def create_mh_model( mcmc_params ):
if mcmc_params["type"] == "mh":
mh_model = BaseMetropolisHastingsModel( mcmc_params )
elif mcmc_params["type"] == "amh":
mh_model = AdaptiveMetropolisHastingsModel( mcmc_params )
elif mcmc_params["type"] == "sgldmh":
mh_model = SGLD_MetropolisHastingsModel( mcmc_params )
else:
raise NotImplementedError
return mh_model
state_params["response_groups"] = [ResponseModel( response_model_params )]
mcmc_params = {}
mcmc_params["priorrand"] = problem.theta_prior_rand
mcmc_params["logprior"] = problem.theta_prior_logpdf
mcmc_params["proposal_rand"] = problem.theta_proposal_rand
mcmc_params["logproposal"] = problem.theta_proposal_logpdf
mcmc_params["is_marginal"] = False
mcmc_params["nbr_samples"] = nbr_samples
mcmc_params["xi"] = 0.1
mcmc_params["M"] = 10
mcmc_params["deltaS"] = 1
mcmc_params["max_nbr_tries"] = 10
mcmc_params["acquisition_model"] = AcquisitionModel(acquistion_params)
model = MH_Model( mcmc_params)
#epsilon = 0.5
theta0 = problem.theta_prior_rand()
#theta0 *=0
#theta0 += 0.1
state = State( theta0, state_params )
recorder = Recorder(record_stats=True)
#recorder.record_state( state, state.nbr_sim_calls, accepted=True )
model.set_current_state( state )
model.set_recorder( recorder )
loglik = state.loglikelihood()
state_params["response_groups"] = [ResponseModel(response_model_params)]
mcmc_params = {}
mcmc_params["priorrand"] = problem.theta_prior_rand
mcmc_params["logprior"] = problem.theta_prior_logpdf
mcmc_params["proposal_rand"] = problem.theta_proposal_rand
mcmc_params["logproposal"] = problem.theta_proposal_logpdf
mcmc_params["is_marginal"] = False
mcmc_params["nbr_samples"] = nbr_samples
mcmc_params["xi"] = 0.4
mcmc_params["M"] = 10
mcmc_params["deltaS"] = 1
mcmc_params["max_nbr_tries"] = 1
mcmc_params["acquisition_model"] = AcquisitionModel(acquistion_params)
model = MH_Model(mcmc_params)
#epsilon = 0.5
theta0 = problem.theta_prior_rand()
#theta0 *=0
#theta0 += 0.1
state = State(theta0, state_params)
recorder = Recorder(record_stats=True)
#recorder.record_state( state, state.nbr_sim_calls, accepted=True )
model.set_current_state(state)
model.set_recorder(recorder)
loglik = state.loglikelihood()