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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 "regressorExample.h"
using namespace std;
void RegressorExample::Train(std::vector< fvec > samples, ivec labels)
{
if(!samples.size()) return;
dim = samples[0].size();
// outputdim is the dimension that we want to estimate, and it might NOT be the last one in the dataset
if(outputDim != -1 && outputDim < dim-1)
{
// we need to swap the current last dimension with the desired output
FOR(i, samples.size())
{
float val = samples[i][dim-1];
samples[i][dim-1] = samples[i][outputDim];
samples[i][outputDim] = val;
}
}
// here you will train the regressor with your data
}
fvec RegressorExample::Test( const fvec &sample)
{
fvec res;
res.resize(2,0);
res[0] = drand48()-0.5; // the regression estimation
res[1] = drand48()*0.1; // the regression confidence (0 if you don't have one)
return res;
}
const char *RegressorExample::GetInfoString()
{
char *text = new char[1024];
sprintf(text, "My Regressor Example\n");
sprintf(text, "\n");
sprintf(text, "Training information:\n");
// here you can fill in whatever information you want
return text;
}
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