# Dot product

Dot product
You are encouraged to solve this task according to the task description, using any language you may know.

Create a function/use an in-built function, to compute the   dot product,   also known as the   scalar product   of two vectors.

If possible, make the vectors of arbitrary length.

As an example, compute the dot product of the vectors:

` [1,  3, -5] `     and
` [4, -2, -1] `

If implementing the dot product of two vectors directly:

•   each vector must be the same length
•   multiply corresponding terms from each vector
•   sum the products   (to produce the answer)

## 360 Assembly

<lang 360asm>* Dot product 03/05/2016 DOTPROD CSECT

```        USING  DOTPROD,R15
SR     R7,R7              p=0
LA     R6,1               i=1
```

LOOPI CH R6,=AL2((B-A)/4) do i=1 to hbound(a)

```        BH     ELOOPI
LR     R1,R6              i
SLA    R1,2               *4
L      R3,A-4(R1)         a(i)
L      R4,B-4(R1)         b(i)
MR     R2,R4              a(i)*b(i)
AR     R7,R3              p=p+a(i)*b(i)
LA     R6,1(R6)           i=i+1
B      LOOPI
```

ELOOPI XDECO R7,PG edit p

```        XPRNT  PG,80              print buffer
XR     R15,R15            rc=0
BR     R14                return
```

A DC F'1',F'3',F'-5' B DC F'4',F'-2',F'-1' PG DC CL80' ' buffer

```        YREGS
END    DOTPROD</lang>
```
Output:
```           3
```

## 8th

<lang Forth>[1,3,-5] [4,-2,-1] ' n:* ' n:+ a:dot . cr</lang>

Output:
`3`

## ABAP

<lang ABAP>report zdot_product data: lv_n type i,

```     lv_sum type i,
lt_a type standard table of i,
lt_b type standard table of i.
```

append: '1' to lt_a, '3' to lt_a, '-5' to lt_a. append: '4' to lt_b, '-2' to lt_b, '-1' to lt_b. describe table lt_a lines lv_n.

perform dot_product using lt_a lt_b lv_n changing lv_sum.

write lv_sum left-justified.

form dot_product using it_a like lt_a

```                      it_b like lt_b
iv_n type i
changing
ev_sum type i.
field-symbols: <wa_a> type i, <wa_b> type i.
```
``` do iv_n times.
read table: it_a assigning <wa_a> index sy-index, it_b assigning <wa_b> index sy-index.
lv_sum = lv_sum + ( <wa_a> * <wa_b> ).
enddo.
```

endform.</lang>

Output:
`3`

## ACL2

<lang Lisp>(defun dotp (v u)

```  (if (or (endp v) (endp u))
0
(+ (* (first v) (first u))
(dotp (rest v) (rest u)))))</lang>
```
```> (dotp '(1 3 -5) '(4 -2 -1))
3```

## ActionScript

<lang ActionScript>function dotProduct(v1:Vector.<Number>, v2:Vector.<Number>):Number { if(v1.length != v2.length) return NaN; var sum:Number = 0; for(var i:uint = 0; i < v1.length; i++) sum += v1[i]*v2[i]; return sum; } trace(dotProduct(Vector.<Number>([1,3,-5]),Vector.<Number>([4,-2,-1])));</lang>

<lang Ada>with Ada.Text_IO; use Ada.Text_IO; procedure dot_product is type vect is array(Positive range <>) of Integer; v1 : vect := (1,3,-5); v2 : vect := (4,-2,-1);

function dotprod(a: vect; b: vect) return Integer is sum : Integer := 0; begin if not (a'Length=b'Length) then raise Constraint_Error; end if; for p in a'Range loop sum := sum + a(p)*b(p); end loop; return sum; end dotprod;

begin put_line(Integer'Image(dotprod(v1,v2))); end dot_product;</lang>

Output:
`3`

## Aime

<lang aime>real dp(list a, list b) {

```   real p, v;
integer i;
```
```   p = 0;
for (i, v in a) {
p += v * b[i];
}
```
```   p;
```

}

integer main(void) {

```   o_(dp(list(1r, 3r, -5r), list(4r, -2r, -1r)), "\n");
```
```   0;
```

}</lang>

Output:
`3`

## ALGOL 68

Translation of: C++
Works with: ALGOL 68 version Standard - with prelude inserted manually
Works with: ALGOL 68G version Any - tested with release mk15-0.8b.fc9.i386
Works with: ELLA ALGOL 68 version Any (with appropriate job cards) - tested with release 1.8.8d.fc9.i386

<lang algol68>MODE DOTFIELD = REAL; MODE DOTVEC = [1:0]DOTFIELD;

1. The "Spread Sheet" way of doing a dot product:
``` o Assume bounds are equal, and start at 1
o Ignore round off error
```

PRIO SSDOT = 7; OP SSDOT = (DOTVEC a,b)DOTFIELD: (

``` DOTFIELD sum := 0;
FOR i TO UPB a DO sum +:= a[i]*b[i] OD;
sum
```

);

1. An improved dot-product version:
``` o Handles sparse vectors
o Improves summation by gathering round off error
with no additional multiplication - or LONG - operations.
```

OP * = (DOTVEC a,b)DOTFIELD: (

``` DOTFIELD sum := 0, round off error:= 0;
FOR i
```
1. Assume bounds may not be equal, empty members are zero (sparse) #
```   FROM LWB (LWB a > LWB b | a | b )
TO UPB (UPB a < UPB b | a | b )
DO
DOTFIELD org = sum, prod = a[i]*b[i];
sum +:= prod;
round off error +:= sum - org - prod
OD;
sum - round off error
```

);

1. Test: #

DOTVEC a=(1,3,-5), b=(4,-2,-1);

print(("a SSDOT b = ",fixed(a SSDOT b,0,real width), new line)); print(("a * b = ",fixed(a * b,0,real width), new line))</lang>

Output:
```a SSDOT b = 3.000000000000000
a   *   b = 3.000000000000000```

## ALGOL W

<lang algolw>begin

```   % computes the dot product of two equal length integer vectors            %
% (single dimension arrays ) the length of the vectors must be specified  %
% in length.                                                              %
integer procedure integerDotProduct( integer array a ( * )
; integer array b ( * )
; integer value length
) ;
begin
integer product;
product := 0;
for i := 1 until length do product := product + ( a(i) * b(i) );
product
end integerDotProduct ;
```
```   % declare two vectors of length 3                                         %
integer array v1, v2 ( 1 :: 3 );
% initialise the vectors                                                  %
v1(1) :=  1; v1(2) :=  3; v1(3) := -5;
v2(1) :=  4; v2(2) := -2; v2(3) := -1;
% output the dot product                                                  %
write( integerDotProduct( v1, v2, 3 ) )
```

end. </lang>

## APL

<lang APL>1 3 ¯5 +.× 4 ¯2 ¯1</lang> Output:

`3`

## AppleScript

Translation of: JavaScript
( functional version )

<lang AppleScript>-- DOT PRODUCT ---------------------------------------------------------------

-- dotProduct :: [Number] -> [Number] -> Number on dotProduct(xs, ys)

```   script product
on |λ|(a, b)
a * b
end |λ|
end script

if length of xs = length of ys then
sum(zipWith(product, xs, ys))
else
missing value -- arrays of differing dimension
end if
```

end dotProduct

-- TEST ---------------------------------------------------------------------- on run

```   dotProduct([1, 3, -5], [4, -2, -1])

--> 3
```

end run

-- GENERIC FUNCTIONS ---------------------------------------------------------

-- foldl :: (a -> b -> a) -> a -> [b] -> a on foldl(f, startValue, xs)

```   tell mReturn(f)
set v to startValue
set lng to length of xs
repeat with i from 1 to lng
set v to |λ|(v, item i of xs, i, xs)
end repeat
return v
end tell
```

end foldl

-- min :: Ord a => a -> a -> a on min(x, y)

```   if y < x then
y
else
x
end if
```

end min

-- Lift 2nd class handler function into 1st class script wrapper -- mReturn :: Handler -> Script on mReturn(f)

```   if class of f is script then
f
else
script
property |λ| : f
end script
end if
```

end mReturn

-- sum :: [Number] -> Number on sum(xs)

```   script add
on |λ|(a, b)
a + b
end |λ|
end script

```

end sum

-- zipWith :: (a -> b -> c) -> [a] -> [b] -> [c] on zipWith(f, xs, ys)

```   set lng to min(length of xs, length of ys)
set lst to {}
tell mReturn(f)
repeat with i from 1 to lng
set end of lst to |λ|(item i of xs, item i of ys)
end repeat
return lst
end tell
```

end zipWith</lang>

Output:

<lang AppleScript>3</lang>

## AutoHotkey

<lang AutoHotkey>Vet1 := "1,3,-5" Vet2 := "4 , -2 , -1" MsgBox % DotProduct( Vet1 , Vet2 )

---------------------------

DotProduct( VectorA , VectorB ) {

``` Sum := 0
StringSplit, ArrayA, VectorA, `,, %A_Space%
StringSplit, ArrayB, VectorB, `,, %A_Space%
If ( ArrayA0 <> ArrayB0 )
Return ERROR
While ( A_Index <= ArrayA0 )
Sum += ArrayA%A_Index% * ArrayB%A_Index%
Return Sum
```

