opennlp.tools.ml.perceptron

## Class PerceptronTrainer

- java.lang.Object
- - opennlp.tools.ml.AbstractTrainer
  - - opennlp.tools.ml.AbstractEventTrainer
    - - opennlp.tools.ml.perceptron.PerceptronTrainer

- All Implemented Interfaces:
    EventTrainer

      public class PerceptronTrainer
      extends AbstractEventTrainer

  Trains models using the perceptron algorithm. Each outcome is represented as a binary perceptron classifier. This supports standard (integer) weighting as well average weighting as described in: Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with the Perceptron Algorithm. Michael Collins, EMNLP 2002.

- - ### Field Summary

    Fields
    Modifier and Type | Field and Description
    --- | ---
    `static String` | `PERCEPTRON_VALUE`
    `static double` | `TOLERANCE_DEFAULT`

    - ### Fields inherited from class opennlp.tools.ml.AbstractEventTrainer

      `DATA_INDEXER_ONE_PASS_REAL_VALUE, DATA_INDEXER_ONE_PASS_VALUE, DATA_INDEXER_PARAM, DATA_INDEXER_TWO_PASS_VALUE`

    - ### Fields inherited from class opennlp.tools.ml.AbstractTrainer

      `ALGORITHM_PARAM, CUTOFF_DEFAULT, CUTOFF_PARAM, ITERATIONS_DEFAULT, ITERATIONS_PARAM, TRAINER_TYPE_PARAM, VERBOSE_DEFAULT, VERBOSE_PARAM`

    - ### Fields inherited from interface opennlp.tools.ml.EventTrainer

      `EVENT_VALUE`

  - ### Constructor Summary

    Constructors
    Constructor and Description
    `PerceptronTrainer()`
    `PerceptronTrainer(TrainingParameters parameters)`

  - ### Method Summary

    All Methods Instance Methods Concrete Methods Deprecated Methods
    Modifier and Type | Method and Description
    --- | ---
    `AbstractModel` | `doTrain(DataIndexer indexer)`
    `boolean` | `isSortAndMerge()`
    `boolean` | `isValid()` Deprecated.
    `void` | `setSkippedAveraging(boolean averaging)` Enables skipped averaging, this flag changes the standard averaging to special averaging instead.
    `void` | `setStepSizeDecrease(double decrease)` Enables and sets step size decrease.
    `void` | `setTolerance(double tolerance)` Specifies the tolerance.
    `AbstractModel` | `trainModel(int iterations, DataIndexer di, int cutoff)`
    `AbstractModel` | `trainModel(int iterations, DataIndexer di, int cutoff, boolean useAverage)`
    `void` | `validate()` Check parameters.

    - ### Methods inherited from class opennlp.tools.ml.AbstractEventTrainer

      `getDataIndexer, train, train`

    - ### Methods inherited from class opennlp.tools.ml.AbstractTrainer

      `getAlgorithm, getCutoff, getIterations, init, init`

    - ### Methods inherited from class java.lang.Object

      `equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait`

    - ### Methods inherited from interface opennlp.tools.ml.EventTrainer

      `init, init`

- - ### Field Detail

    - #### PERCEPTRON_VALUE

          public static final String PERCEPTRON_VALUE

      See Also:
        Constant Field Values

    - #### TOLERANCE_DEFAULT

          public static final double TOLERANCE_DEFAULT

      See Also:
        Constant Field Values

  - ### Constructor Detail

    - #### PerceptronTrainer

          public PerceptronTrainer()

    - #### PerceptronTrainer

          public PerceptronTrainer(TrainingParameters parameters)

  - ### Method Detail

    - #### validate

          public void validate()

      Description copied from class: `AbstractTrainer`
      Check parameters. If subclass overrides this, it should call super.validate();
      Overrides:
        `validate` in class `AbstractEventTrainer`

    - #### isValid

          @Deprecated
          public boolean isValid()

      Deprecated.
      Overrides:
        `isValid` in class `AbstractEventTrainer`
      Returns:

    - #### isSortAndMerge

          public boolean isSortAndMerge()

      Specified by:
        `isSortAndMerge` in class `AbstractEventTrainer`

    - #### doTrain

          public AbstractModel doTrain(DataIndexer indexer)
                                throws IOException

      Specified by:
        `doTrain` in class `AbstractEventTrainer`
      Throws:
        `IOException`

    - #### setTolerance

          public void setTolerance(double tolerance)

      Specifies the tolerance. If the change in training set accuracy is less than this, stop iterating.
      Parameters:
        `tolerance` -

    - #### setStepSizeDecrease

          public void setStepSizeDecrease(double decrease)

      Enables and sets step size decrease. The step size is decreased every iteration by the specified value.
      Parameters:
        `decrease` - - step size decrease in percent

    - #### setSkippedAveraging

          public void setSkippedAveraging(boolean averaging)

      Enables skipped averaging, this flag changes the standard averaging to special averaging instead.

      If we are doing averaging, and the current iteration is one of the first 20 or it is a perfect square, then updated the summed parameters.

      The reason we don't take all of them is that the parameters change less toward the end of training, so they drown out the contributions of the more volatile early iterations. The use of perfect squares allows us to sample from successively farther apart iterations.

      Parameters:
        `averaging` - averaging flag

    - #### trainModel

          public AbstractModel trainModel(int iterations,
                                          DataIndexer di,
                                          int cutoff)

    - #### trainModel

          public AbstractModel trainModel(int iterations,
                                          DataIndexer di,
                                          int cutoff,
                                          boolean useAverage)

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