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iCAPs_v2/packages/gspbox/3rdparty/sources/flann-1.8.4-src/test/test_nn_autotune.py
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test_nn_autotune.py
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#!/usr/bin/env python
import sys
from os.path import *
import os
from pyflann import *
from copy import copy
from numpy import *
from numpy.random import *
import unittest
class Test_PyFLANN_nn(unittest.TestCase):
def setUp(self):
self.nn = FLANN(log_level="warning")
################################################################################
# The typical
def test_nn_2d_10pt(self):
self.__nd_random_test_autotune(2, 2)
def test_nn_autotune_2d_1000pt(self):
self.__nd_random_test_autotune(2, 1000)
def test_nn_autotune_100d_1000pt(self):
self.__nd_random_test_autotune(100, 1000)
def test_nn_autotune_500d_100pt(self):
self.__nd_random_test_autotune(500, 100)
#
# ##########################################################################################
# # Stress it should handle
#
def test_nn_stress_1d_1pt_kmeans_autotune(self):
self.__nd_random_test_autotune(1, 1)
def __ensure_list(self,arg):
if type(arg)!=list:
return [arg]
else:
return arg
def __nd_random_test_autotune(self, dim, N, num_neighbors = 1, **kwargs):
"""
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)
xq = rand(N, dim)
perm = permutation(N)
# compute ground truth nearest neighbors
gt_idx, gt_dist = self.nn.nn(x,xq,
algorithm='linear',
num_neighbors=num_neighbors)
for tp in [0.70, 0.80, 0.90]:
nidx,ndist = self.nn.nn(x, xq,
algorithm='autotuned',
sample_fraction=1.0,
num_neighbors = num_neighbors,
target_precision = tp, checks=-2, **kwargs)
correctness = 0.0
for i in range(N):
l1 = self.__ensure_list(nidx[i])
l2 = self.__ensure_list(gt_idx[i])
correctness += float(len(set(l1).intersection(l2)))/num_neighbors
correctness /= N
self.assertTrue(correctness >= tp*0.9,
'failed #1: targ_prec=%f, N=%d,correctness=%f' % (tp, N, correctness))
if __name__ == '__main__':
unittest.main()
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