def GenerateRichDataFromTheta(rs, sizeOfTarget, foldername, thetaFile, repeat, save):
exp = Experiments(rs, sizeOfTarget, save, foldername,thetaFile,rs.popsizeCmaes,rs.period)
cost = exp.runRichTrajectories(repeat)
print("Average cost: ", cost)
print("foldername : ", foldername)
if (save):
exp.saveCost()
def GenerateDataFromTheta(rs, sizeOfTarget, foldername, thetaFile, repeat, save):
exp = Experiments(rs, sizeOfTarget, save, foldername,thetaFile,rs.popsizeCmaes,rs.period)
cost, time = exp.runTrajectoriesForResultsGeneration(repeat)
print("Average cost: ", cost)
print("Average time: ", time)
print("foldername : ", foldername)
if (save):
exp.saveCost()
def GenerateRichDataFromTheta(rs, sizeOfTarget, foldername, thetaFile, repeat,
save):
exp = Experiments(rs, sizeOfTarget, save, foldername, thetaFile,
rs.popsizeCmaes, rs.period)
cost = exp.runRichTrajectories(repeat)
print("Average cost: ", cost)
print("foldername : ", foldername)
if (save):
exp.saveCost()
def GenerateDataFromTheta(rs, sizeOfTarget, foldername, thetaFile, repeat,
save):
exp = Experiments(rs, sizeOfTarget, save, foldername, thetaFile,
rs.popsizeCmaes, rs.period)
cost, time = exp.runTrajectoriesForResultsGeneration(repeat)
print("Average cost: ", cost)
print("Average time: ", time)
print("foldername : ", foldername)
if (save):
exp.saveCost()
def GenerateDataFromThetaOnePoint(rs, target_size, foldername, thetaFile, repeat, point):
'''
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, True, foldername,thetaFile,rs.popsizeCmaes,rs.period)
cost, time = exp.runTrajectoriesForResultsGenerationOnePoint(repeat,point)
print("Average cost: ", cost)
print("Average time: ", time)
print("foldername : ", foldername)
def GenerateRichDataFromTheta(rs, target_size, foldername, thetaFile, repeat, save):
'''
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")
exp = Experiments(rs, target_size, save, foldername,thetaFile,rs.popsizeCmaes,rs.period)
cost = exp.runRichTrajectories(repeat)
print("Average cost: ", cost)
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 GenerateDataFromThetaOnePoint(rs, target_size, foldername, thetaFile,
repeat, point):
'''
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, True, foldername, thetaFile,
rs.popsizeCmaes, rs.period)
cost, time = exp.runTrajectoriesForResultsGenerationOnePoint(repeat, point)
print("Average cost: ", cost)
print("Average time: ", time)
print("foldername : ", foldername)
def launchCMAESForSpecificTargetSize(sizeOfTarget, thetaFile, save):
'''
Run cmaes for a specific target size
Input: -sizeOfTarget, size of the target, float
'''
print("Starting the CMAES Optimization for target " + str(sizeOfTarget) +
" !")
rs = ReadSetupFile()
foldername = rs.CMAESpath + str(sizeOfTarget) + "/"
thetaname = foldername + thetaFile
if save:
copyRBFNtoCMAES(rs, thetaFile, sizeOfTarget)
#Initializes all the class used to generate trajectory
exp = Experiments(rs, sizeOfTarget, False, foldername, thetaname,
rs.popsizeCmaes, rs.period)
theta = exp.tm.controller.theta
thetaCMA = theta.flatten()
#run the optimization (cmaes)
resCma = cma.fmin(exp.runTrajectoriesCMAES,
thetaCMA,
rs.sigmaCmaes,
options={
'maxiter': rs.maxIterCmaes,
'popsize': rs.popsizeCmaes,
'CMA_diagonal': True,
'verb_log': 50,
'verb_disp': 1,
'termination_callback': term()
})
print("End of optimization for target " + str(sizeOfTarget) + " !")
def GenerateRichDataFromTheta(rs, target_size, foldername, thetaFile, repeat,
save):
'''
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")
exp = Experiments(rs, target_size, save, foldername, thetaFile,
rs.popsizeCmaes, rs.period)
cost = exp.runRichTrajectories(repeat)
print("Average cost: ", cost)
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 launchCMAESMissing(rs, save, gamma, point):
'''
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
'''
rs.gamma = gamma / 10.0
pos = point[0]
x = point[1][0]
y = point[1][1]
target_size = point[2]
print("Starting the CMAES Optimization for gamma = " + str(gamma) +
",target " + str(target_size) + " for point " + str(pos) + " !")
foldername = rs.OPTIpathMissing + "gamma" + str(gamma) + "/" + str(
target_size) + "/" + str(pos) + "/"
thetaname = foldername + "Best"
if save:
checkIfFolderExists(foldername)
copyfile(
rs.OPTIpathMissing + "gamma" + str(gamma) + "/" +
str(target_size) + "/" + "Best.theta", foldername + "Best.theta")
elif save == None:
thetaname = None
#Initializes all the classes used to generate trajectory
exp = Experiments(rs, target_size, False, foldername, thetaname,
rs.popsizeCmaes, rs.period)
theta = exp.tm.controller.getTheta()
thetaCMA = theta.flatten()
#run the optimization (cmaes)
cma.fmin(partial(exp.runTrajectoriesCMAESOnePointMulti, 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 gamma = " + str(gamma) + ",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 launchCMAESForSpecificTargetSize(target_size, rs, save):
'''
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
'''
print("Starting the CMAES Optimization for target " + str(target_size) +
" !")
foldername = rs.OPTIpath + str(target_size) + "/"
if save:
thetaname = foldername + rs.thetaFile
copyRegressiontoCMAES(rs, rs.thetaFile, target_size)
else:
thetaname = foldername + "Best"
#Initializes all the class used to generate trajectory
exp = Experiments(rs, target_size, False, foldername, thetaname,
rs.popsizeCmaes, rs.period)
theta = exp.tm.controller.getTheta()
thetaCMA = theta.flatten()
#run the optimization (cmaes)
cma.fmin(exp.runTrajectoriesCMAES,
thetaCMA,
rs.sigmaCmaes,
options={
'maxiter': rs.maxIterCmaes,
'popsize': rs.popsizeCmaes,
'CMA_diagonal': True,
'verb_log': 50,
'verb_disp': 1,
'termination_callback': term()
})
print("End of optimization for target " + str(target_size) + " !")
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()
from Utils.Chrono import Chrono
from Utils.ReadXmlFile import ReadXmlFile
from Utils.FileReading import loadTrajForModel
from Experiments.Experiments import Experiments
from GlobalVariables import pathDataFolder
from Regression.NeuraNetTF import NeuralNetTF
import numpy as np
rs=ReadXmlFile("setupNN1th.xml")
foldername = rs.CMAESpath + str(0.04) + "/"
thetaname = foldername + rs.thetaFile
exp = Experiments(rs, 0.04, False, foldername, thetaname,rs.popsizeCmaes,rs.period, "Inv")
#exp2 = Experiments(rs, 0.04, False, foldername, thetaname,rs.popsizeCmaes,rs.period,"Reg")
exp3 = Experiments(rs, 0.04, False, foldername, thetaname,rs.popsizeCmaes,rs.period,"Hyb")
'''
c=Chrono()
for i in range(30):
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)
