def CalculatePathBasedOnCurrentAlgorithm(tree, start, end, window):
isCurrentAlgorithmAStar = Commons.CurrentAlgorithm == Commons.A_ALGORITHM
queue = PriorityQueue()
exploredPositions = set([])
queue.put((0, start, [start]))
exploredPositions.add(start)
pathWeight = start.weight.__neg__()
positionDictionary = {}
positionDictionary[start] = 0
while queue:
weight, position, currentPath = queue.get()
position.ColorPosition(Commons.ORANGE)
if position != start and isCurrentAlgorithmAStar:
weight -= CalculateManhattanDistance(position, end)
if position == end:
for pos in currentPath:
pos.ColorPosition(Commons.YELLOW)
pathWeight += pos.weight
visitedNodes = (exploredPositions.__len__() -
queue._qsize()).__str__()
RenderTexts(window, visitedNodes, pathWeight.__str__())
GameWindow.DrawWindow(window, tree)
return
for i in range(position.neighbors.__len__()):
if isCurrentAlgorithmAStar:
cost = weight + position.neighbors[
i].weight + CalculateManhattanDistance(
position.neighbors[i], end)
else:
cost = weight + position.neighbors[i].weight
if position.neighbors[i] not in exploredPositions:
queue.put((cost, position.neighbors[i],
currentPath + [position.neighbors[i]]))
exploredPositions.add(position.neighbors[i])
position.neighbors[i].ColorPosition(Commons.BLACK)
positionDictionary[position.neighbors[i]] = cost
elif positionDictionary[
position.neighbors[i]] > cost and isCurrentAlgorithmAStar:
queue.put((cost, position.neighbors[i],
currentPath + [position.neighbors[i]]))
position.neighbors[i].ColorPosition(Commons.BLACK)
positionDictionary[position.neighbors[i]] = cost
thread = threading.Thread(target=GameWindow.DrawWindow,
args=[window, tree, False])
thread.start()
thread.join()
for event in pygame.event.get():
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_ESCAPE:
Commons.QuitGame()
if event.type == pygame.QUIT:
Commons.QuitGame()
for strat in starting_strats:
perms = list(set(itertools.permutations(tuple(strat))))
set_of_strats_as_tuples.update(perms)
list_of_strats_as_tuples = list(set_of_strats_as_tuples)
best_strats = PriorityQueue()
total_strats = 0
for strat_as_tuple in list_of_strats_as_tuples:
strat_as_array = np.asarray(strat_as_tuple)
score = calc_score(strat_as_array, orig_strats)
if score > cutoff_score:
best_strats.put((-score, strat_as_tuple))
total_strats += 1
print('total strats to look at = ' + str(total_strats))
starting_strats = set()
strat_counter = 0
for _ in range(best_strats._qsize()): # all from the priority queue
if strat_counter % 100 == 0:
print('looking at strat ' + str(strat_counter))
x = best_strats.get()
strat_as_tuple = x[1]
for l in range(3): # different number of explorations
new_strat = np.asarray(strat_as_tuple)
best_new_strat = None
for k in range(
5 + l * 10
): # different number of initial random changes to the strat
new_strat = change_to_random_new_strat(new_strat)
current_score = calc_score(new_strat, orig_strats)
for j in range(100): # greedy algorithm to search better strat
old_strat = np.copy(new_strat)
new_strat = change_to_random_new_strat(new_strat)
