This Raku package has functions for generating random strings, words, pet names, vectors, arrays, and (tabular) datasets.
The primary motivation for this package is to have simple, intuitively named functions for generating random vectors (lists) and datasets of different objects.
Although, Raku has a fairly good support of random vector generation, it is assumed that commands like the following are easier to use:
say random-string(6, chars => 4, ranges => [ <y n Y N>, "0".."9" ] ).raku;
The function random-string
generates random strings.
Here is a random string:
use Data::Generators;
random-string
# rNa0FuC75aoA
Here we generate a vector of random strings with length 4 and characters that belong to specified ranges:
say random-string(6, chars => 4, ranges => [ <y n Y N>, "0".."9" ] ).raku;
# ("333N", "5N1y", "n0Y7", "7085", "6502", "y0Y7")
The function random-word
generates random words.
Here is a random word:
random-word
# psychopathy
Here we generate a list with 12 random words:
random-word(12)
# (drive-in joyless opportunistic kalian fertilize barium Malawian dingy reprobate wannabe penitential compaction)
Here we generate a table of random words of different types:
use Data::Reshapers;
my @dfWords = do for <Any Common Known Stop> -> $wt { $wt => random-word(6, type => $wt) };
say to-pretty-table(@dfWords);
# +--------+---------------+---------+-----------+----------------+-------------+--------------+
# | | 4 | 0 | 2 | 3 | 5 | 1 |
# +--------+---------------+---------+-----------+----------------+-------------+--------------+
# | Any | Anoectochilus | coltan | parhelion | heartburning | therewithal | columniation |
# | Common | explicit | beastly | cycle | overindulgence | extenuate | anaphora |
# | Known | spiritism | talaria | feeling | grapy | guru | epigon |
# | Stop | a | i've | 0 | shouldn't | X | few |
# +--------+---------------+---------+-----------+----------------+-------------+--------------+
Remark: Whatever
can be used instead of 'Any'
.
Remark: The function to-pretty-table
is from the package
Data::Reshapers.
All word data can be retrieved with the resources object:
my $ra = Data::Generators::ResourceAccess.instance();
$ra.get-word-data().elems;
# 84996
The function random-pet-name
generates random pet names.
The pet names are taken from publicly available data of pet license registrations in the years 2015–2020 in Seattle, WA, USA. See [DG1].
Here is a random pet name:
random-pet-name
# Murphy
The following command generates a list of six random pet names:
srand(32);
random-pet-name(6).raku
# ("Zoe", "Bandit", "Cody", "Barb", "Barack", "Cooper")
The named argument species
can be used to specify specie of the random pet names.
(According to the specie-name relationships in [DG1].)
Here we generate a table of random pet names of different species:
my @dfPetNames = do for <Any Cat Dog Goat Pig> -> $wt { $wt => random-pet-name(6, species => $wt) };
say to-pretty-table(@dfPetNames);
# +------+----------+------------------+----------+---------+----------+---------+
# | | 0 | 1 | 2 | 4 | 5 | 3 |
# +------+----------+------------------+----------+---------+----------+---------+
# | Any | Hamilton | Tyson Zeus Brown | Georgia | Ivy | Mochi | Ellla |
# | Cat | Ace | Felix | Dean | Georgia | Little B | Lulu |
# | Dog | Louis | Wiley | Moya | Cleo | Barkley | Buster |
# | Goat | Grayson | Junebug | Winnipeg | Lula | Abelard | Grace |
# | Pig | Atticus | Atticus | Millie | Atticus | Atticus | Atticus |
# +------+----------+------------------+----------+---------+----------+---------+
Remark: Whatever
can be used instead of 'Any'
.
The named argument (adverb) weighted
can be used to specify random pet name choice
based on known real-life number of occurrences:
srand(32);
say random-pet-name(6, :weighted).raku
# ("Claire", "Ilsa", "Tinkerbelle", "Remy", "Zoe", "Bandit")
The weights used correspond to the counts from [DG1].
