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object BayesClassifier

The BayesClassifier object provides factory methods for building Bayesian 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. val RANDOMIZED: Boolean

    Use randomized cross-validation

  5. val XFOLD: Int

    Perform XFOLD cross-validation

  6. def apply(xy: MatriI, fn: Strings, k: Int, cn: Strings, vc: Array[Int], 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: Strings, k: Int, cn: Strings, vc: Array[Int], 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: Strings, k: Int, cn: Strings, me: Float, vc: Array[Int]): 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: Strings, k: Int, cn: Strings, me: Float, vc: Array[Int]): 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: Strings, k: Int, cn: Strings, vc: Array[Int], 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: Strings, k: Int, cn: Strings, vc: Array[Int], 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: Strings, k: Int, cn: Strings, vc: Array[Int], 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: Strings, k: Int, cn: Strings, vc: Array[Int], 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
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  15. def clone(): AnyRef
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  16. final def eq(arg0: AnyRef): Boolean
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  17. def equals(arg0: Any): Boolean
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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. 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

  22. val me_default: Float

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

  23. final def ne(arg0: AnyRef): Boolean
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  24. final def notify(): Unit
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    @native() @HotSpotIntrinsicCandidate()
  25. final def notifyAll(): Unit
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    @native() @HotSpotIntrinsicCandidate()
  26. final def synchronized[T0](arg0: ⇒ T0): T0
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  27. 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

  28. def toString(): String
    Definition Classes
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  29. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  30. final def wait(arg0: Long): Unit
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  31. final def wait(): Unit
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    @throws( ... )

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  1. def finalize(): Unit
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