The Burrows Delta algorithm is primarily used for authorship attribution, but
can be used for other applications. This abstract class can be used to
implement different variants of Burrows' Delta. The input type for this
algorithm is always Vector. Each element in the vectors should correspond
to a feature and all vectors should be of the same size
and their elements should correspond to the same features. Each element in
the vectors is expected to be the number of times the corresponding feature
occurs in the text that the vector was generated from divided by the total number
of features in that text. This is referred to as relative feature frequency in
much of the literature. You may have to read a paper on Burrows' Delta to
understand how to construct the vectors correctly.
If this algorithm is going to be used for other applications the most important
constraint to still obey is that all vectors should be of the same size
and their elements should correspond to the same thing.
This abstract method should implement evaluation aspect of this general
algorithm. That is, given an unknownVector, this method should return
a discriminant value paired with the corresponding most likely category.
The discriminant value should be the score.