# Probabilistic choice

Given a mapping between items and their required probability of occurrence, generate a million items randomly subject to the given probabilities and compare the target probability of occurrence versus the generated values.

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

The total of all the probabilities should equal one. (Because floating point arithmetic is involved, this is subject to rounding errors).

Use the following mapping to test your programs:
```aleph   1/5.0
beth    1/6.0
gimel   1/7.0
daleth  1/8.0
he      1/9.0
waw     1/10.0
zayin   1/11.0

```with Ada.Numerics.Float_Random;  use Ada.Numerics.Float_Random;

procedure Random_Distribution is
Trials : constant := 1_000_000;
type Outcome is (Aleph, Beth, Gimel, Daleth, He, Waw, Zayin, Heth);
Pr : constant array (Outcome) of Uniformly_Distributed :=
(1.0/5.0, 1.0/6.0, 1.0/7.0, 1.0/8.0, 1.0/9.0, 1.0/10.0, 1.0/11.0, 1.0);
Samples : array (Outcome) of Natural := (others => 0);
Value   : Uniformly_Distributed;
Dice    : Generator;
begin
for Try in 1..Trials loop
Value := Random (Dice);
for I in Pr'Range loop
if Value <= Pr (I) then
Samples (I) := Samples (I) + 1;
exit;
else
Value := Value - Pr (I);
end if;
end loop;
end loop;
-- Printing the results
for I in Pr'Range loop
Put (Outcome'Image (I) & Character'Val (9));
Put (Float'Image (Float (Samples (I)) / Float (Trials)) & Character'Val (9));
if I = Heth then
Put_Line (" rest");
else
Put_Line (Uniformly_Distributed'Image (Pr (I)));
end if;
end loop;
end Random_Distribution;
```

Sample output:

```ALEPH    2.00167E-01     2.00000E-01
BETH     1.67212E-01     1.66667E-01
GIMEL    1.42290E-01     1.42857E-01
DALETH   1.24186E-01     1.25000E-01
HE       1.11455E-01     1.11111E-01
WAW      1.00325E-01     1.00000E-01
ZAYIN    9.10220E-02     9.09091E-02
HETH     6.33430E-02     rest
```

## ALGOL 68

Translation of: C
Works with: ALGOL 68 version Revision 1 - no extensions to language used
Works with: ALGOL 68G version Any - tested with release 1.18.0-9h.tiny
```INT trials = 1 000 000;

MODE LREAL = LONG REAL;

MODE ITEM = STRUCT(
STRING name,
INT prob count,
LREAL expect,
mapping
);
INT col width = 9;
FORMAT real repr = \$g(-col width+1, 6)\$,
item repr = \$"Name: "g", Prob count: "g(0)", Expect: "f(real repr)", Mapping: ", f(real repr)l\$;

[8]ITEM items := (
( "aleph",  0, ~, ~ ),
( "beth",   0, ~, ~ ),
( "gimel",  0, ~, ~ ),
( "daleth", 0, ~, ~ ),
( "he",     0, ~, ~ ),
( "waw",    0, ~, ~ ),
( "zayin",  0, ~, ~ ),
( "heth",   0, ~, ~ )
);

main:
(
LREAL offset = 5; # const #

# initialise items #
LREAL total sum := 0;
FOR i FROM LWB items TO UPB items - 1 DO
expect OF items[i] := 1/(i-1+offset);
total sum +:= expect OF items[i]
OD;
expect OF items[UPB items] := 1 - total sum;

mapping OF items[LWB items] := expect OF items[LWB items];
FOR i FROM LWB items + 1 TO UPB items DO
mapping OF items[i] := mapping OF items[i-1] + expect OF items[i]
OD;

# printf((item repr, items)) #

# perform the sampling #
PROC sample = (REF[]LREAL mapping)INT:(
INT out;
LREAL rand real = random;
FOR j FROM LWB items TO UPB items DO
IF rand real < mapping[j] THEN
out := j;
done
FI
OD;
done: out
);

FOR i TO trials DO
prob count OF items[sample(mapping OF items)] +:= 1
OD;

FORMAT indent = \$17k\$;

# print the results #
printf((\$"Trials: "g(0)l\$, trials));
printf((\$"Items:"\$,indent));
FOR i FROM LWB items TO UPB items DO printf((\$gn(col width)k" "\$, name OF items[i])) OD;
printf((\$l"Target prob.:"\$, indent, \$f(real repr)" "\$, expect OF items));
printf((\$l"Attained prob.:"\$, indent));
FOR i FROM LWB items TO UPB items DO printf((\$f(real repr)" "\$, prob count OF items[i]/trials)) OD;
printf(\$l\$)
)```

Sample output:

```Trials: 1000000
Items:          aleph    beth     gimel    daleth   he       waw      zayin    heth
Target prob.:   0.200000 0.166667 0.142857 0.125000 0.111111 0.100000 0.090909 0.063456
Attained prob.: 0.199987 0.166917 0.142531 0.124203 0.111338 0.099702 0.091660 0.063662
```

## AppleScript

AppleScript does have a random number command, but this is located in the StandardAdditions OSAX and invoking it a million times can take quite a while. Since Mac OS X 10.11, it's been possible to use the randomising features of the system's "GameplayKit" framework, which are faster to access.

```use AppleScript version "2.5" -- Mac OS X 10.11 (El Capitan) or later.
use framework "Foundation"
use framework "GameplayKit"

on probabilisticChoices(mapping, picks)
script o
property mapping : {}
end script

-- Make versions of the mapping records with additional 'actual' properties …
set mapping to current application's class "NSMutableArray"'s arrayWithArray:(mapping)
-- … ensuring that they're sorted (for accuracy) in descending order (for efficiency) of probability.
set descriptor to current application's class "NSSortDescriptor"'s sortDescriptorWithKey:("probability") ascending:(false)
tell mapping to sortUsingDescriptors:({descriptor})
set o's mapping to mapping as list

set rndGenerator to current application's class "GKRandomDistribution"'s distributionForDieWithSideCount:(picks)
set onePickth to 1 / picks
repeat picks times
-- Get a random number between 0.0 and 1.0.
set r to rndGenerator's nextUniform()
-- Interpret the probability of the number occurring in the range it does
-- as picking the item with the same probability of being picked.
repeat with thisRecord in o's mapping
set r to r - (thisRecord's probability)
if (r ≤ 0) then
set thisRecord's actual to (thisRecord's actual) + onePickth
exit repeat
end if
end repeat
end repeat

return o's mapping
end probabilisticChoices

set mapping to {{|item|:"aleph", probability:1 / 5}, {|item|:"beth", probability:1 / 6}, ¬
{|item|:"gimel", probability:1 / 7}, {|item|:"daleth", probability:1 / 8}, ¬
{|item|:"he", probability:1 / 9}, {|item|:"waw", probability:1 / 10}, ¬
{|item|:"zayin", probability:1 / 11}, {|item|:"heth", probability:1759 / 27720}}
set picks to 1000000
set theResults to probabilisticChoices(mapping, picks)
set output to {}
set template to {"|item|:", missing value, ", probability:", missing value, ", actual:", missing value}
set astid to AppleScript's text item delimiters
set AppleScript's text item delimiters to ""
repeat with thisRecord in theResults
set {|item|:item 2 of template, probability:item 4 of template, actual:item 6 of template} to thisRecord
set {|item|:template's second item, probability:template's fourth item, actual:template's sixth item} to thisRecord
set end of output to template as text
end repeat
set AppleScript's text item delimiters to "}, ¬
{"
set output to "{ ¬
{" & output & "} ¬
}"
set AppleScript's text item delimiters to astid
return output

```
Output:
```"{ ¬
{|item|:aleph, probability:0.2, actual:0.20033}, ¬
{|item|:beth, probability:0.166666666667, actual:0.166744}, ¬
{|item|:gimel, probability:0.142857142857, actual:0.142403}, ¬
{|item|:daleth, probability:0.125, actual:0.125195}, ¬
{|item|:he, probability:0.111111111111, actual:0.110284}, ¬
{|item|:waw, probability:0.1, actual:0.100505}, ¬
{|item|:zayin, probability:0.090909090909, actual:0.090721}, ¬
{|item|:heth, probability:0.063455988456, actual:0.063817} ¬
}"
```

## Arturo

```nofTrials: 10000
probabilities: #[
aleph: to :rational [1 5]
beth: to :rational [1 6]
gimel: to :rational [1 7]
daleth: to :rational [1 8]
he: to :rational [1 9]
waw: to :rational [1 10]
zayin: to :rational [1 11]
heth: to :rational [1759 27720]
]

samples: #[]

loop 1..nofTrials 'x [
z: random 0.0 1.0
loop probabilities [item,prob][
if? z < prob [
unless key? samples item -> samples\[item]: 0
samples\[item]: samples\[item] + 1
break
]
else [
z: z - prob
]
]
]

[s1, s2]: 0.0

print repeat "-" 50
loop probabilities [item,prob][
r: samples\[item] // nofTrials
s1: s1 + r*100
s2: s2 + prob*100

print [
pad to :string round.to:4 r 10
pad to :string round.to:4 to :floating 100*1-r//prob 13 "%"
]
]
print repeat "-" 50
print [
pad to :string to :floating s2 10
]
```
Output:
```Item           Target    Tesults     Differences
--------------------------------------------------
aleph             1/5     0.1954           2.3 %
beth              1/6     0.1654          0.76 %
gimel             1/7     0.1397          2.21 %
daleth            1/8     0.1234          1.28 %
he                1/9      0.112          -0.8 %
waw              1/10     0.1032          -3.2 %
zayin            1/11     0.0946         -4.06 %
heth       1759/27720     0.0663       -4.4819 %
--------------------------------------------------
Total:          100.0      100.0```

## AutoHotkey

contributed by Laszlo on the ahk forum

```s1 := "aleph",   p1 := 1/5.0                       ; Input
s2 := "beth",    p2 := 1/6.0
s3 := "gimel",   p3 := 1/7.0
s4 := "daleth",  p4 := 1/8.0
s5 := "he",      p5 := 1/9.0
s6 := "waw",     p6 := 1/10.0
s7 := "zayin",   p7 := 1/11.0
s8 := "heth",    p8 := 1-p1-p2-p3-p4-p5-p6-p7
n := 8, r0 := 0, r%n% := 1                         ; auxiliary data

Loop % n-1
i := A_Index-1, r%A_Index% := r%i% + p%A_Index% ; cummulative distribution

Loop 1000000 {
Random R, 0, 1.0
Loop %n%                                        ; linear search
If (R < r%A_Index%) {
c%A_Index%++
Break
}
}
; Output
Loop %n%
t .= s%A_Index% "`t" p%A_Index% "`t" c%A_Index%*1.0e-6 "`n"
Msgbox %t%

/*
output:
---------------------------
aleph  0.200000   0.199960
beth   0.166667   0.166146
gimel  0.142857   0.142624
daleth 0.125000   0.124924
he     0.111111   0.111226
waw    0.100000   0.100434
zayin  0.090909   0.091344
heth   0.063456   0.063342
---------------------------
*/
```

## AWK

```#!/usr/bin/awk -f

BEGIN {
ITERATIONS = 1000000
delete symbMap
delete probMap
delete counts
initData();

for (i = 0; i < ITERATIONS; i++) {
distribute(rand())
}
showDistributions()

exit
}

function distribute(rnd,    cnt, symNum, sym, symPrb) {
cnt = length(symbMap)
for (symNum = 1; symNum <= cnt; symNum++) {
sym = symbMap[symNum];
symPrb = probMap[sym];
rnd -= symPrb;
if (rnd <= 0) {
counts[sym]++
return;
}
}
}

function showDistributions(    s, sym, prb, actSum, expSum, totItr) {
actSum = 0.0
expSum = 0.0
totItr = 0
printf "%-7s  %-7s  %-5s  %-5s\n", "symb", "num.", "act.", "expt."
print  "-------  -------  -----  -----"
for (s = 1; s <= length(symbMap); s++) {
sym = symbMap[s]
prb = counts[sym]/ITERATIONS
actSum += prb
expSum += probMap[sym]
totItr += counts[sym]
printf "%-7s  %7d  %1.3f  %1.3f\n", sym, counts[sym], prb, probMap[sym]
}
print  "-------  -------  -----  -----"
printf "Totals:  %7d  %1.3f  %1.3f\n", totItr, actSum, expSum
}

function initData(    sym) {
srand()

probMap["aleph"]  = 1.0 / 5.0
probMap["beth"]   = 1.0 / 6.0
probMap["gimel"]  = 1.0 / 7.0
probMap["daleth"] = 1.0 / 8.0
probMap["he"]     = 1.0 / 9.0
probMap["waw"]    = 1.0 / 10.0
probMap["zyin"]   = 1.0 / 11.0
probMap["heth"]   = 1759.0 / 27720.0

symbMap[1] = "aleph"
symbMap[2] = "beth"
symbMap[3] = "gimel"
symbMap[4] = "daleth"
symbMap[5] = "he"
symbMap[6] = "waw"
symbMap[7] = "zyin"
symbMap[8] = "heth"

for (sym in probMap)
counts[sym] = 0;
}
```

Example output:

```symb     num.     act.   expt.
-------  -------  -----  -----
aleph     200598  0.201  0.200
beth      166317  0.166  0.167
gimel     142391  0.142  0.143
daleth    125051  0.125  0.125
he        110658  0.111  0.111
waw       100464  0.100  0.100
zyin       90649  0.091  0.091
heth       63872  0.064  0.063
-------  -------  -----  -----
Totals:  1000000  1.000  1.000
```

Rounding off makes the results look perfect.

## BASIC256

Translation of: FreeBASIC
```dim letters\$ = {"aleph", "beth", "gimel", "daleth", "he", "waw", "zayin", "heth"}
dim actual(8) fill 0 ## all zero by default
dim probs = {1/5.0, 1/6.0, 1/7.0, 1/8.0, 1/9.0, 1/10.0, 1/11.0, 0}
dim cumProbs(8)

cumProbs[0] = probs[0]
for i = 1 to 6
cumProbs[i] = cumProbs[i - 1] + probs[i]
next i
cumProbs[7] = 1.0
probs[7] = 1.0 - cumProbs[6]

n = 1000000
sum = 0.0

for i = 1 to n
rnd = rand  ## random number where 0 <= rand < 1
begin case
case rnd <= cumProbs[0]
actual[0] += 1
case rnd <= cumProbs[1]
actual[1] += 1
case rnd <= cumProbs[2]
actual[2] += 1
case rnd <= cumProbs[3]
actual[3] += 1
case rnd <= cumProbs[4]
actual[4] += 1
case rnd <= cumProbs[5]
actual[5] += 1
case rnd <= cumProbs[6]
actual[6] += 1
else
actual[7] += 1
end case
next i

sumActual = 0

print "Letter", " Actual", "Expected"
print "------", "--------", "--------"
for i = 0 to 7
print ljust(letters\$[i],14," ");
print ljust(actual[i]/n,8,"0"); "      ";
sumActual += actual[i]/n
print ljust(probs[i],8,"0")
next i

print "      ", "--------", "--------"
print "      ", ljust(sumActual,8,"0"), "1.000000"
end```

## BBC BASIC

```      DIM item\$(7), prob(7), cnt%(7)
item\$() = "aleph","beth","gimel","daleth","he","waw","zayin","heth"
prob()  = 1/5.0, 1/6.0, 1/7.0, 1/8.0, 1/9.0, 1/10.0, 1/11.0, 1759/27720
IF ABS(SUM(prob())-1) > 1E-6 ERROR 100, "Probabilities don't sum to 1"

FOR trial% = 1 TO 1E6
r = RND(1)
p = 0
FOR i% = 0 TO DIM(prob(),1)
p += prob(i%)
IF r < p cnt%(i%) += 1 : EXIT FOR
NEXT
NEXT

@% = &2060A
PRINT "Item        actual    theoretical"
FOR i% = 0 TO DIM(item\$(),1)
PRINT item\$(i%), cnt%(i%)/1E6, prob(i%)
NEXT
```

Output:

```Item        actual    theoretical
aleph       0.200306  0.200000
beth        0.165963  0.166667
gimel       0.143089  0.142857
daleth      0.125387  0.125000
he          0.111057  0.111111
waw         0.100098  0.100000
zayin       0.091031  0.090909
heth        0.063069  0.063456
```

## C

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

/* pick a random index from 0 to n-1, according to probablities listed
in p[] which is assumed to have a sum of 1. The values in the probablity
list matters up to the point where the sum goes over 1 */
int rand_idx(double *p, int n)
{
double s = rand() / (RAND_MAX + 1.0);
int i;
for (i = 0; i < n - 1 && (s -= p[i]) >= 0; i++);
return i;
}

