Random numbers: Difference between revisions
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==={{header|Applesoft BASIC}}=== |
==={{header|Applesoft BASIC}}=== |
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The [[Commodore_BASIC|Commodore BASIC]] code works in Applesoft BASIC. |
The [[Random_numbers#Commodore_BASIC|Commodore BASIC]] code works in Applesoft BASIC. |
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==={{header|BASIC256}}=== |
==={{header|BASIC256}}=== |
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{{trans|FreeBASIC}} |
{{trans|FreeBASIC}} |
Revision as of 05:35, 13 June 2022
You are encouraged to solve this task according to the task description, using any language you may know.
- Task
Generate a collection filled with 1000 normally distributed random (or pseudo-random) numbers with a mean of 1.0 and a standard deviation of 0.5
Many libraries only generate uniformly distributed random numbers. If so, you may use one of these algorithms.
- Related task
Ada
<lang ada>with Ada.Numerics; use Ada.Numerics; with Ada.Numerics.Float_Random; use Ada.Numerics.Float_Random; with Ada.Numerics.Elementary_Functions; use Ada.Numerics.Elementary_Functions;
procedure Normal_Random is
function Normal_Distribution ( Seed : Generator; Mu : Float := 1.0; Sigma : Float := 0.5 ) return Float is begin return Mu + (Sigma * Sqrt (-2.0 * Log (Random (Seed), 10.0)) * Cos (2.0 * Pi * Random (Seed))); end Normal_Distribution; Seed : Generator; Distribution : array (1..1_000) of Float;
begin
Reset (Seed); for I in Distribution'Range loop Distribution (I) := Normal_Distribution (Seed); end loop;
end Normal_Random;</lang>
ALGOL 68
<lang algol68>PROC random normal = REAL: # normal distribution, centered on 0, std dev 1 # (
sqrt(-2*log(random)) * cos(2*pi*random)
);
test:(
[1000]REAL rands; FOR i TO UPB rands DO rands[i] := 1 + random normal/2 OD; INT limit=10; printf(($"("n(limit-1)(-d.6d",")-d.5d" ... )"$, rands[:limit]))
)</lang>
- Output:
( 0.693461, 0.948424, 0.482261, 1.045939, 0.890818, 1.467935, 0.604153, 0.804811, 0.690227, 0.83462 ... )
Arturo
<lang rebol>rnd: function []-> (random 0 10000)//10000
rands: map 1..1000 'x [
1 + (sqrt neg 2 * ln rnd) * (cos 2 * pi * rnd)
]
print rands</lang>
- Output:
0.6219537961087694 1.279396486161406 1.619019280815647 2.119538294228789 -0.1598383851981044 2.67797211803156 0.9304703037226587 1.629254364659528 -0.4171704717398712 0.9082342931486092 -0.5929704390625219 2.117000897984871 -0.1981633787460266 0.01132471973856153 2.102359263212924 0.2408823232884222 2.046195035792376 0.6374831627030295 0.000839808324124558 1.117061838266626 0.7413355299469649 0.4485598815755762 2.999434800016997 2.560580541932842 1.703197984879731 2.889159248353575 -1.800157205708138 1.756810020187321 0.7136708180852145 0.5929151678321705 0.332519993787973 2.660212054362758 0.5835660585480075 0.8527946892567934 1.640573993747053 0.09471843345263908 1.051402997891346 1.116149156137905 -0.7400139019343499 1.782572831979232 2.531779039786426 0.5240268064639871 0.07099232630526586 -0.854892656700071 1.54381929430469 -0.4416899008614745 0.4274356035015117 0.7350027625573482 2.153583935076981 1.461215281535983 -1.041723064151266 2.338060763553139 -0.1492967916030414 0.3799517724040202 0.4577924541353815 0.673317567666373 -2.27731583876462 1.28480355806061 -0.6925811023772748 -0.2642224122781984 0.6590513830891744 2.55537133425143 1.67933335469247 0.8659013395355968 -1.211026941441126 0.9524579534222226 -0.1931750631835656 -0.5119479869237693 0.1814749003063878 3.03139579963414 ...
AutoHotkey
contributed by Laszlo on the ahk forum <lang AutoHotkey>Loop 40
R .= RandN(1,0.5) "`n" ; mean = 1.0, standard deviation = 0.5
MsgBox %R%
RandN(m,s) { ; Normally distributed random numbers of mean = m, std.dev = s by Box-Muller method
Static i, Y If (i := !i) { ; every other call Random U, 0, 1.0 Random V, 0, 6.2831853071795862 U := sqrt(-2*ln(U))*s Y := m + U*sin(V) Return m + U*cos(V) } Return Y
}</lang>
Avail
<lang Avail>Method "U(_,_)" is [ lower : number, upper : number | divisor ::= ((1<<32)) ÷ (upper - lower)→double; map a pRNG through [i : integer | (i ÷ divisor) + lower] ];
Method "a Marsaglia polar sampler" is [ generator for [ yield : [double]→⊤ | source ::= U(-1, 1); Repeat [ x ::= take 1 from source[1]; y ::= take 1 from source[1]; s ::= x^2 + y^2; If 0 < s < 1 then [ factor ::= ((-2 × ln s) ÷ s) ^ 0.5; yield(x × factor); yield(y × factor); ]; ] ] ];
// the default distribution has mean 0 and std dev 1.0, so we scale the values sampler ::= map a Marsaglia polar sampler through [d : double | d ÷ 2.0 + 1.0]; values ::= take 1000 from sampler;</lang>
AWK
One-liner: <lang awk>$ awk 'func r(){return sqrt(-2*log(rand()))*cos(6.2831853*rand())}BEGIN{for(i=0;i<1000;i++)s=s" "1+0.5*r();print s}'</lang>
Readable version: <lang awk> function r() {
return sqrt( -2*log( rand() ) ) * cos(6.2831853*rand() )
}
BEGIN {
n=1000 for(i=0;i<n;i++) { x = 1 + 0.5*r() s = s" "x } print s
} </lang>
- Output:
first few values only
0.783753 1.16682 1.17989 1.14975 1.34784 0.29296 0.979227 1.04402 0.567835 1.58812 0.465559 1.27186 0.324533 0.725827 -0.0626549 0.632273 1.0145 1.3387 0.861667 1.04147 1.2576 1.02497 0.58453 0.9619 1.26902 0.851048 -0.126259 0.863256
...
BASIC
RANDOMIZE TIMER 'seeds random number generator with the system time pi = 3.141592653589793# DIM a(1 TO 1000) AS DOUBLE CLS FOR i = 1 TO 1000 a(i) = 1 + SQR(-2 * LOG(RND)) * COS(2 * pi * RND) NEXT i
Applesoft BASIC
The Commodore BASIC code works in Applesoft BASIC.
BASIC256
<lang BASIC256># Generates normally distributed random numbers with mean 0 and standard deviation 1 function randomNormal() return cos(2.0 * pi * rand) * sqr(-2.0 * log(rand)) end function
dim r(1000) sum = 0.0
- Generate 1000 normally distributed random numbers
- with mean 1 and standard deviation 0.5
- and calculate their sum
for i = 0 to 999 r[i] = 1.0 + randomNormal() / 2.0 sum += r[i] next i
mean = sum / 1000.0 sum = 0.0
- Now calculate their standard deviation
for i = 0 to 999 sum += (r[i] - mean) ^ 2.0 next i sd = sqr(sum/1000.0)
print "Mean is "; mean print "Standard Deviation is "; sd end</lang>
- Output:
Mean is 1.002092 Standard Deviation is 0.4838570687
Commodore BASIC
<lang bbcbasic> 10 DIM AR(999): DIM DE(999) 20 FOR N = 0 TO 999 30 AR(N)= 0 + SQR(-1.3*LOG(RND(1))) * COS(1.2*PI*RND(1)) 40 NEXT N 50 : 60 REM SUM 70 LET SU = 0 80 FOR N = 0 TO 999 90 LET SU = SU + AR(N) 100 NEXT N 110 : 120 REM MEAN 130 LET ME= 0 140 LET ME = SU/1000 150 : 160 REM DEVIATION 170 FOR N = 0 TO 999 180 T = AR(N)-ME: REM SUBTRACT MEAN FROM NUMBER 190 T = T * T: REM SQUARE THE RESULT 200 DE(N) = T : REM STORE IN DEVIATION ARRAY 210 NEXT N 220 LET DS=0: REM SUM OF DEVIATION ARRAY 230 FOR N = 0 TO 999 240 LET DS = DS + DE(N) 250 NEXT N 260 LET DM=0: REM MEAN OF DEVIATION ARRAY 270 LET DM = DS / 1000 280 LET DE = 0: 290 LET DE = SQR(DM) 300 : 310 PRINT "MEAN = "ME 320 PRINT "STANDARD DEVIATION ="DE 330 END </lang>
BBC BASIC
<lang bbcbasic> DIM array(999)
FOR number% = 0 TO 999 array(number%) = 1.0 + 0.5 * SQR(-2*LN(RND(1))) * COS(2*PI*RND(1)) NEXT mean = SUM(array()) / (DIM(array(),1) + 1) array() -= mean stdev = MOD(array()) / SQR(DIM(array(),1) + 1) PRINT "Mean = " ; mean PRINT "Standard deviation = " ; stdev</lang>
- Output:
Mean = 1.01848064 Standard deviation = 0.503551814
C
<lang c>#include <stdlib.h>
- include <math.h>
- ifndef M_PI
- define M_PI 3.14159265358979323846
- endif
double drand() /* uniform distribution, (0..1] */ {
return (rand()+1.0)/(RAND_MAX+1.0);
} double random_normal() /* normal distribution, centered on 0, std dev 1 */ {
return sqrt(-2*log(drand())) * cos(2*M_PI*drand());
} int main() {
int i; double rands[1000]; for (i=0; i<1000; i++) rands[i] = 1.0 + 0.5*random_normal(); return 0;
}</lang>
C#
<lang csharp> private static double randomNormal() { return Math.Cos(2 * Math.PI * tRand.NextDouble()) * Math.Sqrt(-2 * Math.Log(tRand.NextDouble())); } </lang>
Then the methods in Random numbers#Metafont are used to calculate the average and the Standard Deviation: <lang csharp> static Random tRand = new Random();
static void Main(string[] args) { double[] a = new double[1000];
double tAvg = 0; for (int x = 0; x < a.Length; x++) { a[x] = randomNormal() / 2 + 1; tAvg += a[x]; }
tAvg /= a.Length; Console.WriteLine("Average: " + tAvg.ToString());
double s = 0; for (int x = 0; x < a.Length; x++) { s += Math.Pow((a[x] - tAvg), 2); } s = Math.Sqrt(s / 1000);
Console.WriteLine("Standard Deviation: " + s.ToString());
Console.ReadLine(); } </lang>
An example result:
Average: 1,00510073053613 Standard Deviation: 0,502540443430955
C++
The new C++ standard looks very similar to the Boost library example below.
