def run_main():
initialPopulationSize = 51
generations = 1500
conf = LoadSystemConfiguration() #TODO make an object to encapsulate this kind of information
# Genome instance
genome = G2DList.G2DList(400, 18)
genome.setParams(rangemin=0, rangemax=360)
# The evaluator function (objective function)
genome.evaluator.set(eval_func)
genome.crossover.set(Crossovers.G2DListCrossoverSingleHPoint)
#genome.mutator.set(Mutators.G2DListMutatorIntegerRange)
genome.mutator.set(Mutators.G2DListMutatorIntegerGaussian)
# Genetic Algorithm Instance
ga = GSimpleGA.GSimpleGA(genome)
ga.setGenerations(generations) #TODO class atribute
ga.setPopulationSize(initialPopulationSize) #TODO class atribute
ga.setMutationRate(0.2)
ga.selector.set(Selectors.GRankSelector)
#ga.selector.set(Selectors.GTournamentSelector)
#ga.selector.set(Selectors.GRouletteWheel)
ga.setElitism(True)
ga.setElitismReplacement(initialPopulationSize/3)
#ga.terminationCriteria.set(ConvergenceCriteria)
# Create DB Adapter and set as adapter
sqlite_adapter = DBAdapters.DBSQLite(identify="Lucy walk", resetDB=True)
ga.setDBAdapter(sqlite_adapter)
#callback to persist best individual of each generation
ga.stepCallback.set(generationCallback)
#keep a reference to the genetic algorithm engine
global gaEngine
gaEngine = ga
# Do the evolution, with stats dump
# frequency of 2 generations
ga.evolve(freq_stats=1)
# Best individual
best = ga.bestIndividual()
score = best.getRawScore()
timestr = time.strftime("%Y%m%d-%H%M%S")
filename = str(score) + "-" + timestr + "-" + str(generations) + ".xml"
prop = DTIndividualPropertyVanilla() #TODO create a vanilla property as default argument in Individual constructor
bestIndividual = Individual(prop, DTIndividualGeneticMatrix(chromosomeToLucyGeneticMatrix(best)))
geneticPoolDir = os.getcwd()+conf.getDirectory("Genetic Pool")
bestIndividual.persist(geneticPoolDir + filename)
#TODO store all the population, not only the fitest
print ga.getStatistics()
def generationCallback(ga_engine):
# persist best individual at the moment
conf = LoadSystemConfiguration() #TODO make an object to encapsulate this kind of information
geneticPoolDir = os.getcwd()+conf.getDirectory("Genetic Pool")
gen = ga_engine.getCurrentGeneration()
best = ga_engine.bestIndividual()
score = best.getRawScore()
timestr = time.strftime("%Y%m%d-%H%M%S")
filename = str(score) + "-" + timestr + "-" + str(gen) + ".xml"
prop = DTIndividualPropertyVanilla() #TODO create a vanilla property as default argument in Individual constructor
bestIndividual = Individual(prop, DTIndividualGeneticMatrix(chromosomeToLucyGeneticMatrix(best)))
bestIndividual.persist(geneticPoolDir + filename)
ga_engine.getDBAdapter().commit()
##population = ga_engine.getPopulation()
##averagePopulation = getPopulationAverage(population)
#averageGeneration = getPopulationAverage(gen)
##print "current population raw score average: ", averagePopulation
#print "current generation raw score average: ", averageGeneration
return False
def generationCallback(ga_engine):
# persist best individual at the moment
conf = LoadSystemConfiguration() #TODO make an object to encapsulate this kind of information
geneticPoolDir = os.getcwd()+conf.getDirectory("Genetic Pool")
gen = ga_engine.getCurrentGeneration()
best = ga_engine.bestIndividual()
score = best.getRawScore()
#check if the convergence criteria has been reached
global max_score, max_score_generation, convergenceCriteria
if score > max_score:
max_score = score
max_score_generation = gen
else:
#if the score doesn't improve in NUMBER_GENERATIONS_CONVERGENCE_CRITERIA generations then there is no reason to continue and we have reach a convergence
if gen - max_score_generation > NUMBER_GENERATIONS_CONVERGENCE_CRITERIA:
convergenceCriteria = True
timestr = time.strftime("%Y%m%d-%H%M%S")
filename = str(score) + "-" + timestr + "-" + str(gen) + ".xml"
#at generation 0 is the firs time to create de directory and store the GA parameters
if gen == 0:
global experimentDir
experimentDir = geneticPoolDir + timestr
global experimentTime
experimentTime = timestr
os.mkdir(experimentDir)
storeExperimentGAparameters()
prop = DTIndividualPropertyVanilla()
bestIndividual = Individual(prop, DTIndividualGeneticMatrix(chromosomeToLucyGeneticMatrix(best)))
bestIndividual.persist(os.path.join(experimentDir, filename))
ga_engine.getDBAdapter().commit()
#population = ga_engine.getPopulation()
#popSize = len(population)
print "generation executed!, best fit of generation: ", score, "fittest: ", max_score, "reached in generation: ", max_score_generation
return False
def run_main():
conf = LoadSystemConfiguration()
initialPopulationSize = int(conf.getProperty("Population size"))
generations = int(conf.getProperty("Number of generations"))
# Genome instance
framesQty = sysConstants.GENOMA_MAX_LENGTH
genome = G2DList.G2DList(framesQty, 18)
genome.setParams(rangemin=0, rangemax=360)
genome.setParams(gauss_sigma=sysConstants.MUTATION_SIGMA, gauss_mu=0)
# The evaluator function (objective function)
genome.evaluator.set(eval_func)
