def test_simple_successor():
tree = BSTree()
root = BSTreeNode(4)
tree.add_node(root)
random.seed('test')
data = map(lambda _: random.randint(0, 1000), range(0, 100))
nodes = [tree.add_node(BSTreeNode(i)) for i in data]
sorted_nodes = list(BSTreeNode.inorder_walk(root))
for i in range(100):
current_node = sorted_nodes[i]
next_index = i + 1 if i != 100 else 100
previous_index = i - 1 if i != 0 else 0
expected_successor_node = sorted_nodes[next_index]
actual_successor_node = BSTreeNode.get_successor_for_node(current_node)
assert expected_successor_node.key == actual_successor_node.key
expected_predecessor_node = sorted_nodes[previous_index]
actual_predecessor_node = BSTreeNode.get_predecessor_for_node(
current_node)
assert actual_predecessor_node and (
expected_predecessor_node.key
== actual_predecessor_node.key) or True
def test_simple_successor():
tree = BSTree()
root = BSTreeNode(4)
tree.add_node(root)
random.seed('test')
data = map(lambda _: random.randint(0, 1000), range(0, 100))
nodes = [tree.add_node(BSTreeNode(i)) for i in data]
sorted_nodes = list(BSTreeNode.inorder_walk(root))
for i in range(100):
current_node = sorted_nodes[i]
next_index = i+1 if i!=100 else 100
previous_index = i-1 if i != 0 else 0
expected_successor_node = sorted_nodes[next_index]
actual_successor_node = BSTreeNode.get_successor_for_node(current_node)
assert expected_successor_node.key == actual_successor_node.key
expected_predecessor_node = sorted_nodes[previous_index]
actual_predecessor_node = BSTreeNode.get_predecessor_for_node(current_node)
assert actual_predecessor_node and (expected_predecessor_node.key == actual_predecessor_node.key) or True
def get_diagram_elements(a_root_node):
result = [
(
_.key,
_.depth,
BSTreeNode.get_relationship_between_nodes(_, _.parent))
for _ in BSTreeNode.preorder_walk(a_root_node, False)
]
return result
def test_begin():
tree = BSTree()
root = BSTreeNode(5)
tree.add_node(root)
left_leaf = BSTreeNode(5)
right_leaf = BSTreeNode(6)
tree.add_node(left_leaf)
tree.add_node(right_leaf)
assert left_leaf == root.left_child
assert right_leaf == root.right_child
def test_insertion():
root = BSTreeNode(4)
tree = BSTree()
tree.add_node(root)
values = [2, 7, 6, 12, 3, 14, 5, 2, 8, 3, 4, 15, 3]
[tree.add_node(BSTreeNode(_)) for _ in values]
expected = sorted(values + [root.key])
actual = [_.key for _ in BSTreeNode.inorder_walk(root, False)]
assert expected == actual
def test_basic_match():
root = BSTreeNode(4)
tree = BSTree()
tree.add_node(root)
values = [3, 5]
[tree.add_node(BSTreeNode(_)) for _ in values]
expected_results = [(4, 0, None), (3, 1, "left_child"),
(5, 1, "right_child")]
actual_results = get_diagram_elements(root)
assert expected_results == actual_results
def test_easy_delete2():
root = BSTreeNode(4)
tree = BSTree()
tree.add_node(root)
values = [3, 5]
[tree.add_node(BSTreeNode(_)) for _ in values]
expected_results = [(4, 0, None), (3, 1, "left_child")]
node_5 = next(tree.search_for_key(5))
BSTreeNode.delete_node(node_5)
actual_results = get_diagram_elements(root)
assert expected_results == actual_results
def test_inorder_walk_non_recursive():
tree = BSTree()
root = BSTreeNode(4)
tree.add_node(root)
values = [2, 7, 4, 15, 3]
for i in values:
tree.add_node(BSTreeNode(i))
expected = sorted(values + [root.key])
actual = [_.key for _ in BSTreeNode.inorder_walk(root, False)]
assert expected == actual
def test_relationship_identification():
root = BSTreeNode(4)
tree = BSTree()
tree.add_node(root)
values = [3, 5]
[tree.add_node(BSTreeNode(_)) for _ in values]
node_3 = next(tree.search_for_key(3))
node_4 = next(tree.search_for_key(4))
node_5 = next(tree.search_for_key(5))
rel3_4 = BSTreeNode.get_relationship_between_nodes(node_3, node_4)
assert rel3_4 == "left_child"
rel4_5 = BSTreeNode.get_relationship_between_nodes(node_4, node_5)
assert rel4_5 == "parent"
rel5_4 = BSTreeNode.get_relationship_between_nodes(node_5, node_4)
assert rel5_4 == "right_child"
def test_relationship_identification():
root = BSTreeNode(4)
tree = BSTree()
tree.add_node(root)
values = [3, 5]
[tree.add_node(BSTreeNode(_)) for _ in values]
node_3 = next(tree.search_for_key(3))
