kernel.h

使用KernelBDA实现三维模型相关反馈的算法注：1、使用本系统必须在本地计算机上安装matlab 2、算法使用的三维模型特征向量是从PSB模型库中自动提取的DESIRE三维模型特征向量 3、本

#pragma once
#include <math.h>

typedef float Qfloat;

template <class T> inline void swap(T& x, T& y) { T t=x; x=y; y=t; }
template <class S, class T> inline void clone(T*& dst, S* src, int n)
{
	dst = new T[n];
	memcpy((void *)dst,(void *)src,sizeof(T)*n);
}


enum { LINEAR, POLY, RBF, SIGMOID };	/* kernel_type */
//////////////////////////////////////////////////////////////////////////
//kernel必须的一些东西都在这里了，主要就是k_function
//////////////////////////////////////////////////////////////////////////


struct svm_node
{
	int index;
	double value;
};

struct svm_parameter
{
	int svm_type;
	int kernel_type;
	double degree;	/* for poly */
	double gamma;	/* for poly/rbf/sigmoid */
	double coef0;	/* for poly/sigmoid */

	/* these are for training only */
	double cache_size; /* in MB */
	double eps;	/* stopping criteria */
	double C;	/* for C_SVC, EPSILON_SVR and NU_SVR */
	int nr_weight;		/* for C_SVC */
	int *weight_label;	/* for C_SVC */
	double* weight;		/* for C_SVC */
	double nu;	/* for NU_SVC, ONE_CLASS, and NU_SVR */
	double p;	/* for EPSILON_SVR */
	int shrinking;	/* use the shrinking heuristics */
	int probability; /* do probability estimates */
};


class QMatrix {
public:
	virtual Qfloat *get_Q(int column, int len) const = 0;
	virtual Qfloat *get_QD() const = 0;
	virtual void swap_index(int i, int j) const = 0;
	virtual ~QMatrix() {}
};

//class CKernel
//{
//public:
//	CKernel(void);
//	~CKernel(void);
//};

class Kernel: public QMatrix {
public:
	Kernel(int l, svm_node * const * x, const svm_parameter& param);
	virtual ~Kernel();

	static double k_function(const svm_node *x, const svm_node *y,
		const svm_parameter& param);
	virtual Qfloat *get_Q(int column, int len) const = 0;
	virtual Qfloat *get_QD() const = 0;
	virtual void swap_index(int i, int j) const	// no so const...
	{
		swap(x[i],x[j]);
		if(x_square) swap(x_square[i],x_square[j]);
	}
protected:

	double (Kernel::*kernel_function)(int i, int j) const;

private:
	const svm_node **x;
	double *x_square;

	// svm_parameter
	const int kernel_type;
	const double degree;
	const double gamma;
	const double coef0;

	static double dot(const svm_node *px, const svm_node *py);
	double kernel_linear(int i, int j) const
	{
		return dot(x[i],x[j]);
	}
	double kernel_poly(int i, int j) const
	{
		return pow(gamma*dot(x[i],x[j])+coef0,degree);
	}
	double kernel_rbf(int i, int j) const
	{
		return exp(-gamma*(x_square[i]+x_square[j]-2*dot(x[i],x[j])));
	}
	double kernel_sigmoid(int i, int j) const
	{
		return tanh(gamma*dot(x[i],x[j])+coef0);
	}
};