September 2017 Update

Task/Random-numbers/Kotlin/random-numbers.kotlin

@@ -0,0 +1,14 @@
+// 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")
+}

Task/Random-numbers/Lua/random-numbers.lua

@@ -0,0 +1,4 @@
+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

Task/Random-numbers/Nimrod/random-numbers.nimrod

@@ -1,14 +0,0 @@
-import math, strutils
-
-const  precisn = 5
-var rs: TRunningStat
-
-proc normGauss: float {.inline.} = 1 + 0.76 * cos(2*PI*random(1.0)) * sqrt(-2*log10(random(1.0)))
-
-randomize()
-
-for j in 0..5:
-   for i in 0..1000:
-      rs.push(normGauss())
-   echo("mean: ", $formatFloat(rs.mean,ffDecimal,precisn),
-        " stdDev: ", $formatFloat(rs.standardDeviation(),ffDecimal,precisn))

Task/Random-numbers/OoRexx/random-numbers-1.rexx

@@ -0,0 +1,42 @@
+/*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

Task/Random-numbers/OoRexx/random-numbers-2.rexx

@@ -0,0 +1,46 @@
+/*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

Task/Random-numbers/Sidef/random-numbers.sidef

@@ -1,2 +1,2 @@
-var arr = 1000.of { 1 + (0.5 * (-2 * 1.rand.log -> sqrt) * (Number.tau * 1.rand -> cos)) }
+var arr = 1000.of { 1 + (0.5 * sqrt(-2 * 1.rand.log) * cos(Num.tau * 1.rand)) }
 arr.each { .say }

Task/Random-numbers/Stata/random-numbers.stata

@@ -0,0 +1,3 @@
+clear all
+set obs 1000
+gen x=rnormal(1,0.5)

Task/Random-numbers/Zkl/random-numbers-1.zkl

@@ -0,0 +1,5 @@
+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 })
+}

Task/Random-numbers/Zkl/random-numbers-2.zkl

@@ -0,0 +1,5 @@
+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