SVMLinearVectorClassifierTrainer

Value Members

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

   Definition Classes

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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. val baseTrainer: SVMMulticlassTrainer

8. def clone(): AnyRef

   Attributes

   protected[java.lang]

   Definition Classes

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   @throws( ... )

9. 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

10. 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

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

    Definition Classes

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12. def equals(arg0: Any): Boolean

    Definition Classes

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13. 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

14. def finalize(): Unit

    Attributes

    protected[java.lang]

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

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16. def hashCode(): Int

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17. final def isInstanceOf[T0]: Boolean

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18. val maxIterations: Int

    Definition Classes

    OptimizingLinearVectorClassifierTrainer

19. val miniBatch: Int

    Definition Classes

    OptimizingLinearVectorClassifierTrainer

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

    Definition Classes

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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.

    Attributes

    protected

    Definition Classes

    LinearVectorClassifierTrainer

22. final def notify(): Unit

    Definition Classes

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23. final def notifyAll(): Unit

    Definition Classes

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24. val objective: Multiclass

    Definition Classes

    OptimizingLinearVectorClassifierTrainer

25. val optimizer: GradientOptimizer

    Definition Classes

    OptimizingLinearVectorClassifierTrainer

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

    Definition Classes

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27. def toString(): String

    Definition Classes

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28. 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

    SVMLinearVectorClassifierTrainer → OptimizingLinearVectorClassifierTrainer

29. 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

30. 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

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

    Definition Classes

    OptimizingLinearVectorClassifierTrainer

33. val useParallelTrainer: Boolean

    Definition Classes

    OptimizingLinearVectorClassifierTrainer

34. final def wait(): Unit

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

    Definition Classes

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

    Definition Classes

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    @throws( ... )