#define LEN 8
#define N 1000000
int main()
{
const char *names[LEN] = { "aleph", "beth", "gimel", "daleth",
"he", "waw", "zayin", "heth" };
double s, p[LEN] = { 1./5, 1./6, 1./7, 1./8, 1./9, 1./10, 1./11, 1e300 };
int i, count[LEN] = {0};

for (i = 0; i < N; i++) count[rand_idx(p, LEN)] ++;

printf("  Name  Count    Ratio Expected\n");
for (i = 0, s = 1; i < LEN; s -= p[i++])
printf("%6s%7d %7.4f%% %7.4f%%\n",
names[i], count[i],
(double)count[i] / N * 100,
((i < LEN - 1) ? p[i] : s) * 100);

return 0;
}
```
output
```  Name  Count    Ratio Expected
aleph 199928 19.9928% 20.0000%
beth 166489 16.6489% 16.6667%
gimel 143211 14.3211% 14.2857%
daleth 125257 12.5257% 12.5000%
he 110849 11.0849% 11.1111%
waw  99935  9.9935% 10.0000%
zayin  91001  9.1001%  9.0909%
heth  63330  6.3330%  6.3456%
```

## C#

Translation of: Java
```using System;

class Program
{
static long TRIALS = 1000000L;
private class Expv
{
public string name;
public int probcount;
public double expect;
public double mapping;

public Expv(string name, int probcount, double expect, double mapping)
{
this.name = name;
this.probcount = probcount;
this.expect = expect;
this.mapping = mapping;
}
}

static Expv[] items = {
new Expv("aleph", 0, 0.0, 0.0), new Expv("beth", 0, 0.0, 0.0),
new Expv("gimel", 0, 0.0, 0.0), new Expv("daleth", 0, 0.0, 0.0),
new Expv("he", 0, 0.0, 0.0),    new Expv("waw", 0, 0.0, 0.0),
new Expv("zayin", 0, 0.0, 0.0), new Expv("heth", 0, 0.0, 0.0)
};

static void Main(string[] args)
{
double rnum, tsum = 0.0;
Random random = new Random();

for (int i = 0, rnum = 5.0; i < 7; i++, rnum += 1.0)
{
items[i].expect = 1.0 / rnum;
tsum += items[i].expect;
}
items[7].expect = 1.0 - tsum;

items[0].mapping = 1.0 / 5.0;
for (int i = 1; i < 7; i++)
items[i].mapping = items[i - 1].mapping + 1.0 / ((double)i + 5.0);
items[7].mapping = 1.0;

for (int i = 0; i < TRIALS; i++)
{
rnum = random.NextDouble();
for (int j = 0; j < 8; j++)
if (rnum < items[j].mapping)
{
items[j].probcount++;
break;
}
}

Console.WriteLine("Trials: {0}", TRIALS);
Console.Write("Items:          ");
for (int i = 0; i < 8; i++)
Console.WriteLine();
Console.Write("Target prob.:   ");
for (int i = 0; i < 8; i++)
Console.Write("{0:0.000000} ", items[i].expect);
Console.WriteLine();
Console.Write("Attained prob.: ");
for (int i = 0; i < 8; i++)
Console.Write("{0:0.000000} ", (double)items[i].probcount / (double)TRIALS);
Console.WriteLine();
}
}
```

Output:

```Trials: 1000000
Items:          aleph    beth     gimel    daleth   he       waw      zayin    heth
Target prob.:   0.200000 0.166667 0.142857 0.125000 0.111111 0.100000 0.090909 0.063456
Attained prob.: 0.199975 0.166460 0.142290 0.125510 0.111374 0.100018 0.090746 0.063627```

## C++

```#include <cstdlib>
#include <iostream>
#include <vector>
#include <utility>
#include <algorithm>
#include <ctime>
#include <iomanip>

int main( ) {
typedef std::vector<std::pair<std::string, double> >::const_iterator SPI ;
typedef std::vector<std::pair<std::string , double> > ProbType ;
ProbType probabilities ;
probabilities.push_back( std::make_pair( "aleph" , 1/5.0 ) ) ;
probabilities.push_back( std::make_pair( "beth" , 1/6.0 ) ) ;
probabilities.push_back( std::make_pair( "gimel" , 1/7.0 ) ) ;
probabilities.push_back( std::make_pair( "daleth" , 1/8.0 ) ) ;
probabilities.push_back( std::make_pair( "he" , 1/9.0 ) ) ;
probabilities.push_back( std::make_pair( "waw" , 1/10.0 ) ) ;
probabilities.push_back( std::make_pair( "zayin" , 1/11.0 ) ) ;
probabilities.push_back( std::make_pair( "heth" , 1759/27720.0 ) ) ;
std::vector<std::string> generated ; //for the strings that are generatod
std::vector<int> decider ; //holds the numbers that determine the choice of letters
for ( int i = 0 ; i < probabilities.size( ) ; i++ ) {
if ( i == 0 ) {
decider.push_back( 27720 * (probabilities[ i ].second) ) ;
}
else {
int number = 0 ;
for ( int j = 0 ; j < i ; j++ ) {
number +=  27720 * ( probabilities[ j ].second ) ;
}
number += 27720 * probabilities[ i ].second ;
decider.push_back( number ) ;
}
}
srand( time( 0 ) ) ;
for ( int i = 0 ; i < 1000000 ; i++ ) {
int randnumber = rand( ) % 27721 ;
int j = 0 ;
while ( randnumber > decider[ j ] )
j++ ;
generated.push_back( ( probabilities[ j ]).first ) ;
}
std::cout << "letter  frequency attained   frequency expected\n" ;
for ( SPI i = probabilities.begin( ) ; i != probabilities.end( ) ; i++ ) {
std::cout << std::left << std::setw( 8 ) << i->first ;
int found = std::count ( generated.begin( ) , generated.end( ) , i->first ) ;
std::cout << std::left << std::setw( 21 ) << found / 1000000.0 ;
std::cout << std::left << std::setw( 17 ) << i->second << '\n' ;
}
return 0 ;
}
```

Output:

```letter  frequency attained   frequency expected
aleph   0.200089             0.2
beth    0.16695              0.166667
gimel   0.142693             0.142857
daleth  0.124859             0.125
he      0.111258             0.111111
waw     0.099665             0.1
zayin   0.090654             0.0909091
heth    0.063832             0.063456
```

## Clojure

Works by first converting the provided Probability Distribution Function into a Cumulative Distribution Function, so that it can simply scan through the CDF list and return the current item as soon as the CDF at that point is greater than the random number generated. The code could be made more concise by skipping this step and instead tracking the whole PDF for each random number; but this code is both faster and more readable.

It uses the language built-in (frequencies) to count the number of occurrences of each distinct name. Note that while we actually generate a sequence of num-trials random samples, the sequence is lazily generated and lazily consumed. This means that the program will scale to an arbitrarily-large num-trials with no ill effects, by throwing away elements it's already processed.

```(defn to-cdf [pdf]
(reduce
(fn [acc n] (conj acc (+ (or (last acc) 0) n)))
[]
pdf))

(defn choose [cdf]
(let [r (rand)]
(count
(filter (partial > r) cdf))))

(def *names* '[aleph beth gimel daleth he waw zayin heth])
(def *pdf* (map double [1/5 1/6 1/7 1/8 1/9 1/10 1/11 1759/27720]))

(let [num-trials 1000000
cdf (to-cdf *pdf*)
indexes (range (count *names*)) ;; use integer key internally, not name
expected (into (sorted-map) (zipmap indexes *pdf*))
actual (frequencies (repeatedly num-trials #(choose cdf)))]
(doseq [[idx exp] expected]
(println "Expected number of" (*names* idx) "was"
(* num-trials exp) "and actually got" (actual idx))))
```
```Expected number of aleph was 200000.0 and actually got 199300
Expected number of beth was 166666.66666666672 and actually got 166291
Expected number of gimel was 142857.1428571429 and actually got 143297
Expected number of daleth was 125000.0 and actually got 125032
Expected number of he was 111111.11111111111 and actually got 111540
Expected number of waw was 100000.0 and actually got 100062
Expected number of zayin was 90909.09090909091 and actually got 90719
Expected number of heth was 63455.98845598846 and actually got 63759```

## Common Lisp

This is a straightforward, if a little verbose implementation based upon the Perl one.

```(defvar *probabilities* '((aleph  1/5)
(beth   1/6)
(gimel  1/7)
(daleth 1/8)
(he     1/9)
(waw    1/10)
(zayin  1/11)
(heth   1759/27720)))
(defun calculate-probabilities (choices &key (repetitions 1000000))
(assert (= 1 (reduce #'+ choices :key #'second)))
(labels ((make-ranges ()
(loop for (datum probability) in choices
sum (coerce probability 'double-float) into total
collect (list datum total)))
(pick (ranges)
(declare (optimize (speed 3) (safety 0) (debug 0)))
(loop with random = (random 1.0d0)
for (datum below) of-type (t double-float) in ranges
when (< random below)
do (return datum)))
(populate-hash (ranges)
(declare (optimize (speed 3) (safety 0) (debug 0)))
(loop repeat (the fixnum repetitions)
with hash = (make-hash-table)
do (incf (the fixnum (gethash (pick ranges) hash 0)))
finally (return hash)))
(make-table-data (hash)
(loop for (datum probability) in choices
collect (list datum
(float (/ (gethash datum hash)
repetitions))
(float probability)))))
(format t "Datum~10,2TOccured~20,2TExpected~%")
(format t "~{~{~A~10,2T~F~20,2T~F~}~%~}"
(make-table-data (populate-hash (make-ranges))))))

CL-USER> (calculate-probabilities *probabilities*)
Datum     Occured   Expected
ALEPH     0.200156  0.2
BETH      0.166521  0.16666667
GIMEL     0.142936  0.14285715
DALETH    0.124779  0.125
HE        0.111601  0.11111111
WAW       0.100068  0.1
ZAYIN     0.090458  0.09090909
HETH      0.063481  0.06345599
```

## D

### Basic Version

```void main() {
import std.stdio, std.random, std.string, std.range;

enum int nTrials = 1_000_000;
const items = "aleph beth gimel daleth he waw zayin heth".split;
const pr = [1/5., 1/6., 1/7., 1/8., 1/9., 1/10., 1/11., 1759/27720.];

double[pr.length] counts = 0.0;
foreach (immutable _; 0 .. nTrials)
counts[pr.dice]++;

writeln("Item    Target prob  Attained prob");
foreach (name, p, co; zip(items, pr, counts[]))
writefln("%-7s %.8f   %.8f", name, p, co / nTrials);
}
```
Output:
```Item    Target prob  Attained prob
aleph   0.20000000   0.19964000
beth    0.16666667   0.16753600
gimel   0.14285714   0.14283300
daleth  0.12500000   0.12515400
he      0.11111111   0.11074300
waw     0.10000000   0.10025800
zayin   0.09090909   0.09070400
heth    0.06345598   0.06313200```

### A Faster Version

```void main() {
import std.stdio, std.random, std.algorithm, std.range;

enum int nTrials = 1_000_000;
const items = "aleph beth gimel daleth he waw zayin heth".split;
const pr = [1/5., 1/6., 1/7., 1/8., 1/9., 1/10., 1/11., 1759/27720.];

double[pr.length] cumulatives = pr[];
foreach (immutable i, ref c; cumulatives[1 .. \$ - 1])
c += cumulatives[i];
cumulatives[\$ - 1] = 1.0;

double[pr.length] counts = 0.0;
auto rnd = Xorshift(unpredictableSeed);
foreach (immutable _; 0 .. nTrials)
counts[cumulatives[].countUntil!(c => c >= rnd.uniform01)]++;

writeln("Item    Target prob  Attained prob");
foreach (name, p, co; zip(items, pr, counts[]))
writefln("%-7s %.8f   %.8f", name, p, co / nTrials);
}
```

## E

This implementation converts the list of probabilities to sub-intervals of [0.0,1.0), then arranges those intervals in a binary tree for searching based on a random number input.

It is rather verbose, due to using the tree rather than a linear search, and having code to print the tree (which was used to debug it).

`pragma.syntax("0.9")`

First, the algorithm:

```/** Makes leaves of the binary tree */
def leaf(value) {
return def leaf {
to run(_) { return value }
to __printOn(out) { out.print("=> ", value) }
}
}
/** Makes branches of the binary tree */
def split(leastRight, left, right) {
return def tree {
to run(specimen) {
return if (specimen < leastRight) {
left(specimen)
} else {
right(specimen)
}
}
to __printOn(out) {
out.print("    ")
out.indent().print(left)
out.lnPrint("< ")
out.print(leastRight)
out.indent().lnPrint(right)
}
}
}
def makeIntervalTree(assocs :List[Tuple[any, float64]]) {
def size :int := assocs.size()
if (size > 1) {
def midpoint := size // 2
return split(assocs[midpoint][1], makeIntervalTree(assocs.run(0, midpoint)),
makeIntervalTree(assocs.run(midpoint)))
} else {
def [[value, _]] := assocs
return leaf(value)
}
}
def setupProbabilisticChoice(entropy, table :Map[any, float64]) {
var cumulative := 0.0
var intervalTable := []
for value => probability in table {
intervalTable with= [value, cumulative]
cumulative += probability
}
def total := cumulative
def selector := makeIntervalTree(intervalTable)
return def probChoice {
# Multiplying by the total helps correct for any error in the sum of the inputs
to run() { return selector(entropy.nextDouble() * total) }
to __printOn(out) {
out.print("Probabilistic choice using tree:")
out.indent().lnPrint(selector)
}
}
}```

Then the test setup:

```def rosetta := setupProbabilisticChoice(entropy, def probTable := [
"aleph"  => 1/5,
"beth"   => 1/6.0,
"gimel"  => 1/7.0,
"daleth" => 1/8.0,
"he"     => 1/9.0,
"waw"    => 1/10.0,
"zayin"  => 1/11.0,
"heth"   => 0.063455988455988432,
])

var trials := 1000000
var timesFound := [].asMap()
for i in 1..trials {
if (i % 1000 == 0) { print(`\${i//1000} `) }
def value := rosetta()
timesFound with= (value, timesFound.fetch(value, fn { 0 }) + 1)
}
stdout.println()
for item in probTable.domain() {
stdout.print(item, "\t", timesFound[item] / trials, "\t", probTable[item], "\n")
}```

## Elixir

Translation of: Erlang
```defmodule Probabilistic do
@tries 1000000
@probs [aleph:  1/5,
beth:   1/6,
gimel:  1/7,
daleth: 1/8,
he:     1/9,
waw:    1/10,
zayin:  1/11,
heth:   1759/27720]

def test do
trials = for _ <- 1..@tries, do: get_choice(@probs, :rand.uniform)
IO.puts "Item      Expected   Actual"
fmt = " ~-8s ~.6f  ~.6f~n"
Enum.each(@probs, fn {glyph,expected} ->
actual = length(for ^glyph <- trials, do: glyph) / @tries
:io.format fmt, [glyph, expected, actual]
end)
end

defp get_choice([{glyph,_}], _), do: glyph
defp get_choice([{glyph,prob}|_], ran) when ran < prob, do: glyph
defp get_choice([{_,prob}|t], ran), do: get_choice(t, ran - prob)
end

Probabilistic.test
```
Output:
```Item      Expected   Actual
aleph    0.200000  0.200676
beth     0.166667  0.166103
gimel    0.142857  0.142543
daleth   0.125000  0.125055
he       0.111111  0.111165
waw      0.100000  0.100439
zayin    0.090909  0.090894
heth     0.063456  0.063125
```

## Erlang

Translation of: Java

The optimized version of Java.

```-module(probabilistic_choice).

-export([test/0]).

-define(TRIES, 1000000).

test() ->
Probs =
[{aleph,1/5},
{beth,1/6},
{gimel,1/7},
{daleth,1/8},
{he,1/9},
{waw,1/10},
{zayin,1/11},
{heth,1759/27720}],
random:seed(now()),
Trials =
[get_choice(Probs,random:uniform()) || _ <- lists:seq(1,?TRIES)],
[{Glyph,Expected,(length([Glyph || Glyph_ <- Trials, Glyph_ == Glyph])/?TRIES)}
|| {Glyph,Expected} <- Probs].

get_choice([{Glyph,_}],_) ->
Glyph;
get_choice([{Glyph,Prob}|T],Ran) ->
case (Ran < Prob) of
true ->
Glyph;
false ->
get_choice(T,Ran - Prob)
end.
```

Output:

```[{aleph,0.2,0.200325},
{beth,0.16666666666666666,0.167108},
{gimel,0.14285714285714285,0.142246},
{daleth,0.125,0.124851},
{he,0.1111111111111111,0.111345},
{waw,0.1,0.099912},
{zayin,0.09090909090909091,0.091352},
{heth,0.06345598845598846,0.062861}]
```

## ERRE

```PROGRAM PROB_CHOICE

DIM ITEM\$[7],PROB[7],CNT[7]

BEGIN
ITEM\$[]=("aleph","beth","gimel","daleth","he","waw","zayin","heth")

PROB[0]=1/5.0  PROB[1]=1/6.0  PROB[2]=1/7.0   PROB[3]=1/8.0
PROB[4]=1/9.0  PROB[5]=1/10.0 PROB[6]=1/11.0  PROB[7]=1759/27720
SUM=0
FOR I%=0 TO UBOUND(PROB,1) DO
SUM=SUM+PROB[I%]
END FOR

IF ABS(SUM-1)>1E-6 THEN
PRINT("Probabilities don't sum to 1")
ELSE
FOR TRIAL=1 TO 1E6 DO
R=RND(1)
P=0
FOR I%=0 TO UBOUND(PROB,1) DO
P+=PROB[I%]
IF R<P THEN
CNT[I%]+=1
EXIT
END IF
END FOR
END FOR
PRINT("Item        actual    theoretical")
PRINT("---------------------------------")
FOR I%=0 TO UBOUND(ITEM\$,1) DO
WRITE("\      \    #.######  #.######";ITEM\$[I%],CNT[I%]/1E6,PROB[I%])
END FOR
END IF
END PROGRAM```

Output:

```Item        actual    theoretical
---------------------------------
aleph       0.199769  0.200000
beth        0.167277  0.166667
gimel       0.142914  0.142857
daleth      0.124991  0.125000
he          0.111227  0.111111
waw         0.099732  0.100000
zayin       0.090757  0.090909
heth        0.063333  0.063456
```

## Euphoria

Translation of: PureBasic
```constant MAX = #3FFFFFFF
constant times = 1e6
atom d,e
sequence Mapps
Mapps = {
{ "aleph",  1/5,        0},
{ "beth",   1/6,        0},
{ "gimel",  1/7,        0},
{ "daleth", 1/8,        0},
{ "he",     1/9,        0},
{ "waw",    1/10,       0},
{ "zayin",  1/11,       0},
{ "heth",   1759/27720, 0}
}

for i = 1 to times do
d = (rand(MAX)-1)/MAX
e = 0
for j = 1 to length(Mapps) do
e += Mapps[j][2]
if d <= e then
Mapps[j][3] += 1
exit
end if
end for
end for

printf(1,"Sample times: %d\n",times)
for j = 1 to length(Mapps) do
d = Mapps[j][3]/times
printf(1,"%-7s should be %f is %f | Deviatation %6.3f%%\n",
{Mapps[j][1],Mapps[j][2],d,(1-Mapps[j][2]/d)*100})
end for```

Output:

```Sample times: 1000000
aleph   should be 0.200000 is 0.200492 | Deviatation  0.245%
beth    should be 0.166667 is 0.166855 | Deviatation  0.113%
gimel   should be 0.142857 is 0.143169 | Deviatation  0.218%
daleth  should be 0.125000 is 0.124923 | Deviatation -0.062%
he      should be 0.111111 is 0.110511 | Deviatation -0.543%
waw     should be 0.100000 is 0.099963 | Deviatation -0.037%
zayin   should be 0.090909 is 0.090647 | Deviatation -0.289%
heth    should be 0.063456 is 0.063440 | Deviatation -0.025%
```

## Factor

```USING: arrays assocs combinators.random io kernel macros math
math.statistics prettyprint quotations sequences sorting formatting ;
IN: rosettacode.proba

CONSTANT: data
{
{ "aleph"   1/5.0 }
{ "beth"    1/6.0 }
{ "gimel"   1/7.0 }
{ "daleth"  1/8.0 }
{ "he"      1/9.0 }
{ "waw"     1/10.0 }
{ "zayin"   1/11.0 }
{ "heth"    f }
}

MACRO: case-probas ( data -- case-probas )
[ first2 [ swap 1quotation 2array ] [ 1quotation ] if* ] map 1quotation ;

: expected ( name data -- float )
2dup at [ 2nip ] [ nip values sift sum 1 swap - ] if* ;
: generate ( # case-probas -- seq )
H{ } clone
[ [ [ casep ] [ inc-at ] bi* ] 2curry times ] keep ; inline
: normalize ( seq # -- seq )
[ clone ] dip [ /f ] curry assoc-map ;
: summarize1 ( name value data -- )
[ over ] dip expected
"%6s: %10f %10f\n" printf ;
: summarize ( generated data -- )
"Key" "Value" "expected" "%6s  %10s %10s\n" printf
[ summarize1 ] curry assoc-each ;
: generate-normalized ( # proba -- seq )
[ generate ] [ drop normalize ] 2bi ; inline
: example ( # data -- )
[ case-probas generate-normalized ]
[ summarize ] bi ; inline
```

In a REPL:

```USE: rosettacode.proba
1000000 data example
```

outputs

```   Key       Value   expected
heth:   0.063469   0.063456
waw:   0.100226   0.100000
daleth:   0.125844   0.125000
beth:   0.166264   0.166667
zayin:   0.090806   0.090909
he:   0.110562   0.111111
aleph:   0.199868   0.200000
gimel:   0.142961   0.142857
```

## Fermat

```trials:=1000000;

Array probs[8];                         {store the probabilities}
[probs]:=[<i=1,8> 1/(i+4)];
probs[8]:=1-Sigma<i=1,7>[probs[i,1]];

Func Round( a, b ) = (2*a+b)\(2*b).;    {rounds a fraction with numerator a and denominator b}
;    {to the nearest integer (positive fractions only)}

Func Sel =                              {select a number from 1 to 8 according to the}
r:=Rand|27720;                      {specified probabilities}
if r < probs[1]*27720 then Return(1) fi;
if r < Sigma<i=1,2>[probs[i]]*27720 then Return(2) fi;
if r < Sigma<i=1,3>[probs[i]]*27720 then Return(3) fi;
if r < Sigma<i=1,4>[probs[i]]*27720 then Return(4) fi;
if r < Sigma<i=1,5>[probs[i]]*27720 then Return(5) fi;
if r < Sigma<i=1,6>[probs[i]]*27720 then Return(6) fi;
if r < Sigma<i=1,7>[probs[i]]*27720 then Return(7) fi;
Return(8);
.;

Array label[10];                        {strings are not Fermat's strong suit}
Func Letter(n) =                        {assign a Hebrew letter to the numbers 1-8}
[label]:='heth     ';
if n = 1 then [label]:='aleph    '  fi;
if n = 2 then [label]:='beth     '  fi;
if n = 3 then [label]:='gimel    '  fi;
if n = 4 then [label]:='daleth   '  fi;
if n = 5 then [label]:='he       '  fi;
if n = 6 then [label]:='waw      '  fi;
if n = 7 then [label]:='zayin    '  fi;
.;

Array count[8];                         {pick a bunch of random numbers}
for i = 1 to trials do
s:=Sel;
count[s]:=count[s]+1;
od;

for i = 1 to 8 do                       {now display some diagnostics}
Letter(i);
ctp:=count[i]/trials-probs[i];
!([label:char, count[i]/trials,' differs from ',probs[i]);
!(' by ',ctp, ' or about one part in ', Round(Denom(ctp),|Numer(ctp)|));
!!;
od;

!!('The various probabilities add up to ',Sigma<i=1,8>[count[i]/trials]);   {check if our trials add to 1}```
Output:
```
aleph      199939 / 1000000 differs from   1 / 5 by   -61 / 1000000 or about one part in  16393
beth       166361 / 1000000 differs from   1 / 6 by   -917 / 3000000 or about one part in  3272
gimel      142509 / 1000000 differs from   1 / 7 by   -2437 / 7000000 or about one part in  2872
daleth     124917 / 1000000 differs from   1 / 8 by   -83 / 1000000 or about one part in  12048
he         111013 / 1000000 differs from   1 / 9 by   -883 / 9000000 or about one part in  10193
waw        100327 / 1000000 differs from   1 / 10 by   327 / 1000000 or about one part in  3058
zayin      4569 / 50000 differs from   1 / 11 by   259 / 550000 or about one part in  2124
heth       31777 / 500000 differs from   1759 / 27720 by   33961 / 346500000 or about one part in  10203
The various probabilities add up to  1

```

## Forth

```include random.fs

\ common factors of desired probabilities (1/5 .. 1/11)
2 2 * 2 * 3 * 3 * 5 * 7 * 11 * constant denom   \ 27720

\ represent each probability as the numerator with 27720 as the denominator
: ,numerators ( max min -- )
do denom i / , loop ;

\  final item is 27720 - sum(probs)
: ,remainder ( denom addr len -- )
cells bounds do  i @ -  1 cells +loop , ;

create probs 12 5 ,numerators  denom probs 7 ,remainder
create bins 8 cells allot

: choose ( -- 0..7 )
denom random
8 0 do
probs i cells + @ -
dup 0< if drop i unloop exit then
loop
abort" can't get here" ;

: trials ( n -- )
0 do  1  bins choose cells +  +!  loop ;

: str-table
create ( c-str ... n -- ) 0 do , loop
does> ( n -- str len ) swap cells + @ count ;

here ," heth"   here ," zayin" here ," waw"  here ," he"
here ," daleth" here ," gimel" here ," beth" here ," aleph"
8 str-table names

cr ." Name" #tab emit ." Prob" #tab emit ." Actual" #tab emit ." Error" ;
: .result ( n -- )
cr dup names type #tab emit
dup cells probs + @ s>f denom s>f f/ fdup f. #tab emit
dup cells bins  + @ s>f 1e6       f/ fdup f. #tab emit
f- fabs fs. ;

: .results   .header 8 0 do i .result loop ;
```
```bins 8 cells erase
3 set-precision
1000000 trials .results
Name    Prob    Actual  Error
aleph   0.2     0.2     9.90E-5
beth    0.167   0.167   4.51E-4
gimel   0.143   0.142   4.99E-4
daleth  0.125   0.125   1.82E-4
he      0.111   0.111   2.10E-4
waw     0.1     0.1     3.30E-5
zayin   0.0909  0.0912  2.77E-4
heth    0.0635  0.0636  9.70E-5  ok
```

## Fortran

Works with: Fortran version 90 and later
```PROGRAM PROBS

IMPLICIT NONE

INTEGER, PARAMETER :: trials = 1000000
INTEGER :: i, j, probcount(8) = 0
REAL :: expected(8), mapping(8), rnum
CHARACTER(6) :: items(8) = (/ "aleph ", "beth  ", "gimel ", "daleth", "he    ", "waw   ", "zayin ", "heth  " /)

expected(1:7) = (/ (1.0/i, i=5,11) /)
expected(8) = 1.0 - SUM(expected(1:7))
mapping(1) = 1.0 / 5.0
DO i = 2, 7
mapping(i) = mapping(i-1) + 1.0/(i+4.0)
END DO
mapping(8) = 1.0

DO i = 1, trials
CALL RANDOM_NUMBER(rnum)
DO j = 1, 8
IF (rnum < mapping(j)) THEN
probcount(j) = probcount(j) + 1
EXIT
END IF
END DO
END DO

WRITE(*, "(A,I10)") "Trials:             ", trials
WRITE(*, "(A,8A10)") "Items:             ", items
WRITE(*, "(A,8F10.6)") "Target Probability:  ", expected
WRITE(*, "(A,8F10.6)") "Attained Probability:", REAL(probcount) / REAL(trials)

ENDPROGRAM PROBS
```

Sample Output:

```Trials:                1000000
Items:                 aleph     beth      gimel     daleth    he        waw       zayin     heth
Target Probability:    0.200000  0.166667  0.142857  0.125000  0.111111  0.100000  0.090909  0.063456
Attained Probability:  0.199631  0.166907  0.142488  0.124920  0.110906  0.099943  0.091775  0.063430```

## FreeBASIC

```' FB 1.05.0 Win64

Dim letters  (0 To 7) As String = {"aleph", "beth", "gimel", "daleth", "he", "waw", "zayin", "heth"}
Dim actual   (0 To 7) As Integer '' all zero by default
Dim probs (0 To 7)    As Double = {1/5.0, 1/6.0, 1/7.0, 1/8.0, 1/9.0, 1/10.0, 1/11.0}
Dim cumProbs (0 To 7) As Double

cumProbs(0) = probs(0)
For i As Integer = 1 To 6
cumProbs(i) = cumProbs(i - 1) + probs(i)
Next
cumProbs(7) = 1.0
probs(7) = 1.0 - cumProbs(6)

Randomize
Dim rand As Double
Dim n As Double = 1000000
Dim sum As Double = 0.0

For i As Integer = 1 To n
rand = Rnd  '' random number where 0 <= rand < 1
Select case rand
Case Is <= cumProbs(0)
actual(0) += 1
Case Is <= cumProbs(1)
actual(1) += 1
Case Is <= cumProbs(2)
actual(2) += 1
Case Is <= cumProbs(3)
actual(3) += 1
Case Is <= cumProbs(4)
actual(4) += 1
Case Is <= cumProbs(5)
actual(5) += 1
Case Is <= cumProbs(6)
actual(6) += 1
Case Else
actual(7) += 1
End Select
Next

Dim sumActual As Double = 0

Print "Letter", " Actual", "Expected"
Print "------", "--------", "--------"
For i As Integer = 0 To 7
Print letters(i),
Print Using "#.######"; actual(i)/n;
sumActual += actual(i)/n
Print , Using "#.######"; probs(i)
Next

Print , "--------", "--------"
Print , Using "#.######"; sumActual;
Print , Using "#.######"; 1.000000

Print
Print "Press any key to quit"
Sleep
```
Output:
```Letter         Actual       Expected
------        --------      --------
aleph         0.199987      0.200000
beth          0.166663      0.166667
gimel         0.143134      0.142857
daleth        0.125132      0.125000
he            0.110772      0.111111
waw           0.100236      0.100000
>         0.090664      0.090909
heth          0.063412      0.063456
--------      --------
1.000000      1.000000
```

## FutureBasic

```_elements = 8

local fn ProbabilisticChoice
double prob(_elements), cumulative(_elements)
Str15  item(_elements)
double r, p, sum = 0, checksum = 0
long   i, j, samples = 1000000

item(1) = "aleph" : item(2) = "beth" : item(3) = "gimel" : item(4) = "daleth"
item(5) = "he"    : item(6) = "waw"  : item(7) = "zayin" : item(8) = "heth"

prob(1) = 1/5.0 : prob(2) = 1/6.0  : prob(3) = 1/7.0  : prob(4) = 1/8.0
prob(5) = 1/9.0 : prob(6) = 1/10.0 : prob(7) = 1/11.0 : prob(8) = 1759/27720

for i = 1 to _elements
sum += prob(i)
next
if abs(sum-1) > samples then print "Probabilities don't sum to 1." : exit fn

for i = 1 to samples
cln r = (((double)arc4random()/0x100000000));
p = 0
for j = 1 to _elements
p += prob(j)
if (r < p) then cumulative(j) += 1 : exit for
next
next

print
printf @"Item      Actual    Theoretical"
printf @"----      ------    -----------"
for i = 1 to _elements
printf @"%-7s %10.6f %12.6f", item(i), cumulative(i)/samples, prob(i)
checksum += cumulative(i)/samples
next
printf @"         --------   -----------"
printf @"%17.6f %12.6f", checksum, 1.000000
end fn

fn ProbabilisticChoice

HandleEvents```
Output:
```Item      Actual    Theoretical
----      ------    -----------
aleph    0.199966     0.200000
beth     0.166505     0.166667
gimel    0.142958     0.142857
daleth   0.124827     0.125000
he       0.111451     0.111111
waw      0.100095     0.100000
zayin    0.090985     0.090909
heth     0.063213     0.063456
--------   -----------
1.000000     1.000000
```

## Go

```package main

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

type mapping struct {
item string
pr   float64
}

func main() {
// input mapping
m := []mapping{
{"aleph", 1 / 5.},
{"beth", 1 / 6.},
{"gimel", 1 / 7.},
{"daleth", 1 / 8.},
{"he", 1 / 9.},
{"waw", 1 / 10.},
{"zayin", 1 / 11.},
{"heth", 1759 / 27720.}} // adjusted so that probabilities add to 1

// cumulative probability
cpr := make([]float64, len(m)-1)
var c float64
for i := 0; i < len(m)-1; i++ {
c += m[i].pr
cpr[i] = c
}

// generate
const samples = 1e6
occ := make([]int, len(m))
rand.Seed(time.Now().UnixNano())
for i := 0; i < samples; i++ {
r := rand.Float64()
for j := 0; ; j++ {
if r < cpr[j] {
occ[j]++
break
}
if j == len(cpr)-1 {
occ[len(cpr)]++
break
}
}
}

// report
fmt.Println("  Item  Target   Generated")
var totalTarget, totalGenerated float64
for i := 0; i < len(m); i++ {
target := m[i].pr
generated := float64(occ[i]) / samples
fmt.Printf("%6s  %8.6f  %8.6f\n", m[i].item, target, generated)
totalTarget += target
totalGenerated += generated
}
fmt.Printf("Totals  %8.6f  %8.6f\n", totalTarget, totalGenerated)
}
```

Output:

```  Item  Target   Generated
aleph  0.200000  0.199509
beth  0.166667  0.167194
gimel  0.142857  0.143293
daleth  0.125000  0.124869
he  0.111111  0.110896
waw  0.100000  0.099849
zayin  0.090909  0.090789
heth  0.063456  0.063601
Totals  1.000000  1.000000
```

```import System.Random (newStdGen, randomRs)

dataBinCounts :: [Float] -> [Float] -> [Int]
dataBinCounts thresholds range =
zipWith
(-)
(xs <> [sampleSize])
(0 : xs)
where
sampleSize = length range
xs =
(-) sampleSize . length
. flip filter range
. (<)
<\$> thresholds

--------------------------- TEST -------------------------
main :: IO ()
main = do
g <- newStdGen
let fractions = recip <\$> [5 .. 11] :: [Float]
expected = fractions <> [1 - sum fractions]
actual =
(/ 1000000.0) . fromIntegral
<\$> dataBinCounts
(scanl1 (+) expected)
(take 1000000 (randomRs (0, 1) g))
piv n x = take n (x <> repeat ' ');

putStrLn "       expected     actual"
mapM_ putStrLn \$
zipWith3
( \l s c ->
piv 7 l
<> (piv 13 (show s) <> piv 12 (show c))
)
[ "aleph",
"beth",
"gimel",
"daleth",
"he",
"waw",
"zayin",
"heth"
]
expected
actual
```
Output:

Sample

```       expected     actual
aleph  0.2          0.200597
beth   0.16666667   0.167192
gimel  0.14285715   0.142781
daleth 0.125        0.124556
he     0.11111111   0.111128
waw    0.1          9.9671e-2
zayin  9.090909e-2  9.0294e-2
heth   6.345594e-2  6.3781e-2   ```