Remark: The implementation of random-pet-name
is based on the Mathematica implementation
RandomPetName
,
[AAf1].
All pet data can be retrieved with the resources object:
my $ra = Data::Generators::ResourceAccess.instance();
$ra.get-pet-data()>>.elems
# {cat => 7806, dog => 12941, goat => 40, pig => 3}
The function random-pretentious-job-title
generates random pretentious job titles.
Here is a random pretentious job title:
random-pretentious-job-title
# Direct Functionality Director
The following command generates a list of six random pretentious job titles:
random-pretentious-job-title(6).raku
# ("Central Data Agent", "National Team Executive", "Corporate Infrastructure Associate", "Global Accountability Agent", "National Metrics Agent", "Future Mobility Planner")
The named argument number-of-words
can be used to control the number of words in the generated job titles.
The named argument language
can be used to control in which language the generated job titles are in.
At this point, only Bulgarian and English are supported.
Here we generate pretentious job titles using different languages and number of words per title:
my $res = random-pretentious-job-title(12, number-of-words => Whatever, language => Whatever);
say to-pretty-table($res.rotor(3));
# +---------------------------+----------------------------------+----------------------------------+
# | 0 | 1 | 2 |
# +---------------------------+----------------------------------+----------------------------------+
# | Specialist | Администратор | Response Executive |
# | Optimization Designer | Супервайзор по Маркетинг | Interactive Applications Manager |
# | Future Response Associate | Brand Technician | Посредник на Качество |
# | Identity Administrator | Глобален Специалист на Отчетност | Integration Designer |
# +---------------------------+----------------------------------+----------------------------------+
Remark: Whatever
can be used as values for the named arguments number-of-words
and language
.
Remark: The implementation uses the job title phrases in https://www.bullshitjob.com .
It is, more-or-less, based on the Mathematica implementation
RandomPretentiousJobTitle
,
[AAf2].
This module provides the function random-real
that can be used to generate lists of real numbers
using the uniform distribution.
Here is a random real:
say random-real();
# 0.6148375015300324
Here is a random real between 0 and 20:
say random-real(20);
# 13.957487542046149
Here are six random reals between -2 and 12:
say random-real([-2,12], 6);
# (-1.4191627349160865 0.7985910676295189 2.5735598216113056 8.655772458122875 -0.23141703578666983 3.2473529322039427)
Here is a 4-by-3 array of random reals between -3 and 3:
say random-real([-3,3], [4,3]);
# [[-0.7777530943688706 2.633038558227515 0.9960527665422672]
# [0.5438581449111846 0.8538444340370224 -1.2232218597405276]
# [0.24844626265222436 1.6176949918194392 -2.9106836929578517]
# [-1.1519301602208225 -2.168108140257122 1.575345624009456]]
Remark: The signature design follows Mathematica's function
RandomReal
.
This module provides the function random-variate
that can be used to generate lists of real numbers
using distribution specifications.
Here are examples:
say random-variate(BernoulliDistribution.new(:p(0.3)), 1000).BagHash.Hash;
# {0 => 683, 1 => 317}
say random-variate(BinomialDistribution.new(:n(10), :p(0.2)), 10);
# (2 2 4 1 2 2 4 2 1 2)
say random-variate(NormalDistribution.new( µ => 10, σ => 20), 5);
# (-1.0372379191539256 15.88117685892444 2.3800289134125467 -16.077914554672056 21.434725308461598)
say random-variate(UniformDistribution.new(:min(2), :max(60)), 5);
# (33.12620384207506 54.54682825992015 44.703052532365824 18.662929703538268 16.99790622594807)
Remark: Only Normal distribution and Uniform distribution are implemented at this point.
Remark: The signature design follows Mathematica's function
RandomVariate
.
