Generator

Q: How deeply does Functional Programming in Scala, Second Edition cover generator?

Defines Gen[A], develops combinators, and descends to State[RNG, A]; leaves function generation as an open challenge.

As explained in Functional Programming in Scala, Second Edition

A generator is a parameterized representation of a computation that produces test-case values, potentially for any element type.

A generator is a parameterized representation of a computation that produces test-case values. A Gen[A] knows how to generate values of type A, such as lists of random integers, and can be combined with predicates to test properties. section 8.1 section 8.1.2

Generating lists for properties

val intList = Gen.listOf(Gen.choose(0, 100)) val prop = Prop.forAll(intList)(ns => ns.reverse.reverse == ns) && Prop.forAll(intList)(ns => ns.headOption == ns.reverse.lastOption) The generator supplies lists of varying lengths and values. ScalaCheck samples them, reporting success when generated cases satisfy both predicates and printing a counterexample when one fails. section 8.1

Generators become useful through a small, composable API:

Use listOf to lift Gen[A] into Gen[List[A]], keeping the operation polymorphic across element types. section 8.1.2
Represent Gen[A] as a State transition over an RNG, making random generation an explicit functional computation. section 8.1.4
Use flatMap when a generated value determines the next generator; use union and weighted to combine alternative generators. section 8.1.5

opaque type Gen[+A] = State[RNG, A]

Browse the compact representation that connects generators with explicit random-state transitions. section 8.1.4

How Functional Programming in Scala, Second Edition covers it

Cover of Functional Programming in Scala, Second Edition

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Related concepts

generative adversarial networkpart of · A generative adversarial network is a generative model with a generator and discriminator trained in competition, enabling the generator to produce samples resembling a target dataset or distribution.
retrieval-augmented generationused by · Retrieval-augmented generation is a technique that retrieves relevant information from external sources and supplies it as context to a generative model when producing an output.
latent spaceuses · A latent space is a learned representational space in which numerical coordinates encode information or features inferred from observed data.
noiseuses · Noise is variation, content, or data treated as unwanted, irrelevant, or distinct from the signal, pattern, or structure of interest.
language modelkind of · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.

This explainer was generated by Manning's knowledge graph from the text of Functional Programming in Scala, Second Edition. The section links point to the parts of the book it draws on.