[![Build Status](https://travis-ci.org/titsuki/raku-Random-Choice.svg?branch=master)](https://travis-ci.org/titsuki/raku-Random-Choice) NAME ==== Random::Choice - A Raku alias method implementation SYNOPSIS ======== ```perl6 use Random::Choice; say choice(:size(8), :p([0.1, 0.1, 0.1, 0.7])); # (3 1 0 3 3 3 3 3) say choice(:p([0.1, 0.1, 0.1, 0.7])); # 3 ``` DESCRIPTION =========== Random::Choice is a Raku alias method implementation. Alias method is an efficient algorithm for sampling from a discrete probability distribution. METHODS ------- ### choice Defined as: multi sub choice(:@p! --> Int) is export multi sub choice(Int :$size!, :@p! --> List) Returns a sample which is an Int value or a List. Where `:@p` is the probabilities associated with each index and `:$size` is the sample size. FAQ === Is `Random::Choice` faster than Mix.roll? ----------------------------------------- The answer is YES when you roll a large biased dice or try to roll a dice many times; but NO when a biased dice is small or try to roll a dice few times. Why? There are some possible reasons: * `Random::Choice` employs O(N) + O(1) algorithm whereas `Mix.roll` employs O(N) + O(N) algorithm (rakudo 2018.12). * `Mix.roll` is directly written in nqp. In general, nqp-powered code is faster than naive-Raku-powered code when they take small input. * Both algorithms take O(N) initialization cost; however, the actual cost of `Mix.roll` is slightly less than `Random::Choice`. A benchmark result is here (For more info, see `example/bench.p6`): ### A Benchmark Result benchmark result ### The Comparison Table on the Benchmark ```bash $ perl6 example/bench.p6 Benchmark: Timing 1000 iterations of Mix(size=10, @p.elems=10) , Random::Choice(size=10, @p.elems=10)... Mix(size=10, @p.elems=10) : 0.076 wallclock secs (0.086 usr 0.003 sys 0.089 cpu) @ 13154.606/s (n=1000) Random::Choice(size=10, @p.elems=10): 0.122 wallclock secs (0.137 usr 0.008 sys 0.145 cpu) @ 8210.383/s (n=1000) O--------------------------------------O---------O----------------------------O--------------------------------------O | | Rate | Mix(size=10, @p.elems=10) | Random::Choice(size=10, @p.elems=10) | O======================================O=========O============================O======================================O | Mix(size=10, @p.elems=10) | 13155/s | -- | -42% | | Random::Choice(size=10, @p.elems=10) | 8210/s | 73% | -- | O--------------------------------------O---------O----------------------------O--------------------------------------O Benchmark: Timing 1000 iterations of Mix(size=1000, @p.elems=10) , Random::Choice(size=1000, @p.elems=10)... Mix(size=1000, @p.elems=10) : 1.879 wallclock secs (1.892 usr 0.000 sys 1.892 cpu) @ 532.130/s (n=1000) Random::Choice(size=1000, @p.elems=10): 0.097 wallclock secs (0.099 usr 0.002 sys 0.101 cpu) @ 10361.621/s (n=1000) O----------------------------------------O---------O------------------------------O----------------------------------------O | | Rate | Mix(size=1000, @p.elems=10) | Random::Choice(size=1000, @p.elems=10) | O========================================O=========O==============================O========================================O | Mix(size=1000, @p.elems=10) | 532/s | -- | 2141% | | Random::Choice(size=1000, @p.elems=10) | 10362/s | -96% | -- | O----------------------------------------O---------O------------------------------O----------------------------------------O Benchmark: Timing 1000 iterations of Mix(size=10, @p.elems=1000) , Random::Choice(size=10, @p.elems=1000)... Mix(size=10, @p.elems=1000) : 2.576 wallclock secs (2.560 usr 0.020 sys 2.580 cpu) @ 388.182/s (n=1000) Random::Choice(size=10, @p.elems=1000): 6.010 wallclock secs (6.015 usr 0.032 sys 6.047 cpu) @ 166.398/s (n=1000) O----------------------------------------O-------O------------------------------O----------------------------------------O | | Rate | Mix(size=10, @p.elems=1000) | Random::Choice(size=10, @p.elems=1000) | O========================================O=======O==============================O========================================O | Mix(size=10, @p.elems=1000) | 388/s | -- | -57% | | Random::Choice(size=10, @p.elems=1000) | 166/s | 134% | -- | O----------------------------------------O-------O------------------------------O----------------------------------------O Benchmark: Timing 1000 iterations of Mix(size=100, @p.elems=100), Random::Choice(size=100, @p.elems=100)... Mix(size=100, @p.elems=100): 1.505 wallclock secs (1.511 usr 0.000 sys 1.511 cpu) @ 664.420/s (n=1000) Random::Choice(size=100, @p.elems=100): 0.619 wallclock secs (0.624 usr 0.000 sys 0.624 cpu) @ 1616.535/s (n=1000) O----------------------------------------O--------O-----------------------------O----------------------------------------O | | Rate | Mix(size=100, @p.elems=100) | Random::Choice(size=100, @p.elems=100) | O========================================O========O=============================O========================================O | Mix(size=100, @p.elems=100) | 664/s | -- | 146% | | Random::Choice(size=100, @p.elems=100) | 1617/s | -59% | -- | O----------------------------------------O--------O-----------------------------O----------------------------------------O Benchmark: Timing 1000 iterations of Mix(size=1000, @p.elems=1000), Random::Choice(size=1000, @p.elems=1000)... Mix(size=1000, @p.elems=1000): 135.720 wallclock secs (135.946 usr 0.288 sys 136.234 cpu) @ 7.368/s (n=1000) Random::Choice(size=1000, @p.elems=1000): 6.022 wallclock secs (6.031 usr 0.028 sys 6.058 cpu) @ 166.058/s (n=1000) O------------------------------------------O--------O-------------------------------O------------------------------------------O | | Rate | Mix(size=1000, @p.elems=1000) | Random::Choice(size=1000, @p.elems=1000) | O==========================================O========O===============================O==========================================O | Mix(size=1000, @p.elems=1000) | 7.37/s | -- | 2158% | | Random::Choice(size=1000, @p.elems=1000) | 166/s | -96% | -- | O------------------------------------------O--------O-------------------------------O------------------------------------------O ``` ### The Environment on the Benchmark * `CPU` Ryzen7 5800X (8core) * `OS` Debian11 bullseye AUTHOR ====== titsuki COPYRIGHT AND LICENSE ===================== Copyright 2019 titsuki This library is free software; you can redistribute it and/or modify it under the Artistic License 2.0. The algorithm is from: * Vose, Michael D. "A linear algorithm for generating random numbers with a given distribution." IEEE Transactions on software engineering 17.9 (1991): 972-975.