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In [[First-class functions]], a language is showing how its manipulation of functions is similar to its manipulation of other types.
 
This tasks aim is to compare and contrast a languageslanguage's implementation of Firstfirst class functions, with its normal handling of numbers.
 
 
Line 21:
 
<small>To paraphrase the task description: Do what was done before, but with numbers rather than functions</small>
<br><br>
 
=={{header|11l}}==
{{trans|Python}}
 
<syntaxhighlight lang="11l">V (x, xi, y, yi) = (2.0, 0.5, 4.0, 0.25)
V z = x + y
V zi = 1.0 / (x + y)
V multiplier = (n1, n2) -> (m -> @=n1 * @=n2 * m)
V numlist = [x, y, z]
V numlisti = [xi, yi, zi]
print(zip(numlist, numlisti).map((n, inversen) -> multiplier(inversen, n)(.5)))</syntaxhighlight>
 
{{out}}
<pre>
[0.5, 0.5, 0.5]
</pre>
 
=={{header|Ada}}==
<syntaxhighlight lang="ada">with Ada.Text_IO;
procedure Firstclass is
generic
n1, n2 : Float;
function Multiplier (m : Float) return Float;
function Multiplier (m : Float) return Float is
begin
return n1 * n2 * m;
end Multiplier;
num, inv : array (1 .. 3) of Float;
begin
num := (2.0, 4.0, 6.0);
inv := (1.0/2.0, 1.0/4.0, 1.0/6.0);
for i in num'Range loop
declare
function new_function is new Multiplier (num (i), inv (i));
begin
Ada.Text_IO.Put_Line (Float'Image (new_function (0.5)));
end;
end loop;
end Firstclass;</syntaxhighlight>
{{out}}
<pre>5.00000E-01
5.00000E-01
5.00000E-01</pre>
 
=={{header|ALGOL 68}}==
{{trans|python}}{{wont work with|ALGOL 68|Revision 1 - scoping rules forbid exporting a procedure out of it's scope}}
{{wont work with|ALGOL 68G|Any - tested with release [http://sourceforge.net/projects/algol68/files/algol68g/algol68g-1.18.0/algol68g-1.18.0-9h.tiny.el5.centos.fc11.i386.rpm/download 1.18.0-9h.tiny] - scoping rules forbid exporting a procedure out of it's scope - detected at compile time and again at runtime}}
{{works with|ELLA ALGOL 68|Any (with appropriate job cards) - tested with release [http://sourceforge.net/projects/algol68/files/algol68toc/algol68toc-1.8.8d/algol68toc-1.8-8d.fc9.i386.rpm/download 1.8-8d]}}
 
Note: Standard ALGOL 68's scoping rules forbids exporting a '''proc'''[edure] (or '''format''') out of it's scope (closure). Hence this specimen will run on [[ELLA ALGOL 68]], but is non-standard. For a discussion of first-class functions in ALGOL 68 consult [http://www.cs.ru.nl/~kees/home/papers/psi96.pdf "The Making of Algol 68"] - [[wp:Cornelis_H.A._Koster|C.H.A. Koster]] (1993). <!-- Retrieved April 28, 2007 -->
<syntaxhighlight lang="algol68">REAL
x := 2,
xi := 0.5,
y := 4,
yi := 0.25,
z := x + y,
zi := 1 / ( x + y );
 
MODE F = PROC(REAL)REAL;
 
PROC multiplier = (REAL n1, n2)F: ((REAL m)REAL: n1 * n2 * m);
 
# Numbers as members of collections #
[]REAL num list = (x, y, z),
inv num list = (xi, yi, zi);
 
# Apply numbers from list #
FOR key TO UPB num list DO
REAL n = num list[key],
inv n = inv num list[key];
print ((multiplier(inv n, n)(.5), new line))
OD</syntaxhighlight>
Output:
<pre>
+.500000000000000e +0
+.500000000000000e +0
+.500000000000000e +0
</pre>
 
Comparing and contrasting with the First Class Functions example:
 
As noted above, in order to do what is required by this task and the First Class Functions task, extensions to Algol 68 must be used.
 
The First Class Functions example uses C. H. Lindsey's partial parameterization extension to Algol 68 which implemented in Algol 68G but not in algol68toc.
This example uses an alternative (technically, invalid Algol 68 as the author notes) accepted by algol68toc but not Algol 68G.
 
=={{header|Arturo}}==
 
<syntaxhighlight lang="arturo">x: 2.0
xi: 0.5
y: 4.0
yi: 0.25
z: x + y
zi: 1 / z
 
multiplier: function [m n][
function [a] with [m n][
a*m*n
]
]
 
couple @[x y z] @[xi yi zi]
| map 'p -> multiplier p\0 p\1
| map => [call & -> 0.5]
| print</syntaxhighlight>
 
{{out}}
 
<pre>0.5 0.5 0.5</pre>
 
=={{header|Axiom}}==
<syntaxhighlight lang="axiom">(x,xi,y,yi) := (2.0,0.5,4.0,0.25)
(z,zi) := (x+y,1/(x+y))
(numbers,invers) := ([x,y,z],[xi,yi,zi])
multiplier(a:Float,b:Float):(Float->Float) == (m +-> a*b*m)
[multiplier(number,inver) 0.5 for number in numbers for inver in invers]
</syntaxhighlight>Output:
<syntaxhighlight lang="axiom"> [0.5,0.5,0.5]
Type: List(Float)</syntaxhighlight>
We can also curry functions, possibly with function composition, with the same output as before:
<syntaxhighlight lang="axiom">mult(n:Float):(Float->Float) == curryLeft(*$Float,n)$MAPPKG3(Float,Float,Float)
[mult(number*inver) 0.5 for number in numbers for inver in invers]
[(mult(number)*mult(inver)) 0.5 for number in numbers for inver in invers]</syntaxhighlight>
Using the Spad code in [[First-class functions#Axiom]], this can be done more economically using:
<syntaxhighlight lang="axiom">(map(mult,numbers)*map(mult,invers)) 0.5</syntaxhighlight>
For comparison, [[First-class functions#Axiom]] gave:
<syntaxhighlight lang="axiom">fns := [sin$Float, cos$Float, (x:Float):Float +-> x^3]
inv := [asin$Float, acos$Float, (x:Float):Float +-> x^(1/3)]
[(f*g) 0.5 for f in fns for g in inv]
</syntaxhighlight>
- which has the same output.
 
=={{header|BBC BASIC}}==
{{works with|BBC BASIC for Windows}}
<syntaxhighlight lang="bbcbasic"> REM Create some numeric variables:
x = 2 : xi = 1/2
y = 4 : yi = 0.25
z = x + y : zi = 1 / (x + y)
REM Create the collections (here structures are used):
DIM c{x, y, z}
DIM ci{x, y, z}
c.x = x : c.y = y : c.z = z
ci.x = xi : ci.y = yi : ci.z = zi
REM Create some multiplier functions:
multx = FNmultiplier(c.x, ci.x)
multy = FNmultiplier(c.y, ci.y)
multz = FNmultiplier(c.z, ci.z)
REM Test applying the compositions:
x = 1.234567 : PRINT x " ", FN(multx)(x)
x = 2.345678 : PRINT x " ", FN(multy)(x)
x = 3.456789 : PRINT x " ", FN(multz)(x)
END
DEF FNmultiplier(n1,n2)
LOCAL f$, p%
f$ = "(m)=" + STR$n1 + "*" + STR$n2 + "*m"
DIM p% LEN(f$) + 4
$(p%+4) = f$ : !p% = p%+4
= p%</syntaxhighlight>
'''Output:'''
<pre>
1.234567 1.234567
2.345678 2.345678
3.456789 3.45678901
</pre>
Compare with the implementation of First-class functions:
<syntaxhighlight lang="bbcbasic"> REM Create some functions and their inverses:
DEF FNsin(a) = SIN(a)
DEF FNasn(a) = ASN(a)
DEF FNcos(a) = COS(a)
DEF FNacs(a) = ACS(a)
DEF FNcube(a) = a^3
DEF FNroot(a) = a^(1/3)
dummy = FNsin(1)
REM Create the collections (here structures are used):
DIM cA{Sin%, Cos%, Cube%}
DIM cB{Asn%, Acs%, Root%}
cA.Sin% = ^FNsin() : cA.Cos% = ^FNcos() : cA.Cube% = ^FNcube()
cB.Asn% = ^FNasn() : cB.Acs% = ^FNacs() : cB.Root% = ^FNroot()
REM Create some function compositions:
AsnSin% = FNcompose(cB.Asn%, cA.Sin%)
AcsCos% = FNcompose(cB.Acs%, cA.Cos%)
RootCube% = FNcompose(cB.Root%, cA.Cube%)
REM Test applying the compositions:
x = 1.234567 : PRINT x, FN(AsnSin%)(x)
x = 2.345678 : PRINT x, FN(AcsCos%)(x)
x = 3.456789 : PRINT x, FN(RootCube%)(x)
END
DEF FNcompose(f%,g%)
LOCAL f$, p%
f$ = "(x)=" + CHR$&A4 + "(&" + STR$~f% + ")(" + \
\ CHR$&A4 + "(&" + STR$~g% + ")(x))"
DIM p% LEN(f$) + 4
$(p%+4) = f$ : !p% = p%+4
= p%</syntaxhighlight>
 
=={{header|C sharp|C#}}==
{{works with|C#|4.0}}
The structure here is exactly the same as the C# entry in [[First-class functions]]. The "var" keyword allows us to use the same initialization code for an array of doubles as an array of functions. Note that variable names have been changed to correspond with the new functionality.
<syntaxhighlight lang="csharp">using System;
using System.Linq;
 
class Program
{
static void Main(string[] args)
{
double x, xi, y, yi, z, zi;
x = 2.0;
xi = 0.5;
y = 4.0;
yi = 0.25;
z = x + y;
zi = 1.0 / (x + y);
 
var numlist = new[] { x, y, z };
var numlisti = new[] { xi, yi, zi };
var multiplied = numlist.Zip(numlisti, (n1, n2) =>
{
Func<double, double> multiplier = m => n1 * n2 * m;
return multiplier;
});
 
foreach (var multiplier in multiplied)
Console.WriteLine(multiplier(0.5));
}
}
</syntaxhighlight>
 
