data.random - Random data generators ¶This module defines a set of generators and generator makers that yield random data of specific type and distribution.
A part of the functionality of this module is turned into
SRFI-194, but it gives different names/API for the consistency of other SRFIs
(see srfi.194 - Random data generators (SRFI)).
A naming convention: Procedures that takes parameters and returns
a generator is suffixed by $ (e.g. integer$).
Procedures that are generators themselves are not (e.g. fixnums).
Procedures that are combinators, that is, the ones that take
one or more generators and returns a generator, generally ends
with a preposition (e.g. list-of).
{data.random}
Its value must be a SRFI-27 random source (see srfi.27 - Sources of Random Bits).
The initial value is SRFI-27’s default-random-source.
When generators are created with the utilities of this module, it captures the dynamic value of this parameter, from which the random values are generated. Changing the parameter value after generators are created won’t affect their behavior.
Note: Up to 0.9.13, generators created with this module refers to the dynamic value of the current random source every time it generates a random value, so that prebuilt network of random values can be affected by modifying the current random source. However it incurs overhead for each random number generation, and generally it is cheaper to reconstruct a new generators with the different random source. The current behavior is consistent with SRFI-194.
The following two procedures are to swap current random source. They are still supported but no longer effective.
random-data-seed with-random-data-seed
Those generators generate uniformly distributed data.
In the following examples, we use generator->list to show
some concrete data from the generators. It is provided
in gauche.generator module. See gauche.generator - Generators, for more
utilities work on generators.
{data.random}
Create exact integer generators. The first one, integers$, 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)
{data.random}
Creates and returns uniform integer generators.
Each generates integers in fixnum range, and
8/16/32/64bit signed and unsigned integers, respectively.
(generator->list (int8s$) 10) ⇒ (20 -101 50 -99 -111 -28 -19 -61 39 110)
{data.random}
Creates and returns a generator of
boolean values (#f and #t) in equal probability.
(generator->list (booleans$) 10) ⇒ (#f #f #t #f #f #t #f #f #f #f)
{data.random}
These are preconstructed generators by fixnum$ etc. Provided
for the convenience, but note that the random source they use is
fixed to default-random-source.
{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$)) (generator->list alphanumeric-chars 10) ⇒ (#\f #\m #\3 #\S #\z #\m #\x #\S #\l #\y)
{data.random}
Create a generator that generates real numbers uniformly with given range.
The first procedure, reals$, 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)))
Note also that floating point numbers are not uniformly distributed
within the given real number range. When you uniformly sample
from a certain real number range, the returned flonums would very likely
have the same or closer exponent of the bounds of the range.
If that’s what you want, it’s ok. If you want a sample uniformly from
a valid flonum values, see finite-flonums$ below.
Returns a flonum generator that samples uniformly from all possible finite flonums. It is not the same as uniformly sampling real numbers, since floating-point numbers are not distributed evenly. This is more useful if you want feed random flonums for testing, than uniformly distributed real numbers, for the latter has rather skewed distribution of exponents.
Returns a fresh generator of complex numbers uniformly. All the *-lb and *-ub arguments must be real numbers, and each *-ub argument must be greater than or equal to the corresponding *-lb argument. The origin argument is a complex number.
Generators returned by
complexes-rectangular$ sample a rectangular area
where its real value is between re-lb and re-ub inclusive,
and its imaginary value is between im-lb and im-ub inclusive.
Generators returned by
complexes-rectangular$ samples an annulus whose origin is
origin, whose magnitude is between mag-lb and mag-ub
inclusive, and whose angle is between ang-lb and ang-ub
radians inclusive. If origin, ang-lb, and/or ang-ub
are omitted, they’re defaulted to 0+0i, 0, and 2π.
{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)
{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.
{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.
{data.random}
Creates a generator that yields real numbers from exponential distribution
with mean.
NB: The probability density function p(x) is
(* (/ mean) (exp (- (/ x mean)))). Some definition of exponential
distribution uses a decay parameter λ such that p(x) is
(* λ (exp (- (* x λ)))). That is, λ = 1/mean.
{data.random}
Creates a generator that yields finite positive real numbers greater
than or equal
to xmin, and follows the power-law probability density function
p(x) = (α-1)/xmin (x/xmin)^{-α}, where x >= xmin and α > 1.
The power argument specifies α.
The xmin argument must be a positive real numebr, and power must be a real number greater than 1.
{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.
{data.random}
Creates a generator that yields integers from poisson distribution with
mean L, variance L.
{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.
The resulting generator is exhausted when all the input generators are exhausted.
(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.
{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$)))))
(generator->list g 10)
⇒ (195 97 #\j #\W #\5 72 49 143 19 164)
{data.random}
Returns a generator that yields pairs,
whose car is generated from car-gen
and whose cdr is generated from cdr-gen.
(define g (pairs-of int8s booleans)) (generator->list g 10) ⇒ ((113 . #t) (101 . #f) (12 . #t) (68 . #f) (-55 . #f))
{data.random}
Returns a generator that yields lists,
whose i-th element is generated from the i-th argument.
(define g (tuples-of int8s booleans (char$))) (generator->list g 3) ⇒ ((-43 #f #\8) (53 #f #\1) (-114 #f #\i))
{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")
{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).
{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$)) 5)
⇒ ("dTJYVhu" "F" "PXkC" "w" "")
{data.random}
Creates a generator that yields sequences of class class,
whose items are generated by item-gen. The size of each
sequence is determined by sizer, or the value of default-sizer
if omitted; the sizer can be a nonnegative integer, or a generator
that yields nonnegative integers.
The class class must be a subclass of <sequence> and
implement the builder interface.
(generator->list (sequences-of <u8vector> 4 uint8s) 3) ⇒ (#u8(95 203 243 46) #u8(187 199 153 152) #u8(39 114 39 25))
{data.random}
The sizer used by lists-of, vectors-of and strings-of
when sizer argument is omitted.
The value must be either an nonnegative integer, or a generator
of nonnegative integers. The default value is (integers-poisson$ 4).