class test_small(ConsistencyTests):
def setUp(self):
self.data = np.array([[0,0,0],
[0,0,1],
[0,1,0],
[0,1,1],
[1,0,0],
[1,0,1],
[1,1,0],
[1,1,1]])
self.bounds = 1.1 * np.ones(3)
self.kdtree = PeriodicKDTree(self.bounds, self.data)
self.n = self.kdtree.n
self.m = self.kdtree.m
self.x = np.random.randn(3)
self.d = 0.5
self.k = 4
def test_nearest(self):
assert_array_equal(
self.kdtree.query((0,0,0.1), 1),
(0.1,0))
def test_nearest_two(self):
assert_array_almost_equal(
self.kdtree.query((0,0,0.1), 2),
([0.1,np.sqrt(0.1**2 + 0.1**2)],[0,2]))
def test_random_ball_vectorized():
n = 20
m = 5
bounds = np.ones(m)
T = PeriodicKDTree(bounds, np.random.randn(n,m))
r = T.query_ball_point(np.random.randn(2,3,m),1)
assert_equal(r.shape,(2,3))
assert_(isinstance(r[0,0],list))
def test_random_ball_vectorized():
n = 20
m = 5
bounds = np.ones(m)
T = PeriodicKDTree(bounds, np.random.randn(n, m))
r = T.query_ball_point(np.random.randn(2, 3, m), 1)
assert_equal(r.shape, (2, 3))
assert_(isinstance(r[0, 0], list))
def setUp(self):
self.data = np.array([[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1],
[1, 0, 0], [1, 0, 1], [1, 1, 0], [1, 1, 1]])
self.bounds = 1.1 * np.ones(3)
self.kdtree = PeriodicKDTree(self.bounds, self.data)
self.n = self.kdtree.n
self.m = self.kdtree.m
self.x = np.random.randn(3)
self.d = 0.5
self.k = 4
class test_small(ConsistencyTests):
def setUp(self):
self.data = np.array([[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1],
[1, 0, 0], [1, 0, 1], [1, 1, 0], [1, 1, 1]])
self.bounds = 1.1 * np.ones(3)
self.kdtree = PeriodicKDTree(self.bounds, self.data)
self.n = self.kdtree.n
self.m = self.kdtree.m
self.x = np.random.randn(3)
self.d = 0.5
self.k = 4
def test_nearest(self):
assert_array_equal(self.kdtree.query((0, 0, 0.1), 1), (0.1, 0))
def test_nearest_two(self):
assert_array_almost_equal(self.kdtree.query((0, 0, 0.1), 2),
([0.1, np.sqrt(0.1**2 + 0.1**2)], [0, 2]))
def setUp(self):
self.n = 100
self.m = 4
self.data = np.random.randn(self.n, self.m)
self.bounds = np.ones(self.m)
self.kdtree = PeriodicKDTree(self.bounds, self.data, leafsize=2)
self.x = np.random.randn(self.m)
self.d = 0.2
self.k = 10
def setUp(self):
n = 100
m = 4
self.data = np.random.randn(n, m)
self.bounds = np.ones(m)
self.T = PeriodicKDTree(self.bounds, self.data, leafsize=2)
self.x = np.random.randn(m)
self.p = 2.
self.eps = 0
self.d = 0.2
def setUp(self):
self.data = np.array([[0,0,0],
[0,0,1],
[0,1,0],
[0,1,1],
[1,0,0],
[1,0,1],
[1,1,0],
[1,1,1]])
self.bounds = 1.1 * np.ones(3)
self.kdtree = PeriodicKDTree(self.bounds, self.data)
def setUp(self):
self.data = np.array([[0,0,0],
[0,0,1],
[0,1,0],
[0,1,1],
[1,0,0],
[1,0,1],
[1,1,0],
[1,1,1]])
self.bounds = 1.1 * np.ones(3)
self.kdtree = PeriodicKDTree(self.bounds, self.data)
self.n = self.kdtree.n
self.m = self.kdtree.m
self.x = np.random.randn(3)
self.d = 0.5
self.k = 4
class test_vectorization:
def setUp(self):
self.data = np.array([[0,0,0],
[0,0,1],
[0,1,0],
[0,1,1],
[1,0,0],
[1,0,1],
[1,1,0],
[1,1,1]])
self.bounds = 1.1 * np.ones(3)
self.kdtree = PeriodicKDTree(self.bounds, self.data)
def test_single_query(self):
d, i = self.kdtree.query(np.array([0,0,0]))
assert_(isinstance(d,float))
assert_(np.issubdtype(i, int))
def test_vectorized_query(self):
d, i = self.kdtree.query(np.zeros((2,4,3)))
assert_equal(np.shape(d),(2,4))
assert_equal(np.shape(i),(2,4))
def test_single_query_multiple_neighbors(self):
s = 23
kk = 27*self.kdtree.n+s
d, i = self.kdtree.query(np.array([0,0,0]),k=kk)
assert_equal(np.shape(d),(kk,))
assert_equal(np.shape(i),(kk,))
assert_(np.all(~np.isfinite(d[-s:])))
assert_(np.all(i[-s:]==self.kdtree.n))
def test_vectorized_query_multiple_neighbors(self):
s = 23
kk = 27*self.kdtree.n+s
