def run_mimic(t, samples, keep, m):
fill = [N] * N
ranges = array('i', fill)
ef = TravelingSalesmanRouteEvaluationFunction(points)
odd = DiscreteUniformDistribution(ranges)
fname = outfile.format('MIMIC{}_{}_{}'.format(samples, keep, m),
str(t + 1))
base.write_header(fname)
df = DiscreteDependencyTree(m, ranges)
ef = TravelingSalesmanRouteEvaluationFunction(points)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
mimic = MIMIC(samples, keep, pop)
fit = FixedIterationTrainer(mimic, 10)
times = [0]
for i in range(0, maxIters, 10):
start = clock()
fit.train()
elapsed = time.clock() - start
times.append(times[-1] + elapsed)
fevals = ef.fevals
score = ef.value(mimic.getOptimal())
ef.fevals -= 1
st = '{},{},{},{}\n'.format(i, score, times[-1], fevals)
# print st
base.write_to_file(fname, st)
return
def run_sa(t, CE):
fname = outfile.format('SA{}'.format(CE), str(t + 1))
ef = TravelingSalesmanRouteEvaluationFunction(points)
hcp = GenericHillClimbingProblem(ef, odd, nf)
sa = SimulatedAnnealing(1E10, CE, hcp)
fit = FixedIterationTrainer(sa, 10)
times = [0]
for i in range(0, maxIters, 10):
start = clock()
fit.train()
elapsed = time.clock() - start
times.append(times[-1] + elapsed)
fevals = ef.fevals
score = ef.value(sa.getOptimal())
ef.fevals -= 1
st = '{},{},{},{}\n'.format(i, score, times[-1], fevals)
# print st
base.write_to_file(fname, st)
return
def run_rhc(t):
fname = outfile.format('RHC', str(t + 1))
ef = TravelingSalesmanRouteEvaluationFunction(points)
hcp = GenericHillClimbingProblem(ef, odd, nf)
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, 10)
times = [0]
for i in range(0, maxIters, 10):
start = clock()
fit.train()
elapsed = time.clock() - start
times.append(times[-1] + elapsed)
fevals = ef.fevals
score = ef.value(rhc.getOptimal())
ef.fevals -= 1
st = '{},{},{},{}\n'.format(i, score, times[-1], fevals)
# print st
base.write_to_file(fname, st)
return
def run_ga(t, pop, mate, mutate):
fname = outfile.format('GA{}_{}_{}'.format(pop, mate, mutate), str(t + 1))
ef = TravelingSalesmanRouteEvaluationFunction(points)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
ga = StandardGeneticAlgorithm(pop, mate, mutate, gap)
fit = FixedIterationTrainer(ga, 10)
times = [0]
for i in range(0, maxIters, 10):
start = clock()
fit.train()
elapsed = time.clock() - start
times.append(times[-1] + elapsed)
fevals = ef.fevals
score = ef.value(ga.getOptimal())
ef.fevals -= 1
st = '{},{},{},{}\n'.format(i, score, times[-1], fevals)
# print st
base.write_to_file(fname, st)
return
points[i][0] = random.nextDouble()
points[i][1] = random.nextDouble()
ef = TravelingSalesmanRouteEvaluationFunction(points)
odd = DiscretePermutationDistribution(N)
nf = SwapNeighbor()
mf = SwapMutation()
cf = TravelingSalesmanCrossOver(ef)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
start = time.time()
ga = StandardGeneticAlgorithm(param[0], param[1], param[2], gap)
fit = FixedIterationTrainer(ga, num_iterations)
fit.train()
value = str(ef.value(ga.getOptimal()))
print "GA Inverse of Distance: " + value
end = time.time()
print "Route:"
path = []
for x in range(0, N):
path.append(ga.getOptimal().getDiscrete(x))
print path
print "Time -->", end - start
results = {
'num_iterations': num_iterations,
'value': value,
'time': end - start
}
for i in range(0, len(points)):
points[i][0] = random.nextDouble()
points[i][1] = random.nextDouble()
ef = TravelingSalesmanRouteEvaluationFunction(points)
odd = DiscretePermutationDistribution(N)
nf = SwapNeighbor()
mf = SwapMutation()
cf = TravelingSalesmanCrossOver(ef)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, 200000)
fit.train()
print "RHC Inverse of Distance: " + str(ef.value(rhc.getOptimal()))
print "Route:"
path = []
for x in range(0, N):
path.append(rhc.getOptimal().getDiscrete(x))
print path
sa = SimulatedAnnealing(1e12, 0.999, hcp)
fit = FixedIterationTrainer(sa, 200000)
fit.train()
print "SA Inverse of Distance: " + str(ef.value(sa.getOptimal()))
print "Route:"
path = []
for x in range(0, N):
path.append(sa.getOptimal().getDiscrete(x))
print path
for GA_MUTATION in GA_MUTATION_pool:
ga = StandardGeneticAlgorithm(GA_POPULATION, GA_CROSSOVER, GA_MUTATION, gap)
fit_ga = FixedIterationTrainer(ga, n_iteration)
print("calculating for mutations = %d " % GA_MUTATION)
# Training
start_ga = time.time()
fit_ga.train()
end_ga = time.time()
# Result extracting
last_training_time_ga = end_ga - start_ga
