I am composing function literals, though unlike most examples I've seen I'm starting with a multi-argument function that is then curried.
I have:
//types
case class Thing1(v: Double)
case class Thing2(v: Double)
case class Thing3(v: Double)
type Multiplier = Double
//functions
val f1 = (t: Thing1, m: Multiplier) => Thing2(m * t.v)
val f2 = (t: Thing2) => Thing3(t.v)
I want to compose f1 and f2 to get a combined function
Thing1 => (Multiplier => Thing3)
As expected, the following doesn't compile:
val fcomposed1 = f1.curried.andThen(f2) // does not compile
By experimentation, I was able to work out that the following does compile and has the right signature for fcomposed:
val fcomposed2 = f1.curried(_:Thing1).andThen(f2)
I've read various sources like What are all the uses of an underscore in Scala? and possibly relevant Why does Scala apply thunks automatically, sometimes? but unfortunately I still cannot work out exactly step-by-step what is happening here and why it works.
Furthermore, I would expect the above separated into two expressions to work identically to fcomposed2, however instead the second does not compile:
val f1partial = f1.curried(_:Thing1)
val fcomposed3 = f1partial.andThen(f2) // does not compile - same error as fcomposed1
Looks like f1partial returns the same signature as f1.curried, which makes me wonder further how the earlier fcomposed2 works.
Could someone please explain both behaviours step by step?
Here, the _ is acting as syntactical sugar for a lambda expression, but at a level you might not expect.
f1.curried has type Thing1 => Multiplier => Thing2
f1.curried(_:Thing1) is the same as { x: Thing1 => f1.curried(x) }. Since the result of f1.curried(x) has type Multiplier => Thing2, the final type of the whole expression is still Thing1 => Multiplier => Thing2. So it is not valid to call andThen(f2) on the result (f1partial) because the input type of function f2 (Thing2) is not the same as the output of the previous function (Multiplier => Thing2).
By contrast, f1.curried(_:Thing1).andThen(f2) expands to { x: Thing1 => f1.curried(x).andThen(f2) }. f1.curried(x) evaluates to type Multiplier => Thing2, like stated earlier, so you can call andThen(f2) on that, resulting in a Multiplier => Thing3. So then the entire expression evaluates to a Thing1 => Multiplier => Thing3
Perhaps it's more clear if you think about the differences between these two expressions:
val fcomposed1 = { x: Thing1 => f1.curried(x).andThen(f2) } // valid
val fcomposed2 = { x: Thing1 => f1.curried(x) }.andThen(f2) // error
Related
I am new in Scala and I just came across a situation that I would like someone could explain me.
When watching a Martin Odersky's course I found the following script he uses to explain functions which return a function
val tolerance = 0.0001
def isCloseEnough(x : Double, y : Double) = abs((x - y)/ x) / x < tolerance
def fixedPoint(f : Double => Double)(firstGuess: Double) = {
def iterate(guess: Double): Double = {
val next = f(guess)
if(isCloseEnough(guess,next))next
else iterate(next)
}
iterate(firstGuess)
}
fixedPoint(x => 1 + x/2)(1)
def averageDamp(f: Double => Double)(x: Double) = (x + f(x))/2
def sqrt(x : Double) = fixedPoint(averageDamp(y => x/y))(1)
sqrt(2)
I understand perfectly how the scrip works, but I didn't expect this line:
fixedPoint(averageDamp(y => x/y))(1)
I know that thanks to Currying, Scala let us write functions with several parameter list. So the call to fixedPoint passing as parameter the result of avergaDamp and (1) is clear for me.
What I don't understand is how averageDamp uses the second parameter list of fixedPoint when it itself is inside the first parameter list. I though that would be a different scope, so I was expecting something like:
fixedPoint(averageDamp(y => x/y)(1))(1)
What is the property of Scala which allow us to implement the currying in this way? Is something similar to an implicit applied to a parameter list?
Thanks for your time
This is just how multiple parameter lists work: averageDamp(y => x/y) is equivalent to z => averageDamp(y => x/y)(z) and so its type is Double => Double.
If you wrote fixedPoint(averageDamp(y => x/y)(1))(1) as you expect, it would have a type mismatch because averageDamp(y => x/y)(1) has type Double and fixedPoint needs Double => Double.
Implicits aren't relevant here.
This line works because in the following expression:
fixedPoint(averageDamp(y => x/y))(1)
function "averageDamp(y => x/y)" is "passed by name" i.e. it will not be evaluated while passing to function "fixedPoint" but will be evaluated when it is called from inside "fixedPoint".
value "(1)" is just pass to argument "firstGuess" of "fixedPoint" which will be supplied to parameter "guess" inside the function definition in following expression:
val next = f(guess)
I'm new to scala so sorry if this is easy but I've had a hard time finding the answer.
I'm having a hard time understanding what <- does, and what ()=> Unit does. My understanding of these is that -> is sometimes used in foreach, and that => is used in maps. Trying to google "scala "<-" doesn't prove very fruitful. I found http://jim-mcbeath.blogspot.com/2008/12/scala-operator-cheat-sheet.html but it wasn't as helpful as it looks at first glance.
val numbers = List("one", "two", "three","four","five")
def operateOnList() {
for(number <- numbers) {
println(number + ": came out of this crazy thing!")
}
}
def tweener(method: () => Unit) {
method()
}
tweener(operateOnList)
() => Unit means that method is a function that takes no parameter and returns nothing (Unit).
<- is used in the for comprehension as an kind of assignation operator. for comprehension are a little bit specific because they are internally transformed. In your case, that would be transforms as numbers.foreach(i => println(i + ": came out of this crazy thing!"))
<- in the for comprehension means that we will iterate over each element of the numbers list and passed to number.
'<-' could be threated as 'in' so
for(number <- numbers){
...
}
could be translated into english as for each number in numbers do
'<-' has a twin with a different semantics: '->'. Simply it is just a replacement of comma in tuples: (a,b) is an equivalent to (a->b) or just a->b. The meaning after this symbols is that 'a' maps to 'b'. So this is often used in definition of Maps:
Map("a" -> 1,"aba" -> 3)
Map("London" -> "Britain", "Paris" -> "France")
Here you can think about mapping as a projection (or not) via some function (e.g. 'length of string', 'capital of').
Better explanation is here.
Last, but not least is '=>' which is map too, but with a general semantics. '=>' is in use all over the place in anonymous expressions:
scala> List(1,2,3,4).map(current => current+1)
res5: List[Int] = List(2, 3, 4, 5)
Which is for each element map current element of list with function 'plus one'
List(1,2,3,4).map(c => c%2 match {
| case 0 => "even"
| case 1 => "odd"
| }
| )
res6: List[java.lang.String] = List(odd, even, odd, even)
Map current element with provided pattern mathing
In the method
def tweener(method: () => Unit) {
method()
}
the method is called tweener, the parameter is arbitrarily named method, and the type of method is () => Unit, which is a function type, as you can tell from the =>.
Unit is a return type similar to void in Java, and represents no interesting value being returned. For instance, the return type of print is Unit. () represents an empty parameter list.
Confusingly, () is also used to represent an instance of Unit, called the unit value, the only value a Unit can take. But this is not what it means in the function type () => Unit, just as you can't have a function type 42 => Unit.
Back to your example, tweener takes a function of type () => Unit. operateOnList is a method, but it gets partially applied by the compiler to turn it into a function value. You can turn methods into functions yourself like this:
scala> def m = println("hi")
m: Unit
scala> m _
res17: () => Unit = <function0>
operateOnList can be turned into the right type of function because its parameter list is empty (), and its return type is implicity Unit.
As a side-note, if operateOnList were defined without the empty parameter list (as is legal, but more common when the return type is not Unit), you would need to manually partially apply it, else its value will be passed instead:
def f1() {}
def f2 {}
def g(f: () => Unit) {}
g(f1) // OK
g(f2) // error, since we're passing f2's result (),
// rather than partial function () => Unit
g(f2 _) // OK
In Scala, I have progressively lost my Java/C habit of thinking in a control-flow oriented way, and got used to go ahead and get the object I'm interested in first, and then usually apply something like a match or a map() or foreach() for collections. I like it a lot, since it now feels like a more natural and more to-the-point way of structuring my code.
Little by little, I've wished I could program the same way for conditions; i.e., obtain a Boolean value first, and then match it to do various things. A full-blown match, however, does seem a bit overkill for this task.
