dico_test['fom'] = fom_test
dico_test['track_learning'] = False
model_test = bh1d.BH1D(**dico_test)
model_test.control_fun = model.control_fun
optim_params = res['params']
res_test = model_test(optim_params)
#plot func optimal
func_used = model_test.control_fun
import numpy as np
x_to_plot = np.linspace(-0.2, T + 0.1, 500)
func_used.plot_function(x_to_plot)
## Benchmarking results vs Linear
ow = pFunc_base.OwriterYWrap(input_min=[-100, T],
input_max=[0, 100 + T],
output_ow=[0, 1])
linear = ow * pFunc_base.LinearFunc(bias=0, w=1 / T)
dico_test_linear = copy.copy(dico_test)
dico_test_linear['control_obj'] = linear
model_test_linear = bh1d.BH1D(**dico_test_linear)
res_test_linear = model_test_linear([])
## Looking at convergenge
if (optim_type == 'BO2'):
import matplotlib.pylab as plt
def dist(x, y):
d = np.squeeze(x) - np.squeeze(y)
return np.dot(d, d)
'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': 'owbds01_pwl15',
'kblock': 0,
'pblock': 1
}
ow = pFunc_base.OwriterYWrap(input_min=[-np.inf, T_long],
input_max=[0, np.inf],
output_ow=[0, 1])
linear = ow * pFunc_base.LinearFunc(bias=0, w=1 / T_long)
dico_linear = copy.copy(dico_simul)
dico_linear['control_obj'] = linear
dico_linear['T'] = T_long
model_linear = bh1d.BH1D(**dico_linear)
res_test_linear = model_linear([], trunc_res=False)
state_tmp = model_linear.EvolutionPopAdiab(nb_ev=2)
model_linear.plot_pop_adiab(plot_gap=True)
optim_args = {
'algo': 'BO2',
'maxiter': 100,
'num_cores': 4,
'init_obj': 75,
