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iCAPs/packages/gspbox/3rdparty/sources/flann-1.8.4-src/test/test_clustering.py
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test_clustering.py
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#!/usr/bin/env python
import sys
from os.path import *
from pyflann import *
import os
from copy import copy
from numpy import *
from numpy.random import *
import unittest, time
class Test_PyFLANN_clustering(unittest.TestCase):
def setUp(self):
self.nn = FLANN(iterations=11)
################################################################################
def test_Rand(self):
x = rand(100, 10000)
nK = 10
centroids = self.nn.kmeans(x, nK)
self.assertTrue(len(centroids) == nK)
def test2d_small(self):
self.__nd_random_clustering_test(2,2)
def test3d_small(self):
self.__nd_random_clustering_test(3,3)
def test4d_small(self):
self.__nd_random_clustering_test(4,4)
def test3d_large(self):
self.__nd_random_clustering_test(3,3, 1000)
def test10d_large(self):
self.__nd_random_clustering_test(10,2,10)
def test500d(self):
self.__nd_random_clustering_test(500,2, 10)
def __nd_random_clustering_test(self, dim, N, dup=1):
"""
Make a set of random points, then pass the same ones to the
query points. Each point should be closest to itself.
"""
seed(0)
x = rand(N, dim)
xc = concatenate(tuple([x for i in range(dup)]))
if dup > 1: xc += randn(xc.shape[0], xc.shape[1])*0.000001/dim
rnseed = int(time.time())
centroids = self.nn.kmeans(xc[permutation(len(xc))], N, centers_init = "random", random_seed=2)
mindists = array([[ sum((d1-d2)**2) for d1 in x] for d2 in centroids]).min(0)
#print mindists
for m in mindists: self.assertAlmostEqual(m, 0.0, 1)
rnseed = int(time.time())
centroids = self.nn.kmeans(xc[permutation(len(xc))], N, centers_init = "gonzales", random_seed=2)
mindists = array([[ sum((d1-d2)**2) for d1 in x] for d2 in centroids]).min(0)
#print mindists
for m in mindists: self.assertAlmostEqual(m, 0.0, 1)
centroids = self.nn.kmeans(xc[permutation(len(xc))], N, centers_init = "kmeanspp", random_seed=2)
mindists = array([[ sum((d1-d2)**2) for d1 in x] for d2 in centroids]).min(0)
#print mindists
for m in mindists: self.assertAlmostEqual(m, 0.0, 1)
def testrandomnumber_same(self):
data = rand(1000,2) # Random, so we can get a lot of local minima
rnseed = int(time.time())
cl1 = self.nn.kmeans(data, 50, random_seed = rnseed)
cl2 = self.nn.kmeans(data, 50, random_seed = rnseed)
self.assertTrue(all(cl1 == cl2))
def testrandnumber_different(self):
data = rand(1000,100) # Random, so we can get a lot of local minima
rnseed = int(time.time())
cl1 = self.nn.kmeans(data, 50, random_seed = rnseed)
cl2 = self.nn.kmeans(data, 50)
self.assertTrue(any(cl1 != cl2))
if __name__ == '__main__':
unittest.main()
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