Compare:
obj.isSomethingValid match {
case true => doX
case false => doY
}
vs. what I would write with style closer to Java:
if (obj.isSomethingValid)
doX
else
doY
Then I remembered Smalltalk's ifTrue: and ifFalse: messages (and variants thereof). Would it be possible to write something like this in Scala?
obj.isSomethingValid ifTrue doX else doY
with variants:
val v = obj.isSomethingValid ifTrue someVal else someOtherVal
// with side effects
obj.isSomethingValid ifFalse {
numInvalid += 1
println("not valid")
}
Furthermore, could this style be made available to simple, two-state types like Option? I know the more idiomatic way to use Option is to treat it as a collection and call filter(), map(), exists() on it, but often, at the end, I find that I want to perform some doX if it is defined, and some doY if it isn't. Something like:
val ok = resultOpt ifSome { result =>
println("Obtained: " + result)
updateUIWith(result) // returns Boolean
} else {
numInvalid += 1
println("missing end result")
false
}
To me, this (still?) looks better than a full-blown match.
I am providing a base implementation I came up with; general comments on this style/technique and/or better implementations are welcome!
First: we probably cannot reuse else, as it is a keyword, and using the backticks to force it to be seen as an identifier is rather ugly, so I'll use otherwise instead.
Here's an implementation attempt. First, use the pimp-my-library pattern to add ifTrue and ifFalse to Boolean. They are parametrized on the return type R and accept a single by-name parameter, which should be evaluated if the specified condition is realized. But in doing so, we must allow for an otherwise call. So we return a new object called Otherwise0 (why 0 is explained later), which stores a possible intermediate result as a Option[R]. It is defined if the current condition (ifTrue or ifFalse) is realized, and is empty otherwise.
class BooleanWrapper(b: Boolean) {
def ifTrue[R](f: => R) = new Otherwise0[R](if (b) Some(f) else None)
def ifFalse[R](f: => R) = new Otherwise0[R](if (b) None else Some(f))
}
implicit def extendBoolean(b: Boolean): BooleanWrapper = new BooleanWrapper(b)
For now, this works and lets me write
someTest ifTrue {
println("OK")
}
But, without the following otherwise clause, it cannot return a value of type R, of course. So here's the definition of Otherwise0:
class Otherwise0[R](intermediateResult: Option[R]) {
def otherwise[S >: R](f: => S) = intermediateResult.getOrElse(f)
def apply[S >: R](f: => S) = otherwise(f)
}
It evaluates its passed named argument if and only if the intermediate result it got from the preceding ifTrue or ifFalse is undefined, which is exactly what is wanted. The type parametrization [S >: R] has the effect that S is inferred to be the most specific common supertype of the actual type of the named parameters, such that for instance, r in this snippet has an inferred type Fruit:
class Fruit
class Apple extends Fruit
class Orange extends Fruit
val r = someTest ifTrue {
new Apple
} otherwise {
new Orange
}
The apply() alias even allows you to skip the otherwise method name altogether for short chunks of code:
someTest.ifTrue(10).otherwise(3)
// equivalently:
someTest.ifTrue(10)(3)
Finally, here's the corresponding pimp for Option:
class OptionExt[A](option: Option[A]) {
def ifNone[R](f: => R) = new Otherwise1(option match {
case None => Some(f)
case Some(_) => None
}, option.get)
def ifSome[R](f: A => R) = new Otherwise0(option match {
case Some(value) => Some(f(value))
case None => None
})
}
implicit def extendOption[A](opt: Option[A]): OptionExt[A] = new OptionExt[A](opt)
class Otherwise1[R, A1](intermediateResult: Option[R], arg1: => A1) {
def otherwise[S >: R](f: A1 => S) = intermediateResult.getOrElse(f(arg1))
def apply[S >: R](f: A1 => S) = otherwise(f)
}
Note that we now also need Otherwise1 so that we can conveniently passed the unwrapped value not only to the ifSome function argument, but also to the function argument of an otherwise following an ifNone.
You may be looking at the problem too specifically. You would probably be better off with the pipe operator:
class Piping[A](a: A) { def |>[B](f: A => B) = f(a) }
implicit def pipe_everything[A](a: A) = new Piping(a)
Now you can
("fish".length > 5) |> (if (_) println("Hi") else println("Ho"))
which, admittedly, is not quite as elegant as what you're trying to achieve, but it has the great advantage of being amazingly versatile--any time you want to put an argument first (not just with booleans), you can use it.
