if __name__ == '__main__':
from smc_sampler_functions.functions_smc_main import repeat_sampling
samplers_list_dict_adaptive = [hmcdict_ft_adaptive, hmcdict_ours_adaptive, rwdict, maladict]
# define the target distributions
from smc_sampler_functions.target_distributions import priorlogdens, priorgradlogdens, priorsampler
from smc_sampler_functions.target_distributions import targetlogdens_logistic, targetgradlogdens_logistic, f_dict_logistic_regression
from smc_sampler_functions.target_distributions import targetlogdens_probit, targetgradlogdens_probit
priordistribution = {'logdensity' : priorlogdens, 'gradlogdensity' : priorgradlogdens, 'priorsampler': priorsampler}
targetdistribution = {'logdensity' : targetlogdens_logistic, 'gradlogdensity' : targetgradlogdens_logistic, 'target_name': 'logistic'}
parameters_logistic = f_dict_logistic_regression(dim)
parameters.update(parameters_logistic)
target_dist_list = [targetdistribution]
for target_dist in target_dist_list:
temperedist = sequence_distributions(parameters, priordistribution, target_dist)
res_repeated_sampling_adaptive, res_first_iteration_adaptive = repeat_sampling(samplers_list_dict_adaptive, temperedist, parameters, M_num_repetions=M_num_repetions, save_res=True, save_res_intermediate=False, save_name = target_dist['target_name'])
#import ipdb; ipdb.set_trace()
plt.show()
# mean
print(mh_sampler['particles'][0, 0, int(T_time / 2):].mean())
# acceptance rate
print(
np.mean(
np.diff(mh_sampler['particles'][0, 1,
int(T_time / 2):], 1) > 0))
plt.hist(mh_sampler['particles'][0, 1, int(T_time / 2):])
plt.show()
import ipdb
ipdb.set_trace()
from smc_sampler_functions.functions_smc_main import repeat_sampling
#samplers_list_dict = [rwdict, hmcdict2, maladict, rwdict]
samplers_list_dict = [hmcdict2, rwdict, maladict]
#samplers_list_dict = [hmcdict1, hmcdict2]
res_repeated_sampling, res_first_iteration = repeat_sampling(
samplers_list_dict,
temperedist,
parameters,
M_num_repetions=M_num_repetions,
save_res=True,
save_name=targetdistribution['target_name'])
from smc_sampler_functions.functions_smc_plotting import plot_repeated_simulations, plot_results_single_simulation
plot_repeated_simulations(res_repeated_sampling)
plot_results_single_simulation(res_first_iteration)
import ipdb
ipdb.set_trace()