Initial data commit

Task/Statistics-Normal-distribution/PureBasic/statistics-normal-distribution.basic

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+Procedure.f randomf(resolution = 2147483647)
+  ProcedureReturn Random(resolution) / resolution
+EndProcedure
+
+Procedure.f normalDist() ;Box Muller method
+   ProcedureReturn Sqr(-2 * Log(randomf())) * Cos(2 * #PI * randomf())
+EndProcedure
+
+Procedure sample(n, nBins = 50)
+  Protected i, maxBinValue, binNumber
+  Protected.f d, mean, sum, sumSq, mx, mn, range
+
+  Dim dat.f(n)
+  For i = 1 To n
+    dat(i) = normalDist()
+  Next
+
+  ;show mean, standard deviation, find max & min.
+  mx  = -1000
+  mn  =  1000
+  sum = 0
+  sumSq = 0
+  For i = 1 To n
+    d = dat(i)
+    If d > mx: mx = d: EndIf
+    If d < mn: mn = d: EndIf
+    sum + d
+    sumSq + d * d
+  Next
+
+  PrintN(Str(n) + " data terms used.")
+  PrintN("Largest term was " + StrF(mx) + " & smallest was " + StrF(mn))
+  mean = sum / n
+  PrintN("Mean = " + StrF(mean))
+  PrintN("Stddev = " + StrF((sumSq / n) - Sqr(mean * mean)))
+
+  ;show histogram
+  range = mx - mn
+  Dim bins(nBins)
+  For i = 1 To n
+    binNumber = Int(nBins * (dat(i) - mn) / range)
+    bins(binNumber) + 1
+  Next
+
+  maxBinValue = 1
+  For i = 0 To nBins
+    If bins(i) > maxBinValue
+      maxBinValue = bins(i)
+    EndIf
+  Next
+
+  #normalizedMaxValue = 70
+  For binNumber = 0 To nBins
+    tickMarks = Round(bins(binNumber) * #normalizedMaxValue / maxBinValue, #PB_Round_Nearest)
+    PrintN(ReplaceString(Space(tickMarks), " ", "#"))
+  Next
+  PrintN("")
+EndProcedure
+
+If OpenConsole()
+  sample(100000)
+
+  Print(#CRLF$ + #CRLF$ + "Press ENTER to exit"): Input()
+  CloseConsole()
+EndIf