opennlp.tools.ml.maxent

Class GISModel

java.lang.Object

opennlp.tools.ml.model.AbstractModel

opennlp.tools.ml.maxent.GISModel

All Implemented Interfaces:

MaxentModel

public final class GISModel
extends AbstractModel

A maximum entropy model which has been trained using the Generalized Iterative Scaling procedure (implemented in GIS.java).

Nested Class Summary

Nested classes/interfaces inherited from class opennlp.tools.ml.model.AbstractModel

AbstractModel.ModelType

Constructor Summary

Constructors

Constructor and Description
GISModel(Context[] params, String[] predLabels, String[] outcomeNames) Creates a new model with the specified parameters, outcome names, and predicate/feature labels.
GISModel(Context[] params, String[] predLabels, String[] outcomeNames, Prior prior) Creates a new model with the specified parameters, outcome names, and predicate/feature labels.

Method Summary

Modifier and Type	Method and Description
static double[]	eval(int[] context, double[] prior, EvalParameters model) Use this model to evaluate a context and return an array of the likelihood of each outcome given the specified context and the specified parameters.
static double[]	eval(int[] context, float[] values, double[] prior, EvalParameters model) Deprecated.
double[]	eval(String[] context) Use this model to evaluate a context and return an array of the likelihood of each outcome given that context.
double[]	eval(String[] context, double[] outsums) Evaluates a context.
double[]	eval(String[] context, float[] values) Evaluates a contexts with the specified context values.
double[]	eval(String[] context, float[] values, double[] outsums) Use this model to evaluate a context and return an array of the likelihood of each outcome given that context.

Methods inherited from class opennlp.tools.ml.model.AbstractModel

equals, getAllOutcomes, getBestOutcome, getDataStructures, getIndex, getModelType, getNumOutcomes, getOutcome, hashCode

Methods inherited from class java.lang.Object

getClass, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

GISModel

public GISModel(Context[] params,
                String[] predLabels,
                String[] outcomeNames)

Creates a new model with the specified parameters, outcome names, and predicate/feature labels.

Parameters:

params - The parameters of the model.

predLabels - The names of the predicates used in this model.

outcomeNames - The names of the outcomes this model predicts.

GISModel

public GISModel(Context[] params,
                String[] predLabels,
                String[] outcomeNames,
                Prior prior)

Creates a new model with the specified parameters, outcome names, and predicate/feature labels.

Parameters:

params - The parameters of the model.

predLabels - The names of the predicates used in this model.

outcomeNames - The names of the outcomes this model predicts.

prior - The prior to be used with this model.

Method Detail

eval

public final double[] eval(String[] context)

Use this model to evaluate a context and return an array of the likelihood of each outcome given that context.

Parameters:

context - The names of the predicates which have been observed at the present decision point.

Returns:

The normalized probabilities for the outcomes given the context. The indexes of the double[] are the outcome ids, and the actual string representation of the outcomes can be obtained from the method getOutcome(int i).

eval

public final double[] eval(String[] context,
                           float[] values)

Description copied from interface: MaxentModel

Evaluates a contexts with the specified context values.

Parameters:

context - A list 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.

eval

public final double[] eval(String[] context,
                           double[] outsums)

Description copied from interface: MaxentModel

Evaluates a context.

Parameters:

context - A list of String names of the contextual predicates which are to be evaluated together.

outsums - 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

public final double[] eval(String[] context,
                           float[] values,
                           double[] outsums)

Use this model to evaluate a context and return an array of the likelihood of each outcome given that context.

Parameters:

context - The names of the predicates which have been observed at the present decision point.

outsums - This is where the distribution is stored.

Returns:

The normalized probabilities for the outcomes given the context. The indexes of the double[] are the outcome ids, and the actual string representation of the outcomes can be obtained from the method getOutcome(int i).

eval

public static double[] eval(int[] context,
                            double[] prior,
                            EvalParameters model)

Use this model to evaluate a context and return an array of the likelihood of each outcome given the specified context and the specified parameters.

Parameters:

context - The integer values of the predicates which have been observed at the present decision point.

prior - The prior distribution for the specified context.

model - The set of parametes used in this computation.

Returns:

The normalized probabilities for the outcomes given the context. The indexes of the double[] are the outcome ids, and the actual string representation of the outcomes can be obtained from the method getOutcome(int i).

eval

@Deprecated
public static double[] eval(int[] context,
                                        float[] values,
                                        double[] prior,
                                        EvalParameters model)

Deprecated.

Use this model to evaluate a context and return an array of the likelihood of each outcome given the specified context and the specified parameters.

Parameters:

context - The integer values of the predicates which have been observed at the present decision point.

values - The values for each of the parameters.

prior - The prior distribution for the specified context.

model - The set of parametes used in this computation.

Returns:

The normalized probabilities for the outcomes given the context. The indexes of the double[] are the outcome ids, and the actual string representation of the outcomes can be obtained from the method getOutcome(int i).