# 100 prisoners

Show and compare the computed probabilities of success for the two strategies, here, on this page.

100 prisoners
You are encouraged to solve this task according to the task description, using any language you may know.

The Problem
• 100 prisoners are individually numbered 1 to 100
• A room having a cupboard of 100 opaque drawers numbered 1 to 100, that cannot be seen from outside.
• Cards numbered 1 to 100 are placed randomly, one to a drawer, and the drawers all closed; at the start.
• Prisoners start outside the room
• They can decide some strategy before any enter the room.
• Prisoners enter the room one by one, can open a drawer, inspect the card number in the drawer, then close the drawer.
• A prisoner can open no more than 50 drawers.
• A prisoner tries to find his own number.
• A prisoner finding his own number is then held apart from the others.
• If all 100 prisoners find their own numbers then they will all be pardoned. If any don't then all sentences stand.

1. Simulate several thousand instances of the game where the prisoners randomly open drawers
2. Simulate several thousand instances of the game where the prisoners use the optimal strategy mentioned in the Wikipedia article, of:
• First opening the drawer whose outside number is his prisoner number.
• If the card within has his number then he succeeds otherwise he opens the drawer with the same number as that of the revealed card. (until he opens his maximum).

References
1. The unbelievable solution to the 100 prisoner puzzle standupmaths (Video).
2. wp:100 prisoners problem
3. 100 Prisoners Escape Puzzle DataGenetics.
4. Random permutation statistics#One hundred prisoners on Wikipedia.

## 11l

Translation of: Python
```F play_random(n)
V pardoned = 0
V in_drawer = Array(0.<100)
V sampler = Array(0.<100)
L 0 .< n
random:shuffle(&in_drawer)
V found = 0B
L(prisoner) 100
found = 0B
L(reveal) random:sample(sampler, 50)
V card = in_drawer[reveal]
I card == prisoner
found = 1B
L.break
I !found
L.break
I found
pardoned++
R Float(pardoned) / n * 100

F play_optimal(n)
V pardoned = 0
V in_drawer = Array(0.<100)
L 0 .< n
random:shuffle(&in_drawer)
V found = 0B
L(prisoner) 100
V reveal = prisoner
found = 0B
L 50
V card = in_drawer[reveal]
I card == prisoner
found = 1B
L.break
reveal = card
I !found
L.break
I found
pardoned++
R Float(pardoned) / n * 100

V n = 100'000
print(‘ Simulation count: ’n)
print(‘ Random play wins: #2.1% of simulations’.format(play_random(n)))
print(‘Optimal play wins: #2.1% of simulations’.format(play_optimal(n)))```
Output:
``` Simulation count: 100000
Random play wins:  0.0% of simulations
Optimal play wins: 31.1% of simulations
```

## AArch64 Assembly

Works with: as version Raspberry Pi 3B version Buster 64 bits
```/* ARM assembly AARCH64 Raspberry PI 3B */
/*  program prisonniex64.s   */

/*******************************************/
/* Constantes file                         */
/*******************************************/
/* for this file see task include a file in language AArch64 assembly*/
.include "../includeConstantesARM64.inc"

.equ NBDOORS,   100
.equ NBLOOP, 1000

/*********************************/
/* Initialized data              */
/*********************************/
.data
sMessResult:        .asciz "Random strategie  : @ sur 1000 \n"
sMessResultOPT:     .asciz "Optimal strategie : @ sur 1000 \n"
szCarriageReturn:   .asciz "\n"
/*********************************/
/* UnInitialized data            */
/*********************************/
.bss
sZoneConv:        .skip 24
tbDoors:          .skip 8 * NBDOORS
tbTest:           .skip 8 * NBDOORS
/*********************************/
/*  code section                 */
/*********************************/
.text
.global main
main:                                 // entry of program

mov x2,#0
1:                                    // loop init doors table
str x3,[x1,x2,lsl #3]
cmp x2,#NBDOORS
blt 1b

mov x9,#0                         // loop counter
mov x10,#0                        // counter successes random strategie
mov x11,#0                        // counter successes optimal strategie
2:
mov x1,#NBDOORS
bl knuthShuffle

bl aleaStrategie
cmp x0,#NBDOORS
cinc x10,x10,eq

bl optimaStrategie
cmp x0,#NBDOORS
cinc x11,x11,eq

cmp x9,#NBLOOP
blt 2b

mov x0,x10                        // result display
bl conversion10                   // call decimal conversion
ldr x1,qAdrsZoneConv              // insert conversion in message
bl strInsertAtCharInc
bl affichageMess

mov x0,x11                        // result display
bl conversion10                   // call decimal conversion
ldr x1,qAdrsZoneConv              // insert conversion in message
bl strInsertAtCharInc
bl affichageMess

100:                                  // standard end of the program
mov x0,0                          // return code
mov x8,EXIT                       // request to exit program
svc 0                             // perform the system call

/******************************************************************/
/*            random door test strategy                           */
/******************************************************************/
/* x0 contains the address of table */
aleaStrategie:
stp x1,lr,[sp,-16]!          // save  registres
stp x2,x3,[sp,-16]!          // save  registres
stp x4,x5,[sp,-16]!          // save  registres
stp x6,x7,[sp,-16]!          // save  registres
stp x8,x9,[sp,-16]!          // save  registres
mov x8,x0                    // save table doors address
mov x4,#0                    // counter number of successes
mov x2,#0                    // prisonners indice
1:
bl razTable                  // zero to table doors tests
mov x5,#0                    // counter of door tests
2:
mov x0,#1
mov x1,#NBDOORS
bl extRandom                 // random test
ldr x3,[x6,x0,lsl #3]        // doors also tested ?
cmp x3,#0
bne 2b                       // yes
ldr x3,[x8,x0,lsl #3]        // load N° door
cmp x3,x7                    // compar N° door N° prisonner
cinc x4,x4,eq
beq 3f
mov x3,#1                    // top test table item
str x3,[x6,x0,lsl #3]
cmp x5,#NBDOORS / 2          // number tests maxi ?
blt 2b                       // no -> loop
3:
cmp x2,#NBDOORS
blt 1b

mov x0,x4                    // return number of successes
100:
ldp x8,x9,[sp],16           // restaur des  2 registres
ldp x6,x7,[sp],16           // restaur des  2 registres
ldp x4,x5,[sp],16           // restaur des  2 registres
ldp x2,x3,[sp],16           // restaur des  2 registres
ldp x1,lr,[sp],16           // restaur des  2 registres
ret
/******************************************************************/
/*     raz test table                                             */
/******************************************************************/
razTable:
stp x0,lr,[sp,-16]!        // save  registres
stp x1,x2,[sp,-16]!        // save  registres
mov x1,#0                  // item indice
mov x2,#0
1:
str x2,[x0,x1,lsl #3]      // store zero à item
cmp x1,#NBDOORS
blt 1b
100:
ldp x1,x2,[sp],16          // restaur des  2 registres
ldp x0,lr,[sp],16          // restaur des  2 registres
ret
/******************************************************************/
/*            random door test strategy                           */
/******************************************************************/
/* x0 contains the address of table */
optimaStrategie:
stp x1,lr,[sp,-16]!          // save  registres
stp x2,x3,[sp,-16]!          // save  registres
stp x4,x5,[sp,-16]!          // save  registres
mov x4,#0                    // counter number of successes
mov x2,#0                    // counter prisonner
1:
mov x5,#0                    // counter test
mov x1,x2                    // first test = N° prisonner
2:
ldr x3,[x0,x1,lsl #3]        // load N° door
cmp x3,x2
cinc x4,x4,eq                // equal -> succes
beq 3f
mov x1,x3                    // new test with N° door
cmp x5,#NBDOORS / 2          // test number maxi ?
blt 2b
3:
cmp x2,#NBDOORS
blt 1b

mov x0,x4
100:
ldp x4,x5,[sp],16          // restaur des  2 registres
ldp x2,x3,[sp],16          // restaur des  2 registres
ldp x1,lr,[sp],16          // restaur des  2 registres
ret
/******************************************************************/
/*     knuth Shuffle                                  */
/******************************************************************/
/* x0 contains the address of table */
/* x1 contains the number of elements */
knuthShuffle:
stp x1,lr,[sp,-16]!          // save  registres
stp x2,x3,[sp,-16]!          // save  registres
stp x4,x5,[sp,-16]!          // save  registres
stp x6,x7,[sp,-16]!         // save  registers
mov x5,x0                   // save table address
mov x6,x1                   // save number of elements
mov x2,0                    // start index
1:
mov x0,0
mov x1,x2                   // generate aleas
bl extRandom
ldr x3,[x5,x2,lsl #3]        // swap number on the table
ldr x4,[x5,x0,lsl #3]
str x4,[x5,x2,lsl #3]
str x3,[x5,x0,lsl #3]
cmp x2,x6                    // end ?
blt 1b                       // no -> loop
100:
ldp x6,x7,[sp],16           // restaur des  2 registres
ldp x4,x5,[sp],16           // restaur des  2 registres
ldp x2,x3,[sp],16           // restaur des  2 registres
ldp x1,lr,[sp],16           // restaur des  2 registres
ret

/******************************************************************/
/*     random number                                          */
/******************************************************************/
/*  x0 contains inferior value */
/*  x1 contains maxi value */
/*  x0 return random number */
extRandom:
stp x1,lr,[sp,-16]!        // save  registers
stp x2,x8,[sp,-16]!        // save  registers
stp x3,x4,[sp,-16]!        // save  registers
stp x19,x20,[sp,-16]!      // save  registers
sub sp,sp,16               // reserve 16 octets on stack
mov x19,x0
mov x0,sp                  // store result on stack
mov x1,8                   // length 8 bytes
mov x2,0
mov x8,278                 //  call system Linux 64 bits Urandom
svc 0
mov x0,sp                  // load résult on stack
ldr x0,[x0]
sub x2,x20,x19             // calculation of the range of values
udiv x1,x0,x2              // calculation range modulo
msub x0,x1,x2,x0
100:
ldp x19,x20,[sp],16        // restaur  2 registers
ldp x3,x4,[sp],16          // restaur  2 registers
ldp x2,x8,[sp],16          // restaur  2 registers
ldp x1,lr,[sp],16          // restaur  2 registers
/********************************************************/
/*        File Include fonctions                        */
/********************************************************/
/* for this file see task include a file in language AArch64 assembly */
.include "../includeARM64.inc"```
```Random strategie  : 0 sur 1000
Optimal strategie : 305 sur 1000
```

```package Prisoners is

type Win_Percentage is digits 2 range 0.0 .. 100.0;
type Drawers is array (1 .. 100) of Positive;

function Play_Game
(Repetitions : in Positive;
Strategy    :    not null access function
(Cupboard     : in Drawers; Max_Prisoners : Integer;
Max_Attempts :    Integer; Prisoner_Number : Integer) return Boolean)
return Win_Percentage;
-- Play the game with a specified number of repetitions, the chosen strategy
-- is passed to this function

function Optimal_Strategy
(Cupboard : in Drawers; Max_Prisoners : Integer; Max_Attempts : Integer;
Prisoner_Number :    Integer) return Boolean;

function Random_Strategy
(Cupboard : in Drawers; Max_Prisoners : Integer; Max_Attempts : Integer;
Prisoner_Number :    Integer) return Boolean;

end Prisoners;
```
```pragma Ada_2012;

package body Prisoners is

subtype Drawer_Range is Positive range 1 .. 100;
package Random_Drawer is new Ada.Numerics.Discrete_Random (Drawer_Range);
use Random_Drawer;

-- Helper procedures to initialise and shuffle the drawers

procedure Swap (A, B : Positive; Cupboard : in out Drawers) is
Temp : Positive;
begin
Temp         := Cupboard (B);
Cupboard (B) := Cupboard (A);
Cupboard (A) := Temp;
end Swap;

procedure Shuffle (Cupboard : in out Drawers) is
G : Generator;
begin
Reset (G);
for I in Cupboard'Range loop
Swap (I, Random (G), Cupboard);
end loop;
end Shuffle;

procedure Initialise_Drawers (Cupboard : in out Drawers) is
begin
for I in Cupboard'Range loop
Cupboard (I) := I;
end loop;
Shuffle (Cupboard);
end Initialise_Drawers;

-- The two strategies for playing the game

function Optimal_Strategy
(Cupboard : in Drawers; Max_Prisoners : Integer; Max_Attempts : Integer;
Prisoner_Number :    Integer) return Boolean
is
Current_Card : Positive;
begin
Current_Card := Cupboard (Prisoner_Number);
if Current_Card = Prisoner_Number then
return True;
else
for I in Integer range 1 .. Max_Attempts loop
Current_Card := Cupboard (Current_Card);
if Current_Card = Prisoner_Number then
return True;
end if;
end loop;
end if;
return False;
end Optimal_Strategy;

function Random_Strategy
(Cupboard : in Drawers; Max_Prisoners : Integer; Max_Attempts : Integer;
Prisoner_Number :    Integer) return Boolean
is
Current_Card : Positive;
G            : Generator;
begin
Reset (G);
Current_Card := Cupboard (Prisoner_Number);
if Current_Card = Prisoner_Number then
return True;
else
for I in Integer range 1 .. Max_Attempts loop
Current_Card := Cupboard (Random (G));
if Current_Card = Prisoner_Number then
return True;
end if;
end loop;
end if;
return False;
end Random_Strategy;

function Prisoners_Attempts
(Cupboard : in Drawers; Max_Prisoners : Integer; Max_Attempts : Integer;
Strategy :    not null access function
(Cupboard     : in Drawers; Max_Prisoners : Integer;
Max_Attempts :    Integer; Prisoner_Number : Integer) return Boolean)
return Boolean
is
begin
for Prisoner_Number in Integer range 1 .. Max_Prisoners loop
if not Strategy
(Cupboard, Max_Prisoners, Max_Attempts, Prisoner_Number)
then
return False;
end if;
end loop;
return True;
end Prisoners_Attempts;

-- The function to play the game itself

function Play_Game
(Repetitions : in Positive;
Strategy    :    not null access function
(Cupboard     : in Drawers; Max_Prisoners : Integer;
Max_Attempts :    Integer; Prisoner_Number : Integer) return Boolean)
return Win_Percentage
is
Cupboard            : Drawers;
Win, Game_Count     : Natural          := 0;
Number_Of_Prisoners : constant Integer := 100;
Max_Attempts        : constant Integer := 50;
begin
loop
Initialise_Drawers (Cupboard);
if Prisoners_Attempts
(Cupboard     => Cupboard, Max_Prisoners => Number_Of_Prisoners,
Max_Attempts => Max_Attempts, Strategy => Strategy)
then
Win := Win + 1;
end if;
Game_Count := Game_Count + 1;
exit when Game_Count = Repetitions;
end loop;
return Win_Percentage ((Float (Win) / Float (Repetitions)) * 100.0);
end Play_Game;

end Prisoners;
```
```with Prisoners;   use Prisoners;

procedure Main is
Wins : Win_Percentage;
package Win_Percentage_IO is new Float_IO (Win_Percentage);
begin
Wins := Play_Game (100_000, Optimal_Strategy'Access);
Put ("Optimal Strategy = ");
Win_Percentage_IO.Put (Wins, 2, 2, 0);
Put ("%");
New_Line;
Wins := Play_Game (100_000, Random_Strategy'Access);
Put ("Random Strategy = ");
Win_Percentage_IO.Put (Wins, 2, 2, 0);
Put ("%");
end Main;
```
Output:
```Optimal Strategy = 31.80%
Random Strategy =  0.00%
```

## APL

Works with: GNU APL version 1.8
```∇ R ← random Nnc; N; n; c
(N n c) ← Nnc
R ← ∧/{∨/⍵=c[n?N]}¨⍳N
∇

∇ R ← follow Nnc; N; n; c; b
(N n c) ← Nnc
b ← n N⍴⍳N
R ← ∧/∨⌿b={⍺⊢c[⍵]}⍀n N⍴c
∇

∇ R ← M timesSimPrisoners Nn; N; n; m; c; r; s
(N n) ← Nn
R ← 0 0
m ← M
LOOP: c←N?N
r ← random N n c
s ← follow N n c
R ← R + r,s
→((m←m-1)>0)/LOOP
R ← R ÷ M
∇

⎕TS
'>>>>>'
1000 timesSimPrisoners 100 50
'>>>>>'
⎕TS
```
Output:
```2023 3 26 17 43 32 983
>>>>>
0 0.307
>>>>>
2023 3 26 17 53 48 531
```

## Applesoft BASIC

This is modified from the 100_prisoners#Commodore_BASIC listing. Here are some noted differences between the BASICs and platforms:

• UPPER CASE, for the 1970's Apple II and Apple II+
• GET in Applesoft waits for a keypress, so : IF K\$ = "" THEN 1110 is not needed
• CLear Screen: PRINT CHR\$ (147); on Commodore BASIC, HOME in Applesoft
• "{LEFT-CRSR}" is CHR\$(8) on Apple II, but numbers printed in Applesoft don't have spaces appended to them
• but spaces need to be added in front and after numbers in Applesoft
• ; is optional for string concatenation
• Replace bare PRINT statement with M\$ embedded in PRINT statements to visually compact the listing

And, minor speed tweaks:

• Remove REMs, adjust line numbers, move the two compacted methods to the beginning of the program
• Rename some two character variable names to single character names: 's/DR(/D(/' 's/IG(/J(/'
• Start at 0 and go up to 99, but don't regress into off by one bugs
• Inline the shuffle subroutine and hoist it out of the methods
• Embed the results in the loop because feedback can be helpful, otherwise it looks like the program froze

Actual test of 4000 trials for each method were run on the KEGSMAC emulator with MHz set to No Limit.

```0 GOTO 9

1 FOR X = 0 TO N:J(X) = X: NEXT: FOR I = 0 TO N:FOR X = 0 TO N:T = J(X):NP =  INT ( RND (1) * H):J(X) = J(NP):J(NP) = T: NEXT :FOR G = 1 TO W:IF D(J(G)) = I THEN IP = IP + 1: NEXT I: RETURN
2 NEXT G:RETURN

3 FOR I = 0 TO N:NG = I: FOR G = 0 TO W:CD = D(NG):IF CD = I THEN IP = IP + 1: NEXT I: RETURN
4 NG = CD:IF CD = I THEN STOP
5 NEXT G: RETURN

9 H=100:N=H-1:DIM D(99),J(99):FOR I = 0 TO N:D(I) = I: NEXT:W=INT(H/2)-1:M\$=CHR\$(13):M\$(1)="RANDOM GUESSING":M\$(2)="CHAINED NUMBER PICKING"

1000 FOR Q = 0 TO 1 STEP 0 : HOME : PRINT "100 PRISONERS"M\$: INPUT "HOW MANY TRIALS FOR EACH METHOD? ";  TT
1010     VTAB 2:CALL-958:PRINT M\$"RESULTS:"M\$
1020     FOR M = 1 TO 2: SU(M) = 0:FA(M) = 0
1030         FOR TN = 1 TO TT
1040             VTAB 4:PRINT M\$ "   OUT OF " TT " TRIALS, THE RESULTS ARE"M\$"   AS FOLLOWS...";
1050             IP = 0: X =  RND ( - TI): FOR I = 0 TO N:R =  INT ( RND (1) * N):T = D(I):D(I) = D(R):D(R) = T: NEXT
1060             ON M GOSUB 1,3 : SU(M) = SU(M) + (IP = H):FA(M) = FA(M) + (IP < H)
1070             FOR Z = 1 TO 2
1071                 PRINT M\$M\$Z". "M\$(Z)":"M\$
1073                 PRINT "   "SU(Z)" SUCCESSES"TAB(21)
1074                 PRINT "   "FA(Z)" FAILURES"M\$
1075                 PRINT "   "(SU(Z) / TT) * 100"% SUCCESS RATE.";:CALL-868
1090     NEXT Z,TN,M

1100     PRINT M\$M\$"AGAIN?"
1110     GET K\$
1120     Q = K\$ <> "Y" AND K\$ <> CHR\$(ASC("Y") + 32) : NEXT Q
```
Output:
```100 PRISONERS

RESULTS:

OUT OF 4000 TRIALS, THE RESULTS ARE
AS FOLLOWS...

1. RANDOM GUESSING:

0 SUCCESSES         4000 FAILURES

0% SUCCESS RATE.

2. CHAINED NUMBER PICKING:

1278 SUCCESSES      2722 FAILURES

31.95% SUCCESS RATE.
```

## ARM Assembly

Works with: as version Raspberry Pi
```/* ARM assembly Raspberry PI  */
/*  program prisonniers.s   */

/* REMARK 1 : this program use routines in a include file
see task Include a file language arm assembly
for the routine affichageMess conversion10
see at end of this program the instruction include */
/* for constantes see task include a file in arm assembly */
/************************************/
/* Constantes                       */
/************************************/
.include "../constantes.inc"

.equ NBDOORS,   100
.equ NBLOOP, 1000

/*********************************/
/* Initialized data              */
/*********************************/
.data
sMessResult:        .asciz "Random strategie  : @ sur 1000 \n"
sMessResultOPT:     .asciz "Optimal strategie : @ sur 1000 \n"
szCarriageReturn:   .asciz "\n"
.align 4
iGraine:  .int 123456
/*********************************/
/* UnInitialized data            */
/*********************************/
.bss
sZoneConv:        .skip 24
tbDoors:          .skip 4 * NBDOORS
tbTest:           .skip 4 * NBDOORS
/*********************************/
/*  code section                 */
/*********************************/
.text
.global main
main:                                 @ entry of program

mov r2,#0
1:                                    @ loop init doors table
str r3,[r1,r2,lsl #2]
cmp r2,#NBDOORS
blt 1b

mov r9,#0                         @ loop counter
mov r10,#0                        @ counter successes random strategie
mov r11,#0                        @ counter successes optimal strategie
2:
mov r1,#NBDOORS
bl knuthShuffle

bl aleaStrategie
cmp r0,#NBDOORS

bl optimaStrategie
cmp r0,#NBDOORS

cmp r9,#NBLOOP
blt 2b

mov r0,r10                        @ result display
bl conversion10                   @ call decimal conversion
ldr r1,iAdrsZoneConv              @ insert conversion in message
bl strInsertAtCharInc
bl affichageMess

mov r0,r11                        @ result display
bl conversion10                   @ call decimal conversion
ldr r1,iAdrsZoneConv              @ insert conversion in message
bl strInsertAtCharInc
bl affichageMess

100:                                  @ standard end of the program
mov r0, #0                        @ return code
mov r7, #EXIT                     @ request to exit program
svc #0                            @ perform the system call

/******************************************************************/
/*            random door test strategy                           */
/******************************************************************/
/* r0 contains the address of table */
aleaStrategie:
push {r1-r7,lr}              @ save registers
mov r1,r0                    @ save table doors address
mov r4,#0                    @ counter number of successes
mov r2,#0                    @ prisonners indice
1:
bl razTable                  @ zero to table doors tests
mov r5,#0                    @ counter of door tests
2:
mov r0,#NBDOORS - 1
bl genereraleas              @ random test
ldr r3,[r6,r0,lsl #2]        @ doors also tested ?
cmp r3,#0
bne 2b                       @ yes
ldr r3,[r1,r0,lsl #2]        @ load N° door
cmp r3,r7                    @ compar N° door N° prisonner
beq 3f
mov r3,#1                    @ top test table item
str r3,[r6,r0,lsl #2]
cmp r5,#NBDOORS / 2          @ number tests maxi ?
blt 2b                       @ no -> loop
3:
cmp r2,#NBDOORS
blt 1b

mov r0,r4                    @ return number of successes
100:
pop {r1-r7,lr}
bx lr                        @ return
/******************************************************************/
/*     raz test table                                             */
/******************************************************************/
razTable:
push {r0-r2,lr}              @ save registers
mov r1,#0                    @ item indice
mov r2,#0
1:
str r2,[r0,r1,lsl #2]        @ store zero à item
cmp r1,#NBDOORS
blt 1b
100:
pop {r0-r2,lr}
bx lr                        @ return
/******************************************************************/
/*            random door test strategy                           */
/******************************************************************/
/* r0 contains the address of table */
optimaStrategie:
push {r1-r7,lr}              @ save registers
mov r4,#0                    @ counter number of successes
mov r2,#0                    @ counter prisonner
1:
mov r5,#0                    @ counter test
mov r1,r2                    @ first test = N° prisonner
2:
ldr r3,[r0,r1,lsl #2]        @ load N° door
cmp r3,r2
addeq r4,r4,#1               @ equal -> succes
beq 3f
mov r1,r3                    @ new test with N° door
cmp r5,#NBDOORS / 2          @ test number maxi ?
blt 2b
3:
cmp r2,#NBDOORS
blt 1b

mov r0,r4
100:
pop {r1-r7,lr}
bx lr                        @ return
/******************************************************************/
/*     knuth Shuffle                                  */
/******************************************************************/
/* r0 contains the address of table */
/* r1 contains the number of elements */
knuthShuffle:
push {r2-r5,lr}                   @ save registers
mov r5,r0                         @ save table address
mov r2,#0                         @ start index
1:
mov r0,r2                         @ generate aleas
bl genereraleas
ldr r3,[r5,r2,lsl #2]             @ swap number on the table
ldr r4,[r5,r0,lsl #2]
str r4,[r5,r2,lsl #2]
str r3,[r5,r0,lsl #2]
cmp r2,r1                         @ end ?
blt 1b                            @ no -> loop
100:
pop {r2-r5,lr}
bx lr                             @ return
/***************************************************/
/*   Generation random number                  */
/***************************************************/
/* r0 contains limit  */
genereraleas:
push {r1-r4,lr}                   @ save registers
ldr r2,[r4]
ldr r3,iNbDep1
mul r2,r3,r2
ldr r3,iNbDep1
str r2,[r4]                       @ maj de la graine pour l appel suivant
cmp r0,#0
beq 100f
mov r1,r0                         @ divisor
mov r0,r2                         @ dividende
bl division
mov r0,r3                         @ résult = remainder

100:                                  @ end function
pop {r1-r4,lr}                    @ restaur registers
bx lr                             @ return
/*****************************************************/
iNbDep1:     .int 0x343FD
iNbDep2:     .int 0x269EC3
/***************************************************/
/*      ROUTINES INCLUDE                           */
/***************************************************/
.include "../affichage.inc"```
```Random strategie  : 0           sur 1000
Optimal strategie : 303         sur 1000
```

## Arturo

