Averages/Arithmetic mean
Write a program to find the mean (arithmetic average) of a numeric vector.
You are encouraged to solve this task according to the task description, using any language you may know.
In case of a zero-length input, since the mean of an empty set of numbers is ill-defined, the program may choose to behave in any way it deems appropriate, though if the programming language has an established convention for conveying math errors or undefined values, it's preferable to follow it.
0815
<lang 0815> {x{+=<:2:x/%<:d:~$<:01:~><:02:~><:03:~><:04:~><:05:~><:06:~><:07:~><:08: ~><:09:~><:0a:~><:0b:~><:0c:~><:0d:~><:0e:~><:0f:~><:10:~><:11:~><:12:~> <:13:~><:14:~><:15:~><:16:~><:17:~><:18:~><:19:~><:ffffffffffffffff:~>{x {+>}:8f:{&={+>{~>&=x<:ffffffffffffffff:/#:8f:{{=<:19:x/% </lang>
- Output:
0 D
11l
<lang 11l>F average(x)
R sum(x) / Float(x.len)
print(average([0, 0, 3, 1, 4, 1, 5, 9, 0, 0]))</lang>
- Output:
2.3
360 Assembly
Compact and functional. <lang 360asm>AVGP CSECT
USING AVGP,12
LR 12,15
SR 3,3 i=0
SR 6,6 sum=0
LOOP CH 3,=AL2(NN-T-1) for i=1 to nn
BH ENDLOOP
L 2,T(3) t(i)
MH 2,=H'100' scaling factor=2
AR 6,2 sum=sum+t(i)
LA 3,4(3) next i
B LOOP
ENDLOOP LR 5,6 sum
LA 4,0
D 4,NN sum/nn
XDECO 5,Z edit binary
MVC U,Z+10 descale
MVI Z+10,C'.'
MVC Z+11(2),U
XPRNT Z,80 output
XR 15,15
BR 14
T DC F'10',F'9',F'8',F'7',F'6',F'5',F'4',F'3',F'2',F'1' NN DC A((NN-T)/4) Z DC CL80' ' U DS CL2
END AVGP</lang>
- Output:
5.50
6502 Assembly
Called as a subroutine (i.e., JSR ArithmeticMean), this calculates the integer average of up to 255 8-bit unsigned integers. The address of the beginning of the list of integers is in the memory location ArrayPtr and the number of integers is in the memory location NumberInts. The arithmetic mean is returned in the memory location ArithMean.
<lang 6502asm>ArithmeticMean: PHA TYA PHA ;push accumulator and Y register onto stack
LDA #0
STA Temp
STA Temp+1 ;temporary 16-bit storage for total
LDY NumberInts BEQ Done ;if NumberInts = 0 then return an average of zero
DEY ;start with NumberInts-1 AddLoop: LDA (ArrayPtr),Y CLC ADC Temp STA Temp LDA Temp+1 ADC #0 STA Temp+1 DEY CPY #255 BNE AddLoop
LDY #-1 DivideLoop: LDA Temp SEC SBC NumberInts STA Temp LDA Temp+1 SBC #0 STA Temp+1 INY BCS DivideLoop
Done: STY ArithMean ;store result here PLA ;restore accumulator and Y register from stack TAY PLA RTS ;return from routine</lang>
8th
<lang forth>
- avg \ a -- avg(a)
dup ' n:+ 0 a:reduce swap a:len nip n:/ ;
\ test:
[ 1.0, 2.3, 1.1, 5.0, 3, 2.8, 2.01, 3.14159 ] avg . cr
[ ] avg . cr
[ 10 ] avg . cr
bye
</lang>
Output is:
2.54395
NaN
10.00000
ACL2
<lang Lisp>(defun mean-r (xs)
(if (endp xs)
(mv 0 0)
(mv-let (m j)
(mean-r (rest xs))
(mv (+ (first xs) m) (+ j 1)))))
(defun mean (xs)
(if (endp xs)
0
(mv-let (n d)
(mean-r xs)
(/ n d))))</lang>
Action!
<lang Action!>INCLUDE "D2:REAL.ACT" ;from the Action! Tool Kit
PROC Mean(INT ARRAY a INT count REAL POINTER result)
INT i REAL x,sum,tmp
IntToReal(0,sum) FOR i=0 TO count-1 DO IntToReal(a(i),x) RealAdd(sum,x,tmp) RealAssign(tmp,sum) OD IntToReal(count,tmp) RealDiv(sum,tmp,result)
RETURN
PROC Test(INT ARRAY a INT count)
INT i REAL result
Mean(a,count,result)
Print("mean(")
FOR i=0 TO count-1
DO
PrintI(a(i))
IF i<count-1 THEN
Put(',)
FI
OD
Print(")=")
PrintRE(result)
RETURN
PROC Main()
INT ARRAY a1=[1 2 3 4 5 6] INT ARRAY a2=[1 10 100 1000 10000] INT ARRAY a3=[9]
Put(125) PutE() ;clear screen Test(a1,6) Test(a2,5) Test(a3,1) Test(a3,0)
RETURN</lang>
- Output:
Screenshot from Atari 8-bit computer
mean(1,2,3,4,5,6)=3.5 mean(1,10,100,1000,10000)=2222.2 mean(9)=9 mean()=0
ActionScript
<lang ActionScript>function mean(vector:Vector.<Number>):Number { var sum:Number = 0; for(var i:uint = 0; i < vector.length; i++) sum += vector[i]; return vector.length == 0 ? 0 : sum / vector.length; }</lang>
Ada
This example shows how to pass a zero length vector as well as a larger vector. With Ada 2012 it is possible to check that pre conditions are satisfied (otherwise an exception is thrown). So we check that the length is not zero. <lang ada>with Ada.Float_Text_Io; use Ada.Float_Text_Io; with Ada.Text_IO; use Ada.Text_IO;
procedure Mean_Main is
type Vector is array (Positive range <>) of Float;
function Mean (Item : Vector) return float with pre => Item'length > 0;
function Mean (Item : Vector) return Float is
Sum : Float := 0.0;
begin
for I in Item'range loop
Sum := Sum + Item(I);
end loop;
return Sum / Float(Item'Length);
end Mean; A : Vector := (3.0, 1.0, 4.0, 1.0, 5.0, 9.0);
begin
Put(Item => Mean (A), Fore => 1, Exp => 0); New_Line; -- test for zero length vector Put(Item => Mean(A (1..0)), Fore => 1, Exp => 0); New_Line;
end Mean_Main;</lang> Output: 3.83333
raised SYSTEM.ASSERTIONS.ASSERT_FAILURE : failed precondition from mean_main.adb:6
Aime
<lang aime>real mean(list l) {
real sum, x;
sum = 0;
for (, x in l) {
sum += x;
}
sum / ~l;
}
integer main(void) {
o_form("%f\n", mean(list(4.5, 7.25, 5r, 5.75)));
0;
}</lang>
ALGOL 68
<lang algol68>PROC mean = (REF[]REAL p)REAL:
- Calculates the mean of qty REALs beginning at p. #
IF LWB p > UPB p THEN 0.0 ELSE REAL total := 0.0; FOR i FROM LWB p TO UPB p DO total +:= p[i] OD; total / (UPB p - LWB p + 1) FI;
main:(
[6]REAL test := (1.0, 2.0, 5.0, -5.0, 9.5, 3.14159); print((mean(test),new line))
)</lang>
ALGOL W
<lang algolw>begin
% procedure to find the mean of the elements of a vector. %
% As the procedure can't find the bounds of the array for itself, %
% we pass them in lb and ub %
real procedure mean ( real array vector ( * )
; integer value lb
; integer value ub
) ;
begin
real sum;
assert( ub > lb ); % terminate the program if there are no elements %
sum := 0;
for i := lb until ub do sum := sum + vector( i );
sum / ( ( ub + 1 ) - lb )
end mean ;
% test the mean procedure by finding the mean of 1.1, 2.2, 3.3, 4.4, 5.5 % real array numbers ( 1 :: 5 ); for i := 1 until 5 do numbers( i ) := i + ( i / 10 ); r_format := "A"; r_w := 10; r_d := 2; % set fixed point output % write( mean( numbers, 1, 5 ) );
end.</lang>
AmigaE
Because of the way Amiga E handles floating point numbers, the passed list/vector must contain all explicitly floating point values (e.g., you need to write "1.0", not "1") <lang amigae>PROC mean(l:PTR TO LONG)
DEF m, i, ll ll := ListLen(l) IF ll = 0 THEN RETURN 0.0 m := 0.0 FOR i := 0 TO ll-1 DO m := !m + l[i] m := !m / (ll!)
ENDPROC m
PROC main()
DEF s[20] : STRING
WriteF('mean \s\n',
RealF(s,mean([1.0, 2.0, 3.0, 4.0, 5.0]), 2))
ENDPROC</lang>
AntLang
AntLang has a built-in avg function. <lang AntLang>avg[list]</lang>
APL
<lang apl> X←3 1 4 1 5 9
(+/X)÷⍴X
3.833333333</lang>
AppleScript
Vanilla
With vanilla AppleScript, the process is the literal one of adding the numbers and dividing by the list length. It naturally returns results of class real, but it would be simple to return integer-representable results as integers if required.
<lang applescript>on average(listOfNumbers)
set len to (count listOfNumbers)
if (len is 0) then return missing value
set sum to 0
repeat with thisNumber in listOfNumbers
set sum to sum + thisNumber
end repeat
return sum / len
end average
average({2500, 2700, 2400, 2300, 2550, 2650, 2750, 2450, 2600, 2400})</lang>
- Output:
2530.0
ASObjC
The vanilla method above is the more efficient with lists of up to around 100 numbers. But for longer lists, using Foundation methods with AppleScriptObjectC can be useful
<lang applescript>use AppleScript version "2.4" -- OS X 10.10 (Yosemite) or later use framework "Foundation"
on average(listOfNumbers)
if ((count listOfNumbers) is 0) then return missing value
set arrayOfNumbers to current application's class "NSArray"'s arrayWithArray:(listOfNumbers)
return (arrayOfNumbers's valueForKeyPath:("@avg.self")) as real
end average
average({2500, 2700, 2400, 2300, 2550, 2650, 2750, 2450, 2600, 2400})</lang>
- Output:
2530.0
Applesoft BASIC
<lang ApplesoftBasic>REM COLLECTION IN DATA STATEMENTS, EMPTY DATA IS THE END OF THE COLLECTION
0 READ V$ 1 IF LEN(V$) = 0 THEN END 2 N = 0 3 S = 0 4 FOR I = 0 TO 1 STEP 0 5 S = S + VAL(V$) 6 N = N + 1 7 READ V$ 8 IF LEN(V$) THEN NEXT 9 PRINT S / N
10000 DATA1,2,2.718,3,3.142 63999 DATA
REM COLLECTION IN AN ARRAY, ITEM 0 IS THE SIZE OF THE COLLECTION A(0) = 5 : A(1) = 1 : A(2) = 2 : A(3) = 2.718 : A(4) = 3 : A(5) = 3.142 N = A(0) : IF N THEN S = 0 : FOR I = 1 TO N : S = S + A(I) : NEXT : ? S / N </lang>
Arturo
<lang rebol>arr: [1 2 3 4 5 6 7]
print average arr</lang>
- Output:
4.0
Astro
<lang astro>mean([1, 2, 3]) mean(1..10) mean([]) </lang>
AutoHotkey
<lang autohotkey>i = 10 Loop, % i {
Random, v, -3.141592, 3.141592 list .= v "`n" sum += v
} MsgBox, % i ? list "`nmean: " sum/i:0</lang>
AWK
<lang awk>cat mean.awk
- !/usr/local/bin/gawk -f
- User defined function
function mean(v, i,n,sum) {
for (i in v) {
n++
sum += v[i]
}
if (n>0) {
return(sum/n)
} else {
return("zero-length input !")
}
}
BEGIN {
# fill a vector with random numbers
for(i=0; i < 10; i++) {
vett[i] = rand()*10
}
print mean(vett)
print mean(nothing)
} </lang>
- Output:
$ awk -f mean.awk 3.92689 zero-length input !
