def generateFromCMAESonePoint(repeat, rs, thetaFile, saveDir = 'Data', size=0.04, point=0):
c = Chrono()
thetaName = rs.OPTIpath + str(size)+"/"+str(point)+"/"+thetaFile
saveName = rs.OPTIpath + str(size) + "/" + saveDir + "/"
GenerateDataFromThetaOnePoint(rs,size, saveName,thetaName,repeat, point)
c.stop()
print("CMAES:End of generation")
def generateFromCMAES(repeat, rs, thetaFile, saveDir = 'Data'):
for el in rs.target_size:
c = Chrono()
thetaName = rs.OPTIpath + str(el) + "/" + thetaFile
saveName = rs.OPTIpath + str(el) + "/" + saveDir + "/"
GenerateDataFromTheta(rs,el,saveName,thetaName,repeat,True)
c.stop()
print("CMAES:End of generation")
def generateFromDDPGNController(repeat, rs, thetaFile, saveDir="Data"):
for el in rs.sizeOfTarget:
c = Chrono()
thetaName = rs.OPTIpath + str(el) + "/*/" + thetaFile
saveName = rs.OPTIpath + str(el) + "/" + saveDir + "/"
# GenerateDataFromThetaNController(rs,el, saveName,thetaName,repeat,True)
c.stop()
print("DDPG:End of generation")
def generateFromCMAES(repeat, rs, thetaFile, saveDir='Data'):
for el in rs.target_size:
c = Chrono()
thetaName = rs.OPTIpath + str(el) + "/" + thetaFile
saveName = rs.OPTIpath + str(el) + "/" + saveDir + "/"
GenerateDataFromTheta(rs, el, saveName, thetaName, repeat, True)
c.stop()
print("CMAES:End of generation")
def generateFromDDPGNController(repeat, rs, thetaFile, saveDir='Data'):
for el in rs.sizeOfTarget:
c = Chrono()
thetaName = rs.OPTIpath + str(el) + "/*/" + thetaFile
saveName = rs.OPTIpath + str(el) + "/" + saveDir + "/"
#GenerateDataFromThetaNController(rs,el, saveName,thetaName,repeat,True)
c.stop()
print("DDPG:End of generation")
def GenerateRichDataFromTheta(rs, sizeOfTarget, foldername, thetaFile, repeat, save):
exp = Experiments(rs, sizeOfTarget, save, foldername, thetaFile, rs.popsizeCmaes, rs.period)
c = Chrono()
cost = exp.runRichTrajectories(repeat)
c.stop()
print("Average cost: ", cost)
print("foldername : ", foldername)
if save:
exp.saveCost()
def generateFromCMAES(repeat, thetaFile, saveDir='Data'):
rs = ReadSetupFile()
for el in rs.sizeOfTarget:
c = Chrono()
thetaName = rs.CMAESpath + str(el) + "/" + thetaFile
saveName = rs.CMAESpath + str(el) + "/" + saveDir + "/"
GenerateDataFromTheta(rs, el, saveName, thetaName, repeat, True)
c.stop()
print("CMAES:End of generation")
def generateFromCMAES(repeat, thetaFile, saveDir = 'Data'):
rs = ReadSetupFile()
for el in rs.sizeOfTarget:
c = Chrono()
thetaName = rs.CMAESpath + str(el) + "/" + thetaFile
saveName = rs.CMAESpath + str(el) + "/" + saveDir + "/"
GenerateDataFromTheta(rs,el,saveName,thetaName,repeat,True)
c.stop()
print("CMAES:End of generation")
def GenerateRichDataFromTheta(rs, sizeOfTarget, foldername, thetaFile, repeat,
save):
exp = Experiments(rs, sizeOfTarget, save, foldername, thetaFile,
rs.popsizeCmaes, rs.period)
c = Chrono()
cost = exp.runRichTrajectories(repeat)
c.stop()
print("Average cost: ", cost)
print("foldername : ", foldername)
if (save):
exp.saveCost()
def generateFromCMAESonePoint(repeat,
rs,
thetaFile,
saveDir='Data',
size=0.04,
point=0):
c = Chrono()
thetaName = rs.OPTIpath + str(size) + "/" + str(point) + "/" + thetaFile
saveName = rs.OPTIpath + str(size) + "/" + saveDir + "/"
GenerateDataFromThetaOnePoint(rs, size, saveName, thetaName, repeat, point)
c.stop()
print("CMAES:End of generation")
def generateFromDDPG(repeat, rs, thetaFile, saveDir='Data'):
for el in rs.sizeOfTarget:
c = Chrono()
actor = simple_actor_network(rs.inputDim,
rs.outputDim,
l1_size=rs.hiddenLayers[0][1],
l2_size=rs.hiddenLayers[1][1],
learning_rate=rs.learningRate)
env = DDPGEnv(rs, el, rs.thetaFile, actor=actor, saveDir=saveDir)
thetaName = rs.OPTIpath + str(el) + "/" + thetaFile + ".theta"
saveName = rs.OPTIpath + str(el) + "/" + saveDir + "/"
os.system("rm " + saveName + "Log/*.log 2>/dev/null")
parameters = np.loadtxt(thetaName)
actor.load_parameters(parameters)
cost, time = env.saveAllTraj(repeat)
c.stop()
print("Average cost: ", cost)
print("Average time: ", time)
print("foldername : ", saveName)
print("DDPG:End of generation")
def generateFromDDPG(repeat, rs, thetaFile, saveDir="Data"):
for el in rs.sizeOfTarget:
c = Chrono()
actor = simple_actor_network(
rs.inputDim,
rs.outputDim,
l1_size=rs.hiddenLayers[0][1],
l2_size=rs.hiddenLayers[1][1],
learning_rate=rs.learningRate,
)
env = DDPGEnv(rs, el, rs.thetaFile, actor=actor, saveDir=saveDir)
thetaName = rs.OPTIpath + str(el) + "/" + thetaFile + ".theta"
saveName = rs.OPTIpath + str(el) + "/" + saveDir + "/"
os.system("rm " + saveName + "Log/*.log 2>/dev/null")
parameters = np.loadtxt(thetaName)
actor.load_parameters(parameters)
cost, time = env.saveAllTraj(repeat)
c.stop()
print("Average cost: ", cost)
print("Average time: ", time)
print("foldername : ", saveName)
print("DDPG:End of generation")
def chooseFunction(choix, rs):
if choix == 1:
plotVelocityProfile("Brent", rs)
elif choix == 2:
plotArticularPositions("Brent", rs)
elif choix == 3:
plotXYPositions("Brent", rs)
elif choix == 4:
plotMuscularActivations("Brent", rs)
#------------------------------------------- RBFN
elif choix == 5:
c = Chrono()
# run(name,True)
run(rs)
c.stop()
elif choix == 6:
fname = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
c = Chrono()
generateFromRegression(nbret, rs, fname)
