# Adapted from https://github.com/JonathanTay/CS-7641-assignment-2/blob/master/tsp.py
# set N value. This is the number of points
N = 100
random = Random()
maxIters = 3001
numTrials = 5
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()
outfile = OUTPUT_DIRECTORY + '/TSP/TSP_{}_{}_LOG.csv'
ef = TravelingSalesmanRouteEvaluationFunction(points)
odd = DiscretePermutationDistribution(N)
nf = SwapNeighbor()
mf = SwapMutation()
cf = TravelingSalesmanCrossOver(ef)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
# MIMIC
fill = [N] * N
ranges = array('i', fill)
ef = TravelingSalesmanRouteEvaluationFunction(points)
odd = DiscreteUniformDistribution(ranges)
# for t in range(numTrials):
# for samples, keep, m in product([100], [50], [0.1, 0.3, 0.5, 0.7, 0.9]):
# fname = outfile.format('MIMIC{}_{}_{}'.format(samples, keep, m), str(t + 1))
# df = DiscreteDependencyTree(m, ranges)
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]
#RHC
output_directory = "Results/Small/TSP_RHC.csv"
with open(output_directory, 'w') as f:
f.write('iterations,fitness,time\n')
for i in range(len(iteration_list)):
iteration = iteration_list[i]
rhc_total = 0
rhc_time = 0
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
