function [Y, X] = svmlread(fname)% SVMLREAD - Read a data file generated by SVM light% %   Y = SVMLREAD(FNAME)%   FNAME gives the name of an output file generated by SVM light. It%   may contain predicted labels, coefficients alpha, or an input%   (example) file with class values and features. From this file the%   data in the first column (class labels or alphas) is extrated and%   returned in Y.%   [Y, X] = SVMLREAD(FNAME), where FNAME is the name of an input file%   with class values and features, returns both the vector of class%   labels Y and the matrix of examples X. Each line of X corresponds to%   a line in the file.%   Attention: this may take a while...%%   See also SVML, SVM_LEARN, SVM_CLASSIFY, SVMLOPT, SVMLWRITE%% % Copyright (c) by Anton Schwaighofer (2001)% $Revision: 1.6 $ $Date: 2002/02/19 12:26:07 $% mailto:anton.schwaighofer@gmx.net% % This program is released unter the GNU General Public License.% error(nargchk(1, 1, nargin));X = [];Y = [];f = fopen(fname, 'rt');if (f<0),  error(sprintf('Unable to open file %s', fname));endi = 0;fprintf('Scanning ');while ~feof(f),  s = fgetl(f);  [Yi, count, errmsg, nextind] = sscanf(s, '%f', 1);  % read the class label resp. anything else that is in the first column  if (count==1),    i = i+1;    Y(i,1) = Yi;    [Xi, count] = sscanf(s(nextind:end), ' %i:%f');    % scan for the feature:value pairs    if (rem(count,2)==0) & (count~=0),      % if they really come in pairs, then accept      ind = 2:2:count;      if isempty(X),        maxCol = max(Xi(ind-1));        approxSparsity = (count/2)/maxCol;        % a rough estimate of the sparsity, based on the first line of        % data        if approxSparsity>0.5,          approxSparsity = 1;          X = zeros(maxCol, 1000);        else          X = spalloc(maxCol, 1000, round(1000*maxCol*approxSparsity));          % allocate for 1000 data points (lines) beforehand          % We store everything *columnwise* and transpose afterwards,          % this greatly improves performance        end      end      X(Xi(ind-1),i) = Xi(ind);      if (rem(i,100)==0),        fprintf(' %i', i);      end    end  endendfprintf(' done.\n');if ~isempty(X),  X = X(:,1:i)';  sparsity = length(find(X))/prod(size(X));  if sparsity<0.5,    X = sparse(X);    % remove any surplus lines & convert to sparse a second time for    % optimal memory usage  endendfclose(f);