def __init__(self, idividualProperty, individualGeneticMaterial):
self.property = idividualProperty
self.fitness = 0
self.robotConfig = LoadRobotConfiguration()
self.configuration = LoadSystemConfiguration()
self.genomeMatrix = individualGeneticMaterial.getGeneticMatrix()
self.poseSize = len(self.genomeMatrix)
self.genomeMatrixJointNameIDMapping = {}
self.sysConf = LoadSystemConfiguration()
i = 0
for jointName in self.robotConfig.getJointsName():
self.genomeMatrixJointNameIDMapping[jointName] = i
i = i + 1
dontSupportedJoints = self.configuration.getVrepNotImplementedBioloidJoints()
self.robotImplementedJoints = []
robotJoints = self.robotConfig.getJointsName()
for joint in robotJoints:
if joint not in dontSupportedJoints:
self.robotImplementedJoints.append(joint)
# is important to use only supported joints to avoid errors obtaining the handler of a joint that doesn't exists
for i in xrange(self.poseSize):
for joint in self.robotImplementedJoints:
# print "i: ", i, "j: ", joint
value = self.genomeMatrix[i][self.genomeMatrixJointNameIDMapping[joint]] + self.property.getPoseFix(
joint
) # TODO this can be a problem for the physical robot
self.genomeMatrix[i][self.genomeMatrixJointNameIDMapping[joint]] = value
def eval_func(chromosome):
conf = LoadSystemConfiguration()
if not initialPopulationSetted:
setInitialPopulation(gaEngine)
prop = DTIndividualPropertyVanillaEvolutive()
if int(conf.getProperty("Concatenate walk cycles?")):
embryo = DTIndividualGeneticMatrixWalk(chromosomeToLucyGeneticMatrix(chromosome))
else:
embryo = DTIndividualGeneticMatrix(chromosomeToLucyGeneticMatrix(chromosome))
precycleFile = os.getcwd()+"/mocap/cmu_mocap/xml/util/walk_precycle.xml"
preCycleEmbryo = DTIndividualGeneticTimeSerieFile(precycleFile)
preCycleEmbryo.concatenate(embryo)
newEmbryo = preCycleEmbryo
#embryoLength = newEmbryo.getLength()
individual = Individual(prop, newEmbryo)
#individual.setLength(embryoLength)
fitness = individual.execute() #return the fitness resulting from the simulator execution
if int(conf.getProperty("re-evaluate fittest?"))==True:
if fitness > max_score: #is really a better fitness ?
candidateFitness = fitness
fitness = individual.execute()
print "candidateFitness: ", candidateFitness, "fitness: ", fitness
while abs(candidateFitness-fitness) > 0.01:
candidateFitness=fitness
fitness = individual.execute()
print "candidateFitness: ", candidateFitness, "fitness: ", fitness
#the candidateFitness was validated!
fitness = candidateFitness
return fitness
def __init__(self):
conf = LoadSystemConfiguration()
self.geneticMatrix = []
self.cyclesQty = int(conf.getProperty("Concatenate walk cycles?"))
