def RHC():
correctCount = 0
RHC_iters = 10
t=0
totalTime =0
totalIters = 0
global rhc
rhc = RandomizedHillClimbing(hcp)
while correctCount < NUM_RIGHT:
# print str(correctCount)+ " / 20 correct in RHC w/ iters " + str(RHC_iters)
fit = FixedIterationTrainer(rhc, RHC_iters)
start = time.time()
fitness = fit.train()
t = time.time() - start
totalIters+=RHC_iters
totalTime += t;
myWriter.addValue(fitness, "RHC_fitness", runNum)
myWriter.addValue(t, "RHC_searchTimes",runNum)
v = ef.value(rhc.getOptimal())
if v == N:
correctCount += 1
else:
correctCount = 0
#RHC_iters += 1
myWriter.addValue(totalTime,"RHC_times",runNum)
myWriter.addValue(totalIters,"RHC_iters",runNum)
print str(N) + ": RHC: " + str(ef.value(rhc.getOptimal()))+" took "+str(totalTime)+" seconds and " + str(totalIters) + " iterations"
def RHC():
correctCount = 0
RHC_iters = 10
t = 0
totalTime = 0
totalIters = 0
global rhc
rhc = RandomizedHillClimbing(hcp)
while correctCount < NUM_RIGHT:
# print str(correctCount)+ " / 20 correct in RHC w/ iters " + str(RHC_iters)
fit = FixedIterationTrainer(rhc, RHC_iters)
start = time.time()
fitness = fit.train()
t = time.time() - start
totalIters += RHC_iters
totalTime += t
myWriter.addValue(fitness, "RHC_fitness", runNum)
myWriter.addValue(t, "RHC_searchTimes", runNum)
v = ef.value(rhc.getOptimal())
if v == N:
correctCount += 1
else:
correctCount = 0
#RHC_iters += 1
myWriter.addValue(totalTime, "RHC_times", runNum)
myWriter.addValue(totalIters, "RHC_iters", runNum)
print str(N) + ": RHC: " + str(ef.value(
rhc.getOptimal())) + " took " + str(totalTime) + " seconds and " + str(
totalIters) + " iterations"
def run_rhc(t):
fname = outfile.format('RHC', str(t + 1))
with open(fname, 'a+') as f:
content = f.read()
if "fitness" not in content:
f.write('iterations,fitness,time,fevals\n')
ef = FlipFlopEvaluationFunction()
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
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 solveit(oaname, params):
N = 60
T = N / 10
fill = [2] * N
ranges = array('i', fill)
iterations = 10000
tryi = 1
ef = ContinuousPeaksEvaluationFunction(T)
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
mf = DiscreteChangeOneMutation(ranges)
cf = SingleCrossOver()
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
# fit = FixedIterationTrainer(rhc, 200000)
# fit.train()
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':
oa = StandardGeneticAlgorithm(int(params[0]), int(params[1]),
int(params[2]), gap)
if oaname == 'MMC':
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 = float(timeit.default_timer() - starttime)
output.append([str(i), str(score), str(elapsed)])
print 'score: %.3f' % score
print 'train time: %.3f secs' % (int(timeit.default_timer() - starttime))
scsv = 'cp-%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
def run_rhc(hcp, ef, iterations=200000):
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, iterations)
fit.train()
optimal_result = str(ef.value(rhc.getOptimal()))
print "RHC: " + optimal_result
return optimal_result, iterations
def run_experiment(self, opName='TSP'):
"""Run a randomized hill climbing optimization experiment for a given
optimization problem.
Args:
ef (AbstractEvaluationFunction): Evaluation function.
ranges (array): Search space ranges.
op (str): Name of optimization problem.
