Unbias a random generator

Unbias a random generator
You are encouraged to solve this task according to the task description, using any language you may know.
Given a weighted one bit generator of random numbers where the probability of a one occuring, ${\displaystyle P_{1}}$, is not the same as ${\displaystyle P_{0}}$, the probability of a zero occuring, the probability of the occurrence of a one followed by a zero is ${\displaystyle P_{1}}$ × ${\displaystyle P_{0}}$. This is the same as the probability of a zero followed by a one: ${\displaystyle P_{0}}$ × ${\displaystyle P_{1}}$.

• Use your language's random number generator to create a function/method/subroutine/... randN that returns a one or a zero, but with one occurring, on average, 1 out of N times, where N is an integer from the range 3 to 6 inclusive.
• Create a function unbiased that uses only randN as its source of randomness to become an unbiased generator of random ones and zeroes.
• For N over its range, generate and show counts of the outputs of randN and unbiased(randN).

The actual unbiasing should be done by generating two numbers at a time from randN and only returning a 1 or 0 if they are different. As long as you always return the first number or always return the second number, the probabilities discussed above should take over the biased probability of randN.

This task is an implementation of Von Neumann debiasing, first described in a 1951 paper.

`with Ada.Text_IO; with Ada.Numerics.Discrete_Random; procedure Bias_Unbias is    Modulus: constant Integer := 60; -- lcm of {3,4,5,6}   type M is mod Modulus;   package Rand is new Ada.Numerics.Discrete_Random(M);   Gen: Rand.Generator;    subtype Bit is Integer range 0 .. 1;    function Biased_Bit(Bias_Base: Integer) return Bit is   begin      if (Integer(Rand.Random(Gen))* Bias_Base) / Modulus > 0 then         return 0;      else         return 1;      end if;   end Biased_Bit;    function Unbiased_Bit(Bias_Base: Integer) return Bit is      A, B: Bit := 0;   begin      while A = B loop         A := Biased_Bit(Bias_Base);         B := Biased_Bit(Bias_Base);      end loop;      return A;   end Unbiased_Bit;    package FIO is new Ada.Text_IO.Float_IO(Float);    Counter_B, Counter_U: Natural;   Number_Of_Samples: constant Natural := 10_000; begin   Rand.Reset(Gen);   Ada.Text_IO.Put_Line(" I  Biased% UnBiased%");   for I in 3 .. 6 loop      Counter_B := 0;      Counter_U := 0;      for J in 1 .. Number_Of_Samples loop         Counter_B := Counter_B + Biased_Bit(I);         Counter_U := Counter_U + Unbiased_Bit(I);      end loop;      Ada.Text_IO.Put(Integer'Image(I));      FIO.Put(100.0 * Float(Counter_B) / Float(Number_Of_Samples), 5, 2, 0);      FIO.Put(100.0 * Float(Counter_U) / Float(Number_Of_Samples), 5, 2, 0);      Ada.Text_IO.New_Line;   end loop;end Bias_Unbias;`
Output:
``` I  Biased% UnBiased%
3   32.87   49.80
4   24.49   50.22
5   19.73   50.05
6   16.75   50.19
```

Aime

Translation of: C
`integerbiased(integer bias){    1 ^ min(drand(bias - 1), 1);} integerunbiased(integer bias){    integer a;     while ((a = biased(bias)) == biased(bias)) {    }     a;} integermain(void){    integer b, n, cb, cu, i;     n = 10000;    b = 3;    while (b <= 6) {        i = cb = cu = 0;        while ((i += 1) <= n) {            cb += biased(b);            cu += unbiased(b);        }         o_form("bias ~: /d2p2/%% vs /d2p2/%%\n", b, 100r * cb / n,               100r * cu / n);         b += 1;    }     0;}`
Output:
```bias 3: 33.51% vs 50.27%
bias 4: 24.97% vs 49.99%
bias 5: 19.93% vs 49.92%
bias 6: 16.32% vs 49.36%```

AutoHotkey

 This example does not show the output mentioned in the task description on this page (or a page linked to from here). Please ensure that it meets all task requirements and remove this message. Note that phrases in task descriptions such as "print and display" and "print and show" for example, indicate that (reasonable length) output be a part of a language's solution.

`Biased(){   Random, q, 0, 4   return q=4}Unbiased(){   Loop      If ((a := Biased()) != biased())          return a}Loop 1000   t .= biased(), t2 .= unbiased()StringReplace, junk, t2, 1, , UseErrorLevelMsgBox % "Unbiased probability of a 1 occurring: " Errorlevel/1000StringReplace, junk, t, 1, , UseErrorLevelMsgBox % "biased probability of a 1 occurring: " Errorlevel/1000`

BBC BASIC

`      FOR N% = 3 TO 6        biased% = 0        unbiased% = 0        FOR I% = 1 TO 10000          IF FNrandN(N%) biased% += 1          IF FNunbiased(N%) unbiased% += 1        NEXT        PRINT "N = ";N% " : biased = "; biased%/100 "%, unbiased = "; unbiased%/100 "%"      NEXT      END       DEF FNunbiased(N%)      LOCAL A%,B%      REPEAT        A% = FNrandN(N%)        B% = FNrandN(N%)      UNTIL A%<>B%      = A%       DEF FNrandN(N%) = -(RND(N%) = 1)`

Output:

```N = 3 : biased = 33.57%, unbiased = 49.94%
N = 4 : biased = 25.34%, unbiased = 50.76%
N = 5 : biased = 20.06%, unbiased = 50.04%
N = 6 : biased = 16.25%, unbiased = 50.13%
```

C

`#include <stdio.h>#include <stdlib.h> int biased(int bias){	/* balance out the bins, being pedantic */	int r, rand_max = RAND_MAX - (RAND_MAX % bias);	while ((r = rand()) > rand_max);	return r < rand_max / bias;} int unbiased(int bias){	int a;	while ((a = biased(bias)) == biased(bias));	return a;} int main(){	int b, n = 10000, cb, cu, i;	for (b = 3; b <= 6; b++) {		for (i = cb = cu = 0; i < n; i++) {			cb += biased(b);			cu += unbiased(b);		}		printf("bias %d: %5.3f%% vs %5.3f%%\n", b,			100. * cb / n, 100. * cu / n);	} 	return 0;}`

output

```bias 3: 33.090% vs 49.710%
bias 4: 25.130% vs 49.430%
bias 5: 19.760% vs 49.650%
bias 6: 16.740% vs 50.030%```

C#

`using System; namespace Unbias{    internal class Program    {        private static void Main(string[] args)        {            // Demonstrate.            for (int n = 3; n <= 6; n++)            {                int biasedZero = 0, biasedOne = 0, unbiasedZero = 0, unbiasedOne = 0;                for (int i = 0; i < 100000; i++)                {                    if (randN(n))                        biasedOne++;                    else                        biasedZero++;                    if (Unbiased(n))                        unbiasedOne++;                    else                        unbiasedZero++;                }                 Console.WriteLine("(N = {0}):".PadRight(17) + "# of 0\t# of 1\t% of 0\t% of 1", n);                Console.WriteLine("Biased:".PadRight(15) + "{0}\t{1}\t{2}\t{3}",                                  biasedZero, biasedOne,                                  biasedZero/1000, biasedOne/1000);                Console.WriteLine("Unbiased:".PadRight(15) + "{0}\t{1}\t{2}\t{3}",                                  unbiasedZero, unbiasedOne,                                  unbiasedZero/1000, unbiasedOne/1000);            }        }         private static bool Unbiased(int n)        {            bool flip1, flip2;             /* Flip twice, and check if the values are the same.             * If so, flip again. Otherwise, return the value of the first flip. */             do            {                flip1 = randN(n);                flip2 = randN(n);            } while (flip1 == flip2);             return flip1;        }         private static readonly Random random = new Random();         private static bool randN(int n)        {            // Has an 1/n chance of returning 1. Otherwise it returns 0.            return random.Next(0, n) == 0;        }    }}`

