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IM trying to understand the below high order functions in Scala but need some clarifications on the parameters of the functions.
Questions:-
What does the Int => String in the apply function mean?
v: Int indicates that the parameter v is of type Int.
What does the [A](x: A) mean in layout function?
object Demo {
def main(args: Array[String]) {
println( apply( layout, 10) )
}
def apply(f: Int => String, v: Int) = f(v)
def layout[A](x: A) = "[" + x.toString() + "]"
}
f: Int => String means that f is a function with one argument of type Int and with a return type String
def layout[A](x: A) means that parameter x is of type A, which can be of any type. Here are a couple of examples on how to invoke layout:
layout[String]("abc") //returns "[abc]"
layout[Int](123) //returns "[123]"
When main runs it invokes apply with the layout function and the argument 10. This will output "[10]"
The syntax of Int => String means passing a function that accepts Int and returns String.
Here is a useful example for passing function:
case class Person(name: String, lastName: String)
val person = Person("Johnny", "Cage")
def updateName(name: String) = {
updatePerson(_.copy(name = name))
}
def updateLastName(lastName: String) {
updatePerson(_.copy(lastName = lastName))
}
private def updatePerson(transformer: Person => Person): Unit = {
transformer(person)
}
Note how each update function passes the copy constructor.
Suppose x and y are of the same type and can be either Boolean, Int, or Double. Here is the function I want to write:
f(x, y) =
- if x == Boolean ==> !x
- if x == Integer or x == Double ==> x+ y
One way of doing this can be the following. I was wondering if anyone has a better ideas on this.
def fun[T](x: T, y: T): T {
x match {
case xP: Boolean => !xP
case xP: Double => y match { case yP: Double => xP + yP }
case xP: Int => y match { case yP: Int => xP + yP }
}
}
The reason I am not happy with this is that x and y have the same type. I shouldn't need two match-cases; right?
Two other things:
Is it enough to just set [T <: Int, Double, Boolean] in order to restrict the type to only three types?
The output type needs to be again T.
This is precisely the kind of problem that type classes are designed to solve. In your case you could write something like this:
trait Add[A] {
def apply(a: A, b: A): A
}
object Add {
implicit val booleanAdd: Add[Boolean] = new Add[Boolean] {
def apply(a: Boolean, b: Boolean): Boolean = !a
}
implicit def numericAdd[A: Numeric]: Add[A] = new Add[A] {
def apply(a: A, b: A): A = implicitly[Numeric[A]].plus(a, b)
}
}
A value of type Add[X] describes how to add two values of type X. You put implicit "instances" of type Add[X] in scope for every type X that you want to be able to perform this operation on. In this case I've provided instances for Boolean and any type that has an instance of scala.math.Numeric (a type class that's provided by the standard library). If you only wanted instances for Int and Double, you could simply leave out numericAdd and write your own Add[Int] and Add[Double] instances.
You'd write your fun like this:
def fun[T: Add](x: T, y: T) = implicitly[Add[T]].apply(x, y)
And use it like this:
scala> fun(true, false)
res0: Boolean = false
scala> fun(1, 2)
res1: Int = 3
scala> fun(0.01, 1.01)
res2: Double = 1.02
This has the very significant advantage of not blowing up at runtime on types that you haven't defined the operation for. Instead of crashing your program with a MatchError exception when you pass e.g. two strings to fun, you get a nice compilation failure:
scala> fun("a", "b")
<console>:14: error: could not find implicit value for evidence parameter of type Add[String]
fun("a", "b")
^
In general "type case" matching (i.e. matches that look like case x: X => ...) are a bad idea in Scala, and there's almost always a better solution. Often it'll involve type classes.