## HicEst

```REAL :: trials=1E6, n=8, map(n), limit(n), expected(n), outcome(n)

expected = 1 / (\$ + 4)
expected(n) = 1 - SUM(expected) + expected(n)

map = expected
map = map(\$) + map(\$-1)

DO i = 1, trials
random = RAN(1)
limit = random > map
item = INDEX(limit, 0)
outcome(item) = outcome(item) + 1
ENDDO
outcome = outcome / trials

DLG(Text=expected, Text=outcome, Y=0)```

Exported from the spreadsheet-like DLG function:

```0.2        0.199908
0.1666667  0.166169
0.1428571  0.142722
0.125      0.124929
0.1111111  0.111706
0.1        0.099863
0.0909091  0.090965
0.063456   0.063738   ```

## Icon and Unicon

```record Item(value, probability)

procedure find_item (items, v)
sum := 0.0
every item := !items do {
if v < sum+item.probability
then return item.value
else sum +:= item.probability
}
fail # v exceeded 1.0
end

# -- helper procedures

# count the number of occurrences of i in list l,
# assuming the items are strings
procedure count (l, i)
result := 0.0
every x := !l do
if x == i then result +:= 1
return result
end

procedure rand_float ()
return ?1000/1000.0
end

# -- test the procedure
procedure main ()
items := [
Item("aleph",   1/5.0),
Item("beth",    1/6.0),
Item("gimel",   1/7.0),
Item("daleth",  1/8.0),
Item("he",      1/9.0),
Item("waw",     1/10.0),
Item("zayin",   1/11.0),
Item("heth",    1759/27720.0)
]

# collect a sample of results
sample := []
every (1 to 1000000) do push (sample, find_item(items, rand_float ()))

# return comparison of expected vs actual probability
every item := !items do
write (right(item.value, 7) || " " ||
left(item.probability, 15) || " " ||
left(count(sample, item.value)/*sample, 6))
end
```

Output:

```  aleph 0.2             0.1988
beth 0.1666666667    0.1676
gimel 0.1428571429    0.1431
daleth 0.125           0.1249
he 0.1111111111    0.1112
waw 0.1             0.0996
zayin 0.09090909091   0.0908
heth 0.06345598846   0.0636
```

## J

```main=: verb define
hdr=.  '       target   actual  '
lbls=. ; ,:&.> ;:'aleph beth gimel daleth he waw zayin heth'
prtn=. +/\ pt=. (, 1-+/)1r1%5+i.7
da=.   prtn I. ?y # 0
pa=.   y%~ +/ da =/ i.8
hdr, lbls,. 9j6 ": |: pt,:pa
)

Note 'named abbreviations'
lbls (labels)
pt   (target proportions)
prtn (partitions corresponding to target proportions)
da   (distribution of actual values among partitions)
pa   (actual proportions)
)
```

Example use:

```main 1e6
target   actual
aleph  0.200000 0.200344
beth   0.166667 0.166733
gimel  0.142857 0.142611
daleth 0.125000 0.124458
he     0.111111 0.111455
waw    0.100000 0.099751
zayin  0.090909 0.091121
heth   0.063456 0.063527
```

Note that there is no rounding error in summing the proportions, as they are represented as rational numbers, not floating-point approximations.

```   pt=. (, 1-+/)1r1%5+i.7
pt
1r5 1r6 1r7 1r8 1r9 1r10 1r11 1759r27720
+/pt
1
```

## Java

Translation of: C
```public class Prob{
static long TRIALS= 1000000;

private static class Expv{
public String name;
public int probcount;
public double expect;
public double mapping;

public Expv(String name, int probcount, double expect, double mapping){
this.name= name;
this.probcount= probcount;
this.expect= expect;
this.mapping= mapping;
}
}

static Expv[] items=
{new Expv("aleph", 0, 0.0, 0.0), new Expv("beth", 0, 0.0, 0.0),
new Expv("gimel", 0, 0.0, 0.0),
new Expv("daleth", 0, 0.0, 0.0),
new Expv("he", 0, 0.0, 0.0), new Expv("waw", 0, 0.0, 0.0),
new Expv("zayin", 0, 0.0, 0.0),
new Expv("heth", 0, 0.0, 0.0)};

public static void main(String[] args){
int i, j;
double rnum, tsum= 0.0;

for(i= 0, rnum= 5.0;i < 7;i++, rnum+= 1.0){
items[i].expect= 1.0 / rnum;
tsum+= items[i].expect;
}
items[7].expect= 1.0 - tsum;

items[0].mapping= 1.0 / 5.0;
for(i= 1;i < 7;i++){
items[i].mapping= items[i - 1].mapping + 1.0 / ((double)i + 5.0);
}
items[7].mapping= 1.0;

for(i= 0;i < TRIALS;i++){
rnum= Math.random();
for(j= 0;j < 8;j++){
if(rnum < items[j].mapping){
items[j].probcount++;
break;
}
}
}

System.out.printf("Trials: %d\n", TRIALS);
System.out.printf("Items:          ");
for(i= 0;i < 8;i++)
System.out.printf("%-8s ", items[i].name);
System.out.printf("\nTarget prob.:   ");
for(i= 0;i < 8;i++)
System.out.printf("%8.6f ", items[i].expect);
System.out.printf("\nAttained prob.: ");
for(i= 0;i < 8;i++)
System.out.printf("%8.6f ", (double)(items[i].probcount)
/ (double)TRIALS);
System.out.printf("\n");

}
}
```

Output:

```Trials: 1000000
Items:          aleph    beth     gimel    daleth   he       waw      zayin    heth
Target prob.:   0.200000 0.166667 0.142857 0.125000 0.111111 0.100000 0.090909 0.063456
Attained prob.: 0.199615 0.167517 0.142612 0.125211 0.110970 0.099614 0.091002 0.063459 ```
Works with: Java version 1.5+
```import java.util.EnumMap;

public class Prob {
public static long TRIALS= 1000000;
public enum Glyph{
ALEPH, BETH, GIMEL, DALETH, HE, WAW, ZAYIN, HETH;
}

public static EnumMap<Glyph, Double> probs = new EnumMap<Glyph, Double>(Glyph.class){{
put(Glyph.ALEPH,   1/5.0);
put(Glyph.BETH,    1/6.0);
put(Glyph.GIMEL,   1/7.0);
put(Glyph.DALETH,  1/8.0);
put(Glyph.HE,      1/9.0);
put(Glyph.WAW,     1/10.0);
put(Glyph.ZAYIN,   1/11.0);
put(Glyph.HETH,    1759./27720);
}};

public static EnumMap<Glyph, Double> counts = new EnumMap<Glyph, Double>(Glyph.class){{
put(Glyph.ALEPH, 0.);put(Glyph.BETH,   0.);
put(Glyph.GIMEL, 0.);put(Glyph.DALETH, 0.);
put(Glyph.HE,    0.);put(Glyph.WAW,    0.);
put(Glyph.ZAYIN, 0.);put(Glyph.HETH,   0.);
}};

public static void main(String[] args){
System.out.println("Target probabliities:\t" + probs);
for(long i = 0; i < TRIALS; i++){
Glyph choice = getChoice();
counts.put(choice, counts.get(choice) + 1);
}

//correct the counts to probablities in (0..1]
for(Glyph glyph:counts.keySet()){
counts.put(glyph, counts.get(glyph) / TRIALS);
}

System.out.println("Actual probabliities:\t" + counts);
}

private static Glyph getChoice() {
double rand = Math.random();
for(Glyph item:Glyph.values()){
if(rand < probs.get(item)){
return item;
}
rand -= probs.get(item);
}
return null;
}
}
```

Output:

```Target probabliities:	{ALEPH=0.2, BETH=0.16666666666666666, GIMEL=0.14285714285714285, DALETH=0.125, HE=0.1111111111111111, WAW=0.1, ZAYIN=0.09090909090909091, HETH=0.06345598845598846}
Actual probabliities:	{ALEPH=0.200794, BETH=0.165916, GIMEL=0.143286, DALETH=0.124727, HE=0.110818, WAW=0.100168, ZAYIN=0.090878, HETH=0.063413}```

## JavaScript

### ES5

Fortunately, iterating over properties added to an object maintains the insertion order.

```var probabilities = {
aleph:  1/5.0,
beth:   1/6.0,
gimel:  1/7.0,
daleth: 1/8.0,
he:     1/9.0,
waw:    1/10.0,
zayin:  1/11.0,
heth:   1759/27720
};

var sum = 0;
var iterations = 1000000;
var cumulative = {};
var randomly = {};
for (var name in probabilities) {
sum += probabilities[name];
cumulative[name] = sum;
randomly[name] = 0;
}
for (var i = 0; i < iterations; i++) {
var r = Math.random();
for (var name in cumulative) {
if (r <= cumulative[name]) {
randomly[name]++;
break;
}
}
}
for (var name in probabilities)
// using WSH
WScript.Echo(name + "\t" + probabilities[name] + "\t" + randomly[name]/iterations);
```

output:

```aleph   0.2     0.200597
beth    0.16666666666666666     0.166527
gimel   0.14285714285714285     0.142646
daleth  0.125   0.124613
he      0.1111111111111111      0.111342
waw     0.1     0.099888
zayin   0.09090909090909091     0.091141
heth    0.06345598845598846     0.063246```

### ES6

By functional composition:

```(() => {
'use strict';

// GENERIC FUNCTIONS -----------------------------------------------------

// transpose :: [[a]] -> [[a]]
const transpose = xs =>
xs[0].map((_, iCol) => xs.map(row => row[iCol]));

// justifyLeft :: Int -> Char -> Text -> Text
const justifyLeft = (n, cFiller, strText) =>
n > strText.length ? (
(strText + cFiller.repeat(n))
.substr(0, n)
) : strText;

// 2 or more arguments
// curry :: Function -> Function
const curry = (f, ...args) => {
const go = xs => xs.length >= f.length ? (f.apply(null, xs)) :
function () {
return go(xs.concat([].slice.apply(arguments)));
};
return go([].slice.call(args, 1));
};

// zipWith :: (a -> b -> c) -> [a] -> [b] -> [c]
const zipWith = (f, xs, ys) => {
const ny = ys.length;
return (xs.length <= ny ? xs : xs.slice(0, ny))
.map((x, i) => f(x, ys[i]));
};

// subtract :: (Num a) => a -> a -> a
const subtract = (x, y) => y - x;

// scanl1 :: (a -> a -> a) -> [a] -> [a]
const scanl1 = (f, xs) =>
xs.length > 0 ? scanl(f, xs[0], xs.slice(1)) : [];

// scanl :: (b -> a -> b) -> b -> [a] -> [b]
const scanl = (f, startValue, xs) =>
xs.reduce((a, x) => {
const v = f(a.acc, x);
return {
acc: v,
scan: a.scan.concat(v)
};
}, {
acc: startValue,
scan: [startValue]
})
.scan;

// unwords :: [String] -> String
const unwords = xs => xs.join(' ');

// PROBABILISTIC CHOICE --------------------------------------------------

// samples :: Int -> Int -> [Float]
const samples = n =>
Array.from({
length: n
}, Math.random);

// thresholds :: Float
const thresholds = scanl1(
(a, b) => a + b, [5, 6, 7, 8, 9, 10, 11].map(x => 1 / x)
)
.concat(1);

// expected :: Float -> Float
const expected = limits =>
limits.map((x, i, xs) => i > 0 ? (x - xs[i - 1]) : x);

// dataBinCounts :: [Float] -> [Float] -> [Int]
const dataBinCounts = (thresholds, samples) => {
const
lng = samples.length,
xs = thresholds
.map(x => lng - samples.filter(v => v > x)
.length);
return zipWith(subtract, [0].concat(xs), xs.concat(lng));
};

// intSamples :: Integer
const intSamples = 1000000;

// aligned :: a -> String
const aligned = x => justifyLeft(12, ' ', isNaN(x) ? x : x.toFixed(7));

return transpose([
['', 'Aleph', 'Beit', 'Gimel', 'Dalet', 'He', 'Vav', 'Zayin', 'Chet']
.map(curry(justifyLeft)(7, ' ')),

['Expected'].concat(expected(thresholds))
.map(aligned),

['Observed'].concat(dataBinCounts(thresholds, samples(intSamples))
.map(x => x / intSamples))
.map(aligned)
])
.map(unwords)
.join('\n');
})();
```
Output:

Sample:

```        Expected     Observed
Aleph   0.2000000    0.2002440
Beit    0.1666667    0.1665330
Gimel   0.1428571    0.1433880
Dalet   0.1250000    0.1244630
He      0.1111111    0.1112830
Vav     0.1000000    0.0998390
Zayin   0.0909091    0.0909630
Chet    0.0634560    0.0632870   ```

## jq

In the task description, the probabilities are given as rationals, so in this entry, we shall assume the probabilities are given as rationals represented by JSON objects of the form {n: \$numerator, d: denominator}. For simplicity, it is further assumed that the largest denominator is not too large.

Neither the C nor the Go implmentations of jq provides a PRNG, so in the following, /dev/urandom is used as a source of entropy; an appropriate invocation of jq (or gojq) would thus be as follows:

```#!/bin/bash

< /dev/urandom tr -cd '0-9' | fold -w 1 | jq -nr -f probabilistic-choice.jq
```
```# Output: a prn in range(0;\$n) where \$n is `.`
def prn:
if . == 1 then 0
else . as \$n
| ([1, ((\$n-1)|tostring|length)]|max) as \$w
| [limit(\$w; inputs)] | join("") | tonumber
| if . < \$n then . else (\$n | prn) end
end;

# General Utility Functions

# bag of words
def bow(stream):
reduce stream as \$word ({}; .[(\$word|tostring)] += 1);

def lpad(\$len): tostring | (\$len - length) as \$l | (" " * \$l)[:\$l] + .;

def rpad(\$len): tostring | (\$len - length) as \$l | .+ ("0" * \$l)[:\$l];

# Input: a string of digits with up to one "."
# Output: the corresponding string representation with exactly \$n decimal digits
def align_decimal(\$n):
tostring
| index(".") as \$ix
| if \$ix then capture("(?<i>[0-9]*[.])(?<j>[0-9]{0," + (\$n|tostring) + "})") as {\$i, \$j}
else . + "." + ("0" * \$n)
end ;

# Input: a string of digits with up to one embedded "."
# Output: the corresponding string representation with up to \$n decimal digits but aligned at the period
def align_decimal(\$n):
tostring
| index(".") as \$ix
| if \$ix then capture("(?<i>[0-9]*[.])(?<j>[0-9]{0," + (\$n|tostring) + "})") as {\$i, \$j}
else . + ".0" | align_decimal(\$n)
end ;

# least common multiple
# Define the helper function to take advantage of jq tail-recursion optimization
def lcm(\$m; \$n):
def _lcm:
# state is [m, n, i]
if (.[2] % .[1]) == 0 then .[2]
else .[0:2] + [.[2] + \$m] | _lcm
end;
[m, n, m] | _lcm;

def lcm(s): reduce s as \$_ (1; lcm(.; \$_));

# rationals
def r(\$n; \$d): {\$n, \$d};```

```# Given that \$integers is an array of integers to be interpreted as
# relative probabilities, return a corresponding element chosen
# randomly from the input array.
def randomly(\$integers):
def accumulate: reduce .[1:][] as \$i ([.[0]]; . + [\$i + .[-1]]);
if (\$integers | length) != length
then "randomly/1: the array lengths are unequal" | error
else . as \$in
| \$integers
| accumulate as \$p
| (\$sum|prn + 1) as \$random
| \$in[first(range(0; \$p|length) | select( \$random <= \$p[.] ))]
end ;

# Input should be a JSON object giving probabilities of each key as a rational: {n, d}
def choose(\$n):
lcm(.[].d) as \$lcm
| ([.[] | \$lcm * .n / .d]) as \$p
| keys_unsorted as \$items
| range(0; \$n)
| \$items | randomly(\$p);

# Print a table comparing expected, observed and the ratio
# (expected - observed)^2 / expected
def compare( \$expected; \$observed ):

"       : expected observed  (e-o)^2 / e",
( \$expected
| keys_unsorted[] as \$k
| .[\$k] as \$e
| (\$observed[\$k] // 0) as \$o
| "\(\$k|lpad(6)) : \(\$e|p(1)) \(\$o|floor|lpad(8)) \( ((\$e - \$o) | (.*.) / \$e) | p(2))" );

def probabilities:
{ "aleph":   r(1; 5),
"beth":    r(1; 6),
"gimel":   r(1; 7),
"daleth":  r(1; 8),
"he":      r(1; 9),
"waw":     r(1; 10),
"zayin":   r(1; 11),
"heth":    r(1759; 27720)
};

probabilities
| bow(choose(\$n)) as \$observed
| compare( map_values(\$n * .n / .d); \$observed ) ;

'```

Output

```       : expected observed  (e-o)^2 / e
aleph : 200000.0   200247     0.30
beth : 166666.6   166668     1.06
gimel : 142857.1   142787     0.03
daleth : 125000.0   124789     0.35
he : 111111.1   111280     0.25
waw : 100000.0   100165     0.27
zayin :  90909.0    90716     0.41
heth :  63455.9    63348     0.18
```