}</lang>

## AWK

<lang AWK>

1. syntax: GAWK -f DOT_PRODUCT.AWK

BEGIN {

```   v1 = "1,3,-5"
v2 = "4,-2,-1"
if (split(v1,v1arr,",") != split(v2,v2arr,",")) {
print("error: vectors are of unequal lengths")
exit(1)
}
printf("%g\n",dot_product(v1arr,v2arr))
exit(0)
```

} function dot_product(v1,v2, i,sum) {

```   for (i in v1) {
sum += v1[i] * v2[i]
}
return(sum)
```

} </lang>

Output:
`3`

## BASIC

### Applesoft BASIC

Calculates the dot product of two random vectors of length N. <lang basic>

```100 :
110  REM  DOT PRODUCT
120 :
130  REM  INITIALIZE VECTORS OF LENGTH N
140  N = 3
150  DIM V1(N): DIM V2(N)
160  FOR I = 1 TO N
170  V1(I) =  INT ( RND (1) * 20 - 9.5)
180  V2(I) =  INT ( RND (1) * 20 - 9.5)
190  NEXT I
300 :
310  REM  CALCULATE THE DOT PRODUCT
320 :
330  FOR I = 1 TO N:DP = DP + V1(I) * V2(I): NEXT I
400 :
410  REM  DISPLAY RESULT
420 :
430  PRINT "[";: FOR I = 1 TO N: PRINT " ";V1(I);: NEXT I
440  PRINT "] . [";: FOR I = 1 TO N: PRINT " ";V2(I);: NEXT I
450  PRINT "] = ";DP
```

</lang>

Output:
```]RUN
[ 7 2 -2] . [ 7 -5 8] = 23
]RUN
[ -3 -4 -8] . [ -8 7 6] = -52```

### BBC BASIC

BBC BASIC has a built-in dot-product operator: <lang bbcbasic> DIM vec1(2), vec2(2), dot(0)

```     vec1() = 1, 3, -5
vec2() = 4, -2, -1

dot() = vec1() . vec2()
PRINT "Result is "; dot(0)</lang>
```
Output:
`Result is 3`

## bc

<lang bc>/* Calculate the dot product of two vectors a and b (represented as

```* arrays) of size n.
*/
```

define d(a[], b[], n) {

```   auto d, i
```
```   for (i = 0; i < n; i++) {
d += a[i] * b[i]
}
return(d)
```

}

a[0] = 1 a[1] = 3 a[2] = -5 b[0] = 4 b[1] = -2 b[2] = -1 d(a[], b[], 3)</lang>

Output:
`3`

## Befunge 93

<lang befunge> v Space for variables v Space for vector1 v Space for vector2 v http://rosettacode.org/wiki/Dot_product

```                                           >00pv
```

>>55+":htgneL",,,,,,,,&:0` | v,,,,,,,"Length can't be negative."+55< >,,,,,,,,,,,,,,,,,,,@ |!`-10<

```                                     >0.@
```

v,")".g00,,,,,,,,,,,,,,"Vector a(size " < 0v01g00,")".g00,,,,,,,,,,,,,,"Vector b"< 0pvp2g01&p01-1g01< " g>> 10g0`| @.g30<( 1 >03g:-03p>00g1-` |s 0 vp00-1g00p30+g30*g2-1g00g1-1g00 v # z vp1g01&p01-1g01<> ^ e > 10g0` | vp01-1g01.g1<

```              >00g1-10p>10g:01-`   |  "
>  ^
```

</lang>

Output:
```Length:
3
Vector a(size 3 )1
3
-5
1 3 -5 Vector b(size 3 )4
-2
-1

3```

## Bracmat

<lang bracmat> ( dot

``` =   a A z Z
.     !arg:(%?a ?z.%?A ?Z)
& !a*!A+dot\$(!z.!Z)
| 0
)
```

& out\$(dot\$(1 3 -5.4 -2 -1));</lang>

Output:
`3`

## C

<lang c>#include <stdio.h>

1. include <stdlib.h>

int dot_product(int *, int *, size_t);

int main(void) {

```       int a[3] = {1, 3, -5};
int b[3] = {4, -2, -1};
```
```       printf("%d\n", dot_product(a, b, sizeof(a) / sizeof(a[0])));
```
```       return EXIT_SUCCESS;
```

}

int dot_product(int *a, int *b, size_t n) {

```       int sum = 0;
size_t i;
```
```       for (i = 0; i < n; i++) {
sum += a[i] * b[i];
}
```
```       return sum;
```

}</lang>

Output:
`3`

## C#

<lang csharp>static void Main(string[] args) { Console.WriteLine(DotProduct(new decimal[] { 1, 3, -5 }, new decimal[] { 4, -2, -1 })); Console.Read(); }

private static decimal DotProduct(decimal[] vec1, decimal[] vec2) { if (vec1 == null) return 0;

if (vec2 == null) return 0;

if (vec1.Length != vec2.Length) return 0;

decimal tVal = 0; for (int x = 0; x < vec1.Length; x++) { tVal += vec1[x] * vec2[x]; }

return tVal; }</lang>

Output:
`3`

### Alternative using Linq (C# 4)

Works with: C# version 4

<lang csharp>public static decimal DotProduct(decimal[] a, decimal[] b) {

```   return a.Zip(b, (x, y) => x * y).Sum();
```

}</lang>

## C++

<lang cpp>#include <iostream>

1. include <numeric>

int main() {

```   int a[] = { 1, 3, -5 };
int b[] = { 4, -2, -1 };
```
```   std::cout << std::inner_product(a, a + sizeof(a) / sizeof(a[0]), b, 0) << std::endl;
```
```   return 0;
```

}</lang>

Output:
`3`

### Alternative using std::valarray

<lang cpp>

1. include <valarray>
2. include <iostream>

int main() {

```   std::valarray<double> xs = {1,3,-5};
std::valarray<double> ys = {4,-2,-1};
```
```   double result = (xs * ys).sum();
```
```   std::cout << result << '\n';

return 0;
```

}</lang>

Output:
`3`

## Clojure

Works with: Clojure version 1.1

Preconditions are new in 1.1. The actual code also works in older Clojure versions. <lang clojure>(defn dot-product [& matrix]

``` {:pre [(apply == (map count matrix))]}
(apply + (apply map * matrix)))
```

(defn dot-product2 [x y]

```(->> (interleave x y)
(partition 2 2)
(map #(apply * %))
(reduce +)))
```

(defn dot-product3

``` "Dot product of vectors. Tested on version 1.8.0."
[v1 v2]
{:pre [(= (count v1) (count v2))]}
(reduce + (map * v1 v2)))
```
Example Usage

(println (dot-product [1 3 -5] [4 -2 -1])) (println (dot-product2 [1 3 -5] [4 -2 -1])) (println (dot-product3 [1 3 -5] [4 -2 -1])) </lang>

## CoffeeScript

<lang coffeescript>dot_product = (ary1, ary2) ->

``` if ary1.length != ary2.length
throw "can't find dot product: arrays have different lengths"
dotprod = 0
for v, i in ary1
dotprod += v * ary2[i]
dotprod
```

console.log dot_product([ 1, 3, -5 ], [ 4, -2, -1 ]) # 3 try

``` console.log dot_product([ 1, 3, -5 ], [ 4, -2, -1, 0 ]) # exception
```

catch e

``` console.log e</lang>
```
Output:
```> coffee foo.coffee
3

can't find dot product: arrays have different lengths```

## Common Lisp

<lang lisp>(defun dot-product (a b)

``` (apply #'+ (mapcar #'* (coerce a 'list) (coerce b 'list))))</lang>
```

This works with any size vector, and (as usual for Common Lisp) all numeric types (rationals, bignums, complex numbers, etc.).

Maybe it is better to do it without coercing. Then we got a cleaner code. <lang lisp>(defun dot-prod (a b)

``` (reduce #'+ (map 'simple-vector #'* a b)))</lang>
```

## Component Pascal

<lang oberon2> MODULE DotProduct; IMPORT StdLog;

PROCEDURE Calculate*(x,y: ARRAY OF INTEGER): INTEGER; VAR i,sum: INTEGER; BEGIN sum := 0; FOR i:= 0 TO LEN(x) - 1 DO INC(sum,x[i] * y[i]); END; RETURN sum END Calculate;

PROCEDURE Test*; VAR i,sum: INTEGER; v1,v2: ARRAY 3 OF INTEGER; BEGIN v1[0] := 1;v1[1] := 3;v1[2] := -5; v2[0] := 4;v2[1] := -2;v2[2] := -1;

StdLog.Int(Calculate(v1,v2));StdLog.Ln END Test;

END DotProduct. </lang> Execute: ^Q DotProduct.Test

Output:
```3
```

## D

<lang d>void main() {

```   import std.stdio, std.numeric;
```
```   [1.0, 3.0, -5.0].dotProduct([4.0, -2.0, -1.0]).writeln;
```

}</lang>

Output:
`3`

Using an array operation: <lang d>void main() {

```   import std.stdio, std.algorithm;
```
```   double[3] a = [1.0, 3.0, -5.0];
double[3] b = [4.0, -2.0, -1.0];
double[3] c = a[] * b[];
c[].sum.writeln;
```

}</lang>

## Dart

<lang dart>num dot(List<num> A, List<num> B){

``` if (A.length != B.length){
throw new Exception('Vectors must be of equal size');
}
num result = 0;
for (int i = 0; i < A.length; i++){
result += A[i] * B[i];
}
return result;
```

}

void main(){

