BatchOptimizingLinearVectorClassifierTrainer

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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

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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

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11. def equals(arg0: Any): Boolean

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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

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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

    Definition Classes

    OptimizingLinearVectorClassifierTrainer

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

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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

    OptimizingLinearVectorClassifierTrainer → LinearVectorClassifierTrainer

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

    LinearVectorClassifierTrainer → VectorClassifierTrainer

28. val useOnlineTrainer: Boolean

    Definition Classes

    OptimizingLinearVectorClassifierTrainer

29. val useParallelTrainer: Boolean

    Definition Classes

    OptimizingLinearVectorClassifierTrainer

30. final def wait(): Unit

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

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

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