ga_training_time[n].append(last_training_time_ga)
ga_fitness[n].append(ef.value(ga.getOptimal()))
overall_ga_training_time = list_avg(*ga_training_time)
overall_ga_fitness = list_avg(*ga_fitness)
with open(OUTPUT_FILE, "w") as outFile:
for i in range(1):
outFile.write(','.join([
"ga_mutations",
"ga_fitness",
"ga_training_time"]) + '\n')
for i in range(len(GA_MUTATION_pool)):
outFile.write(','.join([
str(GA_MUTATION_pool[i]),
str(overall_ga_fitness[i]),
str(overall_ga_training_time[i])]) + '\n')
df = DiscreteDependencyTree(m, ranges)
f = open(fname, 'w')
f.write('iterations,fitness,time,fevals\n')
f.close()
ef = TravelingSalesmanRouteEvaluationFunction(points)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
mimic = MIMIC(samples, keep, pop)
fit = FixedIterationTrainer(mimic, 10)
times = [0]
for i in range(0, maxIters, 10):
start = clock()
fit.train()
elapsed = time.clock() - start
times.append(times[-1] + elapsed)
fevals = ef.fevals
score = ef.value(mimic.getOptimal())
ef.fevals -= 1
st = '{},{},{},{}\n'.format(i, score, times[-1], fevals)
print st
f = open(fname, 'a')
f.write(st)
f.close()
# RHC
for t in range(numTrials):
fname = outfile.format('RHC', str(t + 1))
f = open(fname, 'w')
f.write('iterations,fitness,time,fevals\n')
f.close()
ef = TravelingSalesmanRouteEvaluationFunction(points)
hcp = GenericHillClimbingProblem(ef, odd, nf)
rhc = RandomizedHillClimbing(hcp)
start_sa = time.time()
fit_sa.train()
end_sa = time.time()
start_ga = time.time()
fit_ga.train()
end_ga = time.time()
start_mimic = time.time()
fit_mimic.train()
end_mimic = time.time()
# Result handling
last_train_time_rhc = end_rhc - start_rhc
rhc_train_time[repetition].append(last_train_time_rhc)
rhc_accuracy[repetition].append(ef.value(rhc.getOptimal()))
last_train_time_sa = end_sa - start_sa
sa_train_time[repetition].append(last_train_time_sa)
sa_accuracy[repetition].append(ef.value(sa.getOptimal()))
last_train_time_ga = end_ga - start_ga
ga_train_time[repetition].append(last_train_time_ga)
ga_accuracy[repetition].append(ef.value(ga.getOptimal()))
last_train_time_mimic = end_mimic - start_mimic
mimic_train_time[repetition].append(last_train_time_mimic)
mimic_accuracy[repetition].append(ef.value(mimic.getOptimal()))
while current_iteration_count <= MAX_ITERATION - ITERATION_STEP:
print("Computing for %d iterations" %
for i in range(0, len(points)):
points[i][0] = random.nextDouble()
points[i][1] = random.nextDouble()
ef = TravelingSalesmanRouteEvaluationFunction(points)
odd = DiscretePermutationDistribution(N)
nf = SwapNeighbor()
mf = SwapMutation()
cf = TravelingSalesmanCrossOver(ef)
hcp = GenericHillClimbingProblem(ef, odd, nf)
start = time.time()
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, num_iterations)
fit.train()
value = str(ef.value(rhc.getOptimal()))
print "RHC Inverse of Distance: " + value
end = time.time()
print "Route:"
path = []
for x in range(0, N):
path.append(rhc.getOptimal().getDiscrete(x))
print path
print "Time -->", end - start
results = {
'num_iterations': num_iterations,
'value': value,
'time': end - start
}
points[i][1] = random.nextDouble()
ef = TravelingSalesmanRouteEvaluationFunction(points)
odd = DiscretePermutationDistribution(N)
nf = SwapNeighbor()
mf = SwapMutation()
cf = TravelingSalesmanCrossOver(ef)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
start = time.time()
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, iteration)
fit.train()
end = time.time()
rhc_fit = ef.value(rhc.getOptimal())
rhc_time = end - start
print "RHC: " + str(rhc_fit)
start = time.time()
sa = SimulatedAnnealing(1E12, .999, hcp)
fit = FixedIterationTrainer(sa, iteration)
fit.train()
end = time.time()
sa_fit = ef.value(sa.getOptimal())
sa_time = end - start
print "SA: " + str(sa_fit)
# if iteration <= 5000:
#
# start = time.time()
for r in range(reps):
# create a new trainer each time
row = {}
for key, value in zip(keys, exper):
row[key] = value
print(row)
fit = factory(row)
# have to reset func eval per run
ef.func_evals = 0
for i in range(0, max_iterations,num_iterations):
start = clock()
fit.train()
stop = clock()
rep_times.append(stop - start)
func_eval = ef.func_evals
fitness = ef.value(fit.trainer.getOptimal())
# log
line = [name,r,i,fitness,rep_times[-1], func_eval] + list(exper)
line = [str(x) for x in line]
out.write(','.join(line) + os.linesep)
print "Done " + name + " trainer..."
