svmkernel.m

LibSVM工具箱

function K = svmkernel(net, X1, X2)
% SVMKERNEL - Compute Support Vector Machine kernel function
% 
%   K = SVMKERNEL(NET, X1, X2)
%   The Support Vector Machine structure NET must contain 2 field
%   NET.kernel and NET.kernelpar, selecting the kernel function and its
%   parameters. X1 and X2 contain one example per row. If X1 is of size
%   [M, NET.nin] and X2 is of size [N, NET.nin], K will be a matrix
%   [M, N]. K(i,j) is the result of the kernelfunction for inputs X1(i,:)
%   and X2(j,:).
%   Currently the only valid kernel functions are
%   NET.kernel = 'linear' 
%       inner product
%   NET.kernel = 'poly'
%       (1+inner product)^NET.kernelpar(1)
%   NET.kernel = 'rbf'
%       radial basis function, common length scale for all inputs is
%       NET.kernelpar(1), scaled with the number of inputs NET.nin
%       K = exp(-sum((X1i-X2i)^2)/(NET.kernelpar(1)*NET.nin))
%   NET.kernel = 'rbffull'
%       radial basis function, different length scale for each input.
%       If NET.kernelpar is a vector of length NET.nin
%       K = exp(-sum((X1i-X2i)^2*NET.kernelpar(i))/NET.nin)
%       If NET.kernelpar is a vector of length NET.nin+1
%       K = exp(NET.kernelpar(end)-sum((X1i-X2i)^2*NET.kernelpar(i))/NET.nin)
%       If NET.kernelpar is a matrix of size [NET.nin, NET.nin]
%       K = exp(-(X1-X2)*NET.kernelpar*(X1-X2)'/NET.nin)
%
%   See also
%   SVM, SVMTRAIN, SVMFWD
%

% 
% Copyright (c) Anton Schwaighofer (2001)
% $Revision: 1.3 $ $Date: 2001/06/18 15:21:55 $
% mailto:[e-mail]
% 
% This program is released unter the GNU General Public License.
% 

errstring = consist(net, 'svm', X1);
if ~isempty(errstring);
  error(errstring);
end
errstring = consist(net, 'svm', X2);
if ~isempty(errstring);
  error(errstring);
end
[N1, d] = size(X1);
[N2, d] = size(X2);

switch net.kernel
  case 'linear'
    K = X1*X2';
  case 'poly'
    K = (1+X1*X2').^net.kernelpar(1);
  case 'rbf'
    dist2 = repmat(sum((X1.^2)', 1), [N2 1])' + ...
            repmat(sum((X2.^2)',1), [N1 1]) - ...
            2*X1*(X2');
    K = exp(-dist2/(net.nin*net.kernelpar(1)));
  case 'rbffull'
    bias = 0;
    if any(all(repmat(size(net.kernelpar), [4 1]) == ...
               [d 1; 1 d; d+1 1; 1 d+1], 2), 1),
      weights = diag(net.kernelpar(1:d));
      if length(net.kernelpar)>d,
        bias = net.kernelpar(end);
      end
    elseif all(size(net.kernelpar)==[d d]),
      weights = net.kernelpar;
    else
      error('Size of NET.kernelpar does not match the chosen kernel ''rbffull''');
    end
    dist2 = (X1.^2)*weights*ones([d N2]) + ...
            ones([N1 d])*weights*(X2.^2)' - ...
            2*X1*weights*(X2');
    K = exp(bias-dist2/net.nin);
  otherwise
    error('Unknown kernel function');
end
K = double(K);
% Convert to full matrix if inputs are sparse