In Functional Programming in Scala, the author asks to express FoldRight via FoldLeft. And then the author offers the following implementation:
def foldRightViaFoldLeftAuthor[A, B](l: List[A], z: B)(f: (A, B) => B): B = {
foldLeft(l, (b: B) => b)((g, a) => b => g(f(a, b)))(z)
}
There have been a couple of questions like this asking to explain the author's solution. And probably a lot of people are still struggling to understand it.
While I was thinking about the task I came up with a different implementation that seems much more readable and easier to grasp at least for me
def foldRightViaFoldLeftMy[A, B](l: List[A], z: B)(f: (A, B) => B): B = {
foldLeft(l, z)(((g: (A, B) => B) => (b: B, a: A) => g(a, b)) (f))
}
So I basically prepare a function that converts f(a,b) to f(b,a) and now I'm able to call foldLeft that is tail-recursive.
So my questions are:
Is there any reason to implement it in the way the author did?
Are there any drawbacks in my implementation in comparison to the author's?
You've written an implementation that has the same signature as foldRight, but it doesn't have the right semantics when the combination operation isn't commutative. To take one example, a right fold with the empty list as zero and cons as the combination operation should be identity:
scala> val input = List(1, 2, 3)
input: List[Int] = List(1, 2, 3)
scala> val f: (Int, List[Int]) => List[Int] = _ :: _
f: (Int, List[Int]) => List[Int] = $$Lambda$1912/991363637#5e9bf744
scala> foldRightViaFoldLeftAuthor(input, List.empty[Int])(f)
res0: List[Int] = List(1, 2, 3)
But your implementation reverses the list:
scala> foldRightViaFoldLeftMy(input, List.empty[Int])(f)
res1: List[Int] = List(3, 2, 1)
This is because you're still folding from left to right, even though you've switched the order of the combination function's arguments. I find the diagrams on the Wikipedia page about fold useful for visualizing the difference. In your implementation the applications happen like this:
scala> f(3, f(2, f(1, Nil)))
res2: List[Int] = List(3, 2, 1)
While in the book's implementation you have something like this:
((b3: List[Int]) =>
((b2: List[Int]) =>
((b1: List[Int]) => identity(f(1, b1)))(f(2, b2)))(f(3, b3)
)
)(Nil)
Which boils down to:
scala> f(1, f(2, f(3, Nil)))
res3: List[Int] = List(1, 2, 3)
So the answer to both of your questions is "yes", there is an important difference between your implementation and the book's.
Related
On the Scala command line, there is no problem writing:
List((1,2),(1,'a'))
But I can't seem to write a function that would convert a Tuple2 to a List, because Tuple2 takes 2 type parameters, but List only one. Any attempt such as:
def tuple2ToList[T1, T2](pair: (T1, T2)): List[Any] = List(pair._1, pair._2)
seems to be bound to lose type information. Is there anything we can do to preserve some type information in the process?
This is a solution proposed by #BenReich in a comment
Simply use one type parameter instead of two, the compiler will
automatically select the least upper bound of the types of the two
tuple elements:
def tupleToList[T](p: (T, T)): List[T] = List(p._1, p._2)
Examples:
scala> tupleToList((Some(42), None))
res4: List[Option[Int]] = List(Some(42), None)
scala> tupleToList((0.9999, 1))
res5: List[AnyVal] = List(0.9999, 1)
scala> tupleToList((Set(1), List(1)))
res6: List[scala.collection.immutable.Iterable[Int] with Int => AnyVal] = List(Set(1), List(1))
This is the old suboptimal solution, I'll leave it here as context for #BenReich's comment.
Define the return type as the least upper bound of T1 and T2:
def tupleToListRescueTypeInfo[R, T1 <: R, T2 <: R](p: (T1, T2)): List[R] =
List(p._1, p._2)
Little test:
scala> tupleToListRescueTypeInfo((2, 3))
res0: List[Int] = List(2, 3)
scala> tupleToListRescueTypeInfo((Some[Int](3), None))
res1: List[Option[Int]] = List(Some(3), None)
scala> tupleToListRescueTypeInfo((List(1,2), Set(1,2)))
res2: List[scala.collection.immutable.Iterable[Int] with Int => AnyVal] =
List(List(1, 2), Set(1, 2))
It obviously cannot preserve all type information, but it at least attempts to rescue as much as possible.
