Essence of the iterator pattern

Essence
ofthe
iteratorpatternMarkus Klink, @joheinz, markus.klink@inoio.de

Goal
“Imperative iterations using the pattern have two
simultaneous aspects: mapping and accumulating.”
Jeremy Gibbons & Bruno C. d. S. Oliveira
Markus Klink, @joheinz, markus.klink@inoio.de

Functionalmappingand
accumulating
trait Traverse[F[_]] extends Functor[F] with Foldable[F] {
def traverse[G[_]: Applicative, A, B](fa: F[A])(f: A => G[B]): G[F[B]]
}
trait Applicative[F[_]] extends Functor[F] {
def ap[A,B](fa: F[A])(f: F[A => B]) : F[B]
def pure(a: A) : F[A]
def map[A,B](fa: F[A])(f: A => B) : F[B] = ap(fa)(pure(f))
}
Traverse takes a structure F[A], injects each element
via the function f: A => G[B] into an Applicative
G[_] and combines the results into G[F[B] using the
applicative instance of G.

Acloser look
trait Foldable[F[_]] {
def foldRight[A, B](fa: F[A], z: => B)(f: (A, B) => B): B
}
trait Traverse[F[_]] extends Functor[F] with Foldable[F] {
}
Look at the similarities!

Traversing is "almost" like
Folding:
» Look! No zero element:
» Look! B is wrapped in an Applicative and our F:

Accumulating

val l = List(1,2,3)
val result: Future[List[String]] = l.traverse(a => Future.successful { a.toString })
How?
// traverse implementation of List, G[_] is the future
override def traverse[G[_]: Applicative, A, B](fa: List[A])(f: (A) => G[B]): G[List[B]] = {
val emptyListInG: G[List[B]] = Applicative[G].pure(List.empty[B]) // if the list is empty we need a Future { List.empty() }
// f(a) gives us another G[B], which we can inject into B => B => Future[List[B]]
foldRight(fa, emptyListInG) { (a: A, fbs: G[List[B]]) => Applicative[G].apply2(f(a), fbs)(_ +: _) }
}
// applicative instance of Future, example...
override def ap[A,B](F: => Future[A])(f: => Future[A => B]) : Future[B] = {
for {
a <- F
g <- f
} yield { g(a) }
}

Gibbons & Oliveiraclaimthatwe
can do:
val x : List[Char]= "1234".toList
val result : Int = x.traverse(c => ???)
assert(4 == result)
The claim is that we can accumulate values, or write
the length function just with Traverse/Applicative

How?
» we need a result type G[List[Int]] which equals
Int
» G needs to be an applicative
» we need to calculate a sum of all the values.
» we need a zero value in case the list is empty,
because of ...
val emptyListInG: G[List[B]] =
Applicative[G].pure(List.empty[B])

Each Monoid gives risetoan
applicative
trait Monoid[F] extends Semigroup[F] {
self =>
/**
* the identity element.
*/
def zero: F
def applicative: Applicative[Lambda[a => F]] = new Applicative[Lambda[a => F]] {
// mapping just returns ourselves
override def map[A, B](fa: F)(f: A => B) : F = fa
// putting any value into this Applicative will put the Monoid.zero in it
def pure[A](a: => A): F = self.zero
// Applying this Applicative combines each value with the append function.
def ap[A, B](fa: => F)(f: => F): F = self.append(f, fa)
}

How2
Part of the trick is this type: Applicative[Lambda[a
=> F]]!
It means that we throw everything away and create a
type G[_] which behaves like F. So...
val x : List[Char]= "1234".toList
val charCounter : Applicative[Lambda[a => Int]] = Monoid[Int].applicative
// we just "reversed" the parameters of traverse
// the summing is done automatically via append
charCounter.traverse(x)(_ => 1)

Counting lines
In the previous example we assigned each char in the
list to a 1 and let the Monoid do the work.
val x : List[Char] = "1233n1234n"
val lineCounter : Applicative[Lambda[a => Int]] = Monoid[Int].applicative
lineCounter.traverse(x){c => if (c == 'n') 1 else 0 }

Products ofApplicatives
Doing bothatthe sametimewithinasingle
traversal
val x : List[Char]= "1234n1234n"
val counter : Applicative[Lambda[a => Int]] = Monoid[Int].applicative
val charLineApp : Applicative[Lambda[a => (Int, Int)]] =
counter.product[Lambda[a => Int](counter)
val (chars, lines) = counter.traverse(x){c => (1 -> if (c == 'n') 1 else 0 }

Countingwords
Counting words cannot work on the current position
alone. We need to track changes from spaces to non
spaces to recognize the beginning of a new word.
def atWordStart(c: Char): State[Boolean, Int] = State { (prev: Boolean) =>
val cur = c != ' '
(cur, if (!prev && cur) 1 else 0)
}
val WordCount : Applicative[Lambda[a => Int]] =
State.stateMonad[Boolean].compose[Lambda[a => Int](counter)
val StateWordCount = WordCount.traverse(text)(c => atWordStart(c))
StateWordCount.eval(false)

Usingthe productofall3
countersto implementwc
val AppCharLinesWord: Applicative[Lambda[a => ((Int, Int), State[Boolean, Int])]] =
Count // char count
.product[Lambda[a => Int]](Count) // line count
.product[Lambda[a => State[Boolean, Int]]](WordCount) // word count
val ((charCount, lineCount), wordCountState) = AppCharLinesWord.traverse(text)((c: Char) =>
((1, if (c == 'n') 1 else 0), atWordStart(c)))
val wordCount: Int = wordCountState.eval(false)

Collecting some
state
and modifying
elements

// start value
public Integer count = 0;
public Collection<Person> collection = ...;
for (e <- collection) {
// or we use an if
count = count + 1;
// side effecting function
// or we map into another collection
e.modify();
}
Obviously in a functional programming language, we
would not want to modify the collection but get back
a new collection.
We also would like to get the (stateful) counter
back.

def collect[G[_]: Applicative, A, B](fa: F[A])(f: A => G[Unit])(g: A => B): G[F[B]] =
{
val G = implicitly[Applicative[G]]
val applicationFn : A => G[B] = a => G.ap(f(a))(G.pure((u: Unit) => g(a)))
self.traverse(fa)(applicationFn)
}
def collectS[S, A, B](fa: F[A])(f: A => State[S, Unit])(g: A => B): State[S, F[B]] =
{
collect[Lambda[a => State[S, a]], A, B](fa)(f)(g)
}
val list : List[Person] = ...
val stateMonad = State.stateMonad[Int]
val (counter, updatedList) = list.collectS{
a => for { count <- get; _ <- put(count + 1) } yield ()}(p => p.modify()).run(0)

Modifying elements
depending on some
state

// start value
public Collection<Person> collection = ...;
for (e <- collection) {
e.modify(stateful());
}
Now the modiﬁcation depends on some state we are
collecting.

def disperse[G[_]: Applicative, A, B, C](fa: F[A])(fb: G[B], g: A => B => C): G[F[C]] =
{
val G = implicitly[Applicative[G]]
val applicationFn: A => G[C] = a => G.ap(fb)(G.pure(g(a)))
self.traverse(fa)(applicationFn)
}
def disperseS[S, A, C](fa: F[A])(fb: State[S, S], g: A => S => C) : State[S,F[C]] =
{
disperse[Lambda[a => State[S, a]], A, S, C](fa)(fb, g)
}

THANKS

Some resources
http://etorreborre.blogspot.de/2011/06/essence-of-
iterator-pattern.html

Essence of the iterator pattern

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