function svmlwrite(fname, X, Y, floatformat)% SVMLWRITE - Write matrix into data file for SVM light% %   SVMLWRITE(FNAME, X) writes out matrix X into file FNAME, in the format%   needed for SVM light. X is a matrix of data points, with one point%   per row.%   SVMLWRITE(FNAME, X, Y) writes out data points X with target values%   given in the column vector Y. In the case of classification, Y(i) may%   either be +1, -1 or 0 (indicating unknown class label, for%   transductive SVM). In the case of regression data, Y(i) is%   real-valued.%   SVMLWRITE(FNAME, X, Y, FLOATFORMAT) uses format string FLOATFORMAT to%   write out the features in X (and in case of regression, also the%   target values Y(i)). Default: '%.16g'%%   See also%   SVM_LEARN, SVMLOPT, SVM_CLASSIFY, SVMLREAD%% % Copyright (c) by Anton Schwaighofer (2001)% $Revision: 1.8 $ $Date: 2002/02/19 12:28:03 $% mailto:anton.schwaighofer@gmx.net% % This program is released unter the GNU General Public License.% error(nargchk(2, 4, nargin));if nargin<4,  floatformat = '%.16g';end[N, d] = size(X);if nargin<3,  Y = zeros(N, 1);endif isempty(Y),  Y = zeros(N, 1);endif ~all(size(Y)==[N 1]),  error('Input parameter Y must be a column vector with length SIZE(X,1)');endf = fopen(fname, 'wt');if (f<0),  error(sprintf('Unable to open file %s', fname));end% Check whether this is a regression or a classification problemuY = unique(Y);if isempty(setdiff(uY, [-1 0 +1])),  % Classification:  labelformat = '%i ';else  % Regression:   labelformat = [floatformat ' '];end% transpose for increased efficiency when working with sparse matricesX = X';fprintf('Writing ');for i = 1:N,  Xi = X(:,i);  % Write label as the first entry  s = sprintf(labelformat, Y(i));  % Then follow 'feature:value' pairs  ind = find(Xi);  s = [s sprintf(['%i:' floatformat ' '], [ind'; full(Xi(ind))'])];  fprintf(f, '%s\n', s);  if (rem(i,100)==0),    fprintf(' %i', i);  endendfprintf(' done.\n');fclose(f);