NAG Library Manual, Mark 29.2
Interfaces:  FL   CL   CPP   AD 

NAG CL Interface Introduction
Example description
/* nag_mv_gaussian_mixture_ld (g03gbc) Example Program.
 *
 * Copyright 2023 Numerical Algorithms Group.
 *
 * Mark 29.2, 2023.
 */
#include <math.h>
#include <nag.h>
#include <stdio.h>
#include <string.h>

int main(void) {
  /* Integer scalar and array declarations */
  Integer exit_status = 0, i, j, lens, m, n, ng, niter, nvar, riter, tdprob,
    pdx, pds, sds, tds, pdf, pdg;
  Integer s1, s2, x1, g1, f1;
  Integer *isx = 0;

  /* Double scalar and array declarations */
  double loglik, tol;
  double *f = 0, *g = 0, *prob = 0, *s = 0, *w = 0, *x = 0;
  /* NAG structures */
  Nag_Boolean popt;
  Nag_VarCovar sopt;
  NagError fail;

  /* Character scalar and array declarations */
  char nag_enum_popt[30 + 1], nag_enum_sopt[30 + 1];

  printf("nag_mv_gaussian_mixture_ld (g03gbc) Example Program Results\n\n");
  fflush(stdout);

  /* Skip heading in data file */
  scanf("%*[^\n] ");

  /* Problem size */
  scanf("%" NAG_IFMT "", &n);
  scanf("%" NAG_IFMT "", &m);
  scanf("%" NAG_IFMT "", &nvar);
  scanf("%*[^\n] ");

  /* Number of groups */
  scanf("%" NAG_IFMT "", &ng);
  scanf("%*[^\n] ");

  /* Scaling option */
  scanf("%30s", nag_enum_sopt);
  scanf("%*[^\n] ");

  /* Initial probabilities option */
  scanf("%30s", nag_enum_popt);
  scanf("%*[^\n] ");

  /* Maximum number of iterations */
  scanf("%" NAG_IFMT "", &niter);
  scanf("%*[^\n] ");

  /* Principal dimensions */
  x1 = m;
  g1 = ng;
  f1 = ng;
  pdx = 2*m;
  tdprob = ng;
  pdf = 2*ng;
  pdg = 2*ng;

  s1 = nvar;
  s2 = nvar;
  tds = ng;

  /* nag_enum_name_to_value (x04nac).
   * Converts NAG enum member name to value
   */
  popt = (Nag_Boolean)nag_enum_name_to_value(nag_enum_popt);
  sopt = (Nag_VarCovar)nag_enum_name_to_value(nag_enum_sopt);

  /* Variance/covariance array */
  switch (sopt) {
  case Nag_GroupCovar:
    break;
  case Nag_PooledCovar:
    break;
  case Nag_GroupVar:
    s2 = ng;
    break;
  case Nag_PooledVar:
    s2 = 1;
    break;
  case Nag_OverallVar:
    s1 = 1;
    s2 = 1;
    break;
  }
  pds = 2*s1;
  sds = 2*s2;
  lens = pds*sds*tds;

  if (!(x = NAG_ALLOC(n * pdx, double)) ||
      !(prob = NAG_ALLOC(n * tdprob, double)) ||
      !(g = NAG_ALLOC(pdg * nvar, double)) || !(w = NAG_ALLOC(2*ng, double)) ||
      !(isx = NAG_ALLOC(m, Integer)) || !(f = NAG_ALLOC(pdf * n, double)) ||
      !(s = NAG_ALLOC(lens, double))) {
    printf("Allocation failure\n");
    exit_status = -1;
    goto END;
  }

  /* Data matrix X */
  for (i = 0; i < n; i++) {
    for (j = 0; j < m; j++) {
      scanf("%lf", &x[i*pdx+j]);
      x[i*pdx+j+m] = x[i*pdx+j];
    }
  }
  scanf("%*[^\n] ");


  /* Included variables */
  if (nvar != m) {
    for (j = 0; j < m; j++)
      scanf("%" NAG_IFMT "", &isx[j]);
    scanf("%*[^\n] ");
  }

  /* Optimization parameters */
  tol = 0.0;
  riter = 5;

  /* Fit the model */
  /* nag_mv_gaussian_mixture_ld (g03gbc).
   * Computes a Gaussian mixture model onto leading submatrices
   * First Call using sopt = Nag_GroupCovar and
   *                  popt = Nag_TRUE (probabilities set internally)
   */