<lang cpp>#include <random>
- include <functional>
- include <vector>
- include <algorithm>
using namespace std;
int main() {
random_device seed; mt19937 engine(seed()); normal_distribution<double> dist(1.0, 0.5); auto rnd = bind(dist, engine);
vector<double> v(1000); generate(v.begin(), v.end(), rnd); return 0;
}</lang>
<lang cpp>#include <cstdlib> // for rand
- include <cmath> // for atan, sqrt, log, cos
- include <algorithm> // for generate_n
double const pi = 4*std::atan(1.0);
// simple functor for normal distribution class normal_distribution { public:
normal_distribution(double m, double s): mu(m), sigma(s) {} double operator() const // returns a single normally distributed number { double r1 = (std::rand() + 1.0)/(RAND_MAX + 1.0); // gives equal distribution in (0, 1] double r2 = (std::rand() + 1.0)/(RAND_MAX + 1.0); return mu + sigma * std::sqrt(-2*std::log(r1))*std::cos(2*pi*r2); }
private:
const double mu, sigma;
};
int main() {
double array[1000]; std::generate_n(array, 1000, normal_distribution(1.0, 0.5)); return 0;
}</lang>
This example used Mersenne Twister generator. It can be changed by changing the typedef.
<lang cpp>
- include <vector>
- include "boost/random.hpp"
- include "boost/generator_iterator.hpp"
- include <boost/random/normal_distribution.hpp>
- include <algorithm>
typedef boost::mt19937 RNGType; ///< mersenne twister generator
int main() {
RNGType rng; boost::normal_distribution<> rdist(1.0,0.5); /**< normal distribution with mean of 1.0 and standard deviation of 0.5 */
boost::variate_generator< RNGType, boost::normal_distribution<> > get_rand(rng, rdist);
std::vector<double> v(1000); generate(v.begin(),v.end(),get_rand); return 0;
} </lang>
Clojure
<lang lisp>(import '(java.util Random)) (def normals
(let [r (Random.)] (take 1000 (repeatedly #(-> r .nextGaussian (* 0.5) (+ 1.0))))))</lang>
COBOL
<lang COBOL>
IDENTIFICATION DIVISION. PROGRAM-ID. RANDOM. AUTHOR. Bill Gunshannon INSTALLATION. Home. DATE-WRITTEN. 14 January 2022. ************************************************************ ** Program Abstract: ** Able to get the Mean to be really close to 1.0 but ** couldn't get the Standard Deviation any closer than ** .3 to .4. ************************************************************ DATA DIVISION. WORKING-STORAGE SECTION. 01 Sample-Size PIC 9(5) VALUE 1000. 01 Total PIC 9(10)V9(5) VALUE 0.0. 01 Arith-Mean PIC 999V999 VALUE 0.0. 01 Std-Dev PIC 999V999 VALUE 0.0. 01 Seed PIC 999V999. 01 TI PIC 9(8).
01 Idx PIC 99999 VALUE 0. 01 Intermediate PIC 9(10)V9(5) VALUE 0.0. 01 Rnd-Work. 05 Rnd-Tbl OCCURS 1 TO 99999 TIMES DEPENDING ON Sample-Size. 10 Rnd PIC 9V9999999 VALUE 0.0. PROCEDURE DIVISION. Main-Program. ACCEPT TI FROM TIME. MOVE FUNCTION RANDOM(TI) TO Seed. PERFORM WITH TEST AFTER VARYING Idx FROM 1 BY 1 UNTIL Idx = Sample-Size COMPUTE Intermediate = (FUNCTION RANDOM() * 2.01) MOVE Intermediate TO Rnd(Idx) END-PERFORM. PERFORM WITH TEST AFTER VARYING Idx FROM 1 BY 1 UNTIL Idx = Sample-Size COMPUTE Total = Total + Rnd(Idx) END-PERFORM.
COMPUTE Arith-Mean = Total / Sample-Size. DISPLAY "Mean: " Arith-Mean.
PERFORM WITH TEST AFTER VARYING Idx FROM 1 BY 1 UNTIL Idx = Sample-Size COMPUTE Intermediate = Intermediate + (Rnd(Idx) - Arith-Mean) ** 2 END-PERFORM. COMPUTE Std-Dev = Intermediate / Sample-Size.
DISPLAY "Std-Dev: " Std-Dev.
STOP RUN. END PROGRAM RANDOM.
</lang>
Common Lisp
<lang lisp>(loop for i from 1 to 1000
collect (1+ (* (sqrt (* -2 (log (random 1.0)))) (cos (* 2 pi (random 1.0))) 0.5)))</lang>
Crystal
<lang ruby>n, mean, sd, tau = 1000, 1, 0.5, (2 * Math::PI) array = Array.new(n) { mean + sd * Math.sqrt(-2 * Math.log(rand)) * Math.cos(tau * rand) }
mean = array.sum / array.size standev = Math.sqrt( array.sum{ |x| (x - mean) ** 2 } / array.size ) puts "mean = #{mean}, standard deviation = #{standev}"</lang>
- Output:
mean = 1.0093442539237896, standard deviation = 0.504694489463623
D
<lang d>import std.stdio, std.random, std.math;
struct NormalRandom {
double mean, stdDev;
// Necessary because it also defines an opCall. this(in double mean_, in double stdDev_) pure nothrow { this.mean = mean_; this.stdDev = stdDev_; }
double opCall() const /*nothrow*/ { immutable r1 = uniform01, r2 = uniform01; // Not nothrow. return mean + stdDev * sqrt(-2 * r1.log) * cos(2 * PI * r2); }
}
void main() {
double[1000] array; auto nRnd = NormalRandom(1.0, 0.5); foreach (ref x; array) //x = nRnd; x = nRnd();
}</lang>
Alternative Version
(Untested)
<lang d>import tango.math.random.Random;
void main() {
double[1000] list; auto r = new Random(); foreach (ref l; list) { r.normalSource!(double)()(l); l = 1.0 + 0.5 * l; }
}</lang>
Delphi
Delphi has RandG function which generates random numbers with normal distribution using Marsaglia-Bray algorithm:
<lang Delphi>program Randoms;
{$APPTYPE CONSOLE}
uses
Math;
var
Values: array[0..999] of Double; I: Integer;
begin // Randomize; Commented to obtain reproducible results
for I:= Low(Values) to High(Values) do Values[I]:= RandG(1.0, 0.5); // Mean = 1.0, StdDev = 0.5 Writeln('Mean = ', Mean(Values):6:4); Writeln('Std Deviation = ', StdDev(Values):6:4); Readln;
end.</lang>
- Output:
Mean = 1.0098 Std deviation = 0.5016
DWScript
<lang delphi>var values : array [0..999] of Float; var i : Integer;
for i := values.Low to values.High do
values[i] := RandG(1, 0.5);</lang>
E
<lang e>accum [] for _ in 1..1000 { _.with(entropy.nextGaussian()) }</lang>
EasyLang
<lang>for i range 1000
a[] &= 1 + 0.5 * sqrt (-2 * logn randomf) * cos (360 * randomf)
. print a[]</lang>
Eiffel
<lang eiffel> class APPLICATION
inherit ARGUMENTS
create make
feature {NONE} -- Initialization
l_time: TIME l_seed: INTEGER math:DOUBLE_MATH rnd:RANDOM Size:INTEGER once Result:= 1000 end
make -- Run application. local ergebnis:ARRAY[DOUBLE] tavg: DOUBLE x: INTEGER tmp: DOUBLE text : STRING
do -- initialize random generator create l_time.make_now
l_seed := l_time.hour l_seed := l_seed * 60 + l_time.minute l_seed := l_seed * 60 + l_time.second l_seed := l_seed * 1000 + l_time.milli_second create rnd.set_seed (l_seed)
-- initialize random number container and math create ergebnis.make_filled (0.0, 1, size) tavg := 0; create math
from x := 1 until x > ergebnis.count loop tmp := randomNormal / 2 + 1 tavg := tavg + tmp ergebnis.enter (tmp , x) x := x + 1 end
tavg := tavg / ergebnis.count text := "Average: " text.append_double (tavg) text.append ("%N") print(text)
tmp := 0 from x:= 1 until x > ergebnis.count loop tmp := tmp + (ergebnis.item (x) - tavg)^2 x := x + 1 end
tmp := math.sqrt (tmp / ergebnis.count) text := "Standard Deviation: " text.append_double (tmp) text.append ("%N") print(text)
end
randomNormal:DOUBLE
local
first: DOUBLE second: DOUBLE
do
rnd.forth first := rnd.double_item rnd.forth second := rnd.double_item
Result := math.cosine (2 * math.pi * first) * math.sqrt (-2 * math.log (second))
end end </lang>
Example Result
Average: 1.0079398405028137 Standard Deviation: 0.49042787564453988
Elena
ELENA 4.1 : <lang elena>import extensions; import extensions'math;
randomNormal() {
^ cos(2 * Pi_value * randomGenerator.nextReal()) * sqrt(-2 * ln(randomGenerator.nextReal()))
}
public program() {
real[] a := new real[](1000); real tAvg := 0; for (int x := 0, x < a.Length, x += 1) { a[x] := (randomNormal()) / 2 + 1; tAvg += a[x] }; tAvg /= a.Length; console.printLine("Average: ", tAvg); real s := 0; for (int x := 0, x < a.Length, x += 1) { s += power(a[x] - tAvg, 2) }; s := sqrt(s / 1000); console.printLine("Standard Deviation: ", s); console.readChar()
}</lang>
- Output:
Average: 0.9842420481571 Standard Deviation: 0.5109070975558
Elixir
<lang elixir>defmodule Random do
def normal(mean, sd) do {a, b} = {:rand.uniform, :rand.uniform} mean + sd * (:math.sqrt(-2 * :math.log(a)) * :math.cos(2 * :math.pi * b)) end
end
std_dev = fn (list) ->
mean = Enum.sum(list) / length(list) sd = Enum.reduce(list, 0, fn x,acc -> acc + (x-mean)*(x-mean) end) / length(list) |> :math.sqrt IO.puts "Mean: #{mean},\tStdDev: #{sd}" end
xs = for _ <- 1..1000, do: Random.normal(1.0, 0.5) std_dev.(xs)</lang>
- Output:
Mean: 1.009079383094275, StdDev: 0.4991894476975088
used Erlang function :rand.normal
<lang elixir>xs = for _ <- 1..1000, do: 1.0 + :rand.normal * 0.5
std_dev.(xs)</lang>
- Output:
Mean: 0.9955701150615597, StdDev: 0.5036412260426065
Erlang
<lang erlang> mean(Values) ->
mean(tl(Values), hd(Values), 1).
mean([], Acc, Length) ->
Acc / Length;
mean(Values, Acc, Length) ->
mean(tl(Values), hd(Values)+Acc, Length+1).
variance(Values) ->
Mean = mean(Values), variance(Values, Mean, 0) / length(Values).
variance([], _, Acc) ->
Acc;
variance(Values, Mean, Acc) ->
Diff = hd(Values) - Mean, DiffSqr = Diff * Diff, variance(tl(Values), Mean, Acc + DiffSqr).
stddev(Values) ->
math:sqrt(variance(Values)).
normal(Mean, StdDev) ->
U = random:uniform(), V = random:uniform(), Mean + StdDev * ( math:sqrt(-2 * math:log(U)) * math:cos(2 * math:pi() * V) ). % Erlang's math:log is the natural logarithm.
main(_) ->
X = [ normal(1.0, 0.5) || _ <- lists:seq(1, 1000) ], io:format("mean = ~w\n", [mean(X)]), io:format("stddev = ~w\n", [stddev(X)]).