```unplanned: function [][
drawers: shuffle @1..100
every? 1..100 'x -> some? 1..50 => [x = sample drawers]
]

planned: function [][
drawers: shuffle @1..100
every? 1..100 'x [
next: x
some? 1..50 => [x = next: <= drawers\[next-1]]
]
]

test: function [f][
count: enumerate 10000 => [call f []]
print [f ~"|mul fdiv count 10000 100|%"]
]

test 'unplanned
test 'planned```
Output:
```unplanned 0.0%
planned 31.43%```

## AutoHotkey

```NumOfTrials := 20000
randomFailTotal := 0, strategyFailTotal := 0
prisoners := [], drawers := [], Cards := []
loop, 100
prisoners[A_Index] := A_Index				; create prisoners
, drawers[A_Index] := true				; create drawers

loop, % NumOfTrials
{
loop, 100
Cards[A_Index] := A_Index			; create cards for this iteration
loop, 100
{
Random, rnd, 1, Cards.count()
drawers[A_Index] := Cards.RemoveAt(rnd)		; randomly place cards in drawers
}
;-------------------------------------------
; randomly open drawers
RandomList := []
loop, 100
RandomList[A_Index] := A_Index
Fail := false
while (A_Index <=100) && !Fail
{
thisPrisoner := A_Index
res := ""
while (thisCard <> thisPrisoner) && !Fail
{
Random, rnd, 1, % RandomList.Count()	; choose random number
NextDrawer := RandomList.RemoveAt(rnd)	; remove drawer from random list (don't choose more than once)
thisCard := drawers[NextDrawer]		; get card from this drawer
if (A_Index > 50)
Fail := true
}
if Fail
randomFailTotal++
}
;-------------------------------------------
; use optimal strategy
Fail := false
while (A_Index <=100) && !Fail
{
counter := 1, thisPrisoner := A_Index
NextDrawer := drawers[thisPrisoner]		; 1st trial, drawer whose outside number is prisoner number
while (drawers[NextDrawer] <> thisPrisoner) && !Fail
{
NextDrawer := drawers[NextDrawer]	; drawer with the same number as that of the revealed card
if ++counter > 50
Fail := true
}
if Fail
strategyFailTotal++
}
}
MsgBox %  "Number Of Trials = " NumOfTrials
. "`nOptimal Strategy:`t" (1 - strategyFailTotal/NumOfTrials) *100 " % success rate"
. "`nRandom Trials:`t" (1 - randomFailTotal/NumOfTrials) *100 " % success rate"
```
Outputs:
```Number Of Trials = 20000
Optimal Strategy:	33.275000 % success rate
Random Trials   :	0.000000 % success rate```

## BASIC

### BASIC256

Works with: BASIC256 version 2.0.0.11
```O = 50
N = 2*O
iterations = 10000

REM From the numbers 0 to N-1 inclusive, pick O of them.
function shuffle(N, O)
dim array(N)
for i = 0 to N-1
array[i] = i
next i
for i = 0 to O-1
swapindex = i + rand*(N-i)
swapvalue = array[swapindex]
array[swapindex] = array[i]
array[i] = swapvalue
next i
return array
end function

REM given N drawers with O to open, prisoner P chooses randomly: does he choose well?
function chooserandom(drawers, N, O, p)
choices = shuffle(N, O)
for i = 0 to O-1
if drawers[choices[i]] = p then return true
next i
return false
end function

REM N prisoners randomly choose O drawers to open: do they all choose well?
function allchooserandom(N, O)
drawers = shuffle(N, N)
for p = 0 to N-1
goodchoice = chooserandom(drawers, N, O, p)
if not goodchoice then return false
next p
return true
end function

REM given N drawers with O to open, prisoner P chooses smartly: does he choose well?
function choosesmart(drawers, N, O, p)
numopened = 0
i = p
while numopened < O
numopened += 1
if drawers[i] = p then return true
i = drawers[i]
end while
return false
end function

REM N prisoners smartly choose O drawers to open: do they all choose well?
function allchoosesmart(N, O)
drawers = shuffle(N, N)
for p = 0 to N-1
goodchoice = choosesmart(drawers, N, O, p)
if not goodchoice then return false
next p
return true
end function

cls
print N; " prisoners choosing ";O;" drawers, ";iterations;" iterations:"

total = 0
for iteration = 1 to iterations
if allchooserandom(N, O) then total += 1
next iteration

print "Random choices: "; total;" out of ";iterations
print "Observed ratio: "; total/iterations; ", expected ratio: "; (O/N)^N

total = 0
for iteration = 1 to iterations
if allchoosesmart(N, O) then total += 1
next iteration

print "Smart choices: "; total;" out of ";iterations
print "Observed ratio: "; total/iterations; ", expected ratio with N=2*O: greater than about 0.30685": REM for N=100, O=50 particularly, about 0.3118```
Output:
```100 prisoners choosing 50 drawers, 10000 iterations:
Random choices: 0 out of 10000
Observed ratio: 0.0, expected ratio: 0.0
Smart choices: 3052 out of 10000
Observed ratio: 0.3052, expected ratio with N=2*O: greater than about 0.30685
```

## BCPL

```get "libhdr"

manifest \$(
seed = 12345   // for pseudorandom number generator
size = 100     // amount of drawers and prisoners
tries = 50     // amount of tries each prisoner may make
simul = 2000   // amount of simulations to run
\$)

let randto(n) = valof
\$(  static \$( state = seed \$)
state := random(state) repeatuntil ((state >> 8) & mask) < n
resultis (state >> 8) & mask
\$)

// initialize drawers
let placeCards(d, n) be
\$(  for i=0 to n-1 do d!i := i;
for i=0 to n-2 do
\$(  let j = i+randto(n-i)
let k = d!i
d!i := d!j
d!j := k
\$)
\$)

// random strategy (prisoner 'p' tries to find his own number)
let randoms(d, p, t) = valof
\$(  for n = 1 to t do
if d!randto(size) = p then resultis true
resultis false
\$)

// optimal strategy
let optimal(d, p, t) = valof
\$(  let last = p
for n = 1 to t do
test d!last = p
then resultis true
else last := d!last
resultis false
\$)

// run a simulation given a strategy
let simulate(d, strat, n, t) = valof
\$(  placeCards(d, n)
for p = 0 to n-1 do
if not strat(d, p, t) then resultis false
resultis true
\$)

// run many simulations and count the successes
let runSimulations(d, strat, n, amt, t) = valof
\$(  let succ = 0
for i = 1 to amt do
if simulate(d, strat, n, t) do
succ := succ + 1
resultis succ
\$)

let run(d, name, strat, n, amt, t) be
\$(  let s = runSimulations(d, strat, n, amt, t);
writef("%S: %I5 of %I5, %N percent.*N", name, s, amt, s*10/(amt/10))
\$)

let start() be
\$(  let d = vec size-1
run(d, " Random", randoms, size, simul, tries)
run(d, "Optimal", optimal, size, simul, tries)
\$)```
Output:
``` Random:     0 of  2000, 0 percent.
Optimal:   698 of  2000, 34 percent.```

## C

```#include<stdbool.h>
#include<stdlib.h>
#include<stdio.h>
#include<time.h>

#define LIBERTY false
#define DEATH true

typedef struct{
int cardNum;
bool hasBeenOpened;
}drawer;

drawer *drawerSet;

void initialize(int prisoners){
int i,j,card;
bool unique;

drawerSet = ((drawer*)malloc(prisoners * sizeof(drawer))) -1;

card = rand()%prisoners + 1;
drawerSet[1] = (drawer){.cardNum = card, .hasBeenOpened = false};

for(i=1 + 1;i<prisoners + 1;i++){
unique = false;
while(unique==false){
for(j=0;j<i;j++){
if(drawerSet[j].cardNum == card){
card = rand()%prisoners + 1;
break;
}
}
if(j==i){
unique = true;
}
}
drawerSet[i] = (drawer){.cardNum = card, .hasBeenOpened = false};
}

}

void closeAllDrawers(int prisoners){
int i;
for(i=1;i<prisoners + 1;i++)
drawerSet[i].hasBeenOpened = false;
}

bool libertyOrDeathAtRandom(int prisoners,int chances){
int i,j,chosenDrawer;

for(i= 1;i<prisoners + 1;i++){
bool foundCard = false;
for(j=0;j<chances;j++){
do{
chosenDrawer = rand()%prisoners + 1;
}while(drawerSet[chosenDrawer].hasBeenOpened==true);
if(drawerSet[chosenDrawer].cardNum == i){
foundCard = true;
break;
}
drawerSet[chosenDrawer].hasBeenOpened = true;
}
closeAllDrawers(prisoners);
if(foundCard == false)
return DEATH;
}

return LIBERTY;
}

bool libertyOrDeathPlanned(int prisoners,int chances){
int i,j,chosenDrawer;
for(i=1;i<prisoners + 1;i++){
chosenDrawer = i;
bool foundCard = false;
for(j=0;j<chances;j++){
drawerSet[chosenDrawer].hasBeenOpened = true;

if(drawerSet[chosenDrawer].cardNum == i){
foundCard = true;
break;
}
if(chosenDrawer == drawerSet[chosenDrawer].cardNum){
do{
chosenDrawer = rand()%prisoners + 1;
}while(drawerSet[chosenDrawer].hasBeenOpened==true);
}
else{
chosenDrawer = drawerSet[chosenDrawer].cardNum;
}

}

closeAllDrawers(prisoners);
if(foundCard == false)
return DEATH;
}

return LIBERTY;
}

int main(int argc,char** argv)
{
int prisoners, chances;
unsigned long long int trials,i,count = 0;
char* end;

if(argc!=4)
return printf("Usage : %s <Number of prisoners> <Number of chances> <Number of trials>",argv[0]);

prisoners = atoi(argv[1]);
chances = atoi(argv[2]);
trials = strtoull(argv[3],&end,10);

srand(time(NULL));

printf("Running random trials...");
for(i=0;i<trials;i+=1L){
initialize(prisoners);

count += libertyOrDeathAtRandom(prisoners,chances)==DEATH?0:1;
}

printf("\n\nGames Played : %llu\nGames Won : %llu\nChances : %lf %% \n\n",trials,count,(100.0*count)/trials);

count = 0;

printf("Running strategic trials...");
for(i=0;i<trials;i+=1L){
initialize(prisoners);

count += libertyOrDeathPlanned(prisoners,chances)==DEATH?0:1;
}

printf("\n\nGames Played : %llu\nGames Won : %llu\nChances : %lf %% \n\n",trials,count,(100.0*count)/trials);
return 0;
}
```
```\$ gcc 100prisoners.c && ./a.out 100 50 10000
Running random trials...

Games Played : 10000
Games Won : 0
Chances : 0.000000 %

Running strategic trials...

Games Played : 10000
Games Won : 3051
Chances : 30.510000 %
```

## C#

Translation of: D
```using System;
using System.Linq;

namespace Prisoners {
class Program {
static bool PlayOptimal() {
var secrets = Enumerable.Range(0, 100).OrderBy(a => Guid.NewGuid()).ToList();

for (int p = 0; p < 100; p++) {
bool success = false;

var choice = p;
for (int i = 0; i < 50; i++) {
if (secrets[choice] == p) {
success = true;
break;
}
choice = secrets[choice];
}

if (!success) {
return false;
}
}

return true;
}

static bool PlayRandom() {
var secrets = Enumerable.Range(0, 100).OrderBy(a => Guid.NewGuid()).ToList();

for (int p = 0; p < 100; p++) {
var choices = Enumerable.Range(0, 100).OrderBy(a => Guid.NewGuid()).ToList();

bool success = false;
for (int i = 0; i < 50; i++) {
if (choices[i] == p) {
success = true;
break;
}
}

if (!success) {
return false;
}
}

return true;
}

static double Exec(uint n, Func<bool> play) {
uint success = 0;
for (uint i = 0; i < n; i++) {
if (play()) {
success++;
}
}
return 100.0 * success / n;
}

static void Main() {
const uint N = 1_000_000;
Console.WriteLine("# of executions: {0}", N);
Console.WriteLine("Optimal play success rate: {0:0.00000000000}%", Exec(N, PlayOptimal));
Console.WriteLine(" Random play success rate: {0:0.00000000000}%", Exec(N, PlayRandom));
}
}
}
```
Output:
```# of executions: 1000000
Optimal play success rate: 31.21310000000%
Random play success rate: 0.00000000000%```

## C++

```#include <cstdlib>   // for rand
#include <algorithm> // for random_shuffle
#include <iostream>  // for output

using namespace std;

class cupboard {
public:
cupboard() {
for (int i = 0; i < 100; i++)
drawers[i] = i;
random_shuffle(drawers, drawers + 100);
}

bool playRandom();
bool playOptimal();

private:
int drawers[100];
};

bool cupboard::playRandom() {
bool openedDrawers[100] = { 0 };
for (int prisonerNum = 0; prisonerNum < 100; prisonerNum++) { // loops through prisoners numbered 0 through 99
bool prisonerSuccess = false;
for (int i = 0; i < 100 / 2; i++) {  // loops through 50 draws for each prisoner
int drawerNum = rand() % 100;
if (!openedDrawers[drawerNum]) {
openedDrawers[drawerNum] = true;
break;
}
if (drawers[drawerNum] == prisonerNum) {
prisonerSuccess = true;
break;
}
}
if (!prisonerSuccess)
return false;
}
return true;
}

bool cupboard::playOptimal() {
for (int prisonerNum = 0; prisonerNum < 100; prisonerNum++) {
bool prisonerSuccess = false;
int checkDrawerNum = prisonerNum;
for (int i = 0; i < 100 / 2; i++) {
if (drawers[checkDrawerNum] == prisonerNum) {
prisonerSuccess = true;
break;
} else
checkDrawerNum = drawers[checkDrawerNum];
}
if (!prisonerSuccess)
return false;
}
return true;
}

double simulate(char strategy) {
int numberOfSuccesses = 0;
for (int i = 0; i < 10000; i++) {
cupboard d;
if ((strategy == 'R' && d.playRandom()) || (strategy == 'O' && d.playOptimal())) // will run playRandom or playOptimal but not both because of short-circuit evaluation
numberOfSuccesses++;
}

return numberOfSuccesses * 100.0 / 10000;
}

int main() {
cout << "Random strategy:  " << simulate('R') << " %" << endl;
cout << "Optimal strategy: " << simulate('O') << " %" << endl;
system("PAUSE"); // for Windows
return 0;
}
```
Output:
```Random strategy:  0 %
Optimal strategy: 31.54 %```

## Clojure

```(ns clojure-sandbox.prisoners)

(defn random-drawers []
"Returns a list of shuffled numbers"
(-> 100
range
shuffle))

(defn search-50-random-drawers [prisoner-number drawers]
"Select 50 random drawers and return true if the prisoner's number was found"
(->> drawers
shuffle ;; Put drawer contents in random order
(take 50) ;; Select first 50, equivalent to selecting 50 random drawers
(filter (fn [x] (= x prisoner-number))) ;; Filter to include only those that match prisoner number
count
(= 1))) ;; Returns true if the number of matching numbers is 1

(defn search-50-optimal-drawers [prisoner-number drawers]
"Open 50 drawers according to the agreed strategy, returning true if prisoner's number was found"
(loop [next-drawer prisoner-number ;; The drawer index to start on is the prisoner's number
drawers-opened 0] ;; To keep track of how many have been opened as 50 is the maximum
(if (= drawers-opened 50)
false ;; If 50 drawers have been opened, the prisoner's number has not been found
(let [result (nth drawers next-drawer)] ;; Open the drawer given by next number
(if (= result prisoner-number) ;; If prisoner number has been found
true ;; No need to keep opening drawers - return true
(recur result (inc drawers-opened))))))) ;; Restart the loop using the resulting number as the drawer number

(defn try-luck [drawers drawer-searching-function]
"Returns 1 if all prisoners find their number otherwise 0"
(if (empty? prisoners) ;; If they've all gone and found their number
1 ;; Return true- they'll all live
(let [res (-> prisoners
first
(drawer-searching-function drawers))] ;; Otherwise, have the first prisoner open drawers according to the specified method
(if (false? res) ;; If this prisoner didn't find their number
0 ;; no prisoners will be freed so we can return false and stop
(recur (rest prisoners))))))) ;; Otherwise they've found the number, so we remove them from the queue and repeat with the others

(defn simulate-100-prisoners []
"Simulates all prisoners searching the same drawers by both strategies, returns map showing whether each was successful"
(let [drawers (random-drawers)] ;; Create 100 drawers with randomly ordered prisoner numbers
{:random (try-luck drawers search-50-random-drawers) ;; True if all prisoners found their number using random strategy
:optimal (try-luck drawers search-50-optimal-drawers)})) ;; True if all prisoners found their number using optimal strategy

(defn simulate-n-runs [n]
"Simulate n runs of the 100 prisoner problem and returns a success count for each search method"
(loop [random-successes 0
optimal-successes 0
run-count 0]
(if (= n run-count) ;; If we've done the loop n times
{:random-successes random-successes ;; return results
:optimal-successes optimal-successes
:run-count run-count}
(let [next-result (simulate-100-prisoners)] ;; Otherwise, run for another batch of prisoners
(recur (+ random-successes (:random next-result)) ;; Add result of run to the total successs count
(+ optimal-successes (:optimal next-result))
(inc run-count)))))) ;; increment run count and run again

(defn -main [& args]
"For 5000 runs, print out the success frequency for both search methods"
(let [{:keys [random-successes optimal-successes run-count]} (simulate-n-runs 5000)]
(println (str "Probability of survival with random search: " (float (/ random-successes run-count))))
(println (str "Probability of survival with ordered search: " (float (/ optimal-successes run-count))))))
```
Output:
```Probability of survival with random search: 0.0
Probability of survival with ordered search: 0.3062
```

## CLU

```% This program needs to be merged with PCLU's "misc" library
% to use the random number generator.
%
% pclu -merge \$CLUHOME/lib/misc.lib -compile prisoners.clu

% Seed the random number generator with the current time
init_rng = proc ()
d: date := now()
seed: int := ((d.hour*60) + d.minute)*60 + d.second
random\$seed(seed)
end init_rng

% Place cards in drawers randomly
make_drawers = proc (n: int) returns (sequence[int])
d: array[int] := array[int]\$predict(1,n)

% place each card in its own drawer
for i: int in int\$from_to(1,n) do
end

% shuffle the cards
for i: int in int\$from_to_by(n,2,-1) do
j: int := random\$next(i)+1
t: int := d[i]
d[i] := d[j]
d[j] := t
end
return(sequence[int]\$a2s(d))
end make_drawers

% Random strategy
rand_strat = proc (p, tries: int, d: sequence[int]) returns (bool)
n: int := sequence[int]\$size(d)
for i: int in int\$from_to(1,tries) do
if p = d[random\$next(n)+1] then return(true) end
end
return(false)
end rand_strat

% Optimal strategy
opt_strat = proc (p, tries: int, d: sequence[int]) returns (bool)
last: int := p
for i: int in int\$from_to(1,tries) do
if d[last]=p then return(true) end
last := d[last]
end
return(false)
end opt_strat

% Run one simulation given a strategy
simulate = proc (n, tries: int,
strat: proctype (int,int,sequence[int]) returns (bool))
returns (bool)
d: sequence[int] := make_drawers(n)
for p: int in int\$from_to(1,n) do
% If one prisoner fails, they all hang
if ~strat(p,tries,d) then return(false) end
end
return(true)
end simulate

% Run many simulations and count the successes
run_simulations = proc (amount, n, tries: int,
strat: proctype (int,int,sequence[int]) returns (bool))
returns (int)
ok: int := 0
for i: int in int\$from_to(1,amount) do
if simulate(n,tries,strat) then
ok := ok + 1
end
end
return(ok)
end run_simulations

% Run simulations and show the results
show = proc (title: string,
amount, n, tries: int,
strat: proctype (int,int,sequence[int]) returns (bool))
po: stream := stream\$primary_output()
stream\$puts(po, title || ": ")

ok: int := run_simulations(amount, n, tries, strat)
perc: real := real\$i2r(ok)*100.0/real\$i2r(amount)

stream\$putright(po, int\$unparse(ok), 7)
stream\$puts(po, " out of ")
stream\$putright(po, int\$unparse(amount), 7)
stream\$putl(po, ", " || f_form(perc, 3, 2) || "%")
end show

start_up = proc ()
prisoners   = 100
tries       = 50
simulations = 50000

init_rng()

show(" Random", simulations, prisoners, tries, rand_strat)
show("Optimal", simulations, prisoners, tries, opt_strat)
end start_up```
Output:
``` Random:       0 out of   50000, 0.00%
Optimal:   15541 out of   50000, 31.08%```

## Commodore BASIC

It should be noted that this is a very time consuming process for a ~1 MHz 8-bit computer. Evaluating 1000 trials of each method with the algorithm below takes about 3.5 hours on the BASIC system clock (TIME\$) of a stock NTSC Commodore 64, even with screen blanking. (Screen blanking seems to achieve only a 3% improvement in speed.) Actual test of 4000 trials for each method were run on the VICE emulator with warp speed engaged, otherwise the user would have had to wait a day and a half for results.

Another concern is when the prisoner's number is found. When this happens it becomes unnecessary to use whatever guesses are remaining; we should simply move on to the next prisoner. Furthermore, if any prisoner uses all 50 guesses with no luck, then everyone is out of luck and the trial is over, which means no other prisoner needs to make the attempt.

This potentially could cause problems on the stack with unfinished guessing (or prisoner) loops, especially where stack limits are extremely small however, a few things are happening to prevent this (See C64-Wiki "NEXT: Early Exits..." for reference.):

1. The prisoner loop, and each prisoner's 50-guesses loop, are contained within a subroutine. The RETURN at the end of either subroutine terminates any unfinished loops and keeps the stack clean.
2. When the NEXT belonging to loop 'i' is encountered, any inner loops ('g') are terminated.
3. Similar to above, any new loop using an existing loop's variable terminates the old loop, and any nested loops within it.

The key here is avoiding the use of GOTO as a means of exiting a loop early.

```10 rem 100 prisoners
20 rem set arrays
30 rem dr = drawers containing card values
40 rem ig = a list of numbers 1 through 100, shuffled to become the
41 rem guess sequence for each inmate - method 1
50 dim dr(100),ig(100)
55 rem initialize drawers with own card in each drawer
60 for i=1 to 100:dr(i)=i:next

1000 print chr\$(147);"how many trials for each method";:input tt
1010 for m=1 to 2:su(m)=0:fa(m)=0
1015 for tn=1 to tt
1020 on m gosub 2000,3000
1025 rem ip = number of inmates who passed
1030 if ip=100 then su(m)=su(m)+1
1040 if ip<100 then fa(m)=fa(m)+1
1045 next tn
1055 next m

1060 print chr\$(147);"Results:":print
1070 print "Out of";tt;"trials, the results are"
1071 print "as follows...":print
1072 print "1. Random Guessing:"
1073 print "  ";su(1);"successes"
1074 print "  ";fa(1);"failures"
1075 print "  ";su(1)/tn;"{left-crsr}% success rate.":print
1077 print "2. Chained Number Picking:"
1078 print "  ";su(2);"successes"
1079 print "  ";fa(2);"failures"
1080 print "  ";(su(2)/tn)*100;"{left-crsr}% success rate.":print
1100 print:print "Again?"
1110 get k\$:if k\$="" then 1110
1120 if k\$="y" then 1000
1500 end

2000 rem random guessing method
2005 for x=1 to 100:ig(x)=x:next:ip=0:gosub 4000
2007 for i=1 to 100
2010 for x=1 to 100:t=ig(x):np=int(rnd(1)*100)+1:ig(x)=ig(np):ig(np)=t:next
2015 for g=1 to 50
2020 if dr(ig(g))=i then ip=ip+1:next i:return
2025 next g
2030 return

3000 rem chained method
3005 ip=0:gosub 4000
3007 rem iterate through each inmate
3010 fori=1to100
3015 ng=i:forg=1to50
3020 cd=dr(ng)
3025 ifcd=ithenip=ip+1:nexti:return
3030 ifcd<>ithenng=cd
3035 nextg:return

4000 rem shuffle the drawer cards randomly
4010 x=rnd(-ti)
4020 for i=1 to 100
4030 r=int(rnd(1)*100)+1:t=dr(i):dr(i)=dr(r):dr(r)=t:next
4040 return
```
Output:
```Results:

Out of 4000 trials the percentage of
success is as follows...

