libshogun-dev 3.2.0-7.5 / usr / include / shogun / classifier / svm / SVMOcas.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) 2007-2009 Vojtech Franc
 * Written (W) 2007-2009 Soeren Sonnenburg
 * Copyright (C) 2007-2009 Fraunhofer Institute FIRST and Max-Planck-Society
 */

#ifndef _SVMOCAS_H___
#define _SVMOCAS_H___

#include <shogun/lib/common.h>
#include <shogun/machine/LinearMachine.h>
#include <shogun/lib/external/libocas.h>
#include <shogun/features/DotFeatures.h>
#include <shogun/labels/Labels.h>

namespace shogun
{
#ifndef DOXYGEN_SHOULD_SKIP_THIS
enum E_SVM_TYPE
{
	SVM_OCAS = 0,
	SVM_BMRM = 1
};
#endif

/** @brief class SVMOcas */
class CSVMOcas : public CLinearMachine
{
	public:

		/** problem type */
		MACHINE_PROBLEM_TYPE(PT_BINARY);

		/** default constructor  */
		CSVMOcas();

		/** constructor
		 *
		 * @param type a E_SVM_TYPE
		 */
		CSVMOcas(E_SVM_TYPE type);

		/** constructor
		 *
		 * @param C constant C
		 * @param traindat training features
		 * @param trainlab labels for training features
		 */
		CSVMOcas(
			float64_t C, CDotFeatures* traindat,
			CLabels* trainlab);
		virtual ~CSVMOcas();

		/** get classifier type
		 *
		 * @return classifier type SVMOCAS
		 */
		virtual EMachineType get_classifier_type() { return CT_SVMOCAS; }

		/** set C
		 *
		 * @param c_neg new C constant for negatively labeled examples
		 * @param c_pos new C constant for positively labeled examples
		 *
		 */
		inline void set_C(float64_t c_neg, float64_t c_pos) { C1=c_neg; C2=c_pos; }

		/** get C1
		 *
		 * @return C1
		 */
		inline float64_t get_C1() { return C1; }

		/** get C2
		 *
		 * @return C2
		 */
		inline float64_t get_C2() { return C2; }

		/** set epsilon
		 *
		 * @param eps new epsilon
		 */
		inline void set_epsilon(float64_t eps) { epsilon=eps; }

		/** get epsilon
		 *
		 * @return epsilon
		 */
		inline float64_t get_epsilon() { return epsilon; }

		/** set if bias shall be enabled
		 *
		 * @param enable_bias if bias shall be enabled
		 */
		inline void set_bias_enabled(bool enable_bias) { use_bias=enable_bias; }

		/** check if bias is enabled
		 *
		 * @return if bias is enabled
		 */
		inline bool get_bias_enabled() { return use_bias; }

		/** set buffer size
		 *
		 * @param sz buffer size
		 */
		inline void set_bufsize(int32_t sz) { bufsize=sz; }

		/** get buffer size
		 *
		 * @return buffer size
		 */
		inline int32_t get_bufsize() { return bufsize; }

		/** compute the primal objective value
		 *
		 * @return the primal objective
		 */
		virtual float64_t compute_primal_objective() const;

	protected:
		/** compute W
		 *
		 * @param sq_norm_W square normed W
		 * @param dp_WoldW dp W old W
		 * @param alpha alpha
		 * @param nSel nSel
		 * @param ptr ptr
		 */
		static void compute_W(
			float64_t *sq_norm_W, float64_t *dp_WoldW, float64_t *alpha,
			uint32_t nSel, void* ptr);

		/** update W
		 *
		 * @param t t
		 * @param ptr ptr
		 * @return something floaty
		 */
		static float64_t update_W(float64_t t, void* ptr );

		/** add new cut
		 *
		 * @param new_col_H new col H
		 * @param new_cut new cut
		 * @param cut_length length of cut
		 * @param nSel nSel
		 * @param ptr ptr
		 */
		static int add_new_cut(
			float64_t *new_col_H, uint32_t *new_cut, uint32_t cut_length,
			uint32_t nSel, void* ptr );

		/** compute output
		 *
		 * @param output output
		 * @param ptr ptr
		 */
		static int compute_output( float64_t *output, void* ptr );

		/** sort
		 *
		 * @param vals vals
		 * @param data data
		 * @param size size
		 */
		static int sort( float64_t* vals, float64_t* data, uint32_t size);

		/** print nothing */
		static inline void print(ocas_return_value_T value)
		{
			  return;
		}

	protected:
		/** train SVM 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);

		/** @return object name */
		inline const char* get_name() const { return "SVMOcas"; }
	private:
		void init();

	protected:
		/** if bias is used */
		bool use_bias;
		/** buffer size */
		int32_t bufsize;
		/** C1 */
		float64_t C1;
		/** C2 */
		float64_t C2;
		/** epsilon */
		float64_t epsilon;
		/** method */
		E_SVM_TYPE method;

		/** old W */
		float64_t* old_w;
		/** old bias */
		float64_t old_bias;
		/** nDim big */
		float64_t* tmp_a_buf;
		/** labels */
		SGVector<float64_t> lab;

		/** sparse representation of
		 * cutting planes */
		float64_t** cp_value;
		/** cutting plane index */
		uint32_t** cp_index;
		/** cutting plane dimensions */
		uint32_t* cp_nz_dims;
		/** bias dimensions */
		float64_t* cp_bias;

		/** primal objective */
		float64_t primal_objective;
};
}
#endif