self.robotConfig = LoadRobotConfiguration()
self.jointNameIDMapping = {}
jointIDCounter = 0
for j in self.robotConfig.getJointsName():
self.jointNameIDMapping[jointIDCounter] = j
jointIDCounter += 1
def __init__(self, file):
self.parser = BvhImport(file)
##dtCycleProp = DTWalkPreCyclePropertyLR(file)
##dtCycleProp = DTWalkCycleStartingLeftFootProperty(file)
#dtCycleProp = DTWalkPreCyclePropertyRL(file)
dtCycleProp = DTWalkCycleStartingRightFootProperty(file)
self.start = dtCycleProp.getIndividualStart()
self.end = dtCycleProp.getIndividualEnd()
self.direction = dtCycleProp.getMoveDirection()
conf = LoadSystemConfiguration()
self.skipping = int(conf.getProperty("number of frames to skip"))
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 getPose(self, frameNumber):
sysconf = LoadSystemConfiguration()
robotConf = LoadRobotConfiguration()
dontSupportedJoints = sysconf.getVrepNotImplementedBioloidJoints()
robotImplementedJoints = []
robotJoints = robotConf.getJointsName()
for joint in robotJoints:
if joint not in dontSupportedJoints:
robotImplementedJoints.append(joint)
frame = self.framelist[frameNumber]
for jointName in robotImplementedJoints:
joint = frame.getElementsByTagName(jointName)[0]
angle = joint.getAttribute("angle")
self.jointAngleMapping[jointName] = float(angle)
return Pose(self.jointAngleMapping)
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 setInitialPopulation (ga_engine):
prop = DTIndividualPropertyCMUDaz()
propVanilla = DTIndividualPropertyVanilla()
balieroProp = DTIndividualPropertyBaliero()
conf = LoadSystemConfiguration()
CMUxmlDir = os.getcwd()+conf.getDirectory("Transformed CMU mocap Files")
GAwalkDir = os.getcwd()+conf.getDirectory("GAWalk Files")
UIBLHDir = os.getcwd()+conf.getDirectory("UIBLH mocap Files")
BalieroDir = os.getcwd()+conf.getDirectory("Baliero transformed walk Files")
ADHOCDir = os.getcwd()+conf.getDirectory("ADHOC Files")
geneticPoolDir = os.getcwd()+conf.getDirectory("Genetic Pool")
population = ga_engine.getPopulation()
popSize = len(population)
individualCounter = 0
# if individualCounter < popSize:
# for filename in glob.glob(os.path.join(GAwalkDir, '*.xml')):
# print individualCounter, " individuals processed!"
# print 'inserting individual: ' + filename + " into the initial population"
# walk = Individual(propVanilla, DTIndividualGeneticTimeSerieFile(filename))
# geneticMatrix = walk.getGenomeMatrix()
# if individualCounter < popSize:
# adan = population[individualCounter]
# for i in xrange(adan.getHeight()):
# if i == len(geneticMatrix):
# break
# for j in xrange(adan.getWidth()):
# adan.setItem(i,j,geneticMatrix[i][j])
# population[individualCounter]=adan
# individualCounter = individualCounter + 1
# else:
# break
if individualCounter < popSize:
for filename in glob.glob(os.path.join(CMUxmlDir, '*.xml')):
print individualCounter, " individuals processed!"
print 'inserting individual: ' + filename + " into the initial population"
walk = Individual(prop, DTIndividualGeneticTimeSerieFile(filename))
geneticMatrix = walk.getGenomeMatrix()
if individualCounter < popSize:
adan = population[individualCounter]
for i in xrange(adan.getHeight()):
if i == len(geneticMatrix):
break
for j in xrange(adan.getWidth()):
adan.setItem(i,j,geneticMatrix[i][j])
population[individualCounter]=adan
individualCounter = individualCounter + 1
else:
break
global initialPopulationSetted
initialPopulationSetted = True
def storeExperimentGAparameters():
conf = LoadSystemConfiguration()
file = open(os.path.join(experimentDir,"info.txt"),"w")
file.write("initialPopulationSize = " + conf.getProperty("Population size") + "\n")
file.write("generations = " + conf.getProperty("Number of generations") + "\n")
file.write("genome.crossover = " + conf.getProperty("Crossover operator") + "\n")
file.write("genome.mutator = " + conf.getProperty("Mutator operator") + "\n")