1. Random Guessing:
0 successes
4000 failures
0% success rate.

2. Chained Number Picking:
1274 successes
2726 failures
31.85% success rate.
```

## Common Lisp

Translation of: Racket
```(defparameter *samples* 10000)
(defparameter *prisoners* 100)
(defparameter *max-guesses* 50)

(defun range (n)
"Returns a list from 0 to N."
(loop
for i below n
collect i))

(defun nshuffle (list)
"Returns a shuffled LIST."
(loop
for i from (length list) downto 2
do (rotatef (nth (random i) list)
(nth (1- i) list)))
list)

(defun build-drawers ()
"Returns a list of shuffled drawers."
(nshuffle (range *prisoners*)))

(defun strategy-1 (drawers p)
"Returns T if P is found in DRAWERS under *MAX-GUESSES* using a random strategy."
(loop
for i below *max-guesses*
thereis (= p (nth (random *prisoners*) drawers))))

(defun strategy-2 (drawers p)
"Returns T if P is found in DRAWERS under *MAX-GUESSES* using an optimal strategy."
(loop
for i below *max-guesses*
for j = p then (nth j drawers)
thereis (= p (nth j drawers))))

(defun 100-prisoners-problem (strategy &aux (drawers (build-drawers)))
"Returns T if all prisoners find their number using the given STRATEGY."
(every (lambda (e) (eql T e))
(mapcar (lambda (p) (funcall strategy drawers p)) (range *prisoners*))))

(defun sampling (strategy)
(loop
repeat *samples*
for result = (100-prisoners-problem strategy)
count result))

(defun compare-strategies ()
(format t "Using a random strategy in ~4,2F % of the cases the prisoners are free.~%" (* (/ (sampling #'strategy-1) *samples*) 100))
(format t "Using an optimal strategy in ~4,2F % of the cases the prisoners are free.~%" (* (/ (sampling #'strategy-2) *samples*) 100)))
```
Output:
```CL-USER> (compare-strategies)
Using a random strategy in 0.00 % of the cases the prisoners are free.
Using an optimal strategy in 31.34 % of the cases the prisoners are free.```

## Cowgol

```include "cowgol.coh";
include "argv.coh";

# Parameters
const Drawers     := 100;   # Amount of drawers (and prisoners)
const Attempts    := 50;    # Amount of attempts a prisoner may make
const Simulations := 2000;  # Amount of simulations to run

typedef NSim is int(0, Simulations);

# Random number generator
record RNG is
x: uint8;
a: uint8;
b: uint8;
c: uint8;
state @at(0): int32;
end record;

sub RandomByte(r: [RNG]): (byte: uint8) is
r.x := r.x + 1;
r.a := r.a ^ r.c ^ r.x;
r.b := r.b + r.a;
r.c := r.c + (r.b >> 1) ^ r.a;
byte := r.c;
end sub;

sub RandomUpTo(r: [RNG], limit: uint8): (rslt: uint8) is
var x: uint8 := 1;
while x < limit loop
x := x << 1;
end loop;
x := x - 1;

loop
rslt := RandomByte(r) & x;
if rslt < limit then
break;
end if;
end loop;
end sub;

# Drawers (though marked 0..99 instead of 1..100)
var drawers: uint8[Drawers];
typedef Drawer is @indexof drawers;
typedef Prisoner is Drawer;

# Place cards randomly in drawers
sub InitDrawers(r: [RNG]) is
var x: Drawer := 0;
while x < Drawers loop
drawers[x] := x;
x := x + 1;
end loop;

x := 0;
while x < Drawers - 1 loop
var y := x + RandomUpTo(r, Drawers-x);
var t := drawers[x];
drawers[x] := drawers[y];
drawers[y] := t;
x := x + 1;
end loop;
end sub;

# A prisoner can apply a strategy and either succeed or not
interface Strategy(p: Prisoner, r: [RNG]): (success: uint8);

# The stupid strategy: open drawers randomly.
sub Stupid implements Strategy is
# Let's assume the prisoner is smart enough not to reopen an open drawer
var opened: Drawer[Drawers];
MemZero(&opened[0], @bytesof opened);

# Open random drawers
success := 0;
var triesLeft: uint8 := Attempts;
while triesLeft != 0 loop
var d := RandomUpTo(r, Drawers); # grab a random drawer
if opened[d] != 0 then
continue; # Ignore it if a drawer was already open
else
triesLeft := triesLeft - 1;
opened[d] := 1;
if drawers[d] == p then # found it!
success := 1;
return;
end if;
end if;
end loop;
end sub;

# The optimal strategy: open the drawer for each number
sub Optimal implements Strategy is
var current := p;
var triesLeft: uint8 := Attempts;
success := 0;
while triesLeft != 0 loop
current := drawers[current];
if current == p then
success := 1;
return;
end if;
triesLeft := triesLeft - 1;
end loop;
end sub;

# Run a simulation
sub Simulate(s: Strategy, r: [RNG]): (success: uint8) is
InitDrawers(r); # place cards randomly in drawer
var p: Prisoner := 0;
success := 1; # if they all succeed the simulation succeeds
while p < Drawers loop # but for each prisoner...
if s(p, r) == 0 then # if he fails, the simulation fails
success := 0;
return;
end if;
p := p + 1;
end loop;
end sub;

# Run an amount of simulations and report the amount of successes
sub Run(n: NSim, s: Strategy, r: [RNG]): (successes: NSim) is
successes := 0;
while n > 0 loop
successes := successes + Simulate(s, r) as NSim;
n := n - 1;
end loop;
end sub;

# Initialize RNG with number given on command line (defaults to 0)
var rng: RNG; rng.state := 0;
ArgvInit();
var arg := ArgvNext();
if arg != 0 as [uint8] then
(rng.state, arg) := AToI(arg);
end if;

sub RunAndPrint(name: [uint8], strat: Strategy) is
print(name);
print(" strategy: ");
var succ := Run(Simulations, strat, &rng) as uint32;
print_i32(succ);
print(" out of ");
print_i32(Simulations);
print(" - ");
print_i32(succ * 100 / Simulations);
print("%\n");
end sub;

RunAndPrint("Stupid", Stupid);
RunAndPrint("Optimal", Optimal);```
Output:
```Stupid strategy: 0 out of 2000 - 0%
Optimal strategy: 634 out of 2000 - 31%```

## Crystal

Based on the Ruby implementation

```prisoners = (1..100).to_a
N = 100_000
generate_rooms = ->{ (1..100).to_a.shuffle }

res = N.times.count do
rooms = generate_rooms.call
prisoners.all? { |pr| rooms[1, 100].sample(50).includes?(pr) }
end
puts "Random strategy : %11.4f %%" % (res.fdiv(N) * 100)

res = N.times.count do
rooms = generate_rooms.call
prisoners.all? do |pr|
cur_room = pr
50.times.any? do
cur_room = rooms[cur_room - 1]
found = (cur_room == pr)
found
end
end
end
puts "Optimal strategy: %11.4f %%" % (res.fdiv(N) * 100)
```
Output:
```Random strategy :      0.0000 %
Optimal strategy:     31.3190 %```

## D

Translation of: Kotlin
```import std.array;
import std.random;
import std.range;
import std.stdio;
import std.traits;

bool playOptimal() {
auto secrets = iota(100).array.randomShuffle();

prisoner:
foreach (p; 0..100) {
auto choice = p;
foreach (_; 0..50) {
if (secrets[choice] == p) continue prisoner;
choice = secrets[choice];
}
return false;
}

return true;
}

bool playRandom() {
auto secrets = iota(100).array.randomShuffle();

prisoner:
foreach (p; 0..100) {
auto choices = iota(100).array.randomShuffle();
foreach (i; 0..50) {
if (choices[i] == p) continue prisoner;
}
return false;
}

return true;
}

double exec(const size_t n, bool function() play) {
size_t success = 0;
for (int i = n; i > 0; i--) {
if (play()) {
success++;
}
}
return 100.0 * success / n;
}

void main() {
enum N = 1_000_000;
writeln("# of executions: ", N);
writefln("Optimal play success rate: %11.8f%%", exec(N, &playOptimal));
writefln(" Random play success rate: %11.8f%%", exec(N, &playRandom));
}
```
Output:
```# of executions: 1000000
Optimal play success rate: 31.16100000%
Random play success rate:  0.00000000%```

See #Pascal.

## EasyLang

```for i = 1 to 100
drawer[] &= i
sampler[] &= i
.
subr shuffle_drawer
for i = len drawer[] downto 2
r = random i
swap drawer[r] drawer[i]
.
.
subr play_random
call shuffle_drawer
for prisoner = 1 to 100
found = 0
for i = 1 to 50
r = random (100 - i)
card = drawer[sampler[r]]
swap sampler[r] sampler[100 - i - 1]
if card = prisoner
found = 1
break 1
.
.
if found = 0
break 1
.
.
.
subr play_optimal
call shuffle_drawer
for prisoner = 1 to 100
reveal = prisoner
found = 0
for i = 1 to 50
card = drawer[reveal]
if card = prisoner
found = 1
break 1
.
reveal = card
.
if found = 0
break 1
.
.
.
n = 10000
win = 0
for _ = 1 to n
call play_random
win += found
.
print "random: " & 100.0 * win / n & "%"
#
win = 0
for _ = 1 to n
call play_optimal
win += found
.
print "optimal: " & 100.0 * win / n & "%"```
Output:
```random: 0.000%
optimal: 30.800%
```

## Elixir

Translation of: Ruby
```defmodule HundredPrisoners do
def optimal_room(_, _, _, []), do: []
def optimal_room(prisoner, current_room, rooms, [_ | tail]) do
found = Enum.at(rooms, current_room - 1) == prisoner
next_room = Enum.at(rooms, current_room - 1)
[found] ++ optimal_room(prisoner, next_room, rooms, tail)
end

def optimal_search(prisoner, rooms) do
Enum.any?(optimal_room(prisoner, prisoner, rooms, Enum.to_list(1..50)))
end
end

prisoners = 1..100
n = 1..10_000
generate_rooms = fn -> Enum.shuffle(1..100) end

random_strategy = Enum.count(n,
fn _ ->
rooms = generate_rooms.()
Enum.all?(prisoners, fn pr -> pr in (rooms |> Enum.take_random(50)) end)
end)

IO.puts "Random strategy: #{random_strategy} / #{n |> Range.size}"

optimal_strategy = Enum.count(n,
fn _ ->
rooms = generate_rooms.()
Enum.all?(prisoners,
fn pr -> HundredPrisoners.optimal_search(pr, rooms) end)
end)

IO.puts "Optimal strategy: #{optimal_strategy} / #{n |> Range.size}"
```
Output:
```Random strategy: 0 / 10000
Optimal strategy: 3110 / 10000
```

## F#

```let rnd = System.Random()
let shuffled min max =
[|min..max|] |> Array.sortBy (fun _ -> rnd.Next(min,max+1))

let drawers () = shuffled 1 100

// strategy randomizing drawer opening
let badChoices (drawers' : int array) =
Seq.init 100 (fun _ -> shuffled 1 100 |> Array.take 50) // selections for each prisoner
|> Seq.map (fun indexes -> indexes |> Array.map(fun index -> drawers'.[index-1])) // transform to cards
|> Seq.mapi (fun i cards -> cards |> Array.contains i) // check if any card matches prisoner number
|> Seq.contains false // true means not all prisoners got their cards
let outcomeOfRandom runs =
let pardons = Seq.init runs (fun _ -> badChoices (drawers ()))
|> Seq.sumBy (fun badChoice -> if badChoice |> not then 1.0 else 0.0)
pardons/ float runs

// strategy optimizing drawer opening
let smartChoice max prisoner (drawers' : int array) =
prisoner
|> Seq.unfold (fun selection ->
let card = drawers'.[selection-1]
Some (card, card))
|> Seq.take max
|> Seq.contains prisoner
let smartChoices (drawers' : int array) =
seq { 1..100 }
|> Seq.map (fun prisoner -> smartChoice 50 prisoner drawers')
|> Seq.filter (fun result -> result |> not) // remove all but false results
|> Seq.isEmpty // empty means all prisoners got their cards
let outcomeOfOptimize runs =
let pardons = Seq.init runs (fun _ -> smartChoices (drawers()))
|> Seq.sumBy (fun smartChoice' -> if smartChoice' then 1.0 else 0.0)
pardons/ float runs

printfn \$"Using Random Strategy: {(outcomeOfRandom 20000):p2}"
printfn \$"Using Optimum Strategy: {(outcomeOfOptimize 20000):p2}"
```
Output:
```Using Random Strategy: 0.00%
Using Optimum Strategy: 31.06%
```

## Factor

```USING: arrays formatting fry io kernel math random sequences ;

: setup ( -- seq seq ) 100 <iota> dup >array randomize ;

: rand ( -- ? )
setup [ 50 sample member? not ] curry find nip >boolean not ;

: trail ( m seq -- n )
50 pick '[ [ nth ] keep over _ = ] replicate [ t = ] any?
2nip ;

: optimal ( -- ? ) setup [ trail ] curry [ and ] map-reduce ;

: simulate ( m quot -- x )
dupd replicate [ t = ] count swap /f 100 * ; inline

"Simulation count: 10,000" print
10,000 [ rand ] simulate "Random play success: "
10,000 [ optimal ] simulate "Optimal play success: "
[ write "%.2f%%\n" printf ] 2bi@
```
Output:
```Simulation count: 10,000
Random play success: 0.00%
Optimal play success: 31.11%
```

## FOCAL

```01.10 T %5.02," RANDOM";S CU=0
01.20 F Z=1,2000;D 5;S CU=CU+SU
01.30 T CU/20,!,"OPTIMAL";S CU=0
01.40 F Z=1,2000;D 6;S CU=CU+SU
01.50 T CU/20,!
01.60 Q

02.01 C-- PUT CARDS IN RANDOM DRAWERS
02.10 F X=1,100;S D(X)=X
02.20 F X=1,99;D 2.3;S B=D(X);S D(X)=D(A);S D(A)=B
02.30 D 2.4;S A=X+FITR(A*(101-X))
02.40 S A=FABS(FRAN()*10);S A=A-FITR(A)

03.01 C-- PRISONER X TRIES UP TO 50 RANDOM DRAWERS
03.10 S TR=50;S SU=0
03.20 D 2.4;I (X-D(A))3.3,3.4,3.3
03.30 S TR=TR-1;I (TR),3.5,3.2
03.40 S SU=1;R
03.50 S SU=0

04.01 C-- PRISONER X TRIES OPTIMAL METHOD
04.10 S TR=50;S SU=0;S A=X
04.20 I (X-D(A))4.3,4.4,4.3
04.30 S TR=TR-1;S A=D(A);I (TR),4.5,4.2
04.40 S SU=1;R
04.50 S SU=0

05.01 C-- PRISONERS TRY RANDOM METHOD UNTIL ONE FAILS
05.10 D 2;S X=1
05.20 I (X-101)5.3,5.4
05.30 D 3;S X=X+1;I (SU),5.4,5.2
05.40 R

06.01 C-- PRISONERS TRY OPTIMAL METHOD UNTIL ONE FAILS
06.10 D 2;S X=1
06.20 I (X-101)6.3,6.4
06.30 D 4;S X=X+1;I (SU),6.4,6.2
06.40 R```
Output:
``` RANDOM=   0.00
OPTIMAL=  30.10```

## Forth

ANS Forth has no in-built facility for random numbers, but libraries are available.

Works with: ANS Forth

Here is a solution using ran4.seq from The Forth Scientific Library, available here.

Run the two strategies (random and follow the card number) 10,000 times each, and show number or successes.

```INCLUDE ran4.seq

100      CONSTANT #drawers
#drawers CONSTANT #players
100000   CONSTANT #tries

CREATE drawers  #drawers CELLS ALLOT                    \ index 0..#drawers-1

: drawer[]                              ( n -- addr )   \ return address of drawer n
CELLS drawers +
;

: random_drawer                         ( -- n )        \ n=0..#drawers-1 random drawer
RAN4 ( d ) XOR ( n ) #drawers MOD
;

: random_drawer[]                       ( -- addr )     \ return address of random drawer
random_drawer drawer[]
;

ROT ! SWAP !                        \ store n1 at addr2 and n2 at addr1
;

: init_drawers                          ( -- ) \ shuffle cards into drawers
#drawers 0 DO
I I drawer[] !                  \ store cards in order
LOOP
#drawers 0 DO
swap_indirect
LOOP
;

: random_turn                           ( player - f )
#drawers 2 / 0 DO
random_drawer
drawer[] @
OVER = IF
DROP TRUE UNLOOP EXIT	\ found his number
THEN
LOOP
DROP FALSE
;

0 VALUE player

: cycle_turn                            ( player - f )
DUP TO player			( next-drawer )
#drawers 2 / 0 DO
drawer[] @
DUP player = IF
DROP TRUE UNLOOP EXIT	\ found his number
THEN
LOOP
DROP FALSE
;

: turn                                  ( strategy player - f )
SWAP 0= IF                          \ random play
random_turn
ELSE
cycle_turn
THEN
;

: play                                  ( strategy -- f ) \ return true if prisioners survived
init_drawers
#players 0 DO
DUP I turn
0= IF
DROP FALSE UNLOOP EXIT 	\ this player did not survive, UNLOOP, return false
THEN
LOOP
DROP TRUE                           \ all survived, return true
;

: trie					( strategy - nr-saved )
0				( strategy nr-saved )
#tries 0 DO
OVER play IF 1+ THEN
LOOP
NIP
;

0 trie . CR	\ random strategy
1 trie . CR	\ follow the card number strategy
```

output:

```0
30009
```

## Fortran

```SUBROUTINE SHUFFLE_ARRAY(INT_ARRAY)
! Takes an input array and shuffles the elements by swapping them
! in pairs in turn 10 times
IMPLICIT NONE

INTEGER, DIMENSION(100), INTENT(INOUT) :: INT_ARRAY
INTEGER, PARAMETER :: N_PASSES = 10
! Local Variables

INTEGER :: TEMP_1, TEMP_2   ! Temporaries for swapping elements
INTEGER :: I, J, PASS       ! Indices variables
REAL :: R                   ! Randomly generator value

CALL RANDOM_SEED()  ! Seed the random number generator

DO PASS=1, N_PASSES
DO I=1, SIZE(INT_ARRAY)

! Get a random index to swap with
CALL RANDOM_NUMBER(R)
J = CEILING(R*SIZE(INT_ARRAY))

! In case generated index value
! exceeds array size
DO WHILE (J > SIZE(INT_ARRAY))
J = CEILING(R*SIZE(INT_ARRAY))
END DO

!  Swap the two elements
TEMP_1 = INT_ARRAY(I)
TEMP_2 = INT_ARRAY(J)
INT_ARRAY(I) = TEMP_2
INT_ARRAY(J) = TEMP_1
ENDDO
ENDDO
END SUBROUTINE SHUFFLE_ARRAY

SUBROUTINE RUN_RANDOM(N_ROUNDS)
! Run the 100 prisoner puzzle simulation N_ROUNDS times
! in the scenario where each prisoner selects a drawer at random
IMPLICIT NONE

INTEGER, INTENT(IN) :: N_ROUNDS ! Number of simulations to run in total

INTEGER :: ROUND, PRISONER, CHOICE, I       ! Iteration variables
INTEGER :: N_SUCCESSES                      ! Number of successful trials
REAL(8) :: TOTAL                            ! Total number of trials as real
LOGICAL :: NUM_FOUND = .FALSE.              ! Prisoner has found their number

INTEGER, DIMENSION(100) :: CARDS, CHOICES   ! Arrays representing card allocations
! to draws and drawer choice order

! Both cards and choices are randomly assigned.
! This being the drawer (allocation represented by index),
! and what drawer to pick for Nth/50 choice
! (take first 50 elements of 100 element array)
CARDS = (/(I, I=1, 100, 1)/)
CHOICES = (/(I, I=1, 100, 1)/)

N_SUCCESSES = 0
TOTAL = REAL(N_ROUNDS)

! Run the simulation for N_ROUNDS rounds
! when a prisoner fails to find their number
! after 50 trials, set that simulation to fail
! and start the next round
ROUNDS_LOOP: DO ROUND=1, N_ROUNDS
CALL SHUFFLE_ARRAY(CARDS)
PRISONERS_LOOP: DO PRISONER=1, 100
NUM_FOUND = .FALSE.
CALL SHUFFLE_ARRAY(CHOICES)
CHOICE_LOOP: DO CHOICE=1, 50
IF(CARDS(CHOICE) == PRISONER) THEN
NUM_FOUND = .TRUE.
EXIT CHOICE_LOOP
ENDIF
ENDDO CHOICE_LOOP
IF(.NOT. NUM_FOUND) THEN
EXIT PRISONERS_LOOP
ENDIF
ENDDO PRISONERS_LOOP
IF(NUM_FOUND) THEN
N_SUCCESSES = N_SUCCESSES + 1
ENDIF
ENDDO ROUNDS_LOOP

WRITE(*, '(A, F0.3, A)') "Random drawer selection method success rate: ", &
100*N_SUCCESSES/TOTAL, "%"

END SUBROUTINE RUN_RANDOM

SUBROUTINE RUN_OPTIMAL(N_ROUNDS)
! Run the 100 prisoner puzzle simulation N_ROUNDS times in the scenario
! where each prisoner selects firstly the drawer with their number and then
! subsequently the drawer matching the number of the card present
! within that current drawer
IMPLICIT NONE

INTEGER, INTENT(IN) :: N_ROUNDS

INTEGER :: ROUND, PRISONER, CHOICE, I   ! Iteration variables
INTEGER :: CURRENT_DRAW                 ! ID of the current draw
INTEGER :: N_SUCCESSES                  ! Number of successful trials
REAL(8) :: TOTAL                        ! Total number of trials as real
LOGICAL :: NUM_FOUND = .FALSE.          ! Prisoner has found their number
INTEGER, DIMENSION(100) :: CARDS        ! Array representing card allocations

! Cards are randomly assigned to a drawer
! (allocation represented by index),
CARDS = (/(I, I=1, 100, 1)/)

N_SUCCESSES = 0
TOTAL = REAL(N_ROUNDS)

! Run the simulation for N_ROUNDS rounds
! when a prisoner fails to find their number
! after 50 trials, set that simulation to fail
! and start the next round
ROUNDS_LOOP: DO ROUND=1, N_ROUNDS
CARDS = (/(I, I=1, 100, 1)/)
CALL SHUFFLE_ARRAY(CARDS)
PRISONERS_LOOP: DO PRISONER=1, 100
CURRENT_DRAW = PRISONER
NUM_FOUND = .FALSE.
CHOICE_LOOP: DO CHOICE=1, 50
IF(CARDS(CURRENT_DRAW) == PRISONER) THEN
NUM_FOUND = .TRUE.
EXIT CHOICE_LOOP
ELSE
CURRENT_DRAW = CARDS(CURRENT_DRAW)
ENDIF
ENDDO CHOICE_LOOP
IF(.NOT. NUM_FOUND) THEN
EXIT PRISONERS_LOOP
ENDIF
ENDDO PRISONERS_LOOP
IF(NUM_FOUND) THEN
N_SUCCESSES = N_SUCCESSES + 1
ENDIF
ENDDO ROUNDS_LOOP
WRITE(*, '(A, F0.3, A)') "Optimal drawer selection method success rate: ", &
100*N_SUCCESSES/TOTAL, "%"

END SUBROUTINE RUN_OPTIMAL

PROGRAM HUNDRED_PRISONERS
! Run the two scenarios for the 100 prisoners puzzle of random choice
! and optimal choice (choice based on drawer contents)
IMPLICIT NONE
INTEGER, PARAMETER :: N_ROUNDS = 50000
WRITE(*,'(A, I0, A)') "Running simulation for ", N_ROUNDS, " trials..."
CALL RUN_RANDOM(N_ROUNDS)
CALL RUN_OPTIMAL(N_ROUNDS)
END PROGRAM HUNDRED_PRISONERS
```

output:

```Running simulation for 50000 trials...
Random drawer selection method success rate: .000%
Optimal drawer selection method success rate: 31.360%
```

## FreeBASIC

```#include once "knuthshuf.bas"   'use the routines in https://rosettacode.org/wiki/Knuth_shuffle#FreeBASIC

function gus( i as long, strat as boolean ) as long
if strat then return i
return 1+int(rnd*100)
end function

sub trials( byref c_success as long, byref c_fail as long, byval strat as boolean )
dim as long i, j, k, guess, drawer(1 to 100)
for i = 1 to 100
drawer(i) = i
next i
for j = 1 to 1000000 'one million trials of prisoners
knuth_up( drawer() )  'shuffles the cards in the drawers
for i = 1 to 100 'prisoner number
guess = gus(i, strat)
for k = 1 to 50 'each prisoner gets 50 tries
if drawer(guess) = i then goto next_prisoner
guess = gus(drawer(guess), strat)
next k
c_fail += 1
goto next_trial
next_prisoner:
next i
c_success += 1
next_trial:
next j
end sub

randomize timer
dim as long c_fail=0, c_success=0

trials( c_success, c_fail, false )

print using "For prisoners guessing randomly we had ####### successes and ####### failures.";c_success;c_fail

c_success = 0
c_fail = 0

trials( c_success, c_fail, true )

print using "For prisoners using the strategy we had ####### successes and ####### failures.";c_success;c_fail
```

## FutureBasic

```include "Tlbx GameplayKit.incl"

