# Dice game probabilities

Dice game probabilities
You are encouraged to solve this task according to the task description, using any language you may know.

Two players have a set of dice each. The first player has nine dice with four faces each, with numbers one to four. The second player has six normal dice with six faces each, each face has the usual numbers from one to six.

They roll their dice and sum the totals of the faces. The player with the highest total wins (it's a draw if the totals are the same). What's the probability of the first player beating the second player?

Later the two players use a different set of dice each. Now the first player has five dice with ten faces each, and the second player has six dice with seven faces each. Now what's the probability of the first player beating the second player?

## 11l

Translation of: C
```F throw_die(n_sides, n_dice, s, [Int] &counts)
I n_dice == 0
counts[s]++
R
L(i) 1..n_sides
throw_die(n_sides, n_dice - 1, s + i, counts)

F beating_probability(n_sides1, n_dice1,
n_sides2, n_dice2)
V len1 = (n_sides1 + 1) * n_dice1
V C1 = [0] * len1
throw_die(n_sides1, n_dice1, 0, &C1)

V len2 = (n_sides2 + 1) * n_dice2
V C2 = [0] * len2
throw_die(n_sides2, n_dice2, 0, &C2)

Float p12 = (n_sides1 ^ n_dice1) * (n_sides2 ^ n_dice2)

V tot = 0.0
L(i) 0 .< len1
L(j) 0 .< min(i, len2)
tot += Float(C1[i]) * C2[j] / p12
R tot

print(‘#.16’.format(beating_probability(4, 9, 6, 6)))
print(‘#.16’.format(beating_probability(10, 5, 7, 6)))```
Output:
```0.5731440767829814
0.6427886287176272
```

## Action!

```INCLUDE "D2:REAL.ACT" ;from the Action! Tool Kit

BYTE FUNC Roll(BYTE sides,dices)
BYTE i,sum

sum=0
FOR i=1 TO dices
DO
sum==+Rand(sides)+1
OD
RETURN (sum)

PROC Test(BYTE sides1,dices1,sides2,dices2)
INT i,count=[10000],sum1,sum2,wins1,wins2,draws
REAL r1,r2,p

wins1=0 wins2=0 draws=0
FOR i=1 TO count
DO
sum1=Roll(sides1,dices1)
sum2=Roll(sides2,dices2)
IF sum1>sum2 THEN
wins1==+1
ELSEIF sum1<sum2 THEN
wins2==+1
ELSE
draws==+1
FI
OD

IntToReal(wins1,r1)
IntToReal(wins2,r2)
RealDiv(r2,r1,p)

PrintF("Tested %I times%E",count)
PrintF("Player 1 with %B dice of %B sides%E",dices1,sides1)
PrintF("Player 2 with %B dice of %B sides%E",dices2,sides2)
PrintF("Player 1 wins %I times%E",wins1)
PrintF("Player 2 wins %I times%E",wins2)
PrintF("Draw %I times%E",draws)
Print("Player 1 beating Player 2 probability:")
PrintRE(p)
PutE()
RETURN

PROC Main()
Put(125) PutE() ;clear the screen

Test(4,9,6,6)
Test(10,5,7,6)
RETURN```
Output:
```Tested 10000 times
Player 1 with 9 dice of 4 sides
Player 2 with 6 dice of 6 sides
Player 1 wins 5770 times
Player 2 wins 3493 times
Draw 737 times
Player 1 beating Player 2 probability:
.6053726169

Tested 10000 times
Player 1 with 5 dice of 10 sides
Player 2 with 6 dice of 7 sides
Player 1 wins 6417 times
Player 2 wins 3140 times
Draw 443 times
Player 1 beating Player 2 probability:
.4893252298
```

```with Ada.Text_IO; use Ada.Text_IO;

procedure Main is
package real_io is new Float_IO (Long_Float);
use real_io;

type Dice is record
Faces    : Positive;
Num_Dice : Positive;
end record;

procedure Roll_Dice (The_Dice : in Dice; Count : out Natural) is
subtype Faces is Integer range 1 .. The_Dice.Faces;
package Die_Random is new Ada.Numerics.Discrete_Random (Faces);
use Die_Random;
Seed : Generator;
begin
Reset (Seed);
Count := 0;
for I in 1 .. The_Dice.Num_Dice loop
Count := Count + Random (Seed);
end loop;
end Roll_Dice;

function Win_Prob
(Dice_1 : Dice; Dice_2 : Dice; Tries : Positive) return Long_Float
is
Count_1      : Natural := 0;
Count_2      : Natural := 0;
Count_1_Wins : Natural := 0;
begin
for I in 1 .. Tries loop
Roll_Dice (Dice_1, Count_1);
Roll_Dice (Dice_2, Count_2);
if Count_1 > Count_2 then
Count_1_Wins := Count_1_Wins + 1;
end if;
end loop;
return Long_Float (Count_1_Wins) / Long_Float (Tries);
end Win_Prob;

D1 : Dice := (Faces => 4, Num_Dice => 9);
D2 : Dice := (Faces => 6, Num_Dice => 6);
D3 : Dice := (Faces => 10, Num_Dice => 5);
D4 : Dice := (Faces => 7, Num_Dice => 6);

P1 : Long_Float := Win_Prob (D1, D2, 1_000_000);
P2 : Long_Float := Win_Prob (D3, D4, 1_000_000);
begin
Put ("Dice D1 wins = ");
Put (Item => P1, Fore => 1, Aft => 7, Exp => 0);
New_Line;
Put ("Dice D2 wins = ");
Put (Item => P2, Fore => 1, Aft => 7, Exp => 0);
New_Line;
end Main;
```
Output:
```Dice D1 wins = 0.5727800
Dice D2 wins = 0.6427660
```

## BASIC

### BASIC256

Translation of: Yabasic
```dado1 = 9: lado1 = 4
total1 = 0: total2 = 0

for i = 0 to 1
for cont = 1 to 100000
total1 = total1 + 1
else
endif
next cont

print "Total victorias jugador1 = "; total1; " => "; left(string(total2 / total1), 9)
print "Total victorias jugador2 = "; total2
print (cont - 1) - (total1 + total2); " empates" + chr(10)

total1 = 0: total2 = 0
next i
end

total = 0

for lanza = 1 to dado
total = total + int(rand * lado) + 1
next lanza
end function```
Output:
```Igual que la entrada de Yabasic.
```

### FreeBASIC

Translation of: Gambas
```Dim As Integer lado, jugador1, jugador2, total1, total2, cont, i

Randomize Timer

Dim As Short lanza, total

For lanza = 1 To dado
total += Int(Rnd * lado) + 1
Next lanza
End Function

For i = 0 To 1
For cont = 1 To 100000
total1 += 1
total2 += 1
End If
Next cont

Print Using "Total victorias jugador1 = &  => #.#######"; total1; (total2 / total1)
Print "Total victorias jugador2 ="; total2
Print (cont - 1) - (total1 + total2); !" empates\n"

total1 = 0: total2 = 0
Next i

Sleep
```
Output:
```Lanzado el dado 100000 veces
Total victorias jugador1 = 57274  => 0.6237211
7003 empates

Total victorias jugador1 = 64093  => 0.4893826
4541 empates
```

### Yabasic

Translation of: FreeBASIC
```dado1 = 9: lado1 = 4
total1 = 0: total2 = 0

for i = 0 to 1
for cont = 1 to 100000
total1 = total1 + 1
total2 = total2 + 1
endif
next cont

print "Total victorias jugador1 = ", total1, " => ", (total2 / total1)
print "Total victorias jugador2 = ", total2
print (cont - 1) - (total1 + total2), " empates\n"

total1 = 0: total2 = 0
next i

local lanza, total

for lanza = 1 to dado
total = total + int(ran(lado)) + 1
next lanza
end sub```
Output:
```Igual que la entrada de FreeBASIC.
```

## C

```#include <stdio.h>
#include <stdint.h>

typedef uint32_t uint;
typedef uint64_t ulong;

ulong ipow(const uint x, const uint y) {
ulong result = 1;
for (uint i = 1; i <= y; i++)
result *= x;
return result;
}

uint min(const uint x, const uint y) {
return (x < y) ? x : y;
}

void throw_die(const uint n_sides, const uint n_dice, const uint s, uint counts[]) {
if (n_dice == 0) {
counts[s]++;
return;
}

for (uint i = 1; i < n_sides + 1; i++)
throw_die(n_sides, n_dice - 1, s + i, counts);
}

double beating_probability(const uint n_sides1, const uint n_dice1,
const uint n_sides2, const uint n_dice2) {
const uint len1 = (n_sides1 + 1) * n_dice1;
uint C1[len1];
for (uint i = 0; i < len1; i++)
C1[i] = 0;
throw_die(n_sides1, n_dice1, 0, C1);

const uint len2 = (n_sides2 + 1) * n_dice2;
uint C2[len2];
for (uint j = 0; j < len2; j++)
C2[j] = 0;
throw_die(n_sides2, n_dice2, 0, C2);

const double p12 = (double)(ipow(n_sides1, n_dice1) * ipow(n_sides2, n_dice2));

double tot = 0;
for (uint i = 0; i < len1; i++)
for (uint j = 0; j < min(i, len2); j++)
tot += (double)C1[i] * C2[j] / p12;
}

int main() {
printf("%1.16f\n", beating_probability(4, 9, 6, 6));
printf("%1.16f\n", beating_probability(10, 5, 7, 6));
return 0;
}
```
Output:
```0.5731440767829801
0.6427886287176260```

## C#

Translation of: Java
```using System;

class Program
{
static uint MinOf(uint x, uint y)
{
return x < y ? x : y;
}

static void ThrowDie(uint nSides, uint nDice, uint s, uint[] counts)
{
if (nDice == 0)
{
counts[s]++;
return;
}
for (uint i = 1; i <= nSides; i++)
{
ThrowDie(nSides, nDice - 1, s + i, counts);
}
}

static double BeatingProbability(uint nSides1, uint nDice1, uint nSides2, uint nDice2)
{
uint len1 = (nSides1 + 1) * nDice1;
uint[] c1 = new uint[len1]; // initialized to zero by default
ThrowDie(nSides1, nDice1, 0, c1);

uint len2 = (nSides2 + 1) * nDice2;
uint[] c2 = new uint[len2];
ThrowDie(nSides2, nDice2, 0, c2);
double p12 = Math.Pow(nSides1, nDice1) * Math.Pow(nSides2, nDice2);

double tot = 0.0;
for (uint i = 0; i < len1; i++)
{
for (uint j = 0; j < MinOf(i, len2); j++)
{
tot += (double)(c1[i] * c2[j]) / p12;
}
}
}

static void Main(string[] args)
{
Console.WriteLine(BeatingProbability(4, 9, 6, 6));
Console.WriteLine(BeatingProbability(10, 5, 7, 6));
}
}
```
Output:
```0.573144076782982
0.642788628717627

```

## C++

```#include <cmath>
#include <cstdint>
#include <iomanip>
#include <iostream>
#include <map>

// Returns map from sum of faces to number of ways it can happen
std::map<uint32_t, uint32_t> get_totals(uint32_t dice, uint32_t faces) {
std::map<uint32_t, uint32_t> result;
for (uint32_t i = 1; i <= faces; ++i)
result.emplace(i, 1);
for (uint32_t d = 2; d <= dice; ++d) {
std::map<uint32_t, uint32_t> tmp;
for (const auto& p : result) {
for (uint32_t i = 1; i <= faces; ++i)
tmp[p.first + i] += p.second;
}
tmp.swap(result);
}
return result;
}

double probability(uint32_t dice1, uint32_t faces1, uint32_t dice2, uint32_t faces2) {
auto totals1 = get_totals(dice1, faces1);
auto totals2 = get_totals(dice2, faces2);
double wins = 0;
for (const auto& p1 : totals1) {
for (const auto& p2 : totals2) {
if (p2.first >= p1.first)
break;
wins += p1.second * p2.second;
}
}
double total = std::pow(faces1, dice1) * std::pow(faces2, dice2);
return wins/total;
}

int main() {
std::cout << std::setprecision(10);
std::cout << probability(9, 4, 6, 6) << '\n';
std::cout << probability(5, 10, 6, 7) << '\n';
return 0;
}
```
Output:
```0.5731440768
0.6427886287
```

## Common Lisp

DRAFT

#### 1. Note

We use total probability rule.

#### 2. Program

Player i gets si points as the sum of rolling ni dices, each with fi faces.

