def radix_tree_insert(tree, string, label):
"""inserts a binary string in a radix tree
args:
tree: Node, radix_tree
string: str, to be inserted ( 01011, 1001, .. )
label: label the end of the string with
"""
curr = tree
for i, char in enumerate(string):
# traverse the binary tree adding nodes if required
if char == '1':
if not curr.right:
curr.right = Node(1)
curr = curr.right
elif char == '0':
if not curr.left:
curr.left = Node(0)
curr = curr.left
# marks the termination of strings
if i == len(string) - 1:
curr.end = label
elif (hasattr(curr, 'end') and curr.end < 0) \
or not hasattr(curr, 'end'):
curr.end = -1
def setUp(self):
tree = Node(5)
tree.left = Node(3)
tree.left.right = Node(5)
tree.left.left = Node(2)
tree.right = Node(7)
tree.right.right = Node(8)
self.tree = tree
def insert(self,
new_node: Any,
current: Node,
parent: Node = None) -> None:
if self.root is None:
self.root = new_node
return
parent = current
if current.key < new_node.key:
current = current.right
else:
current = current.left
if current:
# recursive
self.insert(new_node, current, parent)
else:
if parent.key < new_node.key:
parent.right = new_node
else:
parent.left = new_node
def pQueue(dictionary):
# Instantiate a PriorityStack object
pqueue = PriorityStack()
# Create the nodes for the tree
for char, freq in dictionary.items():
node = Node(freq, char, None) # sorting by keys, so freq is now key
pqueue.push(node.key, node)
# Create the tree
while True:
# Pop the first two values so that they can become children of a single parent node... or not.
freq1, value1 = pqueue.pop()
freq2, value2 = pqueue.pop()
# Return what tree?
if value1 is None:
return None
# return the whole tree
elif value2 is None:
return value1
else:
"""
1. create the key of the parent node of the first two values in the priority queue
2. Create the node, which is empty because it isn't a leaf
3. Set the left and right children of the parent node
4. Push value3 to the priority queue because it isn't a root, and needs to attached to another node
a. Pass the key of the parent node for the sake of the priority stack
b. Pass the node as the value
"""
freq3 = freq1 + freq2
value3 = Node(freq3, None, None)
value3.left_child = value1
value3.right_child = value2
pqueue.push(value3.key, value3)
def main():
args = parse_argument()
log.debug(args)
if args.max_num == 3:
from test_binary_search_tree import three_data as data
elif args.max_num == 5:
from test_binary_search_tree import five_data as data
elif args.max_num == 8:
from test_binary_search_tree import eight_data as data
elif args.max_num == 12:
from test_binary_search_tree import twelve_data as data
elif args.max_num == 17:
from test_binary_search_tree import seventeen_data as data
else:
data = list(random.sample(range(args.max_num), args.max_num))
log.info(data)
first = data.pop(0)
root = Node(first, 'root')
bst = BinarySearchTree(root)
for i in data:
bst.insert(i)
print('=== initial tree structure ===')
bst.show()
if args.target_key is not None:
set_parent(bst.root)
key = args.target_key
node = bst.search(key)
print('search:', node)
if node.attr == 'left' or node.attr == 'root':
print('=== target %d, rotating left ===' % key)
rotate_left(node, bst)
bst.show()
print('=== target %d, rotating right ===' % node.parent.key)
rotate_right(node.parent, bst)
bst.show()
elif node.attr == 'right':
print('=== target %d, rotating right ===' % key)
rotate_right(node, bst)
bst.show()
print('=== target %d, rotating left ===' % node.parent.key)
rotate_left(node.parent, bst)
bst.show()
def tree_insert(tree, val):
"""CLRS pg. 261, insert a new node in the BST
args:
tree: Node BST
val: value to be inserted
returns:
Node: the new tree
"""
prev, res = None, tree
while tree:
prev = tree
if tree.val > val:
tree = tree.left
elif tree.val < val:
tree = tree.right
else:
return res
if prev is None:
return Node(val)
elif prev.val > val:
prev.left = Node(val)
else:
prev.right = Node(val)
return res
def animation():
size = 10
numbers = gen_numbers(size)
root = None
ga = Animation()
# generate tree
for n in numbers:
root = insert(root, Node(n))
add_nodes(ga, root)
ga.highlight_node(n)
ga.next_step(clean=True)
# delete
for n in gen_numbers(size):
add_nodes(ga, root)
ga.highlight_node(n)
ga.next_step(clean=True)
root = delete(root, search(root, n))
# save
graphs = ga.graphs()
files = render(graphs, "figure/figure", 'png')
gif(files, "figure/gif-anim", 50)
def sort_strings(strings):
"""sorts set of binary strings lexicographically
args:
strings: list(strings), binary strings in the form 00101..
