def anneal(init_function,move_operator,objective_function,max_evaluations,\
start_temp,alpha, draw, every):
# wrap the objective function (so we record the best)
objective_function = ObjectiveFunction(objective_function)
current = init_function()
current_score = objective_function(current)
num_evaluations = 0
cl = None
t = None
if draw == True:
cl = visualizer.loadCityFile("./files/cityPixels.txt")
cities = fileio.readCities("./files/cities.txt")
cooling_schedule = kirkpatrick_cooling(start_temp, alpha)
logging.info('anneal started: score=%f', current_score)
for temperature in cooling_schedule:
done = False
# examine moves around our current position
for next in move_operator(current):
if num_evaluations >= max_evaluations:
done = True
break
next_score = objective_function(next)
if draw == True:
if (num_evaluations % every) == 0:
best = objective_function.best
bt = visualizer.makeTour(best, cities)
visualizer.plotTour(bt, cl, outFile = "./images/" + \
str(num_evaluations) + ".png")
num_evaluations += 1
# probablistically accept this solution
# always accepting better solutions
p = P(current_score, next_score, temperature)
if random.random() < p:
current = next
current_score = next_score
break
# see if completely finished
if done: break
best_score = objective_function.best_score
best = objective_function.best
logging.info('final temperature: %f', temperature)
logging.info('anneal finished: num_evaluations=%d, best_score=%f',
num_evaluations, best_score)
return (num_evaluations, best_score, best)
def anneal(init_function, move_operator, objective_function, max_evaluations, start_temp, alpha, draw, every):
# wrap the objective function (so we record the best)
objective_function = ObjectiveFunction(objective_function)
current = init_function()
current_score = objective_function(current)
num_evaluations = 0
cl = None
t = None
if draw == True:
cl = visualizer.loadCityFile("./files/cityPixels.txt")
cities = fileio.readCities("./files/cities.txt")
cooling_schedule = kirkpatrick_cooling(start_temp, alpha)
logging.info("anneal started: score=%f", current_score)
for temperature in cooling_schedule:
done = False
# examine moves around our current position
for next in move_operator(current):
if num_evaluations >= max_evaluations:
done = True
break
next_score = objective_function(next)
if draw == True:
if (num_evaluations % every) == 0:
best = objective_function.best
bt = visualizer.makeTour(best, cities)
visualizer.plotTour(bt, cl, outFile="./images/" + str(num_evaluations) + ".png")
num_evaluations += 1
# probablistically accept this solution
# always accepting better solutions
p = P(current_score, next_score, temperature)
if random.random() < p:
current = next
current_score = next_score
break
# see if completely finished
if done:
break
best_score = objective_function.best_score
best = objective_function.best
logging.info("final temperature: %f", temperature)
logging.info("anneal finished: num_evaluations=%d, best_score=%f", num_evaluations, best_score)
return (num_evaluations, best_score, best)
copy = tour[:]
if i < j:
copy[i:j + 1] = reversed(tour[i:j + 1])
else:
copy[i + 1:] = reversed(tour[:j])
copy[:j] = reversed(tour[i + 1:])
if copy != tour: # no point returning the same tour
yield copy
def run_sa(iterations, dist, start, alpha, draw=True, every=100):
init_function = lambda: init_random_tour(len(dist[0]))
objective_function = lambda tour: -tour_length(dist, tour)
move_operator = reversed_sections
iterations,score,best = sa.anneal(init_function, move_operator, \
objective_function, iterations, \
start, alpha, draw, every)
print iterations, score, best
return best
if __name__ == "__main__":
cities = fileio.readCities("./files/cities.txt")
dist = fileio.readDistances("./files/distMiles.txt")
best = run_sa(5000, dist, 100, 0.90, draw=True, every=100)
bt = visualizer.makeTour(best, cities)
fileio.writeTour("tour.txt", bt)
if i < j:
copy[i:j+1]=reversed(tour[i:j+1])
else:
copy[i+1:]=reversed(tour[:j])
copy[:j]=reversed(tour[i+1:])
if copy != tour: # no point returning the same tour
yield copy
def run_sa(iterations, dist, start, alpha, draw = True, every = 100):
init_function = lambda: init_random_tour(len(dist[0]))
objective_function = lambda tour: -tour_length(dist, tour)
move_operator = reversed_sections
iterations,score,best = sa.anneal(init_function, move_operator, \
objective_function, iterations, \
start, alpha, draw, every)
print iterations, score, best
return best
if __name__ == "__main__":
cities = fileio.readCities("./files/cities.txt")
dist = fileio.readDistances("./files/distMiles.txt")
best = run_sa(5000, dist, 100, 0.90, draw=True, every=100)
bt = visualizer.makeTour(best, cities)
fileio.writeTour("tour.txt", bt)