I have the following map with target node "E":
val map = Map("A" -> "B", "A" -> "C", "C" -> "D", "C" -> "E")
It describe a directed node graph, which looks like:
A
/ \
B C
/ \
D E
I need to enter the graph at any point and generate a route to the target node.
Example 1: Enter at A -> Route: A->C->E
Example 2: Enter at D -> Route: D->C->E
Example 3: Enter at B -> Route: B->A->C->E
Does anyone know of a compact algo which could do this as this must have been attempted before.
Look forward to hearing from you.
Cheers,
Jez
So, here is it:
val map = List("A" -> "B", "A" -> "C", "C" -> "D", "C" -> "E")
def pathOf(tree: Iterable[(String,String)],from: String,to: String, path: List[String] = Nil): List[String] = {
if(from == to) return to::path
tree.filterNot{ case(a,b) => path.contains(a)||path.contains(b) }
.collect{
case (a,b) if a == to => b
case (a,b) if b == to => a
}.map{ x => pathOf(tree,from,x,to::path) }
.find{ _.nonEmpty }
.getOrElse(Nil)
}
Use case:
scala> pathOf(map,"B","E").mkString("->")
res1: String = B->A->C->E
scala> pathOf(map,"C","E").mkString("->")
res2: String = C->E
As relatively new to Scala, I take this problem as a good exercise for myself and would like to share my solution with all of you. Any comments are welcomed!
BTW, the solution given by #Eastsun is a depth-first search which "memorizes" visited nodes in each path, while mine is a breadth-first search where memorization is not required (though you can definitely add this feature to improve efficiency). For trees they yield the same answer but for general graphs they can differ.
The neighbors of each node can also be cached for optimization.
val graph = Vector(("A","B"), ("A","C"), ("C","D"), ("C","E"))
def adjacent(a: String) = {
graph flatMap {
case (`a`, x) => Some(x)
case (x, `a`) => Some(x)
case _ => None
}
}
def go(from: String, to: String) {
def expand(paths: Vector[Vector[String]]) {
paths.find(_.last==to) match {
case Some(x) => println(x); return
case None => expand(paths flatMap { e =>
adjacent(e.last) map (e :+ _)
})
}
}
expand(Vector(Vector(from)))
}
// tests
go("A","E") // Vector(A, C, E)
go("B","E") // Vector(B, A, C, E)
go("D","E") // Vector(D, C, E)
Version with memorization: change
adjacent(e.last) map (e :+ _)
to
adjacent(e.last) filterNot (x => paths.flatten contains x) map (e :+ _)
or put this functionality in the adjacent function.
Related
Newbie question.
I am looping through a list and need keep state in between the items.
For instance
val l = List("a", "1", "2", "3", "b", "4")
var state: String = ""
l.foreach(o => {
if (toInt(o).isEmpty) state = o else println(state + o.toString)
})
what's the alternative for the usage of var here?
You should keep in mind that it's sometimes (read: when it makes the code more readable and maintainable by others) okay to use mutability when performing some operation that's easily expressed with mutable state as long as that mutable state is confined to as little of your program as possible. Using (e.g.) foldLeft to maintain an accumulator here without using a var doesn't gain you much.
That said, here's one way to go about doing this:
val listOfThings: Seq[Either[Char, Int]] = Seq(Left('a'), Right(11), Right(212), Left('b'), Right(89))
val result = listOfThings.foldLeft(Seq[(Char, Seq[Int])]()) {
case (accumulator, Left(nextChar)) => accumulator :+ (nextChar, Seq.empty)
case (accumulator, Right(nextInt)) =>
val (currentChar, currentSequence) = accumulator.last
accumulator.dropRight(1) :+ (currentChar, currentSequence :+ nextInt)
}
result foreach {
case (char, numbers) => println(numbers.map(num => s"$char-$num").mkString(" "))
}
Use foldLeft:
l.foldLeft(""){ (state, o) =>
if(toInt(o).isEmpty) o
else {
println(state + o.toString)
state
}
}
Pass an arg:
scala> def collapse(header: String, vs: List[String]): Unit = vs match {
| case Nil =>
| case h :: t if h.forall(Character.isDigit) => println(s"$header$h") ; collapse(header, t)
| case h :: t => collapse(h, t)
| }
collapse: (header: String, vs: List[String])Unit
scala> collapse("", vs)
a1
a2
a3
b4
As simple as:
val list: List[Int] = List.range(1, 10) // Create list
def updateState(i : Int) : Int = i + 1 // Generate new state, just add one to each position. That will be the state
list.foldRight[List[(Int,Int)]](List())((a, b) => (a, updateState(a)) :: b)
Note that the result is a list of Tuple2: (Element, State), and each state depends on the element of the list.