Also, you already can use options the way you want:
Option("fish").filter(_.length > 5).
map (_ => println("Hi")).
getOrElse(println("Ho"))
Just because these things could take a return value doesn't mean you have to avoid them. It does take a little getting used to the syntax; this may be a valid reason to create your own implicits. But the core functionality is there. (If you do create your own, consider fold[B](f: A => B)(g: => B) instead; once you're used to it the lack of the intervening keyword is actually rather nice.)
Edit: Although the |> notation for pipe is somewhat standard, I actually prefer use as the method name, because then def reuse[B,C](f: A => B)(g: (A,B) => C) = g(a,f(a)) seems more natural.
Why don't just use it like this:
val idiomaticVariable = if (condition) {
firstExpression
} else {
secondExpression
}
?
IMO, its very idiomatic! :)
I found myself writing something like this quite often:
a match {
case `b` => // do stuff
case _ => // do nothing
}
Is there a shorter way to check if some value matches a pattern? I mean, in this case I could just write if (a == b) // do stuff, but what if the pattern is more complex? Like when matching against a list or any pattern of arbitrary complexity. I'd like to be able to write something like this:
if (a matches b) // do stuff
I'm relatively new to Scala, so please pardon, if I'm missing something big :)
This is exactly why I wrote these functions, which are apparently impressively obscure since nobody has mentioned them.
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)
scala> condOpt("abc") { case x if x.length == 4 => x + x }
res2: Option[java.lang.String] = None
The match operator in Scala is most powerful when used in functional style. This means, rather than "doing something" in the case statements, you would return a useful value. Here is an example for an imperative style:
var value:Int = 23
val command:String = ... // we get this from somewhere
command match {
case "duplicate" => value = value * 2
case "negate" => value = -value
case "increment" => value = value + 1
// etc.
case _ => // do nothing
}
println("Result: " + value)
It is very understandable that the "do nothing" above hurts a little, because it seems superflous. However, this is due to the fact that the above is written in imperative style. While constructs like these may sometimes be necessary, in many cases you can refactor your code to functional style:
val value:Int = 23
val command:String = ... // we get this from somewhere
val result:Int = command match {
case "duplicate" => value * 2
case "negate" => -value
case "increment" => value + 1
// etc.
case _ => value
}
println("Result: " + result)
In this case, you use the whole match statement as a value that you can, for example, assign to a variable. And it is also much more obvious that the match statement must return a value in any case; if the last case would be missing, the compiler could not just make something up.
It is a question of taste, but some developers consider this style to be more transparent and easier to handle in more real-world examples. I would bet that the inventors of the Scala programming language had a more functional use in mind for match, and indeed the if statement makes more sense if you only need to decide whether or not a certain action needs to be taken. (On the other hand, you can also use if in the functional way, because it also has a return value...)
This might help:
class Matches(m: Any) {
def matches[R](f: PartialFunction[Any, R]) { if (f.isDefinedAt(m)) f(m) }
}
implicit def any2matches(m: Any) = new Matches(m)
scala> 'c' matches { case x: Int => println("Int") }
scala> 2 matches { case x: Int => println("Int") }
Int
Now, some explanation on the general nature of the problem.
Where may a match happen?
There are three places where pattern matching might happen: val, case and for. The rules for them are:
// throws an exception if it fails
val pattern = value
// filters for pattern, but pattern cannot be "identifier: Type",
// though that can be replaced by "id1 # (id2: Type)" for the same effect
for (pattern <- object providing map/flatMap/filter/withFilter/foreach) ...
// throws an exception if none of the cases match
value match { case ... => ... }
There is, however, another situation where case might appear, which is function and partial function literals. For example:
val f: Any => Unit = { case i: Int => println(i) }
val pf: PartialFunction[Any, Unit] = { case i: Int => println(i) }
Both functions and partial functions will throw an exception if called with an argument that doesn't match any of the case statements. However, partial functions also provide a method called isDefinedAt which can test whether a match can be made or not, as well as a method called lift, which will turn a PartialFunction[T, R] into a Function[T, Option[R]], which means non-matching values will result in None instead of throwing an exception.
What is a match?
