Class

org.apache.predictionio.e2.engine

CategoricalNaiveBayesModel

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case class CategoricalNaiveBayesModel(priors: Map[String, Double], likelihoods: Map[String, Array[Map[String, Double]]]) extends Serializable with Product

Model for naive Bayes classifiers with categorical variables.

priors

log prior probabilities

likelihoods

log likelihood probabilities

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Instance Constructors

  1. new CategoricalNaiveBayesModel(priors: Map[String, Double], likelihoods: Map[String, Array[Map[String, Double]]])

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    priors

    log prior probabilities

    likelihoods

    log likelihood probabilities

Value Members

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

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  2. final def ##(): Int

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

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  7. val featureCount: Int

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  8. def finalize(): Unit

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  11. val likelihoods: Map[String, Array[Map[String, Double]]]

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    log likelihood probabilities

  12. def logScore(point: LabeledPoint, defaultLikelihood: (Seq[Double]) ⇒ Double = ls => Double.NegativeInfinity): Option[Double]

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    Calculate the log score of having the given features and label

    Calculate the log score of having the given features and label

    point

    label and features

    defaultLikelihood

    a function that calculates the likelihood when a feature value is not present. The input to the function is the other feature value likelihoods.

    returns

    log score when label is present. None otherwise.

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

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  14. final def notify(): Unit

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  15. final def notifyAll(): Unit

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  16. def predict(features: Array[String]): String

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    Return the label that yields the highest score

    Return the label that yields the highest score

    features

    features for classification

  17. val priors: Map[String, Double]

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    log prior probabilities

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

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

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