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\demo5_2.m
% DEMO5 Nu Support Vector Regression with LIBSVM. % % This demo shows how to solve a two-dimensional regession problem by a % nu-style SVM (where nu is an alternative to the insensitivity zone). It % also shows how to call LIBSVMOPT with multi-dimensional matrices. The % computed regression function is shown as mesh together with the original % function as transparent surface. % ------------------------------------------------------------------------------ % 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 C = 100; % 拉格朗日乘子上界 nu = 0.01; % nu -> (0,1] 在支持向量数与拟合精度之间进行折衷 %ker = struct('type','linear'); %ker = struct('type','ploy','degree',3,'offset',1); ker = struct('type','gauss','width',1); %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). % ------------------------------------------------------------% % 构造两类训练样本 n = 50; rand('state',42); X = linspace(-4,4,n)'; % 训练样本,n×d的矩阵,n为样本个数,d为样本维数,这里d=1 Ys = (1-X+2*X.^2).*exp(-.5*X.^2); f = 0.2; % 相对误差 Y = Ys+f*max(abs(Ys))*(2*rand(size(Ys))-1)/2; % 训练目标,n×1的矩阵,n为样本个数,值为期望输出 figure plot(X,Ys,'b-',X,Y,'b*'); title('\nu-SVR'); hold on; % ------------------------------------------------------------% % 训练支持向量机 tic svm = libsvmopt(X,Y,C,nu,ker,'style','nu'); 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 % svm.coef % ------------------------------------------------------------% % 测试输出 tic Yd = libsvmsim(svm,X); % 测试输出 t_sim = toc plot(X,Yd,'r--'); hold off;