"""
outdir = 'results/OPT/{}'.format(opName) # get results directory
fname = get_abspath('RHC_results.csv', outdir) # get output filename
# delete existing results file, if it already exists
try:
os.remove(fname)
except Exception as e:
print e
pass
with open(fname, 'w') as f:
f.write('iterations,fitness,time,fevals,trial\n')
# start experiment
for t in range(self.numTrials):
# initialize optimization problem and training functions
ranges, ef = self.op.get_ef()
nf = None
if opName == 'TSP':
nf = SwapNeighbor()
else:
nf = DiscreteChangeOneNeighbor(ranges)
odd = DiscreteUniformDistribution(ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, 10)
# run experiment and train evaluation function
start = time.clock()
for i in range(0, self.maxIters, 10):
fit.train()
elapsed = time.clock() - start
fe = ef.valueCallCount
score = ef.value(rhc.getOptimal())
ef.valueCallCount -= 1
# write results to output file
s = '{},{},{},{},{}\n'.format(i + 10, score, elapsed, fe, t)
with open(fname, 'a+') as f:
f.write(s)
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_rhc(t):
fname = outfile.format('RHC', str(t + 1))
ef = ContinuousPeaksEvaluationFunction(T)
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
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 fname, st
base.write_to_file(fname, st)
return
ranges = array('i', fill)
ef = ContinuousPeaksEvaluationFunction(T)
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
mf = DiscreteChangeOneMutation(ranges)
cf = SingleCrossOver()
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, 200000)
fit.train()
print "RHC: " + str(ef.value(rhc.getOptimal()))
sa = SimulatedAnnealing(1E11, .95, hcp)
fit = FixedIterationTrainer(sa, 200000)
fit.train()
print "SA: " + str(ef.value(sa.getOptimal()))
ga = StandardGeneticAlgorithm(200, 100, 10, gap)
fit = FixedIterationTrainer(ga, 1000)
fit.train()
print "GA: " + str(ef.value(ga.getOptimal()))
mimic = MIMIC(200, 20, pop)
fit = FixedIterationTrainer(mimic, 1000)
fit.train()
print "MIMIC: " + str(ef.value(mimic.getOptimal()))
def main():
# The number of items
NUM_ITEMS = 40
# The number of copies each
COPIES_EACH = 4
# The maximum weight for a single element
MAX_WEIGHT = 50
# The maximum volume for a single element
MAX_VOLUME = 50
iterations = 20000
gaIters = 1000
mimicIters = 1000
gaPop = 200
gaMate = 150
gaMutate = 25
mimicSamples = 200
mimicToKeep = 100
saTemp = 100
saCooling = .95
alg = 'all'
run = 0
settings = []
try:
opts, args = getopt.getopt(sys.argv[1:], "ahrsgmn:N:c:w:v:i:", ["gaIters=", "mimicIters=","gaPop=", "gaMate=", "gaMutate=", "mimicSamples=", "mimicToKeep=", "saTemp=", "saCooling="])
except:
print 'knapsack.py -i <iterations> -n <NUM_ITEMS> -c <COPIES_EACH> -w <MAX_WEIGHT> -v <MAX_VOLUME>'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'knapsack.py -i <iterations> -n <NUM_ITEMS> -c <COPIES_EACH> -w <MAX_WEIGHT> -v <MAX_VOLUME>'
sys.exit(1)
elif opt == '-i':
iterations = int(arg)
elif opt == '-N':
NUM_ITEMS = int(arg)
elif opt == '-c':
COPIES_EACH = int(arg)
elif opt == '-w':
MAX_WEIGHT = int(arg)
elif opt == '-v':
MAX_VOLUME = int(arg)
elif opt == '-n':
run = int(arg)
elif opt == '-r':
alg = 'RHC'
elif opt == '-s':
alg = 'SA'
elif opt == '-g':
alg = 'GA'
elif opt == '-m':
alg = 'MIMIC'
elif opt == '-a':
alg = 'all'
elif opt == '--gaPop':
gaPop = int(arg)
elif opt == '--gaMate':
gaMate = int(arg)
elif opt == '--gaMutate':
gaMutate = int(arg)
elif opt == '--mimicSamples':
mimicSamples = int(arg)
elif opt == '--mimicToKeep':
mimicToKeep = int(arg)
elif opt == '--saTemp':
saTemp = float(arg)
elif opt == '--saCooling':
saCooling = float(arg)
elif opt == '--gaIters':
gaIters = int(arg)
elif opt == '--mimicIters':
mimicIters = int(arg)
vars ={
'NUM_ITEMS' : NUM_ITEMS,
'COPIES_EACH' : COPIES_EACH,
'MAX_WEIGHT' : MAX_WEIGHT,
'MAX_VOLUME' : MAX_VOLUME,
'iterations' : iterations,
'gaIters' : gaIters,
'mimicIters' : mimicIters,
'gaPop' : gaPop,
'gaMate' : gaMate,
'gaMutate' : gaMutate,
'mimicSamples' : mimicSamples,
'mimicToKeep' : mimicToKeep,
'saTemp' : saTemp,
'saCooling' : saCooling,
'alg' : alg,
'run' : run
}
settings = getSettings(alg, settings, vars)
# Random number generator */
random = Random()
# The volume of the knapsack
KNAPSACK_VOLUME = MAX_VOLUME * NUM_ITEMS * COPIES_EACH * .4