Hope this helps
There are two major options to pass a state in functional programming when processing collections (I assume you want to get your result as a variable):
Recursion (classic)
val xs = List("a", "11", "212", "b", "89")
#annotation.tailrec
def fold(seq: ListBuffer[(String, ListBuffer[String])],
xs: Seq[String]): ListBuffer[(String, ListBuffer[String])] = {
(seq, xs) match {
case (_, Nil) =>
seq
case (_, c :: tail) if toInt(c).isEmpty =>
fold(seq :+ ((c, ListBuffer[String]())), tail)
case (init :+ ((c, seq)), i :: tail) =>
fold(init :+ ((c, seq :+ i)), tail)
}
}
val result =
fold(ListBuffer[(String, ListBuffer[String])](), xs)
// Get rid of mutable ListBuffer
.toSeq
.map {
case (c, seq) =>
(c, seq.toSeq)
}
//> List((a,List(11, 212)), (b,List(89)))
foldLeft et al.
val xs = List("a", "11", "212", "b", "89")
val result =
xs.foldLeft(
ListBuffer[(String, ListBuffer[String])]()
) {
case (seq, c) if toInt(c).isEmpty =>
seq :+ ((c, ListBuffer[String]()))
case (init :+ ((c, seq)), i) =>
init :+ ((c, seq :+ i))
}
// Get rid of mutable ListBuffer
.toSeq
.map {
case (c, seq) =>
(c, seq.toSeq)
}
//> List((a,List(11, 212)), (b,List(89)))
Which one is better? Unless you want to abort your processing in the middle of your collection (like e.g. in find) foldLeft is considered a better way and it has slightly less boilerplate, but otherwise they are very similar.
I'm using ListBuffer here to avoid reversing lists.
I am struggling with finding an elegant FP approach to solving the following problem in Scala:
Say I have a set of candidate keys
val validKeys = Set("key1", "key2", "key3")
And a list that
Starts with a key
has some number of non-keys (> 0) between each key
Does not end with a key
For example:
val myList = List("key3", "foo", "bar", "key1", "baz")
I'd like to transform this list into a map by choosing using valid keys as the key and aggregating non-keys as the value. So, in the example above:
("key3" -> "foo\nbar", "key1" -> "baz")
Thanks in advance.
Short and simple:
def create(a: List[String]): Map[String, String] = a match {
case Nil => Map()
case head :: tail =>
val (vals, rest) = tail.span(!validKeys(_))
create(rest) + (head -> vals.mkString("\n"))
}
Traversing a list from left to right, accumulating a result should suggest foldLeft
myList.foldLeft((Map[String, String](), "")) {
case ((m, lk), s) =>
if (validKeys contains s)
(m updated (s, ""), s)
else (m updated (lk, if (m(lk) == "") s else m(lk) + "\n" + s), lk)
}._1
// Map(key3 -> foo\nbar, key1 -> baz)
As a first approximation solution:
def group(list:List[String]):List[(String, List[String])] = {
#tailrec
def grp(list:List[String], key:String, acc:List[String]):List[(String, List[String])] =
list match {
case Nil => List((key, acc.reverse))
case x :: xs if validKeys(x) => (key, acc.reverse)::group(x::xs)
case x :: xs => grp(xs, key, x::acc)
}
list match {
case Nil => Nil
case x::xs => grp(xs, x, List())
}
}
val map = group(myList).toMap
Another option:
list.foldLeft((Map[String, String](), "")) {
case ((map, key), item) if validKeys(item) => (map, item)
case ((map, key), item) =>
(map.updated(key, map.get(key).map(v => v + "\n" + item).getOrElse(item)), key)
}._1
Is there any Scala trick to enable pattern matching against map keys? In other words, I'd like to have an extractor that beside the Map instance accepted also a key value that would mean I want this pattern to match only if the matchable value is an instance of Map and there is an entry with the given key in it and the value for this entry be subject to recursive pattern matching.
Something like this:
myMap match {
case MyMap("a")(a) => // do smth with value a
case MyMap("b")(MyMap("c")(c)) => // do smth with value c
}
Update:
I've found some way to approach closer to the goal, but it's still not perfect because it implies definition of synthetic key-value-holders:
case class MapKey[K](key: K) {
def unapply(o: Any) = o match {
case m: Map[K, _] ⇒ m.get(key)
case _ ⇒ None
}
}
val m1 = Map("a" → "aa", "b" → Map("c" → "cc"))
val m2 = Map("a" → "aa", "d" → "dd")
val b = MapKey("b")
val c = MapKey("c")
val d = MapKey("d")
for (m ← List(m1, m2)) m match {
case b(c(x)) ⇒ println(s"b > c: $x")
case d(x) ⇒ println(s"d: $x")
}
Similar question: Can extractors be customized with parameters in the body of a case statement (or anywhere else that an extractor would be used)?