If you want a generic function for summing numbers, you can make a trait Summable[A] with implicit conversions from the numbers you want to Summable. These conversions can be implicit methods or they can be methods in implicit objects, latter being shown below.
trait Summable[A] {
def +(a: A, b: A): A
}
object Summable {
implicit object SummableBoolean extends Summable[Boolean] {
override def +(a: Boolean, b: Boolean) = !a
}
implicit object SummableInt extends Summable[Int] {
override def +(a: Int, b: Int) = a + b
}
implicit object SummableDouble extends Summable[Double] {
override def +(a: Double, b: Double) = a + b
}
}
def fun[A](a: A, b: A)(implicit ev: Summable[A]) =
ev.+(a, b)
val res1 = fun(true, true) // returns false
val res2 = fun(1, 3) // returns 4
val res3 = fun(1.5, 4.3) // returns "5.8"
This is called a type class pattern. I included the boolean case because you asked for it, but I strongly believe that it has no place in a function which sums elements. One nice rule to follow is to have each function do one thing and one thing only. Then you can easily compose them into bigger functions. Inverting boolean has no place in a function that sums its arguments.
First of all, your example is syntactically wrong (missing case in match). A simple and shorter way I can figure now is something like this:
def fun[T <: AnyVal](x: T, y: T) = {
x match {
case xP: Boolean => !xP
case xP: Double => xP + y.asInstanceOf[Double]
case xP: Int => xP + y.asInstanceOf[Int]
}
}
fun(1, 2) // res0: AnyVal = 3
fun(2.5, 2.6) // res1: AnyVal = 5.1
fun(true, false) // res2: AnyVal = false
Recently I have come across a very useful groupBy function that Groovy has made available on Iterable:
public static Map groupBy(Iterable self, List<Closure> closures)
Which you can use to perform recursive groupBy on Lists and even Maps see example by mrhaki here
I would like to write a function that does the same in Scala. But having just started my Scala journey, I am kind of lost on how I should going about defining and implementing this method. Especially the generics side of the functions and return type on this method's signature are way beyond my level.
I would need more experienced Scala developers to help me out here.
Is this following signature totally wrong or am I in the ball park?
def groupBy[A, K[_]](src: List[A], fs: Seq[(A) ⇒ K[_]]): Map[K[_], List[A]]
Also, how would I implement the recursion with the correct types?
This is simple multigroup implementation:
implicit class GroupOps[A](coll: Seq[A]) {
def groupByKeys[B](fs: (A => B)*): Map[Seq[B], Seq[A]] =
coll.groupBy(elem => fs map (_(elem)))
}
val a = 1 to 20
a.groupByKeys(_ % 3, _ % 2) foreach println
If you really need some recursive type you'll need a wrapper:
sealed trait RecMap[K, V]
case class MapUnit[K, V](elem: V) extends RecMap[K, V] {
override def toString = elem.toString()
}
case class MapLayer[K, V](map: Map[K, RecMap[K, V]]) extends RecMap[K, V] {
override def toString = map.toString()
}
out definition changes to:
implicit class GroupOps[A](coll: Seq[A]) {
def groupByKeys[B](fs: (A => B)*): Map[Seq[B], Seq[A]] =
coll.groupBy(elem => fs map (_(elem)))
def groupRecursive[B](fs: (A => B)*): RecMap[B, Seq[A]] = fs match {
case Seq() => MapUnit(coll)
case f +: fs => MapLayer(coll groupBy f mapValues {_.groupRecursive(fs: _*)})
}
}
and a.groupRecursive(_ % 3, _ % 2) yield something more relevant to question
And finally i rebuild domain definition from referred article:
case class User(name: String, city: String, birthDate: Date) {
override def toString = name
}
implicit val date = new SimpleDateFormat("yyyy-MM-dd").parse(_: String)
val month = new SimpleDateFormat("MMM").format (_:Date)
val users = List(
User(name = "mrhaki", city = "Tilburg" , birthDate = "1973-9-7"),
User(name = "bob" , city = "New York" , birthDate = "1963-3-30"),
User(name = "britt" , city = "Amsterdam", birthDate = "1980-5-12"),
User(name = "kim" , city = "Amsterdam", birthDate = "1983-3-30"),
User(name = "liam" , city = "Tilburg" , birthDate = "2009-3-6")
)
now we can write
users.groupRecursive(_.city, u => month(u.birthDate))
and get
Map(Tilburg -> Map(Mar -> List(liam), Sep -> List(mrhaki)), New York
-> Map(Mar -> List(bob)), Amsterdam -> Map(Mar -> List(kim), May -> List(britt)))
I decided add another answer, due to fully different approach.
