def travelling_salesman():
N = 50
random = Random()
random.setSeed(0)
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()
fill = [N] * N
ranges = array('i', fill)
odd_mimic = DiscreteUniformDistribution(ranges)
odd = DiscretePermutationDistribution(N)
ef = TravelingSalesmanRouteEvaluationFunction(points)
nf = SwapNeighbor()
mf = SwapMutation()
cf = TravelingSalesmanCrossOver(ef)
rhc_generic("TSPrhc50", ef, odd, nf, 1.0, 10000, 10, 5)
sa_generic("TSPsa50", ef, odd, nf, 1.0, 10000, 10, 5,
([1E12, 1E6], [0.999, 0.99, 0.95]))
ga_generic("TSPga50", ef, odd, mf, cf, 50.0, 10000, 10, 1,
([2000, 200], [0.5, 0.25], [0.25, 0.1, 0.02]))
mimic_discrete("TSPmimic50", ef, odd_mimic, ranges, 300.0, 10000, 10, 1,
([200], [100], [0.1, 0.5, 0.9]))
print "TSP all done"
def knapsack():
# Random number generator */
random = Random()
random.setSeed(0)
NUM_ITEMS = 40 # The number of items
COPIES_EACH = 4 # The number of copies each
MAX_WEIGHT = 50 # The maximum weight for a single element
MAX_VOLUME = 50 # The maximum volume for a single element
KNAPSACK_VOLUME = MAX_VOLUME * NUM_ITEMS * COPIES_EACH * .4 # The volume of the knapsack
# 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()
rhc_generic("KnSrhc50", ef, odd, nf, 1.0, 10000, 10, 5)
sa_generic("KnSsa50", ef, odd, nf, 1.0, 10000, 10, 5,
([1E12, 1E6], [0.999, 0.99, 0.95]))
ga_generic("KnSga50", ef, odd, mf, cf, 50.0, 10000, 10, 1,
([2000, 200], [0.5, 0.25], [0.25, 0.1, 0.02]))
mimic_discrete("KnSmimic50", ef, odd, ranges, 300.0, 10000, 10, 1,
([200], [100], [0.1, 0.5, 0.9]))
print "KnS all done"
def get_ef(self):
"""Creates a new travelling salesman route evaluation function with
the specified class variables.
Returns
ranges (array): Array of values as specified by N.
ef (TravelingSalesmanEvaluationFunction): Evaluation function.
"""
random = Random()
points = [[0 for x in xrange(2)] for x in xrange(self.N)]
for i in range(0, len(points)):
points[i][0] = random.nextDouble()
points[i][1] = random.nextDouble()
# create ranges
fill = [self.N] * self.N
ranges = array('i', fill)
if self.subtype == 'route':
return ranges, TravelingSalesmanRouteEvaluationFunction(points)
elif self.subtype == 'sort':
return ranges, TravelingSalesmanSortEvaluationFunction(points)
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
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
# The maximum volume for a single element
MAX_VOLUME = 50
# The volume of the knapsack
KNAPSACK_VOLUME = MAX_VOLUME * NUM_ITEMS * COPIES_EACH * .4
iters = 800000
# 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)
def run_all():
problem = '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)
maxEpochs = 400
columns = ['problem','label','score','epoch','time','avgTrainTime','iterations']
outFile = open(filename+'_all.csv','wb')
fout = csv.writer(outFile,delimiter=',')
fout.writerow(columns)
def run_algo(alg,fit,label,iters):
print(alg)
trainTimes = [0.]
trainTime = []
scores = [0]
deltaScores = []
for epoch in range(0,maxEpochs,1):
st = time.clock()
fit.train()
et = time.clock()
trainTimes.append(trainTimes[-1]+(et-st))
trainTime.append((et-st))
rollingMean = 10
avgTime = (math.fsum(trainTime[-rollingMean:]) / float(rollingMean))
score = ef.value(alg.getOptimal())
scores.append(score)
deltaScores.append(math.fabs(scores[-2] - scores[-1]))
# trialString = '{}-{}-{}-{}'.format(label,score,epoch,trainTimes[-1])
trialData = [problem,label,score,epoch,trainTimes[-1],avgTime,iters]
print(trialData)
fout.writerow(trialData)
iters = 10
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, iters)
run_algo(rhc,fit,'RHC',10)
startTemp = 1E11
coolingFactor = .95
sa = SimulatedAnnealing(startTemp, coolingFactor, hcp)
fit = FixedIterationTrainer(sa, iters)
run_algo(sa,fit,'HCP',10)
population = 300
mates = 100
mutations = 50
ga = StandardGeneticAlgorithm(population, mates, mutations, gap)
fit = FixedIterationTrainer(ga, iters)
run_algo(ga,fit,'GA',10)
samples = 200
keep = 20
mimic = MIMIC(samples, keep, pop)
fit = FixedIterationTrainer(mimic, iters)
run_algo(mimic,fit,'MIMIC',10)
outFile.close()
def knapsackfunc(NUM_ITEMS, iterations):
rhcMult = 600
saMult = 600
gaMult = 4
mimicMult = 3
# 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)
optimalOut = []
timeOut = []
evalsOut = []
for niter in iterations:
iterOptimalOut = [NUM_ITEMS, niter]
iterTimeOut = [NUM_ITEMS, niter]
iterEvals = [NUM_ITEMS, 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(100, .95, 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, 150, 25, 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, 100, 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]
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)
def nextDouble(self):
return Random.nextDouble(self) * self.multiplier
from array import array
"""
Commandline parameter(s):
none
"""
# 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)
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, 200000)
fit.train()
print "RHC Inverse of Distance: " + str(ef.value(rhc.getOptimal()))
print "Route:"
def travelingsalesmanfunc(N, iterations):
rhcMult = 1500
saMult = 1500
gaMult = 1
mimicMult = 3
random = Random()
points = [[0 for x in xrange(2)] for x in xrange(N)]
for i in range(0, len(points)):
points[i][0] = random.nextDouble()
points[i][1] = random.nextDouble()
optimalOut = []
timeOut = []
evalsOut = []
for niter in iterations:
ef = TravelingSalesmanRouteEvaluationFunction(points)
odd = DiscretePermutationDistribution(N)
nf = SwapNeighbor()
mf = SwapMutation()
cf = TravelingSalesmanCrossOver(ef)
hcp = GenericHillClimbingProblem(ef, odd, nf)
gap = GenericGeneticAlgorithmProblem(ef, odd, mf, cf)
iterOptimalOut = [N, niter]
iterTimeOut = [N, niter]
iterEvals = [N, niter]
start = time.time()
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, niter * rhcMult)
fit.train()
end = time.time()
rhcOptimal = ef.value(rhc.getOptimal())
rhcTime = end - start
print "RHC Inverse of Distance: optimum: " + str(rhcOptimal)
print "RHC time: " + str(rhcTime)
#print "RHC Inverse of Distance: " + str(ef.value(rhc.getOptimal()))
print "Route:"
path = []
for x in range(0, N):
path.append(rhc.getOptimal().getDiscrete(x))
print path
iterOptimalOut.append(rhcOptimal)
iterTimeOut.append(rhcTime)
functionEvals = ef.getNumEvals()
ef.zeroEvals()
iterEvals.append(functionEvals)
start = time.time()
sa = SimulatedAnnealing(1E12, .999, hcp)
fit = FixedIterationTrainer(sa, niter * saMult)
fit.train()
end = time.time()
saOptimal = ef.value(sa.getOptimal())
saTime = end - start
print "SA Inverse of Distance optimum: " + str(saOptimal)
print "SA time: " + str(saTime)
#print "SA Inverse of Distance: " + str(ef.value(sa.getOptimal()))
print "Route:"
path = []
for x in range(0, N):
path.append(sa.getOptimal().getDiscrete(x))
print path
iterOptimalOut.append(saOptimal)
iterTimeOut.append(saTime)
functionEvals = ef.getNumEvals()
ef.zeroEvals()
iterEvals.append(functionEvals)
start = time.time()
ga = StandardGeneticAlgorithm(2000, 1500, 250, gap)
fit = FixedIterationTrainer(ga, niter * gaMult)
fit.train()
end = time.time()
gaOptimal = ef.value(ga.getOptimal())
gaTime = end - start
print "GA Inverse of Distance optimum: " + str(gaOptimal)
print "GA time: " + str(gaTime)
#print "GA Inverse of Distance: " + str(ef.value(ga.getOptimal()))
print "Route:"
path = []
for x in range(0, N):
path.append(ga.getOptimal().getDiscrete(x))
print path
iterOptimalOut.append(gaOptimal)
iterTimeOut.append(gaTime)
functionEvals = ef.getNumEvals()
ef.zeroEvals()
iterEvals.append(functionEvals)
start = time.time()
# for mimic we use a sort encoding
ef = TravelingSalesmanSortEvaluationFunction(points)
fill = [N] * N
ranges = array('i', fill)
odd = DiscreteUniformDistribution(ranges)
df = DiscreteDependencyTree(.1, ranges)
pop = GenericProbabilisticOptimizationProblem(ef, odd, df)
start = time.time()
mimic = MIMIC(500, 100, pop)
fit = FixedIterationTrainer(mimic, niter * mimicMult)
fit.train()
end = time.time()
mimicOptimal = ef.value(mimic.getOptimal())
mimicTime = end - start
print "MIMIC Inverse of Distance optimum: " + str(mimicOptimal)
print "MIMIC time: " + str(mimicTime)
#print "MIMIC Inverse of Distance: " + str(ef.value(mimic.getOptimal()))
print "Route:"
path = []
optimal = mimic.getOptimal()
fill = [0] * optimal.size()
ddata = array('d', fill)
for i in range(0, len(ddata)):
ddata[i] = optimal.getContinuous(i)
order = ABAGAILArrays.indices(optimal.size())
