/* nag_robust_corr_estim (g02hkc) Example Program.
 *
 * Copyright 1996 Numerical Algorithms Group.
 *
 * Mark 4, 1996.
 * Mark 8 revised, 2004.
 *
 */

#include <nag.h>
#include <stdio.h>
#include <nag_stdlib.h>
#include <nagg02.h>

#define X(I,J) x[(I-1)*tdx + J-1]
int main(void)
{
  Integer exit_status=0, i, iter, j, k, l1, l2, m, max_iter, n, print_iter;
  Integer tdx;
  NagError fail;
  double *cov=0, eps, *theta=0, tol, *x=0;

  INIT_FAIL(fail);

  Vprintf("nag_robust_corr_estim (g02hkc) Example Program Results\n\n");

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

  /* Read in the dimensions of X */
  Vscanf("%ld %ld %*[^\n]\n", &n, &m);

  if (n>1 && (m>=1 && m<=n))
    {
      if ( !( x = NAG_ALLOC((n)*(m), double)) ||
           !( theta = NAG_ALLOC(m, double)) ||
           !( cov = NAG_ALLOC(m*(m+1)/2, double)) )
        {
          Vprintf("Allocation failure\n");
          exit_status = -1;
          goto END;
        }
      tdx = m;
    }
  else
    {
      Vprintf("Invalid n or m.\n");
      exit_status = 1;
      return exit_status;
    }
  /* Read in the x matrix */
  for (i = 1; i <= n; ++i)
    {
      for (j = 1; j <= m; ++j)
        Vscanf("%lf", &X(i,j));
      Vscanf("%*[^\n]\n");
    }

  /* Read in value of eps */
  Vscanf("%lf%*[^\n]\n", &eps);

  /* Set up remaining parameters */
  max_iter = 100;
  tol = 5e-5;

  /* Set print_iter to positive value for iteration moiteroring */
  print_iter = 1;
  /* nag_robust_corr_estim (g02hkc).
   * Robust estimation of a correlation matrix, Huber's weight
   * function
   */
  nag_robust_corr_estim(n, m, x, tdx, eps, cov, theta, max_iter, print_iter, "",
                        tol, &iter, &fail);
  if (fail.code != NE_NOERROR)
    {
      Vprintf("Error from nag_robust_corr_estim (g02hkc).\n%s\n", fail.message);
      exit_status = 1;
      goto END;
    }

  Vprintf("\n\nnag_robust_corr_estim (g02hkc) required %ld iterations "
          "to converge\n\n", iter);
  Vprintf("Covariance matrix\n");
  l2 = 0;
  for (j = 1; j <= m; ++j)
    {
      l1 = l2 + 1;
      l2 += j;
      for (k = l1; k <= l2; ++k)
        Vprintf("%10.3f", cov[k - 1]);
      Vprintf("\n");
    }
  Vprintf("\n\ntheta\n");
  for (j = 1; j <= m; ++j)
    Vprintf("%10.3f\n", theta[j - 1]);

 END:
  if (x) NAG_FREE(x);
  if (theta) NAG_FREE(theta);
  if (cov) NAG_FREE(cov);
  return exit_status;
}