def Ant(self):
a = ant.Ant(self)
self.ants.append(a)
a.id = "ant" + str(AntFarm.ANT_ID)
AntFarm.ANT_ID += 1
return a
def get_best_path(self, num_ants=2, num_steps=3):
NUM_CITIES = len(self.cities)
NUM_ANTS = num_ants
NUM_STEPS = num_steps
self.calc_attraction()
ants = []
for i in xrange(0, NUM_ANTS):
ants.append(ant.Ant(i, self))
for step in xrange(0, NUM_STEPS):
print "Step: ", step + 1, " of ", NUM_STEPS
path_lens = []
paths = []
argu = self
procs = []
q = Queue()
for a in ants:
p = Process(target=self.mp_tour, args=(
a,
q,
))
procs.append(p)
p.start()
for p in procs:
p.join()
ants = []
while q.empty() == False:
ants.append(q.get())
for a in ants:
#print a.index
path_len = a.calc_path_length()
path_lens.append(path_len)
paths.append(a.path)
self.update_pheromone(a)
self.update_routing_table(a)
best_path_len = min(path_lens)
best_path = paths[path_lens.index(best_path_len)]
print "step best path: ", best_path_len, " step: ", step + 1
self.shortest_paths.append(best_path)
self.shortest_paths_lens.append(best_path_len)
output_index = self.shortest_paths_lens.index(
min(self.shortest_paths_lens))
output_path = self.shortest_paths[output_index]
self.shortest_path_len = self.shortest_paths_lens[output_index]
self.shortest_paths = []
self.shortest_paths_lens = []
return output_path
def __init__(self):
# 实例化
self.map = mp.MapInit()
self.ant = at.Ant()
self.path = self.map.load_csv()
self.pheromone_ini = 8
self.alpha = 0.5
self.beta = 3
def get_best_path(self, num_ants=2, num_steps=3):
ants = []
self.calc_attraction()
shuffle(self.cities)
for i in range(0, num_ants):
ants.append(ant.Ant(i, self))
for step in range(0, num_steps):
print("Step: {} of {}".format(step + 1, num_steps))
path_lens = []
paths = []
procs = []
q = Queue()
for a in ants:
p = Process(target=self.mp_tour, args=(
a,
q,
))
procs.append(p)
p.start()
for p in procs:
p.join()
ants = []
while q.empty() == False:
ants.append(q.get())
for a in ants:
path_len = a.calc_path_length()
path_lens.append(path_len)
paths.append(a.path)
self.update_pheromone(a)
self.update_routing_table(a)
best_path_len = min(path_lens)
best_path = paths[path_lens.index(best_path_len)]
print("Step best path: {} Step: {}".format(best_path_len,
step + 1))
self.shortest_paths.append(best_path)
self.shortest_paths_lens.append(best_path_len)
output_index = self.shortest_paths_lens.index(
min(self.shortest_paths_lens))
output_path = self.shortest_paths[output_index]
self.shortest_path_len = self.shortest_paths_lens[output_index]
self.shortest_paths = []
self.shortest_paths_lens = []
return output_path
def startup(): mapgen() for i in range(9): antList.append(ant.Ant((random.randint(0, 49), 23), 0)) for j in range(2): foodList.append(food.Food(random.randint(0,49), array))
def restart():
print("restarted")
mapgen()
#new units
for i in range(2):
antList.append(ant.Ant((random.randint(0, 49), 23), 0))
#mutated units
for i in range(5):
am = ant.Ant((random.randint(0, 49), 23), 0)
am.neuralNet = winner.neuralNet.makeMutant(1)
antList.append(am)
#original unit
for i in range(2):
ao = ant.Ant((random.randint(0, 49), 23), 0)
ao.neuralNet = winner.neuralNet.makeCopy()
antList.append(ao)
foodList.clear()
for i in range(18):
foodList.append(food.Food(random.randint(0,49), array))
def algorithm():
vertices = {i for i in range(0, 12)}
edges = [(0, 10), (0, 2), (1, 2), (1, 7), (1, 10), (2, 9), (3, 4), (3, 6),
(3, 11), (4, 8), (4, 10), (5, 8), (5, 10), (6, 10), (6, 11),
(7, 9), (7, 11), (0, 5)]
w = world.World(vertices, edges, 0.1)
iterations = 200
number_of_ants = 50
goals = [(i, j) for i in range(0, 12) for j in range(i + 1, 12)]
solutions = {}
for goal in goals:
global_best_solution = None
ants = []
for _ in range(0, number_of_ants):
ants.append(ant.Ant(w, goal[0], goal[1]))
for _ in range(0, iterations):
for a in ants:
# print(a.find_solution())
a.find_solution()
local_best_ant = ant.find_best_ant(ants)
if local_best_ant is not None:
if global_best_solution is None:
global_best_solution = local_best_ant.solution
elif len(local_best_ant.solution) < len(global_best_solution):
global_best_solution = local_best_ant.solution
w.update_pheromone()
for a in ants:
a.update_path()
a.reset()
# pprint(w.edges)
# print(f"best solution {global_best_solution}")
w.reset_edges()
solutions[goal] = global_best_solution
print(f"{goal} - {global_best_solution}")
pprint(solutions)
[1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1]]
# For trails:
trail_value = [[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0]]
# List with chances of each choice
chance_list = []
probability_list = []
city_list = []
not_visited_cities = [1, 2, 3, 4, 5, 6, 7]
testing_coordinates = [[100, 100], [50, 200], [400, 50], [150, 500],
[500, 150], [500, 400], [600, 300], [400, 400]]
ant = ant.Ant(0, 0)
current_city = 0
shortest_path = 1000
which_iteration = 0
trail_checker = 0
# Creates cities according to testing_coordinates list
def show_pheromones():
for element in pheromone_value_matrix:
print(element)
def city_maker():
city_list.clear()
import time
size = int(input("How large a grid do you want? : "))
print("Do you want to step through the positions, or just see a specific position?")
