def aStarManhattanHeuristic(self, val):
if val == 0:
f = lambda x: len(x) + heuristics.manhattanCost(
x[-1][0], self.goal)
h = lambda x: heuristics.manhattanCost(x[-1][0], self.goal)
queue = dataStructure.PriorityQueue((f, h))
return self.aStarSearch(queue, val)
elif val == 1:
#used in computation of XY heuristic.
f = lambda x: len(x) + heuristics.rowManhattan(x[-1][0], self.goal)
h = lambda x: heuristics.rowManhattan(x[-1][0], self.goal)
queue = dataStructure.PriorityQueue((f, h))
return self.aStarSearch(queue, val)
else:
#used in computation of XY heuristic.
f = lambda x: len(x) + heuristics.colManhattan(x[-1][0], self.goal)
h = lambda x: heuristics.colManhattan(x[-1][0], self.goal)
queue = dataStructure.PriorityQueue((f, h))
return self.aStarSearch(queue, val)
def aStarXYHeuristic(self):
f = lambda x: len(x) + heuristics.xyCostHeuristic(x[-1][0], self.goal)
h = lambda x: heuristics.xyCostHeuristic(x[-1][0], self.goal)
queue = dataStructure.PriorityQueue((f, h))
return self.aStarSearch(queue, 0)
def aStarLinearConflicts(self):
f = lambda x: len(x) + heuristics.linearConflicts(x[-1][0], self.goal)
h = lambda x: heuristics.linearConflicts(x[-1][0], self.goal)
queue = dataStructure.PriorityQueue((f, h))
return self.aStarSearch(queue, 0)
def aStarNilssonHeuristic(self):
f = lambda x: len(x) + heuristics.nilssonSequenceScore(
x[-1][0], self.goal)
h = lambda x: heuristics.nilssonSequenceScore(x[-1][0], self.goal)
queue = dataStructure.PriorityQueue((f, h))
return self.aStarSearch(queue, 0)
def aStarMisplacedTiles(self):
f = lambda x: len(x) + heuristics.misplacedTiles(x[-1][0], self.goal)
h = lambda x: heuristics.misplacedTiles(x[-1][0], self.goal)
queue = dataStructure.PriorityQueue((f, h))
return self.aStarSearch(queue, 0)