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`data.random`

- Random data generators- Module:
**data.random**¶ -
This module defines a set of generators and generator makers that yield random data of specific type and distribution.

A naming convention: Procedures that takes parameters and returns
a generator is suffixed by `$`

(e.g. `integer$`

).
Procedures that are generators themselves are not (e.g. `fixnums`

).
Procedures that are combinators, that is, the ones that take
one or more generators and returns a generator, generally ends
with a preposition (e.g. `list-of`

).

All the generators in this module shares a global random state. The random seed is initialized by a fixed value when the module is loaded. You can get and set the random seed by the following procedure.

- Function:
**random-data-seed**¶ - Function:
**(setter random-data-seed)***seed-value*¶ {

`data.random`} Calling`random-data-seed`

(without arguments) returns the random seed value used to initialize the current random state.It can be used with generic setter, to reinitialize the random state with

`seed-value`.Random seed value must be an exact integer. Its lower 32bits are used.

`; reinitialize the random state with a new random seed. (set! (random-data-seed) 1) (random-data-seed) ⇒ 1`

Note: This procedure doesn’t have parameter interface (alter the global value by giving the new value as an argument), since it doesn’t work like a parameter (see Parameters). You can get the random seed value, but you can’t get the current random state itself—if you restore the random seed value again, the internal state is reset, instead of restoring the state at the time you called

`random-data-seed`

.If you want to use different random state temporarily, and ensure to restore original state afterwards, use

`with-random-data-seed`

below.

- Function:
**with-random-data-seed***seed thunk*¶ {

`data.random`} Saves the current global random state, initializes the random state with`seed`, then executes`thunk`. If`thunk`returns or the control exits out of`thunk`, the state at the time`with-random-data-seed`

was called is restored.

Since the default random seed value is fixed, you can get deterministic output when you call the random data generators below without altering the random seed explicitly.

Those generators generate uniformly distributed data.

In the following examples, we use `generator->list`

to show
some concrete data from the generators. It is provided
in `gauche.generator`

module. See `gauche.generator`

- Generators, for more
utilities work on generators.

- Function:
**integers$***size :optional (start 0)*¶ - Function:
**integers-between$***lower-bound upper-bound*¶ {

`data.random`} Create exact integer generators. The first one,`integers$`

, creates a generator that generates integers from start (inclusive) below start+size (exclusive) uniformly. The second one,`integers-between$`

, creates a generator that generates integers between`lower-bound`and`upper-bound`(both inclusive) uniformly.;; A dice roller (define dice (integers$ 6 1)) ;; Roll the dice 10 times (generator->list dice 10) ⇒ (6 6 2 4 2 5 5 1 2 2)

- Function:
**fixnums**¶ - Function:
**int8s**¶ - Function:
**uint8s**¶ - Function:
**int16s**¶ - Function:
**uint16s**¶ - Function:
**int32s**¶ - Function:
**uint32s**¶ - Function:
**int64s**¶ - Function:
**uint64s**¶ {

`data.random`} Uniform integer generators. Generate integers in fixnum range, and 8/16/32/64bit signed and unsigned integers, respectively.(generator->list int8s 10) ⇒ (20 -101 50 -99 -111 -28 -19 -61 39 110)

- Function:
**booleans**¶ {

`data.random`} Generates boolean values (`#f`

and`#t`

) in equal probability.(generator->list booleans 10) ⇒ (#f #f #t #f #f #t #f #f #f #f)

- Function:
**chars$***:optional char-set*¶ {

`data.random`} Creates a generator that generates characters in`char-set`uniformly. The default`char-set`is`#[A-Za-z0-9]`

.(define alphanumeric-chars (chars$)) (generator->list alphanumeric-chars 10) ⇒ (#\f #\m #\3 #\S #\z #\m #\x #\S #\l #\y)

- Function:
**reals$***:optional size start*¶ - Function:
**reals-between$***lower-bound upper-bound*¶ {

`data.random`} Create a generator that generates real numbers uniformly with given range. The first procedure,`reals$`

, returns reals between start and start+size, inclusively. The default of size is 1.0 and start is 0.0. The second procedure,`reals-between$`

, returns reals between lower-bound and upper-bound, inclusively.(define uniform-100 (reals$ 100)) (generator->list uniform-100 10) ⇒ (81.67965004942268 81.84927577572596 53.02443813660833)

Note that a generator from

`reals$`

can generate the upper-bound value start+size, as opposed to`integers$`

. If you need to exclude the bound value, just discard the bound value;`gfilter`

may come handy.(define generate-from-0-below-1 (gfilter (^r (not (= r 1.0))) (reals$ 1.0 0.0)))

- Function:
**samples$***collection*¶ {

`data.random`} Creates a generator that returns randomly chosen item in`collection`at a time.Do not confuse this with

`samples-from`

below, which is to combine multiple generators for sampling.(define coin-toss (samples$ '(head tail))) (generator->list coin-toss 5) ⇒ (head tail tail head tail)

- Function:
**regular-string$***regexp*¶ {

`data.random`} Creates an infinite generator that generates random strings each of which matches the given`regexp`. The`regexp`shouldn’t include conditional patterns and lookahead/behind assertions.Note: It is hard to define how the distribution of the generated strings should look like. For now, we build an NFA from regexp and put the same probability when there are multiple choices, but that may not be really useful for typical use cases (e.g. generate test data). Please assume the current implementation strategy a provisional one.

- Function:
**reals-normal$***:optional mean deviation*¶ {

`data.random`} Creates a generator that yields real numbers from normal distribution with`mean`and`deviation`. The default of`mean`is 0.0 and`deviation`is 1.0.

