opennlp.tools.ml.model

## Interface MaxentModel

- All Known Implementing Classes:
    AbstractModel, GISModel, NaiveBayesModel, PerceptronModel, QNModel

      public interface MaxentModel

  Interface for maximum entropy models.

- - ### Method Summary

    All Methods Instance Methods Abstract Methods
    Modifier and Type | Method and Description
    --- | ---
    `double[]` | `eval(String[] context)` Evaluates a context.
    `double[]` | `eval(String[] context, double[] probs)` Evaluates a context.
    `double[]` | `eval(String[] context, float[] values)` Evaluates a contexts with the specified context values.
    `String` | `getAllOutcomes(double[] outcomes)` Return a string matching all the outcome names with all the probabilities produced by the `eval(String[] context)` method.
    `String` | `getBestOutcome(double[] outcomes)` Simple function to return the outcome associated with the index containing the highest probability in the double[].
    `int` | `getIndex(String outcome)` Gets the index associated with the String name of the given outcome.
    `int` | `getNumOutcomes()` Returns the number of outcomes for this model.
    `String` | `getOutcome(int i)` Gets the String name of the outcome associated with the index i.

- - ### Method Detail

    - #### eval

          double[] eval(String[] context)

      Evaluates a context.
      Parameters:
        `context` - A list 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

          double[] eval(String[] context,
                        double[] probs)

      Evaluates a context.
      Parameters:
        `context` - A list 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

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

      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.

    - #### getBestOutcome

          String getBestOutcome(double[] outcomes)

      Simple function to return the outcome associated with the index containing the highest probability in the double[].
      Parameters:
        `outcomes` - A `double[]` as returned by the `eval(String[] context)` method.
      Returns:
        the String name of the best outcome

    - #### getAllOutcomes

          String getAllOutcomes(double[] outcomes)

      Return a string matching all the outcome names with all the probabilities produced by the `eval(String[] context)` method.
      Parameters:
        `outcomes` - A `double[]` as returned by the `eval(String[] context)` method.
      Returns:
        String containing outcome names paired with the normalized probability (contained in the `double[] ocs`) for each one.

    - #### getOutcome

          String getOutcome(int i)

      Gets the String name of the outcome associated with the index i.
      Parameters:
        `i` - the index for which the name of the associated outcome is desired.
      Returns:
        the String name of the outcome

    - #### getIndex

          int getIndex(String outcome)

      Gets the index associated with the String name of the given outcome.
      Parameters:
        `outcome` - the String name of the outcome for which the index is desired
      Returns:
        the index if the given outcome label exists for this model, -1 if it does not.

    - #### getNumOutcomes

          int getNumOutcomes()

      Returns the number of outcomes for this model.
      Returns:
        The number of outcomes.

Copyright © 2026 The Apache Software Foundation. All rights reserved.