Implementing the divergence from randomness model

In Lucene, divergence from randomness model is implemented as DFRSimilarity. It's made up of three components: BasicModel, AfterEffect, and Normalization. BasicModel is a model of information content, AfterEffect is the first normalization, and Normalization is second (length) normalization. Here is an excerpt from Lucene's Javadoc on DFRSimilarity's components:

1. BasicModel: This is a basic model of information content:

   • BasicModelBE: This is the limiting form of Bose-Einstein

   • BasicModelG: This is the geometric approximation of Bose-Einstein

   • BasicModelP: This is the Poisson approximation of the Binomial

   • BasicModelD: This is the divergence approximation of the Binomial

   • BasicModelIn: This is the inverse document frequency

   • BasicModelIne: This is the inverse expected document frequency (mixture of Poisson and IDF)

   • BasicModelIF: This is the inverse term frequency (approximation of I(ne))

2. AfterEffect: This is the first normalization of information...