```(defun n-ways (n f s &optional (mem (make-array '(999 99) :initial-element -1)))
(cond ((and (= n 0) (= s 0)) 1)
((or (= n 0) (<= s 0)) 0)
((>= (aref mem s n) 0) (aref mem s n))
(t (loop for i from 1 to f
sum (n-ways (1- n) f (- s i) mem) into total
finally (return (setf (aref mem s n) total))))))

(defun winning-probability (n1 f1 n2 f2 &aux (w 0))
(loop for i from n1 to (* n1 f1)
do (loop for j from n2 to (* n2 f2)
do (if (> i j)
(setf w (+ w (* (n-ways n1 f1 i) (n-ways n2 f2 j))))))
finally (return (/ w (* (expt f1 n1) (expt f2 n2))))))
```

#### 3. Execution

```(winning-probability 9 4 6 6)
(winning-probability 5 10 6 7)```
Output:
```48679795/84934656      ; ≈ 0.573
3781171969/5882450000  ; ≈ 0.643```

That's all Folks !

cyril nocton (cyril.nocton@gmail.com) w/ google translate

## D

### version 1

```import std.stdio, std.range, std.algorithm;

void throwDie(in uint nSides, in uint nDice, in uint s, uint[] counts)
pure nothrow @safe @nogc {
if (nDice == 0) {
counts[s]++;
return;
}

foreach (immutable i; 1 .. nSides + 1)
throwDie(nSides, nDice - 1, s + i, counts);
}

real beatingProbability(uint nSides1, uint nDice1,
uint nSides2, uint nDice2)()
pure nothrow @safe /*@nogc*/ {
uint[(nSides1 + 1) * nDice1] C1;
throwDie(nSides1, nDice1, 0, C1);

uint[(nSides2 + 1) * nDice2] C2;
throwDie(nSides2, nDice2, 0, C2);

immutable p12 = real((ulong(nSides1) ^^ nDice1) *
(ulong(nSides2) ^^ nDice2));

return cartesianProduct(C1[].enumerate, C2[].enumerate)
.filter!(p => p[0][0] > p[1][0])
.map!(p => real(p[0][1]) * p[1][1] / p12)
.sum;
}

void main() @safe {
writefln("%1.16f", beatingProbability!(4, 9, 6, 6));
writefln("%1.16f", beatingProbability!(10, 5, 7, 6));
}
```
Output:
```0.5731440767829801
0.6427886287176262```

### version 2 (Faster Alternative Version)

Translation of: Python
```import std.stdio, std.range, std.algorithm;

ulong[] combos(R)(R sides, in uint n) pure nothrow @safe
if (isForwardRange!R) {
if (sides.empty)
return null;
if (!n)
return [1];
auto ret = new typeof(return)(reduce!max(sides[0], sides[1 .. \$]) * n + 1);
foreach (immutable i, immutable v; enumerate(combos(sides, n - 1))) {
if (!v)
continue;
foreach (immutable s; sides)
ret[i + s] += v;
}
return ret;
}

real winning(R)(R sides1, in uint n1, R sides2, in uint n2)
pure nothrow @safe if (isForwardRange!R) {
static void accumulate(T)(T[] arr) pure nothrow @safe @nogc {
foreach (immutable i; 1 .. arr.length)
arr[i] += arr[i - 1];
}

immutable p1 = combos(sides1, n1);
auto p2 = combos(sides2, n2);
immutable s = p1.sum * p2.sum;
accumulate(p2);
ulong win = 0; // 'win' is 1 beating 2.
foreach (immutable i, immutable x1; p1.dropOne.enumerate)
win += x1 * p2[min(i, \$ - 1)];
return win / real(s);
}

void main() @safe {
writefln("%1.16f", winning(iota(1u, 5u),  9, iota(1u, 7u), 6));
writefln("%1.16f", winning(iota(1u, 11u), 5, iota(1u, 8u), 6));
}
```
Output:
```0.5731440767829801
0.6427886287176262```

## EasyLang

Translation of: Phix
```proc throw_die n_sides n_dice s . counts[] .
if n_dice = 0
counts[s] += 1
return
.
for i = 1 to n_sides
throw_die n_sides (n_dice - 1) (s + i) counts[]
.
.
func proba n_sides1 n_dice1 n_sides2 n_dice2 .
len c1[] (n_sides1 + 1) * n_dice1
len c2[] (n_sides2 + 1) * n_dice2
throw_die n_sides1 n_dice1 0 c1[]
throw_die n_sides2 n_dice2 0 c2[]
p12 = pow n_sides1 n_dice1 * pow n_sides2 n_dice2
for i = 1 to len c1[]
for j = 1 to lower (i - 1) len c2[]
tot += c1[i] * c2[j] / p12
.
.
.
numfmt 5 0
print proba 4 9 6 6
print proba 10 5 7 6
```

## Factor

```USING: dice generalizations kernel math prettyprint sequences ;
IN: rosetta-code.dice-probabilities

: winning-prob ( a b c d -- p )
[ [ random-roll ] 2bi@ > ] 4 ncurry [ 100000 ] dip replicate
[ [ t = ] count ] [ length ] bi /f ;

9 4 6 6 winning-prob
5 10 6 7 winning-prob [ . ] bi@
```
Output:
```0.57199
0.64174
```

## Forth

Works with: gforth version 0.7.3

```#! /usr/bin/gforth

\ Dice game probabilities

: min? ( addr -- min )
@
;

: max? ( addr -- max )
cell + @
;

: max+1-min? ( addr -- max+1 min )
dup max? 1+ swap min?
;

over min? - 2 + cells +
;

: weight? ( addr x -- w )
2dup swap min? < IF
2drop 0
ELSE
2dup swap max? > IF
2drop 0
ELSE
THEN
THEN
;

: total-weight?   ( addr -- w )
dup max? 1+   ( addr max+1 )
over min?     ( addr max+1 min )
0 -rot ?DO ( adrr 0 max+1 min )
over i weight? +
LOOP
nip
;

: uniform-aux ( min max x -- addr )
>r 2dup
2dup swap - 3 + cells allocate throw ( min max min max addr )
tuck cell + !                        ( min max min addr )
tuck !                               ( min max addr )
-rot swap                            ( addr max min )
r> -rot                              ( addr x max min )
- 3 + 2 ?DO                          ( addr x )
2dup swap i cells + !
LOOP
drop
;

LOOP
LOOP
;

: even? ( n -- f )
2 mod 0=
;

dup 1 = IF
drop
ELSE
dup even? IF
2/ recurse dup convolve
ELSE
over swap 2/ recurse dup convolve convolve
THEN
THEN
;

: .dist { addr -- }
i 10 .r
addr i weight? dup 20 .r
0 d>f tw 0 d>f f/ ."  " f. cr
LOOP
;

0
j i xt execute IF
* +
THEN
LOOP
LOOP
0 d>f
f/
;

['] > dist-cmp
;

\ creates the uniform distribution with outcomes from min to max
: uniform ( min max -- addr )
1 uniform-aux
;

\ example

1 4 uniform 9 power
1 6 uniform 6 power
dist> f. cr

1 10 uniform 5 power
1  7 uniform 6 power
dist> f. cr

bye
```
Output:
```time ./dice-game-probabilities.fs
0.57314407678298
0.642788628717626

real	0m0,008s
user	0m0,005s
sys	    0m0,003s
```

## FutureBasic

```local fn RollTheDice( dice as long, sides as long ) as long
long i, total = 0

for i = 1 to dice
total += rnd(sides)
next
end fn = total

void local fn CaclulateProbabilities( dice1 as long, sides1 as long, dice2 as long, sides2 as long )
long  i, player1, player2
float total1 = 0, total2 = 0, total3 = 0

for i = 1 to 100000
player1 = fn RollTheDice( dice1, sides1 )
player2 = fn RollTheDice( dice2, sides2 )
if ( player1  > player2 ) then total1++ : continue
if ( player1  < player2 ) then total2++ : continue
if ( player1 == player2 ) then total3++ : continue
next

printf @"Dice cast %ld times.", i - 1
printf @"Player 1 with %ld dice of %ld sides.", dice1, sides1
printf @"Player 2 with %ld dice of %ld sides.", dice2, sides2
printf @"Total wins Player 1 = %.f. Probability of winning: %0.4f%%.", total1, (total1 / i ) * 100
printf @"Total wins Player 2 = %.f", total2
printf @"Number of ties: %.f.", total3
end fn

randomize
print
fn CaclulateProbabilities( 9, 4, 6, 6 )
print
fn CaclulateProbabilities( 5, 10, 6, 7 )
print

HandleEvents```
Output:
```Dice cast 100000 times.
Player 1 with 9 dice of 4 sides.
Player 2 with 6 dice of 6 sides.
Total wins Player 1 = 57301. Probability of winning: 57.3004%.
Total wins Player 2 = 35733
Number of ties: 6966.

Dice cast 100000 times.
Player 1 with 5 dice of 10 sides.
Player 2 with 6 dice of 7 sides.
Total wins Player 1 = 64289. Probability of winning: 64.2884%.
Total wins Player 2 = 31296
Number of ties: 4415.
```

## Gambas

```' Gambas module file

Public Sub Main()
Dim iSides, iPlayer1, iPlayer2, iTotal1, iTotal2, iCount, iCount0 As Integer
Dim iDice1 As Integer = 9
Dim iDice2 As Integer = 6
Dim iSides1 As Integer = 4
Dim iSides2 As Integer = 6

Randomize

For iCount0 = 0 To 1
For iCount = 1 To 100000
iPlayer1 = Roll(iDice1, iSides1)
iPlayer2 = Roll(iDice2, iSides2)
If iPlayer1 > iPlayer2 Then
iTotal1 += 1
Else If iPlayer1 <> iPlayer2 Then
iTotal2 += 1
Endif
Next

Print "Tested " & Str(iCount - 1) & " times"
Print "Player1 with " & Str(iDice1) & " dice of " & Str(iSides1) & " sides"
Print "Player2 with " & Str(iDice2) & " dice of " & Str(iSides2) & " sides"
Print "Total wins Player1 = " & Str(iTotal1) & " - " & Str(iTotal2 / iTotal1)
Print "Total wins Player2 = " & Str(iTotal2)
Print Str((iCount - 1) - (iTotal1 + iTotal2)) & " draws" & gb.NewLine

iDice1 = 5
iDice2 = 6
iSides1 = 10
iSides2 = 7
iTotal1 = 0
iTotal2 = 0
Next

End

Public Sub Roll(iDice As Integer, iSides As Integer) As Integer
Dim iCount, iTotal As Short

For iCount = 1 To iDice
iTotal += Rand(1, iSides)
Next

Return iTotal

End
```

