/*
 * 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) 1999-2010 Soeren Sonnenburg
 * Written (W) 2011 Abhinav Maurya
 * Written (W) 2012 Heiko Strathmann
 * Copyright (C) 1999-2009 Fraunhofer Institute FIRST and Max-Planck-Society
 * Copyright (C) 2010 Berlin Institute of Technology
 */

#ifndef _GAUSSIANKERNEL_H___
#define _GAUSSIANKERNEL_H___

#include <shogun/lib/common.h>
#include <shogun/kernel/Kernel.h>
#include <shogun/kernel/DotKernel.h>
#include <shogun/features/DotFeatures.h>

namespace shogun
{
	class CDotFeatures;
/** @brief The well known Gaussian kernel (swiss army knife for SVMs)
 * computed on CDotFeatures.
 *
 * It is computed as
 *
 * \f[
 * k({\bf x},{\bf x'})= exp(-\frac{||{\bf x}-{\bf x'}||^2}{\tau})
 * \f]
 *
 * where \f$\tau\f$ is the kernel width.
 *
 * The compact version as given in Bart Hamers' thesis <i>Kernel Models for Large Scale Applications</i> (Eq. 4.10) is computed as
 *
 * \f[
 * k({\bf x},{\bf x'})= max(0, (1-\frac{||{\bf x}-{\bf x'}||}{3\tau})^v)) * exp(-\frac{||{\bf x}-{\bf x'}||^2}{\tau})
 * \f]
 *
 * where \f$\tau\f$ is the kernel width.
 *
 */
class CGaussianKernel: public CDotKernel
{
	public:
		/** default constructor
		 *
		 */
		CGaussianKernel();

		/** constructor
		 *
		 * @param size cache size
		 * @param width width
		 */
		CGaussianKernel(int32_t size, float64_t width);

		/** constructor
		 *
		 * @param l features of left-hand side
		 * @param r features of right-hand side
		 * @param width width
		 * @param size cache size
		 */
		CGaussianKernel(CDotFeatures* l, CDotFeatures* r,
			float64_t width, int32_t size=10);

		virtual ~CGaussianKernel();

		/** @param kernel is casted to CGaussianKernel, error if not possible
		 * is SG_REF'ed
		 * @return casted CGaussianKernel object
		 */
		static CGaussianKernel* obtain_from_generic(CKernel* kernel);

		/** Make a shallow copy of the kernel */
		virtual CSGObject *shallow_copy() const;

		/** initialize kernel
		 *
		 * @param l features of left-hand side
		 * @param r features of right-hand side
		 * @return if initializing was successful
		 */
		virtual bool init(CFeatures* l, CFeatures* r);

		/** clean up kernel */
		virtual void cleanup();

		/** return what type of kernel we are
		 *
		 * @return kernel type GAUSSIAN
		 */
		virtual EKernelType get_kernel_type() { return K_GAUSSIAN; }

		/** return the kernel's name
		 *
		 * @return name Gaussian
		 */
		virtual const char* get_name() const { return "GaussianKernel"; }

		/** set the kernel's width
		 *
		 * @param w kernel width
		 */
		virtual void set_width(float64_t w)
		{
			width=w;
		}

		/** return the kernel's width
		 *
		 * @return kernel width
		 */
		virtual float64_t get_width() const
		{
			return width;
		}

		/** set the compact option
		 *
		 * @param compact value of the compact option
		 */
		inline void set_compact_enabled(bool compact)
		{
			m_compact = compact;
		}

		/** return value of the compact option
		 *
		 * @return whether the compact option is enabled
		 */
		inline bool get_compact_enabled()
		{
			return m_compact;
		}
		
		/** return derivative with respect to specified parameter
		 * 
		 * @param param the parameter
		 * @param obj the object that owns the parameter
		 * @param index the index of the element if parameter is a vector
		 *
		 * @return gradient with respect to parameter
		 */
		virtual SGMatrix<float64_t> get_parameter_gradient(TParameter* param,
				CSGObject* obj, index_t index = -1);


	protected:
		/** compute kernel function for features a and b
		 * idx_{a,b} denote the index of the feature vectors
		 * in the corresponding feature object
		 *
		 * @param idx_a index a
		 * @param idx_b index b
		 * @return computed kernel function at indices a,b
		 */
		virtual float64_t compute(int32_t idx_a, int32_t idx_b);

		/** Can (optionally) be overridden to post-initialize some
		 *  member variables which are not PARAMETER::ADD'ed.  Make
		 *  sure that at first the overridden method
		 *  BASE_CLASS::LOAD_SERIALIZABLE_POST is called.
		 *
		 *  @exception ShogunException Will be thrown if an error
		 *                             occurres.
		 */
		virtual void load_serializable_post() throw (ShogunException);

	private:
		/** helper function to compute quadratic terms in
		 * (a-b)^2 (== a^2+b^2-2ab)
		 */
		void precompute_squared();

		/** helper function to compute quadratic terms in
		 * (a-b)^2 (== a^2+b^2-2ab)
		 *
		 * @param buf buffer to store squared terms (will be allocated)
		 * @param df dot feature object based on which k(i,i) is computed
		 * */
		void precompute_squared_helper(float64_t* &buf, CDotFeatures* df);

		void init();

	protected:
		/** width */
		float64_t width;
		/** squared left-hand side */
		float64_t* sq_lhs;
		/** squared right-hand side */
		float64_t* sq_rhs;
		/** whether compact output enabled */
		bool m_compact;
};
}
#endif /* _GAUSSIANKERNEL_H__ */
