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Improve documentation for random (#17015)

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* Improve documentation for random

Use runnableExamples
Minor changes

* Apply suggestions

Remove echo
Use RNG in more places

* Fix skipRandomNumbers example
This commit is contained in:
konsumlamm
2021-02-12 15:10:12 +01:00
committed by GitHub
parent 8053ccde2f
commit f57774e1e7
1 changed files with 168 additions and 185 deletions
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353
lib/pure/random.nim
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@@ -7,9 +7,9 @@
# distribution, for details about the copyright.
#
## Nim's standard random number generator.
## Nim's standard random number generator (RNG).
##
## Its implementation is based on the ``xoroshiro128+``
## Its implementation is based on the `xoroshiro128+`
## (xor/rotate/shift/rotate) library.
## * More information: http://xoroshiro.di.unimi.it
## * C implementation: http://xoroshiro.di.unimi.it/xoroshiro128plus.c
@@ -19,70 +19,63 @@
## Basic usage
## ===========
##
## To get started, here are some examples:
##
## .. code-block::
##
## import random
##
## # Call randomize() once to initialize the default random number generator
## # If this is not called, the same results will occur every time these
## # examples are run
## randomize()
##
## # Pick a number between 0 and 100
## let num = rand(100)
## echo num
##
## # Roll a six-sided die
## let roll = rand(1..6)
## echo roll
##
## # Pick a marble from a bag
## let marbles = ["red", "blue", "green", "yellow", "purple"]
## let pick = sample(marbles)
## echo pick
##
## # Shuffle some cards
## var cards = ["Ace", "King", "Queen", "Jack", "Ten"]
## shuffle(cards)
## echo cards
##
## These examples all use the default random number generator. The
## `Rand type<#Rand>`_ represents the state of a random number generator.
runnableExamples:
# Call randomize() once to initialize the default random number generator.
# If this is not called, the same results will occur every time these
# examples are run.
randomize()
# Pick a number in 0..100.
let num = rand(100)
doAssert num in 0..100
# Roll a six-sided die.
let roll = rand(1..6)
doAssert roll in 1..6
# Pick a marble from a bag.
let marbles = ["red", "blue", "green", "yellow", "purple"]
let pick = sample(marbles)
doAssert pick in marbles
# Shuffle some cards.
var cards = ["Ace", "King", "Queen", "Jack", "Ten"]
shuffle(cards)
doAssert cards.len == 5
## These examples all use the default RNG. The
## `Rand type <#Rand>`_ represents the state of an RNG.
## For convenience, this module contains a default Rand state that corresponds
## to the default random number generator. Most procs in this module which do
## to the default RNG. Most procs in this module which do
## not take in a Rand parameter, including those called in the above examples,
## use the default generator. Those procs are **not** thread-safe.
##
## Note that the default generator always starts in the same state.
## The `randomize proc<#randomize>`_ can be called to initialize the default
## The `randomize proc <#randomize>`_ can be called to initialize the default
## generator with a seed based on the current time, and it only needs to be
## called once before the first usage of procs from this module. If
## ``randomize`` is not called, then the default generator will always produce
## `randomize` is not called, the default generator will always produce
## the same results.
##
## Generators that are independent of the default one can be created with the
## `initRand proc<#initRand,int64>`_.
## RNGs that are independent of the default one can be created with the
## `initRand proc <#initRand,int64>`_.
##
## Again, it is important to remember that this module must **not** be used for
## cryptographic applications.
##
## See also
## ========
## * `std/sysrand module<sysrand.html>`_ for cryptographically secure pseudorandom number generator
## * `math module<math.html>`_ for basic math routines
## * `mersenne module<mersenne.html>`_ for the Mersenne Twister random number
## generator
## * `stats module<stats.html>`_ for statistical analysis
## * `list of cryptographic and hashing modules
## <lib.html#pure-libraries-hashing>`_
## * `std/sysrand module <sysrand.html>`_ for a cryptographically secure pseudorandom number generator
## * `mersenne module <mersenne.html>`_ for the Mersenne Twister random number generator
## * `math module <math.html>`_ for basic math routines
## * `stats module <stats.html>`_ for statistical analysis
## * `list of cryptographic and hashing modules <lib.html#pure-libraries-hashing>`_
## in the standard library
import algorithm, math
import std/[algorithm, math]
import std/private/since
include "system/inclrtl"
include system/inclrtl
{.push debugger: off.}
when defined(js):
@@ -97,12 +90,12 @@ else:
type
Rand* = object ## State of a random number generator.
