Capricious

Generation of random values

Being able to calculate random values is useful for a variety of tasks, not least as test data. Capricious provides a more convenient interface to the standard pseudorandom number generators available on the JVM.

Features

• generalized abstraction of random value generation
• special support for generating random Doubles in a user-specified distribution
• parameterized uniform, Normal (Gaussian) and Gamma distributions are available
• offers a choice of random sources: "default", secure and strongly secure, with optional seed values
• generic derivation of product and sum types

Availability

Getting Started

All terms and types are defined in the capricious package:

import capricious.*

Generating random values

A random instance of some type, Type can be constructed by calling,

val randomInt = random[Int]()
val randomBoolean = random[Boolean]()

or, if the type can be inferred from the context, just,

val randomChar: Char = random()

Random values can only be generated for certain types, but this includes most primitive types, and any type for which an Randomizable typeclass instance exists.

A value generated with random should be unpredictable, since it will be determined from a random 64-bit value provided by the JVM's default random number generator. This is, however, only a pseudorandom number generator, and the sequence it produces will be deterministic, albeit difficult to predict.

Repeatability

Randomness is useful, but it can undermine repeatability. So a more fine-grained mechanism is available for generating random values with the same probability distribution, but using different random number generators (RNGs), including seeded RNGs which will produce a repeatable sequence of values each time.

The code which will generate random values of this form must be delimited in a stochastic block. Within this block, new random values can be generated by calling arbitrary in much the same way as we called random before.

But in order to construct a new stochastic block, a random number generator should be specified, with a seed value if necessary. For now, we will used the default random number generator, with a specified seed value.

For example,

given Seed = Seed(12L)
import randomNumberGenerators.seeded

def main(): Unit = stochastic:
  println(arbitrary[Int]())
  println(arbitrary[Char]())

Note that the sequence of random values generated within a stochastic block will be deterministic, so long as the code is deterministic. This is generally true for single-threaded code, but concurrency can introduce nondeterminism, since multiple threads could cause random values to be generated in a different order across threads, each time the code is run.

Therefore, it is important to initiate a new stochastic block for each thread, using a seed generated from the parent thread, like so:

import parasite.*

given Seed = Seed(42L)
import randomNumberGenerators.seeded

def main(): Unit =
  stochastic:
    val seed1: Seed = arbitrary()
    val seed2: Seed = arbitrary()

    val async1 = Async:
      seed1.stochastic:
        println(arbitrary[Int])

    val async2 = Async:
      seed2.stochastic:
        println(arbitrary[Int])

    async1.await()
    async2.await()

Generating random Doubles

Random Doubles can be generated only if a probability distribution is specified. Since Doubles are a 64-bit approximation of the set of real numbers, which is an infinite set, there is no clear answer for what probability each possible Double value should have of being chosen randomly. Hence, several options are provided, which can be selected by importing them as contextual values:

• import randomDistributions.gaussian - the Gaussian distribution with mean, 0, and variance, 1
• import randomDistributions.uniformUnitInterval - uniform across the interval [0, 1]
• import randomDistributions.uniformSymmetricUnitInterval - uniform across the interval [-1, 1]
• import randomDistributions.binary - uniform across the 64-bit binary representations of IEEE 754 double-precision values
• given Distribution = Gamma(shape, scale) - a Gamma distribution with a specified shape (k) and scale (θ)
• given Distribution = Gaussian(mean, standardDeviation) - a Gaussian (normal) distribution with specified mean (x̄) and standard deviation (σ)
• given Distribution = UniformDistribution(start, end) - a uniform distribution in the range [start, end]

Random sources

Several (pseudo-)random number generators are available, sometimes in seeded and unseeded variants:

• import randomNumberGenerators.unseeded - a "standard" generator, with no seed
• import randomNumberGenerators.seeded - a "standard" generator, requiring a contextual Seed instance
• import randomNumberGenerators.secureUnseeded - a "secure" generator, with no seed
• import randomNumberGenerators.secureSeeded - a "secure" generator, requiring a contextual Seed instance
• import randomNumberGenerators.stronglySecure - a "strongly secure" generator, which cannot be seeded

Those generators which require a seed value can define it, as a Long value, with:

given Seed = Seed(23956242374982L)

or as a byte array of arbitrary length, for example,

given Seed = Seed(Bytes(78, 124, 19, 3, 52, 99, 112, 89, 8, 7, 12))

though different random number generators may only use as much of the seed value as they need.