``` var l = [1,3,-5];
var k = [4,-2,-1];
print(dot(l,k));
```

}</lang>

Output:
`3`

## Delphi

Works with: Lazarus

<lang delphi>program Project1;

{\$APPTYPE CONSOLE}

type

``` doublearray = array of Double;
```

function DotProduct(const A, B : doublearray): Double; var I: integer; begin

``` assert (Length(A) = Length(B), 'Input arrays must be the same length');
Result := 0;
for I := 0 to Length(A) - 1 do
Result := Result + (A[I] * B[I]);
```

end;

var

``` x,y: doublearray;
```

begin

``` SetLength(x, 3);
SetLength(y, 3);
x[0] := 1; x[1] := 3; x[2] := -5;
y[0] := 4; y[1] :=-2; y[2] := -1;
WriteLn(DotProduct(x,y));
```

end.</lang>

Output:
` 3.00000000000000E+0000`

Note: Delphi does not like arrays being declared in procedure headings, so it is necessary to declare it beforehand. To use integers, modify doublearray to be an array of integer.

## Déjà Vu

<lang dejavu>dot a b: if /= len a len b: Raise value-error "dot product needs two vectors with the same length"

0 while a: + * pop-from a pop-from b

!. dot [ 1 3 -5 ] [ 4 -2 -1 ]</lang>

Output:
`3`

## DWScript

For arbitrary length vectors, using a precondition to check vector length: <lang delphi>function DotProduct(a, b : array of Float) : Float; require

```  a.Length = b.Length;
```

var

```  i : Integer;
```

begin

```  Result := 0;
for i := 0 to a.High do
Result += a[i]*b[i];
```

end;

PrintLn(DotProduct([1,3,-5], [4,-2,-1]));</lang> Using built-in 4D Vector type: <lang delphi>var a := Vector(1, 3, -5, 0); var b := Vector(4, -2, -1, 0);

PrintLn(a * b);</lang>

Ouput in both cases:
`3`

## EchoLisp

<lang lisp> (define a #(1 3 -5)) (define b #(4 -2 -1))

function definition

(define ( ⊗ a b) (for/sum ((x a)(y b)) (* x y))) (⊗ a b) → 3

library

(lib 'math) (dot-product a b) → 3 </lang>

## Eiffel

<lang Eiffel>class APPLICATION

create make

feature {NONE} -- Initialization

make -- Run application. do print(dot_product(<<1, 3, -5>>, <<4, -2, -1>>).out) end

feature -- Access

dot_product (a, b: ARRAY[INTEGER]): INTEGER -- Dot product of vectors `a' and `b'. require a.lower = b.lower a.upper = b.upper local i: INTEGER do from i := a.lower until i > a.upper loop Result := Result + a[i] * b[i] i := i + 1 end end end</lang>

Ouput:
`3`

## Ela

<lang ela>open list

dotp a b | length a == length b = sum (zipWith (*) a b)

```        | else = fail "Vector sizes must match."
```

dotp [1,3,-5] [4,-2,-1]</lang>

Output:
`3`

## Elena

ELENA 4.x : <lang elena>import extensions; import system'routines;

extension op {

```   method dotProduct(int[] array)
= self.zipBy(array, (x,y => x * y)).summarize();
```

}

public program() {

```   console.printLine(new int[]{1, 3, -5}.dotProduct(new int[]{4, -2, -1}))
```

}</lang>

Output:
```3
```

## Elixir

Translation of: Erlang

<lang elixir>defmodule Vector do

``` def dot_product(a,b) when length(a)==length(b), do: dot_product(a,b,0)
def dot_product(_,_) do
raise ArgumentError, message: "Vectors must have the same length."
end

defp dot_product([],[],product), do: product
defp dot_product([h1|t1], [h2|t2], product), do: dot_product(t1, t2, product+h1*h2)
```

end

IO.puts Vector.dot_product([1,3,-5],[4,-2,-1])</lang>

Output:
```3
```

## Emacs Lisp

<lang Emacs Lisp> (defun dot-product (v1 v2)

``` (setq res 0)
(dotimes (i (length v1))
(setq res (+ (* (elt v1 i) (elt v2 i) ) res) ))
res)
```

(progn

``` (insert (format "%d\n" (dot-product [1 2 3] [1 2 3]) ))
(insert (format "%d\n" (dot-product '(1 2 3) '(1 2 3) ))))
```

</lang> Output:

```
14
14
```

## Erlang

<lang erlang>dotProduct(A,B) when length(A) == length(B) -> dotProduct(A,B,0); dotProduct(_,_) -> erlang:error('Vectors must have the same length.').

dotProduct([H1|T1],[H2|T2],P) -> dotProduct(T1,T2,P+H1*H2); dotProduct([],[],P) -> P.

dotProduct([1,3,-5],[4,-2,-1]).</lang>

Output:
`3`

## Euphoria

<lang Euphoria>function dotprod(sequence a, sequence b)

```   atom sum
a *= b
sum = 0
for n = 1 to length(a) do
sum += a[n]
end for
return sum
```

end function

? dotprod({1,3,-5},{4,-2,-1})</lang>

Output:
`3`

<lang Euphoria>-- Here is an alternative method, -- using the standard Euphoria Version 4+ Math Library include std/math.e sequence a = {1,3,-5}, b = {4,-2,-1} -- Make them any length you want ? sum(a * b)</lang>

Output:
`3`

## F#

<lang fsharp>let dot_product (a:array<'a>) (b:array<'a>) =

```   if Array.length a <> Array.length b then failwith "invalid argument: vectors must have the same lengths"
Array.fold2 (fun acc i j -> acc + (i * j)) 0 a b</lang>
```
```> dot_product [| 1; 3; -5 |] [| 4; -2; -1 |] ;;
val it : int = 3```

## Factor

The built-in word `v.` is used to compute the dot product. It doesn't enforce that the vectors are of the same length, so here's a wrapper. <lang factor>USING: kernel math.vectors sequences ;

dot-product ( u v -- w )
```   2dup [ length ] bi@ =
[ v. ] [ "Vector lengths must be equal" throw ] if ;</lang>
```
```( scratchpad ) { 1 3 -5 } { 4 -2 -1 } dot-product .
3
```

## FALSE

<lang false>[[\1-\$0=~][\$d;2*1+\-ø\\$d;2+\-ø@*@+]#]p: 3d: {Vectors' length} 1 3 5_ 4 2_ 1_ d;\$1+ø@*p;!%. {Output: 3}</lang>

## Fantom

Dot product of lists of Int: <lang fantom>class DotProduct {

``` static Int dotProduct (Int[] a, Int[] b)
{
Int result := 0
[a.size,b.size].min.times |i|
{
result += a[i] * b[i]
}
return result
}
```
``` public static Void main ()
{
Int[] x := [1,2,3,4]
Int[] y := [2,3,4]
```
```   echo ("Dot product of \$x and \$y is \${dotProduct(x, y)}")
}
```

}</lang>

## Fōrmulæ

Fōrmulæ programs are not textual, visualization/edition of programs is done showing/manipulating structures but not text (more info). Moreover, there can be multiple visual representations of the same program. Even though it is possible to have textual representation —i.e. XML, JSON— they are intended for transportation effects more than visualization and edition.

The option to show Fōrmulæ programs and their results is showing images. Unfortunately images cannot be uploaded in Rosetta Code.

## Forth

<lang forth>: vector create cells allot ;

th cells + ;

3 constant /vector /vector vector a /vector vector b

dotproduct ( a1 a2 -- n)
``` 0 tuck ?do -rot over i th @ over i th @ * >r rot r> + loop nip nip
```
vector! cells over + swap ?do i ! 1 cells +loop ;

-5 3 1 a /vector vector! -1 -2 4 b /vector vector!

a b /vector dotproduct . 3 ok</lang>

## Fortran

<lang fortran>program test_dot_product

``` write (*, '(i0)') dot_product ([1, 3, -5], [4, -2, -1])
```

end program test_dot_product</lang>

Output:
`3`

The intrinsic function `Dot_Product(X,Y)` accepts various precisions of integer, floating-point and complex arrays (for which it is `Sum(Conjg(x)*y)`) and even logical, for which it is `Any(x .AND. y)` returning zero if either array is of length zero, or false for logical types.

