libshogun-dev 1.1.0-4ubuntu2 / usr / include / shogun / classifier / mkl / MKLMultiClass.h

/*
 * This program is free software; you can redistribute it and/or modify
 * it under the terms of the GNU General Public License as published by
 * the Free Software Foundation; either version 3 of the License, or
 * (at your option) any later version.
 *
 * Written (W) 2009 Alexander Binder
 * Copyright (C) 2009 Fraunhofer Institute FIRST and Max-Planck-Society
 */

#ifndef MKLMULTICLASS_H_
#define MKLMULTICLASS_H_

#include <vector>

#include <shogun/base/SGObject.h>
#include <shogun/kernel/Kernel.h>  
#include <shogun/kernel/CombinedKernel.h> 
#include <shogun/classifier/svm/GMNPSVM.h>
#include <shogun/classifier/mkl/MKLMultiClassGLPK.h>
#include <shogun/classifier/mkl/MKLMultiClassGradient.h>


namespace shogun
{
/** @brief MKLMultiClass is a class for L1-norm multiclass MKL.
*
*	It is based on the GMNPSVM Multiclass SVM.
*	Its own parameters are the L2 norm weight change based MKL
*	Its termination criterion set by void set_mkl_epsilon(float64_t eps ); and the maximal number of MKL iterations set by void set_max_num_mkliters(int32_t maxnum); It passes the regularization constants C1 and C2 to GMNPSVM.
 */
class CMKLMultiClass : public CMultiClassSVM
{
public:
	/** Class default Constructor
	 * 
	 */
	CMKLMultiClass();
	/** Class Constructor commonly used in Shogun Toolbox
	 * @param C constant C
	 * @param k kernel
	 * @param lab labels
	 */
	CMKLMultiClass(float64_t C, CKernel* k, CLabels* lab);


	/** Class default Destructor
	 * 
	 */
	virtual ~CMKLMultiClass();

	/** get classifier type
	 *
	 * @return classifier type GMNPMKL
	 */
	virtual inline EClassifierType get_classifier_type()
		{ return CT_MKLMULTICLASS; }
	

	/** returns MKL weights for the different kernels
	 *
	 * @param numweights is output parameter, is set to zero if no weights
	 * have been computed or to the number of MKL weights which is equal to the number of kernels
	 * 
	 * @return NULL if no weights have been computed or otherwise an array
	 * with the weights, caller has to SG_FREE() the output
	 */
	float64_t* getsubkernelweights(int32_t & numweights);
	
	/** sets MKL termination threshold
	 *
	 * @param eps is the desired threshold value
	 * the termination criterion is the L2 norm between the current MKL weights
	 *  and their counterpart from the previous iteration
	 * 
	 */
	void set_mkl_epsilon(float64_t eps ); 

	/** sets maximal number of MKL iterations
	 *
	 * @param maxnum is the desired maximal number of MKL iterations; when it
	 *  is reached the MKL terminates irrespective of the MKL progress
	 * set it to a nonpositive value in order to turn it off
	 * 
	 */
	void set_max_num_mkliters(int32_t maxnum);

	/** set mkl norm
	 * @param norm
	 */
	virtual void set_mkl_norm(float64_t norm);

	
protected:
	/** Class Copy Constructor
	 * protected to avoid its usage 
	 * 
	 */
	CMKLMultiClass( const CMKLMultiClass & cm);
	/** Class Assignment operator
	 * protected to avoid its usage
	 * 
	 */
	CMKLMultiClass operator=( const CMKLMultiClass & cm);

	/** performs some sanity checks (on the provided kernel), inits the
	 * GLPK-based LP solver
	 * 
	 */
	void initlpsolver();

	/** inits the underlying Multiclass SVM
	 * 
	 */
	void initsvm();
	


	
	/** checks MKL for convergence
	 *
	 * @param numberofsilpiterations is the number of currently done iterations
	 * 
	 */
	virtual bool evaluatefinishcriterion(const int32_t
			numberofsilpiterations);
	

	/** adds a constraint to the LP used in MKL
	 *
	 * @param curweights are the current MKL weights
	 *
	 * it uses 
	 * void addingweightsstep( const std::vector<float64_t> & curweights);
	 * and 
	 * float64_t getsumofsignfreealphas();
	 */
	void addingweightsstep( const std::vector<float64_t> & curweights);
	/** computes the first svm-dependent part used for generating MKL constraints
	 * it is
	 * \f$ \sum_y b_y^2-\sum_i \sum_{ y | y \neq y_i} \alpha_{iy}(b_{y_i}-b_y-1) \f$
	 */
	float64_t getsumofsignfreealphas();
	/** computes the second svm-dependent part used for generating MKL
	 * constraints
	 *
	 * @param ind is the index of the kernel for which
	 * to compute \f$ \|w \|^2  \f$
	 */
	float64_t getsquarenormofprimalcoefficients(
			const int32_t ind);
	

	/** train Multiclass MKL classifier
	 *
	 * @param data training data (parameter can be avoided if distance or
	 * kernel-based classifiers are used and distance/kernels are
	 * initialized with train data)
	 *
	 * @return whether training was successful
	 */
	virtual bool train_machine(CFeatures* data=NULL);
	

protected:
	/** the multiclass svm for fixed MKL weights
	*
	*
	*/	
	CGMNPSVM* svm;
	/** the solver wrapper
	*	
	*/
	MKLMultiClassOptimizationBase* lpw; 
	/** stores the last two mkl iteration weights 
	*	
	*/
	::std::vector< std::vector< float64_t> > weightshistory;

	/** MKL termination threshold
	*	is set void set_mkl_epsilon(float64_t eps ); 
	*/
	float64_t mkl_eps; 
	/** maximal number of MKL iterations
	*	is set by void set_max_num_mkliters(int32_t maxnum);
	*/
	int32_t max_num_mkl_iters; 
	/** MKL norm >=1
	*   
	*/
	float64_t pnorm;
	/** stores the term  \| w_l \|^2 ~~~ "alpha o Y K_l Y o alpha"
	*   
	*/
	std::vector<float64_t> normweightssquared;
	
};
}
#endif // GMNPMKL_H_