Sample Output

```(N = 3):       # of 0   # of 1  % of 0  % of 1
Biased:        66867    33133   66      33
Unbiased:      49843    50157   49      50
(N = 4):       # of 0   # of 1  % of 0  % of 1
Biased:        74942    25058   74      25
Unbiased:      50192    49808   50      49
(N = 5):       # of 0   # of 1  % of 0  % of 1
Biased:        80203    19797   80      19
Unbiased:      49928    50072   49      50
(N = 6):       # of 0   # of 1  % of 0  % of 1
Biased:        83205    16795   83      16
Unbiased:      49744    50256   49      50
```

Clojure

`(defn biased [n]  (if (< (rand 2) (/ n)) 0 1)) (defn unbiased [n]  (loop [a 0 b 0]    (if (= a b)      (recur (biased n) (biased n))      a))) (for [n (range 3 7)]  [n   (double (/ (apply + (take 50000 (repeatedly #(biased n)))) 50000))   (double (/ (apply + (take 50000 (repeatedly #(unbiased n)))) 50000))])([3 0.83292 0.50422] [4 0.87684 0.5023] [5 0.90122 0.49728] [6 0.91526 0.5])`

CoffeeScript

` biased_rand_function = (n) ->  # return a function that returns 0/1  with  # 1 appearing only 1/Nth of the time  cap = 1/n  ->    if Math.random() < cap       1    else      0 unbiased_function = (f) ->  ->    while true      [n1, n2] = [f(), f()]      return n1 if n1 + n2 == 1 stats = (label, f) ->  cnt = 0  sample_size = 10000000  for i in [1...sample_size]    cnt += 1 if f() == 1  console.log "ratio of 1s: #{cnt / sample_size} [#{label}]" for n in [3..6]  console.log "\n---------- n = #{n}"  f_biased = biased_rand_function(n)  f_unbiased = unbiased_function f_biased  stats "biased", f_biased  stats "unbiased", f_unbiased `

output

```> coffee unbiased.coffee

---------- n = 3
ratio of 1s: 0.3333343 [biased]
ratio of 1s: 0.4999514 [unbiased]

---------- n = 4
ratio of 1s: 0.2499751 [biased]
ratio of 1s: 0.4998067 [unbiased]

---------- n = 5
ratio of 1s: 0.199729 [biased]
ratio of 1s: 0.5003183 [unbiased]

---------- n = 6
ratio of 1s: 0.1664843 [biased]
ratio of 1s: 0.4997813 [unbiased]
```

Common Lisp

`(defun biased (n) (if (zerop (random n)) 0 1)) (defun unbiased (n)    (loop with x do      (if (/= (setf x (biased n)) (biased n))	    (return x)))) (loop for n from 3 to 6 do      (let ((u (loop repeat 10000 collect (unbiased n)))	    (b (loop repeat 10000 collect (biased n))))	(format t "~a: unbiased ~d biased ~d~%" n (count 0 u) (count 0 b))))`

output

```3: unbiased 4992 biased 3361
4: unbiased 4988 biased 2472
5: unbiased 5019 biased 1987
6: unbiased 4913 biased 1658```

D

`import std.stdio, std.random, std.algorithm, std.range, std.functional; enum biased = (in int n) /*nothrow*/ => uniform01 < (1.0 / n); int unbiased(in int bias) /*nothrow*/ {    int a;    while ((a = bias.biased) == bias.biased) {}    return a;} void main() {    enum M = 500_000;    foreach (immutable n; 3 .. 7)        writefln("%d: %2.3f%%  %2.3f%%", n,                 M.iota.map!(_=> n.biased).sum * 100.0 / M,                 M.iota.map!(_=> n.unbiased).sum * 100.0 / M);}`
Output:
```3: 33.441%  49.964%
4: 24.953%  49.910%
5: 19.958%  49.987%
6: 16.660%  49.890%```

Elena

Translation of: C#

ELENA 4.x :

`import extensions; extension op : IntNumber{    bool randN()        = randomGenerator.nextInt(self) == 0;     get bool Unbiased()    {        bool flip1 := self.randN();        bool flip2 := self.randN();         while (flip1 == flip2)        {            flip1 := self.randN();            flip2 := self.randN()        };         ^ flip1    }} public program(){    for(int n := 3, n <= 6, n += 1)    {        int biasedZero := 0;        int biasedOne := 0;        int unbiasedZero := 0;        int unbiasedOne := 0;         for(int i := 0, i < 100000, i += 1)        {            if(n.randN()) { biasedOne += 1 } else { biasedZero += 1 };            if(n.Unbiased) { unbiasedOne += 1 } else { unbiasedZero += 1 }        };         console            .printLineFormatted("(N = {0}):".padRight(17) + "# of 0"\$9"# of 1"\$9"% of 0"\$9"% of 1", n)            .printLineFormatted("Biased:".padRight(15) + "{0}"\$9"{1}"\$9"{2}"\$9"{3}",                                     biasedZero, biasedOne, biasedZero / 1000, biasedOne / 1000)            .printLineFormatted("Unbiased:".padRight(15) + "{0}"\$9"{1}"\$9"{2}"\$9"{3}",                                     unbiasedZero, unbiasedOne, unbiasedZero / 1000, unbiasedOne / 1000)    }}`
Output:
```(N = 3):       # of 0	# of 1	% of 0	% of 1
Biased:        66793	33207	66	33
Unbiased:      49965	50035	49	50
(N = 4):       # of 0	# of 1	% of 0	% of 1
Biased:        75233	24767	75	24
Unbiased:      50106	49894	50	49
(N = 5):       # of 0	# of 1	% of 0	% of 1
Biased:        80209	19791	80	19
Unbiased:      50080	49920	50	49
(N = 6):       # of 0	# of 1	% of 0	% of 1
Biased:        83349	16651	83	16
Unbiased:      49699	50301	49	50
```

Elixir

`defmodule Random do  def randN(n) do    if :rand.uniform(n) == 1, do: 1, else: 0  end   def unbiased(n) do    {x, y} = {randN(n), randN(n)}    if x != y, do: x, else: unbiased(n)  end end IO.puts "N  biased  unbiased"m = 10000for n <- 3..6 do  xs = for _ <- 1..m, do: Random.randN(n)  ys = for _ <- 1..m, do: Random.unbiased(n)  IO.puts "#{n}  #{Enum.sum(xs) / m}  #{Enum.sum(ys) / m}"end`
Output:
```N  biased  unbiased
3  0.3356  0.5043
4  0.2523  0.4996
5  0.2027  0.5041
6  0.1647  0.4912
```

