Class

com.github.jonnylaw.model

MultivariateNormal

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case class MultivariateNormal(mean: DenseVector[Double], covariance: DenseMatrix[Double])(implicit rand: RandBasis = Rand) extends Rand[DenseVector[Double]] with Product with Serializable

Simulate from a multivariate normal using Eigenvalue decomposition Y = Q * Z + mean, where Q = L^0.5 * M

Linear Supertypes
Serializable, Serializable, Product, Equals, Rand[DenseVector[Double]], AnyRef, Any
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Inherited
  1. MultivariateNormal
  2. Serializable
  3. Serializable
  4. Product
  5. Equals
  6. Rand
  7. AnyRef
  8. Any
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Visibility
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Instance Constructors

  1. new MultivariateNormal(mean: DenseVector[Double], covariance: DenseMatrix[Double])(implicit rand: RandBasis = Rand)

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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 condition(p: (DenseVector[Double]) ⇒ Boolean): Rand[DenseVector[Double]]

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    Definition Classes
    Rand

  7. val covariance: DenseMatrix[Double]

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  8. def draw(): DenseVector[Double]

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    Definition Classes
    MultivariateNormal → Rand

  9. def drawOpt(): Option[DenseVector[Double]]

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    Definition Classes
    Rand

  10. def eigen: DenseEigSym

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

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    Definition Classes
    AnyRef

  12. def filter(p: (DenseVector[Double]) ⇒ Boolean): Rand[DenseVector[Double]]

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    Definition Classes
    Rand

  13. def finalize(): Unit

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

  14. def flatMap[E](f: (DenseVector[Double]) ⇒ Rand[E]): Rand[E]

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    Definition Classes
    Rand

  15. def foreach(f: (DenseVector[Double]) ⇒ Unit): Unit

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    Definition Classes
    Rand

  16. def get(): DenseVector[Double]

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    Definition Classes
    Rand

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

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    Definition Classes
    AnyRef → Any

  18. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  19. def map[E](f: (DenseVector[Double]) ⇒ E): Rand[E]

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    Definition Classes
    Rand

  20. val mean: DenseVector[Double]

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

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    Definition Classes
    AnyRef

  22. final def notify(): Unit

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    Definition Classes
    AnyRef

  23. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  24. def q: DenseMatrix[Double]

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    Normalise the matrix of eigenvalues to build the matrix *

  25. def sample(n: Int): IndexedSeq[DenseVector[Double]]

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    Definition Classes
    Rand

  26. def sample(): DenseVector[Double]

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    Definition Classes
    Rand

  27. def samples: Iterator[DenseVector[Double]]

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    Definition Classes
    Rand

  28. def samplesVector[U >: DenseVector[Double]](size: Int)(implicit m: ClassTag[U]): DenseVector[U]

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    Definition Classes
    Rand

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

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    Definition Classes
    AnyRef

  30. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  33. def withFilter(p: (DenseVector[Double]) ⇒ Boolean): Rand[DenseVector[Double]]

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    Definition Classes
    Rand

Inherited from Serializable

Inherited from Serializable

Inherited from Product

Inherited from Equals

Inherited from Rand[DenseVector[Double]]

Inherited from AnyRef

Inherited from Any

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