=={{header|C++}}==
<syntaxhighlight lang="cpp">#include <array>
#include <iostream>
 
int main()
{
double x = 2.0;
double xi = 0.5;
double y = 4.0;
double yi = 0.25;
double z = x + y;
double zi = 1.0 / ( x + y );
 
const std::array values{x, y, z};
const std::array inverses{xi, yi, zi};
 
auto multiplier = [](double a, double b)
{
return [=](double m){return a * b * m;};
};
 
for(size_t i = 0; i < values.size(); ++i)
{
auto new_function = multiplier(values[i], inverses[i]);
double value = new_function(i + 1.0);
std::cout << value << "\n";
}
}
</syntaxhighlight>
{{out}}
<pre>
1
2
3
</pre>
 
=={{header|Clojure}}==
<syntaxhighlight lang="clojure">(def x 2.0)
(def xi 0.5)
(def y 4.0)
(def yi 0.25)
(def z (+ x y))
(def zi (/ 1.0 (+ x y)))
 
(def numbers [x y z])
(def invers [xi yi zi])
 
(defn multiplier [a b]
(fn [m] (* a b m)))
 
> (for [[n i] (zipmap numbers invers)]
((multiplier n i) 0.5))
(0.5 0.5 0.5)</syntaxhighlight>
For comparison:
<syntaxhighlight lang="clojure">
(use 'clojure.contrib.math)
(let [fns [#(Math/sin %) #(Math/cos %) (fn [x] (* x x x))]
inv [#(Math/asin %) #(Math/acos %) #(expt % 1/3)]]
(map #(% 0.5) (map #(comp %1 %2) fns inv)))
</syntaxhighlight>
Output:
<pre>(0.5 0.4999999999999999 0.5000000000000001)</pre>
 
=={{header|Common Lisp}}==
 
<langsyntaxhighlight lang="lisp">(defun multiplier (f g)
#'(lambda (x) (* f g x)))
 
Line 43 ⟶ 343:
inverse
value
(funcall multiplier value))))</langsyntaxhighlight>
 
Output:
Line 53 ⟶ 353:
The code from [[First-class functions]], for comparison:
 
<langsyntaxhighlight lang="lisp">(defun compose (f g) (lambda (x) (funcall f (funcall g x))))
(defun cube (x) (expt x 3))
(defun cube-root (x) (expt x (/ 3)))
Line 65 ⟶ 365:
function
value
(funcall composed value)))</langsyntaxhighlight>
 
Output:
Line 72 ⟶ 372:
(#<FUNCTION ACOS> ∘ #<FUNCTION COS>)(0.5) = 0.5
(#<FUNCTION CUBE-ROOT> ∘ #<FUNCTION CUBE>)(0.5) = 0.5
 
=={{header|D}}==
<syntaxhighlight lang="d">import std.stdio;
 
auto multiplier(double a, double b)
{
return (double c) => a * b * c;
}
 
void main()
{
double x = 2.0;
double xi = 0.5;
double y = 4.0;
double yi = 0.25;
double z = x + y;
double zi = 1.0 / (z);
 
double[3] f = [x, y, z];
double[3] r = [xi, yi, zi];
 
foreach (i; 0..3)
{
auto mult = multiplier(f[i], r[i]);
writefln("%f * %f * %f == %f", f[i], r[i], 1.0, mult(1));
}
}</syntaxhighlight>
Output:
<pre>2.000000 * 0.500000 * 1.000000 == 1.000000
4.000000 * 0.250000 * 1.000000 == 1.000000
6.000000 * 0.166667 * 1.000000 == 1.000000</pre>
 
=={{header|E}}==
Line 77 ⟶ 408:
This is written to have identical structure to [[First-class functions#E]], though the variable names are different.
 
<langsyntaxhighlight lang="e">def x := 2.0
def xi := 0.5
def y := 4.0
Line 94 ⟶ 425:
def b := reverse[i]
println(`s = $s, a = $a, b = $b, multiplier($a, $b)($s) = ${multiplier(a, b)(s)}`)
}</langsyntaxhighlight>
 
Output:
Line 103 ⟶ 434:
 
Note: <code>def g := reverse[i]</code> is needed here because E as yet has no defined protocol for iterating over collections in parallel. [http://wiki.erights.org/wiki/Parallel_iteration Page for this issue.]
 
=={{header|Elena}}==
{{trans|C#}}
ELENA 6.x :
<syntaxhighlight lang="elena">import system'routines;
import extensions;
public program()
{
real x := 2.0r;
real xi := 0.5r;
real y := 4.0r;
real yi := 0.25r;
real z := x + y;
real zi := 1.0r / (x + y);
var numlist := new real[]{ x, y, z };
var numlisti := new real[]{ xi, yi, zi };
var multiplied := numlist.zipBy(numlisti, (n1,n2 => (m => n1 * n2 * m) )).toArray();
multiplied.forEach::(multiplier){ console.printLine(multiplier(0.5r)) }
}</syntaxhighlight>
{{out}}
<pre>
0.5
0.5
0.5
</pre>
 
=={{header|Erlang}}==
<syntaxhighlight lang="erlang">
-module( first_class_functions_use_numbers ).
 
-export( [task/0] ).
 
task() ->
X = 2.0, Xi = 0.5, Y = 4.0, Yi = 0.25, Z = X + Y, Zi = 1.0 / (X + Y),
As = [X, Y, Z],
Bs = [Xi, Yi, Zi],
[io:fwrite( "Value: 2.5 Result: ~p~n", [(multiplier(A, B))(2.5)]) || {A, B} <- lists:zip(As, Bs)].
 
 
 
multiplier( N1, N2 ) -> fun(M) -> N1 * N2 * M end.
</syntaxhighlight>
{{out}}
<pre>
20> first_class_functions_use_numbers:task().
Value: 2.5 Result: 2.5
Value: 2.5 Result: 2.5
Value: 2.5 Result: 2.5
</pre>
 
=={{header|F_Sharp|F#}}==
<syntaxhighlight lang="fsharp">
let x = 2.0
let xi = 0.5
let y = 4.0
let yi = 0.25
let z = x + y
let zi = 1.0 / ( x + y )
let multiplier (n1,n2) = fun (m:float) -> n1 * n2 * m
 
[x; y; z]
|> List.zip [xi; yi; zi]
|> List.map multiplier
|> List.map ((|>) 0.5)
|> printfn "%A"
</syntaxhighlight>
{{out}}
<pre>
[0.5; 0.5; 0.5]
</pre>
 
=={{header|Factor}}==
Compared to http://rosettacode.org/wiki/First-class_functions, the call to "compose" is replaced with the call to "mutliplier"
<syntaxhighlight lang="factor">USING: arrays kernel literals math prettyprint sequences ;
IN: q
 
CONSTANT: x 2.0
CONSTANT: xi 0.5
CONSTANT: y 4.0
CONSTANT: yi .25
CONSTANT: z $[ $ x $ y + ]
CONSTANT: zi $[ 1 $ x $ y + / ]
 
CONSTANT: A ${ x y z }
CONSTANT: B ${ xi yi zi }
 
: multiplier ( n1 n2 -- q ) [ * * ] 2curry ;
: create-all ( seq1 seq2 -- seq ) [ multiplier ] 2map ;
: example ( -- )
0.5 A B create-all
[ call( x -- y ) ] with map . ;</syntaxhighlight>
{{out}}
<pre>{ 0.5 0.5 0.5 }</pre>
 
=={{header|Fantom}}==
 
<syntaxhighlight lang="fantom">
class Main
{
static |Float -> Float| combine (Float n1, Float n2)
{
return |Float m -> Float| { n1 * n2 * m }
}
 
public static Void main ()
{
Float x := 2f
Float xi := 0.5f
Float y := 4f
Float yi := 0.25f
Float z := x + y
Float zi := 1 / (x + y)
echo (combine(x, xi)(0.5f))
echo (combine(y, yi)(0.5f))
echo (combine(z, zi)(0.5f))
}
}
</syntaxhighlight>
 
The <code>combine</code> function is very similar to the <code>compose</code> function in 'First-class functions'. In both cases a new function is returned:
 
<syntaxhighlight lang="fantom">
static |Obj -> Obj| compose (|Obj -> Obj| fn1, |Obj -> Obj| fn2)
{
return |Obj x -> Obj| { fn2 (fn1 (x)) }
}
</syntaxhighlight>
 
=={{header|FreeBASIC}}==
FreeBASIC does not support first-class functions or function closures, which means that you cannot create a function that returns another function or that has a function defined inside it.
 
However, similar behavior can be achieved with subroutines and global variables.
<syntaxhighlight lang="vbnet">Dim As Double x = 2.0, xi = 0.5
Dim As Double y = 4.0, yi = 0.25
Dim As Double z = x + y, zi = 1.0 / (x + y)
Dim As Double values(2) = {x, y, z}
Dim As Double inverses(2) = {xi, yi, zi}
 
Dim Shared As Double m = 0.5
 
Function multiplier(a As Double, d As Double) As Double
Return a * d * m
End Function
 
For i As Byte = 0 To Ubound(values)
Dim As Double new_function = multiplier(values(i), inverses(i))
Print values(i); " *"; inverses(i); " *"; m; " ="; new_function
Next i
 
Sleep</syntaxhighlight>
{{out}}
<pre> 2 * 0.5 * 0.5 = 0.5
4 * 0.25 * 0.5 = 0.5
6 * 0.1666666666666667 * 0.5 = 0.5</pre>
 
=={{header|Go}}==
==="Number means value"===
Task interpretation 1: "Number" means a numeric value, not any sort of reference. This is the most natural interpretation in Go.
 