# rhc = RandomizedHillClimbing(hcp)
# fit = FixedIterationTrainer(rhc, 200000)
# fit.train()
# print "RHC Inverse of Distance: " + str(ef.value(rhc.getOptimal()))
cf = TravelingSalesmanCrossOver(ef)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
ef = TravelingSalesmanSortEvaluationFunction(points);
fill = [N] * N
ranges = array('i', fill)
odd = DiscreteUniformDistribution(ranges);
df = DiscreteDependencyTree(.1, ranges);
pop = GenericProbabilisticOptimizationProblem(ef, odd, df);
start = time.time()
mimic = MIMIC(param[0], param[1], pop)
fit = FixedIterationTrainer(mimic, num_iterations)
fit.train()
value = str(ef.value(mimic.getOptimal()))
print "MIMIC Inverse of Distance: " + value
end = time.time()
print "Route:"
path = []
optimal = mimic.getOptimal()
fill = [0] * optimal.size()
ddata = array('d', fill)
for i in range(0, len(ddata)):
ddata[i] = optimal.getContinuous(i)
order = ABAGAILArrays.indices(optimal.size())
ABAGAILArrays.quicksort(ddata, order)
print order
end = time.time()
results = {
'num_iterations': num_iterations,
ef = TravelingSalesmanRouteEvaluationFunction(points)
odd = DiscretePermutationDistribution(N)
nf = SwapNeighbor()
mf = SwapMutation()
cf = TravelingSalesmanCrossOver(ef)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, N)
start = time.time()
fit.train()
end = time.time()
training_time = end - start
print "RHC Inverse of Distance: " + str(ef.value(rhc.getOptimal()))
OUTFILE = "%s%s.csv" % (OUTFILE_BASE, "RHC")
with open(OUTFILE, 'a+') as f:
f.write("%d,%f,%f\n" % (N, training_time, ef.value(rhc.getOptimal())))
sa = SimulatedAnnealing(1E12, .999, hcp)
fit = FixedIterationTrainer(sa, N)
start = time.time()
fit.train()
end = time.time()
training_time = end - start
print "SA Inverse of Distance: " + str(ef.value(sa.getOptimal()))
OUTFILE = "%s%s.csv" % (OUTFILE_BASE, "SA")
with open(OUTFILE, 'a+') as f:
for x in range(runs):
ranges = array('i', [2] * N)
fitness = TravelingSalesmanRouteEvaluationFunction(points)
discrete_dist = DiscretePermutationDistribution(N)
discrete_neighbor = SwapNeighbor()
discrete_mutation = SwapMutation()
discrete_dependency = DiscreteDependencyTree(.1, ranges)
hill_problem = GHC(fitness, discrete_dist, discrete_neighbor)
start = time.clock()
rhc_problem = RHC(hill_problem)
fit = FixedIterationTrainer(rhc_problem, iteration)
fit.train()
end = time.clock()
full_time = end - start
rhc_total += fitness.value(rhc_problem.getOptimal())
rhc_time += full_time
rhc_total_avg = rhc_total / runs
rhc_time_avg = rhc_time / runs
data = '{},{},{}\n'.format(iteration, rhc_total_avg, rhc_time_avg)
print(data)
with open(output_directory, 'a') as f:
f.write(data)
#SA
output_directory = "Results/Small/TSP_SA.csv"
with open(output_directory, 'w') as f:
f.write('iterations,fitness,time\n')
def main():
iterations = 200000
alg = 'all'
gaPop = 2000
gaMate = 1500
gaMutate = 250
mimicSamples = 500
mimicToKeep = 100
saTemp = 1E12
saCooling = .999
gaIters = 1000
mimicIters = 1000
run = 0
settings = []
try:
opts, args = getopt.getopt(sys.argv[1:], "ahrsgmn:i:", ["gaIters=", "mimicIters=", "gaPop=", "gaMate=", "gaMutate=", "mimicSamples=", "mimicToKeep=", "saTemp=", "saCooling="])
except:
print 'travelingsalesman.py -i <iterations>'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'travelingsalesman.py -i <iterations>'
sys.exit(1)
elif opt == '-i':
if arg < 1:
print 'Iterations must be greater than 0'
sys.exit(2)
iterations = int(arg)
elif opt == '-a':
alg = 'all'
elif opt == '-r':
alg = 'RHC'
elif opt == '-s':
alg = 'SA'
elif opt == '-g':
alg = 'GA'
elif opt == '-m':
alg = 'MIMIC'
elif opt == '--gaPop':
if arg < 1:
print 'Population must be greater than 0'
sys.exit(2)
gaPop = int(arg)
elif opt == '--gaMate':
if arg < 1:
print 'Mating must be greater than 0'
sys.exit(2)
gaMate = int(arg)
elif opt == '--gaMutate':
if arg < 1:
print 'Mutators must be greater than 0'
sys.exit(2)
gaMutate = int(arg)
elif opt == '--mimicSamples':
if arg < 1:
print 'MIMIC samples must be greater than 0'
sys.exit(2)
mimicSamples = int(arg)
elif opt == '--mimicToKeep':
if arg < 1:
print 'MIMIC to keep must be greater than 0'
sys.exit(2)
mimicToKeep = int(arg)
elif opt == '--saTemp':
saTemp = float(arg)
elif opt == '--saCooling':
saCooling = float(arg)
elif opt == '-n':
run = int(arg)
elif opt == '--gaIters':
