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Author | SHA1 | Date | |
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Bruno BELANYI | 42910bca86 | ||
Bruno BELANYI | 36cb7e080f | ||
Bruno BELANYI | 6d292a3978 | ||
Bruno BELANYI | 7cbbdcad06 | ||
Bruno BELANYI | d89ea56edd | ||
Bruno BELANYI | 021c40f6ac | ||
Bruno BELANYI | 6f813eb787 |
144
content/posts/2024-07-27-treap-revisited/index.md
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content/posts/2024-07-27-treap-revisited/index.md
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---
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title: "Treap, revisited"
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date: 2024-07-27T14:12:27+01:00
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draft: false # I don't care for draft mode, git has branches for that
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description: "An even simpler BST"
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tags:
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- algorithms
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- data structures
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- python
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categories:
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- programming
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series:
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- Cool algorithms
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favorite: false
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disable_feed: false
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---
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My [last post]({{< relref "../2024-07-20-treap/index.md" >}}) about the _Treap_
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showed an implementation using tree rotations, as is commonly done with [AVL
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Trees][avl] and [Red Black Trees][rb].
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But the _Treap_ lends itself well to a simple and elegant implementation with no
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tree rotations. This makes it especially easy to implement the removal of a key,
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rather than the fiddly process of deletion using tree rotations.
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[avl]: https://en.wikipedia.org/wiki/AVL_tree
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[rb]: https://en.wikipedia.org/wiki/Red%E2%80%93black_tree
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<!--more-->
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## Implementation
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All operations on the tree will be implemented in terms of two fundamental
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operations: `split` and `merge`.
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We'll be reusing the same structures as in the last post, so let's skip straight
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to implementing those fundaments, and building on them for `insert` and
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`delete`.
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### Split
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Splitting a tree means taking a key, and getting the following output:
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* a `left` node, root of the tree of all keys lower than the input.
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* an extracted `node` which corresponds to the input `key`.
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* a `right` node, root of the tree of all keys higher than the input.
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```python
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type OptionalNode[K, V] = Node[K, V] | None
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def split(
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root: OptionalNode[K, V],
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key: K,
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) -> tuple[OptionalNode[K, V], OptionalNode[K, V], OptionalNode[K, V]]:
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# Base case, empty tree
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if root is None:
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return None, None, None
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# If we found the key, simply extract left and right
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if root.key == key:
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left, right = root.left, root.right
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root.left, root.right = None, None
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return left, root, right
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# Otherwise, recurse on the corresponding side of the tree
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if root.key < key:
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left, node, right = split(root.right, key)
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root.right = left
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return root, node, right
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if key < root.key:
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left, node, right = split(root.left, key)
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root.left = right
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return left, node, root
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raise RuntimeError("Unreachable")
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```
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### Merge
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Merging a `left` and `right` tree means (cheaply) building a new tree containing
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both of them. A pre-condition for merging is that the `left` tree is composed
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entirely of nodes that are lower than any key in `right` (i.e: as in `left` and
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`right` after a `split`).
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```python
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def merge(
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left: OptionalNode[K, V],
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right: OptionalNode[K, V],
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) -> OptionalNode[K, V]:
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# Base cases, left or right being empty
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if left is None:
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return right
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if right is None:
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return left
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# Left has higher priority, it must become the root node
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if left.priority >= right.priority:
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# We recursively reconstruct its right sub-tree
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left.right = merge(left.right, right)
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return left
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# Right has higher priority, it must become the root node
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if left.priority < right.priority:
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# We recursively reconstruct its left sub-tree
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right.left = merge(left, right.left)
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return right
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raise RuntimeError("Unreachable")
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```
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### Insertion
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Inserting a node into the tree is done in two steps:
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1. `split` the tree to isolate the middle insertion point
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2. `merge` it back up to form a full tree with the inserted key
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```python
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def insert(self, key: K, value: V) -> bool:
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# `left` and `right` come before/after the key
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left, node, right = split(self._root, key)
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was_updated: bool
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# Create the node, or update its value, if the key was already in the tree
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if node is None:
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node = Node(key, value)
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was_updated = False
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else:
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node.value = value
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was_updated = True
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# Rebuild the tree with a couple of merge operations
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self._root = merge(left, merge(node, right))
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# Signal whether the key was already in the key
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return was_updated
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```
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### Removal
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Removing a key from the tree is similar to inserting a new key, and forgetting
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to insert it back: simply `split` the tree and `merge` it back without the
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extracted middle node.
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```python
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def remove(self, key: K) -> bool:
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# `node` contains the key, or `None` if the key wasn't in the tree
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left, node, right = split(self._root, key)
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# Put the tree back together, without the extract node
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self._root = merge(left, right)
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# Signal whether `key` was mapped in the tree
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return node is not None
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```
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