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

Global diffusion metrics based on the user and information pieces features.

Class Summary

Class	Description
AbstractExternalFeatureGlobalSimulationMetric<U extends java.io.Serializable,I extends java.io.Serializable,P>	Abstract class for representing global feature-based metrics which consider those features that the user already knows.
AbstractFeatureGlobalKLDivergence<U extends java.io.Serializable,I extends java.io.Serializable,F>	This global metric computes the KL divergence of the priori distribution of the feature values over the whole set of information pieces, and the frequency of receival of these parameters for the set of users.
ExternalFeatureGlobalGiniComplement<U extends java.io.Serializable,I extends java.io.Serializable,F>	Metric that computes the complement of the Gini coefficient over the different features unknown to the different users.
ExternalFeatureGlobalRate<U extends java.io.Serializable,I extends java.io.Serializable,F>	Metric that computes the rate of features received by the different users which were unknown by the receiver (we understand as external features those information features which are not present in the information pieces created by the users, or those user features different from the receiver's ones).
FeatureGlobalEntropy<U extends java.io.Serializable,I extends java.io.Serializable,F>	Metric that computes the entropy over the number of times each feature has been received.
FeatureGlobalGiniComplement<U extends java.io.Serializable,I extends java.io.Serializable,F>	Metric that computes the complement of the Gini coefficient over the different features.
FeatureGlobalKLDivergence<U extends java.io.Serializable,I extends java.io.Serializable,F>	This global metric computes the number of bytes of information we expect to lose if we approximate the real distribution of features with the estimated distribution obtained from simulating.
FeatureGlobalKLDivergenceInverse<U extends java.io.Serializable,I extends java.io.Serializable,P>	This global metric computes the number of bytes of information we expect to lose if we approximate the observed distribution of the parameters with their prior distribution (i.e.
FeatureGlobalUserEntropy<U extends java.io.Serializable,I extends java.io.Serializable,P>	Computes the entropy over the different features.
FeatureGlobalUserGiniComplement<U extends java.io.Serializable,I extends java.io.Serializable,P>	Computes the complement of the Gini coefficient over the different features.
UserFeatureCount<U extends java.io.Serializable,I extends java.io.Serializable,F>	Metric that computes the number of different (user, feature) pairs which have appeared during the simulation.
UserFeatureGiniComplement<U extends java.io.Serializable,I extends java.io.Serializable,F>	Metric that computes the complement of the Gini coefficient over the (user, feature) pairs.