cc.factorie.optimize

OnlineTrainer

class OnlineTrainer extends Trainer with FastLogging

Learns the parameters of a model by computing the gradient and calling the optimizer one example at a time.

Linear Supertypes
FastLogging, Logging, Trainer, AnyRef, Any
Known Subclasses
SampleRankTrainer
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  1. OnlineTrainer
  2. FastLogging
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Instance Constructors

  1. new OnlineTrainer(weightsSet: WeightsSet, optimizer: GradientOptimizer = ..., maxIterations: Int = 3, logEveryN: Int = -1)

    weightsSet

    The parameters to be optimized

    optimizer

    The optimizer

    maxIterations

    The maximum number of iterations until reporting convergence

    logEveryN

    After this many examples a log will be printed. If set to -1 10 logs will be printed.

Value Members

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

  4. final def ==(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  7. def clone(): AnyRef

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

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

    Definition Classes
    AnyRef

  9. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  10. def finalize(): Unit

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

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

    Definition Classes
    AnyRef → Any

  12. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  13. def isConverged: Boolean

    Would more training help?

    Would more training help?

    Definition Classes
    OnlineTrainer → Trainer

  14. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  15. var iteration: Int

  16. var logEveryN: Int

    After this many examples a log will be printed.

    After this many examples a log will be printed. If set to -1 10 logs will be printed.

  17. val logger: Logger

    Definition Classes
    FastLogging → Logging

  18. val maxIterations: Int

    The maximum number of iterations until reporting convergence

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

    Definition Classes
    AnyRef

  20. final def notify(): Unit

    Definition Classes
    AnyRef

  21. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  22. val optimizer: GradientOptimizer

    The optimizer

  23. def processExamples(examples: Iterable[Example]): Unit

    Process the examples once.

    Process the examples once.

    examples

    Examples to be processed

    Definition Classes
    OnlineTrainer → Trainer

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

    Definition Classes
    AnyRef

  25. def toString(): String

    Definition Classes
    AnyRef → Any

  26. def trainFromExamples(examples: Iterable[Example]): Unit

    Repeatedly process the examples until training has converged.

    Repeatedly process the examples until training has converged.

    Definition Classes
    Trainer

  27. val valueAccumulator: LocalDoubleAccumulator

  28. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
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    @throws( ... )

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

    Definition Classes
    AnyRef
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    @throws( ... )

  31. val weightsSet: WeightsSet

    The parameters to be optimized

Inherited from FastLogging

Inherited from Logging

Inherited from Trainer

Inherited from AnyRef

Inherited from Any

Ungrouped