c.stop()
elif choix == 7:
nameF = raw_input('Folder where the results are saved: ')
plotVelocityProfile("Regression", rs, nameF)
elif choix == 8:
nameF = raw_input('Folder where the results are saved: ')
rorc = input("enter 1 if XY or 2 if Joint results: ")
rorc = int(rorc)
if rorc == 1:
plotXYPositions("Regression", rs, nameF, "All", True) #False)#
else:
plotArticularPositions("Regression", rs, nameF)
elif choix == 9:
nameF = raw_input('Folder where the results are saved: ')
plotMuscularActivations("Regression", rs, nameF)
elif choix == 10:
nameF = raw_input('Folder where the results are saved: ')
plotCostColorMap("Regression", rs, nameF)
elif choix == 11:
nameF = raw_input('Folder where the results are saved: ')
plotXYEstimError("Regression", rs, nameF, "All")
elif choix == 12:
nameF = raw_input('Folder where the results are saved: ')
plotXYEstimErrorOfSpeed("Regression", rs, nameF, "All")
def chooseFunction(choix, rs):
if choix == 1:
plotVelocityProfile("Brent",rs)
elif choix == 2:
plotArticularPositions("Brent",rs)
elif choix == 3:
plotXYPositions("Brent",rs)
elif choix == 4:
plotMuscularActivations("Brent",rs)
#------------------------------------------- RBFN
elif choix == 5:
c = Chrono()
# run(name,True)
run(rs)
c.stop()
elif choix == 6:
fname = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
c = Chrono()
generateFromRegression(nbret, rs, fname)
c.stop()
elif choix == 7:
nameF = raw_input('Folder where the results are saved: ')
plotVelocityProfile("Regression",rs, nameF)
elif choix == 8:
nameF = raw_input('Folder where the results are saved: ')
rorc = input("enter 1 if XY or 2 if Joint results: ")
rorc = int(rorc)
if rorc == 1:
plotXYPositions("Regression", rs, nameF,"All",True)#False)#
else:
plotArticularPositions("Regression",rs, nameF)
elif choix == 9:
nameF = raw_input('Folder where the results are saved: ')
plotMuscularActivations("Regression",rs, nameF)
elif choix == 10:
nameF = raw_input('Folder where the results are saved: ')
plotCostColorMap("Regression", rs, nameF)
elif choix == 11:
nameF = raw_input('Folder where the results are saved: ')
plotXYEstimError("Regression", rs, nameF,"All")
elif choix == 12:
nameF = raw_input('Folder where the results are saved: ')
plotXYEstimErrorOfSpeed("Regression", rs, nameF,"All")
def testRBFNs():
for i in range(3):
c = Chrono()
generateFromRegression(3, "R" + str(i), "SR" + str(i))
c.stop()
def run():
c = Chrono()
generateFromRBFN(2, "Full4", "Test")
c.stop()
from Main.MainCMAES import generateFromRegression
from Regression.RunRegression import run
from Plot.plotFunctions import plotXYPositions
from Main.Test import *
from Utils.Chrono import Chrono
from Utils.ReadXmlFile import ReadXmlFile
setupFile="setupRBFN.xml"
thetaName="ScriptRBFNxml"
folder="TrajRBFNxml"
rs=ReadXmlFile(setupFile)
c = Chrono()
run(rs)
c.stop()
generateFromRegression(1, rs, thetaName, folder)
plotXYPositions("Regression",rs, folder,"All",True)
testRegression(rs, thetaName, folder)
def runTrajectoriesCMAES(self, theta):
'''
Generates all the trajectories of the experimental setup and return the mean cost. This function is used by cmaes to optimize the controller.
Input: -theta: vector of parameters, one dimension normalized numpy array
Ouput: -meanAll: the mean of the cost of all trajectories generated, float
'''
if (self.call==0):
self.localBestCost = -1000000.0
self.localWorstCost = 1000000.0
self.localBestTime = -1000000.0
self.localWorstTime = 1000000.0
self.periodMeanCost = 0.0
self.periodMeanTime = 0.0
c = Chrono()
self.initTheta(theta)
#print "theta avant appel :", theta
#compute all the trajectories x times each, x = numberOfRepeat
meanCost, meanTime = self.runTrajectoriesForResultsGeneration(self.numberOfRepeat)
#cma.plot()
#opt = cma.CMAOptions()
#print "CMAES options :", opt
c.stop()
print("Indiv #: ", self.call, "\n Cost: ", meanCost)
if meanCost>self.localBestCost:
self.localBestCost = meanCost
if meanTime>self.localBestTime:
self.localBestTime = meanTime
if meanCost<self.localWorstCost:
self.localWorstCost = meanCost
if meanTime<self.localWorstTime:
self.localWorstTime = meanTime
if meanCost>self.bestCost:
self.bestCost = meanCost
if meanCost>0:
extension = ".save" + str(meanCost)
filename = findDataFilename(self.foldername+"Theta/", "theta", extension)
np.savetxt(filename, self.theta)
self.periodMeanCost += meanCost
self.periodMeanTime += meanTime
self.call += 1
self.call = self.call%self.period
if (self.call==0):
self.periodMeanCost = self.periodMeanCost/self.period
self.periodMeanTime = self.periodMeanTime/self.period
self.CMAESCostStore.append((self.localWorstCost,self.periodMeanCost,self.localBestCost))
self.CMAESTimeStore.append((self.localWorstTime,self.periodMeanTime,self.localBestTime))
costfoldername = self.foldername+"Cost/"
checkIfFolderExists(costfoldername)
np.savetxt(costfoldername+"cmaesCost.log",self.CMAESCostStore) #Note: inefficient, should rather add to the file
np.savetxt(costfoldername+"cmaesTime.log",self.CMAESTimeStore) #Note: inefficient, should rather add to the file
return 10.0*(self.rs.rhoCF-meanCost)/self.rs.rhoCF
def chooseFunction(choix, rs):
if choix == 1:
c = Chrono()
#TODO: DDPGForAllTargetSizes
#launchDDPGForAllTargetSizesMulti(rs)
c.stop()
elif choix == 2:
#TODO: Choose the kinematic model
nameTheta = raw_input('Name of the controller file: ')
name = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
nbret = int(nbret)
generateFromDDPG(nbret, rs, nameTheta, name)
elif choix == 3:
nameF = raw_input('Folder where the results are saved: ')
plotVelocityProfile("OPTI",rs,nameF)
elif choix == 4:
nameF = raw_input('Folder where the results are saved: ')
rorc = input("enter 1 if XY or 2 if Joint results: ")
rorc = int(rorc)
if rorc == 1:
plotXYPositions("OPTI",rs, nameF,"All",False)
else:
plotArticularPositions("OPTI",rs, nameF)
elif choix == 5:
nameF = raw_input('Folder where the results are saved: ')
tSize = raw_input('Target Size: ')
plotMuscularActivations("OPTI",rs,nameF,tSize)
elif choix == 6:
nameF = raw_input('Folder where the results are saved: ')
#tSize = raw_input('Target Size: ')
#plotCostColorMap("OPTI",nameF,tSize)
plotCostColorMap("OPTI",rs, nameF)
elif choix == 7:
nameF = raw_input('Folder where the results are saved: ')
plotTimeDistanceTarget(nameF, rs)
elif choix == 8:
nameF = raw_input('Folder where the results are saved: ')
plotPerfSizeDist(nameF, rs)
elif choix == 9:
nameF = raw_input('Folder where the results are saved: ')
plotFittsLaw(nameF, rs)
elif choix == 10:
nameF = raw_input('Folder where the results are saved: ')
plotTimeColorMap("OPTI",rs, nameF)
elif choix == 11:
rorc = input("enter 0 if Brent, 1 if Regression or 2 if DDPG results: ")
rorc = int(rorc)
if rorc == 0:
trajectoriesAnimation("Brent", rs)
elif rorc == 1:
nameF = raw_input('Folder where the results are saved: ')
trajectoriesAnimation("RBFN",rs, nameF)
elif rorc == 2:
nameF = raw_input('Folder where the results are saved: ')
tSize = raw_input('Target Size: ')
trajectoriesAnimation("OPTI",rs, nameF, tSize)
elif choix == 12:
nameF = raw_input('Folder where the results are saved: ')
rorc = input("enter 1 if RBFN or 2 if DDPG results: ")
#plotHitDispersion(nameF,"0.05")
rorc = int(rorc)
if rorc == 1:
plotScattergram("RBFN",nameF, rs)
elif rorc == 2:
plotScattergram("OPTI",nameF, rs)
elif choix == 13:
tSize = raw_input('Target Size: ')
c = Chrono()
launchDDPGForSpecificTargetSize(float(tSize),rs)
c.stop()
elif choix == 14:
plotDDPGProgress(rs)
elif choix == 15:
name = raw_input('Name of the Regression controller file: ')
fname = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
c = Chrono()
generateRichDataFromRegression(nbret,rs, name, fname)
c.stop()
elif choix == 16:
nameTheta = raw_input('Name of the controller file: ')
name = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
nbret = int(nbret)
c = Chrono()
generateRichDataFromDDPG(nbret,rs, nameTheta, name)
c.stop()
elif choix == 17:
plotTrajsInRepo()
elif choix == 18:
nameF = raw_input('Folder where the results are saved: ')
plotXYEstimError("OPTI",rs,nameF,"All")
elif choix == 19:
nameF = raw_input('Folder where the results are saved: ')
plotXYEstimErrorOfSpeed("OPTI",rs,nameF,"All")
elif choix == 20:
plotExperimentSetup(rs)
elif choix == 21:
plotManipulability(rs)
elif choix == 22:
plotManipulability2(rs)
elif choix == 23:
plotEstimator(rs, 0.04, 0., 0.25)
elif choix == 24:
tSize = raw_input('Target Size: ')
c = Chrono()
launchDDPGForAllPoint(rs,float(tSize))
#launchDDPGForSpecificTargetSizeAndSpeceficBeginning(float(tSize), rs, [0, [0, 0.2]])
c.stop()
elif choix == 25:
size=raw_input('Target Size: ')
while True:
print(" Enter the number of the point you want sea, q for quit")
point=raw_input('Point: ')
if(point=="q"):
break
plotDDPGOnePointProgress(rs,size, point)
elif choix == 26:
#TODO: Choose the kinematic model
nameTheta = raw_input('Name of the controller file: ')
name = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
nbret = int(nbret)
generateFromDDPGNController(nbret, rs, nameTheta, name)
elif choix == 27:
nameF = raw_input('Folder where the results are saved: ')
rorc = raw_input("Target Size: ")
plotXYPositions("OPTI",rs, nameF,rorc,False)
else :
return 0
return 1
def testRBFNs():
for i in range(3):
c = Chrono()
generateFromRBFN(3, "R"+str(i), "SR"+str(i))
c.stop()
def chooseFunction(choix):
if choix == 1:
plotVelocityProfile("Brent")
elif choix == 2:
plotArticularPositions("Brent")
elif choix == 3:
plotXYPositions("Brent")
elif choix == 4:
plotMuscularActivations("Brent")
#------------------------------------------- RBFN
elif choix == 5:
name = raw_input('Name of file to save the RBFN controller: ')
c = Chrono()
# runRBFN(name,True)
runRBFN(name,False)
c.stop()
elif choix == 6:
name = raw_input('Name of the RBFN controller file: ')
fname = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
c = Chrono()
generateFromRBFN(nbret, name, fname)
c.stop()
elif choix == 7:
nameF = raw_input('Folder where the results are saved: ')
plotVelocityProfile("RBFN",nameF)
elif choix == 8:
nameF = raw_input('Folder where the results are saved: ')
rorc = input("enter 1 if XY or 2 if Joint results: ")
rorc = int(rorc)
if rorc == 1:
plotXYPositions("RBFN",nameF,"All",True)#False)#
else:
plotArticularPositions("RBFN",nameF)
elif choix == 9:
nameF = raw_input('Folder where the results are saved: ')
plotMuscularActivations("RBFN",nameF)
elif choix == 10:
nameF = raw_input('Folder where the results are saved: ')
plotCostColorMap("RBFN",nameF)
elif choix == 28:
nameF = raw_input('Folder where the results are saved: ')
plotXYEstimError("RBFN",nameF,"All")
elif choix == 29:
nameF = raw_input('Folder where the results are saved: ')
plotXYEstimErrorOfSpeed("RBFN",nameF,"All")
#------------------------------------------- CMAES
elif choix == 11:
rorc = input("enter 1 if from RBFN, anything if from previous CMAES: ")
save = False
rorc = int(rorc)
if rorc == 1:
save = True
name = raw_input('Name of the controller file: ')
c = Chrono()
launchCMAESForAllTargetSizes(name,save)
c.stop()
elif choix == 12:
nameTheta = raw_input('Name of the controller file: ')
name = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
nbret = int(nbret)
generateFromCMAES(nbret, nameTheta, name)
elif choix == 13:
nameF = raw_input('Folder where the results are saved: ')
plotVelocityProfile("CMAES",nameF)
elif choix == 14:
nameF = raw_input('Folder where the results are saved: ')
rorc = input("enter 1 if XY or 2 if Joint results: ")
rorc = int(rorc)
if rorc == 1:
plotXYPositions("CMAES",nameF,"All",False)
else:
tSize = raw_input('Target Size: ')
plotArticularPositions("CMAES",nameF,tSize)
elif choix == 15:
nameF = raw_input('Folder where the results are saved: ')
tSize = raw_input('Target Size: ')
plotMuscularActivations("CMAES",nameF,tSize)
elif choix == 16:
nameF = raw_input('Folder where the results are saved: ')
#tSize = raw_input('Target Size: ')
#plotCostColorMap("CMAES",nameF,tSize)
plotCostColorMap("CMAES",nameF)
elif choix == 17:
nameF = raw_input('Folder where the results are saved: ')
plotTimeDistanceTarget(nameF)
elif choix == 18:
nameF = raw_input('Folder where the results are saved: ')
plotPerfSizeDist(nameF)
elif choix == 19:
nameF = raw_input('Folder where the results are saved: ')
plotFittsLaw(nameF)
elif choix == 20:
nameF = raw_input('Folder where the results are saved: ')
plotTimeColorMap("CMAES",nameF)
elif choix == 21:
rorc = input("enter 0 if Brent, 1 if RBFN or 2 if CMAES results: ")
rorc = int(rorc)
if rorc == 0:
trajectoriesAnimation("Brent")
elif rorc == 1:
nameF = raw_input('Folder where the results are saved: ')
trajectoriesAnimation("RBFN",nameF)
elif rorc == 2:
nameF = raw_input('Folder where the results are saved: ')
tSize = raw_input('Target Size: ')
trajectoriesAnimation("CMAES",nameF, tSize)
elif choix == 22:
nameF = raw_input('Folder where the results are saved: ')
rorc = input("enter 1 if RBFN or 2 if CMAES results: ")
#plotHitDispersion(nameF,"0.05")
rorc = int(rorc)
if rorc == 1:
plotScattergram("RBFN",nameF)
elif rorc == 2:
plotScattergram("CMAES",nameF)
elif choix == 23:
name = raw_input('Name of the controller file: ')
rorc = input("enter 1 if from RBFN, anything if from previous CMAES: ")
save = False
rorc = int(rorc)
if rorc == 1:
save = True
tSize = raw_input('Target Size: ')
c = Chrono()
launchCMAESForSpecificTargetSize(float(tSize),name,save)
c.stop()
elif choix == 24:
plotCMAESProgress()
elif choix == 25:
name = raw_input('Name of the RBFN controller file: ')
fname = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
c = Chrono()
generateRichDataFromRBFN(nbret, name, fname)
c.stop()
elif choix == 26:
nameTheta = raw_input('Name of the controller file: ')
name = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
nbret = int(nbret)
c = Chrono()
generateRichDataFromCMAES(nbret, nameTheta, name)
c.stop()
elif choix == 27:
plotTrajsInRepo()
elif choix == 30:
nameF = raw_input('Folder where the results are saved: ')
plotXYEstimError("CMAES",nameF,"All")
elif choix == 31:
nameF = raw_input('Folder where the results are saved: ')
plotXYEstimErrorOfSpeed("CMAES",nameF,"All")
elif choix == 32:
plotExperimentSetup()
elif choix == 33:
plotManipulability()
elif choix == 34:
plotManipulability2()
def generateRBFNs():
for i in range(20):
c = Chrono()
runRBFN("X"+str(i),True)
c.stop()
def run():
c = Chrono()
generateFromNN(1, "Random", "R1")
c.stop()
def generateNNs():
for i in range(20):
c = Chrono()
runNN("X" + str(i), True)
c.stop()
def chooseFunction(choix, rs):
if choix == 1:
rorc = input("enter 1 if from Regression, anything if from previous CMAES: ")
save = False
rorc = int(rorc)
if rorc == 1:
save = True
c = Chrono()
launchCMAESForAllTargetSizesMulti(rs)
c.stop()
elif choix == 2:
# TODO: Choose the kinematic model
nameTheta = raw_input("Name of the controller file: ")
name = raw_input("Folder where you want to save the results: ")
nbret = input("Number of repeat for each trajectory (int): ")
nbret = int(nbret)
generateFromCMAES(nbret, rs, nameTheta, name)
elif choix == 3:
nameF = raw_input("Folder where the results are saved: ")
plotVelocityProfile("OPTI", rs, nameF)
elif choix == 4:
nameF = raw_input("Folder where the results are saved: ")
rorc = input("enter 1 if XY or 2 if Joint results: ")
rorc = int(rorc)
if rorc == 1:
plotXYPositions("OPTI", rs, nameF, "All", False)
else:
plotArticularPositions("OPTI", rs, nameF)
elif choix == 5:
nameF = raw_input("Folder where the results are saved: ")
tSize = raw_input("Target Size: ")
plotMuscularActivations("OPTI", rs, nameF, tSize)
elif choix == 6:
nameF = raw_input("Folder where the results are saved: ")
# tSize = raw_input('Target Size: ')
# plotCostColorMap("OPTI",nameF,tSize)
plotCostColorMap("OPTI", rs, nameF)
elif choix == 7:
nameF = raw_input("Folder where the results are saved: ")
plotTimeDistanceTarget(nameF, rs)
elif choix == 8:
nameF = raw_input("Folder where the results are saved: ")
plotPerfSizeDist(nameF, rs)
elif choix == 9:
nameF = raw_input("Folder where the results are saved: ")
plotFittsLaw(nameF, rs)
elif choix == 10:
nameF = raw_input("Folder where the results are saved: ")
plotTimeColorMap("OPTI", rs, nameF)
elif choix == 11:
rorc = input("enter 0 if Brent, 1 if Regression or 2 if CMAES results: ")
rorc = int(rorc)
if rorc == 0:
trajectoriesAnimation("Brent", rs)
elif rorc == 1:
nameF = raw_input("Folder where the results are saved: ")
trajectoriesAnimation("RBFN", rs, nameF)
elif rorc == 2:
nameF = raw_input("Folder where the results are saved: ")
tSize = raw_input("Target Size: ")
trajectoriesAnimation("OPTI", rs, nameF, tSize)
elif choix == 12:
nameF = raw_input("Folder where the results are saved: ")
rorc = input("enter 1 if RBFN or 2 if CMAES results: ")
# plotHitDispersion(nameF,"0.05")
rorc = int(rorc)
if rorc == 1:
plotScattergram("RBFN", nameF, rs)
elif rorc == 2:
plotScattergram("OPTI", nameF, rs)
elif choix == 13:
rorc = input("enter 1 if from RBFN, anything if from previous CMAES: ")
save = False
rorc = int(rorc)
if rorc == 1:
save = True
tSize = raw_input("Target Size: ")
c = Chrono()
launchCMAESForSpecificTargetSize(float(tSize), rs, save)
c.stop()
elif choix == 14:
plotCMAESProgress(rs)
elif choix == 15:
name = raw_input("Name of the Regression controller file: ")
fname = raw_input("Folder where you want to save the results: ")
nbret = input("Number of repeat for each trajectory (int): ")
c = Chrono()
generateRichDataFromRegression(nbret, rs, name, fname)
c.stop()
elif choix == 16:
nameTheta = raw_input("Name of the controller file: ")
name = raw_input("Folder where you want to save the results: ")
nbret = input("Number of repeat for each trajectory (int): ")
nbret = int(nbret)
c = Chrono()
generateRichDataFromCMAES(nbret, rs, nameTheta, name)
c.stop()
elif choix == 17:
plotTrajsInRepo()
elif choix == 18:
nameF = raw_input("Folder where the results are saved: ")
plotXYEstimError("OPTI", rs, nameF, "All")
elif choix == 19:
nameF = raw_input("Folder where the results are saved: ")
plotXYEstimErrorOfSpeed("OPTI", rs, nameF, "All")
elif choix == 20:
plotExperimentSetup(rs)
elif choix == 21:
plotManipulability(rs)
elif choix == 22:
plotManipulability2(rs)
elif choix == 23:
plotEstimatorPoint(rs, 0.04, 12)
elif choix == 24:
rorc = input("enter 1 if General CMAES, 2 if from scratch, anything if from previous CMAES point: ")
save = False
rorc = int(rorc)
if rorc == 1:
save = True
elif rorc == 2:
save = None
tSize = raw_input("Target Size: ")
c = Chrono()
launchCMAESForAllPoint(rs, float(tSize), save)
c.stop()
elif choix == 25:
# TODO: Test!
rorc = input("enter 1 if General CMAES, 2 if from scratch, anything if from previous CMAES point: ")
save = False
rorc = int(rorc)
if rorc == 1:
save = True
elif rorc == 2:
save = None
tSize = raw_input("Target Size: ")
points = map(int, raw_input("Point(s): ").split())
print points
lauchCMAESForListOfPoints(float(tSize), rs, save, points)
elif choix == 26:
size = raw_input("Target Size: ")
while True:
print (" Enter the number of the point you want sea, q for quit")
point = raw_input("Point: ")
if point == "q":
break
plotCMAESOnePointProgress(rs, size, point)
elif choix == 27:
# TODO: Choose the kinematic model
nameTheta = raw_input("Name of the controller file: ")
name = raw_input("Folder where you want to save the results: ")
nbret = input("Number of repeat for each trajectory (int): ")
nbret = int(nbret)
generateFromCMAESNController(nbret, rs, nameTheta, name)
elif choix == 28:
nameF = raw_input("Folder where the results are saved: ")
rorc = raw_input("Target Size: ")
plotXYPositions("OPTI", rs, nameF, rorc, False)
elif choix == 29:
nameTheta = raw_input("Name of the controller file: ")
name = raw_input("Folder where you want to save the results: ")
nbret = input("Number of repeat for each trajectory (int): ")
nbret = int(nbret)
size = raw_input("Target Size: ")
point = raw_input("Point: ")
generateFromCMAESonePoint(nbret, rs, nameTheta, name, float(size), point)
elif choix == 30:
nameF = raw_input("Folder where the results are saved: ")
# tSize = raw_input('Target Size: ')
# plotCostColorMap("OPTI",nameF,tSize)
plotCostColorMapFor12("OPTI", rs, nameF)
elif choix == 31:
size = raw_input("Target Size: ")
checkAllPoint(rs, size)
else:
return 0
return 1
cost, time = exp.runMultiProcessTrajectories(10)
c.stop()
print("Average cost: ", cost)
print("Average time: ", time)
'''
stateAndCommand, nextState = loadTrajForModel(pathDataFolder + "Brent/", 10)
#stateAndCommand, nextState = loadTrajForModel(pathDataFolder + "CMAEScluster/0.02/cluster/Log/", 10)
state=stateAndCommand[:,:4]
command=stateAndCommand[:,4:]
#exp2.tm.stateEstimator.initStore(state)
exp3.tm.stateEstimator.initStore(state)
nbE=0
error=np.zeros(4)
c=Chrono()
for i in range(state.shape[0]):
if(state[i][0]==0 and state[i][1]==0):
exp.tm.stateEstimator.initStore(state[i])
tmp=0
inferredState = exp.tm.stateEstimator.getEstimState(state[i], command[i][:6])
if i<state.shape[0]-1 and not (state[i+1][0]==0 and state[i+1][1]==0):
error += (inferredState - state[i+1])**2
