axle.ml

NaiveBayesClassifier

class NaiveBayesClassifier[D, TF, TC] extends AnyRef

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Instance Constructors

  1. new NaiveBayesClassifier(data: Seq[D], pFs: List[RandomVariable[TF]], pC: RandomVariable[TC], featureExtractor: (D) ⇒ List[TF], classExtractor: (D) ⇒ TC)

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. val C: RandomVariable0[TC]

  7. val Fs: List[RandomVariable1[TF, TC]]

  8. val N: Int

  9. def argmax[K](ks: Iterable[K], f: (K) ⇒ Double): K

  10. final def asInstanceOf[T0]: T0

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  11. val classTally: Map[TC, Int]

  12. def clone(): AnyRef

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

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  14. def equals(arg0: Any): Boolean

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  15. val featureNames: List[String]

  16. val featureTally: Map[(TC, String, TF), Int]

  17. def finalize(): Unit

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

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  19. def hashCode(): Int

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  20. final def isInstanceOf[T0]: Boolean

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

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  22. final def notify(): Unit

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  23. final def notifyAll(): Unit

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  24. def performance(dit: Iterator[D], k: TC): (Double, Double, Double, Double)

    "performance" returns four measures of classification performance for the given class.

    "performance" returns four measures of classification performance for the given class.

    They are:

    1. Precision 2. Recall 3. Specificity 4. Accuracy

    See http://en.wikipedia.org/wiki/Precision_and_recall for more information.

  25. def predict(d: D): TC

  26. def predictedVsActual(dit: Iterator[D], k: TC): (Int, Int, Int, Int)

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

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  28. def toString(): String

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  29. final def wait(): Unit

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

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

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