/* rstat/test.c

 * 

 * Copyright (C) 2015 Patrick Alken

 * 

 * This program is free software; you can redistribute it and/or modify

 * it under the terms of the GNU General Public License as published by

 * the Free Software Foundation; either version 3 of the License, or (at

 * your option) any later version.

 * 

 * This program is distributed in the hope that it will be useful, but

 * WITHOUT ANY WARRANTY; without even the implied warranty of

 * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU

 * General Public License for more details.

 * 

 * You should have received a copy of the GNU General Public License

 * along with this program; if not, write to the Free Software

 * Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.

 */

#include <config.h>

#include <stdlib.h>

#include <math.h>

#include <string.h>

#include <gsl/gsl_math.h>

#include <gsl/gsl_rstat.h>

#include <gsl/gsl_sort.h>

#include <gsl/gsl_statistics.h>

#include <gsl/gsl_test.h>

#include <gsl/gsl_rng.h>

#include <gsl/gsl_randist.h>

#include <gsl/gsl_ieee_utils.h>

double *

random_data(const size_t n, gsl_rng *r)

{

  size_t i;

  double *data = malloc(n * sizeof(double));

  for (i = 0; i < n; ++i)

    data[i] = gsl_rng_uniform(r);

  return data;

}

void

test_basic(const size_t n, const double data[], const double tol)

{

  gsl_rstat_workspace *rstat_workspace_p = gsl_rstat_alloc();

  const double expected_mean = gsl_stats_mean(data, 1, n);

  const double expected_var = gsl_stats_variance(data, 1, n);

  const double expected_sd = gsl_stats_sd(data, 1, n);

  const double expected_sd_mean = expected_sd / sqrt((double) n);

  const double expected_skew = gsl_stats_skew(data, 1, n);

  const double expected_kurtosis = gsl_stats_kurtosis(data, 1, n);

  double expected_rms = 0.0;

  double mean, var, sd, sd_mean, rms, skew, kurtosis;

  size_t i, num;

  int status;

  /* compute expected rms */

  for (i = 0; i < n; ++i)

    expected_rms += data[i] * data[i];

  expected_rms = sqrt(expected_rms / n);

  /* add data to rstat workspace */

  for (i = 0; i < n; ++i)

    gsl_rstat_add(data[i], rstat_workspace_p);

  mean     = gsl_rstat_mean(rstat_workspace_p);

  var      = gsl_rstat_variance(rstat_workspace_p);

  sd       = gsl_rstat_sd(rstat_workspace_p);

  sd_mean  = gsl_rstat_sd_mean(rstat_workspace_p);

  rms      = gsl_rstat_rms(rstat_workspace_p);

  skew     = gsl_rstat_skew(rstat_workspace_p);

  kurtosis = gsl_rstat_kurtosis(rstat_workspace_p);

  num      = gsl_rstat_n(rstat_workspace_p);

  gsl_test_int(num, n, "n n=%zu" , n);

  gsl_test_rel(mean, expected_mean, tol, "mean n=%zu", n);

  gsl_test_rel(var, expected_var, tol, "variance n=%zu", n);

  gsl_test_rel(sd, expected_sd, tol, "stddev n=%zu", n);

  gsl_test_rel(sd_mean, expected_sd_mean, tol, "stddev_mean n=%zu", n);

  gsl_test_rel(rms, expected_rms, tol, "rms n=%zu", n);

  gsl_test_rel(skew, expected_skew, tol, "skew n=%zu", n);

  gsl_test_rel(kurtosis, expected_kurtosis, tol, "kurtosis n=%zu", n);

  status = gsl_rstat_reset(rstat_workspace_p);

  gsl_test_int(status, GSL_SUCCESS, "rstat returned success");

  num = gsl_rstat_n(rstat_workspace_p);

  gsl_test_int(num, 0, "n n=%zu" , n);

  gsl_rstat_free(rstat_workspace_p);

}

void

test_quantile(const double p, const double data[], const size_t n,

              const double expected, const double tol, const char *desc)

