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#include <apop.h>
#define print_draws(mm) apop_data_print(apop_model_draws(mm, 20),\
.output_name= "draws-" #mm);
int main(){
apop_model *uniform_20 = apop_model_set_parameters(apop_uniform, 0, 20);
apop_data *d = apop_model_draws(uniform_20, 10);
//Estimate a Normal distribution from the data:
apop_model *N = apop_estimate(d, apop_normal);
print_draws(N);
//estimate a one-dimensional multivariate Normal from the data:
apop_model *mvN = apop_estimate(d, apop_multivariate_normal);
print_draws(mvN);
//fixed parameter list:
apop_model *std_normal = apop_model_set_parameters(apop_normal, 0, 1);
print_draws(std_normal);
//variable-size parameter list:
apop_model *std_multinormal = apop_model_copy(apop_multivariate_normal);
std_multinormal->msize1 =
std_multinormal->msize2 =
std_multinormal->vsize =
std_multinormal->dsize = 3;
std_multinormal->parameters = apop_data_falloc((3, 3, 3),
1, 1, 0, 0,
1, 0, 1, 0,
1, 0, 0, 1);
print_draws(std_multinormal);
//estimate a KDE using the defaults:
apop_model *k = apop_estimate(d, apop_kernel_density);
print_draws(k);
/*the documentation tells us that a KDE estimation consists of filling
an apop_kernel_density_settings group, so we can set it to use a
Normal(μ, 2) kernel via: */
apop_model *k2 = apop_model_copy_set(apop_kernel_density, apop_kernel_density,
.base_data=d,
.kernel = apop_model_set_parameters(apop_normal, 0, 2));
print_draws(k2);
}
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