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Readme
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:
pip install heapy
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}
}
FAQs
A priority queue built with an in-place modifiable binary heap
We found that heapy demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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