function svrplot(X,Y,ker,beta,bias,e,mag,xaxis,yaxis,input)%SVRPLOT Support Vector Regression Plotting routine for 1D function%%  Usage: svrplot(X,Y,ker,alpha,bias,e,mag,xaxis,yaxis,input)%%  Parameters: X      - Training inputs%              Y      - Training targets%              ker    - kernel function%              beta   - Difference of Lagrange Multipliers%              bias   - Bias term %              e      - e insensitive value%              mag    - display magnification %              xaxis  - xaxis input (default: 1) %              input  - vector of input values (default: zeros(no_of_inputs))%%  Author: Steve Gunn (srg@ecs.soton.ac.uk)  if (nargin < 5 | nargin > 8) % check correct number of arguments    help svrplot  else    epsilon = 1e-4;      if (nargin < 9) input = zeros(1,size(X,2));, end    if (nargin < 8) xaxis = 1;, end    if (nargin < 7) mag = 0;, end    if (nargin < 6) e = 0;, end        xmin = min(X(:,xaxis));, xmax = max(X(:,xaxis));     xa = (xmax - xmin);, xmin = xmin - mag*xa;, xmax = xmax + mag*xa;    % Plot function value    n = size(X,1);    x = [xmin:(xmax-xmin)/300:xmax];     z = bias*ones(size(x));    for x1 = 1 : length(x)      input(xaxis) = x(x1);      for i = 1 : length(Y)          if (abs(beta(i)) > epsilon)    	    z(x1) = z(x1) + beta(i)*svkernel(ker,input,X(i,:));          end      end    end    plot(x,z,'k','linewidth',2);    axis off    hold on        % Plot e insensitive zone    if (e > 0)       plot(x,z+e,'b:')       plot(x,z-e,'b:')    end     % Plot Training points        for i = 1:size(Y)      plot(X(i),Y(i),'k+')       if (abs(beta(i)) > epsilon)        plot(X(i),Y(i),'ro','markersize',10) % Support Vector      end    end    set(gca,'XLim',[xmin xmax]);      hold off  end