heapy 0.7

An priority queue built with an in-place modifiable binary heap (decrease-key,increase-key).

Operations have the following algorithmic complexities:

• pop - O(log(n))
• push - O(log(n)) (average: O(1))
• peek - O(1)
• remove - O(log(n))

Useful for:

• Dijkstra's shortest path algorithm
• A*
• Rolling median

Install

pip install heapy

Examples

The following is an example implementation of Dijkstra's algorithm, a method for finding the shortest path in a weighted graph. The heap-based priority queue reduces the complexity of the basic algorithm from O(n^2) to O(m*log(n)), for n nodes and m edges.

from heapy import pqueue

def dijkstra(G, r):
	'''
	Find the shortest paths from a given node in a weighted graph
	to every other (connected) node.
	'''

    todo = pqueue() # this is the heap-based priority queue

    distance = {} # distances to all nodes on shortest paths
    parents = { r: None } # parent pointers for all items on known shortest paths

    todo.push((r, 0)) # alternate syntax: todo[r] = 0

    while todo: # automatically checks len(todo) > 0
        (n, d) = todo.pop() # get the minimum element; O(log(n))

        distance[n] = d # save shortest distance

        # update position of children in priority queue, if less
        children = G.get(n, {})
        for (c, w) in children.items():
            if c not in distance and (c not in todo or d + w < todo[c]):

                todo[c] = d + w # add or update distance ~ O(1)
                parents[c] = n # update parent in minimum path distance spanning tree

    return distance

Note that G is a dictionary of dictionaries.

	{
		'a' : {'b' : 2, 'c' : 1},
		'b' : {'a' : 2, 'd' : 3},
		'c' : {'a' : 1},
		'd' : {'b' : 3}
	}