## FunL

<lang funl>import lists.zipWith

def dot( a, b )

``` | a.length() == b.length() = sum( zipWith((*), a, b) )
| otherwise = error( "Vector sizes must match" )
```

println( dot([1, 3, -5], [4, -2, -1]) )</lang>

Output:
`3`

## GAP

<lang gap># Built-in

[1, 3, -5]*[4, -2, -1];

1. 3</lang>

## Go

### Implementation

<lang go>package main

import (

```   "errors"
"fmt"
"log"
```

)

var (

```   v1 = []int{1, 3, -5}
v2 = []int{4, -2, -1}
```

)

func dot(x, y []int) (r int, err error) {

```   if len(x) != len(y) {
return 0, errors.New("incompatible lengths")
}
for i, xi := range x {
r += xi * y[i]
}
return
```

}

func main() {

```   d, err := dot([]int{1, 3, -5}, []int{4, -2, -1})
if err != nil {
log.Fatal(err)
}
fmt.Println(d)
```

}</lang>

Output:
```3
```

### Library gonum/floats

<lang go>package main

import (

```   "fmt"
```
```   "github.com/gonum/floats"
```

)

var (

```   v1 = []float64{1, 3, -5}
v2 = []float64{4, -2, -1}
```

)

func main() {

```   fmt.Println(floats.Dot(v1, v2))
```

}</lang>

Output:
```3
```

## Groovy

Solution: <lang groovy>def dotProduct = { x, y ->

```   assert x && y && x.size() == y.size()
[x, y].transpose().collect{ xx, yy -> xx * yy }.sum()
```

}</lang> Test: <lang groovy>println dotProduct([1, 3, -5], [4, -2, -1])</lang>

Output:
`3`

<lang haskell>dotp :: Num a => [a] -> [a] -> a dotp a b | length a == length b = sum (zipWith (*) a b)

```        | otherwise = error "Vector sizes must match"

```

main = print \$ dotp [1, 3, -5] [4, -2, -1] -- prints 3</lang>

Or, using the Maybe monad to avoid exceptions and keep things composable: <lang haskell>dotp

``` :: Num a
=> [a] -> [a] -> Maybe a
```

dotp a b

``` | length a == length b = Just \$ sum (zipWith (*) a b)
| otherwise = Nothing
```

main :: IO () main = mbPrint \$ dotp [1, 3, -5] [4, -2, -1] -- prints 3

mbPrint

``` :: Show a
=> Maybe a -> IO ()
```

mbPrint (Just x) = print x mbPrint n = print n</lang>

## Hy

<lang clojure>(defn dotp [a b]

``` (assert (= (len a) (len b)))
(sum (genexpr (* aterm bterm)
[(, aterm bterm) (zip a b)])))
```

(assert (= 3 (dotp [1 3 -5] [4 -2 -1])))</lang>

## Icon and Unicon

The procedure below computes the dot product of two vectors of arbitrary length or generates a run time error if its arguments are the wrong type or shape. <lang Icon>procedure main() write("a dot b := ",dotproduct([1, 3, -5],[4, -2, -1])) end

procedure dotproduct(a,b) #: return dot product of vectors a & b or error if *a ~= *b & type(a) == type(b) == "list" then runerr(205,a) # invalid value every (dp := 0) +:= a[i := 1 to *a] * b[i] return dp end</lang>

## IDL

<lang IDL> a = [1, 3, -5] b = [4, -2, -1] c = a#TRANSPOSE(b) c = TOTAL(a*b,/PRESERVE_TYPE) </lang>

## Idris

<lang idris>module Main

import Data.Vect

dotProduct : (Num a) => Vect n a -> Vect n a -> a dotProduct = (sum .) . zipWith (*)

main : IO () main = printLn \$ dotProduct [1,2,3] [1,2,3] </lang>

## J

<lang j> 1 3 _5 +/ . * 4 _2 _1 3

```  dotp=: +/ . *                  NB. Or defined as a verb (function)
1 3 _5  dotp 4 _2 _1
```

3</lang> Note also: The verbs built using the conjunction ` .` generally apply to matricies and arrays of higher dimensions and can be built with verbs (functions) other than sum ( `+/` ) and product ( `*` ).

Spelling issue: The conjunction ` .` needs to be preceded by a space. This is because J's spelling rules say that if the character '.' is preceded by any other character, it is included in the same parser token that included that other character. In other words, `1.23e4`, `'...'` and `/.` are each examples of "parser tokens".

## Java

<lang java>public class DotProduct {

public static void main(String[] args) { double[] a = {1, 3, -5}; double[] b = {4, -2, -1};

System.out.println(dotProd(a,b)); }

public static double dotProd(double[] a, double[] b){ if(a.length != b.length){ throw new IllegalArgumentException("The dimensions have to be equal!"); } double sum = 0; for(int i = 0; i < a.length; i++){ sum += a[i] * b[i]; } return sum; } }</lang>

Output:
`3.0`

## JavaScript

### ES5

<lang javascript>function dot_product(ary1, ary2) {

```   if (ary1.length != ary2.length)
throw "can't find dot product: arrays have different lengths";
var dotprod = 0;
for (var i = 0; i < ary1.length; i++)
dotprod += ary1[i] * ary2[i];
return dotprod;
```

}

print(dot_product([1,3,-5],[4,-2,-1])); // ==> 3 print(dot_product([1,3,-5],[4,-2,-1,0])); // ==> exception</lang>

We could also use map and reduce in lieu of iteration,

<lang javascript>function dotp(x,y) {

```   function dotp_sum(a,b) { return a + b; }
function dotp_times(a,i) { return x[i] * y[i]; }
if (x.length != y.length)
throw "can't find dot product: arrays have different lengths";
return x.map(dotp_times).reduce(dotp_sum,0);
```

}

dotp([1,3,-5],[4,-2,-1]); // ==> 3 dotp([1,3,-5],[4,-2,-1,0]); // ==> exception</lang>

### ES6

Composing functional primitives into a dotProduct() which returns undefined (rather than an error) when the array lengths are unmatched.

<lang JavaScript>(() => {

```   'use strict';
```
```   // dotProduct :: [Int] -> [Int] -> Int
const dotProduct = (xs, ys) => {
const sum = xs => xs ? xs.reduce((a, b) => a + b, 0) : undefined;
```
```       return xs.length === ys.length ? (
sum(zipWith((a, b) => a * b, xs, ys))
) : undefined;
}
```
```   // zipWith :: (a -> b -> c) -> [a] -> [b] -> [c]
const zipWith = (f, xs, ys) => {
const ny = ys.length;
return (xs.length <= ny ? xs : xs.slice(0, ny))
.map((x, i) => f(x, ys[i]));
}
```
```   return dotProduct([1, 3, -5], [4, -2, -1]);
```

})();</lang>

Output:

<lang JavaScript>3</lang>

## jq

The dot-product of two arrays, x and y, can be computed using dot(x;y) defined as follows: <lang jq> def dot(x; y):

``` reduce range(0;x|length) as \$i (0; . + x[\$i] * y[\$i]);
```

</lang>

Suppose however that we are given an array of objects, each of which has an "x" field and a "y" field, and that we wish to compute SIGMA( x * y ) where the sum is taken over the array, and where x and y denote the values in the "x" and "y" fields respectively.

This can most usefully be accomplished in jq with the aid of SIGMA(f) defined as follows:<lang jq>def SIGMA( f ): reduce .[] as \$o (0; . + (\$o | f )) ;</lang> Given the array of objects as input, the dot-product is then simply `SIGMA( .x * .y )`.

Example:<lang jq>dot( [1, 3, -5]; [4, -2, -1]) # => 3

[ {"x": 1, "y": 4}, {"x": 3, "y": -2}, {"x": -5, "y": -1} ]

``` | SIGMA( .x * .y ) # => 3</lang>
```

## Julia

Dot products and many other linear-algebra functions are built-in functions in Julia (and are largely implemented by calling functions from LAPACK). <lang julia>x = [1, 3, -5] y = [4, -2, -1] z = dot(x, y) z = x'*y</lang>

## K

<lang K> +/1 3 -5 * 4 -2 -1 3

```  1 3 -5 _dot 4 -2 -1
```

3</lang>

## Kotlin

Works with: Kotlin version 1.0+

<lang scala>fun dot(v1: Array<Double>, v2: Array<Double>) =

```   v1.zip(v2).map { it.first * it.second }.reduce { a, b -> a + b }
```

fun main(args: Array<String>) {

```   dot(arrayOf(1.0, 3.0, -5.0), arrayOf(4.0, -2.0, -1.0)).let { println(it) }
```

}</lang>

Output:
`3.0`

## LFE

<lang lisp>(defun dot-product (a b)

``` (: lists foldl #'+/2 0
(: lists zipwith #'*/2 a b)))
```

</lang>

## Liberty BASIC

<lang lb>vectorA\$ = "1, 3, -5" vectorB\$ = "4, -2, -1" print "DotProduct of ";vectorA\$;" and "; vectorB\$;" is "; print DotProduct(vectorA\$, vectorB\$)

'arbitrary length vectorA\$ = "3, 14, 15, 9, 26" vectorB\$ = "2, 71, 18, 28, 1" print "DotProduct of ";vectorA\$;" and "; vectorB\$;" is "; print DotProduct(vectorA\$, vectorB\$)

end

function DotProduct(a\$, b\$)

```   DotProduct = 0
i = 1
while 1
x\$=word\$( a\$, i, ",")
y\$=word\$( b\$, i, ",")
if x\$="" or y\$="" then exit function
DotProduct = DotProduct + val(x\$)*val(y\$)
i = i+1
wend
```

end function </lang>

## Logo

<lang logo>to dotprod :a :b

``` output apply "sum (map "product :a :b)
```

end

show dotprod [1 3 -5] [4 -2 -1]  ; 3</lang>

## Logtalk

<lang logtalk>dot_product(A, B, Sum) :-

```   dot_product(A, B, 0, Sum).
```

dot_product([], [], Sum, Sum). dot_product([A| As], [B| Bs], Acc, Sum) :-

```   Acc2 is Acc + A*B,
dot_product(As, Bs, Acc2, Sum).</lang>
```

## Lua

<lang lua>function dotprod(a, b)

``` local ret = 0
for i = 1, #a do
ret = ret + a[i] * b[i]
end
return ret
```

end

print(dotprod({1, 3, -5}, {4, -2, 1}))</lang>

## M2000 Interpreter

<lang M2000 Interpreter> Module dot_product {

```     A=(1,3,-5)
B=(4,-2,-1)
Function Dot(a, b) {
if len(a)<>len(b) Then Error "not same length"
if len(a)=0 then Error "empty vectors"
Let a1=each(a), b1=each(b), sum=0
While a1, b1 {sum+=array(a1)*array(b1)}
=sum
}
Print Dot(A, B)
Print Dot((1,3,-5), (4,-2,-1))
```

