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Quite complex algorith is being applied to list of Spark Dataset's rows (list was obtained using groupByKey and flatMapGroups). Most rows are transformed 1 : 1 from input to output, but in some scenarios require more than one output per each input. The input row schema can change anytime. The map() fits the requirements quite well for the 1:1 transformation, but is there a way to use it producing 1 : n output?
The only work-around I found relies on foreach method which has unpleasant overhed cause by creating the initial empty list (remember, unlike the simplified example below, real-life list structure is changing randomly).
My original problem is too complex to share here, but this example demonstrates the concept. Let's have a list of integers. Each should be transformed into its square value and if the input is even it should also transform into one half of the original value:
val X = Seq(1, 2, 3, 4, 5)
val y = X.map(x => x * x) //map is intended for 1:1 transformation so it works great here
val z = X.map(x => for(n <- 1 to 5) (n, x * x)) //this attempt FAILS - generates list of five rows with emtpy tuples
// this work-around works, but newX definition is problematic
var newX = List[Int]() //in reality defining as head of the input list and dropping result's tail at the end
val za = X.foreach(x => {
newX = x*x :: newX
if(x % 2 == 0) newX = (x / 2) :: newX
})
newX
Is there a better way than foreach construct?
.flatMap produces any number of outputs from a single input.
val X = Seq(1, 2, 3, 4, 5)
X.flatMap { x =>
if (x % 2 == 0) Seq(x*x, x / 2) else Seq(x / 2)
}
#=> Seq[Int] = List(0, 4, 1, 1, 16, 2, 2)
flatMap in more detail
In X.map(f), f is a function that maps each input to a single output. By contrast, in X.flatMap(g), the function g maps each input to a sequence of outputs. flatMap then takes all the sequences produced (one for each element in f) and concatenates them.
The neat thing is .flatMap works not just for sequences, but for all sequence-like objects. For an option, for instance, Option(x)#flatMap(g) will allow g to return an Option. Similarly, Future(x)#flatMap(g) will allow g to return a Future.
Whenever the number of elements you return depends on the input, you should think of flatMap.
I'm looking for an elegant way to combine every element of a Seq with the rest for a large collection.
Example: Seq(1,2,3).someMethod should produce something like
Iterator(
(1,Seq(2,3)),
(2,Seq(1,3)),
(3,Seq(1,2))
)
Order of elements doesn't matter. It doesn't have to be a tuple, a Seq(Seq(1),Seq(2,3)) is also acceptable (although kinda ugly).
Note the emphasis on large collection (which is why my example shows an Iterator).
Also note that this is not combinations.
Ideas?
Edit:
In my use case, the numbers are expected to be unique. If a solution can eliminate the dupes, that's fine, but not at additional cost. Otherwise, dupes are acceptable.
Edit 2: In the end, I went with a nested for-loop, and skipped the case when i == j. No new collections were created. I upvoted the solutions that were correct and simple ("simplicity is the ultimate sophistication" - Leonardo da Vinci), but even the best ones are quadratic just by the nature of the problem, and some create intermediate collections by usage of ++ that I wanted to avoid because the collection I'm dealing with has close to 50000 elements, 2.5 billion when quadratic.
The following code has constant runtime (it does everything lazily), but accessing every element of the resulting collections has constant overhead (when accessing each element, an index shift must be computed every time):
def faceMap(i: Int)(j: Int) = if (j < i) j else j + 1
def facets[A](simplex: Vector[A]): Seq[(A, Seq[A])] = {
val n = simplex.size
(0 until n).view.map { i => (
simplex(i),
(0 until n - 1).view.map(j => simplex(faceMap(i)(j)))
)}
}
Example:
println("Example: facets of a 3-dimensional simplex")
for ((i, v) <- facets((0 to 3).toVector)) {
println(i + " -> " + v.mkString("[", ",", "]"))
}
Output:
Example: facets of a 3-dimensional simplex
0 -> [1,2,3]
1 -> [0,2,3]
2 -> [0,1,3]
3 -> [0,1,2]
This code expresses everything in terms of simplices, because "omitting one index" corresponds exactly to the face maps for a combinatorially described simplex. To further illustrate the idea, here is what the faceMap does:
println("Example: how `faceMap(3)` shifts indices")
for (i <- 0 to 5) {
println(i + " -> " + faceMap(3)(i))
}
gives:
Example: how `faceMap(3)` shifts indices
0 -> 0
1 -> 1
2 -> 2
3 -> 4
4 -> 5
5 -> 6
The facets method uses the faceMaps to create a lazy view of the original collection that omits one element by shifting the indices by one starting from the index of the omitted element.