node_4 = next(tree.search_for_key(4))
node_5 = next(tree.search_for_key(5))
rel3_4 = BSTreeNode.get_relationship_between_nodes(node_3, node_4)
assert rel3_4 == "left_child"
rel4_5 = BSTreeNode.get_relationship_between_nodes(node_4, node_5)
assert rel4_5 == "parent"
rel5_4 = BSTreeNode.get_relationship_between_nodes(node_5, node_4)
assert rel5_4 == "right_child"
def test_bsearch():
tree = BSTree()
root = BSTreeNode(4)
tree.add_node(root)
values = [2, 7, 4, 15, 3]
for i in values:
tree.add_node(BSTreeNode(i))
expected = [4, 4]
actual = [_.key for _ in tree.search_for_key(4)]
assert expected == actual
expected = [7]
actual = [_.key for _ in tree.search_for_key(7)]
assert expected == actual
def test_node_removal():
test_tree = BSTree()
random.seed('test')
data = map(lambda _: random.randint(0, 1000), range(0, 100))
# data = map(lambda _: random.weibullvariate(1, 1), range(0,100))
removal_nodes = []
for value in data:
node = BSTreeNode(value)
test_tree.add_node(node)
removal_nodes.append(node)
random.shuffle(removal_nodes)
for removal_node in removal_nodes:
initial_values = map(lambda x: x.key, test_tree.inorder_walk())
pre_distribution = compute_distribution(initial_values)
expected_removal_count = \
(pre_distribution.get(removal_node.key) or 0) - 1
expected_distribution = pre_distribution.copy()
if expected_removal_count is 0:
expected_distribution.pop(removal_node.key, None)
else:
expected_distribution[removal_node.key] = expected_removal_count
test_tree.delete_node(removal_node)
post_values = map(lambda x: x.key, test_tree.inorder_walk())
post_distribution = compute_distribution(post_values)
assert post_distribution == expected_distribution
def test_min_and_max():
tree = BSTree()
root = BSTreeNode(4)
tree.add_node(root)
values = [2, 7, 4, 15, 3]
for i in values:
tree.add_node(BSTreeNode(i))
expected_min = min(values)
actual_min = BSTreeNode.get_minimum(root).key
assert expected_min == actual_min
expected_max = max(values)
actual_max = BSTreeNode.get_maximum(root).key
assert expected_max == actual_max
def test_bsearch_non_recursive():
from intervaltree.bs_tree_funcs import _search_node_non_recursive
tree = BSTree()
root = BSTreeNode(4)
tree.add_node(root)
values = [2, 7, 4, 15, 3]
for i in values:
tree.add_node(BSTreeNode(i))
expected = [4, 4]
actual = [_.key for _ in _search_node_non_recursive(root, 4)]
assert expected == actual
expected = [7]
actual = [_.key for _ in _search_node_non_recursive(root, 7)]
assert expected == actual
def test_easy_delete2():
root = BSTreeNode(4)
tree = BSTree()
tree.add_node(root)
values = [3, 5]
[tree.add_node(BSTreeNode(_)) for _ in values]
expected_results = [
(4, 0, None),
(3, 1, "left_child")
]
node_5 = next(tree.search_for_key(5))
BSTreeNode.delete_node(node_5)
actual_results = get_diagram_elements(root)
assert expected_results == actual_results
def test_min_and_max():
tree = BSTree()
root = BSTreeNode(4)
tree.add_node(root)
values = [2, 7, 4, 15, 3]
for i in values:
tree.add_node(BSTreeNode(i))
expected_min = min(values)
actual_min = BSTreeNode.get_minimum(root).key
assert expected_min == actual_min
expected_max = max(values)
actual_max = BSTreeNode.get_maximum(root).key
assert expected_max == actual_max
def test_insertion():
root = BSTreeNode(4)
tree = BSTree()
tree.add_node(root)
values = [2, 7, 6, 12, 3, 14, 5, 2, 8, 3, 4, 15, 3]
[tree.add_node(BSTreeNode(_)) for _ in values]
expected = sorted(values + [root.key])
actual = [_.key for _ in BSTreeNode.inorder_walk(root, False)]
assert expected == actual
def test_inorder_walk_non_recursive():
tree = BSTree()
root = BSTreeNode(4)
tree.add_node(root)
values = [2, 7, 4, 15, 3]
for i in values:
tree.add_node(BSTreeNode(i))
expected = sorted(values + [root.key])
actual = [_.key for _ in BSTreeNode.inorder_walk(root, False)]
assert expected == actual
def get_diagram_elements(a_root_node):
result = [(_.key, _.depth,
BSTreeNode.get_relationship_between_nodes(_, _.parent))
for _ in BSTreeNode.preorder_walk(a_root_node, False)]
return result
def delete_node(self, a_node):
BSTreeNode.delete_node(a_node, self)
def inorder_walk(self):
yield from BSTreeNode.inorder_walk(self.root, False)