#!/usr/bin/env python
# encoding=utf-8 (pep 0263)
import heapq
from linked_lists import LinkedList, Cons, Nil, member
# horni zavora pro cenu nejlepsi cesty
biggest = 9999
# Algoritmus funguje analogicky k algoritmu ze souboru
# 4.1_9-priority-queue.py, ale namisto jednotlivych uzlu udrzujeme ve
# fronte skupiny uzlu, ze kterych je nutne se dostat do koncovych uzlu
# pro splneni vsech jiz navstivenych AND uzlu. Na zaver jsou cesty
# jednotlivych uzlu zrekonstruovany do stromu.
def andor(start):
heap = [(0, 0, LinkedList([(0, 0, 0, start, Nil)]), Nil)]
while True:
try:
f, g, nodes, solved = heapq.heappop(heap)
except IndexError: # fronta je prazdna
raise ValueError("Reseni neexistuje.")
if nodes == Nil: # seznam uzlu k vyreseni je prazdny
return reconstruct_search_tree(solved)
_, g1, c, node, path = nodes.head
if is_goal(node):
solved = Cons((node, Cons(node, path)), solved)
heapq.heappush(heap, (f-h(node)-c, g-c, nodes.tail, solved))
elif f <= biggest:
succ = get_successors(node)
if succ is None: # narazili jsme na necilovy uzel
continue
op, successors = succ
path1 = Cons(node, path)
if op == "and":
nodes1 = nodes.tail
for m, c in successors:
if not member(m, path1):
nodes1 = insert((g1+c+h(m), g1+c, c, m, path1), nodes1)
f = g + c + h(m)
g = g + c
heapq.heappush(heap, (f, g, nodes1, solved))
if op == "or":
for m, c in successors:
if not member(m, path1):
nodes1 = insert((g1+c+h(m), g1+c, c, m, path1), nodes.tail)
heapq.heappush(heap, (g+c+h(m), g+c, nodes1, solved))
def reconstruct_search_tree(leaves):
tree = dict()
for node, path in leaves:
tree[node] = "goal"
while path != Nil and path.tail != Nil:
node = path.head
parent = path.tail.head
if parent not in tree:
op, _ = get_successors(parent)
tree[parent] = (op + "_result", LinkedList([node]))
else:
op, nodes = tree[parent]
if not member(node, nodes):
tree[parent] = (op, Cons(node, nodes))
break
path = path.tail
return tree
def insert(node, nodes):
if nodes == Nil:
return LinkedList([node])
f = node[0]
f1 = nodes.head[0]
if f <= f1:
return Cons(node, nodes)
return Cons(nodes.head, insert(node, nodes.tail))
def h(_):
# zavisi na resenem problemu
return 0
graph = dict(
a=("or", LinkedList([("b", 1), ("c", 3)])),
b=("and", LinkedList([("d", 1), ("e", 1)])),
c=("and", LinkedList([("f", 2), ("g", 1)])),
e=("or", LinkedList([("h", 6)])),
f=("or", LinkedList([("h", 2), ("i", 3)])))
goals = dict(d=True, g=True, h=True)
def is_goal(node):
# zavisi na resenem problemu
return node in goals
# tato funkce nahrazuje prologovska fakta tvaru node ---> Op:Subtrees
# a pro zadany node navraci prislusne Op:Subtrees
def get_successors(node):
# zavisi na resenem problemu
if node in graph:
return graph[node]
return None
# demonstracni vypis
if __name__ == "__main__":
print('Prohledavani AND/OR grafu - implementace pomoci prioritni fronty')
print('\n Graf:')
print(' a ---> or:[b/1,c/3].')
print(' b ---> and:[d/1,e/1].')
print(' c ---> and:[f/2,g/1].')
print(' e ---> or:[h/6].')
print(' f ---> or:[h/2,i/3].')
print(' h(X,0).')
print(' goal(d).')
print(' goal(g).')
print(' goal(h).')
print('\nVysledky dotazu andor("a"):')
solution = andor("a")
for key, value in sorted(solution.items()):
print("%s : %s" % (key, value))
Prohledavani AND/OR grafu - implementace pomoci prioritni fronty
Graf:
a ---> or:[b/1,c/3].
b ---> and:[d/1,e/1].
c ---> and:[f/2,g/1].
e ---> or:[h/6].
f ---> or:[h/2,i/3].
h(X,0).
goal(d).
goal(g).
goal(h).
Vysledky dotazu andor("a"):
a : ('or_result', ['c'])
c : ('and_result', ['g', 'f'])
f : ('or_result', ['h'])
g : goal
h : goal
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