If I understand what you want correctly, in terms of handling duplicate values (i.e., duplicate values are to be preserved), here's something that should work. Given the following input:
import scala.util.Random
val nums = Vector.fill(20)(Random.nextInt)
This should get you what you need:
for (i <- Iterator.from(0).take(nums.size)) yield {
nums(i) -> (nums.take(i) ++ nums.drop(i + 1))
}
On the other hand, if you want to remove dups, I'd convert to Sets:
val numsSet = nums.toSet
for (num <- nums) yield {
num -> (numsSet - num)
}
seq.iterator.map { case x => x -> seq.filter(_ != x) }
This is quadratic, but I don't think there is very much you can do about that, because in the end of the day, creating a collection is linear, and you are going to need N of them.
import scala.annotation.tailrec
def prems(s : Seq[Int]):Map[Int,Seq[Int]]={
#tailrec
def p(prev: Seq[Int],s :Seq[Int],res:Map[Int,Seq[Int]]):Map[Int,Seq[Int]] = s match {
case x::Nil => res+(x->prev)
case x::xs=> p(x +: prev,xs, res+(x ->(prev++xs)))
}
p(Seq.empty[Int],s,Map.empty[Int,Seq[Int]])
}
prems(Seq(1,2,3,4))
res0: Map[Int,Seq[Int]] = Map(1 -> List(2, 3, 4), 2 -> List(1, 3, 4), 3 -> List(2, 1, 4),4 -> List(3, 2, 1))
I think you are looking for permutations. You can map the resulting lists into the structure you are looking for:
Seq(1,2,3).permutations.map(p => (p.head, p.tail)).toList
res49: List[(Int, Seq[Int])] = List((1,List(2, 3)), (1,List(3, 2)), (2,List(1, 3)), (2,List(3, 1)), (3,List(1, 2)), (3,List(2, 1)))
Note that the final toList call is only there to trigger the evaluation of the expressions; otherwise, the result is an iterator as you asked for.
In order to get rid of the duplicate heads, toMap seems like the most straight-forward approach:
Seq(1,2,3).permutations.map(p => (p.head, p.tail)).toMap
res50: scala.collection.immutable.Map[Int,Seq[Int]] = Map(1 -> List(3, 2), 2 -> List(3, 1), 3 -> List(2, 1))
I am new to Scala and new OOP too. How can I update a particular element in a list while creating a new list.
val numbers= List(1,2,3,4,5)
val result = numbers.map(_*2)
I need to update third element only -> multiply by 2. How can I do that by using map?
You can use zipWithIndex to map the list into a list of tuples, where each element is accompanied by its index. Then, using map with pattern matching - you single out the third element (index = 2):
val numbers = List(1,2,3,4,5)
val result = numbers.zipWithIndex.map {
case (v, i) if i == 2 => v * 2
case (v, _) => v
}
// result: List[Int] = List(1, 2, 6, 4, 5)
Alternatively - you can use patch, which replaces a sub-sequence with a provided one:
numbers.patch(from = 2, patch = Seq(numbers(2) * 2), replaced = 1)
I think the clearest way of achieving this is by using updated(index: Int, elem: Int). For your example, it could be applied as follows:
val result = numbers.updated(2, numbers(2) * 2)
list.zipWithIndex creates a list of pairs with original element on the left, and index in the list on the right (indices are 0-based, so "third element" is at index 2).
val result = number.zipWithIndex.map {
case (n, 2) => n*2
case n => n
}
This creates an intermediate list holding the pairs, and then maps through it to do your transformation. A bit more efficient approach is to use iterator. Iterators a 'lazy', so, rather than creating an intermediate container, it will generate the pairs one-by-one, and send them straight to the .map:
val result = number.iterator.zipWithIndex.map {
case (n, 2) => n*2
case n => n
}.toList
1st and the foremost scala is FOP and not OOP. You can update any element of a list through the keyword "updated", see the following example for details:
Signature :- updated(index,value)
val numbers= List(1,2,3,4,5)
print(numbers.updated(2,10))
Now here the 1st argument is the index and the 2nd argument is the value. The result of this code will modify the list to:
List(1, 2, 10, 4, 5).
Apologies: I'm well noob
I have an items class
class item(ind:Int,freq:Int,gap:Int){}
I have an ordered list of ints
val listVar = a.toList
where a is an array
I want a list of items called metrics where
ind is the (unique) integer
freq is the number of times that ind appears in list
gap is the minimum gap between ind and the number in the list before it
so far I have:
def metrics = for {
n <- 0 until 255
listVar filter (x == n) count > 0
}
yield new item(n, (listVar filter == n).count,0)
It's crap and I know it - any clues?
