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/*
* Copyright © 2015 Intel Corporation
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the "Software"),
* to deal in the Software without restriction, including without limitation
* the rights to use, copy, modify, merge, publish, distribute, sublicense,
* and/or sell copies of the Software, and to permit persons to whom the
* Software is furnished to do so, subject to the following conditions:
*
* The above copyright notice and this permission notice (including the next
* paragraph) shall be included in all copies or substantial portions of the
* Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
* THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING
* FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS
* IN THE SOFTWARE.
*
*/
#include <math.h>
#include <string.h>
#include "igt_core.h"
#include "igt_stats.h"
/**
* SECTION:igt_stats
* @short_description: Tools for statistical analysis
* @title: Stats
* @include: igt_stats.h
*
* Various tools to make sense of data.
*
* #igt_stats_t is a container of data samples. igt_stats_push() is used to add
* new samples and various results (mean, variance, standard deviation, ...)
* can then be retrieved.
*
* |[
* igt_stats_t stats;
*
* igt_stats_init(&stats, 8);
*
* igt_stats_push(&stats, 2);
* igt_stats_push(&stats, 4);
* igt_stats_push(&stats, 4);
* igt_stats_push(&stats, 4);
* igt_stats_push(&stats, 5);
* igt_stats_push(&stats, 5);
* igt_stats_push(&stats, 7);
* igt_stats_push(&stats, 9);
*
* printf("Mean: %lf\n", igt_stats_get_mean(&stats));
*
* igt_stats_fini(&stats);
* ]|
*/
/**
* igt_stats_init:
* @stats: An #igt_stats_t instance
* @capacity: Number of data samples @stats can contain
*
* Initializes an #igt_stats_t instance to hold @capacity samples.
* igt_stats_fini() must be called once finished with @stats.
*
* We currently assume the user knows how many data samples upfront and there's
* no need to grow the array of values.
*/
void igt_stats_init(igt_stats_t *stats, unsigned int capacity)
{
memset(stats, 0, sizeof(*stats));
stats->values = calloc(capacity, sizeof(*stats->values));
igt_assert(stats->values);
stats->capacity = capacity;
}
/**
* igt_stats_fini:
* @stats: An #igt_stats_t instance
*
* Frees resources allocated in igt_stats_init().
*/
void igt_stats_fini(igt_stats_t *stats)
{
free(stats->values);
}
/**
* igt_stats_set_population:
* @stats: An #igt_stats_t instance
* @full_population: Whether we're dealing with sample data or a full
* population
*
* In statistics, we usually deal with a subset of the full data (which may be
* a continuous or infinite set). Data analysis is then done on a sample of
* this population.
*
* This has some importance as only having a sample of the data leads to
* [biased estimators](https://en.wikipedia.org/wiki/Bias_of_an_estimator). We
* currently used the information given by this method to apply
* [Bessel's correction](https://en.wikipedia.org/wiki/Bessel%27s_correction)
* to the variance.
*
* When giving #true to this function, the data set in @stats is considered a
* full population. It's considered a sample of a bigger population otherwise.
*
* When newly created, @stats defaults to holding sample data.
*/
void igt_stats_set_population(igt_stats_t *stats, bool full_population)
{
if (full_population == stats->is_population)
return;
stats->is_population = full_population;
stats->mean_variance_valid = false;
}
/**
* igt_stats_push:
* @stats: An #igt_stats_t instance
* @value: An integer value
*
* Adds a new value to the @stats dataset.
*/
void igt_stats_push(igt_stats_t *stats, uint64_t value)
{
igt_assert(stats->n_values < stats->capacity);
stats->values[stats->n_values++] = value;
stats->mean_variance_valid = false;
}
/*
* Algorithm popularised by Knuth in:
*
* The Art of Computer Programming, volume 2: Seminumerical Algorithms,
* 3rd edn., p. 232. Boston: Addison-Wesley
*
* Source: https://en.wikipedia.org/wiki/Algorithms_for_calculating_variance
*/
static void igt_stats_knuth_mean_variance(igt_stats_t *stats)
{
double mean = 0., m2 = 0.;
unsigned int i;
if (stats->mean_variance_valid)
return;
for (i = 0; i < stats->n_values; i++) {
double delta = stats->values[i] - mean;
mean += delta / (i + 1);
m2 += delta * (stats->values[i] - mean);
}
stats->mean = mean;
if (stats->n_values > 1 && !stats->is_population)
stats->variance = m2 / (stats->n_values - 1);
else
stats->variance = m2 / stats->n_values;
stats->mean_variance_valid = true;
}
/**
* igt_stats_get_mean:
* @stats: An #igt_stats_t instance
*
* Retrieves the mean of the @stats dataset.
*/
double igt_stats_get_mean(igt_stats_t *stats)
{
igt_stats_knuth_mean_variance(stats);
return stats->mean;
}
/**
* igt_stats_get_variance:
* @stats: An #igt_stats_t instance
*
* Retrieves the variance of the @stats dataset.
*/
double igt_stats_get_variance(igt_stats_t *stats)
{
igt_stats_knuth_mean_variance(stats);
return stats->variance;
}
/**
* igt_stats_get_std_deviation:
* @stats: An #igt_stats_t instance
*
* Retrieves the standard deviation of the @stats dataset.
*/
double igt_stats_get_std_deviation(igt_stats_t *stats)
{
igt_stats_knuth_mean_variance(stats);
return sqrt(stats->variance);
}
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