NICE_VisLearning/classifier/fpclassifier/logisticregression/SLR.h

/** 
 * @file SLR.h
 * @brief implementation of Sparse Logistic Regression (SMLR) Classififier (one vs. all)
 * @author Björn Fröhlich
 * @date 06/23/2009
 */
#ifndef SLRDEF
#define SLRDEF

#include <vislearning/nice.h>

#include "vislearning/classifier/classifierbase/FeaturePoolClassifier.h"
#include "vislearning/cbaselib/FeaturePool.h"
#include "core/algebra/GMSparseVectorMatrix.h"

namespace OBJREC {

class SLR : public NICE::Persistent
{
  //protected:
	public:
	//! the configuration file
	const NICE::Config *conf;

	//! section in the configfile
	std::string confsection;
	
	//! weight vectors
	SparseVector weight;
	
	//! the featurepool
	FeaturePool fp;
	
	//! maximum number of iterations
	int maxiter;
	
	//! Decay rate in the probability of resampling a zero weight.1.0 will immediately decrease to the min_resamp from 1.0, 0.0 will never decrease from 1.0.
	double resamp_decay;
	
	//! convergence criterium for stepwise regression
	double convergence_tol;
	
	//! Minimum resampling probability for zeroed weights"
	double min_resamp;
	
	//! Feature Dimension
	int fdim;
	
	//! The penalty term lambda. Larger values will give rise to	more sparsification
	double lambda;
	
	//! how many samples per class should be used
	double samplesperclass;
	
	//! for normalization
	std::vector<double> minval, maxval;
	
    public:
  
	
	/**
	 * standard constructor
	 * @param conf configfile
	 * @param section section name in configfile for classifier
	 */
	SLR( const NICE::Config *conf, std::string section="SMLR");
      
	
	/**
	 * simple constructor -> does nothing
	 */
	SLR ();

	/**
	 * simple destructor
	 */
	~SLR();

	/**
	 * main classification function
	 * @param pce input feature
	 * @return a classification result
	 */
	double classify ( Example & pce );

	/**
	 * start training
	 * @param fp a featurepool (how to handle which features...)
	 * @param classno which class should be learned
	 * @param examples input features
	 */
	void train ( FeaturePool & _fp, Examples & examples, int classno = 1 );

	/**
	 * clone this object
	 * @return a copy of this object
	 */
	FeaturePoolClassifier *clone () const;

	/**
	 * perform the stepwise regression
	 * @param x input features
	 * @param classno which class should be learned
	 * @return number of iterations
	 */
	int stepwise_regression(Examples &x, int classno = 1);
	
	/** IO functions */
	void restore (std::istream & is, int format = 0);
	void store (std::ostream & os, int format = 0) const;
	void clear ();
};

} // namespace
#endif