Well, some of it is easy:
val freqMap = listVar groupBy identity mapValues (_.size)
This gives you ind and freq. To get gap I'd use a fold:
val gapMap = listVar.sliding(2).foldLeft(Map[Int, Int]()) {
case (map, List(prev, ind)) =>
map + (ind -> (map.getOrElse(ind, Int.MaxValue) min ind - prev))
}
Now you just need to unify them:
freqMap.keys.map( k => new item(k, freqMap(k), gapMap.getOrElse(k, 0)) )
Ideally you want to traverse the list only once and in the course for each different Int, you want to increment a counter (the frequency) as well as keep track of the minimum gap.
You can use a case class to store the frequency and the minimum gap, the value stored will be immutable. Note that minGap may not be defined.
case class Metric(frequency: Int, minGap: Option[Int])
In the general case you can use a Map[Int, Metric] to lookup the Metric immutable object. Looking for the minimum gap is the harder part. To look for gap, you can use the sliding(2) method. It will traverse the list with a sliding window of size two allowing to compare each Int to its previous value so that you can compute the gap.
Finally you need to accumulate and update the information as you traverse the list. This can be done by folding each element of the list into your temporary result until you traverse the whole list and get the complete result.
Putting things together:
listVar.sliding(2).foldLeft(
Map[Int, Metric]().withDefaultValue(Metric(0, None))
) {
case (map, List(a, b)) =>
val metric = map(b)
val newGap = metric.minGap match {
case None => math.abs(b - a)
case Some(gap) => math.min(gap, math.abs(b - a))
}
val newMetric = Metric(metric.frequency + 1, Some(newGap))
map + (b -> newMetric)
case (map, List(a)) =>
map + (a -> Metric(1, None))
case (map, _) =>
map
}
Result for listVar: List[Int] = List(2, 2, 4, 4, 0, 2, 2, 2, 4, 4)
scala.collection.immutable.Map[Int,Metric] = Map(2 -> Metric(4,Some(0)),
4 -> Metric(4,Some(0)), 0 -> Metric(1,Some(4)))
You can then turn the result into your desired item class using map.toSeq.map((i, m) => new Item(i, m.frequency, m.minGap.getOrElse(-1))).
You can also create directly your Item object in the process, but I thought the code would be harder to read.
I'm learning Scala as my first functional-ish language. As one of the problems, I was trying to find a functional way of generating the sequence S up to n places. S is defined so that S(1) = 1, and S(x) = the number of times x appears in the sequence. (I can't remember what this is called, but I've seen it in programming books before.)
In practice, the sequence looks like this:
S = 1, 2, 2, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7 ...
I can generate this sequence pretty easily in Scala using an imperative style like this:
def genSequence(numItems: Int) = {
require(numItems > 0, "numItems must be >= 1")
var list: List[Int] = List(1)
var seq_no = 2
var no = 2
var no_nos = 0
var num_made = 1
while(num_made < numItems) {
if(no_nos < seq_no) {
list = list :+ no
no_nos += 1
num_made += 1
} else if(no % 2 == 0) {
no += 1
no_nos = 0
} else {
no += 1
seq_no += 1
no_nos = 0
}
}
list
}
But I don't really have any idea how to write this without using vars and the while loop.
Thanks!
Pavel's answer has come closest so far, but it's also inefficient. Two flatMaps and a zipWithIndex are overkill here :)
My understanding of the required output:
The results contain all the positive integers (starting from 1) at least once
each number n appears in the output (n/2) + 1 times
As Pavel has rightly noted, the solution is to start with a Stream then use flatMap:
Stream from 1
This generates a Stream, a potentially never-ending sequence that only produces values on demand. In this case, it's generating 1, 2, 3, 4... all the way up to Infinity (in theory) or Integer.MAX_VALUE (in practice)
Streams can be mapped over, as with any other collection. For example: (Stream from 1) map { 2 * _ } generates a Stream of even numbers.
You can also use flatMap on Streams, allowing you to map each input element to zero or more output elements; this is key to solving your problem:
val strm = (Stream from 1) flatMap { n => Stream.fill(n/2 + 1)(n) }
So... How does this work? For the element 3, the lambda { n => Stream.fill(n/2 + 1)(n) } will produce the output stream 3,3. For the first 5 integers you'll get:
1 -> 1
2 -> 2, 2
3 -> 3, 3
4 -> 4, 4, 4
5 -> 5, 5, 5
etc.
and because we're using flatMap, these will be concatenated, yielding:
1, 2, 2, 3, 3, 4, 4, 4, 5, 5, 5, ...
