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Scala underscore minimal function
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Could someone show me the difference between the 2 implementations of function sequence in my code.
I'm using IntelIJ IDEA with sbt.
def traverse[A, B](a : List[A]) (f : A => Option[B]) : Option[List[B]] = {
a.foldRight(Some(List[B]()) : Option[List[B]])(
(x, y) => for {
xx <- f(x)
yy <- y
} yield xx +: yy
)
}
def sequence[A](a: List[Option[A]]): Option[List[A]] = {
traverse(a)(x => x) //worked
//traverse(a)(_) //Expression of type (Option[A] => Option[B_]) => Option[List[Nothing]] doesn't conform to expected type Option[List[A]]
}
I expected the final line to achieve the same, instead it show that I return an Option[List[Nothing]].
TL;DR, f(_) does not equal f(x => x)
As eloquently explained in this relevant SO answer, you're looking at the difference between the "short form" for anonymous function versus partially applied function.
When _ is part of an expression that represents a parameter:
f(_ + 1) // f(x => x + 1)
g(2 * _) // g(x => 2 * x)
When _ is a parameter by itself:
f(_) // x => f(x)
g(1, _) // x => g(1, x)
h(0)(_) // x => h(0)(x)
So I have a generic compose combinator.
Recall that the composition of two functions—f and g-- is h(x) = f(g(x))
def inc(x: Double) = x + 1
def double(x: Double) = 2 * x
def compose[A,B,C](f: B => C, g: A => B, x: A): C = f(g(x))
//TEST
println(compose(double, inc, 2.0))
//OUTPUT
// 6.0
But now I want to implement the self-composition iterator combinator, recursively, using my compose function where:
def selfIter[T](f: T=>T, n: Int) = f composed with itself n times.
I tried doing this:
def selfIter[T](f: T, n: Int): T = {
if(n == 0) f
else f + selfIter(f, n-1)
}
//TEST
println(selfIter(compose(double, inc, 2.0), 2))
I get an error, I know I'm doing something fundamentally wrong, but I cant figure out what I need to do.
In this case, the output should be 14.0 because first call will be 2(2+1) = 6.0, and then second call will be 2(6.0 + 1) = 14.0
Question: How should I refactor my code so that selfIter will compose f with itself n times until we have n == 0, and returns the final value
The easiest way to solve this kind of problems is to use the combinators provided by Scala. Also you should first compose the function you want to use and then apply the input
def compose[A,B,C](f: B => C, g: A => B): A => C = g.andThen(f)
def selfIter[T](f: T=>T, n: Int): T => T = Function.chain(List.fill(n)(f))
println(selfIter(compose(double, inc), 2)(2.0))
If compose signature could not be changed then
def compose[A,B,C](f: B => C, g: A => B, x: A): C = f(g(x))
def selfIter[T](f: T=>T, n: Int): T => T = Function.chain(List.fill(n)(f))
println(selfIter[Double](compose(double, inc, _), 2)(2.0))
But it makes much more sense the first solution
There are a few things going wrong here.
This f + selfIter(f, n-1) says that f (type T) must have a + method that takes another T as an argument. But you don't want to add these things, you want to compose them.
Here's a simpler way to get the result you're after.
Stream.iterate(2.0)(compose(double, inc, _))(2) // res0: Double = 14.0
If you're intent on a recursive method, this appears to achieve your goal.
def selfIter[T](start:T, n:Int)(f:T=>T): T = {
if (n < 2) f(start)
else f(selfIter(start, n-1)(f))
}
selfIter(2.0, 2)(compose(double, inc, _)) // res0: Double = 14.0
I have a Scala pre-examination question which I cannot get through. Probably someone could help.
Does the expression on the right conforms to the declared type, and why?
(a) val x1: B => D = (b: A) => new D
(b) val x2: A => C => D = (a: A) => (b: D) => new C
(c) val x3: (D => B) => A = (db: D => A) => new B
The class hierarchy:
class A
class B extends A
class C
class D extends C
You don't need us for this. Just paste the code into the REPL.
scala> class A
defined class A
scala> class B extends A
defined class B
scala> class C
defined class C
scala> class D extends C
defined class D
scala> val x1: B => D = (b: A) => new D
x1: B => D = <function1>
scala> val x2: A => C => D = (a: A) => (b: D) => new C
<console>:10: error: type mismatch;
found : C
required: D
val x2: A => C => D = (a: A) => (b: D) => new C
^
scala> val x3: (D => B) => A = (db: D => A) => new B
x3: (D => B) => A = <function1>
I guess that there is no correct expression amongst them.