</lang>
- Output:
mean = 1.0118289913718608 stddev = 0.5021636849524854
ERRE
<lang> PROGRAM DISTRIBUTION
! ! for rosettacode.org !
! formulas taken from TI-59 Master Library manual
CONST NUM_ITEM=1000
!VAR SUMX#,SUMX2#,R1#,R2#,Z#,I%
DIM A#[1000]
BEGIN ! seeds random number generator with system time
RANDOMIZE(TIMER)
PRINT(CHR$(12);) !CLS SUMX#=0 SUMX2#=0
FOR I%=1 TO NUM_ITEM DO R1#=RND(1) R2#=RND(1) Z#=SQR(-2*LOG(R1#))*COS(2*π*R2#) A#[I%]=Z#/2+1 ! I want a normal distribution with ! mean=1 and std.dev=0.5 SUMX#+=A#[I%] SUMX2#+=A#[I%]*A#[I%] END FOR
Z#=SUMX#/NUM_ITEM
PRINT("Average is";Z#) PRINT("Standard dev. is";SQR(SUMX2#/NUM_ITEM-Z#*Z#))
END PROGRAM </lang>
Euler Math Toolbox
<lang Euler Math Toolbox> >v=normal(1,1000)*0.5+1; >mean(v), dev(v)
1.00291801071 0.498226876528
</lang>
Euphoria
<lang euphoria>include misc.e
function RandomNormal()
atom x1, x2 x1 = rand(999999) / 1000000 x2 = rand(999999) / 1000000 return sqrt(-2*log(x1)) * cos(2*PI*x2)
end function
constant n = 1000 sequence s s = repeat(0,n) for i = 1 to n do
s[i] = 1 + 0.5 * RandomNormal()
end for</lang>
F#
<lang fsharp> let n = MathNet.Numerics.Distributions.Normal(1.0,0.5) List.init 1000 (fun _->n.Sample()) </lang>
- Output:
[0.734433576; 1.54225304; 0.4407528678; 1.177675412; 0.4318617021; 0.6026656337; 0.769764924; 1.104693934; 0.6297500925; 0.9594598077; 1.684736389; 1.160376323; 0.883354356; 0.9513968363; 0.9727698268; 0.5315570949; 0.9599239266; 1.564976755; 0.7232002879; 1.084139442; 1.220914517; 0.3553085946; 1.112549824; 1.989443553; 0.5752307543; 1.156682549; 0.7886670467; 0.02050745923; 1.532060208; 1.18789591; 1.408946777; 1.038812004; 1.724679503; 1.671565045; 1.266831442; 1.363611654; 1.705819067; 0.5772366328; 0.4503488498; 1.496891481; 0.9831877282; 0.3845460366; 0.8253240671; 1.298969969; 0.4265904553; 0.9303696876; 0.445003361; 0.753175816; 0.6143534043; 1.059982235; 0.7143206784; 0.2233328038; 1.005178481; 0.7697392436; 0.5904948577; 0.5127953044; 0.6467346747; 0.7929387604; -0.1501790761; 0.8750780903; 0.941704369; 1.37941579; 0.4739006145; 1.998886344; 1.219428519; 0.06270791476; 1.097739804; 0.7584232803; 1.042177217; 1.166561247; 1.502357164; 1.171525776; 0.1528807432; 0.2289389756; 1.36208422; 0.3714421124; 1.299571092; 1.171553369; 1.317807265; 1.616662281; 1.724223246; 1.059580642; 1.270520918; -0.1827677907; 1.938593232; 1.420362143; 1.888357595; 0.7851629936; 0.7080554899; 0.7747215818; 1.403719877; 0.5765950249; 1.275206565; 0.6292054813; 1.525562798; 0.6224640457; 0.8524078517; 0.7646595627; 0.6799834691; 0.773111053; ...]
==F# ==
<lang fsharp>let gaussianRand count =
let o = new System.Random() let pi = System.Math.PI let gaussrnd = (fun _ -> 1. + 0.5 * sqrt(-2. * log(o.NextDouble())) * cos(2. * pi * o.NextDouble())) [ for i in {0 .. (int count)} -> gaussrnd() ]</lang>
Factor
<lang factor>USING: random ; 1000 [ 1.0 0.5 normal-random-float ] replicate</lang>
Falcon
<lang falcon>a = [] for i in [0:1000] : a+= norm_rand_num()
function norm_rand_num()
pi = 2*acos(0) return 1 + (cos(2 * pi * random()) * pow(-2 * log(random()) ,1/2)) /2
end</lang>
Fantom
Two solutions. The first uses Fantom's random-number generator, which produces a uniform distribution. So, convert to a normal distribution using a formula:
<lang fantom> class Main {
static const Float PI := 0.0f.acos * 2 // we need to precompute PI
static Float randomNormal () { return (Float.random * PI * 2).cos * (Float.random.log * -2).sqrt }
public static Void main () { mean := 1.0f sd := 0.5f Float[] values := [,] // this is the collection to fill with random numbers 1000.times { values.add (randomNormal * sd + mean) } }
} </lang>
The second calls out to Java's Gaussian random-number generator:
<lang fantom> using [java] java.util::Random
class Main {
Random generator := Random()
Float randomNormal () { return generator.nextGaussian }
public static Void main () { rnd := Main() // create an instance of Main class, which holds the generator mean := 1.0f sd := 0.5f Float[] values := [,] // this is the collection to fill with random numbers 1000.times { values.add (rnd.randomNormal * sd + mean) } }
} </lang>
Forth
<lang forth>require random.fs here to seed
-1. 1 rshift 2constant MAX-D \ or s" MAX-D" ENVIRONMENT? drop
- frnd ( -- f ) \ uniform distribution 0..1
rnd rnd dabs d>f MAX-D d>f f/ ;
- frnd-normal ( -- f ) \ centered on 0, std dev 1
frnd pi f* 2e f* fcos frnd fln -2e f* fsqrt f* ;
- ,normals ( n -- ) \ store many, centered on 1, std dev 0.5
0 do frnd-normal 0.5e f* 1e f+ f, loop ;
create rnd-array 1000 ,normals</lang>
For newer versions of gforth (tested on 0.7.3), it seems you need to use HERE SEED ! instead of HERE TO SEED, because SEED has been made a variable instead of a value.
<lang>rnd rnd dabs d>f</lang> is necessary, but surprising and definitely not well documented / perhaps not compliant.
Fortran
<lang fortran>PROGRAM Random
INTEGER, PARAMETER :: n = 1000 INTEGER :: i REAL :: array(n), pi, temp, mean = 1.0, sd = 0.5
pi = 4.0*ATAN(1.0) CALL RANDOM_NUMBER(array) ! Uniform distribution
! Now convert to normal distribution
DO i = 1, n-1, 2 temp = sd * SQRT(-2.0*LOG(array(i))) * COS(2*pi*array(i+1)) + mean array(i+1) = sd * SQRT(-2.0*LOG(array(i))) * SIN(2*pi*array(i+1)) + mean array(i) = temp END DO
! Check mean and standard deviation
mean = SUM(array)/n sd = SQRT(SUM((array - mean)**2)/n) WRITE(*, "(A,F8.6)") "Mean = ", mean WRITE(*, "(A,F8.6)") "Standard Deviation = ", sd
END PROGRAM Random</lang>
- Output:
Mean = 0.995112 Standard Deviation = 0.503373
Free Pascal
Free Pascal provides the randg function in the RTL math unit that produces Gaussian-distributed random numbers with the Box-Müller algorithm.