_prisoners = 100
_instances = 10000

local fn DrawersArray as CFArrayRef
long index
CFMutableArrayRef temp = fn MutableArrayWithCapacity(100)
for index = 0 to 99
next
end fn = fn ArrayShuffledArray( temp )

local fn RandomResult( drawers as CFArrayRef ) as BOOL
long prisoner, i, drawer, total = 0
MutableIndexSetRef set

for prisoner = 0 to _prisoners - 1
set = fn MutableIndexSetInit
for i = 1 to _prisoners/2
drawer = rnd(_prisoners)-1
while ( fn IndexSetContainsIndex( set, intVal( drawers[drawer] ) ) )
drawer = rnd(_prisoners)-1
wend
MutableIndexSetAddIndex( set, intVal( drawers[drawer] ) )
if ( fn IndexSetContainsIndex( set, prisoner ) )
total++
break
end if
next
next
end fn = ( total == _prisoners )

local fn OptimalResult( drawers as CFArrayRef ) as BOOL
long prisoner, drawer, i, card, total = 0

for prisoner = 0 to _prisoners - 1
drawer = prisoner
for i = 1 to _prisoners/2
card = intVal( drawers[drawer] )
if ( card == prisoner )
total++
break
end if
drawer = card
next
next
end fn = ( total == _prisoners )

void local fn DoIt
static double sTime = 0.0

block TimerRef timer = timerbegin , 0.001, YES
sTime += 0.001
cls
printf @"Compute time: %.3f\n",sTime
timerend

dispatchglobal
long instance, randomTotal = 0, optimalTotal = 0
CFArrayRef drawers

for instance = 1 to _instances
drawers = fn DrawersArray
if ( fn RandomResult( drawers ) ) then randomTotal++
if ( fn OptimalResult( drawers ) ) then optimalTotal++
next

dispatchmain
TimerInvalidate( timer )

cls
print @"Prisoners: "_prisoners
print @"Instances: "_instances
printf @"Random - fail: %ld, success: %ld (%.2f%%)",_instances-randomTotal,randomTotal,(double)randomTotal/(double)_instances*100.0
printf @"Optimal - fail: %ld, success: %ld (%.2f%%)\n",_instances-optimalTotal,optimalTotal,(double)optimalTotal/(double)_instances*100.0

printf @"Compute time: %.3f\n",sTime
dispatchend

dispatchend
end fn

random

window 1, @"100 Prisoners"

fn DoIt

HandleEvents```
Output:
```Prisoners: 100
Instances: 10000
Random - fail: 10000, success: 0 (0.00%)
Optimal - fail: 6896, success: 3104 (31.04%)

Compute time: 7.856
```

## Gambas

Implementation of the '100 Prisoners' program written in VBA. Tested in Gambas 3.15.2

```' Gambas module file

Public DrawerArray As Long[]
Public NumberFromDrawer As Long
Public FoundOwnNumber As Long

Public Sub Main()

Dim NumberOfPrisoners As Long
Dim Selections As Long
Dim Tries As Long

Print "Number of prisoners (default, 100)?"
Try Input NumberOfPrisoners
If Error Then NumberOfPrisoners = 100

Print "Number of selections (default, half of prisoners)?"
Try Input Selections
If Error Then Selections = NumberOfPrisoners / 2

Print "Number of tries (default, 1000)?"
Try Input Tries
If Error Then Tries = 1000

Dim AllFoundOptimal As Long = 0
Dim AllFoundRandom As Long = 0
Dim AllFoundRandomMem As Long = 0

Dim i As Long
Dim OptimalCount As Long
Dim RandomCount As Long
Dim RandomMenCount As Long

Dim fStart As Float = Timer

For i = 1 To Tries
OptimalCount = HundredPrisoners_Optimal(NumberOfPrisoners, Selections)
RandomCount = HundredPrisoners_Random(NumberOfPrisoners, Selections)
RandomMenCount = HundredPrisoners_Random_Mem(NumberOfPrisoners, Selections)

If OptimalCount = NumberOfPrisoners Then AllFoundOptimal += 1
If RandomCount = NumberOfPrisoners Then AllFoundRandom += 1
If RandomMenCount = NumberOfPrisoners Then AllFoundRandomMem += 1
Next

Dim fTime As Float = Timer - fStart
fTime = Round(ftime, -1)

Print
Print "Result with " & NumberOfPrisoners & " prisoners, " & Selections & " selections and " & Tries & " tries. "
Print
Print "Optimal: " & AllFoundOptimal & " of " & Tries & ": " & Str(AllFoundOptimal / Tries * 100) & " %"
Print "Random: " & AllFoundRandom & " of " & Tries & ": " & Str(AllFoundRandom / Tries * 100) & " %"
Print "RandomMem: " & AllFoundRandomMem & " of " & Tries & ": " & Str(AllFoundRandomMem / Tries * 100) & " %"
Print
Print "Elapsed Time: " & fTime & " sec"
Print
Print "Trials/sec: " & Round(Tries / fTime, -1)

End

Function HundredPrisoners_Optimal(NrPrisoners As Long, NrSelections As Long) As Long

DrawerArray = New Long[NrPrisoners]
Dim Counter As Long

For Counter = 0 To DrawerArray.Max
DrawerArray[Counter] = Counter + 1
Next

DrawerArray.Shuffle()

Dim i As Long
Dim j As Long
FoundOwnNumber = 0

For i = 1 To NrPrisoners
For j = 1 To NrSelections
If j = 1 Then NumberFromDrawer = DrawerArray[i - 1]

If NumberFromDrawer = i Then
FoundOwnNumber += 1
Break
Endif
NumberFromDrawer = DrawerArray[NumberFromDrawer - 1]
Next
Next
Return FoundOwnNumber

End

Function HundredPrisoners_Random(NrPrisoners As Long, NrSelections As Long) As Long

Dim RandomDrawer As Long
Dim Counter As Long

DrawerArray = New Long[NrPrisoners]

For Counter = 0 To DrawerArray.Max
DrawerArray[Counter] = Counter + 1
Next

DrawerArray.Shuffle()

Dim i As Long
Dim j As Long
FoundOwnNumber = 0

Randomize

For i = 1 To NrPrisoners
For j = 1 To NrSelections
RandomDrawer = CLong(Rand(NrPrisoners - 1))
NumberFromDrawer = DrawerArray[RandomDrawer]
If NumberFromDrawer = i Then
FoundOwnNumber += 1
Break
Endif
Next
Next
Return FoundOwnNumber

End

Function HundredPrisoners_Random_Mem(NrPrisoners As Long, NrSelections As Long) As Long

Dim SelectionArray As New Long[NrPrisoners]
Dim Counter As Long

DrawerArray = New Long[NrPrisoners]

For Counter = 0 To DrawerArray.Max
DrawerArray[Counter] = Counter + 1

Next

For Counter = 0 To SelectionArray.Max
SelectionArray[Counter] = Counter + 1

Next

DrawerArray.Shuffle()

Dim i As Long
Dim j As Long
FoundOwnNumber = 0

For i = 1 To NrPrisoners
SelectionArray.Shuffle()
For j = 1 To NrSelections
NumberFromDrawer = DrawerArray[SelectionArray[j - 1] - 1]
If NumberFromDrawer = i Then
FoundOwnNumber += 1
Break
Endif
NumberFromDrawer = DrawerArray[NumberFromDrawer - 1]
Next
Next
Return FoundOwnNumber

End
```
Output:
```Number of prisoners (default, 100)?
100
Number of selections (default, half of prisoners)?
50
Number of tries (default, 1000)?

Result with 100 prisoners, 50 selections and 1000 tries.

Optimal: 311 of 1000: 31,1 %
Random: 0 of 1000: 0 %
RandomMem: 0 of 1000: 0 %

Elapsed Time: 8.7 sec

Trials/sec: 114.9```

## GDScript

Works with: Godot version 4.0
```extends MainLoop

enum Strategy {Random, Optimal}

const prisoner_count := 100

func get_random_drawers() -> Array[int]:
var drawers: Array[int] = []
drawers.resize(prisoner_count)
for i in range(0, prisoner_count):
drawers[i] = i + 1
drawers.shuffle()
return drawers

var random_strategy = func(drawers: Array[int], prisoner: int) -> bool:
# Randomly selecting 50 drawers is equivalent to shuffling and picking the first 50
var drawerCopy: Array[int] = drawers.duplicate()
drawerCopy.shuffle()
for i in range(50):
if drawers[drawerCopy[i]-1] == prisoner:
return true
return false

var optimal_strategy = func(drawers: Array[int], prisoner: int) -> bool:
var choice: int = prisoner
for _i in range(50):
var drawer_value: int = drawers[choice-1]
if drawer_value == prisoner:
return true
choice = drawer_value
return false

func play_all(drawers: Array[int], strategy: Callable) -> bool:
for prisoner in range(1, prisoner_count+1):
if not strategy.call(drawers, prisoner):
return false
return true

func _process(_delta: float) -> bool:
# Constant seed for reproducibility, call randomize() in real use
seed(1234)

const SAMPLE_SIZE: int = 10_000

var random_successes: int = 0
for i in range(SAMPLE_SIZE):
if play_all(get_random_drawers(), random_strategy):
random_successes += 1

var optimal_successes: int = 0
for i in range(SAMPLE_SIZE):
if play_all(get_random_drawers(), optimal_strategy):
optimal_successes += 1

print("Random play: %%%f" % (100.0 * random_successes/SAMPLE_SIZE))
print("Optimal play: %%%f" % (100.0 * optimal_successes/SAMPLE_SIZE))

return true # Exit
```
Output:
```Random play: %0.000000
Optimal play: %31.700000
```

## Go

```package main

import (
"fmt"
"math/rand"
"time"
)

// Uses 0-based numbering rather than 1-based numbering throughout.
func doTrials(trials, np int, strategy string) {
pardoned := 0
trial:
for t := 0; t < trials; t++ {
var drawers [100]int
for i := 0; i < 100; i++ {
drawers[i] = i
}
rand.Shuffle(100, func(i, j int) {
drawers[i], drawers[j] = drawers[j], drawers[i]
})
prisoner:
for p := 0; p < np; p++ {
if strategy == "optimal" {
prev := p
for d := 0; d < 50; d++ {
this := drawers[prev]
if this == p {
continue prisoner
}
prev = this
}
} else {
// Assumes a prisoner remembers previous drawers (s)he opened
// and chooses at random from the others.
var opened [100]bool
for d := 0; d < 50; d++ {
var n int
for {
n = rand.Intn(100)
if !opened[n] {
opened[n] = true
break
}
}
if drawers[n] == p {
continue prisoner
}
}
}
continue trial
}
pardoned++
}
rf := float64(pardoned) / float64(trials) * 100
fmt.Printf("  strategy = %-7s  pardoned = %-6d relative frequency = %5.2f%%\n\n", strategy, pardoned, rf)
}

func main() {
rand.Seed(time.Now().UnixNano())
const trials = 100000
for _, np := range []int{10, 100} {
fmt.Printf("Results from %d trials with %d prisoners:\n\n", trials, np)
for _, strategy := range [2]string{"random", "optimal"} {
doTrials(trials, np, strategy)
}
}
}
```
Output:
```Results from 100000 trials with 10 prisoners:

strategy = random   pardoned = 99     relative frequency =  0.10%

strategy = optimal  pardoned = 31205  relative frequency = 31.20%

Results from 100000 trials with 100 prisoners:

strategy = random   pardoned = 0      relative frequency =  0.00%

strategy = optimal  pardoned = 31154  relative frequency = 31.15%
```

## Groovy

Translation of: Java
```import java.util.function.Function
import java.util.stream.Collectors
import java.util.stream.IntStream

class Prisoners {
private static boolean playOptimal(int n) {
List<Integer> secretList = IntStream.range(0, n).boxed().collect(Collectors.toList())
Collections.shuffle(secretList)

prisoner:
for (int i = 0; i < secretList.size(); ++i) {
int prev = i
for (int j = 0; j < secretList.size() / 2; ++j) {
if (secretList.get(prev) == i) {
continue prisoner
}
prev = secretList.get(prev)
}
return false
}
return true
}

private static boolean playRandom(int n) {
List<Integer> secretList = IntStream.range(0, n).boxed().collect(Collectors.toList())
Collections.shuffle(secretList)

prisoner:
for (Integer i : secretList) {
List<Integer> trialList = IntStream.range(0, n).boxed().collect(Collectors.toList())
Collections.shuffle(trialList)

for (int j = 0; j < trialList.size() / 2; ++j) {
if (Objects.equals(trialList.get(j), i)) {
continue prisoner
}
}

return false
}
return true
}

private static double exec(int n, int p, Function<Integer, Boolean> play) {
int succ = 0
for (int i = 0; i < n; ++i) {
if (play.apply(p)) {
succ++
}
}
return (succ * 100.0) / n
}

static void main(String[] args) {
final int n = 100_000
final int p = 100
System.out.printf("# of executions: %d\n", n)
System.out.printf("Optimal play success rate: %f%%\n", exec(n, p, Prisoners.&playOptimal))
System.out.printf("Random play success rate: %f%%\n", exec(n, p, Prisoners.&playRandom))
}
}
```
Output:
```# of executions: 100000
Optimal play success rate: 31.215000%
Random play success rate: 0.000000%```

```import System.Random

numRuns        = 10000
numPrisoners   = 100
numDrawerTries = 50
type Drawers   = [Int]
type Prisoner  =  Int
type Prisoners = [Int]

main = do
gen <- getStdGen
putStrLn \$ "Chance of winning when choosing randomly: "  ++ (show \$ evalState runRandomly gen)
putStrLn \$ "Chance of winning when choosing optimally: " ++ (show \$ evalState runOptimally gen)

runRandomly :: State StdGen Double
runRandomly =
let runResults = replicateM numRuns \$ do
drawers <- state \$ shuffle [1..numPrisoners]
allM (\prisoner -> openDrawersRandomly drawers prisoner numDrawerTries) [1..numPrisoners]
in  ((/ fromIntegral numRuns) . fromIntegral . sum . map fromEnum) `liftM` runResults

openDrawersRandomly :: Drawers -> Prisoner -> Int -> State StdGen Bool
openDrawersRandomly drawers prisoner triesLeft = go triesLeft []
where go 0 _ = return False
go triesLeft seenDrawers = do
try <- state \$ randomR (1, numPrisoners)
case try of
x | x == prisoner        -> return True
| x `elem` seenDrawers -> go triesLeft seenDrawers
| otherwise            -> go (triesLeft - 1) (x:seenDrawers)

runOptimally :: State StdGen Double
runOptimally =
let runResults = replicateM numRuns \$ do
drawers <- state \$ shuffle [1..numPrisoners]
return \$ all (\prisoner -> openDrawersOptimally drawers prisoner numDrawerTries) [1..numPrisoners]
in  ((/ fromIntegral numRuns) . fromIntegral . sum . map fromEnum) `liftM` runResults

openDrawersOptimally :: Drawers -> Prisoner -> Int -> Bool
openDrawersOptimally drawers prisoner triesLeft = go triesLeft prisoner
where go 0 _ = False
go triesLeft drawerToTry =
let thisDrawer = drawers !! (drawerToTry - 1)
in if thisDrawer == prisoner then True else go (triesLeft - 1) thisDrawer

-- Haskel stdlib is lacking big time, so here some necessary 'library' functions

-- make a list of 'len' random values in range 'range' from 'gen'
randomLR :: Integral a => Random b => a -> (b, b) -> StdGen -> ([b], StdGen)
randomLR 0 range gen = ([], gen)
randomLR len range gen =
let (x, newGen) = randomR range gen
(xs, lastGen) = randomLR (len - 1) range newGen
in (x : xs, lastGen)

-- shuffle a list by a generator
shuffle :: [a] -> StdGen -> ([a], StdGen)
shuffle list gen = (shuffleByNumbers numbers list, finalGen)
where
n = length list
(numbers, finalGen) = randomLR n (0, n-1) gen
shuffleByNumbers :: [Int] -> [a] -> [a]
shuffleByNumbers [] _ = []
shuffleByNumbers _ [] = []
shuffleByNumbers (i:is) xs = let (start, x:rest) = splitAt (i `mod` length xs) xs
in x : shuffleByNumbers is (start ++ rest)

allM :: Monad m => (a -> m Bool) -> [a] -> m Bool
allM func [] = return True
allM func (x:xs) = func x >>= \res -> if res then allM func xs else return False
```
Output:
```Chance of winning when choosing randomly: 0.0
Chance of winning when choosing optimally: 0.3188```

## J

```NB. game is solvable by optimal strategy when the length (#) of the
NB. longest (>./) cycle (C.) is at most 50.
opt=: 50 >: [: >./ [: > [: #&.> C.

NB. for each prisoner randomly open 50 boxes ((50?100){y) and see if
NB. the right card is there (p&e.). if not return 0.
for_p. i.100 do. if. -.p e.(50?100){y do. 0 return. end.
end. 1
)

NB. use both strategies on the same shuffles y times.
'o r'=. y %~ 100 * +/ ((rand,opt)@?~)"0 y # 100
('strategy';'win rate'),('random';(":o),'%'),:'optimal';(":r),'%'
)
```
Output:
```   simulate 10000000
┌────────┬────────┐
│strategy│win rate│
├────────┼────────┤
│random  │0%      │
├────────┼────────┤
│optimal │31.1816%│
└────────┴────────┘
```

## Janet

```(math/seedrandom (os/cryptorand 8))

(defn drawers
"create list and shuffle it"
[prisoners]
(var x (seq [i :range [0 prisoners]] i))
(loop [i :down [(- prisoners 1) 0]]
(var j (math/floor (* (math/random) (+ i 1))))
(var k (get x i))
(put x i (get x j))
(put x j k))
x)

(defn optimal-play
"optimal decision path"
[prisoners drawers]
(var result 0)
(loop [i :range [0 prisoners]]
(var choice i)
(loop [j :range [0 50] :until (= (get drawers choice) i)]
(set choice (get drawers choice)))
(cond
(= (get drawers choice) i) (++ result)
(break)))
result)

(defn random-play
"random decision path"
[prisoners d]
(var result 0)
(var options (drawers prisoners))
(loop [i :range [0 prisoners]]
(var choice 0)
(loop [j :range [0 (/ prisoners 2)] :until (= (get d j) (get options i))]
(set choice j))
(cond
(= (get d choice) (get options i)) (++ result)
(break)))
result)

(defn main [& args]
(def prisoners 100)
(var optimal-success 0)
(var random-success 0)
(var sims 10000)
(for i 0 sims
(var d (drawers prisoners))
(if (= (optimal-play prisoners d) prisoners)
(++ optimal-success))
(if (= (random-play prisoners d) prisoners)
(++ random-success)))
(printf "Simulation count:  %d" sims)
(printf "Optimal play wins: %.1f%%" (* (/ optimal-success sims) 100))
(printf "Random play wins:  %.1f%%" (* (/ random-success sims) 100)))```

Output:

```Simulation count:  10000
Optimal play wins: 33.1%
Random play wins:  0.0%
```

## Java

Translation of: Kotlin
```import java.util.Collections;
import java.util.List;
import java.util.Objects;
import java.util.function.Function;
import java.util.function.Supplier;
import java.util.stream.Collectors;
import java.util.stream.IntStream;

public class Main {
private static boolean playOptimal(int n) {
List<Integer> secretList = IntStream.range(0, n).boxed().collect(Collectors.toList());
Collections.shuffle(secretList);

prisoner:
for (int i = 0; i < secretList.size(); ++i) {
int prev = i;
for (int j = 0; j < secretList.size() / 2; ++j) {
if (secretList.get(prev) == i) {
continue prisoner;
}
prev = secretList.get(prev);
}
return false;
}
return true;
}

private static boolean playRandom(int n) {
List<Integer> secretList = IntStream.range(0, n).boxed().collect(Collectors.toList());
Collections.shuffle(secretList);

prisoner:
for (Integer i : secretList) {
List<Integer> trialList = IntStream.range(0, n).boxed().collect(Collectors.toList());
Collections.shuffle(trialList);

for (int j = 0; j < trialList.size() / 2; ++j) {
if (Objects.equals(trialList.get(j), i)) {
continue prisoner;
}
}

return false;
}
return true;
}

private static double exec(int n, int p, Function<Integer, Boolean> play) {
int succ = 0;
for (int i = 0; i < n; ++i) {
if (play.apply(p)) {
succ++;
}
}
return (succ * 100.0) / n;
}

public static void main(String[] args) {
final int n = 100_000;
final int p = 100;
System.out.printf("# of executions: %d\n", n);
System.out.printf("Optimal play success rate: %f%%\n", exec(n, p, Main::playOptimal));
System.out.printf("Random play success rate: %f%%\n", exec(n, p, Main::playRandom));
}
}
```
Output:
```# of executions: 100000
Optimal play success rate: 31.343000%
Random play success rate: 0.000000%```

## JavaScript

Translation of: C#
Works with: Node.js
```const _ = require('lodash');

const numPlays = 100000;

const setupSecrets = () => {
// setup the drawers with random cards
let secrets = [];

for (let i = 0; i < 100; i++) {
secrets.push(i);
}

return _.shuffle(secrets);
}

const playOptimal = () => {

let secrets = setupSecrets();

// Iterate once per prisoner
loop1:
for (let p = 0; p < 100; p++) {

// whether the prisoner succeedss
let success = false;

// the drawer number the prisoner chose
let choice = p;

// The prisoner can choose up to 50 cards
loop2:
for (let i = 0; i < 50; i++) {

// if the card in the drawer that the prisoner chose is his card
if (secrets[choice] === p){
success = true;
break loop2;
}

// the next drawer the prisoner chooses will be the number of the card he has.
choice = secrets[choice];

}	// each prisoner gets 50 chances

if (!success) return false;

} // iterate for each prisoner

return true;
}

const playRandom = () => {

let secrets = setupSecrets();

// iterate for each prisoner
for (let p = 0; p < 100; p++) {

let choices = setupSecrets();

let success = false;

for (let i = 0; i < 50; i++) {

if (choices[i] === p) {
success = true;
break;
}
}

if (!success) return false;
}

return true;
}

const execOptimal = () => {

let success = 0;

for (let i = 0; i < numPlays; i++) {

if (playOptimal()) success++;

}

return 100.0 * success / 100000;
}

const execRandom = () => {

let success = 0;

for (let i = 0; i < numPlays; i++) {

if (playRandom()) success++;

}

return 100.0 * success / 100000;
}

console.log("# of executions: " + numPlays);
console.log("Optimal Play Success Rate: " + execOptimal());
console.log("Random Play Success Rate: " + execRandom());
```

### School example

Works with: JavaScript version Node.js 16.13.0 (LTS)
```"use strict";

// Simulate several thousand instances of the game:
const gamesCount = 2000;

// ...where the prisoners randomly open drawers.
const randomResults = playGame(gamesCount, randomStrategy);

// ...where the prisoners use the optimal strategy mentioned in the Wikipedia article.
const optimalResults = playGame(gamesCount, optimalStrategy);

// Show and compare the computed probabilities of success for the two strategies.
console.log(`Games count: \${gamesCount}`);
console.log(`Probability of success with "random" strategy: \${computeProbability(randomResults, gamesCount)}`);
console.log(`Probability of success with "optimal" strategy: \${computeProbability(optimalResults, gamesCount)}`);

function playGame(gamesCount, strategy, prisonersCount = 100) {
const results = new Array();

for (let game = 1; game <= gamesCount; game++) {
// A room having a cupboard of 100 opaque drawers numbered 1 to 100, that cannot be seen from outside.
// Cards numbered 1 to 100 are placed randomly, one to a drawer, and the drawers all closed; at the start.
const drawers = initDrawers(prisonersCount);

// A prisoner tries to find his own number.
// Prisoners start outside the room.
// They can decide some strategy before any enter the room.
let found = 0;
for (let prisoner = 1; prisoner <= prisonersCount; prisoner++, found++)
if (!find(prisoner, drawers, strategy)) break;

// If all 100 findings find their own numbers then they will all be pardoned. If any don't then all sentences stand.
results.push(found == prisonersCount);
}

return results;
}

function find(prisoner, drawers, strategy) {
// A prisoner can open no more than 50 drawers.
const openMax = Math.floor(drawers.length / 2);

// Prisoners start outside the room.
let card;
for (let open = 0; open < openMax; open++) {
// A prisoner tries to find his own number.
card = strategy(prisoner, drawers, card);

// A prisoner finding his own number is then held apart from the others.
if (card == prisoner)
break;
}

return (card == prisoner);
}

function randomStrategy(prisoner, drawers, card) {
// Simulate the game where the prisoners randomly open drawers.

const min = 0;
const max = drawers.length - 1;

return drawers[draw(min, max)];
}

function optimalStrategy(prisoner, drawers, card) {
// Simulate the game where the prisoners use the optimal strategy mentioned in the Wikipedia article.

