Constructing an instance of EnumeratedDistribution in Scala

Constructing an instance of EnumeratedDistribution in Scala

By : tremendo
Date : November 20 2020, 01:01 AM
hop of those help? Your problem comes from the Double type: java.lang.Double is expected while you use scala.Double.
Simply try:
code :
val mapping = new java.util.ArrayList[Pair[Long, java.lang.Double]]()

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Constructing priority_queue instance with Compare instance of different type

Constructing priority_queue instance with Compare instance of different type

By : user3805000
Date : March 29 2020, 07:55 AM
will be helpful for those in need Could anybody please explain why my compiler (GCC) accepts the code below as valid? , The below statement:
code :
priority_queue<int, deque<int>, less<int>> pq(greater<int>());
priority_queue<int, deque<int>, less<int> > pq((greater<int>()));
//                                             ^              ^ 
priority_queue<int, deque<int>, less<int>> pq(greater<int>{});
//                                                        ^^ 
No instance of play.api.libs.json.Format is available for scala.Predef.Map[java.lang.String, scala.Option[scala.Double]]

No instance of play.api.libs.json.Format is available for scala.Predef.Map[java.lang.String, scala.Option[scala.Double]]

By : GanapatiKor
Date : March 29 2020, 07:55 AM
it helps some times Trying to write a json format for an entity which contains a Map of Option. It throws following error , You can define implicits:
code :
  import scala.collection.Map

  object Person {
    implicit object MapReads extends Reads[Map[String, Option[Double]]] {
      def reads(jsValue: JsValue): JsResult[Map[String, Option[Double]]] = jsValue match {
        case JsObject(map) => JsSuccess(
          map.mapValues {
            case JsNumber(d) => Some(d.toDouble)
            case _ => None
        case _ => JsError()

    implicit object MapWrites extends Writes[Map[String, Option[Double]]] {
      def writes(map: Map[String, Option[Double]]): JsValue =
        JsObject(map.mapValues(optd => JsNumber(optd.getOrElse[Double](0.0))))

    implicit val personFormat: OFormat[Person] = Json.format[Person]

How do I default a property of a Matlab instance with a size based on an instance variable that is set upon constructing

How do I default a property of a Matlab instance with a size based on an instance variable that is set upon constructing

By : user3320925
Date : March 29 2020, 07:55 AM
To fix the issue you can do I have a problem in which I want to model some physical phenomenon. This phenomenon consists of several phases, each of which may have its own governing equations (dynamics and thus no. of variables, constraints, variable boundaries, etc.). In order to group this phase's properties, I have written a class phase. An instance of this class has, a.o., a property called nVars, the number of variables in the governing equations (which may thus vary amongst instances of this class).
code :
classdef phase < handle
        nVars       double ;
        boundaries  boundaries ;
        function obj = phase(nVars)
            %Some constructor method
            obj.nVars = nVars;
            obj.boundaries = boundaries(obj);

classdef boundaries
        parent phase
        upper   double ;
        lower   double ;
        function obj = boundaries( parent )
            % Assign the parent handle
            obj.parent = parent ;
            % Initialise default values for the properties
            obj.upper =  Inf * ones([1 parent.nVars]);
            obj.lower = -obj.upper ;
>> a = phase(5)
a = 
  phase with properties:

         nVars: 5
    boundaries: [1x1 boundaries]
>> a.boundaries
ans = 
  boundaries with properties:

    parent: [1x1 phase]
     upper: [Inf Inf Inf Inf Inf]
     lower: [-Inf -Inf -Inf -Inf -Inf]
How to efficiently update probabilities within an EnumeratedDistribution instance?

How to efficiently update probabilities within an EnumeratedDistribution instance?

By : user3383876
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further Unfortunately it is not possible to update the existing EnumeratedIntegerDistribution. I have had similar issue in the past and I ended up re-creating the instance everytime I need to update the chances.
I won't worry too much about the memory allocations as those will be short-lived objects. These are micro-optimisations you should not worry about.
code :
public class DistributedProbabilityGeneratorBuilder<T extends DistributedProbabilityGeneratorBuilder.ProbableItem> {

    private static final DistributedProbabilityGenerator EMPTY = () -> {
        throw new UnsupportedOperationException("Not supported");

    private final Map<Integer, T> distribution = new HashMap<>();

    private DistributedProbabilityGeneratorBuilder() {

    public static <T extends ProbableItem> DistributedProbabilityGeneratorBuilder<T> newBuilder() {
        return new DistributedProbabilityGeneratorBuilder<>();

    public DistributedProbabilityGenerator build() {
        return build(ProbableItem::getChances);

     * Returns a new instance of probability generator at every call.
     * @param chanceChangeFunction - Function to modify existing chances
    public DistributedProbabilityGenerator build(Function<T, Double> chanceChangeFunction) {
        if (distribution.isEmpty()) {
            return EMPTY;
        } else {
            return new NonEmptyProbabilityGenerator(createPairList(chanceChangeFunction));

    private List<Pair<Integer, Double>> createPairList(Function<T, Double> chanceChangeFunction) {
        return distribution.entrySet().stream()
                .map(entry -> Pair.create(entry.getKey(), chanceChangeFunction.apply(entry.getValue())))

    public DistributedProbabilityGeneratorBuilder<T> add(int id, T item) {
        if (distribution.containsKey(id)) {
            throw new IllegalArgumentException("Id " + id + " already present.");

        this.distribution.put(id, item);
        return this;

    public interface ProbableItem {

        double getChances();

    public interface DistributedProbabilityGenerator {

        int generateId();

    public static class NonEmptyProbabilityGenerator implements DistributedProbabilityGenerator {

        private final EnumeratedDistribution<Integer> enumeratedDistribution;

        NonEmptyProbabilityGenerator(List<Pair<Integer, Double>> pairs) {
            this.enumeratedDistribution = new EnumeratedDistribution<>(pairs);

        public int generateId() {
            return enumeratedDistribution.sample();

    public static ProbableItem ofDouble(double chances) {
        return () -> chances;
DistributedProbabilityGenerator distributedProbabilityGenerator = DistributedProbabilityGeneratorBuilder.newBuilder()
                .add(0, ofDouble(10))
                .add(1, ofDouble(45))
                .add(2, ofDouble(45))

int generatedObjectId = distributedProbabilityGenerator.generateId();
Constructing a rectangle in Scala

Constructing a rectangle in Scala

By : Mark Player
Date : September 30 2020, 06:00 PM
this one helps. Here is the implementation of the first 4 requirements as an attempt to show how to write idiomatic Scala code.
The last requirement is left as an exercise for the reader.
code :
// Part a.
final case class Point(x: Int, y: Int) {
  // Part b.
  def isLeftOf(that: Point): Boolean =
    this.x < that.x

  def isAboveOf(that: Point): Boolean =
    this.y > that.y

  def isRightOf(that: Point): Boolean =

  def isBelowOf(that: Point): Boolean =

// Part c.
final case class Rectangle(topLeft: Point, bottomRight: Point) {
  // Part d.
  def contains(point: Point): Boolean =
    (point isBelowOf this.topLeft) &&
    (point isRightOf this.topLeft) &&
    (point isAboveOf this.bottomRight) &&
    (point isLeftOf this.bottomRight)

Rectangle(Point(0, 0), Point(5, 5)) contains Point(3, 3)
// res: Boolean = true
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