/usr/lib/python3/dist-packages/pygraph/algorithms/heuristics/euclidean.py is in python3-pygraph 1.8.2-6.
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"""
A* heuristic for euclidean graphs.
"""
# Imports
class euclidean(object):
"""
A* heuristic for Euclidean graphs.
This heuristic has three requirements:
1. All nodes should have the attribute 'position';
2. The weight of all edges should be the euclidean distance between the nodes it links;
3. The C{optimize()} method should be called before the heuristic search.
A small example for clarification:
>>> g = graph.graph()
>>> g.add_nodes(['A','B','C'])
>>> g.add_node_attribute('A', ('position',(0,0)))
>>> g.add_node_attribute('B', ('position',(1,1)))
>>> g.add_node_attribute('C', ('position',(0,2)))
>>> g.add_edge('A','B', wt=2)
>>> g.add_edge('B','C', wt=2)
>>> g.add_edge('A','C', wt=4)
>>> h = graph.heuristics.euclidean()
>>> h.optimize(g)
>>> g.heuristic_search('A', 'C', h)
"""
def __init__(self):
"""
Initialize the heuristic object.
"""
self.distances = {}
def optimize(self, graph):
"""
Build a dictionary mapping each pair of nodes to a number (the distance between them).
@type graph: graph
@param graph: Graph.
"""
for start in graph.nodes():
for end in graph.nodes():
for each in graph.node_attributes(start):
if (each[0] == 'position'):
start_attr = each[1]
break
for each in graph.node_attributes(end):
if (each[0] == 'position'):
end_attr = each[1]
break
dist = 0
for i in range(len(start_attr)):
dist = dist + (float(start_attr[i]) - float(end_attr[i]))**2
self.distances[(start,end)] = dist
def __call__(self, start, end):
"""
Estimate how far start is from end.
@type start: node
@param start: Start node.
@type end: node
@param end: End node.
"""
assert len(list(self.distances.keys())) > 0, "You need to optimize this heuristic for your graph before it can be used to estimate."
return self.distances[(start,end)]
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