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 main():
"""Run algorithms on the abalone dataset."""
instances = initialize_instances()
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(instances)
networks = [] # BackPropagationNetwork
nnop = [] # NeuralNetworkOptimizationProblem
oa = [] # OptimizationAlgorithm
oa_names = ["RHC", "SA", "GA"]
results = ""
for name in oa_names:
classification_network = factory.createClassificationNetwork([INPUT_LAYER, HIDDEN_LAYER, OUTPUT_LAYER])
networks.append(classification_network)
nnop.append(NeuralNetworkOptimizationProblem(data_set, classification_network, measure))
oa.append(RandomizedHillClimbing(nnop[0]))
oa.append(SimulatedAnnealing(1E11, .95, nnop[1]))
oa.append(StandardGeneticAlgorithm(200, 100, 10, nnop[2]))
for i, name in enumerate(oa_names):
start = time.time()
correct = 0
incorrect = 0
train(oa[i], networks[i], oa_names[i], instances, measure)
end = time.time()
training_time = end - start
optimal_instance = oa[i].getOptimal()
networks[i].setWeights(optimal_instance.getData())
start = time.time()
for instance in instances:
networks[i].setInputValues(instance.getData())
networks[i].run()
actual = instance.getLabel().getContinuous()
predicted = networks[i].getOutputValues().get(0)
if abs(predicted - actual) < 0.5:
correct += 1
else:
incorrect += 1
end = time.time()
testing_time = end - start
results += "\nResults for %s: \nCorrectly classified %d instances." % (name, 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,)
print results
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 main():
"""Run this experiment"""
training_ints = initialize_instances('Cryo_train.csv')
testing_ints = initialize_instances('Cryo_test.csv')
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(training_ints)
acti = LogisticSigmoid()
rule = RPROPUpdateRule()
classification_network = factory.createClassificationNetwork([INPUT_LAYER, HIDDEN_LAYER1, OUTPUT_LAYER],acti)
nnop = NeuralNetworkOptimizationProblem(data_set, classification_network, measure)
oa = RandomizedHillClimbing(nnop)
train(oa, classification_network, 'RHC', training_ints, testing_ints, measure)
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
def main():
"""Run this experiment"""
training_ints = initialize_instances('./../data/x_train_val.csv')
testing_ints = initialize_instances('./../data/x_test.csv')
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(training_ints)
acti = HyperbolicTangentSigmoid()
rule = RPROPUpdateRule()
classification_network = factory.createClassificationNetwork([INPUT_LAYER, HIDDEN_LAYER1,HIDDEN_LAYER2,HIDDEN_LAYER3,HIDDEN_LAYER4, OUTPUT_LAYER],acti)
nnop = NeuralNetworkOptimizationProblem(data_set, classification_network, measure)
oa = RandomizedHillClimbing(nnop)
train(oa, classification_network, 'RHC', training_ints, testing_ints, measure, TRAINING_ITERATIONS, OUTFILE)
def main():
"""Run algorithms on the adult dataset."""
train_instances = initialize_instances(trainX)
test_instances = initialize_instances(testX)
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(train_instances)
networks = [] # BackPropagationNetwork
nnop = [] # NeuralNetworkOptimizationProblem
oa = [] # OptimizationAlgorithm
oa_names = ["RHC", "SA", "GA", "BP"]
print(sys.argv)
if len(sys.argv) > 1:
oa_names = [sys.argv[1]]
set_num = sys.argv[2]
# results = ""
for name in oa_names:
classification_network = factory.createClassificationNetwork(
[INPUT_LAYER, HIDDEN_LAYER, OUTPUT_LAYER], RELU())
networks.append(classification_network)
if name != "BP":
nnop.append(
NeuralNetworkOptimizationProblem(data_set,
classification_network,
measure))
else:
print("adding backprop")
rule = RPROPUpdateRule()
nnop.append(
BatchBackPropagationTrainer(data_set, classification_network,
measure, rule))
if "RHC" in oa_names:
rhc_index = oa_names.index("RHC")
oa.append(RandomizedHillClimbing(nnop[rhc_index]))
if "SA" in oa_names:
sa_index = oa_names.index("SA")
oa.append(SimulatedAnnealing(1E11, .95, nnop[sa_index]))
if "GA" in oa_names:
ga_index = oa_names.index("GA")
oa.append(StandardGeneticAlgorithm(100, 50, 10, nnop[ga_index]))
if "BP" in oa_names:
rule = RPROPUpdateRule()
bp_index = oa_names.index("BP")
oa.append(nnop[bp_index])
for i, name in enumerate(oa_names):
train(oa[i], networks[i], oa_names[i], train_instances, test_instances,
measure)
def main():
"""Run algorithms on the abalone dataset."""