Babel
<lang babel>(3 24 18 427 483 49 14 4294 2 41) dup len <- sum ! -> / itod <<</lang>
- Output:
535
BASIC
Assume the numbers are in an array named "nums". <lang qbasic>mean = 0 sum = 0; FOR i = LBOUND(nums) TO UBOUND(nums)
sum = sum + nums(i);
NEXT i size = UBOUND(nums) - LBOUND(nums) + 1 PRINT "The mean is: "; IF size <> 0 THEN
PRINT (sum / size)
ELSE
PRINT 0
END IF</lang>
BBC BASIC
To calculate the mean of an array: <lang BBC BASIC>
REM specific functions for the array/vector types
REM Byte Array
DEF FN_Mean_Arithmetic&(n&())
= SUM(n&()) / (DIM(n&(),1)+1)
REM Integer Array
DEF FN_Mean_Arithmetic%(n%())
= SUM(n%()) / (DIM(n%(),1)+1)
REM Float 40 array
DEF FN_Mean_Arithmetic(n())
= SUM(n()) / (DIM(n(),1)+1)
REM A String array
DEF FN_Mean_Arithmetic$(n$())
LOCAL I%, S%, sum, Q%
S% = DIM(n$(),1)
FOR I% = 0 TO S%
Q% = TRUE
ON ERROR LOCAL Q% = FALSE
IF Q% sum += EVAL(n$(I%))
NEXT
= sum / (S%+1)
REM Float 64 array
DEF FN_Mean_Arithmetic#(n#())
= SUM(n#()) / (DIM(n#(),1)+1)
</lang> Michael Hutton 14:02, 29 May 2011 (UTC)
IS-BASIC
<lang IS-BASIC>100 NUMERIC ARR(3 TO 8) 110 LET ARR(3)=3:LET ARR(4)=1:LET ARR(5)=4:LET ARR(6)=1:LET ARR(7)=5:LET ARR(8)=9 120 PRINT AM(ARR) 130 DEF AM(REF A) 140 LET T=0 150 FOR I=LBOUND(A) TO UBOUND(A) 160 LET T=T+A(I) 170 NEXT 180 LET AM=T/SIZE(A) 190 END DEF</lang>
bc
Uses the current scale for calculating the mean. <lang bc>define m(a[], n) {
auto i, s
for (i = 0; i < n; i++) {
s += a[i]
}
return(s / n)
}</lang>
Befunge
The first input is the length of the vector. If a length of 0 is entered, the result is equal to 0/0.
<lang befunge>&:0\:!v!:-1<
@./\$_\&+\^</lang>
blz
<lang blz>
- mean(vec)
vec.fold_left(0, (x, y -> x + y)) / vec.length()
end</lang>
Bracmat
Here are two solutions. The first uses a while loop, the second scans the input by backtracking. <lang bracmat> (mean1=
sum length n
. 0:?sum:?length
& whl
' ( !arg:%?n ?arg
& 1+!length:?length
& !n+!sum:?sum
)
& !sum*!length^-1
);
(mean2=
sum length n
. 0:?sum:?length
& !arg
: ?
( #%@?n
& 1+!length:?length
& !n+!sum:?sum
& ~
)
?
| !sum*!length^-1
); </lang> To test with a list of all numbers 1 .. 999999: <lang bracmat> ( :?test & 1000000:?Length & whl'(!Length+-1:?Length:>0&!Length !test:?test) & out$mean1$!test & out$mean2$!test )</lang>
Brat
<lang brat>mean = { list |
true? list.empty?, 0, { list.reduce(0, :+) / list.length }
}
p mean 1.to 10 #Prints 5.5</lang>
Burlesque
<lang burlesque> blsq ) {1 2 2.718 3 3.142}av 2.372 blsq ) {}av NaN </lang>
BQN
Defines a tacit Avg function which works on any simple numeric list.
<lang bqn>Avg ← +´÷≠
Avg 1‿2‿3‿4</lang> <lang>2.5</lang>
C
Compute mean of a double array of given length. If length is zero, does whatever 0.0/0 does (usually means returning NaN).
<lang c>#include <stdio.h>
double mean(double *v, int len) { double sum = 0; int i; for (i = 0; i < len; i++) sum += v[i]; return sum / len; }
int main(void) { double v[] = {1, 2, 2.718, 3, 3.142}; int i, len; for (len = 5; len >= 0; len--) { printf("mean["); for (i = 0; i < len; i++) printf(i ? ", %g" : "%g", v[i]); printf("] = %g\n", mean(v, len)); }
return 0;
}</lang>
- Output:
mean[1, 2, 2.718, 3, 3.142] = 2.372 mean[1, 2, 2.718, 3] = 2.1795 mean[1, 2, 2.718] = 1.906 mean[1, 2] = 1.5 mean[1] = 1 mean[] = -nan
C#
<lang csharp>using System; using System.Linq;
class Program {
static void Main()
{
Console.WriteLine(new[] { 1, 2, 3 }.Average());
}
}</lang>
Alternative version (not using the built-in function): <lang csharp>using System;
class Program {
static void Main(string[] args)
{
double average = 0;
double[] numArray = { 1, 2, 3, 4, 5 };
average = Average(numArray);
Console.WriteLine(average); // Output is 3
// Alternative use
average = Average(1, 2, 3, 4, 5);
Console.WriteLine(average); // Output is still 3
Console.ReadLine();
}
static double Average(params double[] nums)
{
double d = 0;
foreach (double num in nums)
d += num;
return d / nums.Length;
}
}</lang>
C++
<lang cpp>#include <vector>
double mean(const std::vector<double>& numbers) {
if (numbers.size() == 0)
return 0;
double sum = 0;
for (std::vector<double>::iterator i = numbers.begin(); i != numbers.end(); i++)
sum += *i;
return sum / numbers.size();
}</lang>
Shorter (and more idiomatic) version:
<lang cpp>#include <vector>
- include <algorithm>
double mean(const std::vector<double>& numbers) {
if (numbers.empty())
return 0;
return std::accumulate(numbers.begin(), numbers.end(), 0.0) / numbers.size();
}</lang>
Idiomatic version templated on any kind of iterator:
<lang cpp>#include <iterator>
- include <algorithm>
template <typename Iterator> double mean(Iterator begin, Iterator end) {
if (begin == end)
return 0;
return std::accumulate(begin, end, 0.0) / std::distance(begin, end);
}</lang>
Chef
<lang Chef>Mean.
Chef has no way to detect EOF, so rather than interpreting some arbitrary number as meaning "end of input", this program expects the first input to be the sample size. Pass in the samples themselves as the other inputs. For example, if you wanted to compute the mean of 10, 100, 47, you could pass in 3, 10, 100, and 47. To test the "zero-length vector" case, you need to pass in 0.
Ingredients. 0 g Sample Size 0 g Counter 0 g Current Sample
Method. Take Sample Size from refrigerator. Put Sample Size into mixing bowl. Fold Counter into mixing bowl. Put Current Sample into mixing bowl. Loop Counter. Take Current Sample from refrigerator. Add Current Sample into mixing bowl. Endloop Counter until looped. If Sample Size. Divide Sample Size into mixing bowl. Put Counter into 2nd mixing bowl. Fold Sample Size into 2nd mixing bowl. Endif until ifed. Pour contents of mixing bowl into baking dish.
Serves 1.</lang>
Clojure
Returns a ratio: <lang lisp>(defn mean [sq]
(if (empty? sq)
0
(/ (reduce + sq) (count sq))))</lang>
Returns a float: <lang lisp>(defn mean [sq]
(if (empty? sq)
0
(float (/ (reduce + sq) (count sq)))))</lang>
COBOL
Intrinsic function: <lang cobol>FUNCTION MEAN(some-table (ALL))</lang>
Sample implementation: <lang cobol> IDENTIFICATION DIVISION.
PROGRAM-ID. find-mean.
DATA DIVISION.
LOCAL-STORAGE SECTION.
01 i PIC 9(4).
01 summ USAGE FLOAT-LONG.
LINKAGE SECTION.
01 nums-area.
03 nums-len PIC 9(4).
03 nums USAGE FLOAT-LONG
OCCURS 0 TO 1000 TIMES
DEPENDING ON nums-len.
01 result USAGE FLOAT-LONG.
PROCEDURE DIVISION USING nums-area, result.
IF nums-len = 0
MOVE 0 TO result
GOBACK
END-IF
DIVIDE FUNCTION SUM(nums (ALL)) BY nums-len GIVING result
GOBACK
.</lang>
Cobra
<lang cobra> class Rosetta def mean(ns as List<of number>) as number if ns.count == 0 return 0 else sum = 0.0 for n in ns sum += n return sum / ns.count
def main print "mean of [[]] is [.mean(List<of number>())]" print "mean of 1,2,3,4 is [.mean([1.0,2.0,3.0,4.0])]" </lang>
Output:
mean of [] is 0 mean of [1, 2, 3, 4] is 2.5
CoffeeScript
<lang coffeescript> mean = (array) ->
return 0 if array.length is 0 sum = array.reduce (s,i,0) -> s += i sum / array.length
alert mean [1] </lang>
Common Lisp
With Reduce
<lang lisp>(defun mean (&rest sequence)
(when sequence (/ (reduce #'+ sequence) (length sequence))))</lang>
With Loop <lang lisp>(defun mean (list)
(when list
(/ (loop for i in list sum i)
(length list))))</lang>
Crystal
<lang ruby># Crystal will return NaN if an empty array is passed def mean(arr) : Float64
arr.sum / arr.size.to_f
end</lang>
D
Imperative Version
<lang d>real mean(Range)(Range r) pure nothrow @nogc {
real sum = 0.0; int count;
foreach (item; r) {
sum += item;
count++;
}
if (count == 0)
return 0.0;
else
return sum / count;
}
void main() {
import std.stdio;
int[] data;
writeln("Mean: ", data.mean);
data = [3, 1, 4, 1, 5, 9];
writeln("Mean: ", data.mean);
}</lang>
- Output:
mean: 0 mean: 3.83333
More Functional Version
<lang d>import std.stdio, std.algorithm, std.range;
real mean(Range)(Range r) pure nothrow @nogc {
return r.sum / max(1.0L, r.count);
}
void main() {
writeln("Mean: ", (int[]).init.mean);
writeln("Mean: ", [3, 1, 4, 1, 5, 9].mean);
}</lang>
- Output:
Mean: 0 Mean: 3.83333
More Precise Version
A (naive?) version that tries to minimize precision loss (but already the sum algorithm applied to a random access range of floating point values uses a more precise summing strategy): <lang d>import std.stdio, std.conv, std.algorithm, std.math, std.traits;
CommonType!(T, real) mean(T)(T[] n ...) if (isNumeric!T) {
alias E = CommonType!(T, real); auto num = n.dup; num.schwartzSort!(abs, "a > b"); return num.map!(to!E).sum(0.0L) / max(1, num.length);
}
void main() {
writefln("%8.5f", mean((int[]).init));
writefln("%8.5f", mean( 0, 3, 1, 4, 1, 5, 9, 0));
writefln("%8.5f", mean([-1e20, 3, 1, 4, 1, 5, 9, 1e20]));
}</lang>
- Output:
0.00000 2.87500 2.87500
Dart
<lang d>num mean(List<num> l) => l.reduce((num p, num n) => p + n) / l.length;
void main(){
print(mean([1,2,3,4,5,6,7]));
}</lang>
- Output:
4.0
dc
This is not a translation of the bc solution. Array handling would add some complexity. This one-liner is similar to the K solution.
<lang dc>1 2 3 5 7 zsn1k[+z1<+]ds+xln/p 3.6</lang>
An expanded example, identifying an empty sample set, could be created as a file, e.g., amean.cd:
<lang dc>[[Nada Mean: ]Ppq]sq zd0=qsn [stack length = n]sz 1k [precision can be altered]sz [+z1<+]ds+x[Sum: ]Pp ln/[Mean: ]Pp [Sample size: ]Plnp</lang>
By saving the sample set "1 2 3 5 7" in a file (sample.dc), the routine, listing summary information, could be called in a command line:
<lang dc>$ dc sample.dc amean.cd Sum: 18 Mean: 3.6 Sample size: 5 $</lang>
Delphi
<lang Delphi>program AveragesArithmeticMean;
{$APPTYPE CONSOLE}
uses Types;
function ArithmeticMean(aArray: TDoubleDynArray): Double; var
lValue: Double;
begin
Result := 0;
for lValue in aArray do Result := Result + lValue; if Result > 0 then Result := Result / Length(aArray);
end;
begin
Writeln(Mean(TDoubleDynArray.Create())); Writeln(Mean(TDoubleDynArray.Create(1,2,3,4,5)));
end.</lang>
Dyalect
<lang dyalect>func avg(args...) {
var acc = .0
var len = 0
for x in args {
len += 1
acc += x
}
acc / len
}
avg(1, 2, 3, 4, 5, 6)</lang>
E
Slightly generalized to support any object that allows iteration.
