Package com.opengamma.strata.math.impl.interpolation

Class PolynomialsLeastSquaresFitter

java.lang.Object

com.opengamma.strata.math.impl.interpolation.PolynomialsLeastSquaresFitter

public class PolynomialsLeastSquaresFitter
extends Object

Derive coefficients of n-degree polynomial that minimizes least squares error of fit by using QR decomposition and back substitution.

Constructor Summary

Constructors

Constructor	Description
PolynomialsLeastSquaresFitter()

Method Summary

Modifier and Type	Method	Description
LeastSquaresRegressionResult	regress(double[] xData, double[] yData, int degree)	Given a set of data (X_i, Y_i) and degrees of a polynomial, determines optimal coefficients of the polynomial.
PolynomialsLeastSquaresFitterResult	regressVerbose(double[] xData, double[] yData, int degree, boolean normalize)	Alternative regression method with different output.

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

PolynomialsLeastSquaresFitter

public PolynomialsLeastSquaresFitter()

Method Detail

regress

public LeastSquaresRegressionResult regress(double[] xData,
                                            double[] yData,
                                            int degree)

Given a set of data (X_i, Y_i) and degrees of a polynomial, determines optimal coefficients of the polynomial.

Parameters:

xData - X values of data

yData - Y values of data

degree - Degree of polynomial which fits the given data

Returns:

LeastSquaresRegressionResult Containing optimal coefficients of the polynomial and difference between yData[i] and f(xData[i]), where f() is the polynomial with the derived coefficients

regressVerbose

public PolynomialsLeastSquaresFitterResult regressVerbose(double[] xData,
                                                          double[] yData,
                                                          int degree,
                                                          boolean normalize)

Alternative regression method with different output.

Parameters:

xData - X values of data

yData - Y values of data

degree - Degree of polynomial which fits the given data

normalize - Normalize xData by mean and standard deviation if normalize == true

Returns:

PolynomialsLeastSquaresRegressionResult containing coefficients, rMatrix, degrees of freedom, norm of residuals, and mean, standard deviation