svmsim.m

这是我的一个作业

function Yd = svmSim(svm,Xt)
% 输入参数:
% svm  支持向量机(结构体变量)
% the following fields:
%   type - 支持向量机类型 {'svc_c', 'svr_epsilon' }
%   ker - 核参数
%       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).
%   x - 训练样本
%   y - 训练目标;
%   a - 拉格朗日乘子
% Xt  测试样本,n×d的矩阵,n为样本个数,d为样本维数
% 输出参数:
% Yd  测试输出,n×1的矩阵,n为样本个数,值为+1或-1
% ------------------------------------------------------------%
type = svm.type;
ker = svm.ker;
X = svm.x;
Y = svm.y;
a = svm.a;
% ------------------------------------------------------------%
% 测试输出
epsilon=1e-6;                  % 如果小于此值则认为是0
i_sv = find(abs(a)>epsilon);          % 支持向量下标
switch type
    case 'svc_c',
        tmp = ((a(i_sv,:).*Y(i_sv,:))'*kernel(ker,X(i_sv,:),X(i_sv,:)))'; % 行向量
        b = 1./Y(i_sv)-tmp;
        b = mean(b);
        tmp=((a.*Y)'*kernel(ker,X,Xt))';
        Yd = sign(tmp+b);
    case 'svr_epsilon',
        tmp = (a(i_sv)'*kernel(ker,X(i_sv,:),X(i_sv,:)))';   % 列向量
        b = Y(i_sv)-tmp; 
        if length(b)~=0
           b = mean(b); 
        else
            b=0;
        end
        tmp=(a'*kernel(ker,X,Xt))';
        Yd=tmp+b; 
    otherwise,
end