<lang pascal> function randg(mean,stddev: float): float; </lang>
FreeBASIC
<lang freebasic>' FB 1.05.0 Win64
Const pi As Double = 3.141592653589793 Randomize
' Generates normally distributed random numbers with mean 0 and standard deviation 1 Function randomNormal() As Double
Return Cos(2.0 * pi * Rnd) * Sqr(-2.0 * Log(Rnd))
End Function
Dim r(0 To 999) As Double Dim sum As Double = 0.0
' Generate 1000 normally distributed random numbers ' with mean 1 and standard deviation 0.5 ' and calculate their sum For i As Integer = 0 To 999
r(i) = 1.0 + randomNormal/2.0 sum += r(i)
Next
Dim mean As Double = sum / 1000.0
Dim sd As Double sum = 0.0 ' Now calculate their standard deviation For i As Integer = 0 To 999
sum += (r(i) - mean) ^ 2.0
Next sd = Sqr(sum/1000.0)
Print "Mean is "; mean Print "Standard Deviation is"; sd Print Print "Press any key to quit" Sleep</lang> Sample result:
- Output:
Mean is 1.000763573902885 Standard Deviation is 0.500653063426955
Frink
<lang frink>a = new array[[1000], {|x| randomGaussian[1, 0.5]}]</lang>
FutureBasic
Note: To generate the random number, rather than using FB's native "rnd" function, this code wraps C code into the RandomZeroToOne function. <lang futurebasic> include "ConsoleWindow"
local fn RandomZeroToOne as double dim as double result BeginCCode
result = (double)( (rand() % 100000 ) * 0.00001 );
EndC end fn = result
local fn RandomGaussian as double dim as double r
r = fn RandomZeroToOne end fn = 1 + .5 * ( sqr( -2 * log(r) ) * cos( 2 * pi * r ) )
dim as long i dim as double mean, std, a(1000)
for i = 1 to 1000
a(i) = fn RandomGaussian mean += a(i)
next mean = mean / 1000
for i = 1 to 1000
std += ( a(i) - mean )^2
next std = std / 1000
print " Average:"; mean print "Standard Deviation:"; std </lang> Output:
Average: 1.0258434498 Standard Deviation: 0.2771047023
Go
This solution uses math/rand package in the standard library. See also though the subrepository rand package at https://godoc.org/golang.org/x/exp/rand, which also has a NormFloat64 and has a rand source with a number of advantages over the one in standard library. <lang go>package main
import (
"fmt" "math" "math/rand" "strings" "time"
)
const mean = 1.0 const stdv = .5 const n = 1000
func main() {
var list [n]float64 rand.Seed(time.Now().UnixNano()) for i := range list { list[i] = mean + stdv*rand.NormFloat64() } // show computed mean and stdv of list var s, sq float64 for _, v := range list { s += v } cm := s / n for _, v := range list { d := v - cm sq += d * d } fmt.Printf("mean %.3f, stdv %.3f\n", cm, math.Sqrt(sq/(n-1))) // show histogram by hdiv divisions per stdv over +/-hrange stdv const hdiv = 3 const hrange = 2 var h [1 + 2*hrange*hdiv]int for _, v := range list { bin := hrange*hdiv + int(math.Floor((v-mean)/stdv*hdiv+.5)) if bin >= 0 && bin < len(h) { h[bin]++ } } const hscale = 10 for _, c := range h { fmt.Println(strings.Repeat("*", (c+hscale/2)/hscale)) }
}</lang>
- Output:
mean 0.995, stdv 0.503 ** **** ****** ******** ************ ************ ************* ************ ********** ******** ***** *** **
Groovy
<lang groovy>rnd = new Random() result = (1..1000).inject([]) { r, i -> r << rnd.nextGaussian() }</lang>
Haskell
<lang haskell>import System.Random
pairs :: [a] -> [(a,a)] pairs (x:y:zs) = (x,y):pairs zs pairs _ = []
gauss mu sigma (r1,r2) =
mu + sigma * sqrt (-2 * log r1) * cos (2 * pi * r2)
gaussians :: (RandomGen g, Random a, Floating a) => Int -> g -> [a] gaussians n g = take n $ map (gauss 1.0 0.5) $ pairs $ randoms g
result :: IO [Double] result = getStdGen >>= \g -> return $ gaussians 1000 g</lang>
Or using Data.Random from random-fu package: <lang haskell>replicateM 1000 $ normal 1 0.5</lang> To print them: <lang haskell>import Data.Random import Control.Monad
thousandRandomNumbers :: RVar [Double] thousandRandomNumbers = replicateM 1000 $ normal 1 0.5
main = do
x <- sample thousandRandomNumbers print x</lang>
HicEst
<lang hicest>REAL :: n=1000, m=1, s=0.5, array(n)
pi = 4 * ATAN(1) array = s * (-2*LOG(RAN(1)))^0.5 * COS(2*pi*RAN(1)) + m </lang>
Icon and Unicon
The seed &random may be assigned in either language; either to randomly seed or to pick a fixed starting point. ?i is the random number generator, returning an integer from 0 to i - 1 for non-zero integer i. As a special case, ?0 yields a random floating point number from 0.0 <= r < 1.0
Note that Unicon randomly seeds it's generator. <lang icon> procedure main()
local L L := list(1000) every L[1 to 1000] := 1.0 + 0.5 * sqrt(-2.0 * log(?0)) * cos(2.0 * &pi * ?0) every write(!L)
end </lang>
IDL
<lang idl>result = 1.0 + 0.5*randomn(seed,1000)</lang>
J
Solution: <lang j>urand=: ?@$ 0: zrand=: (2 o. 2p1 * urand) * [: %: _2 * [: ^. urand
1 + 0.5 * zrand 100</lang>
Alternative Solution:
Using the normal script from the stats/distribs addon.
<lang j> require 'stats/distribs/normal'
1 0.5 rnorm 1000
1.44868803 1.21548637 0.812460657 1.54295452 1.2470606 ...</lang>
Java
<lang java>double[] list = new double[1000]; double mean = 1.0, std = 0.5; Random rng = new Random(); for(int i = 0;i<list.length;i++) {
list[i] = mean + std * rng.nextGaussian();
}</lang>
JavaScript
<lang javascript>function randomNormal() {
return Math.cos(2 * Math.PI * Math.random()) * Math.sqrt(-2 * Math.log(Math.random()))
}
var a = [] for (var i=0; i < 1000; i++){
a[i] = randomNormal() / 2 + 1
}</lang>
jq
Since jq is a purely functional language, it is convenient to define the pseudo-random number generator functions as filters whose inputs and outputs are arrays containing a "seed".
The following uses the same pseudo-random number generator as the Microsoft C Runtime (see Linear congruential generator).
'A Pseudo-Random Number Generator' <lang jq># 15-bit integers generated using the same formula as rand() from the Microsoft C Runtime.
- The random numbers are in [0 -- 32767] inclusive.
- Input: an array of length at least 2 interpreted as [count, state, ...]
- Output: [count+1, newstate, r] where r is the next pseudo-random number.
def next_rand_Microsoft:
.[0] as $count | .[1] as $state | ( (214013 * $state) + 2531011) % 2147483648 # mod 2^31 | [$count+1 , ., (. / 65536 | floor) ] ;</lang>
'Box-Muller Method' <lang jq># Generate a single number following the normal distribution with mean 0, variance 1,
- using the Box-Muller method: X = sqrt(-2 ln U) * cos(2 pi V) where U and V are uniform on [0,1].
- Input: [n, state]
- Output [n+1, nextstate, r]
def next_rand_normal:
def u: next_rand_Microsoft | .[2] /= 32767; u as $u1 | ($u1 | u) as $u2 | ((( (8*(1|atan)) * $u1[2]) | cos) * ((-2 * (($u2[2]) | log)) | sqrt)) as $r | [ (.[0]+1), $u2[1], $r] ;
- Generate "count" arrays, each containing a random normal variate with the given mean and standard deviation.
- Input: [count, state]
- Output: [updatedcount, updatedstate, rnv]
- where "state" is a seed and "updatedstate" can be used as a seed.
def random_normal_variate(mean; sd; count):
next_rand_normal | recurse( if .[0] < count then next_rand_normal else empty end) | .[2] = (.[2] * sd) + mean;</lang>
Example The task can be completed using: [0,1] | random_normal_variate(1; 0.5; 1000) | .[2]
We show just the sample average and standard deviation: <lang jq>def summary:
length as $l | add as $sum | ($sum/$l) as $a | reduce .[] as $x (0; . + ( ($x - $a) | .*. )) | [ $a, (./$l | sqrt)] ;
[ [0,1] | random_normal_variate(1; 0.5; 1000) | .[2] ] | summary</lang>
- Output:
$ jq -n -c -f Random_numbers.jq [0.9932830741018853,0.4977760644490579]
Julia
Julia's standard library provides a randn
function to generate normally distributed random numbers (with mean 0 and standard deviation 0.5, which can be easily rescaled to any desired values):
<lang julia>randn(1000) * 0.5 + 1</lang>
Kotlin
<lang scala>// version 1.0.6
import java.util.Random
fun main(args: Array<String>) {
val r = Random() val da = DoubleArray(1000) for (i in 0 until 1000) da[i] = 1.0 + 0.5 * r.nextGaussian() // now check actual mean and SD val mean = da.average() val sd = Math.sqrt(da.map { (it - mean) * (it - mean) }.average()) println("Mean is $mean") println("S.D. is $sd")
}</lang> Sample output:
- Output:
Mean is 1.0071784073168768 S.D. is 0.48567118114896807
LabVIEW
Liberty BASIC
<lang lb>dim a(1000) mean =1 sd =0.5 for i = 1 to 1000 ' throw 1000 normal variates
a( i) =mean +sd *( sqr( -2 * log( rnd( 0))) * cos( 2 * pi * rnd( 0)))
next i</lang>
Lingo
<lang Lingo>-- Returns a random float value in range 0..1 on randf ()
n = random(the maxinteger)-1 return n / float(the maxinteger-1)
end</lang>
<lang Lingo>normal = [] repeat with i = 1 to 1000
normal.add(1 + sqrt(-2 * log(randf())) * cos(2 * PI * randf()) / 2)
end repeat</lang>
Lobster
Uses built-in rnd_gaussian
<lang Lobster>
let mean = 1.0
let stdv = 0.5
let count = 1000
// stats computes a running mean and variance // See Knuth TAOCP vol 2, 3rd edition, page 232
def stats(xs: [float]) -> float, float: // variance, mean
var M = xs[0] var S = 0.0 var n = 1.0 for(xs.length - 1) i: let x = xs[i + 1] n = n + 1.0 let mm = (x - M) M += mm / n S += mm * (x - M) return (if n > 0.0: S / n else: 0.0), M
def test_random_normal() -> [float]:
rnd_seed(floor(seconds_elapsed() * 1000000)) let r = vector_reserve(typeof return, count) for (count): r.push(rnd_gaussian() * stdv + mean) let cvar, cmean = stats(r) let cstdv = sqrt(cvar) print concat_string(["Mean: ", string(cmean), ", Std.Deviation: ", string(cstdv)], "")
test_random_normal() </lang>
Logo
The earliest Logos only have a RANDOM function for picking a random non-negative integer. Many modern Logos have floating point random generators built-in. <lang logo>to random.float ; 0..1
localmake "max.int lshift -1 -1 output quotient random :max.int :max.int
end
to random.gaussian
output product cos random 360 sqrt -2 / ln random.float
end
make "randoms cascade 1000 [fput random.gaussian / 2 + 1 ?] []</lang>
Lua
<lang lua>local list = {} for i = 1, 1000 do
list[i] = 1 + math.sqrt(-2 * math.log(math.random())) * math.cos(2 * math.pi * math.random()) / 2
end</lang>
M2000 Interpreter
M2000 use a Wichmann - Hill Pseudo Random Number Generator. <lang M2000 Interpreter> Module CheckIt {
Function StdDev (A()) { \\ A() has a copy of values N=Len(A()) if N<1 then Error "Empty Array" M=Each(A()) k=0 \\ make sum, dev same type as A(k) sum=A(k)-A(k) dev=sum \\ find mean While M { sum+=Array(M) } Mean=sum/N \\ make a pointet to A() P=A() \\ subtruct from each item P-=Mean M=Each(P) While M { dev+=Array(M)*Array(M) } \\ as pointer to arrray =(if(dev>0->Sqrt(dev/N), 0), Mean) } Function randomNormal { \\ by default all numbers are double \\ cos() get degrees =1+Cos(360 * rnd) * Sqrt(-2 * Ln(rnd)) /2 } \\ fill array calling randomNormal() for each item Dim A(1000)<<randomNormal() \\ we can pass a pointer to array and place it to stack of values DisplayMeanAndStdDeviation(A()) ' mean ~ 1 std deviation ~0.5 \\ check M2000 rnd only Dim B(1000)<<rnd DisplayMeanAndStdDeviation(B()) ' mean ~ 0.5 std deviation ~0.28
DisplayMeanAndStdDeviation((0,0,14,14)) ' mean = 7 std deviation = 7 DisplayMeanAndStdDeviation((0,6,8,14)) ' mean = 7 std deviation = 5 DisplayMeanAndStdDeviation((6,6,8,8)) ' mean = 7 std deviation = 1 Sub DisplayMeanAndStdDeviation(A) \\ push to stack all items of an array (need an array pointer) Push ! StdDev(A) \\ read from strack two numbers Print "Mean is "; Number Print "Standard Deviation is "; Number End Sub
} Checkit </lang>
Maple
<lang maple>with(Statistics): Sample(Normal(1, 0.5), 1000);</lang>
or
<lang maple>1+0.5*ArrayTools[RandomArray](1000,1,distribution=normal);</lang>
Mathematica /Wolfram Language
Built-in function RandomReal with built-in distribution NormalDistribution as an argument: <lang Mathematica>RandomReal[NormalDistribution[1, 1/2], 1000]</lang>
MATLAB
Native support : <lang MATLAB> mu = 1; sd = 0.5;
x = randn(1000,1) * sd + mu;
</lang>
The statistics toolbox provides this function <lang MATLAB> x = normrnd(mu, sd, [1000,1]); </lang>
This script uses the Box-Mueller Transform to transform a number from the uniform distribution to a normal distribution of mean = mu0 and standard deviation = chi2.
