kernel.m

支持向量机matlab程序 (1) Main_SVC_C.m --- C_SVC二类分类算法 (2) Main_SVC_Nu.m --- Nu_SVC二类分类算法 (3) Main_SVM_On

function [K] = kernel(ker,x,y)
% Calculate kernel function.   
%
% x: 输入样本,d×n1的矩阵,n1为样本个数,d为样本维数
% y: 输入样本,d×n2的矩阵,n2为样本个数,d为样本维数
%
% ker  核参数(结构体变量)
% the following fields:
%   type   - linear :  k(x,y) = x'*y
%            poly   :  k(x,y) = (x'*y+c)^d
%            gauss  :  k(x,y) = exp(-0.5*(norm(x-y)/s)^2)
%            tanh   :  k(x,y) = tanh(g*x'*y+c)
%   degree - Degree d of polynomial kernel (positive scalar).
%   offset - Offset c of polynomial and tanh kernel (scalar, negative for tanh).
%   width  - Width s of Gauss kernel (positive scalar).
%   gamma  - Slope g of the tanh kernel (positive scalar).
%
% ker = struct('type','linear');
% ker = struct('type','ploy','degree',d,'offset',c);
% ker = struct('type','gauss','width',s);
% ker = struct('type','tanh','gamma',g,'offset',c);
%
% K: 输出核参数,n1×n2的矩阵

%-------------------------------------------------------------%

switch ker.type
    case 'linear'
        K = x'*y;
    case 'ploy'
        d = ker.degree;
        c = ker.offset;
        K = (x'*y+c).^d;
    case 'gauss'
        
        s = ker.width;
        rows = size(x,2);
        cols = size(y,2);   
        tmp = zeros(rows,cols);
        for i = 1:rows
            for j = 1:cols
                tmp(i,j) = norm(x(:,i)-y(:,j));
            end
        end        
        K = exp(-0.5*(tmp/s).^2);

    case 'tanh'
        g = ker.gamma;
        c = ker.offset;
        K = tanh(g*x'*y+c);
    otherwise
        K = 0;
end