Just another update
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Ingy döt Net
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The goal of this task is to generate a collection filled with 1000 normally distributed random (or pseudorandom) numbers with a mean of 1.0 and a [[wp:Standard_deviation|standard deviation]] of 0.5
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{{omit from|GUISS}}
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{{omit from|UNIX Shell|From the shell, we simply invoke the awk solution}}
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Many libraries only generate uniformly distributed random numbers. If so, use [[wp:Normal_distribution#Generating_values_from_normal_distribution|this formula]] to convert them to a normal distribution.
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The goal of this task is to generate a collection filled with
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1000 normally distributed random (or pseudorandom) numbers
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with a mean of 1.0 and a [[wp:Standard_deviation|standard deviation]] of 0.5
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Many libraries only generate uniformly distributed random numbers.
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If so, use [[wp:Normal_distribution#Generating_values_from_normal_distribution|this formula]] to convert them to a normal distribution.
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;See also:
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* [[Standard deviation]]
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---
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category:
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- Probability and statistics
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- Randomness
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note: Basic language learning
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12
Task/Random-numbers/AWK/random-numbers-2.awk
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12
Task/Random-numbers/AWK/random-numbers-2.awk
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@ -0,0 +1,12 @@
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function r() {
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return sqrt( -2*log( rand() ) ) * cos(6.2831853*rand() )
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}
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BEGIN {
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n=1000
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for(i=0;i<n;i++) {
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x = 1 + 0.5*r()
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s = s" "x
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}
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print s
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}
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@ -10,8 +10,7 @@ struct NormalRandom {
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}
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double opCall() const /*nothrow*/ {
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immutable r1 = uniform(0.0, 1.0), // Not nothrow.
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r2 = uniform(0.0, 1.0);
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immutable r1 = uniform01, r2 = uniform01; // Not nothrow.
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return mean + stdDev * sqrt(-2 * r1.log) * cos(2 * PI * r2);
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}
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}
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12
Task/Random-numbers/Elixir/random-numbers.elixir
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Task/Random-numbers/Elixir/random-numbers.elixir
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defmodule Random do
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def init() do
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:random.seed(:erlang.now())
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end
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def normal(mean, sd) do
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{a, b} = {:random.uniform(), :random.uniform()}
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mean + sd * (:math.sqrt(-2 * :math.log(a)) * :math.cos(2 * :math.pi * b))
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end
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end
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Random.init()
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xs = for _ <- 1..1000, do: Random.normal(1.0, 0.5)
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create or replace
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PROCEDURE PROCEDURE1 AS
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TYPE numsColl is TABLE OF NUMBER;
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nums numsColl;
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FUNCTION GenNums(n IN NUMBER) RETURN numsColl AS
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PI NUMBER := ACOS (-1);
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BEGIN
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nums := numsColl();
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nums.extend(n);
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FOR i in 1 .. n LOOP
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nums(i) := 1 + .5 * (sqrt(-2 * log(dbms_random.value, 10)) * cos(2 * PI * dbms_random.value));
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END LOOP;
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RETURN nums;
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END GenNums;
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DECLARE
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--The desired collection
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type t_coll is table of number index by binary_integer;
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l_coll t_coll;
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c_max pls_integer := 1000;
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BEGIN
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nums := GenNums(10);
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FOR i in 1 .. 10 LOOP
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DBMS_OUTPUT.PUT_LINE(nums(i));
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END LOOP;
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END PROCEDURE1;
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FOR l_counter IN 1 .. c_max
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LOOP
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-- dbms_random.normal delivers normal distributed random numbers with a mean of 0 and a variance of 1
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-- We just adjust the values and get the desired result:
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l_coll(l_counter) := DBMS_RANDOM.normal * 0.5 + 1;
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DBMS_OUTPUT.put_line (l_coll(l_counter));
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END LOOP;
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END;
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9
Task/Random-numbers/Pascal/random-numbers.pascal
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Task/Random-numbers/Pascal/random-numbers.pascal
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function rnorm (mean, sd: real): real;
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{Calculates Gaussian random numbers according to the Box-Müller approach}
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var
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u1, u2: real;
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begin
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u1 := random;
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u2 := random;
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rnorm := mean * abs(1 + sqrt(-2 * (ln(u1))) * cos(2 * pi * u2) * sd);
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end;
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@ -1,3 +1,3 @@
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import random
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values = [random.gauss(1, .5) for i in range(1000)]
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# or [ random.normalvariate(1, 0.5) for i in range(1000)]
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>>> import random
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>>> values = [random.gauss(1, .5) for i in range(1000)]
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>>>
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>>> mean = sum(values)/1000
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>>> sdeviation = (sum((i - mean)**2 for i in values)/1000)**0.5
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>>> mean, sdeviation
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(1.0127861555468178, 0.5006682783828207)
