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')
ny = args.ny
if not ny == dataset.data['Y'].shape[1]:
new_sequences = {'Y': dataset.data['Y'][:, :1]}
dataset.add_data(new_sequences, overwrite=True)
dataset.min_max_norms['Ymin'] = dataset.min_max_norms['Ymin'][0]
dataset.min_max_norms['Ymax'] = dataset.min_max_norms['Ymax'][0]
nsim = dataset.data['Y'].shape[0]
nu = dataset.data['U'].shape[1]
# new_sequences = {'Y_max': 0.8 * np.ones([nsim, 1]), 'Y_min': 0.2 * np.ones([nsim, 1]),
new_sequences = {'Y_max': psl.Periodic(nx=1, nsim=nsim, numPeriods=30, xmax=0.9, xmin=0.6),
'Y_min': psl.Periodic(nx=1, nsim=nsim, numPeriods=25, xmax=0.4, xmin=0.1),
'U_max': np.ones([nsim, nu]), 'U_min': np.zeros([nsim, nu]),
# 'R': psl.Steps(nx=1, nsim=nsim, randsteps=60, xmax=0.7, xmin=0.3),
'R': psl.Periodic(nx=1, nsim=nsim, numPeriods=20, xmax=0.8, xmin=0.2)
# 'Y_ctrl_': psl.RandomWalk(nx=ny, nsim=nsim, xmax=[1.0] * ny, xmin=[0.0] * ny, sigma=0.05)}
#'Y_ctrl_': psl.WhiteNoise(nx=ny, nsim=nsim, xmax=[1.0] * ny, xmin=[0.0] * ny)}
}
dataset.add_data(new_sequences)
# dataset.dims['Rf'] = (9000, 1)
return dataset
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)
else:
dataset = FileDataset(system=args.system, nsim=args.nsim,
norm=args.norm, nsteps=args.nsteps, device=device, savedir=args.savedir)
return dataset
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)
else:
dataset = FileDataset(system=args.system, nsim=args.nsim,
norm=args.norm, nsteps=args.nsteps, device=device, savedir=args.savedir)
new_sequences = {'Y': dataset.data['Y'][:, :1]}
dataset.min_max_norms['Ymin'] = dataset.min_max_norms['Ymin'][0]
dataset.min_max_norms['Ymax'] = dataset.min_max_norms['Ymax'][0]
dataset.add_data(new_sequences, overwrite=True)
return dataset
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
nsim) + SG2.sequence_generator(nsim)
if __name__ == '__main__':
model_file = './datasets/Flexy_air/best_model_flexy1.pth'
load_model = torch.load(model_file,
pickle_module=dill,
map_location=torch.device('cpu'))
estimator = load_model.components[0]
dynamics = load_model.components[1]
nsteps = 32
ny = load_model.components[1].fy.out_features
dataset = FileDataset(system='flexy_air',
nsim=10000,
norm=['U', 'D', 'Y'],
nsteps=nsteps,
device='cpu')
dataset.min_max_norms['Ymin'] = dataset.min_max_norms['Ymin'][0]
dataset.min_max_norms['Ymax'] = dataset.min_max_norms['Ymax'][0]
nsim = dataset.data['Y'].shape[0]
nu = dataset.data['U'].shape[1]
new_sequences = {
'Y_max': 0.8 * np.ones([nsim, 1]),
'Y_min': 0.2 * np.ones([nsim, 1]),
'U_max': np.ones([nsim, nu]),
'U_min': np.zeros([nsim, nu]),
'R': psl.Periodic(nx=1, nsim=nsim, numPeriods=20, xmax=0.7, xmin=0.3)
}
dataset.add_data(new_sequences)
dynamics_generator = SignalGeneratorDynamics(dynamics,
D.append(d) if d is not None else None
R.append(r) if r is not None else None
Ymin.append(ymin) if ymin is not None else None
Ymax.append(ymax) if ymax is not None else None
Umin.append(umin) if umin is not None else None
Umax.append(umax) if umax is not None else None
return {
'Y': np.concatenate(Y, 0),
'X': np.concatenate(X, 0),
'U': np.concatenate(U, 0),
'D': np.concatenate(D, 0) if D else None,
'R': np.concatenate(R, 0) if R else None,
'Ymin': np.concatenate(Ymin, 0) if Ymin else None,
'Ymax': np.concatenate(Ymax, 0) if Ymax else None,
'Umin': np.concatenate(Umin, 0) if Umin else None,
'Umax': np.concatenate(Umax, 0) if Umax else None
}
if __name__ == '__main__':
systems = {'Reno_full': 'emulator'}
for system, data_type in systems.items():
if data_type == 'emulator':
dataset = EmulatorDataset(system)
elif data_type == 'datafile':
dataset = FileDataset(system)
nsim, ny = dataset.data['Y'].shape
nu = dataset.data['U'].shape[1]
pickle_module=dill)
policy = torch.load('../datasets/Flexy_air/device_test_models/' +
model + '/best_policy_flexy.pth',
pickle_module=dill)
estimator = torch.load('../datasets/Flexy_air/device_test_models/' +
model + '/best_estimator_flexy.pth',
pickle_module=dill)
estimator.input_keys[0] = 'Yp'
policy.input_keys[0] = 'Yp'
HW_emulator = Simulator(estimator=estimator, dynamics=dynamics)
# dataset
nsim = 3000
dataset = FileDataset(system='flexy_air',
nsim=nsim,
norm=['U', 'D', 'Y'],
nsteps=estimator.nsteps)
ny = 1
nu = 1
nsteps = policy.nsteps
R = {
'steps':
psl.Steps(nx=1, nsim=nsim, randsteps=30, xmax=0.7, xmin=0.3),
'periodic':
psl.Periodic(nx=1, nsim=nsim, numPeriods=20, xmax=0.7, xmin=0.3)
}[args.ref_type]
if args.dynamic_constraints:
bounds_reference = {
'Y_max':
pickle_module=dill)
estimator = torch.load('../datasets/Flexy_air/device_test_models/' +
model + '/best_estimator_flexy.pth',
pickle_module=dill)
estimator.input_keys[0] = 'Yp'
policy.input_keys[0] = 'Yp'
# temporary fix to be compatible with latest model change
dynamics.fyu = None
HW_emulator = Simulator(estimator=estimator, dynamics=dynamics)
# dataset
nsim = 9000
dataset = FileDataset(system='flexy_air',
nsim=nsim,
norm=['U', 'D', 'Y'],
nsteps=estimator.nsteps)
ny = 1
nu = 1
nsteps = policy.nsteps
# mean the disturbance
disturb_static = 0.44
# dataset.data['D'] = np.mean(dataset.data['D']) * np.ones((dataset.data['D']).shape)
dataset.data['D'] = disturb_static * np.ones((dataset.data['D']).shape)
# list(np.random.random(10))
R = {
'static':
0.5 * np.ones([nsim, 1]),
# 'spline': psl.SplineSignal(nsim=nsim, values=[0.3, 0.7, 0.6, 0.8, 0.5, 0.7, 0.4, 0.75, 0.3]).reshape(nsim, 1),