K-D tree nearest neighbor search in n-dimensions

gistfile1.txt

import numpy as np
from scipy.spatial import KDTree

def run_kdtree(ndim=8,npnts=500000):
    """ 
    given a set of random n-dimensional points, 
    find the closest one to a given point
    """
    print "Creating %d %d-dimensional points" % (npnts, ndim)

    allpoints = np.random.random_sample((npnts, ndim)) * 100
    tree = KDTree(allpoints)
    print "------------- DATA ----------------"
    print tree.data

    querypoints = np.random.random_sample((1, ndim)) * 100
    print "------------- QUERY FOR ----------------"
    print querypoints

    result = tree.query(querypoints) 
    print "------------- RESULTS ----------------"
    print "Closest is point #", result[1][0], "... distance", result[0][0]
    print "Closest point =",  allpoints[result[1][0]]

run_kdtree()