Output:

```Tested 100000 times
Player1 with 9 dice of 4 sides
Player2 with 6 dice of 6 sides
Total wins Player1 = 56980 - 0.62823797823798
Total wins Player2 = 35797
7223 draws

Tested 100000 times
Player1 with 5 dice of 10 sides
Player2 with 6 dice of 7 sides
Total wins Player1 = 64276 - 0.48548447320928
Total wins Player2 = 31205
4519 draws
```

## Go

Translation of: C
```package main

import(
"math"
"fmt"
)

func minOf(x, y uint) uint {
if x < y {
return x
}
return y
}

func throwDie(nSides, nDice, s uint, counts []uint) {
if nDice == 0 {
counts[s]++
return
}
for i := uint(1); i <= nSides; i++ {
throwDie(nSides, nDice - 1, s + i, counts)
}
}

func beatingProbability(nSides1, nDice1, nSides2, nDice2 uint) float64 {
len1 := (nSides1 + 1) * nDice1
c1 := make([]uint, len1)  // all elements zero by default
throwDie(nSides1, nDice1, 0, c1)

len2 := (nSides2 + 1) * nDice2
c2 := make([]uint, len2)
throwDie(nSides2, nDice2, 0, c2)
p12 := math.Pow(float64(nSides1), float64(nDice1)) *
math.Pow(float64(nSides2), float64(nDice2))

tot := 0.0
for i := uint(0); i < len1; i++ {
for j := uint(0); j < minOf(i, len2); j++ {
tot += float64(c1[i] * c2[j]) / p12
}
}
}

func main() {
fmt.Println(beatingProbability(4, 9, 6, 6))
fmt.Println(beatingProbability(10, 5, 7, 6))
}
```
Output:
```0.5731440767829815
0.6427886287176273
```

More idiomatic go:

```package main

import (
"fmt"
"math/rand"
)

type set struct {
n, s int
}

func (s set) roll() (sum int) {
for i := 0; i < s.n; i++ {
sum += rand.Intn(s.s) + 1
}
return
}

func (s set) beats(o set, n int) (p float64) {
for i := 0; i < n; i++ {
if s.roll() > o.roll() {
p = p + 1.0
}
}
p = p / float64(n)
return
}

func main() {
fmt.Println(set{9, 4}.beats(set{6, 6}, 1000))
fmt.Println(set{5, 10}.beats(set{6, 7}, 1000))
}
```
Output:
```0.576
0.639
```

Translation of: Python
```import Control.Monad (replicateM)
import Data.List (group, sort)

succeeds :: (Int, Int) -> (Int, Int) -> Double
succeeds p1 p2 =
sum
[ realToFrac (c1 * c2) / totalOutcomes
| (s1, c1) <- countSums p1
, (s2, c2) <- countSums p2
, s1 > s2 ]
where
totalOutcomes = realToFrac \$ uncurry (^) p1 * uncurry (^) p2
countSums (nFaces, nDice) = f [1 .. nFaces]
where
f =
fmap (((,) . head) <*> (pred . length)) .
group . sort . fmap sum . replicateM nDice

main :: IO ()
main = do
print \$ (4, 9) `succeeds` (6, 6)
print \$ (10, 5) `succeeds` (7, 6)
```
Output:
```0.5731440767829815
0.6427886287176273```

## J

Solution:

```gen_dict =: (({. , #)/.~@:,@:(+/&>)@:{@:(# <@:>:@:i.)~ ; ^)&x:

'C0 P0' =. gen_dict/ x
'C1 P1' =. gen_dict/ y
(C0 +/@:,@:(>/&:({."1) * */&:({:"1)) C1) % (P0 * P1)
)
```

Example Usage:

```   10 5 (;x:inv)@:beating_probability 7 6
┌─────────────────────┬────────┐
│3781171969r5882450000│0.642789│
└─────────────────────┴────────┘
4 9 (;x:inv)@:beating_probability 6 6
┌─────────────────┬────────┐
│48679795r84934656│0.573144│
└─────────────────┴────────┘
```

gen_dict explanation:
`gen_dict` is akin to `gen_dict` in the python solution and returns a table and total number of combinations, order matters. The table has 2 columns. The first column is the pip count on all dice, the second column is the number of ways this many pips can occur.

`({. , #)/.~` make a vector having items head and tally of each group of like items in this case pip count and occurrences operating on
`,` raveled data (a vector) made of
`+/&>` the sum of each of the
`{` Cartesian products of the
`(# <@:>:@:i.)` equi-probable die values
`&x:` but first use extended integers
`; ^` links the total possibilities to the result.

The verb `beating_probability` is akin to the python solution function having same name.
`C0 >/&:({."1) C1` is a binary table where the pips of first player exceed pips of second player. "Make a greater than table but first take the head of each item."
`C0 */&:({:"1) C1` is the corresponding table of occurrences
`*` naturally we multiply the two tables (atom by atom, not a matrix product)
`+/@:,@:` sum the raveled table
`% (P0 * P1)` after which divide by the all possible rolls.

## Java

```import java.util.Random;

public class Dice{
private static int roll(int nDice, int nSides){
int sum = 0;
Random rand = new Random();
for(int i = 0; i < nDice; i++){
sum += rand.nextInt(nSides) + 1;
}
return sum;
}

private static int diceGame(int p1Dice, int p1Sides, int p2Dice, int p2Sides, int rolls){
int p1Wins = 0;
for(int i = 0; i < rolls; i++){
int p1Roll = roll(p1Dice, p1Sides);
int p2Roll = roll(p2Dice, p2Sides);
if(p1Roll > p2Roll) p1Wins++;
}
return p1Wins;
}

public static void main(String[] args){
int p1Dice = 9; int p1Sides = 4;
int p2Dice = 6; int p2Sides = 6;
int rolls = 10000;
int p1Wins = diceGame(p1Dice, p1Sides, p2Dice, p2Sides, rolls);
System.out.println(rolls + " rolls, p1 = " + p1Dice + "d" + p1Sides + ", p2 = " + p2Dice + "d" + p2Sides);
System.out.println("p1 wins " + (100.0 * p1Wins / rolls) + "% of the time");

System.out.println();

p1Dice = 5; p1Sides = 10;
p2Dice = 6; p2Sides = 7;
rolls = 10000;
p1Wins = diceGame(p1Dice, p1Sides, p2Dice, p2Sides, rolls);
System.out.println(rolls + " rolls, p1 = " + p1Dice + "d" + p1Sides + ", p2 = " + p2Dice + "d" + p2Sides);
System.out.println("p1 wins " + (100.0 * p1Wins / rolls) + "% of the time");

System.out.println();

p1Dice = 9; p1Sides = 4;
p2Dice = 6; p2Sides = 6;
rolls = 1000000;
p1Wins = diceGame(p1Dice, p1Sides, p2Dice, p2Sides, rolls);
System.out.println(rolls + " rolls, p1 = " + p1Dice + "d" + p1Sides + ", p2 = " + p2Dice + "d" + p2Sides);
System.out.println("p1 wins " + (100.0 * p1Wins / rolls) + "% of the time");

System.out.println();

p1Dice = 5; p1Sides = 10;
p2Dice = 6; p2Sides = 7;
rolls = 1000000;
p1Wins = diceGame(p1Dice, p1Sides, p2Dice, p2Sides, rolls);
System.out.println(rolls + " rolls, p1 = " + p1Dice + "d" + p1Sides + ", p2 = " + p2Dice + "d" + p2Sides);
System.out.println("p1 wins " + (100.0 * p1Wins / rolls) + "% of the time");
}
}
```
Output:
```10000 rolls, p1 = 9d4, p2 = 6d6
p1 wins 57.56% of the time

10000 rolls, p1 = 5d10, p2 = 6d7
p1 wins 64.28% of the time

1000000 rolls, p1 = 9d4, p2 = 6d6
p1 wins 57.3563% of the time

1000000 rolls, p1 = 5d10, p2 = 6d7
p1 wins 64.279% of the time```

## JavaScript

### simulating

```let Player = function(dice, faces) {
this.dice = dice;
this.faces = faces;
this.roll = function() {
let results = [];
for (let x = 0; x < dice; x++)
results.push(Math.floor(Math.random() * faces +1));
return eval(results.join('+'));
}
}

function contest(player1, player2, rounds) {
let res = [0, 0, 0];
for (let x = 1; x <= rounds; x++) {
let a = player1.roll(),
b = player2.roll();
switch (true) {
case (a > b): res[0]++; break;
case (a < b): res[1]++; break;
case (a == b): res[2]++; break;
}
}
document.write(`
<p>
<b>Player 1</b> (\${player1.dice} × d\${player1.faces}): \${res[0]} wins<br>
<b>Player 2</b> (\${player2.dice} × d\${player2.faces}): \${res[1]} wins<br>
<b>Draws:</b> \${res[2]}<br>
Chances for Player 1 to win:
~\${Math.round(res[0] / eval(res.join('+')) * 100)} %
</p>
`);
}

let p1, p2;

p1 = new Player(9, 4),
p2 = new Player(6, 6);
contest(p1, p2, 1e6);

p1 = new Player(5, 10);
p2 = new Player(6, 7);
contest(p1, p2, 1e6);
```
Output:
```Player 1 (9 × d4): 572753 wins
Player 2 (6 × d6): 356478 wins
Draws: 70769
Chances for Player 1 to win: ~57 %

Player 1 (5 × d10): 643127 wins
Player 2 (6 × d7): 312151 wins
Draws: 44722
Chances for Player 1 to win: ~64 %
```

## jq

Translation of: Wren
Works with: jq

Works with gojq, the Go implementation of jq

```# To take advantage of gojq's arbitrary-precision integer arithmetic:
def power(\$b): . as \$in | reduce range(0;\$b) as \$i (1; . * \$in);

# Input: an array (aka: counts)
def throwDie(\$nSides; \$nDice; \$s):
if \$nDice == 0
then .[\$s] +=  1
else reduce range(1; \$nSides + 1) as \$i (.;
throwDie(\$nSides; \$nDice-1; \$s + \$i) )
end ;

def beatingProbability(nSides1; nDice1; nSides2; nDice2):
def a: [range(0; .) | 0];

((nSides1 + 1) * nDice1) as \$len1
| (\$len1 | a | throwDie(nSides1; nDice1; 0)) as \$c1

| ((nSides2 + 1) * nDice2) as \$len2
| (\$len2 | a | throwDie(nSides2; nDice2; 0)) as \$c2

|((nSides1|power(nDice1)) * (nSides2|power(nDice2))) as \$p12

| reduce range(0; \$len1) as \$i (0;
reduce range(0; [\$i, \$len2] | min) as \$j (.;
. + (\$c1[\$i] * \$c2[\$j] / \$p12) ) ) ;

beatingProbability(4; 9; 6; 6),
beatingProbability(10; 5; 7; 6)```
Output:
```0.5731440767829815
0.6427886287176273
```

## Julia

Works with: Julia version 0.6
```play(ndices::Integer, nfaces::Integer) = (nfaces, ndices) ∋ 0 ? 0 : sum(rand(1:nfaces) for i in 1:ndices)

simulate(d1::Integer, f1::Integer, d2::Integer, f2::Integer; nrep::Integer=1_000_000) =
mean(play(d1, f1) > play(d2, f2) for _ in 1:nrep)

println("\nPlayer 1: 9 dices, 4 faces\nPlayer 2: 6 dices, 6 faces\nP(Player1 wins) = ", simulate(9, 4, 6, 6))
println("\nPlayer 1: 5 dices, 10 faces\nPlayer 2: 6 dices, 7 faces\nP(Player1 wins) = ", simulate(5, 10, 6, 7))
```
Output:
```Player 1: 9 dices, 4 faces
Player 2: 6 dices, 6 faces
P(Player1 wins) = 0.572805

