'mu': 0,
'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 = {
dico_simul = {
'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',
'L': L,
'Nb': L,
'mu': 0,
'T': T,
'dt': 0.01,
'flag_intermediate': False,
'setup': '1',
'state_init': 'GS_i',
'state_tgt': 'GS_inf',
'fom': fom,
'sps': sps,
'fom_print': True,
'track_learning': True,
'ctl_shortcut': 'owbds01_linear'
}
dico_model = learner1DBH._process_controler(dico_model)
dico_model['control_obj'] = learner1DBH._build_control_from_string(
dico_model['control_obj'], None, context_dico=dico_model)
### Run it
model = bh1d.BH1D(**dico_model)
res = model([], trunc_res=False)
basis = model._ss
state_t = model.EvolutionPopAdiab(nb_ev=basis.Ns)
### Get data
t = model.t_array
st = state_t
en = model.adiab_en
cf = model.adiab_cf
EV = model.adiab_evect
dico_linear['control_obj'] = linear
dico_linear['T'] = T_long
model_linear = bh1d.BH1D(**dico_linear)
model_linear._ss.Ns
res_test_linear = model_linear([], trunc_res=False)
min_gap = model_linear.FindMinDelta()
print(min_gap)
state_tmp = model_linear.EvolutionPopAdiab(nb_ev=10)
model_linear.plot_pop_adiab(plot_gap=True)
#plot_pop_adiab(model_linear)
#==============================================================================
# Try to reach GS at the end
#==============================================================================
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_args = {
'algo': 'BO2',
'maxiter': 100,
'num_cores': 4,
'init_obj': 75,
'exploit_steps': 49,
'acq': 'EI',
'optim_num_anchor': 25,
'optim_num_samples': 10000
}
optim = Learner.learner_Opt(model=model, **optim_args)
resBO2 = optim(track_learning=True)
import sys
sys.path.append("../../../QuantumSimulation")
from QuantumSimulation.ToyModels.BH.learn_1DBH import learner1DBH
from QuantumSimulation.ToyModels.BH import BH1D as bh1d
from QuantumSimulation.Utility.Optim import Learner, pFunc_base
import copy
optim_type = 'BO2'
# Create a model
fom = ['f2t2:neg_fluence:0.0001_smooth:0.05']
T=4.889428431607287
dico_simul = {'L':5, 'Nb':5, 'mu':0, 'sps':5, '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_pwlr15'}
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)
#==============================================================================
# ***OPTIMIZATION***
#==============================================================================
optim_args = {'algo': 'BO2', 'maxiter':500, 'num_cores':4, 'init_obj':75, 'exploit_steps':49,
'acq':'EI', 'optim_num_anchor':25, 'optim_num_samples':10000}
optim = Learner.learner_Opt(model = model, **optim_args)
resBO2 = optim(track_learning=True)
resBO2['last_func'] = model.control_fun
res = resBO2
print(res.keys())
