libsvm代码阅读:关于Kernel类分析

update:2014-2-27 LinJM @HQU  『 libsvm专栏地址:http://blog.csdn.net/column/details/libsvm.html 』

这一篇博文来分析下Kernel类,代码上很简单,一般都能看懂。Kernel类主要是为SVM的核函数服务的,里面实现了SVM常用的核函数,通过函数指针来使用这些核函数。

其中几个常用核函数如下所示:(一般情况下,使用RBF核函数能取得很好的效果)

libsvm代码阅读:关于Kernel类分析_第1张图片

关于基类QMatrix在Kernel中的作用并不明显,只是定义了一些纯虚函数,Kernel继承这些函数,Kernel只对swap_index进行了定义。其余的get_Q和get_QD在Kernel并没有用到。

class QMatrix {
public:
	virtual Qfloat *get_Q(int column, int len) const = 0;//纯虚函数,在子类中实现,important!
	virtual double *get_QD() const = 0;
	virtual void swap_index(int i, int j) const = 0;
	virtual ~QMatrix() {}
};


Kernel类的定义函数,比较简单就不细说。

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 double *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;//用来指向样本数据,每次数据传入时通过克隆函数来实现,完全重新分配内存,主要是为处理多类着想
	double *x_square;//使用RBF 核才使用

	// svm_parameter
	const int kernel_type;
	const int 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 powi(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);
	}
	double kernel_precomputed(int i, int j) const
	{
		return x[i][(int)(x[j][0].value)].value;
	}
};


这个Kernel类的函数比较清晰,我直接把它的全部代码贴出。

全部代码如下:

//
// Kernel evaluation
//
// the static method k_function is for doing single kernel evaluation
// the constructor of Kernel prepares to calculate the l*l kernel matrix
// the member function get_Q is for getting one column from the Q Matrix
//
class QMatrix {
public:
	virtual Qfloat *get_Q(int column, int len) const = 0;
	virtual double *get_QD() const = 0;
	virtual void swap_index(int i, int j) const = 0;
	virtual ~QMatrix() {}
};

class Kernel: public QMatrix {
public:
	Kernel(int l, svm_node * const * x, const svm_parameter& param);//构造函数
	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 double *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;//用来指向样本数据,每次数据传入时通过克隆函数来实现,完全重新分配内存,主要是为处理多类着想
	double *x_square;//使用RBF 核才使用

	// svm_parameter
	const int kernel_type;
	const int 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 powi(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);
	}
	double kernel_precomputed(int i, int j) const
	{
		return x[i][(int)(x[j][0].value)].value;
	}
};

//构造函数,初始化类中的部分常量,指定核函数,克隆样本数据。如果使用RBF核函数,则计算x_square[i]
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;
		case PRECOMPUTED:
			kernel_function = &Kernel::kernel_precomputed;
			break;
	}

	clone(x,x_,l);//void clone(T*& dst, S* src, int n)

	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;
}

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 powi(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);
		case PRECOMPUTED:  //x: test (validation), y: SV
			return x[(int)(y->value)].value;
		default:
			return 0;  // Unreachable 
	}
}

本文地址:http://blog.csdn.net/linj_m/article/details/19574623

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