ABAGAILArrays.quicksort(ddata, order)
print order
iterOptimalOut.append(mimicOptimal)
iterTimeOut.append(mimicTime)
functionEvals = ef.getNumEvals()
ef.zeroEvals()
iterEvals.append(functionEvals)
optimalOut.append(iterOptimalOut)
timeOut.append(iterTimeOut)
evalsOut.append(iterEvals)
return [optimalOut, timeOut, evalsOut]
# NeuralNetwork. Build a bayesian Self-Organizing Map. Example I
from java.util import Random
from jhplot import *
h1 = H1D("Data",20, -100.0, 300.0)
r = Random()
for i in range(2000):
h1.fill(100+r.nextGaussian()*100)
h1.fill(100+r.nextDouble()*100)
p1d=P1D(h1,0,0)
# write to a file
p1d.toFile("data.txt")
bs=HBsom()
bs.setNPoints(30)
bs.setData(p1d)
bs.visible()
# @SciViewService svs
# An example script that opens SciView and generates 25 random colored cubes
from java.util import Random
from cleargl import GLVector
from array import array
sv = svs.getOrCreateActiveSciView()
num_points = 25
rng = Random()
points = [[
rng.nextDouble() * 10,
rng.nextDouble() * 10,
rng.nextDouble() * 10
] for k in range(num_points)]
for point in points:
box = sv.addBox()
box.setPosition(GLVector(array('f', [point[0], point[1], point[2]])))
box.getMaterial().setDiffuse(
GLVector(
array('f', [rng.nextDouble(),
rng.nextDouble(),
rng.nextDouble()])))
"""
Commandline parameter(s):
none
"""
# Random number generator */
random = Random()
# dimension
N = 2
# number of peaks
K = 50
# means of k-peaks
mean = [[50*random.nextDouble() for x in xrange(N)] for x in xrange(K)]
# standard deviations of k-peaks
std = [[20*random.nextDouble() for x in xrange(N)] for x in xrange(K)]
# heights of k-peaks
height = [1000*random.nextDouble() for x in xrange(K)];
# range of bit strings
fill = [100] * N
ranges = array('i', fill)
ef = KHillsEvaluationFunction(mean, std, height)
odd = DiscreteUniformDistribution(ranges)
nf = DiscreteChangeOneNeighbor(ranges)
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 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
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
print "####"
return ef.value(alg_func.getOptimal())
"""
Commandline parameter(s):
none
"""
# set N value. This is the number of points
N = 25
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)
# #Try 5 rounds of RHC
# expt = "expt_Restarts"
# for i in range(5):
# rhc = RandomizedHillClimbing(hcp)
# train(rhc, "RHC", ef, 20000, "round=" + str(i), expt)
def run_all_2(N=40,fout=None):
maxEpochs = 10**5
maxTime = 300 #5 minutes
problem = 'knapsack'
# Random number generator */
random = Random()
# The number of items
NUM_ITEMS = N
# 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)
def run_algo(alg,fit,label,difficulty,iters):
trainTimes = [0.]
trainTime = []
scoreChange = [0.]
stuckCount = 10**3
prev = 0.
for epoch in range(0,maxEpochs,1):
st = time.clock()
fit.train()
et = time.clock()
trainTimes.append(trainTimes[-1]+(et-st))
trainTime.append((et-st))
rollingMean = 10
avgTime = (math.fsum(trainTime[-rollingMean:]) / float(rollingMean))
score = ef.value(alg.getOptimal())
# trialString = '{}-{}-{}-{}'.format(label,score,epoch,trainTimes[-1])
trialData = [problem,difficulty,label,score,epoch,trainTimes[-1],avgTime,iters]
# print(trialData)
# fout.writerow(trialData)
# print(trialData)
print(trialData,max(scoreChange))
# print(max(scoreChange))
# optimum = (difficulty-1-T) + difficulty
# if score >= optimum: break
scoreChange.append(abs(score-prev))
prev = score
scoreChange = scoreChange[-stuckCount:]
# print(scoreChange)
if max(scoreChange) < 1.0: break
if trainTimes[-1] > maxTime: break
# print(trialData)
fout.writerow(trialData)
iters = 1000
rhc = RandomizedHillClimbing(hcp)
fit = FixedIterationTrainer(rhc, iters)
run_algo(rhc,fit,'RHC',N,iters)
iters = 1000
startTemp = 1E10
coolingFactor = .99
sa = SimulatedAnnealing(startTemp, coolingFactor, hcp)
fit = FixedIterationTrainer(sa, iters)
run_algo(sa,fit,'SA',N,iters)
iters = 10
population = 300
mates = 100
mutations = 50
ga = StandardGeneticAlgorithm(population, mates, mutations, gap)
fit = FixedIterationTrainer(ga, iters)
run_algo(ga,fit,'GA',N,iters)
iters = 10
samples = 200
keep = 20
mimic = MIMIC(samples, keep, pop)
fit = FixedIterationTrainer(mimic, iters)
run_algo(mimic,fit,'MIMIC',N,iters)
