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object BayesClassifier

The BayesClassifier object provides factory methods for building Bayes classifiers.

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  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  4. def apply(xy: MatriI, fn: Array[String], k: Int, cn: Array[String], vc: Array[Int], thres: Double, me: Double, PARALLELISM: Int): TwoBAN_OS

    Build a Bayesian Network 2 classification model, passing 'x' and 'y' together in one matrix.

    Build a Bayesian Network 2 classification model, passing 'x' and 'y' together in one matrix.

    xy

    the data vectors along with their classifications stored as rows of a matrix

    fn

    the names of the features

    k

    the number of classes

    vc

    the value count (number of distinct values) for each feature

    thres

    the correlation threshold between 2 features for possible parent-child relationship

    me

    use m-estimates (me == 0 => regular MLE estimates)

  5. def apply(x: MatriI, y: VectoI, fn: Array[String], k: Int, cn: Array[String], vc: Array[Int], thres: Double, me: Double, PARALLELISM: Int): TwoBAN_OS

    Create a Bayesian Network 2 classification model.

    Create a Bayesian Network 2 classification model.

    x

    the integer-valued data vectors stored as rows of a matrix

    y

    the class vector, where y(l) = class for row l of the matrix, x(l)

    fn

    the names for all features/variables

    k

    the number of classes

    cn

    the names for all classes

    vc

    the value count (number of distinct values) for each feature

    thres

    the correlation threshold between 2 features for possible parent-child relationship

    me

    use m-estimates (me == 0 => regular MLE estimates)

  6. def apply(xy: MatriI, fn: Array[String], k: Int, cn: Array[String], me: Double, vc: Array[Int], PARALLELISM: Int): TANBayes

    Build a Tree Augmented Naive Bayes Classification model, passing 'x' and 'y' together in one matrix.

    Build a Tree Augmented Naive Bayes Classification model, passing 'x' and 'y' together in one matrix.

    xy

    the data vectors along with their classifications stored as rows of a matrix

    fn

    the names of the features

    k

    the number of classes

    me

    use m-estimates (me == 0 => regular MLE estimates)

    vc

    the value count (number of distinct values) for each feature

  7. def apply(x: MatriI, y: VectoI, fn: Array[String], k: Int, cn: Array[String], me: Double, vc: Array[Int], PARALLELISM: Int): TANBayes

    Build a Tree Augmented Naive Bayes Classification model

    Build a Tree Augmented Naive Bayes Classification model

    x

    the integer-valued data vectors stored as rows of a matrix

    y

    the class vector, where y(l) = class for row l of the matrix, x(l)

    fn

    the names for all features/variables

    k

    the number of classes

    cn

    the names for all classes

    me

    use m-estimates (me == 0 => regular MLE estimates)

    vc

    the value count (number of distinct values) for each feature

  8. def apply(xy: MatriI, fn: Array[String], k: Int, cn: Array[String], vc: Array[Int], me: Double, PARALLELISM: Int): NaiveBayes

    Build a Naive Bayes classification model, passing 'x' and 'y' together in one matrix.

    Build a Naive Bayes classification model, passing 'x' and 'y' together in one matrix.

    xy

    the data vectors along with their classifications stored as rows of a matrix

    fn

    the names for all features/variables

    k

    the number of classes

    cn

    the names for all classes

    vc

    the value count (number of distinct values) for each feature

    me

    use m-estimates (me == 0 => regular MLE estimates)

  9. def apply(x: MatriI, y: VectoI, fn: Array[String], k: Int, cn: Array[String], vc: Array[Int], me: Double, PARALLELISM: Int): NaiveBayes

    Build a Naive Bayes classification model.

    Build a Naive Bayes classification model.

    x

    the integer-valued data vectors stored as rows of a matrix

    y

    the class vector, where y(l) = class for row l of the matrix x, x(l)

    fn

    the names for all features/variables

    k

    the number of classes

    cn

    the names for all classes

    vc

    the value count (number of distinct values) for each feature

    me

    use m-estimates (me == 0 => regular MLE estimates)

  10. final def asInstanceOf[T0]: T0
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  11. def clone(): AnyRef
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  12. final def eq(arg0: AnyRef): Boolean
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  13. def equals(arg0: Any): Boolean
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  14. def finalize(): Unit
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  15. final def getClass(): Class[_]
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    @native()
  16. def hashCode(): Int
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    @native()
  17. final def isInstanceOf[T0]: Boolean
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  18. def list2Array(list: ListBuffer[Int], n: Int): Array[Boolean]

    Convert a selected feature set from a list to a Boolean array representation.

    Convert a selected feature set from a list to a Boolean array representation.

    list

    the list of selected features, e.g., (1, 3, 5)

    n

    the total number (selected or not) of features

  19. val me_default: Double

    The default value for m-estimates (me == 0 => regular MLE estimates) me == 1 => no divide by 0, close to MLE estimates)

  20. final def ne(arg0: AnyRef): Boolean
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  21. final def notify(): Unit
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    @native()
  22. final def notifyAll(): Unit
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  23. final def synchronized[T0](arg0: ⇒ T0): T0
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  24. def test(bc: BayesClassifier, name: String): Double

    Create and test a Bayes Classifier

    Create and test a Bayes Classifier

    bc

    the Bayes Classifier

    name

    name of the classifier

  25. def toString(): String
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  26. final def wait(): Unit
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    @throws( ... )
  27. final def wait(arg0: Long, arg1: Int): Unit
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  28. final def wait(arg0: Long): Unit
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