function write_svr(x_train,beta,pp,bias,ker,filename) 
% This function creates an analytic SVR equation for a given set of 
% training points and SVR parameters. This does not build the metamodel, 
% it writes a text file of a prebuilt metamodel.SVR parameters can 
% be easily taken from an SVR model built using the MATLAB toolbox 
% developed by Gunn 1998. 
% This function is set up to use MATLAB syntax for the output equation. 
%
% ---Variable Descriptions--- 
% x_train = normalized design(training points): in an 
% [num_of_points by num_of_variables] matrix 
% beta = SVR parameter("Difference of Lagrange Multipliers" - Gunn Toolbox) 
% pp = SVR Kernel parameter 
% bias = SVR bias 
% ker = SVR kernel function 
% --Currently Supported Kernel Functions-- 
% ker = 'rbfal' = referred to as "RBF" in literature and not included in 
% the base Gunn SVR toolbox 
% ker = 'rbf' = referred to as "Gaussian RBF" or "Gaussian" in the 
% literature and called "RBF" in Gunn's toolbox 
% ker = 'linear'= referred to as "linear" in literature and Gunn 
% filename = string containing the filname to save the equation as, 
% must be "filename.txt" 
% SVR parameters pn = num2str(pp); 
% number of design variables and number of training points. 
ndv = size(x_train,2); 
ntrn = size(x_train,1); 
% open output file with specified filename 
fout = fopen(filename,'w'); 
i = 0; 
for n = 1:ntrn 
  for m = 1:ndv 
    i = i + 1; 
% here p0 p1 p2 and p3 represent portions of each line in the text 
% file in sequential order. 
    if i == 1 
      p0 = 'y_svr='; 
    else 
      p0 = '+'; 
    end 
% write the SVR based on which Kernel function is specified 
if strcmp(ker,'rbfal') == 1; 
% typically referred to as RBF 
% in the literature 
    if m == 1 
      p1 = [num2str(beta(n)) '.*exp(-' num2str(pn) '.*((']; 
      p3 = '...'; 
    elseif m == ndv p3 = '))...'; 
    else p1 = '('; 
      p3 = '...'; 
    end 
    p2 = ['(x' num2str(m) '-' num2str(x_train(n,m),12) ').^2)']; 
    if m == ndv && n == ntrn 
      p3 = ['))+' num2str(bias)]; 
    end 
    elseif strcmp(ker,'rbf') == 1; 
% typically referred to as Gaussian 
% RBF or Guassian in the literature 
% (gunn's SVR toolbox uses rbf) 
    if m == 1 
      p1 = [num2str(beta(n)) '.*exp(-((']; p3 = '...'; 
    elseif m == ndv p3 = [')./(2*' num2str(pn) '^2))...']; 
    else p1 = '('; p3 = '...'; 
    end 
    p2 = ['(x' num2str(m) '-' num2str(x_train(n,m),12) ').^2)']; 
    if m == ndv && n == ntrn 
      p3 = [')./(2*' num2str(pn) '^2))+' num2str(bias)]; 
    end 
    elseif strcmp(ker,'linear') == 1; 
    if m == 1 
      p1 = [num2str(beta(n)) '.*(((']; 
      p3 = '...'; 
    elseif m == ndv p3 = '))...'; 
    else p1 = '('; p3 = '...'; 
    end 
    p2 = ['(x' num2str(m) '.*' num2str(x_train(n,m),12) '))']; 
    if m == ndv && n == ntrn 
      p3 = ['))+' num2str(bias)]; 
    end 
  end 
  fprintf(fout,'%s\n',[p0 p1 p2 p3]); 
  end 
end 
fclose(fout)