A match is a combination of many different tests:
// assign anything to x
case x
// only accepts values of type X
case x: X
// only accepts values matches by pattern
case x # pattern
// only accepts a value equal to the value X (upper case here makes a difference)
case X
// only accepts a value equal to the value of x
case `x`
// only accept a tuple of the same arity
case (x, y, ..., z)
// only accepts if extractor(value) returns true of Some(Seq()) (some empty sequence)
case extractor()
// only accepts if extractor(value) returns Some something
case extractor(x)
// only accepts if extractor(value) returns Some Seq or Tuple of the same arity
case extractor(x, y, ..., z)
// only accepts if extractor(value) returns Some Tuple2 or Some Seq with arity 2
case x extractor y
// accepts if any of the patterns is accepted (patterns may not contain assignable identifiers)
case x | y | ... | z
Now, extractors are the methods unapply or unapplySeq, the first returning Boolean or Option[T], and the second returning Option[Seq[T]], where None means no match is made, and Some(result) will try to match result as described above.
So there are all kinds of syntactic alternatives here, which just aren't possible without the use of one of the three constructions where pattern matches may happen. You may able to emulate some of the features, like value equality and extractors, but not all of them.
Patterns can also be used in for expressions. Your code sample
a match {
case b => // do stuff
case _ => // do nothing
}
can then be expressed as
for(b <- Some(a)) //do stuff
The trick is to wrap a to make it a valid enumerator. E.g. List(a) would also work, but I think Some(a) is closest to your intended meaning.
The best I can come up with is this:
def matches[A](a:A)(f:PartialFunction[A, Unit]) = f.isDefinedAt(a)
if (matches(a){case ... =>}) {
//do stuff
}
This won't win you any style points though.
Kim's answer can be “improved” to better match your requirement:
class AnyWrapper[A](wrapped: A) {
def matches(f: PartialFunction[A, Unit]) = f.isDefinedAt(wrapped)
}
implicit def any2wrapper[A](wrapped: A) = new AnyWrapper(wrapped)
then:
val a = "a" :: Nil
if (a matches { case "a" :: Nil => }) {
println("match")
}
I wouldn't do it, however. The => }) { sequence is really ugly here, and the whole code looks much less clear than a normal match. Plus, you get the compile-time overhead of looking up the implicit conversion, and the run-time overhead of wrapping the match in a PartialFunction (not counting the conflicts you could get with other, already defined matches methods, like the one in String).
To look a little bit better (and be less verbose), you could add this def to AnyWrapper:
def ifMatch(f: PartialFunction[A, Unit]): Unit = if (f.isDefinedAt(wrapped)) f(wrapped)
and use it like this:
a ifMatch { case "a" :: Nil => println("match") }
which saves you your case _ => line, but requires double braces if you want a block instead of a single statement... Not so nice.
Note that this construct is not really in the spirit of functional programming, as it can only be used to execute something that has side effects. We can't easily use it to return a value (therefore the Unit return value), as the function is partial — we'd need a default value, or we could return an Option instance. But here again, we would probably unwrap it with a match, so we'd gain nothing.
Frankly, you're better off getting used to seeing and using those match frequently, and moving away from this kind of imperative-style constructs (following Madoc's nice explanation).
I have a recursive function that takes a Map as single parameter. It then adds new entries to that Map and calls itself with this larger Map. Please ignore the return values for now. The function isn't finished yet. Here's the code:
def breadthFirstHelper( found: Map[AIS_State,(Option[AIS_State], Int)] ): List[AIS_State] = {
val extension =
for(
(s, v) <- found;
next <- this.expand(s) if (! (found contains next) )
) yield (next -> (Some(s), 0))
if ( extension.exists( (s -> (p,c)) => this.isGoal( s ) ) )
List(this.getStart)
else
breadthFirstHelper( found ++ extension )
}
In extension are the new entries that shall get added to the map. Note that the for-statement generates an iterable, not a map. But those entries shall later get added to the original map for the recursive call. In the break condition, I need to test whether a certain value has been generated inside extension. I try to do this by using the exists method on extension. But the syntax for extracting values from the map entries (the stuff following the yield) doesn't work.
Questions:
How do I get my break condition (the boolean statement to the if) to work?
Is it a good idea to do recursive work on a immutable Map like this? Is this good functional style?