file.write("MutationRate = " + conf.getProperty("MutationRate") + "\n")
file.write("selector = " + conf.getProperty("Selection operator") + "\n")
file.write("CrossoverRate = " + conf.getProperty("CrossoverRate") + "\n")
file.write("ElitismReplacement percentage = " + conf.getProperty("Elitism replacement percentage") + "\n")
file.close()
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 __init__(self, idividualProperty, individualGeneticMaterial, modelReposeValue=DTModelVrepReda()):
self.property = idividualProperty
self.modelReposeValue = modelReposeValue
self.fitness = 0
self.robotConfig = LoadRobotConfiguration()
self.configuration = LoadSystemConfiguration()
self.genomeMatrix = individualGeneticMaterial.getGeneticMatrix()
self.length = individualGeneticMaterial.getLength()
self.genomeMatrixJointNameIDMapping = {}
self.sysConf = LoadSystemConfiguration()
self.individualGeneticMaterial = individualGeneticMaterial
self.moveArmFirstTime = True
self.precycleLength = 0
self.cycleLength = 0 #Not used yet
i=0
for jointName in self.robotConfig.getJointsName():
self.genomeMatrixJointNameIDMapping[jointName]=i
i=i+1
dontSupportedJoints = self.configuration.getVrepNotImplementedBioloidJoints()
self.robotImplementedJoints = []
robotJoints = self.robotConfig.getJointsName()
for joint in robotJoints:
if joint not in dontSupportedJoints:
self.robotImplementedJoints.append(joint)
#is important to use only supported joints to avoid errors obtaining the handler of a joint that doesn't exists
for i in xrange(self.length):
for joint in self.robotImplementedJoints:
#print "i: ", i, "j: ", joint
if self.property.avoidJoint(joint):
value = modelReposeValue.getReposeValue(joint)
else:
value = self.genomeMatrix[i][self.genomeMatrixJointNameIDMapping[joint]] + self.property.getPoseFix(joint) #TODO this can be a problem for the physical robot
self.genomeMatrix[i][self.genomeMatrixJointNameIDMapping[joint]]=value
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
import os
import glob
import ntpath
from parser.BvhImport import BvhImport
import matplotlib.pyplot as plt
from configuration.LoadSystemConfiguration import LoadSystemConfiguration
import numpy as np
from scipy.signal import argrelextrema
from collections import Counter
sysConf = LoadSystemConfiguration()
BVHDir = os.getcwd() + sysConf.getDirectory("CMU mocap Files")
Y_THREADHOLD = 11 #TODO calculate this as the average of the steps_highs
X_THREADHOLD = 36
def firstMax(values1, values2):
res=0
for i in range(len(values1)-2):
if values1[i] < values1[i+1] and values1[i+1] > values1[i+2]: #i+1 is a local maximun
if (values1[i] - values2[i]) > THREADHOLD:
res=i+1
elif values1[i] < values1[i+1] < values1[i+2]: #i is a local maximun
if (values1[i] - values2[i]) > THREADHOLD:
res=i
def storeExperimentGAparameters():
conf = LoadSystemConfiguration()
file = open(os.path.join(experimentDir,"info.txt"),"w")
file.write("initialPopulationSize = " + conf.getProperty("Population size") + "\n")
file.write("generations = " + conf.getProperty("Number of generations") + "\n")
file.write("genome.crossover = " + conf.getProperty("Crossover operator") + "\n")
file.write("genome.mutator = " + conf.getProperty("Mutator operator") + "\n")
file.write("MutationRate = " + conf.getProperty("MutationRate") + "\n")
file.write("selector = " + conf.getProperty("Selection operator") + "\n")
file.write("CrossoverRate = " + conf.getProperty("CrossoverRate") + "\n")
file.write("ElitismReplacement percentage = " + conf.getProperty("Elitism replacement percentage") + "\n")
file.write("Concatenate walk cycles = " + conf.getProperty("Concatenate walk cycles?") + "\n")
file.write("concatenate external cycle file = " + conf.getProperty("concatenate external cycle file?") + "\n")
file.write("Convergence criteria enable? = " + conf.getProperty("Convergence criteria enable?") + "\n")