<lang e>def meanOrZero(numbers) {
var count := 0
var sum := 0
for x in numbers {
sum += x
count += 1
}
return sum / 1.max(count)
}</lang>
EasyLang
<lang>func mean . f[] r .
for i range len f[] s += f[i] . r = s / len f[]
. f[] = [ 1 2 3 4 5 6 7 8 ] call mean f[] r print r</lang>
EchoLisp
(mean values) is included in math.lib. values may be a list, vector, sequence, or any kind of procrastinator. <lang scheme> (lib 'math) (mean '(1 2 3 4)) ;; mean of a list
→ 2.5
(mean #(1 2 3 4)) ;; mean of a vector
→ 2.5
(lib 'sequences) (mean [1 3 .. 10]) ;; mean of a sequence
→ 5
- error handling
(mean 'elvis)
⛔ error: mean : expected sequence : elvis
(mean ())
💣 error: mean : null is not an object
(mean #())
😐 warning: mean : zero-divide : empty-vector → 0
(mean [2 2 .. 2])
😁 warning: mean : zero-divide : empty-sequence → 0
</lang>
ECL
<lang ecl> AveVal(SET OF INTEGER s) := AVE(s);
//example usage
SetVals := [14,9,16,20,91]; AveVal(SetVals) //returns 30.0 ; </lang>
Elena
ELENA 5.0: <lang elena>import extensions;
extension op {
average()
{
real sum := 0;
int count := 0;
var enumerator := self.enumerator();
while (enumerator.next())
{
sum += enumerator.get();
count += 1;
};
^ sum / count
}
}
public program() {
var array := new int[]{1, 2, 3, 4, 5, 6, 7, 8};
console.printLine(
"Arithmetic mean of {",array.asEnumerable(),"} is ",
array.average()).readChar()
}</lang>
- Output:
Arithmetic mean of {1,2,3,4,5,6,7,8} is 4.5
Elixir
<lang elixir>defmodule Average do
def mean(list), do: Enum.sum(list) / length(list)
end</lang>
Emacs Lisp
<lang lisp>(defun mean (lst)
(/ (float (apply '+ lst)) (length lst)))
(mean '(1 2 3 4))</lang>
<lang lisp>(let ((x '(1 2 3 4)))
(calc-eval "vmean($1)" nil (append '(vec) x)))</lang>
Erlang
<lang erlang>mean([]) -> 0; mean(L) -> lists:sum(L)/erlang:length(L).</lang>
Euphoria
<lang Euphoria>function mean(sequence s)
atom sum
if length(s) = 0 then
return 0
else
sum = 0
for i = 1 to length(s) do
sum += s[i]
end for
return sum/length(s)
end if
end function
sequence test test = {1.0, 2.0, 5.0, -5.0, 9.5, 3.14159} ? mean(test)</lang>
Excel
Assuming the values are entered in the A column, type into any cell which will not be part of the list:
<lang excel>=AVERAGE(A1:A10)</lang>
Assuming 10 values will be entered, alternatively, you can just type:
<lang excel>=AVERAGE(</lang>
and then select the start and end cells, not necessarily in the same row or column.
The output for the first expression, for the set {x | 1 <= x <= 10, x E N} is
1 5,5 2 3 4 5 6 7 8 9 10
F#
The following computes the running mean using a tail-recursive approach. If we just sum all the values then divide by the number of values then we will suffer from overflow problems for large lists. See wikipedia about the moving average computation. <lang fsharp>let avg (a:float) (v:float) n =
a + (1. / ((float n) + 1.)) * (v - a)
let mean_series list =
let a, _ = List.fold_left (fun (a, n) h -> avg a (float h) n, n + 1) (0., 0) list in a</lang>
Checking this: <lang fsharp> > mean_series [1; 8; 2; 8; 1; 7; 1; 8; 2; 7; 3; 6; 1; 8; 100] ;;
val it : float = 10.86666667 > mean_series [] ;; val it : float = 0.0</lang>
We can also make do with the built-in List.average function: <lang fsharp>List.average [4;1;7;5;8;4;5;2;1;5;2;5]</lang>
Factor
<lang factor>USING: math math.statistics ;
- arithmetic-mean ( seq -- n )
[ 0 ] [ mean ] if-empty ;</lang>
Tests:
<lang factor>( scratchpad ) { 2 3 5 } arithmetic-mean >float 3.333333333333333</lang>
Fantom
<lang fantom> class Main {
static Float average (Float[] nums)
{
if (nums.size == 0) return 0.0f
Float sum := 0f
nums.each |num| { sum += num }
return sum / nums.size.toFloat
}
public static Void main ()
{
[[,], [1f], [1f,2f,3f,4f]].each |Float[] i|
{
echo ("Average of $i is: " + average(i))
}
}
} </lang>
Fish
<lang Fish>!vl0=?vl1=?vl&! v< +<>0n; >n; >l1)?^&,n;</lang> Must be called with the values pre-populated on the stack, which can be done in the fish.py interpreter with the -v switch:
fish.py mean.fish -v 10 100 47 207.4
which generates:
91.1
Forth
<lang forth>: fmean ( addr n -- f )
0e dup 0= if 2drop exit then tuck floats bounds do i f@ f+ 1 floats +loop 0 d>f f/ ;
create test 3e f, 1e f, 4e f, 1e f, 5e f, 9e f, test 6 fmean f. \ 3.83333333333333</lang>
Fortran
In ISO Fortran 90 or later, use the SUM intrinsic, the SIZE intrinsic and the MAX intrinsic (to avoid divide by zero): <lang fortran>real, target, dimension(100) :: a = (/ (i, i=1, 100) /) real, dimension(5,20) :: b = reshape( a, (/ 5,20 /) ) real, pointer, dimension(:) :: p => a(2:1) ! pointer to zero-length array real :: mean, zmean, bmean real, dimension(20) :: colmeans real, dimension(5) :: rowmeans
mean = sum(a)/size(a) ! SUM of A's elements divided by SIZE of A mean = sum(a)/max(size(a),1) ! Same result, but safer code
! MAX of SIZE and 1 prevents divide by zero if SIZE == 0 (zero-length array)
zmean = sum(p)/max(size(p),1) ! Here the safety check pays off. Since P is a zero-length array,
! expression becomes "0 / MAX( 0, 1 ) -> 0 / 1 -> 0", rather than "0 / 0 -> NaN"
bmean = sum(b)/max(size(b),1) ! multidimensional SUM over multidimensional SIZE
rowmeans = sum(b,1)/max(size(b,2),1) ! SUM elements in each row (dimension 1)
! dividing by the length of the row, which is the number of columns (SIZE of dimension 2)
colmeans = sum(b,2)/max(size(b,1),1) ! SUM elements in each column (dimension 2)
! dividing by the length of the column, which is the number of rows (SIZE of dimension 1)</lang>
FreeBASIC
<lang freebasic> ' FB 1.05.0 Win64
Function Mean(array() As Double) As Double
Dim length As Integer = Ubound(array) - Lbound(array) + 1 If length = 0 Then Return 0.0/0.0 'NaN End If Dim As Double sum = 0.0 For i As Integer = LBound(array) To UBound(array) sum += array(i) Next Return sum/length
End Function
Function IsNaN(number As Double) As Boolean
Return Str(number) = "-1.#IND" ' NaN as a string in FB
End Function
Dim As Integer n, i Dim As Double num Print "Sample input and output" Print Do
Input "How many numbers are to be input ? : ", n
Loop Until n > 0 Dim vector(1 To N) As Double Print For i = 1 to n
Print " Number #"; i; " : "; Input "", vector(i)
Next Print Print "Mean is"; Mean(vector()) Print Erase vector num = Mean(vector()) If IsNaN(num) Then
Print "After clearing the vector, the mean is 'NaN'"
End If Print Print "Press any key to quit the program" Sleep </lang>
- Output:
Sample input and output How many numbers are to be input ? : 6 Number # 1 : 12 Number # 2 : 18 Number # 3 : 5.6 Number # 4 : 6 Number # 5 : 23 Number # 6 : 17 Mean is 13.6 After clearing the vector, the mean is 'NaN'
Frink
The following works on arrays or sets. If the collection is empty, this returns the special value undef.
<lang frink>
mean[x] := length[x] > 0 ? sum[x] / length[x] : undef
</lang>
GAP
<lang gap>Mean := function(v)
local n; n := Length(v); if n = 0 then return 0; else return Sum(v)/n; fi;
end;
Mean([3, 1, 4, 1, 5, 9]);
- 23/6</lang>
GEORGE
<lang GEORGE>R (n) P ; 0 1, n rep (i)
R P +
] n div P</lang> Output:
7.000000000000000 1.500000000000000E+0001 1.300000000000000E+0001 8.000000000000000 2.500000000000000E+0001 7.400000000000000E+0001 3.100000000000000E+0001 2.900000000000000E+0001 1.700000000000000E+0001 4.300000000000000E+0001 2.620000000000000E+0001
GFA Basic
This works for arrays of integers.
<lang> DIM a%(10) FOR i%=0 TO 10
a%(i%)=i%*2 PRINT "element ";i%;" is ";a%(i%)
NEXT i% PRINT "mean is ";@mean(a%) ' FUNCTION mean(a%)
LOCAL i%,size%,sum ' find size of array, size%=DIM?(a%()) ' return 0 for empty arrays IF size%<=0 RETURN 0 ENDIF ' find sum of all elements sum=0 FOR i%=0 TO size%-1 sum=sum+a%(i%) NEXT i% ' mean is sum over size RETURN sum/size%
ENDFUNC </lang>
Go
A little more elaborate that the task requires. The function "mean" fulfills the task of "a program to find the mean." As a Go idiom, it returns an ok value of true if result m is valid. An ok value of false means the input "vector" (a Go slice) was empty. The fancy accuracy preserving algorithm is a little more than was called more. The program main is a test program demonstrating the ok idiom and several data cases.
<lang go>package main
import (
"fmt" "math"
)
func mean(v []float64) (m float64, ok bool) {
if len(v) == 0 {
return
}
// an algorithm that attempts to retain accuracy
// with widely different values.
var parts []float64
for _, x := range v {
var i int
for _, p := range parts {
sum := p + x
var err float64
switch ax, ap := math.Abs(x), math.Abs(p); {
case ax < ap:
err = x - (sum - p)
case ap < ax:
err = p - (sum - x)
}
if err != 0 {
parts[i] = err
i++
}
x = sum
}
parts = append(parts[:i], x)
}
var sum float64
for _, x := range parts {
sum += x
}
return sum / float64(len(v)), true
}
func main() {
for _, v := range [][]float64{
[]float64{}, // mean returns ok = false
[]float64{math.Inf(1), math.Inf(1)}, // answer is +Inf
// answer is NaN, and mean returns ok = true, indicating NaN
// is the correct result
[]float64{math.Inf(1), math.Inf(-1)},
[]float64{3, 1, 4, 1, 5, 9},
// large magnitude numbers cancel. answer is mean of small numbers.
[]float64{1e20, 3, 1, 4, 1, 5, 9, -1e20},
[]float64{10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0, .11},
[]float64{10, 20, 30, 40, 50, -100, 4.7, -11e2},
} {
fmt.Println("Vector:", v)
if m, ok := mean(v); ok {
fmt.Printf("Mean of %d numbers is %g\n\n", len(v), m)
} else {
fmt.Println("Mean undefined\n")
}
}
}</lang>
- Output:
Vector: [] Mean undefined Vector: [+Inf +Inf] Mean of 2 numbers is +Inf Vector: [+Inf -Inf] Mean of 2 numbers is NaN Vector: [3 1 4 1 5 9] Mean of 6 numbers is 3.8333333333333335 Vector: [1e+20 3 1 4 1 5 9 -1e+20] Mean of 8 numbers is 2.875 Vector: [10 9 8 7 6 5 4 3 2 1 0 0 0 0 0.11] Mean of 15 numbers is 3.674 Vector: [10 20 30 40 50 -100 4.7 -1100] Mean of 8 numbers is -130.6625
Groovy
<lang groovy>def avg = { list -> list == [] ? 0 : list.sum() / list.size() }</lang>
Test Program: <lang groovy>println avg(0..9) println avg([2,2,2,4,2]) println avg ([])</lang>
Output:
4.5 2.4 0
Haskell
This function works if the element type is an instance of Fractional: <lang haskell>mean :: (Fractional a) => [a] -> a mean [] = 0 mean xs = sum xs / Data.List.genericLength xs</lang>
But some types, e.g. integers, are not Fractional; the following function works for all Real types: <lang haskell>meanReals :: (Real a, Fractional b) => [a] -> b meanReals = mean . map realToFrac</lang>
If you want to avoid keeping the list in memory and traversing it twice:
<lang haskell>{-# LANGUAGE BangPatterns #-}
import Data.List (foldl') --'
mean
:: (Real n, Fractional m) => [n] -> m
mean xs =
let (s, l) =
foldl' --'
f
(0, 0)
xs
in realToFrac s / l
where
f (!s, !l) x = (s + x, l + 1)
main :: IO () main = print $ mean [1 .. 100]</lang>
HicEst
<lang hicest>REAL :: vec(100) ! no zero-length arrays in HicEst
vec = $ - 1/2 ! 0.5 ... 99.5 mean = SUM(vec) / LEN(vec) ! 50
END </lang>
Hy
Returns None if the input is of length zero. <lang clojure>(defn arithmetic-mean [xs]
(if xs
(/ (sum xs) (len xs))))</lang>
Icon and Unicon
<lang icon>procedure main(args)
every (s := 0) +:= !args write((real(s)/(0 ~= *args)) | 0)
end</lang>
Sample outputs:
->am 1 2 3 4 5 6 7 4.0 ->am 0 ->
IDL
If truly only the mean is wanted, one could use
<lang idl>x = [3,1,4,1,5,9] print,mean(x)</lang>
But mean() is just a thin wrapper returning the zeroth element of moment() :
<lang idl>print,moment(x)
- ==>
3.83333 8.96667 0.580037 -1.25081</lang>
which are mean, variance, skewness and kurtosis.
