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/*********************************************************************
MLDemos: A User-Friendly visualization toolkit for machine learning
Copyright (C) 2010 Basilio Noris
Contact: mldemos@b4silio.com
This library 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 2.1 of the License, or (at your option) any later version.
This library 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
Library General Public License for more details.
You should have received a copy of the GNU Lesser General Public
License along with this library; if not, write to the Free
Software Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
*********************************************************************/
#include <public.h>
#include "clustererSVR.h"
using namespace std;
ClustererSVR::ClustererSVR()
: svm(0)
{
// default values
param.svm_type = ONE_CLASS;
//param.svm_type = NU_SVR;
param.kernel_type = RBF;
param.gamma = 0.1;
param.C = 100;
param.nu = 0.1;
param.p = 0.3;
param.degree = 1;
param.coef0 = 0;
param.shrinking = 1;
param.probability = 0;
param.eps = 1e-6;
param.cache_size = 400;
param.nr_weight = 0;
param.weight_label = NULL;
param.weight = NULL;
param.kernel_weight = NULL;
param.kernel_dim = 0;
param.kernel_norm = 1.;
param.normalizeKernel = false;
}
ClustererSVR::~ClustererSVR()
{
DEL(svm);
}
void ClustererSVR::Train(std::vector< fvec > samples)
{
svm_problem problem;
svm_node *x_space;
int data_dimension = samples[0].size();
problem.l = samples.size();
problem.y = new double[problem.l];
problem.x = new svm_node *[problem.l];
x_space = new svm_node[(data_dimension+1)*problem.l];
FOR(i, problem.l)
{
FOR(j, data_dimension)
{
x_space[(data_dimension+1)*i + j].index = j+1;
x_space[(data_dimension+1)*i + j].value = samples[i][j];
}
x_space[(data_dimension+1)*i + data_dimension].index = -1;
problem.x[i] = &x_space[(data_dimension+1)*i];
problem.y[i] = 0;
}
if(svm) delete [] svm;
svm = svm_train(&problem, ¶m);
delete [] problem.x;
delete [] problem.y;
}
fvec ClustererSVR::Test( const fvec &sample )
{
int data_dimension = sample.size();
float estimate;
svm_node *x = new svm_node[data_dimension+1];
FOR(i, data_dimension)
{
x[i].index = i+1;
x[i].value = sample[i];
}
x[data_dimension].index = -1;
estimate = (float)svm_predict(svm, x);
delete [] x;
fvec res;
estimate = std::max(-1.f,min(1.f,estimate))/2 + 0.5f;
res.push_back(estimate);
return res;
}
fvec ClustererSVR::Test( const fVec &sample )
{
int data_dimension = 2;
float estimate;
svm_node *x = new svm_node[data_dimension+1];
FOR(i, data_dimension)
{
x[i].index = i+1;
x[i].value = sample._[i];
}
x[data_dimension].index = -1;
estimate = (float)svm_predict(svm, x);
delete [] x;
fvec res;
estimate = std::max(-1.f,min(1.f,estimate))/2 + 0.5f;
res.push_back(estimate);
return res;
}
void ClustererSVR::SetParams(int svmType, float svmC, float svmP, u32 kernelType, float kernelParam)
{
// default values
param.svm_type = svmType;
param.C = svmC;
param.nu = svmC;
param.eps = 0.01;
param.p = svmP;
param.coef0 = 0;
param.gamma = 1;
switch(kernelType)
{
case 0:
param.kernel_type = LINEAR;
param.degree = 1;
break;
case 1:
param.kernel_type = POLY;
param.degree = (u32)kernelParam;
break;
case 2:
param.kernel_type = RBF;
param.gamma = kernelParam;
break;
case 3:
param.kernel_type = SIGMOID;
param.gamma = kernelParam;
break;
}
}
const char *ClustererSVR::GetInfoString()
{
if(!svm) return NULL;
char *text = new char[1024];
sprintf(text, "OneClas SVR\n");
sprintf(text, "%sKernel: ", text);
switch(param.kernel_type)
{
case LINEAR:
sprintf(text, "%s linear\n", text);
break;
case POLY:
sprintf(text, "%s polynomial (deg: %d bias: %f width: %f)\n", text, param.degree, param.coef0, param.gamma);
break;
case RBF:
sprintf(text, "%s rbf (gamma: %f)\n", text, param.gamma);
break;
case SIGMOID:
sprintf(text, "%s sigmoid (%f %f)\n", text, param.gamma, param.coef0);
break;
}
sprintf(text, "%snu: %f\n", text, param.nu);
sprintf(text, "%sSupport Vectors: %d\n", text, svm->l);
return text;
}
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