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// Copyright (C) 2003--2004 Ronan Collobert (collober@idiap.ch)
//
// This file is part of Torch 3.1.
//
// 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.
// 3. The name of the author may not be used to endorse or promote products
// derived from this software without specific prior written permission.
//
// THIS SOFTWARE IS PROVIDED BY THE AUTHOR ``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 AUTHOR 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.
#ifndef SVM_INC
#define SVM_INC
#include "QCMachine.h"
#include "Kernel.h"
#include "DataSet.h"
#include "IOSequenceArray.h"
namespace Torch {
/** Support Vector Machine.
The Q matrix of #QCMachine# is in this case
$q_{ij} = k(x_i, x_j)$, where $k$ is a kernel
and $x_i$ is the i-th example of #data#/
The goal is to looking for the #alpha# and #b#
which are the best in a SVM-sense.
The learning function is
$f(x) = \sum_j y_j alpha_j k(x_i, x) + b$
@author Ronan Collobert (collober@idiap.ch)
*/
class SVM : public QCMachine
{
public:
/// To allocate all stuff related to support vectors.
Allocator *sv_allocator;
/// Give the sequence-format.
IOSequenceArray *io_sequence_array;
/** The dataset associated to the SVM.
*/
DataSet *data;
/// The kernel associated to the SVM.
Kernel *kernel;
///
real b;
/// The support vectors
int *support_vectors;
/// sv_alpha[i] is the weight of the SV i.
real *sv_alpha;
/// SV sequences.
Sequence **sv_sequences;
/// The number of support vectors
int n_support_vectors;
/// The number of support vectors which are at the bound "C"
int n_support_vectors_bound;
//-----
///
SVM(Kernel *kernel_, IOSequenceArray *io_sequence_array_=NULL);
/// Computes the #b#.
bool bCompute();
//-----
virtual void forward(Sequence *inputs);
virtual void loadXFile(XFile *file);
virtual void saveXFile(XFile *file);
virtual ~SVM();
};
}
#endif
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