nbE+=1
tmp+=1
c.stop()
print (np.mean(error/nbE))
'''
nbE=0
error=np.zeros(4)
def run():
c = Chrono()
generateFromRBFN(2, "Full", "Test")
c.stop()
def chooseFunction(choix):
if choix == 1:
plotVelocityProfile("Brent")
elif choix == 2:
plotArticularPositions("Brent")
elif choix == 3:
plotXYPositions("Brent")
elif choix == 4:
plotMuscularActivations("Brent")
#------------------------------------------- NN
elif choix == 5:
name = raw_input('Name of file to save the NN controller: ')
c = Chrono()
#runNN(name)
createNN(name)
c.stop()
elif choix == 6:
name = raw_input('Name of the NN controller file: ')
fname = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
c = Chrono()
generateFromNN(nbret, name, fname)
c.stop()
elif choix == 7:
nameF = raw_input('Folder where the results are saved: ')
plotVelocityProfile("NN", nameF)
elif choix == 8:
nameF = raw_input('Folder where the results are saved: ')
rorc = input("enter 1 if XY or 2 if Joint results: ")
rorc = int(rorc)
if rorc == 1:
plotXYPositions("NN", nameF, "All", True) #False)#
else:
plotArticularPositions("NN", nameF)
elif choix == 9:
nameF = raw_input('Folder where the results are saved: ')
plotMuscularActivations("NN", nameF)
elif choix == 10:
nameF = raw_input('Folder where the results are saved: ')
plotCostColorMap("NN", nameF)
elif choix == 28:
nameF = raw_input('Folder where the results are saved: ')
plotXYEstimError("NN", nameF, "All")
elif choix == 29:
nameF = raw_input('Folder where the results are saved: ')
plotXYEstimErrorOfSpeed("NN", nameF, "All")
#------------------------------------------- CMAES
elif choix == 11:
rorc = input("enter 1 if from NN, anything if from previous CMAES: ")
save = False
rorc = int(rorc)
if rorc == 1:
save = True
name = raw_input('Name of the controller file: ')
c = Chrono()
launchCMAESForAllTargetSizes(name, save)
c.stop()
elif choix == 12:
nameTheta = raw_input('Name of the controller file: ')
name = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
nbret = int(nbret)
generateFromCMAES(nbret, nameTheta, name)
elif choix == 13:
nameF = raw_input('Folder where the results are saved: ')
plotVelocityProfile("CMAES", nameF)
elif choix == 14:
nameF = raw_input('Folder where the results are saved: ')
rorc = input("enter 1 if XY or 2 if Joint results: ")
rorc = int(rorc)
if rorc == 1:
plotXYPositions("CMAES", nameF, "All", False)
else:
tSize = raw_input('Target Size: ')
plotArticularPositions("CMAES", nameF, tSize)
elif choix == 15:
nameF = raw_input('Folder where the results are saved: ')
tSize = raw_input('Target Size: ')
plotMuscularActivations("CMAES", nameF, tSize)
elif choix == 16:
nameF = raw_input('Folder where the results are saved: ')
#tSize = raw_input('Target Size: ')
#plotCostColorMap("CMAES",nameF,tSize)
plotCostColorMap("CMAES", nameF)
elif choix == 17:
nameF = raw_input('Folder where the results are saved: ')
plotTimeDistanceTarget(nameF)
elif choix == 18:
nameF = raw_input('Folder where the results are saved: ')
plotPerfSizeDist(nameF)
elif choix == 19:
nameF = raw_input('Folder where the results are saved: ')
plotFittsLaw(nameF)
elif choix == 20:
nameF = raw_input('Folder where the results are saved: ')
plotTimeColorMap("CMAES", nameF)
elif choix == 21:
rorc = input("enter 0 if Brent, 1 if NN or 2 if CMAES results: ")
rorc = int(rorc)
if rorc == 0:
trajectoriesAnimation("Brent")
elif rorc == 1:
nameF = raw_input('Folder where the results are saved: ')
trajectoriesAnimation("NN", nameF)
elif rorc == 2:
nameF = raw_input('Folder where the results are saved: ')
tSize = raw_input('Target Size: ')
trajectoriesAnimation("CMAES", nameF, tSize)
elif choix == 22:
nameF = raw_input('Folder where the results are saved: ')
rorc = input("enter 1 if NN or 2 if CMAES results: ")
#plotHitDispersion(nameF,"0.05")
rorc = int(rorc)
if rorc == 1:
plotScattergram("NN", nameF)
elif rorc == 2:
plotScattergram("CMAES", nameF)
elif choix == 23:
name = raw_input('Name of the controller file: ')
rorc = input("enter 1 if from NN, anything if from previous CMAES: ")
save = False
rorc = int(rorc)
if rorc == 1:
save = True
tSize = raw_input('Target Size: ')
c = Chrono()
launchCMAESForSpecificTargetSize(float(tSize), name, save)
c.stop()
elif choix == 24:
plotCMAESProgress()
elif choix == 25:
name = raw_input('Name of the NN controller file: ')
fname = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
c = Chrono()
generateRichDataFromNN(nbret, name, fname)
c.stop()
elif choix == 26:
nameTheta = raw_input('Name of the controller file: ')
name = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
nbret = int(nbret)
c = Chrono()
generateRichDataFromCMAES(nbret, nameTheta, name)
c.stop()
elif choix == 27:
plotTrajsInRepo()
elif choix == 30:
nameF = raw_input('Folder where the results are saved: ')
plotXYEstimError("CMAES", nameF, "All")
elif choix == 31:
nameF = raw_input('Folder where the results are saved: ')
plotXYEstimErrorOfSpeed("CMAES", nameF, "All")
elif choix == 32:
plotExperimentSetup()
elif choix == 33:
plotManipulability()
elif choix == 34:
plotManipulability2()
def runTrajectoriesCMAES(self, theta):
'''
Generates all the trajectories of the experimental setup and return the mean cost. This function is used by cmaes to optimize the controller.