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>>> def quick_check(numbers):
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count = len(numbers)
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mean = sum(numbers) / count
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sdeviation = (sum((i - mean)**2 for i in numbers) / count)**0.5
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return mean, sdeviation
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>>> quick_check(values)
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(1.0140373306786599, 0.49943411329234066)
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>>>
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4
Task/Random-numbers/Python/random-numbers-3.py
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Task/Random-numbers/Python/random-numbers-3.py
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>>> values = [ random.normalvariate(1, 0.5) for i in range(1000)]
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>>> quick_check(values)
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(0.990099111944864, 0.5029847005836282)
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>>>
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/*REXX pgm gens 1,000 normally distributed #s: mean=1, standard dev.=½. */
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call pi /*call subroutine to define pi. */
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parse arg n seed . /*allow specification of N | seed*/
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if n=='' | n==',' then n=1000 /* N is the size of the array. */
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if seed\=='' then call random ,,seed /*use seed for repeatable RANDOM#*/
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mean=1 /*desired new mean (arith. avg.) */
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/*REXX pgm gens 1,000 normally distributed #s: mean=1, standard dev.=½.*/
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numeric digits 20 /*greater precision than 9 digits*/
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parse arg n seed . /*allow specification of N & seed*/
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if n=='' | n==',' then n=1000 /*N: is the size of the array.*/
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if seed\=='' then call random ,,seed /*SEED: for repeatable random #'s*/
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newMean=1 /*desired new mean|arithmetic avg*/
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sd=1/2 /*desired new standard deviation.*/
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do g=1 for n /*generate N uniform random nums.*/
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#.g=random(0,1e5)/1e5 /*REXX gens uniform rand integers*/
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end /*g*/
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do g=1 for n /*gen N uniform random #'s (0,1].*/
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#.g = random(1,1e5) / 1e5 /*RANDOM bif generates integers.*/
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end /*g*/ /* [↑] rand integers──►fractions*/
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say ' old mean=' mean()
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say 'old standard deviation=' stddev()
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call pi; pi2=pi+pi /*define pi and also 2*pi. */
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say
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do j=1 to n-1 by 2
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m=j+1
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_=sd*sqrt(-2*ln(#.j))*cos(2*pi*#.m)+mean /*use Box-Muller method*/
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#.m=sd*sqrt(-2*ln(#.j))*sin(2*pi*#.m)+mean /*rand # must be 0──<E29480>1.*/
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#.j=_
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end /*j*/
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do j=1 to n-1 by 2; m=j+1 /*step through iterations by two.*/
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_=sd * sqrt(ln(#.j) * -2) /*calculate used-twice expression*/
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#.j=_ * cos(pi2*#.m) + newMean /*utilize the Box─Muller method.*/
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#.m=_ * sin(pi2*#.m) + newMean /*random number must be: (0,1] */
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end /*j*/
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say ' new mean=' mean()
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say 'new standard deviation=' stddev()
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exit /*stick a fork in it, we're done.*/
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/*──────────────────────────────────subroutines─────────────────────────*/
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mean: _=0; do k=1 for n; _=_+#.k; end; return _/n
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stddev: _avg=mean(); _=0; do k=1 for n; _=_+(#.k-_avg)**2; end; return sqrt(_/n)
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e: e=2.7182818284590452353602874713526624977572470936999595749669676277240766303535; return e
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pi: pi=3.1415926535897932384626433832795028841971693993751058209749445923078164062862; return pi
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r2r: return arg(1)//(2*pi())
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sqrt: procedure;parse arg x; if x=0 then return 0; d=digits(); numeric digits 11; g=.sqrtGuess()
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do j=0 while p>9; m.j=p; p=p%2+1; end; do k=j+5 to 0 by -1; if m.k>11 then numeric digits m.k
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g=.5*(g+x/g); end; numeric digits d; return g/1
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.sqrtGuess: numeric form; m.=11; p=d+d%4+2
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parse value format(x,2,1,,0) 'E0' with g 'E' _ .; return g*.5'E'_%2
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cos: procedure; arg x; x=r2r(x); a=abs(x); numeric fuzz min(9,digits()-9); if a=pi() then return -1
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if a=pi()/2|a=2*pi() then return 0;if a=pi()/3 then return .5;if a=2*pi()/3 then return -.5;return .sincos(1,1,-1)
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sin: procedure; arg x; x=r2r(x); numeric fuzz min(5,digits()-3); if abs(x)=pi() then return 0; return .sincos(x,x,1)
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.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
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ln: procedure; parse arg x,f; call e; ig=x>1.5; is=1-2*(ig\==1); ii=0; xx=x; return .ln_comp()
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/*──────────────────────────────────subroutines──────────────────────────────────────────────────────────────────────*/
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mean: _=0; do k=1 for n; _=_+#.k; end; return _/n
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stddev: _avg=mean(); _=0; do k=1 for n; _=_+(#.k-_avg)**2; end; return sqrt(_/n)
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e: e =2.7182818284590452353602874713526624977572470936999595749669676277240766303535; return e /*digs overkill*/
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pi: pi=3.1415926535897932384626433832795028841971693993751058209749445923078164062862; return pi /* " " */
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r2r: return arg(1) // (2*pi()) /*normalize ang*/
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sqrt: procedure; parse arg x; if x=0 then return 0; d=digits(); numeric digits 11; numeric form; m.=11; p=d+d%4+2
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parse value format(x,2,1,,0) 'E0' with g 'E' _ .; g=g*.5'E'_%2; do j=0 while p>9; m.j=p; p=p%2+1; end
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do k=j+5 to 0 by -1; if m.k>11 then numeric digits m.k; g=.5*(g+x/g); end; numeric digits d; return g/1
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cos: procedure; parse arg x; x=r2r(x); a=abs(x); numeric fuzz min(9,digits()-9); if a=pi then return -1; pi2=pi+pi
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if a=pi*.5|a=pi2 then return 0; if a=pi/3 then return .5; if a=pi2/3 then return -.5; return .sincos(1,1,-1)
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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)
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.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
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ln: procedure; parse arg x,f; call e; ig= x>1.5; is=1-2*(ig\==1); ii=0; xx=x; return .ln_comp()
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.ln_comp: 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
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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
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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
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