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It is also possible to use trained weka classifiert, that are not stored with Odysseus. In this case you will need to use a FilteredClassifier as meta classifier. In options add the wanted classifier. If data are based on strings, add StringToNominalFilter else add NumericToNominalFilter as option. Learn model and store. See following activity diagram (in german).
Original Weka Classifier
This operator has the following optional parameter:
isWekaModel: This factor makes it possible to use a model trained in Weka (outside of Odysseus) as input on port 1. The following example shows a use case, where the Weka model is first loaded from a database and then used in the CLASSIFICATION operator:
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timer = TIMER({PERIOD = 1000000000, SOURCE = 'testdata'})
wekaModel = dbenrich({connection='connection3', query='SELECT id, model_name, labels, model_content, output_attributes FROM trained_models where id=5', multiTupleOutput='false', attributes=[]}, timer)
classified = CLASSIFICATION({isWekaModel='true'}, testdata, wekaModel) |