} Module dot_product </lang>

## Maple

Between Arrays, Vectors, or Matrices you can use the dot operator: <lang Maple><1,2,3> . <4,5,6></lang> <lang Maple>Array([1,2,3]) . Array([4,5,6])</lang>

Between any of the above or lists, you can use the `LinearAlgebra[DotProduct]` function: <lang Maple>LinearAlgebra( <1,2,3>, <4,5,6> )</lang> <lang Maple>LinearAlgebra( Array([1,2,3]), Array([4,5,6]) )</lang> <lang Maple>LinearAlgebra([1,2,3], [4,5,6] )</lang>

## Mathematica / Wolfram Language

<lang Mathematica>{1,3,-5}.{4,-2,-1}</lang>

## MATLAB

The dot product operation is a built-in function that operates on vectors of arbitrary length. <lang matlab>A = [1 3 -5] B = [4 -2 -1] C = dot(A,B)</lang> For the Octave implimentation: <lang matlab>function C = DotPro(A,B)

``` C = sum( A.*B );
```

end</lang>

## Maxima

<lang maxima>[1, 3, -5] . [4, -2, -1]; /* 3 */</lang>

## Mercury

This will cause a software_error/1 exception if the lists are of different lengths. <lang mercury>:- module dot_product.

- interface.
- import_module io.
- pred main(io::di, io::uo) is det.
- implementation.
- import_module int, list.

main(!IO) :-

```   io.write_int([1, 3, -5] `dot_product` [4, -2, -1], !IO),
io.nl(!IO).
```
- func dot_product(list(int), list(int)) = int.

dot_product(As, Bs) =

```   list.foldl_corresponding((func(A, B, Acc) = Acc + A * B), As, Bs, 0).</lang>
```

## МК-61/52

<lang>С/П * ИП0 + П0 С/П БП 00</lang>

Input: В/О x1 С/П x2 С/П y1 С/П y2 С/П ...

## Modula-2

<lang modula2>MODULE DotProduct; FROM RealStr IMPORT RealToStr; FROM Terminal IMPORT WriteString,WriteLn,ReadChar;

TYPE Vector =

```   RECORD
x,y,z : REAL
END;
```

PROCEDURE DotProduct(u,v : Vector) : REAL; BEGIN

```   RETURN u.x*v.x + u.y*v.y + u.z*v.z
```

END DotProduct;

VAR

```   buf : ARRAY[0..63] OF CHAR;
dp : REAL;
```

BEGIN

```   dp := DotProduct(Vector{1.0,3.0,-5.0},Vector{4.0,-2.0,-1.0});
RealToStr(dp, buf);
WriteString(buf);
WriteLn;
```
```   ReadChar
```

END DotProduct.</lang>

## MUMPS

<lang MUMPS>DOTPROD(A,B)

```;Returns the dot product of two vectors. Vectors are assumed to be stored as caret-delimited strings of numbers.
;If the vectors are not of equal length, a null string is returned.
QUIT:\$LENGTH(A,"^")'=\$LENGTH(B,"^") ""
NEW I,SUM
SET SUM=0
FOR I=1:1:\$LENGTH(A,"^") SET SUM=SUM+(\$PIECE(A,"^",I)*\$PIECE(B,"^",I))
KILL I
QUIT SUM</lang>
```

## Nemerle

This will cause an exception if the arrays are different lengths. <lang Nemerle>using System; using System.Console; using Nemerle.Collections.NCollectionsExtensions;

module DotProduct {

```   DotProduct(x : array[int], y : array[int]) : int
{
\$[(a * b)|(a, b) in ZipLazy(x, y)].FoldLeft(0, _+_);
}

Main() : void
{
def arr1 = array[1, 3, -5]; def arr2 = array[4, -2, -1];
WriteLine(DotProduct(arr1, arr2));
}
```

}</lang>

## NetRexx

<lang NetRexx>/* NetRexx */ options replace format comments java crossref savelog symbols binary

whatsTheVectorVictor = [[double 1.0, 3.0, -5.0], [double 4.0, -2.0, -1.0]] dotProduct = Rexx dotProduct(whatsTheVectorVictor) say dotProduct.format(null, 2)

return

method dotProduct(vec1 = double[], vec2 = double[]) public constant returns double signals IllegalArgumentException

``` if vec1.length \= vec2.length then signal IllegalArgumentException('Vectors must be the same length')
```
``` scalarProduct = double 0.0
loop e_ = 0 to vec1.length - 1
scalarProduct = vec1[e_] * vec2[e_] + scalarProduct
end e_
```
``` return scalarProduct
```

method dotProduct(vecs = double[,]) public constant returns double signals IllegalArgumentException

``` return dotProduct(vecs[0], vecs[1])</lang>
```

## newLISP

<lang newLISP>(define (dot-product x y)

``` (apply + (map * x y)))
```

(println (dot-product '(1 3 -5) '(4 -2 -1)))</lang>

## Nim

<lang nim># Compile time error when a and b are differently sized arrays

1. Runtime error when a and b are differently sized seqs

proc dotp[T](a,b: T): int =

``` assert a.len == b.len
for i in a.low..a.high:
result += a[i] * b[i]
```

echo dotp([1,3,-5], [4,-2,-1]) echo dotp(@[1,2,3],@[4,5,6])</lang>

## Oberon-2

Works with: oo2c version 2

<lang oberon2> MODULE DotProduct; IMPORT

``` Out := NPCT:Console;
```

VAR

``` x,y: ARRAY 3 OF LONGINT;
```

PROCEDURE DotProduct(a,b: ARRAY OF LONGINT): LONGINT; VAR

``` resp, i: LONGINT;
```

BEGIN

``` ASSERT(LEN(a) = LEN(b));
resp := 0;
FOR i := 0 TO LEN(x) - 1 DO
INC(resp,x[i]*y[i])
END;
RETURN resp
```

END DotProduct;

BEGIN

``` x[0] := 1;y[0] := 4;
x[1] := 3;y[1] := -2;
x[2] := -5;y[2] := -1;
Out.Int(DotProduct(x,y),0);Out.Ln
```

END DotProduct. </lang>

Output:
```3
```

## Objective-C

<lang objc>#import <stdio.h>

1. import <stdint.h>
2. import <stdlib.h>
3. import <string.h>
4. import <Foundation/Foundation.h>

// this class exists to return a result between two // vectors: if vectors have different "size", valid // must be NO @interface VResult : NSObject {

```@private
double value;
BOOL valid;
```

} +(instancetype)new: (double)v isValid: (BOOL)y; -(instancetype)init: (double)v isValid: (BOOL)y; -(BOOL)isValid; -(double)value; @end

@implementation VResult +(instancetype)new: (double)v isValid: (BOOL)y {

``` return [[self alloc] init: v isValid: y];
```

} -(instancetype)init: (double)v isValid: (BOOL)y {

``` if ((self == [super init])) {
value = v;
valid = y;
}
return self;
```

} -(BOOL)isValid { return valid; } -(double)value { return value; } @end

@interface RCVector : NSObject {

```@private
double *vec;
uint32_t size;
```

} +(instancetype)newWithArray: (double *)v ofLength: (uint32_t)l; -(instancetype)initWithArray: (double *)v ofLength: (uint32_t)l; -(VResult *)dotProductWith: (RCVector *)v; -(uint32_t)size; -(double *)array; -(void)free; @end

@implementation RCVector +(instancetype)newWithArray: (double *)v ofLength: (uint32_t)l {

``` return [[self alloc] initWithArray: v ofLength: l];
```

} -(instancetype)initWithArray: (double *)v ofLength: (uint32_t)l {

``` if ((self = [super init])) {
size = l;
vec = malloc(sizeof(double) * l);
if ( vec == NULL )
return nil;
memcpy(vec, v, sizeof(double)*l);
}
return self;
```

} -(void)dealloc {

``` free(vec);
```

} -(uint32_t)size { return size; } -(double *)array { return vec; } -(VResult *)dotProductWith: (RCVector *)v {

``` double r = 0.0;
uint32_t i, s;
double *v1;
if ( [self size] != [v size] ) return [VResult new: r isValid: NO];
s = [self size];
v1 = [v array];
for(i = 0; i < s; i++) {
r += vec[i] * v1[i];
}
return [VResult new: r isValid: YES];
```

} @end

double val1[] = { 1, 3, -5 }; double val2[] = { 4,-2, -1 };

int main() {

``` @autoreleasepool {
RCVector *v1 = [RCVector newWithArray: val1 ofLength: sizeof(val1)/sizeof(double)];
RCVector *v2 = [RCVector newWithArray: val2 ofLength: sizeof(val1)/sizeof(double)];
VResult *r = [v1 dotProductWith: v2];
if ( [r isValid] ) {
printf("%lf\n", [r value]);
} else {
fprintf(stderr, "length of vectors differ\n");
}
}
return 0;
```

}</lang>

## Objeck

<lang objeck>bundle Default {

``` class DotProduct {
function : Main(args : String[]) ~ Nil {
DotProduct([1, 3, -5], [4, -2, -1])->PrintLine();
}

function : DotProduct(array_a : Int[], array_b : Int[]) ~ Int {
if(array_a = Nil) {
return 0;
};

if(array_b = Nil) {
return 0;
};

if(array_a->Size() <> array_b->Size()) {
return 0;
};

val := 0;
for(x := 0; x < array_a->Size(); x += 1;) {
val += (array_a[x] * array_b[x]);
};

return val;
}
}
```

}</lang>

## OCaml

With lists: <lang ocaml>let dot = List.fold_left2 (fun z x y -> z +. x *. y) 0.

