breeze.classify

NaiveBayes

class NaiveBayes[L, W] extends Classifier[L, Counter[W, Double]] with Serializable

Implements a Naive-Bayes Classifer over bags of words. It automatically trains itself given the collection c of learning examples.

Annotations
@SerialVersionUID( 1L )
Linear Supertypes
Serializable, Serializable, Classifier[L, Counter[W, Double]], (Counter[W, Double]) ⇒ L, AnyRef, Any
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Inherited
  1. NaiveBayes
  2. Serializable
  3. Serializable
  4. Classifier
  5. Function1
  6. AnyRef
  7. Any
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Visibility
  1. Public
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Instance Constructors

  1. new NaiveBayes(c: Iterable[Example[L, Counter[W, Double]]], wordSmoothing: Double = 0.05, classSmoothing: Double = 0.01)

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. def andThen[A](g: (L) ⇒ A): (Counter[W, Double]) ⇒ A

    Definition Classes
    Function1
    Annotations
    @unspecialized()

  7. def apply(o: Counter[W, Double]): L

    Return the most likely label

    Return the most likely label

    Definition Classes
    Classifier → Function1

  8. final def asInstanceOf[T0]: T0

    Definition Classes
    Any

  9. val classSmoothing: Double

  10. def classify(o: Counter[W, Double]): L

    Return the most likely label

    Return the most likely label

    Definition Classes
    Classifier

  11. def clone(): AnyRef

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

  12. def compose[A](g: (A) ⇒ Counter[W, Double]): (A) ⇒ L

    Definition Classes
    Function1
    Annotations
    @unspecialized()

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

    Definition Classes
    AnyRef

  14. def equals(arg0: Any): Boolean

    Definition Classes
    AnyRef → Any

  15. def finalize(): Unit

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

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

    Definition Classes
    AnyRef → Any

  17. def hashCode(): Int

    Definition Classes
    AnyRef → Any

  18. final def isInstanceOf[T0]: Boolean

    Definition Classes
    Any

  19. def map[M](f: (L) ⇒ M): Classifier[M, Counter[W, Double]]

    Transforms output labels L=>M.

    Transforms output labels L=>M. if f(x) is not one-to-one then the max of score from the L's are used.

    Definition Classes
    Classifier

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

    Definition Classes
    AnyRef

  21. final def notify(): Unit

    Definition Classes
    AnyRef

  22. final def notifyAll(): Unit

    Definition Classes
    AnyRef

  23. def scores(o: Counter[W, Double]): Counter[L, Double]

    Returns the unnormalized log probability of each class for the given document.

    Returns the unnormalized log probability of each class for the given document.

    Definition Classes
    NaiveBayes → Classifier

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

    Definition Classes
    AnyRef

  25. def toString(): String

    Definition Classes
    Function1 → AnyRef → Any

  26. final def wait(): Unit

    Definition Classes
    AnyRef
    Annotations
    @throws()

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

    Definition Classes
    AnyRef
    Annotations
    @throws()

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

    Definition Classes
    AnyRef
    Annotations
    @throws()

  29. val wordSmoothing: Double

  30. val wordTotals: Map[L, Double]

Inherited from Serializable

Inherited from Serializable

Inherited from Classifier[L, Counter[W, Double]]

Inherited from (Counter[W, Double]) ⇒ L

Inherited from AnyRef

Inherited from Any

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