step_through = int(input("Type 1 to step, or 0 to jump to a point: "))
if step_through == 0:
steps = int(input("How many steps? : "))
i = 0
else:
print("Press enter repeatedly to continue, or type 'q' to exit")
steps = -1
i = 0
traveler = ant.Ant()
plot = grid.Grid(size)
done = False
while not done:
x = traveler.get_x()
y = traveler.get_y()
#print(x, y, traveler.get_direction())
if plot.get_color(x, y) is 0:
traveler.right()
else:
traveler.left()
plot.change_color(x, y)
def spawnAnt(color, x, y):
ant.Ant(color, x, y)
def AOC_path (nest, food, DecTable, no_of_colonies,no_of_ants=5) :
"""
This function return the best (maximum proability) ant path connecting the nest with food.
"""
#go = time.time()
no_of_critical_ants = 0
cost_goal_generation = 0.3
for generation in range(no_of_colonies) :
#print('colony',no_of_colonies)
#progressBar(value = generation, endvalue=no_of_colonies)
#sys.stdout.write("done: " % generation / no_of_colonies * 100.)
#sys.stdout.flush()
if no_of_critical_ants < 30 :
cost_goal_generation -= 0.001
else : cost_goal_generation += 0.001
ant_colony = []
no_of_critical_ants = 0
for m in range(no_of_ants) :
#print('ant',m)
ant = ANT.Ant(birth_coordinates=nest)
ant_colony.append(ant)
while (ant.location != food) :
loc = ant.location
state_transition_probas = []
neigbours = ANT.GetNeighbours(point=loc)
for n in neigbours :
state_transition_probas.append(DecTable.p_ij(location=loc,destination=n,track=ant.track))
next_state = GRID.GetNextState(state_transition_probas=state_transition_probas,neigbours=neigbours)
ant.AddMove(nextpoint=next_state)
#print(loc,next_state)
colormap_value = 1./ DecTable.eta_ij[loc,next_state]
ant.AddCost(value=colormap_value)
if colormap_value >= cost_goal_generation :
ant.critical_steps += 1
if ant.critical_steps > 1 :
#print 'dead'
no_of_critical_ants += 1
break
# if level reached of the food one, go up or down
if (ant.location[0] == food[0]) :
#print('level reached')
#print('food',food)
while ( loc[1] != food[1] and loc[1] > 1 and loc[1] < 88) :
loc = ant.location
#walk up
if loc[1] < food[1] :
next_state = (loc[0],loc[1]+1)
else :
next_state = (loc[0],loc[1]-1)
#print(next_state)
ant.AddMove(nextpoint=next_state)
#print 1./DecTable.eta_ij[loc,next_state]
colormap_value = 1./ DecTable.eta_ij[loc,next_state]
ant.AddCost(value=colormap_value)
#print colormap_value
if colormap_value >= cost_goal_generation :
ant.critical_steps += 1
if ant.critical_steps > 1 :
#print 'dead'
no_of_critical_ants += 1
break
else :
break
break
#print('ok')
DecTable.UpdatePheromone(colony=ant_colony)
#print('ok')
"""
for n in ANT.GetNeighbours((108,45)) :
print n
print DecTable.p_ij(location=(108,45), destination=n)
"""
#print ('reduced goal', cost_goal_generation)
#print( 'no_of_critical_ants', no_of_critical_ants)
"""
if generation%100 == 0 :
print (generation)
plt.figure(figsize=(11,11))
pl.FlooadPlot(pic=gt_map,flood_level=0.3)
cost_in_generation = []
for a in ant_colony :
track_length = 1.*len(a.track)
pl.PlotTrack(track=a.track)
#plt.pause(0.05)
if a.critical_steps < 2 :
#print 'hi'
cost_in_generation.append(a.cost / track_length)
else :
cost_in_generation.append(1.)
