I am programming in Kotlin and have a MutableList from which I would like to remove the first n elements from that specific list instance. This means that functions like MutableList.drop(n) are out of the question.
One solution would of course be to loop and call MutableList.removeFirst() n times, but this feels inefficient, being O(n). Another way would be to choose another data type, but I would prefer not to clutter my project by implementing my own data type for this, if I can avoid it.
Is there a faster way to do this with a MutableList? If not, is there another built-in data type that can achieve this in less than O(n)?
In my opinion the best way to achieve this is
abstract fun subList(fromIndex: Int, toIndex: Int): List<E>.
https://kotlinlang.org/api/latest/jvm/stdlib/kotlin.collections/-list/sub-list.html
Under the hood it creates a new instance of list(SubList class for AbstractClass) with elements between the selected indexes.
Using:
val yourList = listOf<YourType>(...)
val yourNewList = yourList.subList(5, yourList.size)
// return list from 6th elem to last
One method which seems to be faster if n is sufficiently large seems to be the following:
Store the last listSize - n bytes to keep in a temporary list,
Clear original list instance
Add temporary list to original list
Here is a quick benchmark for some example values that happen to fit my use case:
val numRepetitions = 15_000
val listSize = 1_000
val maxRemove = listSize
val rnd0 = Random(0)
val rnd1 = Random(0)
// 1. Store the last `listSize - n` bytes to keep in a temporary list,
// 2. Clear original list
// 3. Add temporary list to original list
var accumulatedMsClearAddAll = 0L
for (i in 0 until numRepetitions) {
val l = Random.nextBytes(listSize).toMutableList()
val numRemove = rnd0.nextInt(maxRemove)
val numKeep = listSize - numRemove
val startTime = System.currentTimeMillis()
val expectedOutput = l.takeLast(numKeep)
l.clear()
l.addAll(expectedOutput)
val endTime = System.currentTimeMillis()
assert(l == expectedOutput)
accumulatedMsClearAddAll += endTime - startTime
}
// Iteratively remove the first byte `n` times.
var accumulatedMsIterative = 0L
for (i in 0 until numRepetitions) {
val numRemove = rnd1.nextInt(maxRemove)
val l = Random.nextBytes(listSize).toMutableList()
val expectedOutput = l.takeLast(listSize - numRemove)
val startTime = System.currentTimeMillis()
for (ii in 0 until numRemove) {
l.removeFirst()
}
val endTime = System.currentTimeMillis()
assert(l == expectedOutput)
accumulatedMsIterative += endTime - startTime
}
println("clear+addAll removal: $accumulatedMsClearAddAll ms")
println("Iterative removal: $accumulatedMsIterative ms")
Output:
Clear+addAll removal: 478 ms
Iterative removal: 12683 ms
I'm trying to code the fast Non Dominated Sorting algorithm (NDS) of Deb used in NSGA2 in immutable way using Scala.
But the problem seems more difficult than i think, so i simplify here the problem to make a MWE.
Imagine a population of Seq[A], and each A element is decoratedA with a list which contains pointers to other elements of the population Seq[A].
A function evalA(a:decoratedA) take the list of linkedA it contains, and decrement value of each.
Next i take a subset list decoratedAPopulation of population A, and call evalA on each. I have a problem, because between each iteration on element on this subset list decoratedAPopulation, i need to update my population of A with the new decoratedA and the new updated linkedA it contain ...
More problematic, each element of population need an update of 'linkedA' to replace the linked element if it change ...
Hum as you can see, it seem complicated to maintain all linked list synchronized in this way. I propose another solution bottom, which probably need recursion to return after each EvalA a new Population with element replaced.
How can i do that correctly in an immutable way ?
It's easy to code in a mutable way, but i don't find a good way to do this in an immutable way, do you have a path or an idea to do that ?
object test extends App{
case class A(value:Int) {def decrement()= new A(value - 1)}
case class decoratedA(oneAdecorated:A, listOfLinkedA:Seq[A])
// We start algorithm loop with A element with value = 0
val population = Seq(new A(0), new A(0), new A(8), new A(1))
val decoratedApopulation = Seq(new decoratedA(population(1),Seq(population(2),population(3))),
new decoratedA(population(2),Seq(population(1),population(3))))
def evalA(a:decoratedA) = {
val newListOfLinked = a.listOfLinkedA.map{e => e.decrement()
new decoratedA(a.oneAdecorated,newListOfLinked)}
}
def run()= {
//decoratedApopulation.map{
// ?