ERRE

`PROGRAM UNBIAS FUNCTION RANDN(N)   RANDN=INT(1+N*RND(1))=1END FUNCTION PROCEDURE UNBIASED(N->RIS)      LOCAL A,B      REPEAT        A=RANDN(N)        B=RANDN(N)      UNTIL A<>B      RIS=AEND PROCEDURE BEGIN  PRINT(CHR\$(12);) ! CLS  RANDOMIZE(TIMER)   FOR N=3 TO 6 DO        BIASED=0        UNBIASED=0        FOR I=1 TO 10000 DO          IF RANDN(N) THEN biased+=1          UNBIASED(N->RIS)          IF RIS THEN unbiased+=+1        END FOR        PRINT("N =";N;" : biased =";biased/100;", unbiased =";unbiased/100)  END FOREND PROGRAM `
Output:
```N = 3  : biased = 32.66 , unbiased = 49.14
N = 4  : biased = 25.49 , unbiased = 49.92
N = 5  : biased = 20.53 , unbiased = 50
N = 6  : biased = 17.43 , unbiased = 50.43
```

Euphoria

`function randN(integer N)    return rand(N) = 1end function function unbiased(integer N)    integer a    while 1 do        a = randN(N)        if a != randN(N) then            return a        end if    end whileend function constant n = 10000integer cb, cufor b = 3 to 6 do    cb = 0    cu = 0    for i = 1 to n do        cb += randN(b)        cu += unbiased(b)    end for    printf(1, "%d: %5.2f%%  %5.2f%%\n", {b, 100 * cb / n, 100 * cu / n})end for`

Output:

```3: 33.68%  49.94%
4: 24.93%  50.48%
5: 20.32%  49.97%
6: 16.98%  50.05%
```

F#

`open System let random = Random() let randN = random.Next >> (=)0 >> Convert.ToInt32 let rec unbiased n =    let a = randN n    if a <> randN n then a else unbiased n [<EntryPoint>]let main argv =    let n = if argv.Length > 0 then UInt32.Parse(argv.[0]) |> int else 100000    for b = 3 to 6 do        let cb = ref 0        let cu = ref 0        for i = 1 to n do            cb := !cb + randN b            cu := !cu + unbiased b        printfn "%d: %5.2f%%  %5.2f%%"            b (100. * float !cb / float n) (100. * float !cu / float n)    0`
Output:
```3: 33.26%  49.97%
4: 25.02%  50.22%
5: 19.98%  50.00%
6: 16.64%  49.69%```

Factor

`USING: formatting kernel math math.ranges random sequences ;IN: rosetta-code.unbias : randN ( n -- m ) random zero? 1 0 ? ; : unbiased ( n -- m )    dup [ randN ] dup bi 2dup = not    [ drop nip ] [ 2drop unbiased ] if ; : test-generator ( quot -- x )    [ 1,000,000 dup ] dip replicate sum 100 * swap / ; inline : main ( -- )    3 6 [a,b] [        dup [ randN ] [ unbiased ] bi-curry        [ test-generator ] [email protected] "%d: %.2f%%  %.2f%%\n" printf    ] each ; MAIN: main`
Output:
```3: 33.25%  50.03%
4: 24.98%  50.02%
5: 20.03%  50.04%
6: 16.66%  49.99%
```

Fortran

Works with: Fortran version 90 and later
`program Bias_Unbias  implicit none   integer, parameter :: samples = 1000000  integer :: i, j  integer :: c1, c2, rand   do i = 3, 6    c1 = 0    c2 = 0    do j = 1, samples      rand = bias(i)      if (rand == 1) c1 = c1 + 1      rand = unbias(i)      if (rand == 1) c2 = c2 + 1    end do    write(*, "(i2,a,f8.3,a,f8.3,a)") i, ":", real(c1) * 100.0 / real(samples), &                                     "%", real(c2) * 100.0 / real(samples), "%"  end do contains function bias(n)  integer :: bias  integer, intent(in) :: n  real :: r   call random_number(r)  if (r > 1 / real(n)) then    bias = 0  else    bias = 1  end ifend function function unbias(n)  integer :: unbias  integer, intent(in) :: n  integer :: a, b   do    a = bias(n)    b = bias(n)    if (a /= b) exit  end do  unbias = a     end function end program`

Output:

```3:  33.337%  49.971%
4:  24.945%  49.944%
5:  19.971%  49.987%
6:  16.688%  50.097%```

GAP

`RandNGen := function(n)	local v, rand;	v := [1 .. n - 1]*0;	Add(v, 1);	rand := function()		return Random(v);	end;	return rand;end; UnbiasedGen := function(rand)	local unbiased;	unbiased := function()		local a, b;		while true do			a := rand();			b := rand();			if a <> b then				break;			fi;		od;		return a;	end;	return unbiased;end; range := [2 .. 6];v := List(range, RandNGen);w := List(v, UnbiasedGen);apply := gen -> Sum([1 .. 1000000], n -> gen()); # Some tests (2 is added as a witness, since in this case RandN is already unbiased)PrintArray(TransposedMat([range, List(v, apply), List(w, apply)]));# [ [       2,  499991,  499041 ],#   [       3,  333310,  500044 ],#   [       4,  249851,  500663 ],#   [       5,  200532,  500448 ],#   [       6,  166746,  499859 ] ]`

Go

`package main import (    "fmt"    "math/rand") const samples = 1e6 func main() {    fmt.Println("Generator  1 count  0 count  % 1 count")    for n := 3; n <= 6; n++ {        // function randN, per task description        randN := func() int {            if rand.Intn(n) == 0 {                return 1            }            return 0        }        var b [2]int        for x := 0; x < samples; x++ {            b[randN()]++        }        fmt.Printf("randN(%d)   %7d  %7d    %5.2f%%\n",            n, b[1], b[0], float64(b[1])*100/samples)         // function unbiased, per task description        unbiased := func() (b int) {            for b = randN(); b == randN(); b = randN() {            }            return        }        var u [2]int        for x := 0; x < samples; x++ {            u[unbiased()]++        }        fmt.Printf("unbiased   %7d  %7d    %5.2f%%\n",            u[1], u[0], float64(u[1])*100/samples)    }}`

Output:

```Generator  1 count  0 count  % 1 count
randN(3)    332711   667289    33.27%
unbiased    499649   500351    49.96%
randN(4)    249742   750258    24.97%
unbiased    499434   500566    49.94%
randN(5)    200318   799682    20.03%
unbiased    499100   500900    49.91%
randN(6)    166900   833100    16.69%
unbiased    499973   500027    50.00%
```

`import Control.Monad.Randomimport Control.Monadimport Text.Printf randN :: MonadRandom m => Int -> m IntrandN n = fromList [(0, fromIntegral n-1), (1, 1)]`

Examples of use:

```λ> replicateM 20 (randN 2)
[0,0,1,0,0,1,0,1,1,0,0,1,1,1,1,0,1,1,0,0]
λ> replicateM 20 (randN 5)
[0,1,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,1,0]```

The second task. Returns the unbiased generator for any given random generator.

`unbiased :: (MonadRandom m, Eq x) => m x -> m xunbiased g = do x <- g                y <- g                if x /= y then return y else unbiased g`

Examples of use:

```λ> replicateM 20 (unbiased (randN 5))
[0,0,1,0,1,1,1,0,0,0,1,1,1,0,1,1,0,0,1,0]
λ> replicateM 20 (unbiased (fromList [(True,10),(False,1)]))
[True,True,False,True,True,True,False,True,False,True,True,False,False,True,False,True,True,False,False,True]```

`main = forM_ [3..6] showCounts  where    showCounts b = do      r1 <- counts (randN b)      r2 <- counts (unbiased (randN b))      printf "n = %d  biased: %d%%  unbiased: %d%%\n" b r1 r2     counts g = (`div` 100) . length . filter (== 1) <\$> replicateM 10000 g`

Output:

```n = 3  biased: 33%  unbiased: 49%
n = 4  biased: 24%  unbiased: 50%
n = 5  biased: 19%  unbiased: 50%
n = 6  biased: 16%  unbiased: 49%
```

Icon and Unicon

This solution works in both languages. Both randN and unbiased are generators in the Icon/Unicon sense.

`procedure main(A)    iters := \A[1] | 10000    write("ratios of 0 to 1 from ",iters," trials:")    every n := 3 to 6 do {        results_randN := table(0)        results_unbiased := table(0)        every 1 to iters do {            results_randN[randN(n)] +:= 1            results_unbiased[unbiased(n)] +:= 1            }        showResults(n, "randN", results_randN)        showResults(n, "unbiased", results_unbiased)        }end procedure showResults(n, s, t)    write(n," ",left(s,9),":",t[0],"/",t[1]," = ",t[0]/real(t[1]))end procedure unbiased(n)    repeat {        n1 := randN(n)        n2 := randN(n)        if n1 ~= n2 then suspend n1        }end procedure randN(n)    repeat suspend if 1 = ?n then 1 else 0end`

and a sample run:

```->ubrn 100000
ratios of 0 to 1 from 100000 trials:
3 randN    :66804/33196 = 2.012411133871551
3 unbiased :49812/50188 = 0.9925081692834941
4 randN    :75017/24983 = 3.002721850858584
4 unbiased :50000/50000 = 1.0
5 randN    :79990/20010 = 3.997501249375312
5 unbiased :50073/49927 = 1.002924269433373
6 randN    :83305/16695 = 4.989817310572027
6 unbiased :49911/50089 = 0.9964463255405378
->```

J

`randN=: 0 = ?unbiased=: [email protected]# { ::\$: 2 | 0 3 -.~ _2 #.\ 4&* [email protected]# ]`

Example use:

`   randN 10#61 0 0 0 1 0 0 0 0 0   unbiased 10#61 0 0 1 0 0 1 0 1 1`

Some example counts (these are counts of the number of 1s which appear in a test involving 100 random numbers):

`   +/randN 100#330   +/randN 100#420   +/randN 100#518   +/randN 100#618   +/unbiased 100#349   +/unbiased 100#446   +/unbiased 100#549   +/unbiased 100#647`

Note that these results are random. For example, a re-run of `+/randN 100#5` gave 25 as its result, and a re-run of `+/unbiased 100#5` gave 52 as its result.

Java

`public class Bias {    public static boolean biased(int n) {        return Math.random() < 1.0 / n;    }     public static boolean unbiased(int n) {        boolean a, b;        do {            a = biased(n);            b = biased(n);        } while (a == b);        return a;    }     public static void main(String[] args) {        final int M = 50000;        for (int n = 3; n < 7; n++) {            int c1 = 0, c2 = 0;            for (int i = 0; i < M; i++) {                c1 += biased(n) ? 1 : 0;                c2 += unbiased(n) ? 1 : 0;            }            System.out.format("%d: %2.2f%%  %2.2f%%\n",                              n, 100.0*c1/M, 100.0*c2/M);        }    }}`

Output:

```3: 33,11%  50,23%
4: 24.97%  49.78%
5: 20.05%  50.00%
6: 17.00%  49.88%```

Translation of: Java
`// version 1.1.2 fun biased(n: Int) = Math.random() < 1.0 / n fun unbiased(n: Int): Boolean {    var a: Boolean    var b: Boolean    do {        a = biased(n)          b = biased(n)    }    while (a == b)    return a} fun main(args: Array<String>) {    val m = 50_000    val f = "%d: %2.2f%%  %2.2f%%"    for (n in 3..6) {        var c1 = 0        var c2 = 0         for (i in 0 until m) {            if (biased(n)) c1++            if (unbiased(n)) c2++        }        println(f.format(n, 100.0 * c1 / m, 100.0 * c2 / m))    }}`

Sample output:

```3: 33.19%  50.19%
4: 25.29%  49.85%
5: 19.91%  50.07%
6: 16.71%  50.14%
```

Julia

Works with: Julia version 0.6
`randN(N) = () -> rand(1:N) == 1 ? 1 : 0function unbiased(biased::Function)    this, that = biased(), biased()    while this == that this, that = biased(), biased() end    return thisend @printf "%2s | %10s | %5s | %5s | %8s" "N" "bias./unb." "1s" "0s" "pct ratio"const nrep = 10000for N in 3:6    biased = randN(N)     v = collect(biased() for __ in 1:nrep)    v1, v0 = count(v .== 1), count(v .== 0)    @printf("%2i | %10s | %5i | %5i | %5.2f%%\n", N, "biased", v1, v0, 100 * v1 / nrep)     v = collect(unbiased(biased) for __ in 1:nrep)    v1, v0 = count(v .== 1), count(v .== 0)    @printf("%2i | %10s | %5i | %5i | %5.2f%%\n", N, "unbiased", v1, v0, 100 * v1 / nrep)end`
Output:
``` N | bias./unb. |    1s |    0s | pct ratio
3 |     biased |  3286 |  6714 | 32.86%
3 |   unbiased |  4986 |  5014 | 49.86%
4 |     biased |  2473 |  7527 | 24.73%
4 |   unbiased |  4986 |  5014 | 49.86%
5 |     biased |  1992 |  8008 | 19.92%
5 |   unbiased |  5121 |  4879 | 51.21%
6 |     biased |  1663 |  8337 | 16.63%
6 |   unbiased |  5040 |  4960 | 50.40%```

Kotlin

Translation of: Java
`// version 1.1.2 fun biased(n: Int) = Math.random() < 1.0 / n fun unbiased(n: Int): Boolean {    var a: Boolean    var b: Boolean    do {        a = biased(n)          b = biased(n)    }    while (a == b)    return a} fun main(args: Array<String>) {    val m = 50_000    val f = "%d: %2.2f%%  %2.2f%%"    for (n in 3..6) {        var c1 = 0        var c2 = 0         for (i in 0 until m) {            if (biased(n)) c1++            if (unbiased(n)) c2++        }        println(f.format(n, 100.0 * c1 / m, 100.0 * c2 / m))    }}`

Sample output:

