First-class functions/Use numbers analogously: Difference between revisions

 

<small>To paraphrase the task description: Do what was done before, but with numbers rather than functions</small>

 

=={{header|Ada}}==

procedure Firstclass is

generic

end;

end loop;

{{out}}

<pre>5.00000E-01

 

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 -->

x := 2,

xi := 0.5,

inv n = inv num list[key];

print ((multiplier(inv n, n)(.5), new line))

Output:

<pre>

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|Axiom}}==

(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]

We can also curry functions, possibly with function composition, with the same output as before:

[mult(number*inver) 0.5 for number in numbers for inver in invers]

Using the Spad code in [[First-class functions#Axiom]], this can be done more economically using:

For comparison, [[First-class functions#Axiom]] gave:

inv := [asin$Float, acos$Float, (x:Float):Float +-> x^(1/3)]

[(f*g) 0.5 for f in fns for g in inv]

- which has the same output.

 

=={{header|BBC BASIC}}==

{{works with|BBC BASIC for Windows}}

x = 2 : xi = 1/2

y = 4 : yi = 0.25

DIM p% LEN(f$) + 4

$(p%+4) = f$ : !p% = p%+4

'''Output:'''

<pre>

</pre>

Compare with the implementation of First-class functions:

DEF FNsin(a) = SIN(a)

DEF FNasn(a) = ASN(a)

DIM p% LEN(f$) + 4

$(p%+4) = f$ : !p% = p%+4

 

=={{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.

using System.Linq;

 

}

}

 

=={{header|Clojure}}==

(def xi 0.5)

(def y 4.0)

> (for [[n i] (zipmap numbers invers)]

((multiplier n i) 0.5))

For comparison:

(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)))

Output:

<pre>(0.5 0.4999999999999999 0.5000000000000001)</pre>

=={{header|Common Lisp}}==

 

#'(lambda (x) (* f g x)))

 

inverse

value

 

Output:

The code from [[First-class functions]], for comparison:

 

(defun cube (x) (expt x 3))

(defun cube-root (x) (expt x (/ 3)))

function

value

 

Output:

 

=={{header|D}}==

 

auto multiplier(double a, double b)

writefln("%f * %f * %f == %f", f[i], r[i], 1.0, mult(1));

}

Output:

<pre>2.000000 * 0.500000 * 1.000000 == 1.000000

This is written to have identical structure to [[First-class functions#E]], though the variable names are different.

 

def xi := 0.5

def y := 4.0

def b := reverse[i]

println(`s = $s, a = $a, b = $b, multiplier($a, $b)($s) = ${multiplier(a, b)(s)}`)

 

Output:

 

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|Erlang}}==

-module( first_class_functions_use_numbers ).

 

 

multiplier( N1, N2 ) -> fun(M) -> N1 * N2 * M end.

{{out}}

<pre>

Value: 2.5 Result: 2.5

Value: 2.5 Result: 2.5

</pre>

 

=={{header|Factor}}==

Compared to http://rosettacode.org/wiki/First-class_functions, the call to "compose" is replaced with the call to "mutliplier"

IN: q

 

: example ( -- )

0.5 A B create-all

{{out}}

<pre>{ 0.5 0.5 0.5 }</pre>

=={{header|Fantom}}==

 

class Main

{

}

}

 

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:

 

static |Obj -> Obj| compose (|Obj -> Obj| fn1, |Obj -> Obj| fn2)

{

return |Obj x -> Obj| { fn2 (fn1 (x)) }

}

 

=={{header|Go}}==

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.

 

 

import "fmt"

return n1n2 * m

}

Output:

<pre>

 

[[/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.

 

import "fmt"

panic("unsupported multiplier type")

return 0 // never reached

 

=={{header|Groovy}}==

 

 

def ε = 0.00000001 // tolerance(epsilon): acceptable level of "wrongness" to account for rounding error

}

println()

{{out}}

<pre>2.000 * 0.500 * 1.000 == 1.000

=={{header|Haskell}}==

 

where

 

in printf "%f * %f * 0.5 = %f\n" number inverse (new_function 0.5)

mapM_ print_pair pairs

 

This is very close to the first-class functions example, but given as a full Haskell program rather than an interactive session.

[[First-class functions]] task solution. The solution here

is simpler and more direct since it handles a specific function definition.

 

procedure main(A)

procedure multiplier(n1,n2)

return makeProc { repeat inVal := n1 * n2 * (inVal@&source)[1] }

 

A sample run:

This seems to satisfy the new problem statement:

 

xi =: 0.5

y =: 4.0

rev =: xi,yi,zi

 

 

Example use:

 

 

For contrast, here are the final results from [[First-class functions#J]]:

 

===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.

 

]A=. 2 ; 4 ; (2 + 4) NB. Corresponds to ]A=. box (1&o.)`(2&o.)`(^&3)

└───┴───┴───┘

BA of &> 0.5 NB. Corresponds to BA of &> 0.5 (exactly)

 

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|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.

# zip this and that

 

# The task:

def multiplier(j): .[0] * .[1] * j;

 

{{out}}

$ jq -n -c -f First_class_functions_Use_numbers_analogously.jq

[0.5,0.5,0.5]

 

 

=={{header|Kotlin}}==

 

fun multiplier(n1: Double, n2: Double) = { m: Double -> n1 * n2 * m}

println("${multiplier(a[i], ai[i])(m)} = multiplier(${a[i]}, ${ai[i]})($m)")

}

 

{{out}}

 

=={{header|Lua}}==

-- This function returns another function that

-- keeps n1 and n2 in scope, ie. a closure.

print(v .. " * " .. invs[k] .. " * 0.5 = " .. new_function(0.5))

end

{{out}}

<pre>

4 * 0.25 * 0.5 = 0.5

6 * 0.16666666666667 * 0.5 = 0.5

</pre>

 

This code demonstrates the example using structure similar to function composition, however the composition function is replace with the multiplier function.

