def run_mimic(t, samples, keep, m):
fname = outfile.format('MIMIC{}_{}_{}'.format(samples, keep, m),
str(t + 1))
ef = ContinuousPeaksEvaluationFunction(T)
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
mf = DiscreteChangeOneMutation(ranges)
cf = SingleCrossOver()
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
df = DiscreteDependencyTree(m, ranges)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
mimic = MIMIC(samples, keep, pop)
fit = FixedIterationTrainer(mimic, 10)
times = [0]
for i in range(0, maxIters, 10):
start = clock()
fit.train()
elapsed = time.clock() - start
times.append(times[-1] + elapsed)
fevals = ef.fevals
score = ef.value(mimic.getOptimal())
ef.fevals -= 1
st = '{},{},{},{}\n'.format(i, score, times[-1], fevals)
# print st
base.write_to_file(fname, st)
return
def run_ga(t, pop, mate, mutate):
fname = outfile.format('GA{}_{}_{}'.format(pop, mate, mutate), 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)
mf = DiscreteChangeOneMutation(ranges)
cf = SingleCrossOver()
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
ga = StandardGeneticAlgorithm(pop, mate, mutate, gap)
fit = FixedIterationTrainer(ga, 10)
times = [0]
for i in range(0, maxIters, 10):
start = clock()
fit.train()
elapsed = time.clock() - start
times.append(times[-1] + elapsed)
fevals = ef.fevals
score = ef.value(ga.getOptimal())
ef.fevals -= 1
st = '{},{},{},{}\n'.format(i, score, times[-1], fevals)
# print st
base.write_to_file(fname, st)
return
def run_mimic(t, samples, keep, m):
fill = [N] * N
ranges = array('i', fill)
ef = TravelingSalesmanRouteEvaluationFunction(points)
odd = DiscreteUniformDistribution(ranges)
fname = outfile.format('MIMIC{}_{}_{}'.format(samples, keep, m),
str(t + 1))
base.write_header(fname)
df = DiscreteDependencyTree(m, ranges)
ef = TravelingSalesmanRouteEvaluationFunction(points)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
mimic = MIMIC(samples, keep, pop)
fit = FixedIterationTrainer(mimic, 10)
times = [0]
for i in range(0, maxIters, 10):
start = clock()
fit.train()
elapsed = time.clock() - start
times.append(times[-1] + elapsed)
fevals = ef.fevals
score = ef.value(mimic.getOptimal())
ef.fevals -= 1
st = '{},{},{},{}\n'.format(i, score, times[-1], fevals)
# print st
base.write_to_file(fname, st)
return
def run_sa(t, CE):
fname = outfile.format('SA{}'.format(CE), str(t + 1))
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)
sa = SimulatedAnnealing(1E10, CE, hcp)
fit = FixedIterationTrainer(sa, 10)
times = [0]
for i in range(0, maxIters, 10):
start = clock()
fit.train()
elapsed = time.clock() - start
times.append(times[-1] + elapsed)
fevals = ef.fevals
score = ef.value(sa.getOptimal())
ef.fevals -= 1
st = '{},{},{},{}\n'.format(i, score, times[-1], fevals)
# print st
base.write_to_file(fname, st)
return
def run_rhc(t):
fname = outfile.format('RHC', str(t + 1))
with open(fname, 'a+') as f:
content = f.read()
if "fitness" not in content:
f.write('iterations,fitness,time,fevals\n')
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 run_rhc(t):
fname = outfile.format('RHC', str(t + 1))
ef = TravelingSalesmanRouteEvaluationFunction(points)
hcp = GenericHillClimbingProblem(ef, odd, nf)
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, 10)
times = [0]
for i in range(0, maxIters, 10):
start = clock()
fit.train()
elapsed = time.clock() - start
times.append(times[-1] + elapsed)
fevals = ef.fevals
score = ef.value(rhc.getOptimal())
ef.fevals -= 1
st = '{},{},{},{}\n'.format(i, score, times[-1], fevals)
# print st
base.write_to_file(fname, st)
return
def run_ga(t, pop, mate, mutate):
fname = outfile.format('GA{}_{}_{}'.format(pop, mate, mutate), str(t + 1))
ef = TravelingSalesmanRouteEvaluationFunction(points)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
