one_class_svm_sim.m

各人用Mtalab编写的SVM程序

function Yd = One_Class_SVM_Sim(svm,Xt)
% 输入参数:
% svm 支持向量机(结构体变量)
% the following fields:
% 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

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

ker = svm.ker;
X = svm.x;
a = svm.a;

% ------------------------------------------------------------%
% 求超球半径 R

epsilon = 1e-8; % 如果小于此值则认为是0
i_sv = find(a>epsilon); % 支持向量下标
n_sv = length(i_sv);

tmp1 = zeros(n_sv,1);
for i = 1:n_sv
index = i_sv(i);
tmp1(i) = Calckernel(ker,X(index,:),X(index,:));
end

tmp2 = 2*Calckernel(ker,X(i_sv,:),X)*a; % 列向量
tmp3 = sum(sum(a*a'.*Calckernel(ker,X,X))); 

R_square = tmp1-tmp2+tmp3;
R = mean(sqrt(R_square)); % 超球半径 R (对所有支持向量求平均的结果)

% ------------------------------------------------------------%
% 测试输出

nt = size(Xt,1); % 测试样本数

tmp4 = zeros(nt,1); % 列向量
for i = 1:nt
tmp4(i) = Calckernel(ker,Xt(i,:),Xt(i,:));
end

tmp5 = 2*Calckernel(ker,Xt,X)*a; % 列向量

Yd = sign(tmp4-tmp5+tmp3-R^2);