K nearest neighbour in python

Here is a script comparing scipy.spatial.cKDTree and pyflann.FLANN. See for yourself which one is faster for your application.

import cProfile
import numpy as np
import os
import pyflann
import scipy.spatial

# Config params
dim = 4
data_size = 1000
test_size = 1

# Generate data
np.random.seed(1)
dataset = np.random.rand(data_size, dim)
testset = np.random.rand(test_size, dim)

def test_pyflann_flann(num_reps):
    flann = pyflann.FLANN()
    for rep in range(num_reps):
        params = flann.build_index(dataset, target_precision=0.0, log_level='info')
        result = flann.nn_index(testset, 5, checks=params['checks'])

def test_scipy_spatial_kdtree(num_reps):
    flann = pyflann.FLANN()
    for rep in range(num_reps):
        kdtree = scipy.spatial.cKDTree(dataset, leafsize=10)
        result = kdtree.query(testset, 5)

num_reps = 1000
cProfile.run('test_pyflann_flann(num_reps); test_scipy_spatial_kdtree(num_reps)', 'out.prof')
os.system('runsnake out.prof')

I think that you should use scikit ann.

There is a good tutorial about the nearest neightbour here.

According to the documentation :

ann is a SWIG-generated python wrapper for the Approximate Nearest Neighbor (ANN) Library (http://www.cs.umd.edu/~mount/ANN/), developed by David M. Mount and Sunil Arya. ann provides an immutable kdtree implementation (via ANN) which can perform k-nearest neighbor and approximate k