<lang MATLAB>function randNum = randNorm(mu0,chi2, sz)
radiusSquared = +Inf;
while (radiusSquared >= 1) u = ( 2 * rand(sz) ) - 1; v = ( 2 * rand(sz) ) - 1;
radiusSquared = u.^2 + v.^2; end
scaleFactor = sqrt( ( -2*log(radiusSquared) )./ radiusSquared ); randNum = (v .* scaleFactor .* chi2) + mu0;
end</lang>
Output: <lang MATLAB>>> randNorm(1,.5, [1000,1])
ans =
0.693984121077029</lang>
Maxima
<lang maxima>load(distrib)$
random_normal(1.0, 0.5, 1000);</lang>
MAXScript
<lang maxscript>arr = #() for i in 1 to 1000 do (
a = random 0.0 1.0 b = random 0.0 1.0 c = 1.0 + 0.5 * sqrt (-2*log a) * cos (360*b) -- Maxscript cos takes degrees append arr c
)</lang>
Metafont
Metafont has normaldeviate
which produces pseudorandom normal distributed numbers with mean 0 and variance one. So the following complete the task:
<lang metafont>numeric col[];
m := 0; % m holds the mean, for testing purposes for i = 1 upto 1000:
col[i] := 1 + .5normaldeviate; m := m + col[i];
endfor
% testing m := m / 1000; % finalize the computation of the mean
s := 0; % in s we compute the standard deviation for i = 1 upto 1000:
s := s + (col[i] - m)**2;
endfor s := sqrt(s / 1000);
show m, s; % and let's show that really they get what we wanted end</lang>
A run gave
>> 0.99947 >> 0.50533
Assigning a value to the special variable randomseed will allow to have always the same sequence of pseudorandom numbers
MiniScript
<lang MiniScript>randNormal = function(mean=0, stddev=1)
return mean + sqrt(-2 * log(rnd,2.7182818284)) * cos(2*pi*rnd) * stddev
end function
x = [] for i in range(1,1000)
x.push randNormal(1, 0.5)
end for</lang>
Mirah
<lang mirah>import java.util.Random
list = double[999] mean = 1.0 std = 0.5 rng = Random.new 0.upto(998) do | i |
list[i] = mean + std * rng.nextGaussian
end </lang>
МК-61/52
<lang>П7 <-> П8 1/x П6 ИП6 П9 СЧ П6 1/x ln ИП8 * 2 * КвКор ИП9 2 * пи
- sin * ИП7 + С/П БП 05</lang>
Input: РY - variance, РX - expectation.
Or:
<lang>3 10^x П0 ПП 13 2 / 1 + С/П L0 03 С/П СЧ lg 2 /-/ * КвКор 2 пи ^ СЧ * * cos * В/О</lang>
to generate 1000 numbers with a mean of 1.0 and a standard deviation of 0.5.
Modula-3
<lang modula3>MODULE Rand EXPORTS Main;
IMPORT Random; FROM Math IMPORT log, cos, sqrt, Pi;
VAR rands: ARRAY [1..1000] OF LONGREAL;
(* Normal distribution. *) PROCEDURE RandNorm(): LONGREAL =
BEGIN WITH rand = NEW(Random.Default).init() DO RETURN sqrt(-2.0D0 * log(rand.longreal())) * cos(2.0D0 * Pi * rand.longreal()); END; END RandNorm;
BEGIN
FOR i := FIRST(rands) TO LAST(rands) DO rands[i] := 1.0D0 + 0.5D0 * RandNorm(); END;
END Rand.</lang>
Nanoquery
<lang nanoquery>list = {0} * 1000 mean = 1.0; std = 0.5 rng = new(Nanoquery.Util.Random)
for i in range(0, len(list) - 1)
list[i] = mean + std * rng.getGaussian()
end</lang>
NetRexx
<lang NetRexx>/* NetRexx */ options replace format comments java crossref symbols nobinary
import java.math.BigDecimal import java.math.MathContext
-- prologue numeric digits 20
-- get input, set defaults parse arg dp mu sigma ec . if mu = | mu = '.' then mean = 1.0; else mean = mu if sigma = | sigma = '.' then stdDeviation = 0.5; else stdDeviation = sigma if dp = | dp = '.' then displayPrecision = 1; else displayPrecision = dp if ec = | ec = '.' then elements = 1000; else elements = ec
-- set up RNG = Random() numberList = java.util.List numberList = ArrayList()
-- generate list of random numbers loop for elements
rn = mean + stdDeviation * RNG.nextGaussian() numberList.add(BigDecimal(rn, MathContext.DECIMAL128)) end
-- report say "Mean: " mean say "Standard Deviation:" stdDeviation say "Precision: " displayPrecision say drawBellCurve(numberList, displayPrecision)
return
-- ----------------------------------------------------------------------------- method drawBellCurve(numberList = java.util.List, precision) static
Collections.sort(numberList) val = BigDecimal lastN = nextN = loop val over numberList nextN = Rexx(val.toPlainString()).format(5, precision) select when lastN = then nop when lastN \= nextN then say lastN otherwise nop end say '*\-' lastN = nextN end val say lastN
return
</lang>
- Output:
Mean: 1.0 Standard Deviation: 0.5 Precision: 1 * 2.7 ** 2.5 * 2.4 *** 2.3 ***** 2.2 ******* 2.1 ************* 2.0 ************* 1.9 ***************************** 1.8 ************************* 1.7 ************************************* 1.6 ****************************************************** 1.5 ******************************************** 1.4 ******************************************************************** 1.3 ***************************************************************** 1.2 ************************************************************************** 1.1 ********************************************************************************************* 1.0 ************************************************************* 0.9 ********************************************************************** 0.8 ************************************************************** 0.7 *********************************************************************** 0.6 ************************************************************** 0.5 ****************************************** 0.4 ******************************* 0.3 *************************** 0.2 *************** 0.1 ********* 0.0 ****** -0.1 *** -0.2 *** -0.3 * -0.4 * -0.6 ** -0.7
NewLISP
<lang NewLISP>(normal 1 .5 1000)</lang>
Nim
<lang nim>import random, stats, strformat
var rs: RunningStat
randomize()
for _ in 1..5:
for _ in 1..1000: rs.push gauss(1.0, 0.5) echo &"mean: {rs.mean:.5f} stdDev: {rs.standardDeviation:.5f}"
</lang>
- Output:
mean: 1.01294 stdDev: 0.49692 mean: 1.00262 stdDev: 0.50028 mean: 0.99878 stdDev: 0.49662 mean: 0.99830 stdDev: 0.49820 mean: 1.00658 stdDev: 0.49703
Objeck
<lang objeck>bundle Default {
class RandomNumbers { function : Main(args : String[]) ~ Nil { rands := Float->New[1000]; for(i := 0; i < rands->Size(); i += 1;) { rands[i] := 1.0 + 0.5 * RandomNormal(); };
each(i : rands) { rands[i]->PrintLine(); }; }
function : native : RandomNormal() ~ Float { return (2 * Float->Pi() * Float->Random())->Cos() * (-2 * (Float->Random()->Log()))->SquareRoot(); } }
}</lang>
OCaml
<lang ocaml>let pi = 4. *. atan 1.;; let random_gaussian () =
1. +. sqrt (-2. *. log (Random.float 1.)) *. cos (2. *. pi *. Random.float 1.);;
let a = Array.init 1000 (fun _ -> random_gaussian ());;</lang>
Octave
<lang octave>p = normrnd(1.0, 0.5, 1000, 1); disp(mean(p)); disp(sqrt(sum((p - mean(p)).^2)/numel(p)));</lang>
- Output:
1.0209 0.51048
ooRexx
version 1
<lang oorexx>/*REXX pgm gens 1,000 normally distributed #s: mean=1, standard dev.=0.5*/
pi=RxCalcPi() /* get value of pi */ Parse Arg n seed . /* allow specification of N & seed*/ If n==|n==',' Then n=1000 /* N is the size of the array. */ If seed\== Then Call random,,seed /* use seed for repeatable RANDOM#*/ mean=1 /* desired new mean (arith. avg.) */ sd=1/2 /* desired new standard deviation.*/ Do g=1 For n /* generate N uniform random nums.*/ n.g=random(0,1e5)/1e5 /* REXX gens uniform rand integers*/ End
Say ' old mean=' mean() Say 'old standard deviation=' stddev() Say Do j=1 To n-1 By 2 m=j+1 /*use Box-Muller method */ _=sd*RxCalcPower(-2*RxCalcLog(n.j),.5)*RxCalcCos(2*pi*n.m,,'R')+mean n.m=sd*RxCalcpower(-2*RxCalcLog(n.j),.5)*RxCalcSin(2*pi*n.m,,'R')+, mean /* rand # must be 0???1. */ n.j=_ End /* j */ Say ' new mean=' mean() Say 'new standard deviation=' stddev() Exit
mean:
_=0 Do k=1 For n _=_+n.k End Return _/n
stddev:
_avg=mean() _=0 Do k=1 For n _=_+(n.k-_avg)**2 End Return RxCalcPower(_/n,.5)
- requires rxmath library</lang>
- Output:
old mean= 0.49830002 old standard deviation= 0.283199568 new mean= 1.00377404 new standard deviation= 0.501444536
version 2
Using the nice function names in the algorithm. <lang oorexx>/*REXX pgm gens 1,000 normally distributed #s: mean=1, standard dev.=0.5*/
