org.apache.commons.math.optimization.fitting

Class PolynomialFitter

java.lang.Object

org.apache.commons.math.optimization.fitting.PolynomialFitter

public class PolynomialFitter
extends java.lang.Object

This class implements a curve fitting specialized for polynomials.

Polynomial fitting is a very simple case of curve fitting. The estimated coefficients are the polynomial coefficients. They are searched by a least square estimator.

Since:

2.0

Constructor Summary

Constructors

Constructor and Description
PolynomialFitter(int degree, DifferentiableMultivariateVectorialOptimizer optimizer) Simple constructor.

Method Summary

Modifier and Type	Method and Description
void	addObservedPoint(double weight, double x, double y) Add an observed weighted (x,y) point to the sample.
void	clearObservations() Remove all observations.
PolynomialFunction	fit() Get the polynomial fitting the weighted (x, y) points.

Methods inherited from class java.lang.Object

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

Constructor Detail

PolynomialFitter

public PolynomialFitter(int degree,
                        DifferentiableMultivariateVectorialOptimizer optimizer)

Simple constructor.

The polynomial fitter built this way are complete polynomials, ie. a n-degree polynomial has n+1 coefficients.

Parameters:

degree - maximal degree of the polynomial

optimizer - optimizer to use for the fitting

Method Detail

addObservedPoint

public void addObservedPoint(double weight,
                             double x,
                             double y)

Add an observed weighted (x,y) point to the sample.

Parameters:

weight - weight of the observed point in the fit

x - abscissa of the point

y - observed value of the point at x, after fitting we should have P(x) as close as possible to this value

clearObservations

public void clearObservations()

Remove all observations.

Since:

2.2

fit

public PolynomialFunction fit()
                       throws OptimizationException

Get the polynomial fitting the weighted (x, y) points.

Returns:

polynomial function best fitting the observed points

Throws:

OptimizationException - if the algorithm failed to converge