Scala case class: avoid recomputation of the property - scala

I am asking this because I have encountered this use case many times.
Let's say we have a case class like this:
case class C(xs: Iterable[Int]) {
val max = xs.max
def ++(that: C) = C(xs ++ that.xs)
}
This works fine, but the ++ operation is inefficient, since the collection is needlessly traversed once more to compute the maximum of the result; since we already know the maximums of both collections, we could reuse that - by using something like this:
def ++(that: C) =
C(xs ++ that.xs, max = math.max(max, that.max))
This is just a simple example to demonstrate the purpose - the computation avoided could be a lot more complex, or maybe even a TCP data fetch.
How to avoid this recomputation (see the second code snippet), keeping the code elegant?

Something like this would work
class C private (val xs: Iterable[Int], val max: Int) {
def ++(that: C) = new C(xs ++ that.xs, math.max(this.max, that.max)
}
object C {
def apply(xs: Iterable[Int]) = new C(xs, xs.max)
}
Note that C is no longer a case class to avoid max and xs becoming inconsistent. If C was a case class, you could call e.g. c.copy(max = -1) and get an inconsistent instance.

case class C(xs: Iterable[Int]) {
private var maxOp = Option.empty[Int]
lazy val max = maxOp getOrElse {
maxOp = Some(xs.max)
maxOp.get
}
def ++(that: C) = {
val res = C(xs ++ that.xs)
res.maxOp = Some(math.max(this.max, that.max))
res
}
}

Since max is already a val (in contrast to a method) you could do it this way:
case class C private (xs: Iterable[Int], max: Int) {
def ++(that: C) = C(xs ++ that.xs, math.max(max, that.max))
def copy(_xs: Iterable[Int] = this.xs) = {
if (_xs == this.xs) {
C(xs, max)
} else {
C(_xs)
}
}
}
object C {
def apply(xs: Iterable[Int]): C = C(xs, xs.max)
}
If you are going to pattern match on this case class, then it depends on your use cases, if you can (or must) pattern match on max as well.
Update 1 As pointed out by Rüdiger I have added private to the constructor so that xs and max are consistent.
Update 2 As pointed out by som-snytt, the copy method must be handled as well to to prevent inconsistency.

sealed trait C {
val xs: Iterable[Int]
val max: Int
def ++(that: C) = ComposedC(this, that)
}
case class ValidatedC(xs: Iterable[Int]) extends C {
val max = xs.max
}
case class ComposedC(a: C, b: C) extends C {
val max = math.max(a.max, b.max)
val xs = a.xs ++ b.xs
}
object C {
def apply(xs: Iterable[Int]) = ValidatedC(xs)
}
A simpler solution (which doesn't enforce correctness) -
Introduce a way to provide pre-computed max and an auxiliary constructor that gets 2 Cs.
case class C(xs: Iterable[Int])(val max: Int = xs.max) {
def this(a: C, b: C) = {
this(a.xs ++ b.xs)(math.max(a.max, b.max))
}
def ++(that: C) = new C(this, that)
}

