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This documents an unmaintained version of NetworkX. Please upgrade to a maintained version and see the current NetworkX documentation.

Source code for networkx.algorithms.shortest_paths.astar

# -*- coding: utf-8 -*-
#    Copyright (C) 2004-2019 by
#    Aric Hagberg <hagberg@lanl.gov>
#    Dan Schult <dschult@colgate.edu>
#    Pieter Swart <swart@lanl.gov>
#    All rights reserved.
#    BSD license.
#
# Authors: Salim Fadhley <salimfadhley@gmail.com>
#          Matteo Dell'Amico <matteodellamico@gmail.com>
"""Shortest paths and path lengths using the A* ("A star") algorithm.
"""
from heapq import heappush, heappop
from itertools import count

import networkx as nx
from networkx.utils import not_implemented_for

__all__ = ['astar_path', 'astar_path_length']


[docs]@not_implemented_for('multigraph') def astar_path(G, source, target, heuristic=None, weight='weight'): """Returns a list of nodes in a shortest path between source and target using the A* ("A-star") algorithm. There may be more than one shortest path. This returns only one. Parameters ---------- G : NetworkX graph source : node Starting node for path target : node Ending node for path heuristic : function A function to evaluate the estimate of the distance from the a node to the target. The function takes two nodes arguments and must return a number. weight: string, optional (default='weight') Edge data key corresponding to the edge weight. Raises ------ NetworkXNoPath If no path exists between source and target. Examples -------- >>> G = nx.path_graph(5) >>> print(nx.astar_path(G, 0, 4)) [0, 1, 2, 3, 4] >>> G = nx.grid_graph(dim=[3, 3]) # nodes are two-tuples (x,y) >>> nx.set_edge_attributes(G, {e: e[1][0]*2 for e in G.edges()}, 'cost') >>> def dist(a, b): ... (x1, y1) = a ... (x2, y2) = b ... return ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5 >>> print(nx.astar_path(G, (0, 0), (2, 2), heuristic=dist, weight='cost')) [(0, 0), (0, 1), (0, 2), (1, 2), (2, 2)] See Also -------- shortest_path, dijkstra_path """ if source not in G or target not in G: msg = 'Either source {} or target {} is not in G' raise nx.NodeNotFound(msg.format(source, target)) if heuristic is None: # The default heuristic is h=0 - same as Dijkstra's algorithm def heuristic(u, v): return 0 push = heappush pop = heappop # The queue stores priority, node, cost to reach, and parent. # Uses Python heapq to keep in priority order. # Add a counter to the queue to prevent the underlying heap from # attempting to compare the nodes themselves. The hash breaks ties in the # priority and is guaranteed unique for all nodes in the graph. c = count() queue = [(0, next(c), source, 0, None)] # Maps enqueued nodes to distance of discovered paths and the # computed heuristics to target. We avoid computing the heuristics # more than once and inserting the node into the queue too many times. enqueued = {} # Maps explored nodes to parent closest to the source. explored = {} while queue: # Pop the smallest item from queue. _, __, curnode, dist, parent = pop(queue) if curnode == target: path = [curnode] node = parent while node is not None: path.append(node) node = explored[node] path.reverse() return path if curnode in explored: # Do not override the parent of starting node if explored[curnode] is None: continue # Skip bad paths that were enqueued before finding a better one qcost, h = enqueued[curnode] if qcost < dist: continue explored[curnode] = parent for neighbor, w in G[curnode].items(): ncost = dist + w.get(weight, 1) if neighbor in enqueued: qcost, h = enqueued[neighbor] # if qcost <= ncost, a less costly path from the # neighbor to the source was already determined. # Therefore, we won't attempt to push this neighbor # to the queue if qcost <= ncost: continue else: h = heuristic(neighbor, target) enqueued[neighbor] = ncost, h push(queue, (ncost + h, next(c), neighbor, ncost, curnode)) raise nx.NetworkXNoPath("Node %s not reachable from %s" % (target, source))
[docs]def astar_path_length(G, source, target, heuristic=None, weight='weight'): """Returns the length of the shortest path between source and target using the A* ("A-star") algorithm. Parameters ---------- G : NetworkX graph source : node Starting node for path target : node Ending node for path heuristic : function A function to evaluate the estimate of the distance from the a node to the target. The function takes two nodes arguments and must return a number. Raises ------ NetworkXNoPath If no path exists between source and target. See Also -------- astar_path """ if source not in G or target not in G: msg = 'Either source {} or target {} is not in G' raise nx.NodeNotFound(msg.format(source, target)) path = astar_path(G, source, target, heuristic, weight) return sum(G[u][v].get(weight, 1) for u, v in zip(path[:-1], path[1:]))