// First opening the drawer whose outside number is his prisoner number.
// If the card within has his number then he succeeds...
if (typeof card === "undefined")
return drawers[prisoner - 1];

// ...otherwise he opens the drawer with the same number as that of the revealed card.
return drawers[card - 1];
}

function initDrawers(prisonersCount) {
const drawers = new Array();
for (let card = 1; card <= prisonersCount; card++)
drawers.push(card);

return shuffle(drawers);
}

function shuffle(drawers) {
const min = 0;
const max = drawers.length - 1;
for (let i = min, j; i < max; i++)     {
j = draw(min, max);
if (i != j)
[drawers[i], drawers[j]] = [drawers[j], drawers[i]];
}

return drawers;
}

function draw(min, max) {
// See: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Math/random
return Math.floor(Math.random() * (max - min + 1)) + min;
}

function computeProbability(results, gamesCount) {
return Math.round(results.filter(x => x == true).length * 10000 / gamesCount) / 100;
}
```

Output:

```Games count: 2000
Probability of success with "random" strategy: 0
Probability of success with "optimal" strategy: 33.2```

## jq

Works with: jq

jq does not have a built-in PRNG and so the jq program used here presupposes an external source of entropy such as /dev/urandom. The output shown below was obtained by invoking jq as follows:

```export LC_ALL=C
< /dev/urandom tr -cd '0-9' | fold -w 1 | jq -MRcnr -f 100-prisoners.jq
```

In the following jq program:

• `np` is the number of prisoners
• the number of drawers is `np` and the maximum number of draws per prisoner is `np/2|floor`

Preliminaries

```def count(s): reduce s as \$x (0; .+1);

# Output: a PRN in range(0;\$n) where \$n is .
def prn:
if . == 1 then 0
else . as \$n
| ((\$n-1)|tostring|length) as \$w
| [limit(\$w; inputs)] | join("") | tonumber
| if . < \$n then . else (\$n | prn) end
end;

def knuthShuffle:
length as \$n
| if \$n <= 1 then .
else {i: \$n, a: .}
| until(.i ==  0;
.i += -1
| (.i + 1 | prn) as \$j
| .a[.i] as \$t
| .a[.i] = .a[\$j]
| .a[\$j] = \$t)
| .a
end;```

np Prisoners

```# Output: if all the prisoners succeed, emit true, otherwise false
def optimalStrategy(\$drawers; np):
# Does prisoner \$p succeed?
def succeeds(\$p):
first( foreach range(0; np/2) as \$d ({prev: \$p};
.curr = (\$drawers[.prev])
| if .curr == \$p
then .success = true
else .prev = .curr
end;
select(.success))) // false;

all( range(0; np); succeeds(.) );

# Output: if all the prisoners succeed, emit true, otherwise false
def randomStrategy(\$drawers; np):
(np/2) as \$maxd
# Does prisoner \$p succeed?
| def succeeds(\$p):
{success: false }
| first(.d = 0
| .opened = []
| until( (.d >= \$maxd) or .success;
(np|prn) as \$n
| if .opened[\$n] then .
else .opened[\$n] = true
| .d += 1
| .success = \$drawers[\$n] == \$p
end )
| select(.success) ) // false;

all( range(0; np); succeeds(.) );

def run(strategy; trials; np):
count(range(0; trials)
| ([range(0;np)] | knuthShuffle) as \$drawers
| select (if strategy == "optimal"
then optimalStrategy(\$drawers; np)
else randomStrategy(\$drawers; np)
end ) );

def percent: "\(10000 * . | round / 100)%";
def summary(strategy):
"With \(strategy) strategy: pardoned = \(.), relative frequency = \(./\$trials | percent)";

(10, 100) as \$np
| "Results from \(\$trials) trials with \(\$np) prisoners:",
(run("random";  \$trials; \$np) | summary("random")),
(run("optimal"; \$trials; \$np) | summary("optimal")),
""
;

Output:
```Results from 100000 trials with 10 prisoners:
With random strategy: pardoned = 92, relative frequency = 0.09%
With optimal strategy: pardoned = 31212, relative frequency = 31.21%

Results from 100000 trials with 100 prisoners:
With random strategy: pardoned = 0, relative frequency = 0%
With optimal strategy: pardoned = 31026, relative frequency = 31.03%
```

## Julia

Translation of: Python
```using Random, Formatting

function randomplay(n, numprisoners=100)
pardoned, indrawer, found = 0, collect(1:numprisoners), false
for i in 1:n
shuffle!(indrawer)
for prisoner in 1:numprisoners
found = false
for reveal in randperm(numprisoners)[1:div(numprisoners, 2)]
indrawer[reveal] == prisoner && (found = true) && break
end
!found && break
end
found && (pardoned += 1)
end
return 100.0 * pardoned / n
end

function optimalplay(n, numprisoners=100)
pardoned, indrawer, found = 0, collect(1:numprisoners), false
for i in 1:n
shuffle!(indrawer)
for prisoner in 1:numprisoners
reveal = prisoner
found = false
for j in 1:div(numprisoners, 2)
card = indrawer[reveal]
card == prisoner && (found = true) && break
reveal = card
end
!found && break
end
found && (pardoned += 1)
end
return 100.0 * pardoned / n
end

const N = 100_000
println("Simulation count: \$N")
println("Random play wins: ", format(randomplay(N), precision=8), "% of simulations.")
println("Optimal play wins: ", format(optimalplay(N), precision=8), "% of simulations.")
```
Output:
```Simulation count: 100000
Random play wins: 0.00000000% of simulations.
Optimal play wins: 31.18100000% of simulations.
```

## Kotlin

```val playOptimal: () -> Boolean = {
val secrets = (0..99).toMutableList()
var ret = true
secrets.shuffle()
prisoner@ for(i in 0 until 100){
var prev = i
draw@ for(j in 0 until  50){
if (secrets[prev] == i) continue@prisoner
prev = secrets[prev]
}
ret = false
break@prisoner
}
ret
}

val playRandom: ()->Boolean = {
var ret = true
val secrets = (0..99).toMutableList()
secrets.shuffle()
prisoner@ for(i in 0 until 100){
val opened = mutableListOf<Int>()
val genNum : () ->Int = {
var r = (0..99).random()
while (opened.contains(r)) {
r = (0..99).random()
}
r
}
for(j in 0 until 50){
val draw = genNum()
if ( secrets[draw] == i) continue@prisoner
}
ret = false
break@prisoner
}
ret
}

fun exec(n:Int, play:()->Boolean):Double{
var succ = 0
for (i in IntRange(0, n-1)){
succ += if(play()) 1 else 0
}
return (succ*100.0)/n
}

fun main() {
val N = 100_000
println("# of executions: \$N")
println("Optimal play success rate: \${exec(N, playOptimal)}%")
println("Random play success rate: \${exec(N, playRandom)}%")
}
```
Output:
```# of executions: 100000
Optimal play success rate: 31.451%
Random play success rate: 0.0%
```

## Lua

Translation of: lang
```function shuffle(tbl)
for i = #tbl, 2, -1 do
local j = math.random(i)
tbl[i], tbl[j] = tbl[j], tbl[i]
end
return tbl
end

function playOptimal()
local secrets = {}
for i=1,100 do
secrets[i] = i
end
shuffle(secrets)

for p=1,100 do
local success = false

local choice = p
for i=1,50 do
if secrets[choice] == p then
success = true
break
end
choice = secrets[choice]
end

if not success then
return false
end
end

return true
end

function playRandom()
local secrets = {}
for i=1,100 do
secrets[i] = i
end
shuffle(secrets)

for p=1,100 do
local choices = {}
for i=1,100 do
choices[i] = i
end
shuffle(choices)

local success = false
for i=1,50 do
if choices[i] == p then
success = true
break
end
end

if not success then
return false
end
end

return true
end

function exec(n,play)
local success = 0
for i=1,n do
if play() then
success = success + 1
end
end
return 100.0 * success / n
end

function main()
local N = 1000000
print("# of executions: "..N)
print(string.format("Optimal play success rate: %f", exec(N, playOptimal)))
print(string.format("Random play success rate: %f", exec(N, playRandom)))
end

main()
```
Output:
```# of executions: 1000000
Optimal play success rate: 31.237500
Random play success rate: 0.000000```

## Maple

Don"t bother to simulate the random method: each prisoner has a probability p to win with:

```p:=simplify(1-product(1-1/(2*n-k),k=0..n-1));
# p=1/2```

Since all prisoners' attempts are independent, the probability that they all win is:

```p^100;
evalf(%);

# 1/1267650600228229401496703205376
# 7.888609052e-31```

Even with billions of simulations, chances are we won't find even one successful escape.

On the other hand, if they try the optimal strategy, then their success is governed by the cycle decomposition of the permutation of numbers in boxes. That is, the function f(i)=j where i is the number on the box, and j the number in the box, is a permutation of 1..100. This permutation has a cycle decomposition. It's not difficult to see that all prisoners with a number in the same cycle, need the same number of attempts before finding their number, and it's the cycle length. Hence, for all prisoners to escape, the maximum cycle length must not exceed 50.

Here is a simulation based on this, assuming that the permutation of numbers in boxes is random:

```a:=[seq(max(GroupTheory[PermCycleType](Perm(Statistics[Shuffle]([\$1..100])))),i=1..100000)]:
nops(select(n->n<=50,a))/nops(a);
evalf(%);
# 31239/100000
# 0.3123900000```

The probability of success is now better than 30%, which is far better than the random approach.

It can be proved that the probability with the second strategy is in fact:

```1-(harmonic(100)-harmonic(50));
evalf(%);

# 21740752665556690246055199895649405434183/69720375229712477164533808935312303556800
# 0.3118278207```

## Mathematica/Wolfram Language

```ClearAll[PlayRandom, PlayOptimal]
PlayRandom[n_] :=
Module[{pardoned = 0, sampler, indrawer, found, reveal},
sampler = indrawer = Range[100];
Do[
indrawer //= RandomSample;
found = 0;
Do[
reveal = RandomSample[sampler, 50];
If[MemberQ[indrawer[[reveal]], p],
found++;
]
,
{p, 100}
];
If[found == 100, pardoned++];
,
{n}
];
N[pardoned/n]
]
PlayOptimal[n_] :=
Module[{pardoned = 0, indrawer, reveal, found, card},
indrawer = Range[100];
Do[
indrawer //= RandomSample;
Do[
reveal = p;
found = False;
Do[
card = indrawer[[reveal]];
If[card == p,
found = True;
Break[];
];
reveal = card;
,
{g, 50}
];
If[! found, Break[]];
,
{p, 100}
];
If[found, pardoned++];
,
{n}
];
N[pardoned/n]
];
PlayRandom[1000]
PlayOptimal[10000]
```
Output:
```0.
0.3116```

## MATLAB

```function [randSuccess,idealSuccess]=prisoners(numP,numG,numT)
%numP is the number of prisoners
%numG is the number of guesses
%numT is the number of trials
randSuccess=0;

%Random
for trial=1:numT
drawers=randperm(numP);
won=1;
for i=1:numP
correct=0;
notopened=drawers;
for j=1:numG
ind=randi(numel(notopened));
m=notopened(ind);
if m==i
correct=1;
break;
end
notopened(ind)=[];
end
if correct==0
won=0;
break;
end
end
randSuccess=randSuccess*(trial-1)/trial+won/trial;
end

%Ideal
idealSuccess=0;

for trial=1:numT
drawers=randperm(numP);
won=1;
for i=1:numP
correct=0;
guess=i;
for j=1:numG
m=drawers(guess);
if m==i
correct=1;
break;
end
guess=m;
end
if correct==0
won=0;
break;
end
end
idealSuccess=idealSuccess*(trial-1)/trial+won/trial;
end
disp(['Probability of success with random strategy: ' num2str(randSuccess*100) '%']);
disp(['Probability of success with ideal strategy: ' num2str(idealSuccess*100) '%']);
end
```
Output:
```>> [randSuccess,idealSuccess]=prisoners(100,50,10000);
Probability of success with random strategy: 0%
Probability of success with ideal strategy: 31.93%```

## MiniScript

Translation of: Python
```playRandom = function(n)
// using 0-99 instead of 1-100
pardoned = 0
numInDrawer = range(99)
choiceOrder = range(99)
for round in range(1, n)
numInDrawer.shuffle
choiceOrder.shuffle
for prisoner in range(99)
found = false
for card in choiceOrder[:50]
if card == prisoner then
found = true
break
end if
end for
end for
if found then pardoned = pardoned + 1
end for
return pardoned / n * 100
end function

playOptimal = function(n)
// using 0-99 instead of 1-100
pardoned = 0
numInDrawer = range(99)
for round in range(1, n)
numInDrawer.shuffle
for prisoner in range(99)
found = false
drawer = prisoner
for i in range(1,50)
card = numInDrawer[drawer]
if card == prisoner then
found = true
break
end if
drawer = card
end for
end for
if found then pardoned = pardoned + 1
end for
return pardoned / n * 100
end function

print "Random:  " + playRandom(10000) + "%"
print "Optimal: " + playOptimal(10000) + "%"
```
Output:
```Random:  0%
Optimal: 31.06%```

## Nim

Imperative style.

```import random, sequtils, strutils

type
Sample = tuple
succ: int
fail: int

const
numPrisoners = 100
numDrawsEachPrisoner = numPrisoners div 2
numDrawings: Positive = 1_000_000 div 1

proc `\$`(s: Sample): string =
"Succs: \$#\tFails: \$#\tTotal: \$#\tSuccess Rate: \$#%." % [\$s.succ, \$s.fail, \$(s.succ + s.fail), \$(s.succ.float / (s.succ + s.fail).float * 100.0)]

proc prisonersWillBeReleasedSmart(): bool =
result = true
var drawers = toSeq(0..<numPrisoners)
drawers.shuffle
for prisoner in 0..<numPrisoners:
var drawer = prisoner
block inner:
for _ in 0..<numDrawsEachPrisoner:
if drawers[drawer] == prisoner: break inner
drawer = drawers[drawer]
return false

proc prisonersWillBeReleasedRandom(): bool =
result = true
var drawers = toSeq(0..<numPrisoners)
drawers.shuffle
for prisoner in 0..<numPrisoners:
var selectDrawer = toSeq(0..<numPrisoners)
selectDrawer.shuffle
block inner:
for i in 0..<numDrawsEachPrisoner:
if drawers[selectDrawer[i]] == prisoner: break inner
return false

proc massDrawings(prisonersWillBeReleased: proc(): bool): Sample =
var success = 0
for i in 1..numDrawings:
if prisonersWillBeReleased():
inc(success)
return (success, numDrawings - success)

randomize()
echo \$massDrawings(prisonersWillBeReleasedSmart)
echo \$massDrawings(prisonersWillBeReleasedRandom)
```
Output:
```Succs: 312225   Fails: 687775   Total: 1000000  Success Rate: 31.2225%.
Succs: 0        Fails: 1000000  Total: 1000000  Success Rate: 0.0%.```

## Pascal

Works with: Free Pascal

searching the longest cycle length as stated on talk page and increment an counter for that cycle length.

```program Prisoners100;

const
rounds  = 100000;

type
tValue = Uint32;
tPrisNum = array of tValue;
var
drawers,
PrisonersChoice : tPrisNum;

procedure shuffle(var N:tPrisNum);
var
i,j,lmt : nativeInt;
tmp: tValue;
Begin
lmt := High(N);
For i := lmt downto 1 do
begin
//take on from index i..limit
j := random(i+1);
//exchange with i
tmp := N[i];N[i]:= N[j];N[j]:= tmp;
end;
end;

function PardonedRandom(maxTestNum: NativeInt):boolean;
var
PrisNum,TestNum,Lmt : NativeUint;
Pardoned : boolean;
Begin
IF maxTestNum <=0 then
Begin
PardonedRandom := false;
EXIT;
end;
Lmt := High(drawers);
IF (maxTestNum >= Lmt) then
Begin
PardonedRandom := true;
EXIT;
end;

shuffle(drawers);
PrisNum := 0;
repeat
//every prisoner uses his own list of drawers
shuffle(PrisonersChoice);
TestNum := 0;
repeat
Pardoned := drawers[PrisonersChoice[TestNum]] = PrisNum;
inc(TestNum);
until Pardoned OR (TestNum>=maxTestNum);
IF Not(Pardoned) then
BREAK;
inc(PrisNum);
until PrisNum>=Lmt;
PardonedRandom:= Pardoned;
end;

function PardonedOptimized(maxTestNum: NativeUint):boolean;
var
PrisNum,TestNum,NextNum,Cnt,Lmt : NativeUint;
Pardoned : boolean;
Begin
IF maxTestNum <=0 then
Begin
PardonedOptimized := false;
EXIT;
end;
Lmt := High(drawers);
IF (maxTestNum >= Lmt) then
Begin
PardonedOptimized := true;
EXIT;
end;

shuffle(drawers);
Lmt := High(drawers);
IF maxTestNum >= Lmt then
Begin
PardonedOptimized := true;
EXIT;
end;
PrisNum := 0;
repeat
Cnt := 0;
NextNum := PrisNum;
repeat
TestNum := NextNum;
NextNum := drawers[TestNum];
inc(cnt);
Pardoned := NextNum = PrisNum;
until Pardoned OR (cnt >=maxTestNum);

IF Not(Pardoned) then
BREAK;
inc(PrisNum);
until PrisNum>Lmt;
PardonedOptimized := Pardoned;
end;

procedure CheckRandom(testCount : NativeUint);
var
i,cnt : NativeInt;
Begin
cnt := 0;
For i := 1 to rounds do
IF PardonedRandom(TestCount) then
inc(cnt);
writeln('Randomly  ',cnt/rounds*100:7:2,'% get pardoned out of ',rounds,' checking max ',TestCount);
end;

procedure CheckOptimized(testCount : NativeUint);
var
i,cnt : NativeInt;
Begin
cnt := 0;
For i := 1 to rounds do
IF PardonedOptimized(TestCount) then
inc(cnt);
writeln('Optimized ',cnt/rounds*100:7:2,'% get pardoned out of ',rounds,' checking max ',TestCount);
end;

procedure OneCompareRun(PrisCnt:NativeInt);
var
i,lmt :nativeInt;
begin
setlength(drawers,PrisCnt);
For i := 0 to PrisCnt-1 do
drawers[i] := i;
PrisonersChoice := copy(drawers);

//test
writeln('Checking ',PrisCnt,' prisoners');

lmt := PrisCnt;
repeat
CheckOptimized(lmt);
dec(lmt,PrisCnt DIV 10);
until lmt < 0;
writeln;

lmt := PrisCnt;
repeat
CheckRandom(lmt);
dec(lmt,PrisCnt DIV 10);
until lmt < 0;
writeln;
writeln;
end;

Begin
//init
randomize;
OneCompareRun(20);
OneCompareRun(100);
end.
```
Output:
```Checking 20 prisoners
Optimized  100.00% get pardoned out of 100000 checking max 20
Optimized   89.82% get pardoned out of 100000 checking max 18
Optimized   78.25% get pardoned out of 100000 checking max 16
Optimized   65.31% get pardoned out of 100000 checking max 14
Optimized   50.59% get pardoned out of 100000 checking max 12
Optimized   33.20% get pardoned out of 100000 checking max 10
Optimized   15.28% get pardoned out of 100000 checking max 8
Optimized    3.53% get pardoned out of 100000 checking max 6
Optimized    0.10% get pardoned out of 100000 checking max 4
Optimized    0.00% get pardoned out of 100000 checking max 2
Optimized    0.00% get pardoned out of 100000 checking max 0

Randomly   100.00% get pardoned out of 100000 checking max 20
Randomly    13.55% get pardoned out of 100000 checking max 18
Randomly     1.38% get pardoned out of 100000 checking max 16
Randomly     0.12% get pardoned out of 100000 checking max 14
Randomly     0.00% get pardoned out of 100000 checking max 12
Randomly     0.00% get pardoned out of 100000 checking max 10
Randomly     0.00% get pardoned out of 100000 checking max 8
Randomly     0.00% get pardoned out of 100000 checking max 6
Randomly     0.00% get pardoned out of 100000 checking max 4
Randomly     0.00% get pardoned out of 100000 checking max 2
Randomly     0.00% get pardoned out of 100000 checking max 0

Checking 100 prisoners
Optimized  100.00% get pardoned out of 100000 checking max 100
Optimized   89.48% get pardoned out of 100000 checking max 90
Optimized   77.94% get pardoned out of 100000 checking max 80
Optimized   64.48% get pardoned out of 100000 checking max 70
Optimized   49.35% get pardoned out of 100000 checking max 60
Optimized   31.10% get pardoned out of 100000 checking max 50
Optimized   13.38% get pardoned out of 100000 checking max 40
Optimized    2.50% get pardoned out of 100000 checking max 30
Optimized    0.05% get pardoned out of 100000 checking max 20
Optimized    0.00% get pardoned out of 100000 checking max 10
Optimized    0.00% get pardoned out of 100000 checking max 0

Randomly   100.00% get pardoned out of 100000 checking max 100
Randomly     0.01% get pardoned out of 100000 checking max 90
Randomly     0.00% get pardoned out of 100000 checking max 80
Randomly     0.00% get pardoned out of 100000 checking max 70
Randomly     0.00% get pardoned out of 100000 checking max 60
Randomly     0.00% get pardoned out of 100000 checking max 50
Randomly     0.00% get pardoned out of 100000 checking max 40
Randomly     0.00% get pardoned out of 100000 checking max 30
Randomly     0.00% get pardoned out of 100000 checking max 20
Randomly     0.00% get pardoned out of 100000 checking max 10
Randomly     0.00% get pardoned out of 100000 checking max 0```

### Alternative for optimized

```program Prisoners100;
{\$IFDEF FPC}
{\$MODE DELPHI}{\$OPTIMIZATION ON,ALL}
{\$ELSE}
{\$APPTYPE CONSOLE}
{\$ENDIF}
type
tValue  = NativeUint;
tpValue = pNativeUint;
tPrisNum = array of tValue;

const
rounds  = 1000000;
var
drawers,
Visited,
CntToPardoned : tPrisNum;
PrisCount : NativeInt;

procedure shuffle(var N:tPrisNum;lmt : nativeInt = 0);
var
pN : tpValue;
i,j : nativeInt;
tmp: tValue;
Begin
pN := @N[0];
if lmt = 0 then
lmt := High(N);
For i := lmt downto 1 do
begin
//take one from index [0..i]
j := random(i+1);
//exchange with i
tmp := pN[i];pN[i]:= pN[j];pN[j]:= tmp;
end;
end;

procedure CopyDrawers2Visited;
//drawers and Visited are of same size, so only moving values
Begin
Move(drawers[0],Visited[0],SizeOf(tValue)*PrisCount);
end;

function GetMaxCycleLen:NativeUint;
var
pVisited : tpValue;
cycleLen,MaxCycLen,Num,NumBefore : NativeUInt;
Begin
CopyDrawers2Visited;
pVisited := @Visited[0];
MaxCycLen := 0;
cycleLen := MaxCycLen;
Num := MaxCycLen;
repeat
NumBefore := Num;
Num := pVisited[Num];
inc(cycleLen);
IF (Num= NumBefore) or (Num = cAlreadySeen) then
begin
dec(CycleLen);
IF MaxCycLen < cycleLen then
MaxCycLen := cycleLen;
Num := 0;
while (Num< PrisCount) AND (pVisited[Num] = cAlreadySeen) do
inc(Num);
//all cycles found
IF Num >= PrisCount then
BREAK;
cycleLen :=0;
end;
until false;
GetMaxCycleLen := MaxCycLen-1;
end;

procedure CheckOptimized(testCount : NativeUint);
var
factor: extended;
i,sum,digit,delta : NativeInt;
Begin
For i := 1 to rounds do
begin
shuffle(drawers);
inc(CntToPardoned[GetMaxCycleLen]);
end;

digit := 0;
sum := rounds;
while sum > 100 do
Begin
inc(digit);
sum := sum DIV 10;
end;
factor := 100.0/rounds;

delta :=0;
sum := 0;
For i := 0 to High(drawers) do
Begin
inc(sum,CntToPardoned[i]);
dec(delta);
IF delta <= 0 then
Begin
writeln(sum*factor:Digit+5:Digit,'% get pardoned checking max ',i+1);
delta := delta+Length(drawers) DIV 10;
end;
end;
end;

procedure OneCompareRun(PrisCnt:NativeInt);
var
i,lmt :nativeInt;
begin
PrisCount := PrisCnt;
setlength(drawers,PrisCnt);
For i := 0 to PrisCnt-1 do
drawers[i] := i;
setlength(Visited,PrisCnt);
setlength(CntToPardoned,PrisCnt);
//test
writeln('Checking ',PrisCnt,' prisoners for ',rounds,' rounds');
lmt := PrisCnt;
CheckOptimized(lmt);
writeln;

setlength(CntToPardoned,0);
setlength(Visited,0);
setlength(drawers,0);
end;

Begin
randomize;
OneCompareRun(10);
OneCompareRun(100);
OneCompareRun(1000);
end.
```
Output:
```Checking 10 prisoners for 1000000 rounds
0.0000% get pardoned checking max 1
0.2584% get pardoned checking max 2
4.7431% get pardoned checking max 3
17.4409% get pardoned checking max 4
35.4983% get pardoned checking max 5
52.1617% get pardoned checking max 6
66.4807% get pardoned checking max 7
78.9761% get pardoned checking max 8
90.0488% get pardoned checking max 9
100.0000% get pardoned checking max 10

Checking 100 prisoners for 1000000 rounds
0.0000% get pardoned checking max 1
0.0000% get pardoned checking max 10
0.0459% get pardoned checking max 20
2.5996% get pardoned checking max 30
13.5071% get pardoned checking max 40
31.2258% get pardoned checking max 50
49.3071% get pardoned checking max 60
64.6128% get pardoned checking max 70
77.8715% get pardoned checking max 80
89.5385% get pardoned checking max 90
100.0000% get pardoned checking max 100

Checking 1000 prisoners for 1000000 rounds
0.0000% get pardoned checking max 1
0.0000% get pardoned checking max 100
0.0374% get pardoned checking max 200
2.3842% get pardoned checking max 300
13.1310% get pardoned checking max 400
30.7952% get pardoned checking max 500
48.9710% get pardoned checking max 600
64.3555% get pardoned checking max 700
77.6950% get pardoned checking max 800
89.4515% get pardoned checking max 900
100.0000% get pardoned checking max 1000

real    0m9,975s```

## Perl

Translation of: Raku
```use strict;
use warnings;
use feature 'say';
use List::Util 'shuffle';

sub simulation {
my(\$population,\$trials,\$strategy) = @_;
my \$optimal   = \$strategy =~ /^o/i ? 1 : 0;
my @prisoners = 0..\$population-1;
my \$half      = int \$population / 2;
my \$pardoned  = 0;

for (1..\$trials) {
my @drawers = shuffle @prisoners;
my \$total = 0;
for my \$prisoner (@prisoners) {
my \$found = 0;
if (\$optimal) {
my \$card = \$drawers[\$prisoner];
if (\$card == \$prisoner) {
\$found = 1;
} else {
for (1..\$half-1) {
\$card = \$drawers[\$card];
(\$found = 1, last) if \$card == \$prisoner
}
}
} else {
for my \$card ( (shuffle @drawers)[0..\$half]) {
(\$found = 1, last) if \$card == \$prisoner
}
}
last unless \$found;
\$total++;
}
\$pardoned++ if \$total == \$population;
}
\$pardoned / \$trials * 100
}

my \$population = 100;
my \$trials     = 10000;
say " Simulation count: \$trials\n" .
(sprintf " Random strategy pardons: %6.3f%% of simulations\n", simulation \$population, \$trials, 'random' ) .
(sprintf "Optimal strategy pardons: %6.3f%% of simulations\n", simulation \$population, \$trials, 'optimal');