Related

Abstract a synchronous function with its Future counterpart

I wrote a very simple mechanism that only allows a max number of function calls during a given number of seconds. See it as a basic rate limiter.
It takes the execution to limit as an argument and returns the return value of that original execution.
The problem is that executions can be synchronous (of type => A) or asynchronous (of type => Future[A]) and that leads to two extremely similar functions:
case class Limiter[A](max: Int, seconds: Int) {
private val queue = Queue[Long]()
def limit(value: => A): Option[A] = {
val now = System.currentTimeMillis()
if (queue.length == max) {
val oldest = queue.head
if (now - oldest < seconds * 1000) return None
else queue.dequeue()
}
queue.enqueue(now)
Some(value)
}
def limitFuture(future: => Future[A]): Future[Option[A]] = {
val now = System.currentTimeMillis()
if (queue.length == max) {
val oldest = queue.head
if (now - oldest < seconds * 1000) return Future(None)
else queue.dequeue()
}
future.map { x =>
queue.enqueue(now)
Some(x)
}
}
}
(I am not actually using Option but a set of types I defined, just using Option for simplicity sake)
Examples of execution:
// Prevent more than 5 runs/minute. Useful for example to prevent email spamming
val limit = Limit[Boolean](5, 60)
val result = limitFuture { sendEmail(...) } // `sendEmail` returns a future
// Prevent more than 1 run/hour. Useful for example to cache HTML response
val limit = Limit[String](1, 3600)
val limit { getHTML(...) } // `getHTML` returns the HTML as a string directly
How can I refactor these methods to avoid repetition? Later needs might include other argument types and not only direct type + Futured type, so I'd like to keep my options open if it's possible.
The only "solution" I could come up with so far is to replace limit:
def limit(value: => A): Option[A] = {
Await.result(limitFuture(Future.successful(value)), 5.seconds)
}
Well, it works, but it feels backwards. I would rather have the => A being the base version that other methods extend or, even better, a generic (private) method that both limit and limitFuture could extend.
Actually, it would be even better-er if a single limit function could take care of this regardless of argument but I doubt it's possible.
You can condense this down to one method with an implicit parameter handling the differences:
trait Limitable[A, B] {
type Out
def none: Out
def some(b: B, f: () => Unit): Out
}
implicit def rawLimitable[A]: Limitable[A, A] = new Limitable[A, A] {
type Out = Option[A]
def none = None
def some(a: A, f: () => Unit): Out = {
f()
Some(a)
}
}
implicit def futureLimitable[A]: Limitable[A, Future[A]] = new Limitable[A, Future[A]] {
type Out = Future[Option[A]]
def none = Future(None)
def some(future: Future[A], f: () => Unit): Out = future.map { a =>
f()
Some(a)
}
}
case class Limiter[A](max: Int, seconds: Int) {
private val queue = Queue[Long]()
def limit[B](in: => B)(implicit l: Limitable[A, B]): l.Out = {
val now = System.currentTimeMillis()
if (queue.length == max) {
val oldest = queue.head
if (now - oldest < seconds * 1000) return l.none
else queue.dequeue()
}
l.some(in, {() => queue.enqueue(now)})
}
}
And use it like:
val limit = Limit[String](1, 3600)
limit.limit("foo")
limit.limit(Future("bar"))
You can use Applicative typeclass from cats or scalaz. Applicative, among other things, lets you lift a value into some context F (using pure) and is also a functor, so you can use map on F[A].
Currently you want it for Id and Future types (you need ExecutionContext in scope for Future applicative to work). It will work for things like Vector or Validated, tho you might have problems adding custom collection types.
import cats._, implicits._
import scala.concurrent._
import scala.collection.mutable.Queue
case class Limiter[A](max: Int, seconds: Int) {
private val queue = Queue[Long]()
def limitA[F[_]: Applicative](value: => F[A]): F[Option[A]] = {
val now = System.currentTimeMillis()
if (queue.length == max) {
val oldest = queue.head
if (now - oldest < seconds * 1000) return none[A].pure[F]
else queue.dequeue()
}
value.map { x =>
queue.enqueue(now)
x.some
}
}
// or leave these e.g. for source compatibility
def limit(value: => A): Option[A] = limitA[Id](value)
def limitFuture(future: => Future[A])(implicit ec: ExecutionContext): Future[Option[A]] = limitA(future)
}
Notes:
I'm using none[A] instead of None: Option[A] and a.some instead of Some(a): Option[A]. These helpers are available in both cats and scalaz and you need them because F[_] here is not defined as covariant.
You have to specify Id as a type explicitly, e.g. .limitA[Id](3). This is not the case with Future, however.
You map call is strange. It is parsed as:
future.map {
queue.enqueue(now) // in current thread
x => Some(x)
}
Which is the same as
queue.enqueue(now) // in current thread
future.map {
x => Some(x)
}