if arg < 1:
print 'GA Iterations must be greater than 0'
sys.exit(2)
gaIters = int(arg)
elif opt == '--mimicIters':
if arg < 1:
print 'MIMIC Iterations must be greater than 0'
sys.exit(2)
mimicIters = int(arg)
vars = {
'iterations' : iterations,
'alg' : alg,
'gaPop' : gaPop,
'gaMate' : gaMate,
'gaMutate' : gaMutate,
'mimicSamples' : mimicSamples,
'mimicToKeep' : mimicToKeep,
'saTemp' : saTemp,
'saCooling' : saCooling,
'gaIters' : gaIters,
'mimicIters' : mimicIters,
'run' : run
}
settings = getSettings(alg, settings, vars)
if gaPop < gaMate or gaPop < gaMutate or gaMate < gaMutate:
pebkac({gaPop: 'total population',gaMate : 'mating population', gaMutate : 'mutating population'}, alg, 'total population', settings)
if mimicSamples < mimicToKeep:
pebkac({mimicSamples: 'mimic samples', mimicToKeep : 'mimic to keep'}, alg, 'mimic samples', settings)
prob = 'Traveling Sales Problem'
invDist = {}
cities = CityList()
N = len(cities)
#random = Random()
points = [[0 for x in xrange(2)] for x in xrange(N)]
for i in range(0, len(points)):
coords = cities.getCoords(i)
points[i][0] = coords[0]
points[i][1] = coords[1]
ef = TravelingSalesmanRouteEvaluationFunction(points)
odd = DiscretePermutationDistribution(N)
nf = SwapNeighbor()
mf = SwapMutation()
cf = TravelingSalesmanCrossOver(ef)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
rows = []
if alg == 'RHC' or alg == 'all':
print '\n----------------------------------'
print 'Using Random Hill Climbing'
for label, setting in settings:
print label + ":" + str(setting)
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, iterations)
fit.train()
path = []
for x in range(0,N):
path.append(rhc.getOptimal().getDiscrete(x))
output(prob, 'RHC', path, points, settings)
rows = []
row = []
row.append("Inverse of Distance")
row.append(ef.value(rhc.getOptimal()))
rows.append(row)
invDist['RHC'] = ef.value(rhc.getOptimal())
buildFooter(prob, 'RHC', rows, settings)
outputFooter(prob, 'RHC', rows, settings)
if alg == 'SA' or alg == 'all':
print 'Using Simulated Annealing'
for label, setting in settings:
print label + ":" + str(setting)
sa = SimulatedAnnealing(saTemp, saCooling, hcp)
fit = FixedIterationTrainer(sa, iterations)
fit.train()
path = []
for x in range(0,N):
path.append(sa.getOptimal().getDiscrete(x))
output(prob, 'SA', path, points, settings)
rows = []
row = []
row.append("Inverse of Distance")
row.append(ef.value(sa.getOptimal()))
rows.append(row)
invDist['SA'] = ef.value(sa.getOptimal())
buildFooter(prob, 'SA', rows, settings)
outputFooter(prob, 'SA', rows, settings)
if alg == 'GA' or alg == 'all':
print '\n----------------------------------'
print 'Using Genetic Algorithm'
for label, setting in settings:
print label + ":" + str(setting)
ga = StandardGeneticAlgorithm(gaPop, gaMate, gaMutate, gap)
fit = FixedIterationTrainer(ga, gaIters)
fit.train()
path = []
for x in range(0,N):
path.append(ga.getOptimal().getDiscrete(x))
output(prob, 'GA', path, points, settings)
rows = []
row = []
row.append("Inverse of Distance")
row.append(ef.value(ga.getOptimal()))
rows.append(row)
invDist['GA'] = ef.value(ga.getOptimal())
buildFooter(prob, 'GA', rows, settings)
outputFooter(prob, 'GA', rows, settings)
if alg == 'MIMIC' or alg == 'all':
print '\n----------------------------------'
print 'Using MIMIC'
for label, setting in settings:
print label + ":" + str(setting)
# for mimic we use a sort encoding
ef = TravelingSalesmanSortEvaluationFunction(points);
fill = [N] * N
ranges = array('i', fill)
odd = DiscreteUniformDistribution(ranges);
df = DiscreteDependencyTree(.1, ranges);
pop = GenericProbabilisticOptimizationProblem(ef, odd, df);
mimic = MIMIC(mimicSamples, mimicToKeep, pop)
fit = FixedIterationTrainer(mimic, mimicIters)
fit.train()
path = []
optimal = mimic.getOptimal()
fill = [0] * optimal.size()
ddata = array('d', fill)
for i in range(0,len(ddata)):
ddata[i] = optimal.getContinuous(i)
order = ABAGAILArrays.indices(optimal.size())
ABAGAILArrays.quicksort(ddata, order)
output(prob, 'MIMIC', order, points, settings)
rows = []
row = []
row.append("Inverse of Distance")
row.append(ef.value(mimic.getOptimal()))
rows.append(row)
invDist['MIMIC'] = ef.value(mimic.getOptimal())
buildFooter(prob, 'MIMIC', rows, settings)
outputFooter(prob, 'MIMIC', rows, settings)
