rhc = RandomizedHillClimbing(hcp)
sa = SimulatedAnnealing(100, .95, hcp)
ga = StandardGeneticAlgorithm(20, 20, 0, gap)
mimic = MIMIC(50, 10, pop)
rhc_f = open('out/op/countones/rhc.csv', 'w')
sa_f = open('out/op/countones/sa.csv', 'w')
ga_f = open('out/op/countones/ga.csv', 'w')
mimic_f = open('out/op/countones/mimic.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))
mimic.train()
mimic_fitness = ef.value(mimic.getOptimal())
mimic_f.write('{},{}\n'.format(i, mimic_fitness))
rhc_f.close()
sa_f.close()
ga_f.close()
mimic_f.close()
while iters < 80000:
score = rhc.train()
f.write(str(iters) + "," + str(score) +"\n")
iters += 1
print "RHC: " + str(ef.value(rhc.getOptimal())), "time taken", time() - t0, "Iterations:", iters
f.write("starting SA\n")
sa = SimulatedAnnealing(1E13, .95, hill_climbing_problem)
t0 = time()
iters = 0
score = 0
while iters < 80000:
score = sa.train()
f.write(str(iters) + "," + str(score) + "\n")
iters += 1
print "SA: " + str(ef.value(sa.getOptimal())), "time taken", time() - t0, "Iterations", iters
ga = StandardGeneticAlgorithm(200, 100, 10, genetic_problem)
t0 = time()
iters = 0
score = 0
f.write("starting GA\n")
while iters < 5000:
ga.train()
score = ef.value(ga.getOptimal())
f.write(str(iters) + "," + str(score) +"\n")
rhc = RandomizedHillClimbing(hcp)
max = 0
i = 0
while (max < goal and i < timeout):
rhc.train()
i += 1
max = ef.value(rhc.getOptimal())
#print "rhc,", i,",", max, ',', goal
print "rhc,", i,",", max, ',', goal
# run SA
sa = SimulatedAnnealing(1E11, .95, hcp)
max = 0
i = 0
while (max < goal and i < timeout):
sa.train()
i += 1
max = ef.value(sa.getOptimal())
#print "sa,", i,",", max, ',', goal
print "sa,", i,",", max, ',', goal
# run GA
ga = StandardGeneticAlgorithm(200, 100, 25, gap)
max = 0
i = 0
while (max < goal and i < timeout):
ga.train()
i += 200
max = ef.value(ga.getOptimal())
#print "ga,", i,",", max, ',', goal
print "ga,", i,",", max, ',', goal
