2.5 KiB
title | date | draft | description | tags | categories | series | favorite | disable_feed | graphviz | |||||
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Treap | 2024-07-20T14:12:27+01:00 | false | A simpler BST |
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false | false | true |
The Treap is a mix between a Binary Search Tree and a Heap.
Like a Binary Search Tree, it keeps an ordered set of keys in the shape of a tree, allowing for binary search traversal.
Like a Heap, it associates each node with a priority, making sure that a parent's priority is always higher than any of its children.
What does it do?
By randomizing the priority value of each key at insertion time, we ensure a high likelihook that the tree stays roughly balanced, avoiding degenerating to unbalanced O(N) height.
Here's a sample tree created by inserting integers from 0 to 250 into the tree:
{{< graphviz file="treap.gv" />}}
Implementation
I'll be keeping the theme for this [series] by using Python to implement the
Treap. This leads to somewhat annoying code to handle the left
/right
nodes
which is easier to do in C, using pointers.
[series]: {{< ref "/series/cool-algorithms/" >}}
Representation
Creating a new Treap
is easy: the tree starts off empty, waiting for new nodes
to insert.
Each Node
must keep track of the key
, the mapped value
, and the node's
priority
(which is assigned randomly). Finally it must also allow for storing
two children (left
and right
).
class Node[K, V]:
key: K
value: V
priority: float
left: Node[K, V] | None
righg: Node[K, V] | None
def __init__(self, key: K, value: V):
# Store key and value, like a normal BST node
self.key = key
self.value = value
# Priority is derived randomly
self.priority = random()
self.left = None
self.right = None
class Treap[K, V]:
_root: Node[K, V] | None
def __init__(self):
# The tree starts out empty
self._root = None
Search
Searching the tree is the same as in any other Binary Search Tree.
def get(self, key: K) -> T | None:
node = self._root
# The usual BST traversal
while node is not None:
if node.key == key:
return node.value
elif node.key < key:
node = node.right
else:
node = node.left
return None