cc.factorie.app.classify.backend

NaiveBayes

class NaiveBayes extends MulticlassClassifierTrainer[LinearMulticlassClassifier]

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  1. NaiveBayes
  2. MulticlassClassifierTrainer
  3. BaseLinearTrainer
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Instance Constructors

  1. new NaiveBayes(evidenceSmoothingMass: Double = 1.0)

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. def baseTrain(classifier: LinearMulticlassClassifier, labels: Seq[Int], features: Seq[la.Tensor1], weights: Seq[Double], evaluate: (LinearMulticlassClassifier) ⇒ 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
    NaiveBayesBaseLinearTrainer
  8. def clone(): AnyRef

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

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  10. def equals(arg0: Any): Boolean

    Definition Classes
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  11. var evidenceSmoothingMass: Double

  12. def finalize(): Unit

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

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  14. def hashCode(): Int

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  15. final def isInstanceOf[T0]: Boolean

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

    Definition Classes
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  17. def newModel(featureSize: Int, labelSize: Int): LinearMulticlassClassifier

    Definition Classes
    NaiveBayesBaseLinearTrainer
  18. final def notify(): Unit

    Definition Classes
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  19. final def notifyAll(): Unit

    Definition Classes
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  20. final def simpleTrain(labelSize: Int, featureSize: Int, labels: Seq[Int], features: Seq[la.Tensor1], weights: Seq[Double], evaluate: (LinearMulticlassClassifier) ⇒ Unit): LinearMulticlassClassifier

    Create a Classifier and estimate its parameters.

    Create a Classifier and estimate its parameters.

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

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  22. def toString(): String

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

    Definition Classes
    MulticlassClassifierTrainer
  24. def train[Label <: LabeledDiscreteVar](classifier: LinearMulticlassClassifier, labels: Seq[Label], l2f: (Label) ⇒ VectorVar, testLabels: Seq[Label], l2w: (Label) ⇒ Double = (l: Label) => 1.0): Unit

    Definition Classes
    MulticlassClassifierTrainer
  25. def train(classifier: LinearMulticlassClassifier, labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar], evaluate: (LinearMulticlassClassifier) ⇒ Unit): Unit

    Definition Classes
    MulticlassClassifierTrainer
  26. def train(classifier: LinearMulticlassClassifier, labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar], weights: Seq[Double], evaluate: (LinearMulticlassClassifier) ⇒ Unit): Unit

    Definition Classes
    MulticlassClassifierTrainer
  27. def train(classifier: LinearMulticlassClassifier, labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar]): Unit

    Definition Classes
    MulticlassClassifierTrainer
  28. def train(classifier: LinearMulticlassClassifier, labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar], weights: Seq[Double]): Unit

    Definition Classes
    MulticlassClassifierTrainer
  29. def train(classifier: LinearMulticlassClassifier, labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar], testLabels: Seq[LabeledDiscreteVar], testFeatures: Seq[TensorVar]): Unit

    Definition Classes
    MulticlassClassifierTrainer
  30. def train(classifier: LinearMulticlassClassifier, labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar], weights: Seq[Double], testLabels: Seq[LabeledDiscreteVar], testFeatures: Seq[TensorVar]): Unit

    Definition Classes
    MulticlassClassifierTrainer
  31. def train[Label <: LabeledDiscreteVar](labels: Seq[Label], l2f: (Label) ⇒ VectorVar, l2w: (Label) ⇒ Double): LinearMulticlassClassifier

    Definition Classes
    MulticlassClassifierTrainer
  32. def train[Label <: LabeledDiscreteVar](labels: Seq[Label], l2f: (Label) ⇒ VectorVar, testLabels: Seq[Label], l2w: (Label) ⇒ Double = (l: Label) => 1.0): LinearMulticlassClassifier

    Definition Classes
    MulticlassClassifierTrainer
  33. def train(labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar], evaluate: (LinearMulticlassClassifier) ⇒ Unit): LinearMulticlassClassifier

    Definition Classes
    MulticlassClassifierTrainer
  34. def train(labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar], weights: Seq[Double], evaluate: (LinearMulticlassClassifier) ⇒ Unit): LinearMulticlassClassifier

    Definition Classes
    MulticlassClassifierTrainer
  35. def train(labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar]): LinearMulticlassClassifier

    Definition Classes
    MulticlassClassifierTrainer
  36. def train(labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar], weights: Seq[Double]): LinearMulticlassClassifier

    Definition Classes
    MulticlassClassifierTrainer
  37. def train(labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar], testLabels: Seq[LabeledDiscreteVar], testFeatures: Seq[TensorVar]): LinearMulticlassClassifier

    Definition Classes
    MulticlassClassifierTrainer
  38. def train(labels: Seq[LabeledDiscreteVar], features: Seq[VectorVar], weights: Seq[Double], testLabels: Seq[LabeledDiscreteVar], testFeatures: Seq[TensorVar]): LinearMulticlassClassifier

    Definition Classes
    MulticlassClassifierTrainer
  39. final def wait(): Unit

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

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

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