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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
#include "SVMClassification.h"
namespace Torch {
SVMClassification::SVMClassification(Kernel *kernel_, real *C_) : SVM(kernel_)
{
l = data->n_real_examples;
Cuser = C_;
Cup = (real *)xalloc(sizeof(real)*l);
for(int i = 0; i < l; i++)
Cup[i] = C_cst;
Cdown = (real *)xalloc(sizeof(real)*l);
for(int i = 0; i < l; i++)
Cdown[i] = 0;
alpha = (real *)xalloc(sizeof(real)*l);
grad = (real *)xalloc(sizeof(real)*l);
y = (real *)xalloc(sizeof(real)*l);
l = data->n_examples;
}
void SVMClassification::reset()
{
l = data->n_examples;
if(Cuser)
{
for(int i = 0; i < l; i++)
Cup[i] = Cuser[data->selected_examples[i]];
}
for(int i = 0; i < l; i++)
alpha[i] = 0;
b = 0;
for(int i = 0; i < l; i++)
{
data->setExample(i);
y[i] = ((real *)data->targets)[0];
grad[i] = -1;
}
n_support_vectors = 0;
n_support_vectors_bound = 0;
kernel->reset();
}
void SVMClassification::checkSupportVectors()
{
// La je prie pour que l'utilisateur normal utilise
// un processeur deterministe ///
n_support_vectors = 0;
n_support_vectors_bound = 0;
for(int i = 0; i < l; i++)
{
if(alpha[i] > eps_bornes)
{
if(alpha[i] > Cup[i] - eps_bornes)
n_support_vectors_bound++;
n_support_vectors++;
}
}
support_vectors = (int *)xrealloc(support_vectors, sizeof(int)*n_support_vectors);
real_index = (int *)xrealloc(real_index, sizeof(int)*n_support_vectors);
n_support_vectors = 0;
for(int i = 0; i < l; i++)
{
if(alpha[i] > eps_bornes)
{
support_vectors[n_support_vectors ] = i;
real_index[n_support_vectors++] = data->selected_examples[i];
}
}
if(!bCompute())
{
warning("SVMClassification: b is not unique. It's probably wrong");
warning("SVMClassification: I think you are using silly parameters");
}
}
SVMClassification::~SVMClassification()
{
free(Cup);
free(Cdown);
free(alpha);
free(grad);
free(support_vectors);
free(real_index);
free(y);
}
}
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