def __init__(self, nsteps, nx, xmax=(0.5, 0.1), xmin=0.0, min_period=5, max_period=30, name='period', device='cpu'):
super().__init__(nsteps, nx, xmax, xmin, name=name, device=device)
self.min_period = min_period
self.max_period = max_period
self.white_noise_generator = lambda nsim, xmin, xmax: psl.WhiteNoise(nx=self.nx, nsim=nsim,
xmax=xmax, xmin=xmin)[:nsim]
self.period_generator = lambda nsim, xmin, xmax: psl.Periodic(nx=self.nx, nsim=nsim,
numPeriods=random.randint(self.min_period, self.max_period),
xmax=xmax, xmin=xmin)[:nsim]
# self.sequence_generator = lambda nsim, xmin, xmax: self.period_generator(nsim, xmin, xmax) + self.white_noise_generator(nsim, -0.1*xmax, 0.1*xmax)
self.sequence_generator = lambda nsim, xmin, xmax: self.period_generator(nsim, xmin, xmax) + self.white_noise_generator(nsim, xmin, 1.0 - xmax)
def dataset_load(args, device):
if systems[args.system] == 'emulator':
dataset = EmulatorDataset(system=args.system,
nsim=args.nsim,
norm=args.norm,
nsteps=args.nsteps,
device=device,
savedir=args.savedir,
name='closedloop')
else:
dataset = FileDataset(system=args.system,
nsim=args.nsim,
norm=args.norm,
nsteps=args.nsteps,
device=device,
savedir=args.savedir,
name='closedloop')
nsim, ny = dataset.data['Y'].shape
nu = dataset.data['U'].shape[1]
new_sequences = {
'Y_max':
0.8 * np.ones([nsim, ny]),
'Y_min':
0.2 * np.ones([nsim, ny]),
'U_max':
np.ones([nsim, nu]),
'U_min':
np.zeros([nsim, nu]),
'R':
psl.Steps(nx=1, nsim=nsim, randsteps=30, xmax=1, xmin=0),
# 'R': psl.Periodic(nx=1, nsim=nsim, numPeriods=12, xmax=1, xmin=0),
'Y_ctrl_':
psl.WhiteNoise(nx=ny, nsim=nsim, xmax=[1.0] * ny, xmin=[0.0] * ny)
}
dataset.add_data(new_sequences)
return dataset
def __init__(self, nsteps, nx, xmax, xmin, name='period'):
super().__init__(nsteps, nx, xmax, xmin, name=name)
self.sequence_generator = lambda nsim: psl.WhiteNoise(
nx=self.nx, nsim=nsim, xmax=self.xmax, xmin=self.xmin)
def __init__(self, nsteps, nx, xmax, xmin, name='period', device='cpu'):
super().__init__(nsteps, nx, xmax, xmin, name=name, device=device)
self.sequence_generator = lambda nsim, xmin, xmax: psl.WhiteNoise(nx=self.nx, nsim=nsim,
xmax=xmax, xmin=xmin)