Object

core.dlm.model

SvdFilter

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object SvdFilter

Perform the Kalman Filter by updating the value of the Singular Value Decomp. of the state covariance matrix, C = UDU^T https://arxiv.org/pdf/1611.03686.pdf

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Type Members

  1. case class State(time: Double, mt: DenseVector[Double], dc: DenseVector[Double], uc: DenseMatrix[Double], at: DenseVector[Double], dr: DenseVector[Double], ur: DenseMatrix[Double], ft: DenseVector[Double]) extends Product with Serializable

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Value Members

  1. final def !=(arg0: Any): Boolean

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  2. final def ##(): Int

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

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  4. def advanceState(g: (Double) ⇒ DenseMatrix[Double], dt: Double, mt: DenseVector[Double], dc: DenseVector[Double], uc: DenseMatrix[Double], p: Parameters): (DenseVector[Double], DenseVector[Double], DenseMatrix[Double])

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    Perform the time update by advancing the mean and covariance to the time of the next observation

    Perform the time update by advancing the mean and covariance to the time of the next observation

    g

    the system evolution matrix

    dt

    the time difference between observations

    mt

    the posterior mean at the previous time

    dc

    the diagonal matrix containing the singular values (eigen values) of the posterior covariance matrix at the previous time

    uc

    the unitary matrix containing the eigen vectors of the posterior covariance matrix at the previous time step

  5. final def asInstanceOf[T0]: T0

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  6. def clone(): AnyRef

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

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  8. def equals(arg0: Any): Boolean

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  9. def filter(mod: Model, ys: Vector[Data], p: Parameters): Vector[State]

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  10. def finalize(): Unit

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

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  12. def hashCode(): Int

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  13. def initialiseState(mod: Model, p: Parameters, ys: Vector[Data]): State

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    Initialise the state of the SVD Filter

  14. final def isInstanceOf[T0]: Boolean

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  15. def makeDMatrix(m: Int, n: Int, d: DenseVector[Double]): DenseMatrix[Double]

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

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  17. final def notify(): Unit

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  18. final def notifyAll(): Unit

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  19. def oneStepForecast(f: (Double) ⇒ DenseMatrix[Double], at: DenseVector[Double], time: Double): DenseVector[Double]

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  20. def oneStepMissing(fm: DenseMatrix[Double], at: DenseVector[Double]): DenseVector[Double]

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  21. def sqrtInvSvd(m: DenseMatrix[Double]): DenseMatrix[Double]

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    Calculate the square root inverse of a matrix using the Eigenvalue decomposition of a matrix

    Calculate the square root inverse of a matrix using the Eigenvalue decomposition of a matrix

    m

    a symmetric positive definite matrix

    returns

    the square root inverse of the matrix

  22. def sqrtSvd(m: DenseMatrix[Double]): DenseMatrix[Double]

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    Calculate the square root of a symmetric matrix using eigenvalue decomposition

    Calculate the square root of a symmetric matrix using eigenvalue decomposition

    m

    a symmetric positive definite matrix

    returns

    the square root of a matrix

  23. def step(mod: Model, p: Parameters)(s: State, y: Data): State

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

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  25. def toString(): String

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  26. def updateState(at: DenseVector[Double], dr: DenseVector[Double], ur: DenseMatrix[Double], p: Parameters, f: (Double) ⇒ DenseMatrix[Double], d: Data): (DenseVector[Double], DenseVector[Double], DenseMatrix[Double])

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  27. final def wait(): Unit

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

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

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