Feature request: SI-5435
Maybe you are looking for solution that you don't really need? Can't imagine extractors here. You can use PF if you want to match key-value pairs:
val map = Map[String, String]("a" -> "b")
def matchTuple[A,B,C](map: Map[A,B])(pf: PartialFunction[(A,B), C]) =
map.collectFirst(pf)
matchTuple(map) {
case ("a", b) => println("value for a is " + b)
}
Return type is Option[Unit] because we use collectFirst and println
Given a sequence of elements and a predicate p, I would like to produce a sequence of sequences such that, in each subsequence, either all elements satisfy p or the sequence has length 1. Additionally, calling .flatten on the result should give me back my original sequence (so no re-ordering of elements).
For instance, given:
val l = List(2, 4, -6, 3, 1, 8, 7, 10, 0)
val p = (i : Int) => i % 2 == 0
I would like magic(l,p) to produce:
List(List(2, 4, -6), List(3), List(1), List(8), List(7), List(10, 0))
I know of .span, but that method stops the first time it encounters a value that doesn't satisfy p and just returns a pair.
Below is a candidate implementation. It does what I want, but, well, makes we want to cry. I would love for someone to come up with something slightly more idiomatic.
def magic[T](elems : Seq[T], p : T=>Boolean) : Seq[Seq[T]] = {
val loop = elems.foldLeft[(Boolean,Seq[Seq[T]])]((false,Seq.empty)) { (pr,e) =>
val (lastOK,s) = pr
if(lastOK && p(e)) {
(true, s.init :+ (s.last :+ e))
} else {
(p(e), s :+ Seq(e))
}
}
loop._2
}
(Note that I do not particularly care about preserving the actual type of the Seq.)
I would not use foldLeft. It's just a simple recursion of span with a special rule if the head doesn't match the predicate:
def magic[T](elems: Seq[T], p: T => Boolean): Seq[Seq[T]] =
elems match {
case Seq() => Seq()
case Seq(head, tail # _*) if !p(head) => Seq(head) +: magic(tail, p)
case xs =>
val (prefix, rest) = xs span p
prefix +: magic(rest, p)
}
You could also do it tail-recursive, but you need to remember to reverse the output if you're prepending (as is sensible):
def magic[T](elems: Seq[T], p: T => Boolean): Seq[Seq[T]] = {
def iter(elems: Seq[T], out: Seq[Seq[T]]) : Seq[Seq[T]] =
elems match {
case Seq() => out.reverse
case Seq(head, tail # _*) if !p(head) => iter(tail, Seq(head) +: out)
case xs =>
val (prefix, rest) = xs span p
iter(rest, prefix +: out)
}
iter(elems, Seq())
}
For this task you can use takeWhile and drop combined with a little pattern matching an recursion:
def magic[T](elems : Seq[T], p : T=>Boolean) : Seq[Seq[T]] = {
def magic(elems: Seq[T], result: Seq[Seq[T]]): Seq[Seq[T]] = elems.takeWhile(p) match {
// if elems is Nil, we have a result
case Nil if elems.isEmpty => result
// if it's not, but we don't get any values from takeWhile, we take a single elem
case Nil => magic(elems.tail, result :+ Seq(elems.head))
// takeWhile gave us something, so we add it to the result
// and drop as many elements from elems, as takeWhile gave us
case xs => magic(elems.drop(xs.size), result :+ xs)
}
magic(elems, Seq())
}
Another solution using a fold:
def magicFilter[T](seq: Seq[T], p: T => Boolean): Seq[Seq[T]] = {
val (filtered, current) = (seq foldLeft (Seq[Seq[T]](), Seq[T]())) {
case ((filtered, current), element) if p(element) => (filtered, current :+ element)
case ((filtered, current), element) if !current.isEmpty => (filtered :+ current :+ Seq(element), Seq())
case ((filtered, current), element) => (filtered :+ Seq(element), Seq())
}
if (!current.isEmpty) filtered :+ current else filtered
}
I think this might be a common operation. So maybe it's inside the API but I can't find it. Also I'm interested in an efficient functional/simple solution if not.
Given a sequence of tuples ("a" -> 1, "b" ->2, "c" -> 3) I want to turn it into a map. That's easy using TraversableOnce.toMap. But I want to fail this construction if the resulting map "would contain a contradiction", i.e. different values assigned to the same key. Like in the sequence ("a" -> 1, "a" -> 2). But duplicates shall be allowed.
