class TestKDTree(unittest.TestCase):
def setUp(self):
self.dim = 3
self.count = 20
points = np.random.randint(low=0, high=20,
size=(self.count, self.dim)).tolist()
self.tree = KDTree(self.dim)
for p in points:
self.tree.insert(p)
np.random.shuffle(points)
self.points = points
def test_contain(self):
for p in self.points:
self.assertTrue(self.tree.contain(p))
random_points = np.random.randint(low=30, size=(self.count,
self.dim)).tolist()
for p in random_points:
self.assertFalse(self.tree.contain(p))
def test_find_min(self):
for d in range(self.dim):
sorted_at_dim = sorted(self.points, key=operator.itemgetter(d))
min_at_dim = sorted_at_dim[0][d]
candidate_min_at_dim = list(
filter(lambda point: point[d] == min_at_dim, sorted_at_dim))
self.assertIn(self.tree.find_min(d, 0, self.tree.root),
candidate_min_at_dim)
def test_delete(self):
for p in self.points:
self.tree.delete(p)
self.assertFalse(self.tree.contain(p))
def test_nearest_neighbor(self):
for p in self.points:
self.assertListEqual(self.tree.find_nearest(p), p)
random_points = np.random.randint(low=0, high=20, size=(20, self.dim))
for p in random_points:
nearest_point = self.tree.find_nearest(p)
points = np.array(self.points)
self.assertEqual(min(linalg.norm(points - p, axis=1)),
linalg.norm(nearest_point - p))
def test_k_nearest_neighbor(self):
for p in self.points:
self.assertListEqual(self.tree.find_k_nearest(p, 1), [p])
random_points = np.random.randint(low=0, high=20, size=(20, self.dim))
for p in random_points:
nearest_points = self.tree.find_k_nearest(p, 5)
nearest_points = np.array(nearest_points)
points = np.unique(self.points, axis=0)
n_dists = linalg.norm(nearest_points - p, axis=1)
dists = np.sort(linalg.norm(points - p, axis=1))[:5]
self.assertListEqual(n_dists.tolist(), dists.tolist())
