Module Raw_quickcheck_observer

module Raw_quickcheck_observer: sig .. end

type 'a t 

val branching_factor : 'a t -> int

branching_factor t produces the number of nodes in the decision tree of t, or one less than the number of buckets in t. This value is artificially capped at 2^15-1 in order to avoid intractable function generators and overflow in arithmetic. The result may be an overapproximation for observers constructed with fn or of_fun.

val observe : 'a t ->
       'b Raw_quickcheck_generator.t ->
       sexp_of_rng:('b -> Sexplib.Sexp.t) ->
       branching_factor:int ->
       (('a -> 'b) * (unit -> Sexplib.Sexp.t)) Raw_quickcheck_generator.t

observe t gen ~sexp_of_rng ~branching_factor constructs a generator for a function type using t to observe the domain and gen to generate the range. Each generated function also comes with a (lazy, unmemoized) sexp describing it. The size of the function's decision tree is determined by branching_factor and the sexps of its return values are constructed by sexp_of_rng.

The functions in the resulting generator will all be intensionally unique: no two will make the same set of decisions in the same order. However, as two such functions may make the same set of decisions in a different order, they will not be extensionally unique. While it would be nice to have distributions of extensionally unique functions, implementing such a generator is quite difficult, and likely not worth the effort.

val singleton : unit -> 'a t

maps all values to a single bucket.

val unmap : 'a t ->
       f:('b -> 'a) ->
       f_sexp:(unit -> Sexplib.Sexp.t) -> 'b t

unmap t ~f ~f_sexp applies f to values before observing them using t.

val weighted_union : (float * 'a t) list -> 'a t

Nondeterministic observer. Presents a weighted choice of multiple observers. When observe builds a decision tree, it randomly chooses nodes from any of these observers with probability proportional to the given weights. All weights must be finite and non-negative.

val variant2 : 'a t ->
       'b t ->
       [ `A of 'a | `B of 'b ] t

val tuple2 : 'a t ->
       'b t -> ('a * 'b) t

val tuple3 : 'a t ->
       'b t ->
       'c t -> ('a * 'b * 'c) t

val tuple4 : 'a t ->
       'b t ->
       'c t ->
       'd t -> ('a * 'b * 'c * 'd) t

val tuple5 : 'a t ->
       'b t ->
       'c t ->
       'd t ->
       'e t ->
       ('a * 'b * 'c * 'd * 'e) t

val tuple6 : 'a t ->
       'b t ->
       'c t ->
       'd t ->
       'e t ->
       'f t ->
       ('a * 'b * 'c * 'd * 'e * 'f) t

val fn : ?p:float ->
       'a Raw_quickcheck_generator.t ->
       'b t ->
       sexp_of_dom:('a -> Sexplib.Sexp.t) -> ('a -> 'b) t

Observer for function type. fn ~p gen t ~sexp_of_dom observes a function by generating random inputs from gen, applying the function, and observing the output using t.

When observe builds a single random decision tree node from the result of fn, it chooses between generating a new input and observing a previously generated input. It does the former with probability p and the latter with probability 1. -. p.

p defaults to 0.25, and must be between 0 and 1 (both inclusive).

val of_fun : (unit -> 'a t) -> 'a t

of_fun f produces an observer that lazily applies f.

It is recommended that f should not do a lot of expensive work and should not be memoized. Instead, spread out the work of generating an observer over many of_fun calls combined with, e.g., variant or tuple. This allows lazily generated observers to be garbage collected after each test and the relevant portions cheaply recomputed in subsequent tests, rather than accumulating without bound over time.