svmlread.m

SVM,很好用的用于模式识别中特征分类的咚咚。

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:[e-mail]
% 
% 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));
end

i = 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
  end
end
fprintf(' 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
  end
end

fclose(f);