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

function [X, A, B] = svdatanorm(X,ker,isotropic) 
%SVDATANORM normalises the data X for the kernel ker  
% 
%  Usage: [X A B] = svdatanorm(X,ker) 
% 
%  Parameters: X         - training data 
%              ker       - kernel type 
%              isotropic - isotropic (1:default) or anisotropic (0) scaling 
%               
%  Author: Steve Gunn (srg@ecs.soton.ac.uk) 
 
  if (nargin <2 | nargin>3) % check correct number of arguments 
     help svdatanorm 
  else 
    if (nargin < 3) isotropic = 0;, end 
  
    switch lower(ker) 
      case {'curvspline','spline', 'anovaspline1', 'anovaspline2', 'anovaspline2'} 
        lb = 0;, ub = 1; 
      case {'fourier'} 
        lb = -pi/2;, ub = pi/2; 
      case {'temp'} 
        lb = 0.15;, ub = 0.85; 
      otherwise 
        lb = -1;, ub = 1; 
    end 
     
    n = size(X,2); % input dimension  
    sca = zeros(n,1); 
    mina = zeros(n,1); 
    maxa = zeros(n,1); 
    A = zeros(n,1); 
    B = zeros(n,1); 
    for i=1:n 
       mina(i) = min(X(:,i)); 
       maxa(i) = max(X(:,i)); 
       sca(i) = maxa(i) - mina(i); 
    end 
        
    for i=1:n 
       if (isotropic) 
          sca(i) = max(sca); 
       end 
       if sca(i) 
          A(i) = (ub - lb)/sca(i); 
          B(i) = lb - A(i)*mina(i); 
          X(:,i) = A(i)*X(:,i) + B(i); 
       end 
    end 
     
        
  end