Trains a clusterer on an incoming training dataset (from a container) and then evaluates it on the test set (also from a container).
The clusterer setup being used in the evaluation is a callable 'Clusterer' actor.
If a class attribute is set, a classes-to-clusters evaluation is performed automatically

Additional information

Flow input/output:
- input: adams.flow.container.WekaTrainTestSetContainer
- output: adams.flow.container.WekaClusterEvaluationContainer

Container information:
- adams.flow.container.WekaTrainTestSetContainer:
   - Train: training set; weka.core.Instances
   - Test: test set; weka.core.Instances
   - Seed: seed value; java.lang.Long
   - FoldNumber: current fold (1-based); java.lang.Integer
   - FoldCount: total number of folds; java.lang.Integer
   - Train original indices: original indices (0-based, train); array of int
   - Test original indices: original indices (0-based, test); array of int
- adams.flow.container.WekaClusterEvaluationContainer:
   - Evaluation: cluster evaluation object; weka.clusterers.ClusterEvaluation
   - Model: cluster model; java.lang.Object
   - Log-likelohood: log likelihood of cross-validation; java.lang.Double