org.allenai.nlpstack.parse.poly.fsm

FSMTrainingVectorSource

Related Docs: object FSMTrainingVectorSource | package fsm

abstract class FSMTrainingVectorSource extends AnyRef

Linear Supertypes
AnyRef, Any
Known Subclasses
GoldParseTrainingVectorSource
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. FSMTrainingVectorSource
  2. AnyRef
  3. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Instance Constructors

  1. new FSMTrainingVectorSource(transitionSystemFactory: TransitionSystemFactory, baseCostFunctionFactory: Option[StateCostFunctionFactory])

Abstract Value Members

  1. abstract def getVectorIterator: Iterator[FSMTrainingVector]

Concrete Value Members

  1. final def !=(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  2. final def ##(): Int

    Definition Classes
    AnyRef → Any

  3. final def ==(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  4. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  5. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  6. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  7. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  8. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  9. def generateVectors(sculpture: Sculpture): List[FSMTrainingVector]

    This generates a list of labeled feature vectors from a gold parse tree (for training).

    This generates a list of labeled feature vectors from a gold parse tree (for training). The gold parse tree is reduced to its representation as a list of 2*n transitions, then a TrainingVector is produced for each transition (in order).

    Note that this function is implemented using tail-recursion.

    sculpture

    the sculpture to generate feature vectors from

    returns

    a list of training vectors

    Attributes
    protected

  10. final def getClass(): Class[_]

    Definition Classes
    AnyRef → Any

  11. def groupVectorIteratorsByTask: Iterator[(ClassificationTask, Iterator[FSMTrainingVector])]

  12. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  13. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  14. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  15. final def notify(): Unit

    Definition Classes
    AnyRef

  16. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  17. final def synchronized[T0](arg0: ⇒ T0): T0

    Definition Classes
    AnyRef

  18. lazy val tasks: Set[ClassificationTask]

    Returns the set of tasks associated with the training vectors in this source.

    Returns the set of tasks associated with the training vectors in this source.

    In a perhaps over-careful attempt to avoid having all the non-uniqued tasks being stored in memory simultaneously, this was originally implemented as:

    format: OFF lazy val tasks: Iterable[ClassificationTask] = taskHelper(Set(), getVectorIterator) tailrec private def taskHelper( resultSoFar: Set[ClassificationTask], vectorIter: Iterator[FSMTrainingVector] ): Set[ClassificationTask] = {

    if (!vectorIter.hasNext) { resultSoFar } else { taskHelper(resultSoFar + vectorIter.next().task, vectorIter) } } format: ON

  19. def toString(): String

    Definition Classes
    AnyRef → Any

  20. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  21. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  22. final def wait(arg0: Long): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from AnyRef

Inherited from Any

Ungrouped