train_instances = initialize_instances(TRAIN_FILE)
test_instances = initialize_instances(TEST_FILE)
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(train_instances)
networks = [] # BackPropagationNetwork
nnop = [] # NeuralNetworkOptimizationProblem
oa = [] # OptimizationAlgorithm
oa_names = ["trial_1", "trial_2", "trial_3", "trail_4", "trial_5"]
results = ""
for name in oa_names:
classification_network = factory.createClassificationNetwork(
[INPUT_LAYER, HIDDEN_LAYER_1, HIDDEN_LAYER_1, OUTPUT_LAYER])
networks.append(classification_network)
nnop.append(
NeuralNetworkOptimizationProblem(data_set, classification_network,
measure))
for num, params in enumerate(oa_names):
oa.append(RandomizedHillClimbing(nnop[num]))
result_file = open(WRITE_DIR, "w")
for i, name in enumerate(oa_names):
start = time.time()
train(oa[i], networks[i], oa_names[i], train_instances, measure,
result_file)
end = time.time()
training_time = end - start
optimal_instance = oa[i].getOptimal()
networks[i].setWeights(optimal_instance.getData())
result_file.write("finished_training " + name + " in " +
str(training_time))
test(test_instances, networks[i], name, result_file)
print "finished training, " + name
result_file.close()
def main():
"""Run this experiment"""
training_ints = initialize_instances('spam_trg.csv')
testing_ints = initialize_instances('spam_test.csv')
validation_ints = initialize_instances('spam_val.csv')
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(training_ints)
relu = RELU()
rule = RPROPUpdateRule()
oa_names = ["RHC"]
classification_network = factory.createClassificationNetwork([INPUT_LAYER, HIDDEN_LAYER1,HIDDEN_LAYER2, OUTPUT_LAYER],relu)
for trial in xrange(TRIALS):
oa = RandomizedHillClimbing(NeuralNetworkOptimizationProblem(data_set, classification_network, measure))
train(oa, classification_network, 'RHC', training_ints,validation_ints,testing_ints, measure)
def main():
"""Run this experiment"""
training_ints = initialize_instances(TRAIN_DATA_FILE)
testing_ints = initialize_instances(TEST_DATA_FILE)
validation_ints = initialize_instances(VALIDATE_DATA_FILE)
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(training_ints)
relu = RELU()
# 50 and 0.000001 are the defaults from RPROPUpdateRule.java
rule = RPROPUpdateRule(0.001, 50, 0.000001)
oa_names = ["RHC"]
classification_network = factory.createClassificationNetwork([INPUT_LAYER, HIDDEN_LAYER1, HIDDEN_LAYER2, HIDDEN_LAYER3, OUTPUT_LAYER], relu)
nnop = NeuralNetworkOptimizationProblem(data_set, classification_network, measure)
oa = RandomizedHillClimbing(nnop)
train(oa, classification_network, 'RHC', training_ints, validation_ints, testing_ints, measure, TRAINING_ITERATIONS, OUTFILE.format('RHC'))
def initialize_networks_and_optimization(networks, nnop, oa, instances,
factory=BackPropagationNetworkFactory(),
measure = SumOfSquaresError()):
del networks[:]
del nnop[:]
del oa[:]
data_set = DataSet(instances)
for _ in OA_NAMES:
classification_network = factory.createClassificationNetwork([
INPUT_LAYER, HIDDEN_LAYER, OUTPUT_LAYER], LogisticSigmoid())
networks.append(classification_network)
nnop.append(NeuralNetworkOptimizationProblem(data_set, classification_network, measure))
oa.append(RandomizedHillClimbing(nnop[0]))
oa.append(SimulatedAnnealing(1E11, .95, nnop[1]))
oa.append(StandardGeneticAlgorithm(200, 100, 10, nnop[2]))
def run_experiment(self, train, test, validation):
"""Run experiment
Args:
train (list): List of training instances.
test (list): List of test instances.
validation (list): List of validation instances.
