#include <stdlib.h>
#include <math.h>
#include <gsl/gsl_rstat.h>
#include <gsl/gsl_vector.h>
#include <gsl/gsl_statistics_double.h>

#include "bootstrap.h"


/* Function that compares doubles for sorting:
 * x > y  ⇒ 1
 * x == y ⇒ 0
 * x < y  ⇒ -1
 */
int cmp_double (const void *xp, const void *yp) {
  double x = *(double*)xp,
         y = *(double*)yp;
  return x > y ? 1 : (x == y ? 0 : -1);
}


/* Returns the (rounded) mean index of all
 * components of `v` that are equal to `x`.
 * This function is used to handle duplicate
 * data (called "ties") in `hsm()`.
 */
size_t mean_index(gsl_vector *v, double x) {
  gsl_rstat_workspace *w = gsl_rstat_alloc();

  for (size_t i = 0; i < v->size; i++) {
    if (gsl_vector_get(v, i) == x)
      gsl_rstat_add((double)i, w);
  }
  int mean = gsl_rstat_mean(w);
  gsl_rstat_free(w);

  return round(mean);
}


/* Computes the half-sample mode (also called the Robertson-Cryer
 * mode estimator) of the sample `x` containing `n` observations. 
 *
 * It is based on repeatedly finding the modal interval (interval
 * containing the most observations) of half of the sample.
 * This implementation is based on the `hsm()` function from the
 * modeest[1] R package.
 *
 * [1]: https://rdrr.io/cran/modeest/man/hsm.html
 */
double hsm(double *x, size_t n) {
  int i, k;
  gsl_vector *diffs_full = gsl_vector_calloc(n-n/2);

  /* Divide the sample in two halves and compute
   * the paired differences between the upper and
   * lower halves. The index of the min diff. gives
   * the start of the modal interval. Repeat on the
   * new interval until three or less points are left.
   */
  while (n > 3) {
    k = n/2;

    // lower/upper halves of x
    gsl_vector upper = gsl_vector_view_array(x+k, n-k).vector;
    gsl_vector lower = gsl_vector_view_array(x,   n-k).vector;

    // restrict diffs_full to length n-k
    gsl_vector diffs = gsl_vector_subvector(diffs_full, 0, n-k).vector;

    // compute the difference upper-lower
    gsl_vector_memcpy(&diffs, &upper);
    gsl_vector_sub(&diffs, &lower);

    // find minimum while handling ties
    i = mean_index(&diffs, gsl_vector_min(&diffs));

    /* If the minumium difference is 0 we found
     * the hsm so we set n=1 to break the loop.
     */
    x += i;
    n = (gsl_vector_get(&diffs, i) == 0) ? 1 : k;
  }

  // free memory
  gsl_vector_free(diffs_full);

  /* If the sample is has three points the hsm
   * is the average of the two closer ones.
   */
  if (n == 3) {
    if (2*x[1] - x[0] - x[2] > 0)
      return gsl_stats_mean(x+1, 1, 2);
    return gsl_stats_mean(x, 1, 2);
  }

  /* Otherwise (smaller than 3) the hsm is just
   * the mean of the points.
   */
  return gsl_stats_mean(x, 1, n);
}


/* Computes an approximation to the asymptotic median
 * and its standard deviation by bootstrapping (ie
 * repeated resampling) the original `sample`, `boots`
 * times.
 *
 * The functions returns an `uncert` pair of mean and
 * stdev of the medians computed on each sample.
 */
uncert bootstrap_median(
    const gsl_rng *r,
    double *sample, size_t n,
    int boots) {

  /* We use a running statistics to not 
   * store the full resampled array.
   */
  gsl_rstat_workspace* w = gsl_rstat_alloc();

  double *values = calloc(boots, sizeof(double));

  for (size_t i = 0; i < boots; i++) {
    /* The sampling is simply done by generating
     * an array index uniformly in [0, n-1].
     */
    for (size_t j = 0; j < n; j++) {
      size_t choice = gsl_rng_uniform_int(r, n);
      gsl_rstat_add(sample[choice], w);
    }
    values[i] = gsl_rstat_median(w);
  }

  /* Compute mean and stdev of the medians
   * of each newly bootstrapped sample.
   */
  uncert median;
  median.n = gsl_stats_mean(values, 1, boots);
  median.s = gsl_stats_sd(values, 1, boots);

  // free memory
  free(values);

  return median;
}


/* Computes an approximation to the asymptotic mode
 * and its standard deviation by bootstrapping (ie
 * repeated resampling) the original `sample`, `boots`
 * times.
 *
 * The functions returns an `uncert` pair of mean and
 * stddev of the modes computed on each sample.
 */
uncert bootstrap_mode(
    const gsl_rng *r,
    double *sample, size_t n,
    int boots) {

  double *values = calloc(boots, sizeof(double));
  double *boot = calloc(n, sizeof(double));

  for (size_t i = 0; i < boots; i++) {
    /* The sampling is simply done by generating
     * an array index uniformely in [0, n-1].
     */
    for (size_t j = 0; j < n; j++) {
      size_t choice = gsl_rng_uniform_int(r, n);
      boot[j] = sample[choice];
    }
    qsort(boot, n, sizeof(double), cmp_double);
    values[i] = hsm(boot, n);
  }

  /* Compute mean and stddev of the modes
   * of each newly bootstrapped sample.
   */
  uncert mode;
  mode.n = gsl_stats_mean(values, 1, boots);
  mode.s = gsl_stats_sd(values, 1, boots);

  // free memory
  free(values);
  free(boot);

  return mode;
}