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MultiKernelMMD.cpp
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MultiKernelMMD.cpp
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/*
* Copyright (c) The Shogun Machine Learning Toolbox
* Written (w) 2014 - 2016 Soumyajit De
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
*
* 1. Redistributions of source code must retain the above copyright notice, this
* list of conditions and the following disclaimer.
* 2. Redistributions in binary form must reproduce the above copyright notice,
* this list of conditions and the following disclaimer in the documentation
* and/or other materials provided with the distribution.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
* ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
* The views and conclusions contained in the software and documentation are those
* of the authors and should not be interpreted as representing official policies,
* either expressed or implied, of the Shogun Development Team.
*/
#include <shogun/io/SGIO.h>
#include <shogun/lib/SGMatrix.h>
#include <shogun/mathematics/Math.h>
#include <shogun/kernel/ShiftInvariantKernel.h>
#include <shogun/distance/CustomDistance.h>
#include <shogun/statistical_testing/MMD.h>
#include <shogun/statistical_testing/internals/KernelManager.h>
#include <shogun/statistical_testing/internals/mmd/MultiKernelMMD.h>
using namespace shogun;
using namespace internal;
using namespace mmd;
struct MultiKernelMMD::terms_t
{
std::array<float64_t, 3> term{};
std::array<float64_t, 3> diag{};
};
MultiKernelMMD::MultiKernelMMD(index_t nx, index_t ny, EStatisticType stype) : n_x(nx), n_y(ny), s_type(stype)
{
SG_SDEBUG("number of samples are %d and %d!\n", n_x, n_y);
}
void MultiKernelMMD::set_distance(CCustomDistance* distance)
{
m_distance=std::shared_ptr<CCustomDistance>(distance);
}
void MultiKernelMMD::add_term(terms_t& t, float32_t val, index_t i, index_t j) const
{
if (i<n_x && j<n_x && i<=j)
{
SG_SDEBUG("Adding Kernel(%d,%d)=%f to term_0!\n", i, j, val);
t.term[0]+=val;
if (i==j)
t.diag[0]+=val;
}
else if (i>=n_x && j>=n_x && i<=j)
{
SG_SDEBUG("Adding Kernel(%d,%d)=%f to term_1!\n", i, j, val);
t.term[1]+=val;
if (i==j)
t.diag[1]+=val;
}
else if (i>=n_x && j<n_x)
{
SG_SDEBUG("Adding Kernel(%d,%d)=%f to term_2!\n", i, j, val);
t.term[2]+=val;
if (i-n_x==j)
t.diag[2]+=val;
}
}
SGVector<float64_t> MultiKernelMMD::operator()(const KernelManager& kernel_mgr) const
{
SG_SDEBUG("Entering!\n");
REQUIRE(m_distance, "Distance instace is not set!\n");
kernel_mgr.set_precomputed_distance(m_distance.get());
SGVector<float64_t> result(kernel_mgr.num_kernels());
#pragma omp parallel for
for (size_t k=0; k<kernel_mgr.num_kernels(); ++k)
{
terms_t t;
for (auto j=0; j<n_x+n_y; ++j)
{
for (auto i=0; i<n_x+n_y; ++i)
{
auto kernel=kernel_mgr.kernel_at(k)->kernel(i, j);
add_term(t, kernel, i, j);
}
}
t.term[0]=2*(t.term[0]-t.diag[0]);
t.term[1]=2*(t.term[1]-t.diag[1]);
SG_SDEBUG("term_0 sum (without diagonal) = %f!\n", t.term[0]);
SG_SDEBUG("term_1 sum (without diagonal) = %f!\n", t.term[1]);
if (s_type!=ST_BIASED_FULL)
{
t.term[0]/=n_x*(n_x-1);
t.term[1]/=n_y*(n_y-1);
}
else
{
t.term[0]+=t.diag[0];
t.term[1]+=t.diag[1];
SG_SDEBUG("term_0 sum (with diagonal) = %f!\n", t.term[0]);
SG_SDEBUG("term_1 sum (with diagonal) = %f!\n", t.term[1]);
t.term[0]/=n_x*n_x;
t.term[1]/=n_y*n_y;
}
SG_SDEBUG("term_0 (normalized) = %f!\n", t.term[0]);
SG_SDEBUG("term_1 (normalized) = %f!\n", t.term[1]);
SG_SDEBUG("term_2 sum (with diagonal) = %f!\n", t.term[2]);
if (s_type==ST_UNBIASED_INCOMPLETE)
{
t.term[2]-=t.diag[2];
SG_SDEBUG("term_2 sum (without diagonal) = %f!\n", t.term[2]);
t.term[2]/=n_x*(n_x-1);
}
else
t.term[2]/=n_x*n_y;
SG_SDEBUG("term_2 (normalized) = %f!\n", t.term[2]);
result[k]=t.term[0]+t.term[1]-2*t.term[2];
SG_SDEBUG("result[%d] = %f!\n", k, result[k]);
}
kernel_mgr.unset_precomputed_distance();
SG_SDEBUG("Leaving!\n");
return result;
}