(*

1. dot [1.0; 3.0; -5.0] [4.0; -2.0; -1.0];;

- : float = 3.

• )</lang>

With arrays: <lang ocaml>let dot v u =

``` if Array.length v <> Array.length u
then invalid_arg "Different array lengths";
let times v u =
Array.mapi (fun i v_i -> v_i *. u.(i)) v
in Array.fold_left (+.) 0. (times v u)
```

(*

1. dot [| 1.0; 3.0; -5.0 |] [| 4.0; -2.0; -1.0 |];;

- : float = 3.

• )</lang>

## Octave

See Dot product#MATLAB for an implementation. If we have a row-vector and a column-vector, we can use simply *. <lang octave>a = [1, 3, -5] b = [4, -2, -1] % or [4; -2; -1] and avoid transposition with ' disp( a * b' )  % ' means transpose</lang>

## Oforth

<lang Oforth>: dotProduct zipWith(#*) sum ;</lang>

Output:
```>[ 1, 3, -5] [ 4, -2, -1 ] dotProduct .
3
```

## Ol

<lang scheme> (define (dot-product a b)

``` (apply + (map * a b)))

```

(print (dot-product '(1 3 -5) '(4 -2 -1)))

==> 3

</lang>

## Oz

Vectors are represented as lists in this example. <lang oz>declare

``` fun {DotProduct Xs Ys}
{Length Xs} = {Length Ys} %% assert
{List.foldL {List.zip Xs Ys Number.'*'} Number.'+' 0}
end
```

in

``` {Show {DotProduct [1 3 ~5] [4 ~2 ~1]}}</lang>
```

## PARI/GP

<lang parigp>dot(u,v)={

``` sum(i=1,#u,u[i]*v[i])
```

};</lang>

See Delphi

## Perl

<lang perl>sub dotprod {

```       my(\$vec_a, \$vec_b) = @_;
die "they must have the same size\n" unless @\$vec_a == @\$vec_b;
my \$sum = 0;
\$sum += \$vec_a->[\$_] * \$vec_b->[\$_] for 0..\$#\$vec_a;
return \$sum;
```

}

my @vec_a = (1,3,-5); my @vec_b = (4,-2,-1);

print dotprod(\@vec_a,\@vec_b), "\n"; # 3</lang>

## Perl 6

Works with: Rakudo version 2010.07

We use the square-bracket meta-operator to turn the infix operator `+` into a reducing list operator, and the guillemet meta-operator to vectorize the infix operator `*`. Length validation is automatic in this form. <lang perl6>say [+] (1, 3, -5) »*« (4, -2, -1);</lang>

## Phix

<lang Phix>?sum(sq_mul({1,3,-5},{4,-2,-1}))</lang>

Output:
```3
```

## PHP

<lang php><?php function dot_product(\$v1, \$v2) {

``` if (count(\$v1) != count(\$v2))
throw new Exception('Arrays have different lengths');
return array_sum(array_map('bcmul', \$v1, \$v2));
```

}

echo dot_product(array(1, 3, -5), array(4, -2, -1)), "\n"; ?></lang>

## PicoLisp

<lang PicoLisp>(de dotProduct (A B)

```  (sum * A B) )
```

(dotProduct (1 3 -5) (4 -2 -1))</lang>

Output:
`-> 3`

## PL/I

<lang PL/I>get (n); begin;

```  declare (A(n), B(n)) float;
declare dot_product float;
```
```  get list (A);
get list (B);
dot_product = sum(a*b);
put (dot_product);
```

end;</lang>

## PostScript

<lang postscript>/dotproduct{ /x exch def /y exch def /sum 0 def /i 0 def x length y length eq %Check if both arrays have the same length { x length{ /sum x i get y i get mul sum add def /i i 1 add def }repeat sum == } { -1 == }ifelse }def</lang>

## PowerShell

<lang PowerShell> function dotproduct( \$a, \$b) {

```   \$a | foreach -Begin {\$i = \$res = 0} -Process { \$res += \$_*\$b[\$i++] } -End{\$res}
```

} dotproduct (1..2) (1..2) dotproduct (1..10) (11..20) </lang> Output:

```
5
935
```

## Prolog

Works with SWI-Prolog. <lang Prolog>dot_product(L1, L2, N) :- maplist(mult, L1, L2, P), sumlist(P, N).

mult(A,B,C) :- C is A*B.</lang> Example :

``` ?- dot_product([1,3,-5], [4,-2,-1], N).
N = 3.```

## PureBasic

<lang PureBasic>Procedure dotProduct(Array a(1),Array b(1))

``` Protected i, sum, length = ArraySize(a())
```
``` If ArraySize(a()) = ArraySize(b())
For i = 0 To length
sum + a(i) * b(i)
Next
EndIf
```
``` ProcedureReturn sum
```

EndProcedure

If OpenConsole()

``` Dim a(2)
Dim b(2)

a(0) = 1 : a(1) = 3 : a(2) = -5
b(0) = 4 : b(1) = -2 : b(2) = -1

PrintN(Str(dotProduct(a(),b())))

Print(#CRLF\$ + #CRLF\$ + "Press ENTER to exit"): Input()
CloseConsole()
```

EndIf</lang>

## Python

<lang python>def dotp(a,b):

```   assert len(a) == len(b), 'Vector sizes must match'
return sum(aterm * bterm for aterm,bterm in zip(a, b))
```

if __name__ == '__main__':

```   a, b = [1, 3, -5], [4, -2, -1]
assert dotp(a,b) == 3</lang>
```

## R

Here are several ways to do the task. <lang R>x <- c(1, 3, -5) y <- c(4, -2, -1)

sum(x*y) # compute products, then do the sum x %*% y # inner product

1. loop implementation

dotp <- function(x, y) { n <- length(x) if(length(y) != n) stop("invalid argument") s <- 0 for(i in 1:n) s <- s + x[i]*y[i] s }

dotp(x, y)</lang>

## Racket

<lang Racket>

1. lang racket

(define (dot-product l r) (for/sum ([x l] [y r]) (* x y)))

(dot-product '(1 3 -5) '(4 -2 -1))

dot-product works on sequences such as vectors

(dot-product #(1 2 3) #(4 5 6)) </lang>

## Rascal

<lang Rascal>import List;

public int dotProduct(list[int] L, list[int] M){ result = 0; if(size(L) == size(M)) { while(size(L) >= 1) { result += (head(L) * head(M)); L = tail(L); M = tail(M); } return result; } else { throw "vector sizes must match"; } }</lang>

### Alternative solution

If a matrix is represented by a relation of <x-coordinate, y-coordinate, value>, then function below can be used to find the Dot product. <lang Rascal>import Prelude;

public real matrixDotproduct(rel[real x, real y, real v] column1, rel[real x, real y, real v] column2){ return (0.0 | it + v1*v2 | <x1,y1,v1> <- column1, <x2,y2,v2> <- column2, y1==y2); }

//a matrix, given by a relation of x-coordinate, y-coordinate, value. public rel[real x, real y, real v] matrixA = { <0.0,0.0,12.0>, <0.0,1.0, 6.0>, <0.0,2.0,-4.0>, <1.0,0.0,-51.0>, <1.0,1.0,167.0>, <1.0,2.0,24.0>, <2.0,0.0,4.0>, <2.0,1.0,-68.0>, <2.0,2.0,-41.0> };</lang>

## REBOL

<lang REBOL>REBOL []

a: [1 3 -5] b: [4 -2 -1]

dot-product: function [v1 v2] [sum] [

```   if (length? v1) != (length? v2) [
make error! "error: vector sizes must match"
]
sum: 0
repeat i length? v1 [
sum: sum + ((pick v1 i) * (pick v2 i))
]
```

]

dot-product a b</lang>

## REXX

### no error checking

<lang rexx>/*REXX program computes the dot product of two equal size vectors (of any size).*/

```                    vectorA =  '  1   3  -5  '  /*populate vector  A  with some numbers*/
vectorB =  '  4  -2  -1  '  /*    "       "    B    "    "     "   */
```

say 'vector A = ' vectorA /*display the elements in the vector A.*/ say 'vector B = ' vectorB /* " " " " " " B.*/ p=.Prod(vectorA, vectorB) /*invoke function & compute dot product*/ say /*display a blank line for readability.*/ say 'dot product = ' p /*display the dot product to terminal. */ exit /*stick a fork in it, we're all done. */ /*──────────────────────────────────────────────────────────────────────────────────────*/ .Prod: procedure; parse arg A,B /*this function compute the dot product*/

```       \$=0                                      /*initialize the sum to  0 (zero).     */
do j=1  for words(A)         /*multiply each number in the vectors. */
\$=\$+word(A,j) * word(B,j)    /*  ··· and add the product to the sum.*/
end   /*j*/
return \$                                 /*return the sum to function's invoker.*/</lang>
```

output   using the default (internal) inputs:

```vector A =   1   3  -5
vector B =   4  -2  -1

dot product =  3
```

### with error checking

<lang rexx>/*REXX program computes the dot product of two equal size vectors (of any size).*/