The Randomizable Typeclass

The typeclass, Randomizable, will produce random instances of its type parameter. Given instances are predefined for a few basic types, but custom instances can be constructed by implementing the trait:

trait Randomizable:
  type Self
  def from(random: Random): Self

We can define a new instance for a type, say Color, with a simple given definition such as:

given Color is Randomizable = rnd =>
  Color(rnd[Byte](), rnd[Byte](), rnd[Byte]())

In this example we generate three Byte values from the Random instance, rnd, supplied.

An implementation of Randomizable's from method should call its rnd parameter's methods as many times as necessary to construct a new, arbitrary instance of Self. Although random, the instance of Self should depend deterministically on the values produced by random (and should not take randomness from any other source).

Random sequences

If a type ValueType is Randomizable, then List[ValueType] and IArray[ValueType] are also Randomizable, provided a RandomSize instance is in scope, for example by importing,

import randomization.sizes.uniformUpto1000

Instances of RandomSize exist for other powers of 10, up to 100000.

It's also possible to construct random Set[ValueType]s in the same way, but their sizes may be smaller due to deduplication. For example, whatever the range of RandomSize, a Set[Boolean] would never have more than two elements, true and false.

Product and Sum types

Capricious can construct random instances of product types such as case classes and enumeration cases, and sum types like enums and sealed traits, as long as each field of the product and variant of the sum has a valid Randomizable instance.

This generic-derivation functionality works thanks to Wisteria.

Status

Capricious is classified as fledgling. For reference, Soundness projects are categorized into one of the following five stability levels:

• embryonic: for experimental or demonstrative purposes only, without any guarantees of longevity
• fledgling: of proven utility, seeking contributions, but liable to significant redesigns
• maturescent: major design decisions broady settled, seeking probatory adoption and refinement
• dependable: production-ready, subject to controlled ongoing maintenance and enhancement; tagged as version 1.0.0 or later
• adamantine: proven, reliable and production-ready, with no further breaking changes ever anticipated

Projects at any stability level, even embryonic projects, can still be used, as long as caution is taken to avoid a mismatch between the project's stability level and the required stability and maintainability of your own project.

Capricious is designed to be small. Its entire source code currently consists of 183 lines of code.

Building

Capricious will ultimately be built by Fury, when it is published. In the meantime, two possibilities are offered, however they are acknowledged to be fragile, inadequately tested, and unsuitable for anything more than experimentation. They are provided only for the necessity of providing some answer to the question, "how can I try Capricious?".

1. Copy the sources into your own project

   Read the fury file in the repository root to understand Capricious's build structure, dependencies and source location; the file format should be short and quite intuitive. Copy the sources into a source directory in your own project, then repeat (recursively) for each of the dependencies.

   The sources are compiled against the latest nightly release of Scala 3. There should be no problem to compile the project together with all of its dependencies in a single compilation.

2. Build with Wrath

   Wrath is a bootstrapping script for building Capricious and other projects in the absence of a fully-featured build tool. It is designed to read the fury file in the project directory, and produce a collection of JAR files which can be added to a classpath, by compiling the project and all of its dependencies, including the Scala compiler itself.

   Download the latest version of wrath, make it executable, and add it to your path, for example by copying it to /usr/local/bin/.

   Clone this repository inside an empty directory, so that the build can safely make clones of repositories it depends on as peers of capricious. Run wrath -F in the repository root. This will download and compile the latest version of Scala, as well as all of Capricious's dependencies.

   If the build was successful, the compiled JAR files can be found in the .wrath/dist directory.

Contributing

Contributors to Capricious are welcome and encouraged. New contributors may like to look for issues marked beginner.

We suggest that all contributors read the Contributing Guide to make the process of contributing to Capricious easier.

Please do not contact project maintainers privately with questions unless there is a good reason to keep them private. While it can be tempting to repsond to such questions, private answers cannot be shared with a wider audience, and it can result in duplication of effort.

Author

Capricious was designed and developed by Jon Pretty, and commercial support and training on all aspects of Scala 3 is available from Propensive OÜ.

Name

Something which is capricious is determined by chance, whimsy or impulse.

In general, Soundness project names are always chosen with some rationale, however it is usually frivolous. Each name is chosen for more for its uniqueness and intrigue than its concision or catchiness, and there is no bias towards names with positive or "nice" meanings—since many of the libraries perform some quite unpleasant tasks.

Names should be English words, though many are obscure or archaic, and it should be noted how willingly English adopts foreign words. Names are generally of Greek or Latin origin, and have often arrived in English via a romance language.

Logo

The logo shows a stylized pollen cell, the canonical body used to illustrate Brownian (i.e. random) motion.

License

Capricious is copyright © 2025 Jon Pretty & Propensive OÜ, and is made available under the Apache 2.0 License.