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

    All Methods Static Methods Instance Methods Concrete Methods
    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.
    `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).

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