#print TrackCost(ant=a,cost_map=astar_landscape)
print ('minimal track', np.argmin(cost_in_generation))
print ('cost', ant_colony[np.argmin(cost_in_generation)].cost)
print ('cost per unit length', ant_colony[np.argmin(cost_in_generation)].cost / len(ant_colony[np.argmin(cost_in_generation)].track))
print ('amount of critical steps', ant_colony[np.argmin(cost_in_generation)].critical_steps)
pl.HighlightTrack(ant_colony[np.argmin(cost_in_generation)].track)
plt.show()
"""
stop = time.time()
#print ('total time: ', stop-go)
return GetMaxProbaPath(start=nest,goal=food,decisionTable=DecTable)
def run(self):
pygame.display.set_caption("Ant Simulator")
# saving cords every ... secs
pygame.time.set_timer(pygame.USEREVENT, 3000)
# initialize food
nest = Object(self.width / 2 - 5, self.height / 2 - 5, 10, 10,
(55, 55, 55))
foods = []
ants = []
foods.append(Object(500, 200, 50, 50, (0, 255, 0)))
foods.append(Object(100, 350, 50, 100, (0, 255, 0)))
back_ants = []
run = True
while run:
self.screen.fill((0, 0, 0))
self.clock.tick(self.fps)
for event in pygame.event.get():
if event.type == pygame.QUIT:
run = False
if event.type == pygame.KEYDOWN:
if event.key == pygame.K_ESCAPE:
run = False
if event.type == pygame.USEREVENT and not self.foundFood:
for ant in ants:
ant.saveCoordinates()
# create ants one time
while not self.ant_stop:
for i in range(1500):
ants.append(
ant_file.Ant(self.width / 2, self.height / 2, 1,
random.randrange(3, 12)))
self.ant_stop = True
# draw things
for ant in ants:
ant.update(self.screen, self.width, self.height)
for food in foods:
food.update(self.screen)
self.nest.update(self.screen)
# food collison
for ant in ants:
if not ant.back:
for food in foods:
if ant.x >= food.x and ant.x <= food.x + food.width:
if ant.y >= food.y and ant.y <= food.y + food.height:
# save cords for one last time
ant.saveCoordinates()
back_ants.append(ant)
self.foundFood = True
# move ant back to nest
if self.foundFood:
for ant in back_ants:
#}ant.color = (127, 30, 212)
ant.moveBack(nest, self.fps)
#ant.drawPath(self.screen)
pygame.display.flip()
# S - source node
# D - destination node
S, D = map(int, raw_input().split())
for i in xrange(E):
# a - node
# b - node
# d - weight
a, b, weight = map(int, raw_input().split())
graph.add(a, b, weight)
for node in graph.nodes:
node.initPheromoneTable()
# Debug output - init pheromone table
for node in graph.nodes:
print "\nId: " + str(node.id + 1)
print "Links: "
for entry in node.pheromoneTable:
print "\tId: " + str(entry.link[0].id + 1) + " - " + str(
entry.probability) + '%'
# Generate ants
for i in xrange(globals.ANT_COUNT):
a = ant.Ant('ant_' + str(i) + '@127.0.0.1', globals.PASSWORD, i, graph,
globals.AntDirection.FORWARD_ANT, S - 1, D - 1)
a.start()
time.sleep(globals.TICK_PERIOD)
def __init__(self, numberOfAnts, size):
self.ants = [ant.Ant(i + 1, size) for i in range(numberOfAnts)]
self.size = size
self.board = [[0 for x in range(size[0])] for y in range(size[1])]
import pygame
import ant as antClass
from time import time as epoh
pygame.init()
size = [300, 300]
screen = pygame.display.set_mode(size)
pygame.display.set_caption('')
clock = pygame.time.Clock()
fps = 40
ants = []
for i in range(50):
ants += [(antClass.Ant(i + 125, size[1] - 12))]
ants += [(antClass.Ant(i + 125, size[1] - 10))]
ants += [(antClass.Ant(i + 125, size[1] - 8))]
screen.set_at((i + 125, size[1] - 8), [255, 0, 0])
time = int(epoh())
frames = 0
fireplace = pygame.image.load('fireplace.png')
fireplace = pygame.transform.scale(fireplace, (300, 350))
screen.blit(fireplace, (0, 0))
mona_lisa = pygame.image.load('mona_lisa.jpg')
mona_lisa = pygame.transform.scale(mona_lisa, (50, 70))
screen.blit(mona_lisa, (210, 33))
def test_no_deps(self):
a = ant.Ant()
self.assertEquals(a._dependencies(), [])