There are no zero-length vectors in IDL. Every variable has at least one value or otherwise it is <Undefined>.
J
<lang j>mean=: +/ % #</lang>
That is, sum divided by the number of items. The verb also works on higher-ranked arrays. For example:
<lang j> mean 3 1 4 1 5 9 3.83333
mean $0 NB. $0 is a zero-length vector
0
x=: 20 4 ?@$ 0 NB. a 20-by-4 table of random (0,1) numbers mean x
0.58243 0.402948 0.477066 0.511155</lang>
The computation can also be written as a loop. It is shown here for comparison only and is highly non-preferred compared to the version above.
<lang j>mean1=: 3 : 0
z=. 0
for_i. i.#y do. z=. z+i{y end.
z % #y
)
mean1 3 1 4 1 5 9
3.83333
mean1 $0
0
mean1 x
0.58243 0.402948 0.477066 0.511155</lang>
Java
<lang java5>public static double avg(double... arr) {
double sum = 0.0;
for (double x : arr) {
sum += x;
}
return sum / arr.length;
}</lang>
JavaScript
ES5
<lang javascript>function mean(array) {
var sum = 0, i;
for (i = 0; i < array.length; i++)
{
sum += array[i];
}
return array.length ? sum / array.length : 0;
}
alert( mean( [1,2,3,4,5] ) ); // 3 alert( mean( [] ) ); // 0</lang>
Using the native function `.forEach()`: <lang javascript>function mean(array) {
var sum = 0;
array.forEach(function(value){
sum += value;
});
return array.length ? sum / array.length : 0;
}
alert( mean( [1,2,3,4,5] ) ); // 3</lang>
Using the native function `.reduce()`: <lang javascript>function mean(array) {
return !array.length ? 0
: array.reduce(function(pre, cur, i) {
return (pre * i + cur) / (i + 1);
});
}
alert( mean( [1,2,3,4,5] ) ); // 3 alert( mean( [] ) ); // 0 </lang>
Extending the `Array` prototype: <lang javascript>Array.prototype.mean = function() {
return !this.length ? 0
: this.reduce(function(pre, cur, i) {
return (pre * i + cur) / (i + 1);
});
}
alert( [1,2,3,4,5].mean() ); // 3 alert( [].mean() ); // 0 </lang>
<lang javascript>function mean(a) {
return a.length ? Functional.reduce('+', 0, a) / a.length : 0;
}</lang>
ES6
<lang JavaScript>(sample => {
// mean :: [Num] => (Num | NaN)
let mean = lst => {
let lng = lst.length;
return lng ? (
lst.reduce((a, b) => a + b, 0) / lng
) : NaN;
};
return mean(sample);
})([1, 2, 3, 4, 5, 6, 7, 8, 9]);</lang>
- Output:
<lang JavaScript>5</lang>
jq
The mean of an array of numbers can be computed by simply writing <lang jq>add/length</lang>
This definition raises an error condition if the array is empty, so it may make sense to define mean as follows, null being jq's null value: <lang jq>def mean: if length == 0 then null
else add/length end;</lang>
Julia
Julia's built-in mean function accepts AbstractArrays (vector, matrix, etc.) <lang julia>julia> using Statistics; mean([1,2,3]) 2.0 julia> mean(1:10) 5.5 julia> mean([]) ERROR: mean of empty collection undefined: []</lang>
K
<lang k> mean: {(+/x)%#x}
mean 1 2 3 5 7
3.6
mean@!0 / empty array
0.0</lang>
Kotlin
Kotlin has builtin functions for some collection types. Example: <lang scala>fun main(args: Array<String>) {
val nums = doubleArrayOf(1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0)
println("average = %f".format(nums.average()))
}</lang>
KQL
<lang kql> let dataset = datatable(values:real)[
1, 1.5, 3, 5, 6.5];
dataset|summarize avg(values) </lang>
Output:
avg_values 3.4
LabVIEW
This image is a VI Snippet, an executable image of LabVIEW code. The LabVIEW version is shown on the top-right hand corner. You can download it, then drag-and-drop it onto the LabVIEW block diagram from a file browser, and it will appear as runnable, editable code.
Lambdatalk
<lang scheme> {def mean
{lambda {:s}
{if {S.empty? :s}
then 0
else {/ {+ :s} {S.length :s}}}}}
{mean {S.serie 0 1000}} -> 500 </lang>
langur
The built-in mean() function works with an array, hash, or range of numbers.
We could use fold() to write a function that takes an array and calculates the mean.
<lang langur>val .mean = f(.x) fold(f{+}, .x) / len(.x)
writeln " custom: ", .mean([7, 3, 12]) writeln "built-in: ", mean([7, 3, 12])</lang>
- Output:
custom: 7.333333333333333333333333333333333 built-in: 7.333333333333333333333333333333333
Lasso
<lang Lasso>define average(a::array) => { not #a->size ? return 0 local(x = 0.0) with i in #a do => { #x += #i } return #x / #a->size }
average(array(1,2,5,17,7.4)) //6.48</lang>
LFE
1-Arity
<lang lisp> (defun mean (data)
(/ (lists:sum data)
(length data)))
</lang>
Usage: <lang lisp>> (mean '(1 1)) 1.0 > (mean '(1 2)) 1.5 > (mean '(2 10)) 6.0 > (mean '(6 12 18 24 30 36 42 48 54 60 66 72 78)) 42.0</lang>
n-Arity
Functions in LFE (and Erlang) have set arity, but macros can be used to provide the same use as n-arity functions:
<lang lisp>(defmacro mean args
`(/ (lists:sum ,args)
,(length args)))</lang>
Usage:
<lang lisp>> (mean 42) 42.0 > (mean 18 66) 42.0 > (mean 6 12 18 24 30 36 42 48 54 60 66 72 78) 42.0</lang>
Liberty BASIC
<lang lb>total=17 dim nums(total) for i = 1 to total
nums(i)=i-1
next
for j = 1 to total
sum=sum+nums(j)
next if total=0 then mean=0 else mean=sum/total print "Arithmetic mean: ";mean
</lang>
Limbo
<lang Limbo>implement Command;
include "sys.m"; sys: Sys;
include "draw.m";
include "sh.m";
init(nil: ref Draw->Context, nil: list of string) { sys = load Sys Sys->PATH;
a := array[] of {1.0, 2.0, 500.0, 257.0}; sys->print("mean of a: %f\n", getmean(a)); }
getmean(a: array of real): real { n: real = 0.0; for (i := 0; i < len a; i++) n += a[i]; return n / (real len a); }</lang>
Lingo
<lang Lingo>-- v can be (2D) point, (3D) vector or list of integers/floats on mean (v)
case ilk(v) of
#point: cnt = 2
#vector: cnt = 3
#list: cnt = v.count
otherwise: return
end case
sum = 0
repeat with i = 1 to cnt
sum = sum + v[i]
end repeat
return float(sum)/cnt
end</lang>
<lang Lingo>put mean(point(1, 2.5)) -- 1.7500 put mean(vector(1.2, 4.7, 5.6)) -- 3.8333 put mean([6,12,18,24,30,36,42,48,54,60,66,72,78]) -- 42.0000</lang>
LiveCode
Livecode provides arithmeticMean (avg, average) built-in. <lang LiveCode>average(1,2,3,4,5) -- 3 average(empty) -- 0</lang>
Logo
<lang logo>to average :l
if empty? :l [output 0] output quotient apply "sum :l count :l
end print average [1 2 3 4] ; 2.5</lang>
Logtalk
Logtalk's standard library provides an arithmetic average predicate but we ignore it here. Representing a vector using a list: <lang logtalk>
- - object(averages).
:- public(arithmetic/2).
% fails for empty vectors
arithmetic([X| Xs], Mean) :-
sum_and_count([X| Xs], 0, Sum, 0, Count),
Mean is Sum / Count.
% use accumulators to make the predicate tail-recursive
sum_and_count([], Sum, Sum, Count, Count).
sum_and_count([X| Xs], Sum0, Sum, Count0, Count) :-
Sum1 is Sum0 + X,
Count1 is Count0 + 1,
sum_and_count(Xs, Sum1, Sum, Count1, Count).
- - end_object.
</lang> Sample output: <lang text> | ?- averages::arithmetic([1,2,3,4,5,6,7,8,9,10], Mean). Mean = 5.5 yes </lang>
LSL
<lang LSL>integer MAX_ELEMENTS = 10; integer MAX_VALUE = 100; default {
state_entry() {
list lst = [];
integer x = 0;
for(x=0 ; x<MAX_ELEMENTS ; x++) {
lst += llFrand(MAX_VALUE);
}
llOwnerSay("lst=["+llList2CSV(lst)+"]");
llOwnerSay("Geometric Mean: "+(string)llListStatistics(LIST_STAT_GEOMETRIC_MEAN, lst));
llOwnerSay(" Max: "+(string)llListStatistics(LIST_STAT_MAX, lst));
llOwnerSay(" Mean: "+(string)llListStatistics(LIST_STAT_MEAN, lst));
llOwnerSay(" Median: "+(string)llListStatistics(LIST_STAT_MEDIAN, lst));
llOwnerSay(" Min: "+(string)llListStatistics(LIST_STAT_MIN, lst));
llOwnerSay(" Num Count: "+(string)llListStatistics(LIST_STAT_NUM_COUNT, lst));
llOwnerSay(" Range: "+(string)llListStatistics(LIST_STAT_RANGE, lst));
llOwnerSay(" Std Dev: "+(string)llListStatistics(LIST_STAT_STD_DEV, lst));
llOwnerSay(" Sum: "+(string)llListStatistics(LIST_STAT_SUM, lst));
llOwnerSay(" Sum Squares: "+(string)llListStatistics(LIST_STAT_SUM_SQUARES, lst));
}
}</lang> Output:
lst=[23.815209, 85.890704, 10.811144, 31.522696, 54.619416, 12.211729, 42.964463, 87.367889, 7.106129, 18.711078]
Geometric Mean: 27.325070
Max: 87.367889
Mean: 37.502046
Median: 27.668953
Min: 7.106129
Num Count: 10.000000
Range: 80.261761
Std Dev: 29.819840
Sum: 375.020458
Sum Squares: 22067.040048
Lua
<lang lua>function mean (numlist)
if type(numlist) ~= 'table' then return numlist end num = 0 table.foreach(numlist,function(i,v) num=num+v end) return num / #numlist
end
print (mean({3,1,4,1,5,9}))</lang>
Lucid
<lang lucid>avg(x)
where sum = first(x) fby sum + next(x); n = 1 fby n + 1; avg = sum / n; end</lang>
M4
M4 handle only integers, so in order to have a slightly better math for the mean, we must pass to the mean macro integers multiplied by 100. The macro rmean could embed the macro fmean and extractdec directly, but it is a little bit clearer to keep them separated.