maxn = max(len(key) for key in invDist)
maxd = max(len(str(invDist[key])) for key in invDist)
print "Results"
for result in invDist:
print "%-*s %s %-*s" % (len('Best Alg') + 2, result, ':', maxd, invDist[result])
if alg == 'all':
print "%-*s %s %-*s" % (len('Best Alg') + 2, 'Best Alg', ':', maxd, max(invDist.iterkeys(), key=(lambda key: invDist[key])))
print '----------------------------------'
mf = SwapMutation()
cf = TravelingSalesmanCrossOver(ef)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
rhc = RandomizedHillClimbing(hcp)
sa = SimulatedAnnealing(1E12, .999, hcp)
ga = StandardGeneticAlgorithm(2000, 1500, 250, gap)
rhc_f = open('out/op/salesman/rhc.csv', 'w')
sa_f = open('out/op/salesman/sa.csv', 'w')
ga_f = open('out/op/salesman/ga.csv', 'w')
for i in range(ITERATIONS):
rhc.train()
rhc_fitness = ef.value(rhc.getOptimal())
rhc_f.write('{},{}\n'.format(i, rhc_fitness))
sa.train()
sa_fitness = ef.value(sa.getOptimal())
sa_f.write('{},{}\n'.format(i, sa_fitness))
ga.train()
ga_fitness = ef.value(ga.getOptimal())
ga_f.write('{},{}\n'.format(i, ga_fitness))
rhc_f.close()
sa_f.close()
ga_f.close()
# for mimic we use a sort encoding
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
iterations = range(1000, 50000, 5000)
rhc_results = []
sa_results = []
ga_results = []
import time
rhc_startTime = time.time()
for iteration in iterations:
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, iteration)
fit.train()
rhc_optimalVal = ef.value(rhc.getOptimal())
# print "RHC Inverse of Distance: " + str(ef.value(rhc.getOptimal()))
# print "Route:"
# path = []
# for x in range(0,N):
# path.append(rhc.getOptimal().getDiscrete(x))
# print path
rhc_results.append(rhc_optimalVal)
rhc_endTime = time.time()
print "RHC Completed in: " + str(rhc_endTime - rhc_startTime)
sa_startTime = time.time()
for iteration in iterations:
sa = SimulatedAnnealing(1E12, .999, hcp)
fit = FixedIterationTrainer(sa, iteration)
fit.train()
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
nsample = 10
niters = [50, 100, 200, 500, 1000, 2000, 5000, 10000, 20000, 50000, 100000]
##################################################################################################
#-------------------------------------- R-Hill Climbing ------------------------------------------
rhc = RandomizedHillClimbing(hcp)
for iters in niters:
start = time.time()
fit = FixedIterationTrainer(rhc, iters)
value = 0
for isample in range(nsample):
fit.train()
value += ef.value(rhc.getOptimal())
end = time.time()
clock_time = (end - start) / nsample
value = round(value / nsample, 2)
print "RHC " + str(1 / value), iters, clock_time
path = []
for x in range(0, N):
path.append(rhc.getOptimal().getDiscrete(x))
print "Rout_RHC", path[x], points[path[x]][0], points[path[x]][1]
###################################################################################################
#--------------------------------- Simulated Annealing --------------------------------------------
sa = SimulatedAnnealing(1E12, .999, hcp)
for iters in niters:
start = time.time()
for CE in [0.15, 0.35, 0.55, 0.75, 0.95]:
fname = outfile.replace('@ALG@',
'SA{}'.format(CE)).replace('@N@', str(t + 1))
with open(fname, 'w') as f:
f.write('iterations,fitness,time,fevals\n')
ef = TravelingSalesmanRouteEvaluationFunction(points)
hcp = GenericHillClimbingProblem(ef, odd, nf)
sa = SimulatedAnnealing(1E10, CE, hcp)
fit = FixedIterationTrainer(sa, 10)
times = [0]
for i in range(0, maxIters, 10):
start = clock()
fit.train()
elapsed = time.clock() - start
times.append(times[-1] + elapsed)
score = ef.value(sa.getOptimal())
st = '{},{},{}\n'.format(i, score, times[-1])
print st
with open(fname, 'a') as f:
f.write(st)
'''
#GA
for t in range(numTrials):
for pop,mate,mutate in product([100],[50,30,10],[30,10]):
fname = outfile.replace('@ALG@','GA{}_{}_{}'.format(pop,mate,mutate)).replace('@N@',str(t+1))
with open(fname,'w') as f:
f.write('iterations,fitness,time,fevals\n')
ef = TravelingSalesmanRouteEvaluationFunction(points)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
ga = StandardGeneticAlgorithm(pop, mate, mutate, gap)
fit = FixedIterationTrainer(ga, 10)
def run_traveling_salesman():
# set N value. This is the number of points
N = 50
random = Random()
points = [[0 for x in xrange(2)] for x in xrange(N)]
