cc.factorie.app.classify.backend

MulticlassClassifierTrainer

trait MulticlassClassifierTrainer[C <: MulticlassClassifier[la.Tensor1]] extends BaseLinearTrainer[la.Tensor1, la.Tensor1, Int, C]

Base trait for training multi-class classifiers, but it requires the input to be a Tensor1 feature vector.

Linear Supertypes
BaseLinearTrainer[la.Tensor1, la.Tensor1, Int, C], AnyRef, Any
Known Subclasses
BatchLinearMulticlassTrainer, BoostingMulticlassTrainer, DecisionTreeMulticlassTrainer, LinearMulticlassTrainer, NaiveBayes, OnlineLinearMulticlassTrainer, RandomForestMulticlassTrainer, SVMMulticlassTrainer
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  1. MulticlassClassifierTrainer
  2. BaseLinearTrainer
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Abstract Value Members

  1. abstract def baseTrain(classifier: C, labels: Seq[Int], features: Seq[la.Tensor1], 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
    BaseLinearTrainer

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

    Definition Classes
    BaseLinearTrainer

Concrete Value Members

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

    Definition Classes
    AnyRef

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

    Definition Classes
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  3. final def ##(): Int

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

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

    Definition Classes
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  6. final def asInstanceOf[T0]: T0

    Definition Classes
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  7. def clone(): AnyRef

    Attributes
    protected[java.lang]
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    @throws( ... )

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

    Definition Classes
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  9. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  10. def finalize(): Unit

    Attributes
    protected[java.lang]
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    @throws( classOf[java.lang.Throwable] )

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

    Definition Classes
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  12. def hashCode(): Int

    Definition Classes
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  13. final def isInstanceOf[T0]: Boolean

    Definition Classes
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  14. final def ne(arg0: AnyRef): Boolean

    Definition Classes
    AnyRef

  15. final def notify(): Unit

    Definition Classes
    AnyRef

  16. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  17. final def simpleTrain(labelSize: Int, featureSize: Int, labels: Seq[Int], features: Seq[la.Tensor1], weights: Seq[Double], evaluate: (C) ⇒ Unit): C

    Create a Classifier and estimate its parameters.

    Create a Classifier and estimate its parameters.

    Definition Classes
    BaseLinearTrainer

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

    Definition Classes
    AnyRef

  19. def toString(): String

    Definition Classes
    AnyRef → Any

  20. def train[Label <: LabeledDiscreteVar](classifier: C, labels: Seq[Label], l2f: (Label) ⇒ VectorVar, l2w: (Label) ⇒ Double): Unit

  21. def train[Label <: LabeledDiscreteVar](classifier: C, labels: Seq[Label], l2f: (Label) ⇒ VectorVar, testLabels: Seq[Label], l2w: (Label) ⇒ Double = (l: Label) => 1.0): Unit

  22. def train(classifier: C, labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar], evaluate: (C) ⇒ Unit): Unit

  23. def train(classifier: C, labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar], weights: Seq[Double], evaluate: (C) ⇒ Unit): Unit

  24. def train(classifier: C, labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar]): Unit

  25. def train(classifier: C, labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar], weights: Seq[Double]): Unit

  26. def train(classifier: C, labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar], testLabels: Seq[LabeledDiscreteVar], testFeatures: Seq[TensorVar]): Unit

  27. def train(classifier: C, labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar], weights: Seq[Double], testLabels: Seq[LabeledDiscreteVar], testFeatures: Seq[TensorVar]): Unit

  28. def train[Label <: LabeledDiscreteVar](labels: Seq[Label], l2f: (Label) ⇒ VectorVar, l2w: (Label) ⇒ Double): C

  29. def train[Label <: LabeledDiscreteVar](labels: Seq[Label], l2f: (Label) ⇒ VectorVar, testLabels: Seq[Label], l2w: (Label) ⇒ Double = (l: Label) => 1.0): C

  30. def train(labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar], evaluate: (C) ⇒ Unit): C

  31. def train(labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar], weights: Seq[Double], evaluate: (C) ⇒ Unit): C

  32. def train(labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar]): C

  33. def train(labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar], weights: Seq[Double]): C

  34. def train(labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar], testLabels: Seq[LabeledDiscreteVar], testFeatures: Seq[TensorVar]): C

  35. def train(labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar], weights: Seq[Double], testLabels: Seq[LabeledDiscreteVar], testFeatures: Seq[TensorVar]): C

  36. final def wait(): Unit

    Definition Classes
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    @throws( ... )

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

    Definition Classes
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    @throws( ... )

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

    Definition Classes
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    @throws( ... )

Inherited from BaseLinearTrainer[la.Tensor1, la.Tensor1, Int, C]

Inherited from AnyRef

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