Mercurial > hg > camir-aes2014
view core/tools/machine_learning/scale_ratings.m @ 0:e9a9cd732c1e tip
first hg version after svn
author | wolffd |
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date | Tue, 10 Feb 2015 15:05:51 +0000 |
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function Y = scale_ratings(Y, max_weight) % [Yout, Xout] = scale_ratings(Y, max_weight) % get maximal weight weights = cell2mat(Y(:,3)); max_dataweight = max(weights); valid = ~(cellfun(@isempty, Y(:,1)) | cellfun(@isempty, Y(:,2))); % scale weights to a maximal value of max_weight for i = 1:size(Y, 1) if valid(i) && Y{i,3} > 0 Y{i,3} = min(max_weight, max(1, round((Y{i,3} / max_dataweight) * max_weight))); end end