def execute_current_algorithm():
Adj, source, target = Tile.neighborsDict, Config.source, Config.target
W = {} # weight dictionary that maps each tile to its cost
for tile in Tile.tilesDict.values():
W[tile.id] = tile.W
algDict = {
"BFS" : algorithms.BFS(Adj, source, target),
"DFS" : algorithms.DFS(Adj, source, target),
"Dijkstra" : algorithms.Dijkstra(Adj, W, source, target),
"B_FS" : algorithms.B_FS(Adj, W, source, target),
"A*" : algorithms.A_star(Adj, W, source, target),
}
alg = algDict[Config.currentAlgorithm]
if Config.currentAlgorithm in ["BFS", "DFS"]:
Tile.pathToTargetList, Tile.idToLevelDict, Tile.levelToIdList = alg.search()
Tile.explored_tiles = Tile.idToLevelDict.keys() # to draw explored tiles
Tile.idToLevelAux = Tile.idToLevelDict
elif Config.currentAlgorithm in ["Dijkstra", "B_FS", "A*"]:
Tile.pathToTargetList, Tile.idToCostDict, Tile.levelToIdList, Tile.explored_tiles, Tile.levelToCostList = alg.search()
Tile.idToCostAux = Tile.idToCostDict
GameController.currentAlg = alg
def dijkstra_python():
data = request.json
nodes = data["nodes"]
edges = data["edges"]
startNode = data["startNode"]
dijkstra = algo.Dijkstra(nodes, edges, startNode)
trace = dijkstra.run()
return jsonify(trace)
def rungame(self):
self.creategrid()
running = True # variable is use to keep loop running
# following variables will used in program for different purposes
currentx = 0
currenty = 0
while running:
for event in pygame.event.get(): # checks for the event
if event.type == pygame.QUIT: # if event type is quit, then proceed to quit the window
pygame.quit() # end the pygame instance
quit() # closes the window
# checks if thread exists and is it dead or not
if self.runningthreadname is not None and self.runningthreadname.is_alive(
) is False:
self.isalgorunning = False
self.runningthreadname = None
if event.type == pygame.MOUSEBUTTONDOWN and event.button == 1 and (
self.isalgorunning is
False): # checks if mouse button is pressed
if self.dijkstrabutton.chechkifclicked(
): # checks if the dijkstra button is pressed
self.cleargrid()
g = algorithms.Dijkstra(
self.grid
) # creates an instance of class graph from dijkstra module
g.dijkstra() # gets the list of all visited nodes
self.isalgorunning = True
t1 = threading.Thread(target=self.print, args=[g])
self.runningthreadname = t1
t1.start()
elif self.bfsbutton.chechkifclicked(
): # checks if the bfs button is pressed
self.cleargrid()
b = algorithms.Bfs(self.grid)
b.bfs()
self.isalgorunning = True
t1 = threading.Thread(target=self.print, args=[b])
self.runningthreadname = t1
t1.start()
elif self.dfsbutton.chechkifclicked(
): # checks if the bfs button is pressed
self.cleargrid()
d = algorithms.Dfs(self.grid)
d.dfs()
self.isalgorunning = True
t1 = threading.Thread(target=self.print, args=[d])
self.runningthreadname = t1
t1.start()
elif self.astarbutton.chechkifclicked(
): # checks if the bfs button is pressed
self.cleargrid()
a = algorithms.Astar(self.grid)
a.astar()
self.isalgorunning = True
t1 = threading.Thread(target=self.print, args=[a])
self.runningthreadname = t1
t1.start()
elif self.resetbutton.chechkifclicked():
pygame.display.flip()
pygame.time.wait(100) # add time delay
self.startnoderow = node.start_node_row
self.startnodecolumn = node.start_node_column
self.finishnoderow = node.finish_node_row
self.finishnodecolumn = node.finish_node_column
self.flag = 0
self.grid = []
self.creategrid()
else:
mousex, mousey = pygame.mouse.get_pos(
) # gets the current coordinates of mouse
# by using mouse coordinates, it can be determined if any of the rectangle on screen is clicked
# the following function also returns the starting coordinates of particular rectangle
# if no rectangle is clicked it returns -1
currentx, currenty = self.get_rectanglepos(
mousex, mousey)
# by using rectangle coordinates,
# the type of rectangle/node can be determined start-node, finish-node, normal nodes etc
# according to type of rectangle/node the flag value is set
self.flag = self.checknode(currentx, currenty)
if self.flag == 3:
cnode = self.getnode(
currentx,
currenty) # gets the node related to rectangle
cnode.iswall = True # sets the iswall attribute to true
self.drawrect(
8, currentx,
currenty) # changes the color of rectangle
if event.type == pygame.MOUSEBUTTONUP and event.button == 1 and (
self.flag == 1
or self.flag == 2) and (self.isalgorunning is False):
# checks if mouse button is released
mousex, mousey = pygame.mouse.get_pos(
) # gets the coordinates where mouse was released
newx, newy = self.get_rectanglepos(
mousex, mousey) # gets the coordinates of rectangle
if self.checknode(newx,
newy) == 3: # checks the type of node
self.setpos(newx, newy, currentx, currenty)
if event.type == pygame.MOUSEBUTTONDOWN and event.button == 3 and (
self.isalgorunning is False):
mousex, mousey = pygame.mouse.get_pos()