scala's spire framework : I am unable to operate on a group

I try to use spire, a math framework, but I have an error message:
import spire.algebra._
import spire.implicits._
trait AbGroup[A] extends Group[A]
final class Rationnel_Quadratique(val n1: Int = 2)(val coef: (Int, Int)) {
override def toString = {
coef match {
case (c, i) =>
s"$c + $i√$n"
}
}
def a() = coef._1
def b() = coef._2
def n() = n1
}
object Rationnel_Quadratique {
def apply(coef: (Int, Int),n: Int = 2)= {
new Rationnel_Quadratique(n)(coef)
}
}
object AbGroup {
implicit object RQAbGroup extends AbGroup[Rationnel_Quadratique] {
def +(a: Rationnel_Quadratique, b: Rationnel_Quadratique): Rationnel_Quadratique = Rationnel_Quadratique(coef=(a.a() + b.a(), a.b() + b.b()))
def inverse(a: Rationnel_Quadratique): Rationnel_Quadratique = Rationnel_Quadratique((-a.a(), -a.b()))
def id: Rationnel_Quadratique = Rationnel_Quadratique((0, 0))
}
}
object euler66_2 extends App {
val c = Rationnel_Quadratique((1, 2))
val d = Rationnel_Quadratique((3, 4))
val e = c + d
println(e)
}
the program is expected to add 1+2√2 and 3+4√2, but instead I have this error:
could not find implicit value for evidence parameter of type spire.algebra.AdditiveSemigroup[Rationnel_Quadratique]
val e = c + d
^
I think there is something essential I have missed (usage of implicits?)
It looks like you are not using Spire correctly.
Spire already has an AbGroup type, so you should be using that instead of redefining your own. Here's an example using a simple type I created called X.
import spire.implicits._
import spire.algebra._
case class X(n: BigInt)
object X {
implicit object XAbGroup extends AbGroup[X] {
def id: X = X(BigInt(0))
def op(lhs: X, rhs: X): X = X(lhs.n + rhs.n)
def inverse(lhs: X): X = X(-lhs.n)
}
}
def test(a: X, b: X): X = a |+| b
Note that with groups (as well as semigroups and monoids) you'd use |+| rather than +. To get plus, you'll want to define something with an AdditiveSemigroup (e.g. Semiring, or Ring, or Field or something).
You'll also use .inverse and |-| instead of unary and binary - if that makes sense.
Looking at your code, I am also not sure your actual number type is right. What will happen if I want to add two numbers with different values for n?
Anyway, hope this clears things up for you a bit.
EDIT: Since it seems like you're also getting hung up on Scala syntax, let me try to sketch a few designs that might work. First, there's always a more general solution:
import spire.implicits._
import spire.algebra._
import spire.math._
case class RQ(m: Map[Natural, SafeLong]) {
override def toString: String = m.map {
case (k, v) => if (k == 1) s"$v" else s"$v√$k" }.mkString(" + ")
}
object RQ {
implicit def abgroup[R <: Radical](implicit r: R): AbGroup[RQ] =
new AbGroup[RQ] {
def id: RQ = RQ(Map.empty)
def op(lhs: RQ, rhs: RQ): RQ = RQ(lhs.m + rhs.m)
def inverse(lhs: RQ): RQ = RQ(-lhs.m)
}
}
object Test {
def main(args: Array[String]) {
implicit val radical = _2
val x = RQ(Map(Natural(1) -> 1, Natural(2) -> 2))
val y = RQ(Map(Natural(1) -> 3, Natural(2) -> 4))
println(x)
println(y)
println(x |+| y)
}
}
This allows you to add different roots together without problem, at the cost of some indirection. You could also stick more closely to your design with something like this:
import spire.implicits._
import spire.algebra._
abstract class Radical(val n: Int) { override def toString: String = n.toString }
case object _2 extends Radical(2)
case object _3 extends Radical(3)
case class RQ[R <: Radical](a: Int, b: Int)(implicit r: R) {
override def toString: String = s"$a + $b√$r"
}
object RQ {
implicit def abgroup[R <: Radical](implicit r: R): AbGroup[RQ[R]] =
new AbGroup[RQ[R]] {
def id: RQ[R] = RQ[R](0, 0)
def op(lhs: RQ[R], rhs: RQ[R]): RQ[R] = RQ[R](lhs.a + rhs.a, lhs.b + rhs.b)
def inverse(lhs: RQ[R]): RQ[R] = RQ[R](-lhs.a, -lhs.b)
}
}
object Test {
def main(args: Array[String]) {
implicit val radical = _2
val x = RQ[_2.type](1, 2)
val y = RQ[_2.type](3, 4)
println(x)
println(y)
println(x |+| y)
}
}
This approach creates a fake type to represent whatever radical you are using (e.g. √2) and parameterizes QR on that type. This way you can be sure that no one will try to do additions that are invalid.
Hopefully one of these approaches will work for you.

scala: adding a method to List?