for i in range(0, len(points)):
points[i][0] = random.nextDouble()
points[i][1] = random.nextDouble()
ef = TravelingSalesmanRouteEvaluationFunction(points)
odd = DiscretePermutationDistribution(N)
nf = SwapNeighbor()
mf = SwapMutation()
cf = TravelingSalesmanCrossOver(ef)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
iters = [50, 100, 250, 500, 1000, 2500, 5000, 10000, 25000, 50000, 100000]
num_repeats = 5
rhc_results = []
rhc_times = []
for i in iters:
print(i)
for j in range(num_repeats):
start = time.time()
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, i)
fit.train()
end = time.time()
rhc_results.append(ef.value(rhc.getOptimal()))
rhc_times.append(end - start)
print "RHC Inverse of Distance: " + str(ef.value(rhc.getOptimal()))
# print "Route:"
# path = []
# for x in range(0,N):
# path.append(rhc.getOptimal().getDiscrete(x))
# print path
sa_results = []
sa_times = []
for i in iters:
print(i)
for j in range(num_repeats):
start = time.time()
sa = SimulatedAnnealing(1E12, .999, hcp)
fit = FixedIterationTrainer(sa, i)
fit.train()
sa_results.append(ef.value(sa.getOptimal()))
sa_times.append(end - start)
print "SA Inverse of Distance: " + str(ef.value(sa.getOptimal()))
# print "Route:"
# path = []
# for x in range(0,N):
# path.append(sa.getOptimal().getDiscrete(x))
# print path
ga_results = []
ga_times = []
for i in iters:
print(i)
for j in range(num_repeats):
start = time.time()
ga = StandardGeneticAlgorithm(2000, 1500, 250, gap)
fit = FixedIterationTrainer(ga, i)
fit.train()
end = time.time()
ga_results.append(ef.value(ga.getOptimal()))
print "GA Inverse of Distance: " + str(ef.value(ga.getOptimal()))
ga_times.append(end - start)
# print "Route:"
# path = []
# for x in range(0,N):
# path.append(ga.getOptimal().getDiscrete(x))
# print path
# for mimic we use a sort encoding
ef = TravelingSalesmanSortEvaluationFunction(points)
fill = [N] * N
ranges = array('i', fill)
odd = DiscreteUniformDistribution(ranges)
df = DiscreteDependencyTree(.1, ranges)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
mimic_results = []
mimic_times = []
for i in iters[0:6]:
print(i)
for j in range(num_repeats):
start = time.time()
mimic = MIMIC(500, 100, pop)
fit = FixedIterationTrainer(mimic, i)
fit.train()
end = time.time()
mimic_results.append(ef.value(mimic.getOptimal()))
print "MIMIC Inverse of Distance: " + str(
ef.value(mimic.getOptimal()))
# print "Route:"
# path = []
# optimal = mimic.getOptimal()
# fill = [0] * optimal.size()
# ddata = array('d', fill)
# for i in range(0,len(ddata)):
# ddata[i] = optimal.getContinuous(i)
# order = ABAGAILArrays.indices(optimal.size())
# ABAGAILArrays.quicksort(ddata, order)
# print order
mimic_times.append(end - start)
with open('travelingsalesman.csv', 'w') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(rhc_results)
writer.writerow(rhc_times)
writer.writerow(sa_results)
writer.writerow(sa_times)
writer.writerow(ga_results)
writer.writerow(ga_times)
writer.writerow(mimic_results)
writer.writerow(mimic_times)
return rhc_results, rhc_times, sa_results, sa_times, ga_results, ga_times, mimic_results, mimic_times
def travelingsalesmanfunc(N, iterations):
rhcMult = 1500
saMult = 1500
gaMult = 1
mimicMult = 3
random = Random()
points = [[0 for x in xrange(2)] for x in xrange(N)]
for i in range(0, len(points)):
points[i][0] = random.nextDouble()
points[i][1] = random.nextDouble()
optimalOut = []
timeOut = []
evalsOut = []
for niter in iterations:
ef = TravelingSalesmanRouteEvaluationFunction(points)
odd = DiscretePermutationDistribution(N)
nf = SwapNeighbor()
mf = SwapMutation()
cf = TravelingSalesmanCrossOver(ef)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
iterOptimalOut = [N, niter]
iterTimeOut = [N, niter]
iterEvals = [N, niter]
start = time.time()
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, niter * rhcMult)
fit.train()
end = time.time()
rhcOptimal = ef.value(rhc.getOptimal())
rhcTime = end - start
print "RHC Inverse of Distance: optimum: " + str(rhcOptimal)
print "RHC time: " + str(rhcTime)
#print "RHC Inverse of Distance: " + str(ef.value(rhc.getOptimal()))
print "Route:"
path = []
for x in range(0, N):
path.append(rhc.getOptimal().getDiscrete(x))
print path
iterOptimalOut.append(rhcOptimal)
iterTimeOut.append(rhcTime)
functionEvals = ef.getNumEvals()