# create copies
fill = [COPIES_EACH] * NUM_ITEMS
copies = array('i', fill)
# create weights and volumes
fill = [0] * NUM_ITEMS
weights = array('d', fill)
volumes = array('d', fill)
for i in range(0, NUM_ITEMS):
weights[i] = random.nextDouble() * MAX_WEIGHT
volumes[i] = random.nextDouble() * MAX_VOLUME
# create range
fill = [COPIES_EACH + 1] * NUM_ITEMS
ranges = array('i', fill)
ef = KnapsackEvaluationFunction(weights, volumes, KNAPSACK_VOLUME, copies)
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
mf = DiscreteChangeOneMutation(ranges)
cf = UniformCrossOver()
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
if alg == 'RHC' or alg == 'all':
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, iterations)
fit.train()
print "RHC: " + str(ef.value(rhc.getOptimal()))
rows = []
row = []
row.append("Evaluation Function Value")
row.append(str(ef.value(rhc.getOptimal())))
rows.append(row)
output2('Knapsack', 'RHC', rows, settings)
rows = []
buildFooter("Knapsack", "RHC", rows, settings)
outputFooter("Knapsack", "RHC", rows , settings)
if alg == 'SA' or alg == 'all':
sa = SimulatedAnnealing(saTemp, saCooling, hcp)
fit = FixedIterationTrainer(sa, iterations)
fit.train()
rows = []
row = []
row.append("Evaluation Function Value")
row.append(ef.value(sa.getOptimal()))
rows.append(row)
print "SA: " + str(ef.value(sa.getOptimal()))
output2('Knapsack', 'SA', rows, settings)
rows = []
buildFooter("Knapsack", "SA", rows, settings)
outputFooter("Knapsack", "SA", rows, settings)
if alg == 'GA' or alg == 'all':
ga = StandardGeneticAlgorithm(gaPop, gaMate, gaMutate, gap)
fit = FixedIterationTrainer(ga, gaIters)
fit.train()
rows = []
row = []
row.append("Evaluation Function Value")
row.append(ef.value(ga.getOptimal()))
rows.append(row)
print "GA: " + str(ef.value(ga.getOptimal()))
output2('Knapsack', 'GA', rows, settings)
buildFooter("Knapsack", "GA", rows, settings)
outputFooter("Knapsack", "GA", rows , settings)
if alg == 'MIMIC' or alg == 'all':
mimic = MIMIC(mimicSamples, mimicToKeep, pop)
fit = FixedIterationTrainer(mimic, mimicIters)
fit.train()
print "MIMIC: " + str(ef.value(mimic.getOptimal()))
rows = []
row = []
row.append("Evaluation Function Value")
row.append(ef.value(mimic.getOptimal()))
rows.append(row)
output2('Knapsack', 'MIMIC', rows, settings)
rows = []
buildFooter("Knapsack", "MIMIC", rows, settings)
outputFooter("Knapsack", "MIMIC", rows , settings)
network_bp = factory.createClassificationNetwork([inputLayer, hiddenLayer, outputLayer]) bp = BatchBackPropagationTrainer(set, network_bp, measure, RPROPUpdateRule()) cvt = ConvergenceTrainer(bp) cvt.train() print "\nBP training error:", errorRate(network_bp, train) print "BP training confusion matrix:", confusionMatrix(network_bp, train) print " BP test error:", errorRate(network_bp, test) print " BP test confusion matrix:", confusionMatrix(network_bp, test) # learn weights with randomized hill climbing network_rhc = factory.createClassificationNetwork([inputLayer, hiddenLayer, outputLayer]) nnop_rhc = NeuralNetworkOptimizationProblem(set, network_rhc, measure) rhc = RandomizedHillClimbing(nnop_rhc) fit = FixedIterationTrainer(rhc, it_rhc) fit.train() op = rhc.getOptimal(); network_rhc.setWeights(op.getData()) print "\nRHC training error:", errorRate(network_rhc, train) print "RHC training confusion matrix:", confusionMatrix(network_rhc, train) print " RHC test error:", errorRate(network_rhc, test) print " RHC test confusion matrix:", confusionMatrix(network_rhc, test) # learn weights with simulated annealing network_sa = factory.createClassificationNetwork([inputLayer, hiddenLayer, outputLayer]) nnop_sa = NeuralNetworkOptimizationProblem(set, network_sa, measure) sa = SimulatedAnnealing(1E11, 0.95, nnop_sa) fit = FixedIterationTrainer(sa, it_sa) fit.train() op = sa.getOptimal(); network_sa.setWeights(op.getData()) print "\nSA training error:", errorRate(network_sa, train)
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" %
ranges = array('i', fill)
ef = ContinuousPeaksEvaluationFunction(T)
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
mf = DiscreteChangeOneMutation(ranges)
cf = SingleCrossOver()
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, 200000)
