cc.factorie.app.classify

NaiveBayesClassifierTrainer

class NaiveBayesClassifierTrainer extends LinearVectorClassifierTrainer

Creates a trained naive Bayes classifier by counting feature occurrences, smoothed with pseudo-counts (m-Estimates). Note that contrary to tradition, this naive Bayes classifier does not include a "bias" weight P(class); it only includes the feature weights, P(feature|class). If you want a "bias" weight you must include in your data a feature that always has value 1.0.

Linear Supertypes
LinearVectorClassifierTrainer, VectorClassifierTrainer, AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. NaiveBayesClassifierTrainer
  2. LinearVectorClassifierTrainer
  3. VectorClassifierTrainer
  4. AnyRef
  5. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

Instance Constructors

  1. new NaiveBayesClassifierTrainer(pseudoCount: Double = 0.1)

Value Members

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  3. final def ##(): Int

    Definition Classes
    AnyRef → Any

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

    Definition Classes
    AnyRef

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

    Definition Classes
    Any

  6. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  7. val baseTrainer: NaiveBayes

  8. def clone(): AnyRef

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef

  10. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  11. def finalize(): Unit

    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

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

    Definition Classes
    AnyRef → Any

  13. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  14. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

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

    Definition Classes
    AnyRef

  16. 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

  17. final def notify(): Unit

    Definition Classes
    AnyRef

  18. final def notifyAll(): Unit

    Definition Classes
    AnyRef

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

    Definition Classes
    AnyRef

  20. def toString(): String

    Definition Classes
    AnyRef → Any

  21. def train[C <: LinearVectorClassifier[L, F], L <: LabeledDiscreteVar, F <: VectorVar](classifier: C, trainLabels: Iterable[L], l2f: (L) ⇒ F): C

    Train (and return) an already-created (perhaps already partially-trained) LinearVectorClassifier.

    Train (and return) an already-created (perhaps already partially-trained) LinearVectorClassifier.

    Definition Classes
    NaiveBayesClassifierTrainer → LinearVectorClassifierTrainer

  22. 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

  23. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from LinearVectorClassifierTrainer

Inherited from VectorClassifierTrainer

Inherited from AnyRef

Inherited from Any

Ungrouped