Raymond Hettinger <raymond.hettin...@gmail.com> added the comment:
Almost three years have passed. In the spirit of "perfect is the enemy of good", would it be reasonable to start with a simple, fast implementation using exp-mean-log? Then if someone wants to make it more accurate later, they can do so. In some quick tests, I don't see much of an accuracy loss. It looks to be plenty good enough to use as a starting point: --- Accuracy experiments --- >>> from decimal import Decimal >>> from functools import reduce >>> from operator import mul >>> from random import expovariate, triangular >>> from statistics import fmean >>> # https://www.wolframalpha.com/input/?i=geometric+mean+12,+17,+13,+5,+120,+7 >>> data = [12, 17, 13, 5, 120, 7] >>> print(reduce(mul, map(Decimal, data)) ** (Decimal(1) / len(data))) 14.94412420173971227234687688 >>> exp(fmean(map(log, map(fabs, data)))) 14.944124201739715 >>> data = [expovariate(50.0) for i in range(1_000)] >>> print(reduce(mul, map(Decimal, data)) ** (Decimal(1) / len(data))) 0.01140902688569587677205587938 >>> exp(fmean(map(log, map(fabs, data)))) 0.011409026885695879 >>> data = [triangular(2000.0, 3000.0, 2200.0) for i in range(10_000)] >>> print(reduce(mul, map(Decimal, data)) ** (Decimal(1) / len(data))) 2388.381301718524160840023868 >>> exp(fmean(map(log, map(fabs, data)))) 2388.3813017185225 >>> data = [lognormvariate(20.0, 3.0) for i in range(100_000)] >>> min(data), max(data) (2421.506538652375, 137887726484094.5) >>> print(reduce(mul, map(Decimal, data)) ** (Decimal(1) / len(data))) 484709306.8805352290183838500 >>> exp(fmean(map(log, map(fabs, data)))) 484709306.8805349 ---------- _______________________________________ Python tracker <rep...@bugs.python.org> <https://bugs.python.org/issue27181> _______________________________________ _______________________________________________ Python-bugs-list mailing list Unsubscribe: https://mail.python.org/mailman/options/python-bugs-list/archive%40mail-archive.com