## Julia

I made the solution to this task more difficult than I had anticipated by using the Hebrew characters (rather than their anglicised names) as labels for the sampled collection of objects. In doing so, I encountered an interesting subtlety of bidirectional text in Unicode. Namely, that strong right-to-left characters, such as those of Hebrew, override the directionality of European digits, which have weak directionality. Because of this property of Unicode, my table of items and yields had its lines of data interpreted as if it were entirely Hebrew and output in reverse order (from my English speaking perspective). I was able to get the table to display as I liked on my terminal by preceding the the Hebrew characters by the Unicode RLI (right-to-left isolate) control character (U+2067). However, when I pasted this output into this Rosetta Code entry, the display reverted to the "backwards" version. Rather than continue the struggle, trying to force this entry to display as it does on my terminal, I created an alternative version of the table. This "Displayable Here" table adds "yields" to to each line, and this strong left-to-right text makes the whole line display as left-to-right (without the need for a RLI characer).

```using Printf

p = [1/i for i in 5:11]
plen = length(p)
q = [0.0, [sum(p[1:i]) for i = 1:plen]]
plab = [char(i) for i in 0x05d0:(0x05d0+plen)]
hi = 10^6
push!(p, 1.0 - sum(p))
plen += 1

accum = zeros(Int, plen)

for i in 1:hi
accum[sum(rand() .>= q)] += 1
end

r = accum/hi

println("Rates at which items are selected (", hi, " trials).")
println(" Item  Expected   Actual")
for i in 1:plen
println(@sprintf("   \u2067%s   %8.6f  %8.6f", plab[i], p[i], r[i]))
end

println()
println("Rates at which items are selected (", hi, " trials).")
println(" Item         Count   Expected   Actual")
for i in 1:plen
println(@sprintf("   %s yields  %6d   %8.6f  %8.6f",
plab[i], accum[i], p[i], r[i]))
end
```
Output:

Original

This table displays properly on my terminal, but the lines of data are reversed in this display.

```Rates at which items are selected (1000000 trials).
Item  Expected   Actual
⁧א   0.200000  0.199872
⁧ב   0.166667  0.166618
⁧ג   0.142857  0.143302
⁧ד   0.125000  0.125040
⁧ה   0.111111  0.110602
⁧ו   0.100000  0.099833
⁧ז   0.090909  0.091313
⁧ח   0.063456  0.063420
```

Displayable Here

This is the same data, less elegantly presented but accurately displayed on both my terminal and here at Rosetta Code.

```Rates at which items are selected (1000000 trials).
Item         Count   Expected   Actual
א yields  199872   0.200000  0.199872
ב yields  166618   0.166667  0.166618
ג yields  143302   0.142857  0.143302
ד yields  125040   0.125000  0.125040
ה yields  110602   0.111111  0.110602
ו yields   99833   0.100000  0.099833
ז yields   91313   0.090909  0.091313
ח yields   63420   0.063456  0.063420
```

## Kotlin

Translation of: FreeBASIC
```// version 1.0.6

fun main(args: Array<String>) {
val letters  = arrayOf("aleph", "beth", "gimel", "daleth", "he", "waw", "zayin", "heth")
val actual   = IntArray(8)
val probs    = doubleArrayOf(1/5.0, 1/6.0, 1/7.0, 1/8.0, 1/9.0, 1/10.0, 1/11.0, 0.0)
val cumProbs = DoubleArray(8)

cumProbs[0] = probs[0]
for (i in 1..6) cumProbs[i] = cumProbs[i - 1] + probs[i]
cumProbs[7] = 1.0
probs[7] = 1.0 - cumProbs[6]
val n = 1000000
(1..n).forEach {
val rand = Math.random()
when {
rand <= cumProbs[0] -> actual[0]++
rand <= cumProbs[1] -> actual[1]++
rand <= cumProbs[2] -> actual[2]++
rand <= cumProbs[3] -> actual[3]++
rand <= cumProbs[4] -> actual[4]++
rand <= cumProbs[5] -> actual[5]++
rand <= cumProbs[6] -> actual[6]++
else                -> actual[7]++
}
}

var sumActual = 0.0
println("Letter\t Actual    Expected")
println("------\t--------   --------")
for (i in 0..7) {
val generated = actual[i].toDouble() / n
println("\${letters[i]}\t\${String.format("%8.6f   %8.6f", generated, probs[i])}")
sumActual += generated
}
println("\t--------   --------")
println("\t\${"%8.6f".format(sumActual)}   1.000000")
}
```
Output:
```Letter   Actual    Expected
------  --------   --------
aleph   0.199427   0.200000
beth    0.166862   0.166667
gimel   0.142756   0.142857
daleth  0.125442   0.125000
he      0.110868   0.111111
waw     0.100405   0.100000
zayin   0.090799   0.090909
heth    0.063441   0.063456
--------   --------
1.000000   1.000000
```

## Liberty BASIC

```names\$="aleph beth gimel daleth he waw zayin heth"
dim sum(8)
dim counter(8)

s = 0
for i = 1 to 7
s = s+1/(i+4)
sum(i)=s
next

N =1000000     '  number of throws

for i =1 to N
rand =rnd( 1)
for j = 1 to 7
if sum(j)> rand then exit for
next
counter(j)=counter(j)+1
next

print "Observed", "Intended"
for i = 1 to 8
print word\$(names\$, i), using( "#.#####", counter(i)  /N), using( "#.#####", 1/(i+4))
next```

## Lua

```items = {}
items["aleph"]  = 1/5.0
items["beth"]   = 1/6.0
items["gimel"]  = 1/7.0
items["daleth"] = 1/8.0
items["he"]     = 1/9.0
items["waw"]    = 1/10.0
items["zayin"]  = 1/11.0
items["heth"]   = 1759/27720

num_trials = 1000000

samples = {}
for item, _ in pairs( items ) do
samples[item] = 0
end

math.randomseed( os.time() )
for i = 1, num_trials do
z = math.random()

for item, _ in pairs( items ) do
if z < items[item] then
samples[item] = samples[item] + 1
break;
else
z = z - items[item]
end
end
end

for item, _ in pairs( items ) do
print( item, samples[item]/num_trials, items[item] )
end
```

Output

```gimel	0.142606	0.14285714285714
heth	0.063434	0.063455988455988
beth	0.166788	0.16666666666667
zayin	0.091097	0.090909090909091
daleth	0.124772	0.125
aleph	0.200541	0.2
he	0.1107	        0.11111111111111
waw	0.100062	0.1```

## Mathematica/Wolfram Language

Built-in function can already do a weighted random choosing. Example for making a million random choices would be:

```choices={{"aleph", 1/5},{"beth", 1/6},{"gimel", 1/7},{"daleth", 1/8},{"he", 1/9},{"waw", 1/10},{"zayin", 1/11},{"heth", 1759/27720}};
data=RandomChoice[choices[[All,2]]->choices[[All,1]],10^6];
```

To compare the data we use the following code to make a table:

```Grid[{#[[1]],N[Count[data,#[[1]]]/10^6],N[#[[2]]]}&/@choices]
```

gives back (item, attained prob., target prob.):

```aleph		0.200036	0.2
beth		0.166591	0.166667
gimel		0.142699	0.142857
daleth		0.125018	0.125
he		0.111306	0.111111
waw		0.100433	0.1
zayin		0.090671	0.0909091
heth		0.063246	0.063456```

## MATLAB

Works with: MATLAB version with Statistics Toolbox
```function probChoice
choices = {'aleph' 'beth' 'gimel' 'daleth' 'he' 'waw' 'zayin' 'heth'};
w = [1/5 1/6 1/7 1/8 1/9 1/10 1/11 1759/27720];
R = randsample(length(w), 1e6, true, w);
T = tabulate(R);
fprintf('Value\tCount\tPercent\tGoal\n')
for k = 1:size(T, 1)
fprintf('%6s\t%.f\t%.2f%%\t%.2f%%\n', ...
choices{k}, T(k, 2), T(k, 3), 100*w(k))
end
end
```
Output:
```Value	Count	Percent	Goal
aleph	199635	19.96%	20.00%
beth	166427	16.64%	16.67%
gimel	143342	14.33%	14.29%
daleth	125014	12.50%	12.50%
he	111031	11.10%	11.11%
waw	99920	9.99%	10.00%
zayin	91460	9.15%	9.09%
heth	63171	6.32%	6.35%```
Works with: MATLAB version without toolboxes
```function probChoice
choices = {'aleph' 'beth' 'gimel' 'daleth' 'he' 'waw' 'zayin' 'heth'};
w = [1/5 1/6 1/7 1/8 1/9 1/10 1/11 1759/27720];
nSamp = 1e6;
nChoice = length(w);
R = rand(nSamp, 1);
wCS = cumsum(w);
results = zeros(1, nChoice);
fprintf('Value\tCount\tPercent\tGoal\n')
for k = 1:nChoice
choiceKIdxs = R < wCS(k);
R(choiceKIdxs) = k;
results(k) = sum(choiceKIdxs);
fprintf('%6s\t%.f\t%.2f%%\t%.2f%%\n', ...
choices{k}, results(k), 100*results(k)/nSamp, 100*w(k))
end
end
```
Output:
```Value	Count	Percent	Goal
aleph	200327	20.03%	20.00%
beth	166318	16.63%	16.67%
gimel	143040	14.30%	14.29%
daleth	125136	12.51%	12.50%
he	111251	11.13%	11.11%
waw	99946	9.99%	10.00%
zayin	90974	9.10%	9.09%
heth	63008	6.30%	6.35%```

## Nim

```import tables, random, strformat, times

var start = cpuTime()

const
NumTrials = 1_000_000
Probabilities = {"aleph": 1 / 5, "beth": 1 / 6, "gimel": 1 / 7, "daleth": 1 / 8,
"he": 1 / 9, "waw": 1 / 10, "zayin": 1 / 11, "heth": 1759 / 27720}.toTable

var samples: CountTable[string]

randomize()

for i in 1 .. NumTrials:
var z = rand(1.0)
for item, prob in Probabilities.pairs:
if z < prob:
samples.inc(item)
break
else:
z -= prob

var s1, s2 = 0.0

echo " Item    Target     Results   Differences"
echo "======  ========   ========   ==========="
for item, prob in Probabilities.pairs:
let r = samples[item] / NumTrials
s1 += r * 100
s2 += prob * 100
echo &"{item:<6}  {prob:.6f}   {r:.6f}   {100 * (1 - r / prob):9.6f} %"
echo "======  ========   ======== "
echo &"Total:  {s2:^8.2f}   {s1:^8.2f}"
echo &"\nExecution time: {cpuTime()-start:.2f} s"
```
Output:
``` Item    Target     Results   Differences
======  ========   ========   ===========
he      0.111111   0.111485   -0.336500 %
heth    0.063456   0.063758   -0.475939 %
zayin   0.090909   0.090811    0.107900 %
aleph   0.200000   0.199603    0.198500 %
gimel   0.142857   0.142702    0.108600 %
daleth  0.125000   0.124831    0.135200 %
beth    0.166667   0.167160   -0.296000 %
waw     0.100000   0.099650    0.350000 %
======  ========   ========
Total:   100.00     100.00

Execution time: 0.05 s```

## OCaml

```let p = [
"Aleph",   1.0 /. 5.0;
"Beth",    1.0 /. 6.0;
"Gimel",   1.0 /. 7.0;
"Daleth",  1.0 /. 8.0;
"He",      1.0 /. 9.0;
"Waw",     1.0 /. 10.0;
"Zayin",   1.0 /. 11.0;
"Heth", 1759.0 /. 27720.0;
]

let rec take k = function
| (v, p)::tl -> if k < p then v else take (k -. p) tl
| _ -> invalid_arg "take"

let () =
let n = 1_000_000 in
Random.self_init();
let h = Hashtbl.create 3 in
List.iter (fun (v, _) -> Hashtbl.add h v 0) p;
let tot = List.fold_left (fun acc (_, p) -> acc +. p) 0.0 p in
for i = 1 to n do
let sel = take (Random.float tot) p in
let n = Hashtbl.find h sel in
Hashtbl.replace h sel (succ n)  (* count the number of each item *)
done;
List.iter (fun (v, p) ->
let d = Hashtbl.find h v in
Printf.printf "%s \t %f %f\n" v p (float d /. float n)
) p
```

Output:

```Aleph    0.200000 0.200272
Beth     0.166667 0.166381
Gimel    0.142857 0.142497
Daleth   0.125000 0.125005
He       0.111111 0.111272
Waw      0.100000 0.100069
Zayin    0.090909 0.091136
Heth     0.063456 0.063368```

## PARI/GP

```pc()={
my(v=[5544,10164,14124,17589,20669,23441,25961,27720],u=vector(8),e);
for(i=1,1e6,
my(r=random(27720));
for(j=1,8,
if(r<v[j], u[j]++; break)
)
);
e=precision([1/5,1/6,1/7,1/8,1/9,1/10,1/11,1759/27720]*1e6,9); \\ truncate to 9 decimal places
print("Totals: "u);
print("Expected: "e);
print("Diff: ",u-e);
print("StDev: ",vector(8,i,sqrt(abs(u[i]-v[i])/e[i])));
};```

## Perl

```use List::Util qw(first sum);
use constant TRIALS => 1e6;

sub prob_choice_picker {
my %options = @_;
my (\$n, @a) = 0;
while (my (\$k,\$v) = each %options) {
\$n += \$v;
push @a, [\$n, \$k];
}
return sub {
my \$r = rand;
( first {\$r <= \$_->[0]} @a )->[1];
};
}

my %ps =
(aleph  => 1/5,
beth   => 1/6,
gimel  => 1/7,
daleth => 1/8,
he     => 1/9,
waw    => 1/10,
zayin  => 1/11);
\$ps{heth} = 1 - sum values %ps;

my \$picker = prob_choice_picker %ps;
my %results;
for (my \$n = 0 ; \$n < TRIALS ; ++\$n) {
++\$results{\$picker->()};
}

print "Event   Occurred  Expected  Difference\n";
foreach (sort {\$results{\$b} <=> \$results{\$a}} keys %results) {
printf "%-6s  %f  %f  %f\n",
\$_, \$results{\$_}/TRIALS, \$ps{\$_},
abs(\$results{\$_}/TRIALS - \$ps{\$_});
}
```

Sample output:

```Event   Occurred  Expected  Difference
aleph   0.198915  0.200000  0.001085
beth    0.166804  0.166667  0.000137
gimel   0.142992  0.142857  0.000135
daleth  0.125155  0.125000  0.000155
he      0.111160  0.111111  0.000049
waw     0.100229  0.100000  0.000229
zayin   0.091014  0.090909  0.000105
heth    0.063731  0.063456  0.000275```

## Phix

```with javascript_semantics
constant lim = 1000000,
{names, probs} = columnize({{"aleph",   1/5},
{"beth",    1/6},
{"gimel",   1/7},
{"daleth",  1/8},
{"he",      1/9},
{"waw",     1/10},
{"zayin",   1/11},
{"heth",    1759/27720}})
sequence results = repeat(0,length(names))
for j=1 to lim do
atom r = rnd()
for i=1 to length(probs) do
r -= probs[i]
if r<=0 then
results[i]+=1
exit
end if
end for
end for

printf(1,"  Name   Actual Expected\n")
for i=1 to length(probs) do
printf(1,"%6s %8.6f %8.6f\n",{names[i],results[i]/lim,probs[i]})
end for
```
Output:
```  Name   Actual Expected
aleph 0.201010 0.200000
beth 0.166311 0.166667
gimel 0.143354 0.142857
daleth 0.124841 0.125000
he 0.110544 0.111111
waw 0.100228 0.100000
zayin 0.090270 0.090909
heth 0.063442 0.063456
```

## Phixmonti

```/# Rosetta Code problem: http://rosettacode.org/wiki/Probabilistic_choice
by Galileo, 05/2022 #/

include ..\Utilitys.pmt

( ( "aleph" 0.200000 0 ) ( "beth" 0.166667 0 ) ( "gimel" 0.142857 0 ) ( "daleth" 0.125000 0 )
( "he" 0.111111 0 ) ( "waw" 0.100000 0 ) ( "zayin" 0.090909 0 ) ( "heth" 0.063456 0 ) )
len 1 swap 2 tolist var lprob

1000000 var trial

trial for drop
rand >ps
0 >ps
lprob for var i
( i 2 ) sget ps> +
tps swap dup >ps < if
( i 3 ) sget 1 + ( i 3 ) sset
exitfor
endif
endfor
ps> ps> drop drop
endfor

( "item" "\t" "actual" "\t\t" "theoretical" ) lprint nl nl
lprob for drop
pop swap
1 get "\t" rot 3 get trial / "\t" rot 2 get nip "\n" 6 tolist lprint
endfor```
Output:
```item    actual          theoretical

aleph   0.200018        0.2
beth    0.166987        0.166667
gimel   0.142654        0.142857
daleth  0.125217        0.125
he      0.111668        0.111111
waw     0.099827        0.1
zayin   0.090311        0.090909
heth    0.063318        0.063456

=== Press any key to exit ===```

## PicoLisp

```(let (Count 1000000  Denom 27720  N Denom)
(let Probs
(mapcar
'((I S)
(prog1 (cons N (*/ Count I) 0 S)
(dec 'N (/ Denom I)) ) )
(range 5 12)
'(aleph beth gimel daleth he waw zayin heth) )
(do Count
(inc (cddr (rank (rand 1 Denom) Probs T))) )
(let Fmt (-6 12 12)
(tab Fmt NIL "Probability" "Result")
(for X Probs
(tab Fmt
(cdddr X)
(format (caddr X) 6) ) ) ) ) )```