fit.train()
print "RHC: " + str(ef.value(rhc.getOptimal()))
sa = SimulatedAnnealing(1E11, .95, hcp)
fit = FixedIterationTrainer(sa, 200000)
fit.train()
print "SA: " + str(ef.value(sa.getOptimal()))
ga = StandardGeneticAlgorithm(200, 100, 10, gap)
fit = FixedIterationTrainer(ga, 1000)
fit.train()
print "GA: " + str(ef.value(ga.getOptimal()))
mimic = MIMIC(200, 20, pop)
fit = FixedIterationTrainer(mimic, 1000)
fit.train()
print "MIMIC: " + str(ef.value(mimic.getOptimal()))
odd = DiscreteUniformDistribution(ranges) nf = DiscreteChangeOneNeighbor(ranges) mf = DiscreteChangeOneMutation(ranges) cf = SingleCrossOver() df = DiscreteDependencyTree(.1, ranges) hcp = GenericHillClimbingProblem(ef, odd, nf) gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf) pop = GenericProbabilisticOptimizationProblem(ef, odd, df) from time import time rhc = RandomizedHillClimbing(hcp) fit = FixedIterationTrainer(rhc, 600000) t0 = time() fit.train() print "RHC: " + str(ef.value(rhc.getOptimal())), "time taken", time() - t0 sa = SimulatedAnnealing(1E11, .95, hcp) fit = FixedIterationTrainer(sa, 600000) t0 = time() fit.train() print "SA: " + str(ef.value(sa.getOptimal())), "time taken", time() - t0 ga = StandardGeneticAlgorithm(200, 100, 10, gap) fit = FixedIterationTrainer(ga, 20000) t0 = time() fit.train() print "GA: " + str(ef.value(ga.getOptimal())), "time taken", time() - t0
def run_count_ones_experiments():
OUTPUT_DIRECTORY = './output'
N = 80
fill = [2] * N
ranges = array('i', fill)
ef = CountOnesEvaluationFunction()
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
mf = DiscreteChangeOneMutation(ranges)
cf = SingleCrossOver()
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
max_iter = 5000
outfile = OUTPUT_DIRECTORY + '/count_ones_{}_log.csv'
# Randomized Hill Climber
filename = outfile.format('rhc')
with open(filename, 'w') as f:
f.write('iteration,fitness,time\n')
for it in range(0, max_iter, 10):
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, it)
start_time = time.clock()
fit.train()
elapsed_time = time.clock() - start_time
# fevals = ef.fevals
score = ef.value(rhc.getOptimal())
data = '{},{},{}\n'.format(it, score, elapsed_time)
print(data)
with open(filename, 'a') as f:
f.write(data)
# Simulated Annealing
filename = outfile.format('sa')
with open(filename, 'w') as f:
f.write('iteration,cooling_value,fitness,time\n')
for cooling_value in (.19, .38, .76, .95):
for it in range(0, max_iter, 10):
sa = SimulatedAnnealing(100, cooling_value, hcp)
fit = FixedIterationTrainer(sa, it)
start_time = time.clock()
fit.train()
elapsed_time = time.clock() - start_time
# fevals = ef.fevals
score = ef.value(sa.getOptimal())
data = '{},{},{},{}\n'.format(it, cooling_value, score, elapsed_time)
print(data)
with open(filename, 'a') as f:
f.write(data)
# Genetic Algorithm
filename = outfile.format('ga')
with open(filename, 'w') as f:
f.write('iteration,population_size,to_mate,to_mutate,fitness,time\n')
for population_size, to_mate, to_mutate in itertools.product([20], [4, 8, 16, 20], [0, 2, 4, 6]):
for it in range(0, max_iter, 10):
ga = StandardGeneticAlgorithm(population_size, to_mate, to_mutate, gap)
fit = FixedIterationTrainer(ga, it)
start_time = time.clock()
fit.train()
elapsed_time = time.clock() - start_time
# fevals = ef.fevals
score = ef.value(ga.getOptimal())
data = '{},{},{},{},{},{}\n'.format(it, population_size, to_mate, to_mutate, score, elapsed_time)
print(data)
with open(filename, 'a') as f:
f.write(data)
# MIMIC
filename = outfile.format('mm')
with open(filename, 'w') as f:
f.write('iterations,samples,to_keep,m,fitness,time\n')
for samples, to_keep, m in itertools.product([50], [10], [0.1, 0.3, 0.5, 0.7, 0.9]):
for it in range(0, 500, 10):
df = DiscreteDependencyTree(m, ranges)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
mm = MIMIC(samples, 20, pop)
fit = FixedIterationTrainer(mm, it)
start_time = time.clock()
fit.train()
elapsed_time = time.clock() - start_time
# fevals = ef.fevals
score = ef.value(mm.getOptimal())
data = '{},{},{},{},{},{}\n'.format(it, samples, to_keep, m, score, elapsed_time)
print(data)
with open(filename, 'a') as f:
f.write(data)
def main():
"""Run algorithms on the cancer dataset."""
