The thing is that even if you did sum and divide by the sum, summing the results back may not lead to 1.0. This is always the "issue" in floating point computation.
Cheers, Matthieu 2015-12-17 8:26 GMT+01:00 Ryan R. Rosario <r...@bytemining.com>: > Hi, > > I have a very large dense numpy matrix. To avoid running out of RAM, I use > np.float32 as the dtype instead of the default np.float64 on my system. > > When I do an L1 normalization of the rows (axis=1) in my matrix in-place > (copy=False), I frequently get rows that do not sum to 1. Since these are > probability distributions that I pass to np.random.choice, these must sum to > exactly 1.0. > > pp.normalize(term, norm='l1', axis=1, copy=False) > sums = term.sum(axis=1) > sums[np.where(sums != 1)] > > array([ 0.99999994, 0.99999994, 1.00000012, ..., 0.99999994, > 0.99999994, 0.99999994], dtype=float32) > > I wrote some code to manually add/subtract the small difference from 1 to > each row, and I make some progress, but still all the rows do not sum to 1. > > Is there a way to avoid this problem? > > — Ryan > ------------------------------------------------------------------------------ > _______________________________________________ > Scikit-learn-general mailing list > Scikit-learn-general@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- Information System Engineer, Ph.D. Blog: http://matt.eifelle.com LinkedIn: http://www.linkedin.com/in/matthieubrucher ------------------------------------------------------------------------------ _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
