Sum 2D Numpy Array by Multiple Labels

Question

谢谢你回答我的问题。这是我的第三个。

1. 数据数组的每个元素都是一个坐标(x, y)。
2. 每个坐标有2个标签
3. 目标:对具有相同两个标签的元素求和。

例如，如果输入为

data = numpy.array( [ [1, 2], [3,8], [4,5], [2,9], [1, 3], [7, 2] ] )
label1 = numpy.array([0,0,1,1,2,2])
label2 = numpy.array([0,1,0,0,1,1])

应该给:

array([[[ 1 ,  2 ],
        [ 3 ,  8 ]],
       [[ 6 , 14 ],
        [ 0 ,  0 ]],
       [[ 0 ,  0 ],
        [ 8 ,  5 ]]])

下面是我当前的代码:

import numpy
import ndimage from scipy
data = numpy.array( [ [1, 2], [3,8], [4,5], [2,9], [1, 3], [7, 2] ] )
label1 = numpy.array([0,0,1,1,2,2])
label2 = numpy.array([0,1,0,0,1,1])
kinds_of_label1 = 3
kinds_of_label2 = 2
label1_l = label1.size
label2_l = label2.size
label12 = label1 * 2 + label2
kinds12_range = range(kinds_of_label1 * kinds_of_label2 )
result = numpy.zeros( (num_frame, num_cluster, 2) )
result_T = result.view().reshape( (num_frame * num_cluster, 2) ).T
result_T[0] = ndimage.measurements.sum( data.T[0], label12, index = kinds12_range )
result_T[1] = ndimage.measurements.sum( data.T[1], label12, index = kinds12_range )
counting = numpy.bincount(label12)
print(result)
print(counting)

这是可行的，但是分别求和x和y坐标(如result_T[0]和result_T[1])似乎很糟糕。此外，image.measurements.sum给出了浮点数答案。整数运算比较快。

我们能让它更快更好吗?

Answer 1

#### Wrong Answer.  Do Not Use. ####
import numpy
### Input ###
label1 = numpy.array([0,0,1,1,2,2])
kinds_of_label1 = 3
label2 = numpy.array([0,1,0,0,1,1])
kinds_of_label2 = 2
data = numpy.array( [ [1, 2], [3,8], [4,5], [2,9], [1, 3], [7, 2] ] )
### Processing ####
# this assumes label1 and label2 starts are like 0, 1, 2, 3 ...
label1_and_2 = label1*kinds_of_label2 + label2
result = numpy.zeros( (kinds_of_label1 * kinds_of_label2, 2) )
result[ label1_and_2 ] += data
counting = numpy.bincount( label1_and_2 )
### output ###
print( result.view().reshape(kinds_of_label1, kinds_of_label2, 2) )

>>> array([[[ 1.,  2.],
            [ 3.,  8.]],
           [[ 2.,  9.],
            [ 0.,  0.]],
           [[ 0.,  0.],
            [ 7.,  2.]]])