Class

io.github.mandar2812.dynaml.modelpipe

GPBasisFuncRegressionPipe

Related Doc: package modelpipe

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class GPBasisFuncRegressionPipe[Source, IndexSet] extends ModelPipe[Source, Seq[(IndexSet, Double)], IndexSet, Double, GPBasisFuncRegressionModel[Seq[(IndexSet, Double)], IndexSet]]

GP Basis Function Pipe

A data pipe which can spawn a Gaussian Process Basis Function regression model from a provided training data set.

Source

Input data type

IndexSet

Type of features of each data pattern

Linear Supertypes
ModelPipe[Source, Seq[(IndexSet, Double)], IndexSet, Double, GPBasisFuncRegressionModel[Seq[(IndexSet, Double)], IndexSet]], DataPipe[Source, GPBasisFuncRegressionModel[Seq[(IndexSet, Double)], IndexSet]], Serializable, Serializable, DataPipeConvertible[Source, GPBasisFuncRegressionModel[Seq[(IndexSet, Double)], IndexSet]], AnyRef, Any
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Inherited
  1. GPBasisFuncRegressionPipe
  2. ModelPipe
  3. DataPipe
  4. Serializable
  5. Serializable
  6. DataPipeConvertible
  7. AnyRef
  8. Any
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Visibility
  1. Public
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Instance Constructors

  1. new GPBasisFuncRegressionPipe(pre: (Source) ⇒ Seq[(IndexSet, Double)], cov: LocalScalarKernel[IndexSet], n: LocalScalarKernel[IndexSet], basisFunc: DataPipe[IndexSet, DenseVector[Double]], basis_param_prior: MultGaussianRV)(implicit arg0: ClassTag[IndexSet])

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    pre

    A function which converts the input data into a scala Seq of IndexSet and Double pairs.

    cov

    The covariance function of the resultant GP, as an instance of LocalScalarKernel defined on the IndexSet type.

    n

    The measurement noise of the output signal/data, also as in instance of LocalScalarKernel

    basisFunc

    A basis function representation for the input features, represented as a DataPipe.

    basis_param_prior

    A Gaussian prior on the basis function trend coefficients.

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[GPBasisFuncRegressionModel[Seq[(IndexSet, Double)], IndexSet]]): Basis[Source]

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    Definition Classes
    DataPipe

  4. def *[OtherSource, OtherDestination](that: DataPipe[OtherSource, OtherDestination]): ParallelPipe[Source, GPBasisFuncRegressionModel[Seq[(IndexSet, Double)], IndexSet], OtherSource, OtherDestination]

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    Definition Classes
    DataPipe

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

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    Definition Classes
    AnyRef → Any

  6. def >[Further](that: DataPipeConvertible[GPBasisFuncRegressionModel[Seq[(IndexSet, Double)], IndexSet], Further]): DataPipe[Source, Further]

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    Definition Classes
    DataPipe

  7. def >-<[OtherSource, OtherDestination](that: DataPipe[OtherSource, OtherDestination]): DataPipe2[Source, OtherSource, (GPBasisFuncRegressionModel[Seq[(IndexSet, Double)], IndexSet], OtherDestination)]

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    Definition Classes
    DataPipe

  8. def apply[T <: Traversable[Source]](data: T): T

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    Definition Classes
    DataPipe

  9. def apply(data: Source): GPBasisFuncRegressionModel[Seq[(IndexSet, Double)], IndexSet]

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    Definition Classes
    DataPipe

  10. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any

  11. def clone(): AnyRef

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

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

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    Definition Classes
    AnyRef

  13. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  14. def finalize(): Unit

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

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

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    AnyRef → Any

  16. def hashCode(): Int

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    Definition Classes
    AnyRef → Any

  17. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

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

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    Definition Classes
    AnyRef

  19. final def notify(): Unit

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    Definition Classes
    AnyRef

  20. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  21. val preProcess: (Source) ⇒ Seq[(IndexSet, Double)]

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    Definition Classes
    GPBasisFuncRegressionPipe → ModelPipe

  22. def run(data: Source): GPBasisFuncRegressionModel[Seq[(IndexSet, Double)], IndexSet]

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    Definition Classes
    GPBasisFuncRegressionPipe → ModelPipe → DataPipe

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

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    Definition Classes
    AnyRef

  24. def toPipe: (Source) ⇒ GPBasisFuncRegressionModel[Seq[(IndexSet, Double)], IndexSet]

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    Definition Classes
    DataPipe → DataPipeConvertible

  25. def toString(): String

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    Definition Classes
    AnyRef → Any

  26. implicit val transform: DataPipe[Seq[(IndexSet, Double)], Seq[(IndexSet, Double)]]

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  27. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
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    Annotations
    @throws( ... )

  29. final def wait(arg0: Long): Unit

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    Definition Classes
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    Annotations
    @throws( ... )

Inherited from ModelPipe[Source, Seq[(IndexSet, Double)], IndexSet, Double, GPBasisFuncRegressionModel[Seq[(IndexSet, Double)], IndexSet]]

Inherited from DataPipe[Source, GPBasisFuncRegressionModel[Seq[(IndexSet, Double)], IndexSet]]

Inherited from Serializable

Inherited from Serializable

Inherited from DataPipeConvertible[Source, GPBasisFuncRegressionModel[Seq[(IndexSet, Double)], IndexSet]]

Inherited from AnyRef

Inherited from Any

Ungrouped