minimum_spanning_tree_prims2.py

"""
Prim's (also known as Jarník's) algorithm is a greedy algorithm that finds a minimum
spanning tree for a weighted undirected graph. This means it finds a subset of the
edges that forms a tree that includes every vertex, where the total weight of all the
edges in the tree is minimized. The algorithm operates by building this tree one vertex
at a time, from an arbitrary starting vertex, at each step adding the cheapest possible
connection from the tree to another vertex.
"""

from sys import maxsize
from typing import Dict, Optional, Tuple, Union


def get_parent_position(position: int) -> int:
    """
    heap helper function get the position of the parent of the current node

    >>> get_parent_position(1)
    0
    >>> get_parent_position(2)
    0
    """
    return (position - 1) // 2


def get_child_left_position(position: int) -> int:
    """
    heap helper function get the position of the left child of the current node

    >>> get_child_left_position(0)
    1
    """
    return (2 * position) + 1


def get_child_right_position(position: int) -> int:
    """
    heap helper function get the position of the right child of the current node

    >>> get_child_right_position(0)
    2
    """
    return (2 * position) + 2


class MinPriorityQueue:
    """
    Minimum Priority Queue Class

    Functions:
    is_empty: function to check if the priority queue is empty
    push: function to add an element with given priority to the queue
    extract_min: function to remove and return the element with lowest weight (highest
                 priority)
    update_key: function to update the weight of the given key
    _bubble_up: helper function to place a node at the proper position (upward
                movement)
    _bubble_down: helper function to place a node at the proper position (downward
                movement)
    _swap_nodes: helper function to swap the nodes at the given positions

    >>> queue = MinPriorityQueue()

    >>> queue.push(1, 1000)
    >>> queue.push(2, 100)
    >>> queue.push(3, 4000)
    >>> queue.push(4, 3000)

    >>> print(queue.extract_min())
    2

    >>> queue.update_key(4, 50)

    >>> print(queue.extract_min())
    4
    >>> print(queue.extract_min())
    1
    >>> print(queue.extract_min())
    3
    """

    def __init__(self) -> None:
        self.heap = []
        self.position_map = {}
        self.elements = 0

    def __len__(self) -> int:
        return self.elements

    def __repr__(self) -> str:
        return str(self.heap)

    def is_empty(self) -> bool:
        # Check if the priority queue is empty
        return self.elements == 0

    def push(self, elem: Union[int, str], weight: int) -> None:
        # Add an element with given priority to the queue
        self.heap.append((elem, weight))
        self.position_map[elem] = self.elements
        self.elements += 1
        self._bubble_up(elem)

    def extract_min(self) -> Union[int, str]:
        # Remove and return the element with lowest weight (highest priority)
        if self.elements > 1:
            self._swap_nodes(0, self.elements - 1)
        elem, _ = self.heap.pop()
        del self.position_map[elem]
        self.elements -= 1
        if self.elements > 0:
            bubble_down_elem, _ = self.heap[0]
            self._bubble_down(bubble_down_elem)
        return elem

    def update_key(self, elem: Union[int, str], weight: int) -> None:
        # Update the weight of the given key
        position = self.position_map[elem]
        self.heap[position] = (elem, weight)
        if position > 0:
            parent_position = get_parent_position(position)
            _, parent_weight = self.heap[parent_position]
            if parent_weight > weight:
                self._bubble_up(elem)
            else:
                self._bubble_down(elem)
        else:
            self._bubble_down(elem)

    def _bubble_up(self, elem: Union[int, str]) -> None:
        # Place a node at the proper position (upward movement) [to be used internally
        # only]
        curr_pos = self.position_map[elem]
        if curr_pos == 0:
            return
        parent_position = get_parent_position(curr_pos)
        _, weight = self.heap[curr_pos]
        _, parent_weight = self.heap[parent_position]
        if parent_weight > weight:
            self._swap_nodes(parent_position, curr_pos)
            return self._bubble_up(elem)
        return