instances = initialize_instances()
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(instances)
max_iterations = TRAINING_ITERATIONS
hidden_layer_size = HIDDEN_LAYER
# for _hidden_layer in xrange(HIDDEN_LAYER):
# hidden_layer_size = _hidden_layer + 1
network = None # BackPropagationNetwork
nnop = None # NeuralNetworkOptimizationProblem
oa = None # OptimizationAlgorithm
results = ""
for restarts in xrange(1, 11):
RandomOrderFilter().filter(data_set)
train_test_split = TestTrainSplitFilter(TRAIN_TEST_SPLIT)
train_test_split.filter(data_set)
train_set = train_test_split.getTrainingSet()
test_set = train_test_split.getTestingSet()
network = factory.createClassificationNetwork(
[INPUT_LAYER, hidden_layer_size, OUTPUT_LAYER])
nnop = NeuralNetworkOptimizationProblem(train_set, network, measure)
oa = RandomizedHillClimbing(nnop)
start = time.time()
correct = 0
incorrect = 0
train(oa, network, "RHC", train_set, test_set, measure, restarts)
end = time.time()
training_time = end - start
optimal_instance = oa.getOptimal()
network.setWeights(optimal_instance.getData())
start = time.time()
for instance in test_set.getInstances():
network.setInputValues(instance.getData())
network.run()
predicted = instance.getLabel().getContinuous()
actual = network.getOutputValues().get(0)
if abs(predicted - actual) < 0.5:
correct += 1
else:
incorrect += 1
end = time.time()
testing_time = end - start
_results = ""
_results += "\n[RHC] restarts=%d" % (restarts)
_results += "\nResults for RHC: \nCorrectly classified %d instances." % (
correct)
_results += "\nIncorrectly classified %d instances.\nPercent correctly classified: %0.03f%%" % (
incorrect, float(correct) / (correct + incorrect) * 100.0)
_results += "\nTraining time: %0.03f seconds" % (training_time, )
_results += "\nTesting time: %0.03f seconds\n" % (testing_time, )
with open('out/rhc/restarts-%d.log' % (restarts), 'w') as f:
f.write(_results)
results += _results
print results
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 '----------------------------------'
cf = SingleCrossOver()
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
iters = 5000
t0 = time.time()
calls = []
results = []
for _ in range(runs):
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, iters)
fitness = fit.train()
results.append(ef.value(rhc.getOptimal()))
calls.append(ef.getTotalCalls())
ef.clearCount()
print "RHC, average results , " + str(
sum(results) / float(runs)) + ", countones-%d.txt" % N
print "RHC, average feval calls , " + str(
sum(calls) / float(runs)) + ", countones-%d.txt" % N
t1 = time.time() - t0
print "RHC, average time , " + str(float(t1) / runs) + ", countones-%d.txt" % N
t0 = time.time()
calls = []
results = []
for _ in range(runs):
sa = SimulatedAnnealing(1e10, .95, hcp)
fit = FixedIterationTrainer(sa, iters)
def fourpeaksfunc(N, iterations):
rhcMult = 200
saMult = 200
gaMult = 2
mimicMult = 1
optimalOut = []
timeOut = []
evalsOut = []
T = N / 5
fill = [2] * N
ranges = array('i', fill)
ef = FourPeaksEvaluationFunction(T)
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
mf = DiscreteChangeOneMutation(ranges)
cf = SingleCrossOver()
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
for niter in iterations:
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 optimum: " + str(rhcOptimal)
print "RHC time: " + str(rhcTime)
iterOptimalOut.append(rhcOptimal)
iterTimeOut.append(rhcTime)
functionEvals = ef.getNumEvals()
ef.zeroEvals()
iterEvals.append(functionEvals)
start = time.time()
sa = SimulatedAnnealing(1E20, .8, hcp)
fit = FixedIterationTrainer(sa, niter * saMult)
fit.train()
end = time.time()
saOptimal = ef.value(sa.getOptimal())
saTime = end - start
print "SA optimum: " + str(saOptimal)
print "SA time: " + str(saTime)
iterOptimalOut.append(saOptimal)
iterTimeOut.append(saTime)
functionEvals = ef.getNumEvals()
ef.zeroEvals()
iterEvals.append(functionEvals)
start = time.time()
ga = StandardGeneticAlgorithm(200, 100, 10, gap)
fit = FixedIterationTrainer(ga, niter * gaMult)
fit.train()
end = time.time()
gaOptimal = ef.value(ga.getOptimal())
gaTime = end - start
print "GA optimum: " + str(gaOptimal)
print "GA time: " + str(gaTime)
iterOptimalOut.append(gaOptimal)
iterTimeOut.append(gaTime)
functionEvals = ef.getNumEvals()
ef.zeroEvals()
iterEvals.append(functionEvals)
start = time.time()
mimic = MIMIC(200, 20, pop)
fit = FixedIterationTrainer(mimic, niter * mimicMult)
fit.train()
end = time.time()
mimicOptimal = ef.value(mimic.getOptimal())
mimicTime = end - start
print "MIMIC optimum: " + str(mimicOptimal)
print "MIMIC time: " + str(mimicTime)
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]
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
with open(fname, 'w') as f:
f.write('iterations,fitness,time,fevals\n')
ef = FlipFlopEvaluationFunction()
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
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)
with open(fname, 'a') as f:
f.write(st)
# SA
for t in range(numTrials):
for CE in [0.15, 0.35, 0.55, 0.75, 0.95]:
fname = outfile.format('SA{}'.format(CE), str(t + 1))
with open(fname, 'w') as f:
f.write('iterations,fitness,time,fevals\n')
ef = FlipFlopEvaluationFunction()
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
def main():
N=200
tempDenom = 5
T=N/tempDenom
fill = [2] * N
ranges = array('i', fill)
iterations = 2000
gaIters = 1000
mimicIters = 1000
gaPop = 200
gaMate = 100
gaMutate = 10
mimicSamples = 200
mimicToKeep = 20
saTemp = 1E11
saCooling = .95
alg = 'all'
run = 0
settings = []
try:
opts, args = getopt.getopt(sys.argv[1:], "ahn:rsgN:m:t:i:", ["gaIters=", "mimicIters=","gaPop=", "gaMate=", "gaMutate=", "mimicSamples=", "mimicToKeep=", "saTemp=", "saCooling="])
except:
print 'knapsack.py -i <iterations> -n <NUM_ITEMS> -c <COPIES_EACH> -w <MAX_WEIGHT> -v <MAX_VOLUME>'
sys.exit(2)
for opt, arg in opts:
if opt == '-h':
print 'knapsack.py -i <iterations> -n <NUM_ITEMS> -c <COPIES_EACH> -w <MAX_WEIGHT> -v <MAX_VOLUME>'
sys.exit(1)
elif opt == '-i':
iterations = int(arg)
elif opt == '-N':
N = int(arg)
elif opt == '-t':
T = float(arg)
elif opt == '-d':
tempDenom = int(arg)
elif opt == '-r':
alg = 'RHC'
elif opt == '-a':
alg = 'all'
elif opt == '-s':
alg = 'SA'
elif opt == '-g':
alg = 'GA'
elif opt == '-m':
alg = 'MIMIC'
elif opt == '--gaPop':
gaPop = int(arg)
elif opt == '--gaMate':
gaMate = int(arg)
elif opt == '--gaMutate':
gaMutate = int(arg)
elif opt == '--mimicSamples':
mimicSamples = int(arg)
elif opt == '--mimicToKeep':
mimicToKeep = int(arg)
elif opt == '--saTemp':
saTemp = float(arg)
elif opt == '--saCooling':
saCooling = float(arg)
elif opt == '--gaIters':
gaIters = int(arg)
elif opt == '--mimicIters':
mimicIters = int(arg)
elif opt == '-n':
run = int(arg)
vars = {
'N':N,
'tempDenom':tempDenom,
'T':T,
'fill':fill,
'ranges':ranges,
'iterations' :iterations,
'gaIters':gaIters,
'mimicIters':mimicIters,
'gaPop' :gaPop,
'gaMate' :gaMate,
'gaMutate' :gaMutate,
'mimicSamples' : mimicSamples,
'mimicToKeep' : mimicToKeep,
'saTemp' : saTemp,
'saCooling' : saCooling,
'alg' : alg,
'run' : run
}
settings = getSettings(alg, settings, vars)
T=N/tempDenom
fill = [2] * N
ranges = array('i', fill)
ef = FourPeaksEvaluationFunction(T)
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
mf = DiscreteChangeOneMutation(ranges)
cf = SingleCrossOver()
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
if alg == 'RHC' or alg == 'all':
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, iterations)
fit.train()
rows = []
row = []
row.append("Evaluation Function Value")
row.append(ef.value(rhc.getOptimal()))
rows.append(row)
print "RHC: " + str(ef.value(rhc.getOptimal()))
output2('4Peaks', 'RHC', rows, settings)
rows = []
buildFooter("4Peaks", "RHC", rows, settings),
outputFooter("4Peaks", "RHC", rows, settings)
if alg == 'SA' or alg == 'all':
sa = SimulatedAnnealing(saTemp, saCooling, hcp)
fit = FixedIterationTrainer(sa, iterations)
fit.train()
rows = []
row = []
row.append("Evaluation Function Value")
row.append(ef.value(sa.getOptimal()))
rows.append(row)
print "SA: " + str(ef.value(sa.getOptimal()))
output2('4Peaks', 'SA', rows, settings)
rows = []
buildFooter("4Peaks", "SA", rows, settings)
outputFooter("4Peaks", "SA", rows, settings)
if alg == 'GA' or alg == 'all':
ga = StandardGeneticAlgorithm(gaPop, gaMate, gaMutate, gap)
fit = FixedIterationTrainer(ga, gaIters)
fit.train()
print "GA: " + str(ef.value(ga.getOptimal()))
rows = []
row = []
row.append("Evaluation Function Value")
row.append(ef.value(ga.getOptimal()))
rows.append(row)
output2('4Peaks', 'GA', rows, settings)
rows = []
buildFooter("4Peaks", "GA", rows, settings)
outputFooter("4Peaks", "GA", rows , settings)
if alg == 'MIMIC' or alg == 'all':
mimic = MIMIC(mimicSamples, mimicToKeep, pop)
fit = FixedIterationTrainer(mimic, mimicIters)
fit.train()
print "MIMIC: " + str(ef.value(mimic.getOptimal()))
rows = []
row = []
row.append("Evaluation Function Value")
row.append(ef.value(mimic.getOptimal()))
rows.append(row)
output2('4Peaks', 'MIMIC', rows, settings)
rows = []
buildFooter("4Peaks", "GA", rows, settings)
outputFooter("4Peaks", "MIMIC", rows, settings)
def solveit(oaname, params):
iterations = 10000
tryi = 1
# Random number generator */
random = Random()
# The number of items
NUM_ITEMS = 40
# The number of copies each
COPIES_EACH = 4
# The maximum weight for a single element
MAX_WEIGHT = 50
# The maximum volume for a single element
MAX_VOLUME = 50
# The volume of the knapsack
KNAPSACK_VOLUME = MAX_VOLUME * NUM_ITEMS * COPIES_EACH * .4