Output:

```       Probability      Result
aleph     0.200000    0.199760
beth      0.166667    0.166878
gimel     0.142857    0.142977
daleth    0.125000    0.124983
he        0.111111    0.111200
waw       0.100000    0.100173
zayin     0.090909    0.090591
heth      0.083333    0.063438```

## PL/I

``` probch: Proc Options(main);
Dcl prob(8) Dec Float(15) Init((1/5.0),      /* aleph  */
(1/6.0),      /* beth   */
(1/7.0),      /* gimel  */
(1/8.0),      /* daleth */
(1/9.0),      /* he     */
(1/10.0),     /* waw    */
(1/11.0),     /* zayin  */
(1759/27720));/* heth   */
Dcl what(8) Char(6) Init('aleph ','beth  ','gimel ','daleth',
'he    ','waw   ','zayin ','heth  ');
Dcl ulim(0:8) Dec Float(15) Init((9)0);
Dcl i Bin Fixed(31);
Dcl ifloat Dec Float(15);
Dcl one    Dec Float(15) Init(1);
Dcl num    Dec Float(15) Init(1759);
Dcl denom  Dec Float(15) Init(27720);
Dcl x      Dec Float(15) Init(0);
Dcl pr     Dec Float(15) Init(0);
Dcl (n,nn) Bin Fixed(31);
Dcl cnt(8) Bin Fixed(31) Init((8)0);
nn=1000000;
Do i=1 To 8;
ifloat=i+4;
If i<8 Then
prob(i)=one/ifloat;
Else
prob(i)=num/denom;
Ulim(i)=ulim(i-1)+prob(i);
/* Put Skip list(i,prob(i),ulim(i));*/
End;
Do n=1 To nn;
x=random();
Do i=1 To 8;
If x<ulim(i) Then Leave;
End;
cnt(i)+=1;
End;
Put Edit('letter    occurs    frequency  expected ')(Skip,a);
Put Edit('------    ------   ---------- ----------')(Skip,a);
Do i=1 To 8;
pr=float(cnt(i))/float(nn);
Put Edit(what(i),cnt(i),pr,prob(i))(Skip,a,f(10),x(2),2(f(11,8)));
End;
End;```
Output:
```One million trials
letter    occurs    frequency  expected
------    ------   ---------- ---------
aleph     199989   0.19998900 0.20000000
beth      167338   0.16733800 0.16666667
gimel     142968   0.14296800 0.14285714
daleth    124840   0.12484000 0.12500000
he        110620   0.11062000 0.11111111
waw        99744   0.09974400 0.10000000
zayin      90930   0.09093000 0.09090909
heth       63571   0.06357100 0.06345599

One hundred million trials
letter    occurs    frequency  expected
------    ------   ---------- ----------
aleph   20002222   0.20002222 0.20000000
beth    16665226   0.16665226 0.16666667
gimel   14289674   0.14289674 0.14285714
daleth  12498182   0.12498182 0.12500000
he      11108704   0.11108704 0.11111111
waw     10002442   0.10002442 0.10000000
zayin    9087412   0.09087412 0.09090909
heth     6346138   0.06346138 0.06345599 ```

## PowerShell

Translation of: Java Script

The guts of this script are translated from the Java Script entry. Then I stole the idea to show the actual Hebrew character from Julia.

```\$character = [PSCustomObject]@{
aleph  = [PSCustomObject]@{Expected=1/5       ; Alpha="א"}
beth   = [PSCustomObject]@{Expected=1/6       ; Alpha="ב"}
gimel  = [PSCustomObject]@{Expected=1/7       ; Alpha="ג"}
daleth = [PSCustomObject]@{Expected=1/8       ; Alpha="ד"}
he     = [PSCustomObject]@{Expected=1/9       ; Alpha="ה"}
waw    = [PSCustomObject]@{Expected=1/10      ; Alpha="ו"}
zayin  = [PSCustomObject]@{Expected=1/11      ; Alpha="ז"}
heth   = [PSCustomObject]@{Expected=1759/27720; Alpha="ח"}
}

\$sum        = 0
\$iterations = 1000000
\$cumulative = [ordered]@{}
\$randomly   = [ordered]@{}

foreach (\$name in \$character.PSObject.Properties.Name)
{
\$sum += \$character.\$name.Expected
\$cumulative.\$name = \$sum
\$randomly.\$name = 0
}

for (\$i = 0; \$i -lt \$iterations; \$i++)
{
\$random = Get-Random -Minimum 0.0 -Maximum 1.0

foreach (\$name in \$cumulative.Keys)
{
if (\$random -le \$cumulative.\$name)
{
\$randomly.\$name++
break
}
}
}

foreach (\$name in \$character.PSObject.Properties.Name)
{
[PSCustomObject]@{
Name      = \$name
Expected  = \$character.\$name.Expected
Actual    = \$randomly.\$name / \$iterations
Character = \$character.\$name.Alpha
}
}
```
Output:
```Name             Expected   Actual Character
----             --------   ------ ---------
aleph                 0.2 0.199823 א
beth    0.166666666666667 0.166744 ב
gimel   0.142857142857143 0.143312 ג
daleth              0.125 0.125153 ד
he      0.111111111111111 0.110984 ה
waw                   0.1 0.099667 ו
zayin  0.0909090909090909 0.091135 ז
heth   0.0634559884559885 0.063182 ח
```

## PureBasic

```#times=1000000

Structure Item
name.s
prob.d
Amount.i
EndStructure

If OpenConsole()
Define i, j, d.d, e.d, txt.s
Dim Mapps.Item(7)
Mapps(0)\name="aleph": Mapps(0)\prob=1/5.0
Mapps(1)\name="beth":  Mapps(1)\prob=1/6.0
Mapps(2)\name="gimel": Mapps(2)\prob=1/7.0
Mapps(3)\name="daleth":Mapps(3)\prob=1/8.0
Mapps(4)\name="he":    Mapps(4)\prob=1/9.0
Mapps(5)\name="waw":   Mapps(5)\prob=1/10.0
Mapps(6)\name="zayin": Mapps(6)\prob=1/11.0
Mapps(7)\name="heth":  Mapps(7)\prob=1759/27720.0

For i=1 To #times
d=Random(#MAXLONG)/#MAXLONG  ; Get a random number
e=0.0
For j=0 To ArraySize(Mapps())
e+Mapps(j)\prob            ; Get span for current itme
If d<=e                    ; Check if it is within this span?
Mapps(j)\Amount+1        ; If so, count it.
Break
EndIf
Next j
Next i

PrintN("Sample times: "+Str(#times)+#CRLF\$)
For j=0 To ArraySize(Mapps())
d=Mapps(j)\Amount/#times
txt=LSet(Mapps(j)\name,7)+" should be "+StrD(Mapps(j)\prob)+" is "+StrD(d)
PrintN(txt+" | Deviatation "+RSet(StrD(100.0-100.0*Mapps(j)\prob/d,3),6)+"%")
Next

Print(#CRLF\$+"Press ENTER to exit"):Input()
CloseConsole()
EndIf
```

Output may look like

```Sample times: 1000000

aleph   should be 0.2000000000 is 0.1995520000 | Deviatation -0.225%
beth    should be 0.1666666667 is 0.1673270000 | Deviatation  0.395%
gimel   should be 0.1428571429 is 0.1432040000 | Deviatation  0.242%
daleth  should be 0.1250000000 is 0.1251850000 | Deviatation  0.148%
he      should be 0.1111111111 is 0.1109550000 | Deviatation -0.141%
waw     should be 0.1000000000 is 0.0999220000 | Deviatation -0.078%
zayin   should be 0.0909090909 is 0.0902240000 | Deviatation -0.759%
heth    should be 0.0634559885 is 0.0636310000 | Deviatation  0.275%

Press ENTER to exit
```

## Python

Two different algorithms are coded.

```import random, bisect

def probchoice(items, probs):
'''\
Splits the interval 0.0-1.0 in proportion to probs
then finds where each random.random() choice lies
'''

prob_accumulator = 0
accumulator = []
for p in probs:
prob_accumulator += p
accumulator.append(prob_accumulator)

while True:
r = random.random()
yield items[bisect.bisect(accumulator, r)]

def probchoice2(items, probs, bincount=10000):
'''\
Puts items in bins in proportion to probs
then uses random.choice() to select items.

Larger bincount for more memory use but
higher accuracy (on avarage).
'''

bins = []
for item,prob in zip(items, probs):
bins += [item]*int(bincount*prob)
while True:
yield random.choice(bins)

probs=[0.5, 0.3, 0.2],
trials = 100000
):
def problist2string(probs):
'''\
Turns a list of probabilities into a string
Also rounds FP values
'''
return ",".join('%8.6f' % (p,) for p in probs)

from collections import defaultdict

counter = defaultdict(int)
it = func(items, probs)
for dummy in xrange(trials):
counter[it.next()] += 1
print "\n##\n## %s\n##" % func.func_name.upper()
print "Trials:              ", trials
print "Items:               ", ' '.join(items)
print "Target probability:  ", problist2string(probs)
print "Attained probability:", problist2string(
counter[x]/float(trials) for x in items)

if __name__ == '__main__':
items = 'aleph beth gimel daleth he waw zayin heth'.split()
probs = [1/(float(n)+5) for n in range(len(items))]
probs[-1] = 1-sum(probs[:-1])
tester(probchoice, items, probs, 1000000)
tester(probchoice2, items, probs, 1000000)
```

Sample output:

```##
## PROBCHOICE
##
Trials:               1000000
Items:                aleph beth gimel daleth he waw zayin heth
Target probability:   0.200000,0.166667,0.142857,0.125000,0.111111,0.100000,0.090909,0.063456
Attained probability: 0.200050,0.167109,0.143364,0.124690,0.111237,0.099661,0.090338,0.063551

##
## PROBCHOICE2
##
Trials:               1000000
Items:                aleph beth gimel daleth he waw zayin heth
Target probability:   0.200000,0.166667,0.142857,0.125000,0.111111,0.100000,0.090909,0.063456
Attained probability: 0.199720,0.166424,0.142474,0.124561,0.111511,0.100313,0.091316,0.063681```

## Quackery

Uses point\$ from the bignum rational arithmetic module bigrat.qky. `10 point\$` returns a ratio as a decimal string accurate to 10 decimal places with round-to-nearest on the final digit.

```  [ \$ "bigrat.qky" loadfile ] now!

( --------------- zen object orientation -------------- )

[ immovable
]this[ swap do ]done[ ]     is object     ( [ -->   )

[ ]'[ ]                       is method     (   --> [ )

[ method
[ dup share
swap put ] ]            is localise   (   -->   )

[ method [ release ] ]        is delocalise (   -->   )

( ------------------ rand-gen methods ----------------- )

[ method
[ dup take
2 split drop
' [ 0 0 ] join
swap put ] ]            is reset-gen  (   --> [ )

[ method
[ dup take
dup 2 peek 1+
swap 2 poke
dup 1 peek random
over 0 peek <
if
[ dup 3 peek 1+
swap 3 poke ]
swap put ] ]              is rand-gen   (   --> [ )

[ method
[ dup echo say ": "
share
dup 2 peek dup echo
say " trials" cr
say "    Actual: "
over 3 peek
swap 10 point\$ echo\$ cr
say "  Expected: "
dup 0 peek
swap 1 peek
10 point\$ echo\$ cr
cr ] ]                  is report     (   --> [ )

( ------------------ rand-gen objects ----------------- )

[ object [    1     5 0 0 ] ] is aleph    (   [ -->   )
[ object [    1     6 0 0 ] ] is beth     (   [ -->   )
[ object [    1     7 0 0 ] ] is gimel    (   [ -->   )
[ object [    1     8 0 0 ] ] is daleth   (   [ -->   )
[ object [    1     9 0 0 ] ] is he       (   [ -->   )
[ object [    1    10 0 0 ] ] is waw      (   [ -->   )
[ object [    1    11 0 0 ] ] is zayin    (   [ -->   )
[ object [ 1759 27720 0 0 ] ] is heth     (   [ -->   )

' [ aleph beth gimel daleth he waw zayin heth ]

dup witheach [ reset-gen swap do ]

dup witheach
[ 1000000 times
[ rand-gen over do ]
drop ]

witheach [ report swap do ]```
Output:
```aleph: 1000000 trials
Actual: 0.199454
Expected: 0.2

beth: 1000000 trials
Actual: 0.166854
Expected: 0.1666666667

gimel: 1000000 trials
Actual: 0.142329
Expected: 0.1428571429

daleth: 1000000 trials
Actual: 0.12457
Expected: 0.125

he: 1000000 trials
Actual: 0.111143
Expected: 0.1111111111

waw: 1000000 trials
Actual: 0.100061
Expected: 0.1

zayin: 1000000 trials
Actual: 0.090979
Expected: 0.0909090909

heth: 1000000 trials
Actual: 0.063131
Expected: 0.0634559885```

## R

```prob = c(aleph=1/5, beth=1/6, gimel=1/7, daleth=1/8, he=1/9, waw=1/10, zayin=1/11, heth=1759/27720)
# Note that R doesn't actually require the weights
# vector for rmultinom to sum to 1.
hebrew = c(rmultinom(1, 1e6, prob))
d = data.frame(
Requested = prob,
Obtained = hebrew/sum(hebrew))
print(d)
```

Sample output:

```        Requested Obtained
aleph  0.20000000 0.200311
beth   0.16666667 0.167160
gimel  0.14285714 0.141997
daleth 0.12500000 0.124644
he     0.11111111 0.110984
waw    0.10000000 0.099927
zayin  0.09090909 0.091365
heth   0.06345599 0.063612```

A histogram of the data is also possible using, for example,

```library(ggplot2)
qplot(factor(names(prob), levels = names(prob)), hebrew, geom = "histogram")
```

## Racket

probabalistic-choice uses inexact (float) arithmetic

probabalistic-choice/exact uses fractions and greatest common denominators and the likes

The test submodule is used for unit tests, and is not run when this code is loaded as a module. Either run the program in DrRacket or run `raco test prob-choice.rkt`

```#lang racket
;;; returns a probabalistic choice from the sequence choices
;;; choices generates two values -- the chosen value and a
;;; probability (weight) of the choice.
;;;
;;; Note that a hash where keys are choices and values are probabilities
;;; is such a sequence.
;;;
;;; if the total probability < 1 then choice could return #f
;;; if the total probability > 1 then some choices may be impossible
(define (probabalistic-choice choices)
(let-values
(((_ choice) ;; the fold provides two values, we only need the second
;; the first will always be a negative number showing that
;; I've run out of random steam
(for/fold
((rnd (random))
(choice #f))
(((v p) choices)
#:break (<= rnd 0))
(values (- rnd p) v))))
choice))

;;; ditto, but all probabilities must be exact rationals
;;; the optional lcd
;;;
;;; not the most efficient, since it provides a wrapper (and demo)
;;; for p-c/i-w below
(define (probabalistic-choice/exact
choices
#:gcd (GCD (/ (apply gcd (hash-values choices)))))
(probabalistic-choice/integer-weights
(for/hash (((k v) choices))
(values k (* v GCD)))
#:sum-of-weights GCD))

;;; this proves useful in Rock-Paper-Scissors
(define (probabalistic-choice/integer-weights
choices
#:sum-of-weights (sum-of-weights (apply + (hash-values choices))))
(let-values
(((_ choice)
(for/fold
((rnd (random sum-of-weights))
(choice #f))
(((v p) choices)
#:break (< rnd 0))
(values (- rnd p) v))))
choice))

(module+ test
(define test-samples (make-parameter 1000000))

(define (test-p-c-function f w)
(define test-selection (make-hash))
(for* ((i (in-range 0 (test-samples)))
(c (in-value (f w))))
(when (zero? (modulo i 100000)) (eprintf "~a," (quotient i 100000)))
(printf "~a~%choice\tcount\texpected\tratio\terror~%" f)
(for* (((k v) (in-hash test-selection))
(e (in-value (* (test-samples) (hash-ref w k)))))
(printf "~a\t~a\t~a\t~a\t~a%~%"
k v e
(/ v (test-samples))
(real->decimal-string
(exact->inexact (* 100 (/ (- v e) e)))))))

(define test-weightings/rosetta
(hash
'aleph 1/5
'beth 1/6
'gimel 1/7
'daleth 1/8
'he 1/9
'waw 1/10
'zayin 1/11
))

(define test-weightings/50:50 (hash 'woo 1/2 'yay 1/2))
(define test-weightings/1:2:3 (hash 'woo 1 'yay 2 'foo 3))

(test-p-c-function probabalistic-choice test-weightings/50:50)
(test-p-c-function probabalistic-choice/exact test-weightings/50:50)
(test-p-c-function probabalistic-choice test-weightings/rosetta)
(test-p-c-function probabalistic-choice/exact test-weightings/rosetta))
```

Output (note that the progress counts, which go to standard error, are interleaved with the output on standard out)

