void mcmc_reuse_data(mcmc * m, const mcmc * m_orig) {
IFDEBUGPARSER
debug("reusing data from other struct");
assert(m_orig->data != NULL);
m->data = m_orig->data;
mcmc_check(m);
}
void mcmc_load_data(mcmc * m, const char * datafilename) {
IFDEBUGPARSER
dump_s("looking for data in file", datafilename);
load_data(m, datafilename);
mcmc_check(m);
IFDEBUGPARSER
debug("loading data successful")
}
/**
* needs:
* params file
* BURN_IN_ITERATIONS
* start values (in params file)
**
* does:
* calibrate first chain (beta = 1)
* writes beta, stepwidths and start values as first line in file
* calibration_result
**
* provides:
* stepwidths of first chain (calibration_result)
* new params file (params_suggest)
* new start values (calibration_result)
**/
void calibrate_first() {
mcmc ** chains = setup_chains();
const double desired_acceptance_rate = TARGET_ACCEPTANCE_RATE;
const double max_ar_deviation = MAX_AR_DEVIATION;
const unsigned long burn_in_iterations = BURN_IN_ITERATIONS;
const unsigned long iter_limit = ITER_LIMIT;
const double mul = MUL;
printf("Starting markov chain calibration\n");
fflush(stdout);
calc_model(chains[0], NULL);
mcmc_check(chains[0]);
markov_chain_calibrate(chains[0], burn_in_iterations,
desired_acceptance_rate, max_ar_deviation, iter_limit, mul,
DEFAULT_ADJUST_STEP);
write_calibrations_file(chains, 1);
write_params_file(chains[0]);
}
/**
* needs:
* params file
* BURN_IN_ITERATIONS
* first line in calibration_result
* BETA_ALIGNMENT
* BETA_0
* SKIP_CALIBRATE_ALLCHAINS
**
* does:
* calibrate remaining chains (beta < 1)
* writes all betas, stepwidths and start values in file calibration_result
**
* provides:
* stepwidths of first chain (calibration_result)
* new params file (params_suggest)
* new start values (calibration_result)
**/
void calibrate_rest() {
int n_beta = N_BETA;
const double desired_acceptance_rate = TARGET_ACCEPTANCE_RATE;
const double max_ar_deviation = MAX_AR_DEVIATION;
double beta_0 = BETA_0;
const unsigned long burn_in_iterations = BURN_IN_ITERATIONS;
const unsigned long iter_limit = ITER_LIMIT;
const double mul = MUL;
unsigned int n_par;
int i;
gsl_vector * stepwidth_factors;
mcmc ** chains = setup_chains();
read_calibration_file(chains, 1);
printf("Calibrating chains\n");
fflush(stdout);
n_par = get_n_par(chains[0]);
stepwidth_factors = gsl_vector_alloc(n_par);
gsl_vector_set_all(stepwidth_factors, 1);
i = 1;
if (n_beta > 1) {
if (beta_0 < 0)
set_beta(chains[i], get_chain_beta(i, n_beta, calc_beta_0(
chains[0], stepwidth_factors)));
else
set_beta(chains[i], get_chain_beta(i, n_beta, beta_0));
gsl_vector_free(get_steps(chains[i]));
chains[i]->params_step = dup_vector(get_steps(chains[0]));
gsl_vector_scale(get_steps(chains[i]), pow(get_beta(chains[i]), -0.5));
set_params(chains[i], dup_vector(get_params_best(chains[0])));
calc_model(chains[i], NULL);
mcmc_check(chains[i]);
printf("Calibrating second chain to infer stepwidth factor\n");
printf("\tChain %2d - ", i);
printf("beta = %f\tsteps: ", get_beta(chains[i]));
dump_vectorln(get_steps(chains[i]));
fflush(stdout);
markov_chain_calibrate(chains[i], burn_in_iterations,
desired_acceptance_rate, max_ar_deviation, iter_limit, mul,
DEFAULT_ADJUST_STEP);
gsl_vector_scale(stepwidth_factors, pow(get_beta(chains[i]), -0.5));
gsl_vector_mul(stepwidth_factors, get_steps(chains[0]));
gsl_vector_div(stepwidth_factors, get_steps(chains[i]));
mem_free(chains[i]->additional_data);
}
printf("stepwidth factors: ");
dump_vectorln(stepwidth_factors);
if (beta_0 < 0) {
beta_0 = calc_beta_0(chains[0], stepwidth_factors);
printf("automatic beta_0: %f\n", beta_0);
}
fflush(stdout);
#pragma omp parallel for
for (i = 1; i < n_beta; i++) {
printf("\tChain %2d - ", i);
fflush(stdout);
chains[i]->additional_data
= mem_malloc(sizeof(parallel_tempering_mcmc));
set_beta(chains[i], get_chain_beta(i, n_beta, beta_0));
gsl_vector_free(get_steps(chains[i]));
chains[i]->params_step = dup_vector(get_steps(chains[0]));
gsl_vector_scale(get_steps(chains[i]), pow(get_beta(chains[i]), -0.5));
gsl_vector_mul(get_steps(chains[i]), stepwidth_factors);
set_params(chains[i], dup_vector(get_params_best(chains[0])));
calc_model(chains[i], NULL);
mcmc_check(chains[i]);
printf("beta = %f\tsteps: ", get_beta(chains[i]));
dump_vectorln(get_steps(chains[i]));
fflush(stdout);
#ifndef SKIP_CALIBRATE_ALLCHAINS
markov_chain_calibrate(chains[i], burn_in_iterations,
desired_acceptance_rate, max_ar_deviation, iter_limit, mul,
DEFAULT_ADJUST_STEP);
#else
burn_in(chains[i], burn_in_iterations);
#endif
}
gsl_vector_free(stepwidth_factors);
fflush(stdout);
printf("all chains calibrated.\n");
for (i = 0; i < n_beta; i++) {
printf("\tChain %2d - beta = %f \tsteps: ", i, get_beta(chains[i]));
dump_vectorln(get_steps(chains[i]));
}
write_calibration_summary(chains, n_beta);
write_calibrations_file(chains, n_beta);
}
