TwoDistributionsTestStatistic.cpp

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/*
 * 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.
 *
 * Written (W) 2012 Heiko Strathmann
 */

#include <shogun/statistics/TwoDistributionsTestStatistic.h>
#include <shogun/features/Features.h>

using namespace shogun;

CTwoDistributionsTestStatistic::CTwoDistributionsTestStatistic() :
        CTestStatistic()
{
    init();
}

CTwoDistributionsTestStatistic::CTwoDistributionsTestStatistic(
        CFeatures* p_and_q,
        index_t q_start) : CTestStatistic()
{
    init();

    m_p_and_q=p_and_q;
    SG_REF(m_p_and_q);

    m_q_start=q_start;
}

CTwoDistributionsTestStatistic::CTwoDistributionsTestStatistic(
        CFeatures* p, CFeatures* q) :
        CTestStatistic()
{
    init();

    m_p_and_q=p->create_merged_copy(q);
    SG_REF(m_p_and_q);

    m_q_start=p->get_num_vectors();
}

CTwoDistributionsTestStatistic::~CTwoDistributionsTestStatistic()
{
    SG_UNREF(m_p_and_q);
}

void CTwoDistributionsTestStatistic::init()
{
    SG_ADD((CSGObject**)&m_p_and_q, "p_and_q", "Concatenated samples p and q",
            MS_NOT_AVAILABLE);
    SG_ADD(&m_q_start, "q_start", "Index of first sample of q",
            MS_NOT_AVAILABLE);

    m_p_and_q=NULL;
    m_q_start=0;
}

SGVector<float64_t> CTwoDistributionsTestStatistic::bootstrap_null()
{
    SG_DEBUG("entering CTwoDistributionsTestStatistic::bootstrap_null()\n");

    /* compute bootstrap statistics for null distribution */
    SGVector<float64_t> results(m_bootstrap_iterations);

    /* memory for index permutations, (would slow down loop) */
    SGVector<index_t> ind_permutation(m_p_and_q->get_num_vectors());
    ind_permutation.range_fill();
    m_p_and_q->add_subset(ind_permutation);

    for (index_t i=0; i<m_bootstrap_iterations; ++i)
    {
        /* idea: merge features of p and q, shuffle, and compute statistic.
         * This is done using subsets here */

        /* create index permutation and add as subset. This will mix samples
         * from p and q */
        SGVector<int32_t>::permute_vector(ind_permutation);

        /* compute statistic for this permutation of mixed samples */
        results[i]=compute_statistic();
    }

    /* clean up */
    m_p_and_q->remove_subset();

    SG_DEBUG("leaving CTwoDistributionsTestStatistic::bootstrap_null()\n");
    return results;
}

float64_t CTwoDistributionsTestStatistic::compute_p_value(
        float64_t statistic)
{
    float64_t result=0;

    if (m_null_approximation_method==BOOTSTRAP)
    {
        /* bootstrap a bunch of MMD values from null distribution */
        SGVector<float64_t> values=bootstrap_null();

        /* find out percentile of parameter "statistic" in null distribution */
        CMath::qsort(values);
        float64_t i=CMath::find_position_to_insert(values, statistic);

        /* return corresponding p-value */
        result=1.0-i/values.vlen;
    }
    else
    {
        SG_ERROR("CTwoDistributionsTestStatistics::compute_p_value(): Unknown"
                "method to approximate null distribution!\n");
    }

    return result;
}

float64_t CTwoDistributionsTestStatistic::compute_threshold(
        float64_t alpha)
{
    float64_t result=0;

    if (m_null_approximation_method==BOOTSTRAP)
    {
        /* bootstrap a bunch of MMD values from null distribution */
        SGVector<float64_t> values=bootstrap_null();

        /* return value of (1-alpha) quantile */
        result=values[CMath::floor(values.vlen*(1-alpha))];
    }
    else
    {
        SG_ERROR("CTwoDistributionsTestStatistics::compute_threshold():"
                "Unknown method to approximate null distribution!\n");
    }

    return result;
}