Use C++ armadillo Expectation Maximization for Gaussian mixture model - c++

Question

I am trying to use armadillo's expectation maximization maximization gmm_diag class, but when I try to compile it I get "error gmm_diag was not declared in this scope".
My code is as follows:
#include <stdio.h>
#include <iostream>
#include <vector>
#include <armadillo>
#include <omp.h>
using namespace std;
using namespace arma;
int main()
{
// create synthetic data with 2 Gaussians
uword N = 10000;
uword d = 5;
mat data(d, N, fill::zeros);
vec mean0 = linspace<vec>(1,d,d);
vec mean1 = mean0 + 2;
uword i = 0;
while(i < N)
{
if(i < N) { data.col(i) = mean0 + randn<vec>(d); ++i; }
if(i < N) { data.col(i) = mean0 + randn<vec>(d); ++i; }
if(i < N) { data.col(i) = mean1 + randn<vec>(d); ++i; }
}
gmm_diag model;
model.learn(data, 2, maha_dist, random_subset, 10, 5, 1e-10, true);
model.means.print("means:");
double scalar_likelihood = model.log_p( data.col(0) );
rowvec set_likelihood = model.log_p( data.cols(0,9));
double overall_likelihood = model.avg_log_p(data);
uword gaus_id = model.assign( data.col(0), eucl_dist );
urowvec gaus_ids = model.assign( data.cols(0,9), prob_dist );
urowvec hist1 = model.raw_hist (data, prob_dist);
rowvec hist2 = model.norm_hist(data, eucl_dist);
model.save("my_model.gmm");
// the table is now initialized
}