Packages

c

scalation.analytics.fda

SmoothingB_F

class SmoothingB_F extends Error

The SmoothingB_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. This version just used B-Splines.

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Instance Constructors

  1. new SmoothingB_F(y: VectoD, t: VectoD, ord: Int = 4, lambda: Double = -1, method: SmoothingMethod.SmoothingMethod = ROUGHNESS, technique: RegTechnique.RegTechnique = Cholesky)

    y

    the (raw) data points/vector

    t

    the data time points/vector

    ord

    the order of the basis function (defaults to 4, cubic)

    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
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  2. final def ##(): Int
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  3. final def ==(arg0: Any): Boolean
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  4. final def asInstanceOf[T0]: T0
    Definition Classes
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  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
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    protected[lang]
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    @throws( ... ) @native() @HotSpotIntrinsicCandidate()
  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
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  14. def equals(arg0: Any): Boolean
    Definition Classes
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  15. final def flaw(method: String, message: String): Unit
    Definition Classes
    Error
  16. def getBasis: DB_Spline

    Get the Basis Function object

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

    Predict the the basis functions

    Predict the the basis functions

    tt

    the given vector of time points

  25. 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

  26. 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

  27. def residual: VectoD

    Return the vector of residuals/errors.

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

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

  31. final def wait(arg0: Long, arg1: Int): Unit
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    @throws( ... )
  32. final def wait(arg0: Long): Unit
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  33. final def wait(): Unit
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Deprecated Value Members

  1. def finalize(): Unit
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    Deprecated

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