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// Copyright (C) 2002 Ronan Collobert (collober@iro.umontreal.ca)
//
//
// This file is part of Torch. Release II.
// [The Ultimate Machine Learning Library]
//
// Torch 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 2 of the License, or
// (at your option) any later version.
//
// Torch 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 General Public License for more details.
//
// You should have received a copy of the GNU General Public License
// along with Torch; if not, write to the Free Software
// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA
#ifndef KERNEL_INC
#define KERNEL_INC
#include "general.h"
#include "DataSet.h"
#include "EuclideanDataSet.h"
namespace Torch {
/** Kernel class.
Note that all kernels are based on the #dotProduct#
of their associated #DataSet#.
@author Ronan Collobert (collober@iro.umontreal.ca)
*/
class Kernel : public Object
{
public:
DataSet *data;
//-----
///
Kernel(DataSet *data_);
/** Kernel between the example #i# and #j# of the dataset.
This function takes in account the #selected_examples#
of the dataset.
*/
virtual real eval(int i, int j) = 0;
/** Kernel between the example #i# of the dataset and
one example #y#. The structure of #y# \emph{must}
be the same as those generated in #inputs# by
the dataset #data#.
Note that this function don't care about the #selected_examples#
of the dataset: #i# is a \emph{real} index.
*/
virtual real realEval(int i, List *y) = 0;
/// Reset the kernel (Default, do nothing)
virtual void reset();
//-----
virtual ~Kernel();
};
/** DotProduct
@author Ronan Collobert (collober@iro.umontreal.ca)
*/
class DotKernel : public Kernel
{
public:
EuclideanDataSet *edata;
///
DotKernel(EuclideanDataSet *edata_);
virtual real eval(int i, int j);
virtual real realEval(int i, List *y);
virtual ~DotKernel();
};
/** Polynomial $k(x,y) = (s*x.y+r)^d$.
Options:
\begin{tabular}{lcll}
"degree" & int & degree of the polynome & [2]\\
"mul cst" & real & s & [1]\\
"add cst" & real & r & [1]
\end{tabular}
@author Ronan Collobert (collober@iro.umontreal.ca)
*/
class PolynomialKernel : public Kernel
{
public:
EuclideanDataSet *edata;
int d;
real s, r;
///
PolynomialKernel(EuclideanDataSet *edata_);
virtual real eval(int i, int j);
virtual real realEval(int i, List *y);
virtual ~PolynomialKernel();
};
/** Gaussian $k(x,y) = exp(-g * ||x-y||^2)$
Options:
\begin{tabular}{lcll}
"gamma" & real & g & [0.01]
\end{tabular}
@author Ronan Collobert (collober@iro.umontreal.ca)
*/
class GaussianKernel : public Kernel
{
public:
EuclideanDataSet *edata;
real g;
real *precalc;
real *precalc_real;
bool precalc_alloc;
///
GaussianKernel(EuclideanDataSet *edata_);
virtual void reset();
virtual real eval(int i, int j);
virtual real realEval(int i, List *y);
virtual ~GaussianKernel();
};
/** Sigmoid $k(x,y) = tanh(s*x.y+r)$
Options:
\begin{tabular}{lcll}
"mul cst" & real & s & [1]\\
"add cst" & real & r & [1]
\end{tabular}
@author Ronan Collobert (collober@iro.umontreal.ca)
*/
class SigmoidKernel : public Kernel
{
public:
EuclideanDataSet *edata;
real s, r;
///
SigmoidKernel(EuclideanDataSet *edata_);
virtual real eval(int i, int j);
virtual real realEval(int i, List *y);
virtual ~SigmoidKernel();
};
}
#endif
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