```3: 33.19%  50.19%
4: 25.29%  49.85%
5: 19.91%  50.07%
6: 16.71%  50.14%
```

Lua

` local function randN(n)  return function()    if math.random() < 1/n then return 1 else return 0 end  endend local function unbiased(n)  local biased = randN (n)  return function()    local a, b = biased(), biased()    while a==b do      a, b = biased(), biased()    end    return a  endend local function demonstrate (samples)  for n = 3, 6 do    biased = randN(n)    unbias = unbiased(n)    local bcounts = {[0]=0,[1]=0}    local ucounts = {[0]=0,[1]=0}    for i=1, samples do      local bnum = biased()      local unum = unbias()      bcounts[bnum] = bcounts[bnum]+1      ucounts[unum] = ucounts[unum]+1    end    print(string.format("N = %d",n),      "# 0", "# 1",      "% 0", "% 1")    print("biased", bcounts[0], bcounts[1],      bcounts[0] / samples * 100,      bcounts[1] / samples * 100)    print("unbias", ucounts[0], ucounts[1],      ucounts[0] / samples * 100,      ucounts[1] / samples * 100)  endend demonstrate(100000) `

Output:

```N = 3	# 0	# 1	% 0	% 1
biased	66832	33168	66.832	33.168
unbias	50207	49793	50.207	49.793
N = 4	# 0	# 1	% 0	% 1
biased	75098	24902	75.098	24.902
unbias	49872	50128	49.872	50.128
N = 5	# 0	# 1	% 0	% 1
biased	80142	19858	80.142	19.858
unbias	50049	49951	50.049	49.951
N = 6	# 0	# 1	% 0	% 1
biased	83407	16593	83.407	16.593
unbias	49820	50180	49.82	50.18
```

Mathematica

`rand[bias_, n_] := 1 - [email protected][bias - 1, n] unbiased[bias_, n_] :=  DeleteCases[rand[bias, {n, 2}], {a_, a_}][[All, 1]]`
```count = 1000000;
TableForm[
Table[{n, Total[rand[n, count]]/count // N,
Total[#]/Length[#] &@unbiased[n, count] // N}, {n, 3, 6}],
TableHeadings -> {None, {n, "biased", "unbiased"}}]

n	biased	unbiased
3	0.33312	0.500074
4	0.24932	0.499883
5	0.1998 	0.498421
6	0.16620	0.49805

```

NetRexx

Translation of: Java
`/* NetRexx */options replace format comments java crossref symbols binary runSample(arg)return -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~method biased(n = int) public static returns boolean  return Math.random() < 1.0 / n -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~method unbiased(n = int) public static returns boolean  a = boolean  b = boolean  loop until a \= b    a = biased(n)    b = biased(n)    end  return a -- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~method runSample(arg) private static  parse arg Mx .  if Mx.length <= 0 then Mx = 50000  M = int Mx  loop n = int 3 to 6    c1 = int 0    c2 = int 0    loop for M      if biased(n)   then c1 = c1 + 1      if unbiased(n) then c2 = c2 + 1      end      say Rexx(n).right(3)':' Rexx(100.0 * c1 / M).format(6, 2)'%' Rexx(100.0 * c2 / M).format(6, 2)'%'    end n  return `

Output:

```  3:     32.78%     49.98%
4:     24.72%     50.31%
5:     19.95%     50.34%
6:     17.20%     50.20%
```

Nim

Translation of: Python
`import math, strutilsrandomize() template newSeqWith(len: int, init: expr): expr =  var result {.gensym.} = newSeq[type(init)](len)  for i in 0..<len:    result[i] = init proc randN(n): (proc: range[0..1]) =  proc: range[0..1] = ord(random(n) == 0) proc unbiased(biased): range[0..1] =  result = biased()  var that = biased()  while result == that:    result = biased()    that = biased() for n in 3..6:  var biased = randN(n)  var v = newSeqWith(1_000_000, biased())  var cnt0, cnt1 = 0  for x in v:    if x == 0: inc cnt0    else:      inc cnt1  echo "Biased(",n,")  = count1=",cnt1,", count0=",cnt0,", percent=",       formatFloat(100 * float(cnt1)/float(cnt1+cnt0), ffDecimal, 3)   v = newSeqWith(1_000_000, unbiased(biased))  cnt0 = 0  cnt1 = 0  for x in v:    if x == 0: inc cnt0    else:      inc cnt1  echo "  Unbiased = count1=",cnt1,", count0=",cnt0,", percent=",       formatFloat(100 * float(cnt1)/float(cnt1+cnt0), ffDecimal, 3)`

Output:

```Biased(3)  = count1=332805, count0=667195, percent=33.281
Unbiased = count1=500157, count0=499843, percent=50.016
Biased(4)  = count1=249575, count0=750425, percent=24.957
Unbiased = count1=500072, count0=499928, percent=50.007
Biased(5)  = count1=199537, count0=800463, percent=19.954
Unbiased = count1=499396, count0=500604, percent=49.940
Biased(6)  = count1=166728, count0=833272, percent=16.673
Unbiased = count1=499712, count0=500288, percent=49.971```

OCaml

`let randN n =  if Random.int n = 0 then 1 else 0 let rec unbiased n =  let a = randN n in  if a <> randN n then a else unbiased n let () =  Random.self_init();  let n = 50_000 in  for b = 3 to 6 do    let cb = ref 0 in    let cu = ref 0 in    for i = 1 to n do      cb := !cb + (randN b);      cu := !cu + (unbiased b);    done;    Printf.printf "%d: %5.2f%%  %5.2f%%\n"      b (100.0 *. float !cb /. float n) (100.0 *. float !cu /. float n)  done`

Output:

```3: 33.07%  49.90%
4: 25.11%  49.85%
5: 19.82%  50.09%
6: 16.51%  50.51%```

PARI/GP

GP's random number generation is high-quality, using Brent's XORGEN. Thus this program is slow: the required 400,000 unbiased numbers generated through this bias/unbias scheme take nearly a second. This requires about two million calls to `random`, which in turn generate a total of about three million calls to the underlying random number generator through the rejection strategy. The overall efficiency of the scheme is 0.8% for 32-bit and 0.4% for 64-bit...

`randN(N)=!random(N);unbiased(N)={  my(a,b);  while(1,    a=randN(N);    b=randN(N);    if(a!=b, return(a))  )};for(n=3,6,print(n"\t"sum(k=1,1e5,unbiased(n))"\t"sum(k=1,1e5,randN(n))))`

Output:

```3	49997	33540
4	49988	24714
5	50143	20057
6	49913	16770```

Perl

`sub randn {        my \$n = shift;        return int(rand(\$n) / (\$n - 1));} for my \$n (3 .. 6) {        print "Bias \$n: ";        my (@raw, @fixed);        for (1 .. 10000) {                my \$x = randn(\$n);                \$raw[\$x]++;                \$fixed[\$x]++    if randn(\$n) != \$x        }        print "@raw, ";        printf("%3g+-%.3g%%\tfixed: ", \$raw[0]/100,		100 * sqrt(\$raw[0] * \$raw[1]) / (\$raw[0] + \$raw[1])**1.5);        print "@fixed, ";        printf("%3g+-%.3g%%\n", 100*\$fixed[0]/(\$fixed[0] + \$fixed[1]),		100 * sqrt(\$fixed[0] * \$fixed[1]) / (\$fixed[0] + \$fixed[1])**1.5); }`

