/usr/include/shark/LinAlg/GaussianKernelMatrix.h is in libshark-dev 3.1.3+ds1-2.
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/*!
*
*
* \brief Efficient special case if the kernel is gaussian and the inputs are sparse vectors
*
*
* \par
*
*
*
* \author T. Glasmachers
* \date 2007-2012
*
*
* \par Copyright 1995-2015 Shark Development Team
*
* <BR><HR>
* This file is part of Shark.
* <http://image.diku.dk/shark/>
*
* Shark is free software: you can redistribute it and/or modify
* it under the terms of the GNU Lesser General Public License as published
* by the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* Shark is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU Lesser General Public License for more details.
*
* You should have received a copy of the GNU Lesser General Public License
* along with Shark. If not, see <http://www.gnu.org/licenses/>.
*
*/
//===========================================================================
#ifndef SHARK_LINALG_GAUSSIANKERNELMATRIX_H
#define SHARK_LINALG_GAUSSIANKERNELMATRIX_H
#include <shark/Data/Dataset.h>
#include <shark/LinAlg/Base.h>
#include <vector>
#include <cmath>
namespace shark {
///\brief Efficient special case if the kernel is Gaussian and the inputs are sparse vectors
template <class T, class CacheType>
class GaussianKernelMatrix
{
public:
typedef CacheType QpFloatType;
typedef T InputType;
/// Constructor
/// \param gamma bandwidth parameter of Gaussian kernel
/// \param data data evaluated by the kernel function
GaussianKernelMatrix(
double gamma,
Data<InputType> const& data
)
: m_squaredNorms(data.numberOfElements())
, m_gamma(gamma)
, m_accessCounter( 0 )
{
std::size_t elements = data.numberOfElements();
x.resize(elements);
PointerType iter=data.elements().begin();
for(std::size_t i = 0; i != elements; ++i,++iter){
x[i]=iter;
m_squaredNorms(i) =inner_prod(*x[i],*x[i]);//precompute the norms
}
}
/// return a single matrix entry
QpFloatType operator () (std::size_t i, std::size_t j) const
{ return entry(i, j); }
/// return a single matrix entry
QpFloatType entry(std::size_t i, std::size_t j) const
{
++m_accessCounter;
double distance = m_squaredNorms(i)-2*inner_prod(*x[i], *x[j])+m_squaredNorms(j);
return (QpFloatType)std::exp(- m_gamma * distance);
}
/// \brief Computes the i-th row of the kernel matrix.
///
///The entries start,...,end of the i-th row are computed and stored in storage.
///There must be enough room for this operation preallocated.
void row(std::size_t i, std::size_t start,std::size_t end, QpFloatType* storage) const
{
typename ConstProxyReference<T>::type xi = *x[i];
m_accessCounter +=end-start;
SHARK_PARALLEL_FOR(int j = start; j < (int) end; j++)
{
double distance = m_squaredNorms(i)-2*inner_prod(xi, *x[j])+m_squaredNorms(j);
storage[j-start] = std::exp(- m_gamma * distance);
}
}
/// \brief Computes the kernel-matrix
template<class M>
void matrix(
blas::matrix_expression<M> & storage
) const{
for(std::size_t i = 0; i != size(); ++i){
row(i,0,size(),&storage()(i,0));
}
}
/// swap two variables
void flipColumnsAndRows(std::size_t i, std::size_t j){
using std::swap;
swap(x[i],x[j]);
swap(m_squaredNorms[i],m_squaredNorms[j]);
}
/// return the size of the quadratic matrix
std::size_t size() const
{ return x.size(); }
/// query the kernel access counter
unsigned long long getAccessCount() const
{ return m_accessCounter; }
/// reset the kernel access counter
void resetAccessCount()
{ m_accessCounter = 0; }
protected:
//~ typedef blas::sparse_vector_adaptor<typename T::value_type const,std::size_t> PointerType;
typedef typename Data<InputType>::const_element_range::iterator PointerType;
/// Array of data pointers for kernel evaluations
std::vector<PointerType> x;
RealVector m_squaredNorms;
double m_gamma;
/// counter for the kernel accesses
mutable unsigned long long m_accessCounter;
};
}
#endif
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