I know, this question has been asked already. But I have not understood any of the answers. I think I need a more graphic explanation. I can't understand how to "bridge" FoldLeft with FoldRight.
I don't care if the anwer is not in Functional Programming in Scala.
Thabk you very much in advance.
Just check how those are implemented:
def foldLeft[B](z: B)(op: (B, A) => B): B = {
var result = z
this foreach (x => result = op(result, x))
result
}
def foldRight[B](z: B)(op: (A, B) => B): B =
reversed.foldLeft(z)((x, y) => op(y, x))
foldLeft traverses collection from left to right applying op to the result and current element, while foldRight traverses reversed collection (i.e. from right to left).
When op is symmetric and transitive foldLeft and foldRight are equivalent, for example:
List(1,2,3).foldLeft(0)(_ + _)
List(1,2,3).foldRight(0)(_ + _)
Result:
res0: Int = 6
res1: Int = 6
But otherwise foldLeft and foldRight may produce different results:
List(1,2,3).foldLeft(List[Int]()){case (list, el) => list :+ el }
List(1,2,3).foldRight(List[Int]()){case (el, list) => list :+ el }
Result:
res2: List[Int] = List(1, 2, 3)
res3: List[Int] = List(3, 2, 1)
I am confused on how to write the map function, which maps over two lists:
for example:
def map[A,B,C](f: (A, B) => C, lst1: List[A], lst2: List[B]): List[C]
The input would be 2 lists and the output could be a list that adds the integers alternatively
Test example:
assert(map(add, List(1, 2, 3), List(4, 5, 6)) == List(5, 7, 9))
You could use f.tupled to convert f from a function that accepts to arguments (A, B) => C, to a function that accepts one argument as a tuple ((A, B)) => C. Then, you can zip the lists together (make them one list of tuples) and feed them to f using the traditional map.
def map[A,B,C](f: (A, B) => C, lst1: List[A], lst2: List[B]): List[C] =
(lst1 zip lst2) map f.tupled
scala> def add(a: Int, b: Int): Int = a + b
add: (a: Int, b: Int)Int
scala> map(add, List(1, 2, 3), List(4, 5, 6))
res20: List[Int] = List(5, 7, 9)
Keep in mind that if the two lists are not the same size, then zip will truncate the longer list to match the size of the smaller one.
As stated in m-z's answer you can zip the lists, and then map on the list of tuples. If you want to avoid the use of tupled, you can do the destructure explicitly:
def map[A,B,C](f: (A, B) => C, lst1: List[A], lst2: List[B]): List[C] = {
val zipped = lst1 zip lst2
zipped.map { case (a,b) => f(a,b) }
}
Why does this construction cause a Type Mismatch error in Scala?
for (first <- Some(1); second <- List(1,2,3)) yield (first,second)
<console>:6: error: type mismatch;
found : List[(Int, Int)]
required: Option[?]
for (first <- Some(1); second <- List(1,2,3)) yield (first,second)
If I switch the Some with the List it compiles fine:
for (first <- List(1,2,3); second <- Some(1)) yield (first,second)
res41: List[(Int, Int)] = List((1,1), (2,1), (3,1))
This also works fine:
for (first <- Some(1); second <- Some(2)) yield (first,second)
For comprehensions are converted into calls to the map or flatMap method. For example this one:
for(x <- List(1) ; y <- List(1,2,3)) yield (x,y)
becomes that:
List(1).flatMap(x => List(1,2,3).map(y => (x,y)))
Therefore, the first loop value (in this case, List(1)) will receive the flatMap method call. Since flatMap on a List returns another List, the result of the for comprehension will of course be a List. (This was new to me: For comprehensions don't always result in streams, not even necessarily in Seqs.)