Output:

```Bias 3: 6684 3316, 66.84+-0.471%        fixed: 2188 2228, 49.5471+-0.752%
Bias 4: 7537 2463, 75.37+-0.431%        fixed: 1924 1845, 51.048+-0.814%
Bias 5: 7993 2007, 79.93+-0.401%        fixed: 1564 1597, 49.478+-0.889%
Bias 6: 8309 1691, 83.09+-0.375%        fixed: 1403 1410, 49.8756+-0.943%```

Perl 6

Translation of: Perl
`sub randN ( \$n where 3..6 ) {    return ( \$n.rand / (\$n - 1) ).Int;} sub unbiased ( \$n where 3..6 ) {    my \$n1;    repeat { \$n1 = randN(\$n) } until \$n1 != randN(\$n);    return \$n1;} my \$iterations = 1000;for 3 .. 6 -> \$n {    my ( @raw, @fixed );    for ^\$iterations {        @raw[      randN(\$n) ]++;        @fixed[ unbiased(\$n) ]++;    }    printf "N=%d   randN: %s, %4.1f%%   unbiased: %s, %4.1f%%\n",        \$n, map { .perl, .[1] * 100 / \$iterations }, @raw, @fixed;}`
Output:
```N=3   randN: [676, 324], 32.4%   unbiased: [517, 483], 48.3%
N=4   randN: [734, 266], 26.6%   unbiased: [489, 511], 51.1%
N=5   randN: [792, 208], 20.8%   unbiased: [494, 506], 50.6%
N=6   randN: [834, 166], 16.6%   unbiased: [514, 486], 48.6%```

Phix

Copy of Euphoria

`function randN(integer N)    return rand(N) = 1end function function unbiased(integer N)integer a    while 1 do        a = randN(N)        if a!=randN(N) then            return a        end if    end whileend function constant n = 10000integer cb, cufor b=3 to 6 do    cb = 0    cu = 0    for i=1 to n do        cb += randN(b)        cu += unbiased(b)    end for    printf(1, "%d: %5.2f%%  %5.2f%%\n", {b, 100 * cb / n, 100 * cu / n})end for`
Output:
```3: 32.83%  50.34%
4: 24.78%  50.01%
5: 20.21%  49.71%
6: 16.68%  49.67%
```

PicoLisp

`(de randN (N)   (if (= 1 (rand 1 N)) 1 0) ) (de unbiased (N)   (use (A B)      (while         (=            (setq A (randN N))            (setq B (randN N)) ) )      A ) )`

Test:

`(for N (range 3 6)   (tab (2 1 7 2 7 2)      N ":"      (format         (let S 0 (do 10000 (inc 'S (randN N))))         2 )      "%"      (format         (let S 0 (do 10000 (inc 'S (unbiased N))))         2 )      "%" ) )`

Output:

``` 3:  33.21 %  50.48 %
4:  25.06 %  49.79 %
5:  20.04 %  49.75 %
6:  16.32 %  49.02 %```

PL/I

` test: procedure options (main); /* 20 Nov. 2012 */ randN: procedure(N) returns (bit (1));   declare N fixed (1);   declare random builtin;   declare r fixed (2) external initial (-1);   if r >= 0 then do; r = r-1; return ('0'b); end;   r = random()*2*N;   return ('1'b);end randN; random: procedure returns (bit(1));   declare (r1, r2) bit (1);   do until (r1 ^= r2);      r1 = randN(N); r2 = randN(N);   end;   return (r1);end random;    declare (biasedrn, unbiasedrn) (100) bit (1);   declare N fixed (1);    put ('N     Biased  Unbiased (tally of 100 random numbers)');   do N = 3 to 6;      biasedrn = randN(N); unbiasedrn = random;      put skip edit (N, sum(biasedrn), sum(unbiasedrn)) (F(1), 2 F(10));   end; end test; `

Results:

```N     Biased  Unbiased (tally of 100 random numbers)
3        24        42
4        18        47
5        16        41
6        11        53
```

PowerShell

Works with: PowerShell version 2
` function randN ( [int]\$N )    {    [int]( ( Get-Random -Maximum \$N ) -eq 0 )    } function unbiased ( [int]\$N )    {    do  {        \$X = randN \$N        \$Y = randN \$N        }    While ( \$X -eq \$Y )     return \$X    } `

Note: The [pscustomobject] type accelerator, used to simplify making the test output look pretty, requires version 3.0 or higher.

` \$Tests = 1000ForEach ( \$N in 3..6 )    {    \$Biased   = 0    \$Unbiased = 0     ForEach ( \$Test in 1..\$Tests )        {        \$Biased   += randN \$N        \$Unbiased += unbiased \$N        }    [pscustomobject]@{ N = \$N                       "Biased Ones out of \$Test" = \$Biased                       "Unbiased Ones out of \$Test" = \$Unbiased }    } `
Output:
```N Biased Ones out of 1000 Unbiased Ones out of 1000
- ----------------------- -------------------------
3                     322                       503
4                     273                       518
5                     217                       515
6                     173                       486
```

PureBasic

`Procedure biased(n)  If Random(n) <> 1    ProcedureReturn 0  EndIf   ProcedureReturn 1EndProcedure Procedure unbiased(n)  Protected a, b  Repeat    a = biased(n)    b = biased(n)  Until a <> b  ProcedureReturn aEndProcedure #count = 100000 Define n, m, output.sFor n = 3 To 6  Dim b_count(1)  Dim u_count(1)  For m = 1 To #count    x = biased(n)    b_count(x) + 1    x = unbiased(n)    u_count(x) + 1  Next  output + "N = " + Str(n) + #LF\$  output + "  biased =>" + #tab\$ + "#0=" + Str(b_count(0)) + #tab\$ + "#1=" +Str(b_count(1))  output + #tab\$ + " ratio=" + StrF(b_count(1) / #count * 100, 2) + "%" + #LF\$  output + "  unbiased =>" + #tab\$ + "#0=" + Str(u_count(0)) + #tab\$ + "#1=" + Str(u_count(1))  output + #tab\$ + " ratio=" + StrF(u_count(1) / #count * 100, 2) + "%" + #LF\$NextMessageRequester("Biased and Unbiased random number results", output)`

Sample output:

```---------------------------
Biased and Unbiased random number results
---------------------------
N = 3
biased =>	#0=74856	#1=25144	 ratio=25.14%
unbiased =>	#0=50066	#1=49934	 ratio=49.93%
N = 4
biased =>	#0=80003	#1=19997	 ratio=20.00%
unbiased =>	#0=49819	#1=50181	 ratio=50.18%
N = 5
biased =>	#0=83256	#1=16744	 ratio=16.74%
unbiased =>	#0=50268	#1=49732	 ratio=49.73%
N = 6
biased =>	#0=85853	#1=14147	 ratio=14.15%
unbiased =>	#0=49967	#1=50033	 ratio=50.03%```

