I have this two functions
def pattern(s: String): Option[Pattern] =
try {
Some(Pattern.compile(s))
} catch {
case e: PatternSyntaxException => None
}
and
def mkMatcher(pat: String): Option[String => Boolean] =
pattern(pat) map (p => (s: String) => p.matcher(s).matches)
Map is the higher-order function that applies a given function to each element of a list.
Now I am not getting that how map is working here as per above statement.
Map is the higher-order function that applies a given function to each element of a list.
This is an uncommonly restrictive definition of map.
At any rate, it works because it was defined by someone who did not hold to that.
For example, that someone wrote something akin to
sealed trait Option[+A] {
def map[B](f: A => B): Option[B] = this match {
case Some(value) => Some(f(value))
case None => None
}
}
as part of the standard library. This makes map applicable to Option[A]
It was defined because it makes sense to map many kinds of data structures not just lists.
Mapping is a transformation applied to the elements held by the data structure.
It applies a function to each element.
Option[A] can be thought of as a trivial sequence. It either has zero or one elements. To map it means to apply the function on its element if it has one.
Now it may not make much sense to use this facility all of the time, but there are cases where it is useful.
For example, it is one of a few distinct methods that, when present enable enable For Expressions to operate on a type. Option[A] can be used in for expressions which can be convenient.
For example
val option: Option[Int] = Some(2)
val squared: Option[Int] = for {
n <- option
if n % 2 == 0
} yield n * n
Interestingly, this implies that filter is also defined on Option[A].
If you just have a simple value it may well be clearer to use a less general construct.
Map is working the same way that it does with other collections types like List and Vector. It applies your function to the contents of the collection, potentially changing the type but keeping the collection type the same.
In many cases you can treat an Option just like a collection with either 0 or 1 elements. You can do a lot of the same operations on Option that you can on other collections.
You can modify the value
var opt = Option(1)
opt.map(_ + 3)
opt.map(_ * math.Pi)
opt.filter(_ == 1)
opt.collect({case i if i > 0 => i.toString })
opt.foreach(println)
and you can test the value
opt.contains(3)
opt.forall(_ > 0)
opt.exists(_ > 0)
opt.isEmpty
Using these methods you rarely need to use a match statement to unpick an Option.
Related
I have a fold which iterates through elements, dependently modifies them one by one and at the same time modifies their parent.
Currently I replace the elements in their parent one by one in the fold, they are just few and it is not a real performance issue, but I wonder if there is perhaps a nicer way to express this.
case class Behavior(x: Int) {
def simulate(s: Entity): Behavior = copy(x = x + (if (s.alternate) 2 else 1))
}
case class Entity(alternate: Boolean, behaviors: List[Behavior]) {
def replaceBehavior(o: Behavior, n: Behavior): Entity = {
copy(behaviors = behaviors.patch(behaviors.indexOf(o), Seq(n), 1))
}
def toggleAlternate: Entity = copy(alternate = !alternate)
def simulate: Entity = {
behaviors.foldLeft(this) { (e, b) =>
e.replaceBehavior(b, b.simulate(e)).toggleAlternate
}
}
}
val entity = Entity(false, List(Behavior(10), Behavior(20), Behavior(30)))
entity.simulate
Is there some operation or perhaps some clever use of scan or something like that which would allow me to perform foldLeft and map dependent of the foldLeft result in one step? (I would prefer vanilla standard Scala library, but using functional frameworks is possible too).
Folds (fold, foldLeft, foldRight, ...) usually turn some Collection[A] into B.
You could map over A before folding result to B - foldMap maps A => B and assumed existence of Monoid[B] (this is available in Cats in Foldable typeclass), so you would perform transformation Collection[A] --using f--> Collection[B] --using Monoid[B]--> B (code can optimize it to perform things in one step using e.g. foldLeft internally).
Reversing the order of operations - we fold and then we map - is in general impossible because there is nothing that can let us assume that after fold step we will end up with something that is a Functor.
Depending on your specific use case we might try using foldMap to achieve your goal.
I had spent weeks on trying to understand the idea behind "lifting" in scala.
Originally, it was from the example related to Chapter 4 of book "Functional Programming in Scala"
Then I found below topic "How map work on Options in Scala?"
The selected answer specify that:
def map[B](f: A => B): Option[B] = this match (Let's considered this as (*) )
So, from above code, I assume that function "map" is derived from function match. Hence, the mechanism behind "map"
is a kind of pattern matching to provide a case selection between Some, and None
Then, I created below examples by using function map for Seq, Option, and Map (Let's considered below examples as (**) )
Example 1: map for Seq
val xs = Seq(1, 2, 3)
xs.map(println)
Example 2: map for Option
val a:Option[Int] = Some(5)
a.map(println)
val b:Option[Int] = None
b.map(println)
Example 3: map for Map
val capitals = Map("France" -> "Paris", "Japan" -> "Tokyo")
capitals.map(println)
From (*) and (**), I could not know whether "map" is a pattern matching or an iteration, or both.