Input: -theta: vector of parameters, one dimension normalized numpy array
Ouput: -meanAll: the mean of the cost of all trajectories generated, float
'''
if (self.call == 0):
self.localBestCost = -1000000.0
self.localWorstCost = 1000000.0
self.localBestTime = -1000000.0
self.localWorstTime = 1000000.0
self.periodMeanCost = 0.0
self.periodMeanTime = 0.0
c = Chrono()
self.initTheta(theta)
#print "theta avant appel :", theta
#compute all the trajectories x times each, x = numberOfRepeat
meanCost, meanTime = self.runTrajectoriesForResultsGeneration(
self.numberOfRepeat)
#cma.plot()
#opt = cma.CMAOptions()
#print "CMAES options :", opt
c.stop()
print("Indiv #: ", self.call, "\n Cost: ", meanCost)
if meanCost > self.localBestCost:
self.localBestCost = meanCost
if meanTime > self.localBestTime:
self.localBestTime = meanTime
if meanCost < self.localWorstCost:
self.localWorstCost = meanCost
if meanTime < self.localWorstTime:
self.localWorstTime = meanTime
if meanCost > self.bestCost:
self.bestCost = meanCost
if meanCost > 0:
extension = ".save" + str(meanCost)
filename = findDataFilename(self.foldername + "Theta/",
"theta", extension)
np.savetxt(filename, self.theta)
self.periodMeanCost += meanCost
self.periodMeanTime += meanTime
self.call += 1
self.call = self.call % self.period
if (self.call == 0):
self.periodMeanCost = self.periodMeanCost / self.period
self.periodMeanTime = self.periodMeanTime / self.period
self.CMAESCostStore.append(
(self.localWorstCost, self.periodMeanCost, self.localBestCost))
self.CMAESTimeStore.append(
(self.localWorstTime, self.periodMeanTime, self.localBestTime))
costfoldername = self.foldername + "Cost/"
checkIfFolderExists(costfoldername)
np.savetxt(costfoldername + "cmaesCost.log", self.CMAESCostStore
) #Note: inefficient, should rather add to the file
np.savetxt(costfoldername + "cmaesTime.log", self.CMAESTimeStore
) #Note: inefficient, should rather add to the file
return 10.0 * (self.rs.rhoCF - meanCost) / self.rs.rhoCF
def chooseFunction(choix, rs):
if choix == 1:
rorc = input("enter 1 if from Regression, anything if from previous CMAES: ")
save = False
rorc = int(rorc)
if rorc == 1:
save = True
c = Chrono()
launchCMAESForAllTargetSizesMulti(rs)
c.stop()
elif choix == 2:
#TODO: Choose the kinematic model
nameTheta = raw_input('Name of the controller file: ')
name = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
nbret = int(nbret)
generateFromCMAES(nbret, rs, nameTheta, name)
elif choix == 3:
nameF = raw_input('Folder where the results are saved: ')
plotVelocityProfile("OPTI",rs,nameF)
elif choix == 4:
nameF = raw_input('Folder where the results are saved: ')
rorc = input("enter 1 if XY or 2 if Joint results: ")
rorc = int(rorc)
if rorc == 1:
plotXYPositions("OPTI",rs, nameF,"All",False)
else:
plotArticularPositions("OPTI",rs, nameF)
elif choix == 5:
nameF = raw_input('Folder where the results are saved: ')
tSize = raw_input('Target Size: ')
plotMuscularActivations("OPTI",rs,nameF,tSize)
elif choix == 6:
nameF = raw_input('Folder where the results are saved: ')
#tSize = raw_input('Target Size: ')
#plotCostColorMap("OPTI",nameF,tSize)
plotCostColorMap("OPTI",rs, nameF)
elif choix == 7:
nameF = raw_input('Folder where the results are saved: ')
plotTimeDistanceTarget(nameF, rs)
elif choix == 8:
nameF = raw_input('Folder where the results are saved: ')
plotPerfSizeDist(nameF, rs)
elif choix == 9:
nameF = raw_input('Folder where the results are saved: ')
plotFittsLaw(nameF, rs)
elif choix == 10:
nameF = raw_input('Folder where the results are saved: ')
plotTimeColorMap("OPTI",rs, nameF)
elif choix == 11:
rorc = input("enter 0 if Brent, 1 if Regression or 2 if CMAES results: ")
rorc = int(rorc)
if rorc == 0:
trajectoriesAnimation("Brent", rs)
elif rorc == 1:
nameF = raw_input('Folder where the results are saved: ')
trajectoriesAnimation("RBFN",rs, nameF)
elif rorc == 2:
nameF = raw_input('Folder where the results are saved: ')
tSize = raw_input('Target Size: ')
trajectoriesAnimation("OPTI",rs, nameF, tSize)
elif choix == 12:
nameF = raw_input('Folder where the results are saved: ')
rorc = input("enter 1 if RBFN or 2 if CMAES results: ")
#plotHitDispersion(nameF,"0.05")
rorc = int(rorc)
if rorc == 1:
plotScattergram("RBFN",nameF, rs)
elif rorc == 2:
plotScattergram("OPTI",nameF, rs)
elif choix == 13:
rorc = input("enter 1 if from RBFN, anything if from previous CMAES: ")
save = False
rorc = int(rorc)
if rorc == 1:
save = True
tSize = raw_input('Target Size: ')
c = Chrono()
launchCMAESForSpecificTargetSize(float(tSize),rs,save)
c.stop()
elif choix == 14:
plotCMAESProgress(rs)
elif choix == 15:
name = raw_input('Name of the Regression controller file: ')
fname = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
c = Chrono()
generateRichDataFromRegression(nbret,rs, name, fname)
c.stop()
elif choix == 16:
nameTheta = raw_input('Name of the controller file: ')
name = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
nbret = int(nbret)
c = Chrono()
generateRichDataFromCMAES(nbret,rs, nameTheta, name)
c.stop()
elif choix == 17:
plotTrajsInRepo()
elif choix == 18:
nameF = raw_input('Folder where the results are saved: ')
plotXYEstimError("OPTI",rs,nameF,"All")
elif choix == 19:
nameF = raw_input('Folder where the results are saved: ')
plotXYEstimErrorOfSpeed("OPTI",rs,nameF,"All")
elif choix == 20:
plotExperimentSetup(rs)
elif choix == 21:
plotManipulability(rs)
elif choix == 22:
plotManipulability2(rs)
elif choix == 23:
plotEstimatorPoint(rs, 0.04, 12)
elif choix == 24:
rorc = input("enter 1 if General CMAES, 2 if from scratch, anything if from previous CMAES point: ")
save = False
rorc = int(rorc)
if rorc == 1:
save = True
elif rorc==2:
save=None
tSize = raw_input('Target Size: ')
c = Chrono()
launchCMAESForAllPoint(rs,float(tSize),save)
c.stop()
elif choix == 25:
#TODO: Test!