I was wondering how to go about adding a 'partitionCount' method to Lists, e.g.:
(not tested, shamelessly based on List.scala):
Do I have to create my own sub-class and an implicit type converter?
(My original attempt had a lot of problems, so here is one based on #Easy's answer):
class MyRichList[A](targetList: List[A]) {
def partitionCount(p: A => Boolean): (Int, Int) = {
var btrue = 0
var bfalse = 0
var these = targetList
while (!these.isEmpty) {
if (p(these.head)) { btrue += 1 } else { bfalse += 1 }
these = these.tail
}
(btrue, bfalse)
}
}
and here is a little more general version that's good for Seq[...]:
implicit def seqToRichSeq[T](s: Seq[T]) = new MyRichSeq(s)
class MyRichList[A](targetList: List[A]) {
def partitionCount(p: A => Boolean): (Int, Int) = {
var btrue = 0
var bfalse = 0
var these = targetList
while (!these.isEmpty) {
if (p(these.head)) { btrue += 1 } else { bfalse += 1 }
these = these.tail
}
(btrue, bfalse)
}
}
You can use implicit conversion like this:
implicit def listToMyRichList[T](l: List[T]) = new MyRichList(l)
class MyRichList[T](targetList: List[T]) {
def partitionCount(p: T => Boolean): (Int, Int) = ...
}
and instead of this you need to use targetList. You don't need to extend List. In this example I create simple wrapper MyRichList that would be used implicitly.
You can generalize wrapper further, by defining it for Traversable, so that it will work for may other collection types and not only for Lists:
implicit def listToMyRichTraversable[T](l: Traversable[T]) = new MyRichTraversable(l)
class MyRichTraversable[T](target: Traversable[T]) {
def partitionCount(p: T => Boolean): (Int, Int) = ...
}
Also note, that implicit conversion would be used only if it's in scope. This means, that you need to import it (unless you are using it in the same scope where you have defined it).
As already pointed out by Easy Angel, use implicit conversion:
implicit def listTorichList[A](input: List[A]) = new RichList(input)
class RichList[A](val source: List[A]) {
def partitionCount(p: A => Boolean): (Int, Int) = {
val partitions = source partition(p)
(partitions._1.size, partitions._2.size)
}
}
Also note that you can easily define partitionCount in terms of partinion. Then you can simply use:
val list = List(1, 2, 3, 5, 7, 11)
val (odd, even) = list partitionCount {_ % 2 != 0}
If you are curious how it works, just remove implicit keyword and call the list2richList conversion explicitly (this is what the compiler does transparently for you when implicit is used).
val (odd, even) = list2richList(list) partitionCount {_ % 2 != 0}
Easy Angel is right, but the method seems pretty useless. You have already count in order to get the number of "positives", and of course the number of "negatives" is size minus count.
However, to contribute something positive, here a more functional version of your original method:
def partitionCount[A](iter: Traversable[A], p: A => Boolean): (Int, Int) =
iter.foldLeft ((0,0)) { ((x,y), a) => if (p(a)) (x + 1,y) else (x, y + 1)}

