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refactored mmd internal computation components
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src/shogun/statistical_testing/internals/mmd/ComputeMMD.h
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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. | ||
*/ | ||
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#ifndef COMPUTE_MMD_H_ | ||
#define COMPUTE_MMD_H_ | ||
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#include <array> | ||
#include <shogun/io/SGIO.h> | ||
#include <shogun/lib/config.h> | ||
#include <shogun/statistical_testing/MMD.h> | ||
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namespace shogun | ||
{ | ||
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namespace internal | ||
{ | ||
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namespace mmd | ||
{ | ||
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struct terms_t | ||
{ | ||
std::array<float64_t, 3> term{}; | ||
std::array<float64_t, 3> diag{}; | ||
}; | ||
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/** | ||
* @brief Class Compute blah blah. | ||
*/ | ||
struct ComputeMMD | ||
{ | ||
ComputeMMD(index_t n_x, index_t n_y, EStatisticType stype) : m_n_x(n_x), m_n_y(n_y), m_stype(stype) | ||
{ | ||
SG_SDEBUG("number of samples are %d and %d!\n", n_x, n_y); | ||
} | ||
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template <typename T> | ||
inline void add_term(terms_t& terms, T val, index_t i, index_t j) const | ||
{ | ||
if (i<m_n_x && j<m_n_x && i>=j) | ||
{ | ||
SG_SDEBUG("Adding Kernel(%d,%d)=%f to term_0!\n", i, j, val); | ||
terms.term[0]+=val; | ||
if (i==j) | ||
terms.diag[0]+=val; | ||
} | ||
else if (i>=m_n_x && j>=m_n_x && i>=j) | ||
{ | ||
SG_SDEBUG("Adding Kernel(%d,%d)=%f to term_1!\n", i, j, val); | ||
terms.term[1]+=val; | ||
if (i==j) | ||
terms.diag[1]+=val; | ||
} | ||
else if (i>=m_n_x && j<m_n_x) | ||
{ | ||
SG_SDEBUG("Adding Kernel(%d,%d)=%f to term_2!\n", i, j, val); | ||
terms.term[2]+=val; | ||
if (i-m_n_x==j) | ||
terms.diag[2]+=val; | ||
} | ||
} | ||
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inline float64_t compute(terms_t& terms) const | ||
{ | ||
terms.term[0]=2*(terms.term[0]-terms.diag[0]); | ||
terms.term[1]=2*(terms.term[1]-terms.diag[1]); | ||
SG_SDEBUG("term_0 sum (without diagonal) = %f!\n", terms.term[0]); | ||
SG_SDEBUG("term_1 sum (without diagonal) = %f!\n", terms.term[1]); | ||
if (m_stype!=EStatisticType::ST_BIASED_FULL) | ||
{ | ||
terms.term[0]/=m_n_x*(m_n_x-1); | ||
terms.term[1]/=m_n_y*(m_n_y-1); | ||
} | ||
else | ||
{ | ||
terms.term[0]+=terms.diag[0]; | ||
terms.term[1]+=terms.diag[1]; | ||
SG_SDEBUG("term_0 sum (with diagonal) = %f!\n", terms.term[0]); | ||
SG_SDEBUG("term_1 sum (with diagonal) = %f!\n", terms.term[1]); | ||
terms.term[0]/=m_n_x*m_n_x; | ||
terms.term[1]/=m_n_y*m_n_y; | ||
} | ||
SG_SDEBUG("term_0 (normalized) = %f!\n", terms.term[0]); | ||
SG_SDEBUG("term_1 (normalized) = %f!\n", terms.term[1]); | ||
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SG_SDEBUG("term_2 sum (with diagonal) = %f!\n", terms.term[2]); | ||
if (m_stype==EStatisticType::ST_UNBIASED_INCOMPLETE) | ||
{ | ||
terms.term[2]-=terms.diag[2]; | ||
SG_SDEBUG("term_2 sum (without diagonal) = %f!\n", terms.term[2]); | ||
terms.term[2]/=m_n_x*(m_n_x-1); | ||
} | ||
else | ||
terms.term[2]/=m_n_x*m_n_y; | ||
SG_SDEBUG("term_2 (normalized) = %f!\n", terms.term[2]); | ||
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auto result=terms.term[0]+terms.term[1]-2*terms.term[2]; | ||
SG_SDEBUG("result = %f!\n", result); | ||
return result; | ||
} | ||
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const index_t m_n_x; | ||
const index_t m_n_y; | ||
const EStatisticType m_stype; | ||
}; | ||
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} | ||
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} | ||
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} | ||
#endif // COMPUTE_MMD_H_ |
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src/shogun/statistical_testing/internals/mmd/MultiKernelMMD.cpp
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