if conf.getProperty("Crossover operator") == "crossovers.G2DListCrossoverSingleNearHPoint":
genome.crossover.set(crossovers.G2DListCrossoverSingleNearHPoint)
elif conf.getProperty("Crossover operator") == "Crossovers.G2DListCrossoverSingleHPoint":
genome.crossover.set(Crossovers.G2DListCrossoverSingleHPoint)
#genome.crossover.set(crossovers.G2DListCrossoverSingleNearHPointImprove)
#genome.crossover.set(crossovers.G2DListCrossoverSingleHPoint)
# Genetic Algorithm Instance
ga = GSimpleGA.GSimpleGA(genome)
ga.setGenerations(generations)
ga.setPopulationSize(initialPopulationSize)
#genome.mutator.set(Mutators.G2DListMutatorIntegerRange)
if conf.getProperty("Mutator operator") == "mutators.G2DListMutatorRealGaussianSpline":
genome.mutator.set(mutators.G2DListMutatorRealGaussianSpline)
elif conf.getProperty("Mutator operator") == "Mutators.G2DListMutatorRealGaussianGradient":
genome.mutator.set(Mutators.G2DListMutatorRealGaussianGradient)
ga.setMutationRate(float(conf.getProperty("MutationRate")))
if conf.getProperty("Selection operator") == "Selectors.GRankSelector" :
ga.selector.set(Selectors.GRankSelector)
elif conf.getProperty("Selection operator") == "Selectors.GTournamentSelector" :
ga.selector.set(Selectors.GTournamentSelector)
elif conf.getProperty("Selection operator") == "Selectors.GRouletteWheel" :
ga.selector.set(Selectors.GRouletteWheel)
elif conf.getProperty("Selection operator") == "Selectors.GUniformSelector" :
ga.selector.set(Selectors.GUniformSelector)
'''For crossover probability, maybe it is the ratio of next generation population born by crossover operation.
While the rest of population...maybe by previous selection or you can define it as best fit survivors'''
ga.setCrossoverRate(float(conf.getProperty("CrossoverRate")))
elitism = float(conf.getProperty("Elitism replacement percentage")) > 0
ga.setElitism(True)
'''Set the number of best individuals to copy to the next generation on the elitism'''
if elitism:
numberIndividualsForNextGen = int(initialPopulationSize*float(conf.getProperty("Elitism replacement percentage")))
ga.setElitismReplacement(numberIndividualsForNextGen)
if int(conf.getProperty("Convergence criteria enable?"))==True:
ga.terminationCriteria.set(ConvergenceCriteria)
# Create DB Adapter and set as adapter
sqlite_adapter = DBAdapters.DBSQLite(identify="Lucy walk", resetDB=True)
ga.setDBAdapter(sqlite_adapter)
#callback to persist best individual of each generation
ga.stepCallback.set(generationCallback)
#keep a reference to the genetic algorithm engine
global gaEngine
gaEngine = ga
# Do the evolution, with stats dump
# frequency of every generation
ga.evolve(freq_stats=0)
# Best individual
best = ga.bestIndividual()
score = best.getRawScore()
timestr = time.strftime("%Y%m%d-%H%M%S")
filename = str(score) + "-" + timestr + "-" + str(generations) + ".xml"
prop = DTIndividualPropertyVanilla()
bestIndividual = Individual(prop, DTIndividualGeneticMatrix(chromosomeToLucyGeneticMatrix(best)))
bestIndividual.persist(os.path.join(experimentDir,filename))
#store all the final population, not only the fitest
population = ga.getPopulation()
popSize = len(population)
for pos in range(popSize):
individual = Individual(prop, DTIndividualGeneticMatrix(chromosomeToLucyGeneticMatrix(population[pos])))
timestr = time.strftime("%Y%m%d-%H%M%S")
filename = "final-" + str(pos) + "-" + timestr + ".xml"
individual.persist(os.path.join(experimentDir, filename))
#ga.getDBAdapter().commit()
shutil.copy2('pyevolve.db', experimentDir)
shutil.copy2(conf.getProperty("System Log"), experimentDir)
os.system("pyevolve_graph.py -i \"Lucy walk\" -3 -o gene_pool/experiment_img/" + experimentTime + " -e png")
#do the stats
print ga.getStatistics()
from parser.LoadPoses import LoadPoses
from datatypes.DTIndividualProperty import DTIndividualProperty, DTIndividualPropertyCMUDaz, DTIndividualPropertyVanilla, DTIndividualPropertyBaliero, DTIndividualPropertyVanillaEvolutive, DTIndividualPropertyPhysicalBioloid
from datatypes.DTIndividualGeneticMaterial import DTIndividualGeneticMaterial, DTIndividualGeneticTimeSerieFile, DTIndividualGeneticMatrix
from Pose import Pose
from configuration.LoadSystemConfiguration import LoadSystemConfiguration
from simulator.LoadRobotConfiguration import LoadRobotConfiguration
from Individual import Individual
import os
import glob
import time
import sys
propCMUDaz = DTIndividualPropertyCMUDaz()
propVanilla = DTIndividualPropertyVanilla()
balieroProp = DTIndividualPropertyBaliero()
physicalProp = DTIndividualPropertyPhysicalBioloid()
geneticVanillaProp = DTIndividualPropertyVanillaEvolutive()
conf = LoadSystemConfiguration()
CMUxmlDir = os.getcwd()+conf.getDirectory("Transformed CMU mocap Files")
for filename in glob.glob(os.path.join(CMUxmlDir, '*.xml')):
walk = Individual(propCMUDaz, DTIndividualGeneticTimeSerieFile(filename))
fitness = walk.execute()
length = walk.getLength()
walk.persist("/tmp/"+str(fitness)+".xml")
print "individual: ", filename, "fitness: ", fitness, "length: ", length
