def test_point_types(self):
emptyTree = KDTree.create(dimensions=3)
point1 = (2, 3, 4)
point2 = [4, 5, 6]
Point = collections.namedtuple('Point', 'x y z')
point3 = Point(5, 3, 2)
tree = KDTree.create([point1, point2, point3])
res, dist = tree.search_nn((1, 2, 3))
self.assertEqual(res, KDTree.KDNode((2, 3, 4)))
def test_invalid_child(self):
""" Children on wrong subtree invalidate Tree """
child = KDTree.KDNode((3, 2))
child.axis = 2
tree = KDTree.create([(2, 3)])
tree.left = child
self.assertFalse(tree.is_valid())
tree = KDTree.create([(4, 1)])
tree.right = child
self.assertFalse(tree.is_valid())
def test_search_nn(self, nodes=100):
points = list(islice(random_points(), 0, nodes))
tree = KDTree.create(points)
point = random_point()
nn, dist = tree.search_nn(point)
best, best_dist = self.find_best(tree, point)
self.assertEqual(best_dist,
dist,
msg=', '.join(repr(p)
for p in points) + ' / ' + repr(point))
def do_random_add(self, num_points=100):
points = list(set(islice(random_points(), 0, num_points)))
tree = KDTree.create(dimensions=len(points[0]))
for n, point in enumerate(points, 1):
tree.add(point)
self.assertTrue(tree.is_valid())
self.assertTrue(point in [node.data for node in tree.inorder()])
nodes_in_tree = len(list(tree.inorder()))
self.assertEqual(nodes_in_tree, n)
def test_remove_duplicates(self):
""" creates a tree with only duplicate points, and removes them all """
points = [(1, 1)] * 100
tree = KDTree.create(points)
self.assertTrue(tree.is_valid())
random.shuffle(points)
while points:
point = points.pop(0)
tree = tree.remove(point)
# Check if the Tree is valid after the removal
self.assertTrue(tree.is_valid())
# Check if the removal reduced the number of nodes by 1 (not more, not less)
remaining_points = len(points)
nodes_in_tree = len(list(tree.inorder()))
self.assertEqual(nodes_in_tree, remaining_points)
def do_random_remove(self):
""" Creates a random tree, removes all points in random order """
points = list(set(islice(random_points(), 0, 20)))
tree = KDTree.create(points)
self.assertTrue(tree.is_valid())
random.shuffle(points)
while points:
point = points.pop(0)
tree = tree.remove(point)
# Check if the Tree is valid after the removal
self.assertTrue(tree.is_valid())
# Check if the point has actually been removed
self.assertTrue(point not in [n.data for n in tree.inorder()])
# Check if the removal reduced the number of nodes by 1 (not more, not less)
remaining_points = len(points)
nodes_in_tree = len(list(tree.inorder()))
self.assertEqual(nodes_in_tree, remaining_points)
def test_search_knn(self):
points = [(50, 20), (51, 19), (1, 80)]
tree = KDTree.create(points)
point = (48, 18)
all_dist = []
for p in tree.inorder():
dist = p.dist(point)
all_dist.append([p, dist])
all_dist = sorted(all_dist, key=lambda n: n[1])
result = tree.search_knn(point, 1)
self.assertEqual(result[0][1], all_dist[0][1])
result = tree.search_knn(point, 2)
self.assertEqual(result[0][1], all_dist[0][1])
self.assertEqual(result[1][1], all_dist[1][1])
result = tree.search_knn(point, 3)
self.assertEqual(result[0][1], all_dist[0][1])
self.assertEqual(result[1][1], all_dist[1][1])
self.assertEqual(result[2][1], all_dist[2][1])
import KDTree
from timeit import default_timer as timer
#eisagwgi tou dataset
#tous xronous mporeite na tous deite metaferontas tis 3 entoles tou timer
import csv
results = []
with open("5000dataset.csv") as csvfile:
reader = csv.reader(csvfile) # change contents to floats
for row in reader: # each row is a list
results.append(row)
#dimiourgia tou dentrou
tree = KDTree.create(dimensions=2)
#loop insert ta simeia tou csv
for x in range(5010):
tree.insert([int(results[x][0]), int(results[x][1])])
if tree.is_balanced == False:
tree = tree.rebalance()
#elegxos an to dentro einai balanced
if tree.is_balanced == False:
tree = tree.rebalance()
print(tree)
#diagrafi enos simeiou
start = timer()
def test_remove_empty_tree(self):
tree = KDTree.create(dimensions=2)
tree.remove((1, 2))
self.assertFalse(bool(tree))
def random_tree(nodes=20, dimensions=3, minval=0, maxval=100):
points = list(islice(random_points(), 0, nodes))
tree = KDTree.create(points)
return tree