You could, actually get non-wrapped properly typed maps with huge workarounds. I not very good at this, so it by the chance could be simplified.
Trick - is to create Sequence of typed functions, which is lately producing multi-level map using type classes and type path approach.
So here is the solution
sealed trait KeySeq[-V] {
type values
}
case class KeyNil[V]() extends KeySeq[V] {
type values = Seq[V]
}
case class KeyCons[K, V, Next <: KeySeq[V]](f: V => K, next: Next)
(implicit ev: RecGroup[V, Next]) extends KeySeq[V] {
type values = Map[K, Next#values]
def #:[K1](f: V => K1) = new KeyCons[K1, V, KeyCons[K, V, Next]](f, this)
}
trait RecGroup[V, KS <: KeySeq[V]] {
def group(seq: Seq[V], ks: KS): KS#values
}
implicit def groupNil[V]: RecGroup[V, KeyNil[V]] = new RecGroup[V, KeyNil[V]] {
def group(seq: Seq[V], ks: KeyNil[V]) = seq
}
implicit def groupCons[K, V, Next <: KeySeq[V]](implicit ev: RecGroup[V, Next]): RecGroup[V, KeyCons[K, V, Next]] =
new RecGroup[V, KeyCons[K, V, Next]] {
def group(seq: Seq[V], ks: KeyCons[K, V, Next]) = seq.groupBy(ks.f) mapValues (_ groupRecursive ks.next)
}
implicit def funcAsKey[K, V](f: V => K): KeyCons[K, V, KeyNil[V]] =
new KeyCons[K, V, KeyNil[V]](f, KeyNil[V]())
implicit class GroupOps[V](coll: Seq[V]) {
def groupRecursive[KS <: KeySeq[V]](ks: KS)(implicit g: RecGroup[V, KS]) =
g.group(coll, ks)
}
key functions are composed via #: right-associative operator
so if we define
def mod(m:Int) = (x:Int) => x % m
def even(x:Int) = x % 2 == 0
then
1 to 30 groupRecursive (even _ #: mod(3) #: mod(5) )
would yield proper Map[Boolean,Map[Int,Map[Int,Int]]] !!!
and if from previous question we would like to
users.groupRecursive(((u:User)=> u.city(0)) #: ((u:User) => month(u.birthDate)))
We are building Map[Char,Map[String,User]] !
Let's consider a specific example. I have lots of functions that take a variable number of arguments, and return a Seq[T]. Say:
def nonNeg(start: Int, count: Int): Seq[Int] =
Iterator.from(start).take(count).toSeq
For each one of those function, I need to create a "Java version" of that function, returns a java.util.List[T]. I can create the "Java version" of the above function with:
def javaNonNeg(start: Int, count: Int): java.util.List[Int] =
nonNeg(start, count).asJava
This is somewhat verbose, as the list of parameters is duplicated twice. Instead, I'd like to create a higher level function that takes as a parameter a function of the form of nonNeg (any number and type of arguments, returns a Seq[T]) and returns a function which takes the same arguments, but returns a java.util.List[T]. Assuming that function was called makeJava, I'd then be able to write:
def javaNonNeg = makeJava(nonNeg)
Can makeJava be written using Shapeless ability to abstracting over arity? If it can, how, and it not, why and how else can this be done?