<lang m4>define(`extractdec', `ifelse(eval(`$1%100 < 10'),1,`0',`')eval($1%100)')dnl define(`fmean', `eval(`($2/$1)/100').extractdec(eval(`$2/$1'))')dnl define(`mean', `rmean(`$#', $@)')dnl define(`rmean', `ifelse(`$3', `', `fmean($1,$2)',dnl `rmean($1, eval($2+$3), shift(shift(shift($@))))')')dnl</lang> <lang m4>mean(0,100,200,300,400,500,600,700,800,900,1000)</lang>
Maple
This version accepts any indexable structure, including numeric arrays. We use a call to the "environment variable" (dynamically scoped global) "Normalizer" to provide normalization of symbolic expressions. This can be set by the caller to adjust the strength of normalization desired. <lang Maple> mean := proc( a :: indexable )
local i;
Normalizer( add( i, i in a ) / numelems( a ) )
end proc: </lang> For example: <lang Maple> > mean( { 1/2, 2/3, 3/4, 4/5, 5/6 } ); # set
71
---
100
> mean( [ a, 2, c, 2.3, e ] ); # list
0.8600000000 + a/5 + c/5 + e/5
> mean( Array( [ 1, sin( s ), 3, exp( I*t ), 5 ] ) ); # array
9/5 + 1/5 sin(s) + 1/5 exp(t I)
> mean( [ sin(s)^2, cos(s)^2 ] );
2 2
1/2 sin(s) + 1/2 cos(s)
> Normalizer := simplify: # use a stronger normalizer than the default > mean( [ sin(s)^2, cos(s)^2 ] );
1/2
> mean([]); # empty argument causes an exception to be raised. Error, (in mean) numeric exception: division by zero </lang> A slightly different design computes the mean of all its arguments, instead of requiring a single container argument. This seems a little more Maple-like for a general purpose utility. <lang Maple>mean := () -> Normalizer( `+`( args ) / nargs ):</lang> This can be called as in the following examples. <lang Maple> > mean( 1, 2, 3, 4, 5 );
3
> mean( a + b, b + c, c + d, d + e, e + a );
2 a 2 b 2 c 2 d 2 e
--- + --- + --- + --- + ---
5 5 5 5 5
> mean(); # again, an exception is raised Error, (in mean) numeric exception: division by zero </lang> If desired, we can add argument type-checking as follows. <lang Maple>mean := ( s :: seq(algebraic) ) -> Normalizer( `+`( args ) / nargs ):</lang>
Mathematica / Wolfram Language
Modify the built-in Mean function to give 0 for empty vectors (lists in Mathematica): <lang mathematica>Unprotect[Mean]; Mean[{}] := 0</lang> Examples: <lang mathematica>Mean[{3,4,5}] Mean[{3.2,4.5,5.9}] Mean[{-4, 1.233}] Mean[{}] Mean[{1/2,1/3,1/4,1/5}] Mean[{a,c,Pi,-3,a}]</lang> gives (a set of integers gives back an integer or a rational, a set of floats gives back a float, a set of rationals gives a rational back, a list of symbols and numbers keeps the symbols exact and a mix of exact and approximate numbers gives back an approximate number): <lang mathematica>4 4.53333 -1.3835 0 77/240 1/5 (-3+2 a+c+Pi)</lang>
Mathprog
Summing the vector and then dividing the sum by the vector's length is slightly less boring than calling a builtin function Mean or similar.
Mathprog is never boring so this program finds a number M such that when M is subtracted from each value in the vector a second vector is formed with the property that the sum of the elements in the second vector is zero. In this case M is the Arithmetic Mean.
Euclid proved that for any vector there is only one such number and from this derived the Division Theorem.
To make it more interesting I find the Arithmectic Mean of more than a million Integers.
<lang> /*Arithmetic Mean of a large number of Integers
- or - solve a very large constraint matrix
over 1 million rows and columns
Nigel_Galloway
March 18th., 2008.
- /
param e := 20; set Sample := {1..2**e-1};
var Mean; var E{z in Sample};
/* sum of variances is zero */ zumVariance: sum{z in Sample} E[z] = 0;
/* Mean + variance[n] = Sample[n] */ variances{z in Sample}: Mean + E[z] = z;
solve;
printf "The arithmetic mean of the integers from 1 to %d is %f\n", 2**e-1, Mean;
end; </lang>
When run this produces:
<lang> GLPSOL: GLPK LP/MIP Solver, v4.47 Parameter(s) specified in the command line:
--nopresol --math AM.mprog
Reading model section from AM.mprog... 24 lines were read Generating zumVariance... Generating variances... Model has been successfully generated Scaling...
A: min|aij| = 1.000e+000 max|aij| = 1.000e+000 ratio = 1.000e+000
Problem data seem to be well scaled Constructing initial basis... Size of triangular part = 1048575 GLPK Simplex Optimizer, v4.47 1048576 rows, 1048576 columns, 3145725 non-zeros
0: obj = 0.000000000e+000 infeas = 5.498e+011 (1)
- 1: obj = 0.000000000e+000 infeas = 0.000e+000 (0)
OPTIMAL SOLUTION FOUND Time used: 2.0 secs Memory used: 1393.8 Mb (1461484590 bytes) The arithmetic mean of the integers from 1 to 1048575 is 524288.000000 Model has been successfully processed </lang>
MATLAB
<lang Matlab>function meanValue = findmean(setOfValues)
meanValue = mean(setOfValues);
end</lang>
Maxima
<lang maxima>load("descriptive"); mean([2, 7, 11, 17]);</lang>
MAXScript
<lang maxscript>fn mean data = (
total = 0
for i in data do
(
total += i
)
if data.count == 0 then 0 else total as float/data.count
)
print (mean #(3, 1, 4, 1, 5, 9))</lang>
Mercury
<lang mercury>:- module arithmetic_mean.
- - interface.
- - import_module io.
- - pred main(io::di, io::uo) is det.
- - implementation.
- - import_module float, list, require.
main(!IO) :-
io.print_line(mean([1.0, 2.0, 3.0, 4.0, 5.0]), !IO).
- - func mean(list(float)) = float.
mean([]) = func_error("mean: emtpy list"). mean(Ns @ [_ | _]) = foldl((+), Ns, 0.0) / float(length(Ns)).
- - end_module arithmetic_mean.</lang>
Alternatively, we could use inst subtyping to ensure we get a compilation error if the mean function is called with an empty list.
<lang mercury>:- func mean(list(float)::in(non_empty_list)) = (float::out).
mean(Ns) = foldl((+), Ns, 0.0) / float(length(Ns)).</lang>
min
Returns nan for an empty quotation.
<lang min>(((0 (+) reduce) (size /)) cleave) :mean (2 3 5) mean print</lang>
- Output:
3.333333333333334
MiniScript
<lang MiniScript>arr = [ 1, 3, 7, 8, 9, 1 ]
avg = function(arr)
avgNum = 0
for num in arr
avgNum = avgNum + num
end for
return avgNum / arr.len
end function
print avg(arr)</lang>
МК-61/52
<lang>0 П0 П1 С/П ИП0 ИП1 * + ИП1 1 + П1 / П0 БП 03</lang>
Instruction: В/О С/П Number С/П Number ...
Each time you press the С/П on the indicator would mean already entered numbers.
Modula-2
<lang modula2>PROCEDURE Avg;
VAR avg : REAL;
BEGIN
avg := sx / n;
InOut.WriteString ("Average = ");
InOut.WriteReal (avg, 8, 2);
InOut.WriteLn
END Avg;</lang> OR <lang modula2>PROCEDURE Average (Data : ARRAY OF REAL; Samples : CARDINAL) : REAL;
(* Calculate the average over 'Samples' values, stored in array 'Data'. *)
VAR sum : REAL;
n : CARDINAL;
BEGIN
sum := 0.0; FOR n := 0 TO Samples - 1 DO sum := sum + Data [n] END; RETURN sum / FLOAT(Samples)
END Average;</lang>
MUMPS
<lang MUMPS>MEAN(X)
;X is assumed to be a list of numbers separated by "^" QUIT:'$DATA(X) "No data" QUIT:X="" "Empty Set" NEW S,I SET S=0,I=1 FOR QUIT:I>$L(X,"^") SET S=S+$P(X,"^",I),I=I+1 QUIT (S/$L(X,"^"))</lang>
USER>W $$MEAN^ROSETTA
No data
USER>W $$MEAN^ROSETTA("")
Empty Set
USER>
USER>W $$MEAN^ROSETTA("1^6^12^4")
5.75
Nanoquery
<lang Nanoquery>def sum(lst)
sum = 0
for n in lst
sum += n
end
return sum
end
def average(x)
return sum(x) / len(x)
end</lang>
Nemerle
<lang Nemerle>using System; using System.Console; using Nemerle.Collections;
module Mean {
ArithmeticMean(x : list[int]) : double
{
|[] => 0.0
|_ =>(x.FoldLeft(0, _+_) :> double) / x.Length
}
Main() : void
{
WriteLine("Mean of [1 .. 10]: {0}", ArithmeticMean($[1 .. 10]));
}
}</lang>
NetRexx
<lang NetRexx>/* NetRexx */ options replace format comments java crossref symbols nobinary
launchSample() return
-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ method arithmeticMean(vv = Vector) public static signals DivideException returns Rexx
sum = 0 n_ = Rexx loop n_ over vv sum = sum + n_ end n_ mean = sum / vv.size()
return mean
-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ method launchSample() public static
TRUE_ = 1 == 1
FALSE_ = \TRUE_
tracing = FALSE_
vectors = getSampleData()
loop v_ = 0 to vectors.length - 1
say 'Average of:' vectors[v_].toString()
do
say ' =' arithmeticMean(vectors[v_])
catch dex = DivideException
say 'Caught "Divide By Zero"; bypassing...'
if tracing then dex.printStackTrace()
catch xex = RuntimeException
say 'Caught unspecified run-time exception; bypassing...'
if tracing then xex.printStackTrace()
end
say
end v_
return
-- ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ method getSampleData() private static returns Vector[]
seed = 1066 rng = Random(seed) vectors =[ - Vector(Arrays.asList([Rexx 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])), - Vector(), - Vector(Arrays.asList([Rexx rng.nextInt(seed), rng.nextInt(seed), rng.nextInt(seed), rng.nextInt(seed), rng.nextInt(seed), rng.nextInt(seed)])), - Vector(Arrays.asList([Rexx rng.nextDouble(), rng.nextDouble(), rng.nextDouble(), rng.nextDouble(), rng.nextDouble(), rng.nextDouble(), rng.nextDouble()])), - Vector(Arrays.asList([Rexx '1.0', '2.0', 3.0])), - Vector(Arrays.asList([Rexx '1.0', 'not a number', 3.0])) - ] return vectors
</lang> Output:
Average of: [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
= 5.5
Average of: []
Caught "Divide By Zero"; bypassing...
Average of: [294, 726, 945, 828, 1031, 825]
= 774.833333
Average of: [0.3318379308729921, 0.7612271993941618, 0.9517290891755477, 0.7687823629521795, 0.2201768257213939, 0.1083471020993242, 0.5158554699332363]
= 0.52256514
Average of: [1.0, 2.0, 3.0]
= 2
Average of: [1.0, not a number, 3.0]
Caught unspecified run-time exception; bypassing...
NewLISP
<lang NewLISP>(define (Mean Lst)
(if (empty? Lst)
0
(/ (apply + Lst) (length Lst))))
(Mean (sequence 1 1000))-> 500
(Mean '()) -> 0</lang>
Nial
in the standard way, mean is <lang nial>mean is / [sum, tally]
mean 6 2 4 = 4</lang> but it fails with 0 length vectors. so using a tally with a minimum value 1
<lang nial>dtally is recur [ empty rest, 1 first, 1 first, plus, rest ] mean is / [sum, dtally]
mean [] =0</lang>
Nim
<lang nim>import strutils
proc mean(xs: openArray[float]): float =
for x in xs: result += x result = result / float(xs.len)
var v = @[1.0, 2.0, 2.718, 3.0, 3.142] for i in 0..5:
echo "mean of first ", v.len, " = ", formatFloat(mean(v), precision = 0) if v.len > 0: v.setLen(v.high)</lang>
Output:
mean of first 5 = 2.372 mean of first 4 = 2.1795 mean of first 3 = 1.906 mean of first 2 = 1.5 mean of first 1 = 1 mean of first 0 = -1.#IND
Niue
<lang Niue> [ [ , len 1 - at ! ] len 3 - times swap , ] 'map ; ( a Lisp like map, to sum the stack ) [ len 'n ; [ + ] 0 n swap-at map n / ] 'avg ;
1 2 3 4 5 avg . => 3 3.4 2.3 .01 2.0 2.1 avg . => 1.9619999999999997 </lang>
Oberon-2
Oxford Oberon-2 <lang oberon2> MODULE AvgMean; IMPORT Out; CONST MAXSIZE = 10; PROCEDURE Avg(a: ARRAY OF REAL; items: INTEGER): REAL; VAR i: INTEGER; total: REAL; BEGIN total := 0.0; FOR i := 0 TO LEN(a) - 1 DO total := total + a[i] END; RETURN total/LEN(a) END Avg; VAR ary: ARRAY MAXSIZE OF REAL; BEGIN ary[0] := 10.0; ary[1] := 11.01; ary[2] := 12.02; ary[3] := 13.03; ary[4] := 14.04; ary[5] := 15.05; ary[6] := 16.06; ary[7] := 17.07; ary[8] := 18.08; ary[9] := 19.09; Out.Fixed(Avg(ary),4,2);Out.Ln END AvgMean. </lang> Output:
14.55
Objeck
<lang objeck> function : native : PrintAverage(values : FloatVector) ~ Nil {
values->Average()->PrintLine();
} </lang>
OCaml
These functions return a float:
<lang ocaml>let mean_floats = function
| [] -> 0. | xs -> List.fold_left (+.) 0. xs /. float_of_int (List.length xs)
let mean_ints xs = mean_floats (List.map float_of_int xs)</lang>
the previous code is easier to read and understand, though if you wish the fastest implementation to use in production code notice several points: it is possible to save a call to List.length computing the length through the List.fold_left, and for mean_ints it is possible to save calling float_of_int on every numbers, converting only the result of the addition. (also when using List.map and when the order is not important, you can use List.rev_map instead to save an internal call to List.rev). Also the task asks to return 0 on empty lists, but in OCaml this case would rather be handled by an exception.
