opennlp.tools.ml.model (Apache OpenNLP Tools 2.5.9 API)
Description: declaration: package: opennlp.tools.ml.model

# Package opennlp.tools.ml.model

package opennlp.tools.ml.model
Package related to ML models and feature selection techniques.

- Related Packages
  Package
  Description
  opennlp.tools.ml
  Package related to Machine Learning (ML) features of OpenNLP, the related ML models, and trainers.
  opennlp.tools.ml.maxent
  Package related to ML by means of the Maximum Entropy (ME) algorithm.
  opennlp.tools.ml.naivebayes
  Package related to ML by means of the Naive Bayes algorithm.
  opennlp.tools.ml.perceptron
  Package related to ML by means of the perceptron algorithm.
- Class
  Description
  AbstractDataIndexer
  Abstract `DataIndexer` implementation for collecting event and context counts used in training.
  AbstractModel
  A basic `MaxentModel` implementation.
  AbstractModel.ModelType
  AbstractModelReader
  An abstract, basic implementation of a model reader.
  AbstractModelWriter
  An abstract, basic implementation of a model writer.
  BinaryFileDataReader
  A `DataReader` that reads files from a binary format.
  ChecksumEventStream
  A `Checksum`-based `event stream` implementation.
  ComparableEvent
  A maxent event representation which we can use to sort based on the predicates indexes contained in the events.
  ComparablePredicate
  A maxent predicate representation which we can use to sort based on the outcomes.
  Context
  Class which associates a real valued parameter or expected value with a particular contextual predicate or feature.
  DataIndexer
  Represents an indexer which compresses events in memory and performs feature selection.
  DataIndexerFactory
  A factory that produces `DataIndexer` instances.
  DataReader
  Describes generic ways to read data from a `DataInputStream`.
  DynamicEvalParameters
  EvalParameters
  This class encapsulates the variables used in producing probabilities from a model and facilitates passing these variables to the eval method.
  Event
  The context of a decision point during training.
  FileEventStream
  Class for using a file of `events` as an `event stream`.
  GenericModelReader
  An generic `AbstractModelReader` implementation.
  GenericModelWriter
  An generic `AbstractModelWriter` implementation.
  MaxentModel
  Interface for maximum entropy models.
  ModelParameterChunker
  A helper class that handles Strings with more than 64k (65535 bytes) in length.
  MutableContext
  An extension of `Context` used to store parameters or expected values associated with this context which can be updated or assigned.
  ObjectDataReader
  A `DataReader` implementation based on `ObjectInputStream`.
  OnePassDataIndexer
  A `DataIndexer` for maxent model data which handles cutoffs for uncommon contextual predicates and provides a unique integer index for each of the predicates.
  OnePassRealValueDataIndexer
  A `DataIndexer` for maxent model data which handles cutoffs for uncommon contextual predicates and provides a unique integer index for each of the predicates and maintains event values.
  PlainTextFileDataReader
  A generic `DataReader` implementation for plain text files.
  Prior
  This interface allows one to implement a prior distribution for use in maximum entropy model training.
  RealValueFileEventStream
  Class for using a file of real-valued `events` as an `event stream`.
  Sequence<T>
  Class which models a sequence.
  SequenceClassificationModel
  A classification model that can label an input `Sequence`.
  SequenceStream<S>
  Interface for streams of `sequences` used to train sequence models.
  SequenceStreamEventStream
  Class which turns a `SequenceStream` into an event stream.
  TwoPassDataIndexer
  Collecting event and context counts by making two passes over the events.
  UniformPrior
  Provide a maximum entropy model with a uniform `Prior`.