dataset.m

用于matlab环境下的支持向量机svm的工具箱

function [xapp,yapp,xtest,ytest,xtest1,xtest2]=dataset(n,nbapp,nbtest,sigma)

% Usage
% [xapp,yapp,xtest,ytest,xtest1,xtest2]=dataset(String,nbapp,nbtest,sigma)
%
%
%  possible dataset creation
%  
%    'Mixture'  : 2 classes         
%          First class is a a mixture of gaussian
%          which takes in sandwiches the second class 
%
%           
%  'Gaussian' : is a classical dataset with two gaussianly distributed
%         classes with similar variances but different means
%
%  'Checkers': Checkers
%
%  'MultiClassCheckers'
%          
%  'Clowns' : The Clowns
%
%   'CosExp': sign( 0.95*cos(0.5*(exp(x)-1)))); for x in [0...4]
%       xtest1,xtest2 meshgrid of xtest

xapp=[];
yapp=[];
xtest=[];
ytest=[];
xtest1=[];
xtest2=[];
switch lower(n)
    
case 'mixture'
    
    %------------------Data Learn Generation -------------------------
    if nargin <4
        sigma=0.4;
    end;
    nbapp=round(nbapp/3);
    x1=sigma*randn(1,nbapp)+0.3;
    x2=sigma*randn(1,nbapp)-0.3;
    x3=sigma*randn(1,nbapp)-1;
    y1=sigma*randn(1,nbapp)+0.5;
    y2=sigma*randn(1,nbapp)-0.5;
    y3=sigma*randn(1,nbapp)-1;
    
    xapp=[x1 x2 x3; y1 y2 y3]';
    yapp=[ones(1,nbapp) -ones(1,nbapp) ones(1,nbapp)]';
    
    
    %----------------Data Test Generation ---------------------------
    nbtest=round(nbtest/3);
    xt=sigma*randn(1,nbtest)+0.3;
    xt2=sigma*randn(1,nbtest)-0.3;
    xt3=sigma*randn(1,nbtest)-1;
    yt=sigma*randn(1,nbtest)+0.5+0.7*xt;
    yt2=sigma*randn(1,nbtest)-0.5;
    yt3=sigma*randn(1,nbtest)-1-0.7*xt;
    xtest=[xt xt2 xt3; yt yt2 yt3]';
    
    
    ytest=[ones(1,nbtest) -ones(1,nbtest) ones(1,nbtest)]';
    return
case 'gaussian'
    nbapp=round(nbapp/2);
    nbtest=round(nbtest/2);
    if nargin <4
        sigma=0.2;
    end;
    x1=sigma*randn(1,nbapp)+0.3;
    x2=sigma*randn(1,nbapp)-0.3;
    y1=sigma*randn(1,nbapp)+0.5;
    y2=sigma*randn(1,nbapp)-0.5;
    xapp=[x1 x2; y1 y2]';
    yapp=[ones(1,nbapp) -ones(1,nbapp)]';
    x1=sigma*randn(1,nbtest)+0.3;
    x2=sigma*randn(1,nbtest)-0.3;
    y1=sigma*randn(1,nbtest)+0.5;
    y2=sigma*randn(1,nbtest)-0.5;
    xtest=[x1 x2; y1 y2]';
    ytest=[ones(1,nbtest) -ones(1,nbtest)]';
    
case 'checkers'
    xapp=[];
    yapp=[];
    nb=floor(nbapp/16);
    for i=-2:1;
        for j=-2:1;
            xapp=[xapp; [i+rand(nb,1) j+rand(nb,1)]];
            %(2*rem((i+j+4),2)-1)
            yapp=[yapp; (2*rem((i+j+4),2)-1)*ones(nb,1)];
        end;
    end;
    xtest=[];
    ytest=[];
    nb=floor(nbtest/16);
    for i=-2:1;
        for j=-2:1;
            xtest=[xtest; [i+rand(nb,1) j+rand(nb,1)]];
            %(2*rem((i+j+4),2)-1)
            ytest=[ytest; (2*rem((i+j+4),2)-1)*ones(nb,1)];
        end;
    end;
    %     [xt1,xt2]=meshgrid(linspace(-2,2,sqrt(nbtest)));
    %     [na,nb]=size(xt1);
    %     xtest1=reshape(xt1,na*nb,1);
    %     xtest2=reshape(xt2,na*nb,1);
    %     xtest=[xtest1 xtest2];
    %     ytest=zeros(length(xtest),1);
    %     for i=-2:1
    %         for j=-2:1
    %             pos=find( xtest(:,1)>=i & xtest(:,1)< i+1 & xtest(:,2)>=j & xtest(:,2)< j+1 );
    %             ytest(pos)=(2*rem((i+j+4),2)-1)*ones(length(pos),1);
    %         end;
    %     end;
    %     xtest1=reshape(xt1,na,nb);
    %     xtest2=reshape(xt2,na,nb);
    
    
    
case 'clowns'
    nbapp=round(nbapp/2);
    nbtest=round(nbtest/2);
    
    x1 = (6*rand(nbapp,1)-3); 
    x2 = x1.^2 + randn(nbapp,1); 
    x0 = sigma*randn(nbapp,2)+(ones(nbapp,1)*[0 6]); 
    xapp = [x0;[x1 x2]]; 
    xapp = (xapp-ones(2*nbapp,1)*mean(xapp))*diag(1./std(xapp)); 
    yapp = [ones(nbapp,1); -ones(nbapp,1)]; 
    
    if nbtest>0
        x1 = (6*rand(nbtest,1)-3); 
        x2 = x1.^2 + randn(nbtest,1); 
        x0 = sigma*randn(nbtest,2)+(ones(nbtest,1)*[0 6]); 
        xtest = [x0;[x1 x2]]; 
        xtest = (xtest-ones(2*nbtest,1)*mean(xtest))*diag(1./std(xtest)); 
        ytest = [ones(nbtest,1); -ones(nbtest,1)]; 
    end;
    