 

num={2,4,2+4};

numi=1/num;

multiplierfuncs = multiplier @@@ Transpose[{num, numi}];

 

The resulting functions are unity multipliers:

 

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:

 

=={{header|Nemerle}}==

{{trans|Python}}

using System.Console;

using Nemerle.Collections.NCollectionsExtensions;

WriteLine($[multiplier(n, m) (0.5)|(n, m) in ZipLazy(nums, inums)]);

}

 

=={{header|OCaml}}==

 

 

# let y = 4.0;;

(* or just apply the generated function immediately... *)

# List.map2 (fun n inv -> (multiplier n inv) 0.5) coll inv_coll;;

 

=={{header|Oforth}}==

 

 

: firstClassNum

x y + ->z

x y + inv ->zi

{{out}}

<pre>

 

=={{header|Oz}}==

 

[X Y Z] = [2.0 4.0 Z=X+Y]

do

{Show {{Multiplier N I} 0.5}}

 

"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}}

x -> n1 * n2 * x

};

print(multiplier(y,yi)(0.5));

print(multiplier(z,zi)(0.5));

The two are very similar, though as requested the test numbers are in 6 variables instead of two vectors.

 

=={{header|Perl}}==

my ( $n1, $n2 ) = @_;

sub {

print multiplier( $number, $inverse )->(0.5), "\n";

}

Output:

<pre>0.5

</pre>

The entry in first-class functions uses the same technique:

my ($f, $g) = @_;

...

compose($flist1[$_], $flist2[$_]) -> (0.5)

 

=={{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:

{{out}}

<pre>

 

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.

 

=={{header|PicoLisp}}==

 

(de multiplier (N1 N2)

(prinl (format ((multiplier Inv Num) 0.5) *Scl)) )

(list X Y Z)

Output:

<pre>0.500000

that the function 'multiplier' above accepts two numbers, while 'compose'

below accepts two functions:

 

(de compose (F G)

(prinl (format ((compose Inv Fun) 0.5) *Scl)) )

'(sin cos cube)

With a similar output:

<pre>0.500001

=={{header|Python}}==

This new task:

>>> # Number literals

>>> x,xi, y,yi = 2.0,0.5, 4.0,0.25

>>> [multiplier(inversen, n)(.5) for n, inversen in zip(numlist, numlisti)]

[0.5, 0.5, 0.5]

 

The Python solution to First-class functions for comparison:

>>> from math import sin, cos, acos, asin

>>> # Add a user defined function and its inverse

>>> [compose(inversef, f)(.5) for f, inversef in zip(funclist, funclisti)]

[0.5, 0.4999999999999999, 0.5]

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}}==

x = 2.0

xi = 0.5

Output

[1] 0.5 0.5 0.5

 

Compared to original first class functions

 

=={{header|Racket}}==

 

#lang racket

 

((multiplier n i) 0.5))

;; -> '(0.5 0.5 0.5)

 

=={{header|Ruby}}==

numlist = [x=2, y=4, x+y]

invlist = [0.5, 0.25, 1.0/(x+y)]

p numlist.zip(invlist).map {|n, invn| multiplier[invn, n][0.5]}

 

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}}==

fn main() {

let (x, xi) = (2.0, 0.5);

move |m| x*y*m

}

 

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.

 

=={{header|Scala}}==

x: Double = 2.0

 

0.5

0.5

 

=={{header|Scheme}}==

This implementation closely follows the Scheme implementation of the [[First-class functions]] problem.

(define xi 0.5)

(define y 4.0)

(newline))

n1 n2))

Output:

0.5

 

=={{header|Sidef}}==

func (n3) {

n1 * n2 * n3

for f,g (numbers ~Z inverses) {

say multiplier(f, g)(0.5)

{{out}}

<pre>

=={{header|Slate}}==

{{incorrect|Slate|Compare and contrast the resultant program with the corresponding entry in First-class functions.}}

define: #x -> 2.

define: #y -> 4.

define: #numlisti -> (numlist collect: [| :x | 1.0 / x]).

 

 

=={{header|Tcl}}==

{{works with|Tcl|8.5}}

proc multiplier {a b} {

list apply {{ab m} {expr {$ab*$m}}} [expr {$a*$b}]

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:

apply {{ab m} {expr {$ab*$m}}} 6

% {*}$mult23 5

Formally, for the task:

set xi 0.5

set y 4.0

foreach a $numlist b $numlisti {

puts [format "%g * %g * 0.5 = %g" $a $b [{*}[multiplier $a $b] 0.5]]

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|Ursala}}==

composition operator (+), and is named in compliance

with the task specification.

#import flo

 

#cast %eL

 

The multiplier could have been written in pattern

matching form like this.

The main program might also have been written with an

anonymous function like this.

output:

<pre>

<5.000000e-01,5.000000e-01,5.000000e-01>

</pre>

 

=={{header|zkl}}==

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,

//-->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)

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.

 

{{omit from|Java|Can't pass around expressions like that.}}

{{omit from|PureBasic}}

[[Category:Functions and subroutines]]