##
## Create a new Rand state using the `initRand proc<#initRand,int64>`_.
## Create a new Rand state using the `initRand proc <#initRand,int64>`_.
##
## The module contains a default Rand state for convenience.
## It corresponds to the default random number generator's state.
## It corresponds to the default RNG's state.
## The default Rand state always starts with the same values, but the
## `randomize proc<#randomize>`_ can be used to seed the default generator
## `randomize proc <#randomize>`_ can be used to seed the default generator
## with a value based on the current time.
##
## Many procs have two variations: one that takes in a Rand parameter and
@@ -130,9 +123,9 @@ proc rotl(x, k: Ui): Ui =
result = (x shl k) or (x shr (Ui(64) - k))
proc next*(r: var Rand): uint64 =
## Computes a random ``uint64`` number using the given state.
## Computes a random `uint64` number using the given state.
##
## See also:
## **See also:**
## * `rand proc<#rand,Rand,Natural>`_ that returns an integer between zero and
## a given upper bound
## * `rand proc<#rand,Rand,range[]>`_ that returns a float
@@ -145,6 +138,7 @@ proc next*(r: var Rand): uint64 =
doAssert r.next() == 138_744_656_611_299'u64
doAssert r.next() == 979_810_537_855_049_344'u64
doAssert r.next() == 3_628_232_584_225_300_704'u64
let s0 = r.a0
var s1 = r.a1
result = s0 + s1
@@ -155,12 +149,12 @@ proc next*(r: var Rand): uint64 =
proc skipRandomNumbers*(s: var Rand) =
## The jump function for the generator.
##
## This proc is equivalent to 2^64 calls to `next<#next,Rand>`_, and it can
## be used to generate 2^64 non-overlapping subsequences for parallel
## This proc is equivalent to `2^64` calls to `next <#next,Rand>`_, and it can
## be used to generate `2^64` non-overlapping subsequences for parallel
## computations.
##
## When multiple threads are generating random numbers, each thread must
## own the `Rand<#Rand>`_ state it is using so that the thread can safely
## own the `Rand <#Rand>`_ state it is using so that the thread can safely
## obtain random numbers. However, if each thread creates its own Rand state,
## the subsequences of random numbers that each thread generates may overlap,
## even if the provided seeds are unique. This is more likely to happen as the
@@ -171,34 +165,30 @@ proc skipRandomNumbers*(s: var Rand) =
## Rand state to a thread, call this proc before passing it to the next one.
## By using the Rand state this way, the subsequences of random numbers
## generated in each thread will never overlap as long as no thread generates
## more than 2^64 random numbers.
## more than `2^64` random numbers.
##
## The following example below demonstrates this pattern:
##
## .. code-block::
## # Compile this example with --threads:on
## import random
## import threadpool
##
## const spawns = 4
## const numbers = 100000
##
## proc randomSum(rand: Rand): int =
## var r = rand
## for i in 1..numbers:
## result += rand(1..10)
##
## var r = initRand(2019)
## var vals: array[spawns, FlowVar[int]]
## for val in vals.mitems:
## val = spawn(randomSum(r))
## r.skipRandomNumbers()
##
## for val in vals:
## echo ^val
##
## See also:
## **See also:**
## * `next proc<#next,Rand>`_
runnableExamples("--threads:on"):
import std/[random, threadpool]
const spawns = 4
const numbers = 100000
proc randomSum(r: Rand): int =
var r = r
for i in 1..numbers:
result += r.rand(0..10)
var r = initRand(2019)
var vals: array[spawns, FlowVar[int]]
for val in vals.mitems:
val = spawn randomSum(r)
r.skipRandomNumbers()
for val in vals:
doAssert abs(^val - numbers * 5) / numbers < 0.1
when defined(js):
const helper = [0xbeac0467u32, 0xd86b048bu32]
else:
@@ -218,9 +208,8 @@ proc skipRandomNumbers*(s: var Rand) =
proc rand*(r: var Rand; max: Natural): int {.benign.} =
## Returns a random integer in the range `0..max` using the given state.
##
## See also:
## * `rand proc<#rand,int>`_ that returns an integer using the default
## random number generator
## **See also:**
## * `rand proc<#rand,int>`_ that returns an integer using the default RNG
## * `rand proc<#rand,Rand,range[]>`_ that returns a float
## * `rand proc<#rand,Rand,HSlice[T: Ordinal or float or float32 or float64,T: Ordinal or float or float32 or float64]>`_
## that accepts a slice
@@ -230,22 +219,22 @@ proc rand*(r: var Rand; max: Natural): int {.benign.} =
doAssert r.rand(100) == 0
doAssert r.rand(100) == 96
doAssert r.rand(100) == 66
if max == 0: return
while true:
let x = next(r)
if x <= randMax - (randMax mod Ui(max)):
return int(x mod (uint64(max)+1u64))
return int(x mod (uint64(max) + 1u64))
proc rand*(max: int): int {.benign.} =
## Returns a random integer in the range `0..max`.
##
## If `randomize<#randomize>`_ has not been called, the sequence of random
## If `randomize <#randomize>`_ has not been called, the sequence of random
## numbers returned from this proc will always be the same.
##
## This proc uses the default random number generator. Thus, it is **not**
## thread-safe.
## This proc uses the default RNG. Thus, it is **not** thread-safe.
##
## See also:
## **See also:**
## * `rand proc<#rand,Rand,Natural>`_ that returns an integer using a
## provided state
## * `rand proc<#rand,float>`_ that returns a float
@@ -257,23 +246,23 @@ proc rand*(max: int): int {.benign.} =
doAssert rand(100) == 0
doAssert rand(100) == 96
doAssert rand(100) == 66
rand(state, max)
proc rand*(r: var Rand; max: range[0.0 .. high(float)]): float {.benign.} =
## Returns a random floating point number in the range `0.0..max`
## using the given state.
##
## See also:
## * `rand proc<#rand,float>`_ that returns a float using the default
## random number generator
## **See also:**
## * `rand proc<#rand,float>`_ that returns a float using the default RNG
## * `rand proc<#rand,Rand,Natural>`_ that returns an integer
## * `rand proc<#rand,Rand,HSlice[T: Ordinal or float or float32 or float64,T: Ordinal or float or float32 or float64]>`_
## that accepts a slice
## * `rand proc<#rand,typedesc[T]>`_ that accepts an integer or range type
runnableExamples:
var r = initRand(234)
let f = r.rand(1.0)
## f = 8.717181376738381e-07
let f = r.rand(1.0) # 8.717181376738381e-07
let x = next(r)
when defined(js):
result = (float(x) / float(high(uint32))) * max
@@ -284,13 +273,12 @@ proc rand*(r: var Rand; max: range[0.0 .. high(float)]): float {.benign.} =
proc rand*(max: float): float {.benign.} =
## Returns a random floating point number in the range `0.0..max`.
##
## If `randomize<#randomize>`_ has not been called, the sequence of random
## If `randomize <#randomize>`_ has not been called, the sequence of random
## numbers returned from this proc will always be the same.
##
## This proc uses the default random number generator. Thus, it is **not**
## thread-safe.
## This proc uses the default RNG. Thus, it is **not** thread-safe.
##
## See also:
## **See also:**
## * `rand proc<#rand,Rand,range[]>`_ that returns a float using a
## provided state
## * `rand proc<#rand,int>`_ that returns an integer
@@ -299,8 +287,8 @@ proc rand*(max: float): float {.benign.} =
## * `rand proc<#rand,typedesc[T]>`_ that accepts an integer or range type
runnableExamples:
randomize(234)
let f = rand(1.0)
## f = 8.717181376738381e-07
let f = rand(1.0) # 8.717181376738381e-07
rand(state, max)
proc rand*[T: Ordinal or SomeFloat](r: var Rand; x: HSlice[T, T]): T =
@@ -309,9 +297,9 @@ proc rand*[T: Ordinal or SomeFloat](r: var Rand; x: HSlice[T, T]): T =