```                    vectorA =  '  1   3  -5  '  /*populate vector  A  with some numbers*/
vectorB =  '  4  -2  -1  '  /*    "       "    B    "    "     "   */
```

say 'vector A = ' vectorA /*display the elements in the vector A.*/ say 'vector B = ' vectorB /* " " " " " " B.*/ p=.prod(vectorA, vectorB) /*invoke function & compute dot product*/ say /*display a blank line for readability.*/ say 'dot product = ' p /*display the dot product to terminal. */ exit /*stick a fork in it, we're all done. */ /*──────────────────────────────────────────────────────────────────────────────────────*/ .prod: procedure; parse arg A,B /*this function compute the dot product*/

```      lenA = words(A);           @.1= 'A'       /*the number of numbers in vector  A.  */
lenB = words(B);           @.2= 'B'       /* "     "    "    "     "    "    B.  */
/*Also, define 2 literals to hold names*/
if lenA\==lenB  then do;   say "***error*** vectors aren't the same size:" /*oops*/
say '            vector  A  length = '   lenA
say '            vector  B  length = '   lenB
exit 13        /*exit pgm with bad─boy return code 13.*/
end
\$=0                                       /*initialize the  sum  to   0  (zero). */
do j=1  for lenA                /*multiply each number in the vectors. */
#.1=word(A,j)                   /*use array to hold 2 numbers at a time*/
#.2=word(B,j)
do k=1  for 2;   if datatype(#.k,'N')  then iterate
say "***error*** vector "      @.k      ' element'    j,
" isn't numeric: "     n.k;                  exit 13
end   /*k*/
\$=\$ + #.1 * #.2                 /*  ··· and add the product to the sum.*/
end      /*j*/
return \$                                  /*return the sum to function's invoker.*/</lang>
```

output   is the same as the 1st REXX version.

## Ring

<lang ring> aVector = [2, 3, 5] bVector = [4, 2, 1] sum = 0 see dotProduct(aVector, bVector)

func dotProduct cVector, dVector

```    for n = 1 to len(aVector)
sum = sum + cVector[n] * dVector[n]
next
return sum
```

</lang>

## RLaB

In its simplest form dot product is a composition of two functions: element-by-element multiplication '.*' followed by sumation of an array. Consider an example: <lang RLaB>x = rand(1,10); y = rand(1,10); s = sum( x .* y );</lang> Warning: element-by-element multiplication is matrix optimized. As the interpretation of the matrix optimization is quite general, and unique to RLaB, any two matrices can be so multiplied irrespective of their dimensions. It is up to user to check whether in his/her case the matrix optimization needs to be restricted, and then to implement restrictions in his/her code.

## Ruby

With the standard library, require 'matrix' and call Vector#inner_product. <lang ruby>irb(main):001:0> require 'matrix' => true irb(main):002:0> Vector[1, 3, -5].inner_product Vector[4, -2, -1] => 3</lang> Or implement dot product. <lang ruby>class Array

``` def dot_product(other)
raise "not the same size!" if self.length != other.length
self.zip(other).inject(0) {|dp, (a, b)| dp += a*b}
end
```

end

p [1, 3, -5].dot_product [4, -2, -1] # => 3</lang>

## Run BASIC

<lang runbasic>v1\$ = "1, 3, -5" v2\$ = "4, -2, -1"

print "DotProduct of ";v1\$;" and "; v2\$;" is ";dotProduct(v1\$,v2\$) end

function dotProduct(a\$, b\$)

```   while word\$(a\$,i + 1,",") <> ""
i = i + 1
v1\$=word\$(a\$,i,",")
v2\$=word\$(b\$,i,",")
dotProduct = dotProduct + val(v1\$) * val(v2\$)
wend
```

end function</lang>

## Rust

Implemented as a simple function with check for equal length of vectors. <lang rust>// alternatively, fn dot_product(a: &Vec<u32>, b: &Vec<u32>) // but using slices is more general and rustic fn dot_product(a: &[i32], b: &[i32]) -> Option<i32> {

```   if a.len() != b.len() { return None }
Some(
a.iter()
.zip( b.iter() )
.fold(0, |sum, (el_a, el_b)| sum + el_a*el_b)
)
```

}

fn main() {

```   let v1 = vec![1, 3, -5];
let v2 = vec![4, -2, -1];
```
```   println!("{}", dot_product(&v1, &v2).unwrap());
```

}</lang>

Alternatively as a very generic function which works for any two types that can be multiplied to result in a third type which can be added with itself. Works with any argument convertible to an Iterator of known length (ExactSizeIterator).

Uses an unstable feature. <lang rust>#![feature(zero_one)] // <-- unstable feature use std::ops::{Add, Mul}; use std::num::Zero;

fn dot_product<T1, T2, U, I1, I2>(lhs: I1, rhs: I2) -> Option

```   where T1: Mul<T2, Output = U>,
U: Add<U, Output = U> + Zero,
I1: IntoIterator<Item = T1>,
I2: IntoIterator<Item = T2>,
I1::IntoIter: ExactSizeIterator,
I2::IntoIter: ExactSizeIterator,
```

{

```   let (iter_lhs, iter_rhs) = (lhs.into_iter(), rhs.into_iter());
match (iter_lhs.len(), iter_rhs.len()) {
(0, _) | (_, 0) => None,
(a,b) if a != b => None,
(_,_) => Some( iter_lhs.zip(iter_rhs)
.fold(U::zero(), |sum, (a, b)| sum + (a * b)) )
}
```

}

fn main() {

```   let v1 = vec![1, 3, -5];
let v2 = vec![4, -2, -1];
```
```   println!("{}", dot_product(&v1, &v2).unwrap());
```

}</lang>

## S-lang

<lang S-lang>print(sum([1, 3, -5] * [4, -2, -1]));</lang>

Output:
`3.0`

[sum() returns a double from integer arrays]

## Sather

Built-in class VEC "implements" euclidean (geometric) vectors. <lang sather>class MAIN is

``` main is
x ::= #VEC(|1.0, 3.0, -5.0|);
y ::= #VEC(|4.0, -2.0, -1.0|);
#OUT + x.dot(y) + "\n";
end;
```

end;</lang>

## Scala

Library: Scala
<lang scala>class Dot[T](v1: Seq[T])(implicit n: Numeric[T]) {
``` import n._ // import * operator
def dot(v2: Seq[T]) = {
require(v1.size == v2.size)
(v1 zip v2).map{ Function.tupled(_ * _)}.sum
}
```

}

object Main extends App {

``` implicit def toDot[T: Numeric](v1: Seq[T]) = new Dot(v1)
```
``` val v1 = List(1, 3, -5)
val v2 = List(4, -2, -1)
println(v1 dot v2)
```

}</lang>

## Seed7

<lang seed7>\$ include "seed7_05.s7i";

\$ syntax expr: .().dot.() is -> 6; # priority of dot operator

const func integer: (in array integer: a) dot (in array integer: b) is func

``` result
var integer: sum is 0;
local
var integer: index is 0;
begin
if length(a) <> length(b) then
raise RANGE_ERROR;
else
for index range 1 to length(a) do
sum +:= a[index] * b[index];
end for;
end if;
end func;

```

const proc: main is func

``` begin
writeln([](1, 3, -5) dot [](4, -2, -1));
end func;</lang>
```

## Sidef

<lang ruby>func dot_product(a, b) {

```   (a »*« b)«+»;
```

}; say dot_product([1,3,-5], [4,-2,-1]); # => 3</lang>

## Scheme

Works with: Scheme version R${\displaystyle ^{5}}$RS

<lang scheme>(define (dot-product a b)

``` (apply + (map * a b)))
```

(display (dot-product '(1 3 -5) '(4 -2 -1))) (newline)</lang>

Output:
`3`

## Scilab

<lang Scilab>A = [1 3 -5] B = [4 -2 -1] C = sum(A.*B)</lang>

## Slate

<lang slate>v@(Vector traits) <dot> w@(Vector traits) "Dot-product." [

``` (0 below: (v size min: w size)) inject: 0 into:
[| :sum :index | sum + ((v at: index) * (w at: index))]
```

].</lang>

## Smalltalk

Works with: GNU Smalltalk

<lang smalltalk>Array extend [

``` * anotherArray [
|acc| acc := 0.
self with: anotherArray collect: [ :a :b |
acc := acc + ( a * b )
].
^acc
]
```

]

( #(1 3 -5) * #(4 -2 -1 ) ) printNl.</lang>

## SNOBOL4

<lang snobol4> define("dotp(a,b)sum,i")  :(dotp_end) dotp i = 1; sum = 0 loop sum = sum + (a * b)

```       i = i + 1 ?a :s(loop)
dotp = sum      :(return)
```

dotp_end

```       a = array(3); a<1> = 1; a<2> = 3; a<3> = -5;
b = array(3); b<1> = 4; b<2> = -2; b<3> = -1;
output = dotp(a,b)
```

end</lang>

## SPARK

Works with SPARK GPL 2010 and GPS GPL 2010.

By defining numeric subtypes with suitable ranges we can prove statically that there will be no run-time errors. (The Simplifier leaves 2 VCs unproven, but these are clearly provable by inspection.)

The precondition enforces equality of the ranges of the two vectors. <lang ada>with Spark_IO; --# inherit Spark_IO; --# main_program; procedure Dot_Product_Main --# global in out Spark_IO.Outputs; --# derives Spark_IO.Outputs from *; is