Thank you for helping me to understand this.
#Jwvh provided a more programming based answer but I want to dig a little bit deeper.
I certainly appreciate you trying to understand how things work in Scala, however if you really want to dig that deep, I am afraid you will need to obtain some basic knowledge of Category Theory since there is no "idea behind lifting in scala" but just the "idea behind lifting"
This is also why functions like "map" can be very confusing. Inherently, programmers are taught map etc. as operations on collections, where as they are actually operations that come with Functors and Natural Transformations (this is normally referred to as fmap in Category Theory and also Haskell).
Before I move on, the short answer is it is a pattern matching in the examples you gave and in some of them it is both. Map is defined specifically to the case, the only condition is that it maintains functoriality
Attention: I will not be defining every single term below, since I would need to write a book to build up to some of the following definitions, interested readers are welcome to research them on their own. You should be able to get some basic understanding by following the types
Let's consider these as Functors, the definition will be something along the lines of this:
In (very very) short, we consider types as objects in the category of our language. The functions between these types (type constructors) are morphisms between types in this category. The set of these transformations are called Endo-Functors (take us from the category of Scala and drop us back in the category of Scala). Functors have to have a polymorphic (which actually has a whole different (extra) definition in category theory) map function, that will take some object A, through some type constructor turn it into object B.
implicit val option: Functor[Option] = new Functor[Option] {
override def map[A,B](optA: Option[A])(f: (A) => B): Option[B] = optA match{
case Some(a) => Some(f(a))
case _ => None
}
}
implicit val seq: Functor[Seq[_]] = new Functor[Seq[_]] {
override def map[A,B](sA: Seq[A])(f: (A) => B): Seq[B] = sA match{
case a :: tail => Seq(f(a), map(tail)(f))
case Nil => Nil
}
}
As you can see in the second case, there is a little bit of both (more of a recursion than iteration but still).
Now before the internet blows up on me, I will say you cant pattern match on Seq in Scala. It works here because the default Seq is also a List. I just provided this example because it is simpler to understand. The underlying definition something along the lines of that.
Now hold on a second. If you look at these types, you see that they also have flatMap defined on them. This means they are something more special than plain Functors. They are Monads. So beyond satisfying functoriality, they obey the monadic laws.
Turns out Monad has a different kind of meaning in the core scala, more on that here: What exactly makes Option a monad in Scala?
But again very very short, this means that we are now in a category where the endofunctors from our previous category are the objects and the mappings between them are morphisms (natural transformations), this is slightly more accurate because if you think about it when you take a type and transform it, you take (carry over) all of it's internal type constructors (2-cell or internal morphisms) with it, you do not only take this sole idea of a type without it's functions.
implicit val optionMonad: Monad[Option] = new Monad[Option] {
override def flatMap[A, B](optA: Option[A])(f: (A) => Option[B]): Option[B] = optA match{
case Some(a) => f(a)
case _ => None
}
def pure[A](a: A): Option[A] = Some(a)
//You can define map using pure and flatmap
}
implicit val seqMonad: Monad[Seq[_]] = new Monad[Seq[_]] {
override def flatMap[A, B](sA: Seq[A])(f: (A) => Seq[B]): Seq[B] = sA match{
case x :: xs => f(a).append(flatMap(tail)(f))
case Nil => Nil
}
override def pure[A](a: A): Seq[A] = Seq(a)
//Same warning as above, also you can implement map with the above 2 funcs
}
One thing you can always count on is map being having pattern match (or some if statement). Why?
In order to satisfy the identity laws, we need to have some sort of a "base case", a unit object and in many cases (such as Lists) those types are gonna be what we call either a product or coproduct.
Hopefully, this did not confuse you further. I wish I could get into every detail of this but it would simply take pages, I highly recommend getting into categories to fully understand where these come from.
From the ScalaDocs page we can see that the type profile for the Standard Library map() method is a little different.
def map[B](f: (A) => B): Seq[B]
So the Standard Library map() is the means to transition from a collection of elements of type A to the same collection but the elements are type B. (A and B might be the same type. They aren't required to be different.)
So, yes, it iterates through the collection applying function f() to each element A to create each new element B. And function f() might use pattern matching in its code, but it doesn't have to.
Now consider a.map(println). Every element of a is sent to println which returns Unit. So if a is List[Int] then the result of a.map(println) is List[Unit], which isn't terribly useful.
