import os
from neuromancer import arg
import argparse
from neuromancer import datasets
parser = argparse.ArgumentParser()
parser.add_argument('-gpu', type=int, default=None)
args = parser.parse_args()
if args.gpu is not None:
gpu = f'-gpu {args.gpu}'
p = arg.ArgParser(parents=[
arg.log(),
arg.opt(),
arg.data(),
arg.loss(),
arg.lin(),
arg.ssm()
])
options = {
k: v.choices
for k, v in p._option_string_actions.items()
if v.choices is not None and k != '-norm'
}
systems = list(datasets.systems.keys())
for i, (k, v) in enumerate(options.items()):
for j, opt in enumerate(v):
print(k, opt)
code = os.system(
f'python ../train_scripts/system_id.py -norm Y '
f'{k} {opt} -epochs 1 -nsteps 8 -verbosity 1 -nsim 128 -system {systems[(i*j) % len(systems)]}'
if umax is not None:
u_max = umax * np.ones([nstep + 1, umax.shape[0]])
ax[1].plot(u_max, 'k--', linewidth=2)
ax[1].set(xlabel='time')
ax[1].grid()
ax[1].set_xlim(0, nstep)
plt.tight_layout()
if save_path is not None:
plt.savefig(save_path + '/closed_loop_quadcopter_dpc.pdf')
if __name__ == "__main__":
"""
# # # Arguments
"""
parser = arg.ArgParser(parents=[arg.log(), arg_dpc_problem()])
args, grps = parser.parse_arg_groups()
args.bias = True
"""
# # # 3D quadcopter model
"""
# Discrete time model of a quadcopter
A = np.array(
[[1., 0., 0., 0., 0., 0., 0.1, 0., 0., 0., 0., 0.],
[0., 1., 0., 0., 0., 0., 0., 0.1, 0., 0., 0., 0.],
[0., 0., 1., 0., 0., 0., 0., 0., 0.1, 0., 0., 0.],
[0.0488, 0., 0., 1., 0., 0., 0.0016, 0., 0., 0.0992, 0., 0.],
[0., -0.0488, 0., 0., 1., 0., 0., -0.0016, 0., 0., 0.0992, 0.],
[0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0., 0.0992],
[0., 0., 0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],
"Y_max": np.ones([nsim, ny]),
"Y_min": 0.7 * np.ones([nsim, ny]),
"U_max": np.ones([nsim, nu]),
"U_min": np.zeros([nsim, nu]),
"R": psl.Periodic(nx=ny, nsim=nsim, numPeriods=20, xmax=0.9, xmin=0.8)[:nsim, :]
# 'Y_ctrl_': psl.WhiteNoise(nx=ny, nsim=nsim, xmax=[1.0] * ny, xmin=[0.0] * ny)
})
# indices of controlled states, e.g. [0, 1, 3] out of 5 outputs
dataset.ctrl_outputs = args.controlled_outputs
return dataset
if __name__ == "__main__":
# for available systems in PSL library check: psl.systems.keys()
system = 'CSTR' # keyword of selected system
parser = arg.ArgParser(parents=[arg.log(), arg.log(prefix='sysid_'),
arg.opt(), arg.data(system=system),
arg.loss(), arg.lin(), arg.policy(),
arg.ctrl_loss(), arg.freeze()])
path = './test/CSTR_best_model.pth'
parser.add('-model_file', type=str, default=path,
help='Path to pytorch pickled model.')
args = parser.parse_args()
args.savedir = 'test_control'
log_constructor = MLFlowLogger if args.logger == 'mlflow' else BasicLogger
metrics = ["nstep_dev_loss", "loop_dev_loss", "best_loop_dev_loss",
"nstep_dev_ref_loss", "loop_dev_ref_loss"]
logger = log_constructor(args=args, savedir=args.savedir, verbosity=args.verbosity, stdout=metrics)
# evaluate conficence level given risk tolerance epsilon
confidence = 1-2*np.exp(-2*samples*epsilon**2)
mu = mu_tilde-epsilon
# evaluate risk lower bound given number of samples, and desired confidence
mu_lbound = mu_tilde - np.sqrt(-np.log(delta/2)/(2*samples))
return mu, mu_tilde, confidence, mu_lbound, \
I_s, I_c, X_c_mean, U_c_mean, X_norm, U_norm
if __name__ == "__main__":
"""
# # # Arguments
"""
parser = arg.ArgParser(parents=[arg.log(),
arg_dpc_problem()])
args, grps = parser.parse_arg_groups()
args.bias = True
"""
# # # VTOL aircraft model
"""
# System parameters
m = 4 # mass of aircraft
J = 0.0475 # inertia around pitch axis
r = 0.25 # distance to center of force
g = 9.8 # gravitational constant
c = 0.05 # damping factor (estimated)
# State space dynamics
xe = [0, 0, 0, 0, 0, 0] # equilibrium point of interest