When using a pattern-match (e.g. against a Tuple2) in a function, you need to use braces {} and the case statement.
if (extension.exists { case (s,_) => isGoal(s) } )
The above also uses the fact that it is more clear when matching to use the wildcard _ for any allowable value (which you subsequently do not care about). The case xyz gets compiled into a PartialFunction which in turn extends from Function1 and hence can be used as an argument to the exists method.
As for the style, I am not functional programming expert but this seems like it will be compiled into a iterative form (i.e. it's tail-recursive) by scalac. There's nothing which says "recursion with Maps is bad" so why not?
Note that -> is a method on Any (via implicit conversion) which creates a Tuple2 - it is not a case class like :: or ! and hence cannot be used in a case pattern match statement. This is because:
val l: List[String] = Nil
l match {
case x :: xs =>
}
Is really shorthand/sugar for
case ::(x, xs) =>
Similarly a ! b is equivalent to !(a, b). Of course, you may have written your own case class ->...
Note2: as Daniel says below, you cannot in any case use a pattern-match in a function definition; so while the above partial function is valid, the following function is not:
(x :: xs) =>
This is a bit convoluted for me to follow, whatever Oxbow Lakes might think.
I'd like first to clarify one point: there is no break condition in for-comprehensions. They are not loops like C's (or Java's) for.
What an if in a for-comprehension means is a guard. For instance, let's say I do this:
for {i <- 1 to 10
j <- 1 to 10
if i != j
} yield (i, j)
The loop isn't "stopped" when the condition is false. It simply skips the iterations for which that condition is false, and proceed with the true ones. Here is another example:
for {i <- 1 to 10
j <- 1 to 10
if i % 2 != 0
} yield (i, j)
You said you don't have side-effects, so I can skip a whole chapter about side effects and guards on for-comprehensions. On the other hand, reading a blog post I made recently on Strict Ranges is not a bad idea.
So... give up on break conditions. They can be made to work, but they are not functional. Try to rephrase the problem in a more functional way, and the need for a break condition will be replaced by something else.
Next, Oxbow is correct in that (s -> (p,c) => isn't allowed because there is no extractor defined on an object called ->, but, alas, even (a :: b) => would not be allowed, because there is no pattern matching going on in functional literal parameter declaration. You must simply state the parameters on the left side of =>, without doing any kind of decomposition. You may, however, do this:
if ( extension.exists( t => val (s, (p,c)) = t; this.isGoal( s ) ) )
Note that I replaced -> with ,. This works because a -> b is a syntactic sugar for (a, b), which is, itself, a syntactic sugar for Tuple2(a, b). As you don't use neither p nor c, this works too:
if ( extension.exists( t => val (s, _) = t; this.isGoal( s ) ) )
Finally, your recursive code is perfectly fine, though probably not optimized for tail-recursion. For that, you either make your method final, or you make the recursive function private to the method. Like this:
final def breadthFirstHelper
or
def breadthFirstHelper(...) {
def myRecursiveBreadthFirstHelper(...) { ... }
myRecursiveBreadthFirstHelper(...)
}
On Scala 2.8 there is an annotation called #TailRec which will tell you if the function can be made tail recursive or not. And, in fact, it seems there will be a flag to display warnings about functions that could be made tail-recursive if slightly changed, such as above.
EDIT
Regarding Oxbow's solution using case, that's a function or partial function literal. It's type will depend on what the inference requires. In that case, because that's that exists takes, a function. However, one must be careful to ensure that there will always be a match, otherwise you get an exception. For example:
scala> List(1, 'c') exists { case _: Int => true }
res0: Boolean = true
scala> List(1, 'c') exists { case _: String => true }
scala.MatchError: 1
at $anonfun$1.apply(<console>:5)
... (stack trace elided)
scala> List(1, 'c') exists { case _: String => true; case _ => false }
res3: Boolean = false
scala> ({ case _: Int => true } : PartialFunction[AnyRef,Boolean])
res5: PartialFunction[AnyRef,Boolean] = <function1>
scala> ({ case _: Int => true } : Function1[Int, Boolean])
res6: (Int) => Boolean = <function1>
EDIT 2
The solution Oxbow proposes does use pattern matching, because it is based on function literals using case statements, which do use pattern matching. When I said it was not possible, I was speaking of the syntax x => s.