If we write our binary tree nodes as triples (left subtree, node, right subtree), and the null pointer as None, we can build and search them as follows (in Python):
def binary_tree_insert(treenode, value):
if treenode is None: return (None, value, None)
left, nodevalue, right = treenode
if nodevalue > value:
return (binary_tree_insert(left, value), nodevalue, right)
else:
return (left, nodevalue, binary_tree_insert(right, value))
def build_binary_tree(values):
tree = None
for v in values:
tree = binary_tree_insert(tree, v)
return tree
def search_binary_tree(treenode, value):
if treenode is None: return None # failure
left, nodevalue, right = treenode
if nodevalue > value:
return search_binary_tree(left, value)
elif value > nodevalue:
return search_binary_tree(right, value)
else:
return nodevalue
Note that the worst case of this build_binary_tree routine is O(n2) --- if you feed it a sorted list of values, it chains them into a linked list with no left subtrees. For example, build_binary_tree([1, 2, 3, 4, 5]) yields the tree (None, 1, (None, 2, (None, 3, (None, 4, (None, 5, None))))). There are a variety of schemes for overcoming this flaw with simple binary trees.
Once we have a binary tree in this form, a simple inorder traversal can give us the node values in sorted order:
def traverse_binary_tree(treenode):
if treenode is None: return []
else:
left, value, right = treenode
return (traverse_binary_tree(left) + [value] + traverse_binary_tree(right))
So the binary tree sort algorithm is just the following:
def treesort(array):
array[:] = traverse_binary_tree(build_binary_tree(array))
There are many types of binary search trees. AVL trees and Red-Black trees are both forms of balanced binary search trees. A B-tree grows from the bottom up as elements are inserted. A splay tree is a self-adjusting binary search tree. In a treap ("tree heap"), each node also holds a priority and the parent node has higher priority than its children.
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