Mb there is an answer, which has more information then needed.
when we write T => F -- it means a function type, sugar for a trait Function1[-P, +R] { def apply(p: P): R }
when we write (t: T) => new F it means a lambda function, which has a type T => F, or (alias, sugar) Function1[T, F], as you can notice t is an application argument.
we have relations on types, one of them is sub typing, so you need an explanation of function sub typing in this task.
Let's consider why code, provided here by #ChrisMartin, compiling.
So function, is a trait:
trait Function1[-P, +R] {
def apply(p: P): R
}
That means that function is contravariant (+) by argument and covariant (-) by result.
Let F: P → R, p ∈ P => F(p) ∈ R, and let F' : P' → R' : P' ⊃ P, R' ⊂ R'. Then, p ∈ P => p ∈ P' => F'(p) ∈ R'=> F'(p) ∈ R.
That means that F' function is a special case of F function on its domain. In other words some function from a super type of P to the sub type of R is a subtype of function from P to R.
In your examples:
B => D is a more general function then A => D (A is a super type of B) (check: implicitly[(A => D) <:< (B => D)])
A => C => D is a more specific function then A => D => C (check: implicitly[(A => C => D) <:< (A => D => C)])
(D => B) => A is a more general function then (D => A) => B (check: implicitly[((D => A) => B) <:< ((D => B) => A)])
I want to create a simple Map from math operators to the relevant functions:
var ops = Map("+" -> +, "-" -> -)
How would I do this in Scala?
If you want the functions to be curried, the following is probably the most concise way to do it.
scala> val ops: Map[String, Int => Int => Int] = Map(
| "+" -> (x => y => x + y),
| "-" -> (x => y => x - y)
| )
ops: Map[String,Int => Int => Int] = Map(+ -> <function1>, - -> <function1>)
This map however is only limited to Ints. If you want generic operations, you will have to use Numeric context bound.
scala> def ops[N : Numeric]: Map[String, N => N => N] = {
| import Numeric.Implicits._
| Map(
| "+" -> (x => y => x + y),
| "-" -> (x => y => x - y)
| )
| }
ops: [N](implicit evidence$1: Numeric[N])Map[String,N => N => N]
A major caveat with this approach is that a map gets created every time you call ops.
val ops = Map("+" -> ((_: Int) + (_: Int)), "-" -> ((_: Int) - (_:Int)))
or
val ops = Map[String, (Int, Int) => Int]("+" -> (_+_), "-" -> (_-_))
or even, for actual currying,
val ops = Map("+" -> ((_: Int) + (_: Int)).curried, "-" -> ((_: Int) - (_:Int)).curried)
These functions are all bind to Int. Well, Scala is not a point-free programming language, it's an object oriented one, and one in which there's no superclass common to all numeric types. Anyway, if you object to that, then you have an entirely different problem, which was asked and answered many times here on Stack Overflow (in fact, it was my first Scala question, iirc).
I'm guessing that there must be a better functional way of expressing the following:
def foo(i: Any) : Int
if (foo(a) < foo(b)) a else b
So in this example f == foo and p == _ < _. There's bound to be some masterful cleverness in scalaz for this! I can see that using BooleanW I can write:
p(f(a), f(b)).option(a).getOrElse(b)
But I was sure that I would be able to write some code which only referred to a and b once. If this exists it must be on some combination of Function1W and something else but scalaz is a bit of a mystery to me!
EDIT: I guess what I'm asking here is not "how do I write this?" but "What is the correct name and signature for such a function and does it have anything to do with FP stuff I do not yet understand like Kleisli, Comonad etc?"