file.write("Vrep robot model scene = " + conf.getFile("Lucy vrep model") + "\n")
file.close()
class Individual:
def __init__(self, idividualProperty, individualGeneticMaterial, modelReposeValue=DTModelVrepReda()):
self.property = idividualProperty
self.modelReposeValue = modelReposeValue
self.fitness = 0
self.robotConfig = LoadRobotConfiguration()
self.configuration = LoadSystemConfiguration()
self.genomeMatrix = individualGeneticMaterial.getGeneticMatrix()
self.length = individualGeneticMaterial.getLength()
self.genomeMatrixJointNameIDMapping = {}
self.sysConf = LoadSystemConfiguration()
self.individualGeneticMaterial = individualGeneticMaterial
self.moveArmFirstTime = True
self.precycleLength = 0
self.cycleLength = 0 #Not used yet
i=0
for jointName in self.robotConfig.getJointsName():
self.genomeMatrixJointNameIDMapping[jointName]=i
i=i+1
dontSupportedJoints = self.configuration.getVrepNotImplementedBioloidJoints()
self.robotImplementedJoints = []
robotJoints = self.robotConfig.getJointsName()
for joint in robotJoints:
if joint not in dontSupportedJoints:
self.robotImplementedJoints.append(joint)
#is important to use only supported joints to avoid errors obtaining the handler of a joint that doesn't exists
for i in xrange(self.length):
for joint in self.robotImplementedJoints:
#print "i: ", i, "j: ", joint
if self.property.avoidJoint(joint):
value = modelReposeValue.getReposeValue(joint)
else:
value = self.genomeMatrix[i][self.genomeMatrixJointNameIDMapping[joint]] + self.property.getPoseFix(joint) #TODO this can be a problem for the physical robot
self.genomeMatrix[i][self.genomeMatrixJointNameIDMapping[joint]]=value
def stopLucy(self):
self.lucy.stopLucy()
def execute(self):
#create the corresponding instance of Lucy, depending if it's real or simulated.
if int(self.sysConf.getProperty("Lucy simulated?"))==1:
self.lucy = SimulatedLucy(int(self.configuration.getProperty("Lucy render enable")))
else:
self.lucy = PhysicalLucy()
poseExecute={}
i=0
while (self.lucy.isLucyUp() and i < self.length):
for joint in self.robotConfig.getJointsName():
if not(self.property.avoidJoint(joint)):
value = self.genomeMatrix[i][self.genomeMatrixJointNameIDMapping[joint]]
poseExecute[joint] = value
i = i + self.lucy.getPosesExecutedByStepQty()
self.lucy.executePose(Pose(poseExecute))
'''if self.precycleLength > 0 and i == self.precycleLength - 1:
self.lucy.moveHelperArm()
if self.precycleLength > 0 and i > self.precycleLength - 1:
if (i - self.precycleLength) > 0 and (i - self.precycleLength) % self.cycleLength == 0:
self.lucy.moveHelperArm()'''
startingCyclePose = self.getPrecycleLength()
executionConcatenationGap = self.individualGeneticMaterial.getConcatenationGap(startingCyclePose)
self.fitness = self.lucy.getFitness(self.length, executionConcatenationGap)
self.lucy.stopLucy() #this function also updates time and distance
return self.fitness
def getGenomeMatrix(self):
return self.genomeMatrix
def setGenomeMatrix(self, geneMatrix):
self.genomeMatrix = geneMatrix
dtGenome = DTGenomeFunctions()
self.length = dtGenome.individualLength(geneMatrix)
def persist(self,file):
root = ET.Element("root")
lucy = ET.SubElement(root, "Lucy")
for frameIt in xrange(self.length):
frame = ET.SubElement(lucy, "frame")
frame.set("number" , str(frameIt))
for joint in self.robotImplementedJoints: #TODO because the notImplementedJoints doesn't participate in the simulation and fitness evaluation, they are not stored
xmlJoint = ET.SubElement(frame, joint)
joint_id = self.robotConfig.loadJointId(joint)
pos = self.genomeMatrix[frameIt][self.genomeMatrixJointNameIDMapping[joint]]
xmlJointAngle = xmlJoint.set("angle" , str(pos))
tree = ET.ElementTree(root)
tree.write(file)
def getJointMatrixIDFromName(self, jointName):
return self.genomeMatrixJointNameIDMapping[jointName]
