www.gusucode.com > 支持向量机工具箱 - LIBSVM OSU_SVM LS_SVM源码程序 > 支持向量机工具箱 - LIBSVM OSU_SVM LS_SVM\libsvm\demo3_2.m

    % 一类支持向量机(OCSVM) - 已经调试通过
% DEMO3  One-Class Support Vector Classification with LIBSVM.
%
% The aim of a one-class SVM is to find the boundary of a data sample.
% One-class SVMs are always nu style; this demo uses a SVM with Gauss
% kernels. The solution can be illustrated as contour plot or as decision
% function (dependent on the variable pps).

% ------------------------------------------------------------------------------
% MATLAB Interface for LIBSVM, Version 1.2
%
% Copyright (C) 2004-2005 Michael Vogt
% Written by Michael Vogt, Atanas Ayarov and Bennet Gedan
%
% This program is free software; you can redistribute it and/or modify it
% under the terms of the GNU General Public License as published by the Free
% Software Foundation; either version 2 of the License, or (at your option)
% any later version.
% ------------------------------------------------------------------------------

clc
clear
%close all

% ------------------------------------------------------------%
% 定义核函数及相关参数

nu = 0.2;           % nu -> [0,1] 在支持向量数与错分样本数之间进行折衷
                    % 支持向量机的 nu 参数(取值越小，异常点就越少)

%ker = struct('type','linear');
%ker = struct('type','ploy','degree',3,'offset',1);
ker = struct('type','gauss','width',500);
%ker = struct('type','tanh','gamma',1,'offset',0);

% 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).

% ------------------------------------------------------------%
% 构造一类训练样本

n = 100
randn('state',1);
x1 = randn(floor(n*0.95),2);
x2 = 5+randn(ceil(n*0.05),2);
X = [x1;x2];            % 训练样本,n×d的矩阵,n为样本个数,d为样本维数

figure;
plot(x1(:,1),x1(:,2),'bx',x2(:,1),x2(:,2),'k.');
axis([-5 8 -5 8]); 
title('One-Class SVM');
hold on;

% ------------------------------------------------------------%
% 训练支持向量机

tic
svm = libsvmopt(X,[],nu,ker)
t_train = toc

% svm  支持向量机(结构体变量)
% the following fields:
%   ker - 核参数
%   x - 训练样本
%   y - 训练目标;
%   a - 拉格朗日乘子

% ------------------------------------------------------------%
% 寻找支持向量

vect = svm.vect;
plot(vect(:,1),vect(:,2),'ro');

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

[x1,x2] = meshgrid(-4:0.05:6,-4:0.05:6);
[rows,cols] = size(x1);
nt = rows*cols;                  % 测试样本数
Xt = [reshape(x1,nt,1),reshape(x2,nt,1)];

tic
Yd = libsvmsim(svm,Xt);           % 测试输出
t_sim = toc

Yd = reshape(Yd,rows,cols);
contour(x1,x2,Yd,[0 0],'m');       % 分类面
hold off;