[![Build Status](https://travis-ci.org/titsuki/p6-Random-Choice.svg?branch=master)](https://travis-ci.org/titsuki/p6-Random-Choice) NAME ==== Random::Choice - A Perl 6 alias method implementation SYNOPSIS ======== ```perl6 use Random::Choice; say choice(:size(5), :p([0.1, 0.1, 0.1, 0.7])); # (3 3 3 0 1) say choice(:p([0.1, 0.1, 0.1, 0.7])); # 3 ``` DESCRIPTION =========== Random::Choice is a Perl 6 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-Perl6-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`): ```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) : 0.120 wallclock secs (0.146 usr 0.006 sys 0.152 cpu) @ 8335.278/s (n=1000) Random::Choice(size=10, @p.elems=10): 0.249 wallclock secs (0.286 usr 0.003 sys 0.288 cpu) @ 4015.613/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) | 8335/s | -- | -58% | | Random::Choice(size=10, @p.elems=10) | 4016/s | 140% | -- | 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) : 2.794 wallclock secs (2.792 usr 0.000 sys 2.792 cpu) @ 357.965/s (n=1000) Random::Choice(size=1000, @p.elems=10): 0.238 wallclock secs (0.238 usr 0.004 sys 0.242 cpu) @ 4201.204/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) | 358/s | -- | 1215% | | Random::Choice(size=1000, @p.elems=10) | 4201/s | -92% | -- | 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) : 3.570 wallclock secs (3.539 usr 0.028 sys 3.566 cpu) @ 280.119/s (n=1000) Random::Choice(size=10, @p.elems=1000): 15.011 wallclock secs (14.992 usr 0.012 sys 15.004 cpu) @ 66.619/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) | 280/s | -- | -76% | | Random::Choice(size=10, @p.elems=1000) | 66.6/s | 323% | -- | 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): 2.303 wallclock secs (2.305 usr 0.000 sys 2.305 cpu) @ 434.278/s (n=1000) Random::Choice(size=100, @p.elems=100): 1.578 wallclock secs (1.577 usr 0.000 sys 1.577 cpu) @ 633.811/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) | 434/s | -- | 47% | | Random::Choice(size=100, @p.elems=100) | 634/s | -32% | -- | 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): 186.849 wallclock secs (186.608 usr 0.124 sys 186.731 cpu) @ 5.352/s (n=1000) Random::Choice(size=1000, @p.elems=1000): 14.920 wallclock secs (14.897 usr 0.012 sys 14.909 cpu) @ 67.025/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) | 5.35/s | -- | 1155% | | Random::Choice(size=1000, @p.elems=1000) | 67.0/s | -92% | -- | O------------------------------------------O--------O-------------------------------O------------------------------------------O ``` 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.