Python

`from __future__ import print_functionimport random def randN(N):    " 1,0 random generator factory with 1 appearing 1/N'th of the time"    return lambda: random.randrange(N) == 0 def unbiased(biased):    'uses a biased() generator of 1 or 0, to create an unbiased one'    this, that = biased(), biased()    while this == that: # Loop until 10 or 01        this, that = biased(), biased()    return this         # return the first if __name__ == '__main__':    from collections import namedtuple     Stats = namedtuple('Stats', 'count1 count0 percent')     for N in range(3, 7):        biased = randN(N)        v = [biased() for x in range(1000000)]        v1, v0 = v.count(1), v.count(0)        print ( "Biased(%i)  = %r" % (N, Stats(v1, v0, 100. * v1/(v1 + v0))) )         v = [unbiased(biased) for x in range(1000000)]        v1, v0 = v.count(1), v.count(0)        print ( "  Unbiased = %r" % (Stats(v1, v0, 100. * v1/(v1 + v0)), ) )`

Sample output

```Biased(3)  = Stats(count1=331800, count0=668200, percent=33.18)
Unbiased = Stats(count1=499740, count0=500260, percent=49.973999999999997)
Biased(4)  = Stats(count1=249770, count0=750230, percent=24.977)
Unbiased = Stats(count1=499707, count0=500293, percent=49.970700000000001)
Biased(5)  = Stats(count1=199764, count0=800236, percent=19.976400000000002)
Unbiased = Stats(count1=499456, count0=500544, percent=49.945599999999999)
Biased(6)  = Stats(count1=167561, count0=832439, percent=16.7561)
Unbiased = Stats(count1=499963, count0=500037, percent=49.996299999999998)```

R

`randN = function(N) sample.int(N, 1) == 1 unbiased = function(f)   {while ((x <- f()) == f()) {}    x} samples = 10000print(t(round(d = 2, sapply(3:6, function(N) c(    N = N,    biased = mean(replicate(samples, randN(N))),    unbiased = mean(replicate(samples, unbiased(function() randN(N)))))))))`

Sample output:

```     N biased unbiased
[1,] 3   0.32     0.50
[2,] 4   0.24     0.50
[3,] 5   0.21     0.49
[4,] 6   0.16     0.51```

Racket

` #lang racket;; Using boolean #t/#f instead of 1/0(define ((randN n)) (zero? (random n)))(define ((unbiased biased))  (let loop () (let ([r (biased)]) (if (eq? r (biased)) (loop) r)))) ;; Counts(define N 1000000)(for ([n (in-range 3 7)])  (define (try% R) (round (/ (for/sum ([i N]) (if (R) 1 0)) N 1/100)))  (define biased (randN n))  (printf "Count: ~a => Biased: ~a%; Unbiased: ~a%.\n"          n (try% biased) (try% (unbiased biased)))) `
Output:
```Count: 3 => Biased: 33%; Unbiased: 50%.
Count: 4 => Biased: 25%; Unbiased: 50%.
Count: 5 => Biased: 20%; Unbiased: 50%.
Count: 6 => Biased: 17%; Unbiased: 50%.
```

REXX

`/*REXX program generates  unbiased random numbers  and displays the results to terminal.*/parse arg # R seed .                             /*obtain optional arguments from the CL*/if #==''  |  #==","     then #=1000              /*#:  the number of SAMPLES to be used.*/if R==''  |  R==","     then R=6                 /*R:  the high number for the  range.  */if datatype(seed, 'W')  then call random ,,seed  /*Specified?  Then use for RANDOM seed.*/dash='─';    @b="biased";         @ub='un'@b     /*literals for the SAY column headers. */say left('',5)   ctr("N",5)   ctr(@b)   ctr(@b'%')  ctr(@ub)  ctr(@ub"%")   ctr('samples')dash=       do N=3  to R;      b=0;                u=0         do j=1  for #;   b=b + randN(N);     u=u + unbiased()         end   /*j*/       say  left('', 5)     ctr(N, 5)     ctr(b)    pct(b)    ctr(u)    pct(u)    ctr(#)       end     /*N*/exit                                             /*stick a fork in it,  we're all done. *//*──────────────────────────────────────────────────────────────────────────────────────*/ctr:       return center( arg(1), word(arg(2) 12, 1), left(dash, 1)) /*show hdr│numbers.*/pct:       return ctr( format(arg(1) / # * 100, , 2)'%' )            /*2 decimal digits.*/randN:     parse arg z;            return random(1, z)==z            /*ret 1 if rand==Z.*/unbiased:  do  until x\==randN(N); x=randN(N);  end;       return x  /* "  unbiased RAND*/`
output   when using the default inputs:
```      ──N── ───biased─── ──biased%─── ──unbiased── ─unbiased%── ──samples───
3       348         34.80%        541         54.10%        1000
4       259         25.90%        479         47.90%        1000
5       188         18.80%        475         47.50%        1000
6       178         17.80%        488         48.80%        1000
```
output   when using the input of:   10000
```      ──N── ───biased─── ──biased%─── ──unbiased── ─unbiased%── ──samples───
3       3435        34.35%        4995        49.95%       10000
4       2535        25.35%        4957        49.57%       10000
5       2019        20.19%        4958        49.58%       10000
6       1644        16.44%        4982        49.82%       10000
```
output   when using the input of:   100000   30
```      ──N── ───biased─── ──biased%─── ──unbiased── ─unbiased%── ──samples───
3      33301        33.30%       50066        50.07%       100000
4      25359        25.36%       49401        49.40%       100000
5      20026        20.03%       49966        49.97%       100000
6      16579        16.58%       49956        49.96%       100000
7      14294        14.29%       50008        50.01%       100000
8      12402        12.40%       50479        50.48%       100000
9      11138        11.14%       50099        50.10%       100000
10       9973        9.97%        49988        49.99%       100000
11       9062        9.06%        50009        50.01%       100000
12       8270        8.27%        49929        49.93%       100000
13       7704        7.70%        49876        49.88%       100000
14       7223        7.22%        50414        50.41%       100000
15       6725        6.73%        50043        50.04%       100000
16       6348        6.35%        50252        50.25%       100000
17       5900        5.90%        49977        49.98%       100000
18       5583        5.58%        49991        49.99%       100000
19       5139        5.14%        49958        49.96%       100000
20       4913        4.91%        50198        50.20%       100000
21       4714        4.71%        49892        49.89%       100000
22       4517        4.52%        49760        49.76%       100000
23       4226        4.23%        50021        50.02%       100000
24       4174        4.17%        50141        50.14%       100000
25       4005        4.01%        49816        49.82%       100000
26       3890        3.89%        49819        49.82%       100000
27       3705        3.71%        50036        50.04%       100000
28       3567        3.57%        49665        49.67%       100000
29       3481        3.48%        50094        50.09%       100000
30       3355        3.36%        49831        49.83%       100000
```

Ring

` for n = 3 to 6    biased = 0    unb = 0    for i = 1 to 10000        biased += randN(n)        unb += unbiased(n)     next    see "N = " + n + " : biased = " + biased/100  + "%, unbiased = " + unb/100 + "%" + nlnext func unbiased nr     while 1            a = randN(nr)           if a != randN(nr) return a ok     end func randN m     m = (random(m) = 1)     return m `

Output:

```N = 3 : biased = 25.38%, unbiased = 50.12%
N = 4 : biased = 20.34%, unbiased = 49.17%
N = 5 : biased = 16.65%, unbiased = 48.86%
N = 6 : biased = 13.31%, unbiased = 49.96%
```