ga = StandardGeneticAlgorithm(pop, mate, mutate, gap)
fit = FixedIterationTrainer(ga, 10)
times = [0]
for i in range(0, maxIters, 10):
start = clock()
fit.train()
elapsed = time.clock() - start
times.append(times[-1] + elapsed)
fevals = ef.fevals
score = ef.value(ga.getOptimal())
ef.fevals -= 1
st = '{},{},{},{}\n'.format(i, score, times[-1], fevals)
# print st
base.write_to_file(fname, st)
return
def run_sa(t, CE):
fname = outfile.format('SA{}'.format(CE), str(t + 1))
ef = TravelingSalesmanRouteEvaluationFunction(points)
hcp = GenericHillClimbingProblem(ef, odd, nf)
sa = SimulatedAnnealing(1E10, CE, hcp)
fit = FixedIterationTrainer(sa, 10)
times = [0]
for i in range(0, maxIters, 10):
start = clock()
fit.train()
elapsed = time.clock() - start
times.append(times[-1] + elapsed)
fevals = ef.fevals
score = ef.value(sa.getOptimal())
ef.fevals -= 1
st = '{},{},{},{}\n'.format(i, score, times[-1], fevals)
# print st
base.write_to_file(fname, st)
return
def run_sa(t, CE):
fname = outfile.format('SA{}'.format(CE), str(t + 1))
base.write_header(fname)
ef = ContinuousPeaksEvaluationFunction(T)
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
hcp = GenericHillClimbingProblem(ef, odd, nf)
sa = SimulatedAnnealing(1E10, CE, hcp)
fit = FixedIterationTrainer(sa, 10)
times = [0]
for i in range(0, maxIters, 10):
start = clock()
fit.train()
elapsed = time.clock() - start
times.append(times[-1] + elapsed)
fevals = ef.fevals
score = ef.value(sa.getOptimal())
ef.fevals -= 1
st = '{},{},{},{}\n'.format(i, score, times[-1], fevals)
# print st
base.write_to_file(fname, st)
return
def run_rhc(t):
fname = outfile.format('RHC', str(t + 1))
base.write_header(fname)
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
class RunExperiment(Callable):
def __init__(self, experiment):
self.experiment = experiment
self.started = None
self.completed = None
self.result = None
self.thread_used = None
self.exception = None
def __str__(self):
if self.exception:
return "[%s] %s download error %s in %.2fs" % \
(self.thread_used, self.experiment, self.exception,
self.completed - self.started, ) #, self.result)
elif self.completed:
return "[%s] %s downloaded %dK in %.2fs" % \
(self.thread_used, self.experiment, len(self.result)/1024,
self.completed - self.started, ) #, self.result)
elif self.started:
return "[%s] %s started at %s" % \
(self.thread_used, self.experiment, self.started)
else:
return "[%s] %s not yet scheduled" % \
(self.thread_used, self.experiment)
# needed to implement the Callable interface;
# any exceptions will be wrapped as either ExecutionException
# or InterruptedException
def call(self):
self.thread_used = threading.currentThread().getName()
self.started = time.time()
if self.experiment['kind'] == 'rhc':
exp_args = self.experiment['args']
else:
exp_args = ",".join(map(str, self.experiment['args']))
print "START: {},{},{}".format(self.experiment['type'],
self.experiment['kind'], exp_args)
try:
if self.experiment['type'] == 'continuouspeaks':
if self.experiment['kind'] == 'rhc':
self.result = continuouspeaks.run_rhc(
self.experiment['args'])
if self.experiment['kind'] == 'sa':
self.result = continuouspeaks.run_sa(
*self.experiment['args'])
if self.experiment['kind'] == 'ga':
self.result = continuouspeaks.run_ga(
*self.experiment['args'])
if self.experiment['kind'] == 'mimic':
self.result = continuouspeaks.run_mimic(
*self.experiment['args'])
if self.experiment['type'] == 'flipflop':
if self.experiment['kind'] == 'rhc':
self.result = flipflop.run_rhc(self.experiment['args'])
if self.experiment['kind'] == 'sa':
self.result = flipflop.run_sa(*self.experiment['args'])
if self.experiment['kind'] == 'ga':
self.result = flipflop.run_ga(*self.experiment['args'])
