Random numbers: Difference between revisions
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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 [http://en.wikipedia.org/wiki/Standard_deviation standard deviation] of 0.5 |
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 [http://en.wikipedia.org/wiki/Standard_deviation standard deviation] of 0.5 |
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Many libraries only generate uniformly distributed random numbers. If so, use [http://en.wikipedia.org/wiki/Normal_distribution#Generating_values_for_normal_random_variables this formula] to convert them to a normal distribution. |
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{{header|Ada}} |
{{header|Ada}} |
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{{header|E}} |
{{header|E}} |
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accum [] for _ in 1..1000 { _.with(entropy.nextGaussian()) } |
accum [] for _ in 1..1000 { _.with(entropy.nextGaussian()) } |
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{{header|IDL}} |
{{header|IDL}} |
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result = 1.0 + 0.5*randomn(seed,1000) |
result = 1.0 + 0.5*randomn(seed,1000) |
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{{header|Pop11}} |
{{header|Pop11}} |
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;;; Choose radians as arguments to trigonometic functions |
;;; Choose radians as arguments to trigonometic functions |
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true -> popradians; |
true -> popradians; |
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{{header|Tcl}} |
{{header|Tcl}} |
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proc nrand {} {return [expr sqrt(-2*log(rand()))*cos(4*acos(0)*rand())]} |
proc nrand {} {return [expr sqrt(-2*log(rand()))*cos(4*acos(0)*rand())]} |
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for {set i 0} {$i < 1000} {incr i} {lappend result [expr 1+.5*nrand()]} |
for {set i 0} {$i < 1000} {incr i} {lappend result [expr 1+.5*nrand()]} |
Revision as of 21:47, 17 September 2007
Random numbers
You are encouraged to solve this task according to the task description, using any language you may know.
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.
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;
#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)); }
accum [] for _ in 1..1000 { _.with(entropy.nextGaussian()) }
result = 1.0 + 0.5*randomn(seed,1000)
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() }
randList = #() for i in 1 to 1000 do append randList (random 0.0 2.0)
map {.5 + rand} 1..1000
;;; 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)]
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()]}