It is possible to use Shapeless to avoid the boilerplate—you just need to turn the original method into a FunctionN using plain old eta expansion, then convert to a function taking a single HList argument, and then back to a FunctionN with the new result type:
import java.util.{ List => JList }
import shapeless._, ops.function._
import scala.collection.JavaConverters._
def makeJava[F, A, L, S, R](f: F)(implicit
ftp: FnToProduct.Aux[F, L => S],
ev: S <:< Seq[R],
ffp: FnFromProduct[L => JList[R]]
) = ffp(l => ev(ftp(f)(l)).asJava)
And then:
scala> def nonNeg(start: Int, count: Int): Seq[Int] =
| Iterator.from(start).take(count).toSeq
nonNeg: (start: Int, count: Int)Seq[Int]
scala> val javaNonNeg = makeJava(nonNeg _)
javaNonNeg: (Int, Int) => java.util.List[Int] = <function2>
scala> javaNonNeg(1, 4)
res0: java.util.List[Int] = [1, 2, 3, 4]
javaNonNeg is a Function2, so from Java you can use javaNonNeg.apply(1, 4).
For 2 and more (in code below to 4) parameters you can use implicit parameters feature, for resolve result type by input parameter type
sealed trait FuncRes[F] {
type Param
type Result
def func : F => Param => Result
}
class Func[T, R](fn : T => R) {
trait FR[F, P] extends FuncRes[F] { type Param = P; type Result = R }
implicit def func2[T1,T2] = new FR[(T1,T2) => T, (T1,T2)] {
def func = f => p => fn(f.tupled(p))
}
implicit def func3[T1,T2,T3] = new FR[(T1,T2,T3) => T, (T1,T2,T3)] {
def func = f => p => fn(f.tupled(p))
}
implicit def func4[T1,T2,T3,T4] = new FR[(T1,T2,T3,T4) => T, (T1,T2,T3,T4)] {
def func = f => p => fn(f.tupled(p))
}
def makeFunc[F](f : F)(implicit ev : FuncRes[F]): ev.Param => ev.Result =
ev.func(f)
}
and after your def javaNonNeg = makeJava(nonNeg) function will look like:
object asJavaFunc extends Func((_ : Seq[Int]).asJava)
import asJavaFunc._
def javaNonNeq = makeFunc(nonNeg _)
And of course it has some disadvantages, but generally it satisfy your needs.
What are all the instances of syntactic sugar in Scala?
They are hard to search for since most/all of them are purely symbols and are thus hard to search for without knowing the name of the concept.
TODO:
Implicit conversions
_ syntax for anonymous functions
Other things I'm forgetting
Basics:
a b is equivalent to a.b.
a b c is equivalent to a.b(c), except when b ends in :. In that case, a b c is equivalent to c.b(a).
a(b) is equivalent to a.apply(b) This is why the following definitions for an anonymous functions are identical:
val square1 = (x: Int) => x*x
val square2 = new Function1[Int,Int] {
def apply(x: Int) = x*x
}
When calling square1(y), you are actually calling square1.apply(y) which square1 must have as specified by the Function1 trait (or Function2, etc...)
a(b) = c is equivalent to a.update(b,c). Likewise, a(b,c) = d is equivalent to a.update(b,c,d) and so on.
a.b = c is equivalent to a.b_=(c). When you create a val/var x in a Class/Object, Scala creates the methods x and x_= for you. You can define these yourself, but if you define y_= you must define y or it will not compile, for example:
scala> val b = new Object{ def set_=(a: Int) = println(a) }
b: java.lang.Object{def set_=(Int): Unit} = $anon$1#17e4cec
scala> b.set = 5
<console>:6: error: value set is not a member of java.lang.Object{def set_=(Int): Unit}
b.set = 5
^
scala> val c = new Object{ def set = 0 ; def set_=(a:Int) = println(a) }
c: java.lang.Object{def set: Int; def set_=(Int): Unit} = $anon$1#95a253
scala> c.set = 5
5
-a corresponds to a.unary_-. Likewise for +a,~a, and !a.
a <operator>= b, where <operator> is some set of special characters, is equivalent to a = a <operator> b only if a doesn't have the <operator>= method, for example:
class test(val x:Int) {
def %%(y: Int) = new test(x*y)
}
var a = new test(10)
a.x // 10
a %%= 5 // Equivalent to a = a %% 5
a.x // 50
Special Classes: Tuples and Symbols
As mentioned by Rahul G, tuples and symbols get a slightly special syntax.
