Nearest Neighbor Search in Python without k-d tree

Question

I\'m beginning to learn Python coming from a C++ background. What I am looking for is a quick and easy way to find the closest (nearest neighbor) of some multidimensional query

Answer 1

Broadcasting is very useful for this kind of thing. I'm not sure if this is what you need, but here I use broadcasting to find the displacement between p (one point in 3 space) and X (a set of 10 points in 3-space).

import numpy as np

def closest(X, p):
    disp = X - p
    return np.argmin((disp*disp).sum(1))

X = np.random.random((10, 3))
p = np.random.random(3)

print X
#array([[ 0.68395953,  0.97882991,  0.68826511],
#       [ 0.57938059,  0.24713904,  0.32822283],
#       [ 0.06070267,  0.06561339,  0.62241713],
#       [ 0.93734468,  0.73026772,  0.33755815],
#       [ 0.29370809,  0.76298588,  0.68728743],
#       [ 0.66248449,  0.6023311 ,  0.76704199],
#       [ 0.53490144,  0.96555923,  0.43994738],
#       [ 0.23780428,  0.75525843,  0.46067472],
#       [ 0.84240565,  0.82573202,  0.56029917],
#       [ 0.66751884,  0.31561133,  0.19244683]])
print p
#array([ 0.587416 ,  0.4181857,  0.2539029])
print closest(X, p)
#9