kernel.cpp

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

#include "StdAfx.h"
#include ".\kernel.h"

//CKernel::CKernel(void)
//{
//}
//
//CKernel::~CKernel(void)
//{
//}

Kernel::Kernel(int l, svm_node * const * x_, const svm_parameter& param)
:kernel_type(param.kernel_type), degree(param.degree),
gamma(param.gamma), coef0(param.coef0)
{
	switch(kernel_type)
	{
	case LINEAR:
		kernel_function = &Kernel::kernel_linear;
		break;
	case POLY:
		kernel_function = &Kernel::kernel_poly;
		break;
	case RBF:
		kernel_function = &Kernel::kernel_rbf;
		break;
	case SIGMOID:
		kernel_function = &Kernel::kernel_sigmoid;
		break;
	}

	clone(x,x_,l);

	if(kernel_type == RBF)
	{
		x_square = new double[l];
		for(int i=0;i<l;i++)
			x_square[i] = dot(x[i],x[i]);
	}
	else
		x_square = 0;
}

Kernel::~Kernel()
{
	delete[] x;
	delete[] x_square;
}

double Kernel::dot(const svm_node *px, const svm_node *py)
{
	double sum = 0;
	while(px->index != -1 && py->index != -1)
	{
		if(px->index == py->index)
		{
			sum += px->value * py->value;
			++px;
			++py;
		}
		else
		{
			if(px->index > py->index)
				++py;
			else
				++px;
		}			
	}
	return sum;
}

//////////////////////////////////////////////////////////////////////////
//k_function和kernel_function是一样的，不同在于k_function用于预测时的单个处理
double Kernel::k_function(const svm_node *x, const svm_node *y,
						  const svm_parameter& param)
{
	switch(param.kernel_type)
	{
	case LINEAR:
		return dot(x,y);
	case POLY:
		return pow(param.gamma*dot(x,y)+param.coef0,param.degree);
	case RBF:
		{
			double sum = 0;
			while(x->index != -1 && y->index !=-1)
			{
				if(x->index == y->index)
				{
					double d = x->value - y->value;
					sum += d*d;
					++x;
					++y;
				}
				else
				{
					if(x->index > y->index)
					{	
						sum += y->value * y->value;
						++y;
					}
					else
					{
						sum += x->value * x->value;
						++x;
					}
				}
			}

			while(x->index != -1)
			{
				sum += x->value * x->value;
				++x;
			}

			while(y->index != -1)
			{
				sum += y->value * y->value;
				++y;
			}

			return exp(-param.gamma*sum);
		}
	case SIGMOID:
		return tanh(param.gamma*dot(x,y)+param.coef0);
	default:
		return 0;	/* Unreachable */
	}
}