Symbols: the syntax 'x is short for Symbol("x")
Tuples: (p1,p2,..,pn) is short for a case class Tuplen[T1,T2,..,Tn](p1,p2,..,pn)
For example, the following two are equivalent.
val tuple1 = ("Hello",1)
val tuple2 = Tuple2[String,Int]("Hello",1)
In addition to Jaxkson's answer:
type F[A,B] can be used as A F B.
For example:
type ->[A,B] = (A,B)
def foo(f: String -> String)
Using => type in a method definition makes the compiler wrap expressions inside the method call in a function thunk.
For example
def until(cond: => Boolean)(body: => Unit) = while(!cond) body
var a = 0
until (a > 5) {a += 1}
Extractors:
There are two methods used for extractors, unapply and unapplySeq. These are used in multiple variable assignments and pattern matching.
The first use case is where unapply takes the object it is supposed to match and returns a Boolean based on whether or not it matches, for example,
trait Gender
trait Male extends Gender
trait Female extends Gender
object Male extends Male
object Female extends Female
class Person(val g: Gender, val age: Int)
object Adult {
def unapply(p: Person) = p.age >= 18
}
def check(p: Person) = p match {
case Adult() => println("An Adult")
case _ => println("A Child")
}
//Will print: An Adult since Adult.unapply returns true.
check(new Person(Female, 18))
//Will print: A Child as it falls through to the _ case.
check(new Person(Male, 17))
Honestly, I don't really get the purpose of the above syntax since it can be done almost just as easily by just putting the code in the case statements. Of course if you have a better example, leave a comment below
The general case where unapply takes some fixed-number of parameters and returns either an Option[T] for a single parameter or a Option[(p1,p2,...)] for multiple, i.e. a Tuple with the matched values, for example, continuing from the above code:
object Person {
def apply(g: Gender, age: Int) = new Person(g, age)
def unapply(p: Person) = if(p.age < 0) None else Some((p.g, p.age))
}
//Using Person.apply as described in the Basics section
val alice = Person(Female, 30)
val bob = Person(Male, 25)
//This calls Person.unapply(alice), which returns Some((Female, 30)).
//alice_gender is assigned Female and alice_age 30.
val Person(alice_gender, alice_age) = alice
bob match {
//Calls Person.unapply(bob), but sees that g is Male, so no match.
case Person(Female, _) => println("Hello ma'am")
//Calls Person.unapply(bob) and assigns age = bob.age, but it doesn't pass
//the 'if' statement, so it doesn't match here either.
case Person(Male, age) if age < 18 => println("Hey dude")
//So bob falls through to here
case _ => println("Hello Sir")
}
Person(Male,-1) match {
//Person.unapply(Person.apply(Male,-1)) returns None because p.age < 0.
//Therefore this case will not match.
case Person(_, _) => println("Hello person")
//Thus it falls through to here.
case _ => println("Are you Human?")
}
Note: Case classes do all those apply/unapply definitions for you (as well as other stuff) so use them whenver possible to save time and reduce code.
unapplySeq. This works similarly to unapply as above, except it must return an Option of some kind of sequence.
As a quick example,
scala> List.unapplySeq(List(1,2,3))
res2: Some[List[Int]] = Some(List(1, 2, 3))
Anonymous functions:
_ + _ is short for (a, b) => a + b
Context bounds desugar into implicit parameters, e.g. consider a function that leverages the Monoid type class:
def suml[T: Monoid](xs: List[T]) = {
val T = implicitly[Monoid[T]]
xs.foldLeft(T.mzero)(T.mplus)
}
where the : Monoid part is a context bound, gets translated to:
def suml[T](xs: List[T])(implicit evidence$1: Monoid[T]]) = {
...
}
therefore the following compiles, too:
def suml[T: Monoid](xs: List[T]) = {
val T = evidence$1
...
}