##
## Allowed types for `T` are integers, floats, and enums without holes.
##
## See also:
## **See also:**
## * `rand proc<#rand,HSlice[T: Ordinal or float or float32 or float64,T: Ordinal or float or float32 or float64]>`_
## that accepts a slice and uses the default random number generator
## that accepts a slice and uses the default RNG
## * `rand proc<#rand,Rand,Natural>`_ that returns an integer
## * `rand proc<#rand,Rand,range[]>`_ that returns a float
## * `rand proc<#rand,typedesc[T]>`_ that accepts an integer or range type
@@ -320,8 +308,8 @@ proc rand*[T: Ordinal or SomeFloat](r: var Rand; x: HSlice[T, T]): T =
doAssert r.rand(1..6) == 4
doAssert r.rand(1..6) == 4
doAssert r.rand(1..6) == 6
let f = r.rand(-1.0 .. 1.0)
## f = 0.8741183448756229
let f = r.rand(-1.0 .. 1.0) # 0.8741183448756229
when T is SomeFloat:
result = rand(r, x.b - x.a) + x.a
else: # Integers and Enum types
@@ -332,13 +320,12 @@ proc rand*[T: Ordinal or SomeFloat](x: HSlice[T, T]): T =
##
## Allowed types for `T` are integers, floats, and enums without holes.
##
## If `randomize<#randomize>`_ has not been called, the sequence of random
## If `randomize <#randomize>`_ has not been called, the sequence of random
## numbers returned from this proc will always be the same.
##
## This proc uses the default random number generator. Thus, it is **not**
## thread-safe.
## This proc uses the default RNG. Thus, it is **not** thread-safe.
##
## See also:
## **See also:**
## * `rand proc<#rand,Rand,HSlice[T: Ordinal or float or float32 or float64,T: Ordinal or float or float32 or float64]>`_
## that accepts a slice and uses a provided state
## * `rand proc<#rand,int>`_ that returns an integer
@@ -349,18 +336,18 @@ proc rand*[T: Ordinal or SomeFloat](x: HSlice[T, T]): T =
doAssert rand(1..6) == 4
doAssert rand(1..6) == 4
doAssert rand(1..6) == 6
result = rand(state, x)
proc rand*[T: SomeInteger](t: typedesc[T]): T =
## Returns a random integer in the range `low(T)..high(T)`.
##
## If `randomize<#randomize>`_ has not been called, the sequence of random
## If `randomize <#randomize>`_ has not been called, the sequence of random
## numbers returned from this proc will always be the same.
##
## This proc uses the default random number generator. Thus, it is **not**
## thread-safe.
## This proc uses the default RNG. Thus, it is **not** thread-safe.
##
## See also:
## **See also:**
## * `rand proc<#rand,int>`_ that returns an integer
## * `rand proc<#rand,float>`_ that returns a floating point number
## * `rand proc<#rand,HSlice[T: Ordinal or float or float32 or float64,T: Ordinal or float or float32 or float64]>`_
@@ -376,18 +363,18 @@ proc rand*[T: SomeInteger](t: typedesc[T]): T =
doAssert rand(range[1..16]) == 11
doAssert rand(range[1..16]) == 4
doAssert rand(range[1..16]) == 16
when T is range:
result = rand(state, low(T)..high(T))
else:
result = cast[T](state.next)
proc sample*[T](r: var Rand; s: set[T]): T =
## Returns a random element from the set ``s`` using the given state.
## Returns a random element from the set `s` using the given state.
##
## See also:
## * `sample proc<#sample,set[T]>`_ that uses the default random number
## generator
## * `sample proc<#sample,Rand,openArray[T]>`_ for openarrays
## **See also:**
## * `sample proc<#sample,set[T]>`_ that uses the default RNG
## * `sample proc<#sample,Rand,openArray[T]>`_ for `openArray`s
## * `sample proc<#sample,Rand,openArray[T],openArray[U]>`_ that uses a
## cumulative distribution function
runnableExamples:
@@ -396,6 +383,7 @@ proc sample*[T](r: var Rand; s: set[T]): T =
doAssert r.sample(s) == 5
doAssert r.sample(s) == 7
doAssert r.sample(s) == 1
assert card(s) != 0
var i = rand(r, card(s) - 1)
for e in s:
@@ -403,17 +391,16 @@ proc sample*[T](r: var Rand; s: set[T]): T =
dec(i)