At point A, the six variables have been assigned values, 64 bit floating point numbers, and not references to anything that is evaluated later. Again, at point B, these values have been ''copied'' into the array elements. (The arrays being "ordered collections.") The original six variables could be changed at this point and the array values would stay the same.
 
Multiplier multiplies pairs of values and binds the result to the returned closure. This might be considered a difference from the First-class functions task. In that task, the functions were composed into a new function but not evaluated at the time of composition. They ''could'' have been, but that is not the usual meaning of function composition.
 
This task however, works with numbers, which are not reference types. Specifically, the closure here could could have closed on n1 an n2 individually and delayed multiplication until closure evaluation, but that might seem inconsistent with the task interpretation of working with numbers as values. Also, one would expect that a function named "multiplier" does actually multiply.
 
Multiplier of this task and compose of the First-class function task are similar in that they both return first class function objects, which are closed on free variables. The free variables in compose held the two (unevaluated) functions being composed. The free variable in multiplier holds the ''result'' of multiplication.
 
At point C, numbers and their inverses have been multiplied and bound to first class functions. The ordered collection arrays could be modified at this point and the function objects would be unaffected.
 
<syntaxhighlight lang="go">package main
 
import "fmt"
 
func main() {
x := 2.
xi := .5
y := 4.
yi := .25
z := x + y
zi := 1 / (x + y)
// point A
 
numbers := []float64{x, y, z}
inverses := []float64{xi, yi, zi}
// point B
 
mfs := make([]func(float64) float64, len(numbers))
for i := range mfs {
mfs[i] = multiplier(numbers[i], inverses[i])
}
// point C
 
for _, mf := range mfs {
fmt.Println(mf(1))
}
}
 
func multiplier(n1, n2 float64) func(float64) float64 {
// compute product of n's, store in a new variable
n1n2 := n1 * n2
// close on variable containing product
return func(m float64) float64 {
return n1n2 * m
}
}</syntaxhighlight>
Output:
<pre>
1
1
1
</pre>
==="Number means reference"===
Task interpretation 2: "Number" means something abstract, evaluation of which is delayed as long as possible. This interpretation is suggested by the task wording that this program and and the corresponding First-class functions program "should be close" and "do what was done before...."
 
To implement this behavior, reference types are used for "numbers" and a polymorphic array is used for the ordered collection, allowing both static and computed objects to stored.
 
At point A, the variables z and zi are assigned function literals, first class function objects closed on x and y. Changing the values of x or y at this point will cause z and zi to return different results. The x and y in these function literals reference the same storage as the x and y assigned values 2 and 4. This is more like the the First-class functions task in that we now have functions which we can compose.
 
At point B, we have filled the polymorphic arrays with all reference types. References to numeric typed variables x, xi, y, and yi were created with the & operator. z and zi can already be considered reference types in that they reference x and y. Changes to any of x, xi, y, or yi at this point would still affect later results.
 
Multiplier, in this interpretation of the task, simply composes multiplication of three reference objects, which may be variables or functions. It does not actually multiply and does not even evaluate the three objects. This is very much like the compose function of the First-class functions task.
 
Pursuant to the task description, this version of multiplier "returns the result of n1 * n2 * m" in the sense that it sets up evaluation of n1, n2, and m to be done at the same time, even if that time is not quite yet.
 
At point C, changes to x, xi, y, and yi will still propagate through and affect the results returned by the mfs objects. This can be seen as like the First-class functions task in that nothing (nothing numberic anyway) is evaluated until the final composed objects are evaluated.
 
[[/Go interface type|This can also be done with an interface type]] rather than the "empty interface" (<code>interface{}</code>) for better type safety and to avoid the <code>eval</code> function and type switch.
<syntaxhighlight lang="go">package main
 
import "fmt"
 
func main() {
x := 2.
xi := .5
y := 4.
yi := .25
z := func() float64 { return x + y }
zi := func() float64 { return 1 / (x + y) }
// point A
 
numbers := []interface{}{&x, &y, z}
inverses := []interface{}{&xi, &yi, zi}
// point B
 
mfs := make([]func(n interface{}) float64, len(numbers))
for i := range mfs {
mfs[i] = multiplier(numbers[i], inverses[i])
}
// pointC
 
for _, mf := range mfs {
fmt.Println(mf(1.))
}
}
 
func multiplier(n1, n2 interface{}) func(interface{}) float64 {
return func(m interface{}) float64 {
// close on interface objects n1, n2, and m
return eval(n1) * eval(n2) * eval(m)
}
}
 
// utility function for evaluating multiplier interface objects
func eval(n interface{}) float64 {
switch n.(type) {
case float64:
return n.(float64)
case *float64:
return *n.(*float64)
case func() float64:
return n.(func() float64)()
}
panic("unsupported multiplier type")
return 0 // never reached
}</syntaxhighlight>
 
=={{header|Groovy}}==
 
<syntaxhighlight lang="groovy">def multiplier = { n1, n2 -> { m -> n1 * n2 * m } }
 
def ε = 0.00000001 // tolerance(epsilon): acceptable level of "wrongness" to account for rounding error
[(2.0):0.5, (4.0):0.25, (6.0):(1/6.0)].each { num, inv ->
def new_function = multiplier(num, inv)
(1.0..5.0).each { trial ->
assert (new_function(trial) - trial).abs() < ε
printf('%5.3f * %5.3f * %5.3f == %5.3f\n', num, inv, trial, trial)
}
println()
}</syntaxhighlight>
{{out}}
<pre>2.000 * 0.500 * 1.000 == 1.000
2.000 * 0.500 * 2.000 == 2.000
2.000 * 0.500 * 3.000 == 3.000
2.000 * 0.500 * 4.000 == 4.000
2.000 * 0.500 * 5.000 == 5.000
 
4.000 * 0.250 * 1.000 == 1.000
4.000 * 0.250 * 2.000 == 2.000
4.000 * 0.250 * 3.000 == 3.000
4.000 * 0.250 * 4.000 == 4.000
4.000 * 0.250 * 5.000 == 5.000
 
6.000 * 0.167 * 1.000 == 1.000
6.000 * 0.167 * 2.000 == 2.000
6.000 * 0.167 * 3.000 == 3.000
6.000 * 0.167 * 4.000 == 4.000
6.000 * 0.167 * 5.000 == 5.000</pre>
 
=={{header|Haskell}}==
 
<syntaxhighlight lang="haskell">module Main
where
 
import Text.Printf
 
-- Pseudo code happens to be valid Haskell
x = 2.0
xi = 0.5
y = 4.0
yi = 0.25
z = x + y
zi = 1.0 / ( x + y )
 
-- Multiplier function
multiplier :: Double -> Double -> Double -> Double
multiplier a b = \m -> a * b * m
 
main :: IO ()
main = do
let
numbers = [x, y, z]
inverses = [xi, yi, zi]
pairs = zip numbers inverses
print_pair (number, inverse) =
let new_function = multiplier number inverse
in printf "%f * %f * 0.5 = %f\n" number inverse (new_function 0.5)
mapM_ print_pair pairs
</syntaxhighlight>
 
This is very close to the first-class functions example, but given as a full Haskell program rather than an interactive session.
 
=={{header|Icon}} and {{header|Unicon}}==
 
The following is a Unicon solution. It can be recast in Icon, but only at the cost
of losing the "function-call" syntax on the created "procedure". The solution uses
the same basic foundation that is buried in the "compose" procedure in the
[[First-class functions]] task solution. The solution here
is simpler and more direct since it handles a specific function definition.
<syntaxhighlight lang="unicon">import Utils
 
procedure main(A)
mult := multiplier(get(A),get(A)) # first 2 args define function
every write(mult(!A)) # remaining are passed to new function
end
 
procedure multiplier(n1,n2)
return makeProc { repeat inVal := n1 * n2 * (inVal@&source)[1] }
end</syntaxhighlight>
 
A sample run:
<pre>->mu 2 3 4 5 6 7
24
30
36
42
-></pre>
 
=={{header|J}}==
 
===Explicit version===
This seems to satisfy the current problem statement:
 
This seems to satisfy the new problem statement:
<lang j> multiplier=: conjunction def 'm * n * ]'</lang>
 
<syntaxhighlight lang="j"> x =: 2.0
<lang j> 2 multiplier 0.5 (4)
xi =: 0.5
4
y =: 4.0
0.5 multiplier 4 (0.25)
yi =: 0.25
z =: x + y
zi =: 1.0 % (x + y) NB. / is spelled % in J
 
fwd =: x ,y ,z
rev =: xi,yi,zi
 
multiplier =: 2 : 'm * n * ]'</syntaxhighlight>
 
An equivalent but perhaps prettier definition of multiplier would be:
 
<syntaxhighlight lang="j">multiplier=: {{m*n*]}}</syntaxhighlight>
 
Or, if J's "right bracket is the right identity function" bothers you, you might prefer the slightly more verbose but still equivalent:
 
<syntaxhighlight lang="j">multiplier=: {{m*n*{{y}}}}</syntaxhighlight>
 
Example use:
 
<syntaxhighlight lang="text"> fwd multiplier rev 0.5
0.5 0.5 0.5</syntaxhighlight>
 
For contrast, here are the final results from [[First-class functions#J]]:
<syntaxhighlight lang="text"> BA unqcol 0.5
0.5 0.5 0.5 0.5</syntaxhighlight>
 
===Tacit (unorthodox) version===
Although the pseudo-code to generate the numbers can certainly be written (see above [http://rosettacode.org/wiki/First-class_functions/Use_numbers_analogously#Explicit_version Explicit version] ) this is not done for this version because it would destroy part of the analogy (J encourages, from the programming perspective, to process all components at once as opposed to one component at a time). In addition, this version is done in terms of boxed lists of numbers instead of plain list of numbers, again, to preserve the analogy.
 
