svmtrain.m

这是我的一个作业

function svm = svmTrain(svmType,X,Y,ker,p1,p2)
% SVM Classification:
%   svm = svmTrain('svc_c',x,y,ker,C); 
% SVM Regression:
%   svm = svmTrain('svr_epsilon',x,y,ker,C,e); 
% 输入参数:
% X  训练样本,n×d的矩阵,n为样本个数,d为样本维数
% Y  训练目标,n×1的矩阵,n为样本个数,值为+1或-1
% 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).
% 输出参数:
% 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
%   x - 训练样本,n×d的矩阵,n为样本个数,d为样本维数
%   y - 训练目标,n×1的矩阵,n为样本个数,值为+1或-1
%   a - 拉格朗日乘子,n×1的矩阵
% ------------------------------------------------------------%
%生成/改变优化选项结构
options = optimset;
options.LargeScale = 'off';
options.Display = 'off';
switch svmType
    case 'svc_c',
        C = p1;
        n = length(Y);
        H = (Y*Y').*kernel(ker,X,X);
        f = -ones(1,n);
        A = [];
        b = [];
        Aeq = Y';
        beq = 0;
        lb = zeros(n,1);
        ub = C*ones(n,1);
        a0 = zeros(n,1);
        [a,fval,eXitflag,output,lambda]=quadprog(H,f,A,b,Aeq,beq,lb,ub,a0,options);                    
    case 'svr_epsilon',
        C = p1;
        e = p2;
        n = length(Y);
        Q = kernel(ker,X,X);
        H = [Q,-Q;-Q,Q];
        f = [e*ones(n,1)-Y;e*ones(n,1)+Y]; 
        A = [];
        b = [];
        Aeq = [ones(1,n),-ones(1,n)];
        beq = 0;
        lb = zeros(2*n,1); 
        ub = C*ones(2*n,1);
        a0 = zeros(2*n,1);
        [a,fval,eXitflag,output,lambda]=quadprog(H,f,A,b,Aeq,beq,lb,ub,a0,options); 
        a = a(1:n)-a(n+1:end);
    otherwise,
end
eXitflag;
% ------------------------------------------------------------%
% 输出 svm
svm.type = svmType;
svm.ker = ker;
svm.x = X;
svm.y = Y;
svm.a = a;