# LeastSquaresFittingNuggetProblem

### Related Doc: package interpolation

#### abstract class LeastSquaresFittingNuggetProblem extends AnyRef

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

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