When all we want is the side effect of sending information to StdOut then we use foreach() which doesn't create a new collection: a.foreach(println)
Function map for Option isn't about pattern matching. The match/case used in your referred link is just one of the many ways to define the function. It could've been defined using if/else. In fact, that's how it's defined in Scala 2.13 source of class Option:
sealed abstract class Option[+A] extends IterableOnce[A] with Product with Serializable {
self =>
...
final def map[B](f: A => B): Option[B] =
if (isEmpty) None else Some(f(this.get))
...
}
If you view Option like a "collection" of either one element (Some(x)) or no elements (None), it might be easier to see the resemblance of how map transforms an Option versus, say, a List:
val f: Int => Int = _ + 1
List(42).map(f)
// res1: List[Int] = List(43)
List.empty[Int].map(f)
// res2: List[Int] = List()
Some(42).map(f)
// res3: Option[Int] = Some(43)
None.map(f)
// res4: Option[Int] = None
I have a Set which incorporates a combination of strings, and subSets of strings, like so:
val s = Set(brand1-_test, Set(brand-one, brand_one, brandone), brands-two, brandthree1, Set(brand-three2, brand_three2, brandthree2))
How do I flatten this so that I have one flat set of strings? s.flatten doesn't work with the following error:
error: No implicit view available from Object => scala.collection.GenTraversableOnce[B]
Neither does flatMap. What am I missing here? The Set could just as easily incorporate a subLists or subArrays (they are the result of a previous function), if that makes a difference.
s.flatMap { case x:Iterable[_] => x; case y => Seq(y) }
Try putting it in a REPL:
scala> val s = Set("s1", Set("s2", "s3"))
s: scala.collection.immutable.Set[Object] = Set(s1, Set(s2, s3))
since you are providing two types (Set and String) then scala infers a type which covers both (Object in this case, but probably Any or AnyRef in most cases) which is not a collection and therefore cannot be flattened.
I have a list of business rules and multiple rules can apply to a given input.
type Input = …
type Output = …
type Rule = PartialFunction[Input, Output]
I want to write the method that compute all valid output. I've come up with this implementation :
def applyRules(i: Input, rules: Seq[Rule]) : Seq[Output] = {
rules.flatMap(_.lift.apply(i))
}
Is there a better way ?
One proposed variant of your solution (which I'd say is satisfactory) involves filtering and then mapping over the filtered results. This works but involves two passes over the same collection, which for smaller collection can be good. We can, however, reach the same result with three possible further variants:
using the collect method: rules.collect { case r if r.isDefinedAt(i) => r(i) }
using the lazy withFilter instead of filter: rules.withFilter(_.isDefinedAt(i)).map(_.apply(i)) or
using a for comprehension (semantically identical to the one above but perhaps more readable): for (r <- rule if r isDefinedAt i) r(i)
These solutions may produce slightly less garbage (each call to lift creates a new instance of a function object - here the code), however if the number of Rules is small I'm sure in most cases that's a non-issue.
You can use isDefinedAt to check whether given Input can be applied to Rule:
scala> val pf: PartialFunction[Any, Int] = { case s: String => 42 }
pf: PartialFunction[Any,Int] = <function1>
scala> pf.isDefinedAt(10)
res0: Boolean = false
scala> pf.isDefinedAt("")
res1: Boolean = true
So you can do something like:
val validInputs = rules.filter(_.isDefinedAt(i))
val result = validInputs.map(i)
Also PartialFunction contains methods applyOrElse, orElse, ..., that might increase readability.
Please correct me if I misunderstood your problem.
Is there a standard way of combining existing Scala collection functions to achieve the following? Or is this already defined in some popular extension library like Scalaz?
def partialReduceLeft[T](elements: List[T], nextReduction: List[T] => (T, List[T])): List[T] =
if (elements == Nil)
Nil
else {
val (reduction, residual) = nextReduction(elements)
if (residual.length >= elements.length)
throw new Exception("Residual collection from nextReduction function must be smaller than its input collection.")
if (residual == Nil)
List(reduction)
else
reduction :: partialReduceLeft(residual, nextReduction)
}
The function takes a collection and applies a user-defined function which returns the first reduction, which may consume one or more elements. The method keeps going until all elements are consumed.
The resulting collection may have an equal or lower size to the input collection (I rather unscientifically call this a 'partial reduce left' - for want of knowing the exact term for this type of standard function :)).
My implementation is not tail-recursive, and to be honest, I'd much rather use someone else's code!!
There is similar method in scalaz: unfold.
You could implement your method using unfold this way:
def partialReduceLeft[T](elements: List[T],
nextReduction: List[T] => (T, List[T])): Stream[T] =
unfold(elements){ es => es.nonEmpty option nextReduction(es) }