KernelTwoSampleTest.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) 2012-2013 Heiko Strathmann
 */

#include <shogun/statistics/KernelTwoSampleTest.h>
#include <shogun/features/Features.h>
#include <shogun/kernel/Kernel.h>
#include <shogun/kernel/CustomKernel.h>
#include <shogun/mathematics/Math.h>

using namespace shogun;

CKernelTwoSampleTest::CKernelTwoSampleTest() :
        CTwoSampleTest()
{
    init();
}

CKernelTwoSampleTest::CKernelTwoSampleTest(CKernel* kernel,
        CFeatures* p_and_q, index_t q_start) :
        CTwoSampleTest(p_and_q, q_start)
{
    init();

    m_kernel=kernel;
    SG_REF(kernel);
}

CKernelTwoSampleTest::CKernelTwoSampleTest(CKernel* kernel,
        CFeatures* p, CFeatures* q) : CTwoSampleTest(p, q)
{
    init();

    m_kernel=kernel;
    SG_REF(kernel);
}

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

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

SGVector<float64_t> CKernelTwoSampleTest::sample_null()
{
    SG_DEBUG("entering!\n");

    REQUIRE(m_kernel, "No kernel set!\n");
    REQUIRE(m_kernel->get_kernel_type()==K_CUSTOM || m_p_and_q,
            "No features and no custom kernel set!\n");

    /* compute sample 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_num_null_samples);

        /* in case of custom kernel, there are no features */
        index_t num_data;
        if (m_kernel->get_kernel_type()==K_CUSTOM)
            num_data=m_kernel->get_num_vec_lhs();
        else
            num_data=m_p_and_q->get_num_vectors();

        /* memory for index permutations, (would slow down loop) */
        SGVector<index_t> ind_permutation(num_data);
        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_num_null_samples; ++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 */
            CMath::permute(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=CTwoSampleTest::sample_null();
    }

    SG_DEBUG("leaving!\n");

    return results;
}