<lang ocaml>let mean_floats xs =
if xs = [] then
invalid_arg "empty list"
else
let total, length =
List.fold_left
(fun (tot,len) x -> (x +. tot), len +. 1.)
(0., 0.) xs
in
(total /. length)
let mean_ints xs =
if xs = [] then
invalid_arg "empty list"
else
let total, length =
List.fold_left
(fun (tot,len) x -> (x + tot), len +. 1.)
(0, 0.) xs
in
(float total /. length)
- </lang>
Octave
GNU Octave has a mean function (from statistics package), but it does not handle an empty vector; an implementation that allows that is:
<lang octave>function m = omean(l)
if ( numel(l) == 0 ) m = 0; else m = mean(l); endif
endfunction
disp(omean([])); disp(omean([1,2,3]));</lang>
If the data contains missing value, encoded as non-a-number:
<lang octave>function m = omean(l)
n = sum(~isnan(l));
l(isnan(l))=0;
s = sum(l);
m = s./n;
end;</lang>
Oforth
<lang Oforth>: avg ( x -- avg )
x sum x size dup ifZero: [ 2drop null ] else: [ >float / ]
- </lang>
- Output:
[1, 2, 2.718, 3, 3.142] avg . 2.372 ok [ ] avg . null ok
ooRexx
<lang ooRexx> call testAverage .array~of(10, 9, 8, 7, 6, 5, 4, 3, 2, 1) call testAverage .array~of(10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0, .11) call testAverage .array~of(10, 20, 30, 40, 50, -100, 4.7, -11e2) call testAverage .array~new
- routine testAverage
use arg numbers
say "numbers =" numbers~toString("l", ", ")
say "average =" average(numbers)
say
- routine average
use arg numbers -- return zero for an empty list if numbers~isempty then return 0
sum = 0
do number over numbers
sum += number
end
return sum/numbers~items
</lang> Output:
numbers = 10, 9, 8, 7, 6, 5, 4, 3, 2, 1 average = 5.5 numbers = 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0, .11 average = 3.674 numbers = 10, 20, 30, 40, 50, -100, 4.7, -1100 average = -130.6625 numbers = average = 0
Oz
A version working on floats: <lang oz>declare
fun {Mean Xs}
{FoldL Xs Number.'+' 0.0} / {Int.toFloat {Length Xs}}
end
in
{Show {Mean [3. 1. 4. 1. 5. 9.]}}</lang>
PARI/GP
<lang parigp>avg(v)={
if(#v,vecsum(v)/#v)
};</lang>
Pascal
<lang pascal>Program Mean;
function DoMean(vector: array of double): double;
var
sum: double;
i, len: integer;
begin
sum := 0;
len := length(vector);
if len > 0 then
begin
for i := low(vector) to high(vector) do
sum := sum + vector[i];
sum := sum / len;
end;
DoMean := sum;
end;
const
vector: array [3..8] of double = (3.0, 1.0, 4.0, 1.0, 5.0, 9.0);
var
i: integer;
begin
writeln('Calculating the arithmetic mean of a series of numbers:');
write('Numbers: [ ');
for i := low(vector) to high(vector) do
write (vector[i]:3:1, ' ');
writeln (']');
writeln('Mean: ', DoMean(vector):10:8);
end.</lang>
Output:
Calculating the arithmetic mean of a series of numbers: Numbers: [ 3.0 1.0 4.0 1.0 5.0 9.0 ] Mean: 3.83333333
Alternative version using the Math unit:
<lang pascal>Program DoMean; uses math; const
vector: array [3..8] of double = (3.0, 1.0, 4.0, 1.0, 5.0, 9.0);
var
i: integer; mean: double;
begin
writeln('Calculating the arithmetic mean of a series of numbers:');
write('Numbers: [ ');
for i := low(vector) to high(vector) do
write (vector[i]:3:1, ' ');
writeln (']');
mean := 0;
if length(vector) > 0 then
mean := sum(vector)/length(vector);
writeln('Mean: ', mean:10:8);
end.</lang>
Perl
<lang perl>sub avg {
@_ or return 0; my $sum = 0; $sum += $_ foreach @_; return $sum/@_;
}
print avg(qw(3 1 4 1 5 9)), "\n";</lang>
Phix
with javascript_semantics function mean(sequence s) if length(s)=0 then return 0 end if return sum(s)/length(s) end function ? mean({1, 2, 5, -5, -9.5, 3.14159})
Phixmonti
<lang Phixmonti>1 2 5 -5 -9.5 3.14159 stklen tolist len swap sum swap / print</lang>
PHP
<lang php>$nums = array(3, 1, 4, 1, 5, 9); if ($nums)
echo array_sum($nums) / count($nums), "\n";
else
echo "0\n";</lang>
PicoLisp
<lang PicoLisp>(de mean (Lst)
(if (atom Lst)
0
(/ (apply + Lst) (length Lst)) ) )</lang>
Output:
: (mean (range 1 1000)) -> 500
PL/I
<lang pli>arithmetic_mean = sum(A)/dimension(A,1);</lang>
Plain English
<lang plainenglish>To run: Start up. Demonstrate finding the arithmetic mean. Wait for the escape key. Shut down.
An entry is a thing with a fraction. A list is some entries. A sum is a fraction. A mean is a fraction.
To demonstrate finding the arithmetic mean: Create an example list. Write "A list: " then the example list on the console. Find a mean of the example list. Write "The list's mean: " then the mean on the console. Destroy the example list.
To add a fraction to a list: Allocate memory for an entry. Put the fraction into the entry's fraction. Append the entry to the list.
To create an example list: Add 1/1 to the example list. Add 2/1 to the example list. Add 5-1/3 to the example list. Add 7-1/2 to the example list.
To find a sum of a list: Put 0 into the sum. Get an entry from the list. Loop. If the entry is nil, exit. Add the entry's fraction to the sum. Put the entry's next into the entry. Repeat.
To find a mean of a list: Find a sum of the list. Put the sum divided by the list's count into the mean.
To convert a list to a string: Get an entry from the list. Loop. If the entry is nil, break. Append the entry's fraction to the string. If the entry's next is not nil, append ", " to the string. Put the entry's next into the entry. Repeat.</lang>
- Output:
A list: 1, 2, 5-1/3, 7-1/2 The list's mean: 3-23/24
Pop11
<lang pop11>define mean(v);
lvars n = length(v), i, s = 0;
if n = 0 then
return(0);
else
for i from 1 to n do
s + v(i) -> s;
endfor;
endif;
return(s/n);
enddefine;</lang>
PostScript
<lang> /findmean{ /x exch def /sum 0 def /i 0 def x length 0 eq {} { x length{ /sum sum x i get add def /i i 1 add def }repeat /sum sum x length div def }ifelse sum == }def </lang>
<lang postscript> /avg {
dup length
{0 gt} {
exch 0 {add} fold exch div
} {
exch pop
} ifte
}. </lang>
PowerShell
The hard way by calculating a sum and dividing: <lang powershell>function mean ($x) {
if ($x.Count -eq 0) {
return 0
} else {
$sum = 0
foreach ($i in $x) {
$sum += $i
}
return $sum / $x.Count
}
}</lang>
or, shorter, by using the Measure-Object cmdlet which already knows how to compute an average:
<lang powershell>function mean ($x) {
if ($x.Count -eq 0) {
return 0
} else {
return ($x | Measure-Object -Average).Average
}
}</lang>
Processing
<lang processing>float mean(float[] arr) {
float out = 0;
for (float n : arr) {
out += n;
}
return out / arr.length;
}</lang>
Prolog
<lang prolog> mean(List, Mean) :-
length(List, Length), sumlist(List, Sum), Mean is Sum / Length.
</lang>
PureBasic
<lang PureBasic>Procedure.d mean(List number())
Protected sum=0
ForEach number() sum + number() Next ProcedureReturn sum / ListSize(number()) ; Depends on programm if zero check needed, returns nan on division by zero
EndProcedure</lang>
Python
.
Uses fsum which tracks multiple partial sums to avoid losing precision <lang python>from math import fsum def average(x):
return fsum(x)/float(len(x)) if x else 0
print (average([0,0,3,1,4,1,5,9,0,0])) print (average([1e20,-1e-20,3,1,4,1,5,9,-1e20,1e-20]))</lang>
- Output:
<lang python>2.3 2.3</lang>
<lang python>def average(x):
return sum(x)/float(len(x)) if x else 0
print (average([0,0,3,1,4,1,5,9,0,0])) print (average([1e20,-1e-20,3,1,4,1,5,9,-1e20,1e-20]))</lang>
- Output:
(Notice how the second call gave the wrong result) <lang python>2.3 1e-21</lang>
<lang python>def avg(data):
if len(data)==0:
return 0
else:
return sum(data)/float(len(data))
print avg([0,0,3,1,4,1,5,9,0,0])</lang>
- Output:
<lang python>2.3</lang>
Since 3.4, Python has a [statistics library in the stdlib, which takes care of these precision overflow issues in a way that works for all standard types, not just float, even with values way too big or small to fit in a float. (For Python 2.6-2.7, there's a backport available on PyPI.) <lang python>>>> from statistics import mean >>> mean([1e20,-1e-20,3,1,4,1,5,9,-1e20,1e-20]) 2.3 >>> mean([10**10000, -10**10000, 3, 1, 4, 1, 5, 9, 0, 0]) 2.3 >>> mean([10**10000, -10**10000, 3, 1, 4, 1, 5, 9, Fraction(1, 10**10000), Fraction(-1, 10**10000)]) Fraction(23, 10) >>> big = 10**10000 >>> mean([Decimal(big), Decimal(-big), 3, 1, 4, 1, 5, 9, 1/Decimal(big), -1/Decimal(big)]) Decimal('2.3')</lang>
Q
A built-in solution is avg. An implementation of it could be: <lang q>mean:{(sum x)%count x}</lang>
Quackery
Using the Quackery big number rational arithmetic library bigrat.qky.
<lang Quackery> [ $ 'bigrat.qky' loadfile ] now!
[ [] swap times
[ 20001 random 10000 -
n->v 100 n->v v/
join nested join ] ] is makevector ( --> [ )
[ witheach
[ unpack
2 point$ echo$
i 0 > if
[ say ", " ] ] ] is echodecs ( [ --> )
[ dup size n->v rot
0 n->v rot
witheach
[ unpack v+ ]
2swap v/ ] is arithmean ( [ --> n/d )
[ 5 makevector say "Internal representation of a randomly generated vector" cr say "of five rational numbers. They are distributed between" cr say "-100.00 and +100.00 and are multiples of 0.01." cr cr dup echo cr cr say "Shown as decimal fractions." cr cr dup echodecs cr cr arithmean say "Arithmetic mean of vector as a decimal fraction to" cr say "5 places after the point, as a rounded proper" cr say "fraction with the denominator not exceeding 10, and" cr say "finally as a vulgar fraction without rounding." cr cr 2dup 5 point$ echo$ say ", " 2dup proper 10 round improper proper$ echo$ say ", " vulgar$ echo$ cr cr say "The same, but with a vector of 9973 rational numbers," cr say "20 decimal places and a denominator not exceeding 100." cr cr 9973 makevector arithmean 2dup 20 point$ echo$ say ", " 2dup proper 100 round improper proper$ echo$ say ", " vulgar$ echo$ cr ] is demonstrate ( --> )</lang>
- Output:
Internal representation of a randomly generated vector of five rational numbers. They are distributed between -100.00 and +100.00 and are multiples of 0.01. [ [ -1999 100 ] [ 253 50 ] [ 2867 50 ] [ 3929 50 ] [ -25 2 ] ] Shown as decimal fractions. -19.99, 5.06, 57.34, 78.58, -12.5 Arithmetic mean of vector as a decimal fraction to 5 places after the point, as a rounded proper fraction with the denominator not exceeding 10, and finally as a vulgar fraction without rounding. 21.698, 21 7/10, 10849/500 The same, but with a vector of 9973 rational numbers, 20 decimal places and a denominator not exceeding 100. -0.41664995487817106187, -0 5/12, -16621/39892
R
R has its mean function but it does not allow for NULL (void vectors or whatever) as argument: in this case it raises a warning and the result is NA. An implementation that does not suppress the warning could be:
<lang rsplus>omean <- function(v) {
m <- mean(v) ifelse(is.na(m), 0, m)
}</lang>
Racket
Racket's math library (available in v5.3.2 and newer) comes with a mean function that works on arbitrary sequences.
