SVM.rar_svm 模式识别_机器学习拟合

function SVM_Test clear all % ------------------------------------------------------------% % 构造两类训练数据集 以 y=ax^2+ b*x+c 为分界线 shift % if a==0 then it is a line a=0; b=3; c=0.5; shift=0.5; b1=0.5; xx=-1:0.1:1; n = length(xx); x1=zeros(n,2); x2=zeros(n,2); x1(:,1) = xx'; x2(:,1)=xx'; x1(:,2)=a*x1(:,1).*x1(:,1)+b*x1(:,1)+c + shift+ abs(randn(n,1)) ; x2(:,2)=a*x2(:,1).*x2(:,1)+b*x2(:,1)+c - shift- abs(randn(n,1)) ; y1 = ones(n,1); y2 = -ones(n,1); figure; plot(x1(:,1),x1(:,2),'bx',x2(:,1),x2(:,2),'k.'); hold on; X = [x1;x2]; % 训练样本 Y = [y1;y2]; % 训练目标,n×1的矩阵,n为样本个数,值为+1或-1 % ------------------------------------------------------------% ker=struct('type','linear'); %ker=struct('type','Radial basis kernel','value',1); % -------------FFMVM 预抽取预支持向量 ------------------------% XX=X; YY=Y; [X,Y]=SVM_FFMVM(ker, XX, YY); fprintf('原输入向量规模 \n') sizeX=size(XX) fprintf('抽取预支持向量规模 \n') sizeX=size(X) plot(X(:,1),X(:,2),'*'); % ------------------------------------------------------------% svm=SVM_DivideTwoClass(ker,X,Y) %plot the decision function curve for i=1:n tx1=x1(i,1); tx2=a*tx1*tx1+b*tx1+c; min=1000; for step=-shift:0.01:shift tx=[tx1,tx2+step]; f=CalcValueBySVM(svm,tx); if(abs(f)<min) min=abs(f); decisionCurve(i,:)=tx; end end end plot(decisionCurve(:,1),decisionCurve(:,2)); for i=1:2*n f=CalcValueBySVM(svm,XX(i,:)); YYY(i)=sign(f); end YYY=YYY'