    def _bubble_down(self, elem: Union[int, str]) -> None:
        # Place a node at the proper position (downward movement) [to be used
        # internally only]
        curr_pos = self.position_map[elem]
        _, weight = self.heap[curr_pos]
        child_left_position = get_child_left_position(curr_pos)
        child_right_position = get_child_right_position(curr_pos)
        if child_left_position < self.elements and child_right_position < self.elements:
            _, child_left_weight = self.heap[child_left_position]
            _, child_right_weight = self.heap[child_right_position]
            if child_right_weight < child_left_weight:
                if child_right_weight < weight:
                    self._swap_nodes(child_right_position, curr_pos)
                    return self._bubble_down(elem)
        if child_left_position < self.elements:
            _, child_left_weight = self.heap[child_left_position]
            if child_left_weight < weight:
                self._swap_nodes(child_left_position, curr_pos)
                return self._bubble_down(elem)
        else:
            return
        if child_right_position < self.elements:
            _, child_right_weight = self.heap[child_right_position]
            if child_right_weight < weight:
                self._swap_nodes(child_right_position, curr_pos)
                return self._bubble_down(elem)
        else:
            return

    def _swap_nodes(self, node1_pos: int, node2_pos: int) -> None:
        # Swap the nodes at the given positions
        node1_elem = self.heap[node1_pos][0]
        node2_elem = self.heap[node2_pos][0]
        self.heap[node1_pos], self.heap[node2_pos] = (
            self.heap[node2_pos],
            self.heap[node1_pos],
        )
        self.position_map[node1_elem] = node2_pos
        self.position_map[node2_elem] = node1_pos


class GraphUndirectedWeighted:
    """
    Graph Undirected Weighted Class

    Functions:
    add_node: function to add a node in the graph
    add_edge: function to add an edge between 2 nodes in the graph
    """

    def __init__(self) -> None:
        self.connections = {}
        self.nodes = 0

    def __repr__(self) -> str:
        return str(self.connections)

    def __len__(self) -> int:
        return self.nodes

    def add_node(self, node: Union[int, str]) -> None:
        # Add a node in the graph if it is not in the graph
        if node not in self.connections:
            self.connections[node] = {}
            self.nodes += 1

    def add_edge(
        self, node1: Union[int, str], node2: Union[int, str], weight: int
    ) -> None:
        # Add an edge between 2 nodes in the graph
        self.add_node(node1)
        self.add_node(node2)
        self.connections[node1][node2] = weight
        self.connections[node2][node1] = weight


def prims_algo(
    graph: GraphUndirectedWeighted,
) -> Tuple[Dict[str, int], Dict[str, Optional[str]]]:
    """
    >>> graph = GraphUndirectedWeighted()

    >>> graph.add_edge("a", "b", 3)
    >>> graph.add_edge("b", "c", 10)
    >>> graph.add_edge("c", "d", 5)
    >>> graph.add_edge("a", "c", 15)
    >>> graph.add_edge("b", "d", 100)

    >>> dist, parent = prims_algo(graph)

    >>> abs(dist["a"] - dist["b"])
    3
    >>> abs(dist["d"] - dist["b"])
    15
    >>> abs(dist["a"] - dist["c"])
    13
    """
    # prim's algorithm for minimum spanning tree
    dist = {node: maxsize for node in graph.connections}
    parent = {node: None for node in graph.connections}
    priority_queue = MinPriorityQueue()
    [priority_queue.push(node, weight) for node, weight in dist.items()]
    if priority_queue.is_empty():
        return dist, parent

    # initialization
    node = priority_queue.extract_min()
    dist[node] = 0
    for neighbour in graph.connections[node]:
        if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
            dist[neighbour] = dist[node] + graph.connections[node][neighbour]
            priority_queue.update_key(neighbour, dist[neighbour])
            parent[neighbour] = node
    # running prim's algorithm
    while not priority_queue.is_empty():
        node = priority_queue.extract_min()
        for neighbour in graph.connections[node]:
            if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
                dist[neighbour] = dist[node] + graph.connections[node][neighbour]
                priority_queue.update_key(neighbour, dist[neighbour])
                parent[neighbour] = node
    return dist, parent


if __name__ == "__main__":
    from doctest import testmod

    testmod()