proc sample*[T](s: set[T]): T =
## Returns a random element from the set ``s``.
## Returns a random element from the set `s`.
##
## If `randomize<#randomize>`_ has not been called, the order of outcomes
## If `randomize <#randomize>`_ has not been called, the order of outcomes
## from this proc will always be the same.
##
## This proc uses the default random number generator. Thus, it is **not**
## thread-safe.
## This proc uses the default RNG. Thus, it is **not** thread-safe.
##
## See also:
## **See also:**
## * `sample proc<#sample,Rand,set[T]>`_ that uses a provided state
## * `sample proc<#sample,openArray[T]>`_ for openarrays
## * `sample proc<#sample,openArray[T]>`_ for `openArray`s
## * `sample proc<#sample,openArray[T],openArray[U]>`_ that uses a
## cumulative distribution function
runnableExamples:
@@ -422,14 +409,14 @@ proc sample*[T](s: set[T]): T =
doAssert sample(s) == 5
doAssert sample(s) == 7
doAssert sample(s) == 1
sample(state, s)
proc sample*[T](r: var Rand; a: openArray[T]): T =
## Returns a random element from ``a`` using the given state.
## Returns a random element from `a` using the given state.
##
## See also:
## * `sample proc<#sample,openArray[T]>`_ that uses the default
## random number generator
## **See also:**
## * `sample proc<#sample,openArray[T]>`_ that uses the default RNG
## * `sample proc<#sample,Rand,openArray[T],openArray[U]>`_ that uses a
## cumulative distribution function
## * `sample proc<#sample,Rand,set[T]>`_ for sets
@@ -439,18 +426,18 @@ proc sample*[T](r: var Rand; a: openArray[T]): T =
doAssert r.sample(marbles) == "blue"
doAssert r.sample(marbles) == "yellow"
doAssert r.sample(marbles) == "red"
result = a[r.rand(a.low..a.high)]
proc sample*[T](a: openArray[T]): T =
## Returns a random element from ``a``.
## Returns a random element from `a`.
##
## If `randomize<#randomize>`_ has not been called, the order of outcomes
## If `randomize <#randomize>`_ has not been called, the order of outcomes
## from this proc will always be the same.
##
## This proc uses the default random number generator. Thus, it is **not**
## thread-safe.
## This proc uses the default RNG. Thus, it is **not** thread-safe.
##
## See also:
## **See also:**
## * `sample proc<#sample,Rand,openArray[T]>`_ that uses a provided state
## * `sample proc<#sample,openArray[T],openArray[U]>`_ that uses a
## cumulative distribution function
@@ -461,28 +448,29 @@ proc sample*[T](a: openArray[T]): T =
doAssert sample(marbles) == "blue"
doAssert sample(marbles) == "yellow"
doAssert sample(marbles) == "red"
result = a[rand(a.low..a.high)]
proc sample*[T, U](r: var Rand; a: openArray[T]; cdf: openArray[U]): T =
## Returns an element from ``a`` using a cumulative distribution function
## Returns an element from `a` using a cumulative distribution function
## (CDF) and the given state.
##
## The ``cdf`` argument does not have to be normalized, and it could contain
## any type of elements that can be converted to a ``float``. It must be
## the same length as ``a``. Each element in ``cdf`` should be greater than
## The `cdf` argument does not have to be normalized, and it could contain
## any type of elements that can be converted to a `float`. It must be
## the same length as `a`. Each element in `cdf` should be greater than
## or equal to the previous element.
##
## The outcome of the `cumsum<math.html#cumsum,openArray[T]>`_ proc and the
## return value of the `cumsummed<math.html#cumsummed,openArray[T]>`_ proc,
## which are both in the math module, can be used as the ``cdf`` argument.
## which are both in the math module, can be used as the `cdf` argument.
##
## See also:
## **See also:**
## * `sample proc<#sample,openArray[T],openArray[U]>`_ that also utilizes
## a CDF but uses the default random number generator
## a CDF but uses the default RNG
## * `sample proc<#sample,Rand,openArray[T]>`_ that does not use a CDF
## * `sample proc<#sample,Rand,set[T]>`_ for sets
runnableExamples:
from math import cumsummed
from std/math import cumsummed
let marbles = ["red", "blue", "green", "yellow", "purple"]
let count = [1, 6, 8, 3, 4]
@@ -491,35 +479,34 @@ proc sample*[T, U](r: var Rand; a: openArray[T]; cdf: openArray[U]): T =
doAssert r.sample(marbles, cdf) == "red"
doAssert r.sample(marbles, cdf) == "green"
doAssert r.sample(marbles, cdf) == "blue"
assert(cdf.len == a.len) # Two basic sanity checks.
assert(float(cdf[^1]) > 0.0)
#While we could check cdf[i-1] <= cdf[i] for i in 1..cdf.len, that could get
#awfully expensive even in debugging modes.
