Main_SVR_Epsilon.rar_littleg3t_svr matlab_svr matlab程序_svr 回归_支持

clc clear %close all % ------------------------------------------------------------% % 定义核函数及相关参数 C = 100; % 拉格朗日乘子上界 e = 0.2; % 不敏感损失函数的参数，Epsilon越大，支持向量越少 %ker = struct('type','linear'); %ker = struct('type','ploy','degree',5,'offset',1); ker = struct('type','gauss','width',0.6); %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,d×n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,1×n的矩阵,n为样本个数,值为期望输出 figure; plot(X,Ys,'b-',X,Y,'b*'); title('\epsilon-SVR'); hold on; % ------------------------------------------------------------% % 训练支持向量机 tic svm = svmTrain('svr_epsilon',X,Y,ker,C,e); t_train = toc % svm 支持向量机(结构体变量) % the following fields: % type - 支持向量机类型 {'svc_c','svc_nu','svm_one_class','svr_epsilon','svr_nu'} % ker - 核参数 % x - 训练样本,d×n的矩阵,n为样本个数,d为样本维数 % y - 训练目标,1×n的矩阵,n为样本个数 % a - 拉格朗日乘子,1×n的矩阵 % ------------------------------------------------------------% % 寻找支持向量 a = svm.a; epsilon = 1e-8; % 如果"绝对值"小于此值则认为是0 i_sv = find(abs(a)>epsilon); % 支持向量下标,这里对abs(a)进行判定 plot(X(i_sv),Y(i_sv),'ro'); % ------------------------------------------------------------% % 测试输出 tic Yd = svmSim(svm,X); % 测试输出 t_sim = toc plot(X,Yd,'r--'); hold off;