Package es.uam.eps.ir.relison.diffusion.metrics.features.indiv

Class AbstractFeatureKLDivergence<U extends java.io.Serializable,​I extends java.io.Serializable,​P>

java.lang.Object
es.uam.eps.ir.relison.diffusion.metrics.AbstractIndividualSimulationMetric<U,​I,​P>
es.uam.eps.ir.relison.diffusion.metrics.features.AbstractFeatureIndividualSimulationMetric<U,​I,​P>
es.uam.eps.ir.relison.diffusion.metrics.features.indiv.AbstractFeatureKLDivergence<U,​I,​P>
Type Parameters:
U - type of the users.
I - type of the information pieces.
P - type of the parameters.
All Implemented Interfaces:
IndividualSimulationMetric<U,​I,​P>, SimulationMetric<U,​I,​P>
Direct Known Subclasses:
FeatureIndividualKLDivergence, FeatureKLDivergenceInverse
public abstract class AbstractFeatureKLDivergence<U extends java.io.Serializable,​I extends java.io.Serializable,​P>
extends AbstractFeatureIndividualSimulationMetric<U,​I,​P>
This individual metric computes the KL divergence of the priori distribution of the parameter values over the whole set of information pieces, and the frequency of receival of these parameters for a single user. Depending on the nature of the parameter, we differ two cases:
  • User parameters:
    • Priori distribution: The priori value for an individual user parameter is computed as the sum over the set of users as the product of the number of pieces published by the user and the value of the user parameter for the user. For example, let's suppose we have four different users, with two different communites:
      User 1: Comm 1 (value 1), (i1,i2,i3), User 2: Comm 1 (value 2), (i4,i5), User 3: Comm 1 (value 2), (i6), User 4: Comm 2 (value 3), (i7,i8,i9,i10)
      Then: Comm1 = 3*1 + 2*2 + 1*2 = 9; Comm2 = 3*4 = 12
    • Simulation distribution of a user: In this case, for each received and seen information piece, we sum the value of each parameter for the creator of the piece.
  • Information piece parameters:
    • Priori distribution:The priori value for an individual information piece parameter is computed as the sum over the set of information pieces of the value of the parameter for each parameter. For example, let's suppose we have four information pieces, and four different hashtags:
      Piece 1: (h1,h1,h3), Piece 2:(h1,h2), Piece 3: (h3,h4), Piece 4: (h1,h2,h3,h3,h3)
      Then: h1 = 2 + 1 + 0 + 1 = 4; h2 = 0 + 1 + 0 + 1 = 2; h3 = 1 + 0 + 1 + 3 = 5; h4 = 0 + 0 + 1 + 0 = 1
    • Simulation distribution of a user: In this case, for each received and seen information piece, we sum the value of each parameter for the information piece.
In order to prevent divisions by zero, we apply an additive or Laplace smoothing over the distribution obtained from the simulation. In case of the prior distribution had a zero value, then, it would mean that the parameter would not spread, and, therefore, the probability of obtaining that value during simulation is 0 as well.

  • Field Summary

    Fields 
    Modifier and Type Field Description
    protected java.util.Map<P,​java.lang.Double> pvalues
    The distribution we are trying to approximate.
    protected java.util.Map<P,​java.util.Map<U,​java.lang.Double>> qvalues
    Times each parameter has been received.
    protected double sumP
    The sum of the values we are trying to approximate
    protected java.util.Map<U,​java.lang.Double> sumQ
    The total number of external parameters that have reached each user.
    private boolean unique
    Indicates if a piece of information is considered once (or each time it appears if false).

    Fields inherited from class es.uam.eps.ir.relison.diffusion.metrics.AbstractIndividualSimulationMetric

    data, initialized

  • Constructor Summary

    Constructors 
    Constructor Description
    AbstractFeatureKLDivergence​(java.lang.String name, java.lang.String parameter, boolean userparam, boolean unique)
    Constructor.

  • Method Summary

    Modifier and Type Method Description
    double calculate()
    Calculates the metric for the current state of the simulation.
    void clear()
    Resets the metric.
    protected void initialize()
    Initializes all the variables needed for computing and updating the values of the metric.
    protected void updateInfoFeatures​(Iteration<U,​I,​P> iteration)
    Updates the necessary variables to compute a metric, in case the feature values we are using belong to the information pieces received by the users in the network.
    protected void updateUserFeatures​(Iteration<U,​I,​P> iteration)
    Updates the necessary variables to compute a metric, in case the feature values we are using belongs to the creators of the information pieces received by the users in the network.

    Methods inherited from class es.uam.eps.ir.relison.diffusion.metrics.features.AbstractFeatureIndividualSimulationMetric

    getParameter, update, usesUserParam

    Methods inherited from class es.uam.eps.ir.relison.diffusion.metrics.AbstractIndividualSimulationMetric

    calculateIndividuals, getName, initialize, isInitialized

    Methods inherited from class java.lang.Object

    clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

    Methods inherited from interface es.uam.eps.ir.relison.diffusion.metrics.IndividualSimulationMetric

    calculate, calculate, calculateIndividuals

    Methods inherited from interface es.uam.eps.ir.relison.diffusion.metrics.SimulationMetric

    calculate, initialize

  • Field Details

    • qvalues

      protected final java.util.Map<P,​java.util.Map<U extends java.io.Serializable,​java.lang.Double>> qvalues
      Times each parameter has been received.

    • pvalues

      protected final java.util.Map<P,​java.lang.Double> pvalues
      The distribution we are trying to approximate.

    • sumQ

      protected final java.util.Map<U extends java.io.Serializable,​java.lang.Double> sumQ
      The total number of external parameters that have reached each user.

    • sumP

      protected double sumP
      The sum of the values we are trying to approximate

    • unique

      private final boolean unique
      Indicates if a piece of information is considered once (or each time it appears if false).

  • Constructor Details

    • AbstractFeatureKLDivergence

      public AbstractFeatureKLDivergence​(java.lang.String name, java.lang.String parameter, boolean userparam, boolean unique)
      Constructor.
      Parameters:
      name - the name of the metric.
      userparam - true if we are using a user parameter, false if we are using an information piece parameter.
      parameter - the name of the parameter.
      unique - true if a piece of information is considered once, false if it is considered each time it appears.

  • Method Details