/* nag_fit_dim1_spline_auto (e02bec) Example Program.
 *
 * Copyright 2022 Numerical Algorithms Group.
 *
 * Mark 28.6, 2022.
 *
 */

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

int main(void) {
  Integer exit_status = 0, j, m, nest, r;
  NagError fail;
  Nag_Comm warmstartinf;
  Nag_Spline spline;
  Nag_Start start;
  double fp, s, *sp = 0, txr, *weights = 0, *x = 0, *y = 0;

  INIT_FAIL(fail);

  /* Initialize spline */
  spline.lamda = 0;
  spline.c = 0;

  warmstartinf.nag_w = 0;
  warmstartinf.nag_iw = 0;

  printf("nag_fit_dim1_spline_auto (e02bec) Example Program Results\n");
  scanf("%*[^\n]"); /* Skip heading in data file */
  /* Input the number of data points, followed by the data
   * points x, the function values y and the weights w.
   */
  scanf("%" NAG_IFMT "", &m);
  nest = m + 4;
  if (m >= 4) {
    if (!(weights = NAG_ALLOC(m, double)) || !(x = NAG_ALLOC(m, double)) ||
        !(y = NAG_ALLOC(m, double)) || !(sp = NAG_ALLOC(2 * m - 1, double))) {
      printf("Allocation failure\n");
      exit_status = -1;
      goto END;
    }
  } else {
    printf("Invalid m.\n");
    exit_status = 1;
    return exit_status;
  }
  start = Nag_Cold;
  for (r = 0; r < m; r++)
    scanf("%lf%lf%lf", &x[r], &y[r], &weights[r]);
    /* Read in successive values of s until end of data file. */
  while (scanf("%lf", &s) != EOF)
  {
    /* Determine the spline approximation. */
    /* nag_fit_dim1_spline_auto (e02bec).
     * Least squares cubic spline curve fit, automatic knot
     * placement, one variable
     */
    nag_fit_dim1_spline_auto(start, m, x, y, weights, s, nest, &fp,
                             &warmstartinf, &spline, &fail);
    if (fail.code != NE_NOERROR) {
      printf("Error from nag_fit_dim1_spline_auto (e02bec).\n%s\n",
             fail.message);
      exit_status = 1;
      goto END;
    }

    /* Evaluate the spline at each x point and midway
     * between x points, saving the results in sp.
     */
    for (r = 0; r < m; r++) {
      /* nag_fit_dim1_spline_eval (e02bbc).
       * Evaluation of fitted cubic spline, function only
       */
      nag_fit_dim1_spline_eval(x[r], &sp[(r - 1) * 2 + 2], &spline, &fail);
      if (fail.code != NE_NOERROR) {
        printf("Error from nag_fit_dim1_spline_auto (e02bec).\n%s\n",
               fail.message);
        exit_status = 1;
        goto END;
      }
    }

    for (r = 0; r < m - 1; r++) {
      txr = (x[r] + x[r + 1]) / 2;
      /* nag_fit_dim1_spline_eval (e02bbc), see above. */
      nag_fit_dim1_spline_eval(txr, &sp[r * 2 + 1], &spline, &fail);
      if (fail.code != NE_NOERROR) {
        printf("Error from nag_fit_dim1_spline_eval (e02bbc).\n%s\n",
               fail.message);
        exit_status = 1;
        goto END;
      }
    }
    /* Output the results. */
    printf("\nCalling with smoothing factor s = %12.3e\n", s);
    printf("\nNumber of distinct knots = %" NAG_IFMT "\n\n", spline.n - 6);
    printf("Distinct knots located at \n\n");
    for (j = 3; j < spline.n - 3; j++)
      printf("%8.4f%s", spline.lamda[j],
             (j - 3) % 6 == 5 || j == spline.n - 4 ? "\n" : " ");
    printf("\n\n    J      B-spline coeff c\n\n");
    for (j = 0; j < spline.n - 4; ++j)
      printf("  %3" NAG_IFMT "  %13.4f\n", j + 1, spline.c[j]);
    printf("\nWeighted sum of squared residuals fp = %12.3e\n", fp);
    if (fp == 0.0)
      printf("The spline is an interpolating spline\n");
    else if (spline.n == 8)
      printf("The spline is the weighted least squares cubic"
             "polynomial\n");
    start = Nag_Warm;
  }
  /* Free memory allocated in spline and warmstartinf */
END:
  NAG_FREE(spline.lamda);
  NAG_FREE(spline.c);
  NAG_FREE(warmstartinf.nag_w);
  NAG_FREE(warmstartinf.nag_iw);
  NAG_FREE(weights);
  NAG_FREE(x);
  NAG_FREE(y);
  NAG_FREE(sp);
  return exit_status;
}