uniq should give you unique set of x,ys (x,y will be flattened into a single array) and counter corresponding number of repetitions in the array index

编辑 :

每个OP的评论如下:

xx,yy = np.meshgrid(np.arange(ncol),np.arange(nrow))
idx, counts = np.unique(np.vstack((index.flatten(),np.repeat(yy.flatten(),nk),np.repeat(xx.flatten(),nk))), return_counts=True,axis=1)
counter[tuple(idx)] = counts

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