- All Implemented Interfaces:
public class SplineInterpolator extends Object implements UnivariateRealInterpolatorComputes a natural (also known as "free", "unclamped") cubic spline interpolation for the data set.
interpolate(double, double)method returns a
PolynomialSplineFunctionconsisting of n cubic polynomials, defined over the subintervals determined by the x values, x < x[i] ... < x[n]. The x values are referred to as "knot points."
The value of the PolynomialSplineFunction at a point x that is greater than or equal to the smallest knot point and strictly less than the largest knot point is computed by finding the subinterval to which x belongs and computing the value of the corresponding polynomial at
x - x[i]where
iis the index of the subinterval. See
PolynomialSplineFunctionfor more details.
The interpolating polynomials satisfy:
- The value of the PolynomialSplineFunction at each of the input x values equals the corresponding y value.
- Adjacent polynomials are equal through two derivatives at the knot points (i.e., adjacent polynomials "match up" at the knot points, as do their first and second derivatives).
The cubic spline interpolation algorithm implemented is as described in R.L. Burden, J.D. Faires, Numerical Analysis, 4th Ed., 1989, PWS-Kent, ISBN 0-53491-585-X, pp 126-131.
Constructors Constructor Description
All Methods Instance Methods Concrete Methods Modifier and Type Method Description
interpolate(double x, double y)Computes an interpolating function for the data set.
public PolynomialSplineFunction interpolate(double x, double y)Computes an interpolating function for the data set.
- Specified by:
x- the arguments for the interpolation points
y- the values for the interpolation points
- a function which interpolates the data set
yhave different sizes.
xis not sorted in strict increasing order.
NumberIsTooSmallException- if the size of
xis smaller than 3.