Currently I have this (very imperative) code:
def buildMap[A,B](in: TraversableOnce[(A,B)]): Option[Map[A,B]] = {
val map = new HashMap[A,B]
val it = in.toIterator
var fail = false
while(it.hasNext){
val next = it.next()
val old = map.put(next._1, next._2)
fail = old.isDefined && old.get != next._2
}
if(fail) None else Some(map.toMap)
}
Side Question
Is the final toMap really necessary? I get a type error when omitting it, but I think it should work. The implementation of toMap constructs a new map which I want to avoid.
As always when working with Seq[A] the optimal solution performance-wise depends on the concrete collection type.
A general but not very efficient solution would be to fold over an Option[Map[A,B]]:
def optMap[A,B](in: Iterable[(A,B)]): Option[Map[A,B]] =
in.iterator.foldLeft(Option(Map[A,B]())) {
case (Some(m),e # (k,v)) if m.getOrElse(k, v) == v => Some(m + e)
case _ => None
}
If you restrict yourself to using List[A,B]s an optimized version would be:
#tailrec
def rmap[A,B](in: List[(A,B)], out: Map[A,B] = Map[A,B]()): Option[Map[A,B]] = in match {
case (e # (k,v)) :: tail if out.getOrElse(k,v) == v =>
rmap(tail, out + e)
case Nil =>
Some(out)
case _ => None
}
Additionally a less idiomatic version using mutable maps could be implemented like this:
def mmap[A,B](in: Iterable[(A,B)]): Option[Map[A,B]] = {
val dest = collection.mutable.Map[A,B]()
for (e # (k,v) <- in) {
if (dest.getOrElse(k, v) != v) return None
dest += e
}
Some(dest.toMap)
}
Here is a fail-slowly solution (if creating the entire map and then discarding it is okay):
def uniqueMap[A,B](s: Seq[(A,B)]) = {
val m = s.toMap
if (m.size == s.length) Some(s) else None
}
Here is a mutable fail-fast solution (bail out as soon as the error is detected):
def uniqueMap[A,B](s: Seq[(A,B)]) = {
val h = new collection.mutable.HashMap[A,B]
val i = s.iterator.takeWhile(x => !(h contains x._1)).foreach(h += _)
if (h.size == s.length) Some(h) else None
}
And here's an immutable fail-fast solution:
def uniqueMap[A,B](s: Seq[(A,B)]) = {
def mapUniquely(i: Iterator[(A,B)], m: Map[A,B]): Option[Map[A,B]] = {
if (i.hasNext) {
val j = i.next
if (m contains j._1) None
else mapUniquely(i, m + j)
}
else Some(m)
}
mapUniquely(s.iterator, Map[A,B]())
}
Edit: and here's a solution using put for speed (hopefully):
def uniqueMap[A,B](s: Seq[(A,B)]) = {
val h = new collection.mutable.HashMap[A,B]
val okay = s.iterator.forall(x => {
val y = (h put (x._1,x._2))
y.isEmpty || y.get == x._2
})
if (okay) Some(h) else None
}
Edit: now tested, and it's ~2x as fast on input that works (returns true) than Moritz' or my straightforward solution.
Scala 2.9 is near, so why not to take advantage of the combinations method (inspired by Moritz's answer):
def optMap[A,B](in: List[(A,B)]) = {
if (in.combinations(2).exists {
case List((a,b),(c,d)) => a == c && b != d
case _ => false
}) None else Some(in.toMap)
}
scala> val in = List(1->1,2->3,3->4,4->5,2->3)
in: List[(Int, Int)] = List((1,1), (2,3), (3,4), (4,5), (2,3))
scala> optMap(in)
res29: Option[scala.collection.immutable.Map[Int,Int]] = Some(Map(1 -> 1, 2 -> 3, 3 -> 4, 4 -> 5))
scala> val in = List(1->1,2->3,3->4,4->5,2->3,1->2)
in: List[(Int, Int)] = List((1,1), (2,3), (3,4), (4,5), (2,3), (1,2))
scala> optMap(in)
res30: Option[scala.collection.immutable.Map[Int,Int]] = None
You can also use gourpBy as follows:
val pList = List(1 -> "a", 1 -> "b", 2 -> "c", 3 -> "d")
def optMap[A,B](in: Iterable[(A,B)]): Option[Map[A,B]] = {
Option(in.groupBy(_._1).map{case(_, list) => if(list.size > 1) return None else list.head})
}
println(optMap(pList))
It's efficiency is competitive to the above solutions.
In fact if you examine the gourpBy implementation you will see that it is very similar to some of the solutions suggested.