org.apache.commons.math3.optimization.fitting

Class CurveFitter<T extends ParametricUnivariateFunction>

java.lang.Object

org.apache.commons.math3.optimization.fitting.CurveFitter<T>

Type Parameters:

T - Function to use for the fit.

Direct Known Subclasses:

GaussianFitter, HarmonicFitter, PolynomialFitter

Deprecated.

As of 3.1 (to be removed in 4.0).

@Deprecated
public class CurveFitter<T extends ParametricUnivariateFunction>
extends java.lang.Object

Fitter for parametric univariate real functions y = f(x).
When a univariate real function y = f(x) does depend on some unknown parameters p₀, p₁ ... p_n-1, this class can be used to find these parameters. It does this by fitting the curve so it remains very close to a set of observed points (x₀, y₀), (x₁, y₁) ... (x_k-1, y_k-1). This fitting is done by finding the parameters values that minimizes the objective function ∑(y_i-f(x_i))². This is really a least squares problem.

Since:

2.0

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
private class	CurveFitter.OldTheoreticalValuesFunction Deprecated.
private class	CurveFitter.TheoreticalValuesFunction Deprecated. Vectorial function computing function theoretical values.

Field Summary

Fields

Modifier and Type	Field and Description
private java.util.List<WeightedObservedPoint>	observations Deprecated. Observed points.
private DifferentiableMultivariateVectorOptimizer	oldOptimizer Deprecated. as of 3.1 replaced by optimizer
private MultivariateDifferentiableVectorOptimizer	optimizer Deprecated. Optimizer to use for the fitting.

Constructor Summary

Constructors

Constructor and Description
CurveFitter(DifferentiableMultivariateVectorOptimizer optimizer) Deprecated. as of 3.1 replaced by CurveFitter(MultivariateDifferentiableVectorOptimizer)
CurveFitter(MultivariateDifferentiableVectorOptimizer optimizer) Deprecated. Simple constructor.

Method Summary

Methods

Modifier and Type	Method and Description
void	addObservedPoint(double x, double y) Deprecated. Add an observed (x,y) point to the sample with unit weight.
void	addObservedPoint(double weight, double x, double y) Deprecated. Add an observed weighted (x,y) point to the sample.
void	addObservedPoint(WeightedObservedPoint observed) Deprecated. Add an observed weighted (x,y) point to the sample.
void	clearObservations() Deprecated. Remove all observations.
double[]	fit(int maxEval, T f, double[] initialGuess) Deprecated. Fit a curve.
double[]	fit(T f, double[] initialGuess) Deprecated. Fit a curve.
WeightedObservedPoint[]	getObservations() Deprecated. Get the observed points.

Methods inherited from class java.lang.Object

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

Field Detail

oldOptimizer

@Deprecated
private final DifferentiableMultivariateVectorOptimizer oldOptimizer

Deprecated. as of 3.1 replaced by optimizer

Optimizer to use for the fitting.

optimizer

private final MultivariateDifferentiableVectorOptimizer optimizer

Deprecated.

Optimizer to use for the fitting.

observations

private final java.util.List<WeightedObservedPoint> observations

Deprecated.

Observed points.

Constructor Detail

CurveFitter

@Deprecated
public CurveFitter(DifferentiableMultivariateVectorOptimizer optimizer)

Deprecated. as of 3.1 replaced by CurveFitter(MultivariateDifferentiableVectorOptimizer)

Simple constructor.

Parameters:

optimizer - optimizer to use for the fitting

CurveFitter

public CurveFitter(MultivariateDifferentiableVectorOptimizer optimizer)

Deprecated.

Simple constructor.

Parameters:

optimizer - optimizer to use for the fitting

Since:

3.1

Method Detail

addObservedPoint

public void addObservedPoint(double x,
                    double y)

Deprecated.

Add an observed (x,y) point to the sample with unit weight.

Calling this method is equivalent to call addObservedPoint(1.0, x, y).

Parameters:

x - abscissa of the point

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

See Also:

addObservedPoint(double, double, double), addObservedPoint(WeightedObservedPoint), getObservations()

addObservedPoint

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

Deprecated.

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 f(x) as close as possible to this value

See Also:

addObservedPoint(double, double), addObservedPoint(WeightedObservedPoint), getObservations()

addObservedPoint

public void addObservedPoint(WeightedObservedPoint observed)

Deprecated.

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

Parameters:

observed - observed point to add

See Also:

addObservedPoint(double, double), addObservedPoint(double, double, double), getObservations()

getObservations

public WeightedObservedPoint[] getObservations()

Deprecated.

Get the observed points.

Returns:

observed points

See Also:

addObservedPoint(double, double), addObservedPoint(double, double, double), addObservedPoint(WeightedObservedPoint)

clearObservations

public void clearObservations()

Deprecated.

Remove all observations.

fit

public double[] fit(T f,
           double[] initialGuess)

Deprecated.

Fit a curve. This method compute the coefficients of the curve that best fit the sample of observed points previously given through calls to the addObservedPoint method.

Parameters:

f - parametric function to fit.

initialGuess - first guess of the function parameters.

Returns:

the fitted parameters.

Throws:

DimensionMismatchException - if the start point dimension is wrong.

fit

public double[] fit(int maxEval,
           T f,
           double[] initialGuess)

Deprecated.

Fit a curve. This method compute the coefficients of the curve that best fit the sample of observed points previously given through calls to the addObservedPoint method.

Parameters:

f - parametric function to fit.

initialGuess - first guess of the function parameters.

maxEval - Maximum number of function evaluations.

Returns:

the fitted parameters.

Throws:

TooManyEvaluationsException - if the number of allowed evaluations is exceeded.

DimensionMismatchException - if the start point dimension is wrong.

Since:

3.0