```  Limit : constant := 1000;
type V_Elem is range -Limit .. Limit;
V_Size : constant := 100;
type V_Index is range 1 .. V_Size;
type Vector is array(V_Index range <>) of V_Elem;
```
```  type V_Prod is range -(Limit**2)*V_Size .. (Limit**2)*V_Size;
--# assert V_Prod'Base is Integer;
```
```  subtype Index3 is V_Index range 1 .. 3;
subtype Vector3 is Vector(Index3);
Vect1 : constant Vector3 := Vector3'(1, 3, -5);
Vect2 : constant Vector3 := Vector3'(4, -2, -1);
```
```  function Dot_Product(V1, V2 : Vector) return V_Prod
--# pre  V1'First = V2'First
--#  and V1'Last  = V2'Last;
is
Sum : V_Prod := 0;
begin
for I in V_Index range V1'Range
--# assert Sum in -(Limit**2)*V_Prod(I-1) .. (Limit**2)*V_Prod(I-1);
loop
Sum := Sum + V_Prod(V1(I)) * V_Prod(V2(I));
end loop;
return Sum;
end Dot_Product;
```

begin

```  Spark_IO.Put_Integer(File  => Spark_IO.Standard_Output,
Item  => Integer(Dot_Product(Vect1, Vect2)),
Width => 6,
Base  => 10);
```

end Dot_Product_Main;</lang>

Output:
`     3`

## SQL

ANSI sql does not support functions and is missing some other concepts that would be needed for a general case implementation of inner product (column names and tables would need to be first class in SQL -- capable of being passed to functions).

However, inner product is fairly simple to specify in SQL.

Given two tables `A` and `B` where A has key columns `i` and `j` and B has key columns `j` and `k` and both have value columns `N`, the inner product of A and B would be: <lang sql>select i, k, sum(A.N*B.N) as N

```       from A inner join B on A.j=B.j
group by i, k</lang>
```

## Standard ML

With lists: <lang sml>val dot = ListPair.foldlEq Real.*+ 0.0

(* - dot ([1.0, 3.0, ~5.0], [4.0, ~2.0, ~1.0]); val it = 3.0 : real

• )</lang>

With vectors: <lang sml>fun dot (v, u) = (

``` if Vector.length v <> Vector.length u then
raise ListPair.UnequalLengths
else ();
Vector.foldli (fn (i, v_i, z) => v_i * Vector.sub (u, i) + z) 0.0 v
)
```

(* - dot (#[1.0, 3.0, ~5.0], #[4.0, ~2.0, ~1.0]); val it = 3.0 : real

• )</lang>

## Stata

With row vectors:

<lang stata>matrix a=1,3,-5 matrix b=4,-2,-1 matrix c=a*b' di el("c",1,1)</lang>

With column vectors:

<lang stata>matrix a=1\3\-5 matrix b=4\-2\-1 matrix c=a'*b di el("c",1,1)</lang>

### Mata

With row vectors:

<lang stata>a=1,3,-5 b=4,-2,-1 a*b'</lang>

With column vectors:

<lang stata>a=1\3\-5 b=4\-2\-1 a'*b</lang>

In both cases, one cas also write

<lang stata>sum(a:*b)</lang>

## Swift

Works with: Swift version 1.2+

<lang swift>func dot(v1: [Double], v2: [Double]) -> Double {

``` return reduce(lazy(zip(v1, v2)).map(*), 0, +)
```

}

println(dot([1, 3, -5], [4, -2, -1]))</lang>

Output:
`3.0`

## Tcl

Library: Tcllib (Package: math::linearalgebra)

<lang tcl>package require math::linearalgebra

set a {1 3 -5} set b {4 -2 -1} set dotp [::math::linearalgebra::dotproduct \$a \$b] proc pp vec {return \[[join \$vec ,]\]} puts "[pp \$a] \u2219 [pp \$b] = \$dotp"</lang>

Output:
`[1,3,-5] ∙ [4,-2,-1] = 3.0`

## TI-83 BASIC

To perform a matrix dot product on TI-83, the trick is to use lists (and not to use matrices). <lang ti83b>sum({1,3,–5}*{4,–2,–1})</lang>

Output:
```3
```

## TI-89 BASIC

`dotP([1, 3, –5], [4, –2, –1])`
Output:
```3
```

## Ursala

A standard library function for dot products of floating point numbers exists, but a new one can be defined for integers as shown using the map operator (`*`) with the zip suffix (`p`) to construct a "zipwith" operator (`*p`), which operates on the integer `product` function. A catchable exception is thrown if the list lengths are unequal. This function is then composed (`+`) with a cumulative summation function, which is constructed from the binary `sum` function, and the reduction operator (`:-`) with `0` specified for the vacuous sum. <lang Ursala>#import int

dot = sum:-0+ product*p

1. cast %z

test = dot(<1,3,-5>,<4,-2,-1>)</lang>

Output:
`3`

## VBScript

<lang vb> WScript.Echo DotProduct("1,3,-5","4,-2,-1")

Function DotProduct(vector1,vector2) arrv1 = Split(vector1,",") arrv2 = Split(vector2,",") If UBound(arrv1) <> UBound(arrv2) Then WScript.Echo "The vectors are not of the same length." Exit Function End If DotProduct = 0 For i = 0 To UBound(arrv1) DotProduct = DotProduct + (arrv1(i) * arrv2(i)) Next End Function </lang>

Output:
`3`

## Visual Basic .NET

Translation of: C#

<lang vbnet>Module Module1

```   Function DotProduct(a As Decimal(), b As Decimal()) As Decimal
Return a.Zip(b, Function(x, y) x * y).Sum()
End Function
```
```   Sub Main()
Console.WriteLine(DotProduct({1, 3, -5}, {4, -2, -1}))
End Sub
```

End Module</lang>

Output:
`3`

## Wart

<lang python>def (dot_product x y)

``` (sum+map (*) x y)</lang>
```

`+` is punned (overloaded) here; when applied to functions it denotes composition. Also, `(*)` is used to skip infix expansion.

Output:
```(dot_product '(1 3 -5) '(4 -2 -1))
=> 3```

## X86 Assembly

Using FASM. Targets x64 Microsoft Windows. <lang asm>format PE64 console entry start

```   include 'win64a.inc'
```

```   start:
stdcall dotProduct, vA, vB
invoke printf, msg_num, rax

stdcall dotProduct, vA, vC
invoke printf, msg_num, rax

invoke ExitProcess, 0

proc dotProduct vectorA, vectorB
mov rax, [rcx]
cmp rax, [rdx]
je .calculate

invoke printf, msg_sizeMismatch
mov rax, 0
ret

.calculate:
mov r8, rcx
mov r9, rdx
mov rcx, rax
mov rax, 0
mov rdx, 0

.next:
mov rbx, [r9]
imul rbx, [r8]
loop .next

ret
endp
```

```   msg_num db "%d", 0x0D, 0x0A, 0
msg_sizeMismatch db "Size mismatch; can't calculate.", 0x0D, 0x0A, 0

struc Vector [symbols] {
common
.length dq (.end - .symbols) / 8
.symbols dq symbols
.end:
}

vA Vector 1, 3, -5
vB Vector 4, -2, -1
vC Vector 7, 2, 9, 0

```

section '.idata' import data readable writeable

```   library kernel32, 'KERNEL32.DLL',\
msvcrt, 'MSVCRT.DLL'
```
```   include 'api/kernel32.inc'
```
```   import  msvcrt,\
printf, 'printf'</lang>
```
Output:
<lang>3

Size mismatch; can't calculate. 0</lang>

## XPL0

<lang XPL0>include c:\cxpl\codes;

func DotProd(U, V, L); int U, V, L; int S, I; [S:= 0; for I:= 0 to L-1 do S:= S + U(I)*V(I); return S; ];

[IntOut(0, DotProd([1, 3, -5], [4, -2, -1], 3)); CrLf(0); ]</lang>

Output:
`3`

## Yabasic

<lang Yabasic> sub sq_mul(a(), b(), c()) local n, i

n = arraysize(a(), 1)

for i = 1 to n c(i) = a(i) * b(i) next i end sub

sub sq_sum(a()) local n, i, r

n = arraysize(a(), 1)

for i = 1 to n r = r + a(i) next i return r end sub

dim a(3), b(3), c(3)

a(1) = 1 : a(2) = 3 : a(3) = -5 b(1) = 4 : b(2) = -2 : b(3) = -1 sq_mul(a(), b(), c())

print sq_sum(c()) </lang>

## zkl

<lang zkl>fcn dotp(a,b){Utils.zipWith('*,a,b).sum()}</lang> zipWith stops at the shortest of the lists

Output:
`dotp(T(1,3,-5),T(4,-2,-1,666)) //-->3`

If exact length is a requirement <lang zkl>fcn dotp2(a,b){if(a.len()!=b.len())throw(Exception.ValueError);

```  Utils.zipWith('*,a,b).sum()
```

}</lang>

## ZX Spectrum Basic

<lang zxbasic>10 DIM a(3): LET a(1)=1: LET a(2)=3: LET a(3)=-5 20 DIM b(3): LET b(1)=4: LET b(2)=-2: LET b(3)=-1 30 LET sum=0 40 FOR i=1 TO 3: LET sum=sum+a(i)*b(i): NEXT i 50 PRINT sum</lang>