{

  gsl_rstat_quantile_workspace *w = gsl_rstat_quantile_alloc(p);

  double result;

  size_t i;

  for (i = 0; i < n; ++i)

    gsl_rstat_quantile_add(data[i], w);

  result = gsl_rstat_quantile_get(w);

  if (fabs(expected) < 1.0e-4)

    gsl_test_abs(result, expected, tol, "%s p=%g", desc, p);

  else

    gsl_test_rel(result, expected, tol, "%s p=%g", desc, p);

  gsl_rstat_quantile_free(w);

}

int

main()

{

  gsl_rng *r = gsl_rng_alloc(gsl_rng_default);

  const double tol1 = 1.0e-8;

  const double tol2 = 1.0e-3;

  gsl_ieee_env_setup();

  {

    const size_t N = 2000000;

    double *data = random_data(N, r);

    double data2[] = { 4.0, 7.0, 13.0, 16.0, -5.0 };

    size_t i;

    test_basic(2, data, tol1);

    test_basic(100, data, tol1);

    test_basic(1000, data, tol1);

    test_basic(10000, data, tol1);

    test_basic(50000, data, tol1);

    test_basic(80000, data, tol1);

    test_basic(1500000, data, tol1);

    test_basic(2000000, data, tol1);

    for (i = 0; i < 5; ++i)

      data2[i] += 1.0e9;

    test_basic(5, data2, tol1);

    free(data);

  }

  {

    /* dataset from Jain and Chlamtac paper */

    const size_t n_jain = 20;

    const double data_jain[] = {  0.02,  0.15,  0.74,  3.39,  0.83,

                                  22.37, 10.15, 15.43, 38.62, 15.92,

                                  34.60, 10.28,  1.47,  0.40,  0.05,

                                  11.39,  0.27,  0.42,  0.09, 11.37 };

    double expected_jain = 4.44063435326;

    test_quantile(0.5, data_jain, n_jain, expected_jain, tol1, "jain");

  }

  {

    size_t n = 1000000;

    double *data = malloc(n * sizeof(double));

    double *sorted_data = malloc(n * sizeof(double));

    gsl_rstat_workspace *rstat_workspace_p = gsl_rstat_alloc();

    double p;

    size_t i;

    for (i = 0; i < n; ++i)

      {

        data[i] = gsl_ran_gaussian_tail(r, 1.3, 1.0);

        gsl_rstat_add(data[i], rstat_workspace_p);

      }

    memcpy(sorted_data, data, n * sizeof(double));

    gsl_sort(sorted_data, 1, n);

    /* test quantile calculation */

    for (p = 0.1; p <= 0.9; p += 0.1)

      {

        double expected = gsl_stats_quantile_from_sorted_data(sorted_data, 1, n, p);

        test_quantile(p, data, n, expected, tol2, "gauss");

      }

    /* test mean, variance */

    {

      const double expected_mean = gsl_stats_mean(data, 1, n);

      const double expected_var = gsl_stats_variance(data, 1, n);

      const double expected_sd = gsl_stats_sd(data, 1, n);

      const double expected_skew = gsl_stats_skew(data, 1, n);

      const double expected_kurtosis = gsl_stats_kurtosis(data, 1, n);

      const double expected_median = gsl_stats_quantile_from_sorted_data(sorted_data, 1, n, 0.5);

      const double mean = gsl_rstat_mean(rstat_workspace_p);

      const double var = gsl_rstat_variance(rstat_workspace_p);

      const double sd = gsl_rstat_sd(rstat_workspace_p);

      const double skew = gsl_rstat_skew(rstat_workspace_p);

      const double kurtosis = gsl_rstat_kurtosis(rstat_workspace_p);

      const double median = gsl_rstat_median(rstat_workspace_p);

      gsl_test_rel(mean, expected_mean, tol1, "mean");

      gsl_test_rel(var, expected_var, tol1, "variance");

      gsl_test_rel(sd, expected_sd, tol1, "stddev");

      gsl_test_rel(skew, expected_skew, tol1, "skew");

      gsl_test_rel(kurtosis, expected_kurtosis, tol1, "kurtosis");

      gsl_test_abs(median, expected_median, tol2, "median");

    }

    free(data);

    free(sorted_data);

    gsl_rstat_free(rstat_workspace_p);

  }

  gsl_rng_free(r);

  exit (gsl_test_summary());

}