pi=RxCalcPi() /* get value of pi */ Parse Arg n seed . /* allow specification of N & seed*/ If n==|n==',' Then n=1000 /* N is the size of the array. */ If seed\== Then Call random,,seed /* use seed for repeatable RANDOM#*/ mean=1 /* desired new mean (arith. avg.) */ sd=1/2 /* desired new standard deviation.*/ Do g=1 For n /* generate N uniform random nums.*/ n.g=random(0,1e5)/1e5 /* REXX gens uniform rand integers*/ End
Say ' old mean=' mean() Say 'old standard deviation=' stddev() Say Do j=1 To n-1 By 2 m=j+1 /*use Box-Muller method */ _=sd*sqrt(-2*ln(n.j))*cos(2*pi*n.m)+mean n.m=sd*sqrt(-2*ln(n.j))*sin(2*pi*n.m)+mean n.j=_ End Say ' new mean=' mean() Say 'new standard deviation=' stddev() Exit
mean:
_=0 Do k=1 For n _=_+n.k End Return _/n
stddev:
_avg=mean() _=0 Do k=1 For n _=_+(n.k-_avg)**2 End Return sqrt(_/n)
sqrt: Return RxCalcSqrt(arg(1)) ln: Return RxCalcLog(arg(1)) cos: Return RxCalcCos(arg(1),,'R') sin: Return RxCalcSin(arg(1),,'R')
- requires rxmath library</lang>
PARI/GP
<lang parigp>rnormal()={ my(pr=32*ceil(default(realprecision)*log(10)/log(4294967296)),u1=random(2^pr)*1.>>pr,u2=random(2^pr)*1.>>pr); sqrt(-2*log(u1))*cos(2*Pi*u2) \\ in previous version "u1" instead of "u2" was used --> has given crap distribution \\ Could easily be extended with a second normal at very little cost. }; vector(1000,unused,rnormal()/2+1)</lang>
Pascal
The following function calculates Gaussian-distributed random numbers with the Box-Müller algorithm: <lang pascal> function rnorm (mean, sd: real): real;
{Calculates Gaussian random numbers according to the Box-Müller approach}
var
u1, u2: real;
begin
u1 := random; u2 := random; rnorm := mean * abs(1 + sqrt(-2 * (ln(u1))) * cos(2 * pi * u2) * sd); /* error !?! Shouldn't it be "mean +" instead of "mean *" ? */
end; </lang>
Delphi and Free Pascal support implement a randg function that delivers Gaussian-distributed random numbers.
Perl
<lang perl>my $PI = 2 * atan2 1, 0;
my @nums = map {
1 + 0.5 * sqrt(-2 * log rand) * cos(2 * $PI * rand)
} 1..1000;</lang>
Phix
function RandomNormal() return sqrt(-2*log(rnd())) * cos(2*PI*rnd()) end function sequence s = repeat(0,1000) for i=1 to length(s) do s[i] = 1 + 0.5 * RandomNormal() end for
Phixmonti
<lang Phixmonti>include ..\Utilitys.pmt
def RandomNormal
drop rand log -2 * sqrt 2 pi * rand * cos * 0.5 * 1 +
enddef
1000 var n 0 n repeat
getid RandomNormal map
dup sum n / var mean "Mean: " print mean print nl
0 swap n for
get mean - 2 power rot + swap
endfor swap n / sqrt "Standard deviation: " print print</lang>
PHP
<lang php>function random() {
return mt_rand() / mt_getrandmax();
}
$pi = pi(); // Set PI
$a = array(); for ($i = 0; $i < 1000; $i++) {
$a[$i] = 1.0 + ((sqrt(-2 * log(random())) * cos(2 * $pi * random())) * 0.5);
}</lang>
Picat
<lang Picat>main =>
_ = random2(), % random seed G = [gaussian_dist(1,0.5) : _ in 1..1000], println(first_10=G[1..10]), println([mean=avg(G),stdev=stdev(G)]), nl.
% Gaussian (Normal) distribution, Box-Muller algorithm gaussian01() = Y =>
U = frand(0,1), V = frand(0,1), Y = sqrt(-2*log(U))*sin(2*math.pi*V).
gaussian_dist(Mean,Stdev) = Mean + (gaussian01() * Stdev).
% Variance of Xs variance(Xs) = Variance =>
Mu = avg(Xs), N = Xs.len, Variance = sum([ (X-Mu)**2 : X in Xs ]) / N.
% Standard deviation stdev(Xs) = sqrt(variance(Xs)).</lang>
- Output:
first_10 = [1.639965415776091,0.705425965005482,0.981532402477848,0.309148743347499,1.252800181962738,0.098829881195179,0.74888084504147,0.181494956495445,1.304931340021904,0.595939453660087] [mean = 0.99223677282248,stdev = 0.510336641737154]
PicoLisp
<lang PicoLisp>(load "@lib/math.l")
(de randomNormal () # Normal distribution, centered on 0, std dev 1
(*/ (sqrt (* -2.0 (log (rand 0 1.0)))) (cos (*/ 2.0 pi (rand 0 1.0) `(* 1.0 1.0))) 1.0 ) )
(seed (time)) # Randomize
(let Result
(make # Build list (do 1000 # of 1000 elements (link (+ 1.0 (/ (randomNormal) 2))) ) ) (for N (head 7 Result) # Print first 7 results (prin (format N *Scl) " ") ) )</lang>
- Output:
1.500334 1.212931 1.095283 0.433122 0.459116 1.302446 0.402477
PL/I
<lang PL/I> /* CONVERTED FROM WIKI FORTRAN */ Normal_Random: procedure options (main);
declare (array(1000), pi, temp, mean initial (1.0), sd initial (0.5)) float (18); declare (i, n) fixed binary; n = hbound(array, 1); pi = 4.0*ATAN(1.0); array = random(); /* Uniform distribution */ /* Now convert to normal distribution */ DO i = 1 to n-1 by 2; temp = sd * SQRT(-2.0*LOG(array(i))) * COS(2*pi*array(i+1)) + mean; array(i+1) = sd * SQRT(-2.0*LOG(array(i))) * SIN(2*pi*array(i+1)) + mean; array(i) = temp; END; /* Check mean and standard deviation */ mean = SUM(array)/n; sd = SQRT(SUM((array - mean)**2)/n); put skip edit ( "Mean = ", mean ) (a, F(18,16) ); put skip edit ( "Standard Deviation = ", sd) (a, F(18,16));
END Normal_Random; </lang>
- Output:
Mean = 1.0125630677913652 Standard Deviation = 0.5067289784535284 3 runs with different seeds to random(): Mean = 1.0008390411168471 Standard Deviation = 0.5095810511317908 Mean = 0.9754351286894838 Standard Deviation = 0.4804376530558166 Mean = 1.0177411222687990 Standard Deviation = 0.5165899662493400
PL/SQL
<lang PL/SQL> DECLARE
--The desired collection type t_coll is table of number index by binary_integer; l_coll t_coll;
c_max pls_integer := 1000;
BEGIN
FOR l_counter IN 1 .. c_max LOOP -- dbms_random.normal delivers normal distributed random numbers with a mean of 0 and a variance of 1 -- We just adjust the values and get the desired result: l_coll(l_counter) := DBMS_RANDOM.normal * 0.5 + 1; DBMS_OUTPUT.put_line (l_coll(l_counter)); END LOOP;
END; </lang>
Pop11
<lang pop11>;;; Choose radians as arguments to trigonometic functions true -> popradians;
- procedure generating standard normal distribution
define random_normal() -> result; lvars r1 = random0(1.0), r2 = random0(1.0);
cos(2*pi*r1)*sqrt(-2*log(r2)) -> result
enddefine;
lvars array, i;
- Put numbers on the stack
for i from 1 to 1000 do 1.0+0.5*random_normal() endfor;
- collect them into array
consvector(1000) -> array;</lang>
PowerShell
Equation adapted from Liberty BASIC <lang powershell>function Get-RandomNormal
{ [CmdletBinding()] Param ( [double]$Mean, [double]$StandardDeviation ) $RandomNormal = $Mean + $StandardDeviation * [math]::Sqrt( -2 * [math]::Log( ( Get-Random -Minimum 0.0 -Maximum 1.0 ) ) ) * [math]::Cos( 2 * [math]::PI * ( Get-Random -Minimum 0.0 -Maximum 1.0 ) ) return $RandomNormal }
- Standard deviation function for testing
function Get-StandardDeviation
{ [CmdletBinding()] param ( [double[]]$Numbers ) $Measure = $Numbers | Measure-Object -Average $PopulationDeviation = 0 ForEach ($Number in $Numbers) { $PopulationDeviation += [math]::Pow( ( $Number - $Measure.Average ), 2 ) } $StandardDeviation = [math]::Sqrt( $PopulationDeviation / ( $Measure.Count - 1 ) ) return $StandardDeviation }
- Test
$RandomNormalNumbers = 1..1000 | ForEach { Get-RandomNormal -Mean 1 -StandardDeviation 0.5 }
$Measure = $RandomNormalNumbers | Measure-Object -Average
$Stats = [PSCustomObject]@{
Count = $Measure.Count Average = $Measure.Average StandardDeviation = Get-StandardDeviation -Numbers $RandomNormalNumbers
}
$Stats | Format-List </lang>
- Output:
Count : 1000 Average : 1.01206560135809 StandardDeviation : 0.489099623426272
PureBasic
<lang PureBasic>Procedure.f RandomNormal()
; This procedure can return any real number. Protected.f x1, x2
; random numbers from the open interval ]0, 1[ x1 = (Random(999998)+1) / 1000000 ; must be > 0 because of Log(x1) x2 = (Random(999998)+1) / 1000000
ProcedureReturn Sqr(-2*Log(x1)) * Cos(2*#PI*x2)
EndProcedure
Define i, n=1000
Dim a.q(n-1) For i = 0 To n-1
a(i) = 1 + 0.5 * RandomNormal()
Next</lang>
Python
- Using random.gauss
<lang python>>>> import random >>> values = [random.gauss(1, .5) for i in range(1000)] >>> </lang>
- Quick check of distribution
<lang python>>>> def quick_check(numbers):
count = len(numbers) mean = sum(numbers) / count sdeviation = (sum((i - mean)**2 for i in numbers) / count)**0.5 return mean, sdeviation
>>> quick_check(values) (1.0140373306786599, 0.49943411329234066) >>> </lang>
Note that the random module in the Python standard library supports a number of statistical distribution methods.
