cc.factorie.app.classify.backend

OptimizingBaseLinearTrainer

trait OptimizingBaseLinearTrainer[Input, Prediction, Output, C <: OptimizablePredictor[Prediction, Input] with model.Parameters] extends BaseLinearTrainer[Input, Prediction, Output, C]

Linear Supertypes
BaseLinearTrainer[Input, Prediction, Output, C], AnyRef, Any
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Abstract Value Members

  1. abstract def maxIterations: Int

  2. abstract def miniBatch: Int

  3. abstract def nThreads: Int

  4. abstract def newModel(featureSize: Int, labelSize: Int): C

    Definition Classes
    BaseLinearTrainer
  5. abstract def objective: OptimizableObjective[Prediction, Output]

  6. abstract def optimizer: GradientOptimizer

  7. implicit abstract def random: Random

  8. abstract def useOnlineTrainer: Boolean

  9. abstract def useParallelTrainer: Boolean

Concrete 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. final def baseTrain(classifier: C, labels: Seq[Output], features: Seq[Input], weights: Seq[Double], evaluate: (C) ⇒ Unit): Unit

    Estimate the parameters of a classifier that has already been created.

    Estimate the parameters of a classifier that has already been created.

    Definition Classes
    OptimizingBaseLinearTrainerBaseLinearTrainer
  8. def clone(): AnyRef

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    protected[java.lang]
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  9. final def eq(arg0: AnyRef): Boolean

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  10. def equals(arg0: Any): Boolean

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  11. def finalize(): Unit

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

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  13. def hashCode(): Int

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  14. final def isInstanceOf[T0]: Boolean

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

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  16. final def notify(): Unit

    Definition Classes
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  17. final def notifyAll(): Unit

    Definition Classes
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  18. final def simpleTrain(labelSize: Int, featureSize: Int, labels: Seq[Output], features: Seq[Input], weights: Seq[Double], evaluate: (C) ⇒ Unit): C

    Create a Classifier and estimate its parameters.

    Create a Classifier and estimate its parameters.

    Definition Classes
    BaseLinearTrainer
  19. final def synchronized[T0](arg0: ⇒ T0): T0

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  20. def toString(): String

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  21. final def wait(): Unit

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

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

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Inherited from BaseLinearTrainer[Input, Prediction, Output, C]

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