Class QNModel
java.lang.Object
opennlp.tools.ml.model.AbstractModel
opennlp.tools.ml.maxent.quasinewton.QNModel
- All Implemented Interfaces:
- MaxentModel
A 
maximum entropy model which has been trained via the
 L-BFGS algorithm ,
 which belongs to the group of Quasi Newton (QN) algorithms.- See Also:
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Nested Class SummaryNested classes/interfaces inherited from class opennlp.tools.ml.model.AbstractModelAbstractModel.ModelType
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Constructor SummaryConstructors
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Method SummaryMethods inherited from class opennlp.tools.ml.model.AbstractModelequals, getAllOutcomes, getBestOutcome, getDataStructures, getIndex, getModelType, getOutcome, hashCode
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Constructor Details- 
QNModelInitializes aQNModelwith the specified parameters, predicate/feature labels, and outcome names.- Parameters:
- params- The- parametersof the model.
- predLabels- The names of the predicates used in this model.
- outcomeNames- The names of the outcomes this model predicts.
 
 
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Method Details- 
getNumOutcomespublic int getNumOutcomes()- Specified by:
- getNumOutcomesin interface- MaxentModel
- Overrides:
- getNumOutcomesin class- AbstractModel
- Returns:
- Retrieves the number of outcomes for this model.
 
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evalEvaluates acontext.- Parameters:
- context- An array of String names of the contextual predicates which are to be evaluated together.
- Returns:
- An array of the probabilities for each of the different
         outcomes, all of which sum to 1.
 
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evalEvaluates acontext.- Parameters:
- context- An array of String names of the contextual predicates which are to be evaluated together.
- probs- An array which is populated with the probabilities for each of the different outcomes, all of which sum to 1.
- Returns:
- An array of the probabilities for each of the different
         outcomes, all of which sum to 1.
 
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evalEvaluates acontextwith the specified contextvalues.- Parameters:
- context- An array of String names of the contextual predicates which are to be evaluated together.
- values- The values associated with each context.
- Returns:
- An array of the probabilities for each of the different
         outcomes, all of which sum to 1.
 
 
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