在Python中以最快的方式找到3D中给定点的最接近点 [英] Fastest way to find the closest point to a given point in 3D, in Python

问题描述

所以可以说我在A中有10,000点，在B中有10,000点，并且想找出每个B点中A中最接近的点.

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.

当前，我只是遍历B和A中的每个点，以找出距离最近的那个.即.

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

但是，我确定有更快的方法来执行此操作...有什么想法吗?

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

推荐答案

我通常使用 kd-tree 在这种情况下.

I typically use a kd-tree in such situations.

有一个 C ++实现已包装与SWIG并与BioPython捆绑在一起，易于使用.

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