'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',