void gammaexpo(){
printf("gamma/exponential\n");
apop_model *gamma = apop_model_set_parameters(apop_gamma, 1, 0.4);
apop_model *drawfrom = apop_model_set_parameters(apop_exponential, 0.4);
int draw_ct = 120;
apop_data *draws = apop_model_draws(drawfrom, draw_ct);
apop_model *gammaup = apop_update(draws, gamma, apop_exponential);
apop_model_show(gammaup);
gamma->more = apop_gamma;
gamma->log_likelihood = fake_ll;
Apop_settings_add_group(gamma, apop_mcmc, .burnin=.1, .periods=1e5,
.proposal=apop_model_set_parameters(apop_normal, 1, .001));
apop_model *upd = apop_update(draws, gamma, apop_exponential);
apop_model *gammaed = apop_estimate(upd->data, apop_gamma);
apop_model_show(gammaed);
deciles(gammaed, gammaup, 3);
Apop_settings_add_group(gamma, apop_mcmc, .burnin=.1, .periods=1e5,
.proposal=apop_model_set_parameters(apop_normal, 1, .01));
gamma->log_likelihood = NULL;
apop_model *upd_r = apop_update(draws, gamma, apop_exponential);
apop_model *gammafied2 = apop_estimate(apop_data_pmf_expand(upd_r->data, 2000), apop_gamma);
deciles(gammafied2, gammaup, 5);
}
void betabinom(){
apop_model *beta = apop_model_set_parameters(apop_beta, 10, 5);
apop_model *drawfrom = apop_model_copy(apop_multinomial);
drawfrom->parameters = apop_data_falloc((2), 30, .4);
drawfrom->dsize = 2;
int draw_ct = 80;
apop_data *draws = apop_model_draws(drawfrom, draw_ct);
apop_model *betaup = apop_update(draws, beta, apop_binomial);
apop_model_show(betaup);
beta->more = apop_beta;
beta->log_likelihood = fake_ll;
apop_model *bi = apop_model_fix_params(apop_model_set_parameters(apop_binomial, 30, NAN));
apop_model *upd = apop_update(draws, beta, bi);
apop_model *betaed = apop_estimate(upd->data, apop_beta);
deciles(betaed, betaup, 1);
beta->log_likelihood = NULL;
apop_model *upd_r = apop_update(draws, beta, bi);
betaed = apop_estimate(apop_data_pmf_expand(upd_r->data, 2000), apop_beta);
deciles(betaed, betaup, 1);
apop_data *d2 = apop_model_draws(upd, draw_ct*2);
apop_model *d2m = apop_estimate(d2, apop_beta);
deciles(d2m, betaup, 1);
}
void gammafish(){
printf("gamma/poisson\n");
apop_model *gamma = apop_model_set_parameters(apop_gamma, 1.5, 2.2);
apop_model *drawfrom = apop_model_set_parameters(apop_poisson, 3.1);
int draw_ct = 90;
apop_data *draws = apop_model_draws(drawfrom, draw_ct);
apop_model *gammaup = apop_update(draws, gamma, apop_poisson);
apop_model_show(gammaup);
gamma->more = apop_gamma;
gamma->log_likelihood = fake_ll;
apop_model *proposal = apop_model_fix_params(apop_model_set_parameters(apop_normal, NAN, 1));
proposal->parameters = apop_data_falloc((1), .9);
//apop_data_set(apop_settings_get(gamma, apop_mcmc, proposal)->parameters, .val=.9);
Apop_settings_add_group(gamma, apop_mcmc, .burnin=.1, .periods=1e4, .proposal=proposal);
apop_model *upd = apop_update(draws, gamma, apop_poisson);
apop_model *gammafied = apop_estimate(upd->data, apop_gamma);
deciles(gammafied, gammaup, 5);
//Apop_settings_add_group(beta, apop_mcmc, .burnin=.4, .periods=1e4);
gamma->log_likelihood = NULL;
apop_model *upd_r = apop_update(draws, gamma, apop_poisson);
apop_model *gammafied2 = apop_estimate(apop_data_pmf_expand(upd_r->data, 2000), apop_gamma);
deciles(gammafied2, gammaup, 5);
deciles(gammafied, gammafied2, 5);
}
void one_run(int grid_size, int pop_size){
printf("------ A run with a %i X %i grid and %i agents:\n", grid_size, grid_size, pop_size);
search_sim.dsize = pop_size;
apop_data_set(search_sim.parameters, 0, .val=grid_size);
apop_data_set(search_sim.parameters, 1, .val=pop_size);
apop_model *model_out = apop_estimate(apop_model_draws(&search_sim, 1000), weibull);
apop_model_show(model_out);
}
int main(){
apop_data *locations = apop_data_falloc((5, 2),
1.1, 2.2,
4.8, 7.4,
2.9, 8.6,
-1.3, 3.7,
2.9, 1.1);
Apop_model_add_group(min_distance, apop_mle, .method= "NM simplex",
.tolerance=1e-5);
apop_model *est = apop_estimate(locations, min_distance);
apop_model_show(est);
}
int main(){
char outfile[] = "scatter.gplot";
apop_db_open("data-metro.db");
apop_data *data = apop_query_to_data("select riders, year from riders where station like 'Silver%%' and riders>0");
apop_db_close();
//The regression destroys your data, so copy it first.
apop_data *data_copy = apop_data_copy(data);
//Run OLS, display results on terminal
apop_model *est = apop_estimate(data, apop_OLS);
apop_model_show(est);
//Prep the file with a header, then call the function.
FILE *f = fopen(outfile, "w");
fprintf(f,"set term postscript;\n set output \"scatter.eps\"\n set yrange [0:*]\n");
apop_plot_line_and_scatter(data_copy, est, .output_pipe=f);
fclose(f);
}