KernelTwoSampleTestStatistic.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/KernelTwoSampleTestStatistic.h>
#include <shogun/features/Features.h>
#include <shogun/kernel/Kernel.h>
#include <shogun/kernel/CustomKernel.h>

using namespace shogun;

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

CKernelTwoSampleTestStatistic::CKernelTwoSampleTestStatistic(CKernel* kernel,
        CFeatures* p_and_q, index_t q_start) :
        CTwoDistributionsTestStatistic(p_and_q, q_start)
{
    init();

    m_kernel=kernel;
    SG_REF(kernel);
}

CKernelTwoSampleTestStatistic::CKernelTwoSampleTestStatistic(CKernel* kernel,
        CFeatures* p, CFeatures* q) : CTwoDistributionsTestStatistic(p, q)
{
    init();

    m_kernel=kernel;
    SG_REF(kernel);

    /* init kernel once in the beginning */
    m_kernel->init(m_p_and_q, m_p_and_q);
}

CKernelTwoSampleTestStatistic::~CKernelTwoSampleTestStatistic()
{
    SG_UNREF(m_kernel);
}

void CKernelTwoSampleTestStatistic::init()
{
    SG_ADD((CSGObject**)&m_kernel, "kernel", "Kernel for two sample test",
            MS_AVAILABLE);
    m_kernel=NULL;
}

SGVector<float64_t> CKernelTwoSampleTestStatistic::bootstrap_null()
{
    /* compute bootstrap statistics for null distribution */
    SGVector<float64_t> results;

    /* only do something if a custom kernel is used: use the power of pre-
     * computed kernel matrices
     */
    if (m_kernel->get_kernel_type()==K_CUSTOM)
    {
        /* allocate memory */
        results=SGVector<float64_t>(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();

        /* check if kernel is a custom kernel. In that case, changing features is
         * not what we want but just subsetting the kernel itself */
        CCustomKernel* custom_kernel=(CCustomKernel*)m_kernel;

        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. add to custom kernel since
             * it has no features to subset. CustomKernel has not to be
             * re-initialised after each subset setting */
            SGVector<int32_t>::permute_vector(ind_permutation);

            custom_kernel->add_row_subset(ind_permutation);
            custom_kernel->add_col_subset(ind_permutation);

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

            /* remove subsets */
            custom_kernel->remove_row_subset();
            custom_kernel->remove_col_subset();
        }
    }
    else
    {
        /* in this case, just use superclass method */
        results=CTwoDistributionsTestStatistic::bootstrap_null();
    }

    return results;
}