'T': T,
'dt': 0.01,
'flag_intermediate': False,
'setup': '1',
'state_init': 'GS_i',
'state_tgt': 'GS_inf',
'fom': fom_GS,
'fom_print': True,
'track_learning': True,
'ctl_shortcut': 'owbds01_pwl15',
'kblock': 0,
'pblock': 1
}
dico_GS = learner1DBH._process_controler(dico_GS)
dico_GS['control_obj'] = learner1DBH._build_control_from_string(
dico_GS['control_obj'], None, context_dico=dico_GS)
model_GS = bh1d.BH1D(**dico_GS)
optim_GS = Learner.learner_Opt(model=model_GS, **optim_args)
res_GS = optim_GS(track_learning=True)
model_GS.control_fun.plot_function(np.arange(-0.01, T + 0.01, 0.01))
state_tmp = model_GS.EvolutionPopAdiab(nb_ev=2)
model_GS.plot_pop_adiab(plot_gap=True)
#==============================================================================
# Try to reach ES at the end
#==============================================================================
fom_firstE = ['projSS:neg_fluence:0.0001_smooth:0.05']
dico_firstE = {
'L': 2,
'Nb': 2,
'L': 5,
'Nb': 5,
'mu': 0,
'T': T,
'dt': 0.01,
'flag_intermediate': False,
'setup': '1',
'state_init': 'GS_i',
'state_tgt': 'GS_inf',
'fom': fom,
'fom_print': True,
'track_learning': True,
'ctl_shortcut': 'owbds01r_pwc10'
}
dico_simul = learner1DBH._process_controler(dico_simul)
dico_simul['control_obj'] = learner1DBH._build_control_from_string(
dico_simul['control_obj'], None, context_dico=dico_simul)
model = bh1d.BH1D(**dico_simul)
optim_main = {
'algo': 'BO2',
'maxiter': 50,
'num_cores': 4,
'init_obj': 30,
'exploit_steps': 30,
'acq': 'EI',
'optim_num_anchor': 15,
'optim_num_samples': 10000,
'ARD': False,
'model_type': 'GP',
'number_inducing': 10,
'batch_method': 'local_penalization',