fmeasure.m.svn-base

a function inside machine learning

function fm = fMeasure(trueY, predictedY, rankings) 
% Compute the F-measure on a set of true and predicted labels, 
% calculated as F = 2 * (recall * precision) / (recall + precision) 
%
% Usage: fm = fMeasure(trueY, predictedY, rankings) 
% Inputs/Outputs: 
%   trueY - a column vector of binary labels 
%   predictedY - a column vector of predicted labels 
%   rankings (optional) - rankings of predicted labels 
%
%   fm - the F-measure
%
% Copyright (C) 2006 Charanpal Dhanjal 

% This library is free software; you can redistribute it and/or
% modify it under the terms of the GNU Lesser General Public
% License as published by the Free Software Foundation; either
% version 2.1 of the License, or (at your option) any later version.
% 
% This library is distributed in the hope that it will be useful,
% but WITHOUT ANY WARRANTY; without even the implied warranty of
% MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
% Lesser General Public License for more details.
% 
% You should have received a copy of the GNU Lesser General Public
% License along with this library; if not, write to the Free Software
% Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA  02110-1301
% USA

if (nargin < 2)
    fprintf('%s\n', help('fMeasure'));
    error('Incorrect number of inputs - see above usage instructions.');
end

r = recall(trueY, predictedY);  
p = precision(trueY, predictedY);

if (r+p) ~= 0 
    fm = 2*(r*p)/(r+p);
else
    fm = 0; 
end