Player 1: 5 dices, 10 faces
Player 2: 6 dices, 7 faces
P(Player1 wins) = 0.642727```

## Kotlin

Translation of: C
```// version 1.1.2

fun throwDie(nSides: Int, nDice: Int, s: Int, counts: IntArray) {
if (nDice == 0) {
counts[s]++
return
}
for (i in 1..nSides) throwDie(nSides, nDice - 1, s + i, counts)
}

fun beatingProbability(nSides1: Int, nDice1: Int, nSides2: Int, nDice2: Int): Double {
val len1 = (nSides1 + 1) * nDice1
val c1 = IntArray(len1)  // all elements zero by default
throwDie(nSides1, nDice1, 0, c1)

val len2 = (nSides2 + 1) * nDice2
val c2 = IntArray(len2)
throwDie(nSides2, nDice2, 0, c2)

val p12 = Math.pow(nSides1.toDouble(), nDice1.toDouble()) *
Math.pow(nSides2.toDouble(), nDice2.toDouble())

var tot = 0.0
for (i in 0 until len1) {
for (j in 0 until minOf(i, len2)) {
tot += c1[i] * c2[j] / p12
}
}
}

fun main(args: Array<String>) {
println(beatingProbability(4, 9, 6, 6))
println(beatingProbability(10, 5, 7, 6))
}
```
Output:
```0.5731440767829815
0.6427886287176273
```

## Lua

### Simulated

```local function simu(ndice1, nsides1, ndice2, nsides2)
local function roll(ndice, nsides)
local result = 0;
for i = 1, ndice do
result = result + math.random(nsides)
end
return result
end
local wins, coms = 0, 1000000
for i = 1, coms do
local roll1 = roll(ndice1, nsides1)
local roll2 = roll(ndice2, nsides2)
if (roll1 > roll2) then
wins = wins + 1
end
end
print("simulated:  p1 = "..ndice1.."d"..nsides1..", p2 = "..ndice2.."d"..nsides2..",  prob = "..wins.." / "..coms.." = "..(wins/coms))
end

simu(9, 4, 6, 6)
simu(5, 10, 6, 7)
```
Output:
```simulated:  p1 = 9d4, p2 = 6d6,  prob = 573372 / 1000000 = 0.573372
simulated:  p1 = 5d10, p2 = 6d7,  prob = 642339 / 1000000 = 0.642339
```

### Computed

```local function comp(ndice1, nsides1, ndice2, nsides2)
local function throws(ndice, nsides)
local sums = {}
for i = 1, ndice*nsides do
sums[i] = 0
end
local function throw(ndice, nsides, s)
if (ndice==0) then
sums[s] = sums[s] + 1
else
for i = 1, nsides do
throw(ndice-1, nsides, s+i)
end
end
return sums
end
return throw(ndice, nsides, 0)
end
local p1 = throws(ndice1, nsides1)
local p2 = throws(ndice2, nsides2)
local wins, coms = 0, nsides1^ndice1 * nsides2^ndice2
for k1,v1 in pairs(p1) do
for k2,v2 in pairs(p2) do
if (k1 > k2) then
wins = wins + v1 * v2
end
end
end
print("computed:  p1 = "..ndice1.."d"..nsides1..", p2 = "..ndice2.."d"..nsides2..", prob = "..wins.." / "..coms.." = "..(wins/coms))
end

comp(9, 4, 6, 6)
comp(5, 10, 6, 7)
```
Output:
```computed:  p1 = 9d4, p2 = 6d6, prob = 7009890480 / 12230590464 = 0.57314407678298
computed:  p1 = 5d10, p2 = 6d7, prob = 7562343938 / 11764900000 = 0.64278862871763
```

## M2000 Interpreter

Using a buffer of random numbers for all games.

```Declare random1 lib "advapi32.SystemFunction036" {long lpbuffer, long length}
Flush
Prob(4, 9, 6, 6, 55555)
Prob(10, 5, 7, 6, 55555)
Sub Prob(face1 as long=4 ,dice1=9, face2 as long=6,dice2=6, games=1000)
if face1<1 or face1>256 or face2<1 or face2>256 then Error "Faces out of limits"
profiler
local m=@PlayAll(dice1,dice2, games), tt=timecount
print "Time to fill the buffer with random bytes ";str\$(tt, "#0.00 ms")
local s1=dice1-1, s2=dice2-1, k, l, f1=face1/256, f2=face2/256, i, n1=0&, n2=0&
print "Buffer size ";str\$(len(m)/1024,"#0.#");" Kbyte": Refresh
profiler
for i=0 to games-1
long n1=dice1:for j=0to s1{n1+=eval(m, i!n1!j)*f1}
long n2=dice2:for j=0to s2{n2+=eval(m, i!n2!j)*f2}
if n1>n2 then k++ else if n1<n2 then l++
next
print "Games Total "; games
print "Player 1 wins ";k; " probability of winning:" ;str\$(k/games, "#0.00 %")
print "Player 2 wins ";l; " probability of winning:" ;str\$(l/games, "#0.00 %")
print "Number of ties ";games-k-l
print "Execution time ";str\$(timecount/1000, "#0.0 s")
End Sub
Function PlayAll(dice1, dice2, games as long)
if games<1 then error "games<1"
local onegame
structure onegame {
n1 as byte * dice1
n2 as byte * dice2
}
buffer Alfa as onegame*games
call void random1(alfa(0), len(alfa))
= Alfa
End Function```
Output:
```Time to fill the buffer with random bytes 2.00 ms
Buffer size 813.8 Kbyte
Games Total 55555
Player 1 wins 31870 probability of winning:57.37 %
Player 2 wins 19729 probability of winning:35.51 %
Number of ties 3956
Execution time 179.9 s

Time to fill the buffer with random bytes 1.65 ms
Buffer size 596.8 Kbyte
Games Total 55555
Player 1 wins 35441 probability of winning:63.79 %
Player 2 wins 17605 probability of winning:31.69
Number of ties 2509
Execution time 138.7 s
```

## Mathematica / Wolfram Language

```ClearAll[GetProbability]
GetProbability[{dice1_List, n_Integer}, {dice2_List, m_Integer}] :=
Module[{a, b, lena, lenb},
a = Tuples[dice1, n];
a = Plus @@@ a;
lena = a // Length;
a = Tally[a];
a[[All, 2]] /= lena;

b = Tuples[dice2, m];
b = Plus @@@ b;
lenb = b // Length;
b = Tally[b];
b[[All, 2]] /= lenb;

Total[If[#[[1, 1]] > #[[2, 1]], #[[1, 2]] #[[2, 2]], 0] & /@
Tuples[{a, b}]]
]
GetProbability[{Range[4], 9}, {Range[6], 6}]
GetProbability[{Range[10], 5}, {Range[7], 6}]
```
Output:
```48679795/84934656
3781171969/5882450000```

## Nim

Library: bignum
```import bignum
from math import sum

proc counts(ndices, nsides: int): seq[int] =
result.setlen(ndices * nsides + 1)
for i in 1..nsides:
result[i] = 1
for i in 1..<ndices:
var c = newSeq[int](result.len)
for sum in i..(i * nsides):
for val in 1..nsides:
inc c[sum + val], result[sum]
result = move(c)

proc probabilities(counts: seq[int]): seq[Rat] =
result.setLen(counts.len)
let total = sum(counts)
for i, n in counts:
result[i] = newRat(n, total)

proc beatingProbability(ndices1, nsides1, ndices2, nsides2: int): Rat =
let counts1 = counts(ndices1, nsides1)
let counts2 = counts(ndices2, nsides2)
var p1 = counts1.probabilities()
var p2 = counts2.probabilities()

result = newRat(0)
for sum1 in ndices1..p1.high:
var p = newRat(0)
for sum2 in ndices2..min(sum1 - 1, p2.high):
p += p2[sum2]
result += p1[sum1] * p

echo beatingProbability(9, 4, 6, 6).toFloat
echo beatingProbability(5, 10, 6, 7).toFloat
```
Output:
```0.5731440767829801
0.6427886287176261```

## ooRexx

### Algorithm

```Numeric Digits 30
Call test '9 4 6 6'
Call test '5 10 6 7'
Exit
test:
Parse Arg w1 s1 w2 s2
p1.=0
p2.=0
Call pp 1,w1,s1,p1.,p2.
Call pp 2,w2,s2,p1.,p2.
p2low.=0
Do x=w1 To w1*s1
Do y=0 To x-1
p2low.x+=p2.y
End
End
pwin1=0
Do x=w1 To w1*s1
pwin1+=p1.x*p2low.x
End
Say 'Player 1 has' w1 'dice with' s1 'sides each'
Say 'Player 2 has' w2 'dice with' s2 'sides each'
Say 'Probability for player 1 to win:' pwin1
Say ''
Return

pp: Procedure
/*---------------------------------------------------------------------
* Compute and assign probabilities to get a sum x
* when throwing w dice each having s sides (marked from 1 to s)
* k=1 sets p1.*, k=2 sets p2.*
*--------------------------------------------------------------------*/
Use Arg k,w,s,p1.,p2.
str=''
cnt.=0
Do wi=1 To w
str=str||'Do v'wi'=1 To' s';'
End
str=str||'sum='
Do wi=1 To w-1
str=str||'v'wi'+'
End
str=str||'v'w';'
str=str||'cnt.'sum'+=1;'
Do wi=1 To w
str=str||'End;'
End
Interpret str
psum=0
Do x=0 To w*s
If k=1 Then
p1.x=cnt.x/(s**w)
Else
p2.x=cnt.x/(s**w)
psum+=p1.x
End
Return
```
Output:
```Player 1 has 9 dice with 4 sides each
Player 2 has 6 dice with 6 sides each
Probability for player 1 to win: 0.573144076782980082947530864198

Player 1 has 5 dice with 10 sides each
Player 2 has 6 dice with 7 sides each
Probability for player 1 to win: 0.642788628717626159168373721835   ```