abstracting over a collection

Recently, I wrote an iterator for a cartesian product of Anys, and started with a List of List, but recognized, that I can easily switch to the more abstract trait Seq.
I know, you like to see the code. :)
class Cartesian (val ll: Seq[Seq[_]]) extends Iterator [Seq[_]] {
def combicount: Int = (1 /: ll) (_ * _.length)
val last = combicount
var iter = 0
override def hasNext (): Boolean = iter < last
override def next (): Seq[_] = {
val res = combination (ll, iter)
iter += 1
res
}
def combination (xx: Seq [Seq[_]], i: Int): List[_] = xx match {
case Nil => Nil
case x :: xs => x (i % x.length) :: combination (xs, i / x.length)
}
}
And a client of that class:
object Main extends Application {
val illi = new Cartesian (List ("abc".toList, "xy".toList, "AB".toList))
// val ivvi = new Cartesian (Vector (Vector (1, 2, 3), Vector (10, 20)))
val issi = new Cartesian (Seq (Seq (1, 2, 3), Seq (10, 20)))
// val iaai = new Cartesian (Array (Array (1, 2, 3), Array (10, 20)))
(0 to 5).foreach (dummy => println (illi.next ()))
// (0 to 5).foreach (dummy => println (issi.next ()))
}
/*
List(a, x, A)
List(b, x, A)
List(c, x, A)
List(a, y, A)
List(b, y, A)
List(c, y, A)
*/
The code works well for Seq and Lists (which are Seqs), but of course not for Arrays or Vector, which aren't of type Seq, and don't have a cons-method '::'.
But the logic could be used for such collections too.
I could try to write an implicit conversion to and from Seq for Vector, Array, and such, or try to write an own, similar implementation, or write an Wrapper, which transforms the collection to a Seq of Seq, and calls 'hasNext' and 'next' for the inner collection, and converts the result to an Array, Vector or whatever. (I tried to implement such workarounds, but I have to recognize: it's not that easy. For a real world problem I would probably rewrite the Iterator independently.)
However, the whole thing get's a bit out of control if I have to deal with Arrays of Lists or Lists of Arrays and other mixed cases.
What would be the most elegant way to write the algorithm in the broadest, possible way?
There are two solutions. The first is to not require the containers to be a subclass of some generic super class, but to be convertible to one (by using implicit function arguments). If the container is already a subclass of the required type, there's a predefined identity conversion which only returns it.
import collection.mutable.Builder
import collection.TraversableLike
import collection.generic.CanBuildFrom
import collection.mutable.SeqLike
class Cartesian[T, ST[T], TT[S]](val ll: TT[ST[T]])(implicit cbf: CanBuildFrom[Nothing, T, ST[T]], seqLike: ST[T] => SeqLike[T, ST[T]], traversableLike: TT[ST[T]] => TraversableLike[ST[T], TT[ST[T]]] ) extends Iterator[ST[T]] {
def combicount (): Int = (1 /: ll) (_ * _.length)
val last = combicount - 1
var iter = 0
override def hasNext (): Boolean = iter < last
override def next (): ST[T] = {
val res = combination (ll, iter, cbf())
iter += 1
res
}
def combination (xx: TT[ST[T]], i: Int, builder: Builder[T, ST[T]]): ST[T] =
if (xx.isEmpty) builder.result
else combination (xx.tail, i / xx.head.length, builder += xx.head (i % xx.head.length) )
}
This sort of works:
scala> new Cartesian[String, Vector, Vector](Vector(Vector("a"), Vector("xy"), Vector("AB")))
res0: Cartesian[String,Vector,Vector] = empty iterator
scala> new Cartesian[String, Array, Array](Array(Array("a"), Array("xy"), Array("AB")))
res1: Cartesian[String,Array,Array] = empty iterator
I needed to explicitly pass the types because of bug https://issues.scala-lang.org/browse/SI-3343
One thing to note is that this is better than using existential types, because calling next on the iterator returns the right type, and not Seq[Any].
There are several drawbacks here:
If the container is not a subclass of the required type, it is converted to one, which costs in performance
The algorithm is not completely generic. We need types to be converted to SeqLike or TraversableLike only to use a subset of functionality these types offer. So making a conversion function can be tricky.
What if some capabilities can be interpreted differently in different contexts? For example, a rectangle has two 'length' properties (width and height)
Now for the alternative solution. We note that we don't actually care about the types of collections, just their capabilities:
TT should have foldLeft, get(i: Int) (to get head/tail)
ST should have length, get(i: Int) and a Builder
So we can encode these:
trait HasGet[T, CC[_]] {
def get(cc: CC[T], i: Int): T
}
object HasGet {
implicit def seqLikeHasGet[T, CC[X] <: SeqLike[X, _]] = new HasGet[T, CC] {
def get(cc: CC[T], i: Int): T = cc(i)
}
implicit def arrayHasGet[T] = new HasGet[T, Array] {
def get(cc: Array[T], i: Int): T = cc(i)
}
}
trait HasLength[CC] {
def length(cc: CC): Int
}
object HasLength {
implicit def seqLikeHasLength[CC <: SeqLike[_, _]] = new HasLength[CC] {
def length(cc: CC) = cc.length
}
implicit def arrayHasLength[T] = new HasLength[Array[T]] {
def length(cc: Array[T]) = cc.length
}
}
trait HasFold[T, CC[_]] {
def foldLeft[A](cc: CC[T], zero: A)(op: (A, T) => A): A
}
object HasFold {
implicit def seqLikeHasFold[T, CC[X] <: SeqLike[X, _]] = new HasFold[T, CC] {
def foldLeft[A](cc: CC[T], zero: A)(op: (A, T) => A): A = cc.foldLeft(zero)(op)
}
implicit def arrayHasFold[T] = new HasFold[T, Array] {
def foldLeft[A](cc: Array[T], zero: A)(op: (A, T) => A): A = {
var i = 0
var result = zero
while (i < cc.length) {
result = op(result, cc(i))
i += 1
}
result
}
}
}
(strictly speaking, HasFold is not required since its implementation is in terms of length and get, but i added it here so the algorithm will translate more cleanly)
now the algorithm is:
class Cartesian[T, ST[_], TT[Y]](val ll: TT[ST[T]])(implicit cbf: CanBuildFrom[Nothing, T, ST[T]], stHasLength: HasLength[ST[T]], stHasGet: HasGet[T, ST], ttHasFold: HasFold[ST[T], TT], ttHasGet: HasGet[ST[T], TT], ttHasLength: HasLength[TT[ST[T]]]) extends Iterator[ST[T]] {
def combicount (): Int = ttHasFold.foldLeft(ll, 1)((a,l) => a * stHasLength.length(l))
val last = combicount - 1
var iter = 0
override def hasNext (): Boolean = iter < last
override def next (): ST[T] = {
val res = combination (ll, 0, iter, cbf())
iter += 1
res
}
def combination (xx: TT[ST[T]], j: Int, i: Int, builder: Builder[T, ST[T]]): ST[T] =
if (ttHasLength.length(xx) == j) builder.result
else {
val head = ttHasGet.get(xx, j)
val headLength = stHasLength.length(head)
combination (xx, j + 1, i / headLength, builder += stHasGet.get(head, (i % headLength) ))
}
}
And use:
scala> new Cartesian[String, Vector, List](List(Vector("a"), Vector("xy"), Vector("AB")))
res6: Cartesian[String,Vector,List] = empty iterator
scala> new Cartesian[String, Array, Array](Array(Array("a"), Array("xy"), Array("AB")))
res7: Cartesian[String,Array,Array] = empty iterator
Scalaz probably has all of this predefined for you, unfortunately, I don't know it well.
(again I need to pass the types because inference doesn't infer the right kind)
The benefit is that the algorithm is now completely generic and that there is no need for implicit conversions from Array to WrappedArray in order for it to work
Excercise: define for tuples ;-)