Just in case it's not in Scalaz:
def x[T,R](f : T => R)(p : (R,R) => Boolean)(x : T*) =
x reduceLeft ((l, r) => if(p(f(l),f(r))) r else l)
scala> x(Math.pow(_ : Int,2))(_ < _)(-2, 0, 1)
res0: Int = -2
Alternative with some overhead but nicer syntax.
class MappedExpression[T,R](i : (T,T), m : (R,R)) {
def select(p : (R,R) => Boolean ) = if(p(m._1, m._2)) i._1 else i._2
}
class Expression[T](i : (T,T)){
def map[R](f: T => R) = new MappedExpression(i, (f(i._1), f(i._2)))
}
implicit def tupleTo[T](i : (T,T)) = new Expression(i)
scala> ("a", "bc") map (_.length) select (_ < _)
res0: java.lang.String = a
I don't think that Arrows or any other special type of computation can be useful here. Afterall, you're calculating with normal values and you can usually lift a pure computation that into the special type of computation (using arr for arrows or return for monads).
However, one very simple arrow is arr a b is simply a function a -> b. You could then use arrows to split your code into more primitive operations. However, there is probably no reason for doing that and it only makes your code more complicated.
You could for example lift the call to foo so that it is done separately from the comparison. Here is a simiple definition of arrows in F# - it declares *** and >>> arrow combinators and also arr for turning pure functions into arrows:
type Arr<'a, 'b> = Arr of ('a -> 'b)
let arr f = Arr f
let ( *** ) (Arr fa) (Arr fb) = Arr (fun (a, b) -> (fa a, fb b))
let ( >>> ) (Arr fa) (Arr fb) = Arr (fa >> fb)
Now you can write your code like this:
let calcFoo = arr <| fun a -> (a, foo a)
let compareVals = arr <| fun ((a, fa), (b, fb)) -> if fa < fb then a else b
(calcFoo *** calcFoo) >>> compareVals
The *** combinator takes two inputs and runs the first and second specified function on the first, respectively second argument. >>> then composes this arrow with the one that does comparison.
But as I said - there is probably no reason at all for writing this.
Here's the Arrow based solution, implemented with Scalaz. This requires trunk.
You don't get a huge win from using the arrow abstraction with plain old functions, but it is a good way to learn them before moving to Kleisli or Cokleisli arrows.
import scalaz._
import Scalaz._
def mod(n: Int)(x: Int) = x % n
def mod10 = mod(10) _
def first[A, B](pair: (A, B)): A = pair._1
def selectBy[A](p: (A, A))(f: (A, A) => Boolean): A = if (f.tupled(p)) p._1 else p._2
def selectByFirst[A, B](f: (A, A) => Boolean)(p: ((A, B), (A, B))): (A, B) =
selectBy(p)(f comap first) // comap adapts the input to f with function first.
val pair = (7, 16)
// Using the Function1 arrow to apply two functions to a single value, resulting in a Tuple2
((mod10 &&& identity) apply 16) assert_≟ (6, 16)
// Using the Function1 arrow to perform mod10 and identity respectively on the first and second element of a `Tuple2`.
val pairs = ((mod10 &&& identity) product) apply pair
pairs assert_≟ ((7, 7), (6, 16))
// Select the tuple with the smaller value in the first element.
selectByFirst[Int, Int](_ < _)(pairs)._2 assert_≟ 16
// Using the Function1 Arrow Category to compose the calculation of mod10 with the
// selection of desired element.
val calc = ((mod10 &&& identity) product) ⋙ selectByFirst[Int, Int](_ < _)
calc(pair)._2 assert_≟ 16
Well, I looked up Hoogle for a type signature like the one in Thomas Jung's answer, and there is on. This is what I searched for:
(a -> b) -> (b -> b -> Bool) -> a -> a -> a
Where (a -> b) is the equivalent of foo, (b -> b -> Bool) is the equivalent of <. Unfortunately, the signature for on returns something else:
(b -> b -> c) -> (a -> b) -> a -> a -> c
This is almost the same, if you replace c with Bool and a in the two places it appears, respectively.
So, right now, I suspect it doesn't exist. It occured to me that there's a more general type signature, so I tried it as well:
(a -> b) -> ([b] -> b) -> [a] -> a
This one yielded nothing.