\$population = 10;
\$trials     = 100000;
say " Simulation count: \$trials\n" .
(sprintf " Random strategy pardons: %6.3f%% of simulations\n", simulation \$population, \$trials, 'random' ) .
(sprintf "Optimal strategy pardons: %6.3f%% of simulations\n", simulation \$population, \$trials, 'optimal');
```
Output:
``` Simulation count: 10000
Random strategy pardons:  0.000% of simulations
Optimal strategy pardons: 31.510% of simulations

Simulation count: 1000000
Random strategy pardons:  0.099% of simulations
Optimal strategy pardons: 35.420% of simulations```

## PicoLisp

Built so you could easily build and test your own strategies.

```(de shuffle (Lst)
(by '(NIL (rand)) sort Lst) )

# Extend this class with a `next-guess>` method and a `str>` method.
(class +Strategy +Entity)
(dm prev-drawer> (Num)
(=: prev Num) )

(class +Random +Strategy)
(dm T (Prisoner)
(=: guesses (nth (shuffle (range 1 100)) 51)) )
(dm next-guess> ()
(pop (:: guesses)) )
(dm str> ()
"Random" )

(class +Optimal +Strategy)
(dm T (Prisoner)
(=: prisoner-id Prisoner) )
(dm next-guess> ()
(or (: prev) (: prisoner-id)) )
(dm str> ()
"Optimal/Wikipedia" )

(de test-strategy (Strategy)
"Simulate one round of 100 prisoners who use `Strategy`"
(let Drawers (shuffle (range 1 100))
(for Prisoner (range 1 100)
(NIL # Break and return NIL if any prisoner fails their test.
(let Strat (new (list Strategy) Prisoner)
(do 50 # Try 50 iterations of `Strat`. Break and return T iff success.
(T (= Prisoner (prev-drawer> Strat (get Drawers (next-guess> Strat))))
T ) ) ) )
T ) ) )

(de test-strategy-n-times (Strategy N)
"Simulate `N` rounds of 100 prisoners who use `Strategy`"
(let Successes 0
(do N
(when (test-strategy Strategy)
(inc 'Successes) ) )
(prinl "We have a " (/ (* 100 Successes) N) "% success rate with " N " trials.")
(prinl "This is using the " (str> Strategy) " strategy.") ) )```

Then run

```(test-strategy-n-times '+Random 10000)
(test-strategy-n-times '+Optimal 10000)```
Output:
```We have a 0% success rate with 10000 trials.
This is using the Random strategy.
We have a 31% success rate with 10000 trials.
This is using the Optimal/Wikipedia strategy.```

## Phix

```function play(integer prisoners, iterations, bool optimal)
sequence drawers = shuffle(tagset(prisoners))
integer pardoned = 0
bool found = false
for i=1 to iterations do
drawers = shuffle(drawers)
for prisoner=1 to prisoners do
found = false
integer drawer = iff(optimal?prisoner:rand(prisoners))
for j=1 to prisoners/2 do
drawer = drawers[drawer]
if drawer==prisoner then found = true exit end if
if not optimal then drawer = rand(prisoners) end if
end for
end for
pardoned += found
end for
return 100*pardoned/iterations
end function

constant iterations = 100_000
printf(1,"Simulation count: %d\n",iterations)
for prisoners=10 to 100 by 90 do
atom random = play(prisoners,iterations,false),
optimal = play(prisoners,iterations,true)
printf(1,"Prisoners:%d, random:%g, optimal:%g\n",{prisoners,random,optimal})
end for
```
Output:
```Simulation count: 100000
Prisoners:10, random:0.006, optimal:35.168
Prisoners:100, random:0, optimal:31.098
```

## Phixmonti

Translation of: Yabasic
```/# Rosetta Code problem: http://rosettacode.org/wiki/100_prisoners
by Galileo, 05/2022 #/

include ..\Utilitys.pmt

def random rand * 1 + int enddef

def shuffle
len var l
l for var a
l random var b
b get var p
a get b set
p a set
endfor
enddef

def play var optimal var iterations var prisoners
0 var pardoned

( prisoners for endfor )

iterations for drop
shuffle
prisoners for var prisoner
false var found
optimal if prisoner else prisoners random endif
prisoners 2 / int for drop
get dup prisoner == if true var found exitfor
else
optimal not if drop prisoners random endif
endif
endfor
found not if exitfor endif
drop
endfor
pardoned found + var pardoned
endfor
drop
pardoned 100 * iterations /
enddef

( "Optimal: " 100 10000 true play
" Random: " 100 10000 false play
" Prisoners: " prisoners ) lprint```
Output:
```Please, be patient ...
Optimal: 31.65 Random: 0 Prisoners: 100
=== Press any key to exit ===```

## PL/M

```100H:
/* PARAMETERS */
DECLARE N\$DRAWERS  LITERALLY '100';  /* AMOUNT OF DRAWERS */
DECLARE N\$ATTEMPTS LITERALLY '50';   /* ATTEMPTS PER PRISONER */
DECLARE N\$SIMS     LITERALLY '2000'; /* N. OF SIMULATIONS TO RUN */
DECLARE RAND\$SEED  LITERALLY '193';  /* RANDOM SEED */

/* CP/M CALLS */
BDOS: PROCEDURE (FN, ARG); DECLARE FN BYTE, ARG ADDRESS; GO TO 5; END BDOS;
EXIT: PROCEDURE; CALL BDOS(0, 0); END EXIT;
PRINT: PROCEDURE (S); DECLARE S ADDRESS; CALL BDOS(9, S); END PRINT;

/* PRINT NUMBER */
PRINT\$NUMBER: PROCEDURE (N);
DECLARE S (6) BYTE INITIAL ('.....\$');
DECLARE (P, N) ADDRESS, C BASED P BYTE;
P = .S(5);
DIGIT:
P = P - 1;
C = N MOD 10 + '0';
N = N / 10;
IF N > 0 THEN GO TO DIGIT;
CALL PRINT(P);
END PRINT\$NUMBER;

/* RANDOM NUMBER GENERATOR */
RAND\$BYTE: PROCEDURE BYTE;
DECLARE (X, A, B, C) BYTE
INITIAL (RAND\$SEED, RAND\$SEED, RAND\$SEED, RAND\$SEED);
X = X+1;
A = A XOR C XOR X;
B = B+A;
C = C+SHR(B,1)+A;
RETURN C;
END RAND\$BYTE;

/* GENERATE RANDOM NUMBER FROM 0 TO MAX */
RAND\$MAX: PROCEDURE (MAX) BYTE;
DECLARE (X, R, MAX) BYTE;
X = 1;
DO WHILE X < MAX;
X = SHL(X,1);
END;
X = X-1;
DO WHILE 1;
R = RAND\$BYTE AND X;
IF R < MAX THEN RETURN R;
END;
END RAND\$MAX;

/* PLACE CARDS RANDOMLY IN DRAWERS */
INIT\$DRAWERS: PROCEDURE (DRAWERS);
DECLARE DRAWERS ADDRESS, (D BASED DRAWERS, I, J, K) BYTE;
DO I=0 TO N\$DRAWERS-1;
D(I) = I;
END;
DO I=0 TO N\$DRAWERS-1;
J = I + RAND\$MAX(N\$DRAWERS-I);
K = D(I);
D(I) = D(J);
D(J) = K;
END;
END INIT\$DRAWERS;

/* PRISONER OPENS RANDOM DRAWERS */
RANDOM\$STRATEGY: PROCEDURE (DRAWERS, P) BYTE;
DECLARE DRAWERS ADDRESS, D BASED DRAWERS BYTE;
DECLARE (P, I, TRIES) BYTE;

/* KEEP TRACK OF WHICH DRAWERS HAVE BEEN OPENED */
DECLARE OPEN (N\$DRAWERS) BYTE;
DO I=0 TO N\$DRAWERS-1;
OPEN(I) = 0;
END;

/* OPEN RANDOM DRAWERS */
TRIES = N\$ATTEMPTS;
DO WHILE TRIES > 0;
IF NOT OPEN(I := RAND\$MAX(N\$DRAWERS)) THEN DO;
/* IF WE FIND OUR NUMBER, SUCCESS */
IF D(I) = P THEN RETURN 1;
OPEN(I) = 1;
TRIES = TRIES - 1;
END;
END;

RETURN 0; /* WE DID NOT FIND OUR NUMBER */
END RANDOM\$STRATEGY;

/* PRISONER USES OPTIMAL STRATEGY */
OPTIMAL\$STRATEGY: PROCEDURE (DRAWERS, P) BYTE;
DECLARE DRAWERS ADDRESS, D BASED DRAWERS BYTE;
DECLARE (P, I, TRIES) BYTE;
TRIES = N\$ATTEMPTS;
I = P;
DO WHILE TRIES > 0;
I = D(I); /* OPEN DRAWER W/ CURRENT NUMBER */
IF I = P THEN RETURN 1; /* DID WE FIND IT? */
TRIES = TRIES - 1;
END;
RETURN 0;
END OPTIMAL\$STRATEGY;

/* RUN A SIMULATION */
DECLARE RANDOM LITERALLY '0';
DECLARE OPTIMAL LITERALLY '1';
SIMULATE: PROCEDURE (STRAT) BYTE;
DECLARE (STRAT, P, R) BYTE;

/* PLACE CARDS IN DRAWERS */
DECLARE DRAWERS (N\$DRAWERS) BYTE;
CALL INIT\$DRAWERS(.DRAWERS);

/* TRY EACH PRISONER */
DO P=0 TO N\$DRAWERS-1;
DO CASE STRAT;
R = RANDOM\$STRATEGY(.DRAWERS, P);
R = OPTIMAL\$STRATEGY(.DRAWERS, P);
END;

/* IF ONE PRISONER FAILS THEY ALL HANG */
IF NOT R THEN RETURN 0;
END;

RETURN 1; /* IF THEY ALL SUCCEED NONE HANG */
END SIMULATE;

/* RUN MANY SIMULATIONS AND COUNT THE SUCCESSES */
DECLARE STRAT BYTE, (I, N, SUCC) ADDRESS;
SUCC = 0;
DO I=1 TO N;
SUCC = SUCC + SIMULATE(STRAT);
END;
RETURN SUCC;
END RUN\$SIMULATIONS;

/* RUN AND PRINT SIMULATIONS */
RUN\$AND\$PRINT: PROCEDURE (NAME, STRAT, N);
DECLARE (NAME, N, S) ADDRESS, STRAT BYTE;
CALL PRINT(NAME);
CALL PRINT(.' STRATEGY: \$');
S = RUN\$SIMULATIONS(N, STRAT);
CALL PRINT\$NUMBER(S);
CALL PRINT(.' OUT OF \$');
CALL PRINT\$NUMBER(N);
CALL PRINT(.' - \$');
CALL PRINT\$NUMBER( S*10 / (N/10) );
CALL PRINT(.(37,13,10,'\$'));
END RUN\$AND\$PRINT;

CALL RUN\$AND\$PRINT(.'RANDOM\$', RANDOM, N\$SIMS);
CALL RUN\$AND\$PRINT(.'OPTIMAL\$', OPTIMAL, N\$SIMS);
CALL EXIT;
EOF```
Output:
```RANDOM STRATEGY: 0 OUT OF 2000 - 0%
OPTIMAL STRATEGY: 653 OUT OF 2000 - 32%```

## Pointless

```optimalSeq(drawers, n) =
iterate(ind => drawers[ind - 1], n)
|> takeUntil(ind => drawers[ind - 1] == n)

optimalTrial(drawers) =
range(1, 100)
|> map(optimalSeq(drawers))

randomSeq(drawers, n) =
iterate(ind => randRange(1, 100), randRange(1, 100))
|> takeUntil(ind => drawers[ind - 1] == n)

randomTrial(drawers) =
range(1, 100)
|> map(randomSeq(drawers))

checkLength(seq) =
length(take(51, seq)) <= 50

numTrials = 3000

runTrials(trialFunc) =
for t in range(1, numTrials)
yield
range(1, 100)
|> shuffle
|> toArray
|> trialFunc
|> map(checkLength)
|> all

countSuccess(trialFunc) =
runTrials(trialFunc)
|> filter(id)
|> length

optimalCount = countSuccess(optimalTrial)
randomCount = countSuccess(randomTrial)

output =
format("optimal: {} / {} = {} prob\nrandom: {} / {} = {} prob", [
optimalCount, numTrials, optimalCount / numTrials,
randomCount, numTrials, randomCount / numTrials,
])
|> println
```
Output:
```optimal: 923 / 3000 = 0.30766666666666664 prob
random: 0 / 3000 = 0.0 prob```

## PowerShell

Translation of: Chris
```### Clear Screen from old Output
Clear-Host

Function RandomOpening ()
{
\$Prisoners = 1..100 | Sort-Object {Get-Random}
\$Cupboard = 1..100 | Sort-Object {Get-Random}
## Loop for the Prisoners
\$Survived = \$true
for (\$I=1;\$I -le 100;\$i++)
{
\$OpeningListe = 1..100 | Sort-Object {Get-Random}
\$Gefunden = \$false
## Loop for the trys of every prisoner
for (\$X=1;\$X -le 50;\$X++)
{
\$OpenNumber = \$OpeningListe[\$X]
IF (\$Cupboard[\$OpenNumber] -eq \$Prisoners[\$I])
{
\$Gefunden = \$true
}
## Cancel loop if prisoner found his number (yeah i know, dirty way ^^ )
IF (\$Gefunden)
{
\$X = 55
}
}
IF (\$Gefunden -eq \$false)
{
\$I = 120
\$Survived = \$false
}
}
Return \$Survived
}

Function StrategyOpening ()
{
\$Prisoners = 1..100 | Sort-Object {Get-Random}
\$Cupboard = 1..100 | Sort-Object {Get-Random}
\$Survived = \$true
for (\$I=1;\$I -le 100;\$i++)
{
\$Gefunden = \$false
\$OpeningNumber = \$Prisoners[\$I-1]
for (\$X=1;\$X -le 50;\$X++)
{
IF (\$Cupboard[\$OpeningNumber-1] -eq \$Prisoners[\$I-1])
{
\$Gefunden = \$true
}
else
{
\$OpeningNumber = \$Cupboard[\$OpeningNumber-1]
}
IF (\$Gefunden)
{
\$X = 55
}
}
IF (\$Gefunden -eq \$false)
{
\$I = 120
\$Survived = \$false
}
}
Return \$Survived
}

\$MaxRounds = 10000

Function TestRandom
{
\$WinnerRandom = 0
for (\$Round = 1; \$Round -le \$MaxRounds;\$Round++)
{
IF ((\$Round%1000) -eq 0)
{
\$Time = Get-Date
Write-Host "Currently we are at rount \$Round at \$Time"
}
\$Rueckgabewert = RandomOpening
IF (\$Rueckgabewert)
{
\$WinnerRandom++
}
}

\$Prozent = (100/\$MaxRounds)*\$WinnerRandom
Write-Host "There are \$WinnerRandom survivors whit random opening. This is \$Prozent percent"
}

Function TestStrategy
{
for (\$Round = 1; \$Round -le \$MaxRounds;\$Round++)
{
IF ((\$Round%1000) -eq 0)
{
\$Time = Get-Date
Write-Host "Currently we are at \$Round at \$Time"
}
\$Rueckgabewert = StrategyOpening
IF (\$Rueckgabewert)
{
}
}

Write-Host "There are \$WinnersStrategy survivors whit strategic opening. This is \$Prozent percent"
}

Function Main ()
{
Clear-Host
TestRandom
TestStrategy
}

Main
```
Output:
```# of executions: 10000
There are 0 survivors whit random opening. This is 0 percent
There are 3104 survivors whit strategic opening. This is 31,04 percent"
```

## Processing

```IntList drawers = new IntList();
int trials = 100000;
int succes_count;

void setup() {
for (int i = 0; i < 100; i++) {
drawers.append(i);
}
println(trials + " trials\n");

//Random strategy
println("Random strategy");
succes_count = trials;
for (int i = 0; i < trials; i++) {
drawers.shuffle();
for (int prisoner = 0; prisoner < 100; prisoner++) {
boolean found = false;
for (int attempt = 0; attempt < 50; attempt++) {
if (drawers.get(int(random(drawers.size()))) == prisoner) {
found = true;
break;
}
}
if (!found) {
succes_count--;
break;
}
}
}
println(" Succeses: " + succes_count);
println(" Succes rate: " + 100.0 * succes_count / trials + "%\n");

//Optimal strategy
println("Optimal strategy");
succes_count = trials;
for (int i = 0; i < trials; i++) {
drawers.shuffle();
for (int prisoner = 0; prisoner < 100; prisoner++) {
boolean found = false;
int next = prisoner;
for (int attempt = 0; attempt < 50; attempt++) {
next = drawers.get(next);
if (next == prisoner) {
found = true;
break;
}
}
if (!found) {
succes_count--;
break;
}
}
}
println(" Succeses: " + succes_count);
print(" Succes rate: " + 100.0 * succes_count / trials + "%");
}```
Output:
```100000 trials

Random strategy
Succeses: 0
Succes rate: 0.0%

Optimal strategy
Succeses: 31134
Succes rate: 31.134%```

## PureBasic

```#PRISONERS=100
#DRAWERS  =100
#LOOPS    = 50
#MAXPROBE = 10000
OpenConsole()

Dim p1(#PRISONERS,#DRAWERS)
Dim p2(#PRISONERS,#DRAWERS)
Dim d(#DRAWERS)

For i=1 To #DRAWERS : d(i)=i : Next
Start:
For probe=1 To #MAXPROBE
RandomizeArray(d(),1,100)
c1=0 : c2=0
For m=1 To #PRISONERS
p2(m,1)=d(m) : If d(m)=m : p2(m,0)=1 : EndIf
For n=1 To #LOOPS
p1(m,n)=d(Random(100,1))
If p1(m,n)=m : p1(m,0)=1 : EndIf
If n>1 : p2(m,n)=d(p2(m,n-1)) : If p2(m,n)=m : p2(m,0)=1 : EndIf : EndIf
Next n
Next m

For m=1 To #PRISONERS
If p1(m,0) : c1+1 : p1(m,0)=0 : EndIf
If p2(m,0) : c2+1 : p2(m,0)=0 : EndIf
Next m

If c1=#PRISONERS : w1+1 : EndIf
If c2=#PRISONERS : w2+1 : EndIf
Next probe
Print("TRIALS: "+Str(#MAXPROBE))
Print("  RANDOM= "+StrF(100*w1/#MAXPROBE,2)+"%   STATEGY= "+StrF(100*w2/#MAXPROBE,2)+"%")
PrintN(~"\tFIN =q.") : inp\$=Input()
w1=0 : w2=0
If inp\$<>"q" : Goto Start : EndIf
```
Output:
```TRIALS: 10000  RANDOM= 0.00%   STATEGY= 30.83%	FIN =q.

TRIALS: 10000  RANDOM= 0.00%   STATEGY= 31.60%	FIN =q.

TRIALS: 10000  RANDOM= 0.00%   STATEGY= 31.20%	FIN =q.```

## Python

### Procedural

```import random

def play_random(n):
# using 0-99 instead of ranges 1-100
pardoned = 0
in_drawer = list(range(100))
sampler = list(range(100))
for _round in range(n):
random.shuffle(in_drawer)
found = False
for prisoner in range(100):
found = False
for reveal in random.sample(sampler, 50):
card = in_drawer[reveal]
if card == prisoner:
found = True
break
break
if found:
pardoned += 1
return pardoned / n * 100   # %

def play_optimal(n):
# using 0-99 instead of ranges 1-100
pardoned = 0
in_drawer = list(range(100))
for _round in range(n):
random.shuffle(in_drawer)
for prisoner in range(100):
reveal = prisoner
found = False
for go in range(50):
card = in_drawer[reveal]
if card == prisoner:
found = True
break
reveal = card
break
if found:
pardoned += 1
return pardoned / n * 100   # %

if __name__ == '__main__':
n = 100_000
print(" Simulation count:", n)
print(f" Random play wins: {play_random(n):4.1f}% of simulations")
print(f"Optimal play wins: {play_optimal(n):4.1f}% of simulations")
```
Output:
``` Simulation count: 100000
Random play wins:  0.0% of simulations
Optimal play wins: 31.1% of simulations```

Or, an alternative procedural approach:

```# http://rosettacode.org/wiki/100_prisoners

import random

def main():
NUM_DRAWERS = 10
NUM_REPETITIONS = int(1E5)

print('{:15}: {:5} ({})'.format('approach', 'wins', 'ratio'))
for approach in PrisionersGame.approaches:
num_victories = 0
for _ in range(NUM_REPETITIONS):
game = PrisionersGame(NUM_DRAWERS)
num_victories += PrisionersGame.victory(game.play(approach))

print('{:15}: {:5} ({:.2%})'.format(
approach.__name__, num_victories, num_victories / NUM_REPETITIONS))

class PrisionersGame:
"""docstring for PrisionersGame"""
def __init__(self, num_drawers):
assert num_drawers % 2 == 0
self.num_drawers = num_drawers
self.max_attempts = int(self.num_drawers / 2)
self.drawer_ids = list(range(1, num_drawers + 1))
shuffled = self.drawer_ids[:]
random.shuffle(shuffled)
self.drawers = dict(zip(self.drawer_ids, shuffled))

def play_naive(self, player_number):
""" Randomly open drawers """
for attempt in range(self.max_attempts):
if self.drawers[random.choice(self.drawer_ids)] == player_number:
return True

return False

def play_naive_mem(self, player_number):
""" Randomly open drawers but avoiding repetitions """
not_attemped = self.drawer_ids[:]
for attempt in range(self.max_attempts):
guess = random.choice(not_attemped)
not_attemped.remove(guess)

if self.drawers[guess] == player_number:
return True

return False

def play_optimum(self, player_number):
""" Open the drawer that matches the player number and then open the drawer
with the revealed number.
"""
prev_attempt = player_number
for attempt in range(self.max_attempts):
if self.drawers[prev_attempt] == player_number:
return True
else:
prev_attempt = self.drawers[prev_attempt]

return False

@classmethod
def victory(csl, results):
"""Defines a victory of a game: all players won"""
return all(results)

approaches = [play_naive, play_naive_mem, play_optimum]

def play(self, approach):
"""Plays this game and returns a list of booleans with
True if a player one, False otherwise"""
return [approach(self, player) for player in self.drawer_ids]

if __name__ == '__main__':
main()
```
Output:
```With 10 drawers (100k runs)
approach       : wins  (ratio)
play_naive     :    14 (0.01%)
play_naive_mem :    74 (0.07%)
play_optimum   : 35410 (35.41%)

With 100 drawers (10k runs)
approach       : wins  (ratio)
play_naive     :     0 (0.00%)
play_naive_mem :     0 (0.00%)
play_optimum   :  3084 (30.84%)```

### Functional

There is some inefficiency entailed in repeatedly re-calculating the fixed sequence of drawers defined by index-chasing in the optimal strategy. Parts of the same paths from drawer to drawer are followed by several different prisoners.

We can avoid redundant recalculation by first obtaining the full set of drawer-chasing cycles that are defined by the sequence of any given shuffle.

We may also notice that the collective fate of the prisoners turns on whether any of the cyclical paths formed by a given shuffle are longer than 50 items. If a shuffle produces a single over-sized cycle, then not every prisoner will be able to reach their card in 50 moves.

The computation below returns a survival failure as soon as a cycle of more than 50 items is found for any given shuffle:

Works with: Python version 3.7
```'''100 Prisoners'''

from random import randint, sample

# allChainedPathsAreShort :: Int -> IO (0|1)
def allChainedPathsAreShort(n):
'''1 if none of the index-chasing cycles in a shuffled
sample of [1..n] cards are longer than half the
sample size. Otherwise, 0.
'''
limit = n // 2
xs = range(1, 1 + n)
shuffled = sample(xs, k=n)

# A cycle of boxes, drawn from a shuffled
# sample, which includes the given target.
def cycleIncluding(target):
boxChain = [target]
v = shuffled[target - 1]
while v != target:
boxChain.append(v)
v = shuffled[v - 1]
return boxChain

# Nothing if the target list is empty, or if the cycle which contains the
# first target is larger than half the sample size.
# Otherwise, just a cycle of enchained boxes containing the first target
# in the list, tupled with the residue of any remaining targets which
# fall outside that cycle.
def boxCycle(targets):
if targets:
boxChain = cycleIncluding(targets[0])
return Just((
difference(targets[1:])(boxChain),
boxChain
)) if limit >= len(boxChain) else Nothing()
else:
return Nothing()

# No cycles longer than half of total box count ?
return int(n == sum(map(len, unfoldr(boxCycle)(xs))))

# randomTrialResult :: RandomIO (0|1) -> Int -> (0|1)
def randomTrialResult(coin):
'''1 if every one of the prisoners finds their ticket
in an arbitrary half of the sample. Otherwise 0.
'''
return lambda n: int(all(
coin(x) for x in range(1, 1 + n)
))

# TEST ----------------------------------------------------
# main :: IO ()
def main():
'''Two sampling techniques constrasted with 100 drawers
and 100 prisoners, over 100,000 trial runs.
'''
halfOfDrawers = randomRInt(0)(1)

def optimalDrawerSampling(x):
return allChainedPathsAreShort(x)

def randomDrawerSampling(x):
return randomTrialResult(halfOfDrawers)(x)

# kSamplesWithNBoxes :: Int -> Int -> String
def kSamplesWithNBoxes(k):
tests = range(1, 1 + k)
return lambda n: '\n\n' + fTable(
str(k) + ' tests of optimal vs random drawer-sampling ' +
'with ' + str(n) + ' boxes: \n'
)(fName)(lambda r: '{:.2%}'.format(r))(
lambda f: sum(f(n) for x in tests) / k
)([
optimalDrawerSampling,
randomDrawerSampling,
])

print(kSamplesWithNBoxes(10000)(10))

print(kSamplesWithNBoxes(10000)(100))

print(kSamplesWithNBoxes(100000)(100))