<syntaxhighlight lang="text"> multiplier=. train@:((;:'&*') ;~ an@: *)
]A=. 2 ; 4 ; (2 + 4) NB. Corresponds to ]A=. box (1&o.)`(2&o.)`(^&3)
┌─┬─┬─┐
│2│4│6│
└─┴─┴─┘
]B=. %&.> A NB. Corresponds to ]B =. inverse&.> A
┌───┬────┬────────┐
│0.5│0.25│0.166667│
└───┴────┴────────┘
]BA=. B multiplier&.> A NB. Corresponds to B compose&.> A
┌───┬───┬───┐
│1&*│1&*│1&*│
└───┴───┴───┘
BA of &> 0.5 NB. Corresponds to BA of &> 0.5 (exactly)
0.5 0.5 0.5</syntaxhighlight>
 
Please refer to [http://rosettacode.org/wiki/First-class_functions#Tacit_.28unorthodox.29_version First-class functions tacit (unorthodox) version] for the definitions of the functions train, an and of.
 
=={{header|Java}}==
<syntaxhighlight lang="java">
import java.util.List;
import java.util.function.BiFunction;
import java.util.function.Function;
 
public class FirstClassFunctionsUseNumbersAnalogously {
 
public static void main(String[] args) {
final double x = 2.0, xi = 0.5,
y = 4.0, yi = 0.25,
z = x + y, zi = 1.0 / ( x + y );
 
List<Double> list = List.of( x, y, z );
List<Double> inverseList = List.of( xi, yi, zi );
BiFunction<Double, Double, Function<Double, Double>> multiplier = (a, b) -> product -> a * b * product;
for ( int i = 0; i < list.size(); i++ ) {
Function<Double, Double> multiply = multiplier.apply(list.get(i), inverseList.get(i));
final double argument = (double) ( i + 1 );
System.out.println(multiply.apply(argument));
}
}
 
}
</syntaxhighlight>
{{ out }}
<pre>
1.0
2.0
3.0
</pre>
 
=={{header|jq}}==
It may be helpful to compare the following definition of "multiplier" with its Ruby counterpart [[#Ruby|below]]. Whereas the Ruby definition must name all its positional parameters, the jq equivalent is defined as a filter that obtains them implicitly from its input.
<syntaxhighlight lang="jq"># Infrastructure:
# zip this and that
def zip(that): [., that] | transpose;
 
# The task:
def x: 2.0;
def xi: 0.5;
def y: 4.0;
def yi: 0.25;
def z: x + y;
def zi: 1.0 / (x + y);
 
def numlist: [x,y,z];
 
def invlist: [xi, yi, zi];
 
# Input: [x,y]
def multiplier(j): .[0] * .[1] * j;
 
numlist | zip(invlist) | map( multiplier(0.5) )</syntaxhighlight>
{{out}}
$ jq -n -c -f First_class_functions_Use_numbers_analogously.jq
[0.5,0.5,0.5]
 
<!-- [[First-class functions#jq]]. -->
 
=={{header|JavaScript}}==
<syntaxhighlight lang="javascript">const x = 2.0;
const xi = 0.5;
const y = 4.0;
const yi = 0.25;
const z = x + y;
const zi = 1.0 / (x + y);
const pairs = [[x, xi], [y, yi], [z, zi]];
const testVal = 0.5;
 
const multiplier = (a, b) => m => a * b * m;
 
const test = () => {
return pairs.map(([a, b]) => {
const f = multiplier(a, b);
const result = f(testVal);
return `${a} * ${b} * ${testVal} = ${result}`;
});
}
 
test().join('\n');</syntaxhighlight>
{{out}}
<pre>
2 * 0.5 * 0.5 = 0.5
4 * 0.25 * 0.5 = 0.5
6 * 0.16666666666666666 * 0.5 = 0.5
</pre>
 
=={{header|Julia}}==
{{works with|Julia|0.6}}
 
In Julia, like Python and R, functions can be treated as like as other Types.
 
<syntaxhighlight lang="julia">x, xi = 2.0, 0.5
y, yi = 4.0, 0.25
z, zi = x + y, 1.0 / ( x + y )
 
multiplier = (n1, n2) -> (m) -> n1 * n2 * m
 
numlist = [x , y, z]
numlisti = [xi, yi, zi]
 
@show collect(multiplier(n, invn)(0.5) for (n, invn) in zip(numlist, numlisti))</syntaxhighlight>
 
{{out}}
<pre>collect(((multiplier(n, invn))(0.5) for (n, invn) = zip(numlist, numlisti))) = [0.5, 0.5, 0.5]</pre>
 
=={{header|Kotlin}}==
<syntaxhighlight lang="scala">// version 1.1.2
 
fun multiplier(n1: Double, n2: Double) = { m: Double -> n1 * n2 * m}
 
fun main(args: Array<String>) {
val x = 2.0
val xi = 0.5
val y = 4.0
val yi = 0.25
val z = x + y
val zi = 1.0 / ( x + y)
val a = doubleArrayOf(x, y, z)
val ai = doubleArrayOf(xi, yi, zi)
val m = 0.5
for (i in 0 until a.size) {
println("${multiplier(a[i], ai[i])(m)} = multiplier(${a[i]}, ${ai[i]})($m)")
}
}</syntaxhighlight>
 
{{out}}
<pre>
0.5 = multiplier(2.0, 0.5)(0.5)
0.5 = multiplier(4.0, 0.25)(0.5)
0.5 = multiplier(6.0, 0.16666666666666666)(0.5)
</pre>
 
=={{header|Lua}}==
<syntaxhighlight lang="lua">
-- This function returns another function that
-- keeps n1 and n2 in scope, ie. a closure.
function multiplier (n1, n2)
return function (m)
return n1 * n2 * m
end
end
 
-- Multiple assignment a-go-go
local x, xi, y, yi = 2.0, 0.5, 4.0, 0.25
local z, zi = x + y, 1.0 / ( x + y )
local nums, invs = {x, y, z}, {xi, yi, zi}
 
-- 'new_function' stores the closure and then has the 0.5 applied to it
-- (this 0.5 isn't in the task description but everyone else used it)
for k, v in pairs(nums) do
new_function = multiplier(v, invs[k])
print(v .. " * " .. invs[k] .. " * 0.5 = " .. new_function(0.5))
end
</syntaxhighlight>
{{out}}
<pre>
2 * 0.5 * 0.5 = 0.5
4 * 0.25 * 0.5 = 0.5
6 * 0.16666666666667 * 0.5 = 0.5
</pre>
 
=={{header|M2000 Interpreter}}==
<syntaxhighlight lang="m2000 interpreter">
Module CheckIt {
\\ by default numbers are double
x = 2
xi = 0.5
y = 4
yi = 0.25
z = x + y
zi = 1 / ( x + y )
Composed=lambda (a, b)-> {
=lambda a,b (n)->{
=a*b*n
}
}
numbers=(x,y,z)
inverses=(xi,yi,zi)
Dim Base 0, combo(3)
combo(0)=Composed(x,xi), Composed(y,yi), Composed(z,zi)
num=each(numbers)
inv=each(inverses)
fun=each(combo())
While num, inv, fun {
Print $("0.00"), Array(num);" * ";Array(inv);" * 0.50 = "; combo(fun^)(0.5),$("")
Print
}
}
Checkit
\\ for functions we have this definition
Composed=lambda (f1, f2) -> {
=lambda f1, f2 (x)->{
=f1(f2(x))
}
}
 
</syntaxhighlight>
{{out}}
<pre>
2.00 * 0.50 * 0.50 = 0.50
4.00 * 0.25 * 0.50 = 0.50
6.00 * 0.17 * 0.50 = 0.50
</pre>
 
=={{header|Mathematica}} / {{header|Wolfram Language}}==
 
This code demonstrates the example using structure similar to function composition, however the composition function is replace with the multiplier function.
 
<syntaxhighlight lang="mathematica">multiplier[n1_,n2_]:=n1 n2 #&
num={2,4,2+4};
numi=1/num;
multiplierfuncs = multiplier @@@ Transpose[{num, numi}];
</syntaxhighlight>
 
The resulting functions are unity multipliers:
<pre>Table[i[0.666], {i, multiplierfuncs}]
 
{0.666, 0.666, 0.666}</pre>
 
Note that unlike Composition, the above definition of multiplier only allows for exactly two arguments. The definition can be changed to allow any nonzero number of arguments:
<syntaxhighlight lang="mathematica">multiplier[arg__] := Times[arg, #] &
</syntaxhighlight>
 
=={{header|Nemerle}}==
{{trans|Python}}
<syntaxhighlight lang="nemerle">using System;
using System.Console;
using Nemerle.Collections.NCollectionsExtensions;
 
module FirstClassNums
{
Main() : void
{
def x = 2.0; def xi = 0.5;
def y = 4.0; def yi = 0.25;
def z = x + y; def zi = 1.0 / (x + y);
def multiplier = fun (a, b) {fun (c) {a * b * c}};
def nums = [x, y, z];
def inums = [xi, yi, zi];
WriteLine($[multiplier(n, m) (0.5)|(n, m) in ZipLazy(nums, inums)]);
}
}</syntaxhighlight>
 
=={{header|Never}}==
<syntaxhighlight lang="never">
func multiplier(a : float, b : float) -> (float) -> float {
let func(m : float) -> float { a * b * m }
}
 
func main() -> int {
var x = 2.0;
var xi = 0.5;
var y = 4.0;
var yi = 0.25;
var z = x + y;
var zi = 1.0 / z;
 
var f = [ x, y, z ] : float;
var i = [ xi, yi, zi ] : float;
var c = 0;
var mult = let func(m : float) -> float { 0.0 };
for (c = 0; c < 3; c = c + 1) {
mult = multiplier(f[c], i[c]);
prints(f[c] + " * " + i[c] + " * " + 1.0 + " = " + mult(1) + "\n")
};
 