//}
}
}
Update 1:
About the input / output of the initial algorithm.
The first part of Deb algorithm (Step 1 to Step 3) analyse a list of Individual, and compute for each A : (a) domination count, the number of A which dominate me (the value attribute of A) (b) a list of A i dominate (listOfLinkedA).
So it return a Population of decoratedA totally initialized, and for the entry of Step 4 (my problem) i take the first non dominated front, cf. the subset of elements of decoratedA with A value = 0.
My problem start here, with a list of decoratedA with A value = 0; and i search the next front into this list by computing each listOfLinkedA of each of this A
At each iteration between step 4 to step 6, i need to compute a new B subset list of decoratedA with A value = 0. For each , i decrementing first the domination count attribute of each element into listOfLinkedA, then i filter to get the element equal to 0. A the end of step 6, B is saved to a list List[Seq[DecoratedA]], then i restart to step 4 with B, and compute a new C, etc.
Something like that in my code, i call explore() for each element of B, with Q equal at the end to new subset of decoratedA with value (fitness here) = 0 :
case class PopulationElement(popElement:Seq[Double]){
implicit def poptodouble():Seq[Double] = {
popElement
}
}
class SolutionElement(values: PopulationElement, fitness:Double, dominates: Seq[SolutionElement]) {
def decrement()= if (fitness == 0) this else new SolutionElement(values,fitness - 1, dominates)
def explore(Q:Seq[SolutionElement]):(SolutionElement, Seq[SolutionElement])={
// return all dominates elements with fitness - 1
val newSolutionSet = dominates.map{_.decrement()}
val filteredSolution:Seq[SolutionElement] = newSolutionSet.filter{s => s.fitness == 0.0}.diff{Q}
filteredSolution
}
}
A the end of algorithm, i have a final list of seq of decoratedA List[Seq[DecoratedA]] which contain all my fronts computed.
Update 2
A sample of value extracted from this example.
I take only the pareto front (red) and the {f,h,l} next front with dominated count = 1.
case class p(x: Int, y: Int)
val a = A(p(3.5, 1.0),0)
val b = A(p(3.0, 1.5),0)
val c = A(p(2.0, 2.0),0)
val d = A(p(1.0, 3.0),0)
val e = A(p(0.5, 4.0),0)
val f = A(p(0.5, 4.5),1)
val h = A(p(1.5, 3.5),1)
val l = A(p(4.5, 1.0),1)
case class A(XY:p, value:Int) {def decrement()= new A(XY, value - 1)}
case class ARoot(node:A, children:Seq[A])
val population = Seq(
ARoot(a,Seq(f,h,l),
ARoot(b,Seq(f,h,l)),
ARoot(c,Seq(f,h,l)),
ARoot(d,Seq(f,h,l)),
ARoot(e,Seq(f,h,l)),
ARoot(f,Nil),
ARoot(h,Nil),
ARoot(l,Nil))
Algorithm return List(List(a,b,c,d,e), List(f,h,l))
Update 3
After 2 hour, and some pattern matching problems (Ahum...) i'm comming back with complete example which compute automaticaly the dominated counter, and the children of each ARoot.