- Alternatively using random.normalvariate
<lang python>>>> values = [ random.normalvariate(1, 0.5) for i in range(1000)] >>> quick_check(values) (0.990099111944864, 0.5029847005836282) >>> </lang>
R
<lang r>result <- rnorm(1000, mean=1, sd=0.5)</lang>
Racket
<lang Racket>
- lang racket
(for/list ([i 1000])
(add1 (* (sqrt (* -2 (log (random)))) (cos (* 2 pi (random))) 0.5)))
</lang>
Alternative: <lang Racket>
- lang racket
(require math/distributions) (sample (normal-dist 1.0 0.5) 1000) </lang>
Raku
(formerly Perl 6)
<lang perl6>sub randnorm ($mean, $stddev) {
$mean + $stddev * sqrt(-2 * log rand) * cos(2 * pi * rand)
}
my @nums = randnorm(1, 0.5) xx 1000;
- Checking
say my $mean = @nums R/ [+] @nums; say my $stddev = sqrt $mean**2 R- @nums R/ [+] @nums X** 2; </lang>
Raven
<lang raven>define PI
-1 acos
define rand1
9999999 choose 1 + 10000000.0 /
define randNormal
rand1 PI * 2 * cos rand1 log -2 * sqrt * 2 / 1 +
1000 each drop randNormal "%f\n" print</lang> Quick Check (on linux with code in file rand.rv) <lang raven>raven rand.rv | awk '{sum+=$1; sumsq+=$1*$1;} END {print "stdev = " sqrt(sumsq/NR - (sum/NR)**2); print "mean = " sum/NR}' stdev = 0.497773 mean = 1.01497</lang>
REXX
The REXX language doesn't have any "higher math" functions like SQRT/SIN/COS/LN/LOG/EXP/POW/etc.,
so we hoi polloi REXX programmers have to roll our own.
Programming note: note the range of the random numbers: (0,1]
(that is, random numbers from zero──►unity, excluding zero, including unity).
<lang rexx>/*REXX pgm generates 1,000 normally distributed numbers: mean=1, standard deviation=½.*/
numeric digits 20 /*the default decimal digit precision=9*/
parse arg n seed . /*allow specification of N and the seed*/
if n== | n=="," then n=1000 /*N: is the size of the array. */
if datatype(seed,'W') then call random ,,seed /*SEED: for repeatable random numbers. */
newMean=1 /*the desired new mean (arithmetic avg)*/
sd=1/2 /*the desired new standard deviation. */
do g=1 for n /*generate N uniform random #'s (0,1].*/ #.g = random(1, 1e5) / 1e5 /*REXX's RANDOM BIF generates integers.*/ end /*g*/ /* [↑] random integers ──► fractions. */
say ' old mean=' mean() say 'old standard deviation=' stdDev() call pi; pi2=pi * 2 /*define pi and also 2 * pi. */ say
do j=1 to n-1 by 2; m=j+1 /*step through the iterations by two. */ _=sd * sqrt(ln(#.j) * -2) /*calculate the used-twice expression.*/ #.j=_ * cos(pi2 * #.m) + newMean /*utilize the Box─Muller method. */ #.m=_ * sin(pi2 * #.m) + newMean /*random number must be: (0,1] */ end /*j*/
say ' new mean=' mean() say 'new standard deviation=' stdDev() exit /*stick a fork in it, we're all done. */ /*───────────────────────────────────────────────────────────────────────────────────────────────────────────────────*/ mean: _=0; do k=1 for n; _=_ + #.k; end; return _/n stdDev: _avg=mean(); _=0; do k=1 for n; _=_ + (#.k - _avg)**2; end; return sqrt(_/n) e: e =2.7182818284590452353602874713526624977572470936999595749669676277240766303535; return e /*digs overkill*/ pi: pi=3.1415926535897932384626433832795028841971693993751058209749445923078164062862; return pi /* " " */ r2r: return arg(1) // (pi() * 2) /*normalize ang*/ sin: procedure; parse arg x;x=r2r(x);numeric fuzz min(5,digits()-3);if abs(x)=pi then return 0;return .sincos(x,x,1) .sincos:parse arg z,_,i; x=x*x; p=z; do k=2 by 2; _=-_*x/(k*(k+i)); z=z+_; if z=p then leave; p=z; end; return z /*───────────────────────────────────────────────────────────────────────────────────────────────────────────────────*/ ln: procedure; parse arg x,f; call e; ig= x>1.5; is=1 - 2 * (ig\==1); ii=0; xx=x
do while ig&xx>1.5|\ig&xx<.5;_=e;do k=-1;iz=xx*_**-is;if k>=0&(ig&iz<1|\ig&iz>.5) then leave;_=_*_;izz=iz;end xx=izz;ii=ii+is*2**k;end;x=x*e**-ii-1;z=0;_=-1;p=z;do k=1;_=-_*x;z=z+_/k;if z=p then leave;p=z;end; return z+ii
/*───────────────────────────────────────────────────────────────────────────────────────────────────────────────────*/ cos: procedure; parse arg x; x=r2r(x); a=abs(x); hpi=pi * .5
numeric fuzz min(6, digits() - 3); if a=pi then return -1 if a=hpi | a=hpi*3 then return 0; if a=pi/3 then return .5 if a=pi * 2/3 then return -.5; return .sinCos(1,1,-1)
/*───────────────────────────────────────────────────────────────────────────────────────────────────────────────────*/ sqrt: procedure; parse arg x; if x=0 then return 0; d=digits(); numeric digits; h=d+6
numeric form; parse value format(x,2,1,,0) 'E0' with g 'E' _ .; g=g * .5'e'_ %2 m.=9; do j=0 while h>9; m.j=h; h=h%2 + 1; end /*j*/ do k=j+5 to 0 by -1; numeric digits m.k; g=(g+x/g)*.5; end /*k*/ numeric digits d; return g/1</lang>
output when using the default inputs:
old mean= 0.5015724 old standard deviation= 0.28652466389342471402 new mean= 0.98807025356443262689 new standard deviation= 0.50002924192766720838
Ring
<lang ring> for i = 1 to 10
see random(i) + nl
next i </lang>
Ruby
<lang ruby>Array.new(1000) { 1 + Math.sqrt(-2 * Math.log(rand)) * Math.cos(2 * Math::PI * rand) }</lang>
Run BASIC
<lang runbasic>dim a(1000) pi = 22/7 for i = 1 to 1000
a( i) = 1 + .5 * (sqr(-2 * log(rnd(0))) * cos(2 * pi * rnd(0)))
next i</lang>
Rust
Using a for-loop: <lang rust>extern crate rand; use rand::distributions::{Normal, IndependentSample};
fn main() {
let mut rands = [0.0; 1000]; let normal = Normal::new(1.0, 0.5); let mut rng = rand::thread_rng(); for num in rands.iter_mut() { *num = normal.ind_sample(&mut rng); }
}</lang>
Using iterators: <lang rust>extern crate rand; use rand::distributions::{Normal, IndependentSample};
fn main() {
let rands: Vec<_> = { let normal = Normal::new(1.0, 0.5); let mut rng = rand::thread_rng(); (0..1000).map(|_| normal.ind_sample(&mut rng)).collect() };
}</lang>
SAS
<lang SAS> /* Generate 1000 random numbers with mean 1 and standard deviation 0.5.
SAS version 9.2 was used to create this code.*/
data norm1000;
call streaminit(123456);
/* Set the starting point, so we can replicate results.
If you want different results each time, comment the above line. */ do i=1 to 1000; r=rand('normal',1,0.5); output; end;
run; </lang> Results:
The MEANS Procedure Analysis Variable : r Mean Std Dev ---------------------------- 0.9907408 0.4844051 ----------------------------
Sather
<lang sather>class MAIN is
main is a:ARRAY{FLTD} := #(1000); i:INT;
RND::seed(2010); loop i := 1.upto!(1000) - 1; a[i] := 1.0d + 0.5d * RND::standard_normal; end;
-- testing the distribution mean ::= a.reduce(bind(_.plus(_))) / a.size.fltd; #OUT + "mean " + mean + "\n"; a.map(bind(_.minus(mean))); a.map(bind(_.pow(2.0d))); dev ::= (a.reduce(bind(_.plus(_))) / a.size.fltd).sqrt; #OUT + "dev " + dev + "\n"; end;
end;</lang>
Scala
One liner
<lang scala>List.fill(1000)(1.0 + 0.5 * scala.util.Random.nextGaussian)</lang>
Academic
<lang scala> object RandomNumbers extends App {
val distribution: LazyList[Double] = { def randomNormal: Double = 1.0 + 0.5 * scala.util.Random.nextGaussian
def normalDistribution(a: Double): LazyList[Double] = a #:: normalDistribution(randomNormal)
normalDistribution(randomNormal) }
/* * Let's test it */ def calcAvgAndStddev[T](ts: Iterable[T])(implicit num: Fractional[T]): (T, Double) = { val mean: T = num.div(ts.sum, num.fromInt(ts.size)) // Leaving with type of function T
// Root of mean diffs val stdDev = Math.sqrt(ts.map { x => val diff = num.toDouble(num.minus(x, mean)) diff * diff }.sum / ts.size)
(mean, stdDev) }
println(calcAvgAndStddev(distribution.take(1000))) // e.g. (1.0061433267806525,0.5291834867560893)
} </lang>
Scheme
<lang scheme>; linear congruential generator given in C99 section 7.20.2.1 (define ((c-rand seed)) (set! seed (remainder (+ (* 1103515245 seed) 12345) 2147483648)) (quotient seed 65536))
- uniform real numbers in open interval (0, 1)
(define (unif-rand seed) (let ((r (c-rand seed))) (lambda () (/ (+ (r) 1) 32769.0))))
- Box-Muller method to generate normal distribution
(define (normal-rand unif m s) (let ((? #t) (! 0.0) (twopi (* 2.0 (acos -1.0)))) (lambda ()
(set! ? (not ?)) (if ? ! (let ((a (sqrt (* -2.0 (log (unif))))) (b (* twopi (unif)))) (set! ! (+ m (* s a (sin b)))) (+ m (* s a (cos b))))))))
(define rnorm (normal-rand (unif-rand 0) 1.0 0.5))
- auxiliary function to get a list of 'n random numbers from generator 'r
(define (rand-list r n) = (if (zero? n) '() (cons (r) (rand-list r (- n 1)))))
(define v (rand-list rnorm 1000))
v
- |
(-0.27965824722565835
-0.8870860825789542 0.6499618744638194 0.31336141955110863 ... 0.5648743998193049 0.8282656735558756 0.6399951934564637 0.7699535302478072)
|#
- check mean and standard deviation
(define (mean-sdev v) (let loop ((v v) (a 0) (b 0) (n 0)) (if (null? v)
(let ((mean (/ a n))) (list mean (sqrt (/ (- b (* n mean mean)) (- n 1))))) (let ((x (car v))) (loop (cdr v) (+ a x) (+ b (* x x)) (+ n 1))))))
(mean-sdev v)
- (0.9562156817697293 0.5097087109575911)</lang>
Seed7
<lang seed7>$ include "seed7_05.s7i";
include "float.s7i"; include "math.s7i";
const func float: frand is func # Uniform distribution, (0..1]
result var float: frand is 0.0; begin repeat frand := rand(0.0, 1.0); until frand <> 0.0; end func;
const func float: randomNormal is # Normal distribution, centered on 0, std dev 1
return sqrt(-2.0 * log(frand)) * cos(2.0 * PI * frand);
const proc: main is func
local var integer: i is 0; var array float: rands is 1000 times 0.0; begin for i range 1 to length(rands) do rands[i] := 1.0 + 0.5 * randomNormal; end for; end func;</lang>
Sidef
<lang ruby>var arr = 1000.of { 1 + (0.5 * sqrt(-2 * 1.rand.log) * cos(Num.tau * 1.rand)) } arr.each { .say }</lang>
Standard ML
SML/NJ has two structures for random numbers:
1) Rand (a linear congruential generator).