Here is an example of 2D array generation:
say random-variate(NormalDistribution.new, [3,4]);
# [[1.730441181600127 0.3420465932875971 -0.6275231817898077 0.7933012580974133]
# [0.7546145800662779 -0.1414053357058038 0.06128413616858158 -0.48630656184266835]
# [-0.8339285130645827 -3.0312240538258073 0.18147829328498447 1.1150963106600722]]
The function random-tabular-dataset
can be used generate tabular datasets.
Remark: In this module a dataset is (usually) an array of arrays of pairs.
The dataset data structure resembles closely Mathematica's data structure
[Dataset
]https://reference.wolfram.com/language/ref/Dataset.html), [WRI2].
Remark: The programming languages R and S have a data structure called "data frame" that
corresponds to dataset. (In the Python world the package pandas
provides data frames.)
Data frames, though, are column-centric, not row-centric as datasets.
For example, data frames do not allow a column to have elements of heterogeneous types.
Here are basic calls:
random-tabular-dataset();
random-tabular-dataset(Whatever):row-names;
random-tabular-dataset(Whatever, Whatever);
random-tabular-dataset(12, 4);
random-tabular-dataset(Whatever, 4);
random-tabular-dataset(Whatever, <Col1 Col2 Col3>):!row-names;
Here is example of a generated tabular dataset that column names that are cat pet names:
my @dfRand = random-tabular-dataset(5, 3, column-names-generator => { random-pet-name($_, species => 'Cat') });
say to-pretty-table(@dfRand);
# +-----------+-----------+---------------+
# | Tonks | Bailey | Skipper |
# +-----------+-----------+---------------+
# | -0.425523 | 14.708426 | extirpate |
# | 25.249457 | 4.334753 | humanize |
# | 23.518357 | 9.309680 | eccyesis |
# | 5.089616 | 20.839470 | prevailing |
# | 17.328699 | 11.818831 | irreligionist |
# +-----------+-----------+---------------+
The display function to-pretty-table
is from
Data::Reshapers
.
Remark: At this point only wide format datasets are generated. (The long format implementation is high in my TOOD list.)
Remark: The signature design and implementation are based on the Mathematica implementation
RandomTabularDataset
,
[AAf3].
-
TODO Random tabular datasets generation
- DONE Row spec
- DONE Column spec that takes columns count and column names
- DONE Column names generator
- DONE Wide form implementation only
- DONE Generators of column values
- DONE Column-generator hash
- DONE List of generators
- DONE Single generator
- DONE Turn "generators" that are lists into sampling pure functions
- TODO Long form implementation
- TODO Max number of values
- TODO Min number of values
- TODO Form (long or wide)
- DONE Row names (automatic)
-
DONE Random reals vectors generation
-
TODO Figuring out how to handle and indicate missing values
-
TODO Random reals vectors generation according to distribution specs
- DONE Uniform distribution
- DONE Normal distribution
- TODO Poisson distribution
- TODO Skew-normal distribution
- TODO Triangular distribution
-
DONE
RandomReal
-like implementation- See
random-real
.
- See
-
DONE Selection between
roll
andpick
for:- DONE
RandomWord
- DONE
RandomPetName
- DONE
[AA1] Anton Antonov, "Pets licensing data analysis", (2020), MathematicaForPrediction at WordPress.
[AAf1] Anton Antonov, RandomPetName, (2021), Wolfram Function Repository.
[AAf2] Anton Antonov, RandomPretentiousJobTitle, (2021), Wolfram Function Repository.
[AAf3] Anton Antonov, RandomTabularDataset, (2021), Wolfram Function Repository.
[SHf1] Sander Huisman, RandomString, (2021), Wolfram Function Repository.
[WRI1] Wolfram Research (2010), RandomVariate, Wolfram Language function.
[WRI2] Wolfram Research (2014), Dataset, Wolfram Language function.
[DG1] Data.Gov, Seattle Pet Licenses, catalog.data.gov.