/* nag_mv_gaussian_mixture (g03gac) Example Program.
 *
 * Copyright 2023 Numerical Algorithms Group.
 *
 * Mark 29.0, 2023.
 */
#include <math.h>
#include <nag.h>
#include <stdio.h>
#include <string.h>

#define S(I, J, K)                                                             \
  s[I - 1 + (J - 1) * (sopt == Nag_GroupVar ? ng : nvar) +                     \
    (K - 1) * nvar * nvar]
#define X(I, J) x[(I - 1) * tdx + J - 1]
#define PROB(I, J) prob[(I - 1) * tdprob + J - 1]

int main(void) {
  /* Integer scalar and array declarations */
  Integer exit_status = 0, i, j, lens, m, n, ng, niter, nvar, riter, tdprob,
          tdx;
  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 (g03gac) 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 */
  tdx = nvar;
  tdprob = 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:
    lens = nvar * nvar * ng;
    break;
  case Nag_PooledCovar:
    lens = nvar * nvar;
    break;
  case Nag_GroupVar:
    lens = nvar * ng;
    break;
  case Nag_PooledVar:
    lens = nvar;
    break;
  case Nag_OverallVar:
    lens = 1;
    break;
  }

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

  /* Data matrix X */
  for (i = 1; i <= n; i++)
    for (j = 1; j <= m; j++)
      scanf("%lf", &X(i, j));
  scanf("%*[^\n] ");

  /* Included variables */
  if (nvar != m) {
    for (j = 1; j <= m; j++)
      scanf("%" NAG_IFMT "", &isx[j - 1]);
    scanf("%*[^\n] ");
  }

  /* Optionally read initial probabilities of group membership */
  if (popt == Nag_FALSE) {
    for (i = 1; i <= n; i++)
      for (j = 1; j <= ng; j++)
        scanf("%lf", &PROB(i, j));
    scanf("%*[^\n] ");
  }

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

  /* Fit the model */
  /* nag_mv_gaussian_mixture (g03gac).
   * Computes a Gaussian mixture model
   */
  INIT_FAIL(fail);
  nag_mv_gaussian_mixture(n, m, x, tdx, isx, nvar, ng, popt, prob, tdprob,
                          &niter, riter, w, g, sopt, s, f, tol, &loglik, &fail);

  if (fail.code != NE_NOERROR) {
    printf("nag_mv_gaussian_mixture (g03gac) failed.\n%s\n", fail.message);
    exit_status = 1;
    goto END;
  }

  /* Results */
  /* 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,
                                 "Mixing proportions", NULL, &fail);

  nag_file_print_matrix_real_gen(Nag_RowMajor, Nag_GeneralMatrix,
                                 Nag_NonUnitDiag, nvar, ng, g, ng,
                                 "\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(1, 1, i), nvar, "\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, nvar,
        "\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, ng,
                                   "\n Groupwise Variance", NULL, &fail);
    break;
  case Nag_PooledVar:
    nag_file_print_matrix_real_gen(Nag_RowMajor, Nag_GeneralMatrix,
                                   Nag_NonUnitDiag, nvar, 1, s, 1,
                                   "\n Pooled Variance", NULL, &fail);
    break;
  case Nag_OverallVar:
    printf("\n Overall Variance = %g\n", S(1, 1, 1));
    break;
  }

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

  nag_file_print_matrix_real_gen(Nag_RowMajor, Nag_GeneralMatrix,
                                 Nag_NonUnitDiag, n, ng, prob, ng,
                                 "\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;
}