OptimizingLinearVectorClassifierTrainer

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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

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

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.

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 maxIterations: Int

18. val miniBatch: Int

19. val nThreads: Int

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

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21. 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.

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    Definition Classes

    LinearVectorClassifierTrainer

22. final def notify(): Unit

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23. final def notifyAll(): Unit

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24. val objective: Multiclass

25. val optimizer: GradientOptimizer

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

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27. def toString(): String

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28. 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

29. 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.

30. 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.

31. 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

32. val useOnlineTrainer: Boolean

33. val useParallelTrainer: Boolean

34. final def wait(): Unit

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

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

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