Ruby

`def rand_n(bias)  rand(bias) == 0 ? 1 : 0end def unbiased(bias)  a, b = rand_n(bias), rand_n(bias) until a != b #loop until a and b are 0,1 or 1,0  aend runs = 1_000_000keys = %i(bias biased unbiased) #use [:bias,:biased,:unbiased] in Ruby < 2.0puts keys.join("\t") (3..6).each do |bias|  counter = Hash.new(0) # counter will respond with 0 when key is not known  runs.times do    counter[:biased] += 1 if rand_n(bias) == 1 #the first time, counter has no key for :biased, so it will respond 0    counter[:unbiased] += 1 if unbiased(bias) == 1  end  counter[:bias] = bias  puts counter.values_at(*keys).join("\t")end`
Output:
```bias	biased	unbiased
3	333043	500161
4	249133	499393
5	199767	500354
6	166163	499809
```

Rust

`#![feature(inclusive_range_syntax)] extern crate rand; use rand::Rng; fn rand_n<R: Rng>(rng: &mut R, n: u32) -> usize {    rng.gen_weighted_bool(n) as usize // maps `false` to 0 and `true` to 1} fn unbiased<R: Rng>(rng: &mut R, n: u32) -> usize {    let mut bit = rand_n(rng, n);    while bit == rand_n(rng, n) {        bit = rand_n(rng, n);    }    bit} fn main() {    const SAMPLES: usize = 100_000;    let mut rng = rand::weak_rng();     println!(" Bias    rand_n  unbiased");    for n in 3..=6 {        let mut count_biased = 0;        let mut count_unbiased = 0;        for _ in 0..SAMPLES {            count_biased += rand_n(&mut rng, n);            count_unbiased += unbiased(&mut rng, n);        }         let b_percentage = 100.0 * count_biased as f64 / SAMPLES as f64;        let ub_percentage = 100.0 * count_unbiased as f64 / SAMPLES as f64;        println!(            "bias {}:  {:0.2}%   {:0.2}%",            n, b_percentage, ub_percentage        );    }}`
Output:
``` Bias    rand_n  unbiased
bias 3:  33.32%   49.80%
bias 4:  25.22%   50.16%
bias 5:  19.91%   50.00%
bias 6:  16.66%   49.95%
```

Scala

`def biased( n:Int ) = scala.util.Random.nextFloat < 1.0 / n def unbiased( n:Int ) = { def loop : Boolean = { val a = biased(n); if( a != biased(n) ) a else loop }; loop } for( i <- (3 until 7) ) println {    val m = 50000  var c1,c2 = 0   (0 until m) foreach { j => if( biased(i) ) c1 += 1; if( unbiased(i) ) c2 += 1 }   "%d: %2.2f%%  %2.2f%%".format(i, 100.0*c1/m, 100.0*c2/m)}`
Output:
```3: 33.09%  49.79%
4: 24.92%  49.92%
5: 19.75%  49.92%
6: 16.67%  50.23%```

Seed7

`\$ include "seed7_05.s7i";  include "float.s7i"; const func integer: randN (in integer: n) is  return ord(rand(1, n) = 1); const func integer: unbiased (in integer: n) is func  result    var integer: unbiased is 0;  begin    repeat      unbiased := randN(n);    until unbiased <> randN(n);  end func; const proc: main is func  local    const integer: tests is 50000;    var integer: n is 0;    var integer: sumBiased is 0;    var integer: sumUnbiased is 0;    var integer: count is 0;  begin    for n range 3 to 6 do      sumBiased := 0;      sumUnbiased := 0;      for count range 1 to tests do        sumBiased +:= randN(n);        sumUnbiased +:= unbiased(n);      end for;      writeln(n <& ": " <& flt(100 * sumBiased) / flt(tests) digits 3 lpad 6 <&                   "  " <& flt(100 * sumUnbiased) / flt(tests) digits 3 lpad 6);    end for;  end func;`

Output:

```3: 33.004  50.024
4: 25.158  50.278
5: 20.186  49.978
6: 16.570  49.936
```

Sidef

Translation of: Perl 6
`func randN (n) {    n.rand / (n-1) -> int} func unbiased(n) {    var n1 = nil    do { n1 = randN(n) } while (n1 == randN(n))    return n1} var iterations = 1000 for n in (3..6) {    var raw = []    var fixed = []    iterations.times {          raw[    randN(n) ] := 0 ++        fixed[ unbiased(n) ] := 0 ++    }    printf("N=%d   randN: %s, %4.1f%%   unbiased: %s, %4.1f%%\n",        n, [raw, fixed].map {|a| (a.dump, a[1] * 100 / iterations) }...)}`
Output:
```N=3   randN: [661, 339], 33.9%   unbiased: [497, 503], 50.3%
N=4   randN: [765, 235], 23.5%   unbiased: [493, 507], 50.7%
N=5   randN: [812, 188], 18.8%   unbiased: [509, 491], 49.1%
N=6   randN: [820, 180], 18.0%   unbiased: [510, 490], 49.0%
```

Tcl

`# 1,0 random generator factory with 1 appearing 1/N'th of the timeproc randN n {expr {rand()*\$n < 1}} # uses a biased generator of 1 or 0, to create an unbiased oneproc unbiased {biased} {    while 1 {	if {[set a [eval \$biased]] != [eval \$biased]} {return \$a}    }} for {set n 3} {\$n <= 6} {incr n} {    set biased [list randN \$n]    for {set i 0;array set c {0 0 1 0}} {\$i < 1000000} {incr i} {	incr c([eval \$biased])    }    puts [format "  biased %d => #0=%d #1=%d ratio=%.2f%%" \$n \$c(0) \$c(1) \	      [expr {100.*\$c(1)/\$i}]]    for {set i 0;array set c {0 0 1 0}} {\$i < 1000000} {incr i} {	incr c([unbiased \$biased])    }    puts [format "unbiased %d => #0=%d #1=%d ratio=%.2f%%" \$n \$c(0) \$c(1) \	      [expr {100.*\$c(1)/\$i}]]}`

Sample output:

```  biased 3 => #0=667076 #1=332924 ratio=33.29%
unbiased 3 => #0=500263 #1=499737 ratio=49.97%
biased 4 => #0=750470 #1=249530 ratio=24.95%
unbiased 4 => #0=500644 #1=499356 ratio=49.94%
biased 5 => #0=800243 #1=199757 ratio=19.98%
unbiased 5 => #0=500878 #1=499122 ratio=49.91%
biased 6 => #0=833623 #1=166377 ratio=16.64%
unbiased 6 => #0=500518 #1=499482 ratio=49.95%
```

zkl

`fcn randN(N){ (not (0).random(N)).toInt() }fcn unbiased(randN){ while((a:=randN())==randN()){} a }`
`const Z=0d100_000;foreach N in ([3..6]){   "%d: biased: %3.2f%%, unbiased: %3.2f%%".fmt(N,       (0).reduce(Z,'wrap(s,_){ s+randN(N) },0.0)/Z*100,       (0).reduce(Z,'wrap(s,_){ s+unbiased(randN.fp(N)) },0.0)/Z*100)   .println();}`
Output:
```3: biased: 33.46%, unbiased: 49.80%
4: biased: 24.95%, unbiased: 50.01%
5: biased: 19.89%, unbiased: 50.18%
6: biased: 16.75%, unbiased: 50.22%
```