在Python / Numpy中一次分配相同的数组索引

I want to find a fast way (without for loop) in Python to assign reoccuring indices of an array. This is the desired result using a for loop:

我想在Python中找到一种快速的方法(没有for循环)来分配数组的重新索引索引。这是使用for循环的期望结果:

import numpy as np
a=np.arange(9, dtype=np.float64).reshape((3,3))
# The array indices: [2,3,4] are identical.
Px = np.uint64(np.array([0,1,1,1,2]))
Py = np.uint64(np.array([0,0,0,0,0]))
# The array to be added at the array indices (may also contain random numbers).
x = np.array([.1,.1,.1,.1,.1])

for m in np.arange(len(x)):
    a[Px[m]][Py[m]] += x

print a
%[[ 0.1  1.  2.]
%[ 3.3  4.  5.]
%[ 6.1  7.  8.]]

When I try to add x to a at the indices Px,Py I obviously do not get the same result (3.3 vs. 3.1):

当我尝试在索引Px处添加x时,Py我显然得不到相同的结果(3.3对3.1):

a[Px,Py] += x
print a
%[[ 0.1  1.  2.]
%[ 3.1  4.  5.]
%[ 6.1  7.  8.]]

Is there a way to do this with numpy? Thanks.

有没有办法用numpy做到这一点?谢谢。

1 个解决方案

#1

Yes, it can be done, but it is a little tricky:

是的,它可以完成,但它有点棘手:

# convert yourmulti-dim indices to flat indices
flat_idx = np.ravel_multi_index((Px, Py), dims=a.shape)
# extract the unique indices and their position
unique_idx, idx_idx = np.unique(flat_idx, return_inverse=True)
# Aggregate the repeated indices 
deltas = np.bincount(idx_idx, weights=x)
# Sum them to your array
a.flat[unique_idx] += deltas