Random numbers: Difference between revisions

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=={{header|C}}==

#include <stdlib.h>

<c>#include <stdlib.h>

#include <math.h>

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;

}</c>

}

=={{header|C++}}==

#include <cstdlib> // for rand

<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() // 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:

double mu, sigma;

};

int main()

{

double array[1000];

std::generate_n(array, 1000, normal_distribution(1.0, 0.5));

return 0;

}

}</cpp>

=={{header|E}}==

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=={{header|Java}}==

double[] list = new double[1000];

<java>double[] list = new double[1000];

Random rng = new Random();

for(int i = 0;i<list.length;i++) {

list[i] = 1.0 + 0.5 * rng.nextGaussian()

}</java>

}

=={{header|JavaScript}}==

function randomNormal() {

<javascript>function randomNormal() {

return Math.cos(2 * Math.PI * Math.random()) * Math.sqrt(-2 * Math.log(Math.random()));

}

var a = new Array(1000);

for (var i=0; i<a.length; i++)

a[i] = randomNormal() / 2 + 1;

a[i] = randomNormal() / 2 + 1;</javascript>

=={{header|Logo}}==

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=={{header|OCaml}}==

let pi = 4. *. atan 1.;;

<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 ());;

let a = Array.init 1000 (fun _ -> random_gaussian ());;</ocaml>

=={{header|Perl}}==

use Math::Cephes qw($PI);

<perl>use Math::Cephes qw($PI);

map {

1.0 + 0.5 * sqrt (-2 * log rand) * cos (2 * $PI * rand)

1.0 + sqrt (-2 * log rand) * cos (2 * $PI * rand)

} 1..1000

} 1..1000</perl>

=={{header|PHP}}==

$pi = pi(); // Set PI

<php>$pi = pi(); // Set PI

$a = range(1,1000); // Create array

// Cycle array values

foreach(range(1,1000) as $i){

$a[$i] = 1 + sqrt(-2 * log(mt_rand())) * cos(2 * $pi * mt_rand());

}</php>

}

=={{header|Pop11}}==

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=={{header|Python}}==

import random

<python>import random

randList = [random.gauss(1, .5) for i in range(1000)]

# or [ random.normalvariate(1, 0.5) for i in range(1000)]

# or [ random.normalvariate(1, 0.5) for i in range(1000)]</python>

Note that the ''random'' module in the Python standard library supports a number of statistical distribution methods.

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=={{header|R}}==

result <- rnorm(1000, mean=1, sd=0.5)

=={{header|Ruby}}==

=={{header|Tcl}}==