```0,1,2,3,4,5,6,7,8,9,#<procedure:probabalistic-choice>
choice	count	expected	ratio	error
yay	499744	500000	15617/31250	-0.05%
woo	500256	500000	15633/31250	0.05%
0,1,2,3,4,5,6,7,8,9,#<procedure:probabalistic-choice/exact>
choice	count	expected	ratio	error
yay	499852	500000	124963/250000	-0.03%
woo	500148	500000	125037/250000	0.03%
0,1,2,3,4,5,6,7,8,9,#<procedure:probabalistic-choice>
choice	count	expected	ratio	error
daleth	124964	125000	31241/250000	-0.03%
zayin	90233	1000000/11	90233/1000000	-0.74%
gimel	142494	1000000/7	71247/500000	-0.25%
heth	64045	43975000/693	12809/200000	0.93%
aleph	199690	200000	19969/100000	-0.15%
beth	166861	500000/3	166861/1000000	0.12%
waw	100075	100000	4003/40000	0.07%
he	111638	1000000/9	55819/500000	0.47%
0,1,2,3,4,5,6,7,8,9,#<procedure:probabalistic-choice/exact>
choice	count	expected	ratio	error
beth	166423	500000/3	166423/1000000	-0.15%
heth	63462	43975000/693	31731/500000	0.01%
daleth	125091	125000	125091/1000000	0.07%
waw	99820	100000	4991/50000	-0.18%
aleph	200669	200000	200669/1000000	0.33%
gimel	142782	1000000/7	71391/500000	-0.05%
zayin	90478	1000000/11	45239/500000	-0.47%
he	111275	1000000/9	4451/40000	0.15%```

## Raku

(formerly Perl 6)

Works with: Rakudo version 2018.10
```constant TRIALS = 1e6;

constant @event = <aleph beth gimel daleth he waw zayin heth>;

constant @P = flat (1 X/ 5 .. 11), 1759/27720;
constant @cP = [\+] @P;

my atomicint @results[+@event];
(^TRIALS).race.map: { @results[ @cP.first: { \$_ > once rand }, :k ]⚛++; }

say  'Event    Occurred Expected  Difference';
for ^@results {
my (\$occurred, \$expected) = @results[\$_], @P[\$_] * TRIALS;
printf "%-9s%8.0f%9.1f%12.1f\n",
@event[\$_],
\$occurred,
\$expected,
abs \$occurred - \$expected;
}
```
Output:
```Event    Occurred Expected  Difference
aleph      200369 200000.0       369.0
beth       167005 166666.7       338.3
gimel      142690 142857.1       167.1
daleth     125061 125000.0        61.0
he         110563 111111.1       548.1
waw        100214 100000.0       214.0
zayin       90617  90909.1       292.1
heth        63481  63456.0        25.0```

## ReScript

```let p = [
("Aleph",   1.0 /. 5.0),
("Beth",    1.0 /. 6.0),
("Gimel",   1.0 /. 7.0),
("Daleth",  1.0 /. 8.0),
("He",      1.0 /. 9.0),
("Waw",     1.0 /. 10.0),
("Zayin",   1.0 /. 11.0),
("Heth", 1759.0 /. 27720.0),
]

let prob_take = (arr, k) => {
let rec aux = (i, k) => {
let (v, p) = arr[i]
if k < p { v } else { aux(i+1, (k -. p)) }
}
aux(0, k)
}

{
let n = 1_000_000
let h = Belt.HashMap.String.make(~hintSize=10)

Js.Array2.forEach(p, ((v, _)) =>
Belt.HashMap.String.set(h, v, 0)
)

let tot = Js.Array2.reduce(p, (acc, (_, prob)) => acc +. prob, 0.0)

for _ in 1 to n {
let sel = prob_take(p, tot *. Js.Math.random())
let _n = Belt.HashMap.String.get(h, sel)
let n = Belt.Option.getExn(_n)
Belt.HashMap.String.set(h, sel, (n+1))  /* count the number of each item */
}
Printf.printf("Event    expected occurred\n")
Js.Array2.forEach(p, ((v, p)) => {
let _d = Belt.HashMap.String.get(h, v)
let d = Belt.Option.getExn(_d)
Printf.printf("%s \t %8.5g %8.5g\n", v, p, float(d) /. float(n))
}
)
}```
Output:
```Event    expected occurred
Aleph 	      0.2  0.20042
Beth 	  0.16667  0.16606
Gimel 	  0.14286  0.14324
Daleth 	    0.125   0.1252
He 	  0.11111  0.11085
Waw 	      0.1 0.099557
Zayin 	 0.090909 0.090877
Heth 	 0.063456   0.0638
```

## REXX

Note:   REXX can generate random numbers up to a range of   100,000.

A little extra REXX code was added to provide head and foot titles along with the totals.

```/*REXX program displays results of probabilistic choices, gen random #s per probability.*/
parse arg trials digs seed .                     /*obtain the optional arguments from CL*/
if trials=='' | trials==","  then trials= +1e6   /*Not specified?  Then use the default.*/
if   digs=='' |   digs==","  then   digs=   15   /* "      "         "   "   "     "    */
if datatype(seed, 'W')  then call random ,,seed  /*allows repeatability for RANDOM nums.*/
numeric digits digs                              /*use a specific number of decimal digs*/
names= 'aleph beth gimel daleth he waw zayin heth ───totals───►'   /*names of the cells.*/
hi= 100000                                                         /*max REXX RANDOM num*/
z= words(names);             #= z - 1            /*#: the number of actual/usable names.*/
\$= 0                                             /*initialize sum of the probabilities. */
do n=1  for #;    prob.n= 1 / (n+4);     if n==#  then prob.n= 1759 / 27720
\$= \$ + prob.n;   Hprob.n= prob.n * hi /*spread the range of probabilities.   */
end   /*n*/
prob.z= \$                                        /*define the value of the ───totals───.*/
@.= 0                                            /*initialize all counters in the range.*/
@.z= trials                                      /*define the last counter of  "    "   */
do j=1  for trials;    r= random(hi)  /*gen  TRIAL  number of random numbers.*/
do k=1  for #                     /*for each cell, compute  percentages. */
if r<=Hprob.k  then @.k= @.k + 1  /* "    "    "  range, bump the counter*/
end   /*k*/
end       /*j*/
_= '═'                                           /*_:  padding used by the  CENTER  BIF.*/
w= digs + 6                                      /*W:  display width for the percentages*/
d= 4 + max( length(trials), length('count') )    /* [↓]  display a formatted top header.*/
say center('name',15,_)  center('count',d,_) center('target %',w,_) center('actual %',w,_)

do cell=1  for z                            /*display each of the cells and totals.*/
say  ' '   left( word(names, cell), 13)             right(@.cell, d-2)  " " ,
left( format(   prob.cell   * 100, d),   w-2) ,
left( format( @.cell/trials * 100, d),   w-2)   /* [↓]  foot title. [↓] */
if cell==#  then say  center(_,15,_)   center(_,d,_)    center(_,w,_)   center(_,w,_)
end   /*c*/                                 /*stick a fork in it,  we are all done.*/
```
output   when using the default input:
```═════name══════ ═══count═══ ══════target %═══════ ══════actual %═══════
aleph            200135            20                  20.0135
beth             166912            16.6666666          16.6912
gimel            143222            14.2857142          14.3222
daleth           124991            12.5                12.4991
he               111259            11.1111111          11.1259
waw              100049            10                  10.0049
zayin             90978             9.0909090           9.0978
heth              63278             6.3455988           6.3278
═══════════════ ═══════════ ═════════════════════ ═════════════════════
───totals───►   1000000           100                 100
```

## Ring

```# Project : Probabilistic choice

cnt = list(8)
item = ["aleph","beth","gimel","daleth","he","waw","zayin","heth"]
prob  = [1/5.0, 1/6.0, 1/7.0, 1/8.0, 1/9.0, 1/10.0, 1/11.0, 1759/27720]

for trial = 1 to 1000000
r = random(10)/10
p = 0
for i = 1 to len(prob)
p = p + prob[i]
if r < p
cnt[i] = cnt[i] + 1
loop
ok
next
next

see "item     actual    theoretical" + nl
for i = 1 to len(item)
see "" + item[i] + "    " + cnt[i]/1000000 + "    " + prob[i] + nl
next```

Output:

```item     actual      theoretical
aleph    0.091307    0.200000
beth     0.181073    0.166667
gimel    0.181884    0.142857
daleth   0.090985    0.125000
he       0.090958    0.111111
waw      0.091064    0.100000
zayin    0.091061    0.090909
heth     0           0.063456
```

## Ruby

```probabilities = {
"aleph"  => 1/5.0,
"beth"   => 1/6.0,
"gimel"  => 1/7.0,
"daleth" => 1/8.0,
"he"     => 1/9.0,
"waw"    => 1/10.0,
"zayin"  => 1/11.0,
}
probabilities["heth"] = 1.0 - probabilities.each_value.inject(:+)
ordered_keys = probabilities.keys

sum, sums = 0.0, {}
ordered_keys.each do |key|
sum += probabilities[key]
sums[key] = sum
end

actual = Hash.new(0)

samples = 1_000_000
samples.times do
r = rand
for k in ordered_keys
if r < sums[k]
actual[k] += 1
break
end
end
end

puts  "key     expected    actual        diff"
for k in ordered_keys
act = Float(actual[k]) / samples
val = probabilities[k]
printf "%-8s%.8f  %.8f  %6.3f %%\n", k, val, act, 100*(act-val)/val
end
```
Output:
```key     expected    actual        diff
aleph   0.20000000  0.19949200  -0.254 %
beth    0.16666667  0.16689900   0.139 %
gimel   0.14285714  0.14309300   0.165 %
daleth  0.12500000  0.12494200  -0.046 %
he      0.11111111  0.11037800  -0.660 %
waw     0.10000000  0.10030100   0.301 %
zayin   0.09090909  0.09162700   0.790 %
heth    0.06345599  0.06326800  -0.296 %
```

## Rust

```extern crate rand;

use rand::distributions::{IndependentSample, Sample, Weighted, WeightedChoice};
use rand::{weak_rng, Rng};

const DATA: [(&str, f64); 8] = [
("aleph", 1.0 / 5.0),
("beth", 1.0 / 6.0),
("gimel", 1.0 / 7.0),
("daleth", 1.0 / 8.0),
("he", 1.0 / 9.0),
("waw", 1.0 / 10.0),
("zayin", 1.0 / 11.0),
("heth", 1759.0 / 27720.0),
];

const SAMPLES: usize = 1_000_000;

/// Generate a mapping to be used by `WeightedChoice`
fn gen_mapping() -> Vec<Weighted<usize>> {
DATA.iter()
.enumerate()
.map(|(i, &(_, p))| Weighted {
// `WeightedChoice` requires `u32` weights rather than raw probabilities.  For each
// probability, we convert it to a `u32` weight, and associate it with an index. We
// multiply by a constant because small numbers such as 0.2 when casted to `u32`
// become `0`.  This conversion decreases the accuracy of the mapping, which is why we
// provide an implementation which uses `f64`s for the best accuracy.
weight: (p * 1_000_000_000.0) as u32,
item: i,
})
.collect()
}

/// Generate a mapping of the raw probabilities
fn gen_mapping_float() -> Vec<f64> {
// This does the work of `WeightedChoice::new`, splitting a number into various ranges.  The
// `item` of `Weighted` is represented here merely by the probability's position in the `Vec`.
let mut running_total = 0.0;
DATA.iter()
.map(|&(_, p)| {
running_total += p;
running_total
})
.collect()
}

/// An implementation of `WeightedChoice` which uses probabilities rather than weights.  Refer to
/// the `WeightedChoice` source for serious usage.
struct WcFloat {
mapping: Vec<f64>,
}

impl WcFloat {
fn new(mapping: &[f64]) -> Self {
Self {
mapping: mapping.to_vec(),
}
}

// This is roughly the same logic as `WeightedChoice::ind_sample` (though is likely slower)
fn search(&self, sample_prob: f64) -> usize {
let idx = self.mapping
.binary_search_by(|p| p.partial_cmp(&sample_prob).unwrap());
match idx {
Ok(i) | Err(i) => i,
}
}
}

impl IndependentSample<usize> for WcFloat {
fn ind_sample<R: Rng>(&self, rng: &mut R) -> usize {
// Because we know the total is exactly 1.0, we can merely use a raw float value.
// Otherwise caching `Range::new(0.0, running_total)` and sampling with
// `range.ind_sample(&mut rng)` is recommended.
let sample_prob = rng.next_f64();
self.search(sample_prob)
}
}

impl Sample<usize> for WcFloat {
fn sample<R: Rng>(&mut self, rng: &mut R) -> usize {
self.ind_sample(rng)
}
}

fn take_samples<R: Rng, T>(rng: &mut R, wc: &T) -> [usize; 8]
where
T: IndependentSample<usize>,
{
let mut counts = [0; 8];
for _ in 0..SAMPLES {
let sample = wc.ind_sample(rng);
counts[sample] += 1;
}
counts
}

fn print_mapping(counts: &[usize]) {
println!("Item   | Expected | Actual   ");
println!("-------+----------+----------");
for (&(name, expected), &count) in DATA.iter().zip(counts.iter()) {
let real = count as f64 / SAMPLES as f64;
println!("{:6} | {:.6} | {:.6}", name, expected, real);
}
}

fn main() {
let mut rng = weak_rng();

println!("    ~~~ U32 METHOD ~~~");
let mut mapping = gen_mapping();
let wc = WeightedChoice::new(&mut mapping);

let counts = take_samples(&mut rng, &wc);
print_mapping(&counts);

println!();

println!("   ~~~ FLOAT METHOD ~~~");
// initialize the float version of `WeightedChoice`
let mapping = gen_mapping_float();
let wc = WcFloat::new(&mapping);

let counts = take_samples(&mut rng, &wc);
print_mapping(&counts);
}
```
Output:
```    ~~~ U32 METHOD ~~~
Item   | Expected | Actual
-------+----------+----------
aleph  | 0.200000 | 0.200195
beth   | 0.166667 | 0.166182
gimel  | 0.142857 | 0.142502
daleth | 0.125000 | 0.125503
he     | 0.111111 | 0.110820
waw    | 0.100000 | 0.100166
zayin  | 0.090909 | 0.090927
heth   | 0.063456 | 0.063705

~~~ FLOAT METHOD ~~~
Item   | Expected | Actual
-------+----------+----------
aleph  | 0.200000 | 0.199984
beth   | 0.166667 | 0.166634
gimel  | 0.142857 | 0.143218
daleth | 0.125000 | 0.124956
he     | 0.111111 | 0.111047
waw    | 0.100000 | 0.099805
zayin  | 0.090909 | 0.090513
heth   | 0.063456 | 0.063843```

## Scala

This algorithm consists of a concise two-line tail-recursive loop (def weighted). The rest of the code is for API robustness, testing and display. weightedProb is for the task as stated (0 < p < 1), and weightedFreq is the equivalent based on integer frequencies (f >= 0).

```object ProbabilisticChoice extends App {

def weightedProb[A](prob: LinkedHashMap[A,Double]): A = {
require(prob.forall{case (_, p) => p > 0 && p < 1})
assume(prob.values.sum == 1)
def weighted(todo: Iterator[(A,Double)], rand: Double, accum: Double = 0): A = todo.next match {
case (s, i) if rand < (accum + i) => s
case (_, i) => weighted(todo, rand, accum + i)
}
weighted(prob.toIterator, scala.util.Random.nextDouble)
}

def weightedFreq[A](freq: LinkedHashMap[A,Int]): A = {
require(freq.forall{case (_, f) => f >= 0})
require(freq.values.sum > 0)
def weighted(todo: Iterator[(A,Int)], rand: Int, accum: Int = 0): A = todo.next match {
case (s, i) if rand < (accum + i) => s
case (_, i) => weighted(todo, rand, accum + i)
}
weighted(freq.toIterator, scala.util.Random.nextInt(freq.values.sum))
}

// Tests:

'aleph  -> 1.0/5,
'beth   -> 1.0/6,
'gimel  -> 1.0/7,
'daleth -> 1.0/8,
'he     -> 1.0/9,
'waw    -> 1.0/10,
'zayin  -> 1.0/11,
'heth   -> 1759.0/27720
)

'aleph  -> 200,
'beth   -> 167,
'gimel  -> 143,
'daleth -> 125,
'he     -> 111,
'waw    -> 100,
'zayin  -> 91,
'heth   -> 63
)

def check[A](original: LinkedHashMap[A,Double], results: Seq[A]) {
val freq = results.groupBy(x => x).mapValues(_.size.toDouble/results.size)
original.foreach{case (k, v) =>
val a = v/original.values.sum
val b = freq(k)
val c = if (Math.abs(a - b) < 0.001) "ok" else "**"
println(f"\$k%10s  \$a%.4f  \$b%.4f  \$c")
}
println(" "*10 + f"  \${1}%.4f  \${freq.values.sum}%.4f")
}

println("Checking weighted probabilities:")
check(probabilities, for (i <- 1 to 1000000) yield weightedProb(probabilities))
println
println("Checking weighted frequencies:")
check(frequencies.map{case (a, b) => a -> b.toDouble}, for (i <- 1 to 1000000) yield weightedFreq(frequencies))
}
```
Output:
```Checking weighted probabilities:
'aleph  0.2000  0.2001  ok
'beth  0.1667  0.1665  ok
'gimel  0.1429  0.1430  ok
'daleth  0.1250  0.1248  ok
'he  0.1111  0.1112  ok
'waw  0.1000  0.1000  ok
'zayin  0.0909  0.0911  ok
'heth  0.0635  0.0632  ok
1.0000  1.0000

Checking weighted frequencies:
'aleph  0.2000  0.2000  ok
'beth  0.1670  0.1672  ok
'gimel  0.1430  0.1432  ok
'daleth  0.1250  0.1243  ok
'he  0.1110  0.1105  ok
'waw  0.1000  0.1002  ok
'zayin  0.0910  0.0913  ok
'heth  0.0630  0.0632  ok
1.0000  1.0000```

