Python Learner.learner_Opt示例

示例#1

    'likelihood': 'Binomial_' + str(nb_meas),
    'normalize_Y': False
})
optim_b_max_step = copy.copy(optim_b)
optim_b_max_step['constraints'] = 'step_0.4_0_1'
optim_b_max_smooth = copy.copy(optim_b)
optim_b_max_smooth['constraints'] = 'smoothlin_0.1_0_1'

# ==================================================================
# Without constraints
# with binomial observations ()
#
#===================================================================
if (do_base):
    # perfect measurement
    optim = Learner.learner_Opt(model=model_base, **optim_base)
    res_base = optim(track_learning=True)
    p_base = res_base['params']
    p_base_exp = res_base['params_exp']
    func_base.theta = p_base
    func_base.plot_function(x_plot)
    model_base(p_base)

if (do_proj10):
    # binomial measurement, gaussian likelihood
    optim = Learner.learner_Opt(model=model_proj10, **optim_base)
    res_proj10 = optim(track_learning=True)
    p_proj10 = res_proj10['params']
    p_proj10_exp = res_proj10['params_exp']
    func_base.theta = p_proj10
    func_base.plot_function(x_plot)

示例#2

文件： test_crab.py 项目： FredericSauv/QuantumSimulation

}
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)

if (optim_type == 'BO2'):
    #BO
    optim_args = {
        'algo': 'BO2',
        'maxiter': 30,
        'num_cores': 1,
        'init_obj': 15,
        'acq': 'EI'
    }
    optim = Learner.learner_Opt(model=model, **optim_args)
    resBO2 = optim(track_learning=True)
    resBO2['last_func'] = model.control_fun
    print(resBO2)
    res = resBO2
    resBO2['opt_more']

if (optim_type == 'DE'):
    optim_args = {'algo': 'DE', 'popsize': 5, 'maxiter': 75}
    optim = Learner.learner_Opt(model=model, **optim_args)
    resDE = optim()
    print(resDE)
    res = resDE

if (optim_type == 'BO'):
    optim_args = {'algo': 'BO', 'maxiter': 250}

示例#3

文件： SFtoMI_study.py 项目： FredericSauv/QuantumSimulation

    'state_init': 'GS_i',
    'state_tgt': 'GS_inf',
    'fom': fom,
    'fom_print': True,
    'track_learning': True,
    'ctl_shortcut': 'owbds01_pwl15'
}
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)

try:
    func_used = pFunc_base.pFunc_base.read_func_from_file("SFtoMI_0")
except:
    optim = Learner.learner_Opt(model=model, **optim_args)
    resBO2 = optim(track_learning=True)
    resBO2['last_func'] = model.control_fun
    res = resBO2
    func_used = model.control_fun
    func_used.theta = res['params']
    if (save):
        func_used.save_to_file("SFtoMI_0")

#Testing
fom_test = fom + ['f2t2', 'fluence', 'smooth', 'varN']
dico_test = copy.copy(dico_simul)
dico_test['fom'] = fom_test
dico_test['track_learning'] = False
model_test = bh1d.BH1D(**dico_test)
optim_params = func_used.theta

示例#4

文件： double_well.py 项目： FredericSauv/QuantumSimulation

    '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,
    'mu': 0,
    'T': T,
    'dt': 0.01,

示例#5

# is there a better way
#===================================================================
optim_with_noise = True
optim_ideal = True
optim_with_noise_custom = True
optim_args = {
    'algo': 'BO2',
    'maxiter': 50,
    'num_cores': 4,
    'init_obj': 25,
    'acq': 'EI'
}
func_test = dico_no_noise['control_obj']

if (optim_with_noise):
    optim = Learner.learner_Opt(model=model_ensemble, **optim_args)
    res_ensemble = optim(track_learning=True)
    params_noise = res_ensemble['params']
    params_noise_exp = res_ensemble['params_exp']

if (optim_with_noise_custom):
    optim = Learner.learner_Opt(model=model_ensemble_custom, **optim_args)
    res_ensemble_custom = optim(track_learning=True)
    params_noise_custom = res_ensemble_custom['params']
    params_noise_custom_exp = res_ensemble_custom['params_exp']

if (optim_ideal):
    optim = Learner.learner_Opt(model=model_no_noise, **optim_args)
    res_no_noise = optim(track_learning=True)
    params_no_noise = res_no_noise['params']