def setLength(self, length):
self.length = length
def setPrecycleLength(self, precycleLength):
self.precycleLength = precycleLength
def setCycleLength(self, cycleLength):
self.cycleLength = cycleLength
def getLength(self):
return self.length
def getPrecycleLength(self):
return self.precycleLength
def getCycleLength(self):
return self.cycleLength
import glob
import os
import sys
import time
from configuration.LoadSystemConfiguration import LoadSystemConfiguration
from datatypes.DTIndividualGeneticMaterial import DTIndividualGeneticTimeSerieFile, DTIndividualGeneticMatrix
from datatypes.DTIndividualProperty import DTIndividualPropertyBaliero, DTIndividualPropertyPhysicalBioloid
from Individual import Individual
balieroProp = DTIndividualPropertyBaliero()
physicalProp = DTIndividualPropertyPhysicalBioloid()
conf = LoadSystemConfiguration()
BalieroDir = os.getcwd()+conf.getDirectory("Baliero transformed walk Files")
arguments = len(sys.argv)
def createIndividual(filename):
if int(conf.getProperty("Lucy simulated?"))==1:
walk = Individual(balieroProp, DTIndividualGeneticTimeSerieFile(os.getcwd()+"/"+filename))
else:
walk = Individual(physicalProp, DTIndividualGeneticTimeSerieFile(os.getcwd()+"/"+filename))
return walk
walk = Individual(balieroProp, DTIndividualGeneticMatrix()) #dummy individual to initialise the simulator and enable the time step configuration
walk.execute()
print "please set the proper time step in vrep"
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()
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA 02110-1301 USA
from parser.JointCalculation import JointCalculation
from parser.MocapLucyMapping import MocapLucyMapping
from simulator.LoadRobotConfiguration import LoadRobotConfiguration
from configuration.LoadSystemConfiguration import LoadSystemConfiguration
from string import rstrip
import os
import glob
import ntpath
sysConf = LoadSystemConfiguration()
BVHDir = os.getcwd() + sysConf.getDirectory("CMU mocap Files")
XMLDir = os.getcwd() + sysConf.getDirectory("Transformed CMU mocap Files")
robotConfiguration = LoadRobotConfiguration()
for filename in glob.glob(os.path.join(BVHDir, '*.bvh')):
print "transforming: " + filename + " ..."
lucyFileConversion = MocapLucyMapping(filename, robotConfiguration)
newFile = ntpath.basename(filename)
transformedFileName = newFile
newFile = rstrip(newFile[:-4]) + ".xml"
newFile = XMLDir + newFile
lucyFileConversion.generateFile(newFile)
print "file: " + newFile + " generated!"
os.rename(filename, os.path.join(BVHDir)+"/transformed/"+transformedFileName)
def setInitialPopulation(ga_engine):
propCMUDaz = DTIndividualPropertyCMUDaz()
propVanilla = DTIndividualPropertyVanilla()
balieroProp = DTIndividualPropertyBaliero()
conf = LoadSystemConfiguration()
lucyCycles = os.getcwd()+conf.getDirectory("Lucy evolved walk cycles Files")
CMUxmlDir = os.getcwd()+conf.getDirectory("Transformed CMU mocap Files")
GAwalkDir = os.getcwd()+conf.getDirectory("GAWalk Files")
UIBLHDir = os.getcwd()+conf.getDirectory("UIBLH mocap Files")
BalieroDir = os.getcwd()+conf.getDirectory("Baliero transformed walk Files")
ADHOCDir = os.getcwd()+conf.getDirectory("ADHOC Files")
geneticPoolDir = os.getcwd()+conf.getDirectory("Genetic Pool")
population = ga_engine.getPopulation()
popSize = len(population)
individualCounter = 0
walk = Individual(propVanilla, DTIndividualGeneticMatrix()) #dummy individual to initialise the simulator and enable the time step configuration
walk.execute()
print "please set the proper time step in vrep"
dtgenoma = DTGenomeFunctions()
#the random initia population created is replaced by the imitation motion capture database
if individualCounter < popSize:
for filename in glob.glob(os.path.join(CMUxmlDir, '*.xml')):
if individualCounter < popSize:
print individualCounter, " individuals processed!"