## Scheme

Using guile scheme 2.0.11.

```(use-modules (ice-9 format))

(define (random-choice probs)
(define choice (random 1.0))
(define (helper val prob-lis)
(let ((nval (- val (cadar prob-lis))))
(if
(< nval 0)
(caar prob-lis)
(helper nval (cdr prob-lis)))))
(helper choice probs))

(cond
((null? table) (list (list result delta)))
((eq? (caar table) result)
(cons (list result (+ (cadar table) delta)) (cdr table)))
(#t (cons (car table) (add-result result delta (cdr table))))))

(define (choices trials probs)
(define (helper trial-num freq-table)
(if
(= trial-num trials)
freq-table
(helper
(+ trial-num 1)
(add-result (random-choice probs) (/ 1 trials) freq-table))))
(helper 0 '()))

(define (format-results probs results)
(for-each
(lambda (x)
(format
#t
"~10a~10,5f~10,5f~%"
(car x)
probs))

(define probs
'((aleph 1/5) (beth 1/6) (gimel 1/7) (daleth 1/8)
(he 1/9) (waw 1/10) (zayin 1/11) (heth 1759/27720)))

(format-results probs (choices 1000000 probs))
```

Example output:

```aleph        0.20000   0.20051
beth         0.16667   0.16680
gimel        0.14286   0.14231
daleth       0.12500   0.12538
he           0.11111   0.11136
waw          0.10000   0.09955
zayin        0.09091   0.09096
heth         0.06346   0.06313
```

## Seed7

To reduce the runtime this program should be compiled.

```\$ include "seed7_05.s7i";
include "float.s7i";

const type: letter is new enum
aleph, beth, gimel, daleth, he, waw, zayin, heth
end enum;

const func string: str (in letter: aLetter) is
return [] ("aleph", "beth", "gimel", "daleth", "he", "waw", "zayin", "heth") [succ(ord(aLetter))];

enable_output(letter);

const array [letter] integer: table is [letter] (
5544, 4620, 3960, 3465, 3080, 2772, 2520, 1759);

const func letter: randomLetter is func
result
var letter: resultLetter is aleph;
local
var integer: number is 0;
begin
number := rand(1, 27720);
while number > table[resultLetter] do
number -:= table[resultLetter];
incr(resultLetter);
end while;
end func;

const proc: main is func
local
var integer: count is 0;
var letter: aLetter is aleph;
var array [letter] integer: occurrence is letter times 0;
begin
for count range 1 to 1000000 do
aLetter := randomLetter;
incr(occurrence[aLetter]);
end for;
writeln("Name   Count  Ratio    Expected");
for aLetter range letter.first to letter.last do
flt(occurrence[aLetter]) / 10000.9 digits 4 lpad 8 <& "%" <&
100.0 * flt(table[aLetter]) / 27720.0 digits 4 lpad 8 <& "%");
end for;
end func;```

Outout:

```Name   Count  Ratio    Expected
aleph  199788 19.9770% 20.0000%
beth   166897 16.6882% 16.6667%
gimel  143103 14.3090% 14.2857%
daleth 125060 12.5049% 12.5000%
he     110848 11.0838% 11.1111%
waw     99550  9.9541% 10.0000%
zayin   90918  9.0910%  9.0909%
heth    63836  6.3830%  6.3456%
```

## Sidef

Translation of: Perl
```define TRIALS = 1e4;

func prob_choice_picker(options) {
var n = 0;
var a = [];
options.each { |k,v|
n += v;
a << [n, k];
}
func {
var r = 1.rand;
a.first{|e| r <= e[0] }[1];
}
}

var ps = Hash(
aleph  => 1/5,
beth   => 1/6,
gimel  => 1/7,
daleth => 1/8,
he     => 1/9,
waw    => 1/10,
zayin  => 1/11
)

ps{:heth} = (1 - ps.values.sum)

var picker = prob_choice_picker(ps)
var results = Hash()

TRIALS.times {
results{picker()} := 0 ++;
}

say "Event   Occurred  Expected  Difference";
for k,v in (results.sort_by {|k| results{k} }.reverse) {
printf("%-6s  %f  %f  %f\n",
k, v/TRIALS, ps{k},
abs(v/TRIALS - ps{k})
);
}
```
Output:
```Event   Occurred  Expected  Difference
aleph   0.196300  0.200000  0.003700
beth    0.165600  0.166667  0.001067
gimel   0.143700  0.142857  0.000843
daleth  0.123900  0.125000  0.001100
he      0.111800  0.111111  0.000689
waw     0.101900  0.100000  0.001900
zayin   0.088100  0.090909  0.002809
heth    0.068800  0.063456  0.005344
```

## SparForte

As a structured script.

```#!/usr/local/bin/spar
pragma annotate( summary, "randdist" )
@( description, "Given a mapping between items and their required" )
@( description, "probability of occurrence, generate a million items" )
@( description, "randomly subject to the given probabilities and compare" )
@( description, "the target probability of occurrence versus the" )
@( description, "generated values." )
@( description, "" )
@( description, "The total of all the probabilities should equal one." )
@( description, "(Because floating point arithmetic is involved this is" )
@( description, "subject to rounding errors).  Use the following mapping" )
@( description, "to test your programs: aleph 1/5.0, beth 1/6.0," )
@( description, "gimel 1/7.0, daleth 1/8.0, he 1/9.0,  waw 1/10.0" )
@( description, "zayin 1/11.0, heth 1759/27720 adjusted so that" )
@( description, "probabilities add to 1" )
@( see_also, "http://rosettacode.org/wiki/Probabilistic_choice" )
@( author, "Ken O. Burtch" );

pragma restriction( no_external_commands );

procedure randdist is
trials : constant positive := 1_000_000;
type outcome is (aleph, beth, gimel, daleth, he, waw, zayin, heth);
pr : constant array(aleph..heth) of float :=
(1/5, 1/6, 1/7, 1/8, 1/9, 1/10, 1/11, 1 );
samples : array(aleph..heth) of natural := (0, 0, 0, 0, 0, 0, 0, 0);
random_value : float;
begin
for try in 1..trials loop
random_value := numerics.random;
for i in arrays.first( pr )..arrays.last( pr ) loop
if random_value <= pr(i) then
samples(i) := samples(i) + 1;
exit;
else
random_value := @ - pr(i);
end if;
end loop;
end loop;
-- Show results
for i in arrays.first( pr )..arrays.last( pr ) loop
put( i ) @ ( " " ) @ ( float( samples( i ) ) / float( trials ) );
if i = heth then
put_line( " rest" );
else
put_line( pr(i) );
end if;
end loop;
end randdist;
```
Output:
```\$ spar randdist
aleph  2.00260000000000E-01 2.00000000000000E-01
beth  1.66376000000000E-01 1.66666666666667E-01
gimel  1.42698000000000E-01 1.42857142857143E-01
daleth  1.25408000000000E-01 1.25000000000000E-01
he  1.11311000000000E-01 1.11111111111111E-01
waw  9.97980000000000E-02 1.00000000000000E-01
zayin  9.09570000000000E-02 9.09090909090909E-02
heth  6.31920000000000E-02 rest```

## Stata

```clear
mata
letters="aleph","beth","gimel","daleth","he","waw","zayin","heth"
a=letters[rdiscrete(10000,1,(1/5,1/6,1/7,1/8,1/9,1/10,1/11,1759/27720))]'
st_sstore(.,.,a)
end
```

## Tcl

```package require Tcl 8.5

set map [dict create]
set sum 0.0

foreach name {aleph beth gimel daleth he waw zayin} \
prob {1/5.0 1/6.0 1/7.0 1/8.0 1/9.0 1/10.0 1/11.0} \
{
set prob [expr \$prob]
set sum [expr {\$sum + \$prob}]
dict set map \$name [dict create probability \$prob limit \$sum count 0]
}
dict set map heth [dict create probability [expr {1.0 - \$sum}] limit 1.0 count 0]

set samples 1000000
for {set i 0} {\$i < \$samples} {incr i} {
set n [expr {rand()}]
foreach name [dict keys \$map] {
if {\$n <= [dict get \$map \$name limit]} {
set count [dict get \$map \$name count]
dict set map \$name count [incr count]
break
}
}
}

puts "using \$samples samples:"
puts [format "%-10s %-21s %-9s %s" "" expected actual difference]

dict for {name submap} \$map {
dict with submap {
set actual [expr {\$count * 1.0 / \$samples}]
puts [format "%-10s %-21s %-9s %4.2f%%" \$name \$probability \$actual \
[expr {abs(\$actual - \$probability)/\$probability*100.0}]
]
}
}
```
```using 1000000 samples:
expected              actual    difference
aleph      0.2                   0.199641  0.18%
beth       0.16666666666666666   0.1674    0.44%
gimel      0.14285714285714285   0.143121  0.18%
daleth     0.125                 0.124864  0.11%
he         0.1111111111111111    0.111036  0.07%
waw        0.1                   0.100021  0.02%
zayin      0.09090909090909091   0.09018   0.80%
heth       0.06345598845598843   0.063737  0.44%```

## Ursala

The stochasm library function used here constructs a weighted non-deterministic choice of a set of functions. The pseudo-random number generator is a 64 bit Mersenne twistor implemented by the run time system.

```#import std
#import nat
#import flo

outcomes = <'aleph ','beth  ','gimel ','daleth','he    ','waw   ','zayin ','heth  '>
probabilities = ^lrNCT(~&,minus/1.+ plus:-0) div/*1. float* skip/5 iota12

simulation =

^(~&rn,div+ float~~rmPlX)^*D/~& iota; ^A(~&h,length)*K2+ * stochasm@p/probabilities !* outcomes

format =

:/'        frequency   probability'+  * ^lrlrTPT/~&n (printf/'%12.8f')^~/~&m outcomes-\$probabilities@n

#show+

results = format simulation 1000000```

output:

```        frequency   probability
daleth  0.12484500  0.12500000
beth    0.16680600  0.16666667
aleph   0.19973700  0.20000000
waw     0.10016900  0.10000000
gimel   0.14293100  0.14285714
he      0.11131100  0.11111111
zayin   0.09104700  0.09090909
heth    0.06315400  0.06345599```

## VBScript

Derived from the BBC BASIC version

```item = Array("aleph","beth","gimel","daleth","he","waw","zayin","heth")
prob = Array(1/5.0, 1/6.0, 1/7.0, 1/8.0, 1/9.0, 1/10.0, 1/11.0, 1759/27720)
Dim cnt(7)

'Terminate script if sum of probabilities <> 1.
sum = 0
For i = 0 To UBound(prob)
sum = sum + prob(i)
Next

If sum <> 1 Then
WScript.Quit
End If

For trial = 1 To 1000000
r = Rnd(1)
p = 0
For i = 0 To UBound(prob)
p = p + prob(i)
If r < p Then
cnt(i) = cnt(i) + 1
Exit For
End If
Next
Next

WScript.StdOut.Write "item" & vbTab & "actual" & vbTab & vbTab & "theoretical"
WScript.StdOut.WriteLine
For i = 0 To UBound(item)
WScript.StdOut.Write item(i) & vbTab & FormatNumber(cnt(i)/1000000,6) & vbTab & FormatNumber(prob(i),6)
WScript.StdOut.WriteLine
Next
```
Output:
```item	actual		theoretical
aleph	0.199755	0.200000
beth	0.166861	0.166667
gimel	0.143240	0.142857
daleth	0.124474	0.125000
he	0.110879	0.111111
waw	0.100341	0.100000
zayin	0.090745	0.090909
heth	0.063705	0.063456
```

## Wren

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

var letters  = ["aleph", "beth", "gimel", "daleth", "he", "waw", "zayin", "heth"]
var actual   = [0] * 8
var probs    = [1/5, 1/6, 1/7, 1/8, 1/9, 1/10, 1/11, 0]
var cumProbs = [0] * 8

cumProbs[0] = probs[0]
for (i in 1..6) cumProbs[i] = cumProbs[i-1] + probs[i]
cumProbs[7] = 1
probs[7] = 1 - cumProbs[6]
var n = 1e6
var rand = Random.new()
(1..n).each { |i|
var r = rand.float()
var index = (r <= cumProbs[0]) ? 0 :
(r <= cumProbs[1]) ? 1 :
(r <= cumProbs[2]) ? 2 :
(r <= cumProbs[3]) ? 3 :
(r <= cumProbs[4]) ? 4 :
(r <= cumProbs[5]) ? 5 :
(r <= cumProbs[6]) ? 6 : 7
actual[index] = actual[index] + 1
}

var sumActual = 0
System.print("Letter\t Actual    Expected")
System.print("------\t--------   --------")
for (i in 0..7) {
var generated = actual[i]/n
Fmt.print("\$s\t\$8.6f   \$8.6f", letters[i], generated, probs[i])
sumActual = sumActual + generated
}
System.print("\t--------   --------")
Fmt.print("\t\$8.6f   1.000000", sumActual)
```
Output:

Sample run:

```Letter	 Actual    Expected
------	--------   --------
aleph	0.200037   0.200000
beth	0.166643   0.166667
gimel	0.143012   0.142857
daleth	0.125219   0.125000
he	0.111183   0.111111
waw	0.099510   0.100000
zayin	0.091015   0.090909
heth	0.063381   0.063456
--------   --------
1.000000   1.000000
```

## XPL0

```include c:\cxpl\codes;
def Size = 10_000_000;
int Tbl(12+1);
int I, J, N;
real X, S0, S1;
[for J:= 5 to 12 do Tbl(J):= 0;
for I:= 0 to 1_000_000-1 do                    \generate one million items
[N:= Ran(Size);
for J:= 5 to 11 do
[N:= N - Size/J;
if N < 0 then [Tbl(J):= Tbl(J)+1;  J:= 100];
];
if J=12 then Tbl(12):= Tbl(12)+1;
];
S0:= 0.0;  S1:= 0.0;
for J:= 5 to 11 do
[X:= 1.0/float(J);                      RlOut(0, X);  S0:= S0+X;
X:= float(Tbl(J)) / 1_000_000.0;       RlOut(0, X);  S1:= S1+X;
CrLf(0);
];
X:= 1759.0 / 27720.0;                           RlOut(0, X);  S0:= S0+X;
X:= float(Tbl(12)) / 1_000_000.0;               RlOut(0, X);  S1:= S1+X;
CrLf(0);
Text(0, "    -------    -------
");
RlOut(0, S0);  RlOut(0, S1);
]```

Output:

```    0.20000    0.20012
0.16667    0.16679
0.14286    0.14305
0.12500    0.12510
0.11111    0.11113
0.10000    0.09990
0.09091    0.09077
0.06346    0.06313
-------    -------
1.00000    1.00000
```

## Yabasic

```dim letters\$(7)
data "aleph", "beth", "gimel", "daleth", "he", "waw", "zayin", "heth"
letters\$(0) = "aleph"
letters\$(1) = "beth"
letters\$(2) = "gimel"
letters\$(3) = "daleth"
letters\$(4) = "he"
letters\$(5) = "waw"
letters\$(6) = "zayin"
letters\$(7) = "heth"
dim actual(7)
dim probs(7)
probs(0) = 1/5.0
probs(1) = 1/6.0
probs(2) = 1/7.0
probs(3) = 1/8.0
probs(4) = 1/9.0
probs(5) = 1/10.0
probs(6) = 1/11.0
probs(7) = 1759/27720
dim cumProbs(7)

cumProbs(0) = probs(0)
for i = 1 to 6
cumProbs(i) = cumProbs(i - 1) + probs(i)
next i
cumProbs(7) = 1.0

n = 1000000
for test = 1 to n
r = ran(1)
p = 0.0
for i = 1 to arraysize(probs(),1)
p = p + probs(i)
if r < p then
actual(i) = actual(i) + 1
break
end if
next i
next t

sumActual = 0.0
tab\$ = chr\$(9)
print "Letter          Actual          Expected"
print "------          --------        --------"
for i = 0 to 7
print letters\$(i), tab\$,
print actual(i)/n using "#.######",
sumActual = sumActual + actual(i)/n
print probs(i) using "#.######"
next i

print "                --------        --------"
print "                ", sumActual using "#.######", tab\$, "1.000000"
end```

## zkl

Translation of: C
```var names=T("aleph", "beth", "gimel", "daleth",
"he", "waw", "zayin", "heth");
var ptable=T(5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0).apply('/.fp(1.0));
ptable=ptable.append(1.0-ptable.sum(0.0)); // add last weight to sum to 1.0
var [const] N=ptable.len();

fcn ridx{ i:=0; s:=(0.0).random(1);
while((s-=ptable[i]) > 0) { i+=1 }
i
}

const M=0d1_000_000;
var r=(0).pump(N,List,T(Ref,0));  // list of references to int 0
(0).pump(M,Void,fcn{r[ridx()].inc()}); // 1,000,000 weighted random #s

r=r.apply("value").apply("toFloat"); // (reference to int)-->int-->float

println("  Name  Count    Ratio Expected");
foreach i in (N){
"%6s%7d %7.4f%% %7.4f%%".fmt(names[i], r[i], r[i]/M*100,
ptable[i]*100).println();
}```
Output:
```  Name  Count    Ratio Expected
aleph 200214 20.0214% 20.0000%
beth 166399 16.6399% 16.6667%
gimel 143100 14.3100% 14.2857%
daleth 125197 12.5197% 12.5000%
he 111167 11.1167% 11.1111%
waw 100097 10.0097% 10.0000%
zayin  90692  9.0692%  9.0909%
heth  63162  6.3162%  6.3456%
```