### Algorithm using rational arithmetic

```Numeric Digits 30
Call test '9 4 6 6'
Call test '5 10 6 7'
Exit

test:
Parse Arg w1 s1 w2 s2
p1.='0/1'
p2.='0/1'
Call pp 1,w1,s1,p1.,p2.
Call pp 2,w2,s2,p1.,p2.
p2low.='0/1'
Do x=w1 To w1*s1
Do y=0 To x-1
End
End
pwin1='0/1'
Do x=w1 To w1*s1
End
Say 'Player 1 has' w1 'dice with' s1 'sides each'
Say 'Player 2 has' w2 'dice with' s2 'sides each'
Say 'Probability for player 1 to win:' pwin1
Parse Var pwin1 nom '/' denom
Say '                              ->' (nom/denom)
Say ''
Return

pp: Procedure
/*---------------------------------------------------------------------
* Compute and assign probabilities to get a sum x
* when throwing w dice each having s sides (marked from 1 to s)
* k=1 sets p1.*, k=2 sets p2.*
*--------------------------------------------------------------------*/
Use Arg k,w,s,p1.,p2.
str=''
cnt.=0
Do wi=1 To w
str=str||'Do v'wi'=1 To' s';'
End
str=str||'sum='
Do wi=1 To w-1
str=str||'v'wi'+'
End
str=str||'v'w';'
str=str||'cnt.sum+=1;'
Do wi=1 To w
str=str||'End;'
End
Interpret str

psum='0/1'
Do x=0 To w*s
If k=1 Then
p1.x=cnt.x'/'||(s**w)
Else
p2.x=cnt.x'/'||(s**w)
End
Return

Parse Arg a,b
parse Var a an '/' az
parse Var b bn '/' bz
rn=an*bz+bn*az
rz=az*bz
res=fr_cancel(rn','rz)
Return res

fr_div: Procedure
Parse Arg a,b
parse Var a an '/' az
parse Var b bn '/' bz
rn=an*bz
rz=az*bn
res=fr_cancel(rn','rz)
Return res

fr_mult: Procedure
Parse Arg a,b
parse Var a an '/' az
parse Var b bn '/' bz
rn=an*bn
rz=az*bz
res=fr_cancel(rn','rz)
Return res

fr_cancel: Procedure
Parse Arg n ',' z
k=ggt(n,z)
Return n%k'/'z%k

ggt: Procedure
/**********************************************************************
* ggt (gcd) Greatest common Divisor
* Recursive procedure as shown in PL/I
**********************************************************************/
Parse Arg a,b
if b = 0 then return abs(a)
return ggt(b,a//b)
```
Output:
```Player 1 has 9 dice with 4 sides each
Player 2 has 6 dice with 6 sides each
Probability for player 1 to win: 48679795/84934656
-> 0.573144076782980082947530864198

Player 1 has 5 dice with 10 sides each
Player 2 has 6 dice with 7 sides each
Probability for player 1 to win: 3781171969/5882450000
-> 0.642788628717626159168373721834```

### Algorithm using class fraction

```Numeric Digits 50
Call test '9 4 6 6'
Call test '5 10 6 7'
Exit

test:
Parse Arg w1 s1 w2 s2
p1.=.fraction~new(0,1)
p2.=.fraction~new(0,1)
Call pp 1,w1,s1,p1.,p2.
Call pp 2,w2,s2,p1.,p2.
p2low.=.fraction~new(0,1)
Do x=w1 To w1*s1
Do y=0 To x-1
p2low.x=p2low.x+p2.y
End
End
pwin1=.fraction~new(0,1)
Do x=w1 To w1*s1
pwin1=pwin1+(p1.x*p2low.x)
End
Say 'Player 1 has' w1 'dice with' s1 'sides each'
Say 'Player 2 has' w2 'dice with' s2 'sides each'
Say 'Probability for player 1 to win:' pwin1~string
Say '                              ->' pwin1~tonumber
Say ''
Return

pp: Procedure
/*---------------------------------------------------------------------
* Compute and assign probabilities to get a sum x
* when throwing w dice each having s sides (marked from 1 to s)
* k=1 sets p1.*, k=2 sets p2.*
*--------------------------------------------------------------------*/
Use Arg k,w,s,p1.,p2.
str=''
cnt.=0
Do wi=1 To w
str=str||'Do v'wi'=1 To' s';'
End
str=str||'sum='
Do wi=1 To w-1
str=str||'v'wi'+'
End
str=str||'v'w';'
str=str||'cnt.sum+=1;'
Do wi=1 To w
str=str||'End;'
End
Interpret str

psum=.fraction~new(0,1)
Do x=0 To w*s
If k=1 Then
p1.x=.fraction~new(cnt.x,s**w)
Else
p2.x=.fraction~new(cnt.x,s**w)
psum=psum+p1.x
End
Return

::class fraction inherit orderable
::options Digits 50
::method init
expose numerator denominator
use strict arg numerator, denominator = 1
--Trace ?R
--if numerator == 0 then denominator = 0
--else if denominator == 0 then raise syntax 98.900 array("Fraction denominator cannot be zero")

-- if the denominator is negative, make the numerator carry the sign
if denominator < 0 then do
numerator = -numerator
denominator = - denominator
end

-- find the greatest common denominator and reduce to
-- the simplest form
gcd = self~gcd(numerator~abs, denominator~abs)

numerator /= gcd
denominator /= gcd

-- fraction instances are immutable, so these are

-- calculate the greatest common denominator of a numerator/denominator pair
::method gcd private
use arg x, y
--Say 'gcd:' x y digits()
loop while y \= 0
-- check if they divide evenly
temp = x // y
x = y
y = temp
end
return x

-- calculate the least common multiple of a numerator/denominator pair
::method lcm private
use arg x, y
return x / self~gcd(x, y) * y

::method abs
expose numerator denominator
-- the denominator is always forced to be positive
return self~class~new(numerator~abs, denominator)

-- convert a fraction to regular Rexx number
::method toNumber
expose numerator denominator
if numerator == 0 then return 0
return numerator/denominator

expose numerator denominator
use strict arg other
-- convert to a fraction if a regular number
if \other~isa(.fraction) then other = self~class~new(other, 1)

multiple = self~lcm(denominator, other~denominator)
newa = numerator * multiple / denominator
newb = other~numerator * multiple / other~denominator
return self~class~new(newa + newb, multiple)

::method times
expose numerator denominator
use strict arg other
-- convert to a fraction if a regular number
if \other~isa(.fraction) then other = self~class~new(other, 1)
return self~class~new(numerator * other~numerator, denominator * other~denominator)

-- some operator overrides -- these only work if the left-hand-side of the
-- subexpression is a quaternion
::method "*"
forward message("TIMES")

::method "+"
-- need to check if this is a prefix plus or an addition
if arg() == 0 then
return self  -- we can return this copy since it is imutable
else

::method string
expose numerator denominator
if denominator == 1 then return numerator
return numerator"/"denominator

-- override hashcode for collection class hash uses
::method hashCode
expose numerator denominator
return numerator~hashcode~bitxor(numerator~hashcode)

::attribute numerator GET
::attribute denominator GET

::requires rxmath library
```
Output:
```Player 1 has 9 dice with 4 sides each
Player 2 has 6 dice with 6 sides each
Probability for player 1 to win: 48679795/84934656
-> 0.57314407678298008294753086419753086419753086419753

Player 1 has 5 dice with 10 sides each
Player 2 has 6 dice with 7 sides each
Probability for player 1 to win: 3781171969/5882450000
-> 0.64278862871762615916837372183358974576919480828566```

### Test

Result from 10 million tries.

```oid='diet.xxx'; Call sysFileDelete oid
Call test '9  4 6 6'
Call test '5 10 6 7'
Exit
test:
Parse Arg n1 s1 n2 s2
Call o 'Player 1:' n1 'dice with' s1 'sides each'
Call o 'Player 2:' n2 'dice with' s2 'sides each'
cnt1.=0
cnt2.=0
win.=0
nn=10000000
Call time 'R'
Do i=1 To nn
sum1=sum(n1 s1) ; cnt1.sum1+=1
sum2=sum(n2 s2) ; cnt2.sum2+=1
Select
When sum1>sum2 Then win.1+=1
When sum1<sum2 Then win.2+=1
Otherwise           win.0+=1
End
End
Call o win.1/nn 'player 1 wins'
Call o win.2/nn 'player 2 wins'
Call o win.0/nn 'draws'
/*
Do i=min(n1,n2) To max(n1*s1,n2*s2)
Call o right(i,2) format(cnt1.i,7) format(cnt2.i,7)
End
*/
Call o time('E') 'seconds elapsed'
Return

sum: Parse Arg n s
sum=0
Do k=1 To n
sum+=rand(s)
End
Return sum

rand: Parse Arg n
Return random(n-1)+1

o:
Say arg(1)
Return lineout(oid,arg(1))
```
Output:
```Player 1: 9 dice with 4 sides each
Player 2: 6 dice with 6 sides each
0.5730344 player 1 wins
0.3562464 player 2 wins
0.0707192 draws
186.794000 seconds elapsed
Player 1: 5 dice with 10 sides each
Player 2: 6 dice with 7 sides each
0.6425906 player 1 wins
0.312976 player 2 wins
0.0444334 draws
149.784000 seconds elapsed```

## Perl

Translation of: Python
```use List::Util qw(sum0 max);

sub comb {
my (\$s, \$n) = @_;
\$n || return (1);
my @r = (0) x (\$n - max(@\$s) + 1);
my @c = comb(\$s, \$n - 1);
foreach my \$i (0 .. \$#c) {
\$c[\$i] || next;
foreach my \$k (@\$s) {
\$r[\$i + \$k] += \$c[\$i];
}
}
return @r;
}

sub winning {
my (\$s1, \$n1, \$s2, \$n2) = @_;

my @p1 = comb(\$s1, \$n1);
my @p2 = comb(\$s2, \$n2);

my (\$win, \$loss, \$tie) = (0, 0, 0);

foreach my \$i (0 .. \$#p1) {
\$win  += \$p1[\$i] * sum0(@p2[0    .. \$i - 1]);
\$tie  += \$p1[\$i] * sum0(@p2[\$i   .. \$i    ]);
\$loss += \$p1[\$i] * sum0(@p2[\$i+1 .. \$#p2  ]);
}
my \$total = sum0(@p1) * sum0(@p2);
map { \$_ / \$total } (\$win, \$tie, \$loss);
}

print '(', join(', ', winning([1 ..  4], 9, [1 .. 6], 6)), ")\n";
print '(', join(', ', winning([1 .. 10], 5, [1 .. 7], 6)), ")\n";
```
Output:
```(0.57314407678298, 0.070766169838454, 0.356089753378566)
(0.642788628717626, 0.0444960303104999, 0.312715340971874)
```

## Phix

Translation of: Go
```with javascript_semantics
function throwDie(integer nSides, nDice, s, sequence counts)
if nDice == 0 then
counts[s] += 1
else
for i=1 to nSides do
counts = throwDie(nSides, nDice-1, s+i, counts)
end for
end if
return counts
end function

function beatingProbability(integer nSides1, nDice1, nSides2, nDice2)
integer len1 := (nSides1 + 1) * nDice1,
len2 := (nSides2 + 1) * nDice2
sequence c1 = throwDie(nSides1, nDice1, 0, repeat(0,len1)),
c2 = throwDie(nSides2, nDice2, 0, repeat(0,len2))
atom p12 := power(nSides1, nDice1) * power(nSides2, nDice2),
tot := 0.0
for i=1 to len1 do
for j=1 to min(i-1,len2) do
tot += (c1[i] * c2[j]) / p12
end for
end for
end function

printf(1,"%0.16f\n",beatingProbability(4, 9, 6, 6))
printf(1,"%0.16f\n",beatingProbability(10, 5, 7, 6))
```
Output:

(aside: the following tiny discrepancies are to be expected when using IEEE-754 64/80-bit floats; if you want to read anything into them, it should just be that we are all using the same hardware, and probably showing a couple too many digits of (in)accuracy on 32-bit.)
32 bit, same as Go, Kotlin