"""
factory = BackPropagationNetworkFactory() # instantiate main NN class
params = [
self.input_layer, self.hidden_layer_one, self.hidden_layer_two,
self.output_layer
]
self.network = factory.createClassificationNetwork(params)
dataset = DataSet(train) # setup training instances dataset
nnop = NeuralNetworkOptimizationProblem(dataset, self.network,
self.measure)
oa = None
# get output file name
outpath = 'results/NN'
filename = None
# options for different optimization algorithms
if self.oaName == 'BP':
filename = '{}/results.csv'.format(self.oaName)
rule = RPROPUpdateRule()
oa = BatchBackPropagationTrainer(dataset, self.network,
self.measure, rule)
elif self.oaName == 'RHC':
filename = '{}/results.csv'.format(self.oaName)
oa = RandomizedHillClimbing(nnop)
elif self.oaName == 'SA':
filename = '{}/results_{}_{}.csv'.format(self.oaName, self.SA_T,
self.SA_C)
oa = SimulatedAnnealing(self.SA_T, self.SA_C, nnop)
elif self.oaName == 'GA':
filename = '{}/results_{}_{}_{}.csv'.format(
self.oaName, self.GA_P, self.GA_MA, self.GA_MU)
oa = StandardGeneticAlgorithm(self.GA_P, self.GA_MA, self.GA_MU,
nnop)
# train network
filepath = get_abspath(filename, outpath)
self.train(oa, train, test, validation, filepath)
def main():
"""Run this experiment"""
training_ints = initialize_instances('./clean_data/adult_train.txt')
testing_ints = initialize_instances('./clean_data/adult_test.txt')
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(training_ints)
logunit = LogisticSigmoid()
rule = RPROPUpdateRule()
oa_names = ["RHC"]
classification_network = factory.createClassificationNetwork(
[INPUT_LAYER, HIDDEN_LAYER, OUTPUT_LAYER]) #,logunit)
nnop = NeuralNetworkOptimizationProblem(data_set, classification_network,
measure)
oa = RandomizedHillClimbing(nnop)
train(oa, classification_network, 'RHC', training_ints, testing_ints,
measure)
def main(ds_name):
"""Run this experiment"""
nn_config, train_file, val_file, test_file = get_problemset(ds_name)
training_ints = initialize_instances(train_file)
testing_ints = initialize_instances(test_file)
validation_ints = initialize_instances(val_file)
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(training_ints)
relu = RELU()
# 50 and 0.000001 are the defaults from RPROPUpdateRule.java
rule = RPROPUpdateRule(0.064, 50, 0.000001)
oa_names = "RHC_{}".format(name)
classification_network = factory.createClassificationNetwork(nn_config, relu)
nnop = NeuralNetworkOptimizationProblem(data_set, classification_network, measure)
oa = RandomizedHillClimbing(nnop)
train(oa, classification_network, oa_names, training_ints, validation_ints, testing_ints, measure,
TRAINING_ITERATIONS, OUTFILE.format(oa_names))
def main(layers, training_iterations, test_data_file, train_data_file, validate_data_file, data_name):
"""Run this experiment"""
training_ints = base.initialize_instances(train_data_file)
testing_ints = base.initialize_instances(test_data_file)
validation_ints = base.initialize_instances(validate_data_file)
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(training_ints)
relu = RELU()
# 50 and 0.000001 are the defaults from RPROPUpdateRule.java
rule = RPROPUpdateRule(0.064, 50, 0.000001)
oa_names = ["RHC"]
classification_network = factory.createClassificationNetwork(layers, relu)
nnop = NeuralNetworkOptimizationProblem(
data_set, classification_network, measure)
oa = RandomizedHillClimbing(nnop)
base.train(oa, classification_network, 'RHC', training_ints, validation_ints, testing_ints, measure,
training_iterations, OUTFILE.format(data_name, 'RHC'))
return
def main():
"""Run this experiment"""
training_data = initialize_instances('../data/Pima-train.csv')
testing_data = initialize_instances('../data/Pima-test.csv')
print(len(training_data))
#testing_ints = initialize_instances('m_test.csv')
#validation_ints = initialize_instances('m_val.csv')
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(training_data)
relu = RELU()
rule = RPROPUpdateRule()
oa_names = ["RHC"]
classification_network = factory.createClassificationNetwork(
[INPUT_LAYER, HIDDEN_LAYER1, HIDDEN_LAYER2, OUTPUT_LAYER], relu)
nnop = NeuralNetworkOptimizationProblem(data_set, classification_network,
measure)
