def test_nearest_neighbour_random(self):
for seed in range(1, 10):
random.seed(seed)
points = [self.random_point(100) for _ in range(100)]
point = self.random_point(100)
closest = sorted(points, key=lambda x: distance(point, x))
tree = KdTree(points)
self.assertTupleEqual(
tree.nearest_neighbour(point, 1)[0].point, closest[0])
nearest = tree.nearest_neighbour(point, 10)
for n, c in zip(nearest, closest):
self.assertTupleEqual(n.point, c)
def test_nearest_neighbour_big(self):
# creating tree - 36 s
# finding nn - 0.3 ms
# nth element - 1.37 ms
# sort - 1.2 ms
random.seed(42)
points = [(random.randint(-100000,
100000), random.randint(-100000, 100000),
random.randint(-100000, 100000)) for _ in range(25000)]
point = (random.randint(-100000,
100000), random.randint(-100000, 100000),
random.randint(-100000, 100000))
closest = (-10000000, -10000000, -10000000)
for p in points:
if distance(point, p) < distance(point, closest):
closest = p
start = timeit.default_timer()
tree = KdTree(points)
print('Creating tree', timeit.default_timer() - start)
start = timeit.default_timer()
nearest = tree.nearest_neighbour(point, 100)
print('Searching NN', timeit.default_timer() - start)
self.assertTupleEqual(nearest[0].point, closest)
def test_nearest_neighbour(self):
tree = KdTree([(1, )])
nearest = tree.nearest_neighbour((-3, ), 1)
self.assertTupleEqual(nearest[0].point, (1, ))
tree = KdTree([(1, 3), (2, 4)])
nearest = tree.nearest_neighbour((0, 2), 1)
self.assertTupleEqual(nearest[0].point, (1, 3))
nearest = tree.nearest_neighbour((3, 5), 1)
self.assertTupleEqual(nearest[0].point, (2, 4))
tree = KdTree([(1, 3, 3), (4, 8, 7), (2, -1, 2), (3, 4, 1)])
nearest = tree.nearest_neighbour((1, 2, 0), 1)
self.assertTupleEqual(nearest[0].point, (3, 4, 1))
tree = KdTree([(-1, 1), (0, -2), (1, -2)])
nearest = tree.nearest_neighbour((1, 2), 1)
self.assertTupleEqual(nearest[0].point, (-1, 1))
