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Add another distribution

The current distribution library contains the following distributions: Normal, Lognormal, Pareto, Uniform, Piece Wise Linear, Piece Wise Linear Empirical, Poisson, Negative Binomal.

Purpose

Different companies and users have different preferences and traditions concerning probability distributions. This how-to section enables you to add additional distributions.

Prerequisities

Source code of PillarOne modelling. Most distributions are taken from the SSJ library. SSJ is a Java library for stochastic simulation, developed in the Département d'Informatique et de Recherche Opérationnelle (DIRO), at the Université de Montréal.

Step by step

All classes mentioned below can be found in the org.pillarone.modelling.util package.

We distinguish between random variable and probability distributions. Both types are derived from DistributionType. Before starting to implement a new distribution one should have a look at the following classes in the same package as mentioned above: ContinuousRandomDistributionType, DiscreteRandomDistributionType and ContinuousVariateDistributionType, DiscreteVariateDistributionType. Basically these classes act like an enumeration for the available distribution.

In order to add a new distribution it is necessary to add a new public static final variable, it's string representation and the required parameters in one of the four classes mentioned above. Furthermore the new variable has to be added to the "all" variable.

Adding a triangular distribution requires us to add the following line to the class ContinuousRandomDistributionType:

public static final ContinuousRandomDistributionType TRIANGULAR 
            = new ContinuousRandomDistributionType(
            "Triangular", ["a": 0d, "b": 1d, "m": 0.5])

The parameters of this distribution ('a', 'b', 'm') will be displayed on the UI in the same order as they are defined in this Map.

Furthermore we have to add TRIANGULAR to the "all" property in order to display it in the distribution selection lists:

public static final all = [NORMAL, LOGNORMAL, LOGNORMAL_MU_SIGMA, PARETO, 
            CENSOREDPARETO, TRUNCATEDPARETO, UNIFORM, CONSTANT,
            PIECEWISELINEAREMPIRICAL, PIECEWISELINEAR, TRIANGULAR]

In a next step we need to add the distribution to the RandomNumberGeneratorFactory: First we need a private static getter method in order to instantiate the generator:

private static IRandomNumberGenerator getTriangularGenerator(
    double a, double b, double m) {

    TriangularGen generator = new UniformGen(new F2NL607(), 
                                             new TriangularGen(a, b, m))
    return new RandomNumberGenerator(generator: generator)
}

This function is private as we won't allow direct instantiations. Instead the getGenerator method should be used. Therefore we need to add the new case to this function:

static IRandomNumberGenerator getGenerator(DistributionType type, 
    Map parameters) {

    IRandomNumberGenerator generator
    switch (type) {       
        case ContinuousRandomDistributionType.TRIANGULAR:
            generator = getTriangularGenerator(parameters["a"], 
                                               parameters["b"], 
                                               parameters["c"])
            break

The described steps are sufficient in order to add another distribution that is already available in the SSJ library but not yet wrapped into PillarOne. The newly added distribution is now available in attritional or single claims generators (org.pillarone.modelling.domain.generators.claims).

In order to add a more specific distribution an additional step is required: A new class implementing the umontreal.iro.lecuyer.probdist.Distribution interface has to be written. Our  PiecewiseLinearDistribution is an example of this approach.

 

Your implementation is incomplete as we didn't write a test case for our new distribution. It's necessary to complete at least RandomNumberGeneratorFactoryTests.testCreateTypedGenerator() and RandomNumberGeneratorTests.

Caveat: These tests don't test if the generated sample corresponds to the defined distribution.

 

After completing these steps, the new distribution is part of the list of distributions which appears in the user interface and the parameters are displayed if a user selects the distribution. There is no need to program graphical user interface elements.

Further information

We plan to implement a plugin mechanism to make this processmuch simpler. The plugin mechanism will avoid the modification of core classes and provide a self registration mechanism. This means that the user may specify a new distribution in one file only. The rest will be done using the plugin infrastructure.

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