rorc = input("enter 1 if General CMAES, 2 if from scratch, anything if from previous CMAES point: ")
save = False
rorc = int(rorc)
if rorc == 1:
save = True
elif rorc==2:
save=None
tSize = raw_input('Target Size: ')
points=map(int, raw_input('Point(s): ').split())
print points
lauchCMAESForListOfPoints(float(tSize), rs, save, points)
elif choix == 26:
size=raw_input('Target Size: ')
while True:
print(" Enter the number of the point you want sea, q for quit")
point=raw_input('Point: ')
if(point=="q"):
break
plotCMAESOnePointProgress(rs,size, point)
elif choix == 27:
#TODO: Choose the kinematic model
nameTheta = raw_input('Name of the controller file: ')
name = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
nbret = int(nbret)
generateFromCMAESNController(nbret, rs, nameTheta, name)
elif choix == 28:
nameF = raw_input('Folder where the results are saved: ')
rorc = raw_input("Target Size: ")
plotXYPositions("OPTI",rs, nameF,rorc,False)
elif choix == 29:
nameTheta = raw_input('Name of the controller file: ')
name = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
nbret = int(nbret)
size=raw_input('Target Size: ')
point=raw_input('Point: ')
generateFromCMAESonePoint(nbret, rs, nameTheta, name, float(size), point)
elif choix == 30:
nameF = raw_input('Folder where the results are saved: ')
#tSize = raw_input('Target Size: ')
#plotCostColorMap("OPTI",nameF,tSize)
plotCostColorMapFor12("OPTI",rs, nameF)
elif choix == 31:
size=raw_input('Target Size: ')
checkAllPoint(rs, size)
else :
return 0
return 1
def testNNs():
for i in range(3):
c = Chrono()
generateFromNN(3, "R" + str(i), "SR" + str(i))
c.stop()
def run():
c = Chrono()
generateFromNN(1, "Random", "R1")
c.stop()
from Main.MainCMAES import generateFromRegression
from Regression.RunRegression import run
from Plot.plotFunctions import plotXYPositions
from Main.Test import *
from Utils.Chrono import Chrono
from Utils.ReadXmlFile import ReadXmlFile
setupFile = "setupRBFN.xml"
thetaName = "ScriptRBFNxml"
folder = "TrajRBFNxml"
rs = ReadXmlFile(setupFile)
c = Chrono()
run(rs)
c.stop()
generateFromRegression(1, rs, thetaName, folder)
plotXYPositions("Regression", rs, folder, "All", True)
testRegression(rs, thetaName, folder)
def chooseFunction(choix, rs):
if choix == 1:
c = Chrono()
#TODO: DDPGForAllTargetSizes
#launchDDPGForAllTargetSizesMulti(rs)
c.stop()
elif choix == 2:
#TODO: Choose the kinematic model
nameTheta = raw_input('Name of the controller file: ')
name = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
nbret = int(nbret)
generateFromDDPG(nbret, rs, nameTheta, name)
elif choix == 3:
nameF = raw_input('Folder where the results are saved: ')
plotVelocityProfile("OPTI", rs, nameF)
elif choix == 4:
nameF = raw_input('Folder where the results are saved: ')
rorc = input("enter 1 if XY or 2 if Joint results: ")
rorc = int(rorc)
if rorc == 1:
plotXYPositions("OPTI", rs, nameF, "All", False)
else:
plotArticularPositions("OPTI", rs, nameF)
elif choix == 5:
nameF = raw_input('Folder where the results are saved: ')
tSize = raw_input('Target Size: ')
plotMuscularActivations("OPTI", rs, nameF, tSize)
elif choix == 6:
nameF = raw_input('Folder where the results are saved: ')
#tSize = raw_input('Target Size: ')
#plotCostColorMap("OPTI",nameF,tSize)
plotCostColorMap("OPTI", rs, nameF)
elif choix == 7:
nameF = raw_input('Folder where the results are saved: ')
plotTimeDistanceTarget(nameF, rs)
elif choix == 8:
nameF = raw_input('Folder where the results are saved: ')
plotPerfSizeDist(nameF, rs)
elif choix == 9:
nameF = raw_input('Folder where the results are saved: ')
plotFittsLaw(nameF, rs)
elif choix == 10:
nameF = raw_input('Folder where the results are saved: ')
plotTimeColorMap("OPTI", rs, nameF)
elif choix == 11:
rorc = input(
"enter 0 if Brent, 1 if Regression or 2 if DDPG results: ")
rorc = int(rorc)
if rorc == 0:
trajectoriesAnimation("Brent", rs)
elif rorc == 1:
nameF = raw_input('Folder where the results are saved: ')
trajectoriesAnimation("RBFN", rs, nameF)
elif rorc == 2:
nameF = raw_input('Folder where the results are saved: ')
tSize = raw_input('Target Size: ')
trajectoriesAnimation("OPTI", rs, nameF, tSize)
elif choix == 12:
nameF = raw_input('Folder where the results are saved: ')
rorc = input("enter 1 if RBFN or 2 if DDPG results: ")
#plotHitDispersion(nameF,"0.05")
rorc = int(rorc)
if rorc == 1:
plotScattergram("RBFN", nameF, rs)
elif rorc == 2:
plotScattergram("OPTI", nameF, rs)
elif choix == 13:
tSize = raw_input('Target Size: ')
c = Chrono()
launchDDPGForSpecificTargetSize(float(tSize), rs)
c.stop()
elif choix == 14:
plotDDPGProgress(rs)
elif choix == 15:
name = raw_input('Name of the Regression controller file: ')
fname = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
c = Chrono()
generateRichDataFromRegression(nbret, rs, name, fname)
c.stop()
elif choix == 16:
nameTheta = raw_input('Name of the controller file: ')
name = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
nbret = int(nbret)
c = Chrono()
generateRichDataFromDDPG(nbret, rs, nameTheta, name)
c.stop()
elif choix == 17:
plotTrajsInRepo()
elif choix == 18:
nameF = raw_input('Folder where the results are saved: ')
plotXYEstimError("OPTI", rs, nameF, "All")
elif choix == 19:
nameF = raw_input('Folder where the results are saved: ')
plotXYEstimErrorOfSpeed("OPTI", rs, nameF, "All")
elif choix == 20:
plotExperimentSetup(rs)
elif choix == 21:
plotManipulability(rs)
elif choix == 22:
plotManipulability2(rs)
elif choix == 23:
plotEstimator(rs, 0.04, 0., 0.25)
elif choix == 24:
tSize = raw_input('Target Size: ')
c = Chrono()
launchDDPGForAllPoint(rs, float(tSize))
#launchDDPGForSpecificTargetSizeAndSpeceficBeginning(float(tSize), rs, [0, [0, 0.2]])
c.stop()
elif choix == 25:
size = raw_input('Target Size: ')
while True:
print(" Enter the number of the point you want sea, q for quit")
point = raw_input('Point: ')
if (point == "q"):
break
plotDDPGOnePointProgress(rs, size, point)
elif choix == 26:
#TODO: Choose the kinematic model
nameTheta = raw_input('Name of the controller file: ')
name = raw_input('Folder where you want to save the results: ')
nbret = input("Number of repeat for each trajectory (int): ")
nbret = int(nbret)
generateFromDDPGNController(nbret, rs, nameTheta, name)
elif choix == 27:
nameF = raw_input('Folder where the results are saved: ')
rorc = raw_input("Target Size: ")
plotXYPositions("OPTI", rs, nameF, rorc, False)
else:
return 0
return 1