- Function:
**reals-exponential$***mean*¶ {

`data.random`} Creates a generator that yields real numbers from exponential distribution with`mean`.

- Function:
**integers-geometric$***p*¶ {

`data.random`} Creates a generator that yields integers from geometric distribution with success probability`p`(0 <= p <= 1). The mean is`1/p`

and variance is`(1-p)/p^2`

.

- Function:
**integers-poisson$***L*¶ {

`data.random`} Creates a generator that yields integers from poisson distribution with mean`L`, variance`L`.

- Function:
**samples-from***generators*¶ {

`data.random`} Takes a finite sequence of generators (sequence in the sense of`gauche.sequence`

), and returns a generator. Every time the resulting generator is called, it picks one of the input generators in equal probability, then calls it to get a value.(define g (samples-from (list uint8s (chars$ #[a-z])))) (generator->list g 10) ⇒ (207 107 #\m #\f 199 #\o #\b 57 #\j #\e)

NB: To create a generator that samples from a fixed collection of items, use

`samples$`

described above.

- Function:
**weighted-samples-from***weight&gens*¶ {

`data.random`} The argument is a list of pairs of a nonnegative real number and a generator. The real number determines the weight, or the relative probability that the generator is chosen. The sum of weight doesn’t need to be 1.0.The following example chooses the uint8 generator four times frequently than the character generator.

(define g (weighted-samples-from `((4.0 . ,uint8s) (1.0 . ,(chars$))))) (generator->list g 10) ⇒ (195 97 #\j #\W #\5 72 49 143 19 164)

- Function:
**pairs-of***car-gen cdr-gen*¶ {

`data.random`} Returns a generator that yields pairs, whose car is generated from`car-gen`and whose cdr is generated from`cdr-gen`.(define g (pairs-of int8s booleans)) (generator->list g 10) ⇒ ((113 . #t) (101 . #f) (12 . #t) (68 . #f) (-55 . #f))

- Function:
**tuples-of***gen …*¶ {

`data.random`} Returns a generator that yields lists, whose i-th element is generated from the i-th argument.(define g (tuples-of int8s booleans (char$))) (generator->list g 3) ⇒ ((-43 #f #\8) (53 #f #\1) (-114 #f #\i))

- Function:
**permutations-of***seq*¶ {

`data.random`} Returns a generator that yields a random permutations of`seq`.The type of

`seq`should be a sequence with a builder (see`gauche.sequence`

- Sequence framework). The type of generated objects will be the same as`seq`.(generator->list (permutations-of '(1 2 3)) 3) ⇒ ((1 2 3) (2 3 1) (3 2 1)) (generator->list (permutations-of "abc") 3) ⇒ ("cba" "cba" "cab")

- Function:
**combinations-of***size seq*¶ {

`data.random`} Returns a generator that yields a sequence of`size`elements randomly picked from`seq`.The type of

`seq`should be a sequence with a builder (see`gauche.sequence`

- Sequence framework). The type of generated objects will be the same as`seq`.(generator->list (combinations-of 2 '(a b c)) 5) ⇒ ((a c) (a b) (a c) (b a) (a c)) (generator->list (combinations-of 2 '#(a b c)) 5) ⇒ (#(a c) #(b c) #(c b) #(b a) #(b c))

The following procedures takes optional `sizer` argument, which can
be either a nonnegative integer or a generator of nonnegative integers.
The value of the sizer determines the length of the result data.

Unlike most of Gauche procedures, `sizer` argument comes before
the last argument when it is not omitted.
We couldn’t resist the temptation to
write something like `(lists-of 3 booleans)`

.

If `sizer` is omitted, the default value is taken from
the parameter `default-sizer`

. The default of `default-sizer`

is `(integers-poisson$ 4)`

.

- Function:
**lists-of***item-gen*¶ - Function:
**lists-of***sizer item-gen*¶ - Function:
**vectors-of***item-gen*¶ - Function:
**vectors-of***sizer item-gen*¶ - Function:
**strings-of**¶ - Function:
**strings-of***item-gen*¶ - Function:
**strings-of***sizer item-gen*¶ {

`data.random`} Creates a generator that generates lists, vectors or strings of values from`item-gen`, respectively. The size of each datum is determined by`sizer`.You can also omit

`item-gen`for`strings-of`

. In that case, a generator created by`(chars$)`

is used.(generator->list (lists-of 3 uint8s) 4) ⇒ ((254 46 0) (77 158 46) (1 134 156) (74 5 110)) ;; using the default sizer (generator->list (lists-of uint8s) 4) ⇒ ((93 249) (131 97) (98 206 144 247 241) (126 156 31)) ;; using a generator for the sizer (generator->list (strings-of (integers$ 8) (chars$)) 5) ⇒ ("dTJYVhu" "F" "PXkC" "w" "")

- Function:
**sequences-of***class item-gen*¶ - Function:
**sequences-of***class sizer item-gen*¶ {

`data.random`} Creates a generator that yields sequences of class`class`, whose items are generated by`item-gen`. The size of each sequence is determined by`sizer`, or the value of`default-sizer`

if omitted; the sizer can be a nonnegative integer, or a generator that yields nonnegative integers.The class

`class`must be a subclass of`<sequence>`

and implement the builder interface.(generator->list (sequences-of <u8vector> 4 uint8s) 3) ⇒ (#u8(95 203 243 46) #u8(187 199 153 152) #u8(39 114 39 25))

- Parameter:
**default-sizer**¶ {

`data.random`} The sizer used by`lists-of`

,`vectors-of`

and`strings-of`

when`sizer`argument is omitted.The value must be either an nonnegative integer, or a generator of nonnegative integers.

`data.range`

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