ef.zeroEvals()
iterEvals.append(functionEvals)
start = time.time()
sa = SimulatedAnnealing(1E12, .999, hcp)
fit = FixedIterationTrainer(sa, niter * saMult)
fit.train()
end = time.time()
saOptimal = ef.value(sa.getOptimal())
saTime = end - start
print "SA Inverse of Distance optimum: " + str(saOptimal)
print "SA time: " + str(saTime)
#print "SA Inverse of Distance: " + str(ef.value(sa.getOptimal()))
print "Route:"
path = []
for x in range(0, N):
path.append(sa.getOptimal().getDiscrete(x))
print path
iterOptimalOut.append(saOptimal)
iterTimeOut.append(saTime)
functionEvals = ef.getNumEvals()
ef.zeroEvals()
iterEvals.append(functionEvals)
start = time.time()
ga = StandardGeneticAlgorithm(2000, 1500, 250, gap)
fit = FixedIterationTrainer(ga, niter * gaMult)
fit.train()
end = time.time()
gaOptimal = ef.value(ga.getOptimal())
gaTime = end - start
print "GA Inverse of Distance optimum: " + str(gaOptimal)
print "GA time: " + str(gaTime)
#print "GA Inverse of Distance: " + str(ef.value(ga.getOptimal()))
print "Route:"
path = []
for x in range(0, N):
path.append(ga.getOptimal().getDiscrete(x))
print path
iterOptimalOut.append(gaOptimal)
iterTimeOut.append(gaTime)
functionEvals = ef.getNumEvals()
ef.zeroEvals()
iterEvals.append(functionEvals)
start = time.time()
# for mimic we use a sort encoding
ef = TravelingSalesmanSortEvaluationFunction(points)
fill = [N] * N
ranges = array('i', fill)
odd = DiscreteUniformDistribution(ranges)
df = DiscreteDependencyTree(.1, ranges)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
start = time.time()
mimic = MIMIC(500, 100, pop)
fit = FixedIterationTrainer(mimic, niter * mimicMult)
fit.train()
end = time.time()
mimicOptimal = ef.value(mimic.getOptimal())
mimicTime = end - start
print "MIMIC Inverse of Distance optimum: " + str(mimicOptimal)
print "MIMIC time: " + str(mimicTime)
#print "MIMIC Inverse of Distance: " + str(ef.value(mimic.getOptimal()))
print "Route:"
path = []
optimal = mimic.getOptimal()
fill = [0] * optimal.size()
ddata = array('d', fill)
for i in range(0, len(ddata)):
ddata[i] = optimal.getContinuous(i)
order = ABAGAILArrays.indices(optimal.size())
ABAGAILArrays.quicksort(ddata, order)
print order
iterOptimalOut.append(mimicOptimal)
iterTimeOut.append(mimicTime)
functionEvals = ef.getNumEvals()
ef.zeroEvals()
iterEvals.append(functionEvals)
optimalOut.append(iterOptimalOut)
timeOut.append(iterTimeOut)
evalsOut.append(iterEvals)
return [optimalOut, timeOut, evalsOut]
def solveit(oaname, params):
# set N value. This is the number of points
N = 50
iterations = 1000
tryi = 1
random = Random()
points = [[0 for x in xrange(2)] for x in xrange(N)]
for i in range(0, len(points)):
points[i][0] = random.nextDouble()
points[i][1] = random.nextDouble()
ef = TravelingSalesmanRouteEvaluationFunction(points)
odd = DiscretePermutationDistribution(N)
nf = SwapNeighbor()
mf = SwapMutation()
cf = TravelingSalesmanCrossOver(ef)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
if oaname == "RHC":
iterations = int(params[0])
tryi = int(params[1])
oa = RandomizedHillClimbing(hcp)
if oaname == "SA":
oa = SimulatedAnnealing(float(params[0]), float(params[1]), hcp)
if oaname == "GA":
iterations=1000
oa = StandardGeneticAlgorithm(int(params[0]), int(params[1]), int(params[2]), gap)
if oaname == "MMC":
iterations=1000
# for mimic we use a sort encoding
ef = TravelingSalesmanSortEvaluationFunction(points)
fill = [N] * N
ranges = array('i', fill)
odd = DiscreteUniformDistribution(ranges)
df = DiscreteDependencyTree(.1, ranges)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
oa = MIMIC(int(params[0]), int(params[1]), pop)
print "Running %s using %s for %d iterations, try %d" % (oaname, ','.join(params), iterations, tryi)
print "="*20
starttime = timeit.default_timer()
output = []
for i in range(iterations):
oa.train()
if i%10 == 0:
optimal = oa.getOptimal()
score = ef.value(optimal)
elapsed = int(timeit.default_timer()-starttime)
output.append([str(i), str(score), str(elapsed)])
print 'Inverse of Distance [score]: %.3f' % score
print 'train time: %d secs' % (int(timeit.default_timer()-starttime))
scsv = 'tsp-%s-%s.csv' % (oaname, '-'.join(params))
print "Saving to %s" % (scsv),
with open(scsv, 'w') as csvf:
writer = csv.writer(csvf)
for row in output:
writer.writerow(row)
print "saved."