d, i = self.kdtree.query(np.zeros((2,4,3)),k=kk)
assert_equal(np.shape(d),(2,4,kk))
assert_equal(np.shape(i),(2,4,kk))
assert_(np.all(~np.isfinite(d[:,:,-s:])))
assert_(np.all(i[:,:,-s:]==self.kdtree.n))
def test_single_query_all_neighbors(self):
d, i = self.kdtree.query([0,0,0],k=None,distance_upper_bound=1.1)
assert_(isinstance(d,list))
assert_(isinstance(i,list))
def test_vectorized_query_all_neighbors(self):
d, i = self.kdtree.query(np.zeros((2,4,3)),k=None,distance_upper_bound=1.1)
assert_equal(np.shape(d),(2,4))
assert_equal(np.shape(i),(2,4))
assert_(isinstance(d[0,0],list))
assert_(isinstance(i[0,0],list))
class test_vectorization:
def setUp(self):
self.data = np.array([[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1],
[1, 0, 0], [1, 0, 1], [1, 1, 0], [1, 1, 1]])
self.bounds = 1.1 * np.ones(3)
self.kdtree = PeriodicKDTree(self.bounds, self.data)
def test_single_query(self):
d, i = self.kdtree.query(np.array([0, 0, 0]))
assert_(isinstance(d, float))
assert_(np.issubdtype(i, int))
def test_vectorized_query(self):
d, i = self.kdtree.query(np.zeros((2, 4, 3)))
assert_equal(np.shape(d), (2, 4))
assert_equal(np.shape(i), (2, 4))
def test_single_query_multiple_neighbors(self):
s = 23
kk = 27 * self.kdtree.n + s
d, i = self.kdtree.query(np.array([0, 0, 0]), k=kk)
assert_equal(np.shape(d), (kk, ))
assert_equal(np.shape(i), (kk, ))
assert_(np.all(~np.isfinite(d[-s:])))
assert_(np.all(i[-s:] == self.kdtree.n))
def test_vectorized_query_multiple_neighbors(self):
s = 23
kk = 27 * self.kdtree.n + s
d, i = self.kdtree.query(np.zeros((2, 4, 3)), k=kk)
assert_equal(np.shape(d), (2, 4, kk))
assert_equal(np.shape(i), (2, 4, kk))
assert_(np.all(~np.isfinite(d[:, :, -s:])))
assert_(np.all(i[:, :, -s:] == self.kdtree.n))
def test_single_query_all_neighbors(self):
d, i = self.kdtree.query([0, 0, 0], k=None, distance_upper_bound=1.1)
assert_(isinstance(d, list))
assert_(isinstance(i, list))
def test_vectorized_query_all_neighbors(self):
d, i = self.kdtree.query(np.zeros((2, 4, 3)),
k=None,
distance_upper_bound=1.1)
assert_equal(np.shape(d), (2, 4))
assert_equal(np.shape(i), (2, 4))
assert_(isinstance(d[0, 0], list))
assert_(isinstance(i[0, 0], list))
def setUp(self):
self.data = np.array([[0, 0, 0], [0, 0, 1], [0, 1, 0], [0, 1, 1],
[1, 0, 0], [1, 0, 1], [1, 1, 0], [1, 1, 1]])
self.bounds = 1.1 * np.ones(3)
self.kdtree = PeriodicKDTree(self.bounds, self.data)
def setUp(self):
test_small.setUp(self)
self.kdtree = PeriodicKDTree(self.bounds, self.data, leafsize=1)
from periodic_kdtree import PeriodicKDTree, PeriodicCKDTree
m = 3
n = 10000
r = 1000
bounds = np.ones(m)
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)))
print "dimension %d, %d points" % (m, n)
t = time.time()
T1 = PeriodicKDTree(bounds, data)
print "PeriodicKDTree constructed:\t%g" % (time.time() - t)
t = time.time()
T2 = PeriodicCKDTree(bounds, data)
print "PeriodicCKDTree constructed:\t%g" % (time.time() - t)
t = time.time()
w = T1.query(queries)
print "PeriodicKDTree %d lookups:\t%g" % (r, time.time() - t)
del w
t = time.time()
w = T2.query(queries)
print "PeriodicCKDTree %d lookups:\t%g" % (r, time.time() - t)
del w
m = 3
n = 10000
r = 1000
bounds = np.ones(m)
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)))
print "dimension %d, %d points" % (m,n)
t = time.time()
T1 = PeriodicKDTree(bounds, data)
print "PeriodicKDTree constructed:\t%g" % (time.time()-t)
t = time.time()
T2 = PeriodicCKDTree(bounds, data)
print "PeriodicCKDTree constructed:\t%g" % (time.time()-t)
t = time.time()
w = T1.query(queries)
print "PeriodicKDTree %d lookups:\t%g" % (r, time.time()-t)
del w
t = time.time()
w = T2.query(queries)
print "PeriodicCKDTree %d lookups:\t%g" % (r, time.time()-t)
del w