# ------------------------DISPLAY--------------------------

# fTable :: String -> (a -> String) ->
# (b -> String) -> (a -> b) -> [a] -> String
def fTable(s):
'''Heading -> x display function -> fx display function ->
f -> xs -> tabular string.
'''
def go(xShow, fxShow, f, xs):
ys = [xShow(x) for x in xs]
w = max(map(len, ys))
return s + '\n' + '\n'.join(map(
lambda x, y: y.rjust(w, ' ') + ' -> ' + fxShow(f(x)),
xs, ys
))
return lambda xShow: lambda fxShow: lambda f: lambda xs: go(
xShow, fxShow, f, xs
)

# fname :: (a -> b) -> String
def fName(f):
'''Name bound to the given function.'''
return f.__name__

# ------------------------GENERIC -------------------------

# Just :: a -> Maybe a
def Just(x):
'''Constructor for an inhabited Maybe (option type) value.
Wrapper containing the result of a computation.
'''
return {'type': 'Maybe', 'Nothing': False, 'Just': x}

# Nothing :: Maybe a
def Nothing():
'''Constructor for an empty Maybe (option type) value.
Empty wrapper returned where a computation is not possible.
'''
return {'type': 'Maybe', 'Nothing': True}

# difference :: Eq a => [a] -> [a] -> [a]
def difference(xs):
'''All elements of xs, except any also found in ys.'''
return lambda ys: list(set(xs) - set(ys))

# randomRInt :: Int -> Int -> IO () -> Int
def randomRInt(m):
'''The return value of randomRInt is itself
a function. The returned function, whenever
called, yields a a new pseudo-random integer
in the range [m..n].
'''
return lambda n: lambda _: randint(m, n)

# unfoldr(lambda x: Just((x, x - 1)) if 0 != x else Nothing())(10)
# -> [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]
# unfoldr :: (b -> Maybe (a, b)) -> b -> [a]
def unfoldr(f):
'''Dual to reduce or foldr.
Where catamorphism reduces a list to a summary value,
the anamorphic unfoldr builds a list from a seed value.
As long as f returns Just(a, b), a is prepended to the list,
and the residual b is used as the argument for the next
application of f.
When f returns Nothing, the completed list is returned.
'''
def go(v):
xr = v, v
xs = []
while True:
mb = f(xr[0])
if mb.get('Nothing'):
return xs
else:
xr = mb.get('Just')
xs.append(xr[1])
return xs
return lambda x: go(x)

# MAIN ---
if __name__ == '__main__':
main()
```
Output:
```10000 tests of optimal vs random drawer-sampling with 10 boxes:

optimalDrawerSampling -> 35.47%
randomDrawerSampling -> 0.09%

10000 tests of optimal vs random drawer-sampling with 100 boxes:

optimalDrawerSampling -> 30.40%
randomDrawerSampling -> 0.00%

100000 tests of optimal vs random drawer-sampling with 100 boxes:

optimalDrawerSampling -> 31.17%
randomDrawerSampling -> 0.00%```

## R

```t = 100000 #number of trials
success.r = rep(0,t) #this will keep track of how many prisoners find their ticket on each trial for the random method
success.o = rep(0,t) #this will keep track of how many prisoners find their ticket on each trial for the optimal method

#random method
for(i in 1:t){
escape = rep(F,100)
ticket = sample(1:100)
for(j in 1:length(prisoner)){
escape[j] = j %in% sample(ticket,50)
}
success.r[i] = sum(escape)
}

#optimal method
for(i in 1:t){
escape = rep(F,100)
ticket = sample(1:100)
for(j in 1:100){
boxes = 0
current.box = j
while(boxes<50 && !escape[j]){
boxes=boxes+1
escape[j] = ticket[current.box]==j
current.box = ticket[current.box]
}
}
success.o[i] = sum(escape)
}

cat("Random method resulted in a success rate of ",100*mean(success.r==100),
"%.\nOptimal method resulted in a success rate of ",100*mean(success.o==100),"%.",sep="")
```
Output:
```Random method resulted in a success rate of 0%.
Optimal method resulted in a success rate of 31.129%.```

## QB64

```Const Found = -1, Searching = 0, Status = 1, Tries = 2
Const Attempt = 1, Victories = 2, RandomW = 1, ChainW = 2
Randomize Timer

Dim Shared Prisoners(1 To 100, Status To Tries) As Integer, Drawers(1 To 100) As Integer, Results(1 To 2, 1 To 2) As Integer
Print "100 prisoners"
Print "Random way to search..."
For a = 1 To 2000
Init
Results(RandomW, Attempt) = Results(RandomW, Attempt) + 1
RandomWay
If verify% Then Results(RandomW, Victories) = Results(RandomW, Victories) + 1
Next

Print: Print "Chain way to search..."
For a = 1 To 2000
Init
Results(ChainW, Attempt) = Results(ChainW, Attempt) + 1
ChainWay
If verify% Then Results(ChainW, Victories) = Results(ChainW, Victories) + 1
Next
Print: Print "Results: "
Print " Attempts "; Results(RandomW, Attempt); " "; "Victories "; Results(RandomW, Victories); " Ratio:"; Results(RandomW, Victories); "/"; Results(RandomW, Attempt)
Print
Print " Attempts "; Results(ChainW, Attempt); " "; "Victories "; Results(ChainW, Victories); " Ratio:"; Results(ChainW, Victories); "/"; Results(ChainW, Attempt)
End

Function verify%
Dim In As Integer
Print "veryfing "
verify = 0
For In = 1 To 100
If Prisoners(In, Status) = Searching Then Exit For
Next
If In = 101 Then verify% = Found
End Function

Sub ChainWay
Dim In As Integer, ChainChoice As Integer
Print "Chain search"
For In = 1 To 100
ChainChoice = In
Do
Prisoners(In, Tries) = Prisoners(In, Tries) + 1
If Drawers(ChainChoice) = In Then Prisoners(In, Status) = Found: Exit Do
ChainChoice = Drawers(ChainChoice)
Loop Until Prisoners(In, Tries) = 50
Next In
End Sub

Sub RandomWay
Dim In As Integer, RndChoice As Integer
Print "Random search"
For In = 1 To 100
Do
Prisoners(In, Tries) = Prisoners(In, Tries) + 1
If Drawers(Int(Rnd * 100) + 1) = In Then Prisoners(In, Status) = Found: Exit Do
Loop Until Prisoners(In, Tries) = 50
Next
Print "Executed "
End Sub

Sub Init
Dim I As Integer, I2 As Integer
Print "initialization"
For I = 1 To 100
Prisoners(I, Status) = Searching
Prisoners(I, Tries) = Searching
Do
Drawers(I) = Int(Rnd * 100) + 1
For I2 = 1 To I
If Drawers(I2) = Drawers(I) Then Exit For
Next
If I2 = I Then Exit Do
Loop
Next I
Print "Done "
End Sub```

## Quackery

```  [ this ] is 100prisoners.qky

[ dup size 2 / split ]                          is halve     (   [ --> [ [ )

[ stack ]                                       is successes (     --> s   )

[ [] swap times [ i join ] shuffle ]            is drawers   (   n --> [   )

[ false unrot
temp put
dup shuffle
halve drop
witheach
[ dip dup peek
temp share = if
[ dip not
conclude ] ]
drop
temp release ]                                is naive     ( [ n --> b   )

[ false unrot
dup temp put
over size 2 / times
[ dip dup peek
dup temp share = if
[ rot not unrot
conclude ] ]
2drop
temp release ]                                is smart     ( [ n --> b   )

[ ]'[ temp put
drawers
0 successes put
dup size times
[ dup i temp share do
successes tally ]
size successes take =
temp release ]                                is prisoners (   n --> b   )

[ say "100 naive prisoners were pardoned "
0 10000 times [ 100 prisoners naive + ] echo
say " times out of 10000 simulations." cr

say "100 smart prisoners were pardoned "
0 10000 times [ 100 prisoners smart + ] echo
say " times out of 10000 simulations." cr ]   is simulate  (     -->     )```

Output:

```/O>  [ \$ '100prisoners.qky' loadfile ] now!
...  simulate
...
100 naive prisoners were pardoned 0 times out of 10000 simulations.
100 smart prisoners were pardoned 3158 times out of 10000 simulations.

Stack empty.```

## Racket

```#lang racket
(require srfi/1)

(define current-samples (make-parameter 10000))
(define *prisoners* 100)
(define *max-guesses* 50)

(define (evaluate-strategy instance-solved? strategy (s (current-samples)))
(/ (for/sum ((_ s) #:when (instance-solved? strategy)) 1) s))

(define (build-drawers)
(list->vector (shuffle (range *prisoners*))))

(define (100-prisoners-problem strategy)
(every (strategy (build-drawers)) (range *prisoners*)))

(define ((strategy-1 drawers) p)
(any (λ (_) (= p (vector-ref drawers (random *prisoners*)))) (range *max-guesses*)))

(define ((strategy-2 drawers) p)
(define-values (_ found?)
(for/fold ((d p) (found? #f)) ((_ *max-guesses*)) #:break found?
(let ((card (vector-ref drawers d))) (values card (= card p)))))
found?)

(define (print-sample-percentage caption f (s (current-samples)))
(printf "~a: ~a%~%" caption (real->decimal-string (* 100 f) (- (order-of-magnitude s) 2))))

(module+ main
(print-sample-percentage "random" (evaluate-strategy 100-prisoners-problem strategy-1))
(print-sample-percentage "optimal" (evaluate-strategy 100-prisoners-problem strategy-2)))
```
Output:
```random: 0.00%
optimal: 31.18%```

## Raku

(formerly Perl 6)

Works with: Rakudo version 2019.07.1

Accepts command line parameters to modify the number of prisoners and the number of simulations to run.

Also test with 10 prisoners to verify that the logic is correct for random selection. Random selection should succeed with 10 prisoners at a probability of (1/2)**10, so in 100_000 simulations, should get pardons about .0977 percent of the time.

```unit sub MAIN (:\$prisoners = 100, :\$simulations = 10000);
my @prisoners = ^\$prisoners;
my \$half = floor +@prisoners / 2;

sub random (\$n) {
^\$n .race.map( {
my @drawers = @prisoners.pick: *;
@prisoners.map( -> \$prisoner {
my \$found = 0;
for @drawers.pick(\$half) -> \$card {
\$found = 1 and last if \$card == \$prisoner
}
last unless \$found;
\$found
}
).sum == @prisoners
}
).grep( *.so ).elems / \$n * 100
}

sub optimal (\$n) {
^\$n .race.map( {
my @drawers = @prisoners.pick: *;
@prisoners.map( -> \$prisoner {
my \$found = 0;
my \$card = @drawers[\$prisoner];
if \$card == \$prisoner {
\$found = 1
} else {
for ^(\$half - 1) {
\$card = @drawers[\$card];
\$found = 1 and last if \$card == \$prisoner
}
}
last unless \$found;
\$found
}
).sum == @prisoners
}
).grep( *.so ).elems / \$n * 100
}

say "Testing \$simulations simulations with \$prisoners prisoners.";
printf " Random play wins: %.3f%% of simulations\n", random \$simulations;
printf "Optimal play wins: %.3f%% of simulations\n", optimal \$simulations;
```
Output:

With defaults

```Testing 10000 simulations with 100 prisoners.
Random play wins: 0.000% of simulations
Optimal play wins: 30.510% of simulations```

With passed parameters: --prisoners=10, --simulations=100000

```Testing 100000 simulations with 10 prisoners.
Random play wins: 0.099% of simulations
Optimal play wins: 35.461% of simulations
```

## Red

```Red []

K_runs: 100000
repeat n 100 [append rand_arr: []  n]              ;; define array/series with numbers 1..100

;;-------------------------------
strat_optimal: function [pris ][
;;-------------------------------
loop 50 [
if Board/:locker = pris [ return true ]       ;; locker with prisoner number found
locker: Board/:locker
]
]
;;-------------------------------
strat_rand: function [pris ][
;;-------------------------------
random rand_arr                                                 ;; define set of  random lockers
repeat n 50 [ if Board/(rand_arr/:n) = pris [ return true ]  ]  ;; try first 50, found ? then return success
false
]

;;------------------------------
check_board: function [ strat][
;;------------------------------
repeat pris 100 [                                                   ;; for each prisoner
either  strat = 'optimal [ unless strat_optimal pris [return false ]  ]
[ unless strat_rand pris [return false ]  ]
]
true                                                  ;; all 100 prisoners passed test
]

saved: saved_rand: 0                                    ;; count all saved runs per strategy
loop K_runs [
Board: random copy rand_arr                           ;; new board for every run
if  check_board 'optimal [saved: saved + 1]           ;; optimal stategy
if  check_board 'rand [saved_rand: saved_rand + 1]  ;; random strategy
]

print ["runs" k_runs newline  "Percent saved opt.strategy:" saved * 100.0 / k_runs ]
print ["Percent saved random strategy:" saved_rand * 100.0 / k_runs ]
```
Output:
```
runs 100000
Percent saved opt.strategy: 31.165
Percent saved random strategy: 0.0

```

## REXX

```/*REXX program to simulate the problem of 100 prisoners:  random,  and optimal strategy.*/
parse arg men trials seed .                      /*obtain optional arguments from the CL*/
if    men=='' |    men==","  then    men=    100 /*number of   prisoners   for this run.*/
if trials=='' | trials==","  then trials= 100000 /*  "     "  simulations   "    "   "  */
if datatype(seed, 'W')  then call random ,,seed  /*seed for the random number generator.*/
try= men % 2;                swaps= men * 3      /*number tries for searching for a card*/
\$.1= ' a simple ';           \$.2= "an optimal"   /*literals used for the SAY instruction*/
say center(' running'  commas(trials)   "trials with"  commas(men)  'prisoners ', 70, "═")
say
do strategy=1  for 2;    pardons= 0          /*perform the two types of strategies. */

do trials;             call gCards         /*do trials for a strategy;  gen cards.*/
do p=1  for men  until failure           /*have each prisoner go through process*/
if strategy==1  then failure= simple()   /*Is 1st strategy?  Use simple strategy*/
else failure= picker()   /* " 2nd     "       "  optimal   "    */
end   /*p*/                              /*FAILURE ≡ 1?  Then a prisoner failed.*/
if #==men  then pardons= pardons + 1       /*was there a pardon of all prisoners? */
end     /*trials*/                         /*if 1 prisoner fails, then they all do*/

pc= format( pardons/trials*100, , 3);                           _= left('', pc<10)
say right('Using', 9)  \$.strategy  "strategy yields pardons "   _||pc"%  of the time."
end       /*strategy*/
exit                                             /*stick a fork in it,  we're all done. */
/*──────────────────────────────────────────────────────────────────────────────────────*/
commas:  parse arg _;  do c=length(_)-3  to 1  by -3; _= insert(',', _, c); end;  return _
/*──────────────────────────────────────────────────────────────────────────────────────*/
gCards: #= 0;                do j=1  for men;  @.j= j             /*define seq. of cards*/
end   /*j*/                          /*same as seq. of men.*/
do swaps;             a= random(1, men)            /*get 1st rand number.*/
do until  b\==a;  b= random(1, men)            /* "  2nd   "     "   */
end   /*until*/                                /* [↑] ensure A ¬== B */
parse value  @.a @.b  with  @.b @.a                /*swap 2 random cards.*/
end       /*swaps*/;  return
/*──────────────────────────────────────────────────────────────────────────────────────*/
simple: !.= 0; do try;         do until !.?==0; ?= random(1, men) /*get random card ··· */
end   /*until*/                    /*··· not used before.*/
if @.?==p  then do;   #= #+1;  return 0;  end      /*found his own card? */
!.?= 1                                             /*flag as being used. */
end   /*try*/;        return 1                     /*didn't find his card*/
/*──────────────────────────────────────────────────────────────────────────────────────*/
picker: ?= p;  do try; if @.?==p  then do;   #= #+1;    return 0  /*Found his own card? */
end       /* [↑]  indicate success for prisoner. */
?= @.?                            /*choose next drawer from current card.*/
end   /*try*/;        return 1    /*choose half of the number of drawers.*/
```
output   when using the default inputs:
```══════════════ running 100,000 trials with 100 prisoners ══════════════

Using  a simple  strategy yields pardons   0.000%  of the time.
Using an optimal strategy yields pardons  31.186%  of the time.
```
output   when using the input of:     10
```══════════════ running 100,000 trials with 10 prisoners ══════════════

Using  a simple  strategy yields pardons   0.086%  of the time.
Using an optimal strategy yields pardons  31.204%  of the time.
```

## Ruby

```prisoners = [*1..100]
N = 10_000
generate_rooms = ->{ [nil]+[*1..100].shuffle }

res = N.times.count do
rooms = generate_rooms[]
prisoners.all? {|pr| rooms[1,100].sample(50).include?(pr)}
end
puts "Random strategy : %11.4f %%" % (res.fdiv(N) * 100)

res = N.times.count do
rooms = generate_rooms[]
prisoners.all? do |pr|
cur_room = pr
50.times.any? do
found = (rooms[cur_room] == pr)
cur_room = rooms[cur_room]
found
end
end
end
puts "Optimal strategy: %11.4f %%" % (res.fdiv(N) * 100)
```
Output:
```Random strategy :      0.0000 %
Optimal strategy:     30.7400 %
```

## Rust

Fairly naive implementation. Could probably be made more idiomatic. Depends on extern rand crate.

Cargo.toml

```[dependencies]
rand = '0.7.2'
```

src/main.rs

```extern crate rand;

use rand::prelude::*;

// Do a full run of checking boxes in a random order for a single prisoner
fn check_random_boxes(prisoner: u8, boxes: &[u8]) -> bool {
let checks = {
let mut b: Vec<u8> = (1u8..=100u8).collect();
b
};
checks.into_iter().take(50).any(|check| boxes[check as usize - 1] == prisoner)
}

// Do a full run of checking boxes in the optimized order for a single prisoner
fn check_ordered_boxes(prisoner: u8, boxes: &[u8]) -> bool {
let mut next_check = prisoner;
(0..50).any(|_| {
next_check = boxes[next_check as usize - 1];
next_check == prisoner
})
}

fn main() {
let mut boxes: Vec<u8> = (1u8..=100u8).collect();

let trials = 100000;

let ordered_successes = (0..trials).filter(|_| {
(1u8..=100u8).all(|prisoner| check_ordered_boxes(prisoner, &boxes))
}).count();

let random_successes = (0..trials).filter(|_| {
(1u8..=100u8).all(|prisoner| check_random_boxes(prisoner, &boxes))
}).count();

println!("{} / {} ({:.02}%) successes in ordered", ordered_successes, trials, ordered_successes as f64 * 100.0 / trials as f64);
println!("{} / {} ({:.02}%) successes in random", random_successes, trials, random_successes as f64 * 100.0 / trials as f64);

}
```
Output:
```31106 / 100000 (31.11%) successes in ordered
0 / 100000 (0.00%) successes in random```

## Sather

```class MAIN is
shuffle (a: ARRAY{INT}) is
ARR_PERMUTE_ALG{INT, ARRAY{INT}}::shuffle(a);
end;

try_random (n: INT, drawers: ARRAY{INT}, tries: INT): BOOL is
my_tries ::= drawers.inds; shuffle(my_tries);
loop tries.times!;
if drawers[my_tries.elt!] = n then return true; end;
end;
return false;
end;

try_optimal (n: INT, drawers: ARRAY{INT}, tries: INT): BOOL is
num ::= n;
loop tries.times!;
num := drawers[num];
if num = n then return true; end;
end;
return false;
end;

stats (label: STR, rounds, successes: INT): STR is
return #FMT("<^###########>: <#######> rounds. Successes: <#######> (<##.###>%%)\n",
label, rounds, successes, (successes.flt / rounds.flt)*100.0).str;
end;

try (name: STR, nrounds, ndrawers, npris, ntries: INT,
strategy: ROUT{INT,ARRAY{INT},INT}:BOOL)
is
drawers: ARRAY{INT} := #(ndrawers);
loop drawers.set!(drawers.ind!); end;
successes ::= 0;
loop nrounds.times!;
shuffle(drawers);
success ::= true;
loop
n ::= npris.times!;
if ~strategy.call(n, drawers, ntries) then
success := false;
break!;
end;
end;
if success then successes := successes + 1; end;
end;
#OUT + stats(name, nrounds, successes);
end;

main is
RND::seed := #TIMES.wall_time;
#OUT +"100 prisoners, 100 drawers, 50 tries:\n";
try("random",  100000, 100, 100, 50, bind(try_random(_, _, _)));
try("optimal", 100000, 100, 100, 50, bind(try_optimal(_, _, _)));

#OUT +"\n10 prisoners, 10 drawers, 5 tries:\n";
try("random",  100000, 10, 10, 5, bind(try_random(_, _, _)));
try("optimal", 100000, 10, 10, 5, bind(try_optimal(_, _, _)));
end;
end;```
Output:
```100 prisoners, 100 drawers, 50 tries:
random      :  100000 rounds. Successes:       0 ( 0.000%)
optimal     :  100000 rounds. Successes:   31378 (31.378%)

10 prisoners, 10 drawers, 5 tries:
random      :  100000 rounds. Successes:     113 ( 0.113%)
optimal     :  100000 rounds. Successes:   35633 (35.633%)```

## Scala

Translation of: Java
```import scala.util.Random
import scala.util.control.Breaks._

object Main {
def playOptimal(n: Int): Boolean = {
val secretList = Random.shuffle((0 until n).toBuffer)

for (i <- secretList.indices) {
var prev = i
breakable {
for (_ <- 0 until secretList.size / 2) {
if (secretList(prev) == i) {
break()
}
prev = secretList(prev)
}
return false
}
}

true
}

def playRandom(n: Int): Boolean = {
val secretList = Random.shuffle((0 until n).toBuffer)

for (i <- secretList.indices) {
val trialList = Random.shuffle((0 until n).toBuffer)

breakable {
for (j <- 0 until trialList.size / 2) {
if (trialList(j) == i) {
break()
}
}
return false
}
}

true
}

def exec(n: Int, p: Int, play: Int => Boolean): Double = {
var succ = 0.0
for (_ <- 0 until n) {
if (play(p)) {
succ += 1
}
}
(succ * 100.0) / n
}

def main(args: Array[String]): Unit = {
val n = 100000
val p = 100
printf("# of executions: %,d\n", n)
printf("Optimal play success rate: %f%%\n", exec(n, p, playOptimal))
printf("Random play success rate: %f%%\n", exec(n, p, playRandom))
}
}
```
Output:
```# of executions: 100,000
Optimal play success rate: 31.201000%
Random play success rate: 0.000000%```

## Swift

```import Foundation

struct PrisonersGame {
let strategy: Strategy
let numPrisoners: Int
let drawers: [Int]

init(numPrisoners: Int, strategy: Strategy) {
self.numPrisoners = numPrisoners
self.strategy = strategy
self.drawers = (1...numPrisoners).shuffled()
}

func play() -> Bool {
for num in 1...numPrisoners {
guard findNumber(num) else {
return false
}
}

return true
}

private func findNumber(_ num: Int) -> Bool {
var tries = 0
var nextDrawer = num - 1

while tries < 50 {
tries += 1

switch strategy {
case .random where drawers.randomElement()! == num:
return true
case .optimum where drawers[nextDrawer] == num:
return true
case .optimum:
nextDrawer = drawers[nextDrawer] - 1
case _:
continue
}
}

return false
}

enum Strategy {
case random, optimum
}
}

let numGames = 100_000
let lock = DispatchSemaphore(value: 1)
var done = 0

print("Running \(numGames) games for each strategy")

DispatchQueue.concurrentPerform(iterations: 2) {i in
let strat = i == 0 ? PrisonersGame.Strategy.random : .optimum
var numPardoned = 0

for _ in 0..<numGames {
let game = PrisonersGame(numPrisoners: 100, strategy: strat)

if game.play() {
numPardoned += 1
}
}

print("Probability of pardon with \(strat) strategy: \(Double(numPardoned) / Double(numGames))")

lock.wait()
done += 1
lock.signal()

if done == 2 {
exit(0)
}
}

dispatchMain()
```
Output:
```Running 100000 games for each strategy
Probability of pardon with optimum strategy: 0.31099
Probability of pardon with random strategy: 0.0```

## Tcl

Translation of: Common Lisp
```set Samples 10000
set Prisoners 100
set MaxGuesses 50
set Strategies {random optimal}

# returns a random number between 0 and N-1.
proc random {n} {
expr int(rand()*\$n)
}

# Returns a list from 0 to N-1.
proc range {n} {
set res {}
for {set i 0} {\$i < \$n} {incr i} {
lappend res \$i
}
return \$res
}

# Returns shuffled LIST.
proc nshuffle {list} {
set len [llength \$list]
while {\$len} {
set n [expr {int(\$len * rand())}]
set tmp [lindex \$list \$n]
lset list \$n [lindex \$list [incr len -1]]
lset list \$len \$tmp
}
return \$list
}

# Returns a list of shuffled drawers.
proc buildDrawers {} {
global Prisoners
nshuffle [range \$Prisoners]
}

# Returns true if P is found in DRAWERS within \$MaxGuesses attempts using a
# random strategy.
proc randomStrategy {drawers p} {
global Prisoners MaxGuesses
foreach i [range \$MaxGuesses] {
if {\$p == [lindex \$drawers [random \$Prisoners]]} {
return 1
}
}
return 0
}

# Returns true if P is found in DRAWERS within \$MaxGuesses attempts using an
# optimal strategy.
proc optimalStrategy {drawers p} {
global Prisoners MaxGuesses
set j \$p
foreach i [range \$MaxGuesses] {
set k [lindex \$drawers \$j]
if {\$k == \$p} {
return 1
}
set j \$k
}
return 0
}

