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.  val RANDOMIZED: Boolean

   Use randomized cross-validation

5.  val XFOLD: Int

   Perform XFOLD cross-validation

6.  def apply(xy: MatriI, fn: Array[String], k: Int, cn: Array[String], vc: VectoI, thres: Double, me: Float): TwoBAN_OS

   Build a Bayesian Network (2-BAN-OS) classification model, passing 'x' and 'y' together in one matrix.

   Build a Bayesian Network (2-BAN-OS) 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

   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)

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

   Create a Bayesian Network (2-BAN-OS) classification model.

   Create a Bayesian Network (2-BAN-OS) 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)

8.  def apply(xy: MatriI, fn: Array[String], k: Int, cn: Array[String], me: Float, vc: VectoI): TANBayes

   Build a Tree Augmented Naive Bayes (TAN) classification model, passing 'x' and 'y' together in one matrix.

   Build a Tree Augmented Naive Bayes (TAN) 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

   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

9.  def apply(x: MatriI, y: VectoI, fn: Array[String], k: Int, cn: Array[String], me: Float, vc: VectoI): TANBayes

   Build a Tree Augmented Naive Bayes (TAN) classification model.

   Build a Tree Augmented Naive Bayes (TAN) 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

10.  def apply(xy: MatriI, fn: Array[String], k: Int, cn: Array[String], vc: VectoI, me: Float, thres: Double): OneBAN

    Build a Augmented Naive Bayes (1-BAN) classification model, passing 'x' and 'y' together in one matrix.

    Build a Augmented Naive Bayes (1-BAN) 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)

    thres

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

11.  def apply(x: MatriI, y: VectoI, fn: Array[String], k: Int, cn: Array[String], vc: VectoI, me: Float, thres: Double): OneBAN

    Build a Augmented Naive Bayes (1-BAN) classification model.

    Build a Augmented Naive Bayes (1-BAN) 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

    me

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

    thres

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

12.  def apply(xy: MatriI, fn: Array[String], k: Int, cn: Array[String], vc: VectoI, me: Float): 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)

13.  def apply(x: MatriI, y: VectoI, fn: Array[String], k: Int, cn: Array[String], vc: VectoI, me: Float): 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)

14.  final def asInstanceOf[T0]: T0

    Definition Classes

    Any

15.  def clone(): AnyRef

    Attributes

    protected[java.lang]

    Definition Classes

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    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

17.  def equals(arg0: Any): Boolean

    Definition Classes

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18.  def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

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    Annotations

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

19.  final def getClass(): Class[_]

    Definition Classes

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20.  def hashCode(): Int

    Definition Classes

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21.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

22.  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

23.  val me_default: Float

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

24.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

25.  final def notify(): Unit

    Definition Classes

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26.  final def notifyAll(): Unit

    Definition Classes

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

    Definition Classes

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28.  def test(bc: BayesClassifier, name: String): Double

    Test the given Bayes classifier and return its average accuracy.

    Test the given Bayes classifier and return its average accuracy.

    bc

    the Bayes classifier

    name

    name of the Bayes classifier

29.  def toString(): String

    Definition Classes

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30.  final def wait(): Unit

    Definition Classes

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    Annotations

    @throws( ... )

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

    Definition Classes

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    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )