Min/max with Option[T] for possibly empty Seq? - scala

I'm doing a bit of Scala gymnastics where I have Seq[T] in which I try to find the "smallest" element. This is what I do right now:
val leastOrNone = seq.reduceOption { (best, current) =>
if (current.something < best.something) current
else best
}
It works fine, but I'm not quite satisfied - it's a bit long for such a simple thing, and I don't care much for "if"s. Using minBy would be much more elegant:
val least = seq.minBy(_.something)
... but min and minBy throw exceptions when the sequence is empty. Is there an idiomatic, more elegant way of finding the smallest element of a possibly empty list as an Option?

seq.reduceOption(_ min _)
does what you want?
Edit: Here's an example incorporating your _.something:
case class Foo(a: Int, b: Int)
val seq = Seq(Foo(1,1),Foo(2,0),Foo(0,3))
val ord = Ordering.by((_: Foo).b)
seq.reduceOption(ord.min) //Option[Foo] = Some(Foo(2,0))
or, as generic method:
def minOptionBy[A, B: Ordering](seq: Seq[A])(f: A => B) =
seq reduceOption Ordering.by(f).min
which you could invoke with minOptionBy(seq)(_.something)

Starting Scala 2.13, minByOption/maxByOption is now part of the standard library and returns None if the sequence is empty:
seq.minByOption(_.something)
List((3, 'a'), (1, 'b'), (5, 'c')).minByOption(_._1) // Option[(Int, Char)] = Some((1,b))
List[(Int, Char)]().minByOption(_._1) // Option[(Int, Char)] = None

A safe, compact and O(n) version with Scalaz:
xs.nonEmpty option xs.minBy(_.foo)

Hardly an option for any larger list due to O(nlogn) complexity:
seq.sortBy(_.something).headOption

Also, it is available to do like that
Some(seq).filter(_.nonEmpty).map(_.minBy(_.something))

How about this?
import util.control.Exception._
allCatch opt seq.minBy(_.something)
Or, more verbose, if you don't want to swallow other exceptions:
catching(classOf[UnsupportedOperationException]) opt seq.minBy(_.something)
Alternatively, you can pimp all collections with something like this:
import collection._
class TraversableOnceExt[CC, A](coll: CC, asTraversable: CC => TraversableOnce[A]) {
def minOption(implicit cmp: Ordering[A]): Option[A] = {
val trav = asTraversable(coll)
if (trav.isEmpty) None
else Some(trav.min)
}
def minOptionBy[B](f: A => B)(implicit cmp: Ordering[B]): Option[A] = {
val trav = asTraversable(coll)
if (trav.isEmpty) None
else Some(trav.minBy(f))
}
}
implicit def extendTraversable[A, C[A] <: TraversableOnce[A]](coll: C[A]): TraversableOnceExt[C[A], A] =
new TraversableOnceExt[C[A], A](coll, identity)
implicit def extendStringTraversable(string: String): TraversableOnceExt[String, Char] =
new TraversableOnceExt[String, Char](string, implicitly)
implicit def extendArrayTraversable[A](array: Array[A]): TraversableOnceExt[Array[A], A] =
new TraversableOnceExt[Array[A], A](array, implicitly)
And then just write seq.minOptionBy(_.something).

I have the same problem before, so I extends Ordered and implement the compare function.
here is example:
case class Point(longitude0: String, latitude0: String) extends Ordered [Point]{
def this(point: Point) = this(point.original_longitude,point.original_latitude)
val original_longitude = longitude0
val original_latitude = latitude0
val longitude = parseDouble(longitude0).get
val latitude = parseDouble(latitude0).get
override def toString: String = "longitude: " +original_longitude +", latitude: "+ original_latitude
def parseDouble(s: String): Option[Double] = try { Some(s.toDouble) } catch { case _ => None }
def distance(other: Point): Double =
sqrt(pow(longitude - other.longitude, 2) + pow(latitude - other.latitude, 2))
override def compare(that: Point): Int = {
if (longitude < that.longitude)
return -1
else if (longitude == that.longitude && latitude < that.latitude)
return -1
else
return 1
}
}
so if I have a seq of Point
I can ask for max or min method
var points = Seq[Point]()
val maxPoint = points.max
val minPoint = points.min

You could always do something like:
case class Foo(num: Int)
val foos: Seq[Foo] = Seq(Foo(1), Foo(2), Foo(3))
val noFoos: Seq[Foo] = Seq.empty
def minByOpt(foos: Seq[Foo]): Option[Foo] =
foos.foldLeft(None: Option[Foo]) { (acc, elem) =>
Option((elem +: acc.toSeq).minBy(_.num))
}
Then use like:
scala> minByOpt(foos)
res0: Option[Foo] = Some(Foo(1))
scala> minByOpt(noFoos)
res1: Option[Foo] = None

For scala < 2.13
Try(seq.minBy(_.something)).toOption
For scala 2.13
seq.minByOption(_.something)

In Haskell you'd wrap the minimumBy call as
least f x | Seq.null x = Nothing
| otherwise = Just (Seq.minimumBy f x)

Related

How to find if a Scala String is parseable as a Double or not?

Suppose that I have a string in scala and I want to try to parse a double out of it.
I know that, I can just call toDouble and then catch the java num format exception if this fails, but is there a cleaner way to do this? For example if there was a parseDouble function that returned Option[Double] this would qualify.
I don't want to put this in my own code if it already exists in the standard library and I am just looking for it in the wrong place.
Thanks for any help you can provide.
For Scala 2.13+ see Xavier's answer below. Apparently there's a toDoubleOption method now.
For older versions:
def parseDouble(s: String) = try { Some(s.toDouble) } catch { case _ => None }
Fancy version (edit: don't do this except for amusement value; I was a callow youth years ago when I used to write such monstrosities):
case class ParseOp[T](op: String => T)
implicit val popDouble = ParseOp[Double](_.toDouble)
implicit val popInt = ParseOp[Int](_.toInt)
// etc.
def parse[T: ParseOp](s: String) = try { Some(implicitly[ParseOp[T]].op(s)) }
catch {case _ => None}
scala> parse[Double]("1.23")
res13: Option[Double] = Some(1.23)
scala> parse[Int]("1.23")
res14: Option[Int] = None
scala> parse[Int]("1")
res15: Option[Int] = Some(1)
Scalaz provides an extension method parseDouble on Strings, which gives a value of type Validation[NumberFormatException, Double].
scala> "34.5".parseDouble
res34: scalaz.Validation[NumberFormatException,Double] = Success(34.5)
scala> "34.bad".parseDouble
res35: scalaz.Validation[NumberFormatException,Double] = Failure(java.lang.NumberFormatException: For input string: "34.bad")
You can convert it to Option if so required.
scala> "34.bad".parseDouble.toOption
res36: Option[Double] = None
scala> import scala.util.Try
import scala.util.Try
scala> def parseDouble(s: String): Option[Double] = Try { s.toDouble }.toOption
parseDouble: (s: String)Option[Double]
scala> parseDouble("3.14")
res0: Option[Double] = Some(3.14)
scala> parseDouble("hello")
res1: Option[Double] = None
Scala 2.13 introduced String::toDoubleOption:
"5.7".toDoubleOption // Option[Double] = Some(5.7)
"abc".toDoubleOption // Option[Double] = None
"abc".toDoubleOption.getOrElse(-1d) // Double = -1.0
You could try using util.control.Exception.catching which returns an Either type.
So using the following returns a Left wrapping a NumberFormatException or a Right wrapping a Double
import util.control.Exception._
catching(classOf[NumberFormatException]) either "12.W3".toDouble
Unfortunately, this isn't in the standard library. Here's what I use:
class SafeParsePrimitive(s: String) {
private def nfe[T](t: => T) = {
try { Some(t) }
catch { case nfe: NumberFormatException => None }
}
def booleanOption = s.toLowerCase match {
case "yes" | "true" => Some(true)
case "no" | "false" => Some(false)
case _ => None
}
def byteOption = nfe(s.toByte)
def doubleOption = nfe(s.toDouble)
def floatOption = nfe(s.toFloat)
def hexOption = nfe(java.lang.Integer.valueOf(s,16))
def hexLongOption = nfe(java.lang.Long.valueOf(s,16))
def intOption = nfe(s.toInt)
def longOption = nfe(s.toLong)
def shortOption = nfe(s.toShort)
}
implicit def string_parses_safely(s: String) = new SafeParsePrimitive(s)
There's nothing like this not only in Scala, but even in basic Java.
Here's a piece code that does it without exceptions, though:
def parseDouble(s: String)(implicit nf: NumberFormat) = {
val pp = new ParsePosition(0)
val d = nf.parse(s, pp)
if (pp.getErrorIndex == -1) Some(d.doubleValue) else None
}
Usage:
implicit val formatter = NumberFormat.getInstance(Locale.ENGLISH)
Console println parseDouble("184.33")
Console println parseDouble("hello, world")
I'd usually go with an "in place" Try:
def strTimesTen (s: String) = for (d <- Try(s.toDouble)) yield d * 10
strTimesTen("0.1") match {
Success(d) => println( s"It is $d" )
Failure(ex) => println( "I've asked for a number!" )
}
Note, that you can do further calculation in the for and any exception would project into a Failure(ex). AFAIK this is the idiomatic way of handling a sequence of unreliable operations.

