/usr/include/torch/SVM.h is in libtorch3-dev 3.1-2.2.
This file is owned by root:root, with mode 0o644.
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//
// This file is part of Torch 3.1.
//
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#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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