Now, take a look at how flatMap is declared in Option:
def flatMap [B] (f: (A) ⇒ Option[B]) : Option[B]
Keep this in mind. Let's see how the erroneous for comprehension (the one with Some(1)) gets converted to a sequence of map calls:
Some(1).flatMap(x => List(1,2,3).map(y => (x, y)))
Now, it's easy to see that the parameter of the flatMap call is something that returns a List, but not an Option, as required.
In order to fix the thing, you can do the following:
for(x <- Some(1).toSeq ; y <- List(1,2,3)) yield (x, y)
That compiles just fine. It is worth noting that Option is not a subtype of Seq, as is often assumed.
An easy tip to remember, for comprehensions will try to return the type of the collection of the first generator, Option[Int] in this case. So, if you start with Some(1) you should expect a result of Option[T].
If you want a result of List type, you should start with a List generator.
Why have this restriction and not assume you'll always want some sort of sequence? You can have a situation where it makes sense to return Option. Maybe you have an Option[Int] that you want to combine with something to get a Option[List[Int]], say with the following function: (i:Int) => if (i > 0) List.range(0, i) else None; you could then write this and get None when things don't "make sense":
val f = (i:Int) => if (i > 0) Some(List.range(0, i)) else None
for (i <- Some(5); j <- f(i)) yield j
// returns: Option[List[Int]] = Some(List(0, 1, 2, 3, 4))
for (i <- None; j <- f(i)) yield j
// returns: Option[List[Int]] = None
for (i <- Some(-3); j <- f(i)) yield j
// returns: Option[List[Int]] = None
How for comprehensions are expanded in the general case are in fact a fairly general mechanism to combine an object of type M[T] with a function (T) => M[U] to get an object of type M[U]. In your example, M can be Option or List. In general it has to be the same type M. So you can't combine Option with List. For examples of other things that can be M, look at subclasses of this trait.
Why did combining List[T] with (T) => Option[T] work though when you started with the List? In this case the library use a more general type where it makes sense. So you can combine List with Traversable and there is an implicit conversion from Option to Traversable.
The bottom line is this: think about what type you want the expression to return and start with that type as the first generator. Wrap it in that type if necessary.
It probably has something to do with Option not being an Iterable. The implicit Option.option2Iterable will handle the case where compiler is expecting second to be an Iterable. I expect that the compiler magic is different depending on the type of the loop variable.
I always found this helpful:
scala> val foo: Option[Seq[Int]] = Some(Seq(1, 2, 3, 4, 5))
foo: Option[Seq[Int]] = Some(List(1, 2, 3, 4, 5))
scala> foo.flatten
<console>:13: error: Cannot prove that Seq[Int] <:< Option[B].
foo.flatten
^
scala> val bar: Seq[Seq[Int]] = Seq(Seq(1, 2, 3, 4, 5))
bar: Seq[Seq[Int]] = List(List(1, 2, 3, 4, 5))
scala> bar.flatten
res1: Seq[Int] = List(1, 2, 3, 4, 5)
scala> foo.toSeq.flatten
res2: Seq[Int] = List(1, 2, 3, 4, 5)
Since Scala 2.13 Option was made IterableOnce
sealed abstract class Option[+A] extends IterableOnce[A] with Product with Serializable
so the following for comprehension works without the use of option2Iterable implicit conversion
scala> for {
| a <- List(1)
| b <- Some(41)
| } yield (a + b)
val res35: List[Int] = List(42)
scala> List(1).flatMap
final override def flatMap[B](f: Int => scala.collection.IterableOnce[B]): List[B]
where we see List#flatMap takes a function to IterableOnce. Desugaring above for comprehension we get something like
List(1).flatMap(a => Some(41).map(b => a + b))
which show the absence of the implicit conversion.
However in Scala 2.12 and before Option was not a traversable/iterable entity
sealed abstract class Option[+A] extends Product with Serializable
so the above for comprehension would desugar to something like
List(1).flatMap(a => option2Iterable(Some(41)).map(b => a + b))(List.canBuildFrom[Int])
where we see the implicit conversion.
