Shawn Ligocki <sligo...@gmail.com> added the comment: A major pro for pseudo-random number generators is that they are deterministic, that is, you can save a load the state, start from the same seed and reproduce results, etc. At least in science (and probably other areas) this reproducibility can be vital in a random class.
It really depends on your application though. In my use, I was originally using normal random to produce seeds for another programs random number generator. This ended up producing many identical results and thus not producing an appropriate random sampling. Rather than trying to figure out a proper way to do this with a PRNG I decided to just use a completely random source, urandom was close enough for my needs. I believe that is its strongest value, not having the strange artifacts that PRNGs have. But I'm not completely sure how true that claim is :) ---------- _______________________________________ Python tracker <rep...@bugs.python.org> <http://bugs.python.org/issue7076> _______________________________________ _______________________________________________ Python-bugs-list mailing list Unsubscribe: http://mail.python.org/mailman/options/python-bugs-list/archive%40mail-archive.com