# create copies
fill = [COPIES_EACH] * NUM_ITEMS
copies = array('i', fill)
# create weights and volumes
fill = [0] * NUM_ITEMS
weights = array('d', fill)
volumes = array('d', fill)
for i in range(0, NUM_ITEMS):
weights[i] = random.nextDouble() * MAX_WEIGHT
volumes[i] = random.nextDouble() * MAX_VOLUME
# create range
fill = [COPIES_EACH + 1] * NUM_ITEMS
ranges = array('i', fill)
ef = KnapsackEvaluationFunction(weights, volumes, KNAPSACK_VOLUME, copies)
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
mf = DiscreteChangeOneMutation(ranges)
cf = UniformCrossOver()
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
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
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 'score: %.3f' % score
print 'train time: %d secs' % (int(timeit.default_timer() - starttime))
scsv = 'kn-%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
from time import time
f = open("experiments/results/knapsack_optimal2.txt", "w")
f.write("starting RHC\n")
rhc = RandomizedHillClimbing(hill_climbing_problem)
score = 0
iters = 0
t0 = time()
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)
cf = UniformCrossOver()
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
# -- begin problem
t0 = time.time()
calls = []
results = []
for _ in range(runs):
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, iters)
fitness = fit.train()
results.append(ef.value(rhc.getOptimal()))
calls.append(ef.getTotalCalls())
ef.clearCount()
print "RHC, average results , " + str(sum(results)/float(runs))
print "RHC, average feval calls , " + str(sum(calls)/float(runs))
t1 = time.time() - t0
print "RHC, average time , " + str(float(t1)/runs)
t0 = time.time()
calls = []
results = []
for _ in range(runs):
sa = SimulatedAnnealing(1E11, .95, hcp)
fit = FixedIterationTrainer(sa, iters)
def run_knapsack():
# Random number generator */
random = Random()
# The number of items
NUM_ITEMS = 40
# The number of copies each
COPIES_EACH = 4
# The maximum weight for a single element
MAX_WEIGHT = 50
# The maximum volume for a single element
MAX_VOLUME = 50
# The volume of the knapsack
KNAPSACK_VOLUME = MAX_VOLUME * NUM_ITEMS * COPIES_EACH * .4
# create copies
fill = [COPIES_EACH] * NUM_ITEMS
copies = array('i', fill)
# create weights and volumes
fill = [0] * NUM_ITEMS
weights = array('d', fill)
volumes = array('d', fill)
for i in range(0, NUM_ITEMS):
weights[i] = random.nextDouble() * MAX_WEIGHT
volumes[i] = random.nextDouble() * MAX_VOLUME
# create range
fill = [COPIES_EACH + 1] * NUM_ITEMS
ranges = array('i', fill)
ef = KnapsackEvaluationFunction(weights, volumes, KNAPSACK_VOLUME, copies)
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
mf = DiscreteChangeOneMutation(ranges)
cf = UniformCrossOver()
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
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: " + str(ef.value(rhc.getOptimal()))
sa_results = []
sa_times = []
for i in iters:
print(i)
for j in range(num_repeats):
start = time.time()
sa = SimulatedAnnealing(100, .95, hcp)
fit = FixedIterationTrainer(sa, i)
fit.train()
end = time.time()
sa_results.append(ef.value(sa.getOptimal()))
sa_times.append(end - start)
#print "SA: " + str(ef.value(sa.getOptimal()))
ga_results = []
ga_times = []
for i in iters:
print(i)
for j in range(num_repeats):
start = time.time()
ga = StandardGeneticAlgorithm(200, 150, 25, gap)
fit = FixedIterationTrainer(ga, i)
fit.train()
end = time.time()
ga_results.append(ef.value(sa.getOptimal()))
ga_times.append(end - start)
#print "GA: " + str(ef.value(ga.getOptimal()))
mimic_results = []
mimic_times = []
for i in iters[0:6]:
print(i)
for j in range(num_repeats):
start = time.time()
mimic = MIMIC(200, 100, pop)
fit = FixedIterationTrainer(mimic, i)
fit.train()
end = time.time()
mimic_results.append(ef.value(mimic.getOptimal()))
mimic_times.append(end - start)
#print "MIMIC: " + str(ef.value(mimic.getOptimal()))
with open('knapsack.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
sa_acc = []
ga_times = []
ga_acc = []
mimic_times = []
mimic_acc = []
NUMBER_ITERATIONS = 1000
for iteration in xrange(NUMBER_ITERATIONS):
if iteration % 10 == 0:
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, iteration)
start = time.time()
fit.train()
end = time.time()
rhc_times.append(end - start)
rhc_acc.append(ef.value(rhc.getOptimal()))
print "RHC: " + str(ef.value(rhc.getOptimal()))
sa = SimulatedAnnealing(1E11, .95, hcp)
fit = FixedIterationTrainer(sa, iteration)
start = time.time()
fit.train()
end = time.time()
sa_times.append(end - start)
sa_acc.append(ef.value(sa.getOptimal()))
print "SA: " + str(ef.value(sa.getOptimal()))
ga = StandardGeneticAlgorithm(200, 100, 10, gap)