# While we could check cdf[i-1] <= cdf[i] for i in 1..cdf.len, that could get
# awfully expensive even in debugging modes.
let u = r.rand(float(cdf[^1]))
a[cdf.upperBound(U(u))]
proc sample*[T, U](a: openArray[T]; cdf: openArray[U]): T =
## Returns an element from ``a`` using a cumulative distribution function
## Returns an element from `a` using a cumulative distribution function
## (CDF).
##
## This proc works similarly to
## `sample[T, U](Rand, openArray[T], openArray[U])
## <#sample,Rand,openArray[T],openArray[U]>`_.
## `sample <#sample,Rand,openArray[T],openArray[U]>`_.
## See that proc's documentation for more details.
##
## If `randomize<#randomize>`_ has not been called, the order of outcomes
## If `randomize <#randomize>`_ has not been called, the order of outcomes
## from this proc will always be the same.
##
## This proc uses the default random number generator. Thus, it is **not**
## thread-safe.
## This proc uses the default RNG. Thus, it is **not** thread-safe.
##
## See also:
## **See also:**
## * `sample proc<#sample,Rand,openArray[T],openArray[U]>`_ that also utilizes
## a CDF but uses a provided state
## * `sample proc<#sample,openArray[T]>`_ that does not use a CDF
## * `sample proc<#sample,set[T]>`_ for sets
runnableExamples:
from math import cumsummed
from std/math import cumsummed
let marbles = ["red", "blue", "green", "yellow", "purple"]
let count = [1, 6, 8, 3, 4]
@@ -528,14 +515,15 @@ proc sample*[T, U](a: openArray[T]; cdf: openArray[U]): T =
doAssert sample(marbles, cdf) == "red"
doAssert sample(marbles, cdf) == "green"
doAssert sample(marbles, cdf) == "blue"
state.sample(a, cdf)
proc gauss*(r: var Rand; mu = 0.0; sigma = 1.0): float {.since: (1, 3).} =
## Returns a Gaussian random variate,
## with mean ``mu`` and standard deviation ``sigma``
## with mean `mu` and standard deviation `sigma`
## using the given state.
# Ratio of uniforms method for normal
# http://www2.econ.osaka-u.ac.jp/~tanizaki/class/2013/econome3/13.pdf
# https://www2.econ.osaka-u.ac.jp/~tanizaki/class/2013/econome3/13.pdf
const K = sqrt(2 / E)
var
a = 0.0
@@ -548,37 +536,34 @@ proc gauss*(r: var Rand; mu = 0.0; sigma = 1.0): float {.since: (1, 3).} =
proc gauss*(mu = 0.0, sigma = 1.0): float {.since: (1, 3).} =
## Returns a Gaussian random variate,
## with mean ``mu`` and standard deviation ``sigma``.
## with mean `mu` and standard deviation `sigma`.
##
## If `randomize<#randomize>`_ has not been called, the order of outcomes
## If `randomize <#randomize>`_ has not been called, the order of outcomes
## from this proc will always be the same.
##
## This proc uses the default random number generator. Thus, it is **not**
## thread-safe.
## This proc uses the default RNG. Thus, it is **not** thread-safe.
result = gauss(state, mu, sigma)
proc initRand*(seed: int64): Rand =
## Initializes a new `Rand<#Rand>`_ state using the given seed.
## Initializes a new `Rand <#Rand>`_ state using the given seed.
##
## `seed` must not be zero. Providing a specific seed will produce
## the same results for that seed each time.
##
## The resulting state is independent of the default random number
## generator's state.
## The resulting state is independent of the default RNG's state.
##
## See also:
## **See also:**
## * `initRand proc<#initRand>`_ that uses the current time
## * `randomize proc<#randomize,int64>`_ that accepts a seed for the default
## random number generator
## * `randomize proc<#randomize>`_ that initializes the default random
## number generator using the current time
## * `randomize proc<#randomize,int64>`_ that accepts a seed for the default RNG
## * `randomize proc<#randomize>`_ that initializes the default RNG using the current time
runnableExamples:
from times import getTime, toUnix, nanosecond
from std/times import getTime, toUnix, nanosecond
var r1 = initRand(123)
let now = getTime()
var r2 = initRand(now.toUnix * 1_000_000_000 + now.nanosecond)
doAssert seed != 0 # 0 causes `rand(int)` to always return 0 for example.
