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iCAPs_v2/packages/gspbox/3rdparty/sources/flann-1.8.4-src/test/test_index_save.py
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test_index_save.py
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#!/usr/bin/env python
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()
class Test_PyFLANN_nn_index(unittest.TestCase):
def testnn_index_save_kdtree_1(self):
self.run_nn_index_save_perturbed(64,1000, algorithm="kdtree", trees=1)
def testnn_index_save_kdtree_4(self):
self.run_nn_index_save_perturbed(64,1000, algorithm="kdtree", trees=4)
def testnn_index_save_kdtree_10(self):
self.run_nn_index_save_perturbed(64,1000, algorithm="kdtree", trees=10)
def testnn_index_save_kmeans_2(self):
self.run_nn_index_save_perturbed(64,1000, algorithm="kmeans", branching=2, iterations=11)
def testnn_index_save_kmeans_16(self):
self.run_nn_index_save_perturbed(64,1000, algorithm="kmeans", branching=16, iterations=11)
def testnn_index_save_kmeans_32(self):
self.run_nn_index_save_perturbed(64,1000, algorithm="kmeans", branching=32, iterations=11)
def testnn_index_save_kmeans_64(self):
self.run_nn_index_save_perturbed(64,1000, algorithm="kmeans", branching=64, iterations=11)
def testnn__save_kdtree_1(self):
self.run_nn_index_save_rand(64,10000,1000, algorithm="kdtree", trees=1, checks=128)
def testnn__save_kdtree_4(self):
self.run_nn_index_save_rand(64,10000,1000, algorithm="kdtree", trees=4, checks=128)
def testnn__save_kdtree_10(self):
self.run_nn_index_save_rand(64,10000,1000, algorithm="kdtree", trees=10, checks=128)
def testnn__save_kmeans_2(self):
self.run_nn_index_save_rand(64,1000,1000, algorithm="kmeans", branching=2, iterations=11, checks=64)
def testnn__save_kmeans_8(self):
self.run_nn_index_save_rand(64,10000,1000, algorithm="kmeans", branching=8, iterations=11, checks=32)
def testnn__save_kmeans_16(self):
self.run_nn_index_save_rand(64,10000,1000, algorithm="kmeans", branching=16, iterations=11, checks=40)
def testnn__save_kmeans_32(self):
self.run_nn_index_save_rand(64,10000,1000, algorithm="kmeans", branching=32, iterations=11, checks=56)
def run_nn_index_save_perturbed(self, dim, N, **kwargs):
x = rand(N, dim)
nn = FLANN()
nn.build_index(x, **kwargs)
nn.save_index("index.dat")
nn.delete_index();
nn = FLANN()
nn.load_index("index.dat",x)
x_query = x + randn(x.shape[0], x.shape[1])*0.0001/dim
nnidx, nndist = nn.nn_index(x_query)
correct = all(nnidx == arange(N, dtype = index_type))
nn.delete_index()
self.assertTrue(correct)
def run_nn_index_save_rand(self, dim, N, Nq, **kwargs):
x = rand(N, dim)
x_query = rand(Nq,dim)
# build index, search and delete it
nn = FLANN()
nn.build_index(x, **kwargs)
nnidx, nndist = nn.nn_index(x_query, checks=kwargs["checks"])
nn.save_index("index.dat")
del nn
# now reload index and search again
nn = FLANN()
nn.load_index("index.dat",x)
nnidx2, nndist2 = nn.nn_index(x_query, checks=kwargs["checks"])
del nn
correct = all(nnidx == nnidx2)
self.assertTrue(correct)
if __name__ == '__main__':
unittest.main()
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