Class

io.github.mandar2812.dynaml.models

ContinuousMixtureModel

Related Doc: package models

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abstract class ContinuousMixtureModel[T, I, Y, YDomain, W1 <: ContinuousDistrRV[YDomain], BaseProcesses <: ContinuousProcessModel[T, I, Y, W1]] extends StochasticProcessMixtureModel[I, Y, ContinuousDistrMixture[YDomain, W1]]

A process which is a multinomial mixture of continuous component processes.

I

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

Y

The type of the output label

W1

Implementing class of the posterior distribution for the base processes should inherit from ContinuousMixtureRV

Linear Supertypes
StochasticProcessMixtureModel[I, Y, ContinuousDistrMixture[YDomain, W1]], ContinuousProcessModel[Seq[(I, Y)], I, Y, ContinuousDistrMixture[YDomain, W1]], StochasticProcessModel[Seq[(I, Y)], I, Y, ContinuousDistrMixture[YDomain, W1]], Model[Seq[(I, Y)], I, Y], AnyRef, Any
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Inherited
  1. ContinuousMixtureModel
  2. StochasticProcessMixtureModel
  3. ContinuousProcessModel
  4. StochasticProcessModel
  5. Model
  6. AnyRef
  7. Any
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Visibility
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Instance Constructors

  1. new ContinuousMixtureModel(component_processes: Seq[BaseProcesses], weights: DenseVector[Double])(implicit arg0: ClassTag[I])

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Abstract Value Members

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

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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)

    Definition Classes
    ContinuousProcessModel

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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    Definition Classes
    ContinuousProcessModel
  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. val component_processes: Seq[BaseProcesses]

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  8. def data: Seq[(I, Y)]

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    Definition Classes
    Model
  9. def dataAsIndexSeq(data: Seq[(I, Y)]): 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
  10. final def eq(arg0: AnyRef): Boolean

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

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

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    Definition Classes
    ContinuousProcessModel
  13. def finalize(): Unit

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

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

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

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

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

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

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    Definition Classes
    AnyRef
  20. def predictiveDistribution[U <: Seq[I]](test: U): ContinuousDistrMixture[YDomain, W1]

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

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

    Definition Classes
    ContinuousProcessModel
  23. def toString(): String

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

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

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  27. val weights: DenseVector[Double]

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Inherited from StochasticProcessMixtureModel[I, Y, ContinuousDistrMixture[YDomain, W1]]

Inherited from ContinuousProcessModel[Seq[(I, Y)], I, Y, ContinuousDistrMixture[YDomain, W1]]

Inherited from StochasticProcessModel[Seq[(I, Y)], I, Y, ContinuousDistrMixture[YDomain, W1]]

Inherited from Model[Seq[(I, Y)], I, Y]

Inherited from AnyRef

Inherited from Any

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