```0.5731440767829815
0.6427886287176273
```

64 bit, same as D, Python[last], Ruby, Tcl

```0.5731440767829801
0.6427886287176262
```

## PL/I

### version 1

```*process source attributes xref;
dicegame: Proc Options(main);
Call test(9, 4,6,6);
Call test(5,10,6,7);
test: Proc(w1,s1,w2,s2);
Dcl (w1,s1,w2,s2,x,y) Bin Fixed(31);
Dcl p1(100)    Dec Float(18) Init((100)0);
Dcl p2(100)    Dec Float(18) Init((100)0);
Dcl p2low(100) Dec Float(18) Init((100)0);
Call pp(w1,s1,p1);
Call pp(w2,s2,p2);
Do x=w1 To w1*s1;
Do y=0 To x-1;
p2low(x)+=p2(y);
End;
End;
pwin1=0;
Do x=w1 To w1*s1;
pwin1+=p1(x)*p2low(x);
End;
Put Edit('Player 1 has ',w1,' dice with ',s1,' sides each')
(Skip,3(a,f(2)));
Put Edit('Player 2 has ',w2,' dice with ',s2,' sides each')
(Skip,3(a,f(2)));
Put Edit('Probability for player 1 to win: ',pwin1)(Skip,a,f(20,18));
Put Edit('')(Skip,a);
End;
pp: Proc(w,s,p);
/*--------------------------------------------------------------------
* Compute and assign probabilities to get a sum x
* when throwing w dice each having s sides (marked from 1 to s)
*-------------------------------------------------------------------*/
Dcl (w,s)    Bin Fixed(31);
Dcl p(100)   Dec Float(18);
Dcl cnt(100) Bin Fixed(31);
Dcl (a(12),e(12),v(12),sum,i,n) Bin Fixed(31);
a=0;
e=0;
Do i=1 To w;
a(i)=1;
e(i)=s;
End;
n=0;
cnt=0;
Do v(1)=a(1) To e(1);
Do v(2)=a(2) To e(2);
Do v(3)=a(3) To e(3);
Do v(4)=a(4) To e(4);
Do v(5)=a(5) To e(5);
Do v(6)=a(6) To e(6);
Do v(7)=a(7) To e(7);
Do v(8)=a(8) To e(8);
Do v(9)=a(9) To e(9);
Do v(10)=a(10) To e(10);
sum=0;
Do i=1 To 10;
sum=sum+v(i);
End;
cnt(sum)+=1;
n+=1;
End;
End;
End;
End;
End;
End;
End;
End;
End;
End;
Do k=w To w*s;
p(k)=divide(cnt(k),n,18,16);
End;
End;
End;```
Output:
```Player 1 has  9 dice with  4 sides each
Player 2 has  6 dice with  6 sides each
Probability for player 1 to win: 0.573013663291931152

Player 1 has  5 dice with 10 sides each
Player 2 has  6 dice with  7 sides each
Probability for player 1 to win: 0.642703175544738770```

### version 2 using rational arithmetic

```*process source attributes xref;
dgf: Proc Options(main);
Call test(9, 4,6,6);
Call test(5,10,6,7);
test: Proc(w1,s1,w2,s2);
Dcl (w1,s1,w2,s2,x,y) Dec Float(18);
Dcl 1 p1(100),
2 nom      Dec Float(18) Init((100)0),
2 denom    Dec Float(18) Init((100)1);
Dcl 1 p2(100),
2 nom      Dec Float(18) Init((100)0),
2 denom    Dec Float(18) Init((100)1);
Dcl 1 p2low(100),
2 nom      Dec Float(18) Init((100)0),
2 denom    Dec Float(18) Init((100)1);
Dcl 1 pwin1,
2 nom      Dec Float(18) Init(0),
2 denom    Dec Float(18) Init(1);
Dcl 1 prod Like pwin1;
Call pp(w1,s1,p1);
Call pp(w2,s2,p2);
Do x=w1 To w1*s1;
Do y=0 To x-1;
End;
End;
Do x=w1 To w1*s1;
Call fr_mult(p1(x),p2low(x),prod);
End;
Put Edit('Player 1 has ',w1,' dice with ',s1,' sides each')
(Skip,3(a,f(2)));
Put Edit('Player 2 has ',w2,' dice with ',s2,' sides each')
(Skip,3(a,f(2)));
Put Edit('Probability for player 1 to win: ',
str(pwin1.nom),'/',str(pwin1.denom))(Skip,4(a));
Put Edit('                              -> ',
pwin1.nom/pwin1.denom)(Skip,a,f(20,18));
Put Edit('')(Skip,a);
End;

pp: Proc(w,s,p);
/*--------------------------------------------------------------------
* Compute and assign probabilities to get a sum x
* when throwing w dice each having s sides (marked from 1 to s)
*-------------------------------------------------------------------*/
Dcl (w,s)    Dec Float(18);
Dcl 1 p(100),
2 nom   Dec Float(18),
2 denom Dec Float(18);
Dcl cnt(100) Dec Float(18);
Dcl (a(12),e(12),v(12),sum,i,n) Dec Float(18);
a=0;
e=0;
Do i=1 To w;
a(i)=1;
e(i)=s;
End;
n=0;
cnt=0;
Do v(1)=a(1) To e(1);
Do v(2)=a(2) To e(2);
Do v(3)=a(3) To e(3);
Do v(4)=a(4) To e(4);
Do v(5)=a(5) To e(5);
Do v(6)=a(6) To e(6);
Do v(7)=a(7) To e(7);
Do v(8)=a(8) To e(8);
Do v(9)=a(9) To e(9);
Do v(10)=a(10) To e(10);
sum=0;
Do i=1 To 10;
sum=sum+v(i);
End;
cnt(sum)+=1;
n+=1;
End;
End;
End;
End;
End;
End;
End;
End;
End;
End;
Do k=w To w*s;
p(k).nom=cnt(k);
p(k).denom=n;
End;
End;

Dcl 1 a,
2 nom   Dec Float(18),
2 denom Dec Float(18);
Dcl 1 b Like a;
Dcl res like a;
res.nom=a.nom*b.denom+b.nom*a.denom;
res.denom=a.denom*b.denom;
Call fr_cancel(res,res);
End;

fr_mult: Proc(a,b,res);
Dcl 1 a,
2 nom   Dec Float(18),
2 denom Dec Float(18);
Dcl 1 b Like a;
Dcl res like a;
/* Put Edit('fr_mult',a.nom,a.denom,b.nom,b.denom)(Skip,a,4(f(15)));*/
res.nom=a.nom*b.nom;
res.denom=a.denom*b.denom;
Call fr_cancel(res,res);
End;

fr_cancel: Proc(a,res);
Dcl 1 a,
2 nom   Dec Float(18),
2 denom Dec Float(18);
Dcl k Dec Float(18);
Dcl 1 res like a;
/* Put Edit('fr_cancel',a.nom,a.denom)(Skip,a,4(f(15)));            */
k=ggt(a.nom,a.denom);
res=a/k;
End;

ggt: Proc(a,b) Recursive Returns(Dec Float(18));
/**********************************************************************
* ggt (gcd) Greatest common Divisor
* Recursive Proc(a,b)) as shown in PL/I
**********************************************************************/
Dcl (a,b) Dec Float(18);
if b = 0 then return (abs(a));
return (ggt(b,mod(a,b)));
End;

str: Proc(x) Returns(Char(20) Var);
Dcl x Dec Float(18);
Dcl res Char(20) Var;
Put String(res) Edit(x)(f(20));
Return (trim(res));
End;

End;```
Output:
```Player 1 has  9 dice with  4 sides each
Player 2 has  6 dice with  6 sides each
Probability for player 1 to win: 48679795/84934656
-> 0.573144076782980083

Player 1 has  5 dice with 10 sides each
Player 2 has  6 dice with  7 sides each
Probability for player 1 to win: 3781171969/5882450000
-> 0.642788628717626159```

## Python

```from itertools import product

def gen_dict(n_faces, n_dice):
counts = [0] * ((n_faces + 1) * n_dice)
for t in product(range(1, n_faces + 1), repeat=n_dice):
counts[sum(t)] += 1
return counts, n_faces ** n_dice

def beating_probability(n_sides1, n_dice1, n_sides2, n_dice2):
c1, p1 = gen_dict(n_sides1, n_dice1)
c2, p2 = gen_dict(n_sides2, n_dice2)
p12 = float(p1 * p2)

return sum(p[1] * q[1] / p12
for p, q in product(enumerate(c1), enumerate(c2))
if p[0] > q[0])

print beating_probability(4, 9, 6, 6)
print beating_probability(10, 5, 7, 6)
```
Output:
```0.573144076783
0.642788628718```

To handle larger number of dice (and faster in general):

```from __future__ import print_function, division

def combos(sides, n):
if not n: return [1]
ret = [0] * (max(sides)*n + 1)
for i,v in enumerate(combos(sides, n - 1)):
if not v: continue
for s in sides: ret[i + s] += v
return ret

def winning(sides1, n1, sides2, n2):
p1, p2 = combos(sides1, n1), combos(sides2, n2)
win,loss,tie = 0,0,0 # 'win' is 1 beating 2
for i,x1 in enumerate(p1):
# using accumulated sum on p2 could save some time
win += x1*sum(p2[:i])
tie += x1*sum(p2[i:i+1])
loss+= x1*sum(p2[i+1:])
s = sum(p1)*sum(p2)
return win/s, tie/s, loss/s

print(winning(range(1,5), 9, range(1,7), 6))
print(winning(range(1,11), 5, range(1,8), 6)) # this seem hardly fair

# mountains of dice test case
# print(winning((1, 2, 3, 5, 9), 700, (1, 2, 3, 4, 5, 6), 800))
```
Output:
```(0.5731440767829801, 0.070766169838454, 0.3560897533785659)
(0.6427886287176262, 0.044496030310499875, 0.312715340971874)
```

If we further restrict die faces to be 1 to n instead of arbitrary values, the combo generation can be made much faster:

```from __future__ import division, print_function
from itertools import accumulate # Python3 only

def combos(sides, n):
ret = [1] + [0]*(n + 1)*sides # extra length for negative indices
for p in range(1, n + 1):
rolling_sum = 0
for i in range(p*sides, p - 1, -1):
rolling_sum += ret[i - sides] - ret[i]
ret[i] = rolling_sum
ret[p - 1] = 0
return ret

def winning(d1, n1, d2, n2):
c1, c2 = combos(d1, n1), combos(d2, n2)
ac = list(accumulate(c2 + [0]*(len(c1) - len(c2))))

return sum(v*a for  v,a in zip(c1[1:], ac)) / (ac[-1]*sum(c1))

print(winning(4, 9, 6, 6))
print(winning(5, 10, 6, 7))

#print(winning(6, 700, 8, 540))
```
Output:
```0.5731440767829801
0.6427886287176262
```