result.a0 = Ui(seed shr 16)
result.a1 = Ui(seed and 0xffff)
@@ -590,32 +575,33 @@ proc randomize*(seed: int64) {.benign.} =
## `seed` must not be zero. Providing a specific seed will produce
## the same results for that seed each time.
##
## See also:
## **See also:**
## * `initRand proc<#initRand,int64>`_ that initializes a Rand state
## with a given seed
## * `randomize proc<#randomize>`_ that uses the current time instead
## * `initRand proc<#initRand>`_ that initializes a Rand state using
## the current time
runnableExamples:
from times import getTime, toUnix, nanosecond
from std/times import getTime, toUnix, nanosecond
randomize(123)
let now = getTime()
randomize(now.toUnix * 1_000_000_000 + now.nanosecond)
state = initRand(seed)
proc shuffle*[T](r: var Rand; x: var openArray[T]) =
## Shuffles a sequence of elements in-place using the given state.
##
## See also:
## * `shuffle proc<#shuffle,openArray[T]>`_ that uses the default
## random number generator
## **See also:**
## * `shuffle proc<#shuffle,openArray[T]>`_ that uses the default RNG
runnableExamples:
var cards = ["Ace", "King", "Queen", "Jack", "Ten"]
var r = initRand(678)
r.shuffle(cards)
doAssert cards == ["King", "Ace", "Queen", "Ten", "Jack"]
for i in countdown(x.high, 1):
let j = r.rand(i)
swap(x[i], x[j])
@@ -623,44 +609,42 @@ proc shuffle*[T](r: var Rand; x: var openArray[T]) =
proc shuffle*[T](x: var openArray[T]) =
## Shuffles a sequence of elements in-place.
##
## If `randomize<#randomize>`_ has not been called, the order of outcomes
## If `randomize <#randomize>`_ has not been called, the order of outcomes
## from this proc will always be the same.
##
## This proc uses the default random number generator. Thus, it is **not**
## thread-safe.
## This proc uses the default RNG. Thus, it is **not** thread-safe.
##
## See also:
## **See also:**
## * `shuffle proc<#shuffle,Rand,openArray[T]>`_ that uses a provided state
runnableExamples:
var cards = ["Ace", "King", "Queen", "Jack", "Ten"]
randomize(678)
shuffle(cards)
doAssert cards == ["King", "Ace", "Queen", "Ten", "Jack"]
shuffle(state, x)
when not defined(nimscript) and not defined(standalone):
import times
import std/times
proc initRand(): Rand =
## Initializes a new Rand state with a seed based on the current time.
##
## The resulting state is independent of the default random number generator's state.
## The resulting state is independent of the default RNG's state.
##
## **Note:** Does not work for NimScript or the compile-time VM.
##
## See also:
## * `initRand proc<#initRand,int64>`_ that accepts a seed for a new Rand state
## * `randomize proc<#randomize>`_ that initializes the default random
## number generator using the current time
## * `randomize proc<#randomize,int64>`_ that accepts a seed for the default
## random number generator
## * `randomize proc<#randomize>`_ that initializes the default RNG using the current time
## * `randomize proc<#randomize,int64>`_ that accepts a seed for the default RNG
when defined(js):
let time = int64(times.epochTime() * 1000) and 0x7fff_ffff
result = initRand(time)
else:
let now = times.getTime()
result = initRand(convert(Seconds, Nanoseconds, now.toUnix) + now.nanosecond)
since (1, 5, 1):
export initRand
@@ -669,12 +653,11 @@ when not defined(nimscript) and not defined(standalone):
## the current time.
##
## This proc only needs to be called once, and it should be called before
## the first usage of procs from this module that use the default random
## number generator.
## the first usage of procs from this module that use the default RNG.
##
## **Note:** Does not work for NimScript or the compile-time VM.
##
## See also:
## **See also:**
## * `randomize proc<#randomize,int64>`_ that accepts a seed
## * `initRand proc<#initRand>`_ that initializes a Rand state using
## the current time