oa = RandomizedHillClimbing(nnop)
train(oa, classification_network, 'RHC', training_data, testing_data,
measure)
def main():
#training_ints = initialize_instances('bCancer_trg.csv')
#testing_ints = initialize_instances('bCancer_test.csv')
#validation_ints = initialize_instances('bCancer_val.csv')
training_ints = initialize_instances('winequality_trg.csv')
testing_ints = initialize_instances('winequality_test.csv')
validation_ints = initialize_instances('winequality_val.csv')
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(training_ints)
hts = HyperbolicTangentSigmoid()
rule = RPROPUpdateRule()
oa_names = ["RHC"]
classification_network = factory.createClassificationNetwork(
[INPUT_LAYER, HIDDEN_LAYER1, OUTPUT_LAYER], hts)
nnop = NeuralNetworkOptimizationProblem(data_set, classification_network,
measure)
oa = RandomizedHillClimbing(nnop)
train(oa, classification_network, 'RHC', training_ints, validation_ints,
testing_ints, measure)
def main():
"""Run this experiment"""
training_ints = initialize_instances(
'/Users/Sean/School/GeorgiaTech/CS7641/Assignment2/s_trg.csv')
testing_ints = initialize_instances(
'/Users/Sean/School/GeorgiaTech/CS7641/Assignment2/s_test.csv')
validation_ints = initialize_instances(
'/Users/Sean/School/GeorgiaTech/CS7641/Assignment2/s_val.csv')
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(training_ints)
sig = LogisticSigmoid()
rule = RPROPUpdateRule()
oa_names = ["RHC"]
classification_network = factory.createClassificationNetwork([
INPUT_LAYER, HIDDEN_LAYER1, HIDDEN_LAYER2, HIDDEN_LAYER3, OUTPUT_LAYER
], sig)
for trial in xrange(TRIALS):
oa = RandomizedHillClimbing(
NeuralNetworkOptimizationProblem(data_set, classification_network,
measure))
train(oa, classification_network, 'RHC', training_ints,
validation_ints, testing_ints, measure)
def main():
"""Run this experiment"""
pdb.set_trace()
training_ints = initialize_instances(
'/Users/lijiang/Desktop/yichuan_HW/Archive/train.csv')
testing_ints = initialize_instances(
'/Users/lijiang/Desktop/yichuan_HW/Archive/test.csv')
validation_ints = initialize_instances(
'/Users/lijiang/Desktop/yichuan_HW/Archive/validation.csv')
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(training_ints)
relu = RELU()
rule = RPROPUpdateRule()
oa_names = ["RHC"]
classification_network = factory.createClassificationNetwork([
INPUT_LAYER, HIDDEN_LAYER1, HIDDEN_LAYER2, HIDDEN_LAYER3, OUTPUT_LAYER
], relu)
nnop = NeuralNetworkOptimizationProblem(data_set, classification_network,
measure)
oa = RandomizedHillClimbing(nnop)
train(oa, classification_network, 'RHC', training_ints, validation_ints,
testing_ints, measure)
def Random_hill_climb(out_path, train_inst, test_inst, repeats,
training_iterations):
"""Run this experiment"""
for i in range(repeats):
out_path_ = out_path.replace("RHC_", 'RHC_{}'.format(str(i).zfill(3)))
with open(out_path_, 'w') as f:
f.write('{},{},{},{},{},{}\n'.format('iteration', 'MSE_trg',
'MSE_tst', 'acc_trg',
'acc_tst', 'elapsed'))
factory = BackPropagationNetworkFactory()
measure = SumOfSquaresError()
data_set = DataSet(train_inst)
# acti = LogisticSigmoid()
acti = HyperbolicTangentSigmoid()
rule = RPROPUpdateRule()
classification_network = factory.createClassificationNetwork(
[INPUT_LAYER, HIDDEN_LAYER1, HIDDEN_LAYER2, OUTPUT_LAYER], acti)
nnop = NeuralNetworkOptimizationProblem(data_set,
classification_network,
measure)
oa = RandomizedHillClimbing(nnop)
train(oa, classification_network, 'RHC', train_inst, test_inst,
measure, training_iterations, out_path_)
def rhc_generic(name, ef, odd, nf, iter_time, iters_total, iters_step, n_trials):
for i_trial in range(n_trials):
hcp = GenericHillClimbingProblem(ef, odd, nf)
rhc_instance = RandomizedHillClimbing(hcp)
rhc_trainer = FixedIterationTrainer(rhc_instance, iters_step)
rhc_state = {'problem': rhc_instance,
'trainer': rhc_trainer}
wrapper_rhc = AlgoWrapper(rhc_state,
lambda state: state['trainer'].train(),
lambda state: ef.value(state['problem'].getOptimal()),
lambda state: ef.value(state['problem'].getOptimal())
)
# create name and invalidate if super empty
decorated_name = ""
if name is not None and name != "":
decorated_name = name