0
}
</syntaxhighlight>
{{output}}
<pre>
2.00 * 0.50 * 1.00 = 1.00
4.00 * 0.25 * 1.00 = 1.00
6.00 * 0.17 * 1.00 = 1.00
</pre>
 
=={{header|Nim}}==
<syntaxhighlight lang="nim">
func multiplier(a, b: float): auto =
let ab = a * b
result = func(c: float): float = ab * c
 
let
x = 2.0
xi = 0.5
y = 4.0
yi = 0.25
z = x + y
zi = 1.0 / ( x + y )
 
let list = [x, y, z]
let invlist = [xi, yi, zi]
 
for i in 0..list.high:
# Create a multiplier function...
let f = multiplier(list[i], invlist[i])
# ... and apply it.
echo f(0.5)</syntaxhighlight>
 
{{out}}
<pre>0.5
0.5
0.5</pre>
4 multiplier 0.25 (2+4)
6
0.25 multiplier (2+4) (1%2+4)
0.25
(2+4) multiplier (1%2+4) 2
2</lang>
 
=={{header|Objeck}}==
J uses the concept of ''conjunctions'' to enable the composition of new verbs (functions). Conjunctions can link two verbs as in the [[First-class functions]] problem, analogous to the ''and'' in the phrase "cube and cuberoot". The [[First-class functions]] solution used the primitive conjunction <tt>@</tt> to link two verbs to create a new verb. The solution here uses a user-defined conjunction <tt>multiplier</tt> that links two nouns (numbers in this case) to create a new verb - something like "multiply m and n by y" where m and n are the nouns immediately to the left and right of the conjunction.
Similar however this code does not generate a list of functions.
<syntaxhighlight lang="objeck">use Collection.Generic;
 
class FirstClass {
function : Main(args : String[]) ~ Nil {
x := 2.0;
xi := 0.5;
y := 4.0;
yi := 0.25;
z := x + y;
zi := 1.0 / (x + y);
 
numlist := CompareVector->New()<FloatHolder>;
numlist->AddBack(x); numlist->AddBack(y); numlist->AddBack(z);
 
numlisti := Vector->New()<FloatHolder>;
numlisti->AddBack(xi); numlisti->AddBack(yi); numlisti->AddBack(zi);
each(i : numlist) {
v := numlist->Get(i); vi := numlisti->Get(i);
mult := Multiplier(v, vi);
r := mult(0.5);
"{$v} * {$vi} * 0.5 = {$r}"->PrintLine();
};
}
function : Multiplier(a : FloatHolder, b : FloatHolder) ~ (FloatHolder) ~ FloatHolder {
return \(FloatHolder) ~ FloatHolder : (c) => a * b * c;
}
}
</syntaxhighlight>
 
{{output}}
<pre>
2 * 0.5 * 0.5 = 0.5
4 * 0.25 * 0.5 = 0.5
6 * 0.166667 * 0.5 = 0.5
</pre>
 
=={{header|OCaml}}==
<lang ocaml># let x, xi, y, yi = 2.0, 0.5, 4.0, 0.25 ;;
 
<syntaxhighlight lang="ocaml"># let zx = x +2. y0;;
and zi = 1.0 /. (x +. y) ;;
 
# let multiplier n1 n2y = (fun m -> n1 *4. n2 *. m) 0;;
 
# let numlistz = [x; +. y; z];
and numlisti = [xi; yi; zi] ;;
 
# let coll = [ x; y; z];;
# List.map2 (fun n inv -> (multiplier n inv) 0.5) numlist numlisti ;;
- : float list = [0.5; 0.5; 0.5]</lang>
 
<lang ocaml># let cubeinv_coll = functionList.map (fun x -> x1.0 */. x) coll;;
 
# let crootmultiplier n1 n2 = function(fun xt -> xn1 ** (1. /.n2 3*.0) t);;
 
(* create a list of new functions *)
# let compose = fun f1 f2 -> (fun x -> f1(f2 x)) ;;
# let func_list = List.map2 (fun n inv -> (multiplier n inv)) coll inv_coll;;
 
# List.map (fun f -> f 0.5) func_list;;
# let funclist = [sin; cos; cube]
- : andfloat funclistilist = [asin0.5; acos0.5; croot0.5] ;;
 
(* or just apply the generated function immediately... *)
# List.map2 (fun inversef f -> (compose inversef f) 0.5) funclist funclisti ;;
# List.map2 (fun n inv -> (multiplier n inv) 0.5) coll inv_coll;;
- : float list = [0.5; 0.499999999999999889; 0.629960524947436706]</lang>
- : float list = [0.5; 0.5; 0.5]</syntaxhighlight>
 
=={{header|Oforth}}==
 
<syntaxhighlight lang="oforth">: multiplier(n1, n2) #[ n1 n2 * * ] ;
 
: firstClassNum
| x xi y yi z zi |
2.0 ->x
0.5 ->xi
4.0 ->y
0.25 ->yi
x y + ->z
x y + inv ->zi
[ x, y, z ] [ xi, yi, zi ] zipWith(#multiplier) map(#[ 0.5 swap perform ] ) . ;</syntaxhighlight>
{{out}}
<pre>
[0.5, 0.5, 0.5]
</pre>
 
=={{header|Oz}}==
<syntaxhighlight lang="oz">declare
 
[X Y Z] = [2.0 4.0 Z=X+Y]
[XI YI ZI] = [0.5 0.25 1.0/(X+Y)]
 
fun {Multiplier A B}
fun {$ M}
A * B * M
end
end
 
in
for
N in [X Y Z]
I in [XI YI ZI]
do
{Show {{Multiplier N I} 0.5}}
end</syntaxhighlight>
 
"Multiplier" is like "Compose", but with multiplication instead of function application. Otherwise the code is identical except for the argument types (numbers instead of functions).
 
=={{header|PARI/GP}}==
{{works with|PARI/GP|2.4.2 and above}}
<syntaxhighlight lang="parigp">multiplier(n1,n2)={
x -> n1 * n2 * x
};
 
test()={
my(x = 2.0, xi = 0.5, y = 4.0, yi = 0.25, z = x + y, zi = 1.0 / ( x + y ));
print(multiplier(x,xi)(0.5));
print(multiplier(y,yi)(0.5));
print(multiplier(z,zi)(0.5));
};</syntaxhighlight>
The two are very similar, though as requested the test numbers are in 6 variables instead of two vectors.
 
=={{header|Pascal}}==
Works with FPC (currently only version 3.3.1).
<syntaxhighlight lang="pascal">
program FunTest;
{$mode objfpc}
{$modeswitch functionreferences}
{$modeswitch anonymousfunctions}
uses
SysUtils;
 
type
TMultiplier = reference to function(n: Double): Double;
 
function GetMultiplier(a, b: Double): TMultiplier;
var
prod: Double;
begin
prod := a * b;
Result := function(n: Double): Double begin Result := prod * n end;
end;
 
var
Multiplier: TMultiplier;
I: Integer;
x, xi, y, yi: Double;
Numbers, Inverses: array of Double;
begin
x := 2.0;
xi := 0.5;
y := 4.0;
yi := 0.25;
Numbers := [x, y, x + y];
Inverses := [xi, yi, 1.0 / (x + y)];
for I := 0 to High(Numbers) do begin
Multiplier := GetMultiplier(Numbers[I], Inverses[I]);
WriteLn(Multiplier(0.5));
end;
end.
</syntaxhighlight>
{{out}}
<pre>
5.0000000000000000E-001
5.0000000000000000E-001
5.0000000000000000E-001
</pre>
 
=={{header|Perl}}==
<syntaxhighlight lang="perl">sub multiplier {
my ( $n1, $n2 ) = @_;
sub {
$n1 * $n2 * $_[0];
};
}
 
my $x = 2.0;
my $xi = 0.5;
my $y = 4.0;
my $yi = 0.25;
my $z = $x + $y;
my $zi = 1.0 / ( $x + $y );
 
my %zip;
@zip{ $x, $y, $z } = ( $xi, $yi, $zi );
 
while ( my ( $number, $inverse ) = each %zip ) {
print multiplier( $number, $inverse )->(0.5), "\n";
}
</syntaxhighlight>
Output:
<pre>0.5
0.5
0.5
</pre>
The entry in first-class functions uses the same technique:
<syntaxhighlight lang="perl">sub compose {
my ($f, $g) = @_;
sub {
$f -> ($g -> (@_));
};
}
...
compose($flist1[$_], $flist2[$_]) -> (0.5)
</syntaxhighlight>
 
