Trait

io.github.mandar2812.dynaml.models

SecondOrderProcessModel

Related Doc: package models

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trait SecondOrderProcessModel[T, I, Y, K, M, W] extends StochasticProcessModel[T, I, Y, W]

Processes which can be specified by upto second order statistics i.e. mean and covariance

T

The underlying data structure storing the training & test data.

I

The type of the index set (i.e. Double for time series, DenseVector for GP regression)

Y

The type of the output label

K

The type returned by the kernel function.

M

The data structure holding the kernel/covariance matrix

W

Implementing class of the posterior distribution

Linear Supertypes
StochasticProcessModel[T, I, Y, W], Model[T, I, Y], AnyRef, Any
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  2. StochasticProcessModel
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Abstract Value Members

  1. abstract val covariance: CovarianceFunction[I, K, M]

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    Underlying covariance function of the Gaussian Processes.

  2. abstract def dataAsSeq(data: T): Seq[(I, Y)]

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    Convert from the underlying data structure to Seq[(I, Y)] where I is the index set of the GP and Y is the value/label type.

    Convert from the underlying data structure to Seq[(I, Y)] where I is the index set of the GP and Y is the value/label type.

    Definition Classes
    StochasticProcessModel
  3. abstract val g: T

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    The training data

    The training data

    Attributes
    protected
    Definition Classes
    Model
  4. abstract val mean: DataPipe[I, Y]

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    Mean Function: Takes a member of the index set (input) and returns the corresponding mean of the distribution corresponding to input.

  5. abstract def predict(point: I): Y

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    Predict the value of the target variable given a point.

    Predict the value of the target variable given a point.

    Definition Classes
    Model
  6. abstract def predictiveDistribution[U <: Seq[I]](test: U): W

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    Calculates posterior predictive distribution for a particular set of test data points.

    Calculates posterior predictive distribution for a particular set of test data points.

    test

    A Sequence or Sequence like data structure storing the values of the input patters.

    Definition Classes
    StochasticProcessModel

Concrete 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. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  5. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  6. def data: T

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    Definition Classes
    Model
  7. def dataAsIndexSeq(data: T): Seq[I]

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    Convert from the underlying data structure to Seq[I] where I is the index set of the GP

    Convert from the underlying data structure to Seq[I] where I is the index set of the GP

    Definition Classes
    StochasticProcessModel
  8. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  9. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  10. def finalize(): Unit

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

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    Definition Classes
    AnyRef → Any
  12. def hashCode(): Int

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    Definition Classes
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  13. final def isInstanceOf[T0]: Boolean

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

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

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

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    Definition Classes
    AnyRef
  17. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  18. def toString(): String

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    Definition Classes
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  19. final def wait(): Unit

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

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

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    Definition Classes
    AnyRef
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    @throws( ... )

Inherited from StochasticProcessModel[T, I, Y, W]

Inherited from Model[T, I, Y]

Inherited from AnyRef

Inherited from Any

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