Class

io.github.mandar2812.dynaml.utils

MVGaussianScaler

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case class MVGaussianScaler(mean: DenseVector[Double], sigma: DenseMatrix[Double]) extends ReversibleScaler[DenseVector[Double]] with Product with Serializable

Scales the attributes of a data pattern using the sample mean and covariance matrix calculated on the data set. This allows standardization of multivariate data sets where the covariance of individual data dimensions is not negligible.

mean

Sample mean of data

sigma

Sample covariance matrix of data.

Linear Supertypes
Product, Equals, ReversibleScaler[DenseVector[Double]], Encoder[DenseVector[Double], DenseVector[Double]], Scaler[DenseVector[Double]], DataPipe[DenseVector[Double], DenseVector[Double]], Serializable, Serializable, DataPipeConvertible[DenseVector[Double], DenseVector[Double]], AnyRef, Any
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Inherited
  1. MVGaussianScaler
  2. Product
  3. Equals
  4. ReversibleScaler
  5. Encoder
  6. Scaler
  7. DataPipe
  8. Serializable
  9. Serializable
  10. DataPipeConvertible
  11. AnyRef
  12. Any
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Visibility
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Instance Constructors

  1. new MVGaussianScaler(mean: DenseVector[Double], sigma: DenseMatrix[Double])

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    mean

    Sample mean of data

    sigma

    Sample covariance matrix of data.

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. def %>(that: Basis[DenseVector[Double]]): Basis[DenseVector[Double]]

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    Definition Classes
    DataPipe

  4. def *[T](that: ReversibleScaler[T]): ReversibleScaler[(DenseVector[Double], T)]

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    Definition Classes
    ReversibleScaler

  5. def *[T](that: Scaler[T]): Scaler[(DenseVector[Double], T)]

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    Definition Classes
    Scaler

  6. def *[OtherSource, OtherDestination](that: DataPipe[OtherSource, OtherDestination]): ParallelPipe[DenseVector[Double], DenseVector[Double], OtherSource, OtherDestination]

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    Definition Classes
    DataPipe

  7. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  8. def >(otherRevScaler: ReversibleScaler[DenseVector[Double]]): ReversibleScaler[DenseVector[Double]]

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    Definition Classes
    ReversibleScaler

  9. def >[Further](that: Encoder[DenseVector[Double], Further]): Encoder[DenseVector[Double], Further]

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    Definition Classes
    Encoder

  10. def >(otherScaler: Scaler[DenseVector[Double]]): Scaler[DenseVector[Double]]

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    Definition Classes
    Scaler

  11. def >[Further](that: DataPipeConvertible[DenseVector[Double], Further]): DataPipe[DenseVector[Double], Further]

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    Definition Classes
    DataPipe

  12. def >-<[OtherSource, OtherDestination](that: DataPipe[OtherSource, OtherDestination]): DataPipe2[DenseVector[Double], OtherSource, (DenseVector[Double], OtherDestination)]

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    Definition Classes
    DataPipe

  13. def apply(r: Range): MVGaussianScaler

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  14. def apply[T <: Traversable[DenseVector[Double]]](data: T): T

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    Definition Classes
    ReversibleScaler → Scaler → DataPipe

  15. def apply(data: DenseVector[Double]): DenseVector[Double]

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    Definition Classes
    DataPipe

  16. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  17. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  18. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  19. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  20. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any

  21. val i: Scaler[DenseVector[Double]]

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    Definition Classes
    MVGaussianScaler → ReversibleScaler → Encoder

  22. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  23. val mean: DenseVector[Double]

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    Sample mean of data

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

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    Definition Classes
    AnyRef

  25. final def notify(): Unit

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    Definition Classes
    AnyRef

  26. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  27. def run(data: DenseVector[Double]): DenseVector[Double]

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    Definition Classes
    MVGaussianScaler → DataPipe

  28. val sigma: DenseMatrix[Double]

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    Sample covariance matrix of data.

  29. val sigmaInverse: DenseMatrix[Double]

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

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    Definition Classes
    AnyRef

  31. def toPipe: (DenseVector[Double]) ⇒ DenseVector[Double]

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    Definition Classes
    DataPipe → DataPipeConvertible

  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 Product

Inherited from Equals

Inherited from ReversibleScaler[DenseVector[Double]]

Inherited from Encoder[DenseVector[Double], DenseVector[Double]]

Inherited from Scaler[DenseVector[Double]]

Inherited from DataPipe[DenseVector[Double], DenseVector[Double]]

Inherited from Serializable

Inherited from Serializable

Inherited from DataPipeConvertible[DenseVector[Double], DenseVector[Double]]

Inherited from AnyRef

Inherited from Any

Ungrouped