Class

io.github.mandar2812.dynaml.models

ContinuousProcessModel

Related Doc: package models

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abstract class ContinuousProcessModel[T, I, Y, W <: ContinuousRandomVariable[_]] extends StochasticProcessModel[T, I, Y, W]

Blueprint for a continuous valued stochastic process, abstracts away the behavior common to sub-classes such as io.github.mandar2812.dynaml.models.gp.GPRegression, io.github.mandar2812.dynaml.models.stp.StudentTRegression and others.

Linear Supertypes
StochasticProcessModel[T, I, Y, W], Model[T, I, Y], AnyRef, Any
Known Subclasses
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Inherited
  1. ContinuousProcessModel
  2. StochasticProcessModel
  3. Model
  4. AnyRef
  5. Any
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Visibility
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Instance Constructors

  1. new ContinuousProcessModel()

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Abstract Value Members

  1. 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
  2. abstract val g: T

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    The training data

    The training data

    Attributes
    protected
    Definition Classes
    Model
  3. 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
  4. abstract def predictionWithErrorBars[U <: Seq[I]](testData: U, sigma: Int): Seq[(I, Y, Y, Y)]

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    Draw three predictions from the posterior predictive distribution 1) Mean or MAP estimate Y 2) Y- : The lower error bar estimate (mean - sigma*stdDeviation) 3) Y+ : The upper error bar.

    Draw three predictions from the posterior predictive distribution 1) Mean or MAP estimate Y 2) Y- : The lower error bar estimate (mean - sigma*stdDeviation) 3) Y+ : The upper error bar. (mean + sigma*stdDeviation)

  5. 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. def _errorSigma: Int

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  5. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  6. def clone(): AnyRef

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

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

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

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    Definition Classes
    AnyRef → Any
  11. def errorSigma_(s: Int): Unit

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  12. def finalize(): Unit

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

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

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    Definition Classes
    AnyRef → Any
  15. final def isInstanceOf[T0]: Boolean

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

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

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

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

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    Definition Classes
    AnyRef
  20. def test(testData: T): Seq[(I, Y, Y, Y, Y)]

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    Returns a prediction with error bars for a test set of indexes and labels.

    Returns a prediction with error bars for a test set of indexes and labels. (Index, Actual Value, Prediction, Lower Bar, Higher Bar)

  21. def toString(): String

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    Definition Classes
    AnyRef → Any
  22. final def wait(): Unit

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

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

Inherited from Model[T, I, Y]

Inherited from AnyRef

Inherited from Any

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