<lang racket>
- lang racket
(require math)
(mean (in-range 0 1000)) ; -> 499 1/2 (mean '(2 2 4 4)) ; -> 3 (mean #(3 4 5 8)) ; -> 5 </lang>
Raku
(formerly Perl 6)
<lang perl6>multi mean([]){ Failure.new('mean on empty list is not defined') }; # Failure-objects are lazy exceptions multi mean (@a) { ([+] @a) / @a }</lang>
Rapira
<lang Rapira>fun mean(arr)
sum := 0 for N from 1 to #arr do sum := sum + arr[N] od return (sum / #arr)
end</lang>
REBOL
<lang REBOL>rebol [
Title: "Arithmetic Mean (Average)" URL: http://rosettacode.org/wiki/Average/Arithmetic_mean
]
average: func [v /local sum][ if empty? v [return 0]
sum: 0 forall v [sum: sum + v/1] sum / length? v ]
- Note precision loss as spread increased.
print [mold x: [] "->" average x] print [mold x: [3 1 4 1 5 9] "->" average x] print [mold x: [1000 3 1 4 1 5 9 -1000] "->" average x] print [mold x: [1e20 3 1 4 1 5 9 -1e20] "->" average x]</lang>
Output:
[] -> 0 [3 1 4 1 5 9] -> 3.83333333333333 [1000 3 1 4 1 5 9 -1000] -> 2.875 [1E+20 3 1 4 1 5 9 -1E+20] -> 0.0
Red
Red comes with the average function.
<lang red>Red ["Arithmetic mean"]
print average [] print average [2 3 5]</lang>
- Output:
none 3.333333333333334
The source code for average:
<lang red>average: func [
"Returns the average of all values in a block" block [block! vector! paren! hash!]
][
if empty? block [return none] divide sum block to float! length? block
]</lang>
ReScript
<lang ReScript>let arr = [3, 8, 4, 1, 5, 12]
let num = Js.Array.length(arr) let tot = Js.Array.reduce(\"+", 0, arr) let mean = float_of_int(tot) /. float_of_int(num)
Js.log(Js.Float.toString(mean))</lang>
- Output:
$ bsc arithmean.res > arithmean.js $ node arithmean.js 5.5
REXX
The vectors (list) can contain any valid (REXX) numbers.
A check is made to validate if the numbers in the list are all numeric. <lang rexx>/*REXX program finds the averages/arithmetic mean of several lists (vectors) or CL input*/ parse arg @.1; if @.1= then do; #=6 /*vector from the C.L.?*/
@.1 = 10 9 8 7 6 5 4 3 2 1
@.2 = 10 9 8 7 6 5 4 3 2 1 0 0 0 0 .11
@.3 = '10 20 30 40 50 -100 4.7 -11e2'
@.4 = '1 2 3 4 five 6 7 8 9 10.1. ±2'
@.5 = 'World War I & World War II'
@.6 = /* ◄─── a null value. */
end
else #=1 /*number of CL vectors.*/
do j=1 for #
say ' numbers = ' @.j
say ' average = ' avg(@.j)
say copies('═', 79)
end /*t*/
exit /*stick a fork in it, we're all done. */ /*──────────────────────────────────────────────────────────────────────────────────────*/ avg: procedure; parse arg x; #=words(x) /*#: number of items.*/
if #==0 then return 'N/A: ───[null vector.]' /*No words? Return N/A*/
$=0
do k=1 for #; _=word(x,k) /*obtain a number. */
if datatype(_,'N') then do; $=$+_; iterate; end /*if numeric, then add*/
say left(,40) "***error*** non-numeric: " _; #=#-1 /*error; adjust number*/
end /*k*/
if #==0 then return 'N/A: ───[no numeric values.]' /*No nums? Return N/A*/
return $ / # /*return the average. */</lang>
output when using the (internal) lists:
numbers = 10 9 8 7 6 5 4 3 2 1
average = 5.5
═══════════════════════════════════════════════════════════════════════════════
numbers = 10 9 8 7 6 5 4 3 2 1 0 0 0 0 .11
average = 3.674
═══════════════════════════════════════════════════════════════════════════════
numbers = 10 20 30 40 50 -100 4.7 -11e2
average = -130.6625
═══════════════════════════════════════════════════════════════════════════════
numbers = 1 2 3 4 five 6 7 8 9 10.1. ±2
***error*** non-numeric: five
***error*** non-numeric: 10.1.
***error*** non-numeric: ±2
average = 5
═══════════════════════════════════════════════════════════════════════════════
numbers = World War I & World War II
***error*** non-numeric: World
***error*** non-numeric: War
***error*** non-numeric: I
***error*** non-numeric: &
***error*** non-numeric: World
***error*** non-numeric: War
***error*** non-numeric: II
average = N/A: ───[no numeric values.]
═══════════════════════════════════════════════════════════════════════════════
numbers =
average = N/A: ───[null vector.]
═══════════════════════════════════════════════════════════════════════════════
Ring
<lang ring> nums = [1,2,3,4,5,6,7,8,9,10] sum = 0 see "Average = " + average(nums) + nl
func average numbers
for i = 1 to len(numbers)
sum = sum + nums[i]
next
return sum/len(numbers)
</lang>
RPL/2
This is a simple rewrite of the dc version above. This works on an HP 48. "->" is a single right arrow character on the 48. Feel free to alter this code as necessary to work on RPL/2.
<lang rpl/2>1 2 3 5 7 AMEAN
<< DEPTH DUP 'N' STO ->LIST ΣLIST N / >>
3.6</lang>
Ruby
<lang ruby>def mean(nums)
nums.sum(0.0) / nums.size
end
nums = [3, 1, 4, 1, 5, 9] nums.size.downto(0) do |i|
ary = nums[0,i]
puts "array size #{ary.size} : #{mean(ary)}"
end</lang>
- Output:
array size 6 : 3.8333333333333335 array size 5 : 2.8 array size 4 : 2.25 array size 3 : 2.6666666666666665 array size 2 : 2.0 array size 1 : 3.0 array size 0 : NaN
Run BASIC
<lang runbasic>print "Gimme the number in the array:";input numArray dim value(numArray) for i = 1 to numArray
value(i) = i * 1.5
next
for i = 1 to total
totValue = totValue +value(numArray)
next if totValue <> 0 then mean = totValue/numArray print "The mean is: ";mean</lang>
Rust
<lang rust>fn sum(arr: &[f64]) -> f64 {
arr.iter().fold(0.0, |p,&q| p + q)
}
fn mean(arr: &[f64]) -> f64 {
sum(arr) / arr.len() as f64
}
fn main() {
let v = &[2.0, 3.0, 5.0, 7.0, 13.0, 21.0, 33.0, 54.0];
println!("mean of {:?}: {:?}", v, mean(v));
let w = &[];
println!("mean of {:?}: {:?}", w, mean(w));
}</lang> Output:
mean of [2, 3, 5, 7, 13, 21, 33, 54]: 17.25 mean of []: NaN
Sather
Built to work with VEC, ("geometric" vectors), whose elements must be floats. A 0-dimension vector yields "nan". <lang sather>class VECOPS is
mean(v:VEC):FLT is m ::= 0.0; loop m := m + v.aelt!; end; return m / v.dim.flt; end;
end;
class MAIN is
main is v ::= #VEC(|1.0, 5.0, 7.0|); #OUT + VECOPS::mean(v) + "\n"; end;
end;</lang>
Scala
Using Scala 2.7, this has to be defined for each numeric type:
<lang scala>def mean(s: Seq[Int]) = s.foldLeft(0)(_+_) / s.size</lang>
However, Scala 2.8 gives much more flexibility, but you still have to opt between integral types and fractional types. For example:
<lang scala>def mean[T](s: Seq[T])(implicit n: Integral[T]) = {
import n._ s.foldLeft(zero)(_+_) / fromInt(s.size)
}</lang>
This can be used with any subclass of Sequence on integral types, up to and including BigInt. One can also create singletons extending Integral for user-defined numeric classes. Likewise, Integral can be replaced by Fractional in the code to support fractional types, such as Float and Double.
Alas, Scala 2.8 also simplifies the task in another way:
<lang scala>def mean[T](s: Seq[T])(implicit n: Fractional[T]) = n.div(s.sum, n.fromInt(s.size))</lang>
Here we show a function that supports fractional types. Instead of importing the definitions from n, we are calling them on n itself. And because we did not import them, the implicit definitions that would allow us to use / were not imported as well. Finally, we use sum instead of foldLeft.
Scheme
<lang scheme>(define (mean l)
(if (null? l)
0
(/ (apply + l) (length l))))</lang>
> (mean (list 3 1 4 1 5 9)) 3 5/6
Seed7
<lang seed7>$ include "seed7_05.s7i";
include "float.s7i";
const array float: numVector is [] (1.0, 2.0, 3.0, 4.0, 5.0);
const func float: mean (in array float: numbers) is func
result
var float: result is 0.0;
local
var float: total is 0.0;
var float: num is 0.0;
begin
if length(numbers) <> 0 then
for num range numbers do
total +:= num;
end for;
result := total / flt(length(numbers));
end if;
end func;
const proc: main is func
begin writeln(mean(numVector)); end func;</lang>
SenseTalk
SenseTalk has a built-in average function. <lang sensetalk>put the average of [12,92,-17,66,128]
put average(empty) </lang>
- Output:
56.2
nan
Sidef
<lang ruby>func avg(Array list) {
list.len > 0 || return 0; list.sum / list.len;
}
say avg([Math.inf, Math.inf]); say avg([3,1,4,1,5,9]); say avg([1e+20, 3, 1, 4, 1, 5, 9, -1e+20]); say avg([10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0.11]); say avg([10, 20, 30, 40, 50, -100, 4.7, -1100]);</lang>
- Output:
inf 3.833333333333333333333333333333333333333 2.875 3.674 -130.6625
Slate
<lang slate>[|:list| (list reduce: #+ `er ifEmpty: [0]) / (list isEmpty ifTrue: [1] ifFalse: [list size])] applyWith: #(3 1 4 1 5 9). [|:list| (list reduce: #+ `er ifEmpty: [0]) / (list isEmpty ifTrue: [1] ifFalse: [list size])] applyWith: {}.</lang>
Smalltalk
<lang smalltalk> | numbers |
numbers := #(1 2 3 4 5 6 7 8). (numbers isEmpty
ifTrue:[0]
ifFalse: [
(numbers inject: 0 into: [:sumSoFar :eachElement | sumSoFar + eachElement]) / numbers size ]
) displayNl. </lang> However, the empty check can be omitted, as inject returns the injected value for empty collections, and we probably do not care for the average of nothing (i.e. the division by zero exception): <lang smalltalk> | numbers |
numbers := #(1 2 3 4 5 6 7 8). ( numbers inject: 0 into: [:sumSoFar :eachElement | sumSoFar + eachElement]) / numbers size] ) displayNl. </lang> also, most Smalltalk's collection classes already provide sum and average methods, which makes it:
<lang smalltalk> | numbers |
numbers := #(1 2 3 4 5 6 7 8). (numbers sum / numbers size) displayNl. </lang> or <lang smalltalk> | numbers |
numbers := #(1 2 3 4 5 6 7 8). numbers average displayNl. </lang>
SNOBOL4
<lang SNOBOL4> define('avg(a)i,sum') :(avg_end) avg i = i + 1; sum = sum + a :s(avg)
avg = 1.0 * sum / prototype(a) :(return)
avg_end
- # Fill arrays
str = '1 2 3 4 5 6 7 8 9 10'; arr = array(10)
loop i = i + 1; str len(p) span('0123456789') . arr @p :s(loop)
empty = array(1) ;* Null vector
- # Test and display
output = '[' str '] -> ' avg(arr)
output = '[ ] -> ' avg(empty)
end</lang>
Output:
[1 2 3 4 5 6 7 8 9 10] -> 5.5 [ ] -> 0.
SQL
Tested on Oracle 11gR2, the more limited the tool, the more resourceful one becomes :) <lang SQL> create table "numbers" ("datapoint" integer);
insert into "numbers" select rownum from tab;
select sum("datapoint")/count(*) from "numbers"; </lang> ...or... <lang SQL>select avg("datapoint") from "numbers";</lang>
Standard ML
These functions return a real:
<lang sml>fun mean_reals [] = 0.0
| mean_reals xs = foldl op+ 0.0 xs / real (length xs);
val mean_ints = mean_reals o (map real);</lang>
The previous code is easier to read and understand, though if you want
the fastest implementation to use in production code notice several points:
it is possible to save a call to length computing the length through
the foldl, and for mean_ints it is possible to save calling
real on every numbers, converting only the result of the addition.
Also the task asks to return 0 on empty lists, but in Standard ML this case
would rather be handled by an exception.