Extending collection classes with extra fields in Scala

I'm looking to create a class that is basically a collection with an extra field. However, I keep running into problems and am wondering what the best way of implementing this is. I've tried to follow the pattern given in the Scala book. E.g.
import scala.collection.IndexedSeqLike
import scala.collection.mutable.Builder
import scala.collection.generic.CanBuildFrom
import scala.collection.mutable.ArrayBuffer
class FieldSequence[FT,ST](val field: FT, seq: IndexedSeq[ST] = Vector())
extends IndexedSeq[ST] with IndexedSeqLike[ST,FieldSequence[FT,ST]] {
def apply(index: Int): ST = return seq(index)
def length = seq.length
override def newBuilder: Builder[ST,FieldSequence[FT,ST]]
= FieldSequence.newBuilder[FT,ST](field)
}
object FieldSequence {
def fromSeq[FT,ST](field: FT)(buf: IndexedSeq[ST])
= new FieldSequence(field, buf)
def newBuilder[FT,ST](field: FT): Builder[ST,FieldSequence[FT,ST]]
= new ArrayBuffer mapResult(fromSeq(field))
implicit def canBuildFrom[FT,ST]:
CanBuildFrom[FieldSequence[FT,ST], ST, FieldSequence[FT,ST]] =
new CanBuildFrom[FieldSequence[FT,ST], ST, FieldSequence[FT,ST]] {
def apply(): Builder[ST,FieldSequence[FT,ST]]
= newBuilder[FT,ST]( _ ) // What goes here?
def apply(from: FieldSequence[FT,ST]): Builder[ST,FieldSequence[FT,ST]]
= from.newBuilder
}
}
The problem is the CanBuildFrom that is implicitly defined needs an apply method with no arguments. But in these circumstances this method is meaningless, as a field (of type FT) is needed to construct a FieldSequence. In fact, it should be impossible to construct a FieldSequence, simply from a sequence of type ST. Is the best I can do to throw an exception here?
Then your class doesn't fulfill the requirements to be a Seq, and methods like flatMap (and hence for-comprehensions) can't work for it.
I'm not sure I agree with Landei about flatMap and map. If you replace with throwing an exception like this, most of the operations should work.
def apply(): Builder[ST,FieldSequence[FT,ST]] = sys.error("unsupported")
From what I can see in TraversableLike, map and flatMap and most other ones use the apply(repr) version. So for comprehensions seemingly work. It also feels like it should follow the Monad laws (the field is just carried accross).
Given the code you have, you can do this:
scala> val fs = FieldSequence.fromSeq("str")(Vector(1,2))
fs: FieldSequence[java.lang.String,Int] = FieldSequence(1, 2)
scala> fs.map(1 + _)
res3: FieldSequence[java.lang.String,Int] = FieldSequence(2, 3)
scala> val fs2 = FieldSequence.fromSeq("str1")(Vector(10,20))
fs2: FieldSequence[java.lang.String,Int] = FieldSequence(10, 20)
scala> for (x <- fs if x > 0; y <- fs2) yield (x + y)
res5: FieldSequence[java.lang.String,Int] = FieldSequence(11, 21, 12, 22)
What doesn't work is the following:
scala> fs.map(_ + "!")
// does not return a FieldSequence
scala> List(1,2).map(1 + _)(collection.breakOut): FieldSequence[String, Int]
java.lang.RuntimeException: unsupported
// this is where the apply() is used
For breakOut to work you would need to implement the apply() method. I suspect you could generate a builder with some default value for field: def apply() = newBuilder[FT, ST](getDefault) with some implementation of getDefault that makes sense for your use case.
For the fact that fs.map(_ + "!") does not preserve the type, you need to modify your signature and implementation, so that the compiler can find a CanBuildFrom[FieldSequence[String, Int], String, FieldSequence[String, String]]
