www.pudn.com > myMatlabfenlei.rar > svcplot.m

function svcplot(X,Y,ker,alpha,bias,aspect,mag,xaxis,yaxis,input) 
%SVCPLOT Support Vector Machine Plotting routine 
% 
%  Usage: svcplot(X,Y,ker,alpha,bias,zoom,xaxis,yaxis,input) 
% 
%  Parameters: X      - Training inputs 
%              Y      - Training targets 
%              ker    - kernel function 
%              alpha  - Lagrange Multipliers 
%              bias   - Bias term  
%              aspect - Aspect Ratio (default: 0 (fixed), 1 (variable)) 
%              mag    - display magnification  
%              xaxis  - xaxis input (default: 1)  
%              yaxis  - yaxis input (default: 2) 
%              input  - vector of input values (default: zeros(no_of_inputs)) 
% 
%  Author: Steve Gunn (srg@ecs.soton.ac.uk) 
 
  if (nargin < 5 | nargin > 10) % check correct number of arguments 
    help svcplot 
  else 
 
    epsilon = 1e-5;   
    if (nargin < 10) input = zeros(1,size(X,2));, end 
    if (nargin < 9) yaxis = 2;, end 
    if (nargin < 8) xaxis = 1;, end 
    if (nargin < 7) mag = 0.1;, end 
    if (nargin < 6) aspect = 0;, end 
     
    % Scale the axes 
    xmin = min(X(:,xaxis));, xmax = max(X(:,xaxis));  
    ymin = min(X(:,yaxis));, ymax = max(X(:,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)); 
    wh = waitbar(0,'Plotting...'); 
    for x1 = 1 : size(x,1) 
      for y1 = 1 : size(x,2) 
        input(xaxis) = x(x1,y1);, input(yaxis) = y(x1,y1); 
        for i = 1 : length(Y) 
          if (abs(alpha(i)) > epsilon) 
    	    z(x1,y1) = z(x1,y1) + Y(i)*alpha(i)*svkernel(ker,input,X(i,:)); 
          end 
        end 
      end 
      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:size(Y) 
      if (Y(i) == 1) 
        plot(X(i,xaxis),X(i,yaxis),'b+','LineWidth',4) % Class A 
      else 
        plot(X(i,xaxis),X(i,yaxis),'r+','LineWidth',4) % Class B 
      end 
      if (abs(alpha(i)) > epsilon) 
        plot(X(i,xaxis),X(i,yaxis),'wo') % Support Vector 
      end 
    end  
 
    % Plot Boundary contour 
 
    hold on 
    contour(x,y,z,[0 0],'w') 
    contour(x,y,z,[-1 -1],'w:') 
    contour(x,y,z,[1 1],'w:') 
    hold off 
 
  end