# Returns true if all prisoners find their number using the given STRATEGY.
proc run100prisonersProblem {strategy} {
global Prisoners
set drawers [buildDrawers]
foreach p [range \$Prisoners] {
if {![\$strategy \$drawers \$p]} {
return 0
}
}
return 1
}

# Runs the given STRATEGY \$Samples times and returns the number of times all
# prisoners succeed.
proc sampling {strategy} {
global Samples
set successes 0
foreach s [range \$Samples] {
if {[run100prisonersProblem \$strategy]} {
incr successes
}
}
return \$successes
}

# Returns true if the given STRING starts with a vowel.
proc startsWithVowel {string} {
expr [lsearch -exact {a e i o u} [string index \$string 0]] >= 0
}

# Runs each of the STRATEGIES and prints a report on how well they
# worked.
proc compareStrategies {strategies} {
global Samples
set fmt "Using %s %s strategy, the prisoners were freed in %5.2f%% of the cases."
foreach strategy \$strategies {
set article [expr [startsWithVowel \$strategy] ? {"an"} : {"a"}]
set pct [expr [sampling \${strategy}Strategy] / \$Samples.0 * 100]
puts [format \$fmt \$article \$strategy \$pct]
}
}

compareStrategies \$Strategies
```
Output:
```Using a random strategy, the prisoners were freed in  0.00% of the cases.
Using an optimal strategy, the prisoners were freed in 32.35% of the cases.```

## Transact-SQL

### School example

Works with: Transact-SQL version SQL Server 2017
```USE rosettacode;
GO

SET NOCOUNT ON;
GO

CREATE TABLE dbo.numbers (n INT PRIMARY KEY);
GO

-- NOTE If you want to play more than 10000 games, you need to extend the query generating the numbers table by adding
-- next cross joins. Now the table contains enough values to solve the task and it takes less processing time.

WITH sample100 AS (
SELECT TOP(100) object_id
FROM master.sys.objects
)
INSERT numbers
SELECT ROW_NUMBER() OVER (ORDER BY A.object_id) AS n
FROM sample100 AS A
CROSS JOIN sample100 AS B;
GO

CREATE TABLE dbo.drawers (drawer INT PRIMARY KEY, card INT);
GO

CREATE TABLE dbo.results (strategy VARCHAR(10), game INT, result BIT, PRIMARY KEY (game, strategy));
GO

CREATE PROCEDURE dbo.shuffleDrawers @prisonersCount INT
AS BEGIN
SET NOCOUNT ON;

IF NOT EXISTS (SELECT * FROM drawers)
INSERT drawers (drawer, card)
SELECT n AS drawer, n AS card
FROM numbers
WHERE n <= @prisonersCount;

DECLARE @randoms TABLE (n INT, random INT);
DECLARE @n INT = 1;
WHILE @n <= @prisonersCount BEGIN
INSERT @randoms VALUES (@n, ROUND(RAND() * (@prisonersCount - 1), 0) + 1);

SET @n = @n + 1;
END;

WITH ordered AS (
SELECT ROW_NUMBER() OVER (ORDER BY random ASC) AS drawer,
n AS card
FROM @randoms
)
UPDATE drawers
SET card = o.card
FROM drawers AS s
INNER JOIN ordered AS o
ON o.drawer = s.drawer;
END
GO

CREATE PROCEDURE dbo.find @prisoner INT, @strategy VARCHAR(10)
AS BEGIN
-- A prisoner can open no more than 50 drawers.
DECLARE @drawersCount INT = (SELECT COUNT(*) FROM drawers);
DECLARE @openMax INT = @drawersCount / 2;

-- Prisoners start outside the room.
DECLARE @card INT = NULL;
DECLARE @open INT = 1;
WHILE @open <= @openMax BEGIN
-- A prisoner tries to find his own number.
IF @strategy = 'random' BEGIN
DECLARE @random INT = ROUND(RAND() * (@drawersCount - 1), 0) + 1;
SET @card = (SELECT TOP(1) card FROM drawers WHERE drawer = @random);
END
IF @strategy = 'optimal' BEGIN
IF @card IS NULL BEGIN
SET @card = (SELECT TOP(1) card FROM drawers WHERE drawer = @prisoner);
END ELSE BEGIN
SET @card = (SELECT TOP(1) card FROM drawers WHERE drawer = @card);
END
END

-- A prisoner finding his own number is then held apart from the others.
IF @card = @prisoner
RETURN 1;

SET @open = @open + 1;
END

RETURN 0;
END
GO

CREATE PROCEDURE dbo.playGame @gamesCount INT, @strategy VARCHAR(10), @prisonersCount INT = 100
AS BEGIN
SET NOCOUNT ON;

IF @gamesCount <> (SELECT COUNT(*) FROM results WHERE strategy = @strategy) BEGIN
DELETE results
WHERE strategy = @strategy;

INSERT results (strategy, game, result)
SELECT @strategy AS strategy, n AS game, 0 AS result
FROM numbers
WHERE n <= @gamesCount;
END

UPDATE results
SET result = 0
WHERE strategy = @strategy;

DECLARE @game INT = 1;
WHILE @game <= @gamesCount BEGIN
-- A room having a cupboard of 100 opaque drawers numbered 1 to 100, that cannot be seen from outside.
-- Cards numbered 1 to 100 are placed randomly, one to a drawer, and the drawers all closed; at the start.
EXECUTE shuffleDrawers @prisonersCount;

-- A prisoner tries to find his own number.
-- Prisoners start outside the room.
-- They can decide some strategy before any enter the room.
DECLARE @prisoner INT = 1;
DECLARE @found INT = 0;
WHILE @prisoner <= @prisonersCount BEGIN
EXECUTE @found = find @prisoner, @strategy;
IF @found = 1
SET @prisoner = @prisoner + 1;
ELSE
BREAK;
END;

-- If all 100 findings find their own numbers then they will all be pardoned. If any don't then all sentences stand.
IF @found = 1
UPDATE results SET result = 1 WHERE strategy = @strategy AND game = @game;

SET @game = @game + 1;
END
END
GO

CREATE FUNCTION dbo.computeProbability(@strategy VARCHAR(10))
RETURNS decimal (18, 2)
AS BEGIN
RETURN (
SELECT (SUM(CAST(result AS INT)) * 10000 / COUNT(*)) / 100
FROM results
WHERE strategy = @strategy
);
END
GO

-- Simulate several thousand instances of the game:
DECLARE @gamesCount INT = 2000;

-- ...where the prisoners randomly open drawers.
EXECUTE playGame @gamesCount, 'random';

-- ...where the prisoners use the optimal strategy mentioned in the Wikipedia article.
EXECUTE playGame @gamesCount, 'optimal';

-- Show and compare the computed probabilities of success for the two strategies.
DECLARE @log VARCHAR(max);
SET @log = CONCAT('Games count: ', @gamesCount);
RAISERROR (@log, 0, 1) WITH NOWAIT;
SET @log = CONCAT('Probability of success with "random" strategy: ', dbo.computeProbability('random'));
RAISERROR (@log, 0, 1) WITH NOWAIT;
SET @log = CONCAT('Probability of success with "optimal" strategy: ', dbo.computeProbability('optimal'));
RAISERROR (@log, 0, 1) WITH NOWAIT;
GO

DROP FUNCTION dbo.computeProbability;
DROP PROCEDURE dbo.playGame;
DROP PROCEDURE dbo.find;
DROP PROCEDURE dbo.shuffleDrawers;
DROP TABLE dbo.results;
DROP TABLE dbo.drawers;
DROP TABLE dbo.numbers;
GO```

Output:

```Games count: 2000
Probability of success with "random" strategy: 0.00
Probability of success with "optimal" strategy: 31.00```

## Transd

For checking the correctness of simulation of random selection, we can decrease the number of prisoners and increase the number of runs. Then, for 10 prisoners and 100,000 runs we should get 0.5^10 * 100,000 = about 0.1% of wins.

```#lang transd

MainModule: {
simRandom: (λ numPris Int() nRuns Int()
locals: nSucc 0.0
(for n in Range(nRuns) do
(with draws (for i in Range(numPris) project i) succ 1
(for prisN in Range(numPris) do
(shuffle draws)
(if (not (is-el Range(in: draws 0 (/ numPris 2)) prisN))
(= succ 0) break))
(+= nSucc succ)
)   )
(ret (* (/ nSucc nRuns) 100))
),

simOptimal: (λ numPris Int() nRuns Int()
locals: nSucc 0.0
(for n in Range(nRuns) do
(with draws (for i in Range(numPris) project i) succ 0 nextDraw 0
(shuffle draws)
(for prisN in Range(numPris) do (= nextDraw prisN) (= succ 0)
(for i in Range( (/ numPris 2)) do
(= nextDraw (get draws nextDraw))
(if (== nextDraw prisN) (= succ 1) break))
(if (not succ) break))
(+= nSucc succ)
)   )
(ret (* (/ nSucc nRuns) 100))
),

_start: (λ
(lout prec: 4 :fixed "Random play:    " (simRandom 100 10000) "% of wins")
(lout "Strategic play: " (simOptimal 100 10000)  "% of wins")
(lout "Check random play:    " (simRandom 10 100000)  "% of wins")
)
}
```
Output:
```Random play:    0.0000% of wins
Strategic play: 31.4500% of wins
Check random play:    0.1040% of wins
```

## VBA/Visual Basic

```Sub HundredPrisoners()

NumberOfPrisoners = Int(InputBox("Number of Prisoners", "Prisoners", 100))
Tries = Int(InputBox("Numer of Tries", "Tries", 1000))
Selections = Int(InputBox("Number of Selections", "Selections", NumberOfPrisoners / 2))

StartTime = Timer

AllFoundOptimal = 0
AllFoundRandom = 0
AllFoundRandomMem = 0

For i = 1 To Tries
OptimalCount = HundredPrisoners_Optimal(NumberOfPrisoners, Selections)
RandomCount = HundredPrisoners_Random(NumberOfPrisoners, Selections)
RandomMemCount = HundredPrisoners_Random_Mem(NumberOfPrisoners, Selections)

If OptimalCount = NumberOfPrisoners Then
AllFoundOptimal = AllFoundOptimal + 1
End If
If RandomCount = NumberOfPrisoners Then
AllFoundRandom = AllFoundRandom + 1
End If
If RandomMemCount = NumberOfPrisoners Then
AllFoundRandomMem = AllFoundRandomMem + 1
End If
Next i

ResultString = "Optimal: " & AllFoundOptimal & " of " & Tries & ": " & AllFoundOptimal / Tries * 100 & "%"
ResultString = ResultString & Chr(13) & "Random: " & AllFoundRandom & " of " & Tries & ": " & AllFoundRandom / Tries * 100 & "%"
ResultString = ResultString & Chr(13) & "RandomMem: " & AllFoundRandomMem & " of " & Tries & ": " & AllFoundRandomMem / Tries * 100 & "%"

EndTime = Timer

ResultString = ResultString & Chr(13) & "Elapsed Time: " & Round(EndTime - StartTime, 2) & " s"
ResultString = ResultString & Chr(13) & "Trials/sec: " & Tries / Round(EndTime - StartTime, 2)

MsgBox ResultString, vbOKOnly, "Results"

End Sub

Function HundredPrisoners_Optimal(ByVal NrPrisoners, ByVal NrSelections) As Long
Dim DrawerArray() As Long

ReDim DrawerArray(NrPrisoners - 1)

For Counter = LBound(DrawerArray) To UBound(DrawerArray)
DrawerArray(Counter) = Counter + 1
Next Counter

FisherYates DrawerArray

For i = 1 To NrPrisoners
NumberFromDrawer = DrawerArray(i - 1)
For j = 1 To NrSelections - 1
If NumberFromDrawer = i Then
FoundOwnNumber = FoundOwnNumber + 1
Exit For
End If
NumberFromDrawer = DrawerArray(NumberFromDrawer - 1)
Next j
Next i
HundredPrisoners_Optimal = FoundOwnNumber
End Function

Function HundredPrisoners_Random(ByVal NrPrisoners, ByVal NrSelections) As Long
Dim DrawerArray() As Long
ReDim DrawerArray(NrPrisoners - 1)

FoundOwnNumber = 0

For Counter = LBound(DrawerArray) To UBound(DrawerArray)
DrawerArray(Counter) = Counter + 1
Next Counter

FisherYates DrawerArray

For i = 1 To NrPrisoners
For j = 1 To NrSelections
RandomDrawer = Int(NrPrisoners * Rnd)
NumberFromDrawer = DrawerArray(RandomDrawer)
If NumberFromDrawer = i Then
FoundOwnNumber = FoundOwnNumber + 1
Exit For
End If
Next j
Next i
HundredPrisoners_Random = FoundOwnNumber
End Function

Function HundredPrisoners_Random_Mem(ByVal NrPrisoners, ByVal NrSelections) As Long
Dim DrawerArray() As Long
Dim SelectionArray() As Long
ReDim DrawerArray(NrPrisoners - 1)
ReDim SelectionArray(NrPrisoners - 1)

HundredPrisoners_Random_Mem = 0
FoundOwnNumberMem = 0

For Counter = LBound(DrawerArray) To UBound(DrawerArray)
DrawerArray(Counter) = Counter + 1
Next Counter

For Counter = LBound(SelectionArray) To UBound(SelectionArray)
SelectionArray(Counter) = Counter + 1
Next Counter

FisherYates DrawerArray

For i = 1 To NrPrisoners
FisherYates SelectionArray
For j = 1 To NrSelections
NumberFromDrawer = DrawerArray(SelectionArray(j - 1) - 1)
If NumberFromDrawer = i Then
FoundOwnNumberMem = FoundOwnNumberMem + 1
Exit For
End If
Next j
Next i
HundredPrisoners_Random_Mem = FoundOwnNumberMem
End Function

Sub FisherYates(ByRef InputArray() As Long)

Dim Temp As Long
Dim PosRandom As Long
Dim Counter As Long
Dim Upper As Long
Dim Lower As Long

Lower = LBound(InputArray)
Upper = UBound(InputArray)

Randomize

For Counter = Upper To (Lower + 1) Step -1
PosRandom = CLng(Int((Counter - Lower + 1) * Rnd + Lower))
Temp = InputArray(Counter)
InputArray(Counter) = InputArray(PosRandom)
InputArray(PosRandom) = Temp
Next Counter

End Sub
```
Output:
```Optimal: 29090 of 100000: 29.09%
Random: 0 of 100000: 0%
RandomMem: 0 of 100000: 0%
Elapsed Time: 388.41 s```

## Visual Basic .NET

Translation of: C#
```Module Module1

Function PlayOptimal() As Boolean
Dim secrets = Enumerable.Range(0, 100).OrderBy(Function(a) Guid.NewGuid).ToList

For p = 1 To 100
Dim success = False

Dim choice = p - 1
For i = 1 To 50
If secrets(choice) = p - 1 Then
success = True
Exit For
End If
choice = secrets(choice)
Next

If Not success Then
Return False
End If
Next

Return True
End Function

Function PlayRandom() As Boolean
Dim secrets = Enumerable.Range(0, 100).OrderBy(Function(a) Guid.NewGuid).ToList

For p = 1 To 100
Dim choices = Enumerable.Range(0, 100).OrderBy(Function(a) Guid.NewGuid).ToList

Dim success = False
For i = 1 To 50
If choices(i - 1) = p Then
success = True
Exit For
End If
Next

If Not success Then
Return False
End If
Next

Return True
End Function

Function Exec(n As UInteger, play As Func(Of Boolean))
Dim success As UInteger = 0
For i As UInteger = 1 To n
If play() Then
success += 1
End If
Next
Return 100.0 * success / n
End Function

Sub Main()
Dim N = 1_000_000
Console.WriteLine("# of executions: {0}", N)
Console.WriteLine("Optimal play success rate: {0:0.00000000000}%", Exec(N, AddressOf PlayOptimal))
Console.WriteLine(" Random play success rate: {0:0.00000000000}%", Exec(N, AddressOf PlayRandom))
End Sub

End Module
```
Output:
```# of executions: 1000000
Optimal play success rate: 31.12990000000%
Random play success rate: 0.00000000000%```

## VBScript

```option explicit
const npris=100
const ntries=50
const ntests=1000.
dim drawer(100),opened(100),i
for i=1 to npris: drawer(i)=i:next
shuffle drawer
wscript.echo rf(tests(false)/ntests*100,10," ")  &" % success for random"
wscript.echo rf(tests(true) /ntests*100,10," ")  &" % success for optimal strategy"

function rf(v,n,s) rf=right(string(n,s)& v,n):end function

sub shuffle(d) 'knut's shuffle
dim i,j,t
randomize timer
for i=1 to npris
j=int(rnd()*i+1)
t=d(i):d(i)=d(j):d(j)=t
next
end sub

function tests(strat)
dim cntp,i,j
tests=0
for i=1 to ntests
shuffle drawer
cntp=0
if strat then
for j=1 to npris
if not trystrat(j) then exit for
next
else
for j=1 to npris
if not tryrand(j) then exit for
next
end if
if j>=npris then tests=tests+1
next
end function

function tryrand(pris)
dim i,r
erase opened
for i=1 to ntries
do
r=int(rnd*npris+1)
loop until opened(r)=false
opened(r)=true
if drawer(r)= pris then tryrand=true : exit function
next
tryrand=false
end function

function trystrat(pris)
dim i,r
r=pris
for i=1 to ntries
if drawer(r)= pris then trystrat=true	:exit function
r=drawer(r)
next
trystrat=false
end function
```

Output:

```         0 % success for random
32.9 % success for optimal strategy
```

## V (Vlang)

Translation of: Wren
```import rand
import rand.seed
// Uses 0-based numbering rather than 1-based numbering throughout.
fn do_trials(trials int, np int, strategy string) {
mut pardoned := 0
for _ in 0..trials {
mut drawers := []int{len: 100, init: it}
rand.shuffle<int>(mut drawers) or {panic('shuffle failed')}
mut next_trial := false
for p in 0..np {
mut next_prisoner := false
if strategy == "optimal" {
mut prev := p
for _ in 0..50 {
this := drawers[prev]
if this == p {
next_prisoner = true
break
}
prev = this
}
} else {
// Assumes a prisoner remembers previous drawers (s)he opened
// and chooses at random from the others.
mut opened := [100]bool{}
for _ in 0..50 {
mut n := 0
for {
n = rand.intn(100) or {0}
if !opened[n] {
opened[n] = true
break
}
}
if drawers[n] == p {
next_prisoner = true
break
}
}
}
if !next_prisoner {
next_trial = true
break
}
}
if !next_trial {
pardoned++
}
}
rf := f64(pardoned) / f64(trials) * 100
println("  strategy = \${strategy:-7}  pardoned = \${pardoned:-6} relative frequency = \${rf:-5.2f}%\n")
}

fn main() {
rand.seed(seed.time_seed_array(2))
trials := 100000
for np in [10, 100] {
println("Results from \$trials trials with \$np prisoners:\n")
for strategy in ["random", "optimal"] {
do_trials(trials, np, strategy)
}
}
}```
Output:

Sample run:

```Results from 100000 trials with 10 prisoners:

strategy = random   pardoned = 91     relative frequency = 0.09 %

strategy = optimal  pardoned = 31321  relative frequency = 31.32%

Results from 100000 trials with 100 prisoners:

strategy = random   pardoned = 0      relative frequency = 0.00 %

strategy = optimal  pardoned = 31318  relative frequency = 31.32%
```

## Wren

Translation of: Go
Library: Wren-fmt
```import "random" for Random
import "/fmt" for Fmt

var rand = Random.new()

var doTrials = Fn.new{ |trials, np, strategy|
var pardoned = 0
for (t in 0...trials) {
var drawers = List.filled(100, 0)
for (i in 0..99) drawers[i] = i
rand.shuffle(drawers)
var nextTrial = false
for (p in 0...np) {
var nextPrisoner = false
if (strategy == "optimal") {
var prev = p
for (d in 0..49) {
var curr = drawers[prev]
if (curr == p) {
nextPrisoner = true
break
}
prev = curr
}
} else {
var opened = List.filled(100, false)
for (d in 0..49) {
var n
while (true) {
n = rand.int(100)
if (!opened[n]) {
opened[n] = true
break
}
}
if (drawers[n] == p) {
nextPrisoner = true
break
}
}
}
if (!nextPrisoner) {
nextTrial = true
break
}
}
if (!nextTrial) pardoned = pardoned + 1
}
var rf = pardoned/trials * 100
Fmt.print("  strategy = \$-7s  pardoned = \$,6d relative frequency = \$5.2f\%\n", strategy, pardoned, rf)
}

var trials = 1e5
for (np in [10, 100]) {
Fmt.print("Results from \$,d trials with \$d prisoners:\n", trials, np)
for (strategy in ["random", "optimal"]) doTrials.call(trials, np, strategy)
}
```
Output:

Sample run:

```Results from 100,000 trials with 10 prisoners:

strategy = random   pardoned =     98 relative frequency =  0.10%

strategy = optimal  pardoned = 31,212 relative frequency = 31.21%

Results from 100,000 trials with 100 prisoners:

strategy = random   pardoned =      0 relative frequency =  0.00%

strategy = optimal  pardoned = 31,139 relative frequency = 31.14%
```

## XPL0

```int     Drawer(100);

proc KShuffle;          \Randomly rearrange the cards in the drawers
\(Woe unto thee if Stattolo shuffle is used instead of Knuth shuffle.)
int  I, J, T;
[for I:= 100-1 downto 1 do
[J:= Ran(I+1);      \range [0..I]
T:= Drawer(I);  Drawer(I):= Drawer(J);  Drawer(J):= T;
];
];

func Stategy2;          \Return 'true' if stragegy succeeds
int  Prisoner, Card, Try;
[for Prisoner:= 1 to 100 do
[Card:= Drawer(Prisoner-1);
Try:= 1;
loop [if Card = Prisoner then quit;
if Try >= 50 then return false;
Card:= Drawer(Card-1);
Try:= Try+1;
];
];
return true;
];

func Stategy1;          \Return 'true' if stragegy succeeds
int  Prisoner, I, D(100);
[for Prisoner:= 1 to 100 do
loop [for I:= 0 to 100-1 do D(I):= I+1;
KShuffle;
for I:= 1 to 50 do
if Drawer(D(I-1)) = Prisoner then quit;
return false;
];
return true;
];

proc Strategy(S);
int  S, I, Sample;
real Successes;
[Successes:= 0.;
for Sample:= 1 to 100_000 do
[for I:= 0 to 100-1 do Drawer(I):= I+1;
KShuffle;
case S of
1: if Stategy1 then Successes:= Successes + 1.;
2: if Stategy2 then Successes:= Successes + 1.
other [];
];
RlOut(0, Successes/100_000.*100.);  Text(0, "%^m^j");
];

[Format(3, 12);
Text(0, "Random strategy success rate:  ");
Strategy(1);
Text(0, "Optimal strategy success rate: ");
Strategy(2);
]```
Output:
```Random strategy success rate:    0.000000000000%
Optimal strategy success rate:  31.085000000000%
```

## Yabasic

Translation of: Phix
```// Rosetta Code problem: http://rosettacode.org/wiki/100_prisoners
// by Galileo, 05/2022

sub play(prisoners, iterations, optimal)
local prisoner, pardoned, found, drawer, drawers(prisoners), i, j, k, p, x

for i = 1 to prisoners : drawers(i) = i : next

for i = 1 to iterations
for k = 1 to prisoners : x = ran(prisoners) + 1 : p = drawers(x) : drawers(x) = drawers(k) : drawers(k) = p : next
for prisoner = 1 to prisoners
found = false
if optimal then drawer = prisoner else drawer = ran(prisoners) + 1 end if
for j = 1 to prisoners / 2
drawer = drawers(drawer)
if drawer = prisoner found = true : break
if not optimal drawer = ran(prisoners) + 1
next
next
pardoned = pardoned + found
next

return 100 * pardoned / iterations
end sub

iterations = 10000
print "Simulation count: ", iterations
for prisoners = 10 to 100 step 90
random = play(prisoners, iterations, false)
optimal = play(prisoners, iterations, true)
print "Prisoners: ", prisoners, ", random: ", random, ", optimal: ", optimal
next```
Output:
```Simulation count: 10000
Prisoners: 10, random: 0.01, optimal: 35.83
Prisoners: 100, random: 0, optimal: 31.2
---Program done, press RETURN---```

## zkl

```const SLOTS=100, PRISONERS=100, TRIES=50, N=10_000;
fcn oneHundredJDI{	// just do it strategy
cupboard,picks := [0..SLOTS-1].walk().shuffle(), cupboard.copy();
// if this prisoner can't find their number in TRIES, all fail
foreach p in (PRISONERS){ if(picks.shuffle().find(p)>=TRIES) return(False); }
True		// all found their number
}
fcn oneHundredO{	// Optimal strategy
cupboard := [0..SLOTS-1].walk().shuffle();
foreach p in (PRISONERS){
d:=p;
do(TRIES){ if((d=cupboard[d]) == p) continue(2) }  // found my number
return(False);  // this prisoner failed to find their number, all fail
}
True		// all found their number
}```
```s:=N.pump(Ref(0).incN,oneHundredJDI).value.toFloat()/N*100;
println("Just do it strategy (%,d simulatations): %.2f%%".fmt(N,s));

s:=N.pump(Ref(0).incN,oneHundredO).value.toFloat()/N*100;
println("Optimal strategy    (%,d simulatations): %.2f%%".fmt(N,s));```
Output:
```Just do it strategy (10,000 simulatations): 0.00%
Optimal strategy    (10,000 simulatations): 31.16%
```

And a sanity check (from the Raku entry):

`const SLOTS=100, PRISONERS=10, TRIES=50, N=100_000;`
Output:
```Just do it strategy (100,000 simulatations): 0.09%
Optimal strategy    (100,000 simulatations): 31.13%
```