trajectories_sig = np.zeros([2, nb_simul, nb_iterations])
trajectories_tau = np.zeros([2, nb_simul, nb_iterations])
for idx_simul in range(1, nb_simul + 1):
print(idx_simul)
Y = get_Y(path='../simulations/simulation{0}.csv'.format(idx_simul), T=100)
info = pkl.load(
open('N_20/python_bayesopt{0}_4.pkl'.format(idx_simul), 'rb'))
r = .5 * (10**info[1][0] - 10**0.) / (10**1. - 10**0.)
sigma = .5 * (10**info[1][1] - 10**0.) / (10**1. - 10**0.)
tau = .5 * (10**info[1][2] - 10**0.) / (10**1. - 10**0.)
trajectories_lik[0, idx_simul - 1] = get_traj(
'N_20/python_bayesopt{0}_4.dat'.format(idx_simul))
trajectories_lik_[0, idx_simul - 1] = get_traj_(
'N_20/python_bayesopt{0}_4.dat'.format(idx_simul))
trajectories_lik[1, idx_simul - 1] = np.asarray(
powerpack.traj_r(
'N_20/iterated_filtering_4{0}'.format(idx_simul)))[:, 0][:-1]
trajectories_r[1, idx_simul - 1] = np.exp(
np.asarray(
powerpack.traj_r(
'N_20/iterated_filtering_4{0}'.format(idx_simul)))[:, 2][:-1])
trajectories_sig[1, idx_simul - 1] = np.exp(
np.asarray(
powerpack.traj_r(
'N_20/iterated_filtering_4{0}'.format(idx_simul)))[:, 4][:-1])
trajectories_tau[1, idx_simul - 1] = np.exp(
np.asarray(
powerpack.traj_r(
'N_20/iterated_filtering_4{0}'.format(idx_simul)))[:, 5][:-1])
#indexes = np.asarray(get_traj_index('N_20/python_bayesopt{0}_4.dat'.format(idx_simul)))[:-1]
trajectories_r[0, idx_simul - 1] = get_params(
'N_20/python_bayesopt{0}_4.dat'.format(idx_simul))[0]
