Value Members

1.  final def !=(arg0: Any): Boolean

   Definition Classes

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2.  final def ##(): Int

   Definition Classes

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3.  final def ==(arg0: Any): Boolean

   Definition Classes

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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

    Definition Classes

    Any

11.  def clone(): AnyRef

    Attributes

    protected[java.lang]

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    @native() @throws( ... )

12.  final def eq(arg0: AnyRef): Boolean

    Definition Classes

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13.  def equals(arg0: Any): Boolean

    Definition Classes

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14.  def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

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    Annotations

    @throws( classOf[java.lang.Throwable] )

15.  final def getClass(): Class[_]

    Definition Classes

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    Annotations

    @native()

16.  def hashCode(): Int

    Definition Classes

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    Annotations

    @native()

17.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

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

    Definition Classes

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21.  final def notify(): Unit

    Definition Classes

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    Annotations

    @native()

22.  final def notifyAll(): Unit

    Definition Classes

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    Annotations

    @native()

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

    Definition Classes

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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

    Definition Classes

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26.  final def wait(): Unit

    Definition Classes

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    Annotations

    @throws( ... )

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

    Definition Classes

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    Annotations

    @throws( ... )

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

    Definition Classes

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    Annotations

    @native() @throws( ... )