def launchCMAESForSpecificTargetSizeAndSpecificPoint(target_size,
rs,
save,
point,
noise=None):
'''
Run cmaes for a specific target size
Input: -target_size, size of the target, float
-setuFile, file of setup, string
-save: do we use a previous Best.theta file? True = Yes, False = use current controller, None = random controller
noise: noise on muscle, if None, defalt noise from muscle setup, float
'''
pos = point[0]
x = point[1][0]
y = point[1][1]
print("Starting the CMAES Optimization for target " + str(target_size) +
" for point " + str(pos) + " !")
foldername = rs.OPTIpath + str(target_size) + "/" + str(pos) + "/"
thetaname = foldername + "Best"
if save:
checkIfFolderExists(foldername)
copyfile(rs.OPTIpath + str(target_size) + "/" + "Best.theta",
foldername + "Best.theta")
elif save == None:
thetaname = None
#Initializes all the class used to generate trajectory
exp = Experiments(rs, target_size, False, foldername, thetaname,
rs.popsizeCmaes, rs.period)
if (noise != None):
exp.setNoise(noise)
theta = exp.tm.controller.getTheta()
thetaCMA = theta.flatten()
#run the optimization (cmaes)
cma.fmin(partial(exp.runTrajectoriesCMAESOnePoint, x, y),
thetaCMA,
rs.sigmaCmaes,
options={
'maxiter': rs.maxIterCmaes,
'popsize': rs.popsizeCmaes,
'CMA_diagonal': True,
'verb_log': 0,
'verb_disp': 0,
'termination_callback': term()
})
print("End of optimization for target " + str(target_size) +
" for point " + str(pos) + " !")
def GenerateDataFromThetaNController(rs, target_size, foldername, thetaFile, repeat, save,noise=None):
'''
Generate Data from a given theta file
Input: -target_size, size of the target, float
-save: do we save the results (false when running CMAES)? True = Yes, False = No
'''
os.system("rm "+foldername+"/Log/*.log 2>/dev/null")
exp = Experiments(rs, target_size, save, foldername,None,rs.popsizeCmaes,rs.period)
if(noise!=None): exp.setNoise(noise)
cost, time = exp.runTrajectoriesForResultsGenerationNController(repeat,thetaFile)
print("Average cost: ", cost)
print("Average time: ", time)
print("foldername : ", foldername)
if (save):
exp.saveCost()
def launchCMAESForSpecificTargetSizeAndSpecificPoint(target_size, rs, save, point, noise=None):
'''
Run cmaes for a specific target size
Input: -target_size, size of the target, float
-setuFile, file of setup, string
-save: do we use a previous Best.theta file? True = Yes, False = use current controller, None = random controller
noise: noise on muscle, if None, defalt noise from muscle setup, float
'''
pos=point[0]
x=point[1][0]
y=point[1][1]
print("Starting the CMAES Optimization for target " + str(target_size) + " for point "+ str(pos)+" !")
foldername = rs.OPTIpath + str(target_size)+"/"+str(pos)+"/"
thetaname = foldername + "Best"
if save:
checkIfFolderExists(foldername)
copyfile(rs.OPTIpath + str(target_size)+"/" + "Best.theta",foldername + "Best.theta")
elif save==None:
thetaname=None
#Initializes all the class used to generate trajectory
exp = Experiments(rs, target_size, False, foldername, thetaname,rs.popsizeCmaes,rs.period)
if(noise!=None):
exp.setNoise(noise)
theta = exp.tm.controller.getTheta()
thetaCMA = theta.flatten()
#run the optimization (cmaes)
cma.fmin(partial(exp.runTrajectoriesCMAESOnePoint, x, y), thetaCMA, rs.sigmaCmaes, options={'maxiter':rs.maxIterCmaes, 'popsize':rs.popsizeCmaes, 'CMA_diagonal':True, 'verb_log':0, 'verb_disp':0,'termination_callback':term()})
print("End of optimization for target " + str(target_size) + " for point "+ str(pos)+" !")
def GenerateDataFromThetaNController(rs,
target_size,
foldername,
thetaFile,
repeat,
save,
noise=None):
'''
Generate Data from a given theta file
Input: -target_size, size of the target, float
-save: do we save the results (false when running CMAES)? True = Yes, False = No
'''
os.system("rm " + foldername + "/Log/*.log 2>/dev/null")
exp = Experiments(rs, target_size, save, foldername, None, rs.popsizeCmaes,
rs.period)
if (noise != None): exp.setNoise(noise)
cost, time = exp.runTrajectoriesForResultsGenerationNController(
repeat, thetaFile)
print("Average cost: ", cost)
print("Average time: ", time)
print("foldername : ", foldername)
if (save):
exp.saveCost()
