Class

io.github.edouardfouche.mcde

MWPi

Related Doc: package mcde

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case class MWPi(M: Int = 50, alpha: Double = 0.5, beta: Double = 0.5, parallelize: Int = 0) extends McdeStats with Product with Serializable

Simply like MWP but does not correct ties (but adjust ranks still)

alpha

Expected share of instances in slice (independent dimensions).

beta

Expected share of instances in marginal restriction (reference dimension). Added with respect to the original paper to loose the dependence of beta from alpha.

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Serializable, Serializable, Product, Equals, McdeStats, Stats, AnyRef, Any
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  1. MWPi
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. McdeStats
  7. Stats
  8. AnyRef
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Instance Constructors

  1. new MWPi(M: Int = 50, alpha: Double = 0.5, beta: Double = 0.5, parallelize: Int = 0)

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    alpha

    Expected share of instances in slice (independent dimensions).

    beta

    Expected share of instances in marginal restriction (reference dimension). Added with respect to the original paper to loose the dependence of beta from alpha.

Type Members

  1. type PreprocessedData = AdjustedRankIndex

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    Definition Classes
    MWPiStats
  2. abstract type U

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    Definition Classes
    McdeStats

Value Members

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

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  4. val M: Int

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    Definition Classes
    MWPiMcdeStatsStats
  5. val alpha: Double

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    Expected share of instances in slice (independent dimensions).

    Expected share of instances in slice (independent dimensions).

    Definition Classes
    MWPiMcdeStatsStats
  6. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  7. val beta: Double

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    Expected share of instances in marginal restriction (reference dimension).

    Expected share of instances in marginal restriction (reference dimension). Added with respect to the original paper to loose the dependence of beta from alpha.

    Definition Classes
    MWPiMcdeStatsStats
  8. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  9. def conditionalIndependence(m: PreprocessedData, dimensions: Set[Int]): Double

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    Definition Classes
    McdeStats
  10. def contrast(m: PreprocessedData, dimensions: Set[Int]): Double

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    Compute the contrast of a subspace

    Compute the contrast of a subspace

    m

    The indexes from the original data ordered by the rank of the points

    dimensions

    The dimensions in the subspace, each value should be smaller than the number of arrays in m

    returns

    The contrast of the subspace (value between 0 and 1)

    Definition Classes
    McdeStatsStats
  11. def contrast(m: Array[Array[Double]], dimensions: Set[Int]): Double

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    m

    A data set (row oriented)

    Definition Classes
    Stats
  12. def contrastAlpha(m: PreprocessedData, dimensions: Set[Int]): Double

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    Compute the contrast of a subspace // This is a version where alpha is always choosen at random between 0.1 and 0.9

    Compute the contrast of a subspace // This is a version where alpha is always choosen at random between 0.1 and 0.9

    m

    The indexes from the original data ordered by the rank of the points

    dimensions

    The dimensions in the subspace, each value should be smaller than the number of arrays in m

    returns

    The contrast of the subspace (value between 0 and 1)

    Definition Classes
    McdeStats
  13. def contrastMatrix(m: PreprocessedData): Array[Array[Double]]

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    Compute the pairwise contrast matrix for a given data set Note: This matrix is symmetric

    Compute the pairwise contrast matrix for a given data set Note: This matrix is symmetric

    m

    The indexes from the original data ordered by the rank of the points

    returns

    A 2-D Array contains the contrast for each pairwise dimension

    Definition Classes
    McdeStatsStats
  14. def contrastMatrix(m: Array[Array[Double]]): Array[Array[Double]]

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    m

    A data set (row oriented)

    Definition Classes
    Stats
  15. def deviation(m: Array[Array[Double]], dimensions: Set[Int], referenceDim: Int): Double

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    Definition Classes
    McdeStats
  16. def deviation(m: PreprocessedData, dimensions: Set[Int], referenceDim: Int): Double

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    Compute the deviation of a subspace with respect to a particular dimension

    Compute the deviation of a subspace with respect to a particular dimension

    m

    The indexes from the original data ordered by the rank of the points

    dimensions

    The dimensions in the subspace, each value should be smaller than the number of arrays in m

    referenceDim

    The reference dimensions, should be contained in dimensions

    returns

    A 2-D Array contains the contrast for each pairwise dimension

    Definition Classes
    McdeStats
  17. def deviationAlpha(m: PreprocessedData, dimensions: Set[Int], referenceDim: Int): Double

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    Compute the deviation of a subspace with respect to a particular dimension // This is a version where alpha is always choosen at random between 0.1 and 0.9

    Compute the deviation of a subspace with respect to a particular dimension // This is a version where alpha is always choosen at random between 0.1 and 0.9

    m

    The indexes from the original data ordered by the rank of the points

    dimensions

    The dimensions in the subspace, each value should be smaller than the number of arrays in m

    referenceDim

    The reference dimensions, should be contained in dimensions

    returns

    A 2-D Array contains the contrast for each pairwise dimension

    Definition Classes
    McdeStats
  18. def deviationMatrix(m: PreprocessedData): Array[Array[Double]]

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    Compute the pairwise deviation matrix for a given data set Note: This matrix is asymmetric

    Compute the pairwise deviation matrix for a given data set Note: This matrix is asymmetric

    m

    The indexes from the original data ordered by the rank of the points

    returns

    A 2-D Array contains the deviation for each pairwise dimension

    Definition Classes
    McdeStats
  19. def deviationMatrix(m: Array[Array[Double]]): Array[Array[Double]]

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    Definition Classes
    McdeStats
  20. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  21. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  22. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  23. val id: String

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    Definition Classes
    MWPiMcdeStatsStats
  24. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  25. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  26. final def notify(): Unit

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    Definition Classes
    AnyRef
  27. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  28. var parallelize: Int

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    Definition Classes
    MWPiMcdeStats
  29. def preprocess(input: Array[Array[Double]]): PreprocessedData

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    input

    A data set (row oriented)

    Definition Classes
    MWPiMcdeStatsStats
  30. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  31. def twoSample(index: PreprocessedData, reference: Int, indexSelection: Array[Boolean]): Double

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    Compute a statistical test based on Mann-Whitney U test using a reference vector (the indices of a dimension ordered by the rank) and a set of Int that correspond to the intersection of the position of the element in the slices in the other dimensions.

    Compute a statistical test based on Mann-Whitney U test using a reference vector (the indices of a dimension ordered by the rank) and a set of Int that correspond to the intersection of the position of the element in the slices in the other dimensions.

    reference

    The original position of the elements of a reference dimension ordered by their rank

    indexSelection

    An array of Boolean where true means the value is part of the slice

    returns

    The Mann-Whitney statistic

    Definition Classes
    MWPiMcdeStats
  32. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  33. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  34. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from McdeStats

Inherited from Stats

Inherited from AnyRef

Inherited from Any

Ungrouped