class Test_PyFLANN_clustering(unittest.TestCase):
def setUp(self):
self.nn = FLANN(iterations=11)
##########################################################################
def test_Rand(self):
x = np.random.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.
"""
np.random.seed(0)
x = np.random.rand(N, dim)
xc = np.concatenate(tuple([x for i in range(dup)]))
if dup > 1:
xc += np.random.randn(xc.shape[0], xc.shape[1]) * 0.000001 / dim
# rnseed = int(time.time())
centroids = self.nn.kmeans(
xc[np.random.permutation(len(xc))], N, centers_init='random', random_seed=2,
)
mindists = np.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[np.random.permutation(len(xc))], N, centers_init='gonzales', random_seed=2,
)
mindists = np.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[np.random.permutation(len(xc))], N, centers_init='kmeanspp', random_seed=2,
)
mindists = np.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):
"""
self = Test_PyFLANN_clustering()
self.setUp()
"""
import pytest
pytest.skip('broken, but not worth fixing')
data = np.random.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(np.all(cl1 == cl2))
def testrandnumber_different(self):
data = np.random.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(np.any(cl1 != cl2))
"""
return _VmB('VmRSS:') - since
def stacksize(since=0.0):
"""Return stack size in bytes.
"""
return _VmB('VmStk:') - since
if __name__ == '__main__':
print('Profiling Memory usage for pyflann; CTRL-C to stop.')
print('Increasing total process memory, relative to the python memory, ')
print('implies a memory leak in the external libs.')
print('Increasing python memory implies a memory leak in the python code.')
h = hpy()
while True:
s = str(h.heap())
print('Python: %s; Process Total: %s' % (s[: s.find('\n')], memory()))
X = rand(30000, 2)
pf = FLANN()
cl = pf.kmeans(X, 20)
del X
del cl
del pf
gc.collect()
