NICE_VisLearning/classifier/fpclassifier/logisticregression/FPCSMLR.cpp

#include "vislearning/classifier/fpclassifier/logisticregression/FPCSMLR.h"
#include "vislearning/cbaselib/FeaturePool.h"

#include <iostream>

using namespace OBJREC;
using namespace std;
using namespace NICE;

FPCSMLR::FPCSMLR ()
{
	inpic = false;
}

FPCSMLR::FPCSMLR( const Config *_conf, string section ) : conf(_conf)
{
	confsection = section;
	inpic = conf->gB(section, "inpic", false );
}

FPCSMLR::~FPCSMLR()
{
	//clean up
}

ClassificationResult FPCSMLR::classify ( Example & pce )
{
	FullVector overall_distribution(maxClassNo+1);
	overall_distribution[maxClassNo] = 0.0;

	double maxp = -numeric_limits<double>::max();
	int classno = 0;
	
	double sum  = 0.0;
		
	for(int i = 0; i < maxClassNo; i++)
	{
		overall_distribution[i] = classifiers[i].classify(pce);
		
		sum += overall_distribution[i];
		
		if(maxp < overall_distribution[i])
		{
			classno = i;
			maxp = overall_distribution[i];
		}
	}
	for(int i = 0; i < maxClassNo; i++)
	{
		overall_distribution[i] /= sum;
	}

	return ClassificationResult ( classno, overall_distribution );
}

void FPCSMLR::train ( FeaturePool & _fp, Examples & examples )
{

	cout << "start train" << endl;
	fp = FeaturePool(_fp);
    
	// Anzahl von Merkmalen
	int fanz = examples.size();

	maxClassNo = -1;
	for(int i = 0; i < fanz; i++)
	{
		maxClassNo = std::max(maxClassNo, examples[i].first);
	}
	maxClassNo++;
	
	assert(fanz >= maxClassNo);
	
	classifiers.resize(maxClassNo);
	for(int i = 0; i < maxClassNo; i++)
	{
		cout << "classifier no " << i << " training starts" << endl;
		classifiers[i] = SLR(conf, confsection);

		if(inpic)
		{
	
			vector<bool> classinpic;
			
			for(int j = 0; j < (int)examples.size(); j++)
			{
				if(examples[j].first == i)
				{
					if(examples[j].second.position < (int)classinpic.size())
						classinpic[examples[j].second.position] = true;
					else if(examples[j].second.position == (int)classinpic.size())
						classinpic.push_back(true);
					else
					{
						while(examples[j].second.position > (int)classinpic.size())
						{
							classinpic.push_back(false);
						}
						classinpic.push_back(true);
					}
				}
			}
			
			Examples ex2;

			for(int j = 0; j < (int)examples.size(); j++)
			{
				if(examples[j].second.position >= (int)classinpic.size())
						continue;
				if(classinpic[examples[j].second.position])
				{
					Example e;
					e.svec = examples[j].second.svec;
					e.vec = examples[j].second.vec;
					ex2.push_back ( pair<int, Example> ( examples[j].first, e ) );
				}
			}
			cout << "examples for class " << i << ": " << ex2.size() << endl;
			
			if(ex2.size() <= 2)
				continue;
			
			classifiers[i].train(_fp, ex2, i);
			
			for(int j = 0; j < (int)ex2.size(); j++)
			{
				ex2[j].second.svec = NULL;
			}
		}
		else
		{
			classifiers[i].train(_fp, examples, i);
		}
	}

	cout << "end train" << endl;
}


void FPCSMLR::restore (istream & is, int format)
{
	is >> maxClassNo;
	classifiers.resize(maxClassNo);
	for(int i = 0; i < maxClassNo; i++)
	{
		classifiers[i].restore(is, format);
	}
}

void FPCSMLR::store (ostream & os, int format) const
{

	if(format!=-9999) os << maxClassNo;

	for(int i = 0; i < maxClassNo; i++)
	{
		classifiers[i].store(os, format);
	}
}

void FPCSMLR::clear ()
{
//TODO: einbauen
}

FeaturePoolClassifier *FPCSMLR::clone () const
{
	//TODO: wenn alle Variablen der Klasse bekannt sind, dann übergebe diese mit
	FPCSMLR *o = new FPCSMLR ( conf, confsection );

	o->maxClassNo = maxClassNo;

	return o;
}

void FPCSMLR::setComplexity ( int size )
{
	cerr << "FPCSMLR: no complexity to set" << endl;
}