## Package org.apache.mahout.clustering.lda.cvb

Class Summary
CachingCVB0Mapper: Run ensemble learning via loading the `ModelTrainer` with two `TopicModel` instances: one from the previous iteration, the other empty.
CachingCVB0PerplexityMapper
CVB0DocInferenceMapper
CVB0Driver: See `CachingCVB0Mapper` for more details on scalability and room for improvement.
CVB0Driver.DualDoubleSumReducer: Sums keys and values independently.
CVB0TopicTermVectorNormalizerMapper: Performs L1 normalization of input vectors.
InMemoryCollapsedVariationalBayes0: Runs the same algorithm as `CVB0Driver`, but sequentially, in memory.

ModelTrainer

Multithreaded LDA model trainer class, which primarily operates by running a "map/reduce" operation, all in memory locally (ie not a hadoop job!) : the "map" operation is to take the "read-only" `TopicModel` and use it to iteratively learn the p(topic|term, doc) distribution for documents (this can be done in parallel across many documents, as the "read-only" model is, well, read-only.

TopicModel: Thin wrapper around a `Matrix` of counts of occurrences of (topic, term) pairs.

Enum Summary
CachingCVB0PerplexityMapper.Counters: Hadoop counters for `CachingCVB0PerplexityMapper`, to aid in debugging.

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