/* nag_robust_m_regsn_wts (g02hbc) Example Program.
 *
 * Copyright 2002 Numerical Algorithms Group.
 *
 * Mark 7, 2002.
 * Mark 7b revised, 2004.
 */

#include <math.h>
#include <stdio.h>
#include <nag.h>
#include <nag_stdlib.h>
#include <nagg02.h>
#include <nags.h>
#include <nagx01.h>
#include <nagx02.h>
static double ucv(double t, Nag_Comm *comm);
int main(void)
{

  /* Scalars */
  double bd, bl, tol;
  Integer exit_status, i, j, k, l1, l2, m, maxit, mm, n, nit, nitmon;
  Integer pdx;
  NagError fail;
  Nag_OrderType order;
  Nag_Comm comm;

  /* Arrays */
  double *a=0, *x=0, *z=0;

#ifdef NAG_COLUMN_MAJOR
#define X(I,J) x[(J-1)*pdx + I - 1]
  order = Nag_ColMajor;
#else
#define X(I,J) x[(I-1)*pdx + J - 1]
  order = Nag_RowMajor;
#endif

  INIT_FAIL(fail);
  exit_status = 0;
  Vprintf("nag_robust_m_regsn_wts (g02hbc) Example Program Results\n");

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

  /* Read in the dimensions of X */
  Vscanf("%ld%ld%*[^\n] ", &n, &m);
  /* Allocate memory */
  if ( !(a = NAG_ALLOC(m*(m+1)/2, double)) ||
       !(x = NAG_ALLOC(n * m, double)) ||
       !(z = NAG_ALLOC(n, double)) )
    {
      Vprintf("Allocation failure\n");
      exit_status = -1;
      goto END;
    }

#ifdef NAG_COLUMN_MAJOR
  pdx = n;
#else
  pdx = m;
#endif

  /* Read in the X matrix */
  for (i = 1; i <= n; ++i)
    {
      for (j = 1; j <= m; ++j)
        Vscanf("%lf", &X(i,j));
      Vscanf("%*[^\n] ");
    }
  /* Read in the initial value of A */
  mm = (m + 1) * m / 2;
  for (j = 1; j <= mm; ++j)
    Vscanf("%lf", &a[j - 1]);
  Vscanf("%*[^\n] ");

  /* Set the values remaining parameters */
  bl = 0.9;
  bd = 0.9;
  maxit = 50;
  tol = 5e-5;
  /* Change nitmon to a positive value if monitoring information
   * is required
   */
  nitmon = 0;
  /* nag_robust_m_regsn_wts (g02hbc).
   * Robust regression, compute weights for use with
   * nag_robust_m_regsn_user_fn (g02hdc)
   */
  nag_robust_m_regsn_wts(order, ucv, n, m, x, pdx, a, z, bl, bd, tol, maxit,
                         nitmon, 0, &nit, &comm, &fail);
  if (fail.code != NE_NOERROR)
    {
      Vprintf("Error from nag_robust_m_regsn_wts (g02hbc).\n%s\n",
              fail.message);
      exit_status = 1;
      goto END;
    }

  Vprintf("nag_robust_m_regsn_wts (g02hbc) required %4ld iterations to "
          "converge\n\n", nit);
  Vprintf("Matrix A\n");
  l2 = 0;
  for (j = 1; j <= m; ++j)
    {
      l1 = l2 + 1;
      l2 += j;
      for (k = l1; k <= l2; ++k)
        Vprintf("%9.4f%s", a[k - 1], k%6 == 0 || k == l2 ?"\n":" ");
    }

  Vprintf("\n");
  Vprintf("Vector Z\n");
  for (i = 1; i <= n; ++i)
    Vprintf("%9.4f\n", z[i - 1]);

  /* Calculate Krasker-Welsch weights */
  Vprintf("\n");
  Vprintf("Vector of weights\n");
  for (i = 1; i <= n; ++i)
    {
      z[i - 1] = 1.0 / z[i - 1];
      Vprintf("%9.4f\n", z[i - 1]);
    }

 END:
  if (a) NAG_FREE(a);
  if (x) NAG_FREE(x);
  if (z) NAG_FREE(z);

  return exit_status;
}
static double ucv(double t, Nag_Comm *comm)
{

  /* Scalars */
  double pc, pd, q, q2;
  double ret_val;

  /* ucv function for Krasker-Welsch weights */
  ret_val = 1.0;
  if (t != 0.0)
    {
      q = 2.5 / t;
      q2 = q * q;
      /* nag_cumul_normal (s15abc).
       * Cumulative Normal distribution function P(x)
       */
      pc = nag_cumul_normal(q);
      /* nag_real_smallest_number (x02akc).
       * The smallest positive model number
       */
      if (q2 < -log(X02AKC))
        /* nag_pi (x01aac).
         * pi
         */
        pd = exp(-q2 / 2.0) / sqrt(X01AAC * 2.0);
      else
        pd = 0.0;
      ret_val = (pc * 2.0 - 1.0) * (1.0 - q2) + q2 - q * 2.0 * pd;
    }
  return ret_val;
}