=={{header|Phix}}==
Just as there is no real support for first class functions, not much that is pertinent to this task for numbers either, but the manual way is just as trivial:
<!--<syntaxhighlight lang="phix">(phixonline)-->
<span style="color: #008080;">with</span> <span style="color: #008080;">javascript_semantics</span>
<span style="color: #004080;">sequence</span> <span style="color: #000000;">mtable</span> <span style="color: #0000FF;">=</span> <span style="color: #0000FF;">{}</span>
<span style="color: #008080;">function</span> <span style="color: #000000;">multiplier</span><span style="color: #0000FF;">(</span><span style="color: #004080;">atom</span> <span style="color: #000000;">n1</span><span style="color: #0000FF;">,</span> <span style="color: #004080;">atom</span> <span style="color: #000000;">n2</span><span style="color: #0000FF;">)</span>
<span style="color: #000000;">mtable</span> <span style="color: #0000FF;">=</span> <span style="color: #7060A8;">append</span><span style="color: #0000FF;">(</span><span style="color: #000000;">mtable</span><span style="color: #0000FF;">,{</span><span style="color: #000000;">n1</span><span style="color: #0000FF;">,</span><span style="color: #000000;">n2</span><span style="color: #0000FF;">})</span>
<span style="color: #008080;">return</span> <span style="color: #7060A8;">length</span><span style="color: #0000FF;">(</span><span style="color: #000000;">mtable</span><span style="color: #0000FF;">)</span>
<span style="color: #008080;">end</span> <span style="color: #008080;">function</span>
<span style="color: #008080;">function</span> <span style="color: #000000;">call_multiplier</span><span style="color: #0000FF;">(</span><span style="color: #004080;">integer</span> <span style="color: #000000;">f</span><span style="color: #0000FF;">,</span> <span style="color: #004080;">atom</span> <span style="color: #000000;">m</span><span style="color: #0000FF;">)</span>
<span style="color: #004080;">atom</span> <span style="color: #0000FF;">{</span><span style="color: #000000;">n1</span><span style="color: #0000FF;">,</span><span style="color: #000000;">n2</span><span style="color: #0000FF;">}</span> <span style="color: #0000FF;">=</span> <span style="color: #000000;">mtable</span><span style="color: #0000FF;">[</span><span style="color: #000000;">f</span><span style="color: #0000FF;">]</span>
<span style="color: #008080;">return</span> <span style="color: #000000;">n1</span><span style="color: #0000FF;">*</span><span style="color: #000000;">n2</span><span style="color: #0000FF;">*</span><span style="color: #000000;">m</span>
<span style="color: #008080;">end</span> <span style="color: #008080;">function</span>
<span style="color: #008080;">constant</span> <span style="color: #000000;">x</span> <span style="color: #0000FF;">=</span> <span style="color: #000000;">2</span><span style="color: #0000FF;">,</span>
<span style="color: #000000;">xi</span> <span style="color: #0000FF;">=</span> <span style="color: #000000;">0.5</span><span style="color: #0000FF;">,</span>
<span style="color: #000000;">y</span> <span style="color: #0000FF;">=</span> <span style="color: #000000;">4</span><span style="color: #0000FF;">,</span>
<span style="color: #000000;">yi</span> <span style="color: #0000FF;">=</span> <span style="color: #000000;">0.25</span><span style="color: #0000FF;">,</span>
<span style="color: #000000;">z</span> <span style="color: #0000FF;">=</span> <span style="color: #000000;">x</span> <span style="color: #0000FF;">+</span> <span style="color: #000000;">y</span><span style="color: #0000FF;">,</span>
<span style="color: #000000;">zi</span> <span style="color: #0000FF;">=</span> <span style="color: #000000;">1</span> <span style="color: #0000FF;">/</span> <span style="color: #0000FF;">(</span> <span style="color: #000000;">x</span> <span style="color: #0000FF;">+</span> <span style="color: #000000;">y</span> <span style="color: #0000FF;">)</span>
<span style="color: #0000FF;">?</span><span style="color: #000000;">call_multiplier</span><span style="color: #0000FF;">(</span><span style="color: #000000;">multiplier</span><span style="color: #0000FF;">(</span><span style="color: #000000;">x</span><span style="color: #0000FF;">,</span><span style="color: #000000;">xi</span><span style="color: #0000FF;">),</span><span style="color: #000000;">0.5</span><span style="color: #0000FF;">)</span>
<span style="color: #0000FF;">?</span><span style="color: #000000;">call_multiplier</span><span style="color: #0000FF;">(</span><span style="color: #000000;">multiplier</span><span style="color: #0000FF;">(</span><span style="color: #000000;">y</span><span style="color: #0000FF;">,</span><span style="color: #000000;">yi</span><span style="color: #0000FF;">),</span><span style="color: #000000;">0.5</span><span style="color: #0000FF;">)</span>
<span style="color: #0000FF;">?</span><span style="color: #000000;">call_multiplier</span><span style="color: #0000FF;">(</span><span style="color: #000000;">multiplier</span><span style="color: #0000FF;">(</span><span style="color: #000000;">z</span><span style="color: #0000FF;">,</span><span style="color: #000000;">zi</span><span style="color: #0000FF;">),</span><span style="color: #000000;">0.5</span><span style="color: #0000FF;">)</span>
<!--</syntaxhighlight>-->
{{out}}
<pre>
0.5
0.5
0.5
</pre>
I should perhaps note that output in Phix automatically rounds to the specified precision (10 d.p. if none) so 4.9999 to two decimal places is shown as 5.00, and you can be pretty sure that sort of thing is happening on the last line.
 
Compared to first class functions, there are (as in my view there should be) significant differences in the treatment of numbers and functions, but as mentioned on that page tagging ctable entries should be quite sufficient.
<!--<syntaxhighlight lang="phix">(phixonline)-->
<span style="color: #008080;">with</span> <span style="color: #008080;">javascript_semantics</span>
<span style="color: #004080;">sequence</span> <span style="color: #000000;">ctable</span> <span style="color: #0000FF;">=</span> <span style="color: #0000FF;">{}</span>
<span style="color: #008080;">function</span> <span style="color: #000000;">compose</span><span style="color: #0000FF;">(</span><span style="color: #004080;">integer</span> <span style="color: #000000;">f</span><span style="color: #0000FF;">,</span> <span style="color: #000000;">g</span><span style="color: #0000FF;">)</span>
<span style="color: #000000;">ctable</span> <span style="color: #0000FF;">=</span> <span style="color: #7060A8;">append</span><span style="color: #0000FF;">(</span><span style="color: #000000;">ctable</span><span style="color: #0000FF;">,{</span><span style="color: #000000;">f</span><span style="color: #0000FF;">,</span><span style="color: #000000;">g</span><span style="color: #0000FF;">})</span>
<span style="color: #004080;">integer</span> <span style="color: #000000;">cdx</span> <span style="color: #0000FF;">=</span> <span style="color: #7060A8;">length</span><span style="color: #0000FF;">(</span><span style="color: #000000;">ctable</span><span style="color: #0000FF;">)</span>
<span style="color: #008080;">return</span> <span style="color: #000000;">cdx</span>
<span style="color: #008080;">end</span> <span style="color: #008080;">function</span>
<span style="color: #008080;">function</span> <span style="color: #000000;">call_composite</span><span style="color: #0000FF;">(</span><span style="color: #004080;">integer</span> <span style="color: #000000;">cdx</span><span style="color: #0000FF;">,</span> <span style="color: #004080;">atom</span> <span style="color: #000000;">x</span><span style="color: #0000FF;">)</span>
<span style="color: #004080;">integer</span> <span style="color: #0000FF;">{</span><span style="color: #000000;">f</span><span style="color: #0000FF;">,</span><span style="color: #000000;">g</span><span style="color: #0000FF;">}</span> <span style="color: #0000FF;">=</span> <span style="color: #000000;">ctable</span><span style="color: #0000FF;">[</span><span style="color: #000000;">cdx</span><span style="color: #0000FF;">]</span>
<span style="color: #008080;">return</span> <span style="color: #000000;">f</span><span style="color: #0000FF;">(</span><span style="color: #000000;">g</span><span style="color: #0000FF;">(</span><span style="color: #000000;">x</span><span style="color: #0000FF;">))</span>
<span style="color: #008080;">end</span> <span style="color: #008080;">function</span>
<span style="color: #008080;">function</span> <span style="color: #000000;">plus1</span><span style="color: #0000FF;">(</span><span style="color: #004080;">atom</span> <span style="color: #000000;">x</span><span style="color: #0000FF;">)</span>
<span style="color: #008080;">return</span> <span style="color: #000000;">x</span><span style="color: #0000FF;">+</span><span style="color: #000000;">1</span>
<span style="color: #008080;">end</span> <span style="color: #008080;">function</span>
<span style="color: #008080;">function</span> <span style="color: #000000;">halve</span><span style="color: #0000FF;">(</span><span style="color: #004080;">atom</span> <span style="color: #000000;">x</span><span style="color: #0000FF;">)</span>
<span style="color: #008080;">return</span> <span style="color: #000000;">x</span><span style="color: #0000FF;">/</span><span style="color: #000000;">2</span>
<span style="color: #008080;">end</span> <span style="color: #008080;">function</span>
<span style="color: #008080;">constant</span> <span style="color: #000000;">m</span> <span style="color: #0000FF;">=</span> <span style="color: #000000;">compose</span><span style="color: #0000FF;">(</span><span style="color: #000000;">halve</span><span style="color: #0000FF;">,</span><span style="color: #000000;">plus1</span><span style="color: #0000FF;">)</span>
<span style="color: #0000FF;">?</span><span style="color: #000000;">call_composite</span><span style="color: #0000FF;">(</span><span style="color: #000000;">m</span><span style="color: #0000FF;">,</span><span style="color: #000000;">1</span><span style="color: #0000FF;">)</span> <span style="color: #000080;font-style:italic;">-- displays 1</span>
<span style="color: #0000FF;">?</span><span style="color: #000000;">call_composite</span><span style="color: #0000FF;">(</span><span style="color: #000000;">m</span><span style="color: #0000FF;">,</span><span style="color: #000000;">4</span><span style="color: #0000FF;">)</span> <span style="color: #000080;font-style:italic;">-- displays 2.5</span>
<!--</syntaxhighlight>-->
 
=={{header|PicoLisp}}==
<syntaxhighlight lang="picolisp">(load "@lib/math.l")
 
(de multiplier (N1 N2)
(curry (N1 N2) (X)
(*/ N1 N2 X `(* 1.0 1.0)) ) )
 
(let (X 2.0 Xi 0.5 Y 4.0 Yi 0.25 Z (+ X Y) Zi (*/ 1.0 1.0 Z))
(mapc
'((Num Inv)
(prinl (format ((multiplier Inv Num) 0.5) *Scl)) )
(list X Y Z)
(list Xi Yi Zi) ) )</syntaxhighlight>
Output:
<pre>0.500000
0.500000
0.500001</pre>
This follows the same structure as [[First-class functions#PicoLisp]], just
that the function 'multiplier' above accepts two numbers, while 'compose'
below accepts two functions:
<syntaxhighlight lang="picolisp">(load "@lib/math.l")
 
(de compose (F G)
(curry (F G) (X)
(F (G X)) ) )
 
(de cube (X)
(pow X 3.0) )
 
(de cubeRoot (X)
(pow X 0.3333333) )
 
(mapc
'((Fun Inv)
(prinl (format ((compose Inv Fun) 0.5) *Scl)) )
'(sin cos cube)
'(asin acos cubeRoot) )</syntaxhighlight>
With a similar output:
<pre>0.500001
0.499999
0.500000</pre>
 
=={{header|Python}}==
This new task:
<langsyntaxhighlight lang="python">IDLE 2.6.1
>>> # Number literals
>>> x,xi, y,yi = 2.0,0.5, 4.0,0.25
Line 165 ⟶ 1,498:
>>> [multiplier(inversen, n)(.5) for n, inversen in zip(numlist, numlisti)]
[0.5, 0.5, 0.5]
>>></langsyntaxhighlight>
 
The Python solution to First-class functions for comparison:
<langsyntaxhighlight lang="python">>>> # Some built in functions and their inverses
>>> from math import sin, cos, acos, asin
>>> # Add a user defined function and its inverse
Line 182 ⟶ 1,515:
>>> [compose(inversef, f)(.5) for f, inversef in zip(funclist, funclisti)]
[0.5, 0.4999999999999999, 0.5]
>>></langsyntaxhighlight>
As can be see, the treatment of functions is very close to the treatment of numbers. there are no extra wrappers, or function pointer syntax added, for example.
 
