Package smile.interpolation
Class KrigingInterpolation2D
java.lang.Object
smile.interpolation.KrigingInterpolation2D
- All Implemented Interfaces:
Serializable
,Interpolation2D
Kriging interpolation for the data points irregularly distributed in space.
Kriging belongs to the family of linear least squares estimation algorithms,
also known as Gauss-Markov estimation or Gaussian process regression.
This class implements ordinary kriging for interpolation with power variogram.
- See Also:
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Constructor Summary
ConstructorDescriptionKrigingInterpolation2D
(double[] x1, double[] x2, double[] y) Constructor.KrigingInterpolation2D
(double[] x1, double[] x2, double[] y, double beta) Constructor. -
Method Summary
Modifier and TypeMethodDescriptiondouble
interpolate
(double x1, double x2) Interpolate the data at a given 2-dimensional point.toString()
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Constructor Details
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KrigingInterpolation2D
public KrigingInterpolation2D(double[] x1, double[] x2, double[] y) Constructor. The power variogram is employed for interpolation.- Parameters:
x1
- the 1st dimension of data points.x2
- the 2nd dimension of data points.y
- the function values at(x1, x2)
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KrigingInterpolation2D
public KrigingInterpolation2D(double[] x1, double[] x2, double[] y, double beta) Constructor. The power variogram is employed for interpolation.- Parameters:
x1
- the 1st dimension of data points.x2
- the 2nd dimension of data points.y
- the function values at(x1, x2)
.beta
- the parameter of power variogram. The value of β should be in the range1 <=
β< 2
. A good general choice is 1.5, but for functions with a strong linear trend, we may experiment with values as large as 1.99.
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Method Details
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interpolate
public double interpolate(double x1, double x2) Description copied from interface:Interpolation2D
Interpolate the data at a given 2-dimensional point.- Specified by:
interpolate
in interfaceInterpolation2D
- Parameters:
x1
- the 1st dimension value.x2
- the 2nd dimension value.- Returns:
- the interpolated function value.
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toString
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