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: VectorD, t: VectorD, τ: VectorD = null, ord: Int = 4)

    y

    the (raw) data points/vector

    t

    the data time points/vector

    τ

    the time points/vector for the knots

    ord

    the order (degree+1) of B-Splines (2, 3, 4, 5 or 6)

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
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  5. def clone(): AnyRef
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  6. final def eq(arg0: AnyRef): Boolean
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  7. def equals(arg0: Any): Boolean
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  8. def finalize(): Unit
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  9. final def flaw(method: String, message: String): Unit
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  10. final def getClass(): Class[_]
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  11. def hashCode(): Int
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  12. final def isInstanceOf[T0]: Boolean
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  13. def makeKnots: VectorD
  14. final def ne(arg0: AnyRef): Boolean
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  15. final def notify(): Unit
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    @native()
  16. final def notifyAll(): Unit
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    @native()
  17. def predict(tv: VectorD): VectorD

    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

  18. 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

  19. final def synchronized[T0](arg0: ⇒ T0): T0
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  20. def toString(): String
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  21. def train(): VectoD

    Train the model, i.e., determine the optimal cofficients 'c' for the basis functions.

  22. final def wait(): Unit
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  23. final def wait(arg0: Long, arg1: Int): Unit
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  24. final def wait(arg0: Long): Unit
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