gen-monad: Generators
In hedgehog: Property-Based Testing

Description Usage Arguments Details See Also Examples

A Hedgehog generator is a function, which, using R's random seed, will build a lazy rose tree given a size parameter, which represent a value to test, as well as possible shrinks to try in the event of a failure. Usually, one should compose the provided generators instead of dealing with the gen contructor itself.

gen(t)

gen.and_then(g, f)

gen.bind(f, g)

gen.pure(x)

gen.impure(fg)

gen.with(g, m)

gen.map(m, g)

`t`	a function producing a tree from a size parameter, usually an R function producing random values is used.
`g`	a generator to map or bind over
`f`	a function from a value to new generator, used to build new generators monadically from a generator's output
`x`	a value to use as a generator
`fg`	a function producing a single value from a size parameter
`m`	a function to apply to values produced the generator

Hedgehog generators are functors and monads, allowing one to map over them and use their results to create more complex generators.

A generator can use R's random seed when constructing its value, but all shrinks should be deterministic.

In general, functions which accept a generator can also be provided with a list of generators nested arbitrarily.

Generators which are created from impure values (i.e., have randomness), can be created with gen.impure, which takes a function from size to a value. When using this the function will not shrink, so it is best composed with gen.shrink.

generate for way an alternative, but equally expressive way to compose generators using R's "for" loop.

# Create a generator which produces a number between
# 1 and 30
one_to_30 <- gen.element(1:30)

# Use this to create a simple vector of 6 numbers
# between 1 and 30.
vector_one_to_30 <- gen.c(of = 6, one_to_30)

# Create a matrix 2 by 3 matrix using said vector
gen.map(function(x) matrix(x, ncol=3), vector_one_to_30)

# To create a generator from a normal R random function
# use gen.impure (this generator does not shrink).
g <- gen.impure(function(size) sample(1:10) )
gen.example(g)
# [1]  5  6  3  4  8 10  2  7  9  1

# Composing generators with `gen.bind` and `gen.with` is
# easy. Here we make a generator which first build a length,
# then, elements of that length.
g <- gen.bind(function(x) gen.c(of = x, gen.element(1:10)), gen.element(2:100))
gen.example ( g )
# [1] 8 6 2 7 5 4 2 2 4 6 4 6 6 3 6 7 8 5 4 6