cc.factorie.app.classify

BatchOptimizingLinearVectorClassifierTrainer

class BatchOptimizingLinearVectorClassifierTrainer extends OptimizingLinearVectorClassifierTrainer

An OptimizingLinearVectorClassifierTrainer pre-tuned with default arguments well-suited to batch training, operating on all the gradients of the Examples together.

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  1. BatchOptimizingLinearVectorClassifierTrainer
  2. OptimizingLinearVectorClassifierTrainer
  3. LinearVectorClassifierTrainer
  4. VectorClassifierTrainer
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Instance Constructors

  1. new BatchOptimizingLinearVectorClassifierTrainer(useParallel: Boolean = true, optimizer: GradientOptimizer = ..., objective: Multiclass = ..., maxIterations: Int = 200, nThreads: Int = ...)(implicit random: Random)

Value Members

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

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

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  3. final def ##(): Int

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

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

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  6. final def asInstanceOf[T0]: T0

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  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
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    Annotations
    @throws( ... )
  8. def defaultTestDiagnostic[C <: LinearVectorClassifier[L, F], L <: LabeledDiscreteVar, F <: VectorVar](classifier: LinearVectorClassifier[L, F], trainLabels: Iterable[L], testLabels: Iterable[L]): (C) ⇒ Unit

    Return a function suitable for passing in as the diagnostic to train which prints the accuracy on the testLabels

    Return a function suitable for passing in as the diagnostic to train which prints the accuracy on the testLabels

    Definition Classes
    OptimizingLinearVectorClassifierTrainer
  9. def defaultTrainAndTestDiagnostic[C <: LinearVectorClassifier[L, F], L <: LabeledDiscreteVar, F <: VectorVar](classifier: LinearVectorClassifier[L, F], trainLabels: Iterable[L], testLabels: Iterable[L]): (C) ⇒ Unit

    Return a function suitable for passing in as the diagnostic to train which prints the accuracy on the trainLabels and the testLabels

    Return a function suitable for passing in as the diagnostic to train which prints the accuracy on the trainLabels and the testLabels

    Definition Classes
    OptimizingLinearVectorClassifierTrainer
  10. final def eq(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef
  11. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any
  12. def examples[L <: LabeledDiscreteVar, F <: VectorVar](classifier: LinearVectorClassifier[L, F], labels: Iterable[L], l2f: (L) ⇒ F, objective: Multiclass): Seq[optimize.Example]

    Create a sequence of Example instances for obtaining the gradients used for training.

    Create a sequence of Example instances for obtaining the gradients used for training.

    Definition Classes
    OptimizingLinearVectorClassifierTrainer
  13. def finalize(): Unit

    Attributes
    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )
  14. final def getClass(): Class[_]

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  15. def hashCode(): Int

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  16. final def isInstanceOf[T0]: Boolean

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  17. val miniBatch: Int

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

    Definition Classes
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  19. def newClassifier[L <: LabeledDiscreteVar, F <: VectorVar](labelDomainSize: Int, featureDomainSize: Int, l2f: (L) ⇒ F): LinearVectorClassifier[L, F]

    Create a new LinearVectorClassifier, not yet trained.

    Create a new LinearVectorClassifier, not yet trained.

    Attributes
    protected
    Definition Classes
    LinearVectorClassifierTrainer
  20. final def notify(): Unit

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  21. final def notifyAll(): Unit

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

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  23. def toString(): String

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  24. def train[C <: LinearVectorClassifier[L, F], L <: LabeledDiscreteVar, F <: VectorVar](classifier: C, trainLabels: Iterable[L], l2f: (L) ⇒ F): C

    Train the classifier to convergence, calling no diagnostic function.

    Train the classifier to convergence, calling no diagnostic function.

    Definition Classes
    OptimizingLinearVectorClassifierTrainerLinearVectorClassifierTrainer
  25. def train[C <: LinearVectorClassifier[L, F], L <: LabeledDiscreteVar, F <: VectorVar](classifier: C, trainLabels: Iterable[L], testLabels: Iterable[L], l2f: (L) ⇒ F): C

    Train the classifier to convergence, calling a test-accuracy-printing diagnostic function once after each iteration.

    Train the classifier to convergence, calling a test-accuracy-printing diagnostic function once after each iteration.

    Definition Classes
    OptimizingLinearVectorClassifierTrainer
  26. def train[C <: LinearVectorClassifier[L, F], L <: LabeledDiscreteVar, F <: VectorVar](classifier: C, trainLabels: Iterable[L], l2f: (L) ⇒ F, diagnostic: (C) ⇒ Unit): C

    Train the classifier to convergence, calling the diagnostic function once after each iteration.

    Train the classifier to convergence, calling the diagnostic function once after each iteration. This is the base method called by the other simpler train methods.

    Definition Classes
    OptimizingLinearVectorClassifierTrainer
  27. def train[L <: LabeledDiscreteVar, F <: VectorVar](labels: Iterable[L], l2f: (L) ⇒ F): LinearVectorClassifier[L, F]

    Create, train and return a new LinearVectorClassifier

    Create, train and return a new LinearVectorClassifier

    Definition Classes
    LinearVectorClassifierTrainerVectorClassifierTrainer
  28. val useOnlineTrainer: Boolean

  29. val useParallelTrainer: Boolean

  30. final def wait(): Unit

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    @throws( ... )
  31. final def wait(arg0: Long, arg1: Int): Unit

    Definition Classes
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    @throws( ... )
  32. final def wait(arg0: Long): Unit

    Definition Classes
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    @throws( ... )

Inherited from VectorClassifierTrainer

Inherited from AnyRef

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