if self.experiment['kind'] == 'mimic':
self.result = flipflop.run_mimic(*self.experiment['args'])
if self.experiment['type'] == 'tsp':
if self.experiment['kind'] == 'rhc':
self.result = tsp.run_rhc(self.experiment['args'])
if self.experiment['kind'] == 'sa':
self.result = tsp.run_sa(*self.experiment['args'])
if self.experiment['kind'] == 'ga':
self.result = tsp.run_ga(*self.experiment['args'])
if self.experiment['kind'] == 'mimic':
self.result = tsp.run_mimic(*self.experiment['args'])
if self.experiment['type'] == 'nn':
if self.experiment['kind'] == 'backprop':
self.result = NNbackprop.main(*self.experiment['args'])
if self.experiment['kind'] == 'ga':
self.result = NNga.main(*self.experiment['args'])
if self.experiment['kind'] == 'rhc':
self.result = NNrhc.main(*self.experiment['args'])
if self.experiment['kind'] == 'sa':
self.result = NNsa.main(*self.experiment['args'])
except Exception, ex:
self.exception = ex
self.completed = time.time()
if self.experiment['kind'] == 'rhc':
exp_args = self.experiment['args']
else:
exp_args = ",".join(map(str, self.experiment['args']))
finished = "{},{},{},{}".format(self.experiment['type'],
self.experiment['kind'], exp_args,
self.completed - self.started)
print " END: {}".format(finished)
base.write_to_file(TIMING_FILE, "{}\n".format(finished))
return self
def call(self):
self.thread_used = threading.currentThread().getName()
self.started = time.time()
if self.experiment['kind'] == 'rhc':
exp_args = self.experiment['args']
else:
exp_args = ",".join(map(str, self.experiment['args']))
print("START: {},{},{}".format(self.experiment['type'],
self.experiment['kind'], exp_args))
try:
if self.experiment['type'] == 'continuouspeaks':
if self.experiment['kind'] == 'rhc':
self.result = continuouspeaks.run_rhc(
self.experiment['args'])
if self.experiment['kind'] == 'sa':
self.result = continuouspeaks.run_sa(
*self.experiment['args'])
if self.experiment['kind'] == 'ga':
self.result = continuouspeaks.run_ga(
*self.experiment['args'])
if self.experiment['kind'] == 'mimic':
self.result = continuouspeaks.run_mimic(
*self.experiment['args'])
if self.experiment['type'] == 'flipflop':
if self.experiment['kind'] == 'rhc':
self.result = flipflop.run_rhc(self.experiment['args'])
if self.experiment['kind'] == 'sa':
self.result = flipflop.run_sa(*self.experiment['args'])
if self.experiment['kind'] == 'ga':
self.result = flipflop.run_ga(*self.experiment['args'])
if self.experiment['kind'] == 'mimic':
self.result = flipflop.run_mimic(*self.experiment['args'])
if self.experiment['type'] == 'tsp':
if self.experiment['kind'] == 'rhc':
self.result = tsp.run_rhc(self.experiment['args'])
if self.experiment['kind'] == 'sa':
self.result = tsp.run_sa(*self.experiment['args'])
if self.experiment['kind'] == 'ga':
self.result = tsp.run_ga(*self.experiment['args'])
if self.experiment['kind'] == 'mimic':
self.result = tsp.run_mimic(*self.experiment['args'])
if self.experiment['type'] == 'nn':
if self.experiment['kind'] == 'backprop':
self.result = NNbackprop.main(*self.experiment['args'])
if self.experiment['kind'] == 'ga':
self.result = NNga.main(*self.experiment['args'])
if self.experiment['kind'] == 'rhc':
self.result = NNrhc.main(*self.experiment['args'])
if self.experiment['kind'] == 'sa':
self.result = NNsa.main(*self.experiment['args'])
except Exception:
pass
self.exception = ex
self.completed = time.time()
if self.experiment['kind'] == 'rhc':
exp_args = self.experiment['args']
else:
exp_args = ",".join(map(str, self.experiment['args']))
finished = "{},{},{},{}".format(self.experiment['type'],
self.experiment['kind'], exp_args,
self.completed - self.started)
print(" END: {}".format(finished))
base.write_to_file(TIMING_FILE, "{}\n".format(finished))
return self
