Packages

class Smoothing_F extends Error

The Smoothing_F class fits a time-dependent data vector 'y' to B-Splines.

y(t(i)) = x(t(i)) + ε(t(i)) x(t) = cΦ(t)

where 'x' is the signal, 'ε' is the noise, 'c' is a coefficient vector and 'Φ(t)' is a vector of basis functions.

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Instance Constructors

  1. new Smoothing_F(y: VectoD, t: VectoD, bf: DBasisFunction, lambda: Double = -1, method: SmoothingMethod = ROUGHNESS, technique: RegTechnique = Cholesky)

    y

    the (raw) data points/vector

    t

    the data time points/vector

    bf

    the basis function (with derivatives) object

    lambda

    the regularization parameter (>= 0 or -1 to use GCV)

    method

    the smoothing method

    technique

    the factorization technique

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##(): Int
    Definition Classes
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  3. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  4. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  5. def calcCov(yy: VectorD, k: Int = 1): MatrixD

    Calculate the correlation matrix for the basis functions.

    Calculate the correlation matrix for the basis functions.

    yy

    data vector

    k

    lag parameter for auto-covariance

  6. def clone(): AnyRef
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @native() @throws( ... )
  7. def d1predict(tv: VectoD): VectoD

    Predict the 1st derivative values at all time points in vector 'tv'.

    Predict the 1st derivative values at all time points in vector 'tv'.

    tv

    the given vector of time points

  8. def d1predict(tt: Double): Double

    Predict the 1st derivative value at time point 'tt'.

    Predict the 1st derivative value at time point 'tt'.

    tt

    the given time point

  9. def d2predict(tv: VectoD): VectoD

    Predict the 2nd derivative values at all time points in vector 'tv'.

    Predict the 2nd derivative values at all time points in vector 'tv'.

    tv

    the given vector of time points

  10. def d2predict(tt: Double): Double

    Predict the 2nd derivative value at time point 'tt'.

    Predict the 2nd derivative value at time point 'tt'.

    tt

    the given time point

  11. def dnpredict(n: Int, tv: VectoD): VectoD

    Predict the n-th derivative values at all time points in vector 'tv'.

    Predict the n-th derivative values at all time points in vector 'tv'.

    n

    the n-th derivative to be computed

    tv

    the given vector of time points

  12. def dnpredict(n: Int)(tt: Double): Double

    Predict the n-th derivative value at time point 'tt'.

    Predict the n-th derivative value at time point 'tt'.

    n

    the n-th derivative to be computed

    tt

    the given time point

  13. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  15. def finalize(): Unit
    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
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    @throws( classOf[java.lang.Throwable] )
  16. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  17. def getBasis: DBasisFunction

    Get the Basis Function object

  18. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  19. def getLambda: Double
  20. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  21. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  22. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  23. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  24. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  25. def plotBasis(tt: VectoD = t): Unit

    Predict the the basis functions

    Predict the the basis functions

    tt

    the given vector of time points

  26. def predict(tv: VectoD): VectoD

    Predict the y-values at all time points in vector 'tv'.

    Predict the y-values at all time points in vector 'tv'.

    tv

    the given vector of time points

  27. def predict(tt: Double): Double

    Predict the y-value at time point 'tt'.

    Predict the y-value at time point 'tt'.

    tt

    the given time point

  28. def residual: VectoD

    Return the vector of residuals/errors.

  29. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  30. def toString(): String
    Definition Classes
    AnyRef → Any
  31. def train(): VectoD

    Train the model, i.e., determine the optimal coeifficient 'c' for the basis functions by finding optimal Lamdba to minimize gcv.

  32. final def wait(): Unit
    Definition Classes
    AnyRef
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    @throws( ... )
  33. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
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    @throws( ... )
  34. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
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    @native() @throws( ... )

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