currentx, currenty = self.get_rectanglepos(mousex, mousey)
self.flag = self.checknode(currentx, currenty)
if self.flag == 3:
cnode = self.getnode(currentx, currenty)
cnode.isweight = True
self.drawrect(9, currentx, currenty)
def update(self):
self.sprite_group_all.update()
# catch inputs
keystate = pg.key.get_pressed()
if keystate[pg.K_ESCAPE]:
pg.event.post(pg.event.Event(pg.QUIT))
if keystate[pg.K_TAB]:
self.sprite_group_all.empty()
self.tilemap.randomize_start_goal(self)
if keystate[pg.K_KP0]:
self.neural.train()
if keystate[pg.K_KP1]:
self.neural.save(assets.neural_network_model_folder)
if keystate[pg.K_KP2]:
self.neural.load(assets.neural_network_model_folder)
if keystate[pg.K_q]: # BFS
self.pathqueue = None
self.path = alg.BFS(alg.SquareGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
if keystate[pg.K_a]: # Visual BFS
self.path = alg.BFS(alg.SquareGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
self.visual_helper()
if keystate[pg.K_w]: # DFS
self.pathqueue = None
self.path = alg.DFS(alg.SquareGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
if keystate[pg.K_s]: # Visual DFS
self.path = alg.DFS(alg.SquareGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
self.visual_helper()
if keystate[pg.K_e]: # Dijkstra
self.pathqueue = None
self.path = alg.Dijkstra(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
if keystate[pg.K_d]: # Visual Dijkstra
self.path = alg.Dijkstra(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
self.visual_helper()
if keystate[pg.K_z]: # Dijkstra - only path
self.pathqueue = None
d_path = alg.Dijkstra(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
self.path = alg.ReconstructPath(d_path, self.tilemap.custom_start, self.tilemap.custom_goal)
if keystate[pg.K_x]: # Visual Dijkstra - only path
d_path = alg.Dijkstra(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
self.path = alg.ReconstructPath(d_path, self.tilemap.custom_start, self.tilemap.custom_goal)
self.visual_helper(True)
if keystate[pg.K_r]: # Astar
self.pathqueue = None
self.path, cost = alg.Astar(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
if keystate[pg.K_f]: # Visual Astar
self.path, cost = alg.Astar(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
self.visual_helper()
if keystate[pg.K_c]: # Astar - only path
self.pathqueue = None
d_path, cost = alg.Astar(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
self.path = alg.ReconstructPath(d_path, self.tilemap.custom_start, self.tilemap.custom_goal)
if keystate[pg.K_v]: # Visual Astar - only path
d_path, cost = alg.Astar(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
self.path = alg.ReconstructPath(d_path, self.tilemap.custom_start, self.tilemap.custom_goal)
self.visual_helper(True)
if keystate[pg.K_t]: # Astar NN
self.pathqueue = None
self.path, cost = alg.Astar(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal, self.neural)
if keystate[pg.K_g]: # Visual Astar NN
self.path, cost = alg.Astar(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal, self.neural)
self.visual_helper()
if keystate[pg.K_p]: # test
num_tests=50
def bfs_test():
self.sprite_group_all.empty()
self.tilemap.randomize_start_goal(self)
self.pathqueue = None
self.path = alg.BFS(alg.SquareGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
def dfs_test():
self.sprite_group_all.empty()
self.tilemap.randomize_start_goal(self)
self.pathqueue = None
self.path = alg.DFS(alg.SquareGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
def dijkstra_test():
self.sprite_group_all.empty()
self.tilemap.randomize_start_goal(self)
self.pathqueue = None
self.path = alg.Dijkstra(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
def astar_test():
self.sprite_group_all.empty()
self.tilemap.randomize_start_goal(self)
self.pathqueue = None
self.path, cost = alg.Astar(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)
def neural_test():
self.sprite_group_all.empty()
self.tilemap.randomize_start_goal(self)
self.pathqueue = None
self.path, cost = alg.Astar(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal, self.neural)
bfs_time = timeit.timeit(bfs_test, number=num_tests) / num_tests
print("BFS: " + str(bfs_time))
dfs_time = timeit.timeit(dfs_test, number=num_tests) / num_tests
print("DFS: " + str(dfs_time))
# dijkstra_time = timeit.timeit(dijkstra_test, number=num_tests) / num_tests
# print("Dijkstra: " + str(dijkstra_time))
astar_time = timeit.timeit(astar_test, number=num_tests) / num_tests
print("Astar: " + str(astar_time))
neural_time = timeit.timeit(neural_test, number=num_tests) / num_tests
print("Neural: " + str(neural_time) + "\n\n")
def dijkstra_test():
self.sprite_group_all.empty()
self.tilemap.randomize_start_goal(self)
self.pathqueue = None
self.path = alg.Dijkstra(alg.WeightedGraph(self.tilemap), self.tilemap.custom_start, self.tilemap.custom_goal)