print 'inserting individual: ' + filename + " into the initial population"
walk = Individual(propCMUDaz, DTIndividualGeneticTimeSerieFile(filename))
teacherGeneticMatrix = walk.getGenomeMatrix()
adan = population[individualCounter]
adanIndividualLength=dtgenoma.getIndividualLength(adan)
adanJointLength=dtgenoma.getIndividualFrameLength(adan)
for i in xrange(adanIndividualLength):
for j in xrange(adanJointLength):
if i < len(teacherGeneticMatrix): #if the fixed gnoma representation size is less than the teacher size
adan.setItem(i,j,teacherGeneticMatrix[i][j])
else:
#put a sentinel joint value to mark the end of the individual
adan.setItem(i,j,sysConstants.JOINT_SENTINEL)
population[individualCounter]=adan
individualCounter = individualCounter + 1
else:
break
#uncomment this block and comment the one above to use individuals with vainilla format
'''if individualCounter < popSize:
for filename in glob.glob(os.path.join(CMUxmlDir, '*.xml')):
if individualCounter < popSize:
print individualCounter, " individuals processed!"
print 'inserting individual: ' + filename + " into the initial population"
walk = Individual(propVanilla, DTIndividualGeneticTimeSerieFile(filename))
teacherGeneticMatrix = walk.getGenomeMatrix()
adan = population[individualCounter]
adanIndividualLength=dtgenoma.getIndividualLength(adan)
adanJointLength=dtgenoma.getIndividualFrameLength(adan)
for i in xrange(adanIndividualLength):
for j in xrange(adanJointLength):
if i < len(teacherGeneticMatrix): #if the fixed gnoma representation size is less than the teacher size
adan.setItem(i,j,teacherGeneticMatrix[i][j])
else:
#put a sentinel joint value to mark the end of the individual
adan.setItem(i,j,sysConstants.JOINT_SENTINEL)
population[individualCounter]=adan
individualCounter = individualCounter + 1
else:
break
'''
global initialPopulationSetted
initialPopulationSetted = True
class Individual:
def __init__(self, idividualProperty, individualGeneticMaterial):
self.property = idividualProperty
self.fitness = 0
self.robotConfig = LoadRobotConfiguration()
self.configuration = LoadSystemConfiguration()
self.genomeMatrix = individualGeneticMaterial.getGeneticMatrix()
self.poseSize = len(self.genomeMatrix)
self.genomeMatrixJointNameIDMapping = {}
self.sysConf = LoadSystemConfiguration()
i = 0
for jointName in self.robotConfig.getJointsName():
self.genomeMatrixJointNameIDMapping[jointName] = i
i = i + 1
dontSupportedJoints = self.configuration.getVrepNotImplementedBioloidJoints()
self.robotImplementedJoints = []
robotJoints = self.robotConfig.getJointsName()
for joint in robotJoints:
if joint not in dontSupportedJoints:
self.robotImplementedJoints.append(joint)
# is important to use only supported joints to avoid errors obtaining the handler of a joint that doesn't exists
for i in xrange(self.poseSize):
for joint in self.robotImplementedJoints:
# print "i: ", i, "j: ", joint
value = self.genomeMatrix[i][self.genomeMatrixJointNameIDMapping[joint]] + self.property.getPoseFix(
joint
) # TODO this can be a problem for the physical robot
self.genomeMatrix[i][self.genomeMatrixJointNameIDMapping[joint]] = value
def stopLucy(self):
self.lucy.stopLucy()
def execute(self):
