Random numbers: Difference between revisions
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end loop; |
end loop; |
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end Normal_Random; |
end Normal_Random; |
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=={{header|C}}== |
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#include <stdlib.h> |
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#include <math.h> |
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double drand() /* uniform distribution, (0..1] */ |
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{ |
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return (rand()+1.0)/(RAND_MAX+1.0); |
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} |
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double random_normal() /* normal distribution, centered on 0, std dev 1 */ |
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{ |
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return sqrt(-2*log(drand())) * cos(2*M_PI*drand()); |
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} |
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int main() |
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{ |
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int i; |
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double rands[1000]; |
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for (i=0; i<1000; i++) |
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rands[i] = 1.0 + 0.5*random_normal(); |
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return 0; |
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} |
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=={{header|C plus plus|C++}}== |
=={{header|C plus plus|C++}}== |
Revision as of 06:58, 1 October 2007
![Task](http://static.miraheze.org/rosettacodewiki/thumb/b/ba/Rcode-button-task-crushed.png/64px-Rcode-button-task-crushed.png)
You are encouraged to solve this task according to the task description, using any language you may know.
The goal of this task is to generate a 1000-element array (vector, list, whatever it's called in your language) filled with normally distributed 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, use this formula to convert them to a normal distribution.
Ada
with Ada.Numerics.Float_Random; use Ada.Numerics.Float_Random; with Ada.Numerics.Generic_Elementary_Functions; procedure Normal_Random is Seed : Generator; function Normal_Distribution(Seed : Generator) return Float is package Elementary_Flt is new Ada.Numerics.Generic_Elementary_Functions(Float); use Elementary_Flt; use Ada.Numerics; R1 : Float; R2 : Float; Mu : constant Float := 1.0; Sigma : constant Float := 0.5; begin R1 := Random(Seed); R2 := Random(Seed); return Mu + (Sigma * Sqrt(-2.0 * Log(X => R1, Base => 10.0)) * Cos(2.0 * Pi * R2)); end Normal_Distribution; type Normal_Array is array(1..1000) of Float; Distribution : Normal_Array; begin Reset(Seed); for I in Distribution'range loop Distribution(I) := Normal_Distribution(Seed); end loop; end Normal_Random;
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 plus plus
#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)); }
E
accum [] for _ in 1..1000 { _.with(entropy.nextGaussian()) }
Forth
Interpreter: gforth 0.6.2
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
Groovy
rnd = new Random() result = (1..1000).inject([]) { r, i -> r << rnd.nextGaussian() }
IDL
result = 1.0 + 0.5*randomn(seed,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() }
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 )
Perl
map {.5 + rand} 1..1000
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;
Python
Interpreter: Python 2.5
import random randList = [random.gauss(1, .5) for i in range(1000)]
Tcl
proc nrand {} {return [expr sqrt(-2*log(rand()))*cos(4*acos(0)*rand())]} for {set i 0} {$i < 1000} {incr i} {lappend result [expr 1+.5*nrand()]}