EDIT:
Now I don't think I was that far at all. Consider, for instance, this:
Data.List.maximumBy (on compare length) ["abcd", "ab", "abc"]
The function maximumBy signature is (a -> a -> Ordering) -> [a] -> a, which, combined with on, is pretty close to what you originally specified, given that Ordering is has three values -- almost a boolean! :-)
So, say you wrote on in Scala:
def on[A, B, C](f: ((B, B) => C), g: A => B): (A, A) => C = (a: A, b: A) => f(g(a), g(b))
The you could write select like this:
def select[A](p: (A, A) => Boolean)(a: A, b: A) = if (p(a, b)) a else b
And use it like this:
select(on((_: Int) < (_: Int), (_: String).length))("a", "ab")
Which really works better with currying and dot-free notation. :-) But let's try it with implicits:
implicit def toFor[A, B](g: A => B) = new {
def For[C](f: (B, B) => C) = (a1: A, a2: A) => f(g(a1), g(a2))
}
implicit def toSelect[A](t: (A, A)) = new {
def select(p: (A, A) => Boolean) = t match {
case (a, b) => if (p(a, b)) a else b
}
}
Then you can write
("a", "ab") select (((_: String).length) For (_ < _))
Very close. I haven't figured any way to remove the type qualifier from there, though I suspect it is possible. I mean, without going the way of Thomas answer. But maybe that is the way. In fact, I think on (_.length) select (_ < _) reads better than map (_.length) select (_ < _).
This expression can be written very elegantly in Factor programming language - a language where function composition is the way of doing things, and most code is written in point-free manner. The stack semantics and row polymorphism facilitates this style of programming. This is what the solution to your problem will look like in Factor:
# We find the longer of two lists here. The expression returns { 4 5 6 7 8 }
{ 1 2 3 } { 4 5 6 7 8 } [ [ length ] bi# > ] 2keep ?
# We find the shroter of two lists here. The expression returns { 1 2 3 }.
{ 1 2 3 } { 4 5 6 7 8 } [ [ length ] bi# < ] 2keep ?
Of our interest here is the combinator 2keep. It is a "preserving dataflow-combinator", which means that it retains its inputs after the given function is performed on them.
Let's try to translate (sort of) this solution to Scala.
First of all, we define an arity-2 preserving combinator.
scala> def keep2[A, B, C](f: (A, B) => C)(a: A, b: B) = (f(a, b), a, b)
keep2: [A, B, C](f: (A, B) => C)(a: A, b: B)(C, A, B)
And an eagerIf combinator. if being a control structure cannot be used in function composition; hence this construct.
scala> def eagerIf[A](cond: Boolean, x: A, y: A) = if(cond) x else y
eagerIf: [A](cond: Boolean, x: A, y: A)A
Also, the on combinator. Since it clashes with a method with the same name from Scalaz, I'll name it upon instead.
scala> class RichFunction2[A, B, C](f: (A, B) => C) {
| def upon[D](g: D => A)(implicit eq: A =:= B) = (x: D, y: D) => f(g(x), g(y))
| }
defined class RichFunction2
scala> implicit def enrichFunction2[A, B, C](f: (A, B) => C) = new RichFunction2(f)
enrichFunction2: [A, B, C](f: (A, B) => C)RichFunction2[A,B,C]
And now put this machinery to use!
scala> def length: List[Int] => Int = _.length
length: List[Int] => Int
scala> def smaller: (Int, Int) => Boolean = _ < _
smaller: (Int, Int) => Boolean
scala> keep2(smaller upon length)(List(1, 2), List(3, 4, 5)) |> Function.tupled(eagerIf)
res139: List[Int] = List(1, 2)
scala> def greater: (Int, Int) => Boolean = _ > _
greater: (Int, Int) => Boolean
scala> keep2(greater upon length)(List(1, 2), List(3, 4, 5)) |> Function.tupled(eagerIf)
res140: List[Int] = List(3, 4, 5)
This approach does not look particularly elegant in Scala, but at least it shows you one more way of doing things.
There's a nice-ish way of doing this with on and Monad, but Scala is unfortunately very bad at point-free programming. Your question is basically: "can I reduce the number of points in this program?"
Imagine if on and if were differently curried and tupled:
def on2[A,B,C](f: A => B)(g: (B, B) => C): ((A, A)) => C = {
case (a, b) => f.on(g, a, b)
}
def if2[A](b: Boolean): ((A, A)) => A = {
case (p, q) => if (b) p else q
}
Then you could use the reader monad:
on2(f)(_ < _) >>= if2
The Haskell equivalent would be:
on' (<) f >>= if'
where on' f g = uncurry $ on f g
if' x (y,z) = if x then y else z
Or...
flip =<< flip =<< (if' .) . on (<) f
where if' x y z = if x then y else z