fit = FixedIterationTrainer(ga, iteration)
start = time.time()
fit.train()
hill_climbing_output = "salesman_hill_climbing.csv"
annealing_output = "salesman_annealing.csv"
genetic_output = "salesman_genetic.csv"
mimic_output = "salesman_mimic.csv"
# HILL CLIMBING
for i in range(trials):
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, 200000)
start = clock()
fit.train()
end = clock()
total_time = end - start
max_fit = ef.value(rhc.getOptimal())
time_optimum = [total_time, max_fit]
hill_climbing.append(time_optimum)
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
# ANNEALING
for i in range(trials):
sa = SimulatedAnnealing(1E12, .999, hcp)
fit = FixedIterationTrainer(sa, 200000)
start = clock()
fit.train()
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
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()
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
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
}
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
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
mf = DiscreteChangeOneMutation(ranges)
cf = SingleCrossOver()
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, 100)
start = time.time()
fit.train()
end = time.time()
training_time = end - start
print "RHC: " + 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(1E11, .95, hcp)
fit = FixedIterationTrainer(sa, 100)
start = time.time()
fit.train()
end = time.time()
training_time = end - start
print "SA: " + str(ef.value(sa.getOptimal()))
OUTFILE = "%s%s.csv" % (OUTFILE_BASE, "SA")
with open(OUTFILE, 'a+') as f:
f.write("%d,%f,%f\n" % (N, training_time, ef.value(sa.getOptimal())))
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
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(value), iters, clock_time
#-- Simulated Annealing
sa = SimulatedAnnealing(1E11, .95, hcp)
for iters in niters:
start = time.time()
fit = FixedIterationTrainer(sa, iters)
value = 0
for isample in range(nsample):
fit.train()
value += ef.value(sa.getOptimal())
end = time.time()
# ranges = [random.randint(1, N) for i in range(N)]
ef = NQueensFitnessFunction()
odd = DiscretePermutationDistribution(N)
nf = SwapNeighbor()
mf = SwapMutation()
cf = SingleCrossOver()
df = DiscreteDependencyTree(.1)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, 200000)
fit.train()
rhc_opt = ef.value(rhc.getOptimal())
print("RHC: " + str(rhc_opt))
# print "RHC: Board Position: "
# print(ef.boardPositions())
print("============================")
sa = SimulatedAnnealing(1E1, .1, hcp)
fit = FixedIterationTrainer(sa, 200000)
fit.train()
sa_opt = ef.value(sa.getOptimal())
print("SA: " + str(sa_opt))
# print("SA: Board Position: ")
# print(ef.boardPositions())
print("============================")
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)
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
iters_list = [100, 500, 1000, 2500, 5000, 7500, 10000, 20000]
print "Random Hill Climbing"
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 "Simulated Annealing"
temp = 100000
cooling_rate = 0.85
sa = SimulatedAnnealing(temp, 0.85, hcp)
for iters in iters_list:
fit = FixedIterationTrainer(sa, iters)
start = time.time()
fit.train()
dur = time.time() - start
print "Iters: " + str(iters) + ", Fitness: " + str(
ef.value(sa.getOptimal())) + ", Dur: " + str(dur)
print "Genetic Algorithm"
ga = StandardGeneticAlgorithm(2 * N, 300, 100, gap)
cf = SingleCrossOver()
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
times = ""
print "RHC:"
for x in range(20):
start = time.time()
iterations = (x + 1) * 2500
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, iterations)
fit.train()
print(str(ef.value(rhc.getOptimal())))
end = time.time()
times += "\n%0.03f" % (end - start)
print(times)
times = ""
print "SA:"
for x in range(20):
start = time.time()
iterations = (x + 1) * 2500
sa = SimulatedAnnealing(1E11, .95, hcp)
fit = FixedIterationTrainer(sa, iterations)
fit.train()
print(str(ef.value(sa.getOptimal())))
end = time.time()
times += "\n%0.03f" % (end - start)
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
timeout = 1E6
# first find the global optimum by running for a long time
hcp0 = GenericHillClimbingProblem(ef, odd, nf)
rhc0 = RandomizedHillClimbing(hcp0)
i = 0
max = 0
while (i < timeout/10):
rhc0.train()
i += 1
max = ef.value(rhc0.getOptimal())
print "rhc0,", i,",", max
goal = max
pop0 = GenericProbabilisticOptimizationProblem(ef, odd, df)
mimic0 = MIMIC(200, 100, pop)
i = 0
while ( i< timeout/1000):
mimic0.train()
i += 1
max = ef.value(mimic0.getOptimal())
print "mimic0,", i,",", max
if (max > goal):
goal = max
gap0 = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
ga0 = StandardGeneticAlgorithm(200, 100, 25, gap0)
i = 0