implicit def canBuildFrom[FT,ST_FROM,ST]:
CanBuildFrom[FieldSequence[FT,ST_FROM], ST, FieldSequence[FT,ST]] =
new CanBuildFrom[FieldSequence[FT,ST_FROM], ST, FieldSequence[FT,ST]] {
def apply(): Builder[ST,FieldSequence[FT,ST]]
= sys.error("unsupported")
def apply(from: FieldSequence[FT,ST_FROM]): Builder[ST,FieldSequence[FT,ST]]
= newBuilder[FT, ST](from.field)
}
In the end, my answer was very similar to that in a previous question. The difference with that question and my original and the answer are slight but basically allow anything that has a sequence to be a sequence.
import scala.collection.SeqLike
import scala.collection.mutable.Builder
import scala.collection.mutable.ArrayBuffer
import scala.collection.generic.CanBuildFrom
trait SeqAdapter[+A, Repr[+X] <: SeqAdapter[X,Repr]]
extends Seq[A] with SeqLike[A,Repr[A]] {
val underlyingSeq: Seq[A]
def create[B](seq: Seq[B]): Repr[B]
def apply(index: Int) = underlyingSeq(index)
def length = underlyingSeq.length
def iterator = underlyingSeq.iterator
override protected[this] def newBuilder: Builder[A,Repr[A]] = {
val sac = new SeqAdapterCompanion[Repr] {
def createDefault[B](seq: Seq[B]) = create(seq)
}
sac.newBuilder(create)
}
}
trait SeqAdapterCompanion[Repr[+X] <: SeqAdapter[X,Repr]] {
def createDefault[A](seq: Seq[A]): Repr[A]
def fromSeq[A](creator: (Seq[A]) => Repr[A])(seq: Seq[A]) = creator(seq)
def newBuilder[A](creator: (Seq[A]) => Repr[A]): Builder[A,Repr[A]] =
new ArrayBuffer mapResult fromSeq(creator)
implicit def canBuildFrom[A,B]: CanBuildFrom[Repr[A],B,Repr[B]] =
new CanBuildFrom[Repr[A],B,Repr[B]] {
def apply(): Builder[B,Repr[B]] = newBuilder(createDefault)
def apply(from: Repr[A]) = newBuilder(from.create)
}
}
This fixes all the problems huynhjl brought up. For my original problem, to have a field and a sequence treated as a sequence, a simple class will now do.
trait Field[FT] {
val defaultValue: FT
class FieldSeq[+ST](val field: FT, val underlyingSeq: Seq[ST] = Vector())
extends SeqAdapter[ST,FieldSeq] {
def create[B](seq: Seq[B]) = new FieldSeq[B](field, seq)
}
object FieldSeq extends SeqAdapterCompanion[FieldSeq] {
def createDefault[A](seq: Seq[A]): FieldSeq[A] =
new FieldSeq[A](defaultValue, seq)
override implicit def canBuildFrom[A,B] = super.canBuildFrom[A,B]
}
}
This can be tested as so:
val StringField = new Field[String] { val defaultValue = "Default Value" }
StringField: java.lang.Object with Field[String] = $anon$1#57f5de73
val fs = new StringField.FieldSeq[Int]("str", Vector(1,2))
val fsfield = fs.field
fs: StringField.FieldSeq[Int] = (1, 2)
fsfield: String = str
val fm = fs.map(1 + _)
val fmfield = fm.field
fm: StringField.FieldSeq[Int] = (2, 3)
fmfield: String = str
val fs2 = new StringField.FieldSeq[Int]("str1", Vector(10, 20))
val fs2field = fs2.field
fs2: StringField.FieldSeq[Int] = (10, 20)
fs2field: String = str1
val ffor = for (x <- fs if x > 0; y <- fs2) yield (x + y)
val fforfield = ffor.field
ffor: StringField.FieldSeq[Int] = (11, 21, 12, 22)
fforfield: String = str
val smap = fs.map(_ + "!")
val smapfield = smap.field
smap: StringField.FieldSeq[String] = (1!, 2!)
smapfield: String = str
val break = List(1,2).map(1 + _)(collection.breakOut): StringField.FieldSeq[Int]
val breakfield = break.field
break: StringField.FieldSeq[Int] = (2, 3)
breakfield: String = Default Value
val x: StringField.FieldSeq[Any] = fs
val xfield = x.field
x: StringField.FieldSeq[Any] = (1, 2)
xfield: String = str

