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from __future__ import division, print_function, absolute_import
import sys
from numpy.testing import *
from scipy.spatial import cKDTree, KDTree
import numpy as np
class TestBuild(TestCase):
def bench_build(self):
print()
print(' Constructing kd-tree')
print('=====================================')
print(' dim | # points | KDTree | cKDTree ')
for (m, n, repeat) in [(3,10000,3), (8,10000,3), (16,10000,3)]:
print('%4s | %7s ' % (m, n), end=' ')
sys.stdout.flush()
data = np.concatenate((np.random.randn(n//2,m),
np.random.randn(n-n//2,m)+np.ones(m)))
print('| %6.3fs ' % (measure('T1 = KDTree(data)', repeat) / repeat), end=' ')
sys.stdout.flush()
print('| %6.3fs' % (measure('T2 = cKDTree(data)', repeat) / repeat), end=' ')
sys.stdout.flush()
print('')
class TestQuery(TestCase):
def bench_query(self):
print()
print(' Querying kd-tree')
print('===============================================================')
print(' dim | # points | # queries | KDTree | cKDTree | flat cKDTree')
for (m, n, r, repeat) in [(3,10000,1000,3),
(8,10000,1000,3),
(16,10000,1000,3)]:
print('%4s | %8s | %8s ' % (m, n, r), end=' ')
sys.stdout.flush()
data = np.concatenate((np.random.randn(n//2,m),
np.random.randn(n-n//2,m)+np.ones(m)))
queries = np.concatenate((np.random.randn(r//2,m),
np.random.randn(r-r//2,m)+np.ones(m)))
T1 = KDTree(data)
T2 = cKDTree(data)
T3 = cKDTree(data,leafsize=n)
print('| %6.3fs ' % (measure('T1.query(queries)', 1) / 1), end=' ')
sys.stdout.flush()
print('| %6.3fs' % (measure('T2.query(queries)', repeat) / repeat), end=' ')
sys.stdout.flush()
print('| %6.3fs' % (measure('T3.query(queries)', repeat) / repeat), end=' ')
sys.stdout.flush()
print('')
class TestQueryBallPoint(TestCase):
def bench_query_ball_point(self):
print()
print(' Query ball point kd-tree')
print('===============================================================')
print(' dim | # points | # queries | probe radius | KDTree | cKDTree | flat cKDTree')
for (m, n, r, repeat) in [(3,10000,1000,3)]: # ,
# (8,10000,1000,3),
# (16,10000,1000,3)]:
for probe_radius in (0.2, 0.5):
print('%4s | %8s | %9s | %11.1f ' % (m, n, r, probe_radius), end=' ')
sys.stdout.flush()
data = np.concatenate((np.random.randn(n//2,m),
np.random.randn(n-n//2,m)+np.ones(m)))
queries = np.concatenate((np.random.randn(r//2,m),
np.random.randn(r-r//2,m)+np.ones(m)))
T1 = KDTree(data)
T2 = cKDTree(data)
T3 = cKDTree(data,leafsize=n)
print('| %6.3fs ' % (measure('T1.query_ball_point(queries, probe_radius)', 1) / 1), end=' ')
sys.stdout.flush()
print('| %6.3fs' % (measure('T2.query_ball_point(queries, probe_radius)', repeat) / repeat), end=' ')
sys.stdout.flush()
print('| %6.3fs' % (measure('T3.query_ball_point(queries, probe_radius)', repeat) / repeat), end=' ')
sys.stdout.flush()
print('')
class TestQueryPairs(TestCase):
def bench_query_pairs(self):
print()
print(' Query pairs kd-tree')
print('==================================================================')
print(' dim | # points | probe radius | KDTree | cKDTree | flat cKDTree')
for (m, n, repeat) in [(3,1000,30),
(8,1000,30),
(16,1000,30)]:
for probe_radius in (0.2, 0.5):
print('%4s | %8s | %11.1f ' % (m, n, probe_radius), end=' ')
sys.stdout.flush()
data = np.concatenate((np.random.randn(n//2,m),
np.random.randn(n-n//2,m)+np.ones(m)))
T1 = KDTree(data)
T2 = cKDTree(data)
T3 = cKDTree(data,leafsize=n)
print('| %6.3fs ' % (measure('T1.query_pairs(probe_radius)', 1) / 1), end=' ')
sys.stdout.flush()
print('| %6.3fs' % (measure('T2.query_pairs(probe_radius)', repeat) / repeat), end=' ')
sys.stdout.flush()
print('| %6.3fs' % (measure('T3.query_pairs(probe_radius)', repeat) / repeat), end=' ')
sys.stdout.flush()
print('')
class TestSparseDistanceMatrix(TestCase):
def bench_sparse_distance_matrix(self):
print()
print(' Sparse distance matrix kd-tree')
print('====================================================================')
print(' dim | # points T1 | # points T2 | probe radius | KDTree | cKDTree')
for (m, n1, n2, repeat) in [(3,1000,1000,30),
(8,1000,1000,30),
(16,1000,1000,30)]:
data1 = np.concatenate((np.random.randn(n1//2,m),
np.random.randn(n1-n1//2,m)+np.ones(m)))
data2 = np.concatenate((np.random.randn(n2//2,m),
np.random.randn(n2-n2//2,m)+np.ones(m)))
T1 = KDTree(data1)
T2 = KDTree(data2)
cT1 = cKDTree(data1)
cT2 = cKDTree(data2)
for probe_radius in (0.2, 0.5):
print('%4s | %11s | %11s | %11.1f ' % (m, n1, n2, probe_radius), end=' ')
sys.stdout.flush()
print('| %6.3fs ' % (measure('T1.sparse_distance_matrix(T2, probe_radius)', 1) / 1), end=' ')
sys.stdout.flush()
print('| %6.3fs ' % (measure('cT1.sparse_distance_matrix(cT2, probe_radius)', repeat) / repeat), end=' ')
sys.stdout.flush()
print('')
class TestCountNeighbors(TestCase):
def bench_count_neighbors(self):
print()
print(' Count neighbors kd-tree')
print('====================================================================')
print(' dim | # points T1 | # points T2 | probe radius | KDTree | cKDTree')
for (m, n1, n2, repeat) in [(3,1000,1000,30),
(8,1000,1000,30),
(16,1000,1000,30)]:
data1 = np.concatenate((np.random.randn(n1//2,m),
np.random.randn(n1-n1//2,m)+np.ones(m)))
data2 = np.concatenate((np.random.randn(n2//2,m),
np.random.randn(n2-n2//2,m)+np.ones(m)))
T1 = KDTree(data1)
T2 = KDTree(data2)
cT1 = cKDTree(data1)
cT2 = cKDTree(data2)
for probe_radius in (0.2, 0.5):
print('%4s | %11s | %11s | %11.1f ' % (m, n1, n2, probe_radius), end=' ')
sys.stdout.flush()
print('| %6.3fs ' % (measure('T1.count_neighbors(T2, probe_radius)', 1) / 1), end=' ')
sys.stdout.flush()
print('| %6.3fs ' % (measure('cT1.count_neighbors(cT2, probe_radius)', repeat) / repeat), end=' ')
sys.stdout.flush()
print('')
if __name__ == "__main__":
run_module_suite()
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