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_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_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, rms, skew, kurtosis;
size_t i;
/* 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);
rms = gsl_rstat_rms(rstat_workspace_p);
skew = gsl_rstat_skew(rstat_workspace_p);
kurtosis = gsl_rstat_kurtosis(rstat_workspace_p);
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(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);
gsl_rstat_free(rstat_workspace_p);
}
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 };
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 < 4; ++i)
data2[i] += 1.0e9;
test_basic(4, 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());
}