timed_trainer = TimedTrainer(decorated_name,
wrapper_rhc,
iter_time,
iters_total,
iters_step,
_param_dict={'name':name}
)
timed_trainer.run()
def rhc_network(name, network, measure, train_set, test_set, acc_func, iter_time, iters_total, iters_step, n_trials):
for i_trial in range(n_trials):
network_optimizer = NeuralNetworkOptimizationProblem(train_set, network, measure)
rhc_instance = RandomizedHillClimbing(network_optimizer)
rhc_trainer = FixedIterationTrainer(rhc_instance, iters_step)
nn_state = {'network': network,
'trainer': rhc_trainer}
wrapper_rhc = AlgoWrapper(nn_state,
lambda state: state['trainer'].train(),
lambda state: acc_func(train_set, state['network'], measure),
lambda state: acc_func(test_set, state['network'], measure)
)
# create name and invalidate if super empty
decorated_name = ""
if name is not None and name != "":
decorated_name = name
timed_trainer = TimedTrainer(decorated_name,
wrapper_rhc,
iter_time,
iters_total,
iters_step,
_param_dict={'name':name}
)
timed_trainer.run()
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)
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
T = N / 10
fill = [2] * N
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)
if do_rhc:
rhc = RandomizedHillClimbing(hcp)
for iter in hc_iter:
score, call_count, runtime = helpers.eval_algo(ef, rhc, iter)
rhc_scores.append(score)
rhc_times.append(runtime)
sa_params.append([iter, N])
rhc_results[N][iter]['scores'][trial_num] = score
rhc_results[N][iter]['runtimes'][trial_num] = runtime
# print("RHC best score: ", score, " in time: ", runtime, " with iters: ", iter)
print "\tFinished RHC"
if do_sa:
for t in sa_temp:
for cooling in sa_cooling:
sa = SimulatedAnnealing(t, cooling, hcp)
for iter in sa_iter:
it_ga = 100 # learn weigths with back propagation 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()
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)
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
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 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
N=60
T=N/10
fill = [2] * N
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)
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)
# -- 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 = []
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 = DiscretePermutationDistribution(N)
nf = SwapNeighbor()
mf = SwapMutation()
cf = TravelingSalesmanCrossOver(ef)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
ef2 = TravelingSalesmanSortEvaluationFunction(points)
fill = [N] * N
ranges = array('i', fill)
odd2 = DiscreteUniformDistribution(ranges)
df = DiscreteDependencyTree(.1, ranges)
pop = GenericProbabilisticOptimizationProblem(ef2, odd2, df)
# Algorithm declaration
rhc = RandomizedHillClimbing(hcp)
sa = SimulatedAnnealing(SA_TEMPERATURE, SA_COOLING_FACTOR, hcp)
ga = StandardGeneticAlgorithm(GA_POPULATION, GA_CROSSOVER, GA_MUTATION,
gap)
mimic = MIMIC(MIMIC_SAMPLES, MIMIC_TO_KEEP, pop)
# Trainer declaration
fit_rhc = FixedIterationTrainer(rhc, current_iteration_count)
fit_sa = FixedIterationTrainer(sa, current_iteration_count)
fit_ga = FixedIterationTrainer(ga, current_iteration_count)
fit_mimic = FixedIterationTrainer(mimic, current_iteration_count)
print("Computing for %d iterations" % current_iteration_count)
# Fitting
start_rhc = time.time()
ef = KColorEvaluationFunction(adj)
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)
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())
# repeat a few times to get an average?
trials = 10 # more?
hill_climbing = []
annealing = []
genetic = []
mimic_data = []
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
N = 60
T = N / 10
fill = [2] * N
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)
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)
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 = []
cf = SingleCrossOver()
df = DiscreteDependencyTree(.1, ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
# RHC
for t in range(numTrials):
fname = outfile.format('RHC', str(t + 1))
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)
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 '----------------------------------'
