c

geotrellis.vector.interpolation

LeastSquaresFittingNuggetProblem

abstract class LeastSquaresFittingNuggetProblem extends Serializable

Computes fitting of the given empirical semivariogram while forcing the nugget value to 0 (this is invoked when the normal fitting causes a negative nugget value, which is mathematically pointless in Kriging estimation),

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Instance Constructors

  1. new LeastSquaresFittingNuggetProblem(x: Array[Double], y: Array[Double], start: Array[Double])

    x

    Empirical Semivariogram distance value

    y

    Empirical Semivariogram's corresponding variance values

    start

    Starting point for finding the optimization values of Semivariogram's parameters (range, sill, 0)

Abstract Value Members

  1. abstract def jacobianFuncNugget(variables: Array[Double]): (Double) ⇒ Array[Double]

    Computes the differential values at the current point of Levenberg-Marquard optimization

  2. abstract def valueFuncNugget(r: Double, s: Double): (Double) ⇒ Double

    r

    Denotes current Range of Semivariogram while performing fitting optimization

    s

    Denotes current Sill of Semivariogram while performing fitting optimization

Concrete Value Members

  1. def optimum: Optimum
  2. def retMMF(): MultivariateMatrixFunction
  3. def retMVF(): MultivariateVectorFunction