com.opengamma.strata.math.impl.interpolation

Class PolynomialsLeastSquaresFitterResult

java.lang.Object

com.opengamma.strata.math.impl.interpolation.PolynomialsLeastSquaresFitterResult

public class PolynomialsLeastSquaresFitterResult
extends Object

Contains the result of a least squares regression for polynomial.

Constructor Summary

Constructors

Constructor and Description
PolynomialsLeastSquaresFitterResult(double[] coefficients, DoubleMatrix rMatrix, int dof, double diffNorm)
PolynomialsLeastSquaresFitterResult(double[] coefficients, DoubleMatrix rMatrix, int dof, double diffNorm, double[] meanAndStd)

Method Summary

Modifier and Type	Method and Description
double[]	getCoeff()
double	getDiffNorm()
int	getDof()
double[]	getMeanAndStd()
DoubleMatrix	getRMat()

Methods inherited from class java.lang.Object

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

Constructor Detail

PolynomialsLeastSquaresFitterResult

public PolynomialsLeastSquaresFitterResult(double[] coefficients,
                                           DoubleMatrix rMatrix,
                                           int dof,
                                           double diffNorm)

Parameters:

coefficients - Coefficients of the polynomial

rMatrix - R-matrix of the QR decomposition used in PolynomialsLeastSquaresRegression

dof - Degrees of freedom = Number of data points - (degrees of Polynomial + 1)

diffNorm - Square norm of the vector, "residuals," whose components are yData_i - f(xData_i)

PolynomialsLeastSquaresFitterResult

public PolynomialsLeastSquaresFitterResult(double[] coefficients,
                                           DoubleMatrix rMatrix,
                                           int dof,
                                           double diffNorm,
                                           double[] meanAndStd)

Parameters:

coefficients - Coefficients {a_0, a_1, a_2 ...} of the polynomial a_0 + a_1 x^1 + a_2 x^2 + ....

rMatrix - R-matrix of the QR decomposition used in PolynomialsLeastSquaresRegression

dof - Degrees of freedom = Number of data points - (degrees of Polynomial + 1)

diffNorm - Norm of the vector, "residuals," whose components are yData_i - f(xData_i)

meanAndStd - Vector (mean , standard deviation) used in normalization

Method Detail

getCoeff

public double[] getCoeff()

Returns:

Coefficients {a_0, a_1, a_2 ...} of polynomial a_0 + a_1 x^1 + a_2 x^2 + ....

getRMat

public DoubleMatrix getRMat()

Returns:

R Matrix of QR decomposition

getDof

public int getDof()

Returns:

Degrees of freedom = Number of data points - (degrees of Polynomial + 1)

getDiffNorm

public double getDiffNorm()

Returns:

Norm of the vector, "residuals," whose components are yData_i - f(xData_i)

getMeanAndStd

public double[] getMeanAndStd()

Returns:

Vector (mean , standard deviation) used in normalization