This class implements the DFRee weighting model. DFRee stands for DFR free from parameters.
In particular, the DFRee model computes an average number of extra bits (as information
divergence) that are necessary to code one extra token of the query term with respect to
the probability distribution observed in the document. There are two possible populations
to sample the probability distribution: considering only the document and no other document
in the colection, or the document considered as sample drawn from the entire collection
statistics. DFRee takes an average of these two information measures, that is their inner product.
tf - The term frequency of the term in the document
docLength - the document's length
the score assigned to a document with the given
tf and docLength, and other preset parameters
public final double score(double tf,
This method provides the contract for implementing weighting models.
As of Terrier 3.6, the 5-parameter score method is being deprecated
since it is not used. The two parameter score method should be used
instead. Tagged for removal in a later version.