returns:
list(strings) sorted
"""
# populating the radix tree
tree = Node('#')
for i, string in enumerate(strings):
radix_tree_insert(tree, string, i)
# BFS on the radix tree
res, queue = [], [tree]
while queue:
node = queue.pop()
if hasattr(node, 'end') and not node.end < 0:
res.append(strings[node.end])
if node.right:
queue.insert(0, node.right)
if node.left:
queue.insert(0, node.left)
return res
def setUp(self):
self.tree = Node('#')
left = None
if left and right:
if right < left:
# print("Left greater than right")
raise Exception
else:
return True
is_valid(root.right)
is_valid(root.left)
if __name__ == "__main__":
tree = Tree()
tree.insert(1)
tree.insert(2)
tree.insert(10)
tree.insert(6)
try:
is_valid(tree.root)
except:
print("Tree is invalid")
invalid_root = Node(2)
right = Node(4)
left = Node(5)
invalid_root.right = right
invalid_root.left = left
try:
is_valid(invalid_root)
except:
print("The invalid tree was classified as is invalid")
def create_level_linked_list(root, lists, level):
if (root == None):
return
if (len(lists) == level): ## if the level has not been reached
list = LL_Node(root.item)
lists.append(list)
else:
list = lists[level]
new = LL_Node(root.item)
new.next = list
list = new
lists[level] = list
create_level_linked_list(root.left, lists, level + 1)
create_level_linked_list(root.right, lists, level + 1)
def create_linked_list(root):
lists = []
create_level_linked_list(root, lists, 0)
return lists
tree = Node(1, None, None)
tree.left = Node(2, None, None)
tree.right = Node(3, None, None)
r = create_linked_list(tree)
print(r[1].next)
def insert_tree(data):
root = Node(data[0], 'root')
bst = BinarySearchTree(root)
for i in data[1:]:
bst.insert(i)
return bst
from binary_search_tree import Node
def level_order_traversal(queue, result):
if queue:
node = queue.pop(0)
if node is not None:
if node.left is not None:
queue.append(node.left)
if node.right is not None:
queue.append(node.right)
result.append(node)
result = level_order_traversal(queue, result)
return result
if __name__ == '__main__':
root = Node('A')
queue = [root]
root.left = Node('B')
root.left.left = Node('D')
root.left.right = Node('E')
root.left.left.left = Node('G')
root.left.right.right = Node('H')
root.right = Node('C')
root.right.right = Node('F')
root.right.right.left = Node('I')
print('Level-Order Traversal', level_order_traversal([root], []))
def setUpClass(cls):
cls.bst1_node1 = Node(6)
cls.bst1_node2 = Node(5, cls.bst1_node1)
cls.bst1_node3 = Node(7, cls.bst1_node1)
cls.bst1_node4 = Node(2, cls.bst1_node2)
cls.bst1_node5 = Node(5, cls.bst1_node2)
cls.bst1_node6 = Node(8, cls.bst1_node3)
cls.bst1_node1.left = cls.bst1_node2
cls.bst1_node1.right = cls.bst1_node3
cls.bst1_node2.left = cls.bst1_node4
cls.bst1_node2.right = cls.bst1_node5
cls.bst1_node3.right = cls.bst1_node6
cls.bst1 = BinarySearchTree(cls.bst1_node1)
cls.bst1_inorder_str = "2\n5\n5\n6\n7\n8\n"
cls.bst2_node1 = Node(2)
cls.bst2_node2 = Node(5, cls.bst2_node1)
cls.bst2_node3 = Node(7, cls.bst2_node2)
cls.bst2_node4 = Node(6, cls.bst2_node3)
cls.bst2_node5 = Node(8, cls.bst2_node3)
cls.bst2_node6 = Node(5, cls.bst2_node4)
cls.bst2_node1.right = cls.bst2_node2
cls.bst2_node2.right = cls.bst2_node3
cls.bst2_node3.left = cls.bst2_node4
cls.bst2_node3.right = cls.bst2_node5
cls.bst2_node4.left = cls.bst2_node6
cls.bst2 = BinarySearchTree(cls.bst2_node1)
cls.bst2_inorder_str = "2\n5\n5\n6\n7\n8\n"
def create_node(id_, value):
return Node(id_, value)
def huffman_code(characters, weights):
# this dictionary is used for finding characters also insert new pair of merged characters
address = {}
# to save and hold Nodes and also values
nodes = []
values = []
# for loop to fill address dictionary
for index in range(len(characters)):
address[weights[index]] = characters[index]
# create a heap for minimum calculation
heap = Heap(weights)
# repeat step in the while loop until heap length become one
while heap.length > 1:
# find two most minimum values
min_number_one, min_number_two = heap.min(), heap.min()
# find corresponding characters in address dictionary
char_number_one, char_number_two = address.get(
min_number_one), address.get(min_number_two)
summed_values = min_number_one + min_number_two
# merging characters and insert it to address and also heap
address[summed_values] = ''.join([char_number_one, char_number_two])
heap.insert(summed_values)
# create a Node and insert it to tree
if min_number_one in values:
for node in nodes:
if node.value is min_number_one:
min_number_one = node
else:
values.append(min_number_one)
min_number_one = Node(value=min_number_one)
if min_number_two in values:
for node in nodes:
if node.value is min_number_two:
min_number_two = node
else:
values.append(min_number_two)
min_number_two = Node(value=min_number_two)
new_node = Node(value=summed_values,
left_child=min_number_one,
right_child=min_number_two)
min_number_one.parent, min_number_two.parent = new_node, new_node
nodes.append(min_number_one)
nodes.append(min_number_two)
nodes.append(new_node)
values.append(summed_values)
# cleaning address from waste items and only keep first generation weights
number_of_items_must_be_removed = len(address.items()) - len(weights)
for item in range(number_of_items_must_be_removed):
address.popitem()
# this function is return address of a weight
def node_address(key):
list_of_binaries = []
loop_backed_node = None
for node in nodes:
if node.value is key:
loop_backed_node = node
while loop_backed_node.parent is not None:
parent_node = loop_backed_node.parent
if parent_node.left_child.value is loop_backed_node.value:
list_of_binaries.append(0)
else:
list_of_binaries.append(1)
loop_backed_node = loop_backed_node.parent
return list(reversed(list_of_binaries))
list_of_letter_binary_value = []
for weight in weights:
value = node_address(key=weight)
letter = address.get(weight)
list_of_letter_binary_value.append([letter, value])
return list_of_letter_binary_value
from binary_search_tree import Node node = Node(1) print(node.get()) node.set(2) print(node.get())