But i have the same problem, my children list computation is not totally correct, because each element A is possibly a shared member of another ARoot children list, so i need to think about your answer to modify it :/ At this time i only compute children list of Seq[p], and i need list of seq[A]
case class p(x: Double, y: Double){
def toSeq():Seq[Double] = Seq(x,y)
}
case class A(XY:p, dominatedCounter:Int) {def decrement()= new A(XY, dominatedCounter - 1)}
case class ARoot(node:A, children:Seq[A])
case class ARootRaw(node:A, children:Seq[p])
object test_stackoverflow extends App {
val a = new p(3.5, 1.0)
val b = new p(3.0, 1.5)
val c = new p(2.0, 2.0)
val d = new p(1.0, 3.0)
val e = new p(0.5, 4.0)
val f = new p(0.5, 4.5)
val g = new p(1.5, 4.5)
val h = new p(1.5, 3.5)
val i = new p(2.0, 3.5)
val j = new p(2.5, 3.0)
val k = new p(3.5, 2.0)
val l = new p(4.5, 1.0)
val m = new p(4.5, 2.5)
val n = new p(4.0, 4.0)
val o = new p(3.0, 4.0)
val p = new p(5.0, 4.5)
def isStriclyDominated(p1: p, p2: p): Boolean = {
(p1.toSeq zip p2.toSeq).exists { case (g1, g2) => g1 < g2 }
}
def sortedByRank(population: Seq[p]) = {
def paretoRanking(values: Set[p]) = {
//comment from #dk14: I suppose order of values isn't matter here, otherwise use SortedSet
values.map { v1 =>
val t = (values - v1).filter(isStriclyDominated(v1, _)).toSeq
val a = new A(v1, values.size - t.size - 1)
val root = new ARootRaw(a, t)
println("Root value ", root)
root
}
}
val listOfARootRaw = paretoRanking(population.toSet)
//From #dk14: Here is convertion from Seq[p] to Seq[A]
val dominations: Map[p, Int] = listOfARootRaw.map(a => a.node.XY -> a.node.dominatedCounter) //From #dk14: It's a map with dominatedCounter for each point
val listOfARoot = listOfARootRaw.map(raw => ARoot(raw.node, raw.children.map(p => A(p, dominations.getOrElse(p, 0)))))
listOfARoot.groupBy(_.node.dominatedCounter)
}
//Get the first front, a subset of ARoot, and start the step 4
println(sortedByRank(Seq(a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p)).head)
}
Talking about your problem with distinguishing fronts (after update 2):
val (left,right) = population.partition(_.node.value == 0)
List(left, right.map(_.copy(node = node.copy(value = node.value - 1))))
No need for mutating anything here. copy will copy everything but fields you specified with new values. Talking about the code, the new copy will be linked to the same list of children, but new value = value - 1.
P.S. I have a feeling you may actually want to do something like this:
case class A(id: String, level: Int)
val a = A("a", 1)
val b = A("b", 2)
val c = A("c", 2)
val d = A("d", 3)
clusterize(List(a,b,c,d)) === List(List(a), List(b,c), List(d))
It's simple to implement:
def clusterize(list: List[A]) =
list.groupBy(_.level).toList.sortBy(_._1).map(_._2)
Test:
scala> clusterize(List(A("a", 1), A("b", 2), A("c", 2), A("d", 3)))
res2: List[List[A]] = List(List(A(a,1)), List(A(b,2), A(c,2)), List(A(d,3)))
P.S.2. Please consider better naming conventions, like here.
Talking about "mutating" elements in some complex structure:
The idea of "immutable mutating" some shared (between parts of a structure) value is to separate your "mutation" from the structure. Or simply saying, divide and conquerror:
calculate changes in advance
apply them
The code:
case class A(v: Int)
case class AA(a: A, seq: Seq[A]) //decoratedA
def update(input: Seq[AA]) = {
//shows how to decrement each value wherever it is:
val stats = input.map(_.a).groupBy(identity).mapValues(_.size) //domination count for each A
def upd(a: A) = A(a.v - stats.getOrElse(a, 0)) //apply decrement
input.map(aa => aa.copy(aa = aa.seq.map(upd))) //traverse and "update" original structure
}
So, I've introduced new Map[A, Int] structure, that shows how to modify the original one. This approach is based on highly simplified version of Applicative Functor concept. In general case, it should be Map[A, A => A] or even Map[K, A => B] or even Map[K, Zipper[A] => B] as applicative functor (input <*> map). *Zipper (see 1, 2) actually could give you information about current element's context.
Notes:
I assumed that As with same value are same; that's default behaviour for case classess, otherwise you need to provide some additional id's (or redefine hashCode/equals).
If you need more levels - like AA(AA(AA(...)))) - just make stats and upd recursive, if dеcrement's weight depends on nesting level - just add nesting level as parameter to your recursive function.