You create the generator by calling Rand.mkRandom
with a seed (of word
type).
You can call the generator with ()
repeatedly to get a word in the range [Rand.randMin, Rand.randMax]
.
You can use the Rand.norm
function to transform the output into a real
from 0 to 1, or use the Rand.range (i,j)
function to transform the output into an int
of the given range.
<lang sml>val seed = 0w42;
val gen = Rand.mkRandom seed;
fun random_gaussian () =
1.0 + Math.sqrt (~2.0 * Math.ln (Rand.norm (gen ()))) * Math.cos (2.0 * Math.pi * Rand.norm (gen ()));
val a = List.tabulate (1000, fn _ => random_gaussian ());</lang>
2) Random (a subtract-with-borrow generator). You create the generator by calling Random.rand
with a seed (of a pair of int
s). You can use the Random.randInt
function to generate a random int over its whole range; Random.randNat
to generate a non-negative random int; Random.randReal
to generate a real
between 0 and 1; or Random.randRange (i,j)
to generate an int
in the given range.
<lang sml>val seed = (47,42);
val gen = Random.rand seed;
fun random_gaussian () =
1.0 + Math.sqrt (~2.0 * Math.ln (Random.randReal gen)) * Math.cos (2.0 * Math.pi * Random.randReal gen);
val a = List.tabulate (1000, fn _ => random_gaussian ());</lang>
Other implementations of Standard ML have their own random number generators. For example, Moscow ML has a Random
structure that is different from the one from SML/NJ.
The SML Basis Library does not provide a routine for uniform deviate generation, and PolyML does not have one. Using a routine from "Monte Carlo" by Fishman (Springer), in the function uniformdeviate, and avoiding the slow IntInf's: <lang smlh> val urandomlist = fn seed => fn n => let val uniformdeviate = fn seed => let val in31m = (Real.fromInt o Int32.toInt ) (getOpt (Int32.maxInt,0) ); val in31 = in31m +1.0; val s1 = 41160.0; val s2 = 950665216.0; val v = Real.realFloor seed; val val1 = v*s1; val val2 = v*s2; val next1 = Real.fromLargeInt (Real.toLargeInt IEEEReal.TO_NEGINF (val1/in31)) ; val next2 = Real.rem(Real.realFloor(val2/in31) , in31m ); val valt = val1+val2 - (next1+next2)*in31m; val nextt = Real.realFloor(valt/in31m); val valt = valt - nextt*in31m; in (valt/in31m,valt) end; val store = ref (0.0,0.0); val rec u = fn S => fn 0 => [] | n=> (store:=uniformdeviate S; (#1 (!store)):: (u (#2 (!store)) (n-1))) ; in u seed n end;
local open Math in val bmconv = fn urand => fn vrand => 1.0+0.5*(sqrt(~2.0*ln urand)*cos (2.0*pi*vrand) ) end;
val rec makeNormals = fn once => fn u::v::[] => [once u v] | u::v::rm => (once u v )::(makeNormals once rm );
val anyrealseed=1009.0 ; makeNormals bmconv (urandomlist anyrealseed 2000); </lang>
Stata
<lang stata>clear all set obs 1000 gen x=rnormal(1,0.5)</lang>
Mata
<lang stata>a = rnormal(1000,1,1,0.5)</lang>
Tcl
<lang tcl>package require Tcl 8.5 variable ::pi [expr acos(0)] proc ::tcl::mathfunc::nrand {} {
expr {sqrt(-2*log(rand())) * cos(2*$::pi*rand())}
}
set mean 1.0 set stddev 0.5 for {set i 0} {$i < 1000} {incr i} {
lappend result [expr {$mean + $stddev*nrand()}]
}</lang>
TI-83 BASIC
Builtin function: randNorm()
randNorm(1,.5)
Or by a program:
Calculator symbol translations:
"STO" arrow: →
Square root sign: √
ClrList L1 Radian For(A,1,1000) √(-2*ln(rand))*cos(2*π*A)→L1(A) End
TorqueScript
<lang tqs>for (%i = 0; %i < 1000; %i++) %list[%i] = 1 + mSqrt(-2 * mLog(getRandom())) * mCos(2 * $pi * getRandom());</lang>
Ursala
There are two ways of interpreting the task, either to simulate sampling a population described by the given statistics, or to construct a sample exhibiting the given statistics. Both are illustrated below. The functions parameterized by the mean and standard deviation take a sample size and return a sample of that size, represented as a list of floating point numbers. The Z library function simulates a draw from a standard normal distribution. Mean and standard deviation library functions are also used in this example. <lang Ursala>#import nat
- import flo
pop_stats("mu","sigma") = plus/*"mu"+ times/*"sigma"+ Z*+ iota
sample_stats("mu","sigma") = plus^*D(minus/"mu"+ mean,~&)+ vid^*D(div\"sigma"+ stdev,~&)+ Z*+ iota
- cast %eWL
test =
^(mean,stdev)* <
pop_stats(1.,0.5) 1000, sample_stats(1.,0.5) 1000></lang>
The output shows the mean and standard deviation for both sample vectors, the latter being exact by construction.
< (1.004504e+00,4.915525e-01), (1.000000e+00,5.000000e-01)>
Visual FoxPro
<lang vfp> LOCAL i As Integer, m As Double, n As Integer, sd As Double py = PI() SET TALK OFF SET DECIMALS TO 6 CREATE CURSOR gdev (deviate B(6)) RAND(-1) n = 1000 m = 1 sd = 0.5 CLEAR FOR i = 1 TO n INSERT INTO gdev VALUES (GaussDev(m, 1/sd)) ENDFOR CALCULATE AVG(deviate), STD(deviate) TO m, sd ? "Mean", m, "Std Dev", sd SET TALK ON SET DECIMALS TO
FUNCTION GaussDev(mean As Double, sdev As Double) As Double LOCAL z As Double z = SQRT(-2*LOG(RAND()))*COS(py*RAND()) IF sdev # 0 z = mean + z/sdev ENDIF RETURN z ENDFUNC </lang>
Wren
<lang ecmascript>import "random" for Random
var rand = Random.new()
var randNormal = Fn.new { (-2 * rand.float().log).sqrt * (2 * Num.pi * rand.float()).cos }
var stdDev = Fn.new { |a, m|
var c = a.count return ((a.reduce(0) { |acc, x| acc + x*x } - m*m*c) / c).sqrt
}
var n = 1000 var numbers = List.filled(n, 0) var mu = 1 var sigma = 0.5 var sum = 0 for (i in 0...n) {
numbers[i] = mu + sigma*randNormal.call() sum = sum + numbers[i]
} var mean = sum / n System.print("Actual mean : %(mean)") System.print("Actual std dev: %(stdDev.call(numbers, mean))")</lang>
- Output:
Sample run:
Actual mean : 1.0053988699746 Actual std dev: 0.4961645117026
Yorick
Returns array of count random numbers with mean 0 and standard deviation 1. <lang yorick>func random_normal(count) {
return sqrt(-2*log(random(count))) * cos(2*pi*random(count));
}</lang>
Example of basic use:
> nums = random_normal(1000); // create an array 1000 random numbers > nums(avg); // show the mean 0.00901216 > nums(rms); // show the standard deviation 0.990265
Example with a mean of 1.0 and a standard deviation of 0.5:
> nums = random_normal(1000) * 0.5 + 1; > nums(avg); 1.00612 > nums(rms); 0.496853
zkl
<lang zkl>fcn mkRand(mean,sd){ //normally distributed random w/mean & standard deviation
pi:=(0.0).pi; // using the Box–Muller transform rz1:=fcn{1.0-(0.0).random(1)} // from [0,1) to (0,1] return('wrap(){((-2.0*rz1().log()).sqrt() * (2.0*pi*rz1()).cos())*sd + mean })
}</lang> This creates a new random number generator, now to use it: <lang zkl>var g=mkRand(1,0.5); ns:=(0).pump(1000,List,g); // 1000 rands with mean==1 & sd==1/2 mean:=(ns.sum(0.0)/1000); //-->1.00379
// calc sd of list of numbers:
(ns.reduce('wrap(p,n){p+(n-mean).pow(2)},0.0)/1000).sqrt() //-->0.494844</lang>
ZX Spectrum Basic
Here we have converted the QBasic code to suit the ZX Spectrum:
<lang zxbasic>10 RANDOMIZE 0 : REM seeds random number generator based on uptime 20 DIM a(1000) 30 CLS 40 FOR i = 1 TO 1000 50 LET a(i) = 1 + SQR(-2 * LN(RND)) * COS(2 * PI * RND) 60 NEXT i</lang>
- Programming Tasks
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