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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 "classifierExample.h"
#include <map>
#include <QDebug>
using namespace std;
void ClassifierExample::Train(std::vector< fvec > samples, ivec labels)
{
// we don't want to train anything if we don't have data
if(!samples.size()) return;
// we determine the size of the data
dim = samples[0].size();
// we split the samples into separate lists for each class
vector< fvec > positives, negatives;
classes.clear();
classMap.clear();
inverseMap.clear();
// we go through all the samples and either make a list of classes, or use the "positive vs negative" grouping
int cnt=0;
FOR(i, labels.size()) if(!classMap.count(labels[i])) classMap[labels[i]] = cnt++;
for(map<int,int>::iterator it=classMap.begin(); it != classMap.end(); it++) inverseMap[it->second] = it->first;
ivec newLabels(labels.size());
FOR(i, labels.size()) newLabels[i] = classMap[labels[i]];
for(map<int,int>::iterator it=inverseMap.begin(); it != inverseMap.end(); it++) qDebug() << "inverse: " << it->first << it->second;
for(map<int,int>::iterator it=classMap.begin(); it != classMap.end(); it++) qDebug() << "class: " << it->first << it->second;
std::map<int, vector<fvec> > sampleMap;
FOR(i, samples.size())
{
sampleMap[newLabels[i]].push_back(samples[i]);
if(newLabels[i] == 1) positives.push_back(samples[i]);
else negatives.push_back(samples[i]);
}
// to give an example, we compute the center of each class
centers.clear();
// we iterate through the list of samples split by class
for(map<int,vector<fvec> >::iterator it=sampleMap.begin(); it != sampleMap.end(); it++)
{
qDebug() << "analyzing class #" << it->first;
// we get the list for the current class
vector<fvec> &s = it->second;
// we initialize the mean at zero
fvec mean(dim, 0);
// we compute the mean
FOR(j, s.size())
{
mean += s[j];
}
mean /= s.size();
// and we push it in the list by class
centers[it->first] = mean;
}
}
fvec ClassifierExample::TestMulti(const fvec &sample)
{
fvec res(centers.size(),0);
// we simply compute the distance from the center to the current sample
for(map<int,fvec>::iterator it=centers.begin(); it != centers.end(); it++)
{
// we compute the difference
fvec diff = sample - it->second;
// we compute the dot product
float x = sqrtf(diff*diff);
// we use a simple rbf distance
res[it->first] = expf(-0.5*x);
}
return res;
}
float ClassifierExample::Test(const fvec &sample)
{
fvec likelihood = TestMulti(sample);
if(likelihood.size() < 2) return 0;
float res = log(likelihood[1]) - log(likelihood[0]);
return res;
}
const char *ClassifierExample::GetInfoString()
{
char *text = new char[1024];
sprintf(text, "My Classifier Example\n");
sprintf(text, "%s\n", text);
sprintf(text, "%sTraining information:\n", text);
// here you can fill in whatever information you want
for(map<int,fvec>::iterator it=centers.begin(); it != centers.end(); it++)
{
sprintf(text, "%sCenter for class %d\n", text, it->first);
FOR(d, it->second.size())
{
// write down the dimension as floats with 3 decimals
sprintf(text,"%s %.3f", text, it->second[d]);
}
sprintf(text, "%s\n", text);
}
return text;
}
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