Java Doc for AbstractAggregator.java in  » Search-Engine » mg4j » it » unimi » dsi » mg4j » search » score » Java Source Code / Java DocumentationJava Source Code and Java Documentation

An aggregator combines the results of several scorers following some policy (see, e.g., it.unimi.dsi.mg4j.search.score.LinearAggregator ). In doing so, often the aggregator needs to explore the first scores returned by each scorer, and tune some internal parameters. This procedure, equalisation, is supported by this class: if AbstractAggregator.equalize(int) is provided with a positive number of samples, they will be fetched from the underlying document iterator, scored, and passed to the implementing subclass so that equalisation information can be properly set up.

Additionally, this class ensures that if several scorers need access to intervals, the document iterator to be scored is decorated with a it.unimi.dsi.mg4j.search.CachingDocumentIterator , so that several scorer can access intervals.

Since this class uses the same document iterator for all aggregated scorers, they must be necessarily .

Implementing subclasses must provide the following methods:

• AbstractAggregator.setupEqualizationFactors() , which is called in case equalisation is required and must examine AbstractAggregator.actualSamples elements from AbstractAggregator.sampleScore (each element is a tuple of scores, one for each scorer) and use that information to set the equalisation factors (if AbstractAggregator.samples is zero, default values must be applied);
• AbstractAggregator.score(double[]) , which must compute the equalised aggregated score using the given array of scores (each to be thought as a score coming from the respective scorer).

Additionally, implementing subclasses must remember to call AbstractAggregator.equalize(int) when generating a , so that the state of the aggregator is reproduced correctly.

Implementations should look into AbstractAggregator.sampleScore and set up the equalisation logic.

Implementations should look into AbstractAggregator.sampleScore and set up the equalisation logic. Note that this method is responsible for setting up appropriate equalisation factors even if no equalisation is required (e.g., setting all factors to 1 ).