print "="*20
print "Route:"
if oaname == 'MMC':
optimal = oa.getOptimal()
fill = [0] * optimal.size()
ddata = array('d', fill)
for i in range(0,len(ddata)):
ddata[i] = optimal.getContinuous(i)
order = ABAGAILArrays.indices(optimal.size())
ABAGAILArrays.quicksort(ddata, order)
print order
else:
path = []
for x in range(0,N):
path.append(oa.getOptimal().getDiscrete(x))
print path
start_sa = time.time()
fit_sa.train()
end_sa = time.time()
start_ga = time.time()
fit_ga.train()
end_ga = time.time()
start_mimic = time.time()
fit_mimic.train()
end_mimic = time.time()
# Result extracting
last_training_time_rhc = end_rhc - start_rhc
rhc_training_time[n].append(last_training_time_rhc)
rhc_fitness[n].append(ef.value(rhc.getOptimal()))
last_training_time_sa = end_sa - start_sa
sa_training_time[n].append(last_training_time_sa)
sa_fitness[n].append(ef.value(sa.getOptimal()))
last_training_time_ga = end_ga - start_ga
ga_training_time[n].append(last_training_time_ga)
ga_fitness[n].append(ef.value(ga.getOptimal()))
last_training_time_mimic = end_mimic - start_mimic
mimic_training_time[n].append(last_training_time_mimic)
mimic_fitness[n].append(ef.value(mimic.getOptimal()))
overall_rhc_training_time = list_avg(*rhc_training_time)
overall_rhc_fitness = list_avg(*rhc_fitness)
points[i][1] = random.nextDouble()
ef = TravelingSalesmanRouteEvaluationFunction(points)
odd = DiscretePermutationDistribution(N)
nf = SwapNeighbor()
mf = SwapMutation()
cf = TravelingSalesmanCrossOver(ef)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
expt = "expt_avg"
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, 200000)
score_RHC.append(train(rhc, "RHC", ef, 200000, "test", expt))
print "RHC Inverse of Distance: " + str(ef.value(rhc.getOptimal()))
sa = SimulatedAnnealing(1E9, .98, hcp)
fit = FixedIterationTrainer(sa, 200000)
score_SA.append(train(sa, "SA", ef, 200000, "test", expt))
print "SA Inverse of Distance: " + str(ef.value(sa.getOptimal()))
ga = StandardGeneticAlgorithm(225, 40, 5, gap)
fit = FixedIterationTrainer(ga, 1000)
score_GA.append(train(ga, "GA", ef, 40000, "test", expt))
print "GA Inverse of Distance: " + str(ef.value(ga.getOptimal()))
# for mimic we use a sort encoding
ef = TravelingSalesmanSortEvaluationFunction(points)
fill = [N] * N
ranges = array('i', fill)
mf = SwapMutation()
cf = TravelingSalesmanCrossOver(ef)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
iters_list = [100, 500, 1000, 2500, 5000, 7500, 10000, 20000]
print "Random Hill Climb"
rhc = RandomizedHillClimbing(hcp)
for iters in iters_list:
fit = FixedIterationTrainer(rhc, iters)
start = time.time()
fit.train()
dur = time.time() - start
print "Iters: " + str(iters) + ", Fitness: " + str(
ef.value(rhc.getOptimal())) + ", Dur: " + str(dur)
# print "Route:"
# path = []
# for x in range(0,N):
# path.append(rhc.getOptimal().getDiscrete(x))
# print path
print "Simulated Annealing"
# 1e13, 0.8, 1e12 0.85, ... dang
temp = 1E13
cooling_rate = 0.90
sa = SimulatedAnnealing(temp, cooling_rate, hcp)
for iters in iters_list:
fit = FixedIterationTrainer(sa, iters)
start = time.time()
fit.train()