## R

Solving these sorts of problems by simulation is trivial in R.

```probability <- function(facesCount1, diceCount1, facesCount2, diceCount2)
{
mean(replicate(10^6, sum(sample(facesCount1, diceCount1, replace = TRUE)) > sum(sample(facesCount2, diceCount2, replace = TRUE))))
}
cat("Player 1's probability of victory is", probability(4, 9, 6, 6),
"in the first game and", probability(10, 5, 7, 6), "in the second.")
```
Output:
`Player 1's probability of victory is 0.572652 in the first game and 0.642817 in the second.`

## Racket

```#lang racket

(define probs# (make-hash))

(define (NdD n d)
(hash-ref!
probs# (cons n d)
(λ ()
(cond
[(= n 0) ; every chance of nothing!
(hash 0 1)]
[else
(for*/fold ((hsh (hash))) (((i p) (in-hash (NdD (sub1 n) d))) (r (in-range 1 (+ d 1))))
(hash-update hsh (+ r i) (λ (p+) (+ p+ (/ p d))) 0))]))))

(define (game-probs N1 D1 N2 D2)
(define P1 (NdD N1 D1))
(define P2 (NdD N2 D2))
(define-values (W D L)
(for*/fold ((win 0) (draw 0) (lose 0)) (((r1 p1) (in-hash P1)) ((r2 p2) (in-hash P2)))
(define p (* p1 p2))
(cond
[(< r1 r2) (values win draw (+ lose p))]
[(= r1 r2) (values win (+ draw p) lose)]
[(> r1 r2) (values (+ win p) draw lose)])))

(printf "P(P1 win): ~a~%" (real->decimal-string W 6))
(printf "P(draw):   ~a~%" (real->decimal-string D 6))
(printf "P(P2 win): ~a~%" (real->decimal-string L 6))
(list W D L))

(printf "GAME 1 (9D4 vs 6D6)~%")
(game-probs 9 4 6 6)
(newline)

(printf "GAME 2 (5D10 vs 6D7) [what is a D7?]~%")
(game-probs 5 10 6 7)
```
Output:
```GAME 1 (9D4 vs 6D6)
P(P1 win): 0.573144
P(draw):   0.070766
P(P2 win): 0.356090
(48679795/84934656 144252007/2038431744 725864657/2038431744)

GAME 2 (5D10 vs 6D7) [what is a D7?]
P(P1 win): 0.642789
P(draw):   0.044496
P(P2 win): 0.312715
(3781171969/5882450000 523491347/11764900000 735812943/2352980000)```

## Raku

(formerly Perl 6)

Works with: Rakudo version 2020.08.1
```sub likelihoods (\$roll) {
my (\$dice, \$faces) = \$roll.comb(/\d+/);
my @counts;
@counts[\$_]++ for [X+] |(1..\$faces,) xx \$dice;
return [@counts[]:p], \$faces ** \$dice;
}

sub beating-probability ([\$roll1, \$roll2]) {
my (@c1, \$p1) := likelihoods \$roll1;
my (@c2, \$p2) := likelihoods \$roll2;
my \$p12 = \$p1 * \$p2;

[+] gather for flat @c1 X @c2 -> \$p, \$q {
take \$p.value * \$q.value / \$p12 if \$p.key > \$q.key;
}
}

# We're using standard DnD notation for dice rolls here.
say .gist, "\t", .raku given beating-probability < 9d4 6d6 >;
say .gist, "\t", .raku given beating-probability < 5d10 6d7 >;
```
Output:
```0.573144077	<48679795/84934656>
0.64278862872	<3781171969/5882450000>```

Note that all calculations are in integer and rational arithmetic, so the results in fractional notation are exact.

## REXX

### version 1

Translation of: ooRexx

```/* REXX */
Numeric Digits 30
Call test '9 4 6 6'
Call test '5 10 6 7'
Exit
test:
Parse Arg w1 s1 w2 s2
plist1=pp(w1,s1)
p1.=0
Do x=w1 To w1*s1
Parse Var plist1 p1.x plist1
End
plist2=pp(w2,s2)
p2.=0
Do x=w2 To w2*s2
Parse Var plist2 p2.x plist2
End
p2low.=0
Do x=w1 To w1*s1
Do y=0 To x-1
p2low.x=p2low.x+p2.y
End
End
pwin1=0
Do x=w1 To w1*s1
pwin1=pwin1+p1.x*p2low.x
End
Say 'Player 1 has' w1 'dice with' s1 'sides each'
Say 'Player 2 has' w2 'dice with' s2 'sides each'
Say 'Probability for player 1 to win:' pwin1
Say ''
Return

pp: Procedure
/*---------------------------------------------------------------------
* Compute and return the probabilities to get a sum x
* when throwing w dice each having s sides (marked from 1 to s)
*--------------------------------------------------------------------*/
Parse Arg w,s
str=''
cnt.=0
Do wi=1 To w
str=str||'Do v'wi'=1 To' s';'
End
str=str||'sum='
Do wi=1 To w-1
str=str||'v'wi'+'
End
str=str||'v'w';'
str=str||'cnt.'sum'=cnt.'sum'+1;'
Do wi=1 To w
str=str||'End;'
End
Interpret str
psum=0
Do x=0 To w*s
p.x=cnt.x/(s**w)
psum=psum+p.x
End
res=''
Do x=w To s*w
res=res p.x
End
Return res
```
Output:
```Player 1 has 9 dice with 4 sides each
Player 2 has 6 dice with 6 sides each
Probability for player 1 to win: 0.573144076782980082947530864198

Player 1 has 5 dice with 10 sides each
Player 2 has 6 dice with 7 sides each
Probability for player 1 to win: 0.642788628717626159168373721835
```

### version 2

```/* REXX */
oid='diet.xxx'; 'erase' oid
Call test '9  4 6 6'
Call test '5 10 6 7'
Exit
test:
Parse Arg n1 s1 n2 s2
Call o 'Player 1:' n1 'dice with' s1 'sides each'
Call o 'Player 2:' n2 'dice with' s2 'sides each'
cnt1.=0
cnt2.=0
win.=0
nn=10000
Call time 'R'
Do i=1 To nn
sum1=sum(n1 s1) ; cnt1.sum1=cnt1.sum1+1
sum2=sum(n2 s2) ; cnt2.sum2=cnt2.sum2+1
Select
When sum1>sum2 Then win.1=win.1+1
When sum1<sum2 Then win.2=win.2+1
Otherwise           win.0=win.0+1
End
End
Call o win.1/nn 'player 1 wins'
Call o win.2/nn 'player 2 wins'
Call o win.0/nn 'draws'
/*
Do i=min(n1,n2) To max(n1*s1,n2*s2)
Call o right(i,2) format(cnt1.i,7) format(cnt2.i,7)
End
*/
Call o time('E') 'seconds elapsed'
Return

sum: Parse Arg n s
sum=0
Do k=1 To n
sum=sum+rand(s)
End
Return sum

rand: Parse Arg n
Return random(n-1)+1

o:
Say arg(1)
Return lineout(oid,arg(1))
```
Output:
```Player 1: 9 dice with 4 sides each
Player 2: 6 dice with 6 sides each
0.574 player 1 wins
0.3506 player 2 wins
0.0754 draws
0.109000 seconds elapsed
Player 1: 5 dice with 10 sides each
Player 2: 6 dice with 7 sides each
0.6411 player 1 wins
0.3115 player 2 wins
0.0474 draws
0.078000 seconds elapsed```

### optimized

This REXX version is an optimized and reduced version of the first part of the 1st REXX example.

```/*REXX pgm computes and displays the probabilities of a two─player S─sided, N─dice game.*/
numeric digits 100                               /*increase/decrease to heart's desire. */
call game  9  4, 6  6   /*1st player:  9 dice,  4 sides;   2nd player:  6 dice,  6 sides*/
call game  5 10, 6  7   /* "     "     5   "   10   "       "     "     6   "    7   "  */
exit                                             /*stick a fork in it,  we're all done. */
/*──────────────────────────────────────────────────────────────────────────────────────*/
game: parse arg  w.1  s.1,   w.2  s.2            /*1st player(dice sides), 2nd player···*/
p.= 0
do   j=1  for 2;         @@.j= prob(w.j, s.j)
do k=w.j  to w.j*s.j;  parse var  @@.j   p.j.k  @@.j;  end  /*k*/
end   /*j*/
low.= 0
do   j=w.1  to w.1*s.1
do k=0  for j;         low.j= low.j + p.2.k;           end  /*k*/
end   /*j*/
say '   Player  1  has '       w.1       " dice with "       s.1      ' sides each.'
say '   Player  2  has '       w.2       " dice with "       s.2      ' sides each.'
winP= 0
do   j=w.1  to w.1*s.1;  winP= winP   + p.1.j * low.j
end   /*j*/
say 'The probability for first player to win is '  format(winP*100,,digits()%2) "%."
say                                        /*                               ↑     */
return                                     /*show 1/2 of 100 dec. digits────┘     */
/*──────────────────────────────────────────────────────────────────────────────────────*/
prob: procedure; parse arg n,s,,@ \$;     #.= 0;              pow= s**n
do j=1  for n;     @= @'DO _'j"=1 FOR" s';';   end  /*j*/
@= @'_=';                  do k=1  for n-1;   @= @"_"k'+'             ;   end  /*k*/
interpret  @'_'n";#."_'=#.'_"+1"copies(';END', k)
ns= n*s;                   do j=0  to ns;     p.j= #.j / pow;             end  /*j*/
do k=n  to ns;     \$= \$ p.k;                   end  /*k*/
return \$   /* ◄──────────────── probability of 1st player to win, S─sided, N dice.*/
```
output   when using the default inputs:
```   Player  1  has  9  dice with  4  sides each.
Player  2  has  6  dice with  6  sides each.
The probability for first player to win is  57.31440767829800829475308641975308641975308641975309 %.

