def run(regressionSetup, delay):
stateAndCommand, nextState = loadTrajForModel(pathDataFolder + "Brent/",
delay)
print("nombre d'echantillons: ", len(stateAndCommand))
fa = regressionDict[regressionSetup.regression](regressionSetup)
fa.getTrainingData(stateAndCommand, nextState)
fa.train()
def run(regressionSetup, delay):
stateAndCommand, nextState = loadTrajForModel(pathDataFolder + "Brent/", delay)
print("nombre d'echantillons: ", len(stateAndCommand))
fa = regressionDict[regressionSetup.regression](regressionSetup)
fa.getTrainingData(stateAndCommand, nextState)
fa.train()
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)
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])
