gfclass.m

这是一个径向基函数网络MATLAB的源程序,用于两类线性不可分的数据的分类

%
%

clear all
close all


s=1;
N=100;               % Learning set will be 4*N
N=round(N/4);


% Generate a set of learning sample
% Class one
X00=1.5*[randn(N,1),randn(N,1)]+2*[ones(N,1),ones(N,1)];
X01=1.5*[randn(N,1),randn(N,1)]+2*[-ones(N,1),-ones(N,1)];
X0=[X00;X01];
% Class two
X10=[randn(N,1),randn(N,1)]+3*[-ones(N,1),ones(N,1)];
X11=[randn(N,1),randn(N,1)]+3*[ones(N,1),-ones(N,1)];
X1=[X10;X11];
% Plot double class by distinct colors
figure(1)
whitebg(1,'k');
hold on
plot(X0(:,1),X0(:,2),'ro','linewidth',5)
plot(X1(:,1),X1(:,2),'gs','linewidth',5)

figure(2)   % For another lamda
whitebg(2,'k');
hold on
plot(X0(:,1),X0(:,2),'ro','linewidth',5)
plot(X1(:,1),X1(:,2),'gs','linewidth',5)

figure(3)   % Dull one for depict
whitebg(3,'k');
hold on
plot(X0(:,1),X0(:,2),'ro','linewidth',5)
plot(X1(:,1),X1(:,2),'gs','linewidth',5)



input('anykey to continue')

% Concatenating two class 
X=[X0;X1];

D=[ones(2*N,1);-ones(2*N,1)];

% Evaluating G matrix
for n=1:4*N
    for m=1:4*N
        u=norm(X(n,:)-X(m,:))*2/(2*s^2);
        G(m,n)=exp(-u);
    end
end

figure(1)
L=0.1;
W1=(inv(G-L.*diag(ones(1,4*N))))*D;
n=0;
for x=min(X(:,1))-1:0.25:max(X(:,1))+1
    for y=min(X(:,2))-1:0.25:max(X(:,2))+1
        Xin=[x,y];
        F=0;
        for k=1:4*N
            u=-(norm(Xin-X(k,:))^2/(2*s^2));
            F=F+W1(k)*exp(u);
        end
            if F>0.0,
                plot(x,y,'c.')
            end
        end
end
axis([min(X(:,1))-1,max(X(:,1))+1,min(X(:,2))-1,max(X(:,2))+1]);


figure(2)
L=0.5;
W2=(inv(G-L.*diag(ones(1,4*N))))*D;
n=0;
for x=min(X(:,1))-1:0.25:max(X(:,1))+1
    for y=min(X(:,2))-1:0.25:max(X(:,2))+1
        Xin=[x,y];
        F=0;
        for k=1:4*N
            u=-(norm(Xin-X(k,:))^2/(2*s^2));
            F=F+W2(k)*exp(u);
        end
            if F>0.0,
                plot(x,y,'c.')
            end
    end
end
axis([min(X(:,1))-1,max(X(:,1))+1,min(X(:,2))-1,max(X(:,2))+1]);