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discard """
joinable: false # to avoid messing with global rand state
targets: "c js"
"""
import std/[random, math, os, stats, sets, tables]
randomize(233)
proc main() =
var occur: array[1000, int]
for i in 0..100_000:
let x = rand(high(occur))
inc occur[x]
doAssert max(occur) <= 140 and min(occur) >= 60 # gives some slack
var a = [0, 1]
shuffle(a)
doAssert a in [[0,1], [1,0]]
doAssert rand(0) == 0
doAssert sample("a") == 'a'
when compileOption("rangeChecks"):
doAssertRaises(RangeDefect):
discard rand(-1)
doAssertRaises(RangeDefect):
discard rand(-1.0)
# don't use causes integer overflow
doAssert compiles(rand[int](low(int) .. high(int)))
main()
block:
when not defined(js):
doAssert almostEqual(rand(12.5), 7.355175342026979)
doAssert almostEqual(rand(2233.3322), 499.342386778917)
type DiceRoll = range[0..6]
when not defined(js):
doAssert rand(DiceRoll).int == 3
else:
doAssert rand(DiceRoll).int == 6
var rs: RunningStat
for j in 1..5:
for i in 1 .. 100_000:
rs.push(gauss())
doAssert abs(rs.mean-0) < 0.08, $rs.mean
doAssert abs(rs.standardDeviation()-1.0) < 0.1
let bounds = [3.5, 5.0]
for a in [rs.max, -rs.min]:
doAssert a >= bounds[0] and a <= bounds[1]
rs.clear()
block:
type DiceRoll = range[3..6]
var flag = false
for i in 0..<100:
if rand(5.DiceRoll) < 3:
flag = true
doAssert flag # because of: rand(max: int): int
block: # random int
block: # there might be some randomness
var set = initHashSet[int](128)
for i in 1..1000:
incl(set, rand(high(int)))
doAssert len(set) == 1000
block: # single number bounds work
var rand: int
for i in 1..1000:
rand = rand(1000)
doAssert rand <= 1000
doAssert rand >= 0
block: # slice bounds work
var rand: int
for i in 1..1000:
rand = rand(100..1000)
doAssert rand <= 1000
doAssert rand >= 100
block: # again gives new numbers
var rand1 = rand(1000000)
when not defined(js):
os.sleep(200)
var rand2 = rand(1000000)
doAssert rand1 != rand2
block: # random float
block: # there might be some randomness
var set = initHashSet[float](128)
for i in 1..100:
incl(set, rand(1.0))
doAssert len(set) == 100
block: # single number bounds work
var rand: float
for i in 1..1000:
rand = rand(1000.0)
doAssert rand <= 1000.0
doAssert rand >= 0.0
block: # slice bounds work
var rand: float
for i in 1..1000:
rand = rand(100.0..1000.0)
doAssert rand <= 1000.0
doAssert rand >= 100.0
block: # again gives new numbers
var rand1: float = rand(1000000.0)
when not defined(js):
os.sleep(200)
var rand2: float = rand(1000000.0)
doAssert rand1 != rand2
block: # random sample
block: # "non-uniform array sample unnormalized int CDF
let values = [10, 20, 30, 40, 50] # values
let counts = [4, 3, 2, 1, 0] # weights aka unnormalized probabilities
var histo = initCountTable[int]()
let cdf = counts.cumsummed # unnormalized CDF
for i in 0 ..< 5000:
histo.inc(sample(values, cdf))
doAssert histo.len == 4 # number of non-zero in `counts`
# Any one bin is a binomial random var for n samples, each with prob p of
# adding a count to k; E[k]=p*n, Var k=p*(1-p)*n, approximately Normal for
# big n. So, P(abs(k - p*n)/sqrt(p*(1-p)*n))>3.0) =~ 0.0027, while
# P(wholeTestFails) =~ 1 - P(binPasses)^4 =~ 1 - (1-0.0027)^4 =~ 0.01.
for i, c in counts:
if c == 0:
doAssert values[i] notin histo
continue
let p = float(c) / float(cdf[^1])
let n = 5000.0
let expected = p * n
let stdDev = sqrt(n * p * (1.0 - p))
doAssert abs(float(histo[values[i]]) - expected) <= 3.0 * stdDev
block: # non-uniform array sample normalized float CDF
let values = [10, 20, 30, 40, 50] # values
let counts = [0.4, 0.3, 0.2, 0.1, 0] # probabilities
var histo = initCountTable[int]()
let cdf = counts.cumsummed # normalized CDF
for i in 0 ..< 5000:
histo.inc(sample(values, cdf))
doAssert histo.len == 4 # number of non-zero in ``counts``
for i, c in counts:
if c == 0:
doAssert values[i] notin histo
continue
let p = float(c) / float(cdf[^1])
let n = 5000.0
let expected = p * n
let stdDev = sqrt(n * p * (1.0 - p))
# NOTE: like unnormalized int CDF test, P(wholeTestFails) =~ 0.01.
doAssert abs(float(histo[values[i]]) - expected) <= 3.0 * stdDev
block:
# 0 is a valid seed
var r = initRand(0)
doAssert r.rand(1.0) != r.rand(1.0)
r = initRand(10)
doAssert r.rand(1.0) != r.rand(1.0)
# changing the seed changes the sequence
var r1 = initRand(123)
var r2 = initRand(124)
doAssert r1.rand(1.0) != r2.rand(1.0)
block: # bug #17467
let n = 1000
for i in -n .. n:
var r = initRand(i)
let x = r.rand(1.0)
doAssert x > 1e-4, $(x, i)
# This used to fail for each i in 0..<26844, i.e. the 1st produced value
# was predictable and < 1e-4, skewing distributions.
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