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:
-
Nested Class Summary
Nested classes/interfaces inherited from class opennlp.tools.ml.model.AbstractModel
AbstractModel.ModelType -
Constructor Summary
Constructors -
Method Summary
Methods inherited from class opennlp.tools.ml.model.AbstractModel
equals, getAllOutcomes, getBestOutcome, getDataStructures, getIndex, getModelType, getOutcome, hashCode
-
Constructor Details
-
QNModel
Initializes aQNModelwith the specified parameters, predicate/feature labels, and outcome names.- Parameters:
params- Theparametersof the model.predLabels- The names of the predicates used in this model.outcomeNames- The names of the outcomes this model predicts.
-
-
Method Details
-
getNumOutcomes
public int getNumOutcomes()- Specified by:
getNumOutcomesin interfaceMaxentModel- Overrides:
getNumOutcomesin classAbstractModel- Returns:
- Retrieves the number of outcomes for this model.
-
eval
Evaluates 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.
-
eval
Evaluates 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.
-
eval
Evaluates 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.
-