How can I extend Scala collections with an argmax method?

I would like to add to all collections where it makes sense, an argMax method.
How to do it? Use implicits?
On Scala 2.8, this works:
val list = List(1, 2, 3)
def f(x: Int) = -x
val argMax = list max (Ordering by f)
As pointed by mkneissl, this does not return the set of maximum points. Here's an alternate implementation that does, and tries to reduce the number of calls to f. If calls to f don't matter that much, see mkneissl's answer. Also, note that his answer is curried, which provides superior type inference.
def argMax[A, B: Ordering](input: Iterable[A], f: A => B) = {
val fList = input map f
val maxFList = fList.max
input.view zip fList filter (_._2 == maxFList) map (_._1) toSet
}
scala> argMax(-2 to 2, (x: Int) => x * x)
res15: scala.collection.immutable.Set[Int] = Set(-2, 2)
The argmax function (as I understand it from Wikipedia)
def argMax[A,B](c: Traversable[A])(f: A=>B)(implicit o: Ordering[B]): Traversable[A] = {
val max = (c map f).max(o)
c filter { f(_) == max }
}
If you really want, you can pimp it onto the collections
implicit def enhanceWithArgMax[A](c: Traversable[A]) = new {
def argMax[B](f: A=>B)(implicit o: Ordering[B]): Traversable[A] = ArgMax.argMax(c)(f)(o)
}
and use it like this
val l = -2 to 2
assert (argMax(l)(x => x*x) == List(-2,2))
assert (l.argMax(x => x*x) == List(-2,2))
(Scala 2.8)
Yes, the usual way would be to use the 'pimp my library' pattern to decorate your collection. For example (N.B. just as illustration, not meant to be a correct or working example):
trait PimpedList[A] {
val l: List[A]
//example argMax, not meant to be correct
def argMax[T <% Ordered[T]](f:T => T) = {error("your definition here")}
}
implicit def toPimpedList[A](xs: List[A]) = new PimpedList[A] {
val l = xs
}
scala> def f(i:Int):Int = 10
f: (i: Int) Int
scala> val l = List(1,2,3)
l: List[Int] = List(1, 2, 3)
scala> l.argMax(f)
java.lang.RuntimeException: your definition here
at scala.Predef$.error(Predef.scala:60)
at PimpedList$class.argMax(:12)
//etc etc...
Nice and easy ? :
val l = List(1,0,10,2)
l.zipWithIndex.maxBy(x => x._1)._2
You can add functions to an existing API in Scala by using the Pimp my Library pattern. You do this by defining an implicit conversion function. For example, I have a class Vector3 to represent 3D vectors:
class Vector3 (val x: Float, val y: Float, val z: Float)
Suppose I want to be able to scale a vector by writing something like: 2.5f * v. I can't directly add a * method to class Float ofcourse, but I can supply an implicit conversion function like this:
implicit def scaleVector3WithFloat(f: Float) = new {
def *(v: Vector3) = new Vector3(f * v.x, f * v.y, f * v.z)
}
Note that this returns an object of a structural type (the new { ... } construct) that contains the * method.
I haven't tested it, but I guess you could do something like this:
implicit def argMaxImplicit[A](t: Traversable[A]) = new {
def argMax() = ...
}
Here's a way of doing so with the implicit builder pattern. It has the advantage over the previous solutions that it works with any Traversable, and returns a similar Traversable. Sadly, it's pretty imperative. If anyone wants to, it could probably be turned into a fairly ugly fold instead.
object RichTraversable {
implicit def traversable2RichTraversable[A](t: Traversable[A]) = new RichTraversable[A](t)
}
class RichTraversable[A](t: Traversable[A]) {
def argMax[That, C](g: A => C)(implicit bf : scala.collection.generic.CanBuildFrom[Traversable[A], A, That], ord:Ordering[C]): That = {
var minimum:C = null.asInstanceOf[C]
val repr = t.repr
val builder = bf(repr)
for(a<-t){
val test: C = g(a)
if(test == minimum || minimum == null){
builder += a
minimum = test
}else if (ord.gt(test, minimum)){
builder.clear
builder += a
minimum = test
}
}
builder.result
}
}
Set(-2, -1, 0, 1, 2).argmax(x=>x*x) == Set(-2, 2)
List(-2, -1, 0, 1, 2).argmax(x=>x*x) == List(-2, 2)
Here's a variant loosely based on #Daniel's accepted answer that also works for Sets.
def argMax[A, B: Ordering](input: GenIterable[A], f: A => B) : GenSet[A] = argMaxZip(input, f) map (_._1) toSet
def argMaxZip[A, B: Ordering](input: GenIterable[A], f: A => B): GenIterable[(A, B)] = {
if (input.isEmpty) Nil
else {
val fPairs = input map (x => (x, f(x)))
val maxF = fPairs.map(_._2).max
fPairs filter (_._2 == maxF)
}
}
One could also do a variant that produces (B, Iterable[A]), of course.
Based on other answers, you can pretty easily combine the strengths of each (minimal calls to f(), etc.). Here we have an implicit conversion for all Iterables (so they can just call .argmax() transparently), and a stand-alone method if for some reason that is preferred. ScalaTest tests to boot.
class Argmax[A](col: Iterable[A]) {
def argmax[B](f: A => B)(implicit ord: Ordering[B]): Iterable[A] = {
val mapped = col map f
val max = mapped max ord
(mapped zip col) filter (_._1 == max) map (_._2)
}
}
object MathOps {
implicit def addArgmax[A](col: Iterable[A]) = new Argmax(col)
def argmax[A, B](col: Iterable[A])(f: A => B)(implicit ord: Ordering[B]) = {
new Argmax(col) argmax f
}
}
class MathUtilsTests extends FunSuite {
import MathOps._
test("Can argmax with unique") {
assert((-10 to 0).argmax(_ * -1).toSet === Set(-10))
// or alternate calling syntax
assert(argmax(-10 to 0)(_ * -1).toSet === Set(-10))
}
test("Can argmax with multiple") {
assert((-10 to 10).argmax(math.pow(_, 2)).toSet === Set(-10, 10))
}
}