<lang sml>fun mean_reals [] = raise Empty
| mean_reals xs = let
val (total, length) =
foldl
(fn (x, (tot,len)) => (x + tot, len + 1.0))
(0.0, 0.0) xs
in
(total / length)
end;
fun mean_ints [] = raise Empty
| mean_ints xs = let
val (total, length) =
foldl
(fn (x, (tot,len)) => (x + tot, len + 1.0))
(0, 0.0) xs
in
(real total / length)
end;</lang>
Stata
Mean of a dataset variable
Illustration of the mean on the population (in millions) in january 2016 of a few european countries (source Eurostat). <lang>clear all input str20 country population Belgium 11311.1 Bulgaria 7153.8 "Czech Republic" 10553.8 Denmark 5707.3 Germany 82175.7 Estonia 1315.9 Ireland 4724.7 Greece 10783.7 end
. mean population
Mean estimation Number of obs = 8
| Mean Std. Err. [95% Conf. Interval]
+------------------------------------------------
population | 16715.75 9431.077 -5585.203 39016.7
. tabstat population, statistic(mean)
variable | mean
+----------
population | 16715.75
. quietly summarize population . display r(mean) 16715.75</lang>
Mean in Mata
<lang stata>mata a=11311.1\7153.8\10553.8\5707.3\ 82175.7\1315.9\4724.7\10783.7
mean(a) 16715.75</lang>
Swift
<lang swift>func meanDoubles(s: [Double]) -> Double {
return s.reduce(0, +) / Double(s.count)
} func meanInts(s: [Int]) -> Double {
return meanDoubles(s.map{Double($0)})
}</lang>
Tcl
<lang tcl>package require Tcl 8.5 proc mean args {
if {[set num [llength $args]] == 0} {return 0}
expr {[tcl::mathop::+ {*}$args] / double($num)}
} mean 3 1 4 1 5 9 ;# ==> 3.8333333333333335</lang>
TI-83 BASIC
<lang ti83b>Mean(Ans</lang>
TI-89 BASIC
<lang ti89b>Define rcmean(nums) = when(dim(nums) = 0, 0, mean(nums))</lang>
Trith
<lang trith>: mean dup empty? [drop 0] [dup [+] foldl1 swap length /] branch ;
[3 1 4 1 5 9] mean</lang>
TypeScript
<lang typescript> function mean(numbersArr) {
let arrLen = numbersArr.length;
if (arrLen > 0) {
let sum: number = 0;
for (let i of numbersArr) {
sum += i;
}
return sum/arrLen;
}
else return "Not defined";
}
alert( mean( [1,2,3,4,5] ) ); alert( mean( [] ) ); </lang>
UNIX Shell
1) First solution with bash (V >= 3), works with floats : <lang bash1>echo "`cat f | paste -sd+ | bc -l` / `cat f | wc -l`" | bc -l </lang> <lang bash1>cat f 1 2 4 8 16 -200
echo "`cat f | paste -sd+ | bc -l`/`cat f | wc -l`" | bc -l -28.16666666666666666666
cat f 1.109434 2 4.5 8.45 16 -200 400.56
echo "`cat f | paste -sd+ | bc -l`/`cat f | wc -l`" |bc -l 33.23134771428571428571 </lang>
2) This example uses expr, so it only works with integers. It checks that each string in the list is an integer.
<lang bash>mean() { if expr $# >/dev/null; then (count=0 sum=0 while expr $# \> 0 >/dev/null; do sum=`expr $sum + "$1"` result=$? expr $result \> 1 >/dev/null && exit $result
count=`expr $count + 1` shift done expr $sum / $count) else echo 0 fi }
printf "test 1: "; mean # 0 printf "test 2: "; mean 300 # 300 printf "test 3: "; mean 300 100 400 # 266 printf "test 4: "; mean -400 400 -1300 200 # -275 printf "test 5: "; mean - # expr: syntax error printf "test 6: "; mean 1 2 A 3 # expr: non-numeric argument</lang>
UnixPipes
Uses ksh93-style process substitution. Also overwrites the file named count in the current directory.
<lang bash>term() {
b=$1;res=$2 echo "scale=5;$res+$b" | bc
}
sum() {
(read B; res=$1; test -n "$B" && (term $B $res) || (term 0 $res))
}
fold() {
func=$1
(while read a ; do
fold $func | $func $a
done)
}
mean() {
tee >(wc -l > count) | fold sum | xargs echo "scale=5;(1/" $(cat count) ") * " | bc
}
(echo 3; echo 1; echo 4) | mean</lang>
Ursa
<lang ursa>#
- arithmetic mean
decl int<> input decl int i for (set i 1) (< i (size args)) (inc i)
append (int args) input
end for
out (/ (+ input) (size input)) endl console</lang>
Ursala
There is a library function for means already, although it doesn't cope with empty vectors. A mean function could be defined as shown for this task. <lang Ursala>#import nat
- import flo
mean = ~&?\0.! div^/plus:-0. float+ length
- cast %e
example = mean <5.,3.,-2.,6.,-4.></lang> output:
1.600000e+00
V
<lang v>[mean
[sum 0 [+] fold]. dup sum swap size [[1 <] [1]] when /
].</lang>
Vala
Using array to hold the numbers of the list: <lang vala> double arithmetic(double[] list){ double mean; double sum = 0;
if (list.length == 0) return 0.0; foreach(double number in list){ sum += number; } // foreach
mean = sum / list.length;
return mean; } // end arithmetic mean
public static void main(){ double[] test = {1.0, 2.0, 5.0, -5.0, 9.5, 3.14159}; double[] zero_len = {};
double mean = arithmetic(test); double mean_zero = arithmetic(zero_len);
stdout.printf("%s\n", mean.to_string()); stdout.printf("%s\n", mean_zero.to_string()); } </lang>
Output:
2.6069316666666666 0
VBA
<lang vb>Private Function mean(v() As Double, ByVal leng As Integer) As Variant
Dim sum As Double, i As Integer
sum = 0: i = 0
For i = 0 To leng - 1
sum = sum + vv
Next i
If leng = 0 Then
mean = CVErr(xlErrDiv0)
Else
mean = sum / leng
End If
End Function Public Sub main()
Dim v(4) As Double
Dim i As Integer, leng As Integer
v(0) = 1#
v(1) = 2#
v(2) = 2.178
v(3) = 3#
v(4) = 3.142
For leng = 5 To 0 Step -1
Debug.Print "mean[";
For i = 0 To leng - 1
Debug.Print IIf(i, "; " & v(i), "" & v(i));
Next i
Debug.Print "] = "; mean(v, leng)
Next leng
End Sub</lang>
- Output:
mean[1; 2; 2,178; 3; 3,142] = 0 mean[1; 2; 2,178; 3] = 0 mean[1; 2; 2,178] = 0 mean[1; 2] = 0 mean[1] = 0 mean[] = Fout 2007
VBScript
<lang vb> Function mean(arr) size = UBound(arr) + 1 mean = 0 For i = 0 To UBound(arr) mean = mean + arr(i) Next mean = mean/size End Function
'Example WScript.Echo mean(Array(3,1,4,1,5,9)) </lang>
- Output:
3.83333333333333
Vedit macro language
The numeric data is stored in current edit buffer as ASCII strings, one value per line. <lang vedit>#1 = 0 // Sum
- 2 = 0 // Count
BOF While(!At_EOF) {
#1 += Num_Eval(SIMPLE) #2++ Line(1, ERRBREAK)
} if (#2) { #1 /= #2 } Num_Type(#1)</lang>
Vim Script
Throws an exception if the list is empty. <lang vim>function Mean(lst)
if empty(a:lst)
throw "Empty"
endif
let sum = 0.0
for i in a:lst
let sum += i
endfor
return sum / len(a:lst)
endfunction</lang>
Vlang
<lang vlang>import math import arrays
fn main() {
for v in [
[]f64{}, // mean returns ok = false
[math.inf(1), math.inf(1)], // answer is +Inf
// answer is NaN, and mean returns ok = true, indicating NaN
// is the correct result
[math.inf(1), math.inf(-1)],
[f64(3), 1, 4, 1, 5, 9],
[f64(10), 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0, .11],
[f64(10), 20, 30, 40, 50, -100, 4.7, -11e2],
] {
println("Vector: $v")
m := arrays.fold(v, 0.0, fn(r f64, v f64) f64 { return r+v })/v.len
println("Mean of $v.len numbers is $m\n")
}
}</lang>
- Output:
Vector: [] Mean of 0 numbers is nan Vector: [+inf, +inf] Mean of 2 numbers is +inf Vector: [+inf, -inf] Mean of 2 numbers is nan Vector: [3, 1, 4, 1, 5, 9] Mean of 6 numbers is 3.8333333333333335 Vector: [10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, 0, 0, 0, 0.11] Mean of 15 numbers is 3.674 Vector: [10, 20, 30, 40, 50, -100, 4.7, -1100] Mean of 8 numbers is -130.66
Wart
<lang python>def (mean l)
sum.l / len.l</lang>
Example run:
mean '(1 2 3) => 2
WDTE
<lang WDTE>let s => import 'stream'; let a => import 'arrays';
let mean nums =>
a.stream nums -> s.reduce [0; 0] (@ s p n => [+ (a.at p 0) 1; + (a.at p 1) n]) -> (@ s p => / (a.at p 1) (a.at p 0));</lang>
This is a tad messier than it has to be due to a lack of a way to get the length of an array in WDTE currently.
Usage: <lang WDTE>mean [1; 2; 3] -- io.writeln io.stdout;</lang>
Output:
2
Wortel
<lang wortel>@let {
; using a fork (sum divided-by length) mean1 @(@sum / #)
; using a function with a named argument mean2 &a / @sum a #a
[[ !mean1 [3 1 4 1 5 9 2] !mean2 [3 1 4 1 5 9 2] ]]
}</lang> Returns:
[3.5714285714285716 3.5714285714285716]
Wren
<lang ecmascript>class Arithmetic {
static mean(arr) {
if (arr.count == 0) Fiber.abort("Length must be greater than zero")
return arr.reduce(Fn.new{ |x,y| x+y }) / arr.count
}
} Arithmetic.mean([1,2,3,4,5]) // 3</lang>
XLISP
The specification calls for a function that takes a vector; for convenience, we convert this vector internally to a list. The mean of a zero-length vector is returned as nil, equivalent to the empty list or logical false. <lang lisp>(defun mean (v)
(if (= (vector-length v) 0)
nil
(let ((l (vector->list v)))
(/ (apply + l) (length l)))))</lang>
XPL0
<lang XPL0>code CrLf=9; code real RlOut=48;
func real Mean(A, N); real A; int N; real S; int I; [if N=0 then return 0.0; S:= 0.0; for I:= 0 to N-1 do
S:= S+A(I);
return S/float(N); ]; \Mean
real Test; [Test:= [1.0, 2.0, 5.0, -5.0, 9.5, 3.14159]; RlOut(0, Mean(Test, 6)); CrLf(0); ]</lang>
Output:
2.60693
XSLT
Where $values is some variable indicating a set of nodes containing numbers, the average is given by the XPath expression:
<lang xpath>sum($values) div count($values)</lang>
Runnable example
<lang xml><xsl:stylesheet xmlns:xsl="http://www.w3.org/1999/XSL/Transform" version="1.0"> <xsl:output method="text"/>
<xsl:template match="/"> <xsl:variable name="values" select="/*/*"/> <xsl:value-of select="sum($values) div count($values)"/> </xsl:template> </xsl:stylesheet></lang>
Sample input:
<lang xml><numbers> <number>1</number> <number>1</number> <number>2</number> <number>3</number> <number>5</number> </numbers></lang>
Yorick
<lang yorick>func mean(x) {
if(is_void(x)) return 0; return x(*)(avg);
}</lang>
zkl
Converts int to floats (implicitly): <lang zkl>fcn mean(a,b,c,etc){ z:=vm.arglist; z.reduce('+,0.0)/z.len() } mean(3,1,4,1,5,9); //-->3.83333 mean(); //-->Exception thrown: MathError(NaN (Not a number))</lang> To pass in a vector/list: <lang zkl>fcn meanV(z){ z.reduce('+,0.0)/z.len() } meanV(T(3,1,4,1,5,9)); // --> 3.83333</lang>
Zoea
<lang Zoea> program: average
case: 1 input: [2,3,10] output: 5 case: 2 input: [7,11] output: 9
</lang>
zonnon
<lang zonnon> module Averages; type Vector = array {math} * of real;
procedure ArithmeticMean(x: Vector): real; begin (* sum is a predefined function for mathematical arrays *) return sum(x) end ArithmeticMean; var x: Vector;
begin x := new Vector(4); x := [1.0, 2.3, 3.2, 2.1, 5.3]; write("arithmetic mean: ");writeln(ArithmeticMean(x):10:2) end Averages. </lang>
- Output:
arithmetic mean: 13,9