boofcv.alg.interpolate.array
Class LagrangeFormula
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- boofcv.alg.interpolate.array.LagrangeFormula
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public class LagrangeFormula extends java.lang.ObjectLangrange's formula is a straight forward way to perform polynomial interpolation. It is not the most computationally efficient approach and does not provide any estimate of its accuracy. The order of the polynomial refers to the number of points used in the interpolation minus one.
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Constructor Summary
Constructors Constructor and Description LagrangeFormula()
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Method Summary
All Methods Static Methods Concrete Methods Modifier and Type Method and Description static floatprocess_F32(float sample, float[] x, float[] y, int i0, int i1)UsingLlangrange's formula it interpulates the value of a function at the specified sample point given discrete samples.static doubleprocess_F64(double sample, double[] x, double[] y, int i0, int i1)UsingLlangrange's formula it interpulates the value of a function at the specified sample point given discrete samples.
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Method Detail
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process_F64
public static double process_F64(double sample, double[] x, double[] y, int i0, int i1)UsingLlangrange's formula it interpulates the value of a function at the specified sample point given discrete samples. Which samples are used and the order of the approximation are given by i0 and i1.- Parameters:
sample- Where the estimate is done.x- Where the function was sampled.y- The function's value at the sample pointsi0- The first point considered.i1- The last point considered.- Returns:
- The estimated y value at the sample point.
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process_F32
public static float process_F32(float sample, float[] x, float[] y, int i0, int i1)UsingLlangrange's formula it interpulates the value of a function at the specified sample point given discrete samples. Which samples are used and the order of the approximation are given by i0 and i1. The order is = i1-i0+1.- Parameters:
sample- Where the estimate is done.x- Where the function was sampled.y- The function's value at the sample pointsi0- The first point considered.i1- The last point considered.- Returns:
- The estimated y value at the sample point.
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