在Python中以3D查找最接近给定点的最快方法

Question

So lets say I have 10,000 points in A and 10,000 points in B and want to find out the closest point in A for every B point.

Currently, I simply loop through every point in B and A to find which one is closest in distance. ie.

B = [(.5, 1, 1), (1, .1, 1), (1, 1, .2)]
A = [(1, 1, .3), (1, 0, 1), (.4, 1, 1)]
C = {}
for bp in B:
   closestDist = -1
   for ap in A:
      dist = sum(((bp[0]-ap[0])**2, (bp[1]-ap[1])**2, (bp[2]-ap[2])**2))
      if(closestDist > dist or closestDist == -1):
         C[bp] = ap
         closestDist = dist
print C

However, I am sure there is a faster way to do this... any ideas?

Answer 1

I typically use a kd-tree in such situations.

There is a C++ implementation wrapped with SWIG and bundled with BioPython that's easy to use.

Answer 2

You could use some spatial lookup structure. A simple option is an octree ; fancier ones include the BSP tree .

Answer 3

You could use numpy broadcasting. For example,

from numpy import *
import numpy as np

a=array(A)
b=array(B)
#using looping
for i in b:
    print sum((a-i)**2,1).argmin()

will print 2,1,0 which are the rows in a that are closest to the 1,2,3 rows of B, respectively.

Otherwise, you can use broadcasting:

z = sum((a[:,:, np.newaxis] - b)**2,1)
z.argmin(1) # gives array([2, 1, 0])

I hope that helps.