The reason it does not work the other way around where for comprehension begins with Option and then we try to chain a List
scala> for {
| a <- Option(1)
| b <- List(41)
| } yield (a + b)
b <- List(41)
^
On line 3: error: type mismatch;
found : List[Int]
required: Option[?]
scala> Option(1).flatMap
final def flatMap[B](f: Int => Option[B]): Option[B]
is because Option#flatMap takes a function to Option and converting a List to Option probably does not make sense because we would lose elements for Lists with more than one element.
As szeiger explains
I think the recent Option changes actually make the for comprehensions
use case easier to understand because you do not need an implicit
conversion anymore. Option can be used on the RHS of a flatMap of any
collection type because it is IterableOnce (but not the opposite
because the RHS of Option#flatMap requires Option).
Scalaz provides a method named fold for various ADTs such as Boolean, Option[_], Validation[_, _], Either[_, _] etc. This method basically takes functions corresponding to all possible cases for that given ADT. In other words, a pattern match shown below:
x match {
case Case1(a, b, c) => f(a, b, c)
case Case2(a, b) => g(a, b)
.
.
case CaseN => z
}
is equivalent to:
x.fold(f, g, ..., z)
Some examples:
scala> (9 == 8).fold("foo", "bar")
res0: java.lang.String = bar
scala> 5.some.fold(2 *, 2)
res1: Int = 10
scala> 5.left[String].fold(2 +, "[" +)
res2: Any = 7
scala> 5.fail[String].fold(2 +, "[" +)
res6: Any = 7
At the same time, there is an operation with the same name for the Traversable[_] types, which traverses over the collection performing certain operation on its elements, and accumulating the result value. For example,
scala> List(2, 90, 11).foldLeft("Contents: ")(_ + _.toString + " ")
res9: java.lang.String = "Contents: 2 90 11 "
scala> List(2, 90, 11).fold(0)(_ + _)
res10: Int = 103
scala> List(2, 90, 11).fold(1)(_ * _)
res11: Int = 1980
Why are these two operations identified with the same name - fold/catamorphism? I fail to see any similarities/relation between the two. What am I missing?
I think the problem you are having is that you see these things based on their implementation, not their types. Consider this simple representation of types:
List[A] = Nil
| Cons head: A tail: List[A]
Option[A] = None
| Some el: A
Now, let's consider Option's fold:
fold[B] = (noneCase: => B, someCase: A => B) => B
So, on Option, it reduces every possible case to some value in B, and return that. Now, let's see the same thing for List:
fold[B] = (nilCase: => B, consCase: (A, List[A]) => B) => B
Note, however, that we have a recursive call there, on List[A]. We have to fold that somehow, but we know fold[B] on a List[A] will always return B, so we can rewrite it like this:
fold[B] = (nilCase: => B, consCase: (A, B) => B) => B
In other words, we replaced List[A] by B, because folding it will always return a B, given the type signature of fold. Now, let's see Scala's (use case) type signature for foldRight:
foldRight[B](z: B)(f: (A, B) ⇒ B): B
Say, does that remind you of something?
If you think of "folding" as "condensing all the values in a container through an operation, with a seed value", and you think of an Option as a container that can can have at most one value, then this starts to make sense.
In fact, foldLeft has the same signature and gives you exactly the same results if you use it on an empty list vs None, and on a list with only one element vs Some:
scala> val opt : Option[Int] = Some(10)
opt: Option[Int] = Some(10)
scala> val lst : List[Int] = List(10)
lst: List[Int] = List(10)
scala> opt.foldLeft(1)((a, b) => a + b)
res11: Int = 11
scala> lst.foldLeft(1)((a, b) => a + b)
res12: Int = 11
fold is also defined on both List and Option in the Scala standard library, with the same signature (I believe they both inherit it from a trait, in fact). And again, you get the same results on a singleton list as on Some:
scala> opt.fold(1)((a, b) => a * b)
res25: Int = 10
scala> lst.fold(1)((a, b) => a * b)
res26: Int = 10
I'm not 100% sure about the fold from Scalaz on Option/Either/etc, you raise a good point there. It seems to have quite a different signature and operation from the "folding" I'm used to.