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As discussed in MAHOUT-165 at some point, Vector (and Matrix, but let's put that on a separate ticket) could do with a nice exposure of methods like the following:
// this can get optimized, of course public double aggregate(Vector other, BinaryFunction aggregator, BinaryFunction combiner) { double result = 0; for(int i=0; i<size(); i++) { result = aggregator.apply(result, combiner.apply(getQuick(i), other.getQuick(i))); } return result; }
this is good for generalized inner products and distances. Also nice:
public double aggregate(BinaryFunction aggregator, UnaryFunction map) { double result = 0; for(int i=0; i<size(); i++) { result = aggregator.apply(result, map.apply(getQuick(i)) ); } return result; }
Which generalizes norms and statistics (mean, median, stdDev) and things like that (number of positive values, or negative values, etc...).
These kind of thing exists in Colt, and we could just surface it up to the top.
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These look nearly good enough to commit as they stand. I am sure that there will be 1-2 more patterns that we would like to add in this style, but I think that these are good enough to start with. If we need specialized versions for speed, we can file additional JIRA's (after 0.3).