If decrement depends on parent node (like decrement only A(3)'s, which belongs to A(3)) - add parent node(s) as part of stats's key and analise it during upd.
If there is some dependency between stats calculation (how much to decrement) of let's say input(1) from input(0) - you should use foldLeft with partial stats as accumulator: val stats = input.foldLeft(Map[A, Int]())((partialStats, elem) => partialStats ++ analize(partialStats, elem))
Btw, it takes O(N) here (linear memory and cpu usage)
Example:
scala> val population = Seq(A(3), A(6), A(8), A(3))
population: Seq[A] = List(A(3), A(6), A(8), A(3))
scala> val input = Seq(AA(population(1),Seq(population(2),population(3))), AA(population(2),Seq(population(1),population(3))))
input: Seq[AA] = List(AA(A(6),List(A(8), A(3))), AA(A(8),List(A(6), A(3))))
scala> update(input)
res34: Seq[AA] = List(AA(A(5),List(A(7), A(3))), AA(A(7),List(A(5), A(3))))
I have following list -
List(List(
List(((groupName,group1),(tagMember,["192.168.20.30","192.168.20.20","192.168.20.21"]))),
List(((groupName,group1),(tagMember,["192.168.20.30"]))),
List(((groupName,group1),(tagMember,["192.168.20.30","192.168.20.20"])))))
I want to convert it to -
List((groupName, group1),(tagMember,["192.168.20.30","192.168.20.20","192.168.20.21"]))
I tried to use .flatten but unable to form desired output.
How do I get above mentioned output using scala??
I had to make some changes to your input to make it valid.
Input List:
val ll = List(List(
List((("groupName","group1"),("tagMember", List("192.168.20.30","192.168.20.20","192.168.20.21")))),
List((("groupName","group1"),("tagMember",List("192.168.20.30")))),
List((("groupName","group1"),("tagMember",List("192.168.20.30","192.168.20.20"))))
))
Code below works if the group, and tagMember are the same across all the elements in the list
def getUniqueIpsConstantGroupTagMember(inputList: List[List[List[((String, String), (String, List[String]))]]]) = {
// List[((String, String), (String, List[String]))]
val flattenedList = ll.flatten.flatten
if (flattenedList.size > 0) {
val group = flattenedList(0)._1
val tagMember = flattenedList(0)._2._1
val ips = flattenedList flatMap (_._2._2)
((group), (tagMember, ips.distinct))
}
else List()
}
println(getUniqueIpsConstantGroupTagMember(ll))
Output:
((groupName,group1),(tagMember,List(192.168.20.30, 192.168.20.20, 192.168.20.21)))
Now, let's assume you could have different groupNames.
Sample input:
val listWithVariableGroups = List(List(
List((("groupName","group1"),("tagMember",List("192.168.20.30","192.168.20.20","192.168.20.21")))),
List((("groupName","group1"),("tagMember",List("192.168.20.30")))),
List((("groupName","group1"),("tagMember",List("192.168.20.30","192.168.20.20")))),
List((("groupName","group2"),("tagMember",List("192.168.20.30","192.168.20.10"))))
))
The following code should work.
def getUniqueIpsForMultipleGroups(inputList: List[List[List[((String, String), (String, List[String]))]]]) = {
val flattenedList = inputList.flatten.flatten
// Map[(String, String),List[(String, List[String])]]
val groupedByGroupNameId = flattenedList.groupBy(p => p._1) map {
case (key, value) => (key, ("tagMember", extractUniqueTagIps(value)))
}
groupedByGroupNameId
}
def extractUniqueTagIps(list: List[((String, String), (String, List[String]))]) = {
val ips = list flatMap (_._2._2)
ips.distinct
}
getUniqueIpsForMultipleGroups(listWithVariableGroups).foreach(println)
Output:
((groupName,group1),(tagMember,List(192.168.20.30, 192.168.20.20, 192.168.20.21)))
((groupName,group2),(tagMember,List(192.168.20.30, 192.168.20.10)))
I'm looking every where on the web (dart website, stackoverflow, forums, etc), and I can't find my answer.