    
    
    
case 'cosexp'
    
    
    
    nbtest=floor(sqrt(nbtest));
    [xi]=rand(nbapp,2);
    yapp= sign(0.95*cos(0.5*(exp(4*xi(:,1))-1))-(2*xi(:,2)-1));
    xapp = [4*xi(:,1) (2*xi(:,2)-1)];
    [xtest1 xtest2]  = meshgrid(linspace(0,4,nbtest),linspace(-1,1,nbtest)); 
    nn = length(xtest1); 
    xtest = [reshape(xtest1 ,nn*nn,1) reshape(xtest2 ,nn*nn,1)]; 
    ytest = sign(0.95*cos(0.5*(exp(4*xtest(:,1))-1))-(2*xtest(:,2)-1));
    
case 'multiclasscheckers'
    xapp=[];
    yapp=[];
    nb=floor(nbapp/16);
    % keyboard
    for i=-2:1;
        for j=-2:1;
            xapp=[xapp; [i+rand(nb,1) j+rand(nb,1)]];
            
            
            if rem(abs(i),2)==0 & rem(abs(j),2)==0
                yapp=[yapp; 1*ones(nb,1)];
            elseif rem(abs(i),2)==1 & rem(abs(j),2)==0
                yapp=[yapp; 2*ones(nb,1)];
            elseif rem(abs(i),2)==0 & rem(abs(j),2)==1
                yapp=[yapp; 3*ones(nb,1)];
            elseif rem(abs(i),2)==1 & rem(abs(j),2)==1
                yapp=[yapp; 4*ones(nb,1)];  
            end;
            
        end;
    end;
    xtest=[];
    
    [xt1,xt2]=meshgrid(linspace(-2,2,sqrt(nbtest)));
    [na,nb]=size(xt1);
    xtest1=reshape(xt1,na*nb,1);
    xtest2=reshape(xt2,na*nb,1);
    xtest=[xtest1 xtest2];
    ytest=zeros(length(xtest),1);
    for i=-2:1
        for j=-2:1
            pos=find( xtest(:,1)>=i & xtest(:,1)< i+1 & xtest(:,2)>=j & xtest(:,2)< j+1 );
            %ytest(pos)=(2*rem((i+j+4),2)-1)*ones(length(pos),1);
            if rem(abs(i),2)==0 & rem(abs(j),2)==0
                ytest(pos)=1*ones(length(pos),1);
            elseif rem(abs(i),2)==1 & rem(abs(j),2)==0
                ytest(pos)=2*ones(length(pos),1);
            elseif rem(abs(i),2)==0 & rem(abs(j),2)==1
                ytest(pos)=3*ones(length(pos),1);
            elseif rem(abs(i),2)==1 & rem(abs(j),2)==1
                ytest(pos)=4*ones(length(pos),1);
            end;
            
            
        end;
    end;
    xtest1=reshape(xt1,na,nb);
    xtest2=reshape(xt2,na,nb);
    
    
case 'multiclassgaussian'
    
    
    
    mean1=[1 1];
    mean2=[-1 1];
    mean3= [0 -1];
    x1=sigma*randn(nbapp,2)+ones(nbapp,1)*mean1;
    y1= ones(nbapp,1);
    x2=sigma*randn(nbapp,2)+ones(nbapp,1)*mean2;
    y2=2*ones(nbapp,1);
    x3=sigma*randn(nbapp,2)+ones(nbapp,1)*mean3;
    y3=3*ones(nbapp,1);
    Ytarget=[1 2 3];
    xapp=[x1;x2;x3];
    yapp=[y1;y2;y3];
    nbapp=size(xapp,1);
    
    
    %----------------Data Test Generation ---------------------------
    x1=sigma*randn(nbtest,2)+ones(nbtest,1)*mean1;
    y1= ones(nbtest,1);
    x2=sigma*randn(nbtest,2)+ones(nbtest,1)*mean2;
    y2=2*ones(nbtest,1);
    x3=sigma*randn(nbtest,2)+ones(nbtest,1)*mean3;
    y3=3*ones(nbtest,1);
    Ytarget=[1 2 3];
    xtest=[x1;x2;x3];
    ytest=[y1;y2;y3];
    nbapp=size(xapp,1);
    
case 'westonnonlinear' % dataset used in the Weston FeatSel Paper
    nbapp=round(nbapp/2);
    yapp = [ones(nbapp,1); -ones(nbapp,1)]; 
    
    A=2*(rand(2*nbapp,1)<0.5)-1;
    xapp=randn(2*nbapp,2) + [3*A A.*(yapp*1.875+1.125)];
    xapp = [xapp  20*randn(nbapp*2,8)];
    nbtest=round(nbtest/2);
    ytest = [ones(nbtest,1); -ones(nbtest,1)]; 
    A=2*(rand(2*nbtest,1)<0.5)-1;
    xtest=randn(2*nbtest,2) + [3*A A.*(ytest*1.875+1.125)];
    xtest = [xtest  20*randn(nbtest*2,8)];
    
    
end;