www.pudn.com > osu_svm3.00.zip > SVMPlot2.m

function SVMPlot(AlphaY, SVs, Bias, Parameters,Samples, Labels, aspect,mag,xaxis,yaxis,input) 
% USAGE: 
%   SVMPlot(AlphaY, SVs, Bias, Parameters,Samples, Labels); 
%   SVMPlot(AlphaY, SVs, Bias, Parameters,Samples, Labels, aspect,mag,xaxis,yaxis,input) 
% 
% DESCRIPTION: 
%  Plot a 2-class Support Vector classifier with some labelled samples. 
% 
% INPUTS: 
%  AlphaY: Alpha * Y, where Alpha is the non-zero Lagrange Coefficients 
%                    Y is the corresponding labels. 
%  SVs: support vectors. That is, the patterns corresponding the non-zero 
%       Alphas. 
%  Bias: the bias in the decision function, which is AlphaY*Kernel(SVs',x)-Bias. 
%  Parameters:the paramters required by the training algorithm.  
%             (a 10-element row vector) 
%            +----------------------------------------------------------------- 
%            |Kernel Type| Degree | Gamma | Coefficient | C |Cache size|epsilon|  
%            +----------------------------------------------------------------- 
%				 -------------------------------------------+ 
%            | SVM Type |	nu (nu-svm) |   Percentage   |				 
%				 -------------------------------------------+ 
%            where Kernel Type: 
%                   0 --- Linear 
%                   1 --- Polynomial: (Gamma*+Coefficient)^Degree 
%                   2 --- RBF: (exp(-Gamma*|X(:,i)-X(:,j)|^2)) 
%                   3 --- Sigmoid: tanh(Gamma*+Coefficient) 
%                  Gamma: If the input value is zero, Gamma will be set defautly as 
%                         1/(max_pattern_dimension) in the function. If the input 
%                         value is non-zero, Gamma will remain unchanged in the  
%                         function. 
%                  C: Cost of the constrain violation (for C-SVC & C-SVR), or the  
%                     nu for the nu-svm and 1-svm in the second stage. This  
%                     nu should smaller than the nu defined in Parameter(9) 
%                  Cache Size: as the buffer to hold the  (in MB) 
%                  epsilon: tolerance of termination criterion 
%                  SVM Type: (Recommend only using 0, which is c-SVM classifier) 
%                   0 --- c-SVM classifier 
%                   1 --- nu-SVM classifier 
%                  nu: the nu of nu-SVM used in the boundary finding process 
%						 Percentage: the percentage of input train samples used for boundary finding.	 
%  Samples: all the training patterns. (a row of column vectors) 
%  Labels:  the corresponding class labels for the training patterns in Samples. 
%         where Labels(i) in {1, -1}. (a row vector) 
%  aspect: Aspect Ratio (default: 0 (fixed), 1 (variable)) 
%  mag: display magnification  (default: 0.1) (kind of side margin) 
%  xaxis: xaxis input (default: 1) 
%  yaxis: yaxis input (default: 2) 
%  input: vector of input values (default: zeros(no_of_inputs)) 
% 
%  Orignal Author: Steve Gunn (srg@ecs.soton.ac.uk) 
%  Modified by Junshui Ma 
 
if (nargin < 6 | nargin > 11) % check correct number of arguments 
   help SVMPlot; 
   return; 
else 
    
   [maxLabel, I] = max(Labels); 
   [minLabel, I] = min(Labels); 
    
   if (maxLabel ~= 1) & (minLabel ~=-1) 
       if (maxLabel ~= minLabel)  
           Labels(find(Labels==maxLabel)) = 1; 
           Labels(find(Labels==minLabel)) = -1; 
       end     
   end 
    
   epsilon = 1e-5;   
   if (nargin < 11) input = zeros(size(Samples,1),1);, end 
   if (nargin < 10) yaxis = 2;, end 
   if (nargin < 9) xaxis = 1;, end 
   if (nargin < 8) mag = 0.1;, end 
   if (nargin < 7) aspect = 0;, end 
     
   % Scale the axes 
   xmin = min(Samples(xaxis,:)); 
   xmax = max(Samples(xaxis,:));  
    
   ymin = min(Samples(yaxis,:)); 
   ymax = max(Samples(yaxis,:)); 
    
   xa = (xmax - xmin); 
   ya = (ymax - ymin); 
   if (~aspect) 
       if (0.75*abs(xa) < abs(ya))  
          offadd = 0.5*(ya*4/3 - xa);,  
          xmin = xmin - offadd - mag*0.5*ya; 
          xmax = xmax + offadd + mag*0.5*ya; 
          ymin = ymin - mag*0.5*ya; 
          ymax = ymax + mag*0.5*ya; 
       else 
          offadd = 0.5*(xa*3/4 - ya);,  
          xmin = xmin - mag*0.5*xa; 
          xmax = xmax + mag*0.5*xa; 
          ymin = ymin - offadd - mag*0.5*xa; 
          ymax = ymax + offadd + mag*0.5*xa; 
       end 
    else 
       xmin = xmin - mag*0.5*xa; 
       xmax = xmax + mag*0.5*xa; 
       ymin = ymin - mag*0.5*ya; 
       ymax = ymax + mag*0.5*ya; 
    end 
     
    set(gca,'XLim',[xmin xmax],'YLim',[ymin ymax]);   
 
    % Plot function value 
 
    [x,y] = meshgrid(xmin:(xmax-xmin)/50:xmax,ymin:(ymax-ymin)/50:ymax);  
 
	 z = Bias*ones(size(x)); 
    Inputs = input*ones(1,size(x,2)); 
 
    wh = waitbar(0,'Plotting...'); 
    for x1 = 1 : size(x,1) 
       Inputs(xaxis,:) = x(x1,:); 
       Inputs(yaxis,:) = y(x1,:); 
       [OutLabels, z(x1,:)]= SVMClass(Inputs,AlphaY, SVs, Bias,Parameters); 
       waitbar((x1)/size(x,1)); 
    end 
    close(wh); 
     
    l = (-min(min(z)) + max(max(z)))/2.0; 
    sp = pcolor(x,y,z); 
    shading interp;   
    set(sp,'LineStyle','none'); 
    set(gca,'Clim',[-l  l],'Position',[0 0 1 1]); 
    axis off; 
    load cmap; 
    colormap(colmap); 
 
    % Plot Training points 
 
   hold on 
   for i = 1:length(Labels) 
      if (Labels(i) == 1) 
        plot(Samples(xaxis,i),Samples(yaxis,i),'b+','LineWidth',4); % Class A 
      else 
        plot(Samples(xaxis,i),Samples(yaxis,i),'r+','LineWidth',4); % Class B 
      end 
   end 
   for i = 1:length(AlphaY)  
      plot(SVs(xaxis,i),SVs(yaxis,i),'wo'); % Support Vector 
   end 
    
   % Plot Boundary contour 
   hold on 
   contour(x,y,z,[0 0],'w'); 
   if (Parameters(8) ~=2)  
      contour(x,y,z,[1 1],'k:'); 
      contour(x,y,z,[-1 -1],'k:'); 
   end    
   hold off 
end