Player  1  has  5  dice with  10  sides each.
Player  2  has  6  dice with  7  sides each.
The probability for first player to win is  64.27886287176261591683737218335897457691948082856633 %.
```

## Ruby

```def roll_dice(n_dice, n_faces)
return [[0,1]] if n_dice.zero?
one  = [1] * n_faces
zero = [0] * (n_faces-1)
(1...n_dice).inject(one){|ary,_|
(zero + ary + zero).each_cons(n_faces).map{|a| a.inject(:+)}
}.map.with_index(n_dice){|n,sum| [sum,n]}  # sum: total of the faces
end

def game(dice1, faces1, dice2, faces2)
p1 = roll_dice(dice1, faces1)
p2 = roll_dice(dice2, faces2)
p1.product(p2).each_with_object([0,0,0]) do |((sum1, n1), (sum2, n2)), win|
win[sum1 <=> sum2] += n1 * n2        # [0]:draw, [1]:win, [-1]:lose
end
end

[[9, 4, 6, 6], [5, 10, 6, 7]].each do |d1, f1, d2, f2|
puts "player 1 has #{d1} dice with #{f1} faces each"
puts "player 2 has #{d2} dice with #{f2} faces each"
win = game(d1, f1, d2, f2)
sum = win.inject(:+)
puts "Probability for player 1 to win: #{win[1]} / #{sum}",
"                              -> #{win[1].fdiv(sum)}", ""
end
```
Output:
```player 1 has 9 dice with 4 faces each
player 2 has 6 dice with 6 faces each
Probability for player 1 to win: 7009890480 / 12230590464
-> 0.5731440767829801

player 1 has 5 dice with 10 faces each
player 2 has 6 dice with 7 faces each
Probability for player 1 to win: 7562343938 / 11764900000
-> 0.6427886287176262
```

## Rust

```// Returns a vector containing the number of ways each possible sum of face
// values can occur.
fn get_totals(dice: usize, faces: usize) -> Vec<f64> {
let mut result = vec![1.0; faces + 1];
for d in 2..=dice {
let mut tmp = vec![0.0; d * faces + 1];
for i in d - 1..result.len() {
for j in 1..=faces {
tmp[i + j] += result[i];
}
}
result = tmp;
}
result
}

fn probability(dice1: usize, faces1: usize, dice2: usize, faces2: usize) -> f64 {
let totals1 = get_totals(dice1, faces1);
let totals2 = get_totals(dice2, faces2);
let mut wins = 0.0;
let mut total = 0.0;
for i in dice1..totals1.len() {
for j in dice2..totals2.len() {
let n = totals1[i] * totals2[j];
total += n;
if j < i {
wins += n;
}
}
}
wins / total
}

fn main() {
println!("{}", probability(9, 4, 6, 6));
println!("{}", probability(5, 10, 6, 7));
}
```
Output:
```0.5731440767829801
0.6427886287176262
```

## Sidef

Translation of: Python
```func combos(sides, n) {
n || return [1]
var ret = ([0] * (n*sides.max + 1))
combos(sides, n-1).each_kv { |i,v|
v && for s in sides { ret[i + s] += v }
}
return ret
}

func winning(sides1, n1, sides2, n2) {
var (p1, p2) = (combos(sides1, n1), combos(sides2, n2))
var (win,loss,tie) = (0,0,0)
p1.each_kv { |i, x|
win  += x*p2.first(i).sum
tie  += x*p2.slice(i).first(1).sum
loss += x*p2.slice(i+1).sum
}
[win, tie, loss] »/» p1.sum*p2.sum
}

func display_results(String title, Array res) {
say "=> #{title}"
for name, prob in (%w(p₁\ win tie p₂\ win) ~Z res) {
say "P(#{'%6s' % name}) =~ #{prob.round(-11)} (#{prob.as_frac})"
}
print "\n"
}

display_results('9D4 vs 6D6',  winning(range(1, 4), 9, range(1,6), 6))
display_results('5D10 vs 6D7', winning(range(1,10), 5, range(1,7), 6))
```
Output:
```=> 9D4 vs 6D6
P(p₁ win) =~ 0.57314407678 (48679795/84934656)
P(   tie) =~ 0.07076616984 (144252007/2038431744)
P(p₂ win) =~ 0.35608975338 (725864657/2038431744)

=> 5D10 vs 6D7
P(p₁ win) =~ 0.64278862872 (3781171969/5882450000)
P(   tie) =~ 0.04449603031 (523491347/11764900000)
P(p₂ win) =~ 0.31271534097 (735812943/2352980000)
```

## Tcl

To handle the nested loop in NdK, Tcl's metaprogramming abilities are exercised. The goal is to produce a script that looks like:

```foreach d0 {1 2 3 4 5 6} {
foreach d1 {1 2 3 4 5 6} {
...
foreach dN {1 2 3 4 5 6} {
dict incr sum [::tcl::mathop::+ \$n \$d0 \$d1 ... \$DN]
}
...
}
}
```

See the comments attached to that procedure for a more thorough understanding of how that is achieved (with the caveat that \$d0..\$dN are reversed).

Such metaprogramming is a very powerful technique in Tcl for building scripts where other approaches (in this case, recursion) might not be appealing, and should be in every programmer's toolbox!

```proc range {b} {    ;# a common standard proc:  [range 5] -> {0 1 2 3 4}
set a 0
set res {}
while {\$a < \$b} {
lappend res \$a
incr a
}
return \$res
}

# This proc builds up a nested foreach call, then evaluates it.
#
# The script is built up in \$script, starting with the body using "%%" as
# a placeholder.
#
# For each die, a level is wrapped around it as follows:
#   set script {foreach d0 {1 2 3 4 5 6} \$script}
#   set script {foreach d1 {1 2 3 4 5 6} \$script}
#
# .. and {\$d0 \$d1 ..} are collected in the variable \$vars, which is used
# to replace "%%" at the end.
#
# The script is evaluated with [try] - earlier Tcl's could use [catch] or [eval]
proc NdK {n {k 6}} {    ;# calculate a score histogram for \$n dice of \$k faces
set sum {}
set script {
dict incr sum [::tcl::mathop::+ \$n %%]   ;# add \$n because ranges are 0-based
}   ;# %% is a placeholder
set vars ""
for {set i 0} {\$i < \$n} {incr i} {
set script [list foreach d\$i [range \$k] \$script]
append vars " \\$d\$i"
}
set script [string map [list %% \$vars] \$script]
try \$script
return \$sum
}

proc win_pr {p1 p2} {    ;# calculate the winning probability of player 1 given two score histograms
set P 0
set N 0
dict for {d1 k1} \$p1 {
dict for {d2 k2} \$p2 {
set k [expr {\$k1 * \$k2}]
incr N \$k
incr P [expr {\$k * (\$d1 > \$d2)}]
}
}
expr {\$P * 1.0 / \$N}
}

foreach {p1 p2} {
{9 4}   {6 6}
{5 10}  {6 7}
} {
puts [format "p1 has %dd%d; p2 has %dd%d" {*}\$p1 {*}\$p2]
puts [format " p1 wins with Pr(%s)" [win_pr [NdK {*}\$p1] [NdK {*}\$p2]]]
}
```
Output:
```p1 has 9d4; p2 has 6d6
p1 wins with Pr(0.5731440767829801)
p1 has 5d10; p2 has 6d7
p1 wins with Pr(0.6427886287176262)```

### Factoring out foreach*

The nested-loop generation illustrated above is useful to factor out as a routine by itself. Here it is abstracted as foreach*, with NdK modified to suit.

I include this to emphasise the importance and power of metaprogramming in a Tcler's toolbox, as well as sharing a useful proc.

```package require Tcl 8.6    ;# for [tailcall] - otherwise use [uplevel 1 \$script]
# This proc builds up a nested foreach call, then evaluates it.
#
# this:
#   foreach* a {1 2 3} b {4 5 6} {
#     puts "\$a + \$b"
#   }
#
# becomes:
#   foreach a {1 2 3} {
#     foreach b {4 5 6} {
#       puts "\$a + \$b"
#     }
#   }
proc foreach* {args} {
set script [lindex \$args end]
set args [lrange \$args 0 end-1]
foreach {b a} [lreverse \$args] {
set script [list foreach \$a \$b \$script]
}
tailcall {*}\$script
}

proc NdK {n {k 6}} {    ;# calculate a score histogram for \$n dice of \$k faces

set args {}     ;# arguments to [foreach*]
set vars {}     ;# variables used in [foreach*] arguments that need to be added to sum
set sum {}      ;# this will be the result dictionary

for {set i 0} {\$i < \$n} {incr i} {
lappend args d\$i [range \$k]
lappend vars "\\$d\$i"
}

set vars [join \$vars +]

# [string map] to avoid "Quoting Hell"
set script [string map [list %% \$vars] {
dict incr sum [expr {\$n + %%}]  ;# \$n because [range] is 0-based
}]

foreach* {*}\$args \$script
return \$sum
}
```

## V (Vlang)

Translation of: Go
```import math

fn min_of(x int, y int) int {
if x < y {
return x
}
return y
}

fn throw_die(n_sides int, n_dice int, s int, mut counts []int) {
if n_dice == 0 {
counts[s]++
return
}
for i := int(1); i <= n_sides; i++ {
throw_die(n_sides, n_dice - 1, s + i, mut counts)
}
}

fn beating_probability(n_sides1 int, n_dice1 int, n_sides2 int, n_dice2 int) f64 {
len1 := (n_sides1 + 1) * n_dice1
mut c1 := []int{len: len1}  // all elements zero by default
throw_die(n_sides1, n_dice1, 0, mut c1)

len2 := (n_sides2 + 1) * n_dice2
mut c2 := []int{len: len2}
throw_die(n_sides2, n_dice2, 0, mut c2)
p12 := math.pow(f64(n_sides1), f64(n_dice1)) *
math.pow(f64(n_sides2), f64(n_dice2))

mut tot := 0.0
for i := int(0); i < len1; i++ {
for j := int(0); j < min_of(i, len2); j++ {
tot += f64(c1[i] * c2[j]) / p12
}
}
}

fn main() {
println(beating_probability(4, 9, 6, 6))
println(beating_probability(10, 5, 7, 6))
}```
Output:
```0.57314407678298
0.64278862871763
```

## Wren

Translation of: Kotlin
```var throwDie // recursive
throwDie = Fn.new { |nSides, nDice, s, counts|
if (nDice == 0) {
counts[s] = counts[s] + 1
return
}
for (i in 1..nSides) throwDie.call(nSides, nDice-1, s + i, counts)
}

var beatingProbability = Fn.new { |nSides1, nDice1, nSides2, nDice2|
var len1 = (nSides1 + 1) * nDice1
var c1 = List.filled(len1, 0)
throwDie.call(nSides1, nDice1, 0, c1)

var len2 = (nSides2 + 1) * nDice2
var c2 = List.filled(len2, 0)
throwDie.call(nSides2, nDice2, 0, c2)

var p12 = nSides1.pow(nDice1) * nSides2.pow(nDice2)
var tot = 0
for (i in 0...len1) {
for (j in 0...i.min(len2)) {
tot = tot + c1[i] * c2[j] / p12
}
}
}

System.print(beatingProbability.call(4, 9, 6, 6))
System.print(beatingProbability.call(10, 5, 7, 6))
```
Output:
```0.57314407678298
0.64278862871763
```

## zkl

Translation of: Python
```fcn combos(sides, n){
if(not n) return(T(1));
ret:=((0).max(sides)*n + 1).pump(List(),0);
foreach i,v in (combos(sides, n - 1).enumerate()){
if(not v) continue;
foreach s in (sides){ ret[i + s] += v }
}
ret
}

fcn winning(sides1,n1, sides2,n2){
p1, p2 := combos(sides1, n1), combos(sides2, n2);
win,loss,tie := 0,0,0; # 'win' is 1 beating 2
foreach i,x1 in (p1.enumerate()){
# using accumulated sum on p2 could save some time
win += x1*p2[0,i].sum(0);
tie += x1*p2[i,1].sum(0);  // i>p2.len() but p2[bigi,?]-->[]
loss+= x1*p2[i+1,*].sum(0);
}
s := p1.sum(0)*p2.sum(0);
return(win.toFloat()/s, tie.toFloat()/s, loss.toFloat()/s);
}```
```println(winning([1..4].walk(), 9, [1..6].walk(),6));
println(winning([1..10].walk(),5, [1..7].walk(),6)); # this seem hardly fair```
Output:
```L(0.573144,0.0707662,0.35609)
L(0.642789,0.044496,0.312715)
```