So there is my problem: I need to write a function, that print a random sort of a list, witch is provided as an argument. : In dart as well.
I try with maps, with Sets, with list ... I try the method with assert, with sort, I look at random method with Math on dart librabry ... nothing can do what I wana do.
Can some one help me with this?
Here some draft:
var element03 = query('#exercice03');
var uneliste03 = {'01':'Jean', '02':'Maximilien', '03':'Brigitte', '04':'Sonia', '05':'Jean-Pierre', '06':'Sandra'};
var alluneliste03 = new Map.from(uneliste03);
assert(uneliste03 != alluneliste03);
print(alluneliste03);
var ingredients = new Set();
ingredients.addAll(['Jean', 'Maximilien', 'Brigitte', 'Sonia', 'Jean-Pierre', 'Sandra']);
var alluneliste03 = new Map.from(ingredients);
assert(ingredients != alluneliste03);
//assert(ingredients.length == 4);
print(ingredients);
var fruits = <String>['bananas', 'apples', 'oranges'];
fruits.sort();
print(fruits);
There is a shuffle method in the List class. The methods shuffles the list in place. You can call it without an argument or provide a random number generator instance:
var list = ['a', 'b', 'c', 'd'];
list.shuffle();
print('$list');
The collection package comes with a shuffle function/extension that also supports specifying a sub range to shuffle:
void shuffle (
List list,
[int start = 0,
int end]
)
Here is a basic shuffle function. Note that the resulting shuffle is not cryptographically strong. It uses Dart's Random class, which produces pseudorandom data not suitable for cryptographic use.
import 'dart:math';
List shuffle(List items) {
var random = new Random();
// Go through all elements.
for (var i = items.length - 1; i > 0; i--) {
// Pick a pseudorandom number according to the list length
var n = random.nextInt(i + 1);
var temp = items[i];
items[i] = items[n];
items[n] = temp;
}
return items;
}
main() {
var items = ['foo', 'bar', 'baz', 'qux'];
print(shuffle(items));
}
You can use shuffle() with 2 dots like Vinoth Vino said.
List cities = ["Ankara","London","Paris"];
List mixed = cities..shuffle();
print(mixed);
// [London, Paris, Ankara]
I want to split List of user generic List into its small list with each 5 records.
Ex
I have List: u1,u2,u3,u4,u5,u6,u7,u8,u9,u10,u11,u12,u13,u14,u15.
so must be split into
List1:u1,u2,u3,u4,u5
List2:u6,u7,u8,u9,u10
List3:u11,u12,u13,u14,u15
Any direct method available or need programing logic in c# ?
You can group on the index:
List<List<User>> lists =
list
.Select((u, i) => new { List = i / 5, User = u })
.GroupBy(g => g.List, g => g.User)
.Select(g => g.ToList())
.ToList();
You can also use Range to make a loop and get a part of the list for each iteration:
List<List<User>> lists =
Enumerable.Range(0, (list.Count + 4) / 5)
.Select(n => list.Skip(n * 5).Take(5).ToList())
.ToList();
You can Use Skip(count) and Take(count) methods.
// These are your objects, put here your init code
var list = Enumerable.Range(1, 20).ToList();
var lists = new List<int>[(list.Count + 4) / 5]; // The array of lists you wanted
for (int i = 0; i < lists.Length; i++)
{
lists[i] = list.Skip(i * 5).Take(5).ToList();
}
The (list.Count + 4) / 5 is a method to round UP the division (if I have 6 elements in list, I want two sublists)
If you really need a List of List...
var lists = new List<List<int>>((list.Count + 4) / 5);
for (int i = 0; i < lists.Capacity; i++)
{
lists.Add(list.Skip(i * 5).Take(5).ToList());
}
I think this might be more efficient since you're not doing groupings, orderings and such. This is just a single iteration over the dataset.
var splitList= new List<IEnumerable<User>>();
List<User> currentList = null;
int count = 0;
foreach(var user in users)
{
if (0 == count% 5)
{
currentList = new List<User>(5);
returnValue.Add(currentList);
}
currentList.Add(key);
count++;
}
return returnValue;