  INIT_FAIL(fail);
  g03gbc(n, m, x, pdx, isx, nvar, ng, Nag_TRUE, prob, tdprob, &niter,
	 riter, w, g, pdg, Nag_GroupCovar, s, pds, sds, f, pdf, tol, &loglik,
	 &fail);

  if (fail.code != NE_NOERROR) {
    printf("nag_mv_gaussian_mixture_ld (g03gbc) failed 1.\n%s\n", fail.message);
    exit_status = 1;
    goto END;
  } else {
    printf("First call to nag_mv_gaussian_mixture_ld was successful\n\n");
  }

  /* Optionally read initial probabilities of group membership */
  if (popt == Nag_FALSE) {
    for (i = 0; i < n; i++)
      for (j = 0; j < ng; j++)
        scanf("%lf", &prob[j+i*tdprob]);
    scanf("%*[^\n] ");
  }

  /* Fit the model */
  /* nag_mv_gaussian_mixture_ld (g03gbc).
   * Computes a Gaussian mixture model onto trailing submatrices
   */
  g03gbc(n, m, &x[x1], pdx, isx, nvar, ng, popt, prob, tdprob, &niter, riter,
	 &w[ng], &g[g1], pdg, sopt, &s[s1+s2*pds], pds, sds, &f[f1], pdf, tol,
	 &loglik, &fail);

  if (fail.code != NE_NOERROR) {
    printf("nag_mv_gaussian_mixture_ld (g03gbc) failed 2.\n%s\n", fail.message);
    exit_status = 2;
    goto END;
  }

  /* Results (from second call using trailing submatrices) */
  /* nag_file_print_matrix_real_gen (x04cac).
   * Print real general matrix (easy-to-use)
   */
  nag_file_print_matrix_real_gen(Nag_RowMajor, Nag_GeneralMatrix,
                                 Nag_NonUnitDiag, 1, ng, &w[ng], ng,
                                 "Mixing proportions", NULL, &fail);

  nag_file_print_matrix_real_gen(Nag_RowMajor, Nag_GeneralMatrix,
                                 Nag_NonUnitDiag, nvar, ng, &g[g1], pdg,
                                 "\n Group means", NULL, &fail);

  /* Variance/Covariance */
  switch (sopt) {
  case Nag_GroupCovar:
    for (i = 1; i <= ng; i++) {
      nag_file_print_matrix_real_gen(
          Nag_RowMajor, Nag_GeneralMatrix, Nag_NonUnitDiag, nvar, nvar,
          &s[s1+s2*pds+i*pds*sds], pds, "\n Variance-covariance matrix", NULL,
	  &fail);
    }
    break;
  case Nag_PooledCovar:
    nag_file_print_matrix_real_gen(
        Nag_RowMajor, Nag_GeneralMatrix, Nag_NonUnitDiag, nvar, nvar,
	&s[s1+s2*pds], pds, "\n Pooled Variance-covariance matrix", NULL,
	&fail);
    break;
  case Nag_GroupVar:
    nag_file_print_matrix_real_gen(Nag_RowMajor, Nag_GeneralMatrix,
                                   Nag_NonUnitDiag, nvar, ng, &s[s1+s2*pds],
				   pds, "\n Groupwise Variance", NULL, &fail);
    break;
  case Nag_PooledVar:
    nag_file_print_matrix_real_gen(Nag_RowMajor, Nag_GeneralMatrix,
                                   Nag_NonUnitDiag, nvar, 1, &s[s1+s2*pds],
				   pds, "\n Pooled Variance", NULL, &fail);
    break;
  case Nag_OverallVar:
    printf("\n Overall Variance = %g\n", x[s1+s2*pds]);
    break;
  }

  nag_file_print_matrix_real_gen(Nag_RowMajor, Nag_GeneralMatrix,
                                 Nag_NonUnitDiag, n, ng, &f[f1], pdf,
				 "\n Densities", NULL, &fail);

  nag_file_print_matrix_real_gen(Nag_RowMajor, Nag_GeneralMatrix,
                                 Nag_NonUnitDiag, n, ng, prob, tdprob,
                                 "\n Membership probabilities", NULL, &fail);

  printf("\nNo. iterations: %" NAG_IFMT "\n", niter);
  printf("Log-likelihood: %g\n\n", loglik);

END:
  NAG_FREE(f);
  NAG_FREE(g);
  NAG_FREE(prob);
  NAG_FREE(s);
  NAG_FREE(w);
  NAG_FREE(x);
  NAG_FREE(isx);

  return exit_status;
}