Package opennlp.tools.ml.perceptron

# Class SimplePerceptronSequenceTrainer

java.lang.Object
opennlp.tools.ml.AbstractTrainer
opennlp.tools.ml.AbstractEventModelSequenceTrainer
opennlp.tools.ml.perceptron.SimplePerceptronSequenceTrainer

All Implemented Interfaces:
  `Trainer`, `EventModelSequenceTrainer<Event>`

public class SimplePerceptronSequenceTrainer extends AbstractEventModelSequenceTrainer
Trains `models` with sequences using the perceptron algorithm.

Each outcome is represented as a binary perceptron classifier. This supports standard (integer) weighting as well average weighting.

Sequence information is used in a simplified was to that described in: Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with the Perceptron Algorithm. Michael Collins, EMNLP 2002.

Specifically only updates are applied to tokens which were incorrectly tagged by a sequence tagger rather than to all feature across the sequence which differ from the training sequence.

See Also:
  - `PerceptronModel`
  - `AbstractEventModelSequenceTrainer`

- ## Field Summary

  Fields
  Modifier and Type
  Field
  Description
  `static final String`
  `PERCEPTRON_SEQUENCE_VALUE`

  ### Fields inherited from interface opennlp.tools.ml.EventModelSequenceTrainer

  `SEQUENCE_VALUE`

- ## Constructor Summary

  Constructors
  Constructor
  Description
  `SimplePerceptronSequenceTrainer()`
  Instantiates a `SimplePerceptronSequenceTrainer` with a default configuration of training parameters.

- ## Method Summary

  Modifier and Type
  Method
  Description
  `AbstractModel`
  `doTrain(SequenceStream<Event> events)`
  `void`
  `nextIteration(int iteration)`
  `AbstractModel`
  `trainModel(int iterations, SequenceStream<Event> sequenceStream, int cutoff, boolean useAverage)`
  Trains a `PerceptronModel` with given parameters.
  `void`
  `validate()`
  Checks the configured `parameters`.

  ### Methods inherited from class opennlp.tools.ml.AbstractEventModelSequenceTrainer

  `train`

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

  `getAlgorithm, getCutoff, getIterations, getTrainingConfiguration, init, init`

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

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

  ### Methods inherited from interface opennlp.tools.commons.Trainer

  `init, init`

- ## Field Details

  - ### PERCEPTRON_SEQUENCE_VALUE

    public static final String PERCEPTRON_SEQUENCE_VALUE
    See Also:
      - Constant Field Values

- ## Constructor Details

  - ### SimplePerceptronSequenceTrainer

    public SimplePerceptronSequenceTrainer()
    Instantiates a `SimplePerceptronSequenceTrainer` with a default configuration of training parameters.

- ## Method Details

  - ### validate

    public void validate()
    Checks the configured `parameters`. If a subclass overrides this, it should call `super.validate();`.
    Overrides:
      `validate` in class `AbstractTrainer`
    Throws:
      `IllegalArgumentException` - Thrown if the algorithm name is not equal to {`PERCEPTRON_SEQUENCE_VALUE`}.

  - ### doTrain

    public AbstractModel doTrain(SequenceStream<Event> events) throws IOException
    Specified by:
      `doTrain` in class `AbstractEventModelSequenceTrainer`
    Throws:
      `IOException`

  - ### trainModel

    public AbstractModel trainModel(int iterations, SequenceStream<Event> sequenceStream, int cutoff, boolean useAverage) throws IOException
    Trains a `PerceptronModel` with given parameters.
    Parameters:
      `iterations` - The number of iterations to use for training.
      `sequenceStream` - The `SequenceStream<Event>` used as data input.
      `cutoff` - The {`TrainingParameters.CUTOFF_PARAM`} value to use for training.
      `useAverage` - Whether to use 'averaging', or not.
    Returns:
      A valid, trained `perceptron model`.
    Throws:
      `IOException`

  - ### nextIteration

    public void nextIteration(int iteration) throws IOException
    Throws:
      `IOException`