# create the corresponding instance of Lucy, depending if it's real or simulated.
if int(self.sysConf.getProperty("Lucy simulated?")) == 1:
self.lucy = SimulatedLucy(int(self.configuration.getProperty("Lucy render enable")))
else:
self.lucy = PhysicalLucy()
poseExecute = {}
i = 0
while self.lucy.isLucyUp() and i < self.poseSize:
for joint in self.robotConfig.getJointsName():
if not (self.property.avoidJoint(joint)):
value = self.genomeMatrix[i][self.genomeMatrixJointNameIDMapping[joint]]
poseExecute[joint] = value
i = i + 1
self.lucy.executePose(Pose(poseExecute))
self.lucy.stopLucy() # this function also updates time and distance
if i < self.poseSize:
self.fitness = self.lucy.getFitness()
else:
endFrameExecuted = True
self.fitness = self.lucy.getFitness(endFrameExecuted)
print "fitness: ", self.fitness
return self.fitness
def getPoseQty(self):
return self.lp.getFrameQty()
def getPose(self, poseNumber):
return self.lp.getFramePose(poseNumber)
def getMostSimilarPose(self, pose):
diff = MAX_INT
moreSimilarPose = self.getPose(1)
for i in xrange(self.getPoseQty()):
myPose = getPose(i)
newDiff = pose.diff(myPose)
if newDiff < diff:
diff = newDiff
moreSimilarPose = myPose
return moreSimilarPose
def getGenomeMatrix(self):
return self.genomeMatrix
def setGenomeMatrix(self, geneMatrix):
self.genomeMatrix = geneMatrix
self.poseSize = len(geneMatrix)
def persist(self, file):
root = ET.Element("root")
lucy = ET.SubElement(root, "Lucy")
for frameIt in xrange(self.poseSize):
frame = ET.SubElement(lucy, "frame")
frame.set("number", str(frameIt))
for joint in self.robotImplementedJoints:
xmlJoint = ET.SubElement(frame, joint)
joint_id = self.robotConfig.loadJointId(joint)
pos = self.genomeMatrix[frameIt][self.genomeMatrixJointNameIDMapping[joint]]
xmlJointAngle = xmlJoint.set("angle", str(pos))
tree = ET.ElementTree(root)
tree.write(file)
from configuration.LoadSystemConfiguration import LoadSystemConfiguration
from datatypes.DTIndividualGeneticMaterial import DTIndividualGeneticTimeSerieFile, \
DTIndividualGeneticTimeSerieFileWalk
from datatypes.DTIndividualProperty import DTIndividualPropertyCMUDaz, DTIndividualPropertyVanilla, DTIndividualPropertyBaliero, DTIndividualPropertyVanillaEvolutive, DTIndividualPropertyPhysicalBioloid, DTIndividualPropertyVanillaEvolutiveNoAvoid
from Individual import Individual
propCMUDaz = DTIndividualPropertyCMUDaz()
propVanilla = DTIndividualPropertyVanilla()
balieroProp = DTIndividualPropertyBaliero()
physicalProp = DTIndividualPropertyPhysicalBioloid()
geneticVanillaProp = DTIndividualPropertyVanillaEvolutive()
geneticVanillaPropNothingToAvoid = DTIndividualPropertyVanillaEvolutiveNoAvoid()
conf = LoadSystemConfiguration()
CMUxmlDir = os.getcwd()+conf.getDirectory("Transformed CMU mocap Files")
GAwalkDir = os.getcwd()+conf.getDirectory("GAWalk Files")
UIBLHDir = os.getcwd()+conf.getDirectory("UIBLH mocap Files")
BalieroDir = os.getcwd()+conf.getDirectory("Baliero transformed walk Files")
ADHOCDir = os.getcwd()+conf.getDirectory("ADHOC Files")
geneticPoolDir = os.pardir+conf.getDirectory("Genetic Pool")
arguments = len(sys.argv)
def createIndividual(filename):
if int(conf.getProperty("Lucy simulated?"))==1:
if int(conf.getProperty("Concatenate walk cycles?")):
walkEmbryo = DTIndividualGeneticTimeSerieFileWalk(os.getcwd()+"/"+filename)
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