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 ;-)

Scala, partial functions

Is there any way to create a PartialFunction except through the case statement?
I'm curious, because I'd like to express the following (scala pseudo ahead!)...
val bi = BigInt(_)
if (bi.isValidInt) bi.intValue
... as a partial function, and doing
val toInt : PartialFunction[String, Int] = {
case s if BigInt(s).isValidInt => BigInt(s).intValue
}
seems redundant since I create a BigInt twice.
Not sure I understand the question. But here's my attempt: Why not create an extractor?
object ValidBigInt {
def unapply(s: String): Option[Int] = {
val bi = BigInt(s)
if (bi.isValidInt) Some(bi.intValue) else None
}
}
val toInt: PartialFunction[String, Int] = {
case ValidBigInt(i) => i
}
The other option is (and that may answer the question as to whether one can create PartialFunction other than with a case literal):
val toInt = new PartialFunction[String, Int] {
def isDefinedAt(s: String) = BigInt(s).isValidInt
def apply(s: String) = BigInt(s).intValue
}
However since the idea of a partial function is that it's only partially defined, in the end you will still do redundant things -- you need to create a big int to test whether it's valid, and then in the function application you create the big int again...
I saw a project at Github that tried to come around this by somewhat caching the results from isDefinedAt. If you go down to the benchmarks, you'll see that it turned out to be slower than the default Scala implementation :)
So if you want to get around the double nature of isDefinedAt versus apply, you should just go straight for a (full) function that provides an Option[Int] as result.
I think you're looking for lift/unlift. lift takes a partial function and turns it into a function that returns an Option. Unlift takes a function with one argument that returns an option, and returns a partial function.
import scala.util.control.Exception._
scala> def fn(s: String) = catching(classOf[NumberFormatException]) opt {BigInt(s)}
fn: (s: String)Option[scala.math.BigInt]
scala> val fnPf = Function.unlift(fn)
fnPf: PartialFunction[String,scala.math.BigInt] = <function1>
scala> val fn = fnPf.lift
fn: String => Option[scala.math.BigInt] = <function1>
Closely related, you also want to look at this answer for information about cond and condOpt:
scala> import PartialFunction._
import PartialFunction._
scala> cond("abc") { case "def" => true }
res0: Boolean = false
scala> condOpt("abc") { case x if x.length == 3 => x + x }
res1: Option[java.lang.String] = Some(abcabc)
You can write out a PartialFunction "longhand" if you'd like:
object pf extends PartialFunction[Int,String] {
def isDefinedAt(in: Int) = in % 2 == 0
def apply(in: Int) = {
if (in % 2 == 0)
"even"
else
throw new MatchError(in + " is odd")
}
Okay, I got this
import java.lang.NumberFormatException
import scala.util.control.Exception._
val toInt: PartialFunction[String, Int] = {
catching(classOf[NumberFormatException]) opt BigInt(_) match {
case Some(bi) if bi.isValidInt => bi.intValue
}
}
How about this?
val toInt: PartialFunction[String, Int] = (s: String) => BigInt(s) match {
case bi if bi.isValidInt => bi.intValue
}

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))
}
}