Streams are memoised, so once a given value has been calculated it'll be saved for future reference. However, all the preceeding values have to be calculated at least once. If you want the full sequence then this won't cause any problems, but it does mean that generating S(10796) from a cold start is going to be slow! (a problem shared with your imperative algorithm). If you need to do this, then none of the solutions so far is likely to be appropriate for you.
The following code produces exactly the same sequence as yours:
val seq = Stream.from(1)
.flatMap(Stream.fill(2)(_))
.zipWithIndex
.flatMap(p => Stream.fill(p._1)(p._2))
.tail
However, if you want to produce the Golomb sequence (that complies with the definition, but differs from your sample code result), you may use the following:
val seq = 1 #:: a(2)
def a(n: Int): Stream[Int] = (1 + seq(n - seq(seq(n - 2) - 1) - 1)) #:: a(n + 1)
You may check my article for more examples of how to deal with number sequences in functional style.
Here is a translation of your code to a more functional style:
def genSequence(numItems: Int): List[Int] = {
genSequenceR(numItems, 2, 2, 0, 1, List[Int](1))
}
def genSequenceR(numItems: Int, seq_no: Int, no:Int, no_nos: Int, numMade: Int, list: List[Int]): List[Int] = {
if(numMade < numItems){
if(no_nos < seq_no){
genSequenceR(numItems, seq_no, no, no_nos + 1, numMade + 1, list :+ no)
}else if(no % 2 == 0){
genSequenceR(numItems, seq_no, no + 1, 0, numMade, list)
}else{
genSequenceR(numItems, seq_no + 1, no + 1, 0, numMade, list)
}
}else{
list
}
}
The genSequenceR is the recursive function that accumulates values in the list and calls the function with new values based on the conditions. Like the while loop, it terminates, when numMade is less than numItems and returns the list to genSequence.
This is a fairly rudimentary functional translation of your code. It can be improved and there are better approaches typically used. I'd recommend trying to improve it with pattern matching and then work towards the other solutions that use Stream here.
Here's an attempt from a Scala tyro. Keep in mind I don't really understand Scala, I don't really understand the question, and I don't really understand your algorithm.
def genX_Ys[A](howMany : Int, ofWhat : A) : List[A] = howMany match {
case 1 => List(ofWhat)
case _ => ofWhat :: genX_Ys(howMany - 1, ofWhat)
}
def makeAtLeast(startingWith : List[Int], nextUp : Int, howMany : Int, minimumLength : Int) : List[Int] = {
if (startingWith.size >= minimumLength)
startingWith
else
makeAtLeast(startingWith ++ genX_Ys( howMany, nextUp),
nextUp +1, howMany + (if (nextUp % 2 == 1) 1 else 0), minimumLength)
}
def genSequence(numItems: Int) = makeAtLeast(List(1), 2, 2, numItems).slice(0, numItems)
This seems to work, but re-read the caveats above. In particular, I am sure there is a library function that performs genX_Ys, but I couldn't find it.
EDIT Could be
def genX_Ys[A](howMany : Int, ofWhat : A) : Seq[A] =
(1 to howMany) map { x => ofWhat }
Here is a very direct "translation" of the definition of the Golomb seqence:
val it = Iterator.iterate((1,1,Map(1->1,2->2))){ case (n,i,m) =>
val c = m(n)
if (c == 1) (n+1, i+1, m + (i -> n) - n)
else (n, i+1, m + (i -> n) + (n -> (c-1)))
}.map(_._1)
println(it.take(10).toList)
The tripel (n,i,m) contains the actual number n, the index i and a Map m, which contains how often an n must be repeated. When the counter in the Map for our n reaches 1, we increase n (and can drop n from the map, as it is not longer needed), else we just decrease n's counter in the map and keep n. In every case we add the new pair i -> n into the map, which will be used as counter later (when a subsequent n reaches the value of the current i).
[Edit]
Thinking about it, I realized that I don't need indexes and not even a lookup (because the "counters" are already in the "right" order), which means that I can replace the Map with a Queue:
import collection.immutable.Queue
val it = 1 #:: Iterator.iterate((2, 2, Queue[Int]())){
case (n,1,q) => (n+1, q.head, q.tail + (n+1))
case (n,c,q) => (n,c-1,q + n)
}.map(_._1).toStream
The Iterator works correctly when starting by 2, so I had to add a 1 at the beginning. The second tuple argument is now the counter for the current n (taken from the Queue). The current counter could be kept in the Queue as well, so we have only a pair, but then it's less clear what's going on due to the complicated Queue handling:
val it = 1 #:: Iterator.iterate((2, Queue[Int](2))){
case (n,q) if q.head == 1 => (n+1, q.tail + (n+1))
case (n,q) => (n, ((q.head-1) +: q.tail) + n)
}.map(_._1).toStream