=={{header|R}}==
<syntaxhighlight lang="r">multiplier <- function(n1,n2) { (function(m){n1*n2*m}) }
x = 2.0
xi = 0.5
y = 4.0
yi = 0.25
z = x + y
zi = 1.0 / ( x + y )
num = c(x,y,z)
inv = c(xi,yi,zi)
 
multiplier(num,inv)(0.5)
 
Output
[1] 0.5 0.5 0.5
</syntaxhighlight>
 
Compared to original first class functions
<syntaxhighlight lang="r">sapply(mapply(compose,f1,f2),do.call,list(.5))
[1] 0.5 0.5 0.5</syntaxhighlight>
 
=={{header|Racket}}==
 
<syntaxhighlight lang="racket">
#lang racket
 
(define x 2.0)
(define xi 0.5)
(define y 4.0)
(define yi 0.25)
(define z (+ x y))
(define zi (/ 1.0 (+ x y)))
 
(define ((multiplier x y) z) (* x y z))
 
(define numbers (list x y z))
(define inverses (list xi yi zi))
 
(for/list ([n numbers] [i inverses])
((multiplier n i) 0.5))
;; -> '(0.5 0.5 0.5)
</syntaxhighlight>
 
=={{header|Raku}}==
(formerly Perl 6)
{{works with|Rakudo|2015-09-10}}
<syntaxhighlight lang="raku" line>sub multiplied ($g, $f) { return { $g * $f * $^x } }
my $x = 2.0;
my $xi = 0.5;
my $y = 4.0;
my $yi = 0.25;
my $z = $x + $y;
my $zi = 1.0 / ( $x + $y );
 
my @numbers = $x, $y, $z;
my @inverses = $xi, $yi, $zi;
for flat @numbers Z @inverses { say multiplied($^g, $^f)(.5) }</syntaxhighlight>
Output:
<pre>0.5
0.5
0.5</pre>
The structure of this is identical to first-class function task.
 
=={{header|REXX}}==
The REXX language doesn't have an easy method to call functions by using a variable name,
<br>but the '''interpret''' instruction can be used to provide that capability.
<syntaxhighlight lang="rexx">/*REXX program to use a first-class function to use numbers analogously. */
nums= 2.0 4.0 6.0 /*various numbers, can have fractions.*/
invs= 1/2.0 1/4.0 1/6.0 /*inverses of the above (real) numbers.*/
m= 0.5 /*multiplier when invoking new function*/
do j=1 for words(nums); num= word(nums, j); inv= word(invs, j)
nf= multiplier(num, inv); interpret call nf m /*sets the var RESULT.*/
say 'number=' @(num) 'inverse=' @(inv) 'm=' @(m) 'result=' @(result)
end /*j*/
exit /*stick a fork in it, we're all done. */
/*──────────────────────────────────────────────────────────────────────────────────────*/
@: return left( arg(1) / 1, 15) /*format the number, left justified. */
multiplier: procedure expose n1n2; parse arg n1,n2; n1n2= n1 * n2; return 'a_new_func'
a_new_func: return n1n2 * arg(1)</syntaxhighlight>
{{out|output|text=&nbsp; when using the internal default inputs:}}
<pre>
number= 2 inverse= 0.5 m= 0.5 result= 0.5
number= 4 inverse= 0.25 m= 0.5 result= 0.5
number= 6 inverse= 0.166666667 m= 0.5 result= 0.5
</pre>
 
=={{header|Ruby}}==
<langsyntaxhighlight lang="ruby">multiplier = proc {|n1, n2| proc {|m| n1 * n2 * m}}
numlist = [x=2, y=4, x+y]
numlistiinvlist = [0.5, 0.25, 1.0/(x+y)]
p numlist.zip(numlistiinvlist).map {|n,ni invn| multiplier.call([invn, n,ni).call(][0.5)]}
# => [0.5, 0.5, 0.5]</langsyntaxhighlight>
 
This structure is identical to the treatment of Ruby's [[First-class functions#Ruby|first-class functions]] -- create a Proc object that returns a Proc object (a closure). These examples show that 0.5 times number ''n'' (or passed to function ''f'') times inverse of ''n'' (or passed to inverse of ''f'') returns the original number, 0.5.
 
=={{header|Rust}}==
<syntaxhighlight lang="rust">#![feature(conservative_impl_trait)]
fn main() {
let (x, xi) = (2.0, 0.5);
let (y, yi) = (4.0, 0.25);
let z = x + y;
let zi = 1.0/z;
 
let numlist = [x,y,z];
let invlist = [xi,yi,zi];
 
let result = numlist.iter()
.zip(&invlist)
.map(|(x,y)| multiplier(*x,*y)(0.5))
.collect::<Vec<_>>();
println!("{:?}", result);
}
 
fn multiplier(x: f64, y: f64) -> impl Fn(f64) -> f64 {
move |m| x*y*m
}
</syntaxhighlight>
 
This is very similar to the [[First-class functions#Rust|first-class functions]] implementation save that the type inference works a little bit better here (e.g. when declaring <code>numlist</code> and <code>invlist</code>) and <code>multiplier</code>'s declaration is substantially simpler than <code>compose</code>'s. Both of these boil down to the fact that closures and regular functions are actually different types in Rust so we have to be generic over them but here we are only dealing with 64-bit floats.
This structure is identical to the treatment of Ruby's [[First-class_functions#Ruby|first class functions]] -- create a Proc object that returns a Proc object (a closure). We show that a number (or function) multiplied by its inverse (applied to its inverse function) multiplied by some number (passed some number as an argument) results in that number.
 
=={{header|Scala}}==
<langsyntaxhighlight lang="scala">scala> val x = 2.0
x: Double = 2.0
 
Line 213 ⟶ 1,659:
zi: Double = 0.16666666666666666
 
scala> val numbers = List(x, y, z)
scala> def multiplier(n1: Double, n2: Double) = (m: Double) => n1 * n2 * m
numbers: List[Double] = List(2.0, 4.0, 6.0)
multiplier: (n1: Double,n2: Double)(Double) => Double
 
scala> forval {inverses = List(xi, yi, zi)
inverses: List[Double] = List(0.5, 0.25, 0.16666666666666666)
| (number, inverse) <- numbers zip inverses
 
| new_function = multiplier(number, inverse)
scala> def multiplier = (n1: Double, n2: Double) => (m: Double) => n1 * n2 * m
| } println("(%f * %f)(%f) = %f" format (number, inverse, 0.5, new_function(0.5)))
multiplier: (Double, Double) => (Double) => Double
(2,000000 * 0,500000)(0,500000) = 0,500000
 
(4,000000 * 0,250000)(0,500000) = 0,500000
scala> def comp = numbers zip inverses map multiplier.tupled
(6,000000 * 0,166667)(0,500000) = 0,500000</lang>
comp: List[(Double) => Double]
 
scala> comp.foreach(f=>println(f(0.5)))
0.5
0.5
0.5</syntaxhighlight>
 
=={{header|Scheme}}==
This implementation closely follows the Scheme implementation of the [[First-class functions]] problem.
<langsyntaxhighlight lang="scheme">(define x 2.0)
(define xi 0.5)
(define y 4.0)
Line 240 ⟶ 1,692:
(define m 0.5)
(define (go n1 n2)
(iffor-each (notlambda (orn1 (null? n1n2)
(display (null?(multiplier n1 n2) m))
(begin (display ((multiplier (car n1) (car n2)) m (newline))
n1 (newlinen2))
(go number inverse)</syntaxhighlight>
(go (cdr n1) (cdr n2)))))
 
(go number inverse)</lang>
Output:
0.5
0.5
0.5
 
=={{header|Sidef}}==
<syntaxhighlight lang="ruby">func multiplier(n1, n2) {
func (n3) {
n1 * n2 * n3
}
}
 
var x = 2.0
var xi = 0.5
var y = 4.0
var yi = 0.25
var z = (x + y)
var zi = (1 / (x + y))
 
var numbers = [x, y, z]
var inverses = [xi, yi, zi]
 
for f,g (numbers ~Z inverses) {
say multiplier(f, g)(0.5)
}</syntaxhighlight>
{{out}}
<pre>
0.5
0.5
0.5
</pre>
 
=={{header|Slate}}==
{{incorrect|Slate|Compare and contrast the resultant program with the corresponding entry in First-class functions.}}
<lang slate>define: #multiplier -> [| :n1 :n2 | [| :m | n1 * n2 * m]].
<syntaxhighlight lang="slate">define: #multiplier -> [| :n1 :n2 | [| :m | n1 * n2 * m]].
define: #x -> 2.
define: #y -> 4.
Line 259 ⟶ 1,737:
define: #numlisti -> (numlist collect: [| :x | 1.0 / x]).
 
numlist with: numlisti collect: [| :n1 :n2 | (multiplier applyTo: {n1. n2}) applyWith: 0.5].</langsyntaxhighlight>
 
=={{header|Tcl}}==
{{works with|Tcl|8.5}}
<langsyntaxhighlight lang="tcl">package require Tcl 8.5
proc multiplier {a b} {
list apply {{ab m} {expr {$ab*$m}}} [expr {$a*$b}]
}</langsyntaxhighlight>
Note that, as with Tcl's solution for [[First-class functions#Tcl|First-class functions]], the resulting term must be expanded on application. For example, study this interactive session:
<langsyntaxhighlight lang="tcl">% set mult23 [multiplier 2 3]
apply {{ab m} {expr {$ab*$m}}} 6
% {*}$mult23 5
30</langsyntaxhighlight>
Formally, for the task:
<langsyntaxhighlight lang="tcl">set x 2.0
set xi 0.5
set y 4.0
Line 283 ⟶ 1,761:
foreach a $numlist b $numlisti {
puts [format "%g * %g * 0.5 = %g" $a $b [{*}[multiplier $a $b] 0.5]]
}</langsyntaxhighlight>
Which produces this output:
<pre>2 * 0.5 * 0.5 = 0.5
4 * 0.25 * 0.5 = 0.5
6 * 0.166667 * 0.5 = 0.5</pre>
 
=={{header|TXR}}==
 
This solution seeks a non-strawman interpretation of the exercise: to treat functions and literal numeric terms under the same operations. We develop a three-argument function called <code>binop</code> whose argument is an ordinary function which works on numbers, and two arithmetic arguments which are any combination of functions or numbers. The functions may have any arity from 0 to 2. The <code>binop</code> functions handles all the cases.
 
The basic rules are:
 
* When all required arguments are given to a function, it is expected that a number will be produced.
 
* Zero-argument functions are called to force a number out of them.
 
* When operands are numbers or zero-argument functions, a numeric result is calculated.
 
* Otherwise the operation is a functional combinator, returning a function.
 
<syntaxhighlight lang="txrlisp">(defun binop (numop x y)
(typecase x
(number (typecase y
(number [numop x y])
(fun (caseql (fun-fixparam-count y)
(0 [numop x [y]])
(1 (ret [numop x [y @1]]))
(2 (ret [numop x [y @1 @2]]))
(t (error "~s: right argument has too many params"
%fun% y))))
(t (error "~s: right argument must be function or number"
%fun% y))))
(fun (typecase y
(number (caseql (fun-fixparam-count x)
(0 [numop [x] y])
(1 (ret [numop [x @1] y]))
(2 (ret [numop [x @1 @2] y]))
(t (error "~s: left argument has too many params"
%fun% x))))
(fun (macrolet ((pc (x-param-count y-param-count)
^(+ (* 3 ,x-param-count) ,y-param-count)))
(caseql* (pc (fun-fixparam-count x) (fun-fixparam-count y))
(((pc 0 0)) [numop [x] [y]])
(((pc 0 1)) (ret [numop [x] [y @1]]))
(((pc 0 2)) (ret [numop [x] [y @1 @2]]))
(((pc 1 0)) (ret [numop [x @1] [y]]))
(((pc 1 1)) (ret [numop [x @1] [y @1]]))
(((pc 1 2)) (ret [numop [x @1] [y @1 @2]]))
(((pc 2 0)) (ret [numop [x @1 @2] [y]]))
(((pc 2 1)) (ret [numop [x @1 @2] [y @1]]))
(((pc 2 2)) (ret [numop [x @1 @2] [y @1 @2]]))
(t (error "~s: one or both arguments ~s and ~s\ \
have excess arity" %fun% x y)))))))
(t (error "~s: left argument must be function or number"
%fun% y))))
 
(defun f+ (x y) [binop + x y])
(defun f- (x y) [binop - x y])
(defun f* (x y) [binop * x y])
(defun f/ (x y) [binop / x y])</syntaxhighlight>
 
With this, the following sort of thing is possible:
 
<pre>1> [f* 6 4] ;; ordinary arithmetic.
24
2> [f* f+ f+] ;; product of additions
#<interpreted fun: lambda (#:arg-1-0062 #:arg-2-0063 . #:arg-rest-0061)>
3> [*2 10 20] ;; i.e. (* (+ 10 20) (+ 10 20)) -> (* 30 30)
900
4> [f* 2 f+] ;; doubled addition
#<interpreted fun: lambda (#:arg-1-0017 #:arg-2-0018 . #:arg-rest-0016)>
5> [*4 11 19] ;; i.e. (* 2 (+ 11 19))
60
6> [f* (op f+ 2 @1) (op f+ 3 @1)]
#<interpreted fun: lambda (#:arg-1-0047 . #:arg-rest-0046)>
7> [*6 10 10] ;; i.e. (* (+ 2 10) (+ 3 10)) -> (* 12 13)
156
</pre>
 
So with these definitions, we can solve the task like this, which demonstrates that numbers and functions are handled by the same operations:
 
<syntaxhighlight lang="txrlisp">(let* ((x 2.0)
(xi 0.5)
(y 4.0)
(yi 0.25)
(z (lambda () (f+ x y))) ;; z is obviously function
(zi (f/ 1 z))) ;; also a function
(flet ((multiplier (a b) (op f* @1 (f* a b))))
(each ((n (list x y z))
(v (list xi yi zi)))
(prinl [[multiplier n v] 42.0]))))</syntaxhighlight>
 
{{out}}
 
<pre>42.0
42.0
42.0</pre>
 
=={{header|Ursala}}==
Line 294 ⟶ 1,864:
composition operator (+), and is named in compliance
with the task specification.
<langsyntaxhighlight Ursalalang="ursala">#import std
#import flo
 
Line 304 ⟶ 1,874:
#cast %eL
 
main = (gang multiplier*p\numbers inverses) 0.5</langsyntaxhighlight>
The multiplier could have been written in pattern
matching form like this.
<langsyntaxhighlight Ursalalang="ursala">multiplier("a","b") "c" = times(times("a","b"),"c")</langsyntaxhighlight>
The main program might also have been written with an
anonymous function like this.
<langsyntaxhighlight Ursalalang="ursala">main = (gang (//times+ times)*p\numbers inverses) 0.5</langsyntaxhighlight>
output:
<pre>
Line 316 ⟶ 1,886:
</pre>
 
=={{header|Wren}}==
{{libheader|Wren-fmt}}
<syntaxhighlight lang="wren">import "./fmt" for Fmt
 
var multiplier = Fn.new { |n1, n2| Fn.new { |m| n1 * n2 * m } }
 
var orderedCollection = Fn.new {
var x = 2.0
var xi = 0.5
var y = 4.0
var yi = 0.25
var z = x + y
var zi = 1.0 / ( x + y )
return [[x, y, z], [xi, yi, zi]]
}
 
var oc = orderedCollection.call()
for (i in 0..2) {
var x = oc[0][i]
var y = oc[1][i]
var m = 0.5 // rather than 1 to compare with first-class functions task
Fmt.print("$0.1g * $g * $0.1g = $0.1g", x, y, m, multiplier.call(x, y).call(m))
}</syntaxhighlight>
 
{{out}}
Agreeing with results of first-class functions task:
<pre>
2.0 * 0.5 * 0.5 = 0.5
4.0 * 0.25 * 0.5 = 0.5
6.0 * 0.166667 * 0.5 = 0.5
</pre>
 
=={{header|zkl}}==
<syntaxhighlight lang="zkl">var x=2.0, y=4.0, z=(x+y), c=T(x,y,z).apply(fcn(n){ T(n,1.0/n) });
//-->L(L(2,0.5),L(4,0.25),L(6,0.166667))</syntaxhighlight>
<syntaxhighlight lang="zkl">fcn multiplier(n1,n2){ return('*.fp(n1,n2)) }</syntaxhighlight>
This is actually partial evaluation, multiplier returns n1*n2*X where X isn't known yet. So multiplier(2,3)(4) --> (2*3*)4 --> 24. Even better, multiplier(2,3)(4,5) --> 120, multiplier(2,3)(4,5,6) --> 720, multiplier(2,3)() --> 6.
 
Alternatively,
<syntaxhighlight lang="zkl">fcn multiplier(n1,n2){ fcn(n,X){ n*X }.fp(n1*n2) }</syntaxhighlight>
<syntaxhighlight lang="zkl">var ms=c.apply(fcn([(n1,n2)]){ multiplier(n1,n2) });
//-->L(Deferred,Deferred,Deferred) // lazy eval of n*(1/n)*X
ms.run(True,1.0) //-->L(1,1,1)
ms.run(True,5.0) //-->L(5,5,5)
ms.run(True,0.5) //-->L(0.5,0.5,0.5)</syntaxhighlight>
List.run(True,X), for each item in the list, does i(X) and collects the results into another list. Sort of an inverted map or fold.
 
{{Omitomit Fromfrom|C}}
{{Omitomit Fromfrom|AWK}}
{{Omitomit Fromfrom|FortranBASIC}}
{{omit from|Blast}}
{{omit from|Brlcad}}
{{omit from|Fortran}}
{{omit from|GUISS}}
{{omit from|Java|Can't pass around expressions like that.}}
{{Omitomit Fromfrom|PascalLilypond}}
{{omit from|Openscad}}
{{omit from|TI-83 BASIC}} {{omit from|TI-89 BASIC}} <!-- Cannot do function composition. Function definitions are dynamic, but functions cannot be passed as values. -->
{{omit from|Pascal}}
{{omit from|Processing}}
{{omit from|PureBasic}}
{{omit from|TI-83 BASIC}}
{{omit from|TI-89 BASIC}} <!-- Cannot do function composition. Function definitions are dynamic, but